mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
9cae7277ea
Signed-off-by: Min Yu <171526537+yumin066@users.noreply.github.com> Signed-off-by: Anthony Chang <27950904+rosenrodt@users.noreply.github.com> Co-authored-by: Anthony Chang <27950904+rosenrodt@users.noreply.github.com>
1215 lines
65 KiB
C++
1215 lines
65 KiB
C++
/*
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* Copyright (c) 2022-2025, NVIDIA CORPORATION. All rights reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#if defined(USING_OSS_CUTLASS_MOE_GEMM)
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#include "tensorrt_llm/kernels/cutlass_kernels/include/moe_kernels.h"
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#else
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#include "moe_gemm_kernels.h"
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#include "moe_kernels.h"
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#endif
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// Always include the public header for moe_gemm_kernels.h
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#include "tensorrt_llm/kernels/cutlass_kernels/include/moe_gemm_kernels.h"
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#include "tensorrt_llm/common/config.h"
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#include "tensorrt_llm/common/workspace.h"
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#include "tensorrt_llm/kernels/cutlass_kernels/fp8_blockscale_gemm/fp8_blockscale_gemm.h"
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#include "tensorrt_llm/kernels/cutlass_kernels/include/cutlass_kernel_selector.h"
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#include "tensorrt_llm/runtime/torchUtils.h"
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#include "tensorrt_llm/thop/thUtils.h"
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#include <ATen/native/cuda/Resize.h>
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#include <functional>
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#include <map>
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#define C10_THROW_ERROR_FORMATTED(ErrorType, ...) \
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do \
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{ \
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std::ostringstream oss; \
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oss << __VA_ARGS__; \
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C10_THROW_ERROR(ErrorType, oss.str()); \
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} while (0)
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TRTLLM_NAMESPACE_BEGIN
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namespace torch_ext
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{
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namespace common = tensorrt_llm::common;
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namespace kernels = CUTLASS_MOE_GEMM_KERNELS_NAMESPACE;
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using ActivationParams = CUTLASS_MOE_GEMM_NAMESPACE::ActivationParams;
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using ActivationType = CUTLASS_MOE_GEMM_NAMESPACE::ActivationType;
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using MoeGemmId = CUTLASS_MOE_GEMM_NAMESPACE::MoeGemmId;
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// Always use public header as it is just utility functions and types
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using TmaWarpSpecializedGroupedGemmInput = tensorrt_llm::kernels::cutlass_kernels::TmaWarpSpecializedGroupedGemmInput;
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using profiler_backend = CUTLASS_MOE_GEMM_KERNELS_NAMESPACE::GemmProfilerBackend;
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class FusedMoeRunner : public torch::CustomClassHolder
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{
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public:
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template <typename TypeAct, typename TypeWeight, bool NeedQuant = false>
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std::unique_ptr<kernels::CutlassMoeFCRunnerInterface> switch_output_type(c10::ScalarType output_type)
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{
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switch (output_type)
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{
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case c10::ScalarType::Long: // INT64 == FP4
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case c10::ScalarType::Float8_e4m3fn:
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// TODO We need an atomic FP8 reduction for the finalize fusions
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C10_THROW_ERROR_FORMATTED(NotImplementedError,
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"Outputting " << torch::toString(output_type) << " directly is not currently supported");
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// return std::make_unique<kernels::CutlassMoeFCRunner<Type, Type>>();
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case c10::ScalarType::Half:
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if constexpr (NeedQuant)
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{
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return std::make_unique<kernels::CutlassMoeFCRunner<TypeAct, TypeWeight, half, half>>();
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}
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else
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{
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return std::make_unique<kernels::CutlassMoeFCRunner<TypeAct, TypeWeight, half, TypeAct>>();
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}
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#ifdef ENABLE_BF16
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case c10::ScalarType::BFloat16:
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if constexpr (NeedQuant)
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{
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return std::make_unique<
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kernels::CutlassMoeFCRunner<TypeAct, TypeWeight, __nv_bfloat16, __nv_bfloat16>>();
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}
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else
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{
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return std::make_unique<kernels::CutlassMoeFCRunner<TypeAct, TypeWeight, __nv_bfloat16, TypeAct>>();
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}
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#endif
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default:
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C10_THROW_ERROR_FORMATTED(Error,
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"Invalid output type " << torch::toString(output_type) << " specified for "
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<< torch::toString(mActivationDtype));
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}
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};
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template <typename TypeAct>
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std::unique_ptr<kernels::CutlassMoeFCRunnerInterface> create_weight_quant_runner()
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{
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if (isInt8Quant())
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{
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return std::make_unique<kernels::CutlassMoeFCRunner<TypeAct, uint8_t>>();
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}
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else if (isInt4Quant())
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{
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#ifdef ENABLE_FP8
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if (mUseW4GroupScaling)
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{
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return std::make_unique<
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kernels::CutlassMoeFCRunner<__nv_fp8_e4m3, cutlass::uint4b_t, TypeAct, TypeAct>>();
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}
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#endif
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return std::make_unique<kernels::CutlassMoeFCRunner<TypeAct, cutlass::uint4b_t>>();
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}
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else
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{
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C10_THROW_ERROR_FORMATTED(Error, "Unsupported weight quantization type");
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}
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}
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FusedMoeRunner(c10::ScalarType activation_dtype, c10::ScalarType weight_dtype, c10::ScalarType output_dtype,
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bool use_deepseek_fp8_block_scale, bool use_w4_group_scaling, bool use_int8_woq_per_channel,
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bool use_mxfp8_act_scaling, bool use_fused_finalize)
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{
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mActivationDtype = activation_dtype;
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mWeightDtype = weight_dtype;
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mOutputDtype = output_dtype;
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mUseDeepSeekFP8BlockScaling = use_deepseek_fp8_block_scale;
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mUseW4GroupScaling = use_w4_group_scaling;
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mUseINT8WoqPerChannel = use_int8_woq_per_channel;
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mUseMxfp8ActScaling = use_mxfp8_act_scaling;
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mUseFusedFinalize = use_fused_finalize;
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mInnerDimMultiplier = 1;
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// keep consistent with cpp/tensorrt_llm/plugins/mixtureOfExperts/mixtureOfExpertsPlugin.cpp
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if (mActivationDtype == c10::ScalarType::Half && mWeightDtype == c10::ScalarType::Half)
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{
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mKernelRunner = std::make_shared<kernels::CutlassMoeFCRunner<half, half>>();
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}
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#ifdef ENABLE_BF16
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else if (mActivationDtype == c10::ScalarType::BFloat16 && mWeightDtype == c10::ScalarType::BFloat16)
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{
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mKernelRunner = std::make_shared<kernels::CutlassMoeFCRunner<__nv_bfloat16, __nv_bfloat16>>();
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}
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#ifdef ENABLE_FP8
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else if (mActivationDtype == c10::ScalarType::BFloat16 && mWeightDtype == c10::ScalarType::Float8_e4m3fn)
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{
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mKernelRunner = std::make_unique<kernels::CutlassMoeFCRunner<__nv_bfloat16, __nv_fp8_e4m3>>();
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}
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#endif
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#endif
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#ifdef ENABLE_FP8
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if (isFp8Quant())
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{
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mKernelRunner = switch_output_type<__nv_fp8_e4m3, __nv_fp8_e4m3>(mOutputDtype);
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}
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#endif
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#ifdef ENABLE_FP4
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if (isWMxfp4AMxfp8Quant() || isWMxfp4AFp8Quant())
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{
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mInnerDimMultiplier = 16; // 16 FP4 -> 1 LONG
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mKernelRunner = switch_output_type<__nv_fp8_e4m3, __nv_fp4_e2m1>(mOutputDtype);
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}
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if (isNvfp4Quant())
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{
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mInnerDimMultiplier = 16; // 16 FP4 -> 1 LONG
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switch (mActivationDtype)
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{
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case c10::ScalarType::Half:
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#ifdef ENABLE_BF16
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case c10::ScalarType::BFloat16:
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#endif
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mKernelRunner = switch_output_type<__nv_fp4_e2m1, __nv_fp4_e2m1, true>(mOutputDtype);
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break;
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default: mKernelRunner = switch_output_type<__nv_fp4_e2m1, __nv_fp4_e2m1, false>(mOutputDtype);
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}
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}
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if (isWFP4A16Quant())
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{
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mInnerDimMultiplier = 2;
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if (mActivationDtype == c10::ScalarType::Half)
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{
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mKernelRunner = std::make_shared<kernels::CutlassMoeFCRunner<half, __nv_fp4_e2m1>>();
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}
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#ifdef ENABLE_BF16
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else if (mActivationDtype == c10::ScalarType::BFloat16)
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{
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mKernelRunner = std::make_shared<kernels::CutlassMoeFCRunner<__nv_bfloat16, __nv_fp4_e2m1>>();
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}
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#endif
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}
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#endif
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if (isIntWeightOnlyQuant())
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{
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if (isInt4Quant())
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{
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mInnerDimMultiplier = 2; // 2 INT4 -> 1 INT8
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}
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switch (mActivationDtype)
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{
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#ifdef ENABLE_FP8
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case c10::ScalarType::Float8_e4m3fn:
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{
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if (isInt4Quant() and mUseW4GroupScaling)
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{
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mKernelRunner = std::make_unique<
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kernels::CutlassMoeFCRunner<__nv_fp8_e4m3, cutlass::uint4b_t, __nv_bfloat16, __nv_fp8_e4m3>>();
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}
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else
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{
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C10_THROW_ERROR_FORMATTED(Error, "FP8 activation type is not supported for non-W4A8 quantization");
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}
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break;
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}
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#endif
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case c10::ScalarType::Half: mKernelRunner = create_weight_quant_runner<half>(); break;
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case c10::ScalarType::BFloat16: mKernelRunner = create_weight_quant_runner<__nv_bfloat16>(); break;
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default: C10_THROW_ERROR_FORMATTED(Error, "Unsupported activation type for int-type weight");
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}
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}
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if (!mKernelRunner)
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{
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C10_THROW_ERROR_FORMATTED(Error,
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"Could not construct fused moe op with the requested input combination Activation: "
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<< torch::toString(mActivationDtype) << ", Weight: " << torch::toString(mWeightDtype)
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<< ", Output: " << torch::toString(mOutputDtype));
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}
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mKernelRunner->use_fused_finalize_ = mUseFusedFinalize;
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mProfiler = std::make_shared<kernels::GemmProfilerBackend>();
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mGemm1Profiles = mKernelRunner->getTactics(MoeGemmId::GEMM_1);
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mGemm2Profiles = mKernelRunner->getTactics(MoeGemmId::GEMM_2);
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cuInit(0);
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}
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~FusedMoeRunner()
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{
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if (mProfileWorkspace != nullptr)
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{
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auto const cu_free_status = cudaFree(mProfileWorkspace);
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TORCH_CHECK(
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cu_free_status == cudaSuccess, "Can't free profile workspace during FusedMoeRunner destruction.");
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}
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}
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FusedMoeRunner(FusedMoeRunner const&) = delete;
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void operator=(FusedMoeRunner const&) = delete;
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torch::Tensor runMoe(torch::Tensor const& input, torch::Tensor const& token_selected_experts,
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torch::optional<torch::Tensor> const& token_final_scales, torch::Tensor const& fc1_expert_weights,
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torch::optional<torch::Tensor> const& fc1_expert_biases, torch::Tensor const& fc2_expert_weights,
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torch::optional<torch::Tensor> const& fc2_expert_biases,
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torch::optional<c10::ArrayRef<torch::Tensor>> const& quant_scales,
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torch::optional<torch::Tensor> const& input_sf, bool const swizzled_input_sf,
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torch::optional<torch::Tensor> const& swiglu_alpha, torch::optional<torch::Tensor> const& swiglu_beta,
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torch::optional<torch::Tensor> const& swiglu_limit, int64_t const tp_size, int64_t const tp_rank,
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int64_t const ep_size, int64_t const ep_rank, int64_t const cluster_size, int64_t const cluster_rank,
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bool const enable_alltoall, bool min_latency_mode, torch::optional<c10::ArrayRef<int64_t>> const& profile_ids,
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torch::optional<int64_t> const& activation_type, torch::optional<int64_t> const& unpadded_hidden_size,
