COMPMID-1266 : support for FP16 in CLWinogradConvolutionLayer
Added support for FP16 in CLWinogradConvolutionLayer: 5x5 kernels and 3x3 kernels(COMPMID-937)
Change-Id: I0f394cbdc978dd04176416e9f612aca3986b09e6
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/145537
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp
index bb83f5a..3762e39 100644
--- a/tests/validation/CL/Winograd.cpp
+++ b/tests/validation/CL/Winograd.cpp
@@ -55,7 +55,9 @@
// *INDENT-OFF*
// clang-format off
constexpr AbsoluteTolerance<float> tolerance_f32(0.001f);
+const AbsoluteTolerance<half> tolerance_f16(half(0.5f));
constexpr AbsoluteTolerance<float> tolerance_convolution_layer_f32(0.1f);
+const AbsoluteTolerance<half> tolerance_convolution_layer_f16(half(0.4f));
// Input transform
const auto SmallWinogradInputTransformDatasetNCHW =
@@ -176,13 +178,14 @@
ARM_COMPUTE_EXPECT(bool(CLWinogradInputTransform::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), winograd_info)) == expected, framework::LogLevel::ERRORS);
}
-using CLWinogradInputTransformFixture = WinogradInputTransformValidationFixture<CLTensor, CLAccessor, CLWinogradInputTransform, float>;
+using CLWinogradInputTransformFixtureFP32 = WinogradInputTransformValidationFixture<CLTensor, CLAccessor, CLWinogradInputTransform, float>;
+using CLWinogradInputTransformFixtureFP16 = WinogradInputTransformValidationFixture<CLTensor, CLAccessor, CLWinogradInputTransform, half>;
TEST_SUITE(NCHW)
DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(SmallWinogradInputTransformDatasetNCHW,
LargeWinogradInputTransformDatasetNCHW),
framework::dataset::make("DataLayout", { DataLayout::NCHW })),
- framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("DataType", { DataType::F32, DataType::F16 })),
shape_in, winograd_info, data_layout, data_type)
{
TensorInfo tensor_info_in(shape_in, 1, data_type);
@@ -204,26 +207,44 @@
winograd_input_transform.configure(&in, &out, winograd_info);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixture, framework::DatasetMode::PRECOMMIT, combine(combine(SmallWinogradInputTransformDatasetNCHW,
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixtureFP32, framework::DatasetMode::PRECOMMIT, combine(combine(SmallWinogradInputTransformDatasetNCHW,
framework::dataset::make("DataLayout", { DataLayout::NCHW })),
framework::dataset::make("DataType", { DataType::F32 })))
{
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixture, framework::DatasetMode::NIGHTLY, combine(combine(LargeWinogradInputTransformDatasetNCHW,
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixtureFP32, framework::DatasetMode::NIGHTLY, combine(combine(LargeWinogradInputTransformDatasetNCHW,
framework::dataset::make("DataLayout", { DataLayout::NCHW })),
framework::dataset::make("DataType", { DataType::F32 })))
{
validate(CLAccessor(_target), _reference, tolerance_f32);
}
+TEST_SUITE_END() // FP32
+
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixtureFP16, framework::DatasetMode::PRECOMMIT, combine(combine(SmallWinogradInputTransformDatasetNCHW,
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+ framework::dataset::make("DataType", { DataType::F16 })))
+{
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixtureFP16, framework::DatasetMode::NIGHTLY, combine(combine(LargeWinogradInputTransformDatasetNCHW,
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+ framework::dataset::make("DataType", { DataType::F16 })))
+{
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+TEST_SUITE_END() // FP16
TEST_SUITE_END() // NCHW
TEST_SUITE(NHWC)
DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(SmallWinogradInputTransformDatasetNHWC,
LargeWinogradInputTransformDatasetNHWC),
framework::dataset::make("DataLayout", { DataLayout::NHWC })),
- framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("DataType", { DataType::F32, DataType::F16 })),
shape_in, winograd_info, data_layout, data_type)
{
TensorShape shape_in_nhwc(shape_in);
@@ -251,26 +272,43 @@
