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# Copyright 2024 the LlamaFactory team and the Llamole team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, Any, Dict, Optional, TypedDict
from pathlib import Path
import json
import pandas as pd
import os
import torch
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoModelForVision2Seq,
AutoProcessor,
AutoTokenizer,
)
from trl import AutoModelForCausalLMWithValueHead
from huggingface_hub import hf_hub_download
from ..extras.logging import get_logger
from ..extras.misc import (
count_parameters,
skip_check_imports,
try_download_model_from_ms,
)
from .adapter import init_adapter
from .model_utils.misc import register_autoclass
from .model_utils.mod import convert_pretrained_model_to_mod, load_mod_pretrained_model
from .model_utils.valuehead import load_valuehead_params
from .patcher import patch_config, patch_model, patch_tokenizer, patch_valuehead_model
from .graph_decoder.diffusion_model import GraphDiT
from .graph_encoder.model import GraphCLIP
from .graph_predictor.model import GraphPredictor
if TYPE_CHECKING:
from transformers import (
PretrainedConfig,
PreTrainedModel,
PreTrainedTokenizer,
ProcessorMixin,
)
from ..hparams import FinetuningArguments, ModelArguments
logger = get_logger(__name__)
def download_from_hf(repo_id, filename, local_dir):
os.makedirs(local_dir, exist_ok=True)
return hf_hub_download(repo_id=repo_id, filename=filename, local_dir=local_dir)
class TokenizerModule(TypedDict):
tokenizer: "PreTrainedTokenizer"
processor: Optional["ProcessorMixin"]
def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
r"""
Gets arguments to load config/tokenizer/model.
Note: including inplace operation of model_args.
"""
skip_check_imports()
model_args.model_name_or_path = try_download_model_from_ms(model_args)
return {
"trust_remote_code": True,
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"token": model_args.hf_hub_token,
}
def load_tokenizer(model_args: "ModelArguments", generate_mode=False) -> "TokenizerModule":
r"""
Loads pretrained tokenizer or a pre-saved tokenizer.
Note: including inplace operation of model_args.
"""
init_kwargs = _get_init_kwargs(model_args)
padding_size = 'left' if generate_mode else 'right'
try:
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
use_fast=model_args.use_fast_tokenizer,
split_special_tokens=model_args.split_special_tokens,
padding_side=padding_size,
**init_kwargs,
)
except ValueError: # try the fast one
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
use_fast=True,
padding_side=padding_size,
**init_kwargs,
)
if model_args.new_special_tokens is not None:
num_added_tokens = tokenizer.add_special_tokens(
dict(additional_special_tokens=model_args.new_special_tokens),
replace_additional_special_tokens=False,
)
logger.info(
"Add {} to special tokens.".format(",".join(model_args.new_special_tokens))
)
if num_added_tokens > 0 and not model_args.resize_vocab:
model_args.resize_vocab = True
logger.warning(
"New tokens have been added, changed `resize_vocab` to True."
)
patch_tokenizer(tokenizer)
if model_args.new_special_tokens is not None:
token_id_dict = {}
for elem in model_args.new_special_tokens:
if isinstance(elem, str) and len(elem) != 0:
elem_token_ids = tokenizer.encode(elem, add_special_tokens=False)
token_id_dict[elem] = elem_token_ids
logger.info(f"Dictionary of added tokens and their IDs: {token_id_dict}")
return {"tokenizer": tokenizer, "processor": None}
def load_config(model_args: "ModelArguments") -> "PretrainedConfig":
r"""
Loads model config.
"""
init_kwargs = _get_init_kwargs(model_args)
return AutoConfig.from_pretrained(model_args.model_name_or_path, **init_kwargs)
def load_language_model(
tokenizer: "PreTrainedTokenizer",
model_args: "ModelArguments",
finetuning_args: "FinetuningArguments",
is_trainable: bool = False,
add_valuehead: bool = False,
) -> "PreTrainedModel":
r"""
Loads pretrained model.
"""
init_kwargs = _get_init_kwargs(model_args)
config = load_config(model_args)
patch_config(config, tokenizer, model_args, init_kwargs, is_trainable)
model = None
lazy_load = False
# if model is None and not lazy_load:
init_kwargs["config"] = config
init_kwargs["pretrained_model_name_or_path"] = model_args.model_name_or_path
model = AutoModelForCausalLM.from_pretrained(**init_kwargs)
if not lazy_load:
patch_model(model, tokenizer, model_args, is_trainable, add_valuehead)
register_autoclass(config, model, tokenizer)
model = init_adapter(config, model, model_args, finetuning_args, is_trainable)
if add_valuehead:
model = AutoModelForCausalLMWithValueHead.from_pretrained(model)
patch_valuehead_model(model)
if model_args.adapter_name_or_path is not None:
vhead_path = model_args.adapter_name_or_path[-1]
else:
vhead_path = model_args.model_name_or_path
vhead_params = load_valuehead_params(vhead_path, model_args)
if vhead_params is not None:
model.load_state_dict(vhead_params, strict=False)
logger.info("Loaded valuehead from checkpoint: {}".format(vhead_path))
if not is_trainable:
model.requires_grad_(False)
for param in model.parameters():
if (
param.data.dtype == torch.float32
and model_args.compute_dtype != torch.float32
):
param.data = param.data.to(model_args.compute_dtype)
model.eval()
else:
model.train()
trainable_params, all_param = count_parameters(model)
if is_trainable:
param_stats = "lm trainable params: {:,} || all params: {:,} || trainable%: {:.4f}".format(
trainable_params, all_param, 100 * trainable_params / all_param
)
else:
param_stats = "lm all params: {:,}".format(all_param)
logger.info(param_stats)
if model_args.print_param_status:
for name, param in model.named_parameters():
print(
"name: {}, dtype: {}, device: {}, trainable: {}".format(
name, param.dtype, param.device, param.requires_grad
)
)
return model
def load_graph_decoder(model_args: "ModelArguments", path: str, device: str):
path = Path(path)
config_path = path / "config.yaml"
if not config_path.exists():
logger.info(f"Config not found in {path}. Downloading from Hugging Face.")
repo_id = "liuganghuggingface/Llamole-Pretrained-GraphDiT"
config_path = download_from_hf(repo_id, "config.yaml", path)
download_from_hf(repo_id, "data.meta.json", path)
download_from_hf(repo_id, "model.pt", path)
data_info_path = path / "data.meta.json"
model = GraphDiT(
model_config_path=config_path,
data_info_path=data_info_path,
model_dtype=model_args.compute_dtype,
)
model.init_model(path)
if model_args.disable_graph_model_gradient:
model.disable_grads()
model.to(device)
for param in model.parameters():
if param.dtype == torch.float32 and model_args.compute_dtype != torch.float32:
param.data = param.data.to(model_args.compute_dtype)
trainable_params, all_param = count_parameters(model)
param_stats = "Graph DiT trainable params: {:,} || all params: {:,} || trainable%: {:.4f}".format(
trainable_params, all_param, 100 * trainable_params / all_param
)
logger.info(param_stats)
if model_args.print_param_status:
for name, param in model.named_parameters():
logger.info(
f"name: {name}, dtype: {param.dtype}, device: {param.device}, trainable: {param.requires_grad}"
)
return model
def load_graph_predictor(model_args: "ModelArguments", path: str, device: str):
path = Path(path)
config_path = path / "config.json"
if not config_path.exists():
logger.info(f"Config not found in {path}. Downloading from Hugging Face.")
repo_id = "liuganghuggingface/Llamole-Pretrained-GNNPredictor"
config_path = download_from_hf(repo_id, "config.json", path)
download_from_hf(repo_id, "model.pt", path)
download_from_hf(repo_id, "cost_model.pt", path)
download_from_hf(repo_id, "label_to_template.csv.gz", path)
download_from_hf(repo_id, "available.csv.gz", path)
with open(config_path, "r") as f:
config = json.load(f)
label_to_template_path = path / "label_to_template.csv.gz"
label_to_template_df = pd.read_csv(label_to_template_path, compression='gzip')
label_to_template = dict(zip(label_to_template_df['rule_label'], label_to_template_df['retro_templates']))
available_path = path / "available.csv.gz"
available = pd.read_csv(available_path, compression='gzip')
model = GraphPredictor(
num_layer=config["num_layer"],
hidden_size=config["hidden_size"],
drop_ratio=config["drop_ratio"],
out_dim=config["num_task"],
model_config=config,
label_to_template=label_to_template,
available=available,
)
model.init_model(path)
model.init_neural_cost(path)
if model_args.disable_graph_model_gradient:
model.disable_grads()
model.to(device)
for param in model.parameters():
if param.data.dtype == torch.float32 and model_args.compute_dtype != torch.float32:
param.data = param.data.to(model_args.compute_dtype)
trainable_params, all_param = count_parameters(model)
param_stats = "Graph Predictor trainable params: {:,} || all params: {:,} || trainable%: {:.4f}".format(
trainable_params, all_param, 100 * trainable_params / all_param
)
logger.info(param_stats)
if model_args.print_param_status:
for name, param in model.named_parameters():
logger.info(
f"name: {name}, dtype: {param.dtype}, device: {param.device}, trainable: {param.requires_grad}"
)
return model
def load_graph_encoder(model_args: "ModelArguments", path: str, device: str):
path = Path(path)
config_path = path / "config.json"
if not config_path.exists():
logger.info(f"Config not found in {path}. Downloading from Hugging Face.")
repo_id = "liuganghuggingface/Llamole-Pretrained-GraphEncoder"
config_path = download_from_hf(repo_id, "config.json", path)
download_from_hf(repo_id, "model.pt", path)
download_from_hf(repo_id, "model_proj.pt", path)
with open(config_path, "r") as f:
config = json.load(f)
model = GraphCLIP(
graph_num_layer=config["num_layer"],
graph_hidden_size=config["hidden_size"],
dropout=config["drop_ratio"],
model_config=config,
)
model.init_model(path, verbose=False)
if model_args.disable_graph_model_gradient:
model.disable_grads()
model.to(device)
for param in model.parameters():
if param.data.dtype == torch.float32 and model_args.compute_dtype != torch.float32:
param.data = param.data.to(model_args.compute_dtype)
trainable_params, all_param = count_parameters(model)
param_stats = "Graph CLIP Encoder trainable params: {:,} || all params: {:,} || trainable%: {:.4f}".format(
trainable_params, all_param, 100 * trainable_params / all_param
)
logger.info(param_stats)
if model_args.print_param_status:
for name, param in model.named_parameters():
logger.info(
f"name: {name}, dtype: {param.dtype}, device: {param.device}, trainable: {param.requires_grad}"
)
return model |