mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-02-13 22:43:46 +08:00
244 lines
9.2 KiB
C++
244 lines
9.2 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_PLUGIN_H
|
|
#define TRT_GPT_ATTENTION_PLUGIN_H
|
|
#include "NvInferPlugin.h"
|
|
#include "checkMacrosPlugin.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 "tensorrt_llm/plugins/gptAttentionCommon/gptAttentionCommon.h"
|
|
#include <cassert>
|
|
#include <cstdint>
|
|
#include <set>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
namespace nvinfer1
|
|
{
|
|
namespace plugin
|
|
{
|
|
// batch_size = num_ctx_requests + num_gen_requests * beam_width
|
|
// num_ctx_requests = number of context requests (single sequence per request).
|
|
// num_gen_requests = number of generation requests (beam_width sequences per request).
|
|
// Context sequences have to appear first, generation sequences after
|
|
|
|
// inputs
|
|
// input_tensor [batch_size, seq_len, local_hidden_size + 2 * local_num_kv_heads * head_size]
|
|
// [1, num_tokens, local_hidden_size + 2 * local_num_kv_heads * head_size] when
|
|
// enable_remove_input_padding
|
|
// past_key_value_pool [blocks, 2, local_num_kv_heads, tokens_per_block, head_size] if paged_kv_attention
|
|
// or [batch_size, 2, local_num_kv_heads, max_seq_len, head_size]
|
|
// sequence_length [batch_size]
|
|
// host_past_key_value_lengths [batch_size] (int32)
|
|
// context_lengths [batch_size]
|
|
// cache_indir [num_gen_requests, beam_width, memory_max_len] (required in beamsearch)
|
|
// host_request_types [batch_size] int32. 0: context; 1: generation: 2: none. When not in inflight-batching mode,
|
|
// all elements must be identical.
|
|
// kv_cache_quantization_scale [1] (optional)
|
|
// kv_cache_dequantization_scale [1] (optional)
|
|
// block_pointers [batch_size, 2, max_blocks_per_seq] (optional if paged kv cache)
|
|
// alibi_slopes [num_heads] (optional for ALiBi position embedding)
|
|
// host_context_lengths [batch_size] int32. (optional, required when remove_input_padding is true)
|
|
// qkv_bias (optional) [local_hidden_size * 3]
|
|
//
|
|
// outputs
|
|
// output_tensor [batch_size, seq_len, local_hidden_size]
|
|
// present_key_value_pool [blocks, 2, local_num_kv_heads, tokens_per_block, head_size] if paged_kv_attention
|
|
// or [batch_size, 2, local_num_kv_heads, max_seq_len, head_size]
|
|
|
|
class GPTAttentionPlugin : public GPTAttentionPluginCommon
|
|
{
|
|
public:
|
|
GPTAttentionPlugin(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. 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, bool in_flight_batching, int32_t max_context_length, bool qkv_bias_enabled);
|
|
|
|
GPTAttentionPlugin(const void* data, size_t length);
|
|
|
|
~GPTAttentionPlugin() override = default;
|
|
|
|
// IPluginV2DynamicExt Methods
|
|
nvinfer1::DimsExprs getOutputDimensions(int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs,
|
|
nvinfer1::IExprBuilder& exprBuilder) noexcept override;
|
|
|
|
bool supportsFormatCombination(
|
|
int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs) noexcept override;
|
|
void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs,
|
|
const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) noexcept override;
|
|
size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs,
|
|
const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const noexcept override;
|
|
int enqueue(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc,
|
|
const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) noexcept override;
|
|
|
|
template <typename T, typename KVCacheBuffer>
|
|
int enqueueImpl(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc,
|
|
const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream);
|
|
|
|
template <typename T>
|
|
int enqueueDispatchKVCacheType(const nvinfer1::PluginTensorDesc* inputDesc,
|
|
const nvinfer1::PluginTensorDesc* outputDesc, const void* const* inputs, void* const* outputs, void* workspace,
|
|
cudaStream_t stream);
|
|
|
|
// IPluginV2Ext Methods
|
|
nvinfer1::DataType getOutputDataType(
|
|
int index, const nvinfer1::DataType* inputTypes, int nbInputs) const noexcept override;
|
|
|
|
// IPluginV2 Methods
|
|
const char* getPluginType() const noexcept override;
|
|
const char* getPluginVersion() const noexcept override;
|
|
int getNbOutputs() const 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.
