kalpeshk2011/

rankgen-t5-xl-all

python3.7 -m virtualenv rankgen-venv
source rankgen-venv/bin/activate
pip install rankgen

python -m rankgen.test_rankgen_encoder --model_path kalpeshk2011/rankgen-t5-base-all

### Expected output
0.0009239262409127233
0.0011521980725477804

from rankgen import RankGenEncoder, RankGenGenerator

rankgen_encoder = RankGenEncoder("kalpeshk2011/rankgen-t5-xl-all")

# Encoding vectors
prefix_vectors = rankgen_encoder.encode(["This is a prefix sentence."], vectors_type="prefix")
suffix_vectors = rankgen_encoder.encode(["This is a suffix sentence."], vectors_type="suffix")

# Generating text
# use a HuggingFace compatible language model
generator = RankGenGenerator(rankgen_encoder=rankgen_encoder, language_model="gpt2-medium")

inputs = ["Whatever might be the nature of the tragedy it would be over with long before this, and those moving black spots away yonder to the west, that he had discerned from the bluff, were undoubtedly the departing raiders. There was nothing left for Keith to do except determine the fate of the unfortunates, and give their bodies decent burial. That any had escaped, or yet lived, was altogether unlikely, unless, perchance, women had been in the party, in which case they would have been borne away prisoners."]

# Baseline nucleus sampling
print(generator.generate_single(inputs, top_p=0.9)[0][0])
# Over-generate and re-rank
print(generator.overgenerate_rerank(inputs, top_p=0.9, num_samples=10)[0][0])
# Beam search
print(generator.beam_search(inputs, top_p=0.9, num_samples=10, beam_size=2)[0][0])

from transformers import T5Tokenizer, AutoModel

tokenizer = T5Tokenizer.from_pretrained(f"google/t5-v1_1-xl")
model = AutoModel.from_pretrained("kalpeshk2011/rankgen-t5-xl-all", trust_remote_code=True)

import tqdm
from transformers import T5Tokenizer, T5EncoderModel, AutoModel

class RankGenEncoder():
    def __init__(self, model_path, max_batch_size=32, model_size=None, cache_dir=None):
        assert model_path in ["kalpeshk2011/rankgen-t5-xl-all", "kalpeshk2011/rankgen-t5-xl-pg19", "kalpeshk2011/rankgen-t5-base-all", "kalpeshk2011/rankgen-t5-large-all"]
        self.max_batch_size = max_batch_size
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        if model_size is None:
            if "t5-large" in model_path or "t5_large" in model_path:
                self.model_size = "large"
            elif "t5-xl" in model_path or "t5_xl" in model_path:
                self.model_size = "xl"
            else:
                self.model_size = "base"
        else:
            self.model_size = model_size

        self.tokenizer = T5Tokenizer.from_pretrained(f"google/t5-v1_1-{self.model_size}", cache_dir=cache_dir)
        self.model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
        self.model.to(self.device)
        self.model.eval()

    def encode(self, inputs, vectors_type="prefix", verbose=False, return_input_ids=False):
        tokenizer = self.tokenizer
        max_batch_size = self.max_batch_size
        if isinstance(inputs, str):
            inputs = [inputs]
        if vectors_type == 'prefix':
            inputs = ['pre ' + input for input in inputs]
            max_length = 512
        else:
            inputs = ['suffi ' + input for input in inputs]
            max_length = 128

        all_embeddings = []
        all_input_ids = []
        for i in tqdm.tqdm(range(0, len(inputs), max_batch_size), total=(len(inputs) // max_batch_size) + 1, disable=not verbose, desc=f"Encoding {vectors_type} inputs:"):
            tokenized_inputs = tokenizer(inputs[i:i + max_batch_size], return_tensors="pt", padding=True)
            for k, v in tokenized_inputs.items():
                tokenized_inputs[k] = v[:, :max_length]
            tokenized_inputs = tokenized_inputs.to(self.device)
            with torch.inference_mode():
                batch_embeddings = self.model(**tokenized_inputs)
            all_embeddings.append(batch_embeddings)
            if return_input_ids:
                all_input_ids.extend(tokenized_inputs.input_ids.cpu().tolist())
        return {
            "embeddings": torch.cat(all_embeddings, dim=0),
            "input_ids": all_input_ids
        }