ORT optimizer refactorization

docs/source/onnxruntime/optimization.mdx

@@ -14,6 +14,101 @@ specific language governing permissions and limitations under the License.
 
 🤗 Optimum provides an `optimum.onnxruntime` package that enables you to apply graph optimization on many model hosted on the 🤗 hub using the [ONNX Runtime](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers) model optimization tool.
 
+
+## Creating an `ORTOptimizer`
+
+The `ORTOptimizer` class is used to optimize your ONNX model. The class can be initialized using the `from_pretrained()` method, which supports different checkpoint formats.
+
+1. Using an already initialized `ORTModelForXXX` class.
+
+```python
+>>> from optimum.onnxruntime import ORTOptimizer, ORTModelForTextClassification
+
+# Loading ONNX Model from the Hub
+>>> model = ORTModelForTextClassification.from_pretrained("optimum/distilbert-base-uncased-finetuned-sst-2-english")
+
+# Create an optimizer from an ORTModelForXXX
+>>> optimizer = ORTOptimizer.from_pretrained(model)
+```
+
+2. Using a local ONNX model from a directory.
+
+```python
+>>> from optimum.onnxruntime import ORTOptimizer
+
+# This assumes a model.onnx exists in path/to/model
+>>> optimizer = ORTOptimizer.from_pretrained("path/to/model")
+```
+
+
+## Optimization examples
+
+Below you will find an easy end-to-end example on how to optimize [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).
+
+```python
+>>> from optimum.onnxruntime import ORTOptimizer, ORTModelForSequenceClassification
+>>> from optimum.onnxruntime.configuration import OptimizationConfig
+
+>>> model_id = "distilbert-base-uncased-finetuned-sst-2-english"
+>>> save_dir = "/tmp/outputs"
+
+# Load a PyTorch model and export it to the ONNX format
+>>> model = ORTModelForSequenceClassification.from_pretrained(model_id, from_transformers=True)
+
+# Create the optimizer
+>>> optimizer = ORTOptimizer.from_pretrained(model)
+
+# Define the optimization strategy by creating the appropriate configuration
+>>> optimization_config = OptimizationConfig(
+ optimization_level=2,
+ optimize_with_onnxruntime_only=False,
+ optimize_for_gpu=False,
+)
+
+# Optimize the model
+>>> optimizer.optimize(save_dir=save_dir, optimization_config=optimization_config)
+```
+
+
+Below you will find an easy end-to-end example on how to optimize a Seq2Seq model [sshleifer/distilbart-cnn-12-6"](https://huggingface.co/sshleifer/distilbart-cnn-12-6).
+
+```python
+>>> from optimum.onnxruntime import ORTOptimizer, ORTModelForSeq2SeqLM
+>>> from optimum.onnxruntime.configuration import OptimizationConfig
+>>> from transformers import AutoTokenizer
+
+>>> model_id = "sshleifer/distilbart-cnn-12-6"
+>>> save_dir = "/tmp/outputs"
+
+# Load a PyTorch model and export it to the ONNX format
+>>> model = ORTModelForSeq2SeqLM.from_pretrained(model_id, from_transformers=True)
+
+# Create the optimizer
+>>> optimizer = ORTOptimizer.from_pretrained(model)
+
+# Define the optimization strategy by creating the appropriate configuration
+>>> optimization_config = OptimizationConfig(
+ optimization_level=2,
+ optimize_with_onnxruntime_only=False,
+ optimize_for_gpu=False,
+)
+
+# Optimize the model
+>>> optimizer.optimize(save_dir=save_dir, optimization_config=optimization_config)
+
+# Load the resulting optimized model
+>>> optimized_model = ORTModelForSeq2SeqLM.from_pretrained(
+ save_dir,
+ encoder_file_name="encoder_model_optimized.onnx",
+ decoder_file_name="decoder_model_optimized.onnx",
+ decoder_file_with_past_name="decoder_with_past_model_optimized.onnx",
+)
+>>> tokenizer = AutoTokenizer.from_pretrained(model_id)
+>>> tokens = tokenizer("This is a sample input", return_tensors="pt")
+>>> outputs = optimized_model.generate(**tokens)
+```
+
+
 ## ORTOptimizer
 
