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new 475a0c78690 Fix syntax (#29719)
475a0c78690 is described below
commit 475a0c7869051331a03f2bcb65af45113fd984a3
Author: Ritesh Ghorse <riteshgho...@gmail.com>
AuthorDate: Tue Dec 12 14:08:45 2023 -0500
Fix syntax (#29719)
* correct syntax
* remove unused inference_args
---
.../apache_beam/ml/inference/huggingface_inference.py | 19 +------------------
1 file changed, 1 insertion(+), 18 deletions(-)
diff --git a/sdks/python/apache_beam/ml/inference/huggingface_inference.py
b/sdks/python/apache_beam/ml/inference/huggingface_inference.py
index 1bc92c462c9..25367d22eaa 100644
--- a/sdks/python/apache_beam/ml/inference/huggingface_inference.py
+++ b/sdks/python/apache_beam/ml/inference/huggingface_inference.py
@@ -221,7 +221,6 @@ class
HuggingFaceModelHandlerKeyedTensor(ModelHandler[Dict[str,
*,
inference_fn: Optional[Callable[..., Iterable[PredictionResult]]] = None,
load_model_args: Optional[Dict[str, Any]] = None,
- inference_args: Optional[Dict[str, Any]] = None,
min_batch_size: Optional[int] = None,
max_batch_size: Optional[int] = None,
max_batch_duration_secs: Optional[int] = None,
@@ -250,10 +249,6 @@ class
HuggingFaceModelHandlerKeyedTensor(ModelHandler[Dict[str,
load_model_args (Dict[str, Any]): (Optional) Keyword arguments to provide
load options while loading models from Hugging Face Hub.
Defaults to None.
- inference_args (Dict[str, Any]): (Optional) Non-batchable arguments
- required as inputs to the model's inference function. Unlike Tensors
- in `batch`, these parameters will not be dynamically batched.
- Defaults to None.
min_batch_size: the minimum batch size to use when batching inputs.
max_batch_size: the maximum batch size to use when batching inputs.
max_batch_duration_secs: the maximum amount of time to buffer a batch
@@ -273,7 +268,6 @@ class
HuggingFaceModelHandlerKeyedTensor(ModelHandler[Dict[str,
self._device = device
self._inference_fn = inference_fn
self._model_config_args = load_model_args if load_model_args else {}
- self._inference_args = inference_args if inference_args else {}
self._batching_kwargs = {}
self._env_vars = kwargs.get("env_vars", {})
if min_batch_size is not None:
@@ -293,7 +287,7 @@ class
HuggingFaceModelHandlerKeyedTensor(ModelHandler[Dict[str,
model = self._model_class.from_pretrained(
self._model_uri, **self._model_config_args)
if self._framework == 'pt':
- if self._device == "GPU" and is_gpu_available_torch:
+ if self._device == "GPU" and is_gpu_available_torch():
model.to(torch.device("cuda"))
if callable(getattr(model, 'requires_grad_', None)):
model.requires_grad_(False)
@@ -407,7 +401,6 @@ class
HuggingFaceModelHandlerTensor(ModelHandler[Union[tf.Tensor, torch.Tensor],
*,
inference_fn: Optional[Callable[..., Iterable[PredictionResult]]] = None,
load_model_args: Optional[Dict[str, Any]] = None,
- inference_args: Optional[Dict[str, Any]] = None,
min_batch_size: Optional[int] = None,
max_batch_size: Optional[int] = None,
max_batch_duration_secs: Optional[int] = None,
@@ -436,10 +429,6 @@ class
HuggingFaceModelHandlerTensor(ModelHandler[Union[tf.Tensor, torch.Tensor],
load_model_args (Dict[str, Any]): (Optional) keyword arguments to provide
load options while loading models from Hugging Face Hub.
Defaults to None.
- inference_args (Dict[str, Any]): (Optional) Non-batchable arguments
- required as inputs to the model's inference function. Unlike Tensors
- in `batch`, these parameters will not be dynamically batched.
- Defaults to None.
min_batch_size: the minimum batch size to use when batching inputs.
max_batch_size: the maximum batch size to use when batching inputs.
max_batch_duration_secs: the maximum amount of time to buffer a batch
@@ -459,7 +448,6 @@ class
HuggingFaceModelHandlerTensor(ModelHandler[Union[tf.Tensor, torch.Tensor],
self._device = device
self._inference_fn = inference_fn
self._model_config_args = load_model_args if load_model_args else {}
- self._inference_args = inference_args if inference_args else {}
self._batching_kwargs = {}
self._env_vars = kwargs.get("env_vars", {})
if min_batch_size is not None:
@@ -586,7 +574,6 @@ class HuggingFacePipelineModelHandler(ModelHandler[str,
device: Optional[str] = None,
inference_fn: PipelineInferenceFn = _default_pipeline_inference_fn,
load_pipeline_args: Optional[Dict[str, Any]] = None,
- inference_args: Optional[Dict[str, Any]] = None,
min_batch_size: Optional[int] = None,
max_batch_size: Optional[int] = None,
max_batch_duration_secs: Optional[int] = None,
@@ -623,9 +610,6 @@ class HuggingFacePipelineModelHandler(ModelHandler[str,
Default is _default_pipeline_inference_fn.
load_pipeline_args (Dict[str, Any]): keyword arguments to provide load
options while loading pipelines from Hugging Face. Defaults to None.
- inference_args (Dict[str, Any]): Non-batchable arguments
- required as inputs to the model's inference function.
- Defaults to None.
min_batch_size: the minimum batch size to use when batching inputs.
max_batch_size: the maximum batch size to use when batching inputs.
max_batch_duration_secs: the maximum amount of time to buffer a batch
@@ -644,7 +628,6 @@ class HuggingFacePipelineModelHandler(ModelHandler[str,
self._model = model
self._inference_fn = inference_fn
self._load_pipeline_args = load_pipeline_args if load_pipeline_args else {}
- self._inference_args = inference_args if inference_args else {}
self._batching_kwargs = {}
self._framework = "torch"
self._env_vars = kwargs.get('env_vars', {})
