801 lines
31 KiB
Python
801 lines
31 KiB
Python
# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright:
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# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright:
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# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import copy
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from dataclasses import dataclass, field
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import json
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import logging
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import pathlib
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from typing import Dict, Optional, Sequence, List
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import torch
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import transformers
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from transformers.models.clip.image_processing_clip import CLIPImageProcessor
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from torch.utils.data import Dataset
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from mplug_owl2.train.mplug_owl2_trainer import MPLUGOwl2Trainer
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from mplug_owl2.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
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from mplug_owl2 import conversation as conversation_lib
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from mplug_owl2.model import *
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from mplug_owl2.mm_utils import tokenizer_image_token
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from PIL import Image
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from icecream import ic
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local_rank = None
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def rank0_print(*args):
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if local_rank == 0:
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print(*args)
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@dataclass
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class ModelArguments:
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model_name_or_path: Optional[str] = field(default="facebook/opt-125m")
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version: Optional[str] = field(default="v0")
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freeze_backbone: bool = field(default=False)
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@dataclass
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class DataArguments:
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data_path: str = field(default=None,
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metadata={"help": "Path to the training data."})
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lazy_preprocess: bool = False
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is_multimodal: bool = False
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image_folder: Optional[str] = field(default=None)
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image_aspect_ratio: str = 'square'
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image_grid_pinpoints: Optional[str] = field(default=None)
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@dataclass
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class TrainingArguments(transformers.TrainingArguments):
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cache_dir: Optional[str] = field(default=None)
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optim: str = field(default="adamw_torch")
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remove_unused_columns: bool = field(default=False)
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tune_visual_abstractor: bool = field(default=True)
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freeze_vision_model: bool = field(default=True)
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model_max_length: int = field(
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default=512,
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metadata={
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"help":
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"Maximum sequence length. Sequences will be right padded (and possibly truncated)."
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},
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)
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double_quant: bool = field(
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default=True,
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metadata={"help": "Compress the quantization statistics through double quantization."}
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)
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quant_type: str = field(
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default="nf4",
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metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."}
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)
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bits: int = field(
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default=16,
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metadata={"help": "How many bits to use."}
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)
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lora_enable: bool = False
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lora_r: int = 64
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lora_alpha: int = 16
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lora_dropout: float = 0.05
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lora_weight_path: str = ""
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lora_bias: str = "none"
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visual_abstractor_lr: Optional[float] = None
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group_by_modality_length: bool = field(default=False)
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def maybe_zero_3(param, ignore_status=False, name=None):
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from deepspeed import zero
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from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
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if hasattr(param, "ds_id"):
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if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
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if not ignore_status:
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logging.warning(f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}")
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with zero.GatheredParameters([param]):
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param = param.data.detach().cpu().clone()
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else:
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param = param.detach().cpu().clone()
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return param
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# Borrowed from peft.utils.get_peft_model_state_dict
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def get_peft_state_maybe_zero_3(named_params, bias):
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if bias == "none":
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to_return = {k: t for k, t in named_params if "lora_" in k}
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elif bias == "all":
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to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k}
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elif bias == "lora_only":
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to_return = {}
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maybe_lora_bias = {}
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lora_bias_names = set()
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for k, t in named_params:
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if "lora_" in k:
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to_return[k] = t
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bias_name = k.split("lora_")[0] + "bias"
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lora_bias_names.add(bias_name)
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elif "bias" in k:
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maybe_lora_bias[k] = t
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for k, t in maybe_lora_bias:
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if bias_name in lora_bias_names:
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to_return[bias_name] = t
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else:
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raise NotImplementedError
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to_return = {k: maybe_zero_3(v, ignore_status=True) for k, v in to_return.items()}
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return to_return
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def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only=True):
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to_return = {k: t for k, t in named_params if "lora_" not in k}
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if require_grad_only:
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to_return = {k: t for k, t in to_return.items() if t.requires_grad}
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to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
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return to_return
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def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
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to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)}
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to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
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return to_return
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def find_all_linear_names(model):
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cls = torch.nn.Linear
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lora_module_names = set()
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multimodal_keywords = ['vision_model', 'visual_abstractor']
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for name, module in model.named_modules():
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if any(mm_keyword in name for mm_keyword in multimodal_keywords):
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continue
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if isinstance(module, cls):
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lora_module_names.add(name)
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if 'lm_head' in lora_module_names: # needed for 16-bit
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lora_module_names.remove('lm_head')
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return list(lora_module_names)
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def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
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output_dir: str):
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"""Collects the state dict and dump to disk."""
