最近在写tensorflow代码，几天前写双向lstm的时候，就不明不白出现了一次梯度是NoneType的问题，因为当时比较赶时间就放弃了双向lstm。今天，在写transformer的时候，它再一次出现了，而且还是包裹着不一样的外衣出现的，搞得我险些放弃transformer。最后排查半天发现是同一个问题，，太智障了。
运行tensorflow代码，偶尔会遇到梯度为None的情况，但是并不影响运行，甚至连warning都没有，所以Nonetype的梯度，它悄悄的来，悄悄的走，训模型的你甚至都不知道它的存在～
那么有人就要问了，你是怎么发现梯度为NoneType的呢？
这段代码是这样的：
grads_and_vars = optimizer.compute_gradients(self.cost, var_list=tvars)
var_lr_mult = {}
for var in tvars:
if var.op.name.find(r'biases') > 0:
var_lr_mult[var] = 2.0
elif var.name.startswith('bert'):
var_lr_mult[var] = 0.1
else:
var_lr_mult[var] = 1.0
grads_and_vars = [((g if var_lr_mult[v] == 1 else tf.multiply(var_lr_mult[v], g)), v) for g, v in
grads_and_vars]
我当时想要对transformer进行微调，所以将transformer部分的学习率设为其他部分的0.1倍。然后就红红火火报错了。
Traceback (most recent call last):
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/framework/op_def_library.py", line 528, in _apply_op_helper
preferred_dtype=default_dtype)
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py", line 1297, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py", line 286, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py", line 227, in constant
allow_broadcast=True)
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py", line 265, in _constant_impl
allow_broadcast=allow_broadcast))
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py", line 437, in make_tensor_proto
raise ValueError("None values not supported.")
ValueError: None values not supported.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/framework/op_def_library.py", line 542, in _apply_op_helper
values, as_ref=input_arg.is_ref).dtype.name
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py", line 1297, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py", line 286, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py", line 227, in constant
allow_broadcast=True)
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py", line 265, in _constant_impl
allow_broadcast=allow_broadcast))
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py", line 437, in make_tensor_proto
raise ValueError("None values not supported.")
ValueError: None values not supported.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "trainval_trans.py", line 378, in <module>
pre_emb=args.emb)
File "trainval_trans.py", line 44, in train
batch_size=bs, conv5=conv5)
File "/mnt/data1/htr/projects/CVPR2020/codes/hsf_refseg_py36/get_model.py", line 7, in get_segmentation_model
model = eval(name).LSTM_model(**kwargs)
File "/mnt/data1/htr/projects/CVPR2020/codes/hsf_refseg_py36/bert_models/Mutan_RAGR_transformer_p5.py", line 93, in __init__
self.train_op()
File "/mnt/data1/htr/projects/CVPR2020/codes/hsf_refseg_py36/bert_models/Mutan_RAGR_transformer_p5.py", line 350, in train_op
grads_and_vars]
File "/mnt/data1/htr/projects/CVPR2020/codes/hsf_refseg_py36/bert_models/Mutan_RAGR_transformer_p5.py", line 349, in <listcomp>
grads_and_vars = [((g if var_lr_mult[v] == 1 else tf.multiply(var_lr_mult[v], g)), v) for g, v in
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/util/dispatch.py", line 180, in wrapper
return target(*args, **kwargs)
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py", line 331, in multiply
return gen_math_ops.mul(x, y, name)
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/ops/gen_math_ops.py", line 6701, in mul
"Mul", x=x, y=y, name=name)
File "/home/htr/anaconda3/envs/tf36/lib/python3.6/site-packages/tensorflow_core/python/framework/op_def_library.py", line 546, in _apply_op_helper
(input_name, err))
ValueError: Tried to convert 'y' to a tensor and failed. Error: None values not supported.
问题出在对学习率做乘的部分。报错信息显示，乘法的第二个参数，也就是该变量的梯度，是NoneType。于是我将transformer的学习率改为1，非常神奇地，它就可以运行了。之后我陆续把transformer的学习率改成1， 2， 10等等等等，都不行。
于是我准备曲线救国一下，干脆把transformer的学习率改成1，其他的都是10，初始学习率降10倍得了。。然后又报错了。老夫活了这么大年级，头一次见到这么傲娇的网络，学习率不是1就不走了，一瞬间我有点怀疑人生。
无比气愤的我把变量名以及梯度打出来，发现，作妖的是一个名为bert/pooler/dense/bias的变量。此君乃是transformer中最后一层的全连接层的偏置，该全连接层负责对[CLS] token的embedding进行变换，也就是下面的tf.layers.dense层的bias变量。
with tf.variable_scope("pooler"):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token. We assume that this has been pre-trained
first_token_tensor = tf.squeeze(self.sequence_output[:, 0:1, :], axis=1)
self.pooled_output = tf.layers.dense(
first_token_tensor,
config.hidden_size,
activation=tf.tanh,
kernel_initializer=create_initializer(config.initializer_range))
这让我不由得联想到之前写Bi-LSTM时，想在句子特征后面加一个全连接层，然后屡次受挫，最后不得已自己定义了一个没有bias的全连接层。难道，对于[b, c]尺寸的向量应用全连接层，注定会偏置梯度为None？这也太魔幻了。
改来改去，我发现，如果transformer的学习率倍数跟下面做乘的判断条件中的值相同，就不会报错。想想也是，报错在最后一行，也就是tf.multiply这一行，也就是说，前面即使梯度求出来是NoneType，网络也不会理会，还是会继续运行，只有到了multiply，强制要求每个参数都是tensor的时候，才最终出事。
总之，我最终的解决方案就是，transformer的embedding层以及encoder层学习率都设为0.1，pooler层学习率设为1，反正一个bias而已，训得不好影响也不大，爱谁谁呗。
至此，仿佛问题都解决了。然而我还是心里有些不安，万一bias没有梯度影响了训练怎么办，万一bias没有梯度影响了训练怎么办，万一。。个鬼，反正我也不会用到pooled_output，不会用到就不会给它传梯度回去，训得不好也没影响。
此时，我灵光一闪，突然明白了，就是因为我没有用到pooled_output，这一层的输出根本不在loss梯度的计算路径上，所以根本就没有梯度，所以梯度就是NoneType。之前也是一样，我根本从头到尾都没有用到句子特征，所以梯度也是NoneType。
啊，真相大白了～我真是太机智了*
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