twc_embeddings.py

from transformers import AutoModel, AutoTokenizer
from transformers import AutoModelForCausalLM
from scipy.spatial.distance import cosine
import argparse
import json
import pdb
import torch
import torch.nn.functional as F

def read_text(input_file):
    arr = open(input_file).read().split("\n")
    return arr[:-1]


class CausalLMModel:
    def __init__(self):
        self.model = None
        self.tokenizer = None
        self.debug = False
        print("In CausalLMModel Constructor")

    def init_model(self,model_name = None):
        # Get our models - The package will take care of downloading the models automatically
        # For best performance: Muennighoff/SGPT-5.8B-weightedmean-nli-bitfit
        if (self.debug):
            print("Init model",model_name)
        # For best performance: EleutherAI/gpt-j-6B
        if (model_name is None):
            model_name = "EleutherAI/gpt-neo-125M"
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForCausalLM.from_pretrained(model_name)
        self.model.eval()
        self.prompt = 'Documents are searched to find matches with the same content.\nThe document "{}" is a good search result for "'

    def compute_embeddings(self,input_data,is_file):
        if (self.debug):
            print("Computing embeddings for:", input_data[:20])
        model = self.model
        tokenizer = self.tokenizer

        texts = read_text(input_data) if is_file == True else input_data
        query = texts[0]
        docs = texts[1:]

        # Tokenize input texts

        #print(f"Query: {query}")
        scores = []
        for doc in docs:
            context = self.prompt.format(doc)

            context_enc = tokenizer.encode(context, add_special_tokens=False)
            continuation_enc = tokenizer.encode(query, add_special_tokens=False)
            # Slice off the last token, as we take its probability from the one before
            model_input = torch.tensor(context_enc+continuation_enc[:-1])
            continuation_len = len(continuation_enc)
            input_len, = model_input.shape

            # [seq_len] -> [seq_len, vocab]
            logprobs = torch.nn.functional.log_softmax(model(model_input)[0], dim=-1).cpu()
            # [seq_len, vocab] -> [continuation_len, vocab]
            logprobs = logprobs[input_len-continuation_len:]
            # Gather the log probabilities of the continuation tokens -> [continuation_len]
            logprobs = torch.gather(logprobs, 1, torch.tensor(continuation_enc).unsqueeze(-1)).squeeze(-1)
            score = torch.sum(logprobs)
            scores.append(score.tolist())
        return texts,scores

    def output_results(self,output_file,texts,scores,main_index = 0):
        cosine_dict = {}
        docs = texts[1:]
        if (self.debug):
            print("Total sentences",len(texts))
        assert(len(scores) == len(docs))
        for i in range(len(docs)):
            cosine_dict[docs[i]] = scores[i]

        if (self.debug):
            print("Input sentence:",texts[main_index])
        sorted_dict = dict(sorted(cosine_dict.items(), key=lambda item: item[1],reverse = True))
        if (self.debug):
            for key in sorted_dict:
                print("Document score for \"%s\" is: %.3f" % (key[:100], sorted_dict[key]))
        if (output_file is not None):
            with open(output_file,"w") as fp:
                fp.write(json.dumps(sorted_dict,indent=0))
        return sorted_dict


class SGPTQnAModel:
    def __init__(self):
        self.model = None
        self.tokenizer = None
        self.debug = False
        print("In SGPT Q&A Constructor")


    def init_model(self,model_name = None):
        # Get our models - The package will take care of downloading the models automatically
        # For best performance: Muennighoff/SGPT-5.8B-weightedmean-nli-bitfit
        if (self.debug):
            print("Init model",model_name)
        if (model_name is None):
            model_name = "Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit"
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModel.from_pretrained(model_name)
        self.model.eval()
        self.SPECB_QUE_BOS = self.tokenizer.encode("[", add_special_tokens=False)[0]
        self.SPECB_QUE_EOS = self.tokenizer.encode("]", add_special_tokens=False)[0]

