Hi all,

I am encountering an error that I can’t identify in trying to use a Gaussian Mixture model. I tried to switch to a marginalized mixture to make it easier to compile, but it essentially won’t get off the ground at all. I’m fairly certain the model is well-specified- if I put in the data to scikit-learn’s Gaussian Mixture it will immediately spit out the two peaks on the hist/kde plot for ~160,000 observations. Here is the snippet (I can’t publicly share this specific data but can describe shapes- the graphviz indicates that everything looks kosher on that front)

```
with pm.Model(coords={'cluster': np.arange(2),'batters': mean_in_batters}) as Basic_Mixture_Model:
mu = pm.Normal(
'mu',
mu = 80,
sigma = 10,
initval = [70,90],
transform = pm.distributions.transforms.ordered,
dims = 'cluster'
)
weights = pm.Dirichlet("w", np.ones(2), dims="cluster")
tau = pm.Gamma("tau", 1.0, 1.0, dims="cluster")
y = pm.NormalMixture("x", w=weights, mu=mu, tau = tau, observed=obs_ev_means,dims='batters')
GM_trace = pm.sample_prior_predictive(1000)
GM_trace.extend(pm.sample(chains=2, cores=4, random_seed=0))
pm.sample_posterior_predictive(GM_trace, extend_inferencedata=True)
```

If I just call sample_prior_predictive, it will spit out essentially no separation- giving me two identical means. When I call extend, or try creating the trace directly by using pm.sample, I get the following Assertion Error

```
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
Cell In[72], line 14
11 tau = pm.Gamma("tau", 1.0, 1.0, dims="cluster")
12 y = pm.NormalMixture("x", w=weights, mu=mu, tau = tau, observed=obs_ev_means, dims='batters')
---> 14 GM_trace = pm.sample(draws=1000,tune=1000, chains=2)
File ~\anaconda3\Lib\site-packages\pymc\sampling\mcmc.py:714, in sample(draws, tune, chains, cores, random_seed, progressbar, progressbar_theme, step, var_names, 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, blas_cores, model, **kwargs)
711 auto_nuts_init = False
713 initial_points = None
--> 714 step = assign_step_methods(model, step, methods=pm.STEP_METHODS, step_kwargs=kwargs)
716 if nuts_sampler != "pymc":
717 if not isinstance(step, NUTS):
File ~\anaconda3\Lib\site-packages\pymc\sampling\mcmc.py:223, in assign_step_methods(model, step, methods, step_kwargs)
221 if has_gradient:
222 try:
--> 223 tg.grad(model_logp, var) # type: ignore
224 except (NotImplementedError, tg.NullTypeGradError):
225 has_gradient = False
File ~\anaconda3\Lib\site-packages\pytensor\gradient.py:607, in grad(cost, wrt, consider_constant, disconnected_inputs, add_names, known_grads, return_disconnected, null_gradients)
604 if hasattr(g.type, "dtype"):
605 assert g.type.dtype in pytensor.tensor.type.float_dtypes
--> 607 _rval: Sequence[Variable] = _populate_grad_dict(
608 var_to_app_to_idx, grad_dict, _wrt, cost_name
609 )
611 rval: MutableSequence[Variable | None] = list(_rval)
613 for i in range(len(_rval)):
File ~\anaconda3\Lib\site-packages\pytensor\gradient.py:1402, in _populate_grad_dict(var_to_app_to_idx, grad_dict, wrt, cost_name)
1399 # end if cache miss
1400 return grad_dict[var]
-> 1402 rval = [access_grad_cache(elem) for elem in wrt]
1404 return rval
File ~\anaconda3\Lib\site-packages\pytensor\gradient.py:1357, in _populate_grad_dict.<locals>.access_grad_cache(var)
1355 for node in node_to_idx:
1356 for idx in node_to_idx[node]:
-> 1357 term = access_term_cache(node)[idx]
1359 if not isinstance(term, Variable):
1360 raise TypeError(
1361 f"{node.op}.grad returned {type(term)}, expected"
1362 " Variable instance."
1363 )
File ~\anaconda3\Lib\site-packages\pytensor\gradient.py:1032, in _populate_grad_dict.<locals>.access_term_cache(node)
1029 if node not in term_dict:
1030 inputs = node.inputs
-> 1032 output_grads = [access_grad_cache(var) for var in node.outputs]
1034 # list of bools indicating if each output is connected to the cost
1035 outputs_connected = [
1036 not isinstance(g.type, DisconnectedType) for g in output_grads
1037 ]
File ~\anaconda3\Lib\site-packages\pytensor\gradient.py:1357, in _populate_grad_dict.<locals>.access_grad_cache(var)
1355 for node in node_to_idx:
1356 for idx in node_to_idx[node]:
-> 1357 term = access_term_cache(node)[idx]
1359 if not isinstance(term, Variable):
1360 raise TypeError(
1361 f"{node.op}.grad returned {type(term)}, expected"
1362 " Variable instance."
1363 )
File ~\anaconda3\Lib\site-packages\pytensor\gradient.py:1032, in _populate_grad_dict.<locals>.access_term_cache(node)
1029 if node not in term_dict:
1030 inputs = node.inputs
-> 1032 output_grads = [access_grad_cache(var) for var in node.outputs]
1034 # list of bools indicating if each output is connected to the cost
1035 outputs_connected = [
1036 not isinstance(g.type, DisconnectedType) for g in output_grads
1037 ]
File ~\anaconda3\Lib\site-packages\pytensor\gradient.py:1357, in _populate_grad_dict.<locals>.access_grad_cache(var)
1355 for node in node_to_idx:
1356 for idx in node_to_idx[node]:
-> 1357 term = access_term_cache(node)[idx]
1359 if not isinstance(term, Variable):
1360 raise TypeError(
1361 f"{node.op}.grad returned {type(term)}, expected"
1362 " Variable instance."
1363 )
File ~\anaconda3\Lib\site-packages\pytensor\gradient.py:1187, in _populate_grad_dict.<locals>.access_term_cache(node)
1179 if o_shape != g_shape:
1180 raise ValueError(
1181 "Got a gradient of shape "
1182 + str(o_shape)
1183 + " on an output of shape "
1184 + str(g_shape)
1185 )
-> 1187 input_grads = node.op.L_op(inputs, node.outputs, new_output_grads)
1189 if input_grads is None:
1190 raise TypeError(
1191 f"{node.op}.grad returned NoneType, expected iterable."
1192 )
File ~\anaconda3\Lib\site-packages\pytensor\graph\op.py:398, in Op.L_op(self, inputs, outputs, output_grads)
371 def L_op(
372 self,
373 inputs: Sequence[Variable],
374 outputs: Sequence[Variable],
375 output_grads: Sequence[Variable],
376 ) -> list[Variable]:
377 r"""Construct a graph for the L-operator.
378
379 The L-operator computes a row vector times the Jacobian.
(...)
396
397 """
--> 398 return self.grad(inputs, output_grads)
File ~\anaconda3\Lib\site-packages\pytensor\tensor\subtensor.py:1897, in IncSubtensor.grad(self, inputs, grads)
1895 gx = g_output
1896 gy = Subtensor(idx_list=self.idx_list)(g_output, *idx_list)
-> 1897 gy = _sum_grad_over_bcasted_dims(y, gy)
1899 return [gx, gy] + [DisconnectedType()()] * len(idx_list)
File ~\anaconda3\Lib\site-packages\pytensor\tensor\subtensor.py:1933, in _sum_grad_over_bcasted_dims(x, gx)
1931 x_dim_added = gx.ndim - x.ndim
1932 x_broad = (True,) * x_dim_added + x.broadcastable
-> 1933 assert sum(gx.broadcastable) <= sum(x_broad)
1934 axis_to_sum = []
1935 for i in range(gx.ndim):
AssertionError:
```

I very straightforwardly followed the examples so I’m not sure exactly what’s going wrong. Thanks for your help in advance.