Bump transformers to 4.29.2

.github/workflows/build_pr_documentation.yml

@@ -39,7 +39,7 @@ jobs:
           cd doc-builder
           git pull origin main
           pip install .
-          pip install black
+          pip install .[quality]
           cd ..
 
       - name: Make documentation

.github/workflows/check_code_quality.yml

@@ -42,13 +42,12 @@ jobs:
       run: |
         source venv/bin/activate
         pip install --upgrade pip
-        pip install isort
-        pip install black
+        pip install .[quality]
     - name: Check style with black
       run: |
         source venv/bin/activate
-        black --check .
-    - name: Check style with isort
+        black --check examples tests optimum
+    - name: Check style with ruff
       run: |
         source venv/bin/activate
-        isort --check .
+        ruff examples tests optimum

Makefile

@@ -22,14 +22,16 @@ REAL_CLONE_NAME = $(if $(CLONE_NAME),$(CLONE_NAME),$(DEFAULT_CLONE_NAME))
 
 .PHONY:	style test
 
+check_dirs := examples tests optimum
+
 # Run code quality checks
 style_check:
-	black --check .
-	isort --check .
+	black --check $(check_dirs)
+	ruff $(check_dirs)
 
 style:
-	black .
-	isort .
+	black $(check_dirs)
+	ruff $(check_dirs) --fix
 
 # Run tests for the library
 test:

examples/audio-classification/run_audio_classification.py

@@ -23,23 +23,23 @@
 from typing import Optional
 
 import datasets
-import numpy as np
-from datasets import DatasetDict, load_dataset
-
 import evaluate
+import numpy as np
 import transformers
-from optimum.graphcore import IPUConfig, IPUTrainer
-from optimum.graphcore import IPUTrainingArguments as TrainingArguments
+from datasets import DatasetDict, load_dataset
 from transformers import AutoConfig, AutoFeatureExtractor, AutoModelForAudioClassification, HfArgumentParser, set_seed
 from transformers.trainer_utils import get_last_checkpoint
 from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
+from optimum.graphcore import IPUConfig, IPUTrainer
+from optimum.graphcore import IPUTrainingArguments as TrainingArguments
+
 
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.25.0")
+check_min_version("4.29.0")
 
 require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
 
@@ -203,6 +203,10 @@ def main():
         handlers=[logging.StreamHandler(sys.stdout)],
     )
 
+    if training_args.should_log:
+        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
+        transformers.utils.logging.set_verbosity_info()
+
     log_level = training_args.get_process_log_level()
     logger.setLevel(log_level)
     transformers.utils.logging.set_verbosity(log_level)
@@ -278,7 +282,7 @@ def main():
     # Prepare label mappings.
     # We'll include these in the model's config to get human readable labels in the Inference API.
     labels = raw_datasets["train"].features[data_args.label_column_name].names
-    label2id, id2label = dict(), dict()
+    label2id, id2label = {}, {}
     for i, label in enumerate(labels):
         label2id[label] = str(i)
         id2label[str(i)] = label

examples/image-classification/run_image_classification.py

@@ -19,8 +19,10 @@
 from dataclasses import dataclass, field
 from typing import Optional
 
+import evaluate
 import numpy as np
 import torch
+import transformers
 from datasets import load_dataset
 from PIL import Image
 from torchvision.transforms import (
@@ -32,35 +34,32 @@
     Resize,
     ToTensor,
 )
-
-import evaluate
-import transformers
-from optimum.graphcore import IPUConfig, IPUTrainer
-from optimum.graphcore import IPUTrainingArguments as TrainingArguments
-from optimum.graphcore.utils import check_min_version
 from transformers import (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     AutoConfig,
-    AutoFeatureExtractor,
+    AutoImageProcessor,
     AutoModelForImageClassification,
     HfArgumentParser,
     set_seed,
 )
 from transformers.trainer_utils import get_last_checkpoint
-from transformers.utils import check_min_version as tf_check_min_version
-from transformers.utils import send_example_telemetry
+from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
+from optimum.graphcore import IPUConfig, IPUTrainer
+from optimum.graphcore import IPUTrainingArguments as TrainingArguments
+from optimum.graphcore.utils import check_min_version as gc_check_min_version
+
 
 """ Fine-tuning a 🤗 Transformers model for image classification"""
 
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-tf_check_min_version("4.25.0")
+check_min_version("4.29.0")
 
 # Will error if the minimal version of Optimum Graphcore is not installed. Remove at your own risks.
-check_min_version("0.2.4.dev0")
+gc_check_min_version("0.2.4.dev0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
 
@@ -146,7 +145,7 @@ class ModelArguments:
         default="main",
         metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
     )
-    feature_extractor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
+    image_processor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
     use_auth_token: bool = field(
         default=False,
         metadata={
@@ -212,6 +211,10 @@ def main():
         handlers=[logging.StreamHandler(sys.stdout)],
     )
 