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if trainer.deepspeed:
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torch.cuda.synchronize()
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trainer.save_model(output_dir)
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return
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state_dict = trainer.model.state_dict()
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if trainer.args.should_save:
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cpu_state_dict = {
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key: value.cpu()
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for key, value in state_dict.items()
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}
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del state_dict
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trainer._save(output_dir, state_dict=cpu_state_dict) # noqa
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def smart_tokenizer_and_embedding_resize(
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special_tokens_dict: Dict,
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tokenizer: transformers.PreTrainedTokenizer,
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model: transformers.PreTrainedModel,
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):
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"""Resize tokenizer and embedding.
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Note: This is the unoptimized version that may make your embedding size not be divisible by 64.
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"""
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num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict)
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model.resize_token_embeddings(len(tokenizer))
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if num_new_tokens > 0:
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input_embeddings = model.get_input_embeddings().weight.data
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output_embeddings = model.get_output_embeddings().weight.data
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input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
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dim=0, keepdim=True)
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output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
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dim=0, keepdim=True)
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input_embeddings[-num_new_tokens:] = input_embeddings_avg
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output_embeddings[-num_new_tokens:] = output_embeddings_avg
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def _tokenize_fn(strings: Sequence[str],
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tokenizer: transformers.PreTrainedTokenizer) -> Dict:
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"""Tokenize a list of strings."""
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tokenized_list = [
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tokenizer(
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text,
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return_tensors="pt",
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padding="longest",
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max_length=tokenizer.model_max_length,
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truncation=True,
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) for text in strings
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]
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input_ids = labels = [
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tokenized.input_ids[0] for tokenized in tokenized_list
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]
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input_ids_lens = labels_lens = [
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tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item()
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for tokenized in tokenized_list
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]
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return dict(
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input_ids=input_ids,
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labels=labels,
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input_ids_lens=input_ids_lens,
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labels_lens=labels_lens,
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)
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def _mask_targets(target, tokenized_lens, speakers):
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# cur_idx = 0
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cur_idx = tokenized_lens[0]
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tokenized_lens = tokenized_lens[1:]
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target[:cur_idx] = IGNORE_INDEX
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for tokenized_len, speaker in zip(tokenized_lens, speakers):
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if speaker == "human":
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target[cur_idx+2:cur_idx + tokenized_len] = IGNORE_INDEX
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cur_idx += tokenized_len
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def _add_speaker_and_signal(header, source, get_conversation=True):
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"""Add speaker and start/end signal on each round."""
