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https://github.com/NVIDIA/TensorRT-LLM.git
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e06f537e08
* Update TensorRT-LLM --------- Co-authored-by: erenup <ping.nie@pku.edu.cn> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
514 lines
21 KiB
C++
514 lines
21 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 "tensorrt_llm/kernels/decoderMaskedMultiheadAttention/decoderXQAImplPrecompiled.h"
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#include "tensorrt_llm/common/cudaDriverWrapper.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/decoderXQARunner.h"
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#include "tensorrt_llm/kernels/unfusedAttentionKernels.h"
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#include <cassert>
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#include <cuda.h>
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#include <functional>
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#include <memory>
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#include <mutex>
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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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struct XQAKernelLoadHashKey
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{
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Data_type data_type;
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unsigned int sm;
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bool operator==(const XQAKernelLoadHashKey other) const
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{
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return data_type == other.data_type && sm == other.sm;
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}
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};
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struct XQAKernelLoadHasher
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{
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size_t operator()(const XQAKernelLoadHashKey& s) const
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{
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size_t key = s.data_type;
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key <<= 16;
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key ^= s.sm;
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return key;
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}
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};
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struct XQAKernelRuntimeHashKey
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{
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Data_type kv_data_type;
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unsigned int head_size;
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unsigned int beam_size;
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unsigned int num_q_heads_per_kv;
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unsigned int m_tilesize;
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unsigned int tokens_per_page;
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bool paged_kv_cache;
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bool multi_query_tokens;
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bool operator==(const XQAKernelRuntimeHashKey other) const
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{
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return kv_data_type == other.kv_data_type && head_size == other.head_size
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&& num_q_heads_per_kv == other.num_q_heads_per_kv && beam_size == other.beam_size
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&& multi_query_tokens == other.multi_query_tokens && m_tilesize == other.m_tilesize
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&& tokens_per_page == other.tokens_per_page && paged_kv_cache == other.paged_kv_cache;
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}
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};
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struct XQAKernelRuntimeHasher
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{
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size_t operator()(const XQAKernelRuntimeHashKey& s) const
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{
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size_t key = s.kv_data_type;
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key <<= 16;
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key ^= s.head_size;
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key <<= 8;
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key ^= s.num_q_heads_per_kv;
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key <<= 8;
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key ^= s.beam_size;
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key <<= 6;
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key ^= s.m_tilesize;
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key <<= 10;
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key ^= s.tokens_per_page;
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key <<= 1;
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key ^= s.paged_kv_cache;
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key <<= 1;
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key ^= s.multi_query_tokens;
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return key;
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}
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};
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// NOTE: we use int32_t sequence lengths as gpt attention plugins use int32_t for that.
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// XQA kernels assume all length should use uint32_t.
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// NOTE: Linear KV cache and paged KV cache uses the same structure.
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struct KVCache
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{
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void* data;
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int32_t const* sequence_lengths;
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// NOTE: max_num_blocks_per_sequence for paged kv cache, max_sequence_length for linear kv cache.
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uint32_t capacity;
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};
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struct BeamSearchParams
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{
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int32_t const* indices; // cacheIndir with shape: [batchSize][beamWidth][capacity]
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int32_t capacity;
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int32_t const* ctxLenList; // shape: [batchSize][beamWidth]. Should be [batchSize] but we have to match trt-llm API.
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};
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// XQA kernels assume all integer values should use uint32_t.
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struct XQALaunchParam
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{
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uint32_t num_k_heads;
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void* output;
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const void* qkv;
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KVCache kvCacheParams;
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std::optional<BeamSearchParams> beamSearchParams;
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uint32_t batch_size;
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const float* kv_scale_quant_orig = nullptr;
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void* scratch = nullptr;
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};
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// Setup launch params.
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template <typename KVCacheBuffer>
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void buildXQALaunchParams(XQALaunchParam& launchParams, const XQAParams& params, KVCacheBuffer kv_cache_buffer)
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{
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TLLM_CHECK_WITH_INFO(
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params.data_type == DATA_TYPE_FP16 || params.data_type == DATA_TYPE_BF16, "Only fp16 or bf16 supported now.");
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memset(&launchParams, 0, sizeof(XQALaunchParam));
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launchParams.num_k_heads = params.num_kv_heads;
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launchParams.output = static_cast<uint8_t*>(params.output);
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launchParams.qkv = static_cast<const uint8_t*>(params.qkv);
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launchParams.batch_size = params.batch_size;
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launchParams.kv_scale_quant_orig = params.kv_scale_quant_orig;
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launchParams.scratch = params.workspaces;
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launchParams.kvCacheParams.data = kv_cache_buffer.data;
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launchParams.kvCacheParams.sequence_lengths = params.sequence_lengths;
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launchParams.kvCacheParams.capacity
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= params.paged_kv_cache ? params.max_blocks_per_sequence : params.max_attention_window_size;
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// TODO: beam searching has not been implemented yet.
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if (params.beam_width > 1)
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{
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launchParams.beamSearchParams
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= BeamSearchParams{params.cache_indir, params.max_attention_window_size, params.context_lengths};
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}
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else
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{
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launchParams.beamSearchParams = std::nullopt;
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}
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}
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class XQAKernelList
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{
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public:
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using TKernelMeta = XQAKernelMetaInfo;
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XQAKernelList(Data_type type, unsigned int sm)
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: mDataType(type)
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, mKernelMetaCount(sizeof(sXqaKernelMetaInfo) / sizeof(sXqaKernelMetaInfo[0]))
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, mKernelMeta(&sXqaKernelMetaInfo[0])
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, mSM(sm)
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{
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mForceXQA = forceXQAKernels();
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}
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void loadXQAKernels()
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{
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if (!mFunctions.empty())
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{
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return;
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}
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for (unsigned int i = 0; i < mKernelMetaCount; ++i)
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{
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const auto& kernelMeta = mKernelMeta[i];
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if (kernelMeta.mSM != mSM || kernelMeta.mDataType != mDataType)
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continue;
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CUmodule hmod{0};
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auto findModuleIter = mModules.find(kernelMeta.mCubin);
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if (findModuleIter != mModules.end())
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{
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hmod = findModuleIter->second;
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}
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else
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{
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cuErrCheck(mDriver.cuModuleLoadData(&hmod, kernelMeta.mCubin), mDriver);
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mModules.insert(std::make_pair(kernelMeta.mCubin, hmod));
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}
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XQAKernelFuncInfo funcInfo{};
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funcInfo.mMetaInfoIndex = i;
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cuErrCheck(mDriver.cuModuleGetFunction(&funcInfo.mDeviceFunction, hmod, kernelMeta.mFuncName), mDriver);
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unsigned int* shmem_dev_ptr = nullptr;
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cuErrCheck(
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mDriver.cuModuleGetGlobal(reinterpret_cast<CUdeviceptr*>(&shmem_dev_ptr), nullptr, hmod, "smemSize"),
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mDriver);
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check_cuda_error(
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cudaMemcpy(&funcInfo.mSharedMemBytes, shmem_dev_ptr, sizeof(unsigned int), cudaMemcpyDeviceToHost));
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/* Set 46KB threshold here because we have to take static/driver shared memory into consideration. */
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if (funcInfo.mSharedMemBytes >= 46 * 1024)
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{
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cuErrCheck(mDriver.cuFuncSetAttribute(funcInfo.mDeviceFunction,
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CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, funcInfo.mSharedMemBytes),
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mDriver);
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}
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XQAKernelRuntimeHashKey hash_key{kernelMeta.mKVDataType, kernelMeta.mHeadDim, kernelMeta.mBeamWidth,
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kernelMeta.mNumQHeadsOverKV, kernelMeta.mMTileSize, kernelMeta.mTokensPerPage, kernelMeta.mPagedKVCache,
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kernelMeta.mMultiQueryTokens};
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mFunctions.insert(std::make_pair(hash_key, funcInfo));
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}
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}
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bool supportConfig(const XQAParams& xqaParams) const
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{
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unsigned int head_size = xqaParams.head_size;
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int num_q_heads = xqaParams.num_q_heads;
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int num_kv_heads = xqaParams.num_kv_heads;
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TLLM_CHECK_WITH_INFO(num_q_heads % num_kv_heads == 0, "numQHeads should be multiple of numKVHeads.");
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unsigned int num_q_heads_over_kv = num_q_heads / num_kv_heads;
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unsigned int beam_width = xqaParams.beam_width;
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// MultiQueryToken kernels can support any num_q_heads_over_kv that is power of 2.
