| /* |
| * 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. |
| */ |
| |
| #include <cstdlib> |
| #include <memory> |
| |
| #include "crt/kelvin.h" |
| #include "tensorflow/lite/kernels/internal/common.h" |
| #include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h" |
| #include "tensorflow/lite/kernels/internal/runtime_shape.h" |
| #include "tensorflow/lite/kernels/internal/types.h" |
| #include "tflm/opt/opt.h" |
| #include "tflm/opt/util.h" |
| |
| namespace kelvin::opt { |
| |
| namespace { |
| constexpr int kFilterInputChannelIndex = 3; |
| constexpr int kOutputWidthIndex = 2; |
| constexpr int kOutputChannelIndex = 3; |
| |
| // Convert: input [zo][ky][kx][zi] (N,4,4,M) |
| // output [ky][kx][zi_hi=M/4][zo=8][zi_lo=4] |
| // output [3][3][16][8][4] |
| void Filter_8_H_W_M(const int8_t* input, int8_t* output, int H, int W, int M) { |
| const int8_t(&in)[8][H][W][M] = *(int8_t(*)[8][H][W][M])input; |
| int8_t(&out)[H][W][M / 4][8][4] = *(int8_t(*)[H][W][M / 4][8][4]) output; |
| assert(M >= 4); |
| for (int zo = 0; zo < 8; ++zo) { |
| for (int ky = 0; ky < H; ++ky) { |
| for (int kx = 0; kx < W; ++kx) { |
| for (int zi = 0; zi < M; ++zi) { |
| const int zi_hi = zi >> 2; // div4 |
| const int zi_lo = zi & 3; // rem4 |
| out[ky][kx][zi_hi][zo][zi_lo] = in[zo][ky][kx][zi]; |
| } |
| } |
| } |
| } |
| } |
| |
| void Swizzle(const int32_t* input, int32_t* output, int N) { |
| const int32_t(&in)[N] = *(int32_t(*)[N])input; |
| int32_t(&out)[N * 4] = *(int32_t(*)[N * 4]) output; |
| // Convert to accumulator swizzle pattern. |
| for (int i = 0; i < N / 8; ++i) { |
| int32_t* out0 = out + i * 32 + 0; |
| int32_t* out1 = out + i * 32 + 16; |
| int32_t* out2 = out + i * 32 + 8; |
| int32_t* out3 = out + i * 32 + 24; |
| for (int j = 0; j < 4; ++j) { |
| const int32_t* p_in = in + i * 8; |
| for (int k = 0; k < 2; ++k) { |
| *out0++ = *p_in++; |
| *out1++ = *p_in++; |
| *out2++ = *p_in++; |
| *out3++ = *p_in++; |
| } |
| } |
| } |
| } |
| |
| } // namespace |
| |
| void conv_per_channel_pw1_ow8_id8_filterd32( |
| 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) { |
| // Get parameters. |
| const int32_t input_offset = params.input_offset; // r = s(q - Z) |
| const int stride_width = params.stride_width; |
| const int stride_height = params.stride_height; |
| const int dilation_width_factor = params.dilation_width_factor; |
| const int dilation_height_factor = params.dilation_height_factor; |
| const int pad_width = params.padding_values.width; |
| const int pad_height = params.padding_values.height; |
| const int32_t output_offset = params.output_offset; |
| |
| // Set min and max value of the output. |
| const int32_t output_activation_min = params.quantized_activation_min; |
| const int32_t output_activation_max = params.quantized_activation_max; |
| |
| // Consistency check. |
| TFLITE_DCHECK_LE(output_activation_min, output_activation_max); |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const int input_depth = input_shape.Dims(3); |
| const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); |
| if (bias_data) { |
| TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); |
| } |
| |
| // Check dimensions of the tensors. |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| const int filter_height = filter_shape.Dims(1); |
