From d24affe0abefe8f4a83c7d4487386920895fd2e7 Mon Sep 17 00:00:00 2001 From: Sang-Hoon Park Date: Tue, 8 Oct 2019 18:07:23 +0100 Subject: COMPMID-2265 add support for Log Softmax to NEON Kernel (NEON/reference), validation tests, function and fixture are updated to add support for Log Softmax Change-Id: I641dbf1552f4128c691af8875949ebf88da71ee8 Signed-off-by: Sang-Hoon Park Reviewed-on: https://review.mlplatform.org/c/2075 Comments-Addressed: Arm Jenkins Reviewed-by: Michele Di Giorgio Tested-by: Arm Jenkins --- .../core/NEON/kernels/NESoftmaxLayerKernel.h | 12 +- .../runtime/NEON/functions/NESoftmaxLayer.h | 44 +++--- src/core/NEON/kernels/NESoftmaxLayerKernel.cpp | 162 ++++++++++++++++---- src/runtime/NEON/functions/NESoftmaxLayer.cpp | 23 ++- tests/validation/NEON/LogSoftmaxLayer.cpp | 165 +++++++++++++++++++++ tests/validation/fixtures/SoftmaxLayerFixture.h | 42 +++--- tests/validation/reference/LogSoftmaxLayer.cpp | 61 ++++++++ tests/validation/reference/LogSoftmaxLayer.h | 47 ++++++ tests/validation/reference/SoftmaxLayer.cpp | 37 ++++- tests/validation/reference/SoftmaxLayer.h | 5 +- 10 files changed, 517 insertions(+), 81 deletions(-) create mode 100644 tests/validation/NEON/LogSoftmaxLayer.cpp create mode 100644 tests/validation/reference/LogSoftmaxLayer.cpp create mode 100644 tests/validation/reference/LogSoftmaxLayer.h diff --git a/arm_compute/core/NEON/kernels/NESoftmaxLayerKernel.h b/arm_compute/core/NEON/kernels/NESoftmaxLayerKernel.h index 25c3196e34..fb650794fa 100644 --- a/arm_compute/core/NEON/kernels/NESoftmaxLayerKernel.h +++ b/arm_compute/core/NEON/kernels/NESoftmaxLayerKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -69,12 +69,20 @@ private: }; /** Interface for softmax computation for QASYMM8 with pre-computed max. */ +template class NELogits1DSoftmaxKernel : public INEKernel { public: const char *name() const override { - return "NELogits1DSoftmaxKernel"; + if(IS_LOG) + { + return "NELogits1DSoftmaxKernel"; + } + else + { + return "NELogits1DLogSoftmaxKernel"; + } } /** Default constructor */ NELogits1DSoftmaxKernel(); diff --git a/arm_compute/runtime/NEON/functions/NESoftmaxLayer.h b/arm_compute/runtime/NEON/functions/NESoftmaxLayer.h index 4932aeff5a..9cc7088ae2 100644 --- a/arm_compute/runtime/NEON/functions/NESoftmaxLayer.h +++ b/arm_compute/runtime/NEON/functions/NESoftmaxLayer.h @@ -36,29 +36,33 @@ namespace arm_compute { class ITensor; -/** Basic function to compute a SoftmaxLayer. +/** Basic function to compute a SoftmaxLayer and a Log SoftmaxLayer. * * Softmax is calculated by : * @f[ out = \frac{e^{x - max(x)}}{\sum{e^{x - max(x)}}} @f] * + * Log Softmax is calculated by : + * @f[ out = (x - max(x)) - \sum{e^{x - max(x)}} @f] + * * This function runs the following kernels: * -# @ref NEFillBorderKernel * -# @ref NELogits1DMaxKernel * -# @ref NELogits1DSoftmaxKernel */ -class NESoftmaxLayer : public IFunction +template +class NESoftmaxLayerGeneric : public IFunction { public: /** Constructor */ - NESoftmaxLayer(std::shared_ptr memory_manager = nullptr); + NESoftmaxLayerGeneric(std::shared_ptr memory_manager = nullptr); /** Prevent instances of this class from being copied (As this class contains pointers) */ - NESoftmaxLayer(const NESoftmaxLayer &) = delete; + NESoftmaxLayerGeneric(const NESoftmaxLayerGeneric &) = delete; /** Default move constructor */ - NESoftmaxLayer(NESoftmaxLayer &&) = default; + NESoftmaxLayerGeneric(NESoftmaxLayerGeneric &&) = default; /** Prevent instances of this class from being copied (As this class contains pointers) */ - NESoftmaxLayer &operator=(const NESoftmaxLayer &) = delete; + NESoftmaxLayerGeneric &operator=(const NESoftmaxLayerGeneric &) = delete; /** Default move assignment operator */ - NESoftmaxLayer &operator=(NESoftmaxLayer &&) = default; + NESoftmaxLayerGeneric &operator=(NESoftmaxLayerGeneric &&) = default; /** Set the input and output tensors. * * @param[in,out] input Source tensor. Data types supported: QASYMM8/F16/F32. If the width is not a @@ -103,17 +107,21 @@ private: */ void configure_reshape_input_kernel(const ITensor *input, const ITensor *output, size_t axis); - MemoryGroup _memory_group; - NELogits1DMaxKernel _max_kernel; - NELogits1DSoftmaxKernel _softmax_kernel; - std::unique_ptr _flat_or_reshape_kernel_ptr; - NEFillBorderKernel _fill_border_kernel; - NEReshapeLayerKernel _reshape_kernel; - Tensor _max; - Tensor _tmp; - Tensor _input_flattened; - Tensor _output_flattened; - bool _needs_flattening; + MemoryGroup _memory_group; + NELogits1DMaxKernel _max_kernel; + NELogits1DSoftmaxKernel _softmax_kernel; + std::unique_ptr _flat_or_reshape_kernel_ptr; + NEFillBorderKernel _fill_border_kernel; + NEReshapeLayerKernel _reshape_kernel; + Tensor _max; + Tensor _tmp; + Tensor _input_flattened; + Tensor _output_flattened; + bool _needs_flattening; }; + +using NESoftmaxLayer = NESoftmaxLayerGeneric; +using NELogSoftmaxLayer = NESoftmaxLayerGeneric; + } // namespace arm_compute #endif /* __ARM_COMPUTE_NESOFTMAXLAYER_H__ */ diff --git a/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp b/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp index 4144a1877b..1003ebd2e3 100644 --- a/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp +++ b/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp @@ -333,6 +333,19 @@ float32x4x4_t vadd(float32x4x4_t a, float32x4x4_t b) return res; } +float32x4x4_t vsub_n(float32x4x4_t a, float val) +{ + auto scalar_vector = vdup_n(val); + float32x4x4_t res = { { + vsubq_f32(a.val[0], scalar_vector.val[0]), + vsubq_f32(a.val[1], scalar_vector.val[1]), + vsubq_f32(a.val[2], scalar_vector.val[2]), + vsubq_f32(a.val[3], scalar_vector.val[3]) + } + }; + return res; +} + namespace { Status validate_arguments_logits_1d_max(const ITensorInfo &input, const ITensorInfo &output) @@ -590,6 +603,7 @@ elem_type_t reduce_add(F add_fn, V vec) return reduce_add_impl < elem_type_t, N, 0, N - 1 >::reduce(add_fn, vec); } +template void logits_1d_softmax_qasymm8(const ITensor &in, const ITensor &max, void *const tmp, ITensor &out, const float beta, const Window &window) { const int start_x = in.info()->valid_region().anchor.x(); @@ -608,7 +622,8 @@ void logits_1d_softmax_qasymm8(const ITensor &in, const ITensor &max, void *cons const auto out_ptr = reinterpret_cast(out_it.ptr()) + start_x; const auto tmp_ptr = reinterpret_cast(tmp); - float sum_inversed; + float sum{}; + float sum_inversed{}; /* Compute exponentials and sum */ { @@ -622,33 +637,55 @@ void logits_1d_softmax_qasymm8(const ITensor &in, const ITensor &max, void *cons /* Loop over row and compute exponentials and sum */ int i = 0; constexpr int vec_size = vec_size_of(vec_max); + for(; i <= (input_width - vec_size); i += vec_size) { auto vec_elements = vld>(in_ptr + i); vec_elements = vsubq_u8(vec_max, vec_elements); auto vec_elements_flt = vcvt(vec_elements); - vec_elements_flt = vexp(vmul_n(vec_elements_flt, scale_beta)); - - vec_sum = vadd(vec_sum, vec_elements_flt); + if(is_log) + { + vec_elements_flt = vmul_n(vec_elements_flt, scale_beta); + vec_sum = vadd(vec_sum, vexp(vec_elements_flt)); + } + else + { + vec_elements_flt = vexp(vmul_n(vec_elements_flt, scale_beta)); + vec_sum = vadd(vec_sum, vec_elements_flt); + } vst4q_f32(tmp_ptr + i, vec_elements_flt); } + /* Reduce sum */ const auto sum_16_byte = vaddq_f32(vaddq_f32(vec_sum.val[0], vec_sum.val[1]), vaddq_f32(vec_sum.val[2], vec_sum.val[3])); const