I tried this as a workaround, the pm.sample_prior_predictive() works but not pm.sample() unfortunately…
Here is the code to generate the dummy data in case you do want to try it:
import string
def generate_dummy_data():
# dummy data:
rng = np.random.default_rng(seed=88)
n_predictors = 7
predictors = [pred.upper() for pred in string.ascii_letters[:n_predictors]]
N = 100
n_groups = 2
group = rng.binomial(n=1, p=.5, size=N)
# manually make corr matrix
sigma = pm.HalfCauchy.dist(1, shape=(n_predictors, n_groups)).eval()
corr_packed = pm.LKJCorr.dist(n=n_predictors, eta=1).eval()
triu_idx = np.triu_indices(n_predictors, k=1)
corr_upper = pt.set_subtensor(pt.zeros((n_predictors, n_predictors))[triu_idx], corr_packed)
corr = pt.eye(n_predictors) + corr_upper + corr_upper.T
sigma_diag = (pt.eye(n_predictors)[:,:,None] * sigma).T
cov = pt.stack([sigma_diag[i] @ corr @ sigma_diag[i] for i in range(n_groups)]).eval()
obs = pm.MvNormal.dist(0, cov=cov[group]).eval()
return obs
obs = generate_dummy_data()
Run model on dummy data:
coords = {
"obs_id": np.arange(N),
"predictors": predictors,
"predictors_I": predictors,
"groups": np.arange(n_groups)
}
def lkj(n, eta, size: TensorVariable,) -> TensorVariable:
return pm.LKJCorr.dist(n=n, eta=eta, size=size)
with pm.Model(coords=coords) as m:
corr_packed = pm.CustomDist("corr_packed", n_predictors, 1, dist=lkj)
sigma = pm.HalfCauchy("sigma", 1, dims=("groups", "predictors"))
triu_idx = pt.triu_indices(n_predictors, k=1)
corr_upper = pt.set_subtensor(pt.zeros((n_predictors, n_predictors))[triu_idx], corr_packed)
corr = pm.Deterministic("corr", pt.eye(n_predictors) + corr_upper + corr_upper.T, dims=("predictors", "predictors_I"))
# n_groups x n_predictors x n_predictors
sigma_diag = pt.stack([pt.eye(n_predictors) * sigma[i] for i in range(n_groups)])
cov = pm.Deterministic("cov",
pt.stack([sigma_diag[i] @ corr @ sigma_diag[i] \
for i in range(n_groups)]),
dims=("groups", "predictors", "predictors_I"))
# batched dim on the far left
# cov[group]: N x n_predictors x n_predictors (n_groups disappear)
# obs: N x n_predictors (n_groups disappear)
likelihood = pm.MvNormal("likelihood", mu=0, cov=cov[group], observed=obs, dims=("obs_id", "predictors"))
with m:
idata_prior = pm.sample_prior_predictive()
idata = pm.sample(tune=1000, draws=1000, nuts_sampler="numpyro")
And now getting this error:
{
"name": "ImportError",
"message": "/home/cao/.pytensor/compiledir_Linux-5.15-microsoft-standard-WSL2-x86_64-with-glibc2.31-x86_64-3.11.5-64/tmpl0m7osww/m8c7f7341b546dd88fd9d5c257900e34b310c0b1b35fe7d5c475a6f74bd303f07.so: undefined symbol: dgemm_
Apply node that caused the error: BatchedDot(ExpandDims{axis=2}.0, ExpandDims{axis=1}.0)
Toposort index: 72
Inputs types: [TensorType(float64, shape=(100, 7, 1)), TensorType(float64, shape=(100, 1, 7))]
Backtrace when the node is created (use PyTensor flag traceback__limit=N to make it longer):
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/gradient.py\", line 1037, in <listcomp>
output_grads = [access_grad_cache(var) for var in node.outputs]
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/gradient.py\", line 1362, in access_grad_cache
term = access_term_cache(node)[idx]
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/gradient.py\", line 1192, in access_term_cache
input_grads = node.op.L_op(inputs, node.outputs, new_output_grads)
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/tensor/blockwise.py\", line 265, in L_op
rval = self._bgrad(inputs, outs, ograds)
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/tensor/blockwise.py\", line 248, in _bgrad
igrads = vectorize_graph(
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/graph/replace.py\", line 307, in vectorize_graph
vect_node = vectorize_node(node, *vect_inputs)
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/graph/replace.py\", line 221, in vectorize_node
return _vectorize_node(op, node, *batched_inputs)
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/functools.py\", line 909, in wrapper
return dispatch(args[0].__class__)(*args, **kw)
HINT: Use a linker other than the C linker to print the inputs' shapes and strides.
