Arm backend: Add INT64 support to to_copy

Partition to_copy from INT64 as these will be computed AOT and cast to
INT32. Also disables partitioning of int-to-int casts for FP-profile.

Signed-off-by: Oscar Andersson <[email protected]>
Change-Id: I6de8d9baf2c8dc8c729536a36acc2a2049057555

Author: oscarandersson8218

backends/arm/operator_support/to_copy_support.py

@@ -20,6 +20,8 @@
 
 logger = logging.getLogger(__name__)
 
+SupportedTypeDict = dict[torch.dtype, list[torch.dtype]]
+
 
 @register_tosa_support_check
 class ToCopySupported(SupportedTOSAOperatorCheck):
@@ -33,8 +35,6 @@ class ToCopySupported(SupportedTOSAOperatorCheck):
         TosaSpecification.create_from_string("TOSA-1.0+FP"),
     ]
 
-    SupportedTypeDict = dict[torch.dtype, list[torch.dtype]]
-
     @staticmethod
     def _merge_supported_types(
         # pyre-ignore[11]
@@ -53,11 +53,22 @@ def _merge_supported_types(
         torch.int8: [torch.bool, torch.int16, torch.int32],
         torch.int16: [torch.bool, torch.int8, torch.int32],
         torch.int32: [torch.bool, torch.int8, torch.int16],
+        torch.int64: [torch.bool, torch.int8, torch.int16, torch.int32],
     }
     SUPPORTED_FLOAT_TYPES: SupportedTypeDict = {
         torch.int8: [torch.float16, torch.bfloat16, torch.float32],
         torch.int16: [torch.float16, torch.bfloat16, torch.float32],
         torch.int32: [torch.float16, torch.bfloat16, torch.float32],
+        # INT64 inputs to casts *should* be ok, since they should be rejected by
+        # CheckInt64InputsAndOutputs if the cast can't be done AOT.
+        torch.int64: [
+            torch.int8,
+            torch.int16,
+            torch.int32,
+            torch.float16,
+            torch.bfloat16,
+            torch.float32,
+        ],
         torch.bfloat16: [torch.int8, torch.int16, torch.int32, torch.float32],
         torch.float16: [torch.int8, torch.int16, torch.int32, torch.float32],
         torch.float32: [
@@ -71,22 +82,20 @@ def _merge_supported_types(
     ALL_SUPPORTED_TYPES = _merge_supported_types(
         SUPPORTED_INT_TYPES, SUPPORTED_FLOAT_TYPES
     )
-    POSSIBLE_TYPE_CONVERSIONS = {torch.int64: torch.int32}
 
     def is_node_tosa_supported(
         self, node: fx.Node, tosa_spec: TosaSpecification
     ) -> bool:
-        supported_dtypes = (
-            self.ALL_SUPPORTED_TYPES
-            if tosa_spec.support_float()
-            else self.SUPPORTED_INT_TYPES
-        )
-        # Take into account possible type conversions
-        supported_dtypes.update(
-            (k, supported_dtypes[v])
-            for k, v in self.POSSIBLE_TYPE_CONVERSIONS.items()
-            if v in supported_dtypes
-        )
+
+        supported_dtypes: SupportedTypeDict = {}
+        if tosa_spec.support_integer():
+            supported_dtypes = self._merge_supported_types(
+                self.SUPPORTED_INT_TYPES, supported_dtypes
+            )
+        if tosa_spec.support_float():
+            supported_dtypes = self._merge_supported_types(
+                self.SUPPORTED_FLOAT_TYPES, supported_dtypes
+            )
 
         if len(node.all_input_nodes) != 1:
             self.reporter.report_reject(
@@ -156,7 +165,7 @@ def is_node_tosa_supported(
         if "dim_order" in node.kwargs:
             dim_order = node.kwargs["dim_order"]
             # pyre-ignore[6]
-            if dim_order != list(range(len(dim_order))):  # type: ignore[arg-type]
+            if dim_order is not None and dim_order != list(range(len(dim_order))):  # type: ignore[arg-type]
                 self.reporter.report_reject(
                     node,
                     (

backends/arm/test/models/stable_diffusion/test_T5EncoderModel.py

@@ -33,8 +33,9 @@ class TestT5EncoderModel(unittest.TestCase):
     # .to_executorch step, i.e. after Arm partitioner.
     ops_after_partitioner = {
         "executorch_exir_dialects_edge__ops_aten__to_copy_default": 2,
+        "executorch_exir_dialects_edge__ops_dim_order_ops__to_dim_order_copy_default": 1,
         "executorch_exir_dialects_edge__ops_aten_view_copy_default": 1,
-        "torch.ops.higher_order.executorch_call_delegate": 2,
+        "torch.ops.higher_order.executorch_call_delegate": 3,
     }
 
     def _prepare_inputs(

backends/arm/test/ops/test_to_copy.py

@@ -70,6 +70,16 @@ def test_copy_tosa_FP(test_data: Tuple):
         aten_op=[],
         exir_op=[],
     )
+    # int to int cast is not supported in TOSA+FP profile
+    if not new_dtype.is_floating_point and not torch.is_floating_point(test_tensor):
+        pipeline.change_args(
+            "check_count.exir",
+            {
+                "torch.ops.higher_order.executorch_call_delegate": 0,
+                "executorch_exir_dialects_edge__ops_dim_order_ops__to_dim_order_copy_default": 1,
+            },
+        )
+        pipeline.pop_stage("run_method_and_compare_outputs")
     pipeline.run()
 
 
@@ -84,6 +94,15 @@ def test_copy_vgf_FP(test_data: Tuple):
         exir_op=[],
         tosa_version="TOSA-1.0+FP",
     )
+    # int to int cast is not supported in TOSA+FP profile
+    if not new_dtype.is_floating_point and not torch.is_floating_point(test_tensor):
+        pipeline.change_args(
+            "check_count.exir",
+            {
+                "torch.ops.higher_order.executorch_call_delegate": 0,
+                "executorch_exir_dialects_edge__ops_dim_order_ops__to_dim_order_copy_default": 1,
+            },
+        )
     pipeline.run()
 
 

backends/arm/test/passes/test_convert_int64_output_ops_to_int32.py

@@ -32,17 +32,8 @@ def forward(self, x: torch.Tensor):
 test_data_suite_convert = {
     "fp32_input": lambda: (torch.rand((1, 2, 3, 4), dtype=torch.float32), torch.int64),
     "fp16_input": lambda: (torch.rand((1, 2, 3, 4), dtype=torch.float16), torch.int64),
-    "int16_input": lambda: (
-        torch.randint(-127, 128, (1, 2, 3, 4), dtype=torch.int16),
-        torch.int64,
-    ),
-    "int8_input": lambda: (
-        torch.randint(-127, 128, (1, 2, 3, 4), dtype=torch.int8),
-        torch.int64,
-    ),
 }
 
-
 test_data_suite_remove = {
     "int32_input": lambda: (
         torch.randint(-127, 128, (1, 2, 3, 4), dtype=torch.int32),
@@ -52,7 +43,7 @@ def forward(self, x: torch.Tensor):
 
 
 @common.parametrize("test_data", test_data_suite_convert)
-def test_convert_or_remove_casting_to_int64_covnert_tosa_FP(test_data: Tuple):
+def test_convert_or_remove_casting_to_int64_convert_tosa_FP(test_data: Tuple):
     test_tensor, target_dtype = test_data()
     module = CastingToInt64Model(target_dtype)