 [[autodoc]] onnxruntime.optimization.ORTOptimizer

docs/source/onnxruntime/quantization.mdx

@@ -124,7 +124,7 @@ The `ORTQuantizer` class can be used to statically quantize your ONNX model. Bel
 )
 ```
 
-## Quantize Seq2Seq models.
+## Quantize Seq2Seq models
 
 The `ORTQuantizer` currently doesn't support multi-file models, like `ORTModelForSeq2SeqLM`. If you want to quantize a Seq2Seq model, you have to quantize each model's component individually using the `ORTQuantizer` class. Currently, only dynamic quantization is supported for Seq2Seq model.
 

examples/onnxruntime/optimization/multiple-choice/run_swag.py

@@ -32,13 +32,14 @@
 import torch
 import transformers
 from datasets import load_dataset
-from transformers import HfArgumentParser, TrainingArguments
+from transformers import AutoTokenizer, HfArgumentParser, TrainingArguments
 from transformers.tokenization_utils_base import PreTrainedTokenizerBase
 from transformers.trainer_utils import get_last_checkpoint
 from transformers.utils import check_min_version
 
-from optimum.onnxruntime import ORTModel, ORTOptimizer
+from optimum.onnxruntime import ORTModelForMultipleChoice, ORTOptimizer
 from optimum.onnxruntime.configuration import OptimizationConfig, ORTConfig
+from optimum.onnxruntime.model import ORTModel
 
 
 # Will error if the minimal version of Transformers is not installed. The version of transformers must be >= 4.19.0
@@ -132,10 +133,6 @@ class OptimizationArguments:
  Arguments pertaining to what type of optimization we are going to apply on the model.
  """
 
- opset: Optional[int] = field(
- default=None,
- metadata={"help": "ONNX opset version to export the model with."},
- )
  optimization_level: Optional[int] = field(
  default=1,
  metadata={
@@ -224,7 +221,9 @@ def main():
 
  os.makedirs(training_args.output_dir, exist_ok=True)
  model_path = os.path.join(training_args.output_dir, "model.onnx")
- optimized_model_path = os.path.join(training_args.output_dir, "model-optimized.onnx")
+ optimized_model_path = os.path.join(training_args.output_dir, "model_optimized.onnx")
+
+ tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name or model_args.model_name_or_path)
 
  # Create the optimization configuration containing all the optimization parameters
  optimization_config = OptimizationConfig(
@@ -233,22 +232,14 @@ def main():
  optimize_for_gpu=optim_args.optimize_for_gpu,
  )
 
- # Create the optimizer
- optimizer = ORTOptimizer.from_pretrained(
- model_args.model_name_or_path, feature="multiple-choice", opset=optim_args.opset
- )
+ # Export the model
+ model = ORTModelForMultipleChoice.from_pretrained(model_args.model_name_or_path, from_transformers=True)
 
- # Export the optimized model
- optimizer.export(
- onnx_model_path=model_path,
- onnx_optimized_model_output_path=optimized_model_path,
- optimization_config=optimization_config,
- )
+ # Create the optimizer
+ optimizer = ORTOptimizer.from_pretrained(model)
 
- # Create the ONNX Runtime configuration summarizing all the parameters related to ONNX IR export and optimization
- ort_config = ORTConfig(opset=optimizer.opset, optimization=optimization_config)
- # Save the configuration
- ort_config.save_pretrained(training_args.output_dir)
+ # Optimize the model
+ optimizer.optimize(optimization_config=optimization_config, save_dir=training_args.output_dir)
 
  if training_args.do_eval:
  # Prepare the dataset downloading, preprocessing and metric creation to perform the evaluation and / or the
@@ -313,7 +304,7 @@ def preprocess_function(examples, tokenizer: PreTrainedTokenizerBase):
