apivovarov commented on pull request #7513:
URL: https://github.com/apache/tvm/pull/7513#issuecomment-786330018
I prepared a test model which uses both `destination tensor` and `copy_
output tensor` after the `copy_` operator.
PyTorch and TVM outputs are the same
What other test models can we try?
```
import torch
import tvm
from tvm import relay
import numpy as np
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
def forward(self, values):
scale = values.shape[0]
A = torch.zeros(values.shape)
B = torch.stack([A] * scale)
V1 = B + 1
C = B.copy_(values)
V2 = B + 2
V3 = C + 3
D = V1 + V2 + V3
return D
net = Net()
a = torch.tensor([0, 1, 2, 6])
net(a)
traced_net = torch.jit.trace(net, (a))
ctx = tvm.cpu(0)
target = 'llvm'
shape_list = [("input0", [4,]),]
mod, params = relay.frontend.from_pytorch(traced_net, shape_list)
with tvm.transform.PassContext(opt_level=3):
lib = relay.build(mod, target=target, params=params)
func=mod['main']
intrp = relay.create_executor("graph", ctx=ctx, target=target)
ff=intrp.evaluate(func)
ff([0, 1, 2, 6])
<tvm.nd.NDArray shape=(4, 4), cpu(0)>
array([[ 6., 8., 10., 18.],
[ 6., 8., 10., 18.],
[ 6., 8., 10., 18.],
[ 6., 8., 10., 18.]], dtype=float32)
```
Relay graph:
```
print(func)
fn (%input0: Tensor[(4), int64]) {
%0 = full(0, shape=[4], dtype="float32");
%1 = (%0, %0, %0, %0);
%2 = stack(%1);
%3 = add(%2, 1f);
%4 = cast(%input0, dtype="float32");
%5 = (%4, %4, %4, %4);
%6 = stack(%5);
%7 = add(%6, 2f);
%8 = add(%3, %7);
%9 = add(%6, 3f);
add(%8, %9)
}
```
Torch graph:
```
print(traced_net.graph)
graph(%self : __torch__.Net,
%values : Long(4, strides=[1], requires_grad=0, device=cpu)):
%7 : int = prim::Constant[value=0]() # <stdin>:6:0
%8 : int = aten::size(%values, %7) # <stdin>:6:0
%9 : Long(device=cpu) = prim::NumToTensor(%8)
%10 : int = aten::Int(%9)
%11 : int[] = prim::ListConstruct(%10)
%12 : int = prim::Constant[value=6]() # <stdin>:6:0
%13 : None = prim::Constant()
%14 : Device = prim::Constant[value="cpu"]() # <stdin>:6:0
%15 : bool = prim::Constant[value=0]() # <stdin>:6:0
%A : Float(4, strides=[1], requires_grad=0, device=cpu) = aten::zeros(%11,
%12, %13, %14, %15) # <stdin>:6:0
%17 : Tensor[] = prim::ListConstruct(%A, %A, %A, %A)
%18 : int = prim::Constant[value=0]() # <stdin>:7:0
%B.1 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu) =
aten::stack(%17, %18) # <stdin>:7:0
%20 : Long(requires_grad=0, device=cpu) = prim::Constant[value={1}]() #
<stdin>:8:0
%21 : int = prim::Constant[value=1]() # <stdin>:8:0
%V1 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu) =
aten::add(%B.1, %20, %21) # <stdin>:8:0
%23 : bool = prim::Constant[value=0]()
%B : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu) =
aten::copy_(%B.1, %values, %23) # <stdin>:9:0
%25 : Long(requires_grad=0, device=cpu) = prim::Constant[value={2}]() #
<stdin>:10:0
%26 : int = prim::Constant[value=1]() # <stdin>:10:0
%V2 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu) =
aten::add(%B, %25, %26) # <stdin>:10:0
%28 : Long(requires_grad=0, device=cpu) = prim::Constant[value={3}]() #
<stdin>:11:0
%29 : int = prim::Constant[value=1]() # <stdin>:11:0
%V3 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu) =
aten::add(%B, %28, %29) # <stdin>:11:0
%31 : int = prim::Constant[value=1]() # <stdin>:12:0
%32 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu) =
aten::add(%V1, %V2, %31) # <stdin>:12:0
%33 : int = prim::Constant[value=1]() # <stdin>:12:0
%34 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu) =
aten::add(%32, %V3, %33) # <stdin>:12:0
return (%34)
```
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