debugmode — PyTensor dev documentation (original) (raw)

Guide#

The DebugMode evaluation mode includes a number of self-checks and assertions that can help to diagnose several kinds of programmer errors that can lead to incorrect output.

It is much slower to evaluate a function or method with DebugMode than it would be in 'FAST_RUN' or even 'FAST_COMPILE'. We recommended you useDebugMode during development, but not when you launch 1000 processes on a cluster.

DebugMode can be used as follows:

import pytensor from pytensor import tensor as pt from pytensor.compile.debugmode import DebugMode

x = pt.dscalar('x')

f = pytensor.function([x], 10*x, mode='DebugMode')

f(5) f(0) f(7)

It can also be used by setting the configuration variable config.mode, or passing a DebugMode instance, as in

f = pytensor.function([x], 10*x, mode=DebugMode(check_c_code=False))

If any problem is detected, DebugMode will raise an exception according to what went wrong, either at call time (f(5)) or compile time (f = pytensor.function(x, 10*x, mode='DebugMode')). These exceptions should not be ignored; talk to your local PyTensor guru or email the users list if you cannot make the exception go away.

Some kinds of errors can only be detected for certain input value combinations. In the example above, there is no way to guarantee that a future call to say,f(-1) won’t cause a problem. DebugMode is not a silver bullet.

If you use DebugMode by constructing a DebugMode object explicitly, rather than using the keyword mode="DebugMode", you can configure its behaviour via constructor arguments.

Reference#

class pytensor.compile.debugmode.DebugMode(Mode)[source]#

Evaluation Mode that detects internal PyTensor errors.

This mode catches several kinds of internal error:

Each of these exceptions inherits from the more generic DebugModeError.

If there are no internal errors, this mode behaves like FAST_RUN or FAST_COMPILE, but takes a little longer and uses more memory.

If there are internal errors, this mode will raise an DebugModeError exception.

stability_patience = config.DebugMode__patience

When checking the stability of rewrites, recompile the graph this many times. Default 10.

check_c_code = config.DebugMode__check_c

Should we evaluate (and check) the c_code implementations?

True -> yes, False -> no.

Default yes.

check_py_code = config.DebugMode__check_py

Should we evaluate (and check) the perform implementations?

True -> yes, False -> no.

Default yes.

check_isfinite = config.DebugMode__check_finite

Should we check for (and complain about) NaN/Inf ndarray elements?

True -> yes, False -> no.

Default yes.

require_matching_strides = config.DebugMode__check_strides

Check for (and complain about) Ops whose python and C outputs are ndarrays with different strides. (This can catch bugs, but is generally overly strict.)

0 -> no check, 1 -> warn, 2 -> err.

Default warn.

__init__(self, optimizer='fast_run', stability_patience=None, check_c_code=None, check_py_code=None, check_isfinite=None, require_matching_strides=None, linker=None)[source]#

Initialize member variables.

If any of these arguments (except optimizer) is not None, it overrides the class default. The linker arguments is not used. It is set there to allowMode.requiring() and some other functions to work with DebugMode too.

The keyword version of DebugMode (which you get by using mode='DebugMode) is quite strict, and can raise several different Exception types. There following are DebugMode exceptions you might encounter:

class pytensor.compile.debugmode.DebugModeError(Exception)[source]#

This is a generic error. All the other exceptions inherit from this one. This error is typically not raised directly. However, you can use except DebugModeError: ... to catch any of the more specific types of Exceptions.

class pytensor.compile.debugmode.BadThunkOutput(DebugModeError)[source]#

This exception means that different calls to the same Op with the same inputs did not compute the same thing like they were supposed to. For instance, it can happen if the Python (i.e. Op.perform()) and C (i.e. COp.c_code()) implementations of the Op are inconsistent. The problem might be a bug in either Op.perform() or COp.c_code() (or both). It can also happen ifOp.perform() or COp.c_code() does not handle correctly output memory that has been preallocated (for instance, if it did not clear the memory before accumulating into it, or if it assumed the memory layout was C-contiguous even if it is not).

class pytensor.compile.debugmode.BadOptimization(DebugModeError)[source]#

This exception indicates that a rewrite replaced one variable (say V1) with another one (say V2) but at runtime, the values for V1 and V2 were different. This is something that rewrites are not supposed to do.

It can be tricky to identify the one-true-cause of a rewrite error, but this exception provides a lot of guidance. Most of the time, the exception object will indicate which rewrite was at fault. The exception object also contains information such as a snapshot of the before/after graph where the rewrite introduced the error.

class pytensor.compile.debugmode.BadDestroyMap(DebugModeError)[source]#

This happens when an Op.perform() or COp.c_code() modifies an input that it wasn’t supposed to. If either the Op.perform() orCOp.c_code() implementation of an Op might modify any input, it has to advertise that fact via the Op.destroy_map attribute.

For detailed documentation on the Op.destroy_map attribute, see Inplace.

class pytensor.compile.debugmode.BadViewMap(DebugModeError)[source]#

This happens when an Op.perform() or COp.c_code() creates an alias or alias-like dependency between an input and an output, and it didn’t warn the rewrite system via the Op.view_map attribute.

For detailed documentation on the Op.view_map attribute, see Views.

class pytensor.compile.debugmode.StochasticOrder(DebugModeError)[source]#

This happens when an rewrite does not perform the same graph operations in the same order when run several times in a row. This can happen if any steps are ordered by id(object) somehow, such as via the default object hash function. A stochastic rewrite invalidates the pattern of work whereby we debug in DebugMode and then run the full-size jobs in FAST_RUN.

class pytensor.compile.debugmode.InvalidValueError(DebugModeError)[source]#

This happens when some Op.perform() or COp.c_code() implementation computes an output that is invalid with respect to the type of the corresponding output variable. Like if it returned a complex-valued ndarray for a dscalar Type.

This can also be triggered when floating-point values such as NaN and Inf are introduced into the computations. It indicates which Op created the first NaN. These floating-point values can be allowed by passing thecheck_isfinite=False argument to DebugMode.