From 9fc5876b364c1d0d933dc37412e340b9824dc0bc Mon Sep 17 00:00:00 2001
From: liqun Fu <liqfu@microsoft.com>
Date: Fri, 2 Aug 2024 10:20:22 -0700
Subject: [PATCH 01/22] Mlas int4 int8 with avx2/512 (#20687)
MIME-Version: 1.0
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### Description
model: phi-3-mini-4k-instruct
avx2 symmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |49.5|70.0|-29.2%|9.6|10.8|-34.2%
32 |76.8|52.4|9.7%|15.2|14.6|4.1%
64 |78.2|71.4|9.5%|16.6|16.3|1.8%
128 |72.9|70.6|3.2%|17.1|16.8|1.7%
256 |83.7|63.6|31.6%|18.1|17.4|4%

avx2 asymmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |50.7|61.5|-17.5%|9.6|9.2|4.3%
32 |77.4|52.4|47.7%|14.6|13.9|5.0%
64 |78.7|63.0|24.9%|16.2|15.9|1.8%
128 |80.0|61.9|29.2%|17.2|16.9|1.7%
256 |81.5|63.3|28.7%|17.9|17.3|3.4%

avx2vnni symmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |82.9|117.0|-29.0%|15.9|19.3|-17.6%
32 |133.0|100.4|32.4%|26.1|24.5|6.5%
64 |166.9|118.8|40.4%|28.3|27.1|4.4%
128 |165.9|119.6|38.7%|29.3|28.5|2.8%
256 |165.2|119.6|38.1%|30.2|29.0|4.1%

avx2vnni asymmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |80.2|118.9|-32.5%|15.1|16.7|-9.5%
32 |130.7|99.7|31.0%|25.0|23.8|5.0%
64 |168.7|124.9|35.0%|27.3|26.8|1.8%
128 |169.6|123.8|36.9%|29.2|27.9|4.6%
256 |175.0|125.7|39.0%|30.0|29.7|1.0%

avx512 symmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |135.2|156.5|-13.6|25.5|23.8|7.1
32 |150.0|159.5|-5.9|34.9|29.6|17.9
64 |167.5|157.5|6.3|39.7|34.4|15.4
128 |177.8|158.0|12.5|40.3|35.4|13.8
256 |182.6|157.3|16.0|41.7|37.7|10.6

avx512 asymmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |136.1|151.4|-10.1%|26.1|19.9|31.1%
32 |150.0|157.8|-4.9%|34.3|29.3|17.0%
64 |165.7|156.6|5.8%|38.7|30.7|26.0%
128 |180.4|156.6|15.1%|40.2|34.7|15.8%
256 |181.3|158.0|14.7%|41.6|36.6|13.6%

avx512vnni symmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |143.4|155.4|-7.7%|25.6|23.3|9.8%
32 |159.2|157.0|1.4%|34.1|29.8|14.4%
64 |182.0|159.5|14.1%|38.4|34.8|10.3%
128 |221.2|160.8|37.5%|41.0|36.4|12.6%
256 |250.5|162.4|54.2%|41.6|37.7|10.3%

avx512vnni asymmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |142.5|152.3|-6.4%|26.3|19.7|33.5%
32 |158.2|155.0|2.0%|34.3|29.2|17.4%
64 |184.1|156.6|17.5%|38.3|30.9|23.9%
128 |215.8|156.1|17.5%|41.3|35.0|17.9%
256 |249.2|155.9|59.8%|41.1|36.3|13.2%


4bit gemm implementation with avx using tile.

1.
tile size is 2blk by 4. in case of size less then tile, it reduce to
1blk by 4, 2blk by 1 and lastly 1blk by 1.
with internal kernel, weight and activation are loaded based on SIMD
register width and blk length:
avx2 256bit register, 64 weights and activation are loaded.
   blklen16: 4 blks are computed by the internal kernel
   blklen32: 2 blks are computed by the internal kernel
   blklen64: 1 blk are computed by the internal kernel
   blklen128: 1 blks are computed 2 times by the internal kernel
   blklen16: 1 blks are computed 4 times by the internal kernel

avx512 512bit register, 128 weights and activation are loaded.
   blklen16: 8 blks are computed by the internal kernel
   blklen32: 4 blks are computed by the internal kernel
   blklen64: 2 blk are computed by the internal kernel
   blklen128: 1 blks are computed by the internal kernel
   blklen16: 1 blks are computed 2 times by the internal kernel

2.
blksum is precomputed during prepacking.
computation is reformed:
Sum1(scale_a * scale_b * Sum_blk(a_i * b_i)) + Sum2(blksum_a * blksum_b)
  Sum_blk is over one blk
  Sum1 is over all blks for one output
  Sum2 is over all blks for one output
Sum is computed with sgemm with the current implementation. Further
improvement is possible.

 

---------

Signed-off-by: Liqun Fu <liqfu@microsoft.com>
Signed-off-by: liqunfu <liqun.fu@microsoft.com>
Signed-off-by: Liqun Fu <liqun_fu@hotmail.com>
---
 cmake/onnxruntime_mlas.cmake                  |   13 +-
 .../cpu/quantization/matmul_nbits.cc          |   41 +-
 onnxruntime/core/mlas/inc/mlas_qnbit.h        |   56 +-
 onnxruntime/core/mlas/lib/mlasi.h             |    2 +
 onnxruntime/core/mlas/lib/platform.cpp        |    1 +
 onnxruntime/core/mlas/lib/sqnbitgemm.cpp      |  244 +++-
 onnxruntime/core/mlas/lib/sqnbitgemm.h        |  102 ++
 .../core/mlas/lib/sqnbitgemm_kernel_avx2.cpp  |  292 +++-
 .../sqnbitgemm_kernel_avx2_int8_blklen16.h    |  727 ++++++++++
 .../sqnbitgemm_kernel_avx2_int8_blklen32.h    | 1049 +++++++++++++++
 .../sqnbitgemm_kernel_avx2_int8_blklen64.h    |  541 ++++++++
 .../mlas/lib/sqnbitgemm_kernel_avx512.cpp     |  150 ++-
 .../mlas/lib/sqnbitgemm_kernel_avx512_int8.h  | 1171 +++++++++++++++++
 .../sqnbitgemm_kernel_avx512_int8_blklen128.h |  581 ++++++++
 .../sqnbitgemm_kernel_avx512_int8_blklen16.h  |  812 ++++++++++++
 .../sqnbitgemm_kernel_avx512_int8_blklen32.h  |  852 ++++++++++++
 .../sqnbitgemm_kernel_avx512_int8_blklen64.h  |  840 ++++++++++++
 .../mlas/lib/sqnbitgemm_kernel_avx512vnni.cpp |  180 ++-
 .../mlas/lib/sqnbitgemm_kernel_avx_common.h   |  273 +++-
 .../lib/sqnbitgemm_kernel_avx_common_int8.h   |   51 +-
 ...bitgemm_m1_sym_kernel_avx2_int8_blklen32.h |  759 +++++++++++
 ...bitgemm_m1_sym_kernel_avx2_int8_blklen64.h |  312 +++++
 .../test/contrib_ops/matmul_4bits_test.cc     |    5 +-
 onnxruntime/test/mlas/bench/bench_q4dq.cpp    |   24 +-
 .../test/mlas/bench/bench_sqnbitgemm.cpp      |    9 +-
 .../test/mlas/unittest/test_sqnbitgemm.cpp    |   47 +-
 26 files changed, 8834 insertions(+), 300 deletions(-)
 create mode 100644 onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2_int8_blklen16.h
 create mode 100644 onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2_int8_blklen32.h
 create mode 100644 onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2_int8_blklen64.h
 create mode 100644 onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8.h
 create mode 100644 onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen128.h
 create mode 100644 onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen16.h
 create mode 100644 onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen32.h
 create mode 100644 onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen64.h
 create mode 100644 onnxruntime/core/mlas/lib/sqnbitgemm_m1_sym_kernel_avx2_int8_blklen32.h
 create mode 100644 onnxruntime/core/mlas/lib/sqnbitgemm_m1_sym_kernel_avx2_int8_blklen64.h

diff --git a/cmake/onnxruntime_mlas.cmake b/cmake/onnxruntime_mlas.cmake
index 66f4aea606ef5..c02ac2096db2e 100644
--- a/cmake/onnxruntime_mlas.cmake
+++ b/cmake/onnxruntime_mlas.cmake
@@ -555,8 +555,17 @@ else()
           ${MLAS_SRC_DIR}/intrinsics/avx2/qdwconv_avx2.cpp
           ${MLAS_SRC_DIR}/sqnbitgemm_kernel_avx2.cpp
         )
-        set_source_files_properties(${mlas_platform_srcs_avx2} PROPERTIES COMPILE_FLAGS "-mavx2 -mfma")
 
+message(STATUS "CMAKE_CXX_COMPILER_ID: ${CMAKE_CXX_COMPILER_ID}")
+message(STATUS "CMAKE_CXX_COMPILER_VERSION: ${CMAKE_CXX_COMPILER_VERSION}")
+
+if(NOT "${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU" OR CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "10")
+          message(STATUS "Using -mavx2 -mfma -mavxvnni flags")
+          set_source_files_properties(${mlas_platform_srcs_avx2} PROPERTIES COMPILE_FLAGS "-mavx2 -mfma -mavxvnni")
+else()
+          message(STATUS "Using -mavx2 -mfma flags")
+          set_source_files_properties(${mlas_platform_srcs_avx2} PROPERTIES COMPILE_FLAGS "-mavx2 -mfma")
+endif()
         set(mlas_platform_srcs_avx512f
           ${MLAS_SRC_DIR}/x86_64/DgemmKernelAvx512F.S
           ${MLAS_SRC_DIR}/x86_64/SgemmKernelAvx512F.S
@@ -575,7 +584,7 @@ else()
           ${MLAS_SRC_DIR}/x86_64/ConvSymKernelAvx512Core.S
           ${MLAS_SRC_DIR}/sqnbitgemm_kernel_avx512.cpp
         )
-        set_source_files_properties(${mlas_platform_srcs_avx512core} PROPERTIES COMPILE_FLAGS "-mavx512bw -mavx512dq -mavx512vl")
+        set_source_files_properties(${mlas_platform_srcs_avx512core} PROPERTIES COMPILE_FLAGS "-mfma -mavx512vnni -mavx512bw -mavx512dq -mavx512vl")
 
         set(mlas_platform_srcs_avx512vnni
           ${MLAS_SRC_DIR}/sqnbitgemm_kernel_avx512vnni.cpp
diff --git a/onnxruntime/contrib_ops/cpu/quantization/matmul_nbits.cc b/onnxruntime/contrib_ops/cpu/quantization/matmul_nbits.cc
index 995babc857357..5fdd2b017b8a6 100644
--- a/onnxruntime/contrib_ops/cpu/quantization/matmul_nbits.cc
+++ b/onnxruntime/contrib_ops/cpu/quantization/matmul_nbits.cc
@@ -104,6 +104,8 @@ class MatMulNBits final : public OpKernel {
 
     ORT_ENFORCE(nbits_ == 4,
                 "Only 4b quantization is supported for MatMulNBits op, additional bits support is planned.");
+    const Tensor* tensor_zero_point = nullptr;
+    has_zp_input_ = info.TryGetConstantInput(3, &tensor_zero_point);
 #ifdef ORT_NEURAL_SPEED
     const Tensor* tensor_B = nullptr;
     const Tensor* tensor_scale = nullptr;
@@ -139,6 +141,7 @@ class MatMulNBits final : public OpKernel {
   IAllocatorUniquePtr<void> packed_b_{};
   size_t packed_b_size_{0};
 
+  bool has_zp_input_{false};
 #if defined(ORT_NEURAL_SPEED)
 
   bool is_asym_{false};
@@ -207,10 +210,10 @@ Status MatMulNBits::PrePack(const Tensor& tensor, int input_idx, /*out*/ Allocat
     is_packed = true;
   }
 
-#else   // defined(ORT_NEURAL_SPEED)
-
+#else  // defined(ORT_NEURAL_SPEED)
+  ORT_UNUSED_PARAMETER(prepacked_weights);
+  const auto compute_type = static_cast<MLAS_SQNBIT_GEMM_COMPUTE_TYPE>(accuracy_level_);
   if (input_idx == InputIndex::B) {
-    const auto compute_type = static_cast<MLAS_SQNBIT_GEMM_COMPUTE_TYPE>(accuracy_level_);
     if (!MlasIsSQNBitGemmAvailable(nbits_, block_size_, compute_type)) {
       return Status::OK();
     }
@@ -220,12 +223,20 @@ Status MatMulNBits::PrePack(const Tensor& tensor, int input_idx, /*out*/ Allocat
     }
     auto qptr = tensor.DataRaw();
     packed_b_ = IAllocator::MakeUniquePtr<void>(alloc, packed_b_size_, true);
-    MlasSQNBitGemmPackQuantBData(N_, K_, nbits_, block_size_, compute_type, qptr, packed_b_.get());
-    if (prepacked_weights) {
-      prepacked_weights->buffers_.push_back(std::move(packed_b_));
-      prepacked_weights->buffer_sizes_.push_back(packed_b_size_);
-    }
+    MlasSQNBitGemmPackQuantBData(N_, K_, nbits_, block_size_, compute_type, qptr, packed_b_.get(), nullptr, has_zp_input_, nullptr, nullptr);
     is_packed = true;
+  } else if (compute_type == CompInt8) {
+#ifdef MLAS_TARGET_AMD64_IX86
+    if (input_idx == InputIndex::scales && packed_b_ != nullptr) {
+      auto sptr = tensor.Data<float>();
+      MlasSQNBitGemmPackQuantBData(N_, K_, nbits_, block_size_, compute_type, nullptr, packed_b_.get(), sptr, has_zp_input_, nullptr, nullptr);
+      is_packed = false;
+    } else if (input_idx == InputIndex::zero_points && packed_b_ != nullptr) {
+      auto zptr = tensor.Data<uint8_t>();
+      MlasSQNBitGemmPackQuantBData(N_, K_, nbits_, block_size_, compute_type, nullptr, packed_b_.get(), nullptr, has_zp_input_, zptr, nullptr);
+      is_packed = false;
+    }
+#endif
   }
 #endif  // defined(ORT_NEURAL_SPEED)
 
@@ -332,9 +343,9 @@ Status MatMulNBits::Compute(OpKernelContext* ctx) const {
       const auto* bias_data = bias == nullptr ? nullptr : bias->Data<float>();
 
       IAllocatorUniquePtr<std::byte> workspace{};
-      if (const size_t workspace_size = MlasSQNBitGemmBatchWorkspaceSize(M, N, K, batch_count,
-                                                                         nbits_, block_size_, compute_type);
-          workspace_size > 0) {
+      const size_t workspace_size = MlasSQNBitGemmBatchWorkspaceSize(
+          M, N, K, batch_count, nbits_, block_size_, compute_type);
+      if (workspace_size > 0) {
         AllocatorPtr allocator;
         ORT_RETURN_IF_ERROR(ctx->GetTempSpaceAllocator(&allocator));
         workspace = IAllocator::MakeUniquePtr<std::byte>(allocator, workspace_size);
@@ -344,14 +355,18 @@ Status MatMulNBits::Compute(OpKernelContext* ctx) const {
       for (size_t i = 0; i < batch_count; ++i) {
         data[i].A = a_data + helper.LeftOffsets()[i];
         data[i].lda = lda;
-        data[i].QuantBData = packed_b_.get();
+#ifdef MLAS_TARGET_AMD64_IX86
+        if (compute_type == CompInt8) {
+          data[i].QuantBDataWorkspace = packed_b_.get();
+        }
+#endif
+        data[i].PackedQuantBData = static_cast<std::byte*>(packed_b_.get());
         data[i].QuantBScale = scales_data;
         data[i].QuantBZeroPoint = zero_points_data;
         data[i].Bias = bias_data;
         data[i].C = y_data + helper.OutputOffsets()[i];
         data[i].ldc = N;
       }
-
       MlasSQNBitGemmBatch(M, N, K, batch_count, nbits_, block_size_, compute_type, data.data(), workspace.get(),
                           thread_pool);
 
diff --git a/onnxruntime/core/mlas/inc/mlas_qnbit.h b/onnxruntime/core/mlas/inc/mlas_qnbit.h
index 32e9cc98106d5..232bf2261ef4c 100644
--- a/onnxruntime/core/mlas/inc/mlas_qnbit.h
+++ b/onnxruntime/core/mlas/inc/mlas_qnbit.h
@@ -43,14 +43,16 @@ typedef enum {
  * @brief Data parameters for float/n-bit quantized int GEMM routine.
  */
 struct MLAS_SQNBIT_GEMM_DATA_PARAMS {
-    const float* A = nullptr;               ///< address of A (float32 matrix)
-    size_t lda = 0;                         ///< leading dimension of A
-    const void* QuantBData = nullptr;       ///< address of quantized B (quantized n-bit int values)
-    const float* QuantBScale = nullptr;     ///< address of scale values of quantized B, one per block
-    const void* QuantBZeroPoint = nullptr;  ///< optional address of zero point values of quantized B, one per block
-    const float* Bias = nullptr;            ///< optional address of Bias, vector size N
-    float* C = nullptr;                     ///< address of result matrix
-    size_t ldc = 0;                         ///< leading dimension of C
+    const float* A = nullptr;                       ///< address of A (float32 matrix)
+    size_t lda = 0;                                 ///< leading dimension of A
+    const void* QuantBDataWorkspace;                ///< address of quantized B (quantized n-bit int values)
+    const std::byte* PackedQuantBData = nullptr;    /// address of packed quantized B data
+    const float* QuantBScale = nullptr;             ///< address of scale values of quantized B, one per block
+    const void* QuantBZeroPoint = nullptr;          ///< optional address of zero point values of quantized B, one per block
+    const float* QuantBBlkSum = nullptr;            ///< optional address of scale * zp, one per block
+    const float* Bias = nullptr;                    ///< optional address of Bias, vector size N
+    float* C = nullptr;                             ///< address of result matrix
+    size_t ldc = 0;                                 ///< leading dimension of C
 
     ///< optional post processing to apply to result matrix
     MLAS_GEMM_POSTPROCESSOR<float>* PostProcessor = nullptr;
@@ -159,14 +161,29 @@ MlasSQNBitGemmPackQuantBDataSize(
 /**
  * @brief Packs the quantized B data in a format that the kernel expects.
  *
- * @param[in]   N                   column size of matrix B and C
- * @param[in]   K                   column size of matrix A and row size of matrix B
- * @param[in]   BlkBitWidth         quantized value bit width (e.g., 4 means 4 bit ints)
- * @param[in]   BlkLen              number of quantized values per block
- * @param[in]   ComputeType         GEMM compute type (e.g., multiplying float or int8 values)
- * @param[in]   QuantBData          quantized B data
- * @param[out]  PackedQuantBData    packed quantized B data
- * @param[in]   ThreadPool          optional thread pool to use
+ * If the function is called without QuantBScale and QuantBZeroPoint,
+ * it just packs QuantBData into PackedQuantBDataAndOrBlkSum.
+ *
+ * If the function is called with QuantBData, QuantBScale, and QuantBZeroPoint
+ * additional BlkSum (Scale * zeropoint) is computed and stored at the second part of PackedQuantBDataAndOrBlkSum.
+ *
+ * Because ORT OpKernel::PrePack is called for each input (in this case, QuantBData,
+ * QuantBScale, and QuantBZeroPoint) separately, this function may be called 3 times, first with QuantBData,
+ * and then QuantBScale and QuantBZeroPoint. When the function is called with QuantBScale without QuantBZeroPoint,
+ * BlkSum is computed with default zero point 8 and stored at the second part of PackedQuantBDataAndOrBlkSum.
+ * If there is a third call with QuantBZeroPoint, BlkSum is recomputed/adjusted with provided zeropoint.
+ *
+ * @param[in]   N                               column size of matrix B and C
+ * @param[in]   K                               column size of matrix A and row size of matrix B
+ * @param[in]   BlkBitWidth                     quantized value bit width (e.g., 4 means 4 bit ints)
+ * @param[in]   BlkLen                          number of quantized values per block
+ * @param[in]   ComputeType                     GEMM compute type (e.g., multiplying float or int8 values)
+ * @param[in]   QuantBData                      quantized B data
+ * @param[in]   PackedQuantBDataAndOrBlkSum     buffer to store packed quantized B data and/or BlkSum
+ * @param[in]   QuantBScale                     quantized B scale
+ * @param[in]   has_zp_input                    whether QuantBZeroPoint is provided
+ * @param[in]   QuantBZeroPoint                 quantized B zero point
+ * @param[in]   ThreadPool          thread pool to use (no parallel if nullptr)
  */
 void MLASCALL
 MlasSQNBitGemmPackQuantBData(
@@ -176,6 +193,9 @@ MlasSQNBitGemmPackQuantBData(
     size_t BlkLen,
     MLAS_SQNBIT_GEMM_COMPUTE_TYPE ComputeType,
     const void* QuantBData,
-    void* PackedQuantBData,
-    MLAS_THREADPOOL* ThreadPool = nullptr
+    void* PackedQuantBDataAndOrBlkSum,
+    const void* QuantBScale,
+    bool has_zp_input,
+    const void* QuantBZeroPoint,
+    MLAS_THREADPOOL* ThreadPool
 );
diff --git a/onnxruntime/core/mlas/lib/mlasi.h b/onnxruntime/core/mlas/lib/mlasi.h
index 83200187963e1..4239e2ecaeb6e 100644
--- a/onnxruntime/core/mlas/lib/mlasi.h
+++ b/onnxruntime/core/mlas/lib/mlasi.h
@@ -993,6 +993,8 @@ extern const MLAS_SQNBIT_GEMM_DISPATCH MlasSQNBitGemmDispatchNeon;
 
 extern const MLAS_SQNBIT_GEMM_DISPATCH MlasSQNBitGemmDispatchAvx2;
 
+extern const MLAS_SQNBIT_GEMM_DISPATCH MlasSQNBitGemmDispatchAvx2vnni;
+
 extern const MLAS_SQNBIT_GEMM_DISPATCH MlasSQNBitGemmDispatchAvx512;
 
 extern const MLAS_SQNBIT_GEMM_DISPATCH MlasSQNBitGemmDispatchAvx512vnni;
diff --git a/onnxruntime/core/mlas/lib/platform.cpp b/onnxruntime/core/mlas/lib/platform.cpp
index 859b7c2f560a4..ed437f20f7c2a 100644
--- a/onnxruntime/core/mlas/lib/platform.cpp
+++ b/onnxruntime/core/mlas/lib/platform.cpp
@@ -409,6 +409,7 @@ Return Value:
                     this->GemmU8S8Kernel = MlasGemmU8S8KernelAvxVnni;
                     this->GemvU8S8Kernel = MlasGemvU8S8KernelAvxVnni;
                     this->ConvSymU8S8Dispatch = &MlasConvSymDispatchAvxVnni;
+                    this->SQNBitGemmDispatch = &MlasSQNBitGemmDispatchAvx2vnni;
                 }
 
 #if !defined(ORT_MINIMAL_BUILD)
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm.cpp b/onnxruntime/core/mlas/lib/sqnbitgemm.cpp
index 81789386a3200..a45494ef2e04f 100644
--- a/onnxruntime/core/mlas/lib/sqnbitgemm.cpp
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm.cpp
@@ -16,11 +16,10 @@ Module Name:
 --*/
 
 #include "sqnbitgemm.h"
+#include "sqnbitgemm_q8_block.h"
 
 #include <cassert>
 
-#include "sqnbitgemm_q8_block.h"
-
 namespace
 {
 
@@ -80,9 +79,10 @@ MlasIsSQNBitGemmAvailable(
             return Dispatch->SQ4BitGemmM1Kernel_CompFp32 != nullptr &&
                    Dispatch->Q4BitBlkDequantBForSgemm_CompFp32 != nullptr;
         }
-        case SQNBitGemmVariant_BitWidth4_CompInt8: {
-            return Dispatch->SQ4BitGemmKernel_CompInt8 != nullptr &&
-                   Dispatch->QuantizeARow_CompInt8 != nullptr;
+        case SQNBitGemmVariant_BitWidth4_CompInt8: { // SQ4BitGemmKernel_BlkSum_CompInt8
+            return
+              (Dispatch->SQ4BitGemmKernel_CompInt8 != nullptr && Dispatch->QuantizeARow_CompInt8 != nullptr) ||
+              (Dispatch->SQ4BitGemmKernel_BlkSum_CompInt8 != nullptr && Dispatch->QuantizeARowComputeBlkSum_CompInt8 != nullptr);
         }
         default: {
             return false;
@@ -197,6 +197,21 @@ MlasSQNBitGemmPackQuantBDataSize(
     return 0;
 }
 
+struct PerGemmQuantAWorkspace {
+    PerGemmQuantAWorkspace(void* PerGemmWorkspace, size_t M, size_t BlockCountK, size_t BlkLen)
+        : PerGemmWorkspace_(PerGemmWorkspace), M_(M), BlockCountK_(BlockCountK), BlkLen_(BlkLen)
+    {
+        QuantData = (std::byte*)PerGemmWorkspace;
+        QuantScale = (float*)(QuantData + M * BlockCountK * BlkLen);
+        BlockSum = QuantScale + M * BlockCountK;
+    }
+    std::byte* QuantData;     // NxBlockCountKxBlkLen
+    float* QuantScale;        // NxBlockCountK
+    float* BlockSum;          // NxBlockCountK
+    void* PerGemmWorkspace_;  // memory for above data
+    size_t M_, BlockCountK_, BlkLen_;
+};
+
 void MLASCALL
 MlasSQNBitGemmPackQuantBData(
     size_t N,
@@ -205,7 +220,10 @@ MlasSQNBitGemmPackQuantBData(
     size_t BlkLen,
     MLAS_SQNBIT_GEMM_COMPUTE_TYPE ComputeType,
     const void* QuantBData,
-    void* PackedQuantBData,
+    void* PackedQuantBDataAndOrBlkSumWorkspace,
+    const void* QuantBScale,
+    bool has_zp_input,
+    const void* QuantBZeroPoint,
     MLAS_THREADPOOL* ThreadPool
 )
 {
@@ -214,17 +232,37 @@ MlasSQNBitGemmPackQuantBData(
         return;
     }
 
-    if (BlkBitWidth == 4 && Dispatch->SQ4BitGemmPackQuantBData != nullptr) {
-        Dispatch->SQ4BitGemmPackQuantBData(
-            N,
-            K,
-            BlkLen,
-            ComputeType,
-            static_cast<const std::byte*>(QuantBData),
-            static_cast<std::byte*>(PackedQuantBData),
-            ThreadPool
-        );
-        return;
+    if (BlkBitWidth == 4) {
+        if (ComputeType == CompInt8 && Dispatch->SQ4BitGemmPackQuantBDataAndBlkSum != nullptr) {
+            const size_t BlockCountK = MlasDivRoundup(K, BlkLen);
+            PackedQuantBDataStruct packed_quant_b(PackedQuantBDataAndOrBlkSumWorkspace, N, BlockCountK, BlkLen);
+            Dispatch->SQ4BitGemmPackQuantBDataAndBlkSum(
+                N,
+                K,
+                BlkLen,
+                ComputeType,
+                static_cast<const std::byte*>(QuantBData),
+                static_cast<const float*>(QuantBScale),
+                has_zp_input,
+                static_cast<const std::byte*>(QuantBZeroPoint),
+                packed_quant_b,
+                ThreadPool
+            );
+        } else if (Dispatch->SQ4BitGemmPackQuantBData != nullptr) {
+          // TODO: these assertions are true if called from matmul_nbits kernel but not from mlas tests.
+            //assert(QuantBScale == nullptr);
+            //assert(QuantBZeroPoint == nullptr);
+            Dispatch->SQ4BitGemmPackQuantBData(
+                N,
+                K,
+                BlkLen,
+                ComputeType,
+                static_cast<const std::byte*>(QuantBData),
+                static_cast<std::byte*>(PackedQuantBDataAndOrBlkSumWorkspace),
+                ThreadPool
+            );
+            return;
+        }
     }
 }
 
@@ -293,7 +331,7 @@ SQ4BitGemm_CompFp32(
 
     const float* A = DataParams->A + RangeStartM * lda;
 
-    const std::byte* QuantBData = static_cast<const std::byte*>(DataParams->QuantBData) + RangeStartN * ldb;
+    const std::byte* QuantBData = static_cast<const std::byte*>(DataParams->PackedQuantBData) + RangeStartN * ldb;
     const float* QuantBScale = DataParams->QuantBScale + RangeStartN * k_blks;
     const std::byte* QuantBZeroPoint =
         (DataParams->QuantBZeroPoint == nullptr)
@@ -373,7 +411,6 @@ SQ4BitGemm_CompFp32(
             if (bias) {
                 AddBiasForGemm(bias, c_blk, RowsHandled, CountN, ldc);
             }
-
             if (DataParams->PostProcessor != nullptr) {
                 DataParams->PostProcessor->Process(
                     DataParams->C, RangeStartM + RangeCountM - RowsRemaining, RangeStartN + n,
@@ -383,7 +420,6 @@ SQ4BitGemm_CompFp32(
 
             c_blk += ldc * RowsHandled;
             a_row += lda * RowsHandled;
-
             RowsRemaining -= RowsHandled;
         }
     }
@@ -402,16 +438,33 @@ SQ4BitGemm_CompInt8(
 )
 {
 #ifdef MLAS_TARGET_AMD64_IX86
-    if (RangeCountM != 1) {
-        // perf experiment shows fp32 is faster than int8 in M > 1 cases.
-        // route to fp32 compute before int8 compute is improved.
-        SQ4BitGemm_CompFp32(
-            BlkLen,
-            K, DataParams, PerGemmWorkspace, RangeStartM, RangeCountM, RangeStartN, RangeCountN
-        );
-        return;
-    }
-#endif
+    PerGemmQuantAWorkspace* const per_gemm_quant_a_workspace = static_cast<PerGemmQuantAWorkspace*>(PerGemmWorkspace);
+    constexpr size_t BlkBitWidth = 4;
+
+    const size_t k_blks = MlasDivRoundup(K, BlkLen);
+
+    // quant A scale is embedded in QuantData if QuantScale is nullptr.
+    const size_t lda = k_blks * (per_gemm_quant_a_workspace->QuantScale ? BlkLen : Q8BlkSize(BlkLen));
+    const size_t ldc = DataParams->ldc;
+    const size_t ldb = k_blks * MlasQNBitBlkDataSizeInBytes(BlkBitWidth, BlkLen);
+    const size_t k_blks_zp_bytes = MlasQNBitZeroPointsForBlksSizeInBytes<BlkBitWidth>(k_blks);
+
+    const std::byte* QuantA = per_gemm_quant_a_workspace->QuantData + RangeStartM * lda;
+    const float* QuantAScale = per_gemm_quant_a_workspace->QuantScale + RangeStartM * k_blks;
+
+    assert(RangeStartN % 4 == 0);
+    const std::byte* QuantBData = static_cast<const std::byte*>(DataParams->PackedQuantBData) + RangeStartN * ldb;
+    const float* QuantBScale = DataParams->QuantBScale + RangeStartN * k_blks;
+    const std::byte* QuantBZeroPoint =
+        (DataParams->QuantBZeroPoint == nullptr)
+            ? nullptr
+            : static_cast<const std::byte*>(DataParams->QuantBZeroPoint) + RangeStartN * k_blks_zp_bytes;
+    const float* ABlockSum = per_gemm_quant_a_workspace->BlockSum + RangeStartM * k_blks;
+    const float* QuantBBlkSum = DataParams->QuantBBlkSum + RangeStartN * k_blks;
+    float* C = DataParams->C + RangeStartM * ldc + RangeStartN;
+
+    const float* Bias = (DataParams->Bias == nullptr) ? nullptr : DataParams->Bias + RangeStartN;
+#else
     constexpr size_t BlkBitWidth = 4;
 
     const size_t k_blks = MlasDivRoundup(K, BlkLen);
@@ -423,7 +476,7 @@ SQ4BitGemm_CompInt8(
 
     const std::byte* QuantA = static_cast<const std::byte*>(PerGemmWorkspace) + RangeStartM * lda;
 
-    const std::byte* QuantBData = static_cast<const std::byte*>(DataParams->QuantBData) + RangeStartN * ldb;
+    const std::byte* QuantBData = static_cast<const std::byte*>(DataParams->PackedQuantBData) + RangeStartN * ldb;
     const float* QuantBScale = DataParams->QuantBScale + RangeStartN * k_blks;
     const std::byte* QuantBZeroPoint =
         (DataParams->QuantBZeroPoint == nullptr)
@@ -433,6 +486,7 @@ SQ4BitGemm_CompInt8(
     float* C = DataParams->C + RangeStartM * ldc + RangeStartN;
 
     const float* Bias = (DataParams->Bias == nullptr) ? nullptr : DataParams->Bias + RangeStartN;
+#endif
 
     size_t CountN;
     for (size_t n = 0; n < RangeCountN; n += CountN) {
@@ -446,25 +500,57 @@ SQ4BitGemm_CompInt8(
         float* c_blk = C + n;
         const float* bias = (Bias == nullptr) ? nullptr : Bias + n;
 
-        size_t RowsRemaining = RangeCountM;
-        while (RowsRemaining > 0) {
-            const auto RowsHandled = GetMlasPlatform().SQNBitGemmDispatch->SQ4BitGemmKernel_CompInt8(
+        if (GetMlasPlatform().SQNBitGemmDispatch->SQ4BitGemmKernel_CompInt8 != nullptr) {
+            size_t RowsRemaining = RangeCountM;
+            while (RowsRemaining > 0) {
+                const auto RowsHandled = GetMlasPlatform().SQNBitGemmDispatch->SQ4BitGemmKernel_CompInt8(
+                    BlkLen,
+                    a_row, b_col, b_col_scale, b_col_zp, c_blk, RowsRemaining, CountN, K, k_blks, ldc, bias
+                );
+
+                if (DataParams->PostProcessor != nullptr) {
+                    DataParams->PostProcessor->Process(
+                        DataParams->C, RangeStartM + RangeCountM - RowsRemaining, RangeStartN + n,
+                        RowsHandled, CountN, ldc
+                    );
+                }
+
+                c_blk += RowsHandled * ldc;
+                a_row += RowsHandled * lda;
+
+                RowsRemaining -= RowsHandled;
+            }
+        }
+#ifdef MLAS_TARGET_AMD64_IX86
+        else if (GetMlasPlatform().SQNBitGemmDispatch->SQ4BitGemmKernel_BlkSum_CompInt8 != nullptr)
+        {
+            const float* b_blk_sum = QuantBBlkSum + n * k_blks;
+            GetMlasPlatform().SQNBitGemmDispatch->SQ4BitGemmKernel_BlkSum_CompInt8(
                 BlkLen,
-                a_row, b_col, b_col_scale, b_col_zp, c_blk, RowsRemaining, CountN, K, k_blks, ldc, bias
+                QuantA,
+                QuantAScale,
+                b_col,
+                b_col_scale,
+                b_col_zp,
+                c_blk,
+                RangeCountM,
+                CountN,
+                K,
+                k_blks,
+                bias,
+                ldc,
+                ABlockSum,
+                b_blk_sum
             );
 
             if (DataParams->PostProcessor != nullptr) {
                 DataParams->PostProcessor->Process(
-                    DataParams->C, RangeStartM + RangeCountM - RowsRemaining, RangeStartN + n,
-                    RowsHandled, CountN, ldc
+                    DataParams->C, RangeStartM, RangeStartN + n,
+                    RangeCountM, CountN, ldc
                 );
             }
-
-            c_blk += RowsHandled * ldc;
-            a_row += RowsHandled * lda;
-
-            RowsRemaining -= RowsHandled;
         }
+#endif
     }
 }
 
@@ -496,23 +582,44 @@ InitializeWorkspace_CompInt8(
     MLAS_UNREFERENCED_PARAMETER(N);
 
     const auto QuantizeARow = GetMlasPlatform().SQNBitGemmDispatch->QuantizeARow_CompInt8;
+    const auto QuantizeARow2 = GetMlasPlatform().SQNBitGemmDispatch->QuantizeARowComputeBlkSum_CompInt8;
 
     const size_t BlockCountK = MlasDivRoundup(K, BlkLen);
     const size_t QuantAStride = BlockCountK * Q8BlkSize(BlkLen);
 
-    MlasTrySimpleParallel(ThreadPool, BatchN, [&](ptrdiff_t gemm_idx) {
-        const auto& data = DataParams[gemm_idx];
+    // TODO: try parallel on BatchN * M threads because BatchN is usually 1.
+    if (QuantizeARow) {
+        MlasTrySimpleParallel(ThreadPool, BatchN, [&](ptrdiff_t gemm_idx) {
+            const auto& data = DataParams[gemm_idx];
 
-        const float* ARowPtr = data.A;
-        std::byte* QuantARowPtr = static_cast<std::byte*>(Workspace) + gemm_idx * PerGemmWorkspaceStride;
+            const float* ARowPtr = data.A;
+            std::byte* QuantARowPtr = static_cast<std::byte*>(Workspace) + gemm_idx * PerGemmWorkspaceStride;
+            for (size_t m = 0; m < M; ++m) {
+                QuantizeARow(BlkLen, ARowPtr, K, QuantARowPtr);
 
-        for (size_t m = 0; m < M; ++m) {
-            QuantizeARow(BlkLen, ARowPtr, K, QuantARowPtr);
-
-            ARowPtr += data.lda;
-            QuantARowPtr += QuantAStride;
-        }
-    });
+                ARowPtr += data.lda;
+                QuantARowPtr += QuantAStride;
+            }
+        });
+    } else {
+        MlasTrySimpleParallel(ThreadPool, BatchN, [&](ptrdiff_t gemm_idx) {
+            const auto& data = DataParams[gemm_idx];
+            const float* ARowPtr = data.A;
+
+            void* PerGemmWorkspace = static_cast<std::byte*>(Workspace) + gemm_idx * PerGemmWorkspaceStride;
+            PerGemmQuantAWorkspace quant_a_data(PerGemmWorkspace, M, BlockCountK, BlkLen);
+            std::byte* QuantARowPtr = quant_a_data.QuantData;
+            float* QuantARowScalePtr = quant_a_data.QuantScale;
+            float* QuantARowBlkSum = quant_a_data.BlockSum;
+            for (size_t m = 0; m < M; ++m) {
+                QuantizeARow2(BlkLen, ARowPtr, K, QuantARowPtr, QuantARowScalePtr, QuantARowBlkSum);
+                ARowPtr += data.lda;
+                QuantARowPtr += BlockCountK * BlkLen;
+                QuantARowScalePtr += BlockCountK;
+                QuantARowBlkSum += BlockCountK;
+            }
+        });
+    }
 }
 
 struct Operations {
@@ -530,7 +637,6 @@ constexpr auto OperationMap = []() {
 
     return ops;
 }();
-
 }  // namespace
 
 void MLASCALL
@@ -572,12 +678,23 @@ MlasSQNBitGemmBatch(
 
     const auto ComputeOperation = OperationMap[Variant].SQNBitGemm;
 
+    const size_t BlockCountK = MlasDivRoundup(K, BlkLen);
+
     if (ThreadPool == nullptr) {
         for (size_t gemm_i = 0; gemm_i < BatchN; gemm_i++) {
             const auto* Data = &DataParams[gemm_i];
             void* PerGemmWorkspace =
                 reinterpret_cast<std::byte*>(Workspace) + gemm_i * PerGemmWorkspaceStride;
-            ComputeOperation(BlkLen, K, Data, PerGemmWorkspace, 0, M, 0, N);
+            if (ComputeType == CompInt8 && GetMlasPlatform().SQNBitGemmDispatch->SQ4BitGemmPackQuantBDataAndBlkSum != nullptr) {
+                PackedQuantBDataStruct packed_quant_b(const_cast<void*>(Data->QuantBDataWorkspace), N, BlockCountK, BlkLen);
+                const_cast<MLAS_SQNBIT_GEMM_DATA_PARAMS*>(Data)->PackedQuantBData = packed_quant_b.PackedQuantBData;
+                const_cast<MLAS_SQNBIT_GEMM_DATA_PARAMS*>(Data)->QuantBBlkSum = packed_quant_b.QuantBBlkSum;
+                const_cast<MLAS_SQNBIT_GEMM_DATA_PARAMS*>(Data)->QuantBScale = packed_quant_b.PackedQuantBScale;
+                PerGemmQuantAWorkspace per_gemm_quant_a_workspace(PerGemmWorkspace, M, BlockCountK, BlkLen);
+                ComputeOperation(BlkLen, K, Data, &per_gemm_quant_a_workspace, 0, M, 0, N);
+            } else {
+                ComputeOperation(BlkLen, K, Data, PerGemmWorkspace, 0, M, 0, N);
+            }
         }
         return;
     }
@@ -627,9 +744,6 @@ MlasSQNBitGemmBatch(
         const auto gemm_i = tid / ThreadsPerGemm;
         const auto blk_i = tid % ThreadsPerGemm;
         const auto* Data = &DataParams[gemm_i];
-        void* PerGemmWorkspace = reinterpret_cast<void*>(
-            reinterpret_cast<std::byte*>(Workspace) + gemm_i * PerGemmWorkspaceStride
-        );
 
         const ptrdiff_t ThreadIdN = blk_i / ThreadCountM;
         const ptrdiff_t ThreadIdM = blk_i % ThreadCountM;
@@ -640,6 +754,18 @@ MlasSQNBitGemmBatch(
         const size_t RangeStartN = ThreadIdN * StrideN;
         const size_t RangeCountN = std::min(N - RangeStartN, (size_t)StrideN);
 
-        ComputeOperation(BlkLen, K, Data, PerGemmWorkspace, RangeStartM, RangeCountM, RangeStartN, RangeCountN);
+        void* PerGemmWorkspace =
+            reinterpret_cast<std::byte*>(Workspace) + gemm_i * PerGemmWorkspaceStride;
+        if (ComputeType == CompInt8 && GetMlasPlatform().SQNBitGemmDispatch->SQ4BitGemmPackQuantBDataAndBlkSum != nullptr) {
+            PackedQuantBDataStruct packed_quant_b(const_cast<void*>(Data->QuantBDataWorkspace), N, BlockCountK, BlkLen);
+            const_cast<MLAS_SQNBIT_GEMM_DATA_PARAMS*>(Data)->PackedQuantBData = packed_quant_b.PackedQuantBData;
+            const_cast<MLAS_SQNBIT_GEMM_DATA_PARAMS*>(Data)->QuantBBlkSum = packed_quant_b.QuantBBlkSum;
+            const_cast<MLAS_SQNBIT_GEMM_DATA_PARAMS*>(Data)->QuantBScale = packed_quant_b.PackedQuantBScale;
+
+            PerGemmQuantAWorkspace per_gemm_quant_a_workspace(PerGemmWorkspace, M, BlockCountK, BlkLen);
+            ComputeOperation(BlkLen, K, Data, &per_gemm_quant_a_workspace, RangeStartM, RangeCountM, RangeStartN, RangeCountN);
+        } else {
+            ComputeOperation(BlkLen, K, Data, PerGemmWorkspace, RangeStartM, RangeCountM, RangeStartN, RangeCountN);
+        }
     });
 }
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm.h b/onnxruntime/core/mlas/lib/sqnbitgemm.h
index 8321dcc217e9a..2da336ca2f0ec 100644
--- a/onnxruntime/core/mlas/lib/sqnbitgemm.h
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm.h
@@ -25,12 +25,50 @@ Module Name:
 #include "mlas_qnbit.h"
 #include "mlasi.h"
 
+constexpr MLAS_FORCEINLINE size_t
+MlasQNBitQuantBBlkSumAlignment()
+{
+    // 16 floats. this alignment is required by GemmFloatKernel
+    return 16 * sizeof(float);
+}
+
 constexpr MLAS_FORCEINLINE size_t
 MlasQNBitBlkDataSizeInBytes(size_t BlkBitWidth, size_t BlkLen)
 {
     return BlkLen * BlkBitWidth / 8;
 }
 
+MLAS_FORCEINLINE void*
+MlasAlignAddress(void* addr, const size_t alignment)
+{
+    const uintptr_t QuantBBlkSumAddr = reinterpret_cast<uintptr_t>(addr);
+    addr = (void*)((QuantBBlkSumAddr + alignment - 1) & (~(alignment - 1)));
+    return addr;
+}
+
+struct PackedQuantBDataStruct {
+    PackedQuantBDataStruct(void* PackedQuantBWorkspace, size_t N, size_t BlockCountK, size_t BlkLen)
+        : QuantBWorkspace_(PackedQuantBWorkspace), N_(N), BlockCountK_(BlockCountK), BlkLen_(BlkLen)
+    {
+      // TODO: duplicate code from SQ4BitGemmPackQuantBDataSize
+        constexpr size_t BlkBitWidth = 4;
+        const size_t PackedQuantBDataSize = N * BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth, BlkLen);
+        size_t BlkSumSize = MlasDivRoundup(N, 16) * BlockCountK * 16 * sizeof(float);
+
+        // _mm256_load_si256 requires alignment on a 32-byte boundary
+        PackedQuantBData = (std::byte*)MlasAlignAddress(PackedQuantBWorkspace, 32);
+        QuantBBlkSum = (float*)(PackedQuantBData + PackedQuantBDataSize);
+        QuantBBlkSum = (float*)MlasAlignAddress(QuantBBlkSum, MlasQNBitQuantBBlkSumAlignment());
+        PackedQuantBScale = (float*)((std::byte*)QuantBBlkSum + BlkSumSize);
+    }
+    std::byte* PackedQuantBData;
+    float* PackedQuantBScale;
+    float* QuantBBlkSum;
+
+    void* QuantBWorkspace_;
+    size_t N_, BlockCountK_, BlkLen_;
+};
+
 template <size_t BlkBitWidth>
 constexpr MLAS_FORCEINLINE size_t
 MlasQNBitZeroPointsForBlksSizeInBytes(size_t BlkCount)
@@ -74,6 +112,21 @@ struct MLAS_SQNBIT_GEMM_DISPATCH {
 
     SQ4BitGemmPackQuantBData_Fn* SQ4BitGemmPackQuantBData = nullptr;
 
+    typedef void(SQ4BitGemmPackQuantBDataAndSumBlk_Fn)(
+        size_t N,
+        size_t K,
+        size_t BlkLen,
+        MLAS_SQNBIT_GEMM_COMPUTE_TYPE ComputeType,
+        const std::byte* QuantBDataBegin,
+        const float* QuantBScaleBegin,
+        bool has_zp_input,
+        const std::byte* QuantBZPBegin,
+        PackedQuantBDataStruct& packed_quant_b,
+        MLAS_THREADPOOL* ThreadPool
+    );
+
+    SQ4BitGemmPackQuantBDataAndSumBlk_Fn* SQ4BitGemmPackQuantBDataAndBlkSum = nullptr;
+
     //
     // Workspace size calculation function prototypes.
     //
@@ -181,6 +234,45 @@ struct MLAS_SQNBIT_GEMM_DISPATCH {
     // CompInt8 kernel function prototypes.
     //
 
+    /**
+     * @brief Multiply quantized 8-bit integer matrix A with quantized 4-bit integer matrix B.
+     *        A and B are block quantized and B is column major.
+     *
+     * @param       BlkLen              Number of values in a block.
+     * @param       QuantA              Supplies the quantized A matrix.
+                                        Binary data containing block quantized int8 data and scale values.
+     * @param       QuantBData          Supplies the quantized B matrix block data.
+     * @param       QuantBScale         Supplies the quantized B matrix block scale values.
+     * @param       QuantBZeroPoint     Supplies the quantized B matrix block zero point values. Optional.
+     * @param[out]  C                   Supplies the output C matrix.
+     * @param       CountN              Number of columns of B and C.
+     * @param       CountK              Number of columns of A and rows of B.
+     * @param       BlockCountK         Number of blocks between adjacent columns of the quantized B matrix.
+     * @param       Bias                Bias vector of length N.
+     * @param       ldc                 Number of elements between adjacent rows of C..
+     * @param       ABlockSum           Supplies the blksum of A.
+     * @param       QuantBBlkSum        Supplies the blksum of B.
+     */
+    typedef size_t(SQ4BitGemmKernel_BlkSum_CompInt8_Fn)(
+        size_t BlkLen,
+        const std::byte* QuantA,
+        const float* QuantAScale,
+        const std::byte* QuantBData,
+        const float* QuantBScale,
+        const std::byte* QuantBZeroPoint,
+        float* C,
+        size_t CountM,
+        size_t CountN,
+        size_t CountK,
+        size_t BlockCountK,
+        const float* Bias,
+        size_t ldc,
+        const float* ABlockSum,
+        const float* QuantBBlkSum
+    );
+
+    SQ4BitGemmKernel_BlkSum_CompInt8_Fn* SQ4BitGemmKernel_BlkSum_CompInt8 = nullptr;
+
     /**
      * @brief Multiply quantized 8-bit integer matrix A with quantized 4-bit integer matrix B.
      *        A and B are block quantized and B is column major.
@@ -235,4 +327,14 @@ struct MLAS_SQNBIT_GEMM_DISPATCH {
     );
 
     QuantizeARow_CompInt8_Fn* QuantizeARow_CompInt8 = nullptr;
+
+    typedef void(QuantizeARowComputeBlkSum_CompInt8_Fn)(
+        size_t BlkLen,
+        const float* A,
+        size_t CountK,
+        std::byte* QuantA,
+        float* QuantAScale,
+        float* AScaledGroupSum  // scale_k * Sum_blklen(a_i)
+    );
+    QuantizeARowComputeBlkSum_CompInt8_Fn* QuantizeARowComputeBlkSum_CompInt8 = nullptr;
 };
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2.cpp b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2.cpp
index 0922f5ef646be..55d86bb9cc18e 100644
--- a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2.cpp
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2.cpp
@@ -22,6 +22,12 @@ Module Name:
 #include "sqnbitgemm.h"
 #include "sqnbitgemm_kernel_avx_common.h"
 #include "sqnbitgemm_kernel_avx_common_int8.h"
+#include "sqnbitgemm_kernel_avx2_int8_blklen16.h"
+#include "sqnbitgemm_kernel_avx2_int8_blklen32.h"
+#include "sqnbitgemm_kernel_avx2_int8_blklen64.h"
+
+#include "sqnbitgemm_m1_sym_kernel_avx2_int8_blklen32.h"
+#include "sqnbitgemm_m1_sym_kernel_avx2_int8_blklen64.h"
 
 MLAS_FORCEINLINE
 __m256
@@ -338,38 +344,92 @@ Q4BitBlkDequantBForSgemm_CompFp32_avx2(
     }
 }
 
+template<bool vnni>
+MLAS_FORCEINLINE
+void
+SQ4BitGemmKernel_CompInt8_avx2(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t CountK,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    if (BlkLen == 16) {
+        MlasQ4Int8GemmKernelBlkLen16Avx2(
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            CountM,
+            CountN,
+            CountK,
+            BlockCountK,
+            Bias,
+            ldc
+        );
+    } else if (BlkLen == 32) {
+        MlasQ4Int8GemmKernelBlkLen32Avx2<vnni>(
+              QuantA,
+              QuantAScale,
+              QuantBData,
+              QuantBScale,
+              C,
+              CountM,
+              CountN,
+              CountK,
+              BlockCountK,
+              Bias,
+              ldc
+        );
+    } else {
+        MlasQ4Int8GemmKernelBlkLen64Avx2<vnni>(
+            BlkLen,
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            CountM,
+            CountN,
+            BlockCountK,
+            Bias,
+            ldc
+        );
+    }
+}
+
+template<bool vnni>
 MLAS_FORCEINLINE
 void
 SQ4BitGemmM1Kernel_CompInt8_avx2(
     size_t BlkLen,
     const std::byte* QuantA,
+    const float* QuantAScale,
     const std::byte* QuantBData,
     const float* QuantBScale,
     const std::byte* QuantBZeroPoint,
     float* C,
     size_t CountN,
-    size_t CountK,
+    size_t /*CountK*/,
     size_t BlockStrideQuantB,
     const float* Bias
 )
 {
-    if (QuantBZeroPoint != nullptr) {
-        constexpr bool HasZeroPoint = true;
+    if (QuantBZeroPoint) {
         if (BlkLen == 16) {
-            SQ4BitGemmM1Kernel_BlkLen16_CompInt8_Impl<HasZeroPoint>(
-                QuantA,
-                QuantBData,
-                QuantBScale,
-                QuantBZeroPoint,
-                C,
-                CountN,
-                CountK,
-                BlockStrideQuantB,
-                Bias
-            );
         } else if (BlkLen == 32) {
-            SQ4BitGemmM1Kernel_BlkLen32_CompInt8_Impl<HasZeroPoint, accumulate_mul_sum_avx2<HasZeroPoint>>(
+            MlasQ4Int8GemmM1KernelBlkLen32Avx2<true, vnni>(
                 QuantA,
+                QuantAScale,
                 QuantBData,
                 QuantBScale,
                 QuantBZeroPoint,
@@ -379,36 +439,25 @@ SQ4BitGemmM1Kernel_CompInt8_avx2(
                 Bias
             );
         } else {
-            SQ4BitGemmM1Kernel_BlkLen64Plus_CompInt8_Impl<HasZeroPoint, dot_quad_avx2>(
+            MlasQ4Int8GemmKernelBlkLen64Avx2<true>(
                 BlkLen,
                 QuantA,
+                QuantAScale,
                 QuantBData,
                 QuantBScale,
                 QuantBZeroPoint,
                 C,
                 CountN,
-                CountK,
                 BlockStrideQuantB,
                 Bias
             );
         }
     } else {
-        constexpr bool HasZeroPoint = false;
         if (BlkLen == 16) {
-            SQ4BitGemmM1Kernel_BlkLen16_CompInt8_Impl<HasZeroPoint>(
-                QuantA,
-                QuantBData,
-                QuantBScale,
-                QuantBZeroPoint,
-                C,
-                CountN,
-                CountK,
-                BlockStrideQuantB,
-                Bias
-            );
         } else if (BlkLen == 32) {
-            SQ4BitGemmM1Kernel_BlkLen32_CompInt8_Impl<HasZeroPoint, accumulate_mul_sum_avx2<HasZeroPoint>>(
+            MlasQ4Int8GemmM1KernelBlkLen32Avx2<false, vnni>(
                 QuantA,
+                QuantAScale,
                 QuantBData,
                 QuantBScale,
                 QuantBZeroPoint,
@@ -418,15 +467,15 @@ SQ4BitGemmM1Kernel_CompInt8_avx2(
                 Bias
             );
         } else {
-            SQ4BitGemmM1Kernel_BlkLen64Plus_CompInt8_Impl<HasZeroPoint, dot_quad_avx2>(
+            MlasQ4Int8GemmKernelBlkLen64Avx2<false>(
                 BlkLen,
                 QuantA,
+                QuantAScale,
                 QuantBData,
                 QuantBScale,
                 QuantBZeroPoint,
                 C,
                 CountN,
-                CountK,
                 BlockStrideQuantB,
                 Bias
             );
@@ -434,10 +483,12 @@ SQ4BitGemmM1Kernel_CompInt8_avx2(
     }
 }
 
+MLAS_FORCEINLINE
 size_t
-SQ4BitGemmKernel_CompInt8_avx2(
-    size_t BlkLen,
+SQ4BitGemmKernel_BlkSum_CompInt8_avx2(
+    const size_t BlkLen,
     const std::byte* QuantA,
+    const float* QuantAScale,
     const std::byte* QuantBData,
     const float* QuantBScale,
     const std::byte* QuantBZeroPoint,
@@ -446,30 +497,101 @@ SQ4BitGemmKernel_CompInt8_avx2(
     size_t CountN,
     size_t CountK,
     size_t BlockCountK,
+    const float* Bias,
     size_t ldc,
-    const float* Bias
+    const float* ABlockSum,
+    const float* QuantBBlkSum
 )
 {
-    MLAS_UNREFERENCED_PARAMETER(ldc);
+    if (BlkLen >= 32 && CountM == 1) {
+        SQ4BitGemmM1Kernel_CompInt8_avx2<false>(BlkLen, QuantA, QuantAScale, QuantBData, QuantBScale, QuantBZeroPoint, C, CountN, CountK, BlockCountK, Bias);
+        return CountM;
+    }
+
+    SQ4BitGemmKernel_CompInt8_avx2<false>(
+        BlkLen,
+        QuantA,
+        QuantAScale,
+        QuantBData,
+        QuantBScale,
+        C,
+        CountM,
+        CountN,
+        CountK,
+        BlockCountK,
+        Bias,
+        ldc
+    );
+    float* c_blk = C;
+    const float* b_blk_sum = QuantBBlkSum;
+
+    size_t RowsRemaining = CountM;
+    const float* a_blksum_row = ABlockSum;
+    while (RowsRemaining > 0) {
+        auto RowsHandled = GetMlasPlatform().GemmFloatKernel(
+            a_blksum_row, b_blk_sum, c_blk, BlockCountK, RowsRemaining, CountN, BlockCountK, ldc, 1.f, false
+        );
 
-    if (CountM == 0) {
-        return 0;
+        c_blk += ldc * RowsHandled;
+        a_blksum_row += BlockCountK * RowsHandled;
+        RowsRemaining -= RowsHandled;
     }
+    return CountM;
+}
 
-    SQ4BitGemmM1Kernel_CompInt8_avx2(
+size_t
+SQ4BitGemmKernel_BlkSum_CompInt8_avx2vnni(
+  const size_t BlkLen,
+  const std::byte* QuantA,
+  const float* QuantAScale,
+  const std::byte* QuantBData,
+  const float* QuantBScale,
+  const std::byte* QuantBZeroPoint,
+  float* C,
+  size_t CountM,
+  size_t CountN,
+  size_t CountK,
+  size_t BlockCountK,
+  const float* Bias,
+  size_t ldc,
+  const float* ABlockSum,
+  const float* QuantBBlkSum
+)
+{
+    if (BlkLen >= 32 && CountM == 1) {
+        SQ4BitGemmM1Kernel_CompInt8_avx2<true>(BlkLen, QuantA, QuantAScale, QuantBData, QuantBScale, QuantBZeroPoint, C, CountN, CountK, BlockCountK, Bias);
+        return CountM;
+    }
+
+    SQ4BitGemmKernel_CompInt8_avx2<true>(
         BlkLen,
         QuantA,
+        QuantAScale,
         QuantBData,
         QuantBScale,
-        QuantBZeroPoint,
         C,
+        CountM,
         CountN,
         CountK,
         BlockCountK,
-        Bias
+        Bias,
+        ldc
     );
+    float* c_blk = C;
+    const float* b_blk_sum = QuantBBlkSum;
+
+    size_t RowsRemaining = CountM;
+    const float* a_blksum_row = ABlockSum;
+    while (RowsRemaining > 0) {
+        auto RowsHandled = GetMlasPlatform().GemmFloatKernel(
+            a_blksum_row, b_blk_sum, c_blk, BlockCountK, RowsRemaining, CountN, BlockCountK, ldc, 1.f, false
+        );
 
-    return 1;
+        c_blk += ldc * RowsHandled;
+        a_blksum_row += BlockCountK * RowsHandled;
+        RowsRemaining -= RowsHandled;
+    }
+    return CountM;
 }
 
 template <size_t NCols, bool HasZeroPoint>
@@ -1053,30 +1175,23 @@ SQ4BitGemmM1Kernel_CompFp32_avx2(
     }
 }
 
-MLAS_FORCEINLINE __m128i
-convert_2_ps_to_epi8(__m256 v0, __m256 v1)
-{
-    __m256i v0_8_epi32 = _mm256_cvtps_epi32(v0);
-    __m256i v1_8_epi32 = _mm256_cvtps_epi32(v1);
-
-    __m128i v0_8_epi16 = _mm_packs_epi32(_mm256_extractf128_si256(v0_8_epi32, 0), _mm256_extractf128_si256(v0_8_epi32, 1));
-    __m128i v1_8_epi16 = _mm_packs_epi32(_mm256_extractf128_si256(v1_8_epi32, 0), _mm256_extractf128_si256(v1_8_epi32, 1));
-
-    return _mm_packs_epi16(v0_8_epi16, v1_8_epi16);
-}
-
 void MLASCALL
 QuantizeARow_CompInt8_avx2(
     size_t BlkLen,
     const float* A,
     size_t CountK,
-    std::byte* QuantA
+    std::byte* QuantA,
+    float* QuantAScale,
+    float* AScaledBlkSum // scale_k * Sum_blklen(a_i)
 )
 {
     // port from MlasQ80BlkQuantRow
     assert(BlkLen % 16 == 0);
     const __m256 signBit = _mm256_set1_ps(-0.0f);
+    const __m256i one_16_epi16 = _mm256_srli_epi16(
+        _mm256_cmpeq_epi16(_mm256_castps_si256(signBit), _mm256_castps_si256(signBit)), 15);
     int8_t* blob = reinterpret_cast<int8_t*>(QuantA);
+    float* scale_ptr = QuantAScale;
     for (size_t k = 0; k < CountK; k += BlkLen) {
         const size_t step = std::min(BlkLen, CountK - k);
 
@@ -1097,13 +1212,14 @@ QuantizeARow_CompInt8_avx2(
 
         // Quantize these floats
         const float scale = maxScalar / 127.f;
-        *reinterpret_cast<float*>(blob) = scale;
-        blob += sizeof(float);
+        *scale_ptr = scale;
+        scale_ptr++;
 
         const float inverse_scale = (maxScalar != 0.0f) ? 127.f / maxScalar : 0.0f;
         const __m256 mul = _mm256_set1_ps(inverse_scale);
         __m128i* dst = reinterpret_cast<__m128i*>(blob);
 
+        __m256i sum_16_epi16 = _mm256_setzero_si256();
         for (size_t kk = 0; kk < step; kk += 16) {
             const int klen = std::min(16, (int)(step - kk));
 
@@ -1122,16 +1238,50 @@ QuantizeARow_CompInt8_avx2(
                 v1 = _mm256_round_ps(v1, _MM_ROUND_NEAREST);
             }
 
-            __m128i i_8 = convert_2_ps_to_epi8(v0, v1);
-            _mm_storeu_si128(dst++, i_8);
+            __m128i i_16_epi8 = convert_2_ps_to_epi8(v0, v1);
+            _mm_storeu_si128(dst++, i_16_epi8);
+
+            // accumulate Sum(a_i)
+            __m256i i_16_epi16 = _mm256_cvtepi8_epi16(i_16_epi8);
+            sum_16_epi16 = _mm256_hadds_epi16(sum_16_epi16, i_16_epi16);
         }
         if (step < BlkLen) {
             memset(blob + step, 0, BlkLen - step);
         }
+
+        const __m256i sum_8_epi32 = _mm256_madd_epi16(one_16_epi16, sum_16_epi16);
+        *AScaledBlkSum = scale * hsum_8_epi32(sum_8_epi32);
+        AScaledBlkSum++;
         blob += BlkLen;
     }
 }
 
+static void
+SQ4BitGemmPackQuantBDataAndBlkSum(
+    size_t N,
+    size_t K,
+    size_t BlkLen,
+    MLAS_SQNBIT_GEMM_COMPUTE_TYPE ComputeType,
+    const std::byte* QuantBDataBegin,
+    const float* QuantBScaleBegin,
+    bool has_zp_input,
+    const std::byte* QuantBZPBegin,
+    PackedQuantBDataStruct& packed_quant_b,
+    MLAS_THREADPOOL* ThreadPool
+)
+{
+    assert(BlkLen >= 16 && BlkLen % 16 == 0);
+
+    const size_t BlockCountK = MlasDivRoundup(K, BlkLen);
+
+    // TODO: always use SubBlkLen = 64 in CompInt8
+    size_t SubBlkLen = (BlkLen == 16) ? 16 : (BlkLen == 32 ? 32 : 64);
+    if (BlkLen == 32 && ComputeType == CompInt8) {
+        SubBlkLen = 64;
+    }
+    PackQuantBDataAndBlkSum(N, BlockCountK, BlkLen, SubBlkLen, QuantBDataBegin, QuantBScaleBegin, has_zp_input, QuantBZPBegin, packed_quant_b, ThreadPool);
+}
+
 //
 // Kernel dispatch structure definition.
 //
@@ -1140,6 +1290,26 @@ const MLAS_SQNBIT_GEMM_DISPATCH MlasSQNBitGemmDispatchAvx2 = []() {
 
     d.SQ4BitGemmPackQuantBDataSize = SQ4BitGemmPackQuantBDataSize;
     d.SQ4BitGemmPackQuantBData = SQ4BitGemmPackQuantBData;
+    d.SQ4BitGemmPackQuantBDataAndBlkSum = SQ4BitGemmPackQuantBDataAndBlkSum;
+
+    d.SQ4BitGemmPerGemmWorkspaceSize = SQ4BitGemmPerGemmWorkspaceSize;
+    d.SQ4BitGemmPerGemmWorkspaceAlignment = SQ4BitGemmPerGemmWorkspaceAlignment;
+
+    d.SQ4BitGemmM1Kernel_CompFp32 = SQ4BitGemmM1Kernel_CompFp32_avx2;
+    d.Q4BitBlkDequantBForSgemm_CompFp32 = Q4BitBlkDequantBForSgemm_CompFp32_avx2;
+
+    d.SQ4BitGemmKernel_BlkSum_CompInt8 = SQ4BitGemmKernel_BlkSum_CompInt8_avx2;
+    d.QuantizeARowComputeBlkSum_CompInt8 = QuantizeARow_CompInt8_avx2;
+
+    return d;
+}();
+
+const MLAS_SQNBIT_GEMM_DISPATCH MlasSQNBitGemmDispatchAvx2vnni = []() {
+    MLAS_SQNBIT_GEMM_DISPATCH d;
+
+    d.SQ4BitGemmPackQuantBDataSize = SQ4BitGemmPackQuantBDataSize;
+    d.SQ4BitGemmPackQuantBData = SQ4BitGemmPackQuantBData;
+    d.SQ4BitGemmPackQuantBDataAndBlkSum = SQ4BitGemmPackQuantBDataAndBlkSum;
 
     d.SQ4BitGemmPerGemmWorkspaceSize = SQ4BitGemmPerGemmWorkspaceSize;
     d.SQ4BitGemmPerGemmWorkspaceAlignment = SQ4BitGemmPerGemmWorkspaceAlignment;
@@ -1147,8 +1317,8 @@ const MLAS_SQNBIT_GEMM_DISPATCH MlasSQNBitGemmDispatchAvx2 = []() {
     d.SQ4BitGemmM1Kernel_CompFp32 = SQ4BitGemmM1Kernel_CompFp32_avx2;
     d.Q4BitBlkDequantBForSgemm_CompFp32 = Q4BitBlkDequantBForSgemm_CompFp32_avx2;
 
-    d.SQ4BitGemmKernel_CompInt8 = SQ4BitGemmKernel_CompInt8_avx2;
-    d.QuantizeARow_CompInt8 = QuantizeARow_CompInt8_avx2;
+    d.SQ4BitGemmKernel_BlkSum_CompInt8 = SQ4BitGemmKernel_BlkSum_CompInt8_avx2vnni;
+    d.QuantizeARowComputeBlkSum_CompInt8 = QuantizeARow_CompInt8_avx2;
 