  # Preprocess the evaluation dataset
  with training_args.main_process_first(desc="Running tokenizer on the validation dataset"):
  eval_dataset = eval_dataset.map(
- partial(preprocess_function, tokenizer=optimizer.preprocessor),
+ partial(preprocess_function, tokenizer=tokenizer),
  batched=True,
  num_proc=data_args.preprocessing_num_workers,
  load_from_cache_file=not data_args.overwrite_cache,
@@ -330,15 +321,14 @@ def compute_metrics(eval_predictions):
 
  ort_model = ORTModel(
  optimized_model_path,
- optimizer._onnx_config,
  execution_provider=optim_args.execution_provider,
  compute_metrics=compute_metrics,
  label_names=["label"],
  )
  outputs = ort_model.evaluation_loop(eval_dataset)
 
  # Save evaluation metrics
- with open(os.path.join(training_args.output_dir, f"eval_results.json"), "w") as f:
+ with open(os.path.join(training_args.output_dir, "eval_results.json"), "w") as f:
  json.dump(outputs.metrics, f, indent=4, sort_keys=True)
 
 

examples/onnxruntime/optimization/question-answering/run_qa.py

@@ -30,13 +30,13 @@
 import datasets
 import transformers
 from datasets import load_dataset, load_metric
-from transformers import EvalPrediction, HfArgumentParser, PreTrainedTokenizer, TrainingArguments
+from transformers import AutoTokenizer, EvalPrediction, HfArgumentParser, PreTrainedTokenizer, TrainingArguments
 from transformers.utils import check_min_version
 from transformers.utils.versions import require_version
 
+from optimum.onnxruntime import ORTModelForQuestionAnswering, ORTOptimizer
 from optimum.onnxruntime.configuration import OptimizationConfig, ORTConfig
 from optimum.onnxruntime.model import ORTModel
-from optimum.onnxruntime.optimization import ORTOptimizer
 from trainer_qa import QuestionAnsweringTrainer
 from utils_qa import postprocess_qa_predictions
 
@@ -81,10 +81,6 @@ class ModelArguments:
  "with private models)."
  },
  )
- execution_provider: str = field(
- default="CPUExecutionProvider",
- metadata={"help": "ONNX Runtime execution provider to use for inference."},
- )
 
 
 @dataclass
@@ -204,10 +200,6 @@ class OptimizationArguments:
  Arguments pertaining to what type of optimization we are going to apply on the model.
  """
 
- opset: Optional[int] = field(
- default=None,
- metadata={"help": "ONNX opset version to export the model with."},
- )
  optimization_level: Optional[int] = field(
  default=1,
  metadata={
@@ -233,6 +225,10 @@ class OptimizationArguments:
  "GPU or CPU only when optimization_level > 1."
  },
  )
+ execution_provider: str = field(
+ default="CPUExecutionProvider",
+ metadata={"help": "ONNX Runtime execution provider to use for inference."},
+ )
 
 
 def main():
@@ -264,7 +260,7 @@ def main():
  if (
  optim_args.optimization_level > 1
  and optim_args.optimize_for_gpu
- and model_args.execution_provider == "CPUExecutionProvider"
+ and optim_args.execution_provider == "CPUExecutionProvider"
  ):
  raise ValueError(
  f"Optimization level is set at {optim_args.optimization_level} and "
@@ -275,7 +271,7 @@ def main():
  if (
  optim_args.optimization_level > 1
  and not optim_args.optimize_for_gpu
- and model_args.execution_provider == "CUDAExecutionProvider"
+ and optim_args.execution_provider == "CUDAExecutionProvider"
  ):
  raise ValueError(
  f"Optimization level is set at {optim_args.optimization_level} and "
@@ -293,7 +289,9 @@ def main():
 
  os.makedirs(training_args.output_dir, exist_ok=True)
  model_path = os.path.join(training_args.output_dir, "model.onnx")
