Fix uninitialized parameter in conformer relative attention. (hugging…

…face#18368)

`torch.Tensor` creates an unitialized tensor (as via `torch.empty`), this leads to undeterministic behavior, poor initialization, and nans if you have unlucky init. The paper does not specify the initialization for bias terms, so I guess zero seems like a good choice - no bias initially. `torch.Tensor` is usually populated with zeros, so this fix will be close to the intended behavior:

```
>>> torch.Tensor(100, 100).sum()
tensor(0.)
>>> torch.Tensor(100, 100).sum()
tensor(nan)
>>> torch.Tensor(100, 100).sum()
tensor(0.)
```

src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py

@@ -670,8 +670,8 @@ def __init__(self, config):
             self.linear_pos = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
             # these two learnable bias are used in matrix c and matrix d
             # as described in https://arxiv.org/abs/1901.02860 Section 3.3
-            self.pos_bias_u = nn.Parameter(torch.Tensor(self.num_heads, self.head_size))
-            self.pos_bias_v = nn.Parameter(torch.Tensor(self.num_heads, self.head_size))
+            self.pos_bias_u = nn.Parameter(torch.zeros(self.num_heads, self.head_size))
+            self.pos_bias_v = nn.Parameter(torch.zeros(self.num_heads, self.head_size))
 
     def forward(
         self,