hipom_data_mapping/analysis/bert/utils.py

import torch
from transformers import (
    AutoTokenizer,
    AutoModelForSequenceClassification,
    DataCollatorWithPadding,
)
import torch.nn.functional as F



class Retriever:
    def __init__(self, input_texts, model_checkpoint):
        # we need to generate the embedding from list of input strings
        self.embeddings = []
        self.inputs = input_texts
        model_checkpoint = model_checkpoint 
        self.tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)

        model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        # device = "cpu"
        model.to(self.device)
        self.model = model.eval()


    def make_embedding(self, batch_size=64):
        all_embeddings = self.embeddings
        input_texts = self.inputs

        for i in range(0, len(input_texts), batch_size):
            batch_texts = input_texts[i:i+batch_size]
            # Tokenize the input text
            inputs = self.tokenizer(batch_texts, return_tensors="pt", padding=True, truncation=True, max_length=64)
            input_ids = inputs.input_ids.to(self.device)
            attention_mask = inputs.attention_mask.to(self.device)


            # Pass the input through the encoder and retrieve the embeddings
            with torch.no_grad():
                encoder_outputs = self.model(input_ids, attention_mask=attention_mask, output_hidden_states=True)
                # get last layer
                embeddings = encoder_outputs.hidden_states[-1]
                # get cls token embedding
                cls_embeddings = embeddings[:, 0, :]  # Shape: (batch_size, hidden_size)
                all_embeddings.append(cls_embeddings)
        
        # remove the batch list and makes a single large tensor, dim=0 increases row-wise
        all_embeddings = torch.cat(all_embeddings, dim=0)

        self.embeddings = all_embeddings

def cosine_similarity_chunked(batch1, batch2, chunk_size=1024):
    device = 'cuda'
    batch1_size = batch1.size(0)
    batch2_size = batch2.size(0)
    batch2.to(device)
    
    # Prepare an empty tensor to store results
    cos_sim = torch.empty(batch1_size, batch2_size, device=device)

    # Process batch1 in chunks
    for i in range(0, batch1_size, chunk_size):
        batch1_chunk = batch1[i:i + chunk_size]  # Get chunk of batch1
        
        batch1_chunk.to(device)
        # Expand batch1 chunk and entire batch2 for comparison
        # batch1_chunk_exp = batch1_chunk.unsqueeze(1)  # Shape: (chunk_size, 1, seq_len)
        # batch2_exp = batch2.unsqueeze(0)  # Shape: (1, batch2_size, seq_len)
        batch2_norms = batch2.norm(dim=1, keepdim=True)

        
        # Compute cosine similarity for the chunk and store it in the final tensor
        # cos_sim[i:i + chunk_size] = F.cosine_similarity(batch1_chunk_exp, batch2_exp, dim=-1)

        # Compute cosine similarity by matrix multiplication and normalizing
        sim_chunk = torch.mm(batch1_chunk, batch2.T) / (batch1_chunk.norm(dim=1, keepdim=True) * batch2_norms.T + 1e-8)
        
        # Store the results in the appropriate part of the final tensor
        cos_sim[i:i + chunk_size] = sim_chunk
    
    return cos_sim