Deep Neural Network Library (DNNL)  1.2.0
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CNN f32 training example

This C API example demonstrates how to build an AlexNet model training.

The example implements a few layers from AlexNet model.

/*******************************************************************************
* Copyright 2016-2019 Intel Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/
// Required for posix_memalign
#define _POSIX_C_SOURCE 200112L
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "dnnl.h"
#include "example_utils.h"
#define BATCH 32
#define IC 3
#define OC 96
#define CONV_IH 227
#define CONV_IW 227
#define CONV_OH 55
#define CONV_OW 55
#define CONV_STRIDE 4
#define CONV_PAD 0
#define POOL_OH 27
#define POOL_OW 27
#define POOL_STRIDE 2
#define POOL_PAD 0
static size_t product(dnnl_dim_t *arr, size_t size) {
size_t prod = 1;
for (size_t i = 0; i < size; ++i)
prod *= arr[i];
return prod;
}
static void init_net_data(float *data, uint32_t dim, const dnnl_dim_t *dims) {
if (dim == 1) {
for (dnnl_dim_t i = 0; i < dims[0]; ++i) {
data[i] = (float)(i % 1637);
}
} else if (dim == 4) {
for (dnnl_dim_t in = 0; in < dims[0]; ++in)
for (dnnl_dim_t ic = 0; ic < dims[1]; ++ic)
for (dnnl_dim_t ih = 0; ih < dims[2]; ++ih)
for (dnnl_dim_t iw = 0; iw < dims[3]; ++iw) {
dnnl_dim_t indx = in * dims[1] * dims[2] * dims[3]
+ ic * dims[2] * dims[3] + ih * dims[3] + iw;
data[indx] = (float)(indx % 1637);
}
}
}
typedef struct {
int nargs;
} args_t;
static void prepare_arg_node(args_t *node, int nargs) {
node->args = (dnnl_exec_arg_t *)malloc(sizeof(dnnl_exec_arg_t) * nargs);
node->nargs = nargs;
}
static void free_arg_node(args_t *node) {
free(node->args);
}
static void set_arg(dnnl_exec_arg_t *arg, int arg_idx, dnnl_memory_t memory) {
arg->arg = arg_idx;
arg->memory = memory;
}
static void init_data_memory(uint32_t dim, const dnnl_dim_t *dims,
dnnl_format_tag_t user_fmt, dnnl_data_type_t data_type,
dnnl_engine_t engine, float *data, dnnl_memory_t *memory) {
&user_md, dim, dims, dnnl_f32, user_fmt));
CHECK(dnnl_memory_create(memory, &user_md, engine, data));
}
dnnl_status_t prepare_reorder(dnnl_memory_t *user_memory,
const dnnl_memory_desc_t *prim_memory_md,
dnnl_engine_t prim_engine,
int dir_is_user_to_prim,
dnnl_memory_t *prim_memory,
dnnl_primitive_t *reorder,
uint32_t *net_index,
dnnl_primitive_t *net, args_t *net_args) {
const dnnl_memory_desc_t *user_memory_md;
dnnl_memory_get_memory_desc(*user_memory, &user_memory_md);
dnnl_engine_t user_mem_engine;
dnnl_memory_get_engine(*user_memory, &user_mem_engine);
if (!dnnl_memory_desc_equal(user_memory_md, prim_memory_md)) {
CHECK(dnnl_memory_create(prim_memory, prim_memory_md, prim_engine,
DNNL_MEMORY_ALLOCATE));
if (dir_is_user_to_prim) {
user_memory_md, user_mem_engine, prim_memory_md,
prim_engine, NULL));
} else {
prim_memory_md, prim_engine, user_memory_md,
user_mem_engine, NULL));
}
CHECK(dnnl_primitive_create(reorder, reorder_pd));
CHECK(dnnl_primitive_desc_destroy(reorder_pd));
net[*net_index] = *reorder;
prepare_arg_node(&net_args[*net_index], 2);
