mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
597 lines
15 KiB
C++
597 lines
15 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/common/quantization.h"
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#include "tensorrt_llm/runtime/common.h"
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#include "tensorrt_llm/runtime/loraModule.h"
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#include "tensorrt_llm/runtime/medusaModule.h"
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#include <NvInferRuntime.h>
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namespace tensorrt_llm::runtime
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{
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class ModelConfig
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{
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public:
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enum class ModelVariant : std::int32_t
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{
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kGpt = 0,
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kGlm = 1, // https://github.com/THUDM/GLM and https://github.com/THUDM/ChatGLM-6B
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kMamba = 2, // https://github.com/state-spaces/mamba
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kRecurrentGemma = 3, // https://github.com/google-deepmind/recurrentgemma
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};
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struct MambaConfig
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{
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SizeType dState = 0;
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SizeType dConv = 0;
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SizeType expand = 0;
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};
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struct RnnConfig
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{
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SizeType dConv = 0;
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SizeType hiddenSize = 0;
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};
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enum class LayerType : std::int32_t
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{
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kATTENTION,
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kRECURRENT,
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};
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explicit ModelConfig(SizeType vocabSize, SizeType nbAttentionLayers, SizeType nbSsmLayers, SizeType nbHeads,
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SizeType hiddenSize, nvinfer1::DataType dtype)
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: mVocabSize(vocabSize)
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, mNbAttentionLayers(nbAttentionLayers)
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, mNbSsmLayers(nbSsmLayers)
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, mNbHeads(nbHeads)
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, mNbKvHeads(nbHeads)
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, mHiddenSize(hiddenSize)
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, mSizePerHead(mHiddenSize / mNbHeads)
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, mDataType(dtype)
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, mUseGptAttentionPlugin(false)
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, mUseMambaConv1dPlugin(false)
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, mInputPacked{false}
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, mPagedKvCache{false}
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, mPagedState{false}
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, mTokensPerBlock{64}
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, mQuantMode{common::QuantMode::none()}
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, mMaxBatchSize(0)
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, mMaxBeamWidth(0)
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, mMaxInputLen(0)
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, mMaxSequenceLen(0)
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, mMaxNumTokens(std::nullopt)
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, mComputeContextLogits(false)
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, mComputeGenerationLogits(false)
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, mModelVariant(ModelVariant::kGpt)
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, mUseCustomAllReduce(false)
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, mMaxPromptEmbeddingTableSize(0)
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, mMaxDraftLen(0)
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, mUseContextFMHAForGeneration(false)
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, mPagedContextFMHA(false)
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, mUseXQA{false}
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, mUseLoraPlugin(false)
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, mMlpHiddenSize(0)
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, mMedusaModule(std::nullopt)
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, mUseCrossAttention(false)
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, mUsePositionEmbedding(true) // TODO: remove these two properties?
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, mUseTokenTypeEmbedding(false)
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{
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}
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[[nodiscard]] SizeType constexpr getVocabSize() const noexcept
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{
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return mVocabSize;
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}
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[[nodiscard]] SizeType constexpr getVocabSizePadded(SizeType worldSize) const noexcept
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{
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return (mVocabSize + worldSize - 1) / worldSize * worldSize;
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}
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[[nodiscard]] SizeType constexpr getNbAttentionLayers(SizeType pipelineParallelism = 1) const
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{
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TLLM_CHECK(mNbAttentionLayers % pipelineParallelism == 0);
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return mNbAttentionLayers / pipelineParallelism;
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}
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[[nodiscard]] SizeType constexpr getNbSsmLayers(SizeType pipelineParallelism = 1) const
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{
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TLLM_CHECK(mNbSsmLayers % pipelineParallelism == 0);
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return mNbSsmLayers / pipelineParallelism;
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}
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[[nodiscard]] SizeType constexpr getNbHeads() const noexcept
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{
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return mNbHeads;
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}
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[[nodiscard]] SizeType constexpr getNbKvHeads() const noexcept
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{
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return mNbKvHeads;
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}
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void constexpr setNbKvHeads(SizeType nbKvHeads) noexcept
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{
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mNbKvHeads = nbKvHeads;
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}
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[[nodiscard]] SizeType constexpr getHiddenSize() const noexcept
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{
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return mHiddenSize;
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}
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[[nodiscard]] SizeType constexpr getSizePerHead() const noexcept
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{
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return mSizePerHead;
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}
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void constexpr setSizePerHead(SizeType sizePerHead) noexcept
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{
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mSizePerHead = sizePerHead;
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}
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[[nodiscard]] nvinfer1::DataType constexpr getDataType() const noexcept
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{
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return mDataType;