        self.SPECB_DOC_BOS = self.tokenizer.encode("{", add_special_tokens=False)[0]
        self.SPECB_DOC_EOS = self.tokenizer.encode("}", add_special_tokens=False)[0]


    def tokenize_with_specb(self,texts, is_query):
        # Tokenize without padding
        batch_tokens = self.tokenizer(texts, padding=False, truncation=True)   
        # Add special brackets & pay attention to them
        for seq, att in zip(batch_tokens["input_ids"], batch_tokens["attention_mask"]):
            if is_query:
                seq.insert(0, self.SPECB_QUE_BOS)
                seq.append(self.SPECB_QUE_EOS)
            else:
                seq.insert(0, self.SPECB_DOC_BOS)
                seq.append(self.SPECB_DOC_EOS)
            att.insert(0, 1)
            att.append(1)
        # Add padding
        batch_tokens = self.tokenizer.pad(batch_tokens, padding=True, return_tensors="pt")
        return batch_tokens

    def get_weightedmean_embedding(self,batch_tokens, model):
        # Get the embeddings
        with torch.no_grad():
            # Get hidden state of shape [bs, seq_len, hid_dim]
            last_hidden_state = self.model(**batch_tokens, output_hidden_states=True, return_dict=True).last_hidden_state

        # Get weights of shape [bs, seq_len, hid_dim]
        weights = (
            torch.arange(start=1, end=last_hidden_state.shape[1] + 1)
            .unsqueeze(0)
            .unsqueeze(-1)
            .expand(last_hidden_state.size())
            .float().to(last_hidden_state.device)
        )

        # Get attn mask of shape [bs, seq_len, hid_dim]
        input_mask_expanded = (
            batch_tokens["attention_mask"]
            .unsqueeze(-1)
            .expand(last_hidden_state.size())
            .float()
        )

        # Perform weighted mean pooling across seq_len: bs, seq_len, hidden_dim -> bs, hidden_dim
        sum_embeddings = torch.sum(last_hidden_state * input_mask_expanded * weights, dim=1)
        sum_mask = torch.sum(input_mask_expanded * weights, dim=1)

        embeddings = sum_embeddings / sum_mask

        return embeddings

    def compute_embeddings(self,input_data,is_file):
        if (self.debug):
            print("Computing embeddings for:", input_data[:20])
        model = self.model
        tokenizer = self.tokenizer

        texts = read_text(input_data) if is_file == True else input_data

        queries = [texts[0]]
        docs = texts[1:]
        query_embeddings = self.get_weightedmean_embedding(self.tokenize_with_specb(queries, is_query=True), self.model)
        doc_embeddings = self.get_weightedmean_embedding(self.tokenize_with_specb(docs, is_query=False), self.model)
        return texts,(query_embeddings,doc_embeddings)



    def output_results(self,output_file,texts,embeddings,main_index = 0):
        # Calculate cosine similarities
        # Cosine similarities are in [-1, 1]. Higher means more similar
        query_embeddings = embeddings[0]
        doc_embeddings = embeddings[1]
        cosine_dict = {}
        queries = [texts[0]]
        docs = texts[1:]
        if (self.debug):
            print("Total sentences",len(texts))
        for i in range(len(docs)):
            cosine_dict[docs[i]] = 1 - cosine(query_embeddings[0], doc_embeddings[i])

        if (self.debug):
            print("Input sentence:",texts[main_index])
        sorted_dict = dict(sorted(cosine_dict.items(), key=lambda item: item[1],reverse = True))
        if (self.debug):
            for key in sorted_dict:
                print("Cosine similarity with  \"%s\" is: %.3f" % (key, sorted_dict[key]))
        if (output_file is not None):
            with open(output_file,"w") as fp:
                fp.write(json.dumps(sorted_dict,indent=0))
        return sorted_dict


