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(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)
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",
" new orleans",
" japan",
" old trafford",
]
self.assertListEqual(outputs, EXPECTED_OUTPUTS)
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)
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