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}
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[[nodiscard]] bool constexpr useGptAttentionPlugin() const noexcept
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{
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return mUseGptAttentionPlugin;
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}
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void constexpr useGptAttentionPlugin(bool useGptAttentionPlugin) noexcept
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{
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mUseGptAttentionPlugin = useGptAttentionPlugin;
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}
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[[nodiscard]] bool constexpr useMambaConv1dPlugin() const noexcept
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{
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return mUseMambaConv1dPlugin;
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}
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void constexpr useMambaConv1dPlugin(bool useMambaConv1dPlugin) noexcept
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{
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mUseMambaConv1dPlugin = useMambaConv1dPlugin;
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}
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[[nodiscard]] bool constexpr usePackedInput() const noexcept
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{
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return mInputPacked;
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}
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void constexpr usePackedInput(bool inputPacked) noexcept
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{
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mInputPacked = inputPacked;
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}
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[[nodiscard]] bool constexpr usePagedKvCache() const noexcept
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{
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return mPagedKvCache;
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}
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void constexpr usePagedKvCache(bool pagedKvCache) noexcept
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{
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mPagedKvCache = pagedKvCache;
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}
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[[nodiscard]] bool constexpr usePagedState() const noexcept
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{
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return mPagedState;
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}
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void constexpr usePagedState(bool pagedState) noexcept
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{
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mPagedState = pagedState;
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}
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[[nodiscard]] SizeType constexpr getTokensPerBlock() const noexcept
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{
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return mTokensPerBlock;
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}
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void constexpr setTokensPerBlock(SizeType TokensPerBlock) noexcept
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{
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mTokensPerBlock = TokensPerBlock;
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}
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[[nodiscard]] common::QuantMode constexpr getQuantMode() const noexcept
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{
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return mQuantMode;
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}
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void constexpr setQuantMode(common::QuantMode QuantMode) noexcept
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{
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mQuantMode = QuantMode;
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}
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[[nodiscard]] bool constexpr supportsInflightBatching() const noexcept
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{
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return (isTransformerBased() && mUseGptAttentionPlugin && mInputPacked && mPagedKvCache)
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|| (isSsmBased() && mUseMambaConv1dPlugin && mInputPacked && mPagedState);
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}
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[[nodiscard]] SizeType constexpr getMaxBatchSize() const noexcept
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{
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return mMaxBatchSize;
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}
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void constexpr setMaxBatchSize(SizeType maxBatchSize) noexcept
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{
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mMaxBatchSize = maxBatchSize;
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}
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[[nodiscard]] SizeType constexpr getMaxBeamWidth() const noexcept
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{
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return mMaxBeamWidth;
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}
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void constexpr setMaxBeamWidth(SizeType maxBeamWidth) noexcept
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{
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mMaxBeamWidth = maxBeamWidth;
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}
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[[nodiscard]] SizeType constexpr getMaxInputLen() const noexcept
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{
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return mMaxInputLen;
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}
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void constexpr setMaxInputLen(SizeType maxInputLen) noexcept
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{
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mMaxInputLen = maxInputLen;
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}
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[[nodiscard]] SizeType constexpr getMaxSequenceLen() const noexcept
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{
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return mMaxSequenceLen;
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}
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void constexpr setMaxSequenceLen(SizeType maxSequenceLen) noexcept
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{
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mMaxSequenceLen = maxSequenceLen;
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}
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[[nodiscard]] std::optional<SizeType> constexpr getMaxNumTokens() const noexcept
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{
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return mMaxNumTokens;
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}
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void constexpr setMaxNumTokens(std::optional<SizeType> maxNumTokens) noexcept
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{
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mMaxNumTokens = maxNumTokens;
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}
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[[nodiscard]] bool constexpr usePromptTuning() const noexcept
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{
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return mMaxPromptEmbeddingTableSize > 0;
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}
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[[nodiscard]] SizeType constexpr getMaxPromptEmbeddingTableSize() const noexcept
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{
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return mMaxPromptEmbeddingTableSize;
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}
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void constexpr setMaxPromptEmbeddingTableSize(SizeType maxPromptEmbeddingTableSize) noexcept
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{
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mMaxPromptEmbeddingTableSize = maxPromptEmbeddingTableSize;
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}
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[[nodiscard]] bool constexpr computeContextLogits() const noexcept
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{
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return mComputeContextLogits;
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}
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void constexpr computeContextLogits(bool computeContextLogits) noexcept
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{
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mComputeContextLogits = computeContextLogits;
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}
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[[nodiscard]] bool constexpr computeGenerationLogits() const noexcept
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{
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return mComputeGenerationLogits;
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}
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void constexpr computeGenerationLogits(bool computeGenerationLogits) noexcept
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{
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mComputeGenerationLogits = computeGenerationLogits;
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}
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[[nodiscard]] ModelVariant getModelVariant() const
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{
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return mModelVariant;
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}
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void setModelVariant(ModelVariant modelVariant)
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{
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mModelVariant = modelVariant;
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}
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[[nodiscard]] bool constexpr useCustomAllReduce() const noexcept
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{
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return mUseCustomAllReduce;
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}
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void constexpr useCustomAllReduce(bool customAllReduce) noexcept
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{
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mUseCustomAllReduce = customAllReduce;
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}
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void constexpr setMaxDraftLen(SizeType maxDraftLen) noexcept
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{
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mMaxDraftLen = maxDraftLen;
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}
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[[nodiscard]] SizeType getMaxDraftLen() const
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{
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return mMaxDraftLen;
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}
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[[nodiscard]] SizeType constexpr getMaxTokensPerStep() const noexcept
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{
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return mMaxDraftLen + 1;
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}
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void constexpr setUseContextFMHAForGeneration(bool useContextFMHAForGeneration) noexcept
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{
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mUseContextFMHAForGeneration = useContextFMHAForGeneration;
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}
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[[nodiscard]] bool constexpr getContextFMHAForGeneration() const noexcept
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{
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return mUseContextFMHAForGeneration;
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}
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void constexpr setPagedContextFMHA(bool pagedContextFMHA) noexcept
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{
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mPagedContextFMHA = pagedContextFMHA;
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}
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[[nodiscard]] bool constexpr getPagedContextFMHA() const noexcept
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{
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return mPagedContextFMHA;
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}
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void constexpr useXQA(bool useXQA) noexcept
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{
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mUseXQA = useXQA;
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}
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[[nodiscard]] bool constexpr useXQA() const noexcept
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{
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return mUseXQA;
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}
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[[nodiscard]] bool constexpr useLoraPlugin() const noexcept
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{
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return mUseLoraPlugin;
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}
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void constexpr useLoraPlugin(bool useLoraPlugin) noexcept
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{
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mUseLoraPlugin = useLoraPlugin;
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}
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[[nodiscard]] std::vector<LoraModule> const& getLoraModules() const noexcept
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{
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return mLoraModules;
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}
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void setLoraModules(std::vector<LoraModule> const& loraModules) noexcept
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{
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mLoraModules = loraModules;
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}
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[[nodiscard]] SizeType constexpr getMlpHiddenSize() const noexcept
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{
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return mMlpHiddenSize;
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}
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void constexpr setMlpHiddenSize(SizeType mlpHiddenSize) noexcept
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{
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mMlpHiddenSize = mlpHiddenSize;
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}
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[[nodiscard]] bool constexpr useCrossAttention() const noexcept
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{
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return mUseCrossAttention;
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}
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void constexpr useCrossAttention(bool newCrossAttention) noexcept
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{
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mUseCrossAttention = newCrossAttention;
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}
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[[nodiscard]] bool constexpr usePositionEmbedding() const noexcept
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{
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return mUsePositionEmbedding;
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}
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void constexpr usePositionEmbedding(bool newPositionEmbedding) noexcept
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{
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mUsePositionEmbedding = newPositionEmbedding;
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}
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[[nodiscard]] bool constexpr useTokenTypeEmbedding() const noexcept
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{
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return mUseTokenTypeEmbedding;
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}
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void constexpr useTokenTypeEmbedding(bool newTokenTypeEmbedding) noexcept
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{
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mUseTokenTypeEmbedding = newTokenTypeEmbedding;
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}
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[[nodiscard]] SizeType constexpr getFfnHiddenSize() const noexcept
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{
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return mFfnHiddenSize;
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}
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void constexpr setFfnHiddenSize(SizeType ffnHiddenSize) noexcept
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{
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mFfnHiddenSize = ffnHiddenSize;
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}
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[[nodiscard]] SizeType constexpr getMaxLoraRank() const noexcept
