PPO on multi-GPU but get Error: Expected all tensors to be on the same device

[Ricardokevins]

I am training alpaca-7B on 4 * A100 80G I am using the provided Deepspeed-zero2 yaml file as the configuration file in the repository and running it. Even when I set the device-map of the model to None and batch size to 2, there is still a memory overflow issue with minibatch size set to 1.

Therefore, I tried setting the device-map to 'auto' so that accelerate can shard the model across different GPUs. However, when the code reaches ppo-trainer.generate, the aforementioned error occurs. Could you please advise on how to resolve this?

the command i use:

accelerate launch --config_file=deepspeed_zero2.yaml --num_processes 4 train.py --reward_fuction $RF --model_name $model_name

the model loading code:

model = AutoModelForCausalLMWithValueHead.from_pretrained(
    script_args.model_name,
    device_map='auto',
    #device_map=None,
    torch_dtype=torch.bfloat16,
)

the PPo config

config = PPOConfig(
    model_name=script_args.model_name,
    log_with=script_args.log_with,
    learning_rate=5e-6,
    batch_size=2,
    mini_batch_size=1,
    gradient_accumulation_steps=2,
    ppo_epochs=4,
    early_stopping=True,
    optimize_cuda_cache=True,
    seed=script_args.seed,
    project_kwargs = project_kwargs,
    remove_unused_columns=False,
)

[Ricardokevins]

@younesbelkada Hi. I apologize for the interruption. I'm currently uncertain whether ppo_trainer.generate supports model parameter sharding across different GPUs. If it indeed doesn't, then flash_attn alone cannot solve all the problems, especially when the model size grows and requires more memory.

[younesbelkada]

Hi @Ricardokevins i see, thanks for the description, I think the fix is to make sure your lm_head is on the same device as the input. Can you print model.pretrained_model.hf_device_map ?

[Ricardokevins]

Hi @Ricardokevins i see, thanks for the description, I think the fix is to make sure your lm_head is on the same device as the input. Can you print model.pretrained_model.hf_device_map ?

Hi，thank you for your reply! @younesbelkada I follow your instruction and try to print, but encounter an new issue: AttributeError: 'LlamaForCausalLM' object has no attribute 'hf_device_map'

is it an issue about transformers version? i use transformers==4.33.1

[younesbelkada]

Hmm this is strange, loading a model with device_map="auto" should add the hf_device_map attribute to the model. Have you loaded your model with device_map="auto" ?

[Ricardokevins]

Hmm this is strange, loading a model with device_map="auto" should add the hf_device_map attribute to the model. Have you loaded your model with device_map="auto" ?

Hi thank you for your help

I check the setting and set the device_map="auto" , the output is following:

{'model.embed_tokens': 0, 'model.layers.0': 0, 'model.layers.1': 0, 'model.layers.2': 0, 'model.layers.3': 0, 'model.layers.4': 0, 'model.layers.5': 0, 'model.layers.6': 0, 'model.layers.7': 1, 'model.layers.8': 1, 'model.layers.9': 1, 'model.layers.10': 1, 'model.layers.11': 1, 'model.layers.12': 1, 'model.layers.13': 1, 'model.layers.14': 1, 'model.layers.15': 1, 'model.layers.16': 2, 'model.layers.17': 2, 'model.layers.18': 2, 'model.layers.19': 2, 'model.layers.20': 2, 'model.layers.21': 2, 'model.layers.22': 2, 'model.layers.23': 2, 'model.layers.24': 2, 'model.layers.25': 3, 'model.layers.26': 3, 'model.layers.27': 3, 'model.layers.28': 3, 'model.layers.29': 3, 'model.layers.30': 3, 'model.layers.31': 3, 'model.norm': 3, 'lm_head': 3}

But still receive error:

  File "/mnt/data/sheshuaijie/anaconda3local/envs/shesj_ds/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py", line 89, in forward
    return self.weight * hidden_states.to(input_dtype)
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cuda:1!

I check the error is caused by the LlamaRMSNorm module In LlamaDecoderLayer(nn.Module), the decoder layer will call : hidden_states = self.input_layernorm(hidden_states) which case the error

[Ricardokevins]

I dive into the code, and find that the model's 7th layer is on gpu0 although in device_map it should be placed on gpu1. @younesbelkada

residual = hidden_states
print("hidden_states.device", hidden_states.device,self.input_layernorm.weight.device,self.self_attn.q_proj.weight.device)
hidden_states = self.input_layernorm(hidden_states)

entering decoder layer  0  of  32
hidden_states.device cuda:0 cuda:0 cuda:0
entering decoder layer  1  of  32
hidden_states.device cuda:0 cuda:0 cuda:0
entering decoder layer  2  of  32
hidden_states.device cuda:0 cuda:0 cuda:0
entering decoder layer  3  of  32
hidden_states.device cuda:0 cuda:0 cuda:0
entering decoder layer  4  of  32
hidden_states.device cuda:0 cuda:0 cuda:0
entering decoder layer  5  of  32
hidden_states.device cuda:0 cuda:0 cuda:0
entering decoder layer  6  of  32
hidden_states.device cuda:0 cuda:0 cuda:0
entering decoder layer  7  of  32
hidden_states.device cuda:1 cuda:0 cuda:0

[allanj]

I have some similar issues about "Expected all tensors to be on the same device" when I run the example in https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama/scripts/rl_training.py

Later I found out using the example command is fine accelerate launch --multi_gpu --num_machines 1 --num_processes 8 examples/stack_llama/scripts/rl_training.py --log_with=wandb --model_name=<LLAMA_SE_MODEL> --reward_model_name=<LLAMA_SE_RM_MODEL> --adafactor=False --tokenizer_name=<LLAMA_TOKENIZER> --save_freq=100 --output_max_length=128 --batch_size=8 --gradient_accumulation_steps=8 --batched_gen=True --ppo_epochs=4 --seed=0 --learning_rate=1.4e-5 --early_stopping=True --output_dir=llama-se-rl-finetune-128-8-8-1.4e-5_adam

But I enable deepspeed zero-2, I have that error

[Ricardokevins]

Anything Update here ? The Problem still exsit

[allanj]

Remove Accelerator() in your code. I fixed the issue now

[Ricardokevins]

Remove Accelerator() in your code. I fixed the issue now

Wow, amazing! I noticed that in the original code, the Accelerator was mainly used to provide the current_device. Have you replaced all the settings for current_device with 'auto'?

[Ricardokevins]

Remove Accelerator() in your code. I fixed the issue now

Thank you for your previous suggestion in resolving the issue. I have implemented the changes based on your advice, but unfortunately, the problem still persists. If it's convenient for you, could you please share the code that you successfully ran, so that I can reference it for further troubleshooting?

Thank you for your help.

[imgremlin]

@Ricardokevins have you solved the issue? I have the same problem

[allanj]

Hi @Ricardokevins @imgremlin , I think you guys can paste the reproducing code snippet for me to check?

[Ricardokevins]

@Ricardokevins have you solved the issue? I have the same problem

No, I can't run the model correctly

[Ricardokevins]

Hi @Ricardokevins @imgremlin , I think you guys can paste the reproducing code snippet for me to check?

sure

from transformers import LlamaForCausalLM, LlamaTokenizer, GenerationConfig
from dataclasses import dataclass, field
from typing import Optional
from transformers.modeling_utils import PreTrainedModel, unwrap_model
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from datasets import load_dataset
from peft import LoraConfig
from tqdm import tqdm
from transformers import BitsAndBytesConfig, HfArgumentParser, LlamaTokenizer,LlamaForCausalLM
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSequenceClassification
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer
from trl.core import LengthSampler
from transformers.trainer import TRAINING_ARGS_NAME, WEIGHTS_NAME
from trl import PreTrainedModelWrapper

VALUE_HEAD_FILE_NAME = "value_head.bin"

def get_state_dict(model: torch.nn.Module, trainable_only: Optional[bool] = True):
    if isinstance(model,AutoModelForCausalLMWithValueHead):
        state_dict = model.pretrained_model.state_dict()
    else:
        state_dict = model.state_dict()
    print("Enter")
    for k, v in state_dict.items():
        print(k)
    filtered_state_dict = {}
    for k, v in model.named_parameters():
        if 'v_head' in k:
            continue
        k = k.replace("pretrained_model.",'')
        print(k)
        filtered_state_dict[k] = state_dict[k].cpu().clone().detach()
    return filtered_state_dict

