Scan requires random variables to be referenced as non sequences

[ferrine]

Works:

import pytensor
import pytensor.tensor as at
import numpy as np

data = at.constant(np.random.randn(64))
srng = pytensor.tensor.random.RandomStream()
index = srng.integers(64, size=10)
datai = data[index]
var = at.vector("var")
scan = pytensor.scan(
    lambda v, *_: ((datai-v)**2).sum(), 
    sequences=var, non_sequences=[index], 
    strict=True
)
print(scan[0].eval({var: np.array([1., 2.])}))
print(pytensor.grad(scan[0].sum(), var).eval({var: [1, 1]}))

Raises an uninformative or fairly informative error

import pytensor
import pytensor.tensor as at
import numpy as np

data = at.constant(np.random.randn(64))
srng = pytensor.tensor.random.RandomStream()
index = srng.integers(64, size=10)
datai = data[index]
var = at.vector("var")
scan = pytensor.scan(
    lambda v: ((datai-v)**2).sum(), 
    sequences=var, 
    strict=True
)
print(scan[0].eval({var: np.array([1., 2.])}))
print(pytensor.grad(scan[0].sum(), var).eval({var: [1, 1]}))

---------------------------------------------------------------------------
MissingInputError                         Traceback (most recent call last)
Cell In [129], line 10
      8 datai = data[index]
      9 var = at.vector("var")
---> 10 scan = aesara.scan(
     11     lambda v, *_: ((datai-v)**2).sum(), 
     12     sequences=var, #non_sequences=[index],
     13     strict=True
     14 )
     15 print(scan[0].eval({var: np.array([1., 2.])}))
     16 print(aesara.grad(scan[0].sum(), var).eval({var: [1, 1]}))

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/scan/basic.py:1140, in scan(fn, sequences, outputs_info, non_sequences, n_steps, truncate_gradient, go_backwards, mode, name, profile, allow_gc, strict, return_list)
   1126     allow_gc = config.scan__allow_gc
   1128 info = ScanInfo(
   1129     n_seqs=n_seqs,
   1130     mit_mot_in_slices=(),
   (...)
   1137     as_while=as_while,
   1138 )
-> 1140 local_op = Scan(
   1141     inner_inputs,
   1142     new_outs,
   1143     info,
   1144     mode=mode,
   1145     truncate_gradient=truncate_gradient,
   1146     name=name,
   1147     profile=profile,
   1148     allow_gc=allow_gc,
   1149     strict=strict,
   1150 )
   1152 ##
   1153 # Step 8. Compute the outputs using the scan op
   1154 ##
   1155 _scan_inputs = (
   1156     scan_seqs
   1157     + mit_mot_scan_inputs
   (...)
   1163     + other_scan_args
   1164 )

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/scan/op.py:859, in Scan.__init__(self, inputs, outputs, info, mode, typeConstructor, truncate_gradient, name, as_while, profile, allow_gc, strict)
    856 self.n_outer_inputs = info.n_outer_inputs
    857 self.n_outer_outputs = info.n_outer_outputs
--> 859 self.fgraph = FunctionGraph(inputs, outputs, clone=False)
    861 _ = self.prepare_fgraph(self.fgraph)
    863 if any(node.op.destroy_map for node in self.fgraph.apply_nodes):

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/graph/fg.py:153, in FunctionGraph.__init__(self, inputs, outputs, features, clone, update_mapping, **clone_kwds)
    150     self.add_input(in_var, check=False)
    152 for output in outputs:
--> 153     self.add_output(output, reason="init")
    155 self.profile = None
    156 self.update_mapping = update_mapping

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/graph/fg.py:163, in FunctionGraph.add_output(self, var, reason, import_missing)
    161 """Add a new variable as an output to this `FunctionGraph`."""
    162 self.outputs.append(var)
--> 163 self.import_var(var, reason=reason, import_missing=import_missing)
    164 self.clients[var].append(("output", len(self.outputs) - 1))

