wehub-resource-sync
833 lines
30 KiB
C++
833 lines
30 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 1993-2026 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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#include <cstring>
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#include <cuda.h>
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#include <memory>
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#include <set>
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#include <string_view>
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#include <vector>
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#include "NvInfer.h"
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#include "common/serialize.hpp"
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#include "embLayerNormVarSeqlenPlugin.h"
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using namespace nvinfer1;
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using namespace nvinfer1::plugin;
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using namespace nvinfer1::plugin::bert;
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namespace
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{
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using namespace std::string_view_literals;
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constexpr char const* kEMB_LAYER_NORM_VAR_SEQLEN_VERSION_HFACE{"4"};
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constexpr char const* kEMB_LAYER_NORM_VAR_SEQLEN_VERSION_MTRON{"5"};
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constexpr char const* kEMB_LAYER_NORM_VAR_SEQLEN_NAME{"CustomEmbLayerNormPluginDynamic"};
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void checkConfigurationInputs(
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PluginTensorDesc const* inputs, int32_t nbInputs, PluginTensorDesc const* outputs, int32_t nbOutputs) noexcept
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{
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// Validate input arguments
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PLUGIN_ASSERT(nbInputs == 4);
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PLUGIN_ASSERT(nbOutputs == 2);
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PLUGIN_ASSERT(inputs[0].dims.nbDims == 1);
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PLUGIN_ASSERT(inputs[1].dims.nbDims == 1);
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PLUGIN_ASSERT(inputs[1].dims.d[0] == inputs[0].dims.d[0]);
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PLUGIN_ASSERT(inputs[2].dims.nbDims == 1);
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PLUGIN_ASSERT(outputs[0].dims.nbDims == 4);
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PLUGIN_ASSERT(static_cast<size_t>(outputs[0].dims.d[0]) == static_cast<size_t>(inputs[0].dims.d[0]));
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PLUGIN_ASSERT(outputs[0].dims.d[2] == 1);
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PLUGIN_ASSERT(outputs[0].dims.d[3] == 1);
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PLUGIN_ASSERT(inputs[0].type == DataType::kINT32);
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PLUGIN_ASSERT(inputs[1].type == DataType::kINT32);
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PLUGIN_ASSERT(inputs[2].type == DataType::kINT32);
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}
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bool initializeFields(char const* name, PluginFieldCollection const* fc, Weights& beta, Weights& gamma,
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Weights& word_emb, Weights& pos_emb, Weights& tok_emb)
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{
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bool output_fp16 = false;
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std::set<std::string> const requiredAttributes{
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"bert_embeddings_layernorm_beta",
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"bert_embeddings_layernorm_gamma",
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"bert_embeddings_word_embeddings",
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"bert_embeddings_token_type_embeddings",
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"bert_embeddings_position_embeddings",
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};
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plugin::validateRequiredAttributesExist(requiredAttributes, fc);
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for (int32_t i = 0; i < fc->nbFields; i++)
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{
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std::string_view const field_name = fc->fields[i].name;
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if (field_name == "bert_embeddings_layernorm_beta"sv)
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{
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BERT_DEBUG_MSG("Building bert_embeddings_layernorm_beta...");
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beta.values = fc->fields[i].data;
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beta.count = fc->fields[i].length;
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beta.type = fieldTypeToDataType(fc->fields[i].type);
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}
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else if (field_name == "bert_embeddings_layernorm_gamma"sv)
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{
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BERT_DEBUG_MSG("Building bert_embeddings_layernorm_gamma...");
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gamma.values = fc->fields[i].data;
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gamma.count = fc->fields[i].length;
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gamma.type = fieldTypeToDataType(fc->fields[i].type);
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}
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else if (field_name == "bert_embeddings_word_embeddings"sv)
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{
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BERT_DEBUG_MSG("Building bert_embeddings_word_embeddings...");
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word_emb.values = fc->fields[i].data;
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word_emb.count = fc->fields[i].length;
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word_emb.type = fieldTypeToDataType(fc->fields[i].type);
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}
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else if (field_name == "bert_embeddings_token_type_embeddings"sv)
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{
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BERT_DEBUG_MSG("Building bert_embeddings_token_type_embeddings...");
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tok_emb.values = fc->fields[i].data;
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tok_emb.count = fc->fields[i].length;
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tok_emb.type = fieldTypeToDataType(fc->fields[i].type);
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}
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else if (field_name == "bert_embeddings_position_embeddings"sv)
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{
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BERT_DEBUG_MSG("Building bert_embeddings_position_embeddings...");
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pos_emb.values = fc->fields[i].data;
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pos_emb.count = fc->fields[i].length;
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pos_emb.type = fieldTypeToDataType(fc->fields[i].type);
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}
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else if (field_name == "output_fp16"sv)
