xinetzone opened a new issue, #15325:
URL: https://github.com/apache/tvm/issues/15325
### Test Case
```python
from torch import nn
class Model(nn.Module):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.conv = nn.Conv2d(3, 16, 3, 1, 1, bias=False)
self.bn = nn.BatchNorm2d(16)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
torch.set_grad_enabled(False)
input_shape = [1, 3, 8, 8]
input_data = torch.rand(input_shape).float()
pt_model = Model().eval().float()
input_shapes = [("data", input_shape)]
traced_model = torch.jit.trace(pt_model, input_data)
# traced_model 翻译为 TVM 前端模型
mod, params = relay.frontend.from_pytorch(traced_model, input_shapes)
mod = relay.transform.InferType()(mod)
run_mod = relay.quantize.prerequisite_optimize(mod, params)
partition_mod = relay.quantize.partition()(run_mod)
prinjt(partition_mod["main"])
```
### Expected behavior
```
fn (%data: Tensor[(1, 3, 8, 8), float32] /* ty=Tensor[(1, 3, 8, 8), float32]
span=aten::_convolution_0.data:0:0 */) -> Tensor[(1, 16, 8, 8), float32] {
%0 = nn.conv2d(%data, meta[relay.Constant][0] /* ty=Tensor[(16, 3, 3, 3),
float32] */, padding=[1, 1, 1, 1], channels=16, kernel_size=[3, 3]) /*
ty=Tensor[(1, 16, 8, 8), float32] span=aten::_convolution_0:0:0 */;
%1 = multiply(%0, meta[relay.Constant][1] /* ty=Tensor[(16, 1, 1),
float32] */) /* ty=Tensor[(1, 16, 8, 8), float32] */;
%2 = add(%1, meta[relay.Constant][2] /* ty=Tensor[(16, 1, 1), float32] */)
/* ty=Tensor[(1, 16, 8, 8), float32] */;
%3 = nn.relu(%2) /* ty=Tensor[(1, 16, 8, 8), float32]
span=aten::relu__0:0:0 */;
%4 = annotation.cast_hint(%3, dtype="int8") /* ty=Tensor[(1, 16, 8, 8),
float32] */;
annotation.stop_fusion(%4) /* ty=Tensor[(1, 16, 8, 8), float32] */
} /* ty=fn (Tensor[(1, 3, 8, 8), float32]) -> Tensor[(1, 16, 8, 8), float32]
*/
```
### Actual behavior
```
fn (%data: Tensor[(1, 3, 8, 8), float32] /* ty=Tensor[(1, 3, 8, 8), float32]
span=aten::_convolution_0.data:0:0 */) -> Tensor[(1, 16, 8, 8), float32] {
%0 = nn.conv2d(%data, meta[relay.Constant][0] /* ty=Tensor[(16, 3, 3, 3),
float32] */, padding=[1, 1, 1, 1], channels=16, kernel_size=[3, 3]) /*
ty=Tensor[(1, 16, 8, 8), float32] span=aten::_convolution_0:0:0 */;
%1 = annotation.cast_hint(%0, dtype="int8") /* ty=Tensor[(1, 16, 8, 8),
float32] */;
%2 = annotation.stop_fusion(%1) /* ty=Tensor[(1, 16, 8, 8), float32] */;
%3 = multiply(%2, meta[relay.Constant][1] /* ty=Tensor[(16, 1, 1),
float32] */) /* ty=Tensor[(1, 16, 8, 8), float32] */;
%4 = add(%3, meta[relay.Constant][2] /* ty=Tensor[(16, 1, 1), float32] */)
/* ty=Tensor[(1, 16, 8, 8), float32] */;
%5 = nn.relu(%4) /* ty=Tensor[(1, 16, 8, 8), float32]
span=aten::relu__0:0:0 */;
%6 = annotation.cast_hint(%5, dtype="int8") /* ty=Tensor[(1, 16, 8, 8),
float32] */;
annotation.stop_fusion(%6) /* ty=Tensor[(1, 16, 8, 8), float32] */
} /* ty=fn (Tensor[(1, 3, 8, 8), float32]) -> Tensor[(1, 16, 8, 8), float32]
*/
```
### My strategy
Change `mul_partition_generic` :
```python
def mul_partition_generic(ref_call, new_args, ctx):
"""Rewrite function for ewise mul for partition for generic devices"""
return add_partition_generic(ref_call, new_args, ctx)
```
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