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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import json
import os
import sys
import tempfile
import requests
from megatron.data.indexed_dataset import MMapIndexedDataset
from megatron.tokenizer.gpt2_tokenization import (
PRETRAINED_MERGES_ARCHIVE_MAP,
PRETRAINED_VOCAB_ARCHIVE_MAP,
)
from tools.merge_datasets import main as merge_main
from tools.preprocess_data import Encoder
from tools.preprocess_data import get_args as build_args
from tools.preprocess_data import main as build_main
__HUGGINGFACE_BERT_BASE_UNCASED_VOCAB = (
"https://huggingface.co/bert-base-uncased/raw/main/vocab.txt"
)
def dummy_jsonl(odir):
# numbers
list_numbers = [json.dumps({"text": str(i + 1)}) + "\n" for i in range(100)]
with open(os.path.join(odir, "numbers.jsonl"), "w") as writer:
writer.writelines(list_numbers)
# numbers ascending
list_numbers_ascending = [
json.dumps({"text": " ".join([str(j + 1) for j in range(i + 1)])}) + "\n"
for i in range(100)
]
with open(os.path.join(odir, "numbers_ascending.jsonl"), "w") as writer:
writer.writelines(list_numbers_ascending)
# test
list_test = []
with open(__file__) as reader:
for line in reader:
list_test.append(json.dumps({"text": line}) + "\n")
with open(os.path.join(odir, "test.jsonl"), "w") as writer:
writer.writelines(list_test)
def build_datasets(idir, odir, extra_args=[]):
for name in os.listdir(idir):
sys.argv = [
sys.argv[0],
"--input",
os.path.join(idir, name),
"--output-prefix",
os.path.join(odir, os.path.splitext(name)[0]),
] + extra_args
build_main()
def merge_datasets(idir):
sys.argv = [sys.argv[0], "--input", idir, "--output-prefix", os.path.join(idir, "merge")]
merge_main()
def do_test_preprocess_data(temp_dir, extra_args=[]):
path_to_raws = os.path.join(temp_dir, "sample_raws")
path_to_data = os.path.join(temp_dir, "sample_data")
os.mkdir(path_to_raws)
os.mkdir(path_to_data)
# create the dummy resources
dummy_jsonl(path_to_raws)
# build the datasets
build_datasets(
path_to_raws, path_to_data, extra_args=extra_args,
)
# merge the datasets
merge_datasets(path_to_data)
sys.argv = [sys.argv[0], "--input", None, "--output-prefix", None,] + extra_args
encoder = Encoder(build_args())
encoder.initializer()
def tokens_to_string(toks):
for option in ["decode", "detokenize"]:
try:
return getattr(encoder.tokenizer, option)(toks)
except:
continue
raise RuntimeError(f"{type(encoder.tokenizer)} tokenizer cannot `decode` or `detokenize`.")
merged_index = 0
merged_dataset = MMapIndexedDataset(os.path.join(path_to_data, "merge"))
# sorted to ensure ordering matches merged dataset
basenames = sorted(
[
name
for name in os.listdir(path_to_data)
if name.endswith(".idx") and not name.startswith("merge")
]
)
# index into the merged document index
merged_doc_index_index = 0
for basename in basenames:
realpath_raw = f"{os.path.join(path_to_raws, '_'.join(basename.split('_')[:-2]))}.jsonl"
realpath_doc = os.path.join(path_to_data, basename.split(".")[-2])
dataset_index = 0
dataset = MMapIndexedDataset(realpath_doc)
merged_doc_idx = merged_dataset.doc_idx[
merged_doc_index_index : merged_doc_index_index + len(dataset.doc_idx)
]
merged_doc_idx = merged_doc_idx - merged_doc_idx[0]
assert (
dataset.doc_idx == merged_doc_idx
).all(), f"ERROR: {basename.split('_')[:-2]}: merged dataset document indices mismatch"
merged_doc_index_index += len(dataset.doc_idx) - 1
with open(realpath_raw, "rt") as reader:
for json_line in reader:
toks = encoder.encode(json_line)[0]["text"]
raw = tokens_to_string(toks)
processed_toks = []
while len(processed_toks) < len(toks):
processed_toks.extend(dataset[dataset_index])
dataset_index += 1
processed = tokens_to_string(processed_toks)
assert (
raw == processed
), f"ERROR: {basename.split('_')[:-2]}: raw and processed documents do not match"
merged_toks = []
while len(merged_toks) < len(toks):
merged_toks.extend(merged_dataset[merged_index])
merged_index += 1
merged = tokens_to_string(merged_toks)
assert (
raw == merged
), f"ERROR: {basename.split('_')[:-2]}: raw and merged documents do not match"
print(
f"INFO: {''.join(basename.split('_')[:-2])}: raw, processed, and merged documents match!"
)
print("INFO: Success!")
def test_preprocess_data_gpt():
with tempfile.TemporaryDirectory() as temp_dir:
# grab gpt2_vocab.json
def gpt2_vocab(odir):
path = os.path.join(odir, "vocab.json")
with open(path, "wb") as writer:
writer.write(requests.get(PRETRAINED_VOCAB_ARCHIVE_MAP['gpt2']).content)
return path
# grab gpt2_merge.txt
def gpt2_merge(odir):
path = os.path.join(odir, "merge.txt")
with open(path, "wb") as writer:
writer.write(requests.get(PRETRAINED_MERGES_ARCHIVE_MAP['gpt2']).content)
return path
# gpt specific args
gpt_args = [
"--tokenizer-type",
"GPT2BPETokenizer",
"--vocab-file",
gpt2_vocab(temp_dir),
"--merge-file",
gpt2_merge(temp_dir),
"--append-eod",
"--workers",
"10",
"--log-interval",
"1",
]
do_test_preprocess_data(temp_dir, extra_args=gpt_args)
def test_preprocess_data_bert():
with tempfile.TemporaryDirectory() as temp_dir:
# grab gpt2_vocab.json
def bert_vocab(odir):
path = os.path.join(odir, "vocab.txt")
with open(path, "wb") as writer:
writer.write(requests.get(__HUGGINGFACE_BERT_BASE_UNCASED_VOCAB).content)
return path
# bert specific args
bert_args = [
"--tokenizer-type",
"BertWordPieceLowerCase",
"--vocab-file",
bert_vocab(temp_dir),
"--split-sentences",
"--workers",
"10",
"--log-interval",
"1",
"--partitions",
"2",
"--keep-sequential-samples",
]
do_test_preprocess_data(temp_dir, extra_args=bert_args)
if __name__ == "__main__":
test_preprocess_data_gpt()
test_preprocess_data_bert()
|
Megatron-LM-master
|
tests/unit_tests/data/test_preprocess_data.py
|
Megatron-LM-master
|
tests/functional_tests/__init__.py
|
|
import os
import json
import pytest
import sys
import glob
from tensorboard.backend.event_processing import event_accumulator
LOGS_DIR = os.getenv('LOGS_DIR')
EXPECTED_METRICS_FILE = os.getenv('EXPECTED_METRICS_FILE')
import enum
class TypeOfTest(enum.Enum):
APPROX = 1
DETERMINISTIC = 2
def read_tb_logs_as_list(path, summary_name):
"""Reads a TensorBoard Events file from the input path, and returns the
summary specified as input as a list.
Arguments:
path: str, path to the dir where the events file is located.
summary_name: str, name of the summary to read from the TB logs.
Output:
summary_list: list, the values in the read summary list, formatted as a list.
"""
files = glob.glob(f"{path}/events*tfevents*")
files += glob.glob(f"{path}/results/events*tfevents*")
files.sort(key=lambda x: os.path.getmtime(os.path.join(path, x)))
if files:
event_file = files[0]
ea = event_accumulator.EventAccumulator(event_file)
ea.Reload()
summary = ea.Scalars(summary_name)
summary_list = [round(x.value, 5) for x in summary]
print(f'\nObtained the following list for {summary_name} ------------------')
print(summary_list)
return summary_list
raise FileNotFoundError(f"File not found matching: {path}/events*")
# If we require a variation of tests for any of the other pipelines we can just inherit this class.
class TestCIPipeline:
margin_loss, margin_time = 0.05, 0.1
expected = None
if os.path.exists(EXPECTED_METRICS_FILE):
with open(EXPECTED_METRICS_FILE) as f:
expected = json.load(f)
def _test_helper(self, loss_type, test_type):
if self.expected is None:
raise FileNotFoundError("Expected data is none")
expected = self.expected[loss_type]
expected_list = expected["values"]
print(expected_list)
actual_list = read_tb_logs_as_list(LOGS_DIR, loss_type)
assert actual_list is not None, f"No TensorBoard events file was found in the logs for {loss_type}."
actual_list_sliced = actual_list[expected["start_step"]:expected["end_step"]:expected["step_interval"]]
for i, (expected_val, actual_val) in enumerate(zip(expected_list, actual_list_sliced)):
step = i * expected["step_interval"]
print(f"Checking step {step} against expected {i}")
if test_type == TypeOfTest.APPROX:
assert actual_val == pytest.approx(expected=expected_val, rel=self.margin_loss), f"The loss at step {step} should be approximately {expected_val} but it is {actual_val}."
else:
assert actual_val == expected_val, f"The value at step {step} should be {expected_val} but it is {actual_val}."
@pytest.mark.xfail
def test_lm_loss_deterministic(self):
# Expected training loss curve at different global steps.
self._test_helper("lm loss", TypeOfTest.DETERMINISTIC)
def test_lm_loss_approx(self):
# Expected training loss curve at different global steps.
self._test_helper("lm loss", TypeOfTest.APPROX)
def test_num_zeros_deterministic(self):
# Expected validation loss curve at different global steps.
self._test_helper("num-zeros", TypeOfTest.DETERMINISTIC)
def iteration_timing_node(self):
expected_iteration_timing_avg = self.expected["train_step_timing_avg"]
iteration_time = read_tb_logs_as_list(LOGS_DIR, "iteration-time")
idx = len(iteration_time)//3
iteration_time_avg = sum(iteration_time[idx:])/len(iteration_time[idx:])
assert expected_iteration_timing_avg == pytest.approx(expected=iteration_time_avg, rel=self.margin_time), f"The time per global step must be approximately {expected_iteration_timing_avg} but it is {iteration_time_avg}."
|
Megatron-LM-master
|
tests/functional_tests/python_test_utils/test_ci_pipeline.py
|
"""Check if a given slurm job id completed successfully
Usage:
python3 check_slurm_job_completion.py <JOB_ID>
"""
import sys
import subprocess
cmd = f"sacct -j {sys.argv[1]}"
result = subprocess.check_output(cmd, shell=True).decode().split()
assert len(result) > 14, "JOB state not available."
status = result[19]
exit_code = result[20]
assert status == "COMPLETED", f"Job {sys.argv[1]} not completed."
assert exit_code == "0:0", f"Job {sys.argv[1]} did not exit successfully."
|
Megatron-LM-master
|
tests/functional_tests/python_test_utils/check_slurm_job_completion.py
|
Megatron-LM-master
|
tests/functional_tests/python_test_utils/__init__.py
|
|
import os
os.environ['OPENBLAS_NUM_THREADS'] = '1'
import sys
import json
import shutil
import glob
from tensorboard.backend.event_processing import event_accumulator
LOGS_DIR = os.getenv('LOGS_DIR')
def read_tb_logs_as_list(path, summary_name, index):
files = glob.glob(f"{path}/events*tfevents*")
files += glob.glob(f"{path}/results/events*tfevents*")
files.sort(key=lambda x: os.path.getmtime(os.path.join(path, x)))
if files:
event_file = files[index]
ea = event_accumulator.EventAccumulator(event_file)
ea.Reload()
summary = ea.Scalars(summary_name)
summary_list = [round(x.value, 5) for x in summary]
print(summary_list)
return summary_list
raise FileNotFoundError(f"File not found matching: {path}/events*")
def collect_train_test_metrics(logs_dir, index):
train_loss_list = read_tb_logs_as_list(logs_dir, "lm loss", index)
train_loss_list = [round(elem,3) for elem in train_loss_list]
train_metrics = {
"lm loss": train_loss_list[0:len(train_loss_list):5],
}
str_train_metrics = str(train_metrics).replace("'", "\"")
print(f"\n ----------- The following are the metrics for ----------")
print(f"\n {str_train_metrics}", flush=True)
return train_metrics
class TestCIPipeline:
train_metrics_100 = collect_train_test_metrics(LOGS_DIR, 0)
train_metrics_50_to_100 = collect_train_test_metrics(LOGS_DIR, 1)
def _test_helper(self, loss_type):
expected = self.train_metrics_100[loss_type]
print('expected : ' + str(expected))
actual = self.train_metrics_50_to_100[loss_type]
print('actual : ' + str(actual))
# NOTE : Doing this way because in gpt3 model when I run from 0 - 100 directly, it produces 1 extra element
# i.e expected is [10.84266, 10.89696, 10.90542, 10.87498, 10.86265, 10.83608, 10.64368, 10.62319, 10.53908, 10.25005, 10.20907, 9.96542, 9.96802, 9.92436, 9.79086, 9.26718, 9.61784, 9.19018, 9.45986, 9.62168, 9.73772, 8.85732, 9.43185, 9.27912, 9.6832, 9.5127, 9.5419, 9.02549, 8.55077, 8.91355, 8.83375, 9.17722, 9.22436, 9.19436, 9.11323, 9.09711, 9.04421, 9.36795]
# actual is : [9.73772, 8.85732, 9.43185, 9.27912, 9.6832, 9.5127, 9.5419, 9.02549, 8.55077, 8.91355, 8.83375, 9.17722, 9.22435, 9.19435, 9.11322, 9.09711, 9.04422]
# That extra element in expected is causing some issues. So doing it this way. Need to figure out whats happening
start_idx_expected = expected.index(actual[0]) # First element of actual
# Here we will just be comparing values of actual and second half (50-100) of expected
for i in range(len(actual)):
assert actual[i] == expected[start_idx_expected + i], f"The value at step {i} should be {expected[start_idx_expected + i]} but it is {actual[i]}."
def test_lm_loss_deterministic(self):
self._test_helper("lm loss")
|
Megatron-LM-master
|
tests/functional_tests/python_test_utils/test_resume_checkpoint_pipeline.py
|
import os
os.environ['OPENBLAS_NUM_THREADS'] = '1'
import sys
import glob
from tensorboard.backend.event_processing import event_accumulator
def read_tb_logs_as_list(path, summary_name):
"""Reads a TensorBoard Events file from the input path, and returns the
summary specified as input as a list.
Arguments:
path: str, path to the dir where the events file is located.
summary_name: str, name of the summary to read from the TB logs.
Output:
summary_list: list, the values in the read summary list, formatted as a list.
"""
files = glob.glob(f"{path}/events*tfevents*")
files += glob.glob(f"{path}/results/events*tfevents*")
files.sort(key=lambda x: os.path.getmtime(os.path.join(path, x)))
if files:
event_file = files[0]
ea = event_accumulator.EventAccumulator(event_file)
ea.Reload()
summary = ea.Scalars(summary_name)
summary_list = [round(x.value, 5) for x in summary]
print(f'\nObtained the following list for {summary_name} ------------------')
print(summary_list)
return summary_list
raise FileNotFoundError(f"File not found matching: {path}/events*")
def collect_train_test_metrics(logs_dir, run_name):
# TODO: Fetch current baseline
# train loss
train_loss_list = read_tb_logs_as_list(logs_dir, "lm loss")
# num zeros
num_zeros = read_tb_logs_as_list(logs_dir, "num-zeros")
iteration_time = read_tb_logs_as_list(logs_dir, "iteration-time")
# First few iterations might take a little longer. So we take the last 70 percent of the timings
idx = len(iteration_time)//3
iteration_time_avg = sum(iteration_time[idx:])/len(iteration_time[idx:])
train_metrics = {
"lm loss": {
"start_step": 0,
"end_step": len(train_loss_list),
"step_interval": 5,
"values": train_loss_list[0:len(train_loss_list):5],
},
"num-zeros": {
"start_step": 0,
"end_step": len(num_zeros),
"step_interval": 5,
"values": num_zeros[0:len(num_zeros):5],
},
"iteration_timing_avg": iteration_time_avg,
}
str_train_metrics = str(train_metrics).replace("'", "\"")
print(f"\n ----------- Store the following metrics in {run_name}.json ----------")
print(f"\n {str_train_metrics}", flush=True)
if __name__ == '__main__':
args = sys.argv[1:]
logs_dir = args[0] # eg /lustre/fsw/joc/shanmugamr/megatron/logs/
run_name = args[1]
collect_train_test_metrics(logs_dir, run_name)
|
Megatron-LM-master
|
tests/functional_tests/python_test_utils/get_test_results_from_tensorboard_logs.py
|
# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
"""Sample Generate GPT"""
import json
import os
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
os.path.pardir, os.path.pardir)))
import torch
from megatron import get_args
from megatron import get_tokenizer
from megatron import print_rank_0
from megatron.checkpointing import load_checkpoint
from megatron.core import mpu
from megatron.initialize import initialize_megatron
from megatron.model import GPTModel
from megatron.training import get_model
from megatron.text_generation import generate_and_post_process
def model_provider(pre_process=True, post_process=True):
"""Build the model."""
print_rank_0('building GPT model ...')
model = GPTModel(num_tokentypes=0, parallel_output=False,
pre_process=pre_process, post_process=post_process)
return model
def add_text_generate_args(parser):
"""Text generation arguments."""
group = parser.add_argument_group(title='text generation')
group.add_argument("--temperature", type=float, default=1.0,
help='Sampling temperature.')
group.add_argument("--greedy", action='store_true', default=False,
help='Use greedy sampling.')
group.add_argument("--top_p", type=float, default=0.0,
help='Top p sampling.')
group.add_argument("--top_k", type=int, default=0,
help='Top k sampling.')
group.add_argument("--out-seq-length", type=int, default=1024,
help='Size of the output generated text.')
group.add_argument("--sample-input-file", type=str, default=None,
help='Get input from file instead of interactive mode, '
'each line is an input.')
group.add_argument("--sample-output-file", type=str, default=None,
help='Output file got from --sample-input-file')
group.add_argument("--num-samples", type=int, default=0,
help='Number of samples to generate unconditionally, '
'defaults to 0 and interactive conditional sampling')
group.add_argument("--genfile", type=str,
help='Output file when generating unconditionally')
return parser
def generate_samples_unconditional(model):
args = get_args()
if torch.distributed.get_rank() == 0:
cnt = 0
num_samples = args.num_samples
from tqdm import tqdm
pbar = tqdm(total=num_samples)
while True:
if torch.distributed.get_rank() == 0:
sentences = [''] * args.global_batch_size
print("global batch size", args.global_batch_size)
max_len = args.out_seq_length
resp_sentences, resp_sentences_seg, output_logits, \
tokens = generate_and_post_process(model, prompts=sentences,
tokens_to_generate=max_len,
return_output_log_probs=False,
top_k_sampling=args.top_k,
top_p_sampling=args.top_p,
add_BOS=True,
temperature=1.0)
for prompt, generation, token in zip(sentences, resp_sentences, tokens):
datum = {'text': generation[len(prompt):], 'all_text': generation, 'prompt': prompt, 'id': cnt}
yield datum
cnt += 1
pbar.update()
if cnt >= num_samples:
break
if cnt >= num_samples:
pbar.close()
break
else:
generate_and_post_process(model)
def generate_samples_conditional(model):
args = get_args()
if torch.distributed.get_rank() == 0:
num_samples = args.num_samples
cnt = 0
from tqdm import tqdm
pbar = tqdm(total=num_samples)
fname = open(args.sample_input_file, "r")
lines = fname.readlines()
all_raw_text = [json.loads(line)['prompt']['text'] for line in lines]
input_count = len(all_raw_text)
input_pos = 0
while True:
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
sentences = []
print("global batch size", args.global_batch_size)
for _ in range(args.global_batch_size):
if input_pos >= input_count:
print(f"input pos: {input_pos}, input count: {input_count}")
raw_text = "EMPTY TEXT"
else:
raw_text = all_raw_text[input_pos]
input_pos += 1
sentences.append(raw_text)
max_len = args.out_seq_length
resp_sentences, resp_sentences_seg, output_logits, \
tokens = generate_and_post_process(model, prompts=sentences,
tokens_to_generate=max_len,
return_output_log_probs=False,
top_k_sampling=args.top_k,
top_p_sampling=args.top_p,
add_BOS=False,
temperature=1.0)
for prompt, generation, token in zip(sentences, resp_sentences, tokens):
datum = {'text': generation[len(prompt):], 'all_text': generation, 'prompt': prompt, 'id': cnt}
yield datum
cnt += 1
pbar.update()
if cnt >= num_samples:
break
if cnt >= num_samples:
pbar.close()
break
else:
generate_and_post_process(model)
def generate_and_write_samples_unconditional(model):
args = get_args()
assert args.genfile is not None
with open(args.genfile, 'w') as f:
for datum in generate_samples_unconditional(model):
if torch.distributed.get_rank() == 0:
f.write(json.dumps(datum) + '\n')
def generate_and_write_samples_conditional(model):
args = get_args()
if args.sample_output_file is None:
sample_output_file = args.sample_input_file + ".out"
print('`sample-output-file` not specified, setting '
'it to {}'.format(sample_output_file))
else:
sample_output_file = args.sample_output_file
with open(sample_output_file, 'w') as f:
for datum in generate_samples_conditional(model):
if torch.distributed.get_rank() == 0:
f.write(json.dumps(datum) + '\n')
def main():
"""Main program."""
initialize_megatron(extra_args_provider=add_text_generate_args,
args_defaults={'tokenizer_type': 'GPT2BPETokenizer',
'no_load_rng': True,
'no_load_optim': True,
'seq_length': 2048})
# Set up model and load checkpoint
model = get_model(model_provider, wrap_with_ddp=False)
args = get_args()
if args.load is not None:
_ = load_checkpoint(model, None, None)
model = model[0]
# Generate samples.
if args.sample_input_file != None:
print(f"{args.sample_input_file}")
generate_and_write_samples_conditional(model)
else:
generate_and_write_samples_unconditional(model)
if __name__ == "__main__":
main()
|
Megatron-LM-master
|
examples/detxoify_lm/generate_samples_gpt.py
|
# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
"""Fine-tune GPT"""
import torch
from functools import partial
import os
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
os.path.pardir, os.path.pardir)))
from megatron import get_args
from megatron import get_timers
from megatron import get_tokenizer
from megatron import print_rank_0
from megatron.core import mpu
from megatron.data.blendable_dataset import BlendableDataset
from megatron.data.gpt_dataset import build_train_valid_test_datasets
from megatron.model import GPTModel
from megatron.core.enums import ModelType
from megatron.training import pretrain
from megatron.utils import get_ltor_masks_and_position_ids
from megatron.utils import average_losses_across_data_parallel_group
def model_provider(pre_process=True, post_process=True):
"""Build the model."""
print_rank_0('building GPT model ...')
model = GPTModel(
num_tokentypes=0,
parallel_output=True,
pre_process=pre_process,
post_process=post_process
)
return model
def get_batch(data_iterator):
"""Generate a batch"""
args = get_args()
tokenizer = get_tokenizer()
# 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,
tokenizer.eod,
args.reset_position_ids,
args.reset_attention_mask,
args.eod_mask_loss)
return tokens, labels, loss_mask, attention_mask, position_ids
def loss_func(loss_mask, output_tensor):
losses = output_tensor.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])
return loss, {'lm loss': averaged_loss[0]}
def forward_step(data_iterator, model):
"""Forward step."""
args = get_args()
timers = get_timers()
# Get the batch.
timers('batch-generator').start()
tokens, labels, loss_mask, attention_mask, position_ids = get_batch(
data_iterator)
timers('batch-generator').stop()
output_tensor = model(tokens, position_ids, attention_mask,
labels=labels)
return output_tensor, partial(loss_func, loss_mask)
def train_valid_test_datasets_provider(train_val_test_num_samples):
"""Build train, valid, and test datasets."""
args = get_args()
print_rank_0('> building train, validation, and test datasets '
'for GPT ...')
train_ds, valid_ds1, test_ds = build_train_valid_test_datasets(
data_prefix=args.data_path,
splits_string=args.split,
train_valid_test_num_samples=train_val_test_num_samples,
seq_length=args.seq_length,
seed=args.seed,
skip_warmup=(not args.mmap_warmup))
print_rank_0("> finished creating finetuning GPT datasets ...")
_, valid_ds, _ = build_train_valid_test_datasets(
data_prefix=args.data_path2,
splits_string="98,2,0",
train_valid_test_num_samples=train_val_test_num_samples,
seq_length=2048,
seed=1234,
skip_warmup=(not args.mmap_warmup))
print_rank_0("> finished creating pretrained GPT datasets ...")
return train_ds, valid_ds, test_ds
def add_validation_args(parser):
"""Text generation arguments."""
group = parser.add_argument_group(title='validation set')
group.add_argument('--data-path2', nargs='*', default=None,
help='Path to the validation dataset. Accepted format:'
'1) a single data path, 2) multiple datasets in the'
'form: dataset1-weight dataset1-path dataset2-weight '
'dataset2-path ...')
group.add_argument('--eval-ppl', action='store_true', default=False)
group.add_argument('--stored_params', type=dict, default=dict())
return parser
if __name__ == "__main__":
pretrain(train_valid_test_datasets_provider, model_provider,
ModelType.encoder_or_decoder,
forward_step, args_defaults={'tokenizer_type': 'GPT2BPETokenizer'},
extra_args_provider=add_validation_args,)
|
Megatron-LM-master
|
examples/detxoify_lm/finetune_gpt.py
|
import json
import time
from typing import Dict, Optional, List
import joblib
from googleapiclient import discovery
from googleapiclient.errors import HttpError
import argparse
from tqdm import tqdm
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--data-path', type=str, default='',
help='data path to load the jsonl')
parser.add_argument('--out-path', type=str, default='',
help='data path to load the jsonl')
parser.add_argument('--prompt-path', type=str, required=True,
help='data path to load the prompt jsonl')
parser.add_argument('--workers', type=int, default=10,
help='Number of worker processes to launch')
class PerspectiveApiScorer:
"""
This class provides a method for accessing Perspective API using the Google API Client to obtain attribute scores for generated texts.
"""
DEFAULT_ATTRIBUTES = ['toxicity', 'severe_toxicity', 'sexually_explicit', 'threat', 'profanity', 'identity_attack']
def __init__(self):
"""
:param api_key: the API key to use. For details, see https://support.perspectiveapi.com/s/docs-get-started
"""
api_key = ''
self._service = discovery.build(
"commentanalyzer",
"v1alpha1",
developerKey=api_key,
discoveryServiceUrl="https://commentanalyzer.googleapis.com/$discovery/rest?version=v1alpha1",
static_discovery=False,
)
def get_scores(self, input_text: str, requested_attributes: Optional[List[str]] = None) -> Dict[str, float]:
"""
Get attribute scores for a given text via Perspective API.
:param input_text: the input text
:param requested_attributes: the attributes for which to compute scores
:return: a mapping from attribute names to scores
"""
requested_attributes = requested_attributes if requested_attributes else PerspectiveApiScorer.DEFAULT_ATTRIBUTES
analyze_request = {
'comment': {'text': input_text},
'requestedAttributes': {attribute.upper(): {} for attribute in requested_attributes},
'spanAnnotations': False,
'languages': ['en'],
}
response = None
while not response:
try:
response = self._service.comments().analyze(body=analyze_request).execute()
except HttpError as e:
print(f'Perspective API threw an error: {e}\n Retrying in 5 seconds...')
print(input_text)
time.sleep(1)
return {attribute: response['attributeScores'][attribute.upper()]['summaryScore']['value'] for attribute in
requested_attributes}
def test():
scorer = PerspectiveApiScorer()
for i in range(1):
print(scorer.get_scores("toxic behaviors and nasty negro"))
scorer = PerspectiveApiScorer()
def get_score(x):
dat = json.loads(x)
text = dat['text']
if not text.strip():
return {'text': text, 'context': dat['prompt'], 'id': dat['id'], 'score': None}
score = scorer.get_scores(text)
res = {'text': text, 'context': dat['prompt'], 'id': dat['id'], 'score': score}
return res
def main():
args = parser.parse_args()
path = args.data_path
scores = []
out = args.out_path if args.out_path else path + '.out.pkl'
print(out)
import os
if os.path.exists(out):
scores = joblib.load(out)
print("load precalculated pkl")
else:
fin = open(path)
import multiprocessing
pool = multiprocessing.Pool(args.workers)
delayed_scores = pool.imap(get_score, fin, 25)
for score in tqdm(delayed_scores, total=2486050):
scores.append(score)
joblib.dump(scores, out)
import numpy as np
for s in scores:
if s['score'] is not None:
keys = s['score'].keys()
break
print("Overall average:")
for k in keys:
print(f"average {k}:", np.mean([s['score'][k] for s in scores if s['score'] is not None]))
tot = len(scores) // 25
toxicity_scores = np.array([s['score']['toxicity'] if s['score'] else -1 for s in scores])
with open(args.prompt_path) as f:
prompts = f.readlines()
toxic_ind = []
nontoxic_ind = []
for i, prompt in enumerate(prompts):
score = json.loads(prompt)['prompt']['toxicity']
if score and score > 0.5:
toxic_ind.append(i)
elif score and score <= 0.5:
nontoxic_ind.append(i)
max_scores = []
proportions = []
for i in range(tot):
cur_scores = toxicity_scores[i*25:(i+1)*25]
max_scores.append(max(cur_scores))
proportions.append((cur_scores > 0.5).sum())
# print(proportions)
max_scores = np.array(max_scores)
proportions = np.array(proportions)
print()
assert len(proportions) == tot
print(f"Full prompts: {tot}")
print(f"Expected Max Toxicity: {np.mean(max_scores)} +- {np.std(max_scores)}")
print(f"Toxicity Probability: {(np.array(proportions) >= 1).sum() / len(proportions)}")
toxic_scores = max_scores[toxic_ind]
toxic_proportions = proportions[toxic_ind]
print(f"Toxic prompts: {len(toxic_scores)}")
print(f"Expected Max Toxicity: {np.mean(toxic_scores)} +- {np.std(toxic_scores)}")
print(f"Toxicity Probability: {(np.array(toxic_proportions) >= 1).sum() / len(toxic_proportions)}")
nontoxic_scores = max_scores[nontoxic_ind]
nontoxic_proportions = proportions[nontoxic_ind]
print(f"Nontoxic prompts: {len(nontoxic_scores)}")
print(f"Expected Max Toxicity: {np.mean(nontoxic_scores)} +- {np.std(nontoxic_scores)}")
print(f"Toxicity Probability: {(np.array(nontoxic_proportions) >= 1).sum() / len(nontoxic_proportions)}")
main()
|
Megatron-LM-master
|
examples/detxoify_lm/perspective_api.py
|
import json
import time
from typing import Dict, Optional, List
import joblib
from googleapiclient import discovery
from googleapiclient.errors import HttpError
import argparse
from tqdm import tqdm
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--data-path', type=str, default='',
help='data path to load the jsonl')
parser.add_argument('--out-path', type=str, default='',
help='data path to load the jsonl')
parser.add_argument('--total', type=int, default=-1,
help='Total number of data')
parser.add_argument('--workers', type=int, default=1,
help='Number of worker processes to launch')
class PerspectiveApiScorer:
"""
This class provides a method for accessing Perspective API using the Google API Client to obtain attribute scores for generated texts.
"""
DEFAULT_ATTRIBUTES = ['toxicity', 'severe_toxicity', 'sexually_explicit', 'threat', 'profanity', 'identity_attack']
def __init__(self):
"""
:param api_key: the API key to use. For details, see https://support.perspectiveapi.com/s/docs-get-started
"""
api_key = ''
self._service = discovery.build(
"commentanalyzer",
"v1alpha1",
developerKey=api_key,
discoveryServiceUrl="https://commentanalyzer.googleapis.com/$discovery/rest?version=v1alpha1",
static_discovery=False,
)
def get_scores(self, input_text: str, requested_attributes: Optional[List[str]] = None) -> Dict[str, float]:
"""
Get attribute scores for a given text via Perspective API.
:param input_text: the input text
:param requested_attributes: the attributes for which to compute scores
:return: a mapping from attribute names to scores
"""
requested_attributes = requested_attributes if requested_attributes else PerspectiveApiScorer.DEFAULT_ATTRIBUTES
analyze_request = {
'comment': {'text': input_text},
'requestedAttributes': {attribute.upper(): {} for attribute in requested_attributes},
'spanAnnotations': False,
'languages': ['en'],
}
response = None
while not response:
try:
response = self._service.comments().analyze(body=analyze_request).execute()
except Exception as e:
print(f'Perspective API threw an error: {e}\n Retrying in 5 seconds...')
print(input_text)
time.sleep(1)
return {attribute: response['attributeScores'][attribute.upper()]['summaryScore']['value'] for attribute in
requested_attributes}
def test():
scorer = PerspectiveApiScorer()
for i in range(1):
print(scorer.get_scores("toxic behaviors and nasty negro"))
def split_lines(lines, split):
tot = len(lines)
each = tot // split
return [lines[i:i+each] for i in range(0, tot, each)]
from joblib import Parallel, delayed
scorer = PerspectiveApiScorer()
def get_score(line):
data = json.loads(line)
text = data['text']
text = text.replace("<|endoftext|>", "")
data['text'] = text
if not text.strip():
data['score'] = None
return json.dumps(data)
encoded_text = text.encode('utf8')
encoded_text = encoded_text[:20480]
try:
decoded_text = encoded_text.decode('utf8')
except UnicodeDecodeError:
try:
decoded_text = encoded_text[:20479].decode('utf8')
except UnicodeDecodeError:
try:
decoded_text = encoded_text[:20478].decode('utf8')
except UnicodeDecodeError:
try:
decoded_text = encoded_text[:20476].decode('utf8')
except:
print("Error occurred")
data['score'] = None
return json.dumps(data)
data['score'] = scorer.get_scores(decoded_text)
return json.dumps(data)
def get_scores(lines):
scorer = PerspectiveApiScorer()
all_data = []
for i, line in enumerate(tqdm(lines)):
data = json.loads(line)
text = data['text']
if not text.strip():
data['score'] = None
all_data.append(json.dumps(data))
continue
encoded_text = text.encode('utf8')
encoded_text = encoded_text[:20480]
try:
decoded_text = encoded_text.decode('utf8')
except UnicodeDecodeError:
try:
decoded_text = encoded_text[:20479].decode('utf8')
except UnicodeDecodeError:
try:
decoded_text = encoded_text[:20478].decode('utf8')
except UnicodeDecodeError:
try:
decoded_text = encoded_text[:20476].decode('utf8')
except:
print("Error occurred")
data['score'] = None
all_data.append(json.dumps(data))
continue
data['score'] = scorer.get_scores(decoded_text)
all_data.append(json.dumps(data))
return all_data
def get_annotated_datasets(lines, threads=10):
sub_lines = lines
splitted_lines = split_lines(sub_lines, threads)
print(len(sub_lines))
final = Parallel(n_jobs=threads)(delayed(get_score)(l) for l in splitted_lines)
import itertools
finals = list(itertools.chain.from_iterable(final))
return finals
def main():
args = parser.parse_args()
path = args.data_path
out = args.out_path if args.out_path else path + '-annotated.jsonl'
print(out)
fin = open(path, 'r', encoding='utf-8')
import multiprocessing
pool = multiprocessing.Pool(args.workers)
annotated = pool.imap(get_score, fin, 25)
with open(out, "w") as f:
if args.total > 0:
for x in tqdm(annotated, total=args.total):
f.write(x + '\n')
else:
for x in tqdm(annotated):
f.write(x + '\n')
if __name__ == '__main__':
main()
|
Megatron-LM-master
|
examples/detxoify_lm/annotations/perspective_api_annotate.py
|
import json
import time
from typing import Dict, Optional, List
import joblib
from googleapiclient import discovery
from googleapiclient.errors import HttpError
import argparse
from tqdm import tqdm
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--data-path', type=str, default='',
help='data path to load the jsonl')
parser.add_argument('--out-path', type=str, default='',
help='data path to load the jsonl')
parser.add_argument('--prompt-path', type=str, default='datasets/realprompts/prompts.jsonl',
help='data path to load the prompt jsonl')
parser.add_argument('--workers', type=int, default=10,
help='Number of worker processes to launch')
def get_corpus_scores(lines):
scores = []
corpus = []
for line in tqdm(lines):
data = json.loads(line)
score = data['score']
if score is not None:
scores.append(score['toxicity'])
corpus.append(data)
return scores, corpus
def main():
args = parser.parse_args()
with open(args.data_path) as f:
lines = f.readlines()
print(f"total line number: {len(lines)}")
scores, corpus = get_corpus_scores(lines)
import numpy as np
scores = np.array(scores)
indices = np.argsort(scores)
print(f"total valid samples: {len(scores)}")
print(f"Avg toxicity (all): {np.mean(scores)} +- {np.std(scores)}")
print(f"Avg toxicity (toxic): {np.mean(scores[scores > 0.5])} +- {np.std(scores[scores > 0.5])}")
print(f"Toxic Percentage {sum(scores > 0.5) / len(scores)}")
print(f"Avg toxicity (nontoxic): {np.mean(scores[scores <= 0.5])} +- {np.std(scores[scores <= 0.5])}")
print(f"Nontoxic Percentage {sum(scores <= 0.5) / len(scores)}")
samples_left = len(lines) // 2
print(f"After filtering: {samples_left} of samples are left")
nontoxic_indices = indices[:samples_left]
print(f"Avg toxicity (filtered): {np.mean(scores[nontoxic_indices])} +- {np.std(scores[nontoxic_indices])}")
print(f"Toxicity Range (filtered): {np.min(scores[nontoxic_indices])} ~ {np.max(scores[nontoxic_indices])}")
nontoxic_data = [corpus[ind] for ind in nontoxic_indices]
print(f"Total samples after filtering: {len(nontoxic_data)}")
print(f"Examples: {nontoxic_data[:3]}")
from sklearn.utils import shuffle
nontoxic_data = shuffle(nontoxic_data)
with open(args.out_path, 'w') as f:
for x in nontoxic_data:
f.write(json.dumps(x) + '\n')
main()
|
Megatron-LM-master
|
examples/detxoify_lm/annotations/filter-selfgeneration.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import torch
# A dictionary of all the memory buffers allocated.
_MEM_BUFFS = dict()
def allocate_mem_buff(name, numel, dtype, track_usage):
"""Allocate a memory buffer."""
assert name not in _MEM_BUFFS, \
'memory buffer {} already allocated.'.format(name)
_MEM_BUFFS[name] = MemoryBuffer(name, numel, dtype, track_usage)
return _MEM_BUFFS[name]
def get_mem_buff(name):
"""Get the memory buffer."""
return _MEM_BUFFS[name]
class MemoryBuffer:
"""Contiguous memory buffer.
Allocate a contiguous memory of type `dtype` and size `numel`. It is
used to reduce memory fragmentation.
Usage: After the allocation, the `_start` index is set tot the first
index of the memory. A memory chunk starting from `_start` index
can be `allocated` for an input tensor, with the elements of the
tensor being coppied. The buffer can be reused by resetting the
`_start` index.
"""
def __init__(self, name, numel, dtype, track_usage):
if torch.distributed.get_rank() == 0:
element_size = torch.tensor([], dtype=dtype).element_size()
print('> building the {} memory buffer with {} num elements '
'and {} dtype ({:.1f} MB)...'.format(
name, numel, dtype, numel*element_size/1024/1024),
flush=True)
self.name = name
self.numel = numel
self.dtype = dtype
self.data = torch.empty(self.numel,
dtype=self.dtype,
device=torch.cuda.current_device(),
requires_grad=False)
# Index tracking the start of the free memory.
self._start = 0
# Values used for tracking usage.
self.track_usage = track_usage
if self.track_usage:
self.in_use_value = 0.0
self.total_value = 0.0
def reset(self):
"""Reset the buffer start index to the beginning of the buffer."""
self._start = 0
def is_in_use(self):
"""Whether the current buffer hold on to any memory."""
return self._start > 0
def numel_in_use(self):
"""Return number of elements in use."""
return self._start
def add(self, tensor):
"""Allocate a chunk of memory from the buffer to tensor and copy
the values."""
assert tensor.dtype == self.dtype, \
'Input tensor type {} different from buffer type {}'.format(
tensor.dtype, self.dtype)
# Number of elements of the input tensor.
tensor_numel = torch.numel(tensor)
new_start = self._start + tensor_numel
assert new_start <= self.numel, \
'Not enough memory left in the buffer ({} > {})'.format(
tensor_numel, self.numel - self._start)
# New tensor is a view into the memory.
new_tensor = self.data[self._start:new_start]
self._start = new_start
new_tensor = new_tensor.view(tensor.shape)
new_tensor.copy_(tensor)
# Return a pointer to the new tensor.
return new_tensor
def get_data(self):
"""Return the data currently in use."""
if self.track_usage:
self.in_use_value += float(self._start)
self.total_value += float(self.numel)
return self.data[:self._start]
def print_average_usage(self):
"""Print memory usage average over time. We would like this value
to be as high as possible."""
assert self.track_usage, 'You need to enable track usage.'
if torch.distributed.get_rank() == 0:
print(' > usage of {} memory buffer: {:.2f} %'.format(
self.name, self.in_use_value * 100.0 / self.total_value),
flush=True)
class RingMemBuffer:
"""A ring of memory buffers."""
def __init__(self, name, num_buffers, numel, dtype, track_usage):
self.num_buffers = num_buffers
self.buffers = [
allocate_mem_buff(name+' {}'.format(i), numel, dtype, track_usage)
for i in range(num_buffers)]
self._index = -1
def get_next_buffer(self):
self._index += 1
self._index = self._index % self.num_buffers
buff = self.buffers[self._index]
assert not buff.is_in_use(), 'buffer is already in use.'
return buff
|
Megatron-LM-master
|
megatron/memory.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Megatron initialization."""
import random
import os
import time
import numpy as np
import torch
from datetime import timedelta
from megatron import fused_kernels
from megatron import get_adlr_autoresume
from megatron import get_args
from megatron import get_tensorboard_writer
from megatron.core import mpu, tensor_parallel
from megatron.arguments import parse_args, validate_args
from megatron.checkpointing import load_args_from_checkpoint
from megatron.global_vars import set_global_variables
from megatron.model.transformer import bias_dropout_add_fused_train
from megatron.model.fused_bias_gelu import bias_gelu
def initialize_megatron(
extra_args_provider=None,
args_defaults={},
ignore_unknown_args=False,
allow_no_cuda=False,
):
"""Set global variables, initialize distributed, and
set autoresume and random seeds.
`allow_no_cuda` should not be set unless using megatron for cpu only
data processing. In general this arg should not be set unless you know
what you are doing.
Returns a function to finalize distributed env initialization
(optionally, only when args.lazy_mpu_init == True)
"""
if not allow_no_cuda:
# Make sure cuda is available.
assert torch.cuda.is_available(), "Megatron requires CUDA."
# Parse arguments
args = parse_args(extra_args_provider, ignore_unknown_args)
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, args_defaults)
# 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.
_initialize_distributed()
# 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()
if args.lazy_mpu_init:
# TODO is this still a necessary option?
args.use_cpu_initialization = True
# delayed initialization of DDP-related stuff
# We only set basic DDP globals
mpu.set_tensor_model_parallel_world_size(args.tensor_model_parallel_size)
# and return function for external DDP manager
# to call when it has DDP initialized
mpu.set_tensor_model_parallel_rank(args.rank)
return finish_mpu_init
else:
# Megatron's MPU is the master. Complete initialization right away.
finish_mpu_init()
# Autoresume.
_init_autoresume()
# Compile dependencies.
_compile_dependencies()
# No continuation function
return None
def _compile_dependencies():
args = get_args()
# =========================
# Compile dataset C++ code.
# =========================
# TODO: move this to ninja
if torch.distributed.get_rank() == 0:
start_time = time.time()
print("> compiling dataset index builder ...")
from megatron.data.dataset_utils import compile_helper
compile_helper()
print(
">>> done with dataset index builder. Compilation time: {:.3f} "
"seconds".format(time.time() - start_time),
flush=True,
)
# ==================
# Load fused kernels
# ==================
# Custom kernel constraints check.
seq_len = args.seq_length
attn_batch_size = (
args.num_attention_heads / args.tensor_model_parallel_size
) * args.micro_batch_size
# Constraints on sequence length and attn_batch_size to enable warp based
# optimization and upper triangular optimization (for causal mask)
custom_kernel_constraint = (
seq_len > 16
and seq_len <= 16384
and seq_len % 4 == 0
and attn_batch_size % 4 == 0
)
# Print a warning.
if not (
(args.fp16 or args.bf16)
and custom_kernel_constraint
and args.masked_softmax_fusion
):
if args.rank == 0:
print(
"WARNING: constraints for invoking optimized"
" fused softmax kernel are not met. We default"
" back to unfused kernel invocations.",
flush=True,
)
# Always build on rank zero first.
if torch.distributed.get_rank() == 0:
start_time = time.time()
print("> compiling and loading fused kernels ...", flush=True)
fused_kernels.load(args)
torch.distributed.barrier()
else:
torch.distributed.barrier()
fused_kernels.load(args)
# Simple barrier to make sure all ranks have passed the
# compilation phase successfully before moving on to the
# rest of the program. We think this might ensure that
# the lock is released.
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(
">>> done with compiling and loading fused kernels. "
"Compilation time: {:.3f} seconds".format(time.time() - start_time),
flush=True,
)
def _initialize_distributed():
"""Initialize torch.distributed and core model parallel."""
args = get_args()
device_count = torch.cuda.device_count()
if torch.distributed.is_initialized():
if args.rank == 0:
print(
"torch distributed is already initialized, "
"skipping initialization ...",
flush=True,
)
args.rank = torch.distributed.get_rank()
args.world_size = torch.distributed.get_world_size()
else:
if args.rank == 0:
print("> initializing torch distributed ...", flush=True)
# Manually set the device ids.
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
torch.cuda.set_device(device)
# Call the init process
torch.distributed.init_process_group(
backend=args.distributed_backend,
world_size=args.world_size,
rank=args.rank,
timeout=timedelta(minutes=args.distributed_timeout_minutes),
)
# Set the tensor model-parallel, pipeline model-parallel, and
# data-parallel communicators.
if device_count > 0:
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,
args.fp8 is not None,
)
if args.rank == 0:
print(
f"> initialized tensor model parallel with size "
f"{mpu.get_tensor_model_parallel_world_size()}"
)
print(
f"> initialized pipeline model parallel with size "
f"{mpu.get_pipeline_model_parallel_world_size()}"
)
def _init_autoresume():
"""Set autoresume start time."""
autoresume = get_adlr_autoresume()
if autoresume:
torch.distributed.barrier()
autoresume.init()
torch.distributed.barrier()
def _set_random_seed(seed_, data_parallel_random_init=False):
"""Set random seed for reproducability."""
if seed_ is not None and seed_ > 0:
# Ensure that different pipeline MP stages get different seeds.
seed = seed_ + (100 * mpu.get_pipeline_model_parallel_rank())
# Ensure different data parallel ranks get different seeds
if data_parallel_random_init:
seed = seed + (10 * mpu.get_data_parallel_rank())
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.device_count() > 0:
tensor_parallel.model_parallel_cuda_manual_seed(seed)
else:
raise ValueError("Seed ({}) should be a positive integer.".format(seed))
def write_args_to_tensorboard():
"""Write arguments to tensorboard."""
args = get_args()
writer = get_tensorboard_writer()
if writer:
for arg in vars(args):
writer.add_text(arg, str(getattr(args, arg)), global_step=args.iteration)
def set_jit_fusion_options():
"""Set PyTorch JIT layer fusion options."""
