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59392d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | import numpy as np
import dataclasses as dc
@dc.dataclass
class CtrlArguments:
train_data: str = dc.field(
default="data/training_cunique_with_distractors.json",
metadata={"help": "A CSV list of training data files"}
)
formulation: str = dc.field(
default="areg_ltr",
metadata={"help": "Type of problem definition: autoregressive (areg) or u-PMLM (upmlm) or mixed (if predict_questions is set)"}
)
context_strategy: str = dc.field(
default="take_first",
metadata={"help": "How to deal with contexts greater than a specified length"}
)
tokenizer_file: str = dc.field(
default="tokenizer.json",
metadata={"help": "A JSON file (in the format provided by HuggingFace's tokenizers library) with a trained tokenizer"}
)
sequence_length: int = dc.field(
default=256,
metadata={"help": "The max sequence length"}
)
force_prepend_control: bool = dc.field(
default=False,
metadata={"help": "If the control code should be prepended for all sliding windows. Otherwise, it is only prepended at the start of the sequence"}
)
class GradientPrinter:
def __init__(self, name):
self.name = name
def __call__(self, grad):
np_grad = grad.cpu().numpy()
print("======== GRAD FOR {} ========".format(self.name))
print("\tGRAD {}".format(grad))
print("\tGRAD NORM {}".format(np.linalg.norm(np_grad)))
print("\tGRAD MEAN {}".format(np.mean(np_grad)))
print() |