where can I download the 176B checkpoint in deepspeed format?

[xuyifan-0731]

Hello, I used the 176B checkpoint of bloom-176B(https://huggingface.co/bigscience/bloom), but had problem in resolving the layer files. Should I download different type of checkpoint to use in this repo, or what code should I use to run the evalution based on bloom-176B checkpoint/ Thanks a lot.

[xuyifan-0731]

@stas00

[stas00]

What are you after the bf16 weights split across TPs?

or the optim states - that's 2.3TB of data!

I don't know what: "had problem in resolving the layer files" means and how you were loading the model - it works with just AutoModel.from_pretrained("bigscience/bloom")

[xuyifan-0731]

I used this: `CHECKPOINT_PATH="/mnt/yrfs/aohan/checkpoints/bloom" VARIANT="bloom-176B"

TP_SIZE=1 PP_SIZE=1

EVAL_MICRO_BATCH_SIZE=6 SEQ_LEN=2048

MEGATRON_REQUIRED_ARGS=" \ --num-layers -1 \ --hidden-size -1 \ --num-attention-heads -1 \ --seq-length -1 \ --max-position-embeddings -1 "

CMD="./tasks/eval_harness/evaluate.py \ --load $CHECKPOINT_PATH \ --results_path $VARIANT-results.json \ --tensor-model-parallel-size $TP_SIZE \ --pipeline-model-parallel-size $PP_SIZE \ --micro-batch-size $EVAL_MICRO_BATCH_SIZE \ --no-load-optim \ --no-load-rng \ --inference \ --deepspeed \ --deepspeed_config ds_config.json \ --seq-length $SEQ_LEN \ --adaptive_seq_len \ --eval_fp32 \ --task_list copa \ $MEGATRON_REQUIRED_ARGS \ "

N_GPUS=1 LAUNCHER="deepspeed --num_gpus $N_GPUS" echo $LAUNCHER $CMD

$LAUNCHER $CMD`

I try to evaluate the 176B model, but I got this error: dir /mnt/yrfs/aohan/checkpoints/bloom self.layer_files ['/mnt/yrfs/aohan/checkpoints/bloom/pytorch_model.bin.index.json'] LAYER_FILE_PREFIX layer_ self.original_tp_degree 0 Traceback (most recent call last): File "./tasks/eval_harness/evaluate.py", line 461, in <module> main() File "./tasks/eval_harness/evaluate.py", line 416, in main model = load_ds_checkpoint_and_setup_megatron(args) File "./tasks/eval_harness/evaluate.py", line 297, in load_ds_checkpoint_and_setup_megatron ds_checkpoint = DeepSpeedCheckpoint(args.load, File "/root/bloom/Megatron-DeepSpeed-main/tools/convert_checkpoint/deepspeed_checkpoint.py", line 39, in __init__ self.original_pp_degree = len(self.mp_rank_files) // self.original_tp_degree ZeroDivisionError: integer division or modulo by zero I think it means i can not use this checkpont(from https://huggingface.co/bigscience/bloom), because the layer files are not existed.

[xuyifan-0731]

I would like to evaluate bloom-176B on MMLU, Big-Bench, FewCLUE

[stas00]

ok, so you do want the original weights - got it - we have a script that converts from Meg-DS to HF, but not the other way around.

I will ask if we can release the Meg-DS weights on the hub.

[xuyifan-0731]

thanks a lot. It looks like I can only use HF checkpoint now. But I face a problem while using it. I have a machine with 8*A100, and i would like to use bloom-176B to generate text, and apply evaluation on different datasets. I use the code in bigscience-workshop/bigscience/sortval repo,bigscience/evaluation/generation/generate.py. But this code only allows the model to run on a graphics card. Apperently single A100 is not enough: RuntimeError: CUDA out of memory. Tried to allocate 1.53 GiB (GPU 0; 79.17 GiB total capacity; 77.14 GiB already allocated; 1.20 GiB free; 77.14 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

How to use bloom-176B to generate or evaluate on Multi-graphics? Should I change the code in generate.py,or use other code? I have tried use accelerate:

from transformers import AutoTokenizer, AutoModel
from transformers import AutoModelForCausalLM
import torch
from accelerate import Accelerator
def generate_from_text(model, text, tokenizer, max_length=200, greedy=False, top_k=0):
    input_ids = tokenizer.encode(text, return_tensors='pt').to("cuda:0")
    max_length = input_ids.size(-1) + max_length

    greedy_output = model.generate(
        input_ids.to('cuda:0'),
        max_length=max_length,
        do_sample=not greedy,
        top_k=None if greedy else top_k,
    )
    return tokenizer.decode(greedy_output[0], skip_special_tokens=True)

tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom")
print("load tokenizer")
accelerator = Accelerator()
device = accelerator.device
model = AutoModelForCausalLM.from_pretrained(
    arg.checkpoints,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    offload_folder="./offload",
)
model = accelerator.prepare(model)
print("load model")

text = 'hello'
output = generate_from_text(model, text, tokenizer, max_length=500, greedy=True, top_k=1)
print(output)

and it shows :

load tokenizer Traceback (most recent call last): File "/mnt/yrfs/xuyifan/bloom/bigscience-sorteval/eva.py", line 28, in model = accelerator.prepare(model) File "/mnt/yrfs/qinkai/miniconda3/envs/bloom12/lib/python3.9/site-packages/accelerate/accelerator.py", line 545, in prepare result = tuple(self._prepare_one(obj, first_pass=True) for obj in args) File "/mnt/yrfs/qinkai/miniconda3/envs/bloom12/lib/python3.9/site-packages/accelerate/accelerator.py", line 545, in result = tuple(self._prepare_one(obj, first_pass=True) for obj in args) File "/mnt/yrfs/qinkai/miniconda3/envs/bloom12/lib/python3.9/site-packages/accelerate/accelerator.py", line 441, in _prepare_one return self.prepare_model(obj) File "/mnt/yrfs/qinkai/miniconda3/envs/bloom12/lib/python3.9/site-packages/accelerate/accelerator.py", line 565, in prepare_model model = model.to(self.device) File "/mnt/yrfs/qinkai/miniconda3/envs/bloom12/lib/python3.9/site-packages/torch/nn/modules/module.py", line 927, in to return self._apply(convert) File "/mnt/yrfs/qinkai/miniconda3/envs/bloom12/lib/python3.9/site-packages/torch/nn/modules/module.py", line 579, in _apply module._apply(fn) File "/mnt/yrfs/qinkai/miniconda3/envs/bloom12/lib/python3.9/site-packages/torch/nn/modules/module.py", line 579, in _apply module._apply(fn) File "/mnt/yrfs/qinkai/miniconda3/envs/bloom12/lib/python3.9/site-packages/torch/nn/modules/module.py", line 579, in _apply module._apply(fn) [Previous line repeated 2 more times] File "/mnt/yrfs/qinkai/miniconda3/envs/bloom12/lib/python3.9/site-packages/torch/nn/modules/module.py", line 602, in _apply param_applied = fn(param) File "/mnt/yrfs/qinkai/miniconda3/envs/bloom12/lib/python3.9/site-packages/torch/nn/modules/module.py", line 925, in convert return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking) RuntimeError: CUDA out of memory. Tried to allocate 1.53 GiB (GPU 0; 79.17 GiB total capacity; 77.14 GiB already allocated; 1.20 GiB free; 77.14 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

I released same question on https://huggingface.co/bigscience/bloom/discussions/62

[stas00]

Please see: https://github.com/bigscience-workshop/Megatron-DeepSpeed/pull/308

[stas00]

The original Meg-DS checkpoint is here: https://huggingface.co/bigscience/bloom-optimizer-states

[mayank31398]

@xuyifanbupt if you are trying to deploy BLOOM 176B as a server deployment, you can find here

[asafkar]

Hi @stas00, Besides the optimizer states, is there an equivalent checkpoint for inference that is in the Meg-DS format, which I can use with deepspeed inference, in order to run inference with Pipeline parallelism? As far as I understand, currently with the HF weights, I would have to re-write the model so it could pipelined, and possibly need to write a custom script to load the weights to the altered model.

Is there an alternative method, or does a such a checkpoint exist publicly?

thanks

[mayank31398]

@asafkar , the ds inference script is compatible with HF checkpoints.

[asafkar]

@mayank31398 I was actually referring to the other way around - i.e. I want to do ds inference, while using pipeline parallelism.

For that to work (if I understand correctly), I would have 2 options -

1. Load the HF checkpoint, and then restructure it for the layers to be sequential (as done here). I'm not sure it's that easy, since the HF forward function would also have to be altered to fit this.
2. Load the Meg-DS checkpoint into the BLOOM model which is instantiated as a GPTModelPipe , and then I can easily use pipeline parallel (although not sure how this would work with DS-inference.)

Not sure which is easier to do, and which one would actually work...

[stas00]

is there an equivalent checkpoint for inference that is in the Meg-DS format,

https://huggingface.co/bigscience/bloom-optimizer-states is the full Meg-DS checkpoint.

edit: hmm, I think you're correct it's incomplete. I will push the rest of the files in - will take a while. I will update here when it's done.

it will appear here once uploaded: https://huggingface.co/bigscience/bloom-megatron-deepspeed

edit: uploaded

[stas00]

@asafkar, so it looks like I created the new repo for nothing, the https://huggingface.co/bigscience/bloom-optimizer-states was already the full checkpoint. Why did you say it only had optim state files and not the rest of the files? They should be all there.

The listing is limited at the moment to just 50 entries so one can't see the remainder of the files on the hub, I made a request to ameliorate that.

Please confirm that https://huggingface.co/bigscience/bloom-optimizer-states has all you need and I will remove the second repo.

[mayank31398]

Wait @asafkar does DS-inference support pipeline parallelism? I thought it was only tensor parallel for generation

[stas00]

• DS-Inference = TP
• DS-ZeRO = TP-like
• Accelerate = PP
• Megatron-Deepspeed = TP+PP

(plus DP in all)

[asafkar]

@stas00 I haven't checked the https://huggingface.co/bigscience/bloom-optimizer-states repo yet, I was merely asking whether it will support what I'm trying to do - I'm sorry if I wasn't clear enough about it. I will give it a shot and let you know.

@mayank31398 regarding DS-inference, https://arxiv.org/pdf/2207.00032.pdf clearly states that they support PP, so I thought it should be covered by the actual package as well. I don't recall actually seeing an example of PP with DS-inference, so I hope I'm not mistaken. Perhaps I will try that first with a toy model to make sure it is supported...