     return d;
 }();
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2_int8_blklen16.h b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2_int8_blklen16.h
new file mode 100644
index 0000000000000..80d67806ea6e8
--- /dev/null
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2_int8_blklen16.h
@@ -0,0 +1,727 @@
+#pragma once
+#include <algorithm>
+#include <cassert>
+#include <utility>
+
+#include "sqnbitgemm.h"
+#include "sqnbitgemm_kernel_avx_common.h"
+
+
+MLAS_FORCEINLINE __m256
+load_and_broadcast_4_scale_2(const float* scale)
+{
+    // 3 2 1 0 3 2 1 0 (7)
+    __m256 scale_2_4_ps = _mm256_broadcast_ps((__m128 const*)scale);
+
+    // 2 1 0 0 2 1 0 0 (1)
+    __m256 scale_2_4_ps_shifted = _mm256_castsi256_ps(
+        _mm256_bslli_epi128(_mm256_castps_si256(scale_2_4_ps), 4)
+    );
+
+    // 3 2 1 0 2 1 0 0: (3) cross lane
+    __m256 scale_2_4_ps_permutted = _mm256_permute2f128_ps(
+        scale_2_4_ps_shifted, scale_2_4_ps, 0b00110000
+    );
+
+    // in accumulate_r1_4blk_dot and accumulate_r2_4blk_dot
+    // _mm256_hadd_epi16 inter leaved dot sum, resulting:
+    // a31b31|a30b30|a11b11|a10b10|a21b21|a20b20|a01b01|a00b00
+    // therefore we need weight to be:
+    // 3 3 1 1 2 2 0 0 (1)
+    return _mm256_permute_ps(scale_2_4_ps_permutted, 0b11110101);
+}
+
+MLAS_FORCEINLINE
+__m256i
+load_16_epi8_as_epi16(const std::byte* ablob)
+{
+    const __m128i av_epi8 = _mm_lddqu_si128(reinterpret_cast<const __m128i*>(ablob));
+    __m256i av_epi16 = _mm256_cvtepi8_epi16(av_epi8);
+    return av_epi16;
+}
+
+MLAS_FORCEINLINE void
+accumulate_r1_4blk_dot(
+  const __m256i& av0_32_epi8, const __m256i& av1_32_epi8,
+  const __m256i& bv0_32_epi8, const __m256i& bv1_32_epi8,
+  const float* scale_a, const float* scale_b,
+  __m256& acc)
+{
+    const __m256i dot0_16_epi16 = _mm256_maddubs_epi16(bv0_32_epi8, av0_32_epi8);
+    const __m256i dot1_16_epi16 = _mm256_maddubs_epi16(bv1_32_epi8, av1_32_epi8);
+    const __m256i sum_16_inter_leaved_epi16 = _mm256_hadd_epi16(dot0_16_epi16, dot1_16_epi16);
+
+    __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv0_32_epi8, bv0_32_epi8), 15);
+    const __m256i sum_8_inter_leaved_epi32 = _mm256_madd_epi16(one_16_epi16, sum_16_inter_leaved_epi16);
+    const __m256 sum_8_inter_leaved_ps = _mm256_cvtepi32_ps(sum_8_inter_leaved_epi32);
+
+    // load 4 scales
+    __m256 scale_a_4_ps = load_and_broadcast_4_scale_2(scale_a);
+    __m256 scale_b_4_ps = load_and_broadcast_4_scale_2(scale_b);
+    __m256 scale_8_ps = _mm256_mul_ps(scale_a_4_ps, scale_b_4_ps);
+    acc = _mm256_fmadd_ps(sum_8_inter_leaved_ps, scale_8_ps, acc);
+}
+
+MLAS_FORCEINLINE void
+accumulate_r2_4blk_dot(
+    const __m256i& av00_32_epi8, const __m256i& av01_32_epi8, const __m256i& av10_32_epi8, const __m256i& av11_32_epi8,
+    const __m256i& bv0_32_epi8, const __m256i& bv1_32_epi8,
+    const float* scale_a0, const float* scale_a1, const float* scale_b,
+    __m256& acc0, __m256& acc1
+)
+{
+    const __m256i dot0_16_epi16 = _mm256_maddubs_epi16(bv0_32_epi8, av00_32_epi8);
+    const __m256i dot1_16_epi16 = _mm256_maddubs_epi16(bv1_32_epi8, av01_32_epi8);
+    const __m256i sum_16_inter_leaved_epi16 = _mm256_hadd_epi16(dot0_16_epi16, dot1_16_epi16);
+
+    __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv0_32_epi8, bv0_32_epi8), 15);
+    const __m256i sum_8_inter_leaved_epi32 = _mm256_madd_epi16(one_16_epi16, sum_16_inter_leaved_epi16);
+    const __m256 sum_8_inter_leaved_ps = _mm256_cvtepi32_ps(sum_8_inter_leaved_epi32);
+
+    // load 4 scales
+    __m256 scale_a0_4_ps = load_and_broadcast_4_scale_2(scale_a0);
+    __m256 scale_b_4_ps = load_and_broadcast_4_scale_2(scale_b);
+    __m256 scale_8_ps = _mm256_mul_ps(scale_a0_4_ps, scale_b_4_ps);
+    acc0 = _mm256_fmadd_ps(sum_8_inter_leaved_ps, scale_8_ps, acc0);
+
+    const __m256i dot0_16_epi16_ = _mm256_maddubs_epi16(bv0_32_epi8, av10_32_epi8);
+    const __m256i dot1_16_epi16_ = _mm256_maddubs_epi16(bv1_32_epi8, av11_32_epi8);
+    const __m256i sum_16_inter_leaved_epi16_ = _mm256_hadd_epi16(dot0_16_epi16_, dot1_16_epi16_);
+    const __m256i sum_8_inter_leaved_epi32_ = _mm256_madd_epi16(one_16_epi16, sum_16_inter_leaved_epi16_);
+    const __m256 sum_inter_leaved_ps_ = _mm256_cvtepi32_ps(sum_8_inter_leaved_epi32_);
+
+    __m256 scale_a1_4_ps = load_and_broadcast_4_scale_2(scale_a1);
+    scale_8_ps = _mm256_mul_ps(scale_a1_4_ps, scale_b_4_ps);
+    acc1 = _mm256_fmadd_ps(sum_inter_leaved_ps_, scale_8_ps, acc1);
+}
+
+static MLAS_FORCEINLINE __m256i
+load_4b_packed_1blk_blklen16(const std::byte* QuantBDataPtr)
+{
+    // | 0 8 |...| 7 15 |
+    const __m128i bv_packed_64 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(QuantBDataPtr));
+    const __m128i low_mask = _mm_set1_epi8(0xF);
+    const __m128i lower_8_epu8 = _mm_and_si128(bv_packed_64, low_mask);                                         // 0~7
+    const __m128i upper_8_epu8 = _mm_bslli_si128(_mm_and_si128(_mm_srli_epi16(bv_packed_64, 4), low_mask), 8);  // 8~15
+    const __m256i bv_16_epu16 = _mm256_cvtepi8_epi16(_mm_add_epi8(upper_8_epu8, lower_8_epu8));                 // 0~15
+    return bv_16_epu16;
+}
+
+static MLAS_FORCEINLINE void
+load_4b_packed_4blk_blklen16(const std::byte* QuantBDataPtr, __m256i& bv0_32_epi8, __m256i& bv1_32_epi8)
+{
+    // | 0 8 |...| 7 15 | 16 24 |...| 23 31 ||| 32 40 |...| 39 47 | 48 56 |...| 55 63 |
+    const __m256i bv_packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(QuantBDataPtr));
+    const __m256i low_mask = _mm256_set1_epi8(0x0F);
+    // 0~7, 16~22, 32~39, 48~55
+    __m256i bv0_32_epi8_ = _mm256_and_si256(bv_packed, low_mask);
+    // 8~15, 24~31, 40~47, 56~63: (1)
+    __m256i bv1_32_epi8_ = _mm256_srli_epi16(_mm256_sub_epi8(bv_packed, bv0_32_epi8_), 4);
+    // 0~7, 32~39, 16~22, 48~55 <- cross lane (3)
+    bv0_32_epi8_ = _mm256_permute4x64_epi64(bv0_32_epi8_, 0b11011000);
+    // 40~47, 8~15, 56~63, 24~31 <- cross lane (3)
+    bv1_32_epi8_ = _mm256_permute4x64_epi64(bv1_32_epi8_, 0b01110010);
+
+    // 0~7, 8~15, 16~22, 24~31: (1)
+    bv0_32_epi8 = _mm256_blend_epi32(bv0_32_epi8_, bv1_32_epi8_, 0b11001100);
+
+    // 40~47, 32~39, 56~63, 48~55: (1)
+    bv1_32_epi8 = _mm256_blend_epi32(bv0_32_epi8_, bv1_32_epi8_, 0b00110011);
+
+    // 32~39, 40~47, 48~55, 56~63: (1)
+    bv1_32_epi8 = _mm256_shuffle_epi32(bv1_32_epi8, 0b01001110);
+}
+
+static MLAS_FORCEINLINE void
+accumulate_blklen16_r2c1blk4_avx2(
+    const __m256i& av00_32_epi8,
+    const __m256i& av01_32_epi8,
+    const __m256i& av10_32_epi8,
+    const __m256i& av11_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a0,
+    const float* scale_a1,
+    const float* scale_b,
+    __m256& acc0,
+    __m256& acc1
+)
+{
+    __m256i bv0_32_epi8, bv1_32_epi8;
+    load_4b_packed_4blk_blklen16(QuantBDataPtr, bv0_32_epi8, bv1_32_epi8);
+    accumulate_r2_4blk_dot(av00_32_epi8, av01_32_epi8, av10_32_epi8, av11_32_epi8, bv0_32_epi8, bv1_32_epi8,
+      scale_a0, scale_a1, scale_b, acc0, acc1);
+}
+
+static MLAS_FORCEINLINE void
+accumulate_blklen16_r1c1blk4_avx2(
+    const __m256i& av0_32_epi8,
+    const __m256i& av1_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a,
+    const float* scale_b,
+    __m256& acc
+)
+{
+    __m256i bv0_32_epi8, bv1_32_epi8;
+    load_4b_packed_4blk_blklen16(QuantBDataPtr, bv0_32_epi8, bv1_32_epi8);
+    accumulate_r1_4blk_dot(av0_32_epi8, av1_32_epi8, bv0_32_epi8, bv1_32_epi8, scale_a, scale_b, acc);
+}
+
+static MLAS_FORCEINLINE void
+accumulate_blklen16_r2c1blk1_avx2(
+    const __m256i& av0_32_epi8,
+    const __m256i& av1_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float& combined_scale0,
+    const float& combined_scale1,
+    __m256& acc0,
+    __m256& acc1
+)
+{
+    const __m256i bv_16_epu16 = load_4b_packed_1blk_blklen16(QuantBDataPtr);
+
+    __m256i prod_8_epi32 = _mm256_madd_epi16(bv_16_epu16, av0_32_epi8);
+    __m256 prod_8_ps = _mm256_cvtepi32_ps(prod_8_epi32);
+    acc0 = _mm256_fmadd_ps(_mm256_set1_ps(combined_scale0), prod_8_ps, acc0);
+
+    prod_8_epi32 = _mm256_madd_epi16(bv_16_epu16, av1_32_epi8);
+    prod_8_ps = _mm256_cvtepi32_ps(prod_8_epi32);
+    acc1 = _mm256_fmadd_ps(_mm256_set1_ps(combined_scale1), prod_8_ps, acc1);
+}
+
+static MLAS_FORCEINLINE void
+accumulate_blklen16_r1c1blk1_avx2(
+    const __m256i& av_16_epi8,
+    const std::byte* QuantBDataPtr,
+    const float& combined_scale,
+    __m256& acc
+)
+{
+    // | v0  v16 | v1  v17 | ... | v14 v30 | v15 v31 |
+    const __m256i bv_16_epu16 = load_4b_packed_1blk_blklen16(QuantBDataPtr);
+
+    __m256i prod_8_epi32 = _mm256_madd_epi16(bv_16_epu16, av_16_epi8);
+    __m256 prod_8_ps = _mm256_cvtepi32_ps(prod_8_epi32);
+    acc = _mm256_fmadd_ps(_mm256_set1_ps(combined_scale), prod_8_ps, acc);
+}
+
+MLAS_FORCEINLINE void
+Q4Int8GemmR2xC4BlkLen16Avx2(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen16 = 16;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes8 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk4 = 4;
+
+    const size_t lda = BlockCountK * BlkLen16;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM % NRows2 == 0);
+    assert(CountN % NCols4 == 0);
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m256 acc[NCols4 * NRows2] = {
+                _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(),
+                _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps()
+            };
+
+            // process 4 blks of 64 4b weights a time
+            size_t k_blks_remaining = BlockCountK;
+            for (; k_blks_remaining > 3; k_blks_remaining -= PerAccuBlk4) {
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantAPtr);
+                const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + 32));
+                const __m256i av_10_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + lda));
+                const __m256i av_11_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + lda + 32));
+
+                accumulate_blklen16_r2c1blk4_avx2(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr,
+                  QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc[0], acc[NCols4]);
+                accumulate_blklen16_r2c1blk4_avx2(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + StrideQuantBData,
+                  QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + StrideQuantBScale, acc[1], acc[NCols4 + 1]);
+                accumulate_blklen16_r2c1blk4_avx2(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + 2 * StrideQuantBData,
+                  QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 2 * StrideQuantBScale, acc[2], acc[NCols4 + 2]);
+                accumulate_blklen16_r2c1blk4_avx2(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + 3 * StrideQuantBData,
+                  QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 3 * StrideQuantBScale, acc[3], acc[NCols4 + 3]);
+
+                QuantAPtr += BlkLen16 * PerAccuBlk4;
+                QuantAScalePtr += PerAccuBlk4;
+                QuantBDataPtr += BlkDataSizeInBytes8 * PerAccuBlk4;
+                QuantBScalePtr += PerAccuBlk4;
+            }
+
+            while (k_blks_remaining-- > 0) {
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av0_16_epi16 = load_16_epi8_as_epi16(QuantABlk0);
+                const __m256i av1_16_epi16 = load_16_epi8_as_epi16(QuantABlk0 + lda);
+
+                const float& scale_a00 = *QuantAScalePtr;
+                const float& scale_a10 = *(QuantAScalePtr + BlockCountK);
+
+                {
+                    const float scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                    const float scale_10 = scale_a10 * (QuantBScalePtr)[0];
+                    accumulate_blklen16_r2c1blk1_avx2(av0_16_epi16, av1_16_epi16, QuantBDataPtr, scale_00, scale_10, acc[0], acc[NCols4]);
+                }
+
+                {
+                    const float scale_00 = scale_a00 * (QuantBScalePtr + StrideQuantBScale)[0];
+                    const float scale_10 = scale_a10 * (QuantBScalePtr + StrideQuantBScale)[0];
+                    accumulate_blklen16_r2c1blk1_avx2(av0_16_epi16, av1_16_epi16, QuantBDataPtr + StrideQuantBData, scale_00, scale_10, acc[1], acc[NCols4 + 1]);
+                }
+
+                {
+                    const float scale_00 = scale_a00 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                    const float scale_10 = scale_a10 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                    accumulate_blklen16_r2c1blk1_avx2(av0_16_epi16, av1_16_epi16, QuantBDataPtr + 2 * StrideQuantBData, scale_00, scale_10, acc[2], acc[NCols4 + 2]);
+                }
+
+                {
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                    const float& scale_10 = scale_a10 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                    accumulate_blklen16_r2c1blk1_avx2(av0_16_epi16, av1_16_epi16, QuantBDataPtr + 3 * StrideQuantBData, scale_00, scale_10, acc[3], acc[NCols4 + 3]);
+                }
+                QuantAPtr += BlkLen16;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes8;
+                QuantBScalePtr++;
+            }
+
+            __m128 acc_r0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+            __m128 acc_r1 = FoldAccumulators(acc[NCols4 + 0], acc[NCols4 + 1], acc[NCols4 + 2], acc[NCols4 + 3]);
+            if (BiasPtr != nullptr) {
+                const __m128 bias_4_ps = _mm_loadu_ps(BiasPtr);
+                acc_r0 = _mm_add_ps(acc_r0, bias_4_ps);
+                acc_r1 = _mm_add_ps(acc_r1, bias_4_ps);
+            }
+            _mm_storeu_ps(SumPtr, acc_r0);
+            _mm_storeu_ps(SumPtr + ldc, acc_r1);
+
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * StrideQuantBData;
+            QuantBScaleColPtr += NCols4 * StrideQuantBScale;
+
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+void MLAS_FORCEINLINE Q4Int8GemmR2xC1BlkLen16Avx2(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc)
+{
+    constexpr size_t BlkLen16 = 16;
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes8 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+
+    // process 4 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk4 = 4;
+
+    const size_t lda = BlockCountK * BlkLen16;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+    assert(CountM % NRows2 == 0);
+    assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        float* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m256 acc0 = _mm256_setzero_ps(), acc1 = _mm256_setzero_ps();
+
+            // process 4 blks of 64 4b weights a time
+            size_t k_blks_remaining = BlockCountK;
+            for (; k_blks_remaining >= PerAccuBlk4; k_blks_remaining -= PerAccuBlk4) {
+                const std::byte* QuantABlk00 = QuantAPtr;
+                const std::byte* QuantABlk01 = QuantABlk00 + 32;
+                const std::byte* QuantABlk10 = QuantAPtr + lda;
+                const std::byte* QuantABlk11 = QuantABlk10 + 32;
+
+                // load A:
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk00);
+                const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk01);
+                const __m256i av_10_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk10);
+                const __m256i av_11_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk11);
+
+                accumulate_blklen16_r2c1blk4_avx2(
+                    av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr,
+                    QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc0, acc1);
+
+                // increment block pointers
+                QuantAPtr += BlkLen16 * PerAccuBlk4;
+                QuantAScalePtr += PerAccuBlk4;
+                QuantBDataPtr += BlkDataSizeInBytes8 * PerAccuBlk4;
+                QuantBScalePtr += PerAccuBlk4;
+            }
+
+            while (k_blks_remaining-- > 0) {
+                // load A
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av0_16_epi16 = load_16_epi8_as_epi16(QuantABlk0);
+                const __m256i av1_16_epi16 = load_16_epi8_as_epi16(QuantABlk0 + lda);
+
+                const float& scale_a00 = *QuantAScalePtr;
+                const float& scale_a10 = *(QuantAScalePtr + BlockCountK);
+
+                const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                const float& scale_10 = scale_a10 * (QuantBScalePtr)[0];
+                accumulate_blklen16_r2c1blk1_avx2(av0_16_epi16, av1_16_epi16, QuantBDataPtr, scale_00, scale_10, acc0, acc1);
+
+                QuantAPtr += BlkLen16;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes8;
+                QuantBScalePtr++;
+            }
+
+            *SumPtr = hsum_float_8(acc0);
+            *(SumPtr + ldc) = hsum_float_8(acc1);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+                *(SumPtr + ldc) += *BiasPtr;
+            }
+
+            // move to next column
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+MLAS_FORCEINLINE void
+Q4Int8GemmR1xC4BlkLen16Avx2(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen16 = 16;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes8 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk4 = 4;
+
+    const size_t lda = BlockCountK * BlkLen16;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM < NRows2);
+    assert(CountN % NCols4 == 0);
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m256 acc[NCols4] = {_mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps()};
+
+            size_t k_blks_remaining = BlockCountK;
+            for (; k_blks_remaining >= PerAccuBlk4; k_blks_remaining -= PerAccuBlk4) {
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantAPtr);
+                const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + 32));
+
+                accumulate_blklen16_r1c1blk4_avx2(av_00_epi8, av_01_epi8, QuantBDataPtr,
+                    QuantAScalePtr, QuantBScalePtr, acc[0]);
+                accumulate_blklen16_r1c1blk4_avx2(av_00_epi8, av_01_epi8, QuantBDataPtr + StrideQuantBData,
+                    QuantAScalePtr, QuantBScalePtr + StrideQuantBScale, acc[1]);
+                accumulate_blklen16_r1c1blk4_avx2(av_00_epi8, av_01_epi8, QuantBDataPtr + 2 * StrideQuantBData,
+                    QuantAScalePtr, QuantBScalePtr + 2 * StrideQuantBScale, acc[2]);
+                accumulate_blklen16_r1c1blk4_avx2(av_00_epi8, av_01_epi8, QuantBDataPtr + 3 * StrideQuantBData,
+                    QuantAScalePtr, QuantBScalePtr + 3 * StrideQuantBScale, acc[3]);
+                // increment block pointers
+                QuantAPtr += BlkLen16 * PerAccuBlk4;
+                QuantAScalePtr += PerAccuBlk4;
+                QuantBDataPtr += BlkDataSizeInBytes8 * PerAccuBlk4;
+                QuantBScalePtr += PerAccuBlk4;
+            }
+
+            while (k_blks_remaining-- > 0) {
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av_00_epi8 = load_16_epi8_as_epi16(QuantABlk0);
+
+                const float& scale_a00 = *QuantAScalePtr;
+                {
+                  // Col0
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                  accumulate_blklen16_r1c1blk1_avx2(av_00_epi8, QuantBDataPtr, scale_00, acc[0]);
+                }
+                {
+                    // Col1
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + StrideQuantBScale)[0];
+                    accumulate_blklen16_r1c1blk1_avx2(av_00_epi8, QuantBDataPtr + StrideQuantBData, scale_00, acc[1]);
+                }
+                {
+                    // Col2
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                    accumulate_blklen16_r1c1blk1_avx2(av_00_epi8, QuantBDataPtr + 2 * StrideQuantBData, scale_00, acc[2]);
+                }
+                {
+                    // Col3
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                    accumulate_blklen16_r1c1blk1_avx2(av_00_epi8, QuantBDataPtr + 3 * StrideQuantBData, scale_00, acc[3]);
+                }
+                QuantAPtr += BlkLen16;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes8;
+                QuantBScalePtr++;
+            }
+
+            __m128 acc_r0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+            if (BiasPtr != nullptr) {
+                acc_r0 = _mm_add_ps(acc_r0, _mm_loadu_ps(BiasPtr));
+            }
+
+            _mm_storeu_ps(SumPtr, acc_r0);
+
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * StrideQuantBData;
+            QuantBScaleColPtr += NCols4 * StrideQuantBScale;
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+MLAS_FORCEINLINE void
+Q4Int8GemmR1xC1BlkLen16Avx2(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen16 = 16;
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes8 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+
+    // process 4 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk4 = 4;
+
+    const size_t lda = BlockCountK * BlkLen16;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+    assert(CountM < NRows2);
+    assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m256 acc0 = _mm256_setzero_ps();
+            size_t k_blks_remaining = BlockCountK;
+            for (; k_blks_remaining >= PerAccuBlk4; k_blks_remaining -= PerAccuBlk4) {
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantAPtr);
+                const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + 32));
+
+                accumulate_blklen16_r1c1blk4_avx2(
+                    av_00_epi8, av_01_epi8, QuantBDataPtr,
+                    QuantAScalePtr, QuantBScalePtr, acc0);
+
+                // increment block pointers
+                QuantAPtr += BlkLen16 * PerAccuBlk4;
+                QuantAScalePtr += PerAccuBlk4;
+                QuantBDataPtr += BlkDataSizeInBytes8 * PerAccuBlk4;
+                QuantBScalePtr += PerAccuBlk4;
+            }
+
+            while (k_blks_remaining-- > 0) {
+                const __m256i av_16_epi16 = load_16_epi8_as_epi16(QuantAPtr);
+                const float& scale_a00 = *QuantAScalePtr;
+                const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                accumulate_blklen16_r1c1blk1_avx2(av_16_epi16, QuantBDataPtr, scale_00, acc0);
+
+                QuantAPtr += BlkLen16;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes8;
+                QuantBScalePtr++;
+            }
+
+             *SumPtr = hsum_float_8(acc0);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+            }
+
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+MLAS_FORCEINLINE
+    size_t
+    MlasQ4Int8GemmKernelBlkLen16Avx2(
+        const std::byte* QuantA,
+        const float* QuantAScale,
+        const std::byte* QuantBData,
+        const float* QuantBScale,
+        float* C,
+        size_t CountM,
+        size_t CountN,
+        size_t /*CountK*/,
+        size_t BlockCountK,
+        const float* Bias,
+        size_t ldc
+    )
+{
+    constexpr size_t BlkLen16 = 16;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+
+    const size_t lda = BlockCountK * BlkLen16 * sizeof(int8_t);
+    const size_t lda_scale = BlockCountK;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+
+    size_t remainingRows = CountM % NRows2;
+    size_t multipleRows = CountM - remainingRows;
+    size_t remainingCols = CountN % NCols4;
+    size_t multipleCols = CountN - remainingCols;
+
+    if (multipleRows > 0 && multipleCols > 0) {
+        Q4Int8GemmR2xC4BlkLen16Avx2(
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            multipleRows,
+            multipleCols,
+            BlockCountK,
+            Bias,
+            ldc
+        );
+    }
+    if (remainingCols > 0 && multipleRows > 0) {
+        Q4Int8GemmR2xC1BlkLen16Avx2(
+            QuantA,
+            QuantAScale,
+            QuantBData + multipleCols * StrideQuantBData,
+            QuantBScale + multipleCols * StrideQuantBScale,
+            C + multipleCols,
+            multipleRows,
+            remainingCols,
+            BlockCountK,
+            Bias ? Bias + multipleCols : nullptr,
+            ldc);
+    }
+
+    if (remainingRows > 0 && multipleCols > 0) {
+        Q4Int8GemmR1xC4BlkLen16Avx2(
+            QuantA + multipleRows * lda,
+            QuantAScale + multipleRows * lda_scale,
+            QuantBData,
+            QuantBScale,
+            C + multipleRows * ldc,
+            remainingRows,
+            multipleCols,
+            BlockCountK,
+            Bias,
+            ldc);
+    }
+
+    if (remainingCols > 0 && remainingRows > 0) {
+        Q4Int8GemmR1xC1BlkLen16Avx2(
+            QuantA + multipleRows * lda,
+            QuantAScale + multipleRows * lda_scale,
+            QuantBData + multipleCols * StrideQuantBData,
+            QuantBScale + multipleCols * StrideQuantBScale,
+            C + multipleRows * ldc + multipleCols,
+            remainingRows,
+            remainingCols,
+            BlockCountK,
+            Bias ? Bias + multipleCols : nullptr,
+            ldc);
+    }
+
+    return CountM;
+}
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2_int8_blklen32.h b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2_int8_blklen32.h
new file mode 100644
index 0000000000000..af6f52090adcb
--- /dev/null
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2_int8_blklen32.h
@@ -0,0 +1,1049 @@
+#pragma once
+#include <algorithm>
+#include <cassert>
+#include <utility>
+
+#include "sqnbitgemm.h"
+#include "sqnbitgemm_kernel_avx_common.h"
+
+
+MLAS_FORCEINLINE void
+accumulate_1blk_dot(const __m256i& av_32_epi8, const __m256i& bv_32_epi8,
+  const float& combined_scale, const __m256i& one_16_epi16, __m256& acc)
+{
+    const __m256i dot_16_epi16 = _mm256_maddubs_epi16(
+        bv_32_epi8, av_32_epi8
+    );
+    const __m256i sum_8_epi32 = _mm256_madd_epi16(one_16_epi16, dot_16_epi16);
+    const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+    acc = _mm256_fmadd_ps(sum_ps, _mm256_set1_ps(combined_scale), acc);
+}
+
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+MLAS_FORCEINLINE void
+accumulate_1blk_dot_vnni(const __m256i& av_32_epi8, const __m256i& bv_32_epi8, const float& combined_scale, __m256& acc)
+{
+    __m256i sum_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), bv_32_epi8, av_32_epi8);
+    const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+    acc = _mm256_fmadd_ps(sum_ps, _mm256_set1_ps(combined_scale), acc);
+}
+#endif
+
+template <bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r2c1blk2_avx2(
+    const __m256i& av00_32_epi8,
+    const __m256i& av01_32_epi8,
+    const __m256i& av10_32_epi8,
+    const __m256i& av11_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a0,
+    const float* scale_a1,
+    const float* scale_b,
+    __m256& acc0,
+    __m256& acc1
+)
+{
+    // | v0  v32 | v1  v33 | ... | v30 v62 | v31 v63 |
+    const __m256i bv_packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(QuantBDataPtr));
+
+    // generating low_mask of 0x0Fs is not as fast as just calling _mm256_set1_epi8(0x0F).
+    const __m256i low_mask = _mm256_set1_epi8(0x0F);
+    //__m256i low_mask = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv_packed, bv_packed), 12);
+    // low_mask = _mm256_packus_epi16(low_mask, low_mask);
+    __m256i bv0_32_epi8 = _mm256_and_si256(bv_packed, low_mask);  // 0~31
+    // TODO: this (the second line below) is faster and does not keep low_mask in use.
+    // const __m256i bv1_32_epi8 = _mm256_and_si256(_mm256_srli_epi16(bv_packed, 4), low_mask);
+    __m256i bv1_32_epi8 = _mm256_srli_epi16(_mm256_sub_epi8(bv_packed, bv0_32_epi8), 4);  // 32~63
+
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    if constexpr (vnni) {
+        __m256 scale_b_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_b));
+        {
+            const __m256i dot0_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), bv0_32_epi8, av00_32_epi8);
+            const __m256i dot1_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), bv1_32_epi8, av01_32_epi8);
+            const __m256i sum_8_epi32 = _mm256_hadd_epi32(dot0_8_epi32, dot1_8_epi32);
+            const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+            __m256 scale_a0_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_a0));
+            // 1 0 1 0 1 0 1 0 -> 1 1 0 0 1 1 0 0
+            __m256 scale_8_ps = _mm256_permute_ps(_mm256_mul_ps(scale_a0_2_ps, scale_b_2_ps), _MM_SHUFFLE(1, 1, 0, 0));
+            acc0 = _mm256_fmadd_ps(sum_ps, scale_8_ps, acc0);
+        }
+        {
+            const __m256i dot0_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), bv0_32_epi8, av10_32_epi8);
+            const __m256i dot1_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), bv1_32_epi8, av11_32_epi8);
+            const __m256i sum_8_epi32 = _mm256_hadd_epi32(dot0_8_epi32, dot1_8_epi32);
+            const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+            __m256 scale_a1_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_a1));
+            __m256 scale_8_ps = _mm256_permute_ps(_mm256_mul_ps(scale_a1_2_ps, scale_b_2_ps), _MM_SHUFFLE(1, 1, 0, 0));
+            acc1 = _mm256_fmadd_ps(sum_ps, scale_8_ps, acc1);
+        }
+    } else {
+#endif
+        //{
+            const __m256i dot0_16_epi16 = _mm256_maddubs_epi16(bv0_32_epi8, av00_32_epi8);
+            const __m256i dot1_16_epi16 = _mm256_maddubs_epi16(bv1_32_epi8, av01_32_epi8);
+            const __m256i sum_16_epi16 = _mm256_hadd_epi16(dot0_16_epi16, dot1_16_epi16);
+
+            // generating constant 1s is faster here.
+            // __m256i one = _mm256_set1_epi16(1);
+            __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv0_32_epi8, bv0_32_epi8), 15);
+            const __m256i sum_8_epi32 = _mm256_madd_epi16(one_16_epi16, sum_16_epi16);
+            const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+            __m256 scale_a0_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_a0));
+            __m256 scale_b_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_b));
+            // 1 0 1 0 1 0 1 0 -> 1 1 0 0 1 1 0 0
+            __m256 scale_8_ps = _mm256_permute_ps(_mm256_mul_ps(scale_a0_2_ps, scale_b_2_ps), _MM_SHUFFLE(1, 1, 0, 0));
+            acc0 = _mm256_fmadd_ps(sum_ps, scale_8_ps, acc0);
+        //}
+        //{
+            const __m256i dot0_16_epi16_ = _mm256_maddubs_epi16(bv0_32_epi8, av10_32_epi8);
+            const __m256i dot1_16_epi16_ = _mm256_maddubs_epi16(bv1_32_epi8, av11_32_epi8);
+            const __m256i sum_16_epi16_ = _mm256_hadd_epi16(dot0_16_epi16_, dot1_16_epi16_);
+            const __m256i sum_8_epi32_ = _mm256_madd_epi16(one_16_epi16, sum_16_epi16_);
+            const __m256 sum_ps_ = _mm256_cvtepi32_ps(sum_8_epi32_);
+
+            __m256 scale_a1_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_a1));
+            __m256 scale_8_ps_ = _mm256_permute_ps(_mm256_mul_ps(scale_a1_2_ps, scale_b_2_ps), _MM_SHUFFLE(1, 1, 0, 0));
+            acc1 = _mm256_fmadd_ps(sum_ps_, scale_8_ps_, acc1);
+        //}
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    }
+#endif
+}
+
+template <bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r1c1blk2_avx2(
+    const __m256i& av00_32_epi8,
+    const __m256i& av01_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a0,
+    const float* scale_b,
+    __m256& acc0
+)
+{
+    // | v0  v32 | v1  v33 | ... | v30 v62 | v31 v63 |
+    const __m256i bv_packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(QuantBDataPtr));
+    const __m256i low_mask = _mm256_set1_epi8(0x0F);
+    __m256i bv0_32_epi8 = _mm256_and_si256(bv_packed, low_mask);  // 0~31
+    __m256i bv1_32_epi8 = _mm256_srli_epi16(_mm256_sub_epi8(bv_packed, bv0_32_epi8), 4);  // 32~63
+
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    if constexpr (vnni) {
+        const __m256i dot0_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), bv0_32_epi8, av00_32_epi8);
+        const __m256i dot1_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), bv1_32_epi8, av01_32_epi8);
+        const __m256i sum_8_epi32 = _mm256_hadd_epi32(dot0_8_epi32, dot1_8_epi32);  // 00110011
+
+        const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+        __m256 scale_a0_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_a0));
+        __m256 scale_b_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_b));
+        // 1 0 1 0 1 0 1 0 -> 1 1 0 0 1 1 0 0
+        __m256 scale_8_ps = _mm256_permute_ps(_mm256_mul_ps(scale_a0_2_ps, scale_b_2_ps), _MM_SHUFFLE(1, 1, 0, 0));
+        acc0 = _mm256_fmadd_ps(sum_ps, scale_8_ps, acc0);
+    } else {
+#endif
+        const __m256i dot0_16_epi16 = _mm256_maddubs_epi16(bv0_32_epi8, av00_32_epi8);
+        const __m256i dot1_16_epi16 = _mm256_maddubs_epi16(bv1_32_epi8, av01_32_epi8);
+        const __m256i sum_16_epi16 = _mm256_hadd_epi16(dot0_16_epi16, dot1_16_epi16);
+
+        __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv0_32_epi8, bv0_32_epi8), 15);
+        const __m256i sum_8_epi32 = _mm256_madd_epi16(one_16_epi16, sum_16_epi16);
+        const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+        __m256 scale_a0_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_a0));
+        __m256 scale_b_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_b));
+        // 1 0 1 0 1 0 1 0 -> 1 1 0 0 1 1 0 0
+        __m256 scale_8_ps = _mm256_permute_ps(_mm256_mul_ps(scale_a0_2_ps, scale_b_2_ps), _MM_SHUFFLE(1, 1, 0, 0));
+        acc0 = _mm256_fmadd_ps(sum_ps, scale_8_ps, acc0);
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    }
+#endif
+}
+
+template<bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r2c1blk1_avx2(
+    const __m256i& av00_32_epi8,
+    const __m256i& av10_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float& combined_scale00,
+    const float& combined_scale10,
+    __m256& acc0,
+    __m256& acc1
+)
+{
+    // | v0  v16 | v1  v17 | ... | v14 v30 | v15 v31 |
+    const __m128i bv_packed0 = _mm_loadu_si128(reinterpret_cast<const __m128i*>(QuantBDataPtr));
+    __m256i bv_32_epi8 = _mm256_set_m128i(_mm_srli_epi16(bv_packed0, 4), bv_packed0);
+    bv_32_epi8 = _mm256_and_si256(_mm256_set1_epi8(0x0F), bv_32_epi8);
+
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    if constexpr (vnni) {
+        accumulate_1blk_dot_vnni(av00_32_epi8, bv_32_epi8, combined_scale00, acc0);
+        accumulate_1blk_dot_vnni(av10_32_epi8, bv_32_epi8, combined_scale10, acc1);
+    } else {
+#endif
+        __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv_32_epi8, bv_32_epi8), 15);
+        accumulate_1blk_dot(av00_32_epi8, bv_32_epi8, combined_scale00, one_16_epi16, acc0);
+        accumulate_1blk_dot(av10_32_epi8, bv_32_epi8, combined_scale10, one_16_epi16, acc1);
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    }
+#endif
+}
+
+template <bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r1c1blk1_avx2(
+    const __m256i& av00_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float& combined_scale00,
+    __m256& acc0
+)
+{
+    // | v0  v16 | v1  v17 | ... | v14 v30 | v15 v31 |
+    const __m128i bv_packed0 = _mm_loadu_si128(reinterpret_cast<const __m128i*>(QuantBDataPtr));
+    __m256i bv_32_epi8 = _mm256_set_m128i(_mm_srli_epi16(bv_packed0, 4), bv_packed0);
+    bv_32_epi8 = _mm256_and_si256(_mm256_set1_epi8(0x0F), bv_32_epi8);
+
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    if constexpr (vnni) {
+        accumulate_1blk_dot_vnni(av00_32_epi8, bv_32_epi8, combined_scale00, acc0);
+    } else {
+#endif
+        __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv_32_epi8, bv_32_epi8), 15);
+        accumulate_1blk_dot(av00_32_epi8, bv_32_epi8, combined_scale00, one_16_epi16, acc0);
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    }
+#endif
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8Gemm2x4x2BlkLen32Avx2(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    const size_t lda = BlockCountK * BlkLen32;
+    //const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    //const size_t StrideQuantBScale = BlockCountK;
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+    assert(CountM % NRows2 == 0);
+    assert(CountN % NCols4 == 0);
+    const size_t StrideQuantBDataCol = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBData2 = 2 * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBData1 = 1 * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale2 = 2;
+    const size_t StrideQuantBScale1 = 1;
+
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m256 acc[NCols4 * NRows2] = {
+                _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(),
+                _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps()
+            };
+
+            size_t k_blks_remaining = BlockCountK;
+            // process 2 blks of 64 4b weights a time
+            for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+                // load A:
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr));
+                const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + BlkLen32));
+                const __m256i av_10_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + lda));
+                const __m256i av_11_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + lda + BlkLen32));
+
+                {
+                    accumulate_blklen32_r2c1blk2_avx2<vnni>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc[0], acc[NCols4]);
+                }
+                {
+                    accumulate_blklen32_r2c1blk2_avx2<vnni>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + StrideQuantBData2, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + StrideQuantBScale2, acc[1], acc[NCols4 + 1]);
+                }
+
+                {
+                    accumulate_blklen32_r2c1blk2_avx2<vnni>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + 2 * StrideQuantBData2, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 2 * StrideQuantBScale2, acc[2], acc[NCols4 + 2]);
+                }
+
+                {
+                    accumulate_blklen32_r2c1blk2_avx2<vnni>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + 3 * StrideQuantBData2, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 3 * StrideQuantBScale2, acc[3], acc[NCols4 + 3]);
+                }
+
+                // increment block pointers
+                QuantAPtr += BlkLen32 * PerAccuBlk2;
+                QuantAScalePtr += PerAccuBlk2;
+                QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk2 * NCols4;
+                QuantBScalePtr += PerAccuBlk2 * NCols4;
+            }  // k_blks_remaining
+
+            // TODO: use a loop in case PerAccuBlk2 is not 2.
+            if (k_blks_remaining > 0) {
+                // load A
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr));
+                const __m256i av_10_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + lda));
+
+                const float& scale_a00 = *QuantAScalePtr;
+                const float& scale_a10 = *(QuantAScalePtr + BlockCountK);
+
+                {
+                    // Col0
+                    const float scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                    const float scale_10 = scale_a10 * (QuantBScalePtr)[0];
+                    accumulate_blklen32_r2c1blk1_avx2<vnni>(av_00_epi8, av_10_epi8, QuantBDataPtr, scale_00, scale_10, acc[0], acc[NCols4]);
+                }
+
+                {
+                    // Col1
+                    const float scale_00 = scale_a00 * (QuantBScalePtr + StrideQuantBScale1)[0];
+                    const float scale_10 = scale_a10 * (QuantBScalePtr + StrideQuantBScale1)[0];
+                    accumulate_blklen32_r2c1blk1_avx2<vnni>(av_00_epi8, av_10_epi8, QuantBDataPtr + StrideQuantBData1, scale_00, scale_10, acc[1], acc[NCols4 + 1]);
+                }
+
+                {
+                    // Col2
+                    const float scale_00 = scale_a00 * (QuantBScalePtr + 2 * StrideQuantBScale1)[0];
+                    const float scale_10 = scale_a10 * (QuantBScalePtr + 2 * StrideQuantBScale1)[0];
+                    accumulate_blklen32_r2c1blk1_avx2<vnni>(av_00_epi8, av_10_epi8, QuantBDataPtr + 2 * StrideQuantBData1, scale_00, scale_10, acc[2], acc[NCols4 + 2]);
+                }
+
+                {
+                    // Col3
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 3 * StrideQuantBScale1)[0];
+                    const float& scale_10 = scale_a10 * (QuantBScalePtr + 3 * StrideQuantBScale1)[0];
+                    accumulate_blklen32_r2c1blk1_avx2<vnni>(av_00_epi8, av_10_epi8, QuantBDataPtr + 3 * StrideQuantBData1, scale_00, scale_10, acc[3], acc[NCols4 + 3]);
+                }
+            }  // k_blks_remaining
+
+            __m128 acc_r0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+            __m128 acc_r1 = FoldAccumulators(acc[NCols4 + 0], acc[NCols4 + 1], acc[NCols4 + 2], acc[NCols4 + 3]);
+            if (BiasPtr != nullptr) {
+                const __m128 bias_4_ps = _mm_loadu_ps(BiasPtr);
+                acc_r0 = _mm_add_ps(acc_r0, bias_4_ps);
+                acc_r1 = _mm_add_ps(acc_r1, bias_4_ps);
+            }
+
+            _mm_storeu_ps(SumPtr, acc_r0);
+            _mm_storeu_ps(SumPtr + ldc, acc_r1);
+
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * StrideQuantBDataCol;
+            QuantBScaleColPtr += NCols4 * BlockCountK;
+
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+template <bool vnni>
+void MLAS_FORCEINLINE Q4Int8Gemm2xXBlkLen32Avx2(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc)
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    const size_t lda = BlockCountK * BlkLen32;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    //const size_t StrideQuantBScale = BlockCountK;
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+    assert(CountM % NRows2 == 0);
+    assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        float* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m256 acc0 = _mm256_setzero_ps(), acc1 = _mm256_setzero_ps();
+
+            size_t k_blks_remaining = BlockCountK;
+            // process 2 blks of 64 4b weights a time
+            for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr));
+                const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + BlkLen32));
+                const __m256i av_10_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + lda));
+                const __m256i av_11_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + lda + BlkLen32));
+
+                accumulate_blklen32_r2c1blk2_avx2<vnni>(
+                    av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr,
+                    QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc0, acc1);
+
+                // increment block pointers
+                QuantAPtr += BlkLen32 * PerAccuBlk2;
+                QuantAScalePtr += PerAccuBlk2;
+                QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk2;
+                QuantBScalePtr += PerAccuBlk2;
+            }
+
+            if (k_blks_remaining > 0) {
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantAPtr);
+                const __m256i av_10_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + lda));
+
+                const float& scale_a00 = *QuantAScalePtr;
+                const float& scale_a10 = *(QuantAScalePtr + BlockCountK);
+
+                const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                const float& scale_10 = scale_a10 * (QuantBScalePtr)[0];
+                accumulate_blklen32_r2c1blk1_avx2<vnni>(av_00_epi8, av_10_epi8, QuantBDataPtr, scale_00, scale_10, acc0, acc1);
+            }
+
+            *SumPtr = hsum_float_8(acc0);
+            *(SumPtr + ldc) = hsum_float_8(acc1);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+                *(SumPtr + ldc) += *BiasPtr;
+            }
+
+            // move to next column
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += BlockCountK;
+
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmXx4BlkLen32Avx2(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    const size_t lda = BlockCountK * BlkLen32;
+    //const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    //const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM < NRows2);
+    assert(CountN % NCols4 == 0);
+    const size_t StrideQuantBDataCol = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBData2 = 2 * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBData1 = 1 * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale2 = 2;
+    const size_t StrideQuantBScale1 = 1;
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m256 acc[NCols4] = {_mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps()};
+            size_t k_blks_remaining = BlockCountK;
+            for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr));
+                const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + BlkLen32));
+
+                {
+                    accumulate_blklen32_r1c1blk2_avx2<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr,
+                      QuantAScalePtr, QuantBScalePtr, acc[0]);
+                }
+                {
+                    accumulate_blklen32_r1c1blk2_avx2<vnni>(
+                        av_00_epi8, av_01_epi8, QuantBDataPtr + StrideQuantBData2,
+                        QuantAScalePtr, QuantBScalePtr + StrideQuantBScale2, acc[1]
+                    );
+                }
+                {
+                    accumulate_blklen32_r1c1blk2_avx2<vnni>(
+                        av_00_epi8, av_01_epi8, QuantBDataPtr + 2 * StrideQuantBData2,
+                        QuantAScalePtr, QuantBScalePtr + 2 * StrideQuantBScale2, acc[2]
+                    );
+                }
+                {
+                    accumulate_blklen32_r1c1blk2_avx2<vnni>(
+                        av_00_epi8, av_01_epi8, QuantBDataPtr + 3 * StrideQuantBData2,
+                        QuantAScalePtr, QuantBScalePtr + 3 * StrideQuantBScale2, acc[3]
+                    );
+                }
+                // increment block pointers
+                QuantAPtr += BlkLen32 * PerAccuBlk2;
+                QuantAScalePtr += PerAccuBlk2;
+                QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk2 * NCols4;
+                QuantBScalePtr += PerAccuBlk2 * NCols4;
+            }
+
+            // TODO: use a loop in case PerAccuBlk2 is not 2.
+            if (k_blks_remaining > 0) {
+                // load A
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk0);
+
+                const float& scale_a00 = *QuantAScalePtr;
+                {
+                    // Col0
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                    accumulate_blklen32_r1c1blk1_avx2<vnni>(av_00_epi8, QuantBDataPtr, scale_00, acc[0]);
+                }
+                {
+                    // Col1
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + StrideQuantBScale1)[0];
+                    accumulate_blklen32_r1c1blk1_avx2<vnni>(av_00_epi8, QuantBDataPtr + StrideQuantBData1, scale_00, acc[1]);
+                }
+                {
+                    // Col2
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 2 * StrideQuantBScale1)[0];
+                    accumulate_blklen32_r1c1blk1_avx2<vnni>(av_00_epi8, QuantBDataPtr + 2 * StrideQuantBData1, scale_00, acc[2]);
+                }
+                {
+                    // Col3
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 3 * StrideQuantBScale1)[0];
+                    accumulate_blklen32_r1c1blk1_avx2<vnni>(av_00_epi8, QuantBDataPtr + 3 * StrideQuantBData1, scale_00, acc[3]);
+                }
+            }
+
+            __m128 acc_r0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+            if (BiasPtr != nullptr) {
+                acc_r0 = _mm_add_ps(acc_r0, _mm_loadu_ps(BiasPtr));
+            }
+
+            _mm_storeu_ps(SumPtr, acc_r0);
+
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * StrideQuantBDataCol;
+            QuantBScaleColPtr += NCols4 * BlockCountK;
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmXxXBlkLen32Avx2(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    const size_t lda = BlockCountK * BlkLen32;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    //const size_t StrideQuantBScale = BlockCountK;
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+    assert(CountM < NRows2);
+    assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m256 acc0 = _mm256_setzero_ps();
+            size_t k_blks_remaining = BlockCountK;
+            for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr));
+                const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + BlkLen32));
+                accumulate_blklen32_r1c1blk2_avx2<vnni>(
+                    av_00_epi8, av_01_epi8, QuantBDataPtr,
+                    QuantAScalePtr, QuantBScalePtr, acc0
+                );
+
+                // increment block pointers
+                QuantAPtr += BlkLen32 * PerAccuBlk2;
+                QuantAScalePtr += PerAccuBlk2;
+                QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk2;
+                QuantBScalePtr += PerAccuBlk2;
+            }
+
+            // TODO: use a loop in case PerAccuBlk2 is not 2.
+            if (k_blks_remaining > 0) {
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantAPtr);
+                const float& scale_a00 = *QuantAScalePtr;
+                const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                accumulate_blklen32_r1c1blk1_avx2<vnni>(av_00_epi8, QuantBDataPtr, scale_00, acc0);
+            }
+
+            *SumPtr = hsum_float_8(acc0);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+            }
+
+            // move to next column
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += BlockCountK;
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE
+    size_t
+    MlasQ4Int8GemmKernelBlkLen32Avx2(
+        const std::byte* QuantA,
+        const float* QuantAScale,
+        const std::byte* QuantBData,
+        const float* QuantBScale,
+        float* C,
+        size_t CountM,
+        size_t CountN,
+        size_t /*CountK*/,
+        size_t BlockCountK,
+        const float* Bias,
+        size_t ldc
+    )
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+
+    const size_t lda = BlockCountK * BlkLen32 * sizeof(int8_t);
+    const size_t lda_scale = BlockCountK;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+
+    size_t remainingRows = CountM % NRows2;
+    size_t multipleRows = CountM - remainingRows;
+    size_t remainingCols = CountN % NCols4;
+    size_t multipleCols = CountN - remainingCols;
+
+    if (multipleRows > 0 && multipleCols > 0) {
+        Q4Int8Gemm2x4x2BlkLen32Avx2<vnni>(
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            multipleRows,
+            multipleCols,
+            BlockCountK,
+            Bias,
+            ldc
+        );
+    }
+    if (remainingCols > 0 && multipleRows > 0) {
+        Q4Int8Gemm2xXBlkLen32Avx2<vnni>(
+            QuantA,
+            QuantAScale,
+            QuantBData + multipleCols * StrideQuantBData,
+            QuantBScale + multipleCols * StrideQuantBScale,
+            C + multipleCols,
+            multipleRows,
+            remainingCols,
+            BlockCountK,
+            Bias ? Bias + multipleCols : nullptr,
+            ldc);
+    }
+
+    if (remainingRows > 0 && multipleCols > 0) {
+        Q4Int8GemmXx4BlkLen32Avx2<vnni>(
+            QuantA + multipleRows * lda,
+            QuantAScale + multipleRows * lda_scale,
+            QuantBData,
+            QuantBScale,
+            C + multipleRows * ldc,
+            remainingRows,
+            multipleCols,
+            BlockCountK,
+            Bias,
+            ldc);
+    }
+
+    if (remainingCols > 0 && remainingRows > 0) {
+        Q4Int8GemmXxXBlkLen32Avx2<vnni>(
+            QuantA + multipleRows * lda,
+            QuantAScale + multipleRows * lda_scale,
+            QuantBData + multipleCols * StrideQuantBData,
+            QuantBScale + multipleCols * StrideQuantBScale,
+            C + multipleRows * ldc + multipleCols,
+            remainingRows,
+            remainingCols,
+            BlockCountK,
+            Bias ? Bias + multipleCols : nullptr,
+            ldc);
+    }
+
+    return CountM;
+}
+
+// this function is to explore larger NCols. With Avx2 it does not improve performance.
+// Leave it here until the same is implemented in avx512.
+template <bool HasZeroPoint, AccumulateFunctionType<HasZeroPoint> accumulator>
+MLAS_FORCEINLINE
+size_t
+MlasQ4Int8TileGemmKernelBlkLen32Avx2(
+    const std::byte* QuantA,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    const std::byte* QuantBZeroPoint,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t /*CountK*/,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t lda,
+    size_t ldc
+)
+{
+    // We process 32 quantized values in a batch.
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale = BlockCountK;
+    const size_t StrideQuantBZeroPoint = MlasQNBitZeroPointsForBlksSizeInBytes<BlkBitWidth4>(BlockCountK);
+
+    const __m256i zero = _mm256_setzero_si256();
+    const __m128i low_mask = _mm_set1_epi8(0xF);
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+
+    for (size_t m = 0; m < CountM; m++) {
+        // for each row of A, reset B pointers
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const std::byte* QuantBZeroPointColPtr = QuantBZeroPoint;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        int64_t nblk = (int64_t)(CountN)-NCols4;
+        while (nblk >= 0) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+            const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
+
+            __m256 acc[NCols4];
+
+            acc[0] = _mm256_setzero_ps();
+            acc[1] = _mm256_setzero_ps();
+            acc[2] = _mm256_setzero_ps();
+            acc[3] = _mm256_setzero_ps();
+
+            if constexpr (NCols4 == 8) {
+                acc[4] = _mm256_setzero_ps();
+                acc[5] = _mm256_setzero_ps();
+                acc[6] = _mm256_setzero_ps();
+                acc[7] = _mm256_setzero_ps();
+            }
+
+            size_t k_blks_remaining = BlockCountK;
+
+            // process 2 blks of 64 4b weights a time
+            for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const std::byte* QuantABlk1 = QuantABlk0 + Q8BlkSize(BlkLen32);
+
+                // load A:
+                const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
+                const __m256i av_1_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk1));
+
+                const float& scale_a0 = Q8BlkScale(QuantABlk0);
+                const float& scale_a1 = Q8BlkScale(QuantABlk1);
+
+                // Col0
+                const float& scale_00 = scale_a0 * QuantBScalePtr[0];
+                const float& scale_01 = scale_a1 * QuantBScalePtr[1];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr), low_mask, zero, QuantBZeroPointPtr, true, scale_00, acc[0]);
+                accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 16), low_mask, zero, QuantBZeroPointPtr, false, scale_01, acc[0]);
+
+                // Col1
+                const float& scale_10 = scale_a0 * (QuantBScalePtr + StrideQuantBScale)[0];
+                const float& scale_11 = scale_a1 * (QuantBScalePtr + StrideQuantBScale)[1];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + StrideQuantBZeroPoint, true, scale_10, acc[1]);
+                accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + StrideQuantBZeroPoint, false, scale_11, acc[1]);
+
+                // Col2
+                const float& scale_20 = scale_a0 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                const float& scale_21 = scale_a1 * (QuantBScalePtr + 2 * StrideQuantBScale)[1];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 2 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, true, scale_20, acc[2]);
+                accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 2 * StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, false, scale_21, acc[2]);
+
+                // Col3
+                const float& scale_30 = scale_a0 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                const float& scale_31 = scale_a1 * (QuantBScalePtr + 3 * StrideQuantBScale)[1];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 3 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, true, scale_30, acc[3]);
+                accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 3 * StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, false, scale_31, acc[3]);
+
+                if constexpr (NCols4 == 8) {
+                    // Col4
+                    const float& scale_40 = scale_a0 * (QuantBScalePtr + 4 * StrideQuantBScale)[0];
+                    const float& scale_41 = scale_a1 * (QuantBScalePtr + 4 * StrideQuantBScale)[1];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 4 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr, true, scale_40, acc[4]);
+                    accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 4 * StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr, false, scale_41, acc[4]);
+
+                    // Col5
+                    const float& scale_50 = scale_a0 * (QuantBScalePtr + 5 * StrideQuantBScale)[0];
+                    const float& scale_51 = scale_a1 * (QuantBScalePtr + 5 * StrideQuantBScale)[1];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 5 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + StrideQuantBZeroPoint, true, scale_50, acc[5]);
+                    accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 5 * StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + StrideQuantBZeroPoint, false, scale_51, acc[5]);
+
+                    // Col6
+                    const float& scale_60 = scale_a0 * (QuantBScalePtr + 6 * StrideQuantBScale)[0];
+                    const float& scale_61 = scale_a1 * (QuantBScalePtr + 6 * StrideQuantBScale)[1];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 6 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 6 * StrideQuantBZeroPoint, true, scale_60, acc[6]);
+                    accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 6 * StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + 6 * StrideQuantBZeroPoint, false, scale_61, acc[6]);
+
+                    // Col7
+                    const float& scale_70 = scale_a0 * (QuantBScalePtr + 7 * StrideQuantBScale)[0];
+                    const float& scale_71 = scale_a1 * (QuantBScalePtr + 7 * StrideQuantBScale)[1];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 7 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 7 * StrideQuantBZeroPoint, true, scale_70, acc[7]);
+                    accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 7 * StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + 7 * StrideQuantBZeroPoint, false, scale_71, acc[7]);
+                }
+
+                // increment block pointers
+                QuantAPtr += Q8BlkSize(BlkLen32) * PerAccuBlk2;
+                QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk2;
+                QuantBScalePtr += PerAccuBlk2;
+                if constexpr (HasZeroPoint) {
+                    QuantBZeroPointPtr += 1;
+                }
+            } // k_blks_remaining
+
+            // TODO: use a loop in case PerAccuBlk2 is not 2.
+            if (k_blks_remaining > 0) {
+                // load A
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
+
+                const float& scale_a0 = Q8BlkScale(QuantABlk0);
+
+                // Col0
+                const float& scale_00 = scale_a0 * QuantBScalePtr[0];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr), low_mask, zero, QuantBZeroPointPtr, true, scale_00, acc[0]);
+
+                // Col1
+                const float& scale_10 = scale_a0 * (QuantBScalePtr + StrideQuantBScale)[0];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + StrideQuantBZeroPoint, true, scale_10, acc[1]);
+
+                // Col2
+                const float& scale_20 = scale_a0 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 2 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, true, scale_20, acc[2]);
+
+                // Col3
+                const float& scale_30 = scale_a0 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 3 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, true, scale_30, acc[3]);
+
+                if constexpr (NCols4 == 8) {
+                    // Col4
+                    const float& scale_40 = scale_a0 * (QuantBScalePtr + 4 * StrideQuantBScale)[0];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 4 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 4 * StrideQuantBZeroPoint, true, scale_40, acc[4]);
+
+                    // Col5
+                    const float& scale_50 = scale_a0 * (QuantBScalePtr + 5 * StrideQuantBScale)[0];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 5 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 5 * StrideQuantBZeroPoint, true, scale_50, acc[5]);
+
+                    // Col6
+                    const float& scale_60 = scale_a0 * (QuantBScalePtr + 6 * StrideQuantBScale)[0];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 6 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 6 * StrideQuantBZeroPoint, true, scale_60, acc[6]);
+
+                    // Col7
+                    const float& scale_70 = scale_a0 * (QuantBScalePtr + 7 * StrideQuantBScale)[0];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 7 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 7 * StrideQuantBZeroPoint, true, scale_70, acc[7]);
+                }
+            }  // k_blks_remaining
+
+            if constexpr (NCols4 == 8) {
+                __m128 acc_0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+                __m128 acc_1 = FoldAccumulators(acc[4], acc[5], acc[6], acc[7]);
+                if (BiasPtr != nullptr) {
+                    acc_0 = _mm_add_ps(acc_0, _mm_loadu_ps(BiasPtr));
+                    acc_1 = _mm_add_ps(acc_1, _mm_loadu_ps(BiasPtr + 4));
+                }
+                _mm_storeu_ps(SumPtr, acc_0);
+                _mm_storeu_ps(SumPtr+4, acc_1);
+            } else {
+                __m128 acc_x = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+                if (BiasPtr != nullptr) {
+                    acc_x = _mm_add_ps(acc_x, _mm_loadu_ps(BiasPtr));
+                }
+                _mm_storeu_ps(SumPtr, acc_x);
+            }
+
+            // move to next NCols columns
+
+            QuantBDataColPtr += NCols4 * StrideQuantBData;
+            QuantBScaleColPtr += NCols4 * StrideQuantBScale;
+            if constexpr (HasZeroPoint) {
+                QuantBZeroPointColPtr += NCols4 * StrideQuantBZeroPoint;
+            }
+
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+            nblk -= NCols4;
+        } // while (nblk >= 0)
+
+        nblk += NCols4;
+        for (int64_t n = 0; n < nblk; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+            const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
+
+            __m256 acc0 = _mm256_setzero_ps();
+
+            size_t k_blks_remaining = BlockCountK;
+            for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const std::byte* QuantABlk1 = QuantABlk0 + Q8BlkSize(BlkLen32);
+
+                // load A:
+                const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
+                const __m256i av_1_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk1));
+
+                const float& scale_a0 = Q8BlkScale(QuantABlk0);
+                const float& scale_a1 = Q8BlkScale(QuantABlk1);
+
+                // Col0
+                const float& scale_00 = scale_a0 * QuantBScalePtr[0];
+                const float& scale_01 = scale_a1 * QuantBScalePtr[1];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr), low_mask, zero, QuantBZeroPointPtr, true, scale_00, acc0);
+                accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 16), low_mask, zero, QuantBZeroPointPtr, false, scale_01, acc0);
+
+                // increment block pointers
+                QuantAPtr += Q8BlkSize(BlkLen32) * PerAccuBlk2;
+                QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk2;
+                QuantBScalePtr += PerAccuBlk2;
+                if constexpr (HasZeroPoint) {
+                    QuantBZeroPointPtr += 1;
+                }
+            }
+
+            // TODO: use a loop in case PerAccuBlk2 is not 2.
+            if (k_blks_remaining > 0) {
+                // load A
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
+
+                const float& scale_a0 = Q8BlkScale(QuantABlk0);
+
+                // Col0
+                const float& scale_00 = scale_a0 * QuantBScalePtr[0];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr), low_mask, zero, QuantBZeroPointPtr, true, scale_00, acc0);
+            }
+
+            *SumPtr = hsum_float_8(acc0);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+            }
+
+            // move to next column
+
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+            if constexpr (HasZeroPoint) {
+                QuantBZeroPointColPtr += StrideQuantBZeroPoint;
+            }
+
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    } // m
+    return CountM;
+}
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2_int8_blklen64.h b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2_int8_blklen64.h
new file mode 100644
index 0000000000000..174ebc580904c
--- /dev/null
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx2_int8_blklen64.h
@@ -0,0 +1,541 @@
+#pragma once
+#include <algorithm>
+#include <cassert>
+#include <utility>
+
+#include "sqnbitgemm.h"
+#include "sqnbitgemm_kernel_avx_common.h"
+
+template<bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen64_r2c1blk1_avx2(
+    const __m256i& av00_32_epi8,
+    const __m256i& av01_32_epi8,
+    const __m256i& av10_32_epi8,
+    const __m256i& av11_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a0,
+    const float* scale_a1,
+    const float* scale_b,
+    __m256& acc0,
+    __m256& acc1
+)
+{
+    const __m256i bv_packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(QuantBDataPtr));
+    const __m256i low_mask = _mm256_set1_epi8(0x0F);
+    __m256i bv0_32_epi8 = _mm256_and_si256(bv_packed, low_mask);  // 0, 1,...30, 31
+    __m256i bv1_32_epi8 = _mm256_srli_epi16(_mm256_sub_epi8(bv_packed, bv0_32_epi8), 4);  // 32, 33,...62, 63
+
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    if constexpr (vnni) {
+        __m256i sum_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), bv0_32_epi8, av00_32_epi8);
+        sum_8_epi32 = _mm256_dpbusds_avx_epi32(sum_8_epi32, bv1_32_epi8, av01_32_epi8);
+        __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+        __m256 scale_a0_ps = _mm256_broadcast_ss(scale_a0);
+        __m256 scale_b_ps = _mm256_broadcast_ss(scale_b);
+
+        acc0 = _mm256_fmadd_ps(sum_ps, _mm256_mul_ps(scale_a0_ps, scale_b_ps), acc0);
+
+        sum_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), bv0_32_epi8, av10_32_epi8);
+        sum_8_epi32 = _mm256_dpbusds_avx_epi32(sum_8_epi32, bv1_32_epi8, av11_32_epi8);
+        sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+        __m256 scale_a1_ps = _mm256_broadcast_ss(scale_a1);
+
+        acc1 = _mm256_fmadd_ps(sum_ps, _mm256_mul_ps(scale_a1_ps, scale_b_ps), acc1);
+
+    } else {
+#endif
+        __m256i dot0_16_epi16 = _mm256_maddubs_epi16(bv0_32_epi8, av00_32_epi8);
+        __m256i dot1_16_epi16 = _mm256_maddubs_epi16(bv1_32_epi8, av01_32_epi8);
+        __m256i sum_16_epi16 = _mm256_hadd_epi16(dot0_16_epi16, dot1_16_epi16);
+
+        const __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv0_32_epi8, bv0_32_epi8), 15);
+
+        __m256i sum_8_epi32 = _mm256_madd_epi16(one_16_epi16, sum_16_epi16);
+        __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+        __m256 scale_a0_ps = _mm256_broadcast_ss(scale_a0);
+        __m256 scale_b_ps = _mm256_broadcast_ss(scale_b);
+
+        acc0 = _mm256_fmadd_ps(sum_ps, _mm256_mul_ps(scale_a0_ps, scale_b_ps), acc0);
+
+        dot0_16_epi16 = _mm256_maddubs_epi16(bv0_32_epi8, av10_32_epi8);
+        dot1_16_epi16 = _mm256_maddubs_epi16(bv1_32_epi8, av11_32_epi8);
+        sum_16_epi16 = _mm256_hadd_epi16(dot0_16_epi16, dot1_16_epi16);
+
+        sum_8_epi32 = _mm256_madd_epi16(one_16_epi16, sum_16_epi16);
+        sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+        __m256 scale_a1_ps = _mm256_broadcast_ss(scale_a1);
+
+        acc1 = _mm256_fmadd_ps(sum_ps, _mm256_mul_ps(scale_a1_ps, scale_b_ps), acc1);
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    }
+#endif
+}
+
+template <bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen64_r1c1blk1_avx2(
+    const __m256i& av00_32_epi8,
+    const __m256i& av01_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a,
+    const float* scale_b,
+    __m256& acc0
+)
+{
+    // | v0  v32 | v1  v33 | ... | v30 v62 | v31 v63 |
+    const __m256i bv_packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(QuantBDataPtr));
+    const __m256i low_mask = _mm256_set1_epi8(0x0F);
+    __m256i bv0_32_epi8 = _mm256_and_si256(bv_packed, low_mask);                          // 0, 1,...30, 31
+    __m256i bv1_32_epi8 = _mm256_srli_epi16(_mm256_sub_epi8(bv_packed, bv0_32_epi8), 4);  // 32, 33,...62, 63
+
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    if constexpr (vnni) {
+        __m256i sum_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), bv0_32_epi8, av00_32_epi8);
+        sum_8_epi32 = _mm256_dpbusds_avx_epi32(sum_8_epi32, bv1_32_epi8, av01_32_epi8);
+        const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+        __m256 scale_a_8_ps = _mm256_broadcast_ss(scale_a);
+        __m256 scale_b_8_ps = _mm256_broadcast_ss(scale_b);
+
+        acc0 = _mm256_fmadd_ps(sum_ps, _mm256_mul_ps(scale_a_8_ps, scale_b_8_ps), acc0);
+    } else {
+#endif
+        const __m256i dot0_16_epi16 = _mm256_maddubs_epi16(bv0_32_epi8, av00_32_epi8);
+        const __m256i dot1_16_epi16 = _mm256_maddubs_epi16(bv1_32_epi8, av01_32_epi8);
+        const __m256i sum_16_epi16 = _mm256_hadd_epi16(dot0_16_epi16, dot1_16_epi16);
+
+        __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv0_32_epi8, bv0_32_epi8), 15);
+        const __m256i sum_8_epi32 = _mm256_madd_epi16(one_16_epi16, sum_16_epi16);
+        const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+        __m256 scale_a_8_ps = _mm256_broadcast_ss(scale_a);
+        __m256 scale_b_8_ps = _mm256_broadcast_ss(scale_b);
+
+        acc0 = _mm256_fmadd_ps(sum_ps, _mm256_mul_ps(scale_a_8_ps, scale_b_8_ps), acc0);
+#if !defined(__GNUC__) || (__GNUC__ > 9)
+    }
+#endif
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmR2xC4BlkLen64Avx2(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    constexpr size_t SubblkLen = 64;
+
+    const size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t PerBlkSubblkCount = BlkLen / SubblkLen;
+    const size_t SubblkDataSizeInBytes = BlkDataSizeInBytes / PerBlkSubblkCount;
+
+    const size_t lda = BlockCountK * BlkLen;
+    const size_t StrideQuantBData = BlockCountK * BlkDataSizeInBytes;
+    //const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM % NRows2 == 0);
+    assert(CountN % NCols4 == 0);
+
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m256 acc[NCols4 * NRows2] = {
+                _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(),
+                _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps()
+            };
+
+            // process 1 blks of 64 4b weights a time
+            for (size_t k = 0; k < BlockCountK; ++k) {
+                for (size_t kk = 0; kk < PerBlkSubblkCount; kk++) {
+                    const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantAPtr);
+                    const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + 32));
+                    const __m256i av_10_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + lda));
+                    const __m256i av_11_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + lda + 32));
+
+                    accumulate_blklen64_r2c1blk1_avx2<vnni>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc[0], acc[NCols4]);
+                    accumulate_blklen64_r2c1blk1_avx2<vnni>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + SubblkDataSizeInBytes, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 1, acc[1], acc[NCols4 + 1]);
+                    accumulate_blklen64_r2c1blk1_avx2<vnni>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + 2 * SubblkDataSizeInBytes, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 2, acc[2], acc[NCols4 + 2]);
+                    accumulate_blklen64_r2c1blk1_avx2<vnni>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + 3 * SubblkDataSizeInBytes, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 3, acc[3], acc[NCols4 + 3]);
+
+                    // increment block pointers
+                    QuantAPtr += SubblkLen;
+                    QuantBDataPtr += NCols4 * SubblkDataSizeInBytes;
+                }
+                QuantAScalePtr++;
+                QuantBScalePtr += NCols4;
+            }  // k_blks_remaining
+
+            __m128 acc_r0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+            __m128 acc_r1 = FoldAccumulators(acc[NCols4 + 0], acc[NCols4 + 1], acc[NCols4 + 2], acc[NCols4 + 3]);
+            if (BiasPtr != nullptr) {
+                const __m128 bias_4_ps = _mm_loadu_ps(BiasPtr);
+                acc_r0 = _mm_add_ps(acc_r0, bias_4_ps);
+                acc_r1 = _mm_add_ps(acc_r1, bias_4_ps);
+            }
+            _mm_storeu_ps(SumPtr, acc_r0);
+            _mm_storeu_ps(SumPtr + ldc, acc_r1);
+
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * StrideQuantBData;
+            QuantBScaleColPtr += NCols4 * BlockCountK;
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+template<bool vnni>
+void MLAS_FORCEINLINE
+Q4Int8GemmR2xC1BlkLen64Avx2(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    constexpr size_t SubblkLen = 64;
+
+    const size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t PerBlkSubblkCount = BlkLen / SubblkLen;
+    const size_t SubblkDataSizeInBytes = BlkDataSizeInBytes / PerBlkSubblkCount;
+
+    const size_t lda = BlockCountK * BlkLen;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM % NRows2 == 0);
+    assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        float* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m256 acc0 = _mm256_setzero_ps(), acc1 = _mm256_setzero_ps();
+
+            for (size_t k = 0; k < BlockCountK; ++k) {
+                for (size_t kk = 0; kk < PerBlkSubblkCount; kk++) {
+                    const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantAPtr);
+                    const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + 32));
+                    const __m256i av_10_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + lda));
+                    const __m256i av_11_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + lda + 32));
+
+                    accumulate_blklen64_r2c1blk1_avx2<vnni>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc0, acc1);
+
+                    // increment block pointers
+                    QuantAPtr += SubblkLen;
+                    QuantBDataPtr += SubblkDataSizeInBytes;
+                }
+                QuantAScalePtr++;
+                QuantBScalePtr++;
+            }
+
+            *SumPtr = hsum_float_8(acc0);
+            *(SumPtr + ldc) = hsum_float_8(acc1);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+                *(SumPtr + ldc) += *BiasPtr;
+            }
+
+            // move to next column
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmR1xC4BlkLen64Avx2(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t SubblkLen = 64;
+
+    const size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t PerBlkSubblkCount = BlkLen / SubblkLen;
+    const size_t SubblkDataSizeInBytes = BlkDataSizeInBytes / PerBlkSubblkCount;
+
+    const size_t lda = BlockCountK * BlkLen;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    //const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM < NRows2);
+    assert(CountN % NCols4 == 0);
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m256 acc[NCols4] = {_mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps()};
+            for (size_t k = 0; k < BlockCountK; ++k) {
+                for (size_t kk = 0; kk < PerBlkSubblkCount; kk++) {
+                    const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantAPtr);
+                    const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + 32));
+                    accumulate_blklen64_r1c1blk1_avx2<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc[0]);
+                    accumulate_blklen64_r1c1blk1_avx2<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr + SubblkDataSizeInBytes, QuantAScalePtr, QuantBScalePtr + 1, acc[1]);
+                    accumulate_blklen64_r1c1blk1_avx2<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr + 2 * SubblkDataSizeInBytes, QuantAScalePtr, QuantBScalePtr + 2, acc[2]);
+                    accumulate_blklen64_r1c1blk1_avx2<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr + 3 * SubblkDataSizeInBytes, QuantAScalePtr, QuantBScalePtr + 3, acc[3]);
+
+                    // increment block pointers
+                    QuantAPtr += SubblkLen;
+                    QuantBDataPtr += NCols4 * SubblkDataSizeInBytes;
+                }
+                QuantAScalePtr++;
+                QuantBScalePtr += NCols4;
+            }
+
+            __m128 acc_r0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+            if (BiasPtr != nullptr) {
+                acc_r0 = _mm_add_ps(acc_r0, _mm_loadu_ps(BiasPtr));
+            }
+
+            _mm_storeu_ps(SumPtr, acc_r0);
+
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * StrideQuantBData;
+            QuantBScaleColPtr += NCols4 * BlockCountK;
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmR1xC1BlkLen64Avx2(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t SubblkLen = 64;
+
+    const size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t PerBlkSubblkCount = BlkLen / SubblkLen;
+    const size_t SubblkDataSizeInBytes = BlkDataSizeInBytes / PerBlkSubblkCount;
+
+    const size_t lda = BlockCountK * BlkLen;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM < NRows2);
+    assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m256 acc0 = _mm256_setzero_ps();
+            for (size_t k = 0; k < BlockCountK; ++k) {
+                for (size_t kk = 0; kk < PerBlkSubblkCount; kk++) {
+                    const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantAPtr);
+                    const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + 32));
+
+                    accumulate_blklen64_r1c1blk1_avx2<vnni>(
+                        av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0
+                    );
+
+                    // increment block pointers
+                    QuantAPtr += SubblkLen;
+                    QuantBDataPtr += SubblkDataSizeInBytes;
+                }
+                QuantAScalePtr++;
+                QuantBScalePtr++;
+            }
+
+            *SumPtr = hsum_float_8(acc0);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+            }
+
+            // move to next column
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE size_t
+MlasQ4Int8GemmKernelBlkLen64Avx2(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+
+    const size_t lda = BlockCountK * BlkLen * sizeof(int8_t);
+    const size_t lda_scale = BlockCountK;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    size_t remainingRows = CountM % NRows2;
+    size_t multipleRows = CountM - remainingRows;
+    size_t remainingCols = CountN % NCols4;
+    size_t multipleCols = CountN - remainingCols;
+
+    if (multipleRows > 0 && multipleCols > 0) {
+        Q4Int8GemmR2xC4BlkLen64Avx2<vnni>(
+            BlkLen,
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            multipleRows,
+            multipleCols,
+            BlockCountK,
+            Bias,
+            ldc
+        );
+    }
+    if (remainingCols > 0 && multipleRows > 0) {
+        Q4Int8GemmR2xC1BlkLen64Avx2<vnni>(
+            BlkLen,
+            QuantA,
+            QuantAScale,
+            QuantBData + multipleCols * StrideQuantBData,
+            QuantBScale + multipleCols * StrideQuantBScale,
+            C + multipleCols,
+            multipleRows,
+            remainingCols,
+            BlockCountK,
+            Bias ? Bias + multipleCols : nullptr,
+            ldc);
+    }
+
+    if (remainingRows > 0 && multipleCols > 0) {
+        Q4Int8GemmR1xC4BlkLen64Avx2<vnni>(
+            BlkLen,
+            QuantA + multipleRows * lda,
+            QuantAScale + multipleRows * lda_scale,
+            QuantBData,
+            QuantBScale,
+            C + multipleRows * ldc,
+            remainingRows,
+            multipleCols,
+            BlockCountK,
+            Bias,
+            ldc);
+    }
+
+    if (remainingCols > 0 && remainingRows > 0) {
+        Q4Int8GemmR1xC1BlkLen64Avx2<vnni>(
+            BlkLen,
+            QuantA + multipleRows * lda,
+            QuantAScale + multipleRows * lda_scale,
+            QuantBData + multipleCols * StrideQuantBData,
+            QuantBScale + multipleCols * StrideQuantBScale,
+            C + multipleRows * ldc + multipleCols,
+            remainingRows,
+            remainingCols,
+            BlockCountK,
+            Bias ? Bias + multipleCols : nullptr,
+            ldc);
+    }
+
+    return CountM;
+}
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512.cpp b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512.cpp
index b868906760701..13bd369a065bb 100644
--- a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512.cpp
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512.cpp
@@ -22,6 +22,10 @@ Module Name:
 #include "sqnbitgemm.h"
 #include "sqnbitgemm_kernel_avx_common.h"
 #include "sqnbitgemm_kernel_avx_common_int8.h"
+#include "sqnbitgemm_kernel_avx512_int8_blklen16.h"
+#include "sqnbitgemm_kernel_avx512_int8_blklen32.h"
+#include "sqnbitgemm_kernel_avx512_int8_blklen64.h"
+#include "sqnbitgemm_kernel_avx512_int8_blklen128.h"
 
 //
 // CompFp32 kernel implementation.
@@ -150,18 +154,115 @@ SQ4BitGemmM1Kernel_CompFp32_avx512(
 // CompInt8 kernel implementation.
 //
 
+MLAS_FORCEINLINE
+size_t
+SQ4BitGemmKernel_BlkSum_CompInt8_avx512(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    const std::byte* /*QuantBZeroPoint*/,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t /*CountK*/,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc,
+    const float* ABlockSum,
+    const float* QuantBBlkSum
+)
+{
+    if (BlkLen == 16) {
+        MlasQ4Int8GemmKernelBlkLen16Avx512<false>(
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            CountM,
+            CountN,
+            BlockCountK,
+            Bias,
+            ldc
+        );
+    } else if (BlkLen == 32) {
+        MlasQ4Int8GemmKernelBlkLen32Avx512<false>(
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            CountM,
+            CountN,
+            BlockCountK,
+            Bias,
+            ldc
+        );
+    } else if (BlkLen == 64) {
+        MlasQ4Int8GemmKernelBlkLen64Avx512<false>(
+            BlkLen,
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            CountM,
+            CountN,
+            BlockCountK,
+            Bias,
+            ldc
+        );
+    } else {
+        MlasQ4Int8GemmKernelBlkLen128Avx512<false>(
+            BlkLen,
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            CountM,
+            CountN,
+            BlockCountK,
+            Bias,
+            ldc
+        );
+    }
+
+    float* c_blk = C;
+    const float* b_blk_sum = QuantBBlkSum;
+
+    size_t RowsRemaining = CountM;
+    const float* a_blksum_row = ABlockSum;
+    while (RowsRemaining > 0) {
+        auto RowsHandled = GetMlasPlatform().GemmFloatKernel(
+            a_blksum_row, b_blk_sum, c_blk, BlockCountK, RowsRemaining, CountN, BlockCountK, ldc, 1.f, false
+        );
+
+        c_blk += ldc * RowsHandled;
+        a_blksum_row += BlockCountK * RowsHandled;
+        RowsRemaining -= RowsHandled;
+    }
+    return CountM;
+}
+
 void MLASCALL
-MlasQ80BlkQuantRow_avx512(
+QuantizeARow_CompInt8_avx512(
     size_t BlkLen,
     const float* A,
     size_t CountK,
-    std::byte* QuantA
+    std::byte* QuantA,
+    float* QuantAScale,
+    float* AScaledBlkSum  // scale_k * Sum_blklen(a_i)
 )
 {
     // port from MlasQ80BlkQuantRow
     assert(BlkLen % 16 == 0);
     const __m512 signBit = _mm512_set1_ps(-0.0f);
+    const __m256i one_16_epi16 = _mm256_set1_epi16(1);
     int8_t* blob = reinterpret_cast<int8_t*>(QuantA);
+    float* scale_ptr = QuantAScale;
     for (size_t k = 0; k < CountK; k += BlkLen) {
         const size_t step = std::min(BlkLen, CountK - k);
 
@@ -185,13 +286,14 @@ MlasQ80BlkQuantRow_avx512(
 
         // Quantize these floats
         const float scale = maxScalar / 127.f;
-        *reinterpret_cast<float*>(blob) = scale;
-        blob += sizeof(float);
+        *scale_ptr = scale;
+        scale_ptr++;
 
         const float inverse_scale = (maxScalar != 0.0f) ? 127.f / maxScalar : 0.0f;
         const __m512 mul = _mm512_set1_ps(inverse_scale);
         __m128i* dst = reinterpret_cast<__m128i*>(blob);
 
+        __m256i sum_16_epi16 = _mm256_setzero_si256();
         for (size_t kk = 0; kk < step; kk += 16) {
             const size_t klen = std::min(size_t(16), step - kk);
 
@@ -208,23 +310,46 @@ MlasQ80BlkQuantRow_avx512(
             // Convert int32 to int8
             __m128i i0_8 = _mm512_cvtepi32_epi8(i0);
             _mm_storeu_si128(dst++, i0_8);
+
+            // accumulate Sum(a_i)
+            __m256i i_16_epi16 = _mm256_cvtepi8_epi16(i0_8);
+            sum_16_epi16 = _mm256_hadds_epi16(sum_16_epi16, i_16_epi16);
+
         }
         if (step < BlkLen) {
             memset(blob + step, 0, BlkLen - step);
         }
+
+        const __m256i sum_8_epi32 = _mm256_madd_epi16(one_16_epi16, sum_16_epi16);
+        *AScaledBlkSum = scale * hsum_8_epi32(sum_8_epi32);
+        AScaledBlkSum++;
         blob += BlkLen;
     }
 }
 
-void MLASCALL
-QuantizeARow_CompInt8_avx512(
+static void
+SQ4BitGemmPackQuantBDataAndBlkSum512(
+    size_t N,
+    size_t K,
     size_t BlkLen,
-    const float* A,
-    size_t CountK,
-    std::byte* QuantA
+    MLAS_SQNBIT_GEMM_COMPUTE_TYPE ComputeType,
+    const std::byte* QuantBDataBegin,
+    const float* QuantBScaleBegin,
+    bool has_zp_input,
+    const std::byte* QuantBZPBegin,
+    PackedQuantBDataStruct& packed_quant_b,
+    MLAS_THREADPOOL* ThreadPool
 )
 {
-    MlasQ80BlkQuantRow_avx512(BlkLen, A, CountK, QuantA);
+    assert(BlkLen >= 16 && BlkLen % 16 == 0);
+
+    const size_t BlockCountK = MlasDivRoundup(K, BlkLen);
+
+    size_t SubBlkLen = (BlkLen == 16) ? 16 : (BlkLen == 32 ? 32 : 64);
+    if (ComputeType == CompInt8) {
+        SubBlkLen = 128;
+    }
+    PackQuantBDataAndBlkSum(N, BlockCountK, BlkLen, SubBlkLen, QuantBDataBegin, QuantBScaleBegin, has_zp_input, QuantBZPBegin, packed_quant_b, ThreadPool);
 }
 
 const MLAS_SQNBIT_GEMM_DISPATCH MlasSQNBitGemmDispatchAvx512 = []() {
@@ -232,6 +357,7 @@ const MLAS_SQNBIT_GEMM_DISPATCH MlasSQNBitGemmDispatchAvx512 = []() {
 
     d.SQ4BitGemmPackQuantBDataSize = SQ4BitGemmPackQuantBDataSize;
     d.SQ4BitGemmPackQuantBData = SQ4BitGemmPackQuantBData;
+    d.SQ4BitGemmPackQuantBDataAndBlkSum = SQ4BitGemmPackQuantBDataAndBlkSum512;
 
     d.SQ4BitGemmPerGemmWorkspaceSize = SQ4BitGemmPerGemmWorkspaceSize;
     d.SQ4BitGemmPerGemmWorkspaceAlignment = SQ4BitGemmPerGemmWorkspaceAlignment;
@@ -239,8 +365,8 @@ const MLAS_SQNBIT_GEMM_DISPATCH MlasSQNBitGemmDispatchAvx512 = []() {
     d.SQ4BitGemmM1Kernel_CompFp32 = SQ4BitGemmM1Kernel_CompFp32_avx512;
     d.Q4BitBlkDequantBForSgemm_CompFp32 = Q4BitBlkDequantBForSgemm_CompFp32_avx2;
 
-    d.SQ4BitGemmKernel_CompInt8 = SQ4BitGemmKernel_CompInt8_avx2;
-    d.QuantizeARow_CompInt8 = QuantizeARow_CompInt8_avx512;
+    d.SQ4BitGemmKernel_BlkSum_CompInt8 = SQ4BitGemmKernel_BlkSum_CompInt8_avx512;
+    d.QuantizeARowComputeBlkSum_CompInt8 = QuantizeARow_CompInt8_avx512;
 
