[MLX] Add leaky_relu op handler

backends/mlx/ops.py

@@ -2786,6 +2786,61 @@ def _relu_handler(P: MLXProgramBuilder, n: Node) -> Slot:
     return out
 
 
+@REGISTRY.register(target=[torch.ops.aten.leaky_relu.default])
+def _leaky_relu_handler(P: MLXProgramBuilder, n: Node) -> Slot:
+    """Handle aten.leaky_relu.default - leaky rectified linear unit.
+
+    leaky_relu(x) = x          if x >= 0
+                  = slope * x  otherwise
+
+    Implemented as where(x >= 0, x, slope * x) so it stays correct for any
+    negative_slope (including values > 1), matching eager PyTorch.
+    """
+    args = P.args(n)
+    require_args(args, 1, 2, "aten.leaky_relu")
+    require_kwargs(P.kwargs(n), set(), "aten.leaky_relu")
+
+    x = args[0]
+    negative_slope = float(args[1]) if len(args) > 1 else 0.01
+
+    x_meta = n.args[0].meta.get("val")
+    if x_meta is None:
+        raise ValueError("Input tensor metadata not found for leaky_relu")
+    dtype = x_meta.dtype
+
+    zero_slot = emit_lifted_constant(P, 0.0, dtype)
+    slope_slot = emit_lifted_constant(P, negative_slope, dtype)
+
+    _, cond_slot = P.make_tmp_slot()
+    P.emit(
+        GreaterEqualNode(
+            a=P.slot_to_tid(x),
+            b=P.slot_to_tid(zero_slot),
+            out=P.slot_to_tid(cond_slot),
+        )
+    )
+
+    _, scaled_slot = P.make_tmp_slot()
+    P.emit(
+        MultiplyNode(
+            a=P.slot_to_tid(slope_slot),
+            b=P.slot_to_tid(x),
+            out=P.slot_to_tid(scaled_slot),
+        )
+    )
+
+    out = P.make_or_get_slot(n)
+    P.emit(
+        WhereNode(
+            condition=P.slot_to_tid(cond_slot),
+            x=P.slot_to_tid(x),
+            y=P.slot_to_tid(scaled_slot),
+            out=P.slot_to_tid(out),
+        )
+    )
+    return out
+
+
 @REGISTRY.register(target=[torch.ops.aten._log_softmax.default])
 def _log_softmax_handler(P: MLXProgramBuilder, n: Node) -> Slot:
     """Handle aten._log_softmax.default - log of softmax.

backends/mlx/test/test_ops.py

@@ -405,6 +405,56 @@ def create_inputs(self) -> Tuple[torch.Tensor, ...]:
         return (x,)
 
 
+class LeakyReLUModel(nn.Module):
+    """Model that applies leaky_relu with a given negative slope."""
+
+    def __init__(self, negative_slope: float = 0.01):
+        super().__init__()
+        self.negative_slope = negative_slope
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.nn.functional.leaky_relu(x, negative_slope=self.negative_slope)
+
+
+@register_test
+class LeakyReLUTest(OpTestCase):
+    """Test case for leaky_relu activation with various negative slopes."""
+
+    name = "leaky_relu"
+    rtol = 1e-5
+    atol = 1e-5
+
+    def __init__(
+        self,
+        shape: Tuple[int, ...] = (2, 3, 4),
+        negative_slope: float = 0.01,
+    ):
+        self.shape = shape
+        self.negative_slope = negative_slope
+        shape_str = "x".join(str(s) for s in shape)
+        self.name = f"leaky_relu_slope{negative_slope}_{shape_str}"
+
+    @classmethod
+    def get_test_configs(cls) -> List["LeakyReLUTest"]:
+        return [
+            cls(shape=(2, 3, 4), negative_slope=0.01),
+            cls(shape=(4, 8), negative_slope=0.1),
+            cls(shape=(10,), negative_slope=0.2),
+            cls(shape=(10,), negative_slope=1.5),
+            cls(shape=(2, 8, 16), negative_slope=0.01),
+        ]
+
+    def create_model(self) -> nn.Module:
+        return LeakyReLUModel(self.negative_slope)
+
+    def create_inputs(self) -> Tuple[torch.Tensor, ...]:
+        numel = 1
+        for size in self.shape:
+            numel *= size
+        x = torch.linspace(-4.0, 4.0, steps=numel).reshape(self.shape)
+        return (x,)
+
+
 class GELUModel(nn.Module):
     """Simple model using GELU activation."""