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83 lines
3.0 KiB
83 lines
3.0 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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from paddle.fluid import core
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from paddle.fluid import layers
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from paddle.fluid.dygraph import base as imperative_base
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from deepspeech.utils.log import Log
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__all__ = ["ClipGradByGlobalNormWithLog"]
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logger = Log(__name__).getlog()
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class ClipGradByGlobalNormWithLog(paddle.nn.ClipGradByGlobalNorm):
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def __init__(self, clip_norm):
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super().__init__(clip_norm)
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@imperative_base.no_grad
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def _dygraph_clip(self, params_grads):
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params_and_grads = []
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sum_square_list = []
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for i, (p, g) in enumerate(params_grads):
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if g is None:
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continue
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if getattr(p, 'need_clip', True) is False:
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continue
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merge_grad = g
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if g.type == core.VarDesc.VarType.SELECTED_ROWS:
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merge_grad = layers.merge_selected_rows(g)
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merge_grad = layers.get_tensor_from_selected_rows(merge_grad)
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square = layers.square(merge_grad)
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sum_square = layers.reduce_sum(square)
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sum_square_list.append(sum_square)
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# debug log
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if i < 10:
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logger.debug(
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f"Grad Before Clip: {p.name}: {float(sum_square.sqrt()) }")
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# all parameters have been filterd out
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if len(sum_square_list) == 0:
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return params_grads
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global_norm_var = layers.concat(sum_square_list)
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global_norm_var = layers.reduce_sum(global_norm_var)
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global_norm_var = layers.sqrt(global_norm_var)
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# debug log
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logger.debug(f"Grad Global Norm: {float(global_norm_var)}!!!!")
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max_global_norm = layers.fill_constant(
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shape=[1], dtype=global_norm_var.dtype, value=self.clip_norm)
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clip_var = layers.elementwise_div(
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x=max_global_norm,
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y=layers.elementwise_max(x=global_norm_var, y=max_global_norm))
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for i, (p, g) in enumerate(params_grads):
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if g is None:
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continue
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if getattr(p, 'need_clip', True) is False:
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params_and_grads.append((p, g))
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continue
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new_grad = layers.elementwise_mul(x=g, y=clip_var)
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params_and_grads.append((p, new_grad))
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# debug log
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if i < 10:
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logger.debug(
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f"Grad After Clip: {p.name}: {float(new_grad.square().sum().sqrt())}"
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)
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return params_and_grads
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