| const int filter_width = filter_shape.Dims(2); |
| const int filter_input_depth = filter_shape.Dims(3); |
| const int groups = input_depth / filter_input_depth; |
| TFLITE_DCHECK_NE(groups, 0); |
| TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0); |
| const int filters_per_group = output_depth / groups; |
| TFLITE_DCHECK_NE(filters_per_group, 0); |
| const int output_height = output_shape.Dims(1); |
| const int output_width = output_shape.Dims(2); |
| |
| 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; |
| |
| const size_t swizzled_filter_data_size = |
| 8 * filter_height * filter_width * filter_input_depth; |
| std::unique_ptr<int8_t> swizzled_filter_data(reinterpret_cast<int8_t*>( |
| ::aligned_alloc(32, swizzled_filter_data_size))); |
| int8_t* p_swizzled_filter_data = swizzled_filter_data.get(); |
| int32_t swizzled_bias_data[32]; |
| int32_t swizzled_mult_data[32]; |
| int32_t swizzled_shift_data[32]; |
| |
| for (int out_channel = 0; out_channel + 8 <= output_depth; out_channel += 8) { |
| Filter_8_H_W_M(filter_data + (out_channel * filter_height * filter_width * |
| filter_input_depth), |
| p_swizzled_filter_data, filter_height, filter_width, |
| filter_input_depth); |
| Swizzle(bias_data + out_channel, swizzled_bias_data, 8); |
| Swizzle(output_multiplier + out_channel, swizzled_mult_data, 8); |
| Swizzle(output_shift + out_channel, swizzled_shift_data, 8); |
| vld_w_x_m(v16, swizzled_bias_data); |
| vld_w_x_m(v20, swizzled_mult_data); |
| vld_w_x_m(v24, swizzled_shift_data); |
| vrsub_w_vx_m(v24, v24, 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 + 8 <= output_width; out_x += 8) { |
| // 8x accumulators |
| vdup_w_x_m(v48, 0); |
| vdup_w_x_m(v52, 0); |
| acset_v(v48, v48); |
| for (int in_channel = 0; in_channel + 32 <= filter_input_depth; |
| in_channel += 32) { |
| for (int filter_y = 0; filter_y < filter_height; ++filter_y) { |
| const int in_y = in_y_origin + dilation_height_factor * filter_y; |
| const bool is_row_inside_input = |
| (in_y >= 0) && (in_y < input_height); |
| if (!is_row_inside_input) { |
| continue; |
| } |
| |
| for (int filter_x = 0; filter_x < filter_width; ++filter_x) { |
| int in_x[8]; |
| bool left_pad = false; |
| bool right_pad = false; |
| for (int i = 0; i < 8; ++i) { |
| const int in_x_origin = |
| ((out_x + i) * stride_width) - pad_width; |
| in_x[i] = in_x_origin + dilation_width_factor * filter_x; |
| if (in_x[i] < 0) { |
| left_pad = true; |
| } |
| if (in_x[i] >= input_width) { |
| right_pad = true; |
| } |
| } |
| |
| if (left_pad) { |
| vdup_b_x(v0, -input_offset); |
| vld_b_s_xx( |
| v1, |
| &input_data[tflite::Offset(input_shape, batch, in_y, |
| in_x[1], in_channel)], |
| input_depth * stride_width); |
| vld_b_s_xx( |
| v2, |
| &input_data[tflite::Offset(input_shape, batch, in_y, |
| in_x[2], in_channel)], |
| input_depth * stride_width); |
| vld_b_s_xx( |
| v3, |
| &input_data[tflite::Offset(input_shape, batch, in_y, |
| in_x[3], in_channel)], |
| input_depth * stride_width); |
| vld_b_s_xx_m( |
| v4, |
| &input_data[tflite::Offset(input_shape, batch, in_y, |
| in_x[4], in_channel)], |
| input_depth * stride_width); |
| } else if (right_pad) { |