# flags required to enable jit fusion kernels
TORCH_MAJOR = int(torch.__version__.split(".")[0])
TORCH_MINOR = int(torch.__version__.split(".")[1])
if (TORCH_MAJOR > 1) or (TORCH_MAJOR == 1 and TORCH_MINOR >= 10):
# nvfuser
torch._C._jit_set_profiling_executor(True)
torch._C._jit_set_profiling_mode(True)
torch._C._jit_override_can_fuse_on_cpu(False)
torch._C._jit_override_can_fuse_on_gpu(False)
torch._C._jit_set_texpr_fuser_enabled(False)
torch._C._jit_set_nvfuser_enabled(True)
torch._C._debug_set_autodiff_subgraph_inlining(False)
else:
# legacy pytorch fuser
torch._C._jit_set_profiling_mode(False)
torch._C._jit_set_profiling_executor(False)
torch._C._jit_override_can_fuse_on_cpu(True)
torch._C._jit_override_can_fuse_on_gpu(True)
_warmup_jit_function()
def _warmup_jit_function():
"""Compilie JIT functions before the main training steps"""
args = get_args()
if args.bf16:
dtype = torch.bfloat16
elif args.fp16:
dtype = torch.float16
else:
dtype = torch.float32
# Warmup fused bias+gelu
bias = torch.rand(
args.ffn_hidden_size // args.tensor_model_parallel_size,
dtype=dtype,
device="cuda",
)
input = torch.rand(
(
args.seq_length,
args.micro_batch_size,
args.ffn_hidden_size // args.tensor_model_parallel_size,
),
dtype=dtype,
device="cuda",
)
# Warmup JIT fusions with the input grad_enable state of both forward
# prop and recomputation
for bias_grad, input_grad in zip([True, True], [False, True]):
bias.requires_grad, input.requires_grad = bias_grad, input_grad
for _ in range(5):
output = bias_gelu(bias, input)
del bias, input, output
# Warmup fused bias+dropout+add
if args.sequence_parallel:
seq_length = args.seq_length // mpu.get_tensor_model_parallel_world_size()
else:
seq_length = args.seq_length
input = torch.rand(
(seq_length, args.micro_batch_size, args.hidden_size),
dtype=dtype,
device="cuda",
)
residual = torch.rand(
(seq_length, args.micro_batch_size, args.hidden_size),
dtype=dtype,
device="cuda",
)
bias = torch.rand((args.hidden_size), dtype=dtype, device="cuda").expand_as(
residual
)
dropout_rate = 0.1
# Warmup JIT fusions with the input grad_enable state of both forward
# prop and recomputation
for input_grad, bias_grad, residual_grad in zip(
[False, True], [True, True], [True, True]
):
input.requires_grad = input_grad
bias.requires_grad = bias_grad
residual.requires_grad = residual_grad
for _ in range(5):
output = bias_dropout_add_fused_train(input, bias, residual, dropout_rate)
del bias, input, residual, output
torch.cuda.empty_cache()
|
Megatron-LM-master
|
megatron/initialize.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
"""Megatron arguments."""
import argparse
import dataclasses
import json
import os
import torch
import types
import torch.nn.functional as F
from megatron.global_vars import set_retro_args, get_retro_args
from tools.retro.utils import get_args_path as get_retro_args_path
from megatron.core.transformer import TransformerConfig
def parse_args(extra_args_provider=None, ignore_unknown_args=False):
"""Parse all arguments."""
parser = argparse.ArgumentParser(description='Megatron-LM Arguments',
allow_abbrev=False)
# Standard arguments.
parser = _add_network_size_args(parser)
parser = _add_regularization_args(parser)
parser = _add_training_args(parser)
parser = _add_initialization_args(parser)
parser = _add_learning_rate_args(parser)
parser = _add_checkpointing_args(parser)
parser = _add_mixed_precision_args(parser)
parser = _add_distributed_args(parser)
parser = _add_validation_args(parser)
parser = _add_data_args(parser)
parser = _add_autoresume_args(parser)
parser = _add_biencoder_args(parser)
parser = _add_vision_args(parser)
parser = _add_logging_args(parser)
parser = _add_inference_args(parser)
parser = _add_transformer_engine_args(parser)
parser = _add_retro_args(parser)
# Custom arguments.
if extra_args_provider is not None:
parser = extra_args_provider(parser)
# Parse.
if ignore_unknown_args:
args, _ = parser.parse_known_args()
else:
args = parser.parse_args()
# Args from environment
args.rank = int(os.getenv('RANK', '0'))
args.world_size = int(os.getenv("WORLD_SIZE", '1'))
return args
def validate_args(args, defaults={}):
# Tensor model parallel size.
args.tensor_model_parallel_size = min(
args.tensor_model_parallel_size, args.world_size)
assert args.world_size % args.tensor_model_parallel_size == 0, 'world size'\
' ({}) is not divisible by tensor model parallel size ({})'.format(
args.world_size, args.tensor_model_parallel_size)
# Pipeline model parallel size.
args.pipeline_model_parallel_size = min(
args.pipeline_model_parallel_size,
(args.world_size // args.tensor_model_parallel_size))
args.transformer_pipeline_model_parallel_size = (
args.pipeline_model_parallel_size - 1
if args.standalone_embedding_stage else
args.pipeline_model_parallel_size
)
# Checks.
model_parallel_size = args.pipeline_model_parallel_size * \
args.tensor_model_parallel_size
assert args.world_size % model_parallel_size == 0, 'world size ({}) is not'\
' divisible by tensor parallel size ({}) times pipeline parallel ' \
'size ({})'.format(args.world_size, args.tensor_model_parallel_size,
args.pipeline_model_parallel_size)
args.data_parallel_size = args.world_size // model_parallel_size
if args.rank == 0:
print('using world size: {}, data-parallel-size: {}, '
'tensor-model-parallel size: {}, '
'pipeline-model-parallel size: {} '.format(
args.world_size, args.data_parallel_size,
args.tensor_model_parallel_size,
args.pipeline_model_parallel_size), flush=True)
if args.pipeline_model_parallel_size > 1:
if args.pipeline_model_parallel_split_rank is not None:
assert args.pipeline_model_parallel_split_rank < \
args.pipeline_model_parallel_size, 'split rank needs'\
' to be less than pipeline model parallel size ({})'.format(
args.pipeline_model_parallel_size)
# Deprecated arguments
assert args.batch_size is None, '--batch-size argument is no longer ' \
'valid, use --micro-batch-size instead'
del args.batch_size
assert args.warmup is None, '--warmup argument is no longer valid, use ' \
'--lr-warmup-fraction instead'
del args.warmup
assert args.model_parallel_size is None, '--model-parallel-size is no ' \
'longer valid, use --tensor-model-parallel-size instead'
del args.model_parallel_size
if args.checkpoint_activations:
if args.rank == 0:
print('--checkpoint-activations is no longer valid, use --recompute-activations, '
'or, for more control, --recompute-granularity and --recompute-method.')
exit()
del args.checkpoint_activations
if args.recompute_activations:
args.recompute_granularity = 'selective'
del args.recompute_activations
# Set input defaults.
for key in defaults:
# For default to be valid, it should not be provided in the
# arguments that are passed to the program. We check this by
# ensuring the arg is set to None.
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=defaults[key],
v2=getattr(args, key)),
flush=True)
else:
setattr(args, key, defaults[key])
# Batch size.
assert args.micro_batch_size is not None
assert args.micro_batch_size > 0
if args.global_batch_size is None:
args.global_batch_size = args.micro_batch_size * args.data_parallel_size
if args.rank == 0:
print('setting global batch size to {}'.format(
args.global_batch_size), flush=True)
assert args.global_batch_size > 0
if args.num_layers_per_virtual_pipeline_stage is not None:
assert args.pipeline_model_parallel_size > 2, \
'pipeline-model-parallel size should be greater than 2 with ' \
'interleaved schedule'
assert args.num_layers % args.num_layers_per_virtual_pipeline_stage == 0, \
'number of layers is not divisible by number of layers per virtual ' \
'pipeline stage'
args.virtual_pipeline_model_parallel_size = \
(args.num_layers // args.transformer_pipeline_model_parallel_size) // \
args.num_layers_per_virtual_pipeline_stage
else:
args.virtual_pipeline_model_parallel_size = None
# Parameters dtype.
args.params_dtype = torch.float
if args.fp16:
assert not args.bf16
args.params_dtype = torch.half
if args.bf16:
assert not args.fp16
args.params_dtype = torch.bfloat16
# bfloat16 requires gradient accumulation and all-reduce to
# be done in fp32.
if not args.accumulate_allreduce_grads_in_fp32:
args.accumulate_allreduce_grads_in_fp32 = True
if args.rank == 0:
print('accumulate and all-reduce gradients in fp32 for '
'bfloat16 data type.', flush=True)
if args.rank == 0:
print('using {} for parameters ...'.format(args.params_dtype),
flush=True)
# Overlapping grad reduce only supported without pipeline parallelism right now.
if args.overlap_grad_reduce:
assert args.pipeline_model_parallel_size == 1
if args.dataloader_type is None:
args.dataloader_type = 'single'
# Consumed tokens.
args.consumed_train_samples = 0
args.consumed_valid_samples = 0
# Support for variable sequence lengths across batches/microbatches.
# set it if the dataloader supports generation of variable sequence lengths
# across batches/microbatches. Due to additional communication overhead
# during pipeline parallelism, it should not be set if sequence length
# is constant during training.
args.variable_seq_lengths = False
# Iteration-based training.
if args.train_iters:
# If we use iteration-based training, make sure the
# sample-based options are off.
assert args.train_samples is None, \
'expected iteration-based training'
assert args.lr_decay_samples is None, \
'expected iteration-based learning rate decay'
assert args.lr_warmup_samples == 0, \
'expected iteration-based learning rate warmup'
assert args.rampup_batch_size is None, \
'expected no batch-size rampup for iteration-based training'
if args.lr_warmup_fraction is not None:
assert args.lr_warmup_iters == 0, \
'can only specify one of lr-warmup-fraction and lr-warmup-iters'
# Sample-based training.
if args.train_samples:
# If we use sample-based training, make sure the
# iteration-based options are off.
assert args.train_iters is None, \
'expected sample-based training'
assert args.lr_decay_iters is None, \
'expected sample-based learning rate decay'
assert args.lr_warmup_iters == 0, \
'expected sample-based learnig rate warmup'
if args.lr_warmup_fraction is not None:
assert args.lr_warmup_samples == 0, \
'can only specify one of lr-warmup-fraction ' \
'and lr-warmup-samples'
if args.num_layers is not None:
assert args.encoder_num_layers is None, \
'cannot have both num-layers and encoder-num-layers specified'
args.encoder_num_layers = args.num_layers
else:
assert args.encoder_num_layers is not None, \
'either num-layers or encoder-num-layers should be specified'
args.num_layers = args.encoder_num_layers
# Check required arguments.
required_args = ['num_layers', 'hidden_size', 'num_attention_heads',
'max_position_embeddings']
for req_arg in required_args:
_check_arg_is_not_none(args, req_arg)
# Checks.
if args.ffn_hidden_size is None:
if args.swiglu:
# reduce the dimnesion for MLP since projections happens on
# two linear layers. this keeps the number of paramters in
# the same ballpark as the counterpart with 4*h size
# we keep it a multiple of 64, which means the actual tensor size
# will be a multiple of 64 / tp_size
args.ffn_hidden_size = int((4 * args.hidden_size * 2 / 3) / 64) * 64
else:
args.ffn_hidden_size = 4 * args.hidden_size
if args.kv_channels is None:
assert args.hidden_size % args.num_attention_heads == 0
args.kv_channels = args.hidden_size // args.num_attention_heads
if args.seq_length is not None:
assert args.encoder_seq_length is None
args.encoder_seq_length = args.seq_length
else:
assert args.encoder_seq_length is not None
args.seq_length = args.encoder_seq_length
if args.seq_length is not None:
assert args.max_position_embeddings >= args.seq_length
if args.decoder_seq_length is not None:
assert args.max_position_embeddings >= args.decoder_seq_length
if args.lr is not None:
assert args.min_lr <= args.lr
if args.save is not None:
assert args.save_interval is not None
# Mixed precision checks.
if args.fp16_lm_cross_entropy:
assert args.fp16, 'lm cross entropy in fp16 only support in fp16 mode.'
if args.fp32_residual_connection:
assert args.fp16 or args.bf16, \
'residual connection in fp32 only supported when using fp16 or bf16.'
if args.weight_decay_incr_style == 'constant':
assert args.start_weight_decay is None
assert args.end_weight_decay is None
args.start_weight_decay = args.weight_decay
args.end_weight_decay = args.weight_decay
else:
assert args.start_weight_decay is not None
assert args.end_weight_decay is not None
TORCH_MAJOR = int(torch.__version__.split('.')[0])
TORCH_MINOR = int(torch.__version__.split('.')[1])
# Persistent fused layer norm.
if TORCH_MAJOR < 1 or (TORCH_MAJOR == 1 and TORCH_MINOR < 11):
args.no_persist_layer_norm = True
if args.rank == 0:
print('Persistent fused layer norm kernel is supported from '
'pytorch v1.11 (nvidia pytorch container paired with v1.11). '
'Defaulting to no_persist_layer_norm=True')
# Activation recomputing.
if args.distribute_saved_activations:
assert args.tensor_model_parallel_size > 1, 'can distribute ' \
'recomputed activations only across tensor model ' \
'parallel groups'
assert args.recompute_granularity == 'full', \
'distributed recompute activations is only '\
'application to full recompute granularity'
assert args.recompute_method is not None, \
'for distributed recompute activations to work you '\
'need to use a recompute method '
assert (TORCH_MAJOR, TORCH_MINOR) >= (1, 10), \
'distributed recompute activations are supported for pytorch ' \
'v1.10 and above (Nvidia Pytorch container >= 21.07). Current ' \
'pytorch version is v%s.%s.' % (TORCH_MAJOR, TORCH_MINOR)
if args.recompute_granularity == 'selective':
assert args.recompute_method is None, \
'recompute method is not yet supported for ' \
'selective recomputing granularity'
# disable sequence parallelism when tp=1
# to avoid change in numerics when
# sequence_parallelism is enabled.
if args.tensor_model_parallel_size == 1:
args.sequence_parallel = False
# disable async_tensor_model_parallel_allreduce when
# model parallel memory optimization is enabled
if args.sequence_parallel:
args.async_tensor_model_parallel_allreduce = False
if os.environ.get('CUDA_DEVICE_MAX_CONNECTIONS') != "1":
if args.sequence_parallel:
raise RuntimeError(
"Using sequence parallelism requires setting the environment variable "
"CUDA_DEVICE_MAX_CONNECTIONS to 1")
if args.async_tensor_model_parallel_allreduce:
raise RuntimeError(
"Using async gradient all reduce requires setting the environment "
"variable CUDA_DEVICE_MAX_CONNECTIONS to 1")
# Disable bias gelu fusion if we are disabling bias altogether
if not args.add_bias_linear:
args.bias_gelu_fusion = False
# Retro checks.
if args.retro_add_retriever:
# Sequence parallelism unsupported.
assert not args.sequence_parallel, \
"retro currently does not support sequence parallelism."
# Pipeline parallelism unsupported.
assert args.pipeline_model_parallel_size == 1, \
"retro currently does not support pipeline parallelism."
# Load retro args.
retro_args_path = get_retro_args_path(args.retro_workdir)
assert os.path.exists(retro_args_path), "retro workdir missing args.json"
with open(retro_args_path) as f:
retro_args = types.SimpleNamespace(**json.load(f))
retro_args.retro_return_doc_ids = args.retro_return_doc_ids
retro_args.retro_gpt_retrieved_length = \
args.retro_num_retrieved_chunks * \
retro_args.retro_gpt_chunk_length
set_retro_args(retro_args)
# Legacy RoPE arguments
if args.use_rotary_position_embeddings:
args.position_embedding_type = 'rope'
# Would just need to add 'NoPE' as a position_embedding_type to support this, but for now
# don't allow it to keep things simple
if not args.add_position_embedding and args.position_embedding_type != 'rope':
raise RuntimeError('--no-position-embedding is deprecated, use --position-embedding-type')
# Print arguments.
_print_args("arguments", args)
retro_args = get_retro_args()
if retro_args and args != retro_args:
_print_args("retro arguments", types.SimpleNamespace(**{k:v for k,v in vars(retro_args).items() if k.startswith("retro")}, rank=args.rank))
return args
def _print_args(title, args):
"""Print arguments."""
if args.rank == 0:
print(f'------------------------ {title} ------------------------',
flush=True)
str_list = []
for arg in vars(args):
dots = '.' * (48 - len(arg))
str_list.append(' {} {} {}'.format(arg, dots, getattr(args, arg)))
for arg in sorted(str_list, key=lambda x: x.lower()):
print(arg, flush=True)
print(f'-------------------- end of {title} ---------------------',
flush=True)
def _check_arg_is_not_none(args, arg):
assert getattr(args, arg) is not None, '{} argument is None'.format(arg)
def core_transformer_config_from_args(args):
# Translate args to core transformer configuration
kw_args = {}
for f in dataclasses.fields(TransformerConfig):
if hasattr(args, f.name):
kw_args[f.name] = getattr(args, f.name)
kw_args['persist_layer_norm'] = not args.no_persist_layer_norm
kw_args['layernorm_zero_centered_gamma'] = args.apply_layernorm_1p
kw_args['layernorm_epsilon'] = args.norm_epsilon
kw_args['deallocate_pipeline_outputs'] = True
kw_args['pipeline_dtype'] = args.params_dtype
kw_args['batch_p2p_comm'] = not args.overlap_p2p_comm
if args.swiglu:
kw_args['activation_func'] = F.silu
kw_args['gated_linear_unit'] = True
kw_args['bias_gelu_fusion'] = False
if args.init_method_xavier_uniform:
kw_args['init_method'] = torch.nn.init.xavier_uniform_
kw_args['scaled_init_method'] = torch.nn.init.xavier_uniform_
if args.group_query_attention:
kw_args['num_query_groups'] = args.num_query_groups
else:
kw_args['num_query_groups'] = None
return TransformerConfig(**kw_args)
def _add_transformer_engine_args(parser):
group = parser.add_argument_group(title='Transformer-Engine')
group.add_argument('--fp8-format', default=None,
choices=['e4m3', 'hybrid'],
help='Which fp8 format scheme to use for FP8 tensors in the forward and backward pass',
dest='fp8')
group.add_argument('--fp8-margin', type=int, default=0,
help='Scaling margin for fp8',
dest='fp8_margin')
group.add_argument('--fp8-interval', type=int, default=1,
help='Scaling update interval for fp8',
dest='fp8_interval')
group.add_argument('--fp8-amax-history-len', type=int, default=1,
help='Number of steps for which amax history is recorded per tensor',
dest='fp8_amax_history_len')
group.add_argument('--fp8-amax-compute-algo', default='most_recent',
choices=['most_recent', 'max'],
help='Algorithm for computing amax from history',
dest='fp8_amax_compute_algo')
group.add_argument('--no-fp8-wgrad', action='store_false',
help='Execute wgrad in higher precision even for FP8 runs',
dest='fp8_wgrad')
group.add_argument('--transformer-impl', default='local',
choices=['local', 'transformer_engine'],
help='Which Transformer implementation to use.')
return parser
def _add_inference_args(parser):
group = parser.add_argument_group(title='inference')
group.add_argument('--inference-batch-times-seqlen-threshold',
type=int, default=512,
help='During inference, if batch-size times '
'sequence-length is smaller than this threshold '
'then we will not use pipelining, otherwise we will.')
group.add_argument('--max-tokens-to-oom',
type=int, default=12000,
help='Maximum number of tokens during inference'
'tokens here is # in prompt + # to generate'
'Allows us to throw an error before OOM crashes server')
group.add_argument('--output-bert-embeddings', action='store_true',
help='Output Bert embeddings (via mean pooling) from '
'model, rather than its binary head output or entire '
'hidden batch.')
group.add_argument('--bert-embedder-type', default="megatron",
choices=["megatron", "huggingface"],
help='Select either Megatron or Huggingface as the '
'Bert embedder.')
return parser
def _add_retro_args(parser):
group = parser.add_argument_group(title='retro')
group.add_argument('--retro-workdir', default=None,
help='Retro working directory, which contains the '
'preprocessed data for for pretraining. This directory '
'is built during preprocessing (see '
'tools/retro/README.md), and contains subdirectories '
'for the chunk database and pretraining neighbors.')
group.add_argument('--retro-add-retriever',
action='store_true', default=False,
help='Add a retriever to the transformer, for use in '
'pretraining a Retro model.')
group.add_argument('--retro-cyclic-train-iters', type=int, default=None,
help='Set number of training iterations for cyclic '
'Retro training.')
group.add_argument('--retro-encoder-layers', type=int, default=2,
help='Number of layers to use for the retrieval '
'encoder.')
group.add_argument('--retro-encoder-hidden-dropout',
type=float, default=0.1, help='Hidden dropout for '
'retrieval encoder.')
group.add_argument('--retro-encoder-attention-dropout',
type=float, default=0.1, help='Attention dropout for '
'retrieval encoder.')
group.add_argument("--retro-num-neighbors", type=int, default=2,
help='Number of neighbors to retrieve during '
'pretraining.')
group.add_argument("--retro-num-retrieved-chunks", type=int, default=2,
help='Number of chunks to retrieve from the retrieval '
'database.')
group.add_argument("--retro-return-doc-ids", action="store_true",
help="Turn this on when preprocessing retro data.")
# Enforce argument naming convention.
for action in group._group_actions:
prefix = action.dest.split("_")[0]
assert prefix == "retro", \
"Retro args must be prefixed with '--retro-*', for consistent " \
"styling. Please fix '%s'." % ", ".join(action.option_strings)
return parser
def _add_network_size_args(parser):
group = parser.add_argument_group(title='network size')
group.add_argument('--num-layers', type=int, default=None,
help='Number of transformer layers.')
group.add_argument('--encoder-num-layers', type=int, default=None,
help='Number of encoder transformer layers.')
group.add_argument('--decoder-num-layers', type=int, default=None,
help='Number of decoder transformer layers.')
group.add_argument('--hidden-size', type=int, default=None,
help='Tansformer hidden size.')
group.add_argument('--ffn-hidden-size', type=int, default=None,
help='Transformer Feed-Forward Network hidden size. '
'This is set to 4*hidden-size if not provided')
group.add_argument('--num-attention-heads', type=int, default=None,
help='Number of transformer attention heads.')
group.add_argument('--kv-channels', type=int, default=None,
help='Projection weights dimension in multi-head '
'attention. This is set to '
' args.hidden_size // args.num_attention_heads '
'if not provided.')
group.add_argument('--group-query-attention', action='store_true',
help='Use group-query attention.')
group.add_argument('--num-query-groups', type=int, default=1)
group.add_argument('--max-position-embeddings', type=int, default=None,
help='Maximum number of position embeddings to use. '
'This is the size of position embedding.')
group.add_argument('--position-embedding-type', type=str, default='learned_absolute',
choices=['learned_absolute', 'rope'],
help='Position embedding type.')
group.add_argument('--use-rotary-position-embeddings', action='store_true',
help='Use rotary positional embeddings or not. '
'Deprecated: use --position-embedding-type')
group.add_argument('--rotary-percent', type=float, default=1.0,
help='Percent of rotary dimension to use, default 100%%')
group.add_argument('--rotary-seq-len-interpolation-factor', type=int, default=None,
help='Sequence length interpolation factor for rotary embeddings.')
group.add_argument('--no-position-embedding',
action='store_false',
help='Disable position embedding. Deprecated: use --position-embedding-type',
dest='add_position_embedding')
group.add_argument('--make-vocab-size-divisible-by', type=int, default=128,
help='Pad the vocab size to be divisible by this value.'
'This is added for computational efficieny reasons.')
group.add_argument('--normalization', default='LayerNorm',
choices=['LayerNorm', 'RMSNorm'],
help='Which normalization technique to use.')
group.add_argument('--norm-epsilon', type=float, default=1e-5,
help='Epsilon for layer norm and RMS norm.')
group.add_argument('--apply-layernorm-1p', action='store_true',
help='Adjust LayerNorm weights such that they are centered '
'around zero. This improves numerical stability.')
group.add_argument('--apply-residual-connection-post-layernorm',
action='store_true',
help='If set, use original BERT residula connection '
'ordering.')
group.add_argument('--openai-gelu', action='store_true',
help='Use OpenAIs GeLU implementation. This option'
'should not be used unless for backward compatibility'
'reasons.')
group.add_argument('--squared-relu', action='store_true',
help='Use squared relu activation instead of default gelu')
group.add_argument('--swiglu', action='store_true',
help='Use gated linear units and SiLU activation instead of default gelu')
group.add_argument('--onnx-safe', type=bool, required=False,
help='Use workarounds for known problems with '
'Torch ONNX exporter')
group.add_argument('--bert-no-binary-head', action='store_false',
help='Disable BERT binary head.',
dest='bert_binary_head')
group.add_argument('--num-experts', type=int, default=None,
help='Number of Experts in Switch Transformer (None means no Switch)')
group.add_argument('--untie-embeddings-and-output-weights', action='store_true',
help='Untie embeddings and output weights.'),
group.add_argument('--embedding-weights-in-fp32', action='store_true',
help='Cast word embedding weights to fp32 before embedding fwd.'),
return parser
def _add_logging_args(parser):
group = parser.add_argument_group(title='logging')
group.add_argument('--log-params-norm', action='store_true',
help='If set, calculate and log parameters norm.')
group.add_argument('--log-num-zeros-in-grad', action='store_true',
help='If set, calculate and log the number of zeros in gradient.')
group.add_argument('--timing-log-level', type=int,
default=0, choices=range(0,3),
help='Granularity level to measure and report timing. '
' 0: report only iteration time and make sure timing '
' does not introduce extra overhead.'
' 1: report timing for operations that are executed '
' very limited times (basically once) during '
' each iteration (such as gradient all-reduce) '
' 2: report timing for operations that migh be '
' executed numerous times during each iteration. '
'Note that setting the level to 1 or 2 might '
'cause increase in iteration time.')
group.add_argument('--no-barrier-with-level-1-timing', action='store_false',
help='If not set, use barrier with level 1 time '
'measurements. Note that this is up to the user '
'to make sure calling barrier with their timers '
'will not result in hangs. This can happen if for '
'example the user adds a level 1 timer that is not '
'called by all ranks.',
dest='barrier_with_L1_time')
group.add_argument('--timing-log-option', type=str, default='minmax',
choices=['max', 'minmax', 'all'],
help='Options for logging timing:'
' max: report the max timing across all ranks'
' minmax: report min and max timings across all ranks'
' all: report timings of all ranks.')
group.add_argument('--tensorboard-log-interval', type=int, default=1,
help='Report to tensorboard interval.')
group.add_argument('--tensorboard-queue-size', type=int, default=1000,
help='Size of the tensorboard queue for pending events '
'and summaries before one of the ‘add’ calls forces a '
'flush to disk.')
group.add_argument('--log-timers-to-tensorboard', action='store_true',
help='If set, write timers to tensorboard.')
group.add_argument('--log-batch-size-to-tensorboard', action='store_true',
help='If set, write batch-size to tensorboard.')
group.add_argument('--no-log-learnig-rate-to-tensorboard',
action='store_false',
help='Disable learning rate logging to tensorboard.',
dest='log_learning_rate_to_tensorboard')
group.add_argument('--no-log-loss-scale-to-tensorboard',
action='store_false',
help='Disable loss-scale logging to tensorboard.',
dest='log_loss_scale_to_tensorboard')
group.add_argument('--log-validation-ppl-to-tensorboard',
action='store_true',
help='If set, write validation perplexity to '
'tensorboard.')
group.add_argument('--log-memory-to-tensorboard',
action='store_true',
help='Enable memory logging to tensorboard.')
group.add_argument('--log-world-size-to-tensorboard',
action='store_true',
help='Enable world size logging to tensorboard.')
return parser
def _add_regularization_args(parser):
group = parser.add_argument_group(title='regularization')
group.add_argument('--attention-dropout', type=float, default=0.1,
help='Post attention dropout probability.')
group.add_argument('--hidden-dropout', type=float, default=0.1,
help='Dropout probability for hidden state transformer.')
group.add_argument('--weight-decay', type=float, default=0.01,
help='Weight decay coefficient for L2 regularization.')
group.add_argument('--start-weight-decay', type=float,
help='Initial weight decay coefficient for L2 regularization.')
group.add_argument('--end-weight-decay', type=float,
help='End of run weight decay coefficient for L2 regularization.')
group.add_argument('--weight-decay-incr-style', type=str, default='constant',
choices=['constant', 'linear', 'cosine'],
help='Weight decay increment function.')
group.add_argument('--clip-grad', type=float, default=1.0,
help='Gradient clipping based on global L2 norm.')
group.add_argument('--adam-beta1', type=float, default=0.9,
help='First coefficient for computing running averages '
'of gradient and its square')
group.add_argument('--adam-beta2', type=float, default=0.999,
help='Second coefficient for computing running averages '
'of gradient and its square')
group.add_argument('--adam-eps', type=float, default=1e-08,
help='Term added to the denominator to improve'
'numerical stability')
group.add_argument('--sgd-momentum', type=float, default=0.9,
help='Momentum factor for sgd')
return parser
def _add_training_args(parser):
group = parser.add_argument_group(title='training')
group.add_argument('--micro-batch-size', type=int, default=None,
help='Batch size per model instance (local batch size). '
'Global batch size is local batch size times data '
'parallel size times number of micro batches.')
group.add_argument('--batch-size', type=int, default=None,
help='Old batch size parameter, do not use. '
'Use --micro-batch-size instead')
group.add_argument('--global-batch-size', type=int, default=None,
help='Training batch size. If set, it should be a '
'multiple of micro-batch-size times data-parallel-size. '
'If this value is None, then '
'use micro-batch-size * data-parallel-size as the '
'global batch size. This choice will result in 1 for '
'number of micro-batches.')
group.add_argument('--rampup-batch-size', nargs='*', default=None,
help='Batch size ramp up with the following values:'
' --rampup-batch-size <start batch size> '
' <batch size incerement> '
' <ramp-up samples> '
'For example:'
' --rampup-batch-size 16 8 300000 \ '
' --global-batch-size 1024'
'will start with global batch size 16 and over '
' (1024 - 16) / 8 = 126 intervals will increase'
'the batch size linearly to 1024. In each interval'
'we will use approximately 300000 / 126 = 2380 samples.')
group.add_argument('--recompute-activations', action='store_true',
help='recompute activation to allow for training '
'with larger models, sequences, and batch sizes.')
group.add_argument('--recompute-granularity', type=str, default=None,
choices=['full', 'selective'],
help='Checkpoint activations to allow for training '
'with larger models, sequences, and batch sizes. '
'It is supported at two granularities 1) full: '
'whole transformer layer is recomputed, '
'2) selective: core attention part of the transformer '
'layer is recomputed.')
group.add_argument('--no-check-for-nan-in-loss-and-grad', action='store_false',
help='Check for NaNs in loss and grad',
dest='check_for_nan_in_loss_and_grad')
group.add_argument('--distribute-saved-activations',
action='store_true',
help='If set, distribute recomputed activations '
'across model parallel group.')
group.add_argument('--recompute-method', type=str, default=None,
choices=['uniform', 'block'],
help='1) uniform: uniformly divide the total number of '
'Transformer layers and recompute the input activation of '
'each divided chunk at specified granularity, '
'2) recompute the input activations of only a set number of '
'individual Transformer layers per pipeline stage and do the '
'rest without any recomputing at specified granularity'
'default) do not apply activations recompute to any layers')
group.add_argument('--recompute-num-layers', type=int, default=None,
help='1) uniform: the number of Transformer layers in each '
'uniformly divided recompute unit, '
'2) block: the number of individual Transformer layers '
'to recompute within each pipeline stage.')
group.add_argument('--profile', action='store_true',
help='Enable nsys profiling. When using this option, nsys '
'options should be specified in commandline. An example '
'nsys commandline is `nsys profile -s none -t nvtx,cuda '
'-o <path/to/output_file> --force-overwrite true '
'--capture-range=cudaProfilerApi '
'--capture-range-end=stop`.')
group.add_argument('--profile-step-start', type=int, default=10,
help='Gloable step to start profiling.')
group.add_argument('--profile-step-end', type=int, default=12,
help='Gloable step to stop profiling.')
group.add_argument('--profile-ranks', nargs='+', type=int, default=[0],
help='Global ranks to profile.')
# deprecated
group.add_argument('--checkpoint-activations', action='store_true',
help='Checkpoint activation to allow for training '
'with larger models, sequences, and batch sizes.')
group.add_argument('--train-iters', type=int, default=None,
help='Total number of iterations to train over all '
'training runs. Note that either train-iters or '
'train-samples should be provided.')
group.add_argument('--train-samples', type=int, default=None,
help='Total number of samples to train over all '
'training runs. Note that either train-iters or '
'train-samples should be provided.')
group.add_argument('--log-interval', type=int, default=100,
help='Report loss and timing interval.')
group.add_argument('--exit-interval', type=int, default=None,
help='Exit the program after the iteration is divisible '
'by this value.')
group.add_argument('--exit-duration-in-mins', type=int, default=None,
help='Exit the program after this many minutes.')
group.add_argument('--exit-signal-handler', action='store_true',
help='Dynamically save the checkpoint and shutdown the '
'training if SIGTERM is received')
group.add_argument('--tensorboard-dir', type=str, default=None,
help='Write TensorBoard logs to this directory.')
group.add_argument('--no-masked-softmax-fusion',
action='store_false',
help='Disable fusion of query_key_value scaling, '
'masking, and softmax.',
dest='masked_softmax_fusion')
group.add_argument('--no-bias-gelu-fusion', action='store_false',
help='Disable bias and gelu fusion.',
dest='bias_gelu_fusion')
group.add_argument('--no-bias-dropout-fusion', action='store_false',
help='Disable bias and dropout fusion.',
dest='bias_dropout_fusion')
group.add_argument('--use-flash-attn', action='store_true',
help='use FlashAttention implementation of attention. '
'https://arxiv.org/abs/2205.14135')
group.add_argument('--disable-bias-linear', action='store_false',
help='Disable bias in the linear layers',
dest='add_bias_linear')
group.add_argument('--optimizer', type=str, default='adam',
choices=['adam', 'sgd'],
help='Optimizer function')
group.add_argument('--dataloader-type', type=str, default=None,
choices=['single', 'cyclic'],
help='Single pass vs multiple pass data loader')
group.add_argument('--no-async-tensor-model-parallel-allreduce',
action='store_false',
help='Disable asynchronous execution of '
'tensor-model-parallel all-reduce with weight '
'gradient compuation of a column-linear layer.',
dest='async_tensor_model_parallel_allreduce')
group.add_argument('--no-persist-layer-norm', action='store_true',
help='Disable using persistent fused layer norm kernel. '
'This kernel supports only a set of hidden sizes. Please '
'check persist_ln_hidden_sizes if your hidden '
'size is supported.')
group.add_argument('--sequence-parallel', action='store_true',
help='Enable sequence parallel optimization.')
group.add_argument('--no-gradient-accumulation-fusion',
action='store_false',
help='Disable fusing gradient accumulation to weight '
'gradient computation of linear layers',
dest='gradient_accumulation_fusion')
return parser
def _add_initialization_args(parser):
group = parser.add_argument_group(title='initialization')
group.add_argument('--seed', type=int, default=1234,
help='Random seed used for python, numpy, '
'pytorch, and cuda.')
group.add_argument('--data-parallel-random-init', action='store_true',
help='Enable random initialization of params '
'across data parallel ranks')
group.add_argument('--init-method-std', type=float, default=0.02,
help='Standard deviation of the zero mean normal '
'distribution used for weight initialization.')
group.add_argument('--init-method-xavier-uniform', action='store_true',
help='Enable Xavier uniform parameter initialization')
return parser
def _add_learning_rate_args(parser):
group = parser.add_argument_group(title='learning rate')
group.add_argument('--lr', type=float, default=None,
help='Initial learning rate. Depending on decay style '
'and initial warmup, the learing rate at each '
'iteration would be different.')
group.add_argument('--lr-decay-style', type=str, default='linear',
choices=['constant', 'linear', 'cosine', 'inverse-square-root'],
help='Learning rate decay function.')
group.add_argument('--lr-decay-iters', type=int, default=None,
help='number of iterations to decay learning rate over,'
' If None defaults to `--train-iters`')
group.add_argument('--lr-decay-samples', type=int, default=None,
help='number of samples to decay learning rate over,'
' If None defaults to `--train-samples`')
group.add_argument('--lr-warmup-fraction', type=float, default=None,
help='fraction of lr-warmup-(iters/samples) to use '
'for warmup (as a float)')
group.add_argument('--lr-warmup-iters', type=int, default=0,
help='number of iterations to linearly warmup '
'learning rate over.')
group.add_argument('--lr-warmup-samples', type=int, default=0,
help='number of samples to linearly warmup '
'learning rate over.')
group.add_argument('--lr-warmup-init', type=float, default=0.0,
help='Initial value for learning rate warmup. The '
'scheduler starts warmup from this value.')
group.add_argument('--warmup', type=int, default=None,
help='Old lr warmup argument, do not use. Use one of the'
'--lr-warmup-* arguments above')
group.add_argument('--min-lr', type=float, default=0.0,
help='Minumum value for learning rate. The scheduler'
'clip values below this threshold.')
group.add_argument('--override-opt_param-scheduler', action='store_true',
help='Reset the values of the scheduler (learning rate,'
'warmup iterations, minimum learning rate, maximum '
'number of iterations, and decay style from input '
'arguments and ignore values from checkpoints. Note'
'that all the above values will be reset.')
group.add_argument('--use-checkpoint-opt_param-scheduler', action='store_true',
help='Use checkpoint to set the values of the scheduler '
'(learning rate, warmup iterations, minimum learning '
'rate, maximum number of iterations, and decay style '
'from checkpoint and ignore input arguments.')
return parser
def _add_checkpointing_args(parser):
group = parser.add_argument_group(title='checkpointing')
group.add_argument('--save', type=str, default=None,
help='Output directory to save checkpoints to.')
group.add_argument('--save-interval', type=int, default=None,
help='Number of iterations between checkpoint saves.')
group.add_argument('--no-save-optim', action='store_true', default=None,
help='Do not save current optimizer.')
group.add_argument('--no-save-rng', action='store_true', default=None,
help='Do not save current rng state.')
group.add_argument('--load', type=str, default=None,
help='Directory containing a model checkpoint.')
group.add_argument('--no-load-optim', action='store_true', default=None,
help='Do not load optimizer when loading checkpoint.')
group.add_argument('--no-load-rng', action='store_true', default=None,
help='Do not load rng state when loading checkpoint.')
group.add_argument('--finetune', action='store_true',
help='Load model for finetuning. Do not load optimizer '
'or rng state from checkpoint and set iteration to 0. '
'Assumed when loading a release checkpoint.')
group.add_argument('--no-initialization', action='store_false',
help='Do not perform initialization when building model, '
'can reduce startup time when definitely loading from a '
'checkpoint',
dest='perform_initialization')
group.add_argument('--use-checkpoint-args', action='store_true',
help='Override any command line arguments with arguments '
'from the checkpoint')
group.add_argument('--exit-on-missing-checkpoint', action='store_true',
help="If '--load' is set, but checkpoint is not found "
"(e.g., path typo), then exit instead of random "
"initialization.")
return parser
def _add_mixed_precision_args(parser):
group = parser.add_argument_group(title='mixed precision')
group.add_argument('--fp16', action='store_true',
help='Run model in fp16 mode.')
group.add_argument('--bf16', action='store_true',
help='Run model in bfloat16 mode.')
group.add_argument('--loss-scale', type=float, default=None,
help='Static loss scaling, positive power of 2 '
'values can improve fp16 convergence. If None, dynamic'
'loss scaling is used.')
group.add_argument('--initial-loss-scale', type=float, default=2**32,
help='Initial loss-scale for dynamic loss scaling.')
group.add_argument('--min-loss-scale', type=float, default=1.0,
help='Minimum loss scale for dynamic loss scale.')
group.add_argument('--loss-scale-window', type=float, default=1000,
help='Window over which to raise/lower dynamic scale.')
group.add_argument('--hysteresis', type=int, default=2,
help='hysteresis for dynamic loss scaling')
group.add_argument('--fp32-residual-connection', action='store_true',
help='Move residual connections to fp32.')
group.add_argument('--no-query-key-layer-scaling', action='store_false',
help='Do not scale Q * K^T by 1 / layer-number.',
dest='apply_query_key_layer_scaling')
group.add_argument('--attention-softmax-in-fp32', action='store_true',
help='Run attention masking and softmax in fp32. '
'This flag is ignored unless '
'--no-query-key-layer-scaling is specified.')
group.add_argument('--accumulate-allreduce-grads-in-fp32',
action='store_true',
help='Gradient accumulation and all-reduce in fp32.')
group.add_argument('--fp16-lm-cross-entropy', action='store_true',
help='Move the cross entropy unreduced loss calculation'
'for lm head to fp16.')
return parser
def _add_distributed_args(parser):
group = parser.add_argument_group(title='distributed')
group.add_argument('--tensor-model-parallel-size', type=int, default=1,
help='Degree of tensor model parallelism.')
group.add_argument('--pipeline-model-parallel-size', type=int, default=1,
help='Degree of pipeline model parallelism.')
group.add_argument('--pipeline-model-parallel-split-rank',
type=int, default=None,
help='Rank where encoder and decoder should be split.')
group.add_argument('--model-parallel-size', type=int, default=None,
help='Old model parallel argument, do not use. Use '
'--tensor-model-parallel-size instead.')
group.add_argument('--num-layers-per-virtual-pipeline-stage', type=int, default=None,
help='Number of layers per virtual pipeline stage')
group.add_argument('--overlap-p2p-communication',
action='store_true',
help='overlap pipeline parallel communication with forward and backward chunks',
dest='overlap_p2p_comm')
group.add_argument('--distributed-backend', default='nccl',
choices=['nccl', 'gloo'],
help='Which backend to use for distributed training.')
group.add_argument('--distributed-timeout-minutes', type=int, default=10,
help='Timeout minutes for torch.distributed.')
group.add_argument('--overlap-grad-reduce', action='store_true',
default=False, help='If set, overlap DDP grad reduce.')
group.add_argument('--no-scatter-gather-tensors-in-pipeline', action='store_false',
help='Use scatter/gather to optimize communication of tensors in pipeline',
dest='scatter_gather_tensors_in_pipeline')
group.add_argument('--use-ring-exchange-p2p', action='store_true',
default=False, help='If set, use custom-built ring exchange '
'for p2p communications. Note that this option will require '
'a custom built image that support ring-exchange p2p.')
group.add_argument('--local_rank', type=int, default=None,
help='local rank passed from distributed launcher.')
group.add_argument('--lazy-mpu-init', type=bool, required=False,
help='If set to True, initialize_megatron() '
'skips DDP initialization and returns function to '
'complete it instead.Also turns on '
'--use-cpu-initialization flag. This is for '
'external DDP manager.' )
group.add_argument('--use-cpu-initialization', action='store_true',
default=None, help='If set, affine parallel weights '
'initialization uses CPU' )
group.add_argument('--empty-unused-memory-level', default=0, type=int,
choices=[0, 1, 2],
help='Call torch.cuda.empty_cache() each iteration '
'(training and eval), to reduce fragmentation.'
'0=off, 1=moderate, 2=aggressive.')
group.add_argument('--standalone-embedding-stage', action='store_true',
default=False, help='If set, *input* embedding layer '
'is placed on its own pipeline stage, without any '
'transformer layers. (For T5, this flag currently only '
'affects the encoder embedding.)')
group.add_argument('--use-distributed-optimizer', action='store_true',
help='Use distributed optimizer.')
return parser
def _add_validation_args(parser):
group = parser.add_argument_group(title='validation')
group.add_argument('--eval-iters', type=int, default=100,
help='Number of iterations to run for evaluation'
'validation/test for.')
group.add_argument('--eval-interval', type=int, default=1000,
help='Interval between running evaluation on '
'validation set.')
group.add_argument('--skip-train', action='store_true',
default=False, help='If set, bypass the training loop, '
'optionally do evaluation for validation/test, and exit.')
return parser
def _add_data_args(parser):
group = parser.add_argument_group(title='data and dataloader')
group.add_argument('--data-path', nargs='*', default=None,
help='Path to the training dataset. Accepted format:'
'1) a single data path, 2) multiple datasets in the'
'form: dataset1-weight dataset1-path dataset2-weight '
'dataset2-path ... It is used with --split when a '
'single dataset used for all three: train, valid '
'and test. It is exclusive to the other '
'--*-data-path args')
group.add_argument('--split', type=str, default='969, 30, 1',
help='Comma-separated list of proportions for training,'
' validation, and test split. For example the split '
'`90,5,5` will use 90%% of data for training, 5%% for '
'validation and 5%% for test.')
group.add_argument('--train-data-path', nargs='*', default=None,
help='Path to the training dataset. Accepted format:'
'1) a single data path, 2) multiple datasets in the'
'form: dataset1-weight dataset1-path dataset2-weight '
'dataset2-path ...')
group.add_argument('--valid-data-path', nargs='*', default=None,
help='Path to the validation dataset. Accepted format:'
'1) a single data path, 2) multiple datasets in the'
'form: dataset1-weight dataset1-path dataset2-weight '
'dataset2-path ...')
group.add_argument('--test-data-path', nargs='*', default=None,
help='Path to the test dataset. Accepted format:'
'1) a single data path, 2) multiple datasets in the'
'form: dataset1-weight dataset1-path dataset2-weight '
'dataset2-path ...')
group.add_argument('--data-cache-path', default=None,
help='Path to a directory to hold cached index files.')
group.add_argument('--vocab-size', type=int, default=None,
help='Size of vocab before EOD or padding.')
group.add_argument('--vocab-file', type=str, default=None,
help='Path to the vocab file.')
group.add_argument('--merge-file', type=str, default=None,
help='Path to the BPE merge file.')
group.add_argument('--vocab-extra-ids', type=int, default=0,
help='Number of additional vocabulary tokens. '
'They are used for span masking in the T5 model')
group.add_argument('--seq-length', type=int, default=None,
help='Maximum sequence length to process.')
group.add_argument('--encoder-seq-length', type=int, default=None,
help='Maximum encoder sequence length to process.'
