Parallel linear Lora #1092

zhangsheng377 · 2023-11-08T00:56:30Z

We implemented the Lora algorithm for megatron's distributed layer ColumnParallelLinear and RowParallelLinear.

Due to the particularity of megatron creating a distributed layer, the required megatron information needs to be injected before executing Lora:

from megatron.arguments import core_transformer_config_from_args
from megatron import get_args
from peft import LoraConfig, get_peft_model

config = core_transformer_config_from_args(get_args())

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=['query_key_value', 'dense', 'dense_h_to_4h', 'dense_4h_to_h'],
    lora_dropout=0.0,
    bias="none",
    megatron_config=config,
    megatron_core="megatron.core",
)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()

It has been verified on the megatron and megatron-deepspeed frameworks.

zhangsheng377 · 2023-11-08T02:42:01Z

@BenjaminBossan @pacman100

src/peft/tuners/lora/tp_layer.py

BenjaminBossan · 2023-11-08T10:08:17Z

Great, thanks for the PR.

Before going through a full review, I have some points/questions:

Could you please provide a bit of more context what users can expect when using this functionality?

Due to the particularity of megatron creating a distributed layer, the required megatron information needs to be injected before executing Lora:

It would be great if we could find a way to avoid that, I can also check later if I have any ideas.

Finally, it would be great to have unit tests for the new feature, or at the very least an example to see it in action.

zhangsheng377 · 2023-11-08T11:00:00Z

Great, thanks for the PR.

Before going through a full review, I have some points/questions:

Could you please provide a bit of more context what users can expect when using this functionality?

For llama or other large models, the deepspeed framework is not easy to use if the model is too large. We now use our own modified megatron-deepspeed framework to train the cluster. Therefore, the model structure uses the ParallelLinear of megatron, but at this time we want to use Lora for fine-tuning, so we want to extend lora to support the ParallelLinear.

Due to the particularity of megatron creating a distributed layer, the required megatron information needs to be injected before executing Lora:

It would be great if we could find a way to avoid that, I can also check later if I have any ideas.

Finally, it would be great to have unit tests for the new feature, or at the very least an example to see it in action.

Ok, I use this script to finetune a llama7B with alpaca on our own magetron-deepspeed framework :

export CUDA_VISIBLE_DEVICES_=0,1,2,3
export ASCEND_RT_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES_}
ASCEND_RT_VISIBLE_DEVICES_ARRAY=(${CUDA_VISIBLE_DEVICES_//,/ })
echo "${ASCEND_RT_VISIBLE_DEVICES_ARRAY[@]}"

# the number of parameters is not aligned
export LD_LIBRARY_PATH=/usr/local/lib:/home/anaconda3/lib:$LD_LIBRARY_PATH
export HCCL_CONNECT_TIMEOUT=1200
export COMBINED_ENABLE=1
source /home/xxx/Ascend/set_env.sh

GPUS_PER_NODE=${#ASCEND_RT_VISIBLE_DEVICES_ARRAY[@]} 
echo $GPUS_PER_NODE
# Change for multinode config
MASTER_ADDR=localhost
MASTER_PORT=6001
NNODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))

TP=1
PP=1

DATA_PATH=xxx
LOAD_CHECKPOINT_PATH=xxx
SAVE_CHECKPOINT_PATH=xxx
TOKENIZER_PATH=xxx

DS_CONFIG=deepspeed_config_13B_1.json
ZERO_STAGE=2

MICRO_BATCH=4
GRADIENT_ACCUMULATION_STEP=8
GLOBAL_BATCH=$(($MICRO_BATCH * $GRADIENT_ACCUMULATION_STEP * $WORLD_SIZE))
EPOCH=2
TRAIN_ITERS=$((52000 / $GLOBAL_BATCH * $EPOCH))
echo $TRAIN_ITERS
SAVE_INTERVAL=$(($TRAIN_ITERS / 2))
echo $SAVE_INTERVAL

export HCCL_OP_BASE_FFTS_MODE_ENABLE=TRUE

cat <<EOT > $DS_CONFIG
{
    "fp16": {
        "enabled": true,
        "loss_scale": 0,
        "loss_scale_window": 1000,
        "initial_scale_power": 8,
        "hysteresis": 2,
        "min_loss_scale": 1
    },

