mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2026-10-13 00:08:26 +08:00
CUDA 13.2 samples update (#432)
- Added Python samples for CUDA Python 1.0 release - Renamed top-level `Samples` directory to `cpp` to accommodate Python samples.
This commit is contained in:
Dheemanth
GitHub
18
cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/.vscode/c_cpp_properties.json
vendored
Normal file
18
cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/.vscode/c_cpp_properties.json
vendored
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@@ -0,0 +1,18 @@
|
||||
{
|
||||
"configurations": [
|
||||
{
|
||||
"name": "Linux",
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||||
"includePath": [
|
||||
"${workspaceFolder}/**",
|
||||
"${workspaceFolder}/../../../Common"
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||||
],
|
||||
"defines": [],
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||||
"compilerPath": "/usr/local/cuda/bin/nvcc",
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||||
"cStandard": "gnu17",
|
||||
"cppStandard": "gnu++14",
|
||||
"intelliSenseMode": "linux-gcc-x64",
|
||||
"configurationProvider": "ms-vscode.makefile-tools"
|
||||
}
|
||||
],
|
||||
"version": 4
|
||||
}
|
||||
7
cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/.vscode/extensions.json
vendored
Normal file
7
cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/.vscode/extensions.json
vendored
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@@ -0,0 +1,7 @@
|
||||
{
|
||||
"recommendations": [
|
||||
"nvidia.nsight-vscode-edition",
|
||||
"ms-vscode.cpptools",
|
||||
"ms-vscode.makefile-tools"
|
||||
]
|
||||
}
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||||
@@ -0,0 +1,53 @@
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cmake_minimum_required(VERSION 3.20)
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list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/Modules")
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project(cuDLALayerwiseStatsHybrid LANGUAGES C CXX CUDA)
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find_package(CUDAToolkit REQUIRED)
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set(CMAKE_POSITION_INDEPENDENT_CODE ON)
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set(CMAKE_CUDA_ARCHITECTURES 87 110)
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set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets")
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|
||||
if(ENABLE_CUDA_DEBUG)
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set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets)
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else()
|
||||
set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option)
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endif()
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# Include directories and libraries
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include_directories(../../../../Common)
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find_library(CUDLA_LIB cudla PATHS ${CUDAToolkit_LIBRARY_DIR} ${CMAKE_LIBRARY_PATH})
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if(CMAKE_SYSTEM_NAME STREQUAL "Linux")
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if(CUDLA_LIB)
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# Source file
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# Add target for cuDLALayerwiseStatsHybrid
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add_executable(cuDLALayerwiseStatsHybrid main.cu)
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target_compile_options(cuDLALayerwiseStatsHybrid PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda>)
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target_compile_features(cuDLALayerwiseStatsHybrid PRIVATE cxx_std_17 cuda_std_17)
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set_target_properties(cuDLALayerwiseStatsHybrid PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
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target_include_directories(cuDLALayerwiseStatsHybrid PUBLIC
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${CUDAToolkit_INCLUDE_DIRS}
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)
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target_link_libraries(cuDLALayerwiseStatsHybrid
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${CUDLA_LIB}
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)
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else()
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message(STATUS "CUDLA not found - will not build sample 'cuDLALayerwiseStatsHybrid'")
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endif()
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else()
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message(STATUS "Will not build sample cuDLALayerwiseStatsHybrid - requires Linux OS")
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endif()
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|
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# Include installation configuration
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include(${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/InstallSamples.cmake)
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setup_samples_install()
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@@ -0,0 +1,32 @@
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# cuDLALayerwiseStatsHybrid - cuDLA Layerwise statistics HybridMode
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## Description
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||||
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This sample is used to provide layerwise statistics to the application in the cuDLA hybrid mode wherein DLA is programmed using CUDA.
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|
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## Key Concepts
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||||
|
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cuDLA, Data Parallel Algorithms, Image Processing
|
||||
|
||||
## Supported SM Architectures
|
||||
|
||||
[SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus)
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|
||||
## Supported OSes
|
||||
|
||||
Linux, QNX
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||||
|
||||
## Supported CPU Architecture
|
||||
|
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aarch64
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|
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## CUDA APIs involved
|
||||
|
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### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
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cudaStreamCreateWithFlags, cudaStreamDestroy, cudaFree, cudaGetErrorName, cudaSetDevice, cudaStreamSynchronize, cudaMalloc, cudaMemsetAsync, cudaMemcpyAsync
|
||||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
|
||||
## References (for more details)
|
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899
cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/main.cu
Normal file
899
cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/main.cu
Normal file
@@ -0,0 +1,899 @@
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/* Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions
|
||||
* are met:
|
||||
* * Redistributions of source code must retain the above copyright
|
||||
* notice, this list of conditions and the following disclaimer.
|
||||
* * Redistributions in binary form must reproduce the above copyright
|
||||
* notice, this list of conditions and the following disclaimer in the
|
||||
* documentation and/or other materials provided with the distribution.
