mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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16d2467ea8
* Update TensorRT-LLM --------- Co-authored-by: Denis Kayshev <topenkoff@gmail.com> Co-authored-by: akhoroshev <arthoroshev@gmail.com> Co-authored-by: Patrick Reiter Horn <patrick.horn@gmail.com> Update
255 lines
14 KiB
C++
255 lines
14 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/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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namespace tensorrt_llm::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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};
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template <typename T>
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std::size_t hash_combine(std::size_t seed, T const& value)
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{
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std::hash<T> hasher;
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seed ^= hasher(value) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
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return seed;
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}
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template <typename T>
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struct TupleHash;
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template <typename... Args>
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struct TupleHash<std::tuple<Args...>>
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{
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std::size_t operator()(std::tuple<Args...> const& tuple) const noexcept
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{
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std::size_t seed = static_cast<std::size_t>(672807365);
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return std::apply(
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[&seed](auto const&... args)
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{
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((seed = hash_combine(seed, args)), ...);
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return seed;
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},
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tuple);
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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 tensorrt_llm::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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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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#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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