I am not able to train larger model on two GPUs, does anyone know how to fix this with deepspeed? I tried to dispatch large model with my own dispatcher, but received OOM error. from accelerate import dispatch_model device_map = auto_configure_device_map(4) print(device_map) model = ...
- when running in 4 and 8 bit it goes OOM because it only utilizes the first gpu. - when running the model in full, it goes OOM because it is too big anyway for 2 GPUs. Hmmm, I'll report this with bitsandbytes as well. I would not consider the 8bit and 4bit flags as corner...
But not when using multiple GPUs. Different results because of data movement maybe acceptable to an extent where they're not leading black images in this case. On my side, even without device_map, I get different latent space each time. Can you confirm that this is not the case on your...
System Info I'm using the accelerate Python API, on a machine with 4 T4 GPUs. - `Accelerate` version: 0.19.0 - Platform: Linux-5.19.0-1024-aws-x86_64-with-glibc2.35 - Python version: 3.10.6 - Numpy version: 1.24.1 - PyTorch version (GPU?...
Feature request How to make SiglipVisionModel can support Auto map to map to multiple GPUs. Motivation Currently, using MLLM with siglip, the whole model might need automap, since the vision encoder part is SiglipVisionModel, if it doesn...
Setting device_map={'':torch.cuda.current_device()}, it means the model is copied to both GPUs. Setting device_map="auto", I see the model to split into two parts: However, I found the latter method consumes nearly the save GPU memories per GPU as the first method. Why? I thought...
If I omit the device_map and use device=torch.device (my gpu) the models are loaded fine. But because the above will need to be run in multiple computes with different gpus I want to use accelerate's device_map to optimally split the models. ...
(80G A100) node, I figure out that fitting each whole model into a GPU with independent process achieves the best speed. But when trying to use int8, I can't useautoin device_map(it will shard model into different gpus, which I don't want) and I'll have to design a device_map ...
{"": Accelerator().local_process_index},device_map="auto", ) I have 4x4090 GPU and I wanna train Llama7B model across each one of these GPUs. Each 4090GPU has 24 GB, but loading 7B model will take 54GB of memory, so, holding it on single GPU won't work. So, How should I ...
torch 1.13.1 transformers 4.25.1 accelerate 0.15.0 # of gpus: 2 The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results. Setting `pad_token_id` to `eos_token_id`...