[GitHub] [incubator-tvm] yongwww edited a comment on pull request #6472: Add several op mapping in PyTorch frontend

[GitBox <gi...@apache.org>]

yongwww edited a comment on pull request #6472:
URL: https://github.com/apache/incubator-tvm/pull/6472#issuecomment-692568164


   > > Seems PyTorch already has a set of passes to remove mutation
   > 
   > Note fat warning at the top of the class, though.
   > 
   > A typical use of copy is something like this:
   > 
   > ```
   > In [1]: import torch
   > 
   > In [2]: @torch.jit.script
   >    ...: def update(x, y):
   >    ...:     x[0] = y
   >    ...:     return x
   >    ...: 
   > 
   > In [3]: update.graph
   > Out[3]: 
   > graph(%x.1 : Tensor,
   >       %y.1 : Tensor):
   >   %7 : bool = prim::Constant[value=0]()
   >   %4 : int = prim::Constant[value=0]() # <ipython-input-2-e9bf1f9acfa9>:3:6
   >   %6 : Tensor = aten::select(%x.1, %4, %4) # <ipython-input-2-e9bf1f9acfa9>:3:4
   >   %8 : Tensor = aten::copy_(%6, %y.1, %7) # <ipython-input-2-e9bf1f9acfa9>:3:4
   >   return (%x.1)
   > ```
   
   just tested with a similar test case as the one you provided, the slicing assignment doesn't take effect in jit graph.
   
          ```
           # test case
           def forward(self, *args):
               a = args[0]
               b = args[1]
               a[0] = b
               return a
           ```
   related graph:
   
   ```
   graph(%self : __torch__.Copy,
         %a : Float(2, 3),
         %b : Float(1, 3)):
     %3 : int = prim::Constant[value=0]() 
     %4 : int = prim::Constant[value=0]() 
     %5 : Float(3) = aten::select(%a, %3, %4) # 
     %6 : int = prim::Constant[value=3]() 
     %7 : int[] = prim::ListConstruct(%6)
     %8 : Float(3) = aten::view(%b, %7) 
     %9 : bool = prim::Constant[value=0]()
     %10 : Float(3) = aten::copy_(%5, %8, %9) 
     return (%a)
   ```
   
   


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