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cuda-samples/Samples/batchCUBLAS/batchCUBLAS.cpp
Rutwik Choughule 1f76a2d110 add and update samples for CUDA 11.5
2021-10-21 16:34:49 +05:30

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/* Copyright (c) 2021, 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.
*/
/*
* This example demonstrates how to get better performance by
* batching CUBLAS calls with the use of using streams
*/
#include <ctype.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
#include <float.h>
#endif
/* Using updated (v2) interfaces to cublas and cusparse */
#include <cublas_v2.h>
#include <cuda_runtime.h>
// Utilities and system includes
#include <helper_cuda.h>
#include "batchCUBLAS.h"
const char *sSDKname = "batchCUBLAS";
//==============================================================================
// Device information utilities
//==============================================================================
#if defined(__cplusplus)
extern "C" {
#endif /* __cplusplus */
int getDeviceVersion(void) {
int device;
struct cudaDeviceProp properties;
if (cudaGetDevice(&device) != cudaSuccess) {
printf("failed to get device\n");
return 0;
}
if (cudaGetDeviceProperties(&properties, device) != cudaSuccess) {
printf("failed to get properties\n");
return 0;
}
return properties.major * 100 + properties.minor * 10;
}
size_t getDeviceMemory(void) {
struct cudaDeviceProp properties;
int device;
if (cudaGetDevice(&device) != cudaSuccess) {
return 0;
}
if (cudaGetDeviceProperties(&properties, device) != cudaSuccess) {
return 0;
}
return properties.totalGlobalMem;
}
#if defined(__cplusplus)
}
#endif /* __cplusplus */
//==============================================================================
// random utilities
//==============================================================================
template <typename T_ELEM>
void fillupMatrix(T_ELEM *A, int lda, int rows, int cols, int seed = 0);
template <typename T_ELEM>
void fillupMatrix(T_ELEM *A, int lda, int rows, int cols, int seed) {
for (int j = 0; j < cols; j++) {
for (int i = 0; i < rows; i++) {
A[i + lda * j] = cuGet<T_ELEM>(
((double)(((lda * i + j + seed) % 253) + 1)) / 256.0,
((double)((((cols * i + j) + 123 + seed) % 253) + 1)) / 256.0);
}
}
}
/* Explicit instantiation */
template void fillupMatrix<float>(float *A, int lda, int rows, int cols,
int seed);
template void fillupMatrix<double>(double *A, int lda, int rows, int cols,
int seed);
/* For debugging */
void printCuType(const char *str, float A) {
fprintf(stdout, "%s (0x%08x, %g)", str, floatAsUInt(A), A);
}
void printCuType(const char *str, double A) {
fprintf(stdout, "%s (0x%016llx, %g)", str, doubleAsULL(A), A);
}
//==============================================================================
// defines and structures
//==============================================================================
#define CUBLAS_SGEMM_MAX_ULP_ERR (.3)
#define CUBLAS_DGEMM_MAX_ULP_ERR (1.e-3)
#define CUBLAS_SGEMM_MAX_RELATIVE_ERR (6.e-6)
#define CUBLAS_DGEMM_MAX_RELATIVE_ERR (0.0)
#define CUBLAS_GEMM_TEST_COUNT (30)
#define BENCH_MATRIX_M (128)
#define BENCH_MATRIX_K (128)
#define BENCH_MATRIX_N (128)
#define CLEANUP() \
do { \
if (A) free(A); \
if (B) free(B); \
if (C) free(C); \
for (int i = 0; i < opts.N; ++i) { \
if (devPtrA[i]) cudaFree(devPtrA[i]); \
if (devPtrB[i]) cudaFree(devPtrB[i]); \
if (devPtrC[i]) cudaFree(devPtrC[i]); \
} \
if (devPtrA) free(devPtrA); \
if (devPtrB) free(devPtrB); \
if (devPtrC) free(devPtrC); \
if (devPtrA_dev) cudaFree(devPtrA_dev); \
