Repository: pytorch
Issue: Grabbing dtype from NamedTuple vs Tuple for cast causes graph breaks "Unexpected type in sourceless builder."
Labels: triaged,oncall: pt2,module: dynamo,module: graph breaks

### 🐛 Describe the bug

It took me a while to make the reproduction for this one, since I really have no idea what is going on. It seems, that using a NamedTuple instead of a Tuple and casting to a dtype grabbed from that NamedTuple causes a graph break.

The interesting part is it seems to graph break on the part where tensor.custom_dtype is accessed, rather than when it is passed to the CustomTensor constructor. So if tensor.custom_dtype was put on a separate line in create_custom_tensor, it would error on that line. 

reproducer (2 graph breaks):
```python
import torch
from collections import namedtuple

CustomDtype = namedtuple("CustomDtype", ["dtype", "higher_dtype"])

class CustomTensor(torch.Tensor):

    _data: torch.Tensor
    custom_dtype: CustomDtype
    __torch_function__ = torch._C._disabled_torch_function_impl
    __slots__ = [
        "_data",
        "custom_dtype",
    ]

    def __new__(
        cls,
        data: torch.Tensor,
        custom_dtype: CustomDtype,
    ):
        self = torch.Tensor._make_wrapper_subclass(
            cls,
            data.size(),
            strides=data.stride(),
            storage_offset=data.storage_offset(),
            dtype=custom_dtype.dtype,
            layout=data.layout,
            requires_grad=data.requires_grad,
            device=data.device,
        )
        self._data = data
        self.custom_dtype = custom_dtype
        return self

    def __tensor_flatten__(self):
        meta = {
            "custom_dtype": self.custom_dtype,
        }
        return ["_data"], meta

    @staticmethod
    def __tensor_unflatten__(inner_tensors: dict, metadata, outer_size, outer_stride):
        return CustomTensor(
            inner_tensors["_data"],
            metadata["custom_dtype"],
        )

    @classmethod
    def __torch_dispatch__(cls, func, types, args=(), kwargs={}):
        return func(*args, **kwargs)

def maybe_cast_up(tensor):
    return CustomTensor(tensor._data.to(tensor.custom_dtype.higher_dtype), tensor.custom_dtype)

def maybe_cast_down(tensor):
    return CustomTensor(tensor._data.to(tensor.custom_dtype.dtype), tensor.custom_dtype)

def create_custom_tensor(tensor):
    return CustomTensor(tensor._data, tensor.custom_dtype)

@torch.compile
def create_custom_tensor_cast(tensor):
    tensor = maybe_cast_up(tensor)
    ret = create_custom_tensor(tensor)
    return maybe_cast_down(ret)

print(torch._dynamo.explain(create_custom_tensor_cast)(CustomTensor(torch.randn(1000, dtype=torch.float16), CustomDtype(torch.float16, torch.float32))))
```

working version (0 graph breaks):
```python
import torch

class CustomTensor(torch.Tensor):

    _data: torch.Tensor
    custom_dtype: tuple
    __torch_function__ = torch._C._disabled_torch_function_impl
    __slots__ = [
        "_data",
        "custom_dtype",
    ]

    def __new__(
        cls,
        data: torch.Tensor,
        custom_dtype: tuple,
    ):
        self = torch.Tensor._make_wrapper_subclass(
            cls,
            data.size(),
            strides=data.stride(),
            storage_offset=data.storage_offset(),
            dtype=custom_dtype[0],
            layout=data.layout,
            requires_grad=data.requires_grad,
            device=data.device,
        )
        self._data = data
        self.custom_dtype = custom_dtype
        return self

    def __tensor_flatten__(self):
        meta = {
            "custom_dtype": self.custom_dtype,
        }
        return ["_data"], meta

    @staticmethod
    def __tensor_unflatten__(inner_tensors: dict, metadata, outer_size, outer_stride):
        return CustomTensor(
            inner_tensors["_data"],
            metadata["custom_dtype"],
        )

    @classmethod
    def __torch_dispatch__(cls, func, types, args=(), kwargs={}):
        return func(*args, **kwargs)

def maybe_cast_up(tensor):
    return CustomTensor(tensor._data.to(tensor.custom_dtype[1]), tensor.custom_dtype)

def maybe_cast_down(tensor):
    return CustomTensor(tensor._data.to(tensor.custom_dtype[0]), tensor.custom_dtype)

def create_custom_tensor(tensor):
    return CustomTensor(tensor._data, tensor.custom_dtype)

@torch.compile
def create_custom_tensor_cast(tensor):
    tensor = maybe_cast_up(tensor)
    ret = create_custom_tensor(tensor)
    return maybe_cast_down(ret)

print(torch._dynamo.explain(create_custom_tensor_cast)(CustomTensor(torch.randn(1000, dtype=torch.float16), (torch.float16, torch.float32))))
```

tlparse log:
[dedicated_log_torch_trace_9b3b9v03.log](https://github.com/user-attachments/files/17790752/dedicated_log_torch_trace_9b3b9v03.log)


### Error logs

_No response_

### Versions

Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Arch Linux (x86_64)
GCC version: (GCC) 14.2.1 20240910
Clang version: 18.1.8
CMake version: version 3.30.3
Libc version: glibc-2.40

Python version: 3.11.10 (main, Sep  9 2024, 22:11:19) [Clang 18.1.8 ] (64-bit runtime)
Python platform: Linux-6.6.43-273-tkg-bore-x86_64-with-glibc2.40
Is CUDA available: True
CUDA runtime version: 12.6.68
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA RTX 6000 Ada Generation
GPU 1: NVIDIA RTX 6000 Ada Generation

Nvidia driver version: 560.35.03
cuDNN version: Probably one of the following:
/usr/lib/libcudnn.so.9.2.1
/usr/lib/libcudnn_adv.so.9.2.1
/usr/lib/libcudnn_cnn.so.9.2.1
/usr/lib/libcudnn_engines_precompiled.so.9.2.1
/usr/lib/libcudnn_engines_runtime_compiled.so.9.2.1
/usr/lib/libcudnn_graph.so.9.2.1
/usr/lib/libcudnn_heuristic.so.9.2.1
/usr/lib/libcudnn_ops.so.9.2.1
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
Address sizes:                        46 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               24
On-line CPU(s) list:                  0-23
Vendor ID:                            GenuineIntel
Model name:                           12th Gen Intel(R) Core(TM) i9-12900K
CPU family:                           6
Model:                                151
Thread(s) per core:                   2
Core(s) per socket:                   16
Socket(s):                            1
Stepping:                             2
CPU(s) scaling MHz:                   21%
CPU max MHz:                          5300.0000
CPU min MHz:                          800.0000
BogoMIPS:                             6374.40
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect user_shstk avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities
Virtualization:                       VT-x
L1d cache:                            640 KiB (16 instances)
L1i cache:                            768 KiB (16 instances)
L2 cache:                             14 MiB (10 instances)
L3 cache:                             30 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-23
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Not affected
Vulnerability Reg file data sampling: Mitigation; Clear Register File
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] numpy==2.1.3
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-cusparselt-cu12==0.6.2
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] pytorch-triton==3.1.0+cf34004b8a
[pip3] torch==2.5.1
[pip3] torch-xla==2.5.0
[pip3] torchaudio==2.5.1
[pip3] torchvision==0.20.1
[pip3] triton==3.1.0
[conda] No relevant packages


cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @amjames @ezyang