- optimized_model_path = os.path.join(training_args.output_dir, "model-optimized.onnx")
+ optimized_model_path = os.path.join(training_args.output_dir, "model_optimized.onnx")
+
+ tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name or model_args.model_name_or_path)
 
  # Create the optimization configuration containing all the optimization parameters
  optimization_config = OptimizationConfig(
@@ -302,22 +300,14 @@ def main():
  optimize_for_gpu=optim_args.optimize_for_gpu,
  )
 
- # Create the optimizer
- optimizer = ORTOptimizer.from_pretrained(
- model_args.model_name_or_path, feature="question-answering", opset=optim_args.opset
- )
+ # Export the model
+ model = ORTModelForQuestionAnswering.from_pretrained(model_args.model_name_or_path, from_transformers=True)
 
- # Export the optimized model
- optimizer.export(
- onnx_model_path=model_path,
- onnx_optimized_model_output_path=optimized_model_path,
- optimization_config=optimization_config,
- )
+ # Create the optimizer
+ optimizer = ORTOptimizer.from_pretrained(model)
 
- # Create the ONNX Runtime configuration summarizing all the parameters related to ONNX IR export and optimization
- ort_config = ORTConfig(opset=optimizer.opset, optimization=optimization_config)
- # Save the configuration
- ort_config.save_pretrained(training_args.output_dir)
+ # Optimize the model
+ optimizer.optimize(optimization_config=optimization_config, save_dir=training_args.output_dir)
 
  # Prepare the dataset downloading, preprocessing and metric creation to perform the evaluation and / or the
  # prediction step(s)
@@ -456,7 +446,7 @@ def compute_metrics(p: EvalPrediction):
  if data_args.max_eval_samples is not None:
  eval_examples = eval_examples.select(range(data_args.max_eval_samples))
  eval_dataset = eval_examples.map(
- partial(prepare_validation_features, tokenizer=optimizer.preprocessor),
+ partial(prepare_validation_features, tokenizer=tokenizer),
  batched=True,
  num_proc=data_args.preprocessing_num_workers,
  remove_columns=column_names,
@@ -469,9 +459,9 @@ def compute_metrics(p: EvalPrediction):
 
  ort_model = ORTModel(
  optimized_model_path,
- optimizer._onnx_config,
- execution_provider=model_args.execution_provider,
+ execution_provider=optim_args.execution_provider,
  compute_metrics=compute_metrics,
+ label_names=["start_positions", "end_positions"],
  )
  outputs = ort_model.evaluation_loop(eval_dataset)
  predictions = post_processing_function(eval_examples, eval_dataset, outputs.predictions)
@@ -492,7 +482,7 @@ def compute_metrics(p: EvalPrediction):
  if data_args.max_predict_samples is not None:
  predict_examples = predict_examples.select(range(data_args.max_predict_samples))
  predict_dataset = predict_examples.map(
- partial(prepare_validation_features, tokenizer=optimizer.preprocessor),
+ partial(prepare_validation_features, tokenizer=tokenizer),
  batched=True,
  num_proc=data_args.preprocessing_num_workers,
  remove_columns=column_names,
@@ -504,7 +494,9 @@ def compute_metrics(p: EvalPrediction):
  predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
 
  ort_model = ORTModel(
- optimized_model_path, optimizer._onnx_config, execution_provider=model_args.execution_provider
+ optimized_model_path,
+ execution_provider=optim_args.execution_provider,
+ label_names=["start_positions", "end_positions"],
  )
  outputs = ort_model.evaluation_loop(predict_dataset)
  predictions = post_processing_function(predict_examples, predict_dataset, outputs.predictions)