This issue tracks the implementation of unified CL Kernels for Binary LOGICAL OPS (AND & OR).
Unified CL kernel can be implemented.
This patch is an implementation of unified CL Kernel for Binary LOGICAL OPS (AND & OR).
Signed-off-by: prasannar <prasanna.r@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_CLBINARYLOGICALOPKERNEL_H__
+#define __ARM_COMPUTE_CLBINARYLOGICALOPKERNEL_H__
+
+#include "arm_compute/core/CL/ICLKernel.h"
+#include "arm_compute/core/TypesEx.h"
+
+namespace arm_compute
+{
+class ICLTensor;
+
+/** OpenCL kernel to return truth values of two input tensors for Binary Logical Op*/
+class CLBinaryLogicalOpKernel : public ICLKernel
+{
+public:
+ /** Default constructor */
+ CLBinaryLogicalOpKernel();
+ /** Prevent instances of this class from being copied (As this class contains pointers). */
+ CLBinaryLogicalOpKernel(const CLBinaryLogicalOpKernel &) = delete;
+ /** Prevent instances of this class from being copied (As this class contains pointers). */
+ CLBinaryLogicalOpKernel &operator=(const CLBinaryLogicalOpKernel &) = delete;
+ /** Allow instances of this class to be moved */
+ CLBinaryLogicalOpKernel(CLBinaryLogicalOpKernel &&) = default;
+ /** Allow instances of this class to be moved */
+ CLBinaryLogicalOpKernel &operator=(CLBinaryLogicalOpKernel &&) = 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,
+ LogicalOperation op);
+
+ // 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_CLBINARYLOGICALOPKERNEL_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_CLBINARYLOGICALOP_H__
+#define __ARM_COMPUTE_CLBINARYLOGICALOP_H__
+
+#include "arm_compute/runtime/CL/ICLSimpleFunction.h"
+#include "arm_compute/core/TypesEx.h"
+
+namespace arm_compute
+{
+class ICLTensor;
+
+class CLBinaryLogicalOp : public ICLSimpleFunction
+{
+public:
+ /** Initialise the function's source and destination.
+ *
+ * @param[in] input1 Source tensor1. Data types supported: U8, QASYMM8.
+ * @param[in] input2 Source tensor2. Data types supported: U8 QASYMM8.
+ * @param[out] output Output tensor. Data types supported: U8, QASYMM8.
+ *
+ */
+ void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, LogicalOperation op);
+};
+
+} // namespace arm_compute
+#endif /*__ARM_COMPUTE_CLBINARYLOGICALOP_H__ */
{"l2_normalize", "l2_normalize.cl"},
{"lktracker_stage0", "optical_flow_pyramid_lk.cl"},
{"lktracker_stage1", "optical_flow_pyramid_lk.cl"},
+ {"logical_op", "binary_logical_op.cl"},
{"magnitude_phase", "magnitude_phase.cl"},
{"mean_stddev_accumulate", "mean_stddev.cl"},
{"minmax", "minmaxloc.cl"},
#include "./cl_kernels/helpers_asymm.hembed"
},
{
+ "binary_logical_op.cl",
+#include "./cl_kernels/binary_logical_op.clembed"
+ },
+ {
"neg_tensor.cl",
#include "./cl_kernels/neg_tensor.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"
+
+#if defined(OP_CODE)
+/** returns truth value of the two input tensors for BINARY LOGICAL OP.
+ * where BINARY LOGICAL OP can be AND, OR.
+ *
+ * @attention Data type can be passed using the -DDATA_TYPE_IN compile flag, e.g. -DDATA_TYPE_IN=uchar
+ * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16
+ *
+ * @param[in] input1_ptr Pointer to the source tensor.
+ * @param[in] input1_stride_x Stride of the source tensor 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 tensor 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 tensor
+ *
+ * @param[in] input2_ptr Pointer to the source tensor.
+ * @param[in] input2_stride_x Stride of the source tensor 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 tensor 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 tensor
+ *
+ * @param[out] output_ptr Pointer to the destination tensor.
+ * @param[in] output_stride_x Stride of the destination tensor 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 tensor 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 destination tensor in Z dimension (in bytes)
+ * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)
+ */
+__kernel void binary_logical_op(
+ 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);
+
+#if OP_CODE == 0 //LOGICAL AND
+ VSTORE(VEC_SIZE)
+ (CONVERT(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)),
+ 0, (__global DATA_TYPE *)output.ptr);
+#elif OP_CODE == 1 //LOGICAL OR
+ VSTORE(VEC_SIZE)
+ (CONVERT(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)),
+ 0, (__global DATA_TYPE *)output.ptr);
+#endif
+}
+#endif //if defined(OP_CODE)
--- /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/CLBinaryLogicalOpKernel.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;
+}
+
+CLBinaryLogicalOpKernel::CLBinaryLogicalOpKernel()
+ : _input1(nullptr), _input2(nullptr), _output(nullptr)
+{
+}
+
+Status validate_parameters(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::U8, DataType::QASYMM8);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::QASYMM8);
+
+ 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::U8,
+ DataType::QASYMM8);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(
+ detail::have_different_dimensions(out_shape, output->tensor_shape(), 0),
+ "Wrong shape for output");
+ }
+ return Status{};
+}
+
+void CLBinaryLogicalOpKernel::configure(const ICLTensor *input1, const ICLTensor *input2,
+ ICLTensor *output, LogicalOperation op)
+{
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_parameters(input1->info(), input2->info(), output->info()));
+
+ _input1 = input1;
+ _input2 = input2;
+ _output = output;
+
+ // Create kernel
+ std::string kernel_name = "logical_op";
+ std::set<std::string> build_opts;
+ build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input1->info()->data_type())));
+
+ int op_code;
+ switch (op)
+ {
+ case LogicalOperation::AND:
+ op_code = 0;
+ break;
+ case LogicalOperation::OR:
+ op_code = 1;
+ break;
+ }
+
+ build_opts.emplace(("-DOP_CODE=" + support::cpp11::to_string(op_code)));
+ build_opts.emplace(
+ ("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)));
+
+ _kernel =
+ static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel(kernel_name, 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;
+
+ 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);
+
+ bool window_changed = 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(win);
+}
+
+void CLBinaryLogicalOpKernel::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 CLBinaryLogicalOpKernel::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/CLBinaryLogicalOp.h"
+
+#include "arm_compute/core/CL/kernels/CLBinaryLogicalOpKernel.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+
+using namespace arm_compute;
+
+void CLBinaryLogicalOp::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output,
+ LogicalOperation op)
+{
+ auto k = arm_compute::support::cpp14::make_unique<CLBinaryLogicalOpKernel>();
+ k->configure(input1, input2, output, op);
+ _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);
+ }
+ }
+}
projects / platform / core / ml / nnfw.git / commitdiff