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fix: embedding nets end in a linear layer, and two bugs in network building - #2118
While I worked on the docs and input checks of the CNN embedding net (PR #2113), a slow embedding test failed for some thread counts. Its cause is a final ReLU in FCEmbedding. This PR fixes it, and two more bugs in network building that came up at the same time.
Embedding nets ended in a ReLU.FCEmbedding applied its activation after the output layer too. So its output was never negative. An output unit that is zero for every input gets no gradient, and it never recovers. CNNEmbedding and PermutationInvariantEmbedding end in an FCEmbedding, and MNLE and MNPE build their default combined embedding with the same final ReLU. All of them now end in the linear layer:
The failing test was test_npe_with_with_iid_embedding_varying_num_trials. After training, up to 3 of its 5 embedding outputs were zero for every input. Its trial range was off by one too: it trained on 1 to 19 trials but checked 20. It now trains on 1 to 20 trials.
The factories crashed on embedding nets without parameters.posterior_nn and the four other factories took the device of the embedding net from its first parameter. nn.Flatten() has none, so they raised a bare StopIteration. The device now comes from the first parameter or buffer, and a net with neither is used as it is. The config classes did not have this bug.
The 1D spline context net shared its hidden weights. When NSF models a 1D variable, a small net maps the condition to the spline parameters. It repeated one layer object for all hidden layers, so with hidden_layers_spline_context above 1 they all used one set of weights. Each hidden layer now has its own weights.
Before
After
output of FCEmbedding, CNNEmbedding, PermutationInvariantEmbedding
zero or positive; units can die
any real value
default combined embedding of MNLE and MNPE
zero or positive
any real value
posterior_nn("maf", embedding_net=nn.Flatten()) and the other four factories
StopIteration
builds
1D spline context net with 3 hidden layers
1 set of hidden weights
3 sets
I also removed the final ReLU from the custom CNN example in the embedding net docs. To benchmark the change, I added --bm-embedding fc to mini-sbibm. It puts an FCEmbedding on x for every task.
This helps everyone who uses these embedding nets, for example for images, time series or iid trials, and everyone who uses MNLE or MNPE with the default settings.
Evidence
The iid test over 20 training seeds, with its trial range fixed (it passes if c2st is at most 0.6):
FCEmbedding output
Failing seeds
Worst c2st
Dead outputs after training
ReLU (main), 5 outputs
7 of 20
0.868
0 to 3 of 5
ReLU (main), 10 outputs
2 of 20
0.643
0 to 4 of 10
linear (this PR), 5 outputs
0 of 20
0.583
0
mini-sbibm, NPE with an FCEmbedding on x, 2000 simulations, 5 seeds, c2st (lower is better):
Task
NSF, ReLU
NSF, linear
MDN, ReLU
MDN, linear
gaussian_linear
0.692
0.682
0.810
0.792
linear_mvg_2d
0.525
0.524
0.504
0.503
slcp
0.907
0.906
0.942
0.943
two_moons
0.658
0.647
0.762
0.764
With more data, and for MNLE:
Benchmark
ReLU
linear
NSF with FCEmbedding, gaussian_linear, 10,000 simulations, 5 seeds
0.578
0.574
NSF with FCEmbedding, slcp, 10,000 simulations, 5 seeds
0.808
0.805
MNLE with default settings, mixed data, 15 seeds
0.588
0.590
In the benchmarks, the linear output is never worse than the spread over seeds (0.01 to 0.02), but it is not better either. The next layer of the density estimator is linear, so it can rebuild signed features from non-negative ones, as long as enough of them stay alive. The benchmark embeddings have 20 outputs (50 in MNLE) for data with 2 to 10 dimensions, so a few dead units cost nothing. The gain shows where the embedding is a narrow bottleneck. In the iid test, 5 outputs per trial are summed over the trials before any later layer sees them, so dead units cannot be compensated. With 10 outputs the ReLU failed less often, and the linear output fixes it at 5.
Every new FCEmbedding, CNNEmbedding and PermutationInvariantEmbedding, and every default MNLE and MNPE estimator, gets a linear output layer, so trained results change. An old trained net cannot be restored from its state dict. With an activation without weights, such as the default ReLU, the state dict loads, but the net gives different outputs. With a trainable activation such as nn.PReLU, loading fails with an unexpected key. Pickled posteriors keep their old layers. There is no option to keep the final ReLU, because it only removes information from the embedding.
A saved spline context net loads with the same weights in each hidden layer, so it gives the same outputs as before.
mini-sbibm had no case that trains an embedding net, so a change to an embedding net could not be benchmarked. --bm-embedding fc adds an FCEmbedding for x on every task. The option needs --bm-mode npe and is listed in docs/mini_sbibm.md. Its rows in the results table carry the suffix -fc, so they do not replace the rows of plain runs with the same label.
FCEmbedding applied its activation after the output layer too, so every embedding was non-negative. An output unit that is zero for every input gets no gradient and never recovers. CNNEmbedding and PermutationInvariantEmbedding end in an FCEmbedding, so their outputs had the same problem.
