[pytorch] Set alias analysis kind to FROM_SCHEMA for qadd, qmul, qclamp, qconcat (#33359)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33359
Updated alias analysis kind to FROM_SCHEMA so input tensors can be marked as nonmutable
when appropriate, allowing for constant folding of these tensors.
Needed to update the schemas of the _out variants with annotations to mark the output input
tensor as aliased and mutable.
Test Plan:
```
import torch
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
def forward(self, x):
w = torch.tensor([3], dtype=torch.float)
w = torch.quantize_per_tensor(w, 1.0, 0, torch.qint8)
y = torch.tensor([3], dtype=torch.float)
y = torch.quantize_per_tensor(w, 1.0, 0, torch.qint8)
return torch.ops.quantized.add_out(x, w, y)
m = torch.jit.script(M())
torch._C._jit_pass_constant_propagation(m.graph)
print(m.graph)
```
```
graph(%self : __torch__.___torch_mangle_9.M,
%x.1 : Tensor):
%11 : int = prim::Constant[value=12]() # <ipython-input-11-1dd94c30cb58>:9:49
%9 : float = prim::Constant[value=1.]() # <ipython-input-11-1dd94c30cb58>:9:41
%10 : int = prim::Constant[value=0]() # <ipython-input-11-1dd94c30cb58>:9:46
%36 : QInt8(1) = prim::Constant[value={3}]()
%y.2 : Tensor = aten::quantize_per_tensor(%36, %9, %10, %11) # <ipython-input-11-1dd94c30cb58>:11:12
%24 : Tensor = quantized::add_out(%x.1, %36, %y.2) # <ipython-input-11-1dd94c30cb58>:12:15
return (%24)
```
As expected, the aten::quantize_per_tensor() for w is now folded. The aten::quantize_per_tensor()
for y is not folded, since that tensor is aliased/modified.
Differential Revision: D19910667
fbshipit-source-id: 127071909573151dc664500d363399e3643441b7
