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# Copyright 2024 Xiaomi Corp. (authors: Zengrui Jin,)
#
# See ../../LICENSE for clarification regarding multiple authors
#
# 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.
def raise_grad_scale_is_too_small_error(cur_grad_scale: float):
raise RuntimeError(
f"""
grad_scale is too small, exiting: {cur_grad_scale}
========================= NOTE =========================
If you see this error, it means that the gradient scale is too small.
The default base_lr is 0.045 / 0.05 (depends on which recipe you are
using), this is an empirical value obtained mostly using 4 * 32GB V100
GPUs with a max_duration of approx. 1,000.
The proper value of base_lr may vary depending on the number of GPUs
and the value of max-duration you are using.
To fix this issue, you may need to adjust the value of base_lr accordingly.
We would suggest you to decrease the value of base_lr by 0.005 (e.g.,
from 0.045 to 0.04), and try again. If the error still exists, you may
repeat the process until base_lr hits 0.02. (Note that this will lead to
certain loss of performance, but it should work. You can compensate this by
increasing the num_epochs.)
If the error still exists, you could try to seek help by raising an issue,
with a detailed description of (a) your computational resources, (b) the
base_lr and (c) the max_duration you are using, (d) detailed configuration
of your model.
========================================================
"""
)
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