|
|
GPTAttentionPlugin* clone() const noexcept override;
|
|
|
|
size_t getSerializationSize() const noexcept override;
|
|
void serialize(void* buffer) const noexcept override;
|
|
|
|
enum class RequestType : int32_t
|
|
{
|
|
kCONTEXT = 0,
|
|
kGENERATION = 1,
|
|
kNONE = 2
|
|
};
|
|
|
|
private:
|
|
bool mInFlightBatching = false;
|
|
|
|
private:
|
|
template <typename T, typename KVCacheBuffer>
|
|
int enqueueSome(int32_t seqIdxBeg, int32_t localNbSeq, int32_t tokenIdxBeg,
|
|
const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc,
|
|
const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream);
|
|
|
|
using IndexType = std::int32_t;
|
|
|
|
IndexType getInputTensorIdx() const
|
|
{
|
|
return 0;
|
|
}
|
|
|
|
IndexType getPastKeyValueIdx() const
|
|
{
|
|
return 1;
|
|
}
|
|
|
|
IndexType getSequenceLengthIdx() const
|
|
{
|
|
return 2;
|
|
}
|
|
|
|
IndexType getHostPastKeyValueLengthsIdx() const
|
|
{
|
|
return 3;
|
|
}
|
|
|
|
IndexType getContextLengthsIdx() const
|
|
{
|
|
return 4;
|
|
}
|
|
|
|
IndexType getCacheIndirIdx() const
|
|
{
|
|
return 5;
|
|
}
|
|
|
|
IndexType getRequestTypesIdx() const
|
|
{
|
|
return 6;
|
|
}
|
|
|
|
IndexType getKVCacheQuantizationScaleIdx() const
|
|
{
|
|
return 7;
|
|
}
|
|
|
|
IndexType getKVCacheDequantizationScaleIdx() const
|
|
{
|
|
return 8;
|
|
}
|
|
|
|
IndexType getKVCacheBlockPointersIdx() const
|
|
{
|
|
return mKVCacheQuantMode.hasKvCacheQuant() ? 9 : 7;
|
|
}
|
|
|
|
IndexType getAlibiSlopesIdx() const
|
|
{
|
|
return (mKVCacheQuantMode.hasKvCacheQuant() ? 9 : 7) + (mPagedKVCache ? 1 : 0);
|
|
}
|
|
|
|
IndexType getHostContextLengthsIdx() const
|
|
{
|
|
PLUGIN_ASSERT(mRemovePadding);
|
|
return (mKVCacheQuantMode.hasKvCacheQuant() ? 9 : 7) + (mPagedKVCache ? 1 : 0) + (isALiBi() ? 1 : 0);
|
|
}
|
|
|
|
IndexType getQKVBiasTensorIdx() const
|
|
{
|
|
PLUGIN_ASSERT(mQKVBiasEnabled);
|
|
return (mKVCacheQuantMode.hasInt8KvCache() ? 9 : 7) + (mPagedKVCache ? 1 : 0) + (isALiBi() ? 1 : 0)
|
|
+ (mRemovePadding ? 1 : 0);
|
|
}
|
|
|
|
int32_t getInputLength(const void* const* inputs, int32_t seqIdx) const
|
|
{
|
|
auto const reqType = static_cast<RequestType const*>(inputs[getRequestTypesIdx()])[seqIdx];
|
|
switch (reqType)
|
|
{
|
|
case RequestType::kCONTEXT: return static_cast<int32_t const*>(inputs[getHostContextLengthsIdx()])[seqIdx];
|
|
case RequestType::kGENERATION: return 1;
|
|
case RequestType::kNONE: return 0;
|
|
}
|
|
PLUGIN_ASSERT(!"Unexpected request type");
|
|
}
|
|
};
|
|
|
|
class GPTAttentionPluginCreator : public GPTAttentionPluginCreatorCommon
|
|
{
|
|
public:
|
|
GPTAttentionPluginCreator();
|
|
|
|
const char* getPluginName() const noexcept override;
|
|
|
|
const char* getPluginVersion() const noexcept override;
|
|
|
|
const nvinfer1::PluginFieldCollection* getFieldNames() noexcept override;
|
|
|
|
nvinfer1::IPluginV2* createPlugin(const char* name, const nvinfer1::PluginFieldCollection* fc) noexcept override;
|
|
|
|
nvinfer1::IPluginV2* deserializePlugin(
|
|
const char* name, const void* serialData, size_t serialLength) noexcept override;
|
|
|
|
void setPluginNamespace(const char* pluginNamespace) noexcept override;
|
|
|
|
const char* getPluginNamespace() const noexcept override;
|
|
};
|
|
|
|
} // namespace plugin
|
|
} // namespace nvinfer1
|
|
|
|
#endif // TRT_GPT_ATTENTION_PLUGIN_H
|