masahi edited a comment on pull request #7231:
URL: https://github.com/apache/tvm/pull/7231#issuecomment-756716845
For the first case,
```
def foo(x):
x[:5] = 0
return 2 * x
```
Using the two passes I mentioned, I get this graph:
```
graph(%x : Float(10:1, requires_grad=0, device=cpu)):
%1 : int = prim::Constant[value=0]() # test.py:8:0
%2 : int = prim::Constant[value=0]() # test.py:8:0
%3 : int = prim::Constant[value=5]() # test.py:8:0
%4 : int = prim::Constant[value=1]() # test.py:8:0
%5 : Float(5:1, requires_grad=0, device=cpu) = aten::slice(%x, %1, %2, %3,
%4) # test.py:8:0
%6 : Float(requires_grad=0, device=cpu) = prim::Constant[value={0}]() #
test.py:8:0
%7 : int[] = prim::ListConstruct()
%8 : Float(requires_grad=0, device=cpu) = aten::view(%6, %7) # test.py:8:0
%9 : int = prim::Constant[value=5]() # test.py:8:0
%10 : int[] = prim::ListConstruct(%9)
%11 : bool = prim::Constant[value=1]() # test.py:8:0
%12 : Float(5:0, requires_grad=0, device=cpu) = aten::expand(%8, %10, %11)
# test.py:8:0
%13 : bool = prim::Constant[value=0]()
%18 : Float(5:0, requires_grad=0, device=cpu) = aten::expand_as(%12, %5) #
test.py:8:0
%20 : int = prim::Constant[value=0]()
%21 : int = aten::size(%x, %20)
%22 : int = prim::Constant[value=4]()
%23 : None = prim::Constant()
%24 : None = prim::Constant()
%25 : None = prim::Constant()
%26 : Tensor = aten::arange(%21, %22, %23, %24, %25)
%27 : int = prim::Constant[value=0]()
%28 : Tensor = aten::slice(%26, %27, %2, %3, %4)
%30 : int[] = prim::Constant[value=[-1]]()
%31 : Tensor = aten::view(%28, %30)
%32 : Tensor?[] = prim::ListConstruct(%31)
%33 : Float(5:1, requires_grad=0, device=cpu) = aten::index_put(%x, %32,
%18, %13)
%15 : Long(requires_grad=0, device=cpu) = prim::Constant[value={2}]() #
test.py:9:0
%16 : Float(10:1, requires_grad=0, device=cpu) = aten::mul(%33, %15) #
test.py:9:0
return (%16)
```
i.e. It seems `torch._C._jit_pass_onnx_prepare_inplace_ops_for_onnx` does
some of the requirement you mentioned (first and third, not sure about second)?
If we convert this graph to relay using `scatter_nd`, I think the output
tensor would be correct but we cannot modify the input like torch does.
But right, your second example results in
```
graph(%x : Float(10:1, requires_grad=0, device=cpu)):
%20 : Long(requires_grad=0, device=cpu) = prim::Constant[value={2}]() #
test.py:15:0
%21 : Float(10:1, requires_grad=0, device=cpu) = aten::mul(%x, %20) #
test.py:15:0
return (%21)
```
because `torch._C._jit_pass_dce_allow_deleting_nodes_with_side_effects` will
consider side-effect only operation as dead code (`y` is not used to compute
output).
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