[aesara error] Error when trying to use aesara.scan with aesara==2.8.7 pymc==4.2.2 #aesara #pymc

aesara-error.txt

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/link/basic.py:111, in Container.__set__(self, value)
    109 try:
    110     # Use in-place filtering when/if possible
--> 111     self.storage[0] = self.type.filter_inplace(
    112         value, self.storage[0], **kwargs
    113     )
    114 except NotImplementedError:

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/graph/type.py:130, in Type.filter_inplace(self, value, storage, strict, allow_downcast)
    111 """Return data or an appropriately wrapped/converted data by converting it in-place.
    112 
    113 This method allows one to reuse old allocated memory.  If this method
   (...)
    128 NotImplementedError
    129 """
--> 130 raise NotImplementedError()

NotImplementedError: 

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/link/vm.py:1246, in VMLinker.make_all(self, profiler, input_storage, output_storage, storage_map)
   1242 # no-recycling is done at each VM.__call__ So there is
   1243 # no need to cause duplicate c code by passing
   1244 # no_recycling here.
   1245 thunks.append(
-> 1246     node.op.make_thunk(node, storage_map, compute_map, [], impl=impl)
   1247 )
   1248 linker_make_thunk_time[node] = time.time() - thunk_start

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/scan/op.py:1534, in Scan.make_thunk(self, node, storage_map, compute_map, no_recycling, impl)
   1531 # Analyse the compile inner function to determine which inputs and
   1532 # outputs are on the gpu and speed up some checks during the execution
   1533 outs_is_tensor = [
-> 1534     isinstance(out, TensorVariable) for out in self.fn.maker.fgraph.outputs
   1535 ]
   1537 try:

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/scan/op.py:1466, in Scan.fn(self)
   1464     profile = self.profile
-> 1466 self._fn = pfunc(
   1467     wrapped_inputs,
   1468     wrapped_outputs,
   1469     mode=self.mode_instance,
   1470     accept_inplace=False,
   1471     profile=profile,
   1472     on_unused_input="ignore",
   1473     fgraph=self.fgraph,
   1474 )
   1476 return self._fn

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/compile/function/pfunc.py:371, 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)
    359 inputs, cloned_outputs = construct_pfunc_ins_and_outs(
    360     params,
    361     outputs,
   (...)
    368     fgraph=fgraph,
    369 )
--> 371 return orig_function(
    372     inputs,
    373     cloned_outputs,
    374     mode,
    375     accept_inplace=accept_inplace,
    376     name=name,
    377     profile=profile,
    378     on_unused_input=on_unused_input,
    379     output_keys=output_keys,
    380     fgraph=fgraph,
    381 )

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/compile/function/types.py:1759, in orig_function(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys, fgraph)
   1758     with config.change_flags(compute_test_value="off"):
-> 1759         fn = m.create(defaults)
   1760 finally:

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/compile/function/types.py:1667, in FunctionMaker.create(self, input_storage, trustme, storage_map)
   1665     self.profile.import_time += import_time
-> 1667 fn = self.function_builder(
   1668     _fn,
   1669     _i,
   1670     _o,
   1671     self.indices,
   1672     self.outputs,
   1673     defaults,
   1674     self.unpack_single,
   1675     self.return_none,
   1676     self.output_keys,
   1677     self,
   1678     name=self.name,
   1679 )
   1681 fn.profile = self.profile

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/compile/function/types.py:448, in Function.__init__(self, vm, input_storage, output_storage, indices, outputs, defaults, unpack_single, return_none, output_keys, maker, name)
    447     else:
--> 448         c.value = value
    449 c.required = required

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/link/basic.py:115, in Container.__set__(self, value)
    114     except NotImplementedError:
--> 115         self.storage[0] = self.type.filter(value, **kwargs)
    117 except Exception as e:

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/tensor/type.py:244, in TensorType.filter(self, data, strict, allow_downcast)
    240 if not all(
    241     ds == ts if ts is not None else True
    242     for ds, ts in zip(data.shape, self.shape)
    243 ):
--> 244     raise TypeError(
    245         f"The type's shape ({self.shape}) is not compatible with the data's ({data.shape})"
    246     )
    248 if self.filter_checks_isfinite and not np.all(np.isfinite(data)):

TypeError: ("The type's shape ((844,)) is not compatible with the data's ((0,))", 'Container name "None"')

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
Cell In [40], line 30
     21 mu, _ = aesara.scan(
     22     f,
     23     sequences=[i.T, gaps.T],
     24     outputs_info=y0,
     25     non_sequences=[y_max, b, rho],
     26 )
     28 pm.Normal("y_hat", mu=mu.T, sigma=pm.Exponential("sigma", 1), observed=responses)
---> 30 trace = pm.sample(random_seed=194965)

