Release: v4.52.4

fix qwen

examples/3D_parallel.py

@@ -1,3 +1,16 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """:
 This script is used to test training a model using Tensor Parallelism and Data Parallelism.
 

examples/pytorch/3d_parallel_checks.py

@@ -1,3 +1,16 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """:
 This script is used to test training a model using Tensor Parallelism and Data Parallelism.
 

setup.py

@@ -125,7 +125,7 @@ _deps = [
     "jaxlib>=0.4.1,<=0.4.13",
     "jieba",
     "jinja2>=3.1.0",
-    "kenlm@git+https://github.com/ydshieh/kenlm@78f664fb3dafe1468d868d71faf19534530698d5",
+    "kenlm",
     # Keras pin - this is to make sure Keras 3 doesn't destroy us. Remove or change when we have proper support.
     "keras>2.9,<2.16",
     "keras-nlp>=0.3.1,<0.14.0",  # keras-nlp 0.14 doesn't support keras 2, see pin on keras.
@@ -315,7 +315,7 @@ extras["audio"] = deps_list(
     "librosa",
     "pyctcdecode",
     "phonemizer",
-    "kenlm@git+https://github.com/ydshieh/kenlm@78f664fb3dafe1468d868d71faf19534530698d5",
+    "kenlm",
 )
 # `pip install ".[speech]"` is deprecated and `pip install ".[torch-speech]"` should be used instead
 extras["speech"] = deps_list("torchaudio") + extras["audio"]
@@ -451,7 +451,7 @@ install_requires = [
 
 setup(
     name="transformers",
-    version="4.52.0",  # 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="4.52.4",  # 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__ = "4.52.0"
+__version__ = "4.52.4"
 
 from pathlib import Path
 from typing import TYPE_CHECKING

src/transformers/dependency_versions_table.py

@@ -32,7 +32,7 @@ deps = {
     "jaxlib": "jaxlib>=0.4.1,<=0.4.13",
     "jieba": "jieba",
     "jinja2": "jinja2>=3.1.0",
-    "kenlm@git+https://github.com/ydshieh/kenlm@78f664fb3dafe1468d868d71faf19534530698d5": "kenlm@git+https://github.com/ydshieh/kenlm@78f664fb3dafe1468d868d71faf19534530698d5",
+    "kenlm": "kenlm",
     "keras": "keras>2.9,<2.16",
     "keras-nlp": "keras-nlp>=0.3.1,<0.14.0",
     "kernels": "kernels>=0.4.4,<0.5",

src/transformers/integrations/tensor_parallel.py

@@ -52,6 +52,7 @@ def initialize_tensor_parallelism(tp_plan, tp_size=None):
 
     # Detect the accelerator on the machine. If no accelerator is available, it returns CPU.
     device_type = torch._C._get_accelerator().type
+    current_device = getattr(torch, device_type)
     if not torch.distributed.is_initialized():
         try:
             rank = int(os.environ["RANK"])
@@ -73,6 +74,9 @@ def initialize_tensor_parallelism(tp_plan, tp_size=None):
                 "We tried to initialize torch.distributed for you, but it failed. Make "
                 "sure you init torch distributed in your script to use `tp_plan='auto'`."
             ) from e
+
+    if device_type != "cpu":
+        current_device.set_device(int(os.environ["LOCAL_RANK"]))
     index = current_device.current_device() if device_type != "cpu" else None
     tp_device = torch.device(device_type, index)
 
@@ -729,23 +733,24 @@ class ParallelInterface(MutableMapping):
 
