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 <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/2075
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/arm_compute/core/NEON/kernels/NESoftmaxLayerKernel.h b/arm_compute/core/NEON/kernels/NESoftmaxLayerKernel.h
index 25c3196..fb65079 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 @@
};
/** Interface for softmax computation for QASYMM8 with pre-computed max. */
+template <bool IS_LOG = false>
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 4932aef..9cc7088 100644
--- a/arm_compute/runtime/NEON/functions/NESoftmaxLayer.h
+++ b/arm_compute/runtime/NEON/functions/NESoftmaxLayer.h
@@ -36,29 +36,33 @@
{
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 <bool IS_LOG = false>
+class NESoftmaxLayerGeneric : public IFunction
{
public:
/** Constructor */
- NESoftmaxLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
+ NESoftmaxLayerGeneric(std::shared_ptr<IMemoryManager> 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 @@
*/
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<INEKernel> _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<IS_LOG> _softmax_kernel;
+ std::unique_ptr<INEKernel> _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<false>;
+using NELogSoftmaxLayer = NESoftmaxLayerGeneric<true>;
+
} // 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 4144a18..1003ebd 100644
--- a/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp
+++ b/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp
@@ -333,6 +333,19 @@
return res;
}
+float32x4x4_t vsub_n(float32x4x4_t a, float val)
+{
+ auto scalar_vector = vdup_n<float32x4x4_t>(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 @@
return reduce_add_impl < elem_type_t<V>, N, 0, N - 1 >::reduce(add_fn, vec);
}
+template <bool is_log>
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 @@
const auto out_ptr = reinterpret_cast<qasymm8_t *>(out_it.ptr()) + start_x;
const auto tmp_ptr = reinterpret_cast<float *>(tmp);
- float sum_inversed;
+ float sum{};
+ float sum_inversed{};
/* Compute exponentials and sum */
{
@@ -622,33 +637,55 @@
/* 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<vec_16_byte_t<qasymm8_t>>(in_ptr + i);
vec_elements = vsubq_u8(vec_max, vec_elements);
auto vec_elements_flt = vcvt<float32x4x4_t>(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<float>(), sum_8_byte);
+ sum = reduce_add(std::plus<float>(), 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 @@
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<vec_16_byte_t<qasymm8_t>>(vmul_n(vec_in, sum_inversed));
+ float32x4x4_t vec_in = vld4q_f32(tmp_ptr + i);
+ vec_16_byte_t<qasymm8_t> normalized_value{};
+ if(is_log)
+ {
+ normalized_value = vcvt<vec_16_byte_t<qasymm8_t>>(vsub_n(vec_in, sum));
+ }
+ else
+ {
+ normalized_value = vcvt<vec_16_byte_t<qasymm8_t>>(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<qasymm8_t>(tmp_ptr[i] * sum_inversed);
+ if(is_log)
+ {
+ out_ptr[i] = utils::cast::saturate_cast<qasymm8_t>(tmp_ptr[i] - sum);
+ }
+ else
+ {
+ out_ptr[i] = utils::cast::saturate_cast<qasymm8_t>(tmp_ptr[i] * sum_inversed);
+ }
}
}
},
in_it, max_it, out_it);
}
-template <typename T>
+template <typename T, bool is_log = false>
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 @@
const auto out_ptr = reinterpret_cast<T *>(out_it.ptr()) + start_x;
const auto tmp_ptr = reinterpret_cast<T *>(tmp);
- T sum_inversed;
+ T sum{};
+ T sum_inversed{};
/* Compute exponentials and sum */
{
@@ -706,46 +760,87 @@
/* 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<vec_16_byte_t<T>>(in_ptr + i);
vec_elements = vsub(vec_elements, vec_max);
- vec_elements = vexp(vmul_n(vec_elements, static_cast<T>(beta)));
- vec_sum = vadd(vec_sum, vec_elements);
+ if(is_log)
+ {
+ vec_elements = vmul_n(vec_elements, static_cast<T>(beta));
+ vec_sum = vadd(vec_sum, vexp(vec_elements));
+ }
+ else
+ {
+ vec_elements = vexp(vmul_n(vec_elements, static_cast<T>(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<T> {});
+
for(; i <= (input_width - vec_size); i += vec_size)
{
- auto vec_in = vld<vec_16_byte_t<T>>(tmp_ptr + i);
- vec_16_byte_t<T> normalized_value = vmul_n(vec_in, sum_inversed);
+ auto vec_in = vld<vec_16_byte_t<T>>(tmp_ptr + i);
+ vec_16_byte_t<T> normalized_value{};
+ if(is_log)
+ {
+ normalized_value = vsub(vec_in, vdup_n<vec_16_byte_t<T>>(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 @@
}
} // namespace
-NELogits1DSoftmaxKernel::NELogits1DSoftmaxKernel()
+template <bool IS_LOG>
+NELogits1DSoftmaxKernel<IS_LOG>::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 <bool IS_LOG>
+void NELogits1DSoftmaxKernel<IS_LOG>::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 @@
switch(input->info()->data_type())
{
