Reduce vld calls in ConvS8D4.
Introduced specialized ConvS8W8D4, which unrolls hot inner loop.
Also uses reduced #'s of vld calls.
Change-Id: I77c1e6e633c27801e45846e035399b10f47e9a6b
diff --git a/tflm/opt/conv_s8.cc b/tflm/opt/conv_s8.cc
index 64e22e1..95149b3 100644
--- a/tflm/opt/conv_s8.cc
+++ b/tflm/opt/conv_s8.cc
@@ -224,6 +224,13 @@
RUN_KERNEL(kelvin::opt::ConvPerChannelD1OD24_5x5);
}
+ // special case of filter_depth = 4n, stride 2 and min width
+ if (dilation_width_factor == 1 && dilation_height_factor == 1 &&
+ stride_width == 2 && stride_height == 2 && filter_depth % 4 == 0 &&
+ output_depth >= 8 && output_width >= 8 && pad_width <= 1) {
+ RUN_KERNEL(kelvin::opt::ConvS8W8D4);
+ }
+
// 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 &&
diff --git a/tflm/opt/conv_s8.h b/tflm/opt/conv_s8.h
index 6450537..b79bd65 100644
--- a/tflm/opt/conv_s8.h
+++ b/tflm/opt/conv_s8.h
@@ -54,6 +54,16 @@
const tflite::RuntimeShape& bias_shape, const int32_t* bias_data,
const tflite::RuntimeShape& output_shape, int8_t* output_data);
+// filter depth 4n, W >= 8
+void ConvS8W8D4(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_d4.cc b/tflm/opt/conv_s8_d4.cc
index 26f3885..ba1b960 100644
--- a/tflm/opt/conv_s8_d4.cc
+++ b/tflm/opt/conv_s8_d4.cc
@@ -27,6 +27,7 @@
#include "tensorflow/lite/kernels/internal/runtime_shape.h"
#include "tensorflow/lite/kernels/internal/types.h"
#include "tflm/opt/conv_s8.h"
+#include "tflm/opt/conv_util.h"
#define unlikely(x) (__builtin_expect(false || (x), false))
#define likely(x) (__builtin_expect(false || (x), true))
@@ -34,7 +35,9 @@
namespace kelvin::opt {
namespace {
-void Filter_N_H_W_M(const int8_t* input, int8_t* output, int N, int H, int W, int M) {
+// Version of Filter_N_H_W_M which also pads outputs to 8 and inputs to 4.
+void PaddedFilter_N_H_W_M(const int8_t* input, int8_t* output, int N, 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);
@@ -63,19 +66,6 @@
}
}
-void Swizzle(const int32_t* input, int32_t* output, int N) {
- assert(N <= 8);
- const int32_t(&in)[8] = *(int32_t(*)[8])input;
- int32_t(&out)[32] = *(int32_t(*)[32]) output;
- // Convert to accumulator swizzle pattern.
- memset(out, 0, 32 * sizeof(int32_t));
- int offsets[] = {0, 16, 8, 24, 1, 17, 9, 25};
- for (int i = 0; i < N; ++i) {
- int offset = offsets[i];
- out[0 + offset] = out[2 + offset] = out[4 + offset] = out[6 + offset] = in[i];
- }
-}
-
} // namespace
void ConvS8D4(
@@ -150,10 +140,11 @@
int out_channel = 0;
do {
int out_channels_this_iter = std::min(8, output_depth - out_channel);
- Filter_N_H_W_M(filter_data + (out_channel * filter_height * filter_width *
- filter_input_depth),
- p_swizzled_filter_data, out_channels_this_iter, filter_height, filter_width,
- filter_input_depth);
+ PaddedFilter_N_H_W_M(
+ filter_data + (out_channel * filter_height * filter_width *
+ filter_input_depth),
+ p_swizzled_filter_data, out_channels_this_iter, filter_height,
+ filter_width, filter_input_depth);
Swizzle(bias_data + out_channel, swizzled_bias_data, out_channels_this_iter);