winograd_input_transform.configure(&in, &out, winograd_info);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixture, framework::DatasetMode::PRECOMMIT, combine(combine(SmallWinogradInputTransformDatasetNHWC,
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixtureFP16, framework::DatasetMode::PRECOMMIT, combine(combine(SmallWinogradInputTransformDatasetNHWC,
+ framework::dataset::make("DataLayout", { DataLayout::NHWC })),
+ framework::dataset::make("DataType", { DataType::F16 })))
+{
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixtureFP16, framework::DatasetMode::NIGHTLY, combine(combine(LargeWinogradInputTransformDatasetNHWC,
+ framework::dataset::make("DataLayout", { DataLayout::NHWC })),
+ framework::dataset::make("DataType", { DataType::F16 })))
+{
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+TEST_SUITE_END() // FP16
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixtureFP32, framework::DatasetMode::PRECOMMIT, combine(combine(SmallWinogradInputTransformDatasetNHWC,
framework::dataset::make("DataLayout", { DataLayout::NHWC })),
framework::dataset::make("DataType", { DataType::F32 })))
{
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixture, framework::DatasetMode::NIGHTLY, combine(combine(LargeWinogradInputTransformDatasetNHWC,
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixtureFP32, framework::DatasetMode::NIGHTLY, combine(combine(LargeWinogradInputTransformDatasetNHWC,
framework::dataset::make("DataLayout", { DataLayout::NHWC })),
framework::dataset::make("DataType", { DataType::F32 })))
{
validate(CLAccessor(_target), _reference, tolerance_f32);
}
+TEST_SUITE_END() // FP32
TEST_SUITE_END() // NHWC
TEST_SUITE_END() // InputTransform
TEST_SUITE(FilterTransform)
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
framework::dataset::make("InputInfo",{
- TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F16), // F16 not supported
+ TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F16), // F16 supported
TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::QASYMM8), // QASYMM8 not supported
TensorInfo(TensorShape(5U, 5U, 5U, 3U), 1, DataType::F32), // Kernel size not supported
TensorInfo(TensorShape(3U, 3U), 1, DataType::F32), // Output tile not supported
@@ -296,21 +334,22 @@
WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ ),
WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ )
})),
- framework::dataset::make("Expected", { false, false, false, false, true, true, true })),
+ framework::dataset::make("Expected", { true, false, false, false, true, true, true })),
input_info, output_info, winograd_info, expected)
{
ARM_COMPUTE_EXPECT(bool(CLWinogradFilterTransformKernel::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), winograd_info)) == expected, framework::LogLevel::ERRORS);
}
using CLWinogradFilterTransform = CLSynthetizeFunctionWithZeroConstantBorder<CLWinogradFilterTransformKernel, 0>;
-using CLWinogradFilterTransformFixture = WinogradFilterTransformValidationFixture<CLTensor, CLAccessor, CLWinogradFilterTransform, float>;
+using CLWinogradFilterTransformFixtureFP32 = WinogradFilterTransformValidationFixture<CLTensor, CLAccessor, CLWinogradFilterTransform, float>;
+using CLWinogradFilterTransformFixtureFP16 = WinogradFilterTransformValidationFixture<CLTensor, CLAccessor, CLWinogradFilterTransform, half>;
TEST_SUITE(NCHW)
DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL,
combine(combine(framework::dataset::concat(SmallWinogradFilterTransformDatasetNCHW,
LargeWinogradFilterTransformDatasetNCHW),
framework::dataset::make("DataLayout", { DataLayout::NCHW })),
- framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("DataType", { DataType::F32, DataType::F16 })),
shape_a, output_tile, data_layout, data_type)
{
WinogradInfo winograd_info(output_tile, Size2D(shape_a[0], shape_a[1]), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, data_layout /* Not needed */);
@@ -329,7 +368,8 @@
winograd_filter_transform.configure(&a, &b, winograd_info);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixture, framework::DatasetMode::PRECOMMIT,
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixtureFP32, framework::DatasetMode::PRECOMMIT,
combine(combine(SmallWinogradFilterTransformDatasetNCHW,
framework::dataset::make("DataLayout", { DataLayout::NCHW })),
framework::dataset::make("DataType", { DataType::F32 })))
@@ -338,7 +378,7 @@
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixture, framework::DatasetMode::NIGHTLY,
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixtureFP32, framework::DatasetMode::NIGHTLY,
combine(combine(LargeWinogradFilterTransformDatasetNCHW,
framework::dataset::make("DataLayout", { DataLayout::NCHW })),
framework::dataset::make("DataType", { DataType::F32 })))
@@ -346,6 +386,26 @@
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
+TEST_SUITE_END() // FP32
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixtureFP16, framework::DatasetMode::PRECOMMIT,
+ combine(combine(SmallWinogradFilterTransformDatasetNCHW,
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+ framework::dataset::make("DataType", { DataType::F16 })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixtureFP16, framework::DatasetMode::NIGHTLY,
+ combine(combine(LargeWinogradFilterTransformDatasetNCHW,
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+ framework::dataset::make("DataType", { DataType::F16 })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+TEST_SUITE_END() // FP16
TEST_SUITE_END() // NCHW
TEST_SUITE(NHWC)
@@ -353,7 +413,7 @@
combine(combine(framework::dataset::concat(SmallWinogradFilterTransformDatasetNHWC,
LargeWinogradFilterTransformDatasetNHWC),
framework::dataset::make("DataLayout", { DataLayout::NHWC })),
- framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("DataType", { DataType::F32, DataType::F16 })),
shape_in, output_tile, data_layout, data_type)
{
TensorShape shape_in_nhwc(shape_in);
@@ -381,7 +441,27 @@
winograd_filter_transform.configure(&a, &b, winograd_info);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixture, framework::DatasetMode::PRECOMMIT,
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixtureFP16, framework::DatasetMode::PRECOMMIT,
+ combine(combine(SmallWinogradFilterTransformDatasetNHWC,
+ framework::dataset::make("DataLayout", { DataLayout::NHWC })),
+ framework::dataset::make("DataType", { DataType::F16 })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixtureFP16, framework::DatasetMode::NIGHTLY,
+ combine(combine(LargeWinogradFilterTransformDatasetNHWC,
+ framework::dataset::make("DataLayout", { DataLayout::NHWC })),
+ framework::dataset::make("DataType", { DataType::F16 })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+TEST_SUITE_END() // FP16
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixtureFP32, framework::DatasetMode::PRECOMMIT,
combine(combine(SmallWinogradFilterTransformDatasetNHWC,
framework::dataset::make("DataLayout", { DataLayout::NHWC })),
framework::dataset::make("DataType", { DataType::F32 })))
@@ -390,7 +470,7 @@
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixture, framework::DatasetMode::NIGHTLY,
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixtureFP32, framework::DatasetMode::NIGHTLY,
combine(combine(LargeWinogradFilterTransformDatasetNHWC,
framework::dataset::make("DataLayout", { DataLayout::NHWC })),
framework::dataset::make("DataType", { DataType::F32 })))
@@ -398,13 +478,14 @@
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
+TEST_SUITE_END() // FP32
TEST_SUITE_END() // NHWC
TEST_SUITE_END() // FilterTransform
TEST_SUITE(OutputTransform)
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(
framework::dataset::make("InputInfo",{
- TensorInfo(TensorShape(512U, 49U, 16U, 5U), 1, DataType::F16), // F16 not supported
+ TensorInfo(TensorShape(512U, 49U, 16U, 5U), 1, DataType::F16), // F16 supported
TensorInfo(TensorShape(512U, 49U, 16U, 5U), 1, DataType::QASYMM8), // QASYMM8 not supported
TensorInfo(TensorShape(512U, 49U, 16U, 5U), 1, DataType::F32), // Kernel size not supported
