| /* |
| * 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. |
| */ |
| |
| // Depthwise convolution based on Kelvin ops |
| // Data types: input: s8, filter: s8, bias s32 |
| |
| #include "tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h" |
| #include "tflm/opt/conv_util.h" |
| |
| namespace kelvin::opt { |
| namespace { |
| |
| // Reorders a vector to match the pattern after double-widening. |
| // N must be a multiple of 4. |
| void VectorSwizzle(const int32_t* input, int32_t* output, int N) { |
| assert(N >= 4 && N % 4 == 0); |
| const int32_t(&in)[N] = *(int32_t(*)[N])input; |
| int32_t(&out)[N] = *(int32_t(*)[N]) output; |
| const int32_t* p_in = in; |
| for (int i = 0; i < N / 4; ++i) { |
| int32_t* out0 = out + i + 0; |
| int32_t* out1 = out + i + 16; |
| int32_t* out2 = out + i + 8; |
| int32_t* out3 = out + i + 24; |
| *out0 = *p_in++; |
| *out1 = *p_in++; |
| *out2 = *p_in++; |
| *out3 = *p_in++; |
| } |
| } |
| // special case of input depth = 32n, filter shape of 3x3 |
| void DepthwiseConvS83x3D32( |
| const tflite::DepthwiseParams& 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 int stride_width = params.stride_width; |
| const int stride_height = params.stride_height; |
| const int pad_width = params.padding_values.width; |
| const int pad_height = params.padding_values.height; |
| const int32_t input_offset = params.input_offset; |
| const int32_t output_offset = params.output_offset; |
| const int32_t output_activation_min = params.quantized_activation_min; |
| const int32_t output_activation_max = params.quantized_activation_max; |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| const int input_depth = input_shape.Dims(3); |
| const int output_height = output_shape.Dims(1); |
| const int output_width = output_shape.Dims(2); |
| const int output_depth = output_shape.Dims(3); |
| int32_t swizzled_bias_data[32]; |
| int32_t swizzled_shift_multi[32]; |
| int32_t swizzled_output_multi[32]; |
| |
| for (int in_channel = 0; in_channel + 32 <= input_depth; in_channel += 32) { |
| const int output_channel = in_channel; |
| VectorSwizzle(bias_data + output_channel, swizzled_bias_data, 32); |
| VectorSwizzle(output_multiplier + output_channel, swizzled_output_multi, 32); |
| VectorSwizzle(output_shift + output_channel, swizzled_shift_multi, 32); |
| |
| vld_w_x_m(v52, swizzled_bias_data); |
| vld_w_x_m(v56, swizzled_output_multi); |
| vld_w_x_m(v60, swizzled_shift_multi); |
| vrsub_w_vx_m(v60, v60, 0); |
| |
| union { |
| vdwconv_u8_t dwconv; |
| uint32_t raw; |
| } cmds; |
| cmds.raw = 0; |
| cmds.dwconv.sdata1 = true; |
| cmds.dwconv.sbias1 = input_offset; |
| cmds.dwconv.sdata2 = true; |
| cmds.dwconv.sbias2 = 0; |
| cmds.dwconv.mode = 0; |
| cmds.dwconv.sparsity = 0; |
| cmds.dwconv.regbase = 0; |
| |
| // Don't reorder me, otherwise data will not be |
| // loaded in the correct order |
| // (we can reuse the p_flt* due to the `p` vld variant). |
| const int8_t* p_flt0 = filter_data + in_channel; |
| const int8_t* p_flt1 = p_flt0 + input_depth; |
| const int32_t stride = 2 * input_depth; |
| vld_b_sp_xx(v6, p_flt0, stride); |
| vld_b_sp_xx(v7, p_flt1, stride); |
| vld_b_sp_xx(v8, p_flt0, stride); |
| vld_b_sp_xx(v9, p_flt1, stride); |
| vld_b_sp_xx(v10, p_flt0, stride); |
| vld_b_sp_xx(v11, p_flt1, stride); |
| vld_b_sp_xx(v12, p_flt0, stride); |
