yeandy commented on code in PR #21810:
URL: https://github.com/apache/beam/pull/21810#discussion_r895621888
##########
sdks/python/apache_beam/ml/inference/pytorch_inference.py:
##########
@@ -134,3 +135,185 @@ def get_metrics_namespace(self) -> str:
Returns a namespace for metrics collected by the RunInference transform.
"""
return 'RunInferencePytorch'
+
+
+@experimental()
+class PytorchModelHandlerTensor(ModelHandler[torch.Tensor,
+ PredictionResult,
+ torch.nn.Module]):
+ """ Implementation of the ModelHandler interface for PyTorch.
+
+ NOTE: This API and its implementation are under development and
+ do not provide backward compatibility guarantees.
+ """
+ def __init__(
+ self,
+ state_dict_path: str,
+ model_class: Callable[..., torch.nn.Module],
+ model_params: Dict[str, Any],
+ device: str = 'CPU'):
+ """
+ Initializes a PytorchModelHandler
+ :param state_dict_path: path to the saved dictionary of the model state.
+ :param model_class: class of the Pytorch model that defines the model
+ structure.
+ :param device: the device on which you wish to run the model. If
+ ``device = GPU`` then a GPU device will be used if it is available.
+ Otherwise, it will be CPU.
+
+ See https://pytorch.org/tutorials/beginner/saving_loading_models.html
+ for details
+ """
+ self._state_dict_path = state_dict_path
+ if device == 'GPU' and torch.cuda.is_available():
+ self._device = torch.device('cuda')
+ else:
+ self._device = torch.device('cpu')
+ self._model_class = model_class
+ self._model_params = model_params
+
+ def load_model(self) -> torch.nn.Module:
+ """Loads and initializes a Pytorch model for processing."""
+ model = self._model_class(**self._model_params)
+ model.to(self._device)
+ file = FileSystems.open(self._state_dict_path, 'rb')
+ model.load_state_dict(torch.load(file))
+ model.eval()
+ return model
+
+ def _convert_to_device(self, examples: torch.Tensor) -> torch.Tensor:
+ """
+ Converts samples to a style matching given device.
+
+ Note: A user may pass in device='GPU' but if GPU is not detected in the
+ environment it must be converted back to CPU.
+ """
+ if examples.device != self._device:
+ examples = examples.to(self._device)
+ return examples
+
+ def run_inference(
+ self, batch: List[torch.Tensor], model: torch.nn.Module,
+ **kwargs) -> Iterable[PredictionResult]:
+ """
+ Runs inferences on a batch of Tensors and returns an Iterable of
+ Tensor Predictions.
+
+ This method stacks the list of Tensors in a vectorized format to optimize
+ the inference call.
+ """
+ prediction_params = kwargs.get('prediction_params', {})
+ batched_tensors = torch.stack(batch)
+ batched_tensors = self._convert_to_device(batched_tensors)
+ predictions = model(batched_tensors, **prediction_params)
+ return [PredictionResult(x, y) for x, y in zip(batch, predictions)]
+
+ def get_num_bytes(self, batch: List[torch.Tensor]) -> int:
+ """Returns the number of bytes of data for a batch of Tensors."""
+ return sum((el.element_size() for tensor in batch for el in tensor))
+
+ def get_metrics_namespace(self) -> str:
+ """
+ Returns a namespace for metrics collected by the RunInference transform.
+ """
+ return 'RunInferencePytorch'
+
+
+@experimental()
+class PytorchModelHandlerKeyedTensor(ModelHandler[torch.Tensor,
+ PredictionResult,
+ torch.nn.Module]):
+ """ Implementation of the ModelHandler interface for PyTorch.
+
+ NOTE: This API and its implementation are under development and
+ do not provide backward compatibility guarantees.
+ """
+ def __init__(
+ self,
+ state_dict_path: str,
+ model_class: Callable[..., torch.nn.Module],
+ model_params: Dict[str, Any],
+ device: str = 'CPU'):
+ """
+ Initializes a PytorchModelHandler
+ :param state_dict_path: path to the saved dictionary of the model state.
+ :param model_class: class of the Pytorch model that defines the model
+ structure.
+ :param device: the device on which you wish to run the model. If
+ ``device = GPU`` then a GPU device will be used if it is available.
+ Otherwise, it will be CPU.
+
+ See https://pytorch.org/tutorials/beginner/saving_loading_models.html
+ for details
+ """
+ self._state_dict_path = state_dict_path
+ if device == 'GPU' and torch.cuda.is_available():
+ self._device = torch.device('cuda')
+ else:
+ self._device = torch.device('cpu')
+ self._model_class = model_class
+ self._model_params = model_params
+
+ def load_model(self) -> torch.nn.Module:
+ """Loads and initializes a Pytorch model for processing."""
+ model = self._model_class(**self._model_params)
+ model.to(self._device)
+ file = FileSystems.open(self._state_dict_path, 'rb')
+ model.load_state_dict(torch.load(file))
+ model.eval()
+ return model
Review Comment:
Done.
##########
sdks/python/apache_beam/ml/inference/pytorch_inference.py:
##########
@@ -134,3 +135,185 @@ def get_metrics_namespace(self) -> str:
Returns a namespace for metrics collected by the RunInference transform.
"""
return 'RunInferencePytorch'
+
+
+@experimental()
+class PytorchModelHandlerTensor(ModelHandler[torch.Tensor,
+ PredictionResult,
+ torch.nn.Module]):
+ """ Implementation of the ModelHandler interface for PyTorch.
+
+ NOTE: This API and its implementation are under development and
+ do not provide backward compatibility guarantees.
+ """
+ def __init__(
+ self,
+ state_dict_path: str,
+ model_class: Callable[..., torch.nn.Module],
+ model_params: Dict[str, Any],
+ device: str = 'CPU'):
+ """
+ Initializes a PytorchModelHandler
+ :param state_dict_path: path to the saved dictionary of the model state.
+ :param model_class: class of the Pytorch model that defines the model
+ structure.
+ :param device: the device on which you wish to run the model. If
+ ``device = GPU`` then a GPU device will be used if it is available.
+ Otherwise, it will be CPU.
+
+ See https://pytorch.org/tutorials/beginner/saving_loading_models.html
+ for details
+ """
+ self._state_dict_path = state_dict_path
+ if device == 'GPU' and torch.cuda.is_available():
+ self._device = torch.device('cuda')
+ else:
+ self._device = torch.device('cpu')
+ self._model_class = model_class
+ self._model_params = model_params
+
+ def load_model(self) -> torch.nn.Module:
+ """Loads and initializes a Pytorch model for processing."""
+ model = self._model_class(**self._model_params)
+ model.to(self._device)
+ file = FileSystems.open(self._state_dict_path, 'rb')
+ model.load_state_dict(torch.load(file))
+ model.eval()
+ return model
+
+ def _convert_to_device(self, examples: torch.Tensor) -> torch.Tensor:
+ """
+ Converts samples to a style matching given device.
+
+ Note: A user may pass in device='GPU' but if GPU is not detected in the
+ environment it must be converted back to CPU.
+ """
+ if examples.device != self._device:
+ examples = examples.to(self._device)
+ return examples
+
+ def run_inference(
+ self, batch: List[torch.Tensor], model: torch.nn.Module,
+ **kwargs) -> Iterable[PredictionResult]:
+ """
+ Runs inferences on a batch of Tensors and returns an Iterable of
+ Tensor Predictions.
+
+ This method stacks the list of Tensors in a vectorized format to optimize
+ the inference call.
+ """
+ prediction_params = kwargs.get('prediction_params', {})
+ batched_tensors = torch.stack(batch)
+ batched_tensors = self._convert_to_device(batched_tensors)
+ predictions = model(batched_tensors, **prediction_params)
+ return [PredictionResult(x, y) for x, y in zip(batch, predictions)]
+
+ def get_num_bytes(self, batch: List[torch.Tensor]) -> int:
+ """Returns the number of bytes of data for a batch of Tensors."""
+ return sum((el.element_size() for tensor in batch for el in tensor))
+
+ def get_metrics_namespace(self) -> str:
+ """
+ Returns a namespace for metrics collected by the RunInference transform.
+ """
+ return 'RunInferencePytorch'
+
+
+@experimental()
+class PytorchModelHandlerKeyedTensor(ModelHandler[torch.Tensor,
Review Comment:
Done
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