TensorRT-LLMs/cpp/include/tensorrt_llm/batch_manager/decoderBuffers.h
Robin Kobus 9f9edd783c
refactor: Introduce MpiTag enumeration and update MPI function signatures (#3893)
* refactor: Move executor recv functions into classes

Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>

* refactor: Enhance MPI logging and error handling

- Updated MPI logging to include destination and tag information for better traceability during send and receive operations.
- Added error checking for MPI_Wait and MPI_Cancel calls to ensure proper handling of multi-device requests.
- Improved code structure for clarity and maintainability.

Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>

* refactor: Introduce MpiTag enumeration and update MPI function signatures

- Added a new header file `mpiTags.h` to define an enumeration for MPI tags, improving code readability and maintainability.
- Updated function signatures in `mpiUtils.h` and `mpiUtils.cpp` to use the new `MpiTag` type instead of raw integers for tags.
- Refactored various MPI calls across the codebase to utilize the new `MpiTag` enumeration, enhancing type safety and clarity.
- Removed redundant MPI tag constants from several classes, streamlining the code.

Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>

* fixup! refactor: Introduce MpiTag enumeration and update MPI function signatures

Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>

* refactor: Rename tags for consistency

Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>

---------

Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
2025-05-04 13:24:29 +02:00

185 lines
7.1 KiB
C++

/*
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* 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.
*/
#pragma once
#include "tensorrt_llm/runtime/eagleBuffers.h"
#include "tensorrt_llm/runtime/explicitDraftTokensBuffers.h"
#include "tensorrt_llm/runtime/iTensor.h"
#include "tensorrt_llm/runtime/lookaheadBuffers.h"
#include "tensorrt_llm/runtime/modelConfig.h"
#include "tensorrt_llm/runtime/utils/mpiUtils.h"
#include "tensorrt_llm/runtime/worldConfig.h"
#include <optional>
#include <vector>
namespace tensorrt_llm::batch_manager
{
class DecoderInputBuffers
{
public:
using SizeType32 = runtime::SizeType32;
using TensorPtr = runtime::ITensor::SharedPtr;
explicit DecoderInputBuffers(
SizeType32 maxBatchSize, SizeType32 maxDecoderSteps, runtime::BufferManager const& manager);
// buffers for setup
TensorPtr setupBatchSlots;
TensorPtr inputsIds;
// buffers for forward
TensorPtr forwardBatchSlotsRequestOrder;
TensorPtr forwardBatchSlotsRequestOrderDevice;
TensorPtr fillValues;
TensorPtr fillValuesDevice;
std::vector<TensorPtr> forwardBatchSlots;
};
class DecoderOutputBuffers
{
public:
using SizeType32 = runtime::SizeType32;
using TensorPtr = runtime::ITensor::SharedPtr;
DecoderOutputBuffers(SizeType32 maxNumSequences, SizeType32 maxBeamWidth, SizeType32 maxSeqLen,
SizeType32 maxTokensPerStep, runtime::BufferManager const& manager);
void enableLookaheadDecoding(SizeType32 maxNumSequences, SizeType32 maxTokensPerStep);
void disableLookaheadDecoding(SizeType32 maxNumSequences);
TensorPtr sequenceLengthsHost; // [mMaxNumRequests, beamWidth], pinned host tensor
TensorPtr newOutputTokensHost; // [maxTokensPerStep, mMaxNumRequests, beamWidth]
TensorPtr cumLogProbsHost; // [mMaxNumRequests, beamWidth]
TensorPtr logProbsHost; // [mMaxNumRequests, beamWidth, maxSeqLen]
TensorPtr finishedSumHost; // [mMaxNumRequests], pinned host tensor
TensorPtr finishReasonsHost; // [mMaxNumRequests, beamWidth], pinned host tensor
};
class DecoderBuffers
{
public:
using SizeType32 = runtime::SizeType32;
using TensorPtr = runtime::ITensor::SharedPtr;
std::vector<TensorPtr> logits;
TensorPtr cacheIndirectionInput;
TensorPtr cacheIndirectionOutput;
class DraftBuffers
{
public:
TensorPtr nextDraftTokensDevice; // [mMaxNumRequests, maxTokensPerStep-1]
TensorPtr nextDraftTokensHost; // [mMaxNumRequests, maxTokensPerStep-1]
TensorPtr prevDraftTokensLengthsDevice; // [mMaxNumRequests]
TensorPtr prevDraftTokensLengthsHost; // [mMaxNumRequests]
TensorPtr nextDraftTokensLengthsDevice; // [mMaxNumRequests]
TensorPtr nextDraftTokensLengthsHost; // [mMaxNumRequests]
TensorPtr acceptedLengthsCumSumDevice; // [mMaxNumRequests+1]
TensorPtr acceptedPackedPathsDevice; // [mMaxNumRequests * maxAcceptedTokens]
std::vector<std::vector<runtime::ITensor::SharedPtr>>
predictedDraftLogits; // [mMaxNumRequests][mMaxNumHeads][maxDraftTokens + 1, vocabSize]
void create(SizeType32 maxNumSequences, SizeType32 maxTokensPerStep, runtime::BufferManager const& manager,
runtime::ModelConfig const& modelConfig);
};
DraftBuffers draftBuffers;
runtime::ExplicitDraftTokensBuffers::Inputs explicitDraftTokensBuffers;
runtime::EagleBuffers::Inputs eagleBuffers;
std::optional<runtime::LookaheadDecodingBuffers> lookaheadBuffers;
DecoderBuffers(SizeType32 maxNumSequences, SizeType32 maxBeamWidth, SizeType32 maxAttentionWindow,
SizeType32 maxTokensPerStep, runtime::BufferManager const& manager, runtime::ModelConfig const& modelConfig,
runtime::WorldConfig const& worldConfig);
};
class DecoderStepAsyncSend
{
public:
using SizeType32 = runtime::SizeType32;
using BufferPtr = runtime::IBuffer::SharedPtr;
DecoderStepAsyncSend(DecoderOutputBuffers const& decoderOutputBuffers, DecoderBuffers const& decoderBuffers,
bool returnLogProbs, SizeType32 maxBeamWidth, bool useMedusa, mpi::MpiComm const& commSession, int peer);
~DecoderStepAsyncSend();
static void recv(DecoderOutputBuffers const& decoderOutputBuffers, DecoderBuffers const& decoderBuffers,
bool returnLogProbs, SizeType32 maxBeamWidth, bool useMedusa, mpi::MpiComm const& commSession, int peer);
static void bcast(DecoderOutputBuffers const& decoderOutputBuffers, DecoderBuffers const& decoderBuffers,
bool returnLogProbs, SizeType32 maxBeamWidth, bool useMedusa, mpi::MpiComm const& commSession, int root);
private:
std::shared_ptr<mpi::MpiRequest> mRequest1;
std::shared_ptr<mpi::MpiRequest> mRequest2;
std::shared_ptr<mpi::MpiRequest> mRequest3;
std::shared_ptr<mpi::MpiRequest> mRequest4;
std::shared_ptr<mpi::MpiRequest> mRequest5;
std::shared_ptr<mpi::MpiRequest> mRequest6;
std::shared_ptr<mpi::MpiRequest> mRequest7;
std::shared_ptr<mpi::MpiRequest> mRequest8;
std::shared_ptr<mpi::MpiRequest> mRequest9;
};
class SlotDecoderBuffers
{
public:
using SizeType32 = runtime::SizeType32;
using TensorPtr = runtime::ITensor::SharedPtr;
TensorPtr outputIds; // [beamWidth, maxSeqLen], outputIds of single batch slot
TensorPtr outputIdsHost; // [beamWidth, maxSeqLen], outputIds of single batch slot
TensorPtr sequenceLengths; // [beamWidth]
TensorPtr sequenceLengthsHost; // [beamWidth]
TensorPtr cumLogProbs; // [beamWidth]
TensorPtr cumLogProbsHost; // [beamWidth]
TensorPtr logProbs; // [beamWidth, maxSeqLen]
TensorPtr logProbsHost; // [beamWidth, maxSeqLen]
TensorPtr finishReasonsHost; // [beamWidth]
SlotDecoderBuffers(SizeType32 maxBeamWidth, SizeType32 maxSeqLen, runtime::BufferManager const& manager);
};
class DecoderSlotAsyncSend
{
public:
using TensorPtr = runtime::ITensor::SharedPtr;
DecoderSlotAsyncSend(TensorPtr const& outputIds, TensorPtr const& sequenceLengths, TensorPtr const& cumLogProbs,
TensorPtr const& logProbs, bool returnLogProbs, mpi::MpiComm const& commSession, int peer);
DecoderSlotAsyncSend(
SlotDecoderBuffers const& slotDecoderBuffers, bool returnLogProbs, mpi::MpiComm const& commSession, int peer);
~DecoderSlotAsyncSend();
static void recv(
SlotDecoderBuffers const& slotDecoderBuffers, bool returnLogProbs, mpi::MpiComm const& commSession, int peer);
private:
std::shared_ptr<mpi::MpiRequest> mRequest1;
std::shared_ptr<mpi::MpiRequest> mRequest2;
std::shared_ptr<mpi::MpiRequest> mRequest3;
std::shared_ptr<mpi::MpiRequest> mRequest4;
};
} // namespace tensorrt_llm::batch_manager