TensorInfo(TensorShape(512U, 49U, 16U, 5U), 1, DataType::F32), // Valid
@@ -447,19 +528,20 @@
WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(64U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW),
WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(64U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)
})),
- framework::dataset::make("Expected", { false, false, false, true, false, true, false, true, false })),
+ framework::dataset::make("Expected", { true, false, false, true, false, true, false, true, false })),
input_info, bias_info, output_info, winograd_info, expected)
{
ARM_COMPUTE_EXPECT(bool(CLWinogradOutputTransformKernel::validate(&input_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), winograd_info)) == expected, framework::LogLevel::ERRORS);
}
using CLWinogradOutputTransform = CLSynthetizeFunctionWithZeroConstantBorder<CLWinogradOutputTransformKernel, 0>;
-using CLWinogradOutputTransformFixture = WinogradOutputTransformValidationFixture<CLTensor, CLAccessor, CLWinogradOutputTransform, float>;
+using CLWinogradOutputTransformFixtureFP32 = WinogradOutputTransformValidationFixture<CLTensor, CLAccessor, CLWinogradOutputTransform, float>;
+using CLWinogradOutputTransformFixtureFP16 = WinogradOutputTransformValidationFixture<CLTensor, CLAccessor, CLWinogradOutputTransform, half>;
TEST_SUITE(NCHW)
DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(SmallWinogradOutputTransformDatasetNCHW,
LargeWinogradOutputTransformDatasetNCHW),
- framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("DataType", { DataType::F32, DataType::F16 })),
shape_a, winograd_info, data_type)
{
TensorShape shape_b = compute_winograd_output_transform_shape(TensorInfo(shape_a, 1, data_type), winograd_info);
@@ -475,8 +557,25 @@
CLWinogradOutputTransform winograd_output_transform;
winograd_output_transform.configure(&a, nullptr, &b, winograd_info);
}
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::ALL,
+ combine(SmallWinogradOutputTransformDatasetNCHW,
+ framework::dataset::make("DataType", { DataType::F16 })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
-FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixture, framework::DatasetMode::ALL,
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::NIGHTLY,
+ combine(LargeWinogradOutputTransformDatasetNCHW,
+ framework::dataset::make("DataType", { DataType::F16 })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+TEST_SUITE_END() // FP16
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::ALL,
combine(SmallWinogradOutputTransformDatasetNCHW,
framework::dataset::make("DataType", { DataType::F32 })))
{
@@ -484,19 +583,20 @@
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixture, framework::DatasetMode::NIGHTLY,
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::NIGHTLY,
combine(LargeWinogradOutputTransformDatasetNCHW,
framework::dataset::make("DataType", { DataType::F32 })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
+TEST_SUITE_END() // FP32
TEST_SUITE_END() // NCHW
TEST_SUITE(NHWC)
DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(SmallWinogradOutputTransformDatasetNHWC,
LargeWinogradOutputTransformDatasetNHWC),
- framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("DataType", { DataType::F32, DataType::F16 })),
shape_a, winograd_info, data_type)
{
TensorShape shape_b = compute_winograd_output_transform_shape(TensorInfo(shape_a, 1, data_type), winograd_info);
@@ -513,7 +613,25 @@
winograd_output_transform.configure(&a, nullptr, &b, winograd_info);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixture, framework::DatasetMode::ALL,
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::ALL,
+ combine(SmallWinogradOutputTransformDatasetNHWC,
+ framework::dataset::make("DataType", { DataType::F16 })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::NIGHTLY,
+ combine(LargeWinogradOutputTransformDatasetNHWC,
+ framework::dataset::make("DataType", { DataType::F16 })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+TEST_SUITE_END() // FP16
