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use random values in test
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Signed-off-by: fishbell <[email protected]>
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@songbell
songbell committed Dec 25, 2024
1 parent 293a707 commit 98e39cc
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Showing 2 changed files with 75 additions and 78 deletions.
26 changes: 11 additions & 15 deletions src/plugins/intel_gpu/src/graph/graph_optimizer/reshape_transfer.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -34,22 +34,18 @@ void reshape_transfer::run(program& p) {
is_suitable_parent = [&is_suitable_parent](const cldnn::program_node* node) -> bool {
if (node->get_users().size() != 1 || node->is_dynamic())
return false;
if (node->is_type<convolution>())
return true;
if (node->is_type<reorder>()) {
for (size_t idx = 0; idx < node->get_dependencies().size(); idx++) {
auto& input = node->get_dependency(idx);
if (!input.is_in_data_flow() || input.is_constant())
continue;
if (input.is_type<convolution>()) {
return true;
} else if (input.is_type<eltwise>() && input.get_dependency(1).is_constant()) {
return is_suitable_parent(&input);
} else if (input.is_type<activation>()) {
return is_suitable_parent(&input);
}
return false;
for (size_t idx = 0; idx < node->get_dependencies().size(); idx++) {
auto& input = node->get_dependency(idx);
if (!input.is_in_data_flow() || input.is_constant())
continue;
if (node->is_type<convolution>() || input.is_type<convolution>()) {
return true;
} else if (input.is_type<eltwise>() && input.get_dependency(1).is_constant()) {
return is_suitable_parent(&input);
} else if (input.is_type<activation>()) {
return is_suitable_parent(&input);
}
return false;
}
return false;
};
Expand Down
127 changes: 64 additions & 63 deletions src/plugins/intel_gpu/tests/unit/passes/reorder_reshape_permute.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -17,13 +17,9 @@ TEST(opt_reorder_reshape_permute, no_reshape) {
auto in_layout = layout{ov::PartialShape({1, 2, 4, 6}), data_types::f16, format::bfyx};
auto input = engine.allocate_memory(layout{ov::PartialShape({1, 2, 4, 6}), data_types::f16, format::bfyx});
auto weight = engine.allocate_memory(layout{ov::PartialShape({3, 2, 1, 1}), data_types::f16, format::bfyx});
tests::set_random_values<ov::float16>(input);
tests::set_random_values<ov::float16>(weight);

set_values<ov::float16>(input, {2.0f, 3.0f, 4.0f, 4.0f, 3.0f, 2.0f, 1.f, 2.f, 3.f, 1.f, 2.f, 4.f,
5.f, 1.f, 1.f, 2.f, 1.f, 2.f, 2.0f, 3.0f, 1.0f, 4.0f, 1.0f, 4.0f,
3.0f, 2.0f, 0.0f, 1.0f, 0.0f, 2.0f, 2.f, 4.f, 1.f, 1.f, 2.f, 1.f,
1.f, 2.f, 0.f, 2.f, 5.f, 2.f, 4.0f, 3.0f, 1.0f, 0.0f, 3.0f, 2.0f});

set_values<ov::float16>(weight, {1.f, 1.f, 1.f, 1.f, 1.f, 1.f});
topology topology;
topology.add(input_layout("input", in_layout));
topology.add(data("weight", weight));
Expand Down Expand Up @@ -54,17 +50,23 @@ TEST(opt_reorder_reshape_permute, no_reshape) {
});
ASSERT_NE(it, optimzed_nodes.end());
auto permute_inst = net.get_primitive("permute_inter");
ASSERT_TRUE(permute_inst->can_be_optimized());
if (net.get_primitive("convolution")->get_impl()->is_onednn()) {
ASSERT_TRUE(permute_inst->can_be_optimized());
}
auto out_mem = output.at("softmax").get_memory();
mem_lock<ov::float16> lock(out_mem, get_test_stream());

ref_network.set_input_data("input", input);
auto ref_output = ref_network.execute();
auto ref_out_mem = ref_output.at("softmax").get_memory();
mem_lock<ov::float16> lock_ref(ref_out_mem, get_test_stream());
auto tolerance = default_tolerance(ref_out_mem->get_layout().data_type);
for (size_t i = 0; i < out_mem->count(); i++) {
float actual = lock[i];
ASSERT_EQ(actual, lock_ref[i]);
ASSERT_NEAR(lock[i],lock_ref[i], tolerance)
<< "\ntolerance = " << tolerance
<< "\ni = " << i
<< "\nref[i] = " << lock_ref[i]
<< "\nopt[i] = " << lock[i];
}
}

