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https://github.com/NVIDIA/TensorRT-LLM.git
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20d0649f19
Signed-off-by: Yao Yao <lowsfer@users.noreply.github.com> Signed-off-by: peaceh <103117813+peaceh-nv@users.noreply.github.com> Signed-off-by: Jinyang Yuan <154768711+jinyangyuan-nvidia@users.noreply.github.com> Co-authored-by: Yao Yao <lowsfer@users.noreply.github.com> Co-authored-by: peaceh-nv <103117813+peaceh-nv@users.noreply.github.com>
115 lines
3.4 KiB
C++
115 lines
3.4 KiB
C++
/*
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* Copyright (c) 2020-2024, NVIDIA CORPORATION. All rights reserved.
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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/opUtils.h"
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#include "tensorrt_llm/kernels/decoderMaskedMultiheadAttention/decoderXQARunner.h"
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#include "tensorrt_llm/kernels/kvCacheUtils.h"
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#include "tensorrt_llm/kernels/multiHeadAttentionCommon.h"
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#include "tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaRunner.h"
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using namespace tensorrt_llm::common;
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using tensorrt_llm::common::op::UniqPtrWNullCopy;
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namespace tensorrt_llm::kernels
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{
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////////////////////////////////////////////////////////////////////////////////////////////////////
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struct XqaFixedParams
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{
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// Whether the attention is MLA.
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bool isMLA;
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// The QKV input data type.
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kernels::Data_type inputDataType;
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// The XQA KV cache data type.
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kernels::Data_type kvDataType;
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// The XQA output data type.
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kernels::Data_type outputDataType;
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// The XQA BMM dtype.
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kernels::Data_type mathDataType;
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// The number of Q heads.
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int numQHeads;
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// The number of Kv Heads.
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int numKvHeads;
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// The number of tokens per kv cache block.
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int numTokensPerBlock;
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// The head size.
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int headSize;
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// The scaling applied to bmm1_scale.
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float qScaling;
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// Whether to enable multi block mode.
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bool multiBlockMode;
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// The KV cache layout.
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bool isPagedKv;
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// Is speculative decoding enabled.
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bool isSpecDecoding;
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// Do we apply alibi ?
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bool hasAlibi;
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};
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class XqaDispatcher
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{
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public:
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// Constructor.
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XqaDispatcher(XqaFixedParams fixedParams);
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// Deconstructor.
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~XqaDispatcher() = default;
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// Prepare for DecoderXQARunner.
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void prepare(XQAParams const& params);
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// Check whether XQA is supported.
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bool isSupported();
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// Run the XQA kernel.
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void run(XQAParams const& params, KVLinearBuffer const& kv_cache_buffer);
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void run(XQAParams const& params, KVBlockArray const& kv_cache_buffer);
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int getWorkspaceAlignment();
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size_t getWorkspaceSize(int max_num_tokens);
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bool shouldUse(XQAParams const& params);
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private:
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// The fixed XQA parameters.
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XqaFixedParams mFixedParams;
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// The data type of tensor Q, which determines the Q input data type of fmha kernels.
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Data_type mQDataType;
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// Whether to enable trtllm-gen kernels.
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bool mUseTllmGen;
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// The multi-processor count.
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int mMultiProcessorCount;
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// Runner for decoder XQA kernels (for SM <= 90)
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UniqPtrWNullCopy<DecoderXQARunner> mDecoderXqaRunner;
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// Runner for trtllm-gen XQA kernels (for SM == 100)
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UniqPtrWNullCopy<TllmGenFmhaRunner> mTllmGenFMHARunner;
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protected:
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template <typename T, typename KVCacheBuffer>
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void runImpl(XQAParams params, KVCacheBuffer const& kv_cache_buffer);
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};
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constexpr uint32_t xqaMlaCgaXBufSize = 8704 * 2;
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////////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace tensorrt_llm::kernels
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