/
megatron_lm.py
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/
megatron_lm.py
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# Copyright 2022 The HuggingFace Team. 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 argparse
import math
from abc import ABC
from functools import partial
import torch
import torch.nn.functional as F
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
from .imports import is_megatron_lm_available, is_transformers_available
from .operations import recursively_apply, send_to_device
if is_transformers_available():
from transformers.modeling_outputs import (
CausalLMOutputWithCrossAttentions,
Seq2SeqLMOutput,
SequenceClassifierOutput,
)
if is_megatron_lm_available():
from megatron import (
get_args,
get_num_microbatches,
get_tensorboard_writer,
get_timers,
get_tokenizer,
mpu,
print_rank_0,
print_rank_last,
)
from megatron.arguments import _add_data_args, _add_validation_args, parse_args, validate_args
from megatron.checkpointing import load_args_from_checkpoint, load_checkpoint, save_checkpoint
from megatron.data.data_samplers import MegatronPretrainingRandomSampler, MegatronPretrainingSampler
from megatron.global_vars import set_global_variables
from megatron.initialize import (
_compile_dependencies,
_init_autoresume,
_set_random_seed,
set_jit_fusion_options,
write_args_to_tensorboard,
)
from megatron.model import BertModel
from megatron.model import DistributedDataParallel as LocalDDP
from megatron.model import Float16Module, GPTModel, ModelType, T5Model
from megatron.model.classification import Classification
from megatron.optimizer import get_megatron_optimizer
from megatron.schedules import get_forward_backward_func
from megatron.text_generation.communication import broadcast_int_list, broadcast_tensor
from megatron.text_generation.generation import (
beam_search_and_return_on_first_stage,
generate_tokens_probs_and_return_on_first_stage,
)
from megatron.tokenizer.tokenizer import _vocab_size_with_padding
from megatron.training import get_model, get_optimizer_param_scheduler, training_log
from megatron.utils import (
average_losses_across_data_parallel_group,
calc_params_l2_norm,
get_ltor_masks_and_position_ids,
unwrap_model,
)
# model utilities
def model_provider_func(pre_process=True, post_process=True, add_encoder=True, add_decoder=True):
"""Build the model."""
args = get_args()
mode = "pre-training" if args.pretraining_flag else "fine-tuning"
if args.rank == 0:
print(f"Building {args.model_type_name} model in the {mode} mode.")
print(
"The Megatron LM model weights are initialized at random in `accelerator.prepare`. "
"Please use `accelerator.load_checkpoint` to load a pre-trained checkpoint matching the distributed setup."
)
if args.model_type_name == "bert":
if args.pretraining_flag:
num_tokentypes = 2 if args.bert_binary_head else 0
model = BertModel(
num_tokentypes=num_tokentypes,
add_binary_head=args.bert_binary_head,
parallel_output=True,
pre_process=pre_process,
post_process=post_process,
)
else:
model = Classification(
num_classes=args.num_labels, num_tokentypes=2, pre_process=pre_process, post_process=post_process
)
elif args.model_type_name == "gpt":
model = GPTModel(num_tokentypes=0, parallel_output=True, pre_process=pre_process, post_process=post_process)
elif args.model_type_name == "t5":
model = T5Model(
num_tokentypes=0,
parallel_output=True,
pre_process=pre_process,
post_process=post_process,
add_encoder=add_encoder,
add_decoder=add_decoder,
)
else:
raise ValueError(f"Unsupported model type: {args.model_type_name}")
return model
def prepare_model(accelerator):
accelerator.print("Preparing model")
args = get_args()
if accelerator.state.megatron_lm_plugin.custom_prepare_model_function is not None:
if accelerator.state.megatron_lm_plugin.custom_model_provider_function is None:
raise ValueError(
"You must provide a `custom_model_provider_function` when using a `custom_prepare_model_function`."
)
custom_model_provider_func = accelerator.state.megatron_lm_plugin.custom_model_provider_function
model = accelerator.state.megatron_lm_plugin.custom_prepare_model_function(custom_model_provider_func)
else:
if args.model_type_name in ("bert", "gpt"):
model_type = ModelType.encoder_or_decoder
elif args.model_type_name == "t5":
model_type = ModelType.encoder_and_decoder
if args.pipeline_model_parallel_split_rank is None and args.pipeline_model_parallel_size > 1:
args.pipeline_model_parallel_split_rank = args.pipeline_model_parallel_size // 2
model = get_model(model_provider_func, model_type)
return model
# dataloader utilities
class MegatronLMDummyDataLoader:
"""
Dummy dataloader presents model parameters or param groups, this is primarily used to follow conventional training
Args:
**dataset_kwargs: Megatron data arguments.
"""
def __init__(self, **dataset_kwargs):
parser = argparse.ArgumentParser()
parser = _add_data_args(parser)
parser = _add_validation_args(parser)
data_args = parser.parse_known_args()
self.dataset_args = vars(data_args[0])
self.dataset_args.update(dataset_kwargs)
self.dataset_args["megatron_dataset_flag"] = True
def set_megatron_data_args(self):
args = get_args()
for key, value in self.dataset_args.items():
setattr(args, key, value)
def get_train_valid_test_datasets_provider(self):
def train_valid_test_datasets_provider(train_val_test_num_samples):
"""Build train, valid, and test datasets."""
args = get_args()
dataset_args = {
"data_prefix": args.data_path,
"data_impl": args.data_impl,
"splits_string": args.split,
"train_valid_test_num_samples": train_val_test_num_samples,
"skip_warmup": (not args.mmap_warmup),
"seed": args.seed,
}
if args.model_type_name == "bert":
dataset_args.update(
{
"max_seq_length": args.seq_length,
"masked_lm_prob": args.mask_prob,
"short_seq_prob": args.short_seq_prob,
"binary_head": args.bert_binary_head,
}
)
elif args.model_type_name == "gpt":
dataset_args.update(
{
"seq_length": args.seq_length,
}
)
elif args.model_type_name == "t5":
dataset_args.update(
{
"max_seq_length": args.encoder_seq_length,
"max_seq_length_dec": args.decoder_seq_length,
"masked_lm_prob": args.mask_prob,
"short_seq_prob": args.short_seq_prob,
"dataset_type": "t5",
}
)
else:
raise ValueError(f"Unsupported model type: {args.model_type_name}")
if args.model_type_name == "gpt":
from megatron.data.gpt_dataset import build_train_valid_test_datasets
else:
from megatron.data.dataset_utils import build_train_valid_test_datasets
train_ds, valid_ds, test_ds = build_train_valid_test_datasets(**dataset_args)
return train_ds, valid_ds, test_ds
return train_valid_test_datasets_provider
def build_pretraining_data_loader(self, dataset, consumed_samples):
if dataset is None:
return None
args = get_args()
micro_batch_size = args.micro_batch_size * args.num_micro_batches
# Megatron sampler
if args.dataloader_type == "single":
batch_sampler = MegatronPretrainingSampler(
total_samples=len(dataset),
consumed_samples=consumed_samples,
micro_batch_size=micro_batch_size,
data_parallel_rank=mpu.get_data_parallel_rank(),
data_parallel_size=mpu.get_data_parallel_world_size(),
)
elif args.dataloader_type == "cyclic":
batch_sampler = MegatronPretrainingRandomSampler(
dataset,
total_samples=len(dataset),
consumed_samples=consumed_samples,
micro_batch_size=micro_batch_size,
data_parallel_rank=mpu.get_data_parallel_rank(),
data_parallel_size=mpu.get_data_parallel_world_size(),
data_sharding=args.data_sharding,
)
else:
raise Exception("{} dataloader type is not supported.".format(args.dataloader_type))
# Torch dataloader.
return torch.utils.data.DataLoader(
dataset, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True
)
def build_train_valid_test_data_iterators(self):
def cyclic_iter(iter):
while True:
for x in iter:
yield x
args = get_args()
(train_dataloader, valid_dataloader, test_dataloader) = (None, None, None)
print_rank_0("> building train, validation, and test datasets ...")
