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[IE CLDNN] Improved GWS for 3d fsv16 eltwise #1957

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Aug 27, 2020
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[IE CLDNN] Improved GWS for 3d fsv16 eltwise #1957

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4 changes: 3 additions & 1 deletion ...gine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_base.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -552,7 +552,9 @@ EltwiseKernelBase::DispatchData EltwiseKernelBase::SetDefault(const eltwise_para
auto local = GetOptimalLocalWorkGroupSizes({kd.gws0, kd.gws1, kd.gws2}, params.engineInfo);

const size_t optimal_lws_values[] = {256, 224, 192, 160, 128, 96, 64, 32, 16};
if ((params.output.GetLayout() == DataLayout::b_fs_yx_fsv16 || params.output.GetLayout() == DataLayout::bs_fs_yx_bsv16_fsv16) &&
if ((params.output.GetLayout() == DataLayout::b_fs_yx_fsv16 ||
params.output.GetLayout() == DataLayout::b_fs_zyx_fsv16 ||
params.output.GetLayout() == DataLayout::bs_fs_yx_bsv16_fsv16) &&
params.output.Feature().v % 16 == 0 && kd.gws1 % 16 == 0) {
kd.lws0 = 1;
for (auto lws : optimal_lws_values) {
Expand Down
92 changes: 54 additions & 38 deletions inference-engine/thirdparty/clDNN/tests/test_cases/eltwise_gpu_test.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -3206,7 +3206,7 @@ TEST(DISABLED_eltwise_gpu, generic_random) {
}

// mode, input type, input sizes
using eltwise_test_params = std::tuple<eltwise_mode, data_types, std::vector<tensor>>;
using eltwise_test_params = std::tuple<eltwise_mode, data_types, std::vector<std::vector<int32_t>>>;

template<typename T>
class BaseEltwiseTest : public ::testing::TestWithParam<T> {
Expand Down Expand Up @@ -3264,7 +3264,7 @@ class eltwise_test : public BaseEltwiseTest<eltwise_test_params> {
}
};

TEST_P(eltwise_test, b_fs_yx_fsv16) {
TEST_P(eltwise_test, fsv16) {
auto p = GetParam();

ASSERT_EQ(std::get<2>(p).size(), 2);
Expand All @@ -3274,35 +3274,43 @@ TEST_P(eltwise_test, b_fs_yx_fsv16) {
auto input0_size = std::get<2>(p)[0];
auto input1_size = std::get<2>(p)[1];

int b0 = input0_size.batch[0];
int f0 = input0_size.feature[0];
int y0 = input0_size.spatial[1];
int x0 = input0_size.spatial[0];
int b0 = input0_size[0];
int f0 = input0_size[1];
int z0 = input0_size.size() == 4 ? 1 : input0_size[2];
int y0 = input0_size[input0_size.size() == 4 ? 2 : 3];
int x0 = input0_size[input0_size.size() == 4 ? 3 : 4];

int b1 = input1_size.batch[0];
int f1 = input1_size.feature[0];
int y1 = input1_size.spatial[1];
int x1 = input1_size.spatial[0];
int b1 = input1_size[0];
int f1 = input1_size[1];
int z1 = input1_size.size() == 4 ? 1 : input1_size[2];
int y1 = input1_size[input1_size.size() == 4 ? 2 : 3];
int x1 = input1_size[input1_size.size() == 4 ? 3 : 4];

int min_random = -2, max_random = 2;
VVVVVVF<float> input1_rnd = generate_random_6d<float>(b0, f0, 1, 1, y0, x0, min_random, max_random);
VVVVVVF<float> input2_rnd = generate_random_6d<float>(b1, f1, 1, 1, y1, x1, min_random, max_random);
VVVVVVF<float> input1_rnd = generate_random_6d<float>(b0, f0, 1, z0, y0, x0, min_random, max_random);
VVVVVVF<float> input2_rnd = generate_random_6d<float>(b1, f1, 1, z1, y1, x1, min_random, max_random);
VF<float> input1_rnd_vec = flatten_6d<float>(format::bfwzyx, input1_rnd);
VF<float> input2_rnd_vec = flatten_6d<float>(format::bfwzyx, input2_rnd);

const auto& engine = get_test_engine();
auto input1 = memory::allocate(engine, { data_types::f32, format::bfyx, input0_size });
auto input2 = memory::allocate(engine, { data_types::f32, format::bfyx, input1_size });
auto fmt_pln = input0_size.size() == 4 ? format::bfyx : format::bfzyx;
auto fmt_fsv16 = input0_size.size() == 4 ? format::b_fs_yx_fsv16 : format::b_fs_zyx_fsv16;

auto in0_size = tensor(fmt_pln, input0_size);
auto in1_size = tensor(fmt_pln, input1_size);

auto input1 = memory::allocate(engine, { data_types::f32, fmt_pln, in0_size });
auto input2 = memory::allocate(engine, { data_types::f32, fmt_pln, in1_size });
set_values(input1, input1_rnd_vec);
set_values(input2, input2_rnd_vec);

topology topology;
topology.add(input_layout("input1", input1.get_layout()));
topology.add(input_layout("input2", input2.get_layout()));
topology.add(reorder("reorder1", "input1", format::b_fs_yx_fsv16, dt));
topology.add(reorder("reorder2", "input2", format::b_fs_yx_fsv16, dt));
topology.add(reorder("reorder1", "input1", fmt_fsv16, dt));
topology.add(reorder("reorder2", "input2", fmt_fsv16, dt));
topology.add(eltwise("eltwise", {"reorder1", "reorder2"}, mode));
topology.add(reorder("out", "eltwise", format::bfyx, data_types::f32));
topology.add(reorder("out", "eltwise", fmt_pln, data_types::f32));
primitive_id out_id = "out";

build_options bo;
Expand All @@ -3318,7 +3326,7 @@ TEST_P(eltwise_test, b_fs_yx_fsv16) {
auto output_memory = outputs.at(out_id).get_memory();
auto output_ptr = output_memory.pointer<float>();

VF<float> output_cpu_vec = eltwise_ref(input1_rnd, input2_rnd, input0_size, input1_size, mode);
VF<float> output_cpu_vec = eltwise_ref(input1_rnd, input2_rnd, in0_size, in1_size, mode);
for (size_t i = 0; i < output_cpu_vec.size(); ++i) {
EXPECT_TRUE(!(std::isnan((float)output_cpu_vec[i]) && std::isnan((float)output_ptr[i])));
ASSERT_FLOAT_EQ(output_cpu_vec[i], output_ptr[i]);
Expand All @@ -3327,7 +3335,7 @@ TEST_P(eltwise_test, b_fs_yx_fsv16) {

static std::vector<eltwise_mode> modes = {eltwise_mode::sum, eltwise_mode::prod};
static std::vector<data_types> types = {data_types::f32, data_types::f16};
static std::vector<std::vector<tensor>> inputs = {
static std::vector<std::vector<std::vector<int32_t>>> inputs = {
{{1, 2, 3, 4}, {1, 2, 3, 4}},
{{1, 16, 8, 2}, {1, 16, 8, 2}},
{{1, 128, 16, 8}, {1, 1, 16, 8}},
Expand All @@ -3345,6 +3353,11 @@ static std::vector<std::vector<tensor>> inputs = {
{{1, 16, 1, 1}, {1, 16, 8, 2}},
{{1, 32, 1, 1}, {1, 32, 2, 2}},
{{1, 32, 1, 1}, {8, 32, 4, 5}},

{{1, 16, 8, 2, 4}, {1, 16, 8, 2, 4}},
{{8, 32, 4, 5, 6}, {1, 32, 1, 1, 1}},
{{1, 2, 3, 4, 5}, {1, 2, 3, 4, 5}},
{{1, 32, 1, 1, 1}, {8, 32, 3, 4, 5}},
};

INSTANTIATE_TEST_CASE_P(eltwise, eltwise_test,
Expand All @@ -3365,29 +3378,32 @@ TEST_P(eltwise_test_6d, bfwzyx) {
auto input0_size = std::get<2>(p)[0];
auto input1_size = std::get<2>(p)[1];

int b0 = input0_size.batch[0];
int f0 = input0_size.feature[0];
int w0 = input0_size.spatial[3];
int z0 = input0_size.spatial[2];
int y0 = input0_size.spatial[1];
int x0 = input0_size.spatial[0];
int b0 = input0_size[0];
int f0 = input0_size[1];
int w0 = input0_size[2];
int z0 = input0_size[3];
int y0 = input0_size[4];
int x0 = input0_size[5];

int b1 = input1_size.batch[0];
int f1 = input1_size.feature[0];
int w1 = input1_size.spatial[3];
int z1 = input1_size.spatial[2];
int y1 = input1_size.spatial[1];
int x1 = input1_size.spatial[0];
int b1 = input1_size[0];
int f1 = input1_size[1];
int w1 = input1_size[2];
int z1 = input1_size[3];
int y1 = input1_size[4];
int x1 = input1_size[5];

int min_random = -2, max_random = 2;
VVVVVVF<float> input1_rnd = generate_random_6d<float>(b0, f0, w0, z0, y0, x0, min_random, max_random);
VVVVVVF<float> input2_rnd = generate_random_6d<float>(b1, f1, w1, z1, y1, x1, min_random, max_random);
VF<float> input1_rnd_vec = flatten_6d<float>(format::bfwzyx, input1_rnd);
VF<float> input2_rnd_vec = flatten_6d<float>(format::bfwzyx, input2_rnd);

auto in0_size = tensor(format::bfwzyx, input0_size);
auto in1_size = tensor(format::bfwzyx, input1_size);

const auto& engine = get_test_engine();
auto input1 = memory::allocate(engine, { data_types::f32, format::bfwzyx, input0_size });
auto input2 = memory::allocate(engine, { data_types::f32, format::bfwzyx, input1_size });
auto input1 = memory::allocate(engine, { data_types::f32, format::bfwzyx, in0_size });
auto input2 = memory::allocate(engine, { data_types::f32, format::bfwzyx, in1_size });
set_values(input1, input1_rnd_vec);
set_values(input2, input2_rnd_vec);

Expand All @@ -3413,17 +3429,17 @@ TEST_P(eltwise_test_6d, bfwzyx) {
auto output_memory = outputs.at(out_id).get_memory();
auto output_ptr = output_memory.pointer<float>();

VF<float> output_cpu_vec = eltwise_ref(input1_rnd, input2_rnd, input0_size, input1_size, mode);
VF<float> output_cpu_vec = eltwise_ref(input1_rnd, input2_rnd, in0_size, in1_size, mode);
for (size_t i = 0; i < output_cpu_vec.size(); ++i) {
EXPECT_TRUE(!(std::isnan((float)output_cpu_vec[i]) && std::isnan((float)output_ptr[i])));
ASSERT_FLOAT_EQ(output_cpu_vec[i], output_ptr[i]);
}
}

static std::vector<std::vector<tensor>> inputs_6d = {
{tensor(format::bfwzyx, {1, 2, 3, 4, 5, 6}), tensor(format::bfwzyx, {1, 2, 3, 4, 5, 6})},
{tensor(format::bfwzyx, {1, 32, 1, 1, 1, 1}), tensor(format::bfwzyx, {8, 32, 4, 5, 6, 7})},
{tensor(format::bfwzyx, {1, 32, 1, 1, 1, 7}), tensor(format::bfwzyx, {8, 32, 4, 5, 6, 7})},
static std::vector<std::vector<std::vector<int32_t>>> inputs_6d = {
{{1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6}},
{{1, 32, 1, 1, 1, 1}, {8, 32, 4, 5, 6, 7}},
{{1, 32, 1, 1, 1, 7}, {8, 32, 4, 5, 6, 7}},
};

INSTANTIATE_TEST_CASE_P(eltwise, eltwise_test_6d,
Expand Down