COMPMID-1694: Fuse offset contribution with the output stage when we use NEGEMMLowpMatrixMultiplyCore
Change-Id: Ic1a681e4cc03e1eba3bf8485d9cdb17b3e926047
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/561
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/datasets/GEMMLowpFusedOffsetOutputDataset.h b/tests/datasets/GEMMLowpFusedOffsetOutputDataset.h
new file mode 100644
index 0000000..c94019e
--- /dev/null
+++ b/tests/datasets/GEMMLowpFusedOffsetOutputDataset.h
@@ -0,0 +1,201 @@
+/*
+ * Copyright (c) 2019 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_TEST_GEMMLOWPOUTPUT_DATASET
+#define ARM_COMPUTE_TEST_GEMMLOWPOUTPUT_DATASET
+
+#include "utils/TypePrinter.h"
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Utils.h"
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+namespace test
+{
+namespace datasets
+{
+class GEMMLowpFusedOffsetOutputDataset
+{
+public:
+ using type = std::tuple<TensorShape, TensorShape, TensorShape, int32_t, int32_t, GEMMLowpOutputStageInfo>;
+
+ struct iterator
+ {
+ iterator(std::vector<TensorShape>::const_iterator a_it,
+ std::vector<TensorShape>::const_iterator b_it,
+ std::vector<TensorShape>::const_iterator c_it,
+ std::vector<int32_t>::const_iterator a_offset_it,
+ std::vector<int32_t>::const_iterator b_offset_it,
+ std::vector<GEMMLowpOutputStageInfo>::const_iterator output_stage_it)
+ : _a_it{ std::move(a_it) },
+ _b_it{ std::move(b_it) },
+ _c_it{ std::move(c_it) },
+ _a_offset_it{ std::move(a_offset_it) },
+ _b_offset_it{ std::move(b_offset_it) },
+ _output_stage_it{ std::move(output_stage_it) }
+ {
+ }
+
+ std::string description() const
+ {
+ std::stringstream description;
+ description << "A=" << *_a_it << ":";
+ description << "B=" << *_b_it << ":";
+ description << "C=" << *_c_it << ":";
+ description << "a_offset=" << *_a_offset_it << ":";
+ description << "b_offset=" << *_b_offset_it << ":";
+ description << "output_type=" << string_from_gemmlowp_output_stage((*_output_stage_it).type) << ":";
+ description << "output_offset=" << (*_output_stage_it).gemmlowp_offset << ":";
+ description << "output_multiplier=" << (*_output_stage_it).gemmlowp_multiplier << ":";
+ description << "output_shift=" << (*_output_stage_it).gemmlowp_shift << ":";
+ description << "output_min=" << (*_output_stage_it).gemmlowp_min_bound << ":";
+ description << "output_max=" << (*_output_stage_it).gemmlowp_max_bound << ":";
+
+ return description.str();
+ }
+
+ GEMMLowpFusedOffsetOutputDataset::type operator*() const
+ {
+ return std::make_tuple(*_a_it, *_b_it, *_c_it, *_a_offset_it, *_b_offset_it, *_output_stage_it);
+ }
+
+ iterator &operator++()
+ {
+ ++_a_it;
+ ++_b_it;
+ ++_c_it;
+ ++_a_offset_it;
+ ++_b_offset_it;
+ ++_output_stage_it;
+
+ return *this;
+ }
+
+ private:
+ std::vector<TensorShape>::const_iterator _a_it;
+ std::vector<TensorShape>::const_iterator _b_it;
+ std::vector<TensorShape>::const_iterator _c_it;
+ std::vector<int32_t>::const_iterator _a_offset_it;
+ std::vector<int32_t>::const_iterator _b_offset_it;
+ std::vector<GEMMLowpOutputStageInfo>::const_iterator _output_stage_it;
+ };
+
+ iterator begin() const
+ {
+ return iterator(_a_shapes.begin(), _b_shapes.begin(), _c_shapes.begin(), _a_offset.begin(), _b_offset.begin(), _output_stage.begin());
+ }
+
+ int size() const
+ {
+ return std::min(_a_shapes.size(), std::min(_b_shapes.size(), std::min(_c_shapes.size(), std::min(_a_offset.size(), std::min(_b_offset.size(), _output_stage.size())))));
+ }
+
+ void add_config(TensorShape a, TensorShape b, TensorShape c, int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage)
+ {
+ _a_shapes.emplace_back(std::move(a));
