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from transformers import Qwen2Config |
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import inspect |
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import math |
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import os |
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import warnings |
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from typing import List, Optional, Tuple, Union |
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import torch |
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import torch.nn.functional as F |
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import torch.utils.checkpoint |
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from torch import nn |
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss |
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from transformers import PretrainedConfig |
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from transformers.activations import ACT2FN |
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from transformers.cache_utils import Cache, DynamicCache |
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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 |
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast |
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from transformers.modeling_utils import PreTrainedModel |
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from transformers.utils import ( |
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add_start_docstrings, |
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add_start_docstrings_to_model_forward, |
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is_flash_attn_2_available, |
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is_flash_attn_greater_or_equal_2_10, |
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logging, |
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replace_return_docstrings, |
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) |
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import numpy as np |
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from transformers import Qwen2Config |
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from transformers import Qwen2ForCausalLM |
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import inspect |
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import math |
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import os |
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import warnings |
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from typing import List, Optional, Tuple, Union |
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from tqdm import tqdm, trange |
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import torch |
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import torch.nn.functional as F |
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import torch.utils.checkpoint |
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from torch import nn |
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss |
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from transformers.activations import ACT2FN |
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from transformers.cache_utils import Cache, DynamicCache |
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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 |
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast |
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from transformers.modeling_utils import PreTrainedModel |
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from transformers.utils import ( |
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add_start_docstrings, |
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add_start_docstrings_to_model_forward, |
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is_flash_attn_2_available, |
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is_flash_attn_greater_or_equal_2_10, |
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logging, |
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replace_return_docstrings, |
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) |
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import numpy as np |
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import torch |
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import os |
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import argparse |
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import json |
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from tqdm import tqdm |
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from typing import cast, List, Union, Tuple |
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from transformers import AutoTokenizer, AutoModel |
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from peft import LoraConfig, get_peft_model, TaskType |
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import time |
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import torch.nn.functional as F |
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import sys |
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import time |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import numpy as np |
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from tqdm import tqdm, trange |
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from collections import defaultdict |
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from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, AutoConfig |
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import torch.distributed as dist |
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from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint |
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import sys |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import math |
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import re |
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class MAB_POST(nn.Module): |
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def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False): |
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super(MAB_POST, self).__init__() |
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self.dim_V = dim_V |
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self.num_heads = num_heads |
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self.fc_q = nn.Linear(dim_Q, dim_V) |
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self.fc_k = nn.Linear(dim_K, dim_V) |
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self.fc_v = nn.Linear(dim_K, dim_V) |
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if ln: |
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self.ln0 = nn.LayerNorm(dim_V) |
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self.ln1 = nn.LayerNorm(dim_V) |
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self.fc_o = nn.Linear(dim_V, dim_V) |
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nn.init.xavier_uniform_(self.fc_q.weight) |
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nn.init.xavier_uniform_(self.fc_k.weight) |
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nn.init.xavier_uniform_(self.fc_v.weight) |
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nn.init.xavier_uniform_(self.fc_o.weight) |
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def forward(self, Q, K, pad_mask=None): |
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Q_ = self.fc_q(Q) |
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K_, V_ = self.fc_k(K), self.fc_v(K) |
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dim_split = self.dim_V // self.num_heads |
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Q_ = torch.cat(Q_.split(dim_split, 2), 0) |
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K_ = torch.cat(K_.split(dim_split, 2), 0) |
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V_ = torch.cat(V_.split(dim_split, 2), 0) |
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pad_mask = pad_mask.unsqueeze(1).repeat(self.num_heads, Q.size(1), 1) |
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score = Q_.bmm(K_.transpose(1,2))/math.sqrt(self.dim_V) |
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score = score.masked_fill(pad_mask == 0, -1e12) |
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A = torch.softmax(score, 2) |
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A = A * pad_mask |
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O = torch.cat(A.bmm(V_).split(Q.size(0), 0), 2) |
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O = Q + O |
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O = O if getattr(self, 'ln0', None) is None else self.ln0(O) |
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O = O + F.relu(self.fc_o(O)) |
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O = O if getattr(self, 'ln1', None) is None else self.ln1(O) |
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return O |
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class PMA(nn.Module): |
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def __init__(self, dim, compress_dim, num_heads, num_seeds, ln=False, pma_mode=None): |
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super(PMA, self).__init__() |
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self.S = nn.Parameter(torch.Tensor(1, num_seeds, compress_dim)) |
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nn.init.xavier_uniform_(self.S) |
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if pma_mode == 'post_normal': |
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self.mab = MAB_POST(compress_dim, dim, compress_dim, num_heads, ln=ln) |
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elif pma_mode == 'pre_normal': |
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self.mab = MAB_PRE_NORMAL(compress_dim, dim, compress_dim, num_heads, ln=ln) |
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elif pma_mode == 'pre_gptj': |
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self.mab = MAB_PRE_GPTJ(compress_dim, dim, compress_dim, num_heads, ln=ln) |
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else: |
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raise ValueError(f"Error, the pma_mode {pma_mode} is not implemented !") |
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def forward(self, X, pad_mask): |
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if self.S.dtype != torch.bfloat16: |
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X = X.float() |
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return self.mab(self.S.repeat(X.size(0), 1, 1), X, pad_mask) |
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class CodeFuse_CGE_Large(PreTrainedModel): |
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def __init__(self, config): |
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super().__init__(config) |
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self.plm_model = Qwen2ForCausalLM(config) |
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self.embedding_method = config.embedding_method |
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self.inf_seq_length = config.inf_seq_length |
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self.padding_side = config.padding_side |
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self.keep_max_layer = config.keep_max_layer |
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self.emb_dim = self.plm_model.model.embed_tokens.weight.size(1) |
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self.num_heads = config.pma_num_heads |
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self.ln = config.pma_ln |
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self.norm = config.pma_norm |
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self.compress_dim = config.compress_dim |
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self.pma_mode = config.pma_norm_mode |
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self.mha_pma = PMA(self.emb_dim, self.compress_dim, self.num_heads, 1, ln=self.ln, pma_mode=self.pma_mode) |
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def last_embedding(self, A, index): |
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bs, seq, emb = A.size() |
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res = A[torch.arange(bs), index, :] |
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return res |
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def mean_embedding(self, A, mask): |
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bs, seq, emb = A.size() |
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res = (A * (mask.unsqueeze(-1))).sum(1) / (mask.sum(1).unsqueeze(-1)) |
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return res |
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def weighted_embedding(self, A, mask): |
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weights = (torch.arange(start=1, end=A.size(1) + 1).unsqueeze(0).unsqueeze(-1).expand(A.size()).float()).to(A.device) |
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input_mask_expanded = (mask.squeeze(1).unsqueeze(-1).expand(A.size()).float()).to(A.device) |
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sum_embedding = torch.sum(A * input_mask_expanded * weights, dim=1) |
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sum_mask = torch.sum(input_mask_expanded * weights, dim=1) |
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weighted_embedding = sum_embedding / sum_mask |
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return weighted_embedding |
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def pma_embedding(self, A, mask): |
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res = self.mha_pma(A, mask).squeeze(1) |
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return res |
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def get_sentence_embedding(self, embedding_method, **inputs): |
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outputs = self.plm_model(inputs['input_ids'], inputs['attention_mask'], output_hidden_states=True) |
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if embedding_method == 'last': |
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embedding = outputs.hidden_states[self.keep_max_layer] |
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index = inputs['attention_mask'].sum(-1).long() - 1 |
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res_embedding = self.last_embedding(embedding, index) |
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elif embedding_method == 'mean': |
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embedding = outputs.hidden_states[self.keep_max_layer] |
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res_embedding = self.mean_embedding(embedding, inputs['attention_mask']) |
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elif embedding_method == 'weighted': |
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embedding = outputs.hidden_states[self.keep_max_layer] |
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res_embedding = self.weighted_embedding(embedding, inputs['attention_mask']) |
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elif embedding_method == 'pma': |
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embedding = outputs.hidden_states[self.keep_max_layer] |
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attention_mask = inputs['attention_mask'] |
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res_embedding = self.pma_embedding(embedding, attention_mask) |
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else: |
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logger.debug('Error, no {} way to obtain embbedings'.format(embedding_method)) |
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if not self.norm: |
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res_embedding = torch.nn.functional.normalize(res_embedding, p=2.0, dim=-1, eps=1e-12, out=None) |
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return res_embedding |
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def encode(self, tokenizer, sentences, batch_size=32, convert_to_numpy=True, |
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convert_to_tensor=False, show_progress_bar=True, max_seq_length=None, **kwargs): |
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if max_seq_length is None: |
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max_seq_length = self.inf_seq_length |
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input_is_string = False |
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if isinstance(sentences, str) or not hasattr(sentences, "__len__"): |
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sentences = [sentences] |
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input_is_string = True |
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all_embeddings = [] |
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length_sorted_idx = np.argsort([-len(s) for s in sentences]) |
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sentences_sorted = [sentences[idx] for idx in length_sorted_idx] |
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with torch.no_grad(): |
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for start_index in trange(0, len(sentences), batch_size, desc="Batches", disable=not show_progress_bar): |
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sentences_batch = sentences_sorted[start_index: start_index + batch_size] |
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with torch.no_grad(): |
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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) |
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embeddings = self.get_sentence_embedding(self.embedding_method, **inputs) |
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embeddings = embeddings.detach() |
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if convert_to_numpy: |
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if embeddings.dtype == torch.bfloat16: |
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embeddings = embeddings.cpu().to(torch.float32) |
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else: |
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embeddings = embeddings.cpu() |
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all_embeddings.extend(embeddings) |
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all_embeddings = [all_embeddings[idx] for idx in np.argsort(length_sorted_idx)] |
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if convert_to_tensor: |
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all_embeddings = torch.stack(all_embeddings) |
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elif convert_to_numpy: |
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all_embeddings = np.asarray([emb.numpy() for emb in all_embeddings]) |
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if input_is_string: |
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all_embeddings = all_embeddings[0] |
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return all_embeddings |
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