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{
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BERT_DEBUG_MSG("Building output_fp16...");
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PLUGIN_VALIDATE(fc->fields[i].type == PluginFieldType::kINT32);
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output_fp16 = static_cast<int32_t const*>(fc->fields[i].data)[0] != 0;
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}
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}
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return output_fp16;
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}
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} // namespace
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REGISTER_TENSORRT_PLUGIN(EmbLayerNormVarSeqlenPluginHFaceCreator);
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REGISTER_TENSORRT_PLUGIN(EmbLayerNormVarSeqlenPluginMTronCreator);
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EmbLayerNormVarSeqlenPluginBase::EmbLayerNormVarSeqlenPluginBase(std::string const& name, DataType type,
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Weights const& beta, Weights const& gamma, Weights const& wordEmb, Weights const& posEmb, Weights const& tokEmb,
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DataType maskType)
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: mLayerName(name)
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, mLd(beta.count)
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, mType(type)
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, mMaskType(maskType)
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{
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// Assuming Weights.count is the number of elements and not bytes
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PLUGIN_VALIDATE(beta.count == gamma.count);
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PLUGIN_VALIDATE(mLd > 0U);
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PLUGIN_VALIDATE(wordEmb.count % mLd == 0);
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PLUGIN_VALIDATE(posEmb.count % mLd == 0);
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PLUGIN_VALIDATE(tokEmb.count % mLd == 0);
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mWordVocabSize = wordEmb.count / mLd;
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mPosVocabSize = posEmb.count / mLd;
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mTokVocabSize = tokEmb.count / mLd;
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mBeta.convertAndCopy(beta, nvinfer1::DataType::kFLOAT);
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mGamma.convertAndCopy(gamma, nvinfer1::DataType::kFLOAT);
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mWordEmb.convertAndCopy(wordEmb, mType);
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mTokEmb.convertAndCopy(tokEmb, mType);
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mPosEmb.convertAndCopy(posEmb, mType);
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copyToDevice(mGamma, sizeof(float) * mGamma.count, mGammaDev);
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copyToDevice(mBeta, sizeof(float) * mBeta.count, mBetaDev);
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copyToDevice(mWordEmb, getWeightsSize(mWordEmb, mType), mWordEmbDev);
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copyToDevice(mPosEmb, getWeightsSize(mPosEmb, mType), mPosEmbDev);
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copyToDevice(mTokEmb, getWeightsSize(mTokEmb, mType), mTokEmbDev);
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}
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EmbLayerNormVarSeqlenPluginHFace::EmbLayerNormVarSeqlenPluginHFace(std::string const& name, DataType const type,
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Weights const& beta, Weights const& gamma, Weights const& wordEmb, Weights const& posEmb, Weights const& tokEmb)
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: EmbLayerNormVarSeqlenPluginBase(name, type, beta, gamma, wordEmb, posEmb, tokEmb, DataType::kINT32)
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{
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BERT_DEBUG_MSG("EmbLayerNormVarSeqlenPluginHFace creation");
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}
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EmbLayerNormVarSeqlenPluginMTron::EmbLayerNormVarSeqlenPluginMTron(std::string const& name, DataType const type,
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Weights const& beta, Weights const& gamma, Weights const& wordEmb, Weights const& posEmb, Weights const& tokEmb)
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: EmbLayerNormVarSeqlenPluginBase(name, type, beta, gamma, wordEmb, posEmb, tokEmb, type)
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{
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BERT_DEBUG_MSG("EmbLayerNormVarSeqlenPluginMTron creation");
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}
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EmbLayerNormVarSeqlenPluginBase::~EmbLayerNormVarSeqlenPluginBase()
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{
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try
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{
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// This gets called when the network containing plugin is destroyed
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mGammaDev.reset(nullptr);
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mBetaDev.reset(nullptr);
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mWordEmbDev.reset(nullptr);
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mPosEmbDev.reset(nullptr);
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mTokEmbDev.reset(nullptr);
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// delete this; (TRT will delete this plugin object)
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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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}
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EmbLayerNormVarSeqlenPluginHFace::~EmbLayerNormVarSeqlenPluginHFace()
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{
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BERT_DEBUG_MSG("EmbLayerNormVarSeqlenPluginHFace destruction");
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}
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EmbLayerNormVarSeqlenPluginMTron::~EmbLayerNormVarSeqlenPluginMTron()
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{
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BERT_DEBUG_MSG("EmbLayerNormVarSeqlenPluginMTron destruction");
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}
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//////
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// IPluginV3 method definitions:
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// - getCapabilityInterface() (Base)
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// - clone() (HFace, MTron)
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//////
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IPluginCapability* EmbLayerNormVarSeqlenPluginBase::getCapabilityInterface(PluginCapabilityType type) noexcept
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{
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try
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{
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if (type == PluginCapabilityType::kBUILD)
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{
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return static_cast<IPluginV3OneBuild*>(this);
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}
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if (type == PluginCapabilityType::kRUNTIME)
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{
