def test_rag_token_greedy_search(self):
tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
retriever = RagRetriever.from_pretrained("facebook/rag-token-nq",
index_name="exact",
use_dummy_dataset=True)
rag_token = TFRagTokenForGeneration.from_pretrained(
"facebook/rag-token-nq", retriever=retriever)
# check first two questions
input_dict = tokenizer(
self.test_data_questions[:2],
return_tensors="tf",
padding=True,
truncation=True,
)
input_ids = input_dict.input_ids
attention_mask = input_dict.attention_mask
# make sure only 1 beam is used
rag_token.config.num_beams = 1
output_ids = rag_token.generate(
input_ids,
attention_mask=attention_mask,
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
EXPECTED_OUTPUTS = [
" albert einstein",
" september 22, 2017",
]
self.assertListEqual(outputs, EXPECTED_OUTPUTS)
def test_rag_token_generate_batch(self):
tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
rag_token = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever).to(
torch_device
)
input_dict = tokenizer(
self.test_data_questions,
return_tensors="pt",
padding=True,
truncation=True,
)
input_ids = input_dict.input_ids.to(torch_device)
attention_mask = input_dict.attention_mask.to(torch_device)
output_ids = rag_token.generate(
input_ids,
attention_mask=attention_mask,
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
EXPECTED_OUTPUTS = [
" albert einstein",
" september 22, 2017",
" amplitude modulation",
" stefan persson",
" april 20, 2018",
" the 1970s",
" 7.1. 2",
" 13",
]
self.assertListEqual(outputs, EXPECTED_OUTPUTS)
def test_rag_sequence_generate_batch(self):
tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
retriever = RagRetriever.from_pretrained(
"facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True
)
rag_sequence = TFRagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever)
input_dict = tokenizer(
self.test_data_questions,
return_tensors="tf",
padding=True,
truncation=True,
)
input_ids = input_dict.input_ids
attention_mask = input_dict.attention_mask
output_ids = rag_sequence.generate(
input_ids,
attention_mask=attention_mask,
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
EXPECTED_OUTPUTS = [
" albert einstein",
" june 22, 2018",
" amplitude modulation",
" tim besley ( chairman )",
" june 20, 2018",
" 1980",
" 7.0",
" 8",
]
self.assertListEqual(outputs, EXPECTED_OUTPUTS)
def main(args):
model_kwargs = {}
if args.model_type is None:
args.model_type = infer_model_type(args.model_name_or_path)
assert args.model_type is not None
if args.model_type.startswith("rag"):
model_class = RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
model_kwargs["n_docs"] = args.n_docs
if args.index_name is not None:
model_kwargs["index_name"] = args.index_name
if args.index_path is not None:
model_kwargs["index_path"] = args.index_path
else:
model_class = BartForConditionalGeneration
checkpoints = (
[f.path for f in os.scandir(args.model_name_or_path) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info("Evaluate the following checkpoints: %s", checkpoints)
score_fn = get_scores if args.eval_mode == "e2e" else get_precision_at_k
evaluate_batch_fn = evaluate_batch_e2e if args.eval_mode == "e2e" else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path) and (not args.recalculate):
logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path))
score_fn(args, args.predictions_path, args.gold_data_path)
continue
logger.info("***** Running evaluation for {} *****".format(checkpoint))
logger.info(" Batch size = %d", args.eval_batch_size)
logger.info(" Predictions will be stored under {}".format(args.predictions_path))
if args.model_type.startswith("rag"):
retriever = RagRetriever.from_pretrained(checkpoint, **model_kwargs)
model = model_class.from_pretrained(checkpoint, retriever=retriever, **model_kwargs)
model.retriever.init_retrieval()
else:
model = model_class.from_pretrained(checkpoint, **model_kwargs)
model.to(args.device)
with open(args.evaluation_set, "r") as eval_file, open(args.predictions_path, "w") as preds_file:
questions = []
for line in tqdm(eval_file):
