v4.40.1

setup.py

@@ -429,7 +429,7 @@ install_requires = [
 
 setup(
     name="transformers",
-    version="4.40.0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
+    version="4.40.1",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
     author="The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)",
     author_email="transformers@huggingface.co",
     description="State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow",

src/transformers/__init__.py

@@ -18,7 +18,7 @@
 # to defer the actual importing for when the objects are requested. This way `import transformers` provides the names
 # in the namespace without actually importing anything (and especially none of the backends).
 
-__version__ = "4.40.0"
+__version__ = "4.40.1"
 
 from typing import TYPE_CHECKING
 

src/transformers/generation/stopping_criteria.py

@@ -146,7 +146,18 @@ class EosTokenCriteria(StoppingCriteria):
 
     @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> torch.BoolTensor:
-        is_done = torch.isin(input_ids[:, -1], self.eos_token_id.to(input_ids.device))
+        if input_ids.device.type == "mps":
+            # https://github.com/pytorch/pytorch/issues/77764#issuecomment-2067838075
+            is_done = (
+                input_ids[:, -1]
+                .tile(self.eos_token_id.shape[0], 1)
+                .eq(self.eos_token_id.unsqueeze(1).to(input_ids.device))
+                .sum(dim=0)
+                .bool()
+                .squeeze()
+            )
+        else:
+            is_done = torch.isin(input_ids[:, -1], self.eos_token_id.to(input_ids.device))
         return is_done