which case can report "No configurations found."

[feixuedudiao]

which case can report "No configurations found."

[yehudaorel]

@feixuedudiao can you verify your OpenCL driver version?

adding @kealan-barbieri, @echeresh for input

[kealan-barbieri]

This is due to limited support for gen9 platforms in the optimized conv implementation. I submitted a small PR to provide a workaround for such cases.

[feixuedudiao]

@yehudaorel. Thanks. The driver version is "31.0.101.2125".

[feixuedudiao]

@kealan-barbieri. Thank you, but how can i get this version code?

[vpirogov]

@kealan-barbieri, @feixuedudiao, I'd like to reiterate that support of Intel(R) UHD Graphics 630 is discontinued.

[feixuedudiao]

@vpirogov Thanks. I want to apply oneDNN at HD 630 or lower cpu with fp16 or bf16, which version can support?

[vpirogov]

oneDNN v3.3.6 is the last version with HD 630 support.

[feixuedudiao]

@vpirogov Ok ,thanks, i try it .

[feixuedudiao]

@vpirogov @kealan-barbieri thank you to help me. But i test this problem, when the data type for f16, the dims of src ,weight, bias and dst are {1,4,224,384} ,{32, 4, 3, 3},{32} and {1,32,112,192}, the kind is gpu, it can not crash at auto conv_prim = convolution_forward(conv_pd). i test version with v3.3.6 and v.3.3.4. The code that is listed below:
/*******************************************************************************

• Copyright 2020-2022 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.
  *******************************************************************************/

/// @example convolution.cpp
/// > Annotated version: @ref convolution_example_cpp
///
/// @page convolution_example_cpp_short
///
/// This C++ API example demonstrates how to create and execute a
/// [Convolution](@ref dev_guide_convolution) primitive in forward propagation
/// mode in two configurations - with and without groups.
///
/// Key optimizations included in this example:
/// - Creation of optimized memory format from the primitive descriptor;
/// - Primitive attributes with fused post-ops.
///
/// @page convolution_example_cpp Convolution Primitive Example
/// @copydetails convolution_example_cpp_short
///
/// @include convolution.cpp

#include
#include
#include
#include
#include
#include

#include "example_utils.hpp"
#include "oneapi/dnnl/dnnl.hpp"

using namespace dnnl;

using tag = memory::format_tag;
using dt = memory::data_type;

void convolution_example(dnnl::engine::kind engine_kind) {

// Create execution dnnl::engine.
dnnl::engine engine(engine_kind, 0);

// Create dnnl::stream.
dnnl::stream engine_stream(engine);

// Tensor dimensions.

// Source (src), weights, bias, and destination (dst) tensors
// dimensions.
/*
memory::dims src_dims = {N, IC, IH, IW};
memory::dims weights_dims = {OC, IC, KH, KW};
memory::dims bias_dims = {OC};
memory::dims dst_dims = {N, OC, OH, OW};

// Strides, padding dimensions.
memory::dims strides_dims = {SH, SW};
memory::dims padding_dims_l = {PH_L, PW_L};
memory::dims padding_dims_r = {PH_R, PW_R};

*/

memory::dims src_dims = {1, 4, 224, 384};
memory::dims weights_dims = {32, 4, 3, 3};
memory::dims bias_dims = {32};
memory::dims dst_dims = {1, 32, 112, 192};

// Strides, padding dimensions.
memory::dims strides_dims = {2, 2};
memory::dims padding_dims_l = {1, 1};
memory::dims padding_dims_r = {1, 1};

/*
memory::dims src_dims = {1, 32, 56, 96};
memory::dims weights_dims = {1, 32, 3, 3};
memory::dims bias_dims = {1};
memory::dims dst_dims = {1, 1, 56, 96};

// Strides, padding dimensions.
memory::dims strides_dims = {1, 1};
memory::dims padding_dims_l = {1, 1};
memory::dims padding_dims_r = {1, 1};

*/
// Allocate buffers.
std::vector src_data(product(src_dims));
std::vector weights_data(product(weights_dims));
std::vector bias_data(product(bias_dims));
std::vector dst_data(product(dst_dims));

