feat: support aten.sort dynamo converter

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -2505,3 +2505,27 @@ def upsample_bilinear2d(
         resize_mode="bilinear",
         align_corners=args_bounds_check(args, 2),
     )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.sort.default)
+@enforce_tensor_types(
+    {
+        0: (TRTTensor,),
+    }
+)
+def aten_ops_sort(
+    ctx: ConversionContext,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.topk.sort(
+        ctx,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        dim=args_bounds_check(args, 1, -1),
+        descending=args_bounds_check(args, 2, False),
+    )

py/torch_tensorrt/dynamo/conversion/impl/topk.py

@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Optional, Tuple, Union
 
 import tensorrt as trt
 from torch.fx.node import Target
@@ -101,3 +101,36 @@ def argmin(
     return argmax_argmin(
         ctx, target, source_ir, name, input, trt.TopKOperation.MIN, dim, keep_dim
     )
+
+
+def sort(
+    ctx: ConversionContext,
+    target: Target,
+    source_ir: Optional[SourceIR],
+    name: str,
+    input: TRTTensor,
+    dim: int,
+    descending: bool,
+    return_indices: bool = True,
+) -> Union[TRTTensor, Tuple[TRTTensor, TRTTensor]]:
+    if descending:
+        topk_layer = ctx.net.add_topk(
+            input,
+            trt.TopKOperation.MAX,
+            input.shape[dim],
+            get_axes_for_reduce_op(get_positive_dim(dim, len(input.shape))),
+        )
+    else:
+        topk_layer = ctx.net.add_topk(
+            input,
+            trt.TopKOperation.MIN,
+            input.shape[dim],
+            get_axes_for_reduce_op(get_positive_dim(dim, len(input.shape))),
+        )
+
+    set_layer_name(topk_layer, target, name, source_ir)
+
+    if return_indices:
+        return topk_layer.get_output(0), topk_layer.get_output(1)
+    else:
+        return topk_layer.get_output(0)

tests/py/dynamo/conversion/test_sort_aten.py

@@ -0,0 +1,34 @@
+import torch
+import torch.nn as nn
+from parameterized import parameterized
+from torch.testing._internal.common_utils import run_tests
+
+from .harness import DispatchTestCase
+
+
+class TestSortConverter(DispatchTestCase):
+    @parameterized.expand(
+        [
+            ((3, 2, 4), 0, True),
+            ((2, 3, 4, 5), 1, True),
+            ((2, 3, 4, 5), 2, False),
+            ((6, 7, 5, 4, 5), 4, False),
+            ((1, 5, 2, 1), -1, True),
+            ((1, 2, 5, 3), -2, False),
+            ((6, 2, 1, 3), -4, True),
+        ]
+    )
+    def test_sort(self, input_shape, dim, descending):
+        class Sort(nn.Module):
+            def forward(self, x):
+                return torch.ops.aten.sort.default(x, dim, descending)
+
+        inputs = [torch.randn(*input_shape)]
+        self.run_test(
+            Sort(),
+            inputs,
+        )
+
+
+if __name__ == "__main__":
+    run_tests()