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/graph/fg.py:304, in FunctionGraph.import_var(self, var, reason, import_missing)
    302 # Imports the owners of the variables
    303 if var.owner and var.owner not in self.apply_nodes:
--> 304     self.import_node(var.owner, reason=reason, import_missing=import_missing)
    305 elif (
    306     var.owner is None
    307     and not isinstance(var, AtomicVariable)
    308     and var not in self.inputs
    309 ):
    310     from aesara.graph.null_type import NullType

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/graph/fg.py:369, in FunctionGraph.import_node(self, apply_node, check, reason, import_missing)
    360                 else:
    361                     error_msg = (
    362                         f"Input {node.inputs.index(var)} ({var})"
    363                         " of the graph (indices start "
   (...)
    367                         "for more information on this error."
    368                     )
--> 369                     raise MissingInputError(error_msg, variable=var)
    371 for node in new_nodes:
    372     assert node not in self.apply_nodes

MissingInputError: Input 0 (RandomGeneratorSharedVariable(<Generator(PCG64) at 0x7FCBAFBA03C0>)) of the graph (indices start from 0), used to compute integers_rv{0, (0, 0), int64, False}(RandomGeneratorSharedVariable(<Generator(PCG64) at 0x7FCBAFBA03C0>), TensorConstant{(1,) of 10}, TensorConstant{4}, TensorConstant{0}, TensorConstant{64}), was not provided and not given a value. Use the Aesara flag exception_verbosity='high', for more information on this error.
 
Backtrace when that variable is created:

  File "/home/mkochurov/micromamba/envs/bayes/lib/python3.9/site-packages/ipykernel/zmqshell.py", line 528, in run_cell
    return super().run_cell(*args, **kwargs)
  File "/home/mkochurov/micromamba/envs/bayes/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 2940, in run_cell
    result = self._run_cell(
  File "/home/mkochurov/micromamba/envs/bayes/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 2995, in _run_cell
    return runner(coro)
  File "/home/mkochurov/micromamba/envs/bayes/lib/python3.9/site-packages/IPython/core/async_helpers.py", line 129, in _pseudo_sync_runner
    coro.send(None)
  File "/home/mkochurov/micromamba/envs/bayes/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3194, in run_cell_async
    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
  File "/home/mkochurov/micromamba/envs/bayes/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3373, in run_ast_nodes
    if await self.run_code(code, result, async_=asy):
  File "/home/mkochurov/micromamba/envs/bayes/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3433, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "/tmp/ipykernel_441370/4148489949.py", line 7, in <module>
    index = srng.integers(64, size=10)

Scan silently passes and aesara.grad fails miserably later if strict=False

import pytensor
import pytensor.tensor as at
import numpy as np

data = at.constant(np.random.randn(64))
srng = pytensor.tensor.random.RandomStream()
index = srng.integers(64, size=10)
datai = data[index]
var = at.vector("var")
scan = pytensor.scan(
    lambda v: ((datai-v)**2).sum(), 
    sequences=var 
    strict=False
)
print(scan[0].eval({var: np.array([1., 2.])}))
print(pytensor.grad(scan[0].sum(), var).eval({var: [1, 1]}))

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In [130], line 16
     10 scan = aesara.scan(
     11     lambda v, *_: ((datai-v)**2).sum(), 
     12     sequences=var, #non_sequences=[index],
     13     strict=False
     14 )
     15 print(scan[0].eval({var: np.array([1., 2.])}))
---> 16 print(aesara.grad(scan[0].sum(), var).eval({var: [1, 1]}))

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/gradient.py:623, in grad(cost, wrt, consider_constant, disconnected_inputs, add_names, known_grads, return_disconnected, null_gradients)
    620     if hasattr(g.type, "dtype"):
    621         assert g.type.dtype in aesara.tensor.type.float_dtypes
--> 623 _rval: Sequence[Variable] = _populate_grad_dict(
    624     var_to_app_to_idx, grad_dict, _wrt, cost_name
    625 )
    627 rval: MutableSequence[Optional[Variable]] = list(_rval)
    629 for i in range(len(_rval)):

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/gradient.py:1434, in _populate_grad_dict(var_to_app_to_idx, grad_dict, wrt, cost_name)
   1431     # end if cache miss
   1432     return grad_dict[var]
-> 1434 rval = [access_grad_cache(elem) for elem in wrt]
   1436 return rval

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/gradient.py:1434, in <listcomp>(.0)
   1431     # end if cache miss
   1432     return grad_dict[var]
-> 1434 rval = [access_grad_cache(elem) for elem in wrt]
   1436 return rval

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/gradient.py:1387, in _populate_grad_dict.<locals>.access_grad_cache(var)
   1384 for node in node_to_idx:
   1385     for idx in node_to_idx[node]:
-> 1387         term = access_term_cache(node)[idx]
   1389         if not isinstance(term, Variable):
   1390             raise TypeError(
   1391                 f"{node.op}.grad returned {type(term)}, expected"
   1392                 " Variable instance."
   1393             )

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/gradient.py:1058, in _populate_grad_dict.<locals>.access_term_cache(node)
   1054 if node not in term_dict:
   1056     inputs = node.inputs
-> 1058     output_grads = [access_grad_cache(var) for var in node.outputs]
   1060     # list of bools indicating if each output is connected to the cost
   1061     outputs_connected = [
   1062         not isinstance(g.type, DisconnectedType) for g in output_grads
   1063     ]

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/gradient.py:1058, in <listcomp>(.0)
   1054 if node not in term_dict:
   1056     inputs = node.inputs
-> 1058     output_grads = [access_grad_cache(var) for var in node.outputs]
   1060     # list of bools indicating if each output is connected to the cost
   1061     outputs_connected = [
   1062         not isinstance(g.type, DisconnectedType) for g in output_grads
   1063     ]

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/gradient.py:1387, in _populate_grad_dict.<locals>.access_grad_cache(var)
   1384 for node in node_to_idx:
   1385     for idx in node_to_idx[node]:
-> 1387         term = access_term_cache(node)[idx]
   1389         if not isinstance(term, Variable):
   1390             raise TypeError(
   1391                 f"{node.op}.grad returned {type(term)}, expected"
   1392                 " Variable instance."
   1393             )

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/gradient.py:1058, in _populate_grad_dict.<locals>.access_term_cache(node)
   1054 if node not in term_dict:
   1056     inputs = node.inputs
-> 1058     output_grads = [access_grad_cache(var) for var in node.outputs]
   1060     # list of bools indicating if each output is connected to the cost
   1061     outputs_connected = [
   1062         not isinstance(g.type, DisconnectedType) for g in output_grads
   1063     ]

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/gradient.py:1058, in <listcomp>(.0)
   1054 if node not in term_dict:
   1056     inputs = node.inputs
-> 1058     output_grads = [access_grad_cache(var) for var in node.outputs]
   1060     # list of bools indicating if each output is connected to the cost
   1061     outputs_connected = [
   1062         not isinstance(g.type, DisconnectedType) for g in output_grads
   1063     ]

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/gradient.py:1387, in _populate_grad_dict.<locals>.access_grad_cache(var)
   1384 for node in node_to_idx:
   1385     for idx in node_to_idx[node]:
-> 1387         term = access_term_cache(node)[idx]
   1389         if not isinstance(term, Variable):
   1390             raise TypeError(
   1391                 f"{node.op}.grad returned {type(term)}, expected"
   1392                 " Variable instance."
   1393             )

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/gradient.py:1213, in _populate_grad_dict.<locals>.access_term_cache(node)
   1205         if o_shape != g_shape:
   1206             raise ValueError(
   1207                 "Got a gradient of shape "
   1208                 + str(o_shape)
   1209                 + " on an output of shape "
   1210                 + str(g_shape)
   1211             )
-> 1213 input_grads = node.op.L_op(inputs, node.outputs, new_output_grads)
   1215 if input_grads is None:
   1216     raise TypeError(
   1217         f"{node.op}.grad returned NoneType, expected iterable."
   1218     )

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/scan/op.py:2613, in Scan.L_op(self, inputs, outs, dC_douts)
   2611 for dx in range(len(dC_dinps_t)):
   2612     if not dC_dinps_t[dx]:
-> 2613         dC_dinps_t[dx] = at.zeros_like(diff_inputs[dx])
   2614     else:
   2615         disconnected_dC_dinps_t[dx] = False

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/tensor/basic.py:798, in zeros_like(model, dtype, opt)
    782 def zeros_like(model, dtype=None, opt=False):
    783     """equivalent of numpy.zeros_like
    784     Parameters
    785     ----------
   (...)
    795         tensor the shape of model containing zeros of the type of dtype.
    796     """
--> 798     _model = as_tensor_variable(model)
    800     if dtype is None:
    801         dtype = _model.type.dtype

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/tensor/__init__.py:49, in as_tensor_variable(x, name, ndim, **kwargs)
     17 def as_tensor_variable(
     18     x: TensorLike, name: Optional[str] = None, ndim: Optional[int] = None, **kwargs
     19 ) -> "TensorVariable":
     20     """Convert `x` into an equivalent `TensorVariable`.
     21 
     22     This function can be used to turn ndarrays, numbers, `ScalarType` instances,
   (...)
     47 
     48     """
---> 49     return _as_tensor_variable(x, name, ndim, **kwargs)

File ~/micromamba/envs/bayes/lib/python3.9/functools.py:888, in singledispatch.<locals>.wrapper(*args, **kw)
    884 if not args:
    885     raise TypeError(f'{funcname} requires at least '
    886                     '1 positional argument')
--> 888 return dispatch(args[0].__class__)(*args, **kw)

File ~/micromamba/envs/bayes/lib/python3.9/site-packages/aesara/tensor/basic.py:100, in _as_tensor_Variable(x, name, ndim, **kwargs)
     97 @_as_tensor_variable.register(Variable)
     98 def _as_tensor_Variable(x, name, ndim, **kwargs):
     99     if not isinstance(x.type, TensorType):
--> 100         raise TypeError(
    101             f"Tensor type field must be a TensorType; found {type(x.type)}."
    102         )
    104     if ndim is None:
    105         return x

TypeError: Tensor type field must be a TensorType; found <class 'aesara.tensor.random.type.RandomGeneratorType'>.

**Please provide any additional information below.**

Versions and main components

• Aesara version: '2.8.7'
• Python version: '3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:58:50) [GCC 10.3.0]'
• Operating system: Ubuntu
• How did you install Aesara: (conda/pip) conda

pytensor config:

floatX ({'float64', 'float16', 'float32'}) 
    Doc:  Default floating-point precision for python casts.

Note: float16 support is experimental, use at your own risk.
    Value:  float64

warn_float64 ({'ignore', 'pdb', 'warn', 'raise'}) 
    Doc:  Do an action when a tensor variable with float64 dtype is created.
    Value:  ignore

pickle_test_value (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcc2c19ac10>>) 
    Doc:  Dump test values while pickling model. If True, test values will be dumped with model.
    Value:  True

cast_policy ({'custom', 'numpy+floatX'}) 
    Doc:  Rules for implicit type casting
    Value:  custom

deterministic ({'more', 'default'}) 
    Doc:  If `more`, sometimes we will select some implementation that are more deterministic, but slower.  Also see the dnn.conv.algo* flags to cover more cases.
    Value:  default

device (cpu)
    Doc:  Default device for computations. only cpu is supported for now
    Value:  cpu

force_device (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcc2c19a610>>) 
    Doc:  Raise an error if we can't use the specified device
    Value:  False

conv__assert_shape (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcc2c19a5b0>>) 
    Doc:  If True, AbstractConv* ops will verify that user-provided shapes match the runtime shapes (debugging option, may slow down compilation)
    Value:  False

print_global_stats (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5baa670>>) 
    Doc:  Print some global statistics (time spent) at the end
    Value:  False

assert_no_cpu_op ({'ignore', 'pdb', 'warn', 'raise'}) 
    Doc:  Raise an error/warning if there is a CPU op in the computational graph.
    Value:  ignore

unpickle_function (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5baa850>>) 
    Doc:  Replace unpickled Aesara functions with None. This is useful to unpickle old graphs that pickled them when it shouldn't
    Value:  True

<aesara.configparser.ConfigParam object at 0x7fcbc5baa8b0>
    Doc:  Default compilation mode
    Value:  Mode

cxx (<class 'str'>) 
    Doc:  The C++ compiler to use. Currently only g++ is supported, but supporting additional compilers should not be too difficult. If it is empty, no C++ code is compiled.
    Value:  /usr/bin/g++

linker ({'vm_nogc', 'c|py_nogc', 'c', 'py', 'c|py', 'vm', 'cvm_nogc', 'cvm'}) 
    Doc:  Default linker used if the aesara flags mode is Mode
    Value:  cvm

allow_gc (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5baaa30>>) 
    Doc:  Do we default to delete intermediate results during Aesara function calls? Doing so lowers the memory requirement, but asks that we reallocate memory at the next function call. This is implemented for the default linker, but may not work for all linkers.
    Value:  True

optimizer ({'o2', 'o1', 'o3', 'None', 'fast_compile', 'o4', 'fast_run', 'unsafe', 'merge'}) 
    Doc:  Default optimizer. If not None, will use this optimizer with the Mode
    Value:  o4

optimizer_verbose (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41550>>) 
    Doc:  If True, we print all optimization being applied
    Value:  False

on_opt_error ({'ignore', 'pdb', 'warn', 'raise'}) 
    Doc:  What to do when an optimization crashes: warn and skip it, raise the exception, or fall into the pdb debugger.
    Value:  warn

nocleanup (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b415b0>>) 
    Doc:  Suppress the deletion of code files that did not compile cleanly
    Value:  False

on_unused_input ({'ignore', 'warn', 'raise'}) 
    Doc:  What to do if a variable in the 'inputs' list of  aesara.function() is not used in the graph.
    Value:  raise

gcc__cxxflags (<class 'str'>) 
    Doc:  Extra compiler flags for gcc
    Value:   -Wno-c++11-narrowing -fno-exceptions -fno-unwind-tables -fno-asynchronous-unwind-tables

cmodule__warn_no_version (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41640>>) 
    Doc:  If True, will print a warning when compiling one or more Op with C code that can't be cached because there is no c_code_cache_version() function associated to at least one of those Ops.
    Value:  False

cmodule__remove_gxx_opt (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41400>>) 
    Doc:  If True, will remove the -O* parameter passed to g++.This is useful to debug in gdb modules compiled by Aesara.The parameter -g is passed by default to g++
    Value:  False

cmodule__compilation_warning (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41670>>) 
    Doc:  If True, will print compilation warnings.
    Value:  False

cmodule__preload_cache (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b416a0>>) 
    Doc:  If set to True, will preload the C module cache at import time
    Value:  False

cmodule__age_thresh_use (<class 'int'>) 
    Doc:  In seconds. The time after which Aesara won't reuse a compile c module.
    Value:  2073600

cmodule__debug (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41700>>) 
    Doc:  If True, define a DEBUG macro (if not exists) for any compiled C code.
    Value:  False

compile__wait (<class 'int'>) 
    Doc:  Time to wait before retrying to acquire the compile lock.
    Value:  5

compile__timeout (<class 'int'>) 
    Doc:  In seconds, time that a process will wait before deciding to
    override an existing lock. An override only happens when the existing
    lock is held by the same owner *and* has not been 'refreshed' by this
    owner for more than this period. Refreshes are done every half timeout
    period for running processes.
    Value:  120

ctc__root (<class 'str'>) 
    Doc:  Directory which contains the root of Baidu CTC library. It is assumed         that the compiled library is either inside the build, lib or lib64         subdirectory, and the header inside the include directory.
    Value:  

tensor__cmp_sloppy (<class 'int'>) 
    Doc:  Relax aesara.tensor.math._allclose (0) not at all, (1) a bit, (2) more
    Value:  0

tensor__local_elemwise_fusion (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b418b0>>) 
    Doc:  Enable or not in fast_run mode(fast_run optimization) the elemwise fusion optimization
    Value:  True

lib__amblibm (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41940>>) 
    Doc:  Use amd's amdlibm numerical library
    Value:  False

tensor__insert_inplace_optimizer_validate_nb (<class 'int'>) 
    Doc:  -1: auto, if graph have less then 500 nodes 1, else 10
    Value:  -1

traceback__limit (<class 'int'>) 
    Doc:  The number of stack to trace. -1 mean all.
    Value:  8