set_arg(&net_args[*net_index].args[0], DNNL_ARG_FROM,
dir_is_user_to_prim ? *user_memory : *prim_memory);
set_arg(&net_args[*net_index].args[1], DNNL_ARG_TO,
dir_is_user_to_prim ? *prim_memory : *user_memory);
(*net_index)++;
} else {
*prim_memory = NULL;
*reorder = NULL;
}
return dnnl_success;
}
void simple_net() {
dnnl_engine_t engine;
CHECK(dnnl_engine_create(&engine, dnnl_cpu, 0)); // idx
// build a simple net
uint32_t n_fwd = 0, n_bwd = 0;
dnnl_primitive_t net_fwd[10], net_bwd[10];
args_t net_fwd_args[10], net_bwd_args[10];
dnnl_dim_t net_src_sizes[4] = {BATCH, IC, CONV_IH, CONV_IW};
dnnl_dim_t net_dst_sizes[4] = {BATCH, OC, POOL_OH, POOL_OW};
float *net_src = (float *)malloc(product(net_src_sizes, 4) * sizeof(float));
float *net_dst = (float *)malloc(product(net_dst_sizes, 4) * sizeof(float));
init_net_data(net_src, 4, net_src_sizes);
memset(net_dst, 0, product(net_dst_sizes, 4) * sizeof(float));
//----------------------------------------------------------------------
//----------------- Forward Stream -------------------------------------
// AlexNet: conv
// {BATCH, IC, CONV_IH, CONV_IW} (x) {OC, IC, 11, 11} ->
// {BATCH, OC, CONV_OH, CONV_OW}
// strides: {CONV_STRIDE, CONV_STRIDE}
dnnl_dim_t *conv_user_src_sizes = net_src_sizes;
dnnl_dim_t conv_user_weights_sizes[4] = {OC, IC, 11, 11};
dnnl_dim_t conv_bias_sizes[4] = {OC};
dnnl_dim_t conv_user_dst_sizes[4] = {BATCH, OC, CONV_OH, CONV_OW};
dnnl_dim_t conv_strides[2] = {CONV_STRIDE, CONV_STRIDE};
dnnl_dim_t conv_padding[2] = {CONV_PAD, CONV_PAD};
float *conv_src = net_src;
float *conv_weights = (float *)malloc(
product(conv_user_weights_sizes, 4) * sizeof(float));
float *conv_bias
= (float *)malloc(product(conv_bias_sizes, 1) * sizeof(float));
init_net_data(conv_weights, 4, conv_user_weights_sizes);
init_net_data(conv_bias, 1, conv_bias_sizes);
// create memory for user data
dnnl_memory_t conv_user_src_memory, conv_user_weights_memory,
conv_user_bias_memory;
init_data_memory(4, conv_user_src_sizes, dnnl_nchw, dnnl_f32, engine,
conv_src, &conv_user_src_memory);
init_data_memory(4, conv_user_weights_sizes, dnnl_oihw, dnnl_f32, engine,
conv_weights, &conv_user_weights_memory);
init_data_memory(1, conv_bias_sizes, dnnl_x, dnnl_f32, engine, conv_bias,
&conv_user_bias_memory);
// create a convolution
{
// create data descriptors for convolution w/ no specified format
dnnl_memory_desc_t conv_src_md, conv_weights_md, conv_bias_md,
conv_dst_md;
CHECK(dnnl_memory_desc_init_by_tag(&conv_src_md, 4, conv_user_src_sizes,
dnnl_f32, dnnl_format_tag_any));
CHECK(dnnl_memory_desc_init_by_tag(&conv_weights_md, 4,
conv_user_weights_sizes, dnnl_f32, dnnl_format_tag_any));
&conv_bias_md, 1, conv_bias_sizes, dnnl_f32, dnnl_x));
CHECK(dnnl_memory_desc_init_by_tag(&conv_dst_md, 4, conv_user_dst_sizes,
dnnl_f32, dnnl_format_tag_any));
dnnl_convolution_desc_t conv_any_desc;
dnnl_convolution_direct, &conv_src_md, &conv_weights_md,
&conv_bias_md, &conv_dst_md, conv_strides, conv_padding,
conv_padding));
&conv_pd, &conv_any_desc, NULL, engine, NULL));
}
dnnl_memory_t conv_internal_src_memory, conv_internal_weights_memory,
