tflm/opt/conv_s8_1x1.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
 // Special case for 1x1 filter

 #include "tflm/opt/conv_util.h"

 namespace kelvin::opt {

 void ConvS8K1x1(
     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) {
   // Get parameters.
   const int32_t input_offset = params.input_offset;  // r = s(q - Z)
   const int32_t output_offset = params.output_offset;

   // Set min and max value of the output.
   const int32_t output_activation_min = params.quantized_activation_min;
   const int32_t output_activation_max = params.quantized_activation_max;

   // Consistency check.
   TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
   TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
   TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
   TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
   const int batches = MatchingDim(input_shape, 0, output_shape, 0);
   const int input_depth = input_shape.Dims(3);
   const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3);
   if (bias_data) {
     TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
   }

   // Check dimensions of the tensors.
   const int filter_input_depth = filter_shape.Dims(3);
   const int groups = input_depth / filter_input_depth;
   TFLITE_DCHECK_NE(groups, 0);
   TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0);
   const int filters_per_group = output_depth / groups;
   TFLITE_DCHECK_NE(filters_per_group, 0);
   const int output_height = output_shape.Dims(1);
   const int output_width = output_shape.Dims(2);

   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;

   const size_t swizzled_filter_data_size =
       8 * filter_input_depth;
   std::unique_ptr<int8_t> swizzled_filter_data(reinterpret_cast<int8_t*>(
       ::aligned_alloc(32, swizzled_filter_data_size)));
   int8_t* p_swizzled_filter_data = swizzled_filter_data.get();
   int32_t swizzled_bias_data[32];
   int32_t swizzled_mult_data[32];
   int32_t swizzled_shift_data[32];

   const int n_elems = (output_width * batches * output_height);
   int out_channel = 0;
   do {
     int out_channels_this_iter = std::min(8, output_depth - out_channel);
     Filter_N_H_W_M(filter_data + (out_channel * filter_input_depth),
                    p_swizzled_filter_data, out_channels_this_iter, 1, 1,
                    filter_input_depth);
     if (bias_data) {
       Swizzle(bias_data + out_channel, swizzled_bias_data, out_channels_this_iter);
       vld_w_x_m(v16, swizzled_bias_data);
     } else {
       vdup_w_x_m(v16, 0);
     }
     Swizzle(output_multiplier + out_channel, swizzled_mult_data, out_channels_this_iter);
     Swizzle(output_shift + out_channel, swizzled_shift_data, out_channels_this_iter);

     vld_w_x_m(v20, swizzled_mult_data);
     vld_w_x_m(v24, swizzled_shift_data);
     vrsub_w_vx_m(v24, v24, 0);

     int out = 0;
     for (; out < n_elems; out += 8) {
       int out_this_iter = std::min(8, n_elems - out);

       const int8_t* p_in = input_data + (out * input_depth);
       int8_t* p_out = output_data + (out * output_depth) + out_channel;

       // 8x accumulators
       vmv_v_m(v48, v16);
       vmv_v_m(v52, v16);
       acset_v(v48, v48);
       int in_channel = 0;
       for (; in_channel < filter_input_depth; in_channel += 32) {
         const int8_t* p_input = p_in + in_channel;
         if (out_this_iter < 8) {
           switch (out_this_iter) {
             case 7:
               vld_b_x(v6, p_input + (6 * input_depth));
             case 6:
               vld_b_x(v5, p_input + (5 * input_depth));
             case 5:
               vld_b_x(v4, p_input + (4 * input_depth));
             case 4:
               vld_b_x(v3, p_input + (3 * input_depth));
             case 3:
               vld_b_x(v2, p_input + (2 * input_depth));
             case 2:
               vld_b_x(v1, p_input + input_depth);
             case 1:
               vld_b_x(v0, p_input);
           }
         } else {
           // Inputs
           vld_b_s_xx_m(v0, p_input, input_depth);
           vld_b_s_xx_m(v4, p_input + (4 * input_depth), input_depth);
         }

         int8_t* p_local_filter = p_swizzled_filter_data + (in_channel * 8);
         vld_b_p_x_m(v8, p_local_filter);
         vld_b_x_m(v12, p_local_filter);

         aconv_vxv(v48, v0, cmds, v8);
       }

       vcget(v48);
       INT32_TO_INT8_OUTPUT_PIPELINE_INPLACE(
           v48, v20, v24, output_activation_min, output_activation_max,
           output_offset);
       INT32_TO_INT8_OUTPUT_PIPELINE_INPLACE(
           v52, v20, v24, output_activation_min, output_activation_max,
           output_offset);
       vsraqs_b_vx(v48, v48, 0);
       vsraqs_b_vx(v52, v52, 0);

       int i = 0;
       for (; i < std::min(4, out_this_iter); i++) {
         vst_b_l_xx(v48, p_out, out_channels_this_iter);
         p_out += output_depth;
         vsliden_h_4_vv(v48, v48, v48);
       }
       for (; i < out_this_iter; i++) {
         vst_b_l_xx(v52, p_out, out_channels_this_iter);
         p_out += output_depth;
         vsliden_h_4_vv(v52, v52, v52);
       }
     }

     out_channel += out_channels_this_iter;
   } while (out_channel < output_depth);
 }