auto sum_8_byte = vadd_f32(vget_low(sum_16_byte), vget_high(sum_16_byte)); - float sum = reduce_add(std::plus(), sum_8_byte); + sum = reduce_add(std::plus(), sum_8_byte); /* Run remaining elements */ for(; i < input_width; ++i) { - const float element = std::exp((max_val - in_ptr[i]) * scale_beta); - sum += element; + float element{}; + if(is_log) + { + element = (max_val - in_ptr[i]) * scale_beta; + sum += std::exp(element); + } + else + { + element = std::exp((max_val - in_ptr[i]) * scale_beta); + sum += element; + } + tmp_ptr[i] = element; } - sum_inversed = 256.f / sum; + if(!is_log) + { + sum_inversed = 256.f / sum; + } } /* Normalize exponentials */ @@ -657,24 +694,40 @@ void logits_1d_softmax_qasymm8(const ITensor &in, const ITensor &max, void *cons int i = 0; { constexpr int vec_size = 16; + for(; i <= (input_width - vec_size); i += vec_size) { - float32x4x4_t vec_in = vld4q_f32(tmp_ptr + i); - auto normalized_value = vcvt>(vmul_n(vec_in, sum_inversed)); + float32x4x4_t vec_in = vld4q_f32(tmp_ptr + i); + vec_16_byte_t normalized_value{}; + if(is_log) + { + normalized_value = vcvt>(vsub_n(vec_in, sum)); + } + else + { + normalized_value = vcvt>(vmul_n(vec_in, sum_inversed)); + } vst(out_ptr + i, normalized_value); } } /* Run remaining elements */ for(; i < input_width; ++i) { - out_ptr[i] = utils::cast::saturate_cast(tmp_ptr[i] * sum_inversed); + if(is_log) + { + out_ptr[i] = utils::cast::saturate_cast(tmp_ptr[i] - sum); + } + else + { + out_ptr[i] = utils::cast::saturate_cast(tmp_ptr[i] * sum_inversed); + } } } }, in_it, max_it, out_it); } -template +template void logits_1d_softmax_float(const ITensor &in, const ITensor &max, void *const tmp, ITensor &out, const float beta, const Window &window) { @@ -692,7 +745,8 @@ void logits_1d_softmax_float(const ITensor &in, const ITensor &max, void *const const auto out_ptr = reinterpret_cast(out_it.ptr()) + start_x; const auto tmp_ptr = reinterpret_cast(tmp); - T sum_inversed; + T sum{}; + T sum_inversed{}; /* Compute exponentials and sum */ { @@ -706,46 +760,87 @@ void logits_1d_softmax_float(const ITensor &in, const ITensor &max, void *const /* Loop over row and compute exponentials and sum */ int i = 0; constexpr int vec_size = vec_size_of(vec_sum); + for(; i <= (input_width - vec_size); i += vec_size) { auto vec_elements = vld>(in_ptr + i); vec_elements = vsub(vec_elements, vec_max); - vec_elements = vexp(vmul_n(vec_elements, static_cast(beta))); - vec_sum = vadd(vec_sum, vec_elements); + if(is_log) + { + vec_elements = vmul_n(vec_elements, static_cast(beta)); + vec_sum = vadd(vec_sum, vexp(vec_elements)); + } + else + { + vec_elements = vexp(vmul_n(vec_elements, static_cast(beta))); + vec_sum = vadd(vec_sum, vec_elements); + } vst(tmp_ptr + i, vec_elements); } + /* Reduce sum */ const auto sum_8_byte = vadd(vget_high(vec_sum), vget_low(vec_sum)); - T sum = reduce_add([](T a, T b) -> T { return a + b; }, sum_8_byte); + sum = reduce_add([](T a, T b) -> T { return a + b; }, sum_8_byte); /* Run remaining elements */ + for(; i < input_width; ++i) { - T element = std::exp((in_ptr[i] - max_val) * beta); - sum += element; + T element{}; + + if(is_log) + { + element = (in_ptr[i] - max_val) * beta; + sum += std::exp(element); + } + else + { + element = std::exp((in_ptr[i] - max_val) * beta); + sum += element; + } tmp_ptr[i] = element; } - sum_inversed = T(1) / sum; + if(!is_log) + { + sum_inversed = T(1) / sum; + } } /* Normalize exponentials */ { /* Loop over row and compute softmax */ int i = 0; + { constexpr int vec_size = vec_size_of(vec_16_byte_t {}); + for(; i <= (input_width - vec_size); i += vec_size) { - auto vec_in = vld>(tmp_ptr + i); - vec_16_byte_t normalized_value = vmul_n(vec_in, sum_inversed); + auto vec_in = vld>(tmp_ptr + i); + vec_16_byte_t normalized_value{}; + if(is_log) + { + normalized_value = vsub(vec_in, vdup_n>(sum)); + } + else + { + normalized_value = vmul_n(vec_in, sum_inversed); + } vst(out_ptr + i, normalized_value); } } /* Run remaining elements */ for(; i < input_width; ++i) { - out_ptr[i] = tmp_ptr[i] * sum_inversed; + if(is_log) + { + out_ptr[i] = tmp_ptr[i] - sum; + } + else + { + out_ptr[i] = tmp_ptr[i] * sum_inversed; + } } } }, @@ -753,12 +848,14 @@ void logits_1d_softmax_float(const ITensor &in, const ITensor &max, void *const } } // namespace -NELogits1DSoftmaxKernel::NELogits1DSoftmaxKernel() +template +NELogits1DSoftmaxKernel::NELogits1DSoftmaxKernel() : _func(nullptr), _input(nullptr), _max(nullptr), _output(nullptr), _beta(1.0f), _tmp(nullptr) { } -void NELogits1DSoftmaxKernel::configure(const ITensor *input, const ITensor *max, ITensor *output, const float beta, ITensor *tmp) +template +void NELogits1DSoftmaxKernel::configure(const ITensor *input, const ITensor *max, ITensor *output, const float beta, ITensor *tmp) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, max, output, tmp); ARM_COMPUTE_ERROR_ON_NULLPTR(input->info(), max->info(), output->info(), tmp->info()); @@ -771,15 +868,15 @@ void NELogits1DSoftmaxKernel::configure(const ITensor *input, const ITensor *max switch(input->info()->data_type()) { case DataType::QASYMM8: - _func = &logits_1d_softmax_qasymm8; + _func = &logits_1d_softmax_qasymm8; break; #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: - _func = &logits_1d_softmax_float; + _func = &logits_1d_softmax_float; break; #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ case DataType::F32: - _func = &logits_1d_softmax_float; + _func = &logits_1d_softmax_float; break; default: ARM_COMPUTE_ERROR("Unsupported data type."); @@ -795,8 +892,9 @@ void NELogits1DSoftmaxKernel::configure(const ITensor *input, const ITensor *max INEKernel::configure(win_config.second); } -Status NELogits1DSoftmaxKernel::validate(const ITensorInfo *input, const ITensorInfo *max, - const ITensorInfo *output, const float beta, const ITensorInfo *tmp) +template +Status NELogits1DSoftmaxKernel::validate(const ITensorInfo *input, const ITensorInfo *max, + const ITensorInfo *output, const float beta, const ITensorInfo *tmp) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, max, output, tmp); @@ -806,7 +904,8 @@ Status NELogits1DSoftmaxKernel::validate(const ITensorInfo *input, const ITensor return Status{}; } -void NELogits1DSoftmaxKernel::run(const Window &window, const ThreadInfo &info) +template +void NELogits1DSoftmaxKernel::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); @@ -822,4 +921,7 @@ void NELogits1DSoftmaxKernel::run(const Window &window, const ThreadInfo &info) (*_func)(*_input, *_max, tmp_for_thread, *_output, _beta, window); } +template class NELogits1DSoftmaxKernel; +template class NELogits1DSoftmaxKernel; + } // namespace arm_compute diff --git a/src/runtime/NEON/functions/NESoftmaxLayer.cpp b/src/runtime/NEON/functions/NESoftmaxLayer.cpp index 79a94961d8..f530a87d05 100644 --- a/src/runtime/NEON/functions/NESoftmaxLayer.cpp +++ b/src/runtime/NEON/functions/NESoftmaxLayer.cpp @@ -33,13 +33,15 @@ namespace arm_compute { -NESoftmaxLayer::NESoftmaxLayer(std::shared_ptr memory_manager) +template +NESoftmaxLayerGeneric::NESoftmaxLayerGeneric(std::shared_ptr memory_manager) : _memory_group(std::move(memory_manager)), _max_kernel(), _softmax_kernel(), _flat_or_reshape_kernel_ptr(nullptr), _fill_border_kernel(), _reshape_kernel(), _max(), _tmp(), _input_flattened(), _output_flattened(), _needs_flattening(false) { } -void