feat: Support aten.gelu dynamo converter

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -19,7 +19,7 @@
     get_positive_dim,
     is_only_operator_on_placeholder,
 )
-from torch_tensorrt.fx.types import TRTTensor
+from torch_tensorrt.dynamo.types import TRTTensor
 
 _LOGGER: logging.Logger = logging.getLogger(__name__)
 
@@ -548,6 +548,24 @@ def aten_ops_hard_sigmoid(
     )
 
 
+@dynamo_tensorrt_converter(torch.ops.aten.gelu.default, supports_dynamic_shapes=True)
+def aten_ops_gelu(
+    ctx: ConversionContext,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.activation.gelu(
+        ctx,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        kwargs.get("approximate", "none"),
+    )
+
+
 @dynamo_tensorrt_converter(torch.ops.aten.matmul, supports_dynamic_shapes=True)
 @dynamo_tensorrt_converter(torch.ops.aten.dot.default, supports_dynamic_shapes=True)
 @dynamo_tensorrt_converter(torch.ops.aten.mm.default, supports_dynamic_shapes=True)

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

@@ -7,7 +7,7 @@
 from torch_tensorrt.dynamo._SourceIR import SourceIR
 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
 from torch_tensorrt.dynamo.conversion.impl.activation.base import convert_activation
-from torch_tensorrt.fx.types import TRTTensor
+from torch_tensorrt.dynamo.types import TRTTensor
 
 
 def relu(
@@ -327,3 +327,26 @@ def thresholded_relu_fn(x: float) -> float:
         alpha=alpha,
         dyn_range_fn=thresholded_relu_dyn_range_fn,
     )
+
+
+def gelu(
+    ctx: ConversionContext,
+    target: Target,
+    source_ir: Optional[SourceIR],
+    name: str,
+    input_val: TRTTensor,
+    approximate: str,
+) -> TRTTensor:
+    if approximate == "none":
+        operation_type = trt.ActivationType.GELU_ERF
+    elif approximate == "tanh":
+        operation_type = trt.ActivationType.GELU_TANH
+
+    return convert_activation(
+        ctx,
+        target,
+        source_ir,
+        name,
+        operation_type,
+        input_val,
+    )

py/torch_tensorrt/dynamo/lowering/_decomposition_groups.py

@@ -42,7 +42,6 @@
     aten.fill,
     aten.frac,
     aten._fused_moving_avg_obs_fq_helper,
-    aten.gelu,
     aten.gelu_backward,
     aten.glu_backward,
     aten.hardshrink,

tests/py/dynamo/conversion/test_gelu_aten.py

@@ -1,49 +1,67 @@
-import pytest
 import torch
 import torch.nn as nn
+from parameterized import parameterized
 from torch.testing._internal.common_utils import run_tests
 from torch_tensorrt import Input
 
 from .harness import DispatchTestCase
 
 
-@pytest.mark.skip(reason="This test will be skipped.")
-class TestGeLUConverter(DispatchTestCase):
-    def test_gelu(self):
+class TestGELUConverter(DispatchTestCase):
+    @parameterized.expand(
+        [
+            ("none",),
+            ("tanh",),
+        ]
+    )
+    def test_gelu(self, approximate):
         class TestModule(nn.Module):
             def forward(self, x):
-                return torch.ops.aten.gelu.default(x)
+                return torch.ops.aten.gelu.default(x, approximate=approximate)
 
         inputs = [torch.randn(1, 10)]
         self.run_test(TestModule(), inputs)
 
-    def test_gelu_with_dynamic_shape(self):
+    @parameterized.expand(
+        [
+            ("none",),
+            ("tanh",),
+        ]
+    )
+    def test_gelu_with_dynamic_shape(self, approximate):
         class TestModule(nn.Module):
             def forward(self, x):
-                return torch.ops.aten.gelu.default(x)
+                return torch.ops.aten.gelu.default(x, approximate=approximate)
 
         input_specs = [
             Input(
-                shape=(-1, -1, -1),
+                min_shape=(1, 1, 1),
+                opt_shape=(1, 2, 3),
+                max_shape=(3, 3, 3),
                 dtype=torch.float32,
-                shape_ranges=[((1, 1, 1), (1, 2, 3), (3, 3, 3))],
             ),
         ]
         self.run_test_with_dynamic_shape(TestModule(), input_specs)
 
-    def test_gelu_with_dynamic_shape_four_dimensions(self):
+    @parameterized.expand(
+        [
+            ("none",),
+            ("tanh",),
+        ]
+    )
+    def test_gelu_with_dynamic_shape_four_dimensions(self, approximate):
         class TestModule(nn.Module):
             def forward(self, x):
-                return torch.ops.aten.gelu.default(x)
+                return torch.ops.aten.gelu.default(x, approximate=approximate)
 
         input_specs = [
             Input(
-                shape=(-1, -1, -1, -1),
+                min_shape=(1, 1, 1, 5),
+                opt_shape=(1, 2, 3, 5),
+                max_shape=(3, 3, 3, 5),
                 dtype=torch.float32,
-                shape_ranges=[((1, 1, 1, 5), (1, 2, 3, 5), (3, 3, 3, 5))],
             ),
         ]
-
         self.run_test_with_dynamic_shape(TestModule(), input_specs)