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fix null impl case for optimized concat (#18714)

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Wilson Seok 2023-07-24 13:55:53 +09:00 committed by GitHub
parent 2c889c8b5e
commit 90e3e85233
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2 changed files with 114 additions and 1 deletions
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2
src/plugins/intel_gpu/src/graph/primitive_inst.cpp
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@ -527,7 +527,7 @@ bool primitive_inst::update_impl() {
GPU_DEBUG_TRACE_DETAIL << id() << ": update dynamic impl " << prev_impl_str << " to new shape: " << s.str() << std::endl;
};
if ((_impl != nullptr && _impl->is_cpu()) || can_be_optimized()) {
if (_impl != nullptr && (_impl->is_cpu() || can_be_optimized())) {
// Return false if shape not changed, otherwise return true to trigger realloc_if_needed, but do not change impl itself
return shape_changed();
}

113
src/plugins/intel_gpu/tests/unit/dynamic_execution/optimized_out_execution_test.cpp Normal file
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@ -0,0 +1,113 @@
// Copyright (C) 2023 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include "test_utils.h"
#include <intel_gpu/primitives/input_layout.hpp>
#include <intel_gpu/primitives/reorder.hpp>
#include <intel_gpu/primitives/eltwise.hpp>
#include <intel_gpu/primitives/permute.hpp>
#include <intel_gpu/primitives/concatenation.hpp>
#include <intel_gpu/primitives/data.hpp>
#include "program_wrapper.h"
#include <cmath>
#include <algorithm>
using namespace cldnn;
using namespace ::tests;
namespace optimized_out_execution_test {
TEST(optimized_out_execution_test, concat_blocked_format) {
auto& engine = get_test_engine();
auto input1_layout_dyn = layout{ov::PartialShape::dynamic(4), data_types::f16, format::b_fs_yx_fsv16};
auto input2_layout_dyn = layout{ov::PartialShape::dynamic(4), data_types::f16, format::b_fs_yx_fsv16};
auto input3_layout_dyn = layout{ov::PartialShape::dynamic(4), data_types::f16, format::b_fs_yx_fsv16};
auto input4_layout_dyn = layout{ov::PartialShape::dynamic(4), data_types::f16, format::b_fs_yx_fsv16};
auto input1_layout = layout{ov::PartialShape{1, 16, 2, 1}, data_types::f16, format::b_fs_yx_fsv16};
auto input2_layout = layout{ov::PartialShape{1, 16, 2, 1}, data_types::f16, format::b_fs_yx_fsv16};
auto input3_layout = layout{ov::PartialShape{1, 16, 2, 1}, data_types::f16, format::b_fs_yx_fsv16};
auto input4_layout = layout{ov::PartialShape{1, 16, 2, 1}, data_types::f16, format::b_fs_yx_fsv16};
auto input1 = engine.allocate_memory(input1_layout);
auto input2 = engine.allocate_memory(input2_layout);
auto input3 = engine.allocate_memory(input3_layout);
auto input4 = engine.allocate_memory(input4_layout);
set_values<FLOAT16>(input1, {
1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f,
10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f,
1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f,
10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f
});
set_values<FLOAT16>(input2, {
1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f,
10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f,
1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f,
10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f
});
set_values<FLOAT16>(input3, {
1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f,
10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f,
1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f,
10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f
});
set_values<FLOAT16>(input4, {
1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f,
10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f,
1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f,
10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f
});
topology topology(input_layout("input1", input1_layout_dyn),
input_layout("input2", input2_layout_dyn),
input_layout("input3", input3_layout_dyn),
input_layout("input4", input4_layout_dyn),
eltwise("eltwise1", input_info("input1"), input_info("input2"), eltwise_mode::sum),
eltwise("eltwise2", input_info("input3"), input_info("input4"), eltwise_mode::sum),
concatenation("concat", { input_info("eltwise1"), input_info("eltwise2") }, 1),
permute("permute", input_info("concat"), {0, 2, 3, 1}),
reorder("output", input_info("permute"), format::bfyx, data_types::f16));
ExecutionConfig config = get_test_default_config(engine);
config.set_property(ov::intel_gpu::optimize_data(true));
config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
network network(engine, topology, config);
network.set_input_data("input1", input1);
network.set_input_data("input2", input2);
network.set_input_data("input3", input3);
network.set_input_data("input4", input4);
std::vector<FLOAT16> ref = {
2.0f, 4.0f, 6.0f, 8.0f, 10.0f, 12.0f, 14.0f, 16.0f,
20.0f, 40.0f, 60.0f, 80.0f, 100.0f, 120.0f, 140.0f, 160.0f,
2.0f, 4.0f, 6.0f, 8.0f, 10.0f, 12.0f, 14.0f, 16.0f,
20.0f, 40.0f, 60.0f, 80.0f, 100.0f, 120.0f, 140.0f, 160.0f,
2.0f, 4.0f, 6.0f, 8.0f, 10.0f, 12.0f, 14.0f, 16.0f,
20.0f, 40.0f, 60.0f, 80.0f, 100.0f, 120.0f, 140.0f, 160.0f,
2.0f, 4.0f, 6.0f, 8.0f, 10.0f, 12.0f, 14.0f, 16.0f,
20.0f, 40.0f, 60.0f, 80.0f, 100.0f, 120.0f, 140.0f, 160.0f,
2.0f, 4.0f, 6.0f, 8.0f, 10.0f, 12.0f, 14.0f, 16.0f,
20.0f, 40.0f, 60.0f, 80.0f, 100.0f, 120.0f, 140.0f, 160.0f,
2.0f, 4.0f, 6.0f, 8.0f, 10.0f, 12.0f, 14.0f, 16.0f,
20.0f, 40.0f, 60.0f, 80.0f, 100.0f, 120.0f, 140.0f, 160.0f,
2.0f, 4.0f, 6.0f, 8.0f, 10.0f, 12.0f, 14.0f, 16.0f,
20.0f, 40.0f, 60.0f, 80.0f, 100.0f, 120.0f, 140.0f, 160.0f,
2.0f, 4.0f, 6.0f, 8.0f, 10.0f, 12.0f, 14.0f, 16.0f,
20.0f, 40.0f, 60.0f, 80.0f, 100.0f, 120.0f, 140.0f, 160.0f
};
auto outputs = network.execute();
auto output_mem = outputs.begin()->second.get_memory();
cldnn::mem_lock<FLOAT16> output_mem_ptr(output_mem, get_test_stream());
for (size_t i = 0; i < output_mem->get_layout().get_buffer_size().count(); ++i) {
ASSERT_EQ(output_mem_ptr[i], ref[i]);
}
}
} // is_valid_fusion_tests