import inspect import os import traceback import types from functools import wraps from keras.src import backend from keras.src import tree from keras.src.api_export import keras_export from keras.src.backend.common import global_state _EXCLUDED_PATHS = ( os.path.abspath(os.path.join(__file__, "..", "..")), os.path.join("tensorflow", "python"), ) @keras_export("keras.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.config.disable_traceback_filtering()` and `keras.config.is_traceback_filtering_enabled()`. If you have previously disabled traceback filtering via `keras.config.disable_traceback_filtering()`, you can re-enable it via `keras.config.enable_traceback_filtering()`. """ global_state.set_global_attribute("traceback_filtering", True) @keras_export("keras.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.config.enable_traceback_filtering()` and `keras.config.is_traceback_filtering_enabled()`. If you have previously disabled traceback filtering via `keras.config.disable_traceback_filtering()`, you can re-enable it via `keras.config.enable_traceback_filtering()`. """ global_state.set_global_attribute("traceback_filtering", False) @keras_export("keras.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.config.enable_traceback_filtering()` and `keras.config.disable_traceback_filtering()`. If you have previously disabled traceback filtering via `keras.config.disable_traceback_filtering()`, you can re-enable it via `keras.config.enable_traceback_filtering()`. Returns: Boolean, `True` if traceback filtering is enabled, and `False` otherwise. """ return global_state.get_global_attribute("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.""" @wraps(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.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`. """ if backend.backend() == "tensorflow": from tensorflow import errors as tf_errors else: tf_errors = None @wraps(fn) def error_handler(*args, **kwargs): if not is_traceback_filtering_enabled(): return fn(*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 = tree.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 tf_errors is not None and 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 tf_errors is not None and 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() == "torch": tensor_cls = "torch.Tensor" elif backend.backend() == "numpy": tensor_cls = "np.ndarray" else: tensor_cls = "array" return ( f"{tensor_cls}(shape={value.shape}, " f"dtype={backend.standardize_dtype(value.dtype)})" ) return repr(value)