Reorder between CPU and GPU engines

Intel® oneAPI Deep Neural Network Developer Guide and Reference

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ID 768875

Date 2/28/2024

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Reorder between CPU and GPU engines

This C API example demonstrates programming flow when reordering memory between CPU and GPU engines.

/*******************************************************************************
* Copyright 2019-2022 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.
*******************************************************************************/



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

#include "oneapi/dnnl/dnnl.h"

#include "example_utils.h"

size_t product(int n_dims, const dnnl_dim_t dims[]) {
    size_t n_elems = 1;
    for (int d = 0; d < n_dims; ++d) {
        n_elems *= (size_t)dims[d];
    }
    return n_elems;
}

void fill(dnnl_memory_t mem, int n_dims, const dnnl_dim_t dims[]) {
    const size_t n_elems = product(n_dims, dims);
    float *array = (float *)malloc(n_elems * sizeof(float));
    if (!array) COMPLAIN_EXAMPLE_ERROR_AND_EXIT("%s", "malloc returned NULL");

    for (size_t e = 0; e < n_elems; ++e) {
        array[e] = e % 7 ? 1.0f : -1.0f;
    }

    write_to_dnnl_memory(array, mem);
    free(array);
}

int find_negative(dnnl_memory_t mem, int n_dims, const dnnl_dim_t dims[]) {
    const size_t n_elems = product(n_dims, dims);
    float *array = (float *)malloc(n_elems * sizeof(float));
    if (!array) COMPLAIN_EXAMPLE_ERROR_AND_EXIT("%s", "malloc returned NULL");
    read_from_dnnl_memory(array, mem);

    int negs = 0;
    for (size_t e = 0; e < n_elems; ++e) {
        negs += array[e] < 0.0f;
    }

    free(array);
    return negs;
}

void cross_engine_reorder() {
    dnnl_engine_t engine_cpu, engine_gpu;
    CHECK(dnnl_engine_create(&engine_cpu, validate_engine_kind(dnnl_cpu), 0));
    CHECK(dnnl_engine_create(&engine_gpu, validate_engine_kind(dnnl_gpu), 0));

    const dnnl_dims_t tz = {2, 16, 1, 1};

    dnnl_memory_desc_t m_cpu_md, m_gpu_md;
    CHECK(dnnl_memory_desc_create_with_tag(
            &m_cpu_md, 4, tz, dnnl_f32, dnnl_nchw));
    CHECK(dnnl_memory_desc_create_with_tag(
            &m_gpu_md, 4, tz, dnnl_f32, dnnl_nchw));

    dnnl_memory_t m_cpu, m_gpu;
    CHECK(dnnl_memory_create(
            &m_cpu, m_cpu_md, engine_cpu, DNNL_MEMORY_ALLOCATE));
    CHECK(dnnl_memory_create(
            &m_gpu, m_gpu_md, engine_gpu, DNNL_MEMORY_ALLOCATE));

    fill(m_cpu, 4, tz);
    if (find_negative(m_cpu, 4, tz) == 0)
        COMPLAIN_EXAMPLE_ERROR_AND_EXIT(
                "%s", "incorrect data fill, no negative values found");

    /* reorder cpu -> gpu */
    dnnl_primitive_desc_t r1_pd;
    CHECK(dnnl_reorder_primitive_desc_create(
            &r1_pd, m_cpu_md, engine_cpu, m_gpu_md, engine_gpu, NULL));
    dnnl_primitive_t r1;
    CHECK(dnnl_primitive_create(&r1, r1_pd));

    /* relu gpu */
    dnnl_primitive_desc_t relu_pd;
    CHECK(dnnl_eltwise_forward_primitive_desc_create(&relu_pd, engine_gpu,
            dnnl_forward, dnnl_eltwise_relu, m_gpu_md, m_gpu_md, 0.0f, 0.0f,
            NULL));

    dnnl_primitive_t relu;
    CHECK(dnnl_primitive_create(&relu, relu_pd));

    /* reorder gpu -> cpu */
    dnnl_primitive_desc_t r2_pd;
    CHECK(dnnl_reorder_primitive_desc_create(
            &r2_pd, m_gpu_md, engine_gpu, m_cpu_md, engine_cpu, NULL));
    dnnl_primitive_t r2;
    CHECK(dnnl_primitive_create(&r2, r2_pd));

    dnnl_stream_t stream_gpu;
    CHECK(dnnl_stream_create(
            &stream_gpu, engine_gpu, dnnl_stream_default_flags));

    dnnl_exec_arg_t r1_args[] = {{DNNL_ARG_FROM, m_cpu}, {DNNL_ARG_TO, m_gpu}};
    CHECK(dnnl_primitive_execute(r1, stream_gpu, 2, r1_args));

    dnnl_exec_arg_t relu_args[]
            = {{DNNL_ARG_SRC, m_gpu}, {DNNL_ARG_DST, m_gpu}};
    CHECK(dnnl_primitive_execute(relu, stream_gpu, 2, relu_args));

    dnnl_exec_arg_t r2_args[] = {{DNNL_ARG_FROM, m_gpu}, {DNNL_ARG_TO, m_cpu}};
    CHECK(dnnl_primitive_execute(r2, stream_gpu, 2, r2_args));

    CHECK(dnnl_stream_wait(stream_gpu));

    if (find_negative(m_cpu, 4, tz) != 0)
        COMPLAIN_EXAMPLE_ERROR_AND_EXIT(
                "%s", "found negative values after ReLU applied");

    /* clean up */
    dnnl_primitive_desc_destroy(relu_pd);
    dnnl_primitive_desc_destroy(r1_pd);
    dnnl_primitive_desc_destroy(r2_pd);

    dnnl_primitive_destroy(relu);
    dnnl_primitive_destroy(r1);
    dnnl_primitive_destroy(r2);
    dnnl_memory_destroy(m_cpu);
    dnnl_memory_destroy(m_gpu);
    dnnl_memory_desc_destroy(m_cpu_md);
    dnnl_memory_desc_destroy(m_gpu_md);

    dnnl_stream_destroy(stream_gpu);

    dnnl_engine_destroy(engine_cpu);
    dnnl_engine_destroy(engine_gpu);
}

int main() {
    cross_engine_reorder();
    printf("Example passed on CPU/GPU.\n");
    return 0;
}

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Intel® oneAPI Deep Neural Network Developer Guide and Reference

Reorder between CPU and GPU engines