COMPMID-631: Merge branches/gles_compute branch

Last commit:
commit b25c5f68042b0c81bf611d59a1bb8535e1c42497
Author: Xinghang Zhou <xinghang.zhou@arm.com>
Date:   Wed Oct 25 18:48:10 2017 +0800

    Synced validation's tolerances of GCSoftmax from cl side

Change-Id: Ibe72054205c1c8721845d679a31af7ed0a7c5cf6
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/93283
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>

1 files changed, 210 insertions, 0 deletions

diff --git a/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp
new file mode 100644
index 0000000000..ea9b3874b2
--- /dev/null
+++ b/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp
@@ -0,0 +1,210 @@
+/*
+ * Copyright (c) 2017 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/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/AccessWindowTranspose.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/GLES_COMPUTE/GCHelpers.h"
+#include "arm_compute/core/GLES_COMPUTE/GCKernelLibrary.h"
+#include "arm_compute/core/GLES_COMPUTE/IGCTensor.h"
+#include "arm_compute/core/GLES_COMPUTE/OpenGLES.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <set>
+#include <string>
+
+using namespace arm_compute;
+
+GCGEMMMatrixMultiplyKernel::GCGEMMMatrixMultiplyKernel()
+    : _input0(nullptr), _input1(nullptr), _output(nullptr)
+{
+}
+
+void GCGEMMMatrixMultiplyKernel::configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F32, DataType::F16);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
+
+    if(!is_interleaved_transposed)
+    {
+        ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1));
+    }
+
+    _input0 = input0;
+    _input1 = input1;
+    _output = output;
+
+    std::set<std::string> build_opts;
+    Window                win;
+
+    build_opts.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(1));
+    build_opts.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(1));
+    build_opts.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(1));
+    build_opts.emplace("#define COLS_A " + support::cpp11::to_string(input0->info()->dimension(0)));
+    build_opts.emplace("#define COLS_B " + support::cpp11::to_string(input1->info()->dimension(0)));
+    build_opts.emplace("#define ALPHA " + float_to_string_with_full_precision(alpha));
+
+    // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
+    if(is_interleaved_transposed)
+    {
+        switch(input0->info()->data_type())
+        {
+            case DataType::F16:
+                build_opts.emplace("#define DATA_TYPE_FP16");
+                break;
+
+            case DataType::F32:
+                build_opts.emplace("#define DATA_TYPE_FP32");
+                break;
+
+            default:
+                ARM_COMPUTE_ERROR("Current data type is not supported");
+                break;
+        }
+
+        build_opts.emplace("#define GEMM_MM_INTERLEAVED_TRANSPOSED");
+
+        // Create kernel
+        _kernel = GCKernelLibrary::get().create_kernel(("gemm_mm_interleaved_transposed"), build_opts);
+
+        // Configure window kernel
+        const unsigned int     num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(input0->info()->data_type());
+        constexpr unsigned int num_elems_processed_per_iteration_y = 4;
+
+        win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+        AccessWindowRectangle input0_access(input0->info(), 0, 0, num_elems_processed_per_iteration_y, 1, 1.f, 0.25f);
+        AccessWindowTranspose input1_access(input1->info(), 0, 0, num_elems_processed_per_iteration_x, 1, 0.f, 0.25f);
+        AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+
+        update_window_and_padding(win, input0_access, input1_access, output_access);
+
+        output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->info()->tensor_shape()));
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1));
+
+        // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor
+        unsigned int num_elems_processed_per_iteration_x;
+        unsigned int num_elems_processed_per_iteration_y;
+
+        switch(input0->info()->data_type())
+        {
+            case DataType::F16:
+                num_elems_processed_per_iteration_x = 4;
+                num_elems_processed_per_iteration_y = 1;
+                build_opts.emplace("#define DATA_TYPE_FP16");
+                break;
+
+            case DataType::F32:
+                num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(input0->info()->data_type());
+                num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->info()->dimension(1)), 4);
+                build_opts.emplace("#define DATA_TYPE_FP32");
+                break;
+
+            default:
+                ARM_COMPUTE_ERROR("Current data type is not supported");
+                break;
+        }
+
+        build_opts.emplace("#define GEMM_MM_FLOATING_POINT");
+        build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_X " + support::cpp11::to_string(num_elems_processed_per_iteration_x));
+        build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_Y " + support::cpp11::to_string(num_elems_processed_per_iteration_y));
+
+        // Create kernel
+        _kernel = GCKernelLibrary::get().create_kernel("gemm_mm_floating_point", build_opts);
+
+        win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+        AccessWindowStatic input0_access(input0->info(), 0, 0, ceil_to_multiple(input0->info()->dimension(0), num_elems_processed_per_iteration_x), ceil_to_multiple(input0->info()->dimension(1),
+                                         num_elems_processed_per_iteration_y));
+        AccessWindowStatic    input1_access(input1->info(), 0, 0, ceil_to_multiple(input1->info()->dimension(0), num_elems_processed_per_iteration_x), input1->info()->dimension(1));
+        AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+
+        update_window_and_padding(win, input0_access, input1_access, output_access);
+
+        Coordinates coord;
+        coord.set_num_dimensions(output->info()->num_dimensions());
+        output_access.set_valid_region(win, ValidRegion(coord, output->info()->tensor_shape()));
+    }
+
+    _kernel.clear_params();
+    _kernel.set_shader_params_binding_point(0);
+    IGCKernel::configure(win);
+}
+
+void GCGEMMMatrixMultiplyKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IGCKernel::window(), window);
+
+    _kernel.use();
+
+    Window slice          = window.first_slice_window_2D();
+    Window slice_matrix_b = slice;
+
+    slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
+    slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+    do
+    {
+        Window slice_b = slice;
+        // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
+        // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
+        if(_input1->info()->num_dimensions() < 3)
+        {
+            slice_b = slice_matrix_b;
+        }
+
+        unsigned int idx = 0;
+        switch(_input0->info()->data_type())
+        {
+            case DataType::F16:
+                add_2D_tensor_argument(idx, _input0, BufferParam(1, 2), slice);
+                add_2D_tensor_argument(idx, _input1, BufferParam(2, 3), slice_b);
+                add_2D_tensor_argument(idx, _output, BufferParam(3, 3), slice);
+                break;
+
+            case DataType::F32:
+                add_2D_tensor_argument(idx, _input0, BufferParam(1, 2), slice);
+                add_2D_tensor_argument(idx, _input1, BufferParam(2, 2), slice_b);
+                add_2D_tensor_argument(idx, _output, BufferParam(3, 2), slice);
+                break;
+
+            default:
+                ARM_COMPUTE_ERROR("Current data type is not supported");
+                break;
+        }
+
+        _kernel.update_shader_params();
+        enqueue(*this, slice);
+    }
+    while(window.slide_window_slice_2D(slice));
+}