tflm/opt/conv_s8.cc - sw/kelvin - Git at Google

 /*
  * 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: s8, filter: s8, bias: s32

 #include "tflm/opt/conv_s8.h"

 #include <algorithm>

 #include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h"
 #include "tflm/opt/conv_util.h"

 namespace kelvin::opt {
 namespace {
 void ConvS8Generic(
     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) {
   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;

   if (pad_width > 0 || pad_height > 0 || dilation_width_factor > 1 ||
       dilation_height_factor > 1) {
     // use reference implementation
     tflite::reference_integer_ops::ConvPerChannel(
         params, output_multiplier, output_shift, input_shape, input_data,
         filter_shape, filter_data, bias_shape, bias_data, output_shape,
         output_data);
     return;
   }

   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;

   // Zero out accumulators.
   vdup_b_x(v0, 0);
   acset_v(ACC, v0);
   vdup_b_x_m(ACC0, 0);
   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 += 32*/ ++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;

           for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
             const int in_y = in_y_origin + dilation_height_factor * filter_y;
             const int in_x = in_x_origin + dilation_width_factor * 0;

             // Zero padding by omitting the areas outside the image.
             const bool is_point_inside_image =
                 (in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
                 (in_y < input_height);
             if (!is_point_inside_image) {
               continue;
             }

             int q = filter_width * filter_depth;
             for (int i = 0; i < q; i += 32) {
               int count = std::min(q - i, 32);
               count = std::min(
                   count, static_cast<int>((input_width - in_x) * filter_depth));
               int input_offset = tflite::Offset(input_shape, batch, in_y, in_x,
                                                 group * filter_depth) +
                                  i;
               vdup_w_x_m(vm0, 0);
               vdup_w_x_m(vm1, 0);
               vld_b_l_xx(INA0, &input_data[input_offset], count);
               int filter_offset =
                   tflite::Offset(filter_shape, out_channel, filter_y, 0, 0) + i;
               vdup_w_x_m(FLTA0, 0);
               vdup_w_x_m(FLTA4, 0);
               if (count > 0) {
                 vld_b_l_xx(FLTA0, &filter_data[filter_offset],
                            std::min(count, 4));
               }
               if (count > 4) {
                 vld_b_l_xx(FLTA1, &filter_data[filter_offset + 4],
                            std::min(count - 4, 4));
               }
               if (count > 8) {
                 vld_b_l_xx(FLTA2, &filter_data[filter_offset + 8],
                            std::min(count - 8, 4));
               }
               if (count > 12) {
                 vld_b_l_xx(FLTA3, &filter_data[filter_offset + 12],
                            std::min(count - 12, 4));
               }
               if (count > 16) {
                 vld_b_l_xx(FLTA4, &filter_data[filter_offset + 16],
                            std::min(count - 16, 4));
               }
               if (count > 20) {
                 vld_b_l_xx(FLTA5, &filter_data[filter_offset + 20],
                            std::min(count - 20, 4));
               }
               if (count > 24) {
                 vld_b_l_xx(FLTA6, &filter_data[filter_offset + 24],
                            std::min(count - 24, 4));
               }
               if (count > 28) {
                 vld_b_l_xx(FLTA7, &filter_data[filter_offset + 28],
                            std::min(count - 28, 4));
               }
               aconv_vxv(ACC, INA0, cmds, FLTA0);
             }
           }
           vcget(ACC);
           vadd_w_vx_m(ACC0, ACC0, bias_data[out_channel]);
           vsll_w_vx_m(ACC0, ACC0, LEFT_SHIFT(output_shift[out_channel]));
           vdmulh_w_r_vx_m(ACC0, ACC0, output_multiplier[out_channel]);
           vsha_w_r_vx_m(ACC0, ACC0, RIGHT_SHIFT(output_shift[out_channel]));
           vadd_w_vx_m(ACC0, ACC0, output_offset);
           vmin_w_vx_m(ACC0, ACC0, output_activation_max);
           vmax_w_vx_m(ACC0, ACC0, output_activation_min);
           vsraqs_b_vx(OUT0, ACC0, 0);
           size_t output_offset =
               tflite::Offset(output_shape, batch, out_y, out_x, out_channel);
           vst_b_l_xx(OUT0, &output_data[output_offset], 1);
         }
       }
     }
   }
 }
 }  // namespace

 void ConvS8(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) {
   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 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);

 #define RUN_KERNEL(kernel) {\
   kernel(\
       params, output_multiplier, output_shift, input_shape, input_data,\
       filter_shape, filter_data, bias_shape, bias_data, output_shape,\
       output_data);\
   return; \
 }

