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Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.
https://aesara.readthedocs.io
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Scan requires outer random variables to be referenced in non_sequence #1301

Open ferrine opened 2 years ago

ferrine commented 2 years ago

Description of your problem or feature request

First, carefully read the following to determine whether or not you have a valid Aesara issue:

If the above does not apply, and you have an issue or feature request that's specific to Aesara, provide a minimal, self-contained, and reproducible example (i.e. an MWE):

Works:

import aesara
import aesara.tensor as at
import numpy as np

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

Raises an uninformative or fairly informative error

import aesara
import aesara.tensor as at
import numpy as np

data = at.constant(np.random.randn(64))
srng = aesara.tensor.random.RandomStream()
index = srng.integers(64, size=10)
datai = data[index]
var = at.vector("var")
scan = aesara.scan(
    lambda v: ((datai-v)**2).sum(), 
    sequences=var, 
    strict=True
)
print(scan[0].eval({var: np.array([1., 2.])}))
print(aesara.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()) of the graph (indices start from 0), used to compute integers_rv{0, (0, 0), int64, False}(RandomGeneratorSharedVariable(), 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 index = srng.integers(64, size=10) ```

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

import aesara
import aesara.tensor as at
import numpy as np

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

Please provide any additional information below.

Versions and main components

Aesara 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 (>) 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 (>) Doc: Raise an error if we can't use the specified device Value: False conv__assert_shape (>) 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 (>) 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 (>) Doc: Replace unpickled Aesara functions with None. This is useful to unpickle old graphs that pickled them when it shouldn't Value: True Doc: Default compilation mode Value: Mode cxx () 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 (>) 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 (>) 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 (>) 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 () Doc: Extra compiler flags for gcc Value: -Wno-c++11-narrowing -fno-exceptions -fno-unwind-tables -fno-asynchronous-unwind-tables cmodule__warn_no_version (>) 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 (>) 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 (>) Doc: If True, will print compilation warnings. Value: False cmodule__preload_cache (>) Doc: If set to True, will preload the C module cache at import time Value: False cmodule__age_thresh_use () Doc: In seconds. The time after which Aesara won't reuse a compile c module. Value: 2073600 cmodule__debug (>) Doc: If True, define a DEBUG macro (if not exists) for any compiled C code. Value: False compile__wait () Doc: Time to wait before retrying to acquire the compile lock. Value: 5 compile__timeout () 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 () 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 () Doc: Relax aesara.tensor.math._allclose (0) not at all, (1) a bit, (2) more Value: 0 tensor__local_elemwise_fusion (>) Doc: Enable or not in fast_run mode(fast_run optimization) the elemwise fusion optimization Value: True lib__amblibm (>) Doc: Use amd's amdlibm numerical library Value: False tensor__insert_inplace_optimizer_validate_nb () Doc: -1: auto, if graph have less then 500 nodes 1, else 10 Value: -1 traceback__limit () Doc: The number of stack to trace. -1 mean all. Value: 8 traceback__compile_limit () 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 (>) 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 (>) 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 (>) 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 (>) 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 (>) Doc: Default value for nan_is_error Value: True NanGuardMode__inf_is_error (>) Doc: Default value for inf_is_error Value: True NanGuardMode__big_is_error (>) 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 () Doc: Optimize graph this many times to detect inconsistency Value: 10 DebugMode__check_c (>) Doc: Run C implementations where possible Value: True DebugMode__check_py (>) Doc: Run Python implementations where possible Value: True DebugMode__check_finite (>) Doc: True -> complain about NaN/Inf results Value: True DebugMode__check_strides () 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 (>) 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 () 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 () 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 (>) Doc: Time individual thunks when profiling Value: True profiling__n_apply () Doc: Number of Apply instances to print by default Value: 20 profiling__n_ops () Doc: Number of Ops to print by default Value: 20 profiling__output_line_width () Doc: Max line width for the profiling output Value: 512 profiling__min_memory_size () 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 (>) Doc: The min peak memory usage of the order Value: False profiling__destination () Doc: File destination of the profiling output Value: stderr profiling__debugprint (>) Doc: Do a debugprint of the profiled functions Value: False profiling__ignore_first_call (>) 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 (>) 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 () 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 () Doc: When using the default mode, we will remove optimizer with these tags. Separate tags with ':'. Value: optimizer_including () Doc: When using the default mode, we will add optimizer with these tags. Separate tags with ':'. Value: optimizer_requiring () Doc: When using the default mode, we will require optimizer with these tags. Separate tags with ':'. Value: optdb__position_cutoff () Doc: Where to stop eariler during optimization. It represent the position of the optimizer where to stop. Value: inf optdb__max_use_ratio () 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 () Doc: 0 for silent, 1 for only warnings, 2 for full output withtimings and selected implementation Value: 0 metaopt__optimizer_excluding () Doc: exclude optimizers with these tags. Separate tags with ':'. Value: metaopt__optimizer_including () Doc: include optimizers with these tags. Separate tags with ':'. Value: profile (>) Doc: If VM should collect profile information Value: False profile_optimizer (>) Doc: If VM should collect optimizer profile information Value: False profile_memory (>) Doc: If VM should collect memory profile information and print it Value: False 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 () Doc: Seed to use for randomized unit tests. Special value 'random' means using a seed of None. Value: 666 warn__round (>) 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 (>) Doc: If True, use Numba's fastmath mode. Value: True numba__cache (>) Doc: If True, use Numba's file based caching. Value: True compiledir_format () 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 Doc: platform-independent root directory for compiled modules Value: /home/mkochurov/.aesara 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 () 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 (>) 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 (>) Doc: Allow/disallow gc inside of Scan (default: False) Value: False scan__allow_output_prealloc (>) Doc: Allow/disallow memory preallocation for outputs inside of scan (default: True) Value: True ```
ricardoV94 commented 2 years ago

I think this was somewhat related: https://github.com/aesara-devs/aeppl/issues/173#issuecomment-1236368193

ferrine commented 2 years ago

I tried the snippets from the link above, but non of them worked in my situation

rlouf commented 2 years ago

It is rather rude to close an issue relevant to this repo only to keep it on your fork: https://github.com/pymc-devs/pytensor/issues/6

brandonwillard commented 1 year ago

FYI: I have a fix for this that I'll put in soon, but parts of the problem are ultimately related to the limited Type support mentioned in https://github.com/aesara-devs/aesara/issues/738.