YuhengHuang42 commented on issue #7563:
URL: https://github.com/apache/tvm/issues/7563#issuecomment-811245830
Hi, I'm interested in this bug and did some experiments. Here are some
findings:
1. If you do the FuseOps transform first on the relay graph, i.e.
```
seq = tvm.transform.Sequential(
[
transform.SimplifyInference(),
transform.FuseOps()
]
)
with tvm.transform.PassContext(opt_level=opt_level):
mod = seq(mod)
# build the model
# ...
```
Then the final result is correct.
2. If you use opt_level=4 to build the model, then the final result is also
correct:
```
opt_level = 4
with tvm.transform.PassContext(opt_level=opt_level):
lib = relay.build(mod, target='llvm', params=param)
```
This seems pretty weird to me. So I disable some passes here, trying to dig
deeper.
```
disabled_pass = ["CombineParallelConv2D", "CombineParallelDense",
"CombineParallelBatchMatmul", "FastMath"]
opt_level = 4
with tvm.transform.PassContext(opt_level=opt_level,
disabled_pass=disabled_pass):
lib = relay.build(mod, target='llvm', params=param)
```
As far as I know, these four passes are the only ones that are enabled by
default. However, disable these passes doesn't influence the final result: the
result is still correct.
My environment:
Build from source at 2988a08e3ff4a8956ac9b23e662374f6d8f7f4d9,
OS: macOS 10.15.7
As I'm new to TVM, I'm stuck here and can't dig deeper at present. Hope
these info can help you find the root cause of the bug.
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