if (devPtrB_dev) cudaFree(devPtrB_dev); \
if (devPtrC_dev) cudaFree(devPtrC_dev); \
fflush(stdout); \
} while (0)
enum testMethod { tmRegular, tmStream, tmBatched };
struct gemmOpts {
int m;
int n;
int k;
testMethod test_method;
char *elem_type;
int N; // number of multiplications
};
template <typename T_ELEM>
struct gemmTestParams {
cublasOperation_t transa;
cublasOperation_t transb;
int m;
int n;
int k;
T_ELEM alpha;
T_ELEM beta;
};
//==============================================================================
// template wrappers for cuda functions
//==============================================================================
static inline cublasStatus_t cublasXgemm(cublasHandle_t handle,
cublasOperation_t transa,
cublasOperation_t transb, int m, int n,
int k, float *alpha, const float *A,
int lda, float *B, int ldb,
float *beta, float *C, int ldc) {
return cublasSgemm(handle, transa, transb, m, n, k, alpha, A, lda, B, ldb,
beta, C, ldc);
}
static inline cublasStatus_t cublasXgemm(cublasHandle_t handle,
cublasOperation_t transa,
cublasOperation_t transb, int m, int n,
int k, double *alpha, const double *A,
int lda, double *B, int ldb,
double *beta, double *C, int ldc) {
return cublasDgemm(handle, transa, transb, m, n, k, alpha, A, lda, B, ldb,
beta, C, ldc);
}
static inline cublasStatus_t cublasXgemmBatched(
cublasHandle_t handle, cublasOperation_t transa, cublasOperation_t transb,
int m, int n, int k, float *alpha, const float *Aarray[], int lda,
const float *Barray[], int ldb, float *beta, float *Carray[], int ldc,
int batchCount) {
#if CUDART_VERSION >= 4010
return cublasSgemmBatched(handle, transa, transb, m, n, k, alpha, Aarray, lda,
Barray, ldb, beta, Carray, ldc, batchCount);
#else
return CUBLAS_STATUS_SUCCESS;
#endif
}
static inline cublasStatus_t cublasXgemmBatched(
cublasHandle_t handle, cublasOperation_t transa, cublasOperation_t transb,
int m, int n, int k, double *alpha, const double *Aarray[], int lda,
const double *Barray[], int ldb, double *beta, double *Carray[], int ldc,
int batchCount) {
#if CUDART_VERSION >= 4010
return cublasDgemmBatched(handle, transa, transb, m, n, k, alpha, Aarray, lda,
Barray, ldb, beta, Carray, ldc, batchCount);
#else
return CUBLAS_STATUS_SUCCESS;
#endif
}
//==============================================================================
// Primary Application code
//==============================================================================
static int processArgs(int argc, char *argv[], struct gemmOpts *opts) {
int error = 0;
int oldError;
memset(opts, 0, sizeof(*opts));
static char default_type[] = "d"; // default double
opts->elem_type = default_type;
opts->N = 10;
while (argc) {
oldError = error;
if (*argv[0] == SWITCH_CHAR) {
switch (*(argv[0] + 1)) {
case 'm':
opts->m = (int)atol(argv[0] + 2);
break;
case 'n':
opts->n = (int)atol(argv[0] + 2);
break;
case 'k':
opts->k = (int)atol(argv[0] + 2);
break;
case 'N':
opts->N = (int)atol(argv[0] + 2);
break;
default:
break;
}
}
if (error > oldError) {
fprintf(stderr, "Invalid switch '%c%s'\n", SWITCH_CHAR, argv[0] + 1);
}
argc -= 1;
argv++;
}
return error;
}
template <typename T_ELEM>
static int TESTGEN(gemm)(const struct gemmOpts *opts, int matrixM, int matrixN,
int matrixK, int &numTests,
struct gemmTestParams<T_ELEM> *params) {
static T_ELEM alpha[] = {cuGet<T_ELEM>(0, 0), cuGet<T_ELEM>(-1, -1),
cuGet<T_ELEM>(1, -2), cuGet<T_ELEM>(2, -1),
cuGet<T_ELEM>(0, -3)};
static T_ELEM beta[] = {cuGet<T_ELEM>(0, 0), cuGet<T_ELEM>(-1, -1),
cuGet<T_ELEM>(1, -2), cuGet<T_ELEM>(2, -1),
