mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
205 lines
7.4 KiB
C++
205 lines
7.4 KiB
C++
/*
|
|
* SPDX-FileCopyrightText: Copyright (c) 1993-2022 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.
|
|
*/
|
|
#ifndef TRT_GPT_ATTENTION_COMMON_H
|
|
#define TRT_GPT_ATTENTION_COMMON_H
|
|
#include "NvInferPlugin.h"
|
|
#include "tensorrt_llm/common/cublasMMWrapper.h"
|
|
#include "tensorrt_llm/common/quantization.h"
|
|
#include "tensorrt_llm/kernels/contextFusedMultiHeadAttention/fmhaRunner.h"
|
|
#include "tensorrt_llm/kernels/contextFusedMultiHeadAttention/fused_multihead_attention_common.h"
|
|
#include "tensorrt_llm/kernels/gptKernels.h"
|
|
#include "tensorrt_llm/plugins/common/plugin.h"
|
|
#include <cassert>
|
|
#include <set>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
namespace nvinfer1
|
|
{
|
|
namespace plugin
|
|
{
|
|
|
|
class GPTAttentionPluginCommon : public IPluginV2DynamicExt
|
|
{
|
|
public:
|
|
GPTAttentionPluginCommon() = delete;
|
|
|
|
GPTAttentionPluginCommon(int num_heads, int num_kv_heads, int unidirectional, float q_scaling,
|
|
tensorrt_llm::kernels::PositionEmbeddingType position_embedding_type,
|
|
int rotary_embedding_dim, // for RoPE. Use 0 for non-RoPE
|
|
int tp_size, int tp_rank, // for ALiBi
|
|
tensorrt_llm::kernels::ContextFMHAType context_fmha_type, bool multi_block_mode, int kv_cache_quant_mode,
|
|
bool remove_input_padding, tensorrt_llm::kernels::AttentionMaskType mask_type, bool paged_kv_cache,
|
|
nvinfer1::DataType type, int32_t max_context_length, bool qkv_bias_enabled);
|
|
|
|
GPTAttentionPluginCommon(const void* data, size_t length);
|
|
|
|
~GPTAttentionPluginCommon() override = default;
|
|
|
|
template <typename T>
|
|
int enqueueImpl(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc,
|
|
const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream);
|
|
|
|
//! This is called on every trt Engine creation
|
|
int initialize() noexcept override;
|
|
//! This is called on every trt Engine destroy
|
|
void terminate() noexcept override;
|
|
|
|
//! This is called on every trt ExecutionContext creation by TRT
|
|
//! Note TRT does not call the initialize on cloned plugin, so clone internally should do initialization.
|
|
template <typename T>
|
|
T* cloneImpl() const noexcept;
|
|
|
|
//! This is called on evert trt Engine or ExecutionContext destroy.
|
|
//! None-cloned plugins will call terminate and then call destroy, while the cloned plugins will call destroy only
|
|
//! So plugin should put the resource release inside destroy.
|
|
void destroy() noexcept override;
|
|
|
|
static size_t getCommonSerializationSize() noexcept;
|
|
void serializeCommon(void* buffer) const noexcept;
|
|
void setPluginNamespace(const char* pluginNamespace) noexcept override;
|
|
const char* getPluginNamespace() const noexcept override;
|
|
const int getHeadSize(bool checkInit = true) const;
|
|
|
|
protected:
|
|
int getMaxSeqLenTile(int elemSize) const;
|
|
size_t getWorkspaceSizeForContext(DataType type, int32_t nbReq, int32_t max_input_length) const noexcept;
|
|
// total_num_seq is the sum of beam_width for multiple requests
|
|
size_t getWorkspaceSizeForGeneration(DataType type, int32_t total_num_seq) const noexcept;
|
|
|
|
template <typename T, typename KVCacheBuffer>
|
|
struct EnqueueContextParams
|
|
{
|
|
T const* attention_input;
|
|
T const* qkv_bias;
|
|
int32_t input_seq_length; // padded input length
|
|
int32_t max_seq_length; // cache capacity
|
|
int32_t const* context_lengths;
|
|