Swizzle(output_multiplier + out_channel, swizzled_mult_data, out_channels_this_iter);
Swizzle(output_shift + out_channel, swizzled_shift_data, out_channels_this_iter);
@@ -354,4 +345,518 @@
out_channel += out_channels_this_iter;
} while (out_channel < output_depth);
}
+
+// Optimized for width >= 8
+void ConvS8W8D4(
+ 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];
+
+ int out_channel = 0;
+ do {
+ int out_channels_this_iter = std::min(8, output_depth - out_channel);
+ PaddedFilter_N_H_W_M(
+ filter_data + (out_channel * filter_height * filter_width *
+ filter_input_depth),
+ p_swizzled_filter_data, out_channels_this_iter, filter_height,
+ filter_width, filter_input_depth);
+
+ if (bias_data) {
+ Swizzle(bias_data + out_channel, swizzled_bias_data, 8);
+ vld_w_x_m(v44, swizzled_bias_data);
+ } else {
+ vdup_w_x_m(v44, 0);
+ }
+
+ Swizzle(output_multiplier + out_channel, swizzled_mult_data, out_channels_this_iter);
+ Swizzle(output_shift + out_channel, swizzled_shift_data, out_channels_this_iter);
+
+ vld_w_x_m(v56, swizzled_mult_data);
+ vld_w_x_m(v60, swizzled_shift_data);
+ vrsub_w_vx_m(v60, v60, 0);
+
+ for (int batch = 0; batch < batches; ++batch) {
+ int8_t* p_output =
+ output_data + (batch * output_height * output_width * output_depth) +
+ out_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 out_y_offset = (out_y * output_width * output_depth);
+ int out_x = 0;
+ while ((out_x * stride_width) < pad_width) {
+ int out_xs_this_iter = 8;
+ // 8x accumulators
+ vmv_v_m(v48, v44);
+ vmv_v_m(v52, v44);
+ acset_v(v48, v48);
+
+ int in_channel = 0;
+ while (in_channel < filter_input_depth) {
+ int in_channels_this_iter = std::min(filter_input_depth, 32);
+ // Calculate first valid filter_y
+ int filter_y = 0;
+ {
+ int in_y = in_y_origin;
+ while (in_y < 0) {
+ ++filter_y;
+ in_y += (dilation_height_factor);
+ }
+ }
+ for (; filter_y < filter_height; ++filter_y) {
+ const int y_filter_offset =
+ (filter_y * filter_width * 8 * input_depth);
+ const int in_y = in_y_origin + dilation_height_factor * filter_y;
+ if (in_y >= input_height) {
+ break;
+ }
+ const int8_t* p_in =
+ input_data + in_channel + (in_y * input_width * input_depth) +
+ (batch * input_height * input_width * input_depth);
+
+ int in_x[8];
+#pragma GCC unroll 8
+ for (int i = 0; i < 8; ++i) {
+ in_x[i] = ((out_x + i) * stride_width) - pad_width;
+ }
+
+ for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
+ const int8_t* p_in_x[8];
+
+#pragma GCC unroll 8
+ for (int i = 0; i < 8; ++i) {
+ p_in_x[i] = p_in + (in_x[i] * input_depth);
+ }
+
+ int stride = input_depth * stride_width;
+
+ if (in_x[0] < 0) {
+ vdup_b_x(v0, -input_offset);
+ vld_b_s_xx(v1, p_in_x[1], stride);
+ vld_b_s_xx(v2, p_in_x[2], stride);
+ vld_b_s_xx(v3, p_in_x[3], stride);
+ vld_b_s_xx_m(v4, p_in_x[4], stride);
+ } else {
+ // Inputs
+ vld_b_s_xx_m(v0, p_in_x[0], stride);
+ vld_b_s_xx_m(v4, p_in_x[4], stride);
+ }
+ size_t local_filter_offset = y_filter_offset +
+ (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 = (in_channels_this_iter / 4) - 1;
+ aconv_vxv(v48, v0, cmds, v8);
+
+#pragma GCC unroll 8
+ for (int i = 0; i < 8; ++i) {
+ in_x[i] += dilation_width_factor;
+ }
+ }
+ }
+ in_channel += in_channels_this_iter;
+ }
+
+ vcget(v48);
+ INT32_TO_INT8_OUTPUT_PIPELINE_INPLACE2(
+ v48, v52, v56, v60, output_activation_min, output_activation_max,
+ output_offset);
+ vsraqs_b_vx(v48, v48, 0);
+ vsraqs_b_vx(v52, v52, 0);
+ int i = 0;
+ int8_t* p_out = p_output + out_y_offset + (out_x * output_depth);
+ for (; i < std::min(4, out_xs_this_iter); i++) {
+ vst_b_l_xx(v48, p_out, out_channels_this_iter);
+ p_out += output_depth;
+ vsliden_h_4_vv(v48, v48, v48);
+ }
+ for (; i < out_xs_this_iter; i++) {
+ vst_b_l_xx(v52, p_out, out_channels_this_iter);
+ p_out += output_depth;
+ vsliden_h_4_vv(v52, v52, v52);
+ }
+
+ out_x += out_xs_this_iter;
+ } // ((out_x * stride_width) < pad_width)
+
+ // Hot loop, no x padding
+ int right_x = ((out_x + 7) * stride_width) + filter_width - pad_width;
+ while (right_x < output_width) {
+ int out_xs_this_iter = 8;
+ // 8x accumulators
+ vmv_v_m(v48, v44);
+ vmv_v_m(v52, v44);
+ acset_v(v48, v48);
+ int in_channel = 0;
+ while (in_channel < filter_input_depth) {
+ int in_channels_this_iter = std::min(filter_input_depth, 32);
+ cmds.conv.stop = (in_channels_this_iter / 4) - 1;
+
+ // Calculate first valid filter_y
+ int filter_y = 0;
+ {
+ int in_y = in_y_origin;
+ while (in_y < 0) {
+ ++filter_y;
+ in_y += (dilation_height_factor);
+ }
+ }
+ for (; filter_y < filter_height; ++filter_y) {
+ const int y_filter_offset =
+ (filter_y * filter_width * 8 * input_depth);
+ const int in_y = in_y_origin + dilation_height_factor * filter_y;
+ if (in_y >= input_height) {
+ break;
+ }
+ const int8_t* p_in =
+ input_data + in_channel + (in_y * input_width * input_depth) +
+ (batch * input_height * input_width * input_depth);
+
+ int in_x = (out_x * stride_width) - pad_width;
+
+ for (int s = 0; s < stride_width; s++) {
+ int filter_x = s;
+ int stride = input_depth * stride_width;
+
+ const int8_t* p_in_x0 = p_in +
+ ((in_x + filter_x) * input_depth);
+ vld_b_s_xx_m(v0, p_in_x0, stride);
+ p_in_x0 += 4 * stride;
+ vld_b_s_xx_m(v4, p_in_x0, stride);
+ p_in_x0 += 4 * stride;
+
+ {
+ size_t local_filter_offset = y_filter_offset +
+ (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);
+ filter_x += stride_width;
+
+ for (; filter_x + stride_width < filter_width;
+ filter_x += 2 * stride_width) {
+ // Iteration 1
+ vmv_v(v16, v1);
+ vmv_v(v17, v2);
+ vmv_v(v18, v3);
+ vmv_v(v19, v4);
+ vmv_v(v20, v5);
+ vmv_v(v21, v6);
+ vmv_v(v22, v7);
+ vld_b_l_xx(v23, p_in_x0, in_channels_this_iter);
+ p_in_x0 += stride;
+
+ size_t local_filter_offset0 = y_filter_offset +
+ (filter_x * 8 * input_depth) +
+ (in_channel * 8);
+ int8_t* p_local_filter_start0 =
+ p_swizzled_filter_data + local_filter_offset0;
+ vld_b_x_m(v24, p_local_filter_start0);
+ vld_b_x_m(v28, p_local_filter_start0 + 128);
+
+ aconv_vxv(v48, v16, cmds, v24);
+
+ // Iteration 2
+ vmv_v(v0, v17);
+ vmv_v(v1, v18);
+ vmv_v(v2, v19);
+ vmv_v(v3, v20);
+ vmv_v(v4, v21);
+ vmv_v(v5, v22);
+ vmv_v(v6, v23);
+ vld_b_l_xx(v7, p_in_x0, in_channels_this_iter);