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BEGIN_SIGNAL = "### "
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END_SIGNAL = "\n"
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conversation = header
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for sentence in source:
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from_str = sentence["from"]
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if from_str.lower() == "human":
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from_str = conversation_lib.default_conversation.roles[0]
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elif from_str.lower() == "gpt":
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from_str = conversation_lib.default_conversation.roles[1]
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else:
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from_str = 'unknown'
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sentence["value"] = (BEGIN_SIGNAL + from_str + ": " +
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sentence["value"] + END_SIGNAL)
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if get_conversation:
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conversation += sentence["value"]
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conversation += BEGIN_SIGNAL
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return conversation
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def preprocess_multimodal(
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sources: Sequence[str],
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data_args: DataArguments
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) -> Dict:
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is_multimodal = data_args.is_multimodal
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if not is_multimodal:
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return sources
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for source in sources:
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for sentence in source:
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if DEFAULT_IMAGE_TOKEN in sentence['value']:
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sentence['value'] = sentence['value'].replace(DEFAULT_IMAGE_TOKEN, '').strip()
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sentence['value'] = DEFAULT_IMAGE_TOKEN + '\n' + sentence['value']
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sentence['value'] = sentence['value'].strip()
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replace_token = DEFAULT_IMAGE_TOKEN
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sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, replace_token)
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return sources
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def preprocess_v1(
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sources,
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tokenizer: transformers.PreTrainedTokenizer,
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has_image: bool = False
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) -> Dict:
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conv = conversation_lib.default_conversation.copy()
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roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
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# Apply prompt templates
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conversations = []
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for i, source in enumerate(sources):
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if roles[source[0]["from"]] != conv.roles[0]:
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# Skip the first one if it is not from human
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source = source[1:]
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conv.messages = []
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for j, sentence in enumerate(source):
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role = roles[sentence["from"]]
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assert role == conv.roles[j % 2], f"{i}"
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conv.append_message(role, sentence["value"])
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conversations.append(conv.get_prompt())
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# Tokenize conversations
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if has_image:
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input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
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else:
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input_ids = tokenizer(
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conversations,
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return_tensors="pt",
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padding="longest",
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max_length=tokenizer.model_max_length,
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truncation=True,
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).input_ids
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targets = input_ids.clone()
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assert conv.sep_style == conversation_lib.SeparatorStyle.TWO or conv.sep_style == conversation_lib.SeparatorStyle.TWO_NO_SYS
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# Mask targets
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sep = conv.sep + conv.roles[1] + ": "
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for conversation, target in zip(conversations, targets):
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total_len = int(target.ne(tokenizer.pad_token_id).sum())
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rounds = conversation.split(conv.sep2)
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cur_len = 1
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target[:cur_len] = IGNORE_INDEX
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for i, rou in enumerate(rounds):
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if rou == "":
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break
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parts = rou.split(sep)
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if len(parts) != 2:
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break
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parts[0] += sep
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if has_image:
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round_len = len(tokenizer_image_token(rou, tokenizer))
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instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2
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else:
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round_len = len(tokenizer(rou).input_ids)
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instruction_len = len(tokenizer(parts[0]).input_ids) - 2
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target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
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cur_len += round_len
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target[cur_len:] = IGNORE_INDEX
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if cur_len < tokenizer.model_max_length:
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if cur_len != total_len:
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target[:] = IGNORE_INDEX
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print(
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f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
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f" (ignored)"
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)
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return dict(
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input_ids=input_ids,
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labels=targets,
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)
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def preprocess_plain(
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sources: Sequence[str],
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tokenizer: transformers.PreTrainedTokenizer,
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) -> Dict:
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# add end signal and concatenate together
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conversations = []
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for source in sources:
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assert len(source) == 2
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assert DEFAULT_IMAGE_TOKEN in source[0]['value']
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source[0]['value'] = DEFAULT_IMAGE_TOKEN
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conversation = source[0]['value'] + source[1]['value'] + conversation_lib.default_conversation.sep
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conversations.append(conversation)
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# tokenize conversations
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input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations]
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targets = copy.deepcopy(input_ids)
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for target, source in zip(targets, sources):
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tokenized_len = len(tokenizer_image_token(source[0]['value'], tokenizer))
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target[:tokenized_len] = IGNORE_INDEX
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return dict(input_ids=input_ids, labels=targets)
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def preprocess(
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sources: Sequence[str],
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tokenizer: transformers.PreTrainedTokenizer,
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has_image: bool = False
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) -> Dict:
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"""
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Given a list of sources, each is a conversation list. This transform:
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1. Add signal '### ' at the beginning each sentence, with end signal '\n';
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2. Concatenate conversations together;
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3. Tokenize the concatenated conversation;
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4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX.
