utils.py

import numpy as np
import torch

from typing import Any
from transformers import AutoTokenizer


def splade_max(features, attention_mask):
    """
    SPLADE pooling operation
    """
    relu = torch.nn.ReLU(inplace=False)
    values, ids_ = torch.max(
        torch.log(1 + relu(features)) * attention_mask.unsqueeze(-1), dim=1
    )
    return values, ids_


def encode(
    self,
    sentences: list[str],
    max_length: int = 1024,
    prompt_type: str = "document",
    return_dict: bool = False,
    print_dict: bool = False,
    batch_size: int = 8,
    top_k_q: int = -1,
    top_k_d: int = -1,
    **kwargs: Any,
) -> np.ndarray:
    all_embeddings = []
    for i in range(0, len(sentences), batch_size):
        batch_texts = sentences[i : i + batch_size]
        batch_dict = self.create_batch_dict(batch_texts, max_length)
        batch_dict = {
            key: value.to(self.model.device) for key, value in batch_dict.items()
        }
        with torch.no_grad():
            splare_reps = self(**batch_dict)[0]
            if prompt_type == "query" and top_k_q > 0:
                splare_reps = top_k(splare_reps, top_k_q)
            if prompt_type == "document" and top_k_d > 0:
                splare_reps = top_k(splare_reps, top_k_d)
            all_embeddings.append(splare_reps.cpu().float().numpy())
    if return_dict:
        d = bow_dict(self, np.concatenate(all_embeddings, axis=0))
        if print_dict:
            print_bow_bars(sentences, d)
        return d
    else:
        return np.concatenate(all_embeddings, axis=0)


def bow_dict(self, embeddings):
    out = []
    for vector in embeddings:
        idx = np.nonzero(vector)[0]
        weights = vector[idx]
        d = {k: v for k, v in zip(idx.tolist(), weights.tolist())}
        sorted_d = {
            self.reverse_voc[k]: float(v)
            for k, v in sorted(d.items(), key=lambda item: item[1], reverse=True)
        }
        out.append(sorted_d)
    return out


def print_bow_bars(sentences, bow_list, width=20):
    ascii_header("TOP ACTIVATED WORDS")
    for sent, bow in zip(sentences, bow_list):
        print(f"* INPUT: {sent}\n")
        max_w = max(bow.values())
        for k, v in sorted(bow.items(), key=lambda x: x[1], reverse=True):
            bar = "█" * int(v / max_w * width)
            print(f"{k[:25]:25} | {bar} {v:.2f}")
        print("\n")


def ascii_header(title, width=70):
    title = f" {title} "
    print("+" + "-" * (width - 2) + "+")
    print("|" + title.center(width - 2) + "|")
    print("+" + "-" * (width - 2) + "+")
    print("\n")


def similarity(self, a, b) -> torch.Tensor:
    """
    MTEB eval requires this
    """
    if not isinstance(a, torch.Tensor):
        a = torch.tensor(a)
    if not isinstance(b, torch.Tensor):
        b = torch.tensor(b)

    def _dot_score_core(a_tensor, b_tensor):
        if len(a_tensor.shape) == 1:
            a_tensor = a_tensor.unsqueeze(0)
        if len(b_tensor.shape) == 1:
            b_tensor = b_tensor.unsqueeze(0)
        return a_tensor @ b_tensor.transpose(0, 1)

    return _dot_score_core(a, b)


def prepare_tokenizer(tokenizer_name: str, padding_side="right"):
    """
    loads and prepares tokenizer
    """
    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
    tokenizer.pad_token = (
        tokenizer.bos_token or tokenizer.pad_token or tokenizer.eos_token
    )
    tokenizer.padding_side = padding_side
    return tokenizer


def get_decoder_model(
    model_name_or_path: str, attn_implementation: str, bidirectional: bool, base_cfg, token=None
):
    """
    base_cfg is the pretrained config of the underlying model
    """
    print("WARNING: bidirectional only tested for transformer 4.51.2")
    assert (
        bidirectional is True
    ), "the model has been trained with bi-directional attention!"
    assert (
        attn_implementation == "flash_attention_2"
    ), f"bidir models only support flash_attention_2 for now, not {attn_implementation}!"
    from .modeling_qwen3_bidir import Qwen3BidirForCausalLM

    return Qwen3BidirForCausalLM.from_pretrained(
        model_name_or_path,
        config=base_cfg,
        torch_dtype=torch.bfloat16,
        attn_implementation=attn_implementation,
        token=token,
    )


def top_k(x: torch.Tensor, k: int) -> torch.Tensor:
    """
    zeroes out all but the top-k values in the last dimension of x
    """
    _, topk_indices = x.topk(k, dim=-1)
    # create a zero tensor of the same shape as x
    mask = torch.zeros_like(x, dtype=torch.bool)
    # use scatter along the last dimension
    mask.scatter_(-1, topk_indices, True)
    # zero out all but the top-k
    return x * mask