From 2267823065dd28689f851016908e3d3b534ef9c7 Mon Sep 17 00:00:00 2001 From: wistuba Date: Sun, 25 Aug 2024 12:41:55 +0200 Subject: [PATCH] Support for Python 3.11 (#197) --- .github/workflows/documentation.yml | 2 +- .github/workflows/tests.yml | 6 +- .readthedocs.yaml | 2 +- examples/bring_in_your_own.pct.py | 4 +- fortuna/calib_model/calib_model_calibrator.py | 6 +- fortuna/calib_model/classification.py | 2 +- fortuna/calib_model/config/monitor.py | 2 +- fortuna/calib_model/loss.py | 4 +- fortuna/calib_model/predictive/base.py | 6 +- fortuna/calib_model/predictive/regression.py | 14 +- .../top_label_multicalibrator.py | 1 - fortuna/conformal/multivalid/one_shot/base.py | 2 - fortuna/data/dataset/huggingface_datasets.py | 14 +- fortuna/distribution/gaussian.py | 10 +- fortuna/hallucination/base.py | 1 - fortuna/likelihood/base.py | 12 +- fortuna/likelihood/regression.py | 8 +- fortuna/loss/classification/cross_entropy.py | 6 - fortuna/loss/regression/scaled_mse.py | 2 - fortuna/model/model_manager/base.py | 11 +- fortuna/model/model_manager/classification.py | 22 +- fortuna/model/model_manager/regression.py | 5 +- .../transformers/classification.py | 7 +- fortuna/model/utils/random_features.py | 16 +- fortuna/model/utils/spectral_norm.py | 9 +- fortuna/model_editor/base.py | 6 +- fortuna/output_calib_model/config/monitor.py | 2 +- fortuna/output_calib_model/loss.py | 4 +- .../output_calib_model_calibrator.py | 31 +- fortuna/output_calib_model/predictive/base.py | 8 +- .../predictive/regression.py | 10 +- fortuna/output_calib_model/state.py | 1 - .../output_calib_manager/base.py | 10 +- fortuna/prob_model/calib_config/monitor.py | 2 +- fortuna/prob_model/classification.py | 2 +- fortuna/prob_model/joint/base.py | 8 +- fortuna/prob_model/posterior/base.py | 6 +- .../deep_ensemble/deep_ensemble_posterior.py | 5 +- .../posterior/laplace/laplace_posterior.py | 7 +- .../posterior/laplace/laplace_state.py | 1 - .../prob_model/posterior/map/map_posterior.py | 4 +- fortuna/prob_model/posterior/map/map_state.py | 2 - .../prob_model/posterior/map/map_trainer.py | 6 +- .../advi/advi_architecture.py | 6 +- .../normalizing_flow/advi/advi_posterior.py | 7 +- .../normalizing_flow/advi/advi_state.py | 2 - .../normalizing_flow/advi/advi_trainer.py | 5 +- .../normalizing_flow_trainer.py | 9 +- .../cyclical_sgld/cyclical_sgld_callback.py | 2 - .../cyclical_sgld/cyclical_sgld_integrator.py | 5 +- .../cyclical_sgld/cyclical_sgld_posterior.py | 4 +- .../cyclical_sgld/cyclical_sgld_state.py | 2 - .../sgmcmc/sghmc/sghmc_approximator.py | 2 +- .../posterior/sgmcmc/sghmc/sghmc_callback.py | 2 - .../sgmcmc/sghmc/sghmc_integrator.py | 7 +- .../posterior/sgmcmc/sghmc/sghmc_posterior.py | 5 +- .../posterior/sgmcmc/sghmc/sghmc_state.py | 2 - .../posterior/sgmcmc/sgmcmc_posterior.py | 6 +- .../sgmcmc/sgmcmc_sampling_callback.py | 2 - .../posterior/sngp/sngp_posterior.py | 6 +- .../posterior/swag/swag_posterior.py | 6 +- fortuna/prob_model/predictive/base.py | 72 +- .../prob_model/predictive/classification.py | 44 +- fortuna/prob_model/predictive/regression.py | 24 +- fortuna/prob_model/prior/base.py | 6 +- fortuna/prob_model/prior/gaussian.py | 6 +- fortuna/prob_model/prob_model_calibrator.py | 5 +- fortuna/prob_output_layer/base.py | 6 +- fortuna/prob_output_layer/classification.py | 3 +- fortuna/prob_output_layer/regression.py | 12 +- fortuna/training/mixin.py | 7 +- fortuna/training/output_calibrator.py | 31 +- fortuna/training/train_state.py | 1 - fortuna/training/trainer.py | 35 +- fortuna/utils/probit.py | 7 +- fortuna/utils/random.py | 6 +- fortuna/utils/strings.py | 3 +- poetry.lock | 7008 +++++++++-------- pyproject.toml | 18 +- tests/fortuna/prob_model/test_diagnostic.py | 2 +- tests/fortuna/prob_model/test_prob_model.py | 38 +- tests/fortuna/test_model.py | 18 +- tests/fortuna/test_trainer.py | 16 +- tests/fortuna/utils/freeze.py | 8 +- 84 files changed, 4002 insertions(+), 3743 deletions(-) diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml index ff2f7dd6..413e93d2 100644 --- a/.github/workflows/documentation.yml +++ b/.github/workflows/documentation.yml @@ -32,7 +32,7 @@ jobs: - name: Install and configure Poetry uses: snok/install-poetry@v1 with: - version: 1.2.2 + version: 1.8.3 virtualenvs-create: true virtualenvs-in-project: false installer-parallel: true diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 78a19f7c..60356575 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -13,7 +13,7 @@ jobs: strategy: matrix: # Select the Python versions to test against - python-version: ['3.8', '3.9', '3.10', '3.11'] + python-version: ["3.9", "3.10", "3.11"] steps: - name: Check out the code uses: actions/checkout@v3 @@ -28,7 +28,7 @@ jobs: - name: Install Poetry uses: snok/install-poetry@v1.3.3 with: - version: 1.4.0 + version: 1.8.3 # Configure Poetry to use the virtual environment in the project - name: Setup Poetry @@ -38,7 +38,7 @@ jobs: # Install the dependencies - name: Install Package run: | - poetry install --with tests + poetry install --with test # Run the unit tests and build the coverage report - name: Run Tests diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 2a848f34..47cc6754 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -4,7 +4,7 @@ version: 2 build: os: ubuntu-20.04 tools: - python: '3.10' + python: '3.11' jobs: post_create_environment: # Install poetry diff --git a/examples/bring_in_your_own.pct.py b/examples/bring_in_your_own.pct.py index 8235069d..08196cf0 100644 --- a/examples/bring_in_your_own.pct.py +++ b/examples/bring_in_your_own.pct.py @@ -77,7 +77,7 @@ def __call__(self, x): from fortuna.utils.random import generate_rng_like_tree from jax.flatten_util import ravel_pytree from jax.tree_util import tree_map -from jax._src.prng import PRNGKeyArray +import jax import jax.numpy as jnp @@ -86,7 +86,7 @@ def log_joint_prob(self, params: Params) -> float: v = jnp.mean((ravel_pytree(params)[0] <= 1) & (ravel_pytree(params)[0] >= 0)) return jnp.where(v == 1.0, jnp.array(0), -jnp.inf) - def sample(self, params_like: Params, rng: Optional[PRNGKeyArray] = None) -> Params: + def sample(self, params_like: Params, rng: Optional[jax.Array] = None) -> Params: if rng is None: rng = self.rng.get() keys = generate_rng_like_tree(rng, params_like) diff --git a/fortuna/calib_model/calib_model_calibrator.py b/fortuna/calib_model/calib_model_calibrator.py index 81c58aa3..d22bdda3 100644 --- a/fortuna/calib_model/calib_model_calibrator.py +++ b/fortuna/calib_model/calib_model_calibrator.py @@ -8,7 +8,7 @@ ) from flax.core import FrozenDict -from jax._src.prng import PRNGKeyArray +import jax import jax.numpy as jnp from optax._src.base import PyTree @@ -35,7 +35,7 @@ def training_loss_step( params: Params, batch: Batch, mutable: Mutable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, unravel: Optional[Callable[[any], PyTree]] = None, calib_params: Optional[CalibParams] = None, @@ -71,7 +71,7 @@ def validation_step( state: CalibState, batch: Batch, loss_fun: Callable[[Any], Union[float, Tuple[float, dict]]], - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], float], ...]] = None, unravel: Optional[Callable[[any], PyTree]] = None, diff --git a/fortuna/calib_model/classification.py b/fortuna/calib_model/classification.py index 4226e225..7e4b3f5d 100644 --- a/fortuna/calib_model/classification.py +++ b/fortuna/calib_model/classification.py @@ -124,7 +124,7 @@ def _get_model_manager( ) else: model_manager = model_manager_cls(model, model_editor) - except ModuleNotFoundError as e: + except ModuleNotFoundError: logging.warning( "No module named 'transformer' is installed. " "If you are not working with models from the `transformers` library ignore this warning, otherwise " diff --git a/fortuna/calib_model/config/monitor.py b/fortuna/calib_model/config/monitor.py index ae9b0550..369a2845 100644 --- a/fortuna/calib_model/config/monitor.py +++ b/fortuna/calib_model/config/monitor.py @@ -66,7 +66,7 @@ def __init__( f"All metrics in `metrics` must be callable objects, but {metric} is not." ) if uncertainty_fn is not None and not callable(uncertainty_fn): - raise ValueError(f"`uncertainty_fn` must be a a callable function.") + raise ValueError("`uncertainty_fn` must be a a callable function.") self.metrics = metrics self.uncertainty_fn = uncertainty_fn diff --git a/fortuna/calib_model/loss.py b/fortuna/calib_model/loss.py index f8505ce3..8e675a1e 100644 --- a/fortuna/calib_model/loss.py +++ b/fortuna/calib_model/loss.py @@ -6,7 +6,7 @@ Tuple, ) -from jax._src.prng import PRNGKeyArray +import jax import jax.numpy as jnp from fortuna.likelihood.base import Likelihood @@ -40,7 +40,7 @@ def __call__( return_aux: Optional[List[str]] = None, train: bool = False, outputs: Optional[jnp.ndarray] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> Tuple[jnp.ndarray, Any]: if return_aux is None: diff --git a/fortuna/calib_model/predictive/base.py b/fortuna/calib_model/predictive/base.py index e17baee5..6431921f 100644 --- a/fortuna/calib_model/predictive/base.py +++ b/fortuna/calib_model/predictive/base.py @@ -1,6 +1,6 @@ from typing import Optional -from jax._src.prng import PRNGKeyArray +import jax import jax.numpy as jnp from fortuna.data.loader import ( @@ -60,7 +60,7 @@ def sample( self, inputs_loader: InputsLoader, n_samples: int = 1, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -80,7 +80,7 @@ def sample( A loader of input data points. n_samples : int Number of target samples to sample for each input data point. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. diff --git a/fortuna/calib_model/predictive/regression.py b/fortuna/calib_model/predictive/regression.py index 580b99dd..42f0ccfb 100644 --- a/fortuna/calib_model/predictive/regression.py +++ b/fortuna/calib_model/predictive/regression.py @@ -4,7 +4,7 @@ Union, ) -from jax._src.prng import PRNGKeyArray +import jax import jax.numpy as jnp from fortuna.calib_model.predictive.base import Predictive @@ -21,7 +21,7 @@ def entropy( self, inputs_loader: InputsLoader, n_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -42,7 +42,7 @@ def entropy( A loader of input data points. n_samples : int Number of samples to draw for each input. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -67,7 +67,7 @@ def quantile( q: Union[float, Array, List], inputs_loader: InputsLoader, n_samples: Optional[int] = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> Union[float, jnp.ndarray]: r""" @@ -81,7 +81,7 @@ def quantile( A loader of input data points. n_samples : int Number of target samples to sample for each input data point. - rng: Optional[PRNGKeyArray] + rng: Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -109,7 +109,7 @@ def credible_interval( n_samples: int = 30, error: float = 0.05, interval_type: str = "two-tailed", - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -126,7 +126,7 @@ def credible_interval( `error=0.05` corresponds to a 95% level of credibility. interval_type: str The interval type. We support "two-tailed" (default), "right-tailed" and "left-tailed". - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. diff --git a/fortuna/conformal/multivalid/mixins/classification/top_label_multicalibrator.py b/fortuna/conformal/multivalid/mixins/classification/top_label_multicalibrator.py index b992e503..b241e8a8 100644 --- a/fortuna/conformal/multivalid/mixins/classification/top_label_multicalibrator.py +++ b/fortuna/conformal/multivalid/mixins/classification/top_label_multicalibrator.py @@ -1,6 +1,5 @@ from typing import Optional -from jax import vmap import jax.numpy as jnp from fortuna.conformal.multivalid.mixins.multicalibrator import MulticalibratorMixin diff --git a/fortuna/conformal/multivalid/one_shot/base.py b/fortuna/conformal/multivalid/one_shot/base.py index 4d8f022f..1a73f40f 100644 --- a/fortuna/conformal/multivalid/one_shot/base.py +++ b/fortuna/conformal/multivalid/one_shot/base.py @@ -1,9 +1,7 @@ import abc import logging from typing import ( - Dict, Optional, - Tuple, Union, ) diff --git a/fortuna/data/dataset/huggingface_datasets.py b/fortuna/data/dataset/huggingface_datasets.py index 7d556459..ec536e44 100644 --- a/fortuna/data/dataset/huggingface_datasets.py +++ b/fortuna/data/dataset/huggingface_datasets.py @@ -15,9 +15,9 @@ Dataset, DatasetDict, ) +import jax from jax import numpy as jnp import jax.random -from jax.random import PRNGKeyArray from tqdm import tqdm from transformers import ( BatchEncoding, @@ -90,7 +90,7 @@ def get_data_loader( self, dataset: Dataset, per_device_batch_size: int, - rng: PRNGKeyArray, + rng: jax.Array, shuffle: bool = False, drop_last: bool = False, verbose: bool = False, @@ -105,7 +105,7 @@ def get_data_loader( A tokenizeed dataset (see :meth:`.HuggingFaceClassificationDatasetABC.get_tokenized_datasets`). per_device_batch_size: bool Batch size for each device. - rng: PRNGKeyArray + rng: jax.Array Random number generator. shuffle: bool if True, shuffle the data so that each batch is a ranom sample from the dataset. @@ -141,7 +141,7 @@ def _collate(self, batch: Dict[str, Array], batch_size: int) -> Dict[str, Array] @staticmethod def _get_batches_idxs( - rng: PRNGKeyArray, + rng: jax.Array, dataset_size: int, batch_size: int, shuffle: bool = False, @@ -167,7 +167,7 @@ def _get_data_loader( batch_size: int, shuffle: bool = False, drop_last: bool = False, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, verbose: bool = False, ) -> Union[Iterable[Dict[str, Array]], Iterable[Tuple[Dict[str, Array], Array]]]: batch_idxs_gen = self._get_batches_idxs( @@ -375,7 +375,7 @@ def __init__( super(HuggingFaceMaskedLMDataset, self).