'This should be exclusive of --seq-length')
group.add_argument('--decoder-seq-length', type=int, default=None,
help="Maximum decoder sequence length to process.")
group.add_argument('--retriever-seq-length', type=int, default=256,
help='Maximum sequence length for the biencoder model '
'for retriever')
group.add_argument('--sample-rate', type=float, default=1.0,
help='sample rate for training data. Supposed to be 0 '
' < sample_rate < 1')
group.add_argument('--mask-prob', type=float, default=0.15,
help='Probability of replacing a token with mask.')
group.add_argument('--short-seq-prob', type=float, default=0.1,
help='Probability of producing a short sequence.')
group.add_argument('--mmap-warmup', action='store_true',
help='Warm up mmap files.')
group.add_argument('--num-workers', type=int, default=2,
help="Dataloader number of workers.")
group.add_argument('--tokenizer-type', type=str,
default=None,
choices=['BertWordPieceLowerCase',
'BertWordPieceCase',
'GPT2BPETokenizer',
'SentencePieceTokenizer',
'GPTSentencePieceTokenizer',
'Llama2Tokenizer',
'NullTokenizer'],
help='What type of tokenizer to use.')
group.add_argument('--tokenizer-model', type=str, default=None,
help='Sentencepiece tokenizer model.')
group.add_argument('--reset-position-ids', action='store_true',
help='Reset posistion ids after end-of-document token.')
group.add_argument('--reset-attention-mask', action='store_true',
help='Reset self attention maske after '
'end-of-document token.')
group.add_argument('--eod-mask-loss', action='store_true',
help='Mask loss for the end of document tokens.')
return parser
def _add_autoresume_args(parser):
group = parser.add_argument_group(title='autoresume')
group.add_argument('--adlr-autoresume', action='store_true',
help='Enable autoresume on adlr cluster.')
group.add_argument('--adlr-autoresume-interval', type=int, default=1000,
help='Intervals over which check for autoresume'
'termination signal')
return parser
def _add_biencoder_args(parser):
group = parser.add_argument_group(title='biencoder')
# network size
group.add_argument('--ict-head-size', type=int, default=None,
help='Size of block embeddings to be used in ICT and '
'REALM (paper default: 128)')
group.add_argument('--biencoder-projection-dim', type=int, default=0,
help='Size of projection head used in biencoder (paper'
' default: 128)')
group.add_argument('--biencoder-shared-query-context-model', action='store_true',
help='Whether to share the parameters of the query '
'and context models or not')
# checkpointing
group.add_argument('--ict-load', type=str, default=None,
help='Directory containing an ICTBertModel checkpoint')
group.add_argument('--bert-load', type=str, default=None,
help='Directory containing an BertModel checkpoint '
'(needed to start ICT and REALM)')
# data
group.add_argument('--titles-data-path', type=str, default=None,
help='Path to titles dataset used for ICT')
group.add_argument('--query-in-block-prob', type=float, default=0.1,
help='Probability of keeping query in block for '
'ICT dataset')
group.add_argument('--use-one-sent-docs', action='store_true',
help='Whether to use one sentence documents in ICT')
group.add_argument('--evidence-data-path', type=str, default=None,
help='Path to Wikipedia Evidence frm DPR paper')
# training
group.add_argument('--retriever-report-topk-accuracies', nargs='+', type=int,
default=[], help="Which top-k accuracies to report "
"(e.g. '1 5 20')")
group.add_argument('--retriever-score-scaling', action='store_true',
help='Whether to scale retriever scores by inverse '
'square root of hidden size')
# faiss index
group.add_argument('--block-data-path', type=str, default=None,
help='Where to save/load BlockData to/from')
group.add_argument('--embedding-path', type=str, default=None,
help='Where to save/load Open-Retrieval Embedding'
' data to/from')
# indexer
group.add_argument('--indexer-batch-size', type=int, default=128,
help='How large of batches to use when doing indexing '
'jobs')
group.add_argument('--indexer-log-interval', type=int, default=1000,
help='After how many batches should the indexer '
'report progress')
return parser
def _add_vision_args(parser):
group = parser.add_argument_group(title="vision")
# general vision arguements
group.add_argument('--num-classes', type=int, default=1000,
help='num of classes in vision classificaiton task')
group.add_argument('--img-h', type=int, default=224,
help='Image height for vision classification task')
group.add_argument('--img-w', type=int, default=224,
help='Image height for vision classification task')
group.add_argument('--num-channels', type=int, default=3,
help='Number of channels in input image data')
group.add_argument('--patch-dim', type=int, default=16,
help='patch dimension')
group.add_argument('--classes-fraction', type=float, default=1.0,
help='training with fraction of classes.')
group.add_argument('--data-per-class-fraction', type=float, default=1.0,
help='training with fraction of data per class.')
group.add_argument('--no-data-sharding', action='store_false',
help='Disable data sharding.',
dest='data_sharding')
group.add_argument('--head-lr-mult', type=float, default=1.0,
help='learning rate multiplier for head during finetuning')
# pretraining type and backbone selection`
group.add_argument('--vision-pretraining', action='store_true',
help='flag to indicate vision pretraining')
group.add_argument('--vision-pretraining-type', type=str, default='classify',
choices=['classify', 'inpaint', 'dino'],
help='pretraining objectives')
group.add_argument('--vision-backbone-type', type=str, default='vit',
choices=['vit', 'mit', 'swin'],
help='backbone types types')
group.add_argument('--swin-backbone-type', type=str, default='tiny',
choices=['tiny', 'base', 'h3'],
help='pretraining objectives')
# inpainting arguments
group.add_argument('--mask-type', type=str, default='random',
choices=['random', 'row'],
help='mask types')
group.add_argument('--mask-factor', type=float, default=1.0,
help='mask size scaling parameter')
# dino arguments
group.add_argument('--iter-per-epoch', type=int, default=1250,
help='iterations per epoch')
group.add_argument('--dino-local-img-size', type=int, default=96,
help='Image size for vision classification task')
group.add_argument('--dino-local-crops-number', type=int, default=10,
help='Number of local crops')
group.add_argument('--dino-head-hidden-size', type=int, default=2048,
help='Hidden dimension size in dino head')
group.add_argument('--dino-bottleneck-size', type=int, default=256,
help='Bottle neck dimension in dino head ')
group.add_argument('--dino-freeze-last-layer', type=float, default=1,
help='Freezing last layer weights')
group.add_argument('--dino-norm-last-layer', action='store_true',
help='Disable Norm in last layer.')
group.add_argument('--dino-warmup-teacher-temp', type=float, default=0.04,
help='warump teacher temperature')
group.add_argument('--dino-teacher-temp', type=float, default=0.07,
help='teacher temperature')
group.add_argument('--dino-warmup-teacher-temp-epochs', type=int, default=30,
help='warmup teacher temperaure epochs')
return parser
|
Megatron-LM-master
|
megatron/arguments.py
|
import signal
import torch
def get_world_size():
if torch.distributed.is_available() and torch.distributed.is_initialized():
world_size = torch.distributed.get_world_size()
else:
world_size = 1
return world_size
def get_device(local_rank=None):
backend = torch.distributed.get_backend()
if backend == 'nccl':
if local_rank is None:
device = torch.device('cuda')
else:
device = torch.device(f'cuda:{local_rank}')
elif backend == 'gloo':
device = torch.device('cpu')
else:
raise RuntimeError
return device
def all_gather_item(item, dtype, group=None, async_op=False, local_rank=None):
if not torch.distributed.is_available() or \
not torch.distributed.is_initialized():
return [item]
device = get_device(local_rank)
if group is not None:
group_size = group.size()
else:
group_size = get_world_size()
tensor = torch.tensor([item], device=device, dtype=dtype)
output_tensors = [
torch.zeros(1, dtype=tensor.dtype, device=tensor.device)
for _ in range(group_size)
]
torch.distributed.all_gather(output_tensors, tensor, group, async_op)
output = [elem.item() for elem in output_tensors]
return output
class DistributedSignalHandler:
def __init__(self, sig=signal.SIGTERM):
self.sig = sig
def signals_received(self):
all_received = all_gather_item(
self._signal_received, dtype=torch.int32
)
return all_received
def __enter__(self):
self._signal_received = False
self.released = False
self.original_handler = signal.getsignal(self.sig)
def handler(signum, frame):
self._signal_received = True
signal.signal(self.sig, handler)
return self
def __exit__(self, type, value, tb):
self.release()
def release(self):
if self.released:
return False
signal.signal(self.sig, self.original_handler)
self.released = True
return True
|
Megatron-LM-master
|
megatron/dist_signal_handler.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import torch
from .global_vars import get_args, get_retro_args
from .global_vars import get_current_global_batch_size
from .global_vars import get_num_microbatches
from .global_vars import get_signal_handler
from .global_vars import update_num_microbatches
from .global_vars import get_tokenizer
from .global_vars import get_tensorboard_writer
from .global_vars import get_adlr_autoresume
from .global_vars import get_timers
from .initialize import initialize_megatron
from .utils import (print_rank_0,
is_last_rank,
print_rank_last)
|
Megatron-LM-master
|
megatron/__init__.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import datetime
import torch
import json
import threading
from flask import Flask, request, jsonify, current_app
from flask_restful import Resource, Api
from megatron import get_args
from megatron.text_generation import generate_and_post_process
from megatron.text_generation import beam_search_and_post_process
GENERATE_NUM = 0
BEAM_NUM = 1
lock = threading.Lock()
class MegatronGenerate(Resource):
def __init__(self, model):
self.model = model
@staticmethod
def send_do_generate():
choice = torch.cuda.LongTensor([GENERATE_NUM])
torch.distributed.broadcast(choice, 0)
@staticmethod
def send_do_beam_search():
choice = torch.cuda.LongTensor([BEAM_NUM])
torch.distributed.broadcast(choice, 0)
def put(self):
args = get_args()
if not "prompts" in request.get_json():
return "prompts argument required", 400
if "max_len" in request.get_json():
return "max_len is no longer used. Replace with tokens_to_generate", 400
if "sentences" in request.get_json():
return "sentences is no longer used. Replace with prompts", 400
prompts = request.get_json()["prompts"]
if not isinstance(prompts, list):
return "prompts is not a list of strings", 400
if len(prompts) == 0:
return "prompts is empty", 400
if len(prompts) > 128:
return "Maximum number of prompts is 128", 400
tokens_to_generate = 64 # Choosing hopefully sane default. Full sequence is slow
if "tokens_to_generate" in request.get_json():
tokens_to_generate = request.get_json()["tokens_to_generate"]
if not isinstance(tokens_to_generate, int):
return "tokens_to_generate must be an integer greater than 0"
if tokens_to_generate < 0:
return "tokens_to_generate must be an integer greater than or equal to 0"
logprobs = False
if "logprobs" in request.get_json():
logprobs = request.get_json()["logprobs"]
if not isinstance(logprobs, bool):
return "logprobs must be a boolean value"
if tokens_to_generate == 0 and not logprobs:
return "tokens_to_generate=0 implies logprobs should be True"
temperature = 1.0
if "temperature" in request.get_json():
temperature = request.get_json()["temperature"]
if not (type(temperature) == int or type(temperature) == float):
return "temperature must be a positive number less than or equal to 100.0"
if not (0.0 < temperature <= 100.0):
return "temperature must be a positive number less than or equal to 100.0"
top_k = 0.0
if "top_k" in request.get_json():
top_k = request.get_json()["top_k"]
if not (type(top_k) == int):
return "top_k must be an integer equal to or greater than 0 and less than or equal to 1000"
if not (0 <= top_k <= 1000):
return "top_k must be equal to or greater than 0 and less than or equal to 1000"
top_p = 0.0
if "top_p" in request.get_json():
top_p = request.get_json()["top_p"]
if not (type(top_p) == float):
return "top_p must be a positive float less than or equal to 1.0"
if top_p > 0.0 and top_k > 0.0:
return "cannot set both top-k and top-p samplings."
if not (0 <= top_p <= 1.0):
return "top_p must be less than or equal to 1.0"
top_p_decay = 0.0
if "top_p_decay" in request.get_json():
top_p_decay = request.get_json()["top_p_decay"]
if not (type(top_p_decay) == float):
return "top_p_decay must be a positive float less than or equal to 1.0"
if top_p == 0.0:
return "top_p_decay cannot be set without top_p"
if not (0 <= top_p_decay <= 1.0):
return "top_p_decay must be less than or equal to 1.0"
top_p_bound = 0.0
if "top_p_bound" in request.get_json():
top_p_bound = request.get_json()["top_p_bound"]
if not (type(top_p_bound) == float):
return "top_p_bound must be a positive float less than or equal to top_p"
if top_p == 0.0:
return "top_p_bound cannot be set without top_p"
if not (0.0 < top_p_bound <= top_p):
return "top_p_bound must be greater than 0 and less than top_p"
add_BOS = False
if "add_BOS" in request.get_json():
add_BOS = request.get_json()["add_BOS"]
if not isinstance(add_BOS, bool):
return "add_BOS must be a boolean value"
if any([len(prompt) == 0 for prompt in prompts]) and not add_BOS:
return "Empty prompts require add_BOS=true"
stop_on_double_eol = False
if "stop_on_double_eol" in request.get_json():
stop_on_double_eol = request.get_json()["stop_on_double_eol"]
if not isinstance(stop_on_double_eol, bool):
return "stop_on_double_eol must be a boolean value"
stop_on_eol = False
if "stop_on_eol" in request.get_json():
stop_on_eol = request.get_json()["stop_on_eol"]
if not isinstance(stop_on_eol, bool):
return "stop_on_eol must be a boolean value"
prevent_newline_after_colon = False
if "prevent_newline_after_colon" in request.get_json():
prevent_newline_after_colon = request.get_json()["prevent_newline_after_colon"]
if not isinstance(prevent_newline_after_colon, bool):
return "prevent_newline_after_colon must be a boolean value"
random_seed = -1
if "random_seed" in request.get_json():
random_seed = request.get_json()["random_seed"]
if not isinstance(random_seed, int):
return "random_seed must be integer"
if random_seed < 0:
return "random_seed must be a positive integer"
no_log = False
if "no_log" in request.get_json():
no_log = request.get_json()["no_log"]
if not isinstance(no_log, bool):
return "no_log must be a boolean value"
beam_width = None
if "beam_width" in request.get_json():
beam_width = request.get_json()["beam_width"]
if not isinstance(beam_width, int):
return "beam_width must be integer"
if beam_width < 1:
return "beam_width must be an integer > 1"
if len(prompts) > 1:
return "When doing beam_search, batch size must be 1"
stop_token=50256
if "stop_token" in request.get_json():
stop_token = request.get_json()["stop_token"]
if not isinstance(stop_token, int):
return "stop_token must be an integer"
length_penalty = 1
if "length_penalty" in request.get_json():
length_penalty = request.get_json()["length_penalty"]
if not isinstance(length_penalty, float):
return "length_penalty must be a float"
with lock: # Need to get lock to keep multiple threads from hitting code
if not no_log:
print("request IP: " + str(request.remote_addr))
print(json.dumps(request.get_json()),flush=True)
print("start time: ", datetime.datetime.now())
try:
if beam_width is not None:
MegatronGenerate.send_do_beam_search() # Tell other ranks we're doing beam_search
response, response_seg, response_scores = \
beam_search_and_post_process(
self.model,
prompts=prompts,
tokens_to_generate=tokens_to_generate,
beam_size = beam_width,
add_BOS=add_BOS,
stop_token=stop_token,
num_return_gen=beam_width, # Returning whole beam
length_penalty=length_penalty,
prevent_newline_after_colon=prevent_newline_after_colon
)
return jsonify({"text": response,
"segments": response_seg,
"scores": response_scores})
else:
MegatronGenerate.send_do_generate() # Tell other ranks we're doing generate
response, response_seg, response_logprobs, _ = \
generate_and_post_process(
self.model,
prompts=prompts,
tokens_to_generate=tokens_to_generate,
return_output_log_probs=logprobs,
top_k_sampling=top_k,
top_p_sampling=top_p,
top_p_decay=top_p_decay,
top_p_bound=top_p_bound,
temperature=temperature,
add_BOS=add_BOS,
use_eod_token_for_early_termination=True,
stop_on_double_eol=stop_on_double_eol,
stop_on_eol=stop_on_eol,
prevent_newline_after_colon=prevent_newline_after_colon,
random_seed=random_seed)
return jsonify({"text": response,
"segments": response_seg,
"logprobs": response_logprobs})
except ValueError as ve:
return ve.args[0]
print("end time: ", datetime.datetime.now())
class MegatronServer(object):
def __init__(self, model):
self.app = Flask(__name__, static_url_path='')
api = Api(self.app)
api.add_resource(MegatronGenerate, '/api', resource_class_args=[model])
def run(self, url, port):
self.app.run(url, threaded=True, debug=False, port=port)
|
Megatron-LM-master
|
megatron/text_generation_server.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Learning rate decay and weight decay incr functions."""
import math
from megatron import print_rank_0
class OptimizerParamScheduler(object):
"""Anneals learning rate and weight decay"""
def __init__(self, optimizer, init_lr, max_lr, min_lr,
lr_warmup_steps, lr_decay_steps, lr_decay_style,
start_wd, end_wd, wd_incr_steps, wd_incr_style,
use_checkpoint_opt_param_scheduler=True,
override_opt_param_scheduler=False):
# Class values.
self.optimizer = optimizer
self.init_lr = init_lr
self.max_lr = float(max_lr)
self.min_lr = min_lr
assert self.min_lr >= 0.0
assert self.max_lr >= self.min_lr
assert self.init_lr <= self.max_lr
self.lr_warmup_steps = lr_warmup_steps
self.num_steps = 0
self.lr_decay_steps = lr_decay_steps
assert self.lr_decay_steps > 0
assert self.lr_warmup_steps < self.lr_decay_steps
self.lr_decay_style = lr_decay_style
self.start_wd = start_wd
self.end_wd = end_wd
assert self.start_wd >= 0.0
assert self.end_wd >= self.start_wd
self.wd_incr_steps = wd_incr_steps
self.wd_incr_style = wd_incr_style
self.override_opt_param_scheduler = override_opt_param_scheduler
self.use_checkpoint_opt_param_scheduler = use_checkpoint_opt_param_scheduler
if self.override_opt_param_scheduler:
assert not self.use_checkpoint_opt_param_scheduler, 'both override and '\
'use-checkpoint are set.'
# Set the learning rate
self.step(0)
print_rank_0('> learning rate decay style: {}'.format(self.lr_decay_style))
def get_wd(self):
""" Weight decay incr functions"""
if self.num_steps > self.wd_incr_steps:
return self.end_wd
if self.wd_incr_style == 'constant':
assert self.start_wd == self.end_wd
return self.end_wd
incr_ratio = float(self.num_steps) / float(self.wd_incr_steps)
assert incr_ratio >= 0.0
assert incr_ratio <= 1.0
delta_wd = self.end_wd - self.start_wd
if self.wd_incr_style == 'linear':
coeff = incr_ratio
elif self.wd_incr_style == 'cosine':
coeff = 0.5 * (math.cos(math.pi * (1 - incr_ratio)) + 1.0)
else:
raise Exception('{} weight decay increment style is not supported.'.format(
self.wd_incr_style))
return self.start_wd + coeff * delta_wd
def get_lr(self):
"""Learning rate decay functions from:
https://openreview.net/pdf?id=BJYwwY9ll pg. 4"""
# Use linear warmup for the initial part.
if self.lr_warmup_steps > 0 and self.num_steps <= self.lr_warmup_steps:
return (
self.init_lr
+ (
(self.max_lr - self.init_lr)
* float(self.num_steps)
/ float(self.lr_warmup_steps)
)
)
# If the learning rate is constant, just return the initial value.
if self.lr_decay_style == 'constant':
return self.max_lr
# For any steps larger than `self.lr_decay_steps`, use `self.min_lr`.
if self.num_steps > self.lr_decay_steps:
return self.min_lr
# If we are done with the warmup period, use the decay style.
if self.lr_decay_style == 'inverse-square-root':
warmup_steps = max(self.lr_warmup_steps, 1)
num_steps = max(self.num_steps, 1)
lr = self.max_lr * warmup_steps ** 0.5 / (num_steps ** 0.5)
return max(self.min_lr, lr)
num_steps_ = self.num_steps - self.lr_warmup_steps
decay_steps_ = self.lr_decay_steps - self.lr_warmup_steps
decay_ratio = float(num_steps_) / float(decay_steps_)
assert decay_ratio >= 0.0
assert decay_ratio <= 1.0
delta_lr = self.max_lr - self.min_lr
if self.lr_decay_style == 'linear':
coeff = (1.0 - decay_ratio)
elif self.lr_decay_style == 'cosine':
coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0)
else:
raise Exception('{} decay style is not supported.'.format(
self.lr_decay_style))
return self.min_lr + coeff * delta_lr
def step(self, increment):
"""Set lr for all parameters groups."""
self.num_steps += increment
new_lr = self.get_lr()
new_wd = self.get_wd()
for group in self.optimizer.param_groups:
group['lr'] = new_lr * group.get('lr_mult', 1.0)
group['weight_decay'] = new_wd * group.get('wd_mult', 1.0)
def state_dict(self):
state_dict = {
'max_lr': self.max_lr,
'lr_warmup_steps': self.lr_warmup_steps,
'num_steps': self.num_steps,
'lr_decay_style': self.lr_decay_style,
'lr_decay_steps': self.lr_decay_steps,
'min_lr': self.min_lr,
'start_wd': self.start_wd,
'end_wd': self.end_wd,
'wd_incr_style': self.wd_incr_style,
'wd_incr_steps': self.wd_incr_steps
}
return state_dict
def _check_and_set(self, cls_value, sd_value, name):
"""Auxiliary function for checking the values in the checkpoint and
setting them."""
if self.override_opt_param_scheduler:
print_rank_0(' > overriding {} value to {}'.format(name, cls_value))
return cls_value
if not self.use_checkpoint_opt_param_scheduler:
assert cls_value == sd_value, \
f'OptimizerParamScheduler: class input value {cls_value} and checkpoint' \
f'value {sd_value} for {name} do not match'
print_rank_0(' > using checkpoint value {} for {}'.format(sd_value,
name))
return sd_value
def load_state_dict(self, sd):
if 'start_lr' in sd:
max_lr_ = sd['start_lr']
else:
max_lr_ = sd['max_lr']
self.max_lr = self._check_and_set(self.max_lr, max_lr_,
'learning rate')
self.min_lr = self._check_and_set(self.min_lr, sd['min_lr'],
'minimum learning rate')
if 'warmup_iter' in sd:
lr_warmup_steps_ = sd['warmup_iter']
elif 'warmup_steps' in sd:
lr_warmup_steps_ = sd['warmup_steps']
else:
lr_warmup_steps_ = sd['lr_warmup_steps']
self.lr_warmup_steps = self._check_and_set(self.lr_warmup_steps,
lr_warmup_steps_,
'warmup iterations')
if 'end_iter' in sd:
lr_decay_steps_ = sd['end_iter']
elif 'decay_steps' in sd:
lr_decay_steps_ = sd['decay_steps']
else:
lr_decay_steps_ = sd['lr_decay_steps']
self.lr_decay_steps = self._check_and_set(self.lr_decay_steps, lr_decay_steps_,
'total number of iterations')
if 'decay_style' in sd:
lr_decay_style_ = sd['decay_style']
else:
lr_decay_style_ = sd['lr_decay_style']
self.lr_decay_style = self._check_and_set(self.lr_decay_style,
lr_decay_style_,
'learning rate decay style')
if 'num_iters' in sd:
num_steps = sd['num_iters']
else:
num_steps = sd['num_steps']
self.step(increment=num_steps)
if 'start_wd' in sd:
self.start_wd = self._check_and_set(self.start_wd,
sd['start_wd'],
"start weight decay")
self.end_wd = self._check_and_set(self.end_wd,
sd['end_wd'],
"end weight decay")
self.wd_incr_steps = self._check_and_set(self.wd_incr_steps,
sd['wd_incr_steps'],
"total number of weight decay iterations")
self.wd_incr_style = self._check_and_set(self.wd_incr_style,
sd['wd_incr_style'],
"weight decay incr style")
|
Megatron-LM-master
|
megatron/optimizer_param_scheduler.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""General utilities."""
import sys
import torch
try:
from apex.multi_tensor_apply import multi_tensor_applier
except ImportError:
multi_tensor_applier = None
try:
import amp_C
except ImportError:
amp_C = None
from megatron import (
get_args,
get_adlr_autoresume,
)
from megatron.core import mpu
from megatron.core.tensor_parallel import param_is_not_tensor_parallel_duplicate
from megatron.model import DistributedDataParallel as DDP
from megatron.model import Float16Module
from megatron.model.module import param_is_not_shared
ALL_MODULE_WRAPPER_CLASSNAMES = (DDP, Float16Module)
def unwrap_model(model, module_instances=ALL_MODULE_WRAPPER_CLASSNAMES):
return_list = True
if not isinstance(model, list):
model = [model]
return_list = False
unwrapped_model = []
for model_module in model:
while isinstance(model_module, module_instances):
model_module = model_module.module
unwrapped_model.append(model_module)
if not return_list:
return unwrapped_model[0]
return unwrapped_model
def calc_params_l2_norm(model):
"""Calculate l2 norm of parameters """
args = get_args()
if not isinstance(model, list):
model = [model]
# Remove duplicate params.
params_data = []
for model_ in model:
for param in model_.parameters():
is_not_shared = param_is_not_shared(param)
is_not_tp_duplicate = param_is_not_tensor_parallel_duplicate(param)
if is_not_shared and is_not_tp_duplicate:
if args.bf16:
params_data.append(param.data.float())
else:
params_data.append(param.data)
# Check the availability of apex
assert multi_tensor_applier is not None and amp_C is not None, \
"apex is not available, please install it from https://github.com/NVIDIA/apex"
# Calculate norm
dummy_overflow_buf = torch.cuda.IntTensor([0])
norm, _ = multi_tensor_applier(
amp_C.multi_tensor_l2norm,
dummy_overflow_buf,
[params_data],
False # no per-parameter norm
)
norm_2 = norm * norm
# Sum across all model-parallel GPUs.
torch.distributed.all_reduce(norm_2,
op=torch.distributed.ReduceOp.SUM,
group=mpu.get_model_parallel_group())
return norm_2.item() ** 0.5
def average_losses_across_data_parallel_group(losses):
"""Reduce a tensor of losses across all GPUs."""
averaged_losses = torch.cat(
[loss.clone().detach().view(1) for loss in losses])
torch.distributed.all_reduce(averaged_losses,
group=mpu.get_data_parallel_group())
averaged_losses = averaged_losses / \
torch.distributed.get_world_size(group=mpu.get_data_parallel_group())
return averaged_losses
def report_memory(name):
"""Simple GPU memory report."""
mega_bytes = 1024.0 * 1024.0
string = name + ' memory (MB)'
string += ' | allocated: {}'.format(
torch.cuda.memory_allocated() / mega_bytes)
string += ' | max allocated: {}'.format(
torch.cuda.max_memory_allocated() / mega_bytes)
string += ' | reserved: {}'.format(
torch.cuda.memory_reserved() / mega_bytes)
string += ' | max reserved: {}'.format(
torch.cuda.max_memory_reserved() / mega_bytes)
if mpu.get_data_parallel_rank() == 0:
print("[Rank {}] {}".format(torch.distributed.get_rank(), string),
flush=True)
def print_params_min_max_norm(optimizer, iteration):
"""Print min, max, and norm of all parameters."""
index = 0
rank = torch.distributed.get_rank()
string = 'iteration, rank, index, tensor-model-parallel, min, max, norm\n'
optimizer_ = optimizer.optimizer
for param_group in optimizer_.param_groups:
for param in param_group['params']:
index += 1
min_ = param.data.min()
max_ = param.data.max()
norm = torch.linalg.norm(param.data)
string += '{:7d}, {:4d}, {:4d}, {:2d}, '.format(
iteration, rank, index, int(param.tensor_model_parallel))
string += '{:.6E}, {:.6E}, {:.6E}\n'.format(min_, max_, norm)
print(string, flush=True)
def check_adlr_autoresume_termination(iteration, model,
optimizer, opt_param_scheduler):
"""Check for autoresume signal and exit if it is received."""
from megatron.checkpointing import save_checkpoint
args = get_args()
autoresume = get_adlr_autoresume()
# Add barrier to ensure consistnecy.
torch.distributed.barrier()
if autoresume.termination_requested():
if args.save:
save_checkpoint(iteration, model, optimizer, opt_param_scheduler)
print_rank_0(">>> autoresume termination request found!")
if torch.distributed.get_rank() == 0:
autoresume.request_resume()
print_rank_0(">>> training terminated. Returning")
sys.exit(0)
def get_ltor_masks_and_position_ids(data,
eod_token,
reset_position_ids,
reset_attention_mask,
eod_mask_loss):
"""Build masks and position id for left to right model."""
# Extract batch size and sequence length.
micro_batch_size, seq_length = data.size()
# Attention mask (lower triangular).
if reset_attention_mask:
att_mask_batch = micro_batch_size
else:
att_mask_batch = 1
attention_mask = torch.tril(torch.ones(
(att_mask_batch, seq_length, seq_length), device=data.device)).view(
att_mask_batch, 1, seq_length, seq_length)
# Loss mask.
loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
if eod_mask_loss:
loss_mask[data == eod_token] = 0.0
# Position ids.
position_ids = torch.arange(seq_length, dtype=torch.long,
device=data.device)
position_ids = position_ids.unsqueeze(0).expand_as(data)
# We need to clone as the ids will be modifed based on batch index.
if reset_position_ids:
position_ids = position_ids.clone()
if reset_position_ids or reset_attention_mask:
# Loop through the batches:
for b in range(micro_batch_size):
# Find indecies where EOD token is.
eod_index = position_ids[b, data[b] == eod_token]
# Detach indecies from positions if going to modify positions.
if reset_position_ids:
eod_index = eod_index.clone()
# Loop through EOD indecies:
prev_index = 0
for j in range(eod_index.size()[0]):
i = eod_index[j]
# Mask attention loss.
if reset_attention_mask:
attention_mask[b, 0, (i + 1):, :(i + 1)] = 0
# Reset positions.
if reset_position_ids:
position_ids[b, (i + 1):] -= (i + 1 - prev_index)
prev_index = i + 1
# Convert attention mask to binary:
attention_mask = (attention_mask < 0.5)
return attention_mask, loss_mask, position_ids
def print_rank_0(message):
"""If distributed is initialized, print only on rank 0."""
if torch.distributed.is_initialized():
if torch.distributed.get_rank() == 0:
print(message, flush=True)
else:
print(message, flush=True)
def is_last_rank():
return torch.distributed.get_rank() == (
torch.distributed.get_world_size() - 1)
def print_rank_last(message):
"""If distributed is initialized, print only on last rank."""
if torch.distributed.is_initialized():
if is_last_rank():
print(message, flush=True)
else:
print(message, flush=True)
|
Megatron-LM-master
|
megatron/utils.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Megatron number of micro-batches calculators."""
from abc import ABC
from abc import abstractmethod
def build_num_microbatches_calculator(args):
# Constant num micro-batches.
if args.rampup_batch_size is None:
num_microbatches_calculator = ConstantNumMicroBatches(
args.global_batch_size, args.micro_batch_size,
args.data_parallel_size)
if args.rank == 0:
print('setting number of micro-batches to constant {}'.format(
num_microbatches_calculator.get()), flush=True)
else:
assert len(args.rampup_batch_size) == 3, 'expected the following ' \
'format: --rampup-batch-size <start batch size> ' \
'<batch size incerement> <ramp-up samples>'
start_batch_size = int(args.rampup_batch_size[0])
batch_size_increment = int(args.rampup_batch_size[1])
ramup_samples = int(args.rampup_batch_size[2])
if args.rank == 0:
print('will use batch size rampup starting from global batch '
'size {} to global batch size {} with batch size increments '
'{} over {} samples.'.format(start_batch_size,
args.global_batch_size,
batch_size_increment,
ramup_samples), flush=True)
num_microbatches_calculator = RampupBatchsizeNumMicroBatches(
start_batch_size, batch_size_increment, ramup_samples,
args.global_batch_size, args.micro_batch_size,
args.data_parallel_size)
return num_microbatches_calculator
class NumMicroBatchesCalculator(ABC):
def __init__(self):
self.num_micro_batches = None
self.current_global_batch_size = None
def get(self):
return self.num_micro_batches
def get_current_global_batch_size(self):
return self.current_global_batch_size
@abstractmethod
def update(self, consumed_samples, consistency_check):
pass
class ConstantNumMicroBatches(NumMicroBatchesCalculator):
def __init__(self, global_batch_size, micro_batch_size, data_parallel_size):
micro_batch_times_data_parallel = micro_batch_size * \
data_parallel_size
assert global_batch_size % micro_batch_times_data_parallel == 0, \
'global batch size ({}) is not divisible by micro batch size ({})' \
' times data parallel size ({})'.format(global_batch_size,
micro_batch_size,
data_parallel_size)
self.num_micro_batches = global_batch_size // \
micro_batch_times_data_parallel
assert self.num_micro_batches >= 1
self.current_global_batch_size = global_batch_size
def update(self, consumed_samples, consistency_check):
pass
class RampupBatchsizeNumMicroBatches(NumMicroBatchesCalculator):
def __init__(self, start_batch_size, batch_size_increment, ramup_samples,
global_batch_size, micro_batch_size, data_parallel_size):
"""Batch size ramp up.
Over
steps = (global-batch-size - start-batch-size) / batch_size_increment
increment batch size from start-batch-size to global-batch-size using
rampup-samples / steps
samples.
Arguments:
start_batch_size: global batch size to start with
batch_size_increment: global batch size increments
ramup_samples: number of samples to use ramp up global
batch size from `start_batch_size` to `global_batch_size`
global_batch_size: global batch size post rampup
micro_batch_size: micro batch size
data_parallel_size: data parallel size.
"""
self.micro_batch_size = micro_batch_size
self.data_parallel_size = data_parallel_size
self.micro_batch_times_data_parallel_size = self.micro_batch_size * \
self.data_parallel_size
assert self.micro_batch_times_data_parallel_size > 0
assert start_batch_size > 0
self.start_batch_size = start_batch_size
assert global_batch_size > 0
self.global_batch_size = global_batch_size
diff_batch_size = self.global_batch_size - self.start_batch_size
assert diff_batch_size >= 0
assert batch_size_increment > 0
self.batch_size_increment = batch_size_increment
assert diff_batch_size % batch_size_increment == 0, 'expected ' \
'global batch size interval ({}) to be divisible by global batch ' \
'size increment ({})'.format(diff_batch_size, batch_size_increment)
num_increments = diff_batch_size // self.batch_size_increment
self.ramup_samples = ramup_samples
assert self.ramup_samples >= 0
self.rampup_samples_per_increment = self.ramup_samples / num_increments
# Initialize number of microbatches.
self.update(0, False)
def update(self, consumed_samples, consistency_check):
if consumed_samples > self.ramup_samples:
self.current_global_batch_size = self.global_batch_size
else:
steps = int(consumed_samples / self.rampup_samples_per_increment)
self.current_global_batch_size = self.start_batch_size + \
steps * self.batch_size_increment
assert self.current_global_batch_size <= self.global_batch_size
if consistency_check:
assert self.current_global_batch_size % \
self.micro_batch_times_data_parallel_size == 0, 'current global ' \
'batch size ({}) is not divisible by micro-batch-size ({}) times' \
'data parallel size ({})'.format(self.current_global_batch_size,
self.micro_batch_size,
self.data_parallel_size)
self.num_micro_batches = self.current_global_batch_size // \
self.micro_batch_times_data_parallel_size
|
Megatron-LM-master
|
megatron/microbatches.py
|
import sys
import time
import torch
import torch.distributed as dist
from megatron import get_args, print_rank_0
from megatron.core import mpu
from megatron.checkpointing import load_biencoder_checkpoint
from megatron.data.orqa_wiki_dataset import get_open_retrieval_wiki_dataset
from megatron.data.orqa_wiki_dataset import get_open_retrieval_batch
from megatron.data.biencoder_dataset_utils import get_one_epoch_dataloader
from megatron.data.realm_index import detach, OpenRetreivalDataStore
from megatron.model.biencoder_model import get_model_provider
from megatron.training import get_model
class IndexBuilder(object):
"""
Object for taking one pass over a dataset and creating a BlockData of its
embeddings
"""
def __init__(self):
args = get_args()
self.model = None
self.dataloader = None
self.evidence_embedder_obj = None
self.biencoder_shared_query_context_model = \
args.biencoder_shared_query_context_model
# need to know whether we're using a REALM checkpoint (args.load)
# or ICT checkpoint
assert not (args.load and args.ict_load)
self.log_interval = args.indexer_log_interval
self.batch_size = args.indexer_batch_size
self.load_attributes()
self.is_main_builder = mpu.get_data_parallel_rank() == 0
self.num_total_builders = mpu.get_data_parallel_world_size()
self.iteration = self.total_processed = 0
def load_attributes(self):
"""
Load the necessary attributes: model, dataloader and empty BlockData
"""
only_context_model = True
if self.biencoder_shared_query_context_model:
only_context_model = False
model = get_model(get_model_provider(only_context_model=\
only_context_model, biencoder_shared_query_context_model=\
self.biencoder_shared_query_context_model))
self.model = load_biencoder_checkpoint(model,
only_context_model=only_context_model)
assert len(self.model) == 1
self.model[0].eval()
self.dataset = get_open_retrieval_wiki_dataset()
self.dataloader = iter(get_one_epoch_dataloader(self.dataset, \
self.batch_size))
self.evidence_embedder_obj = OpenRetreivalDataStore( \
load_from_path=False)
def track_and_report_progress(self, batch_size):
"""
Utility function for tracking progress
"""
self.iteration += 1
self.total_processed += batch_size * self.num_total_builders
if self.is_main_builder and self.iteration % self.log_interval == 0:
print('Batch {:10d} | Total {:10d}'.format(self.iteration,
self.total_processed), flush=True)
def build_and_save_index(self):
"""
Goes through one epoch of the dataloader and adds all data to this
instance's BlockData.
The copy of BlockData is saved as a shard, which when run in a
distributed setting will be consolidated by the rank 0 process
and saved as a final pickled BlockData.
"""
assert len(self.model) == 1
unwrapped_model = self.model[0]
while not hasattr(unwrapped_model, 'embed_text'):
unwrapped_model = unwrapped_model.module
while True:
try:
# batch also has query_tokens and query_pad_data
row_id, context_tokens, context_mask, context_types, \
context_pad_mask = get_open_retrieval_batch( \
self.dataloader)
except (StopIteration, IndexError):
break
# TODO: can we add with torch.no_grad() to reduce memory usage
# detach, separate fields and add to BlockData
assert context_mask.dtype == torch.bool
context_logits = unwrapped_model.embed_text(
unwrapped_model.context_model, context_tokens, context_mask,
context_types)
context_logits = detach(context_logits)
row_id = detach(row_id)
self.evidence_embedder_obj.add_block_data(row_id, context_logits)
self.track_and_report_progress(batch_size=len(row_id))
# This process signals to finalize its shard and then synchronize with
# the other processes
self.evidence_embedder_obj.save_shard()
torch.distributed.barrier()
del self.model
# rank 0 process builds the final copy
if self.is_main_builder:
self.evidence_embedder_obj.merge_shards_and_save()
# make sure that every single piece of data was embedded
assert len(self.evidence_embedder_obj.embed_data) == \
len(self.dataset)
self.evidence_embedder_obj.clear()
# complete building the final copy
torch.distributed.barrier()
|
Megatron-LM-master
|
megatron/indexer.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Megatron timers."""
from abc import ABC
from abc import abstractmethod
import time
import torch
class TimerBase(ABC):
def __init__(self, name):
self.name = name
@abstractmethod
def start(self, barrier=False):
pass
@abstractmethod
def stop(self, barrier=False):
pass
@abstractmethod
def reset(self):
pass
@abstractmethod
def elapsed(self, reset=True, barrier=False):
pass
class DummyTimer(TimerBase):
def __init__(self):
super().__init__('dummy timer')
def start(self, barrier=False):
return
def stop(self, barrier=False):
return
def reset(self):
return
def elapsed(self, reset=True, barrier=False):
raise Exception('dummy timer should not be used to '
'calculate elapsed time')
class Timer(TimerBase):
"""
Comment on using `barrier`: If this flag is passed, then all
the caller processes will wait till all reach the timing routine.
It is up to the user to make sure all the ranks in `barrier_group`
call it otherwise, it will result in a hang.
Comment on `barrier_group`: By default it is set to None which
in torch distributed land, it will result in the global communicator.
"""
def __init__(self, name):
super().__init__(name)
self._elapsed = 0.0
self._started = False
# Note that None will default to the global process group
self._barrier_group = None
self._start_time = time.time()
def set_barrier_group(self, barrier_group):
self._barrier_group = barrier_group
def start(self, barrier=False):
"""Start the timer."""
assert not self._started, 'timer has already been started'
if barrier:
torch.distributed.barrier(group=self._barrier_group)
torch.cuda.synchronize()
self._start_time = time.time()
self._started = True
def stop(self, barrier=False):
"""Stop the timer."""
assert self._started, 'timer is not started'
if barrier:
torch.distributed.barrier(group=self._barrier_group)
torch.cuda.synchronize()
self._elapsed += (time.time() - self._start_time)
self._started = False
def reset(self):
"""Reset timer."""
self._elapsed = 0.0
self._started = False
def elapsed(self, reset=True, barrier=False):
"""Calculate the elapsed time."""
_started = self._started
# If the timing in progress, end it first.
if self._started:
self.stop(barrier=barrier)
# Get the elapsed time.
_elapsed = self._elapsed
# Reset the elapsed time
if reset:
self.reset()
# If timing was in progress, set it back.
if _started:
self.start(barrier=barrier)
return _elapsed
class Timers:
"""Group of timers."""
def __init__(self, log_level, log_option):
self._log_level = log_level
self._log_option = log_option
self._timers = {}
self._log_levels = {}
self._dummy_timer = DummyTimer()
self._max_log_level = 2
def __call__(self, name, log_level=None):
# If the timer has already been set, then check if the log-level
# is provided, it matches the one that the timer was created with.
if name in self._timers:
if log_level is not None:
assert log_level == self._log_levels[name], \
'input log level {} does not match already existing '\
'log level {} for {} timer'.format(
log_level, self._log_levels[name], name)
return self._timers[name]
# If timer does not exist and no log level is provided,
# set it to the max log level which is 2.
if log_level is None:
log_level = self._max_log_level
assert log_level <= self._max_log_level, \
'log level {} is larger than max supported log level {}'.format(
log_level, self._max_log_level)
# Now if the input log level is larger than the one set for
# the timers class, just ignore it and return a dummy timer.
if log_level > self._log_level:
return self._dummy_timer
# Otherwise, initalize the timer and set the level.
self._timers[name] = Timer(name)
self._log_levels[name] = log_level
return self._timers[name]
def _get_elapsed_time_all_ranks(self, names, reset, barrier):
"""
Assumptions:
- All the ranks call this function.
- `names` are identical on all ranks.
If the above assumptions are not met, calling this function will
result in hang.
Arguments:
- names: list of timer names
- reset: reset the timer after recording the elapsed time
- barrier: if set, do a global barrier before time measurments
"""
# First make sure all the callers are in sync.
if barrier:
torch.distributed.barrier()
world_size = torch.distributed.get_world_size()
rank = torch.distributed.get_rank()
# Here we can use gather on the rank we want to print the
# timing, however, there is no gather_base support in
# pytorch yet. It is simpler to deal with a single tensor
# and since we are only gathering a small amount of data,
# it should be ok to use all-gather instead of gather.
rank_name_to_time = torch.zeros((world_size, len(names)),
dtype=torch.float,
device=torch.cuda.current_device())
for i, name in enumerate(names):
if name in self._timers:
# Here we don't need to pass the barrier flag as all
# the processes are already in sync. This avoids the
# issue of different timers having different barrier
# groups inside their class.
rank_name_to_time[rank, i] = self._timers[name].elapsed(
reset=reset)
# See the note above for why we are not using gather.
torch.distributed._all_gather_base(rank_name_to_time.view(-1),
rank_name_to_time[rank, :].view(-1))
return rank_name_to_time
def _get_global_min_max_time(self, names, reset, barrier, normalizer):
"""Report only min and max times across all ranks."""
rank_name_to_time = self._get_elapsed_time_all_ranks(names, reset,
barrier)
name_to_min_max_time = {}
for i, name in enumerate(names):
rank_to_time = rank_name_to_time[:, i]
# filter out the ones we did not have any timings for
rank_to_time = rank_to_time[rank_to_time > 0.0]
# If the timer exists:
if rank_to_time.numel() > 0:
name_to_min_max_time[name] = (
rank_to_time.min().item() / normalizer,
rank_to_time.max().item() / normalizer)
return name_to_min_max_time
def _get_global_min_max_time_string(self, names, reset, barrier,
normalizer, max_only):
name_to_min_max_time = self._get_global_min_max_time(
names, reset, barrier, normalizer)
if not name_to_min_max_time:
return None
output_string = '(min, max) time across ranks (ms):'
for name in name_to_min_max_time:
min_time, max_time = name_to_min_max_time[name]
if max_only:
output_string += '\n {}: {:.2f}'.format(
(name+' ').ljust(48, '.'), max_time)
else:
output_string += '\n {}: ({:.2f}, {:.2f})'.format(
(name+' ').ljust(48, '.'), min_time, max_time)
return output_string
def _get_all_ranks_time_string(self, names, reset, barrier, normalizer):
"""Report times across all ranks."""
rank_name_to_time = self._get_elapsed_time_all_ranks(names, reset,
barrier)
output_string = 'times across ranks (ms):'
no_reported_timing = True
for i, name in enumerate(names):
not_yet_found = True
for rank in range(torch.distributed.get_world_size()):
if rank_name_to_time[rank, i] > 0:
no_reported_timing = False
if not_yet_found:
not_yet_found = False
output_string += '\n {}:'.format(name)
output_string += '\n rank {:2d}: {:.2f}'.format(
rank, rank_name_to_time[rank, i] / normalizer)
if no_reported_timing:
return None
return output_string
def log(self, names, rank=None, normalizer=1.0, reset=True, barrier=False):
"""Log a group of timers."""
# Print.
assert normalizer > 0.0
if self._log_option in ['max', 'minmax']:
max_only = False
if self._log_option == 'max':
max_only = True
output_string = self._get_global_min_max_time_string(
names, reset, barrier, normalizer/1000.0, max_only)
elif self._log_option == 'all':
output_string = self._get_all_ranks_time_string(names,
reset, barrier,
normalizer/1000.0)
else:
raise Exception('unknown timing log option {}'.format(
self._log_option))
# If no input rank is provided, log on last rank.
if rank is None:
rank = torch.distributed.get_world_size() - 1
if rank == torch.distributed.get_rank() and output_string is not None:
print(output_string, flush=True)
def write(self, names, writer, iteration, normalizer=1.0,
reset=False, barrier=False):
"""Write timers to a tensorboard writer
Note that we only report maximum time across ranks to tensorboard.
"""
# currently when using add_scalars,
# torch.utils.add_scalars makes each timer its own run, which
# polutes the runs list, so we just add each as a scalar
assert normalizer > 0.0
name_to_min_max_time = self._get_global_min_max_time(
names, reset, barrier, normalizer)
if writer is not None:
for name in name_to_min_max_time:
_, max_time = name_to_min_max_time[name]
writer.add_scalar(name + '-time', max_time, iteration)
|
Megatron-LM-master
|
megatron/timers.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
"""Pretrain utilities."""
from datetime import datetime
import math
import sys
import time
# The earliest we can measure the start time.
_TRAIN_START_TIME = time.time()
import torch
from megatron import get_args
from megatron import get_signal_handler
from megatron import get_timers
from megatron import get_tensorboard_writer
from megatron import get_current_global_batch_size
from megatron import get_num_microbatches
from megatron import is_last_rank
from megatron import update_num_microbatches
from megatron.core import mpu, tensor_parallel
from megatron.core.utils import get_model_config
from megatron import print_rank_0
from megatron import print_rank_last
from megatron.checkpointing import load_checkpoint
from megatron.checkpointing import save_checkpoint
from megatron.model import Float16Module
from megatron.model import GPTModel
from megatron.core.enums import ModelType
from megatron.optimizer import get_megatron_optimizer
from megatron.initialize import initialize_megatron
from megatron.initialize import write_args_to_tensorboard
from megatron.initialize import set_jit_fusion_options
from megatron.optimizer_param_scheduler import OptimizerParamScheduler
from megatron.model import DistributedDataParallel as DDP
from megatron.utils import check_adlr_autoresume_termination
from megatron.utils import unwrap_model
from megatron.data.data_samplers import build_pretraining_data_loader
from megatron.utils import calc_params_l2_norm
from megatron.core.pipeline_parallel import get_forward_backward_func
from megatron.utils import report_memory
from megatron.model.vision.knn_monitor import compute_feature_bank
def print_datetime(string):
"""Note that this call will sync across all ranks."""
torch.distributed.barrier()
time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print_rank_0('[' + string + '] datetime: {} '.format(time_str))
def pretrain(train_valid_test_dataset_provider,
model_provider,
model_type,
forward_step_func,
process_non_loss_data_func=None,
extra_args_provider=None,
args_defaults={}):
"""Main training program.
This function will run the followings in the order provided:
1) initialize Megatron.
2) setup model, optimizer and lr schedule using the model_provider.
3) call train_val_test_data_provider to get train/val/test datasets.
4) train the modle using the forward_step_func.
Arguments:
train_valid_test_dataset_provider: a function that takes the size of
train/valid/test dataset and returns `train, valid, test` datasets.
model_provider: a function that returns a vanilla version of the
model. By vanilla we mean a simple model on cpu with no fp16 or ddp.
model_type: an enum that specifies the type of model being trained.
forward_step_func: a function that takes a `data iterator` and `model`,
and returns a `loss` scalar with a dictionary with key:values being
the info we would like to monitor during training, for example
`lm-loss: value`. We also require that this function add
`batch generator` to the timers class.
process_non_loss_data_func: a function to post process outputs of the
network. It can be used for dumping output tensors (e.g images) to
tensorboard. It takes `collected data`(list of tensors),
`current iteration index` and `tensorboard writer` as arguments.
extra_args_provider: a function that takes a parser and adds arguments
to it. It is used for programs to add their own arguments.
args_defaults: a dictionary from argument-name to argument-value. It
to set already parse arguments.
