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fix: docs and input checks of the CNN and permutation-invariant embedding nets - #2113
The CNNEmbedding docstring and two of its error messages name arguments that do not exist. The real arguments are num_conv_layers and pool_kernel_size. The default channel list also fits only two layers. So num_conv_layers=3 alone always fails, and the error points to an argument that users cannot pass.
This PR fixes the docs, the default and the size check of CNNEmbedding. It gives CausalCNNEmbedding the same argument checks, and it fixes two bugs that I found in the same embedding nets.
Before
After
CNNEmbedding docstring
names num_cnn_layers and pool_size; "first two layers"; MaxPool1d only
real argument names; any number of layers; 1D and 2D pooling; output size per layer
CNNEmbedding, num_conv_layers=3, default channels
fails: "out_channels needs as many entries as num_cnn_layers."
builds with channels [6, 12, 24]
CNNEmbedding, 2D input, kernel much larger than the input
builds, then fails in the first forward pass with a torch error
raises at construction and names the arguments to change
CausalCNNEmbedding, wrong-length channel or dilation list, or zero layers
IndexError, "max() arg is an empty sequence", or a silently cut list
ValueError that names the argument
CausalCNNEmbedding with the default aggregator
prints intermediate shapes on every build
prints nothing
PermutationInvariantEmbedding, mean aggregation, observation with only padded trials
"AssertionError: NaNs in embedding."
zero embedding, as with sum aggregation
Invalid input to an embedding net
AssertionError, or no check at all under python -O
TypeError for a wrong type, ValueError for a wrong value
The default channels now start at 6 and double per layer. For the default two layers this is [6, 12] as before, so existing networks do not change.
The size check caught only an output size of exactly zero. A kernel much larger than the padded input can give a negative size. In 2D, two negative sizes give a positive product, so the network built. The check now requires every size to be positive.
The permutation-invariant net already warns that it treats an observation with only padded trials as one with zero trials. Sum aggregation did that, but mean aggregation divided by zero. The mean now divides by at least one.
The exceptions follow the other embedding nets. Checks on values that the classes compute themselves stay asserts. The NaN check of the permutation-invariant net is the exception: an inf in the input causes it, so it now raises a ValueError.
I also fixed typos in the CausalCNNEmbedding docstrings and removed the printed shapes from the stored output of the time-series how-to guide.
This helps everyone who reads the embedding API reference, changes the number of convolutional layers, or uses mean aggregation with padded trials.
Evidence
New tests check that the errors for the number of layers, the list lengths and the sizes name the argument that fixes them, that the default channels follow the number of layers, that the aggregator leaves its arguments alone, and that mean aggregation handles an observation without real trials. They fail on main and pass now.
CNNEmbedding((32,), num_conv_layers=3)
# main: AssertionError: out_channels needs as many entries as num_cnn_layers.# now: builds, with channels [6, 12, 24]CNNEmbedding((4, 4), kernel_size=9)
# main: builds; the first forward pass raises a torch RuntimeError# now: ValueError: CNN output size (-1, -1) is not positive at layer 1. Use a smaller kernel_size or pool_kernel_size.
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Valid configurations build the same networks as before. A CausalCNNEmbedding with more dilations than num_conv_layers, or with more channels and a custom aggregator, used to drop the extra entries; it now raises a ValueError. Code that catches AssertionError from an embedding net now has to catch ValueError or TypeError; nothing in sbi relies on that.
Thanks to @Ashwini8686, who reported the wrong argument names in issue #1536 and offered to fix them. The issue stays open for its other docs to-dos.
The docstring and two assertion messages named num_cnn_layers and pool_size, which are not arguments of CNNEmbedding; the real ones are num_conv_layers and pool_kernel_size. The docstring also said the first two layers are convolutional and that pooling uses MaxPool1d, which is wrong for num_conv_layers != 2 and for 2D inputs.
The messages now name the real arguments, state the default out_channels_per_layer=[6, 12] that only fits two layers, and no longer carry the line breaks and indentation of triple-quoted strings. A test checks that each message names the argument that fixes the error.
The size guard in CNNEmbedding only caught an output size of exactly zero. A kernel much larger than the padded input can give a negative size, which passed the guard. For 2D inputs two negative sizes multiply to a positive linear input size, so the network was built without error and failed only in the first forward pass, with a torch error instead of the guard's message.
The guard now requires every size to be positive and reports the computed size.
The default out_channels_per_layer was [6, 12], so setting num_conv_layers to anything but 2 without also passing out_channels_per_layer always failed. The default now starts at 6 channels and doubles per layer. For the default two layers it is still [6, 12], so existing networks do not change.