__init__(*args, **kwargs) if not self.tokenizer.is_fast: logger.warning( - f"You are not using a Fast Tokenizer, so whole words cannot be masked, only tokens." + "You are not using a Fast Tokenizer, so whole words cannot be masked, only tokens." ) self.mlm = mlm self.mlm_probability = mlm_probability @@ -407,7 +407,7 @@ def get_tokenized_datasets( ), "Only one text column should be passed when the task is MaskedLM." def _tokenize_fn( - batch: Dict[str, List[Union[str, int]]] + batch: Dict[str, List[Union[str, int]]], ) -> Dict[str, List[int]]: tokenized_inputs = self.tokenizer( *[batch[col] for col in text_columns], diff --git a/fortuna/distribution/gaussian.py b/fortuna/distribution/gaussian.py index 959deadc..8c441b7d 100755 --- a/fortuna/distribution/gaussian.py +++ b/fortuna/distribution/gaussian.py @@ -1,10 +1,10 @@ from typing import Union +import jax from jax import ( random, vmap, ) -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp from jax.scipy.stats import ( multivariate_normal, @@ -30,11 +30,11 @@ def __init__(self, mean: Union[float, Array], std: Union[float, Array]): self.std = std self.dim = 1 if type(mean) in [int, float] else len(mean) - def sample(self, rng: PRNGKeyArray, n_samples: int = 1) -> jnp.ndarray: + def sample(self, rng: jax.Array, n_samples: int = 1) -> jnp.ndarray: """ Sample from the diagonal Gaussian. - :param rng: PRNGKeyArray + :param rng: jax.Array Random number generator. :param n_samples: int Number of samples. @@ -72,11 +72,11 @@ def __init__(self, mean: Array, cov: Array): self.cov = cov self.dim = len(mean) - def sample(self, rng: PRNGKeyArray, n_samples: int = 1) -> jnp.ndarray: + def sample(self, rng: jax.Array, n_samples: int = 1) -> jnp.ndarray: """ Sample from the multivariate Gaussian. - :param rng: PRNGKeyArray + :param rng: jax.Array Random number generator. :param n_samples: int Number of samples. diff --git a/fortuna/hallucination/base.py b/fortuna/hallucination/base.py index 8b2fee51..193628f6 100644 --- a/fortuna/hallucination/base.py +++ b/fortuna/hallucination/base.py @@ -6,7 +6,6 @@ List, Optional, Tuple, - Union, ) import numpy as np diff --git a/fortuna/likelihood/base.py b/fortuna/likelihood/base.py index 52b4b0ef..37b392c2 100644 --- a/fortuna/likelihood/base.py +++ b/fortuna/likelihood/base.py @@ -8,11 +8,11 @@ Union, ) +import jax from jax import ( jit, pmap, ) -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp from fortuna.data.loader import ( @@ -133,7 +133,7 @@ def _batched_log_joint_prob( return_aux: Optional[List[str]] = None, train: bool = False, outputs: Optional[jnp.ndarray] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Any]]: """ @@ -161,7 +161,7 @@ def _batched_log_joint_prob( Whether the method is called during training. outputs : Optional[jnp.ndarray] Pre-computed batch of outputs. - rng: Optional[PRNGKeyArray] + rng: Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. Returns @@ -272,7 +272,7 @@ def sample( calib_params: Optional[CalibParams] = None, calib_mutable: Optional[CalibMutable] = None, return_aux: Optional[List[str]] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, **kwargs, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, dict]]: @@ -296,7 +296,7 @@ def sample( return_aux : Optional[List[str]] The auxiliary objects to return. We support 'outputs'. If this argument is not given, no auxiliary object is returned. - rng: Optional[PRNGKeyArray] + rng: Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -345,7 +345,7 @@ def _batched_sample( calib_params: Optional[CalibParams] = None, calib_mutable: Optional[CalibMutable] = None, return_aux: Optional[List[str]] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, dict]]: if return_aux is None: diff --git a/fortuna/likelihood/regression.py b/fortuna/likelihood/regression.py index c57b0d11..32ba58f1 100644 --- a/fortuna/likelihood/regression.py +++ b/fortuna/likelihood/regression.py @@ -3,8 +3,8 @@ Union, ) +import jax from jax import vmap -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp import numpy as np @@ -106,7 +106,7 @@ def entropy( calib_params: Optional[CalibParams] = None, calib_mutable: Optional[CalibMutable] = None, n_target_samples: Optional[int] = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, **kwargs, ) -> jnp.ndarray: @@ -139,7 +139,7 @@ def quantile( calib_mutable: Optional[CalibMutable] = None, n_target_samples: Optional[int] = 30, target_samples: Optional[jnp.ndarray] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, **kwargs, ) -> Union[float, jnp.ndarray]: @@ -164,7 +164,7 @@ def quantile( Number of target samples to sample for each input data point. target_samples: Optional[jnp.ndarray] = None Samples of the target variable for each input, used to estimate the quantiles. - rng: Optional[PRNGKeyArray] + rng: Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. diff --git a/fortuna/loss/classification/cross_entropy.py b/fortuna/loss/classification/cross_entropy.py index ef9b5f1a..2a72ddb6 100644 --- a/fortuna/loss/classification/cross_entropy.py +++ b/fortuna/loss/classification/cross_entropy.py @@ -1,9 +1,3 @@ -from typing import ( - Any, - Callable, - Tuple, -) - import jax import jax.numpy as jnp import jax.scipy as jsp diff --git a/fortuna/loss/regression/scaled_mse.py b/fortuna/loss/regression/scaled_mse.py index 9f40d542..e0653a64 100644 --- a/fortuna/loss/regression/scaled_mse.py +++ b/fortuna/loss/regression/scaled_mse.py @@ -1,5 +1,3 @@ -from typing import Tuple - import jax.numpy as jnp from fortuna.typing import Array diff --git a/fortuna/model/model_manager/base.py b/fortuna/model/model_manager/base.py index 77f90773..62b8ce34 100755 --- a/fortuna/model/model_manager/base.py +++ b/fortuna/model/model_manager/base.py @@ -8,9 +8,8 @@ ) from flax import linen as nn -from flax.core import FrozenDict from flax.training.checkpoints import PyTree -from jax._src.prng import PRNGKeyArray +import jax import jax.numpy as jnp from fortuna.typing import ( @@ -38,7 +37,7 @@ def apply( inputs: InputData, mutable: Optional[Mutable] = None, train: bool = False, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, PyTree]]: """ Apply the models' forward pass. @@ -53,7 +52,7 @@ def apply( The mutable objects used to evaluate the models. train : bool Whether the method is called during training. - rng: Optional[PRNGKeyArray] + rng: Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. @@ -67,7 +66,7 @@ def apply( @abc.abstractmethod def init( - self, input_shape: Tuple[int, ...], rng: Optional[PRNGKeyArray] = None, **kwargs + self, input_shape: Tuple[int, ...], rng: Optional[jax.Array] = None, **kwargs ) -> Dict[str, Mapping]: """ Initialize random parameters and mutable objects. @@ -76,7 +75,7 @@ def init( ---------- input_shape : Tuple The shape of the input variable. - rng: Optional[PRNGKeyArray] + rng: Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. diff --git a/fortuna/model/model_manager/classification.py b/fortuna/model/model_manager/classification.py index df4c85fe..ba92d3a0 100644 --- a/fortuna/model/model_manager/classification.py +++ b/fortuna/model/model_manager/classification.py @@ -8,12 +8,14 @@ Union, ) -from flax.core import FrozenDict +from flax.core import ( + FrozenDict, + unfreeze, +) import flax.linen as nn from flax.training.checkpoints import PyTree import jax from jax import random -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp from fortuna.model.model_manager.base import ModelManager @@ -50,7 +52,7 @@ def apply( inputs: Array, mutable: Optional[Mutable] = None, train: bool = False, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, PyTree]]: variables = params["model"].unfreeze() @@ -97,7 +99,7 @@ def apply_fn(v, x): return outputs def init( - self, input_shape: Tuple[int, ...], rng: Optional[PRNGKeyArray] = None, **kwargs + self, input_shape: Tuple[int, ...], rng: Optional[jax.Array] = None, **kwargs ) -> Dict[str, Mapping]: if rng is None: rng = self.rng.get() @@ -215,7 +217,7 @@ def _mean_field_logits( """ # Implementation adapted from https://github.com/google/edward2/blob/520e28285e905e0021e49b52b982ee5ea170071c/edward2/tensorflow/layers/utils.py#L379 if self.mean_field_factor < 0: - raise ValueError(f"mean_field_factor cannot be < 0.") + raise ValueError("mean_field_factor cannot be < 0.") # Compute standard deviation. if covariance is None: @@ -238,7 +240,7 @@ def apply( inputs: Array, mutable: Optional[Mutable] = None, train: bool = False, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, PyTree]]: if mutable and mutable is not None: deep_feature_extractor_mutable = { @@ -294,7 +296,7 @@ def apply_fn(p, x): outputs = outputs[0] if gp_mutable is not None: mutable = deep_feature_extractor_mutable["mutable"]["model"].unfreeze() - mutable.update(gp_mutable.unfreeze()) + mutable.update(unfreeze(gp_mutable)) mutable = {"mutable": FrozenDict({"model": mutable})} return outputs, mutable else: @@ -325,7 +327,7 @@ def __init__(self, model: nn.Module, *args, **kwargs): super(SNGPClassificationModelManager, self).__init__(model, *args, **kwargs) def init( - self, input_shape: Tuple[int, ...], rng: Optional[PRNGKeyArray] = None, **kwargs + self, input_shape: Tuple[int, ...], rng: Optional[jax.Array] = None, **kwargs ) -> Dict[str, FrozenDict]: if rng is None: rng = self.rng.get() @@ -345,9 +347,7 @@ def init( ) gp_params = self._gp_output_model.init(rngs, jnp.zeros(output_shape), **kwargs) params = dict( - model=FrozenDict( - nested_update(model_params.unfreeze(), gp_params.unfreeze()) - ) + model=FrozenDict(nested_update(unfreeze(model_params), unfreeze(gp_params))) ) if self.model_editor is not None: diff --git a/fortuna/model/model_manager/regression.py b/fortuna/model/model_manager/regression.py index 691aa738..d915c4b1 100644 --- a/fortuna/model/model_manager/regression.py +++ b/fortuna/model/model_manager/regression.py @@ -10,7 +10,6 @@ from flax.training.checkpoints import PyTree import jax from jax import random -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp from fortuna.model.model_manager.base import ModelManager @@ -53,7 +52,7 @@ def apply( inputs: Array, mutable: Optional[Mutable] = None, train: bool = False, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, PyTree]]: # setup dropout key if rng is not None: @@ -141,7 +140,7 @@ def apply_fn(p, x, m_mutable, llv_mutable): return outputs def init( - self, input_shape: Tuple, rng: Optional[PRNGKeyArray] = None, **kwargs + self, input_shape: Tuple, rng: Optional[jax.Array] = None, **kwargs ) -> Dict[str, FrozenDict]: if rng is None: rng = self.rng.get() diff --git a/fortuna/model/model_manager/transformers/classification.py b/fortuna/model/model_manager/transformers/classification.py index 6b793433..7af0118e 100644 --- a/fortuna/model/model_manager/transformers/classification.py +++ b/fortuna/model/model_manager/transformers/classification.py @@ -14,7 +14,6 @@ numpy as jnp, random, ) -from jax._src.prng import PRNGKeyArray from fortuna.model.model_manager.classification import ( ClassificationModelManager, @@ -37,7 +36,7 @@ def apply( inputs: Dict[str, Array], mutable: Optional[Mutable] = None, train: bool = False, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, PyTree]]: # setup dropout key @@ -87,7 +86,7 @@ def apply_fn(p, x): return outputs def init( - self, input_shape: Tuple[int, ...], rng: Optional[PRNGKeyArray] = None, **kwargs + self, input_shape: Tuple[int, ...], rng: Optional[jax.Array] = None, **kwargs ) -> Dict[str, Mapping]: assert self.model._is_initialized, ( "At the moment Fortuna supports models from Hugging Face that are loaded via " @@ -135,7 +134,7 @@ def __init__( ) def init( - self, input_shape: Tuple[int, ...], rng: Optional[PRNGKeyArray] = None, **kwargs + self, input_shape: Tuple[int, ...], rng: Optional[jax.Array] = None, **kwargs ) -> Dict[str, FrozenDict]: if rng is None: rng = self.rng.get() diff --git a/fortuna/model/utils/random_features.py b/fortuna/model/utils/random_features.py index be02e4e7..adb79f9c 100644 --- a/fortuna/model/utils/random_features.py +++ b/fortuna/model/utils/random_features.py @@ -12,12 +12,12 @@ ) import flax.linen as nn +import jax from jax import ( lax, random, ) import jax.numpy as jnp -from jax.random import PRNGKeyArray from fortuna.typing import ( Array, @@ -29,7 +29,7 @@ # Default config for random features. default_rbf_bias_init = nn.initializers.uniform(scale=2.0 * jnp.pi) # Using "he_normal" style random feature distribution (see https://arxiv.org/abs/1502.01852). -# Effectively, this is equivalent to approximating a RBF kernel but with the input standardized by +# Effectively, this is equivalent to approximating an RBF kernel but with the input standardized by # its dimensionality (i.e., input_scaled = input * sqrt(2. / dim_input)) and # empirically leads to better performance for neural network inputs. # default_rbf_kernel_init = nn.initializers.variance_scaling( @@ -154,10 +154,10 @@ class RandomFourierFeatures(nn.Module): Scale to apply to the output. When using GP layer as the output layer of a nerual network, it is recommended to set this to 1. to prevent it from changing the learning rate to the hidden layers. - kernel_init: Callable[[PRNGKeyArray, Shape, Type], Array] - Callable[[PRNGKeyArray, Shape, Type], Array] function for the weight matrix. - bias_init: Callable[[PRNGKeyArray, Shape, Type], Array] - Callable[[PRNGKeyArray, Shape, Type], Array] function for the bias. + kernel_init: Callable[[jax.Array, Shape, Type], Array] + Callable[[jax.Array, Shape, Type], Array] function for the weight matrix. + bias_init: Callable[[jax.Array, Shape, Type], Array] + Callable[[jax.Array, Shape, Type], Array] function for the bias. seed: int Random seed for generating random features. This will override the external RNGs. dtype: Type @@ -167,8 +167,8 @@ class RandomFourierFeatures(nn.Module): features: int kernel_scale: Optional[float] = 1.0 feature_scale: Optional[float] = 1.0 - kernel_init: Callable[[PRNGKeyArray, Shape, Type], Array] = default_rbf_kernel_init - bias_init: Callable[[PRNGKeyArray, Shape, Type], Array] = default_rbf_bias_init + kernel_init: Callable[[jax.Array, Shape, Type], Array] = default_rbf_kernel_init + bias_init: Callable[[jax.Array, Shape, Type], Array] = default_rbf_bias_init seed: int = 0 dtype: Type = jnp.float32 collection_name: str = "random_features" diff --git a/fortuna/model/utils/spectral_norm.py b/fortuna/model/utils/spectral_norm.py index 7da95823..6c8da7e6 100644 --- a/fortuna/model/utils/spectral_norm.py +++ b/fortuna/model/utils/spectral_norm.py @@ -16,7 +16,6 @@ import flax.linen as nn import jax import jax.numpy as jnp -from jax.random import PRNGKeyArray import numpy as np from fortuna.typing import ( @@ -44,9 +43,9 @@ class SpectralNormalization(nn.Module): norm_multiplier: float Multiplicative constant to threshold the normalization. Usually under normalization, the singular value will converge to this value. - u_init: Callable[[PRNGKeyArray, Shape, Type], Array] + u_init: Callable[[jax.Array, Shape, Type], Array] Initializer function for the first left singular vectors of the kernel. - v_init: Callable[[PRNGKeyArray, Shape, Type], Array] + v_init: Callable[[jax.Array, Shape, Type], Array] Initializer function for the first right singular vectors of the kernel. kernel_apply_kwargs: Optional[Mapping[str, Any]] Updated keyword arguments to clone the input layer. @@ -70,10 +69,10 @@ class SpectralNormalization(nn.Module): layer: nn.Module iteration: int = 1 norm_multiplier: float = 0.95 - u_init: Callable[[PRNGKeyArray, Shape, Type], Array] = nn.initializers.normal( + u_init: Callable[[jax.Array, Shape, Type], Array] = nn.initializers.normal( stddev=0.05 ) - v_init: Callable[[PRNGKeyArray, Shape, Type], Array] = nn.initializers.normal( + v_init: Callable[[jax.Array, Shape, Type], Array] = nn.initializers.normal( stddev=0.05 ) kernel_apply_kwargs: Optional[Mapping[str, Any]] = None diff --git a/fortuna/model_editor/base.py b/fortuna/model_editor/base.py index c24b8c65..d4e58cb3 100644 --- a/fortuna/model_editor/base.py +++ b/fortuna/model_editor/base.py @@ -2,7 +2,6 @@ Any, Callable, Dict, - Optional, Tuple, Union, ) @@ -10,10 +9,7 @@ import flax.linen as nn import jax.numpy as jnp -from fortuna.typing import ( - InputData, - Mutable, -) +from fortuna.typing import InputData class ModelEditor(nn.Module): diff --git a/fortuna/output_calib_model/config/monitor.py b/fortuna/output_calib_model/config/monitor.py index ae9b0550..369a2845 100644 --- a/fortuna/output_calib_model/config/monitor.py +++ b/fortuna/output_calib_model/config/monitor.py @@ -66,7 +66,7 @@ def __init__( f"All metrics in `metrics` must be callable objects, but {metric} is not." ) if uncertainty_fn is not None and not callable(uncertainty_fn): - raise ValueError(f"`uncertainty_fn` must be a a callable function.") + raise ValueError("`uncertainty_fn` must be a a callable function.") self.metrics = metrics self.uncertainty_fn = uncertainty_fn diff --git a/fortuna/output_calib_model/loss.py b/fortuna/output_calib_model/loss.py index 529dc8cd..cac3eb05 100644 --- a/fortuna/output_calib_model/loss.py +++ b/fortuna/output_calib_model/loss.py @@ -7,7 +7,7 @@ Union, ) -from jax._src.prng import PRNGKeyArray +import jax import jax.numpy as jnp from fortuna.output_calib_model.predictive.base import Predictive @@ -34,7 +34,7 @@ def __call__( targets: Array, outputs: Array, mutable: Optional[CalibMutable] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, return_aux: Optional[List[str]] = None, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Any]]: if return_aux is None: diff --git a/fortuna/output_calib_model/output_calib_model_calibrator.py b/fortuna/output_calib_model/output_calib_model_calibrator.py index d18ea327..2dfb5a93 100644 --- a/fortuna/output_calib_model/output_calib_model_calibrator.py +++ b/fortuna/output_calib_model/output_calib_model_calibrator.py @@ -20,7 +20,6 @@ random, value_and_grad, ) -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp from jax.tree_util import tree_map from tqdm import trange @@ -85,7 +84,7 @@ def __init__( def train( self, - rng: PRNGKeyArray, + rng: jax.Array, state: OutputCalibState, loss_fun: Callable, n_epochs: int = 1, @@ -175,7 +174,7 @@ def _training_loop( metrics: Optional[ Tuple[Callable[[jnp.ndarray, jnp.ndarray, Array], Array], ...] ], - rng: PRNGKeyArray, + rng: jax.Array, state: OutputCalibState, targets: Array, outputs: Array, @@ -228,7 +227,7 @@ def training_step( targets: Array, outputs: Array, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, ) -> Tuple[OutputCalibState, Dict[str, Any]]: # ensure to use a different key at each step model_key = random.fold_in(rng, state.step) @@ -264,7 +263,7 @@ def training_loss_step( targets: Array, outputs: Array, mutable: CalibMutable, - rng: PRNGKeyArray, + rng: jax.Array, ) -> Tuple[jnp.ndarray, Dict[str, Any]]: return_aux = ["outputs"] if mutable is not None: @@ -340,7 +339,7 @@ def _val_loop( metrics: Optional[ Tuple[Callable[[jnp.ndarray, jnp.ndarray, Array], Array], ...] ], - rng: PRNGKeyArray, + rng: jax.Array, state: OutputCalibState, targets: Array, outputs: Array, @@ -379,7 +378,7 @@ def val_step( targets: Array, outputs: Array, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, metrics: Optional[ Tuple[Callable[[jnp.ndarray, jnp.ndarray, Array], Array], ...] ] = None, @@ -394,7 +393,7 @@ def val_loss_step( targets: Array, outputs: Array, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, ) -> Tuple[jnp.ndarray, Dict[str, jnp.ndarray]]: loss, aux = loss_fun( params=state.params, @@ -473,8 +472,8 @@ def on_train_start( state: OutputCalibState, targets: List[Array], outputs: List[Array], - rng: PRNGKeyArray, - ) -> Tuple[OutputCalibState, List[Array], List[Array], PRNGKeyArray]: + rng: jax.Array, + ) -> Tuple[OutputCalibState, List[Array], List[Array], jax.Array]: return state, targets, outputs, rng def on_train_end(self, state: OutputCalibState) -> OutputCalibState: @@ -512,7 +511,7 @@ def training_step( targets: Array, outputs: Array, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, ) -> Tuple[OutputCalibState, Dict[str, Any]]: return super().training_step(state, targets, outputs, loss_fun, rng) @@ -523,7 +522,7 @@ def val_loss_step( targets: Array, outputs: Array, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, ) -> Dict[str, jnp.ndarray]: return super().val_loss_step(state, targets, outputs, loss_fun, rng) @@ -580,8 +579,8 @@ def on_train_start( state: OutputCalibState, targets: List[Array], outputs: List[Array], - rng: PRNGKeyArray, - ) -> Tuple[OutputCalibState, List[DataLoader], List[TargetsLoader], PRNGKeyArray]: + rng: jax.Array, + ) -> Tuple[OutputCalibState, List[DataLoader], List[TargetsLoader], jax.Array]: state, targets, outputs, rng = super(MultiDeviceMixin, self).on_train_start( state, targets, outputs, rng ) @@ -608,7 +607,7 @@ def training_step( targets: Array, outputs: Array, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, ) -> Tuple[OutputCalibState, Dict[str, Any]]: return super().training_step(state, targets, outputs, loss_fun, rng) @@ -638,7 +637,7 @@ def val_loss_step( targets: Array, outputs: Array, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, ) -> Dict[str, jnp.ndarray]: val_losses = super().val_loss_step(state, targets, outputs, loss_fun, rng) return lax.pmean(val_losses, axis_name="batch") diff --git a/fortuna/output_calib_model/predictive/base.py b/fortuna/output_calib_model/predictive/base.py index 0d1b20ef..a0707fbb 100644 --- a/fortuna/output_calib_model/predictive/base.py +++ b/fortuna/output_calib_model/predictive/base.py @@ -7,7 +7,7 @@ Union, ) -from jax._src.prng import PRNGKeyArray +import jax import jax.numpy as jnp from fortuna.output_calibrator.output_calib_manager.base import OutputCalibManager @@ -70,7 +70,7 @@ def sample( self, n_samples: int, outputs: Array, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, calibrated: bool = True, **kwargs, ) -> jnp.ndarray: @@ -83,7 +83,7 @@ def sample( The number of target samples to draw for each of the outputs. outputs : Array Calibrated outputs. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. calibrated : bool Whether the outputs should be calibrated when computing this method. If `calibrated` is set to True, the @@ -266,7 +266,7 @@ def _log_joint_prob( targets: Array, outputs: Array, mutable: Optional[CalibMutable] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, return_aux: Optional[List[str]] = None, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Any]]: if return_aux is None: diff --git a/fortuna/output_calib_model/predictive/regression.py b/fortuna/output_calib_model/predictive/regression.py index fdef4a55..a4f7ff13 100644 --- a/fortuna/output_calib_model/predictive/regression.py +++ b/fortuna/output_calib_model/predictive/regression.py @@ -4,7 +4,7 @@ Union, ) -from jax._src.prng import PRNGKeyArray +import jax import jax.numpy as jnp from fortuna.output_calib_model.predictive.base import Predictive @@ -29,7 +29,7 @@ def quantile( q: Union[float, Array, List], outputs: Array, n_samples: Optional[int] = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, calibrated: bool = True, ) -> jnp.ndarray: """ @@ -43,7 +43,7 @@ def quantile( Model outputs. n_samples: Optional[int] Number of target samples to draw when computing quantiles. - rng: Optional[PRNGKeyArray] + rng: Optional[jax.Array] A random number generator. calibrated : bool Whether the outputs should be calibrated when computing this method. If `calibrated` is set to True, the @@ -70,7 +70,7 @@ def credible_interval( n_samples: int = 30, error: float = 0.05, interval_type: str = "two-tailed", - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, calibrated: bool = True, ) -> jnp.ndarray: """ @@ -88,7 +88,7 @@ def credible_interval( `error=0.05` corresponds to a 95% level of credibility. interval_type: str The interval type. We support "two-tailed" (default), "right-tailed" and "left-tailed". - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. calibrated : bool Whether the outputs should be calibrated when computing this method. If `calibrated` is set to True, the diff --git a/fortuna/output_calib_model/state.py b/fortuna/output_calib_model/state.py index c69352d3..9d10be68 100644 --- a/fortuna/output_calib_model/state.py +++ b/fortuna/output_calib_model/state.py @@ -8,7 +8,6 @@ ) from flax.core import FrozenDict -import jax.numpy as jnp from fortuna.training.train_state import TrainState from fortuna.typing import ( diff --git a/fortuna/output_calibrator/output_calib_manager/base.py b/fortuna/output_calibrator/output_calib_manager/base.py index fcc4b50d..069e9ecf 100644 --- a/fortuna/output_calibrator/output_calib_manager/base.py +++ b/fortuna/output_calibrator/output_calib_manager/base.py @@ -7,8 +7,8 @@ from flax.core import FrozenDict import flax.linen as nn from flax.training.checkpoints import PyTree +import jax from jax import random -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp from fortuna.typing import ( @@ -29,7 +29,7 @@ def apply( outputs: Array, mutable: Optional[CalibMutable] = None, calib: bool = False, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, PyTree]]: """ Apply the output calibrator forward pass. @@ -44,7 +44,7 @@ def apply( The mutable objects used to evaluate the models. calib : bool Whether the method is called during calibration. - rng: Optional[PRNGKeyArray] + rng: Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. @@ -82,7 +82,7 @@ def apply( ) def init( - self, output_dim: int, rng: Optional[PRNGKeyArray] = None, **kwargs + self, output_dim: int, rng: Optional[jax.Array] = None, **kwargs ) -> Optional[FrozenDict]: """ Initialize random parameters and mutable objects. @@ -91,7 +91,7 @@ def init( ---------- output_dim: int The output dimension. - rng: Optional[PRNGKeyArray] + rng: Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. diff --git a/fortuna/prob_model/calib_config/monitor.py b/fortuna/prob_model/calib_config/monitor.py index f3a6e4a8..edf67099 100644 --- a/fortuna/prob_model/calib_config/monitor.py +++ b/fortuna/prob_model/calib_config/monitor.py @@ -68,7 +68,7 @@ def __init__( f"All metrics in `metrics` must be callable objects, but {metric} is not." ) if uncertainty_fn is not None and not callable(uncertainty_fn): - raise ValueError(f"`uncertainty_fn` must be a a callable function.") + raise ValueError("`uncertainty_fn` must be a a callable function.") self.metrics = metrics self.uncertainty_fn = uncertainty_fn diff --git a/fortuna/prob_model/classification.py b/fortuna/prob_model/classification.py index 7ee36a46..66bc57ad 100644 --- a/fortuna/prob_model/classification.py +++ b/fortuna/prob_model/classification.py @@ -201,7 +201,7 @@ def _get_model_manager( model_editor=model_editor, **posterior_approximator.posterior_method_kwargs, ) - except ModuleNotFoundError as e: + except ModuleNotFoundError: logging.warning( "No module named 'transformer' is installed. " "If you are not working with models from the `transformers` library ignore this warning, otherwise " diff --git a/fortuna/prob_model/joint/base.py b/fortuna/prob_model/joint/base.py index b113c548..7af4d41a 100755 --- a/fortuna/prob_model/joint/base.py +++ b/fortuna/prob_model/joint/base.py @@ -6,7 +6,7 @@ ) from flax.core import FrozenDict -from jax._src.prng import PRNGKeyArray +import jax import jax.numpy as jnp from fortuna.likelihood.base import Likelihood @@ -62,7 +62,7 @@ def _batched_log_joint_prob( return_aux: Optional[List[str]] = None, train: Optional[bool] = False, outputs: Optional[jnp.ndarray] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> Union[float, Tuple[float, dict]]: """ @@ -90,7 +90,7 @@ def _batched_log_joint_prob( Whether the method is called during training. outputs : Optional[jnp.ndarray] Pre-computed batch of outputs. - rng: Optional[PRNGKeyArray] + rng: Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. Returns @@ -132,7 +132,7 @@ def _batched_negative_log_joint_prob( return_aux: Optional[List[str]] = None, train: Optional[bool] = False, outputs: Optional[jnp.ndarray] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> Union[float, Tuple[float, dict]]: outs = self._batched_log_joint_prob( diff --git a/fortuna/prob_model/posterior/base.py b/fortuna/prob_model/posterior/base.py index 0676bb9f..98237d64 100755 --- a/fortuna/prob_model/posterior/base.py +++ b/fortuna/prob_model/posterior/base.py @@ -9,7 +9,7 @@ ) from flax.core import FrozenDict -from jax._src.prng import PRNGKeyArray +import jax from fortuna.data.loader import DataLoader from fortuna.prob_model.fit_config.base import FitConfig @@ -149,13 +149,13 @@ def fit( pass @abc.abstractmethod - def sample(self, rng: Optional[PRNGKeyArray] = None, *args, **kwargs) -> JointState: + def sample(self, rng: Optional[jax.Array] = None, *args, **kwargs) -> JointState: """ Sample from the posterior distribution. Parameters ---------- - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. Returns diff --git a/fortuna/prob_model/posterior/deep_ensemble/deep_ensemble_posterior.py b/fortuna/prob_model/posterior/deep_ensemble/deep_ensemble_posterior.py index 557614f8..bf72cdb0 100755 --- a/fortuna/prob_model/posterior/deep_ensemble/deep_ensemble_posterior.py +++ b/fortuna/prob_model/posterior/deep_ensemble/deep_ensemble_posterior.py @@ -1,7 +1,6 @@ from __future__ import annotations import logging -import os import pathlib from typing import ( List, @@ -11,11 +10,11 @@ ) from flax.core import FrozenDict +import jax from jax import ( pure_callback, random, ) -from jax._src.prng import PRNGKeyArray from fortuna.data.loader import DataLoader from fortuna.prob_model.fit_config.base import FitConfig @@ -192,7 +191,7 @@ def _fit(i): logging.info("Fit completed.") return status - def sample(self, rng: Optional[PRNGKeyArray] = None, **kwargs) -> JointState: + def sample(self, rng: Optional[jax.Array] = None, **kwargs) -> JointState: if rng is None: rng = self.rng.get() state = pure_callback( diff --git a/fortuna/prob_model/posterior/laplace/laplace_posterior.py b/fortuna/prob_model/posterior/laplace/laplace_posterior.py index 6f56116f..5e6376f9 100755 --- a/fortuna/prob_model/posterior/laplace/laplace_posterior.py +++ b/fortuna/prob_model/posterior/laplace/laplace_posterior.py @@ -23,7 +23,6 @@ pmap, vjp, ) -from jax._src.prng import PRNGKeyArray from jax.flatten_util import ravel_pytree import jax.numpy as jnp from jax.tree_util import tree_map @@ -316,7 +315,7 @@ def fit( def sample( self, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> JointState: if rng is None: @@ -426,7 +425,7 @@ def _batched_log_prob( batch, prior_log_var: float, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, dict]]: import jax.random as random @@ -472,7 +471,7 @@ def prior_log_variance_tuning( ) elif mode == "marginal_lik": raise NotImplementedError( - f"Optimizing the prior log variance via marginal likelihood maximization is not yet available." + "Optimizing the prior log variance via marginal likelihood maximization is not yet available." ) else: raise ValueError(f"Unrecognized mode={mode} for prior log variance tuning.") diff --git a/fortuna/prob_model/posterior/laplace/laplace_state.py b/fortuna/prob_model/posterior/laplace/laplace_state.py index 2970811b..97354082 100644 --- a/fortuna/prob_model/posterior/laplace/laplace_state.py +++ b/fortuna/prob_model/posterior/laplace/laplace_state.py @@ -9,7 +9,6 @@ ) from flax.core import FrozenDict -import jax.numpy as jnp from fortuna.prob_model.posterior.map.map_state import MAPState from fortuna.prob_model.posterior.state import PosteriorState diff --git a/fortuna/prob_model/posterior/map/map_posterior.py b/fortuna/prob_model/posterior/map/map_posterior.py index b36013cc..331f0129 100755 --- a/fortuna/prob_model/posterior/map/map_posterior.py +++ b/fortuna/prob_model/posterior/map/map_posterior.py @@ -1,7 +1,7 @@ import logging from typing import Optional -from jax._src.prng import PRNGKeyArray +import jax from fortuna.data.loader import DataLoader from fortuna.prob_model.fit_config.base import FitConfig @@ -118,7 +118,7 @@ def fit( logging.info("Fit completed.") return status - def sample(self, rng: Optional[PRNGKeyArray] = None, **kwargs) -> JointState: + def sample(self, rng: Optional[jax.Array] = None, **kwargs) -> JointState: state = self.state.get() return JointState( params=state.params, diff --git a/fortuna/prob_model/posterior/map/map_state.py b/fortuna/prob_model/posterior/map/map_state.py index 159233da..9c361ed6 100644 --- a/fortuna/prob_model/posterior/map/map_state.py +++ b/fortuna/prob_model/posterior/map/map_state.py @@ -1,7 +1,5 @@ from __future__ import annotations -import jax.numpy as jnp - from fortuna.prob_model.posterior.state import PosteriorState from fortuna.utils.strings import convert_string_to_tuple diff --git a/fortuna/prob_model/posterior/map/map_trainer.py b/fortuna/prob_model/posterior/map/map_trainer.py index d1d7d955..a99487a1 100644 --- a/fortuna/prob_model/posterior/map/map_trainer.py +++ b/fortuna/prob_model/posterior/map/map_trainer.py @@ -10,7 +10,7 @@ ) from flax.core import FrozenDict -from jax._src.prng import PRNGKeyArray +import jax import jax.numpy as jnp from optax._src.base import PyTree @@ -38,7 +38,7 @@ def training_loss_step( params: Params, batch: Batch, mutable: Mutable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, unravel: Optional[Callable[[any], PyTree]] = None, calib_params: Optional[CalibParams] = None, @@ -74,7 +74,7 @@ def validation_step( state: MAPState, batch: Batch, loss_fun: Callable[[Any], Union[float, Tuple[float, dict]]], - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], float], ...]] = None, unravel: Optional[Callable[[any], PyTree]] = None, diff --git a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_architecture.py b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_architecture.py index 932043d4..a737315a 100644 --- a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_architecture.py +++ b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_architecture.py @@ -6,8 +6,8 @@ Tuple, ) +import jax from jax import random -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp from fortuna.utils.builtins import HashableMixin @@ -85,7 +85,7 @@ def backward( def init_params( self, - rng: PRNGKeyArray, + rng: jax.Array, mean: Optional[jnp.ndarray] = None, log_std: Optional[jnp.ndarray] = None, ) -> Dict[str, jnp.ndarray]: @@ -94,7 +94,7 @@ def init_params( Parameters ---------- - rng: PRNGKeyArray + rng: jax.Array Random number generator. mean: jnp.ndarray If the main model has already been initialized calling `model.init`, the already diff --git a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_posterior.py b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_posterior.py index 65ced66f..cde86b88 100755 --- a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_posterior.py +++ b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_posterior.py @@ -11,10 +11,9 @@ ) from flax.core import FrozenDict -from jax._src.prng import PRNGKeyArray +import jax from jax.flatten_util import ravel_pytree import jax.numpy as jnp -import numpy as np from fortuna.data.loader import ( DataLoader, @@ -267,7 +266,7 @@ def load_state(self, checkpoint_path: Path) -> None: def sample( self, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, input_shape: Optional[Tuple[int, ...]] = None, inputs_loader: Optional[InputsLoader] = None, inputs: Optional[Array] = None, @@ -277,7 +276,7 @@ def sample( Sample from the posterior distribution. Either `input_shape` or `_inputs_loader` must be passed. Parameters ---------- - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. input_shape: Optional[Tuple[int, ...]] Shape of a single input. diff --git a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_state.py b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_state.py index 2db8bb78..4efe3d4d 100644 --- a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_state.py +++ b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_state.py @@ -6,8 +6,6 @@ Optional, ) -import jax.numpy as jnp - from fortuna.prob_model.posterior.normalizing_flow.normalizing_flow_state import ( NormalizingFlowState, ) diff --git a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_trainer.py b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_trainer.py index 995276dd..a2fa1612 100644 --- a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_trainer.py +++ b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_trainer.py @@ -5,7 +5,6 @@ from flax.core import FrozenDict import jax -from jax._src.prng import PRNGKeyArray from jax.flatten_util import ravel_pytree from jax.tree_util import tree_map @@ -97,8 +96,8 @@ def on_train_start( self, state: NormalizingFlowState, dataloaders: List[DataLoader], - rng: PRNGKeyArray, - ) -> Tuple[NormalizingFlowState, List[DataLoader], PRNGKeyArray]: + rng: jax.Array, + ) -> Tuple[NormalizingFlowState, List[DataLoader], jax.Array]: if self.freeze_fun is not None: frozen_paths = get_frozen_paths( self._unravel(state.params["mean"]), self.freeze_fun diff --git a/fortuna/prob_model/posterior/normalizing_flow/normalizing_flow_trainer.py b/fortuna/prob_model/posterior/normalizing_flow/normalizing_flow_trainer.py index be6a237e..eeb76fcf 100644 --- a/fortuna/prob_model/posterior/normalizing_flow/normalizing_flow_trainer.py +++ b/fortuna/prob_model/posterior/normalizing_flow/normalizing_flow_trainer.py @@ -12,11 +12,11 @@ ) from flax.core import FrozenDict +import jax from jax import ( random, vmap, ) -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp from jax.tree_util import tree_map from optax._src.base import PyTree @@ -32,7 +32,6 @@ CalibParams, Mutable, Params, - Path, ) from fortuna.utils.strings import encode_tuple_of_lists_of_strings_to_numpy @@ -48,7 +47,7 @@ def sample_forward(self, rng: jnp.array, params: any, n_samples: int) -> tuple: Parameters ---------- - rng: PRNGKeyArray + rng: jax.Array Random number generator. params: Params Transformation parameters. @@ -99,7 +98,7 @@ def training_loss_step( params: Params, batch: Batch, mutable: Mutable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, unravel: Optional[Callable[[any], PyTree]] = None, calib_params: Optional[CalibParams] = None, @@ -197,7 +196,7 @@ def validation_step( state: PosteriorState, batch: Batch, loss_fun: Callable[[Any], Union[float, Tuple[float, dict]]], - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], float]], ...] = None, unravel: Optional[Callable[[any], PyTree]] = None, diff --git a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_callback.py b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_callback.py index fed81699..16aebc57 100644 --- a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_callback.py +++ b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_callback.py @@ -1,8 +1,6 @@ from fortuna.prob_model.posterior.sgmcmc.sgmcmc_sampling_callback import ( SGMCMCSamplingCallback, ) -from fortuna.training.callback import Callback -from fortuna.training.train_state import TrainState from fortuna.training.train_state_repository import TrainStateRepository from fortuna.training.trainer import TrainerABC diff --git a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_integrator.py b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_integrator.py index a6e525c6..2843f8f0 100644 --- a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_integrator.py +++ b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_integrator.py @@ -1,7 +1,6 @@ from typing import NamedTuple import jax -from jax._src.prng import PRNGKeyArray import optax from optax import GradientTransformation @@ -20,7 +19,7 @@ class OptaxCyclicalSGLDState(NamedTuple): def cyclical_sgld_integrator( - rng_key: PRNGKeyArray, + rng_key: jax.Array, init_step_size: float, cycle_length: int, exploration_ratio: float, @@ -30,7 +29,7 @@ def cyclical_sgld_integrator( Parameters ---------- - rng_key: PRNGKeyArray + rng_key: jax.Array An initial random number generator. init_step_size: float The initial step size. diff --git a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_posterior.py b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_posterior.py index 780817c9..59c945d4 100644 --- a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_posterior.py +++ b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_posterior.py @@ -96,7 +96,7 @@ def fit( ) if fit_config.optimizer.method is not None: - logging.info(f"`FitOptimizer` method in CyclicalSGLD is ignored.") + logging.info("`FitOptimizer` method in CyclicalSGLD is ignored.") fit_config.optimizer.method = cyclical_sgld_integrator( rng_key=self.rng.get(), @@ -171,7 +171,7 @@ def fit( keep_top_n_checkpoints=fit_config.checkpointer.keep_top_n_checkpoints, ) - logging.info(f"Run CyclicalSGLD.") + logging.info("Run CyclicalSGLD.") state, status = trainer.train( rng=self.rng.get(), state=state, diff --git a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_state.py b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_state.py index 5aec1731..de078074 100644 --- a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_state.py +++ b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_state.py @@ -7,8 +7,6 @@ Tuple, ) -import jax.numpy as jnp - from fortuna.prob_model.posterior.map.map_state import MAPState from fortuna.prob_model.posterior.state import PosteriorState from fortuna.typing import ( diff --git a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_approximator.py b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_approximator.py index 02083a06..b28a8abf 100644 --- a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_approximator.py +++ b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_approximator.py @@ -57,7 +57,7 @@ def __init__( if isinstance(step_schedule, float): step_schedule = constant_schedule(step_schedule) elif not callable(step_schedule): - raise ValueError(f"`step_schedule` must be a a callable function.") + raise ValueError("`step_schedule` must be a a callable function.") self.burnin_length = burnin_length self.momentum_decay = momentum_decay self.step_schedule = step_schedule diff --git a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_callback.py b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_callback.py index c9dfa12e..30ad00e2 100644 --- a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_callback.py +++ b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_callback.py @@ -1,8 +1,6 @@ from fortuna.prob_model.posterior.sgmcmc.sgmcmc_sampling_callback import ( SGMCMCSamplingCallback, ) -from fortuna.training.callback import Callback -from fortuna.training.train_state import TrainState from fortuna.training.train_state_repository import TrainStateRepository from fortuna.training.trainer import TrainerABC diff --git a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_integrator.py b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_integrator.py index 769ad617..5e3ca9ed 100644 --- a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_integrator.py +++ b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_integrator.py @@ -4,7 +4,6 @@ ) import jax -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp from optax import GradientTransformation from optax._src.base import PyTree @@ -22,7 +21,7 @@ class OptaxSGHMCState(NamedTuple): """Optax state for the SGHMC integrator.""" count: Array - rng_key: PRNGKeyArray + rng_key: jax.Array momentum: PyTree preconditioner_state: PreconditionerState @@ -30,7 +29,7 @@ class OptaxSGHMCState(NamedTuple): def sghmc_integrator( momentum_decay: float, momentum_resample_steps: Optional[int], - rng_key: PRNGKeyArray, + rng_key: jax.Array, step_schedule: StepSchedule, preconditioner: Preconditioner, ) -> GradientTransformation: @@ -40,7 +39,7 @@ def sghmc_integrator( ---------- momentum_decay: float The momentum decay parameter. - rng_key: PRNGKeyArray + rng_key: jax.Array An initial random number generator. step_schedule: StepSchedule A function that takes training step as input and returns the step size. diff --git a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_posterior.py b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_posterior.py index 59ab9464..cb84d464 100644 --- a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_posterior.py +++ b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_posterior.py @@ -7,7 +7,6 @@ from fortuna.data.loader import DataLoader from fortuna.prob_model.fit_config.base import FitConfig from fortuna.prob_model.joint.base import Joint -from fortuna.prob_model.posterior.map.map_posterior import MAPPosterior from fortuna.prob_model.posterior.map.map_state import MAPState from fortuna.prob_model.posterior.map.map_trainer import ( JittedMAPTrainer, @@ -93,7 +92,7 @@ def fit( ) if fit_config.optimizer.method is not None: - logging.info(f"`FitOptimizer` method in SGHMC is ignored.") + logging.info("`FitOptimizer` method in SGHMC is ignored.") fit_config.optimizer.method = sghmc_integrator( momentum_decay=self.posterior_approximator.momentum_decay, @@ -167,7 +166,7 @@ def fit( keep_top_n_checkpoints=fit_config.checkpointer.keep_top_n_checkpoints, ) - logging.info(f"Run SGHMC.") + logging.info("Run SGHMC.") state, status["sghmc"] = trainer.train( rng=self.rng.get(), state=state, diff --git a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_state.py b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_state.py index 28605cb4..836e7bd4 100644 --- a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_state.py +++ b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_state.py @@ -7,8 +7,6 @@ Tuple, ) -import jax.numpy as jnp - from fortuna.prob_model.posterior.map.map_state import MAPState from fortuna.prob_model.posterior.state import PosteriorState from fortuna.typing import ( diff --git a/fortuna/prob_model/posterior/sgmcmc/sgmcmc_posterior.py b/fortuna/prob_model/posterior/sgmcmc/sgmcmc_posterior.py index ac48e64c..b6de41e2 100644 --- a/fortuna/prob_model/posterior/sgmcmc/sgmcmc_posterior.py +++ b/fortuna/prob_model/posterior/sgmcmc/sgmcmc_posterior.py @@ -5,11 +5,11 @@ Type, ) +import jax from jax import ( pure_callback, random, ) -from jax._src.prng import PRNGKeyArray from fortuna.prob_model.fit_config.base import FitConfig from fortuna.prob_model.joint.state import JointState @@ -27,7 +27,7 @@ class SGMCMCPosterior(Posterior): def sample( self, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> JointState: """ @@ -35,7 +35,7 @@ def sample( Parameters ---------- - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. Returns diff --git a/fortuna/prob_model/posterior/sgmcmc/sgmcmc_sampling_callback.py b/fortuna/prob_model/posterior/sgmcmc/sgmcmc_sampling_callback.py index 7641ba84..77a40d1f 100644 --- a/fortuna/prob_model/posterior/sgmcmc/sgmcmc_sampling_callback.py +++ b/fortuna/prob_model/posterior/sgmcmc/sgmcmc_sampling_callback.py @@ -1,5 +1,3 @@ -from typing import Optional - from fortuna.training.callback import Callback from fortuna.training.train_state import TrainState from fortuna.training.train_state_repository import TrainStateRepository diff --git a/fortuna/prob_model/posterior/sngp/sngp_posterior.py b/fortuna/prob_model/posterior/sngp/sngp_posterior.py index 2a40efb1..fc067141 100755 --- a/fortuna/prob_model/posterior/sngp/sngp_posterior.py +++ b/fortuna/prob_model/posterior/sngp/sngp_posterior.py @@ -65,7 +65,7 @@ def _check_state(state: PosteriorState) -> None: path = find_one_path_to_key(state.mutable, "spectral_stats") if len(path) == 0: raise ValueError( - f"It looks like your deep feature extractor does not have Spectral Normalization, " - f"which is required by SNGP. Please include spectral normalization in your model." - f"Check out `fortuna.model.utils.spectral_norm.WithSpectralNorm` for more details." + "It looks like your deep feature extractor does not have Spectral Normalization, " + "which is required by SNGP. Please include spectral normalization in your model." + "Check out `fortuna.model.utils.spectral_norm.WithSpectralNorm` for more details." ) diff --git a/fortuna/prob_model/posterior/swag/swag_posterior.py b/fortuna/prob_model/posterior/swag/swag_posterior.py index 81dfec91..d04e58f4 100755 --- a/fortuna/prob_model/posterior/swag/swag_posterior.py +++ b/fortuna/prob_model/posterior/swag/swag_posterior.py @@ -4,8 +4,8 @@ from typing import Optional from flax.core import FrozenDict +import jax from jax import random -from jax._src.prng import PRNGKeyArray from jax.flatten_util import ravel_pytree import jax.numpy as jnp @@ -179,7 +179,7 @@ def fit( def sample( self, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, inputs_loader: Optional[InputsLoader] = None, inputs: Optional[Array] = None, **kwargs, @@ -189,7 +189,7 @@ def sample( Parameters ---------- - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. inputs_loader: Optional[InputsLoader] Input data loader. This or `inputs` is required if the posterior state includes mutable objects. diff --git a/fortuna/prob_model/predictive/base.py b/fortuna/prob_model/predictive/base.py index 2aeedf64..e6983d1e 100644 --- a/fortuna/prob_model/predictive/base.py +++ b/fortuna/prob_model/predictive/base.py @@ -9,13 +9,13 @@ ) from flax.training.common_utils import shard_prng_key +import jax from jax import ( jit, lax, pmap, random, ) -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp import jax.scipy as jsp from jax.tree_util import tree_map @@ -54,7 +54,7 @@ def ensemble_log_prob( self, data_loader: DataLoader, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, **kwargs, ) -> jnp.ndarray: @@ -76,7 +76,7 @@ def ensemble_log_prob( n_posterior_samples : int Number of posterior samples to draw in order to compute the log -ikelihood. that would be produced using the posterior distribution state. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -105,7 +105,7 @@ def _batched_ensemble_log_prob( self, batch: Batch, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, dict]]: if rng is None: @@ -132,7 +132,7 @@ def log_prob( self, data_loader: DataLoader, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, **kwargs, ) -> jnp.ndarray: @@ -154,7 +154,7 @@ def log_prob( n_posterior_samples : int Number of posterior samples to draw in order to approximate the predictive log-pdf. that would be produced using the posterior distribution state. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -180,7 +180,7 @@ def _batched_log_prob( self, batch: Batch, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, dict]]: if rng is None: @@ -211,7 +211,7 @@ def _batched_log_joint_prob( ensemble_outputs: Optional[jnp.ndarray] = None, calib_params: Optional[CalibParams] = None, calib_mutable: Optional[CalibMutable] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Dict]]: if return_aux is None: @@ -295,7 +295,7 @@ def _batched_negative_log_joint_prob( ensemble_outputs: Optional[jnp.ndarray] = None, calib_params: Optional[CalibParams] = None, calib_mutable: Optional[CalibMutable] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Dict]]: outs = self._batched_log_joint_prob( @@ -320,7 +320,7 @@ def sample( inputs_loader: InputsLoader, n_target_samples: int = 1, return_aux: Optional[List[str]] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, **kwargs, ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Dict[str, jnp.ndarray]]]: @@ -343,7 +343,7 @@ def sample( Number of target samples to sample for each input data point. return_aux : Optional[List[str]] Return auxiliary objects. We currently support 'outputs'. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -408,7 +408,7 @@ def _batched_sample( inputs: Array, n_target_samples: int = 1, return_aux: Optional[List[str]] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> jnp.ndarray: if return_aux is None: @@ -443,7 +443,7 @@ def sample_calibrated_outputs( self, inputs_loader: InputsLoader, n_output_samples: int = 1, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -455,7 +455,7 @@ def sample_calibrated_outputs( A loader of input data points. n_output_samples : int Number of output samples to draw for each input. - rng: Optional[PRNGKeyArray] + rng: Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -480,7 +480,7 @@ def _sample_batched_calibrated_outputs( self, inputs: Array, n_output_samples: int = 1, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, ) -> jnp.ndarray: if rng is None: rng = self.rng.get() @@ -502,7 +502,7 @@ def _sample_outputs( self, inputs_loader: InputsLoader, n_output_samples: int = 1, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: if rng is None: @@ -520,7 +520,7 @@ def _sample_batched_outputs( self, inputs: Array, n_output_samples: int = 1, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, ) -> jnp.ndarray: if rng is None: rng = self.rng.get() @@ -538,7 +538,7 @@ def _sample_outputs_loader( self, inputs_loader: InputsLoader, n_output_samples: int = 1, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, return_size: bool = False, distribute: bool = True, ) -> Union[TargetsLoader, Tuple[TargetsLoader, int]]: @@ -583,7 +583,7 @@ def mean( self, inputs_loader: InputsLoader, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -604,7 +604,7 @@ def mean( A loader of input data points. n_posterior_samples : int Number of samples to draw from the posterior distribution for each input. - rng: Optional[PRNGKeyArray] + rng: Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -625,7 +625,7 @@ def _batched_mean( self, inputs: Array, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, ) -> jnp.ndarray: if rng is None: rng = self.rng.get() @@ -652,7 +652,7 @@ def mode( inputs_loader: InputsLoader, n_posterior_samples: int = 30, means: Optional[jnp.ndarray] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -674,7 +674,7 @@ def mode( Number of samples to draw from the posterior distribution for each input. means : Optional[jnp.ndarray] = None An estimate of the predictive mean. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -690,7 +690,7 @@ def aleatoric_variance( self, inputs_loader: InputsLoader, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -711,7 +711,7 @@ def aleatoric_variance( A loader of input data points. n_posterior_samples : int Number of samples to draw from the posterior distribution for each input. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -736,7 +736,7 @@ def _batched_aleatoric_variance( self, inputs: Array, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, ) -> jnp.ndarray: if rng is None: rng = self.rng.get() @@ -761,7 +761,7 @@ def epistemic_variance( self, inputs_loader: InputsLoader, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -782,7 +782,7 @@ def epistemic_variance( A loader of input data points. n_posterior_samples : int Number of samples to draw from the posterior distribution for each input. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -807,7 +807,7 @@ def _batched_epistemic_variance( self, inputs: Array, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, ) -> jnp.ndarray: if rng is None: rng = self.rng.get() @@ -841,7 +841,7 @@ def variance( n_posterior_samples: int = 30, aleatoric_variances: Optional[jnp.ndarray] = None, epistemic_variances: Optional[jnp.ndarray] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -867,7 +867,7 @@ def variance( An estimate of the aleatoric predictive variance for each input. epistemic_variances: Optional[jnp.ndarray] An estimate of the epistemic predictive variance for each input. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -902,7 +902,7 @@ def std( inputs_loader: InputsLoader, n_posterior_samples: int = 30, variances: Optional[jnp.ndarray] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -924,7 +924,7 @@ def std( Number of samples to draw from the posterior distribution for each input. variances: Optional[jnp.ndarray] An estimate of the predictive variance. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -954,7 +954,7 @@ def _loop_fun_through_inputs_loader( fun: Callable, inputs_loader: InputsLoader, n_posterior_samples: int, - rng: PRNGKeyArray, + rng: jax.Array, distribute: bool = True, **kwargs, ) -> Array: @@ -979,7 +979,7 @@ def _loop_fun_through_data_loader( fun: Callable, data_loader: DataLoader, n_posterior_samples: int, - rng: PRNGKeyArray, + rng: jax.Array, distribute: bool = True, **kwargs, ) -> Array: @@ -1001,7 +1001,7 @@ def _loop_ensemble_fun_through_inputs_loader( fun: Callable, inputs_loader: InputsLoader, n_posterior_samples: int, - rng: PRNGKeyArray, + rng: jax.Array, distribute: bool = True, **kwargs, ) -> Array: diff --git a/fortuna/prob_model/predictive/classification.py b/fortuna/prob_model/predictive/classification.py index 5d4c0297..8119cd55 100644 --- a/fortuna/prob_model/predictive/classification.py +++ b/fortuna/prob_model/predictive/classification.py @@ -1,11 +1,11 @@ from typing import Optional +import jax from jax import ( jit, lax, vmap, ) -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp import jax.scipy as jsp import numpy as np @@ -35,7 +35,7 @@ def mean( self, inputs_loader: InputsLoader, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -56,7 +56,7 @@ def mean( A loader of input data points. n_posterior_samples : int Number of samples to draw from the posterior distribution for each input. - rng: Optional[PRNGKeyArray] + rng: Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -73,7 +73,7 @@ def mode( inputs_loader: InputsLoader, n_posterior_samples: int = 30, means: Optional[jnp.ndarray] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: if means is None: @@ -89,7 +89,7 @@ def aleatoric_variance( self, inputs_loader: InputsLoader, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -110,7 +110,7 @@ def aleatoric_variance( A loader of input data points. n_posterior_samples : int Number of samples to draw from the posterior distribution for each input. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -128,7 +128,7 @@ def epistemic_variance( self, inputs_loader: InputsLoader, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, **kwargs, ) -> jnp.ndarray: @@ -150,7 +150,7 @@ def epistemic_variance( A loader of input data points. n_posterior_samples : int Number of samples to draw from the posterior distribution for each input. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -170,7 +170,7 @@ def variance( n_posterior_samples: int = 30, aleatoric_variances: Optional[jnp.ndarray] = None, epistemic_variances: Optional[jnp.ndarray] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -194,7 +194,7 @@ def variance( An estimate of the aleatoric predictive variance. epistemic_variances: Optional[jnp.ndarray] An estimate of the epistemic predictive variance. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -218,7 +218,7 @@ def std( inputs_loader: InputsLoader, n_posterior_samples: int = 30, variances: Optional[jnp.ndarray] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -240,7 +240,7 @@ def std( Number of samples to draw from the posterior distribution for each input. variances: Optional[jnp.ndarray] An estimate of the predictive variance. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -258,7 +258,7 @@ def aleatoric_entropy( self, inputs_loader: InputsLoader, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -279,7 +279,7 @@ def aleatoric_entropy( A loader of input data points. n_posterior_samples : int Number of samples to draw from the posterior distribution for each input. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -313,7 +313,7 @@ def epistemic_entropy( self, inputs_loader: InputsLoader, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -338,7 +338,7 @@ def epistemic_entropy( A loader of input data points. n_posterior_samples : int Number of samples to draw from the posterior distribution for each input. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -377,7 +377,7 @@ def entropy( self, inputs_loader: InputsLoader, n_posterior_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -398,7 +398,7 @@ def entropy( A loader of input data points. n_posterior_samples : int Number of samples to draw from the posterior distribution for each input. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -436,7 +436,7 @@ def credible_set( inputs_loader: InputsLoader, n_posterior_samples: int = 30, error: float = 0.05, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -452,7 +452,7 @@ def credible_set( error: float The set error. This must be a number between 0 and 1, extremes included. For example, `error=0.05` corresponds to a 95% level of credibility. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -488,7 +488,7 @@ def conformal_set( test_inputs_loader: InputsLoader, n_posterior_samples: int = 30, error: float = 0.05, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, return_ess: bool = False, ) -> jnp.ndarray: @@ -506,7 +506,7 @@ def conformal_set( error: float The set error. This must be a number between 0 and 1, extremes included. For example, `error=0.05` corresponds to a 95% level of confidence. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. diff --git a/fortuna/prob_model/predictive/regression.py b/fortuna/prob_model/predictive/regression.py index c66d5380..4eef7dce 100644 --- a/fortuna/prob_model/predictive/regression.py +++ b/fortuna/prob_model/predictive/regression.py @@ -4,12 +4,12 @@ Union, ) +import jax from jax import ( lax, random, vmap, ) -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp import jax.scipy as jsp @@ -36,7 +36,7 @@ def mode( inputs_loader: InputsLoader, n_posterior_samples: int = 30, means: Optional[jnp.ndarray] = None, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: if means is not None: @@ -53,7 +53,7 @@ def aleatoric_entropy( inputs_loader: InputsLoader, n_posterior_samples: int = 30, n_target_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -76,7 +76,7 @@ def aleatoric_entropy( Number of target samples to draw for each input. n_posterior_samples : int Number of samples to draw from the posterior distribution for each input. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -120,7 +120,7 @@ def epistemic_entropy( inputs_loader: InputsLoader, n_posterior_samples: int = 30, n_target_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -147,7 +147,7 @@ def epistemic_entropy( Number of samples to draw from the posterior distribution for each input. n_target_samples: int Number of target samples to draw for each input. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -199,7 +199,7 @@ def entropy( inputs_loader: InputsLoader, n_posterior_samples: int = 30, n_target_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -222,7 +222,7 @@ def entropy( Number of target samples to draw for each input. n_posterior_samples : int Number of samples to draw from the posterior distribution for each input. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -272,7 +272,7 @@ def credible_interval( n_target_samples: int = 30, error: float = 0.05, interval_type: str = "two-tailed", - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> jnp.ndarray: r""" @@ -289,7 +289,7 @@ def credible_interval( `error=0.05` corresponds to a 95% level of credibility. interval_type: str The interval type. We support "two-tailed" (default), "right-tailed" and "left-tailed". - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. @@ -333,7 +333,7 @@ def quantile( q: Union[float, Array, List], inputs_loader: InputsLoader, n_target_samples: Optional[int] = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, distribute: bool = True, ) -> Union[float, jnp.ndarray]: r""" @@ -347,7 +347,7 @@ def quantile( A loader of input data points. n_target_samples : int Number of target samples to sample for each input data point. - rng: Optional[PRNGKeyArray] + rng: Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. distribute: bool Whether to distribute computation over multiple devices, if available. diff --git a/fortuna/prob_model/prior/base.py b/fortuna/prob_model/prior/base.py index 572504eb..5f9e1dee 100755 --- a/fortuna/prob_model/prior/base.py +++ b/fortuna/prob_model/prior/base.py @@ -1,7 +1,7 @@ import abc from typing import Optional -from jax._src.prng import PRNGKeyArray +import jax from fortuna.typing import Params from fortuna.utils.random import WithRNG @@ -30,7 +30,7 @@ def log_joint_prob(self, params: Params) -> float: pass @abc.abstractmethod - def sample(self, params_like: Params, rng: Optional[PRNGKeyArray] = None) -> Params: + def sample(self, params_like: Params, rng: Optional[jax.Array] = None) -> Params: """ Sample parameters from the prior distribution. @@ -38,7 +38,7 @@ def sample(self, params_like: Params, rng: Optional[PRNGKeyArray] = None) -> Par ---------- params_like : PyTree An PyTree object with the same structure as the parameters to sample. - rng: Optional[PRNGKeyArray] + rng: Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. Returns diff --git a/fortuna/prob_model/prior/gaussian.py b/fortuna/prob_model/prior/gaussian.py index e2b510db..389b5b2f 100644 --- a/fortuna/prob_model/prior/gaussian.py +++ b/fortuna/prob_model/prior/gaussian.py @@ -1,7 +1,7 @@ from typing import Optional +import jax from jax import random -from jax._src.prng import PRNGKeyArray from jax.flatten_util import ravel_pytree import jax.numpy as jnp @@ -31,7 +31,7 @@ def log_joint_prob(self, params: Params) -> float: n = len(rav) return -0.5 * (self.prec * jnp.sum(rav**2) + n * (self.log2pi + self.log_var)) - def sample(self, params_like: Params, rng: Optional[PRNGKeyArray] = None) -> Params: + def sample(self, params_like: Params, rng: Optional[jax.Array] = None) -> Params: dummy_rav, unravel = ravel_pytree(params_like) n = len(dummy_rav) if rng is None: @@ -60,7 +60,7 @@ def log_joint_prob(self, params: Params) -> float: jnp.exp(-self.log_var) * rav**2 + self.log2pi + self.log_var ) - def sample(self, params_like: Params, rng: Optional[PRNGKeyArray] = None) -> Params: + def sample(self, params_like: Params, rng: Optional[jax.Array] = None) -> Params: dummy_rav, unravel = ravel_pytree(params_like) n = len(dummy_rav) if rng is None: diff --git a/fortuna/prob_model/prob_model_calibrator.py b/fortuna/prob_model/prob_model_calibrator.py index 24cc1692..0b77a255 100644 --- a/fortuna/prob_model/prob_model_calibrator.py +++ b/fortuna/prob_model/prob_model_calibrator.py @@ -8,7 +8,6 @@ from flax import jax_utils import jax -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp from jax.tree_util import tree_map @@ -35,7 +34,7 @@ def training_loss_step( batch: Batch, outputs: Array, mutable: CalibMutable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, ) -> Tuple[jnp.ndarray, Dict[str, Any]]: return_aux = ["outputs"] @@ -66,7 +65,7 @@ def val_loss_step( batch: Batch, outputs: Array, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, ) -> Tuple[jnp.ndarray, Dict[str, jnp.ndarray]]: loss, aux = loss_fun( diff --git a/fortuna/prob_output_layer/base.py b/fortuna/prob_output_layer/base.py index ff5ec0e7..94236665 100755 --- a/fortuna/prob_output_layer/base.py +++ b/fortuna/prob_output_layer/base.py @@ -1,7 +1,7 @@ import abc from typing import Optional -from jax._src.prng import PRNGKeyArray +import jax import jax.numpy as jnp from fortuna.typing import Array @@ -56,7 +56,7 @@ def sample( self, n_target_samples: int, outputs: Array, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> jnp.ndarray: """ @@ -68,7 +68,7 @@ def sample( The number of target samples to draw for each of the outputs. outputs : Array Calibrated outputs. - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. Returns diff --git a/fortuna/prob_output_layer/classification.py b/fortuna/prob_output_layer/classification.py index 67b4bfbc..c98225da 100644 --- a/fortuna/prob_output_layer/classification.py +++ b/fortuna/prob_output_layer/classification.py @@ -5,7 +5,6 @@ random, vmap, ) -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp import jax.scipy as jsp @@ -35,7 +34,7 @@ def sample( self, n_target_samples: int, outputs: Array, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> jnp.ndarray: probs = ( diff --git a/fortuna/prob_output_layer/regression.py b/fortuna/prob_output_layer/regression.py index ff9a08d7..6577fb81 100644 --- a/fortuna/prob_output_layer/regression.py +++ b/fortuna/prob_output_layer/regression.py @@ -4,11 +4,11 @@ Union, ) +import jax from jax import ( random, vmap, ) -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp from fortuna.prob_output_layer.base import ProbOutputLayer @@ -40,7 +40,7 @@ def sample( self, n_target_samples: int, outputs: Array, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> jnp.ndarray: if rng is None: @@ -55,7 +55,7 @@ def quantile( q: Union[float, Array, List], outputs: Array, n_target_samples: Optional[int] = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, ) -> jnp.ndarray: if type(q) == list: q = jnp.array(q) @@ -77,7 +77,7 @@ def entropy( self, outputs: Array, n_target_samples: int = 30, - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, **kwargs, ) -> jnp.ndarray: samples = self.sample(n_target_samples, outputs, rng=rng, **kwargs) @@ -94,7 +94,7 @@ def credible_interval( n_target_samples: int = 30, error: float = 0.05, interval_type: str = "two-tailed", - rng: Optional[PRNGKeyArray] = None, + rng: Optional[jax.Array] = None, ) -> jnp.ndarray: r""" Estimate credible intervals for the target variable. This is supported only if the target variable is scalar. @@ -110,7 +110,7 @@ def credible_interval( `error=0.05` corresponds to a 95% level of credibility. interval_type: str The interval type. We support "two-tailed" (default), "right-tailed" and "left-tailed". - rng : Optional[PRNGKeyArray] + rng : Optional[jax.Array] A random number generator. If not passed, this will be taken from the attributes of this class. Returns diff --git a/fortuna/training/mixin.py b/fortuna/training/mixin.py index 353c8aa6..3147da60 100755 --- a/fortuna/training/mixin.py +++ b/fortuna/training/mixin.py @@ -109,7 +109,7 @@ def __init__( if early_stopping_patience is None or early_stopping_patience <= 0: if early_stopping_verbose: logging.info( - f"Early stopping not enabled. Set `early_stopping_patience>=0` to enable it." + "Early stopping not enabled. Set `early_stopping_patience>=0` to enable it." ) elif self.early_stopping_mode is None or self.early_stopping_mode not in ( "min", @@ -146,9 +146,8 @@ def early_stopping_update( early_stopping_monitor = validation_metrics[self.early_stopping_monitor] if self.early_stopping_mode == "max": early_stopping_monitor = -early_stopping_monitor - improved, self._early_stopping = self._early_stopping.update( - early_stopping_monitor - ) + self._early_stopping = self._early_stopping.update(early_stopping_monitor) + improved = self._early_stopping.has_improved return improved diff --git a/fortuna/training/output_calibrator.py b/fortuna/training/output_calibrator.py index ca6e7373..7e7a7740 100644 --- a/fortuna/training/output_calibrator.py +++ b/fortuna/training/output_calibrator.py @@ -20,7 +20,6 @@ random, value_and_grad, ) -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp from jax.tree_util import tree_map from tqdm import trange @@ -82,7 +81,7 @@ def __init__( def train( self, - rng: PRNGKeyArray, + rng: jax.Array, state: OutputCalibState, loss_fun: Callable, training_data_loader: DataLoader, @@ -178,7 +177,7 @@ def _training_loop( metrics: Optional[ Tuple[Callable[[jnp.ndarray, jnp.ndarray, Array], Array], ...] ], - rng: PRNGKeyArray, + rng: jax.Array, state: OutputCalibState, training_data_loader: DataLoader, calib_outputs_loader: TargetsLoader, @@ -237,7 +236,7 @@ def training_step( batch: Batch, outputs: Array, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, ) -> Tuple[OutputCalibState, Dict[str, Any]]: # ensure to use a different key at each step @@ -270,7 +269,7 @@ def training_loss_step( batch: Batch, outputs: Array, mutable: CalibMutable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, ) -> Tuple[jnp.ndarray, Dict[str, Any]]: pass @@ -328,7 +327,7 @@ def _val_loop( metrics: Optional[ Tuple[Callable[[jnp.ndarray, jnp.ndarray, Array], Array], ...] ], - rng: PRNGKeyArray, + rng: jax.Array, state: OutputCalibState, val_data_loader: DataLoader, val_outputs_loader: TargetsLoader, @@ -370,7 +369,7 @@ def val_step( batch: Batch, outputs: Array, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, metrics: Optional[ Tuple[Callable[[jnp.ndarray, jnp.ndarray, Array], Array], ...] @@ -387,7 +386,7 @@ def val_loss_step( batch: Batch, outputs: Array, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, ) -> Tuple[jnp.ndarray, Dict[str, jnp.ndarray]]: pass @@ -459,8 +458,8 @@ def on_train_start( state: OutputCalibState, data_loaders: List[DataLoader], outputs_loaders: List[TargetsLoader], - rng: PRNGKeyArray, - ) -> Tuple[OutputCalibState, List[DataLoader], List[TargetsLoader], PRNGKeyArray]: + rng: jax.Array, + ) -> Tuple[OutputCalibState, List[DataLoader], List[TargetsLoader], jax.Array]: return state, data_loaders, outputs_loaders, rng def on_train_end(self, state: OutputCalibState) -> OutputCalibState: @@ -498,7 +497,7 @@ def training_step( batch: Batch, outputs: Array, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, ) -> Tuple[OutputCalibState, Dict[str, Any]]: return super().training_step(state, batch, outputs, loss_fun, rng, n_data) @@ -510,7 +509,7 @@ def val_loss_step( batch: Batch, outputs: Array, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, ) -> Dict[str, jnp.ndarray]: return super().val_loss_step(state, batch, outputs, loss_fun, rng, n_data) @@ -616,8 +615,8 @@ def on_train_start( state: OutputCalibState, data_loaders: List[DataLoader], outputs_loaders: List[TargetsLoader], - rng: PRNGKeyArray, - ) -> Tuple[OutputCalibState, List[DataLoader], List[TargetsLoader], PRNGKeyArray]: + rng: jax.Array, + ) -> Tuple[OutputCalibState, List[DataLoader], List[TargetsLoader], jax.Array]: state, data_loaders, outputs_loaders, rng = super( MultiDeviceMixin, self ).on_train_start(state, data_loaders, outputs_loaders, rng) @@ -644,7 +643,7 @@ def training_step( batch: Batch, outputs: Array, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, ) -> Tuple[OutputCalibState, Dict[str, Any]]: return super().training_step(state, batch, outputs, loss_fun, rng, n_data) @@ -675,7 +674,7 @@ def val_loss_step( batch: Batch, outputs: Array, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, ) -> Dict[str, jnp.ndarray]: val_losses = super().val_loss_step(state, batch, outputs, loss_fun, rng, n_data) diff --git a/fortuna/training/train_state.py b/fortuna/training/train_state.py index 0707cdb7..7d45afc5 100644 --- a/fortuna/training/train_state.py +++ b/fortuna/training/train_state.py @@ -9,7 +9,6 @@ dynamic_scale, train_state, ) -import jax.numpy as jnp from fortuna.typing import Params from fortuna.utils.strings import convert_string_to_tuple diff --git a/fortuna/training/trainer.py b/fortuna/training/trainer.py index 680415b6..77033685 100755 --- a/fortuna/training/trainer.py +++ b/fortuna/training/trainer.py @@ -21,7 +21,6 @@ random, value_and_grad, ) -from jax._src.prng import PRNGKeyArray import jax.numpy as jnp from jax.tree_util import tree_map from optax._src.base import PyTree @@ -100,7 +99,7 @@ def training_loss_step( params: Params, batch: Batch, mutable: Mutable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, unravel: Optional[Callable[[any], PyTree]] = None, calib_params: Optional[CalibParams] = None, @@ -114,7 +113,7 @@ def training_step( state: TrainState, batch: Batch, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, unravel: Optional[Callable[[any], PyTree]] = None, kwargs: FrozenDict[str, Any] = FrozenDict(), @@ -232,7 +231,7 @@ def validation_step( state: TrainState, batch: Batch, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], float], ...]] = None, unravel: Optional[Callable[[any], PyTree]] = None, @@ -354,7 +353,7 @@ def validation_epoch_end( def train( self, - rng: PRNGKeyArray, + rng: jax.Array, state: TrainState, loss_fun: Callable, training_dataloader: DataLoader, @@ -458,7 +457,7 @@ def _training_loop( current_epoch: int, loss_fun: Callable, metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], jnp.ndarray], ...]], - rng: PRNGKeyArray, + rng: jax.Array, state: TrainState, training_dataloader: DataLoader, training_dataset_size: int, @@ -536,7 +535,7 @@ def _validation_loop( self, loss_fun: Callable, metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], float], ...]], - rng: PRNGKeyArray, + rng: jax.Array, state: TrainState, training_kwargs: FrozenDict[str, Any], validation_dataloader: DataLoader, @@ -606,8 +605,8 @@ def on_train_start( self, state: TrainState, dataloaders: List[DataLoader], - rng: PRNGKeyArray, - ) -> Tuple[TrainState, List[DataLoader], PRNGKeyArray]: + rng: jax.Array, + ) -> Tuple[TrainState, List[DataLoader], jax.Array]: if self.freeze_fun is not None: frozen_paths = get_frozen_paths(state.params, self.freeze_fun) trainable_paths = get_trainable_paths(state.params, self.freeze_fun) @@ -675,8 +674,8 @@ def compute_metrics( return metrics_vals def training_step_start( - self, rng: PRNGKeyArray, step: Union[int, jax.Array] - ) -> PRNGKeyArray: + self, rng: jax.Array, step: Union[int, jax.Array] + ) -> jax.Array: step = step if isinstance(step, int) or step.ndim == 0 else step[0] return random.fold_in(rng, step) @@ -723,7 +722,7 @@ def training_step( state: TrainState, batch: Batch, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, unravel: Optional[Callable[[any], PyTree]] = None, kwargs: FrozenDict[str, Any] = FrozenDict(), @@ -738,7 +737,7 @@ def validation_step( state: TrainState, batch: Batch, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], float], ...]] = None, unravel: Optional[Callable[[any], PyTree]] = None, @@ -800,8 +799,8 @@ def _sync_state(self, state: TrainState) -> TrainState: return jax.device_get(tree_map(lambda x: x[0], state)) def on_train_start( - self, state: TrainState, dataloaders: List[DataLoader], rng: PRNGKeyArray - ) -> Tuple[TrainState, List[DataLoader], PRNGKeyArray]: + self, state: TrainState, dataloaders: List[DataLoader], rng: jax.Array + ) -> Tuple[TrainState, List[DataLoader], jax.Array]: state, dataloaders, rng = super(MultiDeviceMixin, self).on_train_start( state, dataloaders, rng ) @@ -816,7 +815,7 @@ def on_train_end(self, state: TrainState) -> TrainState: state = super(MultiDeviceMixin, self).on_train_end(state) return jax.device_get(tree_map(lambda x: x[0], state)) - def training_step_start(self, rng: PRNGKeyArray, step: int) -> PRNGKeyArray: + def training_step_start(self, rng: jax.Array, step: int) -> jax.Array: step = step if isinstance(step, int) or step.ndim == 0 else step[0] return jax.vmap(lambda r: random.fold_in(r, step))(rng) @@ -826,7 +825,7 @@ def training_step( state: TrainState, batch: Batch, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, unravel: Optional[Callable[[any], PyTree]] = None, kwargs: FrozenDict[str, Any] = FrozenDict(), @@ -863,7 +862,7 @@ def validation_step( state: TrainState, batch: Batch, loss_fun: Callable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], float], ...]] = None, unravel: Optional[Callable[[any], PyTree]] = None, diff --git a/fortuna/utils/probit.py b/fortuna/utils/probit.py index ee31aa06..fdb6199a 100644 --- a/fortuna/utils/probit.py +++ b/fortuna/utils/probit.py @@ -8,19 +8,14 @@ from flax.core import FrozenDict from jax import ( - ShapeDtypeStruct, jit, jvp, lax, - pure_callback, vjp, vmap, ) import jax.numpy as jnp -from jax.tree_util import ( - tree_map, - tree_reduce, -) +from jax.tree_util import tree_map from fortuna.typing import ( AnyKey, diff --git a/fortuna/utils/random.py b/fortuna/utils/random.py index be674de3..a34dd09d 100644 --- a/fortuna/utils/random.py +++ b/fortuna/utils/random.py @@ -1,5 +1,5 @@ +import jax from jax import random -from jax._src.prng import PRNGKeyArray from jax.tree_util import ( tree_map, tree_structure, @@ -35,14 +35,14 @@ def __init__(self, seed: int): """ self._rng = random.PRNGKey(seed) - def get(self) -> PRNGKeyArray: + def get(self) -> jax.Array: """ Get the internal random number generator key. Whenever this function is called, the random number generator key is updated. Returns ------- - PRNGKeyArray + jax.Array A random number generator key. """ self._rng = random.split(self._rng)[0] diff --git a/fortuna/utils/strings.py b/fortuna/utils/strings.py index 5dae4eaf..f54e50aa 100644 --- a/fortuna/utils/strings.py +++ b/fortuna/utils/strings.py @@ -5,7 +5,6 @@ Tuple, ) -import jax.numpy as jnp from jax.tree_util import tree_map import numpy as np @@ -31,7 +30,7 @@ def encode_tuple_of_lists_of_strings_to_numpy( def decode_encoded_tuple_of_lists_of_strings_to_array( - encoded: Optional[Dict[str, List[Array]]] + encoded: Optional[Dict[str, List[Array]]], ) -> Optional[Tuple[List[str], ...]]: if encoded is None: return None diff --git a/poetry.lock b/poetry.lock index 43e8537f..18e971c0 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,14 +1,14 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. [[package]] name = "absl-py" -version = "1.4.0" +version = "2.1.0" description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" files = [ - {file = "absl-py-1.4.0.tar.gz", hash = "sha256:d2c244d01048ba476e7c080bd2c6df5e141d211de80223460d5b3b8a2a58433d"}, - {file = "absl_py-1.4.0-py3-none-any.whl", hash = "sha256:0d3fe606adfa4f7db64792dd4c7aee4ee0c38ab75dfd353b7a83ed3e957fcb47"}, + {file = "absl-py-2.1.0.tar.gz", hash = "sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff"}, + {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"}, ] [[package]] @@ -24,87 +24,87 @@ files = [ [[package]] name = "aiohttp" -version = "3.9.3" +version = "3.9.5" description = "Async http client/server framework (asyncio)" optional = true python-versions = ">=3.8" files = [ - {file = 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assert jnp.alltrue(0 <= ess) and jnp.alltrue(ess <= len(samp1_flat)) + assert jnp.all(0 <= ess) and jnp.all(ess <= len(samp1_flat)) samp1_tree = self.unflatten(samp1_flat) ess_tree = effective_sample_size(samp1_tree) vals, _unravel_fn = ravel_pytree(ess_tree) diff --git a/tests/fortuna/prob_model/test_prob_model.py b/tests/fortuna/prob_model/test_prob_model.py index a5e8f401..4faea295 100755 --- a/tests/fortuna/prob_model/test_prob_model.py +++ b/tests/fortuna/prob_model/test_prob_model.py @@ -105,30 +105,30 @@ def test_rng(self): prior=IsotropicGaussianPrior(), output_calibrator=ClassificationTemperatureScaler(), ) - assert jnp.alltrue(prob_model.rng._rng == jnp.array([0.0, 0.0])) - assert jnp.alltrue(prob_model.rng._rng == prob_model.posterior.rng._rng) - assert jnp.alltrue(prob_model.rng._rng == prob_model.likelihood.rng._rng) - assert jnp.alltrue(prob_model.rng._rng == prob_model.joint.rng._rng) - assert jnp.alltrue(prob_model.rng._rng == prob_model.prior.rng._rng) - assert jnp.alltrue(prob_model.rng._rng == prob_model.predictive.rng._rng) - assert jnp.alltrue( + assert jnp.all(prob_model.rng._rng == jnp.array([0.0, 0.0])) + assert jnp.all(prob_model.rng._rng == prob_model.posterior.rng._rng) + assert jnp.all(prob_model.rng._rng == prob_model.likelihood.rng._rng) + assert jnp.all(prob_model.rng._rng == prob_model.joint.rng._rng) + assert jnp.all(prob_model.rng._rng == prob_model.prior.rng._rng) + assert jnp.all(prob_model.rng._rng == prob_model.predictive.rng._rng) + assert jnp.all( prob_model.rng._rng == prob_model.output_calib_manager.rng._rng ) prob_model.rng.get() - assert jnp.alltrue(prob_model.rng._rng == prob_model.posterior.rng._rng) - assert jnp.alltrue(prob_model.rng._rng == prob_model.likelihood.rng._rng) - assert jnp.alltrue(prob_model.rng._rng == prob_model.joint.rng._rng) - assert jnp.alltrue(prob_model.rng._rng == prob_model.prior.rng._rng) - assert jnp.alltrue(prob_model.rng._rng == prob_model.predictive.rng._rng) - assert jnp.alltrue( + assert jnp.all(prob_model.rng._rng == prob_model.posterior.rng._rng) + assert jnp.all(prob_model.rng._rng == prob_model.likelihood.rng._rng) + assert jnp.all(prob_model.rng._rng == prob_model.joint.rng._rng) + assert jnp.all(prob_model.rng._rng == prob_model.prior.rng._rng) + assert jnp.all(prob_model.rng._rng == prob_model.predictive.rng._rng) + assert jnp.all( prob_model.rng._rng == prob_model.output_calib_manager.rng._rng ) prob_model.posterior.rng.get() - assert jnp.alltrue(prob_model.rng._rng == prob_model.posterior.rng._rng) - assert jnp.alltrue(prob_model.rng._rng == prob_model.likelihood.rng._rng) - assert jnp.alltrue(prob_model.rng._rng == prob_model.joint.rng._rng) - assert jnp.alltrue(prob_model.rng._rng == prob_model.prior.rng._rng) - assert jnp.alltrue(prob_model.rng._rng == prob_model.predictive.rng._rng) - assert jnp.alltrue( + assert jnp.all(prob_model.rng._rng == prob_model.posterior.rng._rng) + assert jnp.all(prob_model.rng._rng == prob_model.likelihood.rng._rng) + assert jnp.all(prob_model.rng._rng == prob_model.joint.rng._rng) + assert jnp.all(prob_model.rng._rng == prob_model.prior.rng._rng) + assert jnp.all(prob_model.rng._rng == prob_model.predictive.rng._rng) + assert jnp.all( prob_model.rng._rng == prob_model.output_calib_manager.rng._rng ) diff --git a/tests/fortuna/test_model.py b/tests/fortuna/test_model.py index 33b8e59c..e9e07059 100755 --- a/tests/fortuna/test_model.py +++ b/tests/fortuna/test_model.py @@ -129,8 +129,8 @@ def __init__(self, *args, **kwargs): def test_constant(self): constant = ConstantModel(output_dim=self.output_dim) params = constant.init(self.rng, self.inputs) - assert jnp.alltrue(params["params"]["constant"] == jnp.zeros(self.output_dim)) - assert jnp.alltrue( + assert jnp.all(params["params"]["constant"] == jnp.zeros(self.output_dim)) + assert jnp.all( params["params"]["constant"] == constant.apply(params, self.inputs) ) @@ -138,7 +138,7 @@ def test_constant(self): output_dim=self.output_dim, initializer_fun=nn.initializers.ones ) params = constant.init(self.rng, self.inputs) - assert jnp.alltrue(params["params"]["constant"] == jnp.ones(self.output_dim)) + assert jnp.all(params["params"]["constant"] == jnp.ones(self.output_dim)) class TestScalarConstant(unittest.TestCase): @@ -155,8 +155,8 @@ def __init__(self, *args, **kwargs): def test_scalar_constant(self): scalar = ScalarConstantModel(output_dim=self.output_dim) params = scalar.init(self.rng, self.inputs) - assert jnp.alltrue(params["params"]["scalar"] == jnp.zeros(self.output_dim)) - assert jnp.alltrue( + assert jnp.all(params["params"]["scalar"] == jnp.zeros(self.output_dim)) + assert jnp.all( params["params"]["scalar"] == scalar.apply(params, self.inputs) ) @@ -176,8 +176,8 @@ def test_hyper(self): value = jnp.array([1.0, 2.0]) hyper = HyperparameterModel(value=value) params = hyper.init(self.rng, self.inputs) - assert jnp.alltrue(params["params"]["none"] == jnp.zeros(0)) - assert jnp.alltrue(hyper.apply(params, self.inputs) == value) + assert jnp.all(params["params"]["none"] == jnp.zeros(0)) + assert jnp.all(hyper.apply(params, self.inputs) == value) class TestScalarHyperparameter(unittest.TestCase): @@ -195,8 +195,8 @@ def test_scalar_hyper(self): value = 3.0 scalar = ScalarHyperparameterModel(output_dim=2, value=value) params = scalar.init(self.rng, self.inputs) - assert jnp.alltrue(params["params"]["none"] == jnp.zeros(0)) - assert jnp.alltrue( + assert jnp.all(params["params"]["none"] == jnp.zeros(0)) + assert jnp.all( scalar.apply(params, self.inputs) == value * jnp.ones((self.inputs.shape[0], self.output_dim)) ) diff --git a/tests/fortuna/test_trainer.py b/tests/fortuna/test_trainer.py index fd243915..15d03d23 100755 --- a/tests/fortuna/test_trainer.py +++ b/tests/fortuna/test_trainer.py @@ -13,7 +13,7 @@ from flax.core import FrozenDict import flax.linen as nn from jax import numpy as jnp -from jax._src.prng import PRNGKeyArray +import jax import jax.random import numpy as np import optax @@ -72,7 +72,7 @@ def training_loss_step( params: Params, batch: Batch, mutable: Mutable, - rng: PRNGKeyArray, + rng: jax.Array, n_data: int, unravel: Optional[Callable[[any], PyTree]] = None, calib_params: Optional[CalibParams] = None, @@ -684,7 +684,7 @@ def loss_fn(params, batch, **kwargs): None, FrozenDict({"max_grad_norm": max_grad_norm}), ) - loss = float(aux["loss"]) + loss = float(aux["loss"][0]) # grad acc update optimizer = optax.sgd(learning_rate=1e-4) @@ -693,8 +693,8 @@ def loss_fn(params, batch, **kwargs): loss_grad_acc = 0.0 batches = [ ( - inputs[start : start + gradient_accumulation_steps][None], - targets[start : start + gradient_accumulation_steps][None], + inputs[start: start + gradient_accumulation_steps][None], + targets[start: start + gradient_accumulation_steps][None], ) for start in range(0, 9, gradient_accumulation_steps) ] @@ -715,8 +715,8 @@ def loss_fn(params, batch, **kwargs): } ), ) - loss_grad_acc += float(aux_grad_acc["loss"]) - chex.assert_tree_all_close(new_state.params, new_state_acc.params, atol=1e-7) + loss_grad_acc += float(aux_grad_acc["loss"][0]) + chex.assert_trees_all_close(new_state.params, new_state_acc.params, atol=1e-7) self.assertAlmostEqual(loss, loss_grad_acc / (i + 1), places=6) def test_grad_acc_no_clipping(self): @@ -775,5 +775,5 @@ def loss_fn(params, batch, **kwargs): ), ) loss_grad_acc += float(aux_grad_acc["loss"]) - chex.assert_tree_all_close(new_state.params, new_state_acc.params, atol=1e-7) + chex.assert_trees_all_close(new_state.params, new_state_acc.params, atol=1e-7) self.assertAlmostEqual(loss, loss_grad_acc / 3, places=6) diff --git a/tests/fortuna/utils/freeze.py b/tests/fortuna/utils/freeze.py index c6be05f4..e0952928 100644 --- a/tests/fortuna/utils/freeze.py +++ b/tests/fortuna/utils/freeze.py @@ -61,7 +61,7 @@ def test_freeze_optimizer(self): opt_state = optimizer.init(params) updates, opt_state = optimizer.update(grads, opt_state, params) updated_params = optax.apply_updates(params, updates) - assert jnp.alltrue(params["a"]["b"]["c"] == updated_params["a"]["b"]["c"]) - assert jnp.alltrue(params["a"]["b"]["d"] != updated_params["a"]["b"]["d"]) - assert jnp.alltrue(params["a"]["f"]["g"] == updated_params["a"]["f"]["g"]) - assert jnp.alltrue(params["h"] != updated_params["h"]) + assert jnp.all(params["a"]["b"]["c"] == updated_params["a"]["b"]["c"]) + assert jnp.all(params["a"]["b"]["d"] != updated_params["a"]["b"]["d"]) + assert jnp.all(params["a"]["f"]["g"] == updated_params["a"]["f"]["g"]) + assert jnp.all(params["h"] != updated_params["h"])