+ p_in_x0 += stride;
+
+ size_t local_filter_offset1 = y_filter_offset +
+ ((filter_x + stride_width) * 8 * input_depth) +
+ (in_channel * 8);
+ int8_t* p_local_filter_start1 =
+ p_swizzled_filter_data + local_filter_offset1;
+ vld_b_x_m(v8, p_local_filter_start1);
+ vld_b_x_m(v12, p_local_filter_start1 + 128);
+
+ aconv_vxv(v48, v0, cmds, v8);
+ }
+
+ for (; filter_x < filter_width; filter_x += stride_width) {
+ // Iteration 1
+ vmv_v(v16, v1);
+ vmv_v(v17, v2);
+ vmv_v(v18, v3);
+ vmv_v(v19, v4);
+ vmv_v(v20, v5);
+ vmv_v(v21, v6);
+ vmv_v(v22, v7);
+ vld_b_l_xx(v23, p_in_x0, in_channels_this_iter);
+ p_in_x0 += stride;
+
+ size_t local_filter_offset = y_filter_offset +
+ (filter_x * 8 * input_depth) +
+ (in_channel * 8);
+ int8_t* p_local_filter_start =
+ p_swizzled_filter_data + local_filter_offset;
+ vld_b_x_m(v24, p_local_filter_start);
+ vld_b_x_m(v28, p_local_filter_start + 128);
+
+ aconv_vxv(v48, v16, cmds, v24);
+ }
+ }
+ }
+ in_channel += in_channels_this_iter;
+ } // while (in_channel < filter_input_depth);
+ vcget(v48);
+ INT32_TO_INT8_OUTPUT_PIPELINE_INPLACE2(
+ v48, v52, v56, v60, output_activation_min, output_activation_max,
+ output_offset);
+
+ vsraqs_b_vx(v48, v48, 0);
+ vsraqs_b_vx(v52, v52, 0);
+ int i = 0;
+ int8_t* p_out = p_output + out_y_offset + (out_x * output_depth);
+ for (; i < std::min(4, out_xs_this_iter); i++) {
+ vst_b_l_xx(v48, p_out, out_channels_this_iter);
+ p_out += output_depth;
+ vsliden_h_4_vv(v48, v48, v48);
+ }
+ for (; i < out_xs_this_iter; i++) {
+ vst_b_l_xx(v52, p_out, out_channels_this_iter);
+ p_out += output_depth;
+ vsliden_h_4_vv(v52, v52, v52);
+ }
+
+ right_x += out_xs_this_iter * stride_width;
+ out_x += out_xs_this_iter;
+ }
+
+ while (out_x < output_width) {
+ int out_xs_this_iter = std::min(8, output_width - out_x);
+ // 8x accumulators
+ vmv_v_m(v48, v44);
+ vmv_v_m(v52, v44);
+ acset_v(v48, v48);
+ int in_channel = 0;
+
+ while (in_channel < filter_input_depth) {
+ int in_channels_this_iter = std::min(filter_input_depth, 32);
+ // Calculate first valid filter_y
+ int filter_y = 0;
+ {
+ int in_y = in_y_origin;
+ while (in_y < 0) {
+ ++filter_y;
+ in_y += (dilation_height_factor);
+ }
+ }
+ for (; filter_y < filter_height; ++filter_y) {
+ const int y_filter_offset =
+ (filter_y * filter_width * 8 * input_depth);
+ const int in_y = in_y_origin + dilation_height_factor * filter_y;
+ if (in_y >= input_height) {
+ break;
+ }
+ const int8_t* p_in =
+ input_data + in_channel + (in_y * input_width * input_depth) +
+ (batch * input_height * input_width * input_depth);
+
+ int in_x[8];
+#pragma GCC unroll 8
+ for (int i = 0; i < 8; ++i) {
+ in_x[i] = ((out_x + i) * stride_width) - pad_width;
+ }
+ for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
+ const int8_t* p_in_x[8];
+ int first_right_pad = -1;
+
+#pragma GCC unroll 8
+ for (int i = 0; i < 8; ++i) {
+ p_in_x[i] = p_in + (in_x[i] * input_depth);
+ }
+
+#pragma GCC unroll 8
+ for (int i = 7; i >= 0; --i) {
+ if (in_x[i] < input_width) {
+ break;
+ }
+ first_right_pad = i;
+ }
+ bool left_pad = (in_x[0] < 0);