+ _b_shapes.emplace_back(std::move(b));
+ _c_shapes.emplace_back(std::move(c));
+ _a_offset.emplace_back(std::move(a_offset));
+ _b_offset.emplace_back(std::move(b_offset));
+ _output_stage.emplace_back(std::move(output_stage));
+ }
+
+ GEMMLowpOutputStageInfo OutputStageInfo(GEMMLowpOutputStageType type, int32_t offset, int32_t multiplier, int32_t shift, int32_t min, int32_t max)
+ {
+ GEMMLowpOutputStageInfo output_stage = GEMMLowpOutputStageInfo();
+ output_stage.type = type;
+ output_stage.gemmlowp_offset = offset;
+ output_stage.gemmlowp_multiplier = multiplier;
+ output_stage.gemmlowp_shift = shift;
+ output_stage.gemmlowp_min_bound = min;
+ output_stage.gemmlowp_max_bound = max;
+ return output_stage;
+ }
+
+protected:
+ GEMMLowpFusedOffsetOutputDataset() = default;
+ GEMMLowpFusedOffsetOutputDataset(GEMMLowpFusedOffsetOutputDataset &&) = default;
+
+private:
+ std::vector<TensorShape> _a_shapes{};
+ std::vector<TensorShape> _b_shapes{};
+ std::vector<TensorShape> _c_shapes{};
+ std::vector<int32_t> _a_offset{};
+ std::vector<int32_t> _b_offset{};
+ std::vector<GEMMLowpOutputStageInfo> _output_stage{};
+};
+
+class SmallGEMMLowpFusedOffsetOutputDataset final : public GEMMLowpFusedOffsetOutputDataset
+{
+public:
+ SmallGEMMLowpFusedOffsetOutputDataset()
+ {
+ add_config(TensorShape(21U, 1U), TensorShape(43U, 21U), TensorShape(43U, 1U), 0, 0, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, -200, 2, 13, 10, 210));
+ add_config(TensorShape(21U, 13U), TensorShape(33U, 21U), TensorShape(33U, 13U), 0, 0, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, -100, 2, 13, 10, 210));
+ add_config(TensorShape(31U, 3U), TensorShape(72U, 31U), TensorShape(72U, 3U), -2, 13, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, 0, 2, 13, 10, 210));
+ add_config(TensorShape(52U, 13U), TensorShape(33U, 52U), TensorShape(33U, 13U), 0, 4, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, 100, 2, 13, 10, 210));
+ add_config(TensorShape(52U, 26U), TensorShape(33U, 52U), TensorShape(33U, 26U), -2, 0, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, 0, 2, 13, 10, 210));
+ add_config(TensorShape(31U, 27U), TensorShape(23U, 31U), TensorShape(23U, 27U), 18, 23, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, 200, 2, 13, 10, 210));
+ add_config(TensorShape(38U, 43U), TensorShape(21U, 38U), TensorShape(21U, 43U), -3, -2, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, -200, 2, 13, 10, 210));
+ add_config(TensorShape(32U, 72U), TensorShape(17U, 32U), TensorShape(17U, 72U), -9, 1, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, -100, 2, 13, 10, 210));
+
+ add_config(TensorShape(21U, 1U), TensorShape(43U, 21U), TensorShape(43U, 1U), 0, 0, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT, -2, 254601600, 10, 10, 210));
+ add_config(TensorShape(21U, 13U), TensorShape(33U, 21U), TensorShape(33U, 13U), 0, 0, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT, -1, 254601600, 10, 10, 210));
+ add_config(TensorShape(31U, 3U), TensorShape(72U, 31U), TensorShape(72U, 3U), -2, 13, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT, 0, 254601600, 10, 10, 210));
+ add_config(TensorShape(52U, 26U), TensorShape(33U, 52U), TensorShape(33U, 26U), -2, 0, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT, 1, 254601600, 10, 10, 210));
+ add_config(TensorShape(31U, 27U), TensorShape(23U, 31U), TensorShape(23U, 27U), 5, 13, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT, 2, 254601602, 10, 10, 210));
+ add_config(TensorShape(38U, 43U), TensorShape(21U, 38U), TensorShape(21U, 43U), -3, -2, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT, -2, 254601602, 10, 10, 210));