| vld_b_sp_xx(v13, p_flt1, stride); |
| vld_b_sp_xx(v14, p_flt0, stride); |
| |
| for (int batch = 0; batch < batches; ++batch) { |
| const int8_t* p_output = output_data + (batch * output_width * output_height * output_depth) + output_channel; |
| for (int out_y = 0; out_y < output_height; ++out_y) { |
| const int in_y_origin = (out_y * stride_height) - pad_height; |
| const int y_offset = (output_depth * output_width * out_y); |
| for (int out_x = 0; out_x < output_width; ++out_x) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| |
| // Initialize accumulators w/ bias data. |
| vmv_v_m(v48, v52); |
| |
| bool top_pad = in_y_origin < 0; |
| bool left_pad = in_x_origin < 0; |
| bool bottom_pad = (in_y_origin + 2) >= input_height; |
| bool right_pad = (in_x_origin + 2) >= input_width; |
| bool padding_required = top_pad || left_pad || bottom_pad || right_pad; |
| const int8_t* p_in_0 = input_data + |
| (batch * input_height * input_width * input_depth) + |
| (in_y_origin * input_width * input_depth) + |
| (in_x_origin * input_depth) + |
| in_channel; |
| const int8_t* p_in_1 = p_in_0 + (input_width * input_depth); |
| const int8_t* p_in_2 = p_in_1 + (input_width * input_depth); |
| if (!padding_required) { |
| vld_b_sp_xx(v15, p_in_0, input_depth); |
| vld_b_sp_xx(v16, p_in_0, input_depth); |
| vld_b_sp_xx(v17, p_in_0, input_depth); |
| vld_b_sp_xx(v18, p_in_1, input_depth); |
| vld_b_sp_xx(v19, p_in_1, input_depth); |
| vld_b_sp_xx(v20, p_in_1, input_depth); |
| vld_b_sp_xx(v21, p_in_2, input_depth); |
| vld_b_sp_xx(v22, p_in_2, input_depth); |
| vld_b_sp_xx(v23, p_in_2, input_depth); |
| } else { |
| // Top row |
| if (top_pad || left_pad) { |
| vdup_b_x(v15, -input_offset); |
| } else { |
| vld_b_x(v15, p_in_0); |
| } |
| if (top_pad) { |
| vdup_b_x(v16, -input_offset); |
| } else { |
| vld_b_x(v16, p_in_0 + input_depth); |
| } |
| if (top_pad || right_pad) { |
| vdup_b_x(v17, -input_offset); |
| } else { |
| vld_b_x(v17, p_in_0 + (2 * input_depth)); |
| } |
| // Middle row |
| if (left_pad) { |
| vdup_b_x(v18, -input_offset); |
| } else { |
| vld_b_x(v18, p_in_1); |
| } |
| vld_b_x(v19, p_in_1 + input_depth); |
| if (right_pad) { |
| vdup_b_x(v20, -input_offset); |
| } else { |
| vld_b_x(v20, p_in_1 + (2 * input_depth)); |
| } |
| // Bottom row |
| if (bottom_pad || left_pad) { |
| vdup_b_x(v21, -input_offset); |
| } else { |
| vld_b_x(v21, p_in_2); |
| } |
| if (bottom_pad) { |
| vdup_b_x(v22, -input_offset); |
| } else { |
| vld_b_x(v22, p_in_2 + input_depth); |
| } |
| if (bottom_pad || right_pad) { |
| vdup_b_x(v23, -input_offset); |
| } else { |
| vld_b_x(v23, p_in_2 + (2 * input_depth)); |
| } |
| } |
| |
| adwinit_v(v48, v48); |
| adwconv_vxv(v48, v15, cmds, v6); |
| adwconv_vxv(v48, v18, cmds, v9); |
| vdwconv_vxv(v48, v21, cmds, v12); |
| |
| vdmulh_w_rn_vv_m(v48, v48, v56); |
| vsha_w_r_vv_m(v48, v48, v60); |
| vadd_w_vx_m(v48, v48, output_offset); |
| vmax_w_vx_m(v48, v48, output_activation_min); |
| vmin_w_vx_m(v48, v48, output_activation_max); |
| vsraqs_b_vx(v48, v48, 0); |
| vst_b_x(v48, p_output + (out_x * output_depth) + y_offset); |
| } |
| } |
| } |
| } |
| } |
| |
| // special case of input depth = 32n, filter shape of 5x5, stride == 1 |
| void DepthwiseConvS85x5D32_Stride1( |
| const tflite::DepthwiseParams& 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 int stride_width = params.stride_width; |
