Arm Backend: Add support for split_copy.default

Signed-off-by: Agrima Khare <[email protected]>
Change-Id: I9320188aceb5778de9649f414bf9fc7fc062b407

Author: agrima1304

backends/arm/_passes/convert_split_to_slice.py

@@ -46,13 +46,24 @@ def call(self, graph_module: torch.fx.GraphModule):
             dim = (dim + rank) % rank
 
             # Validate that split lengths cover the entire dimension
-            length_sum = sum(split_lengths)
+
             dim_size = shape[dim]
-            if length_sum != dim_size:
-                raise ValueError(
-                    f"Split sizes {split_lengths} sum to {length_sum}, "
-                    f"but dimension {dim} has size {dim_size}"
-                )
+            if isinstance(split_lengths, int):
+                if split_lengths <= 0:
+                    raise ValueError(
+                        f"Split size must be positive, got {split_lengths}"
+                    )
+                full_chunks, remainder = divmod(dim_size, split_lengths)
+                split_lengths = [split_lengths] * full_chunks
+                if remainder:
+                    split_lengths.append(remainder)
+            else:
+                length_sum = sum(split_lengths)
+                if length_sum != dim_size:
+                    raise ValueError(
+                        f"Split sizes {split_lengths} sum to {length_sum}, "
+                        f"but dimension {dim} has size {dim_size}"
+                    )
 
             # Convert split argument 'split_lengths' to slice arguments start and end.
             starts = [0] * len(split_lengths)

backends/arm/operator_support/tosa_profile_supported_op_lists.py

@@ -56,6 +56,7 @@
     exir_ops.edge.aten.log.default,
     exir_ops.edge.aten.linear.default,
     exir_ops.edge.aten.split_with_sizes_copy.default,
+    exir_ops.edge.aten.split_copy.Tensor,
     exir_ops.edge.aten.floor.default,
     exir_ops.edge.aten.full.default,
     exir_ops.edge.aten.full_like.default,
@@ -172,6 +173,7 @@
     exir_ops.edge.aten.log.default,
     exir_ops.edge.aten.linear.default,
     exir_ops.edge.aten.split_with_sizes_copy.default,
+    exir_ops.edge.aten.split_copy.Tensor,
     exir_ops.edge.aten.floor.default,
     exir_ops.edge.aten.full.default,
     exir_ops.edge.aten.full_like.default,

backends/arm/quantizer/quantization_annotator.py

@@ -323,6 +323,7 @@ def _match_pattern(
     torch.ops.aten.slice_copy.Tensor,
     torch.ops.aten.split.Tensor,
     torch.ops.aten.split_with_sizes.default,
+    torch.ops.aten.split_copy.Tensor,
     torch.ops.aten.transpose.Dimname,
     torch.ops.aten.transpose.int,
     torch.ops.aten.transpose_copy.int,

backends/arm/test/ops/test_split.py

@@ -22,7 +22,6 @@
 
 
 class Split(torch.nn.Module):
-
     test_data = {
         "split_1d_2_size_0_dim": lambda: (torch.rand(10), 2, 0),
         "split_2d_3_size_1_dim": lambda: (torch.rand(10, 10), 3, 1),
@@ -60,12 +59,24 @@ def forward(
         return x.split(split_size=split_size_or_sections, dim=dim)[1:3]
 
 
+class SplitCopy(torch.nn.Module):
+    aten_op = "torch.ops.aten.split_copy.Tensor"
+    exir_op = "executorch_exir_dialects_edge__ops_aten_split_copy_Tensor"
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        split_size: int,
+        dim: int,
+    ):
+        return torch.split_copy(x, split_size=split_size, dim=dim)
+
+
 @common.parametrize(
     "test_data",
     (Split.test_data | Split.test_data_list),
 )
 def test_split_with_sizes_tosa_FP(test_data: input_t1):
-
     pipeline = TosaPipelineFP[input_t1](
         Split(),
         test_data(),
@@ -77,7 +88,6 @@ def test_split_with_sizes_tosa_FP(test_data: input_t1):
 
