mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
624 lines
24 KiB
Plaintext
624 lines
24 KiB
Plaintext
/*
|
|
* Copyright (c) 2022-2024, NVIDIA CORPORATION. All rights reserved.
|
|
*
|
|
* 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.
|
|
*/
|
|
|
|
#include "DevKernel.h"
|
|
|
|
#include <cutlass/cutlass.h>
|
|
#include <cutlass/numeric_types.h>
|
|
|
|
#include <cub/cub.cuh>
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
namespace moe::dev
|
|
{
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
namespace activation
|
|
{
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
namespace tg = batchedGemm::trtllm::gen;
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
inline __device__ float silu(float x)
|
|
{
|
|
return x / (1.0f + expf(-x));
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename KernelParams>
|
|
__global__ void activationKernel(KernelParams params)
|
|
{
|
|
using Type = typename KernelParams::Type;
|
|
|
|
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
|
|
// immediately trigger the secondary kernel when using PDL, then wait on primary
|
|
if constexpr (KernelParams::UsePdl)
|
|
{
|
|
cudaTriggerProgrammaticLaunchCompletion();
|
|
cudaGridDependencySynchronize();
|
|
}
|
|
#endif
|
|
|
|
for (int tokenIdx = blockIdx.z; tokenIdx < params.numTokens; tokenIdx += gridDim.z)
|
|
{
|
|
// Look over experts per token
|
|
for (int k = blockIdx.y; k < params.topK; k += gridDim.y)
|
|
{
|
|
int const expandedIdx = tokenIdx * params.topK + k;
|
|
int const permutedIdx = params.expandedIdxToPermutedIdx[expandedIdx];
|
|
if (permutedIdx == -1)
|
|
continue;
|
|
|
|
// Loop over hidden dim
|
|
for (int hiddenIdx = threadIdx.x + blockDim.x * blockIdx.x; hiddenIdx < params.innerDim / 2;
|
|
hiddenIdx += blockDim.x * gridDim.x)
|
|
{
|
|
int const baseIdx = permutedIdx * params.innerDim + hiddenIdx;
|
|
|
|
float x1 = (float) params.inPtr[baseIdx];
|
|
float x2 = (float) params.inPtr[baseIdx + params.innerDim / 2];
|
|
|
|
float act = silu(x2);
|
|
Type out = (Type) (act * x1);
|
|
|
|
int const outIdx = permutedIdx * (params.innerDim / 2) + hiddenIdx;
|
|
params.outPtr[outIdx] = out;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename KernelParams>
|
|
__global__ void activationDeepSeekKernel(KernelParams params)
|
|
{
|
|
using Type = typename KernelParams::Type;
|
|
using BlockReduce = cub::BlockReduce<float, 128>;
|
|
|
|
__shared__ float s_scaleOut;
|
|
__shared__ typename BlockReduce::TempStorage temp_storage;
|
|
|
|
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
|
|
// immediately trigger the secondary kernel when using PDL, then wait on primary
|
|
if constexpr (KernelParams::UsePdl)
|
|
{
|
|
cudaTriggerProgrammaticLaunchCompletion();
|
|
cudaGridDependencySynchronize();
|
|
}
|
|
#endif
|
|
// Loop over tokens
|
|
for (int tokenIdx = blockIdx.z; tokenIdx < params.numTokens; tokenIdx += gridDim.z)
|
|
{
|
|
// Look over experts per token
|
|
for (int k = blockIdx.y; k < params.topK; k += gridDim.y)
|
|
{
|
|
int const expandedIdx = tokenIdx * params.topK + k;
|
|
int const permutedIdx = params.expandedIdxToPermutedIdx[expandedIdx];
|
|
|
|
// Needed for expert parallelism
|
|
if (permutedIdx == -1)
|
|
continue;
|
|
|
|
// Loop over hidden dim
|
|
for (int hiddenIdx = threadIdx.x + blockDim.x * blockIdx.x; hiddenIdx < params.innerDim / 2;
|
|
hiddenIdx += blockDim.x * gridDim.x)
|
|
{
|
|
int const baseIdx = permutedIdx * params.innerDim + hiddenIdx;
|
|
|
|
int const totalNumPaddedTokens = params.totalNumPaddedTokens[0];
|
|
|
|
int const scale1_idx = permutedIdx + totalNumPaddedTokens * (hiddenIdx / 128);
|
|
int const scale2_idx
|
|
= permutedIdx + totalNumPaddedTokens * ((hiddenIdx / 128) + (params.innerDim / 2 / 128));
|
|
float const scale1 = params.inDqSfsPtr[scale1_idx];
|
|
float const scale2 = params.inDqSfsPtr[scale2_idx];
|
|
|
|
float x1 = scale1 * (float) params.inPtr[baseIdx];
|
|
float x2 = scale2 * (float) params.inPtr[baseIdx + params.innerDim / 2];
|
|
|
|
float act = silu(x2);
|
|
float out = act * x1;
|
|
|
|
// The largest (finite) value that can be represented using E4m3.
