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/*
* 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: s16, filter: s8, bias s64
#include "tflm/opt/conv_util.h"
namespace kelvin::opt {
namespace {
// Accumulates in v0-v7. [v0-v3], [v4-v7] are sub accumulators for two outputs.
// Load/swizzle filters use [v52-v63].
// Input activations use [v32-v33].
// No clobbers.
void ConvUkernelS8S16(const int16_t* input_data0, const int8_t* filter_data0,
const int8_t* filter_data1, size_t n) {
n = n >> 5;
while (n > 0) {
// Load filters 0 to v58, v59
vld_b_p_x(v52, filter_data0);
vaddw_h_vx(v56, v52, 0);
vzip_h_vv(v58, v56, v57);
// Load activations
vld_h_p_x(v32, input_data0);
vld_h_p_x(v33, input_data0);
// Multiply filters0 * activations
vmulw_w_vv(v16, v58, v32);
vmulw_w_vv(v18, v59, v33);
// Accumulate v0
vadd_w_vv_m(v0, v0, v16);
// Load filters 1 to v62, v63
vld_b_p_x(v53, filter_data1);
vaddw_h_vx(v60, v53, 0);
vzip_h_vv(v62, v60, v61);
// Multiply filters1 * activations
vmulw_w_vv(v20, v62, v32);
vmulw_w_vv(v22, v63, v33);
// Accumulate v4
vadd_w_vv_m(v4, v4, v20);
n--;
}
}
void ConvS16B64K1x1(
const tflite::ConvParams& params, const int32_t* output_multiplier,
const int32_t* output_shift, const tflite::RuntimeShape& input_shape,
const int16_t* input_data, const tflite::RuntimeShape& filter_shape,
const int8_t* filter_data, const tflite::RuntimeShape& bias_shape,
const int64_t* bias_data, const tflite::RuntimeShape& output_shape,
int16_t* output_data) {
const auto batches = MatchingDim(input_shape, 0, output_shape, 0);
const auto input_height = input_shape.Dims(1);
const auto input_width = input_shape.Dims(2);
const auto input_depth = input_shape.Dims(3);
const auto filter_input_depth = filter_shape.Dims(3);
const auto output_depth = output_shape.Dims(3);
const auto output_offset = params.output_offset;
const auto output_activation_min = params.quantized_activation_min;
const auto output_activation_max = params.quantized_activation_max;
const auto groups = input_depth / filter_input_depth;
const auto output_filters_per_group = output_depth / groups;
int32_t accumulators[8];
for (int bhw = 0; bhw < batches * input_height * input_width; bhw++) {
const int16_t* local_input = input_data + (bhw * input_depth);
int16_t* local_output = output_data + (bhw * output_depth);
for (int g = 0; g < groups; g++) {
const int16_t* group_input = local_input + (g * filter_input_depth);
for (int gc = 0; gc + 2 <= output_filters_per_group; gc += 2) {
int oc = (g * output_filters_per_group) + gc;
const int8_t* local_filters0 = filter_data + (oc * filter_input_depth);
const int8_t* local_filters1 = local_filters0 + filter_input_depth;
vdup_w_x_m(v0, 0);
vdup_w_x_m(v4, 0);
ConvUkernelS8S16(group_input, local_filters0, local_filters1,
filter_input_depth);
// sum accumulators
vadd_w_vv(v0, v0, v1);
vadd_w_vv(v2, v2, v3);
vadd_w_vv(v0, v0, v2);
vadd_w_vv(v4, v4, v5);
vadd_w_vv(v6, v6, v7);
vadd_w_vv(v4, v4, v6);
{
vst_w_x(v0, accumulators);
int64_t acc64 = bias_data[oc];
for (int i = 0; i < 8; i++) {
acc64 += accumulators[i];
}
int32_t acc = tflite::MultiplyByQuantizedMultiplier(
acc64, output_multiplier[oc], output_shift[oc]);
acc += output_offset;
acc = std::clamp(acc, output_activation_min, output_activation_max);
local_output[oc] = static_cast<int16_t>(acc);
}
{
vst_w_x(v4, accumulators);
int64_t acc64 = bias_data[oc + 1];
for (int i = 0; i < 8; i++) {
acc64 += accumulators[i];
