Open
Description
Describe the issue:
I'm using pymc and pytensor.tensor.fft to do uncertainty quantification in my signal processing. Since the pytensor.tensor.fft functions seem to still be geared toward neural networks, they all require a batch dimension in addition to the spatial dimensions. It should be easy enough to add a batch dimension to my tensors before feeding them into the fft functions and then removing that dimension afterward. However, calculating the gradient on such a function fails if that batch dimension's size is 1, saying Cannot drop a non-broadcastable dimension.
In the minimal example below, I've shown the three different ways I've tried of adding a batch dimension. calling shape_padleft and stacking the tensor with itself both cause this error to appear. Stacking the tensor with itself twice, though, works fine. This indicates to me that it's the size of the batch dimension and not merely the number or broadcastability of the dimensions that causes the problem. It's definitely a bug, then, since it seems to me that the size of the dimension shouldn't change the behavior to this degree.
Reproducible code example:
from pytensor import tensor
from pytensor.tensor import fft
import numpy as np
def add_batch_dim(x):
# comment all but one of the following three lines to verify that it's the size of the first axis that determines whether an error is thrown
return tensor.shape_padleft(x, 1) # throws an error
# return tensor.stack([x], axis=0) # throws an error
# return tensor.stack([x, x], axis=0) # works correctly
# define some data and a spectral response function
data = np.random.random((5, 5))
response = np.random.random((5, 3, 2))
# define an unknown source and Fourier transform it
source = tensor.tensor("source", dtype="float64", shape=(5, 5))
source_spectrum = fft.rfft(add_batch_dim(source))[0, :, :] # add a batch dimension before rfft and then immediately remove it
# compute the image by multiplying the source spectrum by the spectral response function
image_spectrum = tensor.stack([
source_spectrum[:, :, 0]*response[:, :, 0] - source_spectrum[:, :, 1]*response[:, :, 1],
source_spectrum[:, :, 0]*response[:, :, 1] + source_spectrum[:, :, 1]*response[:, :, 0],
], axis=-1)
image = fft.irfft(add_batch_dim(image_spectrum), is_odd=True)[0, :, :] # add a batch dimension before irfft and then immediately remove it
cost = tensor.sum((image - data)**2)
# call the autodifferentiator
print(tensor.grad(cost, source))Error message:
Traceback (most recent call last):
File "C:\Users\justi\Desktop\minimal_example.py", line 28, in <module>
print(tensor.grad(cost, source))
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 607, in grad
_rval: Sequence[Variable] = _populate_grad_dict(
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1407, in _populate_grad_dict
rval = [access_grad_cache(elem) for elem in wrt]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1407, in <listcomp>
rval = [access_grad_cache(elem) for elem in wrt]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1362, in access_grad_cache
term = access_term_cache(node)[idx]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in <listcomp>
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1362, in access_grad_cache
term = access_term_cache(node)[idx]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in <listcomp>
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1362, in access_grad_cache
term = access_term_cache(node)[idx]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in <listcomp>
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1362, in access_grad_cache
term = access_term_cache(node)[idx]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in <listcomp>
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1362, in access_grad_cache
term = access_term_cache(node)[idx]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in <listcomp>
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1362, in access_grad_cache
term = access_term_cache(node)[idx]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in <listcomp>
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1362, in access_grad_cache
term = access_term_cache(node)[idx]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in <listcomp>
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1362, in access_grad_cache
term = access_term_cache(node)[idx]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in <listcomp>
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1362, in access_grad_cache
term = access_term_cache(node)[idx]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1037, in <listcomp>
output_grads = [access_grad_cache(var) for var in node.outputs]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1362, in access_grad_cache
term = access_term_cache(node)[idx]
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\gradient.py", line 1192, in access_term_cache
input_grads = node.op.L_op(inputs, node.outputs, new_output_grads)
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\graph\op.py", line 392, in L_op
return self.grad(inputs, output_grads)
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\tensor\elemwise.py", line 287, in grad
DimShuffle(tuple(s == 1 for s in gz.type.shape), grad_order)(
File "C:\Users\justi\AppData\Local\Programs\Python\Python310\lib\site-packages\pytensor\tensor\elemwise.py", line 172, in __init__
raise ValueError(
ValueError: Cannot drop a non-broadcastable dimension: (False, False, False, False), [1, 2, 3]PyTensor version information:
Running Pytensor 2.18.6 (installed using pip) on Python 3.10.7.
Configuration details:
floatX ({'float32', 'float16', 'float64'})
Doc: Default floating-point precision for python casts.
Note: float16 support is experimental, use at your own risk.
Value: float64
warn_float64 ({'pdb', 'warn', 'raise', 'ignore'})
Doc: Do an action when a tensor variable with float64 dtype is created.
Value: ignore
pickle_test_value (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C72380>>)
Doc: Dump test values while pickling model. If True, test values will be dumped with model.
Value: True
cast_policy ({'numpy+floatX', 'custom'})
Doc: Rules for implicit type casting
Value: custom
deterministic ({'default', 'more'})
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 <pytensor.configparser.BoolParam object at 0x000001A723C724D0>>)
Doc: Raise an error if we can't use the specified device
Value: False
conv__assert_shape (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C72530>>)
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 <pytensor.configparser.BoolParam object at 0x000001A723C72560>>)
Doc: Print some global statistics (time spent) at the end
Value: False
assert_no_cpu_op ({'pdb', 'warn', 'raise', 'ignore'})
Doc: Raise an error/warning if there is a CPU op in the computational graph.
Value: ignore
unpickle_function (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C725F0>>)
Doc: Replace unpickled PyTensor functions with None. This is useful to unpickle old graphs that pickled them when it shouldn't
Value: True
<pytensor.configparser.ConfigParam object at 0x000001A723C72620>
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: "C:\Program Files\msys64\ucrt64\bin\g++.exe"
linker ({'c', 'py', 'cvm', 'c|py_nogc', 'vm', 'vm_nogc', 'c|py', 'cvm_nogc'})
Doc: Default linker used if the pytensor flags mode is Mode
Value: cvm
allow_gc (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C72890>>)
Doc: Do we default to delete intermediate results during PyTensor 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 ({'unsafe', 'fast_run', 'o1', 'o4', 'fast_compile', 'o2', 'o3', 'None', 'merge'})
Doc: Default optimizer. If not None, will use this optimizer with the Mode
Value: o4
optimizer_verbose (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C72950>>)
Doc: If True, we print all optimization being applied
Value: False
on_opt_error ({'pdb', 'warn', 'raise', 'ignore'})
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 <pytensor.configparser.BoolParam object at 0x000001A723C726E0>>)
Doc: Suppress the deletion of code files that did not compile cleanly
Value: False
on_unused_input ({'warn', 'raise', 'ignore'})
Doc: What to do if a variable in the 'inputs' list of pytensor.function() is not used in the graph.
Value: raise
gcc__cxxflags (<class 'str'>)
Doc: Extra compiler flags for gcc
Value:
cmodule__warn_no_version (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C72920>>)
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 <pytensor.configparser.BoolParam object at 0x000001A723C72770>>)
Doc: If True, will remove the -O* parameter passed to g++.This is useful to debug in gdb modules compiled by PyTensor.The parameter -g is passed by default to g++
Value: False
cmodule__compilation_warning (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C727A0>>)
Doc: If True, will print compilation warnings.
Value: False
cmodule__preload_cache (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C727D0>>)
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 PyTensor won't reuse a compile c module.
Value: 2073600
cmodule__debug (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C72860>>)
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 pytensor.tensor.math._allclose (0) not at all, (1) a bit, (2) more
Value: 0
lib__amblibm (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C729E0>>)
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 PyTensor internal stack trace.
