Add quantized fully connected ops

backends/cadence/aot/ops_registrations.py

@@ -1771,6 +1771,7 @@ def quantized_fully_connected_meta(
     # src comes in shape [leading_dims, in_dim]
     # weight comes in shape [out_dim, in_dim]
     # output comes in empty with shape [leading_dims, out_dim]
+    assert src.shape[0] == 1
     out_size = list(src.size())
     weight_size = list(weight.size())
     assert len(weight_size) == 2
@@ -1793,6 +1794,7 @@ def quantized_fully_connected_per_tensor_meta(
     # src comes in shape [leading_dims, in_dim]
     # weight comes in shape [out_dim, in_dim]
     # output comes in empty with shape [leading_dims, out_dim]
+    assert src.shape[0] == 1
     out_size = list(src.size())
     weight_size = list(weight.size())
     assert len(weight_size) == 2
@@ -1815,6 +1817,7 @@ def quantized_fully_connected_asym8sxasym8s_asym8s_per_tensor_meta(
     # src comes in shape [leading_dims, in_dim]
     # weight comes in shape [out_dim, in_dim]
     # output comes in empty with shape [leading_dims, out_dim]
+    assert src.shape[0] == 1
     out_size = list(src.size())
     weight_size = list(weight.size())
     assert len(weight_size) == 2
@@ -1837,6 +1840,7 @@ def quantized_fully_connected_asym8uxasym8u_asym8u_per_tensor_meta(
     # src comes in shape [leading_dims, in_dim]
     # weight comes in shape [out_dim, in_dim]
     # output comes in empty with shape [leading_dims, out_dim]
+    assert src.shape[0] == 1
     out_size = list(src.size())
     weight_size = list(weight.size())
     assert len(weight_size) == 2

backends/cadence/aot/ref_implementations.py

@@ -249,6 +249,7 @@ def quantized_linear_common(
 
 def quantized_linear_variant(
     per_tensor: bool,
+    fully_connected: bool,
     src_dtype: torch.dtype | None = None,
     weight_dtype: torch.dtype | None = None,
 ) -> Callable[[Callable[..., torch.Tensor]], Callable[..., torch.Tensor]]:
@@ -265,6 +266,10 @@ def variant(
             out_zero_point: int,
             offset: torch.Tensor | None = None,
         ) -> torch.Tensor:
+            if fully_connected and src.shape[0] != 1:
+                raise ValueError(
+                    "Fully connected quantized linear only supports batch size of 1"
+                )
             if src_dtype and src.dtype != src_dtype:
                 raise ValueError(
                     f"src dtype must be {src_dtype}. Got {src.dtype} instead"
@@ -317,25 +322,45 @@ def variant(
 
 
 @impl(m, "quantized_linear")
-@quantized_linear_variant(False)
+@quantized_linear_variant(False, False)
 def quantized_linear() -> torch.Tensor: ...
 
 
 @impl(m, "quantized_linear.per_tensor")
-@quantized_linear_variant(True)
+@quantized_linear_variant(True, False)
 def quantized_linear_per_tensor() -> torch.Tensor: ...
 
 
 @impl(m, "quantized_linear_asym8sxasym8s_asym8s.per_tensor")
-@quantized_linear_variant(True, torch.int8, torch.int8)
+@quantized_linear_variant(True, False, torch.int8, torch.int8)
 def quantized_linear_asym8sxasym8s_asym8s_per_tensor() -> torch.Tensor: ...
 
 
 @impl(m, "quantized_linear_asym8uxasym8u_asym8u.per_tensor")
-@quantized_linear_variant(True, torch.uint8, torch.uint8)
+@quantized_linear_variant(True, False, torch.uint8, torch.uint8)
 def quantized_linear_asym8uxasym8u_asym8u_per_tensor() -> torch.Tensor: ...
 
 
+@impl(m, "quantized_fully_connected")
+@quantized_linear_variant(False, True)
+def quantized_fully_connected() -> torch.Tensor: ...
+
+
+@impl(m, "quantized_fully_connected.per_tensor")
+@quantized_linear_variant(True, True)
+def quantized_fully_connected_per_tensor() -> torch.Tensor: ...
+
+
+@impl(m, "quantized_fully_connected_asym8sxasym8s_asym8s.per_tensor")
+@quantized_linear_variant(True, True, torch.int8, torch.int8)
+def quantized_fully_connected_asym8sxasym8s_asym8s_per_tensor() -> torch.Tensor: ...
+
+
+@impl(m, "quantized_fully_connected_asym8uxasym8u_asym8u.per_tensor")
+@quantized_linear_variant(True, True, torch.uint8, torch.uint8)
+def quantized_fully_connected_asym8uxasym8u_asym8u_per_tensor() -> torch.Tensor: ...
+
+
 @impl(m, "quantized_layer_norm.per_tensor")
 def quantized_layer_norm_per_tensor(
     input_tensor: torch.Tensor,

backends/cadence/aot/tests/test_ref_implementations.py

@@ -307,36 +307,45 @@ def test_quantized_linear(
         if per_tensor:
             match expected_output.dtype:
                 case torch.int8:
-                    linear_op = (
-                        torch.ops.cadence.quantized_linear_asym8sxasym8s_asym8s.per_tensor
+                    linear_ops = (
+                        torch.ops.cadence.quantized_linear_asym8sxasym8s_asym8s.per_tensor,
+                        torch.ops.cadence.quantized_fully_connected_asym8sxasym8s_asym8s.per_tensor,
                     )
                 case torch.uint8:
-                    linear_op = (
-                        torch.ops.cadence.quantized_linear_asym8uxasym8u_asym8u.per_tensor
+                    linear_ops = (
+                        torch.ops.cadence.quantized_linear_asym8uxasym8u_asym8u.per_tensor,
+                        torch.ops.cadence.quantized_fully_connected_asym8uxasym8u_asym8u.per_tensor,
                     )
                 case _:
-                    linear_op = torch.ops.cadence.quantized_linear.per_tensor
+                    linear_ops = (
+                        torch.ops.cadence.quantized_linear.per_tensor,
+                        torch.ops.cadence.quantized_fully_connected.per_tensor,
+                    )
         else:
-            linear_op = torch.ops.cadence.quantized_linear
+            linear_ops = (
+                torch.ops.cadence.quantized_linear,
+                torch.ops.cadence.quantized_fully_connected,
+            )
 
-        output = linear_op(
-            src,
-            weight,
-            bias,
-            in_zero_point,
-            weight_zero_point,
-            out_multiplier,
-            out_shift,
-            out_zero_point,
-            typing.cast(torch.Tensor, None),
-        )
+        for linear_op in linear_ops:
+            output = linear_op(
+                src,
+                weight,
+                bias,
+                in_zero_point,
+                weight_zero_point,
+                out_multiplier,
+                out_shift,
+                out_zero_point,
+                typing.cast(torch.Tensor, None),
+            )
 
-        self.assertTrue(output.dtype == expected_output.dtype, "Dtype mismatch")
+            self.assertTrue(output.dtype == expected_output.dtype, "Dtype mismatch")
 
-        self.assertTrue(
-            torch.equal(output, expected_output),
-            f"Values don't match: got {output}, expected {expected_output}",
-        )
+            self.assertTrue(
+                torch.equal(output, expected_output),
+                f"Values don't match: got {output}, expected {expected_output}",
+            )
 
     @expand(
         [

backends/cadence/aot/tests/test_type_dispatch_passes.py

@@ -20,7 +20,7 @@
 class TestTypeDispatchPasses(unittest.TestCase):
     def test_int8_dispatch_quantized_fully_connected(self) -> None:
         """Test int8 x int8 inputs should dispatch to asym8sxasym8s_asym8s variant"""
-        x = torch.randint(-128, 127, (2, 3), dtype=torch.int8)
+        x = torch.randint(-128, 127, (1, 3), dtype=torch.int8)
         w = torch.randint(-128, 127, (4, 3), dtype=torch.int8)
         b = torch.randint(-2147483648, 2147483647, (4,), dtype=torch.int32)
         gm = single_op_builder(
@@ -46,7 +46,7 @@ def test_int8_dispatch_quantized_fully_connected(self) -> None:
 
     def test_uint8_dispatch_quantized_fully_connected(self) -> None:
         """Test uint8 x uint8 inputs should dispatch to asym8uxasym8u_asym8u variant"""
-        x = torch.randint(0, 255, (2, 3), dtype=torch.uint8)
+        x = torch.randint(0, 255, (1, 3), dtype=torch.uint8)
         w = torch.randint(0, 255, (4, 3), dtype=torch.uint8)
         b = torch.randint(-2147483648, 2147483647, (4,), dtype=torch.int32)
         gm = single_op_builder(
@@ -124,7 +124,7 @@ def test_uint8_quantized_linear_dispatch(self) -> None:
 
     def test_mixed_types_error(self) -> None:
         """Test mixed int8/uint8 inputs should raise RuntimeError"""
-        x = torch.randint(-128, 127, (2, 3), dtype=torch.int8)
+        x = torch.randint(-128, 127, (1, 3), dtype=torch.int8)
         w = torch.randint(0, 255, (4, 3), dtype=torch.uint8)
         b = torch.randint(-2147483648, 2147483647, (4,), dtype=torch.int32)
         gm = single_op_builder(