questions.append(line.strip())
if len(questions) == args.eval_batch_size:
answers = evaluate_batch_fn(args, model, questions)
preds_file.write("\n".join(answers) + "\n")
preds_file.flush()
questions = []
if len(questions) > 0:
answers = evaluate_batch_fn(args, model, questions)
preds_file.write("\n".join(answers))
preds_file.flush()
score_fn(args, args.predictions_path, args.gold_data_path)
def load_model(self) -> None:
logger.debug('loading rag retriever: %s', self.name)
retriever = RagRetriever.from_pretrained(self.rag_sequence,
index_name='custom',
indexed_dataset=self.dataset)
logger.debug('loading rag model: %s', self.name)
self.model = RagSequenceForGeneration.from_pretrained(
self.rag_sequence, retriever=retriever)
def test_rag_sequence_generate_batch_from_context_input_ids(self):
tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq",
index_name="exact",
use_dummy_dataset=True)
rag_sequence = RagSequenceForGeneration.from_pretrained(
"facebook/rag-sequence-nq", retriever=retriever).to(torch_device)
input_dict = tokenizer(
self.test_data_questions,
return_tensors="pt",
padding=True,
truncation=True,
)
input_ids = input_dict.input_ids.to(torch_device)
attention_mask = input_dict.attention_mask.to(torch_device)
question_hidden_states = rag_sequence.question_encoder(
input_ids, attention_mask=attention_mask)[0]
docs_dict = retriever(input_ids.cpu().detach().numpy(),
question_hidden_states.cpu().detach().numpy(),
return_tensors="pt")
doc_scores = torch.bmm(
question_hidden_states.unsqueeze(1),
docs_dict["retrieved_doc_embeds"].to(
torch_device).float().transpose(1, 2),
).squeeze(1)
output_ids = rag_sequence.generate(
context_input_ids=docs_dict["context_input_ids"].to(torch_device),
context_attention_mask=docs_dict["context_attention_mask"].to(
torch_device),
doc_scores=doc_scores.to(torch_device),
do_deduplication=True,
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
EXPECTED_OUTPUTS = [
" albert einstein",
" june 22, 2018",
" amplitude modulation",
" tim besley ( chairman )",
" june 20, 2018",
" 1980",
" 7.0",
" 8",
" reticular formation",
" walls of the abdomen",
" spodumene",
" obama",
" new orleans",
" japan",
" old trafford",
]
self.assertListEqual(outputs, EXPECTED_OUTPUTS)
def __init__(self, **args):
super(RagTrainer, self).__init__()
self.save_hyperparameters()
self.rag_retriever = RagRetriever.from_pretrained(
self.hparams['rag_ckpt_path'],
index_name='custom',
passages_path=self.hparams['wiki_ds_path'],
index_path=self.hparams['wiki_index_path'])
self.rag = RagSequenceForGeneration.from_pretrained(
self.hparams['rag_ckpt_path'], retriever=self.rag_retriever)
def test_rag_sequence_generate_batch_from_context_input_ids(self):
tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq",
index_name="exact",
use_dummy_dataset=True)
rag_sequence = TFRagSequenceForGeneration.from_pretrained(
"facebook/rag-sequence-nq", retriever=retriever)
input_dict = tokenizer(
self.test_data_questions,
return_tensors="tf",
padding=True,
truncation=True,
)
input_ids = input_dict.input_ids
question_hidden_states = rag_sequence.question_encoder(input_ids)[0]
docs_dict = retriever(input_ids.numpy(),
question_hidden_states.numpy(),
return_tensors="tf")
doc_scores = tf.squeeze(
tf.matmul(tf.expand_dims(question_hidden_states, axis=[1]),
docs_dict["retrieved_doc_embeds"],
transpose_b=True),
axis=[1],
)
output_ids = rag_sequence.generate(
context_input_ids=docs_dict["context_input_ids"],
context_attention_mask=docs_dict["context_attention_mask"],
doc_scores=doc_scores,
do_deduplication=True,
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
EXPECTED_OUTPUTS = [
" albert einstein",
" june 22, 2018",
" amplitude modulation",
" tim besley ( chairman )",
" june 20, 2018",
" 1980",
" 7.0",
" 8",
]
self.assertListEqual(outputs, EXPECTED_OUTPUTS)
def test_rag_token_generate_batch(self):
# NOTE: gold labels comes from num_beam=4 -- if change gold labels to greedy-generated, test will pass
tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
retriever = RagRetriever.from_pretrained("facebook/rag-token-nq",