+ add_config(TensorShape(32U, 72U), TensorShape(17U, 32U), TensorShape(17U, 72U), -9, 1, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT, -1, 254601602, 10, 10, 210));
+ }
+};
+
+class LargeGEMMLowpFusedOffsetOutputDataset final : public GEMMLowpFusedOffsetOutputDataset
+{
+public:
+ LargeGEMMLowpFusedOffsetOutputDataset()
+ {
+ add_config(TensorShape(923U, 1U), TensorShape(871U, 923U), TensorShape(871U, 1U), 0, 0, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, -200, 2, 18, 10, 210));
+ add_config(TensorShape(923U, 429U), TensorShape(871U, 923U), TensorShape(871U, 429U), 0, 0, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, -100, 2, 18, 10, 210));
+ add_config(TensorShape(873U, 7U), TensorShape(784U, 873U), TensorShape(784U, 7U), -1, 3, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, 0, 2, 18, 10, 210));
+ add_config(TensorShape(873U, 513U), TensorShape(784U, 873U), TensorShape(784U, 513U), 0, 4, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, 100, 2, 18, 10, 210));
+ add_config(TensorShape(697U, 872U), TensorShape(563U, 697U), TensorShape(563U, 872U), -2, 0, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, 0, 2, 18, 10, 210));
+ add_config(TensorShape(1021U, 973U), TensorShape(783U, 1021U), TensorShape(783U, 973U), 5, 13, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, 200, 2, 18, 10, 210));
+ add_config(TensorShape(681U, 1023U), TensorShape(213U, 681U), TensorShape(213U, 1023U), -3, -2, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, -200, 2, 18, 10, 210));
+ add_config(TensorShape(941U, 1011U), TensorShape(623U, 941U), TensorShape(623U, 1011U), -9, 1, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN, -100, 2, 18, 10, 210));
+
+ add_config(TensorShape(923U, 1U), TensorShape(871U, 923U), TensorShape(871U, 1U), 0, 0, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT, -2, 254601600, 15, 10, 210));
+ add_config(TensorShape(923U, 429U), TensorShape(871U, 923U), TensorShape(871U, 429U), 0, 0, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT, -1, 254601600, 15, 10, 210));
+ add_config(TensorShape(873U, 7U), TensorShape(784U, 873U), TensorShape(784U, 7U), -1, 3, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT, 0, 254601600, 15, 10, 210));
+ add_config(TensorShape(873U, 513U), TensorShape(784U, 873U), TensorShape(784U, 513U), 0, 4, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT, 1, 254601600, 15, 10, 210));
+ add_config(TensorShape(697U, 872U), TensorShape(563U, 697U), TensorShape(563U, 872U), -2, 0, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT, 2, 254601602, 15, 10, 210));
+ add_config(TensorShape(1021U, 973U), TensorShape(783U, 1021U), TensorShape(783U, 973U), 5, 13, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT, -2, 254601602, 15, 10, 210));
+ add_config(TensorShape(681U, 1023U), TensorShape(213U, 681U), TensorShape(213U, 1023U), -3, -2, OutputStageInfo(GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT, -1, 254601602, 15, 10, 210));
+ }
+};
+} // namespace datasets
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_GEMMLOWPOUTPUT_DATASET */
diff --git a/tests/validation/CL/GEMMLowp.cpp b/tests/validation/CL/GEMMLowp.cpp
index 08641db..efefbd6 100644
--- a/tests/validation/CL/GEMMLowp.cpp
+++ b/tests/validation/CL/GEMMLowp.cpp
@@ -28,6 +28,7 @@
#include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h"
#include "tests/CL/CLAccessor.h"
#include "tests/PaddingCalculator.h"
+#include "tests/datasets/GEMMLowpFusedOffsetOutputDataset.h"
#include "tests/datasets/LargeGEMMLowpDataset.h"
#include "tests/datasets/ShapeDatasets.h"
#include "tests/datasets/SmallGEMMLowpDataset.h"
@@ -83,6 +84,21 @@
validate(CLAccessor(_target), _reference);
}
+using CLGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture = GEMMLowpMatrixMultiplyCoreFusedOffsetOutputValidationFixture<CLTensor, CLAccessor, CLGEMMLowpMatrixMultiplyCore>;
+TEST_SUITE(FusedOffsetOutput)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture, framework::DatasetMode::ALL, datasets::SmallGEMMLowpFusedOffsetOutputDataset())
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture, framework::DatasetMode::NIGHTLY, datasets::LargeGEMMLowpFusedOffsetOutputDataset())
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END() // FusedOffsetOutput
+
TEST_SUITE(Output3D)
using CLGEMMLowpMatrixMultiplyCoreOutput3DFixture = GEMMLowpMatrixMultiplyCoreValidationFixture<CLTensor, CLAccessor, CLGEMMLowpMatrixMultiplyCore, false, true>;
FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMLowpMatrixMultiplyCoreOutput3DFixture, framework::DatasetMode::PRECOMMIT, datasets::SmallGEMMLowpOutput3DDataset())
diff --git a/tests/validation/NEON/GEMMLowp.cpp b/tests/validation/NEON/GEMMLowp.cpp
index 57067f1..f0460b4 100644
--- a/tests/validation/NEON/GEMMLowp.cpp
+++ b/tests/validation/NEON/GEMMLowp.cpp
@@ -30,6 +30,7 @@
#include "tests/NEON/Accessor.h"
#include "tests/NEON/Helper.h"
#include "tests/PaddingCalculator.h"
+#include "tests/datasets/GEMMLowpFusedOffsetOutputDataset.h"
#include "tests/datasets/LargeGEMMLowpDataset.h"
#include "tests/datasets/ShapeDatasets.h"
#include "tests/datasets/SmallGEMMLowpDataset.h"
@@ -144,6 +145,20 @@
validate(Accessor(_target), _reference);
}
+using NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture = GEMMLowpMatrixMultiplyCoreFusedOffsetOutputValidationFixture<Tensor, Accessor, NEGEMMLowpMatrixMultiplyCore>;
+TEST_SUITE(FusedOffsetOutput)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture, framework::DatasetMode::ALL, datasets::SmallGEMMLowpFusedOffsetOutputDataset())
+{
+ // Validate output
+ validate(Accessor(_target), _reference);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture, framework::DatasetMode::NIGHTLY, datasets::LargeGEMMLowpFusedOffsetOutputDataset())
+{
+ // Validate output
+ validate(Accessor(_target), _reference);
+}
+TEST_SUITE_END() // FusedOffsetOutput
TEST_SUITE_END() // MatrixMultiplyCore
TEST_SUITE(OutputStage)
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h
index 836f8ed..90a4b5c 100644
--- a/tests/validation/fixtures/GEMMLowpFixture.h
+++ b/tests/validation/fixtures/GEMMLowpFixture.h
@@ -42,88 +42,166 @@
{
namespace validation
{
+namespace
+{
+template <typename U>
+void fill(U &&tensor, int i)
+{
+ // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
+ std::uniform_int_distribution<> distribution(1, 254);
+ library->fill(tensor, distribution, i);
+}
+
+template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d, bool reinterpret_output_as_3d, typename OutputType, bool is_fused = false>
+TensorType compute_gemmlowp_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset,
+ GEMMLowpOutputStageInfo output_stage = GEMMLowpOutputStageInfo())
+{
+ // Create tensors
+ TensorType a = create_tensor<TensorType>(shape_a, DataType::QASYMM8, 1);
+ TensorType b = create_tensor<TensorType>(shape_b, DataType::QASYMM8, 1);
+ TensorType output = create_tensor<TensorType>(shape_output, output_stage.type == GEMMLowpOutputStageType::NONE ? DataType::S32 : DataType::QASYMM8, 1);
+
+ a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset));
+ b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset));
+
+ TensorType bias;
+ if(is_fused)
+ {
+ TensorShape bias_shape(shape_b[0]);
+ bias = create_tensor<TensorType>(bias_shape, DataType::S32, 1);
+ }
