| /* |
| * Copyright 2024 Google LLC |
| * |
| * Licensed under the Apache License, Version 2.0 (the "License"); |
| * you may not use this file except in compliance with the License. |
| * You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| // Convolution based on Kelvin ops |
| // Data types: input: s8, filter: s8, bias: s32 |
| |
| #include "tflm/opt/conv_s8.h" |
| |
| #include <algorithm> |
| |
| #include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h" |
| #include "tflm/opt/conv_util.h" |
| |
| namespace kelvin::opt { |
| namespace { |
| void ConvS8Generic( |
| const tflite::ConvParams& params, const int32_t* output_multiplier, |
| const int32_t* output_shift, const tflite::RuntimeShape& input_shape, |
| const int8_t* input_data, const tflite::RuntimeShape& filter_shape, |
| const int8_t* filter_data, const tflite::RuntimeShape& bias_shape, |
| const int32_t* bias_data, const tflite::RuntimeShape& output_shape, |
| int8_t* output_data) { |
| const auto batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const auto stride_width = params.stride_width; |
| const auto stride_height = params.stride_height; |
| const auto dilation_width_factor = params.dilation_width_factor; |
| const auto dilation_height_factor = params.dilation_height_factor; |
| const auto pad_width = params.padding_values.width; |
| const auto pad_height = params.padding_values.height; |
| const auto input_height = input_shape.Dims(1); |
| const auto input_width = input_shape.Dims(2); |
| const auto input_depth = input_shape.Dims(3); |
| const auto input_offset = params.input_offset; |
| const auto filter_height = filter_shape.Dims(1); |
| const auto filter_width = filter_shape.Dims(2); |
| const auto filter_depth = filter_shape.Dims(3); |
| const auto output_height = output_shape.Dims(1); |
| const auto output_width = output_shape.Dims(2); |
| const auto output_depth = output_shape.Dims(3); |
| const auto output_offset = params.output_offset; |
| const auto output_activation_min = params.quantized_activation_min; |
| const auto output_activation_max = params.quantized_activation_max; |
| const auto groups = input_depth / filter_depth; |
| const auto filters_per_group = output_depth / groups; |
| |
| if (pad_width > 0 || pad_height > 0 || dilation_width_factor > 1 || |
| dilation_height_factor > 1) { |
| // use reference implementation |
| tflite::reference_integer_ops::ConvPerChannel( |
| params, output_multiplier, output_shift, input_shape, input_data, |
| filter_shape, filter_data, bias_shape, bias_data, output_shape, |
| output_data); |
| return; |
| } |
| |
| union { |
| vconv_u8_t conv; |
| uint32_t raw; |
| } cmds; |
| cmds.conv.mode = 0; |
| cmds.conv.start = 0; |
| cmds.conv.stop = 7; |
| cmds.conv.sbias1 = input_offset; |
| cmds.conv.sdata1 = true; |
| cmds.conv.sbias2 = 0; |
| cmds.conv.sdata2 = true; |
| |
| // Zero out accumulators. |
| vdup_b_x(v0, 0); |
| acset_v(ACC, v0); |
| vdup_b_x_m(ACC0, 0); |
| for (int batch = 0; batch < batches; ++batch) { |
| for (int out_y = 0; out_y < output_height; ++out_y) { |
| const int in_y_origin = (out_y * stride_height) - pad_height; |
