Spaces:
Sleeping
Sleeping
File size: 9,507 Bytes
76b1ec5 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 |
"""
import os
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs
from transformers import get_scheduler
from aux import SameLineLogger, log
from data import DataState
from langconv import is_dec_only_llm
from modelops import save_all_models, report_devices
from translate import encode
raise NotImplementedError("This is a backup package, do not run or import from it")
def chain_params(coupling_specs):
for spec in coupling_specs:
yield from spec.model.parameters()
class TrainLossList:
def __init__(self):
self.data = []
def append(self, loss_val, src_k, tgt_k):
self.data.append((loss_val, src_k, tgt_k))
def state_dict(self):
return {'data': self.data}
def load_state_dict(self, state_dict):
self.data = state_dict['data']
class SwitchingAccelerator:
def __init__(self, coupling_specs, train_set, train_kwargs):
self.coupling_specs = coupling_specs
self.train_set = train_set
self.kwargs = train_kwargs
self.is_generative = is_dec_only_llm(self.coupling_specs[0].tokenizer)
self.train_loss_list = TrainLossList()
self.data_state = DataState(epoch_idx=0)
self._init_acc_and_stuff()
def _init_acc_and_stuff(self):
#self.accelerator = Accelerator(gradient_accumulation_steps=self.kwargs.accum_steps, kwargs_handlers=[DistributedDataParallelKwargs(find_unused_parameters=True)])
#self.accelerator = Accelerator(gradient_accumulation_steps=self.kwargs.accum_steps)
self.accelerator = Accelerator()
epoch_len = len(self.train_set)
train_len = epoch_len * self.kwargs.epochs
num_warmup = int(train_len * 0.01)
log(f"Warmup steps: {num_warmup}, epoch len: {epoch_len}, train len: {train_len}", accelerator=self.accelerator)
opt = torch.optim.AdamW(chain_params(self.coupling_specs), lr=self.kwargs.lr)
lr_scheduler = get_scheduler("linear", optimizer=opt, num_warmup_steps=num_warmup,
num_training_steps=train_len * self.accelerator.num_processes)
models = [s.model for s in self.coupling_specs]
self.optimizer, self.lr_scheduler, *self.models = self.accelerator.prepare(opt, lr_scheduler, *models)
self.accelerator.register_for_checkpointing(self.lr_scheduler, self.data_state, self.train_loss_list)
if self.kwargs.continue_training:
self.accelerator.load_state(self.kwargs.mdl_id)
log(f"Reloaded data state: {self.data_state}", accelerator=self.accelerator)
def train(self):
try:
self._main_loop()
except Exception as e:
#in multi-process scenarios it is hard to read the stack trace, so just show one:
if self.accelerator.is_main_process:
raise e
self.accelerator.wait_for_everyone()
unwr_coupled_model = self.accelerator.unwrap_model(self.models[0])
return unwr_coupled_model, self.train_loss_list
def _split_batch_and_bin_idxs(self, batch_with_idxs):
if self.is_generative:
batch, _ = batch_with_idxs
src_k = 0
tgt_k = 0
else:
batch, src_k, tgt_k, _ = batch_with_idxs
return batch, src_k, tgt_k
def _prepare_inputs(self, batch, sub_batch_idx, sub_batch_size, proc_batch_size):
from_proc_idx = proc_batch_size * self.accelerator.process_index + sub_batch_size * sub_batch_idx
to_proc_idx = from_proc_idx + sub_batch_size
#log(f"----> DEBUG for sub_b idx {sub_batch_idx}, proc {self.accelerator.process_index}: {from_proc_idx}:{to_proc_idx}")
return {k: batch[k][from_proc_idx:to_proc_idx].to(self.accelerator.device) for k in batch}
def _get_split_batch_params(self, batch):
batch_nr_snts = batch['input_ids'].size()[0]
snt_nr_words = batch['input_ids'].size()[1]
assert batch_nr_snts % self.accelerator.num_processes == 0, "Batch size must be divisible by number of processes."
