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# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import math
import os
from typing import Any, Dict, List
from transformers.trainer import TRAINER_STATE_NAME
from .logging import get_logger
from .packages import is_matplotlib_available
if is_matplotlib_available():
import matplotlib.figure
import matplotlib.pyplot as plt
logger = get_logger(__name__)
def smooth(scalars: List[float]) -> List[float]:
r"""
EMA implementation according to TensorBoard.
"""
if len(scalars) == 0:
return []
last = scalars[0]
smoothed = []
weight = 1.8 * (1 / (1 + math.exp(-0.05 * len(scalars))) - 0.5) # a sigmoid function
for next_val in scalars:
smoothed_val = last * weight + (1 - weight) * next_val
smoothed.append(smoothed_val)
last = smoothed_val
return smoothed
def gen_loss_plot(trainer_log: List[Dict[str, Any]]) -> "matplotlib.figure.Figure":
r"""
Plots loss curves in LlamaBoard.
"""
plt.close("all")
plt.switch_backend("agg")
fig = plt.figure()
ax = fig.add_subplot(111)
steps, losses = [], []
for log in trainer_log:
if log.get("loss", None):
steps.append(log["current_steps"])
losses.append(log["loss"])
ax.plot(steps, losses, color="#1f77b4", alpha=0.4, label="original")
ax.plot(steps, smooth(losses), color="#1f77b4", label="smoothed")
ax.legend()
ax.set_xlabel("step")
ax.set_ylabel("loss")
return fig
def plot_loss(save_dictionary: str, keys: List[str] = ["loss"]) -> None:
r"""
Plots loss curves and saves the image.
"""
plt.switch_backend("agg")
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 = [], []
for i in range(len(data["log_history"])):
if key in data["log_history"][i]:
steps.append(data["log_history"][i]["step"])
metrics.append(data["log_history"][i][key])
if len(metrics) == 0:
logger.warning(f"No metric {key} to plot.")
continue
plt.figure()
plt.plot(steps, metrics, color="#1f77b4", alpha=0.4, label="original")
plt.plot(steps, smooth(metrics), color="#1f77b4", label="smoothed")
plt.title("training {} of {}".format(key, save_dictionary))
plt.xlabel("step")
plt.ylabel(key)
plt.legend()
figure_path = os.path.join(save_dictionary, "training_{}.png".format(key.replace("/", "_")))
plt.savefig(figure_path, format="png", dpi=100)
print("Figure saved at:", figure_path)
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