conv_internal_dst_memory;
// create memory for dst data, we don't need to reorder it to user data
const dnnl_memory_desc_t *conv_dst_md
CHECK(dnnl_memory_create(&conv_internal_dst_memory, conv_dst_md, engine,
DNNL_MEMORY_ALLOCATE));
// create reorder primitives between user data and convolution srcs
// if required
dnnl_primitive_t conv_reorder_src, conv_reorder_weights;
const dnnl_memory_desc_t *conv_src_md
CHECK(prepare_reorder(&conv_user_src_memory, conv_src_md, engine, 1,
&conv_internal_src_memory, &conv_reorder_src, &n_fwd, net_fwd,
net_fwd_args));
const dnnl_memory_desc_t *conv_weights_md
CHECK(prepare_reorder(&conv_user_weights_memory, conv_weights_md, engine, 1,
&conv_internal_weights_memory, &conv_reorder_weights, &n_fwd,
net_fwd, net_fwd_args));
dnnl_memory_t conv_src_memory = conv_internal_src_memory
? conv_internal_src_memory
: conv_user_src_memory;
dnnl_memory_t conv_weights_memory = conv_internal_weights_memory
? conv_internal_weights_memory
: conv_user_weights_memory;
// finally create a convolution primitive
CHECK(dnnl_primitive_create(&conv, conv_pd));
net_fwd[n_fwd] = conv;
prepare_arg_node(&net_fwd_args[n_fwd], 4);
set_arg(&net_fwd_args[n_fwd].args[0], DNNL_ARG_SRC, conv_src_memory);
set_arg(&net_fwd_args[n_fwd].args[1], DNNL_ARG_WEIGHTS,
conv_weights_memory);
set_arg(&net_fwd_args[n_fwd].args[2], DNNL_ARG_BIAS, conv_user_bias_memory);
set_arg(&net_fwd_args[n_fwd].args[3], DNNL_ARG_DST,
conv_internal_dst_memory);
n_fwd++;
// AlexNet: relu
// {BATCH, OC, CONV_OH, CONV_OW} -> {BATCH, OC, CONV_OH, CONV_OW}
float negative_slope = 1.0f;
// keep memory format of source same as the format of convolution
// output in order to avoid reorder
const dnnl_memory_desc_t *relu_src_md = conv_dst_md;
// create a relu primitive descriptor
dnnl_eltwise_relu, relu_src_md, negative_slope, 0));
CHECK(dnnl_primitive_desc_create(&relu_pd, &relu_desc, NULL, engine, NULL));
// create relu dst memory
dnnl_memory_t relu_dst_memory;
const dnnl_memory_desc_t *relu_dst_md
&relu_dst_memory, relu_dst_md, engine, DNNL_MEMORY_ALLOCATE));
// finally create a relu primitive
CHECK(dnnl_primitive_create(&relu, relu_pd));
net_fwd[n_fwd] = relu;
prepare_arg_node(&net_fwd_args[n_fwd], 2);
set_arg(&net_fwd_args[n_fwd].args[0], DNNL_ARG_SRC,
conv_internal_dst_memory);
set_arg(&net_fwd_args[n_fwd].args[1], DNNL_ARG_DST, relu_dst_memory);
n_fwd++;
// AlexNet: lrn
// {BATCH, OC, CONV_OH, CONV_OW} -> {BATCH, OC, CONV_OH, CONV_OW}
// local size: 5
// alpha: 0.0001
// beta: 0.75
// k: 1.0
uint32_t local_size = 5;
float alpha = 0.0001f;
float beta = 0.75f;
float k = 1.0f;
// create lrn src memory descriptor using dst memory descriptor
// from previous primitive
const dnnl_memory_desc_t *lrn_src_md = relu_dst_md;
// create a lrn primitive descriptor
dnnl_lrn_desc_t lrn_desc;
dnnl_lrn_across_channels, lrn_src_md, local_size, alpha, beta, k));
CHECK(dnnl_primitive_desc_create(&lrn_pd, &lrn_desc, NULL, engine, NULL));
// create primitives for lrn dst and workspace memory
dnnl_memory_t lrn_dst_memory, lrn_ws_memory;
const dnnl_memory_desc_t *lrn_dst_md
&lrn_dst_memory, lrn_dst_md, engine, DNNL_MEMORY_ALLOCATE));
// create workspace only in training and only for forward primitive
// query lrn_pd for workspace, this memory will be shared with forward lrn
const dnnl_memory_desc_t *lrn_ws_md
&lrn_ws_memory, lrn_ws_md, engine, DNNL_MEMORY_ALLOCATE));
// finally create a lrn primitive
CHECK(dnnl_primitive_create(&lrn, lrn_pd));
net_fwd[n_fwd] = lrn;
prepare_arg_node(&net_fwd_args[n_fwd], 3);
set_arg(&net_fwd_args[n_fwd].args[0], DNNL_ARG_SRC, relu_dst_memory);
set_arg(&net_fwd_args[n_fwd].args[1], DNNL_ARG_DST, lrn_dst_memory);
set_arg(&net_fwd_args[n_fwd].args[2], DNNL_ARG_WORKSPACE, lrn_ws_memory);
n_fwd++;
// AlexNet: pool
// {BATCH, OC, CONV_OH, CONV_OW} -> {BATCH, OC, POOL_OH, POOL_OW}
// kernel: {3, 3}
// strides: {POOL_STRIDE, POOL_STRIDE}
dnnl_dim_t *pool_dst_sizes = net_dst_sizes;
dnnl_dim_t pool_kernel[2] = {3, 3};
dnnl_dim_t pool_strides[2] = {POOL_STRIDE, POOL_STRIDE};
dnnl_dim_t pool_padding[2] = {POOL_PAD, POOL_PAD};
// create memory for user dst data
dnnl_memory_t pool_user_dst_memory;
init_data_memory(4, pool_dst_sizes, dnnl_nchw, dnnl_f32, engine, net_dst,
&pool_user_dst_memory);
// create a pooling primitive descriptor
{
// create pooling src memory descriptor using dst descriptor
// from previous primitive
const dnnl_memory_desc_t *pool_src_md = lrn_dst_md;
// create descriptors for dst pooling data
dnnl_memory_desc_t pool_dst_md;
CHECK(dnnl_memory_desc_init_by_tag(&pool_dst_md, 4, pool_dst_sizes,
dnnl_f32, dnnl_format_tag_any));
dnnl_pooling_max, pool_src_md, &pool_dst_md, pool_strides,
pool_kernel, pool_padding, pool_padding));
&pool_pd, &pool_desc, NULL, engine, NULL));
}
// create memory for workspace
dnnl_memory_t pool_ws_memory;
const dnnl_memory_desc_t *pool_ws_md
&pool_ws_memory, pool_ws_md, engine, DNNL_MEMORY_ALLOCATE));
// create reorder primitives between pooling dsts and user format dst
// if required
dnnl_primitive_t pool_reorder_dst;
dnnl_memory_t pool_internal_dst_memory;
const dnnl_memory_desc_t *pool_dst_md
n_fwd += 1; // tentative workaround: preserve space for pooling that should
// happen before the reorder
CHECK(prepare_reorder(&pool_user_dst_memory, pool_dst_md, engine, 0,
&pool_internal_dst_memory, &pool_reorder_dst, &n_fwd, net_fwd,
net_fwd_args));
n_fwd -= pool_reorder_dst ? 2 : 1;
dnnl_memory_t pool_dst_memory = pool_internal_dst_memory
? pool_internal_dst_memory
: pool_user_dst_memory;
// finally create a pooling primitive
CHECK(dnnl_primitive_create(&pool, pool_pd));
net_fwd[n_fwd] = pool;
prepare_arg_node(&net_fwd_args[n_fwd], 3);
set_arg(&net_fwd_args[n_fwd].args[0], DNNL_ARG_SRC, lrn_dst_memory);
set_arg(&net_fwd_args[n_fwd].args[1], DNNL_ARG_DST, pool_dst_memory);
set_arg(&net_fwd_args[n_fwd].args[2], DNNL_ARG_WORKSPACE, pool_ws_memory);
n_fwd++;
if (pool_reorder_dst) n_fwd += 1;
//-----------------------------------------------------------------------
//----------------- Backward Stream -------------------------------------
//-----------------------------------------------------------------------
// ... user diff_data ...
float *net_diff_dst
= (float *)malloc(product(pool_dst_sizes, 4) * sizeof(float));
init_net_data(net_diff_dst, 4, pool_dst_sizes);
// create memory for user diff dst data
dnnl_memory_t pool_user_diff_dst_memory;
init_data_memory(4, pool_dst_sizes, dnnl_nchw, dnnl_f32, engine,
net_diff_dst, &pool_user_diff_dst_memory);
// Pooling Backward
// pooling diff src memory descriptor
const dnnl_memory_desc_t *pool_diff_src_md = lrn_dst_md;
// pooling diff dst memory descriptor
const dnnl_memory_desc_t *pool_diff_dst_md = pool_dst_md;
// create backward pooling descriptor
dnnl_pooling_desc_t pool_bwd_desc;
pool_diff_src_md, pool_diff_dst_md, pool_strides, pool_kernel,
pool_padding, pool_padding));
// backward primitive descriptor needs to hint forward descriptor
dnnl_primitive_desc_t pool_bwd_pd;
&pool_bwd_pd, &pool_bwd_desc, NULL, engine, pool_pd));
// create reorder primitive between user diff dst and pool diff dst
// if required
dnnl_memory_t pool_diff_dst_memory, pool_internal_diff_dst_memory;
dnnl_primitive_t pool_reorder_diff_dst;
CHECK(prepare_reorder(&pool_user_diff_dst_memory, pool_diff_dst_md, engine,
1, &pool_internal_diff_dst_memory, &pool_reorder_diff_dst, &n_bwd,
net_bwd, net_bwd_args));
pool_diff_dst_memory = pool_internal_diff_dst_memory
? pool_internal_diff_dst_memory
: pool_user_diff_dst_memory;
// create memory for pool diff src data
dnnl_memory_t pool_diff_src_memory;
CHECK(dnnl_memory_create(&pool_diff_src_memory, pool_diff_src_md, engine,
DNNL_MEMORY_ALLOCATE));
// finally create backward pooling primitive
dnnl_primitive_t pool_bwd;
CHECK(dnnl_primitive_create(&pool_bwd, pool_bwd_pd));
net_bwd[n_bwd] = pool_bwd;
prepare_arg_node(&net_bwd_args[n_bwd], 3);
set_arg(&net_bwd_args[n_bwd].args[0], DNNL_ARG_DIFF_DST,
pool_diff_dst_memory);
set_arg(&net_bwd_args[n_bwd].args[1], DNNL_ARG_WORKSPACE, pool_ws_memory);
set_arg(&net_bwd_args[n_bwd].args[2], DNNL_ARG_DIFF_SRC,
pool_diff_src_memory);
n_bwd++;
// Backward lrn
const dnnl_memory_desc_t *lrn_diff_dst_md = pool_diff_src_md;
// create backward lrn descriptor
dnnl_lrn_desc_t lrn_bwd_desc;
lrn_diff_dst_md, lrn_src_md, local_size, alpha, beta, k));
&lrn_bwd_pd, &lrn_bwd_desc, NULL, engine, lrn_pd));
// create memory for lrn diff src
dnnl_memory_t lrn_diff_src_memory;
lrn_bwd_pd, dnnl_query_diff_src_md, 0);
CHECK(dnnl_memory_create(&lrn_diff_src_memory, lrn_diff_src_md, engine,
DNNL_MEMORY_ALLOCATE));
// finally create backward lrn primitive
CHECK(dnnl_primitive_create(&lrn_bwd, lrn_bwd_pd));
net_bwd[n_bwd] = lrn_bwd;
prepare_arg_node(&net_bwd_args[n_bwd], 4);
set_arg(&net_bwd_args[n_bwd].args[0], DNNL_ARG_SRC, relu_dst_memory);
set_arg(&net_bwd_args[n_bwd].args[1], DNNL_ARG_DIFF_DST,
pool_diff_src_memory);
set_arg(&net_bwd_args[n_bwd].args[2], DNNL_ARG_WORKSPACE, lrn_ws_memory);
set_arg(&net_bwd_args[n_bwd].args[3], DNNL_ARG_DIFF_SRC,
lrn_diff_src_memory);
n_bwd++;
// Backward relu
const dnnl_memory_desc_t *relu_diff_dst_md = lrn_diff_src_md;
// create backward relu descriptor
dnnl_eltwise_desc_t relu_bwd_desc;
relu_diff_dst_md, relu_src_md, negative_slope, 0));
dnnl_primitive_desc_t relu_bwd_pd;
&relu_bwd_pd, &relu_bwd_desc, NULL, engine, relu_pd));
// create memory for relu diff src
dnnl_memory_t relu_diff_src_memory;
relu_bwd_pd, dnnl_query_diff_src_md, 0);
CHECK(dnnl_memory_create(&relu_diff_src_memory, relu_diff_src_md, engine,
DNNL_MEMORY_ALLOCATE));
// finally create backward relu primitive
dnnl_primitive_t relu_bwd;
CHECK(dnnl_primitive_create(&relu_bwd, relu_bwd_pd));
net_bwd[n_bwd] = relu_bwd;
prepare_arg_node(&net_bwd_args[n_bwd], 3);
set_arg(&net_bwd_args[n_bwd].args[0], DNNL_ARG_SRC,
conv_internal_dst_memory);
set_arg(&net_bwd_args[n_bwd].args[1], DNNL_ARG_DIFF_DST,
lrn_diff_src_memory);
set_arg(&net_bwd_args[n_bwd].args[2], DNNL_ARG_DIFF_SRC,
relu_diff_src_memory);
n_bwd++;
// Backward convolution with respect to weights
float *conv_diff_bias_buffer
= (float *)malloc(product(conv_bias_sizes, 1) * sizeof(float));
float *conv_user_diff_weights_buffer = (float *)malloc(
product(conv_user_weights_sizes, 4) * sizeof(float));
// initialize memory for diff weights in user format
dnnl_memory_t conv_user_diff_weights_memory;
init_data_memory(4, conv_user_weights_sizes, dnnl_oihw, dnnl_f32, engine,
conv_user_diff_weights_buffer, &conv_user_diff_weights_memory);
// create backward convolution primitive descriptor
dnnl_primitive_desc_t conv_bwd_weights_pd;
{
// memory descriptors should be in format `any` to allow backward
// convolution for
// weights to chose the format it prefers for best performance
dnnl_memory_desc_t conv_diff_src_md, conv_diff_weights_md,
conv_diff_bias_md, conv_diff_dst_md;
CHECK(dnnl_memory_desc_init_by_tag(&conv_diff_src_md, 4,
conv_user_src_sizes, dnnl_f32, dnnl_format_tag_any));
CHECK(dnnl_memory_desc_init_by_tag(&conv_diff_weights_md, 4,
conv_user_weights_sizes, dnnl_f32, dnnl_format_tag_any));
&conv_diff_bias_md, 1, conv_bias_sizes, dnnl_f32, dnnl_x));
CHECK(dnnl_memory_desc_init_by_tag(&conv_diff_dst_md, 4,
conv_user_dst_sizes, dnnl_f32, dnnl_format_tag_any));
// create backward convolution descriptor
dnnl_convolution_desc_t conv_bwd_weights_desc;
&conv_bwd_weights_desc, dnnl_convolution_direct,
&conv_diff_src_md, &conv_diff_weights_md, &conv_diff_bias_md,
&conv_diff_dst_md, conv_strides, conv_padding, conv_padding));
CHECK(dnnl_primitive_desc_create(&conv_bwd_weights_pd,
&conv_bwd_weights_desc, NULL, engine, conv_pd));
}
// for best performance convolution backward might chose
// different memory format for src and diff_dst
// than the memory formats preferred by forward convolution
// for src and dst respectively
// create reorder primitives for src from forward convolution to the
// format chosen by backward convolution
dnnl_primitive_t conv_bwd_reorder_src;
dnnl_memory_t conv_bwd_internal_src_memory;
conv_bwd_weights_pd, dnnl_query_src_md, 0);