     return d;
 }();
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8.h b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8.h
new file mode 100644
index 0000000000000..7d9dc36854621
--- /dev/null
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8.h
@@ -0,0 +1,1171 @@
+#pragma once
+#include <algorithm>
+#include <cassert>
+#include <utility>
+
+#include "sqnbitgemm.h"
+#include "sqnbitgemm_kernel_avx_common.h"
+
+
+MLAS_FORCEINLINE void
+accumulate_1blk_dot(const __m256i& av_32_epi8, const __m256i& bv_32_epi8,
+  const float& combined_scale, const __m256i& one_16_epi16, __m256& acc)
+{
+    const __m256i dot_16_epi16 = _mm256_maddubs_epi16(
+        _mm256_sign_epi8(bv_32_epi8, bv_32_epi8), _mm256_sign_epi8(av_32_epi8, bv_32_epi8)
+    );
+    const __m256i sum_8_epi32 = _mm256_madd_epi16(one_16_epi16, dot_16_epi16);
+    const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+    acc = _mm256_fmadd_ps(sum_ps, _mm256_set1_ps(combined_scale), acc);
+}
+
+MLAS_FORCEINLINE void
+accumulate_2blk_dot(
+  const __m256i& av0_32_epi8, const __m256i& av1_32_epi8,
+  const __m256i& bv0_32_epi8, const __m256i& bv1_32_epi8,
+  const float& combined_scale0, const float& combined_scale1,
+  const __m256i& one_16_epi16,
+  __m256& acc)
+{
+    const __m256i dot0_16_epi16 = _mm256_maddubs_epi16(
+        _mm256_sign_epi8(bv0_32_epi8, bv0_32_epi8), _mm256_sign_epi8(av0_32_epi8, bv0_32_epi8)
+    );
+    const __m256i dot1_16_epi16 = _mm256_maddubs_epi16(
+        _mm256_sign_epi8(bv1_32_epi8, bv1_32_epi8), _mm256_sign_epi8(av1_32_epi8, bv1_32_epi8)
+    );
+    const __m256i sum_16_epi16 = _mm256_hadd_epi16(dot0_16_epi16, dot1_16_epi16);
+    const __m256i sum_8_epi32 = _mm256_madd_epi16(one_16_epi16, sum_16_epi16);
+
+    const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+    const __m256 scale_8_ps = _mm256_set_ps(
+        combined_scale1, combined_scale1, combined_scale0, combined_scale0,
+        combined_scale1, combined_scale1, combined_scale0, combined_scale0
+    );
+    acc = _mm256_fmadd_ps(sum_ps, scale_8_ps, acc);
+}
+
+template <bool HasZeroPoint>
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r2c1blk2_avx2(
+    const __m256i& av00_32_epi8,
+    const __m256i& av01_32_epi8,
+    const __m256i& av10_32_epi8,
+    const __m256i& av11_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const std::byte* QuantBZeroPointPtr,
+    const float* scale_a0,
+    const float* scale_a1,
+    const float* scale_b,
+    __m256& acc0,
+    __m256& acc1
+)
+{
+    // | v0  v16 | v1  v17 | ... | v14 v30 | v15 v31 | v32 v48 | v33 v49 | ... | v46 v62 | v47 v63 |
+    const __m256i bv_packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(QuantBDataPtr));
+    const __m256i low_mask = _mm256_set1_epi8(0x0F);
+    __m256i bv0_32_epi8 = _mm256_and_si256(bv_packed, low_mask);  // 0, 1,...30, 31
+    // TODO: will this (the second line below) be faster and not keep low_mask in use?
+    __m256i bv1_32_epi8 = _mm256_srli_epi16(_mm256_sub_epi8(bv_packed, bv0_32_epi8), 4);  // 32, 33,...62, 63
+
+    int8_t zp0, zp1;
+    get_2_zps<HasZeroPoint>(QuantBZeroPointPtr, zp0, zp1);
+    bv0_32_epi8 = _mm256_sub_epi8(bv0_32_epi8, _mm256_set1_epi8(zp0));
+    bv1_32_epi8 = _mm256_sub_epi8(bv1_32_epi8, _mm256_set1_epi8(zp1));
+
+    //accumulate_2blk_dot(av00_32_epi8, av01_32_epi8, bv0_32_epi8, bv1_32_epi8, combined_scale00, combined_scale01, one_16_epi16, acc0);
+    //accumulate_2blk_dot(av10_32_epi8, av11_32_epi8, bv0_32_epi8, bv1_32_epi8, combined_scale10, combined_scale11, one_16_epi16, acc1);
+    const __m256i dot0_16_epi16 = _mm256_maddubs_epi16(
+        _mm256_sign_epi8(bv0_32_epi8, bv0_32_epi8), _mm256_sign_epi8(av00_32_epi8, bv0_32_epi8)
+    );
+    const __m256i dot1_16_epi16 = _mm256_maddubs_epi16(
+        _mm256_sign_epi8(bv1_32_epi8, bv1_32_epi8), _mm256_sign_epi8(av01_32_epi8, bv1_32_epi8)
+    );
+    const __m256i sum_16_epi16 = _mm256_hadd_epi16(dot0_16_epi16, dot1_16_epi16);
+    
+    __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv0_32_epi8, bv0_32_epi8), 15);
+    const __m256i sum_8_epi32 = _mm256_madd_epi16(one_16_epi16, sum_16_epi16);
+    const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+    __m256d scale_a0_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_a0));
+    __m256 scale_b_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_b));
+    // 1 0 1 0 1 0 1 0 -> 1 1 0 0 1 1 0 0
+    __m256 scale_8_ps = _mm256_mul(
+      _mm256_permute_ps(scale_a0_2_ps, _MM_SHUFFLE(1, 1, 0, 0)),
+      _mm256_permute_ps(scale_b_2_ps, _MM_SHUFFLE(1, 1, 0, 0)));
+
+    acc0 = _mm256_fmadd_ps(sum_ps, scale_8_ps, acc0);
+
+
+    const __m256i dot0_16_epi16_ = _mm256_maddubs_epi16(
+        _mm256_sign_epi8(bv0_32_epi8, bv0_32_epi8), _mm256_sign_epi8(av10_32_epi8, bv0_32_epi8)
+    );
+    const __m256i dot1_16_epi16_ = _mm256_maddubs_epi16(
+        _mm256_sign_epi8(bv1_32_epi8, bv1_32_epi8), _mm256_sign_epi8(av11_32_epi8, bv1_32_epi8)
+    );
+    const __m256i sum_16_epi16_ = _mm256_hadd_epi16(dot0_16_epi16_, dot1_16_epi16_);
+    const __m256i sum_8_epi32_ = _mm256_madd_epi16(one_16_epi16, sum_16_epi16_);
+    const __m256 sum_ps_ = _mm256_cvtepi32_ps(sum_8_epi32_);
+
+    __m256d scale_a1_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_a1));
+    __m256 scale_8_ps_ = _mm256_mul(
+      _mm256_permute_ps(scale_a1_2_ps, _MM_SHUFFLE(1, 1, 0, 0)),
+      _mm256_permute_ps(scale_b_2_ps, _MM_SHUFFLE(1, 1, 0, 0)));
+    acc1 = _mm256_fmadd_ps(sum_ps, scale_8_ps, acc1);
+}
+
+template <bool HasZeroPoint>
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r2c1blk2_avx2(
+  const __m256i& av00_32_epi8,
+  const __m256i& av01_32_epi8,
+  const __m256i& av10_32_epi8,
+  const __m256i& av11_32_epi8,
+  const std::byte* QuantBDataPtr,
+  const std::byte* QuantBZeroPointPtr,
+  const float& combined_scale00,
+  const float& combined_scale01,
+  const float& combined_scale10,
+  const float& combined_scale11,
+  __m256& acc0,
+  __m256& acc1
+)
+{
+    // | v0  v16 | v1  v17 | ... | v14 v30 | v15 v31 | v32 v48 | v33 v49 | ... | v46 v62 | v47 v63 |
+    const __m256i bv_packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(QuantBDataPtr));
+
+    // generating low_mask of 0x0Fs is not as fast as just calling _mm256_set1_epi8(0x0F).
+    // however, it is faster to generate one_16_epi16 than calling _mm256_set1_ep16(1);
+    const __m256i low_mask = _mm256_set1_epi8(0x0F);
+    //__m256i low_mask = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv_packed, bv_packed), 12);
+    //low_mask = _mm256_packus_epi16(low_mask, low_mask);
+    __m256i bv0_32_epi8 = _mm256_and_si256(bv_packed, low_mask);  // 0, 1,...14, 15, 32, 33,...46, 47
+    // TODO: will this (the second line below) be faster and not keep low_mask in use?
+    // const __m256i bv1 = _mm256_and_si256(_mm256_srli_epi16(bv_packed, 4), low_mask);  // 16, 17,...30, 31, 48, 49,...,62, 63
+    __m256i bv1_32_epi8 = _mm256_srli_epi16(_mm256_sub_epi8(bv_packed, bv0_32_epi8), 4);  // 16, 17,...30, 31, 48, 49,...,62, 63
+
+    //__m256i bv0_32_epi8 = _mm256_set_m128i(_mm256_castsi256_si128(bv1), _mm256_castsi256_si128(bv0)); 
+
+    //// This (the second line below) saves one _mm256_extracti128_si256 against using _mm256_set_m128i.
+    ////__m256i bv1_32_epi8 = _mm256_set_m128i(_mm256_extracti128_si256(bv1, 1), _mm256_extracti128_si256(bv0, 1));
+    //__m256i bv1_32_epi8 = _mm256_insertf128_si256(bv1, _mm256_extracti128_si256(bv0, 1), 0);
+
+    int8_t zp0, zp1;
+    get_2_zps<HasZeroPoint>(QuantBZeroPointPtr, zp0, zp1);
+    bv0_32_epi8 = _mm256_sub_epi8(bv0_32_epi8, _mm256_set1_epi8(zp0));
+    bv1_32_epi8 = _mm256_sub_epi8(bv1_32_epi8, _mm256_set1_epi8(zp1));
+
+    // generating constant 1s is fater here.
+    // __m256i one = _mm256_set1_epi16(1);
+    __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv0_32_epi8, bv0_32_epi8), 15);
+
+    // performance gains 7% by calling this (accumulate_2blk_dot) instead of 2 accumulate_1blk_dot calls.
+    // accumulate_1blk_dot(av00_32_epi8, bv0_32_epi8, combined_scale00, one_16_epi16, acc0);
+    // accumulate_1blk_dot(av01_32_epi8, bv1_32_epi8, combined_scale01, one_16_epi16, acc0);
+    // accumulate_1blk_dot(av10_32_epi8, bv0_32_epi8, combined_scale10, one_16_epi16, acc1);
+    // accumulate_1blk_dot(av11_32_epi8, bv1_32_epi8, combined_scale11, one_16_epi16, acc1);
+    accumulate_2blk_dot(av00_32_epi8, av01_32_epi8, bv0_32_epi8, bv1_32_epi8, combined_scale00, combined_scale01, one_16_epi16, acc0);
+    accumulate_2blk_dot(av10_32_epi8, av11_32_epi8, bv0_32_epi8, bv1_32_epi8, combined_scale10, combined_scale11, one_16_epi16, acc1);
+}
+
+template <bool HasZeroPoint>
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r2c1blk1_avx2(
+    const __m256i& av00_32_epi8,
+    const __m256i& av10_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const std::byte* QuantBZeroPointPtr,
+    const float& combined_scale00,
+    const float& combined_scale10,
+    __m256& acc0,
+    __m256& acc1
+)
+{
+    // | v0  v16 | v1  v17 | ... | v14 v30 | v15 v31 |
+    const __m128i bv_packed0 = _mm_loadu_si128(reinterpret_cast<const __m128i*>(QuantBDataPtr));
+    __m256i bv_32_epi8 = _mm256_set_m128i(_mm_srli_epi16(bv_packed0, 4), bv_packed0);
+    bv_32_epi8 = _mm256_and_si256(_mm256_set1_epi8(0x0F), bv_32_epi8);
+    
+    const int8_t zp = get_zp<HasZeroPoint>(true, QuantBZeroPointPtr);
+    const __m256i bzp = _mm256_set1_epi8(zp);
+    bv_32_epi8 = _mm256_sub_epi8(bv_32_epi8, bzp);
+
+    __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv_32_epi8, bv_32_epi8), 15);
+    accumulate_1blk_dot(av00_32_epi8, bv_32_epi8, combined_scale00, one_16_epi16, acc0);
+    accumulate_1blk_dot(av10_32_epi8, bv_32_epi8, combined_scale10, one_16_epi16, acc1);
+}
+
+template <bool HasZeroPoint>
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r1c1blk2_avx2(
+    const __m256i& av00_32_epi8,
+    const __m256i& av01_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const std::byte* QuantBZeroPointPtr,
+    const float& combined_scale00,
+    const float& combined_scale01,
+    __m256& acc0)
+{
+    // | v0  v16 | v1  v17 | ... | v14 v30 | v15 v31 | v32 v48 | v33 v49 | ... | v46 v62 | v47 v63 |
+    const __m256i bv_packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(QuantBDataPtr));
+
+    const __m256i low_mask = _mm256_set1_epi8(0x0F);
+    __m256i bv0_32_epi8 = _mm256_and_si256(bv_packed, low_mask);  // 0, 1,...14, 15, 32, 33,...46, 47
+    // TODO: will this be faster and save a use of low_mask?
+    // const __m256i bv1 = _mm256_and_si256(_mm256_srli_epi16(bv_packed, 4), low_mask);  // 16, 17,...30, 31, 48, 49,...,62, 63
+    __m256i bv1_32_epi8 = _mm256_srli_epi16(_mm256_sub_epi8(bv_packed, bv0_32_epi8), 4);  // 16, 17,...30, 31, 48, 49,...,62, 63
+
+    //__m256i bv0_32_epi8 = _mm256_set_m128i(_mm256_castsi256_si128(bv1), _mm256_castsi256_si128(bv0));
+
+    //// This saves one _mm256_extracti128_si256 against using _mm256_set_m128i.
+    ////__m256i bv1_32_epi8 = _mm256_set_m128i(_mm256_extracti128_si256(bv1, 1), _mm256_extracti128_si256(bv0, 1));
+    //__m256i bv1_32_epi8 = _mm256_insertf128_si256(bv1, _mm256_extracti128_si256(bv0, 1), 0);
+
+    int8_t zp0, zp1;
+    get_2_zps<HasZeroPoint>(QuantBZeroPointPtr, zp0, zp1);
+    bv0_32_epi8 = _mm256_sub_epi8(bv0_32_epi8, _mm256_set1_epi8(zp0));
+    bv1_32_epi8 = _mm256_sub_epi8(bv1_32_epi8, _mm256_set1_epi8(zp1));
+
+    __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv0_32_epi8, bv0_32_epi8), 15);
+    //accumulate_1blk_dot(av00_32_epi8, bv0_32_epi8, combined_scale00, one_16_epi16, acc0);
+    //accumulate_1blk_dot(av01_32_epi8, bv1_32_epi8, combined_scale01, one_16_epi16, acc0);
+    accumulate_2blk_dot(av00_32_epi8, av01_32_epi8, bv0_32_epi8, bv1_32_epi8, combined_scale00, combined_scale01, one_16_epi16, acc0);
+}
+
+template <bool HasZeroPoint>
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r1c1blk1_avx2(
+    const __m256i& av00_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const std::byte* QuantBZeroPointPtr,
+    const float& combined_scale00,
+    __m256& acc0
+)
+{
+    // | v0  v16 | v1  v17 | ... | v14 v30 | v15 v31 |
+    const __m128i bv_packed0 = _mm_loadu_si128(reinterpret_cast<const __m128i*>(QuantBDataPtr));
+    __m256i bv_32_epi8 = _mm256_set_m128i(_mm_srli_epi16(bv_packed0, 4), bv_packed0);
+    bv_32_epi8 = _mm256_and_si256(_mm256_set1_epi8(0x0F), bv_32_epi8);
+
+    const int8_t zp = get_zp<HasZeroPoint>(true, QuantBZeroPointPtr);
+    const __m256i bzp = _mm256_set1_epi8(zp);
+    bv_32_epi8 = _mm256_sub_epi8(bv_32_epi8, bzp);
+
+    __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv_32_epi8, bv_32_epi8), 15);
+    accumulate_1blk_dot(av00_32_epi8, bv_32_epi8, combined_scale00, one_16_epi16, acc0);
+}
+
+template <bool HasZeroPoint>
+MLAS_FORCEINLINE void
+Q4Int8Gemm2x4BlkLen32Avx2(
+    const std::byte* QuantA,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    const std::byte* QuantBZeroPoint,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t lda,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    constexpr size_t Q8Blk32Size = Q8BlkSize(BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale = BlockCountK;
+    const size_t StrideQuantBZeroPoint = MlasQNBitZeroPointsForBlksSizeInBytes<BlkBitWidth4>(BlockCountK);
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+    assert(CountM % NRows2 == 0);
+    assert(CountN % NCols4 == 0);
+
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const std::byte* QuantBZeroPointColPtr = QuantBZeroPoint;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+            const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
+
+            __m256 acc[NCols4 * NRows2] = {
+              _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(),
+              _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps()
+            };
+
+            size_t k_blks_remaining = BlockCountK;
+
+            // process 2 blks of 64 4b weights a time
+            for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+                const std::byte* QuantABlk00 = QuantAPtr;
+                const std::byte* QuantABlk01 = QuantABlk00 + Q8Blk32Size;
+                const std::byte* QuantABlk10 = QuantAPtr + lda;
+                const std::byte* QuantABlk11 = QuantABlk10 + Q8Blk32Size;
+
+                // load A:
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk00));
+                const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk01));
+                const __m256i av_10_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk10));
+                const __m256i av_11_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk11));
+
+                const float& scale_a00 = Q8BlkScale(QuantABlk00);
+                const float& scale_a01 = Q8BlkScale(QuantABlk01);
+                const float& scale_a10 = Q8BlkScale(QuantABlk10);
+                const float& scale_a11 = Q8BlkScale(QuantABlk11);
+
+                {
+                    // Col0
+                    const float& scale_00 = scale_a00 * QuantBScalePtr[0];
+                    const float& scale_01 = scale_a01 * QuantBScalePtr[1];
+                    const float& scale_10 = scale_a10 * QuantBScalePtr[0];
+                    const float& scale_11 = scale_a11 * QuantBScalePtr[1];
+                    accumulate_blklen32_r2c1blk2_avx2<HasZeroPoint>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr, QuantBZeroPointPtr, scale_00, scale_01, scale_10, scale_11, acc[0], acc[NCols4]);
+                }
+
+                {
+                    // Col1
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + StrideQuantBScale)[0];
+                    const float& scale_01 = scale_a01 * (QuantBScalePtr + StrideQuantBScale)[1];
+                    const float& scale_10 = scale_a10 * (QuantBScalePtr + StrideQuantBScale)[0];
+                    const float& scale_11 = scale_a11 * (QuantBScalePtr + StrideQuantBScale)[1];
+                    accumulate_blklen32_r2c1blk2_avx2<HasZeroPoint>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + StrideQuantBData, QuantBZeroPointPtr + StrideQuantBZeroPoint, scale_00, scale_01, scale_10, scale_11, acc[1], acc[NCols4 + 1]);
+                }
+
+                {
+                    // Col2
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                    const float& scale_01 = scale_a01 * (QuantBScalePtr + 2 * StrideQuantBScale)[1];
+                    const float& scale_10 = scale_a10 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                    const float& scale_11 = scale_a11 * (QuantBScalePtr + 2 * StrideQuantBScale)[1];
+                    accumulate_blklen32_r2c1blk2_avx2<HasZeroPoint>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + 2 * StrideQuantBData, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, scale_00, scale_01, scale_10, scale_11, acc[2], acc[NCols4 + 2]);
+                }
+
+                {
+                    // Col3
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                    const float& scale_01 = scale_a01 * (QuantBScalePtr + 3 * StrideQuantBScale)[1];
+                    const float& scale_10 = scale_a10 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                    const float& scale_11 = scale_a11 * (QuantBScalePtr + 3 * StrideQuantBScale)[1];
+                    accumulate_blklen32_r2c1blk2_avx2<HasZeroPoint>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + 3 * StrideQuantBData, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, scale_00, scale_01, scale_10, scale_11, acc[3], acc[NCols4 + 3]);
+                }
+
+                // increment block pointers
+                QuantAPtr += Q8Blk32Size * PerAccuBlk2;
+                QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk2;
+                QuantBScalePtr += PerAccuBlk2;
+                if constexpr (HasZeroPoint) {
+                    QuantBZeroPointPtr += 1;
+                }
+            }  // k_blks_remaining
+
+            // TODO: use a loop in case PerAccuBlk2 is not 2.
+            if (k_blks_remaining > 0) {
+                // load A
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
+                const __m256i av_10_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0 + lda));
+
+                const float& scale_a00 = Q8BlkScale(QuantABlk0);
+                const float& scale_a10 = Q8BlkScale(QuantABlk0 + lda);
+
+                {
+                    // Col0
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                    const float& scale_10 = scale_a10 * (QuantBScalePtr)[0];
+                    accumulate_blklen32_r2c1blk1_avx2<HasZeroPoint>(av_00_epi8, av_10_epi8, QuantBDataPtr, QuantBZeroPointPtr, scale_00, scale_10, acc[0], acc[NCols4]);
+                }
+
+                {
+                    // Col1
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + StrideQuantBScale)[0];
+                    const float& scale_10 = scale_a10 * (QuantBScalePtr + StrideQuantBScale)[0];
+                    accumulate_blklen32_r2c1blk1_avx2<HasZeroPoint>(av_00_epi8, av_10_epi8, QuantBDataPtr + StrideQuantBData, QuantBZeroPointPtr + StrideQuantBZeroPoint, scale_00, scale_10, acc[1], acc[NCols4 + 1]);
+                }
+
+                {
+                    // Col2
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                    const float& scale_10 = scale_a10 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                    accumulate_blklen32_r2c1blk1_avx2<HasZeroPoint>(av_00_epi8, av_10_epi8, QuantBDataPtr + 2 * StrideQuantBData, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, scale_00, scale_10, acc[2], acc[NCols4 + 2]);
+                }
+
+                {
+                    // Col3
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                    const float& scale_10 = scale_a10 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                    accumulate_blklen32_r2c1blk1_avx2<HasZeroPoint>(av_00_epi8, av_10_epi8, QuantBDataPtr + 3 * StrideQuantBData, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, scale_00, scale_10, acc[3], acc[NCols4 + 3]);
+                }
+            }  // k_blks_remaining
+
+            __m128 acc_r0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+            __m128 acc_r1 = FoldAccumulators(acc[NCols4 + 0], acc[NCols4 + 1], acc[NCols4 + 2], acc[NCols4 + 3]);
+            if (BiasPtr != nullptr) {
+                const __m128 bias_4_ps = _mm_loadu_ps(BiasPtr);
+                acc_r0 = _mm_add_ps(acc_r0, bias_4_ps);
+                acc_r1 = _mm_add_ps(acc_r1, bias_4_ps);
+            }
+            _mm_storeu_ps(SumPtr, acc_r0);
+            _mm_storeu_ps(SumPtr + ldc, acc_r1);
+
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * StrideQuantBData;
+            QuantBScaleColPtr += NCols4 * StrideQuantBScale;
+            if constexpr (HasZeroPoint) {
+                QuantBZeroPointColPtr += NCols4 * StrideQuantBZeroPoint;
+            }
+
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+template <bool HasZeroPoint> 
+void MLAS_FORCEINLINE Q4Int8Gemm2xXBlkLen32Avx2(
+    const std::byte* QuantA,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    const std::byte* QuantBZeroPoint,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t lda,
+    size_t ldc)
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale = BlockCountK;
+    const size_t StrideQuantBZeroPoint = MlasQNBitZeroPointsForBlksSizeInBytes<BlkBitWidth4>(BlockCountK);
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+    assert(CountM % NRows2 == 0);
+    assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const std::byte* QuantBZeroPointColPtr = QuantBZeroPoint;
+        const float* BiasPtr = Bias;
+        float* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            // accumulate_blklen32_r2c1_avx2
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+            const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
+
+            __m256 acc0 = _mm256_setzero_ps(), acc1 = _mm256_setzero_ps();
+
+            size_t k_blks_remaining = BlockCountK;
+            for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+                const std::byte* QuantABlk00 = QuantAPtr;
+                const std::byte* QuantABlk01 = QuantABlk00 + Q8BlkSize(BlkLen32);
+                const std::byte* QuantABlk10 = QuantAPtr + lda;
+                const std::byte* QuantABlk11 = QuantABlk10 + Q8BlkSize(BlkLen32);
+
+                // load A:
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk00));
+                const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk01));
+                const __m256i av_10_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk10));
+                const __m256i av_11_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk11));
+
+                const float& scale_a00 = Q8BlkScale(QuantABlk00);
+                const float& scale_a01 = Q8BlkScale(QuantABlk01);
+                const float& scale_a10 = Q8BlkScale(QuantABlk10);
+                const float& scale_a11 = Q8BlkScale(QuantABlk11);
+
+                const float& scale_00 = scale_a00 * QuantBScalePtr[0];
+                const float& scale_01 = scale_a01 * QuantBScalePtr[1];
+                const float& scale_10 = scale_a10 * QuantBScalePtr[0];
+                const float& scale_11 = scale_a11 * QuantBScalePtr[1];
+                accumulate_blklen32_r2c1blk2_avx2<HasZeroPoint>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr, QuantBZeroPointPtr, scale_00, scale_01, scale_10, scale_11, acc0, acc1);
+
+                // increment block pointers
+                QuantAPtr += Q8BlkSize(BlkLen32) * PerAccuBlk2;
+                QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk2;
+                QuantBScalePtr += PerAccuBlk2;
+                if constexpr (HasZeroPoint) {
+                    QuantBZeroPointPtr += 1;
+                }
+            }
+
+            // TODO: use a loop in case PerAccuBlk2 is not 2.
+            if (k_blks_remaining > 0) {
+                // load A
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
+                const __m256i av_10_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0 + lda));
+
+                const float& scale_a00 = Q8BlkScale(QuantABlk0);
+                const float& scale_a10 = Q8BlkScale(QuantABlk0 + lda);
+
+                const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                const float& scale_10 = scale_a10 * (QuantBScalePtr)[0];
+                accumulate_blklen32_r2c1blk1_avx2<HasZeroPoint>(av_00_epi8, av_10_epi8, QuantBDataPtr, QuantBZeroPointPtr, scale_00, scale_10, acc0, acc1);
+            }
+
+            *SumPtr = hsum_float_8(acc0);
+            *(SumPtr + ldc) = hsum_float_8(acc1);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+                *(SumPtr + ldc) += *BiasPtr;
+            }
+
+            // move to next column
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+            if constexpr (HasZeroPoint) {
+                QuantBZeroPointColPtr += StrideQuantBZeroPoint;
+            }
+
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+template <bool HasZeroPoint>
+MLAS_FORCEINLINE void
+Q4Int8GemmXx4BlkLen32Avx2(
+    const std::byte* QuantA,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    const std::byte* QuantBZeroPoint,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t lda,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale = BlockCountK;
+    const size_t StrideQuantBZeroPoint = MlasQNBitZeroPointsForBlksSizeInBytes<BlkBitWidth4>(BlockCountK);
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+    assert(CountM < NRows2);
+    assert(CountN % NCols4 == 0);
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const std::byte* QuantBZeroPointColPtr = QuantBZeroPoint;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+            const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
+
+            __m256 acc[NCols4] = {_mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps()};
+            size_t k_blks_remaining = BlockCountK;
+            for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+                const std::byte* QuantABlk00 = QuantAPtr;
+                const std::byte* QuantABlk01 = QuantABlk00 + Q8BlkSize(BlkLen32);
+
+                // load A:
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk00));
+                const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk01));
+
+                const float& scale_a00 = Q8BlkScale(QuantABlk00);
+                const float& scale_a01 = Q8BlkScale(QuantABlk01);
+                {
+                  // Col0
+                    const float& scale_00 = scale_a00 * QuantBScalePtr[0];
+                    const float& scale_01 = scale_a01 * QuantBScalePtr[1];
+                    accumulate_blklen32_r1c1blk2_avx2<HasZeroPoint>(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantBZeroPointPtr, scale_00, scale_01, acc[0]);
+                }
+                {
+                    // Col1
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + StrideQuantBScale)[0];
+                    const float& scale_01 = scale_a01 * (QuantBScalePtr + StrideQuantBScale)[1];
+                    accumulate_blklen32_r1c1blk2_avx2<HasZeroPoint>(av_00_epi8, av_01_epi8, QuantBDataPtr + StrideQuantBData, QuantBZeroPointPtr + 1 * StrideQuantBZeroPoint, scale_00, scale_01, acc[1]);
+                }
+                {
+                    // Col2
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                    const float& scale_01 = scale_a01 * (QuantBScalePtr + 2 * StrideQuantBScale)[1];
+                    accumulate_blklen32_r1c1blk2_avx2<HasZeroPoint>(av_00_epi8, av_01_epi8, QuantBDataPtr + 2 * StrideQuantBData, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, scale_00, scale_01, acc[2]);
+                }
+                {
+                    // Col3
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                    const float& scale_01 = scale_a01 * (QuantBScalePtr + 3 * StrideQuantBScale)[1];
+                    accumulate_blklen32_r1c1blk2_avx2<HasZeroPoint>(av_00_epi8, av_01_epi8, QuantBDataPtr + 3 * StrideQuantBData, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, scale_00, scale_01, acc[3]);
+                }
+                // increment block pointers
+                QuantAPtr += Q8BlkSize(BlkLen32) * PerAccuBlk2;
+                QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk2;
+                QuantBScalePtr += PerAccuBlk2;
+                if constexpr (HasZeroPoint) {
+                    QuantBZeroPointPtr += 1;
+                }
+            }
+
+            // TODO: use a loop in case PerAccuBlk2 is not 2.
+            if (k_blks_remaining > 0) {
+                // load A
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
+
+                const float& scale_a00 = Q8BlkScale(QuantABlk0);
+                {
+                  // Col0
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                  accumulate_blklen32_r1c1blk1_avx2<HasZeroPoint>(av_00_epi8, QuantBDataPtr, QuantBZeroPointPtr, scale_00, acc[0]);
+                }
+                {
+                    // Col1
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + StrideQuantBScale)[0];
+                    accumulate_blklen32_r1c1blk1_avx2<HasZeroPoint>(av_00_epi8, QuantBDataPtr + StrideQuantBData, QuantBZeroPointPtr + StrideQuantBZeroPoint, scale_00, acc[1]);
+                }
+                {
+                    // Col2
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                    accumulate_blklen32_r1c1blk1_avx2<HasZeroPoint>(av_00_epi8, QuantBDataPtr + 2 * StrideQuantBData, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, scale_00, acc[2]);
+                }
+                {
+                    // Col3
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                    accumulate_blklen32_r1c1blk1_avx2<HasZeroPoint>(av_00_epi8, QuantBDataPtr + 3 * StrideQuantBData, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, scale_00, acc[3]);
+                }
+            }
+
+            __m128 acc_r0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+            if (BiasPtr != nullptr) {
+                acc_r0 = _mm_add_ps(acc_r0, _mm_loadu_ps(BiasPtr));
+            }
+            _mm_storeu_ps(SumPtr, acc_r0);
+
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * StrideQuantBData;
+            QuantBScaleColPtr += NCols4 * StrideQuantBScale;
+            if constexpr (HasZeroPoint) {
+                QuantBZeroPointColPtr += NCols4 * StrideQuantBZeroPoint;
+            }
+
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+template <bool HasZeroPoint>
+MLAS_FORCEINLINE void
+Q4Int8GemmXxXBlkLen32Avx2(
+    const std::byte* QuantA,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    const std::byte* QuantBZeroPoint,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t lda,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale = BlockCountK;
+    const size_t StrideQuantBZeroPoint = MlasQNBitZeroPointsForBlksSizeInBytes<BlkBitWidth4>(BlockCountK);
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+    assert(CountM < NRows2);
+    assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const std::byte* QuantBZeroPointColPtr = QuantBZeroPoint;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+            const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
+
+            __m256 acc0 = _mm256_setzero_ps();
+            size_t k_blks_remaining = BlockCountK;
+            for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+                const std::byte* QuantABlk00 = QuantAPtr;
+                const std::byte* QuantABlk01 = QuantABlk00 + Q8BlkSize(BlkLen32);
+
+                // load A:
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk00));
+                const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk01));
+
+                const float& scale_a00 = Q8BlkScale(QuantABlk00);
+                const float& scale_a01 = Q8BlkScale(QuantABlk01);
+
+                const float& scale_00 = scale_a00 * QuantBScalePtr[0];
+                const float& scale_01 = scale_a01 * QuantBScalePtr[1];
+                accumulate_blklen32_r1c1blk2_avx2<HasZeroPoint>(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantBZeroPointPtr, scale_00, scale_01, acc0);
+
+                // increment block pointers
+                QuantAPtr += Q8BlkSize(BlkLen32) * PerAccuBlk2;
+                QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk2;
+                QuantBScalePtr += PerAccuBlk2;
+                if constexpr (HasZeroPoint) {
+                    QuantBZeroPointPtr += 1;
+                }
+            }
+
+            // TODO: use a loop in case PerAccuBlk2 is not 2.
+            if (k_blks_remaining > 0) {
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
+
+                const float& scale_a00 = Q8BlkScale(QuantABlk0);
+
+                const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                accumulate_blklen32_r1c1blk1_avx2<HasZeroPoint>(av_00_epi8, QuantBDataPtr, QuantBZeroPointPtr, scale_00, acc0);
+            }
+
+            *SumPtr = hsum_float_8(acc0);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+            }
+
+            // move to next column
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+            if constexpr (HasZeroPoint) {
+                QuantBZeroPointColPtr += StrideQuantBZeroPoint;
+            }
+
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+template <bool HasZeroPoint>
+MLAS_FORCEINLINE
+    size_t
+    MlasQ4Int8TileGemmKernelBlkLen32Avx2(
+        const std::byte* QuantA,
+        const std::byte* QuantBData,
+        const float* QuantBScale,
+        const std::byte* QuantBZeroPoint,
+        float* C,
+        size_t CountM,
+        size_t CountN,
+        size_t /*CountK*/,
+        size_t BlockCountK,
+        const float* Bias,
+        size_t lda,
+        size_t ldc
+    )
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale = BlockCountK;
+    const size_t StrideQuantBZeroPoint = MlasQNBitZeroPointsForBlksSizeInBytes<BlkBitWidth4>(BlockCountK);
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+
+    size_t remainingRows = CountM % NRows2;
+    size_t multipleRows = CountM - remainingRows;
+    size_t remainingCols = CountN % NCols4;
+    size_t multipleCols = CountN - remainingCols;
+
+    if (multipleRows > 0 && multipleCols > 0) {
+        Q4Int8Gemm2x4BlkLen32Avx2<HasZeroPoint>(
+            QuantA,
+            QuantBData,
+            QuantBScale,
+            QuantBZeroPoint,
+            C,
+            multipleRows,
+            multipleCols,
+            BlockCountK,
+            Bias,
+            lda,
+            ldc
+        );
+    }
+    if (remainingCols > 0 && multipleRows > 0) {
+        Q4Int8Gemm2xXBlkLen32Avx2<HasZeroPoint>(
+          QuantA,
+          QuantBData + multipleCols * StrideQuantBData,
+          QuantBScale + multipleCols * StrideQuantBScale,
+          QuantBZeroPoint + multipleCols * StrideQuantBZeroPoint,
+          C + multipleCols,
+          multipleRows,
+          remainingCols,
+          BlockCountK,
+          Bias ? Bias + multipleCols : nullptr,
+          lda,
+          ldc);
+    }
+
+    if (remainingRows > 0 && multipleCols > 0) {
+        Q4Int8GemmXx4BlkLen32Avx2<HasZeroPoint>(
+          QuantA + multipleRows * lda,
+          QuantBData,
+          QuantBScale,
+          QuantBZeroPoint,
+          C + multipleRows * ldc,
+          remainingRows,
+          multipleCols,
+          BlockCountK,
+          Bias,
+          lda,
+          ldc);
+    }
+
+    if (remainingCols > 0 && remainingRows > 0) {
+        Q4Int8GemmXxXBlkLen32Avx2<HasZeroPoint>(
+          QuantA + multipleRows * lda,
+          QuantBData + multipleCols * StrideQuantBData,
+          QuantBScale + multipleCols * StrideQuantBScale,
+          QuantBZeroPoint + multipleCols * StrideQuantBZeroPoint,
+          C + multipleRows * ldc + multipleCols,
+          remainingRows,
+          remainingCols, 
+          BlockCountK,
+          Bias ? Bias + multipleCols : nullptr,
+          lda,
+          ldc);
+    }
+
+    return CountM;
+}
+
+// this function is to explore larger NCols. With Avx2 it does not improve performance.
+// Leave it here until the same is implemented in avx512.
+template <bool HasZeroPoint, AccumulateFunctionType<HasZeroPoint> accumulator>
+MLAS_FORCEINLINE
+size_t
+MlasQ4Int8GemmKernelBlkLen32Avx2(
+    const std::byte* QuantA,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    const std::byte* QuantBZeroPoint,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t /*CountK*/,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t lda,
+    size_t ldc
+)
+{
+    // We process 32 quantized values in a batch.
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale = BlockCountK;
+    const size_t StrideQuantBZeroPoint = MlasQNBitZeroPointsForBlksSizeInBytes<BlkBitWidth4>(BlockCountK);
+
+    const __m256i zero = _mm256_setzero_si256();
+    const __m128i low_mask = _mm_set1_epi8(0xF);
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+
+    for (size_t m = 0; m < CountM; m++) {
+        // for each row of A, reset B pointers
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const std::byte* QuantBZeroPointColPtr = QuantBZeroPoint;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        int64_t nblk = (int64_t)(CountN)-NCols4;
+        while (nblk >= 0) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+            const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
+
+            __m256 acc[NCols4];
+
+            acc[0] = _mm256_setzero_ps();
+            acc[1] = _mm256_setzero_ps();
+            acc[2] = _mm256_setzero_ps();
+            acc[3] = _mm256_setzero_ps();
+
+            if constexpr (NCols4 == 8) {
+                acc[4] = _mm256_setzero_ps();
+                acc[5] = _mm256_setzero_ps();
+                acc[6] = _mm256_setzero_ps();
+                acc[7] = _mm256_setzero_ps();
+            }
+
+            size_t k_blks_remaining = BlockCountK;
+
+            // process 2 blks of 64 4b weights a time
+            for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const std::byte* QuantABlk1 = QuantABlk0 + Q8BlkSize(BlkLen32);
+
+                // load A:
+                const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
+                const __m256i av_1_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk1));
+
+                const float& scale_a0 = Q8BlkScale(QuantABlk0);
+                const float& scale_a1 = Q8BlkScale(QuantABlk1);
+
+                // Col0
+                const float& scale_00 = scale_a0 * QuantBScalePtr[0];
+                const float& scale_01 = scale_a1 * QuantBScalePtr[1];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr), low_mask, zero, QuantBZeroPointPtr, true, scale_00, acc[0]);
+                accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 16), low_mask, zero, QuantBZeroPointPtr, false, scale_01, acc[0]);
+
+                // Col1
+                const float& scale_10 = scale_a0 * (QuantBScalePtr + StrideQuantBScale)[0];
+                const float& scale_11 = scale_a1 * (QuantBScalePtr + StrideQuantBScale)[1];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + StrideQuantBZeroPoint, true, scale_10, acc[1]);
+                accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + StrideQuantBZeroPoint, false, scale_11, acc[1]);
+
+                // Col2
+                const float& scale_20 = scale_a0 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                const float& scale_21 = scale_a1 * (QuantBScalePtr + 2 * StrideQuantBScale)[1];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 2 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, true, scale_20, acc[2]);
+                accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 2 * StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, false, scale_21, acc[2]);
+
+                // Col3
+                const float& scale_30 = scale_a0 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                const float& scale_31 = scale_a1 * (QuantBScalePtr + 3 * StrideQuantBScale)[1];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 3 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, true, scale_30, acc[3]);
+                accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 3 * StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, false, scale_31, acc[3]);
+
+                if constexpr (NCols4 == 8) {
+                    // Col4
+                    const float& scale_40 = scale_a0 * (QuantBScalePtr + 4 * StrideQuantBScale)[0];
+                    const float& scale_41 = scale_a1 * (QuantBScalePtr + 4 * StrideQuantBScale)[1];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 4 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr, true, scale_40, acc[4]);
+                    accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 4 * StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr, false, scale_41, acc[4]);
+
+                    // Col5
+                    const float& scale_50 = scale_a0 * (QuantBScalePtr + 5 * StrideQuantBScale)[0];
+                    const float& scale_51 = scale_a1 * (QuantBScalePtr + 5 * StrideQuantBScale)[1];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 5 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + StrideQuantBZeroPoint, true, scale_50, acc[5]);
+                    accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 5 * StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + StrideQuantBZeroPoint, false, scale_51, acc[5]);
+
+                    // Col6
+                    const float& scale_60 = scale_a0 * (QuantBScalePtr + 6 * StrideQuantBScale)[0];
+                    const float& scale_61 = scale_a1 * (QuantBScalePtr + 6 * StrideQuantBScale)[1];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 6 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 6 * StrideQuantBZeroPoint, true, scale_60, acc[6]);
+                    accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 6 * StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + 6 * StrideQuantBZeroPoint, false, scale_61, acc[6]);
+
+                    // Col7
+                    const float& scale_70 = scale_a0 * (QuantBScalePtr + 7 * StrideQuantBScale)[0];
+                    const float& scale_71 = scale_a1 * (QuantBScalePtr + 7 * StrideQuantBScale)[1];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 7 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 7 * StrideQuantBZeroPoint, true, scale_70, acc[7]);
+                    accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 7 * StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + 7 * StrideQuantBZeroPoint, false, scale_71, acc[7]);
+                }
+
+                // increment block pointers
+                QuantAPtr += Q8BlkSize(BlkLen32) * PerAccuBlk2;
+                QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk2;
+                QuantBScalePtr += PerAccuBlk2;
+                if constexpr (HasZeroPoint) {
+                    QuantBZeroPointPtr += 1;
+                }
+            } // k_blks_remaining
+
+            // TODO: use a loop in case PerAccuBlk2 is not 2.
+            if (k_blks_remaining > 0) {
+                // load A
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
+
+                const float& scale_a0 = Q8BlkScale(QuantABlk0);
+
+                // Col0
+                const float& scale_00 = scale_a0 * QuantBScalePtr[0];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr), low_mask, zero, QuantBZeroPointPtr, true, scale_00, acc[0]);
+
+                // Col1
+                const float& scale_10 = scale_a0 * (QuantBScalePtr + StrideQuantBScale)[0];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + StrideQuantBZeroPoint, true, scale_10, acc[1]);
+
+                // Col2
+                const float& scale_20 = scale_a0 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 2 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, true, scale_20, acc[2]);
+
+                // Col3
+                const float& scale_30 = scale_a0 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 3 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, true, scale_30, acc[3]);
+
+                if constexpr (NCols4 == 8) {
+                    // Col4
+                    const float& scale_40 = scale_a0 * (QuantBScalePtr + 4 * StrideQuantBScale)[0];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 4 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 4 * StrideQuantBZeroPoint, true, scale_40, acc[4]);
+
+                    // Col5
+                    const float& scale_50 = scale_a0 * (QuantBScalePtr + 5 * StrideQuantBScale)[0];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 5 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 5 * StrideQuantBZeroPoint, true, scale_50, acc[5]);
+
+                    // Col6
+                    const float& scale_60 = scale_a0 * (QuantBScalePtr + 6 * StrideQuantBScale)[0];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 6 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 6 * StrideQuantBZeroPoint, true, scale_60, acc[6]);
+
+                    // Col7
+                    const float& scale_70 = scale_a0 * (QuantBScalePtr + 7 * StrideQuantBScale)[0];
+                    accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 7 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 7 * StrideQuantBZeroPoint, true, scale_70, acc[7]);
+                }
+            }  // k_blks_remaining
+
+            if constexpr (NCols4 == 8) {
+                __m128 acc_0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+                __m128 acc_1 = FoldAccumulators(acc[4], acc[5], acc[6], acc[7]);
+                if (BiasPtr != nullptr) {
+                    acc_0 = _mm_add_ps(acc_0, _mm_loadu_ps(BiasPtr));
+                    acc_1 = _mm_add_ps(acc_1, _mm_loadu_ps(BiasPtr + 4));
+                }
+                _mm_storeu_ps(SumPtr, acc_0);
+                _mm_storeu_ps(SumPtr+4, acc_1);
+            } else {
+                __m128 acc_x = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+                if (BiasPtr != nullptr) {
+                    acc_x = _mm_add_ps(acc_x, _mm_loadu_ps(BiasPtr));
+                }
+                _mm_storeu_ps(SumPtr, acc_x);
+            }
+
+            // move to next NCols columns
+
+            QuantBDataColPtr += NCols4 * StrideQuantBData;
+            QuantBScaleColPtr += NCols4 * StrideQuantBScale;
+            if constexpr (HasZeroPoint) {
+                QuantBZeroPointColPtr += NCols4 * StrideQuantBZeroPoint;
+            }
+
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+            nblk -= NCols4;
+        } // while (nblk >= 0)
+
+        nblk += NCols4;
+        for (int64_t n = 0; n < nblk; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+            const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
+
+            __m256 acc0 = _mm256_setzero_ps();
+
+            size_t k_blks_remaining = BlockCountK;
+            for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const std::byte* QuantABlk1 = QuantABlk0 + Q8BlkSize(BlkLen32);
+
+                // load A:
+                const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
+                const __m256i av_1_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk1));
+
+                const float& scale_a0 = Q8BlkScale(QuantABlk0);
+                const float& scale_a1 = Q8BlkScale(QuantABlk1);
+
+                // Col0
+                const float& scale_00 = scale_a0 * QuantBScalePtr[0];
+                const float& scale_01 = scale_a1 * QuantBScalePtr[1];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr), low_mask, zero, QuantBZeroPointPtr, true, scale_00, acc0);
+                accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 16), low_mask, zero, QuantBZeroPointPtr, false, scale_01, acc0);
+
+                // increment block pointers
+                QuantAPtr += Q8BlkSize(BlkLen32) * PerAccuBlk2;
+                QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk2;
+                QuantBScalePtr += PerAccuBlk2;
+                if constexpr (HasZeroPoint) {
+                    QuantBZeroPointPtr += 1;
+                }
+            }
+
+            // TODO: use a loop in case PerAccuBlk2 is not 2.
+            if (k_blks_remaining > 0) {
+                // load A
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
+
+                const float& scale_a0 = Q8BlkScale(QuantABlk0);
+
+                // Col0
+                const float& scale_00 = scale_a0 * QuantBScalePtr[0];
+                accumulator(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr), low_mask, zero, QuantBZeroPointPtr, true, scale_00, acc0);
+            }
+
+            *SumPtr = hsum_float_8(acc0);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+            }
+
+            // move to next column
+
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+            if constexpr (HasZeroPoint) {
+                QuantBZeroPointColPtr += StrideQuantBZeroPoint;
+            }
+
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    } // m
+    return CountM;
+}
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen128.h b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen128.h
new file mode 100644
index 0000000000000..60a887345d0e0
--- /dev/null
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen128.h
@@ -0,0 +1,581 @@
+#pragma once
+#include <algorithm>
+#include <cassert>
+#include <utility>
+
+#include "sqnbitgemm.h"
+#include "sqnbitgemm_kernel_avx_common.h"
+#include "sqnbitgemm_kernel_avx512_int8_blklen64.h"
+
+//static MLAS_FORCEINLINE __m512i
+//combine_two_m256i_to_m512i(const __m256i& a, const __m256i& b)
+//{
+//    __m512i result = _mm512_castsi256_si512(a);
+//    result = _mm512_inserti64x4(result, b, 1);
+//    return result;
+//}
+
+//static MLAS_FORCEINLINE void
+//load_2blk_4b_packed_blklen64(const std::byte* QuantBDataPtr, __m512i& bv0_64_epi8, __m512i& bv1_64_epi8)
+//{
+//    // | v0 v32 | v1 v33 | ... | v30 v62 | v31 v63 | v64 v96 | ... | v95 v127 |
+//    const __m512i bv_packed = _mm512_loadu_si512(reinterpret_cast<const __m512i*>(QuantBDataPtr));
+//    const __m512i low_mask = _mm512_set1_epi8(0x0F);
+//    __m512i bv0_64_epi8_ = _mm512_and_si512(bv_packed, low_mask); // 0~31, 64~95
+//    __m512i bv1_64_epi8_ = _mm512_srli_epi16(_mm512_sub_epi8(bv_packed, bv0_64_epi8), 4);  // 32~63, 96~127
+//
+//    // Extract lower and higher 256 bits from bv0_64_epi8 and bv1_64_epi8
+//    __m256i bv0_lower = _mm512_castsi512_si256(bv0_64_epi8_);
+//    __m256i bv0_higher = _mm512_extracti64x4_epi64(bv0_64_epi8_, 1);
+//    __m256i bv1_lower = _mm512_castsi512_si256(bv1_64_epi8_);
+//    __m256i bv1_higher = _mm512_extracti64x4_epi64(bv1_64_epi8_, 1);
+//
+//    // Compose new __m512i variables
+//    bv0_64_epi8 = _mm512_inserti64x4(_mm512_castsi256_si512(bv0_lower), bv1_lower, 1);
+//    bv1_64_epi8 = _mm512_inserti64x4(_mm512_castsi256_si512(bv0_higher), bv1_higher, 1);
+//}
+
+static MLAS_FORCEINLINE void
+dot_accumulate_1blk(
+    const __m512i& bv0_64_epi8,
+    const __m512i& bv1_64_epi8,
+    const __m512i& av0_64_epi8,
+    const __m512i& av1_64_epi8,
+    const float combined_scale,
+    __m512& acc
+)
+{
+    __m512i dot0_32_epi16 = _mm512_maddubs_epi16(bv0_64_epi8, av0_64_epi8);
+    __m512i dot1_32_epi16 = _mm512_maddubs_epi16(bv1_64_epi8, av1_64_epi8);
+    __m512i t1 = _mm512_unpacklo_epi32(dot0_32_epi16, dot1_32_epi16);
+    __m512i t2 = _mm512_unpackhi_epi32(dot0_32_epi16, dot1_32_epi16);
+    __m512i sum_32_epi16 = _mm512_add_epi16(t1, t2);
+    const __m512i zeros = _mm512_setzero_si512();
+    const __m512i one_32_epi16 = _mm512_srli_epi16(_mm512_ternarylogic_epi32(zeros, zeros, zeros, 1), 15);
+    __m512i sum_16_epi32 = _mm512_madd_epi16(one_32_epi16, sum_32_epi16);
+    __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+    acc = _mm512_fmadd_ps(sum_16_ps, _mm512_set1_ps(combined_scale), acc);
+}
+
+static MLAS_FORCEINLINE void
+dot_accumulate_1blkvnni(
+    const __m512i& bv0_64_epi8,
+    const __m512i& bv1_64_epi8,
+    const __m512i& av0_64_epi8,
+    const __m512i& av1_64_epi8,
+    const float combined_scale,
+    __m512& acc
+)
+{
+    __m512i dot0_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv0_64_epi8, av0_64_epi8);
+    __m512i dot1_16_epi32 = _mm512_dpbusd_epi32(dot0_16_epi32, bv1_64_epi8, av1_64_epi8);
+    __m512 sum_16_ps = _mm512_cvtepi32_ps(dot1_16_epi32);
+    acc = _mm512_fmadd_ps(sum_16_ps, _mm512_set1_ps(combined_scale), acc);
+}
+
+template<bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen128_r1c1blk1_avx512(
+    const __m512i& av00_64_epi8,
+    const __m512i& av01_64_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a,
+    const float* scale_b,
+    __m512& acc
+)
+{
+    __m512i bv0_64_epi8, bv1_64_epi8;
+    load_2blk_4b_packed_blklen64(QuantBDataPtr, bv0_64_epi8, bv1_64_epi8);
+
+    if constexpr (vnni) {
+        dot_accumulate_1blkvnni(
+            bv0_64_epi8, bv1_64_epi8, av00_64_epi8, av01_64_epi8,
+            (*scale_a) * (*scale_b), acc
+        );
+    } else {
+        dot_accumulate_1blk(
+            bv0_64_epi8, bv1_64_epi8, av00_64_epi8, av01_64_epi8,
+            (*scale_a) * (*scale_b), acc
+        );
+    }
+}
+
+template <bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen128_r2c1blk1_avx512(
+    const __m512i& av00_64_epi8,
+    const __m512i& av01_64_epi8,
+    const __m512i& av10_64_epi8,
+    const __m512i& av11_64_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a0,
+    const float* scale_a1,
+    const float* scale_b,
+    __m512& acc0,
+    __m512& acc1
+)
+{
+    __m512i bv0_64_epi8, bv1_64_epi8;
+    load_2blk_4b_packed_blklen64(QuantBDataPtr, bv0_64_epi8, bv1_64_epi8);
+
+    if constexpr (vnni) {
+        dot_accumulate_1blkvnni(
+            bv0_64_epi8, bv1_64_epi8, av00_64_epi8, av01_64_epi8,
+            (*scale_a0) * (*scale_b), acc0
+        );
+        dot_accumulate_1blkvnni(
+            bv0_64_epi8, bv1_64_epi8, av10_64_epi8, av11_64_epi8,
+            (*scale_a1) * (*scale_b), acc1
+        );
+    } else {
+        dot_accumulate_1blk(
+            bv0_64_epi8, bv1_64_epi8, av00_64_epi8, av01_64_epi8,
+            (*scale_a0) * (*scale_b), acc0
+        );
+        dot_accumulate_1blk(
+            bv0_64_epi8, bv1_64_epi8, av10_64_epi8, av11_64_epi8,
+            (*scale_a1) * (*scale_b), acc1
+        );
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmR2xC4BlkLen128Avx512(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    constexpr size_t SubblkLen = 128;
+    const size_t PerBlkSubblkCount = BlkLen / SubblkLen;
+    const size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t SubblkDataSizeInBytes = BlkDataSizeInBytes / PerBlkSubblkCount;
+
+    const size_t lda = BlockCountK * BlkLen;
+    const size_t StrideQuantBData = BlockCountK * BlkDataSizeInBytes;
+    //const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM % NRows2 == 0);
+    assert(CountN % NCols4 == 0);
+
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc[NCols4 * NRows2] = {
+                _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(),
+                _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps()
+            };
+
+            // process 1 blks of 64 4b weights a time
+            for (size_t k = 0; k < BlockCountK; ++k) {
+                for (size_t kk = 0; kk < PerBlkSubblkCount; kk++) {
+                    const __m512i av00_64_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                    const __m512i av01_64_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + SubblkLen / 2));
+                    const __m512i av10_64_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda));
+                    const __m512i av11_64_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda + SubblkLen / 2));
+
+                    accumulate_blklen128_r2c1blk1_avx512<vnni>(av00_64_epi8, av01_64_epi8, av10_64_epi8, av11_64_epi8, QuantBDataPtr, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc[0], acc[NCols4]);
+                    accumulate_blklen128_r2c1blk1_avx512<vnni>(av00_64_epi8, av01_64_epi8, av10_64_epi8, av11_64_epi8, QuantBDataPtr + SubblkDataSizeInBytes, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 1, acc[1], acc[NCols4 + 1]);
+                    accumulate_blklen128_r2c1blk1_avx512<vnni>(av00_64_epi8, av01_64_epi8, av10_64_epi8, av11_64_epi8, QuantBDataPtr + 2 * SubblkDataSizeInBytes, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 2, acc[2], acc[NCols4 + 2]);
+                    accumulate_blklen128_r2c1blk1_avx512<vnni>(av00_64_epi8, av01_64_epi8, av10_64_epi8, av11_64_epi8, QuantBDataPtr + 3 * SubblkDataSizeInBytes, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 3, acc[3], acc[NCols4 + 3]);
+
+                    // increment block pointers
+                    QuantAPtr += SubblkLen;
+                    QuantBDataPtr += NCols4 * SubblkDataSizeInBytes;
+                }
+                QuantAScalePtr++;
+                QuantBScalePtr += NCols4;
+            }  // k_blks_remaining
+
+#if 1
+            *SumPtr = _mm512_reduce_add_ps(acc[0]);
+            *(SumPtr + 1) = _mm512_reduce_add_ps(acc[1]);
+            *(SumPtr + 2) = _mm512_reduce_add_ps(acc[2]);
+            *(SumPtr + 3) = _mm512_reduce_add_ps(acc[3]);
+            *(SumPtr + ldc) = _mm512_reduce_add_ps(acc[NCols4]);
+            *(SumPtr + ldc + 1) = _mm512_reduce_add_ps(acc[NCols4 + 1]);
+            *(SumPtr + ldc + 2) = _mm512_reduce_add_ps(acc[NCols4 + 2]);
+            *(SumPtr + ldc + 3) = _mm512_reduce_add_ps(acc[NCols4 + 3]);
+            if (BiasPtr != nullptr) {
+                *SumPtr += *BiasPtr;
+                *(SumPtr + 1) += *(BiasPtr + 1);
+                *(SumPtr + 2) += *(BiasPtr + 2);
+                *(SumPtr + 3) += *(BiasPtr + 3);
+                *(SumPtr + ldc) += *BiasPtr;
+                *(SumPtr + ldc + 1) += *(BiasPtr + 1);
+                *(SumPtr + ldc + 2) += *(BiasPtr + 2);
+                *(SumPtr + ldc + 3) += *(BiasPtr + 3);
+            }
+#else
+            __m128 acc_r0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+            __m128 acc_r1 = FoldAccumulators(acc[NCols4 + 0], acc[NCols4 + 1], acc[NCols4 + 2], acc[NCols4 + 3]);
+            if (BiasPtr != nullptr) {
+                const __m128 bias_4_ps = _mm_loadu_ps(BiasPtr);
+                acc_r0 = _mm_add_ps(acc_r0, bias_4_ps);
+                acc_r1 = _mm_add_ps(acc_r1, bias_4_ps);
+            }
+            const __m128 level_r0 = _mm_loadu_ps(SumPtr);
+            _mm_storeu_ps(SumPtr, _mm_sub_ps(acc_r0, level_r0));
+
+            const __m128 level_r1 = _mm_loadu_ps(SumPtr + ldc);
+            _mm_storeu_ps(SumPtr + ldc, _mm_sub_ps(acc_r1, level_r1));
+#endif
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * StrideQuantBData;
+            QuantBScaleColPtr += NCols4 * BlockCountK;
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+template <bool vnni>
+void MLAS_FORCEINLINE
+Q4Int8GemmR2xC1BlkLen128Avx512(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    constexpr size_t SubblkLen = 128;
+
+    const size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t PerBlkSubblkCount = BlkLen / SubblkLen;
+    const size_t SubblkDataSizeInBytes = BlkDataSizeInBytes / PerBlkSubblkCount;
+
+    const size_t lda = BlockCountK * BlkLen;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM % NRows2 == 0);
+    assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        float* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc0 = _mm512_setzero_ps(), acc1 = _mm512_setzero_ps();
+
+            for (size_t k = 0; k < BlockCountK; ++k) {
+                for (size_t kk = 0; kk < PerBlkSubblkCount; kk++) {
+                    const __m512i av00_64_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                    const __m512i av01_64_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + SubblkLen / 2));
+                    const __m512i av10_64_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda));
+                    const __m512i av11_64_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda + SubblkLen / 2));
+
+                    accumulate_blklen128_r2c1blk1_avx512<vnni>(av00_64_epi8, av01_64_epi8, av10_64_epi8, av11_64_epi8,
+                      QuantBDataPtr, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc0, acc1);
+
+                    // increment block pointers
+                    QuantAPtr += SubblkLen;
+                    QuantBDataPtr += SubblkDataSizeInBytes;
+                }
+                QuantAScalePtr++;
+                QuantBScalePtr++;
+            }
+
+            *SumPtr = hsum_float_16(acc0);
+            *(SumPtr + ldc) = hsum_float_16(acc1);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+                *(SumPtr + ldc) += *BiasPtr;
+            }
+
+            // move to next column
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmR1xC4BlkLen128Avx512(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t SubblkLen = 128;
+
+    const size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t PerBlkSubblkCount = BlkLen / SubblkLen;
+    const size_t SubblkDataSizeInBytes = BlkDataSizeInBytes / PerBlkSubblkCount;
+
+    const size_t lda = BlockCountK * BlkLen;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    //const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM < NRows2);
+    assert(CountN % NCols4 == 0);
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc[NCols4] = {_mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps()};
+            for (size_t k = 0; k < BlockCountK; ++k) {
+                for (size_t kk = 0; kk < PerBlkSubblkCount; kk++) {
+                    const __m512i av0_64_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                    const __m512i av1_64_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + SubblkLen / 2));
+                    accumulate_blklen128_r1c1blk1_avx512<vnni>(av0_64_epi8, av1_64_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc[0]);
+                    accumulate_blklen128_r1c1blk1_avx512<vnni>(av0_64_epi8, av1_64_epi8, QuantBDataPtr + SubblkDataSizeInBytes, QuantAScalePtr, QuantBScalePtr + 1, acc[1]);
+                    accumulate_blklen128_r1c1blk1_avx512<vnni>(av0_64_epi8, av1_64_epi8, QuantBDataPtr + 2 * SubblkDataSizeInBytes, QuantAScalePtr, QuantBScalePtr + 2, acc[2]);
+                    accumulate_blklen128_r1c1blk1_avx512<vnni>(av0_64_epi8, av1_64_epi8, QuantBDataPtr + 3 * SubblkDataSizeInBytes, QuantAScalePtr, QuantBScalePtr + 3, acc[3]);
+
+                    // increment block pointers
+                    QuantAPtr += SubblkLen;
+                    QuantBDataPtr += NCols4 * SubblkDataSizeInBytes;
+                }
+                QuantAScalePtr++;
+                QuantBScalePtr +=NCols4;
+            }
+
+            __m128 acc_r0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+            if (BiasPtr != nullptr) {
+                acc_r0 = _mm_add_ps(acc_r0, _mm_loadu_ps(BiasPtr));
+            }
+
+            _mm_storeu_ps(SumPtr, acc_r0);
+
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * StrideQuantBData;
+            QuantBScaleColPtr += NCols4 * BlockCountK;
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmR1xC1BlkLen128Avx512(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t SubblkLen = 128;
+
+    const size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t PerBlkSubblkCount = BlkLen / SubblkLen;
+    const size_t SubblkDataSizeInBytes = BlkDataSizeInBytes / PerBlkSubblkCount;
+
+    const size_t lda = BlockCountK * BlkLen;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM < NRows2);
+    assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc0 = _mm512_setzero_ps();
+            for (size_t k = 0; k < BlockCountK; ++k) {
+                for (size_t kk = 0; kk < PerBlkSubblkCount; kk++) {
+                    const __m512i av0_64_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                    const __m512i av1_64_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + SubblkLen / 2));
+
+                    accumulate_blklen128_r1c1blk1_avx512<vnni>(
+                        av0_64_epi8, av1_64_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0
+                    );
+
+                    // increment block pointers
+                    QuantAPtr += SubblkLen;
+                    QuantBDataPtr += SubblkDataSizeInBytes;
+                }
+                QuantAScalePtr++;
+                QuantBScalePtr++;
+            }
+
+            *SumPtr = hsum_float_16(acc0);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+            }
+
+            // move to next column
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+template<bool vnni>
+MLAS_FORCEINLINE size_t
+MlasQ4Int8GemmKernelBlkLen128Avx512(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+
+    const size_t lda = BlockCountK * BlkLen * sizeof(int8_t);
+    const size_t lda_scale = BlockCountK;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    size_t remainingRows = CountM % NRows2;
+    size_t multipleRows = CountM - remainingRows;
+    size_t remainingCols = CountN % NCols4;
+    size_t multipleCols = CountN - remainingCols;
+
+    if (multipleRows > 0 && multipleCols > 0) {
+        Q4Int8GemmR2xC4BlkLen128Avx512<vnni>(
+            BlkLen,
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            multipleRows,
+            multipleCols,
+            BlockCountK,
+            Bias,
+            ldc
+        );
+    }
+    if (remainingCols > 0 && multipleRows > 0) {
+        Q4Int8GemmR2xC1BlkLen128Avx512<vnni>(
+            BlkLen,
+            QuantA,
+            QuantAScale,
+            QuantBData + multipleCols * StrideQuantBData,
+            QuantBScale + multipleCols * StrideQuantBScale,
+            C + multipleCols,
+            multipleRows,
+            remainingCols,
+            BlockCountK,
+            Bias ? Bias + multipleCols : nullptr,
+            ldc);
+    }
+
+    if (remainingRows > 0 && multipleCols > 0) {
+        Q4Int8GemmR1xC4BlkLen128Avx512<vnni>(
+            BlkLen,
+            QuantA + multipleRows * lda,
+            QuantAScale + multipleRows * lda_scale,
+            QuantBData,
+            QuantBScale,
+            C + multipleRows * ldc,
+            remainingRows,
+            multipleCols,
+            BlockCountK,
+            Bias,
+            ldc);
+    }
+
+    if (remainingCols > 0 && remainingRows > 0) {
+        Q4Int8GemmR1xC1BlkLen128Avx512<vnni>(
+            BlkLen,
+            QuantA + multipleRows * lda,
+            QuantAScale + multipleRows * lda_scale,
+            QuantBData + multipleCols * StrideQuantBData,
+            QuantBScale + multipleCols * StrideQuantBScale,
+            C + multipleRows * ldc + multipleCols,
+            remainingRows,
+            remainingCols,
+            BlockCountK,
+            Bias ? Bias + multipleCols : nullptr,
+            ldc);
+    }
+
+    return CountM;
+}
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen16.h b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen16.h
new file mode 100644
index 0000000000000..3cd610796a5e3
--- /dev/null
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen16.h
@@ -0,0 +1,812 @@
+#pragma once
+#include <algorithm>
+#include <cassert>
+#include <utility>
+
+#include "sqnbitgemm.h"
+#include "sqnbitgemm_kernel_avx_common.h"
+#include "sqnbitgemm_kernel_avx2_int8_blklen16.h"
+#include "sqnbitgemm_kernel_avx512_int8_blklen32.h"
+#include "sqnbitgemm_kernel_avx512_int8_blklen64.h"
+
+
+
+static MLAS_FORCEINLINE void
+load_4blk_4b_packed_blklen16(const std::byte* QuantBDataPtr, __m512i& bv0_64_epi8, __m512i& bv1_64_epi8)
+{
+    // | v0 v64 | v1 v65 | ... | v62 v126 | v63 v127 |
+    const __m512i bv_packed = _mm512_loadu_si512(reinterpret_cast<const __m512i*>(QuantBDataPtr));
+    const __m512i low_mask = _mm512_set1_epi8(0x0F);
+    bv0_64_epi8 = _mm512_and_si512(bv_packed, low_mask);                          // 0~63
+    bv1_64_epi8 = _mm512_srli_epi16(_mm512_sub_epi8(bv_packed, bv0_64_epi8), 4);  // 64~127
+}
+
+static MLAS_FORCEINLINE void
+accumulate_blklen16_r1c1blk8_avx512(
+  const __m512i& av0_64_epi8,
+  const __m512i& av1_64_epi8,
+  const std::byte* QuantBDataPtr,
+  const float* scale_a,
+  const float* scale_b,
+  __m512& acc0)
+{
+    __m512i bv0_64_epi8, bv1_64_epi8;
+    load_4blk_4b_packed_blklen16(QuantBDataPtr, bv0_64_epi8, bv1_64_epi8);
+
+    const __m256 scale_b_ps = _mm256_loadu_ps(scale_b);  // 01234567
+    {
+        const __m256 scale_a0_ps = _mm256_loadu_ps(scale_a);  // 01234567
+        const __m256 scale_a0b_ps = _mm256_mul_ps(scale_b_ps, scale_a0_ps);
+        __m512 scale_a0b_16_ps = _mm512_castsi512_ps(
+            _mm512_broadcast_i64x4(_mm256_castps_si256(scale_a0b_ps))
+        ); // 0123456701234567
+
+        __m512i idx = _mm512_set_epi32(7, 7, 3, 3, 6, 6, 2, 2, 5, 5, 1, 1, 4, 4, 0, 0);
+        scale_a0b_16_ps = _mm512_permutexvar_ps(idx, scale_a0b_16_ps);  // 0044115522663377
+
+        const __m512i dot0_32_epi16 = _mm512_maddubs_epi16(bv0_64_epi8, av0_64_epi8);  // 0~0,1~1,2~2,3~3
+        const __m512i dot1_32_epi16 = _mm512_maddubs_epi16(bv1_64_epi8, av1_64_epi8);  // 4~4,5~5,6~6,7~7
+
+        const __m512i t1 = _mm512_unpacklo_epi64(dot0_32_epi16, dot1_32_epi16);  // 00004444111155552222666633337777
+        const __m512i t2 = _mm512_unpackhi_epi64(dot0_32_epi16, dot1_32_epi16);  // 00004444111155552222666633337777
+        const __m512i sum_32_epi16 = _mm512_add_epi16(t1, t2);
+        const __m512i one_32_epi16 = generate_ones_32_epi16();
+        const __m512i sum_16_epi32 = _mm512_madd_epi16(one_32_epi16, sum_32_epi16);  // 0044115522663377
+        const __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+        acc0 = _mm512_fmadd_ps(sum_16_ps, scale_a0b_16_ps, acc0);
+    }
+}
+
+static MLAS_FORCEINLINE void
+accumulate_blklen16_r2c1blk4_avx512(
+    const __m512i& av00_64_epi8,
+    const __m512i& av01_64_epi8,
+    const __m512i& av10_64_epi8,
+    const __m512i& av11_64_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a0,
+    const float* scale_a1,
+    const float* scale_b,
+    __m512& acc0,
+    __m512& acc1
+)
+{
+    __m512i bv0_64_epi8, bv1_64_epi8;
+    load_2blk_4b_packed_blklen64(QuantBDataPtr, bv0_64_epi8, bv1_64_epi8);
+
+    const __m256 scale_b_ps = _mm256_loadu_ps(scale_b);  // 01234567
+    {
+        const __m256 scale_a0_ps = _mm256_loadu_ps(scale_a0);  // 01234567
+        const __m256 scale_a0b_ps = _mm256_mul_ps(scale_b_ps, scale_a0_ps);
+        __m512 scale_a0b_16_ps = _mm512_castsi512_ps(
+            _mm512_broadcast_i64x4(_mm256_castps_si256(scale_a0b_ps))
+        );  // 0123456701234567
+
+        // TODO: load from memory
+        __m512i idx = _mm512_set_epi32(7, 7, 3, 3, 6, 6, 2, 2, 5, 5, 1, 1, 4, 4, 0, 0);
+        scale_a0b_16_ps = _mm512_permutexvar_ps(idx, scale_a0b_16_ps);
+
+        const __m512i dot0_32_epi16 = _mm512_maddubs_epi16(bv0_64_epi8, av00_64_epi8);
+        const __m512i dot1_32_epi16 = _mm512_maddubs_epi16(bv1_64_epi8, av01_64_epi8);
+
+        const __m512i t1 = _mm512_unpacklo_epi64(dot0_32_epi16, dot1_32_epi16);
+        const __m512i t2 = _mm512_unpackhi_epi64(dot0_32_epi16, dot1_32_epi16);
+        const __m512i sum_32_epi16 = _mm512_add_epi16(t1, t2);
+        const __m512i one_32_epi16 = generate_ones_32_epi16();
+        const __m512i sum_16_epi32 = _mm512_madd_epi16(one_32_epi16, sum_32_epi16);
+        const __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+        acc0 = _mm512_fmadd_ps(sum_16_ps, scale_a0b_16_ps, acc0);
+    }
+    {
+        const __m256 scale_a1_ps = _mm256_loadu_ps(scale_a1);  // 01234567
+        const __m256 scale_a1b_ps = _mm256_mul_ps(scale_b_ps, scale_a1_ps);
+        __m512 scale_a1b_16_ps = _mm512_castsi512_ps(
+            _mm512_broadcast_i64x4(_mm256_castps_si256(scale_a1b_ps))
+        );  // 0123456701234567
+
+        __m512i idx = _mm512_set_epi32(7, 7, 3, 3, 6, 6, 2, 2, 5, 5, 1, 1, 4, 4, 0, 0);
+        scale_a1b_16_ps = _mm512_permutexvar_ps(idx, scale_a1b_16_ps);
+
+        const __m512i dot0_32_epi16 = _mm512_maddubs_epi16(bv0_64_epi8, av10_64_epi8);
+        const __m512i dot1_32_epi16 = _mm512_maddubs_epi16(bv1_64_epi8, av11_64_epi8);
+
+        const __m512i t1 = _mm512_unpacklo_epi64(dot0_32_epi16, dot1_32_epi16);
+        const __m512i t2 = _mm512_unpackhi_epi64(dot0_32_epi16, dot1_32_epi16);
+        const __m512i sum_32_epi16 = _mm512_add_epi16(t1, t2);
+        const __m512i one_32_epi16 = generate_ones_32_epi16();
+        const __m512i sum_16_epi32 = _mm512_madd_epi16(one_32_epi16, sum_32_epi16);
+        const __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+        acc1 = _mm512_fmadd_ps(sum_16_ps, scale_a1b_16_ps, acc1);
+    }
+}
+
+static MLAS_FORCEINLINE void
+accumulate_blklen16_r1c1blk8_avx512vnni(
+    const __m512i& av0_64_epi8,
+    const __m512i& av1_64_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a,
+    const float* scale_b,
+    __m512& acc0
+)
+{
+    __m512i bv0_64_epi8, bv1_64_epi8;
+    load_4blk_4b_packed_blklen16(QuantBDataPtr, bv0_64_epi8, bv1_64_epi8);
+
+    const __m256 scale_b_ps = _mm256_loadu_ps(scale_b);  // 01234567
+    {
+        const __m256 scale_a0_ps = _mm256_loadu_ps(scale_a);  // 01234567
+        const __m256 scale_a0b_ps = _mm256_mul_ps(scale_b_ps, scale_a0_ps);
+        __m512 scale_a0b_16_ps = _mm512_castsi512_ps(
+            _mm512_broadcast_i64x4(_mm256_castps_si256(scale_a0b_ps))
+        );  // 0123456701234567
+
+        __m512i idx = _mm512_set_epi32(7, 7, 3, 3, 6, 6, 2, 2, 5, 5, 1, 1, 4, 4, 0, 0);
+        scale_a0b_16_ps = _mm512_permutexvar_ps(idx, scale_a0b_16_ps);  // 0044115522663377
+
+        const __m512i dot0_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv0_64_epi8, av0_64_epi8);  // 0000111122223333
+        const __m512i dot1_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv1_64_epi8, av1_64_epi8);  // 4444555566667777
+
+        const __m512i t1_16_epi32 = _mm512_unpacklo_epi64(dot0_16_epi32, dot1_16_epi32);  // 0044115522663377
+        const __m512i t2_16_epi32 = _mm512_unpackhi_epi64(dot0_16_epi32, dot1_16_epi32);  // 0044115522663377
+        const __m512i sum_16_epi32 = _mm512_add_epi32(t1_16_epi32, t2_16_epi32);
+        const __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+        acc0 = _mm512_fmadd_ps(sum_16_ps, scale_a0b_16_ps, acc0);
+    }
+}
+
+static MLAS_FORCEINLINE void
+accumulate_blklen16_r2c1blk4_avx512vnni(
+    const __m512i& av00_64_epi8,
+    const __m512i& av01_64_epi8,
+    const __m512i& av10_64_epi8,
+    const __m512i& av11_64_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a0,
+    const float* scale_a1,
+    const float* scale_b,
+    __m512& acc0,
+    __m512& acc1
+)
+{
+    __m512i bv0_64_epi8, bv1_64_epi8;
+    load_2blk_4b_packed_blklen64(QuantBDataPtr, bv0_64_epi8, bv1_64_epi8);
+
+    const __m256 scale_b_ps = _mm256_loadu_ps(scale_b);  // 01234567
+    {
+        const __m256 scale_a0_ps = _mm256_loadu_ps(scale_a0);  // 01234567
+        const __m256 scale_a0b_ps = _mm256_mul_ps(scale_b_ps, scale_a0_ps);
+        __m512 scale_a0b_16_ps = _mm512_castsi512_ps(
+            _mm512_broadcast_i64x4(_mm256_castps_si256(scale_a0b_ps))
+        );  // 0123456701234567
+
+        // TODO: load from memory
+        __m512i idx = _mm512_set_epi32(7, 7, 3, 3, 6, 6, 2, 2, 5, 5, 1, 1, 4, 4, 0, 0);
+        scale_a0b_16_ps = _mm512_permutexvar_ps(idx, scale_a0b_16_ps);
+
+        const __m512i dot0_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv0_64_epi8, av00_64_epi8);  // 0000111122223333
+        const __m512i dot1_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv1_64_epi8, av01_64_epi8);  // 4444555566667777
+
+        const __m512i t1_16_epi32 = _mm512_unpacklo_epi64(dot0_16_epi32, dot1_16_epi32);
+        const __m512i t2_16_epi32 = _mm512_unpackhi_epi64(dot0_16_epi32, dot1_16_epi32);
+        const __m512i sum_16_epi32 = _mm512_add_epi32(t1_16_epi32, t2_16_epi32);
+        const __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+        acc0 = _mm512_fmadd_ps(sum_16_ps, scale_a0b_16_ps, acc0);
+    }
+    {
+        const __m256 scale_a1_ps = _mm256_loadu_ps(scale_a1);  // 01234567
+        const __m256 scale_a1b_ps = _mm256_mul_ps(scale_b_ps, scale_a1_ps);
+        __m512 scale_a1b_16_ps = _mm512_castsi512_ps(
+            _mm512_broadcast_i64x4(_mm256_castps_si256(scale_a1b_ps))
+        );  // 0123456701234567
+
+        __m512i idx = _mm512_set_epi32(7, 7, 3, 3, 6, 6, 2, 2, 5, 5, 1, 1, 4, 4, 0, 0);
+        scale_a1b_16_ps = _mm512_permutexvar_ps(idx, scale_a1b_16_ps);
+
+        const __m512i dot0_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv0_64_epi8, av10_64_epi8);  // 0000111122223333
+        const __m512i dot1_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv1_64_epi8, av11_64_epi8);  // 4444555566667777
+
+        const __m512i t1_16_epi32 = _mm512_unpacklo_epi64(dot0_16_epi32, dot1_16_epi32);
+        const __m512i t2_16_epi32 = _mm512_unpackhi_epi64(dot0_16_epi32, dot1_16_epi32);
+        const __m512i sum_16_epi32 = _mm512_add_epi32(t1_16_epi32, t2_16_epi32);
+        const __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+        acc1 = _mm512_fmadd_ps(sum_16_ps, scale_a1b_16_ps, acc1);
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmR2xC4BlkLen16Avx512(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen16 = 16;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk8 = 8;
+
+    const size_t lda = BlockCountK * BlkLen16;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+    assert(CountM % NRows2 == 0);
+    assert(CountN % NCols4 == 0);
+
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc[NCols4 * NRows2] = {
+                _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(),
+                _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps()
+            };
+
+            size_t k_blks_remaining = BlockCountK;
+            // process 2 blks of 64 4b weights a time
+            for (; k_blks_remaining >= PerAccuBlk8; k_blks_remaining -= PerAccuBlk8) {
+                const __m512i av_00_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                const __m512i av_01_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + 64));
+                const __m512i av_10_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda));
+                const __m512i av_11_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda + 64));
+
+                if constexpr (vnni) {
+                    accumulate_blklen16_r2c1blk4_avx512vnni(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8,
+                        QuantBDataPtr, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr,
+                        acc[0], acc[NCols4]
+                    );
+                    accumulate_blklen16_r2c1blk4_avx512vnni(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + StrideQuantBData,
+                        QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + StrideQuantBScale,
+                        acc[1], acc[NCols4 + 1]
+                    );
+                    accumulate_blklen16_r2c1blk4_avx512vnni(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8,
+                        QuantBDataPtr + 2 * StrideQuantBData, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 2 * StrideQuantBScale,
+                        acc[2], acc[NCols4 + 2]
+                    );
+                    accumulate_blklen16_r2c1blk4_avx512vnni(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8,
+                        QuantBDataPtr + 3 * StrideQuantBData, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 3 * StrideQuantBScale,
+                        acc[3], acc[NCols4 + 3]
+                    );
+                } else {
+                    accumulate_blklen16_r2c1blk4_avx512(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8,
+                        QuantBDataPtr, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr,
+                        acc[0], acc[NCols4]
+                    );
+                    accumulate_blklen16_r2c1blk4_avx512(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + StrideQuantBData,
+                        QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + StrideQuantBScale,
+                        acc[1], acc[NCols4 + 1]
+                    );
+                    accumulate_blklen16_r2c1blk4_avx512(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8,
+                        QuantBDataPtr + 2 * StrideQuantBData, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 2 * StrideQuantBScale,
+                        acc[2], acc[NCols4 + 2]
+                    );
+                    accumulate_blklen16_r2c1blk4_avx512(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8,
+                        QuantBDataPtr + 3 * StrideQuantBData, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 3 * StrideQuantBScale,
+                        acc[3], acc[NCols4 + 3]
+                    );
+                }
+
+                // increment block pointers
+                QuantAPtr += BlkLen16 * PerAccuBlk8;
+                QuantAScalePtr += PerAccuBlk8;
+                QuantBDataPtr += BlkDataSizeInBytes * PerAccuBlk8;
+                QuantBScalePtr += PerAccuBlk8;
+            }  // k_blks_remaining
+
+            __m256 acc2[NCols4 * NRows2] = {
+                h_add_512(acc[0]),
+                h_add_512(acc[1]),
+                h_add_512(acc[2]),
+                h_add_512(acc[3]),
+                h_add_512(acc[4]),
+                h_add_512(acc[5]),
+                h_add_512(acc[6]),
+                h_add_512(acc[7])
+            };
+
+            while (k_blks_remaining-- > 0) {
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av0_16_epi16 = load_16_epi8_as_epi16(QuantABlk0);
+                const __m256i av1_16_epi16 = load_16_epi8_as_epi16(QuantABlk0 + lda);
+
+                const float& scale_a00 = *QuantAScalePtr;
+                const float& scale_a10 = *(QuantAScalePtr + BlockCountK);
+
+                {
+                    // Col0
+                    const float scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                    const float scale_10 = scale_a10 * (QuantBScalePtr)[0];
+                    accumulate_blklen16_r2c1blk1_avx2(av0_16_epi16, av1_16_epi16, QuantBDataPtr, scale_00, scale_10, acc2[0], acc2[NCols4]);
+                }
+
+                {
+                    // Col1
+                    const float scale_00 = scale_a00 * (QuantBScalePtr + StrideQuantBScale)[0];
+                    const float scale_10 = scale_a10 * (QuantBScalePtr + StrideQuantBScale)[0];
+                    accumulate_blklen16_r2c1blk1_avx2(av0_16_epi16, av1_16_epi16, QuantBDataPtr + StrideQuantBData, scale_00, scale_10,
+                      acc2[1], acc2[NCols4 + 1]);
+                }
+
+                {
+                    // Col2
+                    const float scale_00 = scale_a00 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                    const float scale_10 = scale_a10 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                    accumulate_blklen16_r2c1blk1_avx2(av0_16_epi16, av1_16_epi16, QuantBDataPtr + 2 * StrideQuantBData, scale_00, scale_10,
+                      acc2[2], acc2[NCols4 + 2]);
+                }
+
+                {
+                    // Col3
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                    const float& scale_10 = scale_a10 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                    accumulate_blklen16_r2c1blk1_avx2(
+                        av0_16_epi16, av1_16_epi16, QuantBDataPtr + 3 * StrideQuantBData, scale_00, scale_10,
+                      acc2[3], acc2[NCols4 + 3]);
+                }
+                QuantAPtr += BlkLen16;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes;
+                QuantBScalePtr++;
+            }  // k_blks_remaining
+
+            __m128 acc_r0 = FoldAccumulators(acc2[0], acc2[1], acc2[2], acc2[3]);
+            __m128 acc_r1 = FoldAccumulators(acc2[NCols4 + 0], acc2[NCols4 + 1], acc2[NCols4 + 2], acc2[NCols4 + 3]);
+            if (BiasPtr != nullptr) {
+                const __m128 bias_4_ps = _mm_loadu_ps(BiasPtr);
+                acc_r0 = _mm_add_ps(acc_r0, bias_4_ps);
+                acc_r1 = _mm_add_ps(acc_r1, bias_4_ps);
+            }
+            _mm_storeu_ps(SumPtr, acc_r0);
+            _mm_storeu_ps(SumPtr + ldc, acc_r1);
+
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * StrideQuantBData;
+            QuantBScaleColPtr += NCols4 * StrideQuantBScale;
+
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+template <bool vnni>
+void MLAS_FORCEINLINE
+Q4Int8GemmR2C1BlkLen16Avx512(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc)
+{
+    constexpr size_t BlkLen16 = 16;
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk8 = 8;
+
+    const size_t lda = BlockCountK * BlkLen16;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+    assert(CountM % NRows2 == 0);
+    assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        float* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc0 = _mm512_setzero_ps(), acc1 = _mm512_setzero_ps();
+
+            size_t k_blks_remaining = BlockCountK;
+            // process 2 blks of 64 4b weights a time
+            for (; k_blks_remaining >= PerAccuBlk8; k_blks_remaining -= PerAccuBlk8) {
+                const __m512i av_00_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                const __m512i av_01_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + 64));
+                const __m512i av_10_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda));
+                const __m512i av_11_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda + 64));
+
+                if constexpr (vnni) {
+                    accumulate_blklen16_r2c1blk4_avx512vnni(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr,
+                        QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc0, acc1
+                    );
+                } else {
+                    accumulate_blklen16_r2c1blk4_avx512(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr,
+                        QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc0, acc1
+                    );
+                }
+
+                // increment block pointers
+                QuantAPtr += BlkLen16 * PerAccuBlk8;
+                QuantAScalePtr += PerAccuBlk8;
+                QuantBDataPtr += BlkDataSizeInBytes * PerAccuBlk8;
+                QuantBScalePtr += PerAccuBlk8;
+            }
+
+            __m256 acc20 = h_add_512(acc0);
+            __m256 acc21 = h_add_512(acc1);
+            while (k_blks_remaining-- > 0) {
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av0_16_epi16 = load_16_epi8_as_epi16(QuantABlk0);
+                const __m256i av1_16_epi16 = load_16_epi8_as_epi16(QuantABlk0 + lda);
+
+                const float& scale_a00 = *QuantAScalePtr;
+                const float& scale_a10 = *(QuantAScalePtr + BlockCountK);
+
+                const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                const float& scale_10 = scale_a10 * (QuantBScalePtr)[0];
+                accumulate_blklen16_r2c1blk1_avx2(av0_16_epi16, av1_16_epi16, QuantBDataPtr, scale_00, scale_10, acc20, acc21);
+
+                QuantAPtr += BlkLen16;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes;
+                QuantBScalePtr++;
+            }
+
+            *SumPtr = hsum_float_8(acc20);
+            *(SumPtr + ldc) = hsum_float_8(acc21);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+                *(SumPtr + ldc) += *BiasPtr;
+            }
+
+            // move to next column
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmR1xC4BlkLen16Avx512(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen16 = 16;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk8 = 8;
+
+    const size_t lda = BlockCountK * BlkLen16;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM < NRows2);
+    assert(CountN % NCols4 == 0);
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc[NCols4] = {
+              _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps()
+            };
+            size_t k_blks_remaining = BlockCountK;
+            for (; k_blks_remaining >= PerAccuBlk8; k_blks_remaining -= PerAccuBlk8) {
+                const __m512i av_00_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                const __m512i av_01_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + 64));
+
+                if constexpr (vnni) {
+                    accumulate_blklen16_r1c1blk8_avx512vnni(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc[0]);
+                    accumulate_blklen16_r1c1blk8_avx512vnni(av_00_epi8, av_01_epi8, QuantBDataPtr + StrideQuantBData, QuantAScalePtr, QuantBScalePtr + StrideQuantBScale, acc[1]);
+                    accumulate_blklen16_r1c1blk8_avx512vnni(av_00_epi8, av_01_epi8, QuantBDataPtr + 2 * StrideQuantBData, QuantAScalePtr, QuantBScalePtr + 2 * StrideQuantBScale, acc[2]);
+                    accumulate_blklen16_r1c1blk8_avx512vnni(av_00_epi8, av_01_epi8, QuantBDataPtr + 3 * StrideQuantBData, QuantAScalePtr, QuantBScalePtr + 3 * StrideQuantBScale, acc[3]);
+                } else {
+                    accumulate_blklen16_r1c1blk8_avx512(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc[0]);
+                    accumulate_blklen16_r1c1blk8_avx512(av_00_epi8, av_01_epi8, QuantBDataPtr + StrideQuantBData, QuantAScalePtr, QuantBScalePtr + StrideQuantBScale, acc[1]);
+                    accumulate_blklen16_r1c1blk8_avx512(av_00_epi8, av_01_epi8, QuantBDataPtr + 2 * StrideQuantBData, QuantAScalePtr, QuantBScalePtr + 2 * StrideQuantBScale, acc[2]);
+                    accumulate_blklen16_r1c1blk8_avx512(av_00_epi8, av_01_epi8, QuantBDataPtr + 3 * StrideQuantBData, QuantAScalePtr, QuantBScalePtr + 3 * StrideQuantBScale, acc[3]);
+                }
+
+                QuantAPtr += BlkLen16 * PerAccuBlk8;
+                QuantAScalePtr += PerAccuBlk8;
+                QuantBDataPtr += BlkDataSizeInBytes * PerAccuBlk8;
+                QuantBScalePtr += PerAccuBlk8;
+            }
+
+            __m256 acc2[NCols4] = {
+                h_add_512(acc[0]), h_add_512(acc[1]), h_add_512(acc[2]), h_add_512(acc[3])
+            };
+
+            while (k_blks_remaining-- > 0) {
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av_00_epi8 = load_16_epi8_as_epi16(QuantABlk0);
+
+                const float& scale_a00 = *QuantAScalePtr;
+                {
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                    accumulate_blklen16_r1c1blk1_avx2(av_00_epi8, QuantBDataPtr, scale_00, acc2[0]);
+                }
+                {
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + StrideQuantBScale)[0];
+                    accumulate_blklen16_r1c1blk1_avx2(av_00_epi8, QuantBDataPtr + StrideQuantBData, scale_00, acc2[1]);
+                }
+                {
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 2 * StrideQuantBScale)[0];
+                    accumulate_blklen16_r1c1blk1_avx2(av_00_epi8, QuantBDataPtr + 2 * StrideQuantBData, scale_00, acc2[2]);
+                }
+                {
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 3 * StrideQuantBScale)[0];
+                    accumulate_blklen16_r1c1blk1_avx2(av_00_epi8, QuantBDataPtr + 3 * StrideQuantBData, scale_00, acc2[3]);
+                }
+
+                QuantAPtr += BlkLen16;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes;
+                QuantBScalePtr++;
+
+            }
+
+            __m128 acc_r0 = FoldAccumulators(acc2[0], acc2[1], acc2[2], acc2[3]);
+            if (BiasPtr != nullptr) {
+                acc_r0 = _mm_add_ps(acc_r0, _mm_loadu_ps(BiasPtr));
+            }
+
+            _mm_storeu_ps(SumPtr, acc_r0);
+
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * StrideQuantBData;
+            QuantBScaleColPtr += NCols4 * StrideQuantBScale;
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmR1xC1BlkLen16Avx512(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen16 = 16;
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk8 = 8;
+
+    const size_t lda = BlockCountK * BlkLen16;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+    assert(CountM < NRows2);
+    assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc0 = _mm512_setzero_ps();
+            size_t k_blks_remaining = BlockCountK;
+            for (; k_blks_remaining >= PerAccuBlk8; k_blks_remaining -= PerAccuBlk8) {
+                const __m512i av_00_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                const __m512i av_01_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + 64));
+
+                if constexpr (vnni) {
+                    accumulate_blklen16_r1c1blk8_avx512(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0);
+                } else {
+                    accumulate_blklen16_r1c1blk8_avx512vnni(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0);
+                }
+
+                QuantAPtr += BlkLen16 * PerAccuBlk8;
+                QuantAScalePtr += PerAccuBlk8;
+                QuantBDataPtr += BlkDataSizeInBytes * PerAccuBlk8;
+                QuantBScalePtr += PerAccuBlk8;
+            }
+
+            __m256 acc2 = h_add_512(acc0);
+            while (k_blks_remaining-- > 0) {
+                const __m256i av_00_epi8 = load_16_epi8_as_epi16(QuantAPtr);
+
+                const float& scale_a00 = *QuantAScalePtr;
+                const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                accumulate_blklen16_r1c1blk1_avx2(av_00_epi8, QuantBDataPtr, scale_00, acc2);
+
+                QuantAPtr += BlkLen16;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes;
+                QuantBScalePtr++;
+            }
+
+            *SumPtr = hsum_float_8(acc2);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+            }
+
+            // move to next column
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+template<bool vnni>
+MLAS_FORCEINLINE
+    size_t
+MlasQ4Int8GemmKernelBlkLen16Avx512(
+        const std::byte* QuantA,
+        const float* QuantAScale,
+        const std::byte* QuantBData,
+        const float* QuantBScale,
+        float* C,
+        size_t CountM,
+        size_t CountN,
+        size_t BlockCountK,
+        const float* Bias,
+        size_t ldc
+    )
+{
+    constexpr size_t BlkLen16 = 16;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+
+    const size_t lda = BlockCountK * BlkLen16 * sizeof(int8_t);
+    const size_t lda_scale = BlockCountK;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen16);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+
+    size_t remainingRows = CountM % NRows2;
+    size_t multipleRows = CountM - remainingRows;
+    size_t remainingCols = CountN % NCols4;
+    size_t multipleCols = CountN - remainingCols;
+
+    if (multipleRows > 0 && multipleCols > 0) {
+        Q4Int8GemmR2xC4BlkLen16Avx512<vnni>(
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            multipleRows,
+            multipleCols,
+            BlockCountK,
+            Bias,
+            ldc
+        );
+    }
+    if (remainingCols > 0 && multipleRows > 0) {
+        Q4Int8GemmR2C1BlkLen16Avx512<vnni>(
+            QuantA,
+            QuantAScale,
+            QuantBData + multipleCols * StrideQuantBData,
+            QuantBScale + multipleCols * StrideQuantBScale,
+            C + multipleCols,
+            multipleRows,
+            remainingCols,
+            BlockCountK,
+            Bias ? Bias + multipleCols : nullptr,
+            ldc);
+    }
+
+    if (remainingRows > 0 && multipleCols > 0) {
+        Q4Int8GemmR1xC4BlkLen16Avx512<vnni>(
+            QuantA + multipleRows * lda,
+            QuantAScale + multipleRows * lda_scale,
+            QuantBData,
+            QuantBScale,
+            C + multipleRows * ldc,
+            remainingRows,
+            multipleCols,
+            BlockCountK,
+            Bias,
+            ldc);
+    }
+
+    if (remainingCols > 0 && remainingRows > 0) {
+        Q4Int8GemmR1xC1BlkLen16Avx512<vnni>(
+            QuantA + multipleRows * lda,
+            QuantAScale + multipleRows * lda_scale,
+            QuantBData + multipleCols * StrideQuantBData,
+            QuantBScale + multipleCols * StrideQuantBScale,
+            C + multipleRows * ldc + multipleCols,
+            remainingRows,
+            remainingCols,
+            BlockCountK,
+            Bias ? Bias + multipleCols : nullptr,
+            ldc);
+    }
+
+    return CountM;
+}
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen32.h b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen32.h
new file mode 100644
index 0000000000000..ca12cc14a7875
--- /dev/null
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen32.h
@@ -0,0 +1,852 @@
+#pragma once
+#include <algorithm>
+#include <cassert>
+#include <utility>
+
+#include "sqnbitgemm.h"
+#include "sqnbitgemm_kernel_avx_common.h"
+#include "sqnbitgemm_kernel_avx2_int8_blklen32.h"
+#include "sqnbitgemm_kernel_avx512_int8_blklen64.h"
+
+static MLAS_FORCEINLINE void
+load_4blk_4b_packed_blklen32(const std::byte* QuantBDataPtr, __m512i& bv0_64_epi8, __m512i& bv1_64_epi8)
+{
+    // | v0 v64 | v1 v65 | ... | v62 v126 | v63 v127 |
+    const __m512i bv_packed = _mm512_loadu_si512(reinterpret_cast<const __m512i*>(QuantBDataPtr));
+    const __m512i low_mask = _mm512_set1_epi8(0x0F);
+    bv0_64_epi8 = _mm512_and_si512(bv_packed, low_mask);                          // 0~63
+    bv1_64_epi8 = _mm512_srli_epi16(_mm512_sub_epi8(bv_packed, bv0_64_epi8), 4);  // 64~127
+}
+
+static const uint32_t index_array[16] = {0, 0, 2, 2, 0, 0, 2, 2, 1, 1, 3, 3, 1, 1, 3, 3};
+
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r1c1blk4_avx512(
+  const __m512i& av0_64_epi8,
+  const __m512i& av1_64_epi8,
+  const std::byte* QuantBDataPtr,
+  const float* scale_a,
+  const float* scale_b,
+  __m512& acc0)
+{
+    __m512i bv0_64_epi8, bv1_64_epi8;
+    load_4blk_4b_packed_blklen32(QuantBDataPtr, bv0_64_epi8, bv1_64_epi8);
+
+    const __m128 scale_b_ps = _mm_loadu_ps(scale_b);  // 0123
+    {
+        const __m128 scale_a0_ps = _mm_loadu_ps(scale_a);  // 0123
+        const __m128 scale_a0b_ps = _mm_mul_ps(scale_b_ps, scale_a0_ps);
+        __m512 scale_a0b_16_ps = _mm512_broadcast_f32x4(scale_a0b_ps);  // 0123012301230123
+
+        __m512i idx = _mm512_set_epi32(3, 3, 1, 1, 3, 3, 1, 1, 2, 2, 0, 0, 2, 2, 0, 0);
+        // __m512i idx = _mm512_loadu_epi8(&index_array[0]);
+        scale_a0b_16_ps = _mm512_permutexvar_ps(idx, scale_a0b_16_ps);  // 0022002211331133
+
+        const __m512i dot0_32_epi16 = _mm512_maddubs_epi16(bv0_64_epi8, av0_64_epi8);  // 0~0,1~1
+        const __m512i dot1_32_epi16 = _mm512_maddubs_epi16(bv1_64_epi8, av1_64_epi8);  // 2~2,3~3
+
+        const __m512i t1 = _mm512_unpacklo_epi64(dot0_32_epi16, dot1_32_epi16);  // 00002222000022221111333311113333
+        const __m512i t2 = _mm512_unpackhi_epi64(dot0_32_epi16, dot1_32_epi16);  // 00002222000022221111333311113333
+        const __m512i sum_32_epi16 = _mm512_add_epi16(t1, t2);                   // 00002222000022221111333311113333
+        const __m512i one_32_epi16 = generate_ones_32_epi16();
+        const __m512i sum_16_epi32 = _mm512_madd_epi16(one_32_epi16, sum_32_epi16);  // 0022002211331133
+        const __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+        acc0 = _mm512_fmadd_ps(sum_16_ps, scale_a0b_16_ps, acc0);
+    }
+}
+
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r2c1blk4_avx512(
+    const __m512i& av00_64_epi8,
+    const __m512i& av01_64_epi8,
+    const __m512i& av10_64_epi8,
+    const __m512i& av11_64_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a0,
+    const float* scale_a1,
+    const float* scale_b,
+    __m512& acc0,
+    __m512& acc1
+)
+{
+    __m512i bv0_64_epi8, bv1_64_epi8;
+    load_2blk_4b_packed_blklen64(QuantBDataPtr, bv0_64_epi8, bv1_64_epi8);
+
+    const __m128 scale_b_ps = _mm_loadu_ps(scale_b); // 0123
+    {
+        const __m128 scale_a0_ps = _mm_loadu_ps(scale_a0);  // 0123
+        const __m128 scale_a0b_ps = _mm_mul_ps(scale_b_ps, scale_a0_ps);
+        __m512 scale_a0b_16_ps = _mm512_broadcast_f32x4(scale_a0b_ps);  // 0123012301230123
+
+        __m512i idx = _mm512_set_epi32(3, 3, 1, 1, 3, 3, 1, 1, 2, 2, 0, 0, 2, 2, 0, 0);
+        // __m512i idx = _mm512_loadu_epi8(&index_array[0]);
+        scale_a0b_16_ps = _mm512_permutexvar_ps(idx, scale_a0b_16_ps);  // 0022002211331133
+
+        const __m512i dot0_32_epi16 = _mm512_maddubs_epi16(bv0_64_epi8, av00_64_epi8);  // 0~0,1~1
+        const __m512i dot1_32_epi16 = _mm512_maddubs_epi16(bv1_64_epi8, av01_64_epi8);  // 2~2,3~3
+
+        const __m512i t1 = _mm512_unpacklo_epi64(dot0_32_epi16, dot1_32_epi16);  // 00002222000022221111333311113333
+        const __m512i t2 = _mm512_unpackhi_epi64(dot0_32_epi16, dot1_32_epi16);  // 00002222000022221111333311113333
+        const __m512i sum_32_epi16 = _mm512_add_epi16(t1, t2);                   // 00002222000022221111333311113333
+        const __m512i one_32_epi16 = generate_ones_32_epi16();
+        const __m512i sum_16_epi32 = _mm512_madd_epi16(one_32_epi16, sum_32_epi16);  // 0022002211331133
+        const __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+        acc0 = _mm512_fmadd_ps(sum_16_ps, scale_a0b_16_ps, acc0);
+    }
+    {
+        const __m128 scale_a1_ps = _mm_loadu_ps(scale_a1);  // 0123
+        const __m128 scale_a1b_ps = _mm_mul_ps(scale_b_ps, scale_a1_ps);
+        __m512 scale_a1b_16_ps = _mm512_broadcast_f32x4(scale_a1b_ps);  // 0123012301230123
+
+        __m512i idx = _mm512_set_epi32(3, 3, 1, 1, 3, 3, 1, 1, 2, 2, 0, 0, 2, 2, 0, 0);
+        // __m512i idx = _mm512_loadu_epi8(&index_array[0]);
+        scale_a1b_16_ps = _mm512_permutexvar_ps(idx, scale_a1b_16_ps);  // 0022002211331133
+
+        const __m512i dot0_32_epi16 = _mm512_maddubs_epi16(bv0_64_epi8, av10_64_epi8);  // 0~0,1~1
+        const __m512i dot1_32_epi16 = _mm512_maddubs_epi16(bv1_64_epi8, av11_64_epi8);  // 2~2,3~3
+
+        const __m512i t1 = _mm512_unpacklo_epi64(dot0_32_epi16, dot1_32_epi16);  // 00002222000022221111333311113333
+        const __m512i t2 = _mm512_unpackhi_epi64(dot0_32_epi16, dot1_32_epi16);  // 00002222000022221111333311113333
+        const __m512i sum_32_epi16 = _mm512_add_epi16(t1, t2);                   // 00002222000022221111333311113333
+        const __m512i one_32_epi16 = generate_ones_32_epi16();
+        const __m512i sum_16_epi32 = _mm512_madd_epi16(one_32_epi16, sum_32_epi16);  // 0022002211331133
+        const __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+        acc1 = _mm512_fmadd_ps(sum_16_ps, scale_a1b_16_ps, acc1);
+    }
+}
+
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r1c1blk4_avx512vnni(
+    const __m512i& av0_64_epi8,
+    const __m512i& av1_64_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a,
+    const float* scale_b,
+    __m512& acc0
+)
+{
+    __m512i bv0_64_epi8, bv1_64_epi8;
+    load_4blk_4b_packed_blklen32(QuantBDataPtr, bv0_64_epi8, bv1_64_epi8);
+
+    const __m128 scale_b_ps = _mm_loadu_ps(scale_b);  // 0123
+    {
+        const __m128 scale_a0_ps = _mm_loadu_ps(scale_a);  // 0123
+        const __m128 scale_a0b_ps = _mm_mul_ps(scale_b_ps, scale_a0_ps);
+        __m512 scale_a0b_16_ps = _mm512_broadcast_f32x4(scale_a0b_ps);  // 0123012301230123
+
+        __m512i idx = _mm512_set_epi32(3, 3, 1, 1, 3, 3, 1, 1, 2, 2, 0, 0, 2, 2, 0, 0);
+        //__m512i idx = _mm512_loadu_epi8(&index_array[0]);
+        scale_a0b_16_ps = _mm512_permutexvar_ps(idx, scale_a0b_16_ps);  // 0022002211331133
+
+        const __m512i dot0_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv0_64_epi8, av0_64_epi8);  // 0000000011111111
+        const __m512i dot1_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv1_64_epi8, av1_64_epi8);  // 2222222233333333
+
+        const __m512i t1_16_epi32 = _mm512_unpacklo_epi64(dot0_16_epi32, dot1_16_epi32);  // 0022002211331133
+        const __m512i t2_16_epi32 = _mm512_unpackhi_epi64(dot0_16_epi32, dot1_16_epi32);  // 0022002211331133
+        const __m512i sum_16_epi32 = _mm512_add_epi32(t1_16_epi32, t2_16_epi32);          // 0022002211331133
+        const __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+        acc0 = _mm512_fmadd_ps(sum_16_ps, scale_a0b_16_ps, acc0);
+    }
+}
+
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r2c1blk4_avx512vnni(
+    const __m512i& av00_64_epi8,
+    const __m512i& av01_64_epi8,
+    const __m512i& av10_64_epi8,
+    const __m512i& av11_64_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a0,
+    const float* scale_a1,
+    const float* scale_b,
+    __m512& acc0,
+    __m512& acc1
+)
+{
+    __m512i bv0_64_epi8, bv1_64_epi8;
+    load_2blk_4b_packed_blklen64(QuantBDataPtr, bv0_64_epi8, bv1_64_epi8);
+    __m512i idx = _mm512_set_epi32(3, 3, 1, 1, 3, 3, 1, 1, 2, 2, 0, 0, 2, 2, 0, 0);
+    //__m512i idx = _mm512_loadu_epi8(&index_array[0]);
+
+    const __m128 scale_b_ps = _mm_loadu_ps(scale_b);  // 0123
+    {
+        const __m128 scale_a0_ps = _mm_loadu_ps(scale_a0);  // 0123
+        const __m128 scale_a0b_ps = _mm_mul_ps(scale_b_ps, scale_a0_ps);
+        __m512 scale_a0b_16_ps = _mm512_broadcast_f32x4(scale_a0b_ps);  // 0123012301230123
+
+        scale_a0b_16_ps = _mm512_permutexvar_ps(idx, scale_a0b_16_ps);  // 0022002211331133
+
+        const __m512i dot0_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv0_64_epi8, av00_64_epi8);  // 0000000011111111
+        const __m512i dot1_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv1_64_epi8, av01_64_epi8);  // 2222222233333333
+
+        const __m512i t1_16_epi32 = _mm512_unpacklo_epi64(dot0_16_epi32, dot1_16_epi32);  // 0022002211331133
+        const __m512i t2_16_epi32 = _mm512_unpackhi_epi64(dot0_16_epi32, dot1_16_epi32);  // 0022002211331133
+        const __m512i sum_16_epi32 = _mm512_add_epi32(t1_16_epi32, t2_16_epi32);          // 0022002211331133
+        const __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+        acc0 = _mm512_fmadd_ps(sum_16_ps, scale_a0b_16_ps, acc0);
+    }
+    {
+        const __m128 scale_a1_ps = _mm_loadu_ps(scale_a1);  // 0123
+        const __m128 scale_a1b_ps = _mm_mul_ps(scale_b_ps, scale_a1_ps);
+        __m512 scale_a1b_16_ps = _mm512_broadcast_f32x4(scale_a1b_ps);  // 0123012301230123
+
+        scale_a1b_16_ps = _mm512_permutexvar_ps(idx, scale_a1b_16_ps);  // 0022002211331133
+
+        const __m512i dot0_32_epi16 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv0_64_epi8, av10_64_epi8);  // 0000000011111111
+        const __m512i dot1_32_epi16 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv1_64_epi8, av11_64_epi8);  // 2222222233333333
+
+        const __m512i t1_16_epi32 = _mm512_unpacklo_epi64(dot0_32_epi16, dot1_32_epi16);  // 0022002211331133
+        const __m512i t2_16_epi32 = _mm512_unpackhi_epi64(dot0_32_epi16, dot1_32_epi16);  // 0022002211331133
+        const __m512i sum_16_epi32 = _mm512_add_epi32(t1_16_epi32, t2_16_epi32);          // 0022002211331133
+        const __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+        acc1 = _mm512_fmadd_ps(sum_16_ps, scale_a1b_16_ps, acc1);
+    }
+}
+
+MLAS_FORCEINLINE void
+accumulate_1blk_dot_avx512vnni(const __m256i& av_32_epi8, const __m256i& bv_32_epi8, const float& combined_scale, __m256& acc)
+{
+    __m256i sum_8_epi32 = _mm256_dpbusd_epi32(_mm256_setzero_si256(), bv_32_epi8, av_32_epi8);
+    const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+    acc = _mm256_fmadd_ps(sum_ps, _mm256_set1_ps(combined_scale), acc);
+}
+
+template <bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r1c1blk1_avx512(
+    const __m256i& av00_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float& combined_scale00,
+    __m256& acc0
+)
+{
+    if constexpr (vnni) {
+        // | v0  v16 | v1  v17 | ... | v14 v30 | v15 v31 |
+        const __m128i bv_packed0 = _mm_loadu_si128(reinterpret_cast<const __m128i*>(QuantBDataPtr));
+        __m256i bv_32_epi8 = _mm256_set_m128i(_mm_srli_epi16(bv_packed0, 4), bv_packed0);
+        bv_32_epi8 = _mm256_and_si256(_mm256_set1_epi8(0x0F), bv_32_epi8);
+        accumulate_1blk_dot_avx512vnni(av00_32_epi8, bv_32_epi8, combined_scale00, acc0);
+    } else {
+        accumulate_blklen32_r1c1blk1_avx2<false>(av00_32_epi8, QuantBDataPtr, combined_scale00, acc0);
+    }
+}
+
+template <bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r2c1blk1_avx512(
+    const __m256i& av00_32_epi8,
+    const __m256i& av10_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float& combined_scale00,
+    const float& combined_scale10,
+    __m256& acc0,
+    __m256& acc1
+)
+{
+    if constexpr (vnni) {
+        // | v0  v16 | v1  v17 | ... | v14 v30 | v15 v31 |
+        const __m128i bv_packed0 = _mm_loadu_si128(reinterpret_cast<const __m128i*>(QuantBDataPtr));
+        __m256i bv_32_epi8 = _mm256_set_m128i(_mm_srli_epi16(bv_packed0, 4), bv_packed0);
+        bv_32_epi8 = _mm256_and_si256(_mm256_set1_epi8(0x0F), bv_32_epi8);
+
+        accumulate_1blk_dot_avx512vnni(av00_32_epi8, bv_32_epi8, combined_scale00, acc0);
+        accumulate_1blk_dot_avx512vnni(av10_32_epi8, bv_32_epi8, combined_scale10, acc1);
+    } else {
+        accumulate_blklen32_r2c1blk1_avx2<false>(av00_32_epi8, av10_32_epi8, QuantBDataPtr, combined_scale00, combined_scale10, acc0, acc1);
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmR2xC4BlkLen32Avx512(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk4 = 4;
+
+    const size_t lda = BlockCountK * BlkLen32;
+    const size_t StrideQuantBData = PerAccuBlk4 * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    //const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM % NRows2 == 0);
+    assert(CountN % NCols4 == 0);
+
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc[NCols4 * NRows2] = {
+                _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(),
+                _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps()
+            };
+
+            size_t k_blks_remaining = BlockCountK;
+            // process 2 blks of 64 4b weights a time
+            for (; k_blks_remaining >= PerAccuBlk4; k_blks_remaining -= PerAccuBlk4) {
+                const __m512i av_00_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                const __m512i av_01_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + 64));
+                const __m512i av_10_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda));
+                const __m512i av_11_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda + 64));
+
+                if constexpr (vnni) {
+                    accumulate_blklen32_r2c1blk4_avx512vnni(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8,
+                        QuantBDataPtr, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr,
+                        acc[0], acc[NCols4]
+                    );
+                    accumulate_blklen32_r2c1blk4_avx512vnni(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + StrideQuantBData,
+                        QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + PerAccuBlk4,
+                        acc[1], acc[NCols4 + 1]
+                    );
+                    accumulate_blklen32_r2c1blk4_avx512vnni(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8,
+                        QuantBDataPtr + 2 * StrideQuantBData, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 2 * PerAccuBlk4,
+                        acc[2], acc[NCols4 + 2]
+                    );
+                    accumulate_blklen32_r2c1blk4_avx512vnni(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8,
+                        QuantBDataPtr + 3 * StrideQuantBData, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 3 * PerAccuBlk4,
+                        acc[3], acc[NCols4 + 3]
+                    );
+                } else {
+                    accumulate_blklen32_r2c1blk4_avx512(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8,
+                        QuantBDataPtr, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr,
+                        acc[0], acc[NCols4]
+                    );
+                    accumulate_blklen32_r2c1blk4_avx512(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + StrideQuantBData,
+                        QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + PerAccuBlk4,
+                        acc[1], acc[NCols4 + 1]
+                    );
+                    accumulate_blklen32_r2c1blk4_avx512(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8,
+                        QuantBDataPtr + 2 * StrideQuantBData, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 2 * PerAccuBlk4,
+                        acc[2], acc[NCols4 + 2]
+                    );
+                    accumulate_blklen32_r2c1blk4_avx512(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8,
+                        QuantBDataPtr + 3 * StrideQuantBData, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 3 * PerAccuBlk4,
+                        acc[3], acc[NCols4 + 3]
+                    );
+                }
+
+                // increment block pointers
+                QuantAPtr += BlkLen32 * PerAccuBlk4;
+                QuantAScalePtr += PerAccuBlk4;
+                QuantBDataPtr += StrideQuantBData * NCols4;
+                QuantBScalePtr += PerAccuBlk4 * NCols4;
+            }  // k_blks_remaining
+
+            __m256 acc2[NCols4 * NRows2] = {
+                h_add_512(acc[0]),
+                h_add_512(acc[1]),
+                h_add_512(acc[2]),
+                h_add_512(acc[3]),
+                h_add_512(acc[4]),
+                h_add_512(acc[5]),
+                h_add_512(acc[6]),
+                h_add_512(acc[7])
+            };
+
+            while (k_blks_remaining-- > 0) {
+                // load A
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk0);
+                const __m256i av_10_epi8 = _mm256_loadu_si256((const __m256i*)(QuantABlk0 + lda));
+
+                const float& scale_a00 = *QuantAScalePtr;
+                const float& scale_a10 = *(QuantAScalePtr + BlockCountK);
+
+                {
+                    // Col0
+                    const float scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                    const float scale_10 = scale_a10 * (QuantBScalePtr)[0];
+                    accumulate_blklen32_r2c1blk1_avx512<vnni>(av_00_epi8, av_10_epi8, QuantBDataPtr, scale_00, scale_10, acc2[0], acc2[NCols4]);
+                }
+
+                {
+                    // Col1
+                    const float scale_00 = scale_a00 * (QuantBScalePtr + 1)[0];
+                    const float scale_10 = scale_a10 * (QuantBScalePtr + 1)[0];
+                    accumulate_blklen32_r2c1blk1_avx512<vnni>(av_00_epi8, av_10_epi8, QuantBDataPtr + BlkDataSizeInBytes16, scale_00, scale_10, acc2[1], acc2[NCols4 + 1]);
+                }
+
+                {
+                    // Col2
+                    const float scale_00 = scale_a00 * (QuantBScalePtr + 2)[0];
+                    const float scale_10 = scale_a10 * (QuantBScalePtr + 2)[0];
+                    accumulate_blklen32_r2c1blk1_avx512<vnni>(av_00_epi8, av_10_epi8, QuantBDataPtr + 2 * BlkDataSizeInBytes16, scale_00, scale_10, acc2[2], acc2[NCols4 + 2]);
+                }
+
+                {
+                    // Col3
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 3)[0];
+                    const float& scale_10 = scale_a10 * (QuantBScalePtr + 3)[0];
+                    accumulate_blklen32_r2c1blk1_avx512<vnni>(av_00_epi8, av_10_epi8, QuantBDataPtr + 3 * BlkDataSizeInBytes16, scale_00, scale_10, acc2[3], acc2[NCols4 + 3]);
+                }
+                QuantAPtr += BlkLen32;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes16 * NCols4;
+                QuantBScalePtr += NCols4;
+            }  // k_blks_remaining
+
+            __m128 acc_r0 = FoldAccumulators(acc2[0], acc2[1], acc2[2], acc2[3]);
+            __m128 acc_r1 = FoldAccumulators(acc2[NCols4 + 0], acc2[NCols4 + 1], acc2[NCols4 + 2], acc2[NCols4 + 3]);
+            if (BiasPtr != nullptr) {
+                const __m128 bias_4_ps = _mm_loadu_ps(BiasPtr);
+                acc_r0 = _mm_add_ps(acc_r0, bias_4_ps);
+                acc_r1 = _mm_add_ps(acc_r1, bias_4_ps);
+            }
+            _mm_storeu_ps(SumPtr, acc_r0);
+            _mm_storeu_ps(SumPtr + ldc, acc_r1);
+
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * BlockCountK * BlkDataSizeInBytes16;
+            QuantBScaleColPtr += NCols4 * BlockCountK;
+
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+template <bool vnni>
+void MLAS_FORCEINLINE
+Q4Int8GemmR2C1BlkLen32Avx512(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc)
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk4 = 4;
+
+    const size_t lda = BlockCountK * BlkLen32;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM % NRows2 == 0);
+    assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        float* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc0 = _mm512_setzero_ps(), acc1 = _mm512_setzero_ps();
+
+            size_t k_blks_remaining = BlockCountK;
+            // process 2 blks of 64 4b weights a time
+            for (; k_blks_remaining >= PerAccuBlk4; k_blks_remaining -= PerAccuBlk4) {
+                const __m512i av_00_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                const __m512i av_01_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + 64));
+                const __m512i av_10_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda));
+                const __m512i av_11_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda + 64));
+
+                if constexpr (vnni) {
+                    accumulate_blklen32_r2c1blk4_avx512vnni(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr,
+                        QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc0, acc1
+                    );
+                } else {
+                    accumulate_blklen32_r2c1blk4_avx512(
+                        av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr,
+                        QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc0, acc1
+                    );
+                }
+
+                // increment block pointers
+                QuantAPtr += BlkLen32 * PerAccuBlk4;
+                QuantAScalePtr += PerAccuBlk4;
+                QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk4;
+                QuantBScalePtr += PerAccuBlk4;
+            }
+
+            __m256 acc20 = h_add_512(acc0);
+            __m256 acc21 = h_add_512(acc1);
+            while (k_blks_remaining-- > 0) {
+                // load A
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk0);
+                const __m256i av_10_epi8 = _mm256_loadu_si256((const __m256i*)(QuantABlk0 + lda));
+
+                const float& scale_a00 = *QuantAScalePtr;
+                const float& scale_a10 = *(QuantAScalePtr + BlockCountK);
+
+                const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                const float& scale_10 = scale_a10 * (QuantBScalePtr)[0];
+                accumulate_blklen32_r2c1blk1_avx512<vnni>(av_00_epi8, av_10_epi8, QuantBDataPtr, scale_00, scale_10, acc20, acc21);
+
+                QuantAPtr += BlkLen32;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes16;
+                QuantBScalePtr++;
+            }
+
+            *SumPtr = hsum_float_8(acc20);
+            *(SumPtr + ldc) = hsum_float_8(acc21);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+                *(SumPtr + ldc) += *BiasPtr;
+            }
+
+            // move to next column
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmR1xC4BlkLen32Avx512(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk4 = 4;
+
+    const size_t lda = BlockCountK * BlkLen32;
+    //const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    //const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM < NRows2);
+    assert(CountN % NCols4 == 0);
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc[NCols4] = {
+              _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps()
+            };
+            size_t k_blks_remaining = BlockCountK;
+            for (; k_blks_remaining >= PerAccuBlk4; k_blks_remaining -= PerAccuBlk4) {
+                const __m512i av_00_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                const __m512i av_01_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + 64));
+
+                if constexpr (vnni) {
+                    accumulate_blklen32_r1c1blk4_avx512vnni(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc[0]);
+                    accumulate_blklen32_r1c1blk4_avx512vnni(av_00_epi8, av_01_epi8, QuantBDataPtr + PerAccuBlk4 * BlkDataSizeInBytes16, QuantAScalePtr, QuantBScalePtr + PerAccuBlk4, acc[1]);
+                    accumulate_blklen32_r1c1blk4_avx512vnni(av_00_epi8, av_01_epi8, QuantBDataPtr + 2 * PerAccuBlk4 * BlkDataSizeInBytes16, QuantAScalePtr, QuantBScalePtr + 2 * PerAccuBlk4, acc[2]);
+                    accumulate_blklen32_r1c1blk4_avx512vnni(av_00_epi8, av_01_epi8, QuantBDataPtr + 3 * PerAccuBlk4 * BlkDataSizeInBytes16, QuantAScalePtr, QuantBScalePtr + 3 * PerAccuBlk4, acc[3]);
+                } else {
+                    accumulate_blklen32_r1c1blk4_avx512(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc[0]);
+                    accumulate_blklen32_r1c1blk4_avx512(av_00_epi8, av_01_epi8, QuantBDataPtr + PerAccuBlk4 * BlkDataSizeInBytes16, QuantAScalePtr, QuantBScalePtr + PerAccuBlk4, acc[1]);
+                    accumulate_blklen32_r1c1blk4_avx512(av_00_epi8, av_01_epi8, QuantBDataPtr + 2 * PerAccuBlk4 * BlkDataSizeInBytes16, QuantAScalePtr, QuantBScalePtr + 2 * PerAccuBlk4, acc[2]);
+                    accumulate_blklen32_r1c1blk4_avx512(av_00_epi8, av_01_epi8, QuantBDataPtr + 3 * PerAccuBlk4 * BlkDataSizeInBytes16, QuantAScalePtr, QuantBScalePtr + 3 * PerAccuBlk4, acc[3]);
+                }
+
+                QuantAPtr += BlkLen32 * PerAccuBlk4;
+                QuantAScalePtr += PerAccuBlk4;
+                QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk4 * NCols4;
+                QuantBScalePtr += PerAccuBlk4 * NCols4;
+            }
+
+            __m256 acc2[NCols4] = {
+                h_add_512(acc[0]), h_add_512(acc[1]), h_add_512(acc[2]), h_add_512(acc[3])
+            };
+
+            while (k_blks_remaining-- > 0) {
+                // load A
+                const std::byte* QuantABlk0 = QuantAPtr;
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk0);
+
+                const float& scale_a00 = *QuantAScalePtr;
+                {
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                    accumulate_blklen32_r1c1blk1_avx512<vnni>(av_00_epi8, QuantBDataPtr, scale_00, acc2[0]);
+                }
+                {
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 1)[0];
+                    accumulate_blklen32_r1c1blk1_avx512<vnni>(av_00_epi8, QuantBDataPtr + BlkDataSizeInBytes16, scale_00, acc2[1]);
+                }
+                {
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 2)[0];
+                    accumulate_blklen32_r1c1blk1_avx512<vnni>(av_00_epi8, QuantBDataPtr + 2 * BlkDataSizeInBytes16, scale_00, acc2[2]);
+                }
+                {
+                    const float& scale_00 = scale_a00 * (QuantBScalePtr + 3)[0];
+                    accumulate_blklen32_r1c1blk1_avx512<vnni>(av_00_epi8, QuantBDataPtr + 3 * BlkDataSizeInBytes16, scale_00, acc2[3]);
+                }
+
+                QuantAPtr += BlkLen32;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes16 * NCols4;
+                QuantBScalePtr += NCols4;
+
+            }
+
+            __m128 acc_r0 = FoldAccumulators(acc2[0], acc2[1], acc2[2], acc2[3]);
+            if (BiasPtr != nullptr) {
+                acc_r0 = _mm_add_ps(acc_r0, _mm_loadu_ps(BiasPtr));
+            }
+
+            _mm_storeu_ps(SumPtr, acc_r0);
+
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * BlockCountK * BlkDataSizeInBytes16;
+            QuantBScaleColPtr += NCols4 * BlockCountK;
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmR1xC1BlkLen32Avx512(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk4 = 4;
+
+    const size_t lda = BlockCountK * BlkLen32;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+    assert(CountM < NRows2);
+    assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc0 = _mm512_setzero_ps();
+            size_t k_blks_remaining = BlockCountK;
+            for (; k_blks_remaining >= PerAccuBlk4; k_blks_remaining -= PerAccuBlk4) {
+                const __m512i av_00_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                const __m512i av_01_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + 64));
+
+                if constexpr (vnni) {
+                    accumulate_blklen32_r1c1blk4_avx512vnni(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0);
+                }
+                else {
+                    accumulate_blklen32_r1c1blk4_avx512vnni(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0);
+                }
+
+                QuantAPtr += BlkLen32 * PerAccuBlk4;
+                QuantAScalePtr += PerAccuBlk4;
+                QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk4;
+                QuantBScalePtr += PerAccuBlk4;
+            }
+
+            __m256 acc2 = h_add_512(acc0);
+            while (k_blks_remaining-- > 0) {
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantAPtr);
+
+                const float& scale_a00 = *QuantAScalePtr;
+                const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                accumulate_blklen32_r1c1blk1_avx512<vnni>(av_00_epi8, QuantBDataPtr, scale_00, acc2);
+
+                QuantAPtr += BlkLen32;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes16;
+                QuantBScalePtr++;
+            }
+
+            *SumPtr = hsum_float_8(acc2);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+            }
+
+            // move to next column
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+template<bool vnni>
+MLAS_FORCEINLINE
+size_t
+MlasQ4Int8GemmKernelBlkLen32Avx512(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+
+    const size_t lda = BlockCountK * BlkLen32 * sizeof(int8_t);
+    const size_t lda_scale = BlockCountK;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+
+    size_t remainingRows = CountM % NRows2;
+    size_t multipleRows = CountM - remainingRows;
+    size_t remainingCols = CountN % NCols4;
+    size_t multipleCols = CountN - remainingCols;
+
+    if (multipleRows > 0 && multipleCols > 0) {
+        Q4Int8GemmR2xC4BlkLen32Avx512<vnni>(
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            multipleRows,
+            multipleCols,
+            BlockCountK,
+            Bias,
+            ldc
+        );
+    }
+    if (remainingCols > 0 && multipleRows > 0) {
+        Q4Int8GemmR2C1BlkLen32Avx512<vnni>(
+            QuantA,
+            QuantAScale,
+            QuantBData + multipleCols * StrideQuantBData,
+            QuantBScale + multipleCols * StrideQuantBScale,
+            C + multipleCols,
+            multipleRows,
+            remainingCols,
+            BlockCountK,
+            Bias ? Bias + multipleCols : nullptr,
+            ldc);
+    }
+
+    if (remainingRows > 0 && multipleCols > 0) {
+        Q4Int8GemmR1xC4BlkLen32Avx512<vnni>(
+            QuantA + multipleRows * lda,
+            QuantAScale + multipleRows * lda_scale,
+            QuantBData,
+            QuantBScale,
+            C + multipleRows * ldc,
+            remainingRows,
+            multipleCols,
+            BlockCountK,
+            Bias,
+            ldc);
+    }
+
+    if (remainingCols > 0 && remainingRows > 0) {
+        Q4Int8GemmR1xC1BlkLen32Avx512<vnni>(
+            QuantA + multipleRows * lda,
+            QuantAScale + multipleRows * lda_scale,
+            QuantBData + multipleCols * StrideQuantBData,
+            QuantBScale + multipleCols * StrideQuantBScale,
+            C + multipleRows * ldc + multipleCols,
+            remainingRows,
+            remainingCols,
+            BlockCountK,
+            Bias ? Bias + multipleCols : nullptr,
+            ldc);
+    }
+
+    return CountM;
+}
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen64.h b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen64.h
new file mode 100644
index 0000000000000..2a65ac4af0c1d
--- /dev/null
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen64.h
@@ -0,0 +1,840 @@
+#pragma once
+#include <algorithm>
+#include <cassert>
+#include <utility>
+
+#include "sqnbitgemm.h"
+#include "sqnbitgemm_kernel_avx_common.h"
+
+static MLAS_FORCEINLINE __m256
+h_add_512(__m512 a)
+{
+    return _mm256_add_ps(_mm512_castps512_ps256(a), _mm512_extractf32x8_ps(a, 1));
+}
+
+static MLAS_FORCEINLINE float
+hsum_float_16(const __m512 x)
+{
+    __m256 hi = h_add_512(x);
+    __m128 hi128 = _mm256_extractf128_ps(hi, 1);
+    __m128 lo128 = _mm256_castps256_ps128(hi);
+    hi128 = _mm_add_ps(hi128, lo128);
+    hi128 = _mm_add_ps(hi128, _mm_movehl_ps(hi128, hi128));
+    hi128 = _mm_add_ss(hi128, _mm_movehdup_ps(hi128));
+    return _mm_cvtss_f32(hi128);
+}
+
+static MLAS_FORCEINLINE __m512i
+combine_two_m256i_to_m512i(const __m256i& a, const __m256i& b)
+{
+    __m512i result = _mm512_castsi256_si512(a);
+    result = _mm512_inserti64x4(result, b, 1);
+    return result;
+}
+
+static MLAS_FORCEINLINE void
+load_2blk_4b_packed_blklen64(const std::byte* QuantBDataPtr, __m512i& bv0_64_epi8, __m512i& bv1_64_epi8)
+{
+    // | v0 v64 | v1 v65 | ... | v62 v126 | v63 v127 |
+    const __m512i bv_packed = _mm512_loadu_si512(reinterpret_cast<const __m512i*>(QuantBDataPtr));
+    const __m512i low_mask = _mm512_set1_epi8(0x0F);
+    bv0_64_epi8 = _mm512_and_si512(bv_packed, low_mask); // 0~63
+    bv1_64_epi8 = _mm512_srli_epi16(_mm512_sub_epi8(bv_packed, bv0_64_epi8), 4);  // 64~127
+
+    //// Extract lower and higher 256 bits from bv0_64_epi8 and bv1_64_epi8
+    //__m256i bv0_lower = _mm512_castsi512_si256(bv0_64_epi8_);
+    //__m256i bv0_higher = _mm512_extracti64x4_epi64(bv0_64_epi8_, 1);
+    //__m256i bv1_lower = _mm512_castsi512_si256(bv1_64_epi8_);
+    //__m256i bv1_higher = _mm512_extracti64x4_epi64(bv1_64_epi8_, 1);
+
+    //// Compose new __m512i variables
+    //bv0_64_epi8 = _mm512_inserti64x4(_mm512_castsi256_si512(bv0_lower), bv1_lower, 1);
+    //bv1_64_epi8 = _mm512_inserti64x4(_mm512_castsi256_si512(bv0_higher), bv1_higher, 1);
+}
+
+static MLAS_FORCEINLINE __m512i
+load_1blk_4b_packed_blklen64(const std::byte* QuantBDataPtr)
+{
+    // | v0 v32 | v1 v33 | ... | v30 v62 | v31 v63 |
+    const __m256i bv_packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(QuantBDataPtr));
+    const __m256i low_mask = _mm256_set1_epi8(0x0F);
+    __m256i bv0_32_epi8 = _mm256_and_si256(bv_packed, low_mask); // 0, 1,...30, 31
+    __m256i bv1_32_epi8 = _mm256_srli_epi16(
+      _mm256_sub_epi8(bv_packed, bv0_32_epi8), 4);  // 32, 33,...62, 63
+    __m512i bv_64_epi8 = combine_two_m256i_to_m512i(bv0_32_epi8, bv1_32_epi8);
+    return bv_64_epi8;
+}
+
+static MLAS_FORCEINLINE __m512i
+horizontal_add_epi32(__m512i a, __m512i b)
+{
+    __m512i t1 = _mm512_unpacklo_epi32(a, b);
+    __m512i t2 = _mm512_unpackhi_epi32(a, b);
+    __m512i sum = _mm512_add_epi32(t1, t2);
+    return sum;
+}
+
+static MLAS_FORCEINLINE __m512i
+generate_ones_32_epi16()
+{
+    const __m512i zeros = _mm512_setzero_si512();
+    return _mm512_srli_epi16(_mm512_ternarylogic_epi64(zeros, zeros, zeros, 1), 15);
+}
+
+static MLAS_FORCEINLINE void
+dot_accumulate_2blk(
+  const __m512i& av0_64_epi8,
+  const __m512i& av1_64_epi8,
+  const float* scale_a,
+  const __m512i& bv0_64_epi8,
+  const __m512i& bv1_64_epi8,
+  const __m512& scale_b_16_ps,
+  //const __m512i& one_32_epi16,
+  __m512& acc)
+{
+    __m512i dot0_32_epi16 = _mm512_maddubs_epi16(bv0_64_epi8, av0_64_epi8);
+    __m512i dot1_32_epi16 = _mm512_maddubs_epi16(bv1_64_epi8, av1_64_epi8);
+
+    __m512i t1 = _mm512_unpacklo_epi32(dot0_32_epi16, dot1_32_epi16);
+    __m512i t2 = _mm512_unpackhi_epi32(dot0_32_epi16, dot1_32_epi16);
+    __m512i sum_32_epi16 = _mm512_add_epi16(t1, t2);  // sum for blk: 0 0 1 1 0 0 1 1...
+    __m512i one_32_epi16 = generate_ones_32_epi16();
+    __m512i sum_16_epi32 = _mm512_madd_epi16(one_32_epi16, sum_32_epi16);  // sum for blk: 0 1 0 1...
+    __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+
+    __m256 scale_a_8_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_a));
+    __m512 scale_a_16_ps = _mm512_broadcast_f32x8(scale_a_8_ps);
+
+    acc = _mm512_fmadd_ps(sum_16_ps, _mm512_mul_ps(scale_a_16_ps, scale_b_16_ps), acc);
+}
+
+static MLAS_FORCEINLINE void
+dot_accumulate_2blkvnni(
+    const __m512i& av0_64_epi8,
+    const __m512i& av1_64_epi8,
+    const float* scale_a,
+    const __m512i& bv0_64_epi8,
+    const __m512i& bv1_64_epi8,
+    const __m512& scale_b_16_ps,
+    // const __m512i& one_32_epi16,
+    __m512& acc
+)
+{
+    __m512i dot0_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv0_64_epi8, av0_64_epi8);
+    __m512i dot1_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv1_64_epi8, av1_64_epi8);
+
+    __m512i t1_16_epi32 = _mm512_unpacklo_epi32(dot0_16_epi32, dot1_16_epi32);
+    __m512i t2_16_epi32 = _mm512_unpackhi_epi32(dot0_16_epi32, dot1_16_epi32);
+    __m512i sum_16_epi32 = _mm512_add_epi32(t1_16_epi32, t2_16_epi32);  // sum for blk: 0 0 1 1 0 0 1 1...
+    __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+
+    __m256 scale_a_8_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_a));
+    __m512 scale_a_16_ps = _mm512_broadcast_f32x8(scale_a_8_ps);
+
+    acc = _mm512_fmadd_ps(sum_16_ps, _mm512_mul_ps(scale_a_16_ps, scale_b_16_ps), acc);
+}
+
+template <bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen64_r2c1blk2_avx512(
+    const __m512i& av00_64_epi8,
+    const __m512i& av01_64_epi8,
+    const __m512i& av10_64_epi8,
+    const __m512i& av11_64_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a0,
+    const float* scale_a1,
+    const float* scale_b,
+    __m512& acc0,
+    __m512& acc1
+)
+{
+    __m512i bv0_64_epi8, bv1_64_epi8;
+    load_2blk_4b_packed_blklen64(QuantBDataPtr, bv0_64_epi8, bv1_64_epi8);
+
+    const __m256 scale_b_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_b));
+    const __m512 scale_b_16_ps = _mm512_broadcast_f32x8(scale_b_ps);
+
+    if constexpr (vnni) {
+        dot_accumulate_2blkvnni(
+            av00_64_epi8, av01_64_epi8, scale_a0,
+            bv0_64_epi8, bv1_64_epi8, scale_b_16_ps,
+            acc0
+        );
+
+        dot_accumulate_2blkvnni(
+            av10_64_epi8, av11_64_epi8, scale_a1,
+            bv0_64_epi8, bv1_64_epi8, scale_b_16_ps,
+            acc1
+        );
+    } else {
+        dot_accumulate_2blk(
+            av00_64_epi8, av01_64_epi8, scale_a0,
+            bv0_64_epi8, bv1_64_epi8, scale_b_16_ps,
+            acc0
+        );
+
+        dot_accumulate_2blk(
+            av10_64_epi8, av11_64_epi8, scale_a1,
+            bv0_64_epi8, bv1_64_epi8, scale_b_16_ps,
+            acc1
+        );
+    }
+}
+
+template<bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen64_r1c1blk2_avx512(
+    const __m512i& av0_64_epi8,
+    const __m512i& av1_64_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a,
+    const float* scale_b,
+    __m512& acc
+)
+{
+    __m512i bv0_64_epi8, bv1_64_epi8;
+    load_2blk_4b_packed_blklen64(QuantBDataPtr, bv0_64_epi8, bv1_64_epi8);
+
+    const __m256 scale_b_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_b));
+    const __m512 scale_b_16_ps = _mm512_broadcast_f32x8(scale_b_ps);
+
+    if constexpr (vnni) {
+        dot_accumulate_2blkvnni(
+            av0_64_epi8, av1_64_epi8, scale_a,
+            bv0_64_epi8, bv1_64_epi8, scale_b_16_ps,
+            acc
+        );
+    } else {
+        dot_accumulate_2blk(
+            av0_64_epi8, av1_64_epi8, scale_a,
+            bv0_64_epi8, bv1_64_epi8, scale_b_16_ps,
+            acc
+        );
+    }
+}
+
+template <bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen64_r2c1blk1_avx512(
+    const __m512i& av0_64_epi8,
+    const __m512i& av1_64_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a0,
+    const float* scale_a1,
+    const float* scale_b,
+    __m512& acc0,
+    __m512& acc1
+)
+{
+    __m512i bv_64_epi8 = load_1blk_4b_packed_blklen64(QuantBDataPtr);
+
+    const __m128 scale_b_ps = _mm_broadcast_ss(scale_b);
+    const __m512 scale_b_16_ps = _mm512_broadcast_f32x2(scale_b_ps);
+
+    if constexpr (vnni) {
+        {
+            __m512i dot_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv_64_epi8, av0_64_epi8);
+            __m512 sum_16_ps = _mm512_cvtepi32_ps(dot_16_epi32);
+
+            __m128 scale_a0_ps = _mm_broadcast_ss(scale_a0);
+            __m512 scale_a0_16_ps = _mm512_broadcast_f32x2(scale_a0_ps);
+
+            acc0 = _mm512_fmadd_ps(sum_16_ps, _mm512_mul_ps(scale_a0_16_ps, scale_b_16_ps), acc0);
+        }
+
+        {
+            __m512i dot_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv_64_epi8, av1_64_epi8);
+            __m512 sum_16_ps = _mm512_cvtepi32_ps(dot_16_epi32);
+
+            __m128 scale_a1_ps = _mm_broadcast_ss(scale_a1);
+            __m512 scale_a1_16_ps = _mm512_broadcast_f32x2(scale_a1_ps);
+
+            acc1 = _mm512_fmadd_ps(sum_16_ps, _mm512_mul_ps(scale_a1_16_ps, scale_b_16_ps), acc1);
+        }
+    } else {
+        const __m512i zeros = _mm512_setzero_si512();
+        // const __m512i one_32_epi16_ = _mm512_andnot_epi32(zeros, zeros);
+        // const __m512i one_32_epi16 = _mm512_srli_epi16(_mm512_andnot_epi32(zeros, zeros), 15);
+
+        const __m512i one_32_epi16 = _mm512_srli_epi16(_mm512_ternarylogic_epi32(zeros, zeros, zeros, 1), 15);
+        {
+            __m512i dot_32_epi16 = _mm512_maddubs_epi16(bv_64_epi8, av0_64_epi8);
+            __m512i sum_16_epi32 = _mm512_madd_epi16(one_32_epi16, dot_32_epi16);
+
+            __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+
+            __m128 scale_a0_ps = _mm_broadcast_ss(scale_a0);
+            __m512 scale_a0_16_ps = _mm512_broadcast_f32x2(scale_a0_ps);
+
+            acc0 = _mm512_fmadd_ps(sum_16_ps, _mm512_mul_ps(scale_a0_16_ps, scale_b_16_ps), acc0);
+        }
+
+        {
+            __m512i dot_32_epi16 = _mm512_maddubs_epi16(bv_64_epi8, av1_64_epi8);
+            __m512i sum_16_epi32 = _mm512_madd_epi16(one_32_epi16, dot_32_epi16);
+            __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+
+            __m128 scale_a1_ps = _mm_broadcast_ss(scale_a1);
+            __m512 scale_a1_16_ps = _mm512_broadcast_f32x2(scale_a1_ps);
+
+            acc1 = _mm512_fmadd_ps(sum_16_ps, _mm512_mul_ps(scale_a1_16_ps, scale_b_16_ps), acc1);
+        }
+    }
+}
+
+template <bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen64_r1c1blk1_avx512(
+    const __m512i& av_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a,
+    const float* scale_b,
+    __m512& acc
+)
+{
+    __m512i bv_64_epi8 = load_1blk_4b_packed_blklen64(QuantBDataPtr);
+
+    const __m128 scale_b_ps = _mm_broadcast_ss(scale_b);
+    const __m512 scale_b_16_ps = _mm512_broadcast_f32x2(scale_b_ps);
+
+    if constexpr (vnni) {
+        __m512i dot_16_epi32 = _mm512_dpbusd_epi32(_mm512_setzero_epi32(), bv_64_epi8, av_32_epi8);
+        __m512 sum_16_ps = _mm512_cvtepi32_ps(dot_16_epi32);
+
+        __m128 scale_a_ps = _mm_broadcast_ss(scale_a);
+        __m512 scale_a_16_ps = _mm512_broadcast_f32x2(scale_a_ps);
+
+        acc = _mm512_fmadd_ps(sum_16_ps, _mm512_mul_ps(scale_a_16_ps, scale_b_16_ps), acc);
+    } else {
+        const __m512i one_32_epi16 = _mm512_set1_epi16(1);
+
+        __m512i dot_32_epi16 = _mm512_maddubs_epi16(bv_64_epi8, av_32_epi8);
+        __m512i sum_16_epi32 = _mm512_madd_epi16(one_32_epi16, dot_32_epi16);
+
+        __m512 sum_16_ps = _mm512_cvtepi32_ps(sum_16_epi32);
+
+        __m128 scale_a_ps = _mm_broadcast_ss(scale_a);
+        __m512 scale_a_16_ps = _mm512_broadcast_f32x2(scale_a_ps);
+
+        acc = _mm512_fmadd_ps(sum_16_ps, _mm512_mul_ps(scale_a_16_ps, scale_b_16_ps), acc);
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmR2xC4BlkLen64Avx512(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkLen64 = 64;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    const size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen64);
+
+    // process 2 blks of 128 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    const size_t lda = BlockCountK * BlkLen64;
+    const size_t StrideQuantBData = PerAccuBlk2 * BlkDataSizeInBytes;
+    //const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountM % NRows2 == 0);
+    assert(CountN % NCols4 == 0);
+
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc[NCols4 * NRows2] = {
+                _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(),
+                _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps()
+            };
+
+            size_t k_blks_remaining = BlockCountK;
+            // process 2 blks of 128 4b weights a time
+            for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+                const __m512i av_00_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                const __m512i av_01_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + 64));
+                const __m512i av_10_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda));
+                const __m512i av_11_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda + 64));
+
+                accumulate_blklen64_r2c1blk2_avx512<vnni>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc[0], acc[NCols4]);
+                accumulate_blklen64_r2c1blk2_avx512<vnni>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + StrideQuantBData, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + PerAccuBlk2, acc[1], acc[NCols4 + 1]);
+                accumulate_blklen64_r2c1blk2_avx512<vnni>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + 2 * StrideQuantBData, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 2 * PerAccuBlk2, acc[2], acc[NCols4 + 2]);
+                accumulate_blklen64_r2c1blk2_avx512<vnni>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr + 3 * StrideQuantBData, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 3 * PerAccuBlk2, acc[3], acc[NCols4 + 3]);
+
+                // increment block pointers
+                QuantAPtr += BlkLen64 * PerAccuBlk2;
+                QuantAScalePtr += PerAccuBlk2;
+                QuantBDataPtr += StrideQuantBData * NCols4;
+                QuantBScalePtr += PerAccuBlk2 * NCols4;
+            }  // k_blks_remaining
+
+            while (k_blks_remaining-- > 0) {
+                const __m512i av_00_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                const __m512i av_10_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda));
+
+                accumulate_blklen64_r2c1blk1_avx512<vnni>(av_00_epi8, av_10_epi8,
+                  QuantBDataPtr, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc[0], acc[NCols4]);
+                accumulate_blklen64_r2c1blk1_avx512<vnni>(av_00_epi8, av_10_epi8,
+                  QuantBDataPtr + BlkDataSizeInBytes, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 1, acc[1], acc[NCols4 + 1]);
+                accumulate_blklen64_r2c1blk1_avx512<vnni>(av_00_epi8, av_10_epi8,
+                  QuantBDataPtr + 2 * BlkDataSizeInBytes, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 2, acc[2], acc[NCols4 + 2]);
+                accumulate_blklen64_r2c1blk1_avx512<vnni>(av_00_epi8, av_10_epi8,
+                  QuantBDataPtr + 3 * BlkDataSizeInBytes, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr + 3, acc[3], acc[NCols4 + 3]);
+
+                QuantAPtr += BlkLen64;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes * NCols4;
+                QuantBScalePtr += NCols4;
+            }
+
+#if 1
+            *SumPtr = _mm512_reduce_add_ps(acc[0]);
+            *(SumPtr + 1) = _mm512_reduce_add_ps(acc[1]);
+            *(SumPtr + 2) = _mm512_reduce_add_ps(acc[2]);
+            *(SumPtr + 3) = _mm512_reduce_add_ps(acc[3]);
+            *(SumPtr + ldc) = _mm512_reduce_add_ps(acc[NCols4]);
+            *(SumPtr + ldc + 1) = _mm512_reduce_add_ps(acc[NCols4 + 1]);
+            *(SumPtr + ldc + 2) = _mm512_reduce_add_ps(acc[NCols4 + 2]);
+            *(SumPtr + ldc + 3) = _mm512_reduce_add_ps(acc[NCols4 + 3]);
+            if (BiasPtr != nullptr) {
+                *SumPtr += *BiasPtr;
+                *(SumPtr + 1) += *(BiasPtr + 1);
+                *(SumPtr + 2) += *(BiasPtr + 2);
+                *(SumPtr + 3) += *(BiasPtr + 3);
+                *(SumPtr + ldc) += *BiasPtr;
+                *(SumPtr + ldc + 1) += *(BiasPtr + 1);
+                *(SumPtr + ldc + 2) += *(BiasPtr + 2);
+                *(SumPtr + ldc + 3) += *(BiasPtr + 3);
+            }
+#else
+            __m128 acc_r0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+            __m128 acc_r1 = FoldAccumulators(acc[NCols4 + 0], acc[NCols4 + 1], acc[NCols4 + 2], acc[NCols4 + 3]);
+            if (BiasPtr != nullptr) {
+                const __m128 bias_4_ps = _mm_loadu_ps(BiasPtr);
+                acc_r0 = _mm_add_ps(acc_r0, bias_4_ps);
+                acc_r1 = _mm_add_ps(acc_r1, bias_4_ps);
+            }
+            _mm_storeu_ps(SumPtr, acc_r0);
+            _mm_storeu_ps(SumPtr + ldc, acc_r1);
+#endif
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * BlockCountK * BlkDataSizeInBytes;
+            QuantBScaleColPtr += NCols4 * BlockCountK;
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+template <bool vnni>
+void MLAS_FORCEINLINE
+Q4Int8GemmR2xC1BlkLen64Avx512(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+    constexpr size_t BlkLen64 = 64;
+    const size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+
+    // process 2 blks of 128 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    const size_t lda = BlockCountK * BlkLen;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    //assert(CountM % NRows2 == 0);
+    //assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m += NRows2) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        float* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc0 = _mm512_setzero_ps(), acc1 = _mm512_setzero_ps();
+
+            size_t k_blks_remaining = BlockCountK;
+            // process 2 blks of 128 4b weights a time
+            for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+                const __m512i av_00_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                const __m512i av_01_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + 64));
+                const __m512i av_10_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda));
+                const __m512i av_11_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda + 64));
+
+                accumulate_blklen64_r2c1blk2_avx512<vnni>(av_00_epi8, av_01_epi8, av_10_epi8, av_11_epi8, QuantBDataPtr, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc0, acc1);
+
+                // increment block pointers
+                QuantAPtr += BlkLen64 * PerAccuBlk2;
+                QuantBDataPtr += BlkDataSizeInBytes * PerAccuBlk2;
+                QuantAScalePtr += PerAccuBlk2;
+                QuantBScalePtr += PerAccuBlk2;
+            }
+
+            while (k_blks_remaining-- > 0) {
+                const __m512i av_00_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                const __m512i av_10_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + lda));
+
+                accumulate_blklen64_r2c1blk1_avx512<vnni>(av_00_epi8, av_10_epi8, QuantBDataPtr, QuantAScalePtr, QuantAScalePtr + BlockCountK, QuantBScalePtr, acc0, acc1);
+
+                QuantAPtr += BlkLen64;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes;
+                QuantBScalePtr++;
+            }
+
+            *SumPtr = hsum_float_16(acc0);
+            *(SumPtr + ldc) = hsum_float_16(acc1);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+                *(SumPtr + ldc) += *BiasPtr;
+            }
+
+            // move to next column
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmR1xC4BlkLen64Avx512(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t BlkLen64 = 64;
+    const size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+
+    // process 2 blks of 128 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    const size_t lda = BlockCountK * BlkLen;
+    //const size_t StrideQuantBData = PerAccuBlk2 * BlkDataSizeInBytes;
+    //const size_t StrideQuantBScale = BlockCountK;
+
+    //assert(CountM < NRows2);
+    //assert(CountN % NCols4 == 0);
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n += NCols4) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc[NCols4] = {_mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps(), _mm512_setzero_ps()};
+            size_t k_blks_remaining = BlockCountK;
+            // process 2 blks of 128 4b weights a time
+            for (; k_blks_remaining >= PerAccuBlk2; k_blks_remaining -= PerAccuBlk2) {
+                const __m512i av0_64_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                const __m512i av1_64_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + 64));
+                accumulate_blklen64_r1c1blk2_avx512<vnni>(av0_64_epi8, av1_64_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc[0]);
+                accumulate_blklen64_r1c1blk2_avx512<vnni>(av0_64_epi8, av1_64_epi8, QuantBDataPtr + PerAccuBlk2 * BlkDataSizeInBytes, QuantAScalePtr, QuantBScalePtr + PerAccuBlk2, acc[1]);
+                accumulate_blklen64_r1c1blk2_avx512<vnni>(av0_64_epi8, av1_64_epi8, QuantBDataPtr + 2 * PerAccuBlk2 * BlkDataSizeInBytes, QuantAScalePtr, QuantBScalePtr + 2 * PerAccuBlk2, acc[2]);
+                accumulate_blklen64_r1c1blk2_avx512<vnni>(av0_64_epi8, av1_64_epi8, QuantBDataPtr + 3 * PerAccuBlk2 * BlkDataSizeInBytes, QuantAScalePtr, QuantBScalePtr + 3 * PerAccuBlk2, acc[3]);
+
+                // increment block pointers
+                QuantAPtr += BlkLen64 * PerAccuBlk2;
+                QuantAScalePtr += PerAccuBlk2;
+                QuantBDataPtr += PerAccuBlk2 * BlkDataSizeInBytes * NCols4;
+                QuantBScalePtr += PerAccuBlk2 * NCols4;
+            }
+
+            while (k_blks_remaining-- > 0) {
+                const __m512i av_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+
+                accumulate_blklen64_r1c1blk1_avx512<vnni>(av_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc[0]);
+                accumulate_blklen64_r1c1blk1_avx512<vnni>(av_epi8, QuantBDataPtr + BlkDataSizeInBytes, QuantAScalePtr, QuantBScalePtr + 1, acc[1]);
+                accumulate_blklen64_r1c1blk1_avx512<vnni>(av_epi8, QuantBDataPtr + 2 * BlkDataSizeInBytes, QuantAScalePtr, QuantBScalePtr + 2, acc[2]);
+                accumulate_blklen64_r1c1blk1_avx512<vnni>(av_epi8, QuantBDataPtr + 3 * BlkDataSizeInBytes, QuantAScalePtr, QuantBScalePtr + 3, acc[3]);
+
+                QuantAPtr += BlkLen64;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes * NCols4;
+                QuantBScalePtr += NCols4;
+            }
+
+            __m128 acc_r0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+            if (BiasPtr != nullptr) {
+                acc_r0 = _mm_add_ps(acc_r0, _mm_loadu_ps(BiasPtr));
+            }
+
+            _mm_storeu_ps(SumPtr, acc_r0);
+
+            // move to next NCols columns
+            QuantBDataColPtr += NCols4 * BlockCountK * BlkDataSizeInBytes;
+            QuantBScaleColPtr += NCols4 * BlockCountK;
+            BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+            SumPtr += NCols4;
+        }
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmR1xC1BlkLen64Avx512(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t BlkLen64 = 64;
+    const size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+
+    // process 2 blks of 128 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    const size_t lda = BlockCountK * BlkLen;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    //assert(CountM < NRows2);
+    //assert(CountN < NCols4);
+
+    for (size_t m = 0; m < CountM; m++) {
+        const std::byte* QuantBDataColPtr = QuantBData;
+        const float* QuantBScaleColPtr = QuantBScale;
+        const float* BiasPtr = Bias;
+        auto* SumPtr = C + m * ldc;
+
+        for (size_t n = 0; n < CountN; n++) {
+            const std::byte* QuantAPtr = QuantA + m * lda;
+            const float* QuantAScalePtr = QuantAScale + m * BlockCountK;
+            const std::byte* QuantBDataPtr = QuantBDataColPtr;
+            const float* QuantBScalePtr = QuantBScaleColPtr;
+
+            __m512 acc0 = _mm512_setzero_ps();
+            size_t k_blks_remaining = BlockCountK;
+            // process 2 blks of 128 4b weights a time
+            for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+                const __m512i av_00_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+                const __m512i av_01_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + 64));
+
+                accumulate_blklen64_r1c1blk2_avx512<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0);
+
+                // increment block pointers
+                QuantAPtr += BlkLen64 * PerAccuBlk2;
+                QuantBDataPtr += BlkDataSizeInBytes * PerAccuBlk2;
+                QuantAScalePtr += PerAccuBlk2;
+                QuantBScalePtr += PerAccuBlk2;
+            }
+
+            while (k_blks_remaining-- > 0) {
+                const __m512i av_00_epi8 = _mm512_loadu_si512((const __m512i*)QuantAPtr);
+
+                accumulate_blklen64_r1c1blk1_avx512<vnni>(av_00_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0);
+
+                QuantAPtr += BlkLen64;
+                QuantAScalePtr++;
+                QuantBDataPtr += BlkDataSizeInBytes;
+                QuantBScalePtr++;
+            }
+
+            *SumPtr = hsum_float_16(acc0);
+            if (BiasPtr) {
+                *SumPtr += *BiasPtr;
+            }
+
+            // move to next column
+            QuantBDataColPtr += StrideQuantBData;
+            QuantBScaleColPtr += StrideQuantBScale;
+            BiasPtr += BiasPtr != nullptr ? 1 : 0;
+            SumPtr += 1;
+        }
+    }
+}
+
+template <bool vnni>
+MLAS_FORCEINLINE size_t
+MlasQ4Int8GemmKernelBlkLen64Avx512(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    float* C,
+    size_t CountM,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias,
+    size_t ldc
+)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t NRows2 = 2;
+
+    const size_t lda = BlockCountK * BlkLen * sizeof(int8_t);
+    const size_t lda_scale = BlockCountK;
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    size_t remainingRows = CountM % NRows2;
+    size_t multipleRows = CountM - remainingRows;
+    size_t remainingCols = CountN % NCols4;
+    size_t multipleCols = CountN - remainingCols;
+
+    if (multipleRows > 0 && multipleCols > 0) {
+        if (NRows2 == 2)
+            Q4Int8GemmR2xC4BlkLen64Avx512<vnni>(
+              QuantA,
+              QuantAScale,
+              QuantBData,
+              QuantBScale,
+              C,
+              multipleRows,
+              multipleCols,
+              BlockCountK,
+              Bias,
+              ldc
+          );
+        else
+            Q4Int8GemmR1xC4BlkLen64Avx512<vnni>(
+                BlkLen,
+                QuantA,
+                QuantAScale,
+                QuantBData,
+                QuantBScale,
+                C,
+                multipleRows,
+                multipleCols,
+                BlockCountK,
+                Bias,
+                ldc
+            );
+    }
+    if (remainingCols > 0 && multipleRows > 0) {
+        if (NRows2 == 2)
+            Q4Int8GemmR2xC1BlkLen64Avx512<vnni>(
+              BlkLen,
+              QuantA,
+              QuantAScale,
+              QuantBData + multipleCols * StrideQuantBData,
+              QuantBScale + multipleCols * StrideQuantBScale,
+              C + multipleCols,
+              multipleRows,
+              remainingCols,
+              BlockCountK,
+              Bias ? Bias + multipleCols : nullptr,
+              ldc);
+        else
+            Q4Int8GemmR1xC1BlkLen64Avx512<vnni>(
+                BlkLen,
+                QuantA,
+                QuantAScale,
+                QuantBData + multipleCols * StrideQuantBData,
+                QuantBScale + multipleCols * StrideQuantBScale,
+                C + multipleCols,
+                multipleRows,
+                remainingCols,
+                BlockCountK,
+                Bias ? Bias + multipleCols : nullptr,
+                ldc
+            );
+    }
+
+    if (remainingRows > 0 && multipleCols > 0) {
+        Q4Int8GemmR1xC4BlkLen64Avx512<vnni>(
+            BlkLen,
+            QuantA + multipleRows * lda,
+            QuantAScale + multipleRows * lda_scale,
+            QuantBData,
+            QuantBScale,
+            C + multipleRows * ldc,
+            remainingRows,
+            multipleCols,
+            BlockCountK,
+            Bias,
+            ldc);
+    }
+    if (remainingCols > 0 && remainingRows > 0) {
+        Q4Int8GemmR1xC1BlkLen64Avx512<vnni>(
+            BlkLen,
+            QuantA + multipleRows * lda,
+            QuantAScale + multipleRows * lda_scale,
+            QuantBData + multipleCols * StrideQuantBData,
+            QuantBScale + multipleCols * StrideQuantBScale,
+            C + multipleRows * ldc + multipleCols,
+            remainingRows,
+            remainingCols,
+            BlockCountK,
+            Bias ? Bias + multipleCols : nullptr,
+            ldc);
+    }
+
+    return CountM;
+}
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512vnni.cpp b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512vnni.cpp
index 6477a2019b21a..6a5c01162c51b 100644
--- a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512vnni.cpp
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512vnni.cpp
@@ -23,6 +23,10 @@ Module Name:
 #include "sqnbitgemm_kernel_avx_common.h"
 #include "sqnbitgemm_kernel_avx_common_fp32.h"
 #include "sqnbitgemm_kernel_avx_common_int8.h"
+#include "sqnbitgemm_kernel_avx512_int8_blklen16.h"
+#include "sqnbitgemm_kernel_avx512_int8_blklen32.h"
+#include "sqnbitgemm_kernel_avx512_int8_blklen64.h"
+#include "sqnbitgemm_kernel_avx512_int8_blklen128.h"
 
