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|
/******************************************************************************
* Copyright (c) Intel Corporation - All rights reserved. *
* This file is part of the LIBXSMM library. *
* *
* For information on the license, see the LICENSE file. *
* Further information: https://github.com/hfp/libxsmm/ *
* SPDX-License-Identifier: BSD-3-Clause *
******************************************************************************/
/* Alexander Heinecke (Intel Corp.)
******************************************************************************/
#include <libxsmm.h>
#include <stdlib.h>
#include <string.h>
#include <stdio.h>
#include <math.h>
#if defined(_OPENMP)
# include <omp.h>
#endif
/* include c-based dnn library */
#include "../common/dnn_common.h"
#define CHKERR_LIBXSMM_DNN(A) { const int chkerr_libxsmm_dnn_ = A; if (LIBXSMM_DNN_SUCCESS != chkerr_libxsmm_dnn_) { \
fprintf(stderr, "%s\n", libxsmm_dnn_get_error(chkerr_libxsmm_dnn_)); global_status = chkerr_libxsmm_dnn_; } \
}
int main(int argc, char* argv[])
{
float *naive_input, *naive_output, *naive_input_add, *naive_delinput_add, *naive_delinput, *naive_deloutput;
float *naive_input_pad, *naive_output_pad, *naive_input_add_pad, *naive_delinput_add_pad, *naive_delinput_pad, *naive_deloutput_pad;
libxsmm_bfloat16 *naive_input_pad_bf16, *naive_output_pad_bf16, *naive_input_add_pad_bf16, *naive_delinput_add_pad_bf16, *naive_delinput_pad_bf16, *naive_deloutput_pad_bf16;
libxsmm_bfloat16 *naive_libxsmm_output, *naive_libxsmm_delinput, *naive_libxsmm_delinput_add;
float *naive_libxsmm_output_f32, *naive_libxsmm_delinput_f32, *naive_libxsmm_delinput_add_f32;
float *naive_beta, *naive_gamma, *naive_delbeta, *naive_delgamma, *naive_expectval, *naive_rcpstddev, *naive_variance;
libxsmm_bfloat16 *input_libxsmm, *output_libxsmm, *input_add_libxsmm, *delinput_libxsmm, *deloutput_libxsmm, *delinput_add_libxsmm;
float *beta_libxsmm, *gamma_libxsmm, *delbeta_libxsmm, *delgamma_libxsmm, *expectval_libxsmm, *rcpstddev_libxsmm, *variance_libxsmm;
unsigned char* relumask_libxsmm;
int ifhp, ifwp, ofhp, ofwp, ofh, ofw;
int stride_h, stride_w;
naive_fusedbatchnorm_t naive_param;
void* scratch;
size_t scratch_size = 0;
/* some parameters we can overwrite via cli,
default is some inner layer of overfeat */
int iters = 10; /* repetitions of benchmark */
int ifw = 14; /* input width, "W" */
int ifh = 20; /* input height, "H" */
int nImg = 32; /* mini-batch size, "N" */
int nFm = 256; /* number of input feature maps, "C" */
int stride = 1; /* stride when accessing inputs */
int pad_h_in = 0; /* padding mode */
int pad_w_in = 0; /* padding mode */
int pad_h_out = 0; /* padding mode */
int pad_w_out = 0; /* padding mode */
int norm_type = 0; /* 0: full batchnorm, 1: batch scaling only */
int fuse_type = 0; /* 0: nothing fused, 1: relu fused, 2: elementwise fused, 3: relu and elementwise fused */
char type = 'A'; /* 'A': ALL, 'F': FP, 'B': BP, 'U', WU */
char format = 'L';
const char *const env_check = getenv("CHECK");
const double check = LIBXSMM_ABS(0 == env_check ? 1 : atof(env_check));
#if defined(_OPENMP)
int nThreads = omp_get_max_threads(); /* number of threads */
#else
int nThreads = 1; /* number of threads */
#endif
unsigned long long l_start, l_end;
double l_total = 0.0;
double gb = 0.0;
double gib = 0.0;
int i;
int relu_no_match;
libxsmm_dnn_fusedbatchnorm_desc fusedbatchnorm_desc;
libxsmm_dnn_fusedbatchnorm* libxsmm_handle;
libxsmm_dnn_tensor* libxsmm_input;
libxsmm_dnn_tensor* libxsmm_delinput;
libxsmm_dnn_tensor* libxsmm_output;
libxsmm_dnn_tensor* libxsmm_deloutput;
libxsmm_dnn_tensor* libxsmm_input_add;
libxsmm_dnn_tensor* libxsmm_delinput_add;
libxsmm_dnn_tensor* libxsmm_beta;
libxsmm_dnn_tensor* libxsmm_gamma;
libxsmm_dnn_tensor* libxsmm_delbeta;
libxsmm_dnn_tensor* libxsmm_delgamma;
libxsmm_dnn_tensor* libxsmm_expectval;
libxsmm_dnn_tensor* libxsmm_rcpstddev;
libxsmm_dnn_tensor* libxsmm_variance;
libxsmm_dnn_tensor* libxsmm_relumask;
libxsmm_dnn_tensor_datalayout* libxsmm_layout;
libxsmm_dnn_err_t status;
libxsmm_dnn_err_t global_status = LIBXSMM_DNN_SUCCESS;
libxsmm_matdiff_info norms_fwd, norms_bwd, diff;
libxsmm_matdiff_clear(&norms_fwd);
libxsmm_matdiff_clear(&norms_bwd);
libxsmm_matdiff_clear(&diff);
if (argc > 1 && !strncmp(argv[1], "-h", 3)) {
printf("Usage: %s iters inpWidth inpHeight nImg nFm pad_w_in pad_h_in pad_w_out pad_h_out stride type format\n", argv[0]);
return 0;
}
libxsmm_rng_set_seed(1);
/* reading new values from cli */
i = 1;
if (argc > i) iters = atoi(argv[i++]);
if (argc > i) ifw = atoi(argv[i++]);
if (argc > i) ifh = atoi(argv[i++]);
if (argc > i) nImg = atoi(argv[i++]);
if (argc > i) nFm = atoi(argv[i++]);
if (argc > i) pad_w_in = atoi(argv[i++]);
if (argc > i) pad_h_in = atoi(argv[i++]);
if (argc > i) pad_w_out = atoi(argv[i++]);
if (argc > i) pad_h_out = atoi(argv[i++]);
if (argc > i) stride = atoi(argv[i++]);
if (argc > i) norm_type = atoi(argv[i++]);
if (argc > i) fuse_type = atoi(argv[i++]);
if (argc > i) type = *(argv[i++]);
if (type != 'A' && type != 'F' && type != 'B') {