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/pymc/sampling.py:527, in sample(draws, step, init, n_init, initvals, trace, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, **kwargs)
    524         auto_nuts_init = False
    526 initial_points = None
--> 527 step = assign_step_methods(model, step, methods=pm.STEP_METHODS, step_kwargs=kwargs)
    529 if isinstance(step, list):
    530     step = CompoundStep(step)

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/pymc/sampling.py:229, in assign_step_methods(model, step, methods, step_kwargs)
    221         selected = max(
    222             methods,
    223             key=lambda method, var=rv_var, has_gradient=has_gradient: method._competence(
    224                 var, has_gradient
    225             ),
    226         )
    227         selected_steps[selected].append(var)
--> 229 return instantiate_steppers(model, steps, selected_steps, step_kwargs)

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/pymc/sampling.py:147, in instantiate_steppers(model, steps, selected_steps, step_kwargs)
    145         args = step_kwargs.get(step_class.name, {})
    146         used_keys.add(step_class.name)
--> 147         step = step_class(vars=vars, model=model, **args)
    148         steps.append(step)
    150 unused_args = set(step_kwargs).difference(used_keys)

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/pymc/step_methods/hmc/nuts.py:178, in NUTS.__init__(self, vars, max_treedepth, early_max_treedepth, **kwargs)
    120 def __init__(self, vars=None, max_treedepth=10, early_max_treedepth=8, **kwargs):
    121     r"""Set up the No-U-Turn sampler.
    122 
    123     Parameters
   (...)
    176     `pm.sample` to the desired number of tuning steps.
    177     """
--> 178     super().__init__(vars, **kwargs)
    180     self.max_treedepth = max_treedepth
    181     self.early_max_treedepth = early_max_treedepth

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/pymc/step_methods/hmc/base_hmc.py:95, 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, **aesara_kwargs)
     92 else:
     93     vars = [self._model.rvs_to_values.get(var, var) for var in vars]
---> 95 super().__init__(vars, blocked=blocked, model=self._model, dtype=dtype, **aesara_kwargs)
     97 self.adapt_step_size = adapt_step_size
     98 self.Emax = Emax

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/pymc/step_methods/arraystep.py:276, in GradientSharedStep.__init__(self, vars, model, blocked, dtype, logp_dlogp_func, **aesara_kwargs)
    273 model = modelcontext(model)
    275 if logp_dlogp_func is None:
--> 276     func = model.logp_dlogp_function(vars, dtype=dtype, **aesara_kwargs)
    277 else:
    278     func = logp_dlogp_func

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/pymc/model.py:642, in Model.logp_dlogp_function(self, grad_vars, tempered, **kwargs)
    638 ip = self.initial_point(0)
    639 extra_vars_and_values = {
    640     var: ip[var.name] for var in extra_vars if var in input_vars and var not in grad_vars
    641 }
--> 642 return ValueGradFunction(costs, grad_vars, extra_vars_and_values, **kwargs)

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/pymc/model.py:382, in ValueGradFunction.__init__(self, costs, grad_vars, extra_vars_and_values, dtype, casting, compute_grads, **kwargs)
    378     outputs = [cost]
    380 inputs = grad_vars
--> 382 self._aesara_function = compile_pymc(inputs, outputs, givens=givens, **kwargs)

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/pymc/aesaraf.py:970, in compile_pymc(inputs, outputs, random_seed, mode, **kwargs)
    968 opt_qry = mode.provided_optimizer.including("random_make_inplace", check_parameter_opt)
    969 mode = Mode(linker=mode.linker, optimizer=opt_qry)
--> 970 aesara_function = aesara.function(
    971     inputs,
    972     outputs,
    973     updates={**rng_updates, **kwargs.pop("updates", {})},
    974     mode=mode,
    975     **kwargs,
    976 )
    977 return aesara_function

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/compile/function/__init__.py:317, in function(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input)
    311     fn = orig_function(
    312         inputs, outputs, mode=mode, accept_inplace=accept_inplace, name=name
    313     )
    314 else:
    315     # note: pfunc will also call orig_function -- orig_function is
    316     #      a choke point that all compilation must pass through
--> 317     fn = pfunc(
    318         params=inputs,
    319         outputs=outputs,
    320         mode=mode,
    321         updates=updates,
    322         givens=givens,
    323         no_default_updates=no_default_updates,
    324         accept_inplace=accept_inplace,
    325         name=name,
    326         rebuild_strict=rebuild_strict,
    327         allow_input_downcast=allow_input_downcast,
    328         on_unused_input=on_unused_input,
    329         profile=profile,
    330         output_keys=output_keys,
    331     )
    332 return fn

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/compile/function/pfunc.py:371, 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)
    357     profile = ProfileStats(message=profile)
    359 inputs, cloned_outputs = construct_pfunc_ins_and_outs(
    360     params,
    361     outputs,
   (...)
    368     fgraph=fgraph,
    369 )
--> 371 return orig_function(
    372     inputs,
    373     cloned_outputs,
    374     mode,
    375     accept_inplace=accept_inplace,
    376     name=name,
    377     profile=profile,
    378     on_unused_input=on_unused_input,
    379     output_keys=output_keys,
    380     fgraph=fgraph,
    381 )

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/compile/function/types.py:1759, in orig_function(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys, fgraph)
   1747     m = Maker(
   1748         inputs,
   1749         outputs,
   (...)
   1756         fgraph=fgraph,
   1757     )
   1758     with config.change_flags(compute_test_value="off"):
-> 1759         fn = m.create(defaults)
   1760 finally:
   1761     t2 = time.time()

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/compile/function/types.py:1652, in FunctionMaker.create(self, input_storage, trustme, storage_map)
   1649 start_import_time = aesara.link.c.cmodule.import_time
   1651 with config.change_flags(traceback__limit=config.traceback__compile_limit):
-> 1652     _fn, _i, _o = self.linker.make_thunk(
   1653         input_storage=input_storage_lists, storage_map=storage_map
   1654     )
   1656 end_linker = time.time()
   1658 linker_time = end_linker - start_linker

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/link/basic.py:254, in LocalLinker.make_thunk(self, input_storage, output_storage, storage_map, **kwargs)
    247 def make_thunk(
    248     self,
    249     input_storage: Optional["InputStorageType"] = None,
   (...)
    252     **kwargs,
    253 ) -> Tuple["BasicThunkType", "InputStorageType", "OutputStorageType"]:
--> 254     return self.make_all(
    255         input_storage=input_storage,
    256         output_storage=output_storage,
    257         storage_map=storage_map,
    258     )[:3]

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/link/vm.py:1255, in VMLinker.make_all(self, profiler, input_storage, output_storage, storage_map)
   1253             thunks[-1].lazy = False
   1254     except Exception:
-> 1255         raise_with_op(fgraph, node)
   1257 t1 = time.time()
   1259 if self.profile:

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/link/utils.py:534, in raise_with_op(fgraph, node, thunk, exc_info, storage_map)
    529     warnings.warn(
    530         f"{exc_type} error does not allow us to add an extra error message"
    531     )
    532     # Some exception need extra parameter in inputs. So forget the
    533     # extra long error message in that case.
--> 534 raise exc_value.with_traceback(exc_trace)

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/link/vm.py:1246, in VMLinker.make_all(self, profiler, input_storage, output_storage, storage_map)
   1241 thunk_start = time.time()
   1242 # no-recycling is done at each VM.__call__ So there is
   1243 # no need to cause duplicate c code by passing
   1244 # no_recycling here.
   1245 thunks.append(
-> 1246     node.op.make_thunk(node, storage_map, compute_map, [], impl=impl)
   1247 )
   1248 linker_make_thunk_time[node] = time.time() - thunk_start
   1249 if not hasattr(thunks[-1], "lazy"):
   1250     # We don't want all ops maker to think about lazy Ops.
   1251     # So if they didn't specify that its lazy or not, it isn't.
   1252     # If this member isn't present, it will crash later.

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/scan/op.py:1534, in Scan.make_thunk(self, node, storage_map, compute_map, no_recycling, impl)
   1529 node_output_storage = [storage_map[r] for r in node.outputs]
   1531 # Analyse the compile inner function to determine which inputs and
   1532 # outputs are on the gpu and speed up some checks during the execution
   1533 outs_is_tensor = [
-> 1534     isinstance(out, TensorVariable) for out in self.fn.maker.fgraph.outputs
   1535 ]
   1537 try:
   1538     if impl == "py":

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/scan/op.py:1466, in Scan.fn(self)
   1463 elif self.profile:
   1464     profile = self.profile
-> 1466 self._fn = pfunc(
   1467     wrapped_inputs,
   1468     wrapped_outputs,
   1469     mode=self.mode_instance,
   1470     accept_inplace=False,
   1471     profile=profile,
   1472     on_unused_input="ignore",
   1473     fgraph=self.fgraph,
   1474 )
   1476 return self._fn

