Base on
https://tvm-book.readthedocs.io/zh-cn/latest/old/read/transforms/chaos/div-to-mul.html,
I get a test, which show the tvm can do **DivToMul** with
**relay.transform.DivToMul**. But for a more common case, we may have a model
which have many div operators, so it is not convenience to transform it one by
one, and my question：Does tvm support DivToMul without indications，and
transform all the div into mul for better performance ?
* test:
```
import tvm
from tvm import relay
import numpy as np
for dtype, rtol in [("float16", 1e-3), ("float32", 1e-7), ("float64", 1e-12)]:
x = relay.var("x", relay.TensorType((), dtype))
y = relay.Constant(tvm.nd.array(np.array([1.5]).astype(dtype)))
z = x / y
mod = tvm.IRModule.from_expr(z)
transformed = relay.transform.DivToMul()(mod)
assert transformed["main"].body.op.name == "multiply"
np.testing.assert_allclose(transformed["main"].body.args[1].data.numpy()[0], 1
/ 1.5, rtol=rtol)
```
* debug
```
(Pdb) n
> divtomul.py(11)<module>()
-> assert transformed["main"].body.op.name == "multiply"
(Pdb) p transformed
def @main(%x: float16 /* ty=float16 */) -> Tensor[(1), float16] {
multiply(%x, meta[relay.Constant][0] /* ty=Tensor[(1), float16] */) /*
ty=Tensor[(1), float16] */
}
```
---
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