printf("type needs to be 'A' (All), 'F' (FP only), 'B' (BP only)\n");
return -1;
}
if ((norm_type != 0) && (norm_type != 1)) {
printf("norm type needs to be 0 or 1\n");
return -1;
}
if ((fuse_type < 0) || (fuse_type > 5)) {
printf("fuse type needs to be 0, 1, 2, 3, 4 or 5\n");
return -1;
}
stride_w = stride;
stride_h = stride;
/* deriving some values for naive code */
ofh = ifh/stride_h;
ofw = ifw/stride_w;
ifhp = ifh + 2 * pad_h_in;
ifwp = ifw + 2 * pad_w_in;
ofhp = ofh + 2 * pad_h_out;
ofwp = ofw + 2 * pad_w_out;
/* set struct for naive convolution */
naive_param.N = nImg;
naive_param.C = nFm;
naive_param.H = ifh;
naive_param.W = ifw;
naive_param.stride_h = stride_h;
naive_param.stride_w = stride_w;
naive_param.norm_type = norm_type;
naive_param.fuse_type = fuse_type;
#if defined(__SSE3__)
_MM_SET_FLUSH_ZERO_MODE(_MM_FLUSH_ZERO_ON);
_MM_SET_DENORMALS_ZERO_MODE(_MM_DENORMALS_ZERO_ON);
_MM_SET_ROUNDING_MODE(_MM_ROUND_NEAREST);
#endif
/* print some summary */
printf("##########################################\n");
printf("# Setting Up (Common) #\n");
printf("##########################################\n");
printf("PARAMS: W:%d H:%d N:%d C:%d P:%d Q:%d STRIDE:%d\n", ifw, ifh, nImg, nFm, ofh, ofw, stride);
printf("PARAMS: ITERS:%d", iters); if (LIBXSMM_FEQ(0, check)) printf(" Threads:%d\n", nThreads); else printf("\n");
printf(" InImg %dx%d Padded (%dx%d)\n", ifh, ifw, ifhp, ifwp);
printf("OutImg %dx%d Padded (%dx%d)\n", ofh, ofw, ofhp, ofwp);
printf("SIZE Input (MB): %10.2f MiB\n", (double)(nImg*nFm*ifhp*ifwp*sizeof(libxsmm_bfloat16))/(1024.0*1024.0) );
printf("SIZE Output (MB): %10.2f MiB\n", (double)(nImg*nFm*ofhp*ofwp*sizeof(libxsmm_bfloat16))/(1024.0*1024.0) );
printf("SIZE Input (1): %10.2f MiB\n", (double)(1*nFm*ifhp*ifwp* sizeof(libxsmm_bfloat16))/(1024.0*1024.0) );
printf("SIZE Output (1): %10.2f MiB\n", (double)(1*nFm*ofhp*ofwp* sizeof(libxsmm_bfloat16))/(1024.0*1024.0) );
#if defined(USE_OVERWRITE)
printf("Using Overwrite Option\n");
#endif
/* allocate data */
naive_input = (float*)libxsmm_aligned_malloc( nImg*nFm*ifh *ifw *sizeof(float), 2097152);
naive_input_add = (float*)libxsmm_aligned_malloc( nImg*nFm*ifh *ifw *sizeof(float), 2097152);
naive_delinput = (float*)libxsmm_aligned_malloc( nImg*nFm*ifh *ifw *sizeof(float), 2097152);
naive_delinput_add = (float*)libxsmm_aligned_malloc( nImg*nFm*ifh *ifw *sizeof(float), 2097152);
naive_output = (float*)libxsmm_aligned_malloc( nImg*nFm*ofh *ofw *sizeof(float), 2097152);
naive_deloutput = (float*)libxsmm_aligned_malloc( nImg*nFm*ofh *ofw *sizeof(float), 2097152);
naive_input_pad = (float*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(float), 2097152);
naive_input_add_pad = (float*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(float), 2097152);
naive_delinput_pad = (float*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(float), 2097152);
naive_delinput_add_pad = (float*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(float), 2097152);
naive_output_pad = (float*)libxsmm_aligned_malloc( nImg*nFm*ofhp*ofwp*sizeof(float), 2097152);
naive_deloutput_pad = (float*)libxsmm_aligned_malloc( nImg*nFm*ofhp*ofwp*sizeof(float), 2097152);
naive_input_pad_bf16 = (libxsmm_bfloat16*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(libxsmm_bfloat16), 2097152);
naive_input_add_pad_bf16 = (libxsmm_bfloat16*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(libxsmm_bfloat16), 2097152);
naive_delinput_pad_bf16 = (libxsmm_bfloat16*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(libxsmm_bfloat16), 2097152);
naive_delinput_add_pad_bf16 = (libxsmm_bfloat16*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(libxsmm_bfloat16), 2097152);
naive_output_pad_bf16 = (libxsmm_bfloat16*)libxsmm_aligned_malloc( nImg*nFm*ofhp*ofwp*sizeof(libxsmm_bfloat16), 2097152);
naive_deloutput_pad_bf16 = (libxsmm_bfloat16*)libxsmm_aligned_malloc( nImg*nFm*ofhp*ofwp*sizeof(libxsmm_bfloat16), 2097152);
naive_libxsmm_output = (libxsmm_bfloat16*)libxsmm_aligned_malloc( nImg*nFm*ofhp*ofwp*sizeof(libxsmm_bfloat16), 2097152);
naive_libxsmm_delinput = (libxsmm_bfloat16*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(libxsmm_bfloat16), 2097152);
naive_libxsmm_delinput_add = (libxsmm_bfloat16*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(libxsmm_bfloat16), 2097152);
naive_libxsmm_output_f32 = (float*)libxsmm_aligned_malloc( nImg*nFm*ofhp*ofwp*sizeof(float), 2097152);
naive_libxsmm_delinput_f32 = (float*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(float), 2097152);
naive_libxsmm_delinput_add_f32 = (float*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(float), 2097152);
input_libxsmm = (libxsmm_bfloat16*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(libxsmm_bfloat16), 2097152);
delinput_libxsmm = (libxsmm_bfloat16*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(libxsmm_bfloat16), 2097152);
input_add_libxsmm = (libxsmm_bfloat16*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(libxsmm_bfloat16), 2097152);
delinput_add_libxsmm = (libxsmm_bfloat16*)libxsmm_aligned_malloc( nImg*nFm*ifhp*ifwp*sizeof(libxsmm_bfloat16), 2097152);
