haoyang9804 opened a new pull request #10502:
URL: https://github.com/apache/tvm/pull/10502
When `relay.build` a IRModule in which a function whose returned value
includes another function exists, TVM will crash with **segmentation fault**.
Initially I assume this is the behavior disllowed by TVM, but in
`test_analysis_basic_block_normal_form.py`, the following function shows the
tolerance of it.
```python
@pytest.mark.xfail(raises=tvm.error.TVMError)
def test_func():
x = relay.var("x", shape=(1,), dtype="float32") # , a)
y = relay.var("y", shape=(1,), dtype="float32") # , a)
z = relay.var("z", shape=(1,), dtype="float32") # , a)
x2 = relay.add(x, x)
func_a = relay.Function([y], relay.add(x2, y)) # , a, [a])
func_b = relay.Function([z], relay.add(x2, z)) # , a, [a])
body = relay.Tuple([func_a, func_b])
body = relay.Function([x], body)
"""
fn (%x: Tensor[(1), float32]) {
%1 = fn (%y: Tensor[(1), float32]) {
%0 = add(%x, %x);
add(%0, %y)
};
%2 = fn (%z: Tensor[(1), float32]) {
add(%0, %z)
};
(%1, %2)
}
"""
check_basic_block_normal_form(body)
```
So I write the following script with simpler structure to fuzz TVM:
```python
import tvm
from tvm import relay
mod = tvm.IRModule()
x = relay.var("x", shape=(1,), dtype="float32")
x2 = relay.add(x, x)
f = relay.Function([x], x2)
# body = relay.Tuple([f,])
body = relay.Function([], f)
mod['main'] = body
mod = relay.transform.InferType()(mod)
print(mod.astext(show_meta_data=False))
graph, lib, params = relay.build(mod, target='llvm')
```
The relay IR is easy, only including a function whose returned value is
another function
```
#[version = "0.0.5"]
def @main() -> fn (Tensor[(1), float32]) -> Tensor[(1), float32] {
fn (%x: Tensor[(1), float32]) -> Tensor[(1), float32] {
add(%x, %x) /* ty=Tensor[(1), float32] */
}
}
```
As expected, segmentation fault showed up.
By bug localization, I find this problem is caused during memory allocation
in graph_executor_codegen. More concretely, function
`CalculateRelayExprSizeBytes` forgets the situation where the type returned
variable could be of FuncType and directly turns the typenode into
TensorTypeNode. Then `auto shape = tensor_type->shape;` triggers the bug.
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