sunggg opened a new pull request, #15246:
URL: https://github.com/apache/tvm/pull/15246
This PR introduces an utility pass that binds symbolic variables to
user-provided integer values.
For example, say we have a following IRModule.
```Python
@tvm.script.ir_module
class Before:
@R.function
def main(
x: R.Tensor(("m", "n")),
y: R.Tensor(("m", "n"))
) -> R.Tensor(("m", "n")):
m = T.Var("m", "int64")
n = T.Var("m", "int64")
with R.dataflow():
out = R.matmul(x, y)
R.output(out)
return out
```
We can conveniently bind the symbolic variable by applying `After =
relax.transform.BindSymVars("main", {"m": 10, "n": 10})(Before)`.
```Python
@tvm.script.ir_module
class After:
@R.function
def main(
x: R.Tensor((10, 10)),
y: R.Tensor((10, 10))
) -> R.Tensor((10, 10)):
with R.dataflow():
out = R.matmul(x, y)
R.output(out)
return out
```
This would be useful when specializing shape and providing compile-time
shape info (e.g., model params or batch sizes) by eliminating the need to
rewrite the model.
cc. @tqchen @psrivas2
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