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fpA_intB_gemm_template.h
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fpA_intB_gemm_template.h
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/*
* Copyright (c) 2020-2023, 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.
*/
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wstrict-aliasing"
#include "cutlass/gemm/device/gemm_universal_base.h"
#include "cutlass/gemm/kernel/default_gemm.h"
#include "cutlass_extensions/compute_occupancy.h"
#include "cutlass_extensions/epilogue_helpers.h"
#include "cutlass_extensions/ft_gemm_configs.h"
#include "cutlass_extensions/gemm/kernel/default_fpA_intB_traits.h"
#include "cutlass_extensions/gemm/kernel/fpA_intB_gemm.h"
#include "cutlass_extensions/gemm/threadblock/default_mma.h"
#pragma GCC diagnostic pop
#include "src/fastertransformer/kernels/cutlass_kernels/cutlass_heuristic.h"
#include "src/fastertransformer/kernels/cutlass_kernels/fpA_intB_gemm/fpA_intB_gemm.h"
#include "src/fastertransformer/utils/cuda_utils.h"
namespace fastertransformer {
template<typename T,
typename WeightType,
typename arch,
typename EpilogueTag,
typename ThreadblockShape,
typename WarpShape,
int Stages>
void generic_mixed_gemm_kernelLauncher(const T* A,
const WeightType* B,
const T* weight_scales,
const T* biases,
T* C,
int m,
int n,
int k,
CutlassGemmConfig gemm_config,
char* workspace,
size_t workspace_bytes,
cudaStream_t stream,
int* occupancy = nullptr)
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
#ifdef BUILD_CUTLASS_MIXED_GEMM
#ifdef ENABLE_BF16
static_assert(cutlass::platform::is_same<T, __nv_bfloat16>::value || cutlass::platform::is_same<T, half>::value
|| cutlass::platform::is_same<T, float>::value,
"Specialized for bfloat16, half, float");
#else
static_assert(cutlass::platform::is_same<T, half>::value || cutlass::platform::is_same<T, float>::value,
"Specialized for half, float");
#endif
static_assert(cutlass::platform::is_same<T, WeightType>::value
|| cutlass::platform::is_same<WeightType, uint8_t>::value
|| cutlass::platform::is_same<WeightType, cutlass::uint4b_t>::value,
"");
// The cutlass type for the input elements. This is needed to convert to cutlass::half_t if necessary.
using ElementType_ =
typename cutlass::platform::conditional<cutlass::platform::is_same<T, half>::value, cutlass::half_t, T>::type;
#ifdef ENABLE_BF16
using ElementType =
typename cutlass::platform::conditional<cutlass::platform::is_same<ElementType_, __nv_bfloat16>::value,
cutlass::bfloat16_t,
ElementType_>::type;
#else
using ElementType = ElementType_;
#endif
using CutlassWeightType_ = typename cutlass::platform::
conditional<cutlass::platform::is_same<WeightType, half>::value, cutlass::half_t, WeightType>::type;
#ifdef ENABLE_BF16
using CutlassWeightType =
typename cutlass::platform::conditional<cutlass::platform::is_same<CutlassWeightType_, __nv_bfloat16>::value,
cutlass::bfloat16_t,
CutlassWeightType_>::type;
#else
using CutlassWeightType = CutlassWeightType_;
#endif
// We need separate config for each architecture since we will target different tensorcore instructions. For float,
// we do not target TCs.
using MixedGemmArchTraits = cutlass::gemm::kernel::MixedGemmArchTraits<ElementType, CutlassWeightType, arch>;
using ElementAccumulator = typename MixedGemmArchTraits::AccType;
using EpilogueOp =
typename Epilogue<ElementType, MixedGemmArchTraits::ElementsPerAccessC, ElementAccumulator, EpilogueTag>::Op;
using GemmKernel_ = typename cutlass::gemm::kernel::DefaultGemm<
ElementType,
cutlass::layout::RowMajor,
MixedGemmArchTraits::ElementsPerAccessA,
CutlassWeightType,
typename MixedGemmArchTraits::LayoutB,
MixedGemmArchTraits::ElementsPerAccessB,
ElementType,
cutlass::layout::RowMajor,
ElementAccumulator,
cutlass::arch::OpClassTensorOp,
arch,
ThreadblockShape,
WarpShape,
typename MixedGemmArchTraits::InstructionShape,
EpilogueOp,
typename cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
Stages,
true,
typename MixedGemmArchTraits::Operator>::GemmKernel;
using GemmKernel = cutlass::gemm::kernel::GemmFpAIntB<typename GemmKernel_::Mma,
typename GemmKernel_::Epilogue,
typename GemmKernel_::ThreadblockSwizzle,
arch, // Ensure top level arch is used for dispatch
GemmKernel_::kSplitKSerial>;
if (occupancy != nullptr) {
*occupancy = compute_occupancy_for_kernel<GemmKernel>();
return;
}
using Gemm = cutlass::gemm::device::GemmUniversalBase<GemmKernel>;
const int ldb =
cutlass::platform::is_same<cutlass::layout::RowMajor, typename MixedGemmArchTraits::LayoutB>::value ?
