[pt2e] Avoid getting model device once per node #2695
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/2695
Note: Links to docs will display an error until the docs builds have been completed. ❗ 1 Active SEVsThere are 1 currently active SEVs. If your PR is affected, please view them below: ✅ No FailuresAs of commit ae3264e with merge base 418593c ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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| @@ -1885,7 +1885,9 @@ def convert(self, model: torch.fx.GraphModule, observer_node: Node): | |||
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| from torchao.quantization.pt2e.utils import create_getattr_from_value | |||
| from torchao.utils import _assert_and_get_unique_device | |||
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btw, this is still going to be per node, but here it's probably free to get the device from scale tensor? can we do that
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oh you're right, let me try
**Summary:** Previously, we call `assert_and_get_unqiue_device` once per node in both prepare and convert. This is expensive and unnecessary since the model device is the same across all nodes, so we should just call this once in the beginning and reuse the same model device across all the nodes. torchao version of pytorch/pytorch#159901 Note: The prepare path is not completely done yet, since we are blocked on the pytorch PR on being merged. It's different from convert since it still calls utility functions from `torch.ao.quantization.fx`. **Test Plan:** ``` python test/quantization/pt2e/test_quantize_pt2e.py ```
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Summary: Previously, we call
assert_and_get_unqiue_deviceonce per node in both prepare and convert. This is expensive and unnecessary since the model device is the same across all nodes, so we should just call this once in the beginning and reuse the same model device across all the nodes.torchao version of pytorch/pytorch#159901
Note: The prepare path is not completely done yet, since we are blocked on the pytorch PR on being merged. It's different from convert since it still calls utility functions from
torch.ao.quantization.fx.Test Plan: