I made some changes on my local branch; then when committing to my remote fork of the repository, I am getting a git pre-hook error that I really have no clue what it's about 😅.
git commit -m "commit message"
Please provide the full tracebacks for any relevant errors and/or warning messages.
debug statements (python)................................................Passed
check for merge conflicts................................................Passed
black....................................................................Passed
flake8...................................................................Passed
isort....................................................................Passed
autoflake................................................................Passed
mypy.....................................................................Failed
- hook id: mypy
- exit code: 1
aesara/link/c/exceptions.py:1: error: Module "setuptools._distutils.errors" has no attribute "CompileError" [attr-defined]
aesara/graph/destroyhandler.py:361: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/graph/destroyhandler.py:368: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/graph/destroyhandler.py:379: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/graph/destroyhandler.py:387: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/graph/destroyhandler.py:390: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/graph/destroyhandler.py:392: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/graph/destroyhandler.py:394: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/graph/destroyhandler.py:396: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/graph/destroyhandler.py:399: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/graph/destroyhandler.py:401: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/sparse/type.py:94: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/graph/rewriting/basic.py:1166: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/graph/rewriting/basic.py:1167: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/graph/rewriting/basic.py:1168: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/tensor/rewriting/elemwise.py:614: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/tensor/rewriting/shape.py:521: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/tensor/rewriting/shape.py:522: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/tensor/rewriting/shape.py:523: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/scalar/basic.py:917: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/link/basic.py:334: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/link/basic.py:359: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/printing.py:1121: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/printing.py:1122: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/link/numba/dispatch/scan.py:79: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/link/numba/dispatch/scan.py:102: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/link/numba/dispatch/scan.py:120: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/link/numba/dispatch/scan.py:128: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/link/numba/dispatch/scan.py:141: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/link/numba/dispatch/scan.py:145: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/link/numba/dispatch/scan.py:192: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
aesara/link/numba/dispatch/scan.py:202: note: By default the bodies of untyped functions are not checked, consider using --check-untyped-defs [annotation-unchecked]
Found 1 error in 1 file (checked 2 source files)
Please provide any additional information below.
Versions and main components
Aesara version: 2.8.12 (latest)
Python version: 3.10.5
Operating system: Mac M1
How did you install Aesara: (conda/pip) conda then pip via developer guide.
Aesara config:
Place the results of `python -c "import aesara; print(aesara.config)"` here.
```
WARNING (aesara.tensor.blas): Using NumPy C-API based implementation for BLAS functions.
floatX ({'float32', 'float64', 'float16'})
Doc: Default floating-point precision for python casts.
Note: float16 support is experimental, use at your own risk.
Value: float64
warn_float64 ({'raise', 'warn', 'pdb', 'ignore'})
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 ({'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 (>)
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 ({'raise', 'warn', 'pdb', 'ignore'})
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: /Users/pscemama/miniforge3/envs/aesara-dev/bin/clang++
linker ({'c|py_nogc', 'cvm', 'c|py', 'vm', 'vm_nogc', 'py', 'cvm_nogc', 'c'})
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 ({'o1', 'None', 'o2', 'fast_compile', 'fast_run', 'o3', 'o4', '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 ({'raise', 'warn', 'pdb', '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 (>)
Doc: Suppress the deletion of code files that did not compile cleanly
Value: False
on_unused_input ({'raise', 'warn', 'ignore'})
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:
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 ({'1.0.1', '0.10', '1.0.5', '0.4', 'all', '0.6', '0.8.2', '0.4.1', '0.8', '1.0.4', '0.3', '0.7', '1.0.2', '0.8.1', '1.0', 'None', '0.5', '1.0.3', '0.9'})
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 ({'low', 'high'})
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 ({'warn', 'pdb', 'off', 'ignore', 'raise'})
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 ({'warn', 'pdb', 'off', 'ignore', 'raise'})
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 ({'raise', 'warn', 'pdb'})
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 ({'raise', 'warn'})
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 ({'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 ({'off', 'warn', 'log', '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', 'cpu', 'parallel'})
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_version)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: /Users/pscemama/.aesara
Doc: platform-dependent cache directory for compiled modules
Value: /Users/pscemama/.aesara/compiledir_macOS-13.2.1-arm64-arm-64bit-arm-3.10.9-64
blas__ldflags ()
Doc: lib[s] to include for [Fortran] level-3 blas implementation
Value:
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
```
Description of your problem or feature request
I made some changes on my local branch; then when committing to my remote fork of the repository, I am getting a git pre-hook error that I really have no clue what it's about 😅.
Please provide the full tracebacks for any relevant errors and/or warning messages.
Please provide any additional information below.
Versions and main components
Aesara config:
Place the results of `python -c "import aesara; print(aesara.config)"` here. ``` WARNING (aesara.tensor.blas): Using NumPy C-API based implementation for BLAS functions. floatX ({'float32', 'float64', 'float16'}) Doc: Default floating-point precision for python casts. Note: float16 support is experimental, use at your own risk. Value: float64 warn_float64 ({'raise', 'warn', 'pdb', 'ignore'}) Doc: Do an action when a tensor variable with float64 dtype is created. Value: ignore pickle_test_value (