PyMC/PyTensor Implementation of Pathfinder VI #387
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`fit_pathfinder` - Edited `fit_pathfinder` to produce `pathfinder_state`, `pathfinder_info`, `pathfinder_samples` and `pathfinder_idata` for closer examination of the outputs. - Changed the `num_samples` argument name to `num_draws` to avoid `TypeError` got multiple values for keyword argument 'num_samples'. - Initial points are automatically set to jitter as jitter is required for pathfinder. Extras - New function 'get_jaxified_logp_ravel_inputs' to simplify previous code structure in fit_pathfinder. Tests - Added extra test for pathfinder to test pathfinder_info variables and pathfinder_idata are consistent for a given random seed.
Add a new PyMC-based implementation of Pathfinder VI that uses PyTensor operations which provides support for both PyMC and BlackJAX backends in fit_pathfinder.
- Implemented in to support running multiple Pathfinder instances in parallel. - Implemented function in for Pareto Smoothed Importance Resampling (PSIR). - Moved relevant pathfinder files into the directory. - Updated tests to reflect changes in the Pathfinder implementation and added tests for new functionalities.
…race data to InferenceData
… for bfgs_sample
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This version runs much faster than #386, but the codes are messier due to the numerous pytensor symbolic variables created for the compiled pytensor functions (see the lines of code between |
fonnesbeck
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Nov 8, 2024
fonnesbeck
reviewed
Nov 8, 2024
fonnesbeck
reviewed
Nov 8, 2024
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| class LBFGSHistoryManager: |
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Cleaner to use a data class? Don't know.
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yep, I agree. dataclass now added
fonnesbeck
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Nov 8, 2024
Summaryh of changes: - Remove multiprocessing code in favour of reusing compiled for each path - takes only random_seed as argument for each path - Compute graph significantly smaller by using pure pytensor op and symoblic variables - Added LBFGSOp to compile with pytensor.function - Cleaned up codes using pytensor variables
…and . - Corrected the dimensions in comments for matrices Q and R in the function. - Uumerical stability in the calculation by changing from to .
fonnesbeck
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Nov 17, 2024
pymc_experimental/inference/fit.py
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| @@ -31,11 +31,13 @@ def fit(method, **kwargs): | |||
| arviz.InferenceData | |||
| """ | |||
| if method == "pathfinder": | |||
| # TODO: Remove this once we have a pure PyMC implementation | |||
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This PR will provide that, no?
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the latest commit addresses this
Fixed incorrect and inconsistent posterior approximations in the Pathfinder VI algorithm by: 1. Adding missing parentheses in the phi calculation to ensure proper order of operations in matrix multiplications 2. Changing the sign in mu calculation from 'x +' to 'x -' to match Stan's implementation (which differs from the original paper) The resulting changes now make the posterior approximations more reliable.
Implements both sparse and dense BFGS sampling approaches for Pathfinder VI: - Adds bfgs_sample_dense for cases where 2*maxcor >= num_params. - Moved existing and computations to bfgs_sample_sparse, making the sparse use cases more explicit. Other changes: - Sets default maxcor=5 instead of dynamic sizing based on parameters Dense approximations are recommended when the target distribution has higher dependencies among the parameters.
Bigger changes: - Made pmx.fit compatible with method='pathfinder' - Remove JAX dependency when inference_backend='pymc' to support Windows users - Improve runtime performance by setting trust_input=True for compiled functions Minor changes: - Change default num_paths from 1 to 4 for stable and reliable approximations - Change LBFGS code using dataclasses - Update tests to handle both PyMC and BlackJAX backends
- Add LBFGSInitFailed exception for failed LBFGS initialisation - Skip failed paths in multipath_pathfinder and track number of failures - Handle NaN values from Cholesky decompsition in bfgs_sample - Add checks for numericl stabilty in matrix operations Slight performance improvements: - Set allow_gc=False in scan ops - Use FAST_RUN mode consistently
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Another version to draft PR #386 which uses more of PyTensor's symbolic variables and compiling functions.
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