refactor: Reorging to reduce code duplication and seperating TRT impl…

…ementation, example changes with ReLU

Signed-off-by: Naren Dasan <naren@narendasan.com>

py/torch_tensorrt/fx/converters/acc_ops_converters.py

@@ -26,6 +26,7 @@
     trt_transposed_matmul,
 )
 from torch_tensorrt.fx.tracer.acc_tracer.acc_ops import contiguous
+from torch_tensorrt.fx.converters.impl import activation
 
 _LOGGER: logging.Logger = logging.getLogger(__name__)
 
@@ -1004,9 +1005,14 @@ def acc_ops_relu(
     kwargs: Dict[str, Argument],
     name: str,
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
-    input_val = kwargs["input"]
-    operation_type = trt.ActivationType.RELU
-    return add_activation_layer(network, input_val, operation_type, target, name)
+
+    return activation.relu(
+        network,
+        target,
+        SourceIR.ACC,
+        name,
+        kwargs["input"],
+    )
 
 
 @tensorrt_converter(acc_ops.leaky_relu)
@@ -1020,8 +1026,14 @@ def acc_ops_leaky_relu(
     input_val = kwargs["input"]
     negative_slope = kwargs["negative_slope"]
     operation_type = trt.ActivationType.LEAKY_RELU
-    return add_activation_layer(
-        network, input_val, operation_type, target, name, negative_slope
+    return activation.convert_activation(
+        network,
+        target,
+        SourceIR.ACC,
+        name,
+        operation_type,
+        input_val,
+        alpha=negative_slope,
     )
 
 
@@ -1036,7 +1048,9 @@ def acc_ops_elu(
     input_val = kwargs["input"]
     alpha = kwargs["alpha"]
     operation_type = trt.ActivationType.ELU
-    return add_activation_layer(network, input_val, operation_type, target, name, alpha)
+    return activation.convert_activation(
+        network, target, SourceIR.ACC, name, operation_type, input_val, alpha=alpha
+    )
 
 
 @tensorrt_converter(acc_ops.selu)
@@ -1049,7 +1063,14 @@ def acc_ops_selu(
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
     input_val = kwargs["input"]
     operation_type = trt.ActivationType.SELU
-    return add_activation_layer(network, input_val, operation_type, target, name)
+    return activation.convert_activation(
+        network,
+        target,
+        SourceIR.ACC,
+        name,
+        operation_type,
+        input_val,
+    )
 
 
 @tensorrt_converter(acc_ops.softsign)
@@ -1062,7 +1083,14 @@ def acc_ops_softsign(
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
     input_val = kwargs["input"]
     operation_type = trt.ActivationType.SOFTSIGN
-    return add_activation_layer(network, input_val, operation_type, target, name)
+    return activation.convert_activation(
+        network,
+        target,
+        SourceIR.ACC,
+        name,
+        operation_type,
+        input_val,
+    )
 
 
 @tensorrt_converter(acc_ops.sin)
@@ -1140,7 +1168,14 @@ def acc_ops_tanh(
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
     input_val = kwargs["input"]
     operation_type = trt.ActivationType.TANH
-    return add_activation_layer(network, input_val, operation_type, target, name)
+    return activation.convert_activation(
+        network,
+        target,
+        SourceIR.ACC,
+        name,
+        operation_type,
+        input_val,
+    )
 
 
 @tensorrt_converter(acc_ops.asin)
@@ -3137,12 +3172,13 @@ def acc_ops_hard_sigmoid(
             "of the TensorRT region!"
         )
 
-    return add_activation_layer(
+    return activation.convert_activation(
         network,
-        input_val,
-        trt.ActivationType.HARD_SIGMOID,
         target,
+        SourceIR.ACC,
         name,
+        trt.ActivationType.HARD_SIGMOID,
+        input_val,
         alpha=1 / 6,
         beta=0.5,
     )
@@ -3164,8 +3200,13 @@ def acc_ops_sigmoid(
             "of the TensorRT region!"
         )
 
-    return add_activation_layer(
-        network, input_val, trt.ActivationType.SIGMOID, target, name
+    return activation.convert_activation(
+        network,
+        target,
+        SourceIR.ACC,
+        name,
+        trt.ActivationType.SIGMOID,
+        input_val,
     )
 
 
@@ -3557,12 +3598,13 @@ def acc_ops_hardtanh(
             "of the TensorRT region!"
         )
 
-    return add_activation_layer(
+    return activation.convert_activation(
         network,
-        input_val,
-        trt.ActivationType.CLIP,
         target,
+        SourceIR.ACC,
         name,
+        trt.ActivationType.CLIP,
+        input_val,
         alpha=kwargs["min_val"],
         beta=kwargs["max_val"],
     )

py/torch_tensorrt/fx/converters/activation.py

@@ -9,45 +9,6 @@
 from .converter_utils import mark_as_int8_layer
 
 
-def common_activation(
-    network, mod, input_val, activation_type, activation_dyn_range_fn, layer_name
-):
-    layer = network.add_activation(input=input_val, type=activation_type)
-    layer.name = layer_name
-
-    if input_val.dynamic_range:
-        dyn_range = activation_dyn_range_fn(input_val.dynamic_range)
-        mark_as_int8_layer(layer, dyn_range)
-
-    return layer.get_output(0)
-
-
-@tensorrt_converter(torch.nn.functional.relu)
-@tensorrt_converter(torch.nn.modules.activation.ReLU)
-def relu(network, submod, args, kwargs, layer_name):
-    # args/kwargs should have already been normalized to kwargs
-    assert len(args) == 0
-    input_val = kwargs["input"]
-
-    if not isinstance(input_val, trt.tensorrt.ITensor):
-        raise RuntimeError(
-            f"ReLU received input {input_val} that is not part "
-            "of the TensorRT region!"
-        )
-
-    def activation_dyn_range_fn(dyn_range):
-        return max(0, dyn_range[0]), max(0, dyn_range[1])
-
-    return common_activation(
-        network,
-        submod,
-        input_val,
-        trt.ActivationType.RELU,
-        activation_dyn_range_fn,
-        layer_name,
-    )
-
-
 @tensorrt_converter(torch.nn.modules.activation.Sigmoid)
 def sigmoid(network, submod, args, kwargs, layer_name):
     # args/kwargs should have already been normalized to kwargs

py/torch_tensorrt/fx/converters/aten_ops_converters.py

@@ -22,6 +22,7 @@
 
 from .converter_utils import *  # noqa: F403
 import torch_tensorrt.fx.tracer.acc_tracer.acc_utils as acc_utils
+from torch_tensorrt.fx.converters.impl import activation
 
 _LOGGER: logging.Logger = logging.getLogger(__name__)
 
@@ -290,10 +291,14 @@ def aten_ops_relu(
     kwargs: Dict[str, Argument],
     name: str,
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
-    kwargs_new = {
-        "input": args[0],
-    }
-    return acc_ops_converters.acc_ops_relu(network, target, None, kwargs_new, name)
+
+    return activation.relu(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+    )
 
 
 @tensorrt_converter(torch.ops.aten.sub.Tensor)

py/torch_tensorrt/fx/converters/converter_utils.py

@@ -2,6 +2,7 @@
 import warnings
 from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
 
+from enum import Enum, auto
 import numpy as np
 
 # @manual=//deeplearning/trt/python:py_tensorrt
@@ -22,6 +23,26 @@
 from ..utils import torch_dtype_from_trt
 
 
+class SourceIR(Enum):
+    NN = auto()
+    ACC = auto()
+    ATEN = auto()
+    PRIM = auto()
+    UNKNOWN = auto()
+
+    def __str__(self):
+        if self == SourceIR.NN:
+            return "nn"
+        elif self == SourceIR.ACC:
+            return "acc"
+        elif self == SourceIR.ATEN:
+            return "aten"
+        elif self == SourceIR.PRIM:
+            return "prim"
+        else:
+            return "unknown_ir"
+
+
 def get_trt_plugin(
     plugin_name: str,
     field_collection: List[TRTPluginFieldCollection],
@@ -77,7 +98,9 @@ def get_positive_dim(dim: int, dim_size: int) -> int:
     return dim
 
 
-def set_layer_name(layer: TRTLayer, target: Target, name: str) -> None:
+def set_layer_name(
+    layer: TRTLayer, target: Target, name: str, source_ir: Optional[SourceIR] = None
+) -> None:
     """
     Set the TensorRT layer name to "[TensorRT Layer Type]_[Original Op Name]_[FX Node Name with Suffix]"
 
@@ -86,8 +109,16 @@ def set_layer_name(layer: TRTLayer, target: Target, name: str) -> None:
         target (Target): A fx node.target. For call_function node, it's the function that
             the node represents.
         name (str): Consists of fx node.name with optional suffix.
+        source_ir: (Optional[SourceIR]): The IR producing the op.
     """
-    target_name = target if isinstance(target, str) else f"acc_ops.{target.__name__}"
+
+    source_ir = source_ir if source_ir is not None else SourceIR.UNKNOWN
+
+    target_name = (
+        f"{source_ir}_ops.{target}"
+        if isinstance(target, str)
+        else f"{source_ir}_ops.{target.__name__}"
+    )
     layer.name = f"[{layer.type.name}]-[{target_name}]-[{name}]"
 
 
@@ -560,48 +591,6 @@ def add_unary_layer(
     return layer.get_output(0)
 
 
-def add_activation_layer(
-    network: TRTNetwork,
-    input_val: TRTTensor,
-    operation_type: trt.ActivationType,
-    target: Target,
-    name: str,
-    alpha: Optional[Any] = None,
-    beta: Optional[Any] = None,
-) -> TRTTensor:
-    """
-    Add a TensorRT Activation layer to `network`.
-
-    Args:
-        network (TRTNetwork): TensorRT network object.
-        input_val (TRTTensor): Input to the activation op.
-            Must be a TensorRT tensor.
-        op_type (trt.ElementWiseOperation): Type of the TensorRT activation
-            operation.
-        target (Target): Target of fx node.
-        name (str): The name we want to assign to the created TensorRT layer.
-        alpha (Optional[Any]): If not None, we will use it to set the alpha
-            attribute of the created TensorRT activation layer.
-        beta (Optional[Any]): If not None, we will use it to set the beta
-            attribute of the created TensorRT activation layer.
-
-    Returns:
-        The output of TensorRT Activation layer.
-    """
-    if not isinstance(input_val, TRTTensor):
-        raise RuntimeError(
-            f"{operation_type} received input {input_val} that is not part "
-            "of the TensorRT region!"
-        )
-    layer = network.add_activation(input_val, operation_type)
-    if alpha is not None:
-        layer.alpha = alpha
-    if beta is not None:
-        layer.beta = beta
-    set_layer_name(layer, target, name)
-    return layer.get_output(0)
-
-
 def add_reduce_layer(
     network: TRTNetwork,
     target: Target,