COMPMID-477 - Optimized batched case in CLConvolutionLayer
Change-Id: I4ef18f49f1da0cb816aaa0762466b940792c15ed
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/84162
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
index 39526a2..684e323 100644
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
@@ -48,13 +48,13 @@
{
}
-void CLGEMMMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, float alpha)
+void CLGEMMMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, float alpha, bool is_interleaved_transposed)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output);
- if(output->info()->dimension(1) == 1)
+ if(!is_interleaved_transposed)
{
ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1));
}
@@ -72,64 +72,36 @@
_lws_hint = cl::NDRange(8, 8);
}
- std::ostringstream mm_arguments;
- mm_arguments << "-DWIDTH_MATRIX_B=" << input1->info()->dimension(0) << " ";
+ std::set<std::string> build_opts;
+ build_opts.emplace(("-DCOLS_A=" + support::cpp11::to_string(input0->info()->dimension(0))));
+ build_opts.emplace(("-DCOLS_B=" + support::cpp11::to_string(input1->info()->dimension(0))));
+
if(is_data_type_fixed_point(input0->info()->data_type()))
{
- mm_arguments << "-DALPHA=" << (input0->info()->data_type() == DataType::QS8 ?
- sqcvt_qs8_f32(alpha, input0->info()->fixed_point_position()) :
- sqcvt_qs16_f32(alpha, input0->info()->fixed_point_position()))
- << " ";
- mm_arguments << "-DFIXED_POINT_POSITION=" << input0->info()->fixed_point_position() << " ";
+ build_opts.emplace(("-DALPHA=" + support::cpp11::to_string((input0->info()->data_type() == DataType::QS8 ?
+ sqcvt_qs8_f32(alpha, input0->info()->fixed_point_position()) :
+ sqcvt_qs16_f32(alpha, input0->info()->fixed_point_position())))));
+
+ build_opts.emplace(("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input0->info()->fixed_point_position())));
}
else
{
- mm_arguments << "-DALPHA=" << alpha << " ";
+ build_opts.emplace(("-DALPHA=" + float_to_string_with_full_precision(alpha)));
}
- std::set<std::string> build_opts;
- // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
- if(output->info()->dimension(1) == 1)
+ if(is_interleaved_transposed)
{
- mm_arguments << "-DWIDTH_VECTOR_A=" << input0->info()->dimension(0) << " ";
- build_opts.emplace(mm_arguments.str());
-
- // Create kernel
- std::string data_type_name = lower_string(string_from_data_type(input0->info()->data_type()));
- _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(("gemm_vm_" + data_type_name), build_opts));
-
- // Configure window kernel
- const unsigned int num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(input0->info()->data_type());
-
- Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x));
-
- AccessWindowStatic input0_access(input0->info(), 0, 0, input0->info()->tensor_shape().x(), 1);
- AccessWindowHorizontal input1_access(input1->info(), 0, num_elems_processed_per_iteration_x);
- AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration_x);
-
- 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()));
-
- ICLKernel::configure(win);
- }
- else
- {
- build_opts.emplace(mm_arguments.str());
-
// Create kernel
std::string data_type_name = lower_string(string_from_data_type(input0->info()->data_type()));
if(data_type_name == "f32")
{
GPUTarget arch_target = get_arch_from_target(get_target());
- _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemm_mm_f32_" + string_from_target(arch_target), build_opts));
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemm_mm_interleaved_transposed_f32_" + string_from_target(arch_target), build_opts));
}
else
{
- _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemm_mm_" + data_type_name, build_opts));
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemm_mm_interleaved_transposed_" + data_type_name, build_opts));
}
// Configure window kernel
@@ -148,6 +120,44 @@
ICLKernel::configure(win);
}
+ else // The input tensors have not been reshaped
+ {
+ ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1));
+
+ // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor
+ const unsigned int num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(input0->info()->data_type());
+ const unsigned int num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->info()->dimension(1)), 4);
+
+ build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input0->info()->data_type())));
+ build_opts.emplace(("-DNUM_ELEMS_PROCESSED_PER_THREAD_X=" + support::cpp11::to_string(num_elems_processed_per_iteration_x)));
+ build_opts.emplace(("-DNUM_ELEMS_PROCESSED_PER_THREAD_Y=" + support::cpp11::to_string(num_elems_processed_per_iteration_y)));
+
+ // Create kernel
+ if(is_data_type_fixed_point(input0->info()->data_type()))
+ {
+ std::string kernel_name = "gemm_mm_" + lower_string(string_from_data_type(input0->info()->data_type()));
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel((kernel_name), build_opts));
+ }
+ else
+ {
+ std::string kernel_name = "gemm_mm_floating_point";
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel((kernel_name), build_opts));
+ }
+
+ Window 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, input0->info()->dimension(0), 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()));
+
+ ICLKernel::configure(win);
+ }
}
void CLGEMMMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue &queue)
@@ -157,9 +167,9 @@
Window slice = window.first_slice_window_2D();
Window slice_matrix_b = slice;
- slice_matrix_b.set(Window::DimX, Window::Dimension(0, _input1->info()->dimension(0), 1));
- slice_matrix_b.set(Window::DimY, Window::Dimension(0, _input1->info()->dimension(1), 1));
- slice_matrix_b.set(Window::DimZ, Window::Dimension(0, 1, 1));
+
+ slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
+ slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
do
{