mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
417 lines
14 KiB
C++
417 lines
14 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION &
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* AFFILIATES. All rights reserved. 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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#include "qserveGemmPlugin.h"
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#include "tensorrt_llm/kernels/qserveGemm.h"
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#include <cassert>
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#include <numeric>
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using namespace nvinfer1;
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using namespace tensorrt_llm::common;
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using tensorrt_llm::plugins::QServeGemmPluginCreator;
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using tensorrt_llm::plugins::QServeGemmPlugin;
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using tensorrt_llm::plugins::read;
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using tensorrt_llm::plugins::write;
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using namespace tensorrt_llm::kernels::qserve;
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static char const* QSERVE_GEMM_PLUGIN_VERSION{"1"};
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static char const* QSERVE_GEMM_PLUGIN_NAME{"QServeGemm"};
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PluginFieldCollection QServeGemmPluginCreator::mFC{};
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std::vector<nvinfer1::PluginField> QServeGemmPluginCreator::mPluginAttributes;
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namespace tensorrt_llm::plugins
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{
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QServeGemmPlugin::QServeGemmPlugin(
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// QuantMode quantMode,
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nvinfer1::DataType dtype, int groupSize)
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{
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init(dtype, groupSize);
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}
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QServeGemmPlugin::QServeGemmPlugin(void const* data, size_t length)
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{
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char const *d = reinterpret_cast<char const*>(data), *a = d;
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nvinfer1::DataType type;
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unsigned int quantMode;
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int groupSize;
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read(d, quantMode);
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read(d, type);
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read(d, groupSize);
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read(d, mDims);
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// mQuantMode = QuantMode(quantMode);
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init(type, groupSize);
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TLLM_CHECK_WITH_INFO(d == a + length,
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"Expected length (%d) != real length (%d). This is often "
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"caused by using different TensorRT-LLM version to build "
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"engine and run engine.",
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(int) length, (int) (d - a));
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}
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void QServeGemmPlugin::init(nvinfer1::DataType dtype, int groupSize)
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{
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if (groupSize <= 0)
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groupSize = -1; // Per-channel
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mGroupSize = groupSize;
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mType = dtype;
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mRunner = std::make_shared<QServeGemmRunner>();
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}
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// IPluginV2DynamicExt Methods
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nvinfer1::IPluginV2DynamicExt* QServeGemmPlugin::clone() const noexcept
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{
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auto* plugin = new QServeGemmPlugin(*this);
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return plugin;
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}
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nvinfer1::DimsExprs QServeGemmPlugin::getOutputDimensions(
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int outputIndex, nvinfer1::DimsExprs const* inputs, int nbInputs, nvinfer1::IExprBuilder& exprBuilder) noexcept
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{
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try
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{
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TLLM_CHECK(nbInputs == 6);
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TLLM_CHECK(outputIndex == 0);
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int const nbDimsA = inputs[0].nbDims;
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TLLM_CHECK(nbDimsA >= 2);
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DimsExprs ret;
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ret.nbDims = nbDimsA;
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for (int ii = 0; ii < nbDimsA - 1; ++ii)
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{
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ret.d[ii] = inputs[0].d[ii];
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}
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ret.d[nbDimsA - 1] = inputs[1].d[0];
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return ret;
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}
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catch (std::exception const& e)
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{
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caughtError(e);
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}
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return DimsExprs{};
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}
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bool QServeGemmPlugin::supportsFormatCombination(
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int pos, nvinfer1::PluginTensorDesc const* inOut, int nbInputs, int nbOutputs) noexcept
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{
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if (mGroupSize != -1)
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{ // Per-group
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switch (pos)
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{
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case 0:
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// activation
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return inOut[pos].type == nvinfer1::DataType::kINT8 && inOut[pos].format == TensorFormat::kLINEAR;