|
|
float constexpr E4m3MaxVal{448.f};
|
|
|
|
// Compute the absolute max
|
|
float aMax = BlockReduce(temp_storage).Reduce(fabsf(out), cub::Max());
|
|
if (threadIdx.x == 0)
|
|
{
|
|
s_scaleOut = aMax / E4m3MaxVal;
|
|
int const scaleOut_idx = permutedIdx + totalNumPaddedTokens * (hiddenIdx / 128);
|
|
params.outDqSfsPtr[scaleOut_idx] = aMax / E4m3MaxVal;
|
|
}
|
|
__syncthreads();
|
|
float const scaleOut = s_scaleOut;
|
|
__syncthreads();
|
|
int const outIdx = permutedIdx * (params.innerDim / 2) + hiddenIdx;
|
|
params.outPtr[outIdx] = (Type) (out / scaleOut);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
void run(Data const& data, void* stream)
|
|
{
|
|
if (data.mDtypeElt == tg::Dtype::E2m1)
|
|
{
|
|
// Note: this should be unreachable because the options are checked beforehand.
|
|
// E2m1 requires using higher-precision intermediate data (bf16).
|
|
TLLM_CHECK_WITH_INFO(false, "Activation with E2m1_t isn't supported.");
|
|
return;
|
|
}
|
|
|
|
if (data.mUseDeepSeekFp8)
|
|
{
|
|
int const numThreads = 128;
|
|
const dim3 grid(data.innerDim / 128, data.topK, data.numTokens);
|
|
|
|
LAUNCH(data, activationDeepSeekKernel, grid, numThreads, 0, stream);
|
|
}
|
|
else
|
|
{
|
|
int const numThreads = 256;
|
|
const dim3 grid(data.innerDim / 128, data.topK, data.numTokens);
|
|
|
|
LAUNCH(data, activationKernel, grid, numThreads, 0, stream);
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace activation
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
namespace convertsf
|
|
{
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
namespace tg = batchedGemm::trtllm::gen;
|
|
|
|
namespace dev
|
|
{
|
|
// Compute the offset that corresponds to (dataRowIdx, dataBlkColIdx) in the SF tensor where
|
|
// dataRowIdx and dataBlkColIdx are the respective indices of the row and the block of 16 elts
|
|
// from the K dim in the tensor of data.
|
|
inline __device__ int64_t getSfOffset(int32_t dataRowIdx, int32_t dataBlkColIdx, int32_t numDataBlksPerRow)
|
|
{
|
|
|
|
// The number of rows of SF per block.
|
|
static int32_t constexpr NumRowsPerSfBlock = 128;
|
|
// The number of cols of SF per block.
|
|
static int32_t constexpr NumColsPerSfBlock = 4;
|
|
// The size of each SF block.
|
|
static int32_t constexpr NumBytesPerSfBlock = NumRowsPerSfBlock * NumColsPerSfBlock;
|
|
|
|
// The number of rows of data per SF block.
|
|
static int32_t constexpr NumDataRowsPerSfBlock = NumRowsPerSfBlock;
|
|
// The number of cols of blocks of data per SF block.
|
|
static int32_t constexpr NumDataBlkColsPerSfBlock = NumColsPerSfBlock;
|
|
|
|
// The row of the SF block in the SF tensor.
|
|
int sfBlkRowIdx = dataRowIdx / NumDataRowsPerSfBlock;
|
|
// The col of the SF block in the SF tensor.
|
|
int sfBlkColIdx = dataBlkColIdx / NumDataBlkColsPerSfBlock;
|
|
// The blocks are stored row-major in the tensor of scaling factors.
|
|
int sfBlkIdx = sfBlkRowIdx * numDataBlksPerRow / NumDataBlkColsPerSfBlock + sfBlkColIdx;
|
|
|
|
// Find the row in the SF block.