output_libxsmm = (libxsmm_bfloat16*)libxsmm_aligned_malloc( nImg*nFm*ofhp*ofwp*sizeof(libxsmm_bfloat16), 2097152);
deloutput_libxsmm = (libxsmm_bfloat16*)libxsmm_aligned_malloc( nImg*nFm*ofhp*ofwp*sizeof(libxsmm_bfloat16), 2097152);
naive_beta = (float*)libxsmm_aligned_malloc( nFm* sizeof(float), 2097152);
naive_gamma = (float*)libxsmm_aligned_malloc( nFm* sizeof(float), 2097152);
naive_delbeta = (float*)libxsmm_aligned_malloc( nFm* sizeof(float), 2097152);
naive_delgamma = (float*)libxsmm_aligned_malloc( nFm* sizeof(float), 2097152);
naive_expectval = (float*)libxsmm_aligned_malloc( nFm* sizeof(float), 2097152);
naive_rcpstddev = (float*)libxsmm_aligned_malloc( nFm* sizeof(float), 2097152);
naive_variance = (float*)libxsmm_aligned_malloc( nFm* sizeof(float), 2097152);
beta_libxsmm = (float*)libxsmm_aligned_malloc( nFm* sizeof(float), 2097152);
gamma_libxsmm = (float*)libxsmm_aligned_malloc( nFm* sizeof(float), 2097152);
delbeta_libxsmm = (float*)libxsmm_aligned_malloc( nFm* sizeof(float), 2097152);
delgamma_libxsmm = (float*)libxsmm_aligned_malloc( nFm* sizeof(float), 2097152);
expectval_libxsmm = (float*)libxsmm_aligned_malloc( nFm* sizeof(float), 2097152);
rcpstddev_libxsmm = (float*)libxsmm_aligned_malloc( nFm* sizeof(float), 2097152);
variance_libxsmm = (float*)libxsmm_aligned_malloc( nFm* sizeof(float), 2097152);
relumask_libxsmm = (unsigned char*)libxsmm_aligned_malloc( nImg*nFm*ofhp*ofwp*sizeof(unsigned char), 2097152);
/* initialize data */
init_buf( naive_input, nImg*nFm*ifh*ifw, 0, 0 );
copy_internal_nchw( naive_input_pad , naive_input, nImg, nFm, ifh, ifw, pad_h_in, pad_w_in );
init_buf( naive_delinput, nImg*nFm*ifh*ifw, 0, 0 );
copy_internal_nchw( naive_delinput_pad, naive_delinput, nImg, nFm, ifh, ifw, pad_h_in, pad_w_in );
init_buf( naive_input_add, nImg*nFm*ifh*ifw, 0, 0 );
copy_internal_nchw( naive_input_add_pad, naive_input_add, nImg, nFm, ifh, ifw, pad_h_in, pad_w_in );
init_buf( naive_delinput_add, nImg*nFm*ifh*ifw, 0, 0 );
copy_internal_nchw( naive_delinput_add_pad, naive_delinput_add, nImg, nFm, ifh, ifw, pad_h_in, pad_w_in );
init_buf( naive_output, nImg*nFm*ofh*ofw, 0, 0 );
copy_internal_nchw( naive_output_pad, naive_output, nImg, nFm, ofh, ofw, pad_h_out, pad_w_out );
init_buf( naive_deloutput, nImg*nFm*ofh*ofw, 0, 0 );
copy_internal_nchw( naive_deloutput_pad, naive_deloutput, nImg, nFm, ofh, ofw, pad_h_out, pad_w_out );
set_zeropad_nchw(naive_input_pad, nImg, nFm, ifhp, ifwp, pad_h_in, pad_w_in);
set_zeropad_nchw(naive_delinput_pad, nImg, nFm, ifhp, ifwp, pad_h_in, pad_w_in);
set_zeropad_nchw(naive_input_add_pad, nImg, nFm, ifhp, ifwp, pad_h_in, pad_w_in);
set_zeropad_nchw(naive_delinput_add_pad, nImg, nFm, ifhp, ifwp, pad_h_in, pad_w_in);
set_zeropad_nchw(naive_output_pad, nImg, nFm, ofhp, ofwp, pad_h_out, pad_w_out);
set_zeropad_nchw(naive_deloutput_pad, nImg, nFm, ofhp, ofwp, pad_h_out, pad_w_out);
libxsmm_rne_convert_fp32_bf16( naive_input_pad, naive_input_pad_bf16, nImg*nFm*ifhp*ifwp );
libxsmm_rne_convert_fp32_bf16( naive_delinput_pad, naive_delinput_pad_bf16, nImg*nFm*ifhp*ifwp );
libxsmm_rne_convert_fp32_bf16( naive_input_add_pad, naive_input_add_pad_bf16, nImg*nFm*ifhp*ifwp );
libxsmm_rne_convert_fp32_bf16( naive_delinput_add_pad, naive_delinput_add_pad_bf16, nImg*nFm*ifhp*ifwp );
libxsmm_rne_convert_fp32_bf16( naive_output_pad, naive_output_pad_bf16, nImg*nFm*ofhp*ofwp );
libxsmm_rne_convert_fp32_bf16( naive_deloutput_pad, naive_deloutput_pad_bf16, nImg*nFm*ofhp*ofwp );
init_buf(naive_beta, nFm, 0, 0);
init_buf(naive_gamma, nFm, 0, 0);
init_buf(naive_delbeta, nFm, 0, 0);
init_buf(naive_delgamma, nFm, 0, 0);
init_buf(naive_expectval, nFm, 0, 0);
init_buf(naive_rcpstddev, nFm, 0, 0);
init_buf(naive_variance, nFm, 0, 0);
copy_buf(naive_beta, beta_libxsmm, nFm);
copy_buf(naive_gamma, gamma_libxsmm, nFm);
copy_buf(naive_delbeta, delbeta_libxsmm, nFm);
copy_buf(naive_delgamma, delgamma_libxsmm, nFm);
copy_buf(naive_expectval, expectval_libxsmm, nFm);
copy_buf(naive_rcpstddev, rcpstddev_libxsmm, nFm);
copy_buf(naive_variance, variance_libxsmm, nFm);
if (LIBXSMM_NEQ(0, check)) {
printf("##########################################\n");
printf("# Computing Reference ... #\n");
printf("##########################################\n");
if (type == 'A' || type == 'F') {
naive_fusedbatchnorm_fp(&naive_param, naive_input, naive_output, naive_input_add, naive_beta, naive_gamma, naive_expectval, naive_rcpstddev, naive_variance);
}
if (type == 'A' || type == 'B') {
naive_fusedbatchnorm_bp(&naive_param, naive_input, naive_delinput, naive_output, naive_deloutput, naive_delinput_add,
naive_beta, naive_delbeta, naive_gamma, naive_delgamma, naive_expectval, naive_rcpstddev);
}
printf("##########################################\n");
printf("# Computing Reference ... done #\n");
printf("##########################################\n");
}
if (format == 'A' || format == 'L') {
printf("\n");
printf("##########################################\n");
printf("# Setting Up (custom-Storage) #\n");
printf("##########################################\n");