    "optimizer": {
        "type": "Adam"
    },

    "zero_optimization": {
        "stage": $ZERO_STAGE,
        "allgather_partitions": true,
        "allgather_bucket_size": 1e8,
        "overlap_comm": true,
        "reduce_scatter": true,
        "reduce_bucket_size": 1e8,
        "contiguous_gradients": true
    },

    "gradient_accumulation_steps": ${GRADIENT_ACCUMULATION_STEP},
    "train_batch_size": $GLOBAL_BATCH,
    "train_micro_batch_size_per_gpu":$MICRO_BATCH,
    "zero_allow_untested_optimizer": true
}
EOT

ds_args=""
ds_args=" --deepspeed ${ds_args}"
ds_args=" --no-pipeline-parallel ${ds_args}"
ds_args=" --deepspeed_config=$DS_CONFIG ${ds_args}"
ds_args=" --zero-stage=$ZERO_STAGE ${ds_args}"
ds_args=" --deepspeed-activation-checkpointing ${ds_args}"

#deepspeed --master_port ${MASTER_PORT} --include localhost:${CUDA_VISIBLE_DEVICES_} pretrain_llama.py \
deepspeed --master_port ${MASTER_PORT} pretrain_llama.py \
       --DDP-impl local \
       --no-contiguous-buffers-in-local-ddp \
       --tensor-model-parallel-size ${TP} \
       --pipeline-model-parallel-size ${PP} \
       --num-layers 32 \
       --hidden-size 4096 \
       --ffn-hidden-size 11008 \
       --num-attention-heads 32 \
       --micro-batch-size $MICRO_BATCH \
       --global-batch-size $GLOBAL_BATCH \
       --seq-length 1024 \
       --max-position-embeddings 2048 \
       --train-iters ${TRAIN_ITERS} \
       --lr-decay-iters ${TRAIN_ITERS} \
       --save $SAVE_CHECKPOINT_PATH \
       --load $LOAD_CHECKPOINT_PATH \
       --data-path $DATA_PATH \
       --tokenizer-name-or-path $TOKENIZER_PATH \
       --tokenizer-not-use-fast \
       --data-impl mmap \
       --split 949,50,1 \
       --distributed-backend nccl \
       --lr 2e-5 \
       --lr-decay-style cosine \
       --min-lr 0 \
       --weight-decay 0. \
       --clip-grad 1.0 \
       --lr-warmup-iters 100 \
       --checkpoint-activations \
       --log-interval 1 \
       --save-interval ${SAVE_INTERVAL} \
       --eval-interval 1000 \
       --eval-iters 10 \
       --use-cpu-initialization \
       --lora-target-modules query_key_value dense gate_proj up_proj down_proj \
       --lora-r 16 \
       --lora-alpha 32 \
       --is-instruction-dataset \
       --seed 42 \
       $ds_args \
       --optimizer fused_adam \
       --fp16 | tee logs/train_7B_deepspeed.log

We compare it with the model using torch.linear for lora fine-tuning, and the loss error is less than the absolute value 0.001.

This is the inference result of our model:

In fact, if you have the environment, you can use the megatron or megatron-deepspeed framework to run a small model and lora the model after get_model() func. Of course, currently both repoes need to change the parent class initialization method in ParallelLinear. The PRs of the two warehouses are as follows:
microsoft/Megatron-DeepSpeed#284
NVIDIA/Megatron-LM#578

zhangsheng377 · 2023-11-10T03:56:42Z

@BenjaminBossan
Add an unit test to test Lora for the Parallel linear.

You can run it in peft dir:

pytest tests/test_lora_megatron.py

And after modifying these PRs in my local environment, the tests all passed:
microsoft/Megatron-DeepSpeed#284
NVIDIA/Megatron-LM#578
(You will pip install -e . the Megatron-LM from source.)

zhangsheng377 · 2023-11-13T09:43:30Z

@BenjaminBossan @pacman100

zhangsheng377 · 2023-11-15T01:13:53Z

@BenjaminBossan @pacman100 Sorry to bother you, but can you help me review it?

BenjaminBossan

Thanks for making some changes to integrate this better with LoRA and adding tests. I still think we need to find a better way to configure how this feature can be used, I made a suggestion in the comments. What do you think?