@dataclass
class ScriptArguments:
    """
    The name of the Casual LM model we wish to fine with PPO
    """
    local_rank: Optional[int] = field(default=-1, metadata={"help": "Local rank for distributed training (-1: not distributed)"})
    training_config: Optional[str] = field(default=None, metadata={"help": "Path to training config"})
    log_with: Optional[str] = field(default='tensorboard', metadata={"help": "use 'wandb' to log with wandb"})
    seed: Optional[int] = field(default=42, metadata={"help": "Random seed"})
    output_dir : Optional[str] = field(default="")

parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
import json
training_details = {
    "model_name" : "alpaca-7b",
    "lr": 2e-5,
    "batch_size": 4,
    "mini_batch_size" : 1,
    "gradient_accumulation_steps" : 4,
    "ppo_epoch" : 4,
    "version_name" : "LoRA_PPO"
}
script_args.model_name = training_details['model_name']
script_args.reward_functon = training_details['reward_functon']

project_name = "{model}-{setting}".format(model=script_args.model_name.split("/")[-1],setting = script_args.training_config)
script_args.output_dir = script_args.output_dir + training_details['version_name'] + '/'
script_args.output_dir = script_args.output_dir + project_name
script_args.explore_data = "alpaca.json"

tokenizer = LlamaTokenizer.from_pretrained(script_args.model_name,padding_side = "left")
tokenizer.padding_side = "left"
tokenizer.pad_token = tokenizer.eos_token

def tokenize(sample):
    sample['text'] = sample['instruction']
    sample["input_ids"] = tokenizer.encode(sample["text"])
    sample["query"] = tokenizer.decode(sample["input_ids"])
    if 'answer' in sample:
        sample["resonse_label"] = str(sample["answer"])
    else:
        sample["resonse_label"] = "NONE ANSWER"
    return sample

def create_and_prepare_dataset(config):
    ds = load_dataset("json", data_files=config.explore_data)['train'].shuffle(seed=42)
    ds = ds.map(tokenize, batched=False)
    ds.set_format(type="torch")
    return ds

import os
device_map = "auto"
world_size = int(os.environ.get("WORLD_SIZE", 1))
ddp = world_size != 1
if ddp:
    device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}

model = AutoModelForCausalLMWithValueHead.from_pretrained(
    script_args.model_name,
    torch_dtype=torch.bfloat16,
)

tokenizer = LlamaTokenizer.from_pretrained(script_args.model_name,padding_side = "left")
tokenizer.padding_side = "left"
tokenizer.pad_token = tokenizer.eos_token

dataset = create_and_prepare_dataset(script_args)

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

project_kwargs={"logging_dir": script_args.output_dir}

config = PPOConfig(
    model_name=script_args.model_name,
    log_with=script_args.log_with,
    learning_rate=training_details['lr'],
    batch_size=training_details['batch_size'],
    mini_batch_size=training_details['mini_batch_size'],
    gradient_accumulation_steps=training_details['gradient_accumulation_steps'],
    ppo_epochs=training_details['ppo_epoch'],
    early_stopping=True,
    optimize_cuda_cache=True,
    seed=script_args.seed,
    project_kwargs = project_kwargs,
    remove_unused_columns=False,
)
ref_model = AutoModelForCausalLMWithValueHead.from_pretrained(script_args.model_name)
model.gradient_checkpointing_enable()
ppo_trainer = PPOTrainer(
    config,
    model,
    ref_model=ref_model,
    tokenizer=tokenizer,
    dataset=dataset,
    data_collator=collator,
)

device = ppo_trainer.accelerator.device
if ppo_trainer.accelerator.num_processes == 1:
    device = 0 if torch.cuda.is_available() else "cpu"  # to avoid a ` pipeline` bug

generation_config = GenerationConfig(
    top_p=1.0,
    top_k=0,
    max_new_tokens=1024,
    do_sample=True,
)

step = 0
accuracy = 0
total = 0

generated_sample = []
torch.backends.cuda.sdp_kernel(
    enable_flash=True, enable_math=False, enable_mem_efficient=False
)
for i in range(30):
    print("EPOCH ",i)
    for iteration, batch in tqdm(enumerate(ppo_trainer.dataloader)):    
        question_tensors = batch["input_ids"]
        ppo_trainer.accelerator.unwrap_model(model).gradient_checkpointing_disable()
        response_tensors = ppo_trainer.generate(
            question_tensors,
            return_prompt=False,
            generation_config = generation_config,
            #**generation_kwargs,
        )
        ppo_trainer.accelerator.unwrap_model(model).gradient_checkpointing_enable()
        batch["response"] = tokenizer.batch_decode(response_tensors, skip_special_tokens=True)

        labels = batch["resonse_label"]
        texts = [q + r for q, r in zip(batch["query"], batch["response"])]
        rewards = [torch.tensor(1) for _,l in zip(texts,labels)]

        if script_args.local_rank == 0:
            print(batch["query"][0])
            print(batch["resonse_label"][0])
            print(batch["response"][0])
            print(rewards[0])
        stats = ppo_trainer.step(question_tensors, response_tensors, rewards)

        step += 1

        ppo_trainer.log_stats(stats, batch, rewards)   
        if step % 30 == 0:
            ppo_trainer.save_pretrained(script_args.output_dir + f"/ModelSaved/step_{step}")

[Ricardokevins]

Hi @Ricardokevins @imgremlin , I think you guys can paste the reproducing code snippet for me to check?

Here is my script

PORT=$(( $RANDOM % 1000 + 32768 ))
export CUDA_LAUNCH_BLOCKING=1
export OMP_NUM_THREADS=1
export MKL_NUM_THREADS=1
export NCCL_ASYNC_ERROR_HANDLING=1
export GRUB_CMDLINE_LINUX_DEFAULT="iommu=soft"
accelerate launch --config_file="deepspeed_zero2.yaml" --num_processes 4 ppo_full.py

compute_environment: LOCAL_MACHINE
deepspeed_config:
  gradient_accumulation_steps: 1
  offload_optimizer_device: none
  offload_param_device: none
  zero3_init_flag: false
  zero_stage: 2
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: 'bf16'
num_machines: 1
num_processes: 4
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false

[allanj]

The code looks fine to me. Do you have a minimal runnable snippet?

[imgremlin]

This code works well with:

• accelerate launch --config_file=scripts/deepspeed_configs/deepspeed_zero1.yaml --num_processes 1 scripts/deepspeed_repro.py
• accelerate launch --config_file=scripts/deepspeed_configs/multi_gpu.yaml --num_processes 2 scripts/deepspeed_repro.py

But doesn't work with:

accelerate launch --config_file=scripts/deepspeed_configs/deepspeed_zero1.yaml --num_processes 2 scripts/deepspeed_repro.py

import torch
from accelerate import Accelerator
from torch.utils.data import Dataset
from transformers import AutoTokenizer
from trl import AutoModelForSeq2SeqLMWithValueHead, PPOConfig, PPOTrainer
from trl.import_utils import is_xpu_available

MODEL = 'facebook/bart-base'
BS = 32

device_map = {"": Accelerator().local_process_index}

tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSeq2SeqLMWithValueHead.from_pretrained(
    MODEL,
    device_map=device_map
)
tokenizer.pad_token = tokenizer.eos_token

class TextDataset(Dataset):

    def __init__(self):
        self.texts = ['Do you love Paris?'] * 1024

    def __len__(self):
        return len(self.texts)

    def __getitem__(self, idx):
        return self.texts[idx]

dataset = TextDataset()

config = PPOConfig(ppo_epochs=1, batch_size=BS, mini_batch_size=BS//2)
ppo_trainer = PPOTrainer(
    config=config,
    model=model,
    tokenizer=tokenizer,
    dataset=dataset,
)

device = ppo_trainer.accelerator.device

if ppo_trainer.accelerator.num_processes == 1:
    if is_xpu_available():
        device = "xpu:0"
    else:
        device = 0 if torch.cuda.is_available() else "cpu"

for texts in ppo_trainer.dataloader:
    queries_arr = [tokenizer.encode(i, return_tensors='pt').squeeze().to(device) for i in texts]
    response_arr = [ppo_trainer.generate(prompt, return_prompt=False).squeeze() for prompt in queries_arr]
    rewards=[torch.tensor(1.5)] * len(queries_arr)
    stats = ppo_trainer.step(queries_arr, response_arr, rewards)
    print('Batch is done!')

[paraGONG]

Hello! I have the same problem. Have you solved this issue?

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[CHLEE-Leo]

Guys, I am suffering from the same issue abovementioned. Do anybody have solution to this?

[NuoJohnChen]

Guys, I am suffering from the same issue abovementioned. Do anybody have solution to this?