| vld_b_s_xx_m( |
| v0, |
| &input_data[tflite::Offset(input_shape, batch, in_y, |
| in_x[0], in_channel)], |
| input_depth * stride_width); |
| vld_b_s_xx( |
| v4, |
| &input_data[tflite::Offset(input_shape, batch, in_y, |
| in_x[4], in_channel)], |
| input_depth * stride_width); |
| vld_b_s_xx( |
| v5, |
| &input_data[tflite::Offset(input_shape, batch, in_y, |
| in_x[5], in_channel)], |
| input_depth * stride_width); |
| vld_b_s_xx( |
| v6, |
| &input_data[tflite::Offset(input_shape, batch, in_y, |
| in_x[6], in_channel)], |
| input_depth * stride_width); |
| vdup_b_x(v7, -input_offset); |
| } else if (!left_pad && !right_pad) { |
| // Inputs |
| vld_b_s_xx_m( |
| v0, |
| &input_data[tflite::Offset(input_shape, batch, in_y, |
| in_x[0], in_channel)], |
| input_depth * stride_width); |
| vld_b_s_xx_m( |
| v4, |
| &input_data[tflite::Offset(input_shape, batch, in_y, |
| in_x[4], in_channel)], |
| input_depth * stride_width); |
| } else { |
| vdup_b_x(v0, -input_offset); |
| vdup_b_x(v7, -input_offset); |
| vld_b_s_xx_m( |
| v1, |
| &input_data[tflite::Offset(input_shape, batch, in_y, |
| in_x[1], in_channel)], |
| input_depth * stride_width); |
| vld_b_s_xx( |
| v5, |
| &input_data[tflite::Offset(input_shape, batch, in_y, |
| in_x[5], in_channel)], |
| input_depth * stride_width); |
| vld_b_s_xx( |
| v6, |
| &input_data[tflite::Offset(input_shape, batch, in_y, |
| in_x[6], in_channel)], |
| input_depth * stride_width); |
| } |
| size_t local_filter_offset = |
| (filter_y * filter_width * 8 * input_depth) + |
| (filter_x * 8 * input_depth) + (in_channel * 8); |
| int8_t* p_local_filter_start = |
| p_swizzled_filter_data + local_filter_offset; |
| vld_b_p_x_m(v8, p_local_filter_start); |
| vld_b_x_m(v12, p_local_filter_start); |
| |
| aconv_vxv(v48, v0, cmds, v8); |
| } |
| } |
| } |
| vcget(v48); |
| vadd_w_vv_m(v48, v48, v16); |
| vadd_w_vv_m(v52, v52, v16); |
| vdmulh_w_r_vv_m(v48, v48, v20); |
| vdmulh_w_r_vv_m(v52, v52, v20); |
| vsha_w_r_vv_m(v48, v48, v24); |
| vsha_w_r_vv_m(v52, v52, v24); |
| vadd_w_vx_m(v48, v48, output_offset); |
| vadd_w_vx_m(v52, v52, output_offset); |
| vmin_w_vx_m(v48, v48, output_activation_max); |
| vmin_w_vx_m(v52, v52, output_activation_max); |
| vmax_w_vx_m(v48, v48, output_activation_min); |
| vmax_w_vx_m(v52, v52, output_activation_min); |
| vsraqs_b_vx(v56, v48, 0); |
| vsraqs_b_vx(v57, v52, 0); |
| vstq_b_s_xx(v56, |
| &output_data[tflite::Offset(output_shape, batch, out_y, |
| out_x, out_channel)], |
| output_depth); |
| vstq_b_s_xx(v57, |
| &output_data[tflite::Offset(output_shape, batch, out_y, |
| out_x + 4, out_channel)], |
| output_depth); |
| } |
| } |
| } |
| } |
| } |
| |
| // Fixed-point per-channel-quantization convolution reference kernel. |
| void conv_per_channel_filterd32( |
| 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) { |
| // Get parameters. |
| const int32_t input_offset = params.input_offset; // r = s(q - Z) |
| const int stride_width = params.stride_width; |
| const int stride_height = params.stride_height; |
| const int dilation_width_factor = params.dilation_width_factor; |
| const int dilation_height_factor = params.dilation_height_factor; |
| const int pad_width = params.padding_values.width; |
