mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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145 lines
4.7 KiB
C++
145 lines
4.7 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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#pragma once
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#include "cubinObj.h"
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#include "compileEngine.h"
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#include "serializationUtils.h"
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#include "tensorrt_llm/kernels/decoderMaskedMultiheadAttention/decoderXQAImplCommon.h"
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#include <functional>
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#include <unordered_map>
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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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namespace jit
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{
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// A collection of CubinObjs, with caching functionality.
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template <typename Key, class Hash = std::hash<Key>>
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class CubinObjRegistryTemplate
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{
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public:
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CubinObjRegistryTemplate() = default;
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CubinObjRegistryTemplate(void const* buffer_, size_t buffer_size)
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{
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size_t remaining_buffer_size = buffer_size;
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uint8_t const* buffer = static_cast<uint8_t const*>(buffer_);
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// First 4 bytes: num of elements.
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uint32_t n = readFromBuffer<uint32_t>(buffer, remaining_buffer_size);
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for (uint32_t i = 0; i < n; ++i)
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{
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uint32_t key_size = readFromBuffer<uint32_t>(buffer, remaining_buffer_size);
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TLLM_CHECK(key_size <= remaining_buffer_size);
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Key key(buffer, key_size);
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buffer += key_size;
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remaining_buffer_size -= key_size;
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uint32_t obj_size = readFromBuffer<uint32_t>(buffer, remaining_buffer_size);
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TLLM_CHECK(obj_size <= remaining_buffer_size);
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CubinObj obj(buffer, obj_size);
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buffer += obj_size;
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remaining_buffer_size -= obj_size;
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mMap.insert({key, std::move(obj)});
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}
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TLLM_CHECK(remaining_buffer_size == 0);
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}
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std::unique_ptr<CubinObjRegistryTemplate<Key, Hash>> clone() const noexcept
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{
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auto result = std::make_unique<CubinObjRegistryTemplate<Key, Hash>>();
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for (auto const& p : mMap)
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{
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result->mMap.insert(p);
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}
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return result;
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}
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size_t getSerializationSize() const noexcept
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{
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size_t result = sizeof(uint32_t);
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for (auto&& p : mMap)
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{
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result += 2 * sizeof(uint32_t);
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result += p.first.getSerializationSize() + p.second.getSerializationSize();
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}
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return result;
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}
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void serialize(void* buffer_, size_t buffer_size) const noexcept
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{
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size_t remaining_buffer_size = buffer_size;
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uint8_t* buffer = static_cast<uint8_t*>(buffer_);
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uint32_t n = mMap.size();
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writeToBuffer<uint32_t>(n, buffer, remaining_buffer_size);
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for (auto&& p : mMap)
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{
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uint32_t key_size = p.first.getSerializationSize();
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TLLM_CHECK(key_size <= remaining_buffer_size);
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writeToBuffer<uint32_t>(key_size, buffer, remaining_buffer_size);
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p.first.serialize(buffer, key_size);
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buffer += key_size;
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remaining_buffer_size -= key_size;
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uint32_t obj_size = p.second.getSerializationSize();
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TLLM_CHECK(obj_size <= remaining_buffer_size);
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writeToBuffer<uint32_t>(obj_size, buffer, remaining_buffer_size);
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p.second.serialize(buffer, obj_size);
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buffer += obj_size;
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remaining_buffer_size -= obj_size;
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}
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TLLM_CHECK(remaining_buffer_size == 0);
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}
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// Returns directly if the Cubin already exists in the registry, otherwise call compileEngine to compile it.
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//
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// compileEngine may be nullptr.
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CubinObj* getCubin(Key const& key, CompileEngine* compileEngine)
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{
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auto iter = mMap.find(key);
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if (iter != mMap.end())
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{
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return &(iter->second);
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}
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TLLM_CHECK_WITH_INFO(compileEngine != nullptr, "Key not found; compileEngine shouldn't be nullptr.");
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CubinObj obj = compileEngine->compile();
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auto insertResultIter = mMap.insert({key, std::move(obj)}).first;
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return &(insertResultIter->second);
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}
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void clear()
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{
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mMap.clear();
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}
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private:
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std::unordered_map<Key, CubinObj, Hash> mMap;
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};
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using CubinObjKey = XQAKernelFullHashKey;
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using CubinObjHasher = XQAKernelFullHasher;
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using CubinObjRegistry = CubinObjRegistryTemplate<CubinObjKey, CubinObjHasher>;
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} // namespace jit
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} // namespace kernels
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} // namespace tensorrt_llm
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