index_name="exact",
use_dummy_dataset=True)
rag_token = TFRagTokenForGeneration.from_pretrained(
"facebook/rag-token-nq", retriever=retriever, from_pt=True)
input_dict = tokenizer(
self.test_data_questions,
return_tensors="tf",
padding=True,
truncation=True,
)
input_ids = input_dict.input_ids
attention_mask = input_dict.attention_mask
# rag_token.config.num_beams = 1 -> different in 2 answers (obama, united stadium) to num_beams=4 labels
output_ids = rag_token.generate(
input_ids,
attention_mask=attention_mask,
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
EXPECTED_OUTPUTS = [
" albert einstein",
" september 22, 2017",
" amplitude modulation",
" stefan persson",
" april 20, 2018",
" the 1970s",
" 7.1. 2",
" 13",
" step by step",
" stomach",
" spodumene",
" obama",
" northern new jersey",
" india",
" united stadium",
]
self.assertListEqual(outputs, EXPECTED_OUTPUTS)
def test_rag_token_generate_batch(self):
# NOTE: gold labels comes from num_beam=4, so this is effectively beam-search test
tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
retriever = RagRetriever.from_pretrained("facebook/rag-token-nq",
index_name="exact",
use_dummy_dataset=True)
rag_token = TFRagTokenForGeneration.from_pretrained(
"facebook/rag-token-nq", retriever=retriever)
input_dict = tokenizer(
self.test_data_questions,
return_tensors="tf",
padding=True,
truncation=True,
)
input_ids = input_dict.input_ids
attention_mask = input_dict.attention_mask
EXPECTED_OUTPUTS = [
" albert einstein",
" september 22, 2017",
" amplitude modulation",
" stefan persson",
" april 20, 2018",
" the 1970s",
" 7.1. 2",
" 13",
]
# Split into 2 batches of 4 examples to avoid GPU OOM.
output_ids = rag_token.generate(
input_ids[:4],
attention_mask=attention_mask[:4],
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
self.assertListEqual(outputs, EXPECTED_OUTPUTS[:4])
output_ids = rag_token.generate(
input_ids[4:],
attention_mask=attention_mask[4:],
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
self.assertListEqual(outputs, EXPECTED_OUTPUTS[4:])
def test_rag_sequence_generate_batch(self):
tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
retriever = RagRetriever.from_pretrained(
"facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True
)
rag_sequence = RagTokenForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever).to(
torch_device
)
input_dict = tokenizer(
self.test_data_questions,
return_tensors="pt",
padding=True,
truncation=True,
)
input_ids = input_dict.input_ids.to(torch_device)
attention_mask = input_dict.attention_mask.to(torch_device)
output_ids = rag_sequence.generate(
input_ids,
attention_mask=attention_mask,
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
EXPECTED_OUTPUTS = [
" albert einstein",
" june 22, 2018",
" amplitude modulation",
" tim besley ( chairman )",
" june 20, 2018",
" 1980",
" 7.0",
" 8",
" reticular formation",
" walls of the abdomen",
" spodumene",
" obama",
" grainger's compound",
" japan",
" old trafford stadium",
]
self.assertListEqual(outputs, EXPECTED_OUTPUTS)
def test_rag_token_generate_batch(self):
# NOTE: gold labels comes from num_beam=4, so this is effectively beam-search test
tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
retriever = RagRetriever.from_pretrained("facebook/rag-token-nq",
index_name="exact",
use_dummy_dataset=True)
rag_token = TFRagTokenForGeneration.from_pretrained(
"facebook/rag-token-nq", retriever=retriever)
input_dict = tokenizer(
self.test_data_questions,
return_tensors="tf",
padding=True,
truncation=True,
)
input_ids = input_dict.input_ids
attention_mask = input_dict.attention_mask
output_ids = rag_token.generate(
input_ids,
attention_mask=attention_mask,
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
EXPECTED_OUTPUTS = [
" albert einstein",
" september 22, 2017",
" amplitude modulation",
" stefan persson",
" april 20, 2018",
" the 1970s",
" 7.1. 2",
" 13",
" evolution",
" stomach",
" spodumene",
" obama",
" northern new jersey",
" india",
" united stadium",
]
self.assertListEqual(outputs, EXPECTED_OUTPUTS)
def main():
