From bc415af5ee9517fd113e9ea0f01fdc84f9693dc4 Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Thu, 13 Jun 2019 15:58:32 +0100 Subject: COMPMID-2406: Create a new GEMMLowpQuantizeDownInt32ToInt16ScaleKernel for NEON Change-Id: I3f3e247728fd6dafca066e41835f0ef9442d9b7a Signed-off-by: giuros01 Reviewed-on: https://review.mlplatform.org/c/1379 Comments-Addressed: Arm Jenkins Reviewed-by: Georgios Pinitas Reviewed-by: Gian Marco Iodice Tested-by: Arm Jenkins --- tests/validation/NEON/GEMMLowp.cpp | 114 ++++++++++++++++++++++++++++ tests/validation/fixtures/GEMMLowpFixture.h | 92 ++++++++++++++++++++++ tests/validation/reference/GEMMLowp.cpp | 56 +++++++++++++- tests/validation/reference/GEMMLowp.h | 9 ++- 4 files changed, 269 insertions(+), 2 deletions(-) (limited to 'tests/validation') diff --git a/tests/validation/NEON/GEMMLowp.cpp b/tests/validation/NEON/GEMMLowp.cpp index f0460b4a23..2f604c95ea 100644 --- a/tests/validation/NEON/GEMMLowp.cpp +++ b/tests/validation/NEON/GEMMLowp.cpp @@ -294,6 +294,9 @@ const auto quantize_down_int32_to_uint8_scale_by_fixedpoint_relu_cases = framewo using NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointFixture = GEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointValidationFixture; +using NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture = + GEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointValidationFixture; + // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( @@ -404,6 +407,117 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedP TEST_SUITE_END() // BoundedReLu TEST_SUITE_END() // QuantizeDownInt32ToUint8ScaleByFixedPoint + +TEST_SUITE(QuantizeDownInt32ToInt16ScaleByFixedPoint) + +const auto quantize_down_int32_to_int16_scale_by_fixedpoint_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1, + 2) + * framework::dataset::make("min", 0) * framework::dataset::make("max", 0) * framework::dataset::make("addBias", { false, true }); + +const auto quantize_down_int32_to_int16_scale_by_fixedpoint_relu_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1, + 2) + * framework::dataset::make("min", -2, 0) * framework::dataset::make("max", 1, 3) * framework::dataset::make("addBias", { false, true }); + +using NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture = + GEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointValidationFixture; + +// *INDENT-OFF* +// clang-format off +DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( + framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Input not a multiple of 16 + TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Invalid min and max + TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), // Wrong output data type + }), + framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(21U), 1, DataType::S32), + TensorInfo(TensorShape(21U), 1, DataType::S32), + TensorInfo(TensorShape(20U), 1, DataType::S32), + })), + framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(21U, 13U), 1, DataType::QSYMM16), + TensorInfo(TensorShape(21U, 13U), 1, DataType::QSYMM16), + TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), + })), + framework::dataset::make("Min",{ -205, + -60000, + -180, + })), + framework::dataset::make("Max",{ 205, + 60000, + 180, + })), + framework::dataset::make("Expected", { true, false, false })), + a_info, b_info, output_info, min, max, expected) +{ + // Lock tensors + Status status = NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::validate(&a_info.clone()->set_is_resizable(false), + &b_info.clone()->set_is_resizable(false), + &output_info.clone()->set_is_resizable(false), + min, + max); + ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); +} +// clang-format on +// *INDENT-ON* + +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), + quantize_down_int32_to_int16_scale_by_fixedpoint_cases), + shape, result_fixedpoint_multiplier, result_shift, min, max, add_bias) +{ + TensorShape shape_bias(shape[0]); + + // Create tensors + Tensor in = create_tensor(shape, DataType::S32); + Tensor bias = create_tensor(shape_bias, DataType::S32); + Tensor out = create_tensor(shape, DataType::QSYMM16); + + 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 + NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint output_stage; + output_stage.configure(&in, add_bias ? &bias : nullptr, &out, result_fixedpoint_multiplier, result_shift, min, max); + + // 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(0); + validate(in.info()->padding(), padding); + validate(out.info()->padding(), padding); + + if(add_bias) + { + validate(bias.info()->padding(), padding); + } +} + +FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), + quantize_down_int32_to_int16_scale_by_fixedpoint_cases)) +{ + // Validate output + validate(Accessor(_target), _reference); +} + +TEST_SUITE(BoundedReLu) +FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), + quantize_down_int32_to_int16_scale_by_fixedpoint_relu_cases)) +{ + // Validate output + validate(Accessor(_target), _reference); +} +TEST_SUITE_END() // BoundedReLu + +TEST_SUITE_END() // QuantizeDownInt32ToInt16ScaleByFixedPoint + TEST_SUITE_END() // OutputStage TEST_SUITE_END() // GEMMLowp diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h index ad5acfc418..8385221c78 100644 --- a/tests/validation/fixtures/GEMMLowpFixture.h +++ b/tests/validation/fixtures/GEMMLowpFixture.h @@ -385,6 +385,98 @@ protected: SimpleTensor _reference{}; }; +template +class GEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointValidationFixture : public framework::Fixture +{ +public: + template + void setup(TensorShape shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t min, int32_t max, bool add_bias) + { + _target = compute_target(shape, result_fixedpoint_multiplier, result_shift, min, max, add_bias); + _reference = compute_reference(shape, result_fixedpoint_multiplier, result_shift, min, max, add_bias); + } + +protected: + template + void fill(U &&tensor, int i) + { + std::uniform_int_distribution<> distribution(-6000, 6000); + library->fill(tensor, distribution, i); + } + + TensorType compute_target(const TensorShape &shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t min, int32_t max, bool add_bias) + { + TensorShape shape_bias(shape[0]); + + // Create tensors + TensorType a = create_tensor(shape, DataType::S32, 1); + TensorType b = create_tensor(shape_bias, DataType::S32, 1); + TensorType c = create_tensor(shape, DataType::QSYMM16, 1); + + // Create and configure function + FunctionType output_stage; + output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_fixedpoint_multiplier, result_shift, min, max); + + ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + a.allocator()->allocate(); + c.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS); + + // 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 c; + } + + SimpleTensor compute_reference(const TensorShape &shape, int32_t result_fixed_point_multiplier, int32_t result_shift, int32_t min, int32_t max, + bool add_bias) + { + // Create reference + TensorShape shape_bias(shape[0]); + + SimpleTensor a{ shape, DataType::S32, 1 }; + SimpleTensor b{ shape_bias, DataType::S32, 1 }; + + // Fill reference + fill(a, 0); + + if(add_bias) + { + // Fill bias + fill(b, 1); + + return reference::gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(a, b, result_fixed_point_multiplier, result_shift, min, max); + } + else + { + return reference::gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(a, result_fixed_point_multiplier, result_shift, min, max); + } + } + + TensorType _target{}; + SimpleTensor _reference{}; +}; + template class GEMMLowpMatrixMultiplyReshapedValidationFixture : public framework::Fixture { diff --git a/tests/validation/reference/GEMMLowp.cpp b/tests/validation/reference/GEMMLowp.cpp index 9a7e409e8a..97d05327e7 100644 --- a/tests/validation/reference/GEMMLowp.cpp +++ b/tests/validation/reference/GEMMLowp.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -95,6 +95,34 @@ void quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor *in, (*dst)[i] = static_cast(std::max(0, std::min(255, result))); } } + +template +void quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor *in, const SimpleTensor *bias, SimpleTensor *dst, int32_t result_fixedpoint_multiplier, 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]; + + if(bias != nullptr) + { + result += (*bias)[i % cols_in]; + } + + // Fixed point multiplication + result = asymm_rounding_divide_by_pow2(asymm_int_mult(result, result_fixedpoint_multiplier), result_shift); + + // Bounded ReLu + if(min != max) + { + result = std::max(min, std::min(max, result)); + } + + (*dst)[i] = static_cast(std::max(-32768, std::min(32767, result))); + } +} } // namespace template @@ -201,10 +229,36 @@ SimpleTensor gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint( return dst; } +template +SimpleTensor gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor &in, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t min, + int32_t max) +{ + SimpleTensor dst(in.shape(), DataType::QSYMM16); + + quantize_down_int32_to_int16_scale_by_fixedpoint(&in, nullptr, &dst, result_fixedpoint_multiplier, result_shift, min, max); + + return dst; +} + +template +SimpleTensor gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor &in, const SimpleTensor &bias, int32_t result_fixedpoint_multiplier, int32_t result_shift, + int32_t min, int32_t max) +{ + SimpleTensor dst(in.shape(), DataType::QSYMM16); + + quantize_down_int32_to_int16_scale_by_fixedpoint(&in, &bias, &dst, result_fixedpoint_multiplier, result_shift, min, max); + + return dst; +} + template SimpleTensor gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor &a, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); template SimpleTensor gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor &a, const SimpleTensor &b, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); +template SimpleTensor gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor &a, int32_t result_fixedpoint_multiplier, int32_t result_shift, + int32_t min, int32_t max); +template SimpleTensor gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor &a, const SimpleTensor &b, int32_t result_fixedpoint_multiplier, + int32_t result_shift, int32_t min, int32_t max); template SimpleTensor gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor &a, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max); template SimpleTensor gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor &a, const SimpleTensor &b, int32_t result_offset, int32_t result_mult_int, diff --git a/tests/validation/reference/GEMMLowp.h b/tests/validation/reference/GEMMLowp.h index 4396155b96..5581f67652 100644 --- a/tests/validation/reference/GEMMLowp.h +++ b/tests/validation/reference/GEMMLowp.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -58,6 +58,13 @@ SimpleTensor gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint( template SimpleTensor gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor &in, const SimpleTensor &bias, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min = 0, int32_t max = 0); + +template +SimpleTensor gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor &in, int32_t result_fixedpoint_multiplier, int32_t result_shift, + int32_t min, int32_t max); +template +SimpleTensor gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor &in, const SimpleTensor &bias, int32_t result_fixedpoint_multiplier, + int32_t result_shift, int32_t min, int32_t max); } // namespace reference } // namespace validation } // namespace test -- cgit v1.2.1