CNN f32 inference example

Intel® oneAPI Deep Neural Network Developer Guide and Reference

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ID 768875
Date 6/20/2024
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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.
Some key take-aways include:
How tensors are implemented and submitted to primitives.
How primitives are created.
How primitives are sequentially submitted to the network, where the output from primitives is passed as input to the next primitive. The latter specifies a dependency between the primitive input and output data.
Specific ‘inference-only’ configurations.
Limiting the number of reorders performed that are detrimental to performance.
The example implements the AlexNet layers as numbered primitives (for example, conv1, pool1, conv2).
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* Copyright 2016-2022 Intel Corporation
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* 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
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// Required for posix_memalign
#define _POSIX_C_SOURCE 200112L

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

#include "oneapi/dnnl/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;
}

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;
    dnnl_exec_arg_t *args;
} 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_tag, dnnl_engine_t engine, float *data,
        dnnl_memory_t *memory) {
    dnnl_memory_desc_t user_md;
    CHECK(dnnl_memory_desc_create_with_tag(
            &user_md, dim, dims, dnnl_f32, user_tag));
    CHECK(dnnl_memory_create(memory, user_md, engine, DNNL_MEMORY_ALLOCATE));
    CHECK(dnnl_memory_desc_destroy(user_md));
    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)) {
        CHECK(dnnl_memory_create(prim_memory, prim_memory_md, prim_engine,
                DNNL_MEMORY_ALLOCATE));

        dnnl_primitive_desc_t reorder_pd;
        if (dir_is_user_to_prim) {
            CHECK(dnnl_reorder_primitive_desc_create(&reorder_pd,
                    user_memory_md, user_mem_engine, prim_memory_md,
                    prim_engine, NULL));
        } else {
            CHECK(dnnl_reorder_primitive_desc_create(&reorder_pd,
                    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;
    dnnl_primitive_t net[10];
    args_t net_args[10];

    const int ndims = 4;
    dnnl_dims_t net_src_sizes = {BATCH, IC, CONV_IH, CONV_IW};
    dnnl_dims_t net_dst_sizes = {BATCH, OC, POOL_OH, POOL_OW};

    float *net_src
            = (float *)malloc(product(net_src_sizes, ndims) * sizeof(float));
    float *net_dst
            = (float *)malloc(product(net_dst_sizes, ndims) * sizeof(float));

    init_net_data(net_src, ndims, net_src_sizes);
    memset(net_dst, 0, product(net_dst_sizes, ndims) * sizeof(float));

    // 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_dims_t conv_user_src_sizes;
    for (int i = 0; i < ndims; i++)
        conv_user_src_sizes[i] = net_src_sizes[i];
    dnnl_dims_t conv_user_weights_sizes = {OC, IC, 11, 11};
    dnnl_dims_t conv_bias_sizes = {OC};
    dnnl_dims_t conv_user_dst_sizes = {BATCH, OC, CONV_OH, CONV_OW};
    dnnl_dims_t conv_strides = {CONV_STRIDE, CONV_STRIDE};
    dnnl_dims_t conv_dilation = {0, 0};
    dnnl_dims_t conv_padding = {CONV_PAD, CONV_PAD};

    float *conv_src = net_src;
    float *conv_weights = (float *)malloc(
            product(conv_user_weights_sizes, ndims) * sizeof(float));
    float *conv_bias
            = (float *)malloc(product(conv_bias_sizes, 1) * sizeof(float));

    init_net_data(conv_weights, ndims, 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(ndims, conv_user_src_sizes, dnnl_nchw, engine, conv_src,
            &conv_user_src_memory);
    init_data_memory(ndims, conv_user_weights_sizes, dnnl_oihw, engine,
            conv_weights, &conv_user_weights_memory);
    init_data_memory(1, conv_bias_sizes, dnnl_x, 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_create_with_tag(&conv_src_md, ndims,
            conv_user_src_sizes, dnnl_f32, dnnl_format_tag_any));
    CHECK(dnnl_memory_desc_create_with_tag(&conv_weights_md, ndims,
            conv_user_weights_sizes, dnnl_f32, dnnl_format_tag_any));
    CHECK(dnnl_memory_desc_create_with_tag(
            &conv_bias_md, 1, conv_bias_sizes, dnnl_f32, dnnl_x));
    CHECK(dnnl_memory_desc_create_with_tag(&conv_dst_md, ndims,
            conv_user_dst_sizes, dnnl_f32, dnnl_format_tag_any));

    // create a convolution
    dnnl_primitive_desc_t conv_pd;
    CHECK(dnnl_convolution_forward_primitive_desc_create(&conv_pd, engine,
            dnnl_forward, dnnl_convolution_direct, conv_src_md, conv_weights_md,
            conv_bias_md, conv_dst_md, conv_strides, conv_dilation,
            conv_padding, conv_padding, 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
    const_dnnl_memory_desc_t dst_md
            = dnnl_primitive_desc_query_md(conv_pd, dnnl_query_dst_md, 0);
    CHECK(dnnl_memory_create(
            &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;

    const_dnnl_memory_desc_t src_md
            = dnnl_primitive_desc_query_md(conv_pd, dnnl_query_src_md, 0);
    CHECK(prepare_reorder(&conv_user_src_memory, src_md, engine, 1,
            &conv_internal_src_memory, &conv_reorder_src, &n, net, net_args));

    const_dnnl_memory_desc_t weights_md
            = dnnl_primitive_desc_query_md(conv_pd, dnnl_query_weights_md, 0);
    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
    dnnl_primitive_t conv;
    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 = 0.0f;