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torch::optional<int64_t> const& num_valid_tokens, torch::optional<torch::Tensor> const& out_tensor)
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{
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std::lock_guard<std::mutex> lock(mMutex);
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// Free the profile workspace to save memory
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freeProfileWorkspace();
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TORCH_CHECK(cluster_size == 1 && cluster_rank == 0, "smart_router is supported in min_latency mode");
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CHECK_INPUT(input, mActivationDtype)
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CHECK_INPUT(token_selected_experts, at::ScalarType::Int)
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if (token_final_scales)
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{
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CHECK_INPUT(token_final_scales.value(), at::ScalarType::Float)
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}
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CHECK_INPUT(fc1_expert_weights, mWeightDtype)
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CHECK_INPUT(fc2_expert_weights, mWeightDtype)
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TORCH_CHECK(input.dim() == 2, "input must be 2D.");
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TORCH_CHECK(token_selected_experts.dim() == 2, "token_selected_experts must be 2D.");
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TORCH_CHECK(fc1_expert_weights.dim() == 3, "fc1_expert_weights must be 3D.");
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TORCH_CHECK(fc2_expert_weights.dim() == 3, "fc2_expert_weights must be 3D.");
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if (fc1_expert_biases.has_value() || fc2_expert_biases.has_value())
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{
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CHECK_INPUT(fc1_expert_biases.value(), mOutputDtype);
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CHECK_INPUT(fc2_expert_biases.value(), mOutputDtype);
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TORCH_CHECK(fc1_expert_biases.value().dim() == 2, "fc1_expert_biases must be 2D.");
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TORCH_CHECK(fc2_expert_biases.value().dim() == 2, "fc2_expert_biases must be 2D.");
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TORCH_CHECK(fc1_expert_weights.sizes()[0] == fc1_expert_biases.value().sizes()[0],
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"fc1_expert_weights and fc1_expert_biases must have the same number of experts.");
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TORCH_CHECK(fc2_expert_weights.sizes()[0] == fc2_expert_biases.value().sizes()[0],
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"fc2_expert_weights and fc2_expert_biases must have the same number of experts.");
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TORCH_CHECK(fc1_expert_biases.value().sizes()[1] == fc1_expert_weights.sizes()[1],
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"fc1_expert_biases should match fc1_expert_weights output shape.");
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TORCH_CHECK(fc2_expert_biases.value().sizes()[1] == fc2_expert_weights.sizes()[1],
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"fc2_expert_biases should match fc2_expert_weights output shape.");
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}
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if (fc1_expert_biases.has_value() || fc2_expert_biases.has_value())
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{
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CHECK_INPUT(fc1_expert_biases.value(), mOutputDtype);
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CHECK_INPUT(fc2_expert_biases.value(), mOutputDtype);
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TORCH_CHECK(fc1_expert_biases.value().dim() == 2, "fc1_expert_biases must be 2D.");
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TORCH_CHECK(fc2_expert_biases.value().dim() == 2, "fc2_expert_biases must be 2D.");
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TORCH_CHECK(fc1_expert_weights.sizes()[0] == fc1_expert_biases.value().sizes()[0],
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"fc1_expert_weights and fc1_expert_biases must have the same number of experts.");
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TORCH_CHECK(fc2_expert_weights.sizes()[0] == fc2_expert_biases.value().sizes()[0],
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"fc2_expert_weights and fc2_expert_biases must have the same number of experts.");
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TORCH_CHECK(fc1_expert_biases.value().sizes()[1] == fc1_expert_weights.sizes()[1],
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"fc1_expert_biases should match fc1_expert_weights output shape.");
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TORCH_CHECK(fc2_expert_biases.value().sizes()[1] == fc2_expert_weights.sizes()[1],
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"fc2_expert_biases should match fc2_expert_weights output shape.");
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}
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TORCH_CHECK(input.sizes()[0] == token_selected_experts.sizes()[0],
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"input and token_selected_experts must have the same num tokens.");
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if (token_final_scales)
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{
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TORCH_CHECK(token_final_scales.value().dim() == 2, "token_selected_experts_probs must be 2D.");
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TORCH_CHECK(input.sizes()[0] == token_final_scales.value().sizes()[0],
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"input and token_selected_experts_probs must have the same num tokens.");
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TORCH_CHECK(token_selected_experts.sizes()[1] == token_final_scales.value().sizes()[1],
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"token_selected_experts and token_final_scales must have the same number of experts per token.");
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}
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TORCH_CHECK(fc1_expert_weights.sizes()[0] == fc2_expert_weights.sizes()[0],
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"fc1_expert_weights and fc2_expert_weights must have the same number of experts.");
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ActivationType base_activation_type = activation_type.has_value()
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? static_cast<ActivationType>(activation_type.value())
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: ActivationType::Swiglu;
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if (mUseINT8WoqPerChannel)
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{
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// Note: The weight shape for INT8 weight only quantization is different, e.g., fc2_expert_weights:
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// [num_experts, inter_size, hidden_size]
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TORCH_CHECK(fc1_expert_weights.sizes()[2] == fc2_expert_weights.sizes()[1] * mInnerDimMultiplier * 2,
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"fc1_expert_weights inter size must be 2 times fc2_expert_weights inter size.");
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}
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else
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{
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if (isGatedActivation(base_activation_type))
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{
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TORCH_CHECK(fc1_expert_weights.sizes()[1] == fc2_expert_weights.sizes()[2] * mInnerDimMultiplier * 2,
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"fc1_expert_weights inter size must be 2 times fc2_expert_weights inter size.");
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}
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else
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{
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TORCH_CHECK(fc1_expert_weights.sizes()[1] == fc2_expert_weights.sizes()[2] * mInnerDimMultiplier,
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"fc1_expert_weights inter size must be equal to fc2_expert_weights inter size.");
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}
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}
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int experts_per_token = token_selected_experts.sizes()[1];
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int64_t num_rows = input.sizes()[0];
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int64_t hidden_size = fc2_expert_weights.sizes()[1];
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int64_t unpadded_hidden_size_val
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= unpadded_hidden_size.has_value() ? unpadded_hidden_size.value() : hidden_size;
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int64_t inter_size = fc2_expert_weights.sizes()[2] * mInnerDimMultiplier;
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if (mUseINT8WoqPerChannel)
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{
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// Note: The weight shape for INT8 weight only quantization is different, e.g., fc2_expert_weights:
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// [num_experts, inter_size, hidden_size]
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hidden_size = fc2_expert_weights.sizes()[2] * mInnerDimMultiplier;
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inter_size = fc2_expert_weights.sizes()[1];
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}
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if (isWMxfp4AMxfp8Quant() || isWMxfp4AFp8Quant())
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{
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// MXFP4 weights are required to bealigned to 128 bytes
|
|