Embedding nets checked user arguments with assert, and Python drops asserts under -O. The checks on constructor arguments of CNNEmbedding, CausalCNNEmbedding and PermutationInvariantEmbedding, and on user input in the forward passes of SpectralConvEmbedding and LRU, now raise TypeError for a wrong type and ValueError for a wrong value, as the other embedding nets already do.
CNNEmbedding also rejects num_conv_layers < 1. With the default channels that follow num_conv_layers, zero layers gave an empty channel list and an IndexError. Its size error suggests fewer layers only from layer 2 on, since at layer 1 only a smaller kernel_size or pool_kernel_size helps.
Asserts on values that the classes compute themselves stay asserts.
A wrong-length out_channels_per_layer or dilation list, or num_conv_layers=0, failed with an IndexError or Python's 'max() arg is an empty sequence'. A dilation list longer than num_conv_layers was silently cut. These now raise a ValueError that names the argument, and the existing pool size and dilation errors name the arguments and values too.
WaveNetSRLikeAggregator, the default aggregator of CausalCNNEmbedding, printed its intermediate shapes on every build, so the time-series how-to guide showed them in its output. It also appended output_dim to the caller's intermediate_channel_sizes list. Both are gone, the stored notebook output no longer shows the shapes, and its docstring names its arguments correctly.
PermutationInvariantEmbedding warns that it treats an observation with only padded trials as one with zero trials. With aggregation_fn="sum" that held, but "mean" divided by zero and failed with 'AssertionError: NaNs in embedding.'. The mean now divides by at least one, so such observations get a zero embedding with either aggregation.
The remaining NaN check fires only when the trial net returns NaN or inf, and now raises a ValueError that says so.
The PR replaces assertions with explicit exceptions across embedding networks, changes CNNEmbedding default channel counts, and updates permutation-invariant input and aggregation checks. It also removes diagnostic shape prints and three saved notebook output lines.
Changes: Embedding validation and aggregation
Layer / File(s)
Summary
CNN construction and shape validation sbi/neural_nets/embedding_nets/cnn.py, tests/embedding_net_test.py
CNNEmbedding validates input shapes, layer counts, channel counts, and output dimensions. When channel counts are omitted, it generates one per layer, starting at 6 and doubling each layer. Tests cover invalid arguments and the three-layer default.
Causal CNN and aggregator validation sbi/neural_nets/embedding_nets/causal_cnn.py, tests/embedding_net_test.py, docs/how_to_guide/20_time_series_embedding.ipynb
CausalCNNEmbedding adds type and value checks for its arguments and minimum timepoints. WaveNetSRLikeAggregator checks channel and stride-list lengths and removes shape-print statements. Tests cover invalid arguments, list preservation, and silent construction. The notebook output no longer includes three tensor-shape lines.
Other embedding checks sbi/neural_nets/embedding_nets/SC_embedding.py, sbi/neural_nets/embedding_nets/lru.py
SpectralConvEmbedding and LRU raise ValueError for the reported invalid conditions.
Permutation-invariant input and aggregation checks sbi/neural_nets/embedding_nets/permutation_invariant.py, tests/embedding_net_test.py
PermutationInvariantEmbedding rejects infinite inputs and non-finite aggregates, validates aggregation options, and clamps mean-aggregation trial counts to at least one. Tests cover both aggregation options, zero aggregates, and non-finite values.
sbi-dev/sbi#1987: Updates the same notebook output and removes the saved CausalCNN shape lines.
sbi-dev/sbi#2145: Corrects CNNEmbedding parameter names in documentation, which this PR also updates.
sbi-dev/sbi#2118: Changes FCEmbedding output behavior, which is used by embedding classes updated in this PR.
Suggested reviewers:michaeldeistler, aranka-s
Merge Risk: Low · up to 91182
Embedding validation now raises clearer errors, and observations with only padded trials produce a zero mean aggregate instead of failing. Users with a trial network that is non-differentiable at zero could get NaN gradients from such observations. This is narrow and mostly pre-existing, so the change is mergeable with owner awareness.
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The no_real_trial case calls the forward path but checks only the warning count. It would pass if mean aggregation produced a finite nonzero aggregate. Add an assertion that the mean aggregate is zero, or that it matches the sum aggregate for an all-padded observation.
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Review comment at @tests/embedding_net_test.py around lines 633 - 644:
Update test_permutation_invariant_embedding_warns_on_invalid_padding to assert
the embedding output for the no_real_trial case has a zero mean aggregate, or
matches the sum aggregate for the same all-padded observation; keep the existing
warning-count assertion.
