add loss_backward_retain_graph to __init__() #856
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This pull request was exported from Phabricator. Differential Revision: D58901158 |
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This pull request was exported from Phabricator. Differential Revision: D58901158 |
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Summary: Pull Request resolved: pytorch#856 Expose **retain_graph** kwarg in **loss.backward()** by adding a new argument **loss_backward_retain_graph** to **AutoUnit.__init__()** Differential Revision: D58901158
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Summary: Pull Request resolved: pytorch#856 Expose **retain_graph** kwarg in **loss.backward()** by adding a new argument **loss_backward_retain_graph** to **AutoUnit.__init__()** Differential Revision: D58901158
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Summary: Pull Request resolved: pytorch#856 Expose **retain_graph** kwarg in **loss.backward()** by adding a new argument **loss_backward_retain_graph** to **AutoUnit.__init__()** Differential Revision: D58901158
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Summary: Pull Request resolved: pytorch#856 Expose **retain_graph** kwarg in **loss.backward()** by adding a new argument **loss_backward_retain_graph** to **AutoUnit.__init__()** Differential Revision: D58901158
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Summary:
Mask2Former (M2F) Executorch QAT model has its 5 top-level submodules prepared separately (https://fburl.com/code/44qk8qu3).
This is because the model graph during a) QAT training b) QAT evaluation c) ET model export time are different.
Differential Revision: D58901158