Qbits woq ref impl for debug

intel_extension_for_transformers/llm/operator/csrc/dispatcher/include/bestla_packq_impl.hpp

@@ -0,0 +1,32 @@
+//  Copyright (c) 2023 Intel Corporation
+//
+//  Licensed under the Apache License, Version 2.0 (the "License");
+//  you may not use this file except in compliance with the License.
+//  You may obtain a copy of the License at
+//
+//    http://www.apache.org/licenses/LICENSE-2.0
+//
+//  Unless required by applicable law or agreed to in writing, software
+//  distributed under the License is distributed on an "AS IS" BASIS,
+//  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+//  See the License for the specific language governing permissions and
+//  limitations under the License.
+#pragma once
+#include "bestla_weightonly_dispatcher.hpp"
+namespace woq {
+
+enum PACKW_ACQUIRE_TYPE {
+  SIZE = 0,
+  BLOCKSIZE,
+  K,
+  N,
+  ACT_SHUFFLE,
+  G_IDX,
+  WEI_TYPE,
+  CMPT_TYPE,
+  SCALE_TYPE,
+};
+
+void bestla_packq(woq_packq_param* p, woq_packq_ctx* ctx);
+torch::Tensor get_packw_info(torch::Tensor& packw, PACKW_ACQUIRE_TYPE ACQ_T);
+}  // namespace woq

intel_extension_for_transformers/llm/operator/csrc/dispatcher/include/bestla_weightonly_dispatcher.hpp

@@ -69,6 +69,5 @@ static std::map<std::string, BTLA_DTYPE> scale2bestladt_map{
     {"fp32", BTLA_DTYPE::F32}, {"bf16", BTLA_DTYPE::BF16}, {"fp8_e8m0", BTLA_DTYPE::F8_E8M0}};
 
 void dispatch_woq_task(woq_config_param* p, woq_runtime_ctx* ctx, WOQ_TASK task);
-void bestla_packq(woq_packq_param* p, woq_packq_ctx* ctx);
 void set_woq_workspace(torch::Tensor* workspace);
 }  // namespace woq

intel_extension_for_transformers/llm/operator/csrc/dispatcher/src/bestla_packq_impl.cpp

@@ -1,5 +1,5 @@
 #include "bestla/bestla_prologue_b.h"
-#include "../include/bestla_weightonly_dispatcher.hpp"
+#include "../include/bestla_packq_impl.hpp"
 
 namespace woq {
 template <class GemmCore, BTLA_ISA ISA>
@@ -17,6 +17,101 @@ void execute_qpack(woq_packq_param* p, woq_packq_ctx* ctx) {
                   p->asym ? ctx->zp->data_ptr<int8_t>() : nullptr, &qpackw, &dispatcher_utils::DefaultThreading);
 }
 
