switch from pickle/dill to cloudpickle

RELEASE-NOTES.md

@@ -26,6 +26,7 @@
 - Logp method of `Uniform` and `DiscreteUniform` no longer depends on `pymc3.distributions.dist_math.bound` for proper evaluation (see [#4541](https://github.com/pymc-devs/pymc3/pull/4541)).
 - `Model.RV_dims` and `Model.coords` are now read-only properties. To modify the `coords` dictionary use `Model.add_coord`. Also `dims` or coordinate values that are `None` will be auto-completed (see [#4625](https://github.com/pymc-devs/pymc3/pull/4625)).
 - The length of `dims` in the model is now tracked symbolically through `Model.dim_lengths` (see [#4625](https://github.com/pymc-devs/pymc3/pull/4625)).
+- We now include `cloudpickle` as a required dependency, and no longer depend on `dill` (see [#4858](https://github.com/pymc-devs/pymc3/pull/4858)).
 - ...
 
 ## PyMC3 3.11.2 (14 March 2021)

conda-envs/environment-dev-py37.yml

@@ -6,7 +6,7 @@ dependencies:
 - aesara>=2.0.9
 - arviz>=0.11.2
 - cachetools>=4.2.1
-- dill
+- cloudpickle
 - fastprogress>=0.2.0
 - h5py>=2.7
 - ipython>=7.16

conda-envs/environment-dev-py38.yml

@@ -6,7 +6,7 @@ dependencies:
 - aesara>=2.0.9
 - arviz>=0.11.2
 - cachetools>=4.2.1
-- dill
+- cloudpickle
 - fastprogress>=0.2.0
 - h5py>=2.7
 - ipython>=7.16

conda-envs/environment-dev-py39.yml

@@ -6,7 +6,7 @@ dependencies:
 - aesara>=2.0.9
 - arviz>=0.11.2
 - cachetools>=4.2.1
-- dill
+- cloudpickle
 - fastprogress>=0.2.0
 - h5py>=2.7
 - ipython>=7.16

conda-envs/windows-environment-dev-py38.yml

@@ -7,7 +7,7 @@ dependencies:
 - aesara>=2.0.9
 - arviz>=0.11.2
 - cachetools>=4.2.1
-- dill
+- cloudpickle
 - fastprogress>=0.2.0
 - h5py>=2.7
 - libpython

pymc3/distributions/distribution.py

@@ -23,7 +23,6 @@
 
 import aesara
 import aesara.tensor as at
-import dill
 
 from aesara.tensor.random.op import RandomVariable
 from aesara.tensor.random.var import RandomStateSharedVariable
@@ -533,26 +532,5 @@ def __init__(
         self.wrap_random_with_dist_shape = wrap_random_with_dist_shape
         self.check_shape_in_random = check_shape_in_random
 
-    def __getstate__(self):
-        # We use dill to serialize the logp function, as this is almost
-        # always defined in the notebook and won't be pickled correctly.
-        # Fix https://github.com/pymc-devs/pymc3/issues/3844
-        try:
-            logp = dill.dumps(self.logp)
-        except RecursionError as err:
-            if type(self.logp) == types.MethodType:
-                raise ValueError(
-                    "logp for DensityDist is a bound method, leading to RecursionError while serializing"
-                ) from err
-            else:
-                raise err
-        vals = self.__dict__.copy()
-        vals["logp"] = logp
-        return vals
-
-    def __setstate__(self, vals):
-        vals["logp"] = dill.loads(vals["logp"])
-        self.__dict__ = vals
-
     def _distr_parameters_for_repr(self):
         return []

pymc3/parallel_sampling.py

@@ -16,13 +16,13 @@
 import logging
 import multiprocessing
 import multiprocessing.sharedctypes
-import pickle
 import platform
 import time
 import traceback
 
 from collections import namedtuple
 
+import cloudpickle
 import numpy as np
 
 from fastprogress.fastprogress import progress_bar
@@ -93,7 +93,6 @@ def __init__(
         draws: int,
         tune: int,
         seed,
-        pickle_backend,
     ):
         self._msg_pipe = msg_pipe
         self._step_method = step_method
@@ -103,7 +102,6 @@ def __init__(
         self._at_seed = seed + 1
         self._draws = draws
         self._tune = tune
-        self._pickle_backend = pickle_backend
 