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::ALL,
combine(SmallWinogradOutputTransformDatasetNHWC,
framework::dataset::make("DataType", { DataType::F32 })))
{
@@ -521,41 +639,42 @@
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixture, framework::DatasetMode::NIGHTLY,
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::NIGHTLY,
combine(LargeWinogradOutputTransformDatasetNHWC,
framework::dataset::make("DataType", { DataType::F32 })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
+TEST_SUITE_END() // FP32
TEST_SUITE_END() // NHWC
TEST_SUITE_END() // OutputTransform
TEST_SUITE(ConvolutionLayer)
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
framework::dataset::make("InputInfo", {
- TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F16), // FP16 not supported
+ TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F16), // Insufficient padding
TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32), // Datatype mismatch
TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32), // Stride y not supported
TensorInfo(TensorShape(16U, 16U, 8U), 1, DataType::F32), // Padding needed
TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32) // Kernel size not supported
}),
framework::dataset::make("WeightsInfo", {
- TensorInfo(TensorShape(3U, 3U, 2U, 19U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 19U), 1, DataType::F16),
TensorInfo(TensorShape(3U, 3U, 2U, 19U), 1, DataType::QASYMM8),
TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 3U, 8U, 16U), 1, DataType::F32),
TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16)
})),
framework::dataset::make("BiasesInfo", {
- TensorInfo(TensorShape(19U), 1, DataType::F32),
+ TensorInfo(TensorShape(19U), 1, DataType::F16),
TensorInfo(TensorShape(19U), 1, DataType::F32),
TensorInfo(TensorShape(21U), 1, DataType::F32),
TensorInfo(TensorShape(16U), 1, DataType::F32),
TensorInfo(TensorShape(16U), 1, DataType::F32)
})),
framework::dataset::make("OutputInfo", {
- TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F32),
+ TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F16),
TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32),
TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(16U, 16U, 16U), 1, DataType::F32),
@@ -574,6 +693,7 @@
ARM_COMPUTE_EXPECT(bool(CLWinogradConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info)) == expected, framework::LogLevel::ERRORS);
}
+TEST_SUITE(FP32)
using CLWinogradConvolutionLayerFastMathFixture = WinogradConvolutionLayerFastMathValidationFixture<CLTensor, CLAccessor, CLWinogradConvolutionLayer, float>;
TEST_SUITE(Conv3x3)
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
@@ -712,6 +832,151 @@
validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
}
TEST_SUITE_END() // Conv1x5
+TEST_SUITE_END() // FP32
+
+
+TEST_SUITE(FP16)
+
+using CLWinogradConvolutionLayerFastMathFixture16 = WinogradConvolutionLayerFastMathValidationFixture<CLTensor, CLAccessor, CLWinogradConvolutionLayer, half>;
+TEST_SUITE(Conv3x3)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(),
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16);
+}
+TEST_SUITE_END() // Conv3x3
+
+TEST_SUITE(Conv3x1)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(),
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(),
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16);
+}
+TEST_SUITE_END() // Conv3x1
+
+TEST_SUITE(Conv1x3)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(),
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(),
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16);
+}
+TEST_SUITE_END() // Conv1x3
+
+TEST_SUITE(Conv5x5)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(),
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(),
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16);
+}
+TEST_SUITE_END() // Conv5x5
+
+TEST_SUITE(Conv5x1)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(),
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x1Dataset(),
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16);
+}
+TEST_SUITE_END() // Conv5x1
+
+TEST_SUITE(Conv1x5)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(),
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x5Dataset(),
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16);
+}
+TEST_SUITE_END() // Conv1x5
+
+TEST_SUITE_END() // FP16
TEST_SUITE_END() // ConvolutionLayer
TEST_SUITE_END() // Winograd
diff --git a/tests/validation/Helpers.cpp b/tests/validation/Helpers.cpp
index 1044e32..fd034b6 100644
--- a/tests/validation/Helpers.cpp
+++ b/tests/validation/Helpers.cpp
@@ -169,7 +169,8 @@
return dst;
}
-void matrix_multiply(const SimpleTensor<float> &a, const SimpleTensor<float> &b, SimpleTensor<float> &out)
+template <typename T>
+void matrix_multiply(const SimpleTensor<T> &a, const SimpleTensor<T> &b, SimpleTensor<T> &out)
{
ARM_COMPUTE_ERROR_ON(a.shape()[0] != b.shape()[1]);
ARM_COMPUTE_ERROR_ON(a.shape()[1] != out.shape()[1]);
@@ -194,7 +195,8 @@
}
}
-void transpose_matrix(const SimpleTensor<float> &in, SimpleTensor<float> &out)
+template <typename T>
+void transpose_matrix(const SimpleTensor<T> &in, SimpleTensor<T> &out)
{
ARM_COMPUTE_ERROR_ON((in.shape()[0] != out.shape()[1]) || (in.shape()[1] != out.shape()[0]));
@@ -301,7 +303,14 @@
}
template void get_tile(const SimpleTensor<float> &in, SimpleTensor<float> &roi, const Coordinates &coord);
+template void get_tile(const SimpleTensor<half> &in, SimpleTensor<half> &roi, const Coordinates &coord);
template void zeros(SimpleTensor<float> &in, const Coordinates &anchor, const TensorShape &shape);
+template void zeros(SimpleTensor<half> &in, const Coordinates &anchor, const TensorShape &shape);
+template void transpose_matrix(const SimpleTensor<float> &in, SimpleTensor<float> &out);
+template void transpose_matrix(const SimpleTensor<half> &in, SimpleTensor<half> &out);
+template void matrix_multiply(const SimpleTensor<float> &a, const SimpleTensor<float> &b, SimpleTensor<float> &out);
+template void matrix_multiply(const SimpleTensor<half> &a, const SimpleTensor<half> &b, SimpleTensor<half> &out);
+
} // namespace validation
} // namespace test
} // namespace arm_compute
diff --git a/tests/validation/Helpers.h b/tests/validation/Helpers.h
index e5ba148..8b99494 100644
--- a/tests/validation/Helpers.h
+++ b/tests/validation/Helpers.h
@@ -237,7 +237,8 @@
* @param[out] out Output tensor
*
*/
-void matrix_multiply(const SimpleTensor<float> &a, const SimpleTensor<float> &b, SimpleTensor<float> &out);
+template <typename T>
+void matrix_multiply(const SimpleTensor<T> &a, const SimpleTensor<T> &b, SimpleTensor<T> &out);
/** Transpose matrix
*
@@ -245,7 +246,8 @@
* @param[out] out Output tensor
*
*/
-void transpose_matrix(const SimpleTensor<float> &in, SimpleTensor<float> &out);
+template <typename T>
+void transpose_matrix(const SimpleTensor<T> &in, SimpleTensor<T> &out);
/** Get a 2D tile from a tensor
*
diff --git a/tests/validation/fixtures/WinogradConvolutionLayerFixture.h b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
index aba3eff..41f16d3 100644
--- a/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
@@ -70,6 +70,7 @@
{
switch(tensor.data_type())
{
+ case DataType::F16:
case DataType::F32:
{
std::uniform_real_distribution<> distribution(min, max);
@@ -175,6 +176,7 @@
{
switch(tensor.data_type())
{
+ case DataType::F16:
case DataType::F32:
{
std::uniform_real_distribution<> distribution(min, max);
@@ -318,6 +320,7 @@
{
switch(tensor.data_type())
{
+ case DataType::F16:
case DataType::F32:
{
std::uniform_real_distribution<> distribution(min, max);
@@ -401,6 +404,7 @@
{
switch(tensor.data_type())
{
+ case DataType::F16:
case DataType::F32:
{
std::uniform_real_distribution<> distribution(min, max);
@@ -481,6 +485,7 @@
{
switch(tensor.data_type())
{
+ case DataType::F16:
case DataType::F32:
{
std::uniform_real_distribution<> distribution(min, max);
diff --git a/tests/validation/reference/Winograd.cpp b/tests/validation/reference/Winograd.cpp
index 132d252..3c2c11d 100644
--- a/tests/validation/reference/Winograd.cpp
+++ b/tests/validation/reference/Winograd.cpp
@@ -232,7 +232,7 @@
initialize_matrix_transform(matrix, output_tile_size, kernel_size, WinogradTransformType::INPUT);
// Transpose matrix
- transpose_matrix(matrix, matrix_transposed);
+ transpose_matrix<T>(matrix, matrix_transposed);
const int in_w = in.shape().x();
const int in_h = in.shape().y();
@@ -293,14 +293,14 @@
int yi = y * step_y - conv_info.pad_top();
// Get the tile from the input tensor
- get_tile(in, src_tile, Coordinates(xi, yi, z, b));
+ get_tile<T>(in, src_tile, Coordinates(xi, yi, z, b));
// Fill partially with zeros in case of 1D convolution
- zeros(src_tile, anchor_zeros, shape_zeros);
+ zeros<T>(src_tile, anchor_zeros, shape_zeros);
// Compute the transformation
- matrix_multiply(matrix, src_tile, tmp_tile);
- matrix_multiply(tmp_tile, matrix_transposed, dst_tile);
+ matrix_multiply<T>(matrix, src_tile, tmp_tile);
+ matrix_multiply<T>(tmp_tile, matrix_transposed, dst_tile);
// Store the output tile across the channels
for(int i = 0; i < out_d; ++i)
@@ -358,7 +358,7 @@
initialize_matrix_transform(trans_matrix, output_tile_size, kernel_size, WinogradTransformType::FILTER);
// Transpose the transformation matrix
- transpose_matrix(trans_matrix, trans_matrix_transposed);
+ transpose_matrix<T>(trans_matrix, trans_matrix_transposed);
const int num_channels = in.shape()[2];
const int num_filters = in.shape()[3];
@@ -374,13 +374,13 @@
for(int z = 0; z < num_channels; ++z)
{
// Load the tile from the input tensor
- get_tile(in, input_tile, Coordinates(0, 0, z, w, n));
+ get_tile<T>(in, input_tile, Coordinates(0, 0, z, w, n));
// First transformation
- matrix_multiply(trans_matrix, input_tile, tmp_tile);
+ matrix_multiply<T>(trans_matrix, input_tile, tmp_tile);
// Second transformation
- matrix_multiply(tmp_tile, trans_matrix_transposed, transf_tile);
+ matrix_multiply<T>(tmp_tile, trans_matrix_transposed, transf_tile);
// Store the output tile across the channels
const int output_offset = w + z * num_filters;
@@ -451,7 +451,7 @@
initialize_matrix_transform(trans_matrix, output_tile_size, kernel_size, WinogradTransformType::OUTPUT);
// Transpose the transformation matrix
- transpose_matrix(trans_matrix, trans_matrix_transposed);
+ transpose_matrix<T>(trans_matrix, trans_matrix_transposed);
const int w_in = in.shape()[0];
const int h_in = in.shape()[1];
@@ -487,7 +487,7 @@
const int step_y_transf_tile = kernel_size.width == 1 ? 1 : output_tile.shape()[0];
// Initialize with zeros the input tile
- zeros(input_tile, Coordinates(0, 0), input_tile.shape());
+ zeros<T>(input_tile, Coordinates(0, 0), input_tile.shape());
for(int n = 0; n < num_batches; ++n)
{
@@ -502,10 +502,10 @@
}
// First transformation
- matrix_multiply(trans_matrix, input_tile, tmp_tile);
+ matrix_multiply<T>(trans_matrix, input_tile, tmp_tile);
// Second transformation
- matrix_multiply(tmp_tile, trans_matrix_transposed, output_tile);
+ matrix_multiply<T>(tmp_tile, trans_matrix_transposed, output_tile);
// Store the output tile
const int xo = (y % num_tiles_x) * out_tile_w;
@@ -538,6 +538,10 @@
template SimpleTensor<float> winograd_filter_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
template SimpleTensor<float> winograd_input_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
template SimpleTensor<float> winograd_output_transform(const SimpleTensor<float> &in, const SimpleTensor<float> &b, const TensorShape &output_shape, const WinogradInfo &winograd_info);
+template SimpleTensor<half> winograd_filter_transform(const SimpleTensor<half> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
+template SimpleTensor<half> winograd_input_transform(const SimpleTensor<half> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
+template SimpleTensor<half> winograd_output_transform(const SimpleTensor<half> &in, const SimpleTensor<half> &b, const TensorShape &output_shape, const WinogradInfo &winograd_info);
+
} // namespace reference
} // namespace validation
} // namespace test