Check tied parameters

[SunMarc]

What does this PR do ?

This PR fixes two issues user can have when using big inference model:

• Use their own device map but forget that parameters that are tied together should be on the same device. We return an error showing which parameters should be on the same device
• Forget to tie the parameters before using infer_auto_device_map() which can create a bad device_map. We also return an error asking to tie the weights before using this function.

How to test it

Issue 1

import os
import torch
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
from accelerate.utils import find_tied_parameters

checkpoint = "facebook/opt-350m"

device_map_work = {'model.decoder.embed_tokens': 'cpu',
 'model.decoder.embed_positions': 'cpu',
 'model.decoder.project_out': 'cpu',
 'model.decoder.project_in': 'cpu',
 'model.decoder.layers': 'cpu',
 'lm_head': 'cpu'}

device_map_do_no_work = {'model.decoder.embed_tokens': 'cpu',
 'model.decoder.embed_positions': 'cpu',
 'model.decoder.project_out': 'cpu',
 'model.decoder.project_in': 'cpu',
 'model.decoder.layers': 'cpu',
 'lm_head': 'disk'}

model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map = device_map_do_no_work, offload_folder="offload",offload_state_dict = True)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

Issue 2

import torch
from transformers import AutoConfig,AutoModelForCausalLM
from accelerate import init_empty_weights, infer_auto_device_map, load_checkpoint_and_dispatch

checkpoint = "facebook/opt-350m"

config = AutoConfig.from_pretrained(checkpoint)
with init_empty_weights():
    model = AutoModelForCausalLM.from_config(config)
device_map = infer_auto_device_map(model, no_split_module_classes=["OPTDecoderLayer"])

[HuggingFaceDocBuilderDev]

The documentation is not available anymore as the PR was closed or merged.

[sgugger]

Thanks for those QOL improvements!

[sgugger]

I was thinking on testing the class __name__ to avoid the extra dep on Transformers.