n :
k * GemmKernel::kInterleave;
typename Gemm::Arguments args({m, n, k},
{reinterpret_cast<ElementType*>(const_cast<T*>(A)), k},
{reinterpret_cast<CutlassWeightType*>(const_cast<WeightType*>(B)), ldb},
{reinterpret_cast<ElementType*>(const_cast<T*>(weight_scales)), 0},
{reinterpret_cast<ElementType*>(const_cast<T*>(biases)), 0},
{reinterpret_cast<ElementType*>(C), n},
gemm_config.split_k_factor,
{ElementAccumulator(1.f), ElementAccumulator(0.f)});
// This assertion is enabled because because for the column interleaved layout, K MUST be a multiple of
// threadblockK. The reason for this is that the default pitchlinear iterators are used to handle walking over the
// interleaved matrix. The way masking in handled in these do not map to the interleaved layout. We need to write
// our own predicated iterator in order to relax this limitation.
if (GemmKernel::kInterleave > 1
&& ((k % MixedGemmArchTraits::ThreadblockK)
|| ((k / gemm_config.split_k_factor) % MixedGemmArchTraits::ThreadblockK))) {
throw std::runtime_error("Temp assertion: k must be multiple of threadblockK");
}
Gemm gemm;
if (gemm.get_workspace_size(args) > workspace_bytes) {
FT_LOG_WARNING(
"Requested split-k but workspace size insufficient. Falling back to non-split-k implementation.");
// If requested split-k factor will require more workspace bytes, revert to standard gemm.
args.batch_count = 1;
}
auto can_implement = gemm.can_implement(args);
if (can_implement != cutlass::Status::kSuccess) {
std::string err_msg = "fpA_intB cutlass kernel will fail for params. Error: "
+ std::string(cutlassGetStatusString(can_implement));
throw std::runtime_error("[FT Error][fpA_intB Runner] " + err_msg);
}
auto init_status = gemm.initialize(args, workspace, stream);
if (init_status != cutlass::Status::kSuccess) {
std::string err_msg =
"Failed to initialize cutlass fpA_intB gemm. Error: " + std::string(cutlassGetStatusString(init_status));
throw std::runtime_error("[FT Error][fpA_intB Runner] " + err_msg);
}
auto run_status = gemm.run(stream);
if (run_status != cutlass::Status::kSuccess) {
std::string err_msg =
"Failed to run cutlass fpA_intB gemm. Error: " + std::string(cutlassGetStatusString(run_status));
throw std::runtime_error("[FT Error][fpA_intB Runner] " + err_msg);
}
#else
throw std::runtime_error(
"[FT Error][fpA_intB] FasterTransformer was built was mixed gemm support off. Please rebuild with cmake option -DBUILD_CUTLASS_MIXED_GEMM=ON");
#endif
}
template<typename T,
typename WeightType,
typename arch,
typename EpilogueTag,
typename ThreadblockShape,
typename WarpShape,
int Stages,
typename Enable = void>
struct dispatch_stages {
static void dispatch(const T* A,
const WeightType* B,
const T* weight_scales,
const T* biases,
T* C,
int m,
int n,
int k,
CutlassGemmConfig gemm_config,
char* workspace,
size_t workspace_bytes,
cudaStream_t stream,
int* occupancy = nullptr)
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
std::string err_msg = "Cutlass fpA_intB gemm. Not instantiates for arch "
+ std::to_string(arch::kMinComputeCapability) + " with stages set to "
+ std::to_string(Stages);
throw std::runtime_error("[FT Error][dispatch_stages::dispatch] " + err_msg);
}
};
template<typename T,
typename WeightType,
typename arch,
typename EpilogueTag,
typename ThreadblockShape,