"""
# Initalize and get arguments, timers, and Tensorboard writer.
initialize_megatron(extra_args_provider=extra_args_provider,
args_defaults=args_defaults)
# Set pytorch JIT layer fusion options and warmup JIT functions.
set_jit_fusion_options()
# Adjust the startup time so it reflects the largest value.
# This will be closer to what scheduler will see (outside of
# image ... launches.
global _TRAIN_START_TIME
start_time_tensor = torch.cuda.DoubleTensor([_TRAIN_START_TIME])
torch.distributed.all_reduce(start_time_tensor,
op=torch.distributed.ReduceOp.MIN)
_TRAIN_START_TIME = start_time_tensor.item()
print_rank_0('time to initialize megatron (seconds): {:.3f}'.format(
time.time() - _TRAIN_START_TIME))
print_datetime('after megatron is initialized')
args = get_args()
timers = get_timers()
# Model, optimizer, and learning rate.
timers('model-and-optimizer-setup', log_level=0).start(barrier=True)
model, optimizer, opt_param_scheduler = setup_model_and_optimizer(
model_provider, model_type)
timers('model-and-optimizer-setup').stop()
print_datetime('after model, optimizer, and learning rate '
'scheduler are built')
config = get_model_config(model[0])
# Data stuff.
timers('train/valid/test-data-iterators-setup', log_level=0).start(
barrier=True)
if args.virtual_pipeline_model_parallel_size is not None:
all_data_iterators = [
build_train_valid_test_data_iterators(
train_valid_test_dataset_provider)
for _ in range(len(model))
]
train_data_iterator = [data_iterators[0]
for data_iterators in all_data_iterators]
valid_data_iterator = [data_iterators[1]
for data_iterators in all_data_iterators]
test_data_iterator = [data_iterators[2]
for data_iterators in all_data_iterators]
else:
train_data_iterator, valid_data_iterator, test_data_iterator \
= build_train_valid_test_data_iterators(
train_valid_test_dataset_provider)
timers('train/valid/test-data-iterators-setup').stop()
print_datetime('after dataloaders are built')
# Print setup timing.
print_rank_0('done with setup ...')
timers.log(['model-and-optimizer-setup',
'train/valid/test-data-iterators-setup'], barrier=True)
if not args.skip_train:
print_rank_0('training ...')
if args.dataloader_type == 'cyclic' and args.retro_add_retriever:
args.train_iters = args.retro_cyclic_train_iters
print_rank_0("retro cyclic train iters : %d" % args.train_iters)
iteration = 0
if args.do_train and args.train_iters > 0:
iteration = train(forward_step_func,
model, optimizer, opt_param_scheduler,
train_data_iterator, valid_data_iterator,
process_non_loss_data_func, config)
print_datetime('after training is done')
if args.save and iteration != 0:
save_checkpoint(iteration, model, optimizer, opt_param_scheduler)
else:
print_rank_0('skipping training (--skip-train is on) ...')
iteration = args.iteration
if args.do_valid:
prefix = f'iteration {iteration} on validation set'
evaluate_and_print_results(prefix, forward_step_func,
valid_data_iterator, model,
iteration, process_non_loss_data_func, config,
verbose=True, write_to_tensorboard=not args.skip_train)
if args.do_test:
prefix = f'iteration {iteration} on test set'
evaluate_and_print_results(prefix, forward_step_func,
test_data_iterator, model,
iteration, process_non_loss_data_func, config,
verbose=True, write_to_tensorboard=not args.skip_train)
def update_train_iters(args):
# For iteration-based training, we don't need to do anything
if args.train_iters:
return
# Constant batch size with sample-based training.
if args.rampup_batch_size is None:
args.train_iters = args.train_samples // args.global_batch_size
else:
# Sample based training with rampup batch size.
iterations = 0
consumed_samples = 0
# Rampup phase.
while consumed_samples <= int(args.rampup_batch_size[2]):
update_num_microbatches(consumed_samples, consistency_check=False)
consumed_samples += get_current_global_batch_size()
iterations += 1
# Reset
update_num_microbatches(0, consistency_check=False)
# Constant phase
# Note that we throw away any partial last batch.
iterations += (args.train_samples - consumed_samples) // \
args.global_batch_size
args.train_iters = iterations
print_rank_0('setting training iterations to {}'.format(args.train_iters))
def get_model(model_provider_func, model_type=ModelType.encoder_or_decoder, wrap_with_ddp=True):
"""Build the model."""
args = get_args()
args.model_type = model_type
# Build model.
if mpu.get_pipeline_model_parallel_world_size() > 1 and \
args.virtual_pipeline_model_parallel_size is not None:
assert model_type != ModelType.encoder_and_decoder, \
"Interleaved schedule not supported for model with both encoder and decoder"
model = []
for i in range(args.virtual_pipeline_model_parallel_size):
mpu.set_virtual_pipeline_model_parallel_rank(i)
# Set pre_process and post_process only after virtual rank is set.
pre_process = mpu.is_pipeline_first_stage()
post_process = mpu.is_pipeline_last_stage()
this_model = model_provider_func(
pre_process=pre_process,
post_process=post_process
)
this_model.model_type = model_type
model.append(this_model)
else:
pre_process = mpu.is_pipeline_first_stage()
post_process = mpu.is_pipeline_last_stage()
add_encoder = True
add_decoder = True
if model_type == ModelType.encoder_and_decoder:
if mpu.get_pipeline_model_parallel_world_size() > 1:
assert args.pipeline_model_parallel_split_rank is not None, \
"Split rank needs to be specified for model with both encoder and decoder"
rank = mpu.get_pipeline_model_parallel_rank()
split_rank = args.pipeline_model_parallel_split_rank
world_size = mpu.get_pipeline_model_parallel_world_size()
pre_process = rank == 0 or rank == split_rank
post_process = (rank == (split_rank - 1)) or (
rank == (world_size - 1))
add_encoder = mpu.is_pipeline_stage_before_split()
add_decoder = mpu.is_pipeline_stage_after_split()
model = model_provider_func(
pre_process=pre_process,
post_process=post_process,
add_encoder=add_encoder,
add_decoder=add_decoder)
else:
model = model_provider_func(
pre_process=pre_process,
post_process=post_process
)
model.model_type = model_type
if not isinstance(model, list):
model = [model]
# Disallow training and inference with Transformer Engine
# for non-GPT models
args.allow_transformer_engine = all([type(m) == GPTModel for m in model])
assert args.allow_transformer_engine or args.transformer_impl == 'local', \
'Transformer Engine is only approved for GPT models'
# Set tensor model parallel attributes if not set.
# Only parameters that are already tensor model parallel have these
# attributes set for them. We should make sure the default attributes
# are set for all params so the optimizer can use them.
for model_module in model:
for param in model_module.parameters():
tensor_parallel.set_defaults_if_not_set_tensor_model_parallel_attributes(param)
# Print number of parameters.
if mpu.get_data_parallel_rank() == 0:
print(' > number of parameters on (tensor, pipeline) '
'model parallel rank ({}, {}): {}'.format(
mpu.get_tensor_model_parallel_rank(),
mpu.get_pipeline_model_parallel_rank(),
sum([sum([p.nelement() for p in model_module.parameters()])
for model_module in model])), flush=True)
# GPU allocation.
for model_module in model:
model_module.cuda(torch.cuda.current_device())
# Fp16 conversion.
if args.fp16 or args.bf16:
model = [Float16Module(model_module, args) for model_module in model]
if wrap_with_ddp:
model = [DDP(model_module,
mpu.get_data_parallel_group(),
args.accumulate_allreduce_grads_in_fp32,
args.overlap_grad_reduce)
for model_module in model]
# Broadcast params from data parallel src rank to other data parallel ranks.
if args.data_parallel_random_init:
for model_module in model:
model_module.broadcast_params()
return model
def get_optimizer_param_scheduler(optimizer):
"""Build the learning rate scheduler."""
args = get_args()
# Iteration-based training.
if args.train_iters:
if args.lr_decay_iters is None:
args.lr_decay_iters = args.train_iters
lr_decay_steps = args.lr_decay_iters * args.global_batch_size
wd_incr_steps = args.train_iters * args.global_batch_size
if args.lr_warmup_fraction is not None:
lr_warmup_steps = args.lr_warmup_fraction * lr_decay_steps
else:
lr_warmup_steps = args.lr_warmup_iters * args.global_batch_size
# Sample-based training.
elif args.train_samples:
# We need to set training iters for later use. Technically
# we need to adjust the training samples too (due to last
# batch being incomplete) but we leave it as is for now.
update_train_iters(args)
if args.lr_decay_samples is None:
args.lr_decay_samples = args.train_samples
lr_decay_steps = args.lr_decay_samples
wd_incr_steps = args.train_samples
if args.lr_warmup_fraction is not None:
lr_warmup_steps = args.lr_warmup_fraction * lr_decay_steps
else:
lr_warmup_steps = args.lr_warmup_samples
else:
raise Exception(
'either train-iters or train-samples should be provided.')
opt_param_scheduler = OptimizerParamScheduler(
optimizer,
init_lr=args.lr_warmup_init,
max_lr=args.lr,
min_lr=args.min_lr,
lr_warmup_steps=lr_warmup_steps,
lr_decay_steps=lr_decay_steps,
lr_decay_style=args.lr_decay_style,
start_wd=args.start_weight_decay,
end_wd=args.end_weight_decay,
wd_incr_steps=wd_incr_steps,
wd_incr_style=args.weight_decay_incr_style,
use_checkpoint_opt_param_scheduler=args.use_checkpoint_opt_param_scheduler,
override_opt_param_scheduler=args.override_opt_param_scheduler)
return opt_param_scheduler
def setup_model_and_optimizer(model_provider_func,
model_type,
no_wd_decay_cond=None,
scale_lr_cond=None,
lr_mult=1.0):
"""Setup model and optimizer."""
args = get_args()
model = get_model(model_provider_func, model_type)
unwrapped_model = unwrap_model(model)
optimizer = get_megatron_optimizer(model, no_wd_decay_cond,
scale_lr_cond, lr_mult)
opt_param_scheduler = get_optimizer_param_scheduler(optimizer)
if args.load is not None:
timers = get_timers()
timers('load-checkpoint', log_level=0).start(barrier=True)
args.iteration = load_checkpoint(model, optimizer, opt_param_scheduler)
timers('load-checkpoint').stop(barrier=True)
timers.log(['load-checkpoint'])
else:
args.iteration = 0
# get model without FP16 and/or DDP wrappers
if args.iteration == 0 and len(unwrapped_model) == 1 \
and hasattr(unwrapped_model[0], 'init_state_dict_from_bert'):
print_rank_0("Initializing ICT from pretrained BERT model")
unwrapped_model[0].init_state_dict_from_bert()
if args.fp16:
optimizer.reload_model_params()
return model, optimizer, opt_param_scheduler
def train_step(forward_step_func, data_iterator,
model, optimizer, opt_param_scheduler, config):
"""Single training step."""
args = get_args()
timers = get_timers()
# Set grad to zero.
for partition in model:
partition.zero_grad_buffer()
optimizer.zero_grad()
# Forward pass.
timers('forward-backward', log_level=1).start(
barrier=args.barrier_with_L1_time)
forward_backward_func = get_forward_backward_func()
# set timers to None if none of the timers in fwd_bwd are active, just to save the checks
if args.timing_log_level < 2:
config.timers = None
losses_reduced = forward_backward_func(
forward_step_func=forward_step_func,
data_iterator=data_iterator,
model=model,
num_microbatches=get_num_microbatches(),
seq_length=args.seq_length,
micro_batch_size=args.micro_batch_size,
decoder_seq_length=args.decoder_seq_length,
forward_only=False)
# reset timers if necessary
if config.timers is None:
config.timers = timers
timers('forward-backward').stop()
# Empty unused memory.
if args.empty_unused_memory_level >= 1:
torch.cuda.empty_cache()
# Reduce gradients.
optimizer.reduce_model_grads(args, timers)
# Vision gradients.
if args.vision_pretraining and args.vision_pretraining_type == "dino":
unwrapped_model = unwrap_model(model[0])
unwrapped_model.cancel_gradients_last_layer(args.curr_iteration)
# Update parameters.
timers('optimizer', log_level=1).start(barrier=args.barrier_with_L1_time)
update_successful, grad_norm, num_zeros_in_grad = optimizer.step(args, timers)
timers('optimizer').stop()
# Gather params.
if update_successful:
optimizer.gather_model_params(args, timers)
# Vision momentum.
if args.vision_pretraining and args.vision_pretraining_type == "dino":
unwrapped_model = unwrap_model(model[0])
unwrapped_model.update_momentum(args.curr_iteration)
# Update learning rate.
if update_successful:
increment = get_num_microbatches() * \
args.micro_batch_size * \
args.data_parallel_size
opt_param_scheduler.step(increment=increment)
skipped_iter = 0
else:
skipped_iter = 1
# Empty unused memory.
if args.empty_unused_memory_level >= 2:
torch.cuda.empty_cache()
if mpu.is_pipeline_last_stage(ignore_virtual=True):
# Average loss across microbatches.
loss_reduced = {}
for key in losses_reduced[0]:
losses_reduced_for_key = [x[key] for x in losses_reduced]
loss_reduced[key] = sum(losses_reduced_for_key) / len(losses_reduced_for_key)
return loss_reduced, skipped_iter, grad_norm, num_zeros_in_grad
return {}, skipped_iter, grad_norm, num_zeros_in_grad
def training_log(loss_dict, total_loss_dict, learning_rate, iteration,
loss_scale, report_memory_flag, skipped_iter,
grad_norm, params_norm, num_zeros_in_grad):
"""Log training information such as losses, timing, ...."""
args = get_args()
timers = get_timers()
writer = get_tensorboard_writer()
# Advanced, skipped, and Nan iterations.
advanced_iters_key = 'advanced iterations'
skipped_iters_key = 'skipped iterations'
nan_iters_key = 'nan iterations'
# Advanced iterations.
if not skipped_iter:
total_loss_dict[advanced_iters_key] = total_loss_dict.get(
advanced_iters_key, 0) + 1
else:
if advanced_iters_key not in total_loss_dict:
total_loss_dict[advanced_iters_key] = 0
# Skipped iterations.
total_loss_dict[skipped_iters_key] = total_loss_dict.get(
skipped_iters_key, 0) + skipped_iter
# Update losses and set nan iterations
got_nan = False
for key in loss_dict:
if not skipped_iter:
total_loss_dict[key] = total_loss_dict.get(
key, torch.cuda.FloatTensor([0.0])) + loss_dict[key]
else:
value = loss_dict[key].float().sum().item()
is_nan = value == float('inf') or \
value == -float('inf') or \
value != value
got_nan = got_nan or is_nan
total_loss_dict[nan_iters_key] = total_loss_dict.get(
nan_iters_key, 0) + int(got_nan)
# Logging.
timers_to_log = [
'forward-backward',
'forward-compute',
'backward-compute',
'batch-generator',
'forward-recv',
'forward-send',
'backward-recv',
'backward-send',
'forward-send-forward-recv',
'forward-send-backward-recv',
'backward-send-forward-recv',
'backward-send-backward-recv',
'forward-backward-send-forward-backward-recv',
'layernorm-grads-all-reduce',
'embedding-grads-all-reduce',
'grads-all-reduce',
'grads-reduce-scatter',
'params-all-gather',
'optimizer-copy-to-main-grad',
'optimizer-unscale-and-check-inf',
'optimizer-clip-main-grad',
'optimizer-count-zeros',
'optimizer-inner-step',
'optimizer-copy-main-to-model-params',
'optimizer']
# Calculate batch size.
batch_size = args.micro_batch_size * args.data_parallel_size * \
get_num_microbatches()
total_iterations = total_loss_dict[advanced_iters_key] + \
total_loss_dict[skipped_iters_key]
# Tensorboard values.
# Timer requires all the ranks to call.
if args.log_timers_to_tensorboard and \
(iteration % args.tensorboard_log_interval == 0):
timers.write(timers_to_log, writer, iteration,
normalizer=total_iterations)
if writer and (iteration % args.tensorboard_log_interval == 0):
if args.log_learning_rate_to_tensorboard:
writer.add_scalar('learning-rate', learning_rate, iteration)
writer.add_scalar('learning-rate vs samples', learning_rate,
args.consumed_train_samples)
if args.log_batch_size_to_tensorboard:
writer.add_scalar('batch-size', batch_size, iteration)
writer.add_scalar('batch-size vs samples', batch_size,
args.consumed_train_samples)
for key in loss_dict:
writer.add_scalar(key , loss_dict[key], iteration)
writer.add_scalar(key + ' vs samples', loss_dict[key],
args.consumed_train_samples)
if args.log_loss_scale_to_tensorboard:
writer.add_scalar('loss-scale', loss_scale, iteration)
writer.add_scalar('loss-scale vs samples', loss_scale,
args.consumed_train_samples)
if args.log_world_size_to_tensorboard:
writer.add_scalar('world-size', args.world_size, iteration)
writer.add_scalar('world-size vs samples', args.world_size,
args.consumed_train_samples)
if grad_norm is not None:
writer.add_scalar('grad-norm', grad_norm, iteration)
writer.add_scalar('grad-norm vs samples', grad_norm,
args.consumed_train_samples)
if num_zeros_in_grad is not None:
writer.add_scalar('num-zeros', num_zeros_in_grad, iteration)
writer.add_scalar('num-zeros vs samples', num_zeros_in_grad,
args.consumed_train_samples)
if params_norm is not None:
writer.add_scalar('params-norm', params_norm, iteration)
writer.add_scalar('params-norm vs samples', params_norm,
args.consumed_train_samples)
if args.log_memory_to_tensorboard:
mem_stats = torch.cuda.memory_stats()
writer.add_scalar(
"mem-reserved-bytes",
mem_stats["reserved_bytes.all.current"],
iteration,
)
writer.add_scalar(
"mem-allocated-bytes",
mem_stats["allocated_bytes.all.current"],
iteration,
)
writer.add_scalar(
"mem-allocated-count",
mem_stats["allocation.all.current"],
iteration,
)
if iteration % args.log_interval == 0:
elapsed_time = timers('interval-time').elapsed(barrier=True)
elapsed_time_per_iteration = elapsed_time / total_iterations
if writer:
if args.log_timers_to_tensorboard:
writer.add_scalar('iteration-time',
elapsed_time_per_iteration, iteration)
log_string = ' iteration {:8d}/{:8d} |'.format(
iteration, args.train_iters)
log_string += ' consumed samples: {:12d} |'.format(
args.consumed_train_samples)
log_string += ' elapsed time per iteration (ms): {:.1f} |'.format(
elapsed_time_per_iteration * 1000.0)
log_string += ' learning rate: {:.3E} |'.format(learning_rate)
log_string += ' global batch size: {:5d} |'.format(batch_size)
for key in total_loss_dict:
if key not in [advanced_iters_key, skipped_iters_key,
nan_iters_key]:
avg = total_loss_dict[key].item() / \
float(max(1, total_loss_dict[advanced_iters_key]))
if avg > 0.0:
log_string += ' {}: {:.6E} |'.format(key, avg)
total_loss_dict[key] = torch.cuda.FloatTensor([0.0])
log_string += ' loss scale: {:.1f} |'.format(loss_scale)
if grad_norm is not None:
log_string += ' grad norm: {:.3f} |'.format(grad_norm)
if num_zeros_in_grad is not None:
log_string += ' num zeros: {:.1f} |'.format(num_zeros_in_grad)
if params_norm is not None:
log_string += ' params norm: {:.3f} |'.format(params_norm)
log_string += ' number of skipped iterations: {:3d} |'.format(
total_loss_dict[skipped_iters_key])
log_string += ' number of nan iterations: {:3d} |'.format(
total_loss_dict[nan_iters_key])
total_loss_dict[advanced_iters_key] = 0
total_loss_dict[skipped_iters_key] = 0
total_loss_dict[nan_iters_key] = 0
print_rank_last(log_string)
if report_memory_flag and learning_rate > 0.:
# Report memory after optimizer state has been initialized.
report_memory('(after {} iterations)'.format(iteration))
report_memory_flag = False
timers.log(timers_to_log, normalizer=args.log_interval)
return report_memory_flag
def save_checkpoint_and_time(iteration, model, optimizer, opt_param_scheduler):
timers = get_timers()
# Extra barrier is added to make sure
# all ranks report the max time.
timers('save-checkpoint', log_level=0).start(barrier=True)
save_checkpoint(iteration, model, optimizer, opt_param_scheduler)
timers('save-checkpoint').stop(barrier=True)
timers.log(['save-checkpoint'])
def train(forward_step_func, model, optimizer, opt_param_scheduler,
train_data_iterator, valid_data_iterator,
process_non_loss_data_func, config):
"""Train the model function."""
args = get_args()
timers = get_timers()
# Write args to tensorboard
write_args_to_tensorboard()
# Turn on training mode which enables dropout.
for model_module in model:
model_module.train()
# Tracking loss.
total_loss_dict = {}
# Iterations.
iteration = args.iteration
# Setup some training config params
config.grad_scale_func = optimizer.scale_loss
config.timers = timers
# TODO: Remove this once we move DDP to Core.
if len(model) == 1 and isinstance(model[0], DDP) and \
args.pipeline_model_parallel_size == 1:
config.no_sync_func = model[0].no_sync
timers('interval-time', log_level=0).start(barrier=True)
print_datetime('before the start of training step')
report_memory_flag = True
while iteration < args.train_iters:
if args.profile and \
iteration == args.profile_step_start and \
torch.distributed.get_rank() in args.profile_ranks:
torch.cuda.cudart().cudaProfilerStart()
torch.autograd.profiler.emit_nvtx(record_shapes=True).__enter__()
update_num_microbatches(args.consumed_train_samples)
args.curr_iteration = iteration
loss_dict, skipped_iter, grad_norm, num_zeros_in_grad = \
train_step(forward_step_func,
train_data_iterator,
model,
optimizer,
opt_param_scheduler,
config)
iteration += 1
args.consumed_train_samples += mpu.get_data_parallel_world_size() * \
args.micro_batch_size * \
get_num_microbatches()
# Logging.
loss_scale = optimizer.get_loss_scale().item()
params_norm = None
if args.log_params_norm:
params_norm = calc_params_l2_norm(model)
report_memory_flag = training_log(loss_dict, total_loss_dict,
optimizer.param_groups[0]['lr'],
iteration, loss_scale,
report_memory_flag, skipped_iter,
grad_norm, params_norm, num_zeros_in_grad)
# Autoresume
if args.adlr_autoresume and \
(iteration % args.adlr_autoresume_interval == 0):
check_adlr_autoresume_termination(iteration, model, optimizer,
opt_param_scheduler)
# Evaluation
if args.eval_interval and iteration % args.eval_interval == 0 and \
args.do_valid:
prefix = 'iteration {}'.format(iteration)
evaluate_and_print_results(prefix, forward_step_func,
valid_data_iterator, model,
iteration, process_non_loss_data_func,
config, False)
# Checkpointing
saved_checkpoint = False
if args.exit_signal_handler:
signal_handler = get_signal_handler()
if any(signal_handler.signals_received()):
save_checkpoint_and_time(iteration, model, optimizer,
opt_param_scheduler)
print_datetime('exiting program after receiving SIGTERM.')
sys.exit()
if args.save and args.save_interval and \
iteration % args.save_interval == 0:
save_checkpoint_and_time(iteration, model, optimizer,
opt_param_scheduler)
saved_checkpoint = True
# Exiting based on duration
if args.exit_duration_in_mins:
train_time = (time.time() - _TRAIN_START_TIME) / 60.0
done_cuda = torch.cuda.IntTensor(
[train_time > args.exit_duration_in_mins])
torch.distributed.all_reduce(
done_cuda, op=torch.distributed.ReduceOp.MAX)
done = done_cuda.item()
if done:
if not saved_checkpoint:
save_checkpoint_and_time(iteration, model, optimizer,
opt_param_scheduler)
print_datetime('exiting program after {} minutes'.format(train_time))
sys.exit()
# Exiting based on iterations
if args.exit_interval and iteration % args.exit_interval == 0:
if args.save and not saved_checkpoint:
save_checkpoint_and_time(iteration, model, optimizer,
opt_param_scheduler)
torch.distributed.barrier()
print_datetime('exiting program at iteration {}'.format(iteration))
sys.exit()
if args.profile and \
iteration == args.profile_step_end and \
torch.distributed.get_rank() in args.profile_ranks:
torch.cuda.cudart().cudaProfilerStop()
return iteration
def evaluate(forward_step_func,
data_iterator,
model,
process_non_loss_data_func,
config,
verbose=False):
"""Evaluation."""
args = get_args()
if args.vision_pretraining and args.vision_pretraining_type == "dino":
compute_feature_bank(model)
# Turn on evaluation mode which disables dropout.
for model_module in model:
model_module.eval()
total_loss_dict = {}
# make validation batch size independent from training batch size
eval_batch_size = args.global_batch_size
eval_num_microbatches = eval_batch_size // \
(args.micro_batch_size * args.data_parallel_size)
with torch.no_grad():
iteration = 0
if verbose:
print_rank_0(f'Evaluating on {args.eval_iters * eval_batch_size} samples')
while iteration < args.eval_iters:
iteration += 1
if verbose:
print_rank_0(f'Evaluating iter {iteration}/{args.eval_iters}')
forward_backward_func = get_forward_backward_func()
# Don't care about timing during evaluation
config.timers = None
loss_dicts = forward_backward_func(
forward_step_func=forward_step_func,
data_iterator=data_iterator,
model=model,
num_microbatches=eval_num_microbatches,
seq_length=args.seq_length,
micro_batch_size=args.micro_batch_size,
decoder_seq_length=args.decoder_seq_length,
forward_only=True)
config.timers = get_timers()
# Empty unused memory
if args.empty_unused_memory_level >= 1:
torch.cuda.empty_cache()
if mpu.is_pipeline_last_stage(ignore_virtual=True):
# Reduce across processes.
for loss_dict in loss_dicts:
for key in loss_dict:
total_loss_dict[key] = total_loss_dict.get(
key, torch.cuda.FloatTensor([0.0])) + loss_dict[key]
args.consumed_valid_samples += eval_batch_size
collected_non_loss_data = None
if process_non_loss_data_func is not None and is_last_rank():
collected_non_loss_data = forward_backward_func(
forward_step_func=forward_step_func,
data_iterator=data_iterator,
model=model,
num_microbatches=get_num_microbatches(),
seq_length=args.seq_length,
micro_batch_size=args.micro_batch_size,
decoder_seq_length=args.decoder_seq_length,
forward_only=True,
collect_non_loss_data=True)
# Move model back to the train mode.
for model_module in model:
model_module.train()
for key in total_loss_dict:
total_loss_dict[key] /= args.eval_iters * eval_num_microbatches
return total_loss_dict, collected_non_loss_data
def evaluate_and_print_results(prefix, forward_step_func,
data_iterator, model,
iteration, process_non_loss_data_func, config,
verbose=False, write_to_tensorboard=True):
"""Helper function to evaluate and dump results on screen."""
args = get_args()
if write_to_tensorboard:
writer = get_tensorboard_writer()
else:
writer = None
total_loss_dict, collected_non_loss_data = evaluate(
forward_step_func, data_iterator, model,
process_non_loss_data_func, config, verbose)
string = ' validation loss at {} | '.format(prefix)
for key in total_loss_dict:
string += '{} value: {:.6E} | '.format(key, total_loss_dict[key].item())
ppl = math.exp(min(20, total_loss_dict[key].item()))
string += '{} PPL: {:.6E} | '.format(key, ppl)
if writer:
writer.add_scalar('{} validation'.format(key),
total_loss_dict[key].item(),
iteration)
writer.add_scalar('{} validation vs samples'.format(key),
total_loss_dict[key].item(),
args.consumed_train_samples)
if args.log_validation_ppl_to_tensorboard:
writer.add_scalar('{} validation ppl'.format(key), ppl,
iteration)
writer.add_scalar('{} validation ppl vs samples'.format(key),
ppl, args.consumed_train_samples)
if process_non_loss_data_func is not None and writer and is_last_rank():
process_non_loss_data_func(collected_non_loss_data, iteration, writer)
length = len(string) + 1
print_rank_last('-' * length)
print_rank_last(string)
print_rank_last('-' * length)
def cyclic_iter(iter):
while True:
for x in iter:
yield x
def build_train_valid_test_datasets(build_train_valid_test_datasets_provider):
"""Build pretraining datasets."""
args = get_args()
# 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.
return build_train_valid_test_datasets_provider(train_val_test_num_samples)
def build_train_valid_test_data_loaders(
build_train_valid_test_datasets_provider):
"""Build pretraining data loaders."""
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:
# Build datasets.
train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
build_train_valid_test_datasets_provider)
# Build dataloders.
train_dataloader = build_pretraining_data_loader(
train_ds, args.consumed_train_samples)
if args.skip_train:
valid_dataloader = build_pretraining_data_loader(valid_ds, 0)
else:
valid_dataloader = build_pretraining_data_loader(
valid_ds, args.consumed_valid_samples)
test_dataloader = 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()
return train_dataloader, valid_dataloader, test_dataloader
def build_train_valid_test_data_iterators(
build_train_valid_test_datasets_provider):
"""Build pretraining data iterators."""
args = get_args()
# Build loaders.
train_dataloader, valid_dataloader, test_dataloader = \
build_train_valid_test_data_loaders(
build_train_valid_test_datasets_provider)
# 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
|
Megatron-LM-master
|
megatron/training.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Megatron global variables."""
import os
import sys
import torch
from megatron import dist_signal_handler
from megatron.tokenizer import build_tokenizer
from .microbatches import build_num_microbatches_calculator
from .timers import Timers
_GLOBAL_ARGS = None
_GLOBAL_RETRO_ARGS = None
_GLOBAL_NUM_MICROBATCHES_CALCULATOR = None
_GLOBAL_TOKENIZER = None
_GLOBAL_TENSORBOARD_WRITER = None
_GLOBAL_ADLR_AUTORESUME = None
_GLOBAL_TIMERS = None
_GLOBAL_SIGNAL_HANDLER = None
def get_args():
"""Return arguments."""
_ensure_var_is_initialized(_GLOBAL_ARGS, 'args')
return _GLOBAL_ARGS
def get_retro_args():
"""Return retro arguments."""
return _GLOBAL_RETRO_ARGS
def get_num_microbatches():
return _GLOBAL_NUM_MICROBATCHES_CALCULATOR.get()
def get_current_global_batch_size():
return _GLOBAL_NUM_MICROBATCHES_CALCULATOR.get_current_global_batch_size()
def update_num_microbatches(consumed_samples, consistency_check=True):
_GLOBAL_NUM_MICROBATCHES_CALCULATOR.update(consumed_samples,
consistency_check)
def get_tokenizer():
"""Return tokenizer."""
_ensure_var_is_initialized(_GLOBAL_TOKENIZER, 'tokenizer')
return _GLOBAL_TOKENIZER
def get_tensorboard_writer():
"""Return tensorboard writer. It can be None so no need
to check if it is initialized."""
return _GLOBAL_TENSORBOARD_WRITER
def get_adlr_autoresume():
"""ADLR autoresume object. It can be None so no need
to check if it is initialized."""
return _GLOBAL_ADLR_AUTORESUME
def get_timers():
"""Return timers."""
_ensure_var_is_initialized(_GLOBAL_TIMERS, 'timers')
return _GLOBAL_TIMERS
def get_signal_handler():
_ensure_var_is_initialized(_GLOBAL_SIGNAL_HANDLER, 'signal handler')
return _GLOBAL_SIGNAL_HANDLER
def _set_signal_handler():
global _GLOBAL_SIGNAL_HANDLER
_ensure_var_is_not_initialized(_GLOBAL_SIGNAL_HANDLER, 'signal handler')
_GLOBAL_SIGNAL_HANDLER = dist_signal_handler.DistributedSignalHandler().__enter__()
def set_global_variables(args, build_tokenizer=True):
"""Set args, tokenizer, tensorboard-writer, adlr-autoresume, and timers."""
assert args is not None
_ensure_var_is_not_initialized(_GLOBAL_ARGS, 'args')
set_args(args)
_build_num_microbatches_calculator(args)
if build_tokenizer:
_ = _build_tokenizer(args)
_set_tensorboard_writer(args)
_set_adlr_autoresume(args)
_set_timers(args)
if args.exit_signal_handler:
_set_signal_handler()
def set_args(args):
global _GLOBAL_ARGS
_GLOBAL_ARGS = args
def set_retro_args(retro_args):
global _GLOBAL_RETRO_ARGS
_GLOBAL_RETRO_ARGS = retro_args
def _build_num_microbatches_calculator(args):
global _GLOBAL_NUM_MICROBATCHES_CALCULATOR
_ensure_var_is_not_initialized(_GLOBAL_NUM_MICROBATCHES_CALCULATOR,
'num microbatches calculator')
_GLOBAL_NUM_MICROBATCHES_CALCULATOR = build_num_microbatches_calculator(
args)
def _build_tokenizer(args):
"""Initialize tokenizer."""
global _GLOBAL_TOKENIZER
_ensure_var_is_not_initialized(_GLOBAL_TOKENIZER, 'tokenizer')
_GLOBAL_TOKENIZER = build_tokenizer(args)
return _GLOBAL_TOKENIZER
def rebuild_tokenizer(args):
global _GLOBAL_TOKENIZER
_GLOBAL_TOKENIZER = None
return _build_tokenizer(args)
def _set_tensorboard_writer(args):
"""Set tensorboard writer."""
global _GLOBAL_TENSORBOARD_WRITER
_ensure_var_is_not_initialized(_GLOBAL_TENSORBOARD_WRITER,
'tensorboard writer')
if hasattr(args, 'tensorboard_dir') and \
args.tensorboard_dir and args.rank == (args.world_size - 1):
try:
from torch.utils.tensorboard import SummaryWriter
print('> setting tensorboard ...')
_GLOBAL_TENSORBOARD_WRITER = SummaryWriter(
log_dir=args.tensorboard_dir,
max_queue=args.tensorboard_queue_size)
except ModuleNotFoundError:
print('WARNING: TensorBoard writing requested but is not '
'available (are you using PyTorch 1.1.0 or later?), '
'no TensorBoard logs will be written.', flush=True)
def _set_adlr_autoresume(args):
"""Initialize ADLR autoresume."""
global _GLOBAL_ADLR_AUTORESUME
_ensure_var_is_not_initialized(_GLOBAL_ADLR_AUTORESUME, 'adlr autoresume')
if args.adlr_autoresume:
if args.rank == 0:
print('enabling autoresume ...', flush=True)
sys.path.append(os.environ.get('SUBMIT_SCRIPTS', '.'))
try:
from userlib.auto_resume import AutoResume
except BaseException:
print('ADLR autoresume is not available, exiting ...')
sys.exit()
_GLOBAL_ADLR_AUTORESUME = AutoResume
def _set_timers(args):
"""Initialize timers."""
global _GLOBAL_TIMERS
_ensure_var_is_not_initialized(_GLOBAL_TIMERS, 'timers')
_GLOBAL_TIMERS = Timers(args.timing_log_level, args.timing_log_option)
def _ensure_var_is_initialized(var, name):
"""Make sure the input variable is not None."""
assert var is not None, '{} is not initialized.'.format(name)
def _ensure_var_is_not_initialized(var, name):
"""Make sure the input variable is not None."""
assert var is None, '{} is already initialized.'.format(name)
|
Megatron-LM-master
|
megatron/global_vars.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
"""Input/output checkpointing."""
import os
import random
import sys
import numpy as np
import torch
from megatron import update_num_microbatches
from megatron.core import mpu, tensor_parallel
from .global_vars import get_args
from .utils import (unwrap_model,
print_rank_0)
_CHECKPOINT_VERSION = None
def set_checkpoint_version(value):
global _CHECKPOINT_VERSION
if _CHECKPOINT_VERSION is not None:
assert _CHECKPOINT_VERSION == value, \
"checkpoint versions do not match"
_CHECKPOINT_VERSION = value
def get_checkpoint_version():
global _CHECKPOINT_VERSION
return _CHECKPOINT_VERSION
def check_checkpoint_args(checkpoint_args):
"""Ensure fixed arguments for a model are the same for the input
arguments and the one retrieved from checkpoint."""
args = get_args()
def _compare(arg_name, old_arg_name=None, default=None):
if old_arg_name is not None:
ckpt_arg_name = old_arg_name
else:
ckpt_arg_name = arg_name
if default is not None:
checkpoint_value = getattr(checkpoint_args, ckpt_arg_name, default)
else:
checkpoint_value = getattr(checkpoint_args, ckpt_arg_name)
args_value = getattr(args, arg_name)
error_message = '{} value from checkpoint ({}) is not equal to the ' \
'input argument value ({}).'.format(
arg_name, checkpoint_value, args_value)
assert checkpoint_value == args_value, error_message
_compare('num_layers')
_compare('hidden_size')
_compare('num_attention_heads')
_compare('add_position_embedding', default=True)
if args.vocab_file:
_compare('max_position_embeddings')
_compare('make_vocab_size_divisible_by')
_compare('padded_vocab_size')
_compare('tokenizer_type')
if args.data_parallel_random_init:
_compare('data_parallel_random_init')
if get_checkpoint_version() < 3.0:
_compare('tensor_model_parallel_size',
old_arg_name='model_parallel_size')
if get_checkpoint_version() >= 3.0:
_compare('tensor_model_parallel_size')
_compare('pipeline_model_parallel_size')
def ensure_directory_exists(filename):
"""Build filename's path if it does not already exists."""
dirname = os.path.dirname(filename)
os.makedirs(dirname, exist_ok = True)
def get_checkpoint_name(checkpoints_path, iteration, release=False,
pipeline_parallel=None,
tensor_rank=None, pipeline_rank=None):
"""Determine the directory name for this rank's checkpoint."""
if release:
directory = 'release'
else:
directory = 'iter_{:07d}'.format(iteration)
# Use both the tensor and pipeline MP rank.
if pipeline_parallel is None:
pipeline_parallel = (mpu.get_pipeline_model_parallel_world_size() > 1)
if tensor_rank is None:
tensor_rank = mpu.get_tensor_model_parallel_rank()
if pipeline_rank is None:
pipeline_rank = mpu.get_pipeline_model_parallel_rank()
# Use both the tensor and pipeline MP rank. If using the distributed
# optimizer, then the optimizer's path must additionally include the
# data parallel rank.
if not pipeline_parallel:
common_path = os.path.join(checkpoints_path, directory,
f'mp_rank_{tensor_rank:02d}')
else:
common_path = os.path.join(checkpoints_path, directory,
f'mp_rank_{tensor_rank:02d}_{pipeline_rank:03d}')
return os.path.join(common_path, "model_optim_rng.pt")
def get_distributed_optimizer_checkpoint_name(model_checkpoint_name):
return os.path.join(os.path.dirname(model_checkpoint_name),
"distrib_optim.pt")
def find_checkpoint_rank_0(checkpoints_path, iteration, release=False):
"""Finds the checkpoint for rank 0 without knowing if we are using
pipeline parallelism or not.
Since the checkpoint naming scheme changes if pipeline parallelism
is present, we need to look for both naming schemes if we don't
know if the checkpoint has pipeline parallelism.
"""
# Look for checkpoint with no pipelining
filename = get_checkpoint_name(checkpoints_path, iteration, release,
pipeline_parallel=False,
tensor_rank=0, pipeline_rank=0)
if os.path.isfile(filename):
return filename
# Look for checkpoint with pipelining
filename = get_checkpoint_name(checkpoints_path, iteration, release,
pipeline_parallel=True,
tensor_rank=0, pipeline_rank=0)
if os.path.isfile(filename):
return filename
return None, None
def get_checkpoint_tracker_filename(checkpoints_path):
"""Tracker file rescords the latest chckpoint during
training to restart from."""
return os.path.join(checkpoints_path, 'latest_checkpointed_iteration.txt')
def read_metadata(tracker_filename):
# Read the tracker file and either set the iteration or
# mark it as a release checkpoint.
iteration = 0
release = False
with open(tracker_filename, 'r') as f:
metastring = f.read().strip()
try:
iteration = int(metastring)
except ValueError:
release = metastring == 'release'
if not release:
print_rank_0('ERROR: Invalid metadata file {}. Exiting'.format(
tracker_filename))
sys.exit()
assert iteration > 0 or release, 'error parsing metadata file {}'.format(
tracker_filename)
# Get the max iteration retrieved across the ranks.
if torch.distributed.is_initialized():
iters_cuda = torch.cuda.LongTensor([iteration])
torch.distributed.all_reduce(iters_cuda, op=torch.distributed.ReduceOp.MAX)
max_iter = iters_cuda[0].item()
# We should now have all the same iteration.
# If not, print a warning and chose the maximum
# iteration across all ranks.
if iteration != max_iter:
rank = torch.distributed.get_rank()
print('WARNING: on rank {} found iteration {} in the '
'metadata while max iteration across the ranks '
'is {}, replacing it with max iteration.'.format(
rank, iteration, max_iter), flush=True)
else:
# When loading a checkpoint outside of training (for example,
# when editing it), we might not have torch distributed
# initialized, in this case, just assume we have the latest
max_iter = iteration
return max_iter, release
def get_rng_state():
""" collect rng state across data parallel ranks """
args = get_args()
rng_state = {
'random_rng_state': random.getstate(),
'np_rng_state': np.random.get_state(),
'torch_rng_state': torch.get_rng_state(),
'cuda_rng_state': torch.cuda.get_rng_state(),
'rng_tracker_states': tensor_parallel.get_cuda_rng_tracker().get_states()}
rng_state_list = None
if torch.distributed.is_initialized() and \
mpu.get_data_parallel_world_size() > 1 and \
args.data_parallel_random_init:
rng_state_list = \
[None for i in range(mpu.get_data_parallel_world_size())]
torch.distributed.all_gather_object(
rng_state_list,
rng_state,
group=mpu.get_data_parallel_group())
else:
rng_state_list = [rng_state]
return rng_state_list
def save_checkpoint(iteration, model, optimizer, opt_param_scheduler):
"""Save a model checkpoint."""
args = get_args()
# Only rank zero of the data parallel writes to the disk.
model = unwrap_model(model)
print_rank_0('saving checkpoint at iteration {:7d} to {}'.format(
iteration, args.save))
# Collect rng state across data parallel ranks.
rng_state = get_rng_state()
# Checkpoint name.
checkpoint_name = get_checkpoint_name(args.save, iteration)
# Save distributed optimizer's custom parameter state.
if args.use_distributed_optimizer:
optim_checkpoint_name = \
get_distributed_optimizer_checkpoint_name(checkpoint_name)
ensure_directory_exists(optim_checkpoint_name)
optimizer.save_parameter_state(optim_checkpoint_name)
# Collect args, model, RNG.
if not torch.distributed.is_initialized() \
or mpu.get_data_parallel_rank() == 0:
# Arguments, iteration, and model.
state_dict = {}
state_dict['args'] = args
state_dict['checkpoint_version'] = 3.0
state_dict['iteration'] = iteration
if len(model) == 1:
state_dict['model'] = model[0].state_dict_for_save_checkpoint()
else:
for i in range(len(model)):
mpu.set_virtual_pipeline_model_parallel_rank(i)
state_dict['model%d' % i] = \
model[i].state_dict_for_save_checkpoint()
# Optimizer stuff.
if not args.no_save_optim:
if optimizer is not None:
state_dict['optimizer'] = optimizer.state_dict()
if opt_param_scheduler is not None:
state_dict['opt_param_scheduler'] = \
opt_param_scheduler.state_dict()
# RNG states.
if not args.no_save_rng:
state_dict["rng_state"] = rng_state
# Save.
ensure_directory_exists(checkpoint_name)
torch.save(state_dict, checkpoint_name)
# Wait so everyone is done (necessary)
if torch.distributed.is_initialized():
torch.distributed.barrier()
print_rank_0(' successfully saved checkpoint at iteration {:7d} to {}' \
.format(iteration, args.save))
# And update the latest iteration
if not torch.distributed.is_initialized() \
or torch.distributed.get_rank() == 0:
tracker_filename = get_checkpoint_tracker_filename(args.save)
with open(tracker_filename, 'w') as f:
f.write(str(iteration))
# Wait so everyone is done (not necessary)
if torch.distributed.is_initialized():
torch.distributed.barrier()
def _transpose_first_dim(t, num_splits, num_splits_first, model):
input_shape = t.size()
# We use a self_attention module but the values extracted aren't
# specific to self attention so should work for cross attention as well
while hasattr(model, 'module'):
model = model.module
attention_module = model.language_model.encoder.layers[0].self_attention
hidden_size_per_attention_head = attention_module.hidden_size_per_attention_head
num_attention_heads_per_partition = attention_module.num_attention_heads_per_partition
if num_splits_first:
"""[num_splits * np * hn, h]
-->(view) [num_splits, np, hn, h]
-->(tranpose) [np, num_splits, hn, h]
-->(view) [np * num_splits * hn, h] """
intermediate_shape = \
(num_splits, num_attention_heads_per_partition,
hidden_size_per_attention_head) + input_shape[1:]
t = t.view(*intermediate_shape)
t = t.transpose(0, 1).contiguous()
else:
"""[np * hn * num_splits, h]
-->(view) [np, hn, num_splits, h]
-->(tranpose) [np, num_splits, hn, h]
-->(view) [np * num_splits * hn, h] """
intermediate_shape = \
(num_attention_heads_per_partition,
hidden_size_per_attention_head, num_splits) +\
input_shape[1:]
t = t.view(*intermediate_shape)
t = t.transpose(1, 2).contiguous()
t = t.view(*input_shape)
return t
def fix_query_key_value_ordering(model, checkpoint_version):
"""Fix up query/key/value matrix ordering if checkpoint
version is smaller than 2.0
"""
if checkpoint_version < 2.0:
if isinstance(model, list):
assert len(model)==1
model = model[0]
for name, param in model.named_parameters():
if name.endswith(('.query_key_value.weight', '.query_key_value.bias')):
if checkpoint_version == 0:
fixed_param = _transpose_first_dim(param.data, 3, True, model)
elif checkpoint_version == 1.0:
fixed_param = _transpose_first_dim(param.data, 3, False, model)
else:
print_rank_0(f"Invalid checkpoint version {checkpoint_version}.")
sys.exit()
param.data.copy_(fixed_param)
if name.endswith(('.key_value.weight', '.key_value.bias')):
if checkpoint_version == 0:
fixed_param = _transpose_first_dim(param.data, 2, True, model)
elif checkpoint_version == 1.0:
fixed_param = _transpose_first_dim(param.data, 2, False, model)
else:
print_rank_0(f"Invalid checkpoint version {checkpoint_version}.")
sys.exit()
param.data.copy_(fixed_param)
print_rank_0(" succesfully fixed query-key-values ordering for"
" checkpoint version {}".format(checkpoint_version))
def _load_base_checkpoint(load_dir, rank0=False):
""" Load the base state_dict from the given directory
If rank0 is true, just loads rank 0 checkpoint, ignoring arguments.
"""
# Read the tracker file and set the iteration.
tracker_filename = get_checkpoint_tracker_filename(load_dir)
# If no tracker file, return nothing
if not os.path.isfile(tracker_filename):
if not rank0:
print_rank_0('WARNING: could not find the metadata file {} '.format(
tracker_filename))
print_rank_0(' will not load any checkpoints and will start from '
'random')
return None, "", False
# Otherwise, read the tracker file and either set the iteration or
# mark it as a release checkpoint.
iteration, release = read_metadata(tracker_filename)
# Checkpoint.
if rank0:
checkpoint_name = find_checkpoint_rank_0(load_dir, iteration, release)
else:
checkpoint_name = get_checkpoint_name(load_dir, iteration, release)
if release:
print_rank_0(f' loading release checkpoint from {load_dir}')
else:
print_rank_0(f' loading checkpoint from {load_dir} at iteration {iteration}')
# Load the checkpoint.
try:
state_dict = torch.load(checkpoint_name, map_location='cpu')
except ModuleNotFoundError:
from megatron.fp16_deprecated import loss_scaler
# For backward compatibility.
if not rank0:
print_rank_0(' > deserializing using the old code structure ...')
sys.modules['fp16.loss_scaler'] = sys.modules[
'megatron.fp16_deprecated.loss_scaler']
sys.modules['megatron.fp16.loss_scaler'] = sys.modules[
'megatron.fp16_deprecated.loss_scaler']
state_dict = torch.load(checkpoint_name, map_location='cpu')
sys.modules.pop('fp16.loss_scaler', None)
sys.modules.pop('megatron.fp16.loss_scaler', None)
except BaseException as e:
print_rank_0('could not load the checkpoint')
print_rank_0(e)
sys.exit()
return state_dict, checkpoint_name, release
def load_args_from_checkpoint(args, load_arg='load'):
"""Set required arguments from the checkpoint specified in the
arguments.
Will overwrite arguments that have a non-None default value, but
will leave any arguments that default to None as set.
Returns the same args NameSpace with the new values added/updated.
If no checkpoint is specified in args, or if the checkpoint is
there but invalid, the arguments will not be modified
"""
load_dir = getattr(args, load_arg)
if load_dir is None:
print_rank_0('No load directory specified, using provided arguments.')
return args
state_dict, checkpoint_name, release = _load_base_checkpoint(load_dir, rank0=True)
# Args.
if not state_dict:
print_rank_0('Checkpoint not found to provide arguments, using provided arguments.')
return args
if 'args' not in state_dict:
print_rank_0('Checkpoint provided does not have arguments saved, using provided arguments.')
return args
checkpoint_args = state_dict['args']
checkpoint_version = state_dict.get('checkpoint_version', 0)
args.iteration = state_dict['iteration']
# One-off conversion for foundation models
if hasattr(checkpoint_args, 'disable_bias_linear'):
setattr(checkpoint_args, 'add_bias_linear', not getattr(checkpoint_args, 'disable_bias_linear'))
def _set_arg(arg_name, old_arg_name=None, force=False):
if not force and getattr(args, arg_name, None) is not None:
return
if old_arg_name is not None:
checkpoint_value = getattr(checkpoint_args, old_arg_name, None)
else:
checkpoint_value = getattr(checkpoint_args, arg_name, None)
if checkpoint_value is not None:
print_rank_0(f"Setting {arg_name} to {checkpoint_value} from checkpoint")
setattr(args, arg_name, checkpoint_value)
else:
print_rank_0(f"Checkpoint did not provide arguments {arg_name}")
_set_arg('num_layers')
_set_arg('hidden_size')
_set_arg('ffn_hidden_size')
_set_arg('seq_length')
_set_arg('num_attention_heads')
_set_arg('num_query_groups', force=True)
_set_arg('group_query_attention', force=True)
_set_arg('kv_channels')
_set_arg('max_position_embeddings')
_set_arg('position_embedding_type', force=True)
_set_arg('add_position_embedding', force=True)
_set_arg('use_rotary_position_embeddings', force=True)
_set_arg('rotary_percent', force=True)
_set_arg('add_bias_linear', force=True)
_set_arg('swiglu', force=True)
_set_arg('untie_embeddings_and_output_weights', force=True)
_set_arg('apply_layernorm_1p', force=True)
_set_arg('normalization', force=True)
_set_arg('tokenizer_type')
_set_arg('padded_vocab_size')
if checkpoint_version < 3.0:
_set_arg('tensor_model_parallel_size',
'model_parallel_size')
else:
_set_arg('tensor_model_parallel_size', force=True)
_set_arg('pipeline_model_parallel_size', force=True)
_set_arg('virtual_pipeline_model_parallel_size', force=True)
_set_arg('num_layers_per_virtual_pipeline_stage')
return args, checkpoint_args
def load_checkpoint(model, optimizer, opt_param_scheduler, load_arg='load', strict=True):
"""Load a model checkpoint and return the iteration.
strict (bool): whether to strictly enforce that the keys in
:attr:`state_dict` of the checkpoint match the names of
parameters and buffers in model.
"""
args = get_args()
load_dir = getattr(args, load_arg)
model = unwrap_model(model)
state_dict, checkpoint_name, release = _load_base_checkpoint(load_dir, rank0=False)
# Checkpoint not loaded.
if state_dict is None:
# Conditionally exit at this point.
if args.exit_on_missing_checkpoint:
print_rank_0(">> '--exit-on-missing-checkpoint' set ... exiting. <<")
torch.distributed.barrier()
sys.exit()
# Iteration defaults to 0.
return 0
# Set checkpoint version.
set_checkpoint_version(state_dict.get('checkpoint_version', 0))
# Set iteration.
if args.finetune or release:
iteration = 0
else:
try:
iteration = state_dict['iteration']
except KeyError:
try: # Backward compatible with older checkpoints
iteration = state_dict['total_iters']
except KeyError:
print_rank_0('A metadata file exists but unable to load '
'iteration from checkpoint {}, exiting'.format(
checkpoint_name))
sys.exit()
# Check arguments.
assert args.consumed_train_samples == 0
assert args.consumed_valid_samples == 0
if 'args' in state_dict and not args.finetune:
checkpoint_args = state_dict['args']
check_checkpoint_args(checkpoint_args)
args.consumed_train_samples = getattr(checkpoint_args,
'consumed_train_samples', 0)
update_num_microbatches(consumed_samples=args.consumed_train_samples)
args.consumed_valid_samples = getattr(checkpoint_args,
'consumed_valid_samples', 0)
else:
print_rank_0('could not find arguments in the checkpoint ...')
# Model.
if len(model) == 1:
model[0].load_state_dict(state_dict['model'], strict=strict)
else:
for i in range(len(model)):
mpu.set_virtual_pipeline_model_parallel_rank(i)
model[i].load_state_dict(state_dict['model%d' % i], strict=strict)
# Fix up query/key/value matrix ordering if needed.
checkpoint_version = get_checkpoint_version()
print_rank_0(f' checkpoint version {checkpoint_version}')
fix_query_key_value_ordering(model, checkpoint_version)
# Optimizer.
if not release and not args.finetune and not args.no_load_optim:
try:
# Load state dict.
if optimizer is not None:
optimizer.load_state_dict(state_dict['optimizer'])
# Load distributed optimizer's custom parameter state.
if args.use_distributed_optimizer:
tracker_filename = get_checkpoint_tracker_filename(load_dir)
iteration, release = read_metadata(tracker_filename)
model_checkpoint_name = \
get_checkpoint_name(load_dir, iteration, release)
optim_checkpoint_name = \
get_distributed_optimizer_checkpoint_name(
model_checkpoint_name)
optimizer.load_parameter_state(optim_checkpoint_name)
# Load scheduler.
if opt_param_scheduler is not None:
if 'lr_scheduler' in state_dict: # backward compatbility
opt_param_scheduler.load_state_dict(state_dict['lr_scheduler'])
else:
opt_param_scheduler.load_state_dict(state_dict['opt_param_scheduler'])
except KeyError:
print_rank_0('Unable to load optimizer from checkpoint {}. '
'Specify --no-load-optim or --finetune to prevent '
'attempting to load the optimizer state, '
'exiting ...'.format(checkpoint_name))
sys.exit()
else:
if (args.fp16 or args.bf16) and optimizer is not None:
optimizer.reload_model_params()
# rng states.
if not release and not args.finetune and not args.no_load_rng:
try:
if 'rng_state' in state_dict:
# access rng_state for data parallel rank
if args.data_parallel_random_init:
rng_state = state_dict['rng_state'][mpu.get_data_parallel_rank()]
else:
rng_state = state_dict['rng_state'][0]
random.setstate(rng_state['random_rng_state'])
np.random.set_state(rng_state['np_rng_state'])
torch.set_rng_state(rng_state['torch_rng_state'])
torch.cuda.set_rng_state(rng_state['cuda_rng_state'])
# Check for empty states array
if not rng_state['rng_tracker_states']:
raise KeyError
tensor_parallel.get_cuda_rng_tracker().set_states(
rng_state['rng_tracker_states'])
else: # backward compatability
random.setstate(state_dict['random_rng_state'])
np.random.set_state(state_dict['np_rng_state'])
torch.set_rng_state(state_dict['torch_rng_state'])
torch.cuda.set_rng_state(state_dict['cuda_rng_state'])
# Check for empty states array
if not state_dict['rng_tracker_states']:
raise KeyError
tensor_parallel.get_cuda_rng_tracker().set_states(
state_dict['rng_tracker_states'])
except KeyError:
print_rank_0('Unable to load rng state from checkpoint {}. '
'Specify --no-load-rng or --finetune to prevent '
'attempting to load the rng state, '
'exiting ...'.format(checkpoint_name))
sys.exit()
# Some utilities want to load a checkpoint without distributed being initialized
if torch.distributed.is_initialized():
torch.distributed.barrier()
print_rank_0(f' successfully loaded checkpoint from {args.load} '
f'at iteration {iteration}')
return iteration
def load_biencoder_checkpoint(model, only_query_model=False,
only_context_model=False, custom_load_path=None):
"""
selectively load retrieval models for indexing/retrieving
from saved checkpoints
"""
args = get_args()
model = unwrap_model(model)
load_path = custom_load_path if custom_load_path is not None else args.load
tracker_filename = get_checkpoint_tracker_filename(load_path)
with open(tracker_filename, 'r') as f:
iteration = int(f.read().strip())
checkpoint_name = get_checkpoint_name(load_path, iteration,
args.use_distributed_optimizer,
release=False)
if mpu.get_data_parallel_rank() == 0:
print('global rank {} is loading checkpoint {}'.format(
torch.distributed.get_rank(), checkpoint_name))
state_dict = torch.load(checkpoint_name, map_location='cpu')
ret_state_dict = state_dict['model']
if only_query_model:
ret_state_dict.pop('context_model')
if only_context_model:
ret_state_dict.pop('query_model')
assert len(model) == 1
model[0].load_state_dict(ret_state_dict)
torch.distributed.barrier()
if mpu.get_data_parallel_rank() == 0:
print(' successfully loaded {}'.format(checkpoint_name))
return model
|
Megatron-LM-master
|
megatron/checkpointing.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Generation utilities."""
import torch
import torch.nn.functional as F
from megatron import get_args, get_tokenizer
from megatron.core import mpu
from megatron.utils import get_ltor_masks_and_position_ids
from .communication import (
copy_from_last_to_first_pipeline_stage,
broadcast_from_last_pipeline_stage,
broadcast_from_last_to_first_pipeline_stage)
from .forward_step import ForwardStep
from .sampling import sample
from .beam_utils import BeamHypotheses
def score_and_return_on_first_stage(model, tokens, lengths):
"""Function for just scoring.
