modeling_codefuse_cge_large.py

from transformers import Qwen2Config
import inspect
import math
import os
import warnings
from typing import List, Optional, Tuple, Union

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers import PretrainedConfig

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa, _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_flash_attn_2_available,
    is_flash_attn_greater_or_equal_2_10,
    logging,
    replace_return_docstrings,
)
import numpy as np
from transformers import Qwen2Config
from transformers import Qwen2ForCausalLM
import inspect
import math
import os
import warnings
from typing import List, Optional, Tuple, Union
from tqdm import tqdm, trange
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa, _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_flash_attn_2_available,
    is_flash_attn_greater_or_equal_2_10,
    logging,
    replace_return_docstrings,
)
import numpy as np
import torch
import os
import argparse
import json
from tqdm import tqdm
from typing import cast, List, Union, Tuple
from transformers import AutoTokenizer, AutoModel  # pylint: disable=C0413
from peft import LoraConfig, get_peft_model, TaskType
import time
import torch.nn.functional as F
import sys
import time
import torch 
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm, trange
from collections import defaultdict
from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, AutoConfig
import torch.distributed as dist
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import re


class MAB_POST(nn.Module):
    def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
        super(MAB_POST, self).__init__()
        self.dim_V = dim_V
        self.num_heads = num_heads
        self.fc_q = nn.Linear(dim_Q, dim_V)
        self.fc_k = nn.Linear(dim_K, dim_V)
        self.fc_v = nn.Linear(dim_K, dim_V)
        if ln:
            self.ln0 = nn.LayerNorm(dim_V)
            self.ln1 = nn.LayerNorm(dim_V)
        self.fc_o = nn.Linear(dim_V, dim_V)
        nn.init.xavier_uniform_(self.fc_q.weight)
        nn.init.xavier_uniform_(self.fc_k.weight)
        nn.init.xavier_uniform_(self.fc_v.weight)
        nn.init.xavier_uniform_(self.fc_o.weight)

    def forward(self, Q, K, pad_mask=None):

        Q_ = self.fc_q(Q)
        K_, V_ = self.fc_k(K), self.fc_v(K)

        dim_split = self.dim_V // self.num_heads
        Q_ = torch.cat(Q_.split(dim_split, 2), 0)
        K_ = torch.cat(K_.split(dim_split, 2), 0)
        V_ = torch.cat(V_.split(dim_split, 2), 0)

        pad_mask = pad_mask.unsqueeze(1).repeat(self.num_heads, Q.size(1), 1)
        score = Q_.bmm(K_.transpose(1,2))/math.sqrt(self.dim_V)
        score = score.masked_fill(pad_mask == 0, -1e12)
        A = torch.softmax(score, 2)
        A = A * pad_mask
        O = torch.cat(A.bmm(V_).split(Q.size(0), 0), 2) 
        O = Q + O
        O = O if getattr(self, 'ln0', None) is None else self.ln0(O)
        O = O + F.relu(self.fc_o(O))
        O = O if getattr(self, 'ln1', None) is None else self.ln1(O)
        return O


class PMA(nn.Module):
    def __init__(self, dim, compress_dim, num_heads, num_seeds, ln=False, pma_mode=None):
        super(PMA, self).__init__()
        self.S = nn.Parameter(torch.Tensor(1, num_seeds, compress_dim))
        nn.init.xavier_uniform_(self.S)
        if pma_mode == 'post_normal':
            self.mab = MAB_POST(compress_dim, dim, compress_dim, num_heads, ln=ln)
        elif pma_mode == 'pre_normal':
            self.mab = MAB_PRE_NORMAL(compress_dim, dim, compress_dim, num_heads, ln=ln)
        elif pma_mode == 'pre_gptj':
            self.mab = MAB_PRE_GPTJ(compress_dim, dim, compress_dim, num_heads, ln=ln)
        else:
            raise ValueError(f"Error, the pma_mode {pma_mode} is not implemented !")