cuGet<T_ELEM>(0, -3)};
#define NBR_ALPHAS (sizeof(alpha) / sizeof(alpha[0]))
#define NBR_BETAS (sizeof(beta) / sizeof(beta[0]))
static T_ELEM theAlpha;
static T_ELEM theBeta;
static int state;
static int m;
static int n;
static int k;
if (numTests-- <= 0) {
return -1;
}
theAlpha = alpha[cuRand() % NBR_ALPHAS];
theBeta = beta[cuRand() % NBR_BETAS];
params->transa = CUBLAS_OP_N;
params->transb = CUBLAS_OP_N;
m = matrixM;
n = matrixN;
k = matrixK;
params->m = m;
params->n = n;
params->k = k;
params->alpha = theAlpha;
params->beta = theBeta;
printf("#### args: ta=%d tb=%d m=%d n=%d k=%d ", (unsigned int)params->transa,
(unsigned int)params->transb, params->m, params->n, params->k);
printCuType(" alpha =", params->alpha);
printCuType(" beta=", params->beta);
printf("\n");
m = cuRand() % matrixM;
n = cuRand() % matrixN;
k = cuRand() % matrixK;
state = cuRand() % 9;
return 0;
}
template <typename T_ELEM>
void fillupMatrixDebug(T_ELEM *A, int lda, int rows, int cols) {
for (int j = 0; j < cols; j++) {
for (int i = 0; i < rows; i++) {
A[i + lda * j] = cuGet<T_ELEM>(i + j);
}
}
}
template <typename T_ELEM>
int test_gemm_loop(struct gemmOpts &opts, float err, double max_relative_error,
cublasHandle_t handle) {
struct gemmTestParams<T_ELEM> params;
cudaStream_t *streamArray = 0;
cublasStatus_t status1, status2, status3;
T_ELEM *A = NULL;
T_ELEM *B = NULL;
T_ELEM *C = NULL;
T_ELEM **devPtrA = 0;
T_ELEM **devPtrB = 0;
T_ELEM **devPtrC = 0;
T_ELEM **devPtrA_dev = NULL;
T_ELEM **devPtrB_dev = NULL;
T_ELEM **devPtrC_dev = NULL;
int matrixM, matrixN, matrixK;
int rowsA, rowsB, rowsC;
int colsA, colsB, colsC;
int matrixSizeA, matrixSizeB, matrixSizeC;
int errors;
double start, stop;
printf("Testing %cgemm\n", *opts.elem_type);
matrixM = (opts.m) ? opts.m : BENCH_MATRIX_M;
matrixN = (opts.n) ? opts.n : BENCH_MATRIX_N;
matrixK = (opts.k) ? opts.k : BENCH_MATRIX_K;
rowsA = imax(1, matrixM);
colsA = imax(1, matrixK);
rowsB = imax(1, matrixK);
colsB = imax(1, matrixN);
rowsC = imax(1, matrixM);
colsC = imax(1, matrixN);
matrixSizeA = rowsA * colsA;
matrixSizeB = rowsB * colsB;
matrixSizeC = rowsC * colsC;
devPtrA = (T_ELEM **)malloc(opts.N * sizeof(*devPtrA));
devPtrB = (T_ELEM **)malloc(opts.N * sizeof(*devPtrB));
devPtrC = (T_ELEM **)malloc(opts.N * sizeof(*devPtrC));
for (int i = 0; i < opts.N; i++) {
cudaError_t err1 =
cudaMalloc((void **)&devPtrA[i], matrixSizeA * sizeof(devPtrA[0][0]));
cudaError_t err2 =
cudaMalloc((void **)&devPtrB[i], matrixSizeB * sizeof(devPtrB[0][0]));
cudaError_t err3 =
cudaMalloc((void **)&devPtrC[i], matrixSizeC * sizeof(devPtrC[0][0]));
if ((err1 != cudaSuccess) || (err2 != cudaSuccess) ||
(err3 != cudaSuccess)) {
CLEANUP();
fprintf(stderr, "!!!! GPU memory allocation error\n");
return CUBLASTEST_FAILED;
}
}
// For batched processing we need those arrays on the device
if (opts.test_method == tmBatched) {
cudaError_t err1 =
cudaMalloc((void **)&devPtrA_dev, opts.N * sizeof(*devPtrA));
cudaError_t err2 =
cudaMalloc((void **)&devPtrB_dev, opts.N * sizeof(*devPtrB));
cudaError_t err3 =
cudaMalloc((void **)&devPtrC_dev, opts.N * sizeof(*devPtrC));
if ((err1 != cudaSuccess) || (err2 != cudaSuccess) ||
(err3 != cudaSuccess)) {
CLEANUP();
fprintf(stderr, "!!!! GPU memory allocation error\n");
return CUBLASTEST_FAILED;
}
err1 = cudaMemcpy(devPtrA_dev, devPtrA, opts.N * sizeof(*devPtrA),
cudaMemcpyHostToDevice);
err2 = cudaMemcpy(devPtrB_dev, devPtrB, opts.N * sizeof(*devPtrB),
cudaMemcpyHostToDevice);
err3 = cudaMemcpy(devPtrC_dev, devPtrC, opts.N * sizeof(*devPtrC),
