[Relax] [ONNX] Add support for HardSwish (#17088)

add hardswish support to onnx frontend

python/tvm/relax/frontend/onnx/onnx_frontend.py

@@ -1918,6 +1918,22 @@ def _impl_v1(cls, bb, inputs, attr, params):
         ) + relax.op.nn.relu(inputs[0])
 
 
+class HardSwish(OnnxOpConverter):
+    """Converts an onnx HardSwish node into an equivalent Relax expression."""
+
+    @classmethod
+    def _impl_v14(cls, bb, inputs, attr, params):
+        x = inputs[0]
+        dtype = x.struct_info.dtype
+        return relax.op.multiply(
+            x,
+            relax.op.divide(
+                relax.op.clip(relax.op.add(x, relax.const(3, dtype)), 0, 6),
+                relax.expr.const(6, dtype),
+            ),
+        )
+
+
 def _get_convert_map():
     return {
         "MatMul": MatMul,
@@ -1998,6 +2014,7 @@ def _get_convert_map():
         "Reciprocal": Reciprocal,
         "OneHot": OneHot,
         "Elu": Elu,
+        "HardSwish": HardSwish,
     }
 
 

tests/python/relax/test_frontend_onnx.py

@@ -590,6 +590,10 @@ def test_elu():
     verify_unary("Elu", [32, 32])
 
 
+def test_hardswish():
+    verify_unary("HardSwish", [32, 32])
+
+
 def test_conv():
     def _verify_conv(input_shape, weight_shape, output_shape):
         bias_shape = [output_shape[1]]