traceback__compile_limit (<class 'int'>) 
    Doc:  The number of stack to trace to keep during compilation. -1 mean all. If greater then 0, will also make us save Aesara internal stack trace.
    Value:  0

experimental__local_alloc_elemwise (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41ac0>>) 
    Doc:  DEPRECATED: If True, enable the experimental optimization local_alloc_elemwise. Generates error if not True. Use optimizer_excluding=local_alloc_elemwise to disable.
    Value:  True

experimental__local_alloc_elemwise_assert (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41af0>>) 
    Doc:  When the local_alloc_elemwise is applied, add an assert to highlight shape errors.
    Value:  True

warn__ignore_bug_before ({'0.8.2', '0.10', 'all', '0.5', '0.7', '1.0.3', '1.0.5', 'None', '0.8', '1.0.2', '0.3', '1.0', '0.4', '0.6', '0.9', '0.4.1', '1.0.1', '0.8.1', '1.0.4'}) 
    Doc:  If 'None', we warn about all Aesara bugs found by default. If 'all', we don't warn about Aesara bugs found by default. If a version, we print only the warnings relative to Aesara bugs found after that version. Warning for specific bugs can be configured with specific [warn] flags.
    Value:  0.9

exception_verbosity ({'high', 'low'}) 
    Doc:  If 'low', the text of exceptions will generally refer to apply nodes with short names such as Elemwise{add_no_inplace}. If 'high', some exceptions will also refer to apply nodes with long descriptions  like:
        A. Elemwise{add_no_inplace}
                B. log_likelihood_v_given_h
                C. log_likelihood_h
    Value:  low

print_test_value (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41c40>>) 
    Doc:  If 'True', the __eval__ of an Aesara variable will return its test_value when this is available. This has the practical conseguence that, e.g., in debugging `my_var` will print the same as `my_var.tag.test_value` when a test value is defined.
    Value:  False

compute_test_value ({'raise', 'ignore', 'pdb', 'warn', 'off'}) 
    Doc:  If 'True', Aesara will run each op at graph build time, using Constants, SharedVariables and the tag 'test_value' as inputs to the function. This helps the user track down problems in the graph before it gets optimized.
    Value:  off

compute_test_value_opt ({'raise', 'ignore', 'pdb', 'warn', 'off'}) 
    Doc:  For debugging Aesara optimization only. Same as compute_test_value, but is used during Aesara optimization
    Value:  off

check_input (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41cd0>>) 
    Doc:  Specify if types should check their input in their C code. It can be used to speed up compilation, reduce overhead (particularly for scalars) and reduce the number of generated C files.
    Value:  True

NanGuardMode__nan_is_error (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41d00>>) 
    Doc:  Default value for nan_is_error
    Value:  True

NanGuardMode__inf_is_error (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41d30>>) 
    Doc:  Default value for inf_is_error
    Value:  True

NanGuardMode__big_is_error (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41dc0>>) 
    Doc:  Default value for big_is_error
    Value:  True

NanGuardMode__action ({'pdb', 'warn', 'raise'}) 
    Doc:  What NanGuardMode does when it finds a problem
    Value:  raise

DebugMode__patience (<class 'int'>) 
    Doc:  Optimize graph this many times to detect inconsistency
    Value:  10

DebugMode__check_c (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41e50>>) 
    Doc:  Run C implementations where possible
    Value:  True

DebugMode__check_py (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41ee0>>) 
    Doc:  Run Python implementations where possible
    Value:  True

DebugMode__check_finite (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41f10>>) 
    Doc:  True -> complain about NaN/Inf results
    Value:  True

DebugMode__check_strides (<class 'int'>) 
    Doc:  Check that Python- and C-produced ndarrays have same strides. On difference: (0) - ignore, (1) warn, or (2) raise error
    Value:  0

DebugMode__warn_input_not_reused (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41f70>>) 
    Doc:  Generate a warning when destroy_map or view_map says that an op works inplace, but the op did not reuse the input for its output.
    Value:  True

