fix typo

common/checkpointing/snapshot.py

@@ -101,7 +101,7 @@ class Snapshot:
     weight_tensor,
   ) -> None:
     """Loads pretrained embedding from the snapshot to the model.
-       Utilise partial lodaing meachanism from torchsnapshot.
+       Utilise partial lodaing mechanism from torchsnapshot.
     Args:
       embedding_snapshot: Path to the snapshot containing pretrained embeddings (EBC).
       snapshot_emb_name: Name of the layer in the *snapshot* model, containing the EBC.

core/config/config_load.py

@@ -11,7 +11,7 @@ def load_config_from_yaml(config_type: Type[BaseConfig], yaml_path: str):
   """Recommend method to load a config file (a yaml file) and parse it.
 
   Because we have a shared filesystem the recommended route to running jobs it put modified config
-  files with the desired parameters somewhere on the filesytem and run jobs pointing to them.
+  files with the desired parameters somewhere on the filesystem and run jobs pointing to them.
   """
 
   def _substitute(s):

core/custom_training_loop.py

@@ -115,7 +115,7 @@ def train(
     dataset: data iterator for the training set
     evaluation_iterators: data iterators for the different evaluation sets
     scheduler: optional learning rate scheduler
-    output_transform_for_metrics: optional transformation functions to transorm the model
+    output_transform_for_metrics: optional transformation functions to transform the model
                                   output and labels into a format the metrics can understand
   """
 

projects/home/recap/data/config.py

@@ -50,7 +50,7 @@ class DatasetConfig(base_config.BaseConfig):
     None, description="Number of shards to keep."
   )
   repeat_files: bool = pydantic.Field(
-    True, description="DEPRICATED. Files are repeated no matter what this is set to."
+    True, description="DEPRECATED. Files are repeated no matter what this is set to."
   )
   file_batch_size: pydantic.PositiveInt = pydantic.Field(16, description="File batch size")
 
@@ -211,7 +211,7 @@ class Sampler(base_config.BaseConfig):
   Only use this for quick experimentation.
   If samplers are useful, we should sample from upstream data generation.
 
-  DEPRICATED, DO NOT USE.
+  DEPRECATED, DO NOT USE.
   """
 
   name: str
@@ -234,7 +234,7 @@ class RecapDataConfig(DatasetConfig):
 
   sampler: Sampler = pydantic.Field(
     None,
-    description="""DEPRICATED, DO NOT USE. Sampling function for offline experiments.""",
+    description="""DEPRECATED, DO NOT USE. Sampling function for offline experiments.""",
   )
 
   @pydantic.root_validator()

projects/home/recap/data/dataset.py

@@ -398,7 +398,7 @@ class RecapDataset(torch.utils.data.IterableDataset):
         )
     else:
       raise ValueError(
-        "Must specifiy either `inputs`, `explicit_datetime_inputs`, or `explicit_date_inputs` in data_config"
+        "Must specify either `inputs`, `explicit_datetime_inputs`, or `explicit_date_inputs` in data_config"
       )
 
     num_files = len(filenames)

projects/home/recap/data/generate_random_data.py

@@ -9,7 +9,7 @@ from tml.core import config as tml_config_mod
 import tml.projects.home.recap.config as recap_config_mod
 
 flags.DEFINE_string("config_path", None, "Path to hyperparameters for model.")
-flags.DEFINE_integer("n_examples", 100, "Numer of examples to generate.")
+flags.DEFINE_integer("n_examples", 100, "Number of examples to generate.")
 
 FLAGS = flags.FLAGS
 

projects/home/recap/model/config.py

@@ -18,7 +18,7 @@ class DropoutConfig(base_config.BaseConfig):
 
 
 class LayerNormConfig(base_config.BaseConfig):
-  """Configruation for the layer normalization."""
+  """Configuration for the layer normalization."""
 
   epsilon: float = pydantic.Field(
     1e-3, description="Small float added to variance to avoid dividing by zero."
@@ -91,7 +91,7 @@ class ZScoreLogConfig(base_config.BaseConfig):
     False, description="Option to use batch normalization on the inputs."
   )
   use_renorm: bool = pydantic.Field(
-    False, description="Option to use batch renormalization for trainig and serving consistency."
+    False, description="Option to use batch renormalization for training and serving consistency."
   )
   use_bq_stats: bool = pydantic.Field(
     False, description="Option to load the partitioned json files from BQ as statistics."

projects/twhin/data/edges.py

@@ -96,7 +96,7 @@ class EdgesDataset(Dataset):
 
     Returns a KeyedJaggedTensor used to look up all embeddings.
 
-    Note: We treat the lhs and rhs as though they're separate lookups: `len(lenghts) == 2 * bsz * len(tables)`.
+    Note: We treat the lhs and rhs as though they're separate lookups: `len(lengths) == 2 * bsz * len(tables)`.
     This differs from the DLRM pattern where we have `len(lengths) = bsz * len(tables)`.
 
     For the example above: