@junrushao1994 Thank you Junru! It did become faster!
Another related question is: will passes alter the original module (i mean if
`mod` will be modified after applying `new_mod = pass(mod)`). It seems this is
the case and I have to do the copy thing manually if I want to maintain the old
module (I thought it would be COW or something else).
```python
# we have a variable `mod` which is a module
old_mod_str = mod.astext()
_ = ToBasicBlockNormalForm()(mod)
assert old_mod_str == mod.astext() # will fail
```
To reproduce:
```python
from tvm import relay
import tvm
from tvm.relay import testing, transform
import hashlib
def example(batch_dim=1):
out_channels = 32
data = relay.var("data", relay.TensorType((batch_dim, 3, 224, 224),
"float32"))
weight = relay.var("weight")
simple_net = relay.nn.conv2d(
data=data, weight=weight, kernel_size=(5, 5), channels=out_channels,
padding=(1, 1)
)
simple_net = relay.Function(relay.analysis.free_vars(simple_net),
simple_net)
return testing.create_workload(simple_net)
def md5(data):
return hashlib.md5(data).hexdigest()
mod, params = example()
print(f'before apply pass, md5 of mod: {md5(mod.astext().encode())}')
print(mod.astext())
with tvm.transform.PassContext(opt_level=4):
with tvm.target.Target('llvm'):
seq = tvm.transform.Sequential(
passes=[transform.ToBasicBlockNormalForm()],
opt_level=4
)
new_mod = seq(mod)
print(f'after apply pass, md5 of mod: {md5(mod.astext().encode())}')
print(f'after apply pass, md5 of new mod: {md5(new_mod.astext().encode())}')
print(mod.astext())
```
```shell
before apply pass, md5 of mod: 383f47fec6c1b1ad607b5e66671602f0
#[version = "0.0.5"]
def @main(%data: Tensor[(1, 3, 224, 224), float32], %weight: Tensor[(32, 3, 5,
5), float32]) -> Tensor[(1, 32, 222, 222), float32] {
nn.conv2d(%data, %weight, padding=[1, 1, 1, 1], channels=32, kernel_size=[5,
5]) /* ty=Tensor[(1, 32, 222, 222), float32] */
}
after apply pass, md5 of mod: 207a065e002a2e9dcc3873ff51059394
after apply pass, md5 of new mod: 207a065e002a2e9dcc3873ff51059394
#[version = "0.0.5"]
def @main(%data: Tensor[(1, 3, 224, 224), float32], %weight: Tensor[(32, 3, 5,
5), float32]) -> Tensor[(1, 32, 222, 222), float32] {
nn.conv2d(%data, %weight, padding=[1, 1, 1, 1], channels=32, kernel_size=[5,
5])
}
```
---
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