[Torch] Fix converting torch slice op with dynamic slice length (apac…

…he#7549)

* Fix converting torch slice op with dynamic slice length

* use isinstance

Co-authored-by: masa <masa@pop-os.localdomain>

python/tvm/relay/frontend/pytorch.py

@@ -400,7 +400,13 @@ def slice(self, inputs, input_types):
         )
 
         # A fast path when slicing is nop.
-        if target_begin == 0 and target_end >= index_size_limit and stride == 1:
+        if (
+            isinstance(target_begin, int)
+            and isinstance(target_end, int)
+            and target_begin == 0
+            and target_end >= index_size_limit
+            and stride == 1
+        ):
             return data
 
         # Process begin

tests/python/frontend/pytorch/test_forward.py

@@ -1497,13 +1497,22 @@ class SliceWithStride2(torch.nn.Module):
         def forward(self, x):
             return x[0::2, 0::2] + x[1::2, 1::2]
 
+    class DynamicLengthSlice(torch.nn.Module):
+        def forward(self, values, length):
+            return values[0:length]
+
     input_data = torch.rand(input_shape).float()
     verify_model(Slice1(), input_data=input_data)
     verify_model(Slice2(), input_data=input_data)
     verify_model(Slice3(), input_data=input_data)
     verify_model(SliceWithStride(), input_data=torch.randn(1, 4))
     verify_model(SliceWithStride2(), input_data=torch.randn(4, 4))
 
+    inp = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+    slice_len = torch.tensor(2)
+    targets = ["llvm", "cuda"]
+    verify_trace_model(DynamicLengthSlice(), [inp, slice_len], targets)
+
 
 @tvm.testing.uses_gpu
 def test_forward_narrow():