mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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b2fd493c16
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
965 lines
38 KiB
C++
965 lines
38 KiB
C++
/*
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* Copyright (c) 2022-2023, 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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#include "tensorrt_llm/runtime/runtimeBuffers.h"
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#include "tensorrt_llm/batch_manager/kvCacheManager.h"
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#include "tensorrt_llm/common/stlUtils.h"
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#include "tensorrt_llm/runtime/runtimeKernels.h"
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#include "tensorrt_llm/runtime/tllmRuntime.h"
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#include "tensorrt_llm/runtime/utils/sessionUtils.h"
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#include <algorithm>
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#include <iostream>
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using namespace tensorrt_llm::runtime;
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namespace tc = tensorrt_llm::common;
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RuntimeBuffers::GenerationConfig RuntimeBuffers::GenerationConfig::fromInput(ITensor const& inputIds,
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ITensor const& inputLengthsHost, bool const inputPacked, SizeType const beamWidth, SizeType const maxKvCacheLength,
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SizeType const maxSequenceLength)
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{
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TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
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auto const batchSize = static_cast<SizeType>(inputLengthsHost.getSize());
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auto const* inputLengthsPtr = bufferCast<SizeType>(inputLengthsHost);
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SizeType maxInputLength = *std::max_element(inputLengthsPtr, inputLengthsPtr + batchSize);
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auto const& inputShape = inputIds.getShape();
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SizeType inputLengthSum{0};
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if (inputPacked)
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{
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inputLengthSum = std::accumulate(inputLengthsPtr, inputLengthsPtr + batchSize, 0);
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TLLM_CHECK_WITH_INFO(inputShape.d[0] == 1 && inputShape.d[1] == inputLengthSum,
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"Packed input must have shape [1, <sum of input lengths>].");
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}
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else
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{
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TLLM_CHECK_WITH_INFO(inputShape.d[0] == batchSize && inputShape.d[1] >= maxInputLength,
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"Padded input must have shape [batch size, max input length]");
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maxInputLength = inputShape.d[1];
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}
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TLLM_CHECK_WITH_INFO(maxInputLength < maxSequenceLength,
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"Max input length is equal to or larger that maxSequenceLength given in setup. No new tokens can be "
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"generated.");
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TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
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return GenerationConfig{batchSize, beamWidth, maxInputLength, maxKvCacheLength, maxSequenceLength, inputLengthSum};
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}
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void RuntimeBuffers::clear()
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{
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TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
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contextLengthsHost = nullptr;
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contextLengthsDevice = nullptr;
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logits = nullptr;
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sequenceLengths = nullptr;
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pastKeyValueLengths = nullptr;
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attentionMask = nullptr;
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positionIds = nullptr;
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lastTokenIds = nullptr;
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requestTypes = nullptr;
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presentKeysVals.clear();
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presentKeysValsAlt.clear();
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kvCacheBlockPointersHost = nullptr;
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kvCacheBlockPointersDevice = nullptr;
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cacheIndirectionDecoderInput = nullptr;
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cacheIndirectionDecoderOutput = nullptr;
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hiddenStates = nullptr;
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allocated = false;
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TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
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}
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void RuntimeBuffers::clearTensorMaps()
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{
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TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
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for (auto& buffer : inputBuffers)
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buffer.clear();
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for (auto& buffer : outputBuffers)
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buffer.clear();
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TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
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}
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void RuntimeBuffers::create(TllmRuntime& runtime, GptModelConfig const& modelConfig, WorldConfig const& worldConfig)
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{
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TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
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auto& manager = runtime.getBufferManager();
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auto& engine = runtime.getEngine();
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if (worldConfig.isLastPipelineParallelRank())
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{
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auto const logitsType = engine.getTensorDataType("logits");
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logits = manager.emptyTensor(MemoryType::kGPU, logitsType);
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}
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contextLengthsHost = manager.emptyTensor(MemoryType::kPINNED, nvinfer1::DataType::kINT32);
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lastTokenIds = manager.emptyTensor(MemoryType::kGPU, nvinfer1::DataType::kINT32);
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auto const localNbLayers = modelConfig.getNbLayers(worldConfig.getPipelineParallelism());
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auto const firstLayerId = worldConfig.getPipelineParallelRank() * localNbLayers;
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nvinfer1::DataType kvDtype;
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if (modelConfig.usePagedKvCache())
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{
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if (modelConfig.getQuantMode().hasFp8KvCache())
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{
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kvDtype = nvinfer1::DataType::kFP8;
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}
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else if (modelConfig.getQuantMode().hasInt8KvCache())
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{
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kvDtype = nvinfer1::DataType::kINT8;
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}
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else
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{
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kvDtype = modelConfig.getDataType();
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}
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}
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else
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{
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kvDtype = modelConfig.getQuantMode().hasFp8KvCache()
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? nvinfer1::DataType::kFP8
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: engine.getTensorDataType(("present_key_value_" + std::to_string(firstLayerId)).c_str());
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}
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if (modelConfig.usePagedKvCache())
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{
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auto const kvCacheBlockPointersType
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= engine.getTensorDataType(("kv_cache_block_pointers_" + std::to_string(firstLayerId)).c_str());
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kvCacheBlockPointersHost = manager.emptyTensor(MemoryType::kCPU, kvCacheBlockPointersType);
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kvCacheBlockPointersDevice = manager.emptyTensor(MemoryType::kGPU, kvCacheBlockPointersType);
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}
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else
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{
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presentKeysVals = utils::createBufferVector(runtime, localNbLayers, MemoryType::kGPU, kvDtype);
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}
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if (modelConfig.useGptAttentionPlugin())
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{
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pastKeyValueLengths = manager.emptyTensor(MemoryType::kCPU, nvinfer1::DataType::kINT32);
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for (SizeType i = 0; i < modelConfig.getNbLayers(); ++i)
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{
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maxKvCacheLengths.emplace_back(manager.emptyTensor(MemoryType::kCPU, nvinfer1::DataType::kINT32));
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}
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}
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else
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{
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presentKeysValsAlt = utils::createBufferVector(runtime, localNbLayers, MemoryType::kGPU, kvDtype);
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}
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if (modelConfig.useGptAttentionPlugin())
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{
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requestTypes = manager.emptyTensor(MemoryType::kCPU, nvinfer1::DataType::kINT32);
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}
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cacheIndirectionDecoderInput = manager.emptyTensor(MemoryType::kGPU, nvinfer1::DataType::kINT32);
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cacheIndirectionDecoderOutput = manager.emptyTensor(MemoryType::kGPU, nvinfer1::DataType::kINT32);
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nbFinished = BufferManager::pinned(ITensor::makeShape({1}), nvinfer1::DataType::kINT32);
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if (worldConfig.isPipelineParallel())
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{
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hiddenStates = manager.emptyTensor(MemoryType::kGPU, modelConfig.getDataType());
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}
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TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
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}
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void RuntimeBuffers::initFromInput(ITensor const& inputIds, TensorPtr const& inputLengths, bool inputPacked,
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SizeType beamWidth, SizeType maxKvCacheLength, SizeType maxSequenceLength, BufferManager& manager)
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{
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contextLengthsDevice = inputLengths;
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contextLengthsHost->reshape(inputLengths->getShape());
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manager.copy(*contextLengthsDevice, *contextLengthsHost);
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manager.getStream().synchronize(); // wait for context lengths to be copied to host
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generationConfig = RuntimeBuffers::GenerationConfig::fromInput(
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inputIds, *contextLengthsHost, inputPacked, beamWidth, maxKvCacheLength, maxSequenceLength);
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}
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void RuntimeBuffers::reshape(GptModelConfig const& modelConfig, WorldConfig const& worldConfig)
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{
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TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
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auto const batchSize = generationConfig.batchSize;
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auto const beamWidth = generationConfig.beamWidth;
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auto const maxInputLength = generationConfig.maxInputLength;
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auto const maxKvCacheLength = generationConfig.maxKvCacheLength;
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if (worldConfig.isLastPipelineParallelRank() && !modelConfig.computeContextLogits())
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{
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auto const vocabSizePadded = modelConfig.getVocabSizePadded(worldConfig.getSize());
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logits->reshape(ITensor::makeShape({batchSize, 1, vocabSizePadded}));
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}
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lastTokenIds->reshape(ITensor::makeShape({batchSize}));
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auto kvCacheReserve = ITensor::makeShape(
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{batchSize, 2, modelConfig.getNbKvHeads(), maxKvCacheLength, modelConfig.getSizePerHead()});
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auto kvCacheShape
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= ITensor::makeShape({batchSize, 2, modelConfig.getNbKvHeads(), maxInputLength, modelConfig.getSizePerHead()});
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if (modelConfig.usePagedKvCache())
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{
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auto const localNbLayers = modelConfig.getNbLayers(worldConfig.getPipelineParallelism());
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auto const tokensPerBlock = modelConfig.getTokensPerBlock();
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auto const maxBlocksPerSeq = (maxKvCacheLength + tokensPerBlock - 1) / tokensPerBlock;
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// reserve batchSize * beamWidth and resize to batchSize
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auto cacheBlockPointersShape = ITensor::makeShape({localNbLayers, batchSize * beamWidth, 2, maxBlocksPerSeq});
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kvCacheBlockPointersHost->reshape(cacheBlockPointersShape);
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kvCacheBlockPointersDevice->reshape(cacheBlockPointersShape);
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cacheBlockPointersShape.d[1] = batchSize;
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kvCacheBlockPointersHost->reshape(cacheBlockPointersShape);
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kvCacheBlockPointersDevice->reshape(cacheBlockPointersShape);
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}
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else
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{
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utils::reshapeBufferVector(presentKeysVals, kvCacheReserve);
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}
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if (modelConfig.useGptAttentionPlugin())
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{
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pastKeyValueLengths->reshape(ITensor::makeShape({batchSize}));
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for (SizeType i = 0; i < modelConfig.getNbLayers(); ++i)
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{
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maxKvCacheLengths[i]->reshape(ITensor::makeShape({1}));
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}
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requestTypes->reshape(ITensor::makeShape({batchSize}));
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}
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else
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{
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utils::reshapeBufferVector(presentKeysValsAlt, kvCacheReserve);
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// present KV cache tensors will be reshaped by shape inference.
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// reshape to the required shape here to make context batch slicing work correctly.
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utils::reshapeBufferVector(presentKeysVals, kvCacheShape);
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}
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auto const cacheIndirShape = ITensor::makeShape({batchSize, beamWidth, maxKvCacheLength});
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cacheIndirectionDecoderInput->reshape(cacheIndirShape);
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cacheIndirectionDecoderOutput->reshape(cacheIndirShape);
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if (worldConfig.isPipelineParallel())
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{
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// reserve max size
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auto const maxNumTokens = std::max(beamWidth, maxInputLength);
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auto const hiddenSize = modelConfig.getHiddenSize() * worldConfig.getTensorParallelism();
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auto const hiddenStatesShape = ITensor::makeShape({batchSize, maxNumTokens, hiddenSize});
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hiddenStates->reshape(hiddenStatesShape);
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}
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allocated = true;
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TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
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}
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void RuntimeBuffers::reset(BufferManager& manager)
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{
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clearTensorMaps();
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manager.setZero(*cacheIndirectionDecoderInput);
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manager.setZero(*cacheIndirectionDecoderOutput);
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}
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std::vector<RuntimeBuffers> RuntimeBuffers::split(
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SizeType contextBatchSize, GptModelConfig const& modelConfig, WorldConfig const& worldConfig)
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{
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TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
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std::vector<RuntimeBuffers> bufferSlices;
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auto const generationBatchSize = generationConfig.batchSize;
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bufferSlices.reserve(tc::ceilDiv(generationBatchSize, contextBatchSize));
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if (contextBatchSize >= generationBatchSize)
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{
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bufferSlices.emplace_back(*this);
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}
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else
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{
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for (auto offset = 0; offset < generationBatchSize; offset += contextBatchSize)
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{
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auto const batchSize = std::min(contextBatchSize, generationBatchSize - offset);
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auto& buffers = bufferSlices.emplace_back();
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buffers.generationConfig = generationConfig;
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buffers.generationConfig.batchSize = batchSize;
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buffers.contextLengthsHost = ITensor::slice(contextLengthsHost, offset, batchSize);
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buffers.contextLengthsDevice = ITensor::slice(contextLengthsDevice, offset, batchSize);
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if (worldConfig.isLastPipelineParallelRank() && !modelConfig.computeContextLogits())
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{
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buffers.logits = ITensor::slice(logits, offset, batchSize);
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}
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buffers.lastTokenIds = ITensor::slice(lastTokenIds, offset, batchSize);
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if (modelConfig.usePagedKvCache())
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{
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auto const& realCacheBlockPointersShape = kvCacheBlockPointersHost->getShape();
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auto const localNbLayers = realCacheBlockPointersShape.d[0];
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auto const maxBlocksPerSeq = realCacheBlockPointersShape.d[3];
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// enable slicing by moving generationBatchSize to first dim
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auto const fakeCacheBlockPointersShape
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= ITensor::makeShape({generationBatchSize, localNbLayers, 2, maxBlocksPerSeq});
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TensorPtr kvCacheBlockPointersHostView{
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ITensor::view(kvCacheBlockPointersHost, fakeCacheBlockPointersShape)};
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TensorPtr kvCacheBlockPointersDeviceView{
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ITensor::view(kvCacheBlockPointersDevice, fakeCacheBlockPointersShape)};
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// slice and reshape to correct shape
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auto const cacheBlockPointersShape = ITensor::makeShape({localNbLayers, batchSize, 2, maxBlocksPerSeq});
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buffers.kvCacheBlockPointersHost = ITensor::slice(kvCacheBlockPointersHostView, offset, batchSize);
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buffers.kvCacheBlockPointersHost->reshape(cacheBlockPointersShape);
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buffers.kvCacheBlockPointersDevice = ITensor::slice(kvCacheBlockPointersDeviceView, offset, batchSize);
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buffers.kvCacheBlockPointersDevice->reshape(cacheBlockPointersShape);
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}
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else
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{
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buffers.presentKeysVals = utils::sliceBufferVector(presentKeysVals, offset, batchSize);
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}
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if (modelConfig.useGptAttentionPlugin())
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{
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buffers.pastKeyValueLengths = ITensor::slice(pastKeyValueLengths, offset, batchSize);
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buffers.maxKvCacheLengths = maxKvCacheLengths;
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buffers.requestTypes = ITensor::slice(requestTypes, offset, batchSize);
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}
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else
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{
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buffers.presentKeysValsAlt = utils::sliceBufferVector(presentKeysValsAlt, offset, batchSize);
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}
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if (worldConfig.isPipelineParallel())
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{
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buffers.hiddenStates = ITensor::slice(hiddenStates, offset, batchSize);
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}
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buffers.cacheIndirectionDecoderOutput = ITensor::slice(cacheIndirectionDecoderOutput, offset, batchSize);
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if (modelConfig.usePromptTuning())
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{
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auto const& ptuningEnabled = promptTuningParams.promptTuningEnabled;
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buffers.promptTuningParams.promptTuningEnabled
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= std::vector<bool>(ptuningEnabled.begin() + offset, ptuningEnabled.begin() + offset + batchSize);
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buffers.promptTuningParams.tasks = ITensor::slice(promptTuningParams.tasks, offset, batchSize);
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}
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}
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}
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TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
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return bufferSlices;
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}
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void RuntimeBuffers::gatherLastTokenLogits(
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BufferManager& manager, GptModelConfig const& modelConfig, WorldConfig const& worldConfig)
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{
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TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
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TLLM_CHECK_WITH_INFO(modelConfig.computeContextLogits(),
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"Gather last token logits is only necessary when context logits are computed");
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if (worldConfig.isLastPipelineParallelRank())
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{
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auto const batchSize = generationConfig.batchSize;
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auto const beamWidth = generationConfig.beamWidth;
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auto const vocabSizePadded = modelConfig.getVocabSizePadded(worldConfig.getSize());
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auto const tiledTensorShape = ITensor::makeShape({batchSize, beamWidth, vocabSizePadded});
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auto tiledTensor = std::shared_ptr(manager.gpu(tiledTensorShape, logits->getDataType()));
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kernels::gatherLastTokenLogits(*tiledTensor, *logits, *lastTokenIds, manager.getStream());
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manager.getStream().synchronize();
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std::swap(logits, tiledTensor);
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}
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TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
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}
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void RuntimeBuffers::copyAttentionMasks(std::vector<RuntimeBuffers> const& contextBatches, BufferManager& manager)
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{
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TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
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auto const batchSize = generationConfig.batchSize;
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auto const maxInputLength = generationConfig.maxInputLength;
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// TODO(rkobus) include tiling
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attentionMask = manager.gpu(ITensor::makeShape({batchSize, maxInputLength}), nvinfer1::DataType::kINT32);
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auto const numContextBatches = static_cast<SizeType>(contextBatches.size());
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auto offset = 0;
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for (auto contextBatchId = 0; contextBatchId < numContextBatches; ++contextBatchId)
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{
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auto& buffers = contextBatches.at(contextBatchId);
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auto contextBatchSize = buffers.generationConfig.batchSize;
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auto attentionMaskSlice = ITensor::slice(attentionMask, offset, contextBatchSize);
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manager.copy(*buffers.attentionMask, *attentionMaskSlice);
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offset += contextBatchSize;
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}
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TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
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}
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void RuntimeBuffers::tile(BufferManager& manager, GptModelConfig const& modelConfig, WorldConfig const& worldConfig)
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{
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TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
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auto const beamWidth = generationConfig.beamWidth;
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TLLM_CHECK_WITH_INFO(beamWidth > 1, "Tiling is only necessary for beam search.");
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// Note: If computeContextLogits is true, the copy/expansion is performed in gatherLastTokenLogits.
