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alter rewards data type

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Former-commit-id: 3eb7eb2d37525da50fe401ab7c59532e6e1ef984
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hiyouga
2023-06-02 14:19:51 +08:00
parent 896dbfec16
commit e9ab06678f
12 changed files with 40 additions and 50 deletions
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14
src/utils/other.py
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@@ -5,7 +5,6 @@ import torch
import logging
from typing import Dict, List, Optional
from transformers import Seq2SeqTrainingArguments
from transformers.trainer import TRAINER_STATE_NAME
from transformers.modeling_utils import PreTrainedModel
from transformers.generation.utils import LogitsProcessorList
@@ -143,7 +142,7 @@ def load_valuehead_params(model: torch.nn.Module, checkpoint_dir: os.PathLike) -
model.register_buffer("default_head_bias", torch.zeros_like(valuehead_state_dict["summary.bias"]))
def smooth(scalars: List[float], weight: Optional[float] = 0.95) -> List[float]:
def smooth(scalars: List[float], weight: Optional[float] = 0.9) -> List[float]:
"""
EMA implementation according to TensorBoard.
"""
@@ -156,9 +155,10 @@ def smooth(scalars: List[float], weight: Optional[float] = 0.95) -> List[float]:
return smoothed
def plot_loss(training_args: Seq2SeqTrainingArguments, keys: Optional[List[str]] = ["loss"]) -> None:
def plot_loss(save_dictionary: os.PathLike, keys: Optional[List[str]] = ["loss"]) -> None:
import matplotlib.pyplot as plt
data = json.load(open(os.path.join(training_args.output_dir, TRAINER_STATE_NAME), "r"))
with open(os.path.join(save_dictionary, TRAINER_STATE_NAME), "r", encoding="utf-8") as f:
data = json.load(f)
for key in keys:
steps, metrics = [], []
@@ -174,9 +174,9 @@ def plot_loss(training_args: Seq2SeqTrainingArguments, keys: Optional[List[str]]
plt.figure()
plt.plot(steps, metrics, alpha=0.4, label="original")
plt.plot(steps, smooth(metrics), label="smoothed")
plt.title("training {} of {}".format(key, training_args.output_dir))
plt.title("training {} of {}".format(key, save_dictionary))
plt.xlabel("step")
plt.ylabel(key)
plt.legend()
plt.savefig(os.path.join(training_args.output_dir, "training_{}.png".format(key)), format="png", dpi=100)
print("Figure saved:", os.path.join(training_args.output_dir, "training_{}.png".format(key)))
plt.savefig(os.path.join(save_dictionary, "training_{}.png".format(key)), format="png", dpi=100)
print("Figure saved:", os.path.join(save_dictionary, "training_{}.png".format(key)))