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TensorRT-LLMs/cpp/include/tensorrt_llm/common/cudaUtils.h
Dan Blanaru 16d2467ea8 Update TensorRT-LLM (#2755) 
* 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
2025-02-11 03:01:00 +00:00

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/*
* SPDX-FileCopyrightText: Copyright (c) 2022-2024 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/cudaBf16Wrapper.h"
#include "tensorrt_llm/common/cudaDriverWrapper.h"
#include "tensorrt_llm/common/cudaFp8Utils.h"
#include "tensorrt_llm/common/logger.h"
#include "tensorrt_llm/common/tllmException.h"
#include <algorithm>
#include <cinttypes>
#include <cublasLt.h>
#include <cublas_v2.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <driver_types.h>
#include <fstream>
#include <iomanip>
#include <memory>
#include <optional>
#include <sstream>
#include <string>
#ifndef _WIN32 // Linux
#include <sys/sysinfo.h>
#endif // not WIN32
#include <vector>
#ifdef _WIN32 // Windows
#include <windows.h>
#undef ERROR // A Windows header file defines ERROR as 0, but it's used in our logger.h enum. Logging breaks without
// this undef.
#endif // WIN32
namespace tensorrt_llm::common
{
// workspace for cublas gemm : 32MB
#define CUBLAS_WORKSPACE_SIZE 33554432
typedef struct __align__(4)
{
half x, y, z, w;
}
half4;
/* **************************** type definition ***************************** */
enum CublasDataType
{
FLOAT_DATATYPE = 0,
HALF_DATATYPE = 1,
BFLOAT16_DATATYPE = 2,
INT8_DATATYPE = 3,
FP8_DATATYPE = 4
};
enum TRTLLMCudaDataType
{
FP32 = 0,
FP16 = 1,
BF16 = 2,
INT8 = 3,
FP8 = 4
};
enum class OperationType
{
FP32,
FP16,
BF16,
INT8,
FP8
};
/* **************************** debug tools ********************************* */
static char const* _cudaGetErrorEnum(cudaError_t error)
{
return cudaGetErrorString(error);
}
static char const* _cudaGetErrorEnum(cublasStatus_t error)
{
switch (error)
{
case CUBLAS_STATUS_SUCCESS: return "CUBLAS_STATUS_SUCCESS";
case CUBLAS_STATUS_NOT_INITIALIZED: return "CUBLAS_STATUS_NOT_INITIALIZED";
case CUBLAS_STATUS_ALLOC_FAILED: return "CUBLAS_STATUS_ALLOC_FAILED";
case CUBLAS_STATUS_INVALID_VALUE: return "CUBLAS_STATUS_INVALID_VALUE";
case CUBLAS_STATUS_ARCH_MISMATCH: return "CUBLAS_STATUS_ARCH_MISMATCH";
case CUBLAS_STATUS_MAPPING_ERROR: return "CUBLAS_STATUS_MAPPING_ERROR";
case CUBLAS_STATUS_EXECUTION_FAILED: return "CUBLAS_STATUS_EXECUTION_FAILED";
case CUBLAS_STATUS_INTERNAL_ERROR: return "CUBLAS_STATUS_INTERNAL_ERROR";
case CUBLAS_STATUS_NOT_SUPPORTED: return "CUBLAS_STATUS_NOT_SUPPORTED";
case CUBLAS_STATUS_LICENSE_ERROR: return "CUBLAS_STATUS_LICENSE_ERROR";
}
return "<unknown>";
}
template <typename T>
void check(T ptr, char const* const func, char const* const file, int const line)
{
if (ptr)
{
throw TllmException(
file, line, fmtstr("[TensorRT-LLM][ERROR] CUDA runtime error in %s: %s", func, _cudaGetErrorEnum(ptr)));
}
}
template <typename T>
void checkEx(
T ptr, std::initializer_list<T> const& validReturns, char const* const func, char const* const file, int const line)
{
if (std::all_of(std::begin(validReturns), std::end(validReturns), [&ptr](T const& t) { return t != ptr; }))
{
throw TllmException(
file, line, fmtstr("[TensorRT-LLM][ERROR] CUDA runtime error in %s: %s", func, _cudaGetErrorEnum(ptr)));
}
}
#define check_cuda_error(val) check((val), #val, __FILE__, __LINE__)
#define check_cuda_error_2(val, file, line) check((val), #val, file, line)
inline std::optional<bool> isCudaLaunchBlocking()
{
static bool firstCall = true;
static std::optional<bool> ptr = std::nullopt;
if (firstCall)
{
char const* env = std::getenv("CUDA_LAUNCH_BLOCKING");
if (env != nullptr && std::string(env) == "1")
{
ptr = true;
}
else if (env != nullptr && std::string(env) == "0")
{
ptr = false;
}
firstCall = false;
}
return ptr;
}
inline bool doCheckError()
{
auto const cudaLaunchBlocking = isCudaLaunchBlocking();
#ifndef NDEBUG
bool const checkError = cudaLaunchBlocking.value_or(true);
#else
bool const checkError = cudaLaunchBlocking.value_or(false);
#endif
return checkError;
}
inline void syncAndCheck(char const* const file, int const line)
{
if (doCheckError())
{
cudaDeviceSynchronize();