 MLAS_FORCEINLINE void
 SQ4BitGemmM1Kernel_CompFp32(
@@ -146,6 +150,7 @@ void
 SQ4BitGemmM1Kernel_CompInt8_avx512vnni(
     size_t BlkLen,
     const std::byte* QuantA,
+    const float* QuantAScale,
     const std::byte* QuantBData,
     const float* QuantBScale,
     const std::byte* QuantBZeroPoint,
@@ -157,44 +162,7 @@ SQ4BitGemmM1Kernel_CompInt8_avx512vnni(
 )
 {
     if (QuantBZeroPoint != nullptr) {
-        constexpr bool HasZeroPoint = true;
-        if (BlkLen == 16) {
-            SQ4BitGemmM1Kernel_BlkLen16_CompInt8_Impl<HasZeroPoint>(
-                QuantA,
-                QuantBData,
-                QuantBScale,
-                QuantBZeroPoint,
-                C,
-                CountN,
-                CountK,
-                BlockStrideQuantB,
-                Bias
-            );
-        } else if (BlkLen == 32) {
-            SQ4BitGemmM1Kernel_BlkLen32_CompInt8_Impl<HasZeroPoint, accumulate_mul_sum_avx512vnni<HasZeroPoint>>(
-                QuantA,
-                QuantBData,
-                QuantBScale,
-                QuantBZeroPoint,
-                C,
-                CountN,
-                BlockStrideQuantB,
-                Bias
-            );
-        } else {
-            SQ4BitGemmM1Kernel_BlkLen64Plus_CompInt8_Impl<HasZeroPoint, dot_quad_avx512vnni>(
-                BlkLen,
-                QuantA,
-                QuantBData,
-                QuantBScale,
-                QuantBZeroPoint,
-                C,
-                CountN,
-                CountK,
-                BlockStrideQuantB,
-                Bias
-            );
-        }
+        assert(false);
     } else {
         constexpr bool HasZeroPoint = false;
         if (BlkLen == 16) {
@@ -212,6 +180,7 @@ SQ4BitGemmM1Kernel_CompInt8_avx512vnni(
         } else if (BlkLen == 32) {
             SQ4BitGemmM1Kernel_BlkLen32_CompInt8_Impl<HasZeroPoint, accumulate_mul_sum_avx512vnni<HasZeroPoint>>(
                 QuantA,
+                QuantAScale,
                 QuantBData,
                 QuantBScale,
                 QuantBZeroPoint,
@@ -237,52 +206,134 @@ SQ4BitGemmM1Kernel_CompInt8_avx512vnni(
     }
 }
 
+MLAS_FORCEINLINE
 size_t
-SQ4BitGemmKernel_CompInt8_avx512vnni(
-    size_t BlkLen,
+SQ4BitGemmKernel_BlkSum_CompInt8_avx512vnni(
+    const size_t BlkLen,
     const std::byte* QuantA,
+    const float* QuantAScale,
     const std::byte* QuantBData,
     const float* QuantBScale,
-    const std::byte* QuantBZeroPoint,
+    const std::byte* /*QuantBZeroPoint*/,
     float* C,
     size_t CountM,
     size_t CountN,
-    size_t CountK,
+    size_t /*CountK*/,
     size_t BlockCountK,
+    const float* Bias,
     size_t ldc,
-    const float* Bias
+    const float* ABlockSum,
+    const float* QuantBBlkSum
 )
 {
-    MLAS_UNREFERENCED_PARAMETER(ldc);
-
-    if (CountM == 0) {
-        return 0;
+    if (BlkLen == 16) {
+        MlasQ4Int8GemmKernelBlkLen16Avx512<true>(
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            CountM,
+            CountN,
+            BlockCountK,
+            Bias,
+            ldc
+        );
+    } else if (BlkLen == 32) {
+        MlasQ4Int8GemmKernelBlkLen32Avx512<true>(
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            CountM,
+            CountN,
+            BlockCountK,
+            Bias,
+            ldc
+        );
+    } else if (BlkLen == 64) {
+        MlasQ4Int8GemmKernelBlkLen64Avx512<true>(
+            BlkLen,
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            CountM,
+            CountN,
+            BlockCountK,
+            Bias,
+            ldc
+        );
+    } else {
+        MlasQ4Int8GemmKernelBlkLen128Avx512<true>(
+            BlkLen,
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            C,
+            CountM,
+            CountN,
+            BlockCountK,
+            Bias,
+            ldc
+        );
     }
 
-    SQ4BitGemmM1Kernel_CompInt8_avx512vnni(
-        BlkLen,
-        QuantA,
-        QuantBData,
-        QuantBScale,
-        QuantBZeroPoint,
-        C,
-        CountN,
-        CountK,
-        BlockCountK,
-        Bias
-    );
+    float* c_blk = C;
+    const float* b_blk_sum = QuantBBlkSum;
 
-    return 1;
+    size_t RowsRemaining = CountM;
+    const float* a_blksum_row = ABlockSum;
+    while (RowsRemaining > 0) {
+        auto RowsHandled = GetMlasPlatform().GemmFloatKernel(
+            a_blksum_row, b_blk_sum, c_blk, BlockCountK, RowsRemaining, CountN, BlockCountK, ldc, 1.f, false
+        );
+
+        c_blk += ldc * RowsHandled;
+        a_blksum_row += BlockCountK * RowsHandled;
+        RowsRemaining -= RowsHandled;
+    }
+    return CountM;
 }
 
 void MLASCALL
-MlasQ80BlkQuantRow_avx512(
+QuantizeARow_CompInt8_avx512(
     size_t BlkLen,
     const float* A,
     size_t CountK,
-    std::byte* QuantA
+    std::byte* QuantA,
+    float* QuantAScale,
+    float* AScaledBlkSum  // scale_k * Sum_blklen(a_i)
 );
 
+static void
+SQ4BitGemmPackQuantBDataAndBlkSum512vnni(
+    size_t N,
+    size_t K,
+    size_t BlkLen,
+    MLAS_SQNBIT_GEMM_COMPUTE_TYPE ComputeType,
+    const std::byte* QuantBDataBegin,
+    const float* QuantBScaleBegin,
+    bool has_zp_input,
+    const std::byte* QuantBZPBegin,
+    PackedQuantBDataStruct& packed_quant_b,
+    MLAS_THREADPOOL* ThreadPool
+)
+{
+    assert(BlkLen >= 16 && BlkLen % 16 == 0);
+
+    const size_t BlockCountK = MlasDivRoundup(K, BlkLen);
+
+    size_t SubBlkLen = (BlkLen == 16) ? 16 : (BlkLen == 32 ? 32 : 64);
+    if (ComputeType == CompInt8) {
+        SubBlkLen = 128;
+    }
+    PackQuantBDataAndBlkSum(N, BlockCountK, BlkLen, SubBlkLen, QuantBDataBegin, QuantBScaleBegin, has_zp_input, QuantBZPBegin, packed_quant_b, ThreadPool);
+}
+
 //
 // Kernel dispatch structure definition.
 //
@@ -291,6 +342,7 @@ const MLAS_SQNBIT_GEMM_DISPATCH MlasSQNBitGemmDispatchAvx512vnni = []() {
 
     d.SQ4BitGemmPackQuantBDataSize = SQ4BitGemmPackQuantBDataSize;
     d.SQ4BitGemmPackQuantBData = SQ4BitGemmPackQuantBData;
+    d.SQ4BitGemmPackQuantBDataAndBlkSum = SQ4BitGemmPackQuantBDataAndBlkSum512vnni;
 
     d.SQ4BitGemmPerGemmWorkspaceSize = SQ4BitGemmPerGemmWorkspaceSize;
     d.SQ4BitGemmPerGemmWorkspaceAlignment = SQ4BitGemmPerGemmWorkspaceAlignment;
@@ -298,8 +350,8 @@ const MLAS_SQNBIT_GEMM_DISPATCH MlasSQNBitGemmDispatchAvx512vnni = []() {
     d.SQ4BitGemmM1Kernel_CompFp32 = SQ4BitGemmM1Kernel_CompFp32;
     d.Q4BitBlkDequantBForSgemm_CompFp32 = Q4BitBlkDequantBForSgemm_CompFp32_avx2;
 
-    d.SQ4BitGemmKernel_CompInt8 = SQ4BitGemmKernel_CompInt8_avx512vnni;
-    d.QuantizeARow_CompInt8 = MlasQ80BlkQuantRow_avx512;
+    d.SQ4BitGemmKernel_BlkSum_CompInt8 = SQ4BitGemmKernel_BlkSum_CompInt8_avx512vnni;
+    d.QuantizeARowComputeBlkSum_CompInt8 = QuantizeARow_CompInt8_avx512;
 
     return d;
 }();
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx_common.h b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx_common.h
index 706e08fc467ba..177f5518bb891 100644
--- a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx_common.h
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx_common.h
@@ -14,13 +14,24 @@ SQ4BitGemmPackQuantBDataSize(
     MLAS_SQNBIT_GEMM_COMPUTE_TYPE ComputeType
 )
 {
-    MLAS_UNREFERENCED_PARAMETER(ComputeType);  // same size regardless of ComputeType
-
     constexpr size_t BlkBitWidth = 4;
-
     const size_t BlockCountK = MlasDivRoundup(K, BlkLen);
-    const size_t PackedQuantBDataSize = N * BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth, BlkLen);
-    return PackedQuantBDataSize;
+    if (ComputeType == CompInt8) {
+        size_t PackedQuantBDataSize = N * BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth, BlkLen);
+        const size_t ScaleSize = N * BlockCountK * sizeof(float);
+        size_t BlkSumSize = MlasDivRoundup(N, 16) * BlockCountK * 16 * sizeof(float);
+
+        // _mm256_load_si256 requires alignment on a 32-byte boundary
+        constexpr size_t PackedQuantBDataAlignment = 32;
+        PackedQuantBDataSize += PackedQuantBDataAlignment - 1;
+        constexpr size_t BlkSumAlignment = MlasQNBitQuantBBlkSumAlignment();
+        BlkSumSize += BlkSumAlignment - 1;
+
+        return PackedQuantBDataSize + ScaleSize + BlkSumSize;
+    } else {
+        const size_t PackedQuantBDataSize = N * BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth, BlkLen);
+        return PackedQuantBDataSize;
+    }
 }
 
 static void
@@ -100,6 +111,216 @@ SQ4BitGemmPackQuantBData(
     );
 }
 
+static size_t
+GetContinueLayoutOffsetSubBlk(size_t N, const size_t n, const size_t SubOrBlkCountK, const size_t k_sub_or_blk)
+{
+    size_t T = n / 4, t = n % 4;
+    bool te = T == N / 4;
+    size_t scale_dst_offset = T * 4 * SubOrBlkCountK;
+    if (te) {
+        scale_dst_offset += t * SubOrBlkCountK + k_sub_or_blk;
+    } else {
+        scale_dst_offset += k_sub_or_blk * 4 + t;
+    }
+    return scale_dst_offset;
+}
+
+static size_t
+GetContinueLayoutOffsetBlkInSubBlk(size_t N, const size_t n, const size_t BlockCountK, const size_t k_blk, const int blks_per_sub)
+{
+    size_t T = n / 4, t = n % 4, k_subblk = k_blk / blks_per_sub, b = k_blk % blks_per_sub;
+    bool te = T == N / 4, be = k_subblk == BlockCountK / blks_per_sub;
+    size_t scale_dst_offset = T * 4 * BlockCountK;
+    if (te) {
+        scale_dst_offset += t * BlockCountK + k_blk;
+    } else {
+        scale_dst_offset += k_subblk * blks_per_sub * 4;
+        if (be) {
+            scale_dst_offset += b * 4 + t;
+        } else {
+            scale_dst_offset += t * blks_per_sub + b;
+        }
+    }
+    return scale_dst_offset;
+}
+
+static void
+PackQuantB(
+  const std::byte* QuantBDataBegin,
+  std::byte* PackedQuantBDataBegin,
+  MLAS_THREADPOOL* ThreadPool,
+  const size_t N,
+  const size_t BlockCountK,
+  const size_t BlkLen,
+  const size_t SubBlkLen)
+{
+    constexpr size_t BlkBitWidth = 4;
+    const size_t BlkBytePairCount = BlkLen / 4;
+    const size_t BlkDataSize = MlasQNBitBlkDataSizeInBytes(BlkBitWidth, BlkLen);
+
+    const size_t SubBlkDataSize = SubBlkLen / 2;
+    const size_t SubBlkBytePairCount = SubBlkLen / 4;
+    const size_t SubBlkCountK = MlasDivRoundup(BlockCountK * BlkLen, SubBlkLen);
+    const size_t Iterations = N * SubBlkCountK;  // one iteration per sub block
+
+    // for avx2
+    // dst: | v0 v32 | v1 v33 | ... | v30 v62 | v31 v63 |
+    // for the remaining blk, it shall be:
+    // dst blklen32: | v0 v16 | v1 v17 | ... | v14 v30 | v15 v31 |
+    // dst blklen16: | v0 v8 | v1 v9 | v2 v11 | v3 v12 | v4 v13 | v5 v14 | v6 v15 | v7 v16 |
+
+    // for avx512
+    // dst: | v0 v64 | v1 v65 | ... | v62 v126 | v63 v127 |
+    // for the remaining blk, it shall be:
+    // dst blklen64: | v0 v32 | v1 v33 | ... | v30 v62 | v31 v63 |
+    // dst blklen32: | v0 v16 | v1 v17 | ... | v14 v30 | v15 v31 |
+    // dst blklen16: | v0 v8 | v1 v9 | v2 v11 | v3 v12 | v4 v13 | v5 v14 | v6 v15 | v7 v16 |
+    MlasTrySimpleParallel(
+        ThreadPool, Iterations,
+        [&](ptrdiff_t tid) {
+            const size_t n = tid / SubBlkCountK;
+            const size_t k_subblk = tid % SubBlkCountK;
+
+            const size_t src_data_offset = n * BlockCountK * BlkDataSize + k_subblk * SubBlkDataSize;
+            const std::byte* QuantBData = QuantBDataBegin + src_data_offset;
+
+            size_t PackBytePairCount = SubBlkBytePairCount;
+            size_t PackDataSize = SubBlkDataSize;
+
+            auto pack_subblk = [](
+              const std::byte* QuantBData, std::byte* PackedQuantBData,
+              size_t pack_byte_pair_count, size_t pack_data_size) {
+            for (size_t byte_pair_idx = 0; byte_pair_idx < pack_byte_pair_count; ++byte_pair_idx) {
+                const std::byte src0 = QuantBData[byte_pair_idx];
+                const std::byte src1 = QuantBData[byte_pair_idx + pack_data_size / 2];
+
+                std::byte& dst0 = PackedQuantBData[2 * byte_pair_idx];
+                std::byte& dst1 = PackedQuantBData[2 * byte_pair_idx + 1];
+
+                dst0 = (src0 & std::byte{0x0F}) | ((src1 & std::byte{0x0F}) << 4);
+                dst1 = (src0 >> 4) | ((src1 >> 4) << 4);
+            } };
+
+            if (SubBlkLen > BlkLen && k_subblk == SubBlkCountK - 1 &&
+                SubBlkLen * SubBlkCountK > BlkLen * BlockCountK) {
+                // this is the last subblk of the column. check if it extends out of the
+                // BlockCountK. If it does, we shall pack per blocks so that can compute
+                // on each block instead of each subblk.
+                PackBytePairCount = BlkBytePairCount;
+                PackDataSize = BlkDataSize;
+                const size_t k_blks_remaining = BlockCountK - (SubBlkCountK - 1) * SubBlkLen / BlkLen;
+                for (size_t k = 0; k < k_blks_remaining; k++) {
+                    const size_t k_blk = k_subblk * SubBlkLen / BlkLen + k;
+                    if (BlkLen == 16) {
+                      // not to do the compute order layout yet
+                        std::byte* PackedQuantBData = PackedQuantBDataBegin + src_data_offset;
+                        pack_subblk(QuantBData + k * BlkLen / 2, PackedQuantBData + k * BlkLen / 2, PackBytePairCount, PackDataSize);
+                    } else if (BlkLen >= SubBlkLen) {
+                        // shall not reach here with avx2
+                        assert(SubBlkLen == 128);
+                    } else {
+                        int blks_per_sub = (int)(SubBlkLen / BlkLen);
+                        const size_t dst_data_offset = GetContinueLayoutOffsetBlkInSubBlk(N, n, BlockCountK, k_blk, blks_per_sub);
+                        std::byte* PackedQuantBData = PackedQuantBDataBegin + dst_data_offset * BlkLen / 2;
+                        pack_subblk(QuantBData + k * BlkLen / 2, PackedQuantBData, PackBytePairCount, PackDataSize);
+                    }
+                }
+            } else {
+                if (BlkLen == 16) {
+                    // not to do the compute order layout yet
+                    std::byte* PackedQuantBData = PackedQuantBDataBegin + src_data_offset;
+                    pack_subblk(QuantBData, PackedQuantBData, PackBytePairCount, PackDataSize);
+                } else if (BlkLen >= SubBlkLen) {
+                    const size_t dst_data_offset = GetContinueLayoutOffsetSubBlk(N, n, SubBlkCountK, k_subblk);
+                    std::byte* PackedQuantBData = PackedQuantBDataBegin + dst_data_offset * SubBlkDataSize;
+                    pack_subblk(QuantBData, PackedQuantBData, PackBytePairCount, PackDataSize);
+                } else {
+                    int blks_per_sub = (int)(SubBlkLen / BlkLen);
+                    const size_t k_blk = k_subblk * blks_per_sub;
+                    const size_t dst_data_offset = GetContinueLayoutOffsetBlkInSubBlk(N, n, BlockCountK, k_blk, blks_per_sub);
+                    std::byte* PackedQuantBData = PackedQuantBDataBegin + dst_data_offset * BlkLen / 2;
+                    pack_subblk(QuantBData, PackedQuantBData, PackBytePairCount, PackDataSize);
+                }
+            }
+        }
+    );
+}
+
+//#include <iostream>
+
+static void
+ComputePackBlkSum(
+  size_t BlkLen,
+  size_t SubBlkLen,
+  size_t N,
+  float* QuantBScaleBegin,
+  const std::byte* QuantBZPBegin,
+  float* BlockSumBegin,
+  MLAS_THREADPOOL* ThreadPool,
+  const size_t BlockCountK)
+{
+    std::vector<float> QuantBScaleBeginCopy(N * BlockCountK);
+    std::copy(QuantBScaleBegin, QuantBScaleBegin + N * BlockCountK, QuantBScaleBeginCopy.begin());
+    MlasTrySimpleParallel(ThreadPool, N * BlockCountK, [&](ptrdiff_t tid) {
+        const size_t n = tid / BlockCountK;
+        const size_t k_blk = tid % BlockCountK;
+
+        const size_t src_blk_offset = n * BlockCountK + k_blk;
+        const float& QuantBScale = QuantBScaleBeginCopy[src_blk_offset];
+        uint8_t zp = 8;
+        if (QuantBZPBegin) {
+            size_t ZPCountK = MlasDivRoundup(BlockCountK, 2);
+            size_t src_zp_offset = ZPCountK * n + k_blk / 2;
+            bool low_zp = k_blk % 2 == 0;
+            const std::byte* QuantBZP = QuantBZPBegin + src_zp_offset;
+            const std::byte low_mask{0X0F};
+            zp = (uint8_t)(low_zp ? ((*QuantBZP) & low_mask) : ((*QuantBZP) >> 4));
+        }
+
+          // BlockSum is a width 16 row major matrix
+          const size_t dst_offset = ((n / 16) * BlockCountK + k_blk) * 16 + n % 16;
+        *(BlockSumBegin + dst_offset) = -QuantBScale * zp;
+        if (BlkLen == 16) {  // TODO
+
+        } else if (BlkLen >= SubBlkLen) {
+            const size_t scale_dst_offset = GetContinueLayoutOffsetSubBlk(N, n, BlockCountK, k_blk);
+            *(QuantBScaleBegin + scale_dst_offset) = QuantBScale;
+        } else {
+            int blks_per_sub = (int)(SubBlkLen / BlkLen);
+            size_t scale_dst_offset = GetContinueLayoutOffsetBlkInSubBlk(N, n, BlockCountK, k_blk, blks_per_sub);
+            *(QuantBScaleBegin + scale_dst_offset) = QuantBScale;
+        }
+    }
+    );
+}
+
+static void
+PackQuantBDataAndBlkSum(
+    size_t N,
+    size_t BlockCountK,
+    size_t BlkLen,
+    size_t SubBlkLen,
+    const std::byte* QuantBDataBegin,
+    const float* QuantBScaleBegin,
+    bool has_zp_input,
+    const std::byte* QuantBZPBegin,
+    PackedQuantBDataStruct& packed_quant_b,
+    MLAS_THREADPOOL* ThreadPool
+)
+{
+    if (QuantBDataBegin) {
+        PackQuantB(QuantBDataBegin, packed_quant_b.PackedQuantBData, ThreadPool, N, BlockCountK, BlkLen, SubBlkLen);
+    }
+
+    if (QuantBScaleBegin) {
+        std::copy(QuantBScaleBegin, QuantBScaleBegin + N * BlockCountK, packed_quant_b.PackedQuantBScale);
+    }
+
+    if ((QuantBScaleBegin && !has_zp_input) || QuantBZPBegin) {
+        ComputePackBlkSum(BlkLen, SubBlkLen, N, packed_quant_b.PackedQuantBScale, QuantBZPBegin, packed_quant_b.QuantBBlkSum, ThreadPool, BlockCountK);
+    }
+}
+
 //
 // Workspace size calculation function implementation.
 //
@@ -119,7 +340,8 @@ SQ4BitGemmPerGemmWorkspaceSize(
         case CompInt8: {
             // workspace buffer is used for block quantization of A to int8
             const size_t BlockCountK = MlasDivRoundup(K, BlkLen);
-            const size_t PerGemmWorkspaceSize = M * BlockCountK * Q8BlkSize(BlkLen);
+            // QuantData + Scale + BlkSum
+            const size_t PerGemmWorkspaceSize = M * BlockCountK * (Q8BlkSize(BlkLen) + sizeof(float));
             return PerGemmWorkspaceSize;
         }
         default: {
@@ -288,6 +510,20 @@ load_and_mul_sum_s8_quads_with_zp_avx2(
     acc0 = _mm256_fmadd_ps(sum_ps, scale0, acc0);
 }
 
+template <bool HasZeroPoint>
+void MLAS_FORCEINLINE
+get_2_zps(const std::byte* QuantBZeroPointPtr, int8_t& zp0, int8_t& zp1)
+{
+    if constexpr (HasZeroPoint) {
+        zp0 = std::to_integer<int8_t>((*QuantBZeroPointPtr) & std::byte{0x0F});
+        zp1 = std::to_integer<int8_t>((*QuantBZeroPointPtr) >> 4);
+    } else {
+        zp0 = 8;
+        zp1 = 8;
+        (void)QuantBZeroPointPtr;
+    }
+}
+
 template <bool HasZeroPoint>
 int8_t MLAS_FORCEINLINE
 get_zp(bool is_lower_half_byte_zp, const std::byte* QuantBZeroPointPtr)
@@ -375,7 +611,7 @@ FoldAccumulators(const __m256& acc0, const __m256& acc1, const __m256& acc2, con
     return acc_y;
 }
 
-static inline float
+static MLAS_FORCEINLINE float
 hsum_float_8(const __m256 x)
 {
     __m128 res = _mm256_extractf128_ps(x, 1);
@@ -417,4 +653,27 @@ FoldAccumulators(const __m512& acc0, const __m512& acc1, const __m512& acc2, con
         _mm256_add_ps(_mm512_extractf32x8_ps(acc_lo0123, 0), _mm512_extractf32x8_ps(acc_lo0123, 1));
     return _mm_add_ps(_mm256_extractf32x4_ps(acc_y, 0), _mm256_extractf32x4_ps(acc_y, 1));
 }
+
+static MLAS_FORCEINLINE __m128i
+convert_2_ps_to_epi8(__m256 v0, __m256 v1)
+{
+    __m256i v0_8_epi32 = _mm256_cvtps_epi32(v0);
+    __m256i v1_8_epi32 = _mm256_cvtps_epi32(v1);
+
+    __m128i v0_8_epi16 = _mm_packs_epi32(_mm256_extractf128_si256(v0_8_epi32, 0), _mm256_extractf128_si256(v0_8_epi32, 1));
+    __m128i v1_8_epi16 = _mm_packs_epi32(_mm256_extractf128_si256(v1_8_epi32, 0), _mm256_extractf128_si256(v1_8_epi32, 1));
+
+    return _mm_packs_epi16(v0_8_epi16, v1_8_epi16);
+}
+
+// horizontally add 8 int32_t
+static MLAS_FORCEINLINE int
+hsum_8_epi32(const __m256i a_8_epi32)
+{
+    const __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(a_8_epi32), _mm256_extractf128_si256(a_8_epi32, 1));
+    const __m128i hi64 = _mm_unpackhi_epi64(sum128, sum128);
+    const __m128i sum64 = _mm_add_epi32(hi64, sum128);
+    const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1));
+    return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32));
+}
 }  // namespace
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx_common_int8.h b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx_common_int8.h
index 250ffeacd7c2f..895ce6cd091c2 100644
--- a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx_common_int8.h
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx_common_int8.h
@@ -7,20 +7,6 @@
 #include "sqnbitgemm_kernel_avx_common.h"
 #include "sqnbitgemm_q8_block.h"
 
-void
-SQ4BitGemmM1Kernel_CompInt8_avx2(
-    size_t BlkLen,
-    const std::byte* QuantA,
-    const std::byte* QuantBData,
-    const float* QuantBScale,
-    const std::byte* QuantBZeroPoint,
-    float* C,
-    size_t CountN,
-    size_t CountK,
-    size_t BlockStrideQuantB,
-    const float* Bias
-);
-
 template <size_t NCols, bool HasZeroPoint>
 MLAS_FORCEINLINE void
 ComputeDotProducts_BlkBitWidth4_CompInt8_SubBlkLen16(
@@ -240,6 +226,7 @@ template <bool HasZeroPoint, AccumulateFunctionType<HasZeroPoint> accumulator>
 void
 SQ4BitGemmM1Kernel_BlkLen32_CompInt8_Impl(
     const std::byte* QuantA,
+    const float* QuantAScale,
     const std::byte* QuantBData,
     const float* QuantBScale,
     const std::byte* QuantBZeroPoint,
@@ -273,6 +260,7 @@ SQ4BitGemmM1Kernel_BlkLen32_CompInt8_Impl(
     int64_t nblk = (int64_t)(CountN)-4;
     while (nblk >= 0) {
         const std::byte* QuantAPtr = QuantA;
+        const float* QuantAScalePtr = QuantAScale;
         const std::byte* QuantBDataPtr = QuantBDataColPtr;
         const float* QuantBScalePtr = QuantBScaleColPtr;
         const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
@@ -286,14 +274,14 @@ SQ4BitGemmM1Kernel_BlkLen32_CompInt8_Impl(
         size_t k_blks_remaining = BlockCountK;
         for (; k_blks_remaining > 1; k_blks_remaining -= 2) {
             const std::byte* QuantABlk0 = QuantAPtr;
-            const std::byte* QuantABlk1 = QuantABlk0 + Q8BlkSize(BlkLen);
+            const std::byte* QuantABlk1 = QuantABlk0 + BlkLen;
 
             // load A:
-            const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
-            const __m256i av_1_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk1));
+            const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk0);
+            const __m256i av_1_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk1);
 
-            const float& scale_a0 = Q8BlkScale(QuantABlk0);
-            const float& scale_a1 = Q8BlkScale(QuantABlk1);
+            const float& scale_a0 = *QuantAScalePtr;
+            const float& scale_a1 = *(QuantAScalePtr + 1);
 
             // Col0
             const float& scale_00 = scale_a0 * QuantBScalePtr[0];
@@ -320,7 +308,8 @@ SQ4BitGemmM1Kernel_BlkLen32_CompInt8_Impl(
             accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 3 * StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, false, scale_31, acc3);
 
             // increment block pointers
-            QuantAPtr += Q8BlkSize(BlkLen) * 2;
+            QuantAPtr += BlkLen * 2;
+            QuantAScalePtr += 2;
             QuantBDataPtr += 16 * 2;
             QuantBScalePtr += 2;
             if constexpr (HasZeroPoint) {
@@ -331,9 +320,9 @@ SQ4BitGemmM1Kernel_BlkLen32_CompInt8_Impl(
         if (k_blks_remaining > 0) {
             // load A
             const std::byte* QuantABlk0 = QuantAPtr;
-            const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
+            const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk0);
 
-            const float& scale_a0 = Q8BlkScale(QuantABlk0);
+            const float& scale_a0 = *QuantAScalePtr;
 
             // Col0
             const float& scale_00 = scale_a0 * QuantBScalePtr[0];
@@ -374,6 +363,7 @@ SQ4BitGemmM1Kernel_BlkLen32_CompInt8_Impl(
     nblk += NCols;
     for (int64_t n = 0; n < nblk; n++) {
         const std::byte* QuantAPtr = QuantA;
+        const float* QuantAScalePtr = QuantAScale;
         const std::byte* QuantBDataPtr = QuantBDataColPtr;
         const float* QuantBScalePtr = QuantBScaleColPtr;
         const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
@@ -383,14 +373,14 @@ SQ4BitGemmM1Kernel_BlkLen32_CompInt8_Impl(
         size_t k_blks_remaining = BlockCountK;
         for (; k_blks_remaining > 1; k_blks_remaining -= 2) {
             const std::byte* QuantABlk0 = QuantAPtr;
-            const std::byte* QuantABlk1 = QuantABlk0 + Q8BlkSize(BlkLen);
+            const std::byte* QuantABlk1 = QuantABlk0 + BlkLen;
 
             // load A:
-            const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
-            const __m256i av_1_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk1));
+            const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk0);
+            const __m256i av_1_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk1);
 
-            const float& scale_a0 = Q8BlkScale(QuantABlk0);
-            const float& scale_a1 = Q8BlkScale(QuantABlk1);
+            const float& scale_a0 = *QuantAScalePtr;
+            const float& scale_a1 = *(QuantAScalePtr + 1);
 
             // Col0
             const float& scale_00 = scale_a0 * QuantBScalePtr[0];
@@ -399,7 +389,8 @@ SQ4BitGemmM1Kernel_BlkLen32_CompInt8_Impl(
             accumulator(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 16), low_mask, zero, QuantBZeroPointPtr, false, scale_01, acc0);
 
             // increment block pointers
-            QuantAPtr += Q8BlkSize(BlkLen) * 2;
+            QuantAPtr += BlkLen * 2;
+            QuantAScalePtr += 2;
             QuantBDataPtr += 16 * 2;
             QuantBScalePtr += 2;
             if constexpr (HasZeroPoint) {
@@ -410,9 +401,9 @@ SQ4BitGemmM1Kernel_BlkLen32_CompInt8_Impl(
         if (k_blks_remaining > 0) {
             // load A
             const std::byte* QuantABlk0 = QuantAPtr;
-            const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)Q8BlkData(QuantABlk0));
+            const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk0);
 
-            const float& scale_a0 = Q8BlkScale(QuantABlk0);
+            const float& scale_a0 = *QuantAScalePtr;
 
             // Col0
             const float& scale_00 = scale_a0 * QuantBScalePtr[0];
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_m1_sym_kernel_avx2_int8_blklen32.h b/onnxruntime/core/mlas/lib/sqnbitgemm_m1_sym_kernel_avx2_int8_blklen32.h
new file mode 100644
index 0000000000000..45c3963365e6b
--- /dev/null
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_m1_sym_kernel_avx2_int8_blklen32.h
@@ -0,0 +1,759 @@
+#pragma once
+#include <algorithm>
+#include <cassert>
+#include <utility>
+
+#include "sqnbitgemm.h"
+#include "sqnbitgemm_kernel_avx_common.h"
+
+template <bool HasZeroPoint, bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r1c1blk1_zp_avx2(
+    const __m256i& av_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float& combined_scale,
+    const std::byte* QuantBZeroPointPtr,
+    __m256& acc,
+    const __m256i& low_mask
+)
+{
+    // | v0  v16 | v1  v17 | ... | v14 v30 | v15 v31 |
+    const __m128i bv_packed0 = _mm_loadu_si128(reinterpret_cast<const __m128i*>(QuantBDataPtr));
+    __m256i bv_32_epi8 = _mm256_set_m128i(_mm_srli_epi16(bv_packed0, 4), bv_packed0);
+    bv_32_epi8 = _mm256_and_si256(low_mask, bv_32_epi8);
+
+    bv_32_epi8 = _mm256_sub_epi8(bv_32_epi8, _mm256_set1_epi8(get_zp<HasZeroPoint>(true, QuantBZeroPointPtr)));
+
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    if constexpr (vnni) {
+        const __m256i sum_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), _mm256_sign_epi8(bv_32_epi8, bv_32_epi8), _mm256_sign_epi8(av_32_epi8, bv_32_epi8));
+        const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+        acc = _mm256_fmadd_ps(sum_ps, _mm256_set1_ps(combined_scale), acc);
+    } else {
+#endif
+        __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv_32_epi8, bv_32_epi8), 15);
+        const __m256i dot_16_epi16 = _mm256_maddubs_epi16(_mm256_sign_epi8(bv_32_epi8, bv_32_epi8), _mm256_sign_epi8(av_32_epi8, bv_32_epi8));
+        const __m256i sum_8_epi32 = _mm256_madd_epi16(one_16_epi16, dot_16_epi16);
+        const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+        acc = _mm256_fmadd_ps(sum_ps, _mm256_set1_ps(combined_scale), acc);
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    }
+#endif
+}
+
+template<bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r1c1blk2_zp_avx2(
+    const __m256i& av0_32_epi8,
+    const __m256i& av1_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a,
+    const float* scale_b,
+    const std::byte* QuantBZeroPointPtr,
+    __m256& acc0,
+    const __m256i& low_mask
+)
+{
+    const __m256i bv_packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(QuantBDataPtr));
+    __m256i bv0_32_epi8 = _mm256_and_si256(bv_packed, low_mask);                        // 0~31
+    __m256i bv1_32_epi8 = _mm256_and_si256(_mm256_srli_epi16(bv_packed, 4), low_mask);  // 32~63
+
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    if constexpr (vnni) {
+        {
+            bv0_32_epi8 = _mm256_sub_epi8(bv0_32_epi8, _mm256_set1_epi8(get_zp<true>(true, QuantBZeroPointPtr)));
+            __m256i sum_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), _mm256_sign_epi8(bv0_32_epi8, bv0_32_epi8), _mm256_sign_epi8(av0_32_epi8, bv0_32_epi8));
+            const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+            const __m256 scale = _mm256_set1_ps(*(scale_a) * *(scale_b));
+            acc0 = _mm256_fmadd_ps(sum_ps, scale, acc0);
+        }
+
+        {
+            bv1_32_epi8 = _mm256_sub_epi8(bv1_32_epi8, _mm256_set1_epi8(get_zp<true>(false, QuantBZeroPointPtr)));
+            const __m256 scale = _mm256_set1_ps(*(scale_a + 1) * *(scale_b + 1));
+            __m256i sum_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), _mm256_sign_epi8(bv1_32_epi8, bv1_32_epi8), _mm256_sign_epi8(av1_32_epi8, bv1_32_epi8));
+            const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+            acc0 = _mm256_fmadd_ps(sum_ps, scale, acc0);
+        }
+    } else {
+#endif
+        {
+            bv0_32_epi8 = _mm256_sub_epi8(bv0_32_epi8, _mm256_set1_epi8(get_zp<true>(true, QuantBZeroPointPtr)));
+            const __m256 scale = _mm256_set1_ps(*(scale_a) * *(scale_b));
+            __m256i dot_16_epi16 = _mm256_maddubs_epi16(
+                _mm256_sign_epi8(bv0_32_epi8, bv0_32_epi8), _mm256_sign_epi8(av0_32_epi8, bv0_32_epi8)
+            );
+            __m256i sum_8_epi32 = _mm256_madd_epi16(_mm256_set1_epi16(1), dot_16_epi16);
+            const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+            acc0 = _mm256_fmadd_ps(sum_ps, scale, acc0);
+        }
+
+        {
+            bv1_32_epi8 = _mm256_sub_epi8(bv1_32_epi8, _mm256_set1_epi8(get_zp<true>(false, QuantBZeroPointPtr)));
+            const __m256 scale = _mm256_set1_ps(*(scale_a + 1) * *(scale_b + 1));
+            __m256i dot_16_epi16 = _mm256_maddubs_epi16(
+                _mm256_sign_epi8(bv1_32_epi8, bv1_32_epi8), _mm256_sign_epi8(av1_32_epi8, bv1_32_epi8)
+            );
+            __m256i sum_8_epi32 = _mm256_madd_epi16(_mm256_set1_epi16(1), dot_16_epi16);
+            const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+            acc0 = _mm256_fmadd_ps(sum_ps, scale, acc0);
+        }
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    }
+#endif
+}
+
+template<bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r1c1blk2_zp_is_8_avx2(
+    const __m256i& av0_32_epi8,
+    const __m256i& av1_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a,
+    const float* scale_b,
+    __m256& acc0,
+    const __m256i& low_mask,
+    const __m256i& bzp8
+)
+{
+  // accumulate_blklen32_r1c1blk2_zp_is_8_avx2 is much faster than
+  // accumulate_blklen32_r1c1blk2_zp_is_8_no_bc_avx2:
+  // BlkBitWidth:4/BlkLen:32/M:1/N:2560/K:2560/Threads:8/Symmetric:1/HasBias:0/ComputeType:4
+  // 36591 vs 40270 ns (the main is 51836 ns). both are not as good as main with genai.
+    // TODO: consolidate with accumulate_blklen32_r1c1blk2_avx2 using a zp8 template option
+    // | v0 v32 | v1 v33 | ... | v30 v62 | v31 v63 |
+
+    const __m256i bv_packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(QuantBDataPtr));
+    __m256i bv0_32_epi8 = _mm256_and_si256(bv_packed, low_mask);                          // 0~31
+    __m256i bv1_32_epi8 = _mm256_and_si256(_mm256_srli_epi16(bv_packed, 4), low_mask);    // 32~63
+
+    bv0_32_epi8 = _mm256_sub_epi8(bv0_32_epi8, bzp8);
+    bv1_32_epi8 = _mm256_sub_epi8(bv1_32_epi8, bzp8);
+
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    if constexpr (vnni) {
+        __m256i dot0_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), _mm256_sign_epi8(bv0_32_epi8, bv0_32_epi8), _mm256_sign_epi8(av0_32_epi8, bv0_32_epi8));
+        __m256i dot1_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), _mm256_sign_epi8(bv1_32_epi8, bv1_32_epi8), _mm256_sign_epi8(av1_32_epi8, bv1_32_epi8));
+        const __m256i sum_8_epi32 = _mm256_hadd_epi32(dot0_8_epi32, dot1_8_epi32);
+        const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+        __m256 scale_a0_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_a));
+        __m256 scale_b_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_b));
+        // 1 0 1 0 1 0 1 0 -> 1 1 0 0 1 1 0 0
+        __m256 scale_8_ps = _mm256_permute_ps(_mm256_mul_ps(scale_a0_2_ps, scale_b_2_ps), _MM_SHUFFLE(1, 1, 0, 0));
+
+        acc0 = _mm256_fmadd_ps(sum_ps, scale_8_ps, acc0);
+    } else {
+#endif
+        __m256i dot0_16_epi16 = _mm256_maddubs_epi16(_mm256_sign_epi8(bv0_32_epi8, bv0_32_epi8), _mm256_sign_epi8(av0_32_epi8, bv0_32_epi8));
+        __m256i dot1_16_epi16 = _mm256_maddubs_epi16(_mm256_sign_epi8(bv1_32_epi8, bv1_32_epi8), _mm256_sign_epi8(av1_32_epi8, bv1_32_epi8));
+        const __m256i sum_16_epi16 = _mm256_hadd_epi16(dot0_16_epi16, dot1_16_epi16);
+
+        const __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(low_mask, low_mask), 15);
+        const __m256i sum_8_epi32 = _mm256_madd_epi16(one_16_epi16, sum_16_epi16);
+        const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+        __m256 scale_a0_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_a));
+        __m256 scale_b_2_ps = _mm256_castpd_ps(_mm256_broadcast_sd((double*)scale_b));
+        // 1 0 1 0 1 0 1 0 -> 1 1 0 0 1 1 0 0
+        __m256 scale_8_ps = _mm256_permute_ps(
+            _mm256_mul_ps(scale_a0_2_ps, scale_b_2_ps), _MM_SHUFFLE(1, 1, 0, 0)
+        );
+
+        acc0 = _mm256_fmadd_ps(sum_ps, scale_8_ps, acc0);
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    }
+#endif
+}
+
+template <bool vnni>
+static MLAS_FORCEINLINE void
+accumulate_blklen32_r1c1blk2_zp_is_8_no_bc_avx2(
+    const __m256i& av0_32_epi8,
+    const __m256i& av1_32_epi8,
+    const __m256& scale_a0_8_ps,
+    const __m256& scale_a1_8_ps,
+    const std::byte* QuantBDataPtr,
+    const float* scale_b,
+    __m256& acc0,
+    const __m256i& low_mask,
+    const __m256i& bzp8
+)
+{
+    // TODO: consolidate with accumulate_blklen32_r1c1blk2_avx2 using a zp8 template option
+    // | v0 v32 | v1 v33 | ... | v30 v62 | v31 v63 |
+    const __m256i bv_packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(QuantBDataPtr));
+    __m256i bv0_32_epi8 = _mm256_and_si256(bv_packed, low_mask);                          // 0~31
+    __m256i bv1_32_epi8 = _mm256_srli_epi16(_mm256_sub_epi8(bv_packed, bv0_32_epi8), 4);  // 32~63
+
+    bv0_32_epi8 = _mm256_sub_epi8(bv0_32_epi8, bzp8);
+    bv1_32_epi8 = _mm256_sub_epi8(bv1_32_epi8, bzp8);
+
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    if constexpr (vnni) {
+        {
+            __m256i sum_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), _mm256_sign_epi8(bv0_32_epi8, bv0_32_epi8), _mm256_sign_epi8(av0_32_epi8, bv0_32_epi8));
+            const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+            const __m256 scale = _mm256_mul_ps(_mm256_set1_ps(*scale_b), scale_a0_8_ps);
+            acc0 = _mm256_fmadd_ps(sum_ps, scale, acc0);
+        }
+        {
+            __m256i sum_8_epi32 = _mm256_dpbusds_avx_epi32(_mm256_setzero_si256(), _mm256_sign_epi8(bv1_32_epi8, bv1_32_epi8), _mm256_sign_epi8(av1_32_epi8, bv1_32_epi8));
+            const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+            const __m256 scale = _mm256_mul_ps(_mm256_set1_ps(*(scale_b + 1)), scale_a1_8_ps);
+            acc0 = _mm256_fmadd_ps(sum_ps, scale, acc0);
+        }
+    } else {
+#endif
+        {
+            __m256i dot0_16_epi16 = _mm256_maddubs_epi16(_mm256_sign_epi8(bv0_32_epi8, bv0_32_epi8), _mm256_sign_epi8(av0_32_epi8, bv0_32_epi8));
+            __m256i sum_8_epi32 = _mm256_madd_epi16(_mm256_set1_epi16(1), dot0_16_epi16);
+            const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+            const __m256 scale = _mm256_mul_ps(_mm256_set1_ps(*scale_b), scale_a0_8_ps);
+            acc0 = _mm256_fmadd_ps(sum_ps, scale, acc0);
+        }
+        {
+            __m256i dot0_16_epi16 = _mm256_maddubs_epi16(_mm256_sign_epi8(bv1_32_epi8, bv1_32_epi8), _mm256_sign_epi8(av1_32_epi8, bv1_32_epi8));
+            __m256i sum_8_epi32 = _mm256_madd_epi16(_mm256_set1_epi16(1), dot0_16_epi16);
+            const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+            const __m256 scale = _mm256_mul_ps(_mm256_set1_ps(*(scale_b + 1)), scale_a1_8_ps);
+            acc0 = _mm256_fmadd_ps(sum_ps, scale, acc0);
+        }
+#if !defined(__GNUC__) || (__GNUC__ > 10)
+    }
+#endif
+}
+
+template <bool HasZeroPoint, bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmM1C4BlkLen32Avx2(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    const std::byte* QuantBZeroPoint,
+    float* C,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias)
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    //const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    //const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountN % NCols4 == 0);
+
+    const std::byte* QuantBDataColPtr = QuantBData;
+    const float* QuantBScaleColPtr = QuantBScale;
+    const std::byte* QuantBZeroPointColPtr = QuantBZeroPoint;
+    const float* BiasPtr = Bias;
+    auto* SumPtr = C;
+
+    const __m256i low_mask = _mm256_set1_epi8(0x0F);
+    //const __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(low_mask, low_mask), 15);
+    const size_t StrideQuantBDataCol = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBData2 = 2 * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBData1 = 1 * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale2 = 2;
+    const size_t StrideQuantBScale1 = 1;
+    const size_t StrideQuantBZeroPoint = MlasQNBitZeroPointsForBlksSizeInBytes<BlkBitWidth4>(BlockCountK);
+
+
+    for (size_t n = 0; n < CountN; n += NCols4) {
+        const std::byte* QuantAPtr = QuantA;
+        const float* QuantAScalePtr = QuantAScale;
+
+        const std::byte* QuantBDataPtr = QuantBDataColPtr;
+        const float* QuantBScalePtr = QuantBScaleColPtr;
+        const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
+
+        __m256 acc[NCols4] = {_mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps()};
+        size_t k_blks_remaining = BlockCountK;
+        for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+            const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantAPtr);
+            const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + BlkLen32));
+            //const __m256 scale_a0_8_ps = _mm256_set1_ps(Q8BlkScale(QuantAPtr));
+            //const __m256 scale_a1_8_ps = _mm256_set1_ps(Q8BlkScale(QuantAPtr + Q8BlkSize(BlkLen32)));
+
+            //accumulate_blklen32_r1c1blk2_zp_is_8_no_bc_avx2(av_00_epi8, av_01_epi8, scale_a0_8_ps, scale_a1_8_ps, QuantBDataPtr, QuantBScalePtr, acc[0], low_mask, bzp8);
+            //accumulate_blklen32_r1c1blk2_zp_is_8_no_bc_avx2(av_00_epi8, av_01_epi8, scale_a0_8_ps, scale_a1_8_ps, QuantBDataPtr + StrideQuantBData, QuantBScalePtr + StrideQuantBScale, acc[1], low_mask, bzp8);
+            //accumulate_blklen32_r1c1blk2_zp_is_8_no_bc_avx2(av_00_epi8, av_01_epi8, scale_a0_8_ps, scale_a1_8_ps, QuantBDataPtr + 2 * StrideQuantBData, QuantBScalePtr + 2 * StrideQuantBScale, acc[2], low_mask, bzp8);
+            //accumulate_blklen32_r1c1blk2_zp_is_8_no_bc_avx2(av_00_epi8, av_01_epi8, scale_a0_8_ps, scale_a1_8_ps, QuantBDataPtr + 3 * StrideQuantBData, QuantBScalePtr + 3 * StrideQuantBScale, acc[3], low_mask, bzp8);
+            if constexpr (HasZeroPoint) {
+                accumulate_blklen32_r1c1blk2_zp_avx2<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, QuantBZeroPointPtr, acc[0], low_mask);
+                accumulate_blklen32_r1c1blk2_zp_avx2<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr + StrideQuantBData2, QuantAScalePtr, QuantBScalePtr + StrideQuantBScale2, QuantBZeroPointPtr + StrideQuantBZeroPoint, acc[1], low_mask);
+                accumulate_blklen32_r1c1blk2_zp_avx2<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr + 2 * StrideQuantBData2, QuantAScalePtr, QuantBScalePtr + 2 * StrideQuantBScale2, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, acc[2], low_mask);
+                accumulate_blklen32_r1c1blk2_zp_avx2<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr + 3 * StrideQuantBData2, QuantAScalePtr, QuantBScalePtr + 3 * StrideQuantBScale2, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, acc[3], low_mask);
+
+            } else {
+                const __m256i bzp8 = _mm256_set1_epi8(8);
+                accumulate_blklen32_r1c1blk2_zp_is_8_avx2<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc[0], low_mask, bzp8);
+                accumulate_blklen32_r1c1blk2_zp_is_8_avx2<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr + StrideQuantBData2, QuantAScalePtr, QuantBScalePtr + StrideQuantBScale2, acc[1], low_mask, bzp8);
+                accumulate_blklen32_r1c1blk2_zp_is_8_avx2<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr + 2 * StrideQuantBData2, QuantAScalePtr, QuantBScalePtr + 2 * StrideQuantBScale2, acc[2], low_mask, bzp8);
+                accumulate_blklen32_r1c1blk2_zp_is_8_avx2<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr + 3 * StrideQuantBData2, QuantAScalePtr, QuantBScalePtr + 3 * StrideQuantBScale2, acc[3], low_mask, bzp8);
+            }
+            // increment block pointers
+            QuantAPtr += BlkLen32 * PerAccuBlk2;
+            QuantAScalePtr += PerAccuBlk2;
+            QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk2 * NCols4;
+            QuantBScalePtr += PerAccuBlk2 * NCols4;
+            if constexpr (HasZeroPoint) {
+                QuantBZeroPointPtr += 1;
+            }
+        }
+
+        // TODO: use a loop in case PerAccuBlk2 is not 2.
+        if (k_blks_remaining > 0) {
+            // load A
+            const std::byte* QuantABlk0 = QuantAPtr;
+            const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk0);
+            const float& scale_a00 = *QuantAScalePtr;
+            {
+                const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+                accumulate_blklen32_r1c1blk1_zp_avx2<HasZeroPoint, vnni>(av_00_epi8, QuantBDataPtr, scale_00, QuantBZeroPointPtr, acc[0], low_mask);
+            }
+            {
+                const float& scale_00 = scale_a00 * (QuantBScalePtr + StrideQuantBScale1)[0];
+                accumulate_blklen32_r1c1blk1_zp_avx2<HasZeroPoint, vnni>(av_00_epi8, QuantBDataPtr + StrideQuantBData1, scale_00, QuantBZeroPointPtr + StrideQuantBZeroPoint, acc[1], low_mask);
+            }
+            {
+                const float& scale_00 = scale_a00 * (QuantBScalePtr + 2 * StrideQuantBScale1)[0];
+                accumulate_blklen32_r1c1blk1_zp_avx2<HasZeroPoint, vnni>(av_00_epi8, QuantBDataPtr + 2 * StrideQuantBData1, scale_00, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, acc[2], low_mask);
+            }
+            {
+                const float& scale_00 = scale_a00 * (QuantBScalePtr + 3 * StrideQuantBScale1)[0];
+                accumulate_blklen32_r1c1blk1_zp_avx2<HasZeroPoint, vnni>(av_00_epi8, QuantBDataPtr + 3 * StrideQuantBData1, scale_00, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, acc[3], low_mask);
+            }
+        }
+
+        __m128 acc_r0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+        if (BiasPtr != nullptr) {
+            acc_r0 = _mm_add_ps(acc_r0, _mm_loadu_ps(BiasPtr));
+        }
+
+        _mm_storeu_ps(SumPtr, acc_r0);
+
+        // move to next NCols columns
+        QuantBDataColPtr += NCols4 * StrideQuantBDataCol;
+        QuantBScaleColPtr += NCols4 * BlockCountK;
+        if constexpr (HasZeroPoint) {
+            QuantBZeroPointColPtr += NCols4 * StrideQuantBZeroPoint;
+        }
+
+        BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+        SumPtr += NCols4;
+    }
+}
+
+template <bool HasZeroPoint, bool vnni>
+MLAS_FORCEINLINE void
+Q4Int8GemmM1C1BlkLen32Avx2(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    const std::byte* QuantBZeroPoint,
+    float* C,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias
+)
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    constexpr size_t BlkDataSizeInBytes16 = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+
+    // process 2 blks of 64 4b weights a time
+    constexpr size_t PerAccuBlk2 = 2;
+
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale = BlockCountK;
+    const size_t StrideQuantBZeroPoint = MlasQNBitZeroPointsForBlksSizeInBytes<BlkBitWidth4>(BlockCountK);
+
+    [[maybe_unused]] size_t QuantBZeroPointIdx = 0;  // track half byte increments with this index instead of a pointer
+    assert(CountN < NCols4);
+
+    const std::byte* QuantBDataColPtr = QuantBData;
+    const float* QuantBScaleColPtr = QuantBScale;
+    const std::byte* QuantBZeroPointColPtr = QuantBZeroPoint;
+    const float* BiasPtr = Bias;
+    auto* SumPtr = C;
+
+    const __m256i low_mask = _mm256_set1_epi8(0x0F);
+    [[maybe_unused]] const __m256i bzp8 = _mm256_set1_epi8(8);
+    for (size_t n = 0; n < CountN; n++) {
+        const std::byte* QuantAPtr = QuantA;
+        const float* QuantAScalePtr = QuantAScale;
+        const std::byte* QuantBDataPtr = QuantBDataColPtr;
+        const float* QuantBScalePtr = QuantBScaleColPtr;
+        const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
+
+        __m256 acc0 = _mm256_setzero_ps();
+        size_t k_blks_remaining = BlockCountK;
+        for (; k_blks_remaining > 1; k_blks_remaining -= PerAccuBlk2) {
+            const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantAPtr);
+            const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + BlkLen32));
+            //const __m256 scale_a0_8_ps = _mm256_set1_ps(Q8BlkScale(QuantAPtr));
+            //const __m256 scale_a1_8_ps = _mm256_set1_ps(Q8BlkScale(QuantAPtr + Q8BlkSize(BlkLen32)));
+
+            if constexpr (HasZeroPoint) {
+                accumulate_blklen32_r1c1blk2_zp_avx2<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, QuantBZeroPointPtr, acc0, low_mask);
+            } else {
+                accumulate_blklen32_r1c1blk2_zp_is_8_avx2<vnni>(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0, low_mask, bzp8);
+            }
+
+            // increment block pointers
+            QuantAPtr += BlkLen32 * PerAccuBlk2;
+            QuantAScalePtr += PerAccuBlk2;
+            QuantBDataPtr += BlkDataSizeInBytes16 * PerAccuBlk2;
+            QuantBScalePtr += PerAccuBlk2;
+            if constexpr (HasZeroPoint) {
+                QuantBZeroPointPtr += 1;
+            }
+        }
+
+        // TODO: use a loop in case PerAccuBlk2 is not 2.
+        if (k_blks_remaining > 0) {
+            const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantAPtr);
+            const float& scale_a00 = *QuantAScalePtr;
+            const float& scale_00 = scale_a00 * (QuantBScalePtr)[0];
+            accumulate_blklen32_r1c1blk1_zp_avx2<HasZeroPoint, vnni>(av_00_epi8, QuantBDataPtr, scale_00, QuantBZeroPointPtr, acc0, low_mask);
+        }
+
+        *SumPtr = hsum_float_8(acc0);
+        if (BiasPtr) {
+            *SumPtr += *BiasPtr;
+        }
+
+        // move to next column
+        QuantBDataColPtr += StrideQuantBData;
+        QuantBScaleColPtr += StrideQuantBScale;
+        if constexpr (HasZeroPoint) {
+            QuantBZeroPointColPtr += StrideQuantBZeroPoint;
+        }
+
+        BiasPtr += BiasPtr != nullptr ? 1 : 0;
+        SumPtr += 1;
+    }
+}
+
+template <bool HasZeroPoint, bool vnni>
+MLAS_FORCEINLINE
+void
+MlasQ4Int8GemmM1KernelBlkLen32Avx2(
+        const std::byte* QuantA,
+        const float* QuantAScale,
+        const std::byte* QuantBData,
+        const float* QuantBScale,
+        const std::byte* QuantBZeroPoint,
+        float* C,
+        size_t CountN,
+        size_t BlockCountK,
+        const float* Bias
+    )
+{
+    constexpr size_t BlkLen32 = 32;
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen32);
+    const size_t StrideQuantBScale = BlockCountK;
+    const size_t StrideQuantBZeroPoint = MlasQNBitZeroPointsForBlksSizeInBytes<BlkBitWidth4>(BlockCountK);
+
+    size_t remainingCols = CountN % NCols4;
+    size_t multipleCols = CountN - remainingCols;
+
+    if (multipleCols > 0) {
+        Q4Int8GemmM1C4BlkLen32Avx2<HasZeroPoint, vnni>(
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            QuantBZeroPoint,
+            C,
+            multipleCols,
+            BlockCountK,
+            Bias);
+    }
+
+    if (remainingCols > 0) {
+        Q4Int8GemmM1C1BlkLen32Avx2<HasZeroPoint, vnni>(
+            QuantA,
+            QuantAScale,
+            QuantBData + multipleCols * StrideQuantBData,
+            QuantBScale + multipleCols * StrideQuantBScale,
+            QuantBZeroPoint + multipleCols * StrideQuantBZeroPoint,
+            C + multipleCols,
+            remainingCols,
+            BlockCountK,
+            Bias ? Bias + multipleCols : nullptr);
+    }
+}
+
+//#define SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout 1
+void SQ4BitGemmM1Kernel_BlkLen32_CompInt8_Impl2(
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    const std::byte* QuantBZeroPoint,
+    float* C,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias
+)
+{
+    // port from neon implementation
+    constexpr size_t BlkBitWidth = 4;
+    constexpr size_t BlkLen = 32;
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+#else
+    constexpr bool HasZeroPoint = false;
+#endif
+
+    float* CRowPtr = C;
+
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth, BlkLen);
+    const size_t StrideQuantBZeroPoint = MlasQNBitZeroPointsForBlksSizeInBytes<BlkBitWidth>(BlockCountK);
+    //const size_t StrideQuantBScale = BlockCountK;
+    const float* BiasPtr = Bias;
+
+    const std::byte* QuantBDataColPtr = QuantBData;
+    const float* QuantBScaleColPtr = QuantBScale;
+    const std::byte* QuantBZeroPointColPtr = QuantBZeroPoint;
+
+    float* SumPtr = CRowPtr;
+
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+    const __m256i low_mask = _mm256_set1_epi8(0x0F);
+    const __m256i bzp8 = _mm256_set1_epi8(8);
+    const __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(low_mask, low_mask), 15);
+    (void)StrideQuantBZeroPoint;
+#else
+    const __m256i zero = _mm256_setzero_si256();
+    const __m128i low_mask = _mm_set1_epi8(0xF);
+#endif
+    const size_t NCols = 4;
+    constexpr size_t StrideQuantBScale2 = 2;
+    constexpr size_t StrideQuantBScale1 = 1;
+
+    int64_t nblk = (int64_t)(CountN)-4;
+    while (nblk >= 0) {
+        const std::byte* QuantAPtr = QuantA;
+        const float* QuantAScalePtr = QuantAScale;
+        const std::byte* QuantBDataPtr = QuantBDataColPtr;
+        const float* QuantBScalePtr = QuantBScaleColPtr;
+        const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
+
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+        (void)QuantBZeroPointPtr;
+#endif
+        __m256
+            acc0 = _mm256_setzero_ps(),
+            acc1 = _mm256_setzero_ps(),
+            acc2 = _mm256_setzero_ps(),
+            acc3 = _mm256_setzero_ps();
+
+        size_t k_blks_remaining = BlockCountK;
+        for (; k_blks_remaining > 1; k_blks_remaining -= 2) {
+            const std::byte* QuantABlk0 = QuantAPtr;
+            const std::byte* QuantABlk1 = QuantABlk0 + BlkLen;
+
+            // load A:
+            const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk0);
+            const __m256i av_1_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk1);
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+            const __m256 scale_a0_8_ps = _mm256_set1_ps(Q8BlkScale(QuantAPtr));
+            const __m256 scale_a1_8_ps = _mm256_set1_ps(Q8BlkScale(QuantAPtr + Q8BlkSize(BlkLen)));
+#else
+            const float& scale_a0 = QuantAScalePtr[0];
+            const float& scale_a1 = QuantAScalePtr[1];
+#endif
+
+            // Col0
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+            accumulate_blklen32_r1c1blk2_zp_is_8_no_bc_avx2(av_0_epi8, av_1_epi8, scale_a0_8_ps, scale_a1_8_ps, QuantBDataPtr, QuantBScalePtr, acc0, low_mask, bzp8);
+#else
+            const float& scale_00 = scale_a0 * QuantBScalePtr[0];
+            const float& scale_01 = scale_a1 * QuantBScalePtr[1];
+            accumulate_mul_sum_avx2<HasZeroPoint>(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr), low_mask, zero, QuantBZeroPointPtr, true, scale_00, acc0);
+            accumulate_mul_sum_avx2<HasZeroPoint>(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 16), low_mask, zero, QuantBZeroPointPtr, false, scale_01, acc0);
+#endif
+
+            // Col1
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+            accumulate_blklen32_r1c1blk2_zp_is_8_no_bc_avx2(av_0_epi8, av_1_epi8, scale_a0_8_ps, scale_a1_8_ps, QuantBDataPtr + StrideQuantBData, QuantBScalePtr + StrideQuantBScale2, acc1, low_mask, bzp8);
+#else
+            const float& scale_10 = scale_a0 * (QuantBScalePtr + StrideQuantBScale2)[0];
+            const float& scale_11 = scale_a1 * (QuantBScalePtr + StrideQuantBScale2)[1];
+            accumulate_mul_sum_avx2<HasZeroPoint>(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + StrideQuantBZeroPoint, true, scale_10, acc1);
+            accumulate_mul_sum_avx2<HasZeroPoint>(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + StrideQuantBZeroPoint, false, scale_11, acc1);
+#endif
+
+            // Col2
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+            accumulate_blklen32_r1c1blk2_zp_is_8_no_bc_avx2(av_0_epi8, av_1_epi8, scale_a0_8_ps, scale_a1_8_ps, QuantBDataPtr + 2 * StrideQuantBData, QuantBScalePtr + 2 * StrideQuantBScale2, acc2, low_mask, bzp8);
+#else
+            const float& scale_20 = scale_a0 * (QuantBScalePtr + 2 * StrideQuantBScale2)[0];
+            const float& scale_21 = scale_a1 * (QuantBScalePtr + 2 * StrideQuantBScale2)[1];
+            accumulate_mul_sum_avx2<HasZeroPoint>(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 2 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, true, scale_20, acc2);
+            accumulate_mul_sum_avx2<HasZeroPoint>(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 2 * StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, false, scale_21, acc2);
+#endif
+            // Col3
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+            accumulate_blklen32_r1c1blk2_zp_is_8_no_bc_avx2(av_0_epi8, av_1_epi8, scale_a0_8_ps, scale_a1_8_ps, QuantBDataPtr + 3 * StrideQuantBData, QuantBScalePtr + 3 * StrideQuantBScale2, acc3, low_mask, bzp8);
+#else
+            const float& scale_30 = scale_a0 * (QuantBScalePtr + 3 * StrideQuantBScale2)[0];
+            const float& scale_31 = scale_a1 * (QuantBScalePtr + 3 * StrideQuantBScale2)[1];
+            accumulate_mul_sum_avx2<HasZeroPoint>(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 3 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, true, scale_30, acc3);
+            accumulate_mul_sum_avx2<HasZeroPoint>(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 3 * StrideQuantBData + 16), low_mask, zero, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, false, scale_31, acc3);
+#endif
+            // increment block pointers
+            QuantAPtr += BlkLen * 2;
+            QuantAScalePtr += 2;
+            QuantBDataPtr += 16 * 2;
+            QuantBScalePtr += 2 * NCols;
+        }
+
+        if (k_blks_remaining > 0) {
+            // load A
+            const std::byte* QuantABlk0 = QuantAPtr;
+            const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk0);
+
+            const float& scale_a0 = *QuantAScalePtr;
+
+            // Col0
+            const float& scale_0 = scale_a0 * QuantBScalePtr[0];
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+            accumulate_blklen32_r1c1blk1_zp_avx2(av_0_epi8, QuantBDataPtr, scale_0, acc0, low_mask, bzp8);
+#else
+            accumulate_mul_sum_avx2<HasZeroPoint>(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr), low_mask, zero, QuantBZeroPointPtr, true, scale_0, acc0);
+#endif
+
+            // Col1
+            const float& scale_1 = scale_a0 * (QuantBScalePtr + StrideQuantBScale1)[0];
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+            accumulate_blklen32_r1c1blk1_zp_avx2(av_0_epi8, QuantBDataPtr + StrideQuantBData, scale_1, acc1, low_mask, bzp8);
+#else
+            accumulate_mul_sum_avx2<HasZeroPoint>(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + StrideQuantBZeroPoint, true, scale_1, acc1);
+#endif
+
+            // Col2
+            const float& scale_2 = scale_a0 * (QuantBScalePtr + 2 * StrideQuantBScale1)[0];
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+            accumulate_blklen32_r1c1blk1_zp_avx2(av_0_epi8, QuantBDataPtr + 2 * StrideQuantBData, scale_2, acc2, low_mask, bzp8);
+#else
+            accumulate_mul_sum_avx2<HasZeroPoint>(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 2 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, true, scale_2, acc2);
+#endif
+
+            // Col3
+            const float& scale_3 = scale_a0 * (QuantBScalePtr + 3 * StrideQuantBScale1)[0];
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+            accumulate_blklen32_r1c1blk1_zp_avx2(av_0_epi8, QuantBDataPtr + 3 * StrideQuantBData, scale_3, acc3, low_mask, bzp8);
+#else
+            accumulate_mul_sum_avx2<HasZeroPoint>(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 3 * StrideQuantBData), low_mask, zero, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, true, scale_3, acc3);
+#endif
+        }
+
+        __m128 acc_x = FoldAccumulators(acc0, acc1, acc2, acc3);
+        if (BiasPtr != nullptr) {
+            acc_x = _mm_add_ps(acc_x, _mm_loadu_ps(BiasPtr));
+        }
+        _mm_storeu_ps(SumPtr, acc_x);
+
+        // move to next NCols columns
+
+        QuantBDataColPtr += NCols * StrideQuantBData;
+        QuantBScaleColPtr += NCols * BlockCountK;
+
+        BiasPtr += BiasPtr != nullptr ? NCols : 0;
+        SumPtr += NCols;
+        nblk -= NCols;
+    }
+
+    nblk += NCols;
+    for (int64_t n = 0; n < nblk; n++) {
+        const std::byte* QuantAPtr = QuantA;
+        const float* QuantAScalePtr = QuantAScale;
+        const std::byte* QuantBDataPtr = QuantBDataColPtr;
+        const float* QuantBScalePtr = QuantBScaleColPtr;
+        const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
+
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+        (void)QuantBZeroPointPtr;
+#endif
+        __m256 acc0 = _mm256_setzero_ps();
+
+        size_t k_blks_remaining = BlockCountK;
+        for (; k_blks_remaining > 1; k_blks_remaining -= 2) {
+            const std::byte* QuantABlk0 = QuantAPtr;
+            const std::byte* QuantABlk1 = QuantABlk0 + BlkLen;
+
+            // load A:
+            const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk0);
+            const __m256i av_1_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk1);
+
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+            const __m256 scale_a0_8_ps = _mm256_set1_ps(Q8BlkScale(QuantABlk0));
+            const __m256 scale_a1_8_ps = _mm256_set1_ps(Q8BlkScale(QuantABlk1));
+#else
+            const float& scale_a0 = QuantAScalePtr[0];
+            const float& scale_a1 = QuantAScalePtr[1];
+#endif
+
+            // Col0
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+            accumulate_blklen32_r1c1blk2_zp_is_8_no_bc_avx2(av_0_epi8, av_1_epi8, scale_a0_8_ps, scale_a1_8_ps, QuantBDataPtr, QuantBScalePtr, acc0, low_mask, bzp8);
+#else
+            const float& scale_00 = scale_a0 * QuantBScalePtr[0];
+            const float& scale_01 = scale_a1 * QuantBScalePtr[1];
+            accumulate_mul_sum_avx2<HasZeroPoint>(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr), low_mask, zero, QuantBZeroPointPtr, true, scale_00, acc0);
+            accumulate_mul_sum_avx2<HasZeroPoint>(av_1_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr + 16), low_mask, zero, QuantBZeroPointPtr, false, scale_01, acc0);
+#endif
+            // increment block pointers
+            QuantAPtr += BlkLen * 2;
+            QuantAScalePtr += 2;
+            QuantBDataPtr += 16 * 2;
+            QuantBScalePtr += 2;
+        }
+
+        if (k_blks_remaining > 0) {
+            // load A
+            const std::byte* QuantABlk0 = QuantAPtr;
+            const __m256i av_0_epi8 = _mm256_loadu_si256((const __m256i*)QuantABlk0);
+
+            const float& scale_a0 = *QuantAScalePtr;
+
+            // Col0
+            const float& scale_00 = scale_a0 * QuantBScalePtr[0];
+#if defined SQ4BitGemmM1Kernel_BlkLen32_CompInt8_NewLayout
+            accumulate_blklen32_r1c1blk1_zp_avx2(av_0_epi8, QuantBDataPtr, scale_00, acc0, low_mask, bzp8);
+#else
+            accumulate_mul_sum_avx2<HasZeroPoint>(av_0_epi8, reinterpret_cast<const __m128i*>(QuantBDataPtr), low_mask, zero, QuantBZeroPointPtr, true, scale_00, acc0);
+#endif
+        }
+
+        *SumPtr = hsum_float_8(acc0);
+        if (BiasPtr) {
+            *SumPtr += *BiasPtr;
+        }
+
+        // move to next column
+
+        QuantBDataColPtr += StrideQuantBData;
+        QuantBScaleColPtr += BlockCountK;
+
+        BiasPtr += BiasPtr != nullptr ? 1 : 0;
+        SumPtr += 1;
+    }
+}
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_m1_sym_kernel_avx2_int8_blklen64.h b/onnxruntime/core/mlas/lib/sqnbitgemm_m1_sym_kernel_avx2_int8_blklen64.h
new file mode 100644
index 0000000000000..e9c3812bde899
--- /dev/null
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_m1_sym_kernel_avx2_int8_blklen64.h
@@ -0,0 +1,312 @@
+#pragma once
+#include <algorithm>
+#include <cassert>
+#include <utility>
+
+#include "sqnbitgemm.h"
+#include "sqnbitgemm_kernel_avx_common.h"
+
+
+static MLAS_FORCEINLINE void
+accumulate_blklen64_r1c1blk1_zp_avx2(
+    const __m256i& av00_32_epi8,
+    const __m256i& av01_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a,
+    const float* scale_b,
+    const std::byte* QuantBZeroPointPtr,
+    const bool is_lower_half_byte_zp,
+    __m256& acc0,
+    const __m256i& low_mask
+)
+{
+    // | v0  v32 | v1  v33 | ... | v30 v62 | v31 v63 |
+    const __m256i bv_packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(QuantBDataPtr));
+    __m256i bv0_32_epi8 = _mm256_and_si256(bv_packed, low_mask);                          // 0, 1,...30, 31
+    __m256i bv1_32_epi8 = _mm256_srli_epi16(_mm256_sub_epi8(bv_packed, bv0_32_epi8), 4);  // 32, 33,...62, 63
+
+    const __m256i bzp8 = _mm256_set1_epi8(get_zp<true>(is_lower_half_byte_zp, QuantBZeroPointPtr));
+    bv0_32_epi8 = _mm256_sub_epi8(bv0_32_epi8, bzp8);
+    bv1_32_epi8 = _mm256_sub_epi8(bv1_32_epi8, bzp8);
+
+    const __m256i dot0_16_epi16 = _mm256_maddubs_epi16(_mm256_sign_epi8(bv0_32_epi8, bv0_32_epi8), _mm256_sign_epi8(av00_32_epi8, bv0_32_epi8));
+    const __m256i dot1_16_epi16 = _mm256_maddubs_epi16(_mm256_sign_epi8(bv1_32_epi8, bv1_32_epi8), _mm256_sign_epi8(av01_32_epi8, bv1_32_epi8));
+    const __m256i sum_16_epi16 = _mm256_hadd_epi16(dot0_16_epi16, dot1_16_epi16);
+
+    __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv0_32_epi8, bv0_32_epi8), 15);
+    const __m256i sum_8_epi32 = _mm256_madd_epi16(one_16_epi16, sum_16_epi16);
+    const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+    __m256 scale_a_8_ps = _mm256_broadcast_ss(scale_a);
+    __m256 scale_b_8_ps = _mm256_broadcast_ss(scale_b);
+
+    acc0 = _mm256_fmadd_ps(sum_ps, _mm256_mul_ps(scale_a_8_ps, scale_b_8_ps), acc0);
+}
+
+static MLAS_FORCEINLINE void
+accumulate_blklen64_r1c1blk1_zp_is_8_avx2(
+    const __m256i& av00_32_epi8,
+    const __m256i& av01_32_epi8,
+    const std::byte* QuantBDataPtr,
+    const float* scale_a,
+    const float* scale_b,
+    __m256& acc0,
+    const __m256i& low_mask,
+    const __m256i& bzp8
+)
+{
+    // | v0  v32 | v1  v33 | ... | v30 v62 | v31 v63 |
+    const __m256i bv_packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(QuantBDataPtr));
+    __m256i bv0_32_epi8 = _mm256_and_si256(bv_packed, low_mask);                          // 0, 1,...30, 31
+    __m256i bv1_32_epi8 = _mm256_srli_epi16(_mm256_sub_epi8(bv_packed, bv0_32_epi8), 4);  // 32, 33,...62, 63
+
+    bv0_32_epi8 = _mm256_sub_epi8(bv0_32_epi8, bzp8);
+    bv1_32_epi8 = _mm256_sub_epi8(bv1_32_epi8, bzp8);
+
+    const __m256i dot0_16_epi16 = _mm256_maddubs_epi16(_mm256_sign_epi8(bv0_32_epi8, bv0_32_epi8), _mm256_sign_epi8(av00_32_epi8, bv0_32_epi8));
+    const __m256i dot1_16_epi16 = _mm256_maddubs_epi16(_mm256_sign_epi8(bv1_32_epi8, bv1_32_epi8), _mm256_sign_epi8(av01_32_epi8, bv1_32_epi8));
+    const __m256i sum_16_epi16 = _mm256_hadd_epi16(dot0_16_epi16, dot1_16_epi16);
+
+    __m256i one_16_epi16 = _mm256_srli_epi16(_mm256_cmpeq_epi16(bv0_32_epi8, bv0_32_epi8), 15);
+    const __m256i sum_8_epi32 = _mm256_madd_epi16(one_16_epi16, sum_16_epi16);
+    const __m256 sum_ps = _mm256_cvtepi32_ps(sum_8_epi32);
+
+    __m256 scale_a_8_ps = _mm256_broadcast_ss(scale_a);
+    __m256 scale_b_8_ps = _mm256_broadcast_ss(scale_b);
+
+    acc0 = _mm256_fmadd_ps(sum_ps, _mm256_mul_ps(scale_a_8_ps, scale_b_8_ps), acc0);
+}
+
+template <bool HasZeroPoint>
+MLAS_FORCEINLINE void
+Q4Int8GemmM1C4BlkLen64Avx2(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    const std::byte* QuantBZeroPoint,
+    float* C,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+    constexpr size_t SubblkLen64 = 64;
+
+    const size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t PerBlkSubblkCount = BlkLen / SubblkLen64;
+    const size_t SubblkDataSizeInBytes = BlkDataSizeInBytes / PerBlkSubblkCount;
+
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t StrideQuantBScale = BlockCountK;
+
+    assert(CountN % NCols4 == 0);
+
+    const std::byte* QuantBDataColPtr = QuantBData;
+    const float* QuantBScaleColPtr = QuantBScale;
+    const std::byte* QuantBZeroPointColPtr = QuantBZeroPoint;
+    const float* BiasPtr = Bias;
+    auto* SumPtr = C;
+
+    const __m256i low_mask = _mm256_set1_epi8(0x0F);
+    const size_t StrideQuantBData1 = 1 * SubblkDataSizeInBytes;
+    const size_t StrideQuantBScale1 = 1;
+    const size_t StrideQuantBZeroPoint = MlasQNBitZeroPointsForBlksSizeInBytes<BlkBitWidth4>(BlockCountK);
+
+    for (size_t n = 0; n < CountN; n += NCols4) {
+        const std::byte* QuantAPtr = QuantA;
+        const float* QuantAScalePtr = QuantAScale;
+
+        const std::byte* QuantBDataPtr = QuantBDataColPtr;
+        const float* QuantBScalePtr = QuantBScaleColPtr;
+        const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
+
+        __m256 acc[NCols4] = {_mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps(), _mm256_setzero_ps()};
+        for (size_t k = 0; k < BlockCountK; ++k) {
+            [[maybe_unused]] const bool is_lower_half_byte_zp = (k % 2) == 0;
+            for (size_t kk = 0; kk < PerBlkSubblkCount; kk++) {
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantAPtr);
+                const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + 32));
+                if constexpr (HasZeroPoint) {
+                    accumulate_blklen64_r1c1blk1_zp_avx2(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, QuantBZeroPointPtr, is_lower_half_byte_zp, acc[0], low_mask);
+                    accumulate_blklen64_r1c1blk1_zp_avx2(av_00_epi8, av_01_epi8, QuantBDataPtr + StrideQuantBData1, QuantAScalePtr, QuantBScalePtr + StrideQuantBScale1, QuantBZeroPointPtr + StrideQuantBZeroPoint, is_lower_half_byte_zp, acc[1], low_mask);
+                    accumulate_blklen64_r1c1blk1_zp_avx2(av_00_epi8, av_01_epi8, QuantBDataPtr + 2 * StrideQuantBData1, QuantAScalePtr, QuantBScalePtr + 2 * StrideQuantBScale1, QuantBZeroPointPtr + 2 * StrideQuantBZeroPoint, is_lower_half_byte_zp, acc[2], low_mask);
+                    accumulate_blklen64_r1c1blk1_zp_avx2(av_00_epi8, av_01_epi8, QuantBDataPtr + 3 * StrideQuantBData1, QuantAScalePtr, QuantBScalePtr + 3 * StrideQuantBScale1, QuantBZeroPointPtr + 3 * StrideQuantBZeroPoint, is_lower_half_byte_zp, acc[3], low_mask);
+                } else {
+                    const __m256i bzp8 = _mm256_set1_epi8(8);
+                    accumulate_blklen64_r1c1blk1_zp_is_8_avx2(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc[0], low_mask, bzp8);
+                    accumulate_blklen64_r1c1blk1_zp_is_8_avx2(av_00_epi8, av_01_epi8, QuantBDataPtr + StrideQuantBData1, QuantAScalePtr, QuantBScalePtr + StrideQuantBScale1, acc[1], low_mask, bzp8);
+                    accumulate_blklen64_r1c1blk1_zp_is_8_avx2(av_00_epi8, av_01_epi8, QuantBDataPtr + 2 * StrideQuantBData1, QuantAScalePtr, QuantBScalePtr + 2 * StrideQuantBScale1, acc[2], low_mask, bzp8);
+                    accumulate_blklen64_r1c1blk1_zp_is_8_avx2(av_00_epi8, av_01_epi8, QuantBDataPtr + 3 * StrideQuantBData1, QuantAScalePtr, QuantBScalePtr + 3 * StrideQuantBScale1, acc[3], low_mask, bzp8);
+                }
+
+                // increment block pointers
+                QuantAPtr += SubblkLen64;
+                QuantBDataPtr += NCols4 * SubblkDataSizeInBytes;
+            }
+            QuantAScalePtr++;
+            QuantBScalePtr += NCols4;
+            if constexpr (HasZeroPoint) {
+                QuantBZeroPointPtr += k % 2;
+            }
+        }
+
+        __m128 acc_r0 = FoldAccumulators(acc[0], acc[1], acc[2], acc[3]);
+        if (BiasPtr != nullptr) {
+            acc_r0 = _mm_add_ps(acc_r0, _mm_loadu_ps(BiasPtr));
+        }
+
+        _mm_storeu_ps(SumPtr, acc_r0);
+
+        // move to next NCols columns
+        QuantBDataColPtr += NCols4 * StrideQuantBData;
+        QuantBScaleColPtr += NCols4 * StrideQuantBScale;
+        if constexpr (HasZeroPoint) {
+            QuantBZeroPointColPtr += NCols4 * StrideQuantBZeroPoint;
+        }
+        BiasPtr += BiasPtr != nullptr ? NCols4 : 0;
+        SumPtr += NCols4;
+    }
+}
+
+template <bool HasZeroPoint>
+MLAS_FORCEINLINE void
+Q4Int8GemmM1C1BlkLen64Avx2(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    const std::byte* QuantBZeroPoint,
+    float* C,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    [[maybe_unused]] constexpr size_t NCols4 = 4;
+    [[maybe_unused]] constexpr size_t NRows2 = 2;
+    constexpr size_t SubblkLen = 64;
+
+    const size_t BlkDataSizeInBytes = MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t PerBlkSubblkCount = BlkLen / SubblkLen;
+    const size_t SubblkDataSizeInBytes = BlkDataSizeInBytes / PerBlkSubblkCount;
+
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t StrideQuantBScale = BlockCountK;
+    const size_t StrideQuantBZeroPoint = MlasQNBitZeroPointsForBlksSizeInBytes<BlkBitWidth4>(BlockCountK);
+
+    assert(CountN < NCols4);
+
+    const __m256i low_mask = _mm256_set1_epi8(0x0F);
+    [[maybe_unused]] const __m256i bzp8 = _mm256_set1_epi8(8);
+
+    const std::byte* QuantBDataColPtr = QuantBData;
+    const float* QuantBScaleColPtr = QuantBScale;
+    const std::byte* QuantBZeroPointColPtr = QuantBZeroPoint;
+    const float* BiasPtr = Bias;
+    auto* SumPtr = C;
+
+    for (size_t n = 0; n < CountN; n++) {
+        const std::byte* QuantAPtr = QuantA;
+        const float* QuantAScalePtr = QuantAScale;
+        const std::byte* QuantBDataPtr = QuantBDataColPtr;
+        const float* QuantBScalePtr = QuantBScaleColPtr;
+        const std::byte* QuantBZeroPointPtr = QuantBZeroPointColPtr;
+
+        __m256 acc0 = _mm256_setzero_ps();
+        for (size_t k = 0; k < BlockCountK; ++k) {
+            [[maybe_unused]] const bool is_lower_half_byte_zp = (k % 2) == 0;
+            for (size_t kk = 0; kk < PerBlkSubblkCount; kk++) {
+                const __m256i av_00_epi8 = _mm256_loadu_si256((const __m256i*)QuantAPtr);
+                const __m256i av_01_epi8 = _mm256_loadu_si256((const __m256i*)(QuantAPtr + 32));
+
+                if constexpr (HasZeroPoint) {
+                    accumulate_blklen64_r1c1blk1_zp_avx2(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, QuantBZeroPointPtr, is_lower_half_byte_zp, acc0, low_mask);
+                } else {
+                    accumulate_blklen64_r1c1blk1_zp_is_8_avx2(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0, low_mask, bzp8);
+                }
+
+                // increment block pointers
+                QuantAPtr += SubblkLen;
+                QuantBDataPtr += SubblkDataSizeInBytes;
+            }
+            QuantAScalePtr++;
+            QuantBScalePtr++;
+            if constexpr (HasZeroPoint) {
+                QuantBZeroPointPtr += k % 2;
+            }
+        }
+
+        *SumPtr = hsum_float_8(acc0);
+        if (BiasPtr) {
+            *SumPtr += *BiasPtr;
+        }
+
+        // move to next column
+        QuantBDataColPtr += StrideQuantBData;
+        QuantBScaleColPtr += StrideQuantBScale;
+        if constexpr (HasZeroPoint) {
+            QuantBZeroPointColPtr += StrideQuantBZeroPoint;
+        }
+
+        BiasPtr += BiasPtr != nullptr ? 1 : 0;
+        SumPtr += 1;
+    }
+}
+
+template <bool HasZeroPoint>
+MLAS_FORCEINLINE void
+MlasQ4Int8GemmKernelBlkLen64Avx2(
+    const size_t BlkLen,
+    const std::byte* QuantA,
+    const float* QuantAScale,
+    const std::byte* QuantBData,
+    const float* QuantBScale,
+    const std::byte* QuantBZeroPoint,
+    float* C,
+    size_t CountN,
+    size_t BlockCountK,
+    const float* Bias
+)
+{
+    constexpr size_t BlkBitWidth4 = 4;
+    constexpr size_t NCols4 = 4;
+
+    const size_t StrideQuantBData = BlockCountK * MlasQNBitBlkDataSizeInBytes(BlkBitWidth4, BlkLen);
+    const size_t StrideQuantBScale = BlockCountK;
+    const size_t StrideQuantBZeroPoint = MlasQNBitZeroPointsForBlksSizeInBytes<BlkBitWidth4>(BlockCountK);
+
+    size_t remainingCols = CountN % NCols4;
+    size_t multipleCols = CountN - remainingCols;
+
+    if (multipleCols > 0) {
+        Q4Int8GemmM1C4BlkLen64Avx2<HasZeroPoint>(
+            BlkLen,
+            QuantA,
+            QuantAScale,
+            QuantBData,
+            QuantBScale,
+            QuantBZeroPoint,
+            C,
+            multipleCols,
+            BlockCountK,
+            Bias);
+    }
+
+    if (remainingCols > 0) {
+        Q4Int8GemmM1C1BlkLen64Avx2<HasZeroPoint>(
+            BlkLen,
+            QuantA,
+            QuantAScale,
+            QuantBData + multipleCols * StrideQuantBData,
+            QuantBScale + multipleCols * StrideQuantBScale,
+            QuantBZeroPoint + multipleCols * StrideQuantBZeroPoint,
+            C + multipleCols,
+            remainingCols,
+            BlockCountK,
+            Bias ? Bias + multipleCols : nullptr);
+    }
+}
diff --git a/onnxruntime/test/contrib_ops/matmul_4bits_test.cc b/onnxruntime/test/contrib_ops/matmul_4bits_test.cc
index dedc01de9655d..548f24e8ac69e 100644
--- a/onnxruntime/test/contrib_ops/matmul_4bits_test.cc
+++ b/onnxruntime/test/contrib_ops/matmul_4bits_test.cc
@@ -263,9 +263,10 @@ void RunTest(const TestOptions& opts,
 }  // namespace
 
 TEST(MatMulNBits, Float32) {
+  // onnxruntime::profiling::Profiler::Profiler::Instance().StartProfiling<char>("profile.json");
   for (auto M : {1, 2, 100}) {
-    for (auto N : {1, 2, 32, 288}) {
-      for (auto K : {16, 32, 64, 128, 256, 1024, 93, 1234}) {
+    for (auto N : {/*2560, */ 1, 2, 32, 288}) {
+      for (auto K : {/*2560, */ 16, 32, 64, 128, 256, 1024, 93, 1234}) {
         for (auto block_size : {16, 32, 64, 128}) {
           for (auto accuracy_level : {0, 1, 4}) {
             TestOptions base_opts{};
diff --git a/onnxruntime/test/mlas/bench/bench_q4dq.cpp b/onnxruntime/test/mlas/bench/bench_q4dq.cpp
index 9d15c9a6bf994..6d21ed2eef864 100644
--- a/onnxruntime/test/mlas/bench/bench_q4dq.cpp
+++ b/onnxruntime/test/mlas/bench/bench_q4dq.cpp
@@ -9,10 +9,10 @@
 #include "core/util/thread_utils.h"
 
 static void BM_QDQBlockwiseQuantizer_QuantizeColumnwise(benchmark::State& state) {
-  int M = state.range(0);
-  int N = state.range(1);
-  int quant_block_size = state.range(2);
-  int threads = state.range(3);
+  int M = (int)state.range(0);
+  int N = (int)state.range(1);
+  int quant_block_size = (int)state.range(2);
+  int threads = (int)state.range(3);
   size_t scale_size = (M + quant_block_size - 1) / quant_block_size * N;
 
   auto src = RandomVectorUniform(M * N, -16.0f, 14.0f);
@@ -37,10 +37,10 @@ static void BM_QDQBlockwiseQuantizer_QuantizeColumnwise(benchmark::State& state)
 }
 
 static void BM_MlasQuantizeBlockwise(benchmark::State& state) {
-  int M = state.range(0);
-  int N = state.range(1);
-  int quant_block_size = state.range(2);
-  int threads = state.range(3);
+  int M = (int)state.range(0);
+  int N = (int)state.range(1);
+  int quant_block_size = (int)state.range(2);
+  int threads = (int)state.range(3);
   size_t scale_size = (M + quant_block_size - 1) / quant_block_size * N;
 
   auto src = RandomVectorUniform(M * N, -16.0f, 14.0f);
@@ -65,10 +65,10 @@ static void BM_MlasQuantizeBlockwise(benchmark::State& state) {
 }
 
 static void BM_QDQBlockwiseQuantizer_TransposeColumnwise(benchmark::State& state) {
-  int M = state.range(0);
-  int N = state.range(1);
-  int quant_block_size = state.range(2);
-  int threads = state.range(3);
+  int M = (int)state.range(0);
+  int N = (int)state.range(1);
+  int quant_block_size = (int)state.range(2);
+  int threads = (int)state.range(3);
   bool add8 = state.range(4) != 0;
   int quant_num_M = (M + quant_block_size - 1) / quant_block_size;
   int blob_size = (quant_block_size + 1) / 2;
diff --git a/onnxruntime/test/mlas/bench/bench_sqnbitgemm.cpp b/onnxruntime/test/mlas/bench/bench_sqnbitgemm.cpp
index 354621eff42b6..73c78b8cc3d47 100644
--- a/onnxruntime/test/mlas/bench/bench_sqnbitgemm.cpp
+++ b/onnxruntime/test/mlas/bench/bench_sqnbitgemm.cpp
@@ -53,6 +53,7 @@ void RunSQNBitGemmBenchmark(size_t BlkLen,
   std::vector<uint8_t> QuantBData(QuantBDataSizeInBytes);
   std::vector<float> QuantBScale(QuantBScaleSize);
   std::vector<uint8_t> QuantBZeroPoint(Symmetric ? 0 : QuantBZeroPointSizeInBytes);
+  bool has_zp_input = !Symmetric;
 
   MlasQuantizeBlockwise<float, BlkBitWidth>(QuantBData.data(), QuantBScale.data(),
                                             Symmetric ? nullptr : QuantBZeroPoint.data(),
@@ -71,15 +72,17 @@ void RunSQNBitGemmBenchmark(size_t BlkLen,
       PackedQuantBDataSize > 0) {
     PackedQuantBData = std::make_unique<std::byte[]>(PackedQuantBDataSize);
     MlasSQNBitGemmPackQuantBData(N, K, BlkBitWidth, BlkLen, ComputeType, QuantBData.data(), PackedQuantBData.get(),
+                                 QuantBScale.data(), has_zp_input, QuantBZeroPoint.data(),
                                  tp.get());
   }
 
   MLAS_SQNBIT_GEMM_DATA_PARAMS params{};
   params.A = A.data();
   params.lda = K;
-  params.QuantBData = PackedQuantBData != nullptr
-                          ? static_cast<const void*>(PackedQuantBData.get())
-                          : static_cast<const void*>(QuantBData.data());
+  if (PackedQuantBData != nullptr)
+    params.QuantBDataWorkspace = static_cast<const void*>(PackedQuantBData.get());
+  else
+    params.QuantBDataWorkspace = static_cast<const void*>(QuantBData.data());
   params.QuantBScale = QuantBScale.data();
   params.QuantBZeroPoint = Symmetric ? nullptr : QuantBZeroPoint.data();
   params.Bias = HasBias ? Bias.data() : nullptr;
diff --git a/onnxruntime/test/mlas/unittest/test_sqnbitgemm.cpp b/onnxruntime/test/mlas/unittest/test_sqnbitgemm.cpp
index f391027de4d51..0710981fa17c6 100644
--- a/onnxruntime/test/mlas/unittest/test_sqnbitgemm.cpp
+++ b/onnxruntime/test/mlas/unittest/test_sqnbitgemm.cpp
@@ -55,8 +55,8 @@ class MlasSQNBitGemmTest : public MlasTestBase {
                 size_t K,
                 const float* A,
                 size_t lda,
-                const void* QuantBData,
-                const void* PackedQuantBData,
+                const void* /*QuantBData*/,
+                const void* PackedQuantBDataWorkspace,
                 const float* QuantBScale,
                 const void* QuantBZeroPoint,
                 const float* Bias,
@@ -71,7 +71,12 @@ class MlasSQNBitGemmTest : public MlasTestBase {
     params.Bias = Bias;
     params.C = C;
     params.ldc = ldc;
-    params.QuantBData = PackedQuantBData != nullptr ? PackedQuantBData : QuantBData;
+#ifdef MLAS_TARGET_AMD64_IX86
+    if (ComputeType == CompInt8) {
+      params.QuantBDataWorkspace = PackedQuantBDataWorkspace;
+    }
+#endif
+    params.PackedQuantBData = static_cast<const std::byte*>(PackedQuantBDataWorkspace);
     params.QuantBScale = QuantBScale;
     params.QuantBZeroPoint = QuantBZeroPoint;
     params.PostProcessor = nullptr;
@@ -213,12 +218,19 @@ class MlasSQNBitGemmTest : public MlasTestBase {
     auto print_matrix = [](size_t nrows, size_t ncols, const float* data) {
       for (size_t row = 0; row < nrows; ++row) {
         for (size_t col = 0; col < ncols; ++col) {
-          std::cout << data[row * ncols + col] << "\t";
+          std::cout << data[row * ncols + col] << ", ";
         }
         std::cout << "\n";
       }
     };
 
+    auto print_matrix_col = [](size_t nrows, size_t ncols, size_t col, const float* data) {
+      for (size_t row = 0; row < nrows; ++row) {
+        std::cout << data[row * ncols + col] << ", ";
+      }
+      std::cout << "\n";
+    };
+
     std::cout << "A:\n";
     print_matrix(M, K, A);
     std::cout << "B:\n";
@@ -258,14 +270,25 @@ class MlasSQNBitGemmTest : public MlasTestBase {
       Workspace = BufferWorkspace.GetBuffer(WorkspaceSize);
     }
 
-    void* PackedQuantBData = nullptr;
+    void* PackedQuantBDataWorkspace = nullptr;
     if (const auto PackedQuantBDataSize = MlasSQNBitGemmPackQuantBDataSize(N, K, BlkBitWidth, BlkLen, ComputeType);
         PackedQuantBDataSize > 0) {
-      PackedQuantBData = BufferPackedQuantBData.GetBuffer(PackedQuantBDataSize);
-      MlasSQNBitGemmPackQuantBData(N, K, BlkBitWidth, BlkLen, ComputeType, QuantBData, PackedQuantBData,
+      PackedQuantBDataWorkspace = BufferPackedQuantBData.GetBuffer(PackedQuantBDataSize);
+      bool has_zp_input = QuantBZeroPoint != nullptr;
+      MlasSQNBitGemmPackQuantBData(N, K, BlkBitWidth, BlkLen, ComputeType, QuantBData, PackedQuantBDataWorkspace,
+                                   QuantBScale, has_zp_input, QuantBZeroPoint,
                                    GetMlasThreadPool());
     }
 
+    CallGemm(M, N, K,
+             A, /* lda */ K,
+             QuantBData, PackedQuantBDataWorkspace, QuantBScale, QuantBZeroPoint,
+             Bias,
+             C, /* ldc */ N,
+             Workspace,
+             ComputeType,
+             Threadpool);
+
     if (ComputeType == CompFp32) {
       CallReferenceGemm_CompFp32(M, N, K, A, QuantBData, QuantBScale, QuantBZeroPoint, Bias, CReference);
     } else if (ComputeType == CompInt8) {
@@ -275,15 +298,6 @@ class MlasSQNBitGemmTest : public MlasTestBase {
              << ComputeType << " (" << ComputeTypeName(ComputeType) << ")";
     }
 
-    CallGemm(M, N, K,
-             A, /* lda */ K,
-             QuantBData, PackedQuantBData, QuantBScale, QuantBZeroPoint,
-             Bias,
-             C, /* ldc */ N,
-             Workspace,
-             ComputeType,
-             Threadpool);
-
     size_t f = 0;
     for (size_t m = 0; m < M; m++) {
       for (size_t n = 0; n < N; n++, f++) {
@@ -382,7 +396,6 @@ class SQNBitGemmShortExecuteTest : public MlasTestFixture<MlasSQNBitGemmTest<Blk
             tests_registered += RegisterSingleTest(1, b, b, ComputeType, WithThreadpool, Symmetric, false);
           }
           tests_registered += RegisterSingleTest(43, 500, 401, ComputeType, WithThreadpool, Symmetric, true);
-
           tests_registered += RegisterSingleTest(1, 2, 16, ComputeType, WithThreadpool, Symmetric, true);
           tests_registered += RegisterSingleTest(1, 2, 16, ComputeType, WithThreadpool, Symmetric, false);
           tests_registered += RegisterSingleTest(1, 1027, 1031, ComputeType, WithThreadpool, Symmetric, false);

From 188e4530024e17e343c6cb50fd14bf25a99d517e Mon Sep 17 00:00:00 2001
From: Jing Fang <126209182+fajin-corp@users.noreply.github.com>
Date: Tue, 30 Jul 2024 15:22:46 -0700
Subject: [PATCH 02/22] Utilize ext data location to reduce qd matmul memory
 usage (#21451)

### Description

When the graph is quantized to qdq format, the DQ + MatMul is
transformed to MatMulNBits in the level 2 optimizer when the model is
initialized in an inference session.