class SimCSEModel:
    def __init__(self):
        self.model = None
        self.tokenizer = None
        self.debug = False
        print("In SimCSE constructor")

    def init_model(self,model_name = None):
        if (model_name == None):
            model_name = "princeton-nlp/sup-simcse-roberta-large"
        #self.model = SimCSE(model_name)
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModel.from_pretrained(model_name)

    def compute_embeddings(self,input_data,is_file):
        texts = read_text(input_data) if is_file == True else input_data
        inputs = self.tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
        with torch.no_grad():
            embeddings = self.model(**inputs, output_hidden_states=True, return_dict=True).pooler_output
        return texts,embeddings

    def output_results(self,output_file,texts,embeddings,main_index = 0):
        # Calculate cosine similarities
        # Cosine similarities are in [-1, 1]. Higher means more similar
        cosine_dict = {}
        #print("Total sentences",len(texts))
        for i in range(len(texts)):
                cosine_dict[texts[i]] = 1 - cosine(embeddings[main_index], embeddings[i])

        #print("Input sentence:",texts[main_index])
        sorted_dict = dict(sorted(cosine_dict.items(), key=lambda item: item[1],reverse = True))
        if (self.debug):
            for key in sorted_dict:
                print("Cosine similarity with  \"%s\" is: %.3f" % (key, sorted_dict[key]))
        if (output_file is not None):
            with open(output_file,"w") as fp:
                fp.write(json.dumps(sorted_dict,indent=0))
        return sorted_dict



class SGPTModel:
    def __init__(self):
        self.model = None
        self.tokenizer = None
        self.debug = False
        print("In SGPT Constructor")


    def init_model(self,model_name = None):
        # Get our models - The package will take care of downloading the models automatically
        # For best performance: Muennighoff/SGPT-5.8B-weightedmean-nli-bitfit
        if (self.debug):
            print("Init model",model_name)
        if (model_name is None):
            model_name = "Muennighoff/SGPT-125M-weightedmean-nli-bitfit"
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModel.from_pretrained(model_name)
        #self.tokenizer = AutoTokenizer.from_pretrained("Muennighoff/SGPT-1.3B-weightedmean-msmarco-specb-bitfit")
        #self.model = AutoModel.from_pretrained("Muennighoff/SGPT-1.3B-weightedmean-msmarco-specb-bitfit")
        #self.tokenizer = AutoTokenizer.from_pretrained("Muennighoff/SGPT-5.8B-weightedmean-msmarco-specb-bitfit")
        #self.model = AutoModel.from_pretrained("Muennighoff/SGPT-5.8B-weightedmean-msmarco-specb-bitfit")
        # Deactivate Dropout (There is no dropout in the above models so it makes no difference here but other SGPT models may have dropout)
        self.model.eval()

    def compute_embeddings(self,input_data,is_file):
        if (self.debug):
            print("Computing embeddings for:", input_data[:20])
        model = self.model
        tokenizer = self.tokenizer

        texts = read_text(input_data) if is_file == True else input_data

        # Tokenize input texts
        batch_tokens = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")

    # Get the embeddings
        with torch.no_grad():
            # Get hidden state of shape [bs, seq_len, hid_dim]
            last_hidden_state = model(**batch_tokens, output_hidden_states=True, return_dict=True).last_hidden_state

        # Get weights of shape [bs, seq_len, hid_dim]
        weights = (
            torch.arange(start=1, end=last_hidden_state.shape[1] + 1)
            .unsqueeze(0)
            .unsqueeze(-1)
            .expand(last_hidden_state.size())
            .float().to(last_hidden_state.device)
        )

        # Get attn mask of shape [bs, seq_len, hid_dim]
        input_mask_expanded = (
            batch_tokens["attention_mask"]
            .unsqueeze(-1)
            .expand(last_hidden_state.size())
            .float()
        )

