Fixup op_slice negative start arguments

Summary: Fixup op_slice negative start arguments

Reviewed By: digantdesai

Differential Revision: D72728353

Author: 3l1

backends/arm/operators/op_slice.py

@@ -23,6 +23,18 @@ class SliceVisitor(NodeVisitor):
     def __init__(self, *args):
         super().__init__(*args)
 
+    def _fixup_start(self, start, shape, dim):
+        if start.number < 0:
+            return start.number % shape[dim]
+        else:
+            return start.number  
+
+    def _fixup_end(self, end, shape, dim):
+        if end.number < 0:
+            return end.number % shape[dim]
+        else:
+            return min(end.number, shape[dim])
+
     def define_node(
         self,
         node: Node,
@@ -42,17 +54,18 @@ def define_node(
         # Translate and check parameters in Pytorch dim order.
         shape = input_node.shape
         dim = dim.number
-        if end.number < 0:
-            end_index = end.number % shape[dim]
-        else:
-            end_index = min(end.number, shape[dim])
-        size = end_index - start.number
+
+        start_index = self._fixup_start(start, shape, dim)
+        end_index = self._fixup_end(end, shape, dim)
+        size = end_index - start_index
+
         assert size > 0
         assert size <= shape[dim]
 
         # Convert aten args to Tosa's start and size attributes and in TOSA dim order.
         attr = ts.TosaSerializerAttribute()
-        start_attr = [start.number if i == dim else 0 for i in input_node.dim_order]
+
+        start_attr = [self._fixup_start(start, shape, dim) if i == dim else 0 for i in input_node.dim_order]
         size_attr = [size if i == dim else shape[i] for i in input_node.dim_order]
         attr.SliceAttribute(start_attr, size_attr)
 

backends/arm/test/ops/test_slice.py

@@ -16,23 +16,36 @@
 from executorch.exir.backend.compile_spec_schema import CompileSpec
 from parameterized import parameterized
 
+test_data_suite = [
+    (
+        torch.ones(10),
+        [(3, -3)]
+    ),
+    (
+        torch.ones(10),
+        [(-8, 3)]
+    ),
+    (
+        torch.ones(10, 10),
+        [(1, 3), (3, None)]
+    ),
+    (
+        torch.ones(10, 10, 10),
+        [(0, 7), (0, None), (0, 8)]
+    ),
+    (
+        torch.ones((1, 12, 10, 10)),
+        [(None, None), (None, 5), (3, 5), (4, 10)]
+    )
+]
 
 class TestSimpleSlice(unittest.TestCase):
 
     class Slice(torch.nn.Module):
-
-        sizes = [(10), (10, 10), (10, 10, 10), ((1, 12, 10, 10))]
-        test_tensors = [(torch.ones(n),) for n in sizes]
-
-        def forward(self, x: torch.Tensor):
-            if x.dim() == 1:
-                return x[3:-3]
-            elif x.dim() == 2:
-                return x[1:3, 3:]
-            elif x.dim() == 3:
-                return x[0:7, 0:, 0:8]
-            elif x.dim() == 4:
-                return x[:, :5, 3:5, 4:10]
+        def forward(self, x: torch.Tensor, s: list[tuple[int, int]]):
+            slices = [slice(*i) for i in s]
+            return x[slices]
+            
 
     def _test_slice_tosa_MI_pipeline(
         self, module: torch.nn.Module, test_data: torch.Tensor
@@ -111,26 +124,26 @@ def _test_slice_u85_BI_pipeline(
         self._test_slice_ethos_BI_pipeline(
             common.get_u85_compile_spec(), module, test_data
         )
-
-    @parameterized.expand(Slice.test_tensors)
+    
+    @parameterized.expand(test_data_suite)
     @pytest.mark.tosa_ref_model
-    def test_slice_tosa_MI(self, tensor):
-        self._test_slice_tosa_MI_pipeline(self.Slice(), (tensor,))
+    def test_slice_tosa_MI(self, tensor: torch.Tensor, slices: list[tuple[int, int]]):
+        self._test_slice_tosa_MI_pipeline(self.Slice(), (tensor, slices))
 
-    @parameterized.expand(Slice.test_tensors[:2])
+    @parameterized.expand(test_data_suite)
     @pytest.mark.tosa_ref_model
-    def test_slice_nchw_tosa_BI(self, test_tensor: torch.Tensor):
-        self._test_slice_tosa_BI_pipeline(self.Slice(), (test_tensor,))
+    def test_slice_nchw_tosa_BI(self, tensor: torch.Tensor, slices: list[tuple[int, int]]):
+        self._test_slice_tosa_BI_pipeline(self.Slice(), (tensor, slices))
 
-    @parameterized.expand(Slice.test_tensors[2:])
+    @parameterized.expand(test_data_suite)
     @pytest.mark.tosa_ref_model
-    def test_slice_nhwc_tosa_BI(self, test_tensor: torch.Tensor):
-        self._test_slice_tosa_BI_pipeline(self.Slice(), (test_tensor,))
+    def test_slice_nhwc_tosa_BI(self, tensor: torch.Tensor, slices: list[tuple[int, int]]):
+        self._test_slice_tosa_BI_pipeline(self.Slice(), (tensor, slices))
 
-    @parameterized.expand(Slice.test_tensors)
-    def test_slice_u55_BI(self, test_tensor: torch.Tensor):
-        self._test_slice_u55_BI_pipeline(self.Slice(), (test_tensor,))
+    @parameterized.expand(test_data_suite)
+    def test_slice_u55_BI(self, tensor: torch.Tensor, slices: list[tuple[int, int]]):
+        self._test_slice_u55_BI_pipeline(self.Slice(), (tensor, slices))
 
-    @parameterized.expand(Slice.test_tensors)
-    def test_slice_u85_BI(self, test_tensor: torch.Tensor):
-        self._test_slice_u85_BI_pipeline(self.Slice(), (test_tensor,))
+    @parameterized.expand(test_data_suite)
+    def test_slice_u85_BI(self, tensor: torch.Tensor, slices: list[tuple[int, int]]):
+        self._test_slice_u85_BI_pipeline(self.Slice(), (tensor, slices))