FM / converters

/usr/local/lib64/python3.6/site-packages/torch/fx/experimental/fx2trt/converters/

/usr/local/lib64/python3.6/site-packages/torch/fx/experimental/fx2trt/converters

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
acc_ops_converters.py	54261	0644	edit dl rm
activation.py	1988	0644	edit dl rm
adaptive_avgpool.py	1097	0644	edit dl rm
add.py	2261	0644	edit dl rm
batchnorm.py	1737	0644	edit dl rm
convolution.py	3368	0644	edit dl rm
helper_functions.py	1826	0644	edit dl rm
linear.py	2784	0644	edit dl rm
maxpool.py	1374	0644	edit dl rm
mul.py	1435	0644	edit dl rm
quantization.py	1999	0644	edit dl rm
transformation.py	1439	0644	edit dl rm
__init__.py	439	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/fx/experimental/fx2trt/converters/add.py (2261B)

import operator
import torch
import tensorrt as trt
from torch.fx.experimental.fx2trt.fx2trt import tensorrt_converter

from .helper_functions import get_dyn_range, mark_as_int8_layer

@tensorrt_converter(operator.add)
@tensorrt_converter(torch.add)
def add(network, target, args, kwargs, layer_name):
    # operator.add
    if len(kwargs) == 0:
        lhs_val, rhs_val = args
    else:
        # torch.add
        lhs_val, rhs_val = kwargs["input"], kwargs["other"]
        assert kwargs["alpha"] == 1

if not all(isinstance(arg, trt.tensorrt.ITensor) for arg in [lhs_val, rhs_val]):
        raise RuntimeError("add() received an input that is not part of the TensorRT region!")

layer = network.add_elementwise(lhs_val, rhs_val, trt.ElementWiseOperation.SUM)
    layer.name = layer_name

return layer.get_output(0)

@tensorrt_converter(torch.ops.quantized.add)
def quantized_add(network, target, args, kwargs, layer_name):
    lhs_val, rhs_val = kwargs["qa"], kwargs["qb"]

if not all(isinstance(i, trt.tensorrt.ITensor) for i in [lhs_val, rhs_val]):
        raise RuntimeError('Quantized add received an input that is not part of the TensorRT region!')

layer = network.add_elementwise(lhs_val, rhs_val, trt.ElementWiseOperation.SUM)
    layer.name = layer_name
    dyn_range = get_dyn_range(kwargs["scale"], kwargs["zero_point"], torch.quint8)
    mark_as_int8_layer(layer, dyn_range)

return layer.get_output(0)

@tensorrt_converter(torch.ops.quantized.add_relu)
def quantized_add_relu(network, submod, args, kwargs, layer_name):
    lhs_val, rhs_val = kwargs["qa"], kwargs["qb"]

if not all(isinstance(i, trt.tensorrt.ITensor) for i in [lhs_val, rhs_val]):
        raise RuntimeError('Quantized add_relu received an input that is not part of the TensorRT region!')

layer = network.add_elementwise(lhs_val, rhs_val, trt.ElementWiseOperation.SUM)
    layer.name = f"{layer_name}_add"
    dyn_range = get_dyn_range(kwargs["scale"], kwargs["zero_point"], torch.quint8)
    mark_as_int8_layer(layer, dyn_range)

layer = network.add_activation(
        input=layer.get_output(0), type=trt.ActivationType.RELU)
    layer.name = f"{layer_name}_relu"
    mark_as_int8_layer(layer, dyn_range)

return layer.get_output(0)

import operator import torch import tensorrt as trt from torch.fx.experimental.fx2trt.fx2trt import tensorrt_converter from .helper_functions import get_dyn_range, mark_as_int8_layer @tensorrt_converter(operator.add) @tensorrt_converter(torch.add) def add(network, target, args, kwargs, layer_name): # operator.add if len(kwargs) == 0: lhs_val, rhs_val = args else: # torch.add lhs_val, rhs_val = kwargs["input"], kwargs["other"] assert kwargs["alpha"] == 1 if not all(isinstance(arg, trt.tensorrt.ITensor) for arg in [lhs_val, rhs_val]): raise RuntimeError("add() received an input that is not part of the TensorRT region!") layer = network.add_elementwise(lhs_val, rhs_val, trt.ElementWiseOperation.SUM) layer.name = layer_name return layer.get_output(0) @tensorrt_converter(torch.ops.quantized.add) def quantized_add(network, target, args, kwargs, layer_name): lhs_val, rhs_val = kwargs["qa"], kwargs["qb"] if not all(isinstance(i, trt.tensorrt.ITensor) for i in [lhs_val, rhs_val]): raise RuntimeError('Quantized add received an input that is not part of the TensorRT region!') layer = network.add_elementwise(lhs_val, rhs_val, trt.ElementWiseOperation.SUM) layer.name = layer_name dyn_range = get_dyn_range(kwargs["scale"], kwargs["zero_point"], torch.quint8) mark_as_int8_layer(layer, dyn_range) return layer.get_output(0) @tensorrt_converter(torch.ops.quantized.add_relu) def quantized_add_relu(network, submod, args, kwargs, layer_name): lhs_val, rhs_val = kwargs["qa"], kwargs["qb"] if not all(isinstance(i, trt.tensorrt.ITensor) for i in [lhs_val, rhs_val]): raise RuntimeError('Quantized add_relu received an input that is not part of the TensorRT region!') layer = network.add_elementwise(lhs_val, rhs_val, trt.ElementWiseOperation.SUM) layer.name = f"{layer_name}_add" dyn_range = get_dyn_range(kwargs["scale"], kwargs["zero_point"], torch.quint8) mark_as_int8_layer(layer, dyn_range) layer = network.add_activation( input=layer.get_output(0), type=trt.ActivationType.RELU) layer.name = f"{layer_name}_relu" mark_as_int8_layer(layer, dyn_range) return layer.get_output(0)