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test / src /loaders.py
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import functools
import json
from src.enums import t5_type
from src.utils import have_optimum
def get_loaders(model_name, reward_type, llama_type=None,
load_gptq='',
use_autogptq=False,
load_awq='',
load_exllama=False,
config=None,
rope_scaling=None, max_seq_len=None, model_name_exllama_if_no_config='',
exllama_dict=None, gptq_dict=None,
hf_model_dict={},
):
# NOTE: Some models need specific new prompt_type
# E.g. t5_xxl_true_nli_mixture has input format: "premise: PREMISE_TEXT hypothesis: HYPOTHESIS_TEXT".)
if load_exllama:
if exllama_dict is None:
exllama_dict = {}
from src.llm_exllama import H2OExLlamaTokenizer, H2OExLlamaGenerator
from exllama.model import ExLlama, ExLlamaCache, ExLlamaConfig
import os, glob
if config:
# then use HF path
from transformers import TRANSFORMERS_CACHE
model_directory = os.path.join(TRANSFORMERS_CACHE, 'models--' + config.name_or_path.replace('/', '--'),
'snapshots', config._commit_hash)
else:
# then use path in env file
# Directory containing model, tokenizer, generator
model_directory = model_name_exllama_if_no_config
# download model
revision = config._commit_hash
from huggingface_hub import snapshot_download
snapshot_download(repo_id=model_name, revision=revision)
# Locate files we need within that directory
tokenizer_path = os.path.join(model_directory, "tokenizer.model")
assert os.path.isfile(tokenizer_path), "Missing %s" % tokenizer_path
model_config_path = os.path.join(model_directory, "config.json")
assert os.path.isfile(model_config_path), "Missing %s" % model_config_path
st_pattern = os.path.join(model_directory, "*.safetensors")
model_path = glob.glob(st_pattern)[0]
assert os.path.isfile(model_path), "Missing %s" % model_path
# Create config, model, tokenizer and generator
exconfig = ExLlamaConfig(model_config_path) # create config from config.json
rope_scaling = rope_scaling or {}
exconfig.alpha_value = rope_scaling.get('alpha_value', 1) # rope
exconfig.compress_pos_emb = rope_scaling.get('compress_pos_emb', 1) # related rope
# update max_seq_len
assert hasattr(config, 'max_position_embeddings') or hasattr(config,
'max_sequence_length'), "Improve code if no such argument"
if hasattr(config, 'max_position_embeddings'):
exconfig.max_seq_len = int(config.max_position_embeddings * exconfig.alpha_value)
else:
exconfig.max_seq_len = int(config.max_sequence_length * exconfig.alpha_value)
if 'Llama-2'.lower() in model_name.lower():
# override bad defaults
exconfig.max_seq_len = int(4096 * exconfig.alpha_value)
if max_seq_len is not None:
exconfig.max_seq_len = max_seq_len
exconfig.model_path = model_path # supply path to model weights file
for k, v in exllama_dict.items():
setattr(exconfig, k, v)
if 'set_auto_map' in exllama_dict:
exconfig.auto_map = [float(alloc) for alloc in exllama_dict['set_auto_map'].split(",")]
model = ExLlama(exconfig) # create ExLlama instance and load the weights
tokenizer = H2OExLlamaTokenizer(tokenizer_path) # create tokenizer from tokenizer model file
tokenizer.model_max_length = exconfig.max_seq_len
cache = ExLlamaCache(model) # create cache for inference
generator = H2OExLlamaGenerator(model, tokenizer, cache) # create generator
return generator, tokenizer, False
if load_gptq and use_autogptq:
if gptq_dict is None:
gptq_dict = {}
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
if 'use_triton' not in gptq_dict:
gptq_dict['use_triton'] = False
if 'llama-2-70B-chat-GPTQ' in model_name.lower() and 'inject_fused_attention' not in gptq_dict:
gptq_dict.update(dict(inject_fused_attention=False))
model_loader = functools.partial(AutoGPTQForCausalLM.from_quantized,
quantize_config=None,
**gptq_dict,
)
return model_loader, AutoTokenizer, False
if load_gptq and not use_autogptq:
assert have_optimum, "To use HF transformers GPTQ, please: pip install optimum"
if load_awq:
from transformers import AutoTokenizer
from awq import AutoAWQForCausalLM
model_loader = functools.partial(AutoAWQForCausalLM.from_quantized,
fuse_layers=True,
)
return model_loader, AutoTokenizer, False
if llama_type is None:
llama_type = "llama" in model_name.lower()
if llama_type and not load_gptq:
from transformers import LlamaForCausalLM, LlamaTokenizer
return functools.partial(LlamaForCausalLM.from_pretrained, **hf_model_dict), LlamaTokenizer, False
elif 'distilgpt2' in model_name.lower():
from transformers import AutoModelForCausalLM, AutoTokenizer
return functools.partial(AutoModelForCausalLM.from_pretrained, **hf_model_dict), AutoTokenizer, False
elif 'gpt2' in model_name.lower():
from transformers import GPT2LMHeadModel, GPT2Tokenizer
return functools.partial(GPT2LMHeadModel.from_pretrained, **hf_model_dict), GPT2Tokenizer, False
elif 'mbart-' in model_name.lower():
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
return functools.partial(MBartForConditionalGeneration.from_pretrained, **hf_model_dict), MBart50TokenizerFast, True
elif t5_type(model_name):
from transformers import AutoTokenizer, T5ForConditionalGeneration
return functools.partial(T5ForConditionalGeneration.from_pretrained, **hf_model_dict), AutoTokenizer, True
elif 'bigbird' in model_name:
from transformers import BigBirdPegasusForConditionalGeneration, AutoTokenizer
return functools.partial(BigBirdPegasusForConditionalGeneration.from_pretrained, **hf_model_dict), AutoTokenizer, True
elif 'bart-large-cnn-samsum' in model_name or 'flan-t5-base-samsum' in model_name:
from transformers import pipeline
return pipeline, "summarization", False
elif reward_type or 'OpenAssistant/reward-model'.lower() in model_name.lower():
from transformers import AutoModelForSequenceClassification, AutoTokenizer
return functools.partial(AutoModelForSequenceClassification.from_pretrained, **hf_model_dict), AutoTokenizer, False
else:
from transformers import AutoTokenizer, AutoModelForCausalLM
model_loader = functools.partial(AutoModelForCausalLM.from_pretrained, **hf_model_dict)
tokenizer_loader = AutoTokenizer
return model_loader, tokenizer_loader, False
def get_tokenizer(tokenizer_loader, tokenizer_base_model, local_files_only, resume_download, use_auth_token):
tokenizer = tokenizer_loader.from_pretrained(tokenizer_base_model,
local_files_only=local_files_only,
resume_download=resume_download,
token=use_auth_token,
padding_side='left')
tokenizer.pad_token_id = 0 # different from the eos token
# when generating, we will use the logits of right-most token to predict the next token
# so the padding should be on the left,
# e.g. see: https://huggingface.co/transformers/v4.11.3/model_doc/t5.html#inference
tokenizer.padding_side = "left" # Allow batched inference
return tokenizer