TORCH_CHECK(hidden_size % 128 == 0, "hidden_size must be divisible by 128 for MXFP4 weights");
|
|
TORCH_CHECK(inter_size % 128 == 0, "inter_size must be divisible by 128 for MXFP4 weights");
|
|
}
|
|
else
|
|
{
|
|
// TMA requires at least 128 bit alignment
|
|
auto min_alignment
|
|
= 128 / (8 * std::min(c10::elementSize(mActivationDtype), c10::elementSize(mWeightDtype)));
|
|
TORCH_CHECK(hidden_size % min_alignment == 0, "hidden_size ", hidden_size, " must be divisible by ",
|
|
min_alignment, " for weights");
|
|
TORCH_CHECK(inter_size % min_alignment == 0, "inter_size ", inter_size, " must be divisible by ",
|
|
min_alignment, " for weights");
|
|
}
|
|
|
|
int const num_experts_on_rank = fc2_expert_weights.sizes()[0];
|
|
auto const num_experts_total = static_cast<int>(num_experts_on_rank * ep_size);
|
|
auto parallelism_config = kernels::MOEParallelismConfig(tp_size, tp_rank, ep_size, ep_rank);
|
|
|
|
if (swiglu_alpha.has_value())
|
|
{
|
|
CHECK_INPUT(swiglu_alpha.value(), at::ScalarType::Float);
|
|
TORCH_CHECK(swiglu_alpha.value().sizes()[0] == num_experts_on_rank,
|
|
"swiglu_alpha must have num_experts_on_rank elements.");
|
|
base_activation_type = ActivationType::SwigluBias;
|
|
}
|
|
if (swiglu_beta.has_value())
|
|
{
|
|
CHECK_INPUT(swiglu_beta.value(), at::ScalarType::Float);
|
|
TORCH_CHECK(swiglu_beta.value().sizes()[0] == num_experts_on_rank,
|
|
"swiglu_beta must have num_experts_on_rank elements.");
|
|
base_activation_type = ActivationType::SwigluBias;
|
|
}
|
|
if (swiglu_limit.has_value())
|
|
{
|
|
CHECK_INPUT(swiglu_limit.value(), at::ScalarType::Float);
|
|
TORCH_CHECK(swiglu_limit.value().sizes()[0] == num_experts_on_rank,
|
|
"swiglu_limit must have num_experts_on_rank elements.");
|
|
base_activation_type = ActivationType::SwigluBias;
|
|
}
|
|
auto activation_params = ActivationParams(base_activation_type,
|
|
reinterpret_cast<float const*>(swiglu_alpha.has_value() ? swiglu_alpha.value().const_data_ptr() : nullptr),
|
|
reinterpret_cast<float const*>(swiglu_beta.has_value() ? swiglu_beta.value().const_data_ptr() : nullptr),
|
|
reinterpret_cast<float const*>(swiglu_limit.has_value() ? swiglu_limit.value().const_data_ptr() : nullptr));
|
|
|
|
setRunnerProfiles(profile_ids);
|
|
|
|
auto stream = at::cuda::getCurrentCUDAStream(input.get_device());
|
|
|
|
std::vector<int64_t> output_shape = {num_rows, unpadded_hidden_size_val};
|
|
torch::Tensor output;
|
|
if (out_tensor.has_value())
|
|
{
|
|
auto const& provided = out_tensor.value();
|
|
CHECK_INPUT(provided, mOutputDtype);
|
|
TORCH_CHECK(provided.sizes() == output_shape, "Provided out tensor has incorrect shape. Expected ",
|
|
output_shape, ", got ", provided.sizes());
|
|
output = provided;
|
|
}
|
|
else
|
|
{
|
|
output = torch::empty(output_shape, input.options().dtype(mOutputDtype));
|
|
}
|
|
|
|
WorkspaceInfo const& workspace_info = getWorkspaceInfo(num_rows, hidden_size, inter_size, num_experts_total,
|
|
static_cast<int>(experts_per_token), base_activation_type, parallelism_config, min_latency_mode, stream);
|
|
|
|
auto const quant_params
|
|
= getQuantParams(num_experts_on_rank, hidden_size, inter_size, quant_scales, base_activation_type);
|
|
kernels::MoeMinLatencyParams min_latency_params{};
|
|
|
|
// TODO: support lora in the future
|
|
::tensorrt_llm::kernels::LoraParams lora_params{};
|
|
#ifdef USING_OSS_CUTLASS_MOE_GEMM
|
|
mKernelRunner->runMoe(input.const_data_ptr(),
|
|
input_sf.has_value() ? input_sf.value().const_data_ptr() : nullptr, swizzled_input_sf,
|
|
reinterpret_cast<int const*>(token_selected_experts.const_data_ptr()),
|
|
token_final_scales.has_value() ? reinterpret_cast<float const*>(token_final_scales.value().const_data_ptr())
|
|
: nullptr,
|
|
fc1_expert_weights.const_data_ptr(),
|
|
fc1_expert_biases.has_value() ? fc1_expert_biases.value().const_data_ptr() : nullptr, activation_params,
|
|
fc2_expert_weights.const_data_ptr(),
|
|
fc2_expert_biases.has_value() ? fc2_expert_biases.value().const_data_ptr() : nullptr, quant_params,
|
|
num_rows, num_valid_tokens.has_value() ? num_valid_tokens.value() : num_rows, hidden_size,
|
|
unpadded_hidden_size_val, inter_size, num_experts_total, static_cast<int>(experts_per_token),
|
|
static_cast<char*>(workspace_info.workspace.data_ptr()), output.data_ptr(),
|
|
static_cast<int*>(workspace_info.src_to_dest_map), parallelism_config, enable_alltoall, false, lora_params,
|
|
mUseDeepSeekFP8BlockScaling, min_latency_mode, min_latency_params, stream);
|
|
#else
|
|
mKernelRunner->runMoe(input.const_data_ptr(),
|
|
input_sf.has_value() ? input_sf.value().const_data_ptr() : nullptr, swizzled_input_sf,
|
|
reinterpret_cast<int const*>(token_selected_experts.const_data_ptr()),
|
|
token_final_scales.has_value() ? reinterpret_cast<float const*>(token_final_scales.value().const_data_ptr())
|
|
: nullptr,
|
|
fc1_expert_weights.const_data_ptr(),
|
|
fc1_expert_biases.has_value() ? fc1_expert_biases.value().const_data_ptr() : nullptr, activation_params,
|
|
fc2_expert_weights.const_data_ptr(),
|
|
fc2_expert_biases.has_value() ? fc2_expert_biases.value().const_data_ptr() : nullptr, quant_params,
|
|
num_rows, num_valid_tokens.has_value() ? num_valid_tokens.value() : num_rows, hidden_size, inter_size,
|
|
num_experts_total, static_cast<int>(experts_per_token),
|
|
static_cast<char*>(workspace_info.workspace.data_ptr()), output.data_ptr(),
|
|
static_cast<int*>(workspace_info.src_to_dest_map), parallelism_config, false, lora_params,
|
|
mUseDeepSeekFP8BlockScaling, min_latency_mode, min_latency_params, stream);
|
|
#endif
|
|
|
|
return output;
|
|
}
|
|
|
|
std::tuple<torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor> runMoeMinLantency(torch::Tensor const& input,
|
|
torch::Tensor const& token_selected_experts, torch::optional<torch::Tensor> const& token_final_scales,
|
|
torch::Tensor const& fc1_expert_weights, torch::optional<torch::Tensor> const& fc1_expert_biases,
|
|
torch::Tensor const& fc2_expert_weights, torch::optional<torch::Tensor> const& fc2_expert_biases,
|
|
torch::optional<c10::ArrayRef<torch::Tensor>> const& quant_scales,
|
|
torch::optional<torch::Tensor> const& input_sf, bool const swizzled_input_sf,
|
|
torch::optional<torch::Tensor> const& swiglu_alpha, torch::optional<torch::Tensor> const& swiglu_beta,
|
|
torch::optional<torch::Tensor> const& swiglu_limit, int64_t const tp_size, int64_t const tp_rank,
|
|
int64_t const ep_size, int64_t const ep_rank, int64_t const cluster_size, int64_t const cluster_rank,
|
|
bool const enable_alltoall, bool min_latency_mode, torch::optional<c10::ArrayRef<int64_t>> const& profile_ids,
|
|
torch::optional<int64_t> const& activation_type, torch::optional<int64_t> const& unpadded_hidden_size,
|
|
torch::optional<int64_t> const& num_valid_tokens, torch::optional<torch::Tensor> const& out_tensor)
|
|
{
|
|
std::lock_guard<std::mutex> lock(mMutex);
|
|
|
|
// Free the profile workspace to save memory
|
|
freeProfileWorkspace();
|
|
|
|
CHECK_INPUT(input, mActivationDtype)
|
|
CHECK_INPUT(token_selected_experts, at::ScalarType::Int)
|
|
if (token_final_scales)
|
|
{
|
|
CHECK_INPUT(token_final_scales.value(), at::ScalarType::Float)
|
|
}
|
|
CHECK_INPUT(fc1_expert_weights, mWeightDtype)
|
|
CHECK_INPUT(fc2_expert_weights, mWeightDtype)
|
|
|
|
TORCH_CHECK(input.dim() == 2, "input must be 2D.");
|
|
TORCH_CHECK(token_selected_experts.dim() == 2, "token_selected_experts must be 2D.");
|
|
|
|
TORCH_CHECK(fc1_expert_weights.dim() == 3, "fc1_expert_weights must be 3D.");
|
|
TORCH_CHECK(fc2_expert_weights.dim() == 3, "fc2_expert_weights must be 3D.");
|
|
|
|
if (fc1_expert_biases.has_value() || fc2_expert_biases.has_value())
|
|
{
|
|
CHECK_INPUT(fc1_expert_biases.value(), mOutputDtype);
|
|
CHECK_INPUT(fc2_expert_biases.value(), mOutputDtype);
|
|
TORCH_CHECK(fc1_expert_biases.value().dim() == 2, "fc1_expert_biases must be 2D.");
|
|
TORCH_CHECK(fc2_expert_biases.value().dim() == 2, "fc2_expert_biases must be 2D.");
|
|
TORCH_CHECK(fc1_expert_weights.sizes()[0] == fc1_expert_biases.value().sizes()[0],
|
|
"fc1_expert_weights and fc1_expert_biases must have the same number of experts.");
|
|
TORCH_CHECK(fc2_expert_weights.sizes()[0] == fc2_expert_biases.value().sizes()[0],
|
|
"fc2_expert_weights and fc2_expert_biases must have the same number of experts.");
|
|
TORCH_CHECK(fc1_expert_biases.value().sizes()[1] == fc1_expert_weights.sizes()[1],
|
|
"fc1_expert_biases should match fc1_expert_weights output shape.");
|
|
TORCH_CHECK(fc2_expert_biases.value().sizes()[1] == fc2_expert_weights.sizes()[1],
|
|
"fc2_expert_biases should match fc2_expert_weights output shape.");
|
|
}
|
|
|
|
TORCH_CHECK(input.sizes()[0] == token_selected_experts.sizes()[0],
|
|
"input and token_selected_experts must have the same num tokens.");
|
|
if (token_final_scales)
|
|
{
|
|
TORCH_CHECK(token_final_scales.value().dim() == 2, "token_selected_experts_probs must be 2D.");
|
|
TORCH_CHECK(input.sizes()[0] == token_final_scales.value().sizes()[0],
|
|
"input and token_selected_experts_probs must have the same num tokens.");
|
|
TORCH_CHECK(token_selected_experts.sizes()[1] == token_final_scales.value().sizes()[1],
|
|
"token_selected_experts and token_final_scales must have the same number of experts per token.");
|
|
}
|
|
TORCH_CHECK(fc1_expert_weights.sizes()[0] == fc2_expert_weights.sizes()[0],
|
|
"fc1_expert_weights and fc2_expert_weights must have the same number of experts.");
|
|
TORCH_CHECK(fc1_expert_weights.sizes()[1] == fc2_expert_weights.sizes()[2] * mInnerDimMultiplier * 2,
|
|
"fc1_expert_weights inter size must be 2 times fc2_expert_weights inter size.");
|
|
|
|
TORCH_CHECK(!input_sf.has_value() || isWMxfp4AMxfp8Quant() || isNvfp4Quant(),
|
|
"Block-scaling factors provided for non block-scaling quantization");
|
|
|
|
int experts_per_token = token_selected_experts.sizes()[1];
|
|
int64_t num_rows = input.sizes()[0];
|
|
int64_t hidden_size = fc2_expert_weights.sizes()[1];
|
|
int64_t unpadded_hidden_size_val
|
|
= unpadded_hidden_size.has_value() ? unpadded_hidden_size.value() : hidden_size;
|
|
int64_t inter_size = fc2_expert_weights.sizes()[2] * mInnerDimMultiplier;
|
|
int const num_experts_on_rank = fc2_expert_weights.sizes()[0];
|
|
auto const num_experts_total = static_cast<int>(num_experts_on_rank * ep_size);
|
|
auto parallelism_config
|
|
= kernels::MOEParallelismConfig(tp_size, tp_rank, ep_size, ep_rank, cluster_size, cluster_rank);
|
|
ActivationType base_activation_type = activation_type.has_value()
|
|
? static_cast<ActivationType>(activation_type.value())
|
|
: ActivationType::Swiglu;
|
|
if (swiglu_alpha.has_value())
|
|