/* setup LIBXSMM handle */
fusedbatchnorm_desc.partN = nImg;
fusedbatchnorm_desc.fullN = nImg;
fusedbatchnorm_desc.C = nFm;
fusedbatchnorm_desc.H = ifh;
fusedbatchnorm_desc.W = ifw;
fusedbatchnorm_desc.u = stride_h;
fusedbatchnorm_desc.v = stride_w;
fusedbatchnorm_desc.pad_h_in = pad_h_in;
fusedbatchnorm_desc.pad_w_in = pad_w_in;
fusedbatchnorm_desc.pad_h_out = pad_h_out;
fusedbatchnorm_desc.pad_w_out = pad_w_out;
fusedbatchnorm_desc.threads = nThreads;
fusedbatchnorm_desc.datatype_in = LIBXSMM_DNN_DATATYPE_BF16;
fusedbatchnorm_desc.datatype_out = LIBXSMM_DNN_DATATYPE_BF16;
fusedbatchnorm_desc.datatype_stats = LIBXSMM_DNN_DATATYPE_F32;
fusedbatchnorm_desc.buffer_format = LIBXSMM_DNN_TENSOR_FORMAT_LIBXSMM;
fusedbatchnorm_desc.fuse_order = LIBXSMM_DNN_FUSEDBN_ORDER_BN_ELTWISE_RELU;
if ( norm_type == 0 ) {
if ( fuse_type == 0 ) {
fusedbatchnorm_desc.fuse_ops = LIBXSMM_DNN_FUSEDBN_OPS_BN;
} else if ( fuse_type == 1 ) {
fusedbatchnorm_desc.fuse_ops = LIBXSMM_DNN_FUSEDBN_OPS_BN_RELU;
} else if ( fuse_type == 2 ) {
fusedbatchnorm_desc.fuse_ops = LIBXSMM_DNN_FUSEDBN_OPS_BN_ELTWISE;
} else if ( fuse_type == 3 ) {
fusedbatchnorm_desc.fuse_ops = LIBXSMM_DNN_FUSEDBN_OPS_BN_ELTWISE_RELU;
} else if ( fuse_type == 4 ) {
fusedbatchnorm_desc.fuse_ops = LIBXSMM_DNN_FUSEDBN_OPS_BN_RELU_WITH_MASK;
} else if ( fuse_type == 5 ) {
fusedbatchnorm_desc.fuse_ops = LIBXSMM_DNN_FUSEDBN_OPS_BN_ELTWISE_RELU_WITH_MASK;
} else {
/* shouldn't happen */
return -1;
}
} else {
if ( fuse_type == 0 ) {
fusedbatchnorm_desc.fuse_ops = LIBXSMM_DNN_FUSEDBN_OPS_BNSCALE;
} else if ( fuse_type == 1 ) {
fusedbatchnorm_desc.fuse_ops = LIBXSMM_DNN_FUSEDBN_OPS_BNSCALE_RELU;
} else if ( fuse_type == 2 ) {
fusedbatchnorm_desc.fuse_ops = LIBXSMM_DNN_FUSEDBN_OPS_BNSCALE_ELTWISE;
} else if ( fuse_type == 3 ) {
fusedbatchnorm_desc.fuse_ops = LIBXSMM_DNN_FUSEDBN_OPS_BNSCALE_ELTWISE_RELU;
} else if ( fuse_type == 4 ) {
fusedbatchnorm_desc.fuse_ops = LIBXSMM_DNN_FUSEDBN_OPS_BNSCALE_RELU_WITH_MASK;
} else if ( fuse_type == 5 ) {
fusedbatchnorm_desc.fuse_ops = LIBXSMM_DNN_FUSEDBN_OPS_BNSCALE_ELTWISE_RELU_WITH_MASK;
} else {
/* shouldn't happen */
return -1;
}
}
libxsmm_handle = libxsmm_dnn_create_fusedbatchnorm( fusedbatchnorm_desc, &status );
CHKERR_LIBXSMM_DNN( status );
/* setup LIBXSMM buffers */
libxsmm_layout = libxsmm_dnn_fusedbatchnorm_create_tensor_datalayout( libxsmm_handle, LIBXSMM_DNN_REGULAR_INPUT, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_input = libxsmm_dnn_link_tensor( libxsmm_layout, input_libxsmm, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_dnn_destroy_tensor_datalayout( libxsmm_layout );
libxsmm_layout = libxsmm_dnn_fusedbatchnorm_create_tensor_datalayout( libxsmm_handle, LIBXSMM_DNN_GRADIENT_INPUT, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_delinput = libxsmm_dnn_link_tensor( libxsmm_layout, delinput_libxsmm, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_dnn_destroy_tensor_datalayout( libxsmm_layout );
libxsmm_layout = libxsmm_dnn_fusedbatchnorm_create_tensor_datalayout( libxsmm_handle, LIBXSMM_DNN_REGULAR_INPUT_ADD, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_input_add = libxsmm_dnn_link_tensor( libxsmm_layout, input_add_libxsmm, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_dnn_destroy_tensor_datalayout( libxsmm_layout );
libxsmm_layout = libxsmm_dnn_fusedbatchnorm_create_tensor_datalayout( libxsmm_handle, LIBXSMM_DNN_GRADIENT_INPUT_ADD, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_delinput_add = libxsmm_dnn_link_tensor( libxsmm_layout, delinput_add_libxsmm, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_dnn_destroy_tensor_datalayout( libxsmm_layout );
libxsmm_layout = libxsmm_dnn_fusedbatchnorm_create_tensor_datalayout( libxsmm_handle, LIBXSMM_DNN_REGULAR_OUTPUT, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_output = libxsmm_dnn_link_tensor( libxsmm_layout, output_libxsmm, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_dnn_destroy_tensor_datalayout( libxsmm_layout );
libxsmm_layout = libxsmm_dnn_fusedbatchnorm_create_tensor_datalayout( libxsmm_handle, LIBXSMM_DNN_GRADIENT_OUTPUT, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_deloutput = libxsmm_dnn_link_tensor( libxsmm_layout, deloutput_libxsmm, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_dnn_destroy_tensor_datalayout( libxsmm_layout );
libxsmm_layout = libxsmm_dnn_fusedbatchnorm_create_tensor_datalayout( libxsmm_handle, LIBXSMM_DNN_REGULAR_CHANNEL_BETA, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_beta = libxsmm_dnn_link_tensor( libxsmm_layout, beta_libxsmm, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_dnn_destroy_tensor_datalayout( libxsmm_layout );
libxsmm_layout = libxsmm_dnn_fusedbatchnorm_create_tensor_datalayout( libxsmm_handle, LIBXSMM_DNN_GRADIENT_CHANNEL_BETA, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_delbeta = libxsmm_dnn_link_tensor( libxsmm_layout, delbeta_libxsmm, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_dnn_destroy_tensor_datalayout( libxsmm_layout );