+std::string get_dtype_str(BTLA_DTYPE dtype) {
+  switch (dtype) {
+    case BTLA_DTYPE::F32:
+      return "fp32";
+    case BTLA_DTYPE::BF16:
+      return "bf16";
+    case BTLA_DTYPE::S4_CLIP:
+      return "int4_clip";
+    case BTLA_DTYPE::S4_FULLRANGE:
+      return "int4_fullrange";
+    case BTLA_DTYPE::F4_NF4:
+      return "nf4";
+    case BTLA_DTYPE::F4_E2M1:
+      return "fp4_e2m1";
+    case BTLA_DTYPE::F4_BNB:
+      return "fp4_e2m1_bnb";
+    case BTLA_DTYPE::S8:
+      return "int8";
+    case BTLA_DTYPE::F8_E5M2:
+      return "fp8_e5m2";
+    case BTLA_DTYPE::F8_E4M3:
+      return "fp8_e4m3";
+    case BTLA_DTYPE::F8_E8M0:
+      return "fp8_e8m0";
+    default:
+      TORCH_CHECK(false, "QBits: unrecognized data type.")
+      break;
+  }
+}
+
+std::string get_cmpt_str(bestla::gemm::CompType cmpt) {
+  using bestla::gemm::CompType;
+  switch (cmpt) {
+    case CompType::COMP_INT8_US_INT32:
+    case CompType::COMP_INT8_US_FP32:
+      return "int8";
+    case CompType::COMP_FP32:
+      return "fp32";
+    case CompType::COMP_BF16_FP32:
+      return "bf16";
+    default:
+      TORCH_CHECK(false, "QBits: unrecognized compute type.");
+      break;
+  }
+}
+
+std::vector<int> get_ascii_vec(std::string str) {
+  std::vector<int32_t> ret;
+  for (char c : str) ret.push_back(static_cast<int32_t>(c));
+  return ret;
+}
+
+torch::Tensor get_packw_info(torch::Tensor& packw, PACKW_ACQUIRE_TYPE ACQ_T) {
+  torch::Tensor output;
+  auto packw_ptr = dynamic_cast<bestla::storage::gemm::StorageWeightKBlockNInteger*>(
+      bestla::storage::gemm::PackedWeightParser::deserialBuffer(packw.data_ptr()));
+  output = torch::empty(1, torch::kInt64);
+  switch (ACQ_T) {
+    case SIZE:
+      return output.index_put_({0}, static_cast<int64_t>(packw_ptr->mSize));
+    case BLOCKSIZE:
+      return output.index_put_({0}, static_cast<int64_t>(packw_ptr->mBlockSize));
+    case K:
+      return output.index_put_({0}, static_cast<int64_t>(packw_ptr->mK));
+    case N:
+      return output.index_put_({0}, static_cast<int64_t>(packw_ptr->mN));
+    case ACT_SHUFFLE:
+      return output.index_put_({0}, static_cast<int64_t>(packw_ptr->ShfIndice() != nullptr ? 1 : 0));
+    case G_IDX: {
+      auto tensor_size = packw_ptr->mShuffleIndices.size<int>();
+      TORCH_CHECK(packw_ptr->ShfIndice() != nullptr, "QBits: not pack g_idx tensor.");
+      output = torch::empty(tensor_size, torch::kInt32);
+      memcpy(output.data_ptr(), packw_ptr->ShfIndice(), tensor_size * sizeof(int));
+    } break;
+    case WEI_TYPE:
+    case SCALE_TYPE: {
+      BTLA_DTYPE acquire_dt = ACQ_T == WEI_TYPE ? packw_ptr->mDType : packw_ptr->SDtype();
+      auto ascii_vec = get_ascii_vec(get_dtype_str(acquire_dt));
+      output = torch::empty(ascii_vec.size(), torch::kInt32);
+      memcpy(output.data_ptr(), ascii_vec.data(), ascii_vec.size() * sizeof(int));
+    } break;
+    case CMPT_TYPE: {
+      auto CType = bestla::gemm::CoreAttr::get_mask_val(packw_ptr->mCoreId, bestla::gemm::CoreAttr::COMP_MASK,
+                                                        bestla::gemm::CoreAttr::COMP_SHIFT);
+      auto ascii_vec = get_ascii_vec(get_cmpt_str(static_cast<bestla::gemm::CompType>(CType)));
+      output = torch::empty(ascii_vec.size(), torch::kInt32);
+      memcpy(output.data_ptr(), ascii_vec.data(), ascii_vec.size() * sizeof(int));
+    } break;
+    default:
+      TORCH_CHECK(false, "QBits: unsupported acquire_type");
+      break;
+  }
+  return output;
+}
+
 void bestla_packq(woq_packq_param* p, woq_packq_ctx* ctx) {
   TORCH_CHECK(p->weight_type == "int8" || p->weight_type == "int4_clip" || p->weight_type == "int4_fullrange",
               "Qbits: only support Integer WOQ in PACKQ");

intel_extension_for_transformers/llm/operator/csrc/dispatcher/src/bestla_weightonly_dispatcher.cpp

@@ -49,7 +49,6 @@ void woq_dequantize(woq_config_param* p, woq_runtime_ctx* ctx) {
   using PrologueB = typename Launcher::PrologueB;
   using WType = typename Launcher::PrologueB::StorageWeight;
   static PrologueB kernel;
-  // TODO(zhe): using unified StorageWeightKBlockNInteger after sync with neural-speed(with NFloat ProB feature).
   if (ctx->transpose) {
     kernel.unpackTransposeWeight(ctx->deseries_wei->mN, ctx->deseries_wei->mK,
                                  dynamic_cast<bestla::storage::gemm::StorageWeightKBlockNInteger*>(ctx->deseries_wei),

intel_extension_for_transformers/llm/operator/csrc/qbits.cpp

@@ -14,6 +14,7 @@
 #include "dispatcher/include/dispatcher_utils.hpp"
 #include "dispatcher/include/bestla_gemm_dispatcher.hpp"
 #include "dispatcher/include/bestla_weightonly_dispatcher.hpp"
+#include "dispatcher/include/bestla_packq_impl.hpp"
 #include "include/dropout.hpp"
 #include <ATen/core/TensorBody.h>
 #include <c10/core/ScalarType.h>
@@ -45,8 +46,8 @@ static void inline init_woq_config_param(woq::woq_config_param* p, woq::woq_runt
     case woq::WOQ_QUANTIZE:
     case woq::WOQ_DEQUANTIZE:
       p->src_dt = dispatcher_utils::QBITS_FP32;
-      p->dst_dt = dispatcher_utils::QBITS_FP32;  // bestla doesn't care about dst_dt in quantize/dequant task,so set fp32
-                                                 // as default.
+      p->dst_dt = dispatcher_utils::QBITS_FP32;  // bestla doesn't care about dst_dt in quantize/dequant task,so set
+                                                 // fp32 as default.
       break;
     case woq::WOQ_LINEAR:
       p->src_dt = get_qbits_dt(ctx->activation);
@@ -122,7 +123,7 @@ static void set_woq_workspace(const torch::Tensor& workspace) {
 }
 
 static void bestlaop_gemm(const torch::Tensor& matA, const torch::Tensor& matB, const torch::Tensor& matC,
-                         bool matB_trans) {
+                          bool matB_trans) {
   TORCH_CHECK(matA.dim() == 2 && matB.dim() == 2 && matC.dim() == 2,
               "Qbits: only support 2-dim input-tensor in bestla gemm op.");
   bestla_gemm::bestla_gemm_runtime_ctx ctx;
@@ -138,6 +139,10 @@ static void bestlaop_gemm(const torch::Tensor& matA, const torch::Tensor& matB,
   return bestla_gemm::dispatch_bestla_gemm(&ctx);
 }
 
+static torch::Tensor acquire_woq_packw_info(torch::Tensor& packw, int64_t acquire_type) {
+  return woq::get_packw_info(packw, static_cast<woq::PACKW_ACQUIRE_TYPE>(acquire_type));
+}
+
 static torch::Tensor qbits_dropout_fwd(torch::Tensor& output, double p) { return dropout_fwd(output, p); }
 
 static void qbits_dropout_bwd(torch::Tensor& grad, torch::Tensor& scale) { dropout_bwd(grad, scale); }
@@ -149,6 +154,7 @@ TORCH_LIBRARY(bestlaop, m) {
   m.def("woq_packq", &woq_packq);
   m.def("set_woq_workspace", &set_woq_workspace);
   m.def("matmul", &bestlaop_gemm);
+  m.def("acquire_woq_packw_info", &acquire_woq_packw_info);
 }
 
 TORCH_LIBRARY(qbits_customop, m) {

intel_extension_for_transformers/llm/operator/csrc/qbits_ut/test_packq.py

@@ -16,6 +16,7 @@
 # limitations under the License.
 
 from ut_utils import *
+from enum import Enum
 
 
 def convert_idx(g_idx,  k, blocksize):
@@ -27,6 +28,18 @@ def convert_idx(g_idx,  k, blocksize):
     return ret_idx
 
 
+class acquire_type(Enum):
+    SIZE = 0
+    BLOCKSIZE = 1
+    K = 2
+    N = 3
+    ACT_SHUFFLE = 4
+    G_IDX = 5
+    WEI_TYPE = 6
+    CMPT_TYPE = 7
+    SCALE_TYPE = 8
+
+
 @pytest.mark.parametrize("m", [256])
 @pytest.mark.parametrize("n", [1024])
 @pytest.mark.parametrize("k", [512])
@@ -66,3 +79,19 @@ def test(m, k, n, weight_type, scale_type, compute_type, asym, blocksize, dump_t
         assert (abs(ref_dst - tar_dst).max() < 8)
     else:
         assert (abs(ref_dst - tar_dst).max() < 10)
+    packw_size = torch.ops.bestlaop.acquire_woq_packw_info(
+        packw, acquire_type.SIZE.value)[0].item()
+    if packw_size != packw.size()[0]:
+        assert (0)
+    packw_wei_type = torch.ops.bestlaop.acquire_woq_packw_info(
+        packw, acquire_type.WEI_TYPE.value)
+    packw_wei_type_str = ''.join(chr(ascii_code)
+                                 for ascii_code in packw_wei_type.tolist())
+    if packw_wei_type_str != weight_type:
+        assert (0)
+    enable_act_shuffle = torch.ops.bestlaop.acquire_woq_packw_info(
+        packw, acquire_type.ACT_SHUFFLE.value)[0] != 0
+    assert (enable_act_shuffle)
+    acquire_g_idx = packw_wei_type = torch.ops.bestlaop.acquire_woq_packw_info(
+        packw, acquire_type.G_IDX.value)
+    assert (abs(acquire_g_idx-cvt_idx).max() == 0)

intel_extension_for_transformers/llm/quantization/autograd/functions.py

@@ -16,11 +16,49 @@
 # limitations under the License.
 
