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Add traceback filtering.

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This commit is contained in:
Francois Chollet 2023-05-06 14:34:46 -07:00
parent 43e33ab9ab
commit aaa13fd9c6
5 changed files with 310 additions and 40 deletions
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71
keras_core/backend/config.py
View File

@ -1,6 +1,8 @@
import json
import os
from keras_core.api_export import keras_core_export
# The type of float to use throughout a session.
_FLOATX = "float32"
@ -14,6 +16,7 @@ _IMAGE_DATA_FORMAT = "channels_last"
_BACKEND = "tensorflow"
@keras_core_export(["keras_core.config.floatx", "keras_core.backend.floatx"])
def floatx():
"""Return the default float type, as a string.
@ -23,32 +26,34 @@ def floatx():
String, the current default float type.
Example:
>>> keras_core.backend.floatx()
>>> keras_core.config.floatx()
'float32'
"""
return _FLOATX
@keras_core_export(
["keras_core.config.set_floatx", "keras_core.backend.set_floatx"]
)
def set_floatx(value):
"""Set the default float type.
"""Set the default float dtype.
Note: It is not recommended to set this to float16 for training, as this
will likely cause numeric stability issues. Instead, mixed precision, which
is using a mix of float16 and float32, can be used by calling
`keras_core.mixed_precision.set_global_policy('mixed_float16')`. See the
[mixed precision guide](
https://www.tensorflow.org/guide/keras/mixed_precision) for details.
Note: It is not recommended to set this to `"float16"` for training,
as this will likely cause numeric stability issues.
Instead, mixed precision, which leverages
a mix of `float16` and `float32`. It can be configured by calling
`keras_core.mixed_precision.set_global_policy('mixed_float16')`.
Args:
value: String; `'float16'`, `'float32'`, or `'float64'`.
Example:
>>> keras_core.backend.floatx()
>>> keras_core.config.floatx()
'float32'
>>> keras_core.backend.set_floatx('float64')
>>> keras_core.backend.floatx()
>>> keras_core.config.set_floatx('float64')
>>> keras_core.config.floatx()
'float64'
>>> keras_core.backend.set_floatx('float32')
>>> keras_core.config.set_floatx('float32')
Raises:
ValueError: In case of invalid value.
@ -63,6 +68,7 @@ def set_floatx(value):
_FLOATX = str(value)
@keras_core_export(["keras_core.config.epsilon", "keras_core.backend.epsilon"])
def epsilon():
"""Return the value of the fuzz factor used in numeric expressions.
@ -70,12 +76,15 @@ def epsilon():
A float.
Example:
>>> keras_core.backend.epsilon()
>>> keras_core.config.epsilon()
1e-07
"""
return _EPSILON
@keras_core_export(
["keras_core.config.set_epsilon", "keras_core.backend.set_epsilon"]
)
def set_epsilon(value):
"""Set the value of the fuzz factor used in numeric expressions.
@ -83,30 +92,42 @@ def set_epsilon(value):
value: float. New value of epsilon.
Example:
>>> keras_core.backend.epsilon()
>>> keras_core.config.epsilon()
1e-07
>>> keras_core.backend.set_epsilon(1e-5)
>>> keras_core.backend.epsilon()
>>> keras_core.config.set_epsilon(1e-5)
>>> keras_core.config.epsilon()
1e-05
>>> keras_core.backend.set_epsilon(1e-7)
>>> keras_core.config.set_epsilon(1e-7)
"""
global _EPSILON
_EPSILON = value
@keras_core_export(
[
"keras_core.config.image_data_format",
"keras_core.backend.image_data_format",
]
)
def image_data_format():
"""Return the default image data format convention.
Returns:
A string, either `'channels_first'` or `'channels_last'`
A string, either `'channels_first'` or `'channels_last'`.
Example:
>>> keras_core.backend.image_data_format()
>>> keras_core.config.image_data_format()
'channels_last'
"""
return _IMAGE_DATA_FORMAT
@keras_core_export(
[
"keras_core.config.set_image_data_format",
"keras_core.backend.set_image_data_format",
]
)
def set_image_data_format(data_format):
"""Set the value of the image data format convention.
@ -114,15 +135,12 @@ def set_image_data_format(data_format):
data_format: string. `'channels_first'` or `'channels_last'`.
Example:
>>> keras_core.backend.image_data_format()
>>> keras_core.config.image_data_format()
'channels_last'
>>> keras_core.backend.set_image_data_format('channels_first')
>>> keras_core.backend.image_data_format()
>>> keras_core.config.set_image_data_format('channels_first')
>>> keras_core.config.image_data_format()
'channels_first'
>>> keras_core.backend.set_image_data_format('channels_last')
Raises:
ValueError: In case of invalid `data_format` value.
>>> keras_core.config.set_image_data_format('channels_last')
"""
global _IMAGE_DATA_FORMAT
accepted_formats = {"channels_last", "channels_first"}
@ -196,6 +214,7 @@ if "KERAS_BACKEND" in os.environ:
_BACKEND = _backend
@keras_core_export("keras_core.backend.backend")
def backend():
"""Publicly accessible method for determining the current backend.