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if (worldConfig.isLastPipelineParallelRank() && !modelConfig.computeContextLogits())
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{
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// logits needs beamWidth in second dimension
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auto logitsShape = logits->getShape();
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logitsShape.d[1] *= beamWidth;
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utils::tileBufferReplace(logits, beamWidth, manager);
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logits->reshape(logitsShape);
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}
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utils::tileBufferReplace(contextLengthsDevice, beamWidth, manager);
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if (modelConfig.useGptAttentionPlugin())
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{
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utils::tileCpuBufferReplace(contextLengthsHost, beamWidth, manager);
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utils::tileCpuBufferReplace(pastKeyValueLengths, beamWidth, manager);
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}
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else
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{
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utils::tileBufferReplace(attentionMask, beamWidth, manager);
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}
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if (!modelConfig.usePagedKvCache())
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{
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for (auto& buffer : presentKeysVals)
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utils::tileBufferReplace(buffer, beamWidth, manager);
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for (auto& buffer : presentKeysValsAlt)
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utils::tileBufferReplace(buffer, beamWidth, manager);
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}
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TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
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}
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|
|
void RuntimeBuffers::postContextStep(std::vector<RuntimeBuffers> const& contextBuffers, BufferManager& manager,
|
|
GptModelConfig const& modelConfig, WorldConfig const& worldConfig)
|
|
{
|
|
TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
|
|
auto const batchSize = generationConfig.batchSize;
|
|
auto const beamWidth = generationConfig.beamWidth;
|
|
|
|
if (modelConfig.useGptAttentionPlugin())
|
|
{
|
|
requestTypes->reshape(ITensor::makeShape({batchSize * beamWidth}));
|
|
auto hostRequestTypes = bufferCast<int32_t>(*requestTypes);
|
|
std::fill_n(hostRequestTypes, requestTypes->getSize(), 1);
|
|
}
|
|
else
|
|
{
|
|
copyAttentionMasks(contextBuffers, manager);
|
|
}
|
|
|
|
// TODO(rkobus) handle this more gracefully
|
|
positionIds = manager.emptyTensor(MemoryType::kGPU, nvinfer1::DataType::kINT32);
|
|
|
|
if (modelConfig.computeContextLogits())
|
|
{
|
|
gatherLastTokenLogits(manager, modelConfig, worldConfig);
|
|
}
|
|
|
|
if (beamWidth > 1)
|
|
{
|
|
tile(manager, modelConfig, worldConfig);
|
|
}
|
|
|
|
// use output lengths after context step
|
|
manager.copy(*contextLengthsDevice, *outputLengths);
|
|
sequenceLengths = ITensor::view(outputLengths);
|
|
sequenceLengths->reshape(ITensor::makeShape({batchSize * beamWidth}));
|
|
// no need to copy data in lastTokenIds because it is overwritten in prepareNextStep
|
|
lastTokenIds->reshape(ITensor::makeShape({batchSize * beamWidth}));
|
|
|
|
if (modelConfig.useGptAttentionPlugin() && modelConfig.usePagedKvCache())
|
|
{
|
|
auto cacheBlockPointersShape = kvCacheBlockPointersHost->getShape();
|
|
cacheBlockPointersShape.d[1] = batchSize * beamWidth;
|
|
kvCacheBlockPointersHost->reshape(cacheBlockPointersShape);
|
|
kvCacheBlockPointersDevice->reshape(cacheBlockPointersShape);
|
|
}
|
|
|
|
if (modelConfig.usePromptTuning())
|
|
{
|
|
std::vector<SizeType> reqBeamWidths(batchSize, beamWidth);
|
|
//// Note: reqPromptLenghts won't be used
|
|
std::vector<SizeType> reqPromptLengths;
|
|
// Copy the generationInput tasks to host
|
|
promptTuningTasksHost = manager.copyFrom(*promptTuningParams.tasks, MemoryType::kPINNED);
|
|
// Update the promptTuningParams tasks tensor
|
|
promptTuningParams.fillTasksTensor(promptTuningTasksHost, batchSize, 0, reqBeamWidths, reqPromptLengths,
|
|
manager, modelConfig.usePackedInput());
|
|
}
|
|
|
|
TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
void RuntimeBuffers::prepareContextStep(TensorPtr const& inputIds, TokenIdType const padId, BufferManager& manager,
|
|
KvCacheManager const* kvCacheManager, SizeType firstBatchSlotIdx, GptModelConfig const& modelConfig,
|
|
WorldConfig const& worldConfig)
|
|
{
|
|
TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
|
|
auto& stream = manager.getStream();
|
|
SizeType const batchSize = generationConfig.batchSize;
|
|
SizeType const maxInputLength = generationConfig.maxInputLength;
|
|
|
|
// use context lengths only in context step
|
|
sequenceLengths = contextLengthsDevice;
|
|
|
|
if (modelConfig.useGptAttentionPlugin())
|
|
{
|
|
auto pastKeyValueLengthsPtr = bufferCast<SizeType>(*pastKeyValueLengths);
|
|
TLLM_CHECK(pastKeyValueLengths->getSize() == static_cast<std::size_t>(batchSize));
|
|
std::fill_n(pastKeyValueLengthsPtr, batchSize, 0);
|
|
|
|
auto RequestTypesPtr = bufferCast<int32_t>(*requestTypes);
|
|
TLLM_CHECK(requestTypes->getSize() == static_cast<std::size_t>(batchSize));
|
|
std::fill_n(RequestTypesPtr, batchSize, 0);
|
|
|
|
// Set maxKvCacheLengths buffer to the same value currently.
|
|
for (auto layer = 0; layer < modelConfig.getNbLayers(); ++layer)
|
|
{
|
|
bufferCast<SizeType>(*maxKvCacheLengths[layer])[0] = generationConfig.maxKvCacheLength;
|
|
}
|
|
|
|
auto const& inputShape = inputIds->getShape();
|
|
auto const contextLengthsHostPtr = bufferCast<SizeType const>(*contextLengthsHost);
|
|
auto const modelVariant = modelConfig.getModelVariant();
|
|
|
|
if (modelVariant == GptModelConfig::ModelVariant::kGpt)
|
|
{
|
|
auto const inputSize = inputIds->getSize();
|
|
std::vector<SizeType> positionIdsVec(inputSize);
|
|
auto begin = std::begin(positionIdsVec);
|
|
for (SizeType i = 0; i < batchSize; ++i)
|
|
{
|
|
auto end = begin + (modelConfig.usePackedInput() ? contextLengthsHostPtr[i] : maxInputLength);
|
|
std::iota(begin, end, 0);
|
|
begin = end;
|
|
}
|
|
positionIds = manager.copyFrom(positionIdsVec, inputShape, MemoryType::kGPU);
|
|
}
|
|
else if (modelVariant == GptModelConfig::ModelVariant::kGlm)
|
|
{
|
|
auto const positionIdsVec = getPositionIdsContextPhaseGlm(batchSize, maxInputLength, contextLengthsHostPtr,
|
|
modelConfig.useGptAttentionPlugin(), modelConfig.usePackedInput());
|
|
if (modelConfig.usePackedInput())
|
|
{
|
|
int num_tokens = (int) positionIdsVec.size() / 2;
|
|
auto const positionIdsShape = ITensor::makeShape({1, 2, num_tokens});
|
|
positionIds = manager.copyFrom(positionIdsVec, positionIdsShape, MemoryType::kGPU);
|
|
}
|
|
else
|
|
{
|
|
auto const positionIdsShape = ITensor::makeShape({batchSize, 2, maxInputLength});
|
|
positionIds = manager.copyFrom(positionIdsVec, positionIdsShape, MemoryType::kGPU);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
TLLM_THROW("Unsupported model variant");
|
|
}
|
|
|
|
if (worldConfig.isPipelineParallel())
|
|
{
|
|
auto const hiddenSize = hiddenStates->getShape().d[2];
|
|
auto const hiddenStatesShape = ITensor::makeShape({inputShape.d[0], inputShape.d[1], hiddenSize});
|
|
hiddenStates->reshape(hiddenStatesShape);
|
|
}
|
|
|
|
if (modelConfig.usePromptTuning())
|
|
{
|
|
std::vector<SizeType> reqBeamWidths(batchSize, 1);
|
|
std::vector<SizeType> reqPromptLengths;
|
|
for (SizeType i = 0; i < batchSize; ++i)
|
|
{
|
|
reqPromptLengths.push_back(contextLengthsHostPtr[i]);
|
|
}
|
|
|
|
// Copy the generationInput tasks to host
|
|
promptTuningTasksHost = manager.copyFrom(*promptTuningParams.tasks, MemoryType::kPINNED);
|
|
|
|
// Update the tasks tensor
|
|
promptTuningParams.fillTasksTensor(promptTuningTasksHost, batchSize, batchSize, reqBeamWidths,
|
|
reqPromptLengths, manager, modelConfig.usePackedInput());
|
|
}
|
|
}
|
|
else
|
|
{
|
|
attentionMask = manager.copyFrom(*inputIds, MemoryType::kGPU);
|
|
kernels::invokeBuildAttentionMask(*attentionMask, padId, stream);
|
|
|
|
auto attentionMaskHost = manager.copyFrom(*attentionMask, MemoryType::kCPU);
|
|
auto const* attentionMaskData = reinterpret_cast<SizeType const*>(attentionMaskHost->data());
|
|
std::vector<SizeType> positionIdsVec(attentionMask->getSize());
|
|
for (SizeType i = 0; i < batchSize; ++i)
|
|
{
|
|
tc::stl_utils::exclusiveScan(attentionMaskData + i * maxInputLength,
|
|
attentionMaskData + (i + 1) * maxInputLength, std::begin(positionIdsVec) + i * maxInputLength, 0);
|
|
}
|
|
for (std::size_t i = 0; i < positionIdsVec.size(); ++i)
|
|
if (attentionMaskData[i] == 0)
|
|
positionIdsVec[i] = 1;
|
|
positionIds = manager.copyFrom(positionIdsVec, attentionMask->getShape(), MemoryType::kGPU);
|
|
}
|
|
|
|
if (modelConfig.useGptAttentionPlugin() && modelConfig.usePagedKvCache())
|
|
{
|
|
auto constexpr contextBeamWidth = 1;
|
|
kvCacheManager->getBlockPointersOfBatch(
|
|
*kvCacheBlockPointersHost, firstBatchSlotIdx, batchSize, contextBeamWidth);
|
|
manager.copy(*kvCacheBlockPointersHost, *kvCacheBlockPointersDevice);
|
|
}
|
|
|
|
if (modelConfig.usePackedInput())
|
|
{
|
|
kernels::invokeInclusiveSum(*lastTokenIds, *contextLengthsDevice, manager, stream);
|
|
}
|
|
else
|
|
{
|
|
manager.copy(*contextLengthsDevice, *lastTokenIds);
|
|
}
|
|
|
|
TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
RuntimeBuffers::TensorPtr RuntimeBuffers::prepareNextStep(SizeType const step, BufferManager& manager,
|
|
KvCacheManager* kvCacheManager, SizeType firstBatchSlotIdx, GptModelConfig const& modelConfig,
|
|
WorldConfig const& worldConfig)
|
|
{
|
|
TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
|
|
auto& stream = manager.getStream();
|
|
SizeType const batchSize = generationConfig.batchSize;
|
|
SizeType const beamWidth = generationConfig.beamWidth;
|
|
|
|
nvinfer1::Dims inputShape;
|
|
if (modelConfig.usePackedInput())
|
|
{
|
|
// batch in last dim
|
|
inputShape = ITensor::makeShape({1, batchSize * beamWidth});
|
|
}
|
|
else
|
|
{
|
|
// batch in first dim
|
|
inputShape = ITensor::makeShape({batchSize * beamWidth, 1});
|
|
}
|
|
auto nextInputIds = newTokens ? ITensor::view(newTokens, inputShape) : TensorPtr{};
|
|
|
|
if (modelConfig.useGptAttentionPlugin())
|
|
{
|
|
auto const contextLengthsHostPtr = bufferCast<SizeType const>(*contextLengthsHost);
|
|
auto const pastKeyValueLengthsPtr = bufferCast<SizeType>(*pastKeyValueLengths);
|
|
auto const tensorBatchSize = static_cast<SizeType>(pastKeyValueLengths->getSize());
|
|
SizeType const srcStride{modelConfig.useGptAttentionPlugin() ? 1 : beamWidth};
|
|
TLLM_CHECK(static_cast<std::size_t>(tensorBatchSize * srcStride) == contextLengthsDevice->getSize());
|
|
for (SizeType i = 0; i < tensorBatchSize; ++i)
|
|
{
|
|
pastKeyValueLengthsPtr[i] = contextLengthsHostPtr[i * srcStride] + step;
|
|
}
|
|
|
|
auto const modelVariant = modelConfig.getModelVariant();
|
|
|
|
if (modelVariant == GptModelConfig::ModelVariant::kGpt)
|
|
{
|
|
positionIds->reshape(inputShape);
|
|
manager.copy(*contextLengthsDevice, *positionIds);
|
|
kernels::invokeAdd(*positionIds, step, stream);
|
|
}
|
|
else if (modelVariant == GptModelConfig::ModelVariant::kGlm)
|
|
{
|
|
auto const positionIdsVec = getPositionIdsGenerationPhaseGlm(batchSize, beamWidth, step,
|
|
contextLengthsHostPtr, modelConfig.useGptAttentionPlugin(), modelConfig.usePackedInput());
|
|
if (modelConfig.usePackedInput())
|
|
{
|
|
auto const positionIdsShape = ITensor::makeShape({1, 2, batchSize * beamWidth});
|
|
positionIds = manager.copyFrom(positionIdsVec, positionIdsShape, MemoryType::kGPU);
|
|
}
|
|
else
|
|
{
|
|
auto const positionIdsShape = ITensor::makeShape({batchSize * beamWidth, 2, 1});
|
|
positionIds = manager.copyFrom(positionIdsVec, positionIdsShape, MemoryType::kGPU);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
TLLM_THROW("Unsupported model variant");
|
|
}
|
|
|
|
if (worldConfig.isPipelineParallel())
|
|
{
|
|
auto const hiddenSize = hiddenStates->getShape().d[2];
|
|
auto const hiddenStatesShape = ITensor::makeShape({inputShape.d[0], inputShape.d[1], hiddenSize});
|
|
hiddenStates->reshape(hiddenStatesShape);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
auto const& shape = attentionMask->getShape();
|
|
auto const nbInputs = shape.d[0];
|
|
auto const oldLength = shape.d[1];
|
|
auto const newLength = oldLength + 1;
|
|
auto const newShape = ITensor::makeShape({nbInputs, newLength});
|
|
|
|
TensorPtr newAttentionMask = manager.gpu(newShape, attentionMask->getDataType());
|
|
kernels::invokeExtendAttentionMask(*newAttentionMask, *attentionMask, stream);
|
|
attentionMask = newAttentionMask;
|
|
|
|
auto attentionMaskHost = manager.copyFrom(*attentionMask, MemoryType::kCPU);
|
|
auto const* attentionMaskPtr = bufferCast<SizeType>(*attentionMaskHost);
|
|
|
|
// TODO old positionIds could be recovered to avoid scan
|
|
std::vector<SizeType> positionIdsVec(attentionMask->getSize());
|
|
for (SizeType i = 0; i < nbInputs; ++i)
|
|
{
|
|
tc::stl_utils::exclusiveScan(attentionMaskPtr + i * newLength, attentionMaskPtr + (i + 1) * newLength,
|
|
