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ab2f663101
* Reduce memory usage in fused moe op associated with AutoTuning. * Replace pre-defined bucket size strategy with a generating function based on the tune_max_num_tokens. * Add free_memory logic of workspace in min_latency_mode fused moe path. Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com> * Fix fused_moe fallback issue. (#3652) min_latency_mode is only set to False during warmup phase. Thus when it becomes true during inference, all tactics fall back to the default one and thus cause perf regression. Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com> --------- Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
584 lines
29 KiB
C++
584 lines
29 KiB
C++
/*
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* Copyright (c) 2022-2024, 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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#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/internal_cutlass_kernels/include/moe_gemm_kernels.h"
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#include "tensorrt_llm/kernels/internal_cutlass_kernels/include/moe_kernels.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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#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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namespace torch_ext
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{
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namespace common = tensorrt_llm::common;
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namespace kernels = tensorrt_llm::kernels;
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using profiler_backend = kernels::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 Type, 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<Type, Type, 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<Type, Type, half, Type>>();
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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<kernels::CutlassMoeFCRunner<Type, Type, __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<Type, Type, __nv_bfloat16, Type>>();
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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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FusedMoeRunner(c10::ScalarType activation_dtype, c10::ScalarType weight_dtype, c10::ScalarType output_dtype,
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bool use_fp8_block_scaling)
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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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mUseFp8BlockScaling = use_fp8_block_scaling;
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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>(mOutputDtype);
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}
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#endif
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#ifdef ENABLE_FP4
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if (isNvfp4Quant())
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{
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mInnerDimMultiplier = 16;
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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, true>(mOutputDtype);
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break;
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default: mKernelRunner = switch_output_type<__nv_fp4_e2m1, false>(mOutputDtype);
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}
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}
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#endif
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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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mProfiler = std::make_shared<kernels::GemmProfilerBackend>();
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mAllProfiles = mKernelRunner->getTactics();
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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> token_final_scales, torch::Tensor const& fc1_expert_weights,
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torch::Tensor const& fc2_expert_weights, torch::optional<c10::ArrayRef<torch::Tensor>> quant_scales,
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torch::optional<torch::Tensor> input_sf, int64_t const tp_size, int64_t const tp_rank, int64_t const ep_size,
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int64_t const ep_rank, bool min_latency_mode, torch::optional<c10::ArrayRef<int64_t>> profile_ids)
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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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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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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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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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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 inter_size = fc2_expert_weights.sizes()[2] * mInnerDimMultiplier;
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int const num_experts_on_rank = fc2_expert_weights.sizes()[0];
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auto const num_experts_total = static_cast<int>(num_experts_on_rank * ep_size);
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auto parallelism_config = kernels::MOEParallelismConfig(tp_size, tp_rank, ep_size, ep_rank);
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auto activation_type = tensorrt_llm::ActivationType::Swiglu;
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setRunnerProfiles(profile_ids);
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auto stream = at::cuda::getCurrentCUDAStream(input.get_device());
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std::vector<int64_t> output_shape = {num_rows, hidden_size};
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auto output = torch::empty(output_shape, input.options().dtype(mOutputDtype));
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WorkspaceInfo workspace_info = getWorkspaceInfo(num_rows, hidden_size, inter_size, num_experts_total,
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static_cast<int>(experts_per_token), activation_type, parallelism_config, min_latency_mode);
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auto const quant_params = getQuantParams(num_experts_on_rank, hidden_size, inter_size, quant_scales);
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kernels::MoeMinLatencyParams min_latency_params{};
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// TODO: support lora in the future
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kernels::LoraParams lora_params{};
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mKernelRunner->runMoe(input.const_data_ptr(),
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input_sf.has_value() ? input_sf.value().const_data_ptr() : nullptr,
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reinterpret_cast<int const*>(token_selected_experts.const_data_ptr()),
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token_final_scales.has_value() ? reinterpret_cast<float const*>(token_final_scales.value().const_data_ptr())
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: nullptr,
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fc1_expert_weights.const_data_ptr(), nullptr, activation_type, fc2_expert_weights.const_data_ptr(), nullptr,
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quant_params, num_rows, hidden_size, inter_size, num_experts_total, static_cast<int>(experts_per_token),
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static_cast<char*>(workspace_info.workspace), output.data_ptr(),
