gemini-code-assist[bot] commented on code in PR #18631:
URL: https://github.com/apache/tvm/pull/18631#discussion_r2657711678
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python/tvm/relax/frontend/onnx/onnx_frontend.py:
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@@ -3254,7 +3254,11 @@ def _impl_v1(cls, bb, inputs, attr, params):
alpha = relax.const(alpha, dtype=dtype)
beta = float(attr.get("beta", 0.5))
beta = relax.const(beta, dtype=dtype)
- return relax.op.clip(relax.op.add(relax.op.multiply(alpha, x), beta),
0, 1)
+ clipped = relax.op.clip(relax.op.add(relax.op.multiply(alpha, x),
beta), 0, 1)
+ # Preserve NaN values: where x is NaN, return NaN instead of clipped
value
+ is_nan = relax.op.isnan(x)
+ nan_val = relax.op.divide(relax.const(0.0, dtype=dtype),
relax.const(0.0, dtype=dtype))
Review Comment:
For creating a NaN scalar constant, it's more direct and readable to use
`relax.const(float('nan'), dtype=dtype)` instead of dividing zero by zero. This
also avoids creating intermediate `Constant` and `divide` operator nodes in the
graph, making the graph construction slightly more efficient.
```suggestion
nan_val = relax.const(float("nan"), dtype=dtype)
```
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