| const int stride_height = params.stride_height; |
| const int pad_width = params.padding_values.width; |
| const int pad_height = params.padding_values.height; |
| const int32_t input_offset = params.input_offset; |
| const int32_t output_offset = params.output_offset; |
| const int32_t output_activation_min = params.quantized_activation_min; |
| const int32_t output_activation_max = params.quantized_activation_max; |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| const int input_depth = input_shape.Dims(3); |
| const int output_height = output_shape.Dims(1); |
| const int output_width = output_shape.Dims(2); |
| const int output_depth = output_shape.Dims(3); |
| int32_t swizzled_bias_data[32]; |
| int32_t swizzled_shift_multi[32]; |
| int32_t swizzled_output_multi[32]; |
| |
| for (int in_channel = 0; in_channel + 32 <= input_depth; in_channel += 32) { |
| const int output_channel = in_channel; |
| VectorSwizzle(bias_data + output_channel, swizzled_bias_data, 32); |
| VectorSwizzle(output_multiplier + output_channel, swizzled_output_multi, 32); |
| VectorSwizzle(output_shift + output_channel, swizzled_shift_multi, 32); |
| |
| union { |
| vdwconv_u8_t dwconv; |
| uint32_t raw; |
| } cmds; |
| cmds.raw = 0; |
| cmds.dwconv.sdata1 = true; |
| cmds.dwconv.sbias1 = input_offset; |
| cmds.dwconv.sdata2 = true; |
| cmds.dwconv.sbias2 = 0; |
| cmds.dwconv.mode = 0; |
| cmds.dwconv.sparsity = 0; |
| cmds.dwconv.regbase = 0; |
| |
| // Don't reorder me! |
| const int8_t* p_flt0 = filter_data + in_channel; |
| const int32_t stride = input_depth; |
| vld_b_sp_xx_m(v0, p_flt0, stride); |
| vld_b_sp_xx_m(v4, p_flt0, stride); |
| vld_b_sp_xx_m(v8, p_flt0, stride); |
| vld_b_sp_xx_m(v12, p_flt0, stride); |
| vld_b_sp_xx_m(v16, p_flt0, stride); |
| vld_b_sp_xx_m(v20, p_flt0, stride); |
| vld_b_sp_xx(v24, p_flt0, stride); |
| |
| // Extra two registers to get our |
| // total usage to a multiple of 3 for dwconv. |
| vdup_b_x(v25, 0); |
| vdup_b_x(v26, 0); |
| |
| for (int batch = 0; batch < batches; ++batch) { |
| const int8_t* p_output = output_data + (batch * output_height * output_width * output_depth) + output_channel; |
| for (int out_y = 0; out_y < output_height; ++out_y) { |
| const int y_offset = out_y * output_width * output_depth; |
| for (int out_x = 0; out_x < output_width; ++out_x) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| const int in_y_origin = (out_y * stride_height) - pad_height; |
| |
| bool top_pad = in_y_origin < 0; |
| bool left_pad = in_x_origin < 0; |
| int top_pad_count = top_pad ? 0 - in_y_origin : 0; |
| int left_pad_count = left_pad ? 0 - in_x_origin : 0; |
| bool bottom_pad = (in_y_origin + 4) >= input_height; |
| bool right_pad = (in_x_origin + 4) >= input_width; |
| int bottom_pad_count = std::abs(bottom_pad ? (in_y_origin + 4) - input_height + 1: 0); |
| int right_pad_count = std::abs(right_pad ? (in_x_origin + 4) - input_width + 1 : 0); |
| bool padding_required = top_pad || left_pad || bottom_pad || right_pad; |
| assert(top_pad_count <= pad_height); |
| assert(bottom_pad_count <= pad_height); |
| assert(left_pad_count <= pad_width); |
| assert(right_pad_count <= pad_width); |
| assert(!(left_pad && right_pad)); |
| const int8_t* p_in_0 = input_data + |
| (batch * input_height * input_width * input_depth) + |
| (in_y_origin * input_width * input_depth) + |
