a55b2688b6
### Description
<!-- Describe your changes. -->
The model with big Constants tensors size: Estimate size of the RWKV
model: ONNX graph (8MB), initializer tensors(200MB), constants (~5.7GB).
The `onnx.save_model` will got error due to the Constants is not output
in external data. Only the initializer tensors are output as external
data. In this change, expose parameter to support the constants in
external data. Model owner can customize the output behavior and still
keep the default behavior.
Quantize the model and output it to local, got issue due to output size
exceed 2GB even set `use_external_data_format=True`. The
`use_external_data_format` flag only outputs initializer tensors to
external data.
Use the falg `convert_attribute` flag to output all tensors to external
data.
```
def convert_model_to_external_data(
model: ModelProto,
all_tensors_to_one_file: bool = True,
location: Optional[str] = None,
size_threshold: int = 1024,
include_attribute: bool = False,
) -> None:
tensors = _get_initializer_tensors(model)
if include_attribute:
tensors = _get_all_tensors(model)
...
```
The `onnx.external_data_helper.convert_model_to_external_data` support
output the attribute to external with flag `include_attribute=True`.
However, this parameter is hide by the
`onnxruntime\quantization\onnx_model.py` and the constants(`5.7GB)
within the model will got protobuf 2GB limitation issue with default
parameters.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Fix https://github.com/microsoft/onnxruntime/issues/17944
---------
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
Get Started & Resources
-
General Information: onnxruntime.ai
-
Usage documention and tutorials: onnxruntime.ai/docs
-
YouTube video tutorials: youtube.com/@ONNXRuntime
-
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
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