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"""
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if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.PLAIN:
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return preprocess_plain(sources, tokenizer)
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if conversation_lib.default_conversation.version.startswith("v1"):
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return preprocess_v1(sources, tokenizer, has_image=has_image)
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# add end signal and concatenate together
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conversations = []
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for source in sources:
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header = f"{conversation_lib.default_conversation.system}\n\n"
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conversation = _add_speaker_and_signal(header, source)
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conversations.append(conversation)
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# tokenize conversations
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def get_tokenize_len(prompts):
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return [len(tokenizer_image_token(prompt, tokenizer)) for prompt in prompts]
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if has_image:
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input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations]
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else:
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conversations_tokenized = _tokenize_fn(conversations, tokenizer)
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input_ids = conversations_tokenized["input_ids"]
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targets = copy.deepcopy(input_ids)
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for target, source in zip(targets, sources):
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if has_image:
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tokenized_lens = get_tokenize_len([header] + [s["value"] for s in source])
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else:
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tokenized_lens = _tokenize_fn([header] + [s["value"] for s in source], tokenizer)["input_ids_lens"]
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speakers = [sentence["from"] for sentence in source]
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_mask_targets(target, tokenized_lens, speakers)
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return dict(input_ids=input_ids, labels=targets)
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class LazySupervisedDataset(Dataset):
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"""Dataset for supervised fine-tuning."""
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def __init__(self, data_path: str,
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tokenizer: transformers.PreTrainedTokenizer,
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data_args: DataArguments):
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super(LazySupervisedDataset, self).__init__()
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list_data_dict = json.load(open(data_path, "r"))
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rank0_print("Formatting inputs...Skip in lazy mode")
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self.tokenizer = tokenizer
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self.list_data_dict = list_data_dict
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self.data_args = data_args
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def __len__(self):
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return len(self.list_data_dict)
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@property
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def lengths(self):
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length_list = []
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for sample in self.list_data_dict:
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img_tokens = 128 if 'image' in sample else 0
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length_list.append(sum(len(conv['value'].split()) for conv in sample['conversations']) + img_tokens)
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return length_list
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@property
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def modality_lengths(self):
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length_list = []
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for sample in self.list_data_dict:
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cur_len = sum(len(conv['value'].split()) for conv in sample['conversations'])
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cur_len = cur_len if 'image' in sample else -cur_len
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length_list.append(cur_len)
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return length_list
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# def __getitem__(self, i) -> Dict[str, torch.Tensor]:
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# sources = self.list_data_dict[i]
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# if isinstance(i, int):
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# sources = [sources]