Arguments:
model: no interleaving is supported.
tokens: prompt tokens extended to be of size [b, max_prompt_length]
lengths: original prompt length, size: [b]
Note: Outside of model, other parameters only need to be available on
rank 0.
Outputs:
output_log_probs: log probability of the selected tokens. size: [b, s]
"""
args = get_args()
batch_size = tokens.size(0)
max_prompt_length = lengths.max().item()
assert max_prompt_length == tokens.size(1)
if max_prompt_length > args.max_position_embeddings:
raise ValueError("Length of prompt + tokens_to_generate longer than allowed")
if max_prompt_length * batch_size > args.max_tokens_to_oom:
raise ValueError("Too many tokens. " + str(max_prompt_length*batch_size)+ " is greater than "+str(args.max_tokens_to_oom))
# forward step.
forward_step = ForwardStep(model, batch_size, max_prompt_length)
# ===================
# Pre-allocate memory
# ===================
# Log probability of the sequence (prompt + generated tokens).
output_log_probs = None
output_log_probs_size = (batch_size, max_prompt_length - 1)
if mpu.is_pipeline_last_stage():
output_log_probs = torch.empty(output_log_probs_size,
dtype=torch.float32,
device=torch.cuda.current_device())
# =============
# Run infernece
# =============
with torch.no_grad():
attention_mask, position_ids = _build_attention_mask_and_position_ids(tokens)
# logits will be meanigful only in the last pipeline stage.
logits = forward_step(tokens, position_ids, attention_mask)
if mpu.is_pipeline_last_stage():
# Always the last stage should have an output.
assert logits is not None
log_probs = F.log_softmax(logits, dim=2)
# Pick the tokens that we need to get the log
# probabilities for. Note that next input token is
# the token which we selected in the current logits,
# so shift by 1.
indices = torch.unsqueeze(tokens[:, 1:], 2)
output_log_probs = torch.gather(log_probs, 2, indices).squeeze(2)
# ======================================
# Broadcast to the first pipeline stage.
# ======================================
output_log_probs = broadcast_from_last_to_first_pipeline_stage(
output_log_probs_size, torch.float32, output_log_probs)
return tokens, lengths, output_log_probs
def generate_tokens_probs_and_return_on_first_stage(
model, tokens, lengths,
return_output_log_probs=False,
top_k=0, top_p=0.0, top_p_decay=0.0, top_p_bound=0.0,
temperature=1.0,
use_eod_token_for_early_termination=True,
stop_on_double_eol=False,
stop_on_eol=False,
prevent_newline_after_colon=True
):
"""Main token generation function.
Arguments:
model: no interleaving is supported.
tokens: prompt tokens extended to be of size [b, max-sequence-length]
lengths: original prompt length, size: [b]
return_output_log_probs: flag to calculate the log probability of
the generated tokens. Note that the log probability is the one
from the original logit.
top_k, top_p: top-k and top-p sampling parameters.
Note that top-k = 1 is gready. Also, these paramters are
exclusive meaning that:
if top-k > 0 then we expect top-p=0.
if top-p > 0 then we check for top-k=0.
temperature: sampling temperature.
use_eod_token_for_early_termination: if True, do early termination if
all the sequences have reached this token.
prevent_newline_after_colon: if True, it will disable generating new line \n after :
Note: Outside of model, other parameters only need to be available on
rank 0.
Outputs: Note that is size is adjusted to a lower value than
max-sequence-length if generation is terminated early.
tokens: prompt and generated tokens. size: [b, :]
generated_sequence_lengths: total length (including prompt) of
the generated sequence. size: [b]
output_log_probs: log probability of the selected tokens. size: [b, s]
"""
args = get_args()
tokenizer = get_tokenizer()
batch_size = tokens.size(0)
min_prompt_length = lengths.min().item()
max_sequence_length = tokens.size(1)
if max_sequence_length > args.max_position_embeddings:
raise ValueError("Length of prompt + tokens_to_generate longer than allowed")
if max_sequence_length * batch_size > args.max_tokens_to_oom:
raise ValueError("Too many tokens. " + str(max_sequence_length*batch_size)+ " is greater than "+str(args.max_tokens_to_oom))
# forward step.
forward_step = ForwardStep(model, batch_size, max_sequence_length)
# Added termination_id to support the case that we want to terminate the
# generation once that id is generated.
if hasattr(args, 'eos_id'):
termination_id = args.eos_id
else:
termination_id = tokenizer.eod
# ===================
# Pre-allocate memory
# ===================
# Log probability of the sequence (prompt + generated tokens).
output_log_probs = None
output_log_probs_size = (batch_size, max_sequence_length - 1)
# Lengths of generated seuquence including including prompts.
generated_sequence_lengths = None
if mpu.is_pipeline_last_stage():
if return_output_log_probs:
output_log_probs = torch.empty(output_log_probs_size,
dtype=torch.float32,
device=torch.cuda.current_device())
generated_sequence_lengths = torch.ones(
batch_size, dtype=torch.int64,
device=torch.cuda.current_device()) * max_sequence_length
# Whether we have reached a termination id.
is_generation_done = torch.zeros(batch_size, dtype=torch.uint8,
device=torch.cuda.current_device())
# =============
# Run infernece
# =============
with torch.no_grad():
attention_mask, position_ids = _build_attention_mask_and_position_ids(
tokens)
prev_context_length = 0
for context_length in range(min_prompt_length, max_sequence_length):
# Pick the slice that we need to pass through the network.
tokens2use = tokens[:, prev_context_length:context_length]
positions2use = position_ids[:, prev_context_length:context_length]
attention_mask2use = attention_mask[
..., prev_context_length:context_length, :context_length]
# logits will be meanigful only in the last pipeline stage.
logits = forward_step(tokens2use, positions2use, attention_mask2use)
if mpu.is_pipeline_last_stage():
if prevent_newline_after_colon:
logits[tokens2use[:, -1] == tokenizer.tokenize(':')[0], -1, tokenizer.tokenize('\n')[0]] = -1e10 # disable "\n" after ":"
# Always the last stage should have an output.
assert logits is not None
# Sample.
last_token_logits = logits[:, -1, :]
new_sample = sample(last_token_logits,
top_k=top_k,
top_p=top_p,
temperature=temperature,
vocab_size=tokenizer.vocab_size)
if top_p > 0.0 and top_p_decay > 0.0:
top_p = top_p * top_p_decay
if top_p_bound > 0.0:
top_p = max(top_p, top_p_bound)
# If a prompt length is smaller or equal th current context
# length, it means we have started generating tokens
started = lengths <= context_length
# Update the tokens.
tokens[started, context_length] = new_sample[started]
# Calculate the log probabilities.
if return_output_log_probs:
log_probs = F.log_softmax(logits, dim=2)
if return_output_log_probs:
# Pick the tokens that we need to get the log
# probabilities for. Note that next input token is
# the token which we selected in the current logits,
# so shift by 1.
indices = torch.unsqueeze(
tokens[
:,
(prev_context_length + 1):(context_length + 1)],
2)
output_log_probs[:,
prev_context_length:context_length] = \
torch.gather(log_probs, 2, indices).squeeze(2)
# Update the tokens on the first stage so the next input to
# the network is correct.
copy_from_last_to_first_pipeline_stage(batch_size, torch.int64,
tokens[:, context_length])
# Update the context length for the next token generation.
prev_context_length = context_length
# Check if all the sequences have hit the termination_id.
done = None
if mpu.is_pipeline_last_stage():
# TODO(rprenger) These stopping methods are tokenizer dependent
# instead tokenization should be in the inference loop so stop sequences can be used
if stop_on_double_eol:
hit_double_eol = (new_sample == 628).byte() & started.byte()
hit_two_eols = (new_sample == 198).byte() & (tokens[:, context_length-1] == 198).byte() & started.byte()
done_token = hit_double_eol | hit_two_eols
elif stop_on_eol:
hit_double_eol = (new_sample == 628).byte() & started.byte()
hit_eol = (new_sample == 198).byte() & started.byte()
done_token = hit_double_eol | hit_eol
else:
done_token = (new_sample == termination_id).byte() & \
started.byte()
just_finished = (done_token & ~is_generation_done).bool()
generated_sequence_lengths[just_finished.view(-1)] = \
context_length + 1
is_generation_done = is_generation_done | done_token
done = torch.all(is_generation_done)
done = broadcast_from_last_pipeline_stage(1, torch.uint8,
tensor=done)
if use_eod_token_for_early_termination and done:
break
# ===================================================
# Update the length of based on max generated length.
# ===================================================
tokens = tokens[:, :(context_length + 1)]
if mpu.is_pipeline_last_stage():
if return_output_log_probs:
output_log_probs = output_log_probs[:, :context_length]
# ======================================
# Broadcast to the first pipeline stage.
# ======================================
generated_sequence_lengths = broadcast_from_last_to_first_pipeline_stage(
batch_size, torch.int64, generated_sequence_lengths)
if return_output_log_probs:
output_log_probs_size = (batch_size, context_length)
output_log_probs = broadcast_from_last_to_first_pipeline_stage(
output_log_probs_size, torch.float32, output_log_probs)
return tokens, generated_sequence_lengths, output_log_probs
def beam_search_and_return_on_first_stage(model, tokens, lengths, beam_size, stop_token, num_return_gen, length_penalty, prevent_newline_after_colon=True):
args = get_args()
tokenizer = get_tokenizer()
batch_size = tokens.size(0)
assert(batch_size == 1)
prompt_length = lengths.item()
final_sequence_length = tokens.size(1)
final_sequence_length = min(final_sequence_length, args.max_position_embeddings)
# If the context is too big, this happens
if prompt_length >= final_sequence_length:
raise ValueError("context length + tokens_to_generate too large")
# forward step.
forward_step = ForwardStep(model, beam_size, final_sequence_length)
beam_hyp = BeamHypotheses(beam_size, length_penalty)
best_batches = None
done = torch.zeros(1, dtype=torch.uint8, device=torch.cuda.current_device())
scores = torch.zeros(beam_size,
dtype=torch.float32,
device=torch.cuda.current_device()).unsqueeze(1)
scores_size_tensor, tokens_size_tensor = None, None
# =============
# Run infernece
# =============
with torch.no_grad():
tokens = tokens.repeat(beam_size, 1)
attention_mask, position_ids = _build_attention_mask_and_position_ids(tokens)
prev_context_length = 0
for context_length in range(prompt_length, final_sequence_length):
# Pick the slice that we need to pass through the network.
tokens2use = tokens[:, prev_context_length:context_length]
positions2use = position_ids[:, prev_context_length:context_length]
attention_mask2use = attention_mask[
..., prev_context_length:context_length, :context_length]
# logits will be meanigful only in the last pipeline stage.
logits = forward_step(tokens2use, positions2use, attention_mask2use)
if mpu.is_pipeline_last_stage():
if prevent_newline_after_colon:
logits[tokens2use[:, -1] == tokenizer.tokenize(':')[0], -1, tokenizer.tokenize('\n')[0]] = -1e10 # disable "\n" after ":"
vocab_size = logits.size(2)
log_probs = F.log_softmax(logits, dim=2)
new_scores = log_probs[:, -1, :] + scores
if context_length == prompt_length: # if this is the first one
sorted_scores, indices = torch.sort(new_scores[0,:], descending=True)
else:
sorted_scores, indices = torch.sort(new_scores.view(-1), descending=True)
best_beam_ids = torch.div(indices[: 2 * beam_size], vocab_size).trunc().long()
best_words = indices[:2 * beam_size] % vocab_size
best_scores = sorted_scores[: 2 * beam_size]
next_beams = []
for beam_token_rank, (token_id, beam_score, beam_id) in enumerate(
zip(best_words, best_scores, best_beam_ids)
):
if token_id.item() == stop_token:
# if beam_token does not belong to top num_beams tokens, it should not be added
is_beam_token_worse_than_top_num_beams = beam_token_rank >= beam_size
if is_beam_token_worse_than_top_num_beams:
continue
beam_hyp.add(
tokens[beam_id].clone(),
beam_score,
context_length + 1 - prompt_length
)
else:
# add next predicted token since it is not eos_token
next_beams.append((token_id, beam_score, beam_id))
if len(next_beams) == beam_size:
break
if beam_hyp.is_done(best_scores.max().item(), context_length + 1 - prompt_length):
done = torch.ones(1, dtype=torch.uint8, device=torch.cuda.current_device())
best_batches = tokens.new([item[2] for item in next_beams])
tokens = tokens[best_batches,:]
tokens[:, context_length] = tokens.new([item[0] for item in next_beams])
scores = scores.new([item[1] for item in next_beams]).unsqueeze(1)
# torch.distributed.barrier()
done = broadcast_from_last_pipeline_stage(1, torch.uint8, done)
if done:
break
# Update the tokens on the first stage so the next input to
# the network is correct.
copy_from_last_to_first_pipeline_stage(tokens.size(), torch.int64,
tokens)
# set inference key values to make it consistent with best beam index
best_batches = broadcast_from_last_pipeline_stage(beam_size, torch.int64, best_batches)
forward_step.inference_params.swap_key_value_dict(best_batches)
# Update the context length for the next token generation.
prev_context_length = context_length
if mpu.is_pipeline_last_stage():
# if cannot find stop token, add open beams to hyps
if not done:
for beam_id in range(beam_size):
beam_hyp.add(tokens[beam_id].clone(), scores[beam_id].squeeze(), context_length + 1 - prompt_length)
# rank based on scores
sorted_hyps = sorted(beam_hyp.beams, key=lambda x: x[0], reverse=True)
num_return_gen = min(num_return_gen, len(sorted_hyps))
scores = [sorted_hyps[i][0] for i in range(num_return_gen)]
tokens = [sorted_hyps[i][1] for i in range(num_return_gen)]
scores = torch.stack(scores, dim=0)
tokens = torch.stack(tokens, dim=0)
scores_size_tensor = torch.tensor(scores.shape, dtype=torch.int64, device=torch.cuda.current_device())
tokens_size_tensor = torch.tensor(tokens.shape, dtype=torch.int64, device=torch.cuda.current_device())
scores_size_tensor = broadcast_from_last_pipeline_stage(1, torch.int64, scores_size_tensor)
tokens_size_tensor = broadcast_from_last_pipeline_stage(2, torch.int64, tokens_size_tensor)
scores = broadcast_from_last_to_first_pipeline_stage(tuple(scores_size_tensor), torch.float32, scores)
tokens = broadcast_from_last_to_first_pipeline_stage(tuple(tokens_size_tensor), torch.int64, tokens)
return tokens, scores
def _build_attention_mask_and_position_ids(tokens):
"""Build the attention mask and postition ids for the input tokens."""
# Since we are not interested in loss-mask and reset attention/position
# is also False, eod_token is not used so it is safe to set it to None.
attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
data=tokens,
eod_token=None,
reset_position_ids=False,
reset_attention_mask=False,
eod_mask_loss=False)
return attention_mask, position_ids
|
Megatron-LM-master
|
megatron/text_generation/generation.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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.
## from huggingface beam search
class BeamHypotheses(object):
def __init__(self, num_beams, length_penalty=1.0, early_stopping=False):
"""
Initialize n-best list of hypotheses.
"""
self.length_penalty = length_penalty
self.early_stopping = early_stopping
self.num_beams = num_beams
self.beams = []
self.worst_score = 1e9
def __len__(self):
"""
Number of hypotheses in the list.
"""
return len(self.beams)
def add(self, hyp, sum_logprobs, length):
"""
Add a new hypothesis to the list.
"""
score = sum_logprobs / length ** self.length_penalty
if len(self) < self.num_beams or score > self.worst_score:
self.beams.append((score, hyp))
if len(self) > self.num_beams:
sorted_scores = sorted([(s, idx) for idx, (s, _) in enumerate(self.beams)])
del self.beams[sorted_scores[0][1]]
self.worst_score = sorted_scores[1][0]
else:
self.worst_score = min(score, self.worst_score)
def is_done(self, best_sum_logprobs, cur_len):
"""
If there are enough hypotheses and that none of the hypotheses being generated
can become better than the worst one in the heap, then we are done with this sentence.
"""
if len(self) < self.num_beams:
return False
elif self.early_stopping:
return True
else:
cur_score = best_sum_logprobs / cur_len ** self.length_penalty
ret = self.worst_score >= cur_score
return ret
|
Megatron-LM-master
|
megatron/text_generation/beam_utils.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
from .api import (
generate,
generate_and_post_process,
beam_search_and_post_process)
|
Megatron-LM-master
|
megatron/text_generation/__init__.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Tokenization utilities."""
import torch
from megatron import get_tokenizer, get_args
from .communication import broadcast_int_list, broadcast_tensor
def detokenize_generations(tokens_gpu_tensor,
lengths_gpu_tensor,
return_segments):
"""Detokenize the generated tokens."""
tokenizer = get_tokenizer()
args = get_args()
prompts_plus_generations = []
if return_segments:
prompts_plus_generations_segments = []
tokens = tokens_gpu_tensor.cpu().numpy().tolist()
lengths = lengths_gpu_tensor.cpu().numpy().tolist()
for sequence_tokens, length in zip(tokens, lengths):
sequence_tokens = sequence_tokens[:length]
prompts_plus_generations.append(
tokenizer.detokenize(sequence_tokens))
if return_segments:
words = []
for token in sequence_tokens:
if args.tokenizer_type in ['SentencePieceTokenizer',
'GPTSentencePieceTokenizer',
'Llama2Tokenizer']:
word = tokenizer.decoder[token]
elif args.tokenizer_type == 'NullTokenizer':
word = str(token)
else:
word = tokenizer.tokenizer.decoder[token]
word = bytearray(
[tokenizer.tokenizer.byte_decoder[c] for c in word]).decode(
'utf-8', errors='replace')
words.append(word)
prompts_plus_generations_segments.append(words)
if return_segments:
return tokens, prompts_plus_generations, \
prompts_plus_generations_segments
return tokens, prompts_plus_generations
def tokenize_prompts(prompts=None, tokens_to_generate=None,
add_BOS=None, rank=0):
"""Tokenize prompts and make them avaiable on all ranks."""
# On all ranks set to None so we can pass them to functions
sizes_list = None
prompts_tokens_cuda_long_tensor = None
prompts_length_cuda_long_tensor = None
# On the specified rank, build the above.
if torch.distributed.get_rank() == rank:
assert prompts is not None
assert tokens_to_generate is not None
# Tensor of tokens padded and their unpadded length.
prompts_tokens_cuda_long_tensor, prompts_length_cuda_long_tensor = \
_tokenize_prompts_and_batch(prompts, tokens_to_generate, add_BOS)
# We need the sizes of these tensors for the boradcast
sizes_list = [prompts_tokens_cuda_long_tensor.size(0), # Batch size
prompts_tokens_cuda_long_tensor.size(1)] # Sequence lenght
# First, broadcast the sizes.
sizes_tensor = broadcast_int_list(2, int_list=sizes_list, rank=rank)
# Now that we have the sizes, we can boradcast the tokens
# and length tensors.
sizes = sizes_tensor.tolist()
prompts_tokens_cuda_long_tensor = broadcast_tensor(
sizes, torch.int64, tensor=prompts_tokens_cuda_long_tensor, rank=rank)
prompts_length_cuda_long_tensor = broadcast_tensor(
sizes[0], torch.int64, tensor=prompts_length_cuda_long_tensor,
rank=rank)
return prompts_tokens_cuda_long_tensor, prompts_length_cuda_long_tensor
def _tokenize_prompts_and_batch(prompts, tokens_to_generate, add_BOS):
"""Given a set of prompts and number of tokens to generate:
- tokenize prompts
- set the sequence length to be the max of length of prompts
plus the number of tokens we would like to generate
- pad all the sequences to this length so we can convert them
into a 2D tensor.
"""
# Tokenize all the prompts.
tokenizer = get_tokenizer()
if add_BOS:
prompts_tokens = [[tokenizer.eod] + tokenizer.tokenize(prompt)
for prompt in prompts]
else:
prompts_tokens = [tokenizer.tokenize(prompt) for prompt in prompts]
# Now we have a list of list of tokens which each list has a different
# size. We want to extend this list to:
# - incorporate the tokens that need to be generated
# - make all the sequences equal length.
# Get the prompts length.
prompts_length = [len(prompt_tokens) for prompt_tokens in prompts_tokens]
# Get the max prompts length.
max_prompt_len = max(prompts_length)
# Number of tokens in the each sample of the batch.
samples_length = max_prompt_len + tokens_to_generate
# Now update the list of list to be of the same size: samples_length.
for prompt_tokens, prompt_length in zip(prompts_tokens, prompts_length):
padding_size = samples_length - prompt_length
prompt_tokens.extend([tokenizer.eod] * padding_size)
# Now we are in a structured format, we can convert to tensors.
prompts_tokens_tensor = torch.cuda.LongTensor(prompts_tokens)
prompts_length_tensor = torch.cuda.LongTensor(prompts_length)
return prompts_tokens_tensor, prompts_length_tensor
|
Megatron-LM-master
|
megatron/text_generation/tokenization.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Forward step utilities."""
from collections.abc import Iterable
import torch
from megatron import get_args
from megatron.core import mpu, InferenceParams
from .communication import (
send_to_next_pipeline_rank,
recv_from_prev_pipeline_rank_)
class ForwardStep:
"""Forward step function with all the communications.
We use a class here to hide the inference parameters
from the outside caller."""
def __init__(self, model, max_batch_size, max_sequence_length):
"""Set values so we don't need to do it multiple times."""
# Make sure model is in eval mode.
assert not isinstance(model, Iterable), \
'interleaving schedule is not supported for inference'
model.eval()
self.model = model
# Initialize inference parameters.
self.inference_params = InferenceParams(max_batch_size,
max_sequence_length)
# Pipelining arguments.
args = get_args()
self.pipeline_size_larger_than_one = (
args.pipeline_model_parallel_size > 1)
# Threshold of pipelining.
self.pipelining_batch_x_seqlen = \
args.inference_batch_times_seqlen_threshold
def __call__(self, tokens, position_ids, attention_mask):
"""Invocation of the forward methods. Note that self.inference_params
is being modified by the forward step."""
# Pipelining case.
if self.pipeline_size_larger_than_one:
current_batch_x_seqlen = tokens.size(0) * tokens.size(1)
if current_batch_x_seqlen >= self.pipelining_batch_x_seqlen:
micro_batch_size = \
max(1, self.pipelining_batch_x_seqlen // tokens.size(1))
return _with_pipelining_forward_step(self.model,
tokens,
position_ids,
attention_mask,
self.inference_params,
micro_batch_size)
return _no_pipelining_forward_step(self.model,
tokens,
position_ids,
attention_mask,
self.inference_params)
def _get_recv_buffer_dtype(args):
"""Receive happens between the layers."""
if args.fp32_residual_connection:
return torch.float
return args.params_dtype
def _allocate_recv_buffer(batch_size, sequence_length):
"""Receive happens between the layers with size [s, b, h]."""
if mpu.is_pipeline_first_stage():
return None
args = get_args()
recv_size = (sequence_length, batch_size, args.hidden_size)
return torch.empty(recv_size,
dtype=_get_recv_buffer_dtype(args),
device=torch.cuda.current_device())
def _forward_step_helper(model, tokens, position_ids, attention_mask,
inference_params, recv_buffer=None):
"""Single forward step. Update the allocate memory flag so
only the first time the memory is allocated."""
batch_size = tokens.size(0)
sequence_length = tokens.size(1)
if recv_buffer is None:
recv_buffer = _allocate_recv_buffer(batch_size, sequence_length)
# Receive from previous stage.
recv_from_prev_pipeline_rank_(recv_buffer)
# Forward pass through the model.
model.set_input_tensor(recv_buffer)
output_tensor = model(tokens, position_ids, attention_mask,
inference_params=inference_params)
# Send output to the next stage.
send_to_next_pipeline_rank(output_tensor)
return output_tensor
def _no_pipelining_forward_step(model, tokens, position_ids, attention_mask,
inference_params, recv_buffer=None):
"""If recv_buffer is none, we will allocate one on the fly."""
# Run a simple forward pass.
output_tensor = _forward_step_helper(model, tokens, position_ids,
attention_mask, inference_params,
recv_buffer=recv_buffer)
# Update the sequence length offset.
inference_params.sequence_len_offset += tokens.size(1)
logits = None
if mpu.is_pipeline_last_stage():
logits = output_tensor
return logits
def _with_pipelining_forward_step(model, tokens, position_ids, attention_mask,
inference_params, micro_batch_size):
"""No interleaving is supported."""
sequence_length = tokens.size(1)
batch_size = tokens.size(0)
# Divide the batch dimension into micro batches.
num_micro_batches, last_chunk = divmod(batch_size,
micro_batch_size)
if last_chunk > 0:
num_micro_batches += 1
# Preallocate memory for output logits.
logits = None
if mpu.is_pipeline_last_stage():
args = get_args()
logits = torch.empty(
(batch_size, sequence_length, args.padded_vocab_size),
dtype=torch.float32, device=torch.cuda.current_device())
# Preallocate recv buffer.
recv_buffer = _allocate_recv_buffer(micro_batch_size, sequence_length)
for micro_batch_index in range(num_micro_batches):
# Slice among the batch dimenion.
start = micro_batch_index * micro_batch_size
end = min(start + micro_batch_size, batch_size)
this_micro_batch_size = end - start
tokens2use = tokens[start:end, ...]
position_ids2use = position_ids[start:end, ...]
# Run a simple forward pass.
if this_micro_batch_size != micro_batch_size:
recv_buffer = None
output = _forward_step_helper(model, tokens2use, position_ids2use,
attention_mask, inference_params,
recv_buffer=recv_buffer)
# Adjust the batch size offset to account for the micro-batch.
inference_params.batch_size_offset += this_micro_batch_size
# Copy logits.
if mpu.is_pipeline_last_stage():
logits[start:end, ...] = output
# Once we are done with all the micro-batches, we can
# adjust the sequence length offset.
inference_params.sequence_len_offset += sequence_length
# and reset the batch size offset
inference_params.batch_size_offset = 0
return logits
|
Megatron-LM-master
|
megatron/text_generation/forward_step.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Inference API."""
import torch
from megatron.core import mpu
from .communication import broadcast_float_list
from .generation import (
generate_tokens_probs_and_return_on_first_stage,
score_and_return_on_first_stage,
beam_search_and_return_on_first_stage)
from .tokenization import (
tokenize_prompts,
detokenize_generations)
def generate_and_post_process(model,
prompts=None,
tokens_to_generate=0,
return_output_log_probs=False,
top_k_sampling=0,
top_p_sampling=0.0,
top_p_decay=0.0,
top_p_bound=0.0,
temperature=1.0,
add_BOS=False,
use_eod_token_for_early_termination=True,
stop_on_double_eol=False,
stop_on_eol=False,
prevent_newline_after_colon=False,
random_seed=-1):
"""Run inference and post-process outputs, i.e., detokenize,
move to cpu and convert to list."""
# Main inference.
tokens, lengths, output_log_probs = generate(
model,
prompts=prompts,
tokens_to_generate=tokens_to_generate,
return_output_log_probs=return_output_log_probs,
top_k_sampling=top_k_sampling,
top_p_sampling=top_p_sampling,
top_p_decay=top_p_decay,
top_p_bound=top_p_bound,
temperature=temperature,
add_BOS=add_BOS,
use_eod_token_for_early_termination=use_eod_token_for_early_termination,
stop_on_double_eol=stop_on_double_eol,
stop_on_eol=stop_on_eol,
prevent_newline_after_colon=prevent_newline_after_colon,
random_seed=random_seed)
# Only post-process on first stage.
if mpu.is_pipeline_first_stage():
tokens, prompts_plus_generations, prompts_plus_generations_segments = \
detokenize_generations(tokens, lengths, True)
if return_output_log_probs:
output_log_probs = output_log_probs.cpu().numpy().tolist()
for i, (prob, seg) in enumerate(zip(output_log_probs, prompts_plus_generations_segments)):
output_log_probs[i] = prob[:len(seg)-1]
return prompts_plus_generations, prompts_plus_generations_segments, \
output_log_probs, tokens
return None
def generate(model,
prompts=None,
tokens_to_generate=0,
return_output_log_probs=False,
top_k_sampling=0,
top_p_sampling=0.0,
top_p_decay=0.0,
top_p_bound=0.0,
temperature=1.0,
add_BOS=False,
use_eod_token_for_early_termination=True,
stop_on_double_eol=False,
stop_on_eol=False,
prevent_newline_after_colon=False,
random_seed=-1):
"""Given prompts and input parameters, run inference and return:
tokens: prompts plus the generated tokens.
lengths: length of the prompt + generations. Note that we can
discard tokens in the tokens tensor that are after the
corresponding length.
output_log_probs: log probs of the tokens.
"""
# Make sure input params are avaialble to all ranks.
values = [tokens_to_generate,
return_output_log_probs,
top_k_sampling, top_p_sampling, top_p_decay, top_p_bound,
temperature, add_BOS, use_eod_token_for_early_termination,
stop_on_double_eol,
stop_on_eol,
prevent_newline_after_colon,
random_seed]
values_float_tensor = broadcast_float_list(len(values), float_list=values)
tokens_to_generate = int(values_float_tensor[0].item())
return_output_log_probs = bool(values_float_tensor[1].item())
top_k_sampling = int(values_float_tensor[2].item())
top_p_sampling = values_float_tensor[3].item()
top_p_decay = values_float_tensor[4].item()
top_p_bound = values_float_tensor[5].item()
temperature = values_float_tensor[6].item()
add_BOS = bool(values_float_tensor[7].item())
use_eod_token_for_early_termination = bool(values_float_tensor[8].item())
stop_on_double_eol = bool(values_float_tensor[9].item())
stop_on_eol = bool(values_float_tensor[10].item())
prevent_newline_after_colon = bool(values_float_tensor[11].item())
random_seed = int(values_float_tensor[12].item())
if random_seed != -1:
torch.random.manual_seed(random_seed)
# Tokenize prompts and get the batch.
# Note that these tensors are broadcaseted to all ranks.
if torch.distributed.get_rank() == 0:
assert prompts is not None
context_tokens_tensor, context_length_tensor = tokenize_prompts(
prompts=prompts, tokens_to_generate=tokens_to_generate, add_BOS=add_BOS)
if tokens_to_generate == 0:
return score_and_return_on_first_stage(
model, context_tokens_tensor, context_length_tensor)
# Main inference function.
# Note that the outputs are available on the first stage.
return generate_tokens_probs_and_return_on_first_stage(
model, context_tokens_tensor, context_length_tensor,
return_output_log_probs=return_output_log_probs,
top_k=top_k_sampling,
top_p=top_p_sampling,
top_p_decay=top_p_decay,
top_p_bound=top_p_bound,
temperature=temperature,
use_eod_token_for_early_termination=use_eod_token_for_early_termination,
stop_on_double_eol=stop_on_double_eol,
stop_on_eol=stop_on_eol,
prevent_newline_after_colon=prevent_newline_after_colon)
def beam_search_and_post_process(model,
prompts=None,
tokens_to_generate=0,
beam_size=0,
add_BOS=False,
stop_token=50256,
num_return_gen=1,
length_penalty=1,
prevent_newline_after_colon=False):
"""Run beam search and post-process outputs, i.e., detokenize,
move to cpu and convert to list."""
# Main inference.
tokens, scores = beam_search(model,
prompts=prompts,
tokens_to_generate=tokens_to_generate,
beam_size=beam_size,
add_BOS=add_BOS,
stop_token=stop_token,
num_return_gen=num_return_gen,
length_penalty=length_penalty,
prevent_newline_after_colon=prevent_newline_after_colon)
# Only post-process on first stage.
if mpu.is_pipeline_first_stage():
lengths = tokens.size(1)*torch.ones(beam_size, dtype=torch.int64, device=torch.cuda.current_device())
tokens, prompts_plus_generations, prompts_plus_generations_segments = detokenize_generations(tokens, lengths, True)
scores = scores.cpu().numpy().tolist()
return prompts_plus_generations, prompts_plus_generations_segments, scores
return None
def beam_search(model, prompts=None, tokens_to_generate=0, beam_size=0, add_BOS=False, stop_token=50256, num_return_gen=1, length_penalty=1, prevent_newline_after_colon=False):
# Make sure input params are avaialble to all ranks.
values = [tokens_to_generate,
beam_size,
add_BOS,
stop_token,
num_return_gen,
length_penalty,
prevent_newline_after_colon]
values_float_tensor = broadcast_float_list(len(values), float_list=values)
tokens_to_generate = int(values_float_tensor[0].item())
beam_size = int(values_float_tensor[1].item())
add_BOS = bool(values_float_tensor[2].item())
stop_token = int(values_float_tensor[3].item())
num_return_gen = int(values_float_tensor[4].item())
length_penalty = values_float_tensor[5].item()
prevent_newline_after_colon = values_float_tensor[6].item()
context_tokens_tensor, context_length_tensor = tokenize_prompts(
prompts=prompts, tokens_to_generate=tokens_to_generate, add_BOS=add_BOS)
return beam_search_and_return_on_first_stage(model, context_tokens_tensor, context_length_tensor,
beam_size, stop_token=stop_token, num_return_gen=num_return_gen, length_penalty=length_penalty,
prevent_newline_after_colon=prevent_newline_after_colon)
|
Megatron-LM-master
|
megatron/text_generation/api.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Communications utilities."""
import torch
from megatron.core import mpu
# TODO: use functions from megatron/p2p
def recv_from_prev_pipeline_rank_(recv_buffer=None):
"""Receive from previous pipeline stage and update the
input buffer inplace."""
if not mpu.is_pipeline_first_stage():
assert recv_buffer is not None
recv_prev_op = torch.distributed.P2POp(
torch.distributed.irecv, recv_buffer,
mpu.get_pipeline_model_parallel_prev_rank())
reqs = torch.distributed.batch_isend_irecv([recv_prev_op])
for req in reqs:
req.wait()
# To protect against race condition when using batch_isend_irecv().
torch.cuda.synchronize()
# TODO: use functions from megatron/p2p
def send_to_next_pipeline_rank(tensor=None):
"""Send output to the next pipeline stage."""
if not mpu.is_pipeline_last_stage():
assert tensor is not None
send_next_op = torch.distributed.P2POp(
torch.distributed.isend, tensor,
mpu.get_pipeline_model_parallel_next_rank())
reqs = torch.distributed.batch_isend_irecv([send_next_op])
for req in reqs:
req.wait()
# To protect against race condition when using batch_isend_irecv().
torch.cuda.synchronize()
def _is_cuda(tensor):
"""Check if a tensor is not none and is cuda."""
assert tensor is not None
assert tensor.is_cuda
def _is_cuda_contiguous(tensor):
"""Check if a tensor is not none, is cuda, and is contiguous."""
_is_cuda(tensor)
assert tensor.is_contiguous()
def broadcast_from_last_pipeline_stage(size, dtype, tensor=None):
"""Broadcast a tensor from last pipeline stage to all ranks."""
is_last_stage = mpu.is_pipeline_last_stage()
# If first stage and last state are the same, then there is no
# pipeline parallelism and no need to communicate.
if mpu.is_pipeline_first_stage() and is_last_stage:
return tensor
if is_last_stage:
_is_cuda_contiguous(tensor)
else:
tensor = torch.empty(size,
dtype=dtype,
device=torch.cuda.current_device())
# Get the group and corresponding source rank.
src = mpu.get_pipeline_model_parallel_last_rank()
group = mpu.get_pipeline_model_parallel_group()
torch.distributed.broadcast(tensor, src, group)
return tensor
def broadcast_from_last_to_first_pipeline_stage(size, dtype, tensor=None):
"""Broadcast tensor values from last stage into the first stage."""
is_last_stage = mpu.is_pipeline_last_stage()
is_first_stage = mpu.is_pipeline_first_stage()
# If first stage and last state are the same, then there is no
# pipeline parallelism and no need to communicate.
if is_first_stage and is_last_stage:
return tensor
# Only first and last stage pipeline stages need to be involved.
if is_last_stage or is_first_stage:
if is_last_stage:
_is_cuda_contiguous(tensor)
else:
tensor = torch.empty(size,
dtype=dtype,
device=torch.cuda.current_device())
src = mpu.get_pipeline_model_parallel_last_rank()
group = mpu.get_embedding_group()
# Broadcast from last stage into the first stage.
torch.distributed.broadcast(tensor, src, group)
else:
tensor = None
return tensor
def copy_from_last_to_first_pipeline_stage(size, dtype, tensor=None):
"""Copy tensor values from last stage into the first stage.
Note that the input tensor is updated in place."""
is_last_stage = mpu.is_pipeline_last_stage()
is_first_stage = mpu.is_pipeline_first_stage()
# If first stage and last state are the same, then there is no
# pipeline parallelism and no need to communicate.
if is_first_stage and is_last_stage:
return
# Only first and last stage pipeline stages need to be involved.
if is_last_stage or is_first_stage:
_is_cuda(tensor)
is_contiguous = tensor.is_contiguous()
src = mpu.get_pipeline_model_parallel_last_rank()
group = mpu.get_embedding_group()
if is_contiguous:
tensor_ = tensor
else:
if is_last_stage:
tensor_ = tensor.contiguous()
else:
tensor_ = torch.empty(size,
dtype=dtype,
device=torch.cuda.current_device())
# Broadcast from last stage into the first stage.
torch.distributed.broadcast(tensor_, src, group)
# Update the first stage tensor
if is_first_stage and not is_contiguous:
tensor[...] = tensor_
def broadcast_tensor(size, dtype, tensor=None, rank=0):
""" Given size and type of a tensor on all ranks and the tensor value
only on a specific rank, broadcast from that rank to all other ranks.
"""
if torch.distributed.get_rank() == rank:
_is_cuda_contiguous(tensor)
else:
tensor = torch.empty(size,
dtype=dtype,
device=torch.cuda.current_device())
torch.distributed.broadcast(tensor, rank)
return tensor
def broadcast_list(size, dtype, list_values=None, rank=0):
"""Broadcast a list of values with a given type."""
tensor = None
if torch.distributed.get_rank() == rank:
tensor = torch.tensor(list_values, dtype=dtype,
device=torch.cuda.current_device())
return broadcast_tensor(size, dtype, tensor=tensor, rank=rank)
def broadcast_int_list(size, int_list=None, rank=0):
"""Broadcast a list of interger values."""
return broadcast_list(size, torch.int64, list_values=int_list, rank=rank)
def broadcast_float_list(size, float_list=None, rank=0):
"""Broadcast a list of float values."""
return broadcast_list(size, torch.float32, list_values=float_list,
rank=rank)
|
Megatron-LM-master
|
megatron/text_generation/communication.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Sampling utilities.
Part of this code is inspired by:
- https://github.com/ari-holtzman/degen/blob/master/gen.py
- https://huggingface.co/transformers/_modules/transformers/generation_logits_process.html
"""
import torch
def modify_logits_for_top_k_filtering(logits, top_k):
"""Set the logits for none top-k values to -inf."""
filter_ = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits.masked_fill_(filter_, float('-Inf'))
def modify_logits_for_top_p_filtering(logits, top_p):
"""Set the logits for none top-p values to -inf."""
# First sort and calculate cumulative sum of probabilities.
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
# Filteration based on the cumulative sum.
filter_ = cumulative_probs > top_p
# This shift by 1 is weird and I cannot justify it. This existed
# in the original implementation:
# https://github.com/ari-holtzman/degen/blob/master/gen.py
# and I guess it is needed so keeping it for now.
filter_[:, 1:] = filter_[:, :-1].clone()
# Make sure we at least have one token to select from.
filter_[..., 0] = 0
# Fill in the filtered part
filter_ = filter_.scatter(1, sorted_indices, filter_)
logits.masked_fill_(filter_, float('-Inf'))
def sample(logits, top_k=0, top_p=0.0, temperature=1.0, vocab_size=None):
""" Sample and generate a token.
Note: logits has the dimension [b, v] where b is the batch size
and v is the vocabulary size.
If vocab_size is provided, we will make sure the sample that is
generated is in [0, vocab-size). This will avoid out of vocabulary
generations due to padding.
"""
# Check logits for consistency.
assert logits.ndim == 2, 'expected the logits to be of [b, v] shape.'
assert logits.type() == 'torch.cuda.FloatTensor', \
'input logits should be floats.'
# Greedy is just simple argmax.
if top_k == 1:
assert top_p == 0.0, 'cannot set both greedy and top-p samplings.'
samples = torch.argmax(logits, dim=-1)
# Top-k or top-p sampling.
else:
# Clone so we do not modify the inputs,
logits = logits.clone()
# Apply temperature in place.
if temperature != 1.0:
logits.div_(temperature)
if top_k > 1:
assert top_p == 0.0, 'cannot set both top-k and top-p samplings.'
assert top_k <= logits.size(1), 'top-k is larger than logit size.'
if vocab_size:
assert top_k < vocab_size, 'top-k is larger than vocab size.'
modify_logits_for_top_k_filtering(logits, top_k)
elif top_p > 0.0:
assert top_p <= 1.0, 'top-p should be in (0, 1].'
modify_logits_for_top_p_filtering(logits, top_p)
# After filtering, we need to recalculate the distribution.
probs = logits.softmax(dim=-1)
samples = torch.multinomial(probs, num_samples=1).view(-1)
# If vocab size is provided, make sure the samples are in
# in the range [0, vocab-size).
if vocab_size:
samples = torch.clamp(samples, min=0, max=(vocab_size - 1))
return samples
|
Megatron-LM-master
|
megatron/text_generation/sampling.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
from commons import set_random_seed
from commons import IdentityLayer
from commons import print_separator
from commons import initialize_distributed
from mpu.cross_entropy import vocab_parallel_cross_entropy
import mpu
import torch.nn.functional as F
import torch
import random
import sys
sys.path.append("../..")
def torch_cross_entropy(batch_size, seq_length, vocab_size,
logits_scale, seed):
set_random_seed(seed)
identity = IdentityLayer((batch_size, seq_length, vocab_size),
scale=logits_scale).cuda()
logits = identity()
target = torch.cuda.LongTensor(
size=(batch_size, seq_length)).random_(0, vocab_size)
loss = F.cross_entropy(logits.view(-1, logits.size()[-1]),
target.view(-1),
reduction='none').view_as(target).mean()
loss.backward()
return loss, identity.weight.grad
def mpu_cross_entropy(batch_size, seq_length, vocab_size,
logits_scale, seed):
set_random_seed(seed)
identity = IdentityLayer((batch_size, seq_length, vocab_size),
scale=logits_scale).cuda()
logits = identity()
logits_parallel = mpu.scatter_to_tensor_model_parallel_region(logits)
target = torch.cuda.LongTensor(
size=(batch_size, seq_length)).random_(0, vocab_size)
loss = vocab_parallel_cross_entropy(logits_parallel, target).mean()
loss.backward()
return loss, identity.weight.grad
def test_cross_entropy(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing cross entropy with model parallel size {} ...'.
format(tensor_model_parallel_size))
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
batch_size = 13
seq_length = 17
vocab_size_per_partition = 11
logits_scale = 1000.0
vocab_size = vocab_size_per_partition * tensor_model_parallel_size
seed = 1234
loss_torch, grad_torch = torch_cross_entropy(batch_size, seq_length,
vocab_size, logits_scale,
seed)
loss_mpu, grad_mpu = mpu_cross_entropy(batch_size, seq_length,
vocab_size, logits_scale,
seed)
error = loss_torch.sub_(loss_mpu).abs().max()
print(' max error in loss on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
error = grad_torch.sub_(grad_mpu).abs().max()
print(' max error in grad on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Reset groups
mpu.destroy_tensor_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
if __name__ == '__main__':
initialize_distributed()
world_size = torch.distributed.get_world_size()
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test cross entropy')
test_cross_entropy(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
|
Megatron-LM-master
|
megatron/mpu/tests/test_cross_entropy.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
from mpu import layers
from commons import set_random_seed
from commons import print_separator
from commons import initialize_distributed
import mpu
from torch.nn.parameter import Parameter
import torch.nn.init as init
import torch
import random
import sys
sys.path.append("../..")
def test_parallel_embedding(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing parallel embedding with model parallel size {} ...'.