std::begin(positionIdsVec) + i * newLength, 0);
|
|
}
|
|
for (std::size_t i = 0; i < positionIdsVec.size(); ++i)
|
|
if (attentionMaskPtr[i] == 0)
|
|
positionIdsVec[i] = 1;
|
|
std::vector<SizeType> positionIdsEndVec(nbInputs);
|
|
for (SizeType i = 0; i < nbInputs; ++i)
|
|
positionIdsEndVec[i] = positionIdsVec[(i + 1) * newLength - 1];
|
|
|
|
positionIds = manager.copyFrom(positionIdsEndVec, ITensor::makeShape({nbInputs, 1}), MemoryType::kGPU);
|
|
}
|
|
|
|
if (modelConfig.usePagedKvCache())
|
|
{
|
|
for (auto batchIdx = firstBatchSlotIdx; batchIdx < firstBatchSlotIdx + batchSize; ++batchIdx)
|
|
{
|
|
kvCacheManager->addToken(batchIdx);
|
|
}
|
|
kvCacheManager->getBlockPointersOfBatch(*kvCacheBlockPointersHost, firstBatchSlotIdx, batchSize, beamWidth);
|
|
manager.copy(*kvCacheBlockPointersHost, *kvCacheBlockPointersDevice);
|
|
}
|
|
|
|
kernels::invokeFill(*lastTokenIds, 1, stream);
|
|
if (modelConfig.usePackedInput())
|
|
{
|
|
kernels::invokeInclusiveSum(*lastTokenIds, *lastTokenIds, manager, stream);
|
|
}
|
|
TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
|
|
return nextInputIds;
|
|
}
|
|
|
|
void RuntimeBuffers::getRuntimeBuffers(TensorMap& inputBuffers, TensorMap& outputBuffers, SizeType const step,
|
|
TensorPtr const& inputIds, TensorPtr const& commPtrs, GptModelConfig const& modelConfig,
|
|
WorldConfig const& worldConfig) const
|
|
{
|
|
TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);
|
|
inputBuffers.clear();
|
|
outputBuffers.clear();
|
|
|
|
if (worldConfig.isLastPipelineParallelRank())
|
|
{
|
|
// feed a view to TensorRT runtime so reshaping does not change logits buffer
|
|
outputBuffers.insert_or_assign("logits", ITensor::view(logits));
|
|
}
|
|
else
|
|
{
|
|
outputBuffers.insert_or_assign("hidden_states_output", hiddenStates);
|
|
}
|
|
|
|
if (worldConfig.isFirstPipelineParallelRank())
|
|
{
|
|
inputBuffers.insert_or_assign("input_ids", inputIds);
|
|
}
|
|
else
|
|
{
|
|
inputBuffers.insert_or_assign("hidden_states_input", hiddenStates);
|
|
}
|
|
|
|
inputBuffers.insert_or_assign("context_lengths", contextLengthsDevice);
|
|
if (!modelConfig.computeContextLogits())
|
|
{
|
|
inputBuffers.insert_or_assign("last_token_ids", lastTokenIds);
|
|
}
|
|
inputBuffers.insert_or_assign("position_ids", positionIds);
|
|
|
|
auto const localNbLayers = modelConfig.getNbLayers(worldConfig.getPipelineParallelism());
|
|
auto const firstLayerId = worldConfig.getPipelineParallelRank() * localNbLayers;
|
|
|
|
if (modelConfig.useGptAttentionPlugin())
|
|
{
|
|
inputBuffers.insert_or_assign("cache_indirection", cacheIndirectionDecoderOutput);
|
|
inputBuffers.insert_or_assign("host_past_key_value_lengths", pastKeyValueLengths);
|
|
inputBuffers.insert_or_assign("host_request_types", requestTypes);
|
|
inputBuffers.insert_or_assign("sequence_length", sequenceLengths);
|
|
|
|
for (SizeType i = 0; i < modelConfig.getNbLayers(); ++i)
|
|
{
|
|
std::string name = "host_max_kv_cache_length_" + std::to_string(i);
|
|
inputBuffers.insert_or_assign(name, maxKvCacheLengths[i]);
|
|
}
|
|
|
|
if (modelConfig.usePackedInput())
|
|
{
|
|
inputBuffers.insert_or_assign("host_context_lengths", contextLengthsHost);
|
|
}
|
|
if (modelConfig.usePagedKvCache())
|
|
{
|
|
utils::insertTensorSlices(
|
|
inputBuffers, "kv_cache_block_pointers_", kvCacheBlockPointersDevice, firstLayerId);
|
|
}
|
|
else
|
|
{
|
|
utils::insertTensorVector(inputBuffers, "past_key_value_", presentKeysVals, firstLayerId);
|
|
utils::insertTensorVector(outputBuffers, "present_key_value_", presentKeysVals, firstLayerId);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
inputBuffers.insert_or_assign("attention_mask", attentionMask);
|
|
inputBuffers.insert_or_assign("cache_indirection", cacheIndirectionDecoderOutput);
|
|
utils::insertTensorVector(
|
|
outputBuffers, "present_key_value_", (step % 2) ? presentKeysValsAlt : presentKeysVals, firstLayerId);
|
|
|
|
if (step == 0)
|
|
{
|
|
auto kvCacheShape = presentKeysValsAlt.at(0)->getShape();
|
|
kvCacheShape.d[3] = 0;
|
|
|
|
for (SizeType i = firstLayerId; i < firstLayerId + localNbLayers; ++i)
|
|
{
|
|
std::string name = "past_key_value_" + std::to_string(i);
|
|
TensorPtr tmp = ITensor::view(presentKeysValsAlt[i], kvCacheShape);
|
|
inputBuffers.insert_or_assign(name, std::move(tmp));
|
|
}
|
|
}
|
|
else
|
|
{
|
|
utils::insertTensorVector(
|
|
inputBuffers, "past_key_value_", (step % 2) ? presentKeysVals : presentKeysValsAlt, firstLayerId);
|
|
}
|
|
}
|
|
|
|
if (modelConfig.useCustomAllReduce() && worldConfig.getTensorParallelism())
|
|
{
|
|
inputBuffers.insert_or_assign("all_reduce_workspace", commPtrs);
|
|
}
|
|
|
|
if (modelConfig.usePromptTuning())
|
|
{
|
|
inputBuffers.insert_or_assign("prompt_embedding_table", promptTuningParams.embeddingTable);
|
|
inputBuffers.insert_or_assign("tasks", promptTuningParams.tasks);
|
|
inputBuffers.insert_or_assign("prompt_vocab_size", promptTuningParams.vocabSize);
|
|
}
|
|
TLLM_LOG_DEBUG("%s stop", __PRETTY_FUNCTION__);
|
|
}
|
|
|
|
std::vector<SizeType> RuntimeBuffers::getPositionIdsContextPhaseGlm(const SizeType& batchSize,
|
|
const SizeType& maxInputLength, const SizeType* pInputLengths, bool useGptAttentionPlugin, bool usePackedInput)
|
|
{
|
|
TLLM_CHECK(pInputLengths != nullptr);
|
|
|
|
std::vector<SizeType> positionIdsVec(1, 0);
|
|
if (useGptAttentionPlugin)
|
|
{
|
|
if (usePackedInput)
|
|
{
|
|
std::vector<int> pInputLengthsAcc = std::vector<int>(batchSize + 1, 0);
|
|
for (int i = 0; i < batchSize; ++i)
|
|
{
|
|
pInputLengthsAcc[i + 1] = pInputLengthsAcc[i] + pInputLengths[i];
|
|
}
|
|
|
|
auto const size = 1 * 2 * pInputLengthsAcc[batchSize];
|
|
positionIdsVec.resize(size, 0);
|
|
for (SizeType b = 0; b < batchSize; ++b)
|
|
{
|
|
auto* pIdB = positionIdsVec.data() + pInputLengthsAcc[b];
|
|
auto const length = pInputLengths[b];
|
|
std::iota(pIdB, pIdB + length, 0);
|
|
|
|
pIdB[length - 1] = length - 2;
|
|
pIdB[length - 1 + pInputLengthsAcc[batchSize]] = 1;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
auto const size = batchSize * 2 * maxInputLength;
|
|
positionIdsVec.resize(size, 0);
|
|
for (SizeType b = 0; b < batchSize; ++b)
|
|
{
|
|
auto* pIdB = positionIdsVec.data() + b * 2 * maxInputLength;
|
|
auto const length = pInputLengths[b];
|
|
std::iota(pIdB, pIdB + length, 0);
|
|
|
|
pIdB[length - 1] = length - 2;
|
|
pIdB[length - 1 + maxInputLength] = 1;
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
TLLM_THROW("Unsupported model without GPT Attention Plugin");
|
|
}
|
|
|
|
return positionIdsVec;
|
|
}
|
|
|
|
std::vector<SizeType> RuntimeBuffers::getPositionIdsGenerationPhaseGlm(const SizeType& batchSize,
|
|
const SizeType& beamSize, const SizeType& step, const SizeType* pInputLengths, bool useGptAttentionPlugin,
|
|
bool usePackedInput)
|
|
{
|
|
TLLM_CHECK(pInputLengths != nullptr);
|
|
|
|
auto const size = 2 * batchSize * beamSize;
|
|
std::vector<SizeType> positionIdsVec(size, 0);
|
|
if (useGptAttentionPlugin)
|
|
{
|
|
if (usePackedInput)
|
|
{
|
|
for (SizeType b = 0; b < batchSize; ++b)
|
|
{
|
|
auto* pIdB = positionIdsVec.data() + b * beamSize * 2;
|
|
auto const length = pInputLengths[b * beamSize];
|
|
|
|
for (SizeType bm = 0; bm < beamSize; ++bm)
|
|
{
|
|
pIdB[bm * 2 + 0] = length - 2;
|
|
pIdB[bm * 2 + 1] = step + 2;
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
for (SizeType b = 0; b < batchSize; ++b)
|
|
{
|
|
auto* pIdB = positionIdsVec.data() + b * beamSize * 2;
|
|
auto const length = pInputLengths[b * beamSize];
|
|
|
|
for (SizeType bm = 0; bm < beamSize; ++bm)
|
|
{
|
|
pIdB[bm * 2 + 0] = length - 2;
|
|
pIdB[bm * 2 + 1] = step + 2;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
TLLM_THROW("Unsupported model without GPT Attention Plugin");
|
|
}
|
|
|
|
return positionIdsVec;
|
|
}
|