Enable sampling in chunks with external jax samplers

[andrewdipper]

Initial take on extending blackjax and numpyro samplers to be able to sequentially sample multiple chunks. This eliminates the requirement of the gpu having sufficient memory to store all samples at once - they just need to fit in cpu memory.

Changes / features:

• Sampling with one chunk (current behavior) and sampling with multiple chunks will return the exact same samples. This is how the chunking is tested
• As long as the first chunk compiles / samples the remainder should not cause an out of memory error. I have pretty high confidence this holds for blackjax. Numpyro is harder due to the higher level api, but there won't be a big memory jump. Hopefully this prevents annoying errors deep into sampling
• Postprocessing is done on a per chunk basis (and is compiled with sampling for blackjax)
• When num_chunks==1 samples are stored on the sampling device consistent with current behavior. With multiple chunks they are transferred to cpu memory

Some question marks:

• Currently progress through the chunks is just written to the log - I'm not sure if this is the most reasonable solution.
• The postprocessing_backend option is removed. I think this is reasonable as any postprocessing memory requirements should be dominated by the already necessary transpose of the chains and samples dimensions (this is due to vmap(scan) materializing the scan dimension first and subsequently transposing). Unless I'm missing another reason to force the postprocessing backend?

Checklist

• Checked that the pre-commit linting/style checks pass
• Included tests that prove the fix is effective or that the new feature works
• Added necessary documentation (docstrings and/or example notebooks)
• If you are a pro: each commit corresponds to a relevant logical change

Type of change

• New feature / enhancement
• Bug fix
• Documentation
• Maintenance
• Other (please specify):

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📚 Documentation preview 📚: https://pymc--7465.org.readthedocs.build/en/7465/

[andrewdipper]

The test failure seems a bit random - I haven't been able to trigger a failure locally. I get some acceptance_rates pretty far from 0.5 so I'm not sure how stable it's expected to be.

[ricardoV94]

why did the behavior (had to) change?

[andrewdipper]

For postprocessing I needed to be able to calculate the log_likelihood without the final wrapping vmap. It's possible to have it just calculate the likelihood instead of returning a function. However the extra vmap will still be necessary

[andrewdipper]

Changed to calculate the likelihood instead of using a returned likelihood calculator function.

[ricardoV94]

The test failure seems a bit random - I haven't been able to trigger a failure locally. I get some acceptance_rates pretty far from 0.5 so I'm not sure how stable it's expected to be.

I don't think this was failing before so might be related to the changes

[ricardoV94]

@ferrine any opinion on the removal of postprocessing_backend?

[ricardoV94]

We should deprecate before rendering the argument useless or raise already. Also can the message mention the alternative is num_chunks now?

[andrewdipper]

Ok, makes sense - I can add back that functionality - it's just a few extra branches to keep track of

[andrewdipper]

Added back logic for postprocessing_backend. It doesn't have any integration with chunked sampling but restores postprocessing all at once on a different backend.

[andrewdipper]

The test failure seems a bit random - I haven't been able to trigger a failure locally. I get some acceptance_rates pretty far from 0.5 so I'm not sure how stable it's expected to be.

I don't think this was failing before so might be related to the changes

It was splitting the random key 1 extra time as compared to the current behavior. Removing the extra split fixes the failure and I believe the numpyro samples generated will now be identical to those currently generated. However, this means that choosing the wrong key can still trigger the test failure. Based off of pyro-ppl/numpyro#1786, it seems like acceptance_rates won't be super stable.

[ricardoV94]

The test failure seems a bit random - I haven't been able to trigger a failure locally. I get some acceptance_rates pretty far from 0.5 so I'm not sure how stable it's expected to be.

I don't think this was failing before so might be related to the changes

It was splitting the random key 1 extra time as compared to the current behavior. Removing the extra split fixes the failure and I believe the numpyro samples generated will now be identical to those currently generated. However, this means that choosing the wrong key can still trigger the test failure. Based off of pyro-ppl/numpyro#1786, it seems like acceptance_rates won't be super stable.

Okay if it's not stable feel free to choose the best code and pick a seed that happens to works

[ferrine]

@ferrine any opinion on the removal of postprocessing_backend?

What will be different there what what will be memory consumption? What overhead is put on the gpu/ram?

[ferrine]

What if a single sample does not compile on the gpu? Is it realistic? What about num_samples_in_chunk parameter?

[codecov]

Codecov Report

Attention: Patch coverage is 86.48649% with 15 lines in your changes missing coverage. Please review.

Project coverage is 92.40%. Comparing base (cdcdb58) to head (4e6b1fa).

Files with missing lines	Patch %	Lines
pymc/sampling/jax.py	86.48%	15 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #7465      +/-   ##
==========================================
- Coverage   92.44%   92.40%   -0.04%     
==========================================
  Files         103      103              
  Lines       17119    17153      +34     
==========================================
+ Hits        15825    15850      +25     
- Misses       1294     1303       +9     

Files with missing lines	Coverage Δ
pymc/sampling/jax.py	91.83% <86.48%> (-2.96%)	⬇️

[andrewdipper]

@ferrine any opinion on the removal of postprocessing_backend?

What will be different there what what will be memory consumption? What overhead is put on the gpu/ram?

I don't know in general what the memory complexity of the postprocessing transformations can be. However, when sampling with chain_method=vectorized the vmap(scan) seems to always be turned into scan(vmap) with a subsequent transpose on the (chain, samples) dimension. That requires a copy on the leaves of the pytree. My (perhaps invalid) assumption is most transformations fit within that space but I'm not sure on the chain_method="parallel" case.

Practically speaking the postprocessing is jit compiled with the sampling step so if sampling starts then the memory is sufficient (for numpyro if tuning starts then memory is sufficient). I'm not sure I can see a case where postprocessing memory requirements are very high and cpu memory is so dominant of gpu memory that num_chunks cannot get the memory down. And remember that currently all samples must fit on the gpu together.

What if a single sample does not compile on the gpu? Is it realistic? What about num_samples_in_chunk parameter?

I'm not sure on if that happens / what the current resolution would be.

The parameterization is with draws and num_chunks with num_samples_in_chunk = draws / num_chunks. Is there as reason to prefer num_samples_in_chunk?

[andrewdipper]

Is there a proper way to run tests with a gpu backend enabled? My test for postprocessing_backend gets skipped since the backend is not available.

[ricardoV94]

Is there a proper way to run tests with a gpu backend enabled? My test for postprocessing_backend gets skipped since the backend is not available.

No, GitHub actions doesn't include gpu in the free plan

[twiecki]

What's the stauts here, can we merge?

[andrewdipper]

Let me know if anything else needs to be done on my end

[ricardoV94]

I am leaning a bit on "this is too much complexity on our side".

[andrewdipper]

I believe you're talking about higher level complexity. But iirc for blackjax the multi_step function can be replaced by a new (potentially unreleased) run_inference_algorithm from them - that should simplify part of the code a fair bit. The samples would just be generated with different random seeds.

Either way let me know what you decide

[ricardoV94]

@andrewdipper wanna give a try at that simpler approach?

[andrewdipper]

Sure, I'll give it a go

[andrewdipper]

Apologies for the delay - I got caught up.

Switched to using blackjax.util.run_inference_algorithm and tried to clarify things a bit. Let me know if you think it's viable.

I removed the postprocessing test as it doesn't get run and blackjax chunked sampling will no longer be identical to when it's just a single chunk. I plan to swap in some other sampling tests so the code has test coverage.