/* * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ #include "tensorrt_llm/batch_manager/handleGenerationLogits.h" #include "tensorrt_llm/batch_manager/decoderBuffers.h" #include "tensorrt_llm/batch_manager/llmRequest.h" #include "tensorrt_llm/batch_manager/medusaBuffers.h" #include "tensorrt_llm/batch_manager/runtimeBuffers.h" #include "tensorrt_llm/batch_manager/utils/inflightBatchingUtils.h" #include "tensorrt_llm/common/assert.h" #include "tensorrt_llm/common/nvtxUtils.h" #include "tensorrt_llm/runtime/iTensor.h" #include "tensorrt_llm/runtime/utils/debugUtils.h" namespace tr = tensorrt_llm::runtime; namespace tru = tensorrt_llm::runtime::utils; namespace tensorrt_llm::batch_manager { using BufferManager = tensorrt_llm::runtime::BufferManager; using TensorPtr = runtime::ITensor::SharedPtr; using ITensor = runtime::ITensor; using SizeType32 = tensorrt_llm::runtime::SizeType32; namespace { //! @brief Copy logits from generation phase under streaming mode. void copyStreamingGenerationLogits(BufferManager const& bufferManager, LlmRequest& llmReq) { TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__); // If llmRequest is streaming, directly copy to host. // Only one token's logits needs to be copied each time. TLLM_CHECK(llmReq.getGenerationLogitsFragmentsSize() == 1); SizeType32 numGenerationToken = llmReq.getMaxBeamNumTokens() - llmReq.mPromptLen; TensorPtr const& generationLogitsHost = llmReq.getGenerationLogitsHost(); // [mMaxNewTokens (or 1), beamWidth, vocabSizePadded] TensorPtr hostTensorPtr = ITensor::slice(generationLogitsHost, numGenerationToken, 1); // [1, beamWidth, vocabSizePadded] TensorPtr deviceTensorPtr = *(llmReq.getGenerationLogitsFragments().begin()); bufferManager.copy(*deviceTensorPtr, *hostTensorPtr); llmReq.clearGenerationLogitsFragments(); TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__); } void setupMedusaLogits(std::vector& medusaLogitsHeads, TensorPtr const& medusaLogitsDevice, SizeType32 medusaHeads, SizeType32 logitsIndex, SizeType32 numLogits) { for (SizeType32 hi = 0; hi < medusaHeads; ++hi) { TensorPtr logitsHead = ITensor::slice(medusaLogitsDevice, hi, 1); logitsHead->squeeze(0); medusaLogitsHeads[hi] = ITensor::slice(logitsHead, logitsIndex, numLogits); } } } // namespace void HandleGenerationLogits::operator()(DecoderInputBuffers& inputBuffers, RequestVector const& generationRequests, tr::ITensor::SharedPtr const& logits, tr::SizeType32 logitsIndex, tr::ModelConfig const& modelConfig, tr::BufferManager const& manager, OptionalRef genRuntimeBuffers, OptionalRef medusaBuffers) const { TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__); NVTX3_SCOPED_RANGE(HandleGenerationLogits); auto& decoderRequests = inputBuffers.decoderRequests; decoderRequests.reserve(decoderRequests.size() + generationRequests.size()); auto& allDecoderLogits = inputBuffers.decoderLogits; allDecoderLogits.reserve(allDecoderLogits.size() + generationRequests.size()); for (auto const& llmReq : generationRequests) { auto const reqBeamWidth = llmReq->getBeamWidthByIter(); auto const seqSlot = llmReq->mSeqSlot.value(); auto const draftLength = llmReq->getNumDraftTokens(); auto const numLogits = draftLength + reqBeamWidth; TLLM_CHECK(draftLength == 0 || reqBeamWidth == 1); TLLM_LOG_DEBUG("logitsIndex: %d", logitsIndex); TLLM_LOG_DEBUG("draftLength: %d", draftLength); TLLM_LOG_DEBUG("reqBeamWidth: %d", reqBeamWidth); // genRuntimeBuffers.logits shape: [numGen*reqBeamWidth, vocabSize] // logitsView shape: [numLogits, vocabSize] TensorPtr logitsView = ITensor::slice(logits, logitsIndex, numLogits); TLLM_CHECK_DEBUG_WITH_INFO(tru::tensorHasInvalid(*logitsView, manager, "logits") == false, "Found invalid number (NaN or Inf) in logits"); TLLM_CHECK(llmReq->isGenerationInProgressState()); TensorPtr decoderLogits; if (reqBeamWidth > 1) { decoderLogits = logitsView; decoderLogits->unsqueeze(0); } else { decoderLogits = logitsView; decoderLogits->unsqueeze(1); } decoderRequests.push_back(llmReq); allDecoderLogits.emplace_back(std::move(decoderLogits)); if (llmReq->getReturnGenerationLogits()) { TLLM_CHECK_WITH_INFO(modelConfig.getSpeculativeDecodingMode().isNone() || modelConfig.getSpeculativeDecodingMode().isDraftTokensExternal(), "Only speculative decoding with external draft tokens supports returning generation logits"); // Push into fragments vector llmReq->addGenerationLogitsFragment(logitsView); TLLM_CHECK( llmReq->getGenerationLogitsFragmentsSize() <= RuntimeBuffers::GenerationLogitsCache::kCACHE_LENGTH); if (llmReq->isStreaming()) { copyStreamingGenerationLogits(manager, *llmReq); } // Copy back to host for every kCACHE_LENGTH steps to mitigate GPU memory pressure else if (llmReq->getGenerationLogitsFragmentsSize() == RuntimeBuffers::GenerationLogitsCache::kCACHE_LENGTH) { TLLM_CHECK(genRuntimeBuffers); auto constexpr beforeDecoder = true; utils::copyGenerationLogits(genRuntimeBuffers->generationLogitsCache, manager, *llmReq, beforeDecoder); } } if (modelConfig.getSpeculativeDecodingMode().hasDraftLogits()) { auto& medusaLogitsHeads = inputBuffers.predictedDraftLogits.at(seqSlot); TLLM_CHECK(medusaBuffers); setupMedusaLogits(medusaLogitsHeads, medusaBuffers->medusaLogitsDevice, modelConfig.getSpeculativeDecodingModule().getMaxDraftPathLen(), logitsIndex, draftLength); } logitsIndex += numLogits; } TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__); } } // namespace tensorrt_llm::batch_manager