   // special case of filter size 1x1
   if (filter_height == 1 && filter_width == 1 && stride_height == 1 &&
       stride_width == 1 && dilation_height_factor == 1 &&
       dilation_width_factor == 1 && pad_height == 0 && pad_width == 0 &&
       (input_depth == filter_depth)) {
     if ((input_depth == 32) && (batches * input_height * input_width) >= 4) {
       RUN_KERNEL(kelvin::opt::ConvS8K1x1D32);
     }

     if ((output_depth % 8) == 0 && (input_depth % 32) == 0) {
       RUN_KERNEL(kelvin::opt::ConvS8K1x1DMod32);
     }

     // TODO: Relax this kernel for all output_depths
     if ((output_depth < 8) && (input_depth % 32) == 0) {
       RUN_KERNEL(kelvin::opt::ConvS8K1x1DMod32);
     }

     if ((output_depth % 16) == 0 && (input_depth == 16)) {
       RUN_KERNEL(kelvin::opt::ConvS8K1x1D16);
     }
   }

   if (input_depth == 1 && filter_width == 5 && filter_height == 5 &&
       stride_width == 2 && dilation_width_factor == 1 && output_depth == 24 &&
       params.quantized_activation_min == -128 &&
       params.quantized_activation_max == 127 &&
       // Special limitations of the implementation.
       pad_height < 5 && pad_width < 5 &&
       (output_height - 1) * stride_height - pad_height < input_height &&
       (output_width - 1) * stride_width - pad_width < input_width &&
       input_width >= 19) {
     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 &&
       output_depth >= 8 && output_width >= 8 && pad_width <= 1) {
     RUN_KERNEL(kelvin::opt::ConvS8D4);
   }

   // special case of filter depth = 32n
   if (dilation_width_factor == 1 && dilation_height_factor == 1 &&
       stride_width <= 2 && stride_height <= 2 && filter_depth % 32 == 0) {
     RUN_KERNEL(kelvin::opt::ConvS8D32);
   }

   // special case of filter size 48x3x1x48
   if (batches == 1 && filter_height == 3 && filter_width == 1 &&
       input_width == 1 && input_depth == 48 && output_depth == 48 &&
       stride_height == 1 && stride_width == 1 && dilation_height_factor == 1 &&
       dilation_width_factor == 1 && pad_height == 0 && pad_width == 0) {
     RUN_KERNEL(kelvin::opt::ConvS8K3x1D48);
   }

   if (input_depth == 1 && ((output_depth % 4) == 0)) {
     RUN_KERNEL(kelvin::opt::ConvPerChannelD1);
   }

   RUN_KERNEL(ConvS8Generic);
 }

 }  // namespace kelvin::opt
	/*
	* 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: s8, filter: s8, bias: s32

	#include "tflm/opt/conv_s8.h"

	#include <algorithm>

	#include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h"
	#include "tflm/opt/conv_util.h"

	namespace kelvin::opt {
	namespace {
	void ConvS8Generic(
	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) {
	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;

	if (pad_width > 0 \|\| pad_height > 0 \|\| dilation_width_factor > 1 \|\|
	dilation_height_factor > 1) {
	// use reference implementation
	tflite::reference_integer_ops::ConvPerChannel(
	params, output_multiplier, output_shift, input_shape, input_data,
	filter_shape, filter_data, bias_shape, bias_data, output_shape,
	output_data);
	return;
	}

	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;

	// Zero out accumulators.
	vdup_b_x(v0, 0);
	acset_v(ACC, v0);
	vdup_b_x_m(ACC0, 0);
	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 += 32/ ++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;

	for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
	const int in_y = in_y_origin + dilation_height_factor * filter_y;
	const int in_x = in_x_origin + dilation_width_factor * 0;

	// Zero padding by omitting the areas outside the image.
	const bool is_point_inside_image =
	(in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
	(in_y < input_height);
	if (!is_point_inside_image) {
	continue;
	}