Prompt to fix review comments
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
minimal, and validate.
Nitpick comments:
Review comments at @tests/embedding_net_test.py:
- Around line 633-644: Update
test_permutation_invariant_embedding_warns_on_invalid_padding to assert the
embedding output for the no_real_trial case has a zero mean aggregate, or
matches the sum aggregate for the same all-padded observation; keep the existing
warning-count assertion.
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WaveNetSRLikeAggregator checked its intermediate_channel_sizes and stride_sizes lists with assert, so under python -O a list of the wrong length built a net with the wrong output size. Both checks now raise ValueError with the lengths. The stride message also read "same size asthe kernel size list".
The CausalCNNEmbedding errors for an unknown dilation string and a dilation of the wrong type now name dilation and what was passed.
In CNNEmbedding, the hint variable of the size error is renamed to fewer_layers_hint, since it holds message text.
The padding test with an observation of only padded trials checked the warning, but not the embedding. It now checks that both "mean" and "sum" pass a zero aggregate and a zero trial count to the output net.
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Thanks, the docstring and argument-naming fixes look right, and the assert → ValueError conversions are a clear improvement (they survive python -O).
One problem in the new inf check, and one test-precision nit — details inline.
The inf case isn't caught
The new error message on permutation_invariant.py:156 says "the trial_net returned NaN or inf, e.g., because the input contains inf", but the guard on line 154 is torch.isnan, which is False for inf. And nan_to_num(..., nan=0.0) replaces NaN but leaves inf, mapping it to the dtype max:
That is the silent-corruption path the check is meant to close. torch.isfinite(...).all() covers both, and I'd suggest a test that feeds an inf, since the current tests only exercise NaN padding patterns.
Happy to open a branch with the fix and a test if that's useful.
ifnottorch.isfinite(combined_embedding).all():
raiseValueError(
"Non-finite value in the aggregated trial embedding: the trial_net ""returned NaN or inf, e.g., because the input contains inf."
)
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These two cases can pass for the wrong reason: pool_kernel_size appears in both error messages, so the bare-word match doesn't distinguish them.
causal_cnn.py:206 — "pool_kernel_size ({pool_kernel_size}) must not be larger than the ..." (too large)
causal_cnn.py:283 — "After pooling with pool_kernel_size=..., ... Use a smaller pool_kernel_size, ..." (too few timepoints)
So {"pool_kernel_size": 128} and {"pool_kernel_size": 64} would both pass even if the wrong branch raised. Matching on the distinguishing part, e.g. "must not be larger than" vs "aggregator needs at least 2", would pin the intended error.
I went through the latest review comments and noticed two points:
The current torch.isnan() check doesn't catch infinity values. Replacing it with torch.isfinite(combined_embedding).all() would catch both NaN and infinity.
The two test cases could be made more precise by matching the distinguishing part of each expected error message, as suggested by Manuel.
I'd be interested in helping address these points and adding the corresponding tests.
Would you be comfortable with me preparing a proposed fix for your review?
nan_to_num(x, nan=0.0) also turns inf into the float maximum before the trial_net runs, so the NaN check after aggregation never saw it: one inf trial reached the density estimator as an embedding of about 1e37. An isfinite check on the aggregated embedding does not catch it either, because the trial_net shrinks the clamped value to a large finite number.
forward now raises on inf in the input, before nan_to_num. NaN padding is unaffected, since isinf(NaN) is False. The check after aggregation uses isfinite, so a trial_net that returns inf for finite input also raises.
Training with NaN padding needs exclude_invalid_x=False, which also stops dropping inf simulations, so this path is reachable in normal use.
Reported by Manuel Gloeckler in the review of #2113.
Both error messages contain the bare word pool_kernel_size, so either case passed if the other branch raised. Each case now matches the part of its message that the other lacks.
Reported by Manuel Gloeckler in the review of #2113.
@manuelgloeckler thanks for the review, good catch on the inf case! I agree on both points, and the fixes are pushed, so no need for a branch.
The embedding now raises on inf in the input, before the NaN replacement. A check on the aggregated embedding alone would miss your example: nan_to_num clamps the inf before the trial net runs, and the trial net turns it into a large finite value. The aggregated check uses isfinite as well now. NaN padding is unaffected.
The two pool_kernel_size test cases now match the part of each message that the other one lacks.
I also merged main into the branch.
@Ashwini8686 thanks for the offer, this is already done.