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return static_cast<IPluginV3OneRuntime*>(this);
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}
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PLUGIN_ASSERT(type == PluginCapabilityType::kCORE);
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return static_cast<IPluginV3OneCore*>(this);
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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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IPluginV3* EmbLayerNormVarSeqlenPluginHFace::clone() noexcept
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{
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try
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{
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BERT_DEBUG_MSG("EmbLayerNormVarSeqlenPluginHFace clone");
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auto p = std::make_unique<EmbLayerNormVarSeqlenPluginHFace>(
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mLayerName, mType, mBeta, mGamma, mWordEmb, mPosEmb, mTokEmb);
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p->setPluginNamespace(mNamespace.c_str());
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return p.release();
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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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IPluginV3* EmbLayerNormVarSeqlenPluginMTron::clone() noexcept
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{
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try
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{
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BERT_DEBUG_MSG("EmbLayerNormVarSeqlenPluginMTron clone");
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auto p = std::make_unique<EmbLayerNormVarSeqlenPluginMTron>(
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mLayerName, mType, mBeta, mGamma, mWordEmb, mPosEmb, mTokEmb);
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p->setPluginNamespace(mNamespace.c_str());
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return p.release();
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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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// End IPluginV3 method definitions
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//////
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// IPluginV3OneRuntime method definitions:
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// - getFieldsToSerialize() (Base)
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// - onShapeChange() (Base)
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// - attachToContext() (Base)
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// - enqueue() (HFace, MTron)
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/////
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PluginFieldCollection const* EmbLayerNormVarSeqlenPluginBase::getFieldsToSerialize() noexcept
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{
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mDataToSerialize.clear();
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bool output_fp16 = mType == DataType::kHALF;
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mDataToSerialize.emplace_back("output_fp16", &output_fp16, PluginFieldType::kINT32, 1);
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mDataToSerialize.emplace_back("bert_embeddings_layernorm_beta", static_cast<float const*>(mBeta.values),
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PluginFieldType::kFLOAT32, mBeta.count);
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mDataToSerialize.emplace_back("bert_embeddings_layernorm_gamma", static_cast<float const*>(mGamma.values),
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PluginFieldType::kFLOAT32, mGamma.count);
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if (output_fp16)
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{
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mDataToSerialize.emplace_back("bert_embeddings_word_embeddings", static_cast<half const*>(mWordEmb.values),
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PluginFieldType::kFLOAT16, mWordEmb.count);
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mDataToSerialize.emplace_back("bert_embeddings_token_type_embeddings", static_cast<half const*>(mTokEmb.values),
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PluginFieldType::kFLOAT16, mTokEmb.count);
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mDataToSerialize.emplace_back("bert_embeddings_position_embeddings", static_cast<half const*>(mPosEmb.values),
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PluginFieldType::kFLOAT16, mPosEmb.count);
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}
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else
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{
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mDataToSerialize.emplace_back("bert_embeddings_word_embeddings", static_cast<float const*>(mWordEmb.values),
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PluginFieldType::kFLOAT32, mWordEmb.count);
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mDataToSerialize.emplace_back("bert_embeddings_token_type_embeddings",
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static_cast<float const*>(mTokEmb.values), PluginFieldType::kFLOAT32, mTokEmb.count);
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mDataToSerialize.emplace_back("bert_embeddings_position_embeddings", static_cast<float const*>(mPosEmb.values),
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PluginFieldType::kFLOAT32, mPosEmb.count);
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}
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mFCToSerialize.nbFields = mDataToSerialize.size();
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mFCToSerialize.fields = mDataToSerialize.data();
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return &mFCToSerialize;
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}
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int32_t EmbLayerNormVarSeqlenPluginHFace::onShapeChange(
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PluginTensorDesc const* inputs, int32_t nbInputs, PluginTensorDesc const* outputs, int32_t nbOutputs) noexcept
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{
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try
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{
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BERT_DEBUG_MSG("EmbLayerNormVarSeqlenPluginHFace onShapeChange");
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checkConfigurationInputs(inputs, nbInputs, outputs, nbOutputs);
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// output 0 is the embedding
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PLUGIN_ASSERT(static_cast<size_t>(outputs[0].dims.d[1]) == static_cast<size_t>(mLd));
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PLUGIN_ASSERT(outputs[0].type == mType);
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// output 1 is the mask indices (empty for HFace variant)
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PLUGIN_ASSERT(outputs[1].dims.nbDims == 0);
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PLUGIN_ASSERT(outputs[1].type == mMaskType);
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return pluginStatus_t::STATUS_SUCCESS;
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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 pluginStatus_t::STATUS_FAILURE;
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}
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int32_t EmbLayerNormVarSeqlenPluginMTron::onShapeChange(
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PluginTensorDesc const* inputs, int32_t nbInputs, PluginTensorDesc const* outputs, int32_t nbOutputs) noexcept
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{
