maybe not
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@ -614,7 +614,7 @@ class Trainer:
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amp: bool = False # automatic mixed precision
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ddp: bool = False # torch's internal DDP, automatically set if local backend is used and multiple GPUs are requested
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scale_loss: bool = False # whether to perform loss scaling (for FP16 training) (it actually seems more harmful than not for this specific workload)
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#scale_loss: bool = False # whether to perform loss scaling (for FP16 training) (it actually seems more harmful than not for this specific workload)
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load_webui: bool = False # not working, but loads the web UI to allow inferencing during training
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no_logger: bool = False # deprecated, but reroutes some logger calls to normal print statements for when logger broke because of BitNet
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@ -634,14 +634,12 @@ class Trainer:
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return torch.float8_e4m3fn
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return torch.float32
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"""
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@cached_property
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def scale_loss(self):
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# currently cannot feasibly apply loss scaling with DeepSpeed backend (it can handle it itself anyways)
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if self.backend != "local":
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return False
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return self.dtype == torch.float16
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"""
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"""
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@dataclass()
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@ -316,7 +316,7 @@ class AR_NAR(Base):
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def example_usage():
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cfg.trainer.backend = "local"
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# cfg.trainer.backend = "local"
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cfg.hyperparameters.gradient_accumulation_steps = 1
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if cfg.audio_backend == "dac":
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cfg.sample_rate = 44_100
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