mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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336 lines
17 KiB
C++
336 lines
17 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 1993-2024 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 "tensorrt_llm/common/config.h"
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#include "tensorrt_llm/common/cublasMMWrapper.h"
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#include "tensorrt_llm/common/workspace.h"
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#include <NvInferRuntime.h>
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#include <cublasLt.h>
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#include <cublas_v2.h>
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#include <cuda_runtime.h>
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#if ENABLE_MULTI_DEVICE
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#include <nccl.h>
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#endif // ENABLE_MULTI_DEVICE
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#include <cstring>
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#include <map>
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#include <memory>
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#include <nvml.h>
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#include <optional>
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#include <set>
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#include <string>
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#include <unordered_map>
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TRTLLM_NAMESPACE_BEGIN
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namespace common::op
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{
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// Write values into buffer
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template <typename T>
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void write(char*& buffer, T const& val)
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{
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std::memcpy(buffer, &val, sizeof(T));
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buffer += sizeof(T);
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}
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// Read values from buffer
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template <typename T>
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void read(char const*& buffer, T& val)
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{
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auto* valPtr = reinterpret_cast<char*>(&val);
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std::memcpy(valPtr, buffer, sizeof(T));
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buffer += sizeof(T);
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}
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inline cudaDataType_t trtToCublasDtype(nvinfer1::DataType type)
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{
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switch (type)
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{
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case nvinfer1::DataType::kFLOAT: return CUDA_R_32F;
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case nvinfer1::DataType::kHALF: return CUDA_R_16F;
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#if defined(NV_TENSORRT_MAJOR) && NV_TENSORRT_MAJOR >= 9
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case nvinfer1::DataType::kBF16: return CUDA_R_16BF;
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#endif
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default: TLLM_THROW("Not supported data type for cuBLAS");
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}
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}
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// Like std::unique_ptr, but does not prevent generation of default copy constructor when used as class members.
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// The copy constructor produces nullptr. So the plugin default copy constructor will not really copy this, and
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// your clone() implementation is responsible for initializing such data members.
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// With this we can simplify clone() implementation when there are many data members including at least one unique_ptr.
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template <typename T, typename Del = std::default_delete<T>>
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class UniqPtrWNullCopy : public std::unique_ptr<T, Del>
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{
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public:
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using std::unique_ptr<T, Del>::unique_ptr;
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// for compatibility with std::make_unique
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explicit UniqPtrWNullCopy(std::unique_ptr<T, Del>&& src)
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: std::unique_ptr<T, Del>::unique_ptr{std::move(src)}
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{
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}
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// copy constructor produces nullptr
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UniqPtrWNullCopy(UniqPtrWNullCopy const&)
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: std::unique_ptr<T, Del>::unique_ptr{}
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{
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}
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// copy assignment copies nothing
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UniqPtrWNullCopy& operator=(UniqPtrWNullCopy const&)
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{
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return *this;
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}
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};
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namespace
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{
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template <typename T>
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struct hash_helper;
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// Base case: use std::hash for basic types
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template <typename T>
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struct hash_helper
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{
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size_t operator()(T const& v) const
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{
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return std::hash<T>{}(v);
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}
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};
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// Specialization for std::set
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template <typename T>
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struct hash_helper<std::set<T>>
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{
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size_t operator()(std::set<T> const& s) const
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{
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size_t hash_value = 0;
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for (auto const& item : s)
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{
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// Recursively hash each element
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hash_value ^= hash_helper<T>{}(item) + 0x9e3779b9 + (hash_value << 6) + (hash_value >> 2);
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}
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return hash_value;
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}
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};
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// Helper for tuple hashing
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template <typename Tuple, size_t Index = std::tuple_size<Tuple>::value - 1>
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struct tuple_hash_helper
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{
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static size_t hash(Tuple const& tuple)
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{
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size_t hash_value = tuple_hash_helper<Tuple, Index - 1>::hash(tuple);
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return hash_value
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^ (hash_helper<typename std::tuple_element<Index, Tuple>::type>{}(std::get<Index>(tuple)) + 0x9e3779b9
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+ (hash_value << 6) + (hash_value >> 2));
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}
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};
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// Base case for tuple hashing
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template <typename Tuple>
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struct tuple_hash_helper<Tuple, 0>
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{
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static size_t hash(Tuple const& tuple)
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{
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return hash_helper<typename std::tuple_element<0, Tuple>::type>{}(std::get<0>(tuple));
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}
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};
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// Specialization for std::tuple
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template <typename... Args>
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struct hash_helper<std::tuple<Args...>>
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{
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size_t operator()(std::tuple<Args...> const& t) const
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{
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return tuple_hash_helper<std::tuple<Args...>>::hash(t);
