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PaddleSpeech/deepspeech/training/gradclip.py

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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 paddle
from paddle.fluid import core
from paddle.fluid import layers
from paddle.fluid.dygraph import base as imperative_base
from deepspeech.utils.log import Log
__all__ = ["ClipGradByGlobalNormWithLog"]
logger = Log(__name__).getlog()
class ClipGradByGlobalNormWithLog(paddle.nn.ClipGradByGlobalNorm):
def __init__(self, clip_norm):
super().__init__(clip_norm)
def __repr__(self):
return f"{self.__class__.__name__}(global_clip_norm={self.clip_norm})"
@imperative_base.no_grad
def _dygraph_clip(self, params_grads):
params_and_grads = []
sum_square_list = []
for i, (p, g) in enumerate(params_grads):
if g is None:
continue
if getattr(p, 'need_clip', True) is False:
continue
merge_grad = g
if g.type == core.VarDesc.VarType.SELECTED_ROWS:
merge_grad = layers.merge_selected_rows(g)
merge_grad = layers.get_tensor_from_selected_rows(merge_grad)
square = layers.square(merge_grad)
sum_square = layers.reduce_sum(square)
sum_square_list.append(sum_square)
# debug log
logger.debug(
f"Grad Before Clip: {p.name}: {float(sum_square.sqrt()) }")
# all parameters have been filterd out
if len(sum_square_list) == 0:
return params_grads
global_norm_var = layers.concat(sum_square_list)
global_norm_var = layers.reduce_sum(global_norm_var)
global_norm_var = layers.sqrt(global_norm_var)
# debug log
logger.debug(f"Grad Global Norm: {float(global_norm_var)}!!!!")
max_global_norm = layers.fill_constant(
shape=[1], dtype=global_norm_var.dtype, value=self.clip_norm)
clip_var = layers.elementwise_div(
x=max_global_norm,
y=layers.elementwise_max(x=global_norm_var, y=max_global_norm))
for i, (p, g) in enumerate(params_grads):
if g is None:
continue
if getattr(p, 'need_clip', True) is False:
params_and_grads.append((p, g))
continue
new_grad = layers.elementwise_mul(x=g, y=clip_var)
params_and_grads.append((p, new_grad))
# debug log
logger.debug(
f"Grad After Clip: {p.name}: {float(new_grad.square().sum().sqrt())}"
)
return params_and_grads