Do not export sampler for device==mps && dtype==bfloat16 (#208)

As titled.

Seeing this error:

```
E 00:00:13.059103 executorch:et_metal.mm:246] ETMetalShaderLibrary: Failed to compile shader library: program_source:3813:29: error: assigning to 'bfloat' from incompatible type 'float'
                tmp_acc_2 = tmp0;
                            ^~~~

E 00:00:13.059124 executorch:et_metal.mm:263] ETMetalShaderLibrary: Library not compiled
E 00:00:13.059126 executorch:et_metal.mm:301] ETMetalShaderLibrary::getKernelFunction: Failed to get pipeline state for 'generated_kernel'
E 00:00:13.059127 executorch:shim_mps.mm:105] aoti_torch_mps_get_kernel_function: Failed to get kernel function 'generated_kernel'
E 00:00:13.059129 executorch:shim_mps.mm:517] aoti_torch_mps_run_command_block: null function handle
```

When running metal delegated argmax model. This PR disable the sampler export code path, let runner fallback to C++ CPU sampler.

Author: larryliu0820

optimum/exporters/executorch/integrations.py

@@ -919,15 +919,27 @@ def export(
             example_cache_position,
         )
 
-        self.exported_sampler = self._export_sampler(
-            torch.randn((1, 1, self.config.vocab_size), dtype=self.model.dtype, device=self.model.device)
-        )
+        # Skip sampler export for MPS + bfloat16 due to Metal shader compilation error
+        # (assigning float to bfloat in generated shader code)
+        is_mps_bfloat16 = str(self.model.device).startswith("mps") and self.model.dtype == torch.bfloat16
+        if is_mps_bfloat16:
+            logging.warning(
+                "Skipping sampler export for MPS + bfloat16 due to Metal shader compilation issues. "
+                "The runner will use CPU-based sampling instead."
+            )
+            self.exported_sampler = None
+        else:
+            self.exported_sampler = self._export_sampler(
+                torch.randn((1, 1, self.config.vocab_size), dtype=self.model.dtype, device=self.model.device)
+            )
 
-        return {
+        result = {
             "encoder": self.exported_encoder,  # Not called "text_encoder" because the encoder could be non-text too, e.g. Whisper.
             "text_decoder": self.exported_decoder,
-            "sampler": self.exported_sampler,
         }
+        if self.exported_sampler is not None:
+            result["sampler"] = self.exported_sampler
+        return result
 
     def generate(self, prompt_token_ids, max_new_tokens):
         with torch.no_grad():