# Backward compatibility, assume fixed batch size.
if args.iteration > 0 and args.consumed_train_samples == 0:
assert args.train_samples is None, "only backward compatiblity support for iteration-based training"
args.consumed_train_samples = args.iteration * args.global_batch_size
if args.iteration > 0 and args.consumed_valid_samples == 0:
if args.train_samples is None:
args.consumed_valid_samples = (
(args.iteration // args.eval_interval) * args.eval_iters * args.global_batch_size
)
# Data loader only on rank 0 of each model parallel group.
if mpu.get_tensor_model_parallel_rank() == 0:
# Number of train/valid/test samples.
if args.train_samples:
train_samples = args.train_samples
else:
train_samples = args.train_iters * args.global_batch_size
eval_iters = (args.train_iters // args.eval_interval + 1) * args.eval_iters
test_iters = args.eval_iters
train_val_test_num_samples = [
train_samples,
eval_iters * args.global_batch_size,
test_iters * args.global_batch_size,
]
print_rank_0(" > datasets target sizes (minimum size):")
print_rank_0(" train: {}".format(train_val_test_num_samples[0]))
print_rank_0(" validation: {}".format(train_val_test_num_samples[1]))
print_rank_0(" test: {}".format(train_val_test_num_samples[2]))
# Build the datasets.
train_valid_test_datasets_provider = self.get_train_valid_test_datasets_provider()
train_ds, valid_ds, test_ds = train_valid_test_datasets_provider(train_val_test_num_samples)
# Build dataloders.
train_dataloader = self.build_pretraining_data_loader(train_ds, args.consumed_train_samples)
valid_dataloader = self.build_pretraining_data_loader(valid_ds, args.consumed_valid_samples)
test_dataloader = self.build_pretraining_data_loader(test_ds, 0)
# Flags to know if we need to do training/validation/testing.
do_train = train_dataloader is not None and args.train_iters > 0
do_valid = valid_dataloader is not None and args.eval_iters > 0
do_test = test_dataloader is not None and args.eval_iters > 0
# Need to broadcast num_tokens and num_type_tokens.
flags = torch.cuda.LongTensor([int(do_train), int(do_valid), int(do_test)])
else:
flags = torch.cuda.LongTensor([0, 0, 0])
# Broadcast num tokens.
torch.distributed.broadcast(
flags, mpu.get_tensor_model_parallel_src_rank(), group=mpu.get_tensor_model_parallel_group()
)
args.do_train = flags[0].item()
args.do_valid = flags[1].item()
args.do_test = flags[2].item()
# Build iterators.
dl_type = args.dataloader_type
assert dl_type in ["single", "cyclic"]
if train_dataloader is not None:
train_data_iterator = (
iter(train_dataloader) if dl_type == "single" else iter(cyclic_iter(train_dataloader))
)
else:
train_data_iterator = None
if valid_dataloader is not None:
valid_data_iterator = (
iter(valid_dataloader) if dl_type == "single" else iter(cyclic_iter(valid_dataloader))
)
else:
valid_data_iterator = None
if test_dataloader is not None:
test_data_iterator = iter(test_dataloader) if dl_type == "single" else iter(cyclic_iter(test_dataloader))
else:
test_data_iterator = None
return train_data_iterator, valid_data_iterator, test_data_iterator
def prepare_data_loader(accelerator, dataloader):
accelerator.print("Preparing dataloader")
args = get_args()
if not args.megatron_dataset_flag:
from ..data_loader import _PYTORCH_DATALOADER_KWARGS, prepare_data_loader
args = get_args()
micro_batch_size = args.micro_batch_size * args.num_micro_batches
kwargs = {k: getattr(dataloader, k, _PYTORCH_DATALOADER_KWARGS[k]) for k in _PYTORCH_DATALOADER_KWARGS}
if kwargs["batch_size"] is None:
if isinstance(kwargs["sampler"], torch.utils.data.BatchSampler):
kwargs["sampler"].batch_size = micro_batch_size
else:
del kwargs["sampler"]
del kwargs["shuffle"]
del kwargs["batch_size"]
kwargs["batch_sampler"].batch_size = micro_batch_size
