feat: support aten.index_select converter (#2710)

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -2829,3 +2829,28 @@ def aten_ops_roll(
         args[1],
         args_bounds_check(args, 2, []),
     )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.index_select.default)
+@enforce_tensor_types(
+    {
+        0: (TRTTensor,),
+        2: (TRTTensor,),
+    }
+)
+def aten_ops_index_select(
+    ctx: ConversionContext,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.select.index_select(
+        ctx,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+        args[2],
+    )

py/torch_tensorrt/dynamo/conversion/impl/select.py

@@ -90,7 +90,7 @@ def index(
     # is_numpy is a flag to specify if all the indices are numpy or torchTensor.
     # If any is not this flag will be set to False
     _LOGGER.debug(
-        f"Determining whether aten.index constant-index optimization can be invoked"
+        "Determining whether aten.index constant-index optimization can be invoked"
     )
     is_numpy = all(
         isinstance(ind, (torch.Tensor, np.ndarray)) for ind in index if ind is not None
@@ -123,7 +123,7 @@ def index(
         return identity_layer.get_output(0)
     elif len(tensor_indices) == 1:
         indices_tensor = get_trt_tensor(
-            ctx, tensor_indices[0], name + f"_parameter_to_fp32_tensor"
+            ctx, tensor_indices[0], name + "_parameter_to_fp32_tensor"
         )
         index = adv_indx_indices[0]
         _LOGGER.debug(f"The advanced index indices is {adv_indx_indices}")
@@ -204,7 +204,7 @@ def index(
                 cum_adv_index = cum_adv_index + adv_index
                 multiplier = multiplier * input_shape[adv_indx_indices[i]]
             cum_adv_index = get_trt_tensor(
-                ctx, cum_adv_index, name + f"_index_sum_intermediate"
+                ctx, cum_adv_index, name + "_index_sum_intermediate"
             )
         else:
             multiplier = get_trt_tensor(
@@ -263,7 +263,7 @@ def index(
             adv_indx_count
             == adv_indx_indices[adv_indx_count - 1] - adv_indx_indices[0] + 1
         ):
-            _LOGGER.debug(f"The indices are continuous in this case")
+            _LOGGER.debug("The indices are continuous in this case")
             concat_tensor_reshape.append(
                 get_trt_tensor(ctx, -1, name + "_dynamic_concat")
             )
@@ -287,7 +287,7 @@ def index(
                 source_ir,
             )
             unfold_tensor = regular_index_shuffle_layer.get_output(0)
-            _LOGGER.debug(f"The tensor is unfolded now")
+            _LOGGER.debug("The tensor is unfolded now")
             _LOGGER.debug(f"The unfolded tensor shape is {unfold_tensor.shape}")
 
             # Transpose folded advanced indexed axis to its original location.
@@ -342,7 +342,7 @@ def index(
             reshape_output = unfold_advanced_shuffle_layer.get_output(0)
 
         else:
-            _LOGGER.debug(f"The indices are not continuous in this case")
+            _LOGGER.debug("The indices are not continuous in this case")
             concat_final_tensor = []
             concat_final_tensor.append(cum_adv_index_shape_tensor)
             for i in range(0, rank):
@@ -370,3 +370,21 @@ def index(
             reshape_output = reshape_layer.get_output(0)
 
     return reshape_output
+
+
+def index_select(
+    ctx: ConversionContext,
+    target: Target,
+    source_ir: Optional[SourceIR],
+    name: str,
+    input: TRTTensor,
+    dim: int,
+    index: TRTTensor,
+) -> TRTTensor:
+    # The axis parameter specifies the dimension along which to index.
+    dim = get_positive_dim(dim, len(input.shape))
+    gather_layer = ctx.net.add_gather(input, index, axis=dim)
+
+    set_layer_name(gather_layer, target, f"{name}_gather", source_ir)
+
+    return gather_layer.get_output(0)

tests/py/dynamo/conversion/test_index_select_aten.py

@@ -0,0 +1,41 @@
+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 TestIndexSelectConverter(DispatchTestCase):
+    @parameterized.expand(
+        [
+            ("1d_input", (10,), 0, (1,)),
+            ("2d_input_dim_0", (10, 3), 0, (0, 2)),
+            ("2d_input_dim_1", (5, 10), 1, (1, 2, 3)),
+            ("2d_input_dim_-2", (5, 10), -2, (1, 2, 3)),
+            ("3d_input_dim_0", (10, 5, 10), 0, (0, 5)),
+            ("3d_input_dim_2", (10, 5, 10), 2, (3, 3, 4)),
+            ("3d_input_dim_-1", (10, 5, 10), -1, (3, 3, 4)),
+            ("3d_input_dim_-3", (10, 5, 10), -3, (5, 3, 4)),
+        ]
+    )
+    def test_index_select(self, _, source_shape, dim, indices_val):
+        class TestIndexSelect(torch.nn.Module):
+            def forward(self, source_tensor, indices_tensor):
+                return torch.ops.aten.index_select.default(
+                    source_tensor, dim, indices_tensor
+                )
+
+        input = [
+            torch.randn(*source_shape, dtype=torch.float32),
+            torch.tensor([*indices_val], dtype=torch.int32),
+        ]
+
+        self.run_test(
+            TestIndexSelect(),
+            input,
+        )
+
+
+if __name__ == "__main__":
+    run_tests()