+ bool right_pad = (first_right_pad != -1);
+
+ int stride = input_depth * stride_width;
+
+ if (unlikely(left_pad)) {
+ vdup_b_x(v0, -input_offset);
+ vld_b_s_xx(v1, p_in_x[1], stride);
+ vld_b_s_xx(v2, p_in_x[2], stride);
+ vld_b_s_xx(v3, p_in_x[3], stride);
+ vld_b_s_xx_m(v4, p_in_x[4], stride);
+ } else if (unlikely(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, p_in_x[7], stride);
+ case 1:
+ vld_b_s_xx(v6, p_in_x[6], stride);
+ case 2:
+ vld_b_s_xx(v5, p_in_x[5], stride);
+ case 3:
+ vld_b_s_xx(v4, p_in_x[4], stride);
+ case 4:
+ vld_b_s_xx(v3, p_in_x[3], stride);
+ case 5:
+ vld_b_s_xx(v2, p_in_x[2], stride);
+ case 6:
+ vld_b_s_xx(v1, p_in_x[1], stride);
+ case 7:
+ vld_b_s_xx(v0, p_in_x[0], stride);
+ }
+ } else if (likely(!left_pad && !right_pad)) {
+ // Inputs
+ vld_b_s_xx_m(v0, p_in_x[0], stride);
+ vld_b_s_xx_m(v4, p_in_x[4], stride);
+ } else {
+ vdup_b_x(v0, neg_input_offset);
+ vdup_b_x(v7, neg_input_offset);
+ vld_b_s_xx_m(v1, p_in_x[1], stride);
+ vld_b_s_xx(v5, p_in_x[5], stride);
+ vld_b_s_xx(v6, p_in_x[6], stride);
+ }
+ size_t local_filter_offset = y_filter_offset +
+ (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 = (in_channels_this_iter / 4) - 1;
+ aconv_vxv(v48, v0, cmds, v8);
+
+#pragma GCC unroll 8
+ for (int i = 0; i < 8; ++i) {
+ in_x[i] += dilation_width_factor;
+ }
+ }
+ }
+ in_channel += in_channels_this_iter;
+ } // while (in_channel < filter_input_depth);
+
+ vcget(v48);
+ INT32_TO_INT8_OUTPUT_PIPELINE_INPLACE2(
+ v48, v52, v56, v60, output_activation_min, output_activation_max,
+ output_offset);
+ vsraqs_b_vx(v48, v48, 0);
+ vsraqs_b_vx(v52, v52, 0);
+
+ int i = 0;
+ int8_t* p_out = p_output + out_y_offset + (out_x * output_depth);
+ for (; i < std::min(4, out_xs_this_iter); i++) {
+ vst_b_l_xx(v48, p_out, out_channels_this_iter);
+ p_out += output_depth;
+ vsliden_h_4_vv(v48, v48, v48);
+ }
+ for (; i < out_xs_this_iter; i++) {
+ vst_b_l_xx(v52, p_out, out_channels_this_iter);
+ p_out += output_depth;
+ vsliden_h_4_vv(v52, v52, v52);
+ }
+
+ out_x += out_xs_this_iter;
+ } // while (out_x < output_width);
+ }
+ }
+ out_channel += out_channels_this_iter;
+ } while (out_channel < output_depth);
+}
+
} // namespace kelvin::opt
diff --git a/tflm/opt/conv_util.h b/tflm/opt/conv_util.h
index a142c6f..34f3857 100644
--- a/tflm/opt/conv_util.h
+++ b/tflm/opt/conv_util.h
@@ -113,23 +113,15 @@
// Swizzle values, and duplicate 4 times for stripmining.
inline void Swizzle(const int32_t* input, int32_t* output, int N,
bool negate = false) {
- const int32_t(&in)[N] = *(int32_t(*)[N])input;
- int32_t(&out)[N * 4] = *(int32_t(*)[N * 4]) output;
+ assert(N <= 8);
+ const int32_t(&in)[8] = *(int32_t(*)[8])input;
+ int32_t(&out)[32] = *(int32_t(*)[32]) 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++;
- }
- }
+ memset(out, 0, 32 * sizeof(int32_t));
+ int offsets[] = {0, 16, 8, 24, 1, 17, 9, 25};
+ for (int i = 0; i < N; ++i) {
+ int offset = offsets[i];
+ out[0 + offset] = out[2 + offset] = out[4 + offset] = out[6 + offset] = in[i];
}
if (negate) {
for (int i = 0; i < N * 4; ++i) {