| (in_x_origin * input_depth) + |
| in_channel; |
| const int8_t* p_in_1 = p_in_0 + (input_width * input_depth); |
| const int8_t* p_in_2 = p_in_1 + (input_width * input_depth); |
| const int8_t* p_in_3 = p_in_2 + (input_width * input_depth); |
| const int8_t* p_in_4 = p_in_3 + (input_width * input_depth); |
| // Extra two registers to get our |
| // total usage to a multiple of 3 for dwconv. |
| vdup_b_x(v52, -input_offset); |
| vdup_b_x(v53, -input_offset); |
| if (!padding_required) { |
| vld_b_sp_xx(v27, p_in_0, input_depth); |
| vld_b_sp_xx_m(v28, p_in_0, input_depth); |
| vld_b_sp_xx_m(v32, p_in_1, input_depth); |
| vld_b_sp_xx(v36, p_in_1, input_depth); |
| vld_b_sp_xx(v37, p_in_2, input_depth); |
| vld_b_sp_xx(v38, p_in_2, input_depth); |
| vld_b_sp_xx(v39, p_in_2, input_depth); |
| vld_b_sp_xx(v40, p_in_2, input_depth); |
| vld_b_sp_xx(v41, p_in_2, input_depth); |
| vld_b_sp_xx(v42, p_in_3, input_depth); |
| vld_b_sp_xx(v43, p_in_3, input_depth); |
| vld_b_sp_xx(v44, p_in_3, input_depth); |
| vld_b_sp_xx(v45, p_in_3, input_depth); |
| vld_b_sp_xx(v46, p_in_3, input_depth); |
| vld_b_sp_xx(v47, p_in_4, input_depth); |
| vld_b_sp_xx_m(v48, p_in_4, input_depth); |
| } else { |
| // Top row |
| if (top_pad_count >= 1) { |
| vdup_b_x(v27, -input_offset); |
| vdup_b_x_m(v28, -input_offset); |
| } else { |
| switch (left_pad_count) { |
| case 2: |
| vdup_b_x(v28, -input_offset); |
| case 1: |
| vdup_b_x(v27, -input_offset); |
| } |
| switch (left_pad_count) { |
| case 0: |
| vld_b_x(v27, p_in_0); |
| case 1: |
| vld_b_x(v28, p_in_0 + input_depth); |
| } |
| vld_b_x(v29, p_in_0 + (2 * input_depth)); |
| switch (right_pad_count) { |
| case 2: |
| vdup_b_x(v30, -input_offset); |
| case 1: |
| vdup_b_x(v31, -input_offset); |
| } |
| switch (right_pad_count) { |
| case 0: |
| vld_b_x(v31, p_in_0 + (4 * input_depth)); |
| case 1: |
| vld_b_x(v30, p_in_0 + (3 * input_depth)); |
| } |
| } |
| |
| // 2nd row |
| if (top_pad_count == 2) { |
| vdup_b_x_m(v32, -input_offset); |
| vdup_b_x(v36, -input_offset); |
| } else { |
| switch (left_pad_count) { |
| case 2: |
| vdup_b_x(v33, -input_offset); |
| case 1: |
| vdup_b_x(v32, -input_offset); |
| } |
| switch (left_pad_count) { |
| case 0: |
| vld_b_x(v32, p_in_1); |
| case 1: |
| vld_b_x(v33, p_in_1 + input_depth); |
| } |
| vld_b_x(v34, p_in_1 + (2 * input_depth)); |
| switch (right_pad_count) { |
| case 2: |
| vdup_b_x(v35, -input_offset); |
| case 1: |
| vdup_b_x(v36, -input_offset); |
| } |
| switch (right_pad_count) { |
| case 0: |
| vld_b_x(v36, p_in_1 + (4 * input_depth)); |
| case 1: |
| vld_b_x(v35, p_in_1 + (3 * input_depth)); |
| } |
| } |
| |
| // 3rd row |
| switch (left_pad_count) { |
| case 2: |
| vdup_b_x(v38, -input_offset); |
| case 1: |
| vdup_b_x(v37, -input_offset); |
| } |
| switch (left_pad_count) { |
| case 0: |
| vld_b_x(v37, p_in_2); |
| case 1: |
| vld_b_x(v38, p_in_2 + input_depth); |
| } |
| vld_b_x(v39, p_in_2 + (2 * input_depth)); |
| switch (right_pad_count) { |
| case 2: |
| vdup_b_x(v40, -input_offset); |
| case 1: |
| vdup_b_x(v41, -input_offset); |
| } |
| switch (right_pad_count) { |
| case 0: |
| vld_b_x(v41, p_in_2 + (4 * input_depth)); |
| case 1: |
| vld_b_x(v40, p_in_2 + (3 * input_depth)); |
| } |
| |
| // 4th row |
| if (bottom_pad_count == 2) { |
| vdup_b_x(v42, -input_offset); |
| vdup_b_x(v43, -input_offset); |
