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

This C API example demonstrates how to build an AlexNet neural network topology for forward-pass inference.

Example code: cnn_inference_f32.c

Some key take-aways include:

The example implements the AlexNet layers as numbered primitives (for example, conv1, pool1, conv2).

/*******************************************************************************
* 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 <stdlib.h>
#include "dnnl.h"
#include "example_utils.h"
#define BATCH 8
#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;
}
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_engine_t engine, float *data, dnnl_memory_t *memory) {
&user_md, dim, dims, dnnl_f32, user_tag));
CHECK(dnnl_memory_create(memory, &user_md, engine, DNNL_MEMORY_ALLOCATE));
write_to_dnnl_memory(data, *memory);
}
dnnl_status_t prepare_reorder(dnnl_memory_t *user_memory, // in
const dnnl_memory_desc_t *prim_memory_md, // in
dnnl_engine_t prim_engine, // in: primitive's engine
int dir_is_user_to_prim, // in: user -> prim or prim -> user
dnnl_memory_t *prim_memory, // out: primitive's memory created
dnnl_primitive_t *reorder, // out: reorder primitive created
uint32_t *net_index, // primitive index in net (inc if reorder created)
dnnl_primitive_t *net, args_t *net_args) { // net params
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)) {
// memory_create(&p, m, NULL) means allocate memory
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_kind_t engine_kind) {
dnnl_engine_t engine;
CHECK(dnnl_engine_create(&engine, engine_kind, 0));
// build a simple net
uint32_t n = 0;
args_t net_args[10];
float *net_src
= (float *)malloc(BATCH * IC * CONV_IH * CONV_IW * sizeof(float));
float *net_dst
= (float *)malloc(BATCH * OC * POOL_OH * POOL_OW * sizeof(float));
// AlexNet: conv
// {BATCH, IC, CONV_IH, CONV_IW} (x) {OC, IC, CONV_KH, CONV_KW} ->
// {BATCH, OC, CONV_OH, CONV_OW}
// strides: {CONV_STRIDE, CONV_STRIDE}
dnnl_dim_t conv_user_src_sizes[4] = {BATCH, IC, CONV_IH, CONV_IW};
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));
// 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 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));
// create a convolution
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 reorder it to user data
&conv_internal_dst_memory, 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;
CHECK(prepare_reorder(&conv_user_src_memory, src_md, engine, 1,
&conv_internal_src_memory, &conv_reorder_src, &n, net, net_args));
CHECK(prepare_reorder(&conv_user_weights_memory, weights_md, engine, 1,
&conv_internal_weights_memory, &conv_reorder_weights, &n, net,
net_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[n] = conv;
prepare_arg_node(&net_args[n], 4);
set_arg(&net_args[n].args[0], DNNL_ARG_SRC, conv_src_memory);
set_arg(&net_args[n].args[1], DNNL_ARG_WEIGHTS, conv_weights_memory);
set_arg(&net_args[n].args[2], DNNL_ARG_BIAS, conv_user_bias_memory);
set_arg(&net_args[n].args[3], DNNL_ARG_DST, conv_internal_dst_memory);
n++;
// AlexNet: relu
// {BATCH, OC, CONV_OH, CONV_OW} -> {BATCH, OC, CONV_OH, CONV_OW}
float negative_slope = 1.0f;
// create relu memory descriptor on dst memory descriptor
// from previous primitive
const dnnl_memory_desc_t *relu_src_md
// create a relu
dnnl_eltwise_relu, relu_src_md, negative_slope, 0));
CHECK(dnnl_primitive_desc_create(&relu_pd, &relu_desc, NULL, engine, NULL));
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[n] = relu;
prepare_arg_node(&net_args[n], 2);
set_arg(&net_args[n].args[0], DNNL_ARG_SRC, conv_internal_dst_memory);
set_arg(&net_args[n].args[1], DNNL_ARG_DST, relu_dst_memory);
n++;
// AlexNet: lrn
// {BATCH, OC, CONV_OH, CONV_OW} -> {BATCH, OC, CONV_OH, CONV_OW}
// local size: 5
// alpha: 0.0001
// beta: 0.75
uint32_t local_size = 5;
float alpha = 0.0001f;
float beta = 0.75f;
float k = 1.0f;
// create lrn memory descriptor on dst memory descriptor
// from previous primitive
const dnnl_memory_desc_t *lrn_src_md = relu_dst_md;
// create a lrn
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));
dnnl_memory_t lrn_dst_memory;
const dnnl_memory_desc_t *lrn_dst_md
&lrn_dst_memory, lrn_dst_md, engine, DNNL_MEMORY_ALLOCATE));
dnnl_memory_t lrn_ws_memory;
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[n] = lrn;
prepare_arg_node(&net_args[n], 3);
set_arg(&net_args[n].args[0], DNNL_ARG_SRC, relu_dst_memory);
set_arg(&net_args[n].args[1], DNNL_ARG_DST, lrn_dst_memory);
set_arg(&net_args[n].args[2], DNNL_ARG_WORKSPACE, lrn_ws_memory);
n++;
// 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[4] = {BATCH, OC, POOL_OH, POOL_OW};
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 pooling memory descriptor on 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_any_md;
CHECK(dnnl_memory_desc_init_by_tag(&pool_dst_any_md, 4, pool_dst_sizes,
dnnl_f32, dnnl_format_tag_any));
// create memory for user 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
dnnl_pooling_max, pool_src_md, &pool_dst_any_md, pool_strides,
pool_kernel, pool_padding, pool_padding));
CHECK(dnnl_primitive_desc_create(&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));
dnnl_memory_t pool_dst_memory;
// create reorder primitives between user data and pooling dsts
// if required
dnnl_primitive_t pool_reorder_dst;
dnnl_memory_t pool_internal_dst_memory;
const dnnl_memory_desc_t *pool_dst_md
n += 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, net, net_args));
n -= pool_reorder_dst ? 2 : 1;
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[n] = pool;
prepare_arg_node(&net_args[n], 3);
set_arg(&net_args[n].args[0], DNNL_ARG_SRC, lrn_dst_memory);
set_arg(&net_args[n].args[1], DNNL_ARG_DST, pool_dst_memory);
set_arg(&net_args[n].args[2], DNNL_ARG_WORKSPACE, pool_ws_memory);
n++;
if (pool_reorder_dst) n += 1;
dnnl_stream_t stream;
for (uint32_t i = 0; i < n; ++i) {
net[i], stream, net_args[i].nargs, net_args[i].args));
}
CHECK(dnnl_stream_wait(stream));
// clean-up
for (uint32_t i = 0; i < n; ++i)
free_arg_node(&net_args[i]);
CHECK(dnnl_primitive_desc_destroy(conv_pd));
CHECK(dnnl_primitive_desc_destroy(relu_pd));
CHECK(dnnl_primitive_desc_destroy(pool_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);
}
int main(int argc, char **argv) {
dnnl_engine_kind_t engine_kind = parse_engine_kind(argc, argv);
simple_net(engine_kind);
printf("Example passed on %s.\n", engine_kind2str_upper(engine_kind));
return 0;
}