From ebf6b8a00b77ea796d877bc1d0e6850c055318a6 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Mon, 24 Sep 2018 16:31:08 +0100 Subject: COMPMID-1518: Add support for GEMM3D in CLGEMMLowpMatrixMultiplyCore Change-Id: Ib14ac821ee5d4aff80bd602cd3e76e7018abb5e6 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/150268 Tested-by: bsgcomp Reviewed-by: Isabella Gottardi Reviewed-by: Michele DiGiorgio --- .../CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp | 164 ++++++++++++++++----- 1 file changed, 127 insertions(+), 37 deletions(-) (limited to 'src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp') diff --git a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp index 9adf95fa33..cf66ebd5fe 100644 --- a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp +++ b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp @@ -59,17 +59,12 @@ Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_interleaved_transposed && reshape_info.reinterpret_input_as_3d(), "The input tensor cannot be reinterpreted as 3D if is_interleaved_transposed is true"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 2 && reshape_info.reinterpret_input_as_3d(), "The input1 tensor cannot have more than 2 dimensions if input0 has to be reinterpreted as 3D"); if(!is_interleaved_transposed) { ARM_COMPUTE_RETURN_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_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); - } } else { @@ -95,43 +90,82 @@ Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, 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(n)); - ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != static_cast(m)); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); - } + if(output->total_size() != 0) + { + const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_info)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); } return Status{}; } std::pair validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, bool is_interleaved_transposed, - ElementsProcessed &num_elements_processed) + const GEMMReshapeInfo &reshape_info, ElementsProcessed &num_elements_processed) { unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0]; unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1]; + bool reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d(); + bool reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 1); Window win{}; + Window win_out{}; bool window_changed = false; + // In case both input and output have to be reinterpreted as 3D tensors, + // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. + if(reinterpret_input_as_3d == reinterpret_output_as_3d) + { + reinterpret_input_as_3d = false; + reinterpret_output_as_3d = false; + } + + // Output tensor auto inizialitation if not yet initialized + auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_info))); + + TensorInfo tmp_info(*output); + + if(reinterpret_output_as_3d) + { + // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM, + // the window needs to be constructed on the 2D collapsed version of the tensor + TensorShape tmp_shape(output->tensor_shape()); + tmp_shape.collapse(2U, 1U); + tmp_info.set_tensor_shape(tmp_shape); + } + // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication if(is_interleaved_transposed) { + // reinterpret_input_as_3d is not supported if is_interleaved_transposed is set + ARM_COMPUTE_ERROR_ON(reshape_info.reinterpret_input_as_3d()); + // Configure kernel window num_elems_processed_per_iteration_x = 4; 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)); + // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor + // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic + const int m = reshape_info.m(); + const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y; + + win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + win_out = 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); + AccessWindowStatic input1_access(input1, 0, 0, + ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), + ceil_to_multiple(input1->dimension(1), num_elems_processed_per_iteration_y)); + AccessWindowStatic output_access(output, 0, 0, + ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration_x), + output->dimension(1) + bottom_pad); - window_changed = update_window_and_padding(win, input0_access, input1_access, output_access); + window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop + update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor - output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->tensor_shape())); + output_access.set_valid_region(win_out, ValidRegion(Coordinates(0, 0), output->tensor_shape())); } else { @@ -139,27 +173,42 @@ std::pair validate_and_configure_window(ITensorInfo *input0, ITe num_elems_processed_per_iteration_x = 4; num_elems_processed_per_iteration_y = std::min(static_cast(output->dimension(1)), 5); + // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor + // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic + const int m = reinterpret_input_as_3d ? input0->tensor_shape()[1] * input0->tensor_shape()[2] : input0->tensor_shape()[1]; + const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y; + // Configure window - win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - AccessWindowStatic input0_access(input0, 0, 0, input0->dimension(0), 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); + AccessWindowStatic input0_access(input0, 0, 0, input0->dimension(0), input0->dimension(1) + bottom_pad); + AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1)); + AccessWindowStatic