     # Class instance object, so that a call to `register` can be reflected into all other files correctly, even if
     # a new instance is created (in order to locally override a given function)
-    _global_mapping = {
-        "colwise": ColwiseParallel(),
-        "rowwise": RowwiseParallel(),
-        "colwise_rep": ColwiseParallel(output_layouts=Replicate()),
-        "rowwise_rep": RowwiseParallel(input_layouts=Replicate()),
-        "local_colwise": ColwiseParallel(use_dtensor=False),
-        "local_rowwise": RowwiseParallel(use_dtensor=False),
-        "local": IsolatedParallel(),
-        "gather": GatherParallel(),
-        "local_packed_rowwise": PackedRowwiseParallel(use_dtensor=False),
-        "sequence_parallel": SequenceParallel(),
-        "replicate": ReplicateParallel(),
-    }
 
     def __init__(self):
         self._local_mapping = {}
 
+        ParallelInterface._global_mapping = {
+            "colwise": ColwiseParallel(),
+            "rowwise": RowwiseParallel(),
+            "colwise_rep": ColwiseParallel(output_layouts=Replicate()),
+            "rowwise_rep": RowwiseParallel(input_layouts=Replicate()),
+            "local_colwise": ColwiseParallel(use_dtensor=False),
+            "local_rowwise": RowwiseParallel(use_dtensor=False),
+            "local": IsolatedParallel(),
+            "gather": GatherParallel(),
+            "local_packed_rowwise": PackedRowwiseParallel(use_dtensor=False),
+            "sequence_parallel": SequenceParallel(),
+            "replicate": ReplicateParallel(),
+        }
+
     def __getitem__(self, key):
         # First check if instance has a local override
         if key in self._local_mapping:
@@ -775,7 +780,11 @@ class ParallelInterface(MutableMapping):
 
 
 # Global AttentionInterface shared by all models which do not need to overwrite any of the existing ones
-ALL_PARALLEL_STYLES: ParallelInterface = ParallelInterface()
+
+if is_torch_greater_or_equal("2.5") and _torch_distributed_available:
+    ALL_PARALLEL_STYLES: ParallelInterface = ParallelInterface()
+else:
+    ALL_PARALLEL_STYLES = None
 
 
 def convert_local_tensor_to_dtensor(

src/transformers/modeling_utils.py

@@ -319,7 +319,8 @@ def get_torch_context_manager_or_global_device():
     is not "cpu". This is used to infer the correct device to load the model on, in case `device_map` is not provided.
     """
     device_in_context = torch.tensor([]).device
-    default_device = torch.get_default_device()
+    # `get_default_device` was only introduced in torch>=2.3 - use cpu otherwise to align the behavior
+    default_device = torch.get_default_device() if is_torch_greater_or_equal("2.3") else torch.device("cpu")
     # This case means no context manager was used -> we still check if the default that was potentially set is not cpu
     if device_in_context == default_device:
         if default_device != torch.device("cpu"):
@@ -3532,7 +3533,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, PushToHubMixin, PeftAdapterMi
             for key, value in state_dict.items():
                 for pattern, replacement in reverse_key_mapping.items():
                     replacement = replacement.lstrip("^")  # strip off un-needed chars and patterns
-                    replacement = re.sub(r"\(.*?\)", "", pattern)
+                    replacement = re.sub(r"\(.*\)", "", replacement)
                     key, n_replace = re.subn(pattern, replacement, key)
                     # Early exit of the loop
                     if n_replace > 0:
@@ -4177,13 +4178,14 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, PushToHubMixin, PeftAdapterMi
 
         # We need to correctly dispatch the model on the current process device. The easiest way for this is to use a simple
         # `device_map` pointing to the correct device
-        if device_mesh is None:
-            tp_plan, device_map, device_mesh = initialize_tensor_parallelism(tp_plan, tp_size=None)
-        else:
-            # TODO: make device_mesh support multiple dimensions
-            if device_mesh.ndim == 1:
-                raise ValueError("device_mesh must be 1 dimensional and will be used for TP")
-            device_map = torch.device(device_mesh.device_type, int(os.environ["LOCAL_RANK"]))
+        if tp_plan is not None:
+            if device_mesh is None and tp_plan is not None:
+                tp_plan, device_map, device_mesh = initialize_tensor_parallelism(tp_plan, tp_size=None)
+            else:
+                # TODO: make device_mesh support multiple dimensions
+                if device_mesh.ndim == 1:
+                    raise ValueError("device_mesh must be 1 dimensional and will be used for TP")
+                device_map = torch.device(device_mesh.device_type, int(os.environ["LOCAL_RANK"]))
 
         if use_auth_token is not None:
             warnings.warn(

src/transformers/models/opt/modeling_opt.py

@@ -154,7 +154,11 @@ class OPTAttention(nn.Module):
         """Input shape: Batch x Time x Channel"""
         bsz, tgt_len, _ = hidden_states.size()
 