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}
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};
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} // namespace
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// Main hash struct to be used
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template <typename T>
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struct hash
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{
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size_t operator()(T const& v) const
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{
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return hash_helper<T>{}(v);
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}
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};
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// for testing only
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void const* getCommSessionHandle();
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} // namespace common::op
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inline bool isBuilding()
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{
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auto constexpr key = "IS_BUILDING";
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auto const val = getenv(key);
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return val != nullptr && std::string(val) == "1";
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}
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#if ENABLE_MULTI_DEVICE
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#define NCCLCHECK(cmd) \
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do \
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{ \
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ncclResult_t r = cmd; \
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if (r != ncclSuccess) \
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{ \
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printf("Failed, NCCL error %s:%d '%s'\n", __FILE__, __LINE__, ncclGetErrorString(r)); \
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exit(EXIT_FAILURE); \
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} \
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} while (0)
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#define NCCLCHECK_THROW(cmd) \
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do \
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{ \
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ncclResult_t r = cmd; \
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if (TLLM_UNLIKELY(r != ncclSuccess)) \
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{ \
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TLLM_THROW("Failed, NCCL error %s:%d '%s'\n", __FILE__, __LINE__, ncclGetErrorString(r)); \
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} \
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} while (0)
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std::unordered_map<nvinfer1::DataType, ncclDataType_t>* getDtypeMap();
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std::shared_ptr<ncclComm_t> getComm(std::set<int> const& group);
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#endif // ENABLE_MULTI_DEVICE
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//! To save GPU memory, all the plugins share the same cublas and cublasLt handle globally.
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//! Get cublas and cublasLt handle for current cuda context
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std::shared_ptr<cublasHandle_t> getCublasHandle();
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std::shared_ptr<cublasLtHandle_t> getCublasLtHandle();
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TRTLLM_NAMESPACE_END
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#ifndef DEBUG
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#define PLUGIN_CHECK(status) \
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do \
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{ \
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if (status != 0) \
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abort(); \
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} while (0)
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#define ASSERT_PARAM(exp) \
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do \
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{ \
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if (!(exp)) \
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return STATUS_BAD_PARAM; \
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} while (0)
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#define ASSERT_FAILURE(exp) \
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do \
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{ \
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if (!(exp)) \
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return STATUS_FAILURE; \
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} while (0)
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#define CSC(call, err) \
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do \
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{ \
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cudaError_t cudaStatus = call; \
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if (cudaStatus != cudaSuccess) \
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{ \
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return err; \
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} \
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} while (0)
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#define DEBUG_PRINTF(...) \
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do \
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{ \
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} while (0)
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#else
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#define ASSERT_PARAM(exp) \
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do \
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{ \
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if (!(exp)) \
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{ \
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fprintf(stderr, "Bad param - " #exp ", %s:%d\n", __FILE__, __LINE__); \
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return STATUS_BAD_PARAM; \
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} \
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} while (0)
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#define ASSERT_FAILURE(exp) \
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do \
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{ \
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if (!(exp)) \
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{ \
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fprintf(stderr, "Failure - " #exp ", %s:%d\n", __FILE__, __LINE__); \
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return STATUS_FAILURE; \
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} \
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} while (0)
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#define CSC(call, err) \
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do \
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{ \
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cudaError_t cudaStatus = call; \
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if (cudaStatus != cudaSuccess) \
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{ \
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printf("%s %d CUDA FAIL %s\n", __FILE__, __LINE__, cudaGetErrorString(cudaStatus)); \
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return err; \
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} \
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} while (0)
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#define PLUGIN_CHECK(status) \
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{ \
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if (status != 0) \
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{ \
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DEBUG_PRINTF("%s %d CUDA FAIL %s\n", __FILE__, __LINE__, cudaGetErrorString(status)); \
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abort(); \
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} \
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}
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#define DEBUG_PRINTF(...) \
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do \
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{ \
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printf(__VA_ARGS__); \
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} while (0)
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#endif // DEBUG
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#define NVML_CHECK(cmd) \
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do \
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{ \
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nvmlReturn_t r = cmd; \
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if (r != NVML_SUCCESS) \
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{ \
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printf("Failed, NVML error %s:%d '%s'\n", __FILE__, __LINE__, nvmlErrorString(r)); \
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exit(EXIT_FAILURE); \
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} \
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} while (0)
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#define NVML_CHECK_THROW(cmd) \
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do \
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{ \
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nvmlReturn_t r = cmd; \
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if (TLLM_UNLIKELY(r != NVML_SUCCESS)) \
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{ \
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TLLM_THROW("Failed, NVML error %s:%d '%s'\n", __FILE__, __LINE__, nvmlErrorString(r)); \
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} \
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} while (0)
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