    def forward(self, X, pad_mask):
        if self.S.dtype != torch.bfloat16:
            X = X.float()
        return self.mab(self.S.repeat(X.size(0), 1, 1), X, pad_mask)


class CodeFuse_CGE_Large(PreTrainedModel):

    def __init__(self, config):
        super().__init__(config)
        self.plm_model = Qwen2ForCausalLM(config)
        self.embedding_method = config.embedding_method
        self.inf_seq_length = config.inf_seq_length
        self.padding_side = config.padding_side

        self.keep_max_layer = config.keep_max_layer
        self.emb_dim = self.plm_model.model.embed_tokens.weight.size(1)
        self.num_heads = config.pma_num_heads
        self.ln = config.pma_ln
        self.norm = config.pma_norm
        self.compress_dim = config.compress_dim
        self.pma_mode = config.pma_norm_mode
        self.mha_pma = PMA(self.emb_dim, self.compress_dim, self.num_heads, 1, ln=self.ln, pma_mode=self.pma_mode)

    def last_embedding(self, A, index):
        bs, seq, emb = A.size()
        res = A[torch.arange(bs), index, :]
        return res

    def mean_embedding(self, A, mask):
        bs, seq, emb = A.size()
        res = (A * (mask.unsqueeze(-1))).sum(1) / (mask.sum(1).unsqueeze(-1))
        return res

    def weighted_embedding(self, A, mask):
        weights = (torch.arange(start=1, end=A.size(1) + 1).unsqueeze(0).unsqueeze(-1).expand(A.size()).float()).to(A.device)
        input_mask_expanded = (mask.squeeze(1).unsqueeze(-1).expand(A.size()).float()).to(A.device)
        sum_embedding = torch.sum(A * input_mask_expanded * weights, dim=1)
        sum_mask = torch.sum(input_mask_expanded * weights, dim=1)
        weighted_embedding = sum_embedding / sum_mask
        return weighted_embedding

    def pma_embedding(self, A, mask):
        res = self.mha_pma(A, mask).squeeze(1)
        return res

    def get_sentence_embedding(self, embedding_method, **inputs):
        outputs = self.plm_model(inputs['input_ids'], inputs['attention_mask'], output_hidden_states=True)
        if embedding_method == 'last':
            embedding = outputs.hidden_states[self.keep_max_layer]
            index = inputs['attention_mask'].sum(-1).long() - 1
            res_embedding = self.last_embedding(embedding, index)
        elif embedding_method == 'mean':
            embedding = outputs.hidden_states[self.keep_max_layer]
            res_embedding = self.mean_embedding(embedding, inputs['attention_mask'])
        elif embedding_method == 'weighted':
            embedding = outputs.hidden_states[self.keep_max_layer]
            res_embedding = self.weighted_embedding(embedding, inputs['attention_mask'])
        elif embedding_method == 'pma':
            embedding = outputs.hidden_states[self.keep_max_layer]
            attention_mask = inputs['attention_mask']
            res_embedding = self.pma_embedding(embedding, attention_mask)
        else:
            logger.debug('Error, no {} way to obtain embbedings'.format(embedding_method))

        if not self.norm:
            res_embedding = torch.nn.functional.normalize(res_embedding, p=2.0, dim=-1, eps=1e-12, out=None)
        return res_embedding


    def encode(self, tokenizer, sentences, batch_size=32, convert_to_numpy=True,
            convert_to_tensor=False, show_progress_bar=True, max_seq_length=None, **kwargs):

        if max_seq_length is None:
            max_seq_length = self.inf_seq_length

        input_is_string = False        
        if isinstance(sentences, str) or not hasattr(sentences, "__len__"):
            sentences = [sentences]
            input_is_string = True

        all_embeddings = []
        length_sorted_idx = np.argsort([-len(s) for s in sentences])
        sentences_sorted = [sentences[idx] for idx in length_sorted_idx]
        with torch.no_grad():
            for start_index in trange(0, len(sentences), batch_size, desc="Batches", disable=not show_progress_bar):
                sentences_batch = sentences_sorted[start_index: start_index + batch_size]
                with torch.no_grad():
                    inputs = tokenizer(sentences_batch, padding=True, truncation=True, max_length=max_seq_length, add_special_tokens=False, return_tensors='pt').to(self.plm_model.device)
                    embeddings = self.get_sentence_embedding(self.embedding_method, **inputs)
                embeddings = embeddings.detach()
                if convert_to_numpy:
                    if embeddings.dtype == torch.bfloat16:
                        embeddings = embeddings.cpu().to(torch.float32)
                    else:
                        embeddings = embeddings.cpu()
                all_embeddings.extend(embeddings)
        all_embeddings = [all_embeddings[idx] for idx in np.argsort(length_sorted_idx)]
        if convert_to_tensor:
            all_embeddings = torch.stack(all_embeddings)
        elif convert_to_numpy:
            all_embeddings = np.asarray([emb.numpy() for emb in all_embeddings])

        if input_is_string:
            all_embeddings = all_embeddings[0]
        return all_embeddings