File size: 10,684 Bytes
f8e18e6 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 | import os
import torch
import torch.nn as nn
from transformers import Trainer
from transformers.trainer import (
is_sagemaker_mp_enabled,
get_parameter_names,
ALL_LAYERNORM_LAYERS,
is_peft_available,
WEIGHTS_NAME,
TRAINING_ARGS_NAME,
SAFE_WEIGHTS_NAME,
TRAINER_STATE_NAME,
PREFIX_CHECKPOINT_DIR,
logger,
)
import safetensors
from peft import PeftModel
from typing import Optional
import numpy as np
from transformers.processing_utils import ProcessorMixin
from transformers.modeling_utils import PreTrainedModel
from peft import PeftModel
from training.train_utils import get_peft_state_maybe_zero_3, get_peft_state_non_lora_maybe_zero_3
def maybe_zero_3(param, ignore_status=False, name=None):
from deepspeed import zero
from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
if hasattr(param, "ds_id"):
if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
if not ignore_status:
print(name, "no ignore status")
with zero.GatheredParameters([param]):
param = param.data.detach().cpu().clone()
else:
param = param.detach().cpu().clone()
return param
class QwenTrainer(Trainer):
def __init__(self, processor, *args, **kwargs):
super(QwenTrainer, self).__init__(*args, **kwargs)
self.processor = processor
def create_optimizer(self):
"""
Setup the optimizer.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer's init through `optimizers`, or subclass and override this method in a subclass.
"""
if is_sagemaker_mp_enabled():
return super().create_optimizer()
opt_model = self.model
if self.optimizer is None:
decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS)
decay_parameters = [name for name in decay_parameters if "bias" not in name]
lr_mapper = {}
visual_parameters = []
merger_parameters = []
if self.args.vision_lr is not None:
lr_mapper["visual"] = self.args.vision_lr
visual_parameters = [name for name, _ in opt_model.named_parameters() if "visual" in name and "merger" not in name]
if self.args.merger_lr is not None:
lr_mapper["merger"] = self.args.merger_lr
merger_parameters = [name for name, _ in opt_model.named_parameters() if "merger" in name]
if len(lr_mapper) > 0:
special_lr_parameters = merger_parameters + visual_parameters
optimizer_grouped_parameters = [
{
"params": [p for n, p in opt_model.named_parameters() if (n in decay_parameters and n not in special_lr_parameters and p.requires_grad)],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n not in special_lr_parameters and p.requires_grad)],
"weight_decay": 0.0,
},
]
if visual_parameters:
optimizer_grouped_parameters.extend(
[
{
"params": [p for n, p in opt_model.named_parameters() if (n in decay_parameters and n in visual_parameters and p.requires_grad)],
"weight_decay": self.args.weight_decay,
"lr": self.args.vision_lr,
},
{
"params": [p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n in visual_parameters and p.requires_grad)],
"weight_decay": 0.0,
"lr": self.args.vision_lr,
},
]
)
if merger_parameters:
optimizer_grouped_parameters.extend(
[
{
"params": [p for n, p in opt_model.named_parameters() if (n in decay_parameters and n in merger_parameters and p.requires_grad)],
"weight_decay": self.args.weight_decay,
"lr": self.args.merger_lr,
},
{
"params": [p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n in merger_parameters and p.requires_grad)],
"weight_decay": 0.0,
"lr": self.args.merger_lr,
},
]
)
else:
optimizer_grouped_parameters = [
{
"params": [p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad)],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in opt_model.named_parameters() if (n not in decay_parameters and p.requires_grad)],
"weight_decay": 0.0,
},
]
optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(self.args)
self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
if optimizer_cls.__name__ == "Adam8bit":
import bitsandbytes
manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
skipped = 0
for module in opt_model.modules():
if isinstance(module, nn.Embedding):
skipped += sum({p.data_ptr(): p.numel() for p in module.parameters()}.values())
logger.info(f"skipped {module}: {skipped/2**20}M params")
manager.register_module_override(module, "weight", {"optim_bits": 32})
logger.debug(f"bitsandbytes: will optimize {module} in fp32")
logger.info(f"skipped: {skipped/2**20}M params")
return self.optimizer
def _save_checkpoint(self, model, trial):
if self.args.lora_enable:
checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
if self.hp_search_backend is None and trial is None:
self.store_flos()
run_dir = self._get_output_dir(trial=trial)
output_dir = os.path.join(run_dir, checkpoint_folder)
self.save_model(output_dir, _internal_call=True)
non_lora_weights = get_peft_state_non_lora_maybe_zero_3(self.model.named_parameters(), require_grad_only=False)
torch.save(non_lora_weights, os.path.join(output_dir, "non_lora_state_dict.bin"))
if not self.args.save_only_model:
# Save optimizer and scheduler
self._save_optimizer_and_scheduler(output_dir)
# Save RNG state
self._save_rng_state(output_dir)
# Save the Trainer state
if self.args.should_save:
# Update the `TrainerControl` state to where we are currently
self.state.stateful_callbacks["TrainerControl"] = self.control.state()
self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))
if self.args.push_to_hub:
self._push_from_checkpoint(output_dir)
# Maybe delete some older checkpoints.
if self.args.should_save:
# Solely rely on numerical checkpoint id for rotation.
# mtime is not reliable especially on some fuse fs in cloud environments.
self._rotate_checkpoints(use_mtime=False, output_dir=run_dir)
else:
super(QwenTrainer, self)._save_checkpoint(model, trial)
def _save(self, output_dir: Optional[str] = None, state_dict=None):
# If we are executing this function, we are the process zero, so we don't check for that.
output_dir = output_dir if output_dir is not None else self.args.output_dir
os.makedirs(output_dir, exist_ok=True)
logger.info(f"Saving model checkpoint to {output_dir}")
supported_classes = (PreTrainedModel,) if not is_peft_available() else (PreTrainedModel, PeftModel)
# Save a trained model and configuration using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
if not isinstance(self.model, supported_classes):
if state_dict is None:
state_dict = self.model.state_dict()
if isinstance(self.accelerator.unwrap_model(self.model), supported_classes):
self.accelerator.unwrap_model(self.model).save_pretrained(
output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors
)
else:
logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
if self.args.save_safetensors:
safetensors.torch.save_file(
state_dict, os.path.join(output_dir, SAFE_WEIGHTS_NAME), metadata={"format": "pt"}
)
else:
torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
else:
self.model.save_pretrained(
output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors
)
if self.tokenizer is not None:
self.tokenizer.save_pretrained(output_dir)
if self.processor is not None:
self.processor.save_pretrained(output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))
# def training_step(self, model, inputs):
# for name, param in model.named_parameters():
# if 'visual' in name and param.requires_grad:
# print(f"Training parameter {name}")
#
# return super().training_step(model, inputs) |