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unsigned int kernel_num_q_heads_over_kv = xqaParams.multi_query_tokens ? 0 : num_q_heads_over_kv;
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// MultiQueryToken kernels can handle either 16/32 for M direction per CTA.
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unsigned int m_tilesize = xqaParams.multi_query_tokens ? 16 : num_q_heads_over_kv;
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XQAKernelRuntimeHashKey hash_key
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= {xqaParams.kv_cache_data_type, head_size, beam_width, kernel_num_q_heads_over_kv, m_tilesize,
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xqaParams.paged_kv_cache ? static_cast<unsigned int>(xqaParams.tokens_per_block) : 0,
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xqaParams.paged_kv_cache, xqaParams.multi_query_tokens};
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const auto findIter = mFunctions.find(hash_key);
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return findIter != mFunctions.end();
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}
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bool mayHavePerfGain(const XQAParams& xqaParams, int multiprocessor_count) const
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{
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// NOTE: only XQA supports multi_query_tokens (Medusa mode).
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if (mForceXQA || xqaParams.multi_query_tokens)
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{
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return true;
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}
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int num_kv_heads = xqaParams.num_kv_heads;
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int batch_size = static_cast<int>(xqaParams.batch_size);
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int multi_block_count = 1;
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if (xqaParams.multi_block_mode)
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{
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int history_length = xqaParams.timestep;
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multi_block_count = history_length / kMinHistoryTokensPerBlock;
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}
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int block_count = num_kv_heads * batch_size * multi_block_count;
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return static_cast<float>(block_count) * kEnableMinBlockFactor >= static_cast<float>(multiprocessor_count);
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}
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template <typename T, typename KVCacheBuffer>
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void run(const XQAParams& xqaParams, KVCacheBuffer& kv_cache_buffer, int2& rotary_kernel_launch_cache,
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int multiprocessor_count, const cudaStream_t& stream) const
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{
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unsigned int head_size = xqaParams.head_size;
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int num_q_heads = xqaParams.num_q_heads;
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int num_kv_heads = xqaParams.num_kv_heads;
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TLLM_CHECK_WITH_INFO(num_q_heads % num_kv_heads == 0, "numQHeads should be multiple of numKVHeads.");
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unsigned int num_q_heads_over_kv = num_q_heads / num_kv_heads;
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unsigned int beam_width = xqaParams.beam_width;
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const KvCacheDataType cache_type = xqaParams.kv_cache_quant_mode.hasInt8KvCache()
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? KvCacheDataType::INT8
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: (xqaParams.kv_cache_quant_mode.hasFp8KvCache() ? KvCacheDataType::FP8 : KvCacheDataType::BASE);
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// IDEA: Store rotary_processed Q buffer to output buffer.
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// NOTE: MHA kernels should read kv cache that has already been appended with new tokens' kv cache.
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void const* xqa_q_input_ptr = xqaParams.output;
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invokeApplyBiasRopeUpdateKVCache<T, KVCacheBuffer, true>(static_cast<T*>(const_cast<void*>(xqaParams.qkv)),
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static_cast<T*>(const_cast<void*>(xqaParams.output)), kv_cache_buffer,
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static_cast<const T*>(xqaParams.qkv_bias), xqaParams.sequence_lengths, nullptr, nullptr,
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xqaParams.batch_size, xqaParams.generation_input_length, xqaParams.cyclic_attention_window_size,
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xqaParams.sink_token_length, xqaParams.batch_size * beam_width * xqaParams.generation_input_length,
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xqaParams.num_q_heads, xqaParams.num_kv_heads, xqaParams.head_size, xqaParams.rotary_embedding_dim,
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xqaParams.rotary_embedding_base, xqaParams.rotary_embedding_scale_type, xqaParams.rotary_embedding_scale,
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xqaParams.rotary_embedding_max_positions, xqaParams.position_embedding_type,
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xqaParams.medusa_position_offsets, xqaParams.position_shift_enabled, (float*) nullptr, 0, cache_type,
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xqaParams.kv_scale_orig_quant, true, beam_width, rotary_kernel_launch_cache, stream);
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sync_check_cuda_error();
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// Use mTileSize = 16 kernels when qSeqLen <= 16.
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unsigned int qSeqLen = static_cast<unsigned int>(xqaParams.generation_input_length);
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unsigned int mTileSize = qSeqLen <= 16 ? 16 : 32;
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// MultiQueryToken kernels can support any num_q_heads_over_kv that is power of 2.
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unsigned int kernel_num_q_heads_over_kv = xqaParams.multi_query_tokens ? 0 : num_q_heads_over_kv;
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// MultiQueryToken kernels can handle either 16/32 for M direction per CTA.
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unsigned int kernel_m_tilesize = xqaParams.multi_query_tokens ? mTileSize : num_q_heads_over_kv;
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XQAKernelRuntimeHashKey hash_key{xqaParams.kv_cache_data_type, head_size, beam_width,
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kernel_num_q_heads_over_kv, kernel_m_tilesize,
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xqaParams.paged_kv_cache ? static_cast<unsigned int>(xqaParams.tokens_per_block) : 0,
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xqaParams.paged_kv_cache, xqaParams.multi_query_tokens};
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const auto findIter = mFunctions.find(hash_key);
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TLLM_CHECK_WITH_INFO(findIter != mFunctions.end(), "XQAKernelFunc not found.");
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const auto& kernelMeta = mKernelMeta[findIter->second.mMetaInfoIndex];
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const CUfunction func = findIter->second.mDeviceFunction;
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const unsigned int shared_mem_bytes = findIter->second.mSharedMemBytes;
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XQALaunchParam launchParams;
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buildXQALaunchParams(launchParams, xqaParams, kv_cache_buffer);
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if (xqaParams.multi_query_tokens)
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{
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// MultiQueryTokens (generation_input_length > 1) need extra parameters (like qSeqLen, log2HeadGrpSize, and
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// mask). Input parameters for MultiQueryTokens kernels.
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unsigned int log2HeadGrpSize = log2(num_q_heads_over_kv);
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unsigned int nbTokenBlocksPerGrp = divUp(qSeqLen << log2HeadGrpSize, mTileSize);
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int const* maskPtr = xqaParams.medusa_packed_mask;
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// TODO: add fp8/int8 kv cache kernels.
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float kvCacheQuantOrig = 1.0f;
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// TODO: merge SingleQueryToken params and MultiQueryTokens params into one kernelParams.
