mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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4bb65f216f
* Update TensorRT-LLM --------- Co-authored-by: meghagarwal <16129366+megha95@users.noreply.github.com> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
290 lines
12 KiB
C++
290 lines
12 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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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/common/cublasMMWrapper.h"
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#include "tensorrt_llm/common/quantization.h"
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#include "tensorrt_llm/kernels/contextFusedMultiHeadAttention/fmhaRunner.h"
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#include "tensorrt_llm/kernels/contextFusedMultiHeadAttention/fused_multihead_attention_common.h"
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#include "tensorrt_llm/kernels/decoderMaskedMultiheadAttention/decoderXQARunner.h"
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#include "tensorrt_llm/kernels/gptKernels.h"
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#include "tensorrt_llm/plugins/common/plugin.h"
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#include <cassert>
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#include <set>
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#include <string>
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#include <vector>
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namespace tensorrt_llm::plugins
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{
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class GPTAttentionPluginCommon : public BasePlugin
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{
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public:
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GPTAttentionPluginCommon() = delete;
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GPTAttentionPluginCommon(int layer_idx, int num_heads, int num_kv_heads, int head_size, int unidirectional,
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float q_scaling, tensorrt_llm::kernels::PositionEmbeddingType position_embedding_type,
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int rotary_embedding_dim, // for RoPE. Use 0 for non-RoPE
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float rotary_embedding_base, tensorrt_llm::kernels::RotaryScalingType rotary_embedding_scale_type,
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float rotary_embedding_scale, int rotary_embedding_max_positions, int tp_size, int tp_rank, // for ALiBi
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bool unfuse_qkv_gemm, // for AutoPP
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tensorrt_llm::kernels::ContextFMHAType context_fmha_type, bool multi_block_mode, bool enable_xqa,
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int kv_cache_quant_mode, bool remove_input_padding, tensorrt_llm::kernels::AttentionMaskType mask_type,
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bool paged_kv_cache, int tokens_per_block, nvinfer1::DataType type, int32_t max_context_length,
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bool qkv_bias_enabled, bool cross_attention = false, int max_distance = 0, bool pos_shift_enabled = false,
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bool dense_context_fmha = false, bool use_paged_context_fmha = false, bool use_cache = true,
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bool is_medusa_enabled = false);
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GPTAttentionPluginCommon(void const* data, size_t length);
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~GPTAttentionPluginCommon() override = default;
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template <typename T>
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int enqueueImpl(nvinfer1::PluginTensorDesc const* inputDesc, nvinfer1::PluginTensorDesc const* outputDesc,
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void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream);
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//! This is called on every trt Engine creation
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int initialize() noexcept override;
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//! This is called on every trt Engine destroy
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void terminate() noexcept override;
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//! This is called on every trt ExecutionContext creation by TRT
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//! Note TRT does not call the initialize on cloned plugin, so clone internally should do initialization.
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template <typename T>
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T* cloneImpl() const noexcept;
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//! This is called on evert trt Engine or ExecutionContext destroy.
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//! None-cloned plugins will call terminate and then call destroy, while the cloned plugins will call destroy only
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//! So plugin should put the resource release inside destroy.
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void destroy() noexcept override;
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static size_t getCommonSerializationSize() noexcept;
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void serializeCommon(void* buffer) const noexcept;
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int const getHeadSize(bool checkInit = true) const;
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protected:
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int getMaxNumSeqLenTile(int batch_beam_size = 1) const;
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size_t getWorkspaceSizeForContext(nvinfer1::DataType type, int32_t nbReq, int32_t max_input_length,
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int32_t max_kv_cache_len, int32_t cross_qkv_length = 0, int32_t max_num_tokens = 0) const noexcept;
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// total_num_seq is the sum of beam_width for multiple requests
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size_t getWorkspaceSizeForGeneration(
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nvinfer1::DataType type, int32_t total_num_seq, int32_t max_kv_cache_length) const noexcept;
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template <typename T, typename KVCacheBuffer>
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struct EnqueueContextParams
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{
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T const* attention_input;
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T const* qkv_bias;
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int32_t input_seq_length; // padded input length
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int32_t max_past_kv_len;
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// By default, max_attention_window == cyclic_attention_window_size
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// unless each layer has different cyclic kv cache length.
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// Max cache capacity (used to allocate KV cache)
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int32_t max_attention_window;
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// Cyclic kv cache capacity (used to get the cyclic kv cache position for new tokens)
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int32_t cyclic_attention_window_size;
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int32_t sink_token_length;
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int32_t const* q_seq_lengths;
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int32_t const* kv_seq_lengths;
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float const* kv_scale_orig_quant;
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float const* kv_scale_quant_orig;
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T const* alibi_slopes;
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T* context_buf;
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void* key_value_cache;
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void* block_pointers;
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void* host_block_pointers;
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int32_t batch_size;
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int32_t num_tokens;
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int32_t max_blocks_per_sequence;
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void* workspace;
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// optional when relative position
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T const* relative_attention_bias = nullptr;
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int relative_attention_bias_stride = 0;
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// optional when cross attention
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T const* cross_qkv = nullptr;
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int32_t cross_qkv_length = 0;
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int32_t const* encoder_input_lengths = nullptr;
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int32_t num_encoder_tokens = 0;
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};
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template <typename T, typename KVCacheBuffer>
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int enqueueContext(EnqueueContextParams<T, KVCacheBuffer> const& params, cudaStream_t stream);
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template <typename T, typename KVCacheBuffer>
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struct EnqueueGenerationParams
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{
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T const* attention_input;
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T const* qkv_bias;
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// NOTE: input_seq_length might be larger than one in the medusa mode.
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int32_t input_seq_length;
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int32_t const* sequence_lengths;
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int32_t past_kv_length;
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int32_t beam_width;
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int32_t const* context_lengths;
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float const* kv_scale_orig_quant;
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float const* kv_scale_quant_orig;
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T const* alibi_slopes;
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T* context_buf;
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void* key_value_cache;
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void* block_pointers;
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// By default, max_attention_window == cyclic_attention_window_size
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// unless each layer has different cyclic kv cache length.
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// Max cache capacity (used to allocate KV cache)
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int32_t max_attention_window;
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// Cyclic kv cache capacity (used to get the cyclic kv cache position for new tokens)
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int32_t cyclic_attention_window_size;
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int32_t sink_token_length;
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int32_t num_requests;
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int32_t max_blocks_per_sequence;
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int32_t const* cache_indir;
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void* workspace;
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int32_t const* host_past_key_value_lengths;
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// optional when relative position
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T const* relative_attention_bias = nullptr;
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int relative_attention_bias_stride = 0;
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// optional when cross attention
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int32_t const* encoder_input_lengths = nullptr;
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int32_t const* host_context_lengths = nullptr;
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// optional when medusa is used.
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bool const* medusa_mask = nullptr;
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int32_t const* medusa_packed_mask = nullptr;
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int32_t const* medusa_position_offsets = nullptr;
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};
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template <typename T, typename KVCacheBuffer>
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int enqueueGeneration(EnqueueGenerationParams<T, KVCacheBuffer> const& params, cudaStream_t stream);
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// Called in configurePlugin().
