Run multi_adapter_rl_v2.py with multiple gpus error

[ASY246]

In the examples folder, I run the script multi_adapter_rl_v2.py successfully with single GPU by python3 multi_adapter_rl_v2.py.

But when I run with multiple GPUs by
accelerate launch --config_file=../accelerate_configs/multi_gpu.yaml multi_adapter_rl_v2.py.
I meet the following errors:

[2023-09-26 21:14:08] Traceback (most recent call last):
[2023-09-26 21:14:08]   File "/opt/ml/code/examples/scripts/multi_adapter_rl_v2.py", line 167, in <module>
[2023-09-26 21:14:08]     raw_rewards = ppo_trainer.model.compute_reward_score(**inputs)
[2023-09-26 21:14:08]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1630, in __getattr__
[2023-09-26 21:14:08]     raise AttributeError("'{}' object has no attribute '{}'".format(
[2023-09-26 21:14:08] AttributeError: 'DistributedDataParallel' object has no attribute 'compute_reward_score'

So maybe the model warp is changed with multiple GPUs mode?

Here is the python script I used

lora_config = LoraConfig(r=16, lora_alpha=32, bias="none", task_type="CAUSAL_LM", lora_dropout=0.1, modules_to_save=["scores"])
nf4_config = None

model = AutoModelForCausalLMWithValueHead.from_pretrained(
    script_args.model_name,
    device_map={"": current_device},
    peft_config=lora_config,
    quantization_config=nf4_config,
    reward_adapter=script_args.rm_adapter,
    use_safetensors=script_args.use_safetensors,
)

tokenizer = LlamaTokenizer.from_pretrained(script_args.model_name, input_max_text_length=1024)
tokenizer.pad_token = tokenizer.eos_token
dataset = create_and_prepare_dataset(tokenizer)

def collator(data):
    return dict((key, [d[key] for d in data]) for key in data[0])

config = PPOConfig(
    model_name=script_args.model_name,
    log_with=script_args.log_with,
    learning_rate=1e-5,
    batch_size=2,
    mini_batch_size=1,
    gradient_accumulation_steps=2,
    optimize_cuda_cache=True,
    seed=script_args.seed,
    use_score_scaling=script_args.use_score_scaling,
    use_score_norm=script_args.use_score_norm,
    score_clip=script_args.score_clip
)
ppo_trainer = PPOTrainer(
    config,
    model,
    ref_model=None,
    tokenizer=tokenizer,
    dataset=dataset["train"],
    data_collator=collator
)

generation_kwargs = {
    "top_k": 0.0,
    "top_p": 0.9,
    "do_sample": True,
    "pad_token_id": tokenizer.pad_token_id,
}

for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
    question_tensors = batch["input_ids"]
    response_tensors = ppo_trainer.generate(
        question_tensors,
        length_sampler=LengthSampler(input_min_text_length, input_max_text_length),
        return_prompt=False,
        **generation_kwargs,
    )
    batch["response"] = tokenizer.batch_decode(response_tensors, skip_special_tokens=True)
    
    #### Compute reward score
    texts = [q + r for q, r in zip(batch["query"], batch["response"])]
    inputs = tokenizer(texts, padding=True, truncation=True, max_length=256, return_tensors="pt").to(ppo_trainer.accelerator.device)
    raw_rewards = ppo_trainer.model.compute_reward_score(**inputs)
    rewards = [raw_rewards[i, -1, 0] for i in range(len(raw_rewards))]  # take last token， 这个和reward训练阶段的做法需要对应上

    # # Run PPO step
    stats = ppo_trainer.step(question_tensors, response_tensors, rewards)
    ppo_trainer.log_stats(stats, batch, rewards)

[younesbelkada]

Hi @ASY246
I think for multi-gpu we need a small check to make sure the model gets properly un-converted from DDP.
Can you replace the line:

 raw_rewards = ppo_trainer.model.compute_reward_score(**inputs)

with:

 raw_rewards = ppo_trainer.accelerator.unwrap_model(model).compute_reward_score(**inputs)

And let me know if this works?

[ASY246]

@younesbelkada Thanks for your response, I replace the line with
raw_rewards = ppo_trainer.accelerator.unwrap_model(model).compute_reward_score(**inputs)
and I meet another error as follows:

[2023-09-27 20:33:48] Traceback (most recent call last):
[2023-09-27 20:33:48]   File "/opt/ml/code/examples/scripts/multi_adapter_rl_v2.py", line 186, in <module>
[2023-09-27 20:33:48]     stats = ppo_trainer.step(question_tensors, response_tensors, rewards)
[2023-09-27 20:33:48]   File "/usr/lib/python3.10/contextlib.py", line 79, in inner
[2023-09-27 20:33:48]     return func(*args, **kwds)
[2023-09-27 20:33:48]   File "/opt/ml/code/trl/trainer/ppo_trainer.py", line 754, in step
[2023-09-27 20:33:48]     logprobs, logits, vpreds, _ = self.batched_forward_pass(
[2023-09-27 20:33:48]   File "/usr/lib/python3.10/contextlib.py", line 79, in inner
[2023-09-27 20:33:48]     return func(*args, **kwds)
[2023-09-27 20:33:48]   File "/opt/ml/code/trl/trainer/ppo_trainer.py", line 949, in batched_forward_pass
[2023-09-27 20:33:48]     logits, _, values = model(**input_kwargs)
[2023-09-27 20:33:48]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1502, in _wrapped_call_impl
[2023-09-27 20:33:48]     return self._call_impl(*args, **kwargs)
[2023-09-27 20:33:48]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _call_impl
[2023-09-27 20:33:48]     return forward_call(*args, **kwargs)
[2023-09-27 20:33:48]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/parallel/distributed.py", line 1513, in forward
[2023-09-27 20:33:48]     inputs, kwargs = self._pre_forward(*inputs, **kwargs)
[2023-09-27 20:33:48]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/parallel/distributed.py", line 1401, in _pre_forward
[2023-09-27 20:33:48]     if torch.is_grad_enabled() and self.reducer._rebuild_buckets():
[2023-09-27 20:33:48] RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by 
[2023-09-27 20:33:48] making sure all `forward` function outputs participate in calculating loss. 
[2023-09-27 20:33:48] If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable).
[2023-09-27 20:33:48] Parameter indices which did not receive grad for rank 3: 162
[2023-09-27 20:33:48]  In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error

It seems like some modules are not included in the loss calculation, but why it works when I run the script by single GPU and only appears in the multiple GPU mode?

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