proc_batch_nr_snts = batch_nr_snts // self.accelerator.num_processes
if self.kwargs.nr_snts_in_batch > 0:
sub_batch_size = self.kwargs.nr_snts_in_batch
else:
sub_batch_size = max(1, self.kwargs.nr_words_in_batch // snt_nr_words)
#log(f"DEBUG: #words/snt {snt_nr_words} X #snt in sub batch {sub_batch_size} = {snt_nr_words*sub_batch_size} ~ {self.kwargs.nr_words_in_batch}", accelerator=self.accelerator)
nr_steps = -(proc_batch_nr_snts // -sub_batch_size)
#log(f"--> DEBUG: sub_batch {sub_batch_size} X steps {nr_steps} ~ {proc_batch_nr_snts} ({batch_nr_snts} / {self.accelerator.num_processes})", accelerator=self.accelerator)
return sub_batch_size, nr_steps, proc_batch_nr_snts
def _main_loop(self):
#countdown_till_do_it_once = 0
if self.accelerator.is_main_process:
logger = SameLineLogger(len(self.train_set), self.kwargs.epochs)
logger.line_start()
else:
logger = None
self.models[0].train()
self.train_set.thats_where(self.data_state)
for _epoch_idx in range(self.data_state.epoch_idx, self.kwargs.epochs):
for batch_with_bin_idxs, epoch_batch_idx in self.train_set:
batch, src_k, tgt_k = self._split_batch_and_bin_idxs(batch_with_bin_idxs)
sub_batch_size, nr_steps, proc_batch_size = self._get_split_batch_params(batch)
loss = None
for sub_batch_idx in range(nr_steps):
inputs = self._prepare_inputs(batch, sub_batch_idx, sub_batch_size, proc_batch_size)
if self.is_generative:
inputs['labels'] = inputs['input_ids']
outputs = self.models[0](**inputs)
else:
encoder_vecs = encode(self.models[src_k], inputs)
outputs = self.models[tgt_k](attention_mask=inputs['attention_mask'], labels=inputs['labels'], encoder_outputs=encoder_vecs)
loss = outputs.loss
#if countdown_till_do_it_once > 0:
# countdown_till_do_it_once -= 1
#elif countdown_till_do_it_once == 0:
if sub_batch_idx == 5:
batch_size = sum([inputs[k].size()[0] * inputs[k].size()[1] for k in 'input_ids labels attention_mask'.split(' ')])
report_devices(f"training memory usage (batch size: {batch_size}; inputs:" +
f"snts {inputs['input_ids'].size()[0]} X words {inputs['input_ids'].size()[1]})",
self.accelerator, self.models[0])
countdown_till_do_it_once = 0
self.train_loss_list.append(loss.item(), src_k, tgt_k)
self.accelerator.backward(loss)
for k in inputs:
inputs[k] = inputs[k].to('cpu')
self._step_and_perhaps_save(logger, epoch_batch_idx, _epoch_idx, float(loss.item()))
if self.accelerator.is_main_process:
logger.line_break()
def get_total_grad(self):
result = 0
grad_count = 0
all_count = 0
for p in self.models[0].parameters():
if p.grad is not None:
result += p.grad.abs().mean().item()
grad_count += 1
all_count += 1
return result/grad_count if grad_count > 0 else -1
def _step_and_perhaps_save(self, logger, epoch_batch_idx, epoch_i, loss):
epoch_len = len(self.train_set)
global_batch_idx = epoch_batch_idx + epoch_i * epoch_len
self.optimizer.step()
self.lr_scheduler.step()
self.accelerator.wait_for_everyone()
is_end_of_epoch = (epoch_batch_idx == epoch_len)
if self.accelerator.is_main_process and (epoch_batch_idx % self.kwargs.log_steps == 0 or is_end_of_epoch):
grad = self.get_total_grad()
logger.step(global_batch_idx, epoch_batch_idx, epoch_i, loss, self.lr_scheduler.get_last_lr()[0], grad)
self.optimizer.zero_grad()
if (global_batch_idx % self.kwargs.save_steps == 0) or is_end_of_epoch:
self.accelerator.wait_for_everyone()
if self.accelerator.is_main_process:
logger.line_break()
log(f"Saving at {epoch_batch_idx} steps, epoch {epoch_i + 1} ({global_batch_idx} global steps)", accelerator=self.accelerator)
self._save_all(global_batch_idx, epoch_i)
logger.line_start()
def _save_all(self, global_batch_idx, epoch_i):
epoch_len = len(self.train_set)
ckpt_name = (f"checkpoint-e{epoch_i + 1:02}-" +
(f"b{global_batch_idx:07}" if (global_batch_idx % epoch_len) else f"full"))
this_location = os.path.join(self.kwargs.save_location, ckpt_name)
if os.path.exists(this_location):
raise FileExistsError(f"Cannot overwrite existing checkpoint {this_location}!")
self.data_state.copy_from(self.train_set.where_are_we(), epoch_idx=epoch_i)
model_to_save = self.accelerator.unwrap_model(self.models[0])
save_all_models(this_location, model_to_save, self.coupling_specs[0].tokenizer,
self.coupling_specs, trainer=self.accelerator)
""" |