// Initialize src, weights, and dst tensors.
std::generate(src_data.begin(), src_data.end(), []() {
    static int i = 0;
    return std::cos(i++ / 10.f);
});
std::generate(weights_data.begin(), weights_data.end(), []() {
    static int i = 0;
    return std::sin(i++ * 2.f);
});
std::generate(bias_data.begin(), bias_data.end(), []() {
    static int i = 0;
    return std::tanh(float(i++));
});


//memset(&src_data[0], 0, src_data.size() / 3 * sizeof(float));

// Create memory objects for tensor data (src, weights, dst). In this
// example, NCHW layout is assumed for src and dst, and OIHW for weights.
auto user_src_mem = memory({src_dims, dt::f32, tag::nchw}, engine);
auto user_weights_mem = memory({weights_dims, dt::f32, tag::oihw}, engine);
auto user_dst_mem = memory({dst_dims, dt::f32, tag::nchw}, engine);

std::vector<std::unordered_map<int, memory>> conv_args;
std::vector<primitive> convs;

// Create memory descriptors with format_tag::any for the primitive. This
// enables the convolution primitive to choose memory layouts for an
// optimized primitive implementation, and these layouts may differ from the
// ones provided by the user.
auto conv_src_md = memory::desc(src_dims, dt::f16, tag::any);
auto conv_weights_md = memory::desc(weights_dims, dt::f16, tag::any);
auto conv_dst_md = memory::desc(dst_dims, dt::f16, tag::any);

// Create memory descriptor and memory object for input bias.
auto user_bias_md = memory::desc(bias_dims, dt::f16, tag::a);
auto user_bias_mem = memory(user_bias_md, engine);

// Write data to memory object's handle.
write_to_dnnl_memory(src_data.data(), user_src_mem);
write_to_dnnl_memory(weights_data.data(), user_weights_mem);
write_to_dnnl_memory(bias_data.data(), user_bias_mem);

// Create primitive post-ops (ReLU).
const float alpha = 0.166667f;
const float beta = 0.5f;
post_ops conv_ops;
//conv_ops.append_eltwise(algorithm::eltwise_hardswish, alpha, beta);
primitive_attr conv_attr;
conv_attr.set_post_ops(conv_ops);

// Create primitive descriptor.
/*
auto conv_pd = convolution_forward::primitive_desc(engine,
        prop_kind::forward_training, algorithm::convolution_direct,
        conv_src_md, conv_weights_md, user_bias_md, conv_dst_md,
        strides_dims, padding_dims_l, padding_dims_r, conv_attr);

*/
try{
convolution_forward::primitive_desc(engine,
prop_kind::forward_inference, algorithm::convolution_direct,
conv_src_md, conv_weights_md, user_bias_md, conv_dst_md,
strides_dims, padding_dims_l, padding_dims_r, conv_attr);
}
catch(error &e)
{
if (e.status == dnnl_unimplemented)
{
std::cout << " No bf16 or f32 convolution implementation is available for this platform !" << std::endl;
return ;
}

}

auto conv_pd = convolution_forward::primitive_desc(engine,
prop_kind::forward_inference, algorithm::convolution_direct,
conv_src_md, conv_weights_md, user_bias_md, conv_dst_md,
strides_dims, padding_dims_l, padding_dims_r, conv_attr);

// For now, assume that the src, weights, and dst memory layouts generated
// by the primitive and the ones provided by the user are identical.
auto conv_src_mem = user_src_mem;
auto conv_weights_mem = user_weights_mem;
auto conv_dst_mem = user_dst_mem;

// Reorder the data in case the src and weights memory layouts generated by
// the primitive and the ones provided by the user are different. In this
// case, we create additional memory objects with internal buffers that will
// contain the reordered data. The data in dst will be reordered after the
// convolution computation has finalized.
/*
if (conv_pd.src_desc() != user_src_mem.get_desc()) {
    conv_src_mem = memory(conv_pd.src_desc(), engine);
    auto rd = reorder(user_src_mem, conv_src_mem);
    convs.push_back(rd);
    conv_args.push_back({{DNNL_ARG_FROM, user_src_mem},
                       {DNNL_ARG_TO,  conv_src_mem}});
            //.execute(engine_stream, user_src_mem, conv_src_mem);
}
*/


if (conv_pd.weights_desc() != user_weights_mem.get_desc()) {
    conv_weights_mem = memory(conv_pd.weights_desc(), engine);
    reorder(user_weights_mem, conv_weights_mem)
            .execute(engine_stream, user_weights_mem, conv_weights_mem);
}

if (conv_pd.dst_desc() != user_dst_mem.get_desc()) {
    conv_dst_mem = memory(conv_pd.dst_desc(), engine);
}

// Create the primitive.
auto conv_prim = convolution_forward(conv_pd);

// Primitive arguments.