|
|
int sfRowIdx = (dataRowIdx % 32) * 4 + (dataRowIdx % NumDataRowsPerSfBlock) / 32;
|
|
// Find the col in the SF block.
|
|
int sfColIdx = (dataBlkColIdx % 4);
|
|
|
|
// Compute the offset in bytes.
|
|
return sfBlkIdx * NumBytesPerSfBlock + sfRowIdx * NumColsPerSfBlock + sfColIdx;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// Given the GMEM address of an output element, compute the offset of the corresponding scaling
|
|
// factor in the SF tensor. Optionally, a startTokenIndex can be provided if the first token is not
|
|
// the start token in the SF tensor. This is useful when inflight batching is enabled in TRT-LLM,
|
|
// where the context and generation output are stored as one output tensor. In this case, the
|
|
// generation output may not start with zero offset in the SF output tensor.
|
|
template <int32_t NumBitsPerElt>
|
|
inline __device__ int64_t getSfOffset(int64_t gmemOffsetInBytes, int32_t hiddenDim, int32_t startTokenIdx = 0)
|
|
{
|
|
// The number of elements per sf.
|
|
int32_t constexpr NumEltsPerSf = 16;
|
|
// The GMEM offset of the output element.
|
|
int64_t gmemOffset = gmemOffsetInBytes * 8 /*bits*/ / NumBitsPerElt;
|
|
// The row/col indices of the corresponding SF element.
|
|
int32_t sfRowIdx = gmemOffset / hiddenDim + startTokenIdx;
|
|
int32_t sfColIdx = (gmemOffset % hiddenDim) / NumEltsPerSf;
|
|
// Compute the SF offset.
|
|
return getSfOffset(sfRowIdx, sfColIdx, hiddenDim / NumEltsPerSf);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// TODO(tizheng): Refactor to track gmem offset instead of doing pointer subtraction.
|
|
template <int32_t NumBitsPerElt>
|
|
inline __device__ int64_t getSfOffset(
|
|
void const* gmemOutPtr, void const* gmemBasePtr, int32_t hiddenDim, int32_t startTokenIdx = 0)
|
|
{
|
|
return getSfOffset<NumBitsPerElt>(
|
|
reinterpret_cast<char const*>(gmemOutPtr) - reinterpret_cast<char const*>(gmemBasePtr), hiddenDim,
|
|
startTokenIdx);
|
|
}
|
|
|
|
} // namespace dev
|
|
|
|
// TODO: it would be nice to move some of that logic to Fp4Utils.h
|
|
template <tg::SfLayout Layout>
|
|
inline __device__ int32_t getSfOffset(int32_t dataRowIdx, int32_t dataBlkColIdx, int32_t numDataBlksPerRow)
|
|
{
|
|
if constexpr (Layout == tg::SfLayout::Linear)
|
|
{
|
|
return numDataBlksPerRow * dataRowIdx + dataBlkColIdx;
|
|
}
|
|
else if constexpr (Layout == tg::SfLayout::R128c4)
|
|
{
|
|
return static_cast<int32_t>(dev::getSfOffset(dataRowIdx, dataBlkColIdx, numDataBlksPerRow));
|
|
}
|
|
else if constexpr (Layout == tg::SfLayout::R8c4 || Layout == tg::SfLayout::R8c16)
|
|
{
|
|
static int32_t constexpr NumRowsPerSfBlock = 8;
|
|
static int32_t constexpr NumColsPerSfBlock = (Layout == tg::SfLayout::R8c4) ? 4 : 16;
|
|
static int32_t constexpr NumBytesPerSfBlock = NumRowsPerSfBlock * NumColsPerSfBlock;
|
|
int sfBlkRowIdx = dataRowIdx / NumRowsPerSfBlock;
|
|
int sfBlkColIdx = dataBlkColIdx / NumColsPerSfBlock;
|
|
int sfBlkIdx = sfBlkRowIdx * numDataBlksPerRow / NumColsPerSfBlock + sfBlkColIdx;
|
|
int sfRowIdx = dataRowIdx % NumRowsPerSfBlock;
|
|
int sfColIdx = dataBlkColIdx % NumColsPerSfBlock;
|
|
return sfBlkIdx * NumBytesPerSfBlock + sfRowIdx * NumColsPerSfBlock + sfColIdx;
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <tg::SfLayout LayoutSrc, tg::SfLayout LayoutDst, typename KernelParams>
|
|
__device__ void convertSfCommon(KernelParams params)
|
|
{
|
|
// Note: it's assumed that the number of scaling factors per row is a multiple of 4.