    // create relu memory descriptor on dst memory descriptor
    // from previous primitive
    const_dnnl_memory_desc_t relu_src_md
            = dnnl_primitive_desc_query_md(conv_pd, dnnl_query_dst_md, 0);
    const_dnnl_memory_desc_t relu_dst_md = relu_src_md;

    // create a relu
    dnnl_primitive_desc_t relu_pd;
    CHECK(dnnl_eltwise_forward_primitive_desc_create(&relu_pd, engine,
            dnnl_forward, dnnl_eltwise_relu, relu_src_md, relu_dst_md,
            negative_slope, 0, NULL));

    dnnl_memory_t relu_dst_memory;
    CHECK(dnnl_memory_create(
            &relu_dst_memory, relu_dst_md, engine, DNNL_MEMORY_ALLOCATE));

    // finally create a relu primitive
    dnnl_primitive_t relu;
    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
    // 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;
    const_dnnl_memory_desc_t lrn_dst_md = lrn_src_md;

    // create a lrn primitive descriptor
    dnnl_primitive_desc_t lrn_pd;
    CHECK(dnnl_lrn_forward_primitive_desc_create(&lrn_pd, engine, dnnl_forward,
            dnnl_lrn_across_channels, lrn_src_md, lrn_dst_md, local_size, alpha,
            beta, k, NULL));

    // create primitives for lrn dst and workspace memory
    dnnl_memory_t lrn_dst_memory;
    CHECK(dnnl_memory_create(
            &lrn_dst_memory, lrn_dst_md, engine, DNNL_MEMORY_ALLOCATE));
    dnnl_memory_t lrn_ws_memory;
    const_dnnl_memory_desc_t lrn_ws_md
            = dnnl_primitive_desc_query_md(lrn_pd, dnnl_query_workspace_md, 0);
    CHECK(dnnl_memory_create(
            &lrn_ws_memory, lrn_ws_md, engine, DNNL_MEMORY_ALLOCATE));

    // finally create a lrn primitive
    dnnl_primitive_t lrn;
    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}
    // dilation: {0, 0}
    dnnl_dims_t pool_dst_sizes;
    for (int i = 0; i < ndims; i++)
        pool_dst_sizes[i] = net_dst_sizes[i];
    dnnl_dims_t pool_kernel = {3, 3};
    dnnl_dims_t pool_strides = {POOL_STRIDE, POOL_STRIDE};
    dnnl_dims_t pool_padding = {POOL_PAD, POOL_PAD};
    dnnl_dims_t pool_dilation = {0, 0};

    // 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_create_with_tag(&pool_dst_any_md, ndims,
            pool_dst_sizes, dnnl_f32, dnnl_format_tag_any));

    // create memory for user data
    dnnl_memory_t pool_user_dst_memory;
    init_data_memory(ndims, pool_dst_sizes, dnnl_nchw, engine, net_dst,
            &pool_user_dst_memory);

    // create a pooling
    dnnl_primitive_desc_t pool_pd;
    CHECK(dnnl_pooling_forward_primitive_desc_create(&pool_pd, engine,
            dnnl_forward, dnnl_pooling_max, pool_src_md, pool_dst_any_md,
            pool_strides, pool_kernel, pool_dilation, pool_padding,
            pool_padding, NULL));

    // create memory for workspace
    dnnl_memory_t pool_ws_memory;
    const_dnnl_memory_desc_t pool_ws_md
            = dnnl_primitive_desc_query_md(pool_pd, dnnl_query_workspace_md, 0);
    CHECK(dnnl_memory_create(
            &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
            = dnnl_primitive_desc_query_md(pool_pd, dnnl_query_dst_md, 0);
    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
    dnnl_primitive_t pool;
    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;
    CHECK(dnnl_stream_create(&stream, engine, dnnl_stream_default_flags));
    for (uint32_t i = 0; i < n; ++i) {
        CHECK(dnnl_primitive_execute(
                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(lrn_pd));
    CHECK(dnnl_primitive_desc_destroy(pool_pd));

    dnnl_stream_destroy(stream);

    free(net_src);
    free(net_dst);

    dnnl_memory_desc_destroy(conv_src_md);
    dnnl_memory_desc_destroy(conv_weights_md);
    dnnl_memory_desc_destroy(conv_bias_md);
    dnnl_memory_desc_destroy(conv_dst_md);
    dnnl_memory_desc_destroy(pool_dst_any_md);

    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);
    dnnl_primitive_destroy(conv);

    free(conv_weights);
    free(conv_bias);

    dnnl_memory_destroy(relu_dst_memory);
    dnnl_primitive_destroy(relu);

    dnnl_memory_destroy(lrn_ws_memory);
    dnnl_memory_destroy(lrn_dst_memory);
    dnnl_primitive_destroy(lrn);

    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);
    dnnl_primitive_destroy(pool);

    dnnl_engine_destroy(engine);
}

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;
}
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Intel® oneAPI Deep Neural Network Developer Guide and Reference

CNN f32 inference example