CHECK(prepare_reorder(&conv_src_memory, conv_diff_src_md, engine, 1,
&conv_bwd_internal_src_memory, &conv_bwd_reorder_src, &n_bwd,
net_bwd, net_bwd_args));
dnnl_memory_t conv_bwd_weights_src_memory = conv_bwd_internal_src_memory
? conv_bwd_internal_src_memory
: conv_src_memory;
// create reorder primitives for diff_dst between diff_src from relu_bwd
// and format preferred by conv_diff_weights
dnnl_primitive_t conv_reorder_diff_dst;
dnnl_memory_t conv_internal_diff_dst_memory;
conv_bwd_weights_pd, dnnl_query_diff_dst_md, 0);
CHECK(prepare_reorder(&relu_diff_src_memory, conv_diff_dst_md, engine, 1,
&conv_internal_diff_dst_memory, &conv_reorder_diff_dst, &n_bwd,
net_bwd, net_bwd_args));
dnnl_memory_t conv_diff_dst_memory = conv_internal_diff_dst_memory
? conv_internal_diff_dst_memory
: relu_diff_src_memory;
// create reorder primitives for conv diff weights memory
dnnl_primitive_t conv_reorder_diff_weights;
dnnl_memory_t conv_internal_diff_weights_memory;
const dnnl_memory_desc_t *conv_diff_weights_md
conv_bwd_weights_pd, dnnl_query_diff_weights_md, 0);
n_bwd += 1; // tentative workaround: preserve space for conv_bwd_weights
// that should happen before the reorder
CHECK(prepare_reorder(&conv_user_diff_weights_memory, conv_diff_weights_md,
engine, 0, &conv_internal_diff_weights_memory,
&conv_reorder_diff_weights, &n_bwd, net_bwd, net_bwd_args));
n_bwd -= conv_reorder_diff_weights ? 2 : 1;
dnnl_memory_t conv_diff_weights_memory = conv_internal_diff_weights_memory
? conv_internal_diff_weights_memory
: conv_user_diff_weights_memory;
// create memory for diff bias memory
dnnl_memory_t conv_diff_bias_memory;
conv_bwd_weights_pd, dnnl_query_diff_weights_md, 1);
&conv_diff_bias_memory, conv_diff_bias_md, engine, NULL));
conv_diff_bias_memory, conv_diff_bias_buffer));
// finally created backward convolution weights primitive
dnnl_primitive_t conv_bwd_weights;
CHECK(dnnl_primitive_create(&conv_bwd_weights, conv_bwd_weights_pd));
net_bwd[n_bwd] = conv_bwd_weights;
prepare_arg_node(&net_bwd_args[n_bwd], 4);
set_arg(&net_bwd_args[n_bwd].args[0], DNNL_ARG_SRC,
conv_bwd_weights_src_memory);
set_arg(&net_bwd_args[n_bwd].args[1], DNNL_ARG_DIFF_DST,
conv_diff_dst_memory);
set_arg(&net_bwd_args[n_bwd].args[2], DNNL_ARG_DIFF_WEIGHTS,
conv_diff_weights_memory);
set_arg(&net_bwd_args[n_bwd].args[3], DNNL_ARG_DIFF_BIAS,
conv_diff_bias_memory);
n_bwd++;
if (conv_reorder_diff_weights) n_bwd += 1;
// output from backward stream
void *net_diff_weights = NULL;
void *net_diff_bias = NULL;
int n_iter = 10; // number of iterations for training.
dnnl_stream_t stream;
// Execute the net
for (int i = 0; i < n_iter; i++) {
for (uint32_t i = 0; i < n_fwd; ++i)
CHECK(dnnl_primitive_execute(net_fwd[i], stream,
net_fwd_args[i].nargs, net_fwd_args[i].args));
// Update net_diff_dst
void *net_output = NULL; // output from forward stream:
CHECK(dnnl_memory_get_data_handle(pool_user_dst_memory, &net_output));
// ...user updates net_diff_dst using net_output...
