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run_pretrain.py
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run_pretrain.py
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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import copy
import math
import os
import sys
import time
from dataclasses import dataclass, field
from typing import Optional
import paddle
from paddlenlp.data.causal_dataset import (
build_train_valid_test_datasets,
check_data_split,
print_rank_0,
)
from paddlenlp.trainer import (
PdArgumentParser,
Trainer,
TrainingArguments,
get_last_checkpoint,
set_seed,
speed_metrics,
)
from paddlenlp.transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoModelForCausalLMPipe,
AutoTokenizer,
CosineAnnealingWithWarmupDecay,
LinearAnnealingWithWarmupDecay,
register_sequence_parallel_allreduce_hooks,
)
from paddlenlp.transformers.configuration_utils import LlmMetaConfig, llmmetaclass
from paddlenlp.transformers.refined_recompute import update_refined_recompute
from paddlenlp.utils.batch_sampler import DistributedBatchSampler
from paddlenlp.utils.log import logger
from paddlenlp.utils.tools import get_env_device
# Pretaining Environment Variables to support sharding stage1 overlap optimization.
os.environ["USE_CASUAL_MASK"] = "True"
from paddlenlp.trainer.utils.doc import add_start_docstrings
@dataclass
@llmmetaclass
@add_start_docstrings(TrainingArguments.__doc__)
class PreTrainingArguments(TrainingArguments):
min_learning_rate: float = field(
default=1e-5,
metadata={"help": "Minimum learning rate deacyed to."},
)
decay_steps: float = field(
default=None,
metadata={
"help": "The steps use to control the learing rate. If the step > decay_steps, will use the min_learning_rate."
},
)
enable_linear_fused_grad_add: bool = field(
default=False,
metadata={
"help": "Enable fused linear grad add strategy, which will reduce elementwise add for grad accumulation in the backward of nn.Linear ."
},
)
# NOTE(gongenlei): new add autotuner_benchmark
autotuner_benchmark: bool = field(
default=False,
metadata={"help": "Weather to run benchmark by autotuner. True for from_scratch and pad_max_length."},
)
unified_checkpoint: bool = field(
default=True,
metadata={"help": "Enable fused linear grad add strategy."},
)
def __post_init__(self):
super().__post_init__()
# NOTE(gongenlei): new add autotuner_benchmark
from paddlenlp.trainer.trainer_utils import IntervalStrategy
if self.autotuner_benchmark:
self.max_steps = 5
self.do_train = True
self.do_export = False
self.do_predict = False
self.do_eval = False
self.overwrite_output_dir = True
self.load_best_model_at_end = False
self.report_to = []
self.save_strategy = IntervalStrategy.NO
self.evaluation_strategy = IntervalStrategy.NO
self.unified_checkpoint = False
@dataclass
class DataArguments:
"""
Arguments pertaining to what data we are going to input our model for training and evaluating.
Using `PdArgumentParser` we can turn this class into argparse arguments to be able to
specify them on the command line.
"""
input_dir: str = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
split: str = field(default="949,50,1", metadata={"help": "Train/valid/test data split."})
max_seq_length: int = field(
default=1024,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
share_folder: bool = field(
default=False,
metadata={"help": "Use share folder for data dir and output dir on multi machine."},
)
data_impl: str = field(default="mmap", metadata={"help": "The format of the preprocessed data."})
skip_warmup: bool = field(
default=True,
metadata={"help": "Whether to skip the warmup process of mmap files."},
)
data_cache: str = field(default=None, metadata={"help": "The path of the cached dataset."})
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to pre-train from.
"""
model_name_or_path: str = field(
default="__internal_testing__/tiny-random-llama",
metadata={
"help": "Path to pretrained model or model identifier from https://paddlenlp.readthedocs.io/zh/latest/model_zoo/transformers.html"
},
)
tokenizer_name_or_path: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
use_fast_layer_norm: bool = field(
default=False,
metadata={"help": "GPT3 model, use fast layernorm"},
)
hidden_dropout_prob: float = field(default=0.1, metadata={"help": "The hidden dropout prob."})
attention_probs_dropout_prob: float = field(default=0.1, metadata={"help": "The attention hidden dropout prob."})
fuse_attention_qkv: bool = field(
default=None,
metadata={"help": "whether to fuse attention qkv"},
)
fuse_attention_ffn: bool = field(
default=None,
metadata={"help": "whether to fuse first up and gate proj in mlp block"},
)
continue_training: bool = field(
default=False,
metadata={
"help": "Pre-training from existing paddlenlp model weights. Default False and model will train from scratch. If set True, the model_name_or_path argument must exist in the paddlenlp models."
