Make CL Pooling kernels and functions state-less
Resolves COMPMID-4000
Change-Id: I64878f93c033b4928fdefbb964c37c67fdecfaab
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4971
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/src/core/CL/CLKernels.h b/src/core/CL/CLKernels.h
index d31c876..ac05168 100644
--- a/src/core/CL/CLKernels.h
+++ b/src/core/CL/CLKernels.h
@@ -107,7 +107,6 @@
#include "src/core/CL/kernels/CLPadLayerKernel.h"
#include "src/core/CL/kernels/CLPermuteKernel.h"
#include "src/core/CL/kernels/CLPixelWiseMultiplicationKernel.h"
-#include "src/core/CL/kernels/CLPoolingLayerKernel.h"
#include "src/core/CL/kernels/CLPriorBoxLayerKernel.h"
#include "src/core/CL/kernels/CLQLSTMLayerNormalizationKernel.h"
#include "src/core/CL/kernels/CLQuantizationLayerKernel.h"
diff --git a/src/core/CL/kernels/CLMaxUnpoolingLayerKernel.h b/src/core/CL/kernels/CLMaxUnpoolingLayerKernel.h
index 86267ec..cc96cf1 100644
--- a/src/core/CL/kernels/CLMaxUnpoolingLayerKernel.h
+++ b/src/core/CL/kernels/CLMaxUnpoolingLayerKernel.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2020 Arm Limited.
+ * Copyright (c) 2020-2021 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -53,7 +53,7 @@
* @param[in] compile_context The compile context to be used.
* @param[in] input Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
* @param[in] indices Tensor containing the offset to store the input elements in the output tensor.
- * @ref CLPoolingLayerKernel with indices should precede this function in order to
+ * @ref opencl::ClPooling with indices should precede this function in order to
* properly reconstruct the output tensor.
* The tensor shape of this tensor has to be equal to the input tensor shape. Data type supported: U32.
* @param[out] output Destination tensor. Data types supported: Same as @p input.
@@ -65,7 +65,7 @@
* @param[in] input Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
* @param[in] output Destination tensor info. Data types supported: Same as @p input.
* @param[in] indices TensorInfo associated to the tensor containing the offset to store the input elements in the output tensor.
- * @ref CLPoolingLayerKernel with indices should precede this function in order to
+ * @ref opencl::ClPooling with indices should precede this function in order to
* properly reconstruct the output tensor.
* The tensor shape of this tensor has to be equal to the input tensor shape. Data type supported: U32.
* @param[in] pool_info Contains pooling operation information described in @ref PoolingLayerInfo.
diff --git a/src/core/CL/kernels/CLPoolingLayerKernel.h b/src/core/CL/kernels/CLPoolingLayerKernel.h
deleted file mode 100644
index d88402a..0000000
--- a/src/core/CL/kernels/CLPoolingLayerKernel.h
+++ /dev/null
@@ -1,96 +0,0 @@
-/*
- * Copyright (c) 2017-2020 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_CLPOOLINGLAYERKERNEL_H
-#define ARM_COMPUTE_CLPOOLINGLAYERKERNEL_H
-
-#include "src/core/CL/ICLKernel.h"
-
-#include "arm_compute/core/Error.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** Interface for the pooling layer kernel */
-class CLPoolingLayerKernel : public ICLKernel
-{
-public:
- /** Default constructor */
- CLPoolingLayerKernel();
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- CLPoolingLayerKernel(const CLPoolingLayerKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- CLPoolingLayerKernel &operator=(const CLPoolingLayerKernel &) = delete;
- /** Allow instances of this class to be moved */
- CLPoolingLayerKernel(CLPoolingLayerKernel &&) = default;
- /** Allow instances of this class to be moved */
- CLPoolingLayerKernel &operator=(CLPoolingLayerKernel &&) = default;
- /** Default destructor */
- ~CLPoolingLayerKernel() = default;
-
- /** Set the input and output tensors.
- *
- *
- * @param[in] input Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
- * @param[out] output Destination tensor. Data types supported: Same as @p input.
- * @param[in] pool_info Contains pooling operation information described in @ref PoolingLayerInfo.
- * @param[out] indices (optional) The indices of the maximal values. Data type supported: U32.
- */
- void configure(const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info, ICLTensor *indices = nullptr);
- /** Set the input and output tensors.
- *
- *
- * @param[in] compile_context The compile context to be used.
- * @param[in] input Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
- * @param[out] output Destination tensor. Data types supported: Same as @p input.
- * @param[in] pool_info Contains pooling operation information described in @ref PoolingLayerInfo.
- * @param[out] indices (optional) The indices of the maximal values. Data type supported: U32.
- */
- void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info, ICLTensor *indices = nullptr);
- /** Static function to check if given info will lead to a valid configuration of @ref CLPoolingLayerKernel
- *
- * @param[in] input Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
- * @param[in] output Destination tensor info. Data types supported: Same as @p input.
- * @param[in] pool_info Contains pooling operation information described in @ref PoolingLayerInfo.
- * @param[in] indices (optional) The indices of the maximal values. Data type supported: U32.
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, const ITensorInfo *indices = nullptr);
-
- // Inherited methods overridden:
- void run(const Window &window, cl::CommandQueue &queue) override;
- BorderSize border_size() const override;
-
-public:
- const ICLTensor *_input;
- ICLTensor *_output;
- ICLTensor *_indices;
- PoolingLayerInfo _pool_info;
- DataLayout _data_layout;
- BorderSize _border_size;
- unsigned int _num_elems_processed_per_iteration;
-};
-} // namespace arm_compute
-#endif /*ARM_COMPUTE_CLPOOLINGLAYERKERNEL_H */
diff --git a/src/core/CL/kernels/CLPoolingLayerKernel.cpp b/src/core/gpu/cl/kernels/ClPoolingKernel.cpp
similarity index 65%
rename from src/core/CL/kernels/CLPoolingLayerKernel.cpp
rename to src/core/gpu/cl/kernels/ClPoolingKernel.cpp
index 79843cd..567fec2 100644
--- a/src/core/CL/kernels/CLPoolingLayerKernel.cpp
+++ b/src/core/gpu/cl/kernels/ClPoolingKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2020 Arm Limited.
+ * Copyright (c) 2017-2021 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,86 +21,84 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#include "src/core/CL/kernels/CLPoolingLayerKernel.h"
+#include "src/core/gpu/cl/kernels/ClPoolingKernel.h"
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/CL/OpenCL.h"
-#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "src/core/AccessWindowStatic.h"
#include "src/core/CL/CLValidate.h"
-#include "src/core/CL/ICLKernel.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
+#include "support/Cast.h"
#include "support/StringSupport.h"
-#include <set>
-#include <string>
-#include <tuple>
-
namespace arm_compute
{
+namespace opencl
+{
+namespace kernels
+{
using namespace arm_compute::misc::shape_calculator;
namespace
{
// Internal window config info
-using CLPoolingConfig = std::pair<unsigned int, BorderSize>; //num_elems_processed_per_iteration, border_size
+using ClPoolingConfig = std::pair<unsigned int, BorderSize>; //num_elems_processed_per_iteration, border_size
-void auto_init(const ITensorInfo *input, ITensorInfo *output, ITensorInfo *indices, PoolingLayerInfo pool_info)
+void auto_init(const ITensorInfo *src, ITensorInfo *dst, ITensorInfo *indices, PoolingLayerInfo pool_info)
{
- TensorShape out_shape = compute_pool_shape(*input, pool_info);
- auto_init_if_empty(*output, input->clone()->set_tensor_shape(out_shape));
+ TensorShape out_shape = compute_pool_shape(*src, pool_info);
+ auto_init_if_empty(*dst, src->clone()->set_tensor_shape(out_shape));
if(indices)
{
- auto_init_if_empty(*indices, input->clone()->set_tensor_shape(out_shape).set_data_type(DataType::U32));
+ auto_init_if_empty(*indices, src->clone()->set_tensor_shape(out_shape).set_data_type(DataType::U32));
}
}
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, const ITensorInfo *indices)
+Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info, const ITensorInfo *indices)
{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((is_data_type_quantized_asymmetric(input->data_type()) && pool_info.pool_type == PoolingType::L2),
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
+ ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((is_data_type_quantized_asymmetric(src->data_type()) && pool_info.pool_type == PoolingType::L2),
"Unsupported combination of parameters!");
// Check indices
if(indices)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_info.pool_type != PoolingType::MAX, "Pooling indices only supported for MAX pooling method");
ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_info.pool_size != Size2D(2, 2)), "Pooling indices only supported for pool size 2x2");
if(indices->total_size() != 0)
{