        # Perform weighted mean pooling across seq_len: bs, seq_len, hidden_dim -> bs, hidden_dim
        sum_embeddings = torch.sum(last_hidden_state * input_mask_expanded * weights, dim=1)
        sum_mask = torch.sum(input_mask_expanded * weights, dim=1)

        embeddings = sum_embeddings / sum_mask
        return texts,embeddings

    def output_results(self,output_file,texts,embeddings,main_index = 0):
        # Calculate cosine similarities
        # Cosine similarities are in [-1, 1]. Higher means more similar
        cosine_dict = {}
        if (self.debug):
            print("Total sentences",len(texts))
        for i in range(len(texts)):
                cosine_dict[texts[i]] = 1 - cosine(embeddings[main_index], embeddings[i])

        if (self.debug):
            print("Input sentence:",texts[main_index])
        sorted_dict = dict(sorted(cosine_dict.items(), key=lambda item: item[1],reverse = True))
        if (self.debug):
            for key in sorted_dict:
                print("Cosine similarity with  \"%s\" is: %.3f" % (key, sorted_dict[key]))
        if (output_file is not None):
            with open(output_file,"w") as fp:
                fp.write(json.dumps(sorted_dict,indent=0))
        return sorted_dict





class HFModel:
    def __init__(self):
        self.model = None
        self.tokenizer = None
        self.debug = False
        print("In HF Constructor")


    def init_model(self,model_name = None):
        # Get our models - The package will take care of downloading the models automatically
        # For best performance: Muennighoff/SGPT-5.8B-weightedmean-nli-bitfit
        #print("Init model",model_name)
        if (model_name is None):
            model_name = "sentence-transformers/all-MiniLM-L6-v2"
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModel.from_pretrained(model_name)
        self.model.eval()

    def mean_pooling(self,model_output, attention_mask):
        token_embeddings = model_output[0] #First element of model_output contains all token embeddings
        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

    def compute_embeddings(self,input_data,is_file):
        #print("Computing embeddings for:", input_data[:20])
        model = self.model
        tokenizer = self.tokenizer

        texts = read_text(input_data) if is_file == True else input_data

        encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')

        # Compute token embeddings
        with torch.no_grad():
            model_output = model(**encoded_input)

        # Perform pooling
        sentence_embeddings = self.mean_pooling(model_output, encoded_input['attention_mask'])

        # Normalize embeddings
        sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)

        return texts,sentence_embeddings

    def output_results(self,output_file,texts,embeddings,main_index = 0):
        # Calculate cosine similarities
        # Cosine similarities are in [-1, 1]. Higher means more similar
        cosine_dict = {}
        #print("Total sentences",len(texts))
        for i in range(len(texts)):
                cosine_dict[texts[i]] = 1 - cosine(embeddings[main_index], embeddings[i])

        #print("Input sentence:",texts[main_index])
        sorted_dict = dict(sorted(cosine_dict.items(), key=lambda item: item[1],reverse = True))
        if (self.debug):
            for key in sorted_dict:
                print("Cosine similarity with  \"%s\" is: %.3f" % (key, sorted_dict[key]))
        if (output_file is not None):
            with open(output_file,"w") as fp:
                fp.write(json.dumps(sorted_dict,indent=0))
        return sorted_dict



if __name__ == '__main__':
        parser = argparse.ArgumentParser(description='SGPT model for sentence embeddings ',formatter_class=argparse.ArgumentDefaultsHelpFormatter)
        parser.add_argument('-input', action="store", dest="input",required=True,help="Input file with sentences")
        parser.add_argument('-output', action="store", dest="output",default="output.txt",help="Output file with results")
        parser.add_argument('-model', action="store", dest="model",default="sentence-transformers/all-MiniLM-L6-v2",help="model name")

        results = parser.parse_args()
        obj = HFModel()
        obj.init_model(results.model)
        texts, embeddings = obj.compute_embeddings(results.input,is_file = True)
        results = obj.output_results(results.output,texts,embeddings)