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# assert len(sources) == 1, "Don't know why it is wrapped to a list" # FIXME
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# if 'image' in sources[0]:
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# image_file = self.list_data_dict[i]['image']
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# image_folder = self.data_args.image_folder
|
|
# processor = self.data_args.image_processor
|
|
# image = Image.open(os.path.join(image_folder, image_file)).convert('RGB')
|
|
# if self.data_args.image_aspect_ratio == 'pad':
|
|
# def expand2square(pil_img, background_color):
|
|
# width, height = pil_img.size
|
|
# if width == height:
|
|
# return pil_img
|
|
# elif width > height:
|
|
# result = Image.new(pil_img.mode, (width, width), background_color)
|
|
# result.paste(pil_img, (0, (width - height) // 2))
|
|
# return result
|
|
# else:
|
|
# result = Image.new(pil_img.mode, (height, height), background_color)
|
|
# result.paste(pil_img, ((height - width) // 2, 0))
|
|
# return result
|
|
# image = expand2square(image, tuple(int(x*255) for x in processor.image_mean))
|
|
# image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
|
|
# else:
|
|
# image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
|
|
# sources = preprocess_multimodal(
|
|
# copy.deepcopy([e["conversations"] for e in sources]),
|
|
# self.data_args)
|
|
# else:
|
|
# sources = copy.deepcopy([e["conversations"] for e in sources])
|
|
# data_dict = preprocess(
|
|
# sources,
|
|
# self.tokenizer,
|
|
# has_image=('image' in self.list_data_dict[i]))
|
|
# if isinstance(i, int):
|
|
# data_dict = dict(input_ids=data_dict["input_ids"][0],
|
|
# labels=data_dict["labels"][0])
|
|
|
|
# # image exist in the data
|
|
# if 'image' in self.list_data_dict[i]:
|
|
# data_dict['image'] = image
|
|
# elif self.data_args.is_multimodal:
|
|
# # image does not exist in the data, but the model is multimodal
|
|
# crop_size = self.data_args.image_processor.crop_size
|
|
# data_dict['image'] = torch.zeros(3, crop_size['height'], crop_size['width'])
|
|
# return data_dict
|
|
|
|
def next_rand(self):
|
|
import random
|
|
return random.randint(0,len(self)-1)
|
|
|
|
def __getitem__(self, i) -> Dict[str, torch.Tensor]:
|
|
while True:
|
|
sources = self.list_data_dict[i]
|
|
if isinstance(i, int):
|
|
sources = [sources]
|
|
assert len(sources) == 1, "Don't know why it is wrapped to a list" # FIXME
|
|
if 'image' in sources[0]:
|
|
|
|
image_file = self.list_data_dict[i]['image']
|
|
image_folder = self.data_args.image_folder
|
|
processor = self.data_args.image_processor
|
|
from pathlib import Path
|
|
if not Path(os.path.join(image_folder, image_file)).exists():
|
|
i = self.next_rand()
|
|
continue
|
|
image = Image.open(os.path.join(image_folder, image_file)).convert('RGB')
|
|
if self.data_args.image_aspect_ratio == 'pad':
|
|
def expand2square(pil_img, background_color):
|
|
width, height = pil_img.size
|
|
if width == height:
|
|
return pil_img
|
|
elif width > height:
|
|
result = Image.new(pil_img.mode, (width, width), background_color)
|
|
result.paste(pil_img, (0, (width - height) // 2))
|
|
return result
|
|
else:
|
|
result = Image.new(pil_img.mode, (height, height), background_color)
|
|
result.paste(pil_img, ((height - width) // 2, 0))
|
|
return result
|
|
image = expand2square(image, tuple(int(x*255) for x in processor.image_mean))
|
|
image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
|
|
else:
|
|
image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
|
|
sources = preprocess_multimodal(
|
|
copy.deepcopy([e["conversations"] for e in sources]),
|
|
self.data_args)
|
|
else:
|
|
|
|
sources = copy.deepcopy([e["conversations"] for e in sources])
|
|
data_dict = preprocess(
|
|
sources,
|
|
self.tokenizer,
|
|
has_image=('image' in self.list_data_dict[i]))
|
|
if isinstance(i, int):
|
|
data_dict = dict(input_ids=data_dict["input_ids"][0],
|
|
labels=data_dict["labels"][0])
|
|
|
|
# image exist in the data
|
|
if 'image' in self.list_data_dict[i]:
|
|
data_dict['image'] = image
|
|
elif self.data_args.is_multimodal:
|
|
# image does not exist in the data, but the model is multimodal
|
|
crop_size = self.data_args.image_processor.crop_size
|
|
data_dict['image'] = torch.zeros(3, crop_size['height'], crop_size['width'])
|
|
return data_dict
|
|
|
|
|
|
@dataclass
|
|
class DataCollatorForSupervisedDataset(object):
|
|
"""Collate examples for supervised fine-tuning."""
|
|
|
|
tokenizer: transformers.PreTrainedTokenizer
|
|
|
|
def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
|
|
input_ids, labels = tuple([instance[key] for instance in instances]
|
|
for key in ("input_ids", "labels"))
|
|
input_ids = torch.nn.utils.rnn.pad_sequence(
|
|
input_ids,
|
|
batch_first=True,
|
|
padding_value=self.tokenizer.pad_token_id)
|
|
labels = torch.nn.utils.rnn.pad_sequence(labels,
|
|
batch_first=True,
|
|
padding_value=IGNORE_INDEX)
|
|
input_ids = input_ids[:, :self.tokenizer.model_max_length]
|
|
labels = labels[:, :self.tokenizer.model_max_length]
|
|
batch = dict(
|
|
input_ids=input_ids,
|
|
labels=labels,
|
|
attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
|
|
)
|
|
|
|
if 'image' in instances[0]:
|
|
images = [instance['image'] for instance in instances]
|
|
if all(x is not None and x.shape == images[0].shape for x in images):
|
|
batch['images'] = torch.stack(images)
|
|
else:
|
|
batch['images'] = images
|
|
|
|
return batch
|
|
|
|
|
|
def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer,
|
|
data_args) -> Dict:
|
|
"""Make dataset and collator for supervised fine-tuning."""
|
|
train_dataset = LazySupervisedDataset(tokenizer=tokenizer,
|
|
data_path=data_args.data_path,
|
|
data_args=data_args)
|
|
data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
|
|
return dict(train_dataset=train_dataset,
|
|
eval_dataset=None,
|
|
data_collator=data_collator)
|
|
|
|
|
|
def train():
|
|
global local_rank
|
|
|
|
parser = transformers.HfArgumentParser(
|
|
(ModelArguments, DataArguments, TrainingArguments))
|
|
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
|
local_rank = training_args.local_rank
|
|
compute_dtype = (torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
|
|
|
|
bnb_model_from_pretrained_args = {}
|
|
if training_args.bits in [4, 8]:
|
|
from transformers import BitsAndBytesConfig
|
|
bnb_model_from_pretrained_args.update(dict(
|
|
device_map={"": training_args.device},
|
|
load_in_4bit=training_args.bits == 4,
|
|
load_in_8bit=training_args.bits == 8,
|
|
quantization_config=BitsAndBytesConfig(
|
|
load_in_4bit=training_args.bits == 4,
|
|
load_in_8bit=training_args.bits == 8,
|
|
llm_int8_threshold=6.0,
|
|
llm_int8_has_fp16_weight=False,
|
|
bnb_4bit_compute_dtype=compute_dtype,
|
|
bnb_4bit_use_double_quant=training_args.double_quant,
|
|
bnb_4bit_quant_type=training_args.quant_type # {'fp4', 'nf4'}
|
|
)
|
|
))
|
|
|
|
model = MPLUGOwl2LlamaForCausalLM.from_pretrained(
|
|
model_args.model_name_or_path,
|
|
cache_dir=training_args.cache_dir,
|
|
**bnb_model_from_pretrained_args
|
|
)
|
|
model.config.use_cache = False
|
|
|
|
if model_args.freeze_backbone:
|
|
model.model.requires_grad_(False)
|
|
|
|
if training_args.bits in [4, 8]:
|
|
from peft import prepare_model_for_kbit_training
|
|
model.config.torch_dtype=(torch.float32 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
|
|
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=training_args.gradient_checkpointing)
|
|
|
|
if training_args.gradient_checkpointing:
|
|
if hasattr(model, "enable_input_require_grads"):
|
|
model.enable_input_require_grads()
|
|
else:
|
|
def make_inputs_require_grad(module, input, output):
|
|
output.requires_grad_(True)
|
|
model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
|
|
|
|
if training_args.lora_enable:
|
|
from peft import LoraConfig, get_peft_model
|
|
lora_config = LoraConfig(
|
|
r=training_args.lora_r,
|
|
lora_alpha=training_args.lora_alpha,
|
|
target_modules=find_all_linear_names(model),
|
|
lora_dropout=training_args.lora_dropout,
|
|
bias=training_args.lora_bias,
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
if training_args.bits == 16:
|
|
if training_args.bf16:
|
|
model.to(torch.bfloat16)
|
|
if training_args.fp16:
|
|
model.to(torch.float16)
|
|
rank0_print("Adding LoRA adapters...")
