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# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import Callable, Optional
from lightning.pytorch.callbacks import LambdaCallback
class ModelCallback(LambdaCallback):
"""
A callback that extends LambdaCallback to intelligently handle function parameters.
Functions can take either (trainer, pl_module), just (pl_module), or just (trainer).
Supported parameter names:
- trainer, pl_trainer
- model, pl_model, pl_module, module
Example:
>>> # Using with torch.compile
>>> callback = ModelCallback(on_train_start=torch.compile)
>>>
>>> # Using with thunder_compile
>>> callback = ModelCallback(on_train_start=thunder_compile)
>>>
>>> # Mix different callbacks
>>> callback = ModelCallback(
... on_train_start=lambda model: torch.compile(model),
... on_fit_start=lambda trainer, model: print(f"Starting fit with {model}")
... )
"""
TRAINER_PARAMS = {'trainer', 'pl_trainer'}
MODEL_PARAMS = {'model', 'pl_model', 'pl_module', 'module'}
def __init__(
self,
setup: Optional[Callable] = None,
teardown: Optional[Callable] = None,
on_fit_start: Optional[Callable] = None,
on_fit_end: Optional[Callable] = None,
on_sanity_check_start: Optional[Callable] = None,
on_sanity_check_end: Optional[Callable] = None,
on_train_batch_start: Optional[Callable] = None,
on_train_batch_end: Optional[Callable] = None,
on_train_epoch_start: Optional[Callable] = None,
on_train_epoch_end: Optional[Callable] = None,
on_validation_epoch_start: Optional[Callable] = None,
on_validation_epoch_end: Optional[Callable] = None,
on_test_epoch_start: Optional[Callable] = None,
on_test_epoch_end: Optional[Callable] = None,
on_validation_batch_start: Optional[Callable] = None,
on_validation_batch_end: Optional[Callable] = None,
on_test_batch_start: Optional[Callable] = None,
on_test_batch_end: Optional[Callable] = None,
on_train_start: Optional[Callable] = None,
on_train_end: Optional[Callable] = None,
on_validation_start: Optional[Callable] = None,
on_validation_end: Optional[Callable] = None,
on_test_start: Optional[Callable] = None,
on_test_end: Optional[Callable] = None,
on_exception: Optional[Callable] = None,
on_save_checkpoint: Optional[Callable] = None,
on_load_checkpoint: Optional[Callable] = None,
on_before_backward: Optional[Callable] = None,
on_after_backward: Optional[Callable] = None,
on_before_optimizer_step: Optional[Callable] = None,
on_before_zero_grad: Optional[Callable] = None,
on_predict_start: Optional[Callable] = None,
on_predict_end: Optional[Callable] = None,
on_predict_batch_start: Optional[Callable] = None,
on_predict_batch_end: Optional[Callable] = None,
on_predict_epoch_start: Optional[Callable] = None,
on_predict_epoch_end: Optional[Callable] = None,
):
# Create a dictionary of non-None callbacks
callbacks = {
name: self._wrap_func(func)
for name, func in locals().items()
if name != 'self' and name != '__class__' and func is not None
}
super().__init__(**callbacks)
def _get_param_type(self, param_name: str) -> Optional[str]:
"""Determine if a parameter name refers to trainer or model."""
param_name = param_name.lower()
if param_name in self.TRAINER_PARAMS:
return 'trainer'
if param_name in self.MODEL_PARAMS:
return 'model'
return None
def _wrap_func(self, func: Callable) -> Callable:
"""Wraps a function to handle parameter inspection and passing."""
sig = inspect.signature(func)
params = sig.parameters
def wrapped(trainer, pl_module, *args, **kwargs):
call_args = {}
for param_name, param in params.items():
param_type = self._get_param_type(param_name)
if param_type == 'trainer':
call_args[param_name] = trainer
elif param_type == 'model':
call_args[param_name] = pl_module
else:
# If parameter name is not recognized, use position to determine
if len(params) == 1:
call_args[param_name] = pl_module
elif len(params) == 2:
if len(call_args) == 0:
call_args[param_name] = trainer
else:
call_args[param_name] = pl_module
else:
raise ValueError(
f"Unable to determine parameter mapping for '{param_name}'. "
f"Please use recognized parameter names: "
f"trainer/pl_trainer for trainer, "
f"model/pl_model/pl_module/module for model."
)
try:
return func(**call_args)
except TypeError as e:
raise TypeError(
f"Failed to call callback function {func.__name__ if hasattr(func, '__name__') else func}. "
f"Attempted to pass arguments: {call_args.keys()}. Error: {str(e)}"
) from e
return wrapped
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