gradient verification callback

CHANGELOG.md

@@ -23,6 +23,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added metric GIoU ([#347](https://github.com/PyTorchLightning/pytorch-lightning-bolts/pull/347))
 - Added Intersection over Union Metric/Loss ([#469](https://github.com/PyTorchLightning/pytorch-lightning-bolts/pull/469))
 - Added SimSiam model ([#407](https://github.com/PyTorchLightning/pytorch-lightning-bolts/pull/407))
+- Added gradient verification callback ([#465](https://github.com/PyTorchLightning/pytorch-lightning-bolts/pull/465))
 
 ### Changed
 

docs/source/info_callbacks.rst

@@ -64,3 +64,63 @@ You can track all or just a selection of submodules:
 
 This is especially useful for debugging the data flow in complex models and to identify
 numerical instabilities.
+
+
+---------------
+
+Model Verification
+------------------
+
+
+Gradient-Check for Batch-Optimization
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Gradient descent over a batch of samples can not only benefit the optimization but also leverages data parallelism.
+However, one has to be careful not to mix data across the batch dimension.
+Only a small error in a reshape or permutation operation results in the optimization getting stuck and you won't
+even get a runtime error. How can one tell if the model mixes data in the batch?
+A simple trick is to do the following:
+
+1. run the model on an example batch (can be random data)
+2. get the output batch and select the n-th sample (choose n)
+3. compute a dummy loss value of only that sample and compute the gradient w.r.t the entire input batch
+4. observe that only the i-th sample in the input batch has non-zero gradient
+
+|
+
+If the gradient is non-zero for the other samples in the batch, it means the forward pass of the model is mixing data!
+The :class:`~pl_bolts.callbacks.verification.batch_gradient.BatchGradientVerificationCallback`
+does all of that for you before training begins.
+
+.. code-block:: python
+
+    from pytorch_lightning import Trainer
+    from pl_bolts.callbacks import BatchGradientVerificationCallback
+
+    model = YourLightningModule()
+    verification = BatchGradientVerificationCallback()
+    trainer = Trainer(callbacks=[verification])
+    trainer.fit(model)
+
+This Callback will warn the user with the following message in case data mixing inside the batch is detected:
+
+.. code-block::
+
+    Your model is mixing data across the batch dimension.
+    This can lead to wrong gradient updates in the optimizer.
+    Check the operations that reshape and permute tensor dimensions in your model.
+
+
+A non-Callback version
+:class:`~pl_bolts.callbacks.verification.batch_gradient.BatchGradientVerification`
+that works with any PyTorch :class:`~torch.nn.Module` is also available:
+
+.. code-block:: python
+
+    from pl_bolts.utils import BatchGradientVerification
+
+    model = YourPyTorchModel()
+    verification = BatchGradientVerification(model)
+    valid = verification.check(input_array=torch.rand(2, 3, 4), sample_idx=1)
+
+In this example we run the test on a batch size 2 by inspecting gradients on the second sample.

pl_bolts/callbacks/__init__.py

@@ -6,10 +6,12 @@
 from pl_bolts.callbacks.printing import PrintTableMetricsCallback  # noqa: F401
 from pl_bolts.callbacks.ssl_online import SSLOnlineEvaluator  # noqa: F401
 from pl_bolts.callbacks.variational import LatentDimInterpolator  # noqa: F401
+from pl_bolts.callbacks.verification.batch_gradient import BatchGradientVerificationCallback  # type: ignore
 from pl_bolts.callbacks.vision.confused_logit import ConfusedLogitCallback  # noqa: F401
 from pl_bolts.callbacks.vision.image_generation import TensorboardGenerativeModelImageSampler  # noqa: F401
 
