mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
311 lines
15 KiB
C++
311 lines
15 KiB
C++
/*
|
|
* SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
* SPDX-License-Identifier: Apache-2.0
|
|
*
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*/
|
|
|
|
#pragma once
|
|
|
|
#include "tensorrt_llm/common/cublasMMWrapper.h"
|
|
#include "tensorrt_llm/common/workspace.h"
|
|
|
|
#include <NvInferRuntime.h>
|
|
#include <cublasLt.h>
|
|
#include <cublas_v2.h>
|
|
#include <cuda_runtime.h>
|
|
#if ENABLE_MULTI_DEVICE
|
|
#include <nccl.h>
|
|
#endif // ENABLE_MULTI_DEVICE
|
|
|
|
#include <cstring>
|
|
#include <map>
|
|
#include <memory>
|
|
#include <nvml.h>
|
|
#include <optional>
|
|
#include <set>
|
|
#include <string>
|
|
#include <unordered_map>
|
|
|
|
namespace tensorrt_llm::common::op
|
|
{
|
|
|
|
// Write values into buffer
|
|
template <typename T>
|
|
void write(char*& buffer, T const& val)
|
|
{
|
|
std::memcpy(buffer, &val, sizeof(T));
|
|
buffer += sizeof(T);
|
|
}
|
|
|
|
// Read values from buffer
|
|
template <typename T>
|
|
void read(char const*& buffer, T& val)
|
|
{
|
|
auto* valPtr = reinterpret_cast<char*>(&val);
|
|
std::memcpy(valPtr, buffer, sizeof(T));
|
|
buffer += sizeof(T);
|
|
}
|
|
|
|
inline cudaDataType_t trtToCublasDtype(nvinfer1::DataType type)
|
|
{
|
|
switch (type)
|
|
{
|
|
case nvinfer1::DataType::kFLOAT: return CUDA_R_32F;
|
|
case nvinfer1::DataType::kHALF: return CUDA_R_16F;
|
|
#if defined(NV_TENSORRT_MAJOR) && NV_TENSORRT_MAJOR >= 9
|
|
case nvinfer1::DataType::kBF16: return CUDA_R_16BF;
|
|
#endif
|
|
default: TLLM_THROW("Not supported data type for cuBLAS");
|
|
}
|
|
}
|
|
|
|
// Like std::unique_ptr, but does not prevent generation of default copy constructor when used as class members.
|
|
// The copy constructor produces nullptr. So the plugin default copy constructor will not really copy this, and
|
|
// your clone() implementation is responsible for initializing such data members.
|
|
// With this we can simplify clone() implementation when there are many data members including at least one unique_ptr.
|
|
template <typename T, typename Del = std::default_delete<T>>
|
|
class UniqPtrWNullCopy : public std::unique_ptr<T, Del>
|
|
{
|
|
public:
|
|
using std::unique_ptr<T, Del>::unique_ptr;
|
|
|
|
// for compatibility with std::make_unique
|
|
explicit UniqPtrWNullCopy(std::unique_ptr<T, Del>&& src)
|
|
: std::unique_ptr<T, Del>::unique_ptr{std::move(src)}
|
|
{
|
|
}
|
|
|
|
// copy constructor produces nullptr
|
|
UniqPtrWNullCopy(UniqPtrWNullCopy const&)
|
|
: std::unique_ptr<T, Del>::unique_ptr{}
|
|
{
|
|
}
|
|
|
|
// copy assignment copies nothing
|
|
UniqPtrWNullCopy& operator=(UniqPtrWNullCopy const&)
|
|
{
|
|
return *this;
|
|
}
|
|
};
|
|
|
|
namespace
|
|
{
|
|
|
|
template <typename T>
|
|
struct hash_helper;
|
|
|
|
// Base case: use std::hash for basic types
|
|
template <typename T>
|
|
struct hash_helper
|
|
{
|
|
size_t operator()(T const& v) const
|
|
{
|
|
return std::hash<T>{}(v);
|
|
}
|
|
};
|
|
|
|
// Specialization for std::set
|
|
template <typename T>
|
|
struct hash_helper<std::set<T>>
|
|
{
|
|
size_t operator()(std::set<T> const& s) const
|
|
{
|
|
size_t hash_value = 0;
|
|
for (auto const& item : s)
|
|
{
|
|
// Recursively hash each element
|
|
hash_value ^= hash_helper<T>{}(item) + 0x9e3779b9 + (hash_value << 6) + (hash_value >> 2);
|
|
}
|
|
return hash_value;
|
|
}
|
|
};
|
|
|
|
// Helper for tuple hashing
|
|
template <typename Tuple, size_t Index = std::tuple_size<Tuple>::value - 1>
|
|
struct tuple_hash_helper
|
|
{
|
|
static size_t hash(Tuple const& tuple)
|
|
{
|
|
size_t hash_value = tuple_hash_helper<Tuple, Index - 1>::hash(tuple);
|
|
return hash_value
|
|