|
||||
* * Neither the name of NVIDIA CORPORATION nor the names of its
|
||||
* contributors may be used to endorse or promote products derived
|
||||
* from this software without specific prior written permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
|
||||
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
|
||||
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
||||
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
||||
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
|
||||
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
|
||||
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
|
||||
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*/
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||||
|
||||
#include <cstdio>
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||||
#include <cstdlib>
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||||
#include <cstring>
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||||
#include <fstream>
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||||
#include <sstream>
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#include <sys/stat.h>
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||||
#include "cuda_runtime.h"
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||||
#include "cudla.h"
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||||
#include "cudlaExternalEtbl.hpp"
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||||
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||||
#define MAX_FILENAME_LEN 200
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||||
#define RESERVED_SUFFIX_LEN 10
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||||
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||||
#define DPRINTF(...) printf(__VA_ARGS__)
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||||
|
||||
static void printTensorDesc(cudlaModuleTensorDescriptor *tensorDesc)
|
||||
{
|
||||
DPRINTF("\tTENSOR NAME : %s\n", tensorDesc->name);
|
||||
DPRINTF("\tsize: %lu\n", tensorDesc->size);
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||||
|
||||
DPRINTF("\tdims: [%lu, %lu, %lu, %lu]\n", tensorDesc->n, tensorDesc->c, tensorDesc->h, tensorDesc->w);
|
||||
|
||||
DPRINTF("\tdata fmt: %d\n", tensorDesc->dataFormat);
|
||||
DPRINTF("\tdata type: %d\n", tensorDesc->dataType);
|
||||
DPRINTF("\tdata category: %d\n", tensorDesc->dataCategory);
|
||||
DPRINTF("\tpixel fmt: %d\n", tensorDesc->pixelFormat);
|
||||
DPRINTF("\tpixel mapping: %d\n", tensorDesc->pixelMapping);
|
||||
DPRINTF("\tstride[0]: %d\n", tensorDesc->stride[0]);
|
||||
DPRINTF("\tstride[1]: %d\n", tensorDesc->stride[1]);
|
||||
DPRINTF("\tstride[2]: %d\n", tensorDesc->stride[2]);
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||||
DPRINTF("\tstride[3]: %d\n", tensorDesc->stride[3]);
|
||||
}
|
||||
|
||||
typedef struct
|
||||
{
|
||||
cudlaDevHandle devHandle;
|
||||
cudlaModule moduleHandle;
|
||||
unsigned char *loadableData;
|
||||
cudaStream_t stream;
|
||||
uint32_t numInputTensors;
|
||||
uint32_t numOutputTensors;
|
||||
uint32_t numOutputTaskStatistics;
|
||||
unsigned char **inputBuffer;
|
||||
unsigned char **outputBuffer;
|
||||
unsigned char **statisticsOutputBuffer;
|
||||
void **inputBufferGPU;
|
||||
void **outputBufferGPU;
|
||||
void **outputTaskStatisticsGPU;
|
||||
void **csv;
|
||||
cudlaModuleTensorDescriptor *inputTensorDesc;
|
||||
cudlaModuleTensorDescriptor *outputTensorDesc;
|
||||
cudlaModuleTensorDescriptor *outputTaskStatisticsDesc;
|
||||
uint64_t **inputBufferRegisteredPtr;
|
||||
uint64_t **outputBufferRegisteredPtr;
|
||||
uint64_t **outputTaskStatisticsRegisteredPtr;
|
||||
uint64_t **outputStatisticsBufferRegisteredPtr;
|
||||
} ResourceList;
|
||||
|
||||
void cleanUp(ResourceList *resourceList);
|
||||
|
||||
void cleanUp(ResourceList *resourceList)
|
||||
{
|
||||
uint32_t ii = 0;
|
||||
if (resourceList->inputTensorDesc != NULL) {
|
||||
free(resourceList->inputTensorDesc);
|
||||
resourceList->inputTensorDesc = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->outputTensorDesc != NULL) {
|
||||
free(resourceList->outputTensorDesc);
|
||||
resourceList->outputTensorDesc = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->outputTaskStatisticsDesc != NULL) {
|
||||
free(resourceList->outputTaskStatisticsDesc);
|
||||
resourceList->outputTaskStatisticsDesc = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->loadableData != NULL) {
|
||||
free(resourceList->loadableData);
|
||||
resourceList->loadableData = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->moduleHandle != NULL) {
|
||||
cudlaModuleUnload(resourceList->moduleHandle, 0);
|
||||
resourceList->moduleHandle = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->devHandle != NULL) {
|
||||
cudlaDestroyDevice(resourceList->devHandle);
|
||||
resourceList->devHandle = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->inputBufferGPU != NULL) {
|
||||
for (ii = 0; ii < resourceList->numInputTensors; ii++) {
|
||||
