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fe7dc6ad4e
* Update TensorRT-LLM --------- Co-authored-by: Yi Wang <yi.wang.2005@gmail.com> Co-authored-by: lkm2835 <lkm2835@gmail.com>
94 lines
5.0 KiB
C++
94 lines
5.0 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: LicenseRef-NvidiaProprietary
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*
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* NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
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* property and proprietary rights in and to this material, related
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* documentation and any modifications thereto. Any use, reproduction,
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* disclosure or distribution of this material and related documentation
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* without an express license agreement from NVIDIA CORPORATION or
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* its affiliates is strictly prohibited.
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*/
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#include "tensorrt_llm/runtime/loraUtils.h"
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#include "tensorrt_llm/common/assert.h"
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#include "tensorrt_llm/runtime/common.h"
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#include "tensorrt_llm/runtime/iTensor.h"
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#include "tensorrt_llm/runtime/modelConfig.h"
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#include "tensorrt_llm/runtime/worldConfig.h"
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#include <string>
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namespace tensorrt_llm::runtime::lora
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{
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void loraValidateRequestTensorDims(std::optional<ITensor::SharedPtr> const& optReqLoraWeights,
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std::optional<ITensor::SharedPtr> const& optReqLoraConfig)
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{
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TLLM_CHECK_WITH_INFO(optReqLoraWeights.has_value() && optReqLoraConfig.has_value(),
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"Request for LoRA inference must have both lora_weights and lora_keys");
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SizeType32 constexpr expectedBatchSize = 1;
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SizeType32 constexpr expectedWeightsDims = 3;
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SizeType32 constexpr expectedKeysDims = 3;
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auto weights = optReqLoraWeights.value();
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auto keys = optReqLoraConfig.value();
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TLLM_CHECK_WITH_INFO(weights->getShape().nbDims == expectedWeightsDims, "Invalid shape for lora_weights tensor");
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TLLM_CHECK_WITH_INFO(keys->getShape().nbDims == expectedKeysDims, "Invalid shape for lora_keys tensor");
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TLLM_CHECK_WITH_INFO(
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weights->getShape().d[0] == expectedBatchSize, "Expected batch dimension to be 1 for each lora request");
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TLLM_CHECK_WITH_INFO(
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keys->getShape().d[0] == expectedBatchSize, "Expected batch dimension to be 1 for each lora request");
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TLLM_CHECK_WITH_INFO(weights->getMemoryType() != MemoryType::kGPU, "Expected lora weights to be in CPU memory");
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TLLM_CHECK_WITH_INFO(keys->getMemoryType() != MemoryType::kGPU, "Expected lora weights to be in CPU memory");
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TLLM_CHECK_WITH_INFO(keys->getDataType() == nvinfer1::DataType::kINT32,
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"Expected lora keys to have TYPE_INT32 but was " + std::string(keys->getDataTypeName()));
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TLLM_CHECK_WITH_INFO(keys->getShape().d[1] == weights->getShape().d[1],
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"Expected dim1 lora_weights and lora_keys to have the same size");
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TLLM_CHECK_WITH_INFO(keys->getShape().d[2] == kLORA_CONFIG_ROW_SIZE,
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"Expected dim2 of lora_keys to have a size of " + std::to_string(kLORA_CONFIG_ROW_SIZE));
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}
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void loraValidateRequestTensors(std::optional<std::uint64_t> const& optTaskId,
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std::optional<ITensor::SharedPtr> const& optReqLoraWeights,
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std::optional<ITensor::SharedPtr> const& optReqLoraConfig, runtime::ModelConfig const& modelConfig,
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runtime::WorldConfig const& worldConfig)
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{
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TLLM_CHECK_WITH_INFO(optTaskId.has_value(), "lora_task_id must be set for LoRA inference");
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if (optReqLoraWeights.has_value() || optReqLoraConfig.has_value())
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{
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loraValidateRequestTensorDims(optReqLoraWeights, optReqLoraConfig);
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auto weights = optReqLoraWeights.value();
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auto config = optReqLoraConfig.value();
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SizeType32 nbModelLayers = modelConfig.getNbAttentionLayers();
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TLLM_CHECK_WITH_INFO(weights->getDataType() == modelConfig.getDataType(),
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"Expected lora weights to be the same data type as base model");
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auto loraModules = modelConfig.getLoraModules();
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auto configPtr = bufferCast<SizeType32>(*config);
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auto maxAdapterSize = modelConfig.getMaxLoraRank();
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for (SizeType32 row = 0; row < config->getShape().d[1]; ++row)
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{
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auto modId = configPtr[row * kLORA_CONFIG_ROW_SIZE + kLORA_CONFIG_MODULE_OFF];
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auto layerId = configPtr[row * kLORA_CONFIG_ROW_SIZE + kLORA_CONFIG_LAYER_OFF];
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auto adapterSize = configPtr[row * kLORA_CONFIG_ROW_SIZE + kLORA_CONFIG_ADAPTER_SIZE_OFF];
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TLLM_CHECK_WITH_INFO(
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layerId >= 0 && layerId < nbModelLayers, "Expected layerId to be in the range [0, numModelLayers)");
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TLLM_CHECK_WITH_INFO(adapterSize > 0, "Expected adapterSize to be > 0");
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auto it = std::find_if(
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loraModules.begin(), loraModules.end(), [modId](LoraModule const& m) { return m.value() == modId; });
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std::string moduleName(LoraModule::toModuleName(modId));
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TLLM_CHECK_WITH_INFO(it != loraModules.end(), "lora module " + moduleName + " not enabled for this model");
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TLLM_CHECK_WITH_INFO(it->flattenedInOutSize(adapterSize) <= weights->getShape().d[2],
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"lora_weights has to few values for " + moduleName);
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TLLM_CHECK_WITH_INFO(adapterSize <= maxAdapterSize,
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"Invalid low_rank (" + std::to_string(adapterSize) + "). low_rank must be smaller than mMaxLowRank ("
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+ std::to_string(maxAdapterSize) + ")");
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}
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}
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}
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} // namespace tensorrt_llm::runtime::lora
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