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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>
117 lines
3.7 KiB
C++
117 lines
3.7 KiB
C++
/*
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* Copyright (c) 2020-2023, 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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#include "decoderXQARunner.h"
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#include <assert.h>
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#include <string.h>
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#include <mutex>
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#include <unordered_map>
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#include "tensorrt_llm/common/cudaDriverWrapper.h"
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#include "tensorrt_llm/common/cudaUtils.h"
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#include "tensorrt_llm/common/envUtils.h"
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#include "tensorrt_llm/kernels/decoderMaskedMultiheadAttention/cubin/xqa_kernel_cubin.h"
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#include "tensorrt_llm/kernels/decoderMaskedMultiheadAttention/decoderXQAConstants.h"
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#include "tensorrt_llm/kernels/decoderMaskedMultiheadAttention/decoderXQAImpl.h"
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#include "tensorrt_llm/kernels/kvCacheUtils.h"
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#include "tensorrt_llm/kernels/unfusedAttentionKernels.h"
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namespace tensorrt_llm
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{
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namespace kernels
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{
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DecoderXQARunner::DecoderXQARunner(
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const XQADataType data_type, int num_heads, int num_kv_heads, int head_size, bool multi_block_mode)
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: mPrepareCalled(false)
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, mDataType(data_type)
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, mNumHeads(num_heads)
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, mNumKVHeads(num_kv_heads)
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, mHeadSize(head_size)
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, mMultiBlockMode(multi_block_mode)
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{
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mMultiProcessorCount = tensorrt_llm::common::getMultiProcessorCount();
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// The initialization of mImpl must be the last line because *this needs to be fully initialized before calling
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// DecoderXQAImpl::create().
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mImpl = DecoderXQAImpl::create(this, DecoderXQAImpl::ImplType::kPrecompiled);
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}
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DecoderXQARunner::~DecoderXQARunner() = default;
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namespace
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{
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template <typename T>
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constexpr inline T divUp(T a, T b)
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{
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return (a + b - 1) / b;
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}
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template <typename T>
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constexpr inline T roundUp(T a, T b)
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{
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return divUp(a, b) * b;
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}
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} // namespace
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size_t DecoderXQARunner::getWorkspaceSize(int max_batch_beam_size)
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{
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size_t workspace_size = 0;
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if (mMultiBlockMode)
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{
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int workspaces[4];
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int const max_num_request = max_batch_beam_size;
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uint32_t const nbSeq = mNumKVHeads * max_num_request;
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uint32_t const nbSubSeq = kMaxNbCtaPerKVHeadFactor * nbSeq;
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int group_size = mNumHeads / mNumKVHeads;
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workspaces[0] = sizeof(uint32_t) * nbSeq;
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workspaces[1] = sizeof(float) * roundUp(group_size, 32) * nbSubSeq;
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workspaces[2] = sizeof(float) * roundUp(group_size, 32) * nbSubSeq;
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workspaces[3] = sizeof(__half) * group_size * mHeadSize * nbSubSeq;
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workspace_size = roundUp(workspaces[0], 128) + roundUp(workspaces[1], 128) + roundUp(workspaces[2], 128)
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+ roundUp(workspaces[3], 128);
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}
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return workspace_size;
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}
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bool DecoderXQARunner::shouldUseImpl(XQAParams const& xqaParams)
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{
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return mImpl->shouldUse(xqaParams);
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}
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void DecoderXQARunner::prepareForRun(XQAParams const& xqa_params)
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{
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return mImpl->prepare(xqa_params);
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}
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template <typename KVCacheBuffer>
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void DecoderXQARunner::run(XQAParams const& xqa_params, KVCacheBuffer& kv_cache_buffer, cudaStream_t const& stream)
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{
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return mImpl->run(xqa_params, kv_cache_buffer, mLaunchGridBlockCache, stream);
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}
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template void DecoderXQARunner::run(
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XQAParams const& xqa_params, KVLinearBuffer& kv_linear_buffer, cudaStream_t const& stream);
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template void DecoderXQARunner::run(
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XQAParams const& xqa_params, KVBlockArray& kv_block_array, cudaStream_t const& stream);
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} // namespace kernels
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} // namespace tensorrt_llm
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