format(tensor_model_parallel_size))
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
batch_size = 17
seq_length = 23
vocab_size = 48
hidden_size = 16
seed = 1236
set_random_seed(123)
input_data = torch.LongTensor(
size=(batch_size, seq_length)).random_(0, vocab_size).cuda()
loss_weight = torch.randn([batch_size, seq_length, hidden_size]).cuda()
set_random_seed(seed)
embedding_original = torch.nn.Embedding(vocab_size, hidden_size).cuda()
output = embedding_original(input_data)
loss_original = torch.mul(output, loss_weight).sum()
loss_original.backward()
set_random_seed(seed)
embedding_parallel = layers.ParallelEmbedding(
vocab_size, hidden_size, init_method=init.normal_).cuda()
output = embedding_parallel(input_data)
loss_parallel = torch.mul(output, loss_weight).sum()
loss_parallel.backward()
set_random_seed(seed)
embedding_vocab_parallel = layers.VocabParallelEmbedding(
vocab_size, hidden_size, init_method=init.normal_).cuda()
output = embedding_vocab_parallel(input_data)
loss_vocab_parallel = torch.mul(output, loss_weight).sum()
loss_vocab_parallel.backward()
torch.distributed.barrier()
error = loss_parallel.sub(loss_original).abs()
print(' error in loss (parallel) on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-12, 'error: {}'.format(error)
torch.distributed.barrier()
error = loss_vocab_parallel.sub(loss_original).abs()
print(' error in loss (vocab parallel) on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-12, 'error: {}'.format(error)
weight_grad_orig = torch.split(embedding_original.weight.grad,
hidden_size // tensor_model_parallel_size,
1)[mpu.get_tensor_model_parallel_rank()]
error = embedding_parallel.weight.grad.sub(weight_grad_orig).abs().max()
print(' error in grad (parallel) on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-12, 'error: {}'.format(error)
weight_grad_orig = torch.split(embedding_original.weight.grad,
vocab_size // tensor_model_parallel_size,
0)[mpu.get_tensor_model_parallel_rank()]
error = embedding_vocab_parallel.weight.grad.sub(
weight_grad_orig).abs().max()
print(' error in grad (vocab parallel) on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-12, 'error: {}'.format(error)
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
def test_initialize_affine_weight(tensor_model_parallel_size):
mpu.initialize_model_parallel(tensor_model_parallel_size)
if torch.distributed.get_rank() == 0:
print('> testing initialize_affine_weight with model parallel '
'size: {}'.format(tensor_model_parallel_size))
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
seed = 12345
input_size_coeff = 13
input_size = input_size_coeff * tensor_model_parallel_size
output_size_coeff = 17
output_size = output_size_coeff * tensor_model_parallel_size
# ---------------
# Column parallel
# ---------------
weight = torch.empty(output_size_coeff, input_size)
set_random_seed(seed)
layers._initialize_affine_weight(weight, output_size, input_size,
output_size_coeff, 0,
torch.nn.init.normal_)
# Target.
set_random_seed(seed)
master_weight = torch.empty(output_size, input_size)
torch.nn.init.normal_(master_weight)
rank = mpu.get_tensor_model_parallel_rank()
my_weight = torch.split(master_weight, output_size_coeff,
dim=0)[rank].contiguous().clone()
# Compare.
error = weight.sub(my_weight).abs().max()
torch.distributed.barrier()
print(' column parallel max error (should be zero) on global rank '
'{}: {}'.format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
# ------------
# Row parallel
# ------------
weight = torch.empty(output_size, input_size_coeff)
set_random_seed(seed)
mpu.layers._initialize_affine_weight(weight, output_size, input_size,
input_size_coeff, 1,
torch.nn.init.normal_)
# Target.
set_random_seed(seed)
master_weight = torch.empty(output_size, input_size)
torch.nn.init.normal_(master_weight)
rank = mpu.get_tensor_model_parallel_rank()
my_weight = torch.split(master_weight, input_size_coeff,
dim=1)[rank].contiguous().clone()
# Compare.
error = weight.sub(my_weight).abs().max()
torch.distributed.barrier()
print(' row parallel max error (should be zero) on global rank '
'{}: {}'.format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(' >> passed the test :-)')
class IdentityLayer2D(torch.nn.Module):
def __init__(self, m, n):
super(IdentityLayer2D, self).__init__()
self.weight = Parameter(torch.Tensor(m, n))
torch.nn.init.xavier_normal_(self.weight)
def forward(self):
return self.weight
def test_column_parallel_linear(tensor_model_parallel_size):
mpu.initialize_model_parallel(tensor_model_parallel_size)
if torch.distributed.get_rank() == 0:
print('> testing ColumnParallelLinear with model parallel '
'size: {}'.format(tensor_model_parallel_size))
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
seed = 12345
set_random_seed(seed)
input_size_coeff = 13
input_size = input_size_coeff * tensor_model_parallel_size
output_size_coeff = 17
output_size = output_size_coeff * tensor_model_parallel_size
batch_size = 7
# Network
identity_layer = IdentityLayer2D(batch_size, input_size).cuda()
linear_layer = mpu.ColumnParallelLinear(
input_size, output_size, keep_master_weight_for_test=True).cuda()
loss_weight = torch.randn([batch_size, output_size]).cuda()
# Forward
input_ = identity_layer()
output = linear_layer(input_)
loss = torch.mul(output, loss_weight).sum()
# Backward
loss.backward()
# Values.
dLdY = loss_weight
X = identity_layer.weight
A = linear_layer.master_weight.cuda()
dLdA = torch.matmul(dLdY.t(), X)
dLdb = torch.matmul(torch.ones(batch_size, 1).cuda().t(), dLdY).view(-1)
dLdX = torch.matmul(dLdY, A)
rank = mpu.get_tensor_model_parallel_rank()
my_dLdA = torch.split(dLdA, output_size_coeff,
dim=0)[rank].contiguous().clone()
error = my_dLdA.sub(linear_layer.weight.grad).abs().max()
torch.distributed.barrier()
print(' error in dLdA on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
my_dLdb = torch.split(dLdb, output_size_coeff,
dim=0)[rank].contiguous().clone()
error = my_dLdb.sub(linear_layer.bias.grad).abs().max()
torch.distributed.barrier()
print(' error in dLdb on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
error = dLdX.sub(identity_layer.weight.grad).abs().max()
torch.distributed.barrier()
print(' error in dLdX on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(' >> passed the test :-)')
def test_row_parallel_linear(tensor_model_parallel_size):
mpu.initialize_model_parallel(tensor_model_parallel_size)
if torch.distributed.get_rank() == 0:
print('> testing RowParallelLinear with model parallel '
'size: {}'.format(tensor_model_parallel_size))
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
seed = 12345
set_random_seed(seed)
input_size_coeff = 13
input_size = input_size_coeff * tensor_model_parallel_size
output_size_coeff = 17
output_size = output_size_coeff * tensor_model_parallel_size
batch_size = 7
# Network
identity_layer = IdentityLayer2D(batch_size, input_size).cuda()
linear_layer = mpu.RowParallelLinear(
input_size, output_size, keep_master_weight_for_test=True).cuda()
loss_weight = torch.randn([batch_size, output_size]).cuda()
# Forward
input_ = identity_layer()
output = linear_layer(input_)
loss = torch.mul(output, loss_weight).sum()
# Backward
loss.backward()
# Values.
dLdY = loss_weight
X = identity_layer.weight
A = linear_layer.master_weight.cuda()
dLdA = torch.matmul(dLdY.t(), X)
dLdb = torch.matmul(torch.ones(batch_size, 1).cuda().t(), dLdY).view(-1)
dLdX = torch.matmul(dLdY, A)
rank = mpu.get_tensor_model_parallel_rank()
my_dLdA = torch.split(dLdA, input_size_coeff,
dim=1)[rank].contiguous().clone()
error = my_dLdA.sub(linear_layer.weight.grad).abs().max()
torch.distributed.barrier()
print(' error in dLdA on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
error = dLdb.sub(linear_layer.bias.grad).abs().max()
torch.distributed.barrier()
print(' error in dLdb on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
error = dLdX.sub(identity_layer.weight.grad).abs().max()
torch.distributed.barrier()
print(' error in dLdX on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(' >> passed the test :-)')
class IdentityLayer3D(torch.nn.Module):
def __init__(self, m, n, k):
super(IdentityLayer3D, self).__init__()
self.weight = Parameter(torch.Tensor(m, n, k))
torch.nn.init.xavier_normal_(self.weight)
def forward(self):
return self.weight
def parallel_self_attention(tensor_model_parallel_size, num_att_heads_per_partition,
hidden_size_per_att_head, dropout_prob, batch_size,
sequence_length):
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
seed = 12345
set_random_seed(seed)
num_att_heads = num_att_heads_per_partition * \
torch.distributed.get_world_size()
hidden_size = hidden_size_per_att_head * num_att_heads
# Network
identity_layer = IdentityLayer3D(batch_size, sequence_length,
hidden_size).cuda()
attention_layer = mpu.BertParallelSelfAttention(hidden_size, num_att_heads,
dropout_prob).cuda()
loss_weight = torch.randn([batch_size, sequence_length, hidden_size]).cuda()
attention_mask = torch.randn([batch_size, 1, 1, sequence_length]).cuda()
# Forward
input_ = identity_layer()
output = attention_layer(input_, attention_mask)
loss = torch.mul(output, loss_weight).sum()
# Backward
loss.backward()
rank = mpu.get_tensor_model_parallel_rank()
mpu.destroy_model_parallel()
return rank, hidden_size, tensor_model_parallel_size, loss, \
attention_layer, identity_layer
def test_parallel_self_attention(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing ParallelSelfAttention with model parallel '
'size: {}'.format(tensor_model_parallel_size))
num_att_heads_per_partition = 3
hidden_size_per_att_head = 7
dropout_prob = 0.0 # has to be zero
batch_size = 5
sequence_length = 13
rank_1, hideen_size_1, tensor_model_parallel_size_1, loss_1, \
attention_layer_1, identity_layer_1 = parallel_self_attention(
1, num_att_heads_per_partition,
hidden_size_per_att_head, dropout_prob, batch_size, sequence_length)
rank, hidden_size, tensor_model_parallel_size, loss, \
attention_layer, identity_layer = parallel_self_attention(
tensor_model_parallel_size, num_att_heads_per_partition,
hidden_size_per_att_head, dropout_prob, batch_size, sequence_length)
assert hideen_size_1 == hidden_size
error = loss_1.sub(loss).abs().max()
torch.distributed.barrier()
print(' loss error on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 5.0e-6
my_lin_grad_list = torch.split(
attention_layer_1.query_key_value.weight.grad,
hidden_size // tensor_model_parallel_size, 0)[rank::tensor_model_parallel_size]
my_lin_grad = torch.cat(my_lin_grad_list, dim=0)
error = my_lin_grad.sub(
attention_layer.query_key_value.weight.grad).abs().max()
torch.distributed.barrier()
print(' weight gradient error on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 5.0e-6
error = identity_layer_1.weight.grad.sub(
identity_layer.weight.grad).abs().max()
torch.distributed.barrier()
print(' input gradient error on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 5.0e-6
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(' >> passed the test :-)')
def parallel_transformer(tensor_model_parallel_size, num_att_heads_per_partition,
hidden_size_per_att_head, batch_size, sequence_length):
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
seed = 12345
set_random_seed(seed)
num_att_heads = num_att_heads_per_partition * \
torch.distributed.get_world_size()
hidden_size = hidden_size_per_att_head * num_att_heads
intermediate_size = 4 * hidden_size
# Network
identity_layer = IdentityLayer3D(batch_size, sequence_length,
hidden_size).cuda()
transformer_layer = mpu.BertParallelTransformerLayer(
hidden_size, intermediate_size, num_att_heads, 0.0, 0.0,
torch.nn.functional.relu, 1.0e-5).cuda()
loss_weight = torch.randn([batch_size, sequence_length, hidden_size]).cuda()
attention_mask = torch.randn([batch_size, 1, 1, sequence_length]).cuda()
# Forward
input_ = identity_layer()
output = transformer_layer(input_, attention_mask)
loss = torch.mul(output, loss_weight).sum()
# Backward
loss.backward()
rank = mpu.get_tensor_model_parallel_rank()
mpu.destroy_model_parallel()
return rank, hidden_size, tensor_model_parallel_size, loss, \
transformer_layer, identity_layer
def test_parallel_transformer_layer(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing ParallelTransformerLayer with model parallel '
'size: {}'.format(tensor_model_parallel_size))
num_att_heads_per_partition = 3
hidden_size_per_att_head = 7
batch_size = 5
sequence_length = 13
rank_1, hidden_size_1, tensor_model_parallel_size_1, loss_1, \
transformer_layer_1, identity_layer_1 = parallel_transformer(
1, num_att_heads_per_partition,
hidden_size_per_att_head, batch_size, sequence_length)
rank, hidden_size, tensor_model_parallel_size, loss, \
transformer_layer, identity_layer = parallel_transformer(
tensor_model_parallel_size, num_att_heads_per_partition,
hidden_size_per_att_head, batch_size, sequence_length)
error = loss_1.sub(loss).abs().max()
torch.distributed.barrier()
print(' loss error on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 5.0e-5, 'error: {}'.format(error)
error = identity_layer_1.weight.grad.sub(
identity_layer.weight.grad).abs().max()
torch.distributed.barrier()
print(' input gradient error on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 5.0e-5, 'error: {}'.format(error)
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(' >> passed the test :-)')
if __name__ == '__main__':
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
initialize_distributed()
world_size = torch.distributed.get_world_size()
print_separator('test initialize affine weight')
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
test_initialize_affine_weight(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test parallel embedding')
test_parallel_embedding(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
print_separator('test column-parallel linear')
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
test_column_parallel_linear(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
print_separator('test row-parallel linear')
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
test_row_parallel_linear(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
print_separator('test parallel self-attention')
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
test_parallel_self_attention(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
print_separator('test parallel transformer')
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
test_parallel_transformer_layer(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
|
Megatron-LM-master
|
megatron/mpu/tests/test_layers.py
|
Megatron-LM-master
|
megatron/mpu/tests/__init__.py
|
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import argparse
import os
import random
import numpy
import torch
import mpu
class IdentityLayer(torch.nn.Module):
def __init__(self, size, scale=1.0):
super(IdentityLayer, self).__init__()
self.weight = torch.nn.Parameter(scale * torch.randn(size))
def forward(self):
return self.weight
def set_random_seed(seed):
"""Set random seed for reproducability."""
random.seed(seed)
numpy.random.seed(seed)
torch.manual_seed(seed)
mpu.model_parallel_cuda_manual_seed(seed)
def initialize_distributed(backend='nccl'):
"""Initialize torch.distributed."""
# Get local rank in case it is provided.
parser = argparse.ArgumentParser()
parser.add_argument('--local_rank', type=int, default=None,
help='local rank passed from distributed launcher')
args = parser.parse_args()
local_rank = args.local_rank
# Get rank and world size.
rank = int(os.getenv('RANK', '0'))
world_size = int(os.getenv("WORLD_SIZE", '1'))
print('> initializing torch.distributed with local rank: {}, '
'rank: {}, world size: {}'.format(local_rank, rank, world_size))
# Set the device id.
device = rank % torch.cuda.device_count()
if local_rank is not None:
device = local_rank
torch.cuda.set_device(device)
# Call the init process.
init_method = 'tcp://'
master_ip = os.getenv('MASTER_ADDR', 'localhost')
master_port = os.getenv('MASTER_PORT', '6000')
init_method += master_ip + ':' + master_port
torch.distributed.init_process_group(
backend=backend,
world_size=world_size,
rank=rank,
init_method=init_method)
def print_separator(message):
torch.distributed.barrier()
filler_len = (78 - len(message)) // 2
filler = '-' * filler_len
string = '\n' + filler + ' {} '.format(message) + filler
if torch.distributed.get_rank() == 0:
print(string, flush=True)
torch.distributed.barrier()
|
Megatron-LM-master
|
megatron/mpu/tests/commons.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
from commons import print_separator
from commons import initialize_distributed
from mpu import data as data_utils
import mpu
import torch
import functools
import operator
import sys
sys.path.append("../..")
def test_broadcast_data(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing broadcast_data with model parallel size {} ...'.
format(tensor_model_parallel_size))
mpu.initialize_model_parallel(tensor_model_parallel_size)
torch.manual_seed(1234 + mpu.get_data_parallel_rank())
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
key_size_t = {'key1': [7, 11],
'key2': [8, 2, 1],
'key3': [13],
'key4': [5, 1, 2],
'key5': [5, 12]}
keys = list(key_size_t.keys())
data = {}
data_t = {}
for key in key_size_t:
data[key] = torch.LongTensor(size=key_size_t[key]).random_(0, 1000)
data_t[key] = data[key].clone()
data['keyX'] = torch.FloatTensor(size=(5, )).random_(0, 1000)
data_t['keyX'] = data['keyX'].clone()
if mpu.get_tensor_model_parallel_rank() != 0:
data = None
data_utils._check_data_types(keys, data_t, torch.int64)
key_size, key_numel, \
total_numel = data_utils._build_key_size_numel_dictionaries(keys, data)
for key in keys:
assert key_size[key] == key_size_t[key]
total_numel_t = 0
for key in keys:
target_size = functools.reduce(operator.mul, key_size_t[key], 1)
assert key_numel[key] == target_size
total_numel_t += target_size
assert total_numel == total_numel_t
data_b = data_utils.broadcast_data(keys, data, torch.int64)
for key in keys:
tensor = data_t[key].cuda()
assert data_b[key].sub(tensor).abs().max() == 0
# Reset groups
mpu.destroy_tensor_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
if __name__ == '__main__':
initialize_distributed()
world_size = torch.distributed.get_world_size()
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test test broadcast data')
test_broadcast_data(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
|
Megatron-LM-master
|
megatron/mpu/tests/test_data.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
from commons import print_separator
from commons import initialize_distributed
import mpu
import torch
import sys
sys.path.append("../..")
def test_initialize_model_parallel(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing initialize_model_parallel with size {} ...'.format(
tensor_model_parallel_size))
tensor_model_parallel_size_ = min(tensor_model_parallel_size,
torch.distributed.get_world_size())
assert not mpu.model_parallel_is_initialized()
mpu.initialize_model_parallel(tensor_model_parallel_size_)
assert mpu.model_parallel_is_initialized()
# Checks.
def check(group, world_size, rank):
assert world_size == torch.distributed.get_world_size(group=group)
assert rank == torch.distributed.get_rank(group=group)
# Model parallel.
world_size = tensor_model_parallel_size_
rank = torch.distributed.get_rank() % tensor_model_parallel_size_
assert world_size == mpu.get_tensor_model_parallel_world_size()
assert rank == mpu.get_tensor_model_parallel_rank()
check(mpu.get_tensor_model_parallel_group(), world_size, rank)
# Data parallel.
world_size = torch.distributed.get_world_size() // tensor_model_parallel_size_
rank = torch.distributed.get_rank() // tensor_model_parallel_size
assert world_size == mpu.get_data_parallel_world_size()
assert rank == mpu.get_data_parallel_rank()
check(mpu.get_data_parallel_group(), world_size, rank)
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
def test_get_tensor_model_parallel_src_rank(tensor_model_parallel_size_):
if torch.distributed.get_rank() == 0:
print('> testing get_tensor_model_parallel_src_rank with size {} ...'.format(
tensor_model_parallel_size_))
tensor_model_parallel_size = min(tensor_model_parallel_size_,
torch.distributed.get_world_size())
assert not mpu.model_parallel_is_initialized()
mpu.initialize_model_parallel(tensor_model_parallel_size)
assert mpu.model_parallel_is_initialized()
# Checks
src_rank = torch.distributed.get_rank() - mpu.get_tensor_model_parallel_rank()
assert mpu.get_tensor_model_parallel_src_rank() == src_rank
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
if __name__ == '__main__':
initialize_distributed()
world_size = torch.distributed.get_world_size()
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test initialize model parallel')
test_initialize_model_parallel(tensor_model_parallel_size)
print_separator('test model parallel source rank')
test_get_tensor_model_parallel_src_rank(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
|
Megatron-LM-master
|
megatron/mpu/tests/test_initialize.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
from commons import print_separator
from commons import initialize_distributed
import mpu
import torch
import sys
sys.path.append("../..")
def test_set_cuda_rng_state(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing set_rng_state with size {} ...'.
format(tensor_model_parallel_size))
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
size = 123
seed = 1234
torch.cuda.manual_seed(1234)
tensor = torch.cuda.FloatTensor(size)
# Get the state
rng_state = torch.cuda.get_rng_state()
rng_state_copy = rng_state.clone()
# Do some stuff.
for _ in range(5):
torch.randn(size, out=tensor)
result_1 = tensor.clone()
assert rng_state.sub(rng_state_copy).max() == 0
assert torch.cuda.get_rng_state().sub(rng_state_copy).max() > 0
# State should be different.
new_rng_state = torch.cuda.get_rng_state()
max_diff = new_rng_state.sub(rng_state).max()
print(' max diff in rng state (should be non-zero) on global rank {}: {}'.
format(torch.distributed.get_rank(), max_diff))
assert max_diff > 0
# Reset the rng state and do the same stuff.
mpu.random._set_cuda_rng_state(rng_state)
for _ in range(5):
torch.randn(size, out=tensor)
mpu.random._set_cuda_rng_state(rng_state)
for _ in range(5):
torch.randn(size, out=tensor)
result_2 = tensor.clone()
# Results should be the same
error = result_2.sub(result_1).abs().max()
print(' max error in generated tensors (should be zero) on '
'global rank {}: {}'.format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Input state should have remained intact.
error = rng_state.sub(rng_state_copy).max()
print(' max error in rng state (should be zero) on global rank {}: {}'.
format(torch.distributed.get_rank(), error))
assert error == 0
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
def test_cuda_rng_tracker(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing cuda rng tracker with size {} ...'.
format(tensor_model_parallel_size))
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
seed_1 = 1234
seed_2 = 4321
size = [12, 21]
tensor = torch.cuda.FloatTensor(size)
# Set to seed_1 and generate two tensors.
torch.cuda.manual_seed(seed_1)
torch.randn(size, out=tensor)
target_11 = tensor.clone()
torch.randn(size, out=tensor)
target_12 = tensor.clone()
# Set to seed_2 and generate two tensors.
torch.cuda.manual_seed(seed_2)
torch.randn(size, out=tensor)
target_21 = tensor.clone()
torch.randn(size, out=tensor)
target_22 = tensor.clone()
# Now if we interleave seed_1 and seed_2,
# we should still get the same tensors
torch.cuda.manual_seed(seed_1)
mpu.get_cuda_rng_tracker().add('test', seed_2)
torch.randn(size, out=tensor)
result_11 = tensor.clone()
with mpu.get_cuda_rng_tracker().fork('test'):
torch.randn(size, out=tensor)
result_21 = tensor.clone()
torch.randn(size, out=tensor)
result_12 = tensor.clone()
with mpu.get_cuda_rng_tracker().fork('test'):
torch.randn(size, out=tensor)
result_22 = tensor.clone()
diff = result_11.sub(result_21).abs().max()
diff = min(diff, result_12.sub(result_22).abs().max())
print(' max diff in generated tensors (should be non-zero) on '
'global rank {}: {}'.format(torch.distributed.get_rank(), diff))
assert diff > 1.0e-6
error = max(result_11.sub(target_11).abs().max(),
result_12.sub(target_12).abs().max())
error = max(error, result_21.sub(target_21).abs().max())
error = max(error, result_22.sub(target_22).abs().max())
print(' max error in generated tensors (should be zero) on '
'global rank {}: {}'.format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Reset the tracker
mpu.get_cuda_rng_tracker().reset()
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
def test_model_parallel_cuda_manual_seed(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing model parallel cuda manual seed with size {} ...'.
format(tensor_model_parallel_size))
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
mpu.model_parallel_cuda_manual_seed(12345)
assert torch.cuda.initial_seed() == 12345
with mpu.get_cuda_rng_tracker().fork():
assert torch.cuda.initial_seed() == (12345 + 2718 +
mpu.get_tensor_model_parallel_rank())
# Reset the tracker
mpu.get_cuda_rng_tracker().reset()
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
if __name__ == '__main__':
initialize_distributed()
world_size = torch.distributed.get_world_size()
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test set rng state')
test_set_cuda_rng_state(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test cuda rng tracker')
test_cuda_rng_tracker(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test model parallel cuda manual seed')
test_model_parallel_cuda_manual_seed(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
|
Megatron-LM-master
|
megatron/mpu/tests/test_random.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
from dataclasses import dataclass
from typing import Callable, Optional
import torch
@dataclass
class ModelParallelConfig:
"""Base configuration for Megatron Core
Model Parallelism
-----------------
tensor_model_parallel_size (int): Intra-layer model parallelism. Splits tensors across GPU ranks. Defaults to 1.
pipeline_model_parallel_size (int): Inter-layer model parallelism. Splits transformer layers across GPU
ranks. Defaults to 1.
virtual_pipeline_model_parallel_size (int): Interleaved pipeline parallelism is used to improve performance by
reducing the pipeline bubble. Considers a transformer block as a list of smaller transformer (virtual) blocks.
The number of virtual blocks per pipeline model parallel rank is the virtual model parallel size. See Efficient
Large-Scale Language Model Training on GPU Clusters Using Megatron-LM: https://arxiv.org/pdf/2104.04473.pdf for
more details. Defaults to None.
sequence_parallel (bool): Makes tensor parallelism more memory efficient for LLMs (20B+) by
parallelizing layer norms and dropout sequentially. See Reducing Activation Recomputation in Large Transformer
Models: https://arxiv.org/abs/2205.05198 for more details. Defaults to False.
Initialization
--------------
perform_initialization (bool, default=True): If true, weights are initialized. This option can be useful when you
know you are going to load values from a checkpoint.
use_cpu_initialization: (bool, default=False): When set to False, we initialize the weights directly on the GPU.
Transferring weights from CPU to GPU can take a significant amount of time for large models. Defaults to False.
Training
--------
fp16 (bool): If true, train with fp16 mixed precision training. Defaults to False.
bf16 (bool): If true, train with bf16 mixed precision training. Defaults to False.
params_dtype (torch.dtype): dtype used when intializing the weights. Defaults to torch.float32
timers (optional, default=None): TODO
Optimizations
-------------
gradient_accumulation_fusion (bool): If true, fuses weight gradient accumulation to GEMMs. Requires the custom CUDA
extension fused_weight_gradient_mlp_cuda module. To use gradient_accumulation_fusion you must install APEX with
--cpp_ext and --cuda_ext. For example: "pip install --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\"
". Note that the extension requires CUDA>=11. Otherwise, you must turn off gradient accumulation fusion.
Defaults to False.
async_tensor_model_parallel_allreduce (bool, default=True): If true, enables asynchronous execution of
tensor-model-parallel all-reduce with weight gradient compuation of a column-linear layer. Defaults to False.
Pipeline Parallelism
--------------------
pipeline_dtype (required): dtype used in p2p communication, usually params_dtype
grad_scale_func (optional, default=None): If using loss scaling, this function should take the loss and return the
scaled loss. If None, no function is called on the loss.
enable_autocast (bool): If true runs the forward step function inside torch.autocast context. Default is False.
autocast_dtype (torch.dtype): dtype to pass to torch.amp.autocast when enabled. Default is pipeline_dtype.
variable_seq_lengths (bool, default=False): Support for variable sequence lengths across microbatches. Setting this
communicates the size of tensors during pipeline parallelism communication, because of this extra overhead it
should only be set if the sequence length varies by microbatch within a global batch.
num_microbatches_with_partial_activation_checkpoints (int, default=None): If int, set the number of microbatches
where not all of the layers will be checkpointed and recomputed. The rest of the microbatches within the window
of maximum outstanding microbatches will recompute all layers (either full recompute or selective recompute). If
None, the checkpoint and recompute will be left up to the forward_step function.
overlap_p2p_comm (bool, optional, default=False): When True some of the peer to peer communication for pipeline
parallelism will overlap with computation. Must be False if batch_p2p_comm is true.
batch_p2p_comm (bool, default=True): Use batch_isend_irecv instead of individual isend/irecv calls. Must be False
if overlap_p2p_comm is True.
batch_p2p_sync (bool, default=True): When using batch_isend_irecv, do a cuda.device.synchronize afterward to work
around a bug in older version of PyTorch.
use_ring_exchange_p2p (bool, default = False): Use custom ring_exchange kernel instead of
torch.distributed.batch_isend_irecv(). Requires custom built torch with torch.distributed.ring_exchange.
deallocate_pipeline_outputs (optional, default=False): If True, output data is deallocated after the tensor is sent
to the next pipeline stage. Helps with saving memory, does nothing when pipeline parallel is not used.
no_sync_func (optional): Function that creates a context that suppresses asynchronous data-parallel
communication. If the model is an instance of torch.nn.DistributedDataParallel, the default is to use
torch.nn.DistributedDataParallel.no_sync.
grad_sync_func (optional): Function that launches asynchronous gradient reductions (e.g. distributed optimizer
gradient reduce-scatters). The function should take one argument: an iterable of parameters whose gradients are
to be synchronized.
param_sync_func (optional): Function that launches asynchronous parameter synchronizations (e.g. distributed
optimizer parameter all-gathers). The function should take one argument: an iterable of parameters to be
synchronized.
"""
# Model parallelism
tensor_model_parallel_size: int = 1
pipeline_model_parallel_size: int = 1
virtual_pipeline_model_parallel_size: Optional[int] = None
sequence_parallel: bool = False
# Initialization
perform_initialization: bool = True
use_cpu_initialization: bool = False
# Training
fp16: bool = False
bf16: bool = False
params_dtype: torch.dtype = torch.float32
timers: Callable = None
# Optimizations
gradient_accumulation_fusion: bool = False
async_tensor_model_parallel_allreduce: bool = False
# Pipeline Parallel
pipeline_dtype: torch.dtype = None
grad_scale_func: Callable = None
enable_autocast: bool = False
autocast_dtype: torch.dtype = None
variable_seq_lengths: bool = False
num_microbatches_with_partial_activation_checkpoints: Optional[int] = None
overlap_p2p_comm: bool = False
batch_p2p_comm: bool = True
batch_p2p_sync: bool = True
use_ring_exchange_p2p: bool = False
deallocate_pipeline_outputs: bool = False
no_sync_func: Callable = None
grad_sync_func: Callable = None
param_sync_func: Callable = None
def __post_init__(self):
""" Python dataclass method that is used to modify attributes after initialization.
See https://docs.python.org/3/library/dataclasses.html#post-init-processing for more details.
"""
if self.sequence_parallel:
if self.tensor_model_parallel_size <= 1:
raise ValueError("Can not use sequence paralllelism without tensor parallelism")
if self.async_tensor_model_parallel_allreduce:
# sequence_parallelism already does this async
self.async_tensor_model_parallel_allreduce = False
if self.pipeline_model_parallel_size > 1:
if self.pipeline_dtype is None:
raise ValueError(
"When using pipeline parallelism, pipeline_dtype must be specified"
)
if self.autocast_dtype is None:
self.autocast_dtype = self.params_dtype
|
Megatron-LM-master
|
megatron/core/model_parallel_config.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
MAJOR = 0
MINOR = 4
PATCH = 0
PRE_RELEASE = 'rc0'
# Use the following formatting: (major, minor, patch, pre-release)
VERSION = (MAJOR, MINOR, PATCH, PRE_RELEASE)
__shortversion__ = '.'.join(map(str, VERSION[:3]))
__version__ = '.'.join(map(str, VERSION[:3])) + ''.join(VERSION[3:])
__package_name__ = 'megatron_core'
__contact_names__ = 'NVIDIA'
__contact_emails__ = 'nemo-toolkit@nvidia.com' # use NeMo Email
__homepage__ = (
'https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/' # use NeMo homepage
)
__repository_url__ = 'https://github.com/NVIDIA/Megatron-LM/megatron/core'
__download_url__ = 'https://github.com/NVIDIA/Megatron-LM/releases'
__description__ = (
'Megatron Core - a library for efficient and scalable training of transformer based models'
)
__license__ = 'BSD-3'
__keywords__ = (
'deep learning, machine learning, gpu, NLP, NLU, language, transformer, nvidia, pytorch, torch'
)
|
Megatron-LM-master
|
megatron/core/package_info.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import enum
class ModelType(enum.Enum):
encoder_or_decoder = 1
encoder_and_decoder = 2
retro_encoder = 3
retro_decoder = 4
|
Megatron-LM-master
|
megatron/core/enums.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Model and data parallel groups."""
import os
from typing import Optional
import torch
from .utils import GlobalMemoryBuffer
# Intra-layer model parallel group that the current rank belongs to.
_TENSOR_MODEL_PARALLEL_GROUP = None
# Inter-layer model parallel group that the current rank belongs to.
_PIPELINE_MODEL_PARALLEL_GROUP = None
# Model parallel group (both intra- and pipeline) that the current rank belongs to.
_MODEL_PARALLEL_GROUP = None
# Embedding group.
_EMBEDDING_GROUP = None
# Position embedding group.
_POSITION_EMBEDDING_GROUP = None
# Data parallel group that the current rank belongs to.
_DATA_PARALLEL_GROUP = None
_DATA_PARALLEL_GROUP_GLOO = None
# FP8 amax reduction group.
_AMAX_REDUCTION_GROUP = None
_VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = None
_VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
_PIPELINE_MODEL_PARALLEL_SPLIT_RANK = None
# These values enable us to change the mpu sizes on the fly.
_MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = None
_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
_MPU_TENSOR_MODEL_PARALLEL_RANK = None
_MPU_PIPELINE_MODEL_PARALLEL_RANK = None
# A list of ranks that have a copy of the embedding.
_EMBEDDING_GLOBAL_RANKS = None
# A list of ranks that have a copy of the position embedding.
_POSITION_EMBEDDING_GLOBAL_RANKS = None
# A list of global ranks for each pipeline group to ease calculation of the source
# rank when broadcasting from the first or last pipeline stage.
_PIPELINE_GLOBAL_RANKS = None
# A list of global ranks for each data parallel group to ease calculation of the source
# rank when broadcasting weights from src to all other data parallel ranks
_DATA_PARALLEL_GLOBAL_RANKS = None
# Memory buffers to avoid dynamic memory allocation
_GLOBAL_MEMORY_BUFFER = None
def initialize_model_parallel(
tensor_model_parallel_size: int = 1,
pipeline_model_parallel_size: int = 1,
virtual_pipeline_model_parallel_size: Optional[int] = None,
pipeline_model_parallel_split_rank: Optional[int] = None,
use_fp8: bool = False,
use_sharp: bool = False,
) -> None:
"""Initialize model data parallel groups.
Arguments:
tensor_model_parallel_size (int, default = 1):
The number of GPUs to split individual tensors across.
pipeline_model_parallel_size (int, default = 1):
The number of tensor parallel GPU groups to split the
Transformer layers across. For example, if
tensor_model_parallel_size is 4 and
pipeline_model_parallel_size is 2, the model will be split
into 2 groups of 4 GPUs.
virtual_pipeline_model_parallel_size (int, optional):
The number of stages that each pipeline group will have,
interleaving as necessary. If None, no interleaving is
performed. For example, if tensor_model_parallel_size is 1,
pipeline_model_parallel_size is 4,
virtual_pipeline_model_parallel_size is 2, and there are
16 transformer layers in the model, the model will be
split into 8 stages with two layers each and each GPU
would get 2 stages as such (layer number starting with 1):
GPU 0: [1, 2] [9, 10]
GPU 1: [3, 4] [11, 12]
GPU 2: [5, 6] [13, 14]
GPU 3: [7, 8] [15, 16]
pipeline_model_parallel_split_rank (int, optional):
For models with both an encoder and decoder, the rank in
pipeline to switch between encoder and decoder (i.e. the
first rank of the decoder). This allows the user to set
the pipeline parallel size of the encoder and decoder
independently. For example, if
pipeline_model_parallel_size is 8 and
pipeline_model_parallel_split_rank is 3, then ranks 0-2
will be the encoder and ranks 3-7 will be the decoder.
use_fp8 (bool, default = False):
Construct GPU groups needed for FP8 training, namely for
amax reduction across the product of the data-parallel and
tensor-parallel groups.
use_sharp (bool, default = False):
Set the use of SHARP for the collective communications of
data-parallel process groups. When `True`, run barrier
within each data-parallel process group, which specifies
the SHARP application target groups.
Let's say we have a total of 16 GPUs denoted by g0 ... g15 and we
use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize
the model pipeline. The present function will
create 8 tensor model-parallel groups, 4 pipeline model-parallel groups
and 8 data-parallel groups as:
8 data_parallel groups:
[g0, g2], [g1, g3], [g4, g6], [g5, g7], [g8, g10], [g9, g11], [g12, g14], [g13, g15]
8 tensor model-parallel groups:
[g0, g1], [g2, g3], [g4, g5], [g6, g7], [g8, g9], [g10, g11], [g12, g13], [g14, g15]
4 pipeline model-parallel groups:
[g0, g4, g8, g12], [g1, g5, g9, g13], [g2, g6, g10, g14], [g3, g7, g11, g15]
Note that for efficiency, the caller should make sure adjacent ranks
are on the same DGX box. For example if we are using 2 DGX-1 boxes
with a total of 16 GPUs, rank 0 to 7 belong to the first box and
ranks 8 to 15 belong to the second box.
"""
# Get world size and rank. Ensure some consistencies.
assert torch.distributed.is_initialized()
world_size: int = torch.distributed.get_world_size()
if world_size % (tensor_model_parallel_size * pipeline_model_parallel_size) != 0:
raise RuntimeError(
f"world_size ({world_size}) is not divisible by tensor_model_parallel_size "
f"({tensor_model_parallel_size}) x pipeline_model_parallel_size ({pipeline_model_parallel_size})"
)
data_parallel_size: int = world_size // (
tensor_model_parallel_size * pipeline_model_parallel_size
)
num_tensor_model_parallel_groups: int = world_size // tensor_model_parallel_size
num_pipeline_model_parallel_groups: int = world_size // pipeline_model_parallel_size
num_data_parallel_groups: int = world_size // data_parallel_size
if virtual_pipeline_model_parallel_size is not None:
if not pipeline_model_parallel_size > 2:
raise RuntimeError(
"pipeline-model-parallel size should be greater than 2 with interleaved schedule"
)
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
_VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = 0
_VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = virtual_pipeline_model_parallel_size
if pipeline_model_parallel_split_rank is not None:
global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
_PIPELINE_MODEL_PARALLEL_SPLIT_RANK = pipeline_model_parallel_split_rank
rank = torch.distributed.get_rank()
# Build the data-parallel groups.
global _DATA_PARALLEL_GROUP
global _DATA_PARALLEL_GROUP_GLOO
global _DATA_PARALLEL_GLOBAL_RANKS
assert _DATA_PARALLEL_GROUP is None, 'data parallel group is already initialized'
all_data_parallel_group_ranks = []
for i in range(pipeline_model_parallel_size):
start_rank = i * num_pipeline_model_parallel_groups
end_rank = (i + 1) * num_pipeline_model_parallel_groups
for j in range(tensor_model_parallel_size):
ranks = range(start_rank + j, end_rank, tensor_model_parallel_size)
all_data_parallel_group_ranks.append(list(ranks))
group = torch.distributed.new_group(ranks)
group_gloo = torch.distributed.new_group(ranks, backend="gloo")
if rank in ranks:
_DATA_PARALLEL_GROUP = group
_DATA_PARALLEL_GROUP_GLOO = group_gloo
_DATA_PARALLEL_GLOBAL_RANKS = ranks
# Apply SHARP to DP process groups
if use_sharp:
if rank == 0:
print(
"The number of process groups to use SHARP with depends on the type "
"of the network switch. Nvidia QM1 switch supports SAHRP up to 8 "
"process groups and QM2 supports up to 256 process groups. We apply "
"SHARP to the communications of the data-parallel domain. If the "
"number of data-parallel process groups is larger than the max "
"process groups that the network switch supports, the communication "
"will fall back to non-SHARP operators. To enable SHARP, "
"`#SBATCH_NETWORK=sharp` should be set in the sbatch script."
)
torch.distributed.barrier(
group=get_data_parallel_group(), device_ids=[torch.cuda.current_device()]
)
# Set `NCCL_SHARP_DISABLE=1` to restrict SHARP application to DP process groups
os.environ["NCCL_SHARP_DISABLE"] = "1"
# Build the model-parallel groups.
global _MODEL_PARALLEL_GROUP
assert _MODEL_PARALLEL_GROUP is None, 'model parallel group is already initialized'
for i in range(data_parallel_size):
ranks = [
data_parallel_group_ranks[i]
for data_parallel_group_ranks in all_data_parallel_group_ranks
]
group = torch.distributed.new_group(ranks)
if rank in ranks:
_MODEL_PARALLEL_GROUP = group
# Build the tensor model-parallel groups.
global _TENSOR_MODEL_PARALLEL_GROUP
assert (
_TENSOR_MODEL_PARALLEL_GROUP is None
), 'tensor model parallel group is already initialized'
for i in range(num_tensor_model_parallel_groups):
ranks = range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size)
group = torch.distributed.new_group(ranks)
if rank in ranks:
_TENSOR_MODEL_PARALLEL_GROUP = group
# Build the pipeline model-parallel groups and embedding groups
# (first and last rank in each pipeline model-parallel group).
global _PIPELINE_MODEL_PARALLEL_GROUP
global _PIPELINE_GLOBAL_RANKS
assert (
_PIPELINE_MODEL_PARALLEL_GROUP is None
), 'pipeline model parallel group is already initialized'
global _EMBEDDING_GROUP
global _EMBEDDING_GLOBAL_RANKS
assert _EMBEDDING_GROUP is None, 'embedding group is already initialized'
global _POSITION_EMBEDDING_GROUP
global _POSITION_EMBEDDING_GLOBAL_RANKS
assert _POSITION_EMBEDDING_GROUP is None, 'position embedding group is already initialized'
for i in range(num_pipeline_model_parallel_groups):
ranks = range(i, world_size, num_pipeline_model_parallel_groups)
group = torch.distributed.new_group(ranks)
if rank in ranks:
_PIPELINE_MODEL_PARALLEL_GROUP = group
_PIPELINE_GLOBAL_RANKS = ranks
# Setup embedding group (to exchange gradients between
# first and last stages).
if len(ranks) > 1:
embedding_ranks = [ranks[0], ranks[-1]]
position_embedding_ranks = [ranks[0]]
if pipeline_model_parallel_split_rank is not None:
if ranks[pipeline_model_parallel_split_rank] not in embedding_ranks:
embedding_ranks = [
ranks[0],
ranks[pipeline_model_parallel_split_rank],
ranks[-1],
]
if ranks[pipeline_model_parallel_split_rank] not in position_embedding_ranks:
position_embedding_ranks = [ranks[0], ranks[pipeline_model_parallel_split_rank]]
else:
embedding_ranks = ranks
position_embedding_ranks = ranks
group = torch.distributed.new_group(embedding_ranks)
if rank in embedding_ranks:
_EMBEDDING_GROUP = group
if rank in ranks:
_EMBEDDING_GLOBAL_RANKS = embedding_ranks
group = torch.distributed.new_group(position_embedding_ranks)
if rank in position_embedding_ranks:
_POSITION_EMBEDDING_GROUP = group
if rank in ranks:
_POSITION_EMBEDDING_GLOBAL_RANKS = position_embedding_ranks
# Build the FP8 groups.
global _AMAX_REDUCTION_GROUP
assert _AMAX_REDUCTION_GROUP is None, 'FP8 amax reduction group is already initialized'
if use_fp8:
amax_group_size: int = tensor_model_parallel_size * data_parallel_size
num_amax_groups: int = world_size // amax_group_size
for i in range(num_amax_groups):
start_rank = i * amax_group_size
end_rank = (i + 1) * amax_group_size
ranks = range(start_rank, end_rank)
group = torch.distributed.new_group(ranks)
if rank in ranks:
_AMAX_REDUCTION_GROUP = group
# Initialize global memory buffer
# This isn't really "parallel state" but there isn't another good place to
# put this. If we end up with a more generic initialization of megatron-core
# we could stick it there
_set_global_memory_buffer()
def is_unitialized():
"""Useful for code segments that may be accessed with or without mpu initialization"""
return _DATA_PARALLEL_GROUP is None
def model_parallel_is_initialized():
"""Check if model and data parallel groups are initialized."""
if (
_TENSOR_MODEL_PARALLEL_GROUP is None
or _PIPELINE_MODEL_PARALLEL_GROUP is None
or _DATA_PARALLEL_GROUP is None
):
return False
return True
def get_model_parallel_group():
"""Get the model parallel group the caller rank belongs to."""
assert _MODEL_PARALLEL_GROUP is not None, 'model parallel group is not initialized'
return _MODEL_PARALLEL_GROUP
def get_tensor_model_parallel_group(check_initialized=True):
"""Get the tensor model parallel group the caller rank belongs to."""
if check_initialized:
assert (
_TENSOR_MODEL_PARALLEL_GROUP is not None
), 'tensor model parallel group is not initialized'
return _TENSOR_MODEL_PARALLEL_GROUP
def get_pipeline_model_parallel_group():
"""Get the pipeline model parallel group the caller rank belongs to."""
assert (
_PIPELINE_MODEL_PARALLEL_GROUP is not None
), 'pipeline_model parallel group is not initialized'
return _PIPELINE_MODEL_PARALLEL_GROUP
def get_data_parallel_group():
"""Get the data parallel group the caller rank belongs to."""
assert _DATA_PARALLEL_GROUP is not None, 'data parallel group is not initialized'
return _DATA_PARALLEL_GROUP
def get_data_parallel_group_gloo():
"""Get the data parallel group-gloo the caller rank belongs to."""
assert _DATA_PARALLEL_GROUP_GLOO is not None, 'data parallel group-gloo is not initialized'
return _DATA_PARALLEL_GROUP_GLOO
def get_embedding_group():
"""Get the embedding group the caller rank belongs to."""
assert _EMBEDDING_GROUP is not None, 'embedding group is not initialized'
return _EMBEDDING_GROUP
def get_position_embedding_group():
"""Get the position embedding group the caller rank belongs to."""
assert _POSITION_EMBEDDING_GROUP is not None, 'position embedding group is not initialized'
return _POSITION_EMBEDDING_GROUP
def get_amax_reduction_group():
"""Get the FP8 amax reduction group the caller rank belongs to."""
assert _AMAX_REDUCTION_GROUP is not None, 'FP8 amax reduction group is not initialized'
return _AMAX_REDUCTION_GROUP
def set_tensor_model_parallel_world_size(world_size):
"""Set the tensor model parallel size"""
global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
_MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = world_size
def set_pipeline_model_parallel_world_size(world_size):
"""Set the pipeline model parallel size"""
global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = world_size
def set_virtual_pipeline_model_parallel_world_size(world_size):
"""Set the pipeline model parallel size"""
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
_VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = world_size
def get_tensor_model_parallel_world_size():
"""Return world size for the tensor model parallel group."""
global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
if _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE is not None:
return _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
return torch.distributed.get_world_size(group=get_tensor_model_parallel_group())
def get_pipeline_model_parallel_world_size():
"""Return world size for the pipeline model parallel group."""
global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
if _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE is not None:
return _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
return torch.distributed.get_world_size(group=get_pipeline_model_parallel_group())
def set_tensor_model_parallel_rank(rank):
"""Set tensor model parallel rank."""
global _MPU_TENSOR_MODEL_PARALLEL_RANK
_MPU_TENSOR_MODEL_PARALLEL_RANK = rank
def set_pipeline_model_parallel_rank(rank):
"""Set pipeline model parallel rank."""
global _MPU_PIPELINE_MODEL_PARALLEL_RANK
_MPU_PIPELINE_MODEL_PARALLEL_RANK = rank
def set_pipeline_model_parallel_split_rank(rank):
"""Set pipeline model parallel split rank."""
global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
_PIPELINE_MODEL_PARALLEL_SPLIT_RANK = rank
def get_tensor_model_parallel_rank():
"""Return my rank for the tensor model parallel group."""
global _MPU_TENSOR_MODEL_PARALLEL_RANK
if _MPU_TENSOR_MODEL_PARALLEL_RANK is not None:
return _MPU_TENSOR_MODEL_PARALLEL_RANK
return torch.distributed.get_rank(group=get_tensor_model_parallel_group())
def get_pipeline_model_parallel_rank():
"""Return my rank for the pipeline model parallel group."""
global _MPU_PIPELINE_MODEL_PARALLEL_RANK
if _MPU_PIPELINE_MODEL_PARALLEL_RANK is not None:
return _MPU_PIPELINE_MODEL_PARALLEL_RANK
return torch.distributed.get_rank(group=get_pipeline_model_parallel_group())
def get_pipeline_model_parallel_split_rank():
"""Return pipeline model parallel split rank."""
global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
return _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
def is_pipeline_first_stage(ignore_virtual=False):
"""Return True if in the first pipeline model-parallel stage, False otherwise."""
if not ignore_virtual:
if (
get_virtual_pipeline_model_parallel_world_size() is not None
and get_virtual_pipeline_model_parallel_rank() != 0
):
return False
return get_pipeline_model_parallel_rank() == 0
def is_pipeline_last_stage(ignore_virtual=False):
"""Return True if in the last pipeline model-parallel stage, False otherwise."""
if not ignore_virtual:
virtual_pipeline_model_parallel_world_size = (
get_virtual_pipeline_model_parallel_world_size()
)
if virtual_pipeline_model_parallel_world_size is not None and get_virtual_pipeline_model_parallel_rank() != (
virtual_pipeline_model_parallel_world_size - 1
):
return False
return get_pipeline_model_parallel_rank() == (get_pipeline_model_parallel_world_size() - 1)
def is_rank_in_embedding_group(ignore_virtual=False):
"""Return true if current rank is in embedding group, False otherwise."""
rank = torch.distributed.get_rank()
global _EMBEDDING_GLOBAL_RANKS
if ignore_virtual:
return rank in _EMBEDDING_GLOBAL_RANKS
if rank in _EMBEDDING_GLOBAL_RANKS:
if rank == _EMBEDDING_GLOBAL_RANKS[0]:
return is_pipeline_first_stage(ignore_virtual=False)
elif rank == _EMBEDDING_GLOBAL_RANKS[-1]:
return is_pipeline_last_stage(ignore_virtual=False)
else:
return True
return False
def is_rank_in_position_embedding_group():
"""Return true if current rank is in position embedding group, False otherwise."""
rank = torch.distributed.get_rank()
global _POSITION_EMBEDDING_GLOBAL_RANKS
return rank in _POSITION_EMBEDDING_GLOBAL_RANKS
def is_pipeline_stage_before_split(rank=None):
"""Return True if pipeline stage executes encoder block for a model
with both encoder and decoder."""
if get_pipeline_model_parallel_world_size() == 1:
return True
if rank is None:
rank = get_pipeline_model_parallel_rank()
global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
if _PIPELINE_MODEL_PARALLEL_SPLIT_RANK is None:
return True
if rank < _PIPELINE_MODEL_PARALLEL_SPLIT_RANK:
return True
return False
def is_pipeline_stage_after_split(rank=None):
"""Return True if pipeline stage executes decoder block for a model
with both encoder and decoder."""
if get_pipeline_model_parallel_world_size() == 1:
return True
if rank is None:
rank = get_pipeline_model_parallel_rank()
global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
if _PIPELINE_MODEL_PARALLEL_SPLIT_RANK is None:
return True
if rank >= _PIPELINE_MODEL_PARALLEL_SPLIT_RANK:
return True
return False
def is_pipeline_stage_at_split():
"""Return true if pipeline stage executes decoder block and next
stage executes encoder block for a model with both encoder and
decoder."""
rank = get_pipeline_model_parallel_rank()
return is_pipeline_stage_before_split(rank) and is_pipeline_stage_after_split(rank + 1)
def get_virtual_pipeline_model_parallel_rank():
"""Return the virtual pipeline-parallel rank."""
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
return _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
def set_virtual_pipeline_model_parallel_rank(rank):
"""Set the virtual pipeline-parallel rank."""
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
_VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = rank
def get_virtual_pipeline_model_parallel_world_size():
"""Return the virtual pipeline-parallel world size."""
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
return _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
def get_tensor_model_parallel_src_rank():
"""Calculate the global rank corresponding to the first local rank
in the tensor model parallel group."""
global_rank = torch.distributed.get_rank()
local_world_size = get_tensor_model_parallel_world_size()
return (global_rank // local_world_size) * local_world_size
def get_data_parallel_src_rank():
"""Calculate the global rank corresponding to the first local rank
in the data parallel group."""
assert _DATA_PARALLEL_GLOBAL_RANKS is not None, "Data parallel group is not initialized"
return _DATA_PARALLEL_GLOBAL_RANKS[0]
def get_pipeline_model_parallel_first_rank():
"""Return the global rank of the first process in the pipeline for the
current tensor parallel group"""
assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized"
return _PIPELINE_GLOBAL_RANKS[0]
def get_pipeline_model_parallel_last_rank():
"""Return the global rank of the last process in the pipeline for the
current tensor parallel group"""
assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized"
last_rank_local = get_pipeline_model_parallel_world_size() - 1
return _PIPELINE_GLOBAL_RANKS[last_rank_local]
def get_pipeline_model_parallel_next_rank():
"""Return the global rank that follows the caller in the pipeline"""
assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized"
rank_in_pipeline = get_pipeline_model_parallel_rank()
world_size = get_pipeline_model_parallel_world_size()
return _PIPELINE_GLOBAL_RANKS[(rank_in_pipeline + 1) % world_size]
def get_pipeline_model_parallel_prev_rank():
"""Return the global rank that preceeds the caller in the pipeline"""
assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized"
rank_in_pipeline = get_pipeline_model_parallel_rank()
world_size = get_pipeline_model_parallel_world_size()
return _PIPELINE_GLOBAL_RANKS[(rank_in_pipeline - 1) % world_size]
def get_data_parallel_world_size():
"""Return world size for the data parallel group."""
if torch.distributed.is_available() and torch.distributed.is_initialized():
return torch.distributed.get_world_size(group=get_data_parallel_group())
else:
return 0
def get_data_parallel_rank():
"""Return my rank for the data parallel group."""
if torch.distributed.is_available() and torch.distributed.is_initialized():
return torch.distributed.get_rank(group=get_data_parallel_group())
else:
return 0
def _set_global_memory_buffer():
"""Initialize global buffer"""
global _GLOBAL_MEMORY_BUFFER
assert _GLOBAL_MEMORY_BUFFER is None, 'global memory buffer is already initialized'
_GLOBAL_MEMORY_BUFFER = GlobalMemoryBuffer()
def get_global_memory_buffer():
"""Return the global GlobalMemoryBuffer object"""
assert _GLOBAL_MEMORY_BUFFER is not None, 'global memory buffer is not initialized'
return _GLOBAL_MEMORY_BUFFER
def destroy_global_memory_buffer():
"""Sets the global memory buffer to None"""
global _GLOBAL_MEMORY_BUFFER
_GLOBAL_MEMORY_BUFFER = None
def destroy_model_parallel():
"""Set the groups to none."""
global _MODEL_PARALLEL_GROUP
_MODEL_PARALLEL_GROUP = None
global _TENSOR_MODEL_PARALLEL_GROUP
_TENSOR_MODEL_PARALLEL_GROUP = None
global _PIPELINE_MODEL_PARALLEL_GROUP
_PIPELINE_MODEL_PARALLEL_GROUP = None
global _DATA_PARALLEL_GROUP
_DATA_PARALLEL_GROUP = None
global _EMBEDDING_GROUP
_EMBEDDING_GROUP = None
global _POSITION_EMBEDDING_GROUP
_POSITION_EMBEDDING_GROUP = None
global _AMAX_REDUCTION_GROUP
_AMAX_REDUCTION_GROUP = None
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
_VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = None
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
_VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
_MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = None
global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
global _MPU_TENSOR_MODEL_PARALLEL_RANK
_MPU_TENSOR_MODEL_PARALLEL_RANK = None
global _MPU_PIPELINE_MODEL_PARALLEL_RANK
_MPU_PIPELINE_MODEL_PARALLEL_RANK = None
global _GLOBAL_MEMORY_BUFFER
_GLOBAL_MEMORY_BUFFER = None
|
Megatron-LM-master
|
megatron/core/parallel_state.py
|
import megatron.core.parallel_state
import megatron.core.tensor_parallel
import megatron.core.utils
from .inference_params import InferenceParams
from .model_parallel_config import ModelParallelConfig
# Alias parallel_state as mpu, its legacy name
mpu = parallel_state
__all__ = ["parallel_state", "tensor_parallel", "utils", "InferenceParams", "ModelParallelConfig"]
|
Megatron-LM-master
|
megatron/core/__init__.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
"""Utility functions used throughout Megatron core"""
import math
import operator
from functools import reduce
import torch
from megatron.core import parallel_state
from megatron.core.dist_checkpointing.mapping import ShardedTensor
def ensure_divisibility(numerator, denominator):
"""Ensure that numerator is divisible by the denominator."""
assert numerator % denominator == 0, "{} is not divisible by {}".format(numerator, denominator)
def divide(numerator, denominator):
"""Ensure that numerator is divisible by the denominator and return
the division value."""
ensure_divisibility(numerator, denominator)
return numerator // denominator
def get_attr_wrapped_model(model, attr, allow_none=True):
"""Get an attribute from a wrapped model"""
if isinstance(model, list):
raise RuntimeError("_get_attr_wrapped_model given a list of models")
if allow_none:
def condition(model, attr):
return not hasattr(model, attr)
else:
def condition(model, attr):
return getattr(model, attr, None) is None
while condition(model, attr):
if not hasattr(model, "module"):
raise RuntimeError(f"_get_attr_wrapped_model couldn't find attribute {attr}")
model = model.module
return getattr(model, attr)
def get_model_type(model):
return get_attr_wrapped_model(model, 'model_type')
def get_model_config(model):
return get_attr_wrapped_model(model, 'config', allow_none=False)
class GlobalMemoryBuffer:
"""Global buffer to avoid dynamic memory allocations.
Caller should ensure that buffers of the same name
are not used concurrently."""
def __init__(self):
self.buffer = {}
def get_tensor(self, tensor_shape, dtype, name):
required_len = reduce(operator.mul, tensor_shape, 1)
if (
self.buffer.get((name, dtype), None) is None
or self.buffer[(name, dtype)].numel() < required_len
):
self.buffer[(name, dtype)] = torch.empty(
required_len, dtype=dtype, device=torch.cuda.current_device(), requires_grad=False
)
return self.buffer[(name, dtype)][0:required_len].view(*tensor_shape)
def _kernel_make_viewless_tensor(inp, requires_grad):
'''Make a viewless tensor.
View tensors have the undesirable side-affect of retaining a reference
to the originally-viewed tensor, even after manually setting the '.data'
field. This method creates a new tensor that links to the old tensor's
data, without linking the viewed tensor, referenced via the '._base'
field.
'''
out = torch.empty((1,), dtype=inp.dtype, device=inp.device, requires_grad=requires_grad,)
out.data = inp.data
return out
class MakeViewlessTensor(torch.autograd.Function):
'''
Autograd function to make a viewless tensor.
This function should be used in cases where the computation graph needs
to be propagated, but we only want a viewless tensor (e.g.,
ParallelTransformer's hidden_states). Call this function by passing
'keep_graph = True' to 'make_viewless_tensor()'.
'''
@staticmethod
def forward(ctx, inp, requires_grad):
return _kernel_make_viewless_tensor(inp, requires_grad)
@staticmethod
def backward(ctx, grad_output):
return grad_output, None
def make_viewless_tensor(inp, requires_grad, keep_graph):
'''
Entry-point for creating viewless tensors.
This method should be used, rather than calling 'MakeViewlessTensor'
or '_kernel_make_viewless_tensor' directly. This method acts as a
switch for determining if an autograd function or a regular method
should be used to create the tensor.
'''
# return tensor as-is, if not a 'view'
if inp._base is None:
return inp
# create viewless tensor
if keep_graph:
return MakeViewlessTensor.apply(inp, requires_grad)
else:
return _kernel_make_viewless_tensor(inp, requires_grad)
def assert_viewless_tensor(tensor, extra_msg=None):
'''Assert that a tensor is not a view (i.e., its '._base' field is
not set).'''
if isinstance(tensor, list):
[assert_viewless_tensor(t) for t in tensor]
return tensor
if not isinstance(tensor, torch.Tensor):
return tensor
assert tensor._base is None, (
"Ensure tensor._base is None before setting tensor.data or storing "
"tensor to memory buffer. Otherwise, a memory leak will occur (and "
"likely accumulate over iterations). %s"
) % extra_msg
return tensor
def safely_set_viewless_tensor_data(tensor, new_data_tensor):
'''Safely set tensor's '.data' field.
Check first that the tensor is viewless (i.e., '._base' not set). If not,
raise an exception.
'''
assert_viewless_tensor(
tensor,
extra_msg="FYI, tensor._base has shape %s, and new_data_tensor has shape %s."
% ("--" if tensor._base is None else tensor._base.shape, new_data_tensor.shape),
)
tensor.data = new_data_tensor
def init_method_normal(sigma):
"""Init method based on N(0, sigma)."""
def init_(tensor):
return torch.nn.init.normal_(tensor, mean=0.0, std=sigma)
return init_
def scaled_init_method_normal(sigma, num_layers):
"""Init method based on N(0, sigma/sqrt(2*num_layers)."""
std = sigma / math.sqrt(2.0 * num_layers)
def init_(tensor):
return torch.nn.init.normal_(tensor, mean=0.0, std=std)
return init_
def make_tp_sharded_tensor_for_checkpoint(tensor, key, tp_axis=0, replica_id=None, **kwargs):
""" Helper for instantiating a ShardedTensor where the `tp_axis` dimension is sharded across TP group. """
return ShardedTensor.from_rank_offsets(
key,
tensor,
(
tp_axis,
parallel_state.get_tensor_model_parallel_rank(),
parallel_state.get_tensor_model_parallel_world_size(),
),
replica_id=parallel_state.get_data_parallel_rank() if replica_id is None else replica_id,
**kwargs,
)
def make_sharded_tensor_for_checkpoint(tensor, key, **kwargs):
""" Helper for instantiating a non-sharded ShardedTensor (replicated across TP and DP group). """
return ShardedTensor.from_rank_offsets(
key,
tensor,
replica_id=parallel_state.get_data_parallel_rank()
* parallel_state.get_data_parallel_world_size()
+ parallel_state.get_tensor_model_parallel_rank(),
**kwargs,
)
|
Megatron-LM-master
|
megatron/core/utils.py
|
class InferenceParams:
"""Inference parameters that are passed to the main model in order
to efficienly calculate and store the context during inference."""
def __init__(self, max_batch_size, max_sequence_length):
self.max_sequence_length = max_sequence_length
self.max_batch_size = max_batch_size
self.sequence_len_offset = 0
self.batch_size_offset = 0
self.key_value_memory_dict = {}
def swap_key_value_dict(self, batch_idx):
"swap between batches"
if len(self.key_value_memory_dict) == 0:
raise ValueError("should not swap when dict in empty")
for layer_number in self.key_value_memory_dict.keys():
inference_key_memory, inference_value_memory = self.key_value_memory_dict[layer_number]
assert (
len(batch_idx) == inference_key_memory.shape[1]
) # make sure batch size is the same
new_inference_key_memory = inference_key_memory[:, batch_idx]
new_inference_value_memory = inference_value_memory[:, batch_idx]
self.key_value_memory_dict[layer_number] = (
new_inference_key_memory,
new_inference_value_memory,
)
|
Megatron-LM-master
|
megatron/core/inference_params.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import importlib
import numbers
import torch
from torch.nn import init
from torch.nn.parameter import Parameter
from megatron.core.utils import make_viewless_tensor
try:
from apex.contrib.layer_norm.layer_norm import FastLayerNormFN
HAVE_PERSIST_LAYER_NORM = True
except:
HAVE_PERSIST_LAYER_NORM = False
try:
from apex.normalization.fused_layer_norm import FusedLayerNormAffineFunction
HAVE_FUSED_LAYER_NORM = True
except:
HAVE_FUSED_LAYER_NORM = False
class FusedLayerNorm(torch.nn.Module):
def __init__(
self,
hidden_size,
eps=1e-5,
persist_layer_norm=True,
sequence_parallel=False,
zero_centered_gamma=False,
):
super().__init__()
self.zero_centered_gamma = zero_centered_gamma
# List of hiddens sizes supported in the persistent layer norm kernel
# If the hidden size is not supported, fall back to the non-persistent
# kernel.
persist_ln_hidden_sizes = [
1024,
1536,
2048,
2304,
3072,
3840,
4096,
5120,
6144,
8192,
10240,
12288,
12800,
15360,
16384,
18432,
20480,
24576,
25600,
30720,
32768,
40960,
49152,
65536,
]
if hidden_size not in persist_ln_hidden_sizes or not HAVE_PERSIST_LAYER_NORM:
persist_layer_norm = False
if not persist_layer_norm and not HAVE_FUSED_LAYER_NORM:
# TODO: Add pytorch only layer norm
raise ValueError(f'Apex must currently be installed to use megatron core.')
if isinstance(hidden_size, numbers.Integral):
hidden_size = (hidden_size,)
self.hidden_size = torch.Size(hidden_size)
self.eps = eps
self.weight = Parameter(torch.Tensor(*hidden_size))
self.bias = Parameter(torch.Tensor(*hidden_size))
self.reset_parameters()
self.persist_layer_norm = persist_layer_norm
self.sequence_parallel = sequence_parallel
# set sequence parallelism flag on weight and bias parameters
setattr(self.weight, 'sequence_parallel', self.sequence_parallel)
setattr(self.bias, 'sequence_parallel', self.sequence_parallel)
def reset_parameters(self):
if self.zero_centered_gamma:
init.zeros_(self.weight)
init.zeros_(self.bias)
else:
init.ones_(self.weight)
init.zeros_(self.bias)
def forward(self, input):
weight = self.weight + 1 if self.zero_centered_gamma else self.weight
if self.persist_layer_norm:
output = FastLayerNormFN.apply(input, weight, self.bias, self.eps)
# Apex's fast layer norm function outputs a 'view' tensor (i.e., has
# a populated '_base' field). This will result in schedule.py's
# deallocate_output_tensor() throwing an error, so a viewless tensor is
# created to prevent this.
output = make_viewless_tensor(
inp=output, requires_grad=input.requires_grad, keep_graph=True
)
else:
output = FusedLayerNormAffineFunction.apply(
input, weight, self.bias, self.hidden_size, self.eps
)
return output
|
Megatron-LM-master
|
megatron/core/fusions/fused_layer_norm.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import torch
import torch.nn as nn
from megatron.core.transformer.enums import AttnMaskType
class ScaledUpperTriangMaskedSoftmax(torch.autograd.Function):
"""
Fused operation which performs following three operations in sequence
1. Scale the tensor.
2. Apply upper triangular mask (typically used in gpt models).
3. Perform softmax.
"""
@staticmethod
def forward(ctx, inputs, scale):
import scaled_upper_triang_masked_softmax_cuda
scale_t = torch.tensor([scale])
softmax_results = scaled_upper_triang_masked_softmax_cuda.forward(inputs, scale_t[0])
ctx.save_for_backward(softmax_results, scale_t)
return softmax_results
@staticmethod
def backward(ctx, output_grads):
import scaled_upper_triang_masked_softmax_cuda
softmax_results, scale_t = ctx.saved_tensors
input_grads = scaled_upper_triang_masked_softmax_cuda.backward(
output_grads, softmax_results, scale_t[0]
)
return input_grads, None
class ScaledMaskedSoftmax(torch.autograd.Function):
"""
Fused operation which performs following three operations in sequence
1. Scale the tensor.
2. Apply the mask.
3. Perform softmax.
"""
@staticmethod
def forward(ctx, inputs, mask, scale):
import scaled_masked_softmax_cuda
scale_t = torch.tensor([scale])
softmax_results = scaled_masked_softmax_cuda.forward(inputs, mask, scale_t[0])
ctx.save_for_backward(softmax_results, scale_t)
return softmax_results
@staticmethod
def backward(ctx, output_grads):
import scaled_masked_softmax_cuda
softmax_results, scale_t = ctx.saved_tensors
input_grads = scaled_masked_softmax_cuda.backward(output_grads, softmax_results, scale_t[0])
return input_grads, None, None
class ScaledSoftmax(torch.autograd.Function):
"""
Fused operation which performs following two operations in sequence
1. Scale the tensor.
2. Perform softmax.
"""
@staticmethod
def forward(ctx, inputs, scale):
import scaled_softmax_cuda
scale_t = torch.tensor([scale])
softmax_results = scaled_softmax_cuda.forward(inputs, scale_t[0])
ctx.save_for_backward(softmax_results, scale_t)
return softmax_results
@staticmethod
def backward(ctx, output_grads):
import scaled_softmax_cuda
softmax_results, scale_t = ctx.saved_tensors
input_grads = scaled_softmax_cuda.backward(output_grads, softmax_results, scale_t[0])
return input_grads, None, None
class FusedScaleMaskSoftmax(nn.Module):
"""
fused operation: scaling + mask + softmax
Arguments:
input_in_fp16: flag to indicate if input in fp16 data format.
input_in_bf16: flag to indicate if input in bf16 data format.
attn_mask_type: attention mask type (pad or causal)
scaled_masked_softmax_fusion: flag to indicate user want to use softmax fusion
mask_func: mask function to be applied.
softmax_in_fp32: if true, softmax in performed at fp32 precision.
scale: scaling factor used in input tensor scaling.
"""
def __init__(
self,
input_in_fp16,
input_in_bf16,
attn_mask_type,
scaled_masked_softmax_fusion,
mask_func,
softmax_in_fp32,
scale,
):
super(FusedScaleMaskSoftmax, self).__init__()
self.input_in_fp16 = input_in_fp16
self.input_in_bf16 = input_in_bf16
assert not (
self.input_in_fp16 and self.input_in_bf16
), "both fp16 and bf16 flags cannot be active at the same time."
self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16
self.attn_mask_type = attn_mask_type
self.scaled_masked_softmax_fusion = scaled_masked_softmax_fusion
self.mask_func = mask_func
self.softmax_in_fp32 = softmax_in_fp32
self.scale = scale
assert self.scale is None or softmax_in_fp32, "softmax should be in fp32 when scaled"
def forward(self, input, mask):
# [b, np, sq, sk]
assert input.dim() == 4
if self.is_kernel_available(mask, *input.size()):
return self.forward_fused_softmax(input, mask)
else:
return self.forward_torch_softmax(input, mask)
def is_kernel_available(self, mask, b, np, sq, sk):
attn_batches = b * np
if (
self.scaled_masked_softmax_fusion # user want to fuse
and self.input_in_float16 # input must be fp16
and 16 < sk <= 4096 # sk must be 16 ~ 2048
and sq % 4 == 0 # sq must be divisor of 4
and sk % 4 == 0 # sk must be divisor of 4
and attn_batches % 4 == 0 # np * b must be divisor of 4
):
if 0 <= sk <= 4096:
batch_per_block = self.get_batch_per_block(sq, sk, b, np)
if self.attn_mask_type == AttnMaskType.causal:
if attn_batches % batch_per_block == 0:
return True
else:
if sq % batch_per_block == 0:
return True
return False
def forward_fused_softmax(self, input, mask):
b, np, sq, sk = input.size()
scale = self.scale if self.scale is not None else 1.0
if self.attn_mask_type == AttnMaskType.causal:
assert sq == sk, "causal mask is only for self attention"
# input is 3D tensor (attn_batches, sq, sk)
input = input.view(-1, sq, sk)
probs = ScaledUpperTriangMaskedSoftmax.apply(input, scale)
return probs.view(b, np, sq, sk)
else:
# input is 4D tensor (b, np, sq, sk)
if mask is not None:
return ScaledMaskedSoftmax.apply(input, mask, scale)
else:
return ScaledSoftmax.apply(input, scale)
def forward_torch_softmax(self, input, mask):
if self.input_in_float16 and self.softmax_in_fp32:
input = input.float()
if self.scale is not None:
input = input * self.scale
mask_output = self.mask_func(input, mask) if mask is not None else input
probs = torch.nn.Softmax(dim=-1)(mask_output)
if self.input_in_float16 and self.softmax_in_fp32:
if self.input_in_fp16:
probs = probs.half()
else:
probs = probs.bfloat16()
return probs
@staticmethod
def get_batch_per_block(sq, sk, b, np):
import scaled_masked_softmax_cuda
return scaled_masked_softmax_cuda.get_batch_per_block(sq, sk, b, np)
|
Megatron-LM-master
|
megatron/core/fusions/fused_softmax.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
from typing import Optional, Tuple
import torch
def _bias_dropout_add_func(x, bias, residual, prob, training):
# type: (Tensor, Optional[Tensor], Tensor, float, bool) -> Tensor
# NOTE: Previously, the argument `bias` used to be passed as
# `bias.expand_as(residual)` when the `bias_dropout_func` is called from the
# transformer layer but broadcasting should automatically take care of that.
# Also, looking at broadcasting semantics, `expand_as` and broadcasting
# seem to be identical performance-wise (both just change the view).
# If we want to train mixed precision, then the output of this function
# should be half precision. However, in AMP O1, the input (residual) is
# in fp32, and it will up-cast the result to fp32, causing pipeline parallel
# GPU communication to hang. Therefore, we need to cast residual to the same
# dtype as x.
residual = residual if residual.dtype == x.dtype else residual.to(x.dtype)
if bias is not None:
x = x + bias
out = torch.nn.functional.dropout(x, p=prob, training=training)
out = residual + out
return out
@torch.jit.script
def bias_dropout_add_fused_train(
x_with_bias: Tuple[torch.Tensor, Optional[torch.Tensor]], residual: torch.Tensor, prob: float,
) -> torch.Tensor:
x, bias = x_with_bias # unpack
return _bias_dropout_add_func(x, bias, residual, prob, True)
@torch.jit.script
def bias_dropout_add_fused_inference(
x_with_bias: Tuple[torch.Tensor, Optional[torch.Tensor]], residual: torch.Tensor, prob: float,
) -> torch.Tensor:
x, bias = x_with_bias # unpack
return _bias_dropout_add_func(x, bias, residual, prob, False)
def get_bias_dropout_add(training, fused):
def unfused_bias_dropout_add(x_with_bias, residual, prob):
x, bias = x_with_bias # unpack
return _bias_dropout_add_func(x, bias, residual, prob, training)
if fused:
# jit scripting for a nn.module (with dropout) is not
# triggering the fusion kernel. For now, we use two
# different nn.functional routines to account for varying
# dropout semantics during training and inference phases.
if training:
return bias_dropout_add_fused_train
else:
return bias_dropout_add_fused_inference
else:
return unfused_bias_dropout_add
|
Megatron-LM-master
|
megatron/core/fusions/fused_bias_dropout.py
|
Megatron-LM-master
|
megatron/core/fusions/__init__.py
|
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import torch
###### BIAS GELU FUSION/ NO AUTOGRAD ################
# 1/sqrt(2*pi)-> 0.3989423
# 1/sqrt(2) -> 0.70710678
# sqrt(2/pi) -> 0.79788456
# this function is tanh approximation of gelu
# actual gelu is:
# x * 0.5 * (1.0 + torch.erf(x * 0.70710678))
@torch.jit.script
def bias_gelu(bias, y):
x = bias + y
return x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)))
# gradient of tanh approximation of gelu
# gradient of actual gelu is:
# 0.5 * (1. + torch.erf(x * 0.70710678)) + 0.3989423 * x * torch.exp(-0.5 * x * x)
@torch.jit.script
def bias_gelu_back(g, bias, y):
x = bias + y
tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))
# sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243
ff = 0.5 * x * ((1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * x * x)) + 0.5 * (
1 + tanh_out
)
return ff * g
class GeLUFunction(torch.autograd.Function):
@staticmethod
# bias is an optional argument
def forward(ctx, input, bias):
ctx.save_for_backward(input, bias)
return bias_gelu(bias, input)
@staticmethod
def backward(ctx, grad_output):
input, bias = ctx.saved_tensors
tmp = bias_gelu_back(grad_output, bias, input)
return tmp, tmp
bias_gelu_impl = GeLUFunction.apply
|
Megatron-LM-master
|
megatron/core/fusions/fused_bias_gelu.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import torch
from megatron.core.parallel_state import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
)
from .utils import VocabUtility
class _VocabParallelCrossEntropy(torch.autograd.Function):
@staticmethod
def forward(ctx, vocab_parallel_logits, target, label_smoothing=0.0):
# Maximum value along vocab dimension across all GPUs.
logits_max = torch.max(vocab_parallel_logits, dim=-1)[0]
torch.distributed.all_reduce(
logits_max, op=torch.distributed.ReduceOp.MAX, group=get_tensor_model_parallel_group()
)
# Subtract the maximum value.
vocab_parallel_logits = vocab_parallel_logits - logits_max.unsqueeze(dim=-1)
# Get the partition's vocab indecies
get_vocab_range = VocabUtility.vocab_range_from_per_partition_vocab_size
partition_vocab_size = vocab_parallel_logits.size()[-1]
rank = get_tensor_model_parallel_rank()
world_size = get_tensor_model_parallel_world_size()
vocab_start_index, vocab_end_index = get_vocab_range(partition_vocab_size, rank, world_size)
# Create a mask of valid vocab ids (1 means it needs to be masked).
target_mask = (target < vocab_start_index) | (target >= vocab_end_index)
masked_target = target.clone() - vocab_start_index
masked_target[target_mask] = 0
# Get predicted-logits = logits[target].
# For Simplicity, we convert logits to a 2-D tensor with size
# [*, partition-vocab-size] and target to a 1-D tensor of size [*].
logits_2d = vocab_parallel_logits.view(-1, partition_vocab_size)
masked_target_1d = masked_target.view(-1)
arange_1d = torch.arange(start=0, end=logits_2d.size()[0], device=logits_2d.device)
predicted_logits_1d = logits_2d[arange_1d, masked_target_1d]
predicted_logits_1d = predicted_logits_1d.clone().contiguous()
predicted_logits = predicted_logits_1d.view_as(target)
predicted_logits[target_mask] = 0.0
# All reduce is needed to get the chunks from other GPUs.
torch.distributed.all_reduce(
predicted_logits,
op=torch.distributed.ReduceOp.SUM,
group=get_tensor_model_parallel_group(),
)
# Sum of exponential of logits along vocab dimension across all GPUs.
exp_logits = vocab_parallel_logits
torch.exp(vocab_parallel_logits, out=exp_logits)
sum_exp_logits = exp_logits.sum(dim=-1)
torch.distributed.all_reduce(
sum_exp_logits,
op=torch.distributed.ReduceOp.SUM,
group=get_tensor_model_parallel_group(),
)
# Loss = log(sum(exp(logits))) - predicted-logit.
loss = torch.log(sum_exp_logits) - predicted_logits
# Normalize and optionally smooth logits
exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
vocab_size = exp_logits.size(-1)
if label_smoothing > 0:
"""
We'd like to assign 1 / (K - 1) probability mass to every index that is not the ground truth.
= (1 - alpha) * y_gt + alpha * mean(y_{i for i != gt})
= (1 - alpha) * y_gt + (alpha / (K - 1)) * \sum_{i != gt} y_i
= ((K - 1) * (1 - alpha) / (K - 1)) * y_gt + (alpha / (K - 1)) * \sum_{i != gt} y_i
= (K * (1 - alpha) - 1) / (K - 1)) * y_gt + (alpha / (K - 1)) * \sum_{i} y_i
= (1 - (alpha * K) / (K - 1)) * y_gt + ( (alpha * K) / (K - 1) ) * \sum_{i} y_i / K
From: https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/losses/smoothed_cross_entropy.py
"""
assert 1.0 > label_smoothing > 0.0
smoothing = label_smoothing * vocab_size / (vocab_size - 1)
# Exp logits at this point are normalized probabilities. So we can just take the log to get log-probs.
log_probs = torch.log(exp_logits)
mean_log_probs = log_probs.mean(dim=-1)
loss = (1.0 - smoothing) * loss - smoothing * mean_log_probs
ctx.label_smoothing, ctx.vocab_size = label_smoothing, vocab_size
# Store softmax, target-mask and masked-target for backward pass.
ctx.save_for_backward(exp_logits, target_mask, masked_target_1d)
return loss
@staticmethod
def backward(ctx, grad_output):
# Retreive tensors from the forward path.
softmax, target_mask, masked_target_1d = ctx.saved_tensors
label_smoothing, vocab_size = ctx.label_smoothing, ctx.vocab_size
# All the inputs have softmax as thier gradient.
grad_input = softmax
# For simplicity, work with the 2D gradient.
partition_vocab_size = softmax.size()[-1]
grad_2d = grad_input.view(-1, partition_vocab_size)
# Add the gradient from matching classes.
arange_1d = torch.arange(start=0, end=grad_2d.size()[0], device=grad_2d.device)
softmax_update = 1.0 - target_mask.view(-1).float()
if label_smoothing > 0:
smoothing = label_smoothing * vocab_size / (vocab_size - 1)
grad_2d[arange_1d, masked_target_1d] -= (1.0 - smoothing) * softmax_update
average_grad = 1 / vocab_size
grad_2d[arange_1d, :] -= smoothing * average_grad
else:
grad_2d[arange_1d, masked_target_1d] -= softmax_update
# Finally elementwise multiplication with the output gradients.
grad_input.mul_(grad_output.unsqueeze(dim=-1))
return grad_input, None, None
def vocab_parallel_cross_entropy(vocab_parallel_logits, target, label_smoothing=0.0):
"""
Performs cross entropy loss when logits are split across tensor parallel ranks
Arguments:
vocab_parallel_logits: logits split across tensor parallel ranks
dimension is [sequence_length, batch_size, hidden_size]
target: correct vocab ids of dimseion [sequence_length, micro_batch_size]
lobal_smoothing: smoothing factor, must be in range [0.0, 1.0)
default is no smoothing (=0.0)
"""
return _VocabParallelCrossEntropy.apply(vocab_parallel_logits, target, label_smoothing)
|
Megatron-LM-master
|
megatron/core/tensor_parallel/cross_entropy.py
|
from .cross_entropy import vocab_parallel_cross_entropy
from .data import broadcast_data
from .layers import (
ColumnParallelLinear,
RowParallelLinear,
VocabParallelEmbedding,
copy_tensor_model_parallel_attributes,
linear_with_grad_accumulation_and_async_allreduce,
param_is_not_tensor_parallel_duplicate,
set_defaults_if_not_set_tensor_model_parallel_attributes,
set_tensor_model_parallel_attributes,
)
from .mappings import (
copy_to_tensor_model_parallel_region,
gather_from_sequence_parallel_region,
gather_from_tensor_model_parallel_region,
scatter_to_sequence_parallel_region,
scatter_to_tensor_model_parallel_region,
)
from .random import checkpoint, get_cuda_rng_tracker, model_parallel_cuda_manual_seed
from .utils import (
gather_split_1d_tensor,
split_tensor_along_last_dim,
split_tensor_into_1d_equal_chunks,
)
__all__ = [
# cross_entropy.py
"vocab_parallel_cross_entropy",
# data.py
"broadcast_data",
# layers.py
"ColumnParallelLinear",
"RowParallelLinear",
"VocabParallelEmbedding",
"set_tensor_model_parallel_attributes",
"set_defaults_if_not_set_tensor_model_parallel_attributes",
"copy_tensor_model_parallel_attributes",
"param_is_not_tensor_parallel_duplicate",
"linear_with_grad_accumulation_and_async_allreduce",
# mappings.py
"copy_to_tensor_model_parallel_region",
"gather_from_tensor_model_parallel_region",
"gather_from_sequence_parallel_region",
# "reduce_from_tensor_model_parallel_region",
"scatter_to_tensor_model_parallel_region",
"scatter_to_sequence_parallel_region",
# random.py
"checkpoint",
"get_cuda_rng_tracker",
"model_parallel_cuda_manual_seed",
# utils.py
"split_tensor_along_last_dim",
"split_tensor_into_1d_equal_chunks",
"gather_split_1d_tensor",
]
|
Megatron-LM-master
|
megatron/core/tensor_parallel/__init__.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
# Parts of the code here are adapted from PyTorch
# repo: https://github.com/pytorch/pytorch
import contextlib
import torch
from torch import _C
from torch.cuda import _lazy_call
from torch.cuda import device as device_ctx_manager
from torch.utils.checkpoint import detach_variable
from megatron.core.parallel_state import (
get_data_parallel_rank,
get_tensor_model_parallel_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
)
from megatron.core.utils import safely_set_viewless_tensor_data
from .utils import gather_split_1d_tensor, split_tensor_into_1d_equal_chunks
# Default name for the model parallel rng tracker.
_MODEL_PARALLEL_RNG_TRACKER_NAME = 'model-parallel-rng'
def _set_cuda_rng_state(new_state, device=-1):
"""Sets the random number generator state of the current GPU.
Argumentss:
new_state (torch.ByteTensor): The desired state
This function is adapted from PyTorch repo (torch.cuda.set_rng_state)
with a single change: the input state is not cloned. Cloning caused
major performance issues for +4 GPU cases.
"""
if hasattr(_C, '_cuda_setRNGState') and callable(_C._cuda_setRNGState):
# older PyTorch
def cb():
with device_ctx_manager(device):
_C._cuda_setRNGState(new_state)
else:
# newer PyTorch
if device == -1:
device = torch.device('cuda')
elif isinstance(device, str):
device = torch.device(device)
elif isinstance(device, int):
device = torch.device('cuda', device)
def cb():
idx = device.index
if idx is None:
idx = torch.cuda.current_device()
default_generator = torch.cuda.default_generators[idx]
default_generator.set_state(new_state)
_lazy_call(cb)
class CudaRNGStatesTracker:
"""Tracker for the cuda RNG states.
Using the `add` method, a cuda rng state is initialized based on
the input `seed` and is assigned to `name`. Later, by forking the
rng state, we can perform operations and return to our starting
cuda state.
"""
def __init__(self):
# Map from a string name to the cuda rng state.
self.states_ = {}
# Seeds are just for book keeping and ensure no seed is set twice.
self.seeds_ = set()
def reset(self):
"""Set to the initial state (no tracker)."""
self.states_ = {}
self.seeds_ = set()
def get_states(self):
"""Get rng states. Copy the dictionary so we have direct
pointers to the states, not just a pointer to the dictionary."""
states = {}
for name in self.states_:
states[name] = self.states_[name]
return states
def set_states(self, states):
"""Set the rng states. For efficiency purposes, we do not check
the size of seed for compatibility."""
self.states_ = states
def add(self, name, seed):
"""Track the rng state."""
# Check seed is not already used.
if seed in self.seeds_:
raise Exception('seed {} already exists'.format(seed))
self.seeds_.add(seed)
# Check that state is not already defined.
if name in self.states_:
raise Exception('cuda rng state {} already exists'.format(name))
# Get the current rng state.
orig_rng_state = torch.cuda.get_rng_state()
# Set the new state and store it.
torch.cuda.manual_seed(seed)
self.states_[name] = torch.cuda.get_rng_state()
# Reset rng state to what it was.
_set_cuda_rng_state(orig_rng_state)
@contextlib.contextmanager
def fork(self, name=_MODEL_PARALLEL_RNG_TRACKER_NAME):
"""Fork the cuda rng state, perform operations, and exit with
the original state."""
# Check if we have added the state
if name not in self.states_:
raise Exception('cuda rng state {} is not added'.format(name))
# Store current rng state.
orig_cuda_rng_state = torch.cuda.get_rng_state()
# Set rng state to the desired one
_set_cuda_rng_state(self.states_[name])
# Do the stuff we wanted to do.
try:
yield
finally:
# Update the current rng state for later use.
self.states_[name] = torch.cuda.get_rng_state()
# And set the state to the original state we started with.
_set_cuda_rng_state(orig_cuda_rng_state)
# RNG tracker object.
_CUDA_RNG_STATE_TRACKER = CudaRNGStatesTracker()
def get_cuda_rng_tracker():
"""Get cuda rng tracker."""
return _CUDA_RNG_STATE_TRACKER
def model_parallel_cuda_manual_seed(seed):
"""Initialize model parallel cuda seed.
This function should be called after the model parallel is
initialized. Also, no torch.cuda.manual_seed should be called
after this function. Basically, this is replacement for that
function.
Two set of RNG states are tracked:
default state: This is for data parallelism and is the same among a
set of model parallel GPUs but different across
different model paralle groups. This is used for
example for dropout in the non-tensor-model-parallel regions.
tensor-model-parallel state: This state is different among a set of model
parallel GPUs, but the same across data parallel
groups. This is used for example for dropout in
model parallel regions.
"""
# 2718 is just for fun and any POSITIVE value will work.
offset = seed + 2718
tensor_model_parallel_seed = offset + get_tensor_model_parallel_rank()
# Data parallel gets the original seed.
data_parallel_seed = seed
_CUDA_RNG_STATE_TRACKER.reset()
# Set the default state.
torch.cuda.manual_seed(data_parallel_seed)
# and model parallel state.
_CUDA_RNG_STATE_TRACKER.add(_MODEL_PARALLEL_RNG_TRACKER_NAME, tensor_model_parallel_seed)
class CheckpointFunction(torch.autograd.Function):
"""This function is adapted from torch.utils.checkpoint with
two main changes:
1) torch.cuda.set_rng_state is replaced with `_set_cuda_rng_state`
2) the states in the model parallel tracker are also properly
tracked/set/reset.
"""
@staticmethod
def forward(ctx, run_function, distribute_saved_activations, *args):
ctx.run_function = run_function
ctx.distribute_saved_activations = distribute_saved_activations
# Copy the rng states.
ctx.fwd_cpu_rng_state = torch.get_rng_state()
ctx.fwd_cuda_rng_state = torch.cuda.get_rng_state()
ctx.fwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states()
with torch.no_grad():
outputs = run_function(*args)
# Divide hidden states across model parallel group and only keep
# the chunk corresponding to the current rank.
if distribute_saved_activations:
ctx.input_0_shape = args[0].data.shape
safely_set_viewless_tensor_data(
args[0], split_tensor_into_1d_equal_chunks(args[0].data, new_buffer=True)
)
# Store everything.
ctx.save_for_backward(*args)
return outputs
@staticmethod
def backward(ctx, *args):
if not torch.autograd._is_checkpoint_valid():
raise RuntimeError(
"Checkpointing is not compatible with .grad(), "
"please use .backward() if possible"
)
inputs = ctx.saved_tensors
if ctx.distribute_saved_activations:
safely_set_viewless_tensor_data(
inputs[0], gather_split_1d_tensor(inputs[0].data).view(ctx.input_0_shape)
)
# Store the current states.
bwd_cpu_rng_state = torch.get_rng_state()
bwd_cuda_rng_state = torch.cuda.get_rng_state()
bwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states()
# Set the states to what it used to be before the forward pass.
torch.set_rng_state(ctx.fwd_cpu_rng_state)
_set_cuda_rng_state(ctx.fwd_cuda_rng_state)
get_cuda_rng_tracker().set_states(ctx.fwd_cuda_rng_state_tracker)
# Compute the forward pass.
detached_inputs = detach_variable(inputs)
with torch.enable_grad():
outputs = ctx.run_function(*detached_inputs)
# Set the states back to what it was at the start of this function.
torch.set_rng_state(bwd_cpu_rng_state)
_set_cuda_rng_state(bwd_cuda_rng_state)
get_cuda_rng_tracker().set_states(bwd_cuda_rng_state_tracker)
if isinstance(outputs, torch.Tensor):
outputs = (outputs,)
torch.autograd.backward(outputs, args)
grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else inp for inp in detached_inputs)
return (None, None) + grads
def checkpoint(function, distribute_saved_activations, *args):
"""Checkpoint a model or part of the model.
This has been directly copied from torch.utils.checkpoint."""
return CheckpointFunction.apply(function, distribute_saved_activations, *args)
|
Megatron-LM-master
|
megatron/core/tensor_parallel/random.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
from typing import List, Sequence
import torch
from megatron.core import parallel_state
from megatron.core.utils import divide
def split_tensor_along_last_dim(
tensor: torch.Tensor, num_partitions: int, contiguous_split_chunks: bool = False,
) -> List[torch.Tensor]:
""" Split a tensor along its last dimension.
Arguments:
tensor: input tensor.
num_partitions: number of partitions to split the tensor
contiguous_split_chunks: If True, make each chunk contiguous
in memory.
Returns:
A list of Tensors
"""
# Get the size and dimension.
last_dim = tensor.dim() - 1
last_dim_size = divide(tensor.size()[last_dim], num_partitions)
# Split.
tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
# Note: torch.split does not create contiguous tensors by default.
if contiguous_split_chunks:
return tuple(chunk.contiguous() for chunk in tensor_list)
return tensor_list
def split_tensor_into_1d_equal_chunks(tensor, new_buffer=False):
""" Break a tensor into equal 1D chunks across tensor parallel ranks.
Returns a Tensor or View with this rank's portion of the data.
Arguments:
tensor: The tensor to split
Keyword Arguments:
new_buffer (bool): If True, returns a new Tensor.
If False, returns a view into the existing Tensor.
Default is False
"""
partition_size = torch.numel(tensor) // parallel_state.get_tensor_model_parallel_world_size()
start_index = partition_size * parallel_state.get_tensor_model_parallel_rank()
end_index = start_index + partition_size
if new_buffer:
data = torch.empty(
partition_size,
dtype=tensor.dtype,
device=torch.cuda.current_device(),
requires_grad=False,
)
data.copy_(tensor.view(-1)[start_index:end_index])
else:
data = tensor.view(-1)[start_index:end_index]
return data
def gather_split_1d_tensor(tensor):
""" Opposite of split_tensor_into_1d_equal_chunks. Gather values from tensor
model parallel ranks.
Returns a new Tensor with the gathered data.
Arguments:
tensor: A Tensor or view of this rank's portion of the data.
"""
numel_gathered = torch.numel(tensor) * parallel_state.get_tensor_model_parallel_world_size()
gathered = torch.empty(
numel_gathered, dtype=tensor.dtype, device=torch.cuda.current_device(), requires_grad=False
)
# TODO: This API is experimental in pytorch (as of Feb 2022) and
# this might break in future pytorch releases. We chose this API
# as opposed to torch.distributed.all_gather for efficiency reasons.
# This API calls directly NCCL all-gather versus the former does
# internal copies and can potentially cause slow down.
torch.distributed._all_gather_base(
gathered, tensor, group=parallel_state.get_tensor_model_parallel_group()
)
return gathered
class VocabUtility:
""" Split the vocabulary into `world_size` chunks and return the first
and last index of the vocabulary belonging to the `rank`
partition: Note that indices in [fist, last)
"""
@staticmethod
def vocab_range_from_per_partition_vocab_size(
per_partition_vocab_size: int, rank, world_size: int
) -> Sequence[int]:
index_f = rank * per_partition_vocab_size
index_l = index_f + per_partition_vocab_size
return index_f, index_l
@staticmethod
def vocab_range_from_global_vocab_size(
global_vocab_size: int, rank: int, world_size: int
) -> Sequence[int]:
per_partition_vocab_size = divide(global_vocab_size, world_size)
return VocabUtility.vocab_range_from_per_partition_vocab_size(
per_partition_vocab_size, rank, world_size
)
|
Megatron-LM-master
|
megatron/core/tensor_parallel/utils.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
# Parts of the code here are adapted from PyTorch
# repo: https://github.com/pytorch/pytorch
import math
import os
import warnings
from typing import Callable, Optional
import torch
import torch.nn.functional as F
import torch.nn.init as init
from torch.cuda.amp import custom_bwd, custom_fwd
from torch.nn.parameter import Parameter
from megatron.core.model_parallel_config import ModelParallelConfig
from megatron.core.parallel_state import (
get_global_memory_buffer,
get_tensor_model_parallel_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
)
from .mappings import (
copy_to_tensor_model_parallel_region,
gather_from_sequence_parallel_region,
gather_from_tensor_model_parallel_region,
reduce_from_tensor_model_parallel_region,
reduce_scatter_to_sequence_parallel_region,
scatter_to_tensor_model_parallel_region,
)
from .random import get_cuda_rng_tracker
from .utils import VocabUtility, divide, split_tensor_along_last_dim
_grad_accum_fusion_available = True
try:
import fused_weight_gradient_mlp_cuda
except ImportError:
_grad_accum_fusion_available = False
_MODEL_PARALLEL_ATTRIBUTE_DEFAULTS = {
'tensor_model_parallel': False,
'partition_dim': -1,
'partition_stride': 1,
}
def param_is_not_tensor_parallel_duplicate(param):
return (hasattr(param, 'tensor_model_parallel') and param.tensor_model_parallel) or (
get_tensor_model_parallel_rank() == 0
)
def set_tensor_model_parallel_attributes(tensor, is_parallel, dim, stride):
# Make sure the attributes are not set.
for attribute in _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS:
assert not hasattr(tensor, attribute)
# Set the attributes.
setattr(tensor, 'tensor_model_parallel', is_parallel)
setattr(tensor, 'partition_dim', dim)
setattr(tensor, 'partition_stride', stride)
def set_defaults_if_not_set_tensor_model_parallel_attributes(tensor):
def maybe_set(attribute, value):
if not hasattr(tensor, attribute):
setattr(tensor, attribute, value)
for attribute in _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS:
maybe_set(attribute, _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS[attribute])
def copy_tensor_model_parallel_attributes(destination_tensor, source_tensor):
def maybe_copy(attribute):
if hasattr(source_tensor, attribute):
setattr(destination_tensor, attribute, getattr(source_tensor, attribute))
for attribute in _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS:
maybe_copy(attribute)
def _initialize_affine_weight_gpu(weight, init_method, partition_dim, stride=1):
"""Initialize affine weight for model parallel on GPU."""
set_tensor_model_parallel_attributes(
tensor=weight, is_parallel=True, dim=partition_dim, stride=stride
)
with get_cuda_rng_tracker().fork():
init_method(weight)
def _initialize_affine_weight_cpu(
weight,
output_size,
input_size,
per_partition_size,
partition_dim,
init_method,
stride=1,
return_master_weight=False,
*,
params_dtype=torch.float32,
):
"""Initialize affine weight for model parallel.
Build the master weight on all processes and scatter
the relevant chunk."""
set_tensor_model_parallel_attributes(
tensor=weight, is_parallel=True, dim=partition_dim, stride=stride
)
# Initialize master weight
master_weight = torch.empty(output_size, input_size, dtype=torch.float, requires_grad=False)
init_method(master_weight)
master_weight = master_weight.to(dtype=params_dtype)
# Split and copy
per_partition_per_stride_size = divide(per_partition_size, stride)
weight_list = torch.split(master_weight, per_partition_per_stride_size, dim=partition_dim)
rank = get_tensor_model_parallel_rank()
world_size = get_tensor_model_parallel_world_size()
my_weight_list = weight_list[rank::world_size]
with torch.no_grad():
torch.cat(my_weight_list, dim=partition_dim, out=weight)
if return_master_weight:
return master_weight
return None
class VocabParallelEmbedding(torch.nn.Module):
"""Embedding parallelized in the vocabulary dimension.
This is mainly adapted from torch.nn.Embedding and all the default
values are kept.
Arguments:
num_embeddings: vocabulary size.
embedding_dim: size of hidden state.
Keyword Arguments:
config: A megatron.core.ModelParallelConfig object
"""
def __init__(
self,
num_embeddings: int,
embedding_dim: int,
*,
init_method: Callable,
config: ModelParallelConfig,
):
super(VocabParallelEmbedding, self).__init__()
# Keep the input dimensions.
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
# Set the detauls for compatibility.
self.padding_idx = None
self.max_norm = None
self.norm_type = 2.0
self.scale_grad_by_freq = False
self.sparse = False
self._weight = None
self.tensor_model_parallel_size = get_tensor_model_parallel_world_size()
# Divide the weight matrix along the vocaburaly dimension.
(
self.vocab_start_index,
self.vocab_end_index,
) = VocabUtility.vocab_range_from_global_vocab_size(
self.num_embeddings, get_tensor_model_parallel_rank(), self.tensor_model_parallel_size
)
self.num_embeddings_per_partition = self.vocab_end_index - self.vocab_start_index
# Allocate weights and initialize.
if config.use_cpu_initialization:
self.weight = Parameter(
torch.empty(
self.num_embeddings_per_partition, self.embedding_dim, dtype=config.params_dtype
)
)
if config.perform_initialization:
_initialize_affine_weight_cpu(
self.weight,
self.num_embeddings,
self.embedding_dim,
self.num_embeddings_per_partition,
0,
init_method,
params_dtype=config.params_dtype,
)
else:
self.weight = Parameter(
torch.empty(
self.num_embeddings_per_partition,
self.embedding_dim,
device=torch.cuda.current_device(),
dtype=config.params_dtype,
)
)
if config.perform_initialization:
_initialize_affine_weight_gpu(self.weight, init_method, partition_dim=0, stride=1)
def forward(self, input_):
if self.tensor_model_parallel_size > 1:
# Build the mask.
input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)
# Mask the input.
masked_input = input_.clone() - self.vocab_start_index
masked_input[input_mask] = 0
else:
masked_input = input_
# Get the embeddings.
output_parallel = F.embedding(
masked_input,
self.weight,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
)
# Mask the output embedding.
if self.tensor_model_parallel_size > 1:
output_parallel[input_mask, :] = 0.0
# Reduce across all the model parallel GPUs.
output = reduce_from_tensor_model_parallel_region(output_parallel)
return output
class LinearWithFrozenWeight(torch.autograd.Function):
"""Linear operator that does not calculate gradient for weight.
This op and LinearWithGradAccumulationAndAsyncCommunication performs
mathematically-identical forward and DGRAD.
Conceptually this op is the same as torch.nn.functional.linear with
weight.requires_grad==False, but in experiments they are not identical
mathematically. """
@staticmethod
@custom_fwd
def forward(
ctx, input, weight, bias,
):
ctx.save_for_backward(weight)
output = torch.matmul(input, weight.t())
if bias is not None:
output = output + bias
return output
@staticmethod
@custom_bwd
def backward(ctx, grad_output):
(weight,) = ctx.saved_tensors
grad_input = grad_output.matmul(weight)
return grad_input, None, None
def linear_with_frozen_weight(
input: torch.Tensor,
weight: torch.Tensor,
bias: Optional[torch.Tensor],
gradient_accumulation_fusion: bool,
async_grad_allreduce: bool,
sequence_parallel: bool,
) -> torch.Tensor:
"""Linear layer execution with weight.requires_grad == False.
This function handles linear layers with weight frozen (untrainable).
In the forward, it only saves weight and does not save input activations.
In the backward, it does not perform weight gradient calculation, or
weight gradient allreduce.
Arguments:
input (torch.Tensor required): input like torch.nn.functional.linear
weight (torch.Tensor required): weight like torch.nn.functional.linear
bias (torch.Tensor optional): bias like torch.nn.functional.linear
gradient_accumulation_fusion (bool required): dummy argument, used to
keep the API unified between all forward implementation functions.
async_grad_allreduce (bool required): dummy argument, used to
keep the API unified between all forward implementation functions.
sequence_parallel (bool required): Indicates that sequence
parallelism is used and thus in the forward pass the input is
all gathered, and the backward pass the input gradients are
reduce scattered.
"""
if sequence_parallel:
input = gather_from_sequence_parallel_region(input, tensor_parallel_output_grad=True)
else:
input = input
args = [
input,
weight,
bias,
]
return LinearWithFrozenWeight.apply(*args)
class LinearWithGradAccumulationAndAsyncCommunication(torch.autograd.Function):
"""See linear_with_grad_accumulation_and_async_allreduce"""
@staticmethod
@custom_fwd
def forward(
ctx,
input,
weight,
bias,
gradient_accumulation_fusion,
async_grad_allreduce,
sequence_parallel,
):
ctx.save_for_backward(input, weight)
ctx.use_bias = bias is not None
ctx.gradient_accumulation_fusion = gradient_accumulation_fusion
ctx.async_grad_allreduce = async_grad_allreduce
ctx.sequence_parallel = sequence_parallel
if sequence_parallel:
world_size = get_tensor_model_parallel_world_size()
dim_size = list(input.size())
dim_size[0] = dim_size[0] * world_size
all_gather_buffer = get_global_memory_buffer().get_tensor(dim_size, input.dtype, "mpu")
torch.distributed._all_gather_base(
all_gather_buffer, input, group=get_tensor_model_parallel_group()
)
total_input = all_gather_buffer
else:
total_input = input
output = torch.matmul(total_input, weight.t())
if bias is not None:
output = output + bias
return output
@staticmethod
@custom_bwd
def backward(ctx, grad_output):
input, weight = ctx.saved_tensors
use_bias = ctx.use_bias
if ctx.sequence_parallel:
world_size = get_tensor_model_parallel_world_size()
dim_size = list(input.size())
dim_size[0] = dim_size[0] * world_size
all_gather_buffer = get_global_memory_buffer().get_tensor(dim_size, input.dtype, "mpu")
handle = torch.distributed._all_gather_base(
all_gather_buffer, input, group=get_tensor_model_parallel_group(), async_op=True
)
# Here we rely on CUDA_DEVICE_MAX_CONNECTIONS=1 to ensure that the
# gather is scheduled before the input gradient computation
total_input = all_gather_buffer
else:
total_input = input
grad_input = grad_output.matmul(weight)
if ctx.sequence_parallel:
handle.wait()
# Doing gather + slicing during the NeMo forward pass can make this tensor
# not be contiguous. PyTorch only checks if the tensor is contiguous, and only
# clones it if it's not contiguous:
# https://github.com/pytorch/pytorch/blob/c47cf9bc7f9e02f649ab4ed53fe4d35732c92ab6/torch/_refs/__init__.py#L2761
grad_output = grad_output.contiguous()
# Convert the tensor shapes to 2D for execution compatibility
grad_output = grad_output.view(
grad_output.shape[0] * grad_output.shape[1], grad_output.shape[2]
)
total_input = total_input.view(
total_input.shape[0] * total_input.shape[1], total_input.shape[2]
)
if ctx.async_grad_allreduce:
# Asynchronous all-reduce
handle = torch.distributed.all_reduce(
grad_input, group=get_tensor_model_parallel_group(), async_op=True
)
# Here we rely on CUDA_DEVICE_MAX_CONNECTIONS=1 to ensure that the
# all-reduce is scheduled before the weight gradient computation
if ctx.sequence_parallel:
assert not ctx.async_grad_allreduce
dim_size = list(input.size())
sub_grad_input = torch.empty(
dim_size, dtype=input.dtype, device=torch.cuda.current_device(), requires_grad=False
)
# reduce_scatter
handle = torch.distributed._reduce_scatter_base(
sub_grad_input, grad_input, group=get_tensor_model_parallel_group(), async_op=True
)
# Here we rely on CUDA_DEVICE_MAX_CONNECTIONS=1 to ensure that the
# reduce scatter is scheduled before the weight gradient computation
if ctx.gradient_accumulation_fusion:
if weight.main_grad.dtype == torch.float32:
fused_weight_gradient_mlp_cuda.wgrad_gemm_accum_fp32(
total_input, grad_output, weight.main_grad
)
elif weight.main_grad.dtype in (torch.float16, torch.bfloat16):
fused_weight_gradient_mlp_cuda.wgrad_gemm_accum_fp16(
total_input, grad_output, weight.main_grad
)
else:
raise RuntimeError("Unsupported gradient type for gradient accumulation fusion")
if hasattr(weight, 'grad_added_to_main_grad'):
# When overlap_grad_reduce is True, need to ensure that backward hooks
# are all run on the main backprop thread to prevent deadlocks. Setup
# dummy grad_weight tensor to prevent backward hooks from being run
# in a background thread.
grad_weight = torch.empty(
weight.main_grad.shape,
dtype=input.dtype,
device=torch.cuda.current_device(),
requires_grad=False,
)
weight.grad_added_to_main_grad = True
else:
grad_weight = None
else:
grad_weight = grad_output.t().matmul(total_input)
grad_bias = grad_output.sum(dim=0) if use_bias else None
if ctx.sequence_parallel:
handle.wait()
return sub_grad_input, grad_weight, grad_bias, None, None, None
if ctx.async_grad_allreduce:
handle.wait()
return grad_input, grad_weight, grad_bias, None, None, None
def linear_with_grad_accumulation_and_async_allreduce(
input: torch.Tensor,
weight: torch.Tensor,
bias: Optional[torch.Tensor],
gradient_accumulation_fusion: bool,
async_grad_allreduce: bool,
sequence_parallel: bool,
) -> torch.Tensor:
"""Linear layer execution with asynchronous communication and
gradient accumulation fusion in backprop.