global args, best_acc1
args = parser.parse_args()
#########################################################################################
# Create options
#########################################################################################
options = {
'vqa': {
'trainsplit': args.vqa_trainsplit
},
'logs': {
'dir_logs': args.dir_logs
},
'model': {
'arch': args.arch,
'seq2vec': {
'type': args.st_type,
'dropout': args.st_dropout,
'fixed_emb': args.st_fixed_emb
}
},
'optim': {
'lr': args.learning_rate,
'batch_size': args.batch_size,
'epochs': args.epochs
}
}
if args.path_opt is not None:
with open(args.path_opt, 'r') as handle:
options_yaml = yaml.load(handle)
options = utils.update_values(options, options_yaml)
print('## args')
pprint(vars(args))
print('## options')
pprint(options)
if args.help_opt:
return
# Set datasets options
if 'vgenome' not in options:
options['vgenome'] = None
#########################################################################################
# Create needed datasets
#########################################################################################
trainset = datasets.factory_VQA(options['vqa']['trainsplit'],
options['vqa'], options['coco'],
options['vgenome'])
train_loader = trainset.data_loader(
batch_size=options['optim']['batch_size'],
num_workers=args.workers,
shuffle=True)
if options['vqa']['trainsplit'] == 'train':
valset = datasets.factory_VQA('val', options['vqa'], options['coco'])
val_loader = valset.data_loader(batch_size=2, num_workers=args.workers)
if options['vqa']['trainsplit'] == 'trainval' or args.evaluate:
testset = datasets.factory_VQA('test', options['vqa'], options['coco'])
test_loader = testset.data_loader(
batch_size=options['optim']['batch_size'],
num_workers=args.workers)
#########################################################################################
# Create model, criterion and optimizer
#########################################################################################
config = RagConfig.from_pretrained("facebook/rag-token-nq")
config.index_name = "legacy"
config.use_dummy_dataset = False
config.question_encoder.return_dict = True
config.n_docs = 10
# config.n_docs = 15
# import pdb;
# pdb.set_trace ()
if not args.evaluate and not args.resume:
tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-base",
config=config)
retriever = RagRetriever.from_pretrained("facebook/rag-token-base",
config=config)
model = RagTokenForGeneration.from_pretrained(
"facebook/rag-token-base", retriever=retriever, config=config)
else:
tokenizer = RagTokenizer.from_pretrained(os.path.join(
options['logs']['dir_logs'], "epoch_{}".format(args.start_epoch)),
config=config)
retriever = RagRetriever.from_pretrained(os.path.join(
options['logs']['dir_logs'], "epoch_{}".format(args.start_epoch)),
config=config)
model = RagTokenForGeneration.from_pretrained(os.path.join(
options['logs']['dir_logs'], "epoch_{}".format(args.start_epoch)),
retriever=retriever,
config=config)
model.cuda()
criterion = criterions.factory(options['vqa'], cuda=True)
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [
p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)
],
"weight_decay":
0.0,
},
{
"params": [
p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)
],
"weight_decay":
0.0,
},
]
optimizer = AdamW(optimizer_grouped_parameters,
lr=options['optim']['lr'],
eps=1e-8)
# optimizer = torch.optim.SGD(optimizer_grouped_parameters, lr=options['optim']['lr'], momentum=0.9)
#########################################################################################
# args.resume: resume from a checkpoint OR create logs directory
#########################################################################################
exp_logger = None
# Or create logs directory
# os.system('mkdir -p ' + options['logs']['dir_logs'])
path_new_opt = os.path.join(options['logs']['dir_logs'],
os.path.basename(args.path_opt))
path_args = os.path.join(options['logs']['dir_logs'], 'args.yaml')
with open(path_new_opt, 'w') as f:
yaml.dump(options, f, default_flow_style=False)
