Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,219 Bytes
295978e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 |
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import torch
from omegaconf import DictConfig, OmegaConf
from torch import nn
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
CLIPTextModel,
CLIPTokenizerFast,
T5EncoderModel,
T5TokenizerFast,
)
from transformers.tokenization_utils_base import BatchEncoding
from common.fs import download_and_extract
from common.logger import get_logger
logger = get_logger(__name__)
MODEL_TYPES = {
"clip": (CLIPTokenizerFast, CLIPTextModel),
"t5": (T5TokenizerFast, T5EncoderModel),
"llm14b": (AutoTokenizer, AutoModelForCausalLM),
}
@dataclass
class TextEncoderOutput:
embeddings: Union[torch.FloatTensor, List[torch.FloatTensor]]
masks: Union[torch.BoolTensor, List[torch.BoolTensor]]
pooled: Optional[Union[torch.FloatTensor, List[torch.FloatTensor]]]
class TextEncoder(nn.Module):
def __init__(self, config: DictConfig):
super().__init__()
self.config = config
self.tokenizers = []
self.models = nn.ModuleList([])
# Disable tokenizer parallelism since we already use distributed training.
os.environ["TOKENIZERS_PARALLELISM"] = "false"
for model in config.models:
tokenizer_cls, model_cls = MODEL_TYPES[model.type]
path = download_and_extract(model.path)
max_length = model.max_length
if model.type == "llm14b":
tokenizer = tokenizer_cls.from_pretrained(
path,
model_max_length=max_length,
use_fast=False,
trust_remote_code=True,
padding_side="right",
truncation_side="right",
add_eod_token=True,
)
tokenizer.add_special_tokens({"pad_token": "<|endoftext|>"})
model = model_cls.from_pretrained(path, trust_remote_code=True, bf16=True)
else:
tokenizer = tokenizer_cls.from_pretrained(path, model_max_length=max_length)
model = model_cls.from_pretrained(path, torch_dtype=torch.bfloat16)
self.tokenizers.append(tokenizer)
self.models.append(model)
def forward(self, text: Union[str, List[str]]) -> TextEncoderOutput:
embeddings, masks, pooled = [], [], []
for encoder_config, tokenizer, model in zip(
self.config.models, self.tokenizers, self.models
):
if encoder_config.type == "llm14b":
use_mask = encoder_config.get("mask", True)
tokens = tokenizer(
text,
return_tensors="pt",
padding="max_length",
truncation=True,
).to(model.device)
token_ids = tokens["input_ids"]
attention_mask = tokens["attention_mask"]
num_tokens = attention_mask.sum(dim=1)
range_ids = torch.arange(len(token_ids), device=token_ids.device, dtype=torch.long)
token_ids[range_ids, num_tokens.clamp(max=token_ids.size(1) - 1)] = (
tokenizer.pad_token_id
)
attention_mask[range_ids, num_tokens.clamp(max=token_ids.size(1) - 1)] = 1
tokens = BatchEncoding({"input_ids": token_ids, "attention_mask": attention_mask})
output = model.transformer(
input_ids=tokens.input_ids,
attention_mask=attention_mask if use_mask else None,
output_hidden_states=False,
use_cache=False,
)
emb = output.last_hidden_state # batch_size, num_tokens, feat_dim
# emb *= tokens.attention_mask.unsqueeze(-1)
embeddings.append(emb)
masks.append(
tokens.attention_mask.bool() if use_mask else tokens.attention_mask > -1
)
else:
# Tokenizer
tokens = tokenizer(
text=text,
truncation=True,
padding="max_length",
return_tensors="pt",
)
# Encoder
use_mask = encoder_config.get("mask", True)
input_ids = tokens.input_ids.to(model.device)
attention_mask = tokens.attention_mask.to(model.device)
output = model(
input_ids=input_ids,
attention_mask=attention_mask if use_mask else None,
output_hidden_states=True,
)
# Save embeddings from the defined layer.
layer = encoder_config.get("layer", "last")
if layer == "last":
embeddings.append(output.last_hidden_state)
elif layer == "penultimate":
embeddings.append(model.text_model.final_layer_norm(output.hidden_states[-2]))
elif layer == "penultimate_nonorm":
embeddings.append(output.hidden_states[-2])
else:
raise NotImplementedError(f"Unknown layer type: {layer}.")
# Save masks
masks.append(attention_mask.bool() if use_mask else attention_mask > -1)
# Save pooled output if available.
if hasattr(output, "pooler_output"):
pooled.append(output.pooler_output)
output_config = self.config.get("output") or OmegaConf.create()
embedding_output_type = output_config.get("embedding_and_mask", "undefined")
pooled_output_type = output_config.get("pooled", "undefined")
# Select or merge embeddings and mask if needed.
if embedding_output_type == "undefined" and len(self.models) == 1:
embeddings = embeddings[0]
masks = masks[0]
elif embedding_output_type == "channel_concat":
embeddings = torch.cat(embeddings, dim=-1)
masks = sum(masks).bool()
elif embedding_output_type == "last":
embeddings = embeddings[-1]
masks = masks[-1]
else:
raise NotImplementedError(f"output.embedding_and_mask: {embedding_output_type}")
# Select or merge pooled output if needed.
if pooled_output_type == "undefined":
pooled = None
elif pooled_output_type == "channel_concat":
pooled = torch.cat(pooled, dim=-1)
elif pooled_output_type == "first":
pooled = pooled[0]
elif pooled_output_type == "last":
pooled = pooled[-1]
else:
raise NotImplementedError(f"output.pooled: {pooled_output_type}")
# Return final results.
return TextEncoderOutput(embeddings, masks, pooled)
|