if ((resourceList->inputBufferGPU)[ii] != NULL) {
|
||||
cudaFree((resourceList->inputBufferGPU)[ii]);
|
||||
(resourceList->inputBufferGPU)[ii] = NULL;
|
||||
}
|
||||
}
|
||||
free(resourceList->inputBufferGPU);
|
||||
resourceList->inputBufferGPU = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->outputBufferGPU != NULL) {
|
||||
for (ii = 0; ii < resourceList->numOutputTensors; ii++) {
|
||||
if ((resourceList->outputBufferGPU)[ii] != NULL) {
|
||||
cudaFree((resourceList->outputBufferGPU)[ii]);
|
||||
(resourceList->outputBufferGPU)[ii] = NULL;
|
||||
}
|
||||
}
|
||||
free(resourceList->outputBufferGPU);
|
||||
resourceList->outputBufferGPU = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->outputTaskStatisticsGPU != NULL) {
|
||||
for (ii = 0; ii < resourceList->numOutputTaskStatistics; ii++) {
|
||||
if ((resourceList->outputTaskStatisticsGPU)[ii] != NULL) {
|
||||
cudaFree((resourceList->outputTaskStatisticsGPU)[ii]);
|
||||
(resourceList->outputTaskStatisticsGPU)[ii] = NULL;
|
||||
}
|
||||
}
|
||||
free(resourceList->outputTaskStatisticsGPU);
|
||||
resourceList->outputTaskStatisticsGPU = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->csv != NULL) {
|
||||
for (ii = 0; ii < resourceList->numOutputTaskStatistics; ii++) {
|
||||
if ((resourceList->csv)[ii] != NULL) {
|
||||
free((resourceList->csv)[ii]);
|
||||
(resourceList->csv)[ii] = NULL;
|
||||
}
|
||||
}
|
||||
free(resourceList->csv);
|
||||
resourceList->csv = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->inputBuffer != NULL) {
|
||||
for (ii = 0; ii < resourceList->numInputTensors; ii++) {
|
||||
if ((resourceList->inputBuffer)[ii] != NULL) {
|
||||
free((resourceList->inputBuffer)[ii]);
|
||||
(resourceList->inputBuffer)[ii] = NULL;
|
||||
}
|
||||
}
|
||||
free(resourceList->inputBuffer);
|
||||
resourceList->inputBuffer = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->outputBuffer != NULL) {
|
||||
for (ii = 0; ii < resourceList->numOutputTensors; ii++) {
|
||||
if ((resourceList->outputBuffer)[ii] != NULL) {
|
||||
free((resourceList->outputBuffer)[ii]);
|
||||
(resourceList->outputBuffer)[ii] = NULL;
|
||||
}
|
||||
}
|
||||
free(resourceList->outputBuffer);
|
||||
resourceList->outputBuffer = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->statisticsOutputBuffer != NULL) {
|
||||
for (ii = 0; ii < resourceList->numOutputTaskStatistics; ii++) {
|
||||
if ((resourceList->statisticsOutputBuffer)[ii] != NULL) {
|
||||
free((resourceList->statisticsOutputBuffer)[ii]);
|
||||
(resourceList->statisticsOutputBuffer)[ii] = NULL;
|
||||
}
|
||||
}
|
||||
free(resourceList->statisticsOutputBuffer);
|
||||
resourceList->statisticsOutputBuffer = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->stream != NULL) {
|
||||
cudaStreamDestroy(resourceList->stream);
|
||||
resourceList->stream = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->inputBufferRegisteredPtr != NULL) {
|
||||
free(resourceList->inputBufferRegisteredPtr);
|
||||
resourceList->inputBufferRegisteredPtr = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->outputBufferRegisteredPtr != NULL) {
|
||||
free(resourceList->outputBufferRegisteredPtr);
|
||||
resourceList->outputBufferRegisteredPtr = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->outputTaskStatisticsRegisteredPtr != NULL) {
|
||||
free(resourceList->outputTaskStatisticsRegisteredPtr);
|
||||
resourceList->outputTaskStatisticsRegisteredPtr = NULL;
|
||||
}
|
||||
|
||||
if (resourceList->outputStatisticsBufferRegisteredPtr != NULL) {
|
||||
free(resourceList->outputStatisticsBufferRegisteredPtr);
|
||||
resourceList->outputStatisticsBufferRegisteredPtr = NULL;
|
||||
}
|
||||
|
||||
resourceList->numInputTensors = 0;
|
||||
resourceList->numOutputTensors = 0;
|
||||
resourceList->numOutputTaskStatistics = 0;
|
||||
}
|
||||
|
||||
int main(int argc, char **argv)
|
||||
{
|
||||
cudlaDevHandle devHandle;
|
||||
cudlaModule moduleHandle;
|
||||
cudlaStatus err;
|
||||
uint32_t statSupport = 0;
|
||||
uint32_t dlaFreqInMHz = 0;
|
||||
FILE *fp = NULL;
|
||||
struct stat st;
|
||||
size_t file_size;
|
||||
size_t actually_read = 0;
|
||||
unsigned char *loadableData = NULL;
|
||||
char filename[MAX_FILENAME_LEN];
|
||||
const char *suffix = ".csv";
|
||||
|
||||
cudaStream_t stream;
|
||||
cudaError_t result;
|
||||
const char *errPtr = NULL;
|
||||
|
||||
ResourceList resourceList;
|
||||
|
||||
memset(&resourceList, 0x00, sizeof(ResourceList));
|
||||
|
||||
if ((argc != 4) && (argc != 5)) {
|
||||
DPRINTF("Usage : ./test_cudla_layerwise_stats_L0_hybrid_test1 <loadable> <freqMHZ> <statSupport> <filename "
|
||||
"prefix>\n");
|
||||
return 1;
|
||||
}
|
||||
|
||||
if (argc == 5) {
|
||||
if ((strlen(argv[4])) > (MAX_FILENAME_LEN - RESERVED_SUFFIX_LEN)) {
|
||||
DPRINTF("Filename prefix length is too big, greater than maximum permissible prefix length of %u \n",
|
||||
(MAX_FILENAME_LEN - RESERVED_SUFFIX_LEN));
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
// Read loadable into buffer.
