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[GPU] Fix a bug of fusing eltwise sum post-op. (#17078)

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+ When input of eltwise is full-tensor constant layer, use binary add
instead of sum as post-op on oneDNN.
This commit is contained in:
Jade Cho 2023-04-21 20:17:35 +09:00 committed by GitHub
parent 6ad80576b7
commit 8fbd78fb07
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2 changed files with 59 additions and 0 deletions
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1
src/plugins/intel_gpu/src/graph/program_helpers.cpp
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@ -114,6 +114,7 @@ add_fusing_type onednn_add_fusing_helpers::get_add_fusing_type(
&& p_layout.format == d_layout.format && p_layout.get_tensor() == d_layout.get_tensor()
&& p_layout.data_padding == d_layout.data_padding
&& dep_node.get_users().size() == 1
&& !dep_node.is_constant()
&& !p_node.is_type<pooling>()) {
return add_fusing_type::sum;
} else if (p_layout.get_tensor() == d_layout.get_tensor()) {

58
src/plugins/intel_gpu/tests/fusions/convolution_fusion_test.cpp
View File

@ -4378,6 +4378,64 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_before_permute_optimizing, ::testing:
convolution_test_params{ CASE_CONV_FP16_PERMUTE_2, 3, 2, 4 },
}));
class EltwiseSumWithConstantFullTensorFusingTestOneDNN : public BaseFusingTest<convolution_eltw_sum_test_params> {
public:
void execute(convolution_eltw_sum_test_params& p) {
if (!engine.get_device_info().supports_immad)
return;
auto input_prim = get_mem(get_weights_layout(p));
network network_not_fused(this->engine, this->topology_non_fused, cfg_not_fused);
network network_fused(this->engine, this->topology_fused, cfg_fused);
network_fused.set_input_data("input", input_prim);
network_not_fused.set_input_data("input", input_prim);
// Multiple executions of network to increase error of result.
// The output of constant layer will be changed through this iterations bigger.
for (int i = 0; i < 10; i++) {
network_not_fused.execute();
network_fused.execute();
}
compare(network_not_fused, network_fused, p);
}
layout get_input_layout(convolution_eltw_sum_test_params& p) {
auto pad = p.pad;
std::vector<int> pad_ = { 0, 0, static_cast<int>(pad[0]), static_cast<int>(pad[1]) };
return layout{ p.data_type, p.input_format, p.in_shape, padding{ pad_ } };
}
layout get_per_channel_layout(convolution_eltw_sum_test_params& p) {
return layout{ p.default_type, p.default_format, tensor{ 1, p.out_shape.feature[0], 1, 1 } };
}
};
// When dependency of eltwise is full tensor constant, use binary add instead of sum as post-op.
class onednn_replace_full_tensor_sum_to_binary_add : public EltwiseSumWithConstantFullTensorFusingTestOneDNN {};
TEST_P(onednn_replace_full_tensor_sum_to_binary_add, basic) {
auto p = GetParam();
if (engine.get_device_info().supports_immad)
p.expected_fused_primitives = p.expected_fused_primitives_onednn;
create_topologies(
data("src0", get_mem(get_input_layout(p))),
input_layout("input", get_weights_layout(p)), // Input is weights.
data("eltwise_data", get_mem(layout{ p.eltw_type, p.eltw_format, p.out_shape })),
convolution("conv_prim", input_info("src0"), { "input" }, {}, p.groups, p.stride, p.pad, p.dilation, false),
eltwise("sum", { input_info("conv_prim"), input_info("eltwise_data") }, eltwise_mode::sum, p.out_type),
reorder("reorder_bfyx", input_info("sum"), p.default_format, p.default_type)
);
tolerance = 0.01f;
execute(p);
}
// in_shape; out_shape; kernel; stride; pad; dilation; groups; data_type; input_format; weights_type; weights_format; eltw_type; eltw_format; out_type; out_format; default_type; default_format;
#define CASE_CONV_ELTW_SUM_TO_BINARY_ADD { 1, 32, 4, 4 }, { 1, 32, 2, 2 }, { 1, 1, 3, 3 }, { 1, 1 }, { 0, 0 }, { 1, 1 }, 1, data_types::f16, format::bfyx, data_types::f16, format::bfyx, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
INSTANTIATE_TEST_SUITE_P(eltwise_sum_fusings_gpu, onednn_replace_full_tensor_sum_to_binary_add, ::testing::ValuesIn(std::vector<convolution_eltw_sum_test_params>{
convolution_eltw_sum_test_params{ CASE_CONV_ELTW_SUM_TO_BINARY_ADD, 2, 3, 4 },
}));
#endif // ENABLE_ONEDNN_FOR_GPU