Furthermore, could you please run make style?

src/peft/tuners/lora/config.py

tests/test_lora_megatron.py

BenjaminBossan · 2023-11-16T13:02:22Z

Hi @zhangsheng377 I wanted to inform you that we merged #1106, which is a substantial refactor of PEFT and created some merge conflicts for your PR. The most notable change from that PR is that we refactored the adapter layers to take a base_layer argument now, which is a reference to the original module. This means that you have to slightly rewrite LoraParalleLayer and LoraParallelLinear. Take a look at LoraLayer and Linear LoRA layer, I hope that the change should be straightforward. If you have questions, please let me know and I'll help you with the change.

zhangsheng377 · 2023-11-16T13:26:13Z

Hi @zhangsheng377 I wanted to inform you that we merged #1106, which is a substantial refactor of PEFT and created some merge conflicts for your PR. The most notable change from that PR is that we refactored the adapter layers to take a base_layer argument now, which is a reference to the original module. This means that you have to slightly rewrite LoraParalleLayer and LoraParallelLinear. Take a look at LoraLayer and Linear LoRA layer, I hope that the change should be straightforward. If you have questions, please let me know and I'll help you with the change.

Great, I think I need this base_layer. I'll take a closer look when I have time. (After all, we are already at 21 o'clock)

BenjaminBossan · 2023-11-21T11:01:02Z

@zhangsheng377 Thanks a lot for your continued work on this PR. The usability looks much nicer now! There is still a merge conflict left in lora/model.py. Don't hesitate to ask if you need help with resolving it.

zhangsheng377 · 2023-11-22T08:34:02Z

@zhangsheng377 Thanks a lot for your continued work on this PR. The usability looks much nicer now! There is still a merge conflict left in lora/model.py. Don't hesitate to ask if you need help with resolving it.

@BenjaminBossan Ha, thank you for your concern. In fact, the conflict resolution was completed yesterday, but the changes in the main line were quite large, so I trained the llama model again to compare the loss and make sure there were no problems before submitting the code.

HuggingFaceDocBuilderDev · 2023-11-22T14:43:27Z

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint.

zhangsheng377 · 2023-11-23T02:15:26Z

@BenjaminBossan Sorry I forgot make style. Now that it has been reformed, please trigger the workflows again.

PS: Happy Thanksgiving.

zhangsheng377 · 2023-11-23T13:16:34Z

@BenjaminBossan Sorry, I neglected to install ruff this morning, so I mistakenly thought that style was no longer a problem.
But it should be fine now. But unlike the workflow, I did not find the problem of config.py locally. If it continue to report errors, please help me find out where the problem is.

BenjaminBossan

Thank you so much for updating the PR, it looks very good now. I'm not knowledgeable on megatron, so my review focuses more in the integration with PEFT.

As you can see, I left a couple of comments, please take a look. Mostly, I think we need to make some changes to the config to make saving and loading possible. Also, I think we can simplify the class structure. Finally, I have some questions concerning the forward method.

I did not find the problem of config.py locally. If it continue to report errors, please help me find out where the problem is

The issue is that the from types import should come before from typing.

PS: Happy Thanksgiving.

Thanks, but where I am, there is no Thanksgiving :) In case you celebrate it, happy Thanksgiving to you too.

Edit

I forgot to mention, it would be really great if we could have an example or even better an entry in the docs that shows how to use this feature, maybe highlighting why users should use it. That way, the feature is much easier to discover.

src/peft/tuners/lora/config.py

src/peft/tuners/lora/tp_layer.py

tests/test_lora_megatron.py

src/peft/tuners/lora/tp_layer.py

src/peft/tuners/lora/config.py

pacman100

Thank you @zhangsheng377 for working on adding support for PEFT in Megatron. Overall, the PR is in great shape to be merged! 🚀

BenjaminBossan

Thanks so much, I think the implementation is much cleaner now and easier to understand. From my point of view, there are only a few details left, otherwise we should be good to merge.

src/peft/tuners/lora/model.py

src/peft/tuners/lora/config.py

src/peft/tuners/lora/tp_layer.py

tests/test_lora_megatron.py

BenjaminBossan

Thanks a lot for this PR. It looks pretty good now, I have a nit but it's no big deal.

Before merging, I just want to discuss the newly added tests. As is, they would not be run by the Github CI at any point, so in theory bugs could be introduced without us noticing. I have no personal experience, but the requirements for running megatron seem to be quite high, so I'm not sure if we could make it run on our GPU runners. I wonder if there is a way we can still make it work.

src/peft/tuners/lora/tp_layer.py

zhangsheng377 · 2023-11-29T02:17:02Z

Thanks a lot for this PR. It looks pretty good now, I have a nit but it's no big deal.

Before merging, I just want to discuss the newly added tests. As is, they would not be run by the Github CI at any point, so in theory bugs could be introduced without us noticing. I have no personal experience, but the requirements for running megatron seem to be quite high, so I'm not sure if we could make it run on our GPU runners. I wonder if there is a way we can still make it work.

Well, I only used one gpu card to run the newly added unit test locally, and the resource requirements are not large.
I installed Megatron from the source code, but it does have a lot of dependencies and the installation is a bit troublesome, but it should be okay. It should take less than an afternoon for me to configure the environment.

Or you should be able to install Megatron-DeepSpeed, after all, it is actually the backend of our current magic modification.

#1092 (comment)

My local test result:

BenjaminBossan · 2023-11-29T15:35:27Z

Unfortunately, I did not manage to successfully build APEX and it seems I'm not the only one, judging from all the open issues. Therefore, I couldn't test it locally. If you have a recipe to get this all to run, which we could use for a CI job, that would be great.

BenjaminBossan · 2023-11-29T16:09:50Z

Heads up, there is a small merge conflict, should be easy to fix.

zhangsheng377 · 2023-11-30T01:33:00Z

Heads up, there is a small merge conflict, should be easy to fix.

Yes, it's done.

zhangsheng377 · 2023-11-30T01:45:37Z

Unfortunately, I did not manage to successfully build APEX and it seems I'm not the only one, judging from all the open issues. Therefore, I couldn't test it locally. If you have a recipe to get this all to run, which we could use for a CI job, that would be great.

My apex is version 0.1, and it seems that it should be installed directly via pip.
Can you post the error report and take a look?

BenjaminBossan · 2023-11-30T10:27:59Z

My apex is version 0.1, and it seems that it should be installed directly via pip.

Could you tell me how to do that? The apex package on PyPI seems to be unrelated.

What I did is follow the instructions here. Several users reported issues with this. Some suggested solutions included checking out specific tags or commits, but none of those I tried worked for me.

zhangsheng377 · 2023-11-30T12:04:04Z

My apex is version 0.1, and it seems that it should be installed directly via pip.

Could you tell me how to do that? The apex package on PyPI seems to be unrelated.

What I did is follow the instructions here. Several users reported issues with this. Some suggested solutions included checking out specific tags or commits, but none of those I tried worked for me.

I think I did not install it from the source code. You can try pip install apex directly.

BenjaminBossan · 2023-11-30T12:17:26Z

I think I did not install it from the source code. You can try pip install apex directly.

I think this will install the wrong package. When checking on PyPI, the description says:

Authentication, Form Library, I18N/L10N, Flash Message Template (not associated with Pyramid, a Pylons project)

zhangsheng377 · 2023-11-30T12:27:01Z

Authentication, Form Library, I18N/L10N, Flash Message Template (not associated with Pyramid, a Pylons project)

(xx) [root@localhost peft]# pip install apex
Looking in indexes: http://mirrors.aliyun.com/pypi/simple/
Requirement already satisfied: apex in /root/miniconda3/envs/xx/lib/python3.9/site-packages/apex-0.1-py3.9.egg (0.1)
Requirement already satisfied: packaging>20.6 in /root/miniconda3/envs/xx/lib/python3.9/site-packages (from apex) (23.2)

Maybe you can change the index-url? Or specify version?

BenjaminBossan · 2023-11-30T13:12:10Z

Looking in indexes: http://mirrors.aliyun.com/pypi/simple/

Interesting. The host is unfortunately insecure (http) and I don't know who this is. Therefore, we cannot use this index for the CI, as it could start hosting malicious code at any point in the future.

Anyway, even if we cannot find a way right now to make the tests work, we can still proceed. Hopefully, we can find a better way in the future, maybe Nvidia manages to provide a package that can be reliably installed soon.

BenjaminBossan · 2023-11-30T15:25:30Z

@zhangsheng377 Thanks so much for this wonderful PR.

Adds option to use Megatron's ColumnParallelLinear and RowParallelLinear for LoRA linear layers, leading to improved performance when using LoRA with Megatron.

thincal · 2024-02-21T06:02:00Z

on our own magetron-deepspeed framework

@zhangsheng377 so that can we use the latest https://github.com/microsoft/Megatron-DeepSpeed with this peft now ?

zhangsheng377 · 2024-02-21T06:23:08Z

on our own magetron-deepspeed framework

@zhangsheng377 so that can we use the latest https://github.com/microsoft/Megatron-DeepSpeed with this peft now ?

@thincal Yes, you can.

thincal · 2024-02-21T09:03:47Z

Thanks for your information. And another question, with Megatron-DeepSpeed used, what's the format for input model and result model in this LoRA fine-tuning with PEFT ? @zhangsheng377

zhangsheng377 · 2024-02-21T09:07:02Z

Thanks for your information. And another question, with Megatron-DeepSpeed used, what's the format for input model and result model in this LoRA fine-tuning with PEFT ? @zhangsheng377

@thincal In Megatron-DeepSpeed, the base model is megatron's model, so you can see my ut in this pr.

kota-iizuka · 2024-07-04T14:22:42Z

@zhangsheng377 Thank you for your great work. I'd like to try your code, so could you show me the pretrain_llama.py that you mentioned in the comment #1092 (comment) ? I think it's a script that allows us to specify --lora-target-modules from the command line, but I couldn't find anything after a quick search.

zhangsheng377 · 2024-07-04T14:40:38Z

@zhangsheng377 Thank you for your great work. I'd like to try your code, so could you show me the pretrain_llama.py that you mentioned in the comment #1092 (comment) ? I think it's a script that allows us to specify --lora-target-modules from the command line, but I couldn't find anything after a quick search.

Haha, You will need to adapt the Megatron code to PEFT. For example, modify the model_provider function in the Megatron root directory pretrain_gpt.py file.

By the way, '--lora-target-modules' is a parameter I added to Megatron myself, and you can use your own adaptation process.

Or, you can see: https://gitee.com/ascend/MindSpeed

kota-iizuka · 2024-07-05T01:13:52Z

@zhangsheng377 Thank you. I added the conversion to the LoRA model at the end of model_provider() in the example https://github.com/microsoft/Megatron-DeepSpeed/tree/main/examples_deepspeed/finetune_hf_llama, but I got an error when loading the model. This sample includes a process to convert the huggingface model to the megatron model, and I think that the LoRA model conversion is also required there.

zhangsheng377 · 2024-07-05T01:18:57Z

Thank you. I added the conversion to the LoRA model at the end of model_provider() in the example https://github.com/microsoft/Megatron-DeepSpeed/tree/main/examples_deepspeed/finetune_hf_llama, but I got an error when loading the model. This sample includes a process to convert the huggingface model to the megatron model, and I think that the LoRA model conversion is also required there.

Things to note when loading the model: 1. The original hf model may not have lora parameters. 2. If you are converting the lora plug-in, you need to write the conversion code yourself.

kota-iizuka · 2024-07-05T01:28:47Z

If you are converting the lora plug-in, you need to write the conversion code yourself.

I understand that there is no standard way to load weight in LoRA training. I will try converting it now. Thank you.

fearblackcat reviewed Nov 8, 2023

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zhangsheng377 force-pushed the ParallelLinear-Lora branch from d7d8fac to 0ae52fe Compare November 22, 2023 07:06

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zhangsheng377 force-pushed the ParallelLinear-Lora branch from f48fcc2 to e35d46d Compare November 28, 2023 08:47

zhangsheng377 added 3 commits November 28, 2023 16:48

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Merge branch 'main' into ParallelLinear-Lora

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BenjaminBossan merged commit 2674f5e into huggingface:main Nov 30, 2023
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Parallel linear Lora #1092

Parallel linear Lora #1092

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