- TensorInfo idx_info(TensorInfo(compute_pool_shape(*input, pool_info), 1, DataType::U32));
+ TensorInfo idx_info(TensorInfo(compute_pool_shape(*src, pool_info), 1, DataType::U32));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(indices, &idx_info);
}
}
- // Checks performed when output is configured
- if(output->total_size() != 0)
+ // Checks performed when dst is configured
+ if(dst->total_size() != 0)
{
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
- TensorInfo out_info(TensorInfo(compute_pool_shape(*input, pool_info), 1, output->data_type()));
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, dst);
+ TensorInfo out_info(TensorInfo(compute_pool_shape(*src, pool_info), 1, dst->data_type()));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &out_info);
}
return Status{};
}
-std::tuple<Status, Window, CLPoolingConfig> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const PoolingLayerInfo &pool_info, ITensorInfo *indices = nullptr)
+std::tuple<Status, Window, ClPoolingConfig> validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &pool_info, ITensorInfo *indices = nullptr)
{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
// Get data layout
- const DataLayout data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? input->data_layout() : pool_info.data_layout;
+ const DataLayout data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout;
const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
@@ -108,8 +106,8 @@
int pool_stride_y = 0;
unsigned int pooled_w = 0;
unsigned int pooled_h = 0;
- int pool_size_x = pool_info.is_global_pooling ? input->dimension(idx_width) : pool_info.pool_size.width;
- int pool_size_y = pool_info.is_global_pooling ? input->dimension(idx_height) : pool_info.pool_size.height;
+ int pool_size_x = pool_info.is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width;
+ int pool_size_y = pool_info.is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height;
const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
const int pool_pad_right = pad_stride_info.pad_right();
@@ -118,14 +116,14 @@
const int pool_pad_bottom = pad_stride_info.pad_bottom();
BorderSize border_size = BorderSize();
- auto_init(input, output, indices, pool_info);
- pooled_w = output->tensor_shape()[idx_width];
- pooled_h = output->tensor_shape()[idx_height];
+ auto_init(src, dst, indices, pool_info);
+ pooled_w = dst->tensor_shape()[idx_width];
+ pooled_h = dst->tensor_shape()[idx_height];
- const DataType data_type = input->data_type();
+ const DataType data_type = src->data_type();
- const int input_width = input->dimension(idx_width);
- const int input_height = input->dimension(idx_height);
+ const int src_width = src->dimension(idx_width);
+ const int src_height = src->dimension(idx_height);
unsigned int num_elems_processed_per_iteration = 0;
bool window_changed = false;
@@ -146,46 +144,46 @@
const int num_iterations_x = (pooled_w + num_elems_processed_per_iteration - 1) / num_elems_processed_per_iteration;
// Upper limit for the number of right/bottom border elements that are accessed
- const int upper_bound_w = ((num_iterations_x - 1) * num_elems_processed_per_iteration * pool_stride_x - pool_pad_left + num_elems_read_per_iteration) - input_width;
- const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_top + pool_size_y) - input_height;
+ const int upper_bound_w = ((num_iterations_x - 1) * num_elems_processed_per_iteration * pool_stride_x - pool_pad_left + num_elems_read_per_iteration) - src_width;
+ const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_top + pool_size_y) - src_height;
border_size.right = std::max(upper_bound_w, pool_pad_right);
border_size.bottom = std::max(upper_bound_h, pool_pad_bottom);
- win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
+ win = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration));
- AccessWindowRectangle input_access(input, -pool_pad_left, -pool_pad_top, num_elems_read_per_iteration, pool_size_y,
- pool_stride_x, pool_stride_y);
- AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
+ AccessWindowRectangle src_access(src, -pool_pad_left, -pool_pad_top, num_elems_read_per_iteration, pool_size_y,
+ pool_stride_x, pool_stride_y);
+ AccessWindowHorizontal dst_access(dst, 0, num_elems_processed_per_iteration);
// Update indices window
if(indices)
{
AccessWindowHorizontal indices_access(indices, 0, num_elems_processed_per_iteration);
- window_changed = update_window_and_padding(win, input_access, output_access, indices_access);
+ window_changed = update_window_and_padding(win, src_access, dst_access, indices_access);
indices_access.set_valid_region(win, ValidRegion(Coordinates(), indices->tensor_shape()));
