haoyang9804 commented on issue #15004:
URL: https://github.com/apache/tvm/issues/15004#issuecomment-1646876481
If I get it correctly, here is how the error occurs.
In the low-level library of PyTorch, pooling operator is separated into
several sub-operators. For instance, `adaptive_max_pool2d_0_0` pools the first
dimension (the width dimension) while `adaptive_max_pool2d_0_1` pools the
second dimension (the height dimension) in a common 2-d tensor.
However, as for `adaptive_max_pool1d`, PyTorch also uses sub-operators,
which are`adaptive_max_pool1d_0_0` and `adaptive_max_pool1d_0_1` to represent
it. In this representation, `adaptive_max_pool1d_0_0` actually takes charge of
performing pooling while `adaptive_max_pool1d_0_1` does nothing.
I don't know why the PyTorch developers design in this way, but to perfectly
converting `adaptive_max_pool1d` in PyTorch, TVM developers maps this operator
to the a tuple consisting of a call node in the high-level IR and a `None`.
Here is the implementation in `tvm/relay/frontend/pytorch.py`.
```Python
def adaptive_max_pool(self, op, inputs, input_types):
data = inputs[0]
output_size = inputs[1]
for i, item in enumerate(output_size):
if isinstance(item, tvm.relay.expr.Constant):
# convert Constant to int
output_size[i] = item.data.numpy()[()]
# returns dummy indices too
return op(data, output_size=output_size), None
```
`None` will finally flow into the `const` function in `tvm/relay/expr.py`,
which includes the following code:
```Python
if not dtype:
# when dtype is None: int maps to "int32", float maps to "float32"
dtype = {_np.dtype("int64"): _np.int32, _np.dtype("float64"):
_np.float32}.get(
value.dtype, None
)
```
`value` here is `None`, and it certainly does not have `dtype` attribute.
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