Add progressbar to sample_smc and deprecate parallel (#4826)

* Add progressbar to `sample_smc` and deprecate `parallel`

Co-authored-by: Ricardo Vieira <28983449+ricardoV94@users.noreply.github.com>
Co-authored-by: Michael Osthege <michael.osthege@outlook.com>

pymc3/smc/sample_smc.py

@@ -22,6 +22,7 @@
 import numpy as np
 
 from arviz import InferenceData
+from fastprogress.fastprogress import progress_bar
 
 import pymc3
 
@@ -45,12 +46,13 @@ def sample_smc(
     save_log_pseudolikelihood=True,
     model=None,
     random_seed=-1,
-    parallel=False,
+    parallel=None,
     chains=None,
     cores=None,
     compute_convergence_checks=True,
     return_inferencedata=True,
     idata_kwargs=None,
+    progressbar=True,
 ):
     r"""
     Sequential Monte Carlo based sampling.
@@ -90,12 +92,9 @@ def sample_smc(
     model: Model (optional if in ``with`` context)).
     random_seed: int
         random seed
-    parallel: bool
-        Distribute computations across cores if the number of cores is larger than 1.
-        Defaults to False.
     cores : int
         The number of chains to run in parallel. If ``None``, set to the number of CPUs in the
-        system, but at most 4.
+        system.
     chains : int
         The number of chains to sample. Running independent chains is important for some
         convergence statistics. If ``None`` (default), then set to either ``cores`` or 2, whichever
@@ -108,6 +107,9 @@ def sample_smc(
         Defaults to ``True``.
     idata_kwargs : dict, optional
         Keyword arguments for :func:`pymc3.to_inference_data`
+    progressbar : bool, optional default=True
+        Whether or not to display a progress bar in the command line.
+
     Notes
     -----
     SMC works by moving through successive stages. At each stage the inverse temperature
@@ -153,6 +155,16 @@ def sample_smc(
         816-832. `link <http://ascelibrary.org/doi/abs/10.1061/%28ASCE%290733-9399
         %282007%29133:7%28816%29>`__
     """
+
+    if parallel is not None:
+        warnings.warn(
+            "The argument parallel is deprecated, use the argument cores instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        if parallel is False:
+            cores = 1
+
     _log = logging.getLogger("pymc3")
     _log.info("Initializing SMC sampler...")
 
@@ -206,19 +218,26 @@ def sample_smc(
     )
 
     t1 = time.time()
-    if parallel and chains > 1:
-        loggers = [_log] + [None] * (chains - 1)
+    if cores > 1:
+        pbar = progress_bar((), total=100, display=progressbar)
+        pbar.update(0)
+        pbars = [pbar] + [None] * (chains - 1)
+
         pool = mp.Pool(cores)
         results = pool.starmap(
-            sample_smc_int, [(*params, random_seed[i], i, loggers[i]) for i in range(chains)]
+            sample_smc_int, [(*params, random_seed[i], i, pbars[i]) for i in range(chains)]
         )
-
         pool.close()
         pool.join()
+
     else:
         results = []
+        pbar = progress_bar((), total=100 * chains, display=progressbar)
+        pbar.update(0)
         for i in range(chains):
-            results.append(sample_smc_int(*params, random_seed[i], i, _log))
+            pbar.offset = 100 * i
+            pbar.base_comment = f"Chain: {i+1}/{chains}"
+            results.append(sample_smc_int(*params, random_seed[i], i, pbar))
 
     (
         traces,
@@ -310,7 +329,7 @@ def sample_smc_int(
     model,
     random_seed,
     chain,
-    _log,
+    progressbar=None,
 ):
     """Run one SMC instance."""
     smc = SMC(
@@ -331,14 +350,22 @@ def sample_smc_int(
     betas = []
     accept_ratios = []
     nsteps = []
+
+    if progressbar:
+        progressbar.comment = f"{getattr(progressbar, 'base_comment', '')} Stage: 0 Beta: 0"
+        progressbar.update_bar(getattr(progressbar, "offset", 0) + 0)
+
     smc.initialize_population()
     smc.setup_kernel()
     smc.initialize_logp()
 
     while smc.beta < 1:
         smc.update_weights_beta()
-        if _log is not None:
-            _log.info(f"Stage: {stage:3d} Beta: {smc.beta:.3f}")
+        if progressbar:
+            progressbar.comment = (
+                f"{getattr(progressbar, 'base_comment', '')} Stage: {stage} Beta: {smc.beta:.3f}"
+            )
+            progressbar.update_bar(getattr(progressbar, "offset", 0) + int(smc.beta * 100))
         smc.update_proposal()
         smc.resample()
         smc.mutate()

pymc3/tests/test_smc.py

@@ -12,6 +12,8 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
+import time
+
 import aesara
 import aesara.tensor as at
 import numpy as np
@@ -60,9 +62,22 @@ def two_gaussians(x):
 
         self.muref = mu1
 
+        with pm.Model() as self.fast_model:
+            x = pm.Normal("x", 0, 1)
+            y = pm.Normal("y", x, 1, observed=0)
+
+        with pm.Model() as self.slow_model:
+            x = pm.Normal("x", 0, 1)
+            y = pm.Normal("y", x, 1, observed=100)
+
     def test_sample(self):
         with self.SMC_test:
-            mtrace = pm.sample_smc(draws=self.samples, return_inferencedata=False)
+
+            mtrace = pm.sample_smc(
+                draws=self.samples,
+                cores=1,  # Fails in parallel due to #4799
+                return_inferencedata=False,
+            )
 
         x = mtrace["X"]
         mu1d = np.abs(x).mean(axis=0)
@@ -107,39 +122,65 @@ def test_slowdown_warning(self):
                 with pm.Model() as model:
                     a = pm.Poisson("a", 5)
                     y = pm.Normal("y", a, 5, observed=[1, 2, 3, 4])
-                    trace = pm.sample_smc(draws=100, chains=2)
+                    trace = pm.sample_smc(draws=100, chains=2, cores=1)
 
     @pytest.mark.parametrize("chains", (1, 2))
     def test_return_datatype(self, chains):
         draws = 10
 
-        with pm.Model() as m:
-            x = pm.Normal("x", 0, 1)
-            y = pm.Normal("y", x, 1, observed=5)
-
+        with self.fast_model:
             idata = pm.sample_smc(chains=chains, draws=draws)
             mt = pm.sample_smc(chains=chains, draws=draws, return_inferencedata=False)
 
         assert isinstance(idata, InferenceData)
         assert "sample_stats" in idata
-        assert len(idata.posterior.chain) == chains
-        assert len(idata.posterior.draw) == draws
+        assert idata.posterior.dims["chain"] == chains
+        assert idata.posterior.dims["draw"] == draws
 
         assert isinstance(mt, MultiTrace)
         assert mt.nchains == chains
         assert mt["x"].size == chains * draws
 
     def test_convergence_checks(self):
-        with pm.Model() as m:
-            x = pm.Normal("x", 0, 1)
-            y = pm.Normal("y", x, 1, observed=5)
-
+        with self.fast_model:
             with pytest.warns(
                 UserWarning,
                 match="The number of samples is too small",
             ):
                 pm.sample_smc(draws=99)
 
+    def test_parallel_sampling(self):
+        # Cache graph
+        with self.slow_model:
+            _ = pm.sample_smc(draws=10, chains=1, cores=1, return_inferencedata=False)
+
+        chains = 4
+        draws = 100
+
+        t0 = time.time()
+        with self.slow_model:
+            idata = pm.sample_smc(draws=draws, chains=chains, cores=4)
+        t_mp = time.time() - t0
+        assert idata.posterior.dims["chain"] == chains
+        assert idata.posterior.dims["draw"] == draws
+
+        t0 = time.time()
+        with self.slow_model:
+            idata = pm.sample_smc(draws=draws, chains=chains, cores=1)
+        t_seq = time.time() - t0
+        assert idata.posterior.dims["chain"] == chains
+        assert idata.posterior.dims["draw"] == draws
+
+        assert t_mp < t_seq
+
+    def test_depracated_parallel_arg(self):
+        with self.fast_model:
+            with pytest.warns(
+                DeprecationWarning,
+                match="The argument parallel is deprecated",
+            ):
+                pm.sample_smc(draws=10, chains=1, parallel=False)
+
 
 @pytest.mark.xfail(reason="SMC-ABC not refactored yet")
 class TestSMCABC(SeededTest):