typename WarpShape>
struct dispatch_stages<T, WeightType, arch, EpilogueTag, ThreadblockShape, WarpShape, 2> {
static void dispatch(const T* A,
const WeightType* B,
const T* weight_scales,
const T* biases,
T* C,
int m,
int n,
int k,
CutlassGemmConfig gemm_config,
char* workspace,
size_t workspace_bytes,
cudaStream_t stream,
int* occupancy = nullptr)
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
generic_mixed_gemm_kernelLauncher<T, WeightType, arch, EpilogueTag, ThreadblockShape, WarpShape, 2>(
A, B, weight_scales, biases, C, m, n, k, gemm_config, workspace, workspace_bytes, stream, occupancy);
}
};
template<typename T,
typename WeightType,
typename EpilogueTag,
typename ThreadblockShape,
typename WarpShape,
int Stages>
struct dispatch_stages<T,
WeightType,
cutlass::arch::Sm80,
EpilogueTag,
ThreadblockShape,
WarpShape,
Stages,
typename std::enable_if<(Stages > 2)>::type> {
static void dispatch(const T* A,
const WeightType* B,
const T* weight_scales,
const T* biases,
T* C,
int m,
int n,
int k,
CutlassGemmConfig gemm_config,
char* workspace,
size_t workspace_bytes,
cudaStream_t stream,
int* occupancy = nullptr)
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
generic_mixed_gemm_kernelLauncher<T,
WeightType,
cutlass::arch::Sm80,
EpilogueTag,
ThreadblockShape,
WarpShape,
Stages>(
A, B, weight_scales, biases, C, m, n, k, gemm_config, workspace, workspace_bytes, stream, occupancy);
}
};
template<typename T,
typename WeightType,
typename arch,
typename EpilogueTag,
typename ThreadblockShape,
typename WarpShape>
void dispatch_gemm_config(const T* A,
const WeightType* B,
const T* weight_scales,
const T* biases,
T* C,
int m,
int n,
int k,
CutlassGemmConfig gemm_config,
char* workspace,
size_t workspace_bytes,
cudaStream_t stream,
int* occupancy = nullptr)
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
switch (gemm_config.stages) {
case 2:
using DispatcherStages2 = dispatch_stages<T, WeightType, arch, EpilogueTag, ThreadblockShape, WarpShape, 2>;
DispatcherStages2::dispatch(
A, B, weight_scales, biases, C, m, n, k, gemm_config, workspace, workspace_bytes, stream, occupancy);
break;
case 3:
using DispatcherStages3 = dispatch_stages<T, WeightType, arch, EpilogueTag, ThreadblockShape, WarpShape, 3>;
DispatcherStages3::dispatch(
A, B, weight_scales, biases, C, m, n, k, gemm_config, workspace, workspace_bytes, stream, occupancy);
break;
case 4:
using DispatcherStages4 = dispatch_stages<T, WeightType, arch, EpilogueTag, ThreadblockShape, WarpShape, 4>;
DispatcherStages4::dispatch(
A, B, weight_scales, biases, C, m, n, k, gemm_config, workspace, workspace_bytes, stream, occupancy);
break;
default:
std::string err_msg = "dispatch_gemm_config does not support stages " + std::to_string(gemm_config.stages);
throw std::runtime_error("[FT Error][dispatch_gemm_config] " + err_msg);
break;
}
}
template<typename T, typename WeightType, typename arch, typename EpilogueTag>
void dispatch_gemm_to_cutlass(const T* A,
const WeightType* B,
const T* weight_scales,
const T* biases,
T* C,
int m,
int n,
int k,
char* workspace,
size_t workspace_bytes,
CutlassGemmConfig gemm_config,
cudaStream_t stream,
int* occupancy = nullptr)
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
// Note that SIMT configs are omitted here since they are not supported for fpA_intB.