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try
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{
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// Validate input arguments
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BERT_DEBUG_MSG("EmbLayerNormVarSeqlenPluginMTron onShapeChange");
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checkConfigurationInputs(inputs, nbInputs, outputs, nbOutputs);
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PLUGIN_ASSERT(static_cast<size_t>(outputs[0].dims.d[1]) == static_cast<size_t>(mLd));
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PLUGIN_ASSERT(outputs[1].dims.nbDims == 4);
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PLUGIN_ASSERT(static_cast<size_t>(outputs[1].dims.d[0]) == static_cast<size_t>(inputs[0].dims.d[0]));
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PLUGIN_ASSERT(static_cast<size_t>(outputs[1].dims.d[1]) == static_cast<size_t>(mLd));
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PLUGIN_ASSERT(outputs[1].dims.d[2] == 1);
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PLUGIN_ASSERT(outputs[1].dims.d[3] == 1);
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PLUGIN_ASSERT(outputs[0].type == mType);
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PLUGIN_ASSERT(outputs[1].type == mMaskType);
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return pluginStatus_t::STATUS_SUCCESS;
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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 pluginStatus_t::STATUS_FAILURE;
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}
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IPluginV3* EmbLayerNormVarSeqlenPluginBase::attachToContext(IPluginResourceContext* context) noexcept
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{
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return clone();
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}
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int32_t EmbLayerNormVarSeqlenPluginHFace::enqueue(PluginTensorDesc const* inputDesc,
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PluginTensorDesc const* /* outputDesc */, void const* const* inputs, void* const* outputs, void* /* workspace */,
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cudaStream_t stream) noexcept
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{
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try
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{
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PLUGIN_VALIDATE(inputDesc != nullptr && inputs != nullptr && outputs != nullptr);
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int32_t const batchSize = inputDesc[2].dims.d[0] - 1;
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// read out the maximum sequence length from the dummy input
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int32_t const maxSeqlen = inputDesc[3].dims.d[0];
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// There are four versions of the kernel which are optimized for sequence lengths 384, 256, 192 and 128.
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// Find the closest sequence length bigger than the max seq length in this batch.
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int32_t S = 384;
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if (maxSeqlen <= 128)
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{
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S = 128;
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}
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else if (maxSeqlen <= 192)
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{
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S = 192;
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}
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else if (maxSeqlen <= 256)
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{
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S = 256;
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}
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// Our plugin outputs only one tensor
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auto const inputIds = static_cast<int32_t const*>(inputs[0]);
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auto const segmentIds = static_cast<int32_t const*>(inputs[1]);
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int32_t const* cuSeqlens = static_cast<int32_t const*>(inputs[2]);
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float const* beta = mBetaDev.get();
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float const* gamma = mGammaDev.get();
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if (mType == DataType::kFLOAT)
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{
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auto output = static_cast<float*>(outputs[0]);
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auto const wordEmb = static_cast<float const*>(mWordEmbDev.get());
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auto const tokEmb = static_cast<float const*>(mTokEmbDev.get());
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auto const posEmb = static_cast<float const*>(mPosEmbDev.get());
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return embSkipLayerNormHFace<float>(stream, static_cast<int32_t>(mLd), batchSize, S, inputIds, segmentIds,
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cuSeqlens, beta, gamma, wordEmb, posEmb, tokEmb, mWordVocabSize, mTokVocabSize, output);
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}
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if (mType == DataType::kHALF)
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{
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auto output = static_cast<half*>(outputs[0]);
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auto const wordEmb = static_cast<half const*>(mWordEmbDev.get());
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auto const tokEmb = static_cast<half const*>(mTokEmbDev.get());
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auto const posEmb = static_cast<half const*>(mPosEmbDev.get());
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return embSkipLayerNormHFace<half>(stream, static_cast<int32_t>(mLd), batchSize, S, inputIds, segmentIds,
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cuSeqlens, beta, gamma, wordEmb, posEmb, tokEmb, mWordVocabSize, mTokVocabSize, output);
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}
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else
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{
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gLogError << "Unsupported type error, expected [kHALF,kFLOAT], but received " << static_cast<int32_t>(mType)
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<< std::endl;
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return STATUS_NOT_SUPPORTED;
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}
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return STATUS_SUCCESS;
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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 STATUS_FAILURE;
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}
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int32_t EmbLayerNormVarSeqlenPluginMTron::enqueue(PluginTensorDesc const* inputDesc,
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PluginTensorDesc const* /* outputDesc */, void const* const* inputs, void* const* outputs, void* /* workspace */,
|
|
cudaStream_t stream) noexcept
|
|
{
|
|
try
|
|
{
|
|
PLUGIN_VALIDATE(inputDesc != nullptr && inputs != nullptr && outputs != nullptr);
|
|
|
|
int32_t const batchSize = inputDesc[2].dims.d[0] - 1;
|
|
// read out the maximum sequence length from the dummy input
|
|
int32_t const maxSeqlen = inputDesc[3].dims.d[0];
|
|
|
|
// There are four versions of the kernel which are optimized for sequence lengths 384, 256, 192 and 128.