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case 1:
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// uint4 weights packed in int8
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return inOut[pos].type == nvinfer1::DataType::kINT8 && inOut[pos].format == TensorFormat::kLINEAR;
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case 2:
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// int8 weight s2_zeros
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return inOut[pos].type == nvinfer1::DataType::kINT8 && inOut[pos].format == TensorFormat::kLINEAR;
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case 3:
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// int8 weight s2_scales
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return inOut[pos].type == nvinfer1::DataType::kINT8 && inOut[pos].format == TensorFormat::kLINEAR;
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case 4:
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// fp16 weight s1_scales
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return inOut[pos].type == nvinfer1::DataType::kHALF && inOut[pos].format == TensorFormat::kLINEAR;
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case 5:
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// fp16 activation scales
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return inOut[pos].type == nvinfer1::DataType::kHALF && inOut[pos].format == TensorFormat::kLINEAR;
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case 6:
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// fp16 output activation
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return inOut[pos].type == nvinfer1::DataType::kHALF && inOut[pos].format == TensorFormat::kLINEAR;
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default: return false;
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}
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}
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else
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{ // Per-channel
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switch (pos)
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{
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case 0:
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// activation
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return inOut[pos].type == nvinfer1::DataType::kINT8 && inOut[pos].format == TensorFormat::kLINEAR;
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case 1:
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// uint4 weights packed in int8
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return inOut[pos].type == nvinfer1::DataType::kINT8 && inOut[pos].format == TensorFormat::kLINEAR;
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case 2:
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// fp16 s1_scales
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return inOut[pos].type == nvinfer1::DataType::kHALF && inOut[pos].format == TensorFormat::kLINEAR;
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case 3:
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// fp16 s1_szeros
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return inOut[pos].type == nvinfer1::DataType::kHALF && inOut[pos].format == TensorFormat::kLINEAR;
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case 4:
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// fp16 act_sums
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return inOut[pos].type == nvinfer1::DataType::kHALF && inOut[pos].format == TensorFormat::kLINEAR;
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case 5:
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// fp16 act_scales
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return inOut[pos].type == nvinfer1::DataType::kHALF && inOut[pos].format == TensorFormat::kLINEAR;
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case 6:
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// fp16 output activation
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return inOut[pos].type == nvinfer1::DataType::kHALF && inOut[pos].format == TensorFormat::kLINEAR;
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default: return false;
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}
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}
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}
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void QServeGemmPlugin::configurePlugin(nvinfer1::DynamicPluginTensorDesc const* in, int nbInputs,
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nvinfer1::DynamicPluginTensorDesc const* out, int nbOutputs) noexcept
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{
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auto const minM = std::accumulate(in[0].min.d, in[0].min.d + in[0].min.nbDims - 1, 1, std::multiplies<int>());
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auto const maxM = std::accumulate(in[0].max.d, in[0].max.d + in[0].max.nbDims - 1, 1, std::multiplies<int>());
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int const maxK = in[0].max.d[in[0].max.nbDims - 1];
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int const maxN = in[1].max.d[0];
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int const minK = in[0].min.d[in[0].min.nbDims - 1];
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int const minN = in[1].min.d[0];
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TLLM_CHECK_WITH_INFO(minN == maxN, "Variable out channels is not allowed");
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TLLM_CHECK_WITH_INFO(minK == maxK, "Variable in channels is not allowed");
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if (!mDims.isInitialized())
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{
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mDims = {minM, maxM, maxN, maxK};
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}
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m_workspaceMaxSize = mRunner->getWorkspaceSize(maxM, maxN, maxK);
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}
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size_t QServeGemmPlugin::getWorkspaceSize(nvinfer1::PluginTensorDesc const* inputs, int nbInputs,
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nvinfer1::PluginTensorDesc const* outputs, int nbOutputs) const noexcept
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{
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return m_workspaceMaxSize;
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}
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int QServeGemmPlugin::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
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{
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// inputs
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// Per group:
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// activation [M, K] int8_t Quantized sint8 activations
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// weights [N, K/2] int8_t Quantized uint4 weights (packed as int8_t)
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// s2_zeros [K/group_size, N] int8_t Level-2 sint8 scaled zeros of weights
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// s2_scales [K/group_size, N] int8_t Level-2 sint8 scales of weights
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// s1_scales [N] half Level-1 fp16 scales of weights
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// act_scales [M] half Scales of activations
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// Per channel:
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// activation [M, K] int8_t Quantized sint8 activations
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// weights [N, K/2] int8_t Quantized uint4 weights (packed as int8_t)
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// s1_scales [N] half Level-1 scales of weights
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// s1_szeros [N] half Level-1 scaled zeros of weights
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// act_sums [M] half Per-token sums of activations
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// act_scales [M] half Scales of activations
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// outputs
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// mat [M(*), N] half
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int64_t m64 = 1;
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for (int ii = 0; ii < inputDesc[0].dims.nbDims - 1; ++ii)
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{
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m64 *= inputDesc[0].dims.d[ii];
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}
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int const m = TLLM_INT32_CAST(m64);
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int const n = TLLM_INT32_CAST(inputDesc[1].dims.d[0]);
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int const k = TLLM_INT32_CAST(inputDesc[0].dims.d[inputDesc[0].dims.nbDims - 1]);
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// TODO: Implement optimized kernels if (m <= 4)
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if (mGroupSize != -1)
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{
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ParamsPerGroup params = {reinterpret_cast<int8_t const*>(inputs[0]), // A
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reinterpret_cast<int8_t const*>(inputs[1]), // B
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reinterpret_cast<int8_t const*>(inputs[2]), // s2_zeros
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reinterpret_cast<int8_t const*>(inputs[3]), // s2_scales
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reinterpret_cast<half const*>(inputs[4]), // s1_scales
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reinterpret_cast<half const*>(inputs[5]), // act_scales
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reinterpret_cast<half*>(outputs[0]), // C
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m, n, k};
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mRunner->gemmPerGroup(params, stream);
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}
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else
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{
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ParamsPerChannel params = {reinterpret_cast<int8_t const*>(inputs[0]), // A
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reinterpret_cast<int8_t const*>(inputs[1]), // B
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reinterpret_cast<half const*>(inputs[2]), // s1_scales
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reinterpret_cast<half const*>(inputs[3]), // s1_szeros
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reinterpret_cast<half const*>(inputs[4]), // act_sums
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reinterpret_cast<half const*>(inputs[5]), // act_scales
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reinterpret_cast<half*>(outputs[0]), // C
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m, n, k};
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mRunner->gemmPerChannel(params, stream);
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}
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return 0;
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}
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// IPluginV2Ext Methods
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nvinfer1::DataType QServeGemmPlugin::getOutputDataType(
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int index, nvinfer1::DataType const* inputTypes, int nbInputs) const noexcept
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{
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TLLM_CHECK(index == 0);
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return mType;
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}
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// IPluginV2 Methods
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char const* QServeGemmPlugin::getPluginType() const noexcept
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{
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return QSERVE_GEMM_PLUGIN_NAME;
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}
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char const* QServeGemmPlugin::getPluginVersion() const noexcept
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{
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return QSERVE_GEMM_PLUGIN_VERSION;
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}
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int QServeGemmPlugin::getNbOutputs() const noexcept
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{
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return 1;
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}
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int QServeGemmPlugin::initialize() noexcept
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{
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configGemm();
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return 0;
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}
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void QServeGemmPlugin::terminate() noexcept {}
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size_t QServeGemmPlugin::getSerializationSize() const noexcept
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{
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return sizeof(mQuantMode) + // QuantMode
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sizeof(mType) + // dtype
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sizeof(mGroupSize) + // GroupSize
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sizeof(mDims); // Dimensions
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}
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void QServeGemmPlugin::serialize(void* buffer) const noexcept
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{
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char *d = static_cast<char*>(buffer), *a = d;
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write(d, mQuantMode.value());
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write(d, mType);
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write(d, mGroupSize);
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write(d, mDims);
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TLLM_CHECK(d == a + getSerializationSize());
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}
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void QServeGemmPlugin::destroy() noexcept
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{
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// This gets called when the network containing plugin is destroyed
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delete this;
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}
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void QServeGemmPlugin::configGemm() {}
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///////////////
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QServeGemmPluginCreator::QServeGemmPluginCreator()
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{
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// Fill PluginFieldCollection with PluginField arguments metadata
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mPluginAttributes.clear();
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mPluginAttributes.push_back(PluginField("type_id", nullptr, PluginFieldType::kINT32));
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mPluginAttributes.push_back(PluginField("group_size", nullptr, PluginFieldType::kINT32));
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mFC.nbFields = mPluginAttributes.size();
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mFC.fields = mPluginAttributes.data();
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}
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char const* QServeGemmPluginCreator::getPluginName() const noexcept
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{
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return QSERVE_GEMM_PLUGIN_NAME;
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}
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char const* QServeGemmPluginCreator::getPluginVersion() const noexcept
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{
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return QSERVE_GEMM_PLUGIN_VERSION;
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}
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PluginFieldCollection const* QServeGemmPluginCreator::getFieldNames() noexcept
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{
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return &mFC;
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}
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IPluginV2* QServeGemmPluginCreator::createPlugin(char const* name, PluginFieldCollection const* fc) noexcept
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{
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// We do not use any fields for now.
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PluginField const* fields = fc->fields;
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// bool perTokenScaling, perChannelScaling;
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DataType dtype{};
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int group_size = -1;
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// Read configurations from each fields
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for (int i = 0; i < fc->nbFields; ++i)
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{
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char const* attrName = fields[i].name;
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if (!strcmp(attrName, "type_id"))
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{
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TLLM_CHECK(fields[i].type == PluginFieldType::kINT32);
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dtype = static_cast<nvinfer1::DataType>(*(static_cast<nvinfer1::DataType const*>(fields[i].data)));
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// Only supports fp16 for now.
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assert(dtype == nvinfer1::DataType::kHALF);
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}
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else if (!strcmp(attrName, "group_size"))
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{
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TLLM_CHECK(fields[i].type == PluginFieldType::kINT32);
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group_size = *static_cast<int const*>(fields[i].data);
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// Currently only support per-channel or g128.
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assert(group_size == -1 || group_size == 128);
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}
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}
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try
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{
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// QServeGemmPluginCreator is unique and shared for an engine generation
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// Create plugin profiler with shared tactics map
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// auto pluginProfiler = gemmPluginProfileManager.createGemmPluginProfiler(/* inference */ false);
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// QuantMode quantMode = QuantMode::fromQuantAlgo("W4A8_QSERVE");
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auto* obj = new QServeGemmPlugin(dtype, group_size);
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obj->setPluginNamespace(mNamespace.c_str());
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return obj;
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}
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catch (std::exception const& e)
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{
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caughtError(e);
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}
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return nullptr;
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}
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IPluginV2* QServeGemmPluginCreator::deserializePlugin(
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char const* name, void const* serialData, size_t serialLength) noexcept
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{
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// This object will be deleted when the network is destroyed, which will
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// call QServeGemmPlugin::destroy()
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try
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{
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// Create plugin profiler with private tactics map which is read from the serialized engine
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// auto pluginProfiler = gemmPluginProfileManager.createGemmPluginProfiler(/* inference */ true);
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auto* obj = new QServeGemmPlugin(serialData, serialLength);
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obj->setPluginNamespace(mNamespace.c_str());
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return obj;
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}
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catch (std::exception const& e)
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{
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caughtError(e);
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}
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return nullptr;
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}
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} // namespace tensorrt_llm::plugins
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