Fix PiecewiseCompileInterpreter

This PR fixes the other issue discovered in vllm-project#16859 when upgrading from
PyTorch 2.6 to PyTorch 2.7. I don't know why the code used to work in
PyTorch 2.6, but the explanation is:
- when we are running PiecewiseCompileInterpreter, we end up doing
  FakeTensor propagation
- FakeTensor propagation requires `enable_python_dispatcher` to work.
  The mechanism is that some of our "C++ implementations" for
  operations, like matmul, force specialization of dynamic shapes.
  torch.compile works around this by replacing PyTorch's "C++
  implementation" for matmul with a python-based implementation for
  matmul that does not force specialization.

Test Plan:
- Ran `pytest -v tests/models/test_transformers.py -k test_models[meta-llama/Llama-3.2-1B-Instruct-transformers]`
  with PyTorch >= 2.7 and vllm-project#17330, verified that the test passes.

Signed-off-by: rzou <[email protected]>

Author: zou3519

vllm/compilation/backends.py

@@ -11,6 +11,7 @@
 
 import torch
 import torch.fx as fx
+from torch._dispatch.python import enable_python_dispatcher
 
 import vllm.envs as envs
 from vllm.config import CompilationConfig, VllmConfig
@@ -270,7 +271,7 @@ def run(self, *args):
             self.fake_mode.from_tensor(t) if isinstance(t, torch.Tensor) else t
             for t in args
         ]
-        with self.fake_mode:
+        with self.fake_mode, enable_python_dispatcher():
             return super().run(*fake_args)
 
     def call_module(self, target: torch.fx.node.Target,