Expand All @@ -73,12 +75,8 @@ TEST(opt_reorder_reshape_permute, no_reorder) {
auto in_layout = layout{ov::PartialShape({1, 2, 4, 6}), data_types::f16, format::bfyx};
auto input = engine.allocate_memory(layout{ov::PartialShape({1, 2, 4, 6}), data_types::f16, format::bfyx});
auto weight = engine.allocate_memory(layout{ov::PartialShape({3, 2, 1, 1}), data_types::f16, format::bfyx});
set_values<ov::float16>(input, {2.0f, 3.0f, 4.0f, 4.0f, 3.0f, 2.0f, 1.f, 2.f, 3.f, 1.f, 2.f, 4.f,
5.f, 1.f, 1.f, 2.f, 1.f, 2.f, 2.0f, 3.0f, 1.0f, 4.0f, 1.0f, 4.0f,
3.0f, 2.0f, 0.0f, 1.0f, 0.0f, 2.0f, 2.f, 4.f, 1.f, 1.f, 2.f, 1.f,
1.f, 2.f, 0.f, 2.f, 5.f, 2.f, 4.0f, 3.0f, 1.0f, 0.0f, 3.0f, 2.0f});

set_values<ov::float16>(weight, {1.f, 1.f, 1.f, 1.f, 1.f, 1.f});
tests::set_random_values<ov::float16>(input);
tests::set_random_values<ov::float16>(weight);
topology topology;
topology.add(input_layout("input", in_layout));
topology.add(data("weight", weight));
Expand Down Expand Up @@ -110,17 +108,23 @@ TEST(opt_reorder_reshape_permute, no_reorder) {
ASSERT_TRUE(reshape_dist > permute_dist);
// select preferred formats, conv + permute
auto permute_inst = net.get_primitive("permute_inter");
ASSERT_TRUE(permute_inst->can_be_optimized());
if (net.get_primitive("convolution")->get_impl()->is_onednn()) {
ASSERT_TRUE(permute_inst->can_be_optimized());
}
auto out_mem = output.at("softmax").get_memory();
mem_lock<ov::float16> lock(out_mem, get_test_stream());

ref_network.set_input_data("input", input);
auto ref_output = ref_network.execute();
auto ref_out_mem = ref_output.at("softmax").get_memory();
mem_lock<ov::float16> lock_ref(ref_out_mem, get_test_stream());
auto tolerance = default_tolerance(ref_out_mem->get_layout().data_type);
for (size_t i = 0; i < out_mem->count(); i++) {
float actual = lock[i];
ASSERT_EQ(actual, lock_ref[i]);
ASSERT_NEAR(lock[i],lock_ref[i], tolerance)
<< "\ntolerance = " << tolerance
<< "\ni = " << i
<< "\nref[i] = " << lock_ref[i]
<< "\nopt[i] = " << lock[i];
}
}

Expand All @@ -129,12 +133,8 @@ TEST(opt_reorder_reshape_permute, no_reorder_no_reshape) {
auto in_layout = layout{ov::PartialShape({1, 2, 4, 6}), data_types::f16, format::bfyx};
auto input = engine.allocate_memory(layout{ov::PartialShape({1, 2, 4, 6}), data_types::f16, format::bfyx});
auto weight = engine.allocate_memory(layout{ov::PartialShape({3, 2, 1, 1}), data_types::f16, format::bfyx});
set_values<ov::float16>(input, {2.0f, 3.0f, 4.0f, 4.0f, 3.0f, 2.0f, 1.f, 2.f, 3.f, 1.f, 2.f, 4.f,
5.f, 1.f, 1.f, 2.f, 1.f, 2.f, 2.0f, 3.0f, 1.0f, 4.0f, 1.0f, 4.0f,
3.0f, 2.0f, 0.0f, 1.0f, 0.0f, 2.0f, 2.f, 4.f, 1.f, 1.f, 2.f, 1.f,
1.f, 2.f, 0.f, 2.f, 5.f, 2.f, 4.0f, 3.0f, 1.0f, 0.0f, 3.0f, 2.0f});

set_values<ov::float16>(weight, {1.f, 1.f, 1.f, 1.f, 1.f, 1.f});
tests::set_random_values<ov::float16>(input);
tests::set_random_values<ov::float16>(weight);
topology topology;
topology.add(input_layout("input", in_layout));
topology.add(data("weight", weight));
Expand All @@ -157,17 +157,23 @@ TEST(opt_reorder_reshape_permute, no_reorder_no_reshape) {
cldnn::network ref_network(engine, topology, ref_config);
// select preferred formats, conv + permute
auto permute_inst = net.get_primitive("permute_inter");
ASSERT_TRUE(permute_inst->can_be_optimized());
if (net.get_primitive("convolution")->get_impl()->is_onednn()) {
ASSERT_TRUE(permute_inst->can_be_optimized());
}
auto out_mem = output.at("softmax").get_memory();
mem_lock<ov::float16> lock(out_mem, get_test_stream());

ref_network.set_input_data("input", input);
auto ref_output = ref_network.execute();
auto ref_out_mem = ref_output.at("softmax").get_memory();
mem_lock<ov::float16> lock_ref(ref_out_mem, get_test_stream());
auto tolerance = default_tolerance(ref_out_mem->get_layout().data_type);
for (size_t i = 0; i < out_mem->count(); i++) {
float actual = lock[i];
ASSERT_EQ(actual, lock_ref[i]);
ASSERT_NEAR(lock[i],lock_ref[i], tolerance)
<< "\ntolerance = " << tolerance
<< "\ni = " << i
<< "\nref[i] = " << lock_ref[i]
<< "\nopt[i] = " << lock[i];
}
}

Expand All @@ -176,12 +182,8 @@ TEST(opt_reorder_reshape_permute, cutomized_net_yolov6_alike) {
auto in_layout = layout{ov::PartialShape({1, 2, 4, 6}), data_types::f16, format::bfyx};
auto input = engine.allocate_memory(layout{ov::PartialShape({1, 2, 4, 6}), data_types::f16, format::bfyx});
auto weight = engine.allocate_memory(layout{ov::PartialShape({3, 2, 1, 1}), data_types::f16, format::bfyx});
set_values<ov::float16>(input, {2.0f, 3.0f, 4.0f, 4.0f, 3.0f, 2.0f, 1.f, 2.f, 3.f, 1.f, 2.f, 4.f,
5.f, 1.f, 1.f, 2.f, 1.f, 2.f, 2.0f, 3.0f, 1.0f, 4.0f, 1.0f, 4.0f,
3.0f, 2.0f, 0.0f, 1.0f, 0.0f, 2.0f, 2.f, 4.f, 1.f, 1.f, 2.f, 1.f,
1.f, 2.f, 0.f, 2.f, 5.f, 2.f, 4.0f, 3.0f, 1.0f, 0.0f, 3.0f, 2.0f});

set_values<ov::float16>(weight, {1.f, 1.f, 1.f, 1.f, 1.f, 1.f});
tests::set_random_values<ov::float16>(input);
tests::set_random_values<ov::float16>(weight);
topology topology;
topology.add(input_layout("input", in_layout));
topology.add(data("weight", weight));
Expand Down Expand Up @@ -211,7 +213,9 @@ TEST(opt_reorder_reshape_permute, cutomized_net_yolov6_alike) {
});
ASSERT_NE(it, optimzed_nodes.end());
auto permute_inst = net.get_primitive("permute_inter");
ASSERT_TRUE(permute_inst->can_be_optimized());
if (net.get_primitive("convolution")->get_impl()->is_onednn()) {
ASSERT_TRUE(permute_inst->can_be_optimized());
}
auto reshape_inst = net.get_primitive("reshape_inter");
ASSERT_TRUE(reshape_inst->can_be_optimized());

Expand All @@ -231,9 +235,13 @@ TEST(opt_reorder_reshape_permute, cutomized_net_yolov6_alike) {

auto ref_out_mem = ref_output.at("softmax").get_memory();
mem_lock<ov::float16> lock_ref(ref_out_mem, get_test_stream());
auto tolerance = default_tolerance(ref_out_mem->get_layout().data_type);
for (size_t i = 0; i < out_mem->count(); i++) {
float actual = lock[i];
ASSERT_EQ(actual, lock_ref[i]);
ASSERT_NEAR(lock[i],lock_ref[i], tolerance)
<< "\ntolerance = " << tolerance
<< "\ni = " << i
<< "\nref[i] = " << lock_ref[i]
<< "\nopt[i] = " << lock[i];
}
}

Expand All @@ -242,12 +250,8 @@ TEST(opt_reorder_reshape_permute, cutomized_net_yolov6_alike_4d) {
auto in_layout = layout{ov::PartialShape({1, 2, 4, 6}), data_types::f16, format::bfyx};
auto input = engine.allocate_memory(layout{ov::PartialShape({1, 2, 4, 6}), data_types::f16, format::bfyx});
auto weight = engine.allocate_memory(layout{ov::PartialShape({3, 2, 1, 1}), data_types::f16, format::bfyx});
set_values<ov::float16>(input, {2.0f, 3.0f, 4.0f, 4.0f, 3.0f, 2.0f, 1.f, 2.f, 3.f, 1.f, 2.f, 4.f,
5.f, 1.f, 1.f, 2.f, 1.f, 2.f, 2.0f, 3.0f, 1.0f, 4.0f, 1.0f, 4.0f,
3.0f, 2.0f, 0.0f, 1.0f, 0.0f, 2.0f, 2.f, 4.f, 1.f, 1.f, 2.f, 1.f,
1.f, 2.f, 0.f, 2.f, 5.f, 2.f, 4.0f, 3.0f, 1.0f, 0.0f, 3.0f, 2.0f});