| vdup_b_x(v44, -input_offset); |
| vdup_b_x(v45, -input_offset); |
| vdup_b_x(v46, -input_offset); |
| } else { |
| switch (left_pad_count) { |
| case 2: |
| vdup_b_x(v43, -input_offset); |
| case 1: |
| vdup_b_x(v42, -input_offset); |
| } |
| switch (left_pad_count) { |
| case 0: |
| vld_b_x(v42, p_in_3); |
| case 1: |
| vld_b_x(v43, p_in_3 + input_depth); |
| } |
| switch (right_pad_count) { |
| case 2: |
| vdup_b_x(v45, -input_offset); |
| case 1: |
| vdup_b_x(v46, -input_offset); |
| } |
| vld_b_x(v44, p_in_3 + (2 * input_depth)); |
| switch (right_pad_count) { |
| case 0: |
| vld_b_x(v46, p_in_3 + (4 * input_depth)); |
| case 1: |
| vld_b_x(v45, p_in_3 + (3 * input_depth)); |
| } |
| } |
| |
| // 5th row |
| if (bottom_pad_count >= 1) { |
| vdup_b_x(v47, -input_offset); |
| vdup_b_x(v48, -input_offset); |
| vdup_b_x(v49, -input_offset); |
| vdup_b_x(v50, -input_offset); |
| vdup_b_x(v51, -input_offset); |
| } else { |
| switch (left_pad_count) { |
| case 2: |
| vdup_b_x(v48, -input_offset); |
| case 1: |
| vdup_b_x(v47, -input_offset); |
| } |
| switch (left_pad_count) { |
| case 0: |
| vld_b_x(v47, p_in_4); |
| case 1: |
| vld_b_x(v48, p_in_4 + input_depth); |
| } |
| vld_b_x(v49, p_in_4 + (2 * input_depth)); |
| switch (right_pad_count) { |
| case 2: |
| vdup_b_x(v50, -input_offset); |
| case 1: |
| vdup_b_x(v51, -input_offset); |
| } |
| switch (right_pad_count) { |
| case 0: |
| vld_b_x(v51, p_in_4 + (4 * input_depth)); |
| case 1: |
| vld_b_x(v50, p_in_4 + (3 * input_depth)); |
| } |
| } |
| } |
| |
| vld_w_x_m(v60, swizzled_bias_data); |
| adwinit_v(v60, v60); |
| adwconv_vxv(v60, v27, cmds, v0); |
| adwconv_vxv(v60, v30, cmds, v3); |
| adwconv_vxv(v60, v33, cmds, v6); |
| adwconv_vxv(v60, v36, cmds, v9); |
| adwconv_vxv(v60, v39, cmds, v12); |
| adwconv_vxv(v60, v42, cmds, v15); |
| adwconv_vxv(v60, v45, cmds, v18); |
| adwconv_vxv(v60, v48, cmds, v21); |
| vdwconv_vxv(v60, v51, cmds, v24); |
| |
| vld_w_x_m(v56, swizzled_output_multi); |
| vdmulh_w_rn_vv_m(v60, v60, v56); |
| vld_w_x_m(v56, swizzled_shift_multi); |
| vrsub_w_vx_m(v56, v56, 0); |
| vsha_w_r_vv_m(v60, v60, v56); |
| vadd_w_vx_m(v60, v60, output_offset); |
| vmax_w_vx_m(v60, v60, output_activation_min); |
| vmin_w_vx_m(v60, v60, output_activation_max); |
| vsraqs_b_vx(v60, v60, 0); |
| vst_b_x(v60, p_output + y_offset + (out_x * output_depth)); |
| } |
| } |
| } |
| } |
| } |
| |
| // special case of input depth = 32n, filter shape of 5x5 |
| void DepthwiseConvS85x5D32( |
| const tflite::DepthwiseParams& 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 int stride_width = params.stride_width; |
| const int stride_height = params.stride_height; |
| const int pad_width = params.padding_values.width; |
| const int pad_height = params.padding_values.height; |
| const int32_t input_offset = params.input_offset; |
| const int32_t output_offset = params.output_offset; |
| const int32_t output_activation_min = params.quantized_activation_min; |
| const int32_t output_activation_max = params.quantized_activation_max; |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| const int input_depth = input_shape.Dims(3); |
| const int filter_height = filter_shape.Dims(1); |
| const int filter_width = filter_shape.Dims(2); |
| const int output_height = output_shape.Dims(1); |
| const int output_width = output_shape.Dims(2); |