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static_cast<int*>(workspace_info.src_to_dest_map), parallelism_config, false, lora_params,
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mUseFp8BlockScaling, min_latency_mode, min_latency_params, stream);
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return output;
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}
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std::tuple<torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor> runMoeMinLantency(torch::Tensor const& input,
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torch::Tensor const& token_selected_experts, torch::optional<torch::Tensor> token_final_scales,
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torch::Tensor const& fc1_expert_weights, torch::Tensor const& fc2_expert_weights,
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torch::optional<c10::ArrayRef<torch::Tensor>> quant_scales, torch::optional<torch::Tensor> input_sf,
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int64_t const tp_size, int64_t const tp_rank, int64_t const ep_size, int64_t const ep_rank,
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bool min_latency_mode, torch::optional<c10::ArrayRef<int64_t>> profile_ids)
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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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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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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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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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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 inter_size = fc2_expert_weights.sizes()[2] * mInnerDimMultiplier;
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int const num_experts_on_rank = fc2_expert_weights.sizes()[0];
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auto const num_experts_total = static_cast<int>(num_experts_on_rank * ep_size);
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auto parallelism_config = kernels::MOEParallelismConfig(tp_size, tp_rank, ep_size, ep_rank);
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auto activation_type = tensorrt_llm::ActivationType::Swiglu;
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setRunnerProfiles(profile_ids);
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auto stream = at::cuda::getCurrentCUDAStream(input.get_device());
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std::vector<int64_t> output_shape = {num_rows * num_experts_on_rank, hidden_size};
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auto output = torch::empty(output_shape, input.options().dtype(mOutputDtype));
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auto num_active_experts_per_node = torch::empty({1}, input.options().dtype(at::ScalarType::Int));
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auto experts_to_token_score
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= torch::empty({num_experts_on_rank, num_rows}, input.options().dtype(at::ScalarType::Float));
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auto active_expert_global_ids = torch::empty({num_experts_on_rank}, input.options().dtype(at::ScalarType::Int));
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kernels::MoeMinLatencyParams min_latency_params{};
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min_latency_params.num_active_experts_per_node = static_cast<int*>(num_active_experts_per_node.data_ptr());
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min_latency_params.experts_to_token_score = static_cast<float*>(experts_to_token_score.data_ptr());
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min_latency_params.active_expert_global_ids = static_cast<int*>(active_expert_global_ids.data_ptr());
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WorkspaceInfo workspace_info = getWorkspaceInfo(num_rows, hidden_size, inter_size, num_experts_total,
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static_cast<int>(experts_per_token), activation_type, parallelism_config, min_latency_mode);
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auto const quant_params = getQuantParams(num_experts_on_rank, hidden_size, inter_size, quant_scales);
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// TODO: support lora in the future
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kernels::LoraParams lora_params{};
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mKernelRunner->runMoe(input.const_data_ptr(),
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input_sf.has_value() ? input_sf.value().const_data_ptr() : nullptr,
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reinterpret_cast<int const*>(token_selected_experts.const_data_ptr()),
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token_final_scales.has_value() ? reinterpret_cast<float const*>(token_final_scales.value().const_data_ptr())
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: nullptr,
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fc1_expert_weights.const_data_ptr(), nullptr, activation_type, fc2_expert_weights.const_data_ptr(), nullptr,
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quant_params, num_rows, hidden_size, inter_size, num_experts_total, static_cast<int>(experts_per_token),
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static_cast<char*>(workspace_info.workspace), output.data_ptr(),
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static_cast<int*>(workspace_info.src_to_dest_map), parallelism_config, false, lora_params,
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mUseFp8BlockScaling, min_latency_mode, min_latency_params, stream);
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return std::make_tuple(output, num_active_experts_per_node, experts_to_token_score, active_expert_global_ids);
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}
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int64_t getTacticNum()
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{
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std::lock_guard<std::mutex> lock(mMutex);
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return mAllProfiles.size();
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}
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void runGemmProfile(torch::Tensor const& input, torch::Tensor const& fc2_expert_weights, int64_t const top_k,
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int64_t const tp_size, int64_t const tp_rank, int64_t const ep_size, int64_t const ep_rank,
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bool const min_latency_mode, int64_t const gemm_idx, int64_t const profile_id, bool const do_preparation)
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{
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std::lock_guard<std::mutex> lock(mMutex);
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// TODO: support profiling under fp8 block scaling in the future
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if (mUseFp8BlockScaling)
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{
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return;
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}
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int64_t const num_rows = input.sizes()[0];
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int64_t const hidden_size = fc2_expert_weights.sizes()[1];
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int64_t const inter_size = fc2_expert_weights.sizes()[2] * mInnerDimMultiplier;
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int const num_experts = static_cast<int>(fc2_expert_weights.sizes()[0] * ep_size);
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// Get specific profile configs according to the profile_id.