else:
del kwargs["batch_sampler"]
kwargs["batch_size"] = micro_batch_size
dataloader = torch.utils.data.DataLoader(dataloader.dataset, **kwargs)
return prepare_data_loader(
dataloader,
accelerator.device,
num_processes=mpu.get_data_parallel_world_size(),
process_index=mpu.get_data_parallel_rank(),
split_batches=accelerator.split_batches,
put_on_device=True,
rng_types=accelerator.rng_types.copy(),
dispatch_batches=accelerator.dispatch_batches,
)
else:
if args.consumed_samples is not None:
(
args.consumed_train_samples,
args.consumed_valid_samples,
args.consumed_test_samples,
) = args.consumed_samples
else:
args.consumed_train_samples, args.consumed_valid_samples, args.consumed_test_samples = 0, 0, 0
(
train_data_iterator,
valid_data_iterator,
test_data_iterator,
) = dataloader.build_train_valid_test_data_iterators()
return train_data_iterator, valid_data_iterator, test_data_iterator
# optimizer utilities
class MegatronLMOptimizerWrapper(AcceleratedOptimizer):
def __init__(self, optimizer):
super().__init__(optimizer, device_placement=False, scaler=None)
def zero_grad(self, set_to_none=None):
pass # `model(**batch)` is doing that automatically. Therefore, it's implementation is not needed
def step(self):
pass # `model(**batch)` is doing that automatically. Therefore, it's implementation is not needed
@property
def step_was_skipped(self):
"""Whether or not the optimizer step was done, or skipped because of gradient overflow."""
return self.optimizer.skipped_iter
def prepare_optimizer(accelerator, model):
accelerator.print("Preparing optimizer")
args = get_args()
optimizer = get_megatron_optimizer(model, args.no_wd_decay_cond, args.scale_lr_cond, args.lr_mult)
return optimizer
# scheduler utilities
class MegatronLMDummyScheduler:
"""
Dummy scheduler presents model parameters or param groups, this is primarily used to follow conventional training
loop when scheduler config is specified in the deepspeed config file.
Args:
optimizer (`torch.optim.optimizer.Optimizer`):
The optimizer to wrap.
total_num_steps (int):
Total number of steps.
warmup_num_steps (int):
Number of steps for warmup.
**kwargs:
Other arguments.
"""
def __init__(self, optimizer, total_num_steps=None, warmup_num_steps=0, **kwargs):
self.optimizer = optimizer
self.total_num_steps = total_num_steps
self.warmup_num_steps = warmup_num_steps
self.kwargs = kwargs
class MegatronLMSchedulerWrapper(AcceleratedScheduler):
def __init__(self, scheduler, optimizers):
super().__init__(scheduler, optimizers)
def step(self, *args, **kwargs):
return # `model(**batch)` is doing that automatically. Therefore, it's implementation is not needed
def prepare_scheduler(accelerator, optimizer, scheduler):
accelerator.print("Preparing scheduler")
scheduler = get_optimizer_param_scheduler(optimizer)
return scheduler
class AbstractTrainStep(ABC):
"""Abstract class for batching, forward pass and loss handler."""
def __init__(self, name):
super().__init__()
self.name = name
def get_batch_func(self):
pass
def get_forward_step_func(self):
pass
def get_loss_func(self):
pass
class BertTrainStep(AbstractTrainStep):
"""
Bert train step class.
Args:
args (`argparse.Namespace`): Megatron-LM arguments.
"""
def __init__(self, args):
super().__init__("BertTrainStep")
self.get_batch = self.get_batch_func(args.megatron_dataset_flag)
self.loss_func = self.get_loss_func(args.pretraining_flag, args.num_labels)
self.forward_step = self.get_forward_step_func(args.pretraining_flag, args.bert_binary_head)
if not args.model_return_dict:
self.model_output_class = None
else:
self.model_output_class = SequenceClassifierOutput
def get_batch_func(self, megatron_dataset_flag):
def get_batch_megatron(data_iterator):
"""Build the batch."""