{
|
|
CHECK_INPUT(swiglu_alpha.value(), at::ScalarType::Float);
|
|
TORCH_CHECK(swiglu_alpha.value().sizes()[0] == num_experts_on_rank,
|
|
"swiglu_alpha must have num_experts_on_rank elements.");
|
|
base_activation_type = ActivationType::SwigluBias;
|
|
}
|
|
if (swiglu_beta.has_value())
|
|
{
|
|
CHECK_INPUT(swiglu_beta.value(), at::ScalarType::Float);
|
|
TORCH_CHECK(swiglu_beta.value().sizes()[0] == num_experts_on_rank,
|
|
"swiglu_beta must have num_experts_on_rank elements.");
|
|
base_activation_type = ActivationType::SwigluBias;
|
|
}
|
|
if (swiglu_limit.has_value())
|
|
{
|
|
CHECK_INPUT(swiglu_limit.value(), at::ScalarType::Float);
|
|
TORCH_CHECK(swiglu_limit.value().sizes()[0] == num_experts_on_rank,
|
|
"swiglu_limit must have num_experts_on_rank elements.");
|
|
base_activation_type = ActivationType::SwigluBias;
|
|
}
|
|
auto activation_params = ActivationParams(base_activation_type,
|
|
reinterpret_cast<float const*>(swiglu_alpha.has_value() ? swiglu_alpha.value().const_data_ptr() : nullptr),
|
|
reinterpret_cast<float const*>(swiglu_beta.has_value() ? swiglu_beta.value().const_data_ptr() : nullptr),
|
|
reinterpret_cast<float const*>(swiglu_limit.has_value() ? swiglu_limit.value().const_data_ptr() : nullptr));
|
|
|
|
setRunnerProfiles(profile_ids);
|
|
|
|
auto stream = at::cuda::getCurrentCUDAStream(input.get_device());
|
|
|
|
std::vector<int64_t> output_shape = {num_rows * num_experts_on_rank, unpadded_hidden_size_val};
|
|
torch::Tensor output;
|
|
if (out_tensor.has_value())
|
|
{
|
|
auto const& provided = out_tensor.value();
|
|
CHECK_INPUT(provided, mOutputDtype);
|
|
TORCH_CHECK(provided.sizes() == output_shape, "Provided out tensor has incorrect shape. Expected ",
|
|
output_shape, ", got ", provided.sizes());
|
|
output = provided;
|
|
}
|
|
else
|
|
{
|
|
output = torch::empty(output_shape, input.options().dtype(mOutputDtype));
|
|
}
|
|
|
|
auto num_active_experts_per_node = torch::empty({1}, input.options().dtype(at::ScalarType::Int));
|
|
auto experts_to_token_score
|
|
= torch::empty({num_experts_on_rank, num_rows}, input.options().dtype(at::ScalarType::Float));
|
|
auto active_expert_global_ids = torch::empty({num_experts_on_rank}, input.options().dtype(at::ScalarType::Int));
|
|
|
|
kernels::MoeMinLatencyParams min_latency_params{};
|
|
min_latency_params.num_active_experts_per_node = static_cast<int*>(num_active_experts_per_node.data_ptr());
|
|
min_latency_params.experts_to_token_score = static_cast<float*>(experts_to_token_score.data_ptr());
|
|
min_latency_params.active_expert_global_ids = static_cast<int*>(active_expert_global_ids.data_ptr());
|
|
|
|
WorkspaceInfo const& workspace_info = getWorkspaceInfo(num_rows, hidden_size, inter_size, num_experts_total,
|
|
static_cast<int>(experts_per_token), base_activation_type, parallelism_config, min_latency_mode, stream);
|
|
|
|
auto const quant_params
|
|
= getQuantParams(num_experts_on_rank, hidden_size, inter_size, quant_scales, base_activation_type);
|
|
|
|
// TODO: support lora in the future
|
|
::tensorrt_llm::kernels::LoraParams lora_params{};
|
|
#ifdef USING_OSS_CUTLASS_MOE_GEMM
|
|
mKernelRunner->runMoe(input.const_data_ptr(),
|
|
input_sf.has_value() ? input_sf.value().const_data_ptr() : nullptr, swizzled_input_sf,
|
|
reinterpret_cast<int const*>(token_selected_experts.const_data_ptr()),
|
|
token_final_scales.has_value() ? reinterpret_cast<float const*>(token_final_scales.value().const_data_ptr())
|
|
: nullptr,
|
|
fc1_expert_weights.const_data_ptr(),
|
|
fc1_expert_biases.has_value() ? fc1_expert_biases.value().const_data_ptr() : nullptr, activation_params,
|
|
fc2_expert_weights.const_data_ptr(),
|
|
fc2_expert_biases.has_value() ? fc2_expert_biases.value().const_data_ptr() : nullptr, quant_params,
|
|
num_rows, num_valid_tokens.has_value() ? num_valid_tokens.value() : num_rows, hidden_size,
|
|
unpadded_hidden_size_val, inter_size, num_experts_total, static_cast<int>(experts_per_token),
|
|
static_cast<char*>(workspace_info.workspace.data_ptr()), output.data_ptr(),
|
|
static_cast<int*>(workspace_info.src_to_dest_map), parallelism_config, enable_alltoall, false, lora_params,
|
|
mUseDeepSeekFP8BlockScaling, min_latency_mode, min_latency_params, stream);
|
|
#else
|
|
mKernelRunner->runMoe(input.const_data_ptr(),
|
|
input_sf.has_value() ? input_sf.value().const_data_ptr() : nullptr, swizzled_input_sf,
|
|
reinterpret_cast<int const*>(token_selected_experts.const_data_ptr()),
|
|
token_final_scales.has_value() ? reinterpret_cast<float const*>(token_final_scales.value().const_data_ptr())
|
|
: nullptr,
|
|
fc1_expert_weights.const_data_ptr(),
|
|
fc1_expert_biases.has_value() ? fc1_expert_biases.value().const_data_ptr() : nullptr, activation_params,
|
|
fc2_expert_weights.const_data_ptr(),
|
|
fc2_expert_biases.has_value() ? fc2_expert_biases.value().const_data_ptr() : nullptr, quant_params,
|
|
num_rows, num_valid_tokens.has_value() ? num_valid_tokens.value() : num_rows, hidden_size, inter_size,
|
|
num_experts_total, static_cast<int>(experts_per_token),
|
|
static_cast<char*>(workspace_info.workspace.data_ptr()), output.data_ptr(),
|
|
static_cast<int*>(workspace_info.src_to_dest_map), parallelism_config, false, lora_params,
|
|
mUseDeepSeekFP8BlockScaling, min_latency_mode, min_latency_params, stream);
|
|
#endif
|
|
|
|
return std::make_tuple(output, num_active_experts_per_node, experts_to_token_score, active_expert_global_ids);
|
|
}
|
|
|
|
int64_t getTacticNum(int64_t const gemm_idx)
|
|
{
|
|
std::lock_guard<std::mutex> lock(mMutex);
|
|
TORCH_CHECK(gemm_idx == 1 || gemm_idx == 2, "gemm_idx must be 1 or 2");
|
|
return (gemm_idx == 1) ? mGemm1Profiles.size() : mGemm2Profiles.size();
|
|
}
|
|
|
|
// TODO Update this to be able to tell if we are profiling swiglu bias
|
|
void runGemmProfile(torch::Tensor const& input, torch::Tensor const& fc1_expert_weights,
|
|
torch::optional<torch::Tensor> const& fc1_expert_biases, torch::Tensor const& fc2_expert_weights,
|
|
torch::optional<torch::Tensor> const& fc2_expert_biases, int64_t const top_k, int64_t const tp_size,
|
|
int64_t const tp_rank, int64_t const ep_size, int64_t const ep_rank, int64_t const cluster_size,
|
|
int64_t const cluster_rank, bool const enable_alltoall, bool const min_latency_mode, int64_t const gemm_idx,
|
|
int64_t const profile_id, bool const do_preparation, int64_t const activation_type_int,
|
|
int64_t const unpadded_hidden_size)
|
|
{
|
|
std::lock_guard<std::mutex> lock(mMutex);
|
|
|
|
// TODO: support profiling under fp8 block scaling in the future
|
|
if (mUseDeepSeekFP8BlockScaling)
|
|
{
|
|
return;
|
|
}
|
|
ActivationType activation_type = static_cast<ActivationType>(activation_type_int);
|
|
|
|
int64_t const num_rows = input.sizes()[0];
|
|
int64_t hidden_size = fc2_expert_weights.sizes()[1];
|
|
int64_t inter_size = fc2_expert_weights.sizes()[2] * mInnerDimMultiplier;
|
|
if (mUseINT8WoqPerChannel)
|
|
{
|
|
// Note: The weight shape for INT8 weight only quantization is different, e.g., fc2_expert_weights:
|
|
// [num_experts, inter_size, hidden_size]
|
|
hidden_size = fc2_expert_weights.sizes()[2] * mInnerDimMultiplier;
|
|
inter_size = fc2_expert_weights.sizes()[1];
|
|
}
|
|
int64_t const group_size_
|
|
= isInt4Quant() ? TmaWarpSpecializedGroupedGemmInput::INT4GroupwiseParams::int4_group_size : -1;
|
|
int64_t const group_size = isWFP4A16Quant()
|
|
? TmaWarpSpecializedGroupedGemmInput::INT4GroupwiseParams::wfp4a16_group_size
|
|
: group_size_;
|
|
int const num_experts = static_cast<int>(fc2_expert_weights.sizes()[0] * ep_size);
|
|
|
|
auto const gemm_to_profile
|
|
= (gemm_idx == 1) ? profiler_backend::GemmToProfile::GEMM_1 : profiler_backend::GemmToProfile::GEMM_2;
|
|
auto const& profiles = (gemm_idx == 1) ? mGemm1Profiles : mGemm2Profiles;
|
|
|
|
// Get specific profile configs according to the profile_id.
|
|
// Fallback tactic is set to be 0
|
|
// TODO: use the best tactic id found offline for a better default inference perf
|
|
auto const& profile = profile_id == -1 ? profiles.front() : profiles[profile_id];
|
|
|
|
auto stream = at::cuda::getCurrentCUDAStream(input.get_device());
|
|
|
|
auto const* expert_weights_ptr
|
|
= (gemm_idx == 1) ? fc1_expert_weights.const_data_ptr() : fc2_expert_weights.const_data_ptr();
|
|
|
|
// Preparation phase, only enabled during autotuning warmup phase.
|
|
if (do_preparation)
|
|
{
|
|
// Set profiled gemm idx
|
|
mProfiler->mGemmToProfile = gemm_to_profile;
|
|
|
|
// mProfiler init
|
|
auto parallelism_config = kernels::MOEParallelismConfig(static_cast<int>(tp_size),
|
|
static_cast<int>(tp_rank), static_cast<int>(ep_size), static_cast<int>(ep_rank),
|
|
static_cast<int>(cluster_size), static_cast<int>(cluster_rank));
|
|
|
|
bool const USE_BIAS = fc1_expert_biases.has_value() || fc2_expert_biases.has_value();
|
|
bool const USE_LORA = false;
|
|
auto activation_dtype
|
|
= (mUseW4GroupScaling && !isWFP4A16Quant()) ? at::ScalarType::Float8_e4m3fn : mActivationDtype;
|
|
activation_dtype = isNvfp4Quant() ? at::ScalarType::Long : activation_dtype;
|
|
#ifdef USING_OSS_CUTLASS_MOE_GEMM
|
|
mProfiler->init(*mKernelRunner.get(), mProfiler->mGemmToProfile,
|
|
tensorrt_llm::runtime::TorchUtils::dataType(activation_dtype),
|
|
tensorrt_llm::runtime::TorchUtils::dataType(mWeightDtype),
|
|
tensorrt_llm::runtime::TorchUtils::dataType(mOutputDtype), num_experts, static_cast<int>(top_k),
|
|
hidden_size, unpadded_hidden_size > 0 ? unpadded_hidden_size : hidden_size, inter_size, group_size,
|
|
activation_type, USE_BIAS, USE_LORA, min_latency_mode,
|
|
/*need_weights*/ false, parallelism_config, enable_alltoall);
|
|
#else
|
|
mProfiler->init(*mKernelRunner.get(), mProfiler->mGemmToProfile,
|
|
tensorrt_llm::runtime::TorchUtils::dataType(activation_dtype),
|
|
tensorrt_llm::runtime::TorchUtils::dataType(mWeightDtype),
|
|
tensorrt_llm::runtime::TorchUtils::dataType(mOutputDtype), num_experts, static_cast<int>(top_k),
|
|
hidden_size, inter_size, group_size, activation_type, USE_BIAS, USE_LORA, min_latency_mode,
|
|
/*need_weights*/ false, parallelism_config);
|
|
#endif
|
|
|
|
freeProfileWorkspace();
|
|
size_t profile_workspace_size = mProfiler->getWorkspaceSize(num_rows);
|
|
auto const cu_malloc_status = cudaMalloc(&mProfileWorkspace, profile_workspace_size);
|
|
TORCH_CHECK(cu_malloc_status == cudaSuccess, "Can't allocate profile workspace for MoE GEMM profile.");
|
|
|
|
mProfiler->prepare(num_rows, mProfileWorkspace, expert_weights_ptr, stream);
|
|
}
|
|
|
|
// Profile specific tactic. Assuming at least one preparation phase has been executed already.