|
|
constexpr int VecSize = 4;
|
|
using VecType = uint32_t;
|
|
static_assert(sizeof(VecType) == VecSize);
|
|
|
|
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
|
|
// Immediately trigger the secondary kernel when using PDL, then wait on primary.
|
|
if constexpr (KernelParams::UsePdl)
|
|
{
|
|
cudaTriggerProgrammaticLaunchCompletion();
|
|
cudaGridDependencySynchronize();
|
|
}
|
|
#endif
|
|
|
|
// TODO: consider optimizing if used in production.
|
|
// This is a naive kernel. It's not doing coalesced loads.
|
|
|
|
int const numSfPerRow = params.hiddenDimSf;
|
|
|
|
for (int tokenIdx = blockIdx.y; tokenIdx < params.numTokens; tokenIdx += gridDim.y)
|
|
{
|
|
for (int hiddenSfVecIdx = threadIdx.x + blockDim.x * blockIdx.x; hiddenSfVecIdx < numSfPerRow / VecSize;
|
|
hiddenSfVecIdx += blockDim.x * gridDim.x)
|
|
{
|
|
// Index of the first SF in the vector.
|
|
int const hiddenSfIdx = VecSize * hiddenSfVecIdx;
|
|
|
|
// Load scale factors.
|
|
int sfIdxIn = getSfOffset<LayoutSrc>(tokenIdx, hiddenSfIdx, numSfPerRow);
|
|
const VecType sfVec = reinterpret_cast<VecType const*>(params.inSfPtr)[sfIdxIn / VecSize];
|
|
|
|
// Store scale factors.
|
|
int const sfIdxOut = getSfOffset<LayoutDst>(tokenIdx, hiddenSfIdx, numSfPerRow);
|
|
reinterpret_cast<VecType*>(params.outSfPtr)[sfIdxOut / VecSize] = sfVec;
|
|
}
|
|
}
|
|
}
|
|
|
|
#define CONVERT_FP4_SF_KERNEL(LayoutSrc, LayoutDst) \
|
|
template <typename KernelParams> \
|
|
__global__ void convertSf##LayoutSrc##To##LayoutDst##Kernel(KernelParams params) \
|
|
{ \
|
|
convertSfCommon<tg::SfLayout::LayoutSrc, tg::SfLayout::LayoutDst>(params); \
|
|
}
|
|
// We only need a conversion to the linear layout.
|
|
CONVERT_FP4_SF_KERNEL(R128c4, Linear);
|
|
CONVERT_FP4_SF_KERNEL(R8c4, Linear);
|
|
CONVERT_FP4_SF_KERNEL(R8c16, Linear);
|
|
#undef CONVERT_FP4_SF_KERNEL
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
void run(Data const& data, void* stream)
|
|
{
|
|
constexpr int VecSize = 4;
|
|
int const numThreads = 128;
|
|
int const numBlocksX = (data.hiddenDimSf / VecSize - 1 + numThreads) / numThreads;
|
|
int const numBlocksY = data.numTokens;
|
|
dim3 numBlocks(numBlocksX, numBlocksY);
|
|
#define CONVERT_FP4_SF_LAUNCH(LayoutSrc, LayoutDst) \
|
|
if (data.sfLayoutSrc == tg::SfLayout::LayoutSrc && data.sfLayoutDst == tg::SfLayout::LayoutDst) \
|
|
{ \
|
|
LAUNCH_PDL(data, false, cutlass::float_e4m3_t, convertSf##LayoutSrc##To##LayoutDst##Kernel, numBlocks, \
|
|
numThreads, 0, stream); \
|
|
return; \
|
|
}
|
|
CONVERT_FP4_SF_LAUNCH(R128c4, Linear);
|
|
CONVERT_FP4_SF_LAUNCH(R8c4, Linear);
|
|
CONVERT_FP4_SF_LAUNCH(R8c16, Linear);
|
|
#undef CONVERT_FP4_SF_LAUNCH
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace convertsf
|
|
|
|
namespace permute
|
|
{
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
namespace tg = batchedGemm::trtllm::gen;
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename KernelParams>
|
|
__global__ void permuteKernel(KernelParams params)
|
|
{
|
|
using Type = typename KernelParams::Type;
|
|
|
|
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
|
|
// immediately trigger the secondary kernel when using PDL, then wait on primary
|
|
if constexpr (KernelParams::UsePdl)
|
|
{
|
|