In the slow test test_npe_with_with_iid_embedding_varying_num_trials, 0 to 3 of the 5 output units were dead after training. With its trial range fixed (next commit), the test failed for 7 of 20 seeds with the final ReLU and for none without it.
The output layer is now linear. A state dict saved with an activation without weights, such as the default ReLU, still loads, because the linear layers keep their indices, but the network then computes different outputs. With a trainable activation such as PReLU, loading fails with an unexpected key for the removed output activation. So an old trained network cannot be restored from its state dict either way; pickled posteriors keep their old layers. There is no option to keep the final ReLU: it only removes information from the embedding, so an option would keep the bug.
test_npe_with_with_iid_embedding_varying_num_trials drew 1 to 19 trials per simulation, because torch.randint excludes its upper bound, but checked the posterior at 20 trials. Training now covers 1 to 20 trials.
posterior_nn, likelihood_nn, classifier_nn, posterior_score_nn and posterior_flow_nn check the device of the embedding net with the first parameter. A net without parameters, e.g. nn.Flatten(), made them crash with a bare StopIteration; only nn.Identity was exempt. This has been in main since PR #1186.
The device now comes from the first parameter or buffer. A net without either holds no tensors and is returned unchanged. A net that keeps fixed weights only as buffers moves to the cpu, like a net with parameters.
The combined embedding net that MNLE builds by default passed the context of the continuous flow through a final ReLU, the same pattern as in FCEmbedding: the context was never negative, and an output unit that is zero for every input never recovers. The default net now ends in its second linear layer. MNPE builds its default net with the same code, so it changes too. A combined_embedding_net passed by the user is used unchanged.
On the mini-sbibm mnle task with 15 seeds, the change is neutral (c2st 0.590 against 0.588 before).
The example embedding net in the embedding-network how-to guide and advanced tutorial ended in F.relu(self.fc(x)), so it taught users the final ReLU that made FCEmbedding's outputs non-negative. Both now end in the linear layer. No cell with stored output uses this class, so this edit needs no re-run.
ContextSplineMap predicts the spline parameters when NSF models a 1D variable. It built its hidden layers as [Linear, ReLU] * hidden_layers, which repeats one Linear object, so with hidden_layers_spline_context above 1 all hidden layers shared one set of weights. Each hidden layer now gets its own Linear. The default of one hidden layer was not affected.
The changes update embedding output layers, add an optional FC embedding to NPE mini-benchmarks, create distinct hidden modules in ContextSplineMap, and extend check_net_device to inspect buffers and handle networks without tensors.
Changes: Embedding behavior and NPE benchmark
Layer / File(s)
Summary
Linear embedding outputs sbi/neural_nets/embedding_nets/fully_connected.py, sbi/neural_nets/net_builders/mixed_nets.py, docs/advanced_tutorials/04_embedding_networks.ipynb, docs/how_to_guide/04_embedding_networks.ipynb, tests/embedding_net_test.py
FCEmbedding and the default combined embedding network no longer apply a final ReLU. The embedding examples return the fully connected output directly. Tests check for negative outputs and include the maximum trial count.
The benchmark accepts --bm-embedding fc only in NPE mode, passes an FCEmbedding to estimator configuration, and adds the selection to the result method label. The guide documents the option.
ContextSplineMap creates a separate Linear and ReLU pair for each hidden layer. A test checks that its Linear modules have distinct identities.
Network device checks
Layer / File(s)
Summary
Parameter and buffer device detection sbi/utils/nn_utils.py, tests/factory_config_test.py
check_net_device uses the first parameter or buffer to determine device placement. It returns networks with no parameters or buffers unchanged. Tests cover parameter-free and buffer-only networks.
sequenceDiagram
participant Pytest as pytest option
participant Runner as benchmark runner
participant Training as train_and_eval_amortized_inference
participant Estimator as density-estimator config
Pytest->>Runner: provide --bm-embedding value
Runner->>Training: pass embedding_name
Training->>Estimator: provide embedding net
Runner->>Runner: append embedding name to method label
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Suggested reviewers:satwiksps, manuelgloeckler
Merge Risk: Low · previous review 08ea6
Embedding networks now end in a linear layer, so raw state dicts saved from older versions load without error but can give different outputs. Document this compatibility break before or shortly after merge. Pickled posteriors and in-repo training resume are unaffected.
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Actionable comments posted: 1
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Review comments at @sbi/neural_nets/embedding_nets/fully_connected.py:
- Line 44: Document the raw state-dict compatibility change in FCEmbedding near
its network definition: pre-change state dicts retain matching keys but do not
preserve the old ReLU behavior, so they are not semantically compatible and
require retraining or explicit migration.
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Approving. I ran the touched suites on the PR head — tests/embedding_net_test.py and tests/density_estimator_test.py: 1248 passed, 22 xfailed, 0 failed. The three new tests fail on main and pass here, so they are real regression tests.
Ending the embedding nets in a linear layer is the right call: the trailing ReLU silently made every embedding non-negative, which is not something a user would expect from a representation layer.