output_access(output, 0, 0, + ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration_x), + output->dimension(1) + bottom_pad); - window_changed = update_window_and_padding(win, input0_access, input1_access, output_access); + window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop + update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor Coordinates coord; coord.set_num_dimensions(output->num_dimensions()); output_access.set_valid_region(win, ValidRegion(coord, output->tensor_shape())); } + // Collapse along the Z direction + // This collapse needs to be here in order to tune the Z dimension of LWS + Window collapsed = win; + const unsigned int dimension_to_collapse = std::min(static_cast(output->num_dimensions()), 2u); + collapsed = win.collapse(win, dimension_to_collapse); + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_pair(err, win); + return std::make_pair(err, collapsed); } } // namespace CLGEMMLowpMatrixMultiplyKernel::CLGEMMLowpMatrixMultiplyKernel() - : _input0(nullptr), _input1(nullptr), _output(nullptr) + : _input0(nullptr), _input1(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false) { } @@ -176,9 +225,23 @@ void CLGEMMLowpMatrixMultiplyKernel::configure(const ICLTensor *input0, const IC ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info)); - _input0 = input0; - _input1 = input1; - _output = output; + _input0 = input0; + _input1 = input1; + _output = output; + _reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d(); + _reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 1); + + // In case both input and output have to be reinterpreted as 3D tensors, + // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. + if(_reinterpret_input_as_3d == _reinterpret_output_as_3d) + { + _reinterpret_input_as_3d = false; + _reinterpret_output_as_3d = false; + } + + // Check if we need to slide the matrix B + const unsigned int num_dimensions_input0 = _reinterpret_input_as_3d ? _input0->info()->num_dimensions() - 1 : _input0->info()->num_dimensions(); + _slide_matrix_b = (_input1->info()->num_dimensions() >= num_dimensions_input0); ElementsProcessed num_elements_processed{}; @@ -186,15 +249,21 @@ void CLGEMMLowpMatrixMultiplyKernel::configure(const ICLTensor *input0, const IC GPUTarget arch_target = get_arch_from_target(get_target()); // Configure kernel window - auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, num_elements_processed); + auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info, num_elements_processed); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()); // Create build options - CLBuildOptions build_opts; std::string kernel_name(" "); + CLBuildOptions build_opts; + build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D"); + build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D"); + build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1))); + build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(2))); + build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2))); + if(is_interleaved_transposed) { const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width(); @@ -225,6 +294,8 @@ void CLGEMMLowpMatrixMultiplyKernel::configure(const ICLTensor *input0, const IC // Set config_id for enabling LWS tuning _config_id = "gemmlowp_"; _config_id += (is_interleaved_transposed ? "reshaped_" : ""); + _config_id += (_reinterpret_input_as_3d ? "3di_" : ""); + _config_id += (_reinterpret_output_as_3d ? "3do_" : ""); _config_id += lower_string(string_from_data_type(input0->info()->data_type())); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(1)); @@ -242,6 +313,7 @@ Status CLGEMMLowpMatrixMultiplyKernel::validate(const ITensorInfo *input0, const input1->clone().get(), output->clone().get(), is_interleaved_transposed, + reshape_info, num_elements_processed) .first); @@ -253,18 +325,33 @@ void CLGEMMLowpMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); - Window slice = window.first_slice_window_2D(); + Window slice = window.first_slice_window_3D(); 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)); + + if(_reinterpret_input_as_3d) + { + // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor + const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3; + const unsigned int total_cross_plane_pad = _input0->info()->padding().top + _input0->info()->padding().bottom; + _kernel.setArg(idx0, static_cast(total_cross_plane_pad)); + } + + if(_reinterpret_output_as_3d) + { + // Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor + const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0); + const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom; + _kernel.setArg(idx0, static_cast(total_cross_plane_pad)); + } 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) + if(_slide_matrix_b) { slice_b = slice_matrix_b; } @@ -273,7 +360,10 @@ void CLGEMMLowpMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue add_2D_tensor_argument(idx, _input0, slice); add_2D_tensor_argument(idx, _input1, slice_b); add_2D_tensor_argument(idx, _output, slice); + _kernel.setArg(idx++, static_cast(_input0->info()->strides_in_bytes()[2])); + _kernel.setArg(idx++, static_cast(_input1->info()->strides_in_bytes()[2])); + _kernel.setArg(idx++, static_cast(_output->info()->strides_in_bytes()[2])); enqueue(queue, *this, slice, lws_hint()); } - while(window.slide_window_slice_2D(slice)); + while(window.slide_window_slice_3D(slice)); } -- cgit v1.2.1