Use `CLElementwiseSquaredDiff` (introduced in ACL v19.02) instead of custom implementation
Signed-off-by: Sergei Barannikov <s.barannikov@samsung.com>
+++ /dev/null
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright (c) 2016-2018 ARM Limited.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef __ARM_COMPUTE_CLSQUARED_DIFFERENCE_KERNEL_H__
-#define __ARM_COMPUTE_CLSQUARED_DIFFERENCE_KERNEL_H__
-
-#include "arm_compute/core/CL/ICLKernel.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** OpenCL kernel to return squared difference value of two tensors (x-y)^2*/
-class CLSquaredDifferenceKernel : public ICLKernel
-{
-public:
- /** Default constructor */
- CLSquaredDifferenceKernel();
- /** Prevent instances of this class from being copied (As this class contains pointers). */
- CLSquaredDifferenceKernel(const CLSquaredDifferenceKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers). */
- CLSquaredDifferenceKernel &operator=(const CLSquaredDifferenceKernel &) = delete;
- /** Allow instances of this class to be moved */
- CLSquaredDifferenceKernel(CLSquaredDifferenceKernel &&) = default;
- /** Allow instances of this class to be moved */
- CLSquaredDifferenceKernel &operator=(CLSquaredDifferenceKernel &&) = default;
- /** Initialize the kernel's input, output.
- *
- * @param[in] input1 Source tensor1.
- * @param[in] input2 Source tensor2.
- * @param[out] output Output tensor.
- */
- void configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output);
-
- // Inherited methods overridden:
- void run(const Window &window, cl::CommandQueue &queue) override;
-
- BorderSize border_size() const override;
-
-private:
- const ICLTensor *_input1;
- const ICLTensor *_input2;
- ICLTensor *_output;
-};
-} // namespace arm_compute
-#endif /*__ARM_COMPUTE_CLSQUARED_DIFFERENCE_KERNEL_H__ */
#include <arm_compute/runtime/CL/functions/CLSpaceToBatchND.h>
#include <arm_compute/runtime/CL/functions/CLSpaceToDepth.h>
#include <arm_compute/runtime/CL/functions/CLSplit.h>
-#include <arm_compute/runtime/CL/functions/CLSquaredDifference.h>
#include <arm_compute/runtime/CL/functions/CLStridedSliceEx.h>
#include <arm_compute/runtime/CL/functions/CLTopKV2.h>
+++ /dev/null
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright (c) 2016-2018 ARM Limited.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef __ARM_COMPUTE_CLSQUARED_DIFFERENCE_H__
-#define __ARM_COMPUTE_CLSQUARED_DIFFERENCE_H__
-
-#include "arm_compute/runtime/CL/ICLSimpleFunction.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-class CLSquaredDifference : public ICLSimpleFunction
-{
-public:
- /** Initialise the function's source and destination.
- *
- * @param[in] input1 Source tensor1. Data types supported:
- * U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32.
- * @param[in] input2 Source tensor2. Data types supported:
- * U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32.
- * @param[out] output Output tensor. Data types supported: Same as @p input.
- */
- void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output);
-};
-} // namespace arm_compute
-#endif /*__ARM_COMPUTE_CLSQUARED_DIFFERENCE_H__*/
{"prelu_qasymm8", "prelu_quantized.cl"},
{"reduce_min_max", "reduce_operation.cl"},
{"reduce_sum_mean", "reduce_operation.cl"},
- {"squared_difference", "squared_difference.cl"},
{"topkv2_init", "topkv2.cl"},
{"topkv2_find_first_negative", "topkv2.cl"},
{"topkv2_reorder_negatives", "topkv2.cl"},
#include "./cl_kernels/space_to_depth.clembed"
},
{
- "squared_difference.cl",
-#include "./cl_kernels/squared_difference.clembed"
- },
- {
"topkv2.cl",
#include "./cl_kernels/topkv2.clembed"
},
+++ /dev/null
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright (c) 2016-2018 ARM Limited.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "helpers.h"
-
-#ifndef VEC_SIZE
-#define VEC_SIZE 1
-#endif
-
-#if defined(DATA_TYPE)
-/** Returns true value of squared_difference of two tensors.
- *
- * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float
- * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g.
- * -DVEC_SIZE=16
- * @note Can only take floating point data types.
- *
- * @param[in] input1_ptr Pointer to the source image. Supported data
- * types: F16/F32
- * @param[in] input1_stride_x Stride of the source image in X dimension (in
- * bytes)
- * @param[in] input1_step_x input1_stride_x * number of elements along X
- * processed per workitem(in bytes)
- * @param[in] input1_stride_y Stride of the source image in Y dimension (in
- * bytes)
- * @param[in] input1_step_y input1_stride_y * number of elements along Y
- * processed per workitem(in bytes)
- * @param[in] input1_stride_z Stride of the source tensor in Z dimension (in
- * bytes)
- * @param[in] input1_step_z input1_stride_z * number of elements along Z
- * processed per workitem(in bytes)
- * @param[in] input1_offset_first_element_in_bytes The offset of the first element in the source
- * image
- * @param[in] input2_ptr Pointer to the source image. Supported data
- * types: F16/F32
- * @param[in] input2_stride_x Stride of the source image in X dimension (in
- * bytes)
- * @param[in] input2_step_x input2_stride_x * number of elements along X
- * processed per workitem(in bytes)
- * @param[in] input2_stride_y Stride of the source image in Y dimension (in
- * bytes)
- * @param[in] input2_step_y input2_stride_y * number of elements along Y
- * processed per workitem(in bytes)
- * @param[in] input2_stride_z Stride of the source tensor in Z dimension (in
- * bytes)
- * @param[in] input2_step_z input2_stride_z * number of elements along Z
- * processed per workitem(in bytes)
- * @param[in] input2_offset_first_element_in_bytes The offset of the first element in the source
- * image
- * @param[out] output_ptr Pointer to the destination image. Supported
- * data types: F16/F32
- * @param[in] output_stride_x Stride of the destination image in X dimension
- * (in bytes)
- * @param[in] output_step_x output_stride_x * number of elements along X
- * processed per workitem(in bytes)
- * @param[in] output_stride_y Stride of the destination image in Y dimension
- * (in bytes)
- * @param[in] output_step_y output_stride_y * number of elements along Y
- * processed per workitem(in bytes)
- * @param[in] output_stride_z Stride of the source tensor in Z dimension (in
- * bytes)
- * @param[in] output_step_z output_stride_z * number of elements along Z
- * processed per workitem(in bytes)
- * @param[in] output_offset_first_element_in_bytes The offset of the first element in the
- * destination image
- */
-__kernel void squared_difference(TENSOR3D_DECLARATION(input1), TENSOR3D_DECLARATION(input2),
- TENSOR3D_DECLARATION(output))
-{
- Tensor3D input1 = CONVERT_TO_TENSOR3D_STRUCT(input1);
- Tensor3D input2 = CONVERT_TO_TENSOR3D_STRUCT(input2);
- Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output);
-
- VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
- diff = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)input1.ptr) -
- VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)input2.ptr);
-
- VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
- sq_diff = diff * diff;
-
- VSTORE(VEC_SIZE)
- (sq_diff, 0, (__global DATA_TYPE *)output.ptr);
-}
-#endif // defined(DATA_TYPE)
+++ /dev/null
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright (c) 2016-2018 ARM Limited.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "arm_compute/core/CL/kernels/CLSquaredDifferenceKernel.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLKernelLibraryEx.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-
-using namespace arm_compute;
-
-namespace
-{
-constexpr unsigned int num_elems_processed_per_iteration = 16;
-
-Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
-{
- const TensorShape &out_shape =
- TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape());
-
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::F16, DataType::F32);
-
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0,
- "Inputs are not broadcast compatible");
- // Validate in case of configured output
- if (output->total_size() > 0)
- {
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(
- detail::have_different_dimensions(out_shape, output->tensor_shape(), 0),
- "Wrong shape for output");
- }
- return Status{};
-}
-} // namespace
-
-CLSquaredDifferenceKernel::CLSquaredDifferenceKernel()
- : _input1(nullptr), _input2(nullptr), _output(nullptr)
-{
-}
-
-void CLSquaredDifferenceKernel::configure(const ICLTensor *input1, const ICLTensor *input2,
- ICLTensor *output)
-{
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output);
- ARM_COMPUTE_ERROR_THROW_ON(validate(input1->info(), input2->info(), output->info()));
-
- _input1 = input1;
- _input2 = input2;
- _output = output;
-
- // Create kernel
- std::set<std::string> build_opts;
- build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input1->info()->data_type())));
- build_opts.emplace(
- ("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)));
- _kernel = static_cast<cl::Kernel>(