with open(path_args, 'w') as f:
yaml.dump(vars(args), f, default_flow_style=False)
if exp_logger is None:
# Set loggers
exp_name = os.path.basename(
options['logs']['dir_logs']) # add timestamp
exp_logger = logger.Experiment(exp_name, options)
exp_logger.add_meters('train', make_meters())
exp_logger.add_meters('test', make_meters())
if options['vqa']['trainsplit'] == 'train':
exp_logger.add_meters('val', make_meters())
exp_logger.info['model_params'] = utils.params_count(model)
print('Model has {} parameters'.format(
exp_logger.info['model_params']))
#########################################################################################
# args.evaluate: on valset OR/AND on testset
#########################################################################################
if args.evaluate:
path_logger_json = os.path.join(options['logs']['dir_logs'],
'logger.json')
if options['vqa']['trainsplit'] == 'train':
acc1, val_results = engine.validate(val_loader, model, retriever,
tokenizer, criterion,
exp_logger, args.start_epoch,
100)
# save results and compute OpenEnd accuracy
exp_logger.to_json(path_logger_json)
save_results(val_results, args.start_epoch, valset.split_name(),
options['logs']['dir_logs'], options['vqa']['dir'])
return
else:
for epoch in range(args.start_epoch + 1, options['optim']['epochs']):
engine.train(train_loader, model, retriever, tokenizer, criterion,
optimizer, exp_logger, epoch, args.print_freq)
# remember best prec@1 and save checkpoint
is_best = True
best_accs1 = -1
save_checkpoint(
{
'epoch': epoch,
'arch': options['model']['arch'],
'best_acc1': best_acc1,
'exp_logger': exp_logger
}, model, tokenizer, retriever, options['logs']['dir_logs'],
args.save_model, True)
def main(
rag_example_args: "RagExampleArguments",
processing_args: "ProcessingArguments",
index_hnsw_args: "IndexHnswArguments",
):
######################################
logger.info("Step 1 - Create the dataset")
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(
rag_example_args.csv_path), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
dataset = load_dataset("csv",
data_files=[rag_example_args.csv_path],
split="train",
delimiter="\t",
column_names=["title", "text"])
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
dataset = dataset.map(split_documents,
batched=True,
num_proc=processing_args.num_proc)
# And compute the embeddings
ctx_encoder = DPRContextEncoder.from_pretrained(
rag_example_args.dpr_ctx_encoder_model_name).to(device=device)
ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(
rag_example_args.dpr_ctx_encoder_model_name)
dataset = dataset.map(
partial(embed, ctx_encoder=ctx_encoder, ctx_tokenizer=ctx_tokenizer),
batched=True,
batch_size=processing_args.batch_size,
)
# And finally save your dataset
passages_path = os.path.join(rag_example_args.output_dir,
"my_knowledge_dataset")
dataset.save_to_disk(passages_path)
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info("Step 2 - Index the dataset")
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
index = faiss.IndexHNSWFlat(index_hnsw_args.d, index_hnsw_args.m,
faiss.METRIC_INNER_PRODUCT)
dataset.add_faiss_index("embeddings", custom_index=index)
# And save the index
index_path = os.path.join(rag_example_args.output_dir,
"my_knowledge_dataset_hnsw_index.faiss")
dataset.get_index("embeddings").save(index_path)
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
######################################
logger.info("Step 3 - Load RAG")
######################################
# Easy way to load the model
retriever = RagRetriever.from_pretrained(rag_example_args.rag_model_name,
index_name="custom",
indexed_dataset=dataset)
model = RagSequenceForGeneration.from_pretrained(
rag_example_args.rag_model_name, retriever=retriever)
tokenizer = RagTokenizer.from_pretrained(rag_example_args.rag_model_name)
# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)
######################################
logger.info("Step 4 - Have fun")
######################################
question = rag_example_args.question or "What does Moses' rod turn into ?"
