import os import gc import inspect import math import multiprocessing as mp import queue from multiprocessing import Queue import warnings from typing import Any, Union, List, Dict, Literal, Optional 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 import Qwen2Config 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 import logging logging.getLogger().setLevel(logging.INFO) from .configuration_c2llm import C2LLMConfig from transformers.models.qwen2.modeling_qwen2 import Qwen2DecoderLayer, Qwen2Attention 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, compressed_dim, num_heads, num_seeds, ln=False, pma_mode=None): super(PMA, self).__init__() self.S = nn.Parameter(torch.Tensor(1, num_seeds, compressed_dim)) nn.init.xavier_uniform_(self.S) if pma_mode == 'post_normal': self.mab = MAB_POST(compressed_dim, dim, compressed_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 MAB_POST_v2(nn.Module): def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False): super(MAB_POST_v2, 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) # Q(B, num_seed, D), pad_mask (bs, seq) Post-LN def forward(self, Q, K, pad_mask=None): Q_tmp = self.fc_q(Q) # B, num_seed, C K_, V_ = self.fc_k(K), self.fc_v(K) # B, L, C dim_split = self.dim_V // self.num_heads Q_ = torch.cat(Q_tmp.split(dim_split, 2), 0) # (B* num_head, num_seed, C) K_ = torch.cat(K_.split(dim_split, 2), 0) # (B* num_head, L, C) V_ = torch.cat(V_.split(dim_split, 2), 0) # (B* num_head,L, C) pad_mask = pad_mask.unsqueeze(1).repeat(self.num_heads, Q.size(1), 1) # (B*num_head, num_seed, L) score = Q_.bmm(K_.transpose(1,2))/math.sqrt(self.dim_V) # (B*num_head, num_seed, L) score = score.masked_fill(pad_mask == 0, -1e12) # B,num_seed,L A = torch.softmax(score, 2) # (B*num_head, num_seed, L) A = A * pad_mask O = torch.cat(A.bmm(V_).split(Q.size(0), 0), 2) # (B, num_seed, D) O = Q_tmp + O # O = torch.cat((Q_ + A.bmm(V_)).split(Q.size(0), 0), 2) 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_v2(nn.Module): def __init__(self, dim, compressed_dim, num_heads, num_seeds, ln=False): super(PMA_v2, self).__init__() self.S = nn.Parameter(torch.Tensor(1, num_seeds, dim)) nn.init.xavier_uniform_(self.S) # if pma_mode == 'post_normal': self.mab = MAB_POST_v2(dim, dim, compressed_dim, num_heads, ln=ln) # elif pma_mode == 'pre_normal': # self.mab = MAB_PRE_NORMAL(dim, dim, compressed_dim, num_heads, ln=ln) # elif pma_mode == 'pre_gptj': # self.mab = MAB_PRE_GPTJ(dim, dim, compressed_dim, num_heads, ln=ln) # else: # raise ValueError(f"Error, the pma_mode {pma_mode} is not implemented !") # X: (bs, seq, emb), pad_mask: (bs, seq) def forward(self, X, pad_mask): if self.S.dtype != torch.bfloat16: X = X.float() return self.mab(self.S.expand(X.size(0), -1, -1), X, pad_mask) class C2LLMModel(PreTrainedModel): config_class = C2LLMConfig config: C2LLMConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["Qwen2DecoderLayer"] _skip_keys_device_placement = ["past_key_values"] _supports_flash_attn = True _supports_sdpa = True _supports_flex_attn = True _can_compile_fullgraph = True _supports_attention_backend = True _can_record_outputs = { "hidden_states": Qwen2DecoderLayer, "attentions": Qwen2Attention, } class C2LLMForEmbedding(C2LLMModel): config_class = C2LLMConfig model_type = "c2llm" def __init__(self, config): super().__init__(config) qwen_cfg = Qwen2Config.from_dict(config.to_dict()) self.plm_model = AutoModelForCausalLM.from_config(qwen_cfg) self.embedding_method = config.embedding_method self.inf_seq_length = 2048 self.padding_side = config.padding_side self.emb_dim = self.plm_model.model.embed_tokens.weight.size(1) self.keep_max_layer = self.plm_model.config.num_hidden_layers self.num_heads = config.pma_num_heads self.ln = config.pma_ln self.norm = config.pma_norm self.pma_mode = config.pma_norm_mode self.compressed_dim = config.compressed_dim self.mha_pma_disc = PMA_v2(self.emb_dim, self.compressed_dim, self.num_heads, 1, ln=self.ln) self.pool = None self.target_devices = self.get_target_devices(None) self.tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name_or_path, padding_side=config.padding_side) if config.tokenizer_name_or_path is not None else None self.config_class = C2LLMConfig def pma_embedding(self, mha_pma, A, mask): res = mha_pma(A, mask).squeeze(1) return res def get_hidden_states(self, **inputs): outputs = self.plm_model(inputs['input_ids'], inputs['attention_mask'], output_hidden_states=True) return outputs.hidden_states[self.keep_max_layer] def get_sentence_embedding(self, embedding_method, hidden_states, emb_type, attention_mask): if embedding_method == 'pma': if emb_type == 'disc': res_embedding = self.pma_embedding(self.mha_pma_disc, hidden_states, attention_mask) if self.norm: res_embedding = torch.nn.functional.normalize(res_embedding, p=2.0, dim=-1, eps=1e-12, out=None) return res_embedding else: raise NotImplementedError(f"emb type {emb_type} hasn't been implemented") else: raise NotImplementedError(f"embedding method {embedding_method} hasn't been implemented") @staticmethod def get_target_devices(devices: Union[str, int, List[str], List[int]]) -> List[str]: """ Args: devices (Union[str, int, List[str], List[int]]): specified devices, can be `str`, `int`, list of `str`, or list of `int`. Raises: ValueError: Devices should be a string or an integer or a list of strings or a list of integers. Returns: List[str]: A list of target devices in format. """ if devices is None: if torch.cuda.is_available(): return [f"cuda:{i}" for i in range(torch.cuda.device_count())] elif is_torch_npu_available(): return [f"npu:{i}" for i in range(torch.npu.device_count())] elif hasattr(torch, "musa") and torch.musa.is_available(): return [f"musa:{i}" for i in range(torch.musa.device_count())] elif torch.backends.mps.is_available(): try: return [f"mps:{i}" for i in range(torch.mps.device_count())] except: return ["mps"] else: return ["cpu"] elif isinstance(devices, str): return [devices] elif isinstance(devices, int): if hasattr(torch, "musa") and torch.musa.is_available(): return [f"musa:{devices}"] else: return [f"cuda:{devices}"] elif isinstance(devices, list): if isinstance(devices[0], str): return devices elif isinstance(devices[0], int): if hasattr(torch, "musa") and torch.musa.is_available(): return [f"musa:{device}" for device in devices] else: return [f"cuda:{device}" for device in devices] else: raise ValueError("devices should be a string or an integer or a list of strings or a list of integers.") else: raise ValueError("devices should be a string or an integer or a list of strings or a list of integers.") # adapted from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L807 def start_multi_process_pool( self, process_target_func: Any, ) -> Dict[Literal["input", "output", "processes"], Any]: """ Starts a multi-process pool to process the encoding with several independent processes via :meth:`SentenceTransformer.encode_multi_process `. This method is recommended if you want to encode on multiple GPUs or CPUs. It is advised to start only one process per GPU. This method works together with encode_multi_process and stop_multi_process_pool. Returns: Dict[str, Any]: A dictionary with the target processes, an input queue, and an output queue. """ if self.plm_model is None or self.mha_pma_disc is None: raise ValueError("Model is not initialized.") logging.info("Start multi-process pool on devices: {}".format(", ".join(map(str, self.target_devices)))) self.to("cpu") self.share_memory() ctx = mp.get_context("spawn") input_queue = ctx.Queue() output_queue = ctx.Queue() processes = [] for device_id in tqdm(self.target_devices, desc='initial target device'): p = ctx.Process( target=process_target_func, args=(device_id, self, input_queue, output_queue), daemon=True, ) p.start() processes.append(p) return {"input": input_queue, "output": output_queue, "processes": processes} @staticmethod def _encode_multi_process_worker( target_device: str, model: 'C2LLMForEmbedding', input_queue: Queue, results_queue: Queue ) -> None: model = model.to(target_device) while True: try: chunk_id, sentences, kwargs = ( input_queue.get() ) embeddings = model.encode_single_device( sentences, device=target_device, **kwargs ) results_queue.put([chunk_id, embeddings]) except queue.Empty: break def encode_multi_process( self, sentences: List[str], pool: Dict[Literal["input", "output", "processes"], Any], **kwargs ): chunk_size = math.ceil(len(sentences) / len(pool["processes"])) input_queue = pool["input"] last_chunk_id = 0 chunk = [] for sentence in sentences: chunk.append(sentence) if len(chunk) >= chunk_size: input_queue.put( [last_chunk_id, chunk, kwargs] ) last_chunk_id += 1 chunk = [] if len(chunk) > 0: input_queue.put([last_chunk_id, chunk, kwargs]) last_chunk_id += 1 output_queue = pool["output"] results_list = sorted( [output_queue.get() for _ in trange(last_chunk_id, desc="")], key=lambda x: x[0], ) embeddings = self._concatenate_results_from_multi_process([result[1] for result in results_list]) return embeddings def _concatenate_results_from_multi_process(self, results_list: List[Union[torch.Tensor, np.ndarray, Any]]): """concatenate and return the results from all the processes Args: results_list (List[Union[torch.Tensor, np.ndarray, Any]]): A list of results from all the processes. Raises: NotImplementedError: Unsupported type for results_list Returns: Union[torch.Tensor, np.ndarray]: return the embedding vectors in a numpy array or tensor. """ if isinstance(results_list[0], torch.Tensor): # move all tensors to the same device results_list = [res.to(self.target_devices[0]) for res in results_list] return torch.cat(results_list, dim=0) elif isinstance(results_list[0], np.ndarray): return np.concatenate(results_list, axis=0) else: raise NotImplementedError("Unsupported type for results_list") def forward(self, input_ids: torch.Tensor, attention_mask: torch.Tensor, return_dict: bool=True, **kwargs): outputs = self.plm_model(input_ids, attention_mask, output_hidden_states=True) hidden_states = outputs.hidden_states[self.keep_max_layer] embeddings = self.get_sentence_embedding(self.embedding_method, hidden_states, 'disc', attention_mask) if not return_dict: return (embeddings,) return {"sentence_embedding": embeddings} def encode_single_device( self, sentences: Union[List[str], str], batch_size: int = 16, convert_to_numpy: bool = False, convert_to_tensor: bool = True, show_progress_bar: bool = True, max_seq_length: int = 2048, device: Optional[str] = None, **kwargs: Any ): 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] # 大到小重排 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] inputs = self.tokenizer(sentences_batch, padding=True, truncation=True, max_length=max_seq_length, return_tensors='pt').to(self.plm_model.device) hidden_states = self.get_hidden_states(**inputs) embeddings = self.get_sentence_embedding(self.embedding_method, hidden_states, 'disc', inputs['attention_mask']) 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 def encode(self, sentences, batch_size=16, convert_to_numpy=False, convert_to_tensor=True, show_progress_bar=True, max_seq_length=None, **kwargs): if max_seq_length is None: max_seq_length = self.inf_seq_length if convert_to_tensor == convert_to_numpy: convert_to_tensor=True convert_to_numpy=False if isinstance(sentences, str) or len(self.target_devices) == 1: return self.encode_single_device( sentences, batch_size=batch_size, convert_to_numpy=convert_to_numpy, convert_to_tensor=convert_to_tensor, show_progress_bar=show_progress_bar, max_seq_length=max_seq_length, device=self.target_devices[0], **kwargs ) if self.pool is None: self.pool = self.start_multi_process_pool(C2LLMForEmbedding._encode_multi_process_worker) all_embeddings = [] length_sorted_idx = np.argsort([-len(s) for s in sentences]) sentences_sorted = [sentences[idx] for idx in length_sorted_idx] # 大到小重排 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] embeddings_batch = self.encode_multi_process( sentences_batch, self.pool, convert_to_numpy=convert_to_numpy, convert_to_tensor=convert_to_tensor, show_progress_bar=show_progress_bar, max_seq_length=max_seq_length, **kwargs ) embeddings_batch = embeddings_batch.detach() if convert_to_numpy: if embeddings_batch.dtype == torch.bfloat16: embeddings_batch = embeddings_batch.cpu().to(torch.float32) else: embeddings_batch = embeddings_batch.cpu() all_embeddings.extend(embeddings_batch) 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]) return all_embeddings def encode_queries(self, sentences, batch_size=16, convert_to_numpy=False, convert_to_tensor=True, show_progress_bar=True, max_seq_length=None, **kwargs): if max_seq_length is None: max_seq_length = self.inf_seq_length if convert_to_tensor == convert_to_numpy: convert_to_tensor=True convert_to_numpy=False return self.encode( sentences=sentences, batch_size=batch_size, convert_to_numpy=convert_to_numpy, convert_to_tensor=convert_to_tensor, show_progress_bar=show_progress_bar, max_seq_length=max_seq_length, **kwargs ) def encode_corpus(self, sentences, batch_size=16, convert_to_numpy=False, convert_to_tensor=True, show_progress_bar=True, max_seq_length=None, **kwargs): if max_seq_length is None: max_seq_length = self.inf_seq_length if convert_to_tensor == convert_to_numpy: convert_to_tensor=True convert_to_numpy=False sentences = [sentence['title']+' '+sentence['text'] for sentence in sentences] return self.encode( sentences=sentences, batch_size=batch_size, convert_to_numpy=convert_to_numpy, convert_to_tensor=convert_to_tensor, show_progress_bar=show_progress_bar, max_seq_length=max_seq_length, **kwargs ) @staticmethod def stop_multi_process_pool(pool: Dict[Literal["input", "output", "processes"], Any]) -> None: """ Stops all processes started with start_multi_process_pool. Args: pool (Dict[str, object]): A dictionary containing the input queue, output queue, and process list. Returns: None """ for p in pool["processes"]: p.terminate() for p in pool["processes"]: p.join() p.close() pool["input"].close() pool["output"].close() pool = None def stop_self_pool(self): if self.pool is not None: self.stop_multi_process_pool(self.pool) self.pool = None try: self.model.to('cpu') torch.cuda.empty_cache() except: pass if gc is not None and callable(gc.collect): gc.collect() def __del__(self): self.stop_self_pool()