cudaTriggerProgrammaticLaunchCompletion();
|
|
cudaGridDependencySynchronize();
|
|
}
|
|
#endif
|
|
|
|
for (int tokenIdx = blockIdx.y; tokenIdx < params.numTokens; tokenIdx += gridDim.y)
|
|
{
|
|
// Loop over hidden dim
|
|
for (int hiddenIdx = threadIdx.x + blockDim.x * blockIdx.x; hiddenIdx < params.hiddenDim;
|
|
hiddenIdx += blockDim.x * gridDim.x)
|
|
{
|
|
|
|
// Load chunk of token into registers
|
|
const Type data = params.inPtr[tokenIdx * params.hiddenDim + hiddenIdx];
|
|
|
|
// Write to topK places
|
|
for (int k = 0; k < params.topK; k++)
|
|
{
|
|
int const expandedIdx = tokenIdx * params.topK + k;
|
|
int const permutedIdx = params.expandedIdxToPermutedIdx[expandedIdx];
|
|
params.outPtr[permutedIdx * params.hiddenDim + hiddenIdx] = data;
|
|
}
|
|
}
|
|
if (params.useDeepSeekFp8)
|
|
{
|
|
for (int scaleIdx = threadIdx.x + blockDim.x * blockIdx.x; scaleIdx < params.hiddenDim / 128;
|
|
scaleIdx += blockDim.x * gridDim.x)
|
|
{
|
|
for (int k = 0; k < params.topK; k++)
|
|
{
|
|
int const expandedIdx = tokenIdx * params.topK + k;
|
|
int const permutedIdx = params.expandedIdxToPermutedIdx[expandedIdx];
|
|
|
|
int const idx_in = tokenIdx + params.numTokens * scaleIdx;
|
|
int const idx_out = permutedIdx + params.totalNumPaddedTokens[0] * scaleIdx;
|
|
|
|
params.outDqSfsPtr[idx_out] = params.inDqSfsPtr[idx_in];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
void run(Data const& data, void* stream)
|
|
{
|
|
int const numThreads = 256;
|
|
int const numBlocksX = (data.hiddenDim - 1 + numThreads) / numThreads;
|
|
int const numBlocksY = data.numTokens;
|
|
dim3 numBlocks(numBlocksX, numBlocksY);
|
|
|
|
LAUNCH(data, permuteKernel, numBlocks, numThreads, 0, stream);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace permute
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
namespace finalize
|
|
{
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
namespace tg = batchedGemm::trtllm::gen;
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename KernelParams>
|
|
__global__ void finalizeKernel(KernelParams params)
|
|
{
|
|
using Type = typename KernelParams::Type;
|
|
using TypeExpW = typename KernelParams::TypeExpW;
|
|
|
|
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
|
|
// wait on primary kernel when using PDL
|
|
if constexpr (KernelParams::UsePdl)
|
|
{
|
|
cudaGridDependencySynchronize();
|
|
}
|
|
#endif
|
|
|
|
for (int tokenIdx = blockIdx.y; tokenIdx < params.numTokens; tokenIdx += gridDim.y)
|
|
{
|
|
// Loop over hidden dim
|
|
for (int hiddenIdx = threadIdx.x + blockDim.x * blockIdx.x; hiddenIdx < params.hiddenDim;
|
|
hiddenIdx += blockDim.x * gridDim.x)
|
|
{
|
|
|
|
// Accumulate chunk of token into registers
|
|
float data = 0.0F;
|
|
|
|
// Write to topK places
|
|
for (int k = 0; k < params.topK; k++)
|
|
{
|
|
int const expandedIdx = tokenIdx * params.topK + k;
|
|
int const permutedIdx = params.expandedIdxToPermutedIdx[expandedIdx];
|
|
|
|
if (permutedIdx == -1)
|
|
{
|
|
continue;
|
|
}
|
|
|
|
if (params.expertWeightsPtr != nullptr)
|
|
{
|
|
TypeExpW const scale = params.expertWeightsPtr[expandedIdx];
|
|
data += float{scale} * float{params.inPtr[permutedIdx * params.hiddenDim + hiddenIdx]};
|
|
}
|
|
else
|
|
{
|
|
data += float{params.inPtr[permutedIdx * params.hiddenDim + hiddenIdx]};