|
||||
fp = fopen(argv[1], "rb");
|
||||
if (fp == NULL) {
|
||||
DPRINTF("Cannot open file %s\n", argv[1]);
|
||||
return 1;
|
||||
}
|
||||
|
||||
if (stat(argv[1], &st) != 0) {
|
||||
DPRINTF("Cannot stat file\n");
|
||||
return 1;
|
||||
}
|
||||
|
||||
file_size = st.st_size;
|
||||
DPRINTF("The file size = %ld\n", file_size);
|
||||
|
||||
dlaFreqInMHz = atoi(argv[2]);
|
||||
statSupport = atoi(argv[3]);
|
||||
|
||||
loadableData = (unsigned char *)malloc(file_size);
|
||||
if (loadableData == NULL) {
|
||||
DPRINTF("Cannot Allocate memory for loadable\n");
|
||||
return 1;
|
||||
}
|
||||
|
||||
actually_read = fread(loadableData, 1, file_size, fp);
|
||||
if (actually_read != file_size) {
|
||||
free(loadableData);
|
||||
DPRINTF("Read wrong size\n");
|
||||
return 1;
|
||||
}
|
||||
fclose(fp);
|
||||
|
||||
resourceList.loadableData = loadableData;
|
||||
|
||||
// Initialize CUDA.
|
||||
result = cudaFree(0);
|
||||
if (result != cudaSuccess) {
|
||||
errPtr = cudaGetErrorName(result);
|
||||
DPRINTF("Error in creating cudaFree = %s\n", errPtr);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
result = cudaSetDevice(0);
|
||||
if (result != cudaSuccess) {
|
||||
errPtr = cudaGetErrorName(result);
|
||||
DPRINTF("Error in creating cudaSetDevice = %s\n", errPtr);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
err = cudlaCreateDevice(0, &devHandle, CUDLA_CUDA_DLA);
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in cuDLA create device = %d\n", err);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
DPRINTF("Device created successfully\n");
|
||||
resourceList.devHandle = devHandle;
|
||||
|
||||
err = cudlaModuleLoadFromMemory(devHandle, loadableData, file_size, &moduleHandle, 0);
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in cudlaModuleLoadFromMemory = %d\n", err);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
else {
|
||||
DPRINTF("Successfully loaded module\n");
|
||||
}
|
||||
|
||||
resourceList.moduleHandle = moduleHandle;
|
||||
|
||||
// Create CUDA stream.
|
||||
result = cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking);
|
||||
|
||||
if (result != cudaSuccess) {
|
||||
errPtr = cudaGetErrorName(result);
|
||||
DPRINTF("Error in creating cuda stream = %s\n", errPtr);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
resourceList.stream = stream;
|
||||
|
||||
// Get tensor attributes.
|
||||
uint32_t numInputTensors = 0;
|
||||
uint32_t numOutputTensors = 0;
|
||||
uint32_t numOutputTaskStatistics = 0;
|
||||
|
||||
cudlaModuleAttribute attribute;
|
||||
|
||||
err = cudlaModuleGetAttributes(moduleHandle, CUDLA_NUM_INPUT_TENSORS, &attribute);
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in getting numInputTensors = %d\n", err);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
numInputTensors = attribute.numInputTensors;
|
||||
DPRINTF("numInputTensors = %d\n", numInputTensors);
|
||||
|
||||
err = cudlaModuleGetAttributes(moduleHandle, CUDLA_NUM_OUTPUT_TENSORS, &attribute);
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in getting numOutputTensors = %d\n", err);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
numOutputTensors = attribute.numOutputTensors;
|
||||
DPRINTF("numOutputTensors = %d\n", numOutputTensors);
|
||||
|
||||
// using the same attributes to get num_output_task_statistics_tensors
|
||||
attribute.numOutputTensors = 0;
|
||||
|
||||
err = cudlaModuleGetAttributes(moduleHandle, CUDLA_NUM_OUTPUT_TASK_STATISTICS, &attribute);
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in getting numOutputTensors = %d\n", err);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
numOutputTaskStatistics = attribute.numOutputTensors;
|
||||
DPRINTF("numOutputTaskStatistics = %d\n", numOutputTaskStatistics);
|
||||
|
||||
if (numOutputTaskStatistics == 0) {
|
||||
DPRINTF("Layerwise stats is not supported for this Loadable \n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
resourceList.numInputTensors = numInputTensors;
|
||||
resourceList.numOutputTensors = numOutputTensors;
|
||||
resourceList.numOutputTaskStatistics = numOutputTaskStatistics;
|
||||
|
||||
cudlaModuleTensorDescriptor *inputTensorDesc =
|
||||
(cudlaModuleTensorDescriptor *)malloc(sizeof(cudlaModuleTensorDescriptor) * numInputTensors);
|
||||
cudlaModuleTensorDescriptor *outputTensorDesc =
|
||||
(cudlaModuleTensorDescriptor *)malloc(sizeof(cudlaModuleTensorDescriptor) * numOutputTensors);
|
||||
|
||||
if ((inputTensorDesc == NULL) || (outputTensorDesc == NULL)) {
|
||||
if (inputTensorDesc != NULL) {
|
||||
free(inputTensorDesc);
|
||||
inputTensorDesc = NULL;
|
||||
}
|
||||
|
||||
if (outputTensorDesc != NULL) {
|
||||
free(outputTensorDesc);
|
||||
outputTensorDesc = NULL;
|
||||
}
|
||||
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
resourceList.inputTensorDesc = inputTensorDesc;
|
||||
resourceList.outputTensorDesc = outputTensorDesc;
|
||||
|
||||
cudlaModuleTensorDescriptor *outputTaskStatisticsDesc =
|
||||
(cudlaModuleTensorDescriptor *)malloc(sizeof(cudlaModuleTensorDescriptor) * numOutputTaskStatistics);
|
||||
if (outputTaskStatisticsDesc == NULL) {
|
||||
free(outputTaskStatisticsDesc);
|
||||
outputTaskStatisticsDesc = NULL;
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
resourceList.outputTaskStatisticsDesc = outputTaskStatisticsDesc;
|
||||
|
||||
attribute.inputTensorDesc = inputTensorDesc;
|
||||
err = cudlaModuleGetAttributes(moduleHandle, CUDLA_INPUT_TENSOR_DESCRIPTORS, &attribute);
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in getting input tensor descriptor = %d\n", err);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
DPRINTF("Printing input tensor descriptor\n");
|
||||
printTensorDesc(inputTensorDesc);
|
||||
|
||||
attribute.outputTensorDesc = outputTensorDesc;
|
||||
err = cudlaModuleGetAttributes(moduleHandle, CUDLA_OUTPUT_TENSOR_DESCRIPTORS, &attribute);
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in getting output tensor descriptor = %d\n", err);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
DPRINTF("Printing output tensor descriptor\n");
|
||||
printTensorDesc(outputTensorDesc);
|
||||
|
||||
attribute.outputTensorDesc = outputTaskStatisticsDesc;
|
||||
err = cudlaModuleGetAttributes(moduleHandle, CUDLA_OUTPUT_TASK_STATISTICS_DESCRIPTORS, &attribute);
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in getting task statistics descriptor = %d\n", err);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
DPRINTF("Printing output task statistics descriptor size\n");
|
||||
for (uint32_t ii = 0; ii < numOutputTaskStatistics; ii++) {
|
||||
DPRINTF("The size of %u descriptor is %lu\n", ii, outputTaskStatisticsDesc[ii].size);
|
||||
}
|
||||
|
||||
// Setup the input and output buffers which will be used as an input to CUDA.
|
||||
unsigned char **inputBuffer = (unsigned char **)malloc(sizeof(unsigned char *) * numInputTensors);
|
||||
if (inputBuffer == NULL) {
|
||||
DPRINTF("Error in allocating memory for input buffer array\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
memset(inputBuffer, 0x00, sizeof(unsigned char *) * numInputTensors);
|
||||
resourceList.inputBuffer = inputBuffer;
|
||||
for (uint32_t ii = 0; ii < numInputTensors; ii++) {
|
||||
inputBuffer[ii] = (unsigned char *)malloc(inputTensorDesc[ii].size);
|
||||
if (inputBuffer[ii] == NULL) {
|
||||
DPRINTF("Error in allocating input memory\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
memset(inputBuffer[ii], 0x01, inputTensorDesc[ii].size);
|
||||
}
|
||||
|
||||
unsigned char **outputBuffer = (unsigned char **)malloc(sizeof(unsigned char *) * numOutputTensors);
|
||||
if (outputBuffer == NULL) {
|
||||
DPRINTF("Error in allocating memory for output buffer array\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
memset(outputBuffer, 0x00, sizeof(unsigned char *) * numOutputTensors);
|
||||
resourceList.outputBuffer = outputBuffer;
|
||||
|
||||
for (uint32_t ii = 0; ii < numOutputTensors; ii++) {
|
||||
outputBuffer[ii] = (unsigned char *)malloc(outputTensorDesc[ii].size);
|
||||
if (outputBuffer[ii] == NULL) {
|
||||
DPRINTF("Error in allocating output memory\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
memset(outputBuffer[ii], 0x00, outputTensorDesc[ii].size);
|
||||
}
|
||||
|
||||
unsigned char **statisticsOutputBuffer =
|
||||
(unsigned char **)malloc(sizeof(unsigned char *) * numOutputTaskStatistics);
|
||||
if (statisticsOutputBuffer == NULL) {
|
||||
DPRINTF("Error in allocating memory for output buffer array\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
memset(statisticsOutputBuffer, 0x00, sizeof(unsigned char *) * numOutputTaskStatistics);
|
||||
resourceList.statisticsOutputBuffer = statisticsOutputBuffer;
|
||||
|
||||
for (uint32_t ii = 0; ii < numOutputTaskStatistics; ii++) {
|
||||
statisticsOutputBuffer[ii] = (unsigned char *)malloc(outputTaskStatisticsDesc[ii].size);
|
||||
if (outputBuffer[ii] == NULL) {
|
||||
DPRINTF("Error in allocating output memory\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
memset(statisticsOutputBuffer[ii], 0x00, outputTaskStatisticsDesc[ii].size);
|
||||
}
|
||||
|
||||
// Allocate memory on GPU.