# Items and their type.
keys = ["text", "types", "labels", "is_random", "loss_mask", "padding_mask"]
datatype = torch.int64
# Broadcast data.
if data_iterator is not None:
data = next(data_iterator)
else:
data = None
data_b = mpu.broadcast_data(keys, data, datatype)
# Unpack.
tokens = data_b["text"].long()
types = data_b["types"].long()
sentence_order = data_b["is_random"].long()
loss_mask = data_b["loss_mask"].float()
lm_labels = data_b["labels"].long()
padding_mask = data_b["padding_mask"].long()
return tokens, types, sentence_order, loss_mask, lm_labels, padding_mask
def get_batch_transformer(data_iterator):
"""Build the batch."""
data = next(data_iterator)
data = send_to_device(data, torch.cuda.current_device())
# Unpack.
tokens = data["input_ids"].long()
padding_mask = data["attention_mask"].long()
if "token_type_ids" in data:
types = data["token_type_ids"].long()
else:
types = None
if "labels" in data:
lm_labels = data["labels"].long()
loss_mask = (data["labels"] != -100).to(torch.float)
else:
lm_labels = None
loss_mask = None
if "next_sentence_label" in data:
sentence_order = data["next_sentence_label"].long()
else:
sentence_order = None
return tokens, types, sentence_order, loss_mask, lm_labels, padding_mask
if megatron_dataset_flag:
return get_batch_megatron
else:
return get_batch_transformer
def get_loss_func(self, pretraining_flag, num_labels):
def loss_func_pretrain(loss_mask, sentence_order, output_tensor):
lm_loss_, sop_logits = output_tensor
lm_loss_ = lm_loss_.float()
loss_mask = loss_mask.float()
lm_loss = torch.sum(lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum()
if sop_logits is not None:
sop_loss = F.cross_entropy(sop_logits.view(-1, 2).float(), sentence_order.view(-1), ignore_index=-1)
sop_loss = sop_loss.float()
loss = lm_loss + sop_loss
averaged_losses = average_losses_across_data_parallel_group([lm_loss, sop_loss])
return loss, {"lm loss": averaged_losses[0], "sop loss": averaged_losses[1]}
else:
loss = lm_loss
averaged_losses = average_losses_across_data_parallel_group([lm_loss])
return loss, {"lm loss": averaged_losses[0]}
def loss_func_finetune(labels, logits):
if num_labels == 1:
# We are doing regression
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), labels.view(-1))
elif self.num_labels > 1 and (labels.dtype in (torch.long, torch.int)):
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, num_labels), labels.view(-1))
else:
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
averaged_losses = average_losses_across_data_parallel_group([loss])
return loss, {"loss": averaged_losses[0]}
if pretraining_flag:
return loss_func_pretrain
else:
return loss_func_finetune
def get_forward_step_func(self, pretraining_flag, bert_binary_head):
def forward_step(data_iterator, model):
"""Forward step."""
tokens, types, sentence_order, loss_mask, labels, padding_mask = self.get_batch(data_iterator)
if not bert_binary_head:
types = None
# Forward pass through the model.
if pretraining_flag:
output_tensor = model(tokens, padding_mask, tokentype_ids=types, lm_labels=labels)
return output_tensor, partial(self.loss_func, loss_mask, sentence_order)
else:
logits = model(tokens, padding_mask, tokentype_ids=types)
return logits, partial(self.loss_func, labels)
return forward_step
class GPTTrainStep(AbstractTrainStep):
"""
GPT train step class.
Args:
args (`argparse.Namespace`): Megatron-LM arguments.