input_ids = tokenizer.question_encoder(question,
return_tensors="pt")["input_ids"]
generated = model.generate(input_ids)
generated_string = tokenizer.batch_decode(generated,
skip_special_tokens=True)[0]
logger.info("Q: " + question)
logger.info("A: " + generated_string)
k: v.to(args.device)
for k, v in input_batch.items()
}
logits = model(**input_batch)
logits = logits[0]
attention = input_batch["attention_mask"]
argmax = [
l[a == 1].softmax(1).max(1) for l, a in zip(logits, attention)
]
preds = [idx[val >= args.thresh] for val, idx in argmax]
if __name__ == "__main__":
tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-base")
trainset = RAGEDataset(args, tokenizer)
retriever = RagRetriever.from_pretrained("facebook/rag-sequence-base")
model = RagTokenForGeneration.from_pretrained("facebook/rag-sequence-base",
retriever=retriever)
lit_rage = LitRage(args, trainset, model)
trainloader = DataLoader(
trainset,
batch_size=args.batch_size,
num_workers=args.num_workers,
shuffle=True,
collate_fn=trainset.collate,
)
checkpoint = ModelCheckpoint(
filepath=os.path.join(args.output,
"{epoch:02d}-{global_step:02d}-{val_loss:.2f}"),
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration, RagConfig
tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq",
index_name="exact",
use_dummy_dataset=True)
model = RagSequenceForGeneration.from_pretrained(
"facebook/rag-sequence-nq", retriever=retriever).to("cuda:0")
model.add_tokens()
model.config.n_docs = 6
retriever.config.n_docs = 6
model.config.n_docs_splits = 3
retriever.config.n_docs_splits = 3
model.skip_ec = True
model.skip_ec = True
input_dict = tokenizer.prepare_seq2seq_batch("am i a cool person",
return_tensors="pt").to("cuda:0")
generated = model.generate(**input_dict,
extra_context=["Cats are cool animals!"],
num_beams=4)
print(tokenizer.batch_decode(generated, skip_special_tokens=True)[0])
# should give 54 => google says either 44 or 51
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration, BartForConditionalGeneration, RagConfig, DPRQuestionEncoder
import torch
from transformers.models.auto import AutoModel
config = RagConfig.from_pretrained ("facebook/rag-token-nq")
config.index_name = "legacy"
config.use_dummy_dataset = False
config.question_encoder.return_dict = True
print("==> load tokenizer")
tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
print("==> load retriever")
retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", config=config)
print("dataset info")
print(dir(retriever.index))
print("==> load generator")
# question encoder
# question_encoder = DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base')
# generator = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
# config = RagConfig.from_question_encoder_generator_configs(question_encoder.config, generator.config)
# model = RagTokenForGeneration(config, question_encoder=question_encoder,generator=generator, retriever=retriever)
model = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
# input_dict = tokenizer.prepare_seq2seq_batch("USA president in 1999?", return_tensors="pt")
input_dict = tokenizer.prepare_seq2seq_batch("What kind of vehicle uses fire hydrant?", return_tensors="pt")
# input_dict = tokenizer.prepare_seq2seq_batch("what phylum does cat belong to?", return_tensors="pt")
print(input_dict.keys()) # dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'labels'])
input_ids = input_dict['input_ids']
print("==> encode")
question_hidden_states = model.question_encoder(input_ids)[0]
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq",
dataset="wiki_dpr",
index_name='compressed')
model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq",
retriever=retriever)
input_dict = tokenizer.prepare_seq2seq_batch(
"how many countries are in europe", return_tensors="pt")
generated = model.generate(input_ids=input_dict["input_ids"])
print(tokenizer.batch_decode(generated, skip_special_tokens=True)[0])
