Optimize 1x1 group conv. Change-Id: I92f60e1c1b0dfa255063692265176ec2fce65469
diff --git a/tflm/opt/conv.cc b/tflm/opt/conv.cc index 9ea92bc..df4dc89 100644 --- a/tflm/opt/conv.cc +++ b/tflm/opt/conv.cc
@@ -138,6 +138,298 @@ } } +// 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 ukernel_s8_s16(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 conv_per_channel_b64_1x1( + 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 input_offset = params.input_offset; + 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); + ukernel_s8_s16(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 conv_per_channel_b64_filter1xn_group( + 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 input_offset = params.input_offset; + 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); + + ukernel_s8_s16(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 conv_per_channel_b64_filter1xn_non_group( + 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 input_offset = params.input_offset; + 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); + ukernel_s8_s16(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 conv_per_channel_b64( const tflite::ConvParams& params, const int32_t* output_multiplier, const int32_t* output_shift, const tflite::RuntimeShape& input_shape,
diff --git a/tflm/opt/opt.h b/tflm/opt/opt.h index 17e004a..6797f36 100644 --- a/tflm/opt/opt.h +++ b/tflm/opt/opt.h
@@ -70,6 +70,31 @@ const int8_t* filter_data, const tflite::RuntimeShape& bias_shape, const int32_t* bias_data, const tflite::RuntimeShape& output_shape, int16_t* output_data); + +void conv_per_channel_b64_1x1( + 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); + +void conv_per_channel_b64_filter1xn_non_group( + 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); + +void conv_per_channel_b64_filter1xn_group( + 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); + void conv_per_channel_b64( const tflite::ConvParams& params, const int32_t* output_multiplier, const int32_t* output_shift, const tflite::RuntimeShape& input_shape,