RuntimeError: CUDA error: named symbol not found

[mikekgfb]

🐛 Describe the bug

python torchchat.py generate stories110M --quant torchchat/quant_config/cuda.json --prompt "It was a dark and stormy night, and"

Using device=cuda Tesla T4
Loading model...
Time to load model: 0.73 seconds
Quantizing the model with: {'executor': {'accelerator': 'cuda'}, 'precision': {'dtype': 'bf16'}, 'linear:int4': {'groupsize': 256}}
Time to quantize model: 0.35 seconds
Traceback (most recent call last):
File "/content/torchchat-1/torchchat.py", line 88, in
generate_main(args)
File "/content/torchchat-1/torchchat/generate.py", line 1210, in main
gen = Generator(
File "/content/torchchat-1/torchchat/generate.py", line 290, in init
self.model = initialize_model(self.builder_args, self.quantize, self.tokenizer)
File "/content/torchchat-1/torchchat/cli/builder.py", line 574, in initialize_model
quantize_model(
File "/content/torchchat-1/torchchat/utils/quantize.py", line 114, in quantize_model
quantize(model, int4_weight_only(q_kwargs["groupsize"]))
File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/quant_api.py", line 462, in quantize
_replace_with_custom_fn_if_matches_filter(
File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/quant_api.py", line 202, in _replace_with_custom_fn_if_matches_filter
new_child = _replace_with_custom_fn_if_matches_filter(
File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/quant_api.py", line 202, in _replace_with_custom_fn_if_matches_filter
new_child = _replace_with_custom_fn_if_matches_filter(
File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/quant_api.py", line 202, in _replace_with_custom_fn_if_matches_filter
new_child = _replace_with_custom_fn_if_matches_filter(
[Previous line repeated 2 more times]
File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/quant_api.py", line 198, in _replace_with_custom_fn_if_matches_filter
model = replacement_fn(model)
File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/quant_api.py", line 392, in insert_subclass
lin.weight = torch.nn.Parameter(constructor(lin.weight, **kwargs), requires_grad=requires_grad)
File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/quant_api.py", line 553, in apply_int4_weight_only_quant
return to_affine_quantized_intx(weight, mapping_type, block_size, target_dtype, quant_min, quant_max, eps, zero_point_dtype=zero_point_dtype, preserve_zero=preserve_zero, zero_point_domain=zero_point_domain, layout_type=layout_type, use_hqq=use_hqq)
File "/usr/local/lib/python3.10/dist-packages/torchao/dtypes/affine_quantized_tensor.py", line 286, in from_hp_to_intx
layout_tensor = layout_tensor_ctr(data, scale, zero_point, layout_type)
File "/usr/local/lib/python3.10/dist-packages/torchao/dtypes/affine_quantized_tensor.py", line 1033, in from_plain
scale_and_zero = pack_tinygemm_scales_and_zeros(scale, zero_point)
File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/utils.py", line 322, in pack_tinygemm_scales_and_zeros
torch.cat(
RuntimeError: CUDA error: named symbol not found
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.

Versions

/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6
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): 2
On-line CPU(s) list: 0,1
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU @ 2.20GHz
CPU family: 6
Model: 79
Thread(s) per core: 2
Core(s) per socket: 1
Socket(s): 1
Stepping: 0
BogoMIPS: 4399.99
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 32 KiB (1 instance)
L1i cache: 32 KiB (1 instance)
L2 cache: 256 KiB (1 instance)
L3 cache: 55 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0,1
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Vulnerable; SMT Host state unknown
Vulnerability Meltdown: Vulnerable
Vulnerability Mmio stale data: Vulnerable
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Vulnerable
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2: Vulnerable; IBPB: disabled; STIBP: disabled; PBRSB-eIBRS: Not affected; BHI: Vulnerable (Syscall hardening enabled)
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Vulnerable

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] optree==0.13.0
[pip3] pytorch-triton==3.1.0+cf34004b8a
[pip3] torch==2.6.0.dev20241002+cu121
[pip3] torchao==0.5.0
[pip3] torchaudio==2.4.1+cu121
[pip3] torchsummary==1.5.1
[pip3] torchtune==0.3.0.dev20240928+cu121
[pip3] torchvision==0.20.0.dev20241002+cu121
[conda] Could not collect

[desertfire]

The stack dump suggests this is a torchao issue. For RuntimeError: CUDA error: named symbol not found, does it print out what exact symbol is missing? Can you share your installed CUDA version?

[mikekgfb]

@desertfire:

The stack dump suggests this is a torchao issue. For RuntimeError: CUDA error: named symbol not found, does it print out what exact symbol is missing? Can you share your installed CUDA version?

NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2

This ran on Google colab. Detailed trace-back/repro: https://colab.research.google.com/drive/1PRneJBaS5TlJaIgc4Lwv2muiePp6T9Ss?usp=sharing

$nvidia-smi
Thu Oct 17 19:52:30 2024       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |
| N/A   39C    P8               9W /  70W |      0MiB / 15360MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
                                                                                         
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|  No running processes found                                                           |
+---------------------------------------------------------------------------------------+

$ python torchchat.py generate stories110M --quant torchchat/quant_config/cuda.json --prompt "It was a dark and stormy night, and"
2024-10-17 19:52:38.809413: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-10-17 19:52:39.031641: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-10-17 19:52:39.096690: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-10-17 19:52:39.466437: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-10-17 19:52:41.795530: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Downloading builder script: 100% 5.67k/5.67k [00:00<00:00, 18.9MB/s]
Downloading https://huggingface.co/karpathy/tinyllamas/resolve/main/stories110M.pt...
Downloading https://github.com/karpathy/llama2.c/raw/master/tokenizer.model...
Moving model to /root/.torchchat/model-cache/stories110M.
Using device=cuda Tesla T4
Loading model...
Time to load model: 0.40 seconds
Quantizing the model with: {'executor': {'accelerator': 'cuda'}, 'precision': {'dtype': 'bf16'}, 'linear:int4': {'groupsize': 256}}
Time to quantize model: 0.13 seconds
Traceback (most recent call last):
  File "/content/torchchat-1/torchchat.py", line 88, in <module>
    generate_main(args)
  File "/content/torchchat-1/torchchat/generate.py", line 1215, in main
    gen = Generator(
  File "/content/torchchat-1/torchchat/generate.py", line 290, in __init__
    self.model = _initialize_model(self.builder_args, self.quantize, self.tokenizer)
  File "/content/torchchat-1/torchchat/cli/builder.py", line 574, in _initialize_model
    quantize_model(
  File "/content/torchchat-1/torchchat/utils/quantize.py", line 114, in quantize_model
    quantize_(model, int4_weight_only(q_kwargs["groupsize"]))
  File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/quant_api.py", line 462, in quantize_
    _replace_with_custom_fn_if_matches_filter(
  File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/quant_api.py", line 202, in _replace_with_custom_fn_if_matches_filter
    new_child = _replace_with_custom_fn_if_matches_filter(
  File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/quant_api.py", line 202, in _replace_with_custom_fn_if_matches_filter
    new_child = _replace_with_custom_fn_if_matches_filter(
  File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/quant_api.py", line 202, in _replace_with_custom_fn_if_matches_filter
    new_child = _replace_with_custom_fn_if_matches_filter(
  [Previous line repeated 2 more times]
  File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/quant_api.py", line 198, in _replace_with_custom_fn_if_matches_filter
    model = replacement_fn(model)
  File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/quant_api.py", line 392, in insert_subclass
    lin.weight = torch.nn.Parameter(constructor(lin.weight, **kwargs), requires_grad=requires_grad)
  File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/quant_api.py", line 553, in apply_int4_weight_only_quant
    return to_affine_quantized_intx(weight, mapping_type, block_size, target_dtype, quant_min, quant_max, eps, zero_point_dtype=zero_point_dtype, preserve_zero=preserve_zero, zero_point_domain=zero_point_domain, layout_type=layout_type, use_hqq=use_hqq)
  File "/usr/local/lib/python3.10/dist-packages/torchao/dtypes/affine_quantized_tensor.py", line 286, in from_hp_to_intx
    layout_tensor = layout_tensor_ctr(data, scale, zero_point, layout_type)
  File "/usr/local/lib/python3.10/dist-packages/torchao/dtypes/affine_quantized_tensor.py", line 1033, in from_plain
    scale_and_zero = pack_tinygemm_scales_and_zeros(scale, zero_point)
  File "/usr/local/lib/python3.10/dist-packages/torchao/quantization/utils.py", line 322, in pack_tinygemm_scales_and_zeros
    torch.cat(
RuntimeError: CUDA error: named symbol not found
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

[mikekgfb]

The stack dump suggests this is a torchao issue. For RuntimeError: CUDA error: named symbol not found, does it print out what exact symbol is missing? Can you share your installed CUDA version?

It's a torchao issue (see also: pytorch/ao#1110), or more generally a question of expectations whether we should expect BF16 to work on platforms that don't have HW support (see pytorch/issues/124996). On pre-9.0 architecture, PT emulates BF16 (at least for some/most of the kernels), but AO does not for their linear:int4 kernel.