     def _unpickle_step_method(self):
         unpickle_error = (
@@ -112,22 +110,10 @@ def _unpickle_step_method(self):
             "or forkserver."
         )
         if self._step_method_is_pickled:
-            if self._pickle_backend == "pickle":
-                try:
-                    self._step_method = pickle.loads(self._step_method)
-                except Exception:
-                    raise ValueError(unpickle_error)
-            elif self._pickle_backend == "dill":
-                try:
-                    import dill
-                except ImportError:
-                    raise ValueError("dill must be installed for pickle_backend='dill'.")
-                try:
-                    self._step_method = dill.loads(self._step_method)
-                except Exception:
-                    raise ValueError(unpickle_error)
-            else:
-                raise ValueError("Unknown pickle backend")
+            try:
+                self._step_method = cloudpickle.loads(self._step_method)
+            except Exception:
+                raise ValueError(unpickle_error)
 
     def run(self):
         try:
@@ -243,7 +229,6 @@ def __init__(
         seed,
         start,
         mp_ctx,
-        pickle_backend,
     ):
         self.chain = chain
         process_name = "worker_chain_%s" % chain
@@ -287,7 +272,6 @@ def __init__(
                 draws,
                 tune,
                 seed,
-                pickle_backend,
             ),
         )
         self._process.start()
@@ -406,7 +390,6 @@ def __init__(
         start_chain_num: int = 0,
         progressbar: bool = True,
         mp_ctx=None,
-        pickle_backend: str = "pickle",
     ):
 
         if any(len(arg) != chains for arg in [seeds, start_points]):
@@ -420,14 +403,7 @@ def __init__(
 
         step_method_pickled = None
         if mp_ctx.get_start_method() != "fork":
-            if pickle_backend == "pickle":
-                step_method_pickled = pickle.dumps(step_method, protocol=-1)
-            elif pickle_backend == "dill":
-                try:
-                    import dill
-                except ImportError:
-                    raise ValueError("dill must be installed for pickle_backend='dill'.")
-                step_method_pickled = dill.dumps(step_method, protocol=-1)
+            step_method_pickled = cloudpickle.dumps(step_method, protocol=-1)
 
         self._samplers = [
             ProcessAdapter(
@@ -439,7 +415,6 @@ def __init__(
                 seed,
                 start,
                 mp_ctx,
-                pickle_backend,
             )
             for chain, seed, start in zip(range(chains), seeds, start_points)
         ]

pymc3/sampling.py

@@ -26,6 +26,7 @@
 from typing import Any, Dict, Iterable, List, Optional, Set, Union, cast
 
 import aesara.gradient as tg
+import cloudpickle
 import numpy as np
 import xarray
 
@@ -268,7 +269,6 @@ def sample(
     return_inferencedata=None,
     idata_kwargs: dict = None,
     mp_ctx=None,
-    pickle_backend: str = "pickle",
     **kwargs,
 ):
     r"""Draw samples from the posterior using the given step methods.
@@ -362,10 +362,6 @@ def sample(
     mp_ctx : multiprocessing.context.BaseContent
         A multiprocessing context for parallel sampling. See multiprocessing
         documentation for details.
-    pickle_backend : str
-        One of `'pickle'` or `'dill'`. The library used to pickle models
-        in parallel sampling if the multiprocessing context is not of type
-        `fork`.
 
     Returns
     -------
@@ -548,7 +544,6 @@ def sample(
         "discard_tuned_samples": discard_tuned_samples,
     }
     parallel_args = {
-        "pickle_backend": pickle_backend,
         "mp_ctx": mp_ctx,
     }
 
@@ -1100,7 +1095,7 @@ def __init__(self, steppers, parallelize, progressbar=True):
                     enumerate(progress_bar(steppers)) if progressbar else enumerate(steppers)
                 ):
                     secondary_end, primary_end = multiprocessing.Pipe()
-                    stepper_dumps = pickle.dumps(stepper, protocol=4)
+                    stepper_dumps = cloudpickle.dumps(stepper, protocol=4)
                     process = multiprocessing.Process(
                         target=self.__class__._run_secondary,
                         args=(c, stepper_dumps, secondary_end),
@@ -1159,7 +1154,7 @@ def _run_secondary(c, stepper_dumps, secondary_end):
         # re-seed each child process to make them unique
         np.random.seed(None)
         try:
-            stepper = pickle.loads(stepper_dumps)
+            stepper = cloudpickle.loads(stepper_dumps)
             # the stepper is not necessarily a PopulationArraySharedStep itself,
             # but rather a CompoundStep. PopulationArrayStepShared.population
             # has to be updated, therefore we identify the substeppers first.
@@ -1418,7 +1413,6 @@ def _mp_sample(
     callback=None,
     discard_tuned_samples=True,
     mp_ctx=None,
-    pickle_backend="pickle",
     **kwargs,
 ):
     """Main iteration for multiprocess sampling.
@@ -1491,7 +1485,6 @@ def _mp_sample(
         chain,
         progressbar,
         mp_ctx=mp_ctx,
-        pickle_backend=pickle_backend,
     )
     try:
         try:

pymc3/tests/test_distributions.py

@@ -3109,9 +3109,9 @@ def func(x):
         y = pm.DensityDist("y", func)
         pm.sample(draws=5, tune=1, mp_ctx="spawn")
 