libxsmm_layout = libxsmm_dnn_fusedbatchnorm_create_tensor_datalayout( libxsmm_handle, LIBXSMM_DNN_REGULAR_CHANNEL_GAMMA, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_gamma = libxsmm_dnn_link_tensor( libxsmm_layout, gamma_libxsmm, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_dnn_destroy_tensor_datalayout( libxsmm_layout );
libxsmm_layout = libxsmm_dnn_fusedbatchnorm_create_tensor_datalayout( libxsmm_handle, LIBXSMM_DNN_GRADIENT_CHANNEL_GAMMA, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_delgamma = libxsmm_dnn_link_tensor( libxsmm_layout, delgamma_libxsmm, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_dnn_destroy_tensor_datalayout( libxsmm_layout );
libxsmm_layout = libxsmm_dnn_fusedbatchnorm_create_tensor_datalayout( libxsmm_handle, LIBXSMM_DNN_CHANNEL_EXPECTVAL, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_expectval = libxsmm_dnn_link_tensor( libxsmm_layout, expectval_libxsmm, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_dnn_destroy_tensor_datalayout( libxsmm_layout );
libxsmm_layout = libxsmm_dnn_fusedbatchnorm_create_tensor_datalayout( libxsmm_handle, LIBXSMM_DNN_CHANNEL_RCPSTDDEV, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_rcpstddev = libxsmm_dnn_link_tensor( libxsmm_layout, rcpstddev_libxsmm, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_dnn_destroy_tensor_datalayout( libxsmm_layout );
libxsmm_layout = libxsmm_dnn_fusedbatchnorm_create_tensor_datalayout( libxsmm_handle, LIBXSMM_DNN_CHANNEL_VARIANCE, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_variance = libxsmm_dnn_link_tensor( libxsmm_layout, variance_libxsmm, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_dnn_destroy_tensor_datalayout( libxsmm_layout );
libxsmm_layout = libxsmm_dnn_fusedbatchnorm_create_tensor_datalayout( libxsmm_handle, LIBXSMM_DNN_RELU_MASK, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_relumask = libxsmm_dnn_link_tensor( libxsmm_layout, relumask_libxsmm, &status ); CHKERR_LIBXSMM_DNN( status );
libxsmm_dnn_destroy_tensor_datalayout( libxsmm_layout );
/* copy in data to LIBXSMM format */
/* we can also use the layout functions and set the data on our
own external to the library */
CHKERR_LIBXSMM_DNN( libxsmm_dnn_copyin_tensor( libxsmm_input, (void*)naive_input_pad_bf16, LIBXSMM_DNN_TENSOR_FORMAT_NCHW ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_copyin_tensor( libxsmm_output, (void*)naive_output_pad_bf16, LIBXSMM_DNN_TENSOR_FORMAT_NCHW ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_copyin_tensor( libxsmm_input_add, (void*)naive_input_add_pad_bf16, LIBXSMM_DNN_TENSOR_FORMAT_NCHW ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_copyin_tensor( libxsmm_delinput, (void*)naive_delinput_pad_bf16, LIBXSMM_DNN_TENSOR_FORMAT_NCHW ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_copyin_tensor( libxsmm_deloutput, (void*)naive_deloutput_pad_bf16, LIBXSMM_DNN_TENSOR_FORMAT_NCHW ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_copyin_tensor( libxsmm_delinput_add, (void*)naive_delinput_add_pad_bf16, LIBXSMM_DNN_TENSOR_FORMAT_NCHW ) );
/* bind buffers and filter to handle */
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_bind_tensor( libxsmm_handle, libxsmm_input, LIBXSMM_DNN_REGULAR_INPUT ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_bind_tensor( libxsmm_handle, libxsmm_delinput, LIBXSMM_DNN_GRADIENT_INPUT ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_bind_tensor( libxsmm_handle, libxsmm_output, LIBXSMM_DNN_REGULAR_OUTPUT ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_bind_tensor( libxsmm_handle, libxsmm_deloutput, LIBXSMM_DNN_GRADIENT_OUTPUT ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_bind_tensor( libxsmm_handle, libxsmm_input_add, LIBXSMM_DNN_REGULAR_INPUT_ADD ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_bind_tensor( libxsmm_handle, libxsmm_delinput_add, LIBXSMM_DNN_GRADIENT_INPUT_ADD ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_bind_tensor( libxsmm_handle, libxsmm_beta, LIBXSMM_DNN_REGULAR_CHANNEL_BETA ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_bind_tensor( libxsmm_handle, libxsmm_gamma, LIBXSMM_DNN_REGULAR_CHANNEL_GAMMA ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_bind_tensor( libxsmm_handle, libxsmm_delbeta, LIBXSMM_DNN_GRADIENT_CHANNEL_BETA ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_bind_tensor( libxsmm_handle, libxsmm_delgamma, LIBXSMM_DNN_GRADIENT_CHANNEL_GAMMA ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_bind_tensor( libxsmm_handle, libxsmm_expectval, LIBXSMM_DNN_CHANNEL_EXPECTVAL ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_bind_tensor( libxsmm_handle, libxsmm_rcpstddev, LIBXSMM_DNN_CHANNEL_RCPSTDDEV ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_bind_tensor( libxsmm_handle, libxsmm_variance, LIBXSMM_DNN_CHANNEL_VARIANCE ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_bind_tensor( libxsmm_handle, libxsmm_relumask, LIBXSMM_DNN_RELU_MASK ) );
/* let's allocate and bind scratch */
scratch_size = libxsmm_dnn_fusedbatchnorm_get_scratch_size( libxsmm_handle, &status );
CHKERR_LIBXSMM_DNN( status );