|
|
mProfiler->runProfiler(num_rows, profile, mProfileWorkspace, expert_weights_ptr, stream);
|
|
}
|
|
|
|
private:
|
|
struct WorkspaceInfo
|
|
{
|
|
torch::Tensor workspace{};
|
|
void* src_to_dest_map{};
|
|
};
|
|
|
|
std::mutex mMutex;
|
|
std::shared_ptr<kernels::CutlassMoeFCRunnerInterface> mKernelRunner;
|
|
std::shared_ptr<kernels::GemmProfilerBackend> mProfiler;
|
|
c10::ScalarType mActivationDtype;
|
|
c10::ScalarType mWeightDtype;
|
|
c10::ScalarType mOutputDtype;
|
|
// number of elements packed into the inner dimension of a matrix
|
|
// e.g. 16 nvfp4 elements are packed into a single int64 element
|
|
int64_t mInnerDimMultiplier;
|
|
char* mProfileWorkspace = nullptr;
|
|
std::map<cudaStream_t, WorkspaceInfo> mStreamWorkspaces;
|
|
|
|
bool mUseDeepSeekFP8BlockScaling = false;
|
|
bool mUseW4GroupScaling = false;
|
|
bool mUseINT8WoqPerChannel = false;
|
|
bool mUseMxfp8ActScaling = false;
|
|
bool mUseFusedFinalize = true;
|
|
|
|
using Profile = tensorrt_llm::cutlass_extensions::CutlassGemmConfig;
|
|
std::vector<Profile> mGemm1Profiles;
|
|
std::vector<Profile> mGemm2Profiles;
|
|
|
|
void freeProfileWorkspace()
|
|
{
|
|
if (mProfileWorkspace != nullptr)
|
|
{
|
|
auto const cu_free_status = cudaFree(mProfileWorkspace);
|
|
TORCH_CHECK(cu_free_status == cudaSuccess,
|
|
"Can't free profile workspace for MoE GEMM profile during memory reallocation.");
|
|
mProfileWorkspace = nullptr;
|
|
}
|
|
}
|
|
|
|
void setRunnerProfiles(torch::optional<c10::ArrayRef<int64_t>> profile_ids)
|
|
{
|
|
if (mUseDeepSeekFP8BlockScaling)
|
|
{
|
|
auto config = tensorrt_llm::cutlass_extensions::CutlassGemmConfig(
|
|
tensorrt_llm::cutlass_extensions::CutlassTileConfigSM90::CtaShape128x16x128B,
|
|
tensorrt_llm::cutlass_extensions::MainloopScheduleType::AUTO,
|
|
tensorrt_llm::cutlass_extensions::EpilogueScheduleType::AUTO,
|
|
tensorrt_llm::cutlass_extensions::ClusterShape::ClusterShape_1x1x1);
|
|
mKernelRunner->setTactic(config, config);
|
|
return;
|
|
}
|
|
|
|
auto best_gemm1_profile = mGemm1Profiles.front();
|
|
auto best_gemm2_profile = mGemm2Profiles.front();
|
|
if (profile_ids.has_value())
|
|
{
|
|
TORCH_CHECK(profile_ids.value().size() == 2, "Expecting 2 profile ids");
|
|
best_gemm1_profile
|
|
= profile_ids.value()[0] == -1 ? best_gemm1_profile : mGemm1Profiles.at(profile_ids.value()[0]);
|
|
best_gemm2_profile
|
|
= profile_ids.value()[1] == -1 ? best_gemm2_profile : mGemm2Profiles.at(profile_ids.value()[1]);
|
|
}
|
|
mKernelRunner->setTactic(best_gemm1_profile, best_gemm2_profile);
|
|
}
|
|
|
|
WorkspaceInfo const& getWorkspaceInfo(int64_t const num_rows, int64_t const hidden_size, int64_t const inter_size,
|
|
int num_experts, int experts_per_token, ActivationType activation_type,
|
|
kernels::MOEParallelismConfig const& parallelismConfig, bool min_latency_mode, cudaStream_t stream)
|
|
{
|
|
size_t moe_workspace_size = mKernelRunner->getWorkspaceSize(num_rows, hidden_size, inter_size, num_experts,
|
|
experts_per_token, activation_type, parallelismConfig, /* use_lora */ false, mUseDeepSeekFP8BlockScaling,
|
|
min_latency_mode, mUseW4GroupScaling);
|
|
size_t src_to_dest_map_size = experts_per_token * num_rows * sizeof(int);
|
|
auto& workspace_info = mStreamWorkspaces[stream];
|
|
|
|
std::vector<size_t> workspaces{moe_workspace_size, src_to_dest_map_size};
|
|
|
|
int64_t const total_workspace_size = common::calculateTotalWorkspaceSize(workspaces.data(), workspaces.size());
|
|
|
|
bool is_capturing = tensorrt_llm::common::isCapturing(stream);
|
|
// Always allocate workspace when capturing cuda graph to avoid illegal memory access during replay
|
|
if (is_capturing || workspace_info.workspace.numel() < total_workspace_size)
|
|
{
|
|
if (is_capturing)
|
|
{
|
|
TLLM_LOG_DEBUG(
|
|
"Allocating MoE workspace with %ld bytes size during cuda graph capture", total_workspace_size);
|
|
}
|
|
else
|
|
{
|
|
TLLM_LOG_DEBUG("MoE workspace size is not enough, increase the size from %ld bytes to %ld bytes",
|
|
workspace_info.workspace.numel(), total_workspace_size);
|
|
}
|
|
// Release memory first to avoid OOM.
|
|
workspace_info = WorkspaceInfo();
|
|
workspace_info.workspace = torch::empty({static_cast<long>(total_workspace_size)},
|
|
torch::dtype(torch::kInt8).device(torch::kCUDA).requires_grad(false));
|
|
}
|
|
workspace_info.src_to_dest_map
|
|
= common::nextWorkspacePtr(static_cast<int8_t*>(workspace_info.workspace.data_ptr()), moe_workspace_size);
|
|
|
|
return workspace_info;
|
|
}
|
|
|
|
kernels::QuantParams getQuantParams(int64_t const num_experts_on_rank, int64_t const hidden_size,
|
|
int64_t const inter_size, torch::optional<c10::ArrayRef<torch::Tensor>> const& quant_scales,
|
|
ActivationType base_activation_type) const
|
|
{
|
|
int expand_ratio = isGatedActivation(base_activation_type) ? 2 : 1;
|
|
if (isFp8Quant())
|
|
{
|
|
TORCH_CHECK(quant_scales.has_value(), "Expecting quant scales for fp8 quantization");
|
|
TORCH_CHECK(quant_scales.value().size() == 4, "Expecting 4 quant scales for fp8 quantization");
|
|
|
|
auto const fc1_dequant = quant_scales.value()[0];
|
|
auto const fc2_quant = quant_scales.value()[1];
|
|
auto const fc2_dequant = quant_scales.value()[2];
|
|
auto const fc1_input_dequant = quant_scales.value()[3];
|
|
|
|
// Check types
|
|
CHECK_INPUT(fc1_dequant, c10::ScalarType::Float);
|
|
CHECK_INPUT(fc2_quant, c10::ScalarType::Float);
|
|
CHECK_INPUT(fc2_dequant, c10::ScalarType::Float);
|
|
CHECK_INPUT(fc1_input_dequant, c10::ScalarType::Float);
|
|
// Check ranks
|
|
TORCH_CHECK(fc1_dequant.dim() == 1, "fc1 dequant must be 1D");
|
|
TORCH_CHECK(fc2_quant.dim() == 0 || fc2_quant.dim() == 1, "fc2 quant must be a scalar or 1-D tensor");
|
|
TORCH_CHECK(fc2_dequant.dim() == 1, "fc2 quant must be 1D");
|
|
TORCH_CHECK(fc1_input_dequant.dim() == 0, "fc1 input dequant must be a scalar tensor");
|
|
// Check shapes
|
|
TORCH_CHECK(
|
|
fc1_dequant.sizes()[0] == num_experts_on_rank, "fc1 dequant size must be (num_experts_on_rank,)");
|
|
TORCH_CHECK(fc2_quant.dim() == 0 || fc2_quant.sizes()[0] == num_experts_on_rank,
|
|
"fc2 quant must be scalar or (num_experts_on_rank,)");
|
|
TORCH_CHECK(
|
|
fc2_dequant.sizes()[0] == num_experts_on_rank, "fc2 dequant size must be (num_experts_on_rank,)");
|
|
|
|
return kernels::QuantParams::FP8(static_cast<float const*>(fc1_dequant.data_ptr()),
|
|
static_cast<float const*>(fc2_quant.data_ptr()), static_cast<float const*>(fc2_dequant.data_ptr()),
|
|
/* fp8 output quant scale */ nullptr, static_cast<float const*>(fc1_input_dequant.data_ptr()),
|
|
fc2_quant.dim() == 1);
|
|
}
|
|
else if (isWMxfp4AFp8Quant())
|
|
{
|
|
TORCH_CHECK(quant_scales.has_value(), "Expecting quant scales for W4A8_MXFP4_MXF8 quantization");
|
|
TORCH_CHECK(quant_scales.value().size() == 5, "Expecting 5 quant scales for W4A8_MXFP4_FP8 quantization");
|
|
|
|
auto const fc1_weight_block = quant_scales.value()[0];
|
|
auto const fc1_global = quant_scales.value()[1];
|
|
auto const fc2_act_global = quant_scales.value()[2];
|
|
auto const fc2_weight_block = quant_scales.value()[3];
|
|
auto const fc2_global = quant_scales.value()[4];
|
|
|
|
// The input for scale fc1_weight_block / fc2_weight_block is packed into INT32
|
|
constexpr int FP8_PER_INT32 = 4;
|
|
// Check types
|
|
CHECK_INPUT(fc1_weight_block, c10::ScalarType::Int);
|
|
CHECK_INPUT(fc1_global, c10::ScalarType::Float);
|
|
CHECK_INPUT(fc2_act_global, c10::ScalarType::Float);
|
|
CHECK_INPUT(fc2_weight_block, c10::ScalarType::Int);