|
||||
void **inputBufferGPU = (void **)malloc(sizeof(void *) * numInputTensors);
|
||||
if (inputBufferGPU == NULL) {
|
||||
DPRINTF("Error in allocating memory for input buffer GPU array\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
memset(inputBufferGPU, 0x00, sizeof(void *) * numInputTensors);
|
||||
resourceList.inputBufferGPU = inputBufferGPU;
|
||||
|
||||
for (uint32_t ii = 0; ii < numInputTensors; ii++) {
|
||||
result = cudaMalloc(&(inputBufferGPU[ii]), inputTensorDesc[ii].size);
|
||||
if (result != cudaSuccess) {
|
||||
DPRINTF("Error in allocating input memory on GPU\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
void **outputBufferGPU = (void **)malloc(sizeof(void *) * numOutputTensors);
|
||||
if (outputBufferGPU == NULL) {
|
||||
DPRINTF("Error in allocating memory for output buffer GPU array\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
memset(outputBufferGPU, 0x00, sizeof(void *) * numOutputTensors);
|
||||
resourceList.outputBufferGPU = outputBufferGPU;
|
||||
|
||||
for (uint32_t ii = 0; ii < numOutputTensors; ii++) {
|
||||
result = cudaMalloc(&(outputBufferGPU[ii]), outputTensorDesc[ii].size);
|
||||
if (result != cudaSuccess) {
|
||||
DPRINTF("Error in allocating output memory on GPU\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
void **outputTaskStatisticsGPU = (void **)malloc(sizeof(void *) * numOutputTaskStatistics);
|
||||
if (outputTaskStatisticsGPU == NULL) {
|
||||
DPRINTF("Error in allocating memory for output task statistics GPU array\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
memset(outputTaskStatisticsGPU, 0x00, sizeof(void *) * numOutputTaskStatistics);
|
||||
resourceList.outputTaskStatisticsGPU = outputTaskStatisticsGPU;
|
||||
|
||||
for (uint32_t ii = 0; ii < numOutputTaskStatistics; ii++) {
|
||||
result = cudaMalloc(&(outputTaskStatisticsGPU[ii]), outputTaskStatisticsDesc[ii].size);
|
||||
if (result != cudaSuccess) {
|
||||
DPRINTF("Error in allocating task statistics memory on GPU\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
uint64_t **inputBufferRegisteredPtr = (uint64_t **)malloc(sizeof(uint64_t *) * numInputTensors);
|
||||
uint64_t **outputBufferRegisteredPtr = (uint64_t **)malloc(sizeof(uint64_t *) * numOutputTensors);
|
||||
uint64_t **outputTaskStatisticsRegisteredPtr = (uint64_t **)malloc(sizeof(uint64_t *) * numOutputTaskStatistics);
|
||||
|
||||
if ((inputBufferRegisteredPtr == NULL) || (outputBufferRegisteredPtr == NULL)
|
||||
|| (outputTaskStatisticsRegisteredPtr == NULL)) {
|
||||
if (inputBufferRegisteredPtr != NULL) {
|
||||
free(inputBufferRegisteredPtr);
|
||||
inputBufferRegisteredPtr = NULL;
|
||||
}
|
||||
|
||||
if (outputBufferRegisteredPtr != NULL) {
|
||||
free(outputBufferRegisteredPtr);
|
||||
outputBufferRegisteredPtr = NULL;
|
||||
}
|
||||
|
||||
if (outputTaskStatisticsRegisteredPtr != NULL) {
|
||||
free(outputTaskStatisticsRegisteredPtr);
|
||||
outputTaskStatisticsRegisteredPtr = NULL;
|
||||
}
|
||||
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
resourceList.inputBufferRegisteredPtr = inputBufferRegisteredPtr;
|
||||
resourceList.outputBufferRegisteredPtr = outputBufferRegisteredPtr;
|
||||
resourceList.outputTaskStatisticsRegisteredPtr = outputTaskStatisticsRegisteredPtr;
|
||||
|
||||
// Register the CUDA-allocated buffers.
