diff options
author | Gian Marco <gianmarco.iodice@arm.com> | 2017-11-17 09:27:57 +0000 |
---|---|---|
committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:35:24 +0000 |
commit | 6b77e917801b4e979796ea75c538eef740482089 (patch) | |
tree | 0e693ecb1eb0b05018901a992b56781a08b9c266 /tests | |
parent | b3c81cb4100b3a449db5232364e18e649b26df58 (diff) | |
download | ComputeLibrary-6b77e917801b4e979796ea75c538eef740482089.tar.gz |
COMPMID-665 - NEON: Add QASYMM8 in place Activation layer
- Added min and max arguments for QuantizeDownInt32ToUint8Scale in order
to apply bounded relu
- Added support for int32_t biases
- Extended tests
Change-Id: I015dae17faa7284766b5435ca33bcf593c1b2b69
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/96512
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Diffstat (limited to 'tests')
-rw-r--r-- | tests/validation/CPP/GEMMLowp.cpp | 54 | ||||
-rw-r--r-- | tests/validation/CPP/GEMMLowp.h | 9 | ||||
-rw-r--r-- | tests/validation/NEON/GEMMLowp.cpp | 66 | ||||
-rw-r--r-- | tests/validation/fixtures/GEMMLowpFixture.h | 55 |
4 files changed, 152 insertions, 32 deletions
diff --git a/tests/validation/CPP/GEMMLowp.cpp b/tests/validation/CPP/GEMMLowp.cpp index 8670a22a66..bf002cf2b5 100644 --- a/tests/validation/CPP/GEMMLowp.cpp +++ b/tests/validation/CPP/GEMMLowp.cpp @@ -33,6 +33,36 @@ namespace validation { namespace reference { +namespace +{ +template <typename T> +void quantize_down_int32_to_uint8_scale(const SimpleTensor<T> *in, const SimpleTensor<T> *bias, SimpleTensor<uint8_t> *dst, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, + int32_t min, int32_t max) +{ + const int cols_in = in->shape().x(); + + for(int i = 0; i < in->num_elements(); ++i) + { + int32_t result = ((*in)[i] + result_offset) * result_mult_int; + + if(bias != nullptr) + { + result += (*bias)[i % cols_in]; + } + + result >>= result_shift; + + // Bounded ReLu + if(min != max) + { + result = std::max(min, std::min(max, result)); + } + + (*dst)[i] = static_cast<uint8_t>(std::max(0, std::min(255, result))); + } +} +} // namespace + template <typename T> SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<T> &a, const SimpleTensor<T> &b, int32_t a_offset, int32_t b_offset) { @@ -80,21 +110,31 @@ SimpleTensor<int32_t> gemmlowp(const SimpleTensor<int8_t> &a, const SimpleTensor } template <typename T> -SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, int32_t result_offset, int32_t result_mult_int, int32_t result_shift) +SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max) { SimpleTensor<uint8_t> dst(in.shape(), DataType::QASYMM8); - for(int i = 0; i < in.num_elements(); ++i) - { - const int32_t result = ((in[i] + result_offset) * result_mult_int) >> result_shift; - dst[i] = static_cast<uint8_t>(std::max(0, std::min(255, result))); - } + quantize_down_int32_to_uint8_scale<T>(&in, nullptr, &dst, result_offset, result_mult_int, result_shift, min, max); + + return dst; +} + +template <typename T> +SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, + int32_t min, int32_t max) +{ + SimpleTensor<uint8_t> dst(in.shape(), DataType::QASYMM8); + + quantize_down_int32_to_uint8_scale<T>(&in, &bias, &dst, result_offset, result_mult_int, result_shift, min, max); return dst; } template SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b, int32_t a_offset, int32_t b_offset); -template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<int32_t> &a, int32_t result_offset, int32_t result_mult_int, int32_t result_shift); +template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<int32_t> &a, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, + int32_t max); +template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, int32_t result_offset, int32_t result_mult_int, + int32_t result_shift, int32_t min, int32_t max); } // namespace reference } // namespace validation } // namespace test diff --git a/tests/validation/CPP/GEMMLowp.h b/tests/validation/CPP/GEMMLowp.h index cbed2206e3..ee33d8e0c0 100644 --- a/tests/validation/CPP/GEMMLowp.h +++ b/tests/validation/CPP/GEMMLowp.h @@ -35,14 +35,17 @@ namespace validation { namespace reference { +SimpleTensor<int32_t> gemmlowp(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b); + template <typename T> SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<T> &a, const SimpleTensor<T> &b, int32_t a_offset, int32_t b_offset); template <typename T> -SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, int32_t result_offset, int32_t result_mult_int, int32_t result_shift); - -SimpleTensor<int32_t> gemmlowp(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b); +SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min = 0, int32_t max = 0); +template <typename T> +SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, + int32_t min = 0, int32_t max = 0); } // namespace reference } // namespace validation } // namespace test diff --git a/tests/validation/NEON/GEMMLowp.cpp b/tests/validation/NEON/GEMMLowp.cpp index ba91ced443..078096a0dd 100644 --- a/tests/validation/NEON/GEMMLowp.cpp +++ b/tests/validation/NEON/GEMMLowp.cpp @@ -131,34 +131,55 @@ TEST_SUITE(OutputStage) TEST_SUITE(QuantizeDownInt32ToUint8Scale) -using NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture = GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture<Tensor, Accessor, NEGEMMLowpQuantizeDownInt32ToUint8Scale>; +const auto quantize_down_int32_to_uint8_scale_cases = framework::dataset::make("result_offset", -2, 2) * framework::dataset::make("result_mult_int", 1, 2) * framework::dataset::make("result_shift", 2, + 3) + * framework::dataset::make("min", 0) * framework::dataset::make("max", 0) * framework::dataset::make("addBias", { false, true }); + +const auto quantize_down_int32_to_uint8_scale_relu_cases = framework::dataset::make("result_offset", -2, 2) * framework::dataset::make("result_mult_int", 1, + 2) + * framework::dataset::make("result_shift", 2, 3) * framework::dataset::make("min", 0, 2) * framework::dataset::make("max", 171, 174) * framework::dataset::make("addBias", { false, true }); -const auto quantize_down_int32_to_uint8_scale_cases = framework::dataset::make("result_offset", -4, 4) * framework::dataset::make("result_mult_int", 1, 3) * framework::dataset::make("result_shift", 2, - 4); +using NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture = GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture<Tensor, Accessor, NEGEMMLowpQuantizeDownInt32ToUint8Scale>; DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()), quantize_down_int32_to_uint8_scale_cases), - shape, result_offset, result_mult_int, result_shift) + shape, result_offset, result_mult_int, result_shift, min, max, add_bias) { + TensorShape shape_bias(shape[0]); + // Create tensors - Tensor in = create_tensor<Tensor>(shape, DataType::S32); - Tensor out = create_tensor<Tensor>(shape, DataType::QASYMM8); + Tensor in = create_tensor<Tensor>(shape, DataType::S32); + Tensor bias = create_tensor<Tensor>(shape_bias, DataType::S32); + Tensor out = create_tensor<Tensor>(shape, DataType::QASYMM8); ARM_COMPUTE_EXPECT(in.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(out.info()->is_resizable(), framework::LogLevel::ERRORS); // Create and configure function NEGEMMLowpQuantizeDownInt32ToUint8Scale output_stage; - output_stage.configure(&in, &out, result_offset, result_mult_int, result_shift); + output_stage.configure(&in, add_bias ? &bias : nullptr, &out, result_offset, result_mult_int, result_shift, min, max); - // Validate valid region + // Validate valid region input and output const ValidRegion valid_region = shape_to_valid_region(shape); validate(in.info()->valid_region(), valid_region); validate(out.info()->valid_region(), valid_region); + // Validate valid region bias + if(add_bias) + { + const ValidRegion valid_region_bias = shape_to_valid_region(shape_bias); + validate(bias.info()->valid_region(), valid_region_bias); + } + // Validate padding const PaddingSize padding = PaddingCalculator(shape.x(), 16).required_padding(); validate(in.info()->padding(), padding); validate(out.info()->padding(), padding); + + if(add_bias) + { + validate(bias.info()->padding(), padding); + } } FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_cases)) @@ -173,8 +194,35 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, validate(Accessor(_target), _reference); } -TEST_SUITE_END() // QuantizeDownInt32ToUint8Scale +TEST_SUITE(BoundedReLu) +FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_relu_cases)) +{ + // Validate output + validate(Accessor(_target), _reference); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), quantize_down_int32_to_uint8_scale_relu_cases)) +{ + // Validate output + validate(Accessor(_target), _reference); +} +TEST_SUITE_END() // BoundedReLu + +TEST_SUITE(AddBias) +FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_relu_cases)) +{ + // Validate output + validate(Accessor(_target), _reference); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), quantize_down_int32_to_uint8_scale_relu_cases)) +{ + // Validate output + validate(Accessor(_target), _reference); +} +TEST_SUITE_END() // AddBias + +TEST_SUITE_END() // QuantizeDownInt32ToUint8Scale TEST_SUITE_END() // OutputStage TEST_SUITE_END() // GEMMLowp diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h index f9b0dbd959..a99e9323c8 100644 --- a/tests/validation/fixtures/GEMMLowpFixture.h +++ b/tests/validation/fixtures/GEMMLowpFixture.h @@ -122,10 +122,10 @@ class GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture : public framework: { public: template <typename...> - void setup(TensorShape shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift) + void setup(TensorShape shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias) { - _target = compute_target(shape, result_offset, result_mult_int, result_shift); - _reference = compute_reference(shape, result_offset, result_mult_int, result_shift); + _target = compute_target(shape, result_offset, result_mult_int, result_shift, min, max, add_bias); + _reference = compute_reference(shape, result_offset, result_mult_int, result_shift, min, max, add_bias); } protected: @@ -136,43 +136,72 @@ protected: library->fill(tensor, distribution, i); } - TensorType compute_target(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift) + TensorType compute_target(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias) { + TensorShape shape_bias(shape[0]); + // Create tensors TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1); - TensorType b = create_tensor<TensorType>(shape, DataType::QASYMM8, 1); + TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1); + TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8, 1); // Create and configure function FunctionType output_stage; - output_stage.configure(&a, &b, result_offset, result_mult_int, result_shift); + output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_offset, result_mult_int, result_shift, min, max); 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 tensor fill(AccessorType(a), 0); + if(add_bias) + { + ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate bias tensor + b.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensor + fill(AccessorType(b), 1); + } + // Compute GEMM function output_stage.run(); - return b; + return c; } - SimpleTensor<uint8_t> compute_reference(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift) + SimpleTensor<uint8_t> compute_reference(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias) { // Create reference + TensorShape shape_bias(shape[0]); + SimpleTensor<int32_t> a{ shape, DataType::S32, 1 }; + SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 }; // Fill reference fill(a, 0); - return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, result_offset, result_mult_int, result_shift); + if(add_bias) + { + // Fill bias + fill(b, 1); + + return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, b, result_offset, result_mult_int, result_shift, min, max); + } + else + { + return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, result_offset, result_mult_int, result_shift, min, max); + } } TensorType _target{}; |