 @common.parametrize("test_data", Split.test_data_list)
 def test_split_with_sizes_tosa_FP_2(test_data: input_t1):
-
     pipeline = TosaPipelineFP[input_t1](
         SplitWithSizes(),
         test_data(),
@@ -92,7 +102,6 @@ def test_split_with_sizes_tosa_FP_2(test_data: input_t1):
     (Split.test_data | Split.test_data_list),
 )
 def test_split_with_sizes_tosa_FP_one_out(test_data: input_t1):
-
     pipeline = TosaPipelineFP[input_t1](
         SplitSingleOut(),
         test_data(),
@@ -107,7 +116,6 @@ def test_split_with_sizes_tosa_FP_one_out(test_data: input_t1):
     (Split.test_data | Split.test_data_list),
 )
 def test_split_with_sizes_tosa_FP_two_out(test_data: input_t1):
-
     pipeline = TosaPipelineFP[input_t1](
         SplitTwoOut(),
         test_data(),
@@ -122,7 +130,6 @@ def test_split_with_sizes_tosa_FP_two_out(test_data: input_t1):
     (Split.test_data | Split.test_data_list),
 )
 def test_split_with_sizes_tosa_INT(test_data: input_t1):
-
     pipeline = TosaPipelineINT[input_t1](
         Split(),
         test_data(),
@@ -152,7 +159,6 @@ def test_split_with_sizes_u55_INT(test_data: input_t1):
     (Split.test_data | Split.test_data_list),
 )
 def test_split_with_sizes_u85_INT(test_data: input_t1):
-
     pipeline = EthosU85PipelineINT[input_t1](
         Split(),
         test_data(),
@@ -182,7 +188,6 @@ def test_split_with_sizes_vgf_FP(test_data: input_t1):
 @common.parametrize("test_data", Split.test_data_list)
 @common.SkipIfNoModelConverter
 def test_split_with_sizes_vgf_FP_2(test_data: input_t1):
-
     pipeline = VgfPipeline[input_t1](
         SplitWithSizes(),
         test_data(),
@@ -199,7 +204,6 @@ def test_split_with_sizes_vgf_FP_2(test_data: input_t1):
 )
 @common.SkipIfNoModelConverter
 def test_split_with_sizes_vgf_FP_one_out(test_data: input_t1):
-
     pipeline = VgfPipeline[input_t1](
         SplitSingleOut(),
         test_data(),
@@ -216,7 +220,6 @@ def test_split_with_sizes_vgf_FP_one_out(test_data: input_t1):
 )
 @common.SkipIfNoModelConverter
 def test_split_with_sizes_vgf_FP_two_out(test_data: input_t1):
-
     pipeline = VgfPipeline[input_t1](
         SplitTwoOut(),
         test_data(),
@@ -233,7 +236,6 @@ def test_split_with_sizes_vgf_FP_two_out(test_data: input_t1):
 )
 @common.SkipIfNoModelConverter
 def test_split_with_sizes_vgf_INT(test_data: input_t1):
-
     pipeline = VgfPipeline[input_t1](
         Split(),
         test_data(),
@@ -242,3 +244,75 @@ def test_split_with_sizes_vgf_INT(test_data: input_t1):
         tosa_version="TOSA-1.0+INT",
     )
     pipeline.run()
+
+
+@common.parametrize("test_data", Split.test_data)
+def test_split_tensor_tosa_FP(test_data: Tuple):
+    pipeline = TosaPipelineFP[input_t1](
+        SplitCopy(),
+        test_data(),
+        aten_op=SplitCopy.aten_op,
+        exir_op=SplitCopy.exir_op,
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", Split.test_data)
+def test_split_tensor_tosa_INT(test_data: Tuple):
+    pipeline = TosaPipelineINT[input_t1](
+        SplitCopy(),
+        test_data(),
+        aten_op=SplitCopy.aten_op,
+        exir_op=SplitCopy.exir_op,
+    )
+    pipeline.run()
+
+
+@common.XfailIfNoCorstone300
+@common.parametrize("test_data", Split.test_data)
+def test_split_tensor_u55_INT(test_data: Tuple):
+    pipeline = EthosU55PipelineINT[input_t1](
+        SplitCopy(),
+        test_data(),
+        aten_ops=SplitCopy.aten_op,
+        exir_ops=SplitCopy.exir_op,
+    )
+    pipeline.run()
+
+
+@common.XfailIfNoCorstone320
+@common.parametrize("test_data", Split.test_data)
+def test_split_tensor_u85_INT(test_data: Tuple):
+    pipeline = EthosU85PipelineINT[input_t1](
+        SplitCopy(),
+        test_data(),
+        aten_ops=SplitCopy.aten_op,
+        exir_ops=SplitCopy.exir_op,
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", Split.test_data)
+@common.SkipIfNoModelConverter
+def test_split_tensor_vgf_FP(test_data: Tuple):
+    pipeline = VgfPipeline[input_t1](
+        SplitCopy(),
+        test_data(),
+        aten_op=SplitCopy.aten_op,
+        exir_op=SplitCopy.exir_op,
+        tosa_version="TOSA-1.0+FP",
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", Split.test_data)
+@common.SkipIfNoModelConverter
+def test_split_tensor_vgf_INT(test_data: Tuple):
+    pipeline = VgfPipeline[input_t1](
+        SplitCopy(),
+        test_data(),
+        aten_op=SplitCopy.aten_op,
+        exir_op=SplitCopy.exir_op,
+        tosa_version="TOSA-1.0+INT",
+    )
+    pipeline.run()