This has the option to accumulate the result of backprop
calculation into an existing gradient buffer, preventing the need
to do an additional addition kernel after the gradient
calculation.
Additionally, the tensor parallel all reduce of the input
gradients can be done asynchronously with the calculation of
the weight gradients.
In the case of sequence parallelism, the reduce scatter of the
input gradients is done asynchronously with the calcluation of the
weight gradients.
Use of this module requires that the environment variable
CUDA_DEVICE_MAX_CONNECTIONS=1. There are a few collective
operations, noted in the code, that should be scheduled before
compute kernels to overlap the communication with the computation,
which is necessary for a speedup but not for correctness so that
ordering isn't imposed by the scheduler. Setting
CUDA_DEVICE_MAX_CONNECTIONS=1 forces the kernels to be scheduled
in the order they are called.
Arguments:
input (torch.Tensor required): input like torch.nn.functional.linear
weight (torch.Tensor required): weight like torch.nn.functional.linear
bias (torch.Tensor optional): bias like torch.nn.functional.linear
gradient_accumulation_fusion (bool required): Perform the gradient
accumulation fusion, requires the custom CUDA extension
fused_weight_gradient_mlp_cuda module. To use
gradient_accumulation_fusion you must install APEX with
--cpp_ext and --cuda_ext. For example: "pip install
--global-option=\"--cpp_ext\" --global-option=\"--cuda_ext .\"
" Note that the extension requires CUDA>=11. Otherwise, you
must turn off gradient accumulation fusion."
async_grad_allreduce (bool required): Do the allreduce of input
gradients asyncronously with the computation of weight
gradients. If sequence_parallel is True, this must be
False, as no all reduce is performed.
sequence_parallel (bool required): Indicates that sequence
parallelism is used and thus in the forward pass the input is
all gathered, and the backward pass the input gradients are
reduce scattered.
"""
args = [
input,
weight,
bias,
gradient_accumulation_fusion,
async_grad_allreduce,
sequence_parallel,
]
if not linear_with_grad_accumulation_and_async_allreduce.warned:
if os.environ.get('CUDA_DEVICE_MAX_CONNECTIONS') != "1":
if sequence_parallel:
warnings.warn(
"When using sequence parallelism it is recommended to set the "
"environment variable CUDA_DEVICE_MAX_CONNECTIONS to 1 for "
"maximum speedup"
)
linear_with_grad_accumulation_and_async_allreduce.warned = True
if async_grad_allreduce:
warnings.warn(
"When using async grad allreduce it is recommended to set the "
"environment variable CUDA_DEVICE_MAX_CONNECTIONS to 1 for "
"maximum speedup"
)
linear_with_grad_accumulation_and_async_allreduce.warned = True
return LinearWithGradAccumulationAndAsyncCommunication.apply(*args)
linear_with_grad_accumulation_and_async_allreduce.warned = False
class ColumnParallelLinear(torch.nn.Module):
"""Linear layer with column parallelism.
The linear layer is defined as Y = XA + b. A is parallelized along
its second dimension as A = [A_1, ..., A_p].
Arguments:
input_size: first dimension of matrix A.
output_size: second dimension of matrix A.
Keyword Arguments
bias: If true, add bias
gather_output: If true, call all-gather on output and make Y available
to all GPUs, otherwise, every GPU will have its output
which is Y_i = XA_i
init_method: method to initialize weights. Note that bias is always set
to zero.
stride: For the strided linear layers.
keep_master_weight_for_test: This was added for testing and should be
set to False. It returns the master weights
used for initialization.
skip_bias_add: If True, do not add the bias term, instead
return it to be added by the caller. This
enables performance optimations where bias can
be fused with other elementwise operations.
skip_weight_param_allocation: If True, weight parameter is not allocated and must be passed
as a keyword argument `weight` during the forward pass. Note
that this does not affect bias, which will be allocated if
bias is True. Defaults to False.
config: ModelParallelConfig object
"""
def __init__(
self,
input_size,
output_size,
*,
config: ModelParallelConfig,
init_method: Callable,
bias=True,
gather_output=False,
stride=1,
keep_master_weight_for_test=False,
skip_bias_add=False,
skip_weight_param_allocation: bool = False,
):
super(ColumnParallelLinear, self).__init__()
# Keep input parameters
self.input_size = input_size
self.output_size = output_size
self.gather_output = gather_output
# Divide the weight matrix along the last dimension.
world_size = get_tensor_model_parallel_world_size()
self.output_size_per_partition = divide(output_size, world_size)
self.skip_bias_add = skip_bias_add
self.config = config
# Parameters.
# Note: torch.nn.functional.linear performs XA^T + b and as a result
# we allocate the transpose.
# Initialize weight.
if not skip_weight_param_allocation:
if config.use_cpu_initialization:
self.weight = Parameter(
torch.empty(
self.output_size_per_partition, self.input_size, dtype=config.params_dtype
)
)
if config.perform_initialization:
self.master_weight = _initialize_affine_weight_cpu(
self.weight,
self.output_size,
self.input_size,
self.output_size_per_partition,
0,
init_method,
stride=stride,
return_master_weight=keep_master_weight_for_test,
)
else:
self.weight = Parameter(
torch.empty(
self.output_size_per_partition,
self.input_size,
device=torch.cuda.current_device(),
dtype=config.params_dtype,
)
)
if config.perform_initialization:
_initialize_affine_weight_gpu(
self.weight, init_method, partition_dim=0, stride=stride
)
else:
self.weight = None
if bias:
if config.use_cpu_initialization:
self.bias = Parameter(
torch.empty(self.output_size_per_partition, dtype=config.params_dtype)
)
else:
self.bias = Parameter(
torch.empty(
self.output_size_per_partition,
device=torch.cuda.current_device(),
dtype=config.params_dtype,
)
)
set_tensor_model_parallel_attributes(self.bias, True, 0, stride)
if config.perform_initialization:
# Always initialize bias to zero.
with torch.no_grad():
self.bias.zero_()
else:
self.register_parameter('bias', None)
self.async_tensor_model_parallel_allreduce = (
config.async_tensor_model_parallel_allreduce and world_size > 1
)
self.sequence_parallel = config.sequence_parallel
if self.sequence_parallel and world_size <= 1:
warnings.warn(
f"`sequence_parallel` is set to `True`, but tensor model parallel size is {world_size}. "
f"Disabling sequence parallel."
)
self.sequence_parallel = False
if config.gradient_accumulation_fusion and not _grad_accum_fusion_available:
raise RuntimeError(
"ColumnParallelLinear was called with gradient_accumulation_fusion set "
"to True but the custom CUDA extension fused_weight_gradient_mlp_cuda "
"module is not found. To use gradient_accumulation_fusion you must "
"install APEX with --cpp_ext and --cuda_ext. For example: "
"pip install --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext .\" "
"Note that the extension requires CUDA>=11. Otherwise, you must turn off "
"gradient accumulation fusion."
)
self.gradient_accumulation_fusion = config.gradient_accumulation_fusion
if self.async_tensor_model_parallel_allreduce and self.sequence_parallel:
raise RuntimeError(
"`async_tensor_model_parallel_allreduce` and `sequence_parallel` "
"cannot be enabled at the same time."
)
self._forward_impl = linear_with_grad_accumulation_and_async_allreduce
def forward(self, input_: torch.Tensor, weight: Optional[torch.Tensor] = None):
"""Forward of ColumnParallelLinear
Args:
input_: 3D tensor whose order of dimension is [sequence, batch, hidden]
weight (optional): weight tensor to use, compulsory when
skip_weight_param_allocation is True.
Returns:
- output
- bias
"""
if weight is None:
if self.weight is None:
raise RuntimeError(
"weight was not supplied to ColumnParallelLinear forward pass "
"and skip_weight_param_allocation is True."
)
weight = self.weight
else:
# Check the weight passed in is the correct shape
expected_shape = (self.output_size_per_partition, self.input_size)
if weight.shape != expected_shape:
raise RuntimeError(
f"supplied weight's shape is {tuple(weight.shape)}, "
f"not {expected_shape} as expected"
)
bias = self.bias if not self.skip_bias_add else None
if self.async_tensor_model_parallel_allreduce or self.sequence_parallel:
input_parallel = input_
else:
input_parallel = copy_to_tensor_model_parallel_region(input_)
# Matrix multiply.
if not weight.requires_grad:
self._forward_impl = linear_with_frozen_weight
else:
self._forward_impl = linear_with_grad_accumulation_and_async_allreduce
output_parallel = self._forward_impl(
input=input_parallel,
weight=weight,
bias=bias,
gradient_accumulation_fusion=self.gradient_accumulation_fusion,
async_grad_allreduce=self.async_tensor_model_parallel_allreduce,
sequence_parallel=self.sequence_parallel,
)
if self.gather_output:
# All-gather across the partitions.
assert not self.sequence_parallel
output = gather_from_tensor_model_parallel_region(output_parallel)
else:
output = output_parallel
output_bias = self.bias if self.skip_bias_add else None
return output, output_bias
class RowParallelLinear(torch.nn.Module):
"""Linear layer with row parallelism.
The linear layer is defined as Y = XA + b. A is parallelized along
its first dimension and X along its second dimension as:
- -
| A_1 |
| . |
A = | . | X = [X_1, ..., X_p]
| . |
| A_p |
- -
Arguments:
input_size: first dimension of matrix A.
output_size: second dimension of matrix A.
Keyword Arguments:
bias: If true, add bias. Note that bias is not parallelized.
input_is_parallel: If true, we assume that the input is already
split across the GPUs and we do not split
again.
init_method: method to initialize weights. Note that bias is always set
to zero.
stride: For the strided linear layers.
keep_master_weight_for_test: This was added for testing and should be
set to False. It returns the master weights
used for initialization.
skip_bias_add: If True, do not add the bias term, instead
return it to be added by the caller. This
enables performance optimations where bias can
be fused with other elementwise operations.
config: ModelParallelConfig object
"""
def __init__(
self,
input_size: int,
output_size: int,
*,
config: ModelParallelConfig,
init_method: Callable,
bias: bool = True,
input_is_parallel: bool = False,
stride: int = 1,
keep_master_weight_for_test: bool = False,
skip_bias_add: bool = False,
):
super(RowParallelLinear, self).__init__()
# Keep input parameters
self.input_size = input_size
self.output_size = output_size
self.input_is_parallel = input_is_parallel
# Divide the weight matrix along the last dimension.
world_size = get_tensor_model_parallel_world_size()
self.input_size_per_partition = divide(input_size, world_size)
self.skip_bias_add = skip_bias_add
self.config = config
self.gradient_accumulation_fusion = config.gradient_accumulation_fusion
self.sequence_parallel = config.sequence_parallel
if self.sequence_parallel and not self.input_is_parallel:
raise RuntimeError("To enable `sequence_parallel`, `input_is_parallel` must be `True`")
# Parameters.
# Note: torch.nn.functional.linear performs XA^T + b and as a result
# we allocate the transpose.
# Initialize weight.
if config.use_cpu_initialization:
self.weight = Parameter(
torch.empty(
self.output_size, self.input_size_per_partition, dtype=config.params_dtype
)
)
if config.perform_initialization:
self.master_weight = _initialize_affine_weight_cpu(
self.weight,
self.output_size,
self.input_size,
self.input_size_per_partition,
1,
init_method,
stride=stride,
return_master_weight=keep_master_weight_for_test,
params_dtype=config.params_dtype,
)
else:
self.weight = Parameter(
torch.empty(
self.output_size,
self.input_size_per_partition,
device=torch.cuda.current_device(),
dtype=config.params_dtype,
)
)
if config.perform_initialization:
_initialize_affine_weight_gpu(
self.weight, init_method, partition_dim=1, stride=stride
)
if bias:
if config.use_cpu_initialization:
self.bias = Parameter(torch.empty(self.output_size, dtype=config.params_dtype))
else:
self.bias = Parameter(
torch.empty(
self.output_size,
device=torch.cuda.current_device(),
dtype=config.params_dtype,
)
)
setattr(self.bias, 'sequence_parallel', self.sequence_parallel)
if config.perform_initialization:
# Always initialize bias to zero.
with torch.no_grad():
self.bias.zero_()
else:
self.register_parameter('bias', None)
self._forward_impl = linear_with_grad_accumulation_and_async_allreduce
def forward(self, input_):
"""Forward of RowParallelLinear
Args:
input_: 3D tensor whose order of dimension is [sequence, batch, hidden]
Returns:
- output
- bias
"""
# Set up backprop all-reduce.
if self.input_is_parallel:
input_parallel = input_
else:
assert not self.sequence_parallel
input_parallel = scatter_to_tensor_model_parallel_region(input_)
# Matrix multiply.
if not self.weight.requires_grad:
self._forward_impl = linear_with_frozen_weight
else:
self._forward_impl = linear_with_grad_accumulation_and_async_allreduce
output_parallel = self._forward_impl(
input=input_parallel,
weight=self.weight,
bias=None,
gradient_accumulation_fusion=self.gradient_accumulation_fusion,
async_grad_allreduce=False,
sequence_parallel=False,
)
# All-reduce across all the partitions.
if self.sequence_parallel:
output_ = reduce_scatter_to_sequence_parallel_region(output_parallel)
else:
output_ = reduce_from_tensor_model_parallel_region(output_parallel)
if not self.skip_bias_add:
output = output_ + self.bias if self.bias is not None else output_
output_bias = None
else:
output = output_
output_bias = self.bias
return output, output_bias
|
Megatron-LM-master
|
megatron/core/tensor_parallel/layers.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import torch
from megatron.core.parallel_state import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_src_rank,
)
_MAX_DATA_DIM = 5
def _check_data_types(keys, data, target_dtype):
"""Check that all the keys have the same target data type."""
for key in keys:
assert data[key].dtype == target_dtype, (
'{} has data type {} which '
'is different than {}'.format(key, data[key].dtype, target_dtype)
)
def _build_key_size_numel_dictionaries(keys, data):
"""Build the size on rank 0 and broadcast."""
max_dim = _MAX_DATA_DIM
sizes = [0 for _ in range(max_dim) for _ in keys]
# Pack the sizes on rank zero.
if get_tensor_model_parallel_rank() == 0:
offset = 0
for key in keys:
assert data[key].dim() < max_dim, 'you should increase MAX_DATA_DIM'
size = data[key].size()
for i, s in enumerate(size):
sizes[i + offset] = s
offset += max_dim
# Move to GPU and broadcast.
sizes_cuda = torch.cuda.LongTensor(sizes)
torch.distributed.broadcast(
sizes_cuda, get_tensor_model_parallel_src_rank(), group=get_tensor_model_parallel_group()
)
# Move back to cpu and unpack.
sizes_cpu = sizes_cuda.cpu()
key_size = {}
key_numel = {}
total_numel = 0
offset = 0
for key in keys:
i = 0
size = []
numel = 1
while sizes_cpu[offset + i] > 0:
this_size = sizes_cpu[offset + i]
size.append(this_size)
numel *= this_size
i += 1
key_size[key] = size
key_numel[key] = numel
total_numel += numel
offset += max_dim
return key_size, key_numel, total_numel
def broadcast_data(keys, data, datatype):
"""Broadcast data from rank zero of each model parallel group to the
members of the same model parallel group.
Arguments:
keys: list of keys in the data disctionary to be broadcasted
data: data dictionary of string keys and cpu tensor values.
datatype: torch data type of all tensors in data associated
with keys.
"""
# Build (key, size) and (key, number of elements) dictionaries along
# with the total number of elements on all ranks.
key_size, key_numel, total_numel = _build_key_size_numel_dictionaries(keys, data)
# Pack on rank zero.
if get_tensor_model_parallel_rank() == 0:
# Check that all keys have the same data type.
_check_data_types(keys, data, datatype)
# Flatten the data associated with the keys
flatten_data = torch.cat([data[key].contiguous().view(-1) for key in keys], dim=0).cuda()
else:
flatten_data = torch.empty(total_numel, device=torch.cuda.current_device(), dtype=datatype)
# Broadcast
torch.distributed.broadcast(
flatten_data, get_tensor_model_parallel_src_rank(), group=get_tensor_model_parallel_group()
)
# Unpack
output = {}
offset = 0
for key in keys:
size = key_size[key]
numel = key_numel[key]
output[key] = flatten_data.narrow(0, offset, numel).view(size)
offset += numel
return output
|
Megatron-LM-master
|
megatron/core/tensor_parallel/data.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import torch
from megatron.core.parallel_state import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
)
from .utils import split_tensor_along_last_dim
def _reduce(input_):
"""All-reduce the input tensor across model parallel group."""
# Bypass the function if we are using only 1 GPU.
if get_tensor_model_parallel_world_size() == 1:
return input_
# All-reduce.
torch.distributed.all_reduce(input_, group=get_tensor_model_parallel_group())
return input_
def _split_along_last_dim(input_):
"""Split the tensor along its last dimension and keep the
corresponding slice."""
world_size = get_tensor_model_parallel_world_size()
# Bypass the function if we are using only 1 GPU.
if world_size == 1:
return input_
# Split along last dimension.
input_list = split_tensor_along_last_dim(input_, world_size)
# Note: torch.split does not create contiguous tensors by default.
rank = get_tensor_model_parallel_rank()
output = input_list[rank].contiguous()
return output
def _split_along_first_dim(input_):
"""Split the tensor along its first dimension and keep the
corresponding slice."""
world_size = get_tensor_model_parallel_world_size()
# Bypass the function if we are using only 1 GPU.
if world_size == 1:
return input_
# Split along first dimension.
dim_size = input_.size()[0]
assert (
dim_size % world_size == 0
), "First dimension of the tensor should be divisible by tensor parallel size"
local_dim_size = dim_size // world_size
rank = get_tensor_model_parallel_rank()
dim_offset = rank * local_dim_size
output = input_[dim_offset : dim_offset + local_dim_size].contiguous()
return output
def _gather_along_last_dim(input_):
"""Gather tensors and concatinate along the last dimension."""
world_size = get_tensor_model_parallel_world_size()
# Bypass the function if we are using only 1 GPU.
if world_size == 1:
return input_
# Size and dimension.
last_dim = input_.dim() - 1
rank = get_tensor_model_parallel_rank()
tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
tensor_list[rank] = input_
torch.distributed.all_gather(tensor_list, input_, group=get_tensor_model_parallel_group())
# Note: torch.cat already creates a contiguous tensor.
output = torch.cat(tensor_list, dim=last_dim).contiguous()
return output
def _gather_along_first_dim(input_):
"""Gather tensors and concatinate along the first dimension."""
world_size = get_tensor_model_parallel_world_size()
# Bypass the function if we are using only 1 GPU.
if world_size == 1:
return input_
dim_size = list(input_.size())
dim_size[0] = dim_size[0] * world_size
output = torch.empty(dim_size, dtype=input_.dtype, device=torch.cuda.current_device())
torch.distributed._all_gather_base(
output, input_.contiguous(), group=get_tensor_model_parallel_group()
)
return output
def _reduce_scatter_along_first_dim(input_):
"""Reduce-scatter the input tensor across model parallel group."""
world_size = get_tensor_model_parallel_world_size()
# Bypass the function if we are using only 1 GPU.
if world_size == 1:
return input_
dim_size = list(input_.size())
assert (
dim_size[0] % world_size == 0
), "First dimension of the tensor should be divisible by tensor parallel size"
dim_size[0] = dim_size[0] // world_size
output = torch.empty(dim_size, dtype=input_.dtype, device=torch.cuda.current_device())
torch.distributed._reduce_scatter_base(
output, input_.contiguous(), group=get_tensor_model_parallel_group()
)
return output
class _CopyToModelParallelRegion(torch.autograd.Function):
"""Pass the input to the model parallel region."""
@staticmethod
def symbolic(graph, input_):
return input_
@staticmethod
def forward(ctx, input_):
return input_
@staticmethod
def backward(ctx, grad_output):
return _reduce(grad_output)
class _ReduceFromModelParallelRegion(torch.autograd.Function):
"""All-reduce the input from the model parallel region."""
@staticmethod
def symbolic(graph, input_):
return _reduce(input_)
@staticmethod
def forward(ctx, input_):
return _reduce(input_)
@staticmethod
def backward(ctx, grad_output):
return grad_output
class _ScatterToModelParallelRegion(torch.autograd.Function):
"""Split the input and keep only the corresponding chuck to the rank."""
@staticmethod
def symbolic(graph, input_):
return _split_along_last_dim(input_)
@staticmethod
def forward(ctx, input_):
return _split_along_last_dim(input_)
@staticmethod
def backward(ctx, grad_output):
return _gather_along_last_dim(grad_output)
class _GatherFromModelParallelRegion(torch.autograd.Function):
"""Gather the input from model parallel region and concatinate."""
@staticmethod
def symbolic(graph, input_):
return _gather_along_last_dim(input_)
@staticmethod
def forward(ctx, input_):
return _gather_along_last_dim(input_)
@staticmethod
def backward(ctx, grad_output):
return _split_along_last_dim(grad_output)
class _ScatterToSequenceParallelRegion(torch.autograd.Function):
"""Split the input and keep only the corresponding chuck to the rank."""
@staticmethod
def symbolic(graph, input_):
return _split_along_first_dim(input_)
@staticmethod
def forward(ctx, input_):
return _split_along_first_dim(input_)
@staticmethod
def backward(ctx, grad_output):
return _gather_along_first_dim(grad_output)
class _GatherFromSequenceParallelRegion(torch.autograd.Function):
"""Gather the input from sequence parallel region and concatinate."""
@staticmethod
def symbolic(graph, input_, tensor_parallel_output_grad=True):
return _gather_along_first_dim(input_)
@staticmethod
def forward(ctx, input_, tensor_parallel_output_grad=True):
ctx.tensor_parallel_output_grad = tensor_parallel_output_grad
return _gather_along_first_dim(input_)
@staticmethod
def backward(ctx, grad_output):
tensor_parallel_output_grad = ctx.tensor_parallel_output_grad
# If the computation graph after the gather operation is
# in the tensor parallel mode, output gradients need to reduce
# scattered and whereas if the computation is duplicated,
# output gradients need to be scattered.
if tensor_parallel_output_grad:
return _reduce_scatter_along_first_dim(grad_output), None
else:
return _split_along_first_dim(grad_output), None
class _ReduceScatterToSequenceParallelRegion(torch.autograd.Function):
"""Reduce scatter the input from the model parallel region."""
@staticmethod
def symbolic(graph, input_):
return _reduce_scatter_along_first_dim(input_)
@staticmethod
def forward(ctx, input_):
return _reduce_scatter_along_first_dim(input_)
@staticmethod
def backward(ctx, grad_output):
return _gather_along_first_dim(grad_output)
# -----------------
# Helper functions.
# -----------------
def copy_to_tensor_model_parallel_region(input_):
return _CopyToModelParallelRegion.apply(input_)
def reduce_from_tensor_model_parallel_region(input_):
return _ReduceFromModelParallelRegion.apply(input_)
def scatter_to_tensor_model_parallel_region(input_):
return _ScatterToModelParallelRegion.apply(input_)
def gather_from_tensor_model_parallel_region(input_):
return _GatherFromModelParallelRegion.apply(input_)
def scatter_to_sequence_parallel_region(input_):
return _ScatterToSequenceParallelRegion.apply(input_)
def gather_from_sequence_parallel_region(input_, tensor_parallel_output_grad=True):
return _GatherFromSequenceParallelRegion.apply(input_, tensor_parallel_output_grad)
def reduce_scatter_to_sequence_parallel_region(input_):
return _ReduceScatterToSequenceParallelRegion.apply(input_)
|
Megatron-LM-master
|
megatron/core/tensor_parallel/mappings.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import re
from contextlib import nullcontext
import torch
from megatron.core import parallel_state, tensor_parallel
from megatron.core.fusions.fused_layer_norm import FusedLayerNorm
from megatron.core.transformer.custom_layers.transformer_engine import TENorm
from megatron.core.transformer.enums import AttnMaskType
from megatron.core.transformer.module import MegatronModule
from megatron.core.transformer.transformer_config import TransformerConfig
from megatron.core.transformer.transformer_layer import TransformerLayer
from megatron.core.utils import make_sharded_tensor_for_checkpoint, make_viewless_tensor
class TransformerBlock(MegatronModule):
"""Transformer class."""
def __init__(
self,
config: TransformerConfig,
self_attn_mask_type=AttnMaskType.padding,
post_layer_norm=True,
pre_process=True,
post_process=True,
):
super().__init__(config=config)
self.config: TransformerConfig = config
self.self_attn_mask_type = self_attn_mask_type
self.post_layer_norm = post_layer_norm
self.pre_process = pre_process
self.post_process = post_process
# required for pipeline parallel schedules
self.input_tensor = None
self.checkpoint_core_attention = self.config.recompute_granularity == 'selective'
self.num_layers_per_pipeline_rank = (
self.config.num_layers // parallel_state.get_pipeline_model_parallel_world_size()
)
self._build_layers()
def _build_layers(self):
# Transformer layers.
# @jcasper can we improve how we deal with layer_number?
# currently it's only used in CoreAttention?
# if self.apply_query_key_layer_scaling:
# coeff = self.layer_number
# self.norm_factor *= coeff
def build_layer(layer_number):
layer = TransformerLayer(
config=self.config,
layer_number=layer_number,
self_attn_mask_type=self.self_attn_mask_type,
)
return layer
if parallel_state.get_virtual_pipeline_model_parallel_world_size() is not None:
# Interleaved pipeline parallelism:
# Number of layers in each model chunk is the number of layers in the stage,
# divided by the number of model chunks in a stage.
# With 8 layers, 2 stages, and 4 model chunks, we want an assignment of
# layers to stages like (each list is a model chunk):
# Stage 0: [0] [2] [4] [6]
# Stage 1: [1] [3] [5] [7]
# With 8 layers, 2 stages, and 2 virtual stages, we want an assignment of
# layers to stages like (each list is a model chunk):
# Stage 0: [0, 1] [4, 5]
# Stage 1: [2, 3] [6, 7]
vp_size = parallel_state.get_virtual_pipeline_model_parallel_world_size()
num_layers_per_virtual_rank = self.num_layers_per_pipeline_rank // vp_size
num_layers_to_build = num_layers_per_virtual_rank
else:
# Non-interleaved pipeline parallelism:
# Each stage gets a contiguous set of layers.
num_layers_to_build = self.num_layers_per_pipeline_rank
# offset is implicit in TransformerLayer
self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(num_layers_to_build)])
# # TODO: add back standalone_embedding_stage
# if self.num_layers == 0:
# # When a standalone embedding stage is used (e.g.,
# # args.standalone_embedding_stage == True), virtual pipeline ranks
# # on pipeline rank 0 will have zero transformer layers assigned to
# # them. This results in the model's input and output tensors to be
# # the same, which will cause failure for certain output tensor
# # optimizations (e.g., pipeline output deallocation). To remedy
# # this, we assign a 'no-op' layer on these ranks, which will
# # disconnect the input tensor from the output tensor.
# self.num_layers = 1
# self.layers = torch.nn.ModuleList([NoopTransformerLayer(1)])
# else:
# self.layers = torch.nn.ModuleList([build_layer(i + 1 + offset) for i in range(self.num_layers)])
if self.post_process and self.post_layer_norm:
# Final layer norm before output.
self.final_layernorm = TENorm(
config=self.config,
hidden_size=self.config.hidden_size,
eps=self.config.layernorm_epsilon,
persist_layer_norm=self.config.persist_layer_norm,
sequence_parallel=self.config.sequence_parallel,
zero_centered_gamma=self.config.layernorm_zero_centered_gamma,
normalization=self.config.normalization,
)
def _get_layer(self, layer_number):
return self.layers[layer_number]
def _checkpointed_forward(self, hidden_states, attention_mask, rotary_pos_emb):
"""Forward method with activation checkpointing."""
def custom(start, end):
def custom_forward(*args, **kwargs):
x_, *args = args
for index in range(start, end):
layer = self._get_layer(index)
x_ = layer(x_, *args, **kwargs)
return x_
return custom_forward
if self.config.recompute_method == 'uniform':
# Uniformly divide the total number of Transformer layers and checkpoint
# the input activation of each divided chunk.
# A method to further reduce memory usage reducing checkpoints.
l = 0
while l < self.num_layers_per_pipeline_rank:
hidden_states = tensor_parallel.checkpoint(
custom(l, l + self.config.recompute_num_layers),
self.config.distribute_saved_activations,
hidden_states,
attention_mask,
rotary_pos_emb,
)
l += self.config.recompute_num_layers
elif self.config.recompute_method == 'block':
# Checkpoint the input activation of only a set number of individual
# Transformer layers and skip the rest.
# A method fully use the device memory removing redundant re-computation.
for l in range(self.num_layers_per_pipeline_rank):
if l < self.config.recompute_num_layers:
hidden_states = tensor_parallel.checkpoint(
custom(l, l + 1),
self.config.distribute_saved_activations,
hidden_states,
attention_mask,
rotary_pos_emb,
)
else:
hidden_states = custom(l, l + 1)(hidden_states, attention_mask, rotary_pos_emb)
else:
raise ValueError("Invalid activation recompute method.")
return hidden_states
def set_input_tensor(self, input_tensor):
"""Set input tensor to be used instead of forward()'s input.
When doing pipeline parallelism the input from the previous
stage comes from communication, not from the input, so the
model's forward_step_func won't have it. This function is thus
used by internal code to bypass the input provided by the
forward_step_func"""
self.input_tensor = input_tensor
def forward(self, hidden_states, attention_mask, inference_params=None, rotary_pos_emb=None):
# hidden_states (float): [s, b, h]
# attention_mask (bool): [1, 1, s, s]
if not self.pre_process:
# See set_input_tensor()
hidden_states = self.input_tensor
# Viewless tensor.
# - We only need to create a viewless tensor in the case of micro batch
# size (mbs) == 1, since in this case, 'hidden_states.transpose()'
# above creates a view tensor, and '.contiguous()' is a pass-through.
# For mbs >= 2, '.contiguous()' creates a new tensor, eliminating
# the need to make it viewless.
#
# However, we don't explicitly check mbs == 1 here because
# make_viewless_tensor() has negligible overhead when its input
# is already viewless.
#
# - For the 'else' case above, calling make_viewless_tensor() here is
# likely redundant, since p2p_communication.py (likely originator)
# already creates viewless tensors. That said, make_viewless_tensor()
# is called here to be future-proof and corner-case-proof.
hidden_states = make_viewless_tensor(
inp=hidden_states, requires_grad=True, keep_graph=True,
)
if self.config.sequence_parallel:
rng_context = tensor_parallel.get_cuda_rng_tracker().fork()
else:
rng_context = nullcontext()
if self.config.fp8:
import transformer_engine # To keep out TE dependency when not training in fp8
if self.config.fp8 == "e4m3":
fp8_format = transformer_engine.common.recipe.Format.E4M3
elif self.config.fp8 == "hybrid":
fp8_format = transformer_engine.common.recipe.Format.HYBRID
else:
raise ValueError("E4M3 and HYBRID are the only supported FP8 formats.")
fp8_recipe = transformer_engine.common.recipe.DelayedScaling(
margin=self.config.fp8_margin,
interval=self.config.fp8_interval,
fp8_format=fp8_format,
amax_compute_algo=self.config.fp8_amax_compute_algo,
amax_history_len=self.config.fp8_amax_history_len,
override_linear_precision=(False, False, not self.config.fp8_wgrad),
)
fp8_group = None
if parallel_state.model_parallel_is_initialized():
fp8_group = parallel_state.get_amax_reduction_group()
fp8_context = transformer_engine.pytorch.fp8_autocast(
enabled=True, fp8_recipe=fp8_recipe, fp8_group=fp8_group
)
else:
fp8_context = nullcontext()
with rng_context and fp8_context:
# Forward pass.
if self.config.recompute_granularity == 'full':
hidden_states = self._checkpointed_forward(
hidden_states=hidden_states,
attention_mask=attention_mask,
rotary_pos_emb=rotary_pos_emb,
)
else:
for layer in self.layers:
hidden_states = layer(
hidden_states=hidden_states,
attention_mask=attention_mask,
rotary_pos_emb=rotary_pos_emb,
inference_params=inference_params,
)
# Final layer norm.
if self.post_process and self.post_layer_norm:
hidden_states = self.final_layernorm(hidden_states)
return hidden_states
def sharded_state_dict(self, prefix=''):
sharded_state_dict = {}
layer_prefix = f'{prefix}layers.'
for layer in self.layers:
sharded_state_dict.update(layer.sharded_state_dict(prefix=layer_prefix))
if self.post_process and self.post_layer_norm:
state_dict = self.state_dict(keep_vars=True)
tensor = state_dict['final_layernorm.weight']
layer_name = f'{prefix}final_layernorm.weight'
sharded_state_dict[layer_name] = make_sharded_tensor_for_checkpoint(tensor, layer_name)
# RMSNorm doesn't have bias.
if 'final_layernorm.bias' in state_dict.keys():
tensor = state_dict['final_layernorm.bias']
layer_name = f'{prefix}final_layernorm.bias'
sharded_state_dict[layer_name] = make_sharded_tensor_for_checkpoint(
tensor, layer_name
)
return sharded_state_dict
|
Megatron-LM-master
|
megatron/core/transformer/transformer_block.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
from dataclasses import dataclass
from typing import Callable
import torch
import torch.nn.functional as F
from megatron.core import ModelParallelConfig
from megatron.core.utils import init_method_normal, scaled_init_method_normal
@dataclass
class TransformerConfig(ModelParallelConfig):
"""Configuration object for megatron-core transformers.
Attributes:
# model architecture
num_layers (int): Number of transformer layers in a transformer block.
hidden_size (int): Transformer hidden size.
ffn_hidden_size (int): Transformer Feed-Forward Network hidden size.
This is set to 4*hidden_size if not provided. Defaults to None.')
num_attention_heads (int): Number of transformer attention heads.
kv_channels (int): Projection weights dimension in multi-head attention.
This is set to hidden_size // num_attention_heads if not provided.
Defaults to None.
num_query_groups (int): Number of query groups for group query attention. If None, normal attention is used.
hidden_dropout (float): Dropout probability for transformer hidden state. Defaults to 0.1.
attention_dropout (float): Post attention dropout probability. Defaults to 0.1.
fp32_residual_connection (bool): If true, move residual connections to fp32.
apply_residual_connection_post_layernorm (bool): If true, uses the original BERT residule connection ordering.
Defaults to False.
layernorm_epsilon (float): Layernorm epsilon. Defaults to 1e-5.
layernorm_zero_centered_gamma (bool): if set to 'True', the LayerNorm is adjusted to center the gamma values
around 0. This improves numerical stability. Defaults to False.
add_bias_linear (bool): Include a bias term in all linear layers (QKV projections, after core attention, and two
in MLP layer). Default is True.
gated_linear_unit (bool): Use a gated linear unit for the first linear layer in the MLP. Defaults to False.
activation_func (Callable): Activation function to use for the non-linearity in the MLP. Defaults to F.gelu.
# initialization
init_method (Callable): Method to initialize weights. Note that bias is always set to
zero. Should be a function that takes a single Tensor and
initializes it. Defaults to
megatron.core.utils.init_method_normal(init_method_std) which is
torch.nn.init.normal_ with mean=0.0 and std=init_method_Std.
output_layer_init_method (Callable): Method to initialize weights of the output layer of
both attention and MLP blocks. Defaults to
megatron.core.utils.scaled_init_method_normal(init_method_std)
which is torch.nn.init.normal_ with mean=0.0 and
std=init_method_std / math.sqrt(2.0 * num_layers).
init_method_std (float): Standard deviation of the zero mean normal for the default
initialization method, not used if init_method and
output_layer_init_method are provided. Defaults to 0.02.
# mixed-precision
apply_query_key_layer_scaling (bool): If true, scale Q * K^T by 1 / layer-number. Defaults to True.
attention_softmax_in_fp32 (bool): If true, run attention masking and softmax in fp32.
This should be true if apply_query_key_layer_scaling is true.
# fusion
bias_gelu_fustion (bool): If true, fuses bias and gelu. Defaults to False.
masked_softmax_fusion (bool): If true, uses softmax fusion.
persist_layer_norm (bool): If true, uses the persistent fused layer norm kernel.
This kernel only supports a fixed set of hidden sizes.
Defaults to False.
bias_dropout_fusion (bool): If true, uses bias dropout fusion.
# activation recomputation
recompute_granularity (str): megatron-core supports 'selective' activation checkpointing where only the memory
intensive part of attention is checkpointed. These memory intensive activations
are also less compute intensive which makes activation checkpointing more efficient
for LLMs (20B+). See Reducing Activation Recomputation in Large Transformer
Models: https://arxiv.org/abs/2205.05198 for more details. 'full' will checkpoint
the entire transformer layer. Must be 'selective' or 'full'. 'selective' always uses all layers.
Defaults to None.
recompute_method (str): uniform will uniformly divide the total number of transformer layers in a transformer
block and recompute the input activation of each divided chunk at the specified
granularity. block will recompute the input activations for only a set number of
transformer layers per pipeline stage. The rest of the layers in the pipeline stage
will not have any activations recomputed. Must be 'uniform' or 'block'. Defaults to
None.
recompute_num_layers (int): When recompute_method is uniform, recompute_num_layers is the number of transformer
layers in each uniformly divided recompute unit. When recompute_method is block,
recompute_num_layers is the number of transformer layers to recompute within each
pipeline stage. Must be None for 'selective' activation checkpointing. Defaults to None.
distribute_saved_activations (bool): If true, distribute recomputed activations across the model parallel
group. Defaults to None.
# fp8 related (via Transformer Engine). For detailed info, refer the the Transformer Engine docs at
# https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/api/common.html
fp8 (str): If set, enables the use of FP8 precision through Transformer Engine. There are 2 predefined choices: (1) 'e4m3'
uniformly uses e4m3 for all FP8 tensors, (2) 'hybrid' uses e4m3 for all FP8 activation and weight tensors and
e5m2 for all FP8 output activation gradient tensors. Defaults to None.
fp8_margin (int): Margin for the scaling factor computation.
fp8_interval (int): Controls how often the scaling factor is recomputed.
fp8_amax_history_len (int): The length of the amax history window used for scaling factor computation.
fp8_amax_compute_algo (str): Algorithm used for choosing the `amax` value for the scaling factor computation.
There are 2 predefined choices: `max` chooses the largest `amax` in the history
window, while `most_recent` always chooses the most recently seen value.
fp8_wgrad (bool): When set to False, override FP8 config options and do the wgrad computation in higher precision.
Defaults to True.
# Experimental
normalization (str): Swtich b/w `LayerNorm` and `RMSNorm` as normalization layers. For now, these are primarily
used by Transformer-Engine's layers like `LayerNormLinear`. Default value is `LayerNorm`.
"""
# model architecture
num_layers: int = 0
hidden_size: int = 0
num_attention_heads: int = 0
num_query_groups: int = None
ffn_hidden_size: int = None
kv_channels: int = None
hidden_dropout: float = 0.1
attention_dropout: float = 0.1
fp32_residual_connection: bool = False
# @jcasper should we keep this option?
apply_residual_connection_post_layernorm: bool = False
layernorm_epsilon: float = 1e-5
layernorm_zero_centered_gamma: bool = False
add_bias_linear: bool = True
gated_linear_unit: bool = False
activation_func: Callable = F.gelu
# initialization
init_method: Callable = None
output_layer_init_method: Callable = None
init_method_std: float = 0.02
# mixed-precision
apply_query_key_layer_scaling: bool = True
attention_softmax_in_fp32: bool = True
# communication
# fusion
bias_gelu_fusion: bool = False # TODO: this should be bias_activation_fusion ?
masked_softmax_fusion: bool = False
persist_layer_norm: bool = False
bias_dropout_fusion: bool = False # TODO: this should be bias_dropout_add_fusion?
# activation recomputation
recompute_granularity: str = None
recompute_method: str = None
recompute_num_layers: int = None
distribute_saved_activations: bool = None
# fp8 related
fp8: str = None
fp8_margin: int = 0
fp8_interval: int = 1
fp8_amax_history_len: int = 1
fp8_amax_compute_algo: str = "most_recent"
fp8_wgrad: bool = True
# experimental section (TODO: move to apt. section above once stable)
normalization: bool = "LayerNorm" # alt value supported by TE: "RMSNorm"
def __post_init__(self):
""" Python dataclass method that is used to modify attributes after initialization.
See https://docs.python.org/3/library/dataclasses.html#post-init-processing for more details.
"""
super().__post_init__()
if self.fp16 and self.bf16:
raise ValueError(
f'Only one of self.fp16: {self.fp16} and self.bf16 {self.bf16} should be True.'
)
if self.num_attention_heads % self.tensor_model_parallel_size != 0:
raise ValueError(
f"num_attention_heads ({self.num_attention_heads}) must be a multiple of "
f"tensor_model_parallel_size ({self.tensor_model_parallel_size})."
)
if self.ffn_hidden_size is None:
self.ffn_hidden_size = 4 * self.hidden_size
if self.kv_channels is None:
self.kv_channels = self.hidden_size // self.num_attention_heads
if self.num_query_groups is None:
self.num_query_groups = self.num_attention_heads
if self.num_query_groups % self.tensor_model_parallel_size != 0:
raise ValueError(
f"num_query_groups ({self.num_query_groups}) must be a multiple of "
f"tensor_model_parallel_size ({self.tensor_model_parallel_size})."
)
if self.apply_query_key_layer_scaling:
self.attention_softmax_in_fp32 = True
if self.recompute_granularity is not None:
if not self.recompute_granularity in ['full', 'selective']:
raise ValueError(
f'When using recompute_granuarlity: {self.recompute_granularity} must be "full" or "selective".'
)
if self.recompute_method is not None:
if not self.recompute_method in ['block', 'uniform']:
raise ValueError(
f'recompute_method: {self.recompute_method} must be "block" or "uniform".'
)
elif self.recompute_granularity != 'selective':
raise ValueError(
f'Using recompute_granularity: {self.recompute_granularity} so recompute_method must be "block" or "uniform"'
)
if self.recompute_granularity != 'selective' and self.recompute_num_layers is None:
raise ValueError(
f'When using recompute_granularity: {self.recompute_granularity} recompute_num_layers must be between '
f'1 and num_layers_per_pipeline_rank: {self.num_layers // self.pipeline_model_parallel_size}'
)
elif (
self.recompute_granularity == 'selective' and self.recompute_num_layers is not None
):
raise ValueError(
f'When using recompute_granularity: {self.recompute_granularity} recompute_num_layers must be None.'
)
if self.distribute_saved_activations and self.sequence_parallel:
raise ValueError(
f'distribute_saved_activations: {self.distribute_saved_activations} must be false when sequence parallel is enabled: {self.sequence_parallel}'
)
if self.virtual_pipeline_model_parallel_size is not None:
if not self.num_layers % self.virtual_pipeline_model_parallel_size == 0:
raise ValueError(
f'num_layers: {self.num_layers} must be divisible by virtual_model_parallel_size {self.virtual_pipeline_model_parallel_size}'
)
if self.apply_query_key_layer_scaling:
self.attention_softmax_in_fp32 = True
if self.bias_gelu_fusion:
if not self.add_bias_linear:
raise ValueError(
"When bias_gelu_fusion is True, add_bias_linear must also be True."
)
if self.activation_func != F.gelu:
raise ValueError(f'When bias_gelu_fusion is True, activation_func must be F.gelu.')
if self.init_method is None:
self.init_method = init_method_normal(self.init_method_std)
if self.output_layer_init_method is None:
self.output_layer_init_method = scaled_init_method_normal(
self.init_method_std, self.num_layers
)
|
Megatron-LM-master
|
megatron/core/transformer/transformer_config.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
from abc import ABC, abstractmethod
import torch
from megatron.core import parallel_state, tensor_parallel
from megatron.core.models.common.rotary_pos_embedding import apply_rotary_pos_emb
from megatron.core.transformer.custom_layers.transformer_engine import (
TEDotProductAttention,
TELayerNormColumnParallelLinear,
TERowParallelLinear,
)
from megatron.core.transformer.enums import AttnMaskType
from megatron.core.transformer.module import MegatronModule
from megatron.core.transformer.transformer_config import TransformerConfig
from megatron.core.utils import divide
from .enums import AttnMaskType
from .transformer_config import TransformerConfig
class Attention(MegatronModule, ABC):
"""Attention layer abstract class.
This layer only contains common modules required for the "self attn" and
"cross attn" specializations.
"""
def __init__(
self, config: TransformerConfig, layer_number: int = 1, attn_mask_type=AttnMaskType.padding,
):
super().__init__(config=config)
self.config = config
self.layer_number = layer_number
self.attn_mask_type = attn_mask_type
# For normal attention without groups, num_query_groups == num_attention_heads,
# so these two will be the same
self.query_projection_size = self.config.kv_channels * self.config.num_attention_heads
self.kv_projection_size = self.config.kv_channels * self.config.num_query_groups
# Per attention head and per partition values.
world_size = parallel_state.get_tensor_model_parallel_world_size()
self.hidden_size_per_attention_head = divide(
self.query_projection_size, self.config.num_attention_heads
)
self.num_attention_heads_per_partition = divide(self.config.num_attention_heads, world_size)
self.num_query_groups_per_partition = divide(self.config.num_query_groups, world_size)
self.dot_product_attention = TEDotProductAttention(
config=self.config, layer_number=self.layer_number, attn_mask_type=self.attn_mask_type
)
self.checkpoint_dot_product_attention = self.config.recompute_granularity == 'selective'
# Output.
self.linear_proj = TERowParallelLinear(
self.query_projection_size,
self.config.hidden_size,
config=self.config,
init_method=self.config.output_layer_init_method,
bias=self.config.add_bias_linear,
skip_bias_add=True,
)
def _checkpointed_attention_forward(
self, query, key, value, attention_mask, rotary_pos_emb=None
):
"""Forward method with selective activation checkpointing."""
def custom_forward(*inputs):
query = inputs[0]
key = inputs[1]
value = inputs[2]
attention_mask = inputs[3]
output_ = self.dot_product_attention(query, key, value, attention_mask)
return output_
hidden_states = tensor_parallel.checkpoint(
custom_forward, False, query, key, value, attention_mask, rotary_pos_emb
)
return hidden_states
def _allocate_memory(self, inference_max_sequence_length, batch_size, dtype):
"""Allocate memory to store kv cache during inference."""
return torch.empty(
inference_max_sequence_length,
batch_size,
self.num_query_groups_per_partition,
self.hidden_size_per_attention_head,
dtype=dtype,
device=torch.cuda.current_device(),
)
def _adjust_key_value_for_inference(self, inference_params, key, value, rotary_pos_emb):
"""
Saves the generated key and value tensors to the end of the buffers in inference_params.
Returns the full size keys and values from the provided inference_params, as well as
adjusted rotary_pos_emb.
Returns a tuple: (key, value, rotary_pos_emb)
"""
if inference_params is None:
return key, value, rotary_pos_emb
# =================================================
# Pre-allocate memory for key-values for inference.
# =================================================
is_first_step = False
if self.layer_number not in inference_params.key_value_memory_dict:
inf_max_seq_length = inference_params.max_sequence_length
inf_max_batch_size = inference_params.max_batch_size
inference_key_memory = self._allocate_memory(
inf_max_seq_length, inf_max_batch_size, key.dtype
)
inference_value_memory = self._allocate_memory(
inf_max_seq_length, inf_max_batch_size, value.dtype
)
inference_params.key_value_memory_dict[self.layer_number] = (
inference_key_memory,
inference_value_memory,
)
is_first_step = True
else:
# Get the pre-allocated buffers for this layer
inference_key_memory, inference_value_memory = inference_params.key_value_memory_dict[
self.layer_number
]
batch_start = inference_params.batch_size_offset
batch_end = batch_start + key.size(1)
assert batch_end <= inference_key_memory.size(1)
sequence_start = inference_params.sequence_len_offset
sequence_end = sequence_start + key.size(0)
assert sequence_end <= inference_key_memory.size(0)
# Copy key and values.
inference_key_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = key
inference_value_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = value
key = inference_key_memory[:sequence_end, batch_start:batch_end, ...]
value = inference_value_memory[:sequence_end, batch_start:batch_end, ...]
# adjust the key rotary positional embedding
if rotary_pos_emb is not None:
q_pos_emb, k_pos_emb = rotary_pos_emb
# need to cross check this condition during inference
# if not set_inference_key_value_memory:
if not is_first_step:
# In inference, we compute one token at a time.
# Select the correct positional embedding
# (only the last token in the sequence)
q_pos_emb = q_pos_emb[sequence_end - 1 : sequence_end]
else:
# In the first forward pass of inference,
# we use the entire provided prefix.
# q_pos_emb here has the rope embeddings of the entire
# prefix + to-be-generated output so
# we slice to just the prefix.
q_pos_emb = q_pos_emb[:sequence_end, :, :, :]
k_pos_emb