Merge pull request #751 from stan-dev/fix/multichain-profile-file

Fix profile file output when running multiple chains in one process

cmdstanpy/install_cmdstan.py

@@ -523,6 +523,8 @@ def retrieve_version(version: str, progress: bool = True) -> None:
         first = tar.next()
         if first is not None:
             top_dir = first.name
+        else:
+            top_dir = ''
         cmdstan_dir = f'cmdstan-{version}'
         if top_dir != cmdstan_dir:
             raise CmdStanInstallError(

cmdstanpy/install_cxx_toolchain.py

@@ -235,8 +235,10 @@ def get_toolchain_name() -> str:
     return ''
 
 
+# TODO(2.0): drop 3.5 support
 def get_url(version: str) -> str:
     """Return URL for toolchain."""
+    url = ''
     if platform.system() == 'Windows':
         if version == '4.0':
             # pylint: disable=line-too-long
@@ -277,6 +279,8 @@ def run_rtools_install(args: Dict[str, Any]) -> None:
 
     if 'verbose' in args:
         verbose = args['verbose']
+    else:
+        verbose = False
 
     install_dir = args['dir']
     if install_dir is None:

cmdstanpy/stanfit/runset.py

@@ -61,40 +61,40 @@ def __init__(
         self._base_outfile = (
             f'{args.model_name}-{datetime.now().strftime(time_fmt)}'
         )
-        # per-process console messages
+        # per-process outputs
         self._stdout_files = [''] * self._num_procs
+        self._profile_files = [''] * self._num_procs  # optional
         if one_process_per_chain:
             for i in range(chains):
                 self._stdout_files[i] = self.file_path("-stdout.txt", id=i)
+                if args.save_profile:
+                    self._profile_files[i] = self.file_path(
+                        ".csv", extra="-profile", id=chain_ids[i]
+                    )
         else:
             self._stdout_files[0] = self.file_path("-stdout.txt")
+            if args.save_profile:
+                self._profile_files[0] = self.file_path(
+                    ".csv", extra="-profile"
+                )
 
         # per-chain output files
         self._csv_files: List[str] = [''] * chains
         self._diagnostic_files = [''] * chains  # optional
-        self._profile_files = [''] * chains  # optional
 
         if chains == 1:
             self._csv_files[0] = self.file_path(".csv")
             if args.save_latent_dynamics:
                 self._diagnostic_files[0] = self.file_path(
                     ".csv", extra="-diagnostic"
                 )
-            if args.save_profile:
-                self._profile_files[0] = self.file_path(
-                    ".csv", extra="-profile"
-                )
         else:
             for i in range(chains):
                 self._csv_files[i] = self.file_path(".csv", id=chain_ids[i])
                 if args.save_latent_dynamics:
                     self._diagnostic_files[i] = self.file_path(
                         ".csv", extra="-diagnostic", id=chain_ids[i]
                     )
-                if args.save_profile:
-                    self._profile_files[i] = self.file_path(
-                        ".csv", extra="-profile", id=chain_ids[i]
-                    )
 
     def __repr__(self) -> str:
         repr = 'RunSet: chains={}, chain_ids={}, num_processes={}'.format(

cmdstanpy/utils/stancsv.py

@@ -116,6 +116,7 @@ def scan_optimize_csv(path: str, save_iters: bool = False) -> Dict[str, Any]:
                 all_iters[i, :] = [float(x) for x in xs]
             if i == iters - 1:
                 mle: np.ndarray = np.array(xs, dtype=float)
+    # pylint: disable=possibly-used-before-assignment
     dict['mle'] = mle
     if save_iters:
         dict['all_iters'] = all_iters

test/test_sample.py

@@ -1748,33 +1748,36 @@ def test_save_latent_dynamics() -> None:
 
 def test_save_profile() -> None:
     stan = os.path.join(DATAFILES_PATH, 'profile_likelihood.stan')
-    profile_model = CmdStanModel(stan_file=stan)
+    profile_model = CmdStanModel(
+        stan_file=stan, cpp_options={"STAN_THREADS": '1'}, force_compile=True
+    )
+
     profile_fit = profile_model.sample(
         chains=2,
         parallel_chains=2,
+        force_one_process_per_chain=True,
         seed=12345,
         iter_warmup=100,
         iter_sampling=200,
         save_profile=True,
     )
-    for i in range(profile_fit.runset.chains):
-        profile_file = profile_fit.runset.profile_files[i]
+    assert len(profile_fit.runset.profile_files) == 2
+    for profile_file in profile_fit.runset.profile_files:
         assert os.path.exists(profile_file)
 
     profile_fit = profile_model.sample(
         chains=2,
         parallel_chains=2,
+        force_one_process_per_chain=False,
         seed=12345,
+        iter_warmup=100,
         iter_sampling=200,
-        save_latent_dynamics=True,
         save_profile=True,
     )
 
-    for i in range(profile_fit.runset.chains):
-        profile_file = profile_fit.runset.profile_files[i]
+    assert len(profile_fit.runset.profile_files) == 1
+    for profile_file in profile_fit.runset.profile_files:
         assert os.path.exists(profile_file)
-        diagnostics_file = profile_fit.runset.diagnostic_files[i]
-        assert os.path.exists(diagnostics_file)
 
 
 def test_xarray_draws() -> None: