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Prototype reorg of the FX converters to clean dependency chain #1683

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Prototype reorg of the FX converters to clean dependency chain #1683

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110 changes: 11 additions & 99 deletions py/torch_tensorrt/fx/converters/acc_ops_converters.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,7 @@
trt_transposed_matmul,
)
from torch_tensorrt.fx.tracer.acc_tracer.acc_ops import contiguous
import activation

_LOGGER: logging.Logger = logging.getLogger(__name__)

Expand Down Expand Up @@ -1004,9 +1005,7 @@ 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.add_relu(network, target, kwargs, name)


@tensorrt_converter(acc_ops.leaky_relu)
Expand All @@ -1017,12 +1016,7 @@ def acc_ops_leaky_relu(
kwargs: Dict[str, Argument],
name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
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.add_leaky_relu(network, target, kwargs, name)


@tensorrt_converter(acc_ops.elu)
Expand All @@ -1033,11 +1027,7 @@ def acc_ops_elu(
kwargs: Dict[str, Argument],
name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
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.add_elu(network, target, kwargs, name)

@tensorrt_converter(acc_ops.selu)
def acc_ops_selu(
Expand All @@ -1047,9 +1037,7 @@ def acc_ops_selu(
kwargs: Dict[str, Argument],
name: str,
) -> 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.add_selu(network, target, kwargs, name)


@tensorrt_converter(acc_ops.softsign)
Expand All @@ -1060,10 +1048,7 @@ def acc_ops_softsign(
kwargs: Dict[str, Argument],
name: str,
) -> 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.add_softsign(network, target, kwargs, name)

@tensorrt_converter(acc_ops.sin)
def acc_ops_sin(
Expand Down Expand Up @@ -1138,10 +1123,7 @@ def acc_ops_tanh(
kwargs: Dict[str, Argument],
name: str,
) -> 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.add_tanh(network, target, kwargs, name)

@tensorrt_converter(acc_ops.asin)
def acc_ops_asin(
Expand Down Expand Up @@ -3129,23 +3111,7 @@ def acc_ops_hard_sigmoid(
kwargs: Dict[str, Argument],
name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
input_val = kwargs["input"]

if not isinstance(input_val, TRTTensor):
raise RuntimeError(
f"Hard sigmoid received input {input_val} that is not part "
"of the TensorRT region!"
)

return add_activation_layer(
network,
input_val,
trt.ActivationType.HARD_SIGMOID,
target,
name,
alpha=1 / 6,
beta=0.5,
)
return activation.add_hard_sigmoid(network, target, kwargs, name)


@tensorrt_converter(acc_ops.sigmoid)
Expand All @@ -3156,17 +3122,7 @@ def acc_ops_sigmoid(
kwargs: Dict[str, Argument],
name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
input_val = kwargs["input"]

if not isinstance(input_val, TRTTensor):
raise RuntimeError(
f"Sigmoid received input {input_val} that is not part "
"of the TensorRT region!"
)

return add_activation_layer(
network, input_val, trt.ActivationType.SIGMOID, target, name
)
return activation.add_sigmoid(network, target, kwargs, name)


@tensorrt_converter(acc_ops.permute)
Expand Down Expand Up @@ -3367,34 +3323,7 @@ def acc_ops_gelu(
kwargs: Dict[str, Argument],
name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
input_val = kwargs["input"]
approximate = kwargs["approximate"]
if approximate != "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)
set_layer_name(layer, target, name)
return layer.get_output(0)

return activation.add_gelu(network, target, kwargs, name)

@tensorrt_converter(acc_ops.chunk)
def acc_ops_chunk(
Expand Down Expand Up @@ -3549,24 +3478,7 @@ def acc_ops_hardtanh(
kwargs: Dict[str, Argument],
name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
input_val = kwargs["input"]

if not isinstance(input_val, TRTTensor):
raise RuntimeError(
f"hardtanh received input {input_val} that is not part "
"of the TensorRT region!"
)

return add_activation_layer(
network,
input_val,
trt.ActivationType.CLIP,
target,
name,
alpha=kwargs["min_val"],
beta=kwargs["max_val"],
)

return activation.add_hardtanh(network, target, kwargs, name)

@tensorrt_converter(acc_ops.interpolate)
def acc_ops_interpolate(
Expand Down
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