| const int pad_height = params.padding_values.height; |
| const int32_t output_offset = params.output_offset; |
| |
| // Set min and max value of the output. |
| const int32_t output_activation_min = params.quantized_activation_min; |
| const int32_t output_activation_max = params.quantized_activation_max; |
| |
| // Consistency check. |
| TFLITE_DCHECK_LE(output_activation_min, output_activation_max); |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const int input_depth = input_shape.Dims(3); |
| const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); |
| if (bias_data) { |
| TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); |
| } |
| |
| // Check dimensions of the tensors. |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| const int filter_height = filter_shape.Dims(1); |
| const int filter_width = filter_shape.Dims(2); |
| const int filter_input_depth = filter_shape.Dims(3); |
| const int groups = input_depth / filter_input_depth; |
| TFLITE_DCHECK_NE(groups, 0); |
| TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0); |
| const int filters_per_group = output_depth / groups; |
| TFLITE_DCHECK_NE(filters_per_group, 0); |
| const int output_height = output_shape.Dims(1); |
| const int output_width = output_shape.Dims(2); |
| for (int out_channel = 0; out_channel < output_depth; ++out_channel) { |
| 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) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| vdup_w_x_m(v60, 0); |
| int32_t acc = 0; |
| for (int in_channel = 0; in_channel + 32 <= filter_input_depth; |
| in_channel += 32) { |
| for (int filter_y = 0; filter_y < filter_height; ++filter_y) { |
| const int in_y = in_y_origin + dilation_height_factor * filter_y; |
| for (int filter_x = 0; filter_x < filter_width; ++filter_x) { |
| const int in_x = in_x_origin + dilation_width_factor * filter_x; |
| |
| // 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; |
| } |
| |
| vld_b_x(v0, &input_data[tflite::Offset(input_shape, batch, in_y, |
| in_x, in_channel)]); |
| vaddw_h_vx(v0, v0, 0); |
| vadd_h_vx(v0, v0, static_cast<int16_t>(input_offset)); |
| vadd_h_vx(v1, v1, static_cast<int16_t>(input_offset)); |
| vld_b_x(v2, &filter_data[tflite::Offset(filter_shape, |
| out_channel, filter_y, |
| filter_x, in_channel)]); |
| vaddw_h_vx(v2, v2, 0); |
| vmulw_w_vv(v48, v0, v2); |
| vmulw_w_vv(v50, v1, v3); |
| vadd_w_vv_m(v60, v60, v48); |
| } |
| } |
| } |
| int32_t accumulators[32]; |
| vst_w_x_m(v60, accumulators); |
| for (int i = 0; i < 32; ++i) { |
| acc += accumulators[i]; |
| } |
| |
| if (bias_data) { |
| acc += bias_data[out_channel]; |
| } |
| acc = tflite::MultiplyByQuantizedMultiplier( |
| acc, output_multiplier[out_channel], output_shift[out_channel]); |
| acc += output_offset; |
| acc = std::max(acc, output_activation_min); |
| acc = std::min(acc, output_activation_max); |
| output_data[tflite::Offset(output_shape, batch, out_y, out_x, |
| out_channel)] = static_cast<int8_t>(acc); |
| } |
| } |
| } |
| } |
| } |
| |
| void conv_per_channel_generic( |
| 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; |
| 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(v48, v0); |
| vdup_b_x_m(v48, 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(v0, &input_data[input_offset], count); |