In the transformation step, tensors are transposed and new tensor protos
are created. Instead of using protobuf arena allocated memory, the PR
sets the tensor proto to use external buffer, and point the external
location to memory location which contains the tensor buffer allocated
by CPU.

Then, in the step that creates OrtValue using the tensor proto, the
memory buffers in the tensor proto are directly assigned to the tensors
which were originally allocated by Ort Arena.

With these two steps, the peak memory usage of QDQ format model is the
same as usage of QOperator model. Besides, the model initialization time
is significantly reduced. Take
[Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
for example:
|| QOperator Model (MatMulNBits) | QDQ Model (DQ + MatMul, original
code) | QDQ Model (this PR) |
|---|---|---|---|
| peak memory consumption | 2.8 GB | ~4.8 GB | 2.8 GB |
| initialization time | 3 sec | 9 sec | 5 sec |

### Motivation and Context

When the graph is quantized to qdq format, the DQ + MatMul is converted
to MatMulNBits in the level 2 optimizer.

Originally, the newly created tensor proto use memory allocated by
protobuf arena. These memory usage cannot be fully released when the
tensor protos are deleted.
Then, in the tensor proto to OrtValue step, tensors are created using
ORT arena. Later, in the pre-pack step for MatMulNBits, new OrtValues
are created. The tensors in the ORT arena are not fully released as
well.

The two arena memory allocation steps in the DQ + MatMul -> MatMulNBits
transformation will result in almost 2x memory consumption in the model
initialization.
---
 .../core/optimizer/graph_transformer_utils.h  |   9 +-
 onnxruntime/core/framework/session_state.cc   |   3 +-
 onnxruntime/core/framework/session_state.h    |  11 ++
 .../core/framework/session_state_utils.cc     |  41 ++++--
 .../core/framework/session_state_utils.h      |   6 +-
 .../core/framework/tensorprotoutils.cc        |  36 +++++-
 onnxruntime/core/framework/tensorprotoutils.h |  29 +++--
 .../core/optimizer/graph_transformer_utils.cc |  12 +-
 .../selectors_actions/qdq_actions.cc          | 119 +++++++++++-------
 .../selectors_actions/qdq_actions.h           |   6 +-
 .../qdq_selector_action_transformer.cc        |  35 ++++--
 .../qdq_selector_action_transformer.h         |   7 +-
 onnxruntime/core/session/inference_session.cc |  14 ++-
 13 files changed, 239 insertions(+), 89 deletions(-)

diff --git a/include/onnxruntime/core/optimizer/graph_transformer_utils.h b/include/onnxruntime/core/optimizer/graph_transformer_utils.h
index 0bb5c7432f0a7..6cff153c336f0 100644
--- a/include/onnxruntime/core/optimizer/graph_transformer_utils.h
+++ b/include/onnxruntime/core/optimizer/graph_transformer_utils.h
@@ -3,12 +3,15 @@
 
 #pragma once
 
+#include <string>
 #include <memory>
+#include <unordered_map>
 #include <unordered_set>
 #include <vector>
 
 #include "core/common/inlined_containers.h"
 #include "core/framework/session_options.h"
+#include "core/framework/tensor.h"
 #include "core/optimizer/graph_transformer.h"
 #include "core/platform/threadpool.h"
 
@@ -51,7 +54,8 @@ InlinedVector<std::unique_ptr<GraphTransformer>> GenerateTransformers(
     const SessionOptions& session_options,
     const IExecutionProvider& execution_provider /*required by constant folding*/,
     const InlinedHashSet<std::string>& rules_and_transformers_to_disable = {},
-    concurrency::ThreadPool* intra_op_thread_pool = nullptr);
+    concurrency::ThreadPool* intra_op_thread_pool = nullptr,
+    std::unordered_map<std::string, std::unique_ptr<Tensor>>* p_buffered_tensors = nullptr);
 
 #endif  // !defined(ORT_MINIMAL_BUILD)
 
@@ -81,7 +85,8 @@ InlinedVector<std::unique_ptr<GraphTransformer>> GenerateTransformersForMinimalB
     const SatApplyContextVariant& apply_context,
     const IExecutionProvider& cpu_execution_provider,
     const InlinedHashSet<std::string>& rules_and_transformers_to_disable = {},
-    concurrency::ThreadPool* intra_op_thread_pool = nullptr);
+    concurrency::ThreadPool* intra_op_thread_pool = nullptr,
+    std::unordered_map<std::string, std::unique_ptr<Tensor>>* p_buffered_tensors = nullptr);
 
 #endif  // !defined(ORT_MINIMAL_BUILD) || defined(ORT_EXTENDED_MINIMAL_BUILD)
 
diff --git a/onnxruntime/core/framework/session_state.cc b/onnxruntime/core/framework/session_state.cc
index a88f36f63639c..ddb0c3356e544 100644
--- a/onnxruntime/core/framework/session_state.cc
+++ b/onnxruntime/core/framework/session_state.cc
@@ -1486,7 +1486,8 @@ Status SessionState::FinalizeSessionStateImpl(const std::basic_string<PATH_CHAR_
             }
             return Status::OK();
           },
-          logger_, data_transfer_mgr_, *p_seq_exec_plan_, session_options, memory_profile_func));
+          logger_, data_transfer_mgr_, *p_seq_exec_plan_, session_options, memory_profile_func,
+          name_to_buffered_tensor_));
 
 #if !defined(ORT_MINIMAL_BUILD) && defined(ORT_MEMORY_PROFILE)
   // Record Weight allocation info on device
diff --git a/onnxruntime/core/framework/session_state.h b/onnxruntime/core/framework/session_state.h
index b1a7504b283c5..d32f574687480 100644
--- a/onnxruntime/core/framework/session_state.h
+++ b/onnxruntime/core/framework/session_state.h
@@ -6,6 +6,7 @@
 #include <memory>
 #include <map>
 #include <unordered_map>
+#include <string>
 #include <vector>
 
 #include "core/common/flatbuffers.h"
@@ -303,6 +304,10 @@ class SessionState {
   const InlinedHashSet<NodeIndex>* GetToBeExecutedRange(gsl::span<int const> fetch_mlvalue_idxs) const;
 #endif
 
+  std::unordered_map<std::string, std::unique_ptr<Tensor>>* GetMutableBufferedTensors() {
+    return &name_to_buffered_tensor_;
+  }
+
   Status FinalizeSessionState(const std::basic_string<PATH_CHAR_TYPE>& graph_loc,
                               const KernelRegistryManager& kernel_registry_manager,
                               bool remove_initializers = true,
@@ -562,6 +567,12 @@ class SessionState {
   // flag to indicate whether current session using any EP that create device stream dynamically.
   bool has_device_stream_enabled_ep_ = false;
 #endif
+
+  // Holds the tensors which provide memory buffer for TensorProtos
+  // Use case: in optimizer, transform a TensorProto to a new TensorProto whose the memory buffer is
+  // allocated by CPU instead by protobuf's arena. Arena style memory allocators do not fully release
+  // a instance's memory which may result large memory consumption, which is a tradeoff for speed.
+  std::unordered_map<std::string, std::unique_ptr<Tensor>> name_to_buffered_tensor_;
 };
 
 }  // namespace onnxruntime
diff --git a/onnxruntime/core/framework/session_state_utils.cc b/onnxruntime/core/framework/session_state_utils.cc
index 059de8e3c8c4a..b13b0cd27496d 100644
--- a/onnxruntime/core/framework/session_state_utils.cc
+++ b/onnxruntime/core/framework/session_state_utils.cc
@@ -3,6 +3,8 @@
 
 #include <functional>
 #include <limits>
+#include <memory>
+#include <unordered_map>
 #include <utility>
 
 #include <core/common/status.h>
@@ -61,17 +63,23 @@ struct ExtDataValueDeleter {
 
 // given a tensor proto with external data return an OrtValue with a tensor for
 // that data; the pointers for the tensor data and the tensor itself are owned
-// by the OrtValue's deleter
+// by the OrtValue's deleter.
+// If tensor_proto's external file path is kTensorProtoMemoryAddressTag, and
+// buffered_tensor is not null, buffered_tensor holds the real buffer pointed
+// by tensor_proto. buffered_tensor must be the owner of the buffer and deleter
+// should release the buffer when tensor_proto is released.
 static inline common::Status ExtDataTensorProtoToTensor(const Env& env,
                                                         const std::basic_string<PATH_CHAR_TYPE>& proto_path,
                                                         const ONNX_NAMESPACE::TensorProto& tensor_proto,
-                                                        Tensor& tensor, OrtCallback& ext_data_deleter) {
+                                                        Tensor& tensor, OrtCallback& ext_data_deleter,
+                                                        Tensor* buffered_tensor = nullptr) {
   ORT_ENFORCE(utils::HasExternalData(tensor_proto));
 
   void* ext_data_buf = nullptr;
   SafeInt<size_t> ext_data_len = 0;
   ORT_RETURN_IF_ERROR(utils::GetExtDataFromTensorProto(env, proto_path.c_str(), tensor_proto,
-                                                       ext_data_buf, ext_data_len, ext_data_deleter));
+                                                       ext_data_buf, ext_data_len, ext_data_deleter,
+                                                       buffered_tensor));
 
   // NB: creating a do-nothing allocator per tensor is wasteful; can perhaps be
   // avoided if the Tensor class implements the do-nothing behavior when given a
@@ -83,16 +91,24 @@ static inline common::Status ExtDataTensorProtoToTensor(const Env& env,
   return common::Status::OK();
 }
 
+// If tensor_proto's external file path is kTensorProtoMemoryAddressTag, and
+// buffered_tensor is not null, buffered_tensor holds the real buffer pointed
+// by tensor_proto. buffered_tensor must be the owner of the buffer and deleter
+// should release the buffer when tensor_proto is released.
 static common::Status DeserializeTensorProto(const Env& env, const std::basic_string<PATH_CHAR_TYPE>& proto_path,
                                              const ONNX_NAMESPACE::TensorProto& tensor_proto, const MemBuffer* m,
                                              const AllocatorPtr& alloc, const AllocatorPtr& default_cpu_alloc,
                                              OrtValue& ort_value, const DataTransferManager& data_transfer_mgr,
-                                             bool use_device_allocator_for_initializers = false) {
+                                             bool use_device_allocator_for_initializers = false,
+                                             Tensor* buffered_tensor = nullptr) {
   if (bool(alloc) == (m != nullptr)) {
     return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT,
                   "DeserializeTensorProto() takes either pre-allocated buffer or an allocator!");
   }
 
+  ORT_RETURN_IF(buffered_tensor && !utils::HasExternalData(tensor_proto),
+                "With buffered tensor, tensor proto must use external location and point to buffered tensor");
+
   // Get shape and type of the tensor, and allocate the empty tensor
   TensorShape tensor_shape = utils::GetTensorShapeFromTensorProto(tensor_proto);
   const DataTypeImpl* const type = DataTypeImpl::TensorTypeFromONNXEnum(tensor_proto.data_type())->GetElementType();
@@ -123,7 +139,8 @@ static common::Status DeserializeTensorProto(const Env& env, const std::basic_st
       // utilize the mmap'd buffer directly by calling ExtDataTensorProtoToTensor. If we called
       // TensorProtoToTensor it would copy the data, causing unnecessary overhead
       OrtCallback ext_data_deleter;
-      ORT_RETURN_IF_ERROR(ExtDataTensorProtoToTensor(env, proto_path, tensor_proto, *p_tensor, ext_data_deleter));
+      ORT_RETURN_IF_ERROR(ExtDataTensorProtoToTensor(env, proto_path, tensor_proto, *p_tensor,
+                                                     ext_data_deleter, buffered_tensor));
 
       ExtDataValueDeleter deleter{ext_data_deleter, p_tensor.get()};
 
@@ -154,7 +171,7 @@ static common::Status DeserializeTensorProto(const Env& env, const std::basic_st
     std::optional<ScopedOrtCallbackInvoker> scoped_ort_callback_invoker;
     if (utils::HasExternalData(tensor_proto)) {
       ORT_RETURN_IF_ERROR(ExtDataTensorProtoToTensor(env, proto_path, tensor_proto, *p_deserialize_tensor,
-                                                     ext_data_deleter));
+                                                     ext_data_deleter, buffered_tensor));
       scoped_ort_callback_invoker = ScopedOrtCallbackInvoker(ext_data_deleter);
     } else {
       ORT_RETURN_IF_ERROR(utils::TensorProtoToTensor(env, proto_path.c_str(), tensor_proto, *p_deserialize_tensor));
@@ -187,7 +204,8 @@ common::Status SaveInitializedTensors(
     const logging::Logger& logger, const DataTransferManager& data_transfer_mgr,
     const ExecutionPlanBase& exec_plan,
     const SessionOptions& session_options,
-    const MemoryProfileFunction& memory_profile_func) {
+    const MemoryProfileFunction& memory_profile_func,
+    std::unordered_map<std::string, std::unique_ptr<Tensor>>& buffered_tensors) {
   LOGS(logger, INFO) << "Saving initialized tensors.";
   ORT_ENFORCE(ort_value_name_idx_map.MaxIdx() > -1, "OrtValue indexes should have been populated.");
 
@@ -307,9 +325,16 @@ common::Status SaveInitializedTensors(
       bool use_device_allocator_for_initializers =
           session_options.config_options.GetConfigOrDefault(kOrtSessionOptionsUseDeviceAllocatorForInitializers, "0") == "1";
 
+      Tensor* p_tensor = nullptr;
+      if (auto iter = buffered_tensors.find(name);
+          iter != buffered_tensors.end()) {
+        p_tensor = iter->second.release();
+        buffered_tensors.erase(iter);
+      }
+
       Status st = DeserializeTensorProto(env, graph_loc, tensor_proto, (m.has_value()) ? &*m : nullptr, alloc,
                                          default_cpu_alloc, ort_value, data_transfer_mgr,
-                                         use_device_allocator_for_initializers);
+                                         use_device_allocator_for_initializers, p_tensor);
       if (!st.IsOK()) {
         std::ostringstream oss;
         oss << "Deserialize tensor " << name << " failed." << st.ErrorMessage();
diff --git a/onnxruntime/core/framework/session_state_utils.h b/onnxruntime/core/framework/session_state_utils.h
index af44c35fbb7f5..499222b6ec613 100644
--- a/onnxruntime/core/framework/session_state_utils.h
+++ b/onnxruntime/core/framework/session_state_utils.h
@@ -3,6 +3,9 @@
 
 #pragma once
 #include <map>
+#include <memory>
+#include <string>
+#include <unordered_map>
 
 #include "core/common/const_pointer_container.h"
 #include "core/framework/allocator.h"
@@ -44,7 +47,8 @@ common::Status SaveInitializedTensors(
     const DataTransferManager& data_transfer_mgr,
     const ExecutionPlanBase& exec_plan,
     const SessionOptions& session_options,
-    const MemoryProfileFunction& memory_profile_func);
+    const MemoryProfileFunction& memory_profile_func,
+    std::unordered_map<std::string, std::unique_ptr<Tensor>>& buffered_tensors);
 
 common::Status SaveInputOutputNamesToNodeMapping(const GraphViewer& graph,
                                                  SessionState& session_state,
diff --git a/onnxruntime/core/framework/tensorprotoutils.cc b/onnxruntime/core/framework/tensorprotoutils.cc
index 4ecd61962d797..cbd53298ab2ad 100644
--- a/onnxruntime/core/framework/tensorprotoutils.cc
+++ b/onnxruntime/core/framework/tensorprotoutils.cc
@@ -987,7 +987,8 @@ static Status GetFileContent(const Env& env, const std::filesystem::path& file_p
 
 Status GetExtDataFromTensorProto(const Env& env, const std::filesystem::path& model_path,
                                  const ONNX_NAMESPACE::TensorProto& tensor_proto, void*& ext_data_buf,
-                                 SafeInt<size_t>& ext_data_len, OrtCallback& ext_data_deleter) {
+                                 SafeInt<size_t>& ext_data_len, OrtCallback& ext_data_deleter,
+                                 Tensor* buffered_tensor) {
   ORT_ENFORCE(utils::HasExternalData(tensor_proto));
   std::basic_string<ORTCHAR_T> tensor_proto_dir;
   if (!model_path.empty()) {
@@ -1003,7 +1004,12 @@ Status GetExtDataFromTensorProto(const Env& env, const std::filesystem::path& mo
     // the value in location is the memory address of the data
     ext_data_buf = reinterpret_cast<void*>(file_offset);
     ext_data_len = raw_data_safe_len;
-    ext_data_deleter = OrtCallback{nullptr, nullptr};
+    if (buffered_tensor) {
+      ext_data_deleter = OrtCallback{[](void* p) noexcept { delete reinterpret_cast<Tensor*>(p); },
+                                     reinterpret_cast<void*>(buffered_tensor)};
+    } else {
+      ext_data_deleter = OrtCallback{nullptr, nullptr};
+    }
   } else {
 #if defined(__wasm__)
     ORT_RETURN_IF(file_offset < 0 || file_offset + raw_data_safe_len >= 4294967296,
@@ -1241,7 +1247,9 @@ ONNXTensorElementDataType GetTensorElementType(const ONNX_NAMESPACE::TensorProto
   return CApiElementTypeFromProtoType(tensor_proto.data_type());
 }
 
-ONNX_NAMESPACE::TensorProto TensorToTensorProto(const Tensor& tensor, const std::string& tensor_proto_name) {
+ONNX_NAMESPACE::TensorProto TensorToTensorProto(const Tensor& tensor,
+                                                const std::string& tensor_proto_name,
+                                                bool use_tensor_buffer) {
   // Set name, dimensions, type, and data of the TensorProto.
   ONNX_NAMESPACE::TensorProto tensor_proto;
 
@@ -1259,6 +1267,28 @@ ONNX_NAMESPACE::TensorProto TensorToTensorProto(const Tensor& tensor, const std:
     for (; f < end; ++f) {
       *mutable_string_data->Add() = *f;
     }
+  } else if (use_tensor_buffer && tensor.SizeInBytes() > 127) {
+    // The logic aligns with
+    // https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/core/graph/graph_flatbuffers_utils.cc#L302
+    const auto* raw_data = tensor.DataRaw();
+    ORT_ENFORCE(raw_data, "Missing raw data for tensor proto. Invalid tensor.");
+    static_assert(sizeof(void*) <= sizeof(ExternalDataInfo::OFFSET_TYPE));
+    tensor_proto.set_data_location(ONNX_NAMESPACE::TensorProto_DataLocation_EXTERNAL);
+
+    // we reinterpret_cast this back to void* in tensorprotoutils.cc:GetExtDataFromTensorProto.
+    // use intptr_t as OFFSET_TYPE is signed. in theory you could get a weird looking value if the address uses the
+    // high bit, but that should be unlikely in a scenario where we care about memory usage enough to use this path.
+    auto offset = narrow<ExternalDataInfo::OFFSET_TYPE>(reinterpret_cast<intptr_t>(raw_data));
+
+    ONNX_NAMESPACE::StringStringEntryProto* entry = tensor_proto.mutable_external_data()->Add();
+    entry->set_key("location");
+    entry->set_value(ToUTF8String(onnxruntime::utils::kTensorProtoMemoryAddressTag));
+    entry = tensor_proto.mutable_external_data()->Add();
+    entry->set_key("offset");
+    entry->set_value(std::to_string(offset));
+    entry = tensor_proto.mutable_external_data()->Add();
+    entry->set_key("length");
+    entry->set_value(std::to_string(tensor.SizeInBytes()));
   } else {
     utils::SetRawDataInTensorProto(tensor_proto, tensor.DataRaw(), tensor.SizeInBytes());
   }
diff --git a/onnxruntime/core/framework/tensorprotoutils.h b/onnxruntime/core/framework/tensorprotoutils.h
index e5197adcb94ec..2af1f080be7ee 100644
--- a/onnxruntime/core/framework/tensorprotoutils.h
+++ b/onnxruntime/core/framework/tensorprotoutils.h
@@ -114,14 +114,22 @@ common::Status TensorProtoToTensor(const Env& env, const std::filesystem::path&
                                    const ONNX_NAMESPACE::TensorProto& tensor_proto,
                                    Tensor& tensor);
 
-/** Creates a TensorProto from a Tensor.
-    @param[in] tensor the Tensor whose data and shape will be used to create the TensorProto.
-    @param[in] tensor_proto_name the name of the TensorProto.
-    @return the TensorProto.
-
-    Note: Method currently requires that data is in little-endian format.
+/**
+ * @brief Creates a TensorProto from a Tensor.
+ * @param[in] tensor the Tensor whose data and shape will be used to create the TensorProto.
+ * @param[in] tensor_proto_name the name of the TensorProto.
+ * @param[in] use_tensor_buffer the tensor proto is set to use external location, with
+ *                              'location' set to onnxruntime::utils::kTensorProtoMemoryAddressTag
+ *                              'offset' set to tensor's memory location, and 'length' set to tensor's
+ *                              memory size. The caller is responsible to maintain the lifetime of
+ *                              the allocated memory buffer. Use with caution.
+ * @return the TensorProto.
+ *
+ *  Note: Method currently requires that data is in little-endian format.
  */
-ONNX_NAMESPACE::TensorProto TensorToTensorProto(const Tensor& tensor, const std::string& tensor_proto_name);
+ONNX_NAMESPACE::TensorProto TensorToTensorProto(const Tensor& tensor,
+                                                const std::string& tensor_proto_name,
+                                                bool use_tensor_buffer = false);
 
 ONNXTensorElementDataType CApiElementTypeFromProtoType(int type);
 ONNXTensorElementDataType GetTensorElementType(const ONNX_NAMESPACE::TensorProto& tensor_proto);
@@ -141,10 +149,15 @@ constexpr const ORTCHAR_T* kTensorProtoMemoryAddressTag = ORT_TSTR("*/_ORT_MEM_A
 
 // Given a tensor proto with external data obtain a pointer to the data and its length.
 // The ext_data_deleter argument is updated with a callback that owns/releases the data.
+// If tensor_proto's external file path is kTensorProtoMemoryAddressTag, and
+// buffered_tensor is not null, buffered_tensor holds the real buffer pointed
+// by tensor_proto. buffered_tensor must be the owner of the buffer and deleter
+// should release the buffer when tensor_proto is released.
 common::Status GetExtDataFromTensorProto(const Env& env, const std::filesystem::path& model_path,
                                          const ONNX_NAMESPACE::TensorProto& tensor_proto,
                                          void*& ext_data_buf, SafeInt<size_t>& ext_data_len,
-                                         OrtCallback& ext_data_deleter);
+                                         OrtCallback& ext_data_deleter,
+                                         Tensor* buffered_tensor = nullptr);
 
 // Convert the AttributeProto from a Constant node into a TensorProto that can be used as an initializer
 // If AttributeProto contains a TensorProto, this tensor proto is converted as is including the case when the
diff --git a/onnxruntime/core/optimizer/graph_transformer_utils.cc b/onnxruntime/core/optimizer/graph_transformer_utils.cc
index ab1dbaea7b7fd..54bd44ec2dba4 100644
--- a/onnxruntime/core/optimizer/graph_transformer_utils.cc
+++ b/onnxruntime/core/optimizer/graph_transformer_utils.cc
@@ -189,7 +189,8 @@ InlinedVector<std::unique_ptr<GraphTransformer>> GenerateTransformers(
     const SessionOptions& session_options,
     const IExecutionProvider& cpu_execution_provider, /*required by constant folding*/
     const InlinedHashSet<std::string>& rules_and_transformers_to_disable,
-    [[maybe_unused]] concurrency::ThreadPool* intra_op_thread_pool) {
+    [[maybe_unused]] concurrency::ThreadPool* intra_op_thread_pool,
+    std::unordered_map<std::string, std::unique_ptr<Tensor>>* p_buffered_tensors) {
   InlinedVector<std::unique_ptr<GraphTransformer>> transformers;
   const bool disable_quant_qdq =
       session_options.config_options.GetConfigOrDefault(kOrtSessionOptionsDisableQuantQDQ, "0") == "1";
@@ -309,7 +310,8 @@ InlinedVector<std::unique_ptr<GraphTransformer>> GenerateTransformers(
         transformers.emplace_back(std::make_unique<QDQSelectorActionTransformer>(qdq_is_int8_allowed,
                                                                                  SatApplyContextVariant{},
                                                                                  qdq_matmulnbits_accuracy_level,
-                                                                                 intra_op_thread_pool));
+                                                                                 intra_op_thread_pool,
+                                                                                 p_buffered_tensors));
       }
 
       transformers.emplace_back(std::make_unique<GemmActivationFusion>(cpu_ep));
@@ -419,7 +421,8 @@ InlinedVector<std::unique_ptr<GraphTransformer>> GenerateTransformersForMinimalB
     const SatApplyContextVariant& apply_context,
     const IExecutionProvider& cpu_execution_provider,
     const InlinedHashSet<std::string>& rules_and_transformers_to_disable,
-    [[maybe_unused]] concurrency::ThreadPool* intra_op_thread_pool) {
+    [[maybe_unused]] concurrency::ThreadPool* intra_op_thread_pool,
+    std::unordered_map<std::string, std::unique_ptr<Tensor>>* p_buffered_tensors) {
   InlinedVector<std::unique_ptr<GraphTransformer>> transformers;
   const bool saving = std::holds_alternative<SatRuntimeOptimizationSaveContext>(apply_context);
 
@@ -444,7 +447,8 @@ InlinedVector<std::unique_ptr<GraphTransformer>> GenerateTransformersForMinimalB
         transformers.emplace_back(std::make_unique<QDQSelectorActionTransformer>(qdq_is_int8_allowed,
                                                                                  apply_context,
                                                                                  qdq_matmulnbits_accuracy_level,
-                                                                                 intra_op_thread_pool));
+                                                                                 intra_op_thread_pool,
+                                                                                 p_buffered_tensors));
       }
 
       transformers.emplace_back(std::make_unique<ConvActivationFusion>(cpu_ep, apply_context));
diff --git a/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_actions.cc b/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_actions.cc
index 74fecb0427e14..8f99b7409d4fe 100644
--- a/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_actions.cc
+++ b/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_actions.cc
@@ -1,6 +1,9 @@
 // Copyright (c) Microsoft Corporation. All rights reserved.
 // Licensed under the MIT License.
 
+#include <unordered_map>
+#include <utility>
+
 #include "core/optimizer/qdq_transformer/selectors_actions/qdq_actions.h"
 #include "core/optimizer/qdq_transformer/qdq_util.h"
 #include "core/optimizer/initializer.h"
@@ -275,8 +278,10 @@ Status MatMulReplaceWithQLinear::Run(Graph& graph, const NodesToOptimize& select
   }
 }
 
-DQMatMulToMatMulNBitsAction::DQMatMulToMatMulNBitsAction(int64_t accuracy_level,
-                                                         concurrency::ThreadPool* intra_op_thread_pool)
+DQMatMulToMatMulNBitsAction::DQMatMulToMatMulNBitsAction(
+    int64_t accuracy_level,
+    concurrency::ThreadPool* intra_op_thread_pool,
+    std::unordered_map<std::string, std::unique_ptr<Tensor>>* p_buffered_tensors)
     : accuracy_level_{accuracy_level},
       domain_{kMSDomain},
       op_type_{"MatMulNBits"},
@@ -286,7 +291,8 @@ DQMatMulToMatMulNBitsAction::DQMatMulToMatMulNBitsAction(int64_t accuracy_level,
             MoveAndAppend(target, ArgType::kInput, 0, ArgType::kInput),
             MoveAll(target, ArgType::kOutput)};
       }()},
-      intra_op_thread_pool_{intra_op_thread_pool} {
+      intra_op_thread_pool_{intra_op_thread_pool},
+      p_buffered_tensors_{p_buffered_tensors} {
   ORT_ENFORCE(accuracy_level_ >= 0 && accuracy_level_ <= 4, "MatMulNBits accuracy level must be between 0 and 4");
 }
 
@@ -311,6 +317,7 @@ DQMatMulToMatMulNBitsAction::ExtraAttributes(const RuntimeState& runtime_state)
 Status DQMatMulToMatMulNBitsAction::ProcessNewNode(Graph& graph,
                                                    const NodesToOptimize& selected_nodes,
                                                    Node& replacement_node) const {
+  ORT_RETURN_IF_NOT(p_buffered_tensors_, "Buffered tensors map cannot be null");
   const auto* dq_node = selected_nodes.Input(0);
   const auto* weight_arg = dq_node->InputDefs()[0];
   const auto* scale_arg = dq_node->InputDefs()[1];
@@ -338,24 +345,35 @@ Status DQMatMulToMatMulNBitsAction::ProcessNewNode(Graph& graph,
   // to what we need. But it does not handle external data.
   Initializer weight_src(*weight_tensor_proto, graph.ModelPath());
   Initializer scale_src(*scale_tensor_proto, graph.ModelPath());
-  std::optional<Initializer> zp_src;
-  Initializer weight_dst(ONNX_NAMESPACE::TensorProto_DataType_UINT8,
-                         graph.GenerateNodeArgName(weight_arg->Name() + "_T"),
-                         std::vector<int64_t>{N, quant_num, blob_bytes});
-  Initializer scale_dst(static_cast<ONNX_NAMESPACE::TensorProto_DataType>(scale_src.data_type()),
-                        graph.GenerateNodeArgName(scale_arg->Name() + "_T"),
-                        std::vector<int64_t>{N * quant_num});
-  std::optional<Initializer> zp_dst;
+  auto uint8_type = DataTypeImpl::TensorTypeFromONNXEnum(ONNX_NAMESPACE::TensorProto_DataType_UINT8)->GetElementType();
+  auto scale_type = DataTypeImpl::TensorTypeFromONNXEnum(scale_src.data_type())->GetElementType();
+  std::optional<Initializer> zp_src_ptr;
+  auto cpu_allocator = std::make_shared<CPUAllocator>();
+  auto weight_dst_name = graph.GenerateNodeArgName(weight_arg->Name() + "_T");
+  auto weight_dst_ptr = std::make_unique<Tensor>(uint8_type,
+                                                 TensorShape{N, quant_num, blob_bytes},
+                                                 cpu_allocator);
+  auto scale_dst_name = graph.GenerateNodeArgName(scale_arg->Name() + "_T");
+  auto scale_size = (TensorShape{N, quant_num}).Size();
+  auto scale_dst_ptr = std::make_unique<Tensor>(scale_type,
+                                                TensorShape{scale_size},
+                                                cpu_allocator);
+  std::string zp_dst_name;
+  std::unique_ptr<Tensor> zp_dst_ptr;
+  auto zp_size = (TensorShape{N, (quant_num + 1) / 2}).Size();
 
   if (zp_tensor_proto) {
-    zp_src.emplace(*zp_tensor_proto, graph.ModelPath());
-    zp_dst.emplace(ONNX_NAMESPACE::TensorProto_DataType_UINT8,
-                   graph.GenerateNodeArgName(zp_arg->Name() + "_T"),
-                   std::vector<int64_t>{N * ((quant_num + 1) / 2)});
+    zp_src_ptr.emplace(*zp_tensor_proto, graph.ModelPath());
+    zp_dst_name = graph.GenerateNodeArgName(zp_arg->Name() + "_T");
+    zp_dst_ptr = std::make_unique<Tensor>(uint8_type,
+                                          TensorShape{zp_size},
+                                          cpu_allocator);
   } else if (weight_src.data_type() == ONNX_NAMESPACE::TensorProto_DataType_UINT4) {
-    zp_dst.emplace(ONNX_NAMESPACE::TensorProto_DataType_UINT8,
-                   graph.GenerateNodeArgName("fused_DQ_MatMul_zero_point_T"),
-                   std::vector<int64_t>{N * ((quant_num + 1) / 2)});
+    zp_dst_name = graph.GenerateNodeArgName("fused_DQ_MatMul_zero_point_T");
+    zp_dst_ptr = std::make_unique<Tensor>(uint8_type,
+                                          TensorShape{zp_size},
+                                          cpu_allocator);
+    memset(zp_dst_ptr->MutableDataRaw(), 0, zp_dst_ptr->SizeInBytes());
   }
 
   if (scale_src.data_type() == ONNX_NAMESPACE::TensorProto_DataType_FLOAT) {
@@ -363,10 +381,10 @@ Status DQMatMulToMatMulNBitsAction::ProcessNewNode(Graph& graph,
       MlasQDQTransposeBlockwiseQuantized<float, 4, true>(
           weight_src.DataAsByteSpan().data(),
           scale_src.data<float>(),
-          zp_src ? zp_src->DataAsByteSpan().data() : nullptr,
-          weight_dst.data<uint8_t>(),
-          scale_dst.data<float>(),
-          zp_dst ? zp_dst->data<uint8_t>() : nullptr,
+          zp_src_ptr ? zp_src_ptr->DataAsByteSpan().data() : nullptr,
+          weight_dst_ptr->MutableData<uint8_t>(),
+          scale_dst_ptr->MutableData<float>(),
+          zp_dst_ptr ? zp_dst_ptr->MutableData<uint8_t>() : nullptr,
           true,
           static_cast<int>(K),
           static_cast<int>(N),
@@ -376,10 +394,10 @@ Status DQMatMulToMatMulNBitsAction::ProcessNewNode(Graph& graph,
       MlasQDQTransposeBlockwiseQuantized<float, 4, false>(
           weight_src.DataAsByteSpan().data(),
           scale_src.data<float>(),
-          zp_src ? zp_src->DataAsByteSpan().data() : nullptr,
-          weight_dst.data<uint8_t>(),
-          scale_dst.data<float>(),
-          zp_dst ? zp_dst->data<uint8_t>() : nullptr,
+          zp_src_ptr ? zp_src_ptr->DataAsByteSpan().data() : nullptr,
+          weight_dst_ptr->MutableData<uint8_t>(),
+          scale_dst_ptr->MutableData<float>(),
+          zp_dst_ptr ? zp_dst_ptr->MutableData<uint8_t>() : nullptr,
           true,
           static_cast<int>(K),
           static_cast<int>(N),
@@ -391,10 +409,10 @@ Status DQMatMulToMatMulNBitsAction::ProcessNewNode(Graph& graph,
       MlasQDQTransposeBlockwiseQuantized<MLFloat16, 4, true>(
           weight_src.DataAsByteSpan().data(),
           scale_src.data<MLFloat16>(),
-          zp_src ? zp_src->DataAsByteSpan().data() : nullptr,
-          weight_dst.data<uint8_t>(),
-          scale_dst.data<MLFloat16>(),
-          zp_dst ? zp_dst->data<uint8_t>() : nullptr,
+          zp_src_ptr ? zp_src_ptr->DataAsByteSpan().data() : nullptr,
+          weight_dst_ptr->MutableData<uint8_t>(),
+          scale_dst_ptr->MutableData<MLFloat16>(),
+          zp_dst_ptr ? zp_dst_ptr->MutableData<uint8_t>() : nullptr,
           true,
           static_cast<int>(K),
           static_cast<int>(N),
@@ -405,10 +423,10 @@ Status DQMatMulToMatMulNBitsAction::ProcessNewNode(Graph& graph,
       MlasQDQTransposeBlockwiseQuantized<MLFloat16, 4, false>(
           weight_src.DataAsByteSpan().data(),
           scale_src.data<MLFloat16>(),
-          zp_src ? zp_src->DataAsByteSpan().data() : nullptr,
-          weight_dst.data<uint8_t>(),
-          scale_dst.data<MLFloat16>(),
-          zp_dst ? zp_dst->data<uint8_t>() : nullptr,
+          zp_src_ptr ? zp_src_ptr->DataAsByteSpan().data() : nullptr,
+          weight_dst_ptr->MutableData<uint8_t>(),
+          scale_dst_ptr->MutableData<MLFloat16>(),
+          zp_dst_ptr ? zp_dst_ptr->MutableData<uint8_t>() : nullptr,
           true,
           static_cast<int>(K),
           static_cast<int>(N),
@@ -417,28 +435,43 @@ Status DQMatMulToMatMulNBitsAction::ProcessNewNode(Graph& graph,
     }
   }
 
-  ONNX_NAMESPACE::TensorProto weight_T_tp;
-  ONNX_NAMESPACE::TensorProto scale_T_tp;
+  auto weight_T_tp = utils::TensorToTensorProto(*weight_dst_ptr, weight_dst_name, true);
+  auto scale_T_tp = utils::TensorToTensorProto(*scale_dst_ptr, scale_dst_name, true);
   std::optional<ONNX_NAMESPACE::TensorProto> zp_T_tp;
 
-  // TODO(fajin): external_data to memory location to avoid arena allocation
-  // https://github.com/microsoft/onnxruntime/pull/12465
-  weight_dst.ToProto(weight_T_tp);
-  scale_dst.ToProto(scale_T_tp);
-  if (zp_dst) {
-    zp_T_tp.emplace();
-    zp_dst->ToProto(zp_T_tp.value());
+  if (zp_dst_ptr) {
+    zp_T_tp.emplace(utils::TensorToTensorProto(*zp_dst_ptr, zp_dst_name, true));
   }
 
   auto& input_defs = replacement_node.MutableInputDefs();
   input_defs.push_back(&graph_utils::AddInitializer(graph, weight_T_tp));
   replacement_node.MutableInputArgsCount().push_back(1);
+  if (weight_T_tp.data_location() == ONNX_NAMESPACE::TensorProto_DataLocation_EXTERNAL) {
+    // If tensor is too small, tensor proto directly copies data from tensor. The tensor allocated
+    // here can be directly destructed.
+    // Only keep the tensor in p_buffered_tensors_ when the tensor proto is using external data location
+    // and pointing the location to tensor's buffer.
+    ORT_RETURN_IF_NOT(p_buffered_tensors_->emplace(weight_dst_name, std::move(weight_dst_ptr)).second,
+                      "Failed to add buffered tensor ",
+                      weight_dst_name);
+  }
+
   input_defs.push_back(&graph_utils::AddInitializer(graph, scale_T_tp));
   replacement_node.MutableInputArgsCount().push_back(1);
+  if (scale_T_tp.data_location() == ONNX_NAMESPACE::TensorProto_DataLocation_EXTERNAL) {
+    ORT_RETURN_IF_NOT(p_buffered_tensors_->emplace(scale_dst_name, std::move(scale_dst_ptr)).second,
+                      "Failed to add buffered tensor ",
+                      scale_dst_name);
+  }
 
   if (zp_T_tp) {
     input_defs.push_back(&graph_utils::AddInitializer(graph, zp_T_tp.value()));
     replacement_node.MutableInputArgsCount().push_back(1);
+    if (zp_T_tp->data_location() == ONNX_NAMESPACE::TensorProto_DataLocation_EXTERNAL) {
+      ORT_RETURN_IF_NOT(p_buffered_tensors_->emplace(zp_dst_name, std::move(zp_dst_ptr)).second,
+                        "Failed to add buffered tensor ",
+                        zp_dst_name);
+    }
   }
 
   return Status::OK();
diff --git a/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_actions.h b/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_actions.h
index 47821619db65a..d25077ca4b491 100644
--- a/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_actions.h
+++ b/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_actions.h
@@ -5,10 +5,12 @@
 
 #include <memory>
 #include <string>
+#include <unordered_map>
 #include <vector>
 
 #include "core/optimizer/selectors_actions/actions.h"
 #include "core/platform/threadpool.h"
+#include "core/framework/tensor.h"
 
 namespace onnxruntime {
 
@@ -84,7 +86,8 @@ struct MatMulReplaceWithQLinear : public Action {
 // used together with DQMatMulNodeGroupSelector, which does the sanity check
 struct DQMatMulToMatMulNBitsAction : public ReplaceWithNew {
   DQMatMulToMatMulNBitsAction(int64_t accuracy_level,
-                              concurrency::ThreadPool* intra_op_thread_pool);
+                              concurrency::ThreadPool* intra_op_thread_pool,
+                              std::unordered_map<std::string, std::unique_ptr<Tensor>>* p_buffered_tensors);
 
  private:
   std::string OpType(const RuntimeState&) const override { return op_type_; }
@@ -103,6 +106,7 @@ struct DQMatMulToMatMulNBitsAction : public ReplaceWithNew {
   const std::string op_type_;
   const std::vector<NodeAndMoveInfo> value_moves_;
   concurrency::ThreadPool* intra_op_thread_pool_;
+  std::unordered_map<std::string, std::unique_ptr<Tensor>>* p_buffered_tensors_;
 };
 
 struct GemmReplaceWithQuant : public Action {
diff --git a/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.cc b/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.cc
index d81701fdf443b..379d271fbdca7 100644
--- a/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.cc
+++ b/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.cc
@@ -1,8 +1,11 @@
 // Copyright (c) Microsoft Corporation. All rights reserved.
 // Licensed under the MIT License.
 
-#include "core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.h"
 #include <memory>
+#include <string>
+#include <unordered_map>
+
+#include "core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.h"
 #include "core/mlas/inc/mlas.h"
 
 #include "core/optimizer/qdq_transformer/selectors_actions/qdq_actions.h"
@@ -247,7 +250,8 @@ void MatMulQDQRules(SelectorActionRegistry& qdq_selector_action_registry, bool i
 
 void DQMatMulToMatMulNBitsRules(SelectorActionRegistry& qdq_selector_action_registry,
                                 int64_t qdq_matmulnbits_accuracy_level,
-                                concurrency::ThreadPool* intra_op_thread_pool) {
+                                concurrency::ThreadPool* intra_op_thread_pool,
+                                std::unordered_map<std::string, std::unique_ptr<Tensor>>* p_buffered_tensors) {
   // 2 nodes. DQ -> MatMul. DQ is the second input to MatMul.
   // DQ's weight is int4/uint4. DQ's scale is float/float16.
   // DQ is block-quantized along axis 0, with block_size >= 16 and as 2's power.
@@ -255,7 +259,8 @@ void DQMatMulToMatMulNBitsRules(SelectorActionRegistry& qdq_selector_action_regi
 
   std::unique_ptr<Action> action =
       std::make_unique<QDQ::DQMatMulToMatMulNBitsAction>(qdq_matmulnbits_accuracy_level,
-                                                         intra_op_thread_pool);
+                                                         intra_op_thread_pool,
+                                                         p_buffered_tensors);
 
 #if !defined(ORT_MINIMAL_BUILD)
   std::unique_ptr<NodeSelector> selector = std::make_unique<QDQ::DQMatMulToMatMulNBitsSelector>();
@@ -312,9 +317,11 @@ void WhereQDQRules(SelectorActionRegistry& qdq_selector_action_registry) {
 #endif
 }
 
-SelectorActionRegistry CreateSelectorActionRegistry(bool is_int8_allowed,
-                                                    int64_t qdq_matmulnbits_accuracy_level,
-                                                    concurrency::ThreadPool* intra_op_thread_pool) {
+SelectorActionRegistry CreateSelectorActionRegistry(
+    bool is_int8_allowed,
+    int64_t qdq_matmulnbits_accuracy_level,
+    concurrency::ThreadPool* intra_op_thread_pool,
+    std::unordered_map<std::string, std::unique_ptr<Tensor>>* p_buffered_tensors) {
   SelectorActionRegistry qdq_selector_action_registry;
   SplitQDQRules(qdq_selector_action_registry);
   DropQDQNodesRules(qdq_selector_action_registry);
@@ -328,20 +335,24 @@ SelectorActionRegistry CreateSelectorActionRegistry(bool is_int8_allowed,
   WhereQDQRules(qdq_selector_action_registry);
   DQMatMulToMatMulNBitsRules(qdq_selector_action_registry,
                              qdq_matmulnbits_accuracy_level,
-                             intra_op_thread_pool);
+                             intra_op_thread_pool,
+                             p_buffered_tensors);
 
   return qdq_selector_action_registry;
 }
 
 }  // namespace
 
-QDQSelectorActionTransformer::QDQSelectorActionTransformer(bool is_int8_allowed,
-                                                           const SatApplyContextVariant& apply_context,
-                                                           int64_t qdq_matmulnbits_accuracy_level,
-                                                           concurrency::ThreadPool* intra_op_thread_pool)
+QDQSelectorActionTransformer::QDQSelectorActionTransformer(
+    bool is_int8_allowed,
+    const SatApplyContextVariant& apply_context,
+    int64_t qdq_matmulnbits_accuracy_level,
+    concurrency::ThreadPool* intra_op_thread_pool,
+    std::unordered_map<std::string, std::unique_ptr<Tensor>>* p_buffered_tensors)
     : SelectorActionTransformer{
           "QDQSelectorActionTransformer",
-          CreateSelectorActionRegistry(is_int8_allowed, qdq_matmulnbits_accuracy_level, intra_op_thread_pool),
+          CreateSelectorActionRegistry(is_int8_allowed, qdq_matmulnbits_accuracy_level,
+                                       intra_op_thread_pool, p_buffered_tensors),
           apply_context,
           // this transformer is only compatible with the CPU and DML EP
           {kCpuExecutionProvider, kDmlExecutionProvider}} {
diff --git a/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.h b/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.h
index ba636f76d1900..627ddd35b9919 100644
--- a/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.h
+++ b/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.h
@@ -3,6 +3,10 @@
 
 #pragma once
 
+#include <string>
+#include <memory>
+#include <unordered_map>
+
 #include "core/optimizer/selectors_actions/selector_action_transformer.h"
 #include "core/mlas/inc/mlas.h"
 #include "core/platform/threadpool.h"
@@ -25,7 +29,8 @@ class QDQSelectorActionTransformer : public SelectorActionTransformer {
   QDQSelectorActionTransformer(bool is_int8_allowed,
                                const SatApplyContextVariant& apply_context = {},
                                int64_t qdq_matmulnbits_accuracy_level = 4,
-                               concurrency::ThreadPool* intra_op_thread_pool = nullptr);
+                               concurrency::ThreadPool* intra_op_thread_pool = nullptr,
+                               std::unordered_map<std::string, std::unique_ptr<Tensor>>* p_buffered_tensors = nullptr);
 };
 
 }  // namespace onnxruntime
diff --git a/onnxruntime/core/session/inference_session.cc b/onnxruntime/core/session/inference_session.cc
index 5ad2f08467792..5eed7c5c6f2b5 100644
--- a/onnxruntime/core/session/inference_session.cc
+++ b/onnxruntime/core/session/inference_session.cc
@@ -1615,7 +1615,8 @@ Status PartitionOrtFormatModel(onnxruntime::Graph& graph,
 Status ApplyOrtFormatModelRuntimeOptimizations(
     onnxruntime::Graph& graph, const logging::Logger& logger, const SessionOptions& session_options,
     const InlinedHashSet<std::string>& optimizers_to_disable, const IExecutionProvider& cpu_ep,
-    concurrency::ThreadPool* intra_op_thread_pool) {
+    concurrency::ThreadPool* intra_op_thread_pool,
+    std::unordered_map<std::string, std::unique_ptr<Tensor>>* p_buffered_tensors) {
   bool modified = false;
 
   for (int level = static_cast<int>(TransformerLevel::Level2);
@@ -1623,7 +1624,7 @@ Status ApplyOrtFormatModelRuntimeOptimizations(
        ++level) {
     const auto transformers = optimizer_utils::GenerateTransformersForMinimalBuild(
         static_cast<TransformerLevel>(level), session_options, SatRuntimeOptimizationLoadContext{}, cpu_ep,
-        optimizers_to_disable, intra_op_thread_pool);
+        optimizers_to_disable, intra_op_thread_pool, p_buffered_tensors);
 
     for (const auto& transformer : transformers) {
       ORT_RETURN_IF_ERROR(transformer->Apply(graph, modified, logger));
@@ -2012,7 +2013,8 @@ common::Status InferenceSession::Initialize() {
       const auto& cpu_ep = *execution_providers_.Get(onnxruntime::kCpuExecutionProvider);
       ORT_RETURN_IF_ERROR_SESSIONID_(
           ApplyOrtFormatModelRuntimeOptimizations(graph, *session_logger_, session_options_, optimizers_to_disable_,
-                                                  cpu_ep, GetIntraOpThreadPoolToUse()));
+                                                  cpu_ep, GetIntraOpThreadPoolToUse(),
+                                                  session_state_->GetMutableBufferedTensors()));
 #endif  // !defined(ORT_MINIMAL_BUILD) || defined(ORT_EXTENDED_MINIMAL_BUILD)
     }
 
@@ -3175,7 +3177,8 @@ common::Status InferenceSession::AddPredefinedTransformers(
         if (use_full_build_optimizations) {
           return optimizer_utils::GenerateTransformers(level, session_options_, cpu_ep,
                                                        optimizers_to_disable_,
-                                                       GetIntraOpThreadPoolToUse());
+                                                       GetIntraOpThreadPoolToUse(),
+                                                       session_state_->GetMutableBufferedTensors());
         } else {
           const auto sat_context =
               minimal_build_optimization_handling ==
@@ -3185,7 +3188,8 @@ common::Status InferenceSession::AddPredefinedTransformers(
                   : SatApplyContextVariant{SatDirectApplicationContext{}};
           return optimizer_utils::GenerateTransformersForMinimalBuild(level, session_options_, sat_context, cpu_ep,
                                                                       optimizers_to_disable_,
-                                                                      GetIntraOpThreadPoolToUse());
+                                                                      GetIntraOpThreadPoolToUse(),
+                                                                      session_state_->GetMutableBufferedTensors());
         }
       }();
 

From 7cea166f00b1a028908d854a2425b692ba54d384 Mon Sep 17 00:00:00 2001
From: Tianlei Wu <tlwu@microsoft.com>
Date: Wed, 31 Jul 2024 09:01:05 -0700
Subject: [PATCH 03/22] [CUDA] Fix MultiHeadAttention thread safe and bias
 support (#21498)

### Description

#### Issues Fixed
(1) **TRT cross attention not thread safe**. [Core changes like
this](https://github.com/microsoft/onnxruntime/commit/6fd7aba3d4f27089de1a4ead86a2ae7e667c18b6)
are used to make it thread-safe:
* Add an once_flag to CumulatedSequenceLengthCache to make sure it is
only initialized once; and change the cache to be read only after
initialization. Previously, the content is not read-only so it might be
changed by other thread and potentially cause buffer overrun.
* The kernel initialization is not guarded (Although the factory of
kernel loading has static mutex to guard multiple threading), so the
mutable variable might be set by two different threads at the same time.
Add an once_flag to avoid that.

This requires need some workspace computation change as well. So I did
not create a separated pull request.

(2) **Bias for cross attention**

That scenario has assumption that only query has bias, but not for key
and value. However, such assumption is not verified in runtime and there
was no comment of assumption, and there was no test case so the support
of scenario was disabled by mistake. Actually, the scenario is used in
whisper model (TODO: we shall add tests for whisper to CI pipeline, and
also update fusion script to verify such assumptions if needed.)

CUDA/CPU kernels supports bias for cross attention as long as bias is
zero for key and value. I updated the check to support the scenario and
added comments wherever there is such assumption.

(3) **Fallback support**

Previously, unfused kernel did not support packed qkv and packed kv
formats. That means some case might fail since there is no fallback. I
added new AddBiasTranpose cuda kernels for them to support fallback, so
that all supported cases will not fail.

#### Improvements

(4) **QKV workspace size**.

The logic for no_qkv_workspace could be easily out of sync since related
code are scattered in different source files. I refactor the code to
move all related code to one file (attention_prepare_qkv.cu) and add
asserts, so that the logic can be in sync.

(5) **Remove confusing concept of pass past in kv**

parameters.pass_past_in_kv is confusing since the k/v in cross attention
is not past state. Remove it and use parameters.qkv_format ==
Q_K_V_BSNH_BNSH_BNSH instead.

New code does not use past_key/past_value for cross attention, so the
logic is more clear.

(6) **More coverage and less workspace and less transpose of flash and
efficient attention**
Previously, there is one condition does not run flash or efficient
attention:
```
 bool past_no_bias = (pass_key_value_as_past || past_key != nullptr || present_key != nullptr) && bias == nullptr;
```
After this change, we can use flash and efficient attention for the
case, and also less workspace.

For example, cross attention with bias, the original code uses two
additional workspaces:
```
  transpose: past_key (BxNxSxH) => temp_k_workspace (BxSxNxH), past_value (BxNxSxH_v) => temp_v_workspace (BxSxNxH_v)
  add bias: query => q,   temp_k_workspace => k,   temp_v_workspace => v
```

New logic is like
```
   if (has bias)
      Add bias to query, key, value, and store in q, k, v workspace
   else
      Use query, key and value directly as q, k and v in kernel
```

We can see that, we do not need allocate temp_k_workspace and
temp_v_workspace so use less memory. New code saved two transposes in
this case.

Flash and efficient attention supports BSNH or BNSH formats for k and v.
In old code, k/v are also converted to BSNH format. Some is not
necessary. I do some change to convert k/v to BSNH or BNSH case by case.
So that there are more cases can be covered by flash or efficient
attention to improve performance.

(6) **Debugging support**
Previously, there is less debug info. In this change, I add a flag for
debug info in the AttentionData. So that we can output debug info during
the processing.

Also add functions to consolidate the dumping of inputs, QKV processing
and outputs; Add an environment variable `ORT_ENABLE_GPU_DUMP` to allow
disable dumping from cuda kernel.

#### Summary of changes
(1) Refactoring the CheckInputs, and pass in operator type.
(2) Refactoring the PrepareQKV to support fallback for packed qkv or
packed kv inputs.
(3) Change a few case of PrepareQKV to allow more case covered by flash
and efficient attention.
(4) use parameters.qkv_format == Q_K_V_BSNH_BNSH_BNSH to replace
parameters.pass_past_in_kv
(5) Allow bias input for Q_K_V_BSNH_BNSH_BNSH, and add comments of
assumption that key/value has no bias in this case.
(6) Fix thread-safe issue in CumulatedSequenceLengthCache handling.
(7) Add test cases to cover all supported scenarios.

Current support scenarios for MultiHeadAttention for CUDA/CPU:

| Q | K | V | pastK| pastV | presentK| presentV | Bias | Op desc
| ---- | ---- | ---- | ------ | ----- | --------- | -------- |
-----|---------
| BSNH | BLNH| BLNH| - | - | - | - | QKV | not packed
| BLN3H| - | - | - | - | - | - | QKV | qkv packed <br> not support in
CPU
| BSNH | BLN2H| - | - | - | - | - | --- | kv packed <br> not support in
CPU
| BSNH | BNLH| BNLH| - | - | - | - | Q-- | cross attention <br> bias for
Q only
| BSNH | BLNH | BLNH | - | - | BNTH | BNTH | QKV | no past <br> only
present
| BSNH | BLNH | BLNH | BNPH | BNPH | BNTH | BNTH | QKV | past and
present <br> (not share buffer)

### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
https://github.com/microsoft/onnxruntime/issues/18854
---
 .../contrib_ops/cpu/bert/attention_base.cc    |   1 -
 .../contrib_ops/cpu/bert/attention_common.h   |  13 +-
 .../cpu/bert/multihead_attention.cc           |  15 +-
 .../cpu/bert/multihead_attention_helper.h     | 574 +++++++-----
 .../cuda/bert/add_bias_transpose.cu           | 114 +++
 .../cuda/bert/add_bias_transpose.h            |  54 +-
 .../contrib_ops/cuda/bert/attention.cc        |   4 +-
 .../contrib_ops/cuda/bert/attention_impl.cu   | 202 ++--
 .../contrib_ops/cuda/bert/attention_impl.h    |  55 +-
 .../cuda/bert/attention_kernel_options.h      |   1 +
 .../cuda/bert/attention_kv_cache.cu           |  73 +-
 .../cuda/bert/attention_prepare_qkv.cu        | 864 ++++++++++++------
 .../cuda/bert/attention_transpose.cu          |   6 +
 .../decoder_masked_multihead_attention.cc     |  13 +-
 ...decoder_masked_multihead_attention_impl.cu |   3 +
 .../cuda/bert/multihead_attention.cc          | 221 ++---
 .../cuda/bert/multihead_attention.h           |   6 +
 .../cuda/bert/packed_attention_impl.cu        |  22 +-
 .../bert/packed_multihead_attention_impl.cu   |  58 +-
 .../quantization/attention_quantization.cc    |   4 +-
 .../cuda/utils/dump_cuda_tensor.cc            |   9 +
 .../contrib_ops/cuda/utils/dump_cuda_tensor.h |   2 +-
 .../contrib_ops/rocm/bert/attention_impl.cu   |  10 +-
 .../contrib_ops/rocm/bert/attention_impl.h    |   6 -
 .../rocm/bert/multihead_attention.cu          |   8 +-
 .../tools/transformers/fusion_attention.py    |   3 +
 .../test/python/transformers/benchmark_mha.py |  99 +-
 .../test/python/transformers/test_mha.py      | 346 ++++---
 28 files changed, 1729 insertions(+), 1057 deletions(-)

diff --git a/onnxruntime/contrib_ops/cpu/bert/attention_base.cc b/onnxruntime/contrib_ops/cpu/bert/attention_base.cc
index 515a967aa2386..f7d8fedc734e4 100644
--- a/onnxruntime/contrib_ops/cpu/bert/attention_base.cc
+++ b/onnxruntime/contrib_ops/cpu/bert/attention_base.cc
@@ -258,7 +258,6 @@ Status AttentionBase::CheckInputs(const TensorShape& input_shape,
     output_parameters->scale = scale_;
     output_parameters->mask_type = mask_type;
     output_parameters->broadcast_res_pos_bias = broadcast_res_pos_bias;
-    output_parameters->pass_past_in_kv = false;
     output_parameters->qkv_format = Q_K_V_BNSH;
   }
 
diff --git a/onnxruntime/contrib_ops/cpu/bert/attention_common.h b/onnxruntime/contrib_ops/cpu/bert/attention_common.h
index 55292b35e1e38..88127387d08ea 100644
--- a/onnxruntime/contrib_ops/cpu/bert/attention_common.h
+++ b/onnxruntime/contrib_ops/cpu/bert/attention_common.h
@@ -6,6 +6,12 @@
 namespace onnxruntime {
 namespace contrib {
 
+enum AttentionType {
+  kAttention,
+  kMultiHeadAttention,
+  kDecoderMaskedMultiHeadAttention,
+};
+
 enum AttentionMaskType {
   MASK_NONE,                  // No mask
   MASK_1D_KEY_SEQ_LEN,        // [batch_size], key sequence length
@@ -24,10 +30,12 @@ enum AttentionQkvFormat {
   UNKNOWN,               // enum value not set, or depends on qkv projection implementation details
   Q_K_V_BNSH,            // for non-packed qkv, permuted
   Q_K_V_BSNH,            // for non-packed qkv, not permuted, used by memory efficient attention or MultiHeadAttention
-  QKV_BSN3H,             // for TRT fused attention, qkv are packed
+  Q_K_V_BSNH_BNSH_BNSH,  // for cross attention, k and v are permuted
   Q_K_V_BNSH_QKV_BS3NH,  // for TRT fused causal attention, data has two formats (qkv is 3BNSH, gemm_buffer is BS3NH)
-  Q_KV_BSNH_BSN2H,       // for TRT fused cross attention, kv are packed
   Q_K_V_TNH,             // for memory efficient attention, qkv are not packed, and paddings are removed.
+  Q_KV_BSNH_BSN2H,       // for TRT fused cross attention, kv are packed
+  QKV_BSN3H,             // for TRT fused attention, qkv are packed
+  QKV_BS3NH,             // for DecoderMaskedMultiHeadAttention, qkv are packed
   QKV_TN3H,              // for TRT fused attention, qkv are packed and paddings are removed
 };
 
@@ -61,7 +69,6 @@ struct AttentionParameters {
   bool past_present_share_buffer;
   bool do_rotary;
   bool broadcast_res_pos_bias;
-  bool pass_past_in_kv;
   float mask_filter_value;
   float scale;
   bool use_tf32;
diff --git a/onnxruntime/contrib_ops/cpu/bert/multihead_attention.cc b/onnxruntime/contrib_ops/cpu/bert/multihead_attention.cc
index 9677c30f22d8a..0d77376779230 100644
--- a/onnxruntime/contrib_ops/cpu/bert/multihead_attention.cc
+++ b/onnxruntime/contrib_ops/cpu/bert/multihead_attention.cc
@@ -85,7 +85,7 @@ Status MultiHeadAttention<T>::Compute(OpKernelContext* context) const {
                                                                       scale_,
                                                                       is_unidirectional_,
                                                                       past_present_share_buffer,
-                                                                      false));
+                                                                      kMultiHeadAttention));
 
   const int batch_size = parameters.batch_size;
   const int q_sequence_length = parameters.sequence_length;
@@ -121,20 +121,13 @@ Status MultiHeadAttention<T>::Compute(OpKernelContext* context) const {
   AllocatorPtr allocator;
   ORT_RETURN_IF_ERROR(context->GetTempSpaceAllocator(&allocator));
 
-  // For each of Q/K/V, there are multiple scenarios:
-  // 1) Combined QKV bias is null
-  //    a) Q/K/V is (B, S, D)
-  //    b) Q/K/V is (B, S, N, H)
-  // 2) No packed QKV in Q
-  //    a) Q/K/V has seq_len = 1
-  //    b) Q/K/V has seq_len > 1
-
   OrtValue Q;
   ORT_RETURN_IF_ERROR(MaybeTransposeToBNSHAndAddBias<T>(
       context, allocator, batch_size, num_heads_, q_sequence_length, qk_head_size, query, bias, q_bias_offset, Q));
 
-  if (parameters.pass_past_in_kv) {  // key and value in BNSH format
-    assert(bias == nullptr);
+  if (parameters.qkv_format == Q_K_V_BSNH_BNSH_BNSH) {
+    // For cross attention with k and v in BNSH format, we assume that bias for key and value are zeros.
+    // So we don't need to add bias for key and value here.
     assert(past_key == nullptr);
     assert(past_value == nullptr);
     return ApplyAttention(Q.GetMutable<Tensor>()->MutableData<T>(),
diff --git a/onnxruntime/contrib_ops/cpu/bert/multihead_attention_helper.h b/onnxruntime/contrib_ops/cpu/bert/multihead_attention_helper.h
index bd7ab09659170..cfb8d36843777 100644
--- a/onnxruntime/contrib_ops/cpu/bert/multihead_attention_helper.h
+++ b/onnxruntime/contrib_ops/cpu/bert/multihead_attention_helper.h
@@ -11,6 +11,232 @@ namespace onnxruntime {
 namespace contrib {
 namespace multihead_attention_helper {
 
+template <typename T>
+Status Check_QKV(const T* packed_qkv, AttentionQkvFormat& qkv_format) {
+  const auto& query_dims = packed_qkv->Shape().GetDims();
+  if (query_dims.size() == 3) {
+    // Packed qkv used by DecoderMaskedMultiHeadAttention. Query shape is (B, S, 3D), no key and value.
+    qkv_format = AttentionQkvFormat::QKV_BS3NH;
+  } else {
+    assert(query_dims.size() == 5);
+    if (static_cast<int>(query_dims[3]) != 3) {
+      return ORT_MAKE_STATUS(
+          ONNXRUNTIME, INVALID_ARGUMENT,
+          "Expect 'query' shape (batch_size, sequence_length, num_heads, 3, head_size) for packed qkv");
+    }
+
+    qkv_format = AttentionQkvFormat::QKV_BSN3H;
+  }
+
+  return Status::OK();
+}
+
+template <typename T>
+Status Check_Q_KV(const T* query, const T* packed_kv, int num_heads, int head_size,
+                  AttentionQkvFormat& qkv_format, int& kv_sequence_length) {
+  const auto& query_dims = query->Shape().GetDims();
+  const auto& key_dims = packed_kv->Shape().GetDims();
+  if (query_dims.size() != 3) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Expect rank of query be 3 for packed kv");
+  }
+
+  if (key_dims.size() != 5) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Expect rank of key be 5 for packed kv");
+  }
+
+  if (key_dims[0] != query_dims[0] ||
+      static_cast<int>(key_dims[2]) != num_heads ||
+      static_cast<int>(key_dims[3]) != 2 ||
+      static_cast<int>(key_dims[4]) != head_size) {
+    return ORT_MAKE_STATUS(
+        ONNXRUNTIME, INVALID_ARGUMENT,
+        "Expect 'key' shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv");
+  }
+
+  qkv_format = AttentionQkvFormat::Q_KV_BSNH_BSN2H;
+  kv_sequence_length = static_cast<int>(key_dims[1]);
+  return Status::OK();
+}
+
+template <typename T>
+Status Check_Q_K_V(const T* query, const T* key, const T* value, int num_heads, int head_size,
+                   AttentionQkvFormat& qkv_format, int& kv_sequence_length, int& v_hidden_size) {
+  const auto& query_dims = query->Shape().GetDims();
+  const auto& key_dims = key->Shape().GetDims();
+  const auto& value_dims = value->Shape().GetDims();
+  if (query_dims.size() != 3) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Expect rank of query be 3 for packed kv");
+  }
+
+  if (key_dims.size() != value_dims.size() || (key_dims.size() != 3 && value_dims.size() != 4)) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Expect rank of key and value be same, and either 3 or 4");
+  }
+
+  if (key_dims[0] != query_dims[0] || value_dims[0] != query_dims[0]) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'query', 'key' and 'value' shall have same dim 0 (batch_size)");
+  }
+
+  if (key_dims.size() == 3) {
+    if (key_dims[2] != query_dims[2]) {
+      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                             "Input 'query' and 'key' shall have same dim 2 (hidden_size)");
+    }
+
+    if (key_dims[1] != value_dims[1]) {
+      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                             "Input 'key' and 'value' shall have same dim 1 (kv_sequence_length)");
+    }
+
+    qkv_format = AttentionQkvFormat::Q_K_V_BSNH;
+    kv_sequence_length = static_cast<int>(key_dims[1]);
+    v_hidden_size = static_cast<int>(value_dims[2]);
+  } else {  // key_dims.size() == 4
+    if (value->Shape() != key->Shape() ||
+        static_cast<int>(key_dims[1]) != num_heads ||
+        static_cast<int>(key_dims[3]) != head_size) {
+      return ORT_MAKE_STATUS(
+          ONNXRUNTIME, INVALID_ARGUMENT,
+          "Input 'key' and 'value' shall have same shape (batch_size, num_heads, kv_sequence_length, head_size)");
+    }
+
+    qkv_format = AttentionQkvFormat::Q_K_V_BSNH_BNSH_BNSH;
+    kv_sequence_length = static_cast<int>(key_dims[2]);
+    v_hidden_size = static_cast<int>(value_dims[1]) * static_cast<int>(value_dims[3]);
+  }
+
+  return Status::OK();
+}
+
+template <typename T>
+Status CheckPast(const T* past_key, const T* past_value, const T* past_seq_len,
+                 int batch_size, int num_heads, int head_size, bool past_present_share_buffer,
+                 int& past_sequence_length, int& max_sequence_length) {
+  const auto& past_key_dims = past_key->Shape().GetDims();
+  const auto& past_value_dims = past_value->Shape().GetDims();
+
+  if (past_key_dims.size() != 4) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'past_key' is expected to have 4 dimensions, got ",
+                           past_key_dims.size());
+  }
+  if (past_value_dims.size() != 4) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'past_value' is expected to have 4 dimensions, got ",
+                           past_value_dims.size());
+  }
+
+  if (past_key_dims[0] != batch_size) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'past_key' dimension 0 should be batch_size, got ",
+                           past_key_dims[0]);
+  }
+  if (past_value_dims[0] != batch_size) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'past_value' dimension 0 should be batch_size, got ",
+                           past_value_dims[0]);
+  }
+
+  if (past_key_dims[1] != num_heads) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'past_key' dimension 1 should be same as number of heads, got ",
+                           past_key_dims[1]);
+  }
+  if (past_value_dims[1] != num_heads) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'past_value' dimension 1 should be same as number of heads, got ",
+                           past_value_dims[1]);
+  }
+  if (past_key_dims[2] != past_value_dims[2]) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'past_key' and 'past_value' shall have same dim 2 (past_sequence_length). ",
+                           past_key_dims[2], " vs ", past_value_dims[2]);
+  }
+  if (past_key_dims[3] != head_size) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'past_key' dimension 3 should be same as head_size, got ",
+                           past_key_dims[3]);
+  }
+  if (past_value_dims[3] != head_size) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'past_value' dimension 3 should be same as head_size, got ",
+                           past_value_dims[3]);
+  }
+  past_sequence_length = static_cast<int>(past_key_dims[2]);
+  if (past_present_share_buffer) {
+    max_sequence_length = static_cast<int>(past_key_dims[2]);
+    if (past_seq_len == nullptr || !onnxruntime::IsScalarOr1ElementVector(past_seq_len)) {
+      return ORT_MAKE_STATUS(
+          ONNXRUNTIME, INVALID_ARGUMENT,
+          "past_sequence_length tensor must be of one element when past_present_share_buffer is set");
+    }
+    past_sequence_length = *((*past_seq_len).template Data<int32_t>());
+  }
+  return Status::OK();
+}
+
+template <typename T>
+Status CheckRelativePositionBias(
+    const T* relative_position_bias, int batch_size, int num_heads, int sequence_length, int total_sequence_length,
+    bool& broadcast_res_pos_bias) {
+  const auto& relative_position_bias_dims = relative_position_bias->Shape().GetDims();
+
+  if (relative_position_bias_dims.size() != 4) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'relative_position_bias' is expected to have 4 dimensions, got ",
+                           relative_position_bias_dims.size());
+  }
+  if (relative_position_bias_dims[0] != batch_size && relative_position_bias_dims[0] != 1) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'relative_position_bias' dimension 0 should be batch_size or 1, got ",
+                           relative_position_bias_dims[0]);
+  }
+  if (relative_position_bias_dims[0] == 1) {
+    broadcast_res_pos_bias = true;
+  }
+  if (relative_position_bias_dims[1] != num_heads) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'relative_position_bias' dimension 1 should be same as number of heads, got ",
+                           relative_position_bias_dims[1]);
+  }
+  if (relative_position_bias_dims[2] != sequence_length) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'relative_position_bias' dimension 2 should be same as sequence_length, got ",
+                           relative_position_bias_dims[2]);
+  }
+  if (relative_position_bias_dims[3] != total_sequence_length) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'relative_position_bias' dimension 3 should be same as total_sequence_length, got ",
+                           relative_position_bias_dims[3]);
+  }
+  return Status::OK();
+}
+
+template <typename T>
+AttentionMaskType GetMaskType(const T* key_padding_mask, int batch_size, int sequence_length, int total_sequence_length) {
+  AttentionMaskType mask_type = AttentionMaskType::MASK_UNKNOWN;
+  const auto& mask_dims = key_padding_mask->Shape().GetDims();
+  if (mask_dims.size() == 1) {
+    if (mask_dims[0] == static_cast<int64_t>(batch_size)) {
+      mask_type = AttentionMaskType::MASK_1D_KEY_SEQ_LEN;
+    } else if (mask_dims[0] == static_cast<int64_t>(3) * static_cast<int64_t>(batch_size) + static_cast<int64_t>(2)) {
+      mask_type = AttentionMaskType::MASK_1D_KEY_SEQ_LEN_START;
+    }
+  } else if (mask_dims.size() == 2 && mask_dims[0] == static_cast<int64_t>(batch_size) &&
+             mask_dims[1] == static_cast<int64_t>(total_sequence_length)) {
+    mask_type = AttentionMaskType::MASK_2D_KEY_PADDING;
+  } else if (mask_dims.size() == 3 && mask_dims[0] == static_cast<int64_t>(batch_size) &&
+             mask_dims[1] == static_cast<int64_t>(sequence_length) &&
+             mask_dims[2] == static_cast<int64_t>(total_sequence_length)) {
+    mask_type = AttentionMaskType::MASK_3D_ATTENTION;
+  }
+  return mask_type;
+}
+
 template <typename T>
 Status CheckInputs(const T* query,
                    const T* key,
@@ -27,176 +253,128 @@ Status CheckInputs(const T* query,
                    float scale,
                    bool is_unidirectional,
                    bool past_present_share_buffer,
-                   bool dmmha_packing) {
-  //     key_padding_mask (K/V)     : (B) or (2*B + 1) or (B, L) or None
-  //     relative_position_bias     : (B, 1, S, L)
-  //     past_key                   : (B, N, S*, H)
-  //     past_value                 : (B, N, S*, H)
-  // When no packing for q/k/v:
+                   AttentionType operator_type) {
+  // ---------------------------------------------------------------
+  // Notations:
+  //    B: batch_size
+  //    N: num_heads
+  //    H: head_size (V might have different head size than Q and K)
+  //    D: hidden_size = N * H
+  //    S: q_sequence_length
+  //    P: past_sequence_length
+  //    L: kv_sequence_length
+  //    T: total_sequence_length = P + L
+  //    M: max_sequence_length
+  // ---------------------------------------------------------------
+  // MultiHeadAttention inputs:
+  // ---------------------------------------------------------------
+  //  Q_K_V_BSNH - no packing:
   //     query            (Q)       : (B, S, D)
-  //     key              (K)       : (B, L, D) or (B, N, S*, H)
-  //     value            (V)       : (B, L, D_v) or (B, N, S*, H)
-  //     bias             (Q/K/V)   : (D + D + D_v)
-  // When packed kv is used:
+  //     key              (K)       : (B, L, D)
+  //     value            (V)       : (B, L, D_v)
+  //  Q_K_V_BSNH_BNSH_BNSH - cross attention (kv cache is not used, L == T, D == D_v):
   //     query            (Q)       : (B, S, D)
-  //     key              (K)       : (B, L, N, 2, H)
-  //     value            (V)       : None
-  //     bias             (Q/K/V)   : None
-  // When packed qkv is used:
-  //     query            (Q)       : (B, L, N, 3, H) or (B, S, 3*D)
+  //     key              (K)       : (B, N, L, H)
+  //     value            (V)       : (B, N, L, H)
+  //  Q_KV_BSNH_BSN2H - packed kv (kv cache is not used, bias is not allowed for packed kv):
+  //     query            (Q)       : (B, S, D)
+  //     key              (K/V)     : (B, L, N, 2, H)
+  //     value                      : None
+  //  QKV_BSN3H - packed qkv (kv cache is not used, S == L, D == D_v):
+  //     query            (Q/K/V)   : (B, S, N, 3, H)
+  //     key                        : None
+  //     value                      : None
+  //
+  //  Other inputs:
+  //     bias             (Q/K/V)   : None or (D + D + D_v)
+  //     key_padding_mask (K/V)     : (B) or (3 * B + 2) or (B, T) or (B, S, T)
+  //     relative_position_bias     : (B, N, S, T) or (1, N, S, T)
+  //     past_key                   : (B, N, P, H) or None. Past state is only allowed for Q_K_V_BSNH.
+  //     past_value                 : (B, N, P, H) or None. Past state is only allowed for Q_K_V_BSNH.
+  // ---------------------------------------------------------------
+  // DecoderMaskedMultiHeadAttention inputs (S == 1, D == D_v):
+  // ---------------------------------------------------------------
+  //  Q_K_V_BSNH - no packing:
+  //     query            (Q)       : (B, S, D)
+  //     key              (K)       : (B, L, D)
+  //     value            (V)       : (B, L, D)
+  //  Q_K_V_BSNH_BNSH_BNSH - cross attention (kv cache and relative_position_bias are not used. L == T):
+  //     query            (Q)       : (B, S, D)
+  //     key              (K)       : (B, N, L, H)
+  //     value            (V)       : (B, N, L, H)
+  //  QKV_BS3NH - packed qkv (S == L):
+  //     query            (Q)       : (B, S, 3 * D)
   //     key              (K)       : None
   //     value            (V)       : None
-  //     bias             (Q/K/V)   : None or (D + D + D_v)
-
-  AttentionQkvFormat qkv_format;
+  //
+  //  Other inputs:
+  //     bias             (Q/K/V)   : None or (3 * D)
+  //     key_padding_mask (K/V)     : None or (B, T)
+  //     relative_position_bias     : (1, N, S, T), or (B, N, S, T) where only 1 x N x S x T data is used in CUDA.
+  //
+  //  The following inputs are not used in cross attention (so they are None for cross attention):
+  //     past_key                   : (B, N, P, H), or (B, N, M, H) when past_present_share_buffer is True.
+  //                                  For CUDA, past_present_share_buffer is always True. ROCm supports both.
+  //     past_value                 : (B, N, P, H), or (B, N, M, H) when past_present_share_buffer is True.
+  //                                  For CUDA, past_present_share_buffer is always True. ROCm supports both.
+  //     past_sequence_length       : scalar (1) when past_present_share_buffer is True.
+  //  CUDA version has extra inputs (beam_width, cache_indirection) that are not checked in the class.
+  //  For ROCm, see contrib_ops/rocm/bert/batched_gemm_softmax_gemm_permute_pipelines.cuh for more details.
+  // ---------------------------------------------------------------
+  AttentionQkvFormat qkv_format = UNKNOWN;
 
   const auto& query_dims = query->Shape().GetDims();
-  if (query_dims.size() != 3 && query_dims.size() != 5) {
+
+  int query_rank = static_cast<int>(query_dims.size());
+  if (query_rank != 3 && query_rank != 5) {
     return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "Input 'query' is expected to have 3 or 5 dimensions, got ",
-                           query_dims.size());
+                           query_rank);
   }
 
   int batch_size = static_cast<int>(query_dims[0]);
   int sequence_length = static_cast<int>(query_dims[1]);
-  int hidden_size = (query_dims.size() == 3)
+  bool dmmha_packing = operator_type == kDecoderMaskedMultiHeadAttention && key == nullptr && value == nullptr;
+  int hidden_size = (query_rank == 3)
                         ? (dmmha_packing ? (static_cast<int>(query_dims[2]) / 3) : static_cast<int>(query_dims[2]))
                         : (num_heads * static_cast<int>(query_dims[4]));
   int head_size = static_cast<int>(hidden_size) / num_heads;
   int kv_sequence_length = sequence_length;
 
+  int v_hidden_size = hidden_size;
+  if (key != nullptr) {
+    if (value == nullptr) {
+      ORT_RETURN_IF_ERROR(Check_Q_KV<T>(query, key, num_heads, head_size, qkv_format, kv_sequence_length));
+    } else {
+      ORT_RETURN_IF_ERROR(Check_Q_K_V<T>(query, key, value, num_heads, head_size,
+                                         qkv_format, kv_sequence_length, v_hidden_size));
+    }
+  } else if (value == nullptr) {  // no key and value
+    ORT_RETURN_IF_ERROR(Check_QKV<T>(query, qkv_format));
+  } else {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Input 'value' shall absent when 'key' is absent");
+  }
+
   int past_sequence_length = 0;
   int max_sequence_length = 0;
   if (past_key != nullptr && past_value != nullptr) {
-    const auto& past_key_dims = past_key->Shape().GetDims();
-    const auto& past_value_dims = past_value->Shape().GetDims();
-
-    if (past_key_dims.size() != 4) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'past_key' is expected to have 4 dimensions, got ",
-                             past_key_dims.size());
-    }
-    if (past_value_dims.size() != 4) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'past_value' is expected to have 4 dimensions, got ",
-                             past_value_dims.size());
-    }
-
-    if (past_key_dims[0] != batch_size) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'past_key' dimension 0 should be batch_size, got ",
-                             past_key_dims[0]);
-    }
-    if (past_value_dims[0] != batch_size) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'past_value' dimension 0 should be batch_size, got ",
-                             past_value_dims[0]);
-    }
-
-    if (past_key_dims[1] != num_heads) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'past_key' dimension 1 should be same as number of heads, got ",
-                             past_key_dims[1]);
-    }
-    if (past_value_dims[1] != num_heads) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'past_value' dimension 1 should be same as number of heads, got ",
-                             past_value_dims[1]);
-    }
-    if (past_key_dims[2] != past_value_dims[2]) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'past_key' and 'past_value' shall have same dim 2 (past_sequence_length). ",
-                             past_key_dims[2], " vs ", past_value_dims[2]);
-    }
-    if (past_key_dims[3] != head_size) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'past_key' dimension 3 should be same as head_size, got ",
-                             past_key_dims[3]);
-    }
-    if (past_value_dims[3] != head_size) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'past_value' dimension 3 should be same as head_size, got ",
-                             past_value_dims[3]);
-    }
-    past_sequence_length = static_cast<int>(past_key_dims[2]);
-    max_sequence_length = static_cast<int>(past_key_dims[2]);
-    if (past_present_share_buffer) {
-      if (past_seq_len == nullptr || !onnxruntime::IsScalarOr1ElementVector(past_seq_len)) {
-        return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                               "past_sequence_length tensor must be of one element when past_present_share_buffer is set");
-      }
-      past_sequence_length = *((*past_seq_len).template Data<int32_t>());
-    }
+    ORT_RETURN_IF_ERROR(CheckPast(past_key, past_value, past_seq_len,
+                                  batch_size, num_heads, head_size, past_present_share_buffer,
+                                  past_sequence_length, max_sequence_length));
   } else if (past_key != nullptr || past_value != nullptr) {
     return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
                            "Input 'past_key' and 'past_value' shall be both present or both absent");
   }
 
-  if (key != nullptr) {
-    if (query_dims.size() != 3) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "Input 'query' is expected to have 3 dimensions when key is given, got ",
-                             query_dims.size());
-    }
-
-    const auto& key_dims = key->Shape().GetDims();
-    if (key_dims.size() != 3 && key_dims.size() != 4 && key_dims.size() != 5) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "Input 'key' is expected to have 3, 4, or 5 dimensions, got ",
-                             key_dims.size());
-    }
-    if (query_dims[0] != key_dims[0]) {
+  if (operator_type == kMultiHeadAttention) {
+    if (qkv_format == AttentionQkvFormat::QKV_BS3NH) {
       return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'query' and 'key' shall have same dim 0 (batch size)");
+                             "Packed qkv of 3D BS3NH format is not support by MultiHeadAttention");
     }
 
-    if (key_dims.size() == 3) {
-      if (key_dims[2] != query_dims[2]) {
-        return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                               "Input 'query' and 'key' shall have same dim 2 (hidden_size)");
-      }
-
-      qkv_format = Q_K_V_BSNH;
-      kv_sequence_length = static_cast<int>(key_dims[1]);
-    } else if (key_dims.size() == 5) {
-      if (static_cast<int>(key_dims[2]) != num_heads || static_cast<int>(key_dims[3]) != 2 || static_cast<int>(key_dims[4]) != head_size) {
-        return ORT_MAKE_STATUS(
-            ONNXRUNTIME, INVALID_ARGUMENT,
-            "Expect 'key' shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv");
-      }
-      if (value != nullptr) {
-        return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "Expect 'value' be none when 'key' has packed kv format.");
-      }
-
-      qkv_format = Q_KV_BSNH_BSN2H;
-      kv_sequence_length = static_cast<int>(key_dims[1]);
-    } else {  // key_dims.size() == 4 (cross-attention with past_key)
-      if (static_cast<int>(key_dims[1]) != num_heads || static_cast<int>(key_dims[3]) != head_size) {
-        return ORT_MAKE_STATUS(
-            ONNXRUNTIME, INVALID_ARGUMENT,
-            "Expect 'key' shape (batch_size, num_heads, kv_sequence_length, head_size)");
-      }
-
-      if (value == nullptr || value->Shape().GetDims().size() != 4) {
-        return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "Input 'value' shall be 4D when 'key' is 4D");
-      }
-
-      if (bias != nullptr) {
-        return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "Input 'bias' shall be empty when 'key' is 4D");
-      }
-
-      qkv_format = UNKNOWN;
-      kv_sequence_length = static_cast<int>(key_dims[2]);
-    }
-  } else {  // packed QKV
-    if (query_dims.size() != 3 && query_dims.size() != 5) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "Input 'query' is expected to have 3 or 5 dimensions when key is empty, got ",
-                             query_dims.size());
-    }
-    if (query_dims.size() == 5 && (static_cast<int>(query_dims[2]) != num_heads || static_cast<int>(query_dims[3]) != 3)) {
-      return ORT_MAKE_STATUS(
-          ONNXRUNTIME, INVALID_ARGUMENT,
-          "Expect 'query' shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv");
+    if (qkv_format == AttentionQkvFormat::Q_KV_BSNH_BSN2H && bias != nullptr) {
+      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "Input 'bias' shall be empty when packed kv is used");
     }
-
-    qkv_format = QKV_BSN3H;
   }
 
   if (bias != nullptr) {
@@ -206,116 +384,31 @@ Status CheckInputs(const T* query,
                              bias_dims.size());
     }
 
-    if (value == nullptr) {
-      // Currently, bias is not allowed for packed KV. This constraint can be removed later.
-      // Here we assume that fusion tool will not include bias for packed KV.
-      if (query_dims.size() == 5 && query_dims[3] == 2) {
-        return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "'bias' is not allowed for packed kv. ");
-      }
+    int expected_bias_length = 2 * hidden_size + v_hidden_size;
+    if (bias_dims[0] != static_cast<int64_t>(expected_bias_length)) {
+      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "Input 'bias' length is expected to be 2 * hidden_size + hidden_size_v, got ",
+                             bias_dims.size());
     }
   }
 
   int total_sequence_length = past_sequence_length + kv_sequence_length;
   AttentionMaskType mask_type = AttentionMaskType::MASK_NONE;
   if (key_padding_mask != nullptr) {
-    mask_type = AttentionMaskType::MASK_UNKNOWN;
-    const auto& mask_dims = key_padding_mask->Shape().GetDims();
-    if (mask_dims.size() == 1) {
-      if (mask_dims[0] == static_cast<int64_t>(batch_size)) {
-        mask_type = AttentionMaskType::MASK_1D_KEY_SEQ_LEN;
-      } else if (mask_dims[0] == static_cast<int64_t>(3) * static_cast<int64_t>(batch_size) + static_cast<int64_t>(2)) {
-        mask_type = AttentionMaskType::MASK_1D_KEY_SEQ_LEN_START;
-      }
-    } else if (mask_dims.size() == 2 && mask_dims[0] == static_cast<int64_t>(batch_size) &&
-               mask_dims[1] == static_cast<int64_t>(kv_sequence_length)) {
-      mask_type = AttentionMaskType::MASK_2D_KEY_PADDING;
-    } else if (mask_dims.size() == 2 && mask_dims[0] == static_cast<int64_t>(batch_size) &&
-               mask_dims[1] == static_cast<int64_t>(total_sequence_length)) {
-      mask_type = AttentionMaskType::MASK_2D_KEY_PADDING;
-    } else if (mask_dims.size() == 3 && mask_dims[0] == static_cast<int64_t>(batch_size) &&
-               mask_dims[1] == static_cast<int64_t>(sequence_length) &&
-               mask_dims[2] == static_cast<int64_t>(total_sequence_length)) {
-      mask_type = AttentionMaskType::MASK_3D_ATTENTION;
-    }
-
+    mask_type = GetMaskType(key_padding_mask, batch_size, sequence_length, total_sequence_length);
     if (mask_type == AttentionMaskType::MASK_UNKNOWN) {
       return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'key_padding_mask' shape shall be 1D, 2D, or 3D");
-    }
-  }
-
-  // NOTE: In Cross-Attention, we pass the past key and value to 'key' and 'value' instead of 'past_key' and 'past_value'.
-  bool pass_past_in_kv = false;
-  int v_hidden_size = hidden_size;
-  if (value != nullptr) {
-    const auto& value_dims = value->Shape().GetDims();
-    if (value_dims.size() != 3 && value_dims.size() != 4) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "Input 'value' is expected to have 3 or 4 dimensions, got ",
-                             value_dims.size());
-    }
-
-    if (query_dims[0] != value_dims[0]) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'query' and 'value' shall have same dim 0 (batch_size)");
-    }
-
-    if (value_dims.size() == 3) {
-      if (static_cast<int64_t>(kv_sequence_length) != value_dims[1]) {
-        return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                               "Input 'key' and 'value' shall have the same dim 1 (kv_sequence_length)");
-      }
-      v_hidden_size = static_cast<int>(value_dims[2]);
-    } else {  // value_dims.size() == 4
-      if (static_cast<int64_t>(kv_sequence_length) != value_dims[2]) {
-        return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                               "Input 'key' and 'value' shall have the same dim 2 (kv_sequence_length)");
-      }
-
-      if (past_key != nullptr || past_value != nullptr) {
-        return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                               "Input 'past_key' and 'past_value' shall be empty when 'value' is 4D");
-      }
-
-      v_hidden_size = static_cast<int>(value_dims[1]) * static_cast<int>(value_dims[3]);
-      pass_past_in_kv = true;
+                             "Input 'key_padding_mask' shape is not expected.");
     }
   }
 
   bool broadcast_res_pos_bias = false;
   if (relative_position_bias != nullptr) {
-    const auto& relative_position_bias_dims = relative_position_bias->Shape().GetDims();
-
-    if (relative_position_bias_dims.size() != 4) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'relative_position_bias' is expected to have 4 dimensions, got ",
-                             relative_position_bias_dims.size());
-    }
-    if (relative_position_bias_dims[0] != batch_size && relative_position_bias_dims[0] != 1) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'relative_position_bias' dimension 0 should be batch_size or 1, got ",
-                             relative_position_bias_dims[0]);
-    }
-    if (relative_position_bias_dims[0] == 1) {
-      broadcast_res_pos_bias = true;
-    }
-    if (relative_position_bias_dims[1] != num_heads) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'relative_position_bias' dimension 1 should be same as number of heads, got ",
-                             relative_position_bias_dims[1]);
-    }
-    if (relative_position_bias_dims[2] != sequence_length) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'relative_position_bias' dimension 2 should be same as sequence_length, got ",
-                             relative_position_bias_dims[2]);
-    }
-    if (relative_position_bias_dims[3] != total_sequence_length) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                             "Input 'relative_position_bias' dimension 3 should be same as total_sequence_length, got ",
-                             relative_position_bias_dims[3]);
-    }
+    ORT_RETURN_IF_ERROR(CheckRelativePositionBias(
+        relative_position_bias, batch_size, num_heads, sequence_length, total_sequence_length, broadcast_res_pos_bias));
   }
 
-  // TODO: ORT_RETURN_IF(qkv_format == UNKNOWN, "Unrecognized QKV format");
+  assert(qkv_format != UNKNOWN);
+
   if (parameters != nullptr) {
     AttentionParameters* output_parameters = reinterpret_cast<AttentionParameters*>(parameters);
     output_parameters->batch_size = batch_size;
@@ -323,7 +416,7 @@ Status CheckInputs(const T* query,
     output_parameters->past_sequence_length = past_sequence_length;
     output_parameters->kv_sequence_length = kv_sequence_length;
     output_parameters->total_sequence_length = total_sequence_length;
-    output_parameters->max_sequence_length = max_sequence_length;
+    output_parameters->max_sequence_length = past_present_share_buffer ? max_sequence_length : total_sequence_length;
     output_parameters->input_hidden_size = 0;
     output_parameters->hidden_size = hidden_size;
     output_parameters->v_hidden_size = v_hidden_size;
@@ -336,7 +429,6 @@ Status CheckInputs(const T* query,
     output_parameters->mask_type = mask_type;
     output_parameters->scale = scale;
     output_parameters->broadcast_res_pos_bias = broadcast_res_pos_bias;
-    output_parameters->pass_past_in_kv = pass_past_in_kv;
     output_parameters->qkv_format = qkv_format;
   }
 
@@ -359,7 +451,7 @@ Status CheckInputs(const T* query,
                    float scale,
                    bool is_unidirectional,
                    bool past_present_share_buffer,
-                   bool dmmha_packing,
+                   AttentionType operator_type,
                    int max_threads_per_block) {
   if (max_threads_per_block > 0 && num_heads > max_threads_per_block) {
     return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "num_heads should be no larger than ", max_threads_per_block);
@@ -367,7 +459,7 @@ Status CheckInputs(const T* query,
 
   return CheckInputs(query, key, value, bias, key_padding_mask, relative_position_bias, past_key, past_value,
                      past_seq_len, parameters, num_heads, mask_filter_value, scale, is_unidirectional,
-                     past_present_share_buffer, dmmha_packing);
+                     past_present_share_buffer, operator_type);
 }
 
 }  // namespace multihead_attention_helper
diff --git a/onnxruntime/contrib_ops/cuda/bert/add_bias_transpose.cu b/onnxruntime/contrib_ops/cuda/bert/add_bias_transpose.cu
index 9e6752b451868..62d6a723bf32c 100644
--- a/onnxruntime/contrib_ops/cuda/bert/add_bias_transpose.cu
+++ b/onnxruntime/contrib_ops/cuda/bert/add_bias_transpose.cu
@@ -520,6 +520,39 @@ __global__ void AddBiasUnpack(int M, const T* input, const T* biases, T* output)
   }
 }
 
+template <typename T>
+__global__ void AddBiasTransposeUnpack(int M, const T* input, const T* biases, T* output) {
+  // Format 5 to unpack TRT packed input format to BNSH for unfused attention.
+  //     Input:  BxSxNxMxH
+  //     Output: MxBxNxSxH
+  // B is batch_size, S is sequence_length, M is number of matrices, N is num_heads, H is head_size
+  int n = threadIdx.y;
+  int s = blockIdx.x;
+  int b = blockIdx.y;
+  int m = blockIdx.z;  // matrix id
+
+  const int head_size = blockDim.x;
+  const int num_heads = blockDim.y;
+
+  const int sequence_length = gridDim.x;
+  const int batch_size = gridDim.y;
+  const int H = head_size;
+  const int NH = num_heads * head_size;
+  const int NHS = NH * sequence_length;
+
+  int in_offset = m * head_size + n * M * H + (s * NH + b * NHS) * M;
+  const int out_offset = (s + n * sequence_length) * head_size + (b + m * batch_size) * NHS;
+
+  const int h = threadIdx.x;
+  if (h < head_size) {
+    if (biases != nullptr) {
+      output[out_offset + h] = input[in_offset + h] + biases[m * NH + n * H + h];
+    } else {
+      output[out_offset + h] = input[in_offset + h];
+    }
+  }
+}
+
 template <typename T>
 __global__ void AddBiasTransposeCutlass(int M, const T* input, const T* biases, T* output) {
   // Format 3 for cutlass memory efficient attention
@@ -692,6 +725,8 @@ void InvokeAddBiasTranspose(
       }
     } else if (format == 4) {  // format == 4
       AddBiasUnpack<T><<<grid, block, 0, stream>>>(total_matrix_count, input, biases, output);
+    } else if (format == 5) {  // format == 5
+      AddBiasTransposeUnpack<T><<<grid, block, 0, stream>>>(total_matrix_count, input, biases, output);
     } else {  // format == 0
       AddBiasTranspose<T><<<grid, block, 0, stream>>>(input, biases, output);
     }
@@ -716,6 +751,8 @@ void InvokeAddBiasTranspose(
       }
     } else if (format == 4) {  // format == 4
       ORT_THROW("AddBiasTranspose (format 4) not implemented for hidden_size > max_threads_per_block");
+    } else if (format == 5) {  // format == 5
+      ORT_THROW("AddBiasTranspose (format 5) not implemented for hidden_size > max_threads_per_block");
     } else {  // format 0
       AddBiasTransposeLarge<T><<<grid, block, 0, stream>>>(qk_head_size, input, biases, output);
     }
@@ -904,6 +941,7 @@ void InvokeAddBias(
       AddBiasTransposeTrtLarge<T><<<grid, block, 0, stream>>>(head_size, query, biases, q);
     }
   }
+
   // K
   {
     const dim3 grid(kv_sequence_length, batch_size, num_matrices);
@@ -1011,6 +1049,82 @@ void LaunchAddBias(
   }
 }
 
+template <typename T>
+void InvokeAddBias(
+    cudaStream_t stream, const int max_threads_per_block,
+    const int batch_size, const int sequence_length,
+    const int num_heads, const int head_size,
+    const T* biases, const T* query, T* q) {
+  assert(num_heads <= max_threads_per_block);
+  constexpr int num_matrices = 1;
+  const dim3 grid(sequence_length, batch_size, num_matrices);
+  if (head_size * num_heads <= max_threads_per_block) {
+    const dim3 block(head_size, num_heads, 1);
+    AddBiasTransposeTrt<T><<<grid, block, 0, stream>>>(query, biases, q);
+  } else {
+    const dim3 block(max_threads_per_block / num_heads, num_heads, 1);
+    AddBiasTransposeTrtLarge<T><<<grid, block, 0, stream>>>(head_size, query, biases, q);
+  }
+}
+
+template <>
+void LaunchAddBias(
+    cudaStream_t stream, const int max_threads_per_block,
+    const int batch_size, const int sequence_length,
+    const int num_heads, const int head_size,
+    const float* biases, const float* query, float* q) {
+  if (0 == (head_size % 4)) {
+    const int H = head_size / 4;
+    const float4* query2 = reinterpret_cast<const float4*>(query);
+    const float4* biases2 = reinterpret_cast<const float4*>(biases);
+    float4* q2 = reinterpret_cast<float4*>(q);
+    InvokeAddBias<float4>(stream, max_threads_per_block,
+                          batch_size, sequence_length, num_heads, H,
+                          biases2, query2, q2);
+  } else if (0 == (head_size & 1)) {
+    const int H = head_size / 2;
+    const float2* query2 = reinterpret_cast<const float2*>(query);
+    const float2* biases2 = reinterpret_cast<const float2*>(biases);
+    float2* q2 = reinterpret_cast<float2*>(q);
+    InvokeAddBias<float2>(stream, max_threads_per_block,
+                          batch_size, sequence_length, num_heads, H,
+                          biases2, query2, q2);
+  } else {
+    InvokeAddBias<float>(stream, max_threads_per_block,
+                         batch_size, sequence_length, num_heads, head_size,
+                         biases, query, q);
+  }
+}
+
+template <>
+void LaunchAddBias(
+    cudaStream_t stream, const int max_threads_per_block,
+    const int batch_size, const int sequence_length,
+    const int num_heads, const int head_size,
+    const half* biases, const half* query, half* q) {
+  if (0 == (head_size % 4)) {
+    const int H = head_size / 4;
+    const Half4* query2 = reinterpret_cast<const Half4*>(query);
+    const Half4* biases2 = reinterpret_cast<const Half4*>(biases);
+    Half4* q2 = reinterpret_cast<Half4*>(q);
+    InvokeAddBias<Half4>(stream, max_threads_per_block,
+                         batch_size, sequence_length, num_heads, H,
+                         biases2, query2, q2);
+  } else if (0 == (head_size & 1)) {
+    const int H = head_size / 2;
+    const half2* query2 = reinterpret_cast<const half2*>(query);
+    const half2* biases2 = reinterpret_cast<const half2*>(biases);
+    half2* q2 = reinterpret_cast<half2*>(q);
+    InvokeAddBias<half2>(stream, max_threads_per_block,
+                         batch_size, sequence_length, num_heads, H,
+                         biases2, query2, q2);
+  } else {
+    InvokeAddBias<half>(stream, max_threads_per_block,
+                        batch_size, sequence_length, num_heads, head_size,
+                        biases, query, q);
+  }
+}
+
 }  // namespace cuda
 }  // namespace contrib
 }  // namespace onnxruntime
diff --git a/onnxruntime/contrib_ops/cuda/bert/add_bias_transpose.h b/onnxruntime/contrib_ops/cuda/bert/add_bias_transpose.h
index efc31db43bcdb..bd4e123a272bc 100644
--- a/onnxruntime/contrib_ops/cuda/bert/add_bias_transpose.h
+++ b/onnxruntime/contrib_ops/cuda/bert/add_bias_transpose.h
@@ -3,14 +3,15 @@
 
 #pragma once
 #include "core/providers/cuda/shared_inc/cuda_utils.h"
+#include "contrib_ops/cpu/bert/attention_common.h"
 
 namespace onnxruntime {
 namespace contrib {
 namespace cuda {
 
-// Fused kernel of Add (bias) and Transpose.
+// Fused kernel of Add bias (optional, can be None) and Transpose.
 // Shape of inputs and outputs:
-//     biases:  (num_matrices, num_heads * head_size)
+//     biases:  (num_matrices, num_heads * head_size) or None
 // format 0: (requires sequence_length = kv_sequence_length and qk_head_size = v_head_size when num_matrices == 3)
 //     input:   (num_matrices, batch_size, sequence_length, num_heads, head_size)
 //     output:  (num_matrices, batch_size, num_heads, sequence_length, head_size)
@@ -24,9 +25,12 @@ namespace cuda {
 // format 3: (requires sequence_length = kv_sequence_length and qk_head_size = v_head_size when num_matrices == 3)
 //     input:   (batch_size, sequence_length, num_matrices, num_heads, head_size)
 //     output:  (num_matrices, batch_size, sequence_length, num_heads, head_size)
-// format 4: (requires qk_head_size = v_head_size)
+// format 4: (requires qk_head_size == v_head_size)
 //     input:   (batch_size, sequence_length, num_heads, num_matrices, head_size)
 //     output:  (num_matrices, batch_size, sequence_length, num_heads, head_size)
+// format 5: (requires qk_head_size == v_head_size)
+//     input:   (batch_size, sequence_length, num_heads, num_matrices, head_size)
+//     output:  (num_matrices, batch_size, num_heads, sequence_length, head_size)
 
 template <typename T>
 void LaunchAddBiasTranspose(
@@ -35,7 +39,7 @@ void LaunchAddBiasTranspose(
     const T* input, const T* biases, T* output, bool enable_half4, const int v_head_size, T* qkv_add_bias = nullptr,
     int total_matrix_count = -1, bool do_rotary = false, int rotary_embedding = 0, int past_sequence_length = 0);
 
-// Add (bias) and Transpose for separated inputs of Q, K and V, and output Trt format.
+// Add bias (optional, can be None) and Transpose for separated inputs of Q, K and V, and output Trt format.
 // For self attention:
 //   output:  (batch_size, sequence_length, num_heads, 3, head_size)
 //   It assumes sequence_length == kv_sequence_length and head_size == v_head_size.
@@ -50,7 +54,7 @@ void LaunchAddBiasTransposeTrt(
     const T* biases, const T* query, const T* key, const T* value, T* output,
     bool is_cross_attention, int kv_sequence_length = -1);
 