}
else
{
- window_changed = update_window_and_padding(win, input_access, output_access);
+ window_changed = update_window_and_padding(win, src_access, dst_access);
}
- output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
+ dst_access.set_valid_region(win, ValidRegion(Coordinates(), dst->tensor_shape()));
break;
}
case DataLayout::NHWC:
{
// Initialize border size
border_size = BorderSize();
- num_elems_processed_per_iteration = adjust_vec_size(4, output->dimension(0));
- win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
+ num_elems_processed_per_iteration = adjust_vec_size(4, dst->dimension(0));
+ win = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration));
if(indices != nullptr)
{
indices->set_valid_region(ValidRegion(Coordinates(), indices->tensor_shape()));
}
- output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
+ dst->set_valid_region(ValidRegion(Coordinates(), dst->tensor_shape()));
break;
}
default:
@@ -193,37 +191,29 @@
}
Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
- return std::make_tuple(err, win, CLPoolingConfig(num_elems_processed_per_iteration, border_size));
+ return std::make_tuple(err, win, ClPoolingConfig(num_elems_processed_per_iteration, border_size));
}
} // namespace
-CLPoolingLayerKernel::CLPoolingLayerKernel()
- : _input(nullptr), _output(nullptr), _indices(nullptr), _pool_info(), _data_layout(DataLayout::UNKNOWN), _border_size(0), _num_elems_processed_per_iteration(1)
+ClPoolingKernel::ClPoolingKernel()
+ : _pool_info(), _data_layout(DataLayout::UNKNOWN), _border_size(0), _num_elems_processed_per_iteration(1)
{
}
-BorderSize CLPoolingLayerKernel::border_size() const
+BorderSize ClPoolingKernel::border_size() const
{
return _border_size;
}
-void CLPoolingLayerKernel::configure(const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info, ICLTensor *indices)
+void ClPoolingKernel::configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &pool_info, ITensorInfo *indices)
{
- configure(CLKernelLibrary::get().get_compile_context(), input, output, pool_info, indices);
-}
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
-void CLPoolingLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info, ICLTensor *indices)
-{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-
- auto padding_info = get_padding_info({ input, output, indices });
+ auto padding_info = get_padding_info({ src, dst, indices });
// Set instance variables
- _input = input;
- _output = output;
_pool_info = pool_info;
- _data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? input->info()->data_layout() : pool_info.data_layout;
- _indices = indices;
+ _data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout;
int pool_stride_x = 0;
int pool_stride_y = 0;
const PoolingType pool_type = pool_info.pool_type;
@@ -231,8 +221,8 @@
const int idx_height = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
const int idx_channel = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
const int idx_batch_size = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::BATCHES);
- const int pool_size_x = pool_info.is_global_pooling ? input->info()->dimension(idx_width) : pool_info.pool_size.width;
- const int pool_size_y = pool_info.is_global_pooling ? input->info()->dimension(idx_height) : pool_info.pool_size.height;
+ const int pool_size_x = pool_info.is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width;
+ const int pool_size_y = pool_info.is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height;
const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
const bool exclude_padding = pool_info.exclude_padding;
std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
@@ -241,36 +231,36 @@
// Set build options
CLBuildOptions build_opts;
- const DataType data_type = input->info()->data_type();
+ const DataType data_type = src->data_type();
// Configure kernel window
- auto win_config = validate_and_configure_window(input->info(), output->info(), pool_info, (indices ? indices->info() : nullptr));
+ auto win_config = validate_and_configure_window(src, dst, pool_info, indices);
ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));
ICLKernel::configure_internal(std::get<1>(win_config));
- CLPoolingConfig pooling_config = std::get<2>(win_config);
+ ClPoolingConfig pooling_config = std::get<2>(win_config);
_num_elems_processed_per_iteration = pooling_config.first;
_border_size = pooling_config.second;
build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(_num_elems_processed_per_iteration));
// Tensor paddings are used to calculate the indicies for MAX pooling
- if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && _indices && is_data_type_float(data_type))
