I have some simple code that’s behaving drastically differently with torch 1.4.0 and 1.6.0: Conv2D filters are producing different shaped outputs. I can’t see anything in the release notes that would suggest that 2D convolution layers should be constructed differently between those torch releases. The following simple (and possibly buggy) code reproduces the issue:
#!/usr/bin/env python3
import torch
from torch import nn

c = 'circular'
class EmbeddingNetwork(nn.Module):
    def __init__(self):
        super().__init__()
        ksize = 5
        self.embedding = nn.Sequential(
            nn.Conv2d(10, 10, ksize, stride=1, padding=ksize-1, padding_mode=c),
            nn.ReLU())

    def forward(self, x):
        print("x.shape:", x.shape)
        print("embedding model:", self.embedding)
        result =  self.embedding(x)
        print("embedding(x).shape:", result.shape)
        return result

x = torch.zeros(1, 10, 26, 26)
embedding = EmbeddingNetwork()
embedding(x)

Running this with PyTorch 1.6.0, I get:
x.shape: torch.Size([1, 10, 26, 26])
embedding model: Sequential(
  (0): Conv2d(10, 10, kernel_size=(5, 5), stride=(1, 1), padding=(4, 4), padding_mode=circular)
  (1): ReLU()
)
embedding(x).shape: torch.Size([1, 10, 30, 30])

Running with PyTorch 1.4.0, I get:
x.shape: torch.Size([1, 10, 26, 26])
embedding model: Sequential(
  (0): Conv2d(10, 10, kernel_size=(5, 5), stride=(1, 1), padding=(4, 4), padding_mode=circular)
  (1): ReLU()
)
embedding(x).shape: torch.Size([1, 10, 26, 26])