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Apply consistent code formatting across the repo. Add clang-format and pre-commit hooks.

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This commit is contained in:
Rob Armstrong
2025-03-27 10:30:07 -07:00
parent 2cd58fbc9a
commit ceab6e8bcc
782 changed files with 107230 additions and 106548 deletions
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1
Samples/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/README.md
View File

@@ -30,4 +30,3 @@ cudaStreamCreateWithFlags, cudaStreamDestroy, cudaFree, cudaGetErrorName, cudaSe
Download and install the [CUDA Toolkit 12.5](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
## References (for more details)

307
Samples/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/main.cu
View File

@@ -25,28 +25,28 @@
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "cudla.h"
#include "cuda_runtime.h"
#include "cudlaExternalEtbl.hpp"
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <sys/stat.h>
#include <fstream>
#include <sstream>
#include <sys/stat.h>
#define MAX_FILENAME_LEN 200
#include "cuda_runtime.h"
#include "cudla.h"
#include "cudlaExternalEtbl.hpp"
#define MAX_FILENAME_LEN 200
#define RESERVED_SUFFIX_LEN 10
#define DPRINTF(...) printf(__VA_ARGS__)
static void printTensorDesc(cudlaModuleTensorDescriptor* tensorDesc) {
static void printTensorDesc(cudlaModuleTensorDescriptor *tensorDesc)
{
DPRINTF("\tTENSOR NAME : %s\n", tensorDesc->name);
DPRINTF("\tsize: %lu\n", tensorDesc->size);
DPRINTF("\tdims: [%lu, %lu, %lu, %lu]\n", tensorDesc->n, tensorDesc->c,
tensorDesc->h, tensorDesc->w);
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);
@@ -59,33 +59,35 @@ static void printTensorDesc(cudlaModuleTensorDescriptor* tensorDesc) {
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;
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);
void cleanUp(ResourceList* resourceList) {
void cleanUp(ResourceList *resourceList)
{
uint32_t ii = 0;
if (resourceList->inputTensorDesc != NULL) {
free(resourceList->inputTensorDesc);
@@ -152,8 +154,7 @@ void cleanUp(ResourceList* resourceList) {
if (resourceList->csv != NULL) {
for (ii = 0; ii < resourceList->numOutputTaskStatistics; ii++) {
if ((resourceList->csv)[ii] != NULL)
{
if ((resourceList->csv)[ii] != NULL) {
free((resourceList->csv)[ii]);
(resourceList->csv)[ii] = NULL;
}
@@ -175,8 +176,7 @@ void cleanUp(ResourceList* resourceList) {
if (resourceList->outputBuffer != NULL) {
for (ii = 0; ii < resourceList->numOutputTensors; ii++) {
if ((resourceList->outputBuffer)[ii] != NULL)
{
if ((resourceList->outputBuffer)[ii] != NULL) {
free((resourceList->outputBuffer)[ii]);
(resourceList->outputBuffer)[ii] = NULL;
}
@@ -221,42 +221,44 @@ void cleanUp(ResourceList* resourceList) {
resourceList->outputStatisticsBufferRegisteredPtr = NULL;
}
resourceList->numInputTensors = 0;
resourceList->numOutputTensors = 0;
resourceList->numInputTensors = 0;
resourceList->numOutputTensors = 0;
resourceList->numOutputTaskStatistics = 0;
}
int main(int argc, char** argv) {
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";
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;
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");
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));
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;
}
}
@@ -277,7 +279,7 @@ int main(int argc, char** argv) {
DPRINTF("The file size = %ld\n", file_size);
dlaFreqInMHz = atoi(argv[2]);
statSupport = atoi(argv[3]);
statSupport = atoi(argv[3]);
loadableData = (unsigned char *)malloc(file_size);
if (loadableData == NULL) {
@@ -286,7 +288,7 @@ int main(int argc, char** argv) {
}
actually_read = fread(loadableData, 1, file_size, fp);
if ( actually_read != file_size ) {
if (actually_read != file_size) {
free(loadableData);
DPRINTF("Read wrong size\n");
return 1;
@@ -327,8 +329,9 @@ int main(int argc, char** argv) {
DPRINTF("Error in cudlaModuleLoadFromMemory = %d\n", err);
cleanUp(&resourceList);
return 1;
} else {
DPRINTF("Successfully loaded module\n");
}
else {
DPRINTF("Successfully loaded module\n");
}
resourceList.moduleHandle = moduleHandle;
@@ -346,8 +349,8 @@ int main(int argc, char** argv) {
resourceList.stream = stream;
// Get tensor attributes.