NESoftmaxLayer::configure_reshape_input_kernel(const ITensor *input, const ITensor *output, size_t axis) +template +void NESoftmaxLayerGeneric::configure_reshape_input_kernel(const ITensor *input, const ITensor *output, size_t axis) { // Flatten the input const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input->info(), axis); @@ -68,11 +70,12 @@ void NESoftmaxLayer::configure_reshape_input_kernel(const ITensor *input, const auto_init_if_empty(*output->info(), *input->info()->clone()); } -void NESoftmaxLayer::configure(ITensor *input, ITensor *output, float beta, size_t axis) +template +void NESoftmaxLayerGeneric::configure(ITensor *input, ITensor *output, float beta, size_t axis) { // Perform validation step ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_ERROR_THROW_ON(NESoftmaxLayer::validate(input->info(), output->info(), beta, axis)); + ARM_COMPUTE_ERROR_THROW_ON(NESoftmaxLayerGeneric::validate(input->info(), output->info(), beta, axis)); // We don't need flattening only in the case the input is 2D and axis is 1 _needs_flattening = axis != 1; @@ -138,7 +141,8 @@ void NESoftmaxLayer::configure(ITensor *input, ITensor *output, float beta, size _tmp.allocator()->allocate(); } -Status NESoftmaxLayer::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, size_t axis) +template +Status NESoftmaxLayerGeneric::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, size_t axis) { // Perform validation step ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); @@ -173,12 +177,13 @@ Status NESoftmaxLayer::validate(const ITensorInfo *input, const ITensorInfo *out } ARM_COMPUTE_RETURN_ON_ERROR(NELogits1DMaxKernel::validate(input, &tensor_info_max_sum)); - ARM_COMPUTE_RETURN_ON_ERROR(NELogits1DSoftmaxKernel::validate(&tensor_info_tmp, &tensor_info_max_sum, output, beta, &dont_care)); + ARM_COMPUTE_RETURN_ON_ERROR(NELogits1DSoftmaxKernel::validate(&tensor_info_tmp, &tensor_info_max_sum, output, beta, &dont_care)); return Status{}; } -void NESoftmaxLayer::run() +template +void NESoftmaxLayerGeneric::run() { MemoryGroupResourceScope scope_mg(_memory_group); @@ -196,4 +201,8 @@ void NESoftmaxLayer::run() NEScheduler::get().schedule(&_reshape_kernel, Window::DimY); } } + +template class NESoftmaxLayerGeneric; +template class NESoftmaxLayerGeneric; + } // namespace arm_compute \ No newline at end of file diff --git a/tests/validation/NEON/LogSoftmaxLayer.cpp b/tests/validation/NEON/LogSoftmaxLayer.cpp new file mode 100644 index 0000000000..e35c8fd8a2 --- /dev/null +++ b/tests/validation/NEON/LogSoftmaxLayer.cpp @@ -0,0 +1,165 @@ +/* + * 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. + */ +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/NEON/functions/NESoftmaxLayer.h" +#include "arm_compute/runtime/Tensor.h" +#include "arm_compute/runtime/TensorAllocator.h" +#include "tests/NEON/Accessor.h" +#include "tests/PaddingCalculator.h" +#include "tests/datasets/ShapeDatasets.h" +#include "tests/framework/Asserts.h" +#include "tests/framework/Macros.h" +#include "tests/framework/datasets/Datasets.h" +#include "tests/validation/Validation.h" +#include "tests/validation/fixtures/SoftmaxLayerFixture.