PyTorch迁移MindSpore | split后For ‘Mul‘, x.shape and y.shape are supposed to broadcast

Environment(Ascend/GPU/CPU): GPU-GTX3090(24G)
Software Environment:
– MindSpore version (source or binary): 1.7.0
– Python version (e.g., Python 3.7.5): 3.8.13
– OS platform and distribution (e.g., Linux Ubuntu 16.04): Ubuntu 16.04
– CUDA version : 11.0

此代码是ConvLSTM从PyTorch迁移到MindSpore的一部分，下面为报错部分

split = ops.Split(1, 2)
output = split(x)

部分个人信息做遮挡处理

Traceback (most recent call last):
  File "main.py", line 195, in 
    train()
  File "main.py", line 142, in train
    loss = train_network(data, label)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 612, in __call__
    raise err
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 609, in __call__
    output = self._run_construct(cast_inputs, kwargs)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 429, in _run_const
ruct
    output = self.construct(*cast_inputs, **kwargs)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/wrap/cell_wrapper.py", line 373,
in construct
    loss = self.network(*inputs)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 612, in __call__
    raise err
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 609, in __call__
    output = self._run_construct(cast_inputs, kwargs)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 429, in _run_const
ruct
    output = self.construct(*cast_inputs, **kwargs)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/wrap/cell_wrapper.py", line 111,
in construct
    out = self._backbone(data)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 612, in __call__
    raise err
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 609, in __call__
    output = self._run_construct(cast_inputs, kwargs)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 429, in _run_const
ruct
    output = self.construct(*cast_inputs, **kwargs)
  File "/home/xxxlab/zrj/mindspore/ConvLSTM-PyTorch/conv/model.py", line 31, in construct
    state = self.encoder(input)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 612, in __call__
    raise err
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 609, in __call__
    output = self._run_construct(cast_inputs, kwargs)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 429, in _run_const
ruct
    output = self.construct(*cast_inputs, **kwargs)
  File "/home/xxxlab/zrj/mindspore/ConvLSTM-PyTorch/conv/encoder.py", line 42, in construct
    inputs, state_stage = self.forward_by_stage(
  File "/home/xxxlab/zrj/mindspore/ConvLSTM-PyTorch/conv/encoder.py", line 35, in forward_by_stage
    outputs_stage, state_stage = rnn(inputs, None)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 612, in __call__
    raise err
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 609, in __call__
    output = self._run_construct(cast_inputs, kwargs)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 429, in _run_const
ruct
    output = self.construct(*cast_inputs, **kwargs)
  File "/home/xxxlab/zrj/mindspore/ConvLSTM-PyTorch/conv/ConvRNN.py", line 61, in construct
    combined_2 = P.Concat(1)((x, r * htprev))  # h' = tanh(W*(x+r*H_t-1))
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/common/tensor.py", line 278, in __mu
l__
    return tensor_operator_registry.get('__mul__')(self, other)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/ops/composite/multitype_ops/_compile
_utils.py", lin
e 101, in _tensor_mul
    return F.mul(self, other)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/ops/primitive.py", line 294, in __ca
ll__
    return _run_op(self, self.name, args)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/common/api.py", line 90, in wrapper
    results = fn(*arg, **kwargs)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/ops/primitive.py", line 754, in _run
_op
    output = real_run_op(obj, op_name, args)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/ops/primitive.py", line 575, in __in
fer__
    out[track] = fn(*(x[track] for x in args))
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/ops/operations/math_ops.py", line 78
, in infer_shap
e
    return get_broadcast_shape(x_shape, y_shape, self.name)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/ops/_utils/utils.py", line 70, in ge
t_broadcast_sha
pe
    raise ValueError(f"For '{prim_name}', {arg_name1}.shape and {arg_name2}.shape are supposed "
ValueError: For 'Mul', x.shape and y.shape are supposed to broadcast, where broadcast means that x.shape[i] = 1 or -1 or
 y.shape[i] = 1
 or -1 or x.shape[i] = y.shape[i], but now x.shape and y.shape can not broadcast, got i: -3, x.shape: [16, 2, 64, 64], y
.shape: [16, 64
, 64, 64].

当时真的无比疑惑，为什么split出来的维度不是自己想要的呢？还以为输入的维度就错了，从输入开始debug，结果发现前面都没问题，是split出问题了。

一开始我是通过pytorch-mindspore的对照表进行算子映射的。其中torch.split与mindspore.ops.Split相映射，且备注没有额外信息，我自然就以为他们的参数是一样的。但其实不然，翻阅pytorch和mindspore文档就可以知道torch.split中除了tensor和dim的参数是

• split_size_or_sections (int) or (list(int)) – size of a single chunk or list of sizes for each chunk

而mindspore中除了tensor和dim的参数是

• output_num (int) - 指定分割数量。其值为正整数。默认值：1。

  相对而言，mindspore的参数更好操作和理解，而pytorch还需要自己额外计算，所以在迁移时不能单纯把参数复制过来，还要看是否能相对应上。

迁移时要勤翻pytorch和mindspore api的文档，除了利用mindconvert进行自动映射外，还需要注意一下不支持算子的映射。