|
|
model = get_peft_model(model, lora_config)
|
|
|
|
tokenizer = transformers.AutoTokenizer.from_pretrained(
|
|
model_args.model_name_or_path,
|
|
cache_dir=training_args.cache_dir,
|
|
model_max_length=training_args.model_max_length,
|
|
padding_side="right",
|
|
use_fast=False,
|
|
)
|
|
|
|
|
|
tokenizer.pad_token = tokenizer.unk_token
|
|
if model_args.version in conversation_lib.conv_templates:
|
|
conversation_lib.default_conversation = conversation_lib.conv_templates[model_args.version]
|
|
else:
|
|
conversation_lib.default_conversation = conversation_lib.conv_templates["vicuna_v1"]
|
|
|
|
if not training_args.freeze_vision_model and training_args.bits in [4, 8]:
|
|
model.get_model().vision_model.to(dtype=compute_dtype, device=training_args.device)
|
|
else:
|
|
vision_tower = model.get_model().vision_model
|
|
vision_tower.to(dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device)
|
|
|
|
if training_args.tune_visual_abstractor and training_args.bits in [4, 8]:
|
|
model.get_model().visual_abstractor.to(dtype=compute_dtype, device=training_args.device)
|
|
else:
|
|
visual_abstractor = model.get_model().visual_abstractor
|
|
visual_abstractor.to(dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device)
|
|
|
|
data_args.image_processor = CLIPImageProcessor.from_pretrained(model_args.model_name_or_path)
|
|
data_args.is_multimodal = True
|
|
|
|
model.config.image_aspect_ratio = data_args.image_aspect_ratio
|
|
model.config.image_grid_pinpoints = data_args.image_grid_pinpoints
|
|
model.config.tune_visual_abstractor = model_args.tune_visual_abstractor = training_args.tune_visual_abstractor
|
|
ic(training_args.tune_visual_abstractor)
|
|
model.requires_grad_(True)
|
|
if training_args.tune_visual_abstractor:
|
|
# model.requires_grad_(False)
|
|
for p in model.get_model().visual_abstractor.parameters():
|
|
p.requires_grad = True
|
|
|
|
model.config.freeze_vision_model = training_args.freeze_vision_model
|
|
ic(training_args.freeze_vision_model)
|
|
if training_args.freeze_vision_model:
|
|
for p in model.get_model().vision_model.parameters():
|
|
p.requires_grad = False
|
|
|
|
model.config.visual_abstractor_lr = training_args.visual_abstractor_lr
|
|
|
|
|
|
if training_args.bits in [4, 8]:
|
|
from peft.tuners.lora import LoraLayer
|
|
for name, module in model.named_modules():
|
|
if isinstance(module, LoraLayer):
|
|
if training_args.bf16:
|
|
module = module.to(torch.bfloat16)
|
|
if 'norm' in name:
|
|
module = module.to(torch.float32)
|
|
if 'lm_head' in name or 'embed_tokens' in name:
|
|
if hasattr(module, 'weight'):
|
|
if training_args.bf16 and module.weight.dtype == torch.float32:
|
|
module = module.to(torch.bfloat16)
|
|
|
|
data_module = make_supervised_data_module(tokenizer=tokenizer,
|
|
data_args=data_args)
|
|
trainer = MPLUGOwl2Trainer(model=model,
|
|
tokenizer=tokenizer,
|
|
args=training_args,
|
|
**data_module)
|
|
|
|
# if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")):
|
|
# trainer.train(resume_from_checkpoint=True)
|
|
# else:
|
|
# trainer.train()
|
|
|
|
# TODO I dont like auto resume << REMOVE IT AND UNCOMMENT THE ABOVE CODE
|
|
trainer.train()
|
|
|
|
trainer.save_state()
|
|
|
|
model.config.use_cache = True
|
|
|
|
if training_args.lora_enable:
|
|
state_dict = get_peft_state_maybe_zero_3(
|
|
model.named_parameters(), training_args.lora_bias
|
|
)
|
|
non_lora_state_dict = get_peft_state_non_lora_maybe_zero_3(
|
|
model.named_parameters()
|
|
)
|
|
if training_args.local_rank == 0 or training_args.local_rank == -1:
|
|
model.config.save_pretrained(training_args.output_dir)
|
|
model.save_pretrained(training_args.output_dir, state_dict=state_dict)
|
|
torch.save(non_lora_state_dict, os.path.join(training_args.output_dir, 'non_lora_trainables.bin'))
|
|
else:
|
|
safe_save_model_for_hf_trainer(trainer=trainer,
|
|
output_dir=training_args.output_dir)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
train() |