set vocab_size=32 to avoid must be divisible by 16 error (#265)

Summary:
`pytest -s test/test_fsdp2/test_fsdp2_eager.py -k test_transformer_parity_dynamic`

```
E             File "/home/weif/local/pytorch-official/pytorch/torch/testing/_internal/distributed/_tensor/common_dtensor.py", line 205, in forward
E               output = self.output(h).float()
E             File "/home/weif/local/pytorch-official/pytorch/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
E               return self._call_impl(*args, **kwargs)
E             File "/home/weif/local/pytorch-official/pytorch/torch/nn/modules/module.py", line 1541, in _call_impl
E               return forward_call(*args, **kwargs)
E             File "/data/users/weif/float8_experimental/float8_experimental/float8_dynamic_linear.py", line 71, in forward
E               y = torch.nn.functional.linear(x_fp8, w_fp8, self.bias)
E             File "/data/users/weif/float8_experimental/float8_experimental/float8_tensor.py", line 297, in __torch_dispatch__
E               return FLOAT8_OPS_TABLE[func](func, args, kwargs)
E             File "/data/users/weif/float8_experimental/float8_experimental/float8_ops.py", line 151, in float8_mm
E               tensor_out, amax = addmm_float8_unwrapped(
E             File "/data/users/weif/float8_experimental/float8_experimental/float8_python_api.py", line 55, in addmm_float8_unwrapped
E               output, output_amax = torch._scaled_mm(
E           RuntimeError: mat2 shape (768x8 must be divisible by 16
E           Exception raised from _scaled_mm_out_cuda at /data/users/weif/pytorch-official/pytorch/aten/src/ATen/native/cuda/Blas.cpp:874 (most recent call first):
```

Pull Request resolved: #265

Reviewed By: drisspg, awgu

Differential Revision: D57596582

Pulled By: weifengpy

fbshipit-source-id: 8a00601457c4e72271adbba29dd2af8273173aa3

Author: weifengpy

test/test_fsdp2/test_fsdp2_eager.py

@@ -57,7 +57,12 @@ def init_multi_module(self) -> nn.Module:
     def init_transformer(self, weight_tying: bool) -> nn.Module:
         torch.manual_seed(42)
         args = ModelArgs(
-            n_layers=3, dim=768, n_heads=12, dropout_p=0.0, weight_tying=weight_tying
+            n_layers=3,
+            dim=768,
+            n_heads=12,
+            dropout_p=0.0,
+            weight_tying=weight_tying,
+            vocab_size=32,
         )
         module = Transformer(args).cuda()
         self.broadcast_module(module)