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void* kernelParams[] = {&qSeqLen, &launchParams.num_k_heads, &log2HeadGrpSize, &launchParams.output,
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&xqa_q_input_ptr, &maskPtr, &launchParams.kvCacheParams, &launchParams.batch_size, &kvCacheQuantOrig,
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&launchParams.scratch};
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int multi_block = 1;
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if (xqaParams.multi_block_mode)
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{
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multi_block = computeMultiBlockCount(xqaParams, xqaParams.batch_size, multiprocessor_count);
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cudaMemsetAsync(
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xqaParams.workspaces, 0, sizeof(int) * xqaParams.batch_size * xqaParams.num_kv_heads, stream);
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}
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cuErrCheck(mDriver.cuLaunchKernel(func, multi_block, xqaParams.num_kv_heads * nbTokenBlocksPerGrp,
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xqaParams.batch_size, 128, 1, 2, shared_mem_bytes, stream, kernelParams, nullptr),
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mDriver);
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}
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else
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{
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constexpr uint32_t kMAX_NB_KERNEL_PARAMS = 9;
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uint32_t idxNextParam = 0;
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void* kernelParams[kMAX_NB_KERNEL_PARAMS];
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auto appendParam = [&](auto* p) mutable
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{
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TLLM_CHECK(idxNextParam < kMAX_NB_KERNEL_PARAMS);
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kernelParams[idxNextParam++] = p;
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};
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appendParam(&launchParams.num_k_heads);
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appendParam(&launchParams.output);
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appendParam(&xqa_q_input_ptr);
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appendParam(&launchParams.kvCacheParams);
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if (xqaParams.beam_width > 1)
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{
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appendParam(&launchParams.beamSearchParams.value());
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}
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appendParam(&launchParams.batch_size);
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appendParam(&launchParams.kv_scale_quant_orig);
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appendParam(&launchParams.scratch);
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kernelParams[idxNextParam] = nullptr; // one extra nullptr at end as guard.
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int multi_block = 1;
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if (xqaParams.multi_block_mode)
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{
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multi_block = computeMultiBlockCount(xqaParams, xqaParams.batch_size, multiprocessor_count);
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cudaMemsetAsync(
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xqaParams.workspaces, 0, sizeof(int) * xqaParams.batch_size * xqaParams.num_kv_heads, stream);
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}
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cuErrCheck(mDriver.cuLaunchKernel(func, multi_block, xqaParams.num_kv_heads, xqaParams.batch_size, 128, 1,
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2, shared_mem_bytes, stream, kernelParams, nullptr),
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mDriver);
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}
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sync_check_cuda_error();
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}
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static int computeMultiBlockCount(const XQAParams& xqaParams, int batch_size, int multiprocessor_count)
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{
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int multi_block_count = 1;
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int num_kv_heads = xqaParams.num_kv_heads;
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int history_length = xqaParams.timestep;
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multi_block_count = history_length / kMinHistoryTokensPerBlock;
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multi_block_count = std::max(multi_block_count, 1);
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// adjust to kTargetWaveFactor, as already initialized using kMinHistoryTokensPerBlock, only need to decrease.
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double wave_count = (double) batch_size * num_kv_heads * multi_block_count / (double) multiprocessor_count;
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double adj_factor = wave_count / (double) kTargetWaveFactor;
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if (adj_factor > 1.0)
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{
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multi_block_count = floor(multi_block_count / adj_factor);
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}
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multi_block_count = std::max(multi_block_count, 1);
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// add limitation on upper bound.
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multi_block_count = std::min(kMaxNbCtaPerKVHeadFactor, multi_block_count);
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TLLM_CHECK_WITH_INFO(multi_block_count >= 1, "MultiBlock count should be larger than 1");