|
|
CHECK_INPUT(fc2_global, c10::ScalarType::Float);
|
|
// Check ranks
|
|
TORCH_CHECK(fc1_weight_block.dim() == 3, "fc1 weight block must be #D");
|
|
TORCH_CHECK(fc1_global.dim() == 1, "fc1 global must be 1D");
|
|
TORCH_CHECK(fc2_act_global.dim() == 0 || fc2_act_global.dim() == 1,
|
|
"fc2 act global must be a scalar or 1-D tensor");
|
|
TORCH_CHECK(fc2_weight_block.dim() == 3, "fc2 weight block must be 3D");
|
|
TORCH_CHECK(fc2_global.dim() == 1, "fc2 global must be 1D");
|
|
// Check shapes
|
|
TORCH_CHECK(fc1_weight_block.sizes()[0] == num_experts_on_rank
|
|
&& fc1_weight_block.sizes()[1]
|
|
== TmaWarpSpecializedGroupedGemmInput::alignToSfDim(
|
|
inter_size, TmaWarpSpecializedGroupedGemmInput::MinNDimAlignmentMXFPX)
|
|
* expand_ratio
|
|
&& fc1_weight_block.sizes()[2] * FP8_PER_INT32
|
|
* TmaWarpSpecializedGroupedGemmInput::MXFPXBlockScaleVectorSize
|
|
== TmaWarpSpecializedGroupedGemmInput::alignToSfDim(
|
|
hidden_size, TmaWarpSpecializedGroupedGemmInput::MinKDimAlignmentMXFPX),
|
|
"fc1 weight block size must be (num_experts_on_rank, inter_size * expand_ratio, hidden_size // 4 // "
|
|
"block_scale_vector_size)");
|
|
TORCH_CHECK(fc1_global.sizes()[0] == num_experts_on_rank, "fc1 global size must be (num_experts_on_rank,)");
|
|
TORCH_CHECK(fc2_act_global.dim() == 0 || fc2_act_global.sizes()[0] == num_experts_on_rank,
|
|
"fc2 act global must be scalar or (num_experts_on_rank,)");
|
|
TORCH_CHECK(fc2_weight_block.sizes()[0] == num_experts_on_rank
|
|
&& fc2_weight_block.sizes()[1]
|
|
== TmaWarpSpecializedGroupedGemmInput::alignToSfDim(
|
|
hidden_size, TmaWarpSpecializedGroupedGemmInput::MinNDimAlignmentMXFPX)
|
|
&& fc2_weight_block.sizes()[2] * FP8_PER_INT32
|
|
* TmaWarpSpecializedGroupedGemmInput::MXFPXBlockScaleVectorSize
|
|
== TmaWarpSpecializedGroupedGemmInput::alignToSfDim(
|
|
inter_size, TmaWarpSpecializedGroupedGemmInput::MinKDimAlignmentMXFPX),
|
|
"fc2 weight block size must be (num_experts_on_rank, hidden_size, inter_size // 4 // "
|
|
"block_scale_vector_size)");
|
|
TORCH_CHECK(fc2_global.sizes()[0] == num_experts_on_rank, "fc2 global size must be (num_experts_on_rank,)");
|
|
|
|
return kernels::QuantParams::FP8MXFP4(nullptr,
|
|
static_cast<TmaWarpSpecializedGroupedGemmInput::ElementSF*>(fc1_weight_block.data_ptr()),
|
|
static_cast<float const*>(fc1_global.data_ptr()), static_cast<float const*>(fc2_act_global.data_ptr()),
|
|
static_cast<TmaWarpSpecializedGroupedGemmInput::ElementSF*>(fc2_weight_block.data_ptr()),
|
|
static_cast<float const*>(fc2_global.data_ptr()), false, fc2_act_global.dim() == 1);
|
|
}
|
|
else if (isWMxfp4AMxfp8Quant())
|
|
{
|
|
#ifdef USING_OSS_CUTLASS_MOE_GEMM
|
|
TORCH_CHECK(quant_scales.has_value(), "Expecting quant scales for W4A8_MXFP4_MXFP8 quantization");
|
|
TORCH_CHECK(quant_scales.value().size() == 4, "Expecting 4 quant scales for W4A8_MXFP4_MXFP8 quantization");
|
|
|
|
auto const fc1_weight_block = quant_scales.value()[0];
|
|
auto const fc1_global = quant_scales.value()[1];
|
|
auto const fc2_weight_block = quant_scales.value()[2];
|
|
auto const fc2_global = quant_scales.value()[3];
|
|
|
|
// The input for scale fc1_weight_block / fc2_weight_block is packed into INT32
|
|
constexpr int FP8_PER_INT32 = 4;
|
|
CHECK_INPUT(fc1_weight_block, c10::ScalarType::Int);
|
|
CHECK_INPUT(fc1_global, c10::ScalarType::Float);
|
|
CHECK_INPUT(fc2_weight_block, c10::ScalarType::Int);
|
|
CHECK_INPUT(fc2_global, c10::ScalarType::Float);
|
|
TORCH_CHECK(fc1_weight_block.dim() == 3, "fc1 weight block must be #D");
|
|
TORCH_CHECK(fc1_global.dim() == 1, "fc1 global must be 1D");
|
|
TORCH_CHECK(fc2_weight_block.dim() == 3, "fc2 weight block must be 3D");
|
|
TORCH_CHECK(fc2_global.dim() == 1, "fc2 global must be 1D");
|
|
TORCH_CHECK(fc1_weight_block.sizes()[0] == num_experts_on_rank
|
|
&& fc1_weight_block.sizes()[1]
|
|
== TmaWarpSpecializedGroupedGemmInput::alignToSfDim(
|
|
inter_size, TmaWarpSpecializedGroupedGemmInput::MinNDimAlignmentMXFPX)
|
|
* expand_ratio
|
|
&& fc1_weight_block.sizes()[2] * FP8_PER_INT32
|
|
* TmaWarpSpecializedGroupedGemmInput::MXFPXBlockScaleVectorSize
|
|
== TmaWarpSpecializedGroupedGemmInput::alignToSfDim(
|
|
hidden_size, TmaWarpSpecializedGroupedGemmInput::MinKDimAlignmentMXFPX),
|
|
"fc1 weight block size must be (num_experts_on_rank, inter_size * expand_ratio, hidden_size // 4 // "
|
|
"block_scale_vector_size)");
|
|
TORCH_CHECK(fc1_global.sizes()[0] == num_experts_on_rank, "fc1 global size must be (num_experts_on_rank,)");
|
|
TORCH_CHECK(fc2_weight_block.sizes()[0] == num_experts_on_rank
|
|
&& fc2_weight_block.sizes()[1]
|
|
== TmaWarpSpecializedGroupedGemmInput::alignToSfDim(
|
|
hidden_size, TmaWarpSpecializedGroupedGemmInput::MinNDimAlignmentMXFPX)
|
|
&& fc2_weight_block.sizes()[2] * FP8_PER_INT32
|
|
* TmaWarpSpecializedGroupedGemmInput::MXFPXBlockScaleVectorSize
|
|
== TmaWarpSpecializedGroupedGemmInput::alignToSfDim(
|
|
inter_size, TmaWarpSpecializedGroupedGemmInput::MinKDimAlignmentMXFPX),
|
|
"fc2 weight block size must be (num_experts_on_rank, hidden_size, inter_size // 4 // "
|
|
"block_scale_vector_size)");
|
|
TORCH_CHECK(fc2_global.sizes()[0] == num_experts_on_rank, "fc2 global size must be (num_experts_on_rank,)");
|
|
|
|
return kernels::QuantParams::MXFP8MXFP4(
|
|
static_cast<TmaWarpSpecializedGroupedGemmInput::ElementSF*>(fc1_weight_block.data_ptr()),
|
|
static_cast<float const*>(fc1_global.data_ptr()),
|
|
static_cast<TmaWarpSpecializedGroupedGemmInput::ElementSF*>(fc2_weight_block.data_ptr()),
|
|
static_cast<float const*>(fc2_global.data_ptr()));
|
|
#else
|
|
TORCH_CHECK(false, "MXFP8 x MXFP4 quantization is not supported in OSS Cutlass Moe Gemm");
|
|
#endif
|
|
}
|
|
else if (isNvfp4Quant())
|
|
{
|
|
TORCH_CHECK(quant_scales.has_value(), "Expecting quant scales for nvfp4 quantization");
|
|
TORCH_CHECK(quant_scales.value().size() == 6, "Expecting 6 quant scales for nvfp4 quantization");
|
|
|
|
auto const fc1_act_global = quant_scales.value()[0];
|
|
auto const fc1_weight_block = quant_scales.value()[1];
|
|
auto const fc1_global = quant_scales.value()[2];
|
|
auto const fc2_act_global = quant_scales.value()[3];
|
|
auto const fc2_weight_block = quant_scales.value()[4];
|
|
auto const fc2_global = quant_scales.value()[5];
|
|
|
|
// The input for scale fc1_weight_block / fc2_weight_block is packed into INT32
|
|
constexpr int FP8_PER_INT32 = 4;
|
|
// Check types
|
|
CHECK_INPUT(fc1_act_global, c10::ScalarType::Float);
|
|
CHECK_INPUT(fc1_weight_block, c10::ScalarType::Int);
|
|
CHECK_INPUT(fc1_global, c10::ScalarType::Float);
|
|
CHECK_INPUT(fc2_act_global, c10::ScalarType::Float);
|
|
CHECK_INPUT(fc2_weight_block, c10::ScalarType::Int);
|
|
CHECK_INPUT(fc2_global, c10::ScalarType::Float);
|
|
// Check ranks
|
|
TORCH_CHECK(fc1_act_global.dim() == 0 || fc1_act_global.dim() == 1,
|
|
"fc1 act global must be a scalar or 1-D tensor");
|
|
TORCH_CHECK(fc1_weight_block.dim() == 3, "fc1 weight block must be #D");
|
|
TORCH_CHECK(fc1_global.dim() == 1, "fc1 global must be 1D");
|
|
TORCH_CHECK(fc2_act_global.dim() == 0 || fc2_act_global.dim() == 1,
|
|
"fc2 act global must be a scalar or 1-D tensor");
|
|
TORCH_CHECK(fc2_weight_block.dim() == 3, "fc2 weight block must be 3D");
|
|
TORCH_CHECK(fc2_global.dim() == 1, "fc2 global must be 1D");
|
|
// Check shapes
|
|
TORCH_CHECK(fc1_act_global.dim() == 0 || fc1_act_global.sizes()[0] == num_experts_on_rank,
|
|
"fc1 act global must be scalar or (num_experts_on_rank,)");
