gemini-code-assist[bot] commented on code in PR #19841:
URL: https://github.com/apache/tvm/pull/19841#discussion_r3443151062
##########
python/tvm/relax/frontend/onnx/onnx_frontend.py:
##########
@@ -3084,6 +3084,15 @@ def _impl_v1(cls, bb, inputs, attr, params):
return inputs[0]
+class Dropout(OnnxOpConverter):
+ """Converts an onnx Dropout node into an equivalent Relax expression."""
+
+ @classmethod
+ def _impl_v1(cls, bb, inputs, attr, params):
+ ratio = float(attr.get("ratio", 0.5))
+ return relax.op.nn.dropout(inputs[0], ratio)
Review Comment:
In ONNX Opset 12 and later, the `ratio` parameter for `Dropout` is passed as
an optional second input tensor rather than an attribute. The current
implementation only checks the `ratio` attribute, which means any custom
dropout ratio specified as an input in newer ONNX models will be ignored and
default to `0.5`.
We should handle both cases by checking if the second input is provided and
extracting its constant value if available.
```python
def _impl_v1(cls, bb, inputs, attr, params):
ratio = 0.5
if len(inputs) > 1 and inputs[1] is not None:
if isinstance(inputs[1], relax.Constant):
ratio = float(inputs[1].data.numpy().item())
elif isinstance(inputs[1], relax.Var) and inputs[1].name_hint in
params:
ratio = float(params[inputs[1].name_hint].numpy().item())
else:
ratio = float(attr.get("ratio", 0.5))
return relax.op.nn.dropout(inputs[0], ratio)
```
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