| for (int out_x = 0; out_x < output_width; /*out_x += 32*/ ++out_x) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| for (int out_channel = 0; out_channel < output_depth; ++out_channel) { |
| auto group = out_channel / filters_per_group; |
| |
| for (int filter_y = 0; filter_y < filter_height; ++filter_y) { |
| const int in_y = in_y_origin + dilation_height_factor * filter_y; |
| const int in_x = in_x_origin + dilation_width_factor * 0; |
| |
| // Zero padding by omitting the areas outside the image. |
| const bool is_point_inside_image = |
| (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && |
| (in_y < input_height); |
| if (!is_point_inside_image) { |
| continue; |
| } |
| |
| int q = filter_width * filter_depth; |
| for (int i = 0; i < q; i += 32) { |
| int count = std::min(q - i, 32); |
| count = std::min( |
| count, static_cast<int>((input_width - in_x) * filter_depth)); |
| int input_offset = tflite::Offset(input_shape, batch, in_y, in_x, |
| group * filter_depth) + |
| i; |
| vdup_w_x_m(vm0, 0); |
| vdup_w_x_m(vm1, 0); |
| vld_b_l_xx(INA0, &input_data[input_offset], count); |
| int filter_offset = |
| tflite::Offset(filter_shape, out_channel, filter_y, 0, 0) + i; |
| vdup_w_x_m(FLTA0, 0); |
| vdup_w_x_m(FLTA4, 0); |
| if (count > 0) { |
| vld_b_l_xx(FLTA0, &filter_data[filter_offset], |
| std::min(count, 4)); |
| } |
| if (count > 4) { |
| vld_b_l_xx(FLTA1, &filter_data[filter_offset + 4], |
| std::min(count - 4, 4)); |
| } |
| if (count > 8) { |
| vld_b_l_xx(FLTA2, &filter_data[filter_offset + 8], |
| std::min(count - 8, 4)); |
| } |
| if (count > 12) { |
| vld_b_l_xx(FLTA3, &filter_data[filter_offset + 12], |
| std::min(count - 12, 4)); |
| } |
| if (count > 16) { |
| vld_b_l_xx(FLTA4, &filter_data[filter_offset + 16], |
| std::min(count - 16, 4)); |
| } |
| if (count > 20) { |
| vld_b_l_xx(FLTA5, &filter_data[filter_offset + 20], |
| std::min(count - 20, 4)); |
| } |
| if (count > 24) { |
| vld_b_l_xx(FLTA6, &filter_data[filter_offset + 24], |
| std::min(count - 24, 4)); |
| } |
| if (count > 28) { |
| vld_b_l_xx(FLTA7, &filter_data[filter_offset + 28], |
| std::min(count - 28, 4)); |
| } |
| aconv_vxv(ACC, INA0, cmds, FLTA0); |
| } |
| } |
| vcget(ACC); |
| vadd_w_vx_m(ACC0, ACC0, bias_data[out_channel]); |
| vsll_w_vx_m(ACC0, ACC0, LEFT_SHIFT(output_shift[out_channel])); |
| vdmulh_w_r_vx_m(ACC0, ACC0, output_multiplier[out_channel]); |
| vsha_w_r_vx_m(ACC0, ACC0, RIGHT_SHIFT(output_shift[out_channel])); |
| vadd_w_vx_m(ACC0, ACC0, output_offset); |
| vmin_w_vx_m(ACC0, ACC0, output_activation_max); |
| vmax_w_vx_m(ACC0, ACC0, output_activation_min); |
| vsraqs_b_vx(OUT0, ACC0, 0); |
| size_t output_offset = |
| tflite::Offset(output_shape, batch, out_y, out_x, out_channel); |
| vst_b_l_xx(OUT0, &output_data[output_offset], 1); |
| } |
| } |
| } |
| } |
| } |
| } // namespace |
| |
| void ConvS8(const tflite::ConvParams& params, const int32_t* output_multiplier, |
| const int32_t* output_shift, |
| const tflite::RuntimeShape& input_shape, const int8_t* input_data, |
| const tflite::RuntimeShape& filter_shape, const int8_t* filter_data, |
| const tflite::RuntimeShape& bias_shape, const int32_t* bias_data, |
| const tflite::RuntimeShape& output_shape, int8_t* output_data) { |