+ if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && indices && is_data_type_float(data_type))
{
- build_opts.add_option("-DPAD_TENSOR_LEFT=" + support::cpp11::to_string(input->info()->padding().left));
- build_opts.add_option("-DPAD_TENSOR_RIGHT=" + support::cpp11::to_string(input->info()->padding().right));
- build_opts.add_option("-DPAD_TENSOR_TOP=" + support::cpp11::to_string(input->info()->padding().top));
- build_opts.add_option("-DPAD_TENSOR_BOTTOM=" + support::cpp11::to_string(input->info()->padding().bottom));
- build_opts.add_option("-DTENSOR_CHANNEL=" + support::cpp11::to_string(input->info()->dimension(idx_channel)));
- build_opts.add_option("-DTENSOR_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width)));
- build_opts.add_option("-DTENSOR_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height)));
+ build_opts.add_option("-DPAD_TENSOR_LEFT=" + support::cpp11::to_string(src->padding().left));
+ build_opts.add_option("-DPAD_TENSOR_RIGHT=" + support::cpp11::to_string(src->padding().right));
+ build_opts.add_option("-DPAD_TENSOR_TOP=" + support::cpp11::to_string(src->padding().top));
+ build_opts.add_option("-DPAD_TENSOR_BOTTOM=" + support::cpp11::to_string(src->padding().bottom));
+ build_opts.add_option("-DTENSOR_CHANNEL=" + support::cpp11::to_string(src->dimension(idx_channel)));
+ build_opts.add_option("-DTENSOR_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width)));
+ build_opts.add_option("-DTENSOR_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height)));
}
- if(is_data_type_quantized_asymmetric(data_type) && input->info()->quantization_info() != output->info()->quantization_info())
+ if(is_data_type_quantized_asymmetric(data_type) && src->quantization_info() != dst->quantization_info())
{
- const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform();
- const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform();
+ const UniformQuantizationInfo iq_info = src->quantization_info().uniform();
+ const UniformQuantizationInfo oq_info = dst->quantization_info().uniform();
build_opts.add_option("-DOFFSET_IN1=" + float_to_string_with_full_precision(iq_info.offset));
build_opts.add_option("-DOFFSET_OUT=" + float_to_string_with_full_precision(oq_info.offset));
@@ -278,10 +268,10 @@
build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(oq_info.scale));
}
- // Check output dimensions
- auto_init(input->info(), output->info(), indices ? indices->info() : nullptr, pool_info);
+ // Check dst dimensions
+ auto_init(src, dst, indices, pool_info);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info, (indices) ? indices->info() : nullptr));
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, pool_info, indices));
build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
build_opts.add_option("-DPOOL_" + string_from_pooling_type(pool_type));
@@ -312,8 +302,8 @@
build_opts.add_option("-DINITIAL_VALUE=0");
}
- build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width) + (exclude_padding ? 0 : pool_pad_left)));
- build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height) + (exclude_padding ? 0 : pool_pad_top)));
+ build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width) + (exclude_padding ? 0 : pool_pad_left)));
+ build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height) + (exclude_padding ? 0 : pool_pad_top)));
// Create kernel
switch(_data_layout)
@@ -334,14 +324,14 @@
if((pool_size_x == 3) && (pool_size_y == 3) && !is_data_type_quantized_asymmetric(data_type))
{
// Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenCL kernel where
- // each thread computes 4 output elements
+ // each thread computes 4 dst elements
const bool is_pool3x3_stride_le3 = (pool_size_x == 3) && (pool_size_y == 3) && (pool_stride_x <= 3);
std::string kernel_name = ((is_pool3x3_stride_le3) ? "pooling_layer_optimized_" : "pooling_layer_")
+ support::cpp11::to_string(pool_size_x);
_kernel = create_kernel(compile_context, kernel_name, build_opts.options());
}
- else if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && _indices && is_data_type_float(data_type))
+ else if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && indices && is_data_type_float(data_type))
{
// For max pooling with pool2x2, store indicies which will be used in max unpooling
if(data_type == DataType::F32)
@@ -368,8 +358,8 @@
const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision && pool_type != PoolingType::MAX;
// Wider accumulation is required to avoid accuracy loss
- // Case 1: Floating point mixed precision (fp16 input data and fp32 accumulation)
- // Cast 2: Quantized (int8/uint8 input data and int32 accumulation )