 }  // 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
	// Special case for 1x1 filter

	#include "tflm/opt/conv_util.h"

	namespace kelvin::opt {

	void ConvS8K1x1(
	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) {
	// Get parameters.
	const int32_t input_offset = params.input_offset; // r = s(q - Z)
	const int32_t output_offset = params.output_offset;

	// Set min and max value of the output.
	const int32_t output_activation_min = params.quantized_activation_min;
	const int32_t output_activation_max = params.quantized_activation_max;

	// Consistency check.
	TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
	TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
	TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
	TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
	const int batches = MatchingDim(input_shape, 0, output_shape, 0);
	const int input_depth = input_shape.Dims(3);
	const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3);
	if (bias_data) {
	TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
	}

	// Check dimensions of the tensors.
	const int filter_input_depth = filter_shape.Dims(3);
	const int groups = input_depth / filter_input_depth;
	TFLITE_DCHECK_NE(groups, 0);
	TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0);
	const int filters_per_group = output_depth / groups;
	TFLITE_DCHECK_NE(filters_per_group, 0);
	const int output_height = output_shape.Dims(1);
	const int output_width = output_shape.Dims(2);

	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;

	const size_t swizzled_filter_data_size =
	8 * filter_input_depth;
	std::unique_ptr<int8_t> swizzled_filter_data(reinterpret_cast<int8_t*>(
	::aligned_alloc(32, swizzled_filter_data_size)));
	int8_t* p_swizzled_filter_data = swizzled_filter_data.get();
	int32_t swizzled_bias_data[32];
	int32_t swizzled_mult_data[32];
	int32_t swizzled_shift_data[32];

	const int n_elems = (output_width * batches * output_height);
	int out_channel = 0;
	do {
	int out_channels_this_iter = std::min(8, output_depth - out_channel);
	Filter_N_H_W_M(filter_data + (out_channel * filter_input_depth),
	p_swizzled_filter_data, out_channels_this_iter, 1, 1,
	filter_input_depth);
	if (bias_data) {
	Swizzle(bias_data + out_channel, swizzled_bias_data, out_channels_this_iter);
	vld_w_x_m(v16, swizzled_bias_data);
	} else {
	vdup_w_x_m(v16, 0);
	}
	Swizzle(output_multiplier + out_channel, swizzled_mult_data, out_channels_this_iter);
	Swizzle(output_shift + out_channel, swizzled_shift_data, out_channels_this_iter);

	vld_w_x_m(v20, swizzled_mult_data);
	vld_w_x_m(v24, swizzled_shift_data);
	vrsub_w_vx_m(v24, v24, 0);

	int out = 0;
	for (; out < n_elems; out += 8) {
	int out_this_iter = std::min(8, n_elems - out);

	const int8_t* p_in = input_data + (out * input_depth);
	int8_t* p_out = output_data + (out * output_depth) + out_channel;

	// 8x accumulators
	vmv_v_m(v48, v16);
	vmv_v_m(v52, v16);
	acset_v(v48, v48);
	int in_channel = 0;
	for (; in_channel < filter_input_depth; in_channel += 32) {
	const int8_t* p_input = p_in + in_channel;
	if (out_this_iter < 8) {
	switch (out_this_iter) {
	case 7:
	vld_b_x(v6, p_input + (6 * input_depth));
	case 6:
	vld_b_x(v5, p_input + (5 * input_depth));
	case 5:
	vld_b_x(v4, p_input + (4 * input_depth));
	case 4:
	vld_b_x(v3, p_input + (3 * input_depth));
	case 3:
	vld_b_x(v2, p_input + (2 * input_depth));
	case 2:
	vld_b_x(v1, p_input + input_depth);
	case 1:
	vld_b_x(v0, p_input);
	}
	} else {
	// Inputs
	vld_b_s_xx_m(v0, p_input, input_depth);
	vld_b_s_xx_m(v4, p_input + (4 * input_depth), input_depth);
	}

	int8_t* p_local_filter = p_swizzled_filter_data + (in_channel * 8);
	vld_b_p_x_m(v8, p_local_filter);
	vld_b_x_m(v12, p_local_filter);

	aconv_vxv(v48, v0, cmds, v8);
	}

	vcget(v48);
	INT32_TO_INT8_OUTPUT_PIPELINE_INPLACE(
	v48, v20, v24, output_activation_min, output_activation_max,
	output_offset);
	INT32_TO_INT8_OUTPUT_PIPELINE_INPLACE(
	v52, v20, v24, output_activation_min, output_activation_max,
	output_offset);
	vsraqs_b_vx(v48, v48, 0);
	vsraqs_b_vx(v52, v52, 0);

	int i = 0;
	for (; i < std::min(4, out_this_iter); i++) {
	vst_b_l_xx(v48, p_out, out_channels_this_iter);
	p_out += output_depth;
	vsliden_h_4_vv(v48, v48, v48);
	}
	for (; i < out_this_iter; i++) {
	vst_b_l_xx(v52, p_out, out_channels_this_iter);
	p_out += output_depth;
	vsliden_h_4_vv(v52, v52, v52);
	}
	}

	out_channel += out_channels_this_iter;
	} while (out_channel < output_depth);
	}

	} // namespace kelvin::opt