11
keras_core/layers/layer.py
View File

@ -34,6 +34,7 @@ from keras_core.layers import input_spec
from keras_core.metrics.metric import Metric
from keras_core.operations.operation import Operation
from keras_core.utils import summary_utils
from keras_core.utils import traceback_utils
from keras_core.utils.tracking import Tracker
@ -277,6 +278,7 @@ class Layer(Operation):
def compute_mask(self, inputs, previous_mask):
return previous_mask
@traceback_utils.filter_traceback
def __call__(self, *args, **kwargs):
self._check_super_called()
@ -414,6 +416,7 @@ class Layer(Operation):
def call(self, *args, **kwargs):
raise NotImplementedError
@traceback_utils.filter_traceback
def stateless_call(
self,
trainable_variables,
@ -659,8 +662,8 @@ class Layer(Operation):
if not self.built:
raise ValueError(
"You tried to call `count_params` "
f"on layer '{self.name}'"
", but the layer isn't built. "
f"on layer '{self.name}', "
"but the layer isn't built. "
"You can build it manually via: "
f"`layer.build(input_shape)`."
)
@ -714,7 +717,9 @@ class Layer(Operation):
"then the build signature should be "
"`def build(self, x1_shape, x2_shape)`. "
"Keras will not build this layer automatically "
"since it does not conform to this."
"since it does not conform to this. "
"Expected the following build keys: "
f"{list(shapes_dict.keys())}"
)
if failure:
raise ValueError(

27
keras_core/operations/operation.py
View File

@ -8,6 +8,7 @@ from keras_core.backend.common.keras_tensor import any_symbolic_tensors
from keras_core.operations.node import Node
from keras_core.saving import serialization_lib
from keras_core.utils import python_utils
from keras_core.utils import traceback_utils
from keras_core.utils.naming import auto_name
@ -24,7 +25,21 @@ class Operation:
self._inbound_nodes = []
self._outbound_nodes = []
@traceback_utils.filter_traceback
def __call__(self, *args, **kwargs):
if traceback_utils.is_traceback_filtering_enabled():
# Wrap self.call to provide helpful info in case of exception
if any_symbolic_tensors(args, kwargs):
call_fn = self.symbolic_call
else:
call_fn = self.call
call_fn = traceback_utils.inject_argument_info_in_traceback(
call_fn,
object_name=(f"{self.__class__.__name__}.call()"),
)
return call_fn(*args, **kwargs)
# Plain flow.
if any_symbolic_tensors(args, kwargs):
return self.symbolic_call(*args, **kwargs)
return self.call(*args, **kwargs)
@ -50,13 +65,15 @@ class Operation:
try:
return backend.compute_output_spec(self.call, *args, **kwargs)
except Exception as e:
raise RuntimeError(
new_e = RuntimeError(
"Could not automatically infer the output shape / dtype of "
f"operation '{self.name}'. "
"Please implement the `compute_output_spec()` method "
f"on your object ({self.__class__.__name__}). "
f"Error encountered: {e}"
f"'{self.name}' (of type {self.__class__.__name__}). "
f"Either the `{self.__class__.__name__}.call()` method "
f"is incorrect, or you need to implement the "
f"`{self.__class__.__name__}.compute_output_spec()` method. "
f"Error encountered:\n\n{e}"
)
raise new_e.with_traceback(e.__traceback__) from None
def __new__(cls, *args, **kwargs):
"""We override __new__ to saving serializable constructor arguments.

13
keras_core/utils/io_utils.py
View File

@ -6,7 +6,7 @@ from keras_core.api_export import keras_core_export
from keras_core.backend.common import global_state
@keras_core_export("keras_core.utils.enable_interactive_logging")
@keras_core_export("keras_core.config.enable_interactive_logging")
def enable_interactive_logging():
"""Turn on interactive logging.
@ -17,7 +17,7 @@ def enable_interactive_logging():
global_state.set_global_setting("interactive_logging", True)
@keras_core_export("keras_core.utils.disable_interactive_logging")
@keras_core_export("keras_core.config.disable_interactive_logging")
def disable_interactive_logging():
"""Turn off interactive logging.
@ -28,16 +28,17 @@ def disable_interactive_logging():
global_state.set_global_setting("interactive_logging", False)
@keras_core_export("keras_core.utils.is_interactive_logging_enabled")
@keras_core_export("keras_core.config.is_interactive_logging_enabled")
def is_interactive_logging_enabled():
"""Check if interactive logging is enabled.
To switch between writing logs to stdout and `absl.logging`, you may use
`keras.utils.enable_interactive_logging()` and
`keras.utils.disable_interactie_logging()`.
`keras.config.enable_interactive_logging()` and
`keras.config.disable_interactie_logging()`.
Returns:
Boolean (True if interactive logging is enabled and False otherwise).
Boolean, `True` if interactive logging is enabled,
and `False` otherwise.
"""
return global_state.get_global_setting("interactive_logging", True)

