pig-pig-yang commented on issue #12469:
URL: https://github.com/apache/tvm/issues/12469#issuecomment-1219343173
> In the Relay text dump, I'm seeing this
>
> ```
> %57 = clip(%56, a_min=0f, a_max=6f) /* ty=Tensor[(1, 72, 1, 1), float32]
*/;
> %58 = divide(%57, 6f /* ty=float32 */) /* ty=Tensor[(1, 72, 1, 1),
float32] */;
> %59 = multiply(%58, %49) /* ty=Tensor[(1, 72, 28, 28), float32] */;
> %60 = nn.conv2d(%59, %features.4.block.3.0.weight, padding=[0, 0, 0, 0],
channels=40, kernel_size=[1, 1]) /* ty=Tensor[(1, 40, 28, 28), float32] */;
> ```
>
> So it looks all right. @pig-pig-yang Please check your visualization.
There is no conversion error.
Thanks for the reply. Sorry I pasted the wrong codes. I rechecked the codes
and the error did not occur during the front-end conversion. The error occurred
after `tvm.relay.quantize.prerequisite_optimize`. The codes are:
```
import tvm
from tvm import te
import tvm.relay as relay
from tvm.contrib.download import download_testdata
import numpy as np
import os
import torch
import torchvision
from torchvision import transforms
import netron
import onnx
from tvm.relay.quantize import prerequisite_optimize
##################################################
# Preparing relay networks.
# ------------------------------------------------
model_name = "mobilenet_v3"
model = torchvision.models.mobilenet_v3_large(pretrained=True)
model = model.eval()
input_shape = [1, 3, 224, 224]
input_data = torch.randn(input_shape)
scripted_model = torch.jit.trace(model, input_data).eval()
input_name = "input0"
shape_list = [(input_name, (1, 3, 224, 224))]
mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
with tvm.transform.PassContext(opt_level=3):
mod = prerequisite_optimize(mod, params)
print(mod)
```
The `prerequisite_optimize` is consisted of several commonly used pass:
```
def prerequisite_optimize(mod, params=None):
"""Prerequisite optimization passes for quantization. Perform
"SimplifyInference", "FoldScaleAxis", "FoldConstant", and
"CanonicalizeOps" optimization before quantization."""
optimize = tvm.transform.Sequential(
[
_transform.SimplifyInference(),
_transform.FoldConstant(),
_transform.FoldScaleAxis(),
_transform.CanonicalizeOps(),
_transform.FoldConstant(),
]
)
if params:
mod["main"] = _bind_params(mod["main"], params)
mod = optimize(mod)
return mod
```
Then I gets the wrong Relay structure like this:
%66 = multiply(meta[relay.Constant][32] /* ty=Tensor[(40, 120, 1, 1),
float32] */, %65) /* ty=Tensor[(40, 120, 1, 1), float32] */;
%67 = nn.conv2d(%54, %66, padding=[0, 0, 0, 0], channels=40,
kernel_size=[1, 1]) /* ty=Tensor[(1, 40, 28, 28), float32] */;
%68 = add(%67, meta[relay.Constant][37] /* ty=Tensor[(40, 1, 1), float32]
*/) /* ty=Tensor[(1, 40, 28, 28), float32] */;
I haven't confirmed which pass causes this error, I will confirm later.
Thanks!
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