Is your feature request related to a problem? Please describe.
Tried nvidia-ammo 0.5 release.
Tried to torch.compile/torch.export the model with QAT nodes but faced the following error
assert isinstance(
AssertionError: expected FunctionType found method <bound method QuantLinearConvBase.quantized_forward of <class 'ammo.torch.quantization.nn.modules.quant_conv.QuantConv2d'>>
To reproduce
import torch
import ammo.torch.quantization as atq
from vgg16 import vgg16
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch_tensorrt
# Select quantization config
config = atq.INT8_DEFAULT_CFG
model = vgg16(num_classes=10, init_weights=False).eval().cuda()
calib_set = datasets.CIFAR10(
root="./data",
train=False,
download=True,
transform=transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
]
),
)
# Define forward loop for calibration
def forward_loop():
for data in calib_set:
model(data[0].unsqueeze(0).cuda())
# QAT with in-place replacement to quantized modules
atq.quantize(model, config, forward_loop)
x = torch.randn((1, 3, 32, 32), dtype=torch.float32).cuda()
# exp_program = torch.export.export(model, (x,))
compile_spec = {"inputs": [x,],
"debug": True,
"min_block_size": 1}
# Torch compile path
trt_model = torch.compile(model, backend="tensorrt", dynamic=None, options=compile_spec)
Is your feature request related to a problem? Please describe. Tried nvidia-ammo 0.5 release. Tried to torch.compile/torch.export the model with QAT nodes but faced the following error
To reproduce
Describe alternatives you've considered
Additional context