New harder task #405
New harder task #405
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| self._n_modes_via_ids_estimate = float(torch.unique(ids).numel()) | ||
| self._mode_stats_kind = "approx" | ||
| except Exception: | ||
| warnings.warn("+ Warning: Failed to compute mode_stats (skipping).") |
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better to use logger.exception here, to print the exception as well
Also it would be better to avoid catching Exception in general. Why this can fail?
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this would catch the ValueError in "exact" branch as well. Is this what we want? Should we catch at all?
| # Cheap exact threshold (up to ~200k states) | ||
| if self.n_states <= 200_000: | ||
| axes = [ | ||
| torch.arange(self.height, dtype=torch.long) for _ in range(self.ndim) | ||
| ] | ||
| grid = torch.cartesian_prod(*axes) | ||
| rewards = self.reward_fn(grid) |
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how did you come up with this number? Doing the cartesian product seems memory intensive
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this number might need to be lowered. It was arbitrary.
| except Exception: | ||
| # Fall back to heuristic paths below | ||
| pass |
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maybe add a logger
I don't think in general it is a good idea to mask a lot of stuff to the user. Sometimes we compute the exact mode existence, sometimes we use heuristic
| for col in range(m): | ||
| # Find pivot | ||
| piv = None | ||
| for r in range(row, k): | ||
| if A[r, col]: | ||
| piv = r | ||
| break | ||
| if piv is None: | ||
| continue | ||
| # Swap | ||
| if piv != row: | ||
| A[[row, piv]] = A[[piv, row]] | ||
| c[[row, piv]] = c[[piv, row]] | ||
| # Eliminate below | ||
| for r in range(row + 1, k): | ||
| if A[r, col]: | ||
| A[r, :] ^= A[row, :] | ||
| c[r] ^= c[row] | ||
| row += 1 | ||
| if row == k: | ||
| break | ||
| # Check for inconsistency: 0 = 1 rows | ||
| for r in range(k): | ||
| if not A[r, :].any() and c[r]: | ||
| return False | ||
| return True |
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I didn't check the details tbh, but it seems quite inefficient and not easily readable. Can we rely to scipy for these stuffs?
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I'll look into it
| """ | ||
| with torch.no_grad(): | ||
| device = torch.device("cpu") | ||
| B = min(2048, max(128, 8 * self.ndim)) |
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what are these numbers? Maybe use constant to improve clarity
| try: | ||
| all_states = self.all_states | ||
| if all_states is not None: | ||
| mask = self.mode_mask(all_states) | ||
| ids = self.mode_ids(all_states) | ||
| ids = ids[mask] | ||
| ids = ids[ids >= 0] | ||
| return int(torch.unique(ids).numel()) | ||
| except Exception: | ||
| pass | ||
| if self._mode_stats_kind == "exact" and self._n_modes_via_ids_exact is not None: | ||
| return int(self._n_modes_via_ids_exact) | ||
| if ( | ||
| self._mode_stats_kind == "approx" | ||
| and self._n_modes_via_ids_estimate is not None | ||
| ): | ||
| return int(self._n_modes_via_ids_estimate) | ||
|
|
||
| return 2**self.ndim |
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do we need to recompute this every time?
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no you're right it should be stored.
| except Exception: | ||
| pass |
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similar to other comment, this is not nice for debuggability
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Hi @younik - I hear you, this is a big PR. The "splits" would have to be along tasks, though, so the resulting PRs would still be large. I appreciate your comments on the code. I think it would make sense to also look at the tasks (the stuff that's plotted in the notebook) to see if they make sense. I'm not convinced by all of the tasks. I would be open to removing a task or two. I think the one that works best for it's intended purpose is the coprime reward. |
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In the above commit, I fixed the comments of Deceptive Reward and also fixed a pyright error. |
I do think |
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