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import logging
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from typing import Union, Optional, Tuple, List
from pydantic import BaseModel
from tqdm import tqdm
from langchain_text_splitters import RecursiveCharacterTextSplitter
from transformers import ModernBertModel, ModernBertPreTrainedModel, ModernBertConfig
class TextSpan(BaseModel):
s: int
e: int
module_name: str
text: Optional[str] = None
class Instance(BaseModel):
original_text: str
text_spans: List[TextSpan]
def recursive_split(text, chunk_size=256, chunk_overlap=32):
""" recursive split a text by RecursiveCharacterTextSplitter in langchain_text_splitters """
splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
length_function=lambda x: len(x.split()),
separators=["\n\n", "\n", ". ", "? ", "! ", "; "],
)
chunks = splitter.split_text(text)
if not chunks:
logging.error(f"Error, chunks is empty, text:{text}")
return [text], [[0, len(text)]]
chunk_span = [
# TODO a text may have multi same chunks
[text.find(chunk), text.find(chunk) + len(chunk)]
for chunk in chunks
]
assert chunk_span[0][0] == 0
assert all((span[0] >= 0 for span in chunk_span))
return chunks, chunk_span
def make_batch_input_for_prediction(
texts: List[str],
tokenizer,
max_seq_length: int,
chunk_size=256,
chunk_overlap=32,
prompt: str = "",
fast_chunk: bool = False,
batch_text_spans: List[List[TextSpan]] = None,
):
""" prepare input"""
if batch_text_spans is not None:
ipt = tokenizer(
[prompt + i for i in texts],
padding="longest",
truncation=True,
max_length=max_seq_length,
return_tensors="pt"
)
for text_spans, data_len in zip(batch_text_spans, ipt["attention_mask"].sum(dim=1)):
for text_span in text_spans:
assert -1 < text_span.s < text_span.e <= data_len
ipt["batch_text_spans"] = batch_text_spans
return ipt
prompt_len = len(tokenizer.tokenize(prompt))
truncated_texts = [
tokenizer.decode(
tokenizer.encode(text)[:max_seq_length - prompt_len - 2],
skip_special_tokens=True,
clean_up_tokenization_spaces=True
).strip()
for text in texts
]
ipt = tokenizer(
[prompt + i for i in truncated_texts],
padding="longest",
truncation=True,
max_length=max_seq_length,
return_tensors="pt"
)
batch_text_spans = []
for text, data_len in zip(truncated_texts, ipt["attention_mask"].sum(dim=1)):
text_spans = [
TextSpan(
s=0,
e=1,
module_name="cls_linear",
),
TextSpan(
s=1 + prompt_len,
e=data_len - 1,
module_name="chunk_linear",
),
]
if chunk_size > 1 and chunk_overlap > -1:
# chunk_size > 1 means that we need chunk vector
if fast_chunk:
start_pos, end_pos = 1 + prompt_len, data_len - 1
for s in range(start_pos, end_pos, chunk_size):
s -= chunk_overlap
s = max((s, start_pos))
e = min((s + chunk_size, end_pos))
if e - s > 0 and not (s == start_pos and e == end_pos):
text_spans.append(
TextSpan(
s=s,
e=e,
module_name="chunk_linear",
)
)
else:
chunks, chunk_span = recursive_split(text, chunk_size=chunk_size, chunk_overlap=chunk_overlap)
if len(chunks) > 1:
for (s, e), chunk in zip(chunk_span, chunks):
s = len(tokenizer.tokenize(text[:s])) + 1 + prompt_len
e = len(tokenizer.tokenize(text[:e])) + 1 + prompt_len
if s >= e:
continue
# original chunk vector
text_spans.append(
TextSpan(
s=s,
e=e,
module_name="chunk_linear",
text=chunk
)
)
batch_text_spans.append(text_spans)
ipt["batch_text_spans"] = batch_text_spans
return ipt
class DeweyV1(ModernBertPreTrainedModel):
def __init__(self, config: ModernBertConfig):
super().__init__(config)
self.config = config
self.model = ModernBertModel(config)
hidden_size = config.hidden_size
vector_size = config.vector_size
self.linear_dict = nn.ModuleDict(
{
"cls_linear": nn.Linear(hidden_size, vector_size, bias=True),
"chunk_linear": nn.Linear(hidden_size, vector_size, bias=True),
}
)
# Initialize weights and apply final processing
self.post_init()
def get_multi_vectors(
self,
batch_token_embeddings: torch.Tensor,
batch_text_spans: List[List[TextSpan]],
normalize_embeddings: bool = True
) -> List[torch.Tensor]:
multi_vectors = []
for token_embeddings, text_spans in zip(batch_token_embeddings, batch_text_spans):
chunk_vectors = []
for text_span in text_spans:
s, e = text_span.s, text_span.e
if s >= token_embeddings.shape[0] or s >= e:
logging.warning(
f"given span is wrong, s, e, token_embeddings.shape: {s, e, token_embeddings.shape}",
)
s, e = 0, 1
mean_tokens_embs = token_embeddings[s:e, :].mean(dim=0, keepdim=True)
# if torch.isnan(mean_tokens_embs).any():
# logging.error(f"NaNs in token_embeddings.shape: {token_embeddings.shape},s,e:{s, e}")
chunk_vectors.append(
self.linear_dict[text_span.module_name](mean_tokens_embs),
)
chunk_vectors = torch.cat(chunk_vectors, dim=0)
if normalize_embeddings:
multi_vectors.append(F.normalize(chunk_vectors, p=2, dim=-1))
else:
multi_vectors.append(chunk_vectors)
return multi_vectors
def forward(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
batch_text_spans: List[List[TextSpan]],
normalize_embeddings: bool = True,
*args,
**kwargs
) -> List[torch.Tensor]:
batch_token_embeddings = self.model(input_ids=input_ids, attention_mask=attention_mask)[0]
multi_vectors = self.get_multi_vectors(
batch_token_embeddings=batch_token_embeddings,
batch_text_spans=batch_text_spans,
normalize_embeddings=normalize_embeddings
)
return multi_vectors
@torch.no_grad()
def encode(
self,
sentences: str | list[str],
batch_size: int = 32,
use_cuda: bool = True,
show_progress_bar: bool = True,
chunk_size: int = 256,
chunk_overlap: int = 32,
convert_to_tensor: bool = False,
max_seq_length: int = 8192,
normalize_embeddings: bool = True,
prompt: str = "",
fast_chunk: bool = False,
batch_text_spans: List[List[TextSpan]] = None,
*args,
**kwargs
) -> Tuple[List[Union[np.ndarray, torch.Tensor]] | torch.Tensor | np.ndarray, List[List[TextSpan]]]:
"""
encode sentences to multi vectors
Args:
sentences: str | list[str], The sentences to embed
batch_size: int
use_cuda: bool, Whether to use GPU for inference
show_progress_bar: bool, Whether to display the progress bar
chunk_size: int, the number tokens of chunk, The recommended size is between 64-1024. The larger the value,
the faster the speed, but the effect may decrease. The smaller the value, the slower the speed,
and when the value is very small, the effect may also decrease.
chunk_overlap: int, Overlap in characters between chunks
convert_to_tensor: bool, If true: convert to torch fp32 tensor, otherwise will return fp32 ndarray
max_seq_length: int, max length of text
normalize_embeddings: bool, whether to do a L2-normalize for vectors
prompt: str, the prompt for text, the final text to be encoded is "[CLS]{prompt}{sentence}[SEP]",
Note, you CANNOT manually add a prompt before the sentence yourself, as this will affect our length calculation!
fast_chunk: bool, if true, directly chunk on input ids, else using RecursiveCharacterTextSplitter
batch_text_spans: List[List[TextSpan]], default is None, if provided, the model will not chunk text anymore
*args:
**kwargs:
Returns:
List[tensor|ndarray], each text's multi vectors
"""
self.eval()
# remove duplicate
if isinstance(sentences, str):
sentences = [sentences]
deduplicate_sentences = list(set(sentences))
deduplicate_sentences.sort(key=lambda x: len(x), reverse=True)
# encode
vectors_list, text_spans = [], []
for start in tqdm(
range(0, len(deduplicate_sentences), batch_size),
desc="encoding text...",
disable=not show_progress_bar
):
batch = deduplicate_sentences[start:start + batch_size]
ipt = make_batch_input_for_prediction(
batch,
tokenizer=self.tokenizer,
max_seq_length=max_seq_length,
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
prompt=prompt,
fast_chunk=fast_chunk,
batch_text_spans=batch_text_spans
)
text_spans.extend(ipt["batch_text_spans"])
ipt = {k: v.cuda() if use_cuda and isinstance(v, torch.Tensor) else v for k, v in ipt.items()}
vectors_list.extend(self(**ipt, normalize_embeddings=normalize_embeddings))
# print(len(deduplicate_sentences), len(vectors_list), deduplicate_sentences[-1])
assert len(deduplicate_sentences) == len(vectors_list)
sen2vecs = dict(zip(deduplicate_sentences, vectors_list))
sen2spans = dict(zip(deduplicate_sentences, text_spans))
text_spans = [sen2spans[sen] for sen in sentences]
if convert_to_tensor:
result = [sen2vecs[sen].cpu().float() for sen in sentences]
else:
result = [sen2vecs[sen].cpu().float().numpy() for sen in sentences]
return result, text_spans
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