uint32_t numInputTensors = 0;
uint32_t numOutputTensors = 0;
uint32_t numInputTensors = 0;
uint32_t numOutputTensors = 0;
uint32_t numOutputTaskStatistics = 0;
cudlaModuleAttribute attribute;
@@ -382,20 +385,20 @@ int main(int argc, char** argv) {
numOutputTaskStatistics = attribute.numOutputTensors;
DPRINTF("numOutputTaskStatistics = %d\n", numOutputTaskStatistics);
if(numOutputTaskStatistics == 0) {
if (numOutputTaskStatistics == 0) {
DPRINTF("Layerwise stats is not supported for this Loadable \n");
cleanUp(&resourceList);
return 1;
}
resourceList.numInputTensors = numInputTensors;
resourceList.numOutputTensors = numOutputTensors;
resourceList.numInputTensors = numInputTensors;
resourceList.numOutputTensors = numOutputTensors;
resourceList.numOutputTaskStatistics = numOutputTaskStatistics;
cudlaModuleTensorDescriptor* inputTensorDesc =
(cudlaModuleTensorDescriptor*)malloc(sizeof(cudlaModuleTensorDescriptor)*numInputTensors);
cudlaModuleTensorDescriptor* outputTensorDesc =
(cudlaModuleTensorDescriptor*)malloc(sizeof(cudlaModuleTensorDescriptor)*numOutputTensors);
cudlaModuleTensorDescriptor *inputTensorDesc =
(cudlaModuleTensorDescriptor *)malloc(sizeof(cudlaModuleTensorDescriptor) * numInputTensors);
cudlaModuleTensorDescriptor *outputTensorDesc =
(cudlaModuleTensorDescriptor *)malloc(sizeof(cudlaModuleTensorDescriptor) * numOutputTensors);
if ((inputTensorDesc == NULL) || (outputTensorDesc == NULL)) {
if (inputTensorDesc != NULL) {
@@ -412,11 +415,11 @@ int main(int argc, char** argv) {
return 1;
}
resourceList.inputTensorDesc = inputTensorDesc;
resourceList.inputTensorDesc = inputTensorDesc;
resourceList.outputTensorDesc = outputTensorDesc;
cudlaModuleTensorDescriptor* outputTaskStatisticsDesc =
(cudlaModuleTensorDescriptor*)malloc(sizeof(cudlaModuleTensorDescriptor)*numOutputTaskStatistics);
cudlaModuleTensorDescriptor *outputTaskStatisticsDesc =
(cudlaModuleTensorDescriptor *)malloc(sizeof(cudlaModuleTensorDescriptor) * numOutputTaskStatistics);
if (outputTaskStatisticsDesc == NULL) {
free(outputTaskStatisticsDesc);
outputTaskStatisticsDesc = NULL;
@@ -427,9 +430,7 @@ int main(int argc, char** argv) {
resourceList.outputTaskStatisticsDesc = outputTaskStatisticsDesc;
attribute.inputTensorDesc = inputTensorDesc;
err = cudlaModuleGetAttributes(moduleHandle,
CUDLA_INPUT_TENSOR_DESCRIPTORS,
&attribute);
err = cudlaModuleGetAttributes(moduleHandle, CUDLA_INPUT_TENSOR_DESCRIPTORS, &attribute);
if (err != cudlaSuccess) {
DPRINTF("Error in getting input tensor descriptor = %d\n", err);
cleanUp(&resourceList);
@@ -439,9 +440,7 @@ int main(int argc, char** argv) {
printTensorDesc(inputTensorDesc);
attribute.outputTensorDesc = outputTensorDesc;
err = cudlaModuleGetAttributes(moduleHandle,
CUDLA_OUTPUT_TENSOR_DESCRIPTORS,
&attribute);
err = cudlaModuleGetAttributes(moduleHandle, CUDLA_OUTPUT_TENSOR_DESCRIPTORS, &attribute);
if (err != cudlaSuccess) {
DPRINTF("Error in getting output tensor descriptor = %d\n", err);
cleanUp(&resourceList);
@@ -451,9 +450,7 @@ int main(int argc, char** argv) {
printTensorDesc(outputTensorDesc);
attribute.outputTensorDesc = outputTaskStatisticsDesc;
err = cudlaModuleGetAttributes(moduleHandle,
CUDLA_OUTPUT_TASK_STATISTICS_DESCRIPTORS,
&attribute);
err = cudlaModuleGetAttributes(moduleHandle, CUDLA_OUTPUT_TASK_STATISTICS_DESCRIPTORS, &attribute);
if (err != cudlaSuccess) {
DPRINTF("Error in getting task statistics descriptor = %d\n", err);
cleanUp(&resourceList);
@@ -462,20 +459,20 @@ int main(int argc, char** argv) {
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);
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);
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);
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);
inputBuffer[ii] = (unsigned char *)malloc(inputTensorDesc[ii].size);
if (inputBuffer[ii] == NULL) {
DPRINTF("Error in allocating input memory\n");
cleanUp(&resourceList);
@@ -484,17 +481,17 @@ int main(int argc, char** argv) {
memset(inputBuffer[ii], 0x01, inputTensorDesc[ii].size);
}
unsigned char** outputBuffer = (unsigned char **)malloc(sizeof(unsigned char *)*numOutputTensors);
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);
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);
outputBuffer[ii] = (unsigned char *)malloc(outputTensorDesc[ii].size);
if (outputBuffer[ii] == NULL) {
DPRINTF("Error in allocating output memory\n");
cleanUp(&resourceList);
@@ -503,17 +500,18 @@ int main(int argc, char** argv) {
memset(outputBuffer[ii], 0x00, outputTensorDesc[ii].size);
}
unsigned char** statisticsOutputBuffer = (unsigned char **)malloc(sizeof(unsigned char *)*numOutputTaskStatistics);
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);
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);
statisticsOutputBuffer[ii] = (unsigned char *)malloc(outputTaskStatisticsDesc[ii].size);
if (outputBuffer[ii] == NULL) {
DPRINTF("Error in allocating output memory\n");
cleanUp(&resourceList);
@@ -523,32 +521,31 @@ int main(int argc, char** argv) {
}
// Allocate memory on GPU.