| const auto batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const auto stride_width = params.stride_width; |
| const auto stride_height = params.stride_height; |
| const auto dilation_width_factor = params.dilation_width_factor; |
| const auto dilation_height_factor = params.dilation_height_factor; |
| const auto pad_width = params.padding_values.width; |
| const auto pad_height = params.padding_values.height; |
| const auto input_width = input_shape.Dims(2); |
| const auto input_depth = input_shape.Dims(3); |
| const auto filter_height = filter_shape.Dims(1); |
| const auto filter_width = filter_shape.Dims(2); |
| const auto filter_depth = filter_shape.Dims(3); |
| const auto output_width = output_shape.Dims(2); |
| const auto output_depth = output_shape.Dims(3); |
| |
| #define RUN_KERNEL(kernel) {\ |
| kernel(\ |
| params, output_multiplier, output_shift, input_shape, input_data,\ |
| filter_shape, filter_data, bias_shape, bias_data, output_shape,\ |
| output_data);\ |
| return; \ |
| } |
| |
| // special case of filter size 1x1 |
| if (filter_height == 1 && filter_width == 1 && stride_height == 1 && |
| stride_width == 1 && dilation_height_factor == 1 && |
| dilation_width_factor == 1 && pad_height == 0 && pad_width == 0 && |
| (input_depth == filter_depth)) { |
| if ((output_depth % 8) == 0 && (input_depth % 32) == 0) { |
| RUN_KERNEL(kelvin::opt::ConvS8K1x1D32); |
| } |
| |
| // TODO: Relax this kernel for all output_depths |
| if ((output_depth < 8) && (input_depth % 32) == 0) { |
| RUN_KERNEL(kelvin::opt::ConvS8K1x1D32); |
| } |
| |
| if ((output_depth % 16) == 0 && (input_depth == 16)) { |
| RUN_KERNEL(kelvin::opt::ConvS8K1x1D16); |
| } |
| } |
| |
| if (input_depth == 1 && filter_width == 5 && filter_height == 5 && |
| output_depth == 24) { |
| RUN_KERNEL(kelvin::opt::ConvPerChannelD1OD24_5x5); |
| } |
| |
| // special case of filter_depth = 4n, stride 2 and min width |
| if (dilation_width_factor == 1 && dilation_height_factor == 1 && |
| stride_width == 2 && stride_height == 2 && filter_depth % 4 == 0 && |
| output_depth >= 8 && output_width >= 8 && pad_width <= 1) { |
| RUN_KERNEL(kelvin::opt::ConvS8W8D4); |
| } |
| |
| // special case of filter_depth = 4n |
| if (dilation_width_factor == 1 && dilation_height_factor == 1 && |
| stride_width <= 2 && stride_height <= 2 && filter_depth % 4 == 0 && |
| output_depth >= 8 && output_width >= 8 && pad_width <= 1) { |
| RUN_KERNEL(kelvin::opt::ConvS8D4); |
| } |
| |
| // special case of filter depth = 32n |
| if (dilation_width_factor == 1 && dilation_height_factor == 1 && |
| stride_width <= 2 && stride_height <= 2 && filter_depth % 32 == 0) { |
| RUN_KERNEL(kelvin::opt::ConvS8D32); |
| } |
| |
| // special case of filter size 48x3x1x48 |
| if (batches == 1 && filter_height == 3 && filter_width == 1 && |
| input_width == 1 && input_depth == 48 && output_depth == 48 && |
| stride_height == 1 && stride_width == 1 && dilation_height_factor == 1 && |
| dilation_width_factor == 1 && pad_height == 0 && pad_width == 0) { |
| RUN_KERNEL(kelvin::opt::ConvS8K3x1D48); |
| } |
| |
| if (input_depth == 1 && ((output_depth % 4) == 0)) { |
| RUN_KERNEL(kelvin::opt::ConvPerChannelD1); |
| } |
| |
| RUN_KERNEL(ConvS8Generic); |
| } |
| |
| } // namespace kelvin::opt |