|
|
// Find the closest sequence length bigger than the max seq length in this batch.
|
|
int32_t S = 384;
|
|
if (maxSeqlen <= 128)
|
|
{
|
|
S = 128;
|
|
}
|
|
else if (maxSeqlen <= 192)
|
|
{
|
|
S = 192;
|
|
}
|
|
else if (maxSeqlen <= 256)
|
|
{
|
|
S = 256;
|
|
}
|
|
|
|
// Our plugin outputs only one tensor
|
|
auto const inputIds = static_cast<int32_t const*>(inputs[0]);
|
|
auto const segmentIds = static_cast<int32_t const*>(inputs[1]);
|
|
int32_t const* cuSeqlens = static_cast<int32_t const*>(inputs[2]);
|
|
|
|
float const* beta = mBetaDev.get();
|
|
float const* gamma = mGammaDev.get();
|
|
if (mType == DataType::kFLOAT)
|
|
{
|
|
auto output = static_cast<float*>(outputs[0]);
|
|
auto skip = static_cast<float*>(outputs[1]);
|
|
auto const wordEmb = static_cast<float const*>(mWordEmbDev.get());
|
|
auto const tokEmb = static_cast<float const*>(mTokEmbDev.get());
|
|
auto const posEmb = static_cast<float const*>(mPosEmbDev.get());
|
|
|
|
return embSkipLayerNormMTron<float>(stream, static_cast<int32_t>(mLd), batchSize, S, inputIds, segmentIds,
|
|
cuSeqlens, beta, gamma, wordEmb, posEmb, tokEmb, mWordVocabSize, mTokVocabSize, output, skip);
|
|
}
|
|
if (mType == DataType::kHALF)
|
|
{
|
|
auto output = static_cast<half*>(outputs[0]);
|
|
auto skip = static_cast<half*>(outputs[1]);
|
|
auto const wordEmb = static_cast<half const*>(mWordEmbDev.get());
|
|
auto const tokEmb = static_cast<half const*>(mTokEmbDev.get());
|
|
auto const posEmb = static_cast<half const*>(mPosEmbDev.get());
|
|
|
|
return embSkipLayerNormMTron<half>(stream, static_cast<int32_t>(mLd), batchSize, S, inputIds, segmentIds,
|
|
cuSeqlens, beta, gamma, wordEmb, posEmb, tokEmb, mWordVocabSize, mTokVocabSize, output, skip);
|
|
}
|
|
else
|
|
{
|
|
gLogError << "Unsupported type error, expected [kHALF,kFLOAT], but received " << static_cast<int32_t>(mType)
|
|
<< std::endl;
|
|
|
|
return STATUS_NOT_SUPPORTED;
|
|
}
|
|
|
|
return STATUS_SUCCESS;
|
|
}
|
|
catch (std::exception const& e)
|
|
{
|
|
caughtError(e);
|
|
}
|
|
return STATUS_FAILURE;
|
|
}
|
|
|
|
// end IPluginV3OneRuntime method definitions
|
|
|
|
///////
|
|
// IPluginV3OneBuild method definitions
|
|
// - getNbOutputs() (Base)
|
|
// - supportsFormatCombination() (Base)
|
|
// - getOutputShapes (HFace, MTron)
|
|
// - getOutputDataTypes() (Base)
|
|
// - configurePlugin() (Base)
|
|
// - getWorkSpaceSize() (Base)
|
|
//////
|
|
|
|
int32_t EmbLayerNormVarSeqlenPluginBase::getNbOutputs() const noexcept
|
|
{
|
|
return 2;
|
|
}
|
|
|
|
bool EmbLayerNormVarSeqlenPluginBase::supportsFormatCombination(
|
|
int32_t pos, DynamicPluginTensorDesc const* inOut, int32_t nbInputs, int32_t nbOutputs) noexcept
|
|
{
|
|
// The four inputs to this plugin input_ids, segment_ids, cu_seqlens and a dummy input with the
|
|
// size of the max seq length in that order
|
|
PLUGIN_ASSERT(nbInputs == 4);
|
|
// The two outputs of the plugin are embedding and the mask
|
|
PLUGIN_ASSERT(nbOutputs == 2);
|
|
|
|
PluginTensorDesc const& desc = inOut[pos].desc;
|
|
if (desc.format != TensorFormat::kLINEAR)
|
|
{
|
|
return false;
|
|
}
|
|
if (pos == 0 || pos == 2) // input_ids and cu_seqlens
|
|
{
|
|
return desc.type == DataType::kINT32 && desc.dims.nbDims == 1;
|
|
}
|
|
|
|
PluginTensorDesc const& prev = inOut[pos - 1].desc;
|
|
if (pos == 1) // segment ids: check it's the same as input_ids
|
|
{
|
|
return desc.type == DataType::kINT32 && desc.dims.nbDims == 1 && desc.dims.d[0] == prev.dims.d[0];
|
|
}
|
|
|
|
if (pos == 3)
|
|
{
|
|
return desc.dims.nbDims == 1;
|
|
}
|
|
|
|
// embedded sequence
|
|
if (pos == nbInputs)
|
|
{
|
|
return desc.type == mType && desc.dims.nbDims == 4 && desc.dims.d[0] == inOut[0].desc.dims.d[0]
|
|
&& desc.dims.d[2] == 1 && desc.dims.d[3] == 1;
|
|
}
|
|
// mask
|
|
return desc.type == mMaskType;
|
|
}
|
|
|
|
int32_t EmbLayerNormVarSeqlenPluginHFace::getOutputShapes(DimsExprs const* inputs, int32_t nbInputs,
|
|
DimsExprs const* shapeInputs, int32_t nbShapeInputs, DimsExprs* outputs, int32_t nbOutputs,
|
|
IExprBuilder& exprBuilder) noexcept
|
|
{
|
|
try
|
|
{
|
|
PLUGIN_VALIDATE(inputs != nullptr);
|
|
PLUGIN_VALIDATE(outputs != nullptr);
|
|
|
|
// Input should be input ids and token ids and cumulative seqlens
|
|
// Output should be the embeddings tensor and mask indices
|
|
PLUGIN_ASSERT(nbInputs == 4);
|
|
PLUGIN_ASSERT(nbOutputs == 2);
|
|
|
|
PLUGIN_ASSERT(inputs[0].nbDims == 1); // sum of all s
|
|
PLUGIN_ASSERT(inputs[0].nbDims == inputs[1].nbDims);
|
|
|
|
PLUGIN_ASSERT(inputs[2].nbDims == 1); // B+1
|
|
|
|
// output 0 : embedded input
|
|
outputs[0].nbDims = 4;
|
|
outputs[0].d[0] = inputs[0].d[0];
|
|
outputs[0].d[1] = exprBuilder.constant(mLd);
|
|
outputs[0].d[2] = exprBuilder.constant(1);
|
|
outputs[0].d[3] = exprBuilder.constant(1);
|
|
|
|
// Output 1 : maskIdx
|
|
// Return empty tensor since this is dummy output, we do not delete it for backward compatibility.
|
|
outputs[1].nbDims = 0;
|
|
return pluginStatus_t::STATUS_SUCCESS;
|
|
}
|
|
catch (const std::exception& e)
|
|
{
|
|
caughtError(e);
|
|
}
|
|
return pluginStatus_t::STATUS_FAILURE;
|
|
}
|
|
|
|
int32_t EmbLayerNormVarSeqlenPluginMTron::getOutputShapes(DimsExprs const* inputs, int32_t nbInputs,
|
|
DimsExprs const* shapeInputs, int32_t nbShapeInputs, DimsExprs* outputs, int32_t nbOutputs,
|
|
IExprBuilder& exprBuilder) noexcept
|
|
{
|
|
try
|
|
{
|
|
PLUGIN_VALIDATE(inputs != nullptr);
|
|
PLUGIN_VALIDATE(outputs != nullptr);
|
|
// Input should be input ids and token ids and cumulative seqlens
|
|
// Output should be the embeddings tensor and mask indices
|
|
PLUGIN_ASSERT(nbInputs == 4);
|
|
PLUGIN_ASSERT(nbOutputs == 2);
|
|
|
|
PLUGIN_ASSERT(inputs[0].nbDims == 1); // sum of all s
|
|
PLUGIN_ASSERT(inputs[0].nbDims == inputs[1].nbDims);
|
|
PLUGIN_ASSERT(inputs[2].nbDims == 1); // B+1
|
|
|
|
// Output 0 : embedded input
|
|
outputs[0].nbDims = 4;
|
|
outputs[0].d[0] = inputs[0].d[0];
|
|
outputs[0].d[1] = exprBuilder.constant(mLd);
|
|
outputs[0].d[2] = exprBuilder.constant(1);
|
|
outputs[0].d[3] = exprBuilder.constant(1);
|
|
|
|
// Output 1 : maskIdx
|
|
outputs[1].nbDims = 4;
|
|
outputs[1].d[0] = inputs[0].d[0];
|
|
outputs[1].d[1] = exprBuilder.constant(mLd);
|
|
outputs[1].d[2] = exprBuilder.constant(1);
|
|
outputs[1].d[3] = exprBuilder.constant(1);
|
|
|
|
return pluginStatus_t::STATUS_SUCCESS;
|
|
}
|
|
catch (const std::exception& e)
|
|
{
|
|
caughtError(e);
|
|
}
|
|
return pluginStatus_t::STATUS_FAILURE;
|
|
}
|
|
|
|
int32_t EmbLayerNormVarSeqlenPluginBase::getOutputDataTypes(
|
|
DataType* outputTypes, int32_t nbOutputs, DataType const* inputTypes, int32_t nbInputs) const noexcept
|
|
{
|
|
try
|
|
{
|
|
PLUGIN_ASSERT(mType == DataType::kHALF || mType == DataType::kFLOAT);
|
|
outputTypes[0] = mType;
|
|
outputTypes[1] = mMaskType;
|
|
return pluginStatus_t::STATUS_SUCCESS;
|
|
}
|
|
catch (std::exception const& e)
|
|
{
|
|
caughtError(e);
|
|
}
|
|
return pluginStatus_t::STATUS_FAILURE;
|
|
}
|
|
|
|
int32_t EmbLayerNormVarSeqlenPluginBase::configurePlugin(DynamicPluginTensorDesc const* inputs, int32_t nbInputs,
|
|
DynamicPluginTensorDesc const* outputs, int32_t nbOutputs) noexcept
|
|
{
|
|
return pluginStatus_t::STATUS_SUCCESS;
|
|
}
|
|
|
|
size_t EmbLayerNormVarSeqlenPluginBase::getWorkspaceSize(DynamicPluginTensorDesc const* inputs, int32_t nbInputs,
|
|
DynamicPluginTensorDesc const* outputs, int32_t nbOutputs) const noexcept
|
|
{
|
|
return 0;
|
|
}
|
|
// End IPluginV3OneBuild method definitions
|
|
|
|
//////
|
|
// IPluginV3OneCore method definitions
|
|
// - getPluginVersion() (MTron, HFace)
|
|
// - getPluginName() (Base)
|
|
// - getPluginNamespace() (Base)
|
|
// - setPluginNamespace() (Base)
|
|
//////
|
|
char const* EmbLayerNormVarSeqlenPluginHFace::getPluginVersion() const noexcept
|
|
{
|
|
return kEMB_LAYER_NORM_VAR_SEQLEN_VERSION_HFACE;
|
|
}
|
|
|
|
char const* EmbLayerNormVarSeqlenPluginMTron::getPluginVersion() const noexcept
|
|
{
|
|
return kEMB_LAYER_NORM_VAR_SEQLEN_VERSION_MTRON;
|
|
}
|
|
|
|
char const* EmbLayerNormVarSeqlenPluginBase::getPluginName() const noexcept
|
|
{
|
|
return kEMB_LAYER_NORM_VAR_SEQLEN_NAME;
|
|
}
|
|
|
|
char const* EmbLayerNormVarSeqlenPluginBase::getPluginNamespace() const noexcept
|
|
{
|
|
return mNamespace.c_str();
|
|
}
|
|
|
|
void EmbLayerNormVarSeqlenPluginBase::setPluginNamespace(char const* libNamespace) noexcept
|
|
{
|
|
mNamespace = libNamespace;
|
|
}
|
|
// End IPluginV3OneCore method definitions
|
|
|
|
//////////////////////////// Plugin Creator member definitions /////////////////////////////
|
|
|
|
EmbLayerNormVarSeqlenPluginBaseCreator::EmbLayerNormVarSeqlenPluginBaseCreator()
|
|
{
|
|
static std::mutex sMutex;
|
|
std::lock_guard<std::mutex> lock(sMutex);
|
|
mPluginAttributes.clear();
|
|
mPluginAttributes.emplace_back(PluginField("output_fp16", nullptr, PluginFieldType::kINT32, 1));
|
|
// the length of beta, gamma, word_emb, pos_emb, and tok_emb will only be known at the time of plugin creation
|
|
// so we set it to 0 here
|
|
mPluginAttributes.emplace_back(PluginField("bert_embeddings_layernorm_beta", nullptr, PluginFieldType::kFLOAT32, 0));
|
|
mPluginAttributes.emplace_back(PluginField("bert_embeddings_layernorm_gamma", nullptr, PluginFieldType::kFLOAT32, 0));
|
|
// the embeddings datatype is determined by the output_fp16 attribute known at runtime
|
|
// so we set it to kUNKNOWN here
|
|
mPluginAttributes.emplace_back(PluginField("bert_embeddings_word_embeddings", nullptr, PluginFieldType::kUNKNOWN, 0));
|
|
mPluginAttributes.emplace_back(PluginField("bert_embeddings_token_type_embeddings", nullptr, PluginFieldType::kUNKNOWN, 0));
|
|
mPluginAttributes.emplace_back(PluginField("bert_embeddings_position_embeddings", nullptr, PluginFieldType::kUNKNOWN, 0));
|
|
mFC.nbFields = mPluginAttributes.size();
|
|
mFC.fields = mPluginAttributes.data();
|
|
}
|
|
|
|
char const* EmbLayerNormVarSeqlenPluginBaseCreator::getPluginName() const noexcept
|
|
{
|
|
return kEMB_LAYER_NORM_VAR_SEQLEN_NAME;
|
|
}
|
|
|
|
char const* EmbLayerNormVarSeqlenPluginHFaceCreator::getPluginVersion() const noexcept
|
|
{
|
|
return kEMB_LAYER_NORM_VAR_SEQLEN_VERSION_HFACE;