scratch = libxsmm_aligned_scratch( scratch_size, 2097152 );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_bind_scratch( libxsmm_handle, scratch ) );
/* set scratch to bogus to make sure that libxsmm takes care of zeroing internally */
init_buf( (float*)scratch, scratch_size/4, 0, 0 );
if ((type == 'A' || type == 'F') && LIBXSMM_NEQ(0, check)) {
printf("##########################################\n");
printf("# Correctness - FWD (custom-Storage) #\n");
printf("##########################################\n");
/* run LIBXSMM convolutions */
#if defined(_OPENMP)
# pragma omp parallel
#endif
{
#if defined(_OPENMP)
const int tid = omp_get_thread_num();
#else
const int tid = 0;
#endif
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_execute_st( libxsmm_handle, LIBXSMM_DNN_COMPUTE_KIND_FWD, 0, tid ) );
}
/* copy out data */
CHKERR_LIBXSMM_DNN( libxsmm_dnn_copyout_tensor( libxsmm_output, (void*)naive_libxsmm_output, LIBXSMM_DNN_TENSOR_FORMAT_NCHW ) );
libxsmm_convert_bf16_f32( naive_libxsmm_output, naive_libxsmm_output_f32, nImg*nFm*ofhp*ofwp );
copy_internal_nchw( naive_output_pad, naive_output, nImg, nFm, ofh, ofw, pad_h_out, pad_w_out);
/* compare */
printf("rcpstddev:\n");
libxsmm_matdiff(&norms_fwd, LIBXSMM_DATATYPE_F32, nFm, 1, naive_rcpstddev, rcpstddev_libxsmm, 0, 0);
printf("L1 reference : %.25g\n", norms_fwd.l1_ref);
printf("L1 test : %.25g\n", norms_fwd.l1_tst);
printf("L2 abs.error : %.24f\n", norms_fwd.l2_abs);
printf("L2 rel.error : %.24f\n", norms_fwd.l2_rel);
printf("Linf abs.error: %.24f\n", norms_fwd.linf_abs);
printf("Linf rel.error: %.24f\n", norms_fwd.linf_rel);
printf("Check-norm : %.24f\n", norms_fwd.normf_rel);
libxsmm_matdiff_reduce(&diff, &norms_fwd);
printf("variance:\n");
libxsmm_matdiff(&norms_fwd, LIBXSMM_DATATYPE_F32, nFm, 1, naive_variance, variance_libxsmm, 0, 0);
printf("L1 reference : %.25g\n", norms_fwd.l1_ref);
printf("L1 test : %.25g\n", norms_fwd.l1_tst);
printf("L2 abs.error : %.24f\n", norms_fwd.l2_abs);
printf("L2 rel.error : %.24f\n", norms_fwd.l2_rel);
printf("Linf abs.error: %.24f\n", norms_fwd.linf_abs);
printf("Linf rel.error: %.24f\n", norms_fwd.linf_rel);
printf("Check-norm : %.24f\n", norms_fwd.normf_rel);
libxsmm_matdiff_reduce(&diff, &norms_fwd);
printf("expected value:\n");
libxsmm_matdiff(&norms_fwd, LIBXSMM_DATATYPE_F32, nFm, 1, naive_expectval, expectval_libxsmm, 0, 0);
printf("L1 reference : %.25g\n", norms_fwd.l1_ref);
printf("L1 test : %.25g\n", norms_fwd.l1_tst);
printf("L2 abs.error : %.24f\n", norms_fwd.l2_abs);
printf("L2 rel.error : %.24f\n", norms_fwd.l2_rel);
printf("Linf abs.error: %.24f\n", norms_fwd.linf_abs);
printf("Linf rel.error: %.24f\n", norms_fwd.linf_rel);
printf("Check-norm : %.24f\n", norms_fwd.normf_rel);
libxsmm_matdiff_reduce(&diff, &norms_fwd);
printf("output:\n");
libxsmm_matdiff(&norms_fwd, LIBXSMM_DATATYPE_F32, nImg*nFm*ofhp*ofwp, 1, naive_output_pad, naive_libxsmm_output_f32, 0, 0);
printf("L1 reference : %.25g\n", norms_fwd.l1_ref);
printf("L1 test : %.25g\n", norms_fwd.l1_tst);
printf("L2 abs.error : %.24f\n", norms_fwd.l2_abs);
printf("L2 rel.error : %.24f\n", norms_fwd.l2_rel);
printf("Linf abs.error: %.24f\n", norms_fwd.linf_abs);
printf("Linf rel.error: %.24f\n", norms_fwd.linf_rel);
printf("Check-norm : %.24f\n", norms_fwd.normf_rel);
libxsmm_matdiff_reduce(&diff, &norms_fwd);
/* let's check ReLU positions */
relu_no_match = 0;
for ( i = 0; i < nImg*nFm*ofhp*ofwp; ++i ) {
if ( (naive_output_pad[i] == 0.0f && naive_libxsmm_output_f32[i] != 0.0f) ||
(naive_output_pad[i] != 0.0f && naive_libxsmm_output_f32[i] == 0.0f) ) {
relu_no_match++;
}
}
printf("ReLU mismatch count: %i\n", relu_no_match );
}
if ( (type == 'A' || type == 'B') && LIBXSMM_NEQ(0, check) ) {
printf("##########################################\n");
printf("# Correctness - BWD (custom-Storage) #\n");
printf("##########################################\n");
/* run LIBXSMM convolutions */
#if defined(_OPENMP)
# pragma omp parallel
#endif
{
#if defined(_OPENMP)
const int tid = omp_get_thread_num();
#else
const int tid = 0;
#endif
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_execute_st( libxsmm_handle, LIBXSMM_DNN_COMPUTE_KIND_BWD, 0, tid ) );
}
/* copy out data */
CHKERR_LIBXSMM_DNN( libxsmm_dnn_copyout_tensor( libxsmm_delinput, (void*)naive_libxsmm_delinput, LIBXSMM_DNN_TENSOR_FORMAT_NCHW ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_copyout_tensor( libxsmm_delinput_add, (void*)naive_libxsmm_delinput_add, LIBXSMM_DNN_TENSOR_FORMAT_NCHW ) );
libxsmm_convert_bf16_f32( naive_libxsmm_delinput, naive_libxsmm_delinput_f32, nImg*nFm*ifhp*ifwp );
libxsmm_convert_bf16_f32( naive_libxsmm_delinput_add, naive_libxsmm_delinput_add_f32, nImg*nFm*ifhp*ifwp );
copy_internal_nchw( naive_delinput_pad, naive_delinput, nImg, nFm, ifh, ifw, pad_h_in, pad_w_in);
copy_internal_nchw( naive_delinput_add_pad, naive_delinput_add, nImg, nFm, ifh, ifw, pad_h_in, pad_w_in);
/* compare */
printf("delinput_add:\n");
libxsmm_matdiff(&norms_bwd, LIBXSMM_DATATYPE_F32, nImg*nFm*ifhp*ifwp, 1, naive_delinput_add_pad, naive_libxsmm_delinput_add_f32, 0, 0);
printf("L1 reference : %.25g\n", norms_bwd.l1_ref);