-        # get query proj
+        # Scaling is susceptible to floating point arithmetics' inprecisions
+        # which can lead to different results (this is dependent from model
+        # to model, e.g. whisper is one such case). We therefore keep the
+        # original order of scaling to follow the original implementation
+        # and enforce no scaling (1.0) in the attention call below.
         query_states = self.q_proj(hidden_states) * self.scaling
         query_states = query_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)
 
@@ -187,7 +191,7 @@ class OPTAttention(nn.Module):
             value_states,
             attention_mask,
             dropout=0.0 if not self.training else self.dropout,
-            scaling=self.scaling,
+            scaling=1.0,
             **kwargs,
         )
 

src/transformers/models/paligemma/processing_paligemma.py

@@ -310,7 +310,8 @@ class PaliGemmaProcessor(ProcessorMixin):
         return_data = {**inputs, "pixel_values": pixel_values}
 
         if return_token_type_ids:
-            labels = inputs["input_ids"].masked_fill(inputs["token_type_ids"] == 0, -100)
+            labels = np.array(inputs["input_ids"])
+            labels[np.array(inputs["token_type_ids"]) == 0] = -100
             return_data.update({"labels": labels})
         return BatchFeature(data=return_data, tensor_type=return_tensors)
 

src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py

@@ -1797,7 +1797,7 @@ class Qwen2_5_VLForConditionalGeneration(Qwen2_5_VLPreTrainedModel, GenerationMi
     def __init__(self, config):
         super().__init__(config)
         self.model = Qwen2_5_VLModel(config)
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
 
         self.post_init()
 

src/transformers/models/qwen2_vl/modeling_qwen2_vl.py

@@ -1673,7 +1673,7 @@ class Qwen2VLForConditionalGeneration(Qwen2VLPreTrainedModel, GenerationMixin):
     def __init__(self, config):
         super().__init__(config)
         self.model = Qwen2VLModel(config)
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
 
         self.post_init()
 

src/transformers/utils/import_utils.py

@@ -2072,10 +2072,7 @@ class _LazyModule(ModuleType):
         try:
             return importlib.import_module("." + module_name, self.__name__)
         except Exception as e:
-            raise RuntimeError(
-                f"Failed to import {self.__name__}.{module_name} because of the following error (look up to see its"
-                f" traceback):\n{e}"
-            ) from e
+            raise e
 
     def __reduce__(self):
         return (self.__class__, (self._name, self.__file__, self._import_structure))

src/transformers/video_utils.py

@@ -696,11 +696,13 @@ def group_videos_by_shape(
     grouped_videos_index = {}
     for i, video in enumerate(videos):
         shape = video.shape[-2::]
+        num_frames = video.shape[-4]  # video format BTCHW
+        shape = (num_frames, *shape)
         if shape not in grouped_videos:
             grouped_videos[shape] = []
         grouped_videos[shape].append(video)
         grouped_videos_index[i] = (shape, len(grouped_videos[shape]) - 1)
-    # stack videos with the same shape
+    # stack videos with the same size and number of frames
     grouped_videos = {shape: torch.stack(videos, dim=0) for shape, videos in grouped_videos.items()}
     return grouped_videos, grouped_videos_index
 

tests/models/paligemma/test_processor_paligemma.py

@@ -62,6 +62,20 @@ class PaliGemmaProcessorTest(ProcessorTesterMixin, unittest.TestCase):
         )
         self.assertEqual(len(inputs["input_ids"][0]), 112)
 
+    @require_torch
+    def test_call_with_suffix(self):
+        input_str = "lower newer"
+        suffix = "upper older longer string"
+        image_input = self.prepare_image_inputs()
+        processor = self.get_processor()
+        inputs = processor(text=input_str, images=image_input, suffix=suffix)
+        self.assertTrue("labels" in inputs)
+        self.assertEqual(len(inputs["labels"][0]), len(inputs["input_ids"][0]))
+
+        inputs = processor(text=input_str, images=image_input, suffix=suffix, return_tensors="pt")
+        self.assertTrue("labels" in inputs)
+        self.assertEqual(len(inputs["labels"][0]), len(inputs["input_ids"][0]))
+
     def test_text_with_image_tokens(self):
         image_processor = self.get_component("image_processor")
         tokenizer = self.get_component("tokenizer")

tests/utils/test_video_utils.py

@@ -30,7 +30,7 @@ from transformers.testing_utils import (
     require_torchvision,
     require_vision,
 )
-from transformers.video_utils import make_batched_videos
+from transformers.video_utils import group_videos_by_shape, make_batched_videos, reorder_videos
 
 
 if is_torch_available():
@@ -43,9 +43,9 @@ if is_vision_available():
     from transformers.video_utils import VideoMetadata, load_video
 
 
-def get_random_video(height, width, return_torch=False):
+def get_random_video(height, width, num_frames=8, return_torch=False):
     random_frame = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
-    video = np.array(([random_frame] * 8))
+    video = np.array(([random_frame] * num_frames))
     if return_torch:
         # move channel first
         return torch.from_numpy(video).permute(0, 3, 1, 2)
@@ -189,6 +189,53 @@ class BaseVideoProcessorTester(unittest.TestCase):
         rgb_video = video_processor.convert_to_rgb(torch.cat([video, video[:, :1]], dim=1))
         self.assertEqual(rgb_video.shape, (8, 3, 20, 20))
 
+    def test_group_and_reorder_videos(self):
+        """Tests that videos can be grouped by frame size and number of frames"""
+        video_1 = get_random_video(20, 20, num_frames=3, return_torch=True)
+        video_2 = get_random_video(20, 20, num_frames=5, return_torch=True)
+
+        # Group two videos of same size but different number of frames
+        grouped_videos, grouped_videos_index = group_videos_by_shape([video_1, video_2])
+        self.assertEqual(len(grouped_videos), 2)
+
+        regrouped_videos = reorder_videos(grouped_videos, grouped_videos_index)
+        self.assertTrue(len(regrouped_videos), 2)
+        self.assertEqual(video_1.shape, regrouped_videos[0].shape)
+
+        # Group two videos of different size but same number of frames
+        video_3 = get_random_video(15, 20, num_frames=3, return_torch=True)
+        grouped_videos, grouped_videos_index = group_videos_by_shape([video_1, video_3])
+        self.assertEqual(len(grouped_videos), 2)
+
+        regrouped_videos = reorder_videos(grouped_videos, grouped_videos_index)
+        self.assertTrue(len(regrouped_videos), 2)
+        self.assertEqual(video_1.shape, regrouped_videos[0].shape)
+
+        # Group all three videos where some have same size or same frame count
+        # But since none have frames and sizes identical, we'll have 3 groups
+        grouped_videos, grouped_videos_index = group_videos_by_shape([video_1, video_2, video_3])
+        self.assertEqual(len(grouped_videos), 3)
+
+        regrouped_videos = reorder_videos(grouped_videos, grouped_videos_index)
+        self.assertTrue(len(regrouped_videos), 3)
+        self.assertEqual(video_1.shape, regrouped_videos[0].shape)
+
+        # Group if we had some videos with identical shapes
+        grouped_videos, grouped_videos_index = group_videos_by_shape([video_1, video_1, video_3])
+        self.assertEqual(len(grouped_videos), 2)
+
+        regrouped_videos = reorder_videos(grouped_videos, grouped_videos_index)
+        self.assertTrue(len(regrouped_videos), 2)
+        self.assertEqual(video_1.shape, regrouped_videos[0].shape)
+
+        # Group if we had all videos with identical shapes
+        grouped_videos, grouped_videos_index = group_videos_by_shape([video_1, video_1, video_1])
+        self.assertEqual(len(grouped_videos), 1)
+
+        regrouped_videos = reorder_videos(grouped_videos, grouped_videos_index)
+        self.assertTrue(len(regrouped_videos), 1)
+        self.assertEqual(video_1.shape, regrouped_videos[0].shape)
+
 
 @require_vision
 @require_av