|
|
}
|
|
|
|
char const* EmbLayerNormVarSeqlenPluginMTronCreator::getPluginVersion() const noexcept
|
|
{
|
|
return kEMB_LAYER_NORM_VAR_SEQLEN_VERSION_MTRON;
|
|
}
|
|
|
|
PluginFieldCollection const* EmbLayerNormVarSeqlenPluginBaseCreator::getFieldNames() noexcept
|
|
{
|
|
return &mFC;
|
|
}
|
|
|
|
IPluginV3* EmbLayerNormVarSeqlenPluginHFaceCreator::createPlugin(
|
|
char const* name, PluginFieldCollection const* fc, TensorRTPhase phase) noexcept
|
|
{
|
|
try
|
|
{
|
|
BERT_DEBUG_MSG("EmbLayerNormVarSeqlenHFace createPlugin");
|
|
|
|
Weights beta{}; // required attribute: validateRequiredAttributesExist() call in initializeFields() will verify
|
|
// existence
|
|
Weights gamma{}; // required attribute: validateRequiredAttributesExist() call in initializeFields() will verify
|
|
// existence
|
|
Weights word_emb{}; // required attribute: validateRequiredAttributesExist() call in initializeFields() will
|
|
// verify existence
|
|
Weights pos_emb{}; // required attribute: validateRequiredAttributesExist() call in initializeFields() will
|
|
// verify existence
|
|
Weights tok_emb{}; // required attribute: validateRequiredAttributesExist() call in initializeFields() will
|
|
// verify existence
|
|
bool output_fp16 = initializeFields(name, fc, beta, gamma, word_emb, pos_emb, tok_emb);
|
|
|
|
BERT_DEBUG_MSG("Building the Plugin...");
|
|
auto p = std::make_unique<EmbLayerNormVarSeqlenPluginHFace>(
|
|
name, output_fp16 ? DataType::kHALF : DataType::kFLOAT, beta, gamma, word_emb, pos_emb, tok_emb);
|
|
return p.release();
|
|
}
|
|
catch (std::exception const& e)
|
|
{
|
|
caughtError(e);
|
|
}
|
|
return nullptr;
|
|
}
|
|
|
|
IPluginV3* EmbLayerNormVarSeqlenPluginMTronCreator::createPlugin(
|
|
char const* name, PluginFieldCollection const* fc, TensorRTPhase phase) noexcept
|
|
{
|
|
try
|
|
{
|
|
BERT_DEBUG_MSG("EmbLayerNormVarSeqlenMTron createPlugin");
|
|
|
|
Weights beta{}; // required attribute: validateRequiredAttributesExist() call in initializeFields() will verify
|
|
// existence
|
|
Weights gamma{}; // required attribute: validateRequiredAttributesExist() call in initializeFields() will verify
|
|
// existence
|
|
Weights word_emb{}; // required attribute: validateRequiredAttributesExist() call in initializeFields() will
|
|
// verify existence
|
|
Weights pos_emb{}; // required attribute: validateRequiredAttributesExist() call in initializeFields() will
|
|
// verify existence
|
|
Weights tok_emb{}; // required attribute: validateRequiredAttributesExist() call in initializeFields() will
|
|
// verify existence
|
|
bool output_fp16 = initializeFields(name, fc, beta, gamma, word_emb, pos_emb, tok_emb);
|
|
|
|
BERT_DEBUG_MSG("Building the Plugin...");
|
|
auto p = std::make_unique<EmbLayerNormVarSeqlenPluginMTron>(
|
|
name, output_fp16 ? DataType::kHALF : DataType::kFLOAT, beta, gamma, word_emb, pos_emb, tok_emb);
|
|
return p.release();
|
|
}
|
|
catch (std::exception const& e)
|
|
{
|
|
caughtError(e);
|
|
}
|
|
return nullptr;
|
|
}
|
|
|
|
void EmbLayerNormVarSeqlenPluginBaseCreator::setPluginNamespace(char const* libNamespace) noexcept
|
|
{
|
|
try
|
|
{
|
|
mNamespace = libNamespace;
|
|
}
|
|
catch (std::exception const& e)
|
|
{
|
|
caughtError(e);
|
|
}
|
|
}
|
|
|
|
char const* EmbLayerNormVarSeqlenPluginBaseCreator::getPluginNamespace() const noexcept
|
|
{
|
|
return mNamespace.c_str();
|
|
}
|