One non-blocking coverage note, since it's the part with the widest blast radius: the negative-output check is only parametrized over FCEmbedding, but CNNEmbedding and PermutationInvariantEmbedding both end in FCEmbedding and the mixed builder also drops its own trailing nn.ReLU(). A regression that re-added a ReLU inside either of those would leave CI green. Parametrizing the existing assertion over the three concrete nets would close that cheaply — I don't think it should block this PR.
The unrelated comment-vs-code mismatch in flow.py ("Use a non-linearity because otherwise, there will be a linear mapping" now sitting above a block that no longer repeats layers) is worth tidying at some point, but it's cosmetic.
Commit ee1ee88 dropped the trailing ReLU of MNLE's default combined embedding, a hand-written nn.Sequential in mixed_nets.py, without a test. This test fails if the ReLU comes back.
CNNEmbedding and PermutationInvariantEmbedding return the output of their FCEmbedding subnet directly, so the existing FCEmbedding test covers them.
I added a test for MNLE's default combined embedding, which lost its final ReLU in this PR without a test.
I agree with the coverage point. But I did not add the CNN and permutation-invariant nets: both return the output of their FCEmbedding directly, so the existing test covers them.
I also tidied the flow.py comment and merged main into the branch.
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manuelgloeckler
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Summary
While I worked on the docs and input checks of the CNN embedding net (PR #2113), a slow embedding test failed for some thread counts. Its cause is a final ReLU in
FCEmbedding. This PR fixes it, and two more bugs in network building that came up at the same time.Embedding nets ended in a ReLU.
FCEmbeddingapplied its activation after the output layer too. So its output was never negative. An output unit that is zero for every input gets no gradient, and it never recovers.CNNEmbeddingandPermutationInvariantEmbeddingend in anFCEmbedding, and MNLE and MNPE build their default combined embedding with the same final ReLU. All of them now end in the linear layer:layers.append(nn.Linear(num_hiddens, output_dim)) -layers.append(activation())The failing test was
test_npe_with_with_iid_embedding_varying_num_trials. After training, up to 3 of its 5 embedding outputs were zero for every input. Its trial range was off by one too: it trained on 1 to 19 trials but checked 20. It now trains on 1 to 20 trials.The factories crashed on embedding nets without parameters.
posterior_nnand the four other factories took the device of the embedding net from its first parameter.nn.Flatten()has none, so they raised a bareStopIteration. The device now comes from the first parameter or buffer, and a net with neither is used as it is. The config classes did not have this bug.The 1D spline context net shared its hidden weights. When NSF models a 1D variable, a small net maps the condition to the spline parameters. It repeated one layer object for all hidden layers, so with
hidden_layers_spline_contextabove 1 they all used one set of weights. Each hidden layer now has its own weights.FCEmbedding,CNNEmbedding,PermutationInvariantEmbeddingposterior_nn("maf", embedding_net=nn.Flatten())and the other four factoriesStopIterationI also removed the final ReLU from the custom CNN example in the embedding net docs. To benchmark the change, I added
--bm-embedding fcto mini-sbibm. It puts anFCEmbeddingon x for every task.This helps everyone who uses these embedding nets, for example for images, time series or iid trials, and everyone who uses MNLE or MNPE with the default settings.
Evidence
The iid test over 20 training seeds, with its trial range fixed (it passes if c2st is at most 0.6):
FCEmbeddingoutputmain), 5 outputsmain), 10 outputsmini-sbibm, NPE with an
FCEmbeddingon x, 2000 simulations, 5 seeds, c2st (lower is better):With more data, and for MNLE:
FCEmbedding, gaussian_linear, 10,000 simulations, 5 seedsFCEmbedding, slcp, 10,000 simulations, 5 seedsIn the benchmarks, the linear output is never worse than the spread over seeds (0.01 to 0.02), but it is not better either. The next layer of the density estimator is linear, so it can rebuild signed features from non-negative ones, as long as enough of them stay alive. The benchmark embeddings have 20 outputs (50 in MNLE) for data with 2 to 10 dimensions, so a few dead units cost nothing. The gain shows where the embedding is a narrow bottleneck. In the iid test, 5 outputs per trial are summed over the trials before any later layer sees them, so dead units cannot be compensated. With 10 outputs the ReLU failed less often, and the linear output fixes it at 5.
New tests fail on
mainand pass now:Merge Danger
Door: two-way
Blast Radius: wide
Every new
FCEmbedding,CNNEmbeddingandPermutationInvariantEmbedding, and every default MNLE and MNPE estimator, gets a linear output layer, so trained results change. An old trained net cannot be restored from its state dict. With an activation without weights, such as the default ReLU, the state dict loads, but the net gives different outputs. With a trainable activation such asnn.PReLU, loading fails with an unexpected key. Pickled posteriors keep their old layers. There is no option to keep the final ReLU, because it only removes information from the embedding.A saved spline context net loads with the same weights in each hidden layer, so it gives the same outputs as before.