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{
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return mMaxLoraRank;
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}
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void constexpr setMaxLoraRank(SizeType maxLoraRank) noexcept
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{
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mMaxLoraRank = maxLoraRank;
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}
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[[nodiscard]] bool constexpr useMedusa() const noexcept
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{
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return mMedusaModule.has_value();
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}
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[[nodiscard]] std::optional<MedusaModule> getMedusaModule() const noexcept
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{
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return mMedusaModule;
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}
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void setMedusaModule(MedusaModule const& medusaModule) noexcept
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{
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mMedusaModule = medusaModule;
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}
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[[nodiscard]] nvinfer1::DataType getKvDataType() const noexcept
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{
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if (getQuantMode().hasFp8KvCache())
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{
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return nvinfer1::DataType::kFP8;
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}
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else if (getQuantMode().hasInt8KvCache())
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{
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return nvinfer1::DataType::kINT8;
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}
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else
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{
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return getDataType();
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}
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}
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[[nodiscard]] bool constexpr isTransformerBased() const noexcept
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{
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return mModelVariant == ModelVariant::kGpt || mModelVariant == ModelVariant::kGlm
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|| mModelVariant == ModelVariant::kRecurrentGemma;
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}
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[[nodiscard]] bool hasMambaConfig() const noexcept
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{
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return mMambaConfig.has_value();
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}
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[[nodiscard]] std::optional<MambaConfig> getMambaConfig() const noexcept
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{
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return mMambaConfig;
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}
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void setMambaConfig(MambaConfig const& mambaConfig) noexcept
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{
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mMambaConfig = mambaConfig;
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}
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[[nodiscard]] bool constexpr isSsmBased() const noexcept
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{
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return mModelVariant == ModelVariant::kMamba || mModelVariant == ModelVariant::kRecurrentGemma;
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}
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[[nodiscard]] bool hasRnnConfig() const noexcept
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{
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return mRnnConfig.has_value();
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}
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[[nodiscard]] std::optional<RnnConfig> getRnnConfig() const noexcept
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{
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return mRnnConfig;
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}
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void setRnnConfig(RnnConfig const& rnnConfig) noexcept
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{
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mRnnConfig = rnnConfig;
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}
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[[nodiscard]] std::vector<LayerType> const& getLayerTypes() const noexcept
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{
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return mLayerTypes;
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}
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void setLayerTypes(std::vector<LayerType> const& layerTypes) noexcept
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{
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mLayerTypes = layerTypes;
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}
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private:
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SizeType mVocabSize;
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SizeType mNbAttentionLayers;
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SizeType mNbSsmLayers;
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SizeType mNbHeads;
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SizeType mNbKvHeads;
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SizeType mHiddenSize;
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SizeType mSizePerHead;
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nvinfer1::DataType mDataType;
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bool mUseGptAttentionPlugin;
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bool mUseMambaConv1dPlugin;
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bool mInputPacked;
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bool mPagedKvCache;
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bool mPagedState;
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SizeType mTokensPerBlock;
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common::QuantMode mQuantMode;
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SizeType mMaxBatchSize;
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SizeType mMaxBeamWidth;
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SizeType mMaxInputLen;
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SizeType mMaxSequenceLen;
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std::optional<SizeType> mMaxNumTokens;
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bool mComputeContextLogits;
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bool mComputeGenerationLogits;
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ModelVariant mModelVariant;
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bool mUseCustomAllReduce;
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SizeType mMaxPromptEmbeddingTableSize;
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SizeType mMaxDraftLen;
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bool mUseContextFMHAForGeneration;
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bool mPagedContextFMHA;
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bool mUseXQA;
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bool mUseLoraPlugin;
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std::vector<LoraModule> mLoraModules;
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SizeType mMlpHiddenSize;
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SizeType mMaxLoraRank;
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std::optional<MedusaModule> mMedusaModule;
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std::optional<MambaConfig> mMambaConfig;
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// Configs related to encoder / enc-dec models
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bool mUseCrossAttention;
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bool mUsePositionEmbedding;
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bool mUseTokenTypeEmbedding;
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SizeType mFfnHiddenSize; // indicates encoder output hidden size
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std::optional<RnnConfig> mRnnConfig;
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std::vector<LayerType> mLayerTypes;
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};
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} // namespace tensorrt_llm::runtime
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