为什么“conv1d”在 C 代码、python 和 pytorch 中不同

Question

I want to reproduce "Conv1D" results of pytorch in C code.

I tried to implement "Conv1D" using three methods (C code, Python, Pytorch), but the results are different. Only seven fraction digits are reasonable. Assuming there are multiple layers of conv1d in the structure, the fraction digits accuracy will gradually decrease.

According to everyone's recommend, I tried to change the C code type of input data to double but the result is still incorrect. Have I done something wrong?

For example:

The output of Pytorch: 0.2380688339471817017

The output of Python: 0.2380688637495040894

The output of C code (float): 0.2380688637

The output of C code (double): 0.238068885344539680

Here is my current implementation

• Input:

  input dim. = 80, output dim. = 128, kernel size = 5

  Pytorch: Conv1D_input.npy, Conv1D_weight.npy

  Python: Conv1D_input.npy, Conv1D_weight.npy (same as Pytorch)

  C code: Conv1D_input.txt, Conv1D_weight.txt (convert from Pytorch, IEEE 754 single precision)

  Pytorch

  import torch import numpy as np from torch import nn from torch.autograd import Variable import torch.nn.functional as F import argparse import sys import io import time import os class RNN(nn.Module): def __init__(self, input_size, hidden_size): super(RNN, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.c1 = nn.Conv1d(input_size, hidden_size, kernel_size = 5, bias=False) self.c1.weight = torch.nn.Parameter(torch.Tensor(np.load("CONV1D_WEIGHT.npy"))) def forward(self, inputs): c = self.c1(inputs) return c input_size = 80 hidden_size = 128 kernel_size = 5 rnn = RNN(input_size, hidden_size) inputs = torch.nn.Parameter(torch.Tensor(np.load("CONV1D_IN.npy"))) print("inputs", inputs) outputs = rnn(inputs) sub_np456 = outputs[0].cpu().detach().numpy() np.savetxt("Pytorch_CONV1D_OUTPUT.txt", sub_np456) print('outputs', outputs)

  Python

   import struct import numpy as np if __name__ == "__main__": row = 80 col = 327 count = 0 res_out_dim = 128 in_dim = 80 kernel_size = 5 filter = np.zeros((80, 5), dtype = np.float32) featureMaps = np.zeros((128, 323), dtype = np.float32) spectrum = np.load("CONV1D_INPUT.npy") weight = np.load("CONV1D_WEIGHT.npy") spectrum_2d = spectrum.reshape(80, 327) for i in range(res_out_dim): for j in range(in_dim): for k in range(kernel_size): filter[j][k] = weight[i][j][k] while count < (col-kernel_size+1): for j in range(in_dim): for k in range(count, kernel_size+count): featureMaps[i][count] = featureMaps[i][count] + spectrum_2d[j][k]*filter[j][k-count] count = count + 1 count = 0 np.savetxt("Python_CONV1D_OUTPUT.txt", featureMaps)

  C code (float)

   #include<stdio.h> #include<stdlib.h> #include<math.h> #include<time.h> const char CONV1D_WEIGHT[] = "CONV1D_WEIGHT.txt"; const char CONV1D_INPUT[] = "CONV1D_INPUT.txt"; void parameterFree(float **matrix, int row) { int i = 0; for(i=0; i<row; i++) free(matrix[i]); free(matrix); } float** createMatrix_2D(int row, int col) { int i = 0; float **matrix = NULL; matrix = (float**)malloc(sizeof(float*) * row); if(matrix == NULL) printf("Matrix2D malloc failed\\n"); for(i=0; i<row; i++) { matrix[i] = (float*)malloc(sizeof(float) * col); if(matrix[i] == NULL) printf("Matrix2D malloc failed\\n"); } return matrix; } float** conv_1D(const char weightFile[], float **source, int *row, int *col, int in_dim, int res_out_dim, int kernel_size) { float **filter = createMatrix_2D(in_dim, kernel_size); float **featureMaps = createMatrix_2D(res_out_dim, *col-kernel_size+1); int i = 0, j = 0, k = 0, count = 0; char str[10]; float data = 0.0; FILE *fp = fopen(weightFile, "r"); if(fp == NULL) printf("Resnet file open failed\\n"); else { /*initial featureMaps*/ for(i=0; i<res_out_dim; i++) { for(j=0; j<*col-kernel_size+1; j++) { featureMaps[i][j] = 0.0; } } /*next filter*/ for(i=0; i<res_out_dim; i++) { /*read filter*/ for(j=0; j<in_dim; j++) { for(k=0; k<kernel_size; k++) { fscanf(fp, "%s", str); sscanf(str, "%x", &data); filter[j][k] = data; } } /* (part of source * filter) */ while(count < *col-kernel_size+1) { for(j=0; j<in_dim; j++) { for(k=count; k<kernel_size+count; k++) { featureMaps[i][count] += source[j][k]*filter[j][k-count]; } } count++; } count = 0; } fclose(fp); } parameterFree(source, *row); parameterFree(filter, in_dim); *row = res_out_dim; *col = *col-kernel_size+1; return featureMaps; } int main() { int row = 80; int col = 327; int in_dim = 80; int res_out_dim = 128; int kernel_size = 5; int i, j; float data; char str[10]; float **input = createMatrix_2D(row, col); FILE *fp = fopen(CONV1D_INPUT, "r"); FILE *fp2 = fopen("C code_CONV1D_OUTPUT.txt", "w"); if(fp == NULL) printf("File open failed\\n"); else { for(i=0; i<row; i++) { for(j=0; j<col; j++) { fscanf(fp, "%s", str); sscanf(str, "%x", &data); input[i][j] = data; } } } float **CONV1D_ANS = conv_1D(CONV1D_WEIGHT, input, &row, &col, in_dim, res_out_dim, kernel_size); for(i=0; i<row; i++) { for(j=0; j<col; j++) { fprintf(fp2, "[%.12f] ", CONV1D_ANS[i][j]); } fprintf(fp2, "\\n"); } return 0; }

