COMPMID-1716: CL Comparison operations
Adds support for Equal,NotEqual,Less,LessEqual,Greater,GreaterEqual
Change-Id: If0cdf4aae7f95c94709b195eee485f6663f45909
diff --git a/src/core/CL/kernels/CLComparisonKernel.cpp b/src/core/CL/kernels/CLComparisonKernel.cpp
new file mode 100644
index 0000000..f5f5a0f
--- /dev/null
+++ b/src/core/CL/kernels/CLComparisonKernel.cpp
@@ -0,0 +1,224 @@
+/*
+ * Copyright (c) 2018 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/CL/kernels/CLComparisonKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLValidate.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+
+#include <map>
+
+namespace arm_compute
+{
+namespace
+{
+// Create supported comparisons map
+const std::map<ComparisonOperation, std::string> supported_comparison_ops =
+{
+ { ComparisonOperation::Equal, "EQUAL" },
+ { ComparisonOperation::NotEqual, "NOTEQUAL" },
+ { ComparisonOperation::Greater, "GREATER" },
+ { ComparisonOperation::GreaterEqual, "GREATEREQUAL" },
+ { ComparisonOperation::Less, "LESS" },
+ { ComparisonOperation::LessEqual, "LESSEQUAL" },
+};
+
+int calculate_num_elems_processed_per_iteration(const ITensorInfo &input)
+{
+ return 16 / input.element_size();
+}
+
+Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, ComparisonOperation operation)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1,
+ 1,
+ DataType::U8, DataType::S8, DataType::QASYMM8,
+ DataType::U16, DataType::S16,
+ DataType::U32, DataType::S32,
+ DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2);
+ ARM_COMPUTE_RETURN_ERROR_ON(supported_comparison_ops.count(operation) == 0);
+
+ const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());
+ 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);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
+ "Wrong shape for output");
+ }
+
+ return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
+{
+ const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2);
+ const TensorShape &out_shape = broadcast_pair.first;
+ const ValidRegion &valid_region = broadcast_pair.second;
+
+ const unsigned int num_elems_processed_per_iteration = calculate_num_elems_processed_per_iteration(input1);
+
+ // Auto initialize output if not initialized
+ auto_init_if_empty(output, out_shape, 1, DataType::U8, QuantizationInfo());
+
+ Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
+ Window win_input1 = win.broadcast_if_dimension_le_one(input1);
+ Window win_input2 = win.broadcast_if_dimension_le_one(input2);
+
+ AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration);
+ AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration);
+ AccessWindowHorizontal output_access(&output, 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);
+
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, win);
+}
+} // namespace
+
+CLComparisonKernel::CLComparisonKernel()
+ : _input1(nullptr), _input2(nullptr), _output(nullptr)
+{
+}
+
+void CLComparisonKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, ComparisonOperation operation)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info(), operation));
+
+ // Configure kernel window
+ auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info());
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+
+ _input1 = input1;
+ _input2 = input2;
+ _output = output;
+
+ const std::string &operation_name = supported_comparison_ops.at(operation);
+ std::string kernel_name = "compare_" + lower_string(operation_name);
+
+ // Set kernel build options
+ 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(calculate_num_elems_processed_per_iteration(*input1->info())));
+ build_opts.emplace("-DOP=" + operation_name);
+ build_opts.emplace("-DOP_NAME=" + lower_string(operation_name));
+ if(is_data_type_quantized_asymmetric(input1->info()->data_type()))
+ {
+ build_opts.emplace("-DOFFSET_IN1=" + support::cpp11::to_string(input1->info()->quantization_info().offset));
+ build_opts.emplace("-DOFFSET_IN2=" + support::cpp11::to_string(input2->info()->quantization_info().offset));
+ build_opts.emplace("-DSCALE_IN1=" + float_to_string_with_full_precision(input1->info()->quantization_info().scale));
+ build_opts.emplace("-DSCALE_IN2=" + float_to_string_with_full_precision(input2->info()->quantization_info().scale));
+ kernel_name += "_quantized";
+ }
+
+ // Create kernel
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));
+
+ ICLKernel::configure_internal(win_config.second);
+
+ // Set config_id for enabling LWS tuning
+ _config_id = kernel_name;
+ _config_id += "_";
+ _config_id += lower_string(string_from_data_type(input1->info()->data_type()));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(output->info()->dimension(0));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(output->info()->dimension(1));
+ _config_id += lower_string(string_from_data_layout(input1->info()->data_layout()));
+}
+
+Status CLComparisonKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ComparisonOperation operation)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
+
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output, operation));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*input1->clone(), *input2->clone(), *output->clone()).first);
+
+ return Status{};
+}
+
+void CLComparisonKernel::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;
+ const bool is_vector = in_shape1.num_dimensions() == 1 || in_shape2.num_dimensions() == 1;
+ if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1 && !is_vector)
+ {
+ 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, lws_hint());
+
+ collapsed.slide_window_slice_3D(slice_input1);
+ collapsed.slide_window_slice_3D(slice_input2);
+ }
+ while(collapsed.slide_window_slice_3D(slice));
+}
+
+BorderSize CLComparisonKernel::border_size() const
+{
+ const int num_elems_processed_per_iteration = calculate_num_elems_processed_per_iteration(*_input1->info());
+
+ 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);
+}
+} // namespace arm_compute