[NeVa Pretraining] Vision Encoder Created on All GPUs During Pipeline Parallelism
State: closed | Pull request: no
Labels: name: stale

Dear all,

I'm encountering an issue with the vision encoder when using pipeline parallelism. 
The vision encoder appears to be instantiated on all GPUs rather than allocated to GPU 0, which is the first stage of the pipeline.

I tracked the vision encode in `NeMo/nemo/collections/multimodal/models/multimodal_llm/neva/neva_model.py` like this.

```
def create_vision_encoder_and_processor(self, mm_cfg):
    # Initialize vision encoder and freeze it
    if mm_cfg.vision_encoder.get("from_hf", False):
        from transformers import AutoConfig

        config = AutoConfig.from_pretrained(mm_cfg.vision_encoder.from_pretrained)
        if config.architectures[0] == "CLIPVisionModel" or config.architectures[0] == "CLIPModel":
            vision_encoder = CLIPVisionModel.from_pretrained(
                mm_cfg.vision_encoder.from_pretrained,
                torch_dtype=torch.bfloat16,
            ).cuda()
            vision_encoder = vision_encoder.to(torch.bfloat16)
            print(f"vision_encoder is on device: {next(vision_encoder.parameters()).device}")
```
The log shows that the vision encoder is on every device.
Since I used pipeline parallelism, I think the vision encoder must reside in the first stage of the pipeline.
If I misunderstood or my tracking is wrong, let me know.

Thank you.

### Experimental Setup

I used the `nvcr.io/nvidia/nemo:24.07` docker image on testing.

This is the following script to run with NeMo.
```
! torchrun --nproc_per_node=4 \
    /opt/NeMo/examples/multimodal/multimodal_llm/neva/neva_pretrain.py \
    trainer.precision=bf16 \
    trainer.num_nodes=1 \
    trainer.devices=4 \
    trainer.val_check_interval=50 \
    trainer.limit_val_batches=5 \
    trainer.log_every_n_steps=1 \
    trainer.max_steps=1 \
    model.megatron_amp_O2=True \
    model.micro_batch_size=1 \
    model.global_batch_size=16 \
    model.tensor_model_parallel_size=1 \
    model.pipeline_model_parallel_size=4 \
    model.mcore_gpt=True \
    model.transformer_engine=True \
    model.data.data_path=/data/neva/datasets/LLaVA-Pretrain-LCS-558k/blip_laion_cc_sbu_558k.json \
    model.data.image_folder=/data/neva/datasets/LLaVA-Pretrain-LCS-558k/images \
    model.tokenizer.library=sentencepiece \
    model.tokenizer.model=/data/neva/tokenizers/tokenizer_neva.model \
    model.encoder_seq_length=4096 \
    model.num_layers=32 \
    model.hidden_size=4096 \
    model.ffn_hidden_size=11008 \
    model.num_attention_heads=32 \
    model.normalization=rmsnorm \
    model.do_layer_norm_weight_decay=False \
    model.apply_query_key_layer_scaling=True \
    model.bias=False \
    model.activation=fast-swiglu \
    model.headscale=False \
    model.position_embedding_type=rope \
    model.rotary_percentage=1.0 \
    model.num_query_groups=null \
    model.data.num_workers=0 \
    model.mm_cfg.llm.from_pretrained=/data/neva/checkpoints/checkpoints/llama-2-7b-chat.nemo \
    model.mm_cfg.llm.model_type=v1 \
    model.data.conv_template=v1 \
    model.mm_cfg.vision_encoder.from_pretrained='openai/clip-vit-large-patch14' \
    model.mm_cfg.vision_encoder.from_hf=True \
    model.optim.name="fused_adam" \
    exp_manager.create_checkpoint_callback=True \
    exp_manager.checkpoint_callback_params.save_nemo_on_train_end=False \
    exp_manager.create_wandb_logger=False
```

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