|
||||
for (uint32_t ii = 0; ii < numInputTensors; ii++) {
|
||||
err = cudlaMemRegister(
|
||||
devHandle, (uint64_t *)(inputBufferGPU[ii]), inputTensorDesc[ii].size, &(inputBufferRegisteredPtr[ii]), 0);
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in registering input memory = %d\n", err);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
for (uint32_t ii = 0; ii < numOutputTensors; ii++) {
|
||||
err = cudlaMemRegister(devHandle,
|
||||
(uint64_t *)(outputBufferGPU[ii]),
|
||||
outputTensorDesc[ii].size,
|
||||
&(outputBufferRegisteredPtr[ii]),
|
||||
0);
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in registering output memory = %d\n", err);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
for (uint32_t ii = 0; ii < numOutputTaskStatistics; ii++) {
|
||||
err = cudlaMemRegister(devHandle,
|
||||
(uint64_t *)(outputTaskStatisticsGPU[ii]),
|
||||
outputTaskStatisticsDesc[ii].size,
|
||||
&(outputTaskStatisticsRegisteredPtr[ii]),
|
||||
CUDLA_TASK_STATISTICS);
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in registering statistics output memory = %d\n", err);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
DPRINTF("ALL MEMORY REGISTERED SUCCESSFULLY\n");
|
||||
|
||||
// Copy data from CPU buffers to GPU buffers.
|
||||
for (uint32_t ii = 0; ii < numInputTensors; ii++) {
|
||||
result = cudaMemcpyAsync(
|
||||
inputBufferGPU[ii], inputBuffer[ii], inputTensorDesc[ii].size, cudaMemcpyHostToDevice, stream);
|
||||
if (result != cudaSuccess) {
|
||||
DPRINTF("Error in enqueueing memcpy for input\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
for (uint32_t ii = 0; ii < numOutputTensors; ii++) {
|
||||
result = cudaMemsetAsync(outputBufferGPU[ii], 0, outputTensorDesc[ii].size, stream);
|
||||
if (result != cudaSuccess) {
|
||||
DPRINTF("Error in enqueueing memset for output\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
for (uint32_t ii = 0; ii < numOutputTaskStatistics; ii++) {
|
||||
result = cudaMemsetAsync(outputTaskStatisticsGPU[ii], 0, outputTaskStatisticsDesc[ii].size, stream);
|
||||
if (result != cudaSuccess) {
|
||||
DPRINTF("Error in enqueueing memset for statistics output\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
uint64_t *outputStatisticsBufferRegisteredPtr[numOutputTensors + numOutputTaskStatistics] = {0};
|
||||
uint32_t index = 0;
|
||||
for (; index < numOutputTensors; index++) {
|
||||
outputStatisticsBufferRegisteredPtr[index] = ((outputBufferRegisteredPtr[index]));
|
||||
}
|
||||
|
||||
for (uint32_t jj = 0; jj < numOutputTaskStatistics; jj++) {
|
||||
outputStatisticsBufferRegisteredPtr[index++] = ((outputTaskStatisticsRegisteredPtr[jj]));
|
||||
}
|
||||
|
||||
// Enqueue a cuDLA task.
|
||||
cudlaTask task;
|
||||
task.moduleHandle = moduleHandle;
|
||||
task.outputTensor = (uint64_t *const *)&outputStatisticsBufferRegisteredPtr;
|
||||
|
||||
if (statSupport == 1) {
|
||||
task.numOutputTensors = (numOutputTensors + numOutputTaskStatistics);
|
||||
DPRINTF("Layerwise profiling is requested \n");
|
||||
}
|
||||
else {
|
||||
task.numOutputTensors = numOutputTensors;
|
||||
DPRINTF("Layerwise profiling is not requested \n");
|
||||
}
|
||||
|
||||
task.numInputTensors = numInputTensors;
|
||||
task.inputTensor = inputBufferRegisteredPtr;
|
||||
task.waitEvents = NULL;
|
||||
task.signalEvents = NULL;
|
||||
|
||||
err = cudlaSubmitTask(devHandle, &task, 1, stream, 0);
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("no of output tensor %u \n", (task.numOutputTensors));
|
||||
DPRINTF("Error in submitting task\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
DPRINTF("SUBMIT IS DONE !!!\n");
|
||||
|
||||
result = cudaStreamSynchronize(stream);
|
||||
if (result != cudaSuccess) {
|
||||
DPRINTF("Error in synchronizing stream = %s\n", cudaGetErrorName(result));
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
// Wait for stream operations to finish and bring output buffer to CPU.
|
||||
for (uint32_t ii = 0; ii < numOutputTensors; ii++) {
|
||||
result = cudaMemcpyAsync(
|
||||
outputBuffer[ii], outputBufferGPU[ii], outputTensorDesc[ii].size, cudaMemcpyDeviceToHost, stream);
|
||||
if (result != cudaSuccess) {
|
||||
DPRINTF("Error in bringing result back to CPU\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
result = cudaStreamSynchronize(stream);
|
||||
if (result != cudaSuccess) {
|
||||
DPRINTF("Error in synchronizing stream\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
if (statSupport == 1) {
|
||||
// copy statistics data to cpu
|
||||
for (uint32_t ii = 0; ii < numOutputTaskStatistics; ii++) {
|
||||
result = cudaMemcpyAsync(statisticsOutputBuffer[ii],
|
||||
outputTaskStatisticsGPU[ii],
|
||||
outputTaskStatisticsDesc[ii].size,
|
||||
cudaMemcpyDeviceToHost,
|
||||
stream);
|
||||
if (result != cudaSuccess) {
|
||||
DPRINTF("Error in bringing result back to CPU\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
result = cudaStreamSynchronize(stream);
|
||||
if (result != cudaSuccess) {
|
||||
DPRINTF("Error in synchronizing stream\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
// To get the last index of the filename prefix in which statistics will be dumped
|
||||
uint32_t index = 0;
|
||||
if (argc == 5) {
|
||||
while (argv[4][index] != '\0') {
|
||||
index++;
|
||||
}
|
||||
}
|
||||
|
||||
const cudlaExternalEtbl *etbl = NULL;
|
||||
if (cudlaGetExternalExportTable(&etbl, 0) != cudlaSuccess) {
|
||||
DPRINTF("Error in getting export table\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
void **csv = (void **)malloc(sizeof(void *) * numOutputTaskStatistics);
|
||||
if (csv == NULL) {
|
||||
DPRINTF("Error in allocating memory for csv stream\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
memset(csv, 0x00, sizeof(void *) * numOutputTaskStatistics);
|
||||
resourceList.csv = csv;
|
||||
|
||||
for (uint32_t ii = 0; ii < numOutputTaskStatistics; ii++) {
|
||||
cudlaTranslateCsvAttribute csvAttribute;
|
||||
uint64_t csvStreamLength = 0;
|
||||
|
||||
err = etbl->etiTranslateStats(
|
||||
devHandle, statisticsOutputBuffer[ii], dlaFreqInMHz, ii, CUDLA_GET_CSV_LENGTH, &csvAttribute);
|
||||
csv[ii] = (void *)malloc(csvAttribute.csvStreamLength);
|
||||
csvStreamLength = csvAttribute.csvStreamLength;
|
||||
DPRINTF("size for statistics buffer %u is %lu \n", ii, csvStreamLength);
|
||||
|
||||
if (csv[ii] == NULL) {
|
||||
DPRINTF("Error in allocating memory for csv stream\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
memset(csv[ii], 0x00, csvAttribute.csvStreamLength);
|
||||
|
||||
csvAttribute.csvStreamStats = csv[ii];
|
||||
err = etbl->etiTranslateStats(
|
||||
devHandle, statisticsOutputBuffer[ii], dlaFreqInMHz, ii, CUDLA_GET_CSV_STATS, &csvAttribute);
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in translating stats\n");
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
if (argc == 5) {
|
||||
sprintf(filename, "%s%u%s", argv[4], (ii + 1), suffix);
|
||||
fp = fopen(filename, "w+");
|
||||
if (fp == NULL) {
|
||||
DPRINTF("Cannot open file %s\n", filename);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
uint32_t ret_val = fwrite(csv[ii], sizeof(char), csvStreamLength, fp);
|
||||
if (ret_val != csvStreamLength) {
|
||||
DPRINTF("number of elements written to file is %u \n", ret_val);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
fclose(fp);
|
||||
}
|
||||
else {
|
||||
DPRINTF("%s \n", (char *)csv[ii]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// unregister the CUDA-allocated buffers.
|
||||
for (uint32_t ii = 0; ii < numInputTensors; ii++) {
|
||||
err = cudlaMemUnregister(devHandle, (inputBufferRegisteredPtr[ii]));
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in registering input memory = %d\n", err);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
for (uint32_t ii = 0; ii < numOutputTensors; ii++) {
|
||||
err = cudlaMemUnregister(devHandle, (outputBufferRegisteredPtr[ii]));
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in registering output memory = %d\n", err);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
for (uint32_t ii = 0; ii < numOutputTaskStatistics; ii++) {
|
||||
err = cudlaMemUnregister(devHandle, (outputTaskStatisticsRegisteredPtr[ii]));
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in registering output memory = %d\n", err);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
DPRINTF("ALL MEMORY UNREGISTERED SUCCESSFULLY\n");
|
||||
|
||||
result = cudaStreamDestroy(stream);
|
||||
if (result != cudaSuccess) {
|
||||
errPtr = cudaGetErrorName(result);
|
||||
DPRINTF("Error in destroying cuda stream = %s\n", errPtr);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
|
||||
resourceList.stream = NULL;
|
||||
|
||||
err = cudlaModuleUnload(moduleHandle, 0);
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in cudlaModuleUnload = %d\n", err);
|
||||
cleanUp(&resourceList);
|
||||
return 1;
|
||||
}
|
||||
else {
|
||||
DPRINTF("Successfully unloaded module\n");
|
||||
}
|
||||
|
||||
resourceList.moduleHandle = NULL;
|
||||
|
||||
err = cudlaDestroyDevice(devHandle);
|
||||
if (err != cudlaSuccess) {
|
||||
DPRINTF("Error in cuDLA destroy device = %d\n", err);
|
||||
return 1;
|
||||
}
|
||||
DPRINTF("Device destroyed successfully\n");
|
||||
|
||||
resourceList.devHandle = NULL;
|
||||
|
||||
cleanUp(&resourceList);
|
||||
|
||||
DPRINTF("cuDLALayerwiseStatsHybrid DONE !!!\n");
|
||||
|
||||
return 0;
|
||||
}
|
||||
Reference in New Issue
Block a user