COMPMID-1043: Rework GCGEMMMatrixMultiplyKernel interface and allow auto initialization of the tensors
This patch also:
- removes support for already reshaped weights in GCConvolutionLayer
- makes GCConvolutionLayer similar to CLGEMMConvolutionLayer
- enables usage of the GCGEMM function in GCConvolution instead of calling the
GEMM kernels directly
Change-Id: I3e4a64335555e86e18585d38d8fda4bfdb44e265
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/127696
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp
index a5f09e8..b4bb547 100644
--- a/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017, 2018 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -31,37 +31,180 @@
#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/TensorInfo.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 "arm_compute/core/utils/misc/ShapeCalculator.h"
#include <set>
#include <string>
using namespace arm_compute;
using namespace arm_compute::gles_compute;
+using namespace arm_compute::misc::shape_calculator;
+
+namespace
+{
+using ElementsProcessed = Steps;
+
+inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info)
+{
+ ARM_COMPUTE_UNUSED(reshape_info);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
+
+ if(!is_interleaved_transposed)
+ {
+ ARM_COMPUTE_ERROR_ON(input0->dimension(0) != input1->dimension(1));
+
+ if(output->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != output->dimension(0));
+ ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != output->dimension(1));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
+ }
+ }
+ else
+ {
+ const int m = reshape_info.m();
+ const int n = reshape_info.n();
+ const int k = reshape_info.k();
+ const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width();
+ const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
+
+ TensorShape tensor_shape0{ input0->tensor_shape() };
+ tensor_shape0.set(0, k);
+ tensor_shape0.set(1, m);
+
+ TensorShape tensor_shape1{ input1->tensor_shape() };
+ tensor_shape1.set(0, n);
+ tensor_shape1.set(1, k);
+
+ const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0);
+ const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1);
+
+ const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(compute_interleaved_shape(tensor_info0, mult_interleave4x4_height));
+ const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_transpose1xW_with_element_size_shape(tensor_info1, mult_transpose1xW_width));
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);
+
+ if(output->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != static_cast<size_t>(n));
+ ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != static_cast<size_t>(m));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, output);
+ }
+ }
+
+ return Status{};
+}
+
+inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output,
+ bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info,
+ GPUTarget gpu_target, ElementsProcessed &num_elements_processed)
+{
+ ARM_COMPUTE_UNUSED(gpu_target);
+
+ // Output tensor auto inizialitation if not yet initialized
+ TensorShape tensor_shape{ input0->tensor_shape() };
+ tensor_shape.set(0, is_interleaved_transposed ? reshape_info.n() : input1->dimension(0));
+ tensor_shape.set(1, is_interleaved_transposed ? reshape_info.m() : input0->dimension(1));
+
+ auto_init_if_empty(*output, input0->clone()->set_tensor_shape(tensor_shape));
+
+ bool window_changed = false;
+ Window win{};
+
+ const DataType data_type = input0->data_type();
+ unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
+ unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
+
+ if(is_interleaved_transposed)
+ {
+ // Configure window kernel
+ num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(data_type);
+ num_elems_processed_per_iteration_y = 4;
+
+ win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+ AccessWindowRectangle input0_access(input0, 0, 0, num_elems_processed_per_iteration_y, 1, 1.f, 0.25f);
+ AccessWindowTranspose input1_access(input1, 0, 0, num_elems_processed_per_iteration_x, 1, 0.f, 0.25f);
+ AccessWindowRectangle output_access(output, 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(), output->tensor_shape()));
+ }
+ else // The input tensors have not been reshaped
+ {
+ // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor
+
+ switch(data_type)
+ {
+ case DataType::F16:
+ num_elems_processed_per_iteration_x = 4;
+ num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 4);
+ break;
+
+ case DataType::F32:
+ num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(data_type);
+ num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 4);
+ break;
+
+ default:
+ ARM_COMPUTE_ERROR("Current data type is not supported");
