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eba3623a54
* feat/vbws-part4-v1.8: rebase Signed-off-by: wili-65535 <wili-65535@users.noreply.github.com> * feat/vbws-part4-v1.9: fix incorrect output when using short output length Signed-off-by: wili-65535 <wili-65535@users.noreply.github.com> * v1.9.1: remove useless variables Signed-off-by: wili-65535 <wili-65535@users.noreply.github.com> * v1.9.2:fix incorrect output when using short output length Signed-off-by: wili-65535 <wili-65535@users.noreply.github.com> * v1.9.3: rebase Signed-off-by: wili-65535 <wili-65535@users.noreply.github.com> * v1.9.4: rebase Signed-off-by: wili-65535 <wili-65535@users.noreply.github.com> * v1.9.5: remove API change Signed-off-by: wili-65535 <wili-65535@users.noreply.github.com> --------- Signed-off-by: wili-65535 <wili-65535@users.noreply.github.com> Co-authored-by: wili-65535 <wili-65535@users.noreply.github.com>
93 lines
3.5 KiB
C++
93 lines
3.5 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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#include "tensorrt_llm/runtime/promptTuningParams.h"
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namespace tensorrt_llm::runtime
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{
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void PromptTuningParams::fillTasksTensor(TensorPtr tasksHost, SizeType32 const batchSize,
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SizeType32 const numContextRequests, std::vector<SizeType32> const& reqBeamWidths,
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std::vector<SizeType32> const& reqPromptLengths, BufferManager const& manager, bool packedInput)
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{
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auto const& tasksHostShape = tasksHost->getShape();
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TLLM_CHECK_WITH_INFO(tasksHostShape.nbDims == 1, "tasksHost expected to have dimension [batchSize]");
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TLLM_CHECK_WITH_INFO(tasksHostShape.d[0] == batchSize, "tasksHost expected to have dimension [batchSize]");
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auto const tasksHostPtr = bufferCast<SizeType32 const>(*tasksHost);
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bool validInput = packedInput || numContextRequests == batchSize || numContextRequests == 0;
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TLLM_CHECK_WITH_INFO(validInput,
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"fillTasksTensor function with packed inputs must be called with only context requests or only generation "
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"requests.");
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bool validShapes = (static_cast<SizeType32>(reqBeamWidths.size()) == batchSize
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&& static_cast<SizeType32>(reqPromptLengths.size()) == numContextRequests
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&& static_cast<SizeType32>(promptTuningEnabled.size()) == batchSize);
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TLLM_CHECK_WITH_INFO(validShapes,
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"Invalid inputs to fillTasksTensor function. reqBeamWidths and reqPtuningEnabled size must be batchSize and "
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"propmtLenghts size must be numContextRequests");
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SizeType32 totalInputSize = 0;
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std::vector<SizeType32> promptTasksHost;
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for (SizeType32 bid = 0; bid < batchSize; bid++)
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{
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SizeType32 taskId = promptTuningEnabled[bid] ? tasksHostPtr[bid] : 0;
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if (packedInput)
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{
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if (bid < numContextRequests)
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{
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totalInputSize += reqPromptLengths[bid];
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promptTasksHost.insert(promptTasksHost.end(), reqPromptLengths[bid], taskId);
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}
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else
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{
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for (SizeType32 beam = 0; beam < reqBeamWidths[bid]; ++beam)
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{
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promptTasksHost.insert(promptTasksHost.end(), 1, taskId);
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totalInputSize++;
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}
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}
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}
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else
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{
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if (bid < numContextRequests)
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{
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promptTasksHost.push_back(taskId);
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++totalInputSize;
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}
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else
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{
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promptTasksHost.insert(promptTasksHost.end(), reqBeamWidths[bid], taskId);
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totalInputSize += reqBeamWidths[bid];
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}
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}
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}
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if (packedInput)
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{
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tasks = manager.copyFrom(
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promptTasksHost, runtime::ITensor::makeShape({totalInputSize}), runtime::MemoryType::kGPU);
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}
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else
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{
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tasks = manager.copyFrom(
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promptTasksHost, runtime::ITensor::makeShape({totalInputSize, 1}), runtime::MemoryType::kGPU);
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}
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}
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} // namespace tensorrt_llm::runtime
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