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return multi_block_count;
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}
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protected:
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tensorrt_llm::common::CUDADriverWrapper mDriver;
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Data_type mDataType;
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const TKernelMeta* mKernelMeta;
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unsigned int mKernelMetaCount;
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unsigned int mSM;
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std::unordered_map<const unsigned long long*, CUmodule> mModules;
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bool mForceXQA = false;
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|
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struct XQAKernelFuncInfo
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{
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unsigned int mMetaInfoIndex;
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unsigned int mSharedMemBytes;
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CUfunction mDeviceFunction;
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};
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|
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std::unordered_map<XQAKernelRuntimeHashKey, XQAKernelFuncInfo, XQAKernelRuntimeHasher> mFunctions;
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};
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|
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class XQAKernelLoader
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|
{
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public:
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const XQAKernelList* getXQAKernels(Data_type type, unsigned int sm)
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|
{
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static std::mutex s_mutex;
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std::lock_guard<std::mutex> lg(s_mutex);
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|
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XQAKernelLoadHashKey hash_key{type, sm};
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|
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const auto findIter = mKernels.find(hash_key);
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if (findIter == mKernels.end())
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|
{
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XQAKernelList* newKernel = new XQAKernelList{type, sm};
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newKernel->loadXQAKernels();
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mKernels.insert(std::make_pair(hash_key, std::unique_ptr<XQAKernelList>(newKernel)));
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|
return newKernel;
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|
}
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|
return findIter->second.get();
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|
}
|
|
|
|
static XQAKernelLoader& Get()
|
|
{
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|
int device_id = tensorrt_llm::common::getDevice();
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|
static std::unique_ptr<XQAKernelLoader> s_factory[32] = {nullptr};
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|
if (s_factory[device_id] == nullptr)
|
|
{
|
|
assert(device_id <= 32);
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|
s_factory[device_id] = std::make_unique<XQAKernelLoader>(XQAKernelLoader());
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|
}
|
|
|
|
return *(s_factory[device_id]);
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|
}
|
|
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|
private:
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|
XQAKernelLoader() = default;
|
|
|
|
std::unordered_map<XQAKernelLoadHashKey, const std::unique_ptr<XQAKernelList>, XQAKernelLoadHasher> mKernels;
|
|
};
|
|
|
|
inline const XQAKernelList* getXQAKernels(Data_type type, unsigned int sm)
|
|
{
|
|
return XQAKernelLoader::Get().getXQAKernels(type, sm);
|
|
}
|
|
|
|
#define XQA_KERNEL_RUN(DATA_TYPE) \
|
|
xqa_kernel->template run<DATA_TYPE, KVCacheBuffer>( \
|
|
xqa_params, kv_cache_buffer, rotary_kernel_launch_cache, multi_processor_count, stream);
|
|
|
|
template <typename KVCacheBuffer>
|
|
void DecoderXQAImplPrecompiled::runDispatchBuffer(const XQAParams& xqa_params, KVCacheBuffer& kv_cache_buffer,
|
|
int2& rotary_kernel_launch_cache, const cudaStream_t& stream)
|
|
{
|
|
const XQAKernelList* xqa_kernel = getXQAKernels(mRunner->mDataType, tensorrt_llm::common::getSMVersion());
|
|
int multi_processor_count = mRunner->mMultiProcessorCount;
|
|
if (mRunner->mDataType == DATA_TYPE_FP16)
|
|
{
|
|
XQA_KERNEL_RUN(__half);
|
|
}
|
|
else
|
|
{
|
|
XQA_KERNEL_RUN(__nv_bfloat16);
|
|
}
|
|
}
|
|
|
|
#undef XQA_KERNEL_RUN
|
|
|
|
bool DecoderXQAImplPrecompiled::shouldUse(const XQAParams& xqaParams)
|
|
{
|
|
const XQAKernelList* xqa_kernel = getXQAKernels(mRunner->mDataType, tensorrt_llm::common::getSMVersion());
|
|
return xqa_kernel->supportConfig(xqaParams)
|
|
&& xqa_kernel->mayHavePerfGain(xqaParams, mRunner->mMultiProcessorCount);
|
|
}
|
|
|
|
void DecoderXQAImplPrecompiled::prepare(const XQAParams&)
|
|
{
|
|
// Intentionally do nothing.
|
|
}
|
|
|
|
void DecoderXQAImplPrecompiled::runWithKVLinearBuffer(const XQAParams& xqa_params, KVLinearBuffer& kv_linear_buffer,
|
|
int2& rotary_kernel_launch_cache, const cudaStream_t& stream)
|
|
{
|
|
runDispatchBuffer<KVLinearBuffer>(xqa_params, kv_linear_buffer, rotary_kernel_launch_cache, stream);
|
|
}
|
|
|
|
void DecoderXQAImplPrecompiled::runWithKVBlockArray(const XQAParams& xqa_params, KVBlockArray& kv_block_array,
|
|
int2& rotary_kernel_launch_cache, const cudaStream_t& stream)
|
|
{
|
|
runDispatchBuffer<KVBlockArray>(xqa_params, kv_block_array, rotary_kernel_launch_cache, stream);
|
|
}
|
|
|
|
} // namespace kernels
|
|
} // namespace tensorrt_llm
|