src/arch/intsimdmatrix.cpp - main - githubmirror/tesseract

src/arch/intsimdmatrix.cpp
119 строк5 KB
Mahesh Madhav
Facilitate vectorization for generic build (#4223)
16 апр 2024, 20:28
16 апр 2024, 20:28bae520e
///////////////////////////////////////////////////////////////////////
// File:        intsimdmatrix.cpp
// Description: Base class for 8-bit int SIMD matrix multipliers.
// Author:      Ray Smith
//
// (C) Copyright 2017, Google Inc.
// 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 "intsimdmatrix.h"
#include "matrix.h"     // for GENERIC_2D_ARRAY
#include "simddetect.h" // for SIMDDetect

namespace tesseract {

const IntSimdMatrix *IntSimdMatrix::intSimdMatrix = nullptr;

// Computes a reshaped copy of the weight matrix w.
void IntSimdMatrix::Init(const GENERIC_2D_ARRAY<int8_t> &w, std::vector<int8_t> &shaped_w,
                         int32_t &rounded_num_out) const {
  const int num_out = w.dim1();
  const int num_in = w.dim2() - 1;
  // The rounded-up sizes of the reshaped weight matrix, excluding biases.
  int rounded_num_in = Roundup(num_in, num_inputs_per_group_);
  rounded_num_out = RoundOutputs(num_out);
  // Add the bias and compute the required size.
  shaped_w.resize((rounded_num_in + 1) * rounded_num_out, 0);
  int shaped_index = 0;
  int output = 0;
  // Each number of registers needs a different format! Iterates over the
  // different numbers of registers (each a power of 2).
  for (int num_registers = max_output_registers_; num_registers >= 1; num_registers /= 2) {
    // The number of outputs that we will generate with this many registers.
    int num_outputs_per_register_set = num_registers * num_outputs_per_register_;
    // Use the max number of registers until we have to go fewer.
    while (output + num_outputs_per_register_set <= rounded_num_out) {
      // Accumulating outputs in registers saves iterating over the inputs, so
      // we only have to do it once per output register set.
      for (int input = 0; input < num_in; input += num_inputs_per_group_) {
        // Iterate over the number of outputs in a register set.
        for (int j = 0; j < num_outputs_per_register_set; ++j) {
          // Inner-most loop corresponds to the number of inputs in an input
          // group.
          for (int i = 0; i < num_inputs_per_group_; ++i) {
            int8_t weight = 0;
            if (output + j < num_out && input + i < num_in) {
              weight = w(output + j, input + i);
            }
            shaped_w[shaped_index++] = weight;
          }
        }
      }
      // Append the bias weights for the register set.
      for (int j = 0; j < num_outputs_per_register_set; ++j) {
        int8_t weight = 0;
        if (output + j < num_out) {
          weight = w(output + j, num_in);
        }
        shaped_w[shaped_index++] = weight;
      }
      output += num_outputs_per_register_set;
    }
  }
}

// Computes matrix.vector v = Wu.
// u is of size W.dim2() - 1 and the output v is of size W.dim1().
// u is imagined to have an extra element at the end with value 1, to
// implement the bias, but it doesn't actually have it.
void IntSimdMatrix::MatrixDotVector(const GENERIC_2D_ARRAY<int8_t> &w,
                                    const std::vector<TFloat> &scales, const int8_t *u, TFloat *v) {
  int num_out = w.dim1();
  int num_in = w.dim2() - 1;
  // Base implementation.
  int i;
  // Break up into chunks of four to facilitate vectorization
  for (i = 0; i < (num_out / 4) * 4; i += 4) {
    const int8_t *wi0 = w[i + 0];
    const int8_t *wi1 = w[i + 1];
    const int8_t *wi2 = w[i + 2];
    const int8_t *wi3 = w[i + 3];
    int total0 = 0;
    int total1 = 0;
    int total2 = 0;
    int total3 = 0;
    for (int j = 0; j < num_in; ++j) {
      total0 += wi0[j] * u[j];
      total1 += wi1[j] * u[j];
      total2 += wi2[j] * u[j];
      total3 += wi3[j] * u[j];
    }
    // Add in the bias and correct for integer values.
    v[i + 0] = (total0 + wi0[num_in] * INT8_MAX) * scales[i + 0];
    v[i + 1] = (total1 + wi1[num_in] * INT8_MAX) * scales[i + 1];
    v[i + 2] = (total2 + wi2[num_in] * INT8_MAX) * scales[i + 2];
    v[i + 3] = (total3 + wi3[num_in] * INT8_MAX) * scales[i + 3];
  }

  // Capture the remainder mod four
  for (; i < num_out; ++i) {
    const int8_t *wi = w[i];
    int total = 0;
    for (int j = 0; j < num_in; ++j) {
      total += wi[j] * u[j];
    }
    // Add in the bias and correct for integer values.
    v[i] = (total + wi[num_in] * INT8_MAX) * scales[i];
  }
}

} // namespace tesseract