From 368da83fdd7406d629e8cca64f3eb0af05437419 Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Mon, 3 Jul 2017 12:33:49 +0100 Subject: COMPMID-420, COMPMID-414 - Port CLConvolutionLayer and CLFullyConnectedLayer to use 8 bit fixed point Change-Id: I1cb1b4d7711ad7b569ee691e13a5df1b3430292b Reviewed-on: http://mpd-gerrit.cambridge.arm.com/79565 Tested-by: Kaizen Reviewed-by: Georgios Pinitas --- src/runtime/CL/functions/CLConvolutionLayer.cpp | 73 ++++++++++++------------- 1 file changed, 36 insertions(+), 37 deletions(-) (limited to 'src/runtime/CL/functions/CLConvolutionLayer.cpp') diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp index b29bf8f136..96d04dc143 100644 --- a/src/runtime/CL/functions/CLConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp @@ -41,7 +41,7 @@ CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights() void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose1xW) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output); ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output); ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); @@ -63,8 +63,9 @@ void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const const unsigned int mat_weights_cols = weights->info()->dimension(3); const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0); TensorShape shape_wr(mat_weights_cols, mat_weights_rows); - const DataType dt = weights->info()->data_type(); - TensorInfo info_wr(shape_wr, 1, dt); + const DataType dt = weights->info()->data_type(); + const int fixed_point_position = weights->info()->fixed_point_position(); + TensorInfo info_wr(shape_wr, 1, dt, fixed_point_position); _weights_reshaped.allocator()->init(info_wr); _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped); @@ -95,23 +96,27 @@ CLConvolutionLayer::CLConvolutionLayer() void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output); + ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights, output); ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2)); ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); if(biases != nullptr) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); + ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases); ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3)); ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); } + const DataType dt = input->info()->data_type(); + const int fixed_point_position = input->info()->fixed_point_position(); + _has_bias = (biases != nullptr); _are_weights_reshaped = weights_info.are_reshaped(); - // Get parameters for conv_info + // Get parameters from conv_info unsigned int stride_x = 0; unsigned int stride_y = 0; unsigned int pad_x = 0; @@ -123,8 +128,8 @@ void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weig unsigned int conv_w = 0; unsigned int conv_h = 0; - const unsigned int kernel_width = _are_weights_reshaped ? weights_info.kernel_size().first : weights->info()->dimension(0); - const unsigned int kernel_height = _are_weights_reshaped ? weights_info.kernel_size().second : weights->info()->dimension(1); + const unsigned int kernel_width = (_are_weights_reshaped) ? weights_info.kernel_size().first : weights->info()->dimension(0); + const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : weights->info()->dimension(1); std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height, conv_info); ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one"); @@ -132,9 +137,10 @@ void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weig // Check if its a "fully connected" convolution _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1)); - // Create tensor to store the reshaped weights - size_t mat_weights_cols = weights->info()->dimension(3); - size_t mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + ((_has_bias) ? 1 : 0); + unsigned int mat_weights_cols = weights->info()->dimension(3); + unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0); + + // Reshape weights if needed if(_are_weights_reshaped) { mat_weights_cols = output->info()->dimension(2); @@ -147,49 +153,48 @@ void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weig { // Create tensor to store the reshaped weights TensorShape shape_wr(mat_weights_cols, mat_weights_rows); - TensorInfo info_wr(shape_wr, 1, weights->info()->data_type()); + TensorInfo info_wr(shape_wr, 1, dt, fixed_point_position); _weights_reshaped.allocator()->init(info_wr); - _reshape_weights.configure(weights, biases, &_weights_reshaped, false); - weights = &_weights_reshaped; + _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */); } else { // Create tensor to store transposed weights - TensorShape shape_wt(mat_weights_rows * 4, static_cast(std::ceil(mat_weights_cols / 4.f))); - TensorInfo info_wt(shape_wt, 1, weights->info()->data_type()); - _weights_transposed.allocator()->init(info_wt); - _reshape_weights.configure(weights, biases, &_weights_transposed, true); - weights = &_weights_transposed; + const float transpose_width = 16.0f / input->info()->element_size(); + TensorShape shape_wt(mat_weights_rows * static_cast(transpose_width), static_cast(std::ceil(mat_weights_cols / transpose_width))); + TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position); + _weights_reshaped.allocator()->init(info_wt); + _reshape_weights.configure(weights, biases, &_weights_reshaped, true /* 1xW transpose */); } + weights = &_weights_reshaped; } + // Create tensor to store im2col reshaped inputs - const size_t mat_input_cols = mat_weights_rows; - const size_t mat_input_rows = conv_w * conv_h; - TensorShape shape_im2col = input->info()->tensor_shape(); + const unsigned int mat_input_cols = mat_weights_rows; + const unsigned int mat_input_rows = conv_w * conv_h; + TensorShape shape_im2col = input->info()->tensor_shape(); shape_im2col.set(0, mat_input_cols); shape_im2col.set(1, mat_input_rows); shape_im2col.set(2, 1); - _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, input->info()->data_type())); + _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position)); // Create tensor (interleave) to prepare input tensor for GEMM if(!_is_fully_connected_convolution) { TensorShape shape_interleaved = shape_im2col; shape_interleaved.set(0, shape_interleaved.x() * 4); - shape_interleaved.set(1, std::ceil(static_cast(shape_interleaved.y()) / 4.f)); - _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, input->info()->data_type())); + shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f)); + _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position)); } // Create GEMM output tensor TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape(); shape_gemm.set(0, mat_weights_cols); shape_gemm.set(1, mat_input_rows); - _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, input->info()->data_type())); + _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, dt, fixed_point_position)); // Configure kernels _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias); - _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h)); - if(_is_fully_connected_convolution) { _mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f); @@ -199,19 +204,13 @@ void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weig _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped); _mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f); } + _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h)); + // Allocate intermediate tensor if(!_are_weights_reshaped) { - if(!_is_fully_connected_convolution) - { - _weights_transposed.allocator()->allocate(); - } - else - { - _weights_reshaped.allocator()->allocate(); - } + _weights_reshaped.allocator()->allocate(); } - _input_im2col_reshaped.allocator()->allocate(); if(!_is_fully_connected_convolution) { -- cgit v1.2.1