float const* kv_scale_orig_quant;
|
|
float const* kv_scale_quant_orig;
|
|
T const* alibi_slopes;
|
|
T* context_buf;
|
|
void* key_value_cache;
|
|
void* block_pointers;
|
|
int32_t batch_size;
|
|
int32_t num_tokens;
|
|
int32_t tokens_per_block;
|
|
int32_t max_blocks_per_sequence;
|
|
void* workspace;
|
|
};
|
|
|
|
template <typename T, typename KVCacheBuffer>
|
|
int enqueueContext(const EnqueueContextParams<T, KVCacheBuffer>& params, cudaStream_t stream);
|
|
|
|
template <typename T, typename KVCacheBuffer>
|
|
struct EnqueueGenerationParams
|
|
{
|
|
T const* attention_input;
|
|
T const* qkv_bias;
|
|
int32_t const* sequence_lengths;
|
|
int32_t past_kv_length;
|
|
int32_t beam_width;
|
|
int32_t const* context_lengths;
|
|
float const* kv_scale_orig_quant;
|
|
float const* kv_scale_quant_orig;
|
|
T const* alibi_slopes;
|
|
T* context_buf;
|
|
void* key_value_cache;
|
|
void* block_pointers;
|
|
int32_t max_seq_lengths; // cache capacity
|
|
int32_t num_requests;
|
|
int32_t tokens_per_block;
|
|
int32_t max_blocks_per_sequence;
|
|
int32_t const* cache_indir;
|
|
void* workspace;
|
|
};
|
|
|
|
template <typename T, typename KVCacheBuffer>
|
|
int enqueueGeneration(const EnqueueGenerationParams<T, KVCacheBuffer>& params, cudaStream_t stream);
|
|
|
|
bool isALiBi() const
|
|
{
|
|
return mPositionEmbeddingType == tensorrt_llm::kernels::PositionEmbeddingType::kALIBI;
|
|
}
|
|
|
|
bool isRoPE() const
|
|
{
|
|
return mPositionEmbeddingType == tensorrt_llm::kernels::PositionEmbeddingType::kROPE_GPTJ
|
|
|| mPositionEmbeddingType == tensorrt_llm::kernels::PositionEmbeddingType::kROPE_GPT_NEOX;
|
|
}
|
|
|
|
protected:
|
|
const std::string mLayerName;
|
|
std::string mNamespace;
|
|
|
|
int mNumHeads;
|
|
int mNumKVHeads;
|
|
int mHeadSize;
|
|
int mUnidirectional;
|
|
float mQScaling;
|
|
int mRotaryEmbeddingDim;
|
|
tensorrt_llm::kernels::PositionEmbeddingType mPositionEmbeddingType;
|
|
bool mRemovePadding = false;
|
|
tensorrt_llm::kernels::AttentionMaskType mMaskType;
|
|
bool mPagedKVCache = false;
|
|
tensorrt_llm::common::QuantMode mKVCacheQuantMode;
|
|
int mTpSize = 1;
|
|
int mTpRank = 0;
|
|
nvinfer1::DataType mType;
|
|
int32_t mMaxContextLength;
|
|
bool mQKVBiasEnabled;
|
|
|
|
// fmha runner (disable by default)
|
|
// flag: disabled = 0, enabled = 1, enabled with fp32 accumulation = 2
|
|
bool mEnableContextFMHA = false;
|
|
bool mFMHAForceFP32Acc = false;
|
|
int mSM = tensorrt_llm::common::getSMVersion();
|
|
int mMultiProcessorCount = tensorrt_llm::common::getMultiProcessorCount();
|
|
tensorrt_llm::kernels::MHARunner* mFMHARunner;
|
|
|
|
bool mMultiBlockMode;
|
|
int mDeviceId = -1;
|
|
tensorrt_llm::common::cublasAlgoMap* mCublasAlgoMap;
|
|
std::mutex* mCublasWrapperMutex;
|
|
tensorrt_llm::common::cublasMMWrapper* mCublasWrapper;
|
|
};
|
|
|
|
class GPTAttentionPluginCreatorCommon : public IPluginCreator
|
|
{
|
|
public:
|
|
GPTAttentionPluginCreatorCommon();
|
|
|
|
const nvinfer1::PluginFieldCollection* getFieldNames() noexcept override;
|
|
|
|
template <typename T>
|
|
T* deserializePluginImpl(const char* name, const void* serialData, size_t serialLength) noexcept;
|
|
|
|
void setPluginNamespace(const char* pluginNamespace) noexcept override;
|
|
|
|
const char* getPluginNamespace() const noexcept override;
|
|
|
|
protected:
|
|
std::vector<PluginField> mPluginAttributes;
|
|
PluginFieldCollection mFC{};
|
|
std::string mNamespace;
|
|
};
|
|
|
|
} // namespace plugin
|
|
} // namespace nvinfer1
|
|
|
|
#endif // TRT_GPT_ATTENTION_COMMON_H
|