CoreML quantizer does not work if model has nn.Embedding
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- Python
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Description
🚀 The feature, motivation and pitch
CoreML fails during lowering when the model has an embedding op and the CoreML quantizer is used.
quantization_config = LinearQuantizerConfig.from_dict(
{
"global_config": {
"quantization_scheme": QuantizationScheme.symmetric,
"activation_dtype": torch.quint8,
"weight_dtype": torch.qint8,
"weight_per_channel": True,
}
}
)
quantizer = CoreMLQuantizer(quantization_config)
prepared_graph = prepare_pt2e(pre_autograd_aten_dialect, quantizer)
Alternatives
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Additional context
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RFC (Optional)
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Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The report names CoreMLQuantizer, LinearQuantizerConfig, prepare_pt2e, and nn.Embedding; start by reproducing the lowering failure with the shown configuration and tracing the CoreML lowering path for the embedding operation. Done means the same embedding model lowers successfully with CoreML quantization without the reported failure.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning, mobile-dev
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 45/100