}
int32_t acc = tflite::MultiplyByQuantizedMultiplier(
acc64, output_multiplier[oc + 1], output_shift[oc + 1]);
acc += output_offset;
acc = std::clamp(acc, output_activation_min, output_activation_max);
local_output[oc + 1] = static_cast<int16_t>(acc);
}
}
}
}
}
// Optimized for grouped convolutions, no dilation, 1xn filter
void ConvS16B64K1xnGroup(
const tflite::ConvParams& params, const int32_t* output_multiplier,
const int32_t* output_shift, const tflite::RuntimeShape& input_shape,
const int16_t* input_data, const tflite::RuntimeShape& filter_shape,
const int8_t* filter_data, const tflite::RuntimeShape& bias_shape,
const int64_t* bias_data, const tflite::RuntimeShape& output_shape,
int16_t* output_data) {
const auto batches = MatchingDim(input_shape, 0, output_shape, 0);
const auto stride_width = params.stride_width;
const auto pad_width = params.padding_values.width;
const auto input_width = input_shape.Dims(2);
const auto input_depth = input_shape.Dims(3);
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);
const auto output_offset = params.output_offset;
const auto output_activation_min = params.quantized_activation_min;
const auto output_activation_max = params.quantized_activation_max;
const auto groups = input_depth / filter_depth;
const auto output_filters_per_group = output_depth / groups;
int32_t accumulators[8];
for (int g = 0; g < groups; g++) {
for (int gc = 0; gc + 2 <= output_filters_per_group; gc += 2) {
int oc = (g * output_filters_per_group) + gc;
for (int b = 0; b < batches; ++b) {
for (int out_x = 0; out_x < output_width; ++out_x) {
const int in_x_origin = out_x * stride_width - pad_width;
const int8_t* local_filters0 =
filter_data + (oc * filter_width * filter_depth);
const int8_t* local_filters1 =
local_filters0 + (filter_width * filter_depth);
const int16_t* local_input =
input_data + (b * input_width * input_depth) +
(in_x_origin * input_depth) + (g * filter_depth);
int16_t* local_output = output_data +
(b * output_width * output_depth) +
(out_x * output_depth);
int64_t acc64_0 = 0;
int64_t acc64_1 = 0;
vdup_w_x_m(v0, 0);
vdup_w_x_m(v4, 0);
for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
const int8_t* local_filters0x =
local_filters0 + (filter_x * filter_depth);
const int8_t* local_filters1x =
local_filters1 + (filter_x * filter_depth);
const int16_t* local_inputx =
local_input + (filter_x * input_depth);
ConvUkernelS8S16(local_inputx, local_filters0x, local_filters1x,
filter_depth);
}
// sum accumulators
vadd_w_vv(v0, v0, v1);
vadd_w_vv(v2, v2, v3);
vadd_w_vv(v0, v0, v2);
vadd_w_vv(v4, v4, v5);
vadd_w_vv(v6, v6, v7);
vadd_w_vv(v4, v4, v6);
{
vst_w_x(v0, accumulators);
for (int i = 0; i < 8; i++) {
acc64_0 += accumulators[i];
}
acc64_0 += bias_data[oc];
int32_t acc = tflite::MultiplyByQuantizedMultiplier(
acc64_0, output_multiplier[oc], output_shift[oc]);
acc += output_offset;
acc = std::clamp(acc, output_activation_min, output_activation_max);
local_output[oc] = static_cast<int16_t>(acc);
}
{
vst_w_x(v4, accumulators);
for (int i = 0; i < 8; i++) {
acc64_1 += accumulators[i];
}
acc64_1 += bias_data[oc + 1];
int32_t acc = tflite::MultiplyByQuantizedMultiplier(
acc64_1, output_multiplier[oc + 1], output_shift[oc + 1]);
acc += output_offset;
acc = std::clamp(acc, output_activation_min, output_activation_max);
local_output[oc + 1] = static_cast<int16_t>(acc);
}
}
}
}
}
}
// Optimized for no group, no dilation, 1xn filter.
void ConvS16B64K1xnNonGroup(
const tflite::ConvParams& params, const int32_t* output_multiplier,
const int32_t* output_shift, const tflite::RuntimeShape& input_shape,
const int16_t* input_data, const tflite::RuntimeShape& filter_shape,
const int8_t* filter_data, const tflite::RuntimeShape& bias_shape,
const int64_t* bias_data, const tflite::RuntimeShape& output_shape,
int16_t* output_data) {
const auto batches = MatchingDim(input_shape, 0, output_shape, 0);
const auto stride_width = params.stride_width;
const auto pad_width = params.padding_values.width;
const auto input_width = input_shape.Dims(2);
const auto input_depth = input_shape.Dims(3);
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);
const auto output_offset = params.output_offset;
const auto output_activation_min = params.quantized_activation_min;
const auto output_activation_max = params.quantized_activation_max;
int32_t accumulators[8];
for (int oc = 0; oc + 2 <= output_depth; oc += 2) {
for (int batch = 0; batch < batches; ++batch) {
for (int out_x = 0; out_x < output_width; ++out_x) {
const int in_x_origin = out_x * stride_width - pad_width;
const int8_t* local_filters0 =
filter_data + (oc * filter_width * filter_depth);
const int8_t* local_filters1 =
local_filters0 + (filter_width * filter_depth);
const int16_t* local_input = input_data +
(batch * input_width * input_depth) +
(in_x_origin * input_depth);
int16_t* local_output = output_data +
(batch * output_width * output_depth) +
(out_x * output_depth);
vdup_w_x_m(v0, 0);
vdup_w_x_m(v4, 0);
ConvUkernelS8S16(local_input, local_filters0, local_filters1,
filter_width * filter_depth);
// sum accumulators
vadd_w_vv(v0, v0, v1);
vadd_w_vv(v2, v2, v3);
vadd_w_vv(v0, v0, v2);
vadd_w_vv(v4, v4, v5);
vadd_w_vv(v6, v6, v7);
vadd_w_vv(v4, v4, v6);
{
vst_w_x(v0, accumulators);
int64_t acc64 = bias_data[oc];
for (int i = 0; i < 8; i++) {
acc64 += accumulators[i];
}
int32_t acc = tflite::MultiplyByQuantizedMultiplier(
acc64, output_multiplier[oc], output_shift[oc]);
acc += output_offset;
acc = std::clamp(acc, output_activation_min, output_activation_max);
local_output[oc] = static_cast<int16_t>(acc);
}
{
vst_w_x(v4, accumulators);
int64_t acc64 = bias_data[oc + 1];
for (int i = 0; i < 8; i++) {
acc64 += accumulators[i];
}
int32_t acc = tflite::MultiplyByQuantizedMultiplier(
acc64, output_multiplier[oc + 1], output_shift[oc + 1]);
acc += output_offset;
acc = std::clamp(acc, output_activation_min, output_activation_max);
local_output[oc + 1] = static_cast<int16_t>(acc);
}
}
}
}
}
void ConvS16B64Generic(
const tflite::ConvParams& params, const int32_t* output_multiplier,
const int32_t* output_shift, const tflite::RuntimeShape& input_shape,
const int16_t* input_data, const tflite::RuntimeShape& filter_shape,
const int8_t* filter_data, const tflite::RuntimeShape& bias_shape,
const int64_t* bias_data, const tflite::RuntimeShape& output_shape,
int16_t* output_data) {
const auto batches = MatchingDim(input_shape, 0, output_shape, 0);
const auto stride_width = params.stride_width;
const auto stride_height = params.stride_height;
const auto dilation_width_factor = params.dilation_width_factor;
const auto dilation_height_factor = params.dilation_height_factor;
const auto pad_width = params.padding_values.width;
const auto pad_height = params.padding_values.height;
const auto input_height = input_shape.Dims(1);
const auto input_width = input_shape.Dims(2);
const auto input_depth = input_shape.Dims(3);
const auto input_offset = params.input_offset;
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_height = output_shape.Dims(1);