check(cudaGetLastError(), "cudaGetLastError", file, line);
}
}
#define sync_check_cuda_error() tensorrt_llm::common::syncAndCheck(__FILE__, __LINE__)
#define PRINT_FUNC_NAME_() \
do \
{ \
std::cout << "[TensorRT-LLM][CALL] " << __FUNCTION__ << " " << std::endl; \
} while (0)
// clang-format off
template<typename T> struct packed_type;
template <> struct packed_type<float> { using type = float; }; // we don't need to pack float by default
template <> struct packed_type<half> { using type = half2; };
#ifdef ENABLE_BF16
template<>
struct packed_type<__nv_bfloat16> {
using type = __nv_bfloat162;
};
#endif
#ifdef ENABLE_FP8
template<>
struct packed_type<__nv_fp8_e4m3> {
using type = __nv_fp8x2_e4m3;
};
#endif
template<typename T> struct num_elems;
template <> struct num_elems<float> { static constexpr int value = 1; };
template <> struct num_elems<float2> { static constexpr int value = 2; };
template <> struct num_elems<float4> { static constexpr int value = 4; };
template <> struct num_elems<half> { static constexpr int value = 1; };
template <> struct num_elems<half2> { static constexpr int value = 2; };
#ifdef ENABLE_BF16
template <> struct num_elems<__nv_bfloat16> { static constexpr int value = 1; };
template <> struct num_elems<__nv_bfloat162> { static constexpr int value = 2; };
#endif
#ifdef ENABLE_FP8
template <> struct num_elems<__nv_fp8_e4m3> { static constexpr int value = 1; };
template <> struct num_elems<__nv_fp8x2_e4m3> { static constexpr int value = 2; };
#endif
template<typename T, int num> struct packed_as;
template<typename T> struct packed_as<T, 1> { using type = T; };
template<> struct packed_as<half, 2> { using type = half2; };
template<> struct packed_as<float, 2> { using type = float2; };
template<> struct packed_as<int8_t, 2> { using type = int16_t; };
template<> struct packed_as<int32_t, 2> { using type = int2; };
template<> struct packed_as<half2, 1> { using type = half; };
template<> struct packed_as<float2, 1> { using type = float; };
#ifdef ENABLE_BF16
template<> struct packed_as<__nv_bfloat16, 2> { using type = __nv_bfloat162; };
template<> struct packed_as<__nv_bfloat162, 1> { using type = __nv_bfloat16; };
#endif
#ifdef ENABLE_FP8
template<> struct packed_as<__nv_fp8_e4m3, 2> { using type = __nv_fp8x2_e4m3; };
template<> struct packed_as<__nv_fp8x2_e4m3, 1> { using type = __nv_fp8_e4m3; };
template<> struct packed_as<__nv_fp8_e5m2, 2> { using type = __nv_fp8x2_e5m2; };
template<> struct packed_as<__nv_fp8x2_e5m2, 1> { using type = __nv_fp8_e5m2; };
#endif
inline __device__ float2 operator*(float2 a, float2 b) { return make_float2(a.x * b.x, a.y * b.y); }
inline __device__ float2 operator+(float2 a, float2 b) { return make_float2(a.x + b.x, a.y + b.y); }
inline __device__ float2 operator-(float2 a, float2 b) { return make_float2(a.x - b.x, a.y - b.y); }
inline __device__ float2 operator*(float2 a, float b) { return make_float2(a.x * b, a.y * b); }
inline __device__ float2 operator+(float2 a, float b) { return make_float2(a.x + b, a.y + b); }
inline __device__ float2 operator-(float2 a, float b) { return make_float2(a.x - b, a.y - b); }
// clang-format on
template <typename T>
struct CudaDataType
{
};
template <>
struct CudaDataType<float>
{
static constexpr cudaDataType_t value = cudaDataType::CUDA_R_32F;
};
template <>
struct CudaDataType<half>
{
static constexpr cudaDataType_t value = cudaDataType::CUDA_R_16F;
};
#ifdef ENABLE_BF16
template <>
struct CudaDataType<__nv_bfloat16>
{
static constexpr cudaDataType_t value = cudaDataType::CUDA_R_16BF;
};
#endif
inline int getSMVersion()
{
int device{-1};
check_cuda_error(cudaGetDevice(&device));
int sm_major = 0;
int sm_minor = 0;
check_cuda_error(cudaDeviceGetAttribute(&sm_major, cudaDevAttrComputeCapabilityMajor, device));
check_cuda_error(cudaDeviceGetAttribute(&sm_minor, cudaDevAttrComputeCapabilityMinor, device));
return sm_major * 10 + sm_minor;
}
inline int getDevice()
{
int deviceID{0};
check_cuda_error(cudaGetDevice(&deviceID));
return deviceID;
}
inline int getDeviceCount()
{
int count{0};
check_cuda_error(cudaGetDeviceCount(&count));
return count;
}
/// @brief Identifies the memory type of the given pointer.