case DataType::QASYMM8:
- _func = &logits_1d_softmax_qasymm8;
+ _func = &logits_1d_softmax_qasymm8<IS_LOG>;
break;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
- _func = &logits_1d_softmax_float<float16_t>;
+ _func = &logits_1d_softmax_float<float16_t, IS_LOG>;
break;
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
case DataType::F32:
- _func = &logits_1d_softmax_float<float>;
+ _func = &logits_1d_softmax_float<float, IS_LOG>;
break;
default:
ARM_COMPUTE_ERROR("Unsupported data type.");
@@ -795,8 +892,9 @@
INEKernel::configure(win_config.second);
}
-Status NELogits1DSoftmaxKernel::validate(const ITensorInfo *input, const ITensorInfo *max,
- const ITensorInfo *output, const float beta, const ITensorInfo *tmp)
+template <bool IS_LOG>
+Status NELogits1DSoftmaxKernel<IS_LOG>::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 @@
return Status{};
}
-void NELogits1DSoftmaxKernel::run(const Window &window, const ThreadInfo &info)
+template <bool IS_LOG>
+void NELogits1DSoftmaxKernel<IS_LOG>::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
@@ -822,4 +921,7 @@
(*_func)(*_input, *_max, tmp_for_thread, *_output, _beta, window);
}
+template class NELogits1DSoftmaxKernel<true>;
+template class NELogits1DSoftmaxKernel<false>;
+
} // namespace arm_compute
diff --git a/src/runtime/NEON/functions/NESoftmaxLayer.cpp b/src/runtime/NEON/functions/NESoftmaxLayer.cpp
index 79a9496..f530a87 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<IMemoryManager> memory_manager)
+template <bool IS_LOG>
+NESoftmaxLayerGeneric<IS_LOG>::NESoftmaxLayerGeneric(std::shared_ptr<IMemoryManager> 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 <bool IS_LOG>
+void NESoftmaxLayerGeneric<IS_LOG>::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 @@
auto_init_if_empty(*output->info(), *input->info()->clone());
}
-void NESoftmaxLayer::configure(ITensor *input, ITensor *output, float beta, size_t axis)
+template <bool IS_LOG>
+void NESoftmaxLayerGeneric<IS_LOG>::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 @@
_tmp.allocator()->allocate();
}
-Status NESoftmaxLayer::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, size_t axis)
+template <bool IS_LOG>
+Status NESoftmaxLayerGeneric<IS_LOG>::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 @@
}
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<IS_LOG>::validate(&tensor_info_tmp, &tensor_info_max_sum, output, beta, &dont_care));
return Status{};
}
-void NESoftmaxLayer::run()
+template <bool IS_LOG>
+void NESoftmaxLayerGeneric<IS_LOG>::run()
{
MemoryGroupResourceScope scope_mg(_memory_group);
@@ -196,4 +201,8 @@
NEScheduler::get().schedule(&_reshape_kernel, Window::DimY);
}
}
+
+template class NESoftmaxLayerGeneric<false>;
+template class NESoftmaxLayerGeneric<true>;
+
} // 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 0000000..e35c8fd
--- /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<float> tolerance_f32(0.00001f);
+RelativeTolerance<half> tolerance_f16(half(0.2));
+
+/** Tolerance for quantized operations */
+constexpr AbsoluteTolerance<uint8_t> 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 <typename T>
+using NELogSoftmaxLayerFixture = SoftmaxValidationFixture<Tensor, Accessor, NELogSoftmaxLayer, T, true>;
+
+TEST_SUITE(Float)
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, NELogSoftmaxLayerFixture<half>, 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<half>, 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<half>, 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<float>, 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<float>, 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<float>, 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 <typename T>
+using NELogSoftmaxLayerQuantizedFixture = SoftmaxValidationQuantizedFixture<Tensor, Accessor, NELogSoftmaxLayer, T, true>;
+
+TEST_SUITE(Quantized)
+TEST_SUITE(QASYMM8)
+FIXTURE_DATA_TEST_CASE(RunSmall2D, NELogSoftmaxLayerQuantizedFixture<uint8_t>, 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<uint8_t>, 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<uint8_t>, 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 e39ee74..f747ab3 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 <random>
@@ -42,7 +43,7 @@
{
namespace validation
{
-template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool IS_LOG = false>
class SoftmaxValidationGenericFixture : public framework::Fixture
{
public:
@@ -110,7 +111,14 @@
// Fill reference
fill(src);
- return reference::softmax_layer<T>(src, beta, axis);
+ if(IS_LOG)
+ {
+ return reference::log_softmax_layer<T>(src, beta, axis);
+ }
+ else
+ {
+ return reference::softmax_layer<T>(src, beta, axis);
+ }
}
TensorType _target{};
@@ -118,33 +126,33 @@
QuantizationInfo _quantization_info{};
};
-template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class SoftmaxValidationFixture : public SoftmaxValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool IS_LOG = false>
+class SoftmaxValidationFixture : public SoftmaxValidationGenericFixture<TensorType, AccessorType, FunctionType, T, IS_LOG>
{
public:
template <typename...>
void setup(TensorShape shape, DataType data_type, float beta, size_t axis)
{
- SoftmaxValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape,
- data_type,
- QuantizationInfo(),
- beta,
- axis);
+ SoftmaxValidationGenericFixture<TensorType, AccessorType, FunctionType, T, IS_LOG>::setup(shape,
+ data_type,
+ QuantizationInfo(),
+ beta,
+ axis);
}
};
-template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class SoftmaxValidationQuantizedFixture : public SoftmaxValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool IS_LOG = false>
+class SoftmaxValidationQuantizedFixture : public SoftmaxValidationGenericFixture<TensorType, AccessorType, FunctionType, T, IS_LOG>
{
public:
template <typename...>
void setup(TensorShape shape, DataType data_type, QuantizationInfo quantization_info, float beta, size_t axis)
{
- SoftmaxValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape,
- data_type,
- quantization_info,
- beta,
- axis);
+ SoftmaxValidationGenericFixture<TensorType, AccessorType, FunctionType, T, IS_LOG>::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 0000000..3f21d85
--- /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 <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type>
+SimpleTensor<T> log_softmax_layer(const SimpleTensor<T> &src, float beta, size_t axis)
+{
+ return softmax_layer_generic<T>(src, beta, axis, true);
+}
+
+template <typename T, typename std::enable_if<std::is_same<T, uint8_t>::value, int>::type>
+SimpleTensor<T> log_softmax_layer(const SimpleTensor<T> &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<float> src_tmp = convert_from_asymmetric(src);
+ SimpleTensor<float> dst_tmp = log_softmax_layer<float>(src_tmp, beta, axis);
+ SimpleTensor<T> dst = convert_to_asymmetric<uint8_t>(dst_tmp, output_quantization_info);
+ return dst;
+}
+
+template SimpleTensor<float> log_softmax_layer(const SimpleTensor<float> &src, float beta, size_t axis);
+template SimpleTensor<half> log_softmax_layer(const SimpleTensor<half> &src, float beta, size_t axis);
+template SimpleTensor<uint8_t> log_softmax_layer(const SimpleTensor<uint8_t> &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 0000000..35547ca
--- /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 <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0>
+SimpleTensor<T> log_softmax_layer(const SimpleTensor<T> &src, float beta, size_t axis = 1);
+
+template <typename T, typename std::enable_if<std::is_same<T, uint8_t>::value, int>::type = 0>
+SimpleTensor<T> log_softmax_layer(const SimpleTensor<T> &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 fabc62b..ef2468d 100644
--- a/tests/validation/reference/SoftmaxLayer.cpp
+++ b/tests/validation/reference/SoftmaxLayer.cpp
@@ -34,7 +34,7 @@
namespace reference
{
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type>
-SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, size_t axis)
+SimpleTensor<T> softmax_layer_generic(const SimpleTensor<T> &src, float beta, size_t axis, bool is_log)
{
// Create reference
SimpleTensor<T> dst{ src.shape(), src.data_type(), 1 };
@@ -65,23 +65,48 @@
// 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<float> softmax_layer_generic(const SimpleTensor<float> &src, float beta, size_t axis, bool is_log);
+template SimpleTensor<half> softmax_layer_generic(const SimpleTensor<half> &src, float beta, size_t axis, bool is_log);
+
+template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type>
+SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, size_t axis)
+{
+ return softmax_layer_generic<T>(src, beta, axis, false);
+}
+
template <typename T, typename std::enable_if<std::is_same<T, uint8_t>::value, int>::type>
SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, size_t axis)
{
diff --git a/tests/validation/reference/SoftmaxLayer.h b/tests/validation/reference/SoftmaxLayer.h
index d21ca2b..fa9485c 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
*
@@ -36,6 +36,9 @@
namespace reference
{
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0>
+SimpleTensor<T> softmax_layer_generic(const SimpleTensor<T> &src, float beta, size_t axis, bool is_log = false);
+
+template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0>
SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, size_t axis = 1);
template <typename T, typename std::enable_if<std::is_same<T, uint8_t>::value, int>::type = 0>