cudaMemcpyHostToDevice);
if ((err1 != cudaSuccess) || (err2 != cudaSuccess) ||
(err3 != cudaSuccess)) {
CLEANUP();
fprintf(stderr, "!!!! cannot copy pointer array to device\n");
return CUBLASTEST_FAILED;
}
}
A = (T_ELEM *)malloc(matrixSizeA * sizeof(A[0]));
B = (T_ELEM *)malloc(matrixSizeB * sizeof(B[0]));
C = (T_ELEM *)malloc(matrixSizeC * sizeof(C[0]));
if ((!A) || (!B) || (!C)) {
CLEANUP();
fprintf(stderr, "!!!! system memory allocation error\n");
return CUBLASTEST_FAILED;
}
streamArray = (cudaStream_t *)malloc(opts.N * sizeof(cudaStream_t *));
for (int i = 0; i < opts.N; i++) {
if (opts.test_method == tmStream) {
cudaError_t cudaErr = cudaStreamCreate(&streamArray[i]);
if (cudaErr != cudaSuccess) {
CLEANUP();
fprintf(stderr, "!!!! cannot create stream\n");
return CUBLASTEST_FAILED;
}
} else {
streamArray[i] = 0;
}
}
errors = 0;
int numTests = 1;
while (TESTGEN(gemm)(&opts, matrixM, matrixN, matrixK, numTests, &params) ==
0) {
printf("#### args: lda=%d ldb=%d ldc=%d\n", rowsA, rowsB, rowsC);
// fillup with Nan first (so lda padding is full on Nan)
memset(A, 0xFF, matrixSizeA * sizeof(A[0]));
fillupMatrixDebug(A, rowsA, params.m, params.k);
memset(B, 0xFF, matrixSizeB * sizeof(B[0]));
fillupMatrix(B, rowsB, params.k, params.n, 121);
if (!cuEqual(params.beta, cuGet<T_ELEM>(0))) {
fillupMatrix(C, rowsC, params.m, params.n);
} else {
/* fill with SNaNs to make sure ZGEMM doesn't access C */
memset(C, 0xFF, matrixSizeC * sizeof(C[0]));
}
double flopsCoef = 2.0;
for (int i = 0; i < opts.N; i++) {
status1 = cublasSetMatrix(rowsA, colsA, sizeof(A[0]), A, rowsA,
devPtrA[i], rowsA);
status2 = cublasSetMatrix(rowsB, colsB, sizeof(B[0]), B, rowsB,
devPtrB[i], rowsB);
status3 = cublasSetMatrix(rowsC, colsC, sizeof(C[0]), C, rowsC,
devPtrC[i], rowsC);
if ((status1 != CUBLAS_STATUS_SUCCESS) || (status2 != status1) ||
(status3 != status1)) {
CLEANUP();
fprintf(stderr, "!!!! GPU access error (write)\n");
return CUBLASTEST_FAILED;
}
}
start = second();
if (opts.test_method == tmBatched) {
cublasSetStream(handle, streamArray[0]);
status1 = cublasXgemmBatched(handle, params.transa, params.transb,
params.m, params.n, params.k, &params.alpha,
(const T_ELEM **)devPtrA_dev, rowsA,
(const T_ELEM **)devPtrB_dev, rowsB,
&params.beta, devPtrC_dev, rowsC, opts.N);
if (status1 != CUBLAS_STATUS_SUCCESS) {
cudaError_t cudaStatus = cudaGetLastError();
CLEANUP();
fprintf(stderr,
"!!!! GPU program execution error : cublas Error=%d, cuda "
"Error=%d,(%s)\n",
status1, cudaStatus, cudaGetErrorString(cudaStatus));
return CUBLASTEST_FAILED;
}
} else {
for (int i = 0; i < opts.N; i++) {
cublasSetStream(handle, streamArray[i]);
status1 =
cublasXgemm(handle, params.transa, params.transb, params.m,
params.n, params.k, &params.alpha, devPtrA[i], rowsA,
devPtrB[i], rowsB, &params.beta, devPtrC[i], rowsC);
if (status1 != CUBLAS_STATUS_SUCCESS) {
cudaError_t cudaStatus = cudaGetLastError();
CLEANUP();
fprintf(stderr,
"!!!! GPU program execution error : cublas Error=%d, cuda "
"Error=%d,(%s)\n",
status1, cudaStatus, cudaGetErrorString(cudaStatus));
return CUBLASTEST_FAILED;
}
}
}
cudaError_t cudaStatus = cudaDeviceSynchronize();
if (cudaStatus != cudaSuccess) {
CLEANUP();
fprintf(stderr,
"!!!! GPU program execution error on cudaDeviceSynchronize : "
"cudaError=%d,(%s)\n",
cudaStatus, cudaGetErrorString(cudaStatus));
return CUBLASTEST_FAILED;
}
stop = second();
fprintf(stdout, "^^^^ elapsed = %10.8f sec GFLOPS=%g\n", (stop - start),
opts.N * (1e-9 * flopsCoef * params.m * params.n * params.k) /
(stop - start));
} // end while (TESTGEN..
