1 报错描述

1.1 系统环境

Hardware Environment(Ascend/GPU/CPU): GPU

Software Environment:

– MindSpore version (source or binary): 1.6.0

– Python version (e.g., Python 3.7.5): 3.7.6

– OS platform and distribution (e.g., Linux Ubuntu 16.04): Ubuntu 4.15.0-74-generic

– GCC/Compiler version (if compiled from source):

1.2 基本信息

1.2.1 脚本

训练脚本是构建了GRU的单算子网络，通过门控制循环单元更新值。脚本如下：

01 class Net(nn.Cell):
02   def __init__(self):
03     super(Net, self).__init__()
04     self.gru = nn.GRU(10, 16, 1000, has_bias=True, batch_first=True, bidirectional=False)
05
06   def construct(self, x, h0):
07     output = self.gru(x, h0)
08     return output
09
10 net = Net()
11 x = Tensor(np.ones([3, 5, 10]).astype(np.float32))
12 h0 = Tensor(np.ones([1 * 1000, 3, 16]).astype(np.float32))
13 output, hn = net(x, h0)
14 print('output', output.shape))

1.2.2 报错

这里报错信息如下：

Traceback (most recent call last):
 File &quot;demo.py&quot;, line 13, in &lt;module&gt;
  output, hn = net(x, h0)
  …
RuntimeError: mindspore/ccsrc/pipeline/jit/static_analysis/evaluator.cc:198 Eval] Exceed function call depth limit 1000, (function call depth: 1001, simulate call depth: 997).
It's always happened with complex construction of code or infinite recursion or loop.
Please check the code if it's has the infinite recursion or call 'context.set_context(max_call_depth=value)' to adjust this value.
If max_call_depth is set larger, the system max stack depth should be set larger too to avoid stack overflow.
For more details, please refer to the FAQ at https://www.mindspore.cn.
The function call stack (See file ' /rank_0/om/analyze_fail.dat' for more details):
\# 0 In file demo.py(07)
    output = self.gru(x, h0)

2 原因分析

在MindSpore 1.6中，在脚本中第10行代码发现了在construct中创建tensor对象并使用。

接着看报错信息：在RuntimeError中，写到Exceed function call depth limit 1000, (function call depth: 1001, simulate call depth: 997)，意思是超过函数调用深度限制，结合官方接口可知是配置的num_layers太大，导致loop嵌套的数量超过阈值了，因此我们需要将num_layers改小点，或者按照上面提示说的，通过*context.set_context(max_call_depth=value)*调大阈值，但不建议这样做，因为嵌套层次这么多，执行速度会非常慢。

检查代码发现，04行代码num_layers为1000，我们需要将其改为合理范围内。