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https://github.com/NVIDIA/TensorRT-LLM.git
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8681b3a4c0
* Update TensorRT-LLM --------- Co-authored-by: Bhuvanesh Sridharan <bhuvanesh.sridharan@sprinklr.com> Co-authored-by: Qingquan Song <ustcsqq@gmail.com>
103 lines
5.9 KiB
C++
103 lines
5.9 KiB
C++
/*
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* Copyright (c) 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/kernels/decodingCommon.h"
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#include "tensorrt_llm/kernels/speculativeDecoding/common.h"
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#include "tensorrt_llm/runtime/common.h"
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#include <cuda_fp16.h>
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#include <cuda_runtime.h>
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#include <curand_kernel.h>
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namespace tensorrt_llm::kernels::speculative_decoding
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{
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//! \brief Accepts or rejects draft tokens based on their probability distributions or the equality of draft and target
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//! tokens. Corrects targetLogits for the last accepted token
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//! according to https://openreview.net/pdf?id=C9NEblP8vS
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//!
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//! \param batchSize current batch size
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//! \param draftProbs output buffer [maxDraftTokens, batchSize, beamWidth, vocabSize].
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//! Workspace buffer for token probabilities of the draft model.
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//! \param targetProbs output buffer [maxDraftTokens+1, batchSize, beamWidth, vocabSize].
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//! Workspace buffer for token probabilities of the target model.
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//! \param numsDraftTokens input buffer [batchSize]. Number of draft tokens per request
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//! \param batchUseDraftLogits input buffer [batchSize]. Acceptance logic using draft logits or not, per request
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//! \param draftIds input buffer [batchSize, draftTokens]. Pointer to draft token ids.
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//! \param finishedInput input buffer [batchSize, beamWidth].
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//! \param finishedOutput output buffer [batchSize, beamWidth]. At each step sets SKIP_DECODING if token is not
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//! accepted.
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//! \param curandState input buffer [batchSize]. Curand states properly initialized using invokeCurandInitialize
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//! per request.
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//! \param batchSlots input buffer [batchSize], address map from local index to global index [0, batchSize] ->
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//! [0, maxBatchSize].
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//! \param maxDraftTokens maximum number of draft tokens
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//! \param beamWidth beam width (only beamWidth == 1 supported)
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//! \param vocabSizePadded padded vocab size
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//! \param randomThreshold True if use uniformly sampled threshold for token acceptance
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//! \param constantThreshold threshold used to accept tokens if randomThreshold is false
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//! \param step The current step of decoding (draft token id index)
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//! \param batchIsAccepted output buffer [batchSize]. Stores acceptance result for multinomial sampling later or
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//! forwarding next step.
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//! \param targetOutputIds input/output buffer [batchSize]. Stores target sampling output ids for acceptById
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//! logics.
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//! \param stream stream
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template <typename T>
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void invokeAcceptDraftTokens(runtime::SizeType32 batchSize, T* draftProbs, T* targetProbs,
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runtime::SizeType32 const* numsDraftTokens, bool const* batchUseDraftLogits, runtime::TokenIdType const* draftIds,
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FinishedState const* finishedInput, FinishedState* finishedOutput, curandState_t* curandState,
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runtime::SizeType32 const* batchSlots, runtime::SizeType32 maxDraftTokens, runtime::SizeType32 beamWidth,
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runtime::SizeType32 vocabSizePadded, bool randomThreshold, float constantThreshold, runtime::SizeType32 step,
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bool* batchIsAccepted, runtime::SizeType32* targetOutputIds, cudaStream_t stream);
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//! \brief Mask the target logits with -inf for unselected topK/topP token ids.
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//! according to
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//! https://github.com/huggingface/transformers/blob/2e24ee4dfa39cc0bc264b89edbccc373c8337086/src/transformers/generation/utils.py#L4064
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//!
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//! \param batchSize current batch size
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//! \param targetLogits input/output buffer [batchSize][draftTokens+1, beamWidth, vocabSize].
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//! Vector of pointers to the logits. (beamWidth == 1)
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//! Initially contains token logits of the target model.
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//! \param batchSlots input buffer [batchSize], address map from local index to global index [0, batchSize] ->
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//! [0, maxBatchSize].
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//! \param beamWidth beam width (only beamWidth == 1 supported)
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//! \param vocabSizePadded padded vocab size
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//! \param finishedInput input buffer [batchSize, beamWidth].
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//! \param maxBatchSize maximum batch size
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//! \param batchUseDraftLogits input buffer [batchSize]. Acceptance logic using draft logits or not, per request
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//! \param outputIdsAfterSampling input buffer [batchSize, vocabSize]. Stores all selected IDs from sampling for
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//! masking.
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//! \param targetOutputIds input/output buffer [batchSize]. Stores target sampling output ids for acceptById
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//! logics.
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//! \param numsDraftTokens input buffer [batchSize]. Number of draft tokens per request
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//! \param runtimeTopKDevicePtr input buffer [batchSize] the topks in sampling step, for porting topK ids out.
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//! \param maskBuffer input buffer [batchSize, vocabSize] for masking calculation (index value to position).
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//! \param stream stream
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template <typename T>
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void invokeMaskTargetLogits(runtime::SizeType32 batchSize, T* targetLogits, runtime::SizeType32 const* batchSlots,
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runtime::SizeType32 beamWidth, runtime::SizeType32 vocabSizePadded, FinishedState const* finishedInput,
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runtime::SizeType32 maxBatchSize, bool const* batchUseDraftLogits, runtime::SizeType32* outputIdsAfterSampling,
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runtime::SizeType32* targetOutputIds, runtime::SizeType32* runtimeTopKDevicePtr, bool* maskBuffer,
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cudaStream_t stream);
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void invokeForwardAcceptedTokens(runtime::SizeType32 batchSize, runtime::SizeType32 const* batchSlots,
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bool* batchIsAccepted, runtime::SizeType32* outputSequenceLengths, runtime::TokenIdType const* draftIds,
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runtime::TokenIdType** idsPtrs, runtime::SizeType32 step, runtime::SizeType32 maxDraftTokens,
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runtime::TokenIdType const* endIds, FinishedState* finishedOutput, cudaStream_t stream);
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} // namespace tensorrt_llm::kernels::speculative_decoding
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