template <typename T>
cudaMemoryType getPtrCudaMemoryType(T* ptr)
{
cudaPointerAttributes attributes{};
check_cuda_error(cudaPointerGetAttributes(&attributes, ptr));
return attributes.type;
}
/// Get the memory info
/// \return The free and total amount of memory in bytes
inline std::tuple<size_t, size_t> getDeviceMemoryInfo(bool const useUvm)
{
if (useUvm)
{
size_t freeSysMem = 0;
size_t totalSysMem = 0;
#ifndef _WIN32 // Linux
struct sysinfo info
{
};
sysinfo(&info);
totalSysMem = info.totalram * info.mem_unit;
freeSysMem = info.freeram * info.mem_unit;
#else // Windows
MEMORYSTATUSEX memInfo;
memInfo.dwLength = sizeof(memInfo);
GlobalMemoryStatusEx(&memInfo);
totalSysMem = memInfo.ullTotalPhys;
freeSysMem = memInfo.ullAvailPhys;
#endif // WIN32
TLLM_LOG_INFO("Using UVM based system memory for KV cache, total memory %0.2f GB, available memory %0.2f GB",
((double) totalSysMem / 1e9), ((double) freeSysMem / 1e9));
return {freeSysMem, totalSysMem};
}
size_t free = 0;
size_t total = 0;
check_cuda_error(cudaMemGetInfo(&free, &total));
TLLM_LOG_DEBUG("Using GPU memory for KV cache, total memory %0.2f GB, available memory %0.2f GB",
((double) total / 1e9), ((double) free / 1e9));
return {free, total};
}
/// @brief Gets the memory allocation granularity for the current device.
///
/// @return size_t The size of the smallest difference in memory size supported by the current device.
inline size_t getAllocationGranularity()
{
auto const currentDevice = getDevice();
::CUmemAllocationProp prop = {};
prop.type = ::CU_MEM_ALLOCATION_TYPE_PINNED;
prop.location.type = ::CU_MEM_LOCATION_TYPE_DEVICE;
prop.location.id = currentDevice;
prop.requestedHandleTypes = ::CU_MEM_HANDLE_TYPE_NONE;
// Get the minimum granularity supported for allocation with cuMemCreate()
size_t granularity = 0;
TLLM_CU_CHECK(cuMemGetAllocationGranularity(&granularity, &prop, CU_MEM_ALLOC_GRANULARITY_MINIMUM));
return granularity;
}
inline int getMultiProcessorCount()
{
int nSM{0};
int deviceID{0};
check_cuda_error(cudaGetDevice(&deviceID));
check_cuda_error(cudaDeviceGetAttribute(&nSM, cudaDevAttrMultiProcessorCount, deviceID));
return nSM;
}
inline int getMaxSharedMemoryPerSM()
{
int nByteMaxSharedMemoryPerSM{0};
int deviceID{0};
check_cuda_error(cudaGetDevice(&deviceID));
check_cuda_error(
cudaDeviceGetAttribute(&nByteMaxSharedMemoryPerSM, cudaDevAttrMaxSharedMemoryPerMultiprocessor, deviceID));
return nByteMaxSharedMemoryPerSM;
}
inline int getMaxSharedMemoryPerBlockOptin()
{
int nByteMaxSharedMemoryPerBlockOptin{0};
int deviceID{0};
check_cuda_error(cudaGetDevice(&deviceID));
check_cuda_error(
cudaDeviceGetAttribute(&nByteMaxSharedMemoryPerBlockOptin, cudaDevAttrMaxSharedMemoryPerBlockOptin, deviceID));
return nByteMaxSharedMemoryPerBlockOptin;
}
template <typename T1, typename T2>
inline size_t divUp(T1 const& a, T2 const& b)
{
auto const tmp_a = static_cast<size_t>(a);
auto const tmp_b = static_cast<size_t>(b);
return (tmp_a + tmp_b - 1) / tmp_b;
}
inline int roundUp(int a, int b)
{
return divUp(a, b) * b;
}
template <typename T, typename U, typename = std::enable_if_t<std::is_integral<T>::value>,
typename = std::enable_if_t<std::is_integral<U>::value>>
auto constexpr ceilDiv(T numerator, U denominator)
{
return (numerator + denominator - 1) / denominator;
}
template <typename T>
void printArrayInfo(T const* ptr, uint64_t nElement = 1, std::string name = "", bool const bPrintElement = false)
{
if (ptr == nullptr)
{
TLLM_LOG_WARNING("%s is an nullptr, skip!", name.c_str());
return;
}
cudaDeviceSynchronize();
check_cuda_error(cudaGetLastError());
bool const isDevicePtr = (getPtrCudaMemoryType(ptr) == cudaMemoryTypeDevice);
size_t sizeInByte = sizeof(T) * nElement;
TLLM_LOG_TRACE("addr=%p, location=%s, sizeof(T)=%lu, nElement=%d, sizeInByte=%lu\n", ptr,
(isDevicePtr ? "Device" : "Host"), sizeof(T), nElement, sizeInByte);
T* tmp = const_cast<T*>(ptr);
std::vector<T> tmpVec; // For device pointer
if (isDevicePtr)
{
tmpVec.resize(nElement);
tmp = tmpVec.data();
check_cuda_error(cudaMemcpy(tmp, ptr, sizeInByte, cudaMemcpyDeviceToHost));
cudaDeviceSynchronize();
}
size_t nInf = 0;
size_t nNaN = 0;
size_t nZero = 0;
double sum = 0.0;
double sqrSum = 0.0;
double absSum = 0.0;
float allMax = -1.0e6f;
float allMin = 1.0e6f;
float allSad = 0.0f; // Sum Abs of Difference, to distinguish A and its transpose
float old = 0.0f;
for (uint64_t i = 0; i < nElement; i++)
{
float val = (float) tmp[i];
if (std::isinf(val))
{
nInf++;
continue;
}
if (std::isnan(val))
{
nNaN++;
continue;
}
nZero += (val == 0.0f);
sum += val;
sqrSum += val * val;
absSum += expf(val);
allMax = std::max(allMax, val);
allMin = std::min(allMin, val);
allSad += abs(val - old);
old = val;
}
float avg = sum / nElement;
float std = sqrtf(sqrSum / nElement - avg * avg);
TLLM_LOG_INFO("%s", name.c_str());
TLLM_LOG_INFO("size=%u, nInf=%zu, nNaN=%zu, nZero=%zu", nElement, nInf, nNaN, nZero);
TLLM_LOG_INFO("avg=%f, absSum: %f, std=%f, max=%f, min=%f, sad=%f", avg, absSum, std, allMax, allMin, allSad);
if (bPrintElement)
{
uint64_t constexpr nHead = 5;
std::stringstream ss;
ss << std::setw(10) << std::fixed << std::setprecision(3);
for (uint64_t i = 0; i < std::min(nElement, nHead); ++i)
{
ss << (float) tmp[i] << ", ";
}
if (nElement > nHead)
{
ss << " ... ";
for (uint64_t i = nElement - nHead; i < nElement; ++i)
{
ss << (float) tmp[i] << ", ";
}
}
TLLM_LOG_INFO("%s", ss.str().c_str());
}
cudaDeviceSynchronize();
check_cuda_error(cudaGetLastError());
}
template void printArrayInfo(float const* ptr, uint64_t nElement, std::string name, bool const bPrintElement);
template void printArrayInfo(half const* ptr, uint64_t nElement, std::string name, bool const bPrintElement);
#ifdef ENABLE_BF16
template void printArrayInfo(__nv_bfloat16 const* ptr, uint64_t nElement, std::string name, bool const bPrintElement);
#endif
#ifdef ENABLE_FP8
template void printArrayInfo(__nv_fp8_e4m3 const* ptr, uint64_t nElement, std::string name, bool const bPrintElement);
#endif
template void printArrayInfo(uint32_t const* ptr, uint64_t nElement, std::string name, bool const bPrintElement);
template void printArrayInfo(uint64_t const* ptr, uint64_t nElement, std::string name, bool const bPrintElement);
template void printArrayInfo(int const* ptr, uint64_t nElement, std::string name, bool const bPrintElement);
template void printArrayInfo(uint8_t const* ptr, uint64_t nElement, std::string name, bool const bPrintElement);
template <typename T>
void printToStream(T const* ptr, int const nElement, FILE* strm)
{
bool const split_rows = (strm == stdout);
if (ptr == nullptr)
{
TLLM_LOG_WARNING("Nullptr, skip!\n");
return;
}
std::vector<T> tmp(nElement, 0);
check_cuda_error(cudaMemcpy(tmp.data(), ptr, sizeof(T) * nElement, cudaMemcpyDeviceToHost));
for (int i = 0; i < nElement; ++i)
{
fprintf(strm, "%f, ", static_cast<float>(tmp[i]));
if (split_rows && ((i + 1) % 10) == 0)
fprintf(strm, "\n");
}
if (!split_rows || (nElement % 10) != 0)
{
fprintf(strm, "\n");
}
}
template <typename T>
void printToScreen(T const* ptr, int const nElement)
{
printToStream(ptr, nElement, stdout);
}
template <typename T>
void print2dToStream(T const* ptr, int const nRow, int const nCol, int const nStride, FILE* strm)
{
if (ptr == nullptr)
{
TLLM_LOG_WARNING("Nullptr, skip!\n");
return;
}
for (int ri = 0; ri < nRow; ++ri)
{
T const* tmp = ptr + ri * nStride;
printToStream(tmp, nCol, strm);
}
fprintf(strm, "\n");
}
template <typename T>
void print2dToScreen(T const* ptr, int const nRow, int const nCol, int const nStride)
{
print2dToStream(ptr, nRow, nCol, nStride, stdout);
}
template <typename T>
void print2dToFile(std::string fname, T const* ptr, int const nRow, int const nCol, int const nStride)
{
FILE* fp = fopen(fname.c_str(), "wt");
if (fp != nullptr)
{
print2dToStream(ptr, nRow, nCol, nStride, fp);
fclose(fp);
}
}
__host__ __device__ inline void print_float_(float x)
{
printf("%7.3f ", x);
}
__host__ __device__ inline void print_element_(float x)
{
print_float_(x);
}
__host__ __device__ inline void print_element_(half x)
{
print_float_((float) x);
}
#ifdef ENABLE_BF16
__host__ __device__ inline void print_element_(__nv_bfloat16 x)
{
print_float_((float) x);
}
#endif
#ifdef ENABLE_FP8
__host__ __device__ inline void print_element_(__nv_fp8_e4m3 x)
{
print_float_((float) x);
}
#endif
__host__ __device__ inline void print_element_(uint8_t ui)
{
printf("%7" PRIu32 " ", (unsigned int) ui);
}
__host__ __device__ inline void print_element_(uint32_t ul)
{
printf("%7" PRIu32 " ", ul);
}
__host__ __device__ inline void print_element_(uint64_t ull)
{
printf("%7" PRIu64 " ", ull);
}
__host__ __device__ inline void print_element_(int32_t il)
{
printf("%7" PRId32 " ", il);
}
__host__ __device__ inline void print_element_(int64_t ill)
{
printf("%7" PRId64 " ", ill);
}
template <typename T>
__host__ __device__ inline void print_elements(T const* ptr, int nRow, int nCol, int nStride)
{
for (int iRow = -1; iRow < nRow; ++iRow)
{
if (iRow >= 0)
{
printf("%07d|", iRow);
}
else
{
printf(" |"); // heading row
}
for (int iCol = 0; iCol < nCol; iCol += 1)
{
if (iRow >= 0)
{
print_element_(ptr[iRow * nStride + iCol]);
}
else
{
printf("%7d|", iCol); // heading colume
}
}
printf("\n");
}
}
template <typename T>
inline void printMatrix(T const* ptr, int nRow, int nCol, int nStride)
{
// `nRow` is length of row dimension
// `nStride` is length of column dimension
// `nCol` (<= nStride) is length for print per row
if (ptr == nullptr)
{
TLLM_LOG_WARNING("Nullptr, skip!\n");
return;
}
cudaDeviceSynchronize();
check_cuda_error(cudaGetLastError());
bool const isDevicePtr = (getPtrCudaMemoryType(ptr) == cudaMemoryTypeDevice);
size_t sizeInByte = sizeof(T) * nRow * nStride;
TLLM_LOG_TRACE("addr=%p, location=%s, sizeof(T)=%lu, nRow=%d, nStride=%d, sizeInByte=%lu\n", ptr,
(isDevicePtr ? "Device" : "Host"), sizeof(T), nRow, nStride, sizeInByte);
if (isDevicePtr)
{
std::vector<T> tmpVec;
tmpVec.resize(nRow * nStride);
T* tmp = tmpVec.data();
check_cuda_error(cudaMemcpy(tmp, ptr, sizeInByte, cudaMemcpyDeviceToHost));
cudaDeviceSynchronize();
check_cuda_error(cudaGetLastError());
print_elements(tmp, nRow, nCol, nStride);
}
else
{
print_elements(ptr, nRow, nCol, nStride);
}
}
template void printMatrix(float const* ptr, int nRow, int nCol, int nStride);
template void printMatrix(half const* ptr, int nRow, int nCol, int nStride);
#ifdef ENABLE_BF16
template void printMatrix(__nv_bfloat16 const* ptr, int nRow, int nCol, int nStride);
#endif
#ifdef ENABLE_FP8
template void printMatrix(__nv_fp8_e4m3 const* ptr, int nRow, int nCol, int nStride);
#endif
template void printMatrix(uint32_t const* ptr, int nRow, int nCol, int nStride);
template void printMatrix(uint64_t const* ptr, int nRow, int nCol, int nStride);
template void printMatrix(int const* ptr, int nRow, int nCol, int nStride);
template void printMatrix(uint8_t const* ptr, int nRow, int nCol, int nStride);
template <typename T>
__device__ inline void printMatrixDevice(T const* ptr, int nRow, int nCol, int nStride)
{
// `nRow` is length of row dimension
// `nStride` is length of column dimension
// `nCol` (<= nStride) is length for print per row
// Can be called inside kernels by one single thread
if (ptr == nullptr)
{
printf("Nullptr, skip!\n");
return;
}
size_t sizeInByte = sizeof(T) * nRow * nStride;
printf("addr=%p, sizeof(T)=%lu, nRow=%d, nStride=%d, sizeInByte=%lu\n", ptr, sizeof(T), nRow, nStride, sizeInByte);
print_elements(ptr, nRow, nCol, nStride);
}
template __device__ void printMatrixDevice(float const* ptr, int nRow, int nCol, int nStride);
template __device__ void printMatrixDevice(half const* ptr, int nRow, int nCol, int nStride);
#ifdef ENABLE_BF16
template __device__ void printMatrixDevice(__nv_bfloat16 const* ptr, int nRow, int nCol, int nStride);
#endif
#ifdef ENABLE_FP8
template __device__ void printMatrixDevice(__nv_fp8_e4m3 const* ptr, int nRow, int nCol, int nStride);
#endif
template __device__ void printMatrixDevice(uint32_t const* ptr, int nRow, int nCol, int nStride);
template __device__ void printMatrixDevice(uint64_t const* ptr, int nRow, int nCol, int nStride);
template __device__ void printMatrixDevice(int const* ptr, int nRow, int nCol, int nStride);
template __device__ void printMatrixDevice(uint8_t const* ptr, int nRow, int nCol, int nStride);
} // namespace tensorrt_llm::common
/*
* Macros compliant with TensorRT coding conventions
*/
#define TLLM_CUDA_CHECK(stat) \
do \
{ \
tensorrt_llm::common::check((stat), #stat, __FILE__, __LINE__); \
} while (0)
// We use singleton memory pool and the order of destructors depends on the compiler implementation. We find that the
// cudaFree/cudaFreeHost is called after cudaruntime destruction on Windows. There will be an cudaErrorCudartUnloading
// error. However, it is safe to ignore this error because the cuda runtime is already exited, we are no more worried
// about the memory leaks.
#define TLLM_CUDA_CHECK_FREE_RESOURCE(stat) \
do \
{ \
tensorrt_llm::common::checkEx((stat), {cudaSuccess, cudaErrorCudartUnloading}, #stat, __FILE__, __LINE__); \
} while (0)
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