printf("L1 test : %.25g\n", norms_bwd.l1_tst);
printf("L2 abs.error : %.24f\n", norms_bwd.l2_abs);
printf("L2 rel.error : %.24f\n", norms_bwd.l2_rel);
printf("Linf abs.error: %.24f\n", norms_bwd.linf_abs);
printf("Linf rel.error: %.24f\n", norms_bwd.linf_rel);
printf("Check-norm : %.24f\n", norms_bwd.normf_rel);
libxsmm_matdiff_reduce(&diff, &norms_bwd);
printf("delbeta:\n");
libxsmm_matdiff(&norms_bwd, LIBXSMM_DATATYPE_F32, nFm, 1, naive_delbeta, delbeta_libxsmm, 0, 0);
printf("L1 reference : %.25g\n", norms_bwd.l1_ref);
printf("L1 test : %.25g\n", norms_bwd.l1_tst);
printf("L2 abs.error : %.24f\n", norms_bwd.l2_abs);
printf("L2 rel.error : %.24f\n", norms_bwd.l2_rel);
printf("Linf abs.error: %.24f\n", norms_bwd.linf_abs);
printf("Linf rel.error: %.24f\n", norms_bwd.linf_rel);
printf("Check-norm : %.24f\n", norms_bwd.normf_rel);
libxsmm_matdiff_reduce(&diff, &norms_bwd);
printf("delgamma:\n");
libxsmm_matdiff(&norms_bwd, LIBXSMM_DATATYPE_F32, nFm, 1, naive_delgamma, delgamma_libxsmm, 0, 0);
printf("L1 reference : %.25g\n", norms_bwd.l1_ref);
printf("L1 test : %.25g\n", norms_bwd.l1_tst);
printf("L2 abs.error : %.24f\n", norms_bwd.l2_abs);
printf("L2 rel.error : %.24f\n", norms_bwd.l2_rel);
printf("Linf abs.error: %.24f\n", norms_bwd.linf_abs);
printf("Linf rel.error: %.24f\n", norms_bwd.linf_rel);
printf("Check-norm : %.24f\n", norms_bwd.normf_rel);
libxsmm_matdiff_reduce(&diff, &norms_bwd);
printf("delinput:\n");
libxsmm_matdiff(&norms_bwd, LIBXSMM_DATATYPE_F32, nImg*nFm*ifhp*ifwp, 1, naive_delinput_pad, naive_libxsmm_delinput_f32, 0, 0);
printf("L1 reference : %.25g\n", norms_bwd.l1_ref);
printf("L1 test : %.25g\n", norms_bwd.l1_tst);
printf("L2 abs.error : %.24f\n", norms_bwd.l2_abs);
printf("L2 rel.error : %.24f\n", norms_bwd.l2_rel);
printf("Linf abs.error: %.24f\n", norms_bwd.linf_abs);
printf("Linf rel.error: %.24f\n", norms_bwd.linf_rel);
printf("Check-norm : %.24f\n", norms_bwd.normf_rel);
libxsmm_matdiff_reduce(&diff, &norms_bwd);
}
if (type == 'A' || type == 'F') {
printf("##########################################\n");
printf("# Performance - FWD (custom-Storage) #\n");
printf("##########################################\n");
/* run LIBXSMM convolution for performance */
l_start = libxsmm_timer_tick();
#if defined(_OPENMP)
# pragma omp parallel private(i)
#endif
{
#if defined(_OPENMP)
const int tid = omp_get_thread_num();
#else
const int tid = 0;
#endif
for (i = 0; i < iters; ++i) {
libxsmm_dnn_fusedbatchnorm_execute_st( libxsmm_handle, LIBXSMM_DNN_COMPUTE_KIND_FWD, 0, tid );
}
}
l_end = libxsmm_timer_tick();
l_total = libxsmm_timer_duration(l_start, l_end);
gb = ((double)nImg*(double)nFm*(((double)ifh*(double)ifw) + ((double)ofh*(double)ofw))*(double)sizeof(float)*(double)iters) / (1000*1000*1000);
gib = ((double)nImg*(double)nFm*(((double)ifh*(double)ifw) + ((double)ofh*(double)ofw))*(double)sizeof(float)*(double)iters) / (1024*1024*1024);
printf("GB = %.5g\n", gb/(double)iters);
printf("GiB = %.5g\n", gib/(double)iters);
printf("fp time = %.5g\n", ((double)(l_total/iters)));
printf("GB/s = %.5g\n", gb/l_total);
printf("GiB/s = %.5g\n", gib/l_total);
printf("PERFDUMP,FP,%s,%i,%i,%i,%i,%i,%i,%i,%i,%i,%i,%.5g,%.5g,%.5g,%f,%f,%f,%f,%f,%f,%f\n", LIBXSMM_VERSION, nThreads, nImg, nFm,
ifw, ifh, stride, pad_w_in, pad_h_in, pad_w_out, pad_h_out, ((double)(l_total/iters)), gb/l_total, gib/l_total, norms_fwd.l1_ref, norms_fwd.l1_tst,
norms_fwd.l2_abs, norms_fwd.l2_rel, norms_fwd.linf_abs, norms_fwd.linf_rel, norms_fwd.normf_rel);
}
if ( (type == 'A' || type == 'B') ) {
printf("##########################################\n");
printf("# Performance - BWD (custom-Storage) #\n");
printf("##########################################\n");
/* run LIBXSMM convolution for performance */
l_start = libxsmm_timer_tick();
#if defined(_OPENMP)
# pragma omp parallel private(i)
#endif
{
#if defined(_OPENMP)
const int tid = omp_get_thread_num();
#else
const int tid = 0;
#endif
for (i = 0; i < iters; ++i) {
libxsmm_dnn_fusedbatchnorm_execute_st( libxsmm_handle, LIBXSMM_DNN_COMPUTE_KIND_BWD, 0, tid );
}
}
l_end = libxsmm_timer_tick();
l_total = libxsmm_timer_duration(l_start, l_end);
gb = (2.0*(double)nImg*(double)nFm*(((double)ifh*(double)ifw) + (2.0*(double)ofh*(double)ofw))*(double)sizeof(float)*(double)iters) / (1000*1000*1000);
gib = (2.0*(double)nImg*(double)nFm*(((double)ifh*(double)ifw) + (2.0*(double)ofh*(double)ofw))*(double)sizeof(float)*(double)iters) / (1024*1024*1024);
printf("GB = %.5g\n", gb/(double)iters);
printf("GiB = %.5g\n", gib/(double)iters);
printf("fp time = %.5g\n", ((double)(l_total/iters)));
printf("GB/s = %.5g\n", gb/l_total);
printf("GiB/s = %.5g\n", gib/l_total);
printf("PERFDUMP,BP,%s,%i,%i,%i,%i,%i,%i,%i,%i,%i,%i,%.5g,%.5g,%.5g,%f,%f,%f,%f,%f,%f,%f\n", LIBXSMM_VERSION, nThreads, nImg, nFm,
ifw, ifh, stride, pad_w_in, pad_h_in, pad_w_out, pad_h_out, ((double)(l_total/iters)), gb/l_total, gib/l_total, norms_bwd.l1_ref, norms_bwd.l1_tst,
norms_bwd.l2_abs, norms_bwd.l2_rel, norms_bwd.linf_abs, norms_bwd.linf_rel, norms_bwd.normf_rel);
}
/* clean-up */
