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