^ (hash_helper<typename std::tuple_element<Index, Tuple>::type>{}(std::get<Index>(tuple)) + 0x9e3779b9
|
|
+ (hash_value << 6) + (hash_value >> 2));
|
|
}
|
|
};
|
|
|
|
// Base case for tuple hashing
|
|
template <typename Tuple>
|
|
struct tuple_hash_helper<Tuple, 0>
|
|
{
|
|
static size_t hash(Tuple const& tuple)
|
|
{
|
|
return hash_helper<typename std::tuple_element<0, Tuple>::type>{}(std::get<0>(tuple));
|
|
}
|
|
};
|
|
|
|
// Specialization for std::tuple
|
|
template <typename... Args>
|
|
struct hash_helper<std::tuple<Args...>>
|
|
{
|
|
size_t operator()(std::tuple<Args...> const& t) const
|
|
{
|
|
return tuple_hash_helper<std::tuple<Args...>>::hash(t);
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
// Main hash struct to be used
|
|
template <typename T>
|
|
struct hash
|
|
{
|
|
size_t operator()(T const& v) const
|
|
{
|
|
return hash_helper<T>{}(v);
|
|
}
|
|
};
|
|
|
|
// for testing only
|
|
void const* getCommSessionHandle();
|
|
} // namespace tensorrt_llm::common::op
|
|
|
|
inline bool isBuilding()
|
|
{
|
|
auto constexpr key = "IS_BUILDING";
|
|
auto const val = getenv(key);
|
|
return val != nullptr && std::string(val) == "1";
|
|
}
|
|
|
|
#if ENABLE_MULTI_DEVICE
|
|
#define NCCLCHECK(cmd) \
|
|
do \
|
|
{ \
|
|
ncclResult_t r = cmd; \
|
|
if (r != ncclSuccess) \
|
|
{ \
|
|
printf("Failed, NCCL error %s:%d '%s'\n", __FILE__, __LINE__, ncclGetErrorString(r)); \
|
|
exit(EXIT_FAILURE); \
|
|
} \
|
|
} while (0)
|
|
|
|
std::unordered_map<nvinfer1::DataType, ncclDataType_t>* getDtypeMap();
|
|
|
|
std::shared_ptr<ncclComm_t> getComm(std::set<int> const& group);
|
|
|
|
#endif // ENABLE_MULTI_DEVICE
|
|
|
|
//! To save GPU memory, all the plugins share the same cublas and cublasLt handle globally.
|
|
//! Get cublas and cublasLt handle for current cuda context
|
|
std::shared_ptr<cublasHandle_t> getCublasHandle();
|
|
std::shared_ptr<cublasLtHandle_t> getCublasLtHandle();
|
|
|
|
#ifndef DEBUG
|
|
|
|
#define PLUGIN_CHECK(status) \
|
|
do \
|
|
{ \
|
|
if (status != 0) \
|
|
abort(); \
|
|
} while (0)
|
|
|
|
#define ASSERT_PARAM(exp) \
|
|
do \
|
|
{ \
|
|
if (!(exp)) \
|
|
return STATUS_BAD_PARAM; \
|
|
} while (0)
|
|
|
|
#define ASSERT_FAILURE(exp) \
|
|
do \
|
|
{ \
|
|
if (!(exp)) \
|
|
return STATUS_FAILURE; \
|
|
} while (0)
|
|
|
|
#define CSC(call, err) \
|
|
do \
|
|
{ \
|
|
cudaError_t cudaStatus = call; \
|
|
if (cudaStatus != cudaSuccess) \
|
|
{ \
|
|
return err; \
|
|
} \
|
|
} while (0)
|
|
|
|
#define DEBUG_PRINTF(...) \
|
|
do \
|
|
{ \
|
|
} while (0)
|
|
|
|
#else
|
|
|
|
#define ASSERT_PARAM(exp) \
|
|
do \
|
|
{ \
|
|
if (!(exp)) \
|
|
{ \
|
|
fprintf(stderr, "Bad param - " #exp ", %s:%d\n", __FILE__, __LINE__); \
|
|
return STATUS_BAD_PARAM; \
|
|
} \
|
|
} while (0)
|
|
|
|
#define ASSERT_FAILURE(exp) \
|
|
do \
|
|
{ \
|
|
if (!(exp)) \
|
|
{ \
|
|
fprintf(stderr, "Failure - " #exp ", %s:%d\n", __FILE__, __LINE__); \
|
|
return STATUS_FAILURE; \
|
|
} \
|
|
} while (0)
|
|
|
|
#define CSC(call, err) \
|
|
do \
|
|
{ \
|
|
cudaError_t cudaStatus = call; \
|
|
if (cudaStatus != cudaSuccess) \
|
|
{ \
|
|
printf("%s %d CUDA FAIL %s\n", __FILE__, __LINE__, cudaGetErrorString(cudaStatus)); \
|
|
return err; \
|
|
} \
|
|
} while (0)
|
|
|
|
#define PLUGIN_CHECK(status) \
|
|
{ \
|
|
if (status != 0) \
|
|
{ \
|
|
DEBUG_PRINTF("%s %d CUDA FAIL %s\n", __FILE__, __LINE__, cudaGetErrorString(status)); \
|
|
abort(); \
|
|
} \
|
|
}
|
|
|
|
#define DEBUG_PRINTF(...) \
|
|
do \
|
|
{ \
|
|
printf(__VA_ARGS__); \
|
|
} while (0)
|
|
|
|
#endif // DEBUG
|
|
|
|
#define NVML_CHECK(cmd) \
|
|
do \
|
|
{ \
|
|
nvmlReturn_t r = cmd; \
|
|
if (r != NVML_SUCCESS) \
|
|
{ \
|
|
printf("Failed, NVML error %s:%d '%s'\n", __FILE__, __LINE__, nvmlErrorString(r)); \
|
|
exit(EXIT_FAILURE); \
|
|
} \
|
|
} while (0)
|