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_release_scratch( libxsmm_handle ) );
libxsmm_free(scratch);
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_release_tensor( libxsmm_handle, LIBXSMM_DNN_REGULAR_INPUT ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_release_tensor( libxsmm_handle, LIBXSMM_DNN_GRADIENT_INPUT ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_release_tensor( libxsmm_handle, LIBXSMM_DNN_REGULAR_OUTPUT ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_release_tensor( libxsmm_handle, LIBXSMM_DNN_GRADIENT_OUTPUT ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_release_tensor( libxsmm_handle, LIBXSMM_DNN_REGULAR_INPUT_ADD ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_release_tensor( libxsmm_handle, LIBXSMM_DNN_GRADIENT_INPUT_ADD ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_release_tensor( libxsmm_handle, LIBXSMM_DNN_REGULAR_CHANNEL_BETA ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_release_tensor( libxsmm_handle, LIBXSMM_DNN_GRADIENT_CHANNEL_BETA ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_release_tensor( libxsmm_handle, LIBXSMM_DNN_REGULAR_CHANNEL_GAMMA ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_release_tensor( libxsmm_handle, LIBXSMM_DNN_GRADIENT_CHANNEL_GAMMA ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_release_tensor( libxsmm_handle, LIBXSMM_DNN_CHANNEL_EXPECTVAL ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_release_tensor( libxsmm_handle, LIBXSMM_DNN_CHANNEL_RCPSTDDEV ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_release_tensor( libxsmm_handle, LIBXSMM_DNN_CHANNEL_VARIANCE ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_fusedbatchnorm_release_tensor( libxsmm_handle, LIBXSMM_DNN_RELU_MASK) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_destroy_tensor( libxsmm_input ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_destroy_tensor( libxsmm_delinput ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_destroy_tensor( libxsmm_output ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_destroy_tensor( libxsmm_deloutput ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_destroy_tensor( libxsmm_input_add ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_destroy_tensor( libxsmm_delinput_add ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_destroy_tensor( libxsmm_beta ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_destroy_tensor( libxsmm_delbeta ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_destroy_tensor( libxsmm_gamma ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_destroy_tensor( libxsmm_delgamma ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_destroy_tensor( libxsmm_expectval ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_destroy_tensor( libxsmm_rcpstddev ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_destroy_tensor( libxsmm_variance ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_destroy_tensor( libxsmm_relumask ) );
CHKERR_LIBXSMM_DNN( libxsmm_dnn_destroy_fusedbatchnorm( libxsmm_handle ) );
}
/* deallocate data */
libxsmm_free(naive_input);
libxsmm_free(naive_input_add);
libxsmm_free(naive_output);
libxsmm_free(naive_delinput);
libxsmm_free(naive_delinput_add);
libxsmm_free(naive_deloutput);
libxsmm_free(naive_input_pad);
libxsmm_free(naive_input_add_pad);
libxsmm_free(naive_output_pad);
libxsmm_free(naive_delinput_pad);
libxsmm_free(naive_delinput_add_pad);
libxsmm_free(naive_deloutput_pad);
libxsmm_free(naive_input_pad_bf16);
libxsmm_free(naive_input_add_pad_bf16);
libxsmm_free(naive_output_pad_bf16);
libxsmm_free(naive_delinput_pad_bf16);
libxsmm_free(naive_delinput_add_pad_bf16);
libxsmm_free(naive_deloutput_pad_bf16);
libxsmm_free(naive_beta);
libxsmm_free(naive_gamma);
libxsmm_free(naive_delbeta);
libxsmm_free(naive_delgamma);
libxsmm_free(naive_expectval);
libxsmm_free(naive_rcpstddev);
libxsmm_free(naive_variance);
libxsmm_free(naive_libxsmm_output);
libxsmm_free(naive_libxsmm_delinput);
libxsmm_free(naive_libxsmm_delinput_add);
libxsmm_free(naive_libxsmm_output_f32);
libxsmm_free(naive_libxsmm_delinput_f32);
libxsmm_free(naive_libxsmm_delinput_add_f32);
libxsmm_free(input_libxsmm);
libxsmm_free(input_add_libxsmm);
libxsmm_free(output_libxsmm);
libxsmm_free(delinput_libxsmm);
libxsmm_free(delinput_add_libxsmm);
libxsmm_free(deloutput_libxsmm);
libxsmm_free(beta_libxsmm);
libxsmm_free(gamma_libxsmm);
libxsmm_free(delbeta_libxsmm);
libxsmm_free(delgamma_libxsmm);
libxsmm_free(expectval_libxsmm);
libxsmm_free(rcpstddev_libxsmm);
libxsmm_free(variance_libxsmm);
libxsmm_free(relumask_libxsmm);
{ const char *const env_check_scale = getenv("CHECK_SCALE");
const double check_scale = LIBXSMM_ABS(0 == env_check_scale ? 1.0 : atof(env_check_scale));
if (LIBXSMM_NEQ(0, check) && (check < 100.0 * check_scale * diff.normf_rel) && (global_status == LIBXSMM_DNN_SUCCESS)) {
fprintf(stderr, "FAILED with an error of %f%%!\n", 100.0 * diff.normf_rel);
exit(EXIT_FAILURE);
}
}
/* some empty lines at the end */
printf("\n\n\n");
return global_status;
}
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