# torch.nn.Dropout is not converted into Relax

**URL:** <https://discuss.tvm.apache.org/t/torch-nn-dropout-is-not-converted-into-relax/18862>\
**Category:** Questions\
**Created:** [December 22, 2025, 1:43pm UTC](https://discuss.tvm.apache.org/t/torch-nn-dropout-is-not-converted-into-relax/18862 "2025-12-22T13:43:51Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![AugustineYang](https://discuss.tvm.apache.org/user_avatar/discuss.tvm.apache.org/augustineyang/32/4662_2.png) [@AugustineYang](https://discuss.tvm.apache.org/u/AugustineYang)\
**Post date:** [December 22, 2025, 1:43pm UTC](https://discuss.tvm.apache.org/t/torch-nn-dropout-is-not-converted-into-relax/18862/1 "2025-12-22T13:43:51Z")

</div>

Hi. I encountered an issue when trying to convert a PyTorch FCN model into Relax IRModule following the tutorial [Import from existing models](https://tvm.apache.org/docs/get_started/tutorials/ir_module.html#import-from-existing-models): **torch.nn.dropout seems to be discarded**.

In Relay IR, torch.nn.Dropout is converted to relay.nn.dropout. However, in Relax, though relax.op.nn.dropout is defined, torch.nn.Dropout is not converted to it but discarded.

Actually I found a piece of code in `tests/python/relax/test_frontend_from_fx.py`:

```auto
    # dropout
    class Dropout1(Module):
        def __init__ (self):
            super(). __init__ ()
            self.dropout = torch.nn.Dropout(0.5)

        def forward(self, input):
            return self.dropout(input)

    class Dropout2(Module):
        def forward(self, input):
            return torch.dropout(input, 0.5, train=True)

    @tvm.script.ir_module
    class expected_dropout:
        @R.function
        def main(
            input_1: R.Tensor((1, 3, 10, 10), dtype="float32")
        ) -> R.Tensor((1, 3, 10, 10), dtype="float32"):
            # block 0
            with R.dataflow():
                gv: R.Tensor((1, 3, 10, 10), dtype="float32") = input_1
                R.output(gv)
            return gv

    verify_model(Dropout1(), input_info, {}, expected_dropout)
    verify_model(Dropout2(), input_info, {}, expected_dropout)

```

It seems that the specific behaviour is known to TVM group. I’m wondering two questions: Was this change made intentionally, meaning that TVM now only focuses on model inference? Is there any way to reserve these training-related operators or model structures?