[GitHub] [tvm] xinetzone opened a new issue, #15325: `mul_partition_generic` in tvm/python/tvm/relay/quantize/_partition.py[Bug]

["xinetzone (via GitHub)" <gi...@apache.org>]

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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Re: [I] `mul_partition_generic` in tvm/python/tvm/relay/quantize/_partition.py[Bug] [tvm]

["tqchen (via GitHub)" <gi...@apache.org>]

tqchen closed issue #15325: `mul_partition_generic` in tvm/python/tvm/relay/quantize/_partition.py[Bug] 
URL: https://github.com/apache/tvm/issues/15325


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