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[GPU] Fix to enable fc 5d
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@steve-y
steve-y committed Oct 8, 2024
1 parent c62ead7 commit 0c36373
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Showing 2 changed files with 139 additions and 28 deletions.
62 changes: 44 additions & 18 deletions src/plugins/intel_gpu/src/graph/fully_connected.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -64,18 +64,18 @@ format::type get_preferred_format(fully_connected_node const& node, const kernel
}

if (input_layout.data_type == data_types::f32 &&
input_layout.format == format::bfyx &&
(input_layout.format == format::bfyx || input_layout.format == format::bfzyx) &&
no_spatial_padding &&
input_layout.batch() != 8)
return format::bfyx;
return input_layout.format;

auto input_pitches = input_layout.get_pitches();
if (input_layout.data_type == data_types::f16 &&
input_layout.format == format::bfyx &&
(input_layout.format == format::bfyx || input_layout.format == format::bfzyx) &&
no_spatial_padding &&
input_pitches[0] % 2 == 0 &&
input_layout.batch() != 16)
return format::bfyx;
return input_layout.format;

// this condition tests whether our input is batch>1 in bfyx format, if yes there will be
// extra reorder between input and this fc from bfyx to yxfb format (so
Expand Down Expand Up @@ -115,23 +115,49 @@ layout fully_connected_inst::calc_output_layout(fully_connected_node const& node
};

int64_t feature = input_pshape[std::min(desc->input_size, static_cast<size_t>(4)) - 1].get_length();
if (desc->input_size == 3) {
feature = std::max({input_layout.spatial(0), input_layout.spatial(1), input_layout.spatial(2)});
}
auto output_size = tensor();

if ((supports_immad && desc->input_size > 3) || desc->input_size > 4) {
input_layout.set_partial_shape(reshape_to_2d(input_pshape, feature));
}
if (weights_pshape.size() != 2) {
weights_layout.set_partial_shape(reshape_to_2d(weights_pshape, feature));
}
// If immad is supported, spatial dimensions are reshaped to 2d in order to select oneDnn impl,
// because oneDnn doesn't support spatial dimensions for output.
if (supports_immad) {
if (desc->input_size == 3) {
feature = std::max({input_layout.spatial(0), input_layout.spatial(1), input_layout.spatial(2)});
}

auto output_size = tensor(input_layout.batch(), weights_layout.batch(), 1, 1);
if (desc->input_size == 3) {
output_size = tensor(input_layout.batch(), input_layout.feature(), 1, weights_layout.batch());
} else if (!supports_immad && desc->input_size == 4) {
output_size = tensor(input_layout.batch(), input_layout.feature(), weights_layout.batch(), input_layout.spatial(1));
if (desc->input_size > 3) {
input_layout.set_partial_shape(reshape_to_2d(input_pshape, feature));
}
if (weights_pshape.size() != 2) {
weights_layout.set_partial_shape(reshape_to_2d(weights_pshape, feature));
}

output_size = tensor(input_layout.batch(), weights_layout.batch(), 1, 1);
if (desc->input_size == 3) {
output_size = tensor(input_layout.batch(), input_layout.feature(), 1, weights_layout.batch());
}
} else {
feature = input_pshape[std::min(desc->input_size, static_cast<size_t>(5)) - 1].get_length();
if (desc->input_size == 3) {
feature = std::max({input_layout.spatial(0), input_layout.spatial(1), input_layout.spatial(2)});
}

if (desc->input_size > 5) {
input_layout.set_partial_shape(reshape_to_2d(input_pshape, feature));
}
if (weights_pshape.size() != 2) {
weights_layout.set_partial_shape(reshape_to_2d(weights_pshape, feature));
}

output_size = tensor(input_layout.batch(), weights_layout.batch(), 1, 1);
if (desc->input_size == 3) {
output_size = tensor(input_layout.batch(), input_layout.feature(), 1, weights_layout.batch());
} else if (desc->input_size == 4) {
output_size = tensor(input_layout.batch(), input_layout.feature(), weights_layout.batch(), input_layout.spatial(1));
} else if (desc->input_size == 5) {
output_size = tensor(input_layout.batch(), input_layout.feature(), weights_layout.batch(), input_layout.spatial(1), input_layout.spatial(2));
}
}

format output_format = get_preferred_format(node, impl_param);

return layout(output_type, output_format, output_size);
Expand Down
105 changes: 95 additions & 10 deletions src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -376,12 +376,12 @@ TEST(fully_connected_gpu, no_biases_4d_input) {
if (engine.get_device_info().supports_immad)
return;

// Input : 1x256x256x384
// Output : 1x256x256x1536
// Weights: 1536x384x1x1
// Input : 1x8x8x12
// Weights: 48x12x1x1
// Output : 1x8x8x48

const int32_t input_b = 1, input_f = 256, input_y = 256, input_x = 384, // size of the whole input buffer
weight_b = 1536, weight_f = 384, weight_y = 1, weight_x = 1; // size of the whole weights buffer
const int32_t input_b = 1, input_f = 8, input_y = 8, input_x = 12, // size of the whole input buffer
weight_b = 48, weight_f = 12, weight_y = 1, weight_x = 1; // size of the whole weights buffer

auto input_prim = engine.allocate_memory({ data_types::f32, format::bfyx, { input_b, input_f, input_x, input_y } });
auto weights_prim = engine.allocate_memory({ data_types::f32, format::bfyx, { weight_b, weight_f, weight_x, weight_y } });
Expand Down Expand Up @@ -415,12 +415,12 @@ TEST(fully_connected_gpu, no_biases_4d_input_immad) {
if (!engine.get_device_info().supports_immad)
return;

// Input : 1x256x256x384
// Output : 65536x1536x1x1
// Weights: 1536x384x1x1
// Input : 1x8x8x12
// Weights: 48x12x1x1
// Output : 64x48x1x1

const int32_t input_b = 1, input_f = 256, input_y = 256, input_x = 384, // size of the whole input buffer
weight_b = 1536, weight_f = 384, weight_y = 1, weight_x = 1; // size of the whole weights buffer
const int32_t input_b = 1, input_f = 8, input_y = 8, input_x = 12, // size of the whole input buffer
weight_b = 48, weight_f = 12, weight_y = 1, weight_x = 1; // size of the whole weights buffer

auto input_prim = engine.allocate_memory({ data_types::f32, format::bfyx, { input_b, input_f, input_x, input_y } });
auto weights_prim = engine.allocate_memory({ data_types::f32, format::bfyx, { weight_b, weight_f, weight_x, weight_y } });
Expand Down Expand Up @@ -455,6 +455,91 @@ TEST(fully_connected_gpu, no_biases_4d_input_immad) {
ASSERT_EQ(outputs.begin()->second.get_layout().spatial(0), weight_x);
}

TEST(fully_connected_gpu, no_biases_5d_input) {
auto& engine = get_test_engine();
if (engine.get_device_info().supports_immad)
return;

// Input : 1x8x8x8x12
// Weights: 48x12x1x1
// Output : 1x8x8x8x48

const int32_t input_b = 1, input_f = 8, input_z = 8, input_y = 8, input_x = 12, // size of the whole input buffer
weight_b = 48, weight_f = 12, weight_y = 1, weight_x = 1; // size of the whole weights buffer

auto input_prim = engine.allocate_memory({ data_types::f32, format::bfzyx, { input_b, input_f, input_x, input_y, input_z } });
auto weights_prim = engine.allocate_memory({ data_types::f32, format::bfyx, { weight_b, weight_f, weight_x, weight_y } });

std::vector<float> input_data(input_b * input_f * input_z * input_y * input_x, 0);
std::vector<float> weights_data(weight_b * weight_f * weight_y * weight_x, 0);

set_values(input_prim, std::move(input_data));
set_values(weights_prim, std::move(weights_data));

auto input = input_layout("input", input_prim->get_layout());
auto w_data = data("weights", weights_prim);
auto fc = fully_connected("fc_prim", input_info("input"), "weights", "", 5, 2);
topology topology;
topology.add(input);
topology.add(w_data);
topology.add(fc);

network network(engine, topology, get_test_default_config(engine));
network.set_input_data("input", input_prim);

auto outputs = network.execute();
ASSERT_EQ(outputs.begin()->second.get_layout().batch(), input_b);
ASSERT_EQ(outputs.begin()->second.get_layout().feature(), input_f);
ASSERT_EQ(outputs.begin()->second.get_layout().spatial(2), input_z);
ASSERT_EQ(outputs.begin()->second.get_layout().spatial(1), input_y);
ASSERT_EQ(outputs.begin()->second.get_layout().spatial(0), weight_b);
}

TEST(fully_connected_gpu, no_biases_5d_input_immad) {
auto& engine = get_test_engine();
if (!engine.get_device_info().supports_immad)
return;

// Input : 1x8x8x8x12
// Weights: 48x12x1x1
// Output : 512x48x1x1

const int32_t input_b = 1, input_f = 8, input_z = 8, input_y = 8, input_x = 12, // size of the whole input buffer
weight_b = 48, weight_f = 12, weight_y = 1, weight_x = 1; // size of the whole weights buffer

auto input_prim = engine.allocate_memory({ data_types::f32, format::bfzyx, { input_b, input_f, input_x, input_y, input_z } });
auto weights_prim = engine.allocate_memory({ data_types::f32, format::bfyx, { weight_b, weight_f, weight_x, weight_y } });

std::vector<float> input_data(input_b * input_f * input_z * input_y * input_x, 0);
std::vector<float> weights_data(weight_b * weight_f * weight_y * weight_x, 0);

set_values(input_prim, std::move(input_data));
set_values(weights_prim, std::move(weights_data));

auto input = input_layout("input", input_prim->get_layout());
auto w_data = data("weights", weights_prim);
auto fc = fully_connected("fc_prim", input_info("input"), "weights", "", 5, 2);
topology topology;
topology.add(input);
topology.add(w_data);
topology.add(fc);

ExecutionConfig config = get_test_default_config(engine);
config.set_property(ov::intel_gpu::optimize_data(true));
network network(engine, topology, config);
network.set_input_data("input", input_prim);

auto fc_impl = network.get_primitive("fc_prim")->get_impl();
ASSERT_TRUE(fc_impl != nullptr);
ASSERT_TRUE(fc_impl->is_onednn());

auto outputs = network.execute();
ASSERT_EQ(outputs.begin()->second.get_layout().batch(), input_f*input_z*input_y);
ASSERT_EQ(outputs.begin()->second.get_layout().feature(), weight_b);
ASSERT_EQ(outputs.begin()->second.get_layout().spatial(1), weight_y);
ASSERT_EQ(outputs.begin()->second.get_layout().spatial(0), weight_x);
}

TEST(fully_connected_gpu, xb_f32_batch_1) {
// Input : 3x1
// Output : 4x1
Expand Down

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