File size: 9,721 Bytes
a971b09 0394e28 a971b09 0394e28 a971b09 0394e28 a971b09 0394e28 a971b09 0394e28 a971b09 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 |
import pprint
from functools import partial
from tqdm import tqdm, trange
import numpy as np
import mlxu
import jax
import jax.numpy as jnp
from jax.experimental.pjit import pjit
from jax.sharding import PartitionSpec as PS
from flax.training.train_state import TrainState
from EasyLM.data import DatasetFactory
from EasyLM.checkpoint import StreamingCheckpointer
from EasyLM.optimizers import OptimizerFactory
from EasyLM.jax_utils import (
JaxRNG, JaxDistributedConfig, next_rng, match_partition_rules,
cross_entropy_loss_and_accuracy, global_norm, get_float_dtype_by_name,
set_random_seed, average_metrics, get_weight_decay_mask,
make_shard_and_gather_fns, with_sharding_constraint,
)
from EasyLM.models.llama.llama_model import (
LLaMAConfig, FlaxLLaMAForCausalLMModule
)
FLAGS, FLAGS_DEF = mlxu.define_flags_with_default(
seed=42,
mesh_dim='1,-1,1',
dtype='fp32',
total_steps=10000,
load_llama_config='',
update_llama_config='',
load_checkpoint='',
load_dataset_state='',
log_freq=50,
save_model_freq=0,
save_milestone_freq=0,
eval_freq=0,
tokenizer=LLaMAConfig.get_tokenizer_config(),
train_dataset=DatasetFactory.get_default_config(),
eval_dataset=DatasetFactory.get_default_config(),
optimizer=OptimizerFactory.get_default_config(),
checkpointer=StreamingCheckpointer.get_default_config(),
llama=LLaMAConfig.get_default_config(),
logger=mlxu.WandBLogger.get_default_config(),
log_all_worker=False,
jax_distributed=JaxDistributedConfig.get_default_config(),
)
def main(argv):
JaxDistributedConfig.initialize(FLAGS.jax_distributed)
variant = mlxu.get_user_flags(FLAGS, FLAGS_DEF)
flags_config_dict = mlxu.user_flags_to_config_dict(FLAGS, FLAGS_DEF)
logger = mlxu.WandBLogger(
config=FLAGS.logger,
variant=variant,
enable=FLAGS.log_all_worker or (jax.process_index() == 0),
)
set_random_seed(FLAGS.seed)
tokenizer = LLaMAConfig.get_tokenizer(FLAGS.tokenizer)
dataset = DatasetFactory.load_dataset(FLAGS.train_dataset, tokenizer)
if FLAGS.load_dataset_state != '':
dataset.load_state_dict(mlxu.load_pickle(FLAGS.load_dataset_state))
if FLAGS.eval_freq > 0:
eval_dataset = DatasetFactory.load_dataset(
FLAGS.eval_dataset, dataset.tokenizer, eval_dataset=True
)
seq_length = dataset.seq_length
if FLAGS.load_llama_config != '':
llama_config = LLaMAConfig.load_config(FLAGS.load_llama_config)
else:
llama_config = LLaMAConfig(**FLAGS.llama)
if FLAGS.update_llama_config != '':
llama_config.update(dict(eval(FLAGS.update_llama_config)))
llama_config.update(dict(
bos_token_id=dataset.tokenizer.bos_token_id,
eos_token_id=dataset.tokenizer.eos_token_id,
))
if llama_config.vocab_size < dataset.vocab_size:
print("Updating model config vocab size from", llama_config.vocab_size, "to", dataset.vocab_size)
llama_config.update(dict(vocab_size=dataset.vocab_size))
model = FlaxLLaMAForCausalLMModule(
llama_config, dtype=get_float_dtype_by_name(FLAGS.dtype), param_dtype=get_float_dtype_by_name(FLAGS.param_dtype)
)
optimizer, optimizer_info = OptimizerFactory.get_optimizer(
FLAGS.optimizer,
get_weight_decay_mask(LLaMAConfig.get_weight_decay_exclusions())
)
def create_trainstate_from_params(params):
return TrainState.create(params=params, tx=optimizer, apply_fn=None)
def init_fn(rng):
rng_generator = JaxRNG(rng)
params = model.init(
input_ids=jnp.zeros((4, seq_length), dtype=jnp.int32),
position_ids=jnp.zeros((4, seq_length), dtype=jnp.int32),
attention_mask=jnp.ones((4, seq_length), dtype=jnp.int32),
rngs=rng_generator(llama_config.rng_keys()),
)
return TrainState.create(params=params, tx=optimizer, apply_fn=None)
def train_step(train_state, rng, batch):
rng_generator = JaxRNG(rng)
batch = with_sharding_constraint(batch, PS(('dp', 'fsdp')))
def loss_and_accuracy(params):
logits = model.apply(
params, batch['input_tokens'], deterministic=False,
rngs=rng_generator(llama_config.rng_keys()),
).logits
return cross_entropy_loss_and_accuracy(
logits, batch['target_tokens'], batch['loss_masks']