 __all__ = [
+    "BatchGradientVerificationCallback",
     "BYOLMAWeightUpdate",
     "ModuleDataMonitor",
     "TrainingDataMonitor",

pl_bolts/callbacks/data_monitor.py

@@ -18,7 +18,7 @@
     import wandb
 else:  # pragma: no cover
     warn_missing_pkg("wandb")
-    wandb = None
+    wandb = None  # type: ignore
 
 
 class DataMonitorBase(Callback):

pl_bolts/callbacks/verification/base.py

@@ -0,0 +1,123 @@
+# type: ignore
+from abc import abstractmethod
+from copy import deepcopy
+from typing import Any, Optional
+
+import torch.nn as nn
+from pytorch_lightning import Callback
+from pytorch_lightning.core.lightning import LightningModule
+from pytorch_lightning.utilities import move_data_to_device, rank_zero_warn
+
+
+class VerificationBase:
+    """
+    Base class for model verification.
+    All verifications should run with any :class:`torch.nn.Module` unless otherwise stated.
+    """
+
+    def __init__(self, model: nn.Module):
+        """
+        Arguments:
+            model: The model to run verification for.
+        """
+        super().__init__()
+        self.model = model
+
+    @abstractmethod
+    def check(self, *args: Any, **kwargs: Any) -> bool:
+        """ Runs the actual test on the model. All verification classes must implement this.
+
+        Arguments:
+            *args: Any positional arguments that are needed to run the test
+            *kwargs: Keyword arguments that are needed to run the test
+
+        Returns:
+            `True` if the test passes, and `False` otherwise. Some verifications can only be performed
+            with a heuristic accuracy, thus the return value may not always reflect the true state of
+            the system in these cases.
+        """
+
+    def _get_input_array_copy(self, input_array: Optional[Any] = None) -> Any:
+        """
+        Returns a deep copy of the example input array in cases where it is expected that the
+        input changes during the verification process.
+
+        Arguments:
+            input_array: The input to clone.
+        """
+        if input_array is None and isinstance(self.model, LightningModule):
+            input_array = self.model.example_input_array
+        input_array = deepcopy(input_array)
+
+        if isinstance(self.model, LightningModule):
+            input_array = self.model.transfer_batch_to_device(input_array, self.model.device)
+        else:
+            input_array = move_data_to_device(input_array, device=next(self.model.parameters()).device)
+
+        return input_array
+
+    def _model_forward(self, input_array: Any) -> Any:
+        """
+        Feeds the input array to the model via the ``__call__`` method.
+
+        Arguments:
+            input_array: The input that goes into the model. If it is a tuple, it gets
+                interpreted as the sequence of positional arguments and is passed in by tuple unpacking.
+                If it is a dict, the contents get passed in as named parameters by unpacking the dict.
+                Otherwise, the input array gets passed in as a single argument.
+
+        Returns:
+            The output of the model.
+        """
+        if isinstance(input_array, tuple):
+            return self.model(*input_array)
+        if isinstance(input_array, dict):
+            return self.model(**input_array)
+        return self.model(input_array)
+
+
+class VerificationCallbackBase(Callback):
+    """
+    Base class for model verification in form of a callback.
+    This type of verification is expected to only work with
+    :class:`~pytorch_lightning.core.lightning.LightningModule` and will take the input array
+    from :attr:`~pytorch_lightning.core.lightning.LightningModule.example_input_array` if needed.
+    """
+
+    def __init__(self, warn: bool = True, error: bool = False) -> None:
+        """
+        Arguments:
+            warn: If ``True``, prints a warning message when verification fails. Default: ``True``.
+            error: If ``True``, prints an error message when verification fails. Default: ``False``.
+        """
+        self._raise_warning = warn
+        self._raise_error = error
+
+    def message(self, *args: Any, **kwargs: Any) -> str:
+        """
+        The message to be printed when the model does not pass the verification.
+        If the message for warning and error differ, override the
+        :meth:`warning_message` and :meth:`error_message`
+        methods directly.
+
+        Arguments:
+            *args: Any positional arguments that are needed to construct the message.
+            **kwargs: Any keyword arguments that are needed to construct the message.
+
+        Returns:
+            The message as a string.
+        """
+
+    def warning_message(self, *args: Any, **kwargs: Any) -> str:
+        """ The warning message printed when the model does not pass the verification. """
+        return self.message(*args, **kwargs)
+
+    def error_message(self, *args: Any, **kwargs: Any) -> str:
+        """ The error message printed when the model does not pass the verification. """
+        return self.message(*args, **kwargs)
+
+    def _raise(self, *args: Any, **kwargs: Any) -> None:
+        if self._raise_error:
+            raise RuntimeError(self.error_message(*args, **kwargs))
+        if self._raise_warning:
+            rank_zero_warn(self.warning_message(*args, **kwargs))