Fix In-place Assignments for PiecewiseRationalQuadratic Compatibility with functorch and torch2.0#77
Fix In-place Assignments for PiecewiseRationalQuadratic Compatibility with functorch and torch2.0#77HamidrezaKmK wants to merge 1 commit intobayesiains:masterfrom
PiecewiseRationalQuadratic Compatibility with functorch and torch2.0#77Conversation
arturbekasov
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arturbekasov
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Hey Hamid,
Thanks for taking the time for submit the PR. Left a few comments.
I am not against making the code more functional, but I would like to be a bit careful about performance implications of the additional copies. Is there a chance to see some before/after performance stats of the function?
Cheers,
Artur
| min_bin_width=DEFAULT_MIN_BIN_WIDTH, | ||
| min_bin_height=DEFAULT_MIN_BIN_HEIGHT, | ||
| min_derivative=DEFAULT_MIN_DERIVATIVE, | ||
| enable_identity_init=False, |
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Is this on purpose, or we should re-base the changes?
| outputs[outside_interval_mask] = inputs[outside_interval_mask] | ||
| logabsdet[outside_interval_mask] = 0 | ||
| outputs = torch.where(outside_interval_mask, inputs, outputs) |
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Should we drop the zero init above if we're copying here anyway? I.e. outputs = torch.where(outside_interval_mask, inputs, torch.zeros_like(inputs)).
| logabsdet[outside_interval_mask] = 0 | ||
| outputs = torch.where(outside_interval_mask, inputs, outputs) | ||
| logabsdet = torch.where(outside_interval_mask, torch.zeros_like(logabsdet), logabsdet) |
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Actually, does the original line even have an effect? We're assigning zeros to what is already initialized to zeros.
| ( | ||
| outputs[inside_interval_mask], | ||
| logabsdet[inside_interval_mask], | ||
| # outputs[inside_interval_mask], |
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Let's not leave unused code in comments.
| cumwidths[..., -1] = right | ||
| widths = cumwidths[..., 1:] - cumwidths[..., :-1] | ||
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| if enable_identity_init: #flow is the identity if initialized with parameters equal to zero |
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Same as earlier comment: we want to keep this, not sure if this is on purpose.
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| # turn inside_interval_mask into an int tensor |
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Could we simplify below by using masked_scatter?
2 participants
Hello,
While working on my project (OOD Detection using Manifolds), I noticed an issue with the current implementation of the
PiecewiseRationalQuadraticcoupling layers. The in-place masked assignments, specifically:pose challenges when constructing the computation graph in both
functorchandtorch2.0.To address this, I've made necessary modifications in my fork, ensuring a functional adaptation that aligns with these libraries without altering the primary functionality of the layer.
I kindly ask for your review of these changes. If they align with your vision and maintain the library's integrity, I'd appreciate their incorporation into the main branch.
Thank you for your time and consideration.
Best,
Hamid