+ // Case 1: Floating point mixed precision (fp16 src data and fp32 accumulation)
+ // Cast 2: Quantized (int8/uint8 src data and int32 accumulation )
DataType acc_data_type = data_type;
if(use_fp_mixed_precision)
@@ -384,15 +374,15 @@
build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(acc_data_type));
build_opts.add_option_if(use_fp_mixed_precision, "-DFP_MIXED_PRECISION");
build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
- build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width)));
- build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height)));
- build_opts.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(output->info()->dimension(idx_height)));
- build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(output->info()->dimension(idx_channel)));
- build_opts.add_option("-DDST_BATCH_SIZE=" + support::cpp11::to_string(output->info()->dimension(idx_batch_size)));
- build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(input->info()->dimension(0) % _num_elems_processed_per_iteration));
+ build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width)));
+ build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height)));
+ build_opts.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(dst->dimension(idx_height)));
+ build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(dst->dimension(idx_channel)));
+ build_opts.add_option("-DDST_BATCH_SIZE=" + support::cpp11::to_string(dst->dimension(idx_batch_size)));
+ build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(src->dimension(0) % _num_elems_processed_per_iteration));
if(pool_info.pool_size == Size2D(2, 2) && is_data_type_float(data_type))
{
- build_opts.add_option_if(_indices != nullptr && pool_type == PoolingType::MAX, "-DEXTRACT_MAX_INDEX");
+ build_opts.add_option_if(indices != nullptr && pool_type == PoolingType::MAX, "-DEXTRACT_MAX_INDEX");
std::string kernel_name = "pooling_layer_2x2_nhwc";
_kernel = create_kernel(compile_context, kernel_name, build_opts.options());
@@ -414,34 +404,38 @@
_config_id += "_";
_config_id += lower_string(string_from_data_layout(_data_layout));
_config_id += "_";
- _config_id += support::cpp11::to_string(output->info()->dimension(idx_width));
+ _config_id += support::cpp11::to_string(dst->dimension(idx_width));
_config_id += "_";
- _config_id += support::cpp11::to_string(output->info()->dimension(idx_height));
+ _config_id += support::cpp11::to_string(dst->dimension(idx_height));
_config_id += "_";
- _config_id += support::cpp11::to_string(output->info()->dimension(idx_channel));
+ _config_id += support::cpp11::to_string(dst->dimension(idx_channel));
_config_id += "_";
- _config_id += lower_string(string_from_data_layout(input->info()->data_layout()));
+ _config_id += lower_string(string_from_data_layout(src->data_layout()));
- ARM_COMPUTE_ERROR_ON(input->info()->data_layout() == DataLayout::NHWC && has_padding_changed(padding_info));
+ ARM_COMPUTE_ERROR_ON(src->data_layout() == DataLayout::NHWC && has_padding_changed(padding_info));
}
-Status CLPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, const ITensorInfo *indices)
+Status ClPoolingKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info, const ITensorInfo *indices)
{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, pool_info, indices));
- ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), pool_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, pool_info, indices));
+ ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(src->clone().get(), dst->clone().get(), pool_info)));
return Status{};
}
-void CLPoolingLayerKernel::run(const Window &window, cl::CommandQueue &queue)
+void ClPoolingKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
- ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
unsigned int pool_stride_x = 0;
unsigned int pool_stride_y = 0;
std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride();
+ const auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
+ auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_0));
+ auto indices = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_1));
+
// Collapse window
Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
@@ -452,7 +446,7 @@
Window slice = window_collapsed.first_slice_window_3D();
do
{
- // Upsample input by pool size
+ // Upsample src by pool size
Window in_slice(slice);
in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start() - _pool_info.pad_stride_info.pad_left(),
(in_slice.x().end() - _pool_info.pad_stride_info.pad_left()) * pool_stride_x,
@@ -461,13 +455,13 @@
(in_slice.y().end() - _pool_info.pad_stride_info.pad_top()) * pool_stride_y,
pool_stride_y));
- // Set inputs
+ // Set srcs