  C code (double)

   #include<stdio.h> #include<stdlib.h> #include<math.h> #include<time.h> const char CONV1D_WEIGHT[] = "CONV1D_WEIGHT.txt"; const char CONV1D_INPUT[] = "CONV1D_INPUT.txt"; void parameterFree(double **matrix, int row) { int i = 0; for(i=0; i<row; i++) free(matrix[i]); free(matrix); } double** createMatrix_2D(int row, int col) { int i = 0; double **matrix = NULL; matrix = (double**)malloc(sizeof(double*) * row); if(matrix == NULL) printf("Matrix2D malloc failed\\n"); for(i=0; i<row; i++) { matrix[i] = (double*)malloc(sizeof(double) * col); if(matrix[i] == NULL) printf("Matrix2D malloc failed\\n"); } return matrix; } double** conv_1D(const char weightFile[], double **source, int *row, int *col, int in_dim, int res_out_dim, int kernel_size) { double **filter = createMatrix_2D(in_dim, kernel_size); double **featureMaps = createMatrix_2D(res_out_dim, *col-kernel_size+1); int i = 0, j = 0, k = 0, count = 0; char str[10]; float data = 0.0; FILE *fp = fopen(weightFile, "r"); if(fp == NULL) printf("Resnet file open failed\\n"); else { /*initial featureMaps*/ for(i=0; i<res_out_dim; i++) { for(j=0; j<*col-kernel_size+1; j++) { featureMaps[i][j] = 0.0; } } /*next filter*/ for(i=0; i<res_out_dim; i++) { /*read filter*/ for(j=0; j<in_dim; j++) { for(k=0; k<kernel_size; k++) { fscanf(fp, "%s", str); sscanf(str, "%x", &data); filter[j][k] = (double)data; } } /* (part of source * filter) */ while(count < *col-kernel_size+1) { for(j=0; j<in_dim; j++) { for(k=count; k<kernel_size+count; k++) { featureMaps[i][count] += source[j][k]*filter[j][k-count]; } } count++; } count = 0; } fclose(fp); } parameterFree(source, *row); parameterFree(filter, in_dim); *row = res_out_dim; *col = *col-kernel_size+1; return featureMaps; } int main() { int row = 80; int col = 327; int in_dim = 80; int res_out_dim = 128; int kernel_size = 5; int i, j; float data; char str[10]; double **input = createMatrix_2D(row, col); FILE *fp = fopen(CONV1D_INPUT, "r"); FILE *fp2 = fopen("C code_CONV1D_OUTPUT.txt", "w"); if(fp == NULL) printf("File open failed\\n"); else { for(i=0; i<row; i++) { for(j=0; j<col; j++) { fscanf(fp, "%s", str); sscanf(str, "%x", &data); input[i][j] = (double)data; } } } double **CONV1D_ANS = conv_1D(CONV1D_WEIGHT, input, &row, &col, in_dim, res_out_dim, kernel_size); for(i=0; i<row; i++) { for(j=0; j<col; j++) { fprintf(fp2, "[%.18f] ", CONV1D_ANS[i][j]); } fprintf(fp2, "\\n"); } return 0; }

Answer 1

Floating point numbers are not precise (by design). Depending on in which order operations are performed, the results might vary. Even worse, some formulas are straight numerical unstable, whereas another one for the same analytical expression can be stable.

Compilers often rearange statements as an optimization measure. Convolution is an operation which contains notoriously many operations and loops. So unless you directly compare executed bytecode, this speculation is kind of pointless.