Spaces:
Sleeping
Sleeping
File size: 10,562 Bytes
9dd777e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 |
""" Hugging Face utilities for model loading and pipeline creation. """
from typing import Optional, List, Dict, Union
from datasets import Dataset
from transformers import (
AutoTokenizer,
EncoderDecoderModel,
AutoModelForCausalLM,
pipeline,
GenerationConfig,
)
from transformers.pipelines.pt_utils import KeyDataset
from tqdm import tqdm
import torch
def get_encoder_decoder_model(
pretrained_encoder: str = "seyonec/ChemBERTa-zinc-base-v1",
pretrained_decoder: str = "seyonec/ChemBERTa-zinc-base-v1",
max_length: Optional[int] = 512,
tie_encoder_decoder: bool = False,
) -> EncoderDecoderModel:
""" Get the EncoderDecoderModel model for the PROTAC splitter.
Args:
pretrained_encoder (str): The pretrained model to use for the encoder. Default: "seyonec/ChemBERTa-zinc-base-v1"
pretrained_decoder (str): The pretrained model to use for the decoder. Default: "seyonec/ChemBERTa-zinc-base-v1"
max_length (int): The maximum length of the input sequence. Default: 512
tie_encoder_decoder (bool): Whether to tie the encoder and decoder weights. Default: False
Returns:
EncoderDecoderModel: The EncoderDecoderModel model for the PROTAC splitter
"""
bert2bert = EncoderDecoderModel.from_encoder_decoder_pretrained(
pretrained_encoder,
pretrained_decoder,
tie_encoder_decoder=tie_encoder_decoder,
)
print(f"Number of parameters: {bert2bert.num_parameters():,}")
tokenizer = AutoTokenizer.from_pretrained(pretrained_encoder)
# Tokenizer-related configs
bert2bert.config.decoder_start_token_id = tokenizer.cls_token_id
bert2bert.config.eos_token_id = tokenizer.sep_token_id
bert2bert.config.pad_token_id = tokenizer.pad_token_id
bert2bert.config.vocab_size = bert2bert.config.encoder.vocab_size
# Generation configs
# NOTE: See full list of configurations can be found here: https://huggingface.co/docs/transformers/v4.33.3/en/main_classes/text_generation#transformers.GenerationConfig
bert2bert.encoder.config.max_length = max_length
bert2bert.decoder.config.max_length = max_length
def setup_gen(config):
config.do_sample = True
config.num_beams = 5
config.top_k = 20
config.max_length = 512
# config.max_new_tokens = 512
return config
bert2bert.config = setup_gen(bert2bert.config)
bert2bert.encoder.config = setup_gen(bert2bert.encoder.config)
bert2bert.decoder.config = setup_gen(bert2bert.decoder.config)
bert2bert.decoder.config.is_decoder = True
bert2bert.generation_config = setup_gen(bert2bert.generation_config)
# bert2bert.config.do_sample = True
# bert2bert.config.num_beams = 5
# bert2bert.config.top_k = 20
# bert2bert.config.max_length=512
# bert2bert.config.max_new_tokens=512
# bert2bert.generation_config.max_new_tokens = 512
# bert2bert.generation_config.min_new_tokens = 512
# bert2bert.config.max_new_tokens = 514
# bert2bert.config.early_stopping = True
# bert2bert.config.length_penalty = 2.0
# # bert2bert.config.no_repeat_ngram_size = 3 # Default: 0
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
bert2bert.to(device)
return bert2bert
def get_causal_model(
pretrained_model: str = "seyonec/ChemBERTa-zinc-base-v1",
max_length: Optional[int] = 512,
) -> AutoModelForCausalLM:
""" Get the causal language model for the PROTAC splitter.
Args:
pretrained_model (str): The pretrained model to use for the causal language model. Default: "seyonec/ChemBERTa-zinc-base-v1"
max_length (int): The maximum length of the input sequence. Default: 512
Returns:
AutoModelForCausalLM: The causal language model for the PROTAC splitter
"""
model = AutoModelForCausalLM.from_pretrained(pretrained_model, is_decoder=True)
# model.is_decoder = True # It might not be necessary, but it's good to be explicit
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
return model
# REF: https://github.com/huggingface/transformers/blob/v4.44.2/src/transformers/generation/configuration_utils.py#L71
GENERATION_STRATEGY_PARAMS = {
"greedy": {"num_beams": 1, "do_sample": False},
"contrastive_search": {"penalty_alpha": 0.1, "top_k": 10},
"multinomial_sampling": {"num_beams": 1, "do_sample": True},
"beam_search_decoding": {"num_beams": 5, "do_sample": False, "num_return_sequences": 5},
"beam_search_multinomial_sampling": {"num_beams": 5, "do_sample": True, "num_return_sequences": 5},
"diverse_beam_search_decoding": {"num_beams": 5, "num_beam_groups": 5, "diversity_penalty": 1.0, "num_return_sequences": 5},
}
def avail_generation_strategies() -> List[str]:
""" Get the available generation strategies. """
return list(GENERATION_STRATEGY_PARAMS.keys())
def get_generation_config(generation_strategy: str) -> GenerationConfig:
""" Get the generation config for the given generation strategy. """
return GenerationConfig(
max_length=512,
max_new_tokens=512,
**GENERATION_STRATEGY_PARAMS[generation_strategy],
)
def get_pipeline(
model_name: str,
token: str,
is_causal_language_model: bool,
generation_strategy: Optional[str] = None,
num_return_sequences: int = 1,
device: Optional[Union[int, str]] = None,
) -> pipeline:
""" Get the pipeline for the given model name and generation strategy.