"""
def __init__(self, args):
super().__init__("GPTTrainStep")
self.get_batch = self.get_batch_func(args.megatron_dataset_flag)
self.loss_func = self.get_loss_func()
self.forward_step = self.get_forward_step_func()
self.eod_token = args.padded_vocab_size - 1
if args.vocab_file is not None:
tokenizer = get_tokenizer()
self.eod_token = tokenizer.eod
self.reset_position_ids = args.reset_position_ids
self.reset_attention_mask = args.reset_attention_mask
self.eod_mask_loss = args.eod_mask_loss
if not args.model_return_dict:
self.model_output_class = None
else:
self.model_output_class = CausalLMOutputWithCrossAttentions
def get_batch_func(self, megatron_dataset_flag):
def get_batch_megatron(data_iterator):
"""Generate a batch"""
# Items and their type.
keys = ["text"]
datatype = torch.int64
# Broadcast data.
if data_iterator is not None:
data = next(data_iterator)
else:
data = None
data_b = mpu.broadcast_data(keys, data, datatype)
# Unpack.
tokens_ = data_b["text"].long()
labels = tokens_[:, 1:].contiguous()
tokens = tokens_[:, :-1].contiguous()
# Get the masks and postition ids.
attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
tokens, self.eod_token, self.reset_position_ids, self.reset_attention_mask, self.eod_mask_loss
)
return tokens, labels, loss_mask, attention_mask, position_ids
def get_batch_transformer(data_iterator):
data = next(data_iterator)
data = {"input_ids": data["input_ids"]}
data = send_to_device(data, torch.cuda.current_device())
tokens_ = data["input_ids"].long()
padding = torch.zeros((tokens_.shape[0], 1), dtype=tokens_.dtype, device=tokens_.device) + self.eod_token
tokens_ = torch.concat([tokens_, padding], dim=1)
labels = tokens_[:, 1:].contiguous()
tokens = tokens_[:, :-1].contiguous()
# Get the masks and postition ids.
attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
tokens, self.eod_token, self.reset_position_ids, self.reset_attention_mask, True
)
return tokens, labels, loss_mask, attention_mask, position_ids
if megatron_dataset_flag:
return get_batch_megatron
else:
return get_batch_transformer
def get_loss_func(self):
args = get_args()
def loss_func(loss_mask, output_tensor):
if args.return_logits:
losses, logits = output_tensor
else:
losses = output_tensor
losses = losses.float()
loss_mask = loss_mask.view(-1).float()
loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
# Reduce loss for logging.
averaged_loss = average_losses_across_data_parallel_group([loss])
output_dict = {"lm loss": averaged_loss[0]}
if args.return_logits:
output_dict.update({"logits": logits})
return loss, output_dict
return loss_func
def get_forward_step_func(self):
def forward_step(data_iterator, model):
"""Forward step."""
# Get the batch.
tokens, labels, loss_mask, attention_mask, position_ids = self.get_batch(data_iterator)
output_tensor = model(tokens, position_ids, attention_mask, labels=labels)
return output_tensor, partial(self.loss_func, loss_mask)
return forward_step
class T5TrainStep(AbstractTrainStep):
"""
T5 train step class.
Args:
args (`argparse.Namespace`): Megatron-LM arguments.
"""
def __init__(self, args):
super().__init__("T5TrainStep")
self.get_batch = self.get_batch_func(args.megatron_dataset_flag)
self.loss_func = self.get_loss_func()
self.forward_step = self.get_forward_step_func()
if not args.model_return_dict:
self.model_output_class = None
else:
self.model_output_class = Seq2SeqLMOutput
@staticmethod
def attn_mask_postprocess(attention_mask):
# We create a 3D attention mask from a 2D tensor mask.
# [b, 1, s]
attention_mask_b1s = attention_mask.unsqueeze(1)
# [b, s, 1]
attention_mask_bs1 = attention_mask.unsqueeze(2)
# [b, s, s]
attention_mask_bss = attention_mask_b1s * attention_mask_bs1
# Convert attention mask to binary:
extended_attention_mask = attention_mask_bss < 0.5
return extended_attention_mask
@staticmethod
def get_decoder_mask(seq_length, device):
attention_mask = torch.tril(torch.ones((1, seq_length, seq_length), device=device))
attention_mask = attention_mask < 0.5
return attention_mask
@staticmethod
def get_enc_dec_mask(attention_mask, dec_seq_length, device):
batch_size, _ = attention_mask.shape
# We create a 3D attention mask from a 2D tensor mask.
# [b, 1, s]
attention_mask_b1s = attention_mask.unsqueeze(1)
# [b, s, 1]
attention_mask_bs1 = torch.ones((batch_size, dec_seq_length, 1), device=device)
attention_mask_bss = attention_mask_bs1 * attention_mask_b1s
extended_attention_mask = attention_mask_bss < 0.5
return extended_attention_mask
def get_batch_func(self, megatron_dataset_flag):
def get_batch_megatron(data_iterator):
"""Build the batch."""
