Sync Python-side quantized_softmax schema with C++ kernel (add mask_type and pos args) (#18495)

backends/cadence/aot/ops_registrations.py

@@ -472,16 +472,16 @@ def register_fake(
 )
 
 lib.define(
-    "quantized_softmax(Tensor input, Tensor mask, int dim, Tensor in_scale, Tensor in_zero_point, Tensor out_scale, Tensor out_zero_point) -> (Tensor out)"
+    "quantized_softmax(Tensor input, Tensor mask, int dim, int mask_type, Tensor pos, Tensor in_scale, Tensor in_zero_point, Tensor out_scale, Tensor out_zero_point) -> (Tensor out)"
 )
 lib.define(
-    "quantized_softmax.per_tensor(Tensor input, Tensor mask, int dim, float in_scale, int in_zero_point, float out_scale, int out_zero_point) -> (Tensor out)"
+    "quantized_softmax.per_tensor(Tensor input, Tensor mask, int dim, int mask_type, Tensor pos, float in_scale, int in_zero_point, float out_scale, int out_zero_point) -> (Tensor out)"
 )
 lib.define(
-    "quantized_softmax.out(Tensor input, Tensor mask, int dim, Tensor in_scale, Tensor in_zero_point, Tensor out_scale, Tensor out_zero_point, *, Tensor(a!) out) -> Tensor (a!)"
+    "quantized_softmax.out(Tensor input, Tensor mask, int dim, int mask_type, Tensor pos, Tensor in_scale, Tensor in_zero_point, Tensor out_scale, Tensor out_zero_point, *, Tensor(a!) out) -> Tensor (a!)"
 )
 lib.define(
-    "quantized_softmax.per_tensor_out(Tensor input, Tensor mask, int dim, float in_scale, int in_zero_point, float out_scale, int out_zero_point, *, Tensor(a!) out) -> Tensor (a!)"
+    "quantized_softmax.per_tensor_out(Tensor input, Tensor mask, int dim, int mask_type, Tensor pos, float in_scale, int in_zero_point, float out_scale, int out_zero_point, *, Tensor(a!) out) -> Tensor (a!)"
 )
 
 # pack float/bool mask tensor into a bitmask of type uint8 (each element holding 8 bool mask elements)
@@ -2957,6 +2957,8 @@ def quantized_softmax_meta(
     input: torch.Tensor,
     mask: torch.Tensor,
     dim: int,
+    mask_type: int,
+    pos: torch.Tensor,
     in_scale: torch.Tensor,
     in_zero_point: torch.Tensor,
     out_scale: torch.Tensor,
@@ -2970,6 +2972,8 @@ def quantized_softmax_per_tensor_meta(
     input: torch.Tensor,
     mask: torch.Tensor,
     dim: int,
+    mask_type: int,
+    pos: torch.Tensor,
     in_scale: float,
     in_zero_point: int,
     out_scale: float,

backends/cadence/aot/quantizer/fusion_pass.py

@@ -378,6 +378,21 @@ def get_args_and_kwargs_softmax(
     with fake_mode:
         mask_tensor.meta["val"] = torch.full(mask_shape, 0.0, dtype=torch.int32)
     copy_node_metadata(mask_tensor, inputs_inputs[0])
+
+    # Default mask_type=0 (no masking) and dummy pos tensor
+    mask_type = 0
+    pos_tensor = graph_module.graph.call_function(
+        torch.ops.aten.full.default,
+        (
+            [1],
+            0,
+        ),
+        {"dtype": torch.int64},
+    )
+    with fake_mode:
+        pos_tensor.meta["val"] = torch.full([1], 0, dtype=torch.int64)
+    copy_node_metadata(pos_tensor, inputs_inputs[0])
+
     # Make the scale and zero_point tensors
     in_scale = dequants_inputs[0].args[1]
     in_zero_point = dequants_inputs[0].args[2]
@@ -389,6 +404,8 @@ def get_args_and_kwargs_softmax(
         inputs_inputs[0],
         mask_tensor,
         op_node.args[1],
+        mask_type,
+        pos_tensor,
         in_scale,
         in_zero_point,
         out_scale,

backends/cadence/aot/ref_implementations.py

@@ -2480,6 +2480,8 @@ def quantized_softmax_per_tensor_common(
     input_tensor: torch.Tensor,
     mask: torch.Tensor | None,
     dim: int,
+    mask_type: int,
+    pos: torch.Tensor,
     in_scale: float,
     in_zero_point: int,
     out_scale: float,
@@ -2492,13 +2494,18 @@ def quantized_softmax_per_tensor_common(
         - input_tensor (Tensor): The quantized input tensor
         - mask (Tensor): Mask tensor
         - dim (int): The dimension along which softmax is computed
+        - mask_type (int): Masking strategy (0=none, 1=position-based causal)
+        - pos (Tensor): Position tensor for causal masking
         - in_scale (float): The scale of the input quantization
         - in_zero_point (int): The zero point of the input quantization
         - out_scale (float): The scale of the output quantization
         - out_zero_point (int): The zero point of the output quantization
     """
     # TODO: T228751479 - Add support for mask parameter in softmax
     assert mask is None
+    assert (
+        mask_type == 0
+    ), f"Only mask_type=0 (no masking) is supported, got {mask_type}"
     supported_dtypes = [torch.int8, torch.uint8, torch.int16]
     if input_tensor.dtype not in supported_dtypes:
         raise ValueError(
@@ -2531,6 +2538,8 @@ def quantized_softmax_per_tensor(
     input_tensor: torch.Tensor,
     mask: torch.Tensor | None,
     dim: int,
+    mask_type: int,
+    pos: torch.Tensor,
     in_scale: float,
     in_zero_point: int,
     out_scale: float,
@@ -2540,6 +2549,8 @@ def quantized_softmax_per_tensor(
         input_tensor,
         mask,
         dim,
+        mask_type,
+        pos,
         in_scale,
         in_zero_point,
         out_scale,
@@ -2552,6 +2563,8 @@ def quantized_softmax(
     input_tensor: torch.Tensor,
     mask: torch.Tensor | None,
     dim: int,
+    mask_type: int,
+    pos: torch.Tensor,
     in_scale: torch.Tensor,
     in_zero_point: torch.Tensor,
     out_scale: float,
@@ -2561,6 +2574,8 @@ def quantized_softmax(
         input_tensor,
         mask,
         dim,
+        mask_type,
+        pos,
         float(in_scale.item()),
         int(in_zero_point.item()),
         out_scale,

backends/cadence/aot/tests/test_ref_implementations.py

@@ -3152,6 +3152,8 @@ def test_quantized_softmax_per_tensor(
             input_tensor,
             mask,
             dim,
+            0,  # mask_type (no masking)
+            torch.zeros(1, dtype=torch.int64),  # pos
             in_scale,
             in_zero_point,
             out_scale,
@@ -3189,6 +3191,8 @@ def test_quantized_softmax(self) -> None:
             input_tensor,
             None,  # mask
             1,  # dim
+            0,  # mask_type (no masking)
+            torch.zeros(1, dtype=torch.int64),  # pos
             in_scale,
             in_zero_point,
             0.004,  # out_scale