// some user defined func update_diff_dst(net_diff_dst, net_output)
// Backward pass
for (uint32_t i = 0; i < n_bwd; ++i)
CHECK(dnnl_primitive_execute(net_bwd[i], stream,
net_bwd_args[i].nargs, net_bwd_args[i].args));
// ... update weights ...
conv_user_diff_weights_memory, &net_diff_weights));
conv_diff_bias_memory, &net_diff_bias));
// ...user updates weights and bias using diff weights and bias...
// some user defined func update_weights(conv_user_weights_memory,
// conv_bias_memory,
// net_diff_weights, net_diff_bias);
}
CHECK(dnnl_stream_wait(stream));
// clean up nets
for (uint32_t i = 0; i < n_fwd; ++i)
free_arg_node(&net_fwd_args[i]);
for (uint32_t i = 0; i < n_bwd; ++i)
free_arg_node(&net_bwd_args[i]);
// Cleanup forward
CHECK(dnnl_primitive_desc_destroy(pool_pd));
CHECK(dnnl_primitive_desc_destroy(relu_pd));
CHECK(dnnl_primitive_desc_destroy(conv_pd));
free(net_src);
free(net_dst);
dnnl_memory_destroy(conv_user_src_memory);
dnnl_memory_destroy(conv_user_weights_memory);
dnnl_memory_destroy(conv_user_bias_memory);
dnnl_memory_destroy(conv_internal_src_memory);
dnnl_memory_destroy(conv_internal_weights_memory);
dnnl_memory_destroy(conv_internal_dst_memory);
dnnl_primitive_destroy(conv_reorder_src);
dnnl_primitive_destroy(conv_reorder_weights);
free(conv_weights);
free(conv_bias);
dnnl_memory_destroy(relu_dst_memory);
dnnl_memory_destroy(lrn_ws_memory);
dnnl_memory_destroy(lrn_dst_memory);
dnnl_memory_destroy(pool_user_dst_memory);
dnnl_memory_destroy(pool_internal_dst_memory);
dnnl_memory_destroy(pool_ws_memory);
dnnl_primitive_destroy(pool_reorder_dst);
// Cleanup backward
CHECK(dnnl_primitive_desc_destroy(pool_bwd_pd));
CHECK(dnnl_primitive_desc_destroy(lrn_bwd_pd));
CHECK(dnnl_primitive_desc_destroy(relu_bwd_pd));
CHECK(dnnl_primitive_desc_destroy(conv_bwd_weights_pd));
dnnl_memory_destroy(pool_user_diff_dst_memory);
dnnl_memory_destroy(pool_diff_src_memory);
dnnl_memory_destroy(pool_internal_diff_dst_memory);
dnnl_primitive_destroy(pool_reorder_diff_dst);
free(net_diff_dst);
dnnl_memory_destroy(lrn_diff_src_memory);
dnnl_memory_destroy(relu_diff_src_memory);
dnnl_memory_destroy(conv_user_diff_weights_memory);
dnnl_memory_destroy(conv_diff_bias_memory);
dnnl_memory_destroy(conv_bwd_internal_src_memory);
dnnl_primitive_destroy(conv_bwd_reorder_src);
dnnl_memory_destroy(conv_internal_diff_dst_memory);
dnnl_primitive_destroy(conv_reorder_diff_dst);
dnnl_memory_destroy(conv_internal_diff_weights_memory);
dnnl_primitive_destroy(conv_reorder_diff_weights);
dnnl_primitive_destroy(conv_bwd_weights);
free(conv_diff_bias_buffer);
free(conv_user_diff_weights_buffer);
}
int main(int argc, char **argv) {
simple_net();
printf("Example passed on CPU.\n");
return 0;
}