|
|
}
|
|
}
|
|
|
|
params.outPtr[tokenIdx * params.hiddenDim + hiddenIdx] = static_cast<Type>(data);
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename KernelParams>
|
|
__global__ void finalizeDeepSeekKernel(KernelParams params)
|
|
{
|
|
using Type = typename KernelParams::Type;
|
|
using BlockReduce = cub::BlockReduce<float, 128>;
|
|
|
|
__shared__ float s_scaleOut;
|
|
__shared__ typename BlockReduce::TempStorage temp_storage;
|
|
|
|
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
|
|
// wait on primary kernel when using PDL
|
|
if constexpr (KernelParams::UsePdl)
|
|
{
|
|
cudaGridDependencySynchronize();
|
|
}
|
|
#endif
|
|
|
|
for (int tokenIdx = blockIdx.y; tokenIdx < params.numTokens; tokenIdx += gridDim.y)
|
|
{
|
|
// Loop over hidden dim
|
|
for (int hiddenIdx = threadIdx.x + blockDim.x * blockIdx.x; hiddenIdx < params.hiddenDim;
|
|
hiddenIdx += blockDim.x * gridDim.x)
|
|
{
|
|
|
|
// Accumulate chunk of token into registers
|
|
float acc = 0.0f;
|
|
|
|
for (int k = 0; k < params.topK; k++)
|
|
{
|
|
int const expandedIdx = tokenIdx * params.topK + k;
|
|
int const permutedIdx = params.expandedIdxToPermutedIdx[expandedIdx];
|
|
if (permutedIdx == -1)
|
|
continue;
|
|
int const totalNumPaddedTokens = params.totalNumPaddedTokens[0];
|
|
int const scaleIdx = permutedIdx + totalNumPaddedTokens * (hiddenIdx / 128);
|
|
float const blockScale = params.inDqSfsPtr ? params.inDqSfsPtr[scaleIdx] : 1;
|
|
|
|
float const expertProb = (float) params.expertWeightsPtr[tokenIdx * params.topK + k];
|
|
|
|
float const scale = expertProb * blockScale;
|
|
acc += scale * static_cast<float>(params.inPtr[permutedIdx * params.hiddenDim + hiddenIdx]);
|
|
}
|
|
|
|
// The largest (finite) value that can be represented using E4m3.
|
|
float constexpr E4m3MaxVal{448.f};
|
|
|
|
// Compute the absolute max
|
|
float aMax = BlockReduce(temp_storage).Reduce(fabsf(acc), cub::Max());
|
|
|
|
if (threadIdx.x == 0)
|
|
{
|
|
if (params.outDqSfsPtr)
|
|
{
|
|
s_scaleOut = aMax / E4m3MaxVal;
|
|
int const scaleOut_idx = tokenIdx + hiddenIdx / 128 * params.numTokens;
|
|
params.outDqSfsPtr[scaleOut_idx] = aMax / E4m3MaxVal;
|
|
}
|
|
else
|
|
{
|
|
s_scaleOut = 1.0f;
|
|
}
|
|
}
|
|
__syncthreads();
|
|
float const scaleOut = s_scaleOut;
|
|
__syncthreads();
|
|
params.outPtr[tokenIdx * params.hiddenDim + hiddenIdx] = (Type) (acc / scaleOut);
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
void run(Data const& data, void* stream)
|
|
{
|
|
if (data.mUseDeepSeekFp8)
|
|
{
|
|
int const numThreads = 128;
|
|
int const numBlocksX = (data.hiddenDim - 1 + numThreads) / numThreads;
|
|
int const numBlocksY = data.numTokens;
|
|
dim3 numBlocks(numBlocksX, numBlocksY);
|
|
|
|
LAUNCH_EXPW(data, finalizeDeepSeekKernel, numBlocks, numThreads, 0, stream);
|
|
}
|
|
else
|
|
{
|
|
int const numThreads = 256;
|
|
int const numBlocksX = (data.hiddenDim - 1 + numThreads) / numThreads;
|
|
int const numBlocksY = data.numTokens;
|
|
dim3 numBlocks(numBlocksX, numBlocksY);
|
|
|
|
LAUNCH_EXPW(data, finalizeKernel, numBlocks, numThreads, 0, stream);
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace finalize
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace moe::dev
|