HINT: Use the PyTensor flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.",
"stack": "---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/vm.py:1235, in VMLinker.make_all(self, profiler, input_storage, output_storage, storage_map)
1231 # no-recycling is done at each VM.__call__ So there is
1232 # no need to cause duplicate c code by passing
1233 # no_recycling here.
1234 thunks.append(
-> 1235 node.op.make_thunk(node, storage_map, compute_map, [], impl=impl)
1236 )
1237 linker_make_thunk_time[node] = time.perf_counter() - thunk_start
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/op.py:119, in COp.make_thunk(self, node, storage_map, compute_map, no_recycling, impl)
118 try:
--> 119 return self.make_c_thunk(node, storage_map, compute_map, no_recycling)
120 except (NotImplementedError, MethodNotDefined):
121 # We requested the c code, so don't catch the error.
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/op.py:84, in COp.make_c_thunk(self, node, storage_map, compute_map, no_recycling)
83 raise NotImplementedError(\"float16\")
---> 84 outputs = cl.make_thunk(
85 input_storage=node_input_storage, output_storage=node_output_storage
86 )
87 thunk, node_input_filters, node_output_filters = outputs
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/basic.py:1209, in CLinker.make_thunk(self, input_storage, output_storage, storage_map, cache, **kwargs)
1208 init_tasks, tasks = self.get_init_tasks()
-> 1209 cthunk, module, in_storage, out_storage, error_storage = self.__compile__(
1210 input_storage, output_storage, storage_map, cache
1211 )
1213 res = _CThunk(cthunk, init_tasks, tasks, error_storage, module)
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/basic.py:1129, in CLinker.__compile__(self, input_storage, output_storage, storage_map, cache)
1128 output_storage = tuple(output_storage)
-> 1129 thunk, module = self.cthunk_factory(
1130 error_storage,
1131 input_storage,
1132 output_storage,
1133 storage_map,
1134 cache,
1135 )
1136 return (
1137 thunk,
1138 module,
(...)
1147 error_storage,
1148 )
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/basic.py:1653, in CLinker.cthunk_factory(self, error_storage, in_storage, out_storage, storage_map, cache)
1652 cache = get_module_cache()
-> 1653 module = cache.module_from_key(key=key, lnk=self)
1655 vars = self.inputs + self.outputs + self.orphans
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/cmodule.py:1231, in ModuleCache.module_from_key(self, key, lnk)
1230 location = dlimport_workdir(self.dirname)
-> 1231 module = lnk.compile_cmodule(location)
1232 name = module.__file__
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/basic.py:1552, in CLinker.compile_cmodule(self, location)
1551 _logger.debug(f\"LOCATION {location}\")
-> 1552 module = c_compiler.compile_str(
1553 module_name=mod.code_hash,
1554 src_code=src_code,
1555 location=location,
1556 include_dirs=self.header_dirs(),
1557 lib_dirs=self.lib_dirs(),
1558 libs=libs,
1559 preargs=preargs,
1560 )
1561 except Exception as e:
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/cmodule.py:2652, in GCC_compiler.compile_str(module_name, src_code, location, include_dirs, lib_dirs, libs, preargs, py_module, hide_symbols)
2651 assert os.path.isfile(lib_filename)
-> 2652 return dlimport(lib_filename)
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/cmodule.py:332, in dlimport(fullpath, suffix)
331 warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")
--> 332 rval = __import__(module_name, {}, {}, [module_name])
333 t1 = time.perf_counter()
ImportError: /home/cao/.pytensor/compiledir_Linux-5.15-microsoft-standard-WSL2-x86_64-with-glibc2.31-x86_64-3.11.5-64/tmpl0m7osww/m8c7f7341b546dd88fd9d5c257900e34b310c0b1b35fe7d5c475a6f74bd303f07.so: undefined symbol: dgemm_
During handling of the above exception, another exception occurred:
ImportError Traceback (most recent call last)
/home/cao/projects/pymc_experiments/test.ipynb Cell 4 line 3
<a href='vscode-notebook-cell://wsl%2Bubuntu-20.04/home/cao/projects/pymc_experiments/test.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=30'>31</a> with m:
<a href='vscode-notebook-cell://wsl%2Bubuntu-20.04/home/cao/projects/pymc_experiments/test.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=31'>32</a> idata_prior = pm.sample_prior_predictive()
---> <a href='vscode-notebook-cell://wsl%2Bubuntu-20.04/home/cao/projects/pymc_experiments/test.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=32'>33</a> idata = pm.sample(tune=1000, draws=1000, nuts_sampler=\"numpyro\")
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pymc/sampling/mcmc.py:689, in sample(draws, tune, chains, cores, random_seed, progressbar, step, nuts_sampler, initvals, init, jitter_max_retries, n_init, trace, discard_tuned_samples, compute_convergence_checks, keep_warning_stat, return_inferencedata, idata_kwargs, nuts_sampler_kwargs, callback, mp_ctx, model, **kwargs)
686 auto_nuts_init = False
688 initial_points = None
--> 689 step = assign_step_methods(model, step, methods=pm.STEP_METHODS, step_kwargs=kwargs)
691 if nuts_sampler != \"pymc\":
692 if not isinstance(step, NUTS):
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pymc/sampling/mcmc.py:239, in assign_step_methods(model, step, methods, step_kwargs)
231 selected = max(
232 methods_list,
233 key=lambda method, var=rv_var, has_gradient=has_gradient: method._competence( # type: ignore
234 var, has_gradient
235 ),
236 )
237 selected_steps.setdefault(selected, []).append(var)
--> 239 return instantiate_steppers(model, steps, selected_steps, step_kwargs)
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pymc/sampling/mcmc.py:140, in instantiate_steppers(model, steps, selected_steps, step_kwargs)
138 args = step_kwargs.get(name, {})
139 used_keys.add(name)
--> 140 step = step_class(vars=vars, model=model, **args)
141 steps.append(step)
143 unused_args = set(step_kwargs).difference(used_keys)
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pymc/step_methods/hmc/nuts.py:180, in NUTS.__init__(self, vars, max_treedepth, early_max_treedepth, **kwargs)
122 def __init__(self, vars=None, max_treedepth=10, early_max_treedepth=8, **kwargs):
123 r\"\"\"Set up the No-U-Turn sampler.
124
125 Parameters
(...)
178 `pm.sample` to the desired number of tuning steps.
179 \"\"\"
--> 180 super().__init__(vars, **kwargs)
182 self.max_treedepth = max_treedepth
183 self.early_max_treedepth = early_max_treedepth
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pymc/step_methods/hmc/base_hmc.py:109, in BaseHMC.__init__(self, vars, scaling, step_scale, is_cov, model, blocked, potential, dtype, Emax, target_accept, gamma, k, t0, adapt_step_size, step_rand, **pytensor_kwargs)
107 else:
108 vars = get_value_vars_from_user_vars(vars, self._model)
--> 109 super().__init__(vars, blocked=blocked, model=self._model, dtype=dtype, **pytensor_kwargs)
111 self.adapt_step_size = adapt_step_size
112 self.Emax = Emax
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pymc/step_methods/arraystep.py:164, in GradientSharedStep.__init__(self, vars, model, blocked, dtype, logp_dlogp_func, **pytensor_kwargs)
161 model = modelcontext(model)
163 if logp_dlogp_func is None:
--> 164 func = model.logp_dlogp_function(vars, dtype=dtype, **pytensor_kwargs)
165 else:
166 func = logp_dlogp_func
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pymc/model/core.py:618, in Model.logp_dlogp_function(self, grad_vars, tempered, **kwargs)
612 ip = self.initial_point(0)
613 extra_vars_and_values = {
614 var: ip[var.name]
615 for var in self.value_vars
616 if var in input_vars and var not in grad_vars
617 }
--> 618 return ValueGradFunction(costs, grad_vars, extra_vars_and_values, **kwargs)
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pymc/model/core.py:350, in ValueGradFunction.__init__(self, costs, grad_vars, extra_vars_and_values, dtype, casting, compute_grads, **kwargs)
346 outputs = [cost]
348 inputs = grad_vars
--> 350 self._pytensor_function = compile_pymc(inputs, outputs, givens=givens, **kwargs)
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pymc/pytensorf.py:991, in compile_pymc(inputs, outputs, random_seed, mode, **kwargs)
989 opt_qry = mode.provided_optimizer.including(\"random_make_inplace\", check_parameter_opt)
990 mode = Mode(linker=mode.linker, optimizer=opt_qry)
--> 991 pytensor_function = pytensor.function(
992 inputs,
993 outputs,
994 updates={**rng_updates, **kwargs.pop(\"updates\", {})},
995 mode=mode,
996 **kwargs,
997 )
998 return pytensor_function
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/compile/function/__init__.py:315, in function(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input)
309 fn = orig_function(
310 inputs, outputs, mode=mode, accept_inplace=accept_inplace, name=name
311 )
312 else:
313 # note: pfunc will also call orig_function -- orig_function is
314 # a choke point that all compilation must pass through
--> 315 fn = pfunc(
316 params=inputs,
317 outputs=outputs,
318 mode=mode,
319 updates=updates,
320 givens=givens,
321 no_default_updates=no_default_updates,
322 accept_inplace=accept_inplace,
323 name=name,
324 rebuild_strict=rebuild_strict,
325 allow_input_downcast=allow_input_downcast,
326 on_unused_input=on_unused_input,
327 profile=profile,
328 output_keys=output_keys,
329 )
330 return fn
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/compile/function/pfunc.py:469, in pfunc(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys, fgraph)
455 profile = ProfileStats(message=profile)
457 inputs, cloned_outputs = construct_pfunc_ins_and_outs(
458 params,
459 outputs,
(...)
466 fgraph=fgraph,
467 )
--> 469 return orig_function(
470 inputs,
471 cloned_outputs,
472 mode,
473 accept_inplace=accept_inplace,
474 name=name,
475 profile=profile,
476 on_unused_input=on_unused_input,
477 output_keys=output_keys,
478 fgraph=fgraph,
479 )
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/compile/function/types.py:1762, in orig_function(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys, fgraph)
1750 m = Maker(
1751 inputs,
1752 outputs,
(...)
1759 fgraph=fgraph,
1760 )
1761 with config.change_flags(compute_test_value=\"off\"):
-> 1762 fn = m.create(defaults)
1763 finally:
1764 if profile and fn:
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/compile/function/types.py:1654, in FunctionMaker.create(self, input_storage, storage_map)
1651 start_import_time = pytensor.link.c.cmodule.import_time
1653 with config.change_flags(traceback__limit=config.traceback__compile_limit):
-> 1654 _fn, _i, _o = self.linker.make_thunk(
1655 input_storage=input_storage_lists, storage_map=storage_map
1656 )
1658 end_linker = time.perf_counter()
1660 linker_time = end_linker - start_linker
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/basic.py:245, in LocalLinker.make_thunk(self, input_storage, output_storage, storage_map, **kwargs)
238 def make_thunk(
239 self,
240 input_storage: Optional[\"InputStorageType\"] = None,
(...)
243 **kwargs,
244 ) -> tuple[\"BasicThunkType\", \"InputStorageType\", \"OutputStorageType\"]:
--> 245 return self.make_all(
246 input_storage=input_storage,
247 output_storage=output_storage,
248 storage_map=storage_map,
249 )[:3]
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/vm.py:1244, in VMLinker.make_all(self, profiler, input_storage, output_storage, storage_map)
1242 thunks[-1].lazy = False
1243 except Exception:
-> 1244 raise_with_op(fgraph, node)
1246 t1 = time.perf_counter()
1248 if self.profile:
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/utils.py:531, in raise_with_op(fgraph, node, thunk, exc_info, storage_map)
526 warnings.warn(
527 f\"{exc_type} error does not allow us to add an extra error message\"
528 )
529 # Some exception need extra parameter in inputs. So forget the
530 # extra long error message in that case.
--> 531 raise exc_value.with_traceback(exc_trace)
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/vm.py:1235, in VMLinker.make_all(self, profiler, input_storage, output_storage, storage_map)
1230 thunk_start = time.perf_counter()
1231 # no-recycling is done at each VM.__call__ So there is
1232 # no need to cause duplicate c code by passing
1233 # no_recycling here.
1234 thunks.append(
-> 1235 node.op.make_thunk(node, storage_map, compute_map, [], impl=impl)
1236 )
1237 linker_make_thunk_time[node] = time.perf_counter() - thunk_start
1238 if not hasattr(thunks[-1], \"lazy\"):
1239 # We don't want all ops maker to think about lazy Ops.
1240 # So if they didn't specify that its lazy or not, it isn't.
1241 # If this member isn't present, it will crash later.
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/op.py:119, in COp.make_thunk(self, node, storage_map, compute_map, no_recycling, impl)
115 self.prepare_node(
116 node, storage_map=storage_map, compute_map=compute_map, impl=\"c\"
117 )
118 try:
--> 119 return self.make_c_thunk(node, storage_map, compute_map, no_recycling)
120 except (NotImplementedError, MethodNotDefined):
121 # We requested the c code, so don't catch the error.
122 if impl == \"c\":
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/op.py:84, in COp.make_c_thunk(self, node, storage_map, compute_map, no_recycling)
82 print(f\"Disabling C code for {self} due to unsupported float16\")
83 raise NotImplementedError(\"float16\")
---> 84 outputs = cl.make_thunk(
85 input_storage=node_input_storage, output_storage=node_output_storage
86 )
87 thunk, node_input_filters, node_output_filters = outputs
89 @is_cthunk_wrapper_type
90 def rval():
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/basic.py:1209, in CLinker.make_thunk(self, input_storage, output_storage, storage_map, cache, **kwargs)
1174 \"\"\"Compile this linker's `self.fgraph` and return a function that performs the computations.
1175
1176 The return values can be used as follows:
(...)
1206
1207 \"\"\"
1208 init_tasks, tasks = self.get_init_tasks()
-> 1209 cthunk, module, in_storage, out_storage, error_storage = self.__compile__(
1210 input_storage, output_storage, storage_map, cache
1211 )
1213 res = _CThunk(cthunk, init_tasks, tasks, error_storage, module)
1214 res.nodes = self.node_order
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/basic.py:1129, in CLinker.__compile__(self, input_storage, output_storage, storage_map, cache)
1127 input_storage = tuple(input_storage)
1128 output_storage = tuple(output_storage)
-> 1129 thunk, module = self.cthunk_factory(
1130 error_storage,
1131 input_storage,
1132 output_storage,
1133 storage_map,
1134 cache,
1135 )
1136 return (
1137 thunk,
1138 module,
(...)
1147 error_storage,
1148 )
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/basic.py:1653, in CLinker.cthunk_factory(self, error_storage, in_storage, out_storage, storage_map, cache)
1651 if cache is None:
1652 cache = get_module_cache()
-> 1653 module = cache.module_from_key(key=key, lnk=self)
1655 vars = self.inputs + self.outputs + self.orphans
1656 # List of indices that should be ignored when passing the arguments
1657 # (basically, everything that the previous call to uniq eliminated)
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/cmodule.py:1231, in ModuleCache.module_from_key(self, key, lnk)
1229 try:
1230 location = dlimport_workdir(self.dirname)
-> 1231 module = lnk.compile_cmodule(location)
1232 name = module.__file__
1233 assert name.startswith(location)
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/basic.py:1552, in CLinker.compile_cmodule(self, location)
1550 try:
1551 _logger.debug(f\"LOCATION {location}\")
-> 1552 module = c_compiler.compile_str(
1553 module_name=mod.code_hash,
1554 src_code=src_code,
1555 location=location,
1556 include_dirs=self.header_dirs(),
1557 lib_dirs=self.lib_dirs(),
1558 libs=libs,
1559 preargs=preargs,
1560 )
1561 except Exception as e:
1562 e.args += (str(self.fgraph),)
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/cmodule.py:2652, in GCC_compiler.compile_str(module_name, src_code, location, include_dirs, lib_dirs, libs, preargs, py_module, hide_symbols)
2650 pass
2651 assert os.path.isfile(lib_filename)
-> 2652 return dlimport(lib_filename)
File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/link/c/cmodule.py:332, in dlimport(fullpath, suffix)
330 with warnings.catch_warnings():
331 warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")
--> 332 rval = __import__(module_name, {}, {}, [module_name])
333 t1 = time.perf_counter()
334 import_time += t1 - t0
ImportError: /home/cao/.pytensor/compiledir_Linux-5.15-microsoft-standard-WSL2-x86_64-with-glibc2.31-x86_64-3.11.5-64/tmpl0m7osww/m8c7f7341b546dd88fd9d5c257900e34b310c0b1b35fe7d5c475a6f74bd303f07.so: undefined symbol: dgemm_
Apply node that caused the error: BatchedDot(ExpandDims{axis=2}.0, ExpandDims{axis=1}.0)
Toposort index: 72
Inputs types: [TensorType(float64, shape=(100, 7, 1)), TensorType(float64, shape=(100, 1, 7))]
Backtrace when the node is created (use PyTensor flag traceback__limit=N to make it longer):
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/gradient.py\", line 1037, in <listcomp>
output_grads = [access_grad_cache(var) for var in node.outputs]
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/gradient.py\", line 1362, in access_grad_cache
term = access_term_cache(node)[idx]
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/gradient.py\", line 1192, in access_term_cache
input_grads = node.op.L_op(inputs, node.outputs, new_output_grads)
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/tensor/blockwise.py\", line 265, in L_op
rval = self._bgrad(inputs, outs, ograds)
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/tensor/blockwise.py\", line 248, in _bgrad
igrads = vectorize_graph(
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/graph/replace.py\", line 307, in vectorize_graph
vect_node = vectorize_node(node, *vect_inputs)
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/graph/replace.py\", line 221, in vectorize_node
return _vectorize_node(op, node, *batched_inputs)
File \"/home/cao/miniconda3/envs/pymc/lib/python3.11/functools.py\", line 909, in wrapper
return dispatch(args[0].__class__)(*args, **kw)
HINT: Use a linker other than the C linker to print the inputs' shapes and strides.
HINT: Use the PyTensor flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node."
}
I can try to downgrade pymc to another version as a last resort after everything else fails
, so I might look something like this: