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https://github.com/NVIDIA/TensorRT-LLM.git
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16d2467ea8
* 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
97 lines
3.6 KiB
C++
97 lines
3.6 KiB
C++
/*
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* Copyright (c) 2022-2024, NVIDIA CORPORATION. All rights reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#pragma once
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#include "tensorrt_llm/runtime/bufferManager.h"
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#include "tensorrt_llm/runtime/common.h"
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#include "tensorrt_llm/runtime/generationConfig.h"
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#include "tensorrt_llm/runtime/iTensor.h"
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#include "tensorrt_llm/runtime/modelConfig.h"
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#include "tensorrt_llm/runtime/tllmRuntime.h"
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#include "tensorrt_llm/runtime/worldConfig.h"
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namespace tensorrt_llm::runtime
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{
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class RuntimeBuffers;
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class RnnStateBuffers
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{
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public:
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using TensorPtr = ITensor::SharedPtr;
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using TensorMap = StringPtrMap<ITensor>;
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TensorPtr rnnStates; // [layer_count * batch_beam, state_size, rnn_hidden_size]
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TensorPtr convStates; // [layer_count * batch_beam, conv_kernel - 1, rnn_hidden_size]
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TensorPtr convStatesAlt; // [layer_count * batch_beam, conv_kernel - 1, rnn_hidden_size]
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std::vector<TensorPtr> rnnState; // [batch_beam, state_size, rnn_hidden_size] or
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// [batch_beam, num_heads, rnn_hidden_size, rnn_head_size]
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std::vector<TensorPtr> convState; // [batch_beam, conv_kernel - 1, rnn_hidden_size]
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std::vector<TensorPtr> convStateAlt; // [batch_beam, conv_kernel - 1, rnn_hidden_size]
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TensorPtr slotMappingHost; // [batch_size]
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TensorPtr slotMappingDevice; // [batch_size]
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TensorPtr rnnStatePtrs; // [layer_count]
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TensorPtr convStatePtrs; // [layer_count]
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std::vector<TensorPtr> rnnStatePtr; // [1]
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std::vector<TensorPtr> convStatePtr; // [1]
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RnnStateBuffers();
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RnnStateBuffers(
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TllmRuntime const& runtime, runtime::ModelConfig const& modelConfig, runtime::WorldConfig const& worldConfig);
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void reshape(SizeType32 batchSize);
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void reshape(
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GenerationConfig const& generationConfig, ModelConfig const& modelConfig, WorldConfig const& worldConfig);
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void reset(BufferManager& manager);
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RnnStateBuffers sliceTo(SizeType32 offset, SizeType32 size);
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void prepareContextStep(RuntimeBuffers* runtimeBuffers, BufferManager& manager);
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void postContextStep(RuntimeBuffers* runtimeBuffers, std::vector<RuntimeBuffers> const& contextBuffers,
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BufferManager& manager, ModelConfig const& modelConfig, WorldConfig const& worldConfig);
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void getRuntimeBuffers(RuntimeBuffers const* runtimeBuffers, TensorMap& inputBuffers, TensorMap& outputBuffers,
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SizeType32 const step, TensorPtr const& inputIds, ModelConfig const& modelConfig,
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WorldConfig const& worldConfig) const;
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protected:
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void tile(RuntimeBuffers* runtimeBuffers, BufferManager& manager, ModelConfig const& modelConfig,
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WorldConfig const& worldConfig);
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void fillStatePtrs();
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private:
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SizeType32 mConvKernel = 0;
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SizeType32 mStateSize = 0;
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SizeType32 mRnnHiddenSize = 0;
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SizeType32 mRnnHeadSize = 0;
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SizeType32 mRnnConvDimSize = 0;
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int mLocalNbLayers = 0;
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int mMaxBeamWidth = 0;
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bool mUseMambaConv1dPlugin = true;
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};
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} // namespace tensorrt_llm::runtime
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