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/compile/function/pfunc.py:371, 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)
    357     profile = ProfileStats(message=profile)
    359 inputs, cloned_outputs = construct_pfunc_ins_and_outs(
    360     params,
    361     outputs,
   (...)
    368     fgraph=fgraph,
    369 )
--> 371 return orig_function(
    372     inputs,
    373     cloned_outputs,
    374     mode,
    375     accept_inplace=accept_inplace,
    376     name=name,
    377     profile=profile,
    378     on_unused_input=on_unused_input,
    379     output_keys=output_keys,
    380     fgraph=fgraph,
    381 )

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/compile/function/types.py:1759, in orig_function(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys, fgraph)
   1747     m = Maker(
   1748         inputs,
   1749         outputs,
   (...)
   1756         fgraph=fgraph,
   1757     )
   1758     with config.change_flags(compute_test_value="off"):
-> 1759         fn = m.create(defaults)
   1760 finally:
   1761     t2 = time.time()

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/compile/function/types.py:1667, in FunctionMaker.create(self, input_storage, trustme, storage_map)
   1664     import_time = aesara.link.c.cmodule.import_time - start_import_time
   1665     self.profile.import_time += import_time
-> 1667 fn = self.function_builder(
   1668     _fn,
   1669     _i,
   1670     _o,
   1671     self.indices,
   1672     self.outputs,
   1673     defaults,
   1674     self.unpack_single,
   1675     self.return_none,
   1676     self.output_keys,
   1677     self,
   1678     name=self.name,
   1679 )
   1681 fn.profile = self.profile
   1682 return fn

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/compile/function/types.py:448, in Function.__init__(self, vm, input_storage, output_storage, indices, outputs, defaults, unpack_single, return_none, output_keys, maker, name)
    446         assert not refeed
    447     else:
--> 448         c.value = value
    449 c.required = required
    450 c.implicit = input.implicit

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/link/basic.py:115, in Container.__set__(self, value)
    111         self.storage[0] = self.type.filter_inplace(
    112             value, self.storage[0], **kwargs
    113         )
    114     except NotImplementedError:
--> 115         self.storage[0] = self.type.filter(value, **kwargs)
    117 except Exception as e:
    118     e.args = e.args + (f'Container name "{self.name}"',)

File ~/.virtualenvs/mfsera-env/lib/python3.10/site-packages/aesara/tensor/type.py:244, in TensorType.filter(self, data, strict, allow_downcast)
    235     raise TypeError(
    236         "The numpy.ndarray object is not aligned."
    237         " Aesara C code does not support that.",
    238     )
    240 if not all(
    241     ds == ts if ts is not None else True
    242     for ds, ts in zip(data.shape, self.shape)
    243 ):
--> 244     raise TypeError(
    245         f"The type's shape ({self.shape}) is not compatible with the data's ({data.shape})"
    246     )
    248 if self.filter_checks_isfinite and not np.all(np.isfinite(data)):
    249     raise ValueError("Non-finite elements not allowed")

TypeError: ("The type's shape ((844,)) is not compatible with the data's ((0,))", 'Container name "None"')
Apply node that caused the error: for{inplace{0,2,3,},cpu,grad_of_scan_fn}(TensorConstant{4}, Elemwise{pow,no_inplace}.0, Elemwise{mul,no_inplace}.0, Elemwise{Composite{(i0 - (i1 / i2))}}.0, Elemwise{pow}.0, Subtensor{int64:int64:int64}.0, TensorConstant{[[12. 13. .. 11. 10.]]}, Subtensor{int64:int64:int64}.0, Subtensor{::int64}.0, TensorConstant{(2,) of 0.0}, DeepCopyOp.0, DeepCopyOp.0, InplaceDimShuffle{x}.0, InplaceDimShuffle{x}.0, Elemwise{sqr,no_inplace}.0, Elemwise{log}.0, Elemwise{EQ}.0)
Toposort index: 79
Inputs types: [TensorType(int64, ()), TensorType(float64, (None, None)), TensorType(float64, (None, None)), TensorType(float64, (None, None)), TensorType(float64, (None, None)), TensorType(float64, (None, None)), TensorType(float64, (4, 844)), TensorType(float64, (None, None)), TensorType(float64, (None, None)), TensorType(float64, (2,)), TensorType(float64, (2,)), TensorType(float64, (2,)), TensorType(float64, (1,)), TensorType(float64, (1,)), TensorType(float64, (1,)), TensorType(float64, (1,)), TensorType(bool, (1,))]

HINT: Use a linker other than the C linker to print the inputs' shapes and strides.
HINT: Re-running with most Aesara optimizations disabled could provide a back-trace showing when this node was created. This can be done by setting the Aesara flag 'optimizer=fast_compile'. If that does not work, Aesara optimizations can be disabled with 'optimizer=None'.
HINT: Use the Aesara flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.