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +/** Tolerance for float operations */ +constexpr RelativeTolerance tolerance_f32(0.00001f); +RelativeTolerance tolerance_f16(half(0.2)); + +/** Tolerance for quantized operations */ +constexpr AbsoluteTolerance tolerance_qasymm8(1); + +/** CNN data types */ +const auto CNNDataTypes = framework::dataset::make("DataType", +{ +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + DataType::F16, +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + DataType::F32, +}); +} // namespace + +TEST_SUITE(NEON) +TEST_SUITE(LogSoftmaxLayer) + +template +using NELogSoftmaxLayerFixture = SoftmaxValidationFixture; + +TEST_SUITE(Float) +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(RunSmall, NELogSoftmaxLayerFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::Small4DShapes(), + framework::dataset::make("DataType", DataType::F16)), + framework::dataset::make("Beta", { 1.0f, 2.0f })), + framework::dataset::make("Axis", { 1 }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f16); +} +FIXTURE_DATA_TEST_CASE(RunSmall4D, NELogSoftmaxLayerFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::Small4DShapes(), + framework::dataset::make("DataType", DataType::F32)), + framework::dataset::make("Beta", { 1.0f, 2.0f })), + framework::dataset::make("Axis", { 1, 2, 3 }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f32); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NELogSoftmaxLayerFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::SoftmaxLayerLargeShapes(), + framework::dataset::make("DataType", DataType::F16)), + framework::dataset::make("Beta", { 1.0f, 2.0f })), + framework::dataset::make("Axis", { 1 }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f16); +} +TEST_SUITE_END() //FP16 +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + +TEST_SUITE(FP32) +FIXTURE_DATA_TEST_CASE(RunSmall2D, NELogSoftmaxLayerFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SoftmaxLayerSmallShapes(), + framework::dataset::make("DataType", DataType::F32)), + framework::dataset::make("Beta", { 1.0f, 2.0f })), + framework::dataset::make("Axis", { 1 }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f32); +} +FIXTURE_DATA_TEST_CASE(RunSmall4D, NELogSoftmaxLayerFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::Small4DShapes(), + framework::dataset::make("DataType", DataType::F32)), + framework::dataset::make("Beta", { 1.0f, 2.0f })), + framework::dataset::make("Axis", { 1, 2, 3 }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f32); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NELogSoftmaxLayerFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::SoftmaxLayerLargeShapes(), + framework::dataset::make("DataType", DataType::F32)), + framework::dataset::make("Beta", { 1.0f, 2.0f })), + framework::dataset::make("Axis", { 1 }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f32); +} +TEST_SUITE_END() //FP32 +TEST_SUITE_END() //Float + +template +using NELogSoftmaxLayerQuantizedFixture = SoftmaxValidationQuantizedFixture; + +TEST_SUITE(Quantized) +TEST_SUITE(QASYMM8) +FIXTURE_DATA_TEST_CASE(RunSmall2D, NELogSoftmaxLayerQuantizedFixture, framework::DatasetMode::ALL, combine(combine(combine(datasets::SoftmaxLayerSmallShapes(), + framework::dataset::make("DataType", DataType::QASYMM8)), + combine(framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, -10) }), + framework::dataset::make("Beta", { 1.0f, 2.f }))), + framework::dataset::make("Axis", { 1 }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qasymm8); +} +FIXTURE_DATA_TEST_CASE(RunSmall4D, NELogSoftmaxLayerQuantizedFixture, framework::DatasetMode::ALL, combine(combine(combine(datasets::Small4DShapes(), + framework::dataset::make("DataType", DataType::QASYMM8)), + combine(framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, -10) }), + framework::dataset::make("Beta", { 1.0f, 2.f }))), + framework::dataset::make("Axis", { 1, 2, 3 }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qasymm8); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NELogSoftmaxLayerQuantizedFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::SoftmaxLayerLargeShapes(), + framework::dataset::make("DataType", DataType::QASYMM8)), + combine(framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, -10) }), + framework::dataset::make("Beta", { 