Add input_depth == 1 specialized convolution. Change-Id: I4031fe1341b91d462d84e341cd173cf18c4e755d
diff --git a/tflm/opt/BUILD b/tflm/opt/BUILD index 28dde26..3464e0d 100644 --- a/tflm/opt/BUILD +++ b/tflm/opt/BUILD
@@ -24,6 +24,7 @@ "conv_s8_3x1_d48.cc", "conv_s8_d4.cc", "conv_s8_d32.cc", + "conv_s8_d1.cc", "depthwise_conv_s16.cc", "depthwise_conv_s8.cc", "elementwise_add_s16.cc",
diff --git a/tflm/opt/conv_s8.cc b/tflm/opt/conv_s8.cc index 7d7d0ba..2d49dbc 100644 --- a/tflm/opt/conv_s8.cc +++ b/tflm/opt/conv_s8.cc
@@ -225,6 +225,10 @@ fn = kelvin::opt::ConvS8K3x1D48; } + else if (input_depth == 1 && ((output_depth % 4) == 0)) { + fn = kelvin::opt::ConvPerChannelD1; + } + fn(params, output_multiplier, output_shift, input_shape, input_data, filter_shape, filter_data, bias_shape, bias_data, output_shape, output_data);
diff --git a/tflm/opt/conv_s8.h b/tflm/opt/conv_s8.h index 02dd79b..91c535a 100644 --- a/tflm/opt/conv_s8.h +++ b/tflm/opt/conv_s8.h
@@ -62,6 +62,15 @@ const int32_t* bias_data, const tflite::RuntimeShape& output_shape, int8_t* output_data); +// Input depth = 1 +void ConvPerChannelD1( + 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); + } // namespace kelvin::opt #endif // TFLM_OPT_CONV_S8_H_
diff --git a/tflm/opt/conv_s8_d1.cc b/tflm/opt/conv_s8_d1.cc new file mode 100644 index 0000000..80f3a4f --- /dev/null +++ b/tflm/opt/conv_s8_d1.cc
@@ -0,0 +1,215 @@ +/* + * 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 <algorithm> + +#include "tflm/opt/conv_util.h" + +namespace kelvin::opt { +namespace { +void JumptableSwizzle(const int32_t* input, int32_t* output, int n) { + switch (n) { + case 32: + output[7] = input[28]; + output[15] = input[30]; + output[23] = input[29]; + output[31] = input[31]; + case 28: + output[6] = input[24]; + output[14] = input[26]; + output[22] = input[25]; + output[30] = input[27]; + case 24: + output[5] = input[20]; + output[13] = input[22]; + output[21] = input[21]; + output[29] = input[23]; + case 20: + output[4] = input[16]; + output[12] = input[18]; + output[20] = input[17]; + output[28] = input[19]; + case 16: + output[27] = input[15]; + output[19] = input[13]; + output[11] = input[14]; + output[3] = input[12]; + case 12: + output[2] = input[8]; + output[10] = input[10]; + output[18] = input[9]; + output[26] = input[11]; + case 8: + output[1] = input[4]; + output[9] = input[6]; + output[17] = input[5]; + output[25] = input[7]; + case 4: + output[0] = input[0]; + output[8] = input[2]; + output[16] = input[1]; + output[24] = input[3]; + } +} +} // namespace + +void ConvPerChannelD1( + 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 = tflite::MatchingDim(input_shape, 0, output_shape, 0); + const int input_depth = input_shape.Dims(3); + const int output_depth = tflite::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); + + // Scratch pads to juggle data + const size_t swizzled_filter_data_size = 32 * filter_height * filter_width; + std::unique_ptr<int8_t> swizzled_filter_data( + reinterpret_cast<int8_t*>( + ::aligned_alloc(32, swizzled_filter_data_size))); + int32_t swizzled_bias_data[32]; + int32_t swizzled_output_multiplier[32]; + int32_t swizzled_output_shift[32]; + + for (int out_channel = 0; out_channel < output_depth; out_channel += 32) { + int n_channels = std::min(32, output_depth - out_channel); + + // Transpose filter for easy loading + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + for (int i = 0; i < n_channels; i++) { + int filter_location = + (filter_y * filter_width * 32) + (filter_x * 32) + i; + swizzled_filter_data.get()[filter_location] = filter_data[ + tflite::Offset(filter_shape, out_channel + i, filter_y, filter_x, + 0)]; + } + } + } + + if (bias_data) { + JumptableSwizzle(bias_data + out_channel, swizzled_bias_data, n_channels); + vld_w_x_m(v52, swizzled_bias_data); + } else { + vdup_w_x_m(v52, 0); + } + + JumptableSwizzle(output_multiplier + out_channel, + swizzled_output_multiplier, n_channels); + vld_w_x_m(v56, swizzled_output_multiplier); + + JumptableSwizzle(output_shift + out_channel, swizzled_output_shift, + n_channels); + vld_w_x_m(v60, swizzled_output_shift); + vrsub_w_vx_m(v60, v60, 0); + + int8_t* local_output_data = output_data + 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; + + // Accumulator loop + vmv_v_m(v48, v52); + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + const int in_y = in_y_origin + dilation_height_factor * filter_y; + if ((in_y < 0) || (in_y >= input_height)) { + continue; + } + + const int8_t* local_input_data = input_data + + tflite::Offset(input_shape, batch, in_y, 0, 0); + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + const int in_x = in_x_origin + dilation_width_factor * filter_x; + if ((in_x < 0) || (in_x >= input_width)) { + continue; + } + + int16_t input_val = local_input_data[in_x]; + int16_t input_val16 = static_cast<int16_t>( + input_val + input_offset); + vdup_h_x(v32, input_val16); + + const int8_t* local_filter_data = swizzled_filter_data.get() + + (filter_y * filter_width * 32) + (filter_x * 32); + vld_b_l_xx(v0, local_filter_data, n_channels); + vaddw_h_vx(v0, v0, 0); + + // Multiply + vmulw_w_vv(v4, v0, v32); + vmulw_w_vv(v6, v1, v32); + + // Accumulate + vadd_w_vv_m(v48, v48, v4); + } + } + + // Output pipeline + vdmulh_w_rn_vv_m(v48, v48, v56); + vsha_w_r_vv_m(v48, v48, v60); + 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(v48, v48, 0); + vst_b_l_xx(v48, output_data, n_channels); + output_data += output_depth; + } + } + } + } +} + +} // namespace kelvin::opt \ No newline at end of file