[Inference/Feat] Add convert_fp8 op for fp8 test in the future (#5706)

* add convert_fp8 op for fp8 test in the future

* rerun ci

extensions/csrc/kernel/cuda/convert_fp8_kernel.cu

@@ -0,0 +1,127 @@
+#include <torch/extension.h>
+#include <ATen/cuda/Exceptions.h>
+#include <ATen/cuda/CUDAContext.h>
+
+#include <cmath>
+
+#include "common/micros.h"
+#include "utils/vec_copy.h"
+#include "funcs/cast_functor.h"
+
+
+using colossalAI::cuda::utils::copy;
+using colossalAI::cuda::utils::get_vec_size;
+using colossalAI::funcs::CastFunctor;
+
+template <typename InT, typename OutT, int VecSize>
+__global__ void convert_fp8_kernel(const InT* ins_data, OutT* outs_data, int numel, int tail)
+{
+  int64_t idx = static_cast<int64_t>(threadIdx.x) + static_cast<int64_t>(blockIdx.x) * static_cast<int64_t>(blockDim.x);
+  const int64_t grid_size = blockDim.x * gridDim.x;
+  if(idx > numel + tail) {
+    return;
+  }
+
+  for(int64_t i = idx; i < numel; i += grid_size) {
+    copy<InT, OutT, VecSize>(ins_data + i * VecSize, outs_data + i * VecSize);
+  }
+  // Tail process
+  if(threadIdx.x == 0)
+  {
+    for(int i = 0; i < tail; ++i)
+    {
+      outs_data[i + numel * VecSize] = CastFunctor<InT, OutT>()(ins_data[i + numel * VecSize]);
+    }
+  }
+}
+
+template <typename InT, typename OutT>
+void apply_convert_fp8(torch::Tensor& input, torch::Tensor& output)
+{
+  const int kVecSize = get_vec_size<InT>(input);
+  const int kNumel = torch::numel(input);
+
+  const int kVecNumel = (kNumel >> static_cast<int>(std::log2(kVecSize)));
+  const int kTail = kNumel & (kVecSize - 1);
+  int grid_size = kVecNumel ? (kVecNumel + 255) / 256 : 1;
+
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  dim3 grid(grid_size);
+  dim3 block(256);
+
+#define _(VEC_SIZE)                                                   \
+    convert_fp8_kernel<InT, OutT, VEC_SIZE>                           \
+                    <<<grid, block, 0, stream>>>                      \
+                    (reinterpret_cast<const InT*>(input.data_ptr()),  \
+                    reinterpret_cast<OutT*>(output.data_ptr()),       \
+                    kVecNumel,                                        \
+                    kTail)
+
+  switch (kVecSize)
+  {
+  case 1:
+    _(1);
+    break;
+  case 2:
+    _(2);
+    break;
+  case 4:
+    _(4);
+    break;
+  }
+#undef _
+  AT_CUDA_CHECK(cudaGetLastError());
+}
+
+void convert_fp8(torch::Tensor& input, torch::Tensor& output)
+{
+  TORCH_CHECK(input.scalar_type() == at::ScalarType::Byte || output.scalar_type() == at::ScalarType::Byte, "Data type of Input or Output should be torch.uint8 for convert_fp8!");
+  TORCH_CHECK(input.scalar_type() != output.scalar_type(), "Data type of input and output are the same!");
+  TORCH_CHECK(input.scalar_type() == at::ScalarType::Byte ||
+              input.scalar_type() == at::ScalarType::Float ||
+              input.scalar_type() == at::ScalarType::Half ||
+              input.scalar_type() == at::ScalarType::BFloat16, "Unsupported dtype of input!");
+  TORCH_CHECK(output.scalar_type() == at::ScalarType::Byte ||
+              output.scalar_type() == at::ScalarType::Float ||
+              output.scalar_type() == at::ScalarType::Half ||
+              output.scalar_type() == at::ScalarType::BFloat16, "Unsupported dtype of output!");
+  TORCH_CHECK(input.sizes() == output.sizes(), "Shape of input and output should be the same!");
+
+#define _(InT, OutT)                                         \
+    apply_convert_fp8<InT, OutT>(input, output)
+
+
+  if(input.scalar_type() == at::ScalarType::Byte)
+  {
+    if(output.scalar_type() == at::ScalarType::Float)
+    {
+      _(uint8_t, float);
+    }
+    else if(output.scalar_type() == at::ScalarType::Half)
+    {
+      _(uint8_t, half);
+    }
+    else if(output.scalar_type() == at::ScalarType::BFloat16)
+    {
+      _(uint8_t, __nv_bfloat16);
+    }
+  }
+  else
+  {
+    if(input.scalar_type() == at::ScalarType::Float)
+    {
+      _(float, uint8_t);
+    }
+    else if(input.scalar_type() == at::ScalarType::Half)
+    {
+      _(half, uint8_t);
+    }
+    else if(input.scalar_type() == at::ScalarType::BFloat16)
+    {
+      _(__nv_bfloat16, uint8_t);
+    }
+  }
+
+#undef _
+}

extensions/csrc/kernel/cuda/utils/vec_copy.h

@@ -1,9 +1,6 @@
 
 #pragma once
 
-#include <cuda_fp16.h>
-#include <stdint.h>
-
 #include "common/vec_type_traits.h"
 #include "funcs/cast_functor.h"
 