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// Fallback tactic is set to be 0
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// TODO: use the best tactic id found offline for a better default inference perf
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auto const& profile = profile_id == -1 ? mAllProfiles.front() : mAllProfiles[profile_id];
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auto stream = at::cuda::getCurrentCUDAStream(input.get_device());
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// Preparation phase, only enabled during autotuning warmup phase.
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if (do_preparation)
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{
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// Set profiled gemm idx
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mProfiler->mGemmToProfile
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= (gemm_idx == 1) ? profiler_backend::GemmToProfile::GEMM_1 : profiler_backend::GemmToProfile::GEMM_2;
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// mProfiler init
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auto parallelism_config = kernels::MOEParallelismConfig(static_cast<int>(tp_size),
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static_cast<int>(tp_rank), static_cast<int>(ep_size), static_cast<int>(ep_rank));
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int const GROUP_SIZE = -1;
|
|
bool const USE_BIAS = false;
|
|
bool const USE_LORA = false;
|
|
mProfiler->init(*mKernelRunner.get(), mProfiler->mGemmToProfile,
|
|
tensorrt_llm::runtime::TorchUtils::dataType(mActivationDtype),
|
|
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, tensorrt_llm::ActivationType::Swiglu, USE_BIAS, USE_LORA,
|
|
min_latency_mode, parallelism_config);
|
|
|
|
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, stream);
|
|
}
|
|
|
|
// Profile specific tactic. Assuming at least one preparation phase has been executed already.
|
|
mProfiler->runProfiler(num_rows, profile, mProfileWorkspace, stream);
|
|
}
|
|
|
|
private:
|
|
struct WorkspaceInfo
|
|
{
|
|
void* 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;
|
|
|
|
bool mUseFp8BlockScaling = false;
|
|
|
|
using Profile = tensorrt_llm::cutlass_extensions::CutlassGemmConfig;
|
|
std::vector<Profile> mAllProfiles;
|
|
|
|
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 (mUseFp8BlockScaling)
|
|
{
|
|
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 = mAllProfiles.front();
|
|
auto best_gemm2_profile = mAllProfiles.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 : mAllProfiles.at(profile_ids.value()[0]);
|
|
best_gemm2_profile
|
|
= profile_ids.value()[1] == -1 ? best_gemm2_profile : mAllProfiles.at(profile_ids.value()[1]);
|
|
}
|
|
mKernelRunner->setTactic(best_gemm1_profile, best_gemm2_profile);
|
|
}
|
|
|
|
WorkspaceInfo getWorkspaceInfo(int64_t const num_rows, int64_t const hidden_size, int64_t const inter_size,
|
|
int num_experts, int experts_per_token, tensorrt_llm::ActivationType activation_type,
|
|
kernels::MOEParallelismConfig const& parallelismConfig, bool min_latency_mode)
|
|
{
|
|
size_t moe_workspace_size
|
|
= mKernelRunner->getWorkspaceSize(num_rows, hidden_size, inter_size, num_experts, experts_per_token,
|
|
activation_type, parallelismConfig, /* use_lora */ false, mUseFp8BlockScaling, min_latency_mode,
|
|
/* hasExpertPrequantScales */ false);
|
|
size_t src_to_dest_map_size = experts_per_token * num_rows * sizeof(int);
|
|
|
|
std::vector<size_t> workspaces{moe_workspace_size, src_to_dest_map_size};
|
|
|
|
size_t total_workspace_size = common::calculateTotalWorkspaceSize(workspaces.data(), workspaces.size());
|
|
auto workspace = torch::empty({static_cast<long>(total_workspace_size)},
|
|
torch::dtype(torch::kInt8).device(torch::kCUDA).requires_grad(false));
|
|
|
|
WorkspaceInfo info{};
|
|
info.workspace = workspace.data_ptr();
|
|
info.src_to_dest_map = common::nextWorkspacePtr(static_cast<int8_t*>(workspace.data_ptr()), moe_workspace_size);
|
|
|
|
return 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) const
|
|
{
|
|
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_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);
|
|
TORCH_CHECK(fc1_dequant.dim() == 1, "fc1 dequant must be 1D");
|
|
TORCH_CHECK(fc2_quant.dim() == 0, "fc2 quant must be a scalar 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");
|
|
TORCH_CHECK(
|
|
fc1_dequant.sizes()[0] == num_experts_on_rank, "fc1 dequant size must be (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()));
|
|
}
|
|
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_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);
|
|
TORCH_CHECK(fc1_act_global.dim() == 0, "fc1 act global must be a scalar 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 must be a scalar 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");
|
|
TORCH_CHECK(fc1_weight_block.sizes()[0] == num_experts_on_rank
|
|
&& fc1_weight_block.sizes()[1] == inter_size * 2
|
|
&& fc1_weight_block.sizes()[2] * FP8_PER_INT32
|
|
* tensorrt_llm::TmaWarpSpecializedGroupedGemmInput::BlockScaleVectorSize
|
|
== hidden_size,
|
|
"fc1 weight block size must be (num_experts_on_rank, inter_size * 2, 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] == hidden_size
|
|
&& fc2_weight_block.sizes()[2] * FP8_PER_INT32
|
|
* tensorrt_llm::TmaWarpSpecializedGroupedGemmInput::BlockScaleVectorSize
|
|
== inter_size,
|
|
"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<tensorrt_llm::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<tensorrt_llm::TmaWarpSpecializedGroupedGemmInput::ElementSF*>(fc2_weight_block.data_ptr()),
|
|
static_cast<float const*>(fc2_global.data_ptr()));
|
|
}
|
|
else if (mUseFp8BlockScaling)
|
|
{
|
|
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
|
|
{
|
|
return kernels::QuantParams{};
|
|
}
|
|
}
|
|
|
|
bool isFp8Quant() const
|
|
{
|
|
return !mUseFp8BlockScaling && mActivationDtype == c10::ScalarType::Float8_e4m3fn
|
|
&& mWeightDtype == c10::ScalarType::Float8_e4m3fn;
|
|
}
|
|
|
|
bool isNvfp4Quant() const
|
|
{
|
|
return mWeightDtype == c10::ScalarType::Long;
|
|
}
|
|
};
|
|
|
|
} // namespace torch_ext
|
|
|
|
TORCH_LIBRARY(trtllm, m)
|
|
{
|
|
m.class_<torch_ext::FusedMoeRunner>("FusedMoeRunner")
|
|
.def(torch::init<c10::ScalarType, c10::ScalarType, c10::ScalarType, bool>())
|
|
.def("run_gemm_profile", &torch_ext::FusedMoeRunner::runGemmProfile)
|
|
.def("get_tactic_num", &torch_ext::FusedMoeRunner::getTacticNum)
|
|
.def("run_moe", &torch_ext::FusedMoeRunner::runMoe)
|
|
.def("run_moe_min_latency", &torch_ext::FusedMoeRunner::runMoeMinLantency);
|
|
}
|