WIP Depthwise conv for 2d kernels *Draft verison is working *need to add kelvin instructions Change-Id: Id282fed89d333f265c448158e448322988d05b31
diff --git a/tflm/opt/BUILD b/tflm/opt/BUILD index 6481a8a..e4d533b 100644 --- a/tflm/opt/BUILD +++ b/tflm/opt/BUILD
@@ -19,6 +19,7 @@ srcs = [ "conv.cc", "depthwise_conv_s16.cc", + "depthwise_conv_s8.cc", "elementwise_add_s16.cc", "elementwise_add_s32.cc", "elementwise_add_s8.cc", @@ -35,6 +36,7 @@ deps = [ "//crt", "@tflite-micro//tensorflow/lite/kernels/internal:common", + "@tflite-micro//tensorflow/lite/kernels/internal:reference_base", ], alwayslink = True, )
diff --git a/tflm/opt/depthwise_conv_s8.cc b/tflm/opt/depthwise_conv_s8.cc new file mode 100644 index 0000000..c4ee35a --- /dev/null +++ b/tflm/opt/depthwise_conv_s8.cc
@@ -0,0 +1,194 @@ +/* + * Copyright 2023 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. + */ + +#include <algorithm> + +#include "crt/kelvin.h" +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h" +#include "tensorflow/lite/kernels/internal/runtime_shape.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tflm/opt/opt.h" + +namespace kelvin::opt { + +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++; + } + } + } +} + +void DWConv2DKelvin_d32( + const tflite::DepthwiseParams& 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 + +) { + 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 input_offset = params.input_offset; + const int32_t output_offset = params.output_offset; + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int input_depth = input_shape.Dims(3); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + int32_t swizzled_bias_data[32 * 4]; + int32_t swizzled_shift_multi[32 * 4]; + int32_t swizzled_output_multi[32 * 4]; + + for (int in_channel = 0; in_channel + 32 <= input_depth; in_channel += 32) { + const int output_channel = in_channel; + Swizzle(bias_data + output_channel, swizzled_bias_data, 32); + Swizzle(output_multiplier + output_channel, swizzled_output_multi, 32); + Swizzle(output_shift + output_channel, swizzled_shift_multi, 32); + + vld_w_x_m(v20, swizzled_bias_data); + vld_w_x_m(v24, swizzled_output_multi); + vld_w_x_m(v28, swizzled_shift_multi); + vrsub_w_vx_m(v28, v28, 0); + + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + const int in_x_origin = (out_x * stride_width) - pad_width; + const int in_y_origin = (out_y * stride_height) - pad_height; + + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + const int in_y = in_y_origin + filter_y; + if ((in_y < 0) || (in_y >= input_height)) { + continue; + } + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + const int in_x = in_x_origin + filter_x; + if ((in_x < 0) || (in_x >= input_width)) { + continue; + } + + vld_b_x(v0, &input_data[tflite::Offset(input_shape, batch, in_y, + in_x, in_channel)]); // xp + vld_b_x(v4, &filter_data[tflite::Offset(filter_shape, 0, filter_y, + filter_x, in_channel)]); + + vaddw_h_vx(v0, v0, 0); + vadd_h_vx(v0, v0, static_cast<int16_t>(input_offset)); + vadd_h_vx(v1, v1, + static_cast<int16_t>(input_offset)); // v0 v1 input + + vaddw_h_vx(v4, v4, static_cast<int16_t>(0)); + vmulw_w_vv(v8, v0, v4); + vmulw_w_vv(v10, v1, v5); + + vadd_w_vv_m(v48, v48, v8); + } + } + + vadd_w_vv_m(v48, v48, v20); // add bias + vdmulh_w_r_vv_m(v48, v48, v24); + vsha_w_r_vv_m(v48, v48, v28); + vadd_w_vx_m(v48, v48, output_offset); + vmax_w_vx_m(v48, v48, output_activation_min); + vmin_w_vx_m(v48, v48, output_activation_max); + vsraqs_b_vx(v48, v48, 0); + vst_b_x(v48, &output_data[tflite::Offset(output_shape, batch, out_y, + out_x, output_channel)]); + } + } + } + } +} + +void DepthwiseConv2DKelvin( + const tflite::DepthwiseParams& 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. + // TODO(b/141565753): Re-introduce ScopedProfilingLabel on Micro. + 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 int depth_multiplier = params.depth_multiplier; + const int32_t input_offset = params.input_offset; + const int32_t output_offset = params.output_offset; + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + + // Check dimensions of the tensors. + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int input_depth = input_shape.Dims(3); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + + if (depth_multiplier == 1 && pad_height < 2 && pad_width < 2 && + dilation_height_factor == 1 && dilation_width_factor == 1 && + stride_height == 1 && stride_width == 1 && output_depth % 32 == 0) { + DWConv2DKelvin_d32(params, output_multiplier, output_shift, input_shape, + input_data, filter_shape, filter_data, bias_shape, + bias_data, output_shape, output_data); + return; + } + tflite::reference_integer_ops::DepthwiseConvPerChannel( + params, output_multiplier, output_shift, input_shape, input_data, + filter_shape, filter_data, bias_shape, bias_data, output_shape, + output_data); + return; +} +} // namespace kelvin::opt \ No newline at end of file
diff --git a/tflm/opt/opt.h b/tflm/opt/opt.h index 9f83bf6..277f338 100644 --- a/tflm/opt/opt.h +++ b/tflm/opt/opt.h
@@ -24,7 +24,7 @@ /* clang-format on */ namespace kelvin::opt { -void *memcpy(void *dst, const void *src, size_t n); +void* memcpy(void* dst, const void* src, size_t n); void elementwise_add_s8(const int8_t* input1, const int8_t* input2, const int32_t input1_offset, const int32_t input1_mult, const int32_t input1_shift, const int32_t input2_offset, @@ -115,13 +115,29 @@ const int8_t* filter_data, const tflite::RuntimeShape& bias_shape, const int32_t* bias_data, const tflite::RuntimeShape& output_shape, int8_t* output_data); +void DepthwiseConv2DKelvin( + const tflite::DepthwiseParams& 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); +void DWConv2DKelvin_d32( + const tflite::DepthwiseParams& 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); void DepthwiseConv2DKelvinS16K3x1( const int16_t* activations, const int8_t* weights, const int64_t* biases, int channels, int frames, int dilation, const int32_t* output_mult, const int32_t* output_shift, int32_t output_activation_min, int32_t output_activation_max, int16_t* output); -void MaxPoolGeneric(const tflite::PoolParams& params, const tflite::RuntimeShape& input_shape, - const int8_t* input_data, const tflite::RuntimeShape& output_shape, +void MaxPoolGeneric(const tflite::PoolParams& params, + const tflite::RuntimeShape& input_shape, + const int8_t* input_data, + const tflite::RuntimeShape& output_shape, int8_t* output_data); } // namespace kelvin::opt