"""
device = device or ('cuda' if torch.cuda.is_available() else 'cpu')
if is_causal_language_model and generation_strategy is None:
print('Loading pipeline for causal language models...')
tokenizer = AutoTokenizer.from_pretrained(model_name, token=token, padding_side='left')
return pipeline(
"text-generation",
model=model_name,
tokenizer=tokenizer,
token=token,
device=device,
num_return_sequences=num_return_sequences,
)
if is_causal_language_model and generation_strategy is not None:
print('Loading pipeline for causal language models...')
tokenizer = AutoTokenizer.from_pretrained(model_name, token=token, padding_side='left')
return pipeline(
"text-generation",
model=model_name,
tokenizer=tokenizer,
token=token,
device=device,
generation_config=get_generation_config(generation_strategy),
)
if not is_causal_language_model and generation_strategy is None:
print('Loading pipeline for sequence-to-sequence models...')
tokenizer = AutoTokenizer.from_pretrained(model_name, token=token)
return pipeline(
"text2text-generation",
model=model_name,
tokenizer=tokenizer,
token=token,
device=device,
)
if not is_causal_language_model and generation_strategy is not None:
print('Loading pipeline for sequence-to-sequence models...')
tokenizer = AutoTokenizer.from_pretrained(model_name, token=token)
return pipeline(
"text2text-generation",
model=model_name,
tokenizer=tokenizer,
token=token,
device=device,
generation_config=get_generation_config(generation_strategy),
)
def run_causal_pipeline(
pipe: pipeline,
test_ds: Dataset,
batch_size: int,
smiles_column: str = 'prompt',
) -> List[Dict[str, str]]:
""" Run the pipeline for causal language models and return the predictions.
Args:
pipe (pipeline): The pipeline object to use for generating predictions.
test_ds (Dataset): The test dataset to generate predictions for.
batch_size (int): The batch size to use for generating predictions.
Returns:
List[Dict[str, str]]: A list of dictionaries containing the predictions.
"""
preds = []
for pred in tqdm(pipe(KeyDataset(test_ds, smiles_column), batch_size=batch_size, max_length=512), total=len(test_ds) // batch_size):
generated_text = [p['generated_text'] for p in pred]
# Remove the prompt from the generated text
generated_text = ['.'.join(t.split('.')[1:]) for t in generated_text]
# Add the predictions to the list
p = {f'pred_n{i}': t for i, t in enumerate(generated_text)}
preds.append(p)
return preds
def run_seq2seq_pipeline(
pipe: pipeline,
test_ds: Dataset,
batch_size: int,
smiles_column: str = 'text',
) -> List[Dict[str, str]]:
""" Run the pipeline for sequence-to-sequence models and return the predictions.
Args:
pipe (pipeline): The pipeline object to use for generating predictions.
test_ds (Dataset): The test dataset to generate predictions for.
batch_size (int): The batch size to use for generating predictions.
Returns:
List[Dict[str, str]]: A list of dictionaries containing the predictions.
"""
preds = []
for pred in tqdm(pipe(KeyDataset(test_ds, smiles_column), batch_size=batch_size, max_length=512), total=len(test_ds) // batch_size):
p = {f'pred_n{i}': p['generated_text'] for i, p in enumerate(pred)}
preds.append(p)
return preds
def run_pipeline(
pipe: pipeline,
test_ds: Dataset,
batch_size: int,
is_causal_language_model: bool,
smiles_column: str = 'text',
) -> List[Dict[str, str]]:
""" Run the pipeline and return the predictions.
Args:
pipe (pipeline): The pipeline object to use for generating predictions.
test_ds (Dataset): The test dataset to generate predictions for.
batch_size (int): The batch size to use for generating predictions.
is_causal_language_model (bool): Whether the model is a causal language model or not.
smiles_column (str): The column name in the dataset that contains the SMILES strings. Default: 'text'
Returns:
List[Dict[str, str]]: A list of dictionaries containing the beam-size predictions in the format: [{'pred_n0': 'prediction_0', 'pred_n1': 'prediction_1', ...}, ...]
"""
if is_causal_language_model:
return run_causal_pipeline(pipe, test_ds, batch_size, smiles_column)
else:
return run_seq2seq_pipeline(pipe, test_ds, batch_size, smiles_column) |