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fix minor typos in code and docs
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@ -215,7 +215,7 @@ class Chain(ContextModule):
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@staticmethod
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def _pretty_print_args(*args: Any) -> str:
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"""
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Flatten nested tuples and print tensors with their shape and other informations.
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Flatten nested tuples and print tensors with their shape and other information.
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"""
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def _flatten_tuple(t: Tensor | tuple[Any, ...], /) -> list[Any]:
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@ -447,7 +447,7 @@ class IPAdapter(Generic[T], fl.Chain, Adapter[T]):
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assert isinstance(encoder_clone[-3], fl.Lambda) # pooling (classif token)
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for _ in range(3):
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encoder_clone.pop()
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transfomer_layers = encoder_clone[-1]
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assert isinstance(transfomer_layers, fl.Chain) and len(transfomer_layers) == 32
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transfomer_layers.pop()
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transformer_layers = encoder_clone[-1]
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assert isinstance(transformer_layers, fl.Chain) and len(transformer_layers) == 32
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transformer_layers.pop()
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return encoder_clone
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@ -178,7 +178,7 @@ class ModelConfig(BaseModel):
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class GyroDropoutConfig(BaseModel):
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total_subnetworks: int = 512
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concurent_subnetworks: int = 64
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concurrent_subnetworks: int = 64
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iters_per_epoch: int = 512
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num_features_threshold: float = 5e5
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@ -213,7 +213,7 @@ class TrainingClock:
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return int(time.time() - self.start_time)
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@cached_property
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def evalution_interval_steps(self) -> int:
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def evaluation_interval_steps(self) -> int:
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return self.convert_time_unit_to_steps(
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number=self.evaluation_interval["number"], unit=self.evaluation_interval["unit"]
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)
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@ -244,7 +244,7 @@ class TrainingClock:
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@property
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def is_evaluation_step(self) -> bool:
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return self.step % self.evalution_interval_steps == 0
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return self.step % self.evaluation_interval_steps == 0
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@property
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def is_checkpointing_step(self) -> bool:
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