🐛 [Bug] Issues with partitioning showing that the model should be built e2e but running anyway

[narendasan]

Bug Description

There are issues where networks may be fully supported but for some reason partitioning starts running.

To Reproduce

import torch
from torchvision import models
import torch_tensorrt as torchtrt

classification_arches = [
    models.alexnet,
    models.convnext_base,
    models.densenet121,
    models.efficientnet_b0,
    models.efficientnet_v2_s,
    models.googlenet,
    models.inception_v3,
    models.mnasnet0_5,
    models.mobilenet_v2,
    models.mobilenet_v3_small,
    models.regnet_y_400mf,
    models.resnet18,
    models.resnext50_32x4d,
    models.shufflenet_v2_x0_5,
    models.squeezenet1_0,
    models.swin_t,
    models.vgg11_bn,
    models.vit_b_16,
    models.wide_resnet50_2,
]

failures = []

for arch in []:
    model = arch()
    model = torch.jit.script(model)
    model.eval().cuda()

    try:
        print(f"Running {arch.__name__}")
        with torchtrt.logging.errors():
            mod = torchtrt.ts.compile(
                model,
                inputs=[torchtrt.Input((1, 3, 300, 300))],
                truncate_long_and_double=True,
                torch_executed_ops=[
                    "prim::TupleConstruct",
                ]
            )

            x = torch.randn((1, 3, 300, 300)).cuda()

            mod(x)
    except:
        failures.append(arch.__name__)

print(f"Classification Failures: {failures}")

segmentation_arches = [
    models.segmentation.deeplabv3_mobilenet_v3_large,
    models.segmentation.fcn_resnet50,
    models.segmentation.lraspp_mobilenet_v3_large
]

failures = []

for arch in segmentation_arches:
    model = arch()
    model = torch.jit.script(model)
    model.eval().cuda()

    #try:
    print(f"Running {arch.__name__}")
    with torchtrt.logging.graphs():
        mod = torchtrt.ts.compile(
            model,
            inputs=[torchtrt.Input((1, 3, 300, 300))],
            truncate_long_and_double=True,
            min_block_size=1,
            torch_executed_ops=[
                "aten::_set_item",
            ]
        )

        x = torch.randn((1, 3, 300, 300)).cuda()

        mod(x)
    #except:
        #failures.append(arch.__name__)

print(f"Segmentation Failures: {failures}")

Expected behavior

These models should run end to end.

Environment

Build information about Torch-TensorRT can be found by turning on debug messages

• Torch-TensorRT Version (e.g. 1.0.0): 1.2.0a0+master
• PyTorch Version (e.g. 1.0): 1.12
• CPU Architecture: x86
• OS (e.g., Linux): Linux
• How you installed PyTorch (conda, pip, libtorch, source): pip
• Build command you used (if compiling from source): python3 setup.py develop
• Are you using local sources or building from archives: archives
• Python version: 3.9
• CUDA version: 11.3
• GPU models and configuration: TITAN V
• Any other relevant information:

Additional context

[bowang007]

fixed here #1289