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https://github.com/NVIDIA/TensorRT-LLM.git
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* Update TensorRT-LLM --------- Co-authored-by: Puneesh Khanna <puneesh.khanna@tii.ae> Co-authored-by: Ethan Zhang <26497102+ethnzhng@users.noreply.github.com>
169 lines
6.1 KiB
C++
169 lines
6.1 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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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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#pragma once
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#include "tensorrt_llm/common/quantization.h"
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#include "tensorrt_llm/kernels/cutlass_kernels/fpA_intB_gemm/fpA_intB_gemm.h"
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#include "tensorrt_llm/kernels/weightOnlyBatchedGemv/kernelLauncher.h"
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#include "tensorrt_llm/plugins/common/gemmPluginProfiler.h"
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#include "tensorrt_llm/plugins/common/plugin.h"
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#include <cassert>
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#include <cutlass/numeric_types.h>
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#include <memory>
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#include <set>
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#include <string>
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#include <vector>
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// The blank line here is to avoid clang-format -sort-includes option reordering these two cutlass header files and
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// breaking dependencies
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#include "cutlass/integer_subbyte.h"
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namespace tensorrt_llm::plugins
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{
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enum class WeightTypeId
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{
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INT8 = 1,
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INT4 = 2,
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};
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constexpr int32_t FP16_BITS = 16;
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constexpr int32_t INT8_BITS = 8;
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constexpr int32_t INT4_BITS = 4;
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constexpr int32_t INT8_INT4_RATIO = INT8_BITS / INT4_BITS;
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constexpr int32_t FP16_INT4_RATIO = FP16_BITS / INT4_BITS;
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constexpr int32_t FP16_INT8_RATIO = FP16_BITS / INT8_BITS;
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inline int32_t getWeightTypeMultiplier(WeightTypeId weightTypeId)
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{
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return weightTypeId == WeightTypeId::INT8 ? 1 : INT8_INT4_RATIO;
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}
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using WeightOnlyGemmRunner = tensorrt_llm::kernels::cutlass_kernels::CutlassFpAIntBGemmRunnerInterface;
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using WeightOnlyGemmRunnerPtr = std::shared_ptr<WeightOnlyGemmRunner>;
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class WeightOnlyQuantGemmPluginProfiler : public GemmPluginProfiler<tensorrt_llm::cutlass_extensions::CutlassGemmConfig,
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WeightOnlyGemmRunnerPtr, GemmIdCore, GemmIdCoreHash>
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{
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public:
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using Config = tensorrt_llm::cutlass_extensions::CutlassGemmConfig;
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void setWeightTypeId(WeightTypeId weightId)
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{
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mWeightTypeId = weightId;
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}
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protected:
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void runTactic(int m, int n, int k, Config const& tactic, char* workspace, cudaStream_t const& stream) override;
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void computeTmpSize(size_t maxM, size_t n, size_t k) override;
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std::vector<Config> getTactics(int m, int n, int k) const override;
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private:
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WeightTypeId mWeightTypeId;
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};
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class WeightOnlyQuantMatmulPlugin : public BasePlugin
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{
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public:
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using PluginProfilerPtr = std::shared_ptr<WeightOnlyQuantGemmPluginProfiler>;
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WeightOnlyQuantMatmulPlugin() = delete;
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WeightOnlyQuantMatmulPlugin(nvinfer1::DataType type, WeightTypeId weightTypeId, PluginProfilerPtr const& profiler);
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WeightOnlyQuantMatmulPlugin(void const* data, size_t length, PluginProfilerPtr const& profiler);
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~WeightOnlyQuantMatmulPlugin() override = default;
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// IPluginV2DynamicExt Methods
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nvinfer1::IPluginV2DynamicExt* clone() const noexcept override;
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nvinfer1::DimsExprs getOutputDimensions(int outputIndex, nvinfer1::DimsExprs const* inputs, int nbInputs,
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nvinfer1::IExprBuilder& exprBuilder) noexcept override;
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bool supportsFormatCombination(
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int pos, nvinfer1::PluginTensorDesc const* inOut, int nbInputs, int nbOutputs) noexcept override;
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void configurePlugin(nvinfer1::DynamicPluginTensorDesc const* in, int nbInputs,
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nvinfer1::DynamicPluginTensorDesc const* out, int nbOutputs) noexcept override;
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size_t getWorkspaceSize(nvinfer1::PluginTensorDesc const* inputs, int nbInputs,
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nvinfer1::PluginTensorDesc const* outputs, int nbOutputs) const noexcept override;
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int enqueue(nvinfer1::PluginTensorDesc const* inputDesc, nvinfer1::PluginTensorDesc const* outputDesc,
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void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) noexcept override;
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// IPluginV2Ext Methods
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nvinfer1::DataType getOutputDataType(
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int index, nvinfer1::DataType const* inputTypes, int nbInputs) const noexcept override;
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// IPluginV2 Methods
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char const* getPluginType() const noexcept override;
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char const* getPluginVersion() const noexcept override;
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int getNbOutputs() const noexcept override;
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int initialize() noexcept override;
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void terminate() noexcept override;
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size_t getSerializationSize() const noexcept override;
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void serialize(void* buffer) const noexcept override;
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void destroy() noexcept override;
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private:
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void init(nvinfer1::DataType type, WeightTypeId weightTypeId);
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void configGemm();
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private:
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const std::string mLayerName;
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WeightOnlyGemmRunnerPtr m_weightOnlyGemmRunner;
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size_t m_workspaceMaxSize;
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nvinfer1::DataType mType;
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WeightTypeId mWeightTypeId;
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bool mCudaKernelEnabled;
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tensorrt_llm::kernels::weight_only::KernelType mCudaKernelType;
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int mArch;
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// When M is smaller than this value, we trigger a fast path
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// I.e. a tailored kernel instead of cutlass.
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static constexpr int SMALL_M_FAST_PATH = 5;
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GemmDims mDims{};
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GemmIdCore mGemmId{};
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PluginProfilerPtr mPluginProfiler;
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};
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class WeightOnlyQuantMatmulPluginCreator : public BaseCreator
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{
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public:
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WeightOnlyQuantMatmulPluginCreator();
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char const* getPluginName() const noexcept override;
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char const* getPluginVersion() const noexcept override;
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nvinfer1::PluginFieldCollection const* getFieldNames() noexcept override;
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nvinfer1::IPluginV2* createPlugin(char const* name, nvinfer1::PluginFieldCollection const* fc) noexcept override;
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nvinfer1::IPluginV2* deserializePlugin(
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char const* name, void const* serialData, size_t serialLength) noexcept override;
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private:
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GemmPluginProfilerManager<WeightOnlyQuantGemmPluginProfiler> gemmPluginProfileManager;
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static nvinfer1::PluginFieldCollection mFC;
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static std::vector<nvinfer1::PluginField> mPluginAttributes;
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};
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} // namespace tensorrt_llm::plugins
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