1.0f, 2.0f }))), + framework::dataset::make("Axis", { 1 }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qasymm8); +} +TEST_SUITE_END() //QASYMM8 +TEST_SUITE_END() //Quantized + +TEST_SUITE_END() //LogSoftmaxLayer +TEST_SUITE_END() //NEON +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation/fixtures/SoftmaxLayerFixture.h b/tests/validation/fixtures/SoftmaxLayerFixture.h index e39ee74800..f747ab3574 100644 --- a/tests/validation/fixtures/SoftmaxLayerFixture.h +++ b/tests/validation/fixtures/SoftmaxLayerFixture.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -32,6 +32,7 @@ #include "tests/IAccessor.h" #include "tests/framework/Asserts.h" #include "tests/framework/Fixture.h" +#include "tests/validation/reference/LogSoftmaxLayer.h" #include "tests/validation/reference/SoftmaxLayer.h" #include @@ -42,7 +43,7 @@ namespace test { namespace validation { -template +template class SoftmaxValidationGenericFixture : public framework::Fixture { public: @@ -110,7 +111,14 @@ protected: // Fill reference fill(src); - return reference::softmax_layer(src, beta, axis); + if(IS_LOG) + { + return reference::log_softmax_layer(src, beta, axis); + } + else + { + return reference::softmax_layer(src, beta, axis); + } } TensorType _target{}; @@ -118,33 +126,33 @@ protected: QuantizationInfo _quantization_info{}; }; -template -class SoftmaxValidationFixture : public SoftmaxValidationGenericFixture +template +class SoftmaxValidationFixture : public SoftmaxValidationGenericFixture { public: template void setup(TensorShape shape, DataType data_type, float beta, size_t axis) { - SoftmaxValidationGenericFixture::setup(shape, - data_type, - QuantizationInfo(), - beta, - axis); + SoftmaxValidationGenericFixture::setup(shape, + data_type, + QuantizationInfo(), + beta, + axis); } }; -template -class SoftmaxValidationQuantizedFixture : public SoftmaxValidationGenericFixture +template +class SoftmaxValidationQuantizedFixture : public SoftmaxValidationGenericFixture { public: template void setup(TensorShape shape, DataType data_type, QuantizationInfo quantization_info, float beta, size_t axis) { - SoftmaxValidationGenericFixture::setup(shape, - data_type, - quantization_info, - beta, - axis); + SoftmaxValidationGenericFixture::setup(shape, + data_type, + quantization_info, + beta, + axis); } }; } // namespace validation diff --git a/tests/validation/reference/LogSoftmaxLayer.cpp b/tests/validation/reference/LogSoftmaxLayer.cpp new file mode 100644 index 0000000000..3f21d85dd0 --- /dev/null +++ b/tests/validation/reference/LogSoftmaxLayer.cpp @@ -0,0 +1,61 @@ +/* + * 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. + */ +#include "LogSoftmaxLayer.h" +#include "SoftmaxLayer.h" + +#include "arm_compute/core/Types.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace reference +{ +template ::value, int>::type> +SimpleTensor log_softmax_layer(const SimpleTensor &src, float beta, size_t axis) +{ + return softmax_layer_generic(src, beta, axis, true); +} + +template ::value, int>::type> +SimpleTensor log_softmax_layer(const SimpleTensor &src, float beta, size_t axis) +{ + // Note: Output quantization info should always have scale = 1/256 and offset = 0 + const QuantizationInfo output_quantization_info = QuantizationInfo(1.f / 256, 0); + + SimpleTensor src_tmp = convert_from_asymmetric(src); + SimpleTensor dst_tmp = log_softmax_layer(src_tmp, beta, axis); + SimpleTensor dst = convert_to_asymmetric(dst_tmp, output_quantization_info); + return dst; +} + +template SimpleTensor log_softmax_layer(const