-// Add (bias) for separated inputs of Q, K and V.
+// Add bias (required) for separated inputs of Q, K and V.
 //    Q:  (batch_size, sequence_length, num_heads, head_size)
 //    K:  (batch_size, kv_sequence_length, num_heads, head_size)
 //    V:  (batch_size, kv_sequence_length, num_heads, v_head_size)
@@ -61,6 +65,46 @@ void LaunchAddBias(
     const int num_heads, const int head_size, const int v_head_size,
     const T* biases, const T* query, const T* key, const T* value, T* q, T* k, T* v);
 
+// Add bias (required) for Q:  (batch_size, sequence_length, num_heads, head_size)
+template <typename T>
+void LaunchAddBias(
+    cudaStream_t stream, const int max_threads_per_block,
+    const int batch_size, const int sequence_length,
+    const int num_heads, const int head_size,
+    const T* biases, const T* query, T* q);
+
+// Add bias (optional, can be None) transpose kernel defined in packed_multihead_attention_impl.cu.
+// Support the following format transforms (for float and half only).
+// source_format  => target_format:
+//   Q_K_V_TNH    => Q_K_V_BNSH (requires token_offset)
+//   Q_K_V_TNH    => Q_K_V_TNH
+//   Q_K_V_TNH    => QKV_TN3H
+//   QKV_TN3H     => Q_K_V_BNSH (requires token_offset)
+//   QKV_TN3H     => Q_K_V_TNH
+//   QKV_TN3H     => QKV_TN3H
+template <typename T>
+void AddBiasTransposePacked(
+    const T* query, const T* key, const T* value, const T* bias, T* output,
+    const int batch_size, const int sequence_length,
+    const int num_heads, const int qk_head_size, const int v_head_size,
+    AttentionQkvFormat source_format, AttentionQkvFormat target_format,
+    const int32_t* token_offset, int32_t token_count,
+    cudaStream_t stream);
+
+// Add bias (required) transpose kernel defined in packed_attention_impl.cu.
+// Support the following format transforms (for float and half only):
+//     format          transform
+//     Q_K_V_BNSH:     Tx3xNxH => 3xBxNxSxH (requires token_offset)
+//     Q_K_V_BSNH:     Tx3xNxH => 3xTxNxH
+//     QKV_BSN3H:      Tx3xNxH => TxNx3xH
+template <typename T>
+void AddBiasTransposePacked(
+    const T* input, const T* biases, T* output,
+    const int batch_size, const int sequence_length,
+    const int num_heads, const int qk_head_size, const int v_head_size,
+    AttentionQkvFormat format, const int32_t* token_offset, int32_t token_count,
+    cudaStream_t stream);
+
 }  // namespace cuda
 }  // namespace contrib
 }  // namespace onnxruntime
diff --git a/onnxruntime/contrib_ops/cuda/bert/attention.cc b/onnxruntime/contrib_ops/cuda/bert/attention.cc
index 3b7f980ba1881..5c0989bced70c 100644
--- a/onnxruntime/contrib_ops/cuda/bert/attention.cc
+++ b/onnxruntime/contrib_ops/cuda/bert/attention.cc
@@ -260,7 +260,8 @@ Status Attention<T>::ComputeInternal(OpKernelContext* context) const {
                                                    fused_runner,
                                                    use_flash_attention,
                                                    use_fused_cross_attention,
-                                                   use_memory_efficient_attention);
+                                                   use_memory_efficient_attention,
+                                                   false);
   IAllocatorUniquePtr<void> work_space = IAllocator::MakeUniquePtr<void>(allocator, workSpaceSize, false, context->GetComputeStream());
 
   typedef typename ToCudaType<T>::MappedType CudaT;
@@ -281,6 +282,7 @@ Status Attention<T>::ComputeInternal(OpKernelContext* context) const {
   }
   data.has_qkv_workspace = true;
   data.workspace = reinterpret_cast<CudaT*>(work_space.get());
+  data.workspace_bytes = workSpaceSize;
   data.output = reinterpret_cast<CudaT*>(output->MutableData<T>());
   if (nullptr != present) {
     data.present = reinterpret_cast<CudaT*>(present->MutableData<T>());
diff --git a/onnxruntime/contrib_ops/cuda/bert/attention_impl.cu b/onnxruntime/contrib_ops/cuda/bert/attention_impl.cu
index 997493acd9cb7..f9eabe27d97e4 100644
--- a/onnxruntime/contrib_ops/cuda/bert/attention_impl.cu
+++ b/onnxruntime/contrib_ops/cuda/bert/attention_impl.cu
@@ -58,31 +58,25 @@ size_t AlignSize(size_t bytes) {
   return bytesAligned;
 }
 
-void CumulatedSequenceLengthCache::Initialize(int32_t seq_length, cudaStream_t stream) {
-  if (this->sequence_length != seq_length) {
-    ORT_ENFORCE(buffer.get() != nullptr && this->max_batch_size > 0);
-    LaunchTrtSequenceOffset(reinterpret_cast<int32_t*>(buffer.get()), nullptr,
-                            this->max_batch_size, seq_length, stream);
-    this->sequence_length = seq_length;
+const int32_t* CumulatedSequenceLengthCache::TryGet(int batch_size, int32_t seq_len, cudaStream_t stream) {
+  if (this->sequence_length == 0 && seq_len > 0) {
+    // Initialize only once with sequence length in the first request.
+    std::call_once(init_once_flag_, [&]() {
+      ORT_ENFORCE(buffer.get() != nullptr && this->max_batch_size > 0);
+      LaunchTrtSequenceOffset(reinterpret_cast<int32_t*>(buffer.get()), nullptr,
+                              this->max_batch_size, seq_len, stream);
+      // Syncronize to ensure thread-safe since other thread will not wait for the above kernel finish.
+      // Otherwise, the data might be consumed by other threads before it is ready and causes data race issue.
+      cudaStreamSynchronize(stream);
+      this->sequence_length = seq_len;
+    });
   }
-}
 
-int* GetCumulatedSequenceLength(CumulatedSequenceLengthCache* cache,
-                                const int* mask_index,
-                                int batch_size,
-                                int sequence_length,
-                                cudaStream_t stream,
-                                void* scratch_buffer) {
-  if (mask_index == nullptr && cache != nullptr) {
-    if (batch_size <= cache->max_batch_size) {
-      cache->Initialize(sequence_length, stream);
-      return reinterpret_cast<int*>(cache->buffer.get());
-    }
+  if (this->sequence_length == seq_len && batch_size <= this->max_batch_size) {
+    return reinterpret_cast<const int32_t*>(buffer.get());
   }
 
-  int* sequence_offset = reinterpret_cast<int*>(scratch_buffer);
-  LaunchTrtSequenceOffset(sequence_offset, mask_index, batch_size, sequence_length, stream);
-  return sequence_offset;
+  return nullptr;
 }
 
 size_t GetAttentionScratchSize(
@@ -114,10 +108,12 @@ size_t GetAttentionWorkspaceSize(
     void* fused_runner,
     bool use_flash_attention,
     bool use_fused_cross_attention,
-    bool use_memory_efficient_attention) {
+    bool use_memory_efficient_attention,
+    bool no_qkv_workspace) {
   // Note that q, k and v might need alignment for fused attention kernels.
-  const size_t qkv_bytes = element_size * batch_size * num_heads *
-                           ((sequence_length + kv_sequence_length) * qk_head_size + kv_sequence_length * v_head_size);
+  const size_t qkv_size = element_size * batch_size * num_heads *
+                          ((sequence_length + kv_sequence_length) * qk_head_size + kv_sequence_length * v_head_size);
+  const size_t qkv_bytes = no_qkv_workspace ? 0 : qkv_size;
 
 #if USE_FLASH_ATTENTION
   if (use_flash_attention) {
@@ -162,39 +158,44 @@ Status FusedTrtCrossAttention(
   // We only enable fused cross attention when there is no key padding mask.
   // Otherwise, key have effective batch size 2 * batch_size, which is different from batch_size of query.
   assert(data.mask_index == nullptr);
-
+  assert(data.scratch != nullptr);
+  assert(data.q != nullptr);
+  assert(data.k != nullptr);
+
+#ifndef NDEBUG
+  char* scratch_end = reinterpret_cast<char*>(data.scratch) + 2 * GetSequenceOffsetSize(parameters.batch_size, false);
+  char* buffer_end = reinterpret_cast<char*>(data.workspace) + data.workspace_bytes;
+  assert(scratch_end <= buffer_end);
+#endif
   const int batch_size = parameters.batch_size;
   const int sequence_length = parameters.sequence_length;
-  int* q_sequence_offset = GetCumulatedSequenceLength(data.cumulated_sequence_length_q_cache,
-                                                      data.mask_index, batch_size,
-                                                      sequence_length, stream,
-                                                      data.scratch);
 
+  int32_t* q_sequence_offset = const_cast<int32_t*>(data.cumulated_sequence_length_q_cache);
+  if (q_sequence_offset == nullptr) {
+    q_sequence_offset = reinterpret_cast<int*>(data.scratch);
+    LaunchTrtSequenceOffset(q_sequence_offset, data.mask_index, batch_size, sequence_length, stream);
+  }
+
+  CUDA_RETURN_IF_ERROR(cudaGetLastError());
   DUMP_TENSOR_INIT();
   DUMP_TENSOR_D("q_sequence_offset", q_sequence_offset, 1, batch_size + 1);
 
-  int* kv_sequence_offset = q_sequence_offset + (GetSequenceOffsetSize(batch_size, false) / sizeof(int));
-  kv_sequence_offset = GetCumulatedSequenceLength(data.cumulated_sequence_length_kv_cache,
-                                                  data.mask_index, batch_size, parameters.kv_sequence_length, stream,
-                                                  kv_sequence_offset);
-  CUDA_RETURN_IF_ERROR(cudaGetLastError());
+  int32_t* kv_sequence_offset = const_cast<int32_t*>(data.cumulated_sequence_length_kv_cache);
+  if (kv_sequence_offset == nullptr) {
+    int* scratch = reinterpret_cast<int*>(data.scratch) + (GetSequenceOffsetSize(batch_size, false) / sizeof(int));
+    kv_sequence_offset = reinterpret_cast<int*>(scratch);
+    LaunchTrtSequenceOffset(kv_sequence_offset, data.mask_index, batch_size, parameters.kv_sequence_length, stream);
+  }
 
+  CUDA_RETURN_IF_ERROR(cudaGetLastError());
   DUMP_TENSOR_D("kv_sequence_offset", kv_sequence_offset, 1, batch_size + 1);
 
   FusedMultiHeadCrossAttentionKernel const* cross_attention_kernel =
       reinterpret_cast<FusedMultiHeadCrossAttentionKernel const*>(data.fused_cross_attention_kernel);
 
-  // When there is no bias, we can directly use q and packed kv from inputs.
-  void const* query = data.q;
-  void const* packed_kv = data.k;
-  if (data.value == nullptr && data.bias == nullptr) {
-    query = data.query;
-    packed_kv = data.key;
-  }
-
   run_fused_cross_attention(
-      query,                          // Q
-      packed_kv,                      // packed KV
+      data.q,                         // Q
+      data.k,                         // packed KV
       q_sequence_offset,              // cumulated sequence length of Q
       kv_sequence_offset,             // cumulated sequence length of KV
       data.output,                    // output
@@ -206,8 +207,6 @@ Status FusedTrtCrossAttention(
       parameters.kv_sequence_length,  // sequence length of KV
       stream);
 
-  DUMP_TENSOR("trt cross output", data.output,
-              batch_size, sequence_length, parameters.num_heads, parameters.v_head_size);
   return Status::OK();
 }
 
@@ -225,24 +224,33 @@ Status FusedTrtSelfAttention(
     cudaStream_t stream,
     contrib::AttentionParameters& parameters,
     AttentionData<T>& data) {
+  assert(data.scratch != nullptr);
+#ifndef NDEBUG
+  char* scratch_end = reinterpret_cast<char*>(data.scratch) + GetSequenceOffsetSize(parameters.batch_size, false);
+  char* buffer_end = reinterpret_cast<char*>(data.workspace) + data.workspace_bytes;
+  assert(scratch_end <= buffer_end);
+#endif
+
   const int batch_size = parameters.batch_size;
   const int sequence_length = parameters.sequence_length;
   const bool causal = parameters.is_unidirectional;
 
-  int* sequence_offset = reinterpret_cast<int*>(data.scratch);
-
-  DUMP_TENSOR_INIT();
+  const int32_t* sequence_offset = data.cumulated_sequence_length_q_cache;
   if (parameters.mask_type == AttentionMaskType::MASK_2D_KEY_PADDING) {
-    DUMP_TENSOR_D("mask", reinterpret_cast<const int*>(data.mask_index), batch_size, sequence_length);
-    LaunchTrtSequenceOffset2d(sequence_offset, data.mask_index, batch_size, sequence_length, stream);
+    LaunchTrtSequenceOffset2d(reinterpret_cast<int*>(data.scratch), data.mask_index, batch_size, sequence_length, stream);
+    sequence_offset = reinterpret_cast<const int*>(data.scratch);
   } else {
-    sequence_offset = GetCumulatedSequenceLength(data.cumulated_sequence_length_q_cache,
-                                                 data.mask_index, batch_size, sequence_length, stream,
-                                                 sequence_offset);
+    if (sequence_offset == nullptr) {
+      LaunchTrtSequenceOffset(reinterpret_cast<int*>(data.scratch), data.mask_index, batch_size, sequence_length, stream);
+      sequence_offset = reinterpret_cast<const int*>(data.scratch);
+    }
   }
-  DUMP_TENSOR_D("sequence_offset", sequence_offset, 1, (data.mask_index != nullptr ? 2 : 1) * batch_size + 1);
+
   CUDA_RETURN_IF_ERROR(cudaGetLastError());
 
+  DUMP_TENSOR_INIT();
+  DUMP_TENSOR_D("sequence_offset", sequence_offset, 1, (data.mask_index != nullptr ? 2 : 1) * batch_size + 1);
+
   FusedMHARunnerFP16v2* fused_fp16_runner = reinterpret_cast<FusedMHARunnerFP16v2*>(data.fused_runner);
 
   const int s = causal ? sequence_length : fused_fp16_runner->NormalizeSequenceLength(sequence_length);
@@ -252,22 +260,12 @@ Status FusedTrtSelfAttention(
 
   if (!causal) {
     assert(data.qkv_format == AttentionQkvFormat::QKV_BSN3H);
-
-    // When there is no bias, we can directly use packed qkv from inputs.
-    void const* packed_qkv = data.q;
-    if (data.query != nullptr && data.key == nullptr && data.bias == nullptr) {
-      packed_qkv = data.query;
-    }
-
-    fused_fp16_runner->Run(b, s, packed_qkv, sequence_offset, data.output, stream);
-    DUMP_TENSOR("fused output", data.output,
-                batch_size, sequence_length, parameters.num_heads, parameters.v_head_size);
+    fused_fp16_runner->Run(b, s, data.q, sequence_offset, data.output, stream);
   } else {
     assert(data.qkv_format == AttentionQkvFormat::Q_K_V_BNSH_QKV_BS3NH);
     fused_fp16_runner->Run(b, s, data.gemm_buffer, sequence_offset, data.output, stream);
-    DUMP_TENSOR("fused causal output", data.output,
-                batch_size, sequence_length, parameters.num_heads, parameters.v_head_size);
   }
+
   return Status::OK();
 }
 
@@ -289,38 +287,19 @@ Status FlashAttention(
     contrib::AttentionParameters& parameters,
     AttentionData<T>& data,
     float scale) {
-  assert(data.qkv_format == AttentionQkvFormat::Q_K_V_BSNH);
+  assert(data.qkv_format == AttentionQkvFormat::Q_K_V_BSNH ||
+         data.qkv_format == AttentionQkvFormat::Q_K_V_BSNH_BNSH_BNSH);
   assert(nullptr == data.mask_index);
   assert(nullptr == data.relative_position_bias);
   assert(parameters.head_size == parameters.v_head_size);
 
-  void* query = reinterpret_cast<void*>(data.q);
-  void* key = reinterpret_cast<void*>(data.k);
-  void* value = reinterpret_cast<void*>(data.v);
-  // For packed KV, we can use query input directly.
-  if (data.gemm_buffer == nullptr && data.key != nullptr && data.value == nullptr && data.bias == nullptr) {
-    query = reinterpret_cast<void*>(const_cast<T*>(data.query));
-  }
-
-  DUMP_TENSOR_INIT();
-  DUMP_TENSOR_D("q(BSNH)", reinterpret_cast<const T*>(query),
-                parameters.batch_size, parameters.sequence_length, parameters.num_heads, parameters.head_size);
-  DUMP_TENSOR_D("k(BSNH)", data.k,
-                parameters.batch_size, parameters.total_sequence_length, parameters.num_heads, parameters.head_size);
-  DUMP_TENSOR_D("v(BSNH)", data.v,
-                parameters.batch_size, parameters.total_sequence_length,
-                parameters.num_heads, parameters.v_head_size);
-
-  bool is_bf16 = false;
+  constexpr bool is_bf16 = false;
   ORT_RETURN_IF_ERROR(onnxruntime::flash::mha_fwd(
-      device_prop, stream, query, key, value, data.output, reinterpret_cast<void*>(data.scratch),
+      device_prop, stream, data.q, data.k, data.v, data.output, reinterpret_cast<void*>(data.scratch),
       parameters.batch_size, parameters.num_heads, parameters.num_heads, parameters.head_size,
       parameters.sequence_length, parameters.total_sequence_length, scale, parameters.is_unidirectional, is_bf16,
       parameters.num_splits, reinterpret_cast<void*>(data.softmax_lse_accum), reinterpret_cast<void*>(data.out_accum),
-      true));
-
-  DUMP_TENSOR("flash attention output", data.output,
-              parameters.batch_size, parameters.sequence_length, parameters.num_heads, parameters.v_head_size);
+      data.qkv_format == AttentionQkvFormat::Q_K_V_BSNH));
 
   return Status::OK();
 }
@@ -351,25 +330,8 @@ Status EfficientAttention(
     float scale) {
   // We only enable fused cross attention when there is no key padding mask.
   // Otherwise, key have effective batch size 2 * batch_size, which is different from batch_size of query.
-  assert(data.qkv_format == AttentionQkvFormat::Q_K_V_BSNH);
-
-  const void* query = data.q;
-  const void* key = data.k;
-  const void* value = data.v;
-  // For packed KV, we can use query input directly.
-  if (data.gemm_buffer == nullptr && data.key != nullptr && data.value == nullptr) {
-    assert(data.bias == nullptr);
-    query = data.query;
-  }
-
-  DUMP_TENSOR_INIT();
-  DUMP_TENSOR_D("q(BSNH)", reinterpret_cast<const T*>(query),
-                parameters.batch_size, parameters.sequence_length, parameters.num_heads, parameters.head_size);
-  DUMP_TENSOR_D("k(BSNH)", data.k,
-                parameters.batch_size, parameters.total_sequence_length, parameters.num_heads, parameters.head_size);
-  DUMP_TENSOR_D("v(BSNH)", data.v,
-                parameters.batch_size, parameters.total_sequence_length,
-                parameters.num_heads, parameters.v_head_size);
+  assert(data.qkv_format == AttentionQkvFormat::Q_K_V_BSNH ||
+         data.qkv_format == AttentionQkvFormat::Q_K_V_BSNH_BNSH_BNSH);
 
   MemoryEfficientAttentionParams p;
   p.sm = device_prop.major * 10 + device_prop.minor;
@@ -394,21 +356,19 @@ Status EfficientAttention(
                          ? nullptr
                          : const_cast<int32_t*>(reinterpret_cast<const int32_t*>(
                                data.mask_index + 2 * parameters.batch_size + 1));
-  p.query = query;
-  p.key = key;
-  p.value = value;
+  p.query = data.q;
+  p.key = data.k;
+  p.value = data.v;
   p.attn_bias = nullptr == data.relative_position_bias ? nullptr : data.relative_position_bias;
   p.is_attn_bias_batched = !parameters.broadcast_res_pos_bias;
   p.output = data.output;
-  p.is_kv_bsnh = true;
+  p.is_kv_bsnh = data.qkv_format == AttentionQkvFormat::Q_K_V_BSNH;
   p.workspace = MemoryEfficientAttentionParams::need_workspace(parameters.v_head_size, sizeof(T) == sizeof(float))
                     ? data.scratch
                     : nullptr;
   p.stream = stream;
   p.has_custom_right_padding = false;
   run_memory_efficient_attention(p);
-  DUMP_TENSOR("efficient attention output", data.output,
-              parameters.batch_size, parameters.sequence_length, parameters.num_heads, parameters.v_head_size);
 
   return Status::OK();
 }
@@ -449,10 +409,6 @@ Status UnfusedAttention(
 
   cublasSetStream(cublas, stream);
 
-  DUMP_TENSOR_INIT();
-  DUMP_TENSOR_D("q[BNSH]", data.q, batch_size, num_heads, sequence_length, qk_head_size);
-  DUMP_TENSOR_D("k[BNSH]", data.k, batch_size, num_heads, total_sequence_length, qk_head_size);
-
   const int present_sequence_length = parameters.past_present_share_buffer
                                           ? parameters.max_sequence_length
                                           : total_sequence_length;
@@ -467,8 +423,7 @@ Status UnfusedAttention(
       &zero, data.scratch, total_sequence_length, sequence_length * total_sequence_length, batches,
       device_prop, parameters.use_tf32));
 
-  DUMP_TENSOR_D("Q", data.q, batch_size, num_heads, sequence_length, qk_head_size);
-  DUMP_TENSOR_D("K", data.k, batch_size, num_heads, qk_head_size, sequence_length);
+  DUMP_TENSOR_INIT();
   DUMP_TENSOR_D("QK", data.scratch, batch_size, num_heads, sequence_length, total_sequence_length);
 
   constexpr size_t element_size = sizeof(T);
@@ -523,7 +478,6 @@ Status UnfusedAttention(
   // Temp_output is BxNxSxH_v, transpose to output BxSxNxH_v
   Status result = LaunchTransCtx(stream, sequence_length, batch_size, v_head_size, num_heads,
                                  device_prop.maxThreadsPerBlock, false, temp_output, data.output);
-  DUMP_TENSOR("unfused output", data.output, batch_size, sequence_length, num_heads, v_head_size);
   return result;
 }
 
@@ -554,7 +508,7 @@ Status QkvToContext(
 
   if (!parameters.past_present_share_buffer) {
     ORT_RETURN_IF_ERROR(ConcatPastToPresent(batch_size, num_heads, qk_head_size, v_head_size,
-                                            sequence_length, total_sequence_length, parameters.pass_past_in_kv,
+                                            sequence_length, total_sequence_length,
                                             stream, max_threads_per_block, data));
 
   } else {  // past_present_share_buffer
diff --git a/onnxruntime/contrib_ops/cuda/bert/attention_impl.h b/onnxruntime/contrib_ops/cuda/bert/attention_impl.h
index 56836bdda197c..fad353dcfeb07 100644
--- a/onnxruntime/contrib_ops/cuda/bert/attention_impl.h
+++ b/onnxruntime/contrib_ops/cuda/bert/attention_impl.h
@@ -6,6 +6,8 @@
 #include <cuda_fp16.h>
 #include <cublas_v2.h>
 #include <gsl/gsl>
+#include <iostream>
+#include <mutex>
 #include "core/framework/allocator.h"
 #include "contrib_ops/cpu/bert/attention_common.h"
 
@@ -15,13 +17,18 @@ namespace cuda {
 
 constexpr int kCumulatedSequenceLengthCacheMaxBatchSize = 128;
 
+// A cache for cumulated sequence length. It will be initialized in the first request, then become read-only after that.
 struct CumulatedSequenceLengthCache {
   onnxruntime::IAllocatorUniquePtr<void> buffer;
   int32_t max_batch_size;
   int32_t sequence_length;
 
-  CumulatedSequenceLengthCache() : max_batch_size(0), sequence_length(0) {}
-  void Initialize(int32_t sequence_length, cudaStream_t stream);
+  CumulatedSequenceLengthCache() : max_batch_size(kCumulatedSequenceLengthCacheMaxBatchSize), sequence_length(0) {}
+
+  const int32_t* TryGet(int batch_size, int32_t sequence_length, cudaStream_t stream);
+
+  // Use this flag to guard the initializaton only once in multi-threading.
+  mutable std::once_flag init_once_flag_;
 };
 
 size_t
@@ -46,7 +53,8 @@ size_t GetAttentionWorkspaceSize(
     void* fused_runner,
     bool use_flash_attention,
     bool use_fused_cross_attention,
-    bool use_memory_efficient_attention);
+    bool use_memory_efficient_attention,
+    bool no_qkv_workspace);
 
 template <typename T>
 struct AttentionData {
@@ -65,8 +73,6 @@ struct AttentionData {
 
   bool has_qkv_workspace = false;
   T* workspace = nullptr;
-  T* temp_k_workspace = nullptr;
-  T* temp_v_workspace = nullptr;
 
   T* output = nullptr;
   T* present = nullptr;
@@ -79,22 +85,50 @@ struct AttentionData {
   bool use_flash_attention = false;
   bool use_memory_efficient_attention = false;
 
-  mutable CumulatedSequenceLengthCache* cumulated_sequence_length_q_cache = nullptr;
-  mutable CumulatedSequenceLengthCache* cumulated_sequence_length_kv_cache = nullptr;
+  const int32_t* cumulated_sequence_length_q_cache = nullptr;
+  const int32_t* cumulated_sequence_length_kv_cache = nullptr;
 
   // Intermediate data
   T* q = nullptr;
   T* k = nullptr;
   T* v = nullptr;
   T* scratch = nullptr;
-  AttentionQkvFormat qkv_format = AttentionQkvFormat::Q_K_V_BSNH;
+  AttentionQkvFormat qkv_format = AttentionQkvFormat::UNKNOWN;
 
   // Flash buffers
   T* softmax_lse = nullptr;
   T* softmax_lse_accum = nullptr;
   T* out_accum = nullptr;
+
+  // For Debugging
+  size_t workspace_bytes = 0;
+  bool allow_debug_info = false;
+
+  bool IsUnfused() const {
+    return !use_flash_attention && !use_memory_efficient_attention &&
+           (fused_runner == nullptr) && (fused_cross_attention_kernel == nullptr);
+  }
+
+  void PrintDebugInfo() const {
+    std::cout << "flash=" << use_flash_attention
+              << ", efficient=" << use_memory_efficient_attention
+              << ", fused_runner=" << (fused_runner != nullptr)
+              << ", fused_cross=" << (fused_cross_attention_kernel != nullptr)
+              << ", bias=" << (bias != nullptr)
+              << ", attn_bias=" << (relative_position_bias != nullptr)
+              << ", mask_dims=" << mask_index_dims.size()
+              << ", has_qkv_workspace=" << has_qkv_workspace
+              << ", workspace=" << workspace_bytes
+              << ", past=" << (past != nullptr ? 1 : (past_key != nullptr ? 2 : 0))
+              << ", present=" << (present != nullptr ? 1 : (present_key != nullptr ? 2 : 0))
+              << std::endl;
+  }
 };
 
+// Return true if it does not need qkv workspace, false otherwise.
+template <typename T>
+bool NoQkvWorkspace(contrib::AttentionParameters& parameters, AttentionData<T>& data);
+
 template <typename T>
 Status PrepareQkv(contrib::AttentionParameters& parameters,
                   AttentionData<T>& data,
@@ -129,6 +163,9 @@ Status LaunchTransQkv(cudaStream_t stream, const int matrix_num,
                       const int max_threads_per_block, const bool reversed_bs, const half* input, half* output,
                       int total_matrix_count = -1);
 
+Status Transpose_BSNH_to_BNSH(const int batch_size, const int sequence_length, const int num_heads, const int head_size,
+                              const float* input, float* output, cudaStream_t stream, const int max_threads_per_block);
+
 Status Transpose_BSNH_to_BNSH(const int batch_size, const int sequence_length, const int num_heads, const int head_size,
                               const half* input, half* output, cudaStream_t stream, const int max_threads_per_block);
 
@@ -158,7 +195,7 @@ Status LaunchConcatTensorToTensor(cudaStream_t stream,
 
 template <typename T>
 Status ConcatPastToPresent(int batch_size, int num_heads, int qk_head_size, int v_head_size,
-                           int sequence_length, int total_sequence_length, bool pass_past_in_kv,
+                           int sequence_length, int total_sequence_length,
                            cudaStream_t stream,
                            int max_threads_per_block,
                            AttentionData<T>& data);
diff --git a/onnxruntime/contrib_ops/cuda/bert/attention_kernel_options.h b/onnxruntime/contrib_ops/cuda/bert/attention_kernel_options.h
index bd7df5f490c76..aba1e01bfd91b 100644
--- a/onnxruntime/contrib_ops/cuda/bert/attention_kernel_options.h
+++ b/onnxruntime/contrib_ops/cuda/bert/attention_kernel_options.h
@@ -50,6 +50,7 @@ class AttentionKernelOptions {
   bool use_unfused_{true};
 
   bool use_trt_flash_attention_{true};
+
   bool use_trt_cross_attention_{true};
 
   // Causal attention is disabled by default in #14732.
diff --git a/onnxruntime/contrib_ops/cuda/bert/attention_kv_cache.cu b/onnxruntime/contrib_ops/cuda/bert/attention_kv_cache.cu
index 89be0f1115f41..9f0f49348c225 100644
--- a/onnxruntime/contrib_ops/cuda/bert/attention_kv_cache.cu
+++ b/onnxruntime/contrib_ops/cuda/bert/attention_kv_cache.cu
@@ -249,16 +249,15 @@ Status LaunchConcatPastToPresent(cudaStream_t stream,
 
 template <typename T>
 Status ConcatPastToPresent(int batch_size, int num_heads, int qk_head_size, int v_head_size,
-                           int sequence_length, int total_sequence_length, bool pass_past_in_kv,
-                           cudaStream_t stream,
-                           int max_threads_per_block,
+                           int sequence_length, int total_sequence_length,
+                           cudaStream_t stream, int max_threads_per_block,
                            AttentionData<T>& data) {
   // Concat past key value to present (2xBxNxLxH), where L is kv_sequence_length and T is total_sequence_length.
   // past_k (BxNxPxH) + k (BxNxLxH) => present_k (BxNxTxH)
   // past_v (BxNxPxH) + v (BxNxLxH) => present_v (BxNxTxH)
   // When there is past state, the head size for Q/K/V shall be same: H == H_v.
 
-  if (nullptr != data.present) {
+  if (nullptr != data.present) { // Attention op
     assert(data.qkv_format == AttentionQkvFormat::Q_K_V_BNSH ||
            data.qkv_format == AttentionQkvFormat::Q_K_V_BNSH_QKV_BS3NH);
 
@@ -270,58 +269,52 @@ Status ConcatPastToPresent(int batch_size, int num_heads, int qk_head_size, int
     // Update pointers to present_k and present_v.
     data.k = data.present;
     data.v = data.present + batch_size * num_heads * total_sequence_length * qk_head_size;
-  } else if (nullptr != data.past_key || nullptr != data.present_key) {
-    if (nullptr != data.past_key && nullptr == data.present_key) {
-      data.k = const_cast<T*>(data.past_key);
-      data.v = const_cast<T*>(data.past_value);
-    } else if (nullptr == data.past_key && nullptr != data.present_key) {
-      if (data.qkv_format == AttentionQkvFormat::Q_K_V_BNSH) {
+  } else  { // MultiHeadAttention op
+    if (nullptr != data.present_key) {
+      ORT_ENFORCE(data.qkv_format == AttentionQkvFormat::Q_K_V_BNSH ||
+                  data.qkv_format == AttentionQkvFormat::Q_K_V_BSNH_BNSH_BNSH);
+      if (nullptr != data.past_key) {
+        assert(data.past_key != data.k);
+        assert(data.past_value != data.v);
+
+        ORT_RETURN_IF_ERROR(
+            LaunchConcatTensorToTensor(stream, total_sequence_length, sequence_length,
+                                      batch_size, qk_head_size, num_heads,
+                                      max_threads_per_block, 1, data.past_key, data.k, data.present_key));
+        ORT_RETURN_IF_ERROR(
+            LaunchConcatTensorToTensor(stream, total_sequence_length, sequence_length,
+                                      batch_size, v_head_size, num_heads,
+                                      max_threads_per_block, 1, data.past_value, data.v, data.present_value));
+        // Update pointers to present_k and present_v.
         data.k = data.present_key;
         data.v = data.present_value;
-      } else {
-        assert(data.qkv_format == AttentionQkvFormat::Q_K_V_BSNH);
-        data.k = data.temp_k_workspace;
-        data.v = data.temp_v_workspace;
+      } else { // nullptr == data.past_key && nullptr != data.present_key
+        if (data.k != data.present_key) {
+          int64_t k_size = (int64_t)batch_size * num_heads * total_sequence_length * qk_head_size;
+          cudaMemcpyAsync(data.present_key, data.k, k_size * sizeof(T), cudaMemcpyDeviceToDevice, stream);
+        }
+
+        if (data.v != data.present_value) {
+          int64_t v_size = (int64_t)batch_size * num_heads * total_sequence_length * v_head_size;
+          cudaMemcpyAsync(data.present_value, data.v, v_size * sizeof(T), cudaMemcpyDeviceToDevice, stream);
+        }
       }
-    } else if (pass_past_in_kv) {
-      // past_key and past_value are used directly as key and value in attention computations
-      data.k = const_cast<T*>(data.past_key);
-      data.v = const_cast<T*>(data.past_value);
-
-      // This path has a memory copy from past_key and past_value to present_key and present_value
-      // Avoid this path since the memory copy is unnecessary because past_key == present_key and
-      // past_value == present_value
-      int64_t k_size = (int64_t)batch_size * num_heads * total_sequence_length * qk_head_size;
-      int64_t v_size = (int64_t)batch_size * num_heads * total_sequence_length * v_head_size;
-      cudaMemcpyAsync(data.present_key, data.past_key, k_size * sizeof(T), cudaMemcpyDeviceToDevice, stream);
-      cudaMemcpyAsync(data.present_value, data.past_value, v_size * sizeof(T), cudaMemcpyDeviceToDevice, stream);
-    } else {
-      ORT_RETURN_IF_ERROR(
-          LaunchConcatTensorToTensor(stream, total_sequence_length, sequence_length,
-                                     batch_size, qk_head_size, num_heads,
-                                     max_threads_per_block, 1, data.past_key, data.k, data.present_key));
-      ORT_RETURN_IF_ERROR(
-          LaunchConcatTensorToTensor(stream, total_sequence_length, sequence_length,
-                                     batch_size, v_head_size, num_heads,
-                                     max_threads_per_block, 1, data.past_value, data.v, data.present_value));
-      // Update pointers to present_k and present_v.
-      data.k = data.present_key;
-      data.v = data.present_value;
     }
   }
 
+
   return CUDA_CALL(cudaGetLastError());
 }
 
 // Template Instantiation
 template Status ConcatPastToPresent<float>(int batch_size, int num_heads, int qk_head_size, int v_head_size,
-                                           int sequence_length, int total_sequence_length, bool pass_past_in_kv,
+                                           int sequence_length, int total_sequence_length,
                                            cudaStream_t stream,
                                            int max_threads_per_block,
                                            AttentionData<float>& data);
 
 template Status ConcatPastToPresent<half>(int batch_size, int num_heads, int qk_head_size, int v_head_size,
-                                          int sequence_length, int total_sequence_length, bool pass_past_in_kv,
+                                          int sequence_length, int total_sequence_length,
                                           cudaStream_t stream,
                                           int max_threads_per_block,
                                           AttentionData<half>& data);
diff --git a/onnxruntime/contrib_ops/cuda/bert/attention_prepare_qkv.cu b/onnxruntime/contrib_ops/cuda/bert/attention_prepare_qkv.cu
index 040d6124e7456..05c592ec61059 100644
--- a/onnxruntime/contrib_ops/cuda/bert/attention_prepare_qkv.cu
+++ b/onnxruntime/contrib_ops/cuda/bert/attention_prepare_qkv.cu
@@ -12,12 +12,101 @@ namespace onnxruntime {
 namespace contrib {
 namespace cuda {
 
+#if DEBUG_TENSOR_LEVEL > 1
+// Dump the workspace for Q, K, V after processing QKV data.
+template <typename T>
+void DumpQkv(AttentionData<T>& data) {
+  const int batch_size = parameters.batch_size;
+  const int sequence_length = parameters.sequence_length;
+  const int kv_sequence_length = parameters.kv_sequence_length;
+  const int num_heads = parameters.num_heads;
+  const int qk_head_size = parameters.head_size;
+  const int v_head_size = parameters.v_head_size;
+
+  DUMP_TENSOR_INIT();
+  if (data.qkv_format == AttentionQkvFormat::Q_K_V_BNSH) {
+    DUMP_TENSOR_D("q(BNSH)", data.q, batch_size, num_heads, sequence_length, qk_head_size);
+    DUMP_TENSOR_D("k(BNSH)", data.k, batch_size, num_heads, kv_sequence_length, qk_head_size);
+    DUMP_TENSOR_D("v(BNSH)", data.v, batch_size, num_heads, kv_sequence_length, v_head_size);
+  } else if (data.qkv_format == AttentionQkvFormat::Q_K_V_BSNH) {
+    DUMP_TENSOR_D("q(BSNH)", data.q, batch_size, sequence_length, num_heads, qk_head_size);
+    DUMP_TENSOR_D("k(BSNH)", data.k, batch_size, kv_sequence_length, num_heads, qk_head_size);
+    DUMP_TENSOR_D("v(BSNH)", data.v, batch_size, kv_sequence_length, num_heads, v_head_size);
+  } else if (data.qkv_format == AttentionQkvFormat::Q_K_V_BSNH_BNSH_BNSH) {
+    DUMP_TENSOR_D("q(BNSH)", data.q, batch_size, num_heads, sequence_length, qk_head_size);
+    DUMP_TENSOR_D("k(BNSH)", data.k, batch_size, num_heads, kv_sequence_length, qk_head_size);
+    DUMP_TENSOR_D("v(BNSH)", data.v, batch_size, num_heads, kv_sequence_length, v_head_size);
+  } else if (data.qkv_format == AttentionQkvFormat::QKV_BSN3H) {
+    DUMP_TENSOR_D("q(BSN3H)", data.q, batch_size, sequence_length, num_heads * 3, qk_head_size);
+  }
+}
+
+// Dump the inputs before processing QKV data.
+template <typename T>
+void DumpInputs(contrib::AttentionParameters& parameters, AttentionData<T>& data) {
+  const int batch_size = parameters.batch_size;
+  const int sequence_length = parameters.sequence_length;
+  const int kv_sequence_length = parameters.kv_sequence_length;
+  const int num_heads = parameters.num_heads;
+  const int qk_head_size = parameters.head_size;
+  const int v_head_size = parameters.v_head_size;
+
+  DUMP_TENSOR_INIT();
+  if (parameters.qkv_format == AttentionQkvFormat::Q_K_V_BSNH) {
+    DUMP_TENSOR_D("Query(BNSH)", data.query, batch_size, num_heads, sequence_length, qk_head_size);
+    DUMP_TENSOR_D("Key(BNSH)", data.key, batch_size, num_heads, kv_sequence_length, qk_head_size);
+    DUMP_TENSOR_D("Value(BNSH)", data.value, batch_size, num_heads, kv_sequence_length, v_head_size);
+  } else if (data.qkv_format == AttentionQkvFormat::Q_K_V_BSNH) {
+    DUMP_TENSOR_D("Query(BSNH)", data.query, batch_size, sequence_length, num_heads, qk_head_size);
+    DUMP_TENSOR_D("Key(BSNH)", data.key, batch_size, kv_sequence_length, num_heads, qk_head_size);
+    DUMP_TENSOR_D("Value(BSNH)", data.value, batch_size, kv_sequence_length, num_heads, v_head_size);
+  } else if (data.qkv_format == AttentionQkvFormat::Q_K_V_BSNH_BNSH_BNSH) {
+    DUMP_TENSOR_D("Query(BNSH)", data.query, batch_size, num_heads, sequence_length, qk_head_size);
+    DUMP_TENSOR_D("Key(BNSH)", data.key, batch_size, num_heads, kv_sequence_length, qk_head_size);
+    DUMP_TENSOR_D("Value(BNSH)", data.value, batch_size, num_heads, kv_sequence_length, v_head_size);
+  } else if (data.qkv_format == AttentionQkvFormat::QKV_BSN3H) {
+    DUMP_TENSOR_D("Query(BSN3H)", data.query, batch_size, sequence_length, num_heads * 3, qk_head_size);
+  } else if (data.qkv_format == AttentionQkvFormat::Q_KV_BSNH_BSN2H) {
+    DUMP_TENSOR_D("Query(BNSH)", data.query, batch_size, num_heads, sequence_length, qk_head_size);
+    DUMP_TENSOR_D("Value(BSN2H)", data.value, batch_size, sequence_length, num_heads * 2, qk_head_size);
+  }
+
+  if (data.bias != nullptr) {
+    DUMP_TENSOR_D("Q_bias", data.bias, num_heads, qk_head_size);
+    DUMP_TENSOR_D("K_bias", data.bias + num_heads * qk_head_size, num_heads, qk_head_size);
+    DUMP_TENSOR_D("V_bias", data.bias + 2 * num_heads * qk_head_size, num_heads, v_head_size);
+  }
+
+  if (data.relative_position_bias != nullptr) {
+    DUMP_TENSOR_D("relative_position_bias", data.relative_position_bias,
+                  parameters.broadcast_res_pos_bias ? 1 : batch_size,
+                  num_heads, sequence_length, kv_sequence_length);
+  }
+
+  if (data.mask_index != nullptr) {
+    if (parameters.mask_type == AttentionMaskType::MASK_2D_KEY_PADDING) {
+      DUMP_TENSOR_D("mask", data.mask_index, batch_size, parameters.total_sequence_length);
+    }
+    if (parameters.mask_type == AttentionMaskType::MASK_1D_KEY_SEQ_LEN_START) {
+      DUMP_TENSOR_D("mask", data.mask_index, 3 * batch_size + 2, 1);
+    }
+  }
+}
+
+// Dump the kernel outputs
+template <typename T>
+void DumpOutputs(AttentionData<T>& data) {
+  DUMP_TENSOR_INIT();
+  DUMP_TENSOR("output", data.output,
+              parameters.batch_size, parameters.sequence_length, parameters.num_heads, parameters.v_head_size);
+}
+#endif
+
 template <typename T>
 Status PrepareQkv_Attention(contrib::AttentionParameters& parameters,
                             AttentionData<T>& data,
                             cudaStream_t stream,
-                            int max_threads_per_block,
-                            AttentionQkvFormat& qkv_format) {
+                            int max_threads_per_block) {
   const int batch_size = parameters.batch_size;
   const int sequence_length = parameters.sequence_length;
   const int num_heads = parameters.num_heads;
@@ -40,7 +129,7 @@ Status PrepareQkv_Attention(contrib::AttentionParameters& parameters,
     int matrix_to_trans = (past_present_share_buffer ? 1 : 3);
     ORT_RETURN_IF_ERROR(LaunchTransQkv(stream, matrix_to_trans, sequence_length, batch_size, qk_head_size, num_heads,
                                        max_threads_per_block, false, data.gemm_buffer, qkv, 3));
-    qkv_format = AttentionQkvFormat::Q_K_V_BNSH;
+    data.qkv_format = AttentionQkvFormat::Q_K_V_BNSH;
   } else {
     // For fused TRT attention, transpose qkv to BxSxNx3xH (format 2)
     // For flash or memory efficient attention, transpose to 3xBxSxNxH (format 3)
@@ -48,13 +137,13 @@ Status PrepareQkv_Attention(contrib::AttentionParameters& parameters,
     // For fused causal kernel, use format 1 since we need have K and V to update present state,
     //   at the same time, we update gemm_buffer BxSx3xNxH with bias which is used as input for fused causal kernel.
     const int format = (use_fused_kernel ? 2 : (use_flash_or_efficient_attention ? 3 : 1));
-    qkv_format = use_fused_kernel
-                     ? AttentionQkvFormat::QKV_BSN3H
-                     : (use_flash_or_efficient_attention
-                            ? AttentionQkvFormat::Q_K_V_BSNH
-                            : (use_fused_causal
-                                   ? AttentionQkvFormat::Q_K_V_BNSH_QKV_BS3NH
-                                   : AttentionQkvFormat::Q_K_V_BNSH));
+    data.qkv_format = use_fused_kernel
+                          ? AttentionQkvFormat::QKV_BSN3H
+                          : (use_flash_or_efficient_attention
+                                 ? AttentionQkvFormat::Q_K_V_BSNH
+                                 : (use_fused_causal
+                                        ? AttentionQkvFormat::Q_K_V_BNSH_QKV_BS3NH
+                                        : AttentionQkvFormat::Q_K_V_BNSH));
 
     // For fused causal, we will update gemm_buffer with bias directly.
     T* qkv_add_bias = use_fused_causal ? data.gemm_buffer : nullptr;
@@ -71,367 +160,526 @@ Status PrepareQkv_Attention(contrib::AttentionParameters& parameters,
   return Status::OK();
 }
 
-// For MultiHeadAttention with past state
+// Return true if the workspace is not needed for Q, K, V inputs, false otherwise.
+// This shall be in sync with the following function PrepareQkv_MHA_Cross.
 template <typename T>
-Status PrepareQkv_MHA_WithPast(contrib::AttentionParameters& parameters,
-                               AttentionData<T>& data,
-                               cudaStream_t stream,
-                               int max_threads_per_block,
-                               T* q, T* k, T* v, AttentionQkvFormat& qkv_format) {
+bool NoQkvWorkspace_MHA_Cross(AttentionData<T>& data) {
+  // query, key and value are passed as Q, K and V for the following conditions.
+  return (data.use_memory_efficient_attention || data.use_flash_attention) && (data.bias == nullptr);
+}
+
+// For MultiHeadAttention with cross attention (Q_K_V_BSNH_BNSH_BNSH format)
+template <typename T>
+Status PrepareQkv_MHA_Cross(contrib::AttentionParameters& parameters,
+                            AttentionData<T>& data,
+                            cudaStream_t stream,
+                            int max_threads_per_block) {
+  assert(parameters.qkv_format == AttentionQkvFormat::Q_K_V_BSNH_BNSH_BNSH);
+  // past_key or past_value is not supported for cross attention
+  // present_key and present_value can be supported in theory, although we do not allow the senario for now.
+  assert(data.past_key == nullptr);
+  assert(data.past_value == nullptr);
+  assert(data.has_qkv_workspace == !NoQkvWorkspace_MHA_Cross(data));
+
   const int batch_size = parameters.batch_size;
   const int sequence_length = parameters.sequence_length;
-  const int kv_sequence_length = parameters.kv_sequence_length;
   const int num_heads = parameters.num_heads;
   const int qk_head_size = parameters.head_size;
-  const int v_head_size = parameters.v_head_size;
-
-  DUMP_TENSOR_INIT();
 
-  if (data.bias == nullptr) {
-    // Below logic does not support fused attention with past without bias
-    // When there is past state, the format shall be BxNxSxH, so we disable fused attention when there is past.
-
-    // cross attention with past state
-    if (data.past_key != nullptr && data.present_key == nullptr) {
-      assert(data.past_value != nullptr);
-      assert(data.query != nullptr);
-      assert(data.key == nullptr);
-      assert(data.value == nullptr);
-      ORT_RETURN_IF_ERROR(LaunchTransQkv(stream, 1, sequence_length, batch_size, qk_head_size, num_heads,
-                                         max_threads_per_block, false, data.query, q));
+#if USE_MEMORY_EFFICIENT_ATTENTION || USE_FLASH_ATTENTION
+  if (data.use_memory_efficient_attention || data.use_flash_attention) {
+    // Add bias for Q
+    if (data.bias != nullptr) {
+      LaunchAddBias(stream, max_threads_per_block, batch_size, sequence_length, num_heads, qk_head_size,
+                    data.bias, data.query, data.q);
+    } else {
+      data.q = const_cast<T*>(data.query);
     }
-    // cross attention with present state or self attention with present state
-    else if (data.past_key == nullptr && data.present_key != nullptr) {
-      assert(data.past_value == nullptr);
-      assert(data.present_value != nullptr);
-      assert(data.query != nullptr);
-      assert(data.key != nullptr);
-      assert(data.value != nullptr);
-
-      // TODO: supporting packed qkv for self attention may benefit performance
-      ORT_RETURN_IF_ERROR(LaunchTransQkv(stream, 1, sequence_length, batch_size, qk_head_size, num_heads,
-                                         max_threads_per_block, false, data.query, q));
 
-      // TODO: supporting packed kv for cross attention may benefit performance
-      ORT_RETURN_IF_ERROR(LaunchTransQkv(stream, 1, kv_sequence_length, batch_size, qk_head_size, num_heads,
-                                         max_threads_per_block, false, data.key, data.present_key));
-      ORT_RETURN_IF_ERROR(LaunchTransQkv(stream, 1, kv_sequence_length, batch_size, v_head_size, num_heads,
-                                         max_threads_per_block, false, data.value, data.present_value));
-    }
-    // self attention with past and present state
-    else {
-      assert(data.past_key != nullptr);
-      assert(data.past_value != nullptr);
-      assert(data.present_key != nullptr);
-      assert(data.present_value != nullptr);
-      assert(data.query != nullptr);
-      assert(data.key != nullptr);
-      assert(data.value != nullptr);
-      // TODO: supporting packed qkv for self attention may benefit performance
+    // Here we have assumption that there is no bias for key and value when they are in BNSH format.
+    data.k = const_cast<T*>(data.key);
+    data.v = const_cast<T*>(data.value);
+    data.qkv_format = AttentionQkvFormat::Q_K_V_BSNH_BNSH_BNSH;
+  } else
+#endif
+  {  // unfused kernel
+    assert(data.IsUnfused());
+    if (data.bias == nullptr) {
+      // Transpose query from BSNH to BNSH
       ORT_RETURN_IF_ERROR(LaunchTransQkv(stream, 1, sequence_length, batch_size, qk_head_size, num_heads,
-                                         max_threads_per_block, false, data.query, q));
-      ORT_RETURN_IF_ERROR(LaunchTransQkv(stream, 1, kv_sequence_length, batch_size, qk_head_size, num_heads,
-                                         max_threads_per_block, false, data.key, k));
-      ORT_RETURN_IF_ERROR(LaunchTransQkv(stream, 1, kv_sequence_length, batch_size, v_head_size, num_heads,
-                                         max_threads_per_block, false, data.value, v));
+                                         max_threads_per_block, false, data.query, data.q));
+    } else {
+      // Add bias to query, and transpose it: Query (BxSxNxH) => Q (BxNxSxH)
+      constexpr int format = 0;
+      LaunchAddBiasTranspose<T>(stream, 1, format, max_threads_per_block,
+                                batch_size, sequence_length, num_heads, qk_head_size,
+                                data.query, data.bias, data.q,
+                                true, -1);
     }
-    qkv_format = AttentionQkvFormat::Q_K_V_BNSH;
+
+    // Here we have assumption that there is no bias for key and value when they are in BNSH format.
+    // So we do not need to add bias for key and value. Just use the key and value directly.
+    data.k = const_cast<T*>(data.key);
+    data.v = const_cast<T*>(data.value);
+    data.qkv_format = AttentionQkvFormat::Q_K_V_BNSH;
+  }
+  return Status::OK();
+}
+
+template <typename T>
+bool NoQkvWorkspace_MHA_NoPast(AttentionData<T>& data) {
+  // query, key and value are passed as Q, K and V for the following conditions.
+  return (data.use_memory_efficient_attention || data.use_flash_attention) && data.bias == nullptr;
+}
+
+// For MultiHeadAttention without past state, with Q, K and V inputs
+template <typename T>
+Status PrepareQkv_MHA_NoPast(contrib::AttentionParameters& parameters,
+                             AttentionData<T>& data,
+                             cudaStream_t stream,
+                             int max_threads_per_block) {
+  assert(parameters.qkv_format == AttentionQkvFormat::Q_K_V_BSNH);
+  assert(data.query != nullptr);
+  assert(data.key != nullptr);
+  assert(data.value != nullptr);
+  assert(data.past_key == nullptr);
+  assert(data.past_value == nullptr);
+  assert(data.present_key == nullptr);
+  assert(data.present_value == nullptr);
+  assert(!parameters.is_unidirectional);
+  assert(data.has_qkv_workspace == !NoQkvWorkspace_MHA_NoPast(data));
+
+  const int batch_size = parameters.batch_size;
+  const int sequence_length = parameters.sequence_length;
+  const int kv_sequence_length = parameters.kv_sequence_length;
+  const int num_heads = parameters.num_heads;
+  const int qk_head_size = parameters.head_size;
+  const int v_head_size = parameters.v_head_size;
+
+  if (data.fused_cross_attention_kernel != nullptr) {
+    assert(qk_head_size == v_head_size);
+    assert(data.relative_position_bias == nullptr);
+    assert(data.mask_index == nullptr);
+    assert(parameters.hidden_size == parameters.v_hidden_size);
+
+    // For fused cross attention, besides adding bias, K and V needed to be packed:
+    //   Key (BxSxNxH), Value (BxSxNxH) => Q (BxSxNxH), K (BxSxNx2xH)
+    LaunchAddBiasTransposeTrt(
+        stream, max_threads_per_block,
+        batch_size, sequence_length,
+        num_heads, qk_head_size,
+        data.bias, data.query, data.key, data.value, data.q, true, kv_sequence_length);
+    data.v = nullptr;
+    data.qkv_format = AttentionQkvFormat::Q_KV_BSNH_BSN2H;
   }
 #if USE_MEMORY_EFFICIENT_ATTENTION || USE_FLASH_ATTENTION
-  // When past_key/past_value are inputted directly as key/value and there is no present_key/present_value
-  else if ((data.use_memory_efficient_attention || data.use_flash_attention) &&
-           data.past_key != nullptr &&
-           data.past_value != nullptr &&
-           parameters.pass_past_in_kv) {
-    // Transpose past_key and past_value to use memory efficient attention
-
-    // past_key (BxNxSxH) => temp_k_workspace (BxSxNxH)
-    ORT_RETURN_IF_ERROR(LaunchTransCtx(stream, kv_sequence_length, batch_size, qk_head_size, num_heads,
-                                       max_threads_per_block, false, data.past_key, data.temp_k_workspace));
-    // past_value (BxNxSxH_v) => temp_v_workspace (BxSxNxH_v)
-    ORT_RETURN_IF_ERROR(LaunchTransCtx(stream, kv_sequence_length, batch_size, qk_head_size, num_heads,
-                                       max_threads_per_block, false, data.past_value, data.temp_v_workspace));
-
-    // query => q, temp_k_workspace => k, temp_v_workspace => v
-    LaunchAddBias(stream, max_threads_per_block,
-                  batch_size, sequence_length, kv_sequence_length,
-                  num_heads, qk_head_size, v_head_size,
-                  data.bias, data.query, data.temp_k_workspace, data.temp_v_workspace, q, k, v);
-
-    DUMP_TENSOR_D("q(BSNH)", q, batch_size, sequence_length, num_heads, qk_head_size);
-    DUMP_TENSOR_D("k(BSNH)", k, batch_size, kv_sequence_length, num_heads, qk_head_size);
-    DUMP_TENSOR_D("v(BSNH)", v, batch_size, kv_sequence_length, num_heads, v_head_size);
-    qkv_format = AttentionQkvFormat::Q_K_V_BSNH;
-
-    data.past_key = nullptr;
-    data.past_value = nullptr;
+  else if (data.use_memory_efficient_attention || data.use_flash_attention) {
+    if (data.bias != nullptr) {
+      LaunchAddBias(stream, max_threads_per_block,
+                    batch_size, sequence_length, kv_sequence_length,
+                    num_heads, qk_head_size, v_head_size,
+                    data.bias, data.query, data.key, data.value, data.q, data.k, data.v);
+    } else {
+      data.q = const_cast<T*>(data.query);
+      data.k = const_cast<T*>(data.key);
+      data.v = const_cast<T*>(data.value);
+    }
+
+    data.qkv_format = AttentionQkvFormat::Q_K_V_BSNH;
   }
-  // When there is no past_key/past_value and there is present_key/present_value
-  // (e.g. get initial kv to use as past_kv in the next iteration)
-  else if ((data.use_memory_efficient_attention || data.use_flash_attention) &&
-           data.present_key != nullptr &&
-           data.present_value != nullptr) {
-    // Use memory efficient attention kernel
-    LaunchAddBias(stream, max_threads_per_block,
-                  batch_size, sequence_length, kv_sequence_length,
-                  num_heads, qk_head_size, v_head_size,
-                  data.bias, data.query, data.key, data.value, q, data.temp_k_workspace, data.temp_v_workspace);
-
-    // temp_k_workspace (BxSxNxH) => present_k (BxNxSxH)
-    ORT_RETURN_IF_ERROR(LaunchTransQkv(stream, 1, kv_sequence_length, batch_size, qk_head_size, num_heads,
-                                       max_threads_per_block, false, data.temp_k_workspace, data.present_key));
+#endif
+  else if (data.fused_runner != nullptr) {
+    assert(qk_head_size == v_head_size);
+    assert(data.relative_position_bias == nullptr);
+
+    // Query (BxSxNxH), Key (BxSxNxH), Value (BxSxNxH) => Q: BxSxNx(H + H + H)
+    LaunchAddBiasTransposeTrt(
+        stream, max_threads_per_block,
+        batch_size, sequence_length,
+        num_heads, qk_head_size,
+        data.bias, data.query, data.key, data.value, data.q, false, kv_sequence_length);
+    data.k = nullptr;
+    data.v = nullptr;
+
+    data.qkv_format = AttentionQkvFormat::QKV_BSN3H;
+  } else {  // unfused kernel
+    assert(data.IsUnfused());
+    // Query (BxSxNxH) => Q (BxNxSxH)
+    constexpr int format = 0;
+    LaunchAddBiasTranspose<T>(
+        stream, 1, format, max_threads_per_block,
+        batch_size, sequence_length, num_heads, qk_head_size,
+        data.query, data.bias, data.q,
+        true, -1);
+
+    // Key (BxLxNxH) => K (BxNxLxH)
+    LaunchAddBiasTranspose<T>(
+        stream, 1, format, max_threads_per_block,
+        batch_size, kv_sequence_length, num_heads, qk_head_size,
+        data.key, nullptr == data.bias ? nullptr : data.bias + num_heads * qk_head_size, data.k,
+        true, -1);
 
-    // temp_v_workspace (BxSxNxH_v) => present_v (BxNxSxH_v)
+    // Value (BxLxNxH_v) => K (BxNxLxH_v)
+    LaunchAddBiasTranspose<T>(
+        stream, 1, format, max_threads_per_block,
+        batch_size, kv_sequence_length, num_heads, v_head_size,
+        data.value, nullptr == data.bias ? nullptr : data.bias + 2 * num_heads * qk_head_size, data.v,
+        true, -1);
+
+    data.qkv_format = AttentionQkvFormat::Q_K_V_BNSH;
+  }
+
+  return Status::OK();
+}
+
+template <typename T>
+bool NoQkvWorkspace_MHA_WithPast_NoBias(AttentionData<T>& data) {
+  if (data.use_memory_efficient_attention || data.use_flash_attention) {
+    // Q, K and V redirects to query, present_k and present_v, so we do not need extra workspace for QKV.
+    return data.past_key == nullptr && data.present_key != nullptr;
+  }
+  return false;
+}
+
+// For MultiHeadAttention with kv cache (past or present), but no bias
+template <typename T>
+Status PrepareQkv_MHA_WithPast_NoBias(contrib::AttentionParameters& parameters,
+                                      AttentionData<T>& data,
+                                      cudaStream_t stream,
+                                      int max_threads_per_block) {
+  assert(parameters.qkv_format == AttentionQkvFormat::Q_K_V_BSNH);
+  assert(data.query != nullptr);
+  assert(data.key != nullptr);
+  assert(data.value != nullptr);
+  assert(data.bias == nullptr);
+  assert(data.fused_runner == nullptr);
+  assert(data.fused_cross_attention_kernel == nullptr);
+  assert(data.present_key != nullptr);
+  assert(data.present_value != nullptr);
+  assert(data.past_key == nullptr && data.past_value == nullptr ||
+         data.past_key != nullptr && data.past_value != nullptr);
+  assert(data.has_qkv_workspace == !NoQkvWorkspace_MHA_WithPast_NoBias(data));
+
+  const int batch_size = parameters.batch_size;
+  const int sequence_length = parameters.sequence_length;
+  const int kv_sequence_length = parameters.kv_sequence_length;
+  const int num_heads = parameters.num_heads;
+  const int qk_head_size = parameters.head_size;
+  const int v_head_size = parameters.v_head_size;
+
+  // When there is no past state and there is present state, we output K and V directly to present state.
+  if (data.past_key == nullptr && data.present_key != nullptr) {
+    data.k = data.present_key;
+    data.v = data.present_value;
+  }
+
+#if USE_MEMORY_EFFICIENT_ATTENTION || USE_FLASH_ATTENTION
+  if (data.use_memory_efficient_attention || data.use_flash_attention) {
+    // Use oiginal Query (BSNH) since there is no bias.
+    data.q = const_cast<T*>(data.query);
+
+    // Key (BxLxNxH) => K (BxNxLxH)
+    ORT_RETURN_IF_ERROR(LaunchTransQkv(stream, 1, kv_sequence_length, batch_size, qk_head_size, num_heads,
+                                       max_threads_per_block, false, data.key, data.k));
+    // Value (BxLxNxH) => V (BxNxLxH)
     ORT_RETURN_IF_ERROR(LaunchTransQkv(stream, 1, kv_sequence_length, batch_size, v_head_size, num_heads,
-                                       max_threads_per_block, false, data.temp_v_workspace, data.present_value));
+                                       max_threads_per_block, false, data.value, data.v));
+    data.qkv_format = AttentionQkvFormat::Q_K_V_BSNH_BNSH_BNSH;
+  } else
+#endif
+  {  // unfused kernel
+    assert(data.IsUnfused());
+    ORT_RETURN_IF_ERROR(LaunchTransQkv(stream, 1, sequence_length, batch_size, qk_head_size, num_heads,
+                                       max_threads_per_block, false, data.query, data.q));
+    ORT_RETURN_IF_ERROR(LaunchTransQkv(stream, 1, kv_sequence_length, batch_size, qk_head_size, num_heads,
+                                       max_threads_per_block, false, data.key, data.k));
+    ORT_RETURN_IF_ERROR(LaunchTransQkv(stream, 1, kv_sequence_length, batch_size, v_head_size, num_heads,
+                                       max_threads_per_block, false, data.value, data.v));
+    data.qkv_format = AttentionQkvFormat::Q_K_V_BNSH;
+  }
+
+  return Status::OK();
+}
 
-    DUMP_TENSOR_D("q(BSNH)", q, batch_size, sequence_length, num_heads, qk_head_size);
-    DUMP_TENSOR_D("k(BSNH)", data.temp_k_workspace, batch_size, kv_sequence_length, num_heads, qk_head_size);
-    DUMP_TENSOR_D("v(BSNH)", data.temp_v_workspace, batch_size, kv_sequence_length, num_heads, v_head_size);
-    qkv_format = AttentionQkvFormat::Q_K_V_BSNH;
+template <typename T>
+constexpr bool NoQkvWorkspace_MHA_WithPast_Bias(AttentionData<T>& /*data*/) {
+  return false;
+}
+
+// For MultiHeadAttention with both kv cache (past or present) and bias
+template <typename T>
+Status PrepareQkv_MHA_WithPast_Bias(contrib::AttentionParameters& parameters,
+                                    AttentionData<T>& data,
+                                    cudaStream_t stream,
+                                    int max_threads_per_block) {
+  assert(parameters.qkv_format == AttentionQkvFormat::Q_K_V_BSNH);
+  assert(data.bias != nullptr);
+  assert(!(data.past_key != nullptr && data.present_key == nullptr));
+  assert(data.fused_runner == nullptr);
+  assert(data.fused_cross_attention_kernel == nullptr);
+  assert(data.present_key != nullptr);
+  assert(data.present_value != nullptr);
+  assert(data.past_key == nullptr && data.past_value == nullptr ||
+         data.past_key != nullptr && data.past_value != nullptr);
+  assert(data.has_qkv_workspace == !NoQkvWorkspace_MHA_WithPast_Bias(data));
+
+  const int batch_size = parameters.batch_size;
+  const int sequence_length = parameters.sequence_length;
+  const int kv_sequence_length = parameters.kv_sequence_length;
+  const int num_heads = parameters.num_heads;
+  const int qk_head_size = parameters.head_size;
+  const int v_head_size = parameters.v_head_size;
+
+  // When there is no past state and there is present state, we output K and V directly to present state.
+  if (data.past_key == nullptr && data.present_key != nullptr) {
+    data.k = data.present_key;
+    data.v = data.present_value;
   }
+
+#if USE_MEMORY_EFFICIENT_ATTENTION || USE_FLASH_ATTENTION
+  if (data.use_memory_efficient_attention || data.use_flash_attention) {
+    // Query(BxSxNxH) + Bias_Q => Q (BxSxNxH)
+    LaunchAddBias(stream, max_threads_per_block, batch_size, sequence_length, num_heads, qk_head_size,
+                  data.bias, data.query, data.q);
+
+    // Key (BxLxNxH) + Bias_K => K (BxNxLxH)
+    constexpr int format = 0;
+    LaunchAddBiasTranspose<T>(
+        stream, 1, format, max_threads_per_block,
+        batch_size, kv_sequence_length, num_heads, qk_head_size,
+        data.key, data.bias + num_heads * qk_head_size, data.k, true, -1);
+
+    // Key (BxLxNxH) + Bias_K => K (BxNxLxH)
+    LaunchAddBiasTranspose<T>(
+        stream, 1, format, max_threads_per_block,
+        batch_size, kv_sequence_length, num_heads, v_head_size,
+        data.value, data.bias + 2 * num_heads * qk_head_size, data.v, true, -1);
+
+    data.qkv_format = AttentionQkvFormat::Q_K_V_BSNH_BNSH_BNSH;
+  } else
 #endif
-  else {
-    // Use unfused kernel for Q, use unfused kernel for K and V if needed
+  {  // unfused kernel
+    assert(data.IsUnfused());
+
     constexpr int format = 0;
     // Query (BxSxNxH) => Q (BxNxSxH)
     LaunchAddBiasTranspose<T>(stream, 1, format, max_threads_per_block,
                               batch_size, sequence_length, num_heads, qk_head_size,
-                              data.query, data.bias, q,
+                              data.query, data.bias, data.q,
                               true, -1);
 
-    if (!parameters.pass_past_in_kv) {
-      T* k_dest = (data.past_key == nullptr && data.present_key != nullptr) ? data.present_key : k;
-      T* v_dest = (data.past_value == nullptr && data.present_value != nullptr) ? data.present_value : v;
-
-      // Key (BxLxNxH) => K (BxNxLxH)
-      LaunchAddBiasTranspose<T>(stream, 1, format, max_threads_per_block,
-                                batch_size, kv_sequence_length, num_heads, qk_head_size,
-                                data.key, data.bias + num_heads * qk_head_size, k_dest,
-                                true, -1);
+    // Key (BxLxNxH) => K (BxNxLxH)
+    LaunchAddBiasTranspose<T>(stream, 1, format, max_threads_per_block,
+                              batch_size, kv_sequence_length, num_heads, qk_head_size,
+                              data.key, data.bias + num_heads * qk_head_size, data.k,
+                              true, -1);
 
-      // Value (BxLxNxH_v) => V (BxNxLxH_v)
-      LaunchAddBiasTranspose<T>(stream, 1, format, max_threads_per_block,
-                                batch_size, kv_sequence_length, num_heads, v_head_size,
-                                data.value, data.bias + 2 * num_heads * qk_head_size, v_dest,
-                                true, -1);
+    // Value (BxLxNxH_v) => V (BxNxLxH_v)
+    LaunchAddBiasTranspose<T>(stream, 1, format, max_threads_per_block,
+                              batch_size, kv_sequence_length, num_heads, v_head_size,
+                              data.value, data.bias + 2 * num_heads * qk_head_size, data.v,
+                              true, -1);
 
-      DUMP_TENSOR_D("q(BNSH)", q, batch_size, num_heads, sequence_length, qk_head_size);
-      DUMP_TENSOR_D("k(BNSH)", k_dest, batch_size, num_heads, kv_sequence_length, qk_head_size);
-      DUMP_TENSOR_D("v(BNSH)", v_dest, batch_size, num_heads, kv_sequence_length, v_head_size);
-    }
-    qkv_format = AttentionQkvFormat::Q_K_V_BNSH;
+    data.qkv_format = AttentionQkvFormat::Q_K_V_BNSH;
   }
+
   return Status::OK();
 }
 
+template <typename T>
+bool NoQkvWorkspace_MHA_PackedQKV(AttentionData<T>& data) {
+  // query, key and value are passed as Q, K and V for the following conditions.
+  return nullptr != data.fused_runner && data.bias == nullptr;
+}
+
 // For MultiHeadAttention without past state, with packed QKV inputs
 template <typename T>
 Status PrepareQkv_MHA_PackedQKV(contrib::AttentionParameters& parameters,
                                 AttentionData<T>& data,
                                 cudaStream_t stream,
-                                int max_threads_per_block,
-                                AttentionQkvFormat& qkv_format) {
+                                int max_threads_per_block) {
+  assert(parameters.qkv_format == AttentionQkvFormat::QKV_BSN3H);
+  assert(data.past_key == nullptr);
+  assert(data.past_value == nullptr);
+  assert(data.present_key == nullptr);
+  assert(data.present_value == nullptr);
+  assert(parameters.head_size == parameters.v_head_size);
+  assert(data.fused_cross_attention_kernel == nullptr);
+  assert(!parameters.is_unidirectional);
+  assert(data.has_qkv_workspace == !NoQkvWorkspace_MHA_PackedQKV(data));
+
   const int batch_size = parameters.batch_size;
   const int sequence_length = parameters.sequence_length;
   const int num_heads = parameters.num_heads;
   const int qk_head_size = parameters.head_size;
   const int v_head_size = parameters.v_head_size;
-  void* fused_runner = data.fused_runner;
-
-  T* qkv = data.workspace;
-
-  bool use_fused_kernel = (nullptr != fused_runner && !parameters.is_unidirectional);
-
-  assert(data.bias == nullptr);
-  assert(qk_head_size == v_head_size);
-
-  DUMP_TENSOR_INIT();
-  DUMP_TENSOR_D("packed_qkv", data.query, batch_size * sequence_length, num_heads, 3, qk_head_size);
 
   if (data.use_memory_efficient_attention || data.use_flash_attention) {
-    // unpack qkv to BSNH. Note that there is no bias so we need not output query to q.
+    // unpack qkv to BSNH.
     constexpr int format = 4;
     T* qkv_add_bias = nullptr;
     LaunchAddBiasTranspose(stream, 3, format, max_threads_per_block,
                            batch_size, sequence_length, num_heads, qk_head_size,
-                           data.query, data.bias, qkv,
+                           data.query, data.bias, data.q,
                            true, v_head_size, qkv_add_bias, 3);
-    DUMP_TENSOR_D("q(BSNH)", data.q, batch_size, sequence_length, num_heads, qk_head_size);
-    DUMP_TENSOR_D("k(BSNH)", data.k, batch_size, sequence_length, num_heads, qk_head_size);
-    DUMP_TENSOR_D("v(BSNH)", data.v, batch_size, sequence_length, num_heads, v_head_size);
-    qkv_format = AttentionQkvFormat::Q_K_V_BSNH;
-  } else {
-    if (!use_fused_kernel) {
-      return ORT_MAKE_STATUS(
-          ONNXRUNTIME, NOT_IMPLEMENTED,
-          "packed QKV format is not implemented for current GPU. Please disable it in fusion options.");
+    data.qkv_format = AttentionQkvFormat::Q_K_V_BSNH;
+  } else if (nullptr != data.fused_runner) {
+    assert(nullptr == data.relative_position_bias);
+    if (data.bias == nullptr) {
+      // When there is no bias, we can directly use the original packed QKV input.
+      // Need revisit this when we add support for causal.
+      data.q = const_cast<T*>(data.query);
+      data.k = nullptr;
+      data.v = nullptr;
+    } else {  // data.bias != nullptr
+      AddBiasTransposePacked<T>(
+          data.query, data.key, data.value, data.bias, data.q,
+          batch_size, sequence_length,
+          num_heads, qk_head_size, v_head_size,
+          AttentionQkvFormat::QKV_TN3H, AttentionQkvFormat::QKV_TN3H,
+          nullptr, batch_size * sequence_length,
+          stream);
     }
 
-    qkv_format = AttentionQkvFormat::QKV_BSN3H;
+    data.qkv_format = AttentionQkvFormat::QKV_BSN3H;
+  } else {  // unfused kernel
+    assert(data.IsUnfused());
+    // unpack qkv to BNSH
+    constexpr int format = 5;
+    T* qkv_add_bias = nullptr;
+    LaunchAddBiasTranspose(stream, 3, format, max_threads_per_block,
+                           batch_size, sequence_length, num_heads, qk_head_size,
+                           data.query, data.bias, data.q,
+                           true, v_head_size, qkv_add_bias, 3);
+
+    data.qkv_format = AttentionQkvFormat::Q_K_V_BNSH;
   }
+
   return Status::OK();
 }
 
+// This shall be in sync with the following function PrepareQkv_MHA_PackedQKV.
+template <typename T>
+bool NoQkvWorkspace_MHA_PackedKV(AttentionData<T>& data) {
+  return data.fused_cross_attention_kernel != nullptr;
+}
+
 // For MultiHeadAttention without past state, with packed KV inputs
 template <typename T>
 Status PrepareQkv_MHA_PackedKV(contrib::AttentionParameters& parameters,
                                AttentionData<T>& data,
                                cudaStream_t stream,
-                               int max_threads_per_block,
-                               AttentionQkvFormat& qkv_format) {
+                               int max_threads_per_block) {
+  assert(parameters.qkv_format == AttentionQkvFormat::Q_KV_BSNH_BSN2H);
+  assert(data.bias == nullptr);
+  assert(data.past_key == nullptr);
+  assert(data.past_value == nullptr);
+  assert(data.present_key == nullptr);
+  assert(data.present_value == nullptr);
+  assert(parameters.head_size == parameters.v_head_size);
+  assert(data.fused_runner == nullptr);
+  assert(data.has_qkv_workspace == !NoQkvWorkspace_MHA_PackedKV(data));
+
   const int batch_size = parameters.batch_size;
   const int kv_sequence_length = parameters.kv_sequence_length;
   const int num_heads = parameters.num_heads;
   const int qk_head_size = parameters.head_size;
   const int v_head_size = parameters.v_head_size;
 
-  // TODO: unpack kv to BNSH for unfused kernel so that we can remove the following constraint.
-  // CheckInputs verified this constraint.
-  assert(data.bias == nullptr);
-  assert(qk_head_size == v_head_size);
-
-  DUMP_TENSOR_INIT();
-  DUMP_TENSOR_D("packed_kv", data.key, batch_size * kv_sequence_length, num_heads, 2, qk_head_size);
-
   if (data.use_memory_efficient_attention || data.use_flash_attention) {
-    // unpack kv to BSNH. Note that there is no bias so we need not output query to q.
+    // Note that there is no bias so we need not output query to q.
+    data.q = const_cast<T*>(data.query);
+    // Unpack kv to BSNH.
     constexpr int format = 4;
     T* qkv_add_bias = nullptr;
     const T* kv_bias = (data.bias == nullptr ? data.bias : data.bias + parameters.hidden_size);
     LaunchAddBiasTranspose(stream, 2, format, max_threads_per_block,
                            batch_size, kv_sequence_length, num_heads, qk_head_size,
                            data.key, kv_bias, data.k,
-                           true, v_head_size, qkv_add_bias, 2);
-    DUMP_TENSOR_D("k(BSNH)", data.k, batch_size, kv_sequence_length, num_heads, qk_head_size);
-    DUMP_TENSOR_D("v(BSNH)", data.v, batch_size, kv_sequence_length, num_heads, v_head_size);
-    qkv_format = AttentionQkvFormat::Q_K_V_BSNH;
-  } else {
-    if (data.fused_cross_attention_kernel == nullptr) {
-      return ORT_MAKE_STATUS(
-          ONNXRUNTIME, NOT_IMPLEMENTED,
-          "packed KV format is not implemented for current GPU. Please disable packed kv in fusion options.");
-    }
+                           true, v_head_size, qkv_add_bias);
+    data.qkv_format = AttentionQkvFormat::Q_K_V_BSNH;
+  } else if (data.fused_cross_attention_kernel != nullptr) {
+    data.qkv_format = AttentionQkvFormat::Q_KV_BSNH_BSN2H;
+    data.q = const_cast<T*>(data.query);
+    data.k = const_cast<T*>(data.key);
+    data.v = nullptr;
+  } else {  // unfused kernel
+    assert(data.IsUnfused());
+    // Transpose q from BSNH to BNSH. Note that there is no bias.
+    ORT_RETURN_IF_ERROR(Transpose_BSNH_to_BNSH(batch_size, parameters.sequence_length, num_heads, qk_head_size,
+                                               data.query, data.q, stream, max_threads_per_block));
 
-    qkv_format = AttentionQkvFormat::Q_KV_BSNH_BSN2H;
+    // Unpack kv to BNSH.
+    constexpr int format = 5;
+    T* qkv_add_bias = nullptr;
+    const T* kv_bias = (data.bias == nullptr ? data.bias : data.bias + parameters.hidden_size);
+    LaunchAddBiasTranspose(stream, 2, format, max_threads_per_block,
+                           batch_size, kv_sequence_length, num_heads, qk_head_size,
+                           data.key, kv_bias, data.k,
+                           true, v_head_size, qkv_add_bias, 2);
+    data.qkv_format = AttentionQkvFormat::Q_K_V_BNSH;
   }
+
   return Status::OK();
 }
 
-// For MultiHeadAttention without past state, with Q, K and V inputs
+// Prepare Q, K and V for MultiHeadAttention operator.
 template <typename T>
-Status PrepareQkv_MHA_NotPacked(contrib::AttentionParameters& parameters,
-                                AttentionData<T>& data,
-                                cudaStream_t stream,
-                                int max_threads_per_block,
-                                T* q, T* k, T* v, AttentionQkvFormat& qkv_format) {
-  const int batch_size = parameters.batch_size;
-  const int sequence_length = parameters.sequence_length;
-  const int kv_sequence_length = parameters.kv_sequence_length;
-  const int num_heads = parameters.num_heads;
-  const int qk_head_size = parameters.head_size;
-  const int v_head_size = parameters.v_head_size;
-  void* fused_runner = data.fused_runner;
-
-  T* qkv = data.workspace;
-
-  bool use_fused_kernel = (nullptr != fused_runner && !parameters.is_unidirectional);
-  bool use_fused_causal = (nullptr != fused_runner && parameters.is_unidirectional);
-
-  // gemm_buffer == nullptr and not packed
-  assert(data.query != nullptr && data.key != nullptr && data.value != nullptr);
-
-  DUMP_TENSOR_INIT();
-  DUMP_TENSOR_D("query", data.query, batch_size, sequence_length, num_heads, qk_head_size);
-  DUMP_TENSOR_D("key", data.key, batch_size, kv_sequence_length, num_heads, qk_head_size);
-  DUMP_TENSOR_D("value", data.value, batch_size, kv_sequence_length, num_heads, v_head_size);
-
-#if DUMP_TENSOR_LEVEL > 1
-  if (data.bias != nullptr) {
-    DUMP_TENSOR_D("query_bias", data.bias, num_heads, qk_head_size);
-    DUMP_TENSOR_D("key_bias", data.bias + num_heads * qk_head_size, num_heads, qk_head_size);
-    DUMP_TENSOR_D("value_bias", data.bias + 2 * num_heads * qk_head_size, num_heads, v_head_size);
-  }
-#endif
-
-  if (data.relative_position_bias != nullptr && parameters.broadcast_res_pos_bias) {
-    DUMP_TENSOR_D("relative_position_bias", data.relative_position_bias,
-                  num_heads, sequence_length, kv_sequence_length);
-  }
-
-  if (data.mask_index != nullptr && parameters.mask_type == AttentionMaskType::MASK_1D_KEY_SEQ_LEN_START) {
-    DUMP_TENSOR_D("mask_index", data.mask_index, 3 * batch_size + 2, 1);
-  }
-
-  if (data.fused_cross_attention_kernel != nullptr) {
-    assert(qk_head_size == v_head_size);
-
-    // For fused cross attention, besides adding bias, K and V needed to be packed:
-    //   K (BxSxNxH), V (BxSxNxH) => BxSxNx2xH
-    LaunchAddBiasTransposeTrt(
-        stream, max_threads_per_block,
-        batch_size, sequence_length,
-        num_heads, qk_head_size,
-        data.bias, data.query, data.key, data.value, qkv, true, kv_sequence_length);
-
-    qkv_format = AttentionQkvFormat::Q_KV_BSNH_BSN2H;
-  }
-#if USE_MEMORY_EFFICIENT_ATTENTION || USE_FLASH_ATTENTION
-  else if (data.use_memory_efficient_attention || data.use_flash_attention) {
-    LaunchAddBias(stream, max_threads_per_block,
-                  batch_size, sequence_length, kv_sequence_length,
-                  num_heads, qk_head_size, v_head_size,
-                  data.bias, data.query, data.key, data.value, q, k, v);
-
-    DUMP_TENSOR_D("q(BSNH)", q, batch_size, sequence_length, num_heads, qk_head_size);
-    DUMP_TENSOR_D("k(BSNH)", k, batch_size, kv_sequence_length, num_heads, qk_head_size);
-    DUMP_TENSOR_D("v(BSNH)", v, batch_size, kv_sequence_length, num_heads, v_head_size);
-    qkv_format = AttentionQkvFormat::Q_K_V_BSNH;
+Status PrepareQkv_MultiHeadAttention(contrib::AttentionParameters& parameters,
+                                     AttentionData<T>& data,
+                                     cudaStream_t stream,
+                                     int max_threads_per_block) {
+  switch (parameters.qkv_format) {
+    case AttentionQkvFormat::Q_K_V_BSNH_BNSH_BNSH:
+      ORT_RETURN_IF_ERROR(PrepareQkv_MHA_Cross(parameters, data, stream, max_threads_per_block));
+      break;
+    case AttentionQkvFormat::Q_KV_BSNH_BSN2H:
+      ORT_RETURN_IF_ERROR(PrepareQkv_MHA_PackedKV(parameters, data, stream, max_threads_per_block));
+      break;
+    case AttentionQkvFormat::QKV_BSN3H:
+      ORT_RETURN_IF_ERROR(PrepareQkv_MHA_PackedQKV(parameters, data, stream, max_threads_per_block));
+      break;
+    case AttentionQkvFormat::Q_K_V_BSNH:
+      if (data.past_key != nullptr || data.present_key != nullptr) {
+        if (data.bias == nullptr) {
+          ORT_RETURN_IF_ERROR(PrepareQkv_MHA_WithPast_NoBias(parameters, data, stream, max_threads_per_block));
+        } else {
+          ORT_RETURN_IF_ERROR(PrepareQkv_MHA_WithPast_Bias(parameters, data, stream, max_threads_per_block));
+        }
+      } else {  // no past state
+        ORT_RETURN_IF_ERROR(PrepareQkv_MHA_NoPast(parameters, data, stream, max_threads_per_block));
+      }
+      break;
+    default:
+      ORT_THROW("Unsupported QKV format: ", parameters.qkv_format);
   }
-#endif
-  else if (use_fused_kernel) {
-    assert(qk_head_size == v_head_size);
-
-    // Q (BxSxNxH), K (BxSxNxH), V (BxSxNxH) => BxSxNx(H + H + H)
-    LaunchAddBiasTransposeTrt(
-        stream, max_threads_per_block,
-        batch_size, sequence_length,
-        num_heads, qk_head_size,
-        data.bias, data.query, data.key, data.value, qkv, false, kv_sequence_length);
-    DUMP_TENSOR_D("qkv(BSN3H)", qkv, batch_size, sequence_length, num_heads, 2 * qk_head_size + v_head_size);
-
-    qkv_format = AttentionQkvFormat::QKV_BSN3H;
-  } else {  // unfused kernel
-    ORT_ENFORCE(!use_fused_causal, "MultiHeadAttention has not enabled fused causal");
-
-    // Query (BxSxNxH) => Q (BxNxSxH)
-    constexpr int format = 0;
-    LaunchAddBiasTranspose<T>(
-        stream, 1, format, max_threads_per_block,
-        batch_size, sequence_length, num_heads, qk_head_size,
-        data.query, data.bias, q,
-        true, -1);
-
-    // Key (BxLxNxH) => K (BxNxLxH)
-    LaunchAddBiasTranspose<T>(
-        stream, 1, format, max_threads_per_block,
-        batch_size, kv_sequence_length, num_heads, qk_head_size,
-        data.key, nullptr == data.bias ? nullptr : data.bias + num_heads * qk_head_size, k,
-        true, -1);
-
-    // Value (BxLxNxH_v) => K (BxNxLxH_v)
-    LaunchAddBiasTranspose<T>(
-        stream, 1, format, max_threads_per_block,
-        batch_size, kv_sequence_length, num_heads, v_head_size,
-        data.value, nullptr == data.bias ? nullptr : data.bias + 2 * num_heads * qk_head_size, v,
-        true, -1);
+  return Status::OK();
+}
 
-    DUMP_TENSOR_D("q(BNSH)", q, batch_size, num_heads, sequence_length, qk_head_size);
-    DUMP_TENSOR_D("k(BNSH)", k, batch_size, num_heads, kv_sequence_length, qk_head_size);
-    DUMP_TENSOR_D("v(BNSH)", v, batch_size, num_heads, kv_sequence_length, v_head_size);
-    qkv_format = AttentionQkvFormat::Q_K_V_BNSH;
+// Check whether there is no needed to have workspace for Q, K and V for MultiHeadAttention operator.
+// Please make it in sync with PrepareQkv_MultiHeadAttention.
+template <typename T>
+bool NoQkvWorkspace(contrib::AttentionParameters& parameters, AttentionData<T>& data) {
+  switch (parameters.qkv_format) {
+    case AttentionQkvFormat::Q_K_V_BSNH_BNSH_BNSH:
+      return NoQkvWorkspace_MHA_Cross(data);
+    case AttentionQkvFormat::Q_KV_BSNH_BSN2H:
+      return NoQkvWorkspace_MHA_PackedKV(data);
+    case AttentionQkvFormat::QKV_BSN3H:
+      return NoQkvWorkspace_MHA_PackedQKV(data);
+    case AttentionQkvFormat::Q_K_V_BSNH:
+      if (data.past_key != nullptr || data.present_key != nullptr) {
+        if (data.bias == nullptr) {
+          return NoQkvWorkspace_MHA_WithPast_NoBias(data);
+        } else {
+          return NoQkvWorkspace_MHA_WithPast_Bias(data);
+        }
+      } else {  // no past state
+        return NoQkvWorkspace_MHA_NoPast(data);
+      }
+    default:
+      ORT_THROW("Unsupported QKV format: ", parameters.qkv_format);
   }
-  return Status::OK();
 }
 
 template <typename T>
@@ -439,7 +687,6 @@ Status PrepareQkv(contrib::AttentionParameters& parameters,
                   AttentionData<T>& data,
                   cudaStream_t stream,
                   int max_threads_per_block) {
-  data.scratch = data.workspace;
   if (data.has_qkv_workspace) {
     const int size_per_batch_q = parameters.sequence_length * parameters.head_size;
     const int size_per_batch_k = parameters.kv_sequence_length * parameters.head_size;
@@ -452,28 +699,37 @@ Status PrepareQkv(contrib::AttentionParameters& parameters,
     data.k = data.workspace + elements_q;
     data.v = data.k + elements_k;
     data.scratch = data.v + elements_v;
+  } else {
+    data.q = nullptr;
+    data.k = nullptr;
+    data.v = nullptr;
+    data.scratch = data.workspace;
   }
 
+#if DEBUG_TENSOR_LEVEL > 1
+  DumpInputs(parameters, data);
+#endif
+
   if (nullptr != data.gemm_buffer) {  // Attention operator
-    ORT_RETURN_IF_ERROR(PrepareQkv_Attention<T>(parameters, data, stream, max_threads_per_block,
-                                                data.qkv_format));
-  } else if (data.past_key != nullptr || data.present_key != nullptr) {  // mha operator with past/present state
-    ORT_RETURN_IF_ERROR(PrepareQkv_MHA_WithPast(parameters, data, stream, max_threads_per_block,
-                                                data.q, data.k, data.v, data.qkv_format));
-  } else if (data.key == nullptr) {  // multihead attention operator, no past, packed qkv
-    ORT_RETURN_IF_ERROR(PrepareQkv_MHA_PackedQKV(parameters, data, stream, max_threads_per_block, data.qkv_format));
-  } else if (data.value == nullptr) {  // multihead attention operator, no past, packed kv
-    ORT_RETURN_IF_ERROR(PrepareQkv_MHA_PackedKV(parameters, data, stream, max_threads_per_block, data.qkv_format));
-  } else {  // multihead attention operator, no past, separated Q/K/V inputs
-    ORT_RETURN_IF_ERROR(PrepareQkv_MHA_NotPacked(parameters, data, stream, max_threads_per_block,
-                                                 data.q, data.k, data.v, data.qkv_format));
+    ORT_RETURN_IF_ERROR(PrepareQkv_Attention<T>(parameters, data, stream, max_threads_per_block));
+  } else {  // MultiHeadAttention operator
+    ORT_RETURN_IF_ERROR(PrepareQkv_MultiHeadAttention<T>(parameters, data, stream, max_threads_per_block));
   }
 
+  assert(data.qkv_format != AttentionQkvFormat::UNKNOWN);
+
+#if DEBUG_TENSOR_LEVEL > 1
+  DumpQkv(data);
+#endif
+
   CUDA_RETURN_IF_ERROR(cudaGetLastError());
   return Status::OK();
 }
 
 // Template Instantiation
+template bool NoQkvWorkspace<float>(contrib::AttentionParameters& parameters, AttentionData<float>& data);
+template bool NoQkvWorkspace<half>(contrib::AttentionParameters& parameters, AttentionData<half>& data);
+
 template Status PrepareQkv<float>(
     contrib::AttentionParameters& parameters,
     AttentionData<float>& data,
diff --git a/onnxruntime/contrib_ops/cuda/bert/attention_transpose.cu b/onnxruntime/contrib_ops/cuda/bert/attention_transpose.cu
index bd38a21aadfcb..9f3e396b7f949 100644
--- a/onnxruntime/contrib_ops/cuda/bert/attention_transpose.cu
+++ b/onnxruntime/contrib_ops/cuda/bert/attention_transpose.cu
@@ -304,6 +304,12 @@ Status Transpose_BSNH_to_BNSH(const int batch_size, const int sequence_length, c
                         max_threads_per_block, false, input, output);
 }
 
+Status Transpose_BSNH_to_BNSH(const int batch_size, const int sequence_length, const int num_heads, const int head_size,
+                              const float* input, float* output, cudaStream_t stream, const int max_threads_per_block) {
+  return LaunchTransQkv(stream, 1, sequence_length, batch_size, head_size, num_heads,
+                        max_threads_per_block, false, input, output);
+}
+
 }  // namespace cuda
 }  // namespace contrib
 }  // namespace onnxruntime
diff --git a/onnxruntime/contrib_ops/cuda/bert/decoder_masked_multihead_attention.cc b/onnxruntime/contrib_ops/cuda/bert/decoder_masked_multihead_attention.cc
index 66c0aceaed1e7..037a4fdf3d9a0 100644
--- a/onnxruntime/contrib_ops/cuda/bert/decoder_masked_multihead_attention.cc
+++ b/onnxruntime/contrib_ops/cuda/bert/decoder_masked_multihead_attention.cc
@@ -75,7 +75,6 @@ Status DecoderMaskedMultiHeadAttention<T1, T2>::ComputeInternal(OpKernelContext*
       attention::kDecoderMaskedAttentionLoadKVDataInFlight, false);
 
   bool is_unidirectional = false;
-  bool is_dmmha_packing = (key == nullptr && value == nullptr);
   ORT_RETURN_IF_ERROR(multihead_attention_helper::CheckInputs<Tensor>(query,
                                                                       key,
                                                                       value,
@@ -91,7 +90,7 @@ Status DecoderMaskedMultiHeadAttention<T1, T2>::ComputeInternal(OpKernelContext*
                                                                       scale_,
                                                                       is_unidirectional,
                                                                       past_present_share_buffer_,
-                                                                      is_dmmha_packing,  // dmmha_packing
+                                                                      kDecoderMaskedMultiHeadAttention,
                                                                       device_prop.maxThreadsPerBlock));
 
   if (bias) {
@@ -157,7 +156,7 @@ Status DecoderMaskedMultiHeadAttention<T1, T2>::ComputeInternal(OpKernelContext*
     parameters.is_cross_attention = true;
     parameters.total_sequence_length = parameters.kv_sequence_length;
     parameters.max_sequence_length = parameters.kv_sequence_length;
-    // parameters.k and paraneters.v are nullptr
+    // parameters.k and parameters.v are nullptr
     parameters.k_cache = const_cast<T1*>(key->Data<T1>());
     parameters.v_cache = const_cast<T1*>(value->Data<T1>());
     parameters.k_bias = nullptr;
@@ -188,12 +187,14 @@ Status DecoderMaskedMultiHeadAttention<T1, T2>::ComputeInternal(OpKernelContext*
     }
 
     parameters.is_cross_attention = false;
-    parameters.is_packed_qkv = is_dmmha_packing;
 
-    parameters.k = is_dmmha_packing
+    bool is_packed_qkv = (key == nullptr && value == nullptr);
+    parameters.is_packed_qkv = is_packed_qkv;
+
+    parameters.k = is_packed_qkv
                        ? const_cast<T1*>(query->Data<T1>() + parameters.hidden_size)
                        : const_cast<T1*>(key->Data<T1>());
-    parameters.v = is_dmmha_packing
+    parameters.v = is_packed_qkv
                        ? const_cast<T1*>(query->Data<T1>() + 2 * static_cast<size_t>(parameters.hidden_size))
                        : const_cast<T1*>(value->Data<T1>());
     parameters.k_cache = present_key_data;
diff --git a/onnxruntime/contrib_ops/cuda/bert/fastertransformer_decoder_attention/decoder_masked_multihead_attention_impl.cu b/onnxruntime/contrib_ops/cuda/bert/fastertransformer_decoder_attention/decoder_masked_multihead_attention_impl.cu
index 9efb6f08e8e99..2f8d277cb7342 100644
--- a/onnxruntime/contrib_ops/cuda/bert/fastertransformer_decoder_attention/decoder_masked_multihead_attention_impl.cu
+++ b/onnxruntime/contrib_ops/cuda/bert/fastertransformer_decoder_attention/decoder_masked_multihead_attention_impl.cu
@@ -183,6 +183,7 @@ __global__ void masked_multihead_attention_kernel(DecoderMaskedMultiHeadAttentio
     *reinterpret_cast<Qk_vec_k*>(&q_smem[tidx * QK_VEC_SIZE]) = q;
   }
 
+  // This has assumption that key and value does not have bias for cross attention when they are in BNSH format.
   if (!params.is_cross_attention) {
     Qk_vec_k k;
 
@@ -580,6 +581,8 @@ __global__ void masked_multihead_attention_kernel(DecoderMaskedMultiHeadAttentio
 
   // One group of threads computes the product(s) for the current timestep.
   V_vec_k v_bias;
+
+  // This has assumption that key and value does not have bias for cross attention when they are in BNSH format.
   if (params.v_bias && !params.is_cross_attention) {
     zero(v_bias);
 
diff --git a/onnxruntime/contrib_ops/cuda/bert/multihead_attention.cc b/onnxruntime/contrib_ops/cuda/bert/multihead_attention.cc
index 663bd020ddac7..c36abc8e1d624 100644
--- a/onnxruntime/contrib_ops/cuda/bert/multihead_attention.cc
+++ b/onnxruntime/contrib_ops/cuda/bert/multihead_attention.cc
@@ -7,6 +7,7 @@
 #include "contrib_ops/cpu/bert/multihead_attention_helper.h"
 #include "contrib_ops/cuda/bert/cutlass_fmha/memory_efficient_attention.h"
 #include "contrib_ops/cuda/bert/flash_attention/flash_api.h"
+#include "contrib_ops/cuda/utils/dump_cuda_tensor.h"
 
 using namespace onnxruntime::cuda;
 using namespace ::onnxruntime::common;
@@ -44,7 +45,8 @@ MultiHeadAttention<T>::MultiHeadAttention(const OpKernelInfo& info)
 
   scale_ = info.GetAttrOrDefault<float>("scale", 0.0f);
   is_unidirectional_ = info.GetAttrOrDefault<int64_t>("unidirectional", 0) == 1;
-  ORT_ENFORCE(!is_unidirectional_, "Unidirectional MHA does not support CUDA kernel. Consider using Attention or GQA instead.");
+  ORT_ENFORCE(!is_unidirectional_,
+              "MHA support CUDA kernel does not Unidirectional. Consider using Attention or GQA instead.");
 
   kernel_options_ = this->GetAttentionKernelOptions();
 
@@ -95,7 +97,7 @@ Status MultiHeadAttention<T>::ComputeInternal(OpKernelContext* context) const {
                                                                       scale_,
                                                                       is_unidirectional_,
                                                                       false,  // past_present_share_buffer
-                                                                      false,  // dmmha_packing
+                                                                      kMultiHeadAttention,
                                                                       device_prop.maxThreadsPerBlock));
   int sequence_length = parameters.sequence_length;
 
@@ -111,25 +113,43 @@ Status MultiHeadAttention<T>::ComputeInternal(OpKernelContext* context) const {
   Tensor* present_key = context->Output(1, present_shape);
   Tensor* present_value = context->Output(2, present_shape);
 
-  MHARunner* fused_runner = nullptr;
+  int num_past = static_cast<int>(past_key != nullptr) + static_cast<int>(past_value != nullptr);
+  int num_present = static_cast<int>(present_key != nullptr) + static_cast<int>(present_value != nullptr);
+  if (num_past == 0 && num_present == 0) {
+    // It is valid case without past state.
+  } else if ((num_past == 2 && num_present == 2) || (num_past == 0 && num_present == 2)) {
+    if (parameters.qkv_format == AttentionQkvFormat::QKV_BSN3H) {
+      return ORT_MAKE_STATUS(
+          ONNXRUNTIME, INVALID_ARGUMENT,
+          "Inputs 'past_key', 'past_value', 'present_key' and 'present_value' shall be empty for packed QKV format");
+    }
+
+    if (parameters.qkv_format == AttentionQkvFormat::Q_KV_BSNH_BSN2H) {
+      return ORT_MAKE_STATUS(
+          ONNXRUNTIME, INVALID_ARGUMENT,
+          "Inputs 'past_key', 'past_value', 'present_key' and 'present_value' shall be empty for packed KV format");
+    }
+
+    if (parameters.qkv_format == AttentionQkvFormat::Q_K_V_BSNH_BNSH_BNSH) {
+      return ORT_MAKE_STATUS(
+          ONNXRUNTIME, INVALID_ARGUMENT,
+          "Inputs 'past_key', 'past_value', 'present_key' and 'present_value' shall be empty for cross attention");
+    }
+  } else {
+    return ORT_MAKE_STATUS(
+        ONNXRUNTIME, INVALID_ARGUMENT,
+        "Inputs 'past_key', 'past_value', 'present_key' and 'present_value' shall be all provided, "
+        "or all empty, or only present_key and present_value are provided");
+  }
 
+  MHARunner* fused_runner = nullptr;
   const FusedMultiHeadCrossAttentionKernel* fused_cross_attention_kernel = nullptr;
 
   // Check whether we can use fused kernel
   int sm = device_prop.major * 10 + device_prop.minor;
 
-  bool is_mask_1d_seq_len = parameters.mask_type == AttentionMaskType::MASK_1D_KEY_SEQ_LEN;
-
-  const bool pass_key_value_as_past = (parameters.pass_past_in_kv && nullptr != key && nullptr != value);
-
-#if USE_FLASH_ATTENTION || USE_MEMORY_EFFICIENT_ATTENTION
-  // Exclude this case since PrepareQkv will convert the format to BNSH.
-  bool past_no_bias = (pass_key_value_as_past || past_key != nullptr || present_key != nullptr) && bias == nullptr;
-#endif
-
 #if USE_FLASH_ATTENTION
   bool use_flash_attention = !disable_flash_attention_ &&
-                             !past_no_bias &&
                              nullptr == relative_position_bias &&
                              nullptr == key_padding_mask &&
                              parameters.head_size == parameters.v_head_size &&
@@ -138,7 +158,7 @@ Status MultiHeadAttention<T>::ComputeInternal(OpKernelContext* context) const {
                                                               parameters.num_heads,
                                                               parameters.num_heads);
   // When input is packed QKV format, TensorRT kernel might be faster than flash attention when sequence length <= 512.
-  if (use_flash_attention && key == nullptr && value == nullptr &&
+  if (use_flash_attention && parameters.qkv_format == AttentionQkvFormat::QKV_BS3NH &&
       parameters.sequence_length < kernel_options_->MinSeqLenForFlashAttentionPackedQkv()) {
     use_flash_attention = false;
   }
@@ -162,19 +182,21 @@ Status MultiHeadAttention<T>::ComputeInternal(OpKernelContext* context) const {
   auto out_accum_buffer = GetScratchBuffer<void>(0, context->GetComputeStream());          // nullptr
 #endif
 
-  bool use_fused_cross_attention = !use_flash_attention &&
-                                   !disable_fused_cross_attention_ &&
-                                   nullptr == key_padding_mask &&
-                                   nullptr == relative_position_bias &&
-                                   (nullptr == past_key && nullptr == past_value && !parameters.pass_past_in_kv) &&
-                                   key != nullptr &&
-                                   (value != nullptr || bias == nullptr) &&  // TODO: new kernel for adding bias to packed KV
-                                   parameters.hidden_size == parameters.v_hidden_size &&
-                                   has_fused_cross_attention_kernel(sm, parameters.head_size,
-                                                                    parameters.kv_sequence_length);
+  bool use_fused_cross_attention =
+      !use_flash_attention &&
+      !disable_fused_cross_attention_ &&
+      nullptr == key_padding_mask &&
+      nullptr == relative_position_bias &&
+      nullptr == past_key && nullptr == present_key &&
+      (parameters.qkv_format == Q_K_V_BSNH || (parameters.qkv_format == Q_KV_BSNH_BSN2H && bias == nullptr)) &&
+      parameters.hidden_size == parameters.v_hidden_size &&
+      has_fused_cross_attention_kernel(sm, parameters.head_size,
+                                       parameters.kv_sequence_length);
   if (use_fused_cross_attention) {
     if (fused_fp16_cross_attention_kernel_ == nullptr) {
-      fused_fp16_cross_attention_kernel_ = get_fused_cross_attention_kernels(sm);
+      std::call_once(fused_cross_init_once_flag_, [&]() {
+        fused_fp16_cross_attention_kernel_ = get_fused_cross_attention_kernels(sm);
+      });
     }
 