|
|
TORCH_CHECK(fc1_weight_block.sizes()[0] == num_experts_on_rank
|
|
&& fc1_weight_block.sizes()[1]
|
|
== TmaWarpSpecializedGroupedGemmInput::alignToSfDim(
|
|
inter_size, TmaWarpSpecializedGroupedGemmInput::MinKDimAlignmentNVFP4)
|
|
* expand_ratio
|
|
&& fc1_weight_block.sizes()[2] * FP8_PER_INT32
|
|
* TmaWarpSpecializedGroupedGemmInput::NVFP4BlockScaleVectorSize
|
|
== TmaWarpSpecializedGroupedGemmInput::alignToSfDim(
|
|
hidden_size, TmaWarpSpecializedGroupedGemmInput::MinKDimAlignmentNVFP4),
|
|
"fc1 weight block size must be (num_experts_on_rank, inter_size * expand_ratio, hidden_size // 4 // "
|
|
"block_scale_vector_size)");
|
|
TORCH_CHECK(fc1_global.sizes()[0] == num_experts_on_rank, "fc1 global size must be (num_experts_on_rank,)");
|
|
TORCH_CHECK(fc2_act_global.dim() == 0 || fc2_act_global.sizes()[0] == num_experts_on_rank,
|
|
"fc2 act global must be scalar or (num_experts_on_rank,)");
|
|
TORCH_CHECK(fc2_weight_block.sizes()[0] == num_experts_on_rank
|
|
&& fc2_weight_block.sizes()[1]
|
|
== TmaWarpSpecializedGroupedGemmInput::alignToSfDim(
|
|
hidden_size, TmaWarpSpecializedGroupedGemmInput::MinNDimAlignmentNVFP4)
|
|
&& fc2_weight_block.sizes()[2] * FP8_PER_INT32
|
|
* TmaWarpSpecializedGroupedGemmInput::NVFP4BlockScaleVectorSize
|
|
== TmaWarpSpecializedGroupedGemmInput::alignToSfDim(
|
|
inter_size, TmaWarpSpecializedGroupedGemmInput::MinKDimAlignmentNVFP4),
|
|
"fc2 weight block size must be (num_experts_on_rank, hidden_size, inter_size // 4 // "
|
|
"block_scale_vector_size)");
|
|
TORCH_CHECK(fc2_global.sizes()[0] == num_experts_on_rank, "fc2 global size must be (num_experts_on_rank,)");
|
|
|
|
return kernels::QuantParams::FP4(static_cast<float const*>(fc1_act_global.data_ptr()),
|
|
static_cast<TmaWarpSpecializedGroupedGemmInput::ElementSF*>(fc1_weight_block.data_ptr()),
|
|
static_cast<float const*>(fc1_global.data_ptr()), static_cast<float const*>(fc2_act_global.data_ptr()),
|
|
static_cast<TmaWarpSpecializedGroupedGemmInput::ElementSF*>(fc2_weight_block.data_ptr()),
|
|
static_cast<float const*>(fc2_global.data_ptr()), fc1_act_global.dim() == 1, fc2_act_global.dim() == 1);
|
|
}
|
|
else if (mUseDeepSeekFP8BlockScaling)
|
|
{
|
|
auto& fc1_scales = quant_scales.value()[0];
|
|
auto& fc2_scales = quant_scales.value()[1];
|
|
return kernels::QuantParams::FP8BlockScaling(
|
|
static_cast<float const*>(fc1_scales.data_ptr()), static_cast<float const*>(fc2_scales.data_ptr()));
|
|
}
|
|
else if (isWFP4A16Quant())
|
|
{
|
|
TORCH_CHECK(quant_scales.has_value(), "Expecting quant scales for weight only quantization");
|
|
TORCH_CHECK(quant_scales.value().size() == 2, "Expecting 2 quant scales for W4A16 quantization");
|
|
|
|
auto& fc1_weight_scales = quant_scales.value()[0];
|
|
auto& fc2_weight_scales = quant_scales.value()[1];
|
|
int group_size = TmaWarpSpecializedGroupedGemmInput::INT4GroupwiseParams::wfp4a16_group_size;
|
|
return kernels::QuantParams::GroupWise(group_size, static_cast<void const*>(fc1_weight_scales.data_ptr()),
|
|
static_cast<void const*>(fc2_weight_scales.data_ptr()), nullptr, nullptr, nullptr, nullptr, nullptr,
|
|
nullptr);
|
|
}
|
|
else if (isIntWeightOnlyQuant())
|
|
{
|
|
TORCH_CHECK(quant_scales.has_value(), "Expecting quant scales for weight only quantization");
|
|
if (mUseINT8WoqPerChannel)
|
|
{
|
|
TORCH_CHECK(
|
|
quant_scales.value().size() == 2, "Expecting 2 quant scales for INT8 weight only quantization");
|
|
auto& fc1_weight_scales = quant_scales.value()[0];
|
|
auto& fc2_weight_scales = quant_scales.value()[1];
|
|
return kernels::QuantParams::Int(static_cast<float const*>(fc1_weight_scales.data_ptr()),
|
|
static_cast<float const*>(fc2_weight_scales.data_ptr()));
|
|
}
|
|
else if (isInt4Quant() && mUseW4GroupScaling)
|
|
{
|
|
TORCH_CHECK(quant_scales.value().size() == 8, "Expecting 8 quant scales for W4A8 quantization");
|
|
|
|
auto& fc1_weight_scales = quant_scales.value()[0];
|
|
auto& fc2_weight_scales = quant_scales.value()[1];
|
|
auto& fc1_act_scales = quant_scales.value()[2];
|
|
auto& fc2_act_scales = quant_scales.value()[3];
|
|
auto& fc1_weight_zeros = quant_scales.value()[4];
|
|
auto& fc2_weight_zeros = quant_scales.value()[5];
|
|
auto& fc1_alpha = quant_scales.value()[6];
|
|
auto& fc2_alpha = quant_scales.value()[7];
|
|
int group_size = TmaWarpSpecializedGroupedGemmInput::INT4GroupwiseParams::int4_group_size;
|
|
// Whether it is per-expert activation scale
|
|
bool fc1_use_per_expert_act_scale = fc1_act_scales.numel() > hidden_size;
|
|
bool fc2_use_per_expert_act_scale = fc2_act_scales.numel() > inter_size;
|
|
return kernels::QuantParams::GroupWise(group_size,
|
|
static_cast<void const*>(fc1_weight_scales.data_ptr()),
|
|
static_cast<void const*>(fc2_weight_scales.data_ptr()),
|
|
static_cast<void const*>(fc1_act_scales.numel() > 0 ? fc1_act_scales.data_ptr() : nullptr),
|
|
static_cast<void const*>(fc2_act_scales.numel() > 0 ? fc2_act_scales.data_ptr() : nullptr),
|
|
static_cast<void const*>(fc1_weight_zeros.numel() > 0 ? fc1_weight_zeros.data_ptr() : nullptr),
|
|
static_cast<void const*>(fc2_weight_zeros.numel() > 0 ? fc2_weight_zeros.data_ptr() : nullptr),
|
|
static_cast<float const*>(fc1_alpha.numel() > 0 ? fc1_alpha.data_ptr() : nullptr),
|
|
static_cast<float const*>(fc2_alpha.numel() > 0 ? fc2_alpha.data_ptr() : nullptr),
|
|
fc1_use_per_expert_act_scale, fc2_use_per_expert_act_scale);
|
|
}
|
|
else
|
|
{
|
|
TORCH_CHECK(false, "Unsupported weight only quantization");
|
|
}
|
|
}
|
|
else
|
|
{
|
|
return kernels::QuantParams{};
|
|
}
|
|
}
|
|
|
|
bool isFp8Quant() const
|
|
{
|
|
return !mUseDeepSeekFP8BlockScaling && mActivationDtype == c10::ScalarType::Float8_e4m3fn
|
|
&& mWeightDtype == c10::ScalarType::Float8_e4m3fn;
|
|
}
|
|
|
|
bool isNvfp4Quant() const
|
|
{
|
|
return mWeightDtype == c10::ScalarType::Long
|
|
&& mActivationDtype != c10::ScalarType::Float8_e4m3fn; // FP8 activation does not use FP4
|
|
}
|
|
|
|
bool isWFP4A16Quant() const
|
|
{
|
|
return mUseW4GroupScaling && mWeightDtype == c10::ScalarType::Byte;
|
|
}
|
|
|
|
bool isInt8Quant() const
|
|
{
|
|
return mWeightDtype == c10::ScalarType::Char;
|
|
}
|
|
|
|
bool isInt4Quant() const
|
|
{
|
|
return mWeightDtype == c10::ScalarType::QUInt4x2;
|
|
}
|
|
|
|
bool isW4AFp8Quant() const
|
|
{
|
|
return mActivationDtype == c10::ScalarType::Float8_e4m3fn && isInt4Quant();
|
|
}
|
|
|
|
bool isIntWeightOnlyQuant() const
|
|
{
|
|
return isInt8Quant() || isInt4Quant();
|
|
}
|
|
|
|
bool isWMxfp4AFp8Quant() const
|
|
{
|
|
return mActivationDtype == c10::ScalarType::Float8_e4m3fn && mWeightDtype == c10::ScalarType::Long
|
|
&& !mUseMxfp8ActScaling;
|
|
}
|
|
|
|
bool isWMxfp4AMxfp8Quant() const
|
|
{
|
|
return mActivationDtype == c10::ScalarType::Float8_e4m3fn && mWeightDtype == c10::ScalarType::Long
|
|
&& mUseMxfp8ActScaling;
|
|
}
|
|
};
|
|
|
|
} // namespace torch_ext
|
|
|
|
TRTLLM_NAMESPACE_END
|
|
|
|
TORCH_LIBRARY(trtllm, m)
|
|
{
|
|
m.class_<tensorrt_llm::torch_ext::FusedMoeRunner>("FusedMoeRunner")
|
|
.def(torch::init<c10::ScalarType, c10::ScalarType, c10::ScalarType, bool, bool, bool, bool, bool>())
|
|
.def("run_gemm_profile", &tensorrt_llm::torch_ext::FusedMoeRunner::runGemmProfile)
|
|
.def("get_tactic_num", &tensorrt_llm::torch_ext::FusedMoeRunner::getTacticNum)
|
|
.def("run_moe", &tensorrt_llm::torch_ext::FusedMoeRunner::runMoe)
|
|
.def("run_moe_min_latency", &tensorrt_llm::torch_ext::FusedMoeRunner::runMoeMinLantency);
|
|
}
|