Call `symint::sizes()` instead of `sizes()` on convolution error messages. (#89549)
This PR fixes convolution when using `torchdynamo` with dynamic shapes.
**Problem:** there are some `tensor.sizes()` calls in a few error messages. As a result, an uninformative error message was being displayed.
```python
@torch._dynamo.optimize("eager")
def foo(inp, w):
return F.conv2d(inp, w)
inp = torch.rand((1, 1, 32, 32))
w = torch.rand((1, 2, 3, 3))
# |
# |--------- incorrect shape!
foo(inp, w)
```
-----
**Before this PR:**
```python
Traceback (most recent call last):
File "torch/_dynamo/utils.py", line 1076, in run_node
return node.target(*args, **kwargs)
File "torch/_subclasses/fake_tensor.py", line 867, in __torch_dispatch__
op_impl_out = op_impl(self, func, *args, **kwargs)
File "torch/_subclasses/fake_tensor.py", line 445, in conv
conv_backend = torch._C._select_conv_backend(**kwargs)
RuntimeError: Cannot call sizes() on tensor with symbolic sizes/strides
```
**After this PR:**
```python
Traceback (most recent call last):
File "torch/_dynamo/utils.py", line 1076, in run_node
return node.target(*args, **kwargs)
File "torch/_subclasses/fake_tensor.py", line 867, in __torch_dispatch__
op_impl_out = op_impl(self, func, *args, **kwargs)
File "torch/_subclasses/fake_tensor.py", line 445, in conv
conv_backend = torch._C._select_conv_backend(**kwargs)
RuntimeError: Given groups=1, weight of size [1, s1, s2, s2], expected input[1, 1, s0, s0] to have s1 channels, but got 1 channels instead
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89549
Approved by: https://github.com/ezyang