+    if training_args.should_log:
+        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
+        transformers.utils.logging.set_verbosity_info()
+
     log_level = training_args.get_process_log_level()
     logger.setLevel(log_level)
     transformers.utils.logging.set_verbosity(log_level)
@@ -270,7 +273,7 @@ def main():
     # Prepare label mappings.
     # We'll include these in the model's config to get human readable labels in the Inference API.
     labels = dataset["train"].features["labels"].names
-    label2id, id2label = dict(), dict()
+    label2id, id2label = {}, {}
     for i, label in enumerate(labels):
         label2id[label] = str(i)
         id2label[str(i)] = label
@@ -309,19 +312,19 @@ def compute_metrics(p):
         use_auth_token=True if model_args.use_auth_token else None,
         ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
-    feature_extractor = AutoFeatureExtractor.from_pretrained(
-        model_args.feature_extractor_name or model_args.model_name_or_path,
+    image_processor = AutoImageProcessor.from_pretrained(
+        model_args.image_processor_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         use_auth_token=True if model_args.use_auth_token else None,
     )
 
     # Define torchvision transforms to be applied to each image.
-    if "shortest_edge" in feature_extractor.size:
-        size = feature_extractor.size["shortest_edge"]
+    if "shortest_edge" in image_processor.size:
+        size = image_processor.size["shortest_edge"]
     else:
-        size = (feature_extractor.size["height"], feature_extractor.size["width"])
-    normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
+        size = (image_processor.size["height"], image_processor.size["width"])
+    normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
     _train_transforms = [
         RandomResizedCrop(size),
         RandomHorizontalFlip(),
@@ -370,7 +373,7 @@ def compute_metrics(p):
         train_dataset=dataset["train"] if training_args.do_train else None,
         eval_dataset=dataset["validation"] if training_args.do_eval else None,
         compute_metrics=compute_metrics,
-        tokenizer=feature_extractor,
+        tokenizer=image_processor,
         data_collator=collate_fn,
     )
 

examples/language-modeling/prepare_dataset.py

@@ -21,7 +21,6 @@
 from typing import Optional, Sequence, Union
 
 from datasets import Dataset, DatasetDict, concatenate_datasets, load_dataset
-
 from joblib import Parallel, delayed
 
 

examples/language-modeling/run_clm.py

@@ -30,12 +30,9 @@
 from typing import Optional
 
 import datasets
-from datasets import load_dataset
-
+import torch
 import transformers
-from optimum.graphcore import IPUConfig, IPUTrainer
-from optimum.graphcore import IPUTrainingArguments as TrainingArguments
-from optimum.graphcore.data import pad_on_batch_axis
+from datasets import load_dataset
 from transformers import (
     CONFIG_MAPPING,
     MODEL_FOR_CAUSAL_LM_MAPPING,
@@ -48,13 +45,15 @@
 )
 from transformers.testing_utils import CaptureLogger
 from transformers.trainer_utils import get_last_checkpoint
-from transformers.utils import check_min_version as tf_check_min_version
-from transformers.utils import send_example_telemetry
+from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
+from optimum.graphcore import IPUConfig, IPUTrainer
+from optimum.graphcore import IPUTrainingArguments as TrainingArguments
+
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-tf_check_min_version("4.25.0")
+check_min_version("4.29.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 
@@ -119,6 +118,25 @@ class ModelArguments:
             )
         },
     )
+    torch_dtype: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
+                "dtype will be automatically derived from the model's weights."
+            ),
+            "choices": ["auto", "float16", "float32"],
+        },
+    )
+    low_cpu_mem_usage: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded."
+                "set True will benefit LLM loading time and RAM consumption."
+            )
+        },
+    )
 
 
 @dataclass
@@ -220,6 +238,10 @@ def main():
         handlers=[logging.StreamHandler(sys.stdout)],
     )
 
+    if training_args.should_log:
+        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
+        transformers.utils.logging.set_verbosity_info()
+
     log_level = training_args.get_process_log_level()
     logger.setLevel(log_level)
     datasets.utils.logging.set_verbosity(log_level)
@@ -373,17 +395,24 @@ def main():
         )
 
     if model_args.model_name_or_path:
+        torch_dtype = (
+            model_args.torch_dtype
+            if model_args.torch_dtype in ["auto", None]
+            else getattr(torch, model_args.torch_dtype)
+        )
         model = AutoModelForCausalLM.from_pretrained(
             model_args.model_name_or_path,
             from_tf=bool(".ckpt" in model_args.model_name_or_path),
             config=config,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             use_auth_token=True if model_args.use_auth_token else None,
+            torch_dtype=torch_dtype,
+            low_cpu_mem_usage=model_args.low_cpu_mem_usage,
         )
     else:
         model = AutoModelForCausalLM.from_config(config)
-        n_params = sum(dict((p.data_ptr(), p.numel()) for p in model.parameters()).values())
+        n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values())
         logger.info(f"Training new model from scratch - Total size={n_params/2**20:.2f}M params")
 
     # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
@@ -395,9 +424,9 @@ def main():
     # Preprocessing the datasets.
     # First we tokenize all the texts.
     if training_args.do_train:
-        column_names = raw_datasets["train"].column_names
+        column_names = list(raw_datasets["train"].features)
     else:
-        column_names = raw_datasets["validation"].column_names
+        column_names = list(raw_datasets["validation"].features)
     text_column_name = "text" if "text" in column_names else column_names[0]
 
     # since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
@@ -428,8 +457,9 @@ def tokenize_function(examples):
         block_size = tokenizer.model_max_length
         if block_size > 1024:
             logger.warning(
-                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
-                "Picking 1024 instead. You can change that default value by passing --block_size xxx."
+                "The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
+                " of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
+                " override this default with `--block_size xxx`."
             )
             block_size = 1024
     else: