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Update PyTorch PT2E tutorial for PyTorch 2.6+ #871

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copybara-service merged 1 commit into from
Dec 12, 2025
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Update PyTorch PT2E tutorial for PyTorch 2.6+ #871

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11 changes: 7 additions & 4 deletions docs/pytorch_converter/README.md
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Expand Up @@ -120,13 +120,11 @@ edge_model(*inputs_2, signature_name="input2")
## Quantization

Following is the code snippet to quantize a model with [PT2E
quantization](https://pytorch.org/tutorials/prototype/quantization_in_pytorch_2_0_export_tutorial.html)
quantization](https://docs.pytorch.org/ao/stable/tutorials_source/pt2e_quant_ptq.html)
using the `ai_edge_torch` backend.

```python
from torch.ao.quantization.quantize_pt2e import prepare_pt2e, convert_pt2e
from torch._export import capture_pre_autograd_graph

from ai_edge_torch.quantize.pt2e_quantizer import get_symmetric_quantization_config
from ai_edge_torch.quantize.pt2e_quantizer import PT2EQuantizer
from ai_edge_torch.quantize.quant_config import QuantConfig
Expand All @@ -135,7 +133,12 @@ pt2e_quantizer = PT2EQuantizer().set_global(
get_symmetric_quantization_config(is_per_channel=True, is_dynamic=True)
)

pt2e_torch_model = capture_pre_autograd_graph(torch_model, sample_args)
# > For pytorch 2.6+
pt2e_torch_model = torch.export.export(torch_model, sample_args).module()
# > For pytorch 2.5 and before
# from torch._export import capture_pre_autograd_graph
# pt2e_torch_model = capture_pre_autograd_graph(torch_model, sample_args)

pt2e_torch_model = prepare_pt2e(pt2e_torch_model, pt2e_quantizer)

# Run the prepared model with sample input data to ensure that internal observers are populated with correct values
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