 
+import os
 import operator
 import torch
 from functools import reduce
 from torch import Tensor
 from typing import Tuple, Optional, List
+from enum import Enum
+
+
+class qbits_acquire_type(Enum):
+    SIZE = 0
+    BLOCKSIZE = 1
+    K = 2
+    N = 3
+    ACT_SHUFFLE = 4
+    G_IDX = 5
+    WEI_TYPE = 6
+    CMPT_TYPE = 7
+    SCALE_TYPE = 8
+
+
+def qbits_woq_linear_ref_impl(activation, packw,  bias, compute_type, weight_type, scale_type):
+    assert (activation.is_contiguous())
+    assert (packw.is_contiguous())
+    activation = activation.to(torch.float32)
+    n = torch.ops.bestlaop.acquire_woq_packw_info(
+        packw, qbits_acquire_type.N.value)[0].item()
+    k = activation.shape[1]
+    revert_wei = torch.empty(k, n, dtype=torch.float)
+    torch.ops.bestlaop.woq_dequantize(
+        packw, revert_wei, False, compute_type, weight_type, scale_type)
+    enable_act_shuffle = torch.ops.bestlaop.acquire_woq_packw_info(
+        packw, qbits_acquire_type.ACT_SHUFFLE.value)[0] != 0
+    if enable_act_shuffle:
+        g_idx = torch.ops.bestlaop.acquire_woq_packw_info(
+            packw, qbits_acquire_type.G_IDX.value)
+        activation = torch.index_select(activation, 1, g_idx)
+    out = torch.matmul(activation, revert_wei)
+    if bias is not None:
+        assert (bias.is_contiguous())
+        assert (bias.dtype == torch.float32)
+        out += bias
+    return out
 
 
 def prod(iterable):
@@ -64,18 +102,23 @@ def forward(
 
         # 2. Matmul
         # output = torch.nn.functional.linear(A, B_dequant, bias)
-        torch.ops.bestlaop.woq_linear(
-            A,
-            B.data,
-            bias,
-            out,
-            out.shape[-1],
-            bias is not None,
-            compute_dtype,
-            weight_dtype,
-            scale_dtype,
-            False,
-        )
+        qbits_debug_flag = os.getenv('QBITS_DEBUG', 'NULL')
+        if qbits_debug_flag == 'NULL':
+            torch.ops.bestlaop.woq_linear(
+                A,
+                B.data,
+                bias,
+                out,
+                out.shape[-1],
+                bias is not None,
+                compute_dtype,
+                weight_dtype,
+                scale_dtype,
+                False,
+            )
+        else:
+            out = qbits_woq_linear_ref_impl(
+                A, B.data, bias, compute_dtype, weight_dtype, scale_dtype)
         output = out
 
         # 3. Save state
@@ -101,7 +144,8 @@ def forward(
     @staticmethod
     def backward(ctx, grad_output):
         if ctx.is_empty:
-            bias_grad = None if ctx.bias is None else torch.zeros_like(ctx.bias)
+            bias_grad = None if ctx.bias is None else torch.zeros_like(
+                ctx.bias)
             return (
                 torch.zeros_like(ctx.A),
                 torch.zeros_like(ctx.B),
@@ -114,7 +158,8 @@ def backward(ctx, grad_output):
         A, B = ctx.tensors
         grad_A, grad_B, grad_bias = None, None, None
 
-        B_dequant = torch.zeros(grad_output.shape[-1], A.shape[-1], dtype=torch.float)
+        B_dequant = torch.zeros(
+            grad_output.shape[-1], A.shape[-1], dtype=torch.float)
 
         torch.ops.bestlaop.woq_dequantize(
             B, B_dequant, True, ctx.compute_dtype, ctx.weight_dtype, ctx.scale_dtype
@@ -149,17 +194,22 @@ def matmul_kbit(
             A, B, out, bias, compute_dtype, weight_dtype, scale_dtype
         )
     else:
-        torch.ops.bestlaop.woq_linear(
-            A,
-            B.data,
-            bias,
-            out,
-            out.shape[-1],
-            bias is not None,
-            compute_dtype,
-            weight_dtype,
-            scale_dtype,
-            False,
-        )
+        qbits_debug_flag = os.getenv('QBITS_DEBUG', 'NULL')
+        if qbits_debug_flag == 'NULL':
+            torch.ops.bestlaop.woq_linear(
+                A,
+                B.data,
+                bias,
+                out,
+                out.shape[-1],
+                bias is not None,
+                compute_dtype,
+                weight_dtype,
+                scale_dtype,
+                False,
+            )
+        else:
+            out = qbits_woq_linear_ref_impl(
+                A, B.data, bias, compute_dtype, weight_dtype, scale_dtype)
 
         return out