| int filter_offset = |
| tflite::Offset(filter_shape, out_channel, filter_y, 0, 0) + i; |
| vdup_w_x_m(v8, 0); |
| vdup_w_x_m(v12, 0); |
| if (count > 0) { |
| vld_b_l_xx(v8, &filter_data[filter_offset], std::min(count, 4)); |
| } |
| if (count > 4) { |
| vld_b_l_xx(v9, &filter_data[filter_offset + 4], |
| std::min(count - 4, 4)); |
| } |
| if (count > 8) { |
| vld_b_l_xx(v10, &filter_data[filter_offset + 8], |
| std::min(count - 8, 4)); |
| } |
| if (count > 12) { |
| vld_b_l_xx(v11, &filter_data[filter_offset + 12], |
| std::min(count - 12, 4)); |
| } |
| if (count > 16) { |
| vld_b_l_xx(v12, &filter_data[filter_offset + 16], |
| std::min(count - 16, 4)); |
| } |
| if (count > 20) { |
| vld_b_l_xx(v13, &filter_data[filter_offset + 20], |
| std::min(count - 20, 4)); |
| } |
| if (count > 24) { |
| vld_b_l_xx(v14, &filter_data[filter_offset + 24], |
| std::min(count - 24, 4)); |
| } |
| if (count > 28) { |
| vld_b_l_xx(v15, &filter_data[filter_offset + 28], |
| std::min(count - 28, 4)); |
| } |
| aconv_vxv(v48, v0, cmds, v8); |
| } |
| } |
| vcget(v48); |
| vadd_w_vx_m(v48, v48, bias_data[out_channel]); |
| vsll_w_vx_m(v48, v48, LEFT_SHIFT(output_shift[out_channel])); |
| vdmulh_w_r_vx_m(v48, v48, output_multiplier[out_channel]); |
| vsha_w_r_vx_m(v48, v48, RIGHT_SHIFT(output_shift[out_channel])); |
| vadd_w_vx_m(v48, v48, output_offset); |
| vmin_w_vx_m(v48, v48, output_activation_max); |
| vmax_w_vx_m(v48, v48, output_activation_min); |
| vsraqs_b_vx(v56, v48, 0); |
| size_t output_offset = |
| tflite::Offset(output_shape, batch, out_y, out_x, out_channel); |
| vst_b_l_xx(v56, &output_data[output_offset], 1); |
| } |
| } |
| } |
| } |
| } |
| |
| void conv_per_channel_b8( |
| 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 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 int pad_width = params.padding_values.width; |
| const int pad_height = params.padding_values.height; |
| |
| if (dilation_width_factor == 1 && dilation_height_factor == 1 && |
| stride_width <= 2 && stride_height <= 2) { |
| if (filter_shape.Dims(kFilterInputChannelIndex) % 32 == 0 && |
| output_shape.Dims(kOutputChannelIndex) % 8 == 0 && |
| output_shape.Dims(kOutputWidthIndex) % 8 == 0 && pad_width <= 1) { |
| conv_per_channel_pw1_ow8_id8_filterd32( |
| params, output_multiplier, output_shift, input_shape, input_data, |
| filter_shape, filter_data, bias_shape, bias_data, output_shape, |
| output_data); |
| return; |
| } else if (filter_shape.Dims(kFilterInputChannelIndex) % 32 == 0) { |
| conv_per_channel_filterd32(params, output_multiplier, output_shift, |
| input_shape, input_data, filter_shape, |
| filter_data, bias_shape, bias_data, |
| output_shape, output_data); |
| return; |
| } |
| } |
| |
| if (stride_width == 1 && stride_height == 1 && dilation_width_factor == 1 && |
| dilation_height_factor == 1) { |
| if (pad_width == 0 && pad_height == 0) { |
| conv_per_channel_generic(params, output_multiplier, output_shift, |
| input_shape, input_data, filter_shape, |
| filter_data, bias_shape, bias_data, output_shape, |
| output_data); |
| return; |
| } |
| } |
| |
| 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); |
| } |
| |
| } // namespace kelvin::opt |