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template <typename T, typename KVCacheBuffer>
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void prepareEnqueueGeneration(EnqueueGenerationParams<T, KVCacheBuffer> const& params);
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template <typename T, typename KVCacheBuffer>
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bool convertMMHAParamsToXQAParams(tensorrt_llm::kernels::XQAParams& xqaParams,
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EnqueueGenerationParams<T, KVCacheBuffer> const& generationsParams, bool forConfigurePlugin);
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bool isRelativePosition() const
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{
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return mPositionEmbeddingType == tensorrt_llm::kernels::PositionEmbeddingType::kRELATIVE;
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}
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bool isALiBi() const
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{
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return mPositionEmbeddingType == tensorrt_llm::kernels::PositionEmbeddingType::kALIBI
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|| mPositionEmbeddingType == tensorrt_llm::kernels::PositionEmbeddingType::kALIBI_WITH_SCALE;
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}
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bool isAliBiWithScale() const
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{
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return mPositionEmbeddingType == tensorrt_llm::kernels::PositionEmbeddingType::kALIBI_WITH_SCALE;
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}
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bool isRoPE() const
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{
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return mPositionEmbeddingType == tensorrt_llm::kernels::PositionEmbeddingType::kROPE_GPTJ
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|| mPositionEmbeddingType == tensorrt_llm::kernels::PositionEmbeddingType::kROPE_GPT_NEOX;
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}
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bool isCrossAttention() const
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{
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return mCrossAttention;
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}
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bool useKVCache() const
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{
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return mUseKVCache;
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}
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protected:
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static constexpr int kReservedMaxSeqLenTilePerSeq = 64;
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const std::string mLayerName;
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int mLayerIdx;
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int mNumHeads;
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int mNumKVHeads;
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int mHeadSize;
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int mUnidirectional;
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float mQScaling;
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int mRotaryEmbeddingDim;
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float mRotaryEmbeddingBase;
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tensorrt_llm::kernels::RotaryScalingType mRotaryEmbeddingScaleType;
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float mRotaryEmbeddingScale;
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int mRotaryEmbeddingMaxPositions;
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tensorrt_llm::kernels::PositionEmbeddingType mPositionEmbeddingType;
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bool mRemovePadding = false;
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tensorrt_llm::kernels::AttentionMaskType mMaskType;
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// NOTE: default values for paged kv cache.
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bool mPagedKVCache = false;
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int mTokensPerBlock = 0;
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tensorrt_llm::common::QuantMode mKVCacheQuantMode;
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int mTpSize = 1;
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int mTpRank = 0;
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bool mUnfuseQkvGemm = false;
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nvinfer1::DataType mType;
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int32_t mMaxContextLength;
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bool mQKVBiasEnabled;
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bool mCrossAttention = false;
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int mMaxDistance = 0;
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bool mPosShiftEnabled = false;
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bool mPagedContextFMHA = false;
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bool mDenseContextFMHA = false;
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bool mIsMedusaEnabled = false;
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// Medusa packed mask.
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uint4* mMedusaPackedMask;
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uint4* mMedusaPackedHostMask;
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// fmha runner (disable by default)
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// flag: disabled = 0, enabled = 1, enabled with fp32 accumulation = 2
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bool mEnableContextFMHA = false;
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bool mFMHAForceFP32Acc = false;
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int mSM = tensorrt_llm::common::getSMVersion();
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int mMultiProcessorCount = tensorrt_llm::common::getMultiProcessorCount();
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int mMaxSharedMemoryPerBlockOptin = tensorrt_llm::common::getMaxSharedMemoryPerBlockOptin();
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// The default copy constructor will leave it as nullptr. clone() shall initialize it.
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UniqPtrWNullCopy<tensorrt_llm::kernels::MHARunner> mFMHARunner;
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UniqPtrWNullCopy<tensorrt_llm::kernels::DecoderXQARunner> mDecoderXQARunner;
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// Cache the grid_size and block_size that gives the highest occupancy for
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// invokeApplyBiasRopeUpdateKVCache.
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int2 mLaunchGridBlockCache = make_int2(0, 0);
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bool mMultiBlockMode;
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bool mEnableXQA;
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int mDeviceId = -1;
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static bool mForceMultiBlockWarned;
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// The default copy constructor will leave it as nullptr. clone() shall initialize it.
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UniqPtrWNullCopy<tensorrt_llm::common::CublasMMWrapper> mCublasWrapper;
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bool mUseKVCache = true;
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};
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class GPTAttentionPluginCreatorCommon : public BaseCreator
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{
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public:
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GPTAttentionPluginCreatorCommon();
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nvinfer1::PluginFieldCollection const* getFieldNames() noexcept override;
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template <typename T>
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T* deserializePluginImpl(char const* name, void const* serialData, size_t serialLength) noexcept;
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protected:
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std::vector<nvinfer1::PluginField> mPluginAttributes;
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nvinfer1::PluginFieldCollection mFC{};
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};
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} // namespace tensorrt_llm::plugins
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