unsigned int idx = 0;
- add_3D_tensor_argument(idx, _input, in_slice);
- add_3D_tensor_argument(idx, _output, slice);
- if(_indices && is_data_type_float(_input->info()->data_type()) && (_pool_info.pool_size == Size2D(2, 2)))
+ add_3D_tensor_argument(idx, src, in_slice);
+ add_3D_tensor_argument(idx, dst, slice);
+ if(indices && is_data_type_float(src->info()->data_type()) && (_pool_info.pool_size == Size2D(2, 2)))
{
- add_3D_tensor_argument(idx, _indices, slice);
+ add_3D_tensor_argument(idx, indices, slice);
}
enqueue(queue, *this, slice, lws_hint());
}
@@ -476,23 +470,23 @@
}
case DataLayout::NHWC:
{
- const size_t batch_size = _output->info()->tensor_shape().total_size_upper(3);
+ const size_t batch_size = dst->info()->tensor_shape().total_size_upper(3);
Window slice = window_collapsed.first_slice_window_4D();
Window in_slice = window_collapsed.first_slice_window_4D();
- in_slice.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _num_elems_processed_per_iteration));
- in_slice.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), pool_stride_x));
- in_slice.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), pool_stride_y));
+ in_slice.set(Window::DimX, Window::Dimension(0, src->info()->dimension(0), _num_elems_processed_per_iteration));
+ in_slice.set(Window::DimY, Window::Dimension(0, src->info()->dimension(1), pool_stride_x));
+ in_slice.set(Window::DimZ, Window::Dimension(0, src->info()->dimension(2), pool_stride_y));
in_slice.set(3, Window::Dimension(0, batch_size, 1));
do
{
- // Set inputs
+ // Set srcs
unsigned int idx = 0;
- add_4D_tensor_argument(idx, _input, in_slice);
- add_4D_tensor_argument(idx, _output, slice);
- if(_indices && is_data_type_float(_input->info()->data_type()) && (_pool_info.pool_type == PoolingType::MAX) && (_pool_info.pool_size == Size2D(2, 2)))
+ add_4D_tensor_argument(idx, src, in_slice);
+ add_4D_tensor_argument(idx, dst, slice);
+ if(indices && is_data_type_float(src->info()->data_type()) && (_pool_info.pool_type == PoolingType::MAX) && (_pool_info.pool_size == Size2D(2, 2)))
{
- add_4D_tensor_argument(idx, _indices, slice);
+ add_4D_tensor_argument(idx, indices, slice);
}
enqueue(queue, *this, slice, lws_hint());
}
@@ -503,4 +497,6 @@
ARM_COMPUTE_ERROR("Not implemented");
}
}
+} // namespace kernels
+} // namespace opencl
} // namespace arm_compute
diff --git a/src/core/gpu/cl/kernels/ClPoolingKernel.h b/src/core/gpu/cl/kernels/ClPoolingKernel.h
new file mode 100644
index 0000000..c1ce859
--- /dev/null
+++ b/src/core/gpu/cl/kernels/ClPoolingKernel.h
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2017-2021 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_CL_POOLING_KERNEL_H
+#define ARM_COMPUTE_CL_POOLING_KERNEL_H
+
+#include "src/core/common/Macros.h"
+#include "src/core/gpu/cl/ClCompileContext.h"
+#include "src/core/gpu/cl/IClKernel.h"
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+/** Interface for the pooling layer kernel */
+class ClPoolingKernel : public IClKernel
+{
+public:
+ /** Default constructor */
+ ClPoolingKernel();
+ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClPoolingKernel);
+
+ /** Configure kernel for a given list of arguments
+ *
+ *
+ * @param[in] compile_context The compile context to be used.
+ * @param[in] src Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
+ * @param[out] dst Destination tensor info. Data types supported: same as @p src.
+ * @param[in] pool_info Contains pooling operation information described in @ref PoolingLayerInfo.
+ * @param[out] indices (optional) The indices of the maximal values. Data type supported: U32.
+ */
+ void configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &pool_info, ITensorInfo *indices = nullptr);
+ /** Static function to check if given info will lead to a valid configuration of @ref ClPoolingKernel
+ *
+ * @param[in] src Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
+ * @param[in] dst Destination tensor info. Data types supported: same as @p src.
+ * @param[in] pool_info Contains pooling operation information described in @ref PoolingLayerInfo.
+ * @param[in] indices (optional) The indices of the maximal values. Data type supported: U32.
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info, const ITensorInfo *indices = nullptr);
+
+ // Inherited methods overridden:
+ void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
+ BorderSize border_size() const override;
+
+public:
+ PoolingLayerInfo _pool_info;
+ DataLayout _data_layout;
+ BorderSize _border_size;
+ unsigned int _num_elems_processed_per_iteration;
+};
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute
+#endif /*ARM_COMPUTE_CL_POOLING_KERNEL_H */