| const int output_depth = output_shape.Dims(3); |
| int32_t swizzled_bias_data[32]; |
| int32_t swizzled_shift_multi[32]; |
| int32_t swizzled_output_multi[32]; |
| |
| for (int in_channel = 0; in_channel + 32 <= input_depth; in_channel += 32) { |
| const int output_channel = in_channel; |
| VectorSwizzle(bias_data + output_channel, swizzled_bias_data, 32); |
| VectorSwizzle(output_multiplier + output_channel, swizzled_output_multi, 32); |
| VectorSwizzle(output_shift + output_channel, swizzled_shift_multi, 32); |
| |
| vld_w_x_m(v52, swizzled_bias_data); |
| vld_w_x_m(v56, swizzled_output_multi); |
| vld_w_x_m(v60, swizzled_shift_multi); |
| vrsub_w_vx_m(v60, v60, 0); |
| |
| // Don't reorder me! |
| const int8_t* p_flt = filter_data + in_channel; |
| vld_b_sp_xx(v6, p_flt, input_depth); |
| vld_b_sp_xx(v7, p_flt, input_depth); |
| vld_b_sp_xx_m(v8, p_flt, input_depth); |
| vld_b_sp_xx_m(v12, p_flt, input_depth); |
| vld_b_sp_xx_m(v16, p_flt, input_depth); |
| vld_b_sp_xx_m(v20, p_flt, input_depth); |
| vld_b_sp_xx_m(v24, p_flt, input_depth); |
| vld_b_sp_xx(v28, p_flt, input_depth); |
| vld_b_sp_xx(v29, p_flt, input_depth); |
| vld_b_sp_xx(v30, p_flt, input_depth); |
| |
| |
| for (int batch = 0; batch < batches; ++batch) { |
| const int8_t* p_input = input_data + (batch * input_width * input_height * input_depth) + in_channel; |
| const int8_t* p_output = output_data + (batch * output_width * output_height * output_depth) + output_channel; |
| for (int out_y = 0; out_y < output_height; ++out_y) { |
| const int out_y_offset = (out_y * output_width * output_depth); |
| for (int out_x = 0; out_x < output_width; ++out_x) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| const int in_y_origin = (out_y * stride_height) - pad_height; |
| |
| // Initialize accumulators w/ bias_data |
| vmv_v_m(v48, v52); |
| |
| for (int filter_y = 0; filter_y < filter_height; ++filter_y) { |
| const int in_y = in_y_origin + filter_y; |
| if ((in_y < 0) || (in_y >= input_height)) { |
| continue; |
| } |
| switch (filter_y) { |
| case 0: |
| vaddw_h_vx(v31, v6, 0); |
| vaddw_h_vx(v33, v7, 0); |
| vaddw_h_vx(v35, v8, 0); |
| vaddw_h_vx(v37, v9, 0); |
| vaddw_h_vx(v39, v10, 0); |
| break; |
| case 1: |
| vaddw_h_vx(v31, v11, 0); |
| vaddw_h_vx(v33, v12, 0); |
| vaddw_h_vx(v35, v13, 0); |
| vaddw_h_vx(v37, v14, 0); |
| vaddw_h_vx(v39, v15, 0); |
| break; |
| case 2: |
| vaddw_h_vx(v31, v16, 0); |
| vaddw_h_vx(v33, v17, 0); |
| vaddw_h_vx(v35, v18, 0); |
| vaddw_h_vx(v37, v19, 0); |
| vaddw_h_vx(v39, v20, 0); |
| break; |
| case 3: |
| vaddw_h_vx(v31, v21, 0); |
| vaddw_h_vx(v33, v22, 0); |
| vaddw_h_vx(v35, v23, 0); |
| vaddw_h_vx(v37, v24, 0); |
| vaddw_h_vx(v39, v25, 0); |
| break; |
| case 4: |
| vaddw_h_vx(v31, v26, 0); |
| vaddw_h_vx(v33, v27, 0); |
| vaddw_h_vx(v35, v28, 0); |
| vaddw_h_vx(v37, v29, 0); |
| vaddw_h_vx(v39, v30, 0); |
| break; |
| } |
| const int in_y_offset = in_y * input_width * input_depth; |
| for (int filter_x = 0; filter_x < filter_width; ++filter_x) { |
| const int in_x = in_x_origin + filter_x; |
| if ((in_x < 0) || (in_x >= input_width)) { |
| continue; |
| } |
| |
| vld_b_x(v0, p_input + (in_x * input_depth) + in_y_offset); |
| |
| 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)); // v0 v1 input |
| switch (filter_x) { |
| case 0: |
| vmulw_w_vv(v2, v1, v32); |
| vmulw_w_vv(v0, v0, v31); |
