[ORT Training] Some important updates of ONNX Runtime training APIs

examples/onnxruntime/training/docker/Dockerfile-ort-nightly-cu118

@@ -22,6 +22,7 @@ CMD nvidia-smi
 ENV DEBIAN_FRONTEND noninteractive
 
 # Versions
+# available options 3.8, 3.9, 3.10, 3.11
 ARG PYTHON_VERSION=3.9
 ARG TORCH_CUDA_VERSION=cu118
 ARG TORCH_VERSION=2.0.0
@@ -34,7 +35,7 @@ SHELL ["/bin/bash", "-c"]
 # Install and update tools to minimize security vulnerabilities
 RUN apt-get update
 RUN apt-get install -y software-properties-common wget apt-utils patchelf git libprotobuf-dev protobuf-compiler cmake \
-    bzip2 ca-certificates libglib2.0-0 libxext6 libsm6 libxrender1 mercurial subversion libopenmpi-dev && \
+    bzip2 ca-certificates libglib2.0-0 libxext6 libsm6 libxrender1 mercurial subversion libopenmpi-dev ffmpeg && \
     apt-get clean
 RUN unattended-upgrade
 RUN apt-get autoremove -y

examples/onnxruntime/training/docker/Dockerfile-ort1.14.1-cu116

@@ -33,7 +33,7 @@ ARG TORCHVISION_VERSION=0.14.1
 # Install and update tools to minimize security vulnerabilities
 RUN apt-get update
 RUN apt-get install -y software-properties-common wget apt-utils patchelf git libprotobuf-dev protobuf-compiler cmake \
-    bzip2 ca-certificates libglib2.0-0 libxext6 libsm6 libxrender1 mercurial subversion libopenmpi-dev && \
+    bzip2 ca-certificates libglib2.0-0 libxext6 libsm6 libxrender1 mercurial subversion libopenmpi-dev ffmpeg && \
     apt-get clean
 RUN unattended-upgrade
 RUN apt-get autoremove -y

examples/onnxruntime/training/docker/Dockerfile-ort1.15.1-cu118

@@ -34,7 +34,7 @@ ARG TORCHVISION_VERSION=0.15.1
 # Install and update tools to minimize security vulnerabilities
 RUN apt-get update
 RUN apt-get install -y software-properties-common wget apt-utils patchelf git libprotobuf-dev protobuf-compiler cmake \
-    bzip2 ca-certificates libglib2.0-0 libxext6 libsm6 libxrender1 mercurial subversion libopenmpi-dev && \
+    bzip2 ca-certificates libglib2.0-0 libxext6 libsm6 libxrender1 mercurial subversion libopenmpi-dev ffmpeg && \
     apt-get clean
 RUN unattended-upgrade
 RUN apt-get autoremove -y

examples/onnxruntime/training/docker/Dockerfile-ort1.16.1-cu118

@@ -34,7 +34,7 @@ SHELL ["/bin/bash", "-c"]
 # Install and update tools to minimize security vulnerabilities
 RUN apt-get update
 RUN apt-get install -y software-properties-common wget apt-utils patchelf git libprotobuf-dev protobuf-compiler cmake \
-    bzip2 ca-certificates libglib2.0-0 libxext6 libsm6 libxrender1 mercurial subversion libopenmpi-dev && \
+    bzip2 ca-certificates libglib2.0-0 libxext6 libsm6 libxrender1 mercurial subversion libopenmpi-dev ffmpeg && \
     apt-get clean
 RUN unattended-upgrade
 RUN apt-get autoremove -y

examples/onnxruntime/training/image-classification/run_image_classification.py

@@ -16,6 +16,7 @@
 import logging
 import os
 import sys
+import warnings
 from dataclasses import dataclass, field
 from typing import Optional
 
@@ -54,7 +55,7 @@
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.26.0")
+check_min_version("4.34.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
 
@@ -141,12 +142,28 @@ class ModelArguments:
         metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
     )
     image_processor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
     use_auth_token: bool = field(
+        default=None,
+        metadata={
+            "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
+        },
+    )
+    trust_remote_code: bool = field(
         default=False,
         metadata={
             "help": (
-                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
-                "with private models)."
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will "
+                "execute code present on the Hub on your local machine."
             )
         },
     )
@@ -162,32 +179,24 @@ def collate_fn(examples):
     return {"pixel_values": pixel_values, "labels": labels}
 
 
-@dataclass
-class InferenceArguments:
-    """
-    Arguments for inference(evaluate, predict).
-    """
-
-    inference_with_ort: bool = field(
-        default=False,
-        metadata={"help": "Whether use ONNX Runtime as backend for inference. Default set to false."},
-    )
-
-
 def main():
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.
 
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, ORTTrainingArguments, InferenceArguments))
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, ORTTrainingArguments))
     if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
         # If we pass only one argument to the script and it's the path to a json file,
         # let's parse it to get our arguments.
-        model_args, data_args, training_args, inference_args = parser.parse_json_file(
-            json_file=os.path.abspath(sys.argv[1])
-        )
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
     else:
-        model_args, data_args, training_args, inference_args = parser.parse_args_into_dataclasses()
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    if model_args.use_auth_token is not None:
+        warnings.warn("The `use_auth_token` argument is deprecated and will be removed in v4.34.", FutureWarning)
+        if model_args.token is not None:
+            raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
+        model_args.token = model_args.use_auth_token
 
     # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
     # information sent is the one passed as arguments along with your Python/PyTorch versions.
@@ -200,6 +209,10 @@ 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)
@@ -209,7 +222,7 @@ def main():
     # Log on each process the small summary:
     logger.warning(
         f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
-        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
     )
     logger.info(f"Training/evaluation parameters {training_args}")
 
@@ -238,7 +251,7 @@ def main():
             data_args.dataset_config_name,
             cache_dir=model_args.cache_dir,
             task="image-classification",
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=model_args.token,
         )
     else:
         data_files = {}
@@ -285,22 +298,25 @@ def compute_metrics(p):
         finetuning_task="image-classification",
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     model = AutoModelForImageClassification.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,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
         ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
     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,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     # Define torchvision transforms to be applied to each image.
@@ -367,7 +383,6 @@ def val_transforms(example_batch):
         compute_metrics=compute_metrics,
         tokenizer=image_processor,
         data_collator=collate_fn,
-        feature="image-classification",
     )
 
     # Training
@@ -385,7 +400,7 @@ def val_transforms(example_batch):
 
     # Evaluation
     if training_args.do_eval:
-        metrics = trainer.evaluate(inference_with_ort=inference_args.inference_with_ort)
+        metrics = trainer.evaluate()
         trainer.log_metrics("eval", metrics)
         trainer.save_metrics("eval", metrics)