kparzysz-quic commented on PR #15260:
URL: https://github.com/apache/tvm/pull/15260#issuecomment-1638815681
I think I got it to work:
```python
import tvm
from tvm import relax
from tvm.script import relax as R
@R.macro
def alloc_and_shape(dtype: str):
alloc = R.builtin.alloc_tensor(R.shape([4, 4]), runtime_device_index=0,
dtype=dtype)
shape = R.shape_of(alloc)
return shape
@R.function
def foo(x: R.Tensor((4, 4), "float32")):
shape = alloc_and_shape(dtype="float32")
return shape
print(alloc_and_shape)
print()
print(foo)
```
Produces
```python
@R.macro
def alloc_and_shape(dtype: str):
alloc = R.builtin.alloc_tensor(R.shape([4, 4]), runtime_device_index=0,
dtype=dtype)
shape = R.shape_of(alloc)
return shape
# from tvm.script import relax as R
@R.function
def foo(x: R.Tensor((4, 4), dtype="float32")) -> R.Shape([4, 4]):
alloc: R.Tensor((4, 4), dtype="float32") =
R.builtin.alloc_tensor(R.shape([4, 4]), R.dtype("float32"), R.prim_value(0))
shape: R.Shape([4, 4]) = R.shape_of(alloc)
shape_1: R.Shape([4, 4]) = shape
return shape_1
```
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