feat: support tile dynamo converter (#2402)

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -723,7 +723,30 @@ def aten_ops_cumsum(
     )
 
 
-@dynamo_tensorrt_converter(torch.ops.aten.permute.default)
+@dynamo_tensorrt_converter(torch.ops.aten.tile.default)  # type: ignore[misc]
+@enforce_tensor_types(
+    {
+        0: (TRTTensor,),
+    }
+)  # type: ignore[misc]
+def aten_ops_tile(
+    ctx: ConversionContext,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.slice.tile(
+        ctx,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+    )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.permute.default)  # type: ignore[misc]
 @enforce_tensor_types(
     {
         0: (TRTTensor,),

py/torch_tensorrt/dynamo/conversion/impl/slice/ops.py

@@ -1,5 +1,5 @@
 import math
-from typing import Optional
+from typing import Optional, Sequence
 
 import numpy as np
 import tensorrt as trt
@@ -203,3 +203,31 @@ def cumsum(
     set_layer_name(loop_output, target, f"{name}_loop_output", source_ir)
     loop_output.set_input(1, trip_limit)
     return loop_output.get_output(0)
+
+
+def tile(
+    ctx: ConversionContext,
+    target: Target,
+    source_ir: Optional[SourceIR],
+    name: str,
+    input: TRTTensor,
+    dims: Sequence[int],
+) -> TRTTensor:
+    diff = len(dims) - len(input.shape)
+    if diff > 0:
+        # prepend 1 to input.shape
+        new_shape = (1,) * diff + tuple(input.shape)
+        input = impl.shuffle.reshape(
+            ctx, target, source_ir, f"{name}_prepend_input_shape", input, new_shape
+        )
+    elif diff < 0:
+        # prepend 1 to dims
+        dims = (1,) * -diff + tuple(dims)
+
+    shapes = [i * j for i, j in zip(input.shape, dims)]
+    starts = [0] * len(dims)
+    strides = [1] * len(dims)
+    layer = ctx.net.add_slice(input, tuple(starts), tuple(shapes), tuple(strides))
+    layer.mode = trt.SampleMode.WRAP
+    set_layer_name(layer, target, name)
+    return layer.get_output(0)

tests/py/dynamo/conversion/test_tile_aten.py

@@ -0,0 +1,75 @@
+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 TestTileConverter(DispatchTestCase):
+    @parameterized.expand(
+        [
+            ((3,), (1,)),
+            ((3,), (0,)),
+            ((3,), (2,)),
+            ((2,), (2, 2)),
+            ((2,), (0, 2)),
+        ]
+    )
+    def test_tile_1D(self, shape, dims):
+        class Tile(nn.Module):
+            def forward(self, x):
+                return torch.ops.aten.tile.default(x, dims)
+
+        inputs = [torch.randn(shape)]
+        self.run_test(
+            Tile(),
+            inputs,
+        )
+
+    @parameterized.expand(
+        [
+            ((3, 1), (0,)),
+            ((3, 1), (2,)),
+            ((2, 3), (2, 2)),
+            ((2, 3), (1, 0)),
+            ((2, 3), (0, 2)),
+            ((2, 3), (4, 2, 3)),
+            ((2, 3), (0, 0, 3)),
+            ((2, 3), (4, 2, 3, 1, 2)),
+        ]
+    )
+    def test_tile_2D(self, shape, dims):
+        class Tile(nn.Module):
+            def forward(self, x):
+                return torch.ops.aten.tile.default(x, dims)
+
+        inputs = [torch.randn(shape)]
+        self.run_test(
+            Tile(),
+            inputs,
+        )
+
+    @parameterized.expand(
+        [
+            ((4, 2, 3), (2,)),
+            ((4, 2, 3), (1, 2)),
+            ((1, 2, 3), (2, 3)),
+            ((1, 2, 3), (2, 3, 4)),
+            ((1, 2, 3), (2, 3, 4, 5)),
+        ]
+    )
+    def test_tile_3D(self, shape, dims):
+        class Tile(nn.Module):
+            def forward(self, x):
+                return torch.ops.aten.tile.default(x, dims)
+
+        inputs = [torch.randn(shape)]
+        self.run_test(
+            Tile(),
+            inputs,
+        )
+
+
+if __name__ == "__main__":
+    run_tests()