🐛 Describe the bug

Use of functional form of operators in custom modules with keyword args throws off quantization tracing. See minimal repro below.

Error:

RuntimeError: quantized::conv2d_prepack() is missing value for argument 'bias'. Declaration: quantized::conv2d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase

import torch
import torch.nn as nn

from torch.ao.quantization import get_default_qconfig, QConfigMapping
from torch.ao.quantization.quantize_fx import prepare_fx, convert_fx
from torch.nn import functional as F


class myConv2d_1(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size))

    def forward(self, input):
        return F.conv2d(input, self.weight, None, [1, 1], [0, 0], [1, 1], 1)

class myConv2d_2(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size))

    def forward(self, input):
        return F.conv2d(input, self.weight, bias=None, stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1)

# This works
#model = myConv2d_1(8, 64, 3)

# This doesn't work
model = myConv2d_2(8, 64, 3)

model.eval()
qconfig = get_default_qconfig("fbgemm")
qconfig_mapping = QConfigMapping().set_global(qconfig)

example_inputs = torch.randn(1, 8, 224, 224)

prepared_model = prepare_fx(model, qconfig_mapping, example_inputs)
model(example_inputs)
quantized_model = convert_fx(prepared_model)
out_q = quantized_model(example_inputs)
quantized_model.print_readable()

Versions

Collecting environment information...

/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.5.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
Address sizes: 48 bits physical, 48 bits virtual
CPU(s): 64
On-line CPU(s) list: 0-63
Thread(s) per core: 1
Core(s) per socket: 32
Socket(s): 2
NUMA node(s): 8
Vendor ID: AuthenticAMD
CPU family: 25
Model: 1
Model name: AMD EPYC 7513 32-Core Processor
Stepping: 1
Frequency boost: enabled
CPU MHz: 2655.495
CPU max MHz: 2600.0000
CPU min MHz: 1500.0000
BogoMIPS: 5199.82
Virtualization: AMD-V
L1d cache: 2 MiB
L1i cache: 2 MiB
L2 cache: 32 MiB
L3 cache: 256 MiB
NUMA node0 CPU(s): 0-7
NUMA node1 CPU(s): 8-15
NUMA node2 CPU(s): 16-23
NUMA node3 CPU(s): 24-31
NUMA node4 CPU(s): 32-39
NUMA node5 CPU(s): 40-47
NUMA node6 CPU(s): 48-55
NUMA node7 CPU(s): 56-63
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.22.4
[pip3] pytorch-ranger==0.1.1
[pip3] torch==1.13.1
[pip3] torch-optimizer==0.3.0
[pip3] torch-tb-profiler==0.4.1
[pip3] torchmetrics==0.9.3
[pip3] torchtext==0.14.1
[pip3] torchvision==0.14.1
[pip3] vit-pytorch==0.35.8
[conda] No relevant packages

cc @jerryzh168 @jianyuh @raghuramank100 @jamesr66a @vkuzo @jgong5 @Xia-Weiwen @leslie-fang-intel