keys = ["text_enc", "text_dec", "labels", "loss_mask", "enc_mask", "dec_mask", "enc_dec_mask"]
datatype = torch.int64
# Broadcast data.
if data_iterator is not None:
data = next(data_iterator)
else:
data = None
data_b = mpu.broadcast_data(keys, data, datatype)
# Unpack.
tokens_enc = data_b["text_enc"].long()
tokens_dec = data_b["text_dec"].long()
labels = data_b["labels"].long()
loss_mask = data_b["loss_mask"].float()
enc_mask = data_b["enc_mask"] < 0.5
dec_mask = data_b["dec_mask"] < 0.5
enc_dec_mask = data_b["enc_dec_mask"] < 0.5
return tokens_enc, tokens_dec, loss_mask, labels, enc_mask, dec_mask, enc_dec_mask
def get_batch_transformer(data_iterator):
"""Build the batch."""
data = next(data_iterator)
data = send_to_device(data, torch.cuda.current_device())
tokens_enc = data["input_ids"].long()
labels = data["labels"].long()
loss_mask = (labels != -100).to(torch.float)
if "decoder_input_ids" in data:
tokens_dec = data["decoder_input_ids"].long()
else:
tokens_dec = labels.new_zeros(labels.shape, device=labels.device, dtype=torch.long)
tokens_dec[..., 1:] = labels[..., :-1].clone()
tokens_dec[..., 0] = 0
tokens_dec.masked_fill_(tokens_dec == -100, 0)
enc_mask = T5TrainStep.attn_mask_postprocess(data["attention_mask"].long())
dec_mask = T5TrainStep.get_decoder_mask(tokens_dec.shape[1], tokens_dec.device)
enc_dec_mask = T5TrainStep.get_enc_dec_mask(
data["attention_mask"].long(), tokens_dec.shape[1], tokens_dec.device
)
return tokens_enc, tokens_dec, loss_mask, labels, enc_mask, dec_mask, enc_dec_mask
if megatron_dataset_flag:
return get_batch_megatron
else:
return get_batch_transformer
def get_loss_func(self):
def loss_func(loss_mask, output_tensor):
lm_loss_ = output_tensor.float()
lm_loss = torch.sum(lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum()
loss = lm_loss
averaged_losses = average_losses_across_data_parallel_group([lm_loss])
return loss, {"lm loss": averaged_losses[0]}
return loss_func
def get_forward_step_func(self):
def forward_step(data_iterator, model):
"""Forward step."""
# Get the batch.
tokens_enc, tokens_dec, loss_mask, lm_labels, enc_mask, dec_mask, enc_dec_mask = self.get_batch(
data_iterator
)
# Forward model lm_labels
output_tensor = model(
tokens_enc, tokens_dec, enc_mask, dec_mask, enc_dec_mask, tokentype_ids=None, lm_labels=lm_labels
)
return output_tensor, partial(self.loss_func, loss_mask)
return forward_step
# intialize megatron setup
def initialize(accelerator, extra_args_provider=None, args_defaults={}):
accelerator.print("Initializing Megatron-LM")
assert torch.cuda.is_available(), "Megatron requires CUDA."
# Parse arguments
args = parse_args(extra_args_provider, ignore_unknown_args=True)
# Set defaults
for key, value in args_defaults.items():
if getattr(args, key, None) is not None:
if args.rank == 0:
print(
"WARNING: overriding default arguments for {key}:{v} \
with {key}:{v2}".format(
key=key, v=getattr(args, key), v2=value
),
flush=True,
)
setattr(args, key, value)
if args.use_checkpoint_args or args_defaults.get("use_checkpoint_args", False):
assert args.load is not None, "--use-checkpoints-args requires --load argument"
load_args_from_checkpoint(args)
validate_args(args)
# set global args, build tokenizer, and set adlr-autoresume,
# tensorboard-writer, and timers.
set_global_variables(args)
# torch.distributed initialization
def finish_mpu_init():
args = get_args()
# Pytorch distributed.
device_count = torch.cuda.device_count()
args.rank = torch.distributed.get_rank()
args.world_size = torch.distributed.get_world_size()
if device_count > 0:
device = args.rank % device_count
if args.local_rank is not None:
assert args.local_rank == device, "expected local-rank to be the same as rank % device-count."
else:
args.local_rank = device
# Set the tensor model-parallel, pipeline model-parallel, and
# data-parallel communicators.
if mpu.model_parallel_is_initialized():
print("model parallel is already initialized")
else:
mpu.initialize_model_parallel(
args.tensor_model_parallel_size,
args.pipeline_model_parallel_size,
args.virtual_pipeline_model_parallel_size,
args.pipeline_model_parallel_split_rank,
)
# Random seeds for reproducibility.
if args.rank == 0:
print("> setting random seeds to {} ...".format(args.seed))
_set_random_seed(args.seed, args.data_parallel_random_init)
args = get_args()
# Megatron's MPU is the master. Complete initialization right away.
finish_mpu_init()
# Autoresume.
_init_autoresume()
# Compile dependencies.
_compile_dependencies()
# Set pytorch JIT layer fusion options and warmup JIT functions.
set_jit_fusion_options()
args = get_args()
args.padded_vocab_size = _vocab_size_with_padding(args.orig_vocab_size, args)
if args.model_type_name == "bert" and args.pretraining_flag and args.num_labels == 2:
args.bert_binary_head = True
else:
args.bert_binary_head = False
args.iteration = 0
class MegatronEngine(torch.nn.Module):
"""
Megatron-LM model wrapper
Args:
accelerator (:class:`~accelerate.Accelerator`): The accelerator object to use.
model: Megatron-LM model
optimizer: Megatron-LM optimizer
lr_scheduler: Megatron-LM lr scheduler
"""
def __init__(self, accelerator, model, optimizer, scheduler):
super(MegatronEngine, self).__init__()
self.module = model
self.base_model = model[0]
self.optimizer = optimizer
self.scheduler = scheduler
args = get_args()
if accelerator.state.megatron_lm_plugin.custom_train_step_class is not None:
self.train_step_handler = accelerator.state.megatron_lm_plugin.custom_train_step_class(
args, **accelerator.state.megatron_lm_plugin.custom_train_step_kwargs
)
elif args.model_type_name == "bert":
self.train_step_handler = BertTrainStep(args)
elif args.model_type_name == "gpt":
self.train_step_handler = GPTTrainStep(args)
elif args.model_type_name == "t5":
self.train_step_handler = T5TrainStep(args)
else:
raise ValueError(f"Unsupported model type: {args.model_type_name}")
self.optimizer.skipped_iter = False
# Tracking loss.
self.total_loss_dict = {}
self.eval_total_loss_dict = {}
self.iteration = 0
self.report_memory_flag = True
if args.tensorboard_dir is not None:
write_args_to_tensorboard()
def train(self):
for model_module in self.module:
model_module.train()
self.log_eval_results()
def eval(self):
for model_module in self.module:
model_module.eval()
def train_step(self, **batch_data):
"""
Training step for Megatron-LM
Args:
batch_data (:obj:`dict`): The batch data to train on.
"""
args = get_args()
timers = get_timers()
if len(batch_data) > 0:
data_chunks = []
if args.num_micro_batches > 1:
for i in range(0, args.num_micro_batches):
data_chunks.append(
{
k: v[i * args.micro_batch_size : (i + 1) * args.micro_batch_size]
for k, v in batch_data.items()
}
)
else:
data_chunks = [batch_data]
if len(self.module) > 1:
batch_data_iterator = (
[iter(data_chunks) for _ in range(len(self.module))]
if len(batch_data) > 0
else [None] * len(self.module)
)