const auto output_width = output_shape.Dims(2);
const auto output_depth = output_shape.Dims(3);
const auto output_offset = params.output_offset;
const auto output_activation_min = params.quantized_activation_min;
const auto output_activation_max = params.quantized_activation_max;
const auto groups = input_depth / filter_depth;
const auto filters_per_group = output_depth / groups;
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;
for (int out_channel = 0; out_channel < output_depth; ++out_channel) {
auto group = out_channel / filters_per_group;
int64_t acc64 = 0;
for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
const int in_y = in_y_origin + dilation_height_factor * filter_y;
for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
const int in_x = in_x_origin + dilation_width_factor * filter_x;
const bool inside = (in_x >= 0) && (in_x < input_width) &&
(in_y >= 0) && (in_y < input_height);
if (!inside) {
continue;
}
int in_channel = 0;
do {
int load_count = std::min(filter_depth - in_channel, 16L);
int32_t input_swizzled[16];
const int16_t* p_input = &input_data[tflite::Offset(
input_shape, batch, in_y, in_x,
in_channel + group * filter_depth)];
for (int i = 0; i < 16; ++i) {
int swizzle_idx = swizzle[i];
if (swizzle_idx < load_count)
input_swizzled[i] = *(p_input + swizzle_idx) + input_offset;
else
input_swizzled[i] = 0;
}
vld_w_l_xx(v0, input_swizzled, 4);
vld_w_l_xx(v1, input_swizzled + 4, 4);
vld_w_l_xx(v2, input_swizzled + 8, 4);
vld_w_l_xx(v3, input_swizzled + 12, 4);
vld_b_l_xx(v4,
&filter_data[tflite::Offset(filter_shape,
out_channel, filter_y,
filter_x, in_channel)],
load_count);
vaddw_h_vx(v4, v4, 0);
vaddw_w_vx(v6, v5, 0);
vaddw_w_vx(v4, v4, 0);
vmul_w_vv_m(vm0, vm0, vm1);
vadd_w_vv(v0, v0, v1);
vadd_w_vv(v0, v0, v2);
vadd_w_vv(v0, v0, v3);
int32_t acc32[4];
vst_w_l_xx(v0, acc32, 4);
for (int i = 0; i < 4; ++i) {
acc64 += acc32[i];
}
in_channel += 16;
} while (in_channel + 16 <= filter_depth);
}
}
if (bias_data) {
acc64 = acc64 + bias_data[out_channel];
}
int32_t acc = tflite::MultiplyByQuantizedMultiplier(
acc64, output_multiplier[out_channel], output_shift[out_channel]);
acc += output_offset;
acc = std::clamp(acc, output_activation_min, output_activation_max);
output_data[tflite::Offset(output_shape, batch, out_y, out_x,
out_channel)] = static_cast<int16_t>(acc);
}
}
}
}
}
} // namespace
void ConvS16B64(
const tflite::ConvParams& params, const int32_t* output_multiplier,
const int32_t* output_shift, const tflite::RuntimeShape& input_shape,
const int16_t* input_data, const tflite::RuntimeShape& filter_shape,
const int8_t* filter_data, const tflite::RuntimeShape& bias_shape,
const int64_t* bias_data, const tflite::RuntimeShape& output_shape,
int16_t* output_data) {
const auto input_depth = input_shape.Dims(3);
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_depth = output_shape.Dims(3);
// generic implementation by default
auto fn = ConvS16B64Generic;
// special cases
if (filter_height == 1 && output_depth % 2 == 0) {
// 1x1 filter, filter depth = 32n
if (filter_width == 1 && filter_depth % 32 == 0) {
fn = ConvS16B64K1x1;
}
// 1xn non group filter
bool group_conv = !(input_depth == filter_depth);
int32_t fan_in = filter_width * filter_depth;
if (!group_conv && fan_in % 32 == 0) {
fn = ConvS16B64K1xnNonGroup;
}
// 1xn group filter
if (fan_in % 32 == 0) {
fn = ConvS16B64K1xnGroup;
}
}
fn(params, output_multiplier, output_shift, input_shape, input_data,
filter_shape, filter_data, bias_shape, bias_data, output_shape,
output_data);
}
} // namespace kelvin::opt