+ break;
+ }
+
+ win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+ AccessWindowStatic input0_access(input0, 0, 0, ceil_to_multiple(input0->dimension(0), 8), ceil_to_multiple(input0->dimension(1), num_elems_processed_per_iteration_y));
+ AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1));
+ AccessWindowRectangle output_access(output, 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->num_dimensions());
+ output_access.set_valid_region(win, ValidRegion(coord, output->tensor_shape()));
+ }
+
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, win);
+}
+} // namespace
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)
+void GCGEMMMatrixMultiplyKernel::configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info)
{
- 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);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
- if(!is_interleaved_transposed)
- {
- ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1));
- }
+ // Perform validate step
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info));
_input0 = input0;
_input1 = input1;
_output = output;
+ ElementsProcessed num_elements_processed{};
+
+ // Configure kernel window
+ auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info, GPUTarget::UNKNOWN, num_elements_processed);
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ IGCKernel::configure(win_config.second);
+
+ // Create build options
std::set<std::string> build_opts;
+ std::string kernel_name;
Window win;
build_opts.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(1));
@@ -74,6 +217,12 @@
// Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
if(is_interleaved_transposed)
{
+ const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width();
+ const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
+
+ build_opts.emplace("#define MULT_TRANSPOSE1XW_WIDTH " + support::cpp11::to_string(mult_transpose1xW_width));
+ build_opts.emplace("#define MULT_INTERLEAVE4X4_HEIGHT " + support::cpp11::to_string(mult_interleave4x4_height));
+
switch(input0->info()->data_type())
{
case DataType::F16:
@@ -91,56 +240,20 @@
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(), output->info()->tensor_shape()));
+ kernel_name = "gemm_mm_interleaved_transposed";
}
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:
build_opts.emplace("#define DATA_TYPE_FP16");
-
-#define MM_PROCESS_4X_OPTIMIZED
-
-#if defined(MM_PROCESS_4X)
- num_elems_processed_per_iteration_x = 4;
- num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->info()->dimension(1)), 4);
- build_opts.emplace("#define MM_PROCESS_4X");
-#elif defined(MM_PROCESS_4X_OPTIMIZED) /* MM_PROCESS_4X */
- num_elems_processed_per_iteration_x = 4;
- num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->info()->dimension(1)), 4);
build_opts.emplace("#define MM_PROCESS_4X_OPTIMIZED");
-#elif defined(MM_PROCESS_8X) /* MM_PROCESS_4X */
- num_elems_processed_per_iteration_x = 8;
- num_elems_processed_per_iteration_y = 1;
- build_opts.emplace("#define MM_PROCESS_8X");
-#endif /* MM_PROCESS_4X */
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;
@@ -150,31 +263,31 @@
}
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));
+ build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_X " + support::cpp11::to_string(num_elements_processed.x()));
+ build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_Y " + support::cpp11::to_string(num_elements_processed.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));
-
-#if defined(MM_PROCESS_4X_OPTIMIZED)
- AccessWindowStatic input0_access(input0->info(), 0, 0, ceil_to_multiple(input0->info()->dimension(0), 8), ceil_to_multiple(input0->info()->dimension(1), num_elems_processed_per_iteration_y));
-#else /* MM_PROCESS_4X_OPTIMIZED */
- 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));
-#endif /* MM_PROCESS_4X_OPTIMIZED */
- 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_name = "gemm_mm_floating_point";
}
- IGCKernel::configure(win);
+ // Create kernel
+ _kernel = GCKernelLibrary::get().create_kernel(kernel_name, build_opts);
+}
+
+Status GCGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved_transposed,
+ const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target)
+{
+ ARM_COMPUTE_UNUSED(alpha);
+ ElementsProcessed num_elements_processed{};
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, is_interleaved_transposed, reshape_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
+ input1->clone().get(),
+ output->clone().get(),
+ is_interleaved_transposed,
+ reshape_info,
+ gpu_target,
+ num_elements_processed)
+ .first);
+ return Status{};
}
void GCGEMMMatrixMultiplyKernel::run(const Window &window)