mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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186 lines
8.3 KiB
C++
186 lines
8.3 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 "checkMacrosPlugin.h"
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#include "tensorrt_llm/common/cublasMMWrapper.h"
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#include "tensorrt_llm/common/logger.h"
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#include "tensorrt_llm/common/quantization.h"
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#include "tensorrt_llm/common/stringUtils.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/gptKernels.h"
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#include "tensorrt_llm/plugins/common/plugin.h"
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#include "tensorrt_llm/plugins/gptAttentionCommon/gptAttentionCommon.h"
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#include <cassert>
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#include <cstddef>
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#include <cstdint>
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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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// batch_size = num_ctx_requests + num_gen_requests * beam_width
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// num_ctx_requests = number of context requests (single sequence per request).
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// num_gen_requests = number of generation requests (beam_width sequences per request).
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// Context sequences have to appear first, generation sequences after
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// inputs (see GPTAttentionPlugin::isEntryUsed for when each tensor is actually used)
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// 0. input_tensor [batch_size, seq_len, local_hidden_size + 2 * local_num_kv_heads * head_size] or
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// [1, num_tokens, local_hidden_size + 2 * local_num_kv_heads * head_size] when
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// enable_remove_input_padding
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// 1. sequence_length [batch_size] (optional)
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// 2. host_past_key_value_lengths [batch_size] (int32) (optional)
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// 3. host_max_kv_cache_lengths [1] (int32)
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// 4. context_lengths [batch_size]
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// 5. cache_indir [num_gen_requests, beam_width, memory_max_len] (required in beamsearch) (optional)
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// 6. host_request_types [batch_size] int32. 0: context; 1: generation: 2: none. When not in inflight-batching
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// mode,
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// all elements must be identical.
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// 6. past_key_value_pool [batch_size, 2, local_num_kv_heads, max_seq_len, head_size] or
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// block_pointers [batch_size, 2, max_blocks_per_seq] if paged kv cache (optional)
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// 6.1 host_block_pointers [batch_size, 2, max_blocks_per_seq] if paged kv cache (optional)
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// 7. kv_cache_quantization_scale [1] (optional)
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// 8. kv_cache_dequantization_scale [1] (optional)
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// 9. alibi_slopes [num_heads] (optional for ALiBi position embedding)
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// 10. host_context_lengths [batch_size] int32. (optional, required when remove_input_padding is true)
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// 11. qkv_bias (optional) [local_hidden_size * 3]
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//
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// outputs
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// output_tensor [batch_size, seq_len, local_hidden_size]
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// present_key_value_pool (optional if not paged kv cache) [batch_size, 2, local_num_kv_heads, max_seq_len,
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// head_size]
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class GPTAttentionPlugin : public GPTAttentionPluginCommon
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{
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public:
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GPTAttentionPlugin(int num_heads, int num_kv_heads, int head_size, int unidirectional, float q_scaling,
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tensorrt_llm::kernels::PositionEmbeddingType position_embedding_type,
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int rotary_embedding_dim, // for RoPE. 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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tensorrt_llm::kernels::ContextFMHAType context_fmha_type, bool multi_block_mode, int kv_cache_quant_mode,
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bool remove_input_padding, tensorrt_llm::kernels::AttentionMaskType mask_type, bool paged_kv_cache,
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int tokens_per_block, nvinfer1::DataType type, int32_t max_context_length, bool qkv_bias_enabled,
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bool cross_attention = false, int max_distance = 0, bool use_paged_context_fmha = false, bool use_cache = true);
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GPTAttentionPlugin(const void* data, size_t length);
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~GPTAttentionPlugin() override = default;
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// IPluginV2DynamicExt Methods
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nvinfer1::DimsExprs getOutputDimensions(int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs,
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nvinfer1::IExprBuilder& exprBuilder) noexcept override;
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bool supportsFormatCombination(
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int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs) noexcept override;
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void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs,
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const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) noexcept override;
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size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs,
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const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const noexcept override;
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int enqueue(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc,
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const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) noexcept override;
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template <typename T, typename KVCacheBuffer>
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int enqueueImpl(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc,
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const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream);
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template <typename T>
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int enqueueDispatchKVCacheType(const nvinfer1::PluginTensorDesc* inputDesc,
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const nvinfer1::PluginTensorDesc* outputDesc, const void* const* inputs, void* const* outputs, void* workspace,
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cudaStream_t stream);
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// IPluginV2Ext Methods
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nvinfer1::DataType getOutputDataType(
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int index, const nvinfer1::DataType* inputTypes, int nbInputs) const noexcept override;
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// IPluginV2 Methods
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const char* getPluginType() const noexcept override;
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const char* getPluginVersion() const noexcept override;
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int getNbOutputs() const 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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GPTAttentionPlugin* clone() const noexcept override;
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size_t getSerializationSize() const noexcept override;
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void serialize(void* buffer) const noexcept override;
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enum class RequestType : int32_t
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{
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kCONTEXT = 0,
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kGENERATION = 1
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};
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private:
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template <typename T, typename KVCacheBuffer>
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int enqueueSome(int32_t seqIdxBeg, int32_t localNbSeq, int32_t tokenIdxBeg, int32_t localNbTokens,
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const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc,
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const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream);
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using IndexType = std::int32_t;
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std::vector<size_t> mEntryIdx;
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enum class IdxEntry : size_t
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{
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QKV_TENSOR,
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SEQUENCE_LENGTH,
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HOST_PAST_KEY_VALUE_LENGTHS,
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HOST_MAX_KV_CACHE_LENGTH,
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CONTEXT_LENGTHS,
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CACHE_INDIR,
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REQUEST_TYPES,
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KV_CACHE_BLOCK_POINTERS,
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HOST_KV_CACHE_BLOCK_POINTERS,
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PAST_KEY_VALUE,
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KV_CACHE_QUANTIZATION_SCALE,
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KV_CACHE_DEQUANTIZATION_SCALE,
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ALIBI_SLOPES,
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RELATIVE_ATTENTION_BIAS,
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CROSS_QKV,
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CROSS_QKV_LENGTH,
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ENCODER_INPUT_LENGTH,
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HOST_CONTEXT_LENGTH,
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QKV_BIAS_TENSOR,
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ENUM_SIZE,
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};
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bool isEntryUsed(const IdxEntry& entry) const;
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void initEntryIdx();
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IndexType getIdx(const IdxEntry& entry) const;
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};
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class GPTAttentionPluginCreator : public GPTAttentionPluginCreatorCommon
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{
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public:
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GPTAttentionPluginCreator();
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const char* getPluginName() const noexcept override;
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const char* getPluginVersion() const noexcept override;
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const nvinfer1::PluginFieldCollection* getFieldNames() noexcept override;
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nvinfer1::IPluginV2* createPlugin(const char* name, const nvinfer1::PluginFieldCollection* fc) noexcept override;
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nvinfer1::IPluginV2* deserializePlugin(
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const char* name, const void* serialData, size_t serialLength) noexcept override;
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};
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} // namespace tensorrt_llm::plugins
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