void** inputBufferGPU = (void **)malloc(sizeof(void *)*numInputTensors);
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);
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)
{
if (result != cudaSuccess) {
DPRINTF("Error in allocating input memory on GPU\n");
cleanUp(&resourceList);
return 1;
}
}
void** outputBufferGPU = (void **)malloc(sizeof(void *)*numOutputTensors);
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);
memset(outputBufferGPU, 0x00, sizeof(void *) * numOutputTensors);
resourceList.outputBufferGPU = outputBufferGPU;
for (uint32_t ii = 0; ii < numOutputTensors; ii++) {
@@ -560,13 +557,13 @@ int main(int argc, char** argv) {
}
}
void** outputTaskStatisticsGPU = (void **)malloc(sizeof(void *)*numOutputTaskStatistics);
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);
memset(outputTaskStatisticsGPU, 0x00, sizeof(void *) * numOutputTaskStatistics);
resourceList.outputTaskStatisticsGPU = outputTaskStatisticsGPU;
for (uint32_t ii = 0; ii < numOutputTaskStatistics; ii++) {
@@ -578,11 +575,12 @@ int main(int argc, char** argv) {
}
}
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);
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) || (outputBufferRegisteredPtr == NULL)
|| (outputTaskStatisticsRegisteredPtr == NULL)) {
if (inputBufferRegisteredPtr != NULL) {
free(inputBufferRegisteredPtr);
inputBufferRegisteredPtr = NULL;
@@ -602,17 +600,14 @@ int main(int argc, char** argv) {
return 1;
}
resourceList.inputBufferRegisteredPtr = inputBufferRegisteredPtr;
resourceList.outputBufferRegisteredPtr = outputBufferRegisteredPtr;
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);
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);
@@ -622,7 +617,7 @@ int main(int argc, char** argv) {
for (uint32_t ii = 0; ii < numOutputTensors; ii++) {
err = cudlaMemRegister(devHandle,
(uint64_t* )(outputBufferGPU[ii]),
(uint64_t *)(outputBufferGPU[ii]),
outputTensorDesc[ii].size,
&(outputBufferRegisteredPtr[ii]),
0);
@@ -635,7 +630,7 @@ int main(int argc, char** argv) {
for (uint32_t ii = 0; ii < numOutputTaskStatistics; ii++) {
err = cudlaMemRegister(devHandle,
(uint64_t* )(outputTaskStatisticsGPU[ii]),
(uint64_t *)(outputTaskStatisticsGPU[ii]),
outputTaskStatisticsDesc[ii].size,
&(outputTaskStatisticsRegisteredPtr[ii]),
CUDLA_TASK_STATISTICS);
@@ -650,7 +645,8 @@ int main(int argc, char** argv) {
// 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);
result = cudaMemcpyAsync(
inputBufferGPU[ii], inputBuffer[ii], inputTensorDesc[ii].size, cudaMemcpyHostToDevice, stream);
if (result != cudaSuccess) {
DPRINTF("Error in enqueueing memcpy for input\n");
cleanUp(&resourceList);
@@ -677,36 +673,37 @@ int main(int argc, char** argv) {
}
uint64_t *outputStatisticsBufferRegisteredPtr[numOutputTensors + numOutputTaskStatistics] = {0};
uint32_t index = 0;
for (; index < numOutputTensors ; index++) {
uint32_t index = 0;
for (; index < numOutputTensors; index++) {
outputStatisticsBufferRegisteredPtr[index] = ((outputBufferRegisteredPtr[index]));
}
for (uint32_t jj=0; jj < numOutputTaskStatistics ; jj++) {
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;
task.outputTensor = (uint64_t *const *)&outputStatisticsBufferRegisteredPtr;
if(statSupport == 1) {
if (statSupport == 1) {
task.numOutputTensors = (numOutputTensors + numOutputTaskStatistics);
DPRINTF("Layerwise profiling is requested \n");
} else {
task.numOutputTensors = numOutputTensors;
DPRINTF("Layerwise profiling is not 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;
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("no of output tensor %u \n", (task.numOutputTensors));