228
keras_core/utils/traceback_utils.py Normal file
View File

@ -0,0 +1,228 @@
import inspect
import os
import traceback
import types
from tensorflow import errors as tf_errors
from tensorflow import nest
from keras_core import backend
from keras_core.api_export import keras_core_export
from keras_core.backend.common import global_state
_EXCLUDED_PATHS = (
os.path.abspath(os.path.join(__file__, "..", "..")),
os.path.join("tensorflow", "python"),
)
@keras_core_export("keras_core.config.enable_traceback_filtering")
def enable_traceback_filtering():
"""Turn on traceback filtering.
Raw Keras tracebacks (also known as stack traces)
involve many internal frames, which can be
challenging to read through, while not being actionable for end users.
By default, Keras filters internal frames in most exceptions that it
raises, to keep traceback short, readable, and focused on what's
actionable for you (your own code).
See also `keras_core.config.disable_traceback_filtering()` and
`keras_core.config.is_traceback_filtering_enabled()`.
If you have previously disabled traceback filtering via
`keras_core.config.disable_traceback_filtering()`, you can re-enable it via
`keras_core.config.enable_traceback_filtering()`.
"""
global_state.set_global_setting("traceback_filtering", True)
@keras_core_export("keras_core.config.disable_traceback_filtering")
def disable_traceback_filtering():
"""Turn off traceback filtering.
Raw Keras tracebacks (also known as stack traces)
involve many internal frames, which can be
challenging to read through, while not being actionable for end users.
By default, Keras filters internal frames in most exceptions that it
raises, to keep traceback short, readable, and focused on what's
actionable for you (your own code).
See also `keras_core.config.enable_traceback_filtering()` and
`keras_core.config.is_traceback_filtering_enabled()`.
If you have previously disabled traceback filtering via
`keras_core.config.disable_traceback_filtering()`, you can re-enable it via
`keras_core.config.enable_traceback_filtering()`.
"""
global_state.set_global_setting("traceback_filtering", False)
@keras_core_export("keras_core.config.is_traceback_filtering_enabled")
def is_traceback_filtering_enabled():
"""Check if traceback filtering is enabled.
Raw Keras tracebacks (also known as stack traces)
involve many internal frames, which can be
challenging to read through, while not being actionable for end users.
By default, Keras filters internal frames in most exceptions that it
raises, to keep traceback short, readable, and focused on what's
actionable for you (your own code).
See also `keras_core.config.enable_traceback_filtering()` and
`keras_core.config.disable_traceback_filtering()`.
If you have previously disabled traceback filtering via
`keras_core.config.disable_traceback_filtering()`, you can re-enable it via
`keras_core.config.enable_traceback_filtering()`.
Returns:
Boolean, `True` if traceback filtering is enabled,
and `False` otherwise.
"""
return global_state.get_global_setting("traceback_filtering", True)
def include_frame(fname):
for exclusion in _EXCLUDED_PATHS:
if exclusion in fname:
return False
return True
def _process_traceback_frames(tb):
"""Iterate through traceback frames and return a new, filtered traceback."""
last_tb = None
tb_list = list(traceback.walk_tb(tb))
for f, line_no in reversed(tb_list):
if include_frame(f.f_code.co_filename):
last_tb = types.TracebackType(last_tb, f, f.f_lasti, line_no)
if last_tb is None and tb_list:
# If no frames were kept during filtering, create a new traceback
# from the outermost function.
f, line_no = tb_list[-1]
last_tb = types.TracebackType(last_tb, f, f.f_lasti, line_no)
return last_tb
def filter_traceback(fn):
"""Filter out Keras-internal traceback frames in exceptions raised by fn."""
def error_handler(*args, **kwargs):
if not is_traceback_filtering_enabled():
return fn(*args, **kwargs)
filtered_tb = None
try:
return fn(*args, **kwargs)
except Exception as e:
filtered_tb = _process_traceback_frames(e.__traceback__)
# To get the full stack trace, call:
# `keras_core.config.disable_traceback_filtering()`
raise e.with_traceback(filtered_tb) from None
finally:
del filtered_tb
return error_handler
def inject_argument_info_in_traceback(fn, object_name=None):
"""Add information about call argument values to an error message.
Arguments:
fn: Function to wrap. Exceptions raised by the this function will be
re-raised with additional information added to the error message,
displaying the values of the different arguments that the function
was called with.
object_name: String, display name of the class/function being called,
e.g. `'layer "layer_name" (LayerClass)'`.
Returns:
A wrapped version of `fn`.
"""
def error_handler(*args, **kwargs):
signature = None
bound_signature = None
try:
return fn(*args, **kwargs)
except Exception as e:
if hasattr(e, "_keras_call_info_injected"):
# Only inject info for the innermost failing call
raise e
signature = inspect.signature(fn)
try:
# The first argument is `self`, so filter it out
bound_signature = signature.bind(*args, **kwargs)
except TypeError:
# Likely unbindable arguments
raise e
# Add argument context
arguments_context = []
for arg in list(signature.parameters.values()):
if arg.name in bound_signature.arguments:
value = nest.map_structure(
format_argument_value,
bound_signature.arguments[arg.name],
)
else:
value = arg.default
arguments_context.append(f" • {arg.name}={value}")
if arguments_context:
arguments_context = "\n".join(arguments_context)
# Get original error message and append information to it.
if isinstance(e, tf_errors.OpError):
message = e.message
elif e.args:
# Canonically, the 1st argument in an exception is the error
# message. This works for all built-in Python exceptions.
message = e.args[0]
else:
message = ""
display_name = f"{object_name if object_name else fn.__name__}"
message = (
f"Exception encountered when calling {display_name}.\n\n"
f"\x1b[1m{message}\x1b[0m\n\n"
f"Arguments received by {display_name}:\n"
f"{arguments_context}"
)
# Reraise exception, with added context
if isinstance(e, tf_errors.OpError):
new_e = e.__class__(e.node_def, e.op, message, e.error_code)
else:
try:
# For standard exceptions such as ValueError, TypeError,
# etc.
new_e = e.__class__(message)
except TypeError:
# For any custom error that doesn't have a standard
# signature.
new_e = RuntimeError(message)
new_e._keras_call_info_injected = True
else:
new_e = e
raise new_e.with_traceback(e.__traceback__) from None
finally:
del signature
del bound_signature
return error_handler
def format_argument_value(value):
if backend.is_tensor(value):
# Simplified representation for eager / graph tensors
# to keep messages readable
if backend.backend() == "tensorflow":
tensor_cls = "tf.Tensor"
elif backend.backend() == "jax":
tensor_cls = "jnp.ndarray"
elif backend.backend() == "pytorch":
tensor_cls = "torch.Tensor"
else:
tensor_cls = "array"
return f"{tensor_cls}(shape={value.shape}, dtype={value.dtype.name})"
return repr(value)