},
)
num_hidden_layers: Optional[int] = field(
default=None,
metadata={"help": "num_hidden_layers."},
)
def create_pretrained_dataset(
data_args,
training_args,
data_file,
tokenizer,
need_data=True,
):
check_data_split(data_args.split, training_args.do_train, training_args.do_eval, training_args.do_predict)
train_val_test_num_samples = [
training_args.per_device_train_batch_size
* training_args.dataset_world_size
* training_args.max_steps
* training_args.gradient_accumulation_steps,
training_args.per_device_eval_batch_size
* training_args.dataset_world_size
* training_args.eval_iters
* (training_args.max_steps // training_args.eval_steps + 1),
training_args.per_device_eval_batch_size * training_args.dataset_world_size * training_args.test_iters,
]
print_rank_0(" > datasets target sizes (minimum size):")
if training_args.do_train:
print_rank_0(" train: {}".format(train_val_test_num_samples[0]))
if training_args.do_eval:
print_rank_0(" validation: {}".format(train_val_test_num_samples[1]))
if training_args.do_predict:
print_rank_0(" test: {}".format(train_val_test_num_samples[2]))
# Build the datasets.
train_dataset, valid_dataset, test_dataset = build_train_valid_test_datasets(
data_prefix=data_file,
data_impl=data_args.data_impl,
splits_string=data_args.split,
train_val_test_num_samples=train_val_test_num_samples,
seq_length=data_args.max_seq_length,
seed=training_args.seed,
skip_warmup=data_args.skip_warmup,
share_folder=data_args.share_folder,
data_cache_path=data_args.data_cache,
need_data=need_data,
)
def print_dataset(data, mode="train"):
logger.info(f"Sample data for {mode} mode.")
# input_ids, loss_mask, attention_mask, position_ids, labels = data
input_ids = data["text"]
logger.info(tokenizer._decode(list(input_ids)))
from paddlenlp.data import Stack
def _collate_data(data, stack_fn=Stack()):
tokens_ = stack_fn([x["text"] for x in data])
labels = copy.deepcopy(tokens_)[:, 1:]
tokens = tokens_[:, :-1]
return {
"input_ids": tokens,
"labels": labels,
}
if need_data:
if training_args.do_train:
print_dataset(train_dataset[0], "train")
if training_args.do_eval:
print_dataset(valid_dataset[0], "valid")
if training_args.do_predict:
print_dataset(test_dataset[0], "test")
return train_dataset, valid_dataset, test_dataset, _collate_data
def get_train_data_file(args):
if len(args.input_dir.split()) > 1:
# weight-1 data-prefix-1 weight-2 data-prefix-2 ...
return args.input_dir.split()
else:
files = [
os.path.join(args.input_dir, f)
for f in os.listdir(args.input_dir)
if (os.path.isfile(os.path.join(args.input_dir, f)) and ("_idx.npz" in str(f) or ".idx" in str(f)))
]
files = [x.replace("_idx.npz", "") for x in files]
files = [x.replace(".idx", "") for x in files]
if len(files) > 1:
ret = []
logger.info("You are using multi-dataset:")
for x in files:
ret.append(1.0)
ret.append(x)
logger.info(" > set weight of %s dataset to 1.0" % x)
return ret
return files
class PretrainingTrainer(Trainer):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.is_pretraining = True
def evaluate(self, eval_dataset=None, ignore_keys=None, metric_key_prefix: str = "eval"):
# keep eval_dataloader
eval_dataloader = getattr(self, "eval_dataloader", None)
if eval_dataloader is None:
eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset
eval_dataloader = self.get_eval_dataloader(eval_dataset)
# must call data loader, otherwise, it will init many times, cause OOM error.
self.eval_dataloader = eval_dataloader()
start_time = time.time()
# Temporarily disable metric computation, we will do it in the loop here.
compute_metrics = self.compute_metrics
eval_loop = self.evaluation_loop
output = eval_loop(
eval_dataloader,
description="Evaluation",
# No point gathering the predictions if there are no metrics, otherwise we defer to
# self.args.prediction_loss_only
prediction_loss_only=True if compute_metrics is None else None,
ignore_keys=ignore_keys,
# Only evaluate max_eval_iters
max_eval_iters=self.args.eval_iters,
)
total_batch_size = self.args.eval_batch_size * self.args.world_size
output.metrics.update(
speed_metrics(
metric_key_prefix,
start_time,
num_samples=output.num_samples,
num_steps=math.ceil(output.num_samples / total_batch_size),
)
)
self.log(output.metrics)
self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, output.metrics)
return output.metrics
def _get_eval_sampler(self, eval_dataset) -> Optional[paddle.io.Sampler]:
return DistributedBatchSampler(
eval_dataset,
batch_size=self.args.per_device_eval_batch_size,
shuffle=False,
num_replicas=self.args.dataset_world_size,
rank=self.args.dataset_rank,
drop_last=self.args.dataloader_drop_last,
)
def _get_train_sampler(self) -> Optional[paddle.io.Sampler]:
return DistributedBatchSampler(
self.train_dataset,
batch_size=self.args.per_device_train_batch_size,
shuffle=False,
num_replicas=self.args.dataset_world_size,
rank=self.args.dataset_rank,
drop_last=self.args.dataloader_drop_last,
)
def main():
parser = PdArgumentParser((ModelArguments, DataArguments, PreTrainingArguments))
# Support format as "args.json --arg1 value1 --arg2 value2.β
# In case of conflict, command line arguments take precedence.
if len(sys.argv) >= 2 and sys.argv[1].endswith(".json"):
model_args, data_args, training_args = parser.parse_json_file_and_cmd_lines()
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if training_args.no_recompute_layers is not None:
training_args.no_recompute_layers.sort()
if training_args.enable_linear_fused_grad_add:
from utils.fused_layers import mock_layers
mock_layers()
if model_args.tokenizer_name_or_path is None:
model_args.tokenizer_name_or_path = model_args.model_name_or_path
if data_args.data_cache is not None:
os.makedirs(data_args.data_cache, exist_ok=True)
paddle.set_device(training_args.device)
set_seed(seed=training_args.seed)
training_args.eval_iters = 10
training_args.test_iters = training_args.eval_iters * 10
# Log model and data config
training_args.print_config(model_args, "Model")
training_args.print_config(data_args, "Data")
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, world_size: {training_args.world_size}, "
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16 or training_args.bf16}"
)
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
# if last_checkpoint is None and len(
# os.listdir(training_args.output_dir)) > 1:
# raise ValueError(
# f"Output directory ({training_args.output_dir}) already exists and is not empty. "
# "Use --overwrite_output_dir to overcome.")
if last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name_or_path)
config = AutoConfig.from_pretrained(model_args.model_name_or_path)
# set all llm config
LlmMetaConfig.set_llm_config(config, training_args)
config.refined_recompute = update_refined_recompute(
training_args.refined_recompute,
)
config.use_fast_layer_norm = model_args.use_fast_layer_norm
config.seq_length = data_args.max_seq_length
# There are some technique extend RotaryEmbedding context. so don't change max_position_embeddings
if not model_args.continue_training:
config.max_position_embeddings = max(config.max_position_embeddings, data_args.max_seq_length)
if not model_args.continue_training:
config.vocab_size = max(config.vocab_size, ((tokenizer.vocab_size - 1) // 128 + 1) * 128)
logger.info(f"Reset vocab size to {config.vocab_size} for batter amp peformance.")
config.num_hidden_layers = (
model_args.num_hidden_layers if model_args.num_hidden_layers is not None else config.num_hidden_layers
)
# Config for model using dropout, such as GPT.
if hasattr(config, "hidden_dropout_prob"):
config.hidden_dropout_prob = model_args.hidden_dropout_prob
if hasattr(config, "attention_probs_dropout_prob"):
config.attention_probs_dropout_prob = model_args.attention_probs_dropout_prob
if model_args.fuse_attention_qkv is not None:
config.fuse_attention_qkv = model_args.fuse_attention_qkv
if model_args.fuse_attention_ffn is not None:
config.fuse_attention_ffn = model_args.fuse_attention_ffn
if config.sequence_parallel:
assert config.tensor_parallel_degree > 1, "tensor_parallel_degree must be larger than 1 for sequence parallel."
assert (
config.num_attention_heads % config.sep_parallel_degree == 0
), f"num_attention_heads:{config.num_attention_heads} must be divisible by sep_parallel_degree {config.sep_parallel_degree}"
assert (
config.seq_length % config.context_parallel_degree == 0
), f"seq_length:{config.seq_length} must be divisible by context_parallel_degree {config.context_parallel_degree}"
if training_args.sharding_parallel_config is not None:
# for stage1 overlap optimization
if (
"enable_stage1_allgather_overlap" in training_args.sharding_parallel_config
or "enable_stage1_broadcast_overlap" in training_args.sharding_parallel_config
):
from paddle.io.reader import use_pinned_memory
use_pinned_memory(False)
if get_env_device() == "xpu" and training_args.gradient_accumulation_steps > 1:
try:
from paddle_xpu.layers.nn.linear import LinearConfig # noqa: F401
LinearConfig.enable_accumulate_steps_opt()
LinearConfig.set_accumulate_steps(training_args.gradient_accumulation_steps)
except ImportError:
# It's OK, not use accumulate_steps optimization
pass
print("Final pre-training config:", config)
# Set the dtype for loading model
dtype = "float32"
if training_args.fp16_opt_level == "O2":
if training_args.fp16:
dtype = "float16"
if training_args.bf16:
dtype = "bfloat16"
model_class = AutoModelForCausalLM
if training_args.pipeline_parallel_degree > 1:
model_class = AutoModelForCausalLMPipe
if "LLama" in str(config.architectures):
try:
from utils.register_reshard import register_pp_reshard_information
register_pp_reshard_information(config.num_hidden_layers)
except:
print("Not register llama pp reshard information.")
if model_args.continue_training:
# NOTE(gongenlei): new add
if training_args.autotuner_benchmark:
model = model_class.from_config(config, dtype=dtype)
else:
model = model_class.from_pretrained(
model_args.model_name_or_path,
config=config,
dtype=dtype,
)
else:
model = model_class.from_config(config, dtype=dtype)
if training_args.sequence_parallel:
register_sequence_parallel_allreduce_hooks(
model, training_args.gradient_accumulation_steps, training_args.fuse_sequence_parallel_allreduce
)
if training_args.recompute:
model.recompute_enable()
# Create the learning_rate sheduler and optimizer
if training_args.decay_steps is None:
training_args.decay_steps = training_args.max_steps
if training_args.warmup_steps > 0:
warmup_steps = training_args.warmup_steps
else:
warmup_steps = training_args.warmup_ratio * training_args.max_steps
lr_scheduler = None
if training_args.lr_scheduler_type.value == "cosine":
lr_scheduler = CosineAnnealingWithWarmupDecay(
max_lr=training_args.learning_rate,
min_lr=training_args.min_learning_rate,
warmup_step=warmup_steps,
decay_step=training_args.decay_steps,
last_epoch=0,
)
elif training_args.lr_scheduler_type.value == "linear":
lr_scheduler = LinearAnnealingWithWarmupDecay(
max_lr=training_args.learning_rate,
min_lr=training_args.min_learning_rate,
warmup_step=warmup_steps,
decay_step=training_args.decay_steps,
last_epoch=0,
)
data_file = get_train_data_file(data_args)
train_dataset, eval_dataset, test_dataset, data_collator = create_pretrained_dataset(
data_args,
training_args,
data_file,
tokenizer,
need_data=training_args.should_load_dataset,
)
total_effective_tokens = (
training_args.per_device_train_batch_size
* training_args.dataset_world_size
* training_args.max_steps
* training_args.gradient_accumulation_steps
* data_args.max_seq_length
)
trainer = PretrainingTrainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
optimizers=(None, lr_scheduler),
tokenizer=tokenizer,
)
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
# Training
if training_args.do_train:
train_result = trainer.train(resume_from_checkpoint=checkpoint)
# NOTE(gongenlei): new add
if not training_args.autotuner_benchmark:
metrics = train_result.metrics
if not int(os.getenv("test_ci_no_save_model", 0)):
trainer.save_model()
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
if training_args.do_predict:
test_ret = trainer.predict(test_dataset)
trainer.log_metrics("test", test_ret.metrics)
if training_args.do_train and training_args.should_load_dataset:
effective_tokens_per_second = total_effective_tokens / train_result.metrics["train_runtime"]
print(f"Effective Tokens per second: {effective_tokens_per_second:.2f}")
print(f"ips: {effective_tokens_per_second:.2f} tokens/s")
if __name__ == "__main__":
main()