     // In case some kernel not loaded due to shared memory limit, we need to double check here.
@@ -184,17 +206,18 @@ Status MultiHeadAttention<T>::ComputeInternal(OpKernelContext* context) const {
     }
   }
 
-  bool use_fused_runner = !use_flash_attention &&
-                          !disable_fused_self_attention_ &&
-                          fused_cross_attention_kernel == nullptr &&
-                          nullptr == relative_position_bias &&
-                          (value != nullptr || key == nullptr) &&
-                          (nullptr == past_key && nullptr == past_value && !parameters.pass_past_in_kv) &&
-                          (nullptr == key_padding_mask || is_mask_1d_seq_len) &&
-                          parameters.hidden_size == parameters.v_hidden_size &&
-                          parameters.sequence_length == parameters.kv_sequence_length &&
-                          FusedMHARunnerFP16v2::IsSupported(sm, parameters.head_size, sequence_length,
-                                                            enable_trt_flash_attention_, false);
+  bool use_fused_runner =
+      !use_flash_attention &&
+      !disable_fused_self_attention_ &&
+      fused_cross_attention_kernel == nullptr &&
+      nullptr == relative_position_bias &&
+      (parameters.qkv_format == Q_K_V_BSNH || parameters.qkv_format == QKV_BSN3H) &&
+      nullptr == past_key && nullptr == present_key &&
+      (nullptr == key_padding_mask || AttentionMaskType::MASK_1D_KEY_SEQ_LEN) &&
+      parameters.hidden_size == parameters.v_hidden_size &&
+      parameters.sequence_length == parameters.kv_sequence_length &&  // self attention only for fused runner
+      FusedMHARunnerFP16v2::IsSupported(sm, parameters.head_size, sequence_length,
+                                        enable_trt_flash_attention_, false);
   if (use_fused_runner) {
     // Here we assume that num_heads and head_size does not change for a MultiHeadAttention node.
     if (nullptr == fused_fp16_runner_.get()) {
@@ -214,10 +237,11 @@ Status MultiHeadAttention<T>::ComputeInternal(OpKernelContext* context) const {
 
 #if USE_MEMORY_EFFICIENT_ATTENTION
   int length_threshold = this->kernel_options_->MinSeqLenForEfficientAttentionFp32();
-  bool is_long_sequence = sizeof(T) == 2 ||  // sequence length threshold is 0 for FP16
+  bool is_long_sequence = std::is_same<T, MLFloat16>::value ||  // sequence length threshold is 0 for FP16
                           parameters.sequence_length >= length_threshold ||
                           parameters.kv_sequence_length >= length_threshold;
 
+  // Check whether the relative position bias alignment is good for memory efficient attention.
   bool is_good_for_rpb = relative_position_bias != nullptr && parameters.sequence_length % (4 * sizeof(T)) == 0;
 
   bool use_memory_efficient_attention =
@@ -226,82 +250,25 @@ Status MultiHeadAttention<T>::ComputeInternal(OpKernelContext* context) const {
       fused_cross_attention_kernel == nullptr &&
       !disable_memory_efficient_attention_ &&
       is_long_sequence &&
-      !past_no_bias &&
       (relative_position_bias == nullptr || is_good_for_rpb) &&
       (nullptr == key_padding_mask || parameters.mask_type == AttentionMaskType::MASK_1D_KEY_SEQ_LEN_START) &&
-      has_memory_efficient_attention(sm, sizeof(T) == 2, parameters.head_size, parameters.v_head_size);
+      has_memory_efficient_attention(sm, std::is_same<T, MLFloat16>::value,
+                                     parameters.head_size, parameters.v_head_size);
 #else
   constexpr bool use_memory_efficient_attention = false;
 #endif
 
-  if (kernel_options_->AllowDebugInfo()) {
-    AttentionKernelDebugInfo debug_info;
-    debug_info.use_flash_attention = use_flash_attention;
-    debug_info.use_trt_cross_attention = fused_cross_attention_kernel != nullptr;
-    debug_info.use_efficient_attention = use_memory_efficient_attention;
-    if (fused_fp16_runner_ != nullptr) {
-      debug_info.SetTrtFusedKernel(is_unidirectional_, enable_trt_flash_attention_, sequence_length);
-    }
-
-    debug_info.Print("MultiHeadAttention",
-                     this->Node().Name(),
-                     std::is_same<T, MLFloat16>::value,
-                     std::is_same<T, BFloat16>::value);
-  }
-
-  // When packed kv or packed qkv is used, there is no needed for add bias transpose thus no qkv workspace.
-  // TODO(tianleiwu): flash attention or memory efficient attention might not need qkv workspace sometime.
-  bool no_qkv_workspace = nullptr == value &&
-                          (use_fused_cross_attention || (nullptr != fused_runner && nullptr == key)) &&
-                          nullptr == key_padding_mask &&
-                          nullptr == bias;
-
-  size_t workspace_bytes;
-  constexpr size_t element_size = sizeof(T);
-  if (no_qkv_workspace) {
-    workspace_bytes = (parameters.batch_size > kCumulatedSequenceLengthCacheMaxBatchSize) ? 2 * GetSequenceOffsetSize(parameters.batch_size, true) : 0;
-  } else {
-    workspace_bytes = GetAttentionWorkspaceSize(element_size,
-                                                parameters.batch_size,
-                                                parameters.num_heads,
-                                                parameters.head_size,
-                                                parameters.v_head_size,
-                                                parameters.sequence_length,
-                                                parameters.kv_sequence_length,
-                                                parameters.total_sequence_length,
-                                                fused_runner,
-                                                use_flash_attention,
-                                                use_fused_cross_attention,
-                                                use_memory_efficient_attention);
-  }
-
-  auto work_space = GetScratchBuffer<void>(workspace_bytes, context->GetComputeStream());
-
-  const size_t past_k_bytes = element_size * parameters.batch_size * parameters.kv_sequence_length * parameters.num_heads * parameters.head_size;
-  const size_t past_v_bytes = element_size * parameters.batch_size * parameters.kv_sequence_length * parameters.num_heads * parameters.v_head_size;
-  const bool use_temp_k_v_workspace = parameters.pass_past_in_kv || use_memory_efficient_attention || use_flash_attention;
-  auto temp_k_work_space = use_temp_k_v_workspace ? GetScratchBuffer<void>(past_k_bytes, context->GetComputeStream()) : nullptr;
-  auto temp_v_work_space = use_temp_k_v_workspace ? GetScratchBuffer<void>(past_v_bytes, context->GetComputeStream()) : nullptr;
-
   typedef typename ToCudaType<T>::MappedType CudaT;
   AttentionData<CudaT> data;
   data.bias = (nullptr == bias) ? nullptr : reinterpret_cast<const CudaT*>(bias->Data<T>());
   data.query = reinterpret_cast<const CudaT*>(query->Data<T>());
-  data.key = (nullptr == key || parameters.pass_past_in_kv) ? nullptr : reinterpret_cast<const CudaT*>(key->Data<T>());
-  data.value = (nullptr == value || parameters.pass_past_in_kv) ? nullptr : reinterpret_cast<const CudaT*>(value->Data<T>());
+  data.key = (nullptr == key) ? nullptr : reinterpret_cast<const CudaT*>(key->Data<T>());
+  data.value = (nullptr == value) ? nullptr : reinterpret_cast<const CudaT*>(value->Data<T>());
   data.mask_index = (nullptr == key_padding_mask) ? nullptr : key_padding_mask->Data<int>();
   data.mask_index_dims = (nullptr == key_padding_mask) ? gsl::span<const int64_t>() : key_padding_mask->Shape().GetDims();
-  data.past_key = pass_key_value_as_past  ? reinterpret_cast<const CudaT*>(key->Data<T>())
-                  : (nullptr == past_key) ? nullptr
-                                          : reinterpret_cast<const CudaT*>(past_key->Data<T>());
-  data.past_value = pass_key_value_as_past    ? reinterpret_cast<const CudaT*>(value->Data<T>())
-                    : (nullptr == past_value) ? nullptr
-                                              : reinterpret_cast<const CudaT*>(past_value->Data<T>());
+  data.past_key = (nullptr == past_key) ? nullptr : reinterpret_cast<const CudaT*>(past_key->Data<T>());
+  data.past_value = (nullptr == past_value) ? nullptr : reinterpret_cast<const CudaT*>(past_value->Data<T>());
   data.relative_position_bias = (nullptr == relative_position_bias) ? nullptr : reinterpret_cast<const CudaT*>(relative_position_bias->Data<T>());
-  data.has_qkv_workspace = !no_qkv_workspace;
-  data.workspace = reinterpret_cast<CudaT*>(work_space.get());
-  data.temp_k_workspace = use_temp_k_v_workspace ? reinterpret_cast<CudaT*>(temp_k_work_space.get()) : nullptr;
-  data.temp_v_workspace = use_temp_k_v_workspace ? reinterpret_cast<CudaT*>(temp_v_work_space.get()) : nullptr;
   data.output = reinterpret_cast<CudaT*>(output->MutableData<T>());
   data.present_key = (nullptr == present_key) ? nullptr : reinterpret_cast<CudaT*>(present_key->MutableData<T>());
   data.present_value = (nullptr == present_value) ? nullptr : reinterpret_cast<CudaT*>(present_value->MutableData<T>());
@@ -309,8 +276,41 @@ Status MultiHeadAttention<T>::ComputeInternal(OpKernelContext* context) const {
   data.fused_cross_attention_kernel = fused_cross_attention_kernel;
   data.use_flash_attention = use_flash_attention;
   data.use_memory_efficient_attention = use_memory_efficient_attention;
-  data.cumulated_sequence_length_q_cache = &(this->cumulated_sequence_length_q_cache_);
-  data.cumulated_sequence_length_kv_cache = &(this->cumulated_sequence_length_kv_cache_);
+
+  // Cache of cumulated sequence length that could help when sequence length does not change (for example, image model).
+  // The cache will be initialized only once, and become readonly after that.
+  if ((data.fused_cross_attention_kernel != nullptr || data.fused_runner != nullptr) && data.mask_index == nullptr) {
+    cudaStream_t stream = Stream(context);
+    data.cumulated_sequence_length_q_cache = this->cumulated_sequence_length_q_cache_.TryGet(
+        parameters.batch_size, parameters.sequence_length, stream);
+
+    if (data.fused_cross_attention_kernel != nullptr) {
+      data.cumulated_sequence_length_kv_cache = this->cumulated_sequence_length_kv_cache_.TryGet(
+          parameters.batch_size, parameters.kv_sequence_length, stream);
+    }
+  }
+
+  const bool no_qkv_workspace = NoQkvWorkspace(parameters, data);
+  size_t workspace_bytes = GetAttentionWorkspaceSize(sizeof(T),
+                                                     parameters.batch_size,
+                                                     parameters.num_heads,
+                                                     parameters.head_size,
+                                                     parameters.v_head_size,
+                                                     parameters.sequence_length,
+                                                     parameters.kv_sequence_length,
+                                                     parameters.total_sequence_length,
+                                                     fused_runner,
+                                                     use_flash_attention,
+                                                     use_fused_cross_attention,
+                                                     use_memory_efficient_attention,
+                                                     no_qkv_workspace);
+  auto work_space = GetScratchBuffer<void>(workspace_bytes, context->GetComputeStream());
+
+  data.has_qkv_workspace = !no_qkv_workspace;
+  data.workspace = reinterpret_cast<CudaT*>(work_space.get());
+  data.workspace_bytes = workspace_bytes;
+
+  data.allow_debug_info = kernel_options_->AllowDebugInfo();
   if (softmax_lse_accum_buffer != nullptr) {
     data.softmax_lse_accum = reinterpret_cast<CudaT*>(softmax_lse_accum_buffer.get());
   }
@@ -318,8 +318,23 @@ Status MultiHeadAttention<T>::ComputeInternal(OpKernelContext* context) const {
     data.out_accum = reinterpret_cast<CudaT*>(out_accum_buffer.get());
   }
 
-  cublasHandle_t cublas = GetCublasHandle(context);
+  if (data.allow_debug_info) {
+    AttentionKernelDebugInfo debug_info;
+    debug_info.use_flash_attention = use_flash_attention;
+    debug_info.use_trt_cross_attention = fused_cross_attention_kernel != nullptr;
+    debug_info.use_efficient_attention = use_memory_efficient_attention;
+    if (fused_fp16_runner_ != nullptr) {
+      debug_info.SetTrtFusedKernel(is_unidirectional_, enable_trt_flash_attention_, sequence_length);
+    }
+    debug_info.Print("MultiHeadAttention",
+                     this->Node().Name(),
+                     std::is_same<T, MLFloat16>::value,
+                     std::is_same<T, BFloat16>::value);
+
+    data.PrintDebugInfo();
+  }
 
+  cublasHandle_t cublas = GetCublasHandle(context);
   return QkvToContext<CudaT>(
       device_prop, cublas, context->GetComputeStream(), parameters, data);
 }
diff --git a/onnxruntime/contrib_ops/cuda/bert/multihead_attention.h b/onnxruntime/contrib_ops/cuda/bert/multihead_attention.h
index 26e38dbad9fd7..68fd0c9943fca 100644
--- a/onnxruntime/contrib_ops/cuda/bert/multihead_attention.h
+++ b/onnxruntime/contrib_ops/cuda/bert/multihead_attention.h
@@ -4,6 +4,7 @@
 #pragma once
 
 #include <memory>
+#include <mutex>
 #include "core/providers/cuda/cuda_kernel.h"
 #include "contrib_ops/cuda/bert/tensorrt_fused_multihead_attention/mha_runner.h"
 #include "contrib_ops/cuda/bert/tensorrt_fused_multihead_attention/cross_attention/fmha_cross_attention.h"
@@ -32,11 +33,16 @@ class MultiHeadAttention final : public CudaKernel {
   bool disable_fused_cross_attention_;
   bool disable_flash_attention_;
   bool disable_memory_efficient_attention_;
+
+  // These mutable members are readonly after they are initialized so that they can be shared among multiple threads.
+  // Initialization are done only once by the first thread using the resource, so use once_flag to guard each resource.
   mutable std::unique_ptr<MHARunner> fused_fp16_runner_;
   mutable std::once_flag fused_fp16_runner_created_;
   mutable const FusedMultiHeadCrossAttentionKernel* fused_fp16_cross_attention_kernel_;
+  mutable std::once_flag fused_cross_init_once_flag_;
   mutable CumulatedSequenceLengthCache cumulated_sequence_length_q_cache_;
   mutable CumulatedSequenceLengthCache cumulated_sequence_length_kv_cache_;
+
   const AttentionKernelOptions* kernel_options_;
 };
 
diff --git a/onnxruntime/contrib_ops/cuda/bert/packed_attention_impl.cu b/onnxruntime/contrib_ops/cuda/bert/packed_attention_impl.cu
index ac2cb5165a94c..2521cd49b5482 100644
--- a/onnxruntime/contrib_ops/cuda/bert/packed_attention_impl.cu
+++ b/onnxruntime/contrib_ops/cuda/bert/packed_attention_impl.cu
@@ -297,7 +297,7 @@ struct T2<half> {
 };
 
 template <typename T>
-void LaunchAddBiasTranspose(
+void AddBiasTransposePacked(
     const T* input, const T* biases, T* output,
     const int batch_size, const int sequence_length,
     const int num_heads, const int qk_head_size, const int v_head_size,
@@ -452,7 +452,7 @@ Status FusedScaledDotProductAttention(
   void* fused_runner = data.fused_runner;
   ORT_RETURN_IF_NOT(nullptr != fused_runner, "fused_runner cannot be NULL");
 
-  LaunchAddBiasTranspose(data.gemm_buffer, data.bias, data.workspace,
+  AddBiasTransposePacked(data.gemm_buffer, data.bias, data.workspace,
                          batch_size, sequence_length,
                          num_heads, qk_head_size, v_head_size,
                          AttentionQkvFormat::QKV_BSN3H, data.token_offset,
@@ -477,7 +477,7 @@ Status FusedScaledDotProductAttentionCutlass(
   const int num_heads = parameters.num_heads;
   const int qk_head_size = parameters.head_size;
   const int v_head_size = parameters.v_head_size;
-  LaunchAddBiasTranspose(data.gemm_buffer, data.bias, data.workspace,
+  AddBiasTransposePacked(data.gemm_buffer, data.bias, data.workspace,
                          batch_size, sequence_length,
                          num_heads, qk_head_size, v_head_size,
                          AttentionQkvFormat::Q_K_V_BSNH, data.token_offset,
@@ -564,7 +564,7 @@ Status UnfusedScaledDotProductAttention(
   T* k = q + elements_q;
   T* v = k + elements_k;
 
-  LaunchAddBiasTranspose(data.gemm_buffer, data.bias, data.workspace,
+  AddBiasTransposePacked(data.gemm_buffer, data.bias, data.workspace,
                          batch_size, sequence_length,
                          num_heads, qk_head_size, v_head_size,
                          AttentionQkvFormat::Q_K_V_BNSH, data.token_offset,
@@ -657,6 +657,20 @@ Status QkvToContext(
   return UnfusedScaledDotProductAttention<T>(device_prop, cublas, stream, parameters, data);
 }
 
+template void AddBiasTransposePacked<float>(
+    const float* input, const float* biases, float* output,
+    const int batch_size, const int sequence_length,
+    const int num_heads, const int qk_head_size, const int v_head_size,
+    AttentionQkvFormat format, const int32_t* token_offset, int32_t token_count,
+    cudaStream_t stream);
+
+template void AddBiasTransposePacked<half>(
+    const half* input, const half* biases, half* output,
+    const int batch_size, const int sequence_length,
+    const int num_heads, const int qk_head_size, const int v_head_size,
+    AttentionQkvFormat format, const int32_t* token_offset, int32_t token_count,
+    cudaStream_t stream);
+
 template Status QkvToContext<float>(
     const cudaDeviceProp& device_prop,
     cublasHandle_t& cublas,
diff --git a/onnxruntime/contrib_ops/cuda/bert/packed_multihead_attention_impl.cu b/onnxruntime/contrib_ops/cuda/bert/packed_multihead_attention_impl.cu
index b4ca0194b08bc..e5a4c54f48903 100644
--- a/onnxruntime/contrib_ops/cuda/bert/packed_multihead_attention_impl.cu
+++ b/onnxruntime/contrib_ops/cuda/bert/packed_multihead_attention_impl.cu
@@ -502,7 +502,7 @@ struct T2<half> {
 };
 
 template <typename T>
-void LaunchTranspose(
+void AddBiasTransposePacked(
     const T* query, const T* key, const T* value, const T* bias, T* output,
     const int batch_size, const int sequence_length,
     const int num_heads, const int qk_head_size, const int v_head_size,
@@ -566,11 +566,11 @@ Status FusedAttentionTrt(
   // When packed QKV is used, we can directly pass it to fused runner. Otherwise, we need transpose to BSN3H format.
   const T* qkv = data.query;
   if (!data.no_qkv_workspace) {
-    LaunchTranspose(data.query, data.key, data.value, data.bias, data.workspace,
-                    batch_size, sequence_length,
-                    num_heads, qk_head_size, v_head_size,
-                    data.source_qkv_format, AttentionQkvFormat::QKV_TN3H,
-                    data.token_offset, parameters.token_count, stream);
+    AddBiasTransposePacked(data.query, data.key, data.value, data.bias, data.workspace,
+                           batch_size, sequence_length,
+                           num_heads, qk_head_size, v_head_size,
+                           data.source_qkv_format, AttentionQkvFormat::QKV_TN3H,
+                           data.token_offset, parameters.token_count, stream);
     qkv = data.workspace;
   }
 
@@ -601,11 +601,11 @@ Status FlashAttention(
 
   // When separated Q, K, V is used, we can directly use them in Cutlass FMHA. Otherwise, transpose BSN3H to 3BSNH
   if (!data.no_qkv_workspace) {
-    LaunchTranspose(data.query, data.key, data.value, data.bias, data.workspace,
-                    batch_size, sequence_length,
-                    num_heads, qk_head_size, v_head_size,
-                    data.source_qkv_format, AttentionQkvFormat::Q_K_V_TNH,
-                    data.token_offset, parameters.token_count, stream);
+    AddBiasTransposePacked(data.query, data.key, data.value, data.bias, data.workspace,
+                           batch_size, sequence_length,
+                           num_heads, qk_head_size, v_head_size,
+                           data.source_qkv_format, AttentionQkvFormat::Q_K_V_TNH,
+                           data.token_offset, parameters.token_count, stream);
   }
 
   float scale = parameters.scale == 0.0f ? 1.f / sqrt(static_cast<float>(qk_head_size))
@@ -675,11 +675,11 @@ Status FusedAttentionCutlass(
 
   // When separated Q, K, V is used, we can directly use them in Cutlass FMHA. Otherwise, transpose BSN3H to 3BSNH
   if (!data.no_qkv_workspace) {
-    LaunchTranspose(data.query, data.key, data.value, data.bias, data.workspace,
-                    batch_size, sequence_length,
-                    num_heads, qk_head_size, v_head_size,
-                    data.source_qkv_format, AttentionQkvFormat::Q_K_V_TNH,
-                    data.token_offset, parameters.token_count, stream);
+    AddBiasTransposePacked(data.query, data.key, data.value, data.bias, data.workspace,
+                           batch_size, sequence_length,
+                           num_heads, qk_head_size, v_head_size,
+                           data.source_qkv_format, AttentionQkvFormat::Q_K_V_TNH,
+                           data.token_offset, parameters.token_count, stream);
   }
 
   MemoryEfficientAttentionParams p;
@@ -746,11 +746,11 @@ Status UnfusedAttention(
   const size_t elements_v = static_cast<size_t>(batches) * static_cast<size_t>(size_per_batch_v);
 
   // Q, K and V pointers when fused attention is not used
-  LaunchTranspose(data.query, data.key, data.value, data.bias, data.workspace,
-                  batch_size, sequence_length,
-                  num_heads, qk_head_size, v_head_size,
-                  data.source_qkv_format, AttentionQkvFormat::Q_K_V_BNSH,
-                  data.token_offset, parameters.token_count, stream);
+  AddBiasTransposePacked(data.query, data.key, data.value, data.bias, data.workspace,
+                         batch_size, sequence_length,
+                         num_heads, qk_head_size, v_head_size,
+                         data.source_qkv_format, AttentionQkvFormat::Q_K_V_BNSH,
+                         data.token_offset, parameters.token_count, stream);
 
   T* qkv = data.workspace;
   T* q = qkv;
@@ -848,6 +848,22 @@ Status QkvToContext(
   return UnfusedAttention<T>(device_prop, cublas, stream, parameters, data);
 }
 
+template void AddBiasTransposePacked<half>(
+    const half* query, const half* key, const half* value, const half* bias, half* output,
+    const int batch_size, const int sequence_length,
+    const int num_heads, const int qk_head_size, const int v_head_size,
+    AttentionQkvFormat source_format, AttentionQkvFormat target_format,
+    const int32_t* token_offset, int32_t token_count,
+    cudaStream_t stream);
+
+template void AddBiasTransposePacked<float>(
+    const float* query, const float* key, const float* value, const float* bias, float* output,
+    const int batch_size, const int sequence_length,
+    const int num_heads, const int qk_head_size, const int v_head_size,
+    AttentionQkvFormat source_format, AttentionQkvFormat target_format,
+    const int32_t* token_offset, int32_t token_count,
+    cudaStream_t stream);
+
 template Status QkvToContext<float>(
     const cudaDeviceProp& device_prop,
     cublasHandle_t& cublas,
diff --git a/onnxruntime/contrib_ops/cuda/quantization/attention_quantization.cc b/onnxruntime/contrib_ops/cuda/quantization/attention_quantization.cc
index 168c69c69f003..b62e566d43f89 100644
--- a/onnxruntime/contrib_ops/cuda/quantization/attention_quantization.cc
+++ b/onnxruntime/contrib_ops/cuda/quantization/attention_quantization.cc
@@ -190,7 +190,8 @@ Status QAttention<T, int8_t>::ComputeInternal(OpKernelContext* context) const {
                                                    fused_runner,
                                                    use_flash_attention,
                                                    use_fused_cross_attention,
-                                                   use_memory_efficient_attention);
+                                                   use_memory_efficient_attention,
+                                                   true);
 
   auto work_space = GetScratchBuffer<void>(workSpaceSize, context->GetComputeStream());
 
@@ -208,6 +209,7 @@ Status QAttention<T, int8_t>::ComputeInternal(OpKernelContext* context) const {
 
   data.has_qkv_workspace = true;
   data.workspace = reinterpret_cast<CudaT*>(work_space.get());
+  data.workspace_bytes = workSpaceSize;
   data.output = reinterpret_cast<CudaT*>(output->MutableData<T>());
   if (nullptr != present) {
     data.present = reinterpret_cast<CudaT*>(present->MutableData<T>());
diff --git a/onnxruntime/contrib_ops/cuda/utils/dump_cuda_tensor.cc b/onnxruntime/contrib_ops/cuda/utils/dump_cuda_tensor.cc
index e10c2ec63fd51..6d52ff7282799 100644
--- a/onnxruntime/contrib_ops/cuda/utils/dump_cuda_tensor.cc
+++ b/onnxruntime/contrib_ops/cuda/utils/dump_cuda_tensor.cc
@@ -13,6 +13,9 @@ namespace cuda {
 
 #if DUMP_TENSOR_LEVEL > 0
 
+// Environment variable to enable/disable GPU Tensor dumping
+constexpr const char* kEnableGpuTensorDumper = "ORT_ENABLE_GPU_DUMP";
+
 // Total number of elements which trigger snippet rather than full dump (default 200). Value 0 disables snippet.
 constexpr const char* kTensorSnippetThreshold = "ORT_TENSOR_SNIPPET_THRESHOLD";
 
@@ -202,6 +205,10 @@ void DumpGpuTensor(const char* name, const Tensor& tensor) {
   DumpGpuTensor(nullptr, tensor, static_cast<int>(num_rows), static_cast<int>(row_size));
 }
 
+CudaTensorConsoleDumper::CudaTensorConsoleDumper() {
+  is_enabled_ = ParseEnvironmentVariableWithDefault<int>(kEnableGpuTensorDumper, 1) != 0;
+}
+
 void CudaTensorConsoleDumper::Print(const std::string& value) const {
   std::cout << value << std::endl;
 }
@@ -329,6 +336,8 @@ void CudaTensorConsoleDumper::Print(const char* name, const std::string& value,
 }
 
 #else
+CudaTensorConsoleDumper::CudaTensorConsoleDumper() {
+}
 
 void CudaTensorConsoleDumper::Print(const std::string&) const {
 }
diff --git a/onnxruntime/contrib_ops/cuda/utils/dump_cuda_tensor.h b/onnxruntime/contrib_ops/cuda/utils/dump_cuda_tensor.h
index 6ad0ad9a67b75..4f41161cd4a31 100644
--- a/onnxruntime/contrib_ops/cuda/utils/dump_cuda_tensor.h
+++ b/onnxruntime/contrib_ops/cuda/utils/dump_cuda_tensor.h
@@ -13,7 +13,7 @@ namespace cuda {
 
 class CudaTensorConsoleDumper : public onnxruntime::contrib::IConsoleDumper {
  public:
-  CudaTensorConsoleDumper() = default;
+  CudaTensorConsoleDumper();
   virtual ~CudaTensorConsoleDumper() {}
 
   void Print(const char* name, const size_t* tensor, int dim0, int dim1) const override;
diff --git a/onnxruntime/contrib_ops/rocm/bert/attention_impl.cu b/onnxruntime/contrib_ops/rocm/bert/attention_impl.cu
index b0ed3ff82226a..b94971ffd44d5 100644
--- a/onnxruntime/contrib_ops/rocm/bert/attention_impl.cu
+++ b/onnxruntime/contrib_ops/rocm/bert/attention_impl.cu
@@ -119,7 +119,7 @@ Status ClassifyAttentionMode(
       if (attn->qkv_format == Q_K_V_BSNH) {
         attn->mode = BSNH_BLNH_BLNH_NONE_NONE_NONE_NONE;
         return Status::OK();
-      } else if (attn->pass_past_in_kv) {
+      } else if (attn->qkv_format == Q_K_V_BSNH_BNSH_BNSH) {
         attn->mode = BSNH_BNLH_BNLH_NONE_NONE_NONE_NONE;
         return Status::OK();
       }
@@ -128,7 +128,7 @@ Status ClassifyAttentionMode(
         if (attn->qkv_format == Q_K_V_BSNH) {
           attn->mode = BSNH_BLNH_BLNH_NONE_NONE_BNTH_BNTH;
           return Status::OK();
-        } else if (attn->pass_past_in_kv) {
+        } else if (attn->qkv_format == Q_K_V_BSNH_BNSH_BNSH) {
           attn->mode = BSNH_BNLH_BNLH_NONE_NONE_BNTH_BNTH;
           return Status::OK();
         }
@@ -136,7 +136,7 @@ Status ClassifyAttentionMode(
         if (attn->qkv_format == Q_K_V_BSNH) {
           attn->mode = BSNH_BLNH_BLNH_NONE_NONE_BNMH_BNMH;
           return Status::OK();
-        } else if (attn->pass_past_in_kv) {
+        } else if (attn->qkv_format == Q_K_V_BSNH_BNSH_BNSH) {
           attn->mode = BSNH_BNLH_BNLH_NONE_NONE_BNMH_BNMH;
           return Status::OK();
         }
@@ -146,7 +146,7 @@ Status ClassifyAttentionMode(
         if (attn->qkv_format == Q_K_V_BSNH) {
           attn->mode = BSNH_BLNH_BLNH_BNPH_BNPH_BNTH_BNTH;
           return Status::OK();
-        } else if (attn->pass_past_in_kv) {
+        } else if (attn->qkv_format == Q_K_V_BSNH_BNSH_BNSH) {
           attn->mode = BSNH_BNLH_BNLH_BNPH_BNPH_BNTH_BNTH;
           return Status::OK();
         }
@@ -154,7 +154,7 @@ Status ClassifyAttentionMode(
         if (attn->qkv_format == Q_K_V_BSNH) {
           attn->mode = BSNH_BLNH_BLNH_BNMH_BNMH_BNMH_BNMH;
           return Status::OK();
-        } else if (attn->pass_past_in_kv) {
+        } else if (attn->qkv_format == Q_K_V_BSNH_BNSH_BNSH) {
           attn->mode = BSNH_BNLH_BNLH_BNMH_BNMH_BNMH_BNMH;
           return Status::OK();
         }
diff --git a/onnxruntime/contrib_ops/rocm/bert/attention_impl.h b/onnxruntime/contrib_ops/rocm/bert/attention_impl.h
index 349df045becf2..d593bc0012826 100644
--- a/onnxruntime/contrib_ops/rocm/bert/attention_impl.h
+++ b/onnxruntime/contrib_ops/rocm/bert/attention_impl.h
@@ -132,12 +132,6 @@ class CompatRocblasMathModeSetter {
   }
 };
 
-enum AttentionType {
-  kAttention,
-  kMultiHeadAttention,
-  kDecoderMaskedMultiHeadAttention,
-};
-
 enum AttentionMode {
   // Q,K,V,PastK,PastV,PresentK,PresentV
   QFMT_KFMT_VFMT_NONE_NONE_NONE_NONE,
diff --git a/onnxruntime/contrib_ops/rocm/bert/multihead_attention.cu b/onnxruntime/contrib_ops/rocm/bert/multihead_attention.cu
index 09e7d61b71db9..5997daaca6e8a 100644
--- a/onnxruntime/contrib_ops/rocm/bert/multihead_attention.cu
+++ b/onnxruntime/contrib_ops/rocm/bert/multihead_attention.cu
@@ -122,9 +122,11 @@ Status MultiHeadAttention<T>::ComputeInternal(OpKernelContext* context) const {
           query, key, value, bias,
           key_padding_mask, relative_position_bias,
           past_key, past_value, past_seq_len,
-          &attn, num_heads_, 
-          mask_filter_value_, scale_, false, /*is_unidirectional_*/ 
-          past_present_share_buffer_, false, device_prop.maxThreadsPerBlock));
+          &attn, num_heads_,
+          mask_filter_value_, scale_, false, /*is_unidirectional_*/
+          past_present_share_buffer_,
+          attn_type_,
+          device_prop.maxThreadsPerBlock));
 
   if (attn_type_ == kDecoderMaskedMultiHeadAttention && attn.sequence_length != 1) {
     return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
diff --git a/onnxruntime/python/tools/transformers/fusion_attention.py b/onnxruntime/python/tools/transformers/fusion_attention.py
index dc2b38f3928ac..a9ff623fb6967 100644
--- a/onnxruntime/python/tools/transformers/fusion_attention.py
+++ b/onnxruntime/python/tools/transformers/fusion_attention.py
@@ -691,6 +691,9 @@ def create_multihead_attention_node(
             return None
 
         # Add bias to inputs for MHA
+        # Bias for cross attention is not fully supported in DMMHA and cpu MHA kernels since they assume
+        # bias has been added to key and value when they are in BNSH format, so only bias for query is used.
+        # Need add checks if we found such assumption is not true.
         if not self.disable_multi_head_attention_bias:
             bias_name = self.create_combined_qkv_bias(q_add, k_add, v_add, mha_node_name)
             mha_inputs.append(bias_name)
diff --git a/onnxruntime/test/python/transformers/benchmark_mha.py b/onnxruntime/test/python/transformers/benchmark_mha.py
index 715a92431e6bf..ec350874af32c 100644
--- a/onnxruntime/test/python/transformers/benchmark_mha.py
+++ b/onnxruntime/test/python/transformers/benchmark_mha.py
@@ -88,9 +88,11 @@ def __init__(
         enable_cuda_graph: bool = False,
         dtype=torch.float,
         use_kv_cache: bool = False,
+        has_past_input: bool = False,
         share_past_present_buffer: bool = False,
         input_format: int = InputFormats.Q_K_V_BSNH_BSNH_BSNH,
         verbose: bool = False,
+        has_bias: bool = False,
     ):
         self.operator = "MultiHeadAttention"
         self.batch_size = batch_size
@@ -103,15 +105,25 @@ def __init__(
         self.causal = causal
         self.softmax_scale = softmax_scale or (1.0 / (head_size**0.5))
 
+        # Support the case that there is no past but need present output (for prompt case).
+        self.has_past_input = has_past_input
+        if has_past_input:
+            assert use_kv_cache
+        else:  # no past input
+            assert past_sequence_length == 0
+
+        self.has_present_output = use_kv_cache
+
         self.use_kv_cache = use_kv_cache
         if not use_kv_cache:
             assert past_sequence_length == 0
         else:
             assert self.kv_sequence_length == self.sequence_length
 
-        if input_format == InputFormats.Q_K_V_BSNH_BNSH_BNSH:
-            # cross attention does not have past state
-            assert not use_kv_cache
+        # Only BSNH input format supports past state.
+        if input_format != InputFormats.Q_K_V_BSNH_BSNH_BSNH:
+            assert not self.has_past_input
+            assert not self.has_present_output
 
         # Derived values
         self.total_sequence_length = self.kv_sequence_length + past_sequence_length
@@ -130,6 +142,7 @@ def __init__(
         self.is_packed_qkv = input_format == InputFormats.QKV_BSN3H
         self.is_packed_kv = input_format == InputFormats.Q_KV_BSNH_BSN2H
         self.verbose = verbose
+        self.has_bias = has_bias
 
     def __repr__(self):
         return (
@@ -140,7 +153,8 @@ def __repr__(self):
             f"causal={self.causal}), softmax_scale={self.softmax_scale}, use_kv_cache={self.use_kv_cache}, "
             f"share_past_present_buffer={self.share_past_present_buffer}, "
             f"provider={self.provider}, device={self.device}, enable_cuda_graph={self.enable_cuda_graph}, "
-            f"dtype={self.dtype}, input_format={InputFormats.input_format_str(self.input_format)}"
+            f"dtype={self.dtype}, input_format={InputFormats.input_format_str(self.input_format)}, "
+            f"has_bias={self.has_bias}"
         )
 
     def shape_dict(self, input_format=None):
@@ -176,16 +190,23 @@ def shape_dict(self, input_format=None):
                 "value": (self.batch_size, self.num_heads, self.sequence_length, self.head_size),
             }
 
-        if self.use_kv_cache:
-            assert input_format != InputFormats.Q_K_V_BSNH_BNSH_BNSH, "cross attention shall not have past state"
+        if self.has_past_input:
             shapes = {
                 **shapes,
                 "past_key": (self.batch_size, self.num_heads, self.past_buffer_length, self.head_size),
                 "past_value": (self.batch_size, self.num_heads, self.past_buffer_length, self.head_size),
+            }
+
+        if self.has_present_output:
+            shapes = {
+                **shapes,
                 "present_key": (self.batch_size, self.num_heads, self.present_buffer_length, self.head_size),
                 "present_value": (self.batch_size, self.num_heads, self.present_buffer_length, self.head_size),
             }
 
+        if self.has_bias:
+            shapes["bias"] = (3 * self.num_heads * self.head_size,)
+
         return shapes
 
     def symbolic_shape_dict(self, input_format=None):
@@ -221,19 +242,26 @@ def symbolic_shape_dict(self, input_format=None):
                 "value": ("batch_size", self.num_heads, "sequence_length", self.head_size),
             }
 
-        if self.use_kv_cache:
-            assert input_format != InputFormats.Q_K_V_BSNH_BNSH_BNSH, "cross attention shall not have past state"
+        if self.has_past_input:
             shapes = {
                 **shapes,
                 "past_key": ("batch_size", self.num_heads, "past_buffer_length", self.head_size),
                 "past_value": ("batch_size", self.num_heads, "past_buffer_length", self.head_size),
+            }
+
+        if self.has_present_output:
+            shapes = {
+                **shapes,
                 "present_key": ("batch_size", self.num_heads, "present_buffer_length", self.head_size),
                 "present_value": ("batch_size", self.num_heads, "present_buffer_length", self.head_size),
             }
 
+        if self.has_bias:
+            shapes["bias"] = (3 * self.num_heads * self.head_size,)
+
         return shapes
 
-    def random_inputs(self, seed: int = 123):
+    def random_inputs(self, seed: int = 123, no_bias_k_v: bool = False):
         device = self.device
         dtype = self.dtype
 
@@ -246,6 +274,14 @@ def random_inputs(self, seed: int = 123):
         q = torch.empty(shape, device=device, dtype=dtype).normal_(mean=0, std=0.1)
         k = torch.empty(shape, device=device, dtype=dtype).normal_(mean=0, std=0.1)
         v = torch.empty(shape, device=device, dtype=dtype).normal_(mean=0, std=0.1)
+
+        bias_q = torch.empty((self.num_heads * self.head_size,), device=device, dtype=dtype).normal_(mean=0, std=0.1)
+        bias_k = torch.empty((self.num_heads * self.head_size,), device=device, dtype=dtype).normal_(mean=0, std=0.1)
+        bias_v = torch.empty((self.num_heads * self.head_size,), device=device, dtype=dtype).normal_(mean=0, std=0.1)
+        if no_bias_k_v:
+            bias_k = torch.zeros_like(bias_k)
+            bias_v = torch.zeros_like(bias_v)
+
         k_bnsh = k.transpose(1, 2)
         v_bnsh = v.transpose(1, 2)
 
@@ -277,7 +313,7 @@ def random_inputs(self, seed: int = 123):
                 "value": v_bnsh.contiguous(),
             }
 
-        if self.use_kv_cache:
+        if self.has_past_input:
             feeds = {
                 **feeds,
                 "past_key": torch.empty(shape_dict["past_key"], device=device, dtype=dtype).normal_(mean=0, std=0.1),
@@ -286,6 +322,9 @@ def random_inputs(self, seed: int = 123):
                 ),
             }
 
+        if self.has_bias:
+            feeds["bias"] = torch.concat([bias_q, bias_k, bias_v], dim=0).reshape(shape_dict["bias"]).contiguous()
+
         return feeds
 
     def get_input_output_names(self):
@@ -299,15 +338,29 @@ def get_input_output_names(self):
         else:
             inputs, outputs = ["query", "key", "value"], ["output"]
 
-        if self.use_kv_cache:
-            return [*inputs, "past_key", "past_value"], [*outputs, "present_key", "present_value"]
-        else:
-            return inputs, outputs
+        if self.has_bias:
+            inputs = [*inputs, "bias"]
+
+        if self.has_past_input:
+            inputs = [*inputs, "past_key", "past_value"]
+
+        if self.has_present_output:
+            outputs = [*outputs, "present_key", "present_value"]
+
+        return inputs, outputs
 
 
 def fill_optional_mha_inputs(input_names):
     inputs = ["query", "key", "value", "bias", "key_padding_mask", "relative_position_bias", "past_key", "past_value"]
-    return input_names[:-2] + [""] * (len(inputs) - len(input_names)) + input_names[-2:]
+
+    # Remove optional inputs that are not in input_names with empty string
+    inputs_with_optional = [input if input in input_names else "" for input in inputs]
+
+    # Remove empty string at the end of the list.
+    while inputs_with_optional[-1] == "":
+        inputs_with_optional.pop(-1)
+
+    return inputs_with_optional
 
 
 def create_multi_head_attention_onnx_model(config: MultiHeadAttentionConfig, use_symbolic_shape=False):
@@ -317,7 +370,7 @@ def create_multi_head_attention_onnx_model(config: MultiHeadAttentionConfig, use
     nodes = [
         helper.make_node(
             "MultiHeadAttention",
-            fill_optional_mha_inputs(input_names) if config.use_kv_cache else input_names,
+            fill_optional_mha_inputs(input_names),
             output_names,
             "MultiHeadAttention_0",
             num_heads=config.num_heads,
@@ -331,11 +384,13 @@ def create_multi_head_attention_onnx_model(config: MultiHeadAttentionConfig, use
     inputs = [
         helper.make_tensor_value_info(input_name, float_type, list(shape_dict[input_name]))
         for input_name in input_names
+        if input_name
     ]
 
     outputs = [
         helper.make_tensor_value_info(output_name, float_type, list(shape_dict[output_name]))
         for output_name in output_names
+        if output_name
     ]
 
     graph = helper.make_graph(
@@ -355,6 +410,7 @@ def create_ort_session(
     session_options=None,
     attention_kernel=SdpaKernel.DEFAULT,
     use_symbolic_shape: bool = True,
+    use_tf32: bool = True,
 ) -> CudaSession:
     if config.verbose:
         print(f"create session for {vars(config)}")
@@ -364,6 +420,7 @@ def create_ort_session(
         device_id = torch.cuda.current_device() if isinstance(config.device, str) else config.device.index
         provider_options = CudaSession.get_cuda_provider_options(device_id, config.enable_cuda_graph)
         provider_options["sdpa_kernel"] = int(attention_kernel)
+        provider_options["use_tf32"] = int(use_tf32)
         providers = [(config.provider, provider_options), "CPUExecutionProvider"]
     else:
         providers = ["CPUExecutionProvider"]
@@ -373,9 +430,11 @@ def create_ort_session(
 
 
 def create_session(
-    config: MultiHeadAttentionConfig, session_options=None, attention_kernel=SdpaKernel.DEFAULT
+    config: MultiHeadAttentionConfig, session_options=None, attention_kernel=SdpaKernel.DEFAULT, use_tf32: bool = True
 ) -> CudaSession:
-    ort_session = create_ort_session(config, session_options, attention_kernel, use_symbolic_shape=False)
+    ort_session = create_ort_session(
+        config, session_options, attention_kernel, use_symbolic_shape=False, use_tf32=use_tf32
+    )
     cuda_session = CudaSession(ort_session, config.device, config.enable_cuda_graph)
     shape_dict = config.shape_dict()
     cuda_session.allocate_buffers(shape_dict)
@@ -385,8 +444,8 @@ def create_session(
 class OrtMultiHeadAttention:
     """A wrapper of ORT MultiHeadAttention to test relevance and performance."""
 
-    def __init__(self, config: MultiHeadAttentionConfig, session_options=None):
-        self.ort_session = create_session(config, session_options)
+    def __init__(self, config: MultiHeadAttentionConfig, session_options=None, use_tf32: bool = True):
+        self.ort_session = create_session(config, session_options, use_tf32=use_tf32)
         self.feed_dict = config.random_inputs()
 
     def infer(self):
diff --git a/onnxruntime/test/python/transformers/test_mha.py b/onnxruntime/test/python/transformers/test_mha.py
index 0fcbd889847e9..a35d02b0b9d52 100644
--- a/onnxruntime/test/python/transformers/test_mha.py
+++ b/onnxruntime/test/python/transformers/test_mha.py
@@ -21,6 +21,47 @@
 import onnxruntime
 
 
+def get_provider_support_info(provider: str, use_kv_cache: bool):
+    if provider == "CUDAExecutionProvider":
+        if not use_kv_cache:
+            formats = [
+                InputFormats.Q_K_V_BSNH_BSNH_BSNH,
+                InputFormats.Q_KV_BSNH_BSN2H,
+                InputFormats.QKV_BSN3H,
+                InputFormats.Q_K_V_BSNH_BNSH_BNSH,
+            ]
+        else:
+            formats = [InputFormats.Q_K_V_BSNH_BSNH_BSNH]
+
+        device_id = torch.cuda.current_device()
+        device = torch.device("cuda", device_id)
+        dtype = torch.float16
+    else:
+        assert provider == "CPUExecutionProvider"
+        formats = [InputFormats.Q_K_V_BSNH_BSNH_BSNH]
+        if not use_kv_cache:
+            formats.append(InputFormats.Q_K_V_BSNH_BNSH_BNSH)
+        device = torch.device("cpu")
+        dtype = torch.float
+    return device, dtype, formats
+
+
+def get_bias_support(format: InputFormats):
+    if format == InputFormats.Q_K_V_BSNH_BSNH_BSNH:
+        return [True, False]
+
+    if format == InputFormats.Q_K_V_BSNH_BNSH_BNSH:
+        return [True, False]
+
+    if format == InputFormats.Q_KV_BSNH_BSN2H:
+        return [False]
+
+    if format == InputFormats.QKV_BSN3H:
+        return [True, False]
+
+    raise RuntimeError(f"Unknown format: {format}")
+
+
 def attention_reference(
     head_size: int,
     query: torch.Tensor,
@@ -84,8 +125,8 @@ def attention_reference(
 
 def mha_with_past_reference(
     config: MultiHeadAttentionConfig,
-    past_k: torch.Tensor,
-    past_v: torch.Tensor,
+    past_k: Optional[torch.Tensor],
+    past_v: Optional[torch.Tensor],
     q: torch.Tensor,
     k: torch.Tensor,
     v: torch.Tensor,
@@ -94,41 +135,23 @@ def mha_with_past_reference(
 ):
     assert config.kv_sequence_length == config.sequence_length
     assert config.use_kv_cache
-    assert past_k.dim() == 4 and k.dim() == 4 and past_k.size(1) == k.size(1)  # both BNSH format
-    assert past_v.dim() == 4 and v.dim() == 4 and past_v.size(1) == v.size(1)  # both BNSH format
-
-    present_k = torch.cat((past_k, k), dim=2)
-    present_v = torch.cat((past_v, v), dim=2)
+    if past_k is not None:
+        assert (
+            past_k.dim() == 4 and k.dim() == 4 and past_k.size(1) == k.size(1)
+        ), f"expect BNSH format: {past_k.shape=} {k.shape=}"
+
+    if past_v is not None:
+        assert (
+            past_v.dim() == 4 and v.dim() == 4 and past_v.size(1) == v.size(1)
+        ), f"expect BNSH format: {past_v.shape=} {v.shape=}"
+
+    present_k = torch.cat((past_k, k), dim=2) if past_k is not None else k
+    present_v = torch.cat((past_v, v), dim=2) if past_v is not None else v
     out = attention_reference(config.head_size, q, present_k, present_v, scale=scale, mask=mask)
 
     return out, present_k, present_v
 
 
-def get_provider_support_info(provider: str, use_kv_cache: bool):
-    if provider == "CUDAExecutionProvider":
-        if not use_kv_cache:
-            formats = [
-                InputFormats.Q_K_V_BSNH_BSNH_BSNH,
-                InputFormats.Q_KV_BSNH_BSN2H,
-                InputFormats.QKV_BSN3H,
-                InputFormats.Q_K_V_BSNH_BNSH_BNSH,
-            ]
-        else:
-            formats = [InputFormats.Q_K_V_BSNH_BSNH_BSNH]
-
-        device_id = torch.cuda.current_device()
-        device = torch.device("cuda", device_id)
-        dtype = torch.float16
-    else:
-        assert provider == "CPUExecutionProvider"
-        formats = [InputFormats.Q_K_V_BSNH_BSNH_BSNH]
-        if not use_kv_cache:
-            formats.append(InputFormats.Q_K_V_BSNH_BSNH_BSNH)
-        device = torch.device("cpu")
-        dtype = torch.float
-    return device, dtype, formats
-
-
 def get_compute_capability():
     if torch.cuda.is_available() and "CUDAExecutionProvider" in onnxruntime.get_available_providers():
         major, minor = torch.cuda.get_device_capability()
@@ -143,35 +166,38 @@ def no_kv_cache_test_cases(provider: str, comprehensive: bool):
         yield
 
     batch_sizes = [1, 2, 3]
-    sequence_lengths = [1, 16, 127, 128, 255, 256, 383, 384, 2048]
+    sequence_lengths = [1, 16, 127, 128, 255, 256, 383, 384, 512]
     heads = [1, 3, 4, 16]
     head_sizes = [8, 16, 32, 40, 64, 80, 96, 128, 160, 192, 224, 256]
 
     device, dtype, formats = get_provider_support_info(provider, False)
     if comprehensive:
+        sequence_lengths = [*sequence_lengths, 2048]  # Large sequence length is slow and need a lot of memory
         for batch_size in batch_sizes:
             for sequence_length in sequence_lengths:
                 for num_heads in heads:
                     for head_size in head_sizes:
                         for format in formats:
                             for causal in [True, False]:
-                                config = MultiHeadAttentionConfig(
-                                    batch_size=batch_size,
-                                    sequence_length=sequence_length,
-                                    num_heads=num_heads,
-                                    head_size=head_size,
-                                    causal=causal,
-                                    past_sequence_length=0,
-                                    kv_sequence_length=sequence_length,
-                                    max_cache_sequence_length=None,
-                                    provider=provider,
-                                    device=device,
-                                    dtype=dtype,
-                                    use_kv_cache=False,
-                                    share_past_present_buffer=False,
-                                    input_format=format,
-                                )
-                                yield config
+                                for has_bias in get_bias_support(format):
+                                    config = MultiHeadAttentionConfig(
+                                        batch_size=batch_size,
+                                        sequence_length=sequence_length,
+                                        num_heads=num_heads,
+                                        head_size=head_size,
+                                        causal=causal,
+                                        past_sequence_length=0,
+                                        kv_sequence_length=sequence_length,
+                                        max_cache_sequence_length=None,
+                                        provider=provider,
+                                        device=device,
+                                        dtype=dtype,
+                                        use_kv_cache=False,
+                                        share_past_present_buffer=False,
+                                        input_format=format,
+                                        has_bias=has_bias,
+                                    )
+                                    yield config
     else:
         test_cases = max(len(batch_sizes), len(sequence_lengths), len(heads), len(head_sizes))
         for i in range(test_cases):
@@ -179,25 +205,27 @@ def no_kv_cache_test_cases(provider: str, comprehensive: bool):
             sequence_length = sequence_lengths[i % len(sequence_lengths)]
             num_heads = heads[i % len(heads)]
             head_size = head_sizes[i % len(head_sizes)]
-            format = formats[i % len(formats)]
             for causal in [True, False]:
-                config = MultiHeadAttentionConfig(
-                    batch_size=batch_size,
-                    sequence_length=sequence_length,
-                    num_heads=num_heads,
-                    head_size=head_size,
-                    causal=causal,
-                    past_sequence_length=0,
-                    kv_sequence_length=sequence_length,
-                    max_cache_sequence_length=None,
-                    provider=provider,
-                    device=device,
-                    dtype=dtype,
-                    use_kv_cache=False,
-                    share_past_present_buffer=False,
-                    input_format=format,
-                )
-                yield config
+                for format in formats:
+                    for has_bias in get_bias_support(format):
+                        config = MultiHeadAttentionConfig(
+                            batch_size=batch_size,
+                            sequence_length=sequence_length,
+                            num_heads=num_heads,
+                            head_size=head_size,
+                            causal=causal,
+                            past_sequence_length=0,
+                            kv_sequence_length=sequence_length,
+                            max_cache_sequence_length=None,
+                            provider=provider,
+                            device=device,
+                            dtype=dtype,
+                            use_kv_cache=False,
+                            share_past_present_buffer=False,
+                            input_format=format,
+                            has_bias=has_bias,
+                        )
+                        yield config
 
 
 def kv_cache_test_cases(provider: str, comprehensive: bool):
@@ -206,37 +234,42 @@ def kv_cache_test_cases(provider: str, comprehensive: bool):
         yield
 
     batch_sizes = [1, 2, 3]
-    sequence_lengths = [1, 15, 16, 255, 256, 2048]
+    sequence_lengths = [1, 15, 16, 255, 256, 512]
     heads = [1, 3, 4, 16]
     head_sizes = [8, 16, 32, 40, 64, 80, 96, 128, 160, 192, 224, 256]
-
-    sequence_length = 1
     device, dtype, formats = get_provider_support_info(provider, True)
 
     if comprehensive:
+        sequence_lengths = [*sequence_lengths, 2048]  # Large sequence length is slow and need a lot of memory
         for batch_size in batch_sizes:
             for past_sequence_length in sequence_lengths:
                 for num_heads in heads:
                     for head_size in head_sizes:
                         for format in formats:
                             for causal in [True, False]:
-                                config = MultiHeadAttentionConfig(
-                                    batch_size=batch_size,
-                                    sequence_length=sequence_length,
-                                    num_heads=num_heads,
-                                    head_size=head_size,
-                                    causal=causal,
-                                    past_sequence_length=past_sequence_length,
-                                    kv_sequence_length=sequence_length,
-                                    max_cache_sequence_length=None,
-                                    provider=provider,
-                                    device=device,
-                                    dtype=dtype,
-                                    use_kv_cache=True,
-                                    share_past_present_buffer=False,
-                                    input_format=format,
-                                )
-                                yield config
+                                for has_past_input in [True, False]:
+                                    for has_bias in get_bias_support(format):
+                                        sequence_length = 1 if has_past_input else past_sequence_length
+                                        past_seq_len = past_sequence_length if has_past_input else 0
+                                        config = MultiHeadAttentionConfig(
+                                            batch_size=batch_size,
+                                            sequence_length=sequence_length,
+                                            num_heads=num_heads,
+                                            head_size=head_size,
+                                            causal=causal,
+                                            past_sequence_length=past_seq_len,
+                                            kv_sequence_length=sequence_length,
+                                            max_cache_sequence_length=None,
+                                            provider=provider,
+                                            device=device,
+                                            dtype=dtype,
+                                            use_kv_cache=True,
+                                            has_past_input=has_past_input,
+                                            share_past_present_buffer=False,
+                                            input_format=format,
+                                            has_bias=has_bias,
+                                        )
+                                        yield config
     else:
         test_cases = max(len(batch_sizes), len(sequence_lengths), len(heads), len(head_sizes))
         for i in range(test_cases):
@@ -244,31 +277,31 @@ def kv_cache_test_cases(provider: str, comprehensive: bool):
             past_sequence_length = sequence_lengths[i % len(sequence_lengths)]
             num_heads = heads[i % len(heads)]
             head_size = head_sizes[i % len(head_sizes)]
-            format = formats[i % len(formats)]
             for causal in [True, False]:
-                config = MultiHeadAttentionConfig(
-                    batch_size=batch_size,
-                    sequence_length=sequence_length,
-                    num_heads=num_heads,
-                    head_size=head_size,
-                    causal=causal,
-                    past_sequence_length=past_sequence_length,
-                    kv_sequence_length=sequence_length,
-                    max_cache_sequence_length=None,
-                    provider=provider,
-                    device=device,
-                    dtype=dtype,
-                    use_kv_cache=True,
-                    share_past_present_buffer=False,
-                    input_format=format,
-                )
-                yield config
-
-
-def mha_test_cases(provider: str, comprehensive: bool):
-    return itertools.chain(
-        no_kv_cache_test_cases(provider, comprehensive), kv_cache_test_cases(provider, comprehensive)
-    )
+                for format in formats:
+                    for has_past_input in [True, False]:
+                        for has_bias in get_bias_support(format):
+                            sequence_length = 1 if has_past_input else past_sequence_length
+                            past_seq_len = past_sequence_length if has_past_input else 0
+                            config = MultiHeadAttentionConfig(
+                                batch_size=batch_size,
+                                sequence_length=sequence_length,
+                                num_heads=num_heads,
+                                head_size=head_size,
+                                causal=causal,
+                                past_sequence_length=past_seq_len,
+                                kv_sequence_length=sequence_length,
+                                max_cache_sequence_length=None,
+                                provider=provider,
+                                device=device,
+                                dtype=dtype,
+                                use_kv_cache=True,
+                                has_past_input=has_past_input,
+                                share_past_present_buffer=False,
+                                input_format=format,
+                                has_bias=has_bias,
+                            )
+                            yield config
 
 
 def no_kv_cache_multi_thread_test_cases(provider: str, comprehensive: bool):
@@ -343,6 +376,7 @@ def kv_cache_multi_thread_test_cases(provider: str, comprehensive: bool):
                                 device=device,
                                 dtype=dtype,
                                 use_kv_cache=True,
+                                has_past_input=True,
                                 share_past_present_buffer=False,
                                 input_format=format,
                             )
@@ -350,13 +384,6 @@ def kv_cache_multi_thread_test_cases(provider: str, comprehensive: bool):
                     yield configs
 
 
-def multi_thread_test_cases(provider: str, comprehensive: bool):
-    return itertools.chain(
-        no_kv_cache_multi_thread_test_cases(provider, comprehensive),
-        kv_cache_multi_thread_test_cases(provider, comprehensive),
-    )
-
-
 def causal_mask(seqlen_q, seqlen_k, query_padding_mask=None, key_padding_mask=None, device=None):
     row_idx = rearrange(torch.arange(seqlen_q, device=device, dtype=torch.long), "s -> s 1")
     col_idx = torch.arange(seqlen_k, device=device, dtype=torch.long)
@@ -374,28 +401,31 @@ def parity_check_mha(
     if config.causal and config.provider == "CUDAExecutionProvider":
         return
 
-    ort_mha = OrtMultiHeadAttention(config)
+    ort_mha = OrtMultiHeadAttention(config, use_tf32=False)
     ort_outputs = ort_mha.infer()
     out = ort_outputs["output"]
     out = torch.reshape(out, (config.batch_size, config.sequence_length, config.num_heads, config.head_size))
 
+    no_bias_k_v = config.input_format == InputFormats.Q_K_V_BSNH_BNSH_BNSH
     config.input_format = InputFormats.Q_K_V_BSNH_BSNH_BSNH
-    ref_inputs = config.random_inputs()
-    q = (
-        ref_inputs["query"]
-        .reshape((config.batch_size, config.sequence_length, config.num_heads, config.head_size))
-        .transpose(1, 2)
-    )
-    k = (
-        ref_inputs["key"]
-        .reshape((config.batch_size, config.kv_sequence_length, config.num_heads, config.head_size))
-        .transpose(1, 2)
-    )
-    v = (
-        ref_inputs["value"]
-        .reshape((config.batch_size, config.kv_sequence_length, config.num_heads, config.head_size))
-        .transpose(1, 2)
-    )
+    ref_inputs = config.random_inputs(no_bias_k_v=no_bias_k_v)
+    q = ref_inputs["query"].reshape((config.batch_size, config.sequence_length, config.num_heads, config.head_size))
+    k = ref_inputs["key"].reshape((config.batch_size, config.kv_sequence_length, config.num_heads, config.head_size))
+    v = ref_inputs["value"].reshape((config.batch_size, config.kv_sequence_length, config.num_heads, config.head_size))
+
+    if "bias" in ref_inputs:
+        bias = ref_inputs["bias"]
+        bias = bias.reshape((3, config.num_heads, config.head_size))
+        bias_q = bias[0, :, :].reshape(1, 1, config.num_heads, config.head_size)
+        bias_k = bias[1, :, :].reshape(1, 1, config.num_heads, config.head_size)
+        bias_v = bias[2, :, :].reshape(1, 1, config.num_heads, config.head_size)
+        q = q + bias_q
+        k = k + bias_k
+        v = v + bias_v
+
+    q = q.transpose(1, 2)
+    k = k.transpose(1, 2)
+    v = v.transpose(1, 2)
 
     mask = None
     if config.causal:
@@ -404,8 +434,8 @@ def parity_check_mha(
     k_cache = None
     v_cache = None
     if config.use_kv_cache:
-        past_k = ref_inputs["past_key"]
-        past_v = ref_inputs["past_value"]
+        past_k = ref_inputs.get("past_key", None)
+        past_v = ref_inputs.get("past_value", None)
         out_ref, k_cache, v_cache = mha_with_past_reference(config, past_k, past_v, q, k, v, mask=mask)
     else:
         out_ref = attention_reference(config.head_size, q, k, v, mask=mask)
@@ -445,7 +475,7 @@ def parity_check_mha_multi_threading(
     test_inputs: List[Dict],
     rtol: float = 1e-3,
     atol: float = 1e-3,
-    attention_kernel: int = SdpaKernel.DEFAULT,
+    attention_kernel=SdpaKernel.DEFAULT,
     max_threads: int = 5,
     verbose: bool = False,
 ):
@@ -454,6 +484,7 @@ def parity_check_mha_multi_threading(
     # For now, MHA CUDA kernel does not support causal so skip such test cases.
     if config.causal and config.provider == "CUDAExecutionProvider":
         return None
+
     # Some kernel does not support certain input format.
     if attention_kernel not in [
         SdpaKernel.DEFAULT,
@@ -462,7 +493,7 @@ def parity_check_mha_multi_threading(
     ] and config.input_format in [InputFormats.Q_KV_BSNH_BSN2H]:
         return None
 
-    ort_session = create_ort_session(config, attention_kernel=attention_kernel, use_symbolic_shape=True)
+    ort_session = create_ort_session(config, attention_kernel=attention_kernel, use_symbolic_shape=True, use_tf32=False)
 
     def convert_to_ort_inputs(feed_dict):
         ort_inputs = {}
@@ -572,18 +603,32 @@ def check_parity_with_config(i: int):
     return None
 
 
-# Do not run too many tests in CI pipeline. Change it to True to run all combinations in dev machine.
+def mha_test_cases(provider: str, comprehensive: bool):
+    return itertools.chain(
+        no_kv_cache_test_cases(provider, comprehensive),
+        kv_cache_test_cases(provider, comprehensive),
+    )
+
+
+def multi_thread_test_cases(provider: str, comprehensive: bool):
+    return itertools.chain(
+        no_kv_cache_multi_thread_test_cases(provider, comprehensive),
+        kv_cache_multi_thread_test_cases(provider, comprehensive),
+    )
+
+
+# Off by default so that we do not run too many tests in CI pipeline.
 comprehensive_mode = False
 
 
 class TestMultiHeadAttention(unittest.TestCase):
     @parameterized.expand(mha_test_cases("CUDAExecutionProvider", comprehensive_mode), skip_on_empty=True)
     def test_mha_cuda(self, config):
-        parity_check_mha(config)
+        parity_check_mha(config, rtol=5e-3, atol=5e-3)
 
     @parameterized.expand(mha_test_cases("CPUExecutionProvider", comprehensive_mode), skip_on_empty=True)
     def test_mha_cpu(self, config):
-        parity_check_mha(config)
+        parity_check_mha(config, rtol=5e-3, atol=5e-3)
 
     def run_mha_cuda_multi_threading(self, attention_kernel):
         for configs in multi_thread_test_cases("CUDAExecutionProvider", comprehensive_mode):
@@ -604,19 +649,24 @@ def run_mha_cuda_multi_threading(self, attention_kernel):
             assert exception is None, f"{attention_kernel=}, {vars(configs[0])}, {exception}"
 
     def test_mha_cuda_multi_threading(self):
-        self.run_mha_cuda_multi_threading(SdpaKernel.DEFAULT)
+        if get_compute_capability() >= 60:
+            self.run_mha_cuda_multi_threading(SdpaKernel.DEFAULT)
 
     def test_mha_cuda_multi_threading_efficient(self):
-        self.run_mha_cuda_multi_threading(SdpaKernel.EFFICIENT_ATTENTION)
+        if comprehensive_mode and get_compute_capability() >= 60:
+            self.run_mha_cuda_multi_threading(SdpaKernel.EFFICIENT_ATTENTION)
+
+    def test_mha_cuda_multi_threading_math(self):
+        if comprehensive_mode and get_compute_capability() >= 60:
+            self.run_mha_cuda_multi_threading(SdpaKernel.MATH)
 
     def test_mha_cuda_multi_threading_trt(self):
-        sm = get_compute_capability()
-        if sm in [75, 80, 86, 89]:
+        if get_compute_capability() in [75, 80, 86, 89]:
             self.run_mha_cuda_multi_threading(
                 SdpaKernel.TRT_FUSED_ATTENTION
                 | SdpaKernel.TRT_FLASH_ATTENTION
-                | SdpaKernel.TRT_CROSS_ATTENTION
                 | SdpaKernel.TRT_CAUSAL_ATTENTION
+                | SdpaKernel.TRT_CROSS_ATTENTION
             )
 
 

From 866630fc733b14eae178c97decb0e887a800cede Mon Sep 17 00:00:00 2001
From: Jing Fang <126209182+fajin-corp@users.noreply.github.com>
Date: Wed, 31 Jul 2024 15:30:33 -0700
Subject: [PATCH 04/22] Fix quant_format argument for 4bit quantizer (#21581)

### Description
Original argument accepts Enum QuantFormat.QOperator or QuantFormat.QDQ,
but the default value is QOperator.

Change the argument to str to accept QOperator or QDQ and convert to
QuantFormat after parsing.

### Motivation and Context
Bug fix
---
 .../python/tools/quantization/matmul_4bits_quantizer.py     | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/onnxruntime/python/tools/quantization/matmul_4bits_quantizer.py b/onnxruntime/python/tools/quantization/matmul_4bits_quantizer.py
index 40a4a4d26dc1c..cc8bd622df9b1 100644
--- a/onnxruntime/python/tools/quantization/matmul_4bits_quantizer.py
+++ b/onnxruntime/python/tools/quantization/matmul_4bits_quantizer.py
@@ -797,8 +797,8 @@ def parse_args():
     parser.add_argument(
         "--quant_format",
         default="QOperator",
-        type=QuantFormat,
-        choices=list(QuantFormat),
+        type=str,
+        choices=["QOperator", "QDQ"],
         help="QuantFormat {QOperator, QDQ}"
         "QOperator format quantizes the model with quantized operators directly."
         "QDQ format quantize the model by inserting DeQuantizeLinear before the MatMul.",
@@ -814,7 +814,7 @@ def parse_args():
 
     input_model_path = args.input_model
     output_model_path = args.output_model
-    quant_format = args.quant_format
+    quant_format = QuantFormat[args.quant_format]
 
     if os.path.exists(output_model_path):
         logger.error(f"file {output_model_path} already exists")

From 4366daa0a7c27deaf456b9743b0dca4b30bf2950 Mon Sep 17 00:00:00 2001
From: Adrian Lizarraga <adlizarraga@microsoft.com>
Date: Tue, 6 Aug 2024 09:08:48 -0700
Subject: [PATCH 05/22] [QNN EP] Update QNN SDK to 2.25 (#21623)

### Description
- Update pipelines to use QNN SDK 2.25 by default
- Update ifdef condition to apply workaround for QNN LayerNorm
validation bug to QNN SDK 2.25 (as well as 2.24)



### Motivation and Context
Use the latest QNN SDK
---
 .../qnn/builder/opbuilder/layer_norm_op_builder.cc          | 6 +++---
 .../android-arm64-v8a-QNN-crosscompile-ci-pipeline.yml      | 2 +-
 .../azure-pipelines/c-api-noopenmp-packaging-pipelines.yml  | 2 +-
 .../github/azure-pipelines/linux-qnn-ci-pipeline.yml        | 2 +-
 .../github/azure-pipelines/py-packaging-pipeline.yml        | 2 +-
 .../azure-pipelines/qnn-ep-nuget-packaging-pipeline.yml     | 2 +-
 .../templates/jobs/download_linux_qnn_sdk.yml               | 2 +-
 .../azure-pipelines/templates/jobs/download_win_qnn_sdk.yml | 2 +-
 .../github/azure-pipelines/templates/py-packaging-stage.yml | 2 +-
 .../github/azure-pipelines/templates/py-win-arm64-qnn.yml   | 2 +-
 .../github/azure-pipelines/templates/py-win-x64-qnn.yml     | 2 +-
 .../github/azure-pipelines/templates/qnn-ep-win.yml         | 2 +-
 .../github/azure-pipelines/win-qnn-arm64-ci-pipeline.yml    | 2 +-
 .../ci_build/github/azure-pipelines/win-qnn-ci-pipeline.yml | 2 +-
 14 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/onnxruntime/core/providers/qnn/builder/opbuilder/layer_norm_op_builder.cc b/onnxruntime/core/providers/qnn/builder/opbuilder/layer_norm_op_builder.cc
index c667aeeaa61f0..a31b15948cb7f 100644
--- a/onnxruntime/core/providers/qnn/builder/opbuilder/layer_norm_op_builder.cc
+++ b/onnxruntime/core/providers/qnn/builder/opbuilder/layer_norm_op_builder.cc
@@ -87,10 +87,10 @@ Status LayerNormOpBuilder::ProcessInputs(QnnModelWrapper& qnn_model_wrapper,
     ORT_RETURN_IF_ERROR(ProcessInput(qnn_model_wrapper, inputs[BIAS_IDX], logger, input_names));
   }
 
-#if QNN_API_VERSION_MAJOR == 2 && QNN_API_VERSION_MINOR == 17
+#if QNN_API_VERSION_MAJOR == 2 && (QNN_API_VERSION_MINOR == 17 || QNN_API_VERSION_MINOR == 18)
   if (!has_bias_input && IsNpuBackend(qnn_model_wrapper.GetQnnBackendType())) {
-    // Bias is implicit. QNN SDK 2.24 (QNN API version 2.17) has a validation bug for implicit bias inputs, so provide
-    // an explicit bias of all 0 (quantized int32).
+    // Bias is implicit. QNN SDK 2.24/2.25 (QNN API version 2.17/2.18) has a validation bug for implicit bias inputs,
+    // so provide an explicit bias of all 0 (quantized int32).
     TensorInfo x_input_info = {};
     ORT_RETURN_IF_ERROR(qnn_model_wrapper.GetTensorInfo(inputs[X_IDX], x_input_info));
 
diff --git a/tools/ci_build/github/azure-pipelines/android-arm64-v8a-QNN-crosscompile-ci-pipeline.yml b/tools/ci_build/github/azure-pipelines/android-arm64-v8a-QNN-crosscompile-ci-pipeline.yml
index c80092fc82ed5..3fba9f54f2667 100644
--- a/tools/ci_build/github/azure-pipelines/android-arm64-v8a-QNN-crosscompile-ci-pipeline.yml
+++ b/tools/ci_build/github/azure-pipelines/android-arm64-v8a-QNN-crosscompile-ci-pipeline.yml
@@ -31,7 +31,7 @@ parameters:
 - name: QnnSdk
   displayName: QNN SDK version
   type: string
-  default: 2.24.0.240626
+  default: 2.25.0.240728
 
 jobs:
 - job: Build_QNN_EP
diff --git a/tools/ci_build/github/azure-pipelines/c-api-noopenmp-packaging-pipelines.yml b/tools/ci_build/github/azure-pipelines/c-api-noopenmp-packaging-pipelines.yml
index 51b73acd93dc8..c9210b996b84e 100644
--- a/tools/ci_build/github/azure-pipelines/c-api-noopenmp-packaging-pipelines.yml
+++ b/tools/ci_build/github/azure-pipelines/c-api-noopenmp-packaging-pipelines.yml
@@ -62,7 +62,7 @@ parameters:
 - name: QnnSdk
   displayName: QNN SDK Version
   type: string
-  default: 2.24.0.240626
+  default: 2.25.0.240728
 
 resources:
   repositories:
diff --git a/tools/ci_build/github/azure-pipelines/linux-qnn-ci-pipeline.yml b/tools/ci_build/github/azure-pipelines/linux-qnn-ci-pipeline.yml
index 0d67b0947be53..9282792a6b418 100644
--- a/tools/ci_build/github/azure-pipelines/linux-qnn-ci-pipeline.yml
+++ b/tools/ci_build/github/azure-pipelines/linux-qnn-ci-pipeline.yml
@@ -32,7 +32,7 @@ parameters:
 - name: QnnSdk
   displayName: QNN SDK version
   type: string
-  default: 2.24.0.240626
+  default: 2.25.0.240728
 
 jobs:
   - job: Build_QNN_EP
diff --git a/tools/ci_build/github/azure-pipelines/py-packaging-pipeline.yml b/tools/ci_build/github/azure-pipelines/py-packaging-pipeline.yml
index cd3966633d742..c7a1b595a6c6f 100644
--- a/tools/ci_build/github/azure-pipelines/py-packaging-pipeline.yml
+++ b/tools/ci_build/github/azure-pipelines/py-packaging-pipeline.yml
@@ -59,7 +59,7 @@ parameters:
 - name: qnn_sdk_version
   type: string
   displayName: 'QNN SDK version. Only for QNN packages.'
-  default: 2.24.0.240626
+  default: 2.25.0.240728
 
 trigger: none
 
diff --git a/tools/ci_build/github/azure-pipelines/qnn-ep-nuget-packaging-pipeline.yml b/tools/ci_build/github/azure-pipelines/qnn-ep-nuget-packaging-pipeline.yml
index 7229bc5dbd114..25d50f4255cb1 100644
--- a/tools/ci_build/github/azure-pipelines/qnn-ep-nuget-packaging-pipeline.yml
+++ b/tools/ci_build/github/azure-pipelines/qnn-ep-nuget-packaging-pipeline.yml
@@ -2,7 +2,7 @@ parameters:
 - name: QnnSdk
   displayName: QNN SDK Version
   type: string
-  default: 2.24.0.240626
+  default: 2.25.0.240728
 
 - name: build_config
   displayName: Build Configuration
diff --git a/tools/ci_build/github/azure-pipelines/templates/jobs/download_linux_qnn_sdk.yml b/tools/ci_build/github/azure-pipelines/templates/jobs/download_linux_qnn_sdk.yml
index 734ad43e0066d..e727ec4f7ef5c 100644
--- a/tools/ci_build/github/azure-pipelines/templates/jobs/download_linux_qnn_sdk.yml
+++ b/tools/ci_build/github/azure-pipelines/templates/jobs/download_linux_qnn_sdk.yml
@@ -1,7 +1,7 @@
 parameters:
   - name: QnnSDKVersion
     type: string
-    default: '2.24.0.240626'
+    default: '2.25.0.240728'
 
 steps:
   - script: |
diff --git a/tools/ci_build/github/azure-pipelines/templates/jobs/download_win_qnn_sdk.yml b/tools/ci_build/github/azure-pipelines/templates/jobs/download_win_qnn_sdk.yml
index 900adc9690255..912cac6fbb99e 100644
--- a/tools/ci_build/github/azure-pipelines/templates/jobs/download_win_qnn_sdk.yml
+++ b/tools/ci_build/github/azure-pipelines/templates/jobs/download_win_qnn_sdk.yml
@@ -1,7 +1,7 @@
 parameters:
   - name: QnnSDKVersion
     type: string
-    default: '2.24.0.240626'
+    default: '2.25.0.240728'
 
 steps:
   - powershell: |
diff --git a/tools/ci_build/github/azure-pipelines/templates/py-packaging-stage.yml b/tools/ci_build/github/azure-pipelines/templates/py-packaging-stage.yml
index 447e35244eb66..faf453140052b 100644
--- a/tools/ci_build/github/azure-pipelines/templates/py-packaging-stage.yml
+++ b/tools/ci_build/github/azure-pipelines/templates/py-packaging-stage.yml
@@ -63,7 +63,7 @@ parameters:
 - name: qnn_sdk_version
   type: string
   displayName: 'QNN SDK version. Only for QNN packages.'
-  default: 2.24.0.240626
+  default: 2.25.0.240728
 
 stages:
 - ${{ if eq(parameters.enable_windows_cpu, true) }}:
diff --git a/tools/ci_build/github/azure-pipelines/templates/py-win-arm64-qnn.yml b/tools/ci_build/github/azure-pipelines/templates/py-win-arm64-qnn.yml
index 40e8583141df8..c3a2b7be7ebd2 100644
--- a/tools/ci_build/github/azure-pipelines/templates/py-win-arm64-qnn.yml
+++ b/tools/ci_build/github/azure-pipelines/templates/py-win-arm64-qnn.yml
@@ -7,7 +7,7 @@ parameters:
 - name: QNN_SDK
   displayName: QNN SDK Version
   type: string
-  default: 2.24.0.240626
+  default: 2.25.0.240728
 
 - name: PYTHON_VERSION
   type: string
diff --git a/tools/ci_build/github/azure-pipelines/templates/py-win-x64-qnn.yml b/tools/ci_build/github/azure-pipelines/templates/py-win-x64-qnn.yml
index 33335bb2be2dd..5cf03a7cdd100 100644
--- a/tools/ci_build/github/azure-pipelines/templates/py-win-x64-qnn.yml
+++ b/tools/ci_build/github/azure-pipelines/templates/py-win-x64-qnn.yml
@@ -7,7 +7,7 @@ parameters:
 - name: QNN_SDK
   displayName: QNN SDK Version
   type: string
-  default: 2.24.0.240626
+  default: 2.25.0.240728
 
 - name: ENV_SETUP_SCRIPT
   type: string
diff --git a/tools/ci_build/github/azure-pipelines/templates/qnn-ep-win.yml b/tools/ci_build/github/azure-pipelines/templates/qnn-ep-win.yml
index 944745b69ca63..c7fd26712329c 100644
--- a/tools/ci_build/github/azure-pipelines/templates/qnn-ep-win.yml
+++ b/tools/ci_build/github/azure-pipelines/templates/qnn-ep-win.yml
@@ -1,5 +1,5 @@
 parameters:
-  QnnSdk: '2.24.0.240626'
+  QnnSdk: '2.25.0.240728'
   build_config: 'RelWithDebInfo'  
   IsReleaseBuild: false
   DoEsrp: false
diff --git a/tools/ci_build/github/azure-pipelines/win-qnn-arm64-ci-pipeline.yml b/tools/ci_build/github/azure-pipelines/win-qnn-arm64-ci-pipeline.yml
index e1b8b718e9928..31cdbeb99be4f 100644
--- a/tools/ci_build/github/azure-pipelines/win-qnn-arm64-ci-pipeline.yml
+++ b/tools/ci_build/github/azure-pipelines/win-qnn-arm64-ci-pipeline.yml
@@ -32,7 +32,7 @@ parameters:
 - name: QnnSdk
   displayName: QNN SDK version
   type: string
-  default: 2.24.0.240626
+  default: 2.25.0.240728
 
 jobs:
 - job: 'build'
diff --git a/tools/ci_build/github/azure-pipelines/win-qnn-ci-pipeline.yml b/tools/ci_build/github/azure-pipelines/win-qnn-ci-pipeline.yml
index 97c4ab15095c9..54277bcb4039f 100644
--- a/tools/ci_build/github/azure-pipelines/win-qnn-ci-pipeline.yml
+++ b/tools/ci_build/github/azure-pipelines/win-qnn-ci-pipeline.yml
@@ -32,7 +32,7 @@ parameters:
 - name: QnnSdk
   displayName: QNN SDK version
   type: string
-  default: 2.24.0.240626
+  default: 2.25.0.240728
 
 jobs:
 - job: 'build'

From 9a82bd743ac05319d91e729c9287ca0664c4dccc Mon Sep 17 00:00:00 2001
From: liqun Fu <liqfu@microsoft.com>
Date: Mon, 5 Aug 2024 20:52:26 -0700
Subject: [PATCH 06/22] Fix typos so to call correct vnni functions under vnni
 condition (#21625)

### Description
Fix 2 typos in mlas avx 4bit gemm implementation to call correct vnni
functions under vnni condition



### Motivation and Context
needed for 1.19.0 release

Signed-off-by: liqunfu <liqun.fu@microsoft.com>
---
 .../core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen16.h    | 4 ++--
 .../core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen32.h    | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen16.h b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen16.h
index 3cd610796a5e3..bb14babd6c2b1 100644
--- a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen16.h
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen16.h
@@ -679,9 +679,9 @@ Q4Int8GemmR1xC1BlkLen16Avx512(
                 const __m512i av_01_epi8 = _mm512_loadu_si512((const __m512i*)(QuantAPtr + 64));
 
                 if constexpr (vnni) {
-                    accumulate_blklen16_r1c1blk8_avx512(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0);
-                } else {
                     accumulate_blklen16_r1c1blk8_avx512vnni(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0);
+                } else {
+                    accumulate_blklen16_r1c1blk8_avx512(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0);
                 }
 
                 QuantAPtr += BlkLen16 * PerAccuBlk8;
diff --git a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen32.h b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen32.h
index ca12cc14a7875..e9df6b952bd27 100644
--- a/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen32.h
+++ b/onnxruntime/core/mlas/lib/sqnbitgemm_kernel_avx512_int8_blklen32.h
@@ -721,7 +721,7 @@ Q4Int8GemmR1xC1BlkLen32Avx512(
                     accumulate_blklen32_r1c1blk4_avx512vnni(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0);
                 }
                 else {
-                    accumulate_blklen32_r1c1blk4_avx512vnni(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0);
+                    accumulate_blklen32_r1c1blk4_avx512(av_00_epi8, av_01_epi8, QuantBDataPtr, QuantAScalePtr, QuantBScalePtr, acc0);
                 }
 
                 QuantAPtr += BlkLen32 * PerAccuBlk4;

From 083d053cff4dc80e7b3d8ec7610aaae2170a70b7 Mon Sep 17 00:00:00 2001
From: Changming Sun <chasun@microsoft.com>
Date: Thu, 1 Aug 2024 04:23:02 -0700
Subject: [PATCH 07/22] Add CUDA custom op header files to Linux tarball
 (#21551)

### Description
The header files were added in PR #16454.
Then, recently I made a PR #21464 that changed how we packed Linux
tarballs.
The new tarball misses the custom op header files.
Therefore I need to make this change.


### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
---
 cmake/onnxruntime.cmake                | 12 ++++++++----
 cmake/onnxruntime_providers_cpu.cmake  |  1 +
 cmake/onnxruntime_providers_cuda.cmake |  9 ++++++++-
 cmake/onnxruntime_providers_rocm.cmake |  7 ++++++-
 4 files changed, 23 insertions(+), 6 deletions(-)

diff --git a/cmake/onnxruntime.cmake b/cmake/onnxruntime.cmake
index bdb4b00b02a35..927b4ac84b037 100644
--- a/cmake/onnxruntime.cmake
+++ b/cmake/onnxruntime.cmake
@@ -38,10 +38,14 @@ function(get_c_cxx_api_headers HEADERS_VAR)
 
   # need to add header files for enabled EPs
   foreach(f ${ONNXRUNTIME_PROVIDER_NAMES})
-    file(GLOB _provider_headers CONFIGURE_DEPENDS
-      "${REPO_ROOT}/include/onnxruntime/core/providers/${f}/*.h"
-    )
-    list(APPEND _headers ${_provider_headers})
+    # The header files in include/onnxruntime/core/providers/cuda directory cannot be flattened to the same directory 
+    # with onnxruntime_c_api.h . Most other EPs probably also do not work in this way.
+    if((NOT f STREQUAL cuda) AND (NOT f STREQUAL rocm))
+      file(GLOB _provider_headers CONFIGURE_DEPENDS
+        "${REPO_ROOT}/include/onnxruntime/core/providers/${f}/*.h"
+      )
+      list(APPEND _headers ${_provider_headers})
+    endif()
   endforeach()
 
   set(${HEADERS_VAR} ${_headers} PARENT_SCOPE)
diff --git a/cmake/onnxruntime_providers_cpu.cmake b/cmake/onnxruntime_providers_cpu.cmake
index d2afe19f36691..bbcc709b144a0 100644
--- a/cmake/onnxruntime_providers_cpu.cmake
+++ b/cmake/onnxruntime_providers_cpu.cmake
@@ -219,6 +219,7 @@ if (onnxruntime_ENABLE_TRAINING)
 endif()
 
 install(FILES ${PROJECT_SOURCE_DIR}/../include/onnxruntime/core/providers/cpu/cpu_provider_factory.h  DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/onnxruntime/)
+install(FILES ${PROJECT_SOURCE_DIR}/../include/onnxruntime/core/providers/resource.h ${PROJECT_SOURCE_DIR}/../include/onnxruntime/core/providers/custom_op_context.h DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/onnxruntime/core/providers)
 set_target_properties(onnxruntime_providers PROPERTIES LINKER_LANGUAGE CXX)
 set_target_properties(onnxruntime_providers PROPERTIES FOLDER "ONNXRuntime")
 
diff --git a/cmake/onnxruntime_providers_cuda.cmake b/cmake/onnxruntime_providers_cuda.cmake
index 82c31ce6b6b4d..0829be05a3ab0 100644
--- a/cmake/onnxruntime_providers_cuda.cmake
+++ b/cmake/onnxruntime_providers_cuda.cmake
@@ -289,8 +289,15 @@
     config_cuda_provider_shared_module(onnxruntime_providers_cuda_obj)
   endif()
   config_cuda_provider_shared_module(onnxruntime_providers_cuda)
-
+  # Cannot use glob because the file cuda_provider_options.h should not be exposed out.
+  set(ONNXRUNTIME_CUDA_PROVIDER_PUBLIC_HEADERS
+        "${REPO_ROOT}/include/onnxruntime/core/providers/cuda/cuda_context.h"
+        "${REPO_ROOT}/include/onnxruntime/core/providers/cuda/cuda_resource.h"
+      )
+  set_target_properties(onnxruntime_providers_cuda PROPERTIES
+    PUBLIC_HEADER "${ONNXRUNTIME_CUDA_PROVIDER_PUBLIC_HEADERS}")
   install(TARGETS onnxruntime_providers_cuda
+          PUBLIC_HEADER DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/onnxruntime/core/providers/cuda
           ARCHIVE  DESTINATION ${CMAKE_INSTALL_LIBDIR}
           LIBRARY  DESTINATION ${CMAKE_INSTALL_LIBDIR}
           RUNTIME  DESTINATION ${CMAKE_INSTALL_BINDIR})
diff --git a/cmake/onnxruntime_providers_rocm.cmake b/cmake/onnxruntime_providers_rocm.cmake
index 71692ddb9391f..559204bd0df88 100644
--- a/cmake/onnxruntime_providers_rocm.cmake
+++ b/cmake/onnxruntime_providers_rocm.cmake
@@ -223,8 +223,13 @@
   if (onnxruntime_ENABLE_ATEN)
     target_compile_definitions(onnxruntime_providers_rocm PRIVATE ENABLE_ATEN)
   endif()
-
+  file(GLOB ONNXRUNTIME_ROCM_PROVIDER_PUBLIC_HEADERS CONFIGURE_DEPENDS
+        "${REPO_ROOT}/include/onnxruntime/core/providers/rocm/*.h"
+      )
+  set_target_properties(onnxruntime_providers_rocm PROPERTIES
+    PUBLIC_HEADER "${ONNXRUNTIME_ROCM_PROVIDER_PUBLIC_HEADERS}")
   install(TARGETS onnxruntime_providers_rocm
+          PUBLIC_HEADER DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/onnxruntime/core/providers/rocm
           ARCHIVE  DESTINATION ${CMAKE_INSTALL_LIBDIR}
           LIBRARY  DESTINATION ${CMAKE_INSTALL_LIBDIR}
           RUNTIME  DESTINATION ${CMAKE_INSTALL_BINDIR})

From f8c3c7533b49b2546240267f7ec1b3ecac8894f3 Mon Sep 17 00:00:00 2001
From: Sumit Agarwal <sumitagarwal330@gmail.com>
Date: Tue, 30 Jul 2024 09:35:45 -0700
Subject: [PATCH 08/22] Extend Pad Fusion for AveragePool (#21556)

### Description
This extends the existing pad_fusion for AveragePool operator i.e. fuse
Pad if it is followed by AveragePool operator.



### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
---
 onnxruntime/core/optimizer/pad_fusion.cc | 3 ++-
 onnxruntime/core/optimizer/pad_fusion.h  | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/onnxruntime/core/optimizer/pad_fusion.cc b/onnxruntime/core/optimizer/pad_fusion.cc
index a1c7f8de9e6fe..e266946b0d9e0 100644
--- a/onnxruntime/core/optimizer/pad_fusion.cc
+++ b/onnxruntime/core/optimizer/pad_fusion.cc
@@ -12,7 +12,7 @@ namespace onnxruntime {
  * It matches following pattern:
  *     Pad
  *      |
- *   Conv/MaxPool
+ *   Conv/MaxPool/AveragePool
  */
 bool PadFusion::SatisfyCondition(const Graph& graph, const Node& node, const logging::Logger&) const {
   // if Pad has input axis, don't fuse it.
@@ -28,6 +28,7 @@ bool PadFusion::SatisfyCondition(const Graph& graph, const Node& node, const log
 
   const Node& child_node = *node.OutputNodesBegin();
   if (!graph_utils::IsSupportedOptypeVersionAndDomain(child_node, "Conv", {1, 11}) &&
+      !graph_utils::IsSupportedOptypeVersionAndDomain(child_node, "AveragePool", {1, 7, 10, 11, 19}) &&
       !graph_utils::IsSupportedOptypeVersionAndDomain(child_node, "MaxPool", {1, 8, 10, 11, 12})) {
     return false;
   }
diff --git a/onnxruntime/core/optimizer/pad_fusion.h b/onnxruntime/core/optimizer/pad_fusion.h
index a1b6978a83d1e..ca05d219b7e2c 100644
--- a/onnxruntime/core/optimizer/pad_fusion.h
+++ b/onnxruntime/core/optimizer/pad_fusion.h
@@ -8,7 +8,7 @@
 namespace onnxruntime {
 /*
  *   This fusion submerges a Pad operator to it's child
- *   Conv or MaxPool operator, if and only if PadFusion::SatisfyCondition()
+ *   Conv or MaxPool or AveragePool operator, if and only if PadFusion::SatisfyCondition()
  *   is true.
  */
 class PadFusion : public RewriteRule {

From d17b3d5e2cb466eb01bba57990d60a22484be16a Mon Sep 17 00:00:00 2001
From: Tianlei Wu <tlwu@microsoft.com>
Date: Thu, 8 Aug 2024 19:44:15 -0700
Subject: [PATCH 09/22] Unblock migraphx and linux GPU training ci pipelines
 (#21662)

* Fix migraphx build error caused by
https://github.com/microsoft/onnxruntime/pull/21598:
Add a conditional compile on code block that depends on ROCm >= 6.2.
Note that the pipeline uses ROCm 6.0.

Unblock orttraining-linux-gpu-ci-pipeline and
orttraining-ortmodule-distributed and orttraining-amd-gpu-ci-pipeline
pipelines:
* Disable a model test in linux GPU training ci pipelines caused by
https://github.com/microsoft/onnxruntime/pull/19470:
Sometime, cudnn frontend throws exception that cudnn graph does not
support a Conv node of keras_lotus_resnet3D model on V100 GPU.
Note that same test does not throw exception in other GPU pipelines. The
failure might be related to cudnn 8.9 and V100 GPU used in the pipeline
(Amper GPUs and cuDNN 9.x do not have the issue).
The actual fix requires fallback logic, which will take time to
implement, so we temporarily disable the test in training pipelines.
* Force install torch for cuda 11.8. (The docker has torch 2.4.0 for
cuda 12.1 to build torch extension, which it is not compatible cuda
11.8). Note that this is temporary walkround. More elegant fix is to
make sure right torch version in docker build step, that might need
update install_python_deps.sh and corresponding requirements.txt.
* Skip test_gradient_correctness_conv1d since it causes segment fault.
Root cause need more investigation (maybe due to cudnn frontend as
well).
* Skip test_aten_attention since it causes assert failure. Root cause
need more investigation (maybe due to torch version).
* Skip orttraining_ortmodule_distributed_tests.py since it has error
that compiler for torch extension does not support c++17. One possible
fix it to set the following compile argument inside setup.py of
extension fused_adam: extra_compile_args['cxx'] = ['-std=c++17'].
However, due to the urgency of unblocking the pipelines, just disable
the test for now.
* skip test_softmax_bf16_large. For some reason,
torch.cuda.is_bf16_supported() returns True in V100 with torch 2.3.1, so
the test was run in CI, but V100 does not support bf16 natively.
* Fix typo of deterministic

<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
---
 .../providers/migraphx/migraphx_execution_provider.cc  |  6 ++++++
 onnxruntime/test/onnx/TestCase.cc                      |  4 ++++
 .../test/python/orttraining_test_ortmodule_api.py      | 10 +++++++---
 .../test/python/orttraining_test_ortmodule_onnx_ops.py |  2 ++
 ...inux-gpu-ortmodule-distributed-test-ci-pipeline.yml |  2 +-
 .../orttraining-linux-gpu-test-ci-pipeline.yml         |  4 ++--
 6 files changed, 22 insertions(+), 6 deletions(-)

diff --git a/onnxruntime/core/providers/migraphx/migraphx_execution_provider.cc b/onnxruntime/core/providers/migraphx/migraphx_execution_provider.cc
index 097b16ecde536..b28b38e34ab55 100644
--- a/onnxruntime/core/providers/migraphx/migraphx_execution_provider.cc
+++ b/onnxruntime/core/providers/migraphx/migraphx_execution_provider.cc
@@ -16,6 +16,7 @@
 #include "migraphx_allocator.h"
 #include "gpu_data_transfer.h"
 #include "migraphx_inc.h"
+#include <hip/hip_version.h>
 
 #include "migraphx_stream_handle.h"
 
@@ -1297,6 +1298,11 @@ Status MIGraphXExecutionProvider::Compile(const std::vector<FusedNodeAndGraph>&
       if (!input_shape_match) {
         if (!load_precompiled_model(prog, load_compiled_model_, std::string{load_compiled_path_})) {
           LOGS_DEFAULT(VERBOSE) << "No Input shapes mismatch detected. Recompiling" << std::endl;
+#ifndef ENABLE_TRAINING_CORE
+#if HIP_VERSION_MAJOR > 6 || (HIP_VERSION_MAJOR == 6 && HIP_VERSION_MINOR >= 2)
+          cmp_options.set_external_data_path(model_path_.has_parent_path() ? model_path_.parent_path().string() : std::filesystem::current_path().string());
+#endif
+#endif
           prog = migraphx::parse_onnx_buffer(onnx_string, cmp_options);
 
           // Read in the calibration data and map it to an migraphx paramater map for the calibration ops
diff --git a/onnxruntime/test/onnx/TestCase.cc b/onnxruntime/test/onnx/TestCase.cc
index 3319fdd34646b..45aaca1ceae56 100644
--- a/onnxruntime/test/onnx/TestCase.cc
+++ b/onnxruntime/test/onnx/TestCase.cc
@@ -1035,6 +1035,10 @@ std::unique_ptr<std::set<BrokenTest>> GetBrokenTests(const std::string& provider
   // std::set<std::string> broken_tests_keyword_set = {};
 
   if (provider_name == "cuda") {
+#ifdef ENABLE_TRAINING_CORE
+    // cudnn frontend exception in orttraining-linux-gpu-ci-pipeline.
+    broken_tests->insert({"keras_lotus_resnet3D", "Temporarily disabled pending investigation", {}});
+#endif
 #ifdef _WIN32
     broken_tests->insert({"LSTM_Seq_lens_unpacked", "this test fails with new image since Aug 25."});
     broken_tests->insert({"bidaf", "this test fails with new image since Aug 25."});
diff --git a/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py b/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py
index 3615a12705241..0ab441ac936fe 100644
--- a/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py
+++ b/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py
@@ -779,6 +779,8 @@ def run_step(model, rerouted_output, dispatch_mask, expert_output):
 @pytest.mark.parametrize("input_requires_grad", [False, True])
 @pytest.mark.parametrize("conv_algo_search", [None, "EXHAUSTIVE", "HEURISTIC"])
 def test_gradient_correctness_conv1d(use_fp16, input_requires_grad, conv_algo_search):
+    pytest.skip("Temporarily disabled pending investigation (might be related to cudnn frontend).")
+
     class NeuralNetConv1D(torch.nn.Module):
         def __init__(self, in_channels, out_channels, kernel_size, padding=0, groups=1):
             super().__init__()
@@ -6044,7 +6046,7 @@ def test_e2e_padding_elimination():
     torch.manual_seed(seed)
     torch.cuda.manual_seed(seed)
     torch.cuda.manual_seed_all(seed)
-    torch.backends.cudnn.determinstic = True
+    torch.backends.cudnn.deterministic = True
     torch.backends.cudnn.benchmark = False
 
     class OneLayer(torch.nn.Module):
@@ -6773,7 +6775,7 @@ def forward(self, x):
             del os.environ["ORTMODULE_ALLOW_AUTOGRAD_CHECKPOINT"]
 
 
-def test_layerwise_recompute_pythonop_determinstic():
+def test_layerwise_recompute_pythonop_deterministic():
 
     original_val = os.environ.get("ORTMODULE_MEMORY_OPT_LEVEL", None)
 
@@ -6887,7 +6889,7 @@ def generate_inputs(batch_size, max_seq_length, vocab_size):
     os.environ["ORTMODULE_MEMORY_OPT_LEVEL"] = "0"
     ort_model1 = ORTModule(copy.deepcopy(pt_model))
 
-    torch.backends.cudnn.determinstic = True
+    torch.backends.cudnn.deterministic = True
     torch.backends.cudnn.benchmark = False
 
     pt_input, pt_mask = generate_inputs(batch_size, max_seq_length, vocab_size)
@@ -6960,6 +6962,8 @@ def generate_inputs(batch_size, max_seq_length, vocab_size):
     reason="torch.nn.attention module was introduced in PyTorch 2.3.0",
 )
 def test_aten_attention():
+    pytest.skip("Temporarily disabled pending investigation.")
+
     from torch.nn.attention import SDPBackend, sdpa_kernel
 
     class _NeuralNetAttention(torch.nn.Module):
diff --git a/orttraining/orttraining/test/python/orttraining_test_ortmodule_onnx_ops.py b/orttraining/orttraining/test/python/orttraining_test_ortmodule_onnx_ops.py
index 537dcd2ccdb09..35e5bae3ea67e 100644
--- a/orttraining/orttraining/test/python/orttraining_test_ortmodule_onnx_ops.py
+++ b/orttraining/orttraining/test/python/orttraining_test_ortmodule_onnx_ops.py
@@ -150,6 +150,8 @@ def test_onnx_ops(self):
 
     @unittest.skipIf(not torch.cuda.is_bf16_supported(), "Test requires CUDA and BF16 support")
     def test_softmax_bf16_large(self):
+        raise unittest.SkipTest("Temporarily disabled pending investigation")
+
         if torch.version.cuda is None:
             # Only run this test when CUDA is available, as on ROCm BF16 is not supported by MIOpen.
             return
diff --git a/tools/ci_build/github/azure-pipelines/orttraining-linux-gpu-ortmodule-distributed-test-ci-pipeline.yml b/tools/ci_build/github/azure-pipelines/orttraining-linux-gpu-ortmodule-distributed-test-ci-pipeline.yml
index 82aa7b24e7be9..da40be43048c2 100644
--- a/tools/ci_build/github/azure-pipelines/orttraining-linux-gpu-ortmodule-distributed-test-ci-pipeline.yml
+++ b/tools/ci_build/github/azure-pipelines/orttraining-linux-gpu-ortmodule-distributed-test-ci-pipeline.yml
@@ -71,7 +71,7 @@ stages:
           --volume $(Build.BinariesDirectory):/build \
           --volume $(Agent.TempDirectory)/mnist:/mnist \
           onnxruntime_ortmodule_distributed_tests_image \
-            bash -c "rm -rf /build/RelWithDebInfo/onnxruntime/ && python3 -m pip install /build/RelWithDebInfo/dist/onnxruntime*.whl && python3 -m onnxruntime.training.ortmodule.torch_cpp_extensions.install && /build/RelWithDebInfo/launch_test.py --cmd_line_with_args 'python orttraining_ortmodule_distributed_tests.py --mnist /mnist' --cwd /build/RelWithDebInfo" \
+            bash -c "rm -rf /build/RelWithDebInfo/onnxruntime/ && python3 -m pip install /build/RelWithDebInfo/dist/onnxruntime*.whl && python3 -m pip install torch==2.3.1+cu118 --index-url https://download.pytorch.org/whl/cu118 && python3 -m onnxruntime.training.ortmodule.torch_cpp_extensions.install && echo temporarily skip /build/RelWithDebInfo/launch_test.py --cmd_line_with_args 'python orttraining_ortmodule_distributed_tests.py --mnist /mnist' --cwd /build/RelWithDebInfo" \
       displayName: 'Run orttraining_ortmodule_distributed_tests.py'
       condition: succeededOrFailed()
       timeoutInMinutes: 30
diff --git a/tools/ci_build/github/azure-pipelines/templates/orttraining-linux-gpu-test-ci-pipeline.yml b/tools/ci_build/github/azure-pipelines/templates/orttraining-linux-gpu-test-ci-pipeline.yml
index f832315c1f0df..5f073433265fa 100644
--- a/tools/ci_build/github/azure-pipelines/templates/orttraining-linux-gpu-test-ci-pipeline.yml
+++ b/tools/ci_build/github/azure-pipelines/templates/orttraining-linux-gpu-test-ci-pipeline.yml
@@ -21,7 +21,7 @@ steps:
       --volume $(Build.BinariesDirectory)/${{ parameters.BuildConfig }}:/build \
       --volume $(Agent.TempDirectory)/mnist:/mnist \
       ${{ parameters.DockerImageTag }} \
-        bash -c "rm -rf /build/onnxruntime/ && python3 -m pip install /build/dist/onnxruntime*.whl && python3 -m onnxruntime.training.ortmodule.torch_cpp_extensions.install && /build/launch_test.py --cmd_line_with_args 'python orttraining_ortmodule_tests.py --mnist /mnist --bert_data /bert_data/hf_data/glue_data/CoLA/original/raw' --cwd /build" \
+        bash -c "rm -rf /build/onnxruntime/ && python3 -m pip show torch && python3 -m pip install torch==2.3.1+cu118 --index-url https://download.pytorch.org/whl/cu118 && python3 -m pip install /build/dist/onnxruntime*.whl && python3 -m onnxruntime.training.ortmodule.torch_cpp_extensions.install && /build/launch_test.py --cmd_line_with_args 'python orttraining_ortmodule_tests.py --mnist /mnist --bert_data /bert_data/hf_data/glue_data/CoLA/original/raw' --cwd /build" \
   displayName: 'Run orttraining_ortmodule_tests.py'
   condition: succeededOrFailed()
   timeoutInMinutes: 60
@@ -35,7 +35,7 @@ steps:
       --volume $(Build.SourcesDirectory):/onnxruntime_src \
       --volume $(Build.BinariesDirectory)/${{ parameters.BuildConfig }}:/build \
       ${{ parameters.DockerImageTag }} \
-        bash -c "rm -rf /build/onnxruntime/ && python3 -m pip install /build/dist/onnxruntime*.whl && /build/launch_test.py --cmd_line_with_args 'python orttraining_test_ort_apis.py --cwd /build' --cwd /build" \
+        bash -c "rm -rf /build/onnxruntime/ && python3 -m pip install /build/dist/onnxruntime*.whl && python3 -m pip install torch==2.3.1+cu118 --index-url https://download.pytorch.org/whl/cu118 && /build/launch_test.py --cmd_line_with_args 'python orttraining_test_ort_apis.py --cwd /build' --cwd /build" \
   displayName: 'Run ORT Training APIs Tests'
   condition: succeededOrFailed()
   timeoutInMinutes: 120

From 44bcc207cbcf8e75181046f6ad2b7f9f7a5cc9d3 Mon Sep 17 00:00:00 2001
From: vraspar <vrajang@outlook.com>
Date: Sun, 4 Aug 2024 12:47:16 -0700
Subject: [PATCH 10/22] Restructure MacOS framework package to fix malformed
 Framework errors (#21536)

### Description

Refactor framework directory structure for MacOS packages

### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Apple started enforcing specific [framework
structure](https://developer.apple.com/library/archive/documentation/MacOSX/Conceptual/BPFrameworks/Concepts/FrameworkAnatomy.html)
for MacOS packages. We need to change how we package for MacOS to follow
the guidelines

Fixes following issue: [Malformed
Framework](https://github.com/microsoft/onnxruntime-swift-package-manager/issues/19
)
---
 .../assemble_apple_packaging_artifacts.sh     | 28 ++++++++-
 .../github/apple/build_apple_framework.py     | 58 +++++++++++++++----
 2 files changed, 73 insertions(+), 13 deletions(-)

diff --git a/tools/ci_build/github/apple/assemble_apple_packaging_artifacts.sh b/tools/ci_build/github/apple/assemble_apple_packaging_artifacts.sh
index 317048506ac67..f96227a750346 100755
--- a/tools/ci_build/github/apple/assemble_apple_packaging_artifacts.sh
+++ b/tools/ci_build/github/apple/assemble_apple_packaging_artifacts.sh
@@ -23,10 +23,36 @@ ORT_POD_VERSION=${4:?${USAGE_TEXT}}
 POD_ARCHIVE_BASENAME="pod-archive-${POD_NAME}-${ORT_POD_VERSION}.zip"
 PODSPEC_BASENAME="${POD_NAME}.podspec"
 
+
+# Macos requires a different structure for the framework
+# This will create the necessary symlinks for the macos framework before packaging
+# Adding the symlinks here rather than in the build script ensures that symlinks are not lost
+for MACOS_DIR in "${BINARIES_STAGING_DIR}/${POD_NAME}/onnxruntime.xcframework/macos"*; do
+  if [ -d "${MACOS_DIR}" ]; then
+    echo "Creating symlinks for ${MACOS_DIR}"
+    pushd "${MACOS_DIR}/onnxruntime.framework"
+
+    rm -rf Headers Resources onnxruntime
+    rm -rf Versions/Current
+
+    ln -sfn A Versions/Current
+    ln -sfn Versions/Current/Headers Headers
+    ln -sfn Versions/Current/Resources Resources
+    ln -sfn Versions/Current/onnxruntime onnxruntime
+
+    popd
+
+  fi
+done
+
+
+echo "Contents of ${BINARIES_STAGING_DIR}/${POD_NAME}:"
+ls -lR "${BINARIES_STAGING_DIR}/${POD_NAME}"
+
 pushd "${BINARIES_STAGING_DIR}/${POD_NAME}"
 
 # assemble the files in the artifacts staging directory
-zip -r "${ARTIFACTS_STAGING_DIR}/${POD_ARCHIVE_BASENAME}" ./* --exclude "${PODSPEC_BASENAME}"
+zip -r -y "${ARTIFACTS_STAGING_DIR}/${POD_ARCHIVE_BASENAME}" ./* --exclude "${PODSPEC_BASENAME}"
 cp "${PODSPEC_BASENAME}" "${ARTIFACTS_STAGING_DIR}/${PODSPEC_BASENAME}"
 
 popd
diff --git a/tools/ci_build/github/apple/build_apple_framework.py b/tools/ci_build/github/apple/build_apple_framework.py
index 3cd7a3af70622..7270bdd56523c 100644
--- a/tools/ci_build/github/apple/build_apple_framework.py
+++ b/tools/ci_build/github/apple/build_apple_framework.py
@@ -89,18 +89,52 @@ def _build_for_apple_sysroot(
     pathlib.Path(framework_dir).mkdir(parents=True, exist_ok=True)
 
     # copy the Info.plist, framework_info.json, and header files
-    shutil.copy(info_plist_path, framework_dir)
-    shutil.copy(framework_info_path, os.path.dirname(framework_dir))
-    header_dir = os.path.join(framework_dir, "Headers")
-    pathlib.Path(header_dir).mkdir(parents=True, exist_ok=True)
-    for _header in headers:
-        shutil.copy(_header, header_dir)
-
-    # use lipo to create a fat ort library
-    lipo_command = ["lipo", "-create"]
-    lipo_command += ort_libs
-    lipo_command += ["-output", os.path.join(framework_dir, "onnxruntime")]
-    subprocess.run(lipo_command, shell=False, check=True)
+
+    # macos requires different framework structure:
+    # https://developer.apple.com/library/archive/documentation/MacOSX/Conceptual/BPFrameworks/Concepts/FrameworkAnatomy.html
+    if sysroot == "macosx" or sysroot == "macabi":
+        # create headers and resources directory
+        header_dir = os.path.join(framework_dir, "Versions", "A", "Headers")
+        resource_dir = os.path.join(framework_dir, "Versions", "A", "Resources")
+        pathlib.Path(header_dir).mkdir(parents=True, exist_ok=True)
+        pathlib.Path(resource_dir).mkdir(parents=True, exist_ok=True)
+
+        shutil.copy(info_plist_path, resource_dir)
+        shutil.copy(framework_info_path, os.path.dirname(framework_dir))
+
+        for _header in headers:
+            shutil.copy(_header, header_dir)
+
+        # use lipo to create a fat ort library
+        lipo_command = ["lipo", "-create"]
+        lipo_command += ort_libs
+        lipo_command += ["-output", os.path.join(framework_dir, "Versions", "A", "onnxruntime")]
+        subprocess.run(lipo_command, shell=False, check=True)
+
+        # create the symbolic link
+        pathlib.Path(os.path.join(framework_dir, "Versions", "Current")).symlink_to("A", target_is_directory=True)
+        pathlib.Path(os.path.join(framework_dir, "Headers")).symlink_to(
+            "Versions/Current/Headers", target_is_directory=True
+        )
+        pathlib.Path(os.path.join(framework_dir, "Resources")).symlink_to(
+            "Versions/Current/Resources", target_is_directory=True
+        )
+        pathlib.Path(os.path.join(framework_dir, "onnxruntime")).symlink_to("Versions/Current/onnxruntime")
+
+    else:
+        shutil.copy(info_plist_path, framework_dir)
+        shutil.copy(framework_info_path, os.path.dirname(framework_dir))
+        header_dir = os.path.join(framework_dir, "Headers")
+        pathlib.Path(header_dir).mkdir(parents=True, exist_ok=True)
+
+        for _header in headers:
+            shutil.copy(_header, header_dir)
+
+        # use lipo to create a fat ort library
+        lipo_command = ["lipo", "-create"]
+        lipo_command += ort_libs
+        lipo_command += ["-output", os.path.join(framework_dir, "onnxruntime")]
+        subprocess.run(lipo_command, shell=False, check=True)
 
     return framework_dir
 

From 10032f26fc4f9e260c4760885231e747c6732e67 Mon Sep 17 00:00:00 2001
From: Scott McKay <skottmckay@gmail.com>
Date: Tue, 6 Aug 2024 08:50:56 +1000
Subject: [PATCH 11/22] Updates to apple packaging (#21611)

### Description
<!-- Describe your changes. -->
Add ability to test packaging without rebuilding every time.
Add ability to comment out some platforms/architectures without the
scripts to assemble the c/obj-c packages breaking.
Update a couple of commands to preserve symlinks.


### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Make debugging packaging issues faster.
Creates correct package for mac-catalyst and doesn't require setting
symlinks via bash script.
---
 .../assemble_apple_packaging_artifacts.sh     | 23 -----------
 .../apple/build_and_assemble_apple_pods.py    | 11 +++++-
 .../github/apple/build_apple_framework.py     |  2 +-
 .../github/apple/c/assemble_c_pod_package.py  | 13 ++++---
 .../objectivec/assemble_objc_pod_package.py   |  7 +++-
 .../github/apple/package_assembly_utils.py    | 38 +++++++++++++++++++
 .../github/apple/test_apple_packages.py       |  5 ++-
 .../azure-pipelines/templates/c-api-cpu.yml   |  2 +-
 8 files changed, 65 insertions(+), 36 deletions(-)

diff --git a/tools/ci_build/github/apple/assemble_apple_packaging_artifacts.sh b/tools/ci_build/github/apple/assemble_apple_packaging_artifacts.sh
index f96227a750346..a2178337e6876 100755
--- a/tools/ci_build/github/apple/assemble_apple_packaging_artifacts.sh
+++ b/tools/ci_build/github/apple/assemble_apple_packaging_artifacts.sh
@@ -23,29 +23,6 @@ ORT_POD_VERSION=${4:?${USAGE_TEXT}}
 POD_ARCHIVE_BASENAME="pod-archive-${POD_NAME}-${ORT_POD_VERSION}.zip"
 PODSPEC_BASENAME="${POD_NAME}.podspec"
 
-
-# Macos requires a different structure for the framework
-# This will create the necessary symlinks for the macos framework before packaging
-# Adding the symlinks here rather than in the build script ensures that symlinks are not lost
-for MACOS_DIR in "${BINARIES_STAGING_DIR}/${POD_NAME}/onnxruntime.xcframework/macos"*; do
-  if [ -d "${MACOS_DIR}" ]; then
-    echo "Creating symlinks for ${MACOS_DIR}"
-    pushd "${MACOS_DIR}/onnxruntime.framework"
-
-    rm -rf Headers Resources onnxruntime
-    rm -rf Versions/Current
-
-    ln -sfn A Versions/Current
-    ln -sfn Versions/Current/Headers Headers
-    ln -sfn Versions/Current/Resources Resources
-    ln -sfn Versions/Current/onnxruntime onnxruntime
-
-    popd
-
-  fi
-done
-
-
 echo "Contents of ${BINARIES_STAGING_DIR}/${POD_NAME}:"
 ls -lR "${BINARIES_STAGING_DIR}/${POD_NAME}"
 
diff --git a/tools/ci_build/github/apple/build_and_assemble_apple_pods.py b/tools/ci_build/github/apple/build_and_assemble_apple_pods.py
index 5014ba11d983d..71aeb9e7b0304 100755
--- a/tools/ci_build/github/apple/build_and_assemble_apple_pods.py
+++ b/tools/ci_build/github/apple/build_and_assemble_apple_pods.py
@@ -57,6 +57,11 @@ def parse_args():
     )
 
     parser.add_argument("--test", action="store_true", help="Run tests on the framework and pod package files.")
+    parser.add_argument(
+        "--skip-build",
+        action="store_true",
+        help="Use build from previous run. Useful to debug test issues or packaging changes.",
+    )
 
     build_framework_group = parser.add_argument_group(
         title="iOS framework build arguments",
@@ -114,7 +119,8 @@ def main():
 
     build_apple_framework_args += ["--build_dir", str(build_dir), args.build_settings_file]
 
-    run(build_apple_framework_args)
+    if not args.skip_build:
+        run(build_apple_framework_args)
 
     if args.test:
         test_apple_packages_args = [
@@ -171,7 +177,8 @@ def main():
         def move_dir(src, dst):
             if dst.is_dir():
                 shutil.rmtree(dst)
-            shutil.move(src, dst)
+            shutil.copytree(src, dst, symlinks=True)
+            shutil.rmtree(src)
 
         move_dir(c_pod_staging_dir, staging_dir / c_pod_name)
         move_dir(objc_pod_staging_dir, staging_dir / objc_pod_name)
diff --git a/tools/ci_build/github/apple/build_apple_framework.py b/tools/ci_build/github/apple/build_apple_framework.py
index 7270bdd56523c..5a3b242c2a389 100644
--- a/tools/ci_build/github/apple/build_apple_framework.py
+++ b/tools/ci_build/github/apple/build_apple_framework.py
@@ -200,7 +200,7 @@ def _build_package(args):
     xcframework_dir = os.path.join(build_dir, "framework_out")
     pathlib.Path(xcframework_dir).mkdir(parents=True, exist_ok=True)
     shutil.copy(os.path.join(REPO_DIR, "LICENSE"), xcframework_dir)
-    shutil.copytree(public_headers_path, os.path.join(xcframework_dir, "Headers"), dirs_exist_ok=True)
+    shutil.copytree(public_headers_path, os.path.join(xcframework_dir, "Headers"), dirs_exist_ok=True, symlinks=True)
     _merge_framework_info_files(framework_info_files_to_merge, os.path.join(build_dir, "xcframework_info.json"))
 
     # remove existing xcframework if any
diff --git a/tools/ci_build/github/apple/c/assemble_c_pod_package.py b/tools/ci_build/github/apple/c/assemble_c_pod_package.py
index ca4f01cf65bd9..59052734ddd26 100644
--- a/tools/ci_build/github/apple/c/assemble_c_pod_package.py
+++ b/tools/ci_build/github/apple/c/assemble_c_pod_package.py
@@ -16,6 +16,7 @@
     PackageVariant,
     copy_repo_relative_to_dir,
     gen_file_from_template,
+    get_podspec_values,
     load_json_config,
 )
 
@@ -66,23 +67,25 @@ def assemble_c_pod_package(
         print("Warning: staging directory already exists", file=sys.stderr)
 
     # copy the necessary files to the staging directory
-    shutil.copytree(framework_dir, staging_dir / framework_dir.name, dirs_exist_ok=True)
-    shutil.copytree(public_headers_dir, staging_dir / public_headers_dir.name, dirs_exist_ok=True)
+    shutil.copytree(framework_dir, staging_dir / framework_dir.name, dirs_exist_ok=True, symlinks=True)
+    shutil.copytree(public_headers_dir, staging_dir / public_headers_dir.name, dirs_exist_ok=True, symlinks=True)
     copy_repo_relative_to_dir(["LICENSE"], staging_dir)
 
+    (ios_deployment_target, macos_deployment_target, weak_framework) = get_podspec_values(framework_info)
+
     # generate the podspec file from the template
     variable_substitutions = {
         "DESCRIPTION": pod_config["description"],
         # By default, we build both "iphoneos" and "iphonesimulator" architectures, and the deployment target should be the same between these two.
-        "IOS_DEPLOYMENT_TARGET": framework_info["iphonesimulator"]["APPLE_DEPLOYMENT_TARGET"],
-        "MACOSX_DEPLOYMENT_TARGET": framework_info.get("macosx", {}).get("APPLE_DEPLOYMENT_TARGET", ""),
+        "IOS_DEPLOYMENT_TARGET": ios_deployment_target,
+        "MACOSX_DEPLOYMENT_TARGET": macos_deployment_target,
         "LICENSE_FILE": "LICENSE",
         "NAME": pod_name,
         "ORT_C_FRAMEWORK": framework_dir.name,
         "ORT_C_HEADERS_DIR": public_headers_dir.name,
         "SUMMARY": pod_config["summary"],
         "VERSION": pod_version,
-        "WEAK_FRAMEWORK": framework_info["iphonesimulator"]["WEAK_FRAMEWORK"],
+        "WEAK_FRAMEWORK": weak_framework,
     }
 
     podspec_template = _script_dir / "c.podspec.template"
diff --git a/tools/ci_build/github/apple/objectivec/assemble_objc_pod_package.py b/tools/ci_build/github/apple/objectivec/assemble_objc_pod_package.py
index 1e26482440eae..b7eb34cb09219 100755
--- a/tools/ci_build/github/apple/objectivec/assemble_objc_pod_package.py
+++ b/tools/ci_build/github/apple/objectivec/assemble_objc_pod_package.py
@@ -17,6 +17,7 @@
     copy_repo_relative_to_dir,
     filter_files,
     gen_file_from_template,
+    get_podspec_values,
     load_json_config,
 )
 
@@ -147,12 +148,14 @@ def assemble_objc_pod_package(
     def path_patterns_as_variable_value(patterns: list[str]):
         return ", ".join([f'"{pattern}"' for pattern in patterns])
 
+    (ios_deployment_target, macos_deployment_target, _) = get_podspec_values(framework_info)
+
     variable_substitutions = {
         "C_POD_NAME": c_pod_config["name"],
         "DESCRIPTION": pod_config["description"],
         "INCLUDE_DIR_LIST": path_patterns_as_variable_value(include_dirs),
-        "IOS_DEPLOYMENT_TARGET": framework_info["iphonesimulator"]["APPLE_DEPLOYMENT_TARGET"],
-        "MACOSX_DEPLOYMENT_TARGET": framework_info.get("macosx", {}).get("APPLE_DEPLOYMENT_TARGET", ""),
+        "IOS_DEPLOYMENT_TARGET": ios_deployment_target,
+        "MACOSX_DEPLOYMENT_TARGET": macos_deployment_target,
         "LICENSE_FILE": license_file,
         "NAME": pod_name,
         "PUBLIC_HEADER_FILE_LIST": path_patterns_as_variable_value(pod_files["public_header_files"]),
diff --git a/tools/ci_build/github/apple/package_assembly_utils.py b/tools/ci_build/github/apple/package_assembly_utils.py
index 8ab8ccdb3f966..c6822466d73d0 100644
--- a/tools/ci_build/github/apple/package_assembly_utils.py
+++ b/tools/ci_build/github/apple/package_assembly_utils.py
@@ -118,6 +118,44 @@ def load_json_config(json_config_file: pathlib.Path):
         return json.load(config)
 
 
+def get_podspec_values(framework_info):
+    """
+    Get the podspec deployement targets and weak framework info from the dictionary that load_json_config returned.
+    Looks for iphonesimulator, iphoneos and macos settings.
+    Handles missing platforms and checks consistency.
+    Returns empty string for deployment target if that platofrm is not enabled.
+
+    :return (ios_deployment_target, macos_deployment_target, weak_framework)
+    """
+    ios_deployment_target = ""
+    macos_deployment_target = ""
+    weak_framework = ""  # should be the same for all platforms
+    # get info, allowing for a subset of platforms to be specified
+    for framework in ("iphonesimulator", "iphoneos", "macosx"):
+        if framework not in framework_info:
+            continue
+
+        target = framework_info[framework]["APPLE_DEPLOYMENT_TARGET"]
+        weak = framework_info[framework]["WEAK_FRAMEWORK"]
+
+        if not weak_framework:
+            weak_framework = weak
+        else:
+            # should be consistent
+            assert weak == weak_framework
+
+        if framework == "macosx":
+            macos_deployment_target = target
+        else:
+            if not ios_deployment_target:
+                ios_deployment_target = target
+            else:
+                # should be consistent
+                assert ios_deployment_target == target
+
+    return (ios_deployment_target, macos_deployment_target, weak_framework)
+
+
 def get_ort_version():
     """
     Gets the ONNX Runtime version string from the repo.
diff --git a/tools/ci_build/github/apple/test_apple_packages.py b/tools/ci_build/github/apple/test_apple_packages.py
index 8f06d6dd68fbc..14c0b46676ac6 100644
--- a/tools/ci_build/github/apple/test_apple_packages.py
+++ b/tools/ci_build/github/apple/test_apple_packages.py
@@ -89,8 +89,9 @@ def _test_apple_packages(args):
 
         # create a zip file contains the framework
         zip_file_path = local_pods_dir / f"{pod_name}.zip"
-        # shutil.make_archive require target file as full path without extension
-        shutil.make_archive(zip_file_path.with_suffix(""), "zip", root_dir=local_pods_dir)
+
+        # shutil.make_archive doesn't preserve symlinks. we know this is running on macOS so use zip
+        subprocess.run(["zip", "-r", "-y", str(zip_file_path), "."], cwd=local_pods_dir, check=True)
 
         # update the podspec to point to the local framework zip file
         with open(podspec) as file:
diff --git a/tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml b/tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml
index ec97da3786fd9..0368c91290d5e 100644
--- a/tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml
+++ b/tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml
@@ -111,7 +111,7 @@ stages:
         cp -R $(Build.BinariesDirectory)/ios_framework/framework_out/onnxruntime.xcframework \
           $(Build.BinariesDirectory)/artifacts_staging/onnxruntime-ios-xcframework-$(OnnxRuntimeVersion)
         pushd $(Build.BinariesDirectory)/artifacts_staging
-        zip -vr $(Build.BinariesDirectory)/artifacts/onnxruntime_xcframework.zip \
+        zip -vry $(Build.BinariesDirectory)/artifacts/onnxruntime_xcframework.zip \
           onnxruntime-ios-xcframework-$(OnnxRuntimeVersion)
         popd
       displayName: "Build Apple xcframework"

From 5db42faefaec07dda3d06b487abed9a76ad52ef2 Mon Sep 17 00:00:00 2001
From: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Date: Tue, 6 Aug 2024 12:42:57 -0700
Subject: [PATCH 12/22] Fix usability checker CoreML config file path. (#21626)

Fix usability checker CoreML config file path. The files got renamed but one place was still referring to the old name.
---
 tools/python/util/mobile_helpers/usability_checker.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/python/util/mobile_helpers/usability_checker.py b/tools/python/util/mobile_helpers/usability_checker.py
index a8b5021f1387b..e7948c43baa49 100644
--- a/tools/python/util/mobile_helpers/usability_checker.py
+++ b/tools/python/util/mobile_helpers/usability_checker.py
@@ -513,11 +513,11 @@ def check_nnapi_partitions(model, require_fixed_input_sizes: bool):
     return _check_ep_partitioning(model, config_path, require_fixed_input_sizes)
 
 
-def check_coreml_partitions(model: onnx.ModelProto, require_fixed_input_sizes: bool, config_filename):
+def check_coreml_partitions(model: onnx.ModelProto, require_fixed_input_sizes: bool, config_filename: str):
     # if we're running in the ORT python package the file should be local. otherwise assume we're running from the
     # ORT repo
     script_dir = pathlib.Path(__file__).parent
-    local_config = script_dir / "coreml_supported_ops.md"
+    local_config = script_dir / config_filename
     if local_config.exists():
         config_path = local_config
     else:

From 0ad313ff6fe0c07185b417c0c7bfb72b6b9a4b85 Mon Sep 17 00:00:00 2001
From: Scott McKay <skottmckay@gmail.com>
Date: Fri, 9 Aug 2024 17:38:18 +1000
Subject: [PATCH 13/22] Use zipped xcframework in nuget package (#21663)

### Description
<!-- Describe your changes. -->
The xcframework now uses symlinks to have the correct structure
according to Apple requirements. Symlinks are not supported by nuget on
Windows.

In order to work around that we can store a zip of the xcframeworks in
the nuget package.

### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Fix nuget packaging build break
---
 .../targets/net8.0-ios/targets.xml            |  4 ++--
 .../azure-pipelines/templates/c-api-cpu.yml   |  9 +++------
 .../github/windows/extract_nuget_files.ps1    | 20 ++++++++++++++++++-
 .../nuget/generate_nuspec_for_native_nuget.py |  6 ++++--
 4 files changed, 28 insertions(+), 11 deletions(-)

diff --git a/csharp/src/Microsoft.ML.OnnxRuntime/targets/net8.0-ios/targets.xml b/csharp/src/Microsoft.ML.OnnxRuntime/targets/net8.0-ios/targets.xml
index 3eb9720af511f..c6dbba8dfda76 100644
--- a/csharp/src/Microsoft.ML.OnnxRuntime/targets/net8.0-ios/targets.xml
+++ b/csharp/src/Microsoft.ML.OnnxRuntime/targets/net8.0-ios/targets.xml
@@ -1,7 +1,7 @@
 <?xml version="1.0" encoding="utf-8"?>
 <Project ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
   <ItemGroup Condition="('$(OutputType)'!='Library' OR '$(IsAppExtension)'=='True')">
-    <NativeReference Include="$(MSBuildThisFileDirectory)..\..\runtimes\ios\native\onnxruntime.xcframework">
+    <NativeReference Include="$(MSBuildThisFileDirectory)..\..\runtimes\ios\native\onnxruntime.xcframework.zip">
       <Kind>Static</Kind>
       <IsCxx>True</IsCxx>
       <SmartLink>True</SmartLink>
@@ -10,4 +10,4 @@
       <WeakFrameworks>CoreML</WeakFrameworks>
     </NativeReference>
   </ItemGroup>
-</Project>
\ No newline at end of file
+</Project>
diff --git a/tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml b/tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml
index 0368c91290d5e..74fc64fa53a4a 100644
--- a/tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml
+++ b/tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml
@@ -107,12 +107,9 @@ stages:
           --build_dir "$(Build.BinariesDirectory)/ios_framework" \
           tools/ci_build/github/apple/default_full_ios_framework_build_settings.json
         mkdir $(Build.BinariesDirectory)/artifacts
-        mkdir -p $(Build.BinariesDirectory)/artifacts_staging/onnxruntime-ios-xcframework-$(OnnxRuntimeVersion)
-        cp -R $(Build.BinariesDirectory)/ios_framework/framework_out/onnxruntime.xcframework \
-          $(Build.BinariesDirectory)/artifacts_staging/onnxruntime-ios-xcframework-$(OnnxRuntimeVersion)
-        pushd $(Build.BinariesDirectory)/artifacts_staging
-        zip -vry $(Build.BinariesDirectory)/artifacts/onnxruntime_xcframework.zip \
-          onnxruntime-ios-xcframework-$(OnnxRuntimeVersion)
+        pushd $(Build.BinariesDirectory)/ios_framework/framework_out
+        zip -vry $(Build.BinariesDirectory)/artifacts/onnxruntime_ios_xcframework.$(OnnxRuntimeVersion).zip \
+          onnxruntime.xcframework
         popd
       displayName: "Build Apple xcframework"
 
diff --git a/tools/ci_build/github/windows/extract_nuget_files.ps1 b/tools/ci_build/github/windows/extract_nuget_files.ps1
index 68757e25b01f7..095153cb6ad7c 100644
--- a/tools/ci_build/github/windows/extract_nuget_files.ps1
+++ b/tools/ci_build/github/windows/extract_nuget_files.ps1
@@ -10,7 +10,8 @@ New-Item -Path $nuget_artifacts_dir -ItemType directory
 
 ## .zip files
 # unzip directly
-Get-ChildItem $Env:BUILD_BINARIESDIRECTORY\nuget-artifact -Filter *.zip |
+# exclude the iOS xcframework as we need to leave that zipped up to preserve symlinks
+Get-ChildItem -Path $Env:BUILD_BINARIESDIRECTORY\nuget-artifact\* -Include *.zip -Exclude onnxruntime_ios_xcframework.*.zip |
 Foreach-Object {
  $cmd = "7z.exe x $($_.FullName) -y -o$nuget_artifacts_dir"
  Write-Output $cmd
@@ -34,6 +35,23 @@ Foreach-Object {
  Invoke-Expression -Command $cmd
 }
 
+# process iOS xcframework
+$xcframeworks = Get-ChildItem $Env:BUILD_BINARIESDIRECTORY\nuget-artifact -Filter onnxruntime_ios_xcframework.*.zip
+if ($xcframeworks.Count -eq 1) {
+  $xcframework = $xcframeworks[0]
+  $target_dir = "$nuget_artifacts_dir\onnxruntime-ios-xcframework"
+  # remove version info from filename and use required filename format
+  $target_file = "$target_dir\onnxruntime.xcframework.zip"
+  New-Item -Path $target_dir -ItemType directory
+
+  Write-Output "Copy-Item $($xcframework.FullName) $target_file"
+  Copy-Item $xcframework.FullName $target_file
+}
+elseif ($xcframeworks.Count -gt 1) {
+  Write-Error "Expected at most one onnxruntime_ios_xcframework*.zip file but got: [$xcframeworks]"
+}
+
+
 # copy android AAR.
 # for full build of onnxruntime Android AAR, there should only be one .aar file
 # called onnxruntime-android-x.y.z.aar or onnxruntime-training-android-x.y.z.aar but sanity check that
diff --git a/tools/nuget/generate_nuspec_for_native_nuget.py b/tools/nuget/generate_nuspec_for_native_nuget.py
index a005bd4c4b89d..2dda41a5a3bec 100644
--- a/tools/nuget/generate_nuspec_for_native_nuget.py
+++ b/tools/nuget/generate_nuspec_for_native_nuget.py
@@ -105,8 +105,10 @@ def generate_file_list_for_ep(nuget_artifacts_dir, ep, files_list, include_pdbs,
                 if child_file.suffix in [".aar"]:
                     files_list.append('<file src="' + str(child_file) + '" target="runtimes/android/native"/>')
 
-        if child.name == "onnxruntime-ios-xcframework":
-            files_list.append('<file src="' + str(child) + "\\**" '" target="runtimes/ios/native"/>')  # noqa: ISC001
+        if child.name == "onnxruntime-ios":
+            for child_file in child.iterdir():
+                if child_file.suffix in [".zip"]:
+                    files_list.append('<file src="' + str(child_file) + '" target="runtimes/ios/native"/>')
 
 
 def parse_arguments():

From b67af53a51da4cf05b70300569720c18d7f111d6 Mon Sep 17 00:00:00 2001
From: Yi Zhang <zhanyi@microsoft.com>
Date: Wed, 7 Aug 2024 17:47:15 +0800
Subject: [PATCH 14/22] Pin transformer and optimum version (#21650)

### Description
<!-- Describe your changes. -->



### Motivation and Context
To fix whisper test failure
---
 .../python/tools/transformers/models/whisper/requirements.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/onnxruntime/python/tools/transformers/models/whisper/requirements.txt b/onnxruntime/python/tools/transformers/models/whisper/requirements.txt
index 689b14ea9a684..979f872ac4c5e 100644
--- a/onnxruntime/python/tools/transformers/models/whisper/requirements.txt
+++ b/onnxruntime/python/tools/transformers/models/whisper/requirements.txt
@@ -1,11 +1,11 @@
 torch>=1.13.0
-transformers>=4.24.0
+transformers>=4.24.0,<= 4.42.4
 openai-whisper>=20231117
 ffmpeg-python
 datasets
 soundfile
 librosa
-optimum
+optimum<=1.21.2
 onnxruntime-extensions>=0.9.0
 onnx==1.16.1
 protobuf==3.20.2

From ac38c44bfb23895ee8cb2db918523a2f29bc0ea6 Mon Sep 17 00:00:00 2001
From: Tianlei Wu <tlwu@microsoft.com>
Date: Fri, 9 Aug 2024 01:31:00 -0700
Subject: [PATCH 15/22] [CUDA] Fix MHA mask (#21655)

### Description
Fix a check of mask type introduced by me in a recent commit. Add tests.
---
 .../cuda/bert/multihead_attention.cc          |   4 +-
 .../test/python/transformers/benchmark_mha.py |  99 ++++++++-
 .../test/python/transformers/test_mha.py      | 204 ++++++++++++------
 3 files changed, 233 insertions(+), 74 deletions(-)

diff --git a/onnxruntime/contrib_ops/cuda/bert/multihead_attention.cc b/onnxruntime/contrib_ops/cuda/bert/multihead_attention.cc
index c36abc8e1d624..2835192abd298 100644
--- a/onnxruntime/contrib_ops/cuda/bert/multihead_attention.cc
+++ b/onnxruntime/contrib_ops/cuda/bert/multihead_attention.cc
@@ -182,6 +182,8 @@ Status MultiHeadAttention<T>::ComputeInternal(OpKernelContext* context) const {
   auto out_accum_buffer = GetScratchBuffer<void>(0, context->GetComputeStream());          // nullptr
 #endif
 
+  bool is_mask_none_or_1d_k_len = parameters.mask_type == AttentionMaskType::MASK_NONE ||
+                                  parameters.mask_type == AttentionMaskType::MASK_1D_KEY_SEQ_LEN;
   bool use_fused_cross_attention =
       !use_flash_attention &&
       !disable_fused_cross_attention_ &&
@@ -213,7 +215,7 @@ Status MultiHeadAttention<T>::ComputeInternal(OpKernelContext* context) const {
       nullptr == relative_position_bias &&
       (parameters.qkv_format == Q_K_V_BSNH || parameters.qkv_format == QKV_BSN3H) &&
       nullptr == past_key && nullptr == present_key &&
-      (nullptr == key_padding_mask || AttentionMaskType::MASK_1D_KEY_SEQ_LEN) &&
+      is_mask_none_or_1d_k_len &&
       parameters.hidden_size == parameters.v_hidden_size &&
       parameters.sequence_length == parameters.kv_sequence_length &&  // self attention only for fused runner
       FusedMHARunnerFP16v2::IsSupported(sm, parameters.head_size, sequence_length,
diff --git a/onnxruntime/test/python/transformers/benchmark_mha.py b/onnxruntime/test/python/transformers/benchmark_mha.py
index ec350874af32c..0c52ee690af82 100644
--- a/onnxruntime/test/python/transformers/benchmark_mha.py
+++ b/onnxruntime/test/python/transformers/benchmark_mha.py
@@ -71,6 +71,13 @@ class SdpaKernel(IntEnum):
     TRT_CAUSAL_ATTENTION = 128
 
 
+# Since we support attention bias, so we only need support up to 2D mask.
+class AttentionMaskFormat(IntEnum):
+    Mask_None = 0  # No attention mask.
+    Mask_1D_Key_SeqLen = 1  # Shape (batch_size), actual sequence lengths (excluding padding on the right side).
+    Mask_2D_Key_PaddingMask = 2  # Shape (batch_size, total_sequence_length), key padding mask mask.
+
+
 class MultiHeadAttentionConfig:
     def __init__(
         self,
@@ -93,6 +100,7 @@ def __init__(
         input_format: int = InputFormats.Q_K_V_BSNH_BSNH_BSNH,
         verbose: bool = False,
         has_bias: bool = False,
+        mask_format: int = AttentionMaskFormat.Mask_None,
     ):
         self.operator = "MultiHeadAttention"
         self.batch_size = batch_size
@@ -144,6 +152,19 @@ def __init__(
         self.verbose = verbose
         self.has_bias = has_bias
 
+        assert mask_format in [
+            AttentionMaskFormat.Mask_None,
+            AttentionMaskFormat.Mask_1D_Key_SeqLen,
+            AttentionMaskFormat.Mask_2D_Key_PaddingMask,
+        ]
+        self.mask_format = mask_format
+
+        # mask_index_q and mask_index_kv will be updated in random_inputs() if mask_format is not Mask_None.
+        self.mask_index_kv = torch.ones(self.batch_size, dtype=torch.int32, device=self.device) * self.sequence_length
+        self.mask_index_q = (
+            torch.ones(self.batch_size, dtype=torch.int32, device=self.device) * self.total_sequence_length
+        )
+
     def __repr__(self):
         return (
             f"MultiHeadAttentionConfig(batch_size={self.batch_size}, sequence_length={self.sequence_length}, "
@@ -154,7 +175,7 @@ def __repr__(self):
             f"share_past_present_buffer={self.share_past_present_buffer}, "
             f"provider={self.provider}, device={self.device}, enable_cuda_graph={self.enable_cuda_graph}, "
             f"dtype={self.dtype}, input_format={InputFormats.input_format_str(self.input_format)}, "
-            f"has_bias={self.has_bias}"
+            f"has_bias={self.has_bias}, mask_format={self.mask_format}"
         )
 
     def shape_dict(self, input_format=None):
@@ -207,6 +228,13 @@ def shape_dict(self, input_format=None):
         if self.has_bias:
             shapes["bias"] = (3 * self.num_heads * self.head_size,)
 
+        if self.mask_format == AttentionMaskFormat.Mask_1D_Key_SeqLen:
+            shapes["mask"] = (self.batch_size,)
+        elif self.mask_format == AttentionMaskFormat.Mask_2D_Key_PaddingMask:
+            shapes["mask"] = (self.batch_size, self.total_sequence_length)
+        else:
+            assert self.mask_format == AttentionMaskFormat.Mask_None
+
         return shapes
 
     def symbolic_shape_dict(self, input_format=None):
@@ -259,8 +287,35 @@ def symbolic_shape_dict(self, input_format=None):
         if self.has_bias:
             shapes["bias"] = (3 * self.num_heads * self.head_size,)
 
+        if self.mask_format == AttentionMaskFormat.Mask_1D_Key_SeqLen:
+            shapes["mask"] = (self.batch_size,)
+        elif self.mask_format == AttentionMaskFormat.Mask_2D_Key_PaddingMask:
+            shapes["mask"] = (self.batch_size, "total_sequence_length")
+        else:
+            assert self.mask_format == AttentionMaskFormat.Mask_None
+
         return shapes
 
+    def right_side_padding_masks(self):
+        q_mask = torch.ones(self.batch_size, 1, self.sequence_length, 1, dtype=torch.bool, device=self.device)
+        k_mask = torch.ones(self.batch_size, 1, self.total_sequence_length, 1, dtype=torch.bool, device=self.device)
+        mask = torch.ones(
+            self.batch_size,
+            self.num_heads,
+            self.sequence_length,
+            self.total_sequence_length,
+            dtype=torch.bool,
+            device=self.device,
+        )
+
+        if self.mask_format != AttentionMaskFormat.Mask_None:
+            for i, (m, n) in enumerate(zip(self.mask_index_q, self.mask_index_kv)):
+                q_mask[i, :, m:, :] = False
+                k_mask[i, :, n:, :] = False
+                mask[i, :, m:, :] = False
+                mask[i, :, :, n:] = False
+        return q_mask, k_mask, mask
+
     def random_inputs(self, seed: int = 123, no_bias_k_v: bool = False):
         device = self.device
         dtype = self.dtype
@@ -325,13 +380,38 @@ def random_inputs(self, seed: int = 123, no_bias_k_v: bool = False):
         if self.has_bias:
             feeds["bias"] = torch.concat([bias_q, bias_k, bias_v], dim=0).reshape(shape_dict["bias"]).contiguous()
 
+        # Generate padding mask
+        if self.mask_format != AttentionMaskFormat.Mask_None:
+            self.mask_index_kv = torch.randint(
+                1, self.total_sequence_length + 1, (self.batch_size,), dtype=torch.int32, device=self.device
+            )
+            if self.past_sequence_length > 0:
+                self.mask_index_q = (
+                    torch.ones(self.batch_size, dtype=torch.int32, device=self.device) * self.sequence_length
+                )
+            else:  # prompt case
+                self.mask_index_q = self.mask_index_kv.clone()
+
+        mask = None
+        if self.mask_format == AttentionMaskFormat.Mask_1D_Key_SeqLen:
+            mask = self.mask_index_kv.clone()
+        elif self.mask_format == AttentionMaskFormat.Mask_2D_Key_PaddingMask:
+            k_mask = torch.ones(self.batch_size, 1, self.total_sequence_length, 1, dtype=torch.bool, device=self.device)
+            for i, n in enumerate(self.mask_index_kv):
+                k_mask[i, :, n:, :] = False
+            mask = k_mask.reshape(self.batch_size, self.total_sequence_length)
+        else:
+            assert self.mask_format == AttentionMaskFormat.Mask_None
+
+        if mask is not None:
+            feeds = {**feeds, "mask": mask.to(dtype=torch.int32)}  # mask is int32 (not bool) for MultiHeadAttention op.
+
         return feeds
 
     def get_input_output_names(self):
         if self.input_format == InputFormats.Q_K_V_BSNH_BNSH_BNSH:
-            return ["query", "key", "value"], ["output"]
-
-        if self.input_format == InputFormats.QKV_BSN3H:
+            inputs, outputs = ["query", "key", "value"], ["output"]
+        elif self.input_format == InputFormats.QKV_BSN3H:
             inputs, outputs = ["query"], ["output"]
         elif self.input_format == InputFormats.Q_KV_BSNH_BSN2H:
             inputs, outputs = ["query", "key"], ["output"]
@@ -339,8 +419,12 @@ def get_input_output_names(self):
             inputs, outputs = ["query", "key", "value"], ["output"]
 
         if self.has_bias:
+            assert self.input_format != InputFormats.Q_KV_BSNH_BSN2H
             inputs = [*inputs, "bias"]
 
+        if self.mask_format != AttentionMaskFormat.Mask_None:
+            inputs = [*inputs, "mask"]
+
         if self.has_past_input:
             inputs = [*inputs, "past_key", "past_value"]
 
@@ -351,7 +435,7 @@ def get_input_output_names(self):
 
 
 def fill_optional_mha_inputs(input_names):
-    inputs = ["query", "key", "value", "bias", "key_padding_mask", "relative_position_bias", "past_key", "past_value"]
+    inputs = ["query", "key", "value", "bias", "mask", "relative_position_bias", "past_key", "past_value"]
 
     # Remove optional inputs that are not in input_names with empty string
     inputs_with_optional = [input if input in input_names else "" for input in inputs]
@@ -376,13 +460,16 @@ def create_multi_head_attention_onnx_model(config: MultiHeadAttentionConfig, use
             num_heads=config.num_heads,
             unidirectional=int(config.causal),
             scale=config.softmax_scale,
+            mask_filter_value=float("-inf"),
             domain="com.microsoft",
         ),
     ]
 
     shape_dict = config.symbolic_shape_dict() if use_symbolic_shape else config.shape_dict()
     inputs = [
-        helper.make_tensor_value_info(input_name, float_type, list(shape_dict[input_name]))
+        helper.make_tensor_value_info(
+            input_name, TensorProto.INT32 if input_name == "mask" else float_type, list(shape_dict[input_name])
+        )
         for input_name in input_names
         if input_name
     ]
diff --git a/onnxruntime/test/python/transformers/test_mha.py b/onnxruntime/test/python/transformers/test_mha.py
index a35d02b0b9d52..5948f8b1ccfc1 100644
--- a/onnxruntime/test/python/transformers/test_mha.py
+++ b/onnxruntime/test/python/transformers/test_mha.py
@@ -14,9 +14,15 @@
 
 import numpy
 import torch
-from benchmark_mha import InputFormats, MultiHeadAttentionConfig, OrtMultiHeadAttention, SdpaKernel, create_ort_session
+from benchmark_mha import (
+    AttentionMaskFormat,
+    InputFormats,
+    MultiHeadAttentionConfig,
+    OrtMultiHeadAttention,
+    SdpaKernel,
+    create_ort_session,
+)
 from einops import rearrange
-from parameterized import parameterized
 
 import onnxruntime
 
@@ -67,11 +73,11 @@ def attention_reference(
     query: torch.Tensor,
     key: torch.Tensor,
     value: torch.Tensor,
-    scale: Optional[float] = None,
     mask: Optional[torch.Tensor] = None,
+    scale: Optional[float] = None,
     verbose: bool = False,
 ) -> torch.Tensor:
-    """Reference implementation of Dot Product Attention
+    """Reference implementation of SDPA
 
     Args:
         head_size (int): dimension per head
@@ -82,7 +88,7 @@ def attention_reference(
         mask (Optional[torch.Tensor], optional): attention mask. Defaults to None.
 
     Returns:
-        torch.Tensor: result of dot product attention
+        torch.Tensor: result of SDPA
     """
     if scale is None:
         scale = 1.0 / (head_size**0.5)
@@ -93,6 +99,7 @@ def attention_reference(
     assert value.dim() == 4
 
     if verbose:
+        torch.set_printoptions(precision=6, linewidth=200, sci_mode=False)
         print("query(SDPA)", query)
         print("key(SDPA)", key)
         print("value(SDPA)", value)
@@ -101,11 +108,14 @@ def attention_reference(
 
     # Apply multi-head attention.
     attn = torch.einsum("bhmd,bhnd->bhmn", query, key).float() * scale
-    if mask is not None:
-        attn = attn.masked_fill((1 - mask.int()).bool(), float("-inf"))
     if verbose:
         print("QK(SDPA)", attn)
 
+    if mask is not None:
+        attn = attn.masked_fill((1 - mask.int()).bool(), float("-inf"))
+        if verbose:
+            print("masked QK(SDPA)", attn)
+
     attn = attn.softmax(-1)
     if verbose:
         print("Softmax(SDPA)", attn)
@@ -170,6 +180,12 @@ def no_kv_cache_test_cases(provider: str, comprehensive: bool):
     heads = [1, 3, 4, 16]
     head_sizes = [8, 16, 32, 40, 64, 80, 96, 128, 160, 192, 224, 256]
 
+    mask_formats = [
+        AttentionMaskFormat.Mask_None,
+        AttentionMaskFormat.Mask_1D_Key_SeqLen,
+        AttentionMaskFormat.Mask_2D_Key_PaddingMask,
+    ]
+
     device, dtype, formats = get_provider_support_info(provider, False)
     if comprehensive:
         sequence_lengths = [*sequence_lengths, 2048]  # Large sequence length is slow and need a lot of memory
@@ -179,25 +195,27 @@ def no_kv_cache_test_cases(provider: str, comprehensive: bool):
                     for head_size in head_sizes:
                         for format in formats:
                             for causal in [True, False]:
-                                for has_bias in get_bias_support(format):
-                                    config = MultiHeadAttentionConfig(
-                                        batch_size=batch_size,
-                                        sequence_length=sequence_length,
-                                        num_heads=num_heads,
-                                        head_size=head_size,
-                                        causal=causal,
-                                        past_sequence_length=0,
-                                        kv_sequence_length=sequence_length,
-                                        max_cache_sequence_length=None,
-                                        provider=provider,
-                                        device=device,
-                                        dtype=dtype,
-                                        use_kv_cache=False,
-                                        share_past_present_buffer=False,
-                                        input_format=format,
-                                        has_bias=has_bias,
-                                    )
-                                    yield config
+                                for mask_format in mask_formats:
+                                    for has_bias in get_bias_support(format):
+                                        config = MultiHeadAttentionConfig(
+                                            batch_size=batch_size,
+                                            sequence_length=sequence_length,
+                                            num_heads=num_heads,
+                                            head_size=head_size,
+                                            causal=causal,
+                                            past_sequence_length=0,
+                                            kv_sequence_length=sequence_length,
+                                            max_cache_sequence_length=None,
+                                            provider=provider,
+                                            device=device,
+                                            dtype=dtype,
+                                            use_kv_cache=False,
+                                            share_past_present_buffer=False,
+                                            input_format=format,
+                                            has_bias=has_bias,
+                                            mask_format=mask_format,
+                                        )
+                                        yield config
     else:
         test_cases = max(len(batch_sizes), len(sequence_lengths), len(heads), len(head_sizes))
         for i in range(test_cases):
@@ -205,6 +223,7 @@ def no_kv_cache_test_cases(provider: str, comprehensive: bool):
             sequence_length = sequence_lengths[i % len(sequence_lengths)]
             num_heads = heads[i % len(heads)]
             head_size = head_sizes[i % len(head_sizes)]
+            mask_format = mask_formats[i % len(mask_formats)]
             for causal in [True, False]:
                 for format in formats:
                     for has_bias in get_bias_support(format):
@@ -224,6 +243,7 @@ def no_kv_cache_test_cases(provider: str, comprehensive: bool):
                             share_past_present_buffer=False,
                             input_format=format,
                             has_bias=has_bias,
+                            mask_format=mask_format,
                         )
                         yield config
 
@@ -238,6 +258,11 @@ def kv_cache_test_cases(provider: str, comprehensive: bool):
     heads = [1, 3, 4, 16]
     head_sizes = [8, 16, 32, 40, 64, 80, 96, 128, 160, 192, 224, 256]
     device, dtype, formats = get_provider_support_info(provider, True)
+    mask_formats = [
+        AttentionMaskFormat.Mask_None,
+        AttentionMaskFormat.Mask_1D_Key_SeqLen,
+        AttentionMaskFormat.Mask_2D_Key_PaddingMask,
+    ]
 
     if comprehensive:
         sequence_lengths = [*sequence_lengths, 2048]  # Large sequence length is slow and need a lot of memory
@@ -248,28 +273,30 @@ def kv_cache_test_cases(provider: str, comprehensive: bool):
                         for format in formats:
                             for causal in [True, False]:
                                 for has_past_input in [True, False]:
-                                    for has_bias in get_bias_support(format):
-                                        sequence_length = 1 if has_past_input else past_sequence_length
-                                        past_seq_len = past_sequence_length if has_past_input else 0
-                                        config = MultiHeadAttentionConfig(
-                                            batch_size=batch_size,
-                                            sequence_length=sequence_length,
-                                            num_heads=num_heads,
-                                            head_size=head_size,
-                                            causal=causal,
-                                            past_sequence_length=past_seq_len,
-                                            kv_sequence_length=sequence_length,
-                                            max_cache_sequence_length=None,
-                                            provider=provider,
-                                            device=device,
-                                            dtype=dtype,
-                                            use_kv_cache=True,
-                                            has_past_input=has_past_input,
-                                            share_past_present_buffer=False,
-                                            input_format=format,
-                                            has_bias=has_bias,
-                                        )
-                                        yield config
+                                    for mask_format in mask_formats:
+                                        for has_bias in get_bias_support(format):
+                                            sequence_length = 1 if has_past_input else past_sequence_length
+                                            past_seq_len = past_sequence_length if has_past_input else 0
+                                            config = MultiHeadAttentionConfig(
+                                                batch_size=batch_size,
+                                                sequence_length=sequence_length,
+                                                num_heads=num_heads,
+                                                head_size=head_size,
+                                                causal=causal,
+                                                past_sequence_length=past_seq_len,
+                                                kv_sequence_length=sequence_length,
+                                                max_cache_sequence_length=None,
+                                                provider=provider,
+                                                device=device,
+                                                dtype=dtype,
+                                                use_kv_cache=True,
+                                                has_past_input=has_past_input,
+                                                share_past_present_buffer=False,
+                                                input_format=format,
+                                                has_bias=has_bias,
+                                                mask_format=mask_format,
+                                            )
+                                            yield config
     else:
         test_cases = max(len(batch_sizes), len(sequence_lengths), len(heads), len(head_sizes))
         for i in range(test_cases):
@@ -277,6 +304,7 @@ def kv_cache_test_cases(provider: str, comprehensive: bool):
             past_sequence_length = sequence_lengths[i % len(sequence_lengths)]
             num_heads = heads[i % len(heads)]
             head_size = head_sizes[i % len(head_sizes)]
+            mask_format = mask_formats[i % len(mask_formats)]
             for causal in [True, False]:
                 for format in formats:
                     for has_past_input in [True, False]:
@@ -300,6 +328,7 @@ def kv_cache_test_cases(provider: str, comprehensive: bool):
                                 share_past_present_buffer=False,
                                 input_format=format,
                                 has_bias=has_bias,
+                                mask_format=mask_format,
                             )
                             yield config
 
@@ -392,6 +421,23 @@ def causal_mask(seqlen_q, seqlen_k, query_padding_mask=None, key_padding_mask=No
     return col_idx <= row_idx + sk - sq
 
 
+def merge_padding_and_causal_masks(config):
+
+    q_mask, k_mask, mask = config.right_side_padding_masks()
+    if config.causal:
+        query_padding_mask = q_mask.reshape(config.batch_size, config.sequence_length)
+        key_padding_mask = k_mask.reshape(config.batch_size, config.total_sequence_length)
+        mask = causal_mask(
+            config.sequence_length,
+            config.total_sequence_length,
+            query_padding_mask,
+            key_padding_mask,
+            device=config.device,
+        )
+
+    return mask
+
+
 def parity_check_mha(
     config: MultiHeadAttentionConfig,
     rtol=1e-3,
@@ -406,6 +452,7 @@ def parity_check_mha(
     out = ort_outputs["output"]
     out = torch.reshape(out, (config.batch_size, config.sequence_length, config.num_heads, config.head_size))
 
+    ort_input_format = config.input_format
     no_bias_k_v = config.input_format == InputFormats.Q_K_V_BSNH_BNSH_BNSH
     config.input_format = InputFormats.Q_K_V_BSNH_BSNH_BSNH
     ref_inputs = config.random_inputs(no_bias_k_v=no_bias_k_v)
@@ -427,10 +474,7 @@ def parity_check_mha(
     k = k.transpose(1, 2)
     v = v.transpose(1, 2)
 
-    mask = None
-    if config.causal:
-        mask = causal_mask(config.sequence_length, config.total_sequence_length, device=config.device)
-
+    mask = merge_padding_and_causal_masks(config)
     k_cache = None
     v_cache = None
     if config.use_kv_cache:
@@ -440,6 +484,26 @@ def parity_check_mha(
     else:
         out_ref = attention_reference(config.head_size, q, k, v, mask=mask)
 
+    # Fill zeros for the padded kens for comparison.
+    if config.mask_index_q is not None:
+        for i, m in enumerate(config.mask_index_q):
+            out[i, m:, :, :] = 0
+            out_ref[i, m:, :, :] = 0
+
+    if config.mask_index_kv is not None and config.use_kv_cache:
+        assert k_cache is not None
+        assert v_cache is not None
+        present_key = ort_outputs["present_key"]
+        present_value = ort_outputs["present_value"]
+        for i, n in enumerate(config.mask_index_kv):
+            k_cache[i, :, n:, :] = 0
+            present_key[i, :, n:, :] = 0
+            v_cache[i, :, n:, :] = 0
+            present_value[i, :, n:, :] = 0
+
+    # Restore the input format so that it shows up in the error message correctly.
+    config.input_format = ort_input_format
+
     numpy.testing.assert_allclose(
         out.detach().cpu().numpy(),
         out_ref.detach().cpu().numpy(),
@@ -540,10 +604,7 @@ def check_parity_with_config(i: int):
             .transpose(1, 2)
         )
 
-        mask = None
-        if config.causal:
-            mask = causal_mask(config.sequence_length, config.total_sequence_length, device=config.device)
-
+        mask = merge_padding_and_causal_masks(config)
         k_cache = None
         v_cache = None
         if config.use_kv_cache:
@@ -622,13 +683,13 @@ def multi_thread_test_cases(provider: str, comprehensive: bool):
 
 
 class TestMultiHeadAttention(unittest.TestCase):
-    @parameterized.expand(mha_test_cases("CUDAExecutionProvider", comprehensive_mode), skip_on_empty=True)
-    def test_mha_cuda(self, config):
-        parity_check_mha(config, rtol=5e-3, atol=5e-3)
+    def run_mha_cuda(self):
+        for config in mha_test_cases("CUDAExecutionProvider", comprehensive_mode):
+            parity_check_mha(config, rtol=5e-3, atol=5e-3)
 
-    @parameterized.expand(mha_test_cases("CPUExecutionProvider", comprehensive_mode), skip_on_empty=True)
-    def test_mha_cpu(self, config):
-        parity_check_mha(config, rtol=5e-3, atol=5e-3)
+    def run_mha_cpu(self):
+        for config in mha_test_cases("CPUExecutionProvider", comprehensive_mode):
+            parity_check_mha(config, rtol=5e-3, atol=5e-3)
 
     def run_mha_cuda_multi_threading(self, attention_kernel):
         for configs in multi_thread_test_cases("CUDAExecutionProvider", comprehensive_mode):
@@ -646,21 +707,21 @@ def run_mha_cuda_multi_threading(self, attention_kernel):
             exception = parity_check_mha_multi_threading(
                 test_inputs, attention_kernel=attention_kernel, max_threads=len(configs)
             )
-            assert exception is None, f"{attention_kernel=}, {vars(configs[0])}, {exception}"
+            assert exception is None, f"Multi-threading failed: {attention_kernel=}, {vars(configs[0])}, {exception}"
 
-    def test_mha_cuda_multi_threading(self):
+    def run_mha_cuda_multi_threading_default(self):
         if get_compute_capability() >= 60:
             self.run_mha_cuda_multi_threading(SdpaKernel.DEFAULT)
 
-    def test_mha_cuda_multi_threading_efficient(self):
+    def run_mha_cuda_multi_threading_efficient(self):
         if comprehensive_mode and get_compute_capability() >= 60:
             self.run_mha_cuda_multi_threading(SdpaKernel.EFFICIENT_ATTENTION)
 
-    def test_mha_cuda_multi_threading_math(self):
+    def run_mha_cuda_multi_threading_math(self):
         if comprehensive_mode and get_compute_capability() >= 60:
             self.run_mha_cuda_multi_threading(SdpaKernel.MATH)
 
-    def test_mha_cuda_multi_threading_trt(self):
+    def run_mha_cuda_multi_threading_trt(self):
         if get_compute_capability() in [75, 80, 86, 89]:
             self.run_mha_cuda_multi_threading(
                 SdpaKernel.TRT_FUSED_ATTENTION
@@ -669,6 +730,15 @@ def test_mha_cuda_multi_threading_trt(self):
                 | SdpaKernel.TRT_CROSS_ATTENTION
             )
 
+    def test_all(self):
+        # Run tests sequentially to avoid out of memory issue.
+        self.run_mha_cpu()
+        self.run_mha_cuda()
+        self.run_mha_cuda_multi_threading_default()
+        self.run_mha_cuda_multi_threading_efficient()
+        self.run_mha_cuda_multi_threading_math()
+        self.run_mha_cuda_multi_threading_trt()
+
 
 if __name__ == "__main__":
     with torch.no_grad():

From 2792f239c397c799b3a87d45e19c77ca0244194d Mon Sep 17 00:00:00 2001
From: jingyanwangms <47403504+jingyanwangms@users.noreply.github.com>
Date: Sun, 11 Aug 2024 03:28:41 -0700
Subject: [PATCH 16/22] Security Fuzz Test Fixes (#21608)

### Description
Fix address sanitizer and memory access Bug 1, 4, 5, 7, 8 found in
security fuzz test

### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
---
 onnxruntime/core/framework/tensorprotoutils.cc             | 1 +
 onnxruntime/core/optimizer/unsqueeze_elimination.cc        | 4 ++++
 onnxruntime/core/providers/cpu/quantization/qlinearconv.cc | 4 ++--
 3 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/onnxruntime/core/framework/tensorprotoutils.cc b/onnxruntime/core/framework/tensorprotoutils.cc
index cbd53298ab2ad..42f491825462c 100644
--- a/onnxruntime/core/framework/tensorprotoutils.cc
+++ b/onnxruntime/core/framework/tensorprotoutils.cc
@@ -1358,6 +1358,7 @@ common::Status ConstantNodeProtoToTensorProto(const ONNX_NAMESPACE::NodeProto& n
 common::Status ConstantNodeProtoToTensorProto(const ONNX_NAMESPACE::NodeProto& node,
                                               const std::filesystem::path& model_path,
                                               ONNX_NAMESPACE::TensorProto& tensor) {
+  ORT_ENFORCE(node.output_size() == 1, "NodeProto for Constant should have 1 output. Got:", node.output_size());
   return ConstantNodeProtoToTensorProto(node, model_path, tensor, node.output(0));
 }
 
diff --git a/onnxruntime/core/optimizer/unsqueeze_elimination.cc b/onnxruntime/core/optimizer/unsqueeze_elimination.cc
index 4efc8018f0217..d52cc82af02bb 100644
--- a/onnxruntime/core/optimizer/unsqueeze_elimination.cc
+++ b/onnxruntime/core/optimizer/unsqueeze_elimination.cc
@@ -40,6 +40,10 @@ Status UnsqueezeElimination::Apply(Graph& graph, Node& node, RewriteRuleEffect&
   // Generate new dims.
   InlinedVector<int64_t> new_dims(output_rank, 0);
   for (int64_t axis : axes) {
+    if (static_cast<size_t>(axis) >= new_dims.size()) {
+      LOGS(logger, WARNING) << "UnsqueezeElimination cannot remove node due to invalid axes" << node.Name();
+      return Status::OK();
+    }
     new_dims[static_cast<size_t>(axis)] = 1;
   }
 
diff --git a/onnxruntime/core/providers/cpu/quantization/qlinearconv.cc b/onnxruntime/core/providers/cpu/quantization/qlinearconv.cc
index 21a256eee6f14..7797cbe678bd4 100644
--- a/onnxruntime/core/providers/cpu/quantization/qlinearconv.cc
+++ b/onnxruntime/core/providers/cpu/quantization/qlinearconv.cc
@@ -380,8 +380,8 @@ Status QLinearConv<ActType>::PrePack(const Tensor& tensor, int input_idx, Alloca
   const int64_t M = shape[0];
   const int64_t C = shape[1];
 
-  // Verify that the total number of output channels is a multiple of the group count.
-  if (M % conv_attrs_.group != 0) {
+  // Verify that conv_attrs_.group is not 0 and the total number of output channels is a multiple of the group count.
+  if (conv_attrs_.group == 0 || M % conv_attrs_.group != 0) {
     return Status::OK();
   }
 

From 7c2795571c7e73d5538a0c5879e31ddc65bef76c Mon Sep 17 00:00:00 2001
From: Yi Zhang <zhanyi@microsoft.com>
Date: Tue, 6 Aug 2024 21:37:09 +0800
Subject: [PATCH 17/22] Fix docker image layer caching to avoid redundant
 docker building and transient connection exceptions. (#21612)

### Description
Improve docker commands to make docker image layer caching works.
It can make docker building faster and more stable.
So far, A100 pool's system disk is too small to use docker cache.
We won't use pipeline cache for docker image and remove some legacy
code.

### Motivation and Context
There are often an exception of
```
64.58 + curl https://nodejs.org/dist/v18.17.1/node-v18.17.1-linux-x64.tar.gz -sSL --retry 5 --retry-delay 30 --create-dirs -o /tmp/src/node-v18.17.1-linux-x64.tar.gz --fail
286.4 curl: (92) HTTP/2 stream 0 was not closed cleanly: INTERNAL_ERROR (err 2)
```
Because Onnxruntime pipeline have been sending too many requests to
download Nodejs in docker building.
Which is the major reason of pipeline failing now

In fact, docker image layer caching never works.
We can always see the scrips are still running
```
#9 [3/5] RUN cd /tmp/scripts && /tmp/scripts/install_centos.sh && /tmp/scripts/install_deps.sh && rm -rf /tmp/scripts
#9 0.234 /bin/sh: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8)
#9 0.235 /bin/sh: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8)
#9 0.235 /tmp/scripts/install_centos.sh: line 1: !/bin/bash: No such file or directory
#9 0.235 ++ '[' '!' -f /etc/yum.repos.d/microsoft-prod.repo ']'
#9 0.236 +++ tr -dc 0-9.
#9 0.236 +++ cut -d . -f1
#9 0.238 ++ os_major_version=8
....
#9 60.41 + curl https://nodejs.org/dist/v18.17.1/node-v18.17.1-linux-x64.tar.gz -sSL --retry 5 --retry-delay 30 --create-dirs -o /tmp/src/node-v18.17.1-linux-x64.tar.gz --fail
#9 60.59 + return 0
...
```

This PR is improving the docker command to make image layer caching
work.
Thus, CI won't send so many redundant request of downloading NodeJS.
```
#9 [2/5] ADD scripts /tmp/scripts
#9 CACHED

#10 [3/5] RUN cd /tmp/scripts && /tmp/scripts/install_centos.sh && /tmp/scripts/install_deps.sh && rm -rf /tmp/scripts
#10 CACHED

#11 [4/5] RUN adduser --uid 1000 onnxruntimedev
#11 CACHED

#12 [5/5] WORKDIR /home/onnxruntimedev
#12 CACHED
```

###Reference
https://docs.docker.com/build/drivers/

---------

Co-authored-by: Yi Zhang <your@email.com>
---
 tools/ci_build/get_docker_image.py            | 24 ++-----
 .../azure-pipelines/bigmodels-ci-pipeline.yml |  1 +
 .../templates/c-api-linux-cpu.yml             |  8 +--
 .../templates/get-docker-image-steps.yml      | 64 ++++++-------------
 .../inference/aarch64/default/cpu/Dockerfile  |  2 +-
 .../inference/x86_64/default/cpu/Dockerfile   |  2 +-
 6 files changed, 32 insertions(+), 69 deletions(-)

diff --git a/tools/ci_build/get_docker_image.py b/tools/ci_build/get_docker_image.py
index 99ecaf677f339..a3f603b0beda4 100755
--- a/tools/ci_build/get_docker_image.py
+++ b/tools/ci_build/get_docker_image.py
@@ -98,17 +98,19 @@ def main():
         )
 
     if use_container_registry:
+        run(args.docker_path, "buildx", "create", "--driver=docker-container", "--name=container_builder")
         run(
             args.docker_path,
             "--log-level",
             "error",
             "buildx",
             "build",
-            "--push",
+            "--load",
             "--tag",
             full_image_name,
-            "--cache-from",
-            full_image_name,
+            "--cache-from=type=registry,ref=" + full_image_name,
+            "--builder",
+            "container_builder",
             "--build-arg",
             "BUILDKIT_INLINE_CACHE=1",
             *shlex.split(args.docker_build_args),
@@ -116,24 +118,10 @@ def main():
             args.dockerfile,
             args.context,
         )
-    elif args.use_imagecache:
-        log.info("Building image with pipeline cache...")
         run(
             args.docker_path,
-            "--log-level",
-            "error",
-            "buildx",
-            "build",
-            "--tag",
-            full_image_name,
-            "--cache-from",
+            "push",
             full_image_name,
-            "--build-arg",
-            "BUILDKIT_INLINE_CACHE=1",
-            *shlex.split(args.docker_build_args),
-            "-f",
-            args.dockerfile,
-            args.context,
         )
     else:
         log.info("Building image...")
diff --git a/tools/ci_build/github/azure-pipelines/bigmodels-ci-pipeline.yml b/tools/ci_build/github/azure-pipelines/bigmodels-ci-pipeline.yml
index a66828ee5e188..4a3532dd57fa3 100644
--- a/tools/ci_build/github/azure-pipelines/bigmodels-ci-pipeline.yml
+++ b/tools/ci_build/github/azure-pipelines/bigmodels-ci-pipeline.yml
@@ -321,6 +321,7 @@ stages:
         --build-arg TRT_VERSION=${{ variables.linux_trt_version }}
         "
         Repository: onnxruntimeubi8packagestest_torch
+        UseImageCacheContainerRegistry: false
         UpdateDepsTxt: false
 
     - task: DownloadPackage@1
diff --git a/tools/ci_build/github/azure-pipelines/templates/c-api-linux-cpu.yml b/tools/ci_build/github/azure-pipelines/templates/c-api-linux-cpu.yml
index e2b71c5c55fd2..0f4328f75e1bd 100644
--- a/tools/ci_build/github/azure-pipelines/templates/c-api-linux-cpu.yml
+++ b/tools/ci_build/github/azure-pipelines/templates/c-api-linux-cpu.yml
@@ -51,15 +51,15 @@ jobs:
           Dockerfile: tools/ci_build/github/linux/docker/inference/x86_64/default/cpu/Dockerfile
           Context: tools/ci_build/github/linux/docker/inference/x86_64/default/cpu
           DockerBuildArgs: "--build-arg BUILD_UID=$( id -u ) --build-arg BASEIMAGE=${{parameters.BaseImage}}"
-          Repository: onnxruntimecpubuildcentos8${{parameters.OnnxruntimeArch}}
-          
+          Repository: onnxruntimecpubuildcentos8${{parameters.OnnxruntimeArch}}_packaging
+
     - ${{ if eq(parameters.OnnxruntimeArch, 'aarch64') }}:
       - template: get-docker-image-steps.yml
         parameters:
           Dockerfile: tools/ci_build/github/linux/docker/inference/aarch64/default/cpu/Dockerfile
           Context: tools/ci_build/github/linux/docker/inference/aarch64/default/cpu
           DockerBuildArgs: "--build-arg BUILD_UID=$( id -u ) --build-arg BASEIMAGE=${{parameters.BaseImage}}"
-          Repository: onnxruntimecpubuildcentos8${{parameters.OnnxruntimeArch}}
+          Repository: onnxruntimecpubuildcentos8${{parameters.OnnxruntimeArch}}_packaging
           UpdateDepsTxt: false
 
     - task: CmdLine@2
@@ -67,7 +67,7 @@ jobs:
         script: |
           mkdir -p $HOME/.onnx
           docker run --rm --volume /data/onnx:/data/onnx:ro --volume $(Build.SourcesDirectory):/onnxruntime_src --volume $(Build.BinariesDirectory):/build \
-          --volume $HOME/.onnx:/home/onnxruntimedev/.onnx -e NIGHTLY_BUILD onnxruntimecpubuildcentos8${{parameters.OnnxruntimeArch}} /bin/bash -c "python3.9 \
+          --volume $HOME/.onnx:/home/onnxruntimedev/.onnx -e NIGHTLY_BUILD onnxruntimecpubuildcentos8${{parameters.OnnxruntimeArch}}_packaging /bin/bash -c "python3.9 \
           /onnxruntime_src/tools/ci_build/build.py --enable_lto --build_java --build_nodejs --build_dir /build --config Release \
           --skip_submodule_sync --parallel --use_binskim_compliant_compile_flags --build_shared_lib ${{ parameters.AdditionalBuildFlags }} && cd /build/Release && make install DESTDIR=/build/installed"
         workingDirectory: $(Build.SourcesDirectory)
diff --git a/tools/ci_build/github/azure-pipelines/templates/get-docker-image-steps.yml b/tools/ci_build/github/azure-pipelines/templates/get-docker-image-steps.yml
index 94cdf042ec62b..5b6769685a972 100644
--- a/tools/ci_build/github/azure-pipelines/templates/get-docker-image-steps.yml
+++ b/tools/ci_build/github/azure-pipelines/templates/get-docker-image-steps.yml
@@ -53,6 +53,7 @@ steps:
       displayName: patch manylinux
 
 - script: |
+    docker version
     docker image ls
     docker system df
   displayName: Check Docker Images
@@ -71,52 +72,25 @@ steps:
         displayName: "Get ${{ parameters.Repository }} image for ${{ parameters.Dockerfile }}"
       ContainerRegistry: onnxruntimebuildcache
 - ${{ if eq(parameters.UseImageCacheContainerRegistry, false) }}:
-  - task: Cache@2
-    displayName: Cache Docker Image Task
-    inputs:
-      key: ' "${{ parameters.Repository }}" | "$(Build.SourceVersion)" '
-      path: ${{ parameters.IMAGE_CACHE_DIR }}
-      restoreKeys: |
-        "${{ parameters.Repository }}" | "$(Build.SourceVersion)"
-        "${{ parameters.Repository }}"
-      cacheHitVar: CACHE_RESTORED
-    condition: eq('${{ parameters.UsePipelineCache }}', 'true')
-
-  - script: |
-      test -f ${{ parameters.IMAGE_CACHE_DIR }}/cache.tar && docker load -i ${{ parameters.IMAGE_CACHE_DIR }}/cache.tar
-      docker image ls
-    displayName: Docker restore
-    condition: eq('${{ parameters.UsePipelineCache }}', 'true')
-
-  - script: |
-      if [ ${{ parameters.UsePipelineCache}} ]
-      then
-        use_imagecache="--use_imagecache"
-      else
-        use_imagecache=""
-      fi
-      ${{ parameters.ScriptName }} \
-        --dockerfile "${{ parameters.Dockerfile }}" \
-        --context "${{ parameters.Context }}" \
-        --docker-build-args "${{ parameters.DockerBuildArgs }}" \
-        --repository "${{ parameters.Repository }}" \
-        $use_imagecache
-    displayName: "Get ${{ parameters.Repository }} image for ${{ parameters.Dockerfile }}"
-
-  - script: |
-      set -ex
-      mkdir -p "${{ parameters.IMAGE_CACHE_DIR }}"
-      docker save -o "${{ parameters.IMAGE_CACHE_DIR }}/cache.tar" ${{ parameters.Repository }}
-      docker image ls
-      docker system df
-    displayName: Docker save
-    condition: eq('${{ parameters.UsePipelineCache }}', 'true')
+  # the difference is no --container-registry
+  - template: with-container-registry-steps.yml
+    parameters:
+      Steps:
+      - script: |
+          ${{ parameters.ScriptName }} \
+            --dockerfile "${{ parameters.Dockerfile }}" \
+            --context "${{ parameters.Context }}" \
+            --docker-build-args "${{ parameters.DockerBuildArgs }}" \
+            --repository "${{ parameters.Repository }}"
+        displayName: "Get ${{ parameters.Repository }} image for ${{ parameters.Dockerfile }}"
+      ContainerRegistry: onnxruntimebuildcache
 
-  - script: |
-      echo ${{ parameters.IMAGE_CACHE_DIR }}
-      ls -lah ${{ parameters.IMAGE_CACHE_DIR }}
-    displayName: Display docker dir
-    condition: eq('${{ parameters.UsePipelineCache }}', 'true')
+- script: |
+    docker version
+    docker image ls
+    docker system df
+    df -h
+  displayName: Check Docker Images
 
 - ${{ if and(eq(parameters.UpdateDepsTxt, true), or(eq(variables['System.CollectionId'], 'f3ad12f2-e480-4533-baf2-635c95467d29'),eq(variables['System.CollectionId'], 'bc038106-a83b-4dab-9dd3-5a41bc58f34c'))) }}:
   - task: PythonScript@0
diff --git a/tools/ci_build/github/linux/docker/inference/aarch64/default/cpu/Dockerfile b/tools/ci_build/github/linux/docker/inference/aarch64/default/cpu/Dockerfile
index 2cd054e6246bc..ca00050121d67 100644
--- a/tools/ci_build/github/linux/docker/inference/aarch64/default/cpu/Dockerfile
+++ b/tools/ci_build/github/linux/docker/inference/aarch64/default/cpu/Dockerfile
@@ -5,7 +5,7 @@
 ARG BASEIMAGE=arm64v8/almalinux:8
 FROM $BASEIMAGE
 
-ENV PATH /opt/rh/gcc-toolset-12/root/usr/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
+ENV PATH=/opt/rh/gcc-toolset-12/root/usr/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
 ENV LANG=en_US.UTF-8
 ENV LC_ALL=en_US.UTF-8
 
diff --git a/tools/ci_build/github/linux/docker/inference/x86_64/default/cpu/Dockerfile b/tools/ci_build/github/linux/docker/inference/x86_64/default/cpu/Dockerfile
index caf9583807b62..ef28dde67617f 100644
--- a/tools/ci_build/github/linux/docker/inference/x86_64/default/cpu/Dockerfile
+++ b/tools/ci_build/github/linux/docker/inference/x86_64/default/cpu/Dockerfile
@@ -5,7 +5,7 @@
 ARG BASEIMAGE=amd64/almalinux:8
 FROM $BASEIMAGE
 
-ENV PATH /usr/lib/jvm/msopenjdk-11/bin:/opt/rh/gcc-toolset-12/root/usr/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
+ENV PATH=/usr/lib/jvm/msopenjdk-11/bin:/opt/rh/gcc-toolset-12/root/usr/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
 ENV LANG=en_US.UTF-8
 ENV LC_ALL=en_US.UTF-8
 ENV JAVA_HOME=/usr/lib/jvm/msopenjdk-11

From 00daa262200d0da4d7c87e0eae797df641ed4312 Mon Sep 17 00:00:00 2001
From: Sumit Agarwal <sumitagarwal330@gmail.com>
Date: Fri, 9 Aug 2024 06:52:59 -0700
Subject: [PATCH 18/22] Fuse Pad even if Cast is present in-between (#21640)

### Description
This change enhances the existing Pad Fusion to fuse Pad even if a Cast
operator is present between Pad and Conv/MaxPool/AveragePool. It keeps
the Cast as it is.
<pre>
/*
 * Before Fusion:
 *     Pad
 *      |
 *    Cast (Optional)
 *      |
 *   Conv/MaxPool/AveragePool
 *
 * After Fusion:
 *    Cast (Optional)
 *      |
 *   Conv/MaxPool/AveragePool
 */
</pre>


### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
---
 onnxruntime/core/optimizer/pad_fusion.cc | 93 ++++++++++++++++--------
 1 file changed, 62 insertions(+), 31 deletions(-)

diff --git a/onnxruntime/core/optimizer/pad_fusion.cc b/onnxruntime/core/optimizer/pad_fusion.cc
index e266946b0d9e0..3391e20cf0bb7 100644
--- a/onnxruntime/core/optimizer/pad_fusion.cc
+++ b/onnxruntime/core/optimizer/pad_fusion.cc
@@ -8,25 +8,7 @@
 
 namespace onnxruntime {
 
-/*
- * It matches following pattern:
- *     Pad
- *      |
- *   Conv/MaxPool/AveragePool
- */
-bool PadFusion::SatisfyCondition(const Graph& graph, const Node& node, const logging::Logger&) const {
-  // if Pad has input axis, don't fuse it.
-  if (!graph_utils::IsSupportedOptypeVersionAndDomain(node, "Pad", {1, 2, 11, 13, 18, 19}) ||
-      node.GetOutputEdgesCount() != 1 ||
-      node.InputDefs().size() > 3) {
-    return false;
-  }
-
-  if (graph.NodeProducesGraphOutput(node)) {
-    return false;
-  }
-
-  const Node& child_node = *node.OutputNodesBegin();
+bool VerifyNotCastChild(const Node& child_node) {
   if (!graph_utils::IsSupportedOptypeVersionAndDomain(child_node, "Conv", {1, 11}) &&
       !graph_utils::IsSupportedOptypeVersionAndDomain(child_node, "AveragePool", {1, 7, 10, 11, 19}) &&
       !graph_utils::IsSupportedOptypeVersionAndDomain(child_node, "MaxPool", {1, 8, 10, 11, 12})) {
@@ -54,6 +36,45 @@ bool PadFusion::SatisfyCondition(const Graph& graph, const Node& node, const log
     return false;
   }
 
+  return true;
+}
+
+void UpdatePaddingAttribute(Node& child_node, const std::vector<int64_t>& pads_values, const uint32_t pads_size) {
+  auto child_pads = child_node.GetMutableAttributes()["pads"].mutable_ints();
+  uint32_t child_pads_size = static_cast<uint32_t>(child_pads->size());
+
+  for (uint32_t pads_index = 2, child_index = 0; pads_index < pads_size / 2; pads_index++, child_index++) {
+    child_pads->Set(child_index, child_pads->Get(child_index) + pads_values[pads_index]);
+    uint32_t mirrored_child_index = child_index + (child_pads_size / 2);
+    uint32_t mirrored_pad_index = pads_index + (pads_size / 2);
+    child_pads->Set(mirrored_child_index, child_pads->Get(mirrored_child_index) + pads_values[mirrored_pad_index]);
+  }
+}
+/*
+ * Before:
+ *     Pad
+ *      |
+ *    Cast (Optional)
+ *      |
+ *   Conv/MaxPool/AveragePool
+ *
+ * After:
+ *    Cast (Optional)
+ *      |
+ *   Conv/MaxPool/AveragePool
+ */
+bool PadFusion::SatisfyCondition(const Graph& graph, const Node& node, const logging::Logger&) const {
+  // if Pad has input axis, don't fuse it.
+  if (!graph_utils::IsSupportedOptypeVersionAndDomain(node, "Pad", {1, 2, 11, 13, 18, 19}) ||
+      node.GetOutputEdgesCount() != 1 ||
+      node.InputDefs().size() > 3) {
+    return false;
+  }
+
+  if (graph.NodeProducesGraphOutput(node)) {
+    return false;
+  }
+
   const NodeAttributes& pad_attributes = node.GetAttributes();
   if (pad_attributes.find("mode") != pad_attributes.end() &&
       pad_attributes.at("mode").s() != "constant") {
@@ -83,7 +104,19 @@ bool PadFusion::SatisfyCondition(const Graph& graph, const Node& node, const log
     }
   }
 
-  return true;
+  const Node& child_node = *node.OutputNodesBegin();
+  if (graph_utils::IsSupportedOptypeVersionAndDomain(child_node, "Cast", {1, 6, 9, 13})) {
+    if (child_node.GetOutputEdgesCount() != 1) {
+      return false;
+    }
+
+    if (graph.NodeProducesGraphOutput(child_node)) {
+      return false;
+    }
+    return VerifyNotCastChild(*child_node.OutputNodesBegin());
+  } else {
+    return VerifyNotCastChild(child_node);
+  }
 }
 
 /*
@@ -100,8 +133,6 @@ Status PadFusion::Apply(Graph& graph, Node& pad_node, RewriteRuleEffect& rule_ef
     pads_values.assign(pad_node.GetAttributes().at("pads").ints().begin(), pad_node.GetAttributes().at("pads").ints().end());
   }
 
-  assert(static_cast<uint32_t>(pads_values.size()) == (2 * static_cast<uint32_t>(pad_node.InputDefs()[0]->Shape()->dim_size())));
-
   uint32_t pads_size = static_cast<uint32_t>(pads_values.size());
   // check if padding is applied only on feature dims
   if (pads_values[0] != 0 || pads_values[1] != 0 || pads_values[pads_size / 2] != 0 ||
@@ -115,18 +146,18 @@ Status PadFusion::Apply(Graph& graph, Node& pad_node, RewriteRuleEffect& rule_ef
   }
 
   Node& child_node = *graph.GetNode(pad_node.OutputNodesBegin()->Index());
-  auto child_pads = child_node.GetMutableAttributes()["pads"].mutable_ints();
-  uint32_t child_pads_size = static_cast<uint32_t>(child_pads->size());
-
-  for (uint32_t pads_index = 2, child_index = 0; pads_index < pads_size / 2; pads_index++, child_index++) {
-    child_pads->Set(child_index, child_pads->Get(child_index) + pads_values[pads_index]);
-    uint32_t mirrored_child_index = child_index + (child_pads_size / 2);
-    uint32_t mirrored_pad_index = pads_index + (pads_size / 2);
-    child_pads->Set(mirrored_child_index, child_pads->Get(mirrored_child_index) + pads_values[mirrored_pad_index]);
-  }
+  // We don't need to cast the pad_constant_value because this fusion requires that constant_pad_value
+  // to be zero. See PadFusion::SatisfyCondition for details.
+  Node& target_padding_node = (child_node.OpType() == "Cast") ? *graph.GetNode(child_node.OutputNodesBegin()->Index()) : child_node;
+  UpdatePaddingAttribute(target_padding_node, pads_values, pads_size);
 
   graph_utils::RemoveNodeOutputEdges(graph, pad_node);
   graph_utils::ReplaceNodeInput(child_node, 0, *pad_node.MutableInputDefs()[0]);
+  // Un-pad the output shape of Cast node
+  if (child_node.OpType() == "Cast") {
+    auto* cast_output_node_arg = child_node.MutableOutputDefs()[0];
+    cast_output_node_arg->SetShape(*pad_node.MutableInputDefs()[0]->Shape());
+  }
   graph.RemoveNode(pad_node.Index());
   rule_effect = RewriteRuleEffect::kRemovedCurrentNode;
   return Status::OK();

From cd2e3e0ec962aeae592bc74fc89458a97bf1d6d7 Mon Sep 17 00:00:00 2001
From: Chi Lo <54722500+chilo-ms@users.noreply.github.com>
Date: Fri, 9 Aug 2024 17:30:51 -0700
Subject: [PATCH 19/22] [TensorRT EP] No workspace size limit to TRT memory
 pool (#21643)

We saw some models failed to run due to OOM and can be fixed by increase
trt_max_workspace_size.
This PR makes no size limitation by default (max device memory) which is
aligned with trtexec.
---
 .../providers/tensorrt/tensorrt_provider_options.h |  2 +-
 .../tensorrt/tensorrt_execution_provider.cc        | 14 +++++++-------
 .../tensorrt/tensorrt_execution_provider.h         |  3 +--
 .../tensorrt/tensorrt_execution_provider_info.h    |  2 +-
 4 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/include/onnxruntime/core/providers/tensorrt/tensorrt_provider_options.h b/include/onnxruntime/core/providers/tensorrt/tensorrt_provider_options.h
index 816eaaf9bc71a..ec9be80a63574 100644
--- a/include/onnxruntime/core/providers/tensorrt/tensorrt_provider_options.h
+++ b/include/onnxruntime/core/providers/tensorrt/tensorrt_provider_options.h
@@ -19,7 +19,7 @@ struct OrtTensorRTProviderOptionsV2 {
                                                          // can be updated using: UpdateTensorRTProviderOptionsWithValue
   int trt_max_partition_iterations{1000};                // maximum iterations for TensorRT parser to get capability
   int trt_min_subgraph_size{1};                          // minimum size of TensorRT subgraphs
-  size_t trt_max_workspace_size{1 << 30};                // maximum workspace size for TensorRT.
+  size_t trt_max_workspace_size{0};                      // maximum workspace size for TensorRT. Default is 0 means max device memory size
   int trt_fp16_enable{0};                                // enable TensorRT FP16 precision. Default 0 = false, nonzero = true
   int trt_int8_enable{0};                                // enable TensorRT INT8 precision. Default 0 = false, nonzero = true
   const char* trt_int8_calibration_table_name{nullptr};  // TensorRT INT8 calibration table name.
diff --git a/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.cc b/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.cc
index cdbb7bb2a8094..562c32e0231d6 100644
--- a/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.cc
+++ b/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.cc
@@ -1583,10 +1583,6 @@ TensorrtExecutionProvider::TensorrtExecutionProvider(const TensorrtExecutionProv
     LOGS_DEFAULT(WARNING) << "[TensorRT EP] TensorRT option trt_min_subgraph_size must be a positive integer value. Set it to 1";
     min_subgraph_size_ = 1;
   }
-  if (max_workspace_size_ <= 0) {
-    LOGS_DEFAULT(WARNING) << "[TensorRT EP] TensorRT option trt_max_workspace_size must be a positive integer value. Set it to 1073741824 (1GB)";
-    max_workspace_size_ = 1 << 30;
-  }
   if (dla_core_ < 0) {
     LOGS_DEFAULT(WARNING) << "[TensorRT EP] TensorRT option trt_dla_core must be a non-negative integer value. Set it to 0";
     dla_core_ = 0;
@@ -2756,7 +2752,9 @@ Status TensorrtExecutionProvider::CreateNodeComputeInfoFromGraph(const GraphView
   auto trt_config = std::unique_ptr<nvinfer1::IBuilderConfig>(trt_builder->createBuilderConfig());
   auto trt_parser = tensorrt_ptr::unique_pointer<nvonnxparser::IParser>(nvonnxparser::createParser(*trt_network, trt_logger));
   trt_parser->parse(string_buf.data(), string_buf.size(), model_path_);
-  trt_config->setMemoryPoolLimit(nvinfer1::MemoryPoolType::kWORKSPACE, max_workspace_size_);
+  if (max_workspace_size_ > 0) {
+    trt_config->setMemoryPoolLimit(nvinfer1::MemoryPoolType::kWORKSPACE, max_workspace_size_);
+  }
 
   // Force Pow + Reduce ops in layer norm to run in FP32 to avoid overflow
   if (fp16_enable_ && layer_norm_fp32_fallback_) {
@@ -3363,7 +3361,7 @@ Status TensorrtExecutionProvider::CreateNodeComputeInfoFromGraph(const GraphView
           &parsers_[context->node_name], &engines_[context->node_name], &contexts_[context->node_name],
           &networks_[context->node_name], input_info_[context->node_name], output_info_[context->node_name],
           input_shape_ranges_[context->node_name], &tensorrt_mu_, fp16_enable_, int8_enable_, int8_calibration_cache_available_,
-          dla_enable_, dla_core_, &max_workspace_size_, trt_node_name_with_precision,
+          dla_enable_, dla_core_, trt_node_name_with_precision,
           engine_cache_enable_, cache_path_, runtime_.get(), profiles_[context->node_name],
           context_memory_sharing_enable_, &max_ctx_mem_size_, dynamic_range_map, engine_decryption_enable_,
           engine_decryption_, engine_encryption_, timing_cache_enable_, global_cache_path_, force_timing_cache_match_,
@@ -3538,7 +3536,9 @@ Status TensorrtExecutionProvider::CreateNodeComputeInfoFromGraph(const GraphView
       trt_state->context->reset();
       trt_state->engine->reset();
       auto trt_config = std::unique_ptr<nvinfer1::IBuilderConfig>(trt_builder->createBuilderConfig());
-      trt_config->setMemoryPoolLimit(nvinfer1::MemoryPoolType::kWORKSPACE, *(trt_state->max_workspace_size_ptr));
+      if (max_workspace_size_ > 0) {
+        trt_config->setMemoryPoolLimit(nvinfer1::MemoryPoolType::kWORKSPACE, max_workspace_size_);
+      }
       for (auto trt_profile : trt_profiles) {
         trt_config->addOptimizationProfile(trt_profile);
       }
diff --git a/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.h b/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.h
index 3f20314438564..97c9367b0bb61 100644
--- a/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.h
+++ b/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.h
@@ -175,7 +175,6 @@ struct TensorrtFuncState {
   bool int8_calibration_cache_available = false;
   bool dla_enable = false;
   int dla_core = 0;
-  size_t* max_workspace_size_ptr = nullptr;
   std::string trt_node_name_with_precision;
   bool engine_cache_enable = false;
   std::string engine_cache_path;
@@ -290,7 +289,7 @@ class TensorrtExecutionProvider : public IExecutionProvider {
   cudaStream_t stream_ = nullptr;
   int max_partition_iterations_ = 1000;
   size_t min_subgraph_size_ = 1;
-  size_t max_workspace_size_ = 1 << 30;  // 1GB
+  size_t max_workspace_size_ = 0;
   bool fp16_enable_ = false;
   bool int8_enable_ = false;
   bool dla_enable_ = false;
diff --git a/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider_info.h b/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider_info.h
index 50b934fd5fcbc..fa1bbd6d3d7e6 100644
--- a/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider_info.h
+++ b/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider_info.h
@@ -22,7 +22,7 @@ struct TensorrtExecutionProviderInfo {
   bool has_trt_options{false};
   int max_partition_iterations{1000};
   int min_subgraph_size{1};
-  size_t max_workspace_size{1 << 30};
+  size_t max_workspace_size{0};
   bool fp16_enable{false};
   bool int8_enable{false};
   std::string int8_calibration_table_name{""};

From 00cd5b49db190077373a0898ae774ae4c20d04bc Mon Sep 17 00:00:00 2001
From: saurabh <saurabh1.kale@intel.com>
Date: Sat, 10 Aug 2024 02:34:05 +0530
Subject: [PATCH 20/22] fix handling of multiple QuantizeLinear nodes (#21675)

### Description
This fix addresses the issue of handling multiple QLinear nodes as
outputs from the target node in OVEP. Previously, the stripping logic
only supported a single Q node, leading to incorrect stripping of
additional Q nodes.



### Motivation and Context
The OVEP stripping logic was limited to handling a single Q node as an
output from the target node. As a result, additional Q nodes were being
stripped, despite the stripping rules indicating they should be
retained.

With this fix, OVEP can now properly handle multiple Q nodes according
to the specified stripping rules, ensuring that the fate of each Q node
is correctly determined.

---------

Co-authored-by: sfatimar <sahar.fatima@intel.com>
---
 .../qdq_transformations/qdq_stripping.cc      | 33 ++++++++++---------
 1 file changed, 17 insertions(+), 16 deletions(-)

diff --git a/onnxruntime/core/providers/openvino/qdq_transformations/qdq_stripping.cc b/onnxruntime/core/providers/openvino/qdq_transformations/qdq_stripping.cc
index a2b3ed068235b..f1df1abf4c49a 100644
--- a/onnxruntime/core/providers/openvino/qdq_transformations/qdq_stripping.cc
+++ b/onnxruntime/core/providers/openvino/qdq_transformations/qdq_stripping.cc
@@ -583,22 +583,23 @@ static void AddQDQNodeUnit(onnxruntime::Graph& dst_graph,
 
   // Handle Qs in the NodeUnit
   if (!node_unit.GetQNodes().empty()) {
-    ORT_ENFORCE(node_unit.GetQNodes().size() == 1);
-    const auto& q_node = node_unit.GetQNodes().at(0);
-
-    SkipReason reason;
-
-    bool keep_q = CheckQRuleSet(node_unit, q_node, src_graph, reason);
-
-    if (keep_q) {
-      AddNode(initializers_to_keep, src_graph, dst_graph, *q_node);
-      // if keep_q, then output defs of the target node doesn't change
-      output_args.push_back(&dst_graph.GetOrCreateNodeArg(target_node.OutputDefs().at(0)->Name(),
-                                                          target_node.OutputDefs().at(0)->TypeAsProto()));
-    } else {
-      // convert this Q to float
-      output_args.push_back(&ProcessNodeUnitIO(dst_graph, src_graph, initializers_to_keep,
-                                               node_unit_outputs.at(0)));
+    for (size_t i = 0; i < node_unit.GetQNodes().size(); i++) {
+      const auto& q_node = node_unit.GetQNodes().at(i);
+
+      SkipReason reason;
+
+      bool keep_q = CheckQRuleSet(node_unit, q_node, src_graph, reason);
+
+      if (keep_q) {
+        AddNode(initializers_to_keep, src_graph, dst_graph, *q_node);
+        // if keep_q, then output defs of the target node doesn't change
+        output_args.push_back(&dst_graph.GetOrCreateNodeArg(target_node.OutputDefs().at(i)->Name(),
+                                                            target_node.OutputDefs().at(i)->TypeAsProto()));
+      } else {
+        // convert this Q to float
+        output_args.push_back(&ProcessNodeUnitIO(dst_graph, src_graph, initializers_to_keep,
+                                                 node_unit_outputs.at(i)));
+      }
     }
   } else {
     for (const auto& node_unit_output : node_unit_outputs) {

From a3192b84f105eece0361193aeb4935f34c331092 Mon Sep 17 00:00:00 2001
From: Jing Fang <126209182+fajin-corp@users.noreply.github.com>
Date: Fri, 9 Aug 2024 13:50:12 -0700
Subject: [PATCH 21/22] When quantize 4bit mamtul, force upgrade onnx domain
 opset to 21 (#21693)

### Description
When quantize MatMul to DQ + MatMul using 4bit QDQ tool chain,
previously the opsets of domains are not changed.
Now, when quantize MatMul to DQ + MatMul in QDQ format, force upgrade
onnx domain to opset 21.

### Motivation and Context
In QDQ format, DQ with int4 and blocked quantization is used. This
requires DQ with opset >= 21.
When quantize MatMul to DQ + MatMul, force upgrade onnx domain to opset
21.
---
 .../quantization/matmul_4bits_quantizer.py    | 22 ++++++++++++-------
 .../quantization/test_op_matmul_4bits.py      |  3 +++
 2 files changed, 17 insertions(+), 8 deletions(-)

diff --git a/onnxruntime/python/tools/quantization/matmul_4bits_quantizer.py b/onnxruntime/python/tools/quantization/matmul_4bits_quantizer.py
index cc8bd622df9b1..c0cc4f038cd3b 100644
--- a/onnxruntime/python/tools/quantization/matmul_4bits_quantizer.py
+++ b/onnxruntime/python/tools/quantization/matmul_4bits_quantizer.py
@@ -712,14 +712,20 @@ def process(self):
         if self.algo_config.algorithm in ["HQQ", "DEFAULT"]:
             # use a stack to keep track of sub-graphs
             graph_stack = [self.model.graph()]
-            opset_import = self.model.opset_import()
-
-            has_ms_domain = False
-            for opset in opset_import:
-                if opset.domain == "com.microsoft":
-                    has_ms_domain = True
-            if not has_ms_domain:
-                opset_import.extend([onnx.helper.make_opsetid("com.microsoft", 1)])
+
+            # Update domain opset
+            if self.algo_config.quant_format == QuantFormat.QOperator:
+                self.model.set_opset_import("com.microsoft", 1)
+            else:
+                opset_import = self.model.opset_import()
+                for opset in opset_import:
+                    if opset.domain in [None, "ai.onnx", ""] and opset.version < 21:
+                        logger.warning(
+                            "The opset of the input model is under 21 and doesn't support int4 data type. "
+                            "Force to update it to opset 21, but the generated model may not be a valid model."
+                        )
+                        self.model.set_opset_import(opset.domain, 21)
+
             self._process_subgraph(graph_stack)
             self.model.clean_initializers()
         else:
diff --git a/onnxruntime/test/python/quantization/test_op_matmul_4bits.py b/onnxruntime/test/python/quantization/test_op_matmul_4bits.py
index 4cc8a0c151d14..0438d93227524 100644
--- a/onnxruntime/test/python/quantization/test_op_matmul_4bits.py
+++ b/onnxruntime/test/python/quantization/test_op_matmul_4bits.py
@@ -156,6 +156,9 @@ def quant_test(
                 }
             )
             check_qtype_by_node_type(self, model_int4_path, dqnode_io_qtypes)
+            for op in quant.model.opset_import():
+                if op.domain in [None, "", "ai.onnx"] and op.version < 21:
+                    self.fail(f"In QDQ format {op.domain} opset should be >= 21")
 
         data_reader.rewind()
 

From eeeee21a87fe233cf5778f26750199c7f5eef6cb Mon Sep 17 00:00:00 2001
From: Sumit Agarwal <sumitagarwal330@gmail.com>
Date: Mon, 12 Aug 2024 14:16:43 -0700
Subject: [PATCH 22/22] [DML EP] Update DML to 1.15.1 (#21695)

### Description
Update DML runtime binary to 1.15.1



### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
---
 .pipelines/nuget_config/x64/packages.config     | 2 +-
 .pipelines/nuget_config/x86/packages.config     | 2 +-
 cmake/external/dml.cmake                        | 2 +-
 packages.config                                 | 2 +-
 tools/nuget/generate_nuspec_for_native_nuget.py | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.pipelines/nuget_config/x64/packages.config b/.pipelines/nuget_config/x64/packages.config
index 7bf8181b1f838..96bb053a13f29 100644
--- a/.pipelines/nuget_config/x64/packages.config
+++ b/.pipelines/nuget_config/x64/packages.config
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="utf-8"?>
 <packages>
   <package id="python" version="3.9.7" targetFramework="native" />
-  <package id="Microsoft.AI.DirectML" version="1.15.0" targetFramework="native" />
+  <package id="Microsoft.AI.DirectML" version="1.15.1" targetFramework="native" />
   <package id="Microsoft.Windows.CppWinRT" version="2.0.201201.7" targetFramework="native" />
 </packages>
diff --git a/.pipelines/nuget_config/x86/packages.config b/.pipelines/nuget_config/x86/packages.config
index 30f7862a11078..6bf842ac18037 100644
--- a/.pipelines/nuget_config/x86/packages.config
+++ b/.pipelines/nuget_config/x86/packages.config
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="utf-8"?>
 <packages>
   <package id="pythonx86" version="3.9.7" targetFramework="native" />
-  <package id="Microsoft.AI.DirectML" version="1.15.0" targetFramework="native" />
+  <package id="Microsoft.AI.DirectML" version="1.15.1" targetFramework="native" />
   <package id="Microsoft.Windows.CppWinRT" version="2.0.201201.7" targetFramework="native" />
 </packages>
diff --git a/cmake/external/dml.cmake b/cmake/external/dml.cmake
index 54e361ffdb3ae..8b5f602643c0b 100644
--- a/cmake/external/dml.cmake
+++ b/cmake/external/dml.cmake
@@ -41,7 +41,7 @@ if (NOT onnxruntime_USE_CUSTOM_DIRECTML)
   set(NUGET_CONFIG ${PROJECT_SOURCE_DIR}/../NuGet.config)
   set(PACKAGES_CONFIG ${PROJECT_SOURCE_DIR}/../packages.config)
   get_filename_component(PACKAGES_DIR ${CMAKE_CURRENT_BINARY_DIR}/../packages ABSOLUTE)
-  set(DML_PACKAGE_DIR ${PACKAGES_DIR}/Microsoft.AI.DirectML.1.15.0)
+  set(DML_PACKAGE_DIR ${PACKAGES_DIR}/Microsoft.AI.DirectML.1.15.1)
 
   # Restore nuget packages, which will pull down the DirectML redist package.
   add_custom_command(
diff --git a/packages.config b/packages.config
index f69e5b4f27956..24289f36689a7 100644
--- a/packages.config
+++ b/packages.config
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="utf-8"?>
 <packages>
-  <package id="Microsoft.AI.DirectML" version="1.15.0" targetFramework="native" />
+  <package id="Microsoft.AI.DirectML" version="1.15.1" targetFramework="native" />
   <package id="Microsoft.Windows.CppWinRT" version="2.0.201201.7" targetFramework="native" />
   <package id="google.protobuf.tools" version="3.21.12" targetFramework="native" />
 </packages>
diff --git a/tools/nuget/generate_nuspec_for_native_nuget.py b/tools/nuget/generate_nuspec_for_native_nuget.py
index 2dda41a5a3bec..be477bb293249 100644
--- a/tools/nuget/generate_nuspec_for_native_nuget.py
+++ b/tools/nuget/generate_nuspec_for_native_nuget.py
@@ -221,7 +221,7 @@ def add_common_dependencies(xml_text, package_name, version):
 
 
 def generate_dependencies(xml_text, package_name, version):
-    dml_dependency = '<dependency id="Microsoft.AI.DirectML" version="1.15.0"/>'
+    dml_dependency = '<dependency id="Microsoft.AI.DirectML" version="1.15.1"/>'
 
     if package_name == "Microsoft.AI.MachineLearning":
         xml_text.append("<dependencies>")