| break; |
| case 1: |
| vmulw_w_vv(v2, v1, v34); |
| vmulw_w_vv(v0, v0, v33); |
| break; |
| case 2: |
| vmulw_w_vv(v2, v1, v36); |
| vmulw_w_vv(v0, v0, v35); |
| break; |
| case 3: |
| vmulw_w_vv(v2, v1, v38); |
| vmulw_w_vv(v0, v0, v37); |
| break; |
| case 4: |
| vmulw_w_vv(v2, v1, v40); |
| vmulw_w_vv(v0, v0, v39); |
| break; |
| } |
| vadd_w_vv_m(v48, v48, v0); |
| } |
| } |
| |
| vdmulh_w_rn_vv_m(v48, v48, v56); |
| vsha_w_r_vv_m(v48, v48, v60); |
| vadd_w_vx_m(v48, v48, output_offset); |
| vmax_w_vx_m(v48, v48, output_activation_min); |
| vmin_w_vx_m(v48, v48, output_activation_max); |
| vsraqs_b_vx(v48, v48, 0); |
| vst_b_x(v48, p_output + out_y_offset + (out_x * output_depth)); |
| } |
| } |
| } |
| } |
| } |
| |
| // special case of input depth = 32n |
| void DepthwiseConvS8D32( |
| const tflite::DepthwiseParams& 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 int stride_width = params.stride_width; |
| const int stride_height = params.stride_height; |
| const int pad_width = params.padding_values.width; |
| const int pad_height = params.padding_values.height; |
| const int32_t input_offset = params.input_offset; |
| const int32_t output_offset = params.output_offset; |
| const int32_t output_activation_min = params.quantized_activation_min; |
| const int32_t output_activation_max = params.quantized_activation_max; |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| const int input_depth = input_shape.Dims(3); |
| const int filter_height = filter_shape.Dims(1); |
| const int filter_width = filter_shape.Dims(2); |
| const int output_height = output_shape.Dims(1); |
| const int output_width = output_shape.Dims(2); |
| int32_t swizzled_bias_data[32]; |
| int32_t swizzled_shift_multi[32]; |
| int32_t swizzled_output_multi[32]; |
| |
| for (int in_channel = 0; in_channel + 32 <= input_depth; in_channel += 32) { |
| const int output_channel = in_channel; |
| VectorSwizzle(bias_data + output_channel, swizzled_bias_data, 32); |
| VectorSwizzle(output_multiplier + output_channel, swizzled_output_multi, 32); |
| VectorSwizzle(output_shift + output_channel, swizzled_shift_multi, 32); |
| |
| vld_w_x_m(v20, swizzled_bias_data); |
| vld_w_x_m(v24, swizzled_output_multi); |
| vld_w_x_m(v28, swizzled_shift_multi); |
| vrsub_w_vx_m(v28, v28, 0); |
| |
| for (int batch = 0; batch < batches; ++batch) { |
| for (int out_y = 0; out_y < output_height; ++out_y) { |
| for (int out_x = 0; out_x < output_width; ++out_x) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| const int in_y_origin = (out_y * stride_height) - pad_height; |
| |
| vdup_w_x_m(v48, 0); |
| for (int filter_y = 0; filter_y < filter_height; ++filter_y) { |
| const int in_y = in_y_origin + filter_y; |
| if ((in_y < 0) || (in_y >= input_height)) { |
| continue; |
| } |
| for (int filter_x = 0; filter_x < filter_width; ++filter_x) { |
| const int in_x = in_x_origin + filter_x; |
| if ((in_x < 0) || (in_x >= input_width)) { |
| continue; |
| } |
| |
| vld_b_x(v0, &input_data[tflite::Offset(input_shape, batch, in_y, |
| in_x, in_channel)]); // xp |
| vld_b_x(v4, &filter_data[tflite::Offset(filter_shape, 0, filter_y, |
| filter_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)); // v0 v1 input |
| |
| vaddw_h_vx(v4, v4, static_cast<int16_t>(0)); |
| vmulw_w_vv(v8, v0, v4); |
| vmulw_w_vv(v10, v1, v5); |
| |
| vadd_w_vv_m(v48, v48, v8); |
| } |
| } |
| |
| vadd_w_vv_m(v48, v48, v20); // add bias |
| vdmulh_w_rn_vv_m(v48, v48, v24); |
| vsha_w_r_vv_m(v48, v48, v28); |
| vadd_w_vx_m(v48, v48, output_offset); |
| vmax_w_vx_m(v48, v48, output_activation_min); |
| vmin_w_vx_m(v48, v48, output_activation_max); |
| vsraqs_b_vx(v48, v48, 0); |
| vst_b_x(v48, &output_data[tflite::Offset(output_shape, batch, out_y, |
| out_x, output_channel)]); |
| } |
| } |
| } |
| } |
| } |
| |
| // generic implementation based on Kelvin ops |
| void DepthwiseConvS8Generic( |
| const tflite::DepthwiseParams& 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) { |
| // TBD: Use Kelvin implementation to replace the below |
| tflite::reference_integer_ops::DepthwiseConvPerChannel( |
| params, output_multiplier, output_shift, input_shape, input_data, |
| filter_shape, filter_data, bias_shape, bias_data, output_shape, |
| output_data); |
| return; |
| } |
| } // namespace |
| |
| void DepthwiseConvS8( |
| const tflite::DepthwiseParams& 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. |
| // TODO(b/141565753): Re-introduce ScopedProfilingLabel on Micro. |
| const int stride_width = params.stride_width; |
| const int stride_height = params.stride_height; |
| const int pad_width = params.padding_values.width; |
| const int pad_height = params.padding_values.height; |
| const int filter_height = filter_shape.Dims(1); |
| const int filter_width = filter_shape.Dims(2); |
| const int dilation_width_factor = params.dilation_width_factor; |
| const int dilation_height_factor = params.dilation_height_factor; |
| const int depth_multiplier = params.depth_multiplier; |
| const int32_t output_activation_min = params.quantized_activation_min; |
| const int32_t output_activation_max = params.quantized_activation_max; |
| |
| // Check dimensions of the tensors. |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| |
| TFLITE_DCHECK_LE(output_activation_min, output_activation_max); |
| const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); |
| const int input_depth = input_shape.Dims(3); |
| TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); |
| TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); |
| |
| if (depth_multiplier == 1 && |
| dilation_height_factor == 1 && dilation_width_factor == 1 && |
| stride_height <= 2 && stride_width <= 2) { |
| // generic implementation by default |
| auto fn = DepthwiseConvS8Generic; |
| |
| // special case of output depth = 32n |
| if (output_depth % 32 == 0) { |
| if (filter_width == 5 && filter_height == 5) { |
| if (stride_width <= 1 && stride_height <= 1) { |
| fn = DepthwiseConvS85x5D32_Stride1; |
| } else { |
| fn = DepthwiseConvS85x5D32; |
| } |
| } else if (filter_width == 3 && filter_height == 3 && pad_width <= 1 && pad_height <= 1) { |
| fn = DepthwiseConvS83x3D32; |
| } else { |
| fn = DepthwiseConvS8D32; |
| } |
| } |
| |
| fn(params, output_multiplier, output_shift, input_shape, input_data, |
| filter_shape, filter_data, bias_shape, bias_data, output_shape, |
| output_data); |
| return; |
| } |
| |
| // Use reference implementation |
| tflite::reference_integer_ops::DepthwiseConvPerChannel( |
| params, output_multiplier, output_shift, input_shape, input_data, |
| filter_shape, filter_data, bias_shape, bias_data, output_shape, |
| output_data); |
| } |
| |
| } // namespace kelvin::opt |