CLEANUP();
fprintf(stdout, "@@@@ %cgemm test %s\n", *opts.elem_type,
errors ? "FAIL" : "OK");
return CUBLASTEST_PASSED;
}
int main(int argc, char *argv[]) {
struct gemmOpts opts;
int errors, nTimes, nTotalErrors = 0;
int status = CUBLASTEST_PASSED;
printf("%s Starting...\n\n", sSDKname);
int dev = findCudaDevice(argc, (const char **)argv);
if (dev == -1) {
return CUBLASTEST_FAILED;
}
errors = processArgs(argc, argv, &opts);
if (errors) {
fprintf(stdout,
"\n Usage: batchcublas [-mSIZE_M] [-nSIZE_N] [-kSIZE_N] "
"[-NSIZE_NUM_ITERATIONS] [-qatest] [-noprompt]\n");
return CUBLASTEST_FAILED;
}
cublasHandle_t handle;
if (cublasCreate(&handle) != CUBLAS_STATUS_SUCCESS) {
fprintf(stdout, "CUBLAS initialization failed!\n");
exit(EXIT_FAILURE);
}
// Run single kernels
fprintf(stdout, "\n ==== Running single kernels ==== \n\n");
nTimes = opts.N;
opts.N = 1;
*(opts.elem_type) = 's';
status = test_gemm_loop<float>(opts, (float)CUBLAS_SGEMM_MAX_ULP_ERR,
(double)CUBLAS_SGEMM_MAX_RELATIVE_ERR, handle);
// Run Double version
*(opts.elem_type) = 'd';
if (getDeviceVersion() < DEV_VER_DBL_SUPPORT) {
fprintf(stdout, "@@@@ dgemm test WAIVED due to lack of DP support\n");
exit(EXIT_WAIVED);
}
status =
test_gemm_loop<double>(opts, (float)CUBLAS_DGEMM_MAX_ULP_ERR,
(double)CUBLAS_DGEMM_MAX_RELATIVE_ERR, handle);
nTotalErrors += (status == CUBLASTEST_PASSED ? 0 : 1);
opts.N = nTimes;
// Run with and without streams and then batched. The batched functions are a
// feature new feature in 4.1
#if CUDART_VERSION >= 4010
for (int ii = 0; ii < 3; ii++) {
#else
for (int ii = 0; ii < 2; ii++) {
#endif
switch (ii) {
case 0:
opts.test_method = tmRegular;
fprintf(stdout, "\n ==== Running N=%d without streams ==== \n\n",
opts.N);
break;
case 1:
opts.test_method = tmStream;
fprintf(stdout, "\n ==== Running N=%d with streams ==== \n\n", opts.N);
break;
case 2:
opts.test_method = tmBatched;
fprintf(stdout, "\n ==== Running N=%d batched ==== \n\n", opts.N);
break;
}
// Run single version
*(opts.elem_type) = 's';
status =
test_gemm_loop<float>(opts, (float)CUBLAS_SGEMM_MAX_ULP_ERR,
(double)CUBLAS_SGEMM_MAX_RELATIVE_ERR, handle);
nTotalErrors += (status == CUBLASTEST_PASSED ? 0 : 1);
// Run Double version
*(opts.elem_type) = 'd';
// Test doesn't meet minSpec, will will wave the DP test
if (getDeviceVersion() < DEV_VER_DBL_SUPPORT) {
fprintf(stdout, "@@@@ dgemm test WAIVED due to lack of DP support\n");
exit(EXIT_WAIVED);
} else {
status =
test_gemm_loop<double>(opts, (float)CUBLAS_DGEMM_MAX_ULP_ERR,
(double)CUBLAS_DGEMM_MAX_RELATIVE_ERR, handle);
nTotalErrors += (status == CUBLASTEST_PASSED ? 0 : 1);
}
}
cublasDestroy(handle);
printf("\nTest Summary\n");
printf("%d error(s)\n", nTotalErrors);
exit(nTotalErrors == 0 ? EXIT_SUCCESS : EXIT_FAILURE);
}
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