)
grad_fn = jax.value_and_grad(loss_and_accuracy, has_aux=True)
(loss, accuracy), grads = grad_fn(train_state.params)
train_state = train_state.apply_gradients(grads=grads)
metrics = dict(
loss=loss,
accuracy=accuracy,
learning_rate=optimizer_info['learning_rate_schedule'](train_state.step),
gradient_norm=global_norm(grads),
param_norm=global_norm(train_state.params),
)
return train_state, rng_generator(), metrics
def eval_step(train_state, rng, batch):
rng_generator = JaxRNG(rng)
batch = with_sharding_constraint(batch, PS(('dp', 'fsdp')))
logits = model.apply(
train_state.params, batch['input_tokens'], deterministic=True,
rngs=rng_generator(llama_config.rng_keys()),
).logits
loss, accuracy = cross_entropy_loss_and_accuracy(
logits, batch['target_tokens'], batch['loss_masks']
)
metrics = dict(
eval_loss=loss,
eval_accuracy=accuracy,
)
return rng_generator(), metrics
train_state_shapes = jax.eval_shape(init_fn, next_rng())
train_state_partition = match_partition_rules(
LLaMAConfig.get_partition_rules(), train_state_shapes
)
shard_fns, gather_fns = make_shard_and_gather_fns(
train_state_partition, train_state_shapes
)
checkpointer = StreamingCheckpointer(
FLAGS.checkpointer, logger.output_dir,
enable=jax.process_index() == 0,
)
sharded_init_fn = pjit(
init_fn,
in_shardings=PS(),
out_shardings=train_state_partition
)
sharded_create_trainstate_from_params = pjit(
create_trainstate_from_params,
in_shardings=(train_state_partition.params, ),
out_shardings=train_state_partition,
donate_argnums=(0, ),
)
sharded_train_step = pjit(
train_step,
in_shardings=(train_state_partition, PS(), PS()),
out_shardings=(train_state_partition, PS(), PS()),
donate_argnums=(0, 1),
)
sharded_eval_step = pjit(
eval_step,
in_shardings=(train_state_partition, PS(), PS()),
out_shardings=(PS(), PS()),
donate_argnums=(1,),
)
def save_checkpoint(train_state, milestone=False):
step = int(jax.device_get(train_state.step))
metadata = dict(
step=step,
variant=variant,
flags=flags_config_dict,
llama_config=llama_config.to_dict(),
)
checkpointer.save_all(
train_state=train_state,
gather_fns=gather_fns,
metadata=metadata,
dataset=dataset.get_state_dict(),
milestone=milestone,
)
mesh = LLaMAConfig.get_jax_mesh(FLAGS.mesh_dim)
with mesh:
train_state, restored_params = None, None
if FLAGS.load_checkpoint != '':
train_state, restored_params = checkpointer.load_trainstate_checkpoint(
FLAGS.load_checkpoint, train_state_shapes, shard_fns
)
if train_state is None and restored_params is None:
# Initialize from scratch
train_state = sharded_init_fn(next_rng())
elif train_state is None and restored_params is not None:
# Restore from params but initialize train_state
train_state = sharded_create_trainstate_from_params(restored_params)
del restored_params
start_step = int(jax.device_get(train_state.step))
if FLAGS.save_model_freq > 0:
save_checkpoint(train_state)
sharded_rng = next_rng()
step_counter = trange(start_step, FLAGS.total_steps, ncols=0)
for step, (batch, dataset_metrics) in zip(step_counter, dataset):
train_state, sharded_rng, metrics = sharded_train_step(
train_state, sharded_rng, batch
)
if FLAGS.eval_freq > 0 and (step + 1) % FLAGS.eval_freq == 0:
eval_metric_list = []
eval_iterator = iter(eval_dataset)
for eval_batch, _ in eval_iterator:
sharded_rng, eval_metrics = sharded_eval_step(
train_state, sharded_rng, eval_batch
)
eval_metric_list.append(eval_metrics)
metrics.update(average_metrics(eval_metric_list))
if FLAGS.log_freq > 0 and (step + 1) % FLAGS.log_freq == 0:
log_metrics = {"step": step + 1}
log_metrics.update(metrics)
log_metrics.update(dataset_metrics)
log_metrics = jax.device_get(log_metrics)
logger.log(log_metrics)
tqdm.write("\n" + pprint.pformat(log_metrics) + "\n")
if FLAGS.save_milestone_freq > 0 and (step + 1) % FLAGS.save_milestone_freq == 0:
save_checkpoint(train_state, milestone=True)
elif FLAGS.save_model_freq > 0 and (step + 1) % FLAGS.save_model_freq == 0:
save_checkpoint(train_state)
if FLAGS.save_model_freq > 0:
save_checkpoint(train_state)
if __name__ == "__main__":
mlxu.run(main)
|