RuntimeError with Mixed Precision during LoRA Fine-Tuning in LLAVA on Small GPU Machine
Hi everyone,I’m facing an issue while fine-tuning the LLAVA model using LoRA on a machine with limited GPU resources. To accommodate the small GPU, I’ve been experimenting with 4-bit precision. However, I consistently encounter the following error:RuntimeError: expected scalar type BFloat16 but found FloatThis occurs specifically in the vision model, particularly during the LayerNorm operation in the forward pass.Key Configuration:Model:liuhaotian/llava-v1.6-vicuna-7bVision Tower:openai/clip-vit-large-patch14-336LoRA:Enabled withlora_r=128,lora_alpha=256Precision:4-bit (bits=4)Other Settings:bf16=True,gradient_checkpointing=TrueProblem:I’m running into a data type mismatch where some layers (e.g., LayerNorm) expect BFloat16, but are instead using Float32, which triggers the error. When I inspect the model, I find a mix of data types across the layers:166 layers in float32744 layers in bfloat16369 layers in uint8My Situation:I’m trying to modify LLAVA for my own use case and need to run it in a “debug mode” to test and tweak the code. Since I have limited GPU resources, I’m using low precision (4-bit) to make debugging feasible. However, this data type mismatch is proving to be a roadblock.My Questions:How can I debug or fine-tune LLAVA with LoRA on a small GPU without running into these precision-related errors?Should I be manually converting specific layers to avoid the mismatch between bfloat16 and float32?Is there a general approach to running LoRA fine-tuning in a lightweight “debug mode” for code experimentation without worrying about outputs or precision mismatches?Any guidance or suggestions would be greatly appreciated!Thanks in advance!
2024-09-23T16:11:43Z
It seems like torch’s autocast is doing something bad or CUDA version mismatch is the most common cause.github.com/pytorch/pytorch`RuntimeError: expected scalar type BFloat16 but found Float` with `torch.nn.TransformerEncoder`opened05:46AM - 22 Aug 23 UTCjingxu10module: nntriagedoncall: transformer/mhamodule: amp (automated mixed precision)### 🐛 Describe the bug

Runtime error occurred when running `torch.nn.Transfor…merEncoder` in AMP scope. This issue occurs for both when `enable_nested_tensor` is `True` and `False`. 

```python
import torch
encoder_layer = torch.nn.TransformerEncoderLayer(d_model=512, nhead=8, batch_first = True)
model = torch.nn.TransformerEncoder(encoder_layer, num_layers=6, enable_nested_tensor=True)
model.eval()

src_rand = torch.rand(16, 41, 512)
mask_rand =  torch.zeros(16, 41)

with torch.no_grad(), torch.autocast(device_type="cpu", dtype=torch.bfloat16):
    out = model(src_rand, src_key_padding_mask = mask_rand)
```

Error message:
```
Traceback (most recent call last):
  File "/workspace/test/test1.py", line 13, in <module>
    out = model(torch.FloatTensor(src), src_key_padding_mask = mask, is_causal = True)
  File "/root/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/lib/python3.10/site-packages/torch/nn/modules/transformer.py", line 387, in forward
    output = mod(output, src_mask=mask, is_causal=is_causal, src_key_padding_mask=src_key_padding_mask_for_layers)
  File "/root/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/lib/python3.10/site-packages/torch/nn/modules/transformer.py", line 678, in forward
    return torch._transformer_encoder_layer_fwd(
RuntimeError: expected scalar type BFloat16 but found Float
```

### Versions

```
Collecting environment information...                                                                                                                                                       PyTorch version: 2.1.0.dev20230820+cpu                                                                                                                                                      Is debug build: False                                                                                                                                                                       CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: Could not collect
CMake version: version 3.27.2
Libc version: glibc-2.35

Python version: 3.10.9 (main, Jan 11 2023, 15:21:40) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-48-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA                                                                                                                                                               CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA                                                                                                                                                       Nvidia driver version: No CUDA                                                                                                                                                              cuDNN version: No CUDA
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:                   52 bits physical, 57 bits virtual
Byte Order:                      Little Endian
CPU(s):                          64
On-line CPU(s) list:             0-63
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Gold 6346 CPU @ 3.10GHz
CPU family:                      6
Model:                           106
Thread(s) per core:              2
Core(s) per socket:              16
Socket(s):                       2
Stepping:                        6
CPU max MHz:                     3600.0000
CPU min MHz:                     800.0000
BogoMIPS:                        6200.00
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 pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand laREDACTED_TOKEN abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       1.5 MiB (32 instances)
L1i cache:                       1 MiB (32 instances)
L2 cache:                        40 MiB (32 instances)
L3 cache:                        72 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-15,32-47
NUMA node1 CPU(s):               16-31,48-63
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Vulnerable: Clear CPU buffers attempted, no microcode; SMT vulnerable
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; Enhanced IBRS, IBPB conditional, RSB filling
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.25.2
[pip3] torch==2.1.0.dev20230820+cpu
[pip3] torchaudio==2.1.0.dev20230821+cpu
[pip3] torchvision==0.16.0.dev20230821+cpu
[conda] mkl-include               2023.2.0                 pypi_0    pypi
[conda] mkl-static                2023.2.0                 pypi_0    pypi
[conda] numpy                     1.25.2                   pypi_0    pypi
[conda] torch                     2.1.0.dev20230820+cpu          pypi_0    pypi
[conda] torchaudio                2.1.0.dev20230821+cpu          pypi_0    pypi
[conda] torchvision               0.16.0.dev20230821+cpu          pypi_0    pypi
```

cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki @bhosmer @cpuhrsch @erichan1 @drisspg @mcarilli @ptrblck @leslie-fang-intel @jgong5github.com/Stability-AI/stablediffusionRuntimeError: expected scalar type BFloat16 but found Floatopened09:24AM - 10 Apr 23 UTCpicard314Below is the log I have encountered at running "python scripts/txt2img.py --prom…pt "a professional photograph of an astronaut riding a horse" --ckpt <path/to/768model.ckpt/> --config configs/stable-diffusion/v2-inference-v.yaml --H 768 --W 768"

Running DDIM Sampling with 50 timesteps
DDIM Sampler:   0%|                                                                                                                  | 0/50 [00:00<?, ?it/s]
data:   0%|                                                                                                                           | 0/1 [00:00<?, ?it/s]
Sampling:   0%|                                                                                                                       | 0/3 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "scripts/txt2img.py", line 388, in <module>
    main(opt)
  File "scripts/txt2img.py", line 347, in main
    samples, _ = sampler.sample(S=opt.steps,
  File "/root/miniconda3/envs/ldmsd/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/mnt/disk1/swh/git_sd/stablediffusion/ldm/models/diffusion/ddim.py", line 104, in sample
    samples, intermediates = self.ddim_sampling(conditioning, size,
  File "/root/miniconda3/envs/ldmsd/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/mnt/disk1/swh/git_sd/stablediffusion/ldm/models/diffusion/ddim.py", line 164, in ddim_sampling
    outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
  File "/root/miniconda3/envs/ldmsd/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/mnt/disk1/swh/git_sd/stablediffusion/ldm/models/diffusion/ddim.py", line 212, in p_sample_ddim
    model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
  File "/mnt/disk1/swh/git_sd/stablediffusion/ldm/models/diffusion/ddpm.py", line 858, in apply_model
    x_recon = self.model(x_noisy, t, **cond)
  File "/root/miniconda3/envs/ldmsd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/mnt/disk1/swh/git_sd/stablediffusion/ldm/models/diffusion/ddpm.py", line 1335, in forward
    out = self.diffusion_model(x, t, context=cc)
  File "/root/miniconda3/envs/ldmsd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/mnt/disk1/swh/git_sd/stablediffusion/ldm/modules/diffusionmodules/openaimodel.py", line 797, in forward
    h = module(h, emb, context)
  File "/root/miniconda3/envs/ldmsd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/mnt/disk1/swh/git_sd/stablediffusion/ldm/modules/diffusionmodules/openaimodel.py", line 84, in forward
    x = layer(x, context)
  File "/root/miniconda3/envs/ldmsd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/mnt/disk1/swh/git_sd/stablediffusion/ldm/modules/attention.py", line 327, in forward
    x = self.norm(x)
  File "/root/miniconda3/envs/ldmsd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/root/miniconda3/envs/ldmsd/lib/python3.8/site-packages/torch/nn/modules/normalization.py", line 272, in forward
    return F.group_norm(
  File "/root/miniconda3/envs/ldmsd/lib/python3.8/site-packages/torch/nn/functional.py", line 2516, in group_norm
    return torch.group_norm(input, num_groups, weight, bias, eps, torch.backends.cudnn.enabled)
RuntimeError: expected scalar type BFloat16 but found Float


Please, anyone has met the same and had a solution?