| /* |
| * 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 |