oneDNN
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oneapi-src
which case can report "No configurations found."
Summary
Run the example of convolution with the above code : // Create execution dnnl::engine. int isGpu = 1; engine::kind cpu_kind = engine::kind::cpu; engine::kind gpu_kind = engine::kind::gpu;
auto cpu_eng = engine(validate_device(cpu_kind), 0);
auto gpu_eng = engine(validate_device(gpu_kind), 0);
// Create dnnl::stream.
dnnl::stream engine_stream(isGpu ? gpu_eng : cpu_eng);
// Source (src), weights, bias, and destination (dst) tensors
// dimensions.
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<float> src_data(product(src_dims));
std::vector<float> weights_data(product(weights_dims));
std::vector<float> bias_data(1);
std::vector<float> 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++));
});
// 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}, isGpu ? gpu_eng : cpu_eng);
auto user_weights_mem = memory({weights_dims, dt::f32, tag::oihw}, isGpu ? gpu_eng : cpu_eng);
auto user_dst_mem = memory({dst_dims, dt::f32, tag::nchw}, isGpu ? gpu_eng : cpu_eng);
auto user_bias_mem = memory({bias_dims, dt::f32, tag::a}, isGpu ? gpu_eng : cpu_eng);
// 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);
// 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.f;
const float beta = 0.f;
post_ops conv_ops;
conv_ops.append_eltwise(algorithm::eltwise_relu, alpha, beta);
primitive_attr conv_attr;
conv_attr.set_post_ops(conv_ops);
// Create primitive descriptor.
auto conv_pd = convolution_forward::primitive_desc(isGpu ? gpu_eng : cpu_eng,
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_bias_mem = user_bias_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(), isGpu ? gpu_eng : cpu_eng);
reorder(user_src_mem, 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(), isGpu ? gpu_eng : cpu_eng);
reorder(user_weights_mem, conv_weights_mem)
.execute(engine_stream, user_weights_mem, conv_weights_mem);
}
if (conv_pd.bias_desc() != user_bias_mem.get_desc())
{
conv_bias_mem = memory(conv_pd.bias_desc(), isGpu ? gpu_eng : cpu_eng);
auto bias_rd = reorder(user_bias_mem, conv_bias_mem);
bias_rd.execute(engine_stream, user_bias_mem, conv_bias_mem);
}
if (conv_pd.dst_desc() != user_dst_mem.get_desc()) {
conv_dst_mem = memory(conv_pd.dst_desc(), isGpu ? gpu_eng : cpu_eng);
}
// Create the primitive.
auto conv_prim = convolution_forward(conv_pd);
// Primitive arguments.
std::unordered_map<int, memory> conv_args;
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});
// Primitive execution: convolution with ReLU.
conv_prim.execute(engine_stream, conv_args);
// 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(), user_dst_mem);
Environment
windows 10 cpu is i7-10700 @2.90GHz Compiler vs2022 with DNN_GPU_RUNTIME=ocl
Version
v3.3.6
Problem
It reported "No configurations found" when the code is running "auto conv_prim = convolution_forward(conv_pd);", ????????. . .onednn_verbose,info,oneDNN v3.3.6 (commit N/A) onednn_verbose,info,cpu,runtime:sequential,nthr:1 onednn_verbose,info,cpu,isa:Intel AVX2 onednn_verbose,info,gpu,runtime:OpenCL onednn_verbose,info,gpu,engine,0,name:Intel(R) UHD Graphics 630,driver_version:31.0.101,binary_kernels:enabled onednn_verbose,primitive,info,template:operation,engine,primitive,implementation,prop_kind,memory_descriptors,attributes,auxiliary,problem_desc,exec_time onednn_verbose,primitive,exec,gpu,reorder,jit:ir,undef,src_f32::blocked:abcd::f0 dst_f16::blocked:aBcd16b::f0,,,1x32x56x96,0.7471 onednn_verbose,primitive,exec,gpu,reorder,jit:ir,undef,src_f32::blocked:abcd::f0 dst_f16:p:blocked:ABcd16b8a::f0,,,4x32x3x3,0.6235 onednn_verbose,primitive,exec,gpu,reorder,jit:ir,undef,src_f32::blocked:a::f0 dst_f16::blocked:a::f0,,,4,0.5051 Assertion !params_gen_.is_empty() failed at F:\Share\OneDNN\v3.3.6\oneDNN-3.3.6_no_verbose\src\gpu\jit\conv\tiler.cpp:2373 No configurations found.
Note
I test the data type with bf16, it not crashed.
@feixuedudiao can you verify your OpenCL driver version?
adding @kealan-barbieri, @echeresh for input
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.
@kealan-barbieri, @feixuedudiao, I'd like to reiterate that support of Intel(R) UHD Graphics 630 is discontinued.
@vpirogov Thanks. I want to apply oneDNN at HD 630 or lower cpu with fp16 or bf16, which version can support?
@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 "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
// 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);
}