//conv_args.insert({DNNL_ARG_SRC, conv_src_mem});
//conv_args.insert({DNNL_ARG_WEIGHTS, conv_weights_mem});
//conv_args.insert({DNNL_ARG_BIAS, user_bias_mem});
//conv_args.insert({DNNL_ARG_DST, conv_dst_mem});
conv_args.push_back({{DNNL_ARG_SRC, conv_src_mem},
                    {DNNL_ARG_WEIGHTS, conv_weights_mem},
                 { DNNL_ARG_BIAS, user_bias_mem},
                { DNNL_ARG_DST, conv_dst_mem}});
// Primitive execution: convolution with ReLU.
//conv_prim.execute(engine_stream, conv_args);
convs.push_back(conv_prim);

auto hd_swish_pd
        = eltwise_forward::primitive_desc(engine, prop_kind::forward_inference,
                algorithm::eltwise_hardswish, conv_dst_mem.get_desc(),
                conv_dst_mem.get_desc(), alpha, beta);

convs.push_back(eltwise_forward(hd_swish_pd));
conv_args.push_back({{DNNL_ARG_SRC, conv_dst_mem},
        {DNNL_ARG_DST, conv_dst_mem}});

for(int i = 0; i < convs.size(); i++)
{
	convs.at(i).execute(engine_stream, conv_args.at(i));
	std::unordered_map<int,memory> dstVecMem = conv_args.at(i);
    std::string resPath = "conv_result_";
     resPath += std::to_string(i);

    resPath += std::to_string(i) + "_";
	int j = 0 ;
    for(auto it = dstVecMem.begin(); it != dstVecMem.end(); it++)
    {
       j = it->first;
       memory dst_mem = it->second;
       std::string fnlResPath = resPath   + std::to_string(j);

       std::ofstream resStream(fnlResPath + ".txt", std::ios::binary);
       int flag = resStream.is_open();
       if (!flag)
       {
           std::cout <<"failed to open file !"<< std::endl;
       }

       const_dnnl_memory_desc_t md;
       dnnl_memory_get_memory_desc(dnnl_memory_t(dst_mem), &md);
       size_t memSz = dnnl_memory_desc_get_size(md);
       std::vector<float> resVecF(memSz / sizeof(float));
       read_from_dnnl_memory(resVecF.data(), dst_mem);

       for (auto v : resVecF) {
        resStream << v << std::endl;
       }
       resStream.close();

    }

}
// Reorder the data in case the dst memory descriptor generated by the
// primitive and the one provided by the user are different.
if (conv_pd.dst_desc() != user_dst_mem.get_desc()) {
    reorder(conv_dst_mem, user_dst_mem)
            .execute(engine_stream, conv_dst_mem, user_dst_mem);
} else
    user_dst_mem = conv_dst_mem;

// Wait for the computation to finalize.
//engine_stream.wait();

// Read data from memory object's handle.
read_from_dnnl_memory(dst_data.data(), conv_dst_mem);

/*
std::string resPath = "result_";
std::ofstream resStream(resPath + ".txt", std::ios::binary);
int flag = resStream.is_open();
if (!flag)
{
std::cout <<"failed to open file !"<< std::endl;
}
for (auto v : dst_data) {
resStream << v << std::endl;
}
resStream.close();

std::string inPath = "input_";
std::ofstream inStream(inPath + ".txt", std::ios::binary);
flag = inStream.is_open();
if (!flag)
{
    std::cout <<"failed to open file !"<< std::endl;
}
for (auto v : src_data) {
    inStream << v << std::endl;
}
inStream.close();
*/

}

int main(int argc, char **argv) {

engine::kind kind_cpu = engine::kind::gpu;
convolution_example(kind_cpu);

}