+
+ // Create and configure function
+ // The GEMMinfo includes the values of the depth in case of reinterpreted 3d input/output
+ FunctionType gemmlowp;
+ // TODO (COMPMID-1672) - Extending the test to validate add bias in offset contribution
+ gemmlowp.configure(&a, &b, is_fused ? &bias : nullptr, &output, GEMMInfo(false, false, false, (reinterpret_output_as_3d ? shape_output[2] : 0), reinterpret_input_as_3d, false, output_stage));
+
+ ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(output.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate tensors
+ a.allocator()->allocate();
+ b.allocator()->allocate();
+ output.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!output.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensors
+ fill(AccessorType(a), 0);
+ fill(AccessorType(b), 1);
+
+ if(is_fused)
+ {
+ ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ bias.allocator()->allocate();
+ ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ fill(AccessorType(bias), 2);
+ }
+
+ // Compute GEMM function
+ gemmlowp.run();
+ return output;
+}
+
+template <bool reinterpret_input_as_3d>
+SimpleTensor<int32_t> compute_gemmlowp_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset)
+{
+ TensorShape shape_a_to_use = shape_a;
+ if(reinterpret_input_as_3d)
+ {
+ // Collapse the second and third dimension if the input is 3D
+ shape_a_to_use.collapse(2U, 1U);
+ }
+
+ // Create reference
+ SimpleTensor<uint8_t> a{ shape_a_to_use, DataType::QASYMM8, 1 };
+ SimpleTensor<uint8_t> b{ shape_b, DataType::QASYMM8, 1 };
+
+ // Fill reference
+ fill(a, 0);
+ fill(b, 1);
+
+ return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(a, b, shape_output, a_offset, b_offset);
+}
+}
+
template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false>
class GEMMLowpMatrixMultiplyCoreValidationFixture : public framework::Fixture
{
public:
template <typename...>
- void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_c, int32_t a_offset, int32_t b_offset)
+ void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset)
{
- _target = compute_target(shape_a, shape_b, shape_c, a_offset, b_offset);
- _reference = compute_reference(shape_a, shape_b, shape_c, a_offset, b_offset);
+ _target = compute_target(shape_a, shape_b, shape_output, a_offset, b_offset);
+ _reference = compute_reference(shape_a, shape_b, shape_output, a_offset, b_offset);
}
protected:
- template <typename U>
- void fill(U &&tensor, int i)
+ TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset)
{
- // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
- std::uniform_int_distribution<> distribution(1, 254);
- library->fill(tensor, distribution, i);
+ return compute_gemmlowp_target<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, int32_t>(shape_a, shape_b, shape_output, a_offset, b_offset);
}
- TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, int32_t a_offset, int32_t b_offset)
+ SimpleTensor<int32_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset)
{
- // Create tensors
- TensorType a = create_tensor<TensorType>(shape_a, DataType::QASYMM8, 1);
- TensorType b = create_tensor<TensorType>(shape_b, DataType::QASYMM8, 1);
- TensorType c = create_tensor<TensorType>(shape_c, DataType::S32, 1);
-
- a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset));
- b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset));
-
- // Create and configure function
- // The GEMMinfo includes the values of the depth in case of reinterpreted 3d input/output
- FunctionType gemmlowp;
- // TODO (COMPMID-1672) - Extending the test to validate add bias in offset contribution
