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///////////////////////////////////////////////////////////////////////
// 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<double>& scales,
const int8_t* u, double* v) {
int num_out = w.dim1();
int num_in = w.dim2() - 1;
// Base implementation.
for (int i = 0; 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
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