fix type promotion for div in RemoveMixedTypeOperators

The promotion strategy is dependent on the rounding mode (see the div
decomp in PyTorch
https://github.com/pytorch/pytorch/blob/main/torch/_refs/__init__.py#L1214
and then the promotion annotation on each of the
true_divide/trunc_divide/floor_divide functions itcalls).

I had to restructure the test a bit more so that lint didn't complain it was too complex.

ghstack-source-id: b75ace2
ghstack-comment-id: 3026116637
Pull-Request-resolved: #12157

Author: swolchok

exir/passes/remove_mixed_type_operators.py

@@ -23,12 +23,20 @@ def call_operator(self, op, args, kwargs, meta: NodeMetadata):  # noqa: C901
         promotion_type_allow_list = {
             torch.ops.aten.add.Tensor: ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
             torch.ops.aten.mul.Tensor: ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
-            torch.ops.aten.div.Tensor: ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+            # The correct promotion for div depends on the mode! If there is no mode,
+            # it's INT_TO_FLOAT, otherwise it's default.
+            torch.ops.aten.div.Tensor: ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+            torch.ops.aten.div.Tensor_mode: ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
             torch.ops.aten.minimum.default: ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
         }
 
         if op in promotion_type_allow_list:
             promotion_kind = promotion_type_allow_list[op]
+            if (
+                op == torch.ops.aten.div.Tensor_mode
+                and kwargs.get("rounding_mode") is None
+            ):
+                promotion_kind = ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
         else:
             # Not in allow list, do nothing
             return super().call_operator(op, args, kwargs, meta)

exir/tests/test_passes.py

@@ -9,7 +9,7 @@
 import os
 import tempfile
 import unittest
-from typing import List, Optional, Tuple
+from typing import Callable, List, Optional, Tuple
 
 import executorch.exir as exir
 
@@ -71,6 +71,7 @@
 from functorch.experimental import control_flow
 
 from torch import nn
+from torch._prims_common import ELEMENTWISE_TYPE_PROMOTION_KIND
 from torch.export import export
 from torch.export.graph_signature import InputKind, InputSpec, TensorArgument
 from torch.fx import GraphModule, subgraph_rewriter
@@ -121,39 +122,97 @@ def foo_out(
     return a + 1, None
 
 
+def simple_promote_dtype(
+    dtype: torch.dtype, promotion_kind: ELEMENTWISE_TYPE_PROMOTION_KIND
+) -> torch.dtype:
+    if promotion_kind == ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT:
+        return dtype
+    if promotion_kind == ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT:
+        return dtype if dtype.is_floating_point else torch.float
+    else:
+        raise Exception(f"Unsupported promotion kind {promotion_kind}")
+
+
+def count_nodes_with_target_asserting_arguments_have_dtype(
+    self, module, target, arg_dtype
+) -> int:
+    count = 0
+    for node in module.graph.nodes:
+        if node.op == "call_function" and node.target == target:
+            count += 1
+            for arg in node.args:
+                self.assertEqual(arg.meta["val"].dtype, arg_dtype)
+    return count
+
+
 class TestPasses(unittest.TestCase):
     @classmethod
     def setUpClass(cls) -> None:
         register_additional_test_aten_ops()
 
     def test_remove_mixed_type_operators(self) -> None:
-        def count_nodes_with_target_asserting_arguments_have_dtype(
-            new_graph_module, target, arg_dtype
-        ):
-            count = 0
-            for node in new_graph_module.graph.nodes:
-                if node.op == "call_function" and node.target == target:
-                    count += 1
-                    for arg in node.args:
-                        self.assertEqual(arg.meta["val"].dtype, arg_dtype)
-            return count
-
-        class Add(torch.nn.Module):
-            def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-                return (x + y) + x
-
-        class Mult(torch.nn.Module):
-            def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-                return x * y
-
-        class Minimum(torch.nn.Module):
-            def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-                return torch.minimum(x, y)
+        def make_module(fwd: Callable[[torch.Tensor, torch.Tensor], torch.Tensor]):
+            class Module(torch.nn.Module):
+                def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+                    return fwd(x, y)
+
+            return Module
+
+        Add = make_module(lambda x, y: (x + y) + x)
+        Mult = make_module(lambda x, y: x * y)
+        Minimum = make_module(torch.minimum)
+        DivWithoutMode = make_module(torch.div)
+        DivWithNoneMode = make_module(lambda x, y: torch.div(x, y, rounding_mode=None))
+        DivWithTruncMode = make_module(
+            lambda x, y: torch.div(x, y, rounding_mode="trunc")
+        )
+        DivWithFloorMode = make_module(
+            lambda x, y: torch.div(x, y, rounding_mode="floor")
+        )
 
-        for module, op, expected_count in (
-            (Add, exir_ops.edge.aten.add.Tensor, 2),
-            (Mult, exir_ops.edge.aten.mul.Tensor, 1),
-            (Minimum, exir_ops.edge.aten.minimum.default, 1),
+        for module, op, expected_count, promotion_kind in (
+            (
+                Add,
+                exir_ops.edge.aten.add.Tensor,
+                2,
+                ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+            ),
+            (
+                Mult,
+                exir_ops.edge.aten.mul.Tensor,
+                1,
+                ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+            ),
+            (
+                Minimum,
+                exir_ops.edge.aten.minimum.default,
+                1,
+                ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+            ),
+            (
+                DivWithoutMode,
+                exir_ops.edge.aten.div.Tensor,
+                1,
+                ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+            ),
+            (
+                DivWithNoneMode,
+                exir_ops.edge.aten.div.Tensor_mode,
+                1,
+                ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+            ),
+            (
+                DivWithTruncMode,
+                exir_ops.edge.aten.div.Tensor_mode,
+                1,
+                ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+            ),
+            (
+                DivWithFloorMode,
+                exir_ops.edge.aten.div.Tensor_mode,
+                1,
+                ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+            ),
         ):
             for second_arg_dtype in (torch.int64, torch.float, torch.double):
                 int_tensor = torch.tensor([[1, 2, 3]], dtype=torch.int64)
@@ -166,8 +225,9 @@ def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
                 new_graph_module = new_prog.exported_program().graph_module
                 self.assertIsNotNone(new_graph_module)
 
+                promoted_type = simple_promote_dtype(second_arg_dtype, promotion_kind)
                 count = count_nodes_with_target_asserting_arguments_have_dtype(
-                    new_graph_module, op, second_arg_dtype
+                    self, new_graph_module, op, promoted_type
                 )
                 self.assertEqual(count, expected_count)