leezu commented on issue #19286:
URL:
https://github.com/apache/incubator-mxnet/issues/19286#issuecomment-703816274
This bug is related to legacy (non-numpy) reshape operator. Reshape with `0`
implies "copy this dimension from input".
We can see that all shapes are correct prior to infer_shape pass:
```
frame #6: 0x00007fff3bd1cb8b
libmxnet.so`mxnet::imperative::SetShapeType(ctx=0x00007fffffff8f18,
attrs=0x00007fffffff97a8, inputs=size=1, outputs=size=1,
dispatch_mode=0x00007fffffff8f24) at imperative_utils.h:208:26
205 common::ConvertToNumpyShape(&in_shapes);
206 common::ConvertToNumpyShape(&out_shapes);
207 }
-> 208 const bool success = infershape[attrs.op](attrs, &in_shapes,
&out_shapes);
209 if (!success) {
210 std::stringstream os;
211 os << "Operator " << attrs.op->name << " inferring shapes
failed.\n";
(lldb) parray 5 in_shapes[0].data_heap_
(long *) $15 = 0x0000555555f45700 {
(long) [0] = 1
(long) [1] = 2
(long) [2] = 4
(long) [3] = 0
(long) [4] = 128
}
(lldb) p out_shapes
(mxnet::ShapeVector) $16 = size=1 {
[0] = {
mxnet::Tuple<long> = {
ndim_ = 4
num_heap_allocated_ = 0
data_stack_ = ([0] = 2, [1] = 4, [2] = 0, [3] = 128)
data_heap_ = 0x0000000000000000
}
}
}
```
But after infer_shape, `0` is replaced by `4`.
```
frame #1: 0x00007fff4968c1be
libmxnet.so`mxnet::op::ReshapeShape(attrs=0x00007fffffff97a8,
in_attrs=0x00005555569cbb68, out_attrs=0x00005555569cbb80) at
matrix_op-inl.h:235:3
232 << "Target: " << oshape
233 << "\nSource: " << dshape;
234 #endif
-> 235 SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
236 return ReverseReshapeInferShape(&(*in_attrs)[0], (*out_attrs)[0]);
237 }
238
(lldb) p oshape
(mxnet::TShape) $17 = {
mxnet::Tuple<long> = {
ndim_ = 4
num_heap_allocated_ = 0
data_stack_ = ([0] = 2, [1] = 4, [2] = 4, [3] = 128)
data_heap_ = 0x0000000000000000
}
}
```
https://github.com/apache/incubator-mxnet/blob/f732530c8e1f8dcc11134e935430e3793ddbf4c8/src/operator/tensor/matrix_op-inl.h#L194-L237
The root cause is that `MXNDArrayAt`, `MXNDArrayReshape` and
`MXNDArraySlice` do not sufficiently distinguish between the numpy and
non-numpy mode and always record legacy operators.
We need to update the recording step to record numpy / legacy operator based
on if numpy / legacy mode is enabled:
https://github.com/apache/incubator-mxnet/blob/72eff9b66ecc683c3e7f9ad2c0ba69efa8dd423b/src/ndarray/ndarray.cc#L302-L308
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