DebugMode__check_preallocated_output (<class 'str'>) 
    Doc:  Test thunks with pre-allocated memory as output storage. This is a list of strings separated by ":". Valid values are: "initial" (initial storage in storage map, happens with Scan),"previous" (previously-returned memory), "c_contiguous", "f_contiguous", "strided" (positive and negative strides), "wrong_size" (larger and smaller dimensions), and "ALL" (all of the above).
    Value:  

DebugMode__check_preallocated_output_ndim (<class 'int'>) 
    Doc:  When testing with "strided" preallocated output memory, test all combinations of strides over that number of (inner-most) dimensions. You may want to reduce that number to reduce memory or time usage, but it is advised to keep a minimum of 2.
    Value:  4

profiling__time_thunks (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b41e80>>) 
    Doc:  Time individual thunks when profiling
    Value:  True

profiling__n_apply (<class 'int'>) 
    Doc:  Number of Apply instances to print by default
    Value:  20

profiling__n_ops (<class 'int'>) 
    Doc:  Number of Ops to print by default
    Value:  20

profiling__output_line_width (<class 'int'>) 
    Doc:  Max line width for the profiling output
    Value:  512

profiling__min_memory_size (<class 'int'>) 
    Doc:  For the memory profile, do not print Apply nodes if the size
                 of their outputs (in bytes) is lower than this threshold
    Value:  1024

profiling__min_peak_memory (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b52190>>) 
    Doc:  The min peak memory usage of the order
    Value:  False

profiling__destination (<class 'str'>) 
    Doc:  File destination of the profiling output
    Value:  stderr

profiling__debugprint (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b521f0>>) 
    Doc:  Do a debugprint of the profiled functions
    Value:  False

profiling__ignore_first_call (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b52220>>) 
    Doc:  Do we ignore the first call of an Aesara function.
    Value:  False

on_shape_error ({'warn', 'raise'}) 
    Doc:  warn: print a warning and use the default value. raise: raise an error
    Value:  warn

openmp (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b52280>>) 
    Doc:  Allow (or not) parallel computation on the CPU with OpenMP. This is the default value used when creating an Op that supports OpenMP parallelization. It is preferable to define it via the Aesara configuration file ~/.aesararc or with the environment variable AESARA_FLAGS. Parallelization is only done for some operations that implement it, and even for operations that implement parallelism, each operation is free to respect this flag or not. You can control the number of threads used with the environment variable OMP_NUM_THREADS. If it is set to 1, we disable openmp in Aesara by default.
    Value:  False

openmp_elemwise_minsize (<class 'int'>) 
    Doc:  If OpenMP is enabled, this is the minimum size of vectors for which the openmp parallelization is enabled in element wise ops.
    Value:  200000

optimizer_excluding (<class 'str'>) 
    Doc:  When using the default mode, we will remove optimizer with these tags. Separate tags with ':'.
    Value:  

optimizer_including (<class 'str'>) 
    Doc:  When using the default mode, we will add optimizer with these tags. Separate tags with ':'.
    Value:  

optimizer_requiring (<class 'str'>) 
    Doc:  When using the default mode, we will require optimizer with these tags. Separate tags with ':'.
    Value:  

optdb__position_cutoff (<class 'float'>) 
    Doc:  Where to stop eariler during optimization. It represent the position of the optimizer where to stop.
    Value:  inf

optdb__max_use_ratio (<class 'float'>) 
    Doc:  A ratio that prevent infinite loop in EquilibriumGraphRewriter.
    Value:  8.0

cycle_detection ({'fast', 'regular'}) 
    Doc:  If cycle_detection is set to regular, most inplaces are allowed,but it is slower. If cycle_detection is set to faster, less inplacesare allowed, but it makes the compilation faster.The interaction of which one give the lower peak memory usage iscomplicated and not predictable, so if you are close to the peakmemory usage, triyng both could give you a small gain.
    Value:  regular

check_stack_trace ({'log', 'off', 'warn', 'raise'}) 
    Doc:  A flag for checking the stack trace during the optimization process. default (off): does not check the stack trace of any optimization log: inserts a dummy stack trace that identifies the optimizationthat inserted the variable that had an empty stack trace.warn: prints a warning if a stack trace is missing and also a dummystack trace is inserted that indicates which optimization insertedthe variable that had an empty stack trace.raise: raises an exception if a stack trace is missing
    Value:  off

metaopt__verbose (<class 'int'>) 
    Doc:  0 for silent, 1 for only warnings, 2 for full output withtimings and selected implementation
    Value:  0

metaopt__optimizer_excluding (<class 'str'>) 
    Doc:  exclude optimizers with these tags. Separate tags with ':'.
    Value:  

metaopt__optimizer_including (<class 'str'>) 
    Doc:  include optimizers with these tags. Separate tags with ':'.
    Value:  

profile (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b52580>>) 
    Doc:  If VM should collect profile information
    Value:  False

profile_optimizer (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b525b0>>) 
    Doc:  If VM should collect optimizer profile information
    Value:  False

profile_memory (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b525e0>>) 
    Doc:  If VM should collect memory profile information and print it
    Value:  False

<aesara.configparser.ConfigParam object at 0x7fcbc5b52610>
    Doc:  Useful only for the VM Linkers. When lazy is None, auto detect if lazy evaluation is needed and use the appropriate version. If the C loop isn't being used and lazy is True, use the Stack VM; otherwise, use the Loop VM.
    Value:  None

unittests__rseed (<class 'str'>) 
    Doc:  Seed to use for randomized unit tests. Special value 'random' means using a seed of None.
    Value:  666

warn__round (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b526d0>>) 
    Doc:  Warn when using `tensor.round` with the default mode. Round changed its default from `half_away_from_zero` to `half_to_even` to have the same default as NumPy.
    Value:  False

numba__vectorize_target ({'cuda', 'parallel', 'cpu'}) 
    Doc:  Default target for numba.vectorize.
    Value:  cpu

numba__fastmath (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b52790>>) 
    Doc:  If True, use Numba's fastmath mode.
    Value:  True

numba__cache (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbc5b52820>>) 
    Doc:  If True, use Numba's file based caching.
    Value:  True

compiledir_format (<class 'str'>) 
    Doc:  Format string for platform-dependent compiled module subdirectory
(relative to base_compiledir). Available keys: aesara_version, device,
gxx_version, hostname, numpy_version, platform, processor,
python_bitwidth, python_int_bitwidth, python_version, short_platform.
Defaults to compiledir_%(short_platform)s-%(processor)s-%(python_versi
on)s-%(python_bitwidth)s.
    Value:  compiledir_%(short_platform)s-%(processor)s-%(python_version)s-%(python_bitwidth)s

<aesara.configparser.ConfigParam object at 0x7fcbc5b528e0>
    Doc:  platform-independent root directory for compiled modules
    Value:  /home/mkochurov/.aesara

<aesara.configparser.ConfigParam object at 0x7fcbc5b527f0>
    Doc:  platform-dependent cache directory for compiled modules
    Value:  /home/mkochurov/.aesara/compiledir_Linux-5.4--generic-x86_64-with-glibc2.31-x86_64-3.9.13-64

blas__ldflags (<class 'str'>) 
    Doc:  lib[s] to include for [Fortran] level-3 blas implementation
    Value:  -L/home/mkochurov/micromamba/envs/bayes/lib -lmkl_core -lmkl_intel_thread -lmkl_rt -Wl,-rpath,/home/mkochurov/micromamba/envs/bayes/lib

blas__check_openmp (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcc05b91640>>) 
    Doc:  Check for openmp library conflict.
WARNING: Setting this to False leaves you open to wrong results in blas-related operations.
    Value:  True

scan__allow_gc (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcbba30df10>>) 
    Doc:  Allow/disallow gc inside of Scan (default: False)
    Value:  False

scan__allow_output_prealloc (<bound method BoolParam._apply of <aesara.configparser.BoolParam object at 0x7fcc3034d1c0>>) 
    Doc:  Allow/disallow memory preallocation for outputs inside of scan (default: True)
    Value:  True

[ricardoV94]

Scan and OpFromGraph are a bit too eager in how they handle SharedVariables, even if they wouldn't necessarily be needed as inputs of the inner graph. For Scan at least, this is a bit related to the whole updates machinery, so that RandomVariables update across iterations without users saying so manually...