delete gelu

Author: zewenli98

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -152,23 +152,6 @@ def aten_ops_fmod(
     return impl.elementwise.fmod(network, target, SourceIR.ATEN, name, args[0], args[1])
 
 
-@dynamo_tensorrt_converter(torch.ops.aten.gelu.default)  # type: ignore[misc]
-def aten_ops_gelu(
-    network: TRTNetwork,
-    target: Target,
-    args: Tuple[Argument, ...],
-    kwargs: Dict[str, Argument],
-    name: str,
-) -> Union[TRTTensor, Sequence[TRTTensor]]:
-    return impl.activation.gelu(
-        network,
-        target,
-        SourceIR.ATEN,
-        name,
-        args[0],
-    )
-
-
 @dynamo_tensorrt_converter(torch.ops.aten.relu.default)
 def aten_ops_relu(
     network: TRTNetwork,

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

@@ -1,68 +1,15 @@
-import math
-from typing import Any, Optional, Tuple
+from typing import Any, Optional
 
 import numpy as np
 import tensorrt as trt
 import torch
-from torch import Tensor
 from torch.fx.node import Target
 from torch_tensorrt.dynamo._SourceIR import SourceIR
-from torch_tensorrt.fx.converters.converter_utils import (
-    get_trt_plugin,
-    mark_as_int8_layer,
-    set_layer_name,
-)
-from torch_tensorrt.fx.types import TRTNetwork, TRTPluginFieldCollection, TRTTensor
+from torch_tensorrt.fx.types import TRTNetwork, TRTTensor
 
 from .base import convert_activation
 
 
-def gelu(
-    network: TRTNetwork,
-    target: Target,
-    source_ir: Optional[SourceIR],
-    name: str,
-    input_val: TRTTensor,
-    alpha: Optional[Any] = None,
-) -> TRTTensor:
-    approximate = alpha
-    if approximate is not None:
-        raise RuntimeError("GeLU converter currently doesn't support fast gelu compute")
-    if not isinstance(input_val, TRTTensor):
-        raise RuntimeError(
-            f"GELU received input {input_val} that is not part "
-            "of the TensorRT region!"
-        )
-    if network.has_implicit_batch_dimension:
-        raise RuntimeError(
-            "GeLU converter currently doesn't support implicit batch dimension"
-        )
-    plugin_name = "CustomGeluPluginDynamic"
-    # type_id 0 for float32, 1 for  float16
-    type_id = trt.PluginField(
-        "type_id", np.array(0, dtype=np.int32), trt.PluginFieldType.INT32
-    )
-    field_collection = TRTPluginFieldCollection([type_id])
-    plugin_version = "1"
-
-    plugin = get_trt_plugin(plugin_name, field_collection, plugin_version)
-
-    layer = network.add_plugin_v2([input_val], plugin)
-
-    def gelu_dyn_range_fn(
-        dyn_range: Tuple[Tensor, Tensor]
-    ) -> Tuple[Tensor, Tensor]:  # TODO: This probably will not work with fake tensor
-        return (
-            dyn_range[0] * 0.5 * (1.0 + torch.erf(dyn_range[0] / math.sqrt(2.0)))
-        ), (dyn_range[1] * 0.5 * (1.0 + torch.erf(dyn_range[0] / math.sqrt(2.0))))
-
-    if input_val.dynamic_range is not None:
-        dyn_range = gelu_dyn_range_fn(input_val.dynamic_range)
-        mark_as_int8_layer(layer, dyn_range)
-    set_layer_name(layer, target, name)
-    return layer.get_output(0)
-
-
 def relu(
     network: TRTNetwork,
     target: Target,