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https://github.com/NVIDIA/TensorRT-LLM.git
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* Update TensorRT-LLM --------- Co-authored-by: Denis Kayshev <topenkoff@gmail.com> Co-authored-by: akhoroshev <arthoroshev@gmail.com> Co-authored-by: Patrick Reiter Horn <patrick.horn@gmail.com> Update
175 lines
6.5 KiB
C++
175 lines
6.5 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 1993-2024 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/plugins/common/plugin.h"
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#include <cassert>
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#include <set>
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#include <string>
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#include <vector>
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namespace tensorrt_llm::plugins
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{
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class EagleDecodeDraftTokensPlugin : public BasePlugin
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{
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public:
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EagleDecodeDraftTokensPlugin(nvinfer1::DataType type, int32_t layerIdx, int32_t numEagleLayers, bool topKSampling);
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EagleDecodeDraftTokensPlugin(void const* data, size_t length);
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~EagleDecodeDraftTokensPlugin() 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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enum class InputIdxEntry : int32_t
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{
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// 12 inputs
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// [num_input_logits, vocab_size_padded]
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LOGITS = 0,
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// [batch_size, max_decoding_tokens, max_path_len]
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PATHS,
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// [1]
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NUM_VALID_LOGITS,
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// [1]
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USE_DYNAMIC_TREE,
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// [1]
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DYNAMIC_TREE_MAX_TOPK,
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// [batch_size, max_decoding_draft_tokens]
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INPUT_DRAFT_TOKEN_IDS,
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// [batch_size]
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INPUT_DRAFT_LENS,
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// [batch_size, max_decoding_draft_tokens]
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INPUT_PREV_SCORES,
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// [batch_size, max_decoding_draft_tokens]
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INPUT_CURRENT_EXPAND_INDICES,
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// [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
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INPUT_ALL_LAYERS_SCORES,
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// [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
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INPUT_ALL_LAYERS_DRAFT_TOKEN_IDS,
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// [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
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INPUT_ALL_LAYERS_DRAFT_TOKEN_IDS_PREDECESSOR
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};
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enum class OutputIdxEntry : int32_t
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{
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// 8 outputs
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// [batch_size, max_decoding_draft_tokens]
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OUTPUT_DRAFT_TOKEN_IDS = 0,
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// [batch_size]
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OUTPUT_DRAFT_LENS,
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// [batch_size, max_decoding_tokens, max_path_len]
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OUTPUT_PATHS,
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// [batch_size, max_decoding_draft_tokens]
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OUTPUT_CURRENT_SCORES,
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// [batch_size, max_decoding_draft_tokens]
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OUTPUT_NEXT_EXPAND_INDICES,
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// [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
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OUTPUT_ALL_LAYERS_SCORES,
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// [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
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OUTPUT_ALL_LAYERS_DRAFT_TOKEN_IDS,
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// [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
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OUTPUT_ALL_LAYERS_DRAFT_TOKEN_IDS_PREDECESSOR
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};
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int32_t getIdx(InputIdxEntry idx) const
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{
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return static_cast<int32_t>(idx);
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}
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int32_t getIdx(OutputIdxEntry idx) const
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{
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return static_cast<int32_t>(idx);
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}
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private:
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template <typename T>
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size_t getWorkspaceSizeType(nvinfer1::PluginTensorDesc const* inputs, int nbInputs,
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nvinfer1::PluginTensorDesc const* outputs, int nbOutputs) const noexcept;
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template <typename T>
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void enqueueType(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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template <typename T>
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void doTopKSampling(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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private:
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nvinfer1::DataType mDtype; // Logit datatype
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int32_t mLayerIdx{-1}; // Index of eagle layer
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int32_t mNumEagleLayers{-1}; // Number of eagle layers
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bool mTopKSampling; // Use TopK sampling or multinomial sampling
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};
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class EagleDecodeDraftTokensPluginCreator : public BaseCreator
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{
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public:
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EagleDecodeDraftTokensPluginCreator();
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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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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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