@@ -12,9 +9,9 @@ namespace cuda {
 namespace utils {
 
 // Note(LiuYang): Depreciated
-template <typename T, int vec_size>
+template <typename T, int VecSize>
 __device__ __inline__ void copy_vector(T *dst, const T *src) {
-  using VT = typename common::VecTypeTrait<T, vec_size>::Type;
+  using VT = typename common::VecTypeTrait<T, VecSize>::Type;
   *(reinterpret_cast<VT *>(dst)) = *(reinterpret_cast<const VT *>(src));
 }
 
@@ -34,17 +31,17 @@ __device__ __inline__ void copy_zero_vector(T *dst) {
   *(reinterpret_cast<VT *>(dst)) = funcs::CastFunctor<float, VT>()(0.0f);
 }
 
-template <typename SrcT, typename DstT, int vec_size>
+template <typename SrcT, typename DstT, int VecSize>
 __device__ __inline__ void copy(const SrcT *src, DstT *dst) {
-  using SrcVT = typename common::VecTypeTrait<SrcT, vec_size>::Type;
-  using DstVT = typename common::VecTypeTrait<DstT, vec_size>::Type;
+  using SrcVT = typename common::VecTypeTrait<SrcT, VecSize>::Type;
+  using DstVT = typename common::VecTypeTrait<DstT, VecSize>::Type;
   *(reinterpret_cast<DstVT *>(dst)) = funcs::CastFunctor<SrcVT, DstVT>()(
       *(reinterpret_cast<const SrcVT *>(src)));
 }
 
-template <typename T, int vec_size>
+template <typename T, int VecSize>
 __device__ __inline__ void copy(const T *src, T *dst) {
-  using VT = typename common::VecTypeTrait<T, vec_size>::Type;
+  using VT = typename common::VecTypeTrait<T, VecSize>::Type;
   *(reinterpret_cast<VT *>(dst)) = *(reinterpret_cast<const VT *>(src));
 }
 

extensions/pybind/inference/inference.cpp

@@ -75,6 +75,8 @@ void flash_decoding_attention(
     torch::Tensor& tmp_out_lse,  // [num_tokens, num_heads, max_num_partitions]
     const c10::optional<torch::Tensor>& alibi_slopes, float scale);
 
+void convert_fp8(torch::Tensor& input, torch::Tensor& output);
+
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("decode_kv_cache_memcpy", &decode_kv_cache_memcpy,
         "Copy the GPU memory of kvcache during the decode stage.");
@@ -102,4 +104,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("flash_decoding_attention", &flash_decoding_attention,
         "Compute the attention between an input query and the cached "
         "keys/values using PagedAttention.");
+
+  m.def("convert_fp8", &convert_fp8,
+        "Convert input to fp8 output or convert fp8 input to output.");
 }

extensions/pybind/inference/inference_ops_cuda.py

@@ -17,6 +17,7 @@ def sources_files(self):
                 "kernel/cuda/rms_layernorm_kernel.cu",
                 "kernel/cuda/get_cos_and_sin_kernel.cu",
                 "kernel/cuda/flash_decoding_attention_kernel.cu",
+                "kernel/cuda/convert_fp8_kernel.cu",
             ]
         ] + [self.pybind_abs_path("inference/inference.cpp")]
         return ret

tests/test_infer/test_kernels/cuda/test_convert_fp8.py

@@ -0,0 +1,57 @@
+import random
+
+import pytest
+import torch
+
+from colossalai.kernel.kernel_loader import InferenceOpsLoader
+from colossalai.utils import get_current_device
+
+inference_ops = InferenceOpsLoader().load()
+
+DTYPES = [torch.half, torch.bfloat16, torch.float]
+NUM_TOKENS = [42]  # Arbitrary values for testing
+NUM_LAYERS = [1]  # Arbitrary values for testing
+NUM_HEADS = [8]  # Arbitrary values for testing
+HEAD_SIZES = [64, 80, 96, 112, 128, 256]
+BLOCK_SIZES = [8, 16, 32]
+
+
+@pytest.mark.skipif(True, reason="FP8 conversion still needs improvement, now we skip it's relative test!")
+@pytest.mark.parametrize("num_heads", [8])
+@pytest.mark.parametrize("head_size", [64, 80, 96, 112, 128, 256])
+@pytest.mark.parametrize("block_size", [8, 16, 32])
+@pytest.mark.parametrize("num_blocks", [1024, 10000])
+@pytest.mark.parametrize("dtype", [torch.half, torch.bfloat16, torch.float])
+@pytest.mark.parametrize("seed", [0])
+@torch.inference_mode()
+def test_fp8_conversion(
+    num_heads: int,
+    head_size: int,
+    block_size: int,
+    num_blocks: int,
+    dtype: torch.dtype,
+    seed: int,
+) -> None:
+    random.seed(seed)
+    torch.random.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+
+    device = get_current_device()
+
+    low = -224.0
+    high = 224.0
+    shape = (num_blocks, num_heads, head_size, block_size)
+    cache = torch.empty(shape, dtype=dtype, device=device)
+    cache.uniform_(low, high)
+
+    cache_fp8 = torch.empty_like(cache, dtype=torch.uint8)
+    inference_ops.convert_fp8(cache, cache_fp8)
+
+    converted_cache = torch.empty_like(cache)
+    inference_ops.convert_fp8(cache_fp8, converted_cache)
+
+    assert torch.allclose(cache, converted_cache, atol=0.001, rtol=0.1)
+
+
+if __name__ == "__main__":
+    test_fp8_conversion(8, 64, 8, 1024, torch.half, 0)