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Actionable comments posted: 1
Fix comments: Fix CodeRabbit comments on this PR
Prompt to fix all issues
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Inline comments:
Review comments at @sbi/neural_nets/embedding_nets/permutation_invariant.py:
- Line 158: Update the mean aggregation path around trial_counts so it rejects
all-padded observations and prevents padded trials from producing invalid
gradients in trial_net. Ensure padded entries are excluded or made safe before
trial_net evaluates them, including in partially padded observations, while
preserving valid-trial aggregation.
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PyTorch autograd sqrt backward grad result zero division derivatives.yaml 1.13
Result:
Sources consulted:
- Fix `cdist` backward calculation for `p=2` · Pull Request #37337 · pytorch/pytorch — GitHub pull request 37337 in pytorch/pytorch (link omitted to avoid creating a cross-reference)
- Autograd mechanics # — https://docs.pytorch.org/docs/2.13/notes/autograd.html
- Gradient of zero norm is nan — GitHub issue 2421 in pytorch/pytorch (link omitted to avoid creating a cross-reference)
- tools/autograd/derivatives.yaml — https://github.com/pytorch/pytorch/blob/main/tools/autograd/derivatives.yaml
- torchaudio.transforms.Spectrogram with power=1 can easily NaN — GitHub issue 993 in pytorch/audio (link omitted to avoid creating a cross-reference)
Prevent padded trials from corrupting gradients.
If trial_net computes sqrt(weight * x), an all-padded observation now passes the mean aggregation check. Backward can produce a NaN weight.grad, and an optimizer step can corrupt the network.
The previous mean path rejected this input. Partially padded observations already have the same gradient hazard.
Prompt for AI Agents
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Review comment at @sbi/neural_nets/embedding_nets/permutation_invariant.py at line 158:
Update the mean aggregation path around trial_counts so it rejects all-padded observations and prevents padded trials from producing invalid gradients in trial_net. Ensure padded entries are excluded or made safe before trial_net evaluates them, including in partially padded observations, while preserving valid-trial aggregation.
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manuelgloeckler
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Summary
The
CNNEmbeddingdocstring and two of its error messages name arguments that do not exist. The real arguments arenum_conv_layersandpool_kernel_size. The default channel list also fits only two layers. Sonum_conv_layers=3alone always fails, and the error points to an argument that users cannot pass.This PR fixes the docs, the default and the size check of
CNNEmbedding. It givesCausalCNNEmbeddingthe same argument checks, and it fixes two bugs that I found in the same embedding nets.CNNEmbeddingdocstringnum_cnn_layersandpool_size; "first two layers";MaxPool1donlyCNNEmbedding,num_conv_layers=3, default channels[6, 12, 24]CNNEmbedding, 2D input, kernel much larger than the inputCausalCNNEmbedding, wrong-length channel or dilation list, or zero layersIndexError, "max() arg is an empty sequence", or a silently cut listValueErrorthat names the argumentCausalCNNEmbeddingwith the default aggregatorPermutationInvariantEmbedding, mean aggregation, observation with only padded trialsAssertionError, or no check at all underpython -OTypeErrorfor a wrong type,ValueErrorfor a wrong valueThe default channels now start at 6 and double per layer. For the default two layers this is
[6, 12]as before, so existing networks do not change.The size check caught only an output size of exactly zero. A kernel much larger than the padded input can give a negative size. In 2D, two negative sizes give a positive product, so the network built. The check now requires every size to be positive.
The permutation-invariant net already warns that it treats an observation with only padded trials as one with zero trials. Sum aggregation did that, but mean aggregation divided by zero. The mean now divides by at least one.
The exceptions follow the other embedding nets. Checks on values that the classes compute themselves stay asserts. The NaN check of the permutation-invariant net is the exception: an inf in the input causes it, so it now raises a
ValueError.I also fixed typos in the
CausalCNNEmbeddingdocstrings and removed the printed shapes from the stored output of the time-series how-to guide.This helps everyone who reads the embedding API reference, changes the number of convolutional layers, or uses mean aggregation with padded trials.
Evidence
New tests check that the errors for the number of layers, the list lengths and the sizes name the argument that fixes them, that the default channels follow the number of layers, that the aggregator leaves its arguments alone, and that mean aggregation handles an observation without real trials. They fail on
mainand pass now.Merge Danger
Door: two-way
Blast Radius: small
Valid configurations build the same networks as before. A
CausalCNNEmbeddingwith more dilations thannum_conv_layers, or with more channels and a custom aggregator, used to drop the extra entries; it now raises aValueError. Code that catchesAssertionErrorfrom an embedding net now has to catchValueErrororTypeError; nothing in sbi relies on that.Thanks to @Ashwini8686, who reported the wrong argument names in issue #1536 and offered to fix them. The issue stays open for its other docs to-dos.