// We also only instantiate configs here where threadblockShapeM == warpShapeM since those usually perform the best
// for mixed type gemms.
switch (gemm_config.tile_config) {
case CutlassTileConfig::CtaShape32x128x64_WarpShape32x32x64:
dispatch_gemm_config<T,
WeightType,
arch,
EpilogueTag,
cutlass::gemm::GemmShape<32, 128, 64>,
cutlass::gemm::GemmShape<32, 32, 64>>(
A, B, weight_scales, biases, C, m, n, k, gemm_config, workspace, workspace_bytes, stream, occupancy);
break;
case CutlassTileConfig::CtaShape64x128x64_WarpShape64x32x64:
dispatch_gemm_config<T,
WeightType,
arch,
EpilogueTag,
cutlass::gemm::GemmShape<64, 128, 64>,
cutlass::gemm::GemmShape<64, 32, 64>>(
A, B, weight_scales, biases, C, m, n, k, gemm_config, workspace, workspace_bytes, stream, occupancy);
break;
case CutlassTileConfig::CtaShape128x128x64_WarpShape128x32x64:
dispatch_gemm_config<T,
WeightType,
arch,
EpilogueTag,
cutlass::gemm::GemmShape<128, 128, 64>,
cutlass::gemm::GemmShape<128, 32, 64>>(
A, B, weight_scales, biases, C, m, n, k, gemm_config, workspace, workspace_bytes, stream, occupancy);
break;
case CutlassTileConfig::Undefined:
throw std::runtime_error("[FT Error][fpA_intB][dispatch_gemm_to_cutlass] gemm config undefined.");
break;
case CutlassTileConfig::ChooseWithHeuristic:
throw std::runtime_error(
"[FT Error][fpA_intB][dispatch_gemm_to_cutlass] gemm config should have already been set by heuristic.");
break;
default:
throw std::runtime_error(
"[FT Error][fpA_intB][dispatch_gemm_to_cutlass] Config is invalid for mixed type GEMM.");
break;
}
}
template<typename T, typename WeightType>
CutlassFpAIntBGemmRunner<T, WeightType>::CutlassFpAIntBGemmRunner()
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
int device{-1};
check_cuda_error(cudaGetDevice(&device));
sm_ = getSMVersion();
check_cuda_error(cudaDeviceGetAttribute(&multi_processor_count_, cudaDevAttrMultiProcessorCount, device));
}
template<typename T, typename WeightType>
CutlassFpAIntBGemmRunner<T, WeightType>::~CutlassFpAIntBGemmRunner()
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
}
template<typename T, typename WeightType>
template<typename EpilogueTag>
void CutlassFpAIntBGemmRunner<T, WeightType>::dispatch_to_arch<EpilogueTag>(const T* A,
const WeightType* B,
const T* weight_scales,
const T* biases,
T* C,
int m,
int n,
int k,
CutlassGemmConfig gemm_config,
char* workspace_ptr,
const size_t workspace_bytes,
cudaStream_t stream,
int* occupancy)
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
if (sm_ >= 70 && sm_ < 75) {
dispatch_gemm_to_cutlass<T, WeightType, cutlass::arch::Sm70, EpilogueTag>(
A, B, weight_scales, biases, C, m, n, k, workspace_ptr, workspace_bytes, gemm_config, stream, occupancy);
}
else if (sm_ >= 75 && sm_ < 80) {
dispatch_gemm_to_cutlass<T, WeightType, cutlass::arch::Sm75, EpilogueTag>(
A, B, weight_scales, biases, C, m, n, k, workspace_ptr, workspace_bytes, gemm_config, stream, occupancy);
}
else if (sm_ >= 80 && sm_ < 90) {
dispatch_gemm_to_cutlass<T, WeightType, cutlass::arch::Sm80, EpilogueTag>(
A, B, weight_scales, biases, C, m, n, k, workspace_ptr, workspace_bytes, gemm_config, stream, occupancy);
}
else {
throw std::runtime_error(
"[FT Error][CutlassFpAIntBGemmRunner][GEMM Dispatch] Arch unsupported for CUTLASS mixed type GEMM");
}
}
template<typename T, typename WeightType>
template<typename EpilogueTag>
void CutlassFpAIntBGemmRunner<T, WeightType>::run_gemm<EpilogueTag>(const T* A,
const WeightType* B,
const T* weight_scales,
const T* biases,
T* C,
int m,
int n,
int k,
char* workspace_ptr,
const size_t workspace_bytes,
cudaStream_t stream)
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
static constexpr bool is_weight_only = !std::is_same<T, WeightType>::value;
std::vector<CutlassGemmConfig> candidate_configs = get_candidate_configs(sm_, is_weight_only, false);
std::vector<int> occupancies(candidate_configs.size());
for (size_t ii = 0; ii < candidate_configs.size(); ++ii) {
dispatch_to_arch<EpilogueTag>(A,
B,
weight_scales,
biases,
C,
m,
n,
k,
candidate_configs[ii],
workspace_ptr,
workspace_bytes,
stream,
&occupancies[ii]);
}
// Standard GEMM, so 1 "expert". We use the same function for MoE and regular FFN.
static constexpr int num_experts = 1;
CutlassGemmConfig chosen_config = estimate_best_config_from_occupancies(candidate_configs,
occupancies,
m,
n,
k,
num_experts,
split_k_limit,
workspace_bytes,
multi_processor_count_,
is_weight_only);
dispatch_to_arch<EpilogueTag>(
A, B, weight_scales, biases, C, m, n, k, chosen_config, workspace_ptr, workspace_bytes, stream);
}
template<typename T, typename WeightType>
void CutlassFpAIntBGemmRunner<T, WeightType>::gemm_bias_act(const T* A,
const WeightType* B,
const T* weight_scales,
const T* biases,
T* C,
int m,
int n,
int k,
ActivationType activation_type,
char* workspace_ptr,
const size_t workspace_bytes,
cudaStream_t stream)
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
switch (activation_type) {
case ActivationType::Relu:
run_gemm<EpilogueOpBiasReLU>(
A, B, weight_scales, biases, C, m, n, k, workspace_ptr, workspace_bytes, stream);
break;
case ActivationType::Gelu:
run_gemm<EpilogueOpBiasFtGelu>(
A, B, weight_scales, biases, C, m, n, k, workspace_ptr, workspace_bytes, stream);
break;
case ActivationType::Silu:
run_gemm<EpilogueOpBiasSilu>(
A, B, weight_scales, biases, C, m, n, k, workspace_ptr, workspace_bytes, stream);
break;
case ActivationType::Identity:
run_gemm<EpilogueOpBias>(A, B, weight_scales, biases, C, m, n, k, workspace_ptr, workspace_bytes, stream);
break;
case ActivationType::InvalidType:
FT_CHECK_WITH_INFO(false, "Activation type for fpA_intB must be valid.");
break;
default: {
if (isGatedActivation(activation_type)) {
FT_CHECK_WITH_INFO(false, "Fused gated activations not supported");
}
else {
FT_CHECK_WITH_INFO(false, "Invalid activation type.");
}
}
}
}
template<typename T, typename WeightType>
void CutlassFpAIntBGemmRunner<T, WeightType>::gemm(const T* A,
const WeightType* B,
const T* weight_scales,
T* C,
int m,
int n,
int k,
char* workspace_ptr,
const size_t workspace_bytes,
cudaStream_t stream)
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
run_gemm<EpilogueOpNoBias>(A, B, weight_scales, nullptr, C, m, n, k, workspace_ptr, workspace_bytes, stream);
}
template<typename T, typename WeightType>
int CutlassFpAIntBGemmRunner<T, WeightType>::getWorkspaceSize(const int m, const int n, const int k)
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
// These are the min tile sizes for each config, which would launch the maximum number of blocks
const int max_grid_m = (m + 31) / 32;
const int max_grid_n = (n + 127) / 128;
// We need 4 bytes per block in the worst case. We launch split_k_limit in z dim.
return max_grid_m * max_grid_n * split_k_limit * 4;
}
// =============================== Specialization T == WeightType =======================================
template<typename WeightType>
void CutlassFpAIntBGemmRunner<float, WeightType>::gemm_bias_act(const float* A,
const WeightType* B,
const float* weight_scales,
const float* biases,
float* C,
int m,
int n,
int k,
ActivationType activation_type,
char* workspace_ptr,
const size_t workspace_bytes,
cudaStream_t stream)
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
FT_CHECK_WITH_INFO(false, "Attempting to run mixed gemm bias act when the types are the same is an error.");
}
template<typename WeightType>
void CutlassFpAIntBGemmRunner<float, WeightType>::gemm(const float* A,
const WeightType* B,
const float* weight_scales,
float* C,
int m,
int n,
int k,
char* workspace_ptr,
const size_t workspace_bytes,
cudaStream_t stream)
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
FT_CHECK_WITH_INFO(false, "Attempting to run mixed gemm when the types are the same is an error.");
}
template<typename WeightType>
int CutlassFpAIntBGemmRunner<float, WeightType>::getWorkspaceSize(const int m, const int n, const int k)
{
FT_LOG_DEBUG(__PRETTY_FUNCTION__);
return 0;
}
} // namespace fastertransformer