Refactor MiniBatch and stop using deprecated MRG sampler

pymc/distributions/logprob.py

@@ -102,7 +102,8 @@ def _get_scaling(total_size: TOTAL_SIZE, shape, ndim: int) -> TensorVariable:
 
 def _check_no_rvs(logp_terms: Sequence[TensorVariable]):
     # Raise if there are unexpected RandomVariables in the logp graph
-    # Only SimulatorRVs are allowed
+    # Only SimulatorRVs MinibatchIndexRVs are allowed
+    from pymc.data import MinibatchIndexRV
     from pymc.distributions.simulator import SimulatorRV
 
     unexpected_rv_nodes = [
@@ -111,7 +112,7 @@ def _check_no_rvs(logp_terms: Sequence[TensorVariable]):
         if (
             node.owner
             and isinstance(node.owner.op, RandomVariable)
-            and not isinstance(node.owner.op, SimulatorRV)
+            and not isinstance(node.owner.op, (SimulatorRV, MinibatchIndexRV))
         )
     ]
     if unexpected_rv_nodes:

pymc/model.py

@@ -50,7 +50,7 @@
 from pytensor.tensor.var import TensorConstant, TensorVariable
 
 from pymc.blocking import DictToArrayBijection, RaveledVars
-from pymc.data import GenTensorVariable, Minibatch
+from pymc.data import GenTensorVariable, is_minibatch
 from pymc.distributions.logprob import _joint_logp
 from pymc.distributions.transforms import _default_transform
 from pymc.exceptions import (
@@ -1329,14 +1329,15 @@ def register_rv(
         else:
             if (
                 isinstance(observed, Variable)
-                and not isinstance(observed, (GenTensorVariable, Minibatch))
+                and not isinstance(observed, GenTensorVariable)
                 and observed.owner is not None
                 # The only PyTensor operation we allow on observed data is type casting
                 # Although we could allow for any graph that does not depend on other RVs
                 and not (
                     isinstance(observed.owner.op, Elemwise)
                     and isinstance(observed.owner.op.scalar_op, Cast)
                 )
+                and not is_minibatch(observed)
             ):
                 raise TypeError(
                     "Variables that depend on other nodes cannot be used for observed data."

pymc/pytensorf.py

@@ -47,10 +47,10 @@
 )
 from pytensor.graph.fg import FunctionGraph
 from pytensor.graph.op import Op
-from pytensor.sandbox.rng_mrg import MRG_RandomStream as RandomStream
 from pytensor.scalar.basic import Cast
 from pytensor.tensor.basic import _as_tensor_variable
 from pytensor.tensor.elemwise import Elemwise
+from pytensor.tensor.random import RandomStream
 from pytensor.tensor.random.op import RandomVariable
 from pytensor.tensor.random.var import (
     RandomGeneratorSharedVariable,

pymc/tests/distributions/test_continuous.py

@@ -778,10 +778,13 @@ def test_moyal_logp(self):
         reason="PyMC underflows earlier than scipy on float32",
     )
     def test_moyal_logcdf(self):
+        # SciPy has new (?) precision issues at {mu=-2.1, sigma=0.5, x=2.1}
+        # We circumvent it by skipping sigma=0.5:
+        rplusbig = Domain([0, 0.9, 0.99, 1, 1.5, 2, 20, np.inf])
         check_logcdf(
             pm.Moyal,
             R,
-            {"mu": R, "sigma": Rplusbig},
+            {"mu": R, "sigma": rplusbig},
             lambda value, mu, sigma: floatX(st.moyal.logcdf(value, mu, sigma)),
         )
         if pytensor.config.floatX == "float32":

pymc/tests/helpers.py

@@ -26,7 +26,7 @@
 from pytensor.gradient import verify_grad as at_verify_grad
 from pytensor.graph import ancestors
 from pytensor.graph.rewriting.basic import in2out
-from pytensor.sandbox.rng_mrg import MRG_RandomStream as RandomStream
+from pytensor.tensor.random import RandomStream
 from pytensor.tensor.random.op import RandomVariable
 
 import pymc as pm

pymc/tests/test_data.py

@@ -27,6 +27,7 @@
 
 import pymc as pm
 
+from pymc.data import is_minibatch
 from pymc.pytensorf import GeneratorOp, floatX
 from pymc.tests.helpers import SeededTest, select_by_precision
 
@@ -696,15 +697,10 @@ def test_common_errors(self):
 
     def test_mixed1(self):
         with pm.Model():
-            data = np.random.rand(10, 20, 30, 40, 50)
-            mb = pm.Minibatch(data, [2, None, 20, Ellipsis, 10])
-            pm.Normal("n", observed=mb, total_size=(10, None, 30, Ellipsis, 50))
-
-    def test_mixed2(self):
-        with pm.Model():
-            data = np.random.rand(10, 20, 30, 40, 50)
-            mb = pm.Minibatch(data, [2, None, 20])
-            pm.Normal("n", observed=mb, total_size=(10, None, 30))
+            data = np.random.rand(10, 20)
+            mb = pm.Minibatch(data, batch_size=5)
+            v = pm.Normal("n", observed=mb, total_size=10)
+            assert pm.logp(v, 1) is not None, "Check index is allowed in graph"
 
     def test_free_rv(self):
         with pm.Model() as model4:
@@ -719,51 +715,28 @@ def test_free_rv(self):
 
 @pytest.mark.usefixtures("strict_float32")
 class TestMinibatch:
-    data = np.random.rand(30, 10, 40, 10, 50)
+    data = np.random.rand(30, 10)
 
     def test_1d(self):
-        mb = pm.Minibatch(self.data, 20)
-        assert mb.eval().shape == (20, 10, 40, 10, 50)
-
-    def test_2d(self):
-        mb = pm.Minibatch(self.data, [(10, 42), (4, 42)])
-        assert mb.eval().shape == (10, 4, 40, 10, 50)
-
-    @pytest.mark.parametrize(
-        "batch_size, expected",
-        [
-            ([(10, 42), None, (4, 42)], (10, 10, 4, 10, 50)),
-            ([(10, 42), Ellipsis, (4, 42)], (10, 10, 40, 10, 4)),
-            ([(10, 42), None, Ellipsis, (4, 42)], (10, 10, 40, 10, 4)),
-            ([10, None, Ellipsis, (4, 42)], (10, 10, 40, 10, 4)),
-        ],
-    )
-    def test_special_batch_size(self, batch_size, expected):
-        mb = pm.Minibatch(self.data, batch_size)
-        assert mb.eval().shape == expected
-
-    def test_cloning_available(self):
-        gop = pm.Minibatch(np.arange(100), 1)
-        res = gop**2
-        shared = pytensor.shared(np.array([10]))
-        res1 = pytensor.clone_replace(res, {gop: shared})
-        f = pytensor.function([], res1)
-        assert f() == np.array([100])
-
-    def test_align(self):
-        m = pm.Minibatch(np.arange(1000), 1, random_seed=1)
-        n = pm.Minibatch(np.arange(1000), 1, random_seed=1)
-        f = pytensor.function([], [m, n])
-        n.eval()  # not aligned
-        a, b = zip(*(f() for _ in range(1000)))
-        assert a != b
-        pm.align_minibatches()
-        a, b = zip(*(f() for _ in range(1000)))
-        assert a == b
-        n.eval()  # not aligned
-        pm.align_minibatches([m])
-        a, b = zip(*(f() for _ in range(1000)))
-        assert a != b
-        pm.align_minibatches([m, n])
-        a, b = zip(*(f() for _ in range(1000)))
-        assert a == b
+        mb = pm.Minibatch(self.data, batch_size=20)
+        assert is_minibatch(mb)
+        assert mb.eval().shape == (20, 10)
+
+    def test_allowed(self):
+        mb = pm.Minibatch(at.as_tensor(self.data).astype(int), batch_size=20)
+        assert is_minibatch(mb)
+
+    def test_not_allowed(self):
+        with pytest.raises(ValueError, match="not valid for Minibatch"):
+            mb = pm.Minibatch(at.as_tensor(self.data) * 2, batch_size=20)
+
+    def test_not_allowed2(self):
+        with pytest.raises(ValueError, match="not valid for Minibatch"):
+            mb = pm.Minibatch(self.data, at.as_tensor(self.data) * 2, batch_size=20)
+
+    def test_assert(self):
+        with pytest.raises(
+            AssertionError, match=r"All variables shape\[0\] in Minibatch should be equal"
+        ):
+            d1, d2 = pm.Minibatch(self.data, self.data[::2], batch_size=20)
+            d1.eval()

pymc/tests/variational/test_approximations.py

@@ -168,3 +168,15 @@ def test_elbo_beta_kl(aux_total_size):
         np.testing.assert_allclose(
             elbo_via_total_size_scaled.eval(), elbo_via_beta_kl.eval(), rtol=0, atol=1e-1
         )
+
+
+def test_seeding_advi_fit():
+    with pm.Model():
+        x = pm.Normal("x", 0, 10, initval="prior")
+        approx1 = pm.fit(
+            random_seed=42, n=10, method="advi", obj_optimizer=pm.adagrad_window, progressbar=False
+        )
+        approx2 = pm.fit(
+            random_seed=42, n=10, method="advi", obj_optimizer=pm.adagrad_window, progressbar=False
+        )
+        np.testing.assert_allclose(approx1.mean.eval(), approx2.mean.eval())

pymc/tests/variational/test_inference.py

@@ -15,6 +15,7 @@
 import io
 import operator
 
+import cloudpickle
 import numpy as np
 import pytensor
 import pytensor.tensor as at
@@ -26,6 +27,7 @@
 
 from pymc.pytensorf import intX
 from pymc.variational.inference import ADVI, ASVGD, SVGD, FullRankADVI
+from pymc.variational.opvi import NotImplementedInference
 
 pytestmark = pytest.mark.usefixtures("strict_float32", "seeded_test")
 
@@ -60,7 +62,7 @@ def simple_model_data(use_minibatch):
     d = n / sigma**2 + 1 / sigma0**2
     mu_post = (n * np.mean(data) / sigma**2 + mu0 / sigma0**2) / d
     if use_minibatch:
-        data = pm.Minibatch(data)
+        data = pm.Minibatch(data, batch_size=128)
     return dict(
         n=n,
         data=data,
@@ -118,7 +120,7 @@ def init_(**kw):
 @pytest.fixture(scope="function")
 def inference(inference_spec, simple_model):
     with simple_model:
-        return inference_spec()
+        return inference_spec(random_seed=42)
 
 
 @pytest.fixture(scope="function")
@@ -129,7 +131,7 @@ def fit_kwargs(inference, use_minibatch):
             obj_optimizer=pm.adagrad_window(learning_rate=0.01, n_win=50), n=12000
         ),
         (FullRankADVI, "full"): dict(
-            obj_optimizer=pm.adagrad_window(learning_rate=0.01, n_win=50), n=6000
+            obj_optimizer=pm.adagrad_window(learning_rate=0.015, n_win=50), n=6000
         ),
         (FullRankADVI, "mini"): dict(
             obj_optimizer=pm.adagrad_window(learning_rate=0.007, n_win=50), n=12000
@@ -149,6 +151,8 @@ def fit_kwargs(inference, use_minibatch):
         inference.approx.scale_cost_to_minibatch = False
     else:
         key = "full"
+    if (type(inference), key) in {(SVGD, "mini"), (ASVGD, "mini")}:
+        pytest.skip("Not Implemented Inference")
     return _select[(type(inference), key)]
 
 
@@ -179,7 +183,10 @@ def test_fit_start(inference_spec, simple_model):
 
     with simple_model:
         inference = inference_spec(**kw)
-    trace = inference.fit(n=0).sample(10000)
+    try:
+        trace = inference.fit(n=0).sample(10000)
+    except NotImplementedInference as e:
+        pytest.skip(str(e))
     np.testing.assert_allclose(np.mean(trace.posterior["mu"]), mu_init, rtol=0.05)
     if has_start_sigma:
         np.testing.assert_allclose(np.std(trace.posterior["mu"]), mu_sigma_init, rtol=0.05)
@@ -218,6 +225,8 @@ def test_fit_fn_text(method, kwargs, error):
 
 
 def test_profile(inference):
+    if type(inference) in {SVGD, ASVGD}:
+        pytest.skip("Not Implemented Inference")
     inference.run_profiling(n=100).summary()
 
 
@@ -239,8 +248,7 @@ def binomial_model_inference(binomial_model, inference_spec):
 
 @pytest.mark.xfail("pytensor.config.warn_float64 == 'raise'", reason="too strict float32")
 def test_replacements(binomial_model_inference):
-    d = at.bscalar()
-    d.tag.test_value = 1
+    d = pytensor.shared(1)
     approx = binomial_model_inference.approx
     p = approx.model.p
     p_t = p**3
@@ -252,7 +260,7 @@ def test_replacements(binomial_model_inference):
     ), "p should be replaced"
     if pytensor.config.compute_test_value != "off":
         assert p_s.tag.test_value.shape == p_t.tag.test_value.shape
-    sampled = [p_s.eval() for _ in range(100)]
+    sampled = [pm.draw(p_s) for _ in range(100)]
     assert any(map(operator.ne, sampled[1:], sampled[:-1]))  # stochastic
     p_z = approx.sample_node(p_t, deterministic=False, size=10)
     assert p_z.shape.eval() == (10,)
@@ -264,15 +272,17 @@ def test_replacements(binomial_model_inference):
 
     try:
         p_d = approx.sample_node(p_t, deterministic=True)
-        sampled = [p_d.eval() for _ in range(100)]
+        sampled = [pm.draw(p_d) for _ in range(100)]
         assert all(map(operator.eq, sampled[1:], sampled[:-1]))  # deterministic
     except opvi.NotImplementedInference:
         pass
 
     p_r = approx.sample_node(p_t, deterministic=d)
-    sampled = [p_r.eval({d: 1}) for _ in range(100)]
+    d.set_value(1)
+    sampled = [pm.draw(p_r) for _ in range(100)]
     assert all(map(operator.eq, sampled[1:], sampled[:-1]))  # deterministic
-    sampled = [p_r.eval({d: 0}) for _ in range(100)]
+    d.set_value(0)
+    sampled = [pm.draw(p_r) for _ in range(100)]
     assert any(map(operator.ne, sampled[1:], sampled[:-1]))  # stochastic
 
 
@@ -325,8 +335,6 @@ def test_var_replacement():
 
 
 def test_clear_cache():
-    import cloudpickle
-
     with pm.Model():
         pm.Normal("n", 0, 1)
         inference = ADVI()

pymc/variational/approximations.py

@@ -12,6 +12,7 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
+
 import numpy as np
 import pytensor
 
@@ -24,11 +25,13 @@
 
 from pymc.blocking import DictToArrayBijection
 from pymc.distributions.dist_math import rho2sigma
+from pymc.pytensorf import makeiter
 from pymc.variational import opvi
 from pymc.variational.opvi import (
     Approximation,
     Group,
     NotImplementedInference,
+    _known_scan_ignored_inputs,
     node_property,
 )
 
@@ -248,9 +251,12 @@ def randidx(self, size=None):
                 pass
         else:
             size = tuple(np.atleast_1d(size))
-        return self._rng.uniform(
-            size=size, low=pm.floatX(0), high=pm.floatX(self.histogram.shape[0]) - pm.floatX(1e-16)
-        ).astype("int32")
+        return at.random.integers(
+            size=size,
+            low=0,
+            high=self.histogram.shape[0],
+            rng=pytensor.shared(np.random.default_rng()),
+        )
 
     def _new_initial(self, size, deterministic, more_replacements=None):
         pytensor_condition_is_here = isinstance(deterministic, Variable)
@@ -383,8 +389,10 @@ def evaluate_over_trace(self, node):
         """
         node = self.to_flat_input(node)
 
-        def sample(post, node):
+        def sample(post, *_):
             return pytensor.clone_replace(node, {self.input: post})
 
-        nodes, _ = pytensor.scan(sample, self.histogram, non_sequences=[node])
+        nodes, _ = pytensor.scan(
+            sample, self.histogram, non_sequences=_known_scan_ignored_inputs(makeiter(node))
+        )
         return nodes

pymc/variational/inference.py

@@ -631,7 +631,9 @@ def __init__(self, approx=None, estimator=KSD, kernel=test_functions.rbf, **kwar
             "is often **underestimated** when using temperature = 1."
         )
         if approx is None:
-            approx = FullRank(model=kwargs.pop("model", None))
+            approx = FullRank(
+                model=kwargs.pop("model", None), random_seed=kwargs.pop("random_seed", None)
+            )
         super().__init__(estimator=estimator, approx=approx, kernel=kernel, **kwargs)
 
     def fit(