SimpleTensor &src, float beta, size_t axis); +template SimpleTensor log_softmax_layer(const SimpleTensor &src, float beta, size_t axis); +template SimpleTensor log_softmax_layer(const SimpleTensor &src, float beta, size_t axis); +} // namespace reference +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation/reference/LogSoftmaxLayer.h b/tests/validation/reference/LogSoftmaxLayer.h new file mode 100644 index 0000000000..35547cabad --- /dev/null +++ b/tests/validation/reference/LogSoftmaxLayer.h @@ -0,0 +1,47 @@ +/* + * 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_LOG_SOFTMAX_LAYER_H__ +#define __ARM_COMPUTE_TEST_LOG_SOFTMAX_LAYER_H__ + +#include "tests/SimpleTensor.h" +#include "tests/validation/Helpers.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace reference +{ +template ::value, int>::type = 0> +SimpleTensor log_softmax_layer(const SimpleTensor &src, float beta, size_t axis = 1); + +template ::value, int>::type = 0> +SimpleTensor log_softmax_layer(const SimpleTensor &src, float beta, size_t axis = 1); +} // namespace reference +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* __ARM_COMPUTE_TEST_SOFTMAX_LAYER_H__ */ diff --git a/tests/validation/reference/SoftmaxLayer.cpp b/tests/validation/reference/SoftmaxLayer.cpp index fabc62bedb..ef2468df59 100644 --- a/tests/validation/reference/SoftmaxLayer.cpp +++ b/tests/validation/reference/SoftmaxLayer.cpp @@ -34,7 +34,7 @@ namespace validation namespace reference { template ::value, int>::type> -SimpleTensor softmax_layer(const SimpleTensor &src, float beta, size_t axis) +SimpleTensor softmax_layer_generic(const SimpleTensor &src, float beta, size_t axis, bool is_log) { // Create reference SimpleTensor dst{ src.shape(), src.data_type(), 1 }; @@ -65,23 +65,48 @@ SimpleTensor softmax_layer(const SimpleTensor &src, float beta, size_t axi // Regularize T sum(0.f); - std::transform(src_row_ptr, src_row_ptr + lower_dims, dst_row_ptr, [&sum, max, beta](T val) + std::transform(src_row_ptr, src_row_ptr + lower_dims, dst_row_ptr, [&sum, max, beta, is_log](T val) { - const T res(std::exp((val - max) * beta)); - sum += res; + T res{ (val - max) *beta }; + + if(is_log) + { + sum += std::exp(res); + } + else + { + res = std::exp(res); + sum += res; + } return res; }); // Normalize - std::transform(dst_row_ptr, dst_row_ptr + lower_dims, dst_row_ptr, [sum](T val) + std::transform(dst_row_ptr, dst_row_ptr + lower_dims, dst_row_ptr, [sum, is_log](T val) { - return val / sum; + if(is_log) + { + return val - sum; + } + else + { + return val / sum; + } }); } return dst; } +template SimpleTensor softmax_layer_generic(const SimpleTensor &src, float beta, size_t axis, bool is_log); +template SimpleTensor softmax_layer_generic(const SimpleTensor &src, float beta, size_t axis, bool is_log); + +template ::value, int>::type> +SimpleTensor softmax_layer(const SimpleTensor &src, float beta, size_t axis) +{ + return softmax_layer_generic(src, beta, axis, false); +} + template ::value, int>::type> SimpleTensor softmax_layer(const SimpleTensor &src, float beta, size_t axis) { diff --git a/tests/validation/reference/SoftmaxLayer.h b/tests/validation/reference/SoftmaxLayer.h index d21ca2bf20..fa9485ce31 100644 --- a/tests/validation/reference/SoftmaxLayer.h +++ b/tests/validation/reference/SoftmaxLayer.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -35,6 +35,9 @@ namespace validation { namespace reference { +template ::value, int>::type = 0> +SimpleTensor softmax_layer_generic(const SimpleTensor &src, float beta, size_t axis, bool is_log = false); + template ::value, int>::type = 0> SimpleTensor softmax_layer(const SimpleTensor &src, float beta, size_t axis = 1); -- cgit v1.2.1