Value: 0
warn__ignore_bug_before ({'0.4', '1.0.5', '1.0.2', '0.9', '0.6', '1.0.1', '0.8', '0.8.2', '1.0', '0.5', '0.4.1', '0.8.1', '1.0.4', '0.10', '0.3', '1.0.3', 'all', 'None', '0.7'})
Doc: If 'None', we warn about all PyTensor bugs found by default. If 'all', we don't warn about PyTensor bugs found by default. If a version, we print only the warnings relative to PyTensor 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 <pytensor.configparser.BoolParam object at 0x000001A723C72B00>>)
Doc: If 'True', the __eval__ of an PyTensor variable will return its test_value when this is available. This has the practical consequence 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 ({'warn', 'ignore', 'raise', 'off', 'pdb'})
Doc: If 'True', PyTensor 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 ({'warn', 'ignore', 'raise', 'off', 'pdb'})
Doc: For debugging PyTensor optimization only. Same as compute_test_value, but is used during PyTensor optimization
Value: off
check_input (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C72B90>>)
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 <pytensor.configparser.BoolParam object at 0x000001A723C72BC0>>)
Doc: Default value for nan_is_error
Value: True
NanGuardMode__inf_is_error (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C72BF0>>)
Doc: Default value for inf_is_error
Value: True
NanGuardMode__big_is_error (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C72C20>>)
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 <pytensor.configparser.BoolParam object at 0x000001A723C72CB0>>)
Doc: Run C implementations where possible
Value: True
DebugMode__check_py (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C72CE0>>)
Doc: Run Python implementations where possible
Value: True
DebugMode__check_finite (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C72D10>>)
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 <pytensor.configparser.BoolParam object at 0x000001A723C72D70>>)
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 <pytensor.configparser.BoolParam object at 0x000001A723C72E00>>)
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 <pytensor.configparser.BoolParam object at 0x000001A723C72EF0>>)
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 <pytensor.configparser.BoolParam object at 0x000001A723C72F50>>)
Doc: Do a debugprint of the profiled functions
Value: False
profiling__ignore_first_call (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C72F80>>)
Doc: Do we ignore the first call of an PyTensor 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 <pytensor.configparser.BoolParam object at 0x000001A723C72FE0>>)
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 PyTensor configuration file ~/.pytensorrc or with the environment variable PYTENSOR_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 PyTensor 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 earlier 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 ({'regular', 'fast'})
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 ({'raise', 'warn', 'off', 'log'})
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:
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 <pytensor.configparser.BoolParam object at 0x000001A723C73250>>)
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
profile (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C73280>>)
Doc: If VM should collect profile information
Value: False
profile_optimizer (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C732B0>>)
Doc: If VM should collect optimizer profile information
Value: False
profile_memory (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C732E0>>)
Doc: If VM should collect memory profile information and print it
Value: False
<pytensor.configparser.ConfigParam object at 0x000001A723C73310>
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
numba__vectorize_target ({'cpu', 'cuda', 'parallel'})
Doc: Default target for numba.vectorize.
Value: cpu
numba__fastmath (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C73370>>)
Doc: If True, use Numba's fastmath mode.
Value: True
numba__cache (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A723C733A0>>)
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: device, gxx_version,
hostname, numpy_version, platform, processor, pytensor_version,
python_bitwidth, python_int_bitwidth, python_version, short_platform.
Defaults to compiledir_%(short_platform)s-%(processor)s-
%(python_version)s-%(python_bitwidth)s.
Value: compiledir_%(short_platform)s-%(processor)s-%(python_version)s-%(python_bitwidth)s
<pytensor.configparser.ConfigParam object at 0x000001A723C736A0>
Doc: platform-independent root directory for compiled modules
Value: C:\Users\justi\AppData\Local\PyTensor
<pytensor.configparser.ConfigParam object at 0x000001A723C73550>
Doc: platform-dependent cache directory for compiled modules
Value: C:\Users\justi\AppData\Local\PyTensor\compiledir_Windows-10-10.0.22000-SP0-Intel64_Family_6_Model_154_Stepping_4_GenuineIntel-3.10.7-64
blas__ldflags (<class 'str'>)
Doc: lib[s] to include for [Fortran] level-3 blas implementation
Value:
blas__check_openmp (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A7240704C0>>)
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 <pytensor.configparser.BoolParam object at 0x000001A73C6BD300>>)
Doc: Allow/disallow gc inside of Scan (default: False)
Value: False
scan__allow_output_prealloc (<bound method BoolParam._apply of <pytensor.configparser.BoolParam object at 0x000001A73C13AF20>>)
Doc: Allow/disallow memory preallocation for outputs inside of scan (default: True)
Value: True
Context for the issue:
As a workaround, I've found that sometimes, if I keep the batch dimension in all of my variables rather than removing it immediately, the error no longer happens. But I haven't gotten that to work reliably. I suppose ideally the ultimate solution would be for these functions to not require a batch dimension at all (the pytensor.tensor.conv functions are the same way).