- gemmlowp.configure(&a, &b, nullptr, &c, GEMMInfo(false, false, false, (reinterpret_output_as_3d ? shape_c[2] : 0), reinterpret_input_as_3d));
-
- ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS);
-
- // Allocate tensors
- a.allocator()->allocate();
- b.allocator()->allocate();
- c.allocator()->allocate();
-
- ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS);
-
- // Fill tensors
- fill(AccessorType(a), 0);
- fill(AccessorType(b), 1);
-
- // Compute GEMM function
- gemmlowp.run();
- return c;
- }
-
- SimpleTensor<int32_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, int32_t a_offset, int32_t b_offset)
- {
- TensorShape shape_a_to_use = shape_a;
- if(reinterpret_input_as_3d)
- {
- // Collapse the second and third dimension if the input is 3D
- shape_a_to_use.collapse(2U, 1U);
- }
-
- // Create reference
- SimpleTensor<uint8_t> a{ shape_a_to_use, DataType::QASYMM8, 1 };
- SimpleTensor<uint8_t> b{ shape_b, DataType::QASYMM8, 1 };
-
- // Fill reference
- fill(a, 0);
- fill(b, 1);
-
- return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(a, b, shape_c, a_offset, b_offset);
+ return compute_gemmlowp_reference<reinterpret_input_as_3d>(shape_a, shape_b, shape_output, a_offset, b_offset);
}
TensorType _target{};
SimpleTensor<int32_t> _reference{};
};
+template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false>
+class GEMMLowpMatrixMultiplyCoreFusedOffsetOutputValidationFixture : public framework::Fixture
+{
+public:
+ template <typename...>
+ void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage)
+ {
+ ARM_COMPUTE_EXPECT(output_stage.type != GEMMLowpOutputStageType::NONE, framework::LogLevel::ERRORS);
+ _target = compute_target(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage);
+ _reference = compute_reference(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage);
+ }
+
+protected:
+ TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage)
+ {
+ return compute_gemmlowp_target<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, qasymm8_t, true>(shape_a, shape_b, shape_output, a_offset, b_offset,
+ output_stage);
+ }
+
+ SimpleTensor<qasymm8_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset,
+ GEMMLowpOutputStageInfo output_stage)
+ {
+ SimpleTensor<int32_t> output = compute_gemmlowp_reference<reinterpret_input_as_3d>(shape_a, shape_b, shape_output, a_offset, b_offset);
+
+ TensorShape bias_shape(shape_b[0]);
+ SimpleTensor<int32_t> bias{ bias_shape, DataType::S32, 1 };
+ fill(bias, 2);
+
+ switch(output_stage.type)
+ {
+ case GEMMLowpOutputStageType::QUANTIZE_DOWN:
+ return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(output, bias,
+ output_stage.gemmlowp_offset, output_stage.gemmlowp_multiplier, output_stage.gemmlowp_shift, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound);
+ break;
+ case GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT:
+ return reference::gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint<int32_t>(output, bias,
+ output_stage.gemmlowp_multiplier, output_stage.gemmlowp_shift, output_stage.gemmlowp_offset, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound);
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Not Supported!");
+ }
+ }
+
+ TensorType _target{};
+ SimpleTensor<qasymm8_t> _reference{};
+};
+
template <typename TensorType, typename AccessorType, typename FunctionType>
class GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture : public framework::Fixture
{
@@ -536,4 +614,4 @@
} // namespace validation
} // namespace test
} // namespace arm_compute
-#endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */
\ No newline at end of file
+#endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */