Relax restrictions on aconv-based Conv2D
- channel_depth is now mod4
- output_width is only required to be at least 8
Change-Id: I7989f17260d84c666247ccb84d000b2219856389
diff --git a/tflm/opt/BUILD b/tflm/opt/BUILD
index 277eef2..28dde26 100644
--- a/tflm/opt/BUILD
+++ b/tflm/opt/BUILD
@@ -22,6 +22,7 @@
"conv_s8.cc",
"conv_s8_1x1.cc",
"conv_s8_3x1_d48.cc",
+ "conv_s8_d4.cc",
"conv_s8_d32.cc",
"depthwise_conv_s16.cc",
"depthwise_conv_s8.cc",
diff --git a/tflm/opt/conv_s8.cc b/tflm/opt/conv_s8.cc
index 4582b9f..7d7d0ba 100644
--- a/tflm/opt/conv_s8.cc
+++ b/tflm/opt/conv_s8.cc
@@ -189,19 +189,27 @@
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);
// use generic implementation by default
auto fn = ConvS8Generic;
- // special case of filter depth = 32n
+ // 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 == 0 && output_width >= 8 && pad_width <= 1) {
+ fn = kelvin::opt::ConvS8D4;
+ }
+
+ // special case of filter depth = 32n
+ else if (dilation_width_factor == 1 && dilation_height_factor == 1 &&
stride_width <= 2 && stride_height <= 2 && filter_depth % 32 == 0) {
fn = kelvin::opt::ConvS8D32;
}
// special case of filter size 1x1
- if (filter_height == 1 && filter_width == 1 && stride_height == 1 &&
+ else 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 &&
(output_depth % 8) == 0 && (input_depth % 32) == 0) {
@@ -210,7 +218,7 @@
}
// special case of filter size 48x3x1x48
- if (batches == 1 && filter_height == 3 && filter_width == 1 &&
+ else 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) {
diff --git a/tflm/opt/conv_s8.h b/tflm/opt/conv_s8.h
index e1d88ef..02dd79b 100644
--- a/tflm/opt/conv_s8.h
+++ b/tflm/opt/conv_s8.h
@@ -33,6 +33,16 @@
const int32_t* bias_data,
const tflite::RuntimeShape& output_shape, int8_t* output_data);
+// filter depth 4n
+void ConvS8D4(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);
+
// filter depth 32n
void ConvS8D32(const tflite::ConvParams& params,
const int32_t* output_multiplier, const int32_t* output_shift,
diff --git a/tflm/opt/conv_s8_d32.cc b/tflm/opt/conv_s8_d32.cc
index e3e7e10..6572ae8 100644
--- a/tflm/opt/conv_s8_d32.cc
+++ b/tflm/opt/conv_s8_d32.cc
@@ -21,239 +21,6 @@
#include "tflm/opt/conv_util.h"
namespace kelvin::opt {
-namespace {
-void ConvS8D32Pw1Ow8Id8(
- 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_N_H_W_M<8>(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);
- }
- }
- }
- }
-}
-
-} // namespace
// Fixed-point per-channel-quantization convolution reference kernel.
void ConvS8D32(const tflite::ConvParams& params,
@@ -304,14 +71,6 @@
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
- // filter_depth = 32n && input_channels = 8n && output_width = 8n
- if (output_depth % 8 == 0 && output_width % 8 == 0 && pad_width <= 1) {
- ConvS8D32Pw1Ow8Id8(params, output_multiplier, output_shift, input_shape,
- input_data, filter_shape, filter_data, bias_shape,
- bias_data, output_shape, output_data);
- return;
- }
-
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) {
diff --git a/tflm/opt/conv_s8_d4.cc b/tflm/opt/conv_s8_d4.cc
new file mode 100644
index 0000000..0dd3e50
--- /dev/null
+++ b/tflm/opt/conv_s8_d4.cc
@@ -0,0 +1,384 @@
+/*
+ * 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
+// Special case for filter depth = 4n
+
+#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"
+
+namespace kelvin::opt {
+namespace {
+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 ConvS8D4(
+ 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 int32_t neg_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;
+ int out_x = 0;
+ do {
+ int out_xs_this_iter = std::min(8, output_width - out_x);
+ // 8x accumulators
+ vdup_w_x_m(v48, 0);
+ vdup_w_x_m(v52, 0);
+ acset_v(v48, v48);
+ int in_channel = 0;
+ do {
+ int channels_this_iter = std::min(filter_input_depth, 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 right_pad = false;
+ int first_right_pad = -1;
+ 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;
+ }
+ bool left_pad = (in_x[0] < 0);
+ for (int i = 7; i >= 0; --i) {
+ if (in_x[i] < input_width) {
+ break;
+ }
+ right_pad = true;
+ first_right_pad = i;
+ }
+
+ 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) {
+ int first_pad = std::min(first_right_pad, out_xs_this_iter);
+ switch (first_pad) {
+ case 0:
+ vdup_b_x(v0, neg_input_offset);
+ case 1:
+ vdup_b_x(v1, neg_input_offset);
+ case 2:
+ vdup_b_x(v2, neg_input_offset);
+ case 3:
+ vdup_b_x(v3, neg_input_offset);
+ case 4:
+ vdup_b_x(v4, neg_input_offset);
+ case 5:
+ vdup_b_x(v5, neg_input_offset);
+ case 6:
+ vdup_b_x(v6, neg_input_offset);
+ case 7:
+ vdup_b_x(v7, neg_input_offset);
+ }
+ switch (8 - first_pad) { // rest (stripmines?)
+ case 0:
+ vld_b_s_xx(
+ v7,
+ &input_data[tflite::Offset(input_shape, batch, in_y,
+ in_x[7], in_channel)],
+ input_depth * stride_width);
+ case 1:
+ vld_b_s_xx(
+ v6,
+ &input_data[tflite::Offset(input_shape, batch, in_y,
+ in_x[6], in_channel)],
+ input_depth * stride_width);
+ case 2:
+ vld_b_s_xx(
+ v5,
+ &input_data[tflite::Offset(input_shape, batch, in_y,
+ in_x[5], in_channel)],
+ input_depth * stride_width);
+ case 3:
+ vld_b_s_xx(
+ v4,
+ &input_data[tflite::Offset(input_shape, batch, in_y,
+ in_x[4], in_channel)],
+ input_depth * stride_width);
+ case 4:
+ vld_b_s_xx(
+ v3,
+ &input_data[tflite::Offset(input_shape, batch, in_y,
+ in_x[3], in_channel)],
+ input_depth * stride_width);
+ case 5:
+ vld_b_s_xx(
+ v2,
+ &input_data[tflite::Offset(input_shape, batch, in_y,
+ in_x[2], in_channel)],
+ input_depth * stride_width);
+ case 6:
+ vld_b_s_xx(
+ v1,
+ &input_data[tflite::Offset(input_shape, batch, in_y,
+ in_x[1], in_channel)],
+ input_depth * stride_width);
+ case 7:
+ vld_b_s_xx(
+ v0,
+ &input_data[tflite::Offset(input_shape, batch, in_y,
+ in_x[0], in_channel)],
+ input_depth * stride_width);
+ }
+ } 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);
+
+ cmds.conv.stop = (channels_this_iter / 4) - 1;
+ aconv_vxv(v48, v0, cmds, v8);
+ }
+ }
+ in_channel += channels_this_iter;
+ } while (in_channel < filter_input_depth);
+ vcget(v48);
+ vadd_w_vv_m(v48, v48, v16);
+ vadd_w_vv_m(v52, v52, v16);
+ vdmulh_w_rn_vv_m(v48, v48, v20);
+ vdmulh_w_rn_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);
+ if (out_xs_this_iter >= 4) {
+ vstq_b_s_xx(v56,
+ &output_data[tflite::Offset(output_shape, batch, out_y,
+ out_x, out_channel)],
+ output_depth);
+ } else {
+ for (int i = 0; i < out_xs_this_iter; ++i) {
+ if (i > 0) {
+ vsliden_b_4_vv(v58, v56, v0);
+ vsliden_b_4_vv(v56, v58, v0);
+ }
+ vst_b_l_xx(v56,
+ &output_data[tflite::Offset(output_shape, batch, out_y,
+ out_x + i, out_channel)],
+ 8);
+ }
+ }
+ if (out_xs_this_iter == 8) {
+ vstq_b_s_xx(v57,
+ &output_data[tflite::Offset(output_shape, batch, out_y,
+ out_x + 4, out_channel)],
+ output_depth);
+ } else if (out_xs_this_iter > 4) {
+ for (int i = 4; i < out_xs_this_iter; ++i) {
+ if (i > 4) {
+ vsliden_b_4_vv(v58, v57, v0);
+ vsliden_b_4_vv(v57, v58, v0);
+ }
+ vst_b_l_xx(v57,
+ &output_data[tflite::Offset(output_shape, batch, out_y,
+ out_x + i, out_channel)],
+ 8);
+ }
+ }
+ out_x += out_xs_this_iter;
+ } while (out_x < output_width);
+ }
+ }
+ }
+}
+} // namespace kelvin::opt