	int q = filter_width * filter_depth;
	for (int i = 0; i < q; i += 32) {
	int count = std::min(q - i, 32);
	count = std::min(
	count, static_cast<int>((input_width - in_x) * filter_depth));
	int input_offset = tflite::Offset(input_shape, batch, in_y, in_x,
	group * filter_depth) +
	i;
	vdup_w_x_m(vm0, 0);
	vdup_w_x_m(vm1, 0);
	vld_b_l_xx(INA0, &input_data[input_offset], count);
	int filter_offset =
	tflite::Offset(filter_shape, out_channel, filter_y, 0, 0) + i;
	vdup_w_x_m(FLTA0, 0);
	vdup_w_x_m(FLTA4, 0);
	if (count > 0) {
	vld_b_l_xx(FLTA0, &filter_data[filter_offset],
	std::min(count, 4));
	}
	if (count > 4) {
	vld_b_l_xx(FLTA1, &filter_data[filter_offset + 4],
	std::min(count - 4, 4));
	}
	if (count > 8) {
	vld_b_l_xx(FLTA2, &filter_data[filter_offset + 8],
	std::min(count - 8, 4));
	}
	if (count > 12) {
	vld_b_l_xx(FLTA3, &filter_data[filter_offset + 12],
	std::min(count - 12, 4));
	}
	if (count > 16) {
	vld_b_l_xx(FLTA4, &filter_data[filter_offset + 16],
	std::min(count - 16, 4));
	}
	if (count > 20) {
	vld_b_l_xx(FLTA5, &filter_data[filter_offset + 20],
	std::min(count - 20, 4));
	}
	if (count > 24) {
	vld_b_l_xx(FLTA6, &filter_data[filter_offset + 24],
	std::min(count - 24, 4));
	}
	if (count > 28) {
	vld_b_l_xx(FLTA7, &filter_data[filter_offset + 28],
	std::min(count - 28, 4));
	}
	aconv_vxv(ACC, INA0, cmds, FLTA0);
	}
	}
	vcget(ACC);
	vadd_w_vx_m(ACC0, ACC0, bias_data[out_channel]);
	vsll_w_vx_m(ACC0, ACC0, LEFT_SHIFT(output_shift[out_channel]));
	vdmulh_w_r_vx_m(ACC0, ACC0, output_multiplier[out_channel]);
	vsha_w_r_vx_m(ACC0, ACC0, RIGHT_SHIFT(output_shift[out_channel]));
	vadd_w_vx_m(ACC0, ACC0, output_offset);
	vmin_w_vx_m(ACC0, ACC0, output_activation_max);
	vmax_w_vx_m(ACC0, ACC0, output_activation_min);
	vsraqs_b_vx(OUT0, ACC0, 0);
	size_t output_offset =
	tflite::Offset(output_shape, batch, out_y, out_x, out_channel);
	vst_b_l_xx(OUT0, &output_data[output_offset], 1);
	}
	}
	}
	}
	}
	} // namespace

	void ConvS8(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) {
	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 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);

	#define RUN_KERNEL(kernel) {\
	kernel(\
	params, output_multiplier, output_shift, input_shape, input_data,\
	filter_shape, filter_data, bias_shape, bias_data, output_shape,\
	output_data);\
	return; \
	}

	// special case of filter size 1x1
	if (filter_height == 1 && filter_width == 1 && stride_height == 1 &&
	stride_width == 1 && dilation_height_factor == 1 &&
	dilation_width_factor == 1 && pad_height == 0 && pad_width == 0 &&
	(input_depth == filter_depth)) {
	if ((input_depth == 32) && (batches * input_height * input_width) >= 4) {
	RUN_KERNEL(kelvin::opt::ConvS8K1x1D32);
	}

	if ((output_depth % 8) == 0 && (input_depth % 32) == 0) {
	RUN_KERNEL(kelvin::opt::ConvS8K1x1DMod32);
	}

	// TODO: Relax this kernel for all output_depths
	if ((output_depth < 8) && (input_depth % 32) == 0) {
	RUN_KERNEL(kelvin::opt::ConvS8K1x1DMod32);
	}

	if ((output_depth % 16) == 0 && (input_depth == 16)) {
	RUN_KERNEL(kelvin::opt::ConvS8K1x1D16);
	}
	}

	if (input_depth == 1 && filter_width == 5 && filter_height == 5 &&
	stride_width == 2 && dilation_width_factor == 1 && output_depth == 24 &&
	params.quantized_activation_min == -128 &&
	params.quantized_activation_max == 127 &&
	// Special limitations of the implementation.
	pad_height < 5 && pad_width < 5 &&
	(output_height - 1) * stride_height - pad_height < input_height &&
	(output_width - 1) * stride_width - pad_width < input_width &&
	input_width >= 19) {
	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 &&
	output_depth >= 8 && output_width >= 8 && pad_width <= 1) {
	RUN_KERNEL(kelvin::opt::ConvS8D4);
	}

	// special case of filter depth = 32n
	if (dilation_width_factor == 1 && dilation_height_factor == 1 &&
	stride_width <= 2 && stride_height <= 2 && filter_depth % 32 == 0) {
	RUN_KERNEL(kelvin::opt::ConvS8D32);
	}

	// special case of filter size 48x3x1x48
	if (batches == 1 && filter_height == 3 && filter_width == 1 &&
	input_width == 1 && input_depth == 48 && output_depth == 48 &&
	stride_height == 1 && stride_width == 1 && dilation_height_factor == 1 &&
	dilation_width_factor == 1 && pad_height == 0 && pad_width == 0) {
	RUN_KERNEL(kelvin::opt::ConvS8K3x1D48);
	}

	if (input_depth == 1 && ((output_depth % 4) == 0)) {
	RUN_KERNEL(kelvin::opt::ConvPerChannelD1);
	}

	RUN_KERNEL(ConvS8Generic);
	}

	} // namespace kelvin::opt