set_values<ov::float16>(weight, {1.f, 1.f, 1.f, 1.f, 1.f, 1.f});
tests::set_random_values<ov::float16>(input);
tests::set_random_values<ov::float16>(weight);
topology topology;
topology.add(input_layout("input", in_layout));
topology.add(data("weight", weight));
Expand Down Expand Up @@ -277,7 +281,9 @@ TEST(opt_reorder_reshape_permute, cutomized_net_yolov6_alike_4d) {
});
ASSERT_NE(it, optimzed_nodes.end());
auto permute_inst = net.get_primitive("permute_inter");
ASSERT_TRUE(permute_inst->can_be_optimized());
if (net.get_primitive("convolution")->get_impl()->is_onednn()) {
ASSERT_TRUE(permute_inst->can_be_optimized());
}
auto reshape_inst = net.get_primitive("reshape_inter");
ASSERT_TRUE(reshape_inst->can_be_optimized());

Expand All @@ -297,9 +303,13 @@ TEST(opt_reorder_reshape_permute, cutomized_net_yolov6_alike_4d) {

auto ref_out_mem = ref_output.at("softmax").get_memory();
mem_lock<ov::float16> lock_ref(ref_out_mem, get_test_stream());
auto tolerance = default_tolerance(ref_out_mem->get_layout().data_type);
for (size_t i = 0; i < out_mem->count(); i++) {
float actual = lock[i];
ASSERT_EQ(actual, lock_ref[i]);
ASSERT_NEAR(lock[i],lock_ref[i], tolerance)
<< "\ntolerance = " << tolerance
<< "\ni = " << i
<< "\nref[i] = " << lock_ref[i]
<< "\nopt[i] = " << lock[i];
}
}

Expand All @@ -308,12 +318,8 @@ TEST(opt_reorder_reshape_permute, not_sinking_reshape) {
auto in_layout = layout{ov::PartialShape({1, 2, 4, 6}), data_types::f16, format::bfyx};
auto input = engine.allocate_memory(layout{ov::PartialShape({1, 2, 4, 6}), data_types::f16, format::bfyx});
auto weight = engine.allocate_memory(layout{ov::PartialShape({3, 2, 1, 1}), data_types::f16, format::bfyx});
set_values<ov::float16>(input, {2.0f, 3.0f, 4.0f, 4.0f, 3.0f, 2.0f, 1.f, 2.f, 3.f, 1.f, 2.f, 4.f,
5.f, 1.f, 1.f, 2.f, 1.f, 2.f, 2.0f, 3.0f, 1.0f, 4.0f, 1.0f, 4.0f,
3.0f, 2.0f, 0.0f, 1.0f, 0.0f, 2.0f, 2.f, 4.f, 1.f, 1.f, 2.f, 1.f,
1.f, 2.f, 0.f, 2.f, 5.f, 2.f, 4.0f, 3.0f, 1.0f, 0.0f, 3.0f, 2.0f});

set_values<ov::float16>(weight, {1.f, 1.f, 1.f, 1.f, 1.f, 1.f});
tests::set_random_values<ov::float16>(input);
tests::set_random_values<ov::float16>(weight);
topology topology;
topology.add(input_layout("input", in_layout));
topology.add(data("weight", weight));
Expand All @@ -336,16 +342,8 @@ TEST(opt_reorder_reshape_permute, not_sinking_reshape) {

net.set_input_data("input", input);
auto output = net.execute();
auto optimzed_nodes = net.get_program()->get_optimized();
auto it =
std::find_if(std::begin(optimzed_nodes), std::end(optimzed_nodes), [&](cldnn::program::optimized_info& oi) {
return oi.first == "reorder_inter";
});
ASSERT_NE(it, optimzed_nodes.end());
auto permute_inst = net.get_primitive("permute_inter");
ASSERT_FALSE(permute_inst->can_be_optimized());
auto reshape_inst = net.get_primitive("reshape_inter");
ASSERT_FALSE(reshape_inst->can_be_optimized());

auto& processing_order = prog->get_processing_order();

Expand All @@ -363,9 +361,12 @@ TEST(opt_reorder_reshape_permute, not_sinking_reshape) {

auto ref_out_mem = ref_output.at("softmax").get_memory();
mem_lock<ov::float16> lock_ref(ref_out_mem, get_test_stream());
auto tolerance = default_tolerance(ref_out_mem->get_layout().data_type);
for (size_t i = 0; i < out_mem->count(); i++) {
float actual = lock[i];
std::cout << actual << ", " << std::endl;
ASSERT_EQ(actual, lock_ref[i]);
ASSERT_NEAR(lock[i],lock_ref[i], tolerance)
<< "\ntolerance = " << tolerance
<< "\ni = " << i
<< "\nref[i] = " << lock_ref[i]
<< "\nopt[i] = " << lock[i];
}
}
}

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