-    import pickle
+    import cloudpickle
 
-    pickle.loads(pickle.dumps(y))
+    cloudpickle.loads(cloudpickle.dumps(y))
 
 
 def test_distinct_rvs():

pymc3/tests/test_minibatches.py

@@ -13,9 +13,9 @@
 #   limitations under the License.
 
 import itertools
-import pickle
 
 import aesara
+import cloudpickle
 import numpy as np
 import pytest
 
@@ -132,10 +132,10 @@ def gen():
 
     def test_pickling(self, datagen):
         gen = generator(datagen)
-        pickle.loads(pickle.dumps(gen))
+        cloudpickle.loads(cloudpickle.dumps(gen))
         bad_gen = generator(integers())
-        with pytest.raises(Exception):
-            pickle.dumps(bad_gen)
+        with pytest.raises(TypeError):
+            cloudpickle.dumps(bad_gen)
 
     def test_gen_cloning_with_shape_change(self, datagen):
         gen = generator(datagen)

pymc3/tests/test_model.py

@@ -11,14 +11,14 @@
 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
-import pickle
 import unittest
 
 from functools import reduce
 
 import aesara
 import aesara.sparse as sparse
 import aesara.tensor as at
+import cloudpickle
 import numpy as np
 import numpy.ma as ma
 import numpy.testing as npt
@@ -407,9 +407,7 @@ def test_model_pickle(tmpdir):
         x = pm.Normal("x")
         pm.Normal("y", observed=1)
 
-    file_path = tmpdir.join("model.p")
-    with open(file_path, "wb") as buff:
-        pickle.dump(model, buff)
+    cloudpickle.loads(cloudpickle.dumps(model))
 
 
 def test_model_pickle_deterministic(tmpdir):
@@ -420,9 +418,7 @@ def test_model_pickle_deterministic(tmpdir):
         pm.Deterministic("w", x / z)
         pm.Normal("y", observed=1)
 
-    file_path = tmpdir.join("model.p")
-    with open(file_path, "wb") as buff:
-        pickle.dump(model, buff)
+    cloudpickle.loads(cloudpickle.dumps(model))
 
 
 def test_model_vars():

pymc3/tests/test_parallel_sampling.py

@@ -71,12 +71,6 @@ def _crash_remote_process(a, master_pid):
     return 2 * np.array(a)
 
 
-def test_dill():
-    with pm.Model():
-        pm.Normal("x")
-        pm.sample(tune=1, draws=1, chains=2, cores=2, pickle_backend="dill", mp_ctx="spawn")
-
-
 def test_remote_pipe_closed():
     master_pid = os.getpid()
     with pm.Model():
@@ -112,7 +106,6 @@ def test_abort():
             mp_ctx=ctx,
             start={"a": np.array([1.0]), "b_log__": np.array(2.0)},
             step_method_pickled=None,
-            pickle_backend="pickle",
         )
         proc.start()
         while True:
@@ -147,7 +140,6 @@ def test_explicit_sample():
         mp_ctx=ctx,
         start={"a": np.array([1.0]), "b_log__": np.array(2.0)},
         step_method_pickled=None,
-        pickle_backend="pickle",
     )
     proc.start()
     while True:

pymc3/tests/test_pickling.py

@@ -15,6 +15,8 @@
 import pickle
 import traceback
 
+import cloudpickle
+
 from pymc3.tests.models import simple_model
 
 
@@ -26,8 +28,8 @@ def test_model_roundtrip(self):
         m = self.model
         for proto in range(pickle.HIGHEST_PROTOCOL + 1):
             try:
-                s = pickle.dumps(m, proto)
-                pickle.loads(s)
+                s = cloudpickle.dumps(m, proto)
+                cloudpickle.loads(s)
             except Exception:
                 raise AssertionError(
                     "Exception while trying roundtrip with pickle protocol %d:\n" % proto