Skipping cudagraphs for unknown reason

[ken012git]

System Info

Copy-and-paste the text below in your GitHub issue and FILL OUT the two last points.

• transformers version: 4.41.2
• Platform: Linux-5.15.0-112-generic-x86_64-with-glibc2.35
• Python version: 3.10.13
• Huggingface_hub version: 0.23.4
• Safetensors version: 0.4.2
• Accelerate version: 0.28.0
• Accelerate config: not found
• PyTorch version (GPU?): 2.1.2 (True)
• Tensorflow version (GPU?): not installed (NA)
• Flax version (CPU?/GPU?/TPU?): not installed (NA)
• Jax version: not installed
• JaxLib version: not installed
• Using GPU in script?:
• Using distributed or parallel set-up in script?:

Who can help?

@ArthurZucker

Information

• The official example scripts
• My own modified scripts

Tasks

• An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
• My own task or dataset (give details below)

Reproduction

I read issue 30055 and issue 30351, and Llama works well with cache_implementation="static". However, I am trying to use torch.compile for other models such as pythia and phi-2 where the cache_implementation="static" is not appliable, and it will produce errors like:

[2024-06-26 18:18:57,065] [0/0] torch._inductor.fx_passes.split_cat: [WARNING] example value absent for node: cat_3                                        
[2024-06-26 18:18:57,065] [0/0] torch._inductor.fx_passes.split_cat: [WARNING] example value absent for node: cat_2                                        
[2024-06-26 18:18:57,065] [0/0] torch._inductor.fx_passes.split_cat: [WARNING] example value absent for node: cat_1
[2024-06-26 18:18:57,065] [0/0] torch._inductor.fx_passes.split_cat: [WARNING] example value absent for node: cat
skipping cudagraphs for unknown reason
...
...
  File "/tmp/torchinductor_hc29225/bi/cbiig6bqpidhtncuswvfxwqqjwoiiswlwlrnh7eobbwm4wjlvpts.py", line 15465, in call
    extern_kernels.addmm(arg4_1, reinterpret_tensor(buf3, (16, 2560), (2560, 1), 0), reinterpret_tensor(arg3_1, (2560, 2560), (1, 2560), 0), alpha=1, beta=
1, out=buf4)
RuntimeError: "addmm_impl_cpu_" not implemented for 'Half'

Here is my code for reproducing the errors.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Llama works well with  cache_implementation="static", but other types of models do not have the configuration.
# model     = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, attn_implementation="sdpa", token=access_token).cuda().eval()
# tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf", token=access_token)

# model     = AutoModelForCausalLM.from_pretrained("EleutherAI/pythia-2.8b" , torch_dtype=torch.float16, trust_remote_code=True).cuda().eval()
# tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-2.8b", trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype=torch.float16, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)

model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)

inputs = tokenizer(["<s> [INST] Write an essay about large language models [/INST]"], return_tensors="pt").to(model.device)

max_new_tokens = 64
fn = lambda: model.generate(
      **inputs,
      do_sample=False,
      # cache_implementation="static", # this only works for Llama-2
      max_new_tokens=max_new_tokens,
      pad_token_id=tokenizer.pad_token_id,
      temperature=None,
      top_p=None
)
fn() 

Expected behavior

The models such as pythia and phi-2 can run with torch.compile and a clear latency improvement can be observed.

[amyeroberts]

cc @gante too :)

[ArthurZucker]

Hey! These models are not supported yet! We could actually open this to the community and get all decoder models to support compile soon-ish.
Do you want to open a PR?