- CLKernelLibraryEx::get().create_kernel("squared_difference", build_opts));
-
- const std::pair<TensorShape, ValidRegion> broadcast_pair =
- ITensorInfo::broadcast_shape_and_valid_region(*input1->info(), *input2->info());
-
- const TensorShape &out_shape = broadcast_pair.first;
- const ValidRegion &valid_region = broadcast_pair.second;
-
- // Auto initialize output if not initialized
- {
- set_shape_if_empty(*output->info(), out_shape);
-
- if (input1->info()->data_type() == DataType::F16 &&
- input2->info()->data_type() == DataType::F16)
- {
- set_format_if_unknown(*output->info(), Format::F16);
- }
- else if (input1->info()->data_type() == DataType::F32 ||
- input2->info()->data_type() == DataType::F32)
- {
- set_format_if_unknown(*output->info(), Format::F32);
- }
- }
-
- Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
- Window win_input1 = win.broadcast_if_dimension_le_one(*input1->info());
- Window win_input2 = win.broadcast_if_dimension_le_one(*input2->info());
-
- AccessWindowHorizontal input1_access(input1->info(), 0, num_elems_processed_per_iteration);
- AccessWindowHorizontal input2_access(input2->info(), 0, num_elems_processed_per_iteration);
- AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
-
- update_window_and_padding(win_input1, input1_access) ||
- update_window_and_padding(win_input2, input2_access) ||
- update_window_and_padding(win, output_access);
-
- output_access.set_valid_region(win, valid_region);
-
- ICLKernel::configure_internal(win);
-}
-
-void CLSquaredDifferenceKernel::run(const Window &window, cl::CommandQueue &queue)
-{
- ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
- ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
-
- const TensorShape &in_shape1 = _input1->info()->tensor_shape();
- const TensorShape &in_shape2 = _input2->info()->tensor_shape();
- const TensorShape &out_shape = _output->info()->tensor_shape();
-
- bool can_collapse = true;
- if (std::min(in_shape1.total_size(), in_shape2.total_size()) > 1)
- {
- can_collapse =
- (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
- for (size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++)
- {
- can_collapse = (in_shape1[d] == in_shape2[d]);
- }
- }
-
- bool has_collapsed = false;
- Window collapsed =
- can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed)
- : window;
-
- const TensorShape &in_shape1_collapsed =
- has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
- const TensorShape &in_shape2_collapsed =
- has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;
-
- Window slice = collapsed.first_slice_window_3D();
- Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
- Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);
-
- do
- {
- unsigned int idx = 0;
- add_3D_tensor_argument(idx, _input1, slice_input1);
- add_3D_tensor_argument(idx, _input2, slice_input2);
- add_3D_tensor_argument(idx, _output, slice);
-
- enqueue(queue, *this, slice);
-
- collapsed.slide_window_slice_3D(slice_input1);
- collapsed.slide_window_slice_3D(slice_input2);
- } while (collapsed.slide_window_slice_3D(slice));
-}
-
-BorderSize CLSquaredDifferenceKernel::border_size() const
-{
- const unsigned int replicateSize =
- _output->info()->dimension(0) -
- std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
- const unsigned int border =
- std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
- return BorderSize(0, border, 0, 0);
-}
+++ /dev/null
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright (c) 2016-2018 ARM Limited.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "arm_compute/runtime/CL/functions/CLSquaredDifference.h"
-
-#include "arm_compute/core/CL/kernels/CLSquaredDifferenceKernel.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-
-using namespace arm_compute;
-
-void CLSquaredDifference::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output)
-{
- auto k = arm_compute::support::cpp14::make_unique<CLSquaredDifferenceKernel>();
- k->configure(input1, input2, output);
- _kernel = std::move(k);
-
- if (output->info()->dimension(0) > 1)
- {
- ICLTensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2;
-
- if (broadcasted_info->info()->dimension(0) == 1)
- {
- _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE);
- }
- }
-}
std::unique_ptr<::arm_compute::IFunction> fn;
- auto l = nnfw::cpp14::make_unique<::arm_compute::CLSquaredDifference>();
+ auto l = nnfw::cpp14::make_unique<::arm_compute::CLElementwiseSquaredDiff>();
l->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle());
if (::internal::arm_compute::isGpuMode())
{
- auto fn = nnfw::cpp14::make_unique<::arm_compute::CLSquaredDifference>();
+ auto fn = nnfw::cpp14::make_unique<::arm_compute::CLElementwiseSquaredDiff>();
fn->configure(CAST_CL(lhs_alloc), CAST_CL(rhs_alloc), CAST_CL(ofm_alloc));
builder.append("SquaredDifference", std::move(fn));
projects / platform / core / ml / nnfw.git / commitdiff