DPRINTF("Error in submitting task\n");
cleanUp(&resourceList);
return 1;
@@ -722,8 +719,8 @@ int main(int argc, char** argv) {
// 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);
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);
@@ -738,11 +735,14 @@ int main(int argc, char** argv) {
return 1;
}
if(statSupport == 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);
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);
@@ -760,35 +760,36 @@ int main(int argc, char** argv) {
// 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') {
while (argv[4][index] != '\0') {
index++;
}
}
const cudlaExternalEtbl* etbl = NULL;
if (cudlaGetExternalExportTable(&etbl,0) != cudlaSuccess) {
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);
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);
memset(csv, 0x00, sizeof(void *) * numOutputTaskStatistics);
resourceList.csv = csv;
for (uint32_t ii = 0; ii < numOutputTaskStatistics; ii++) {
cudlaTranslateCsvAttribute csvAttribute;
uint64_t csvStreamLength = 0;
uint64_t csvStreamLength = 0;
err = etbl->etiTranslateStats(devHandle,statisticsOutputBuffer[ii],dlaFreqInMHz,ii,CUDLA_GET_CSV_LENGTH,&csvAttribute);
csv[ii] = (void* )malloc(csvAttribute.csvStreamLength);
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);
DPRINTF("size for statistics buffer %u is %lu \n", ii, csvStreamLength);
if (csv[ii] == NULL) {
DPRINTF("Error in allocating memory for csv stream\n");
@@ -798,7 +799,8 @@ int main(int argc, char** argv) {
memset(csv[ii], 0x00, csvAttribute.csvStreamLength);
csvAttribute.csvStreamStats = csv[ii];
err = etbl->etiTranslateStats(devHandle,statisticsOutputBuffer[ii],dlaFreqInMHz,ii,CUDLA_GET_CSV_STATS,&csvAttribute);
err = etbl->etiTranslateStats(
devHandle, statisticsOutputBuffer[ii], dlaFreqInMHz, ii, CUDLA_GET_CSV_STATS, &csvAttribute);
if (err != cudlaSuccess) {
DPRINTF("Error in translating stats\n");
cleanUp(&resourceList);
@@ -806,7 +808,7 @@ int main(int argc, char** argv) {
}
if (argc == 5) {
sprintf(filename,"%s%u%s", argv[4],(ii+1),suffix);
sprintf(filename, "%s%u%s", argv[4], (ii + 1), suffix);
fp = fopen(filename, "w+");
if (fp == NULL) {
DPRINTF("Cannot open file %s\n", filename);
@@ -814,23 +816,23 @@ int main(int argc, char** argv) {
return 1;
}
uint32_t ret_val = fwrite(csv[ii],sizeof(char),csvStreamLength,fp);
if(ret_val != csvStreamLength) {
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]);
}
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]));
err = cudlaMemUnregister(devHandle, (inputBufferRegisteredPtr[ii]));
if (err != cudlaSuccess) {
DPRINTF("Error in registering input memory = %d\n", err);
cleanUp(&resourceList);
@@ -839,8 +841,7 @@ int main(int argc, char** argv) {
}
for (uint32_t ii = 0; ii < numOutputTensors; ii++) {
err = cudlaMemUnregister(devHandle,
(outputBufferRegisteredPtr[ii]));
err = cudlaMemUnregister(devHandle, (outputBufferRegisteredPtr[ii]));
if (err != cudlaSuccess) {
DPRINTF("Error in registering output memory = %d\n", err);
cleanUp(&resourceList);
@@ -849,8 +850,7 @@ int main(int argc, char** argv) {
}
for (uint32_t ii = 0; ii < numOutputTaskStatistics; ii++) {
err = cudlaMemUnregister(devHandle,
(outputTaskStatisticsRegisteredPtr[ii]));
err = cudlaMemUnregister(devHandle, (outputTaskStatisticsRegisteredPtr[ii]));
if (err != cudlaSuccess) {
DPRINTF("Error in registering output memory = %d\n", err);
cleanUp(&resourceList);
@@ -875,7 +875,8 @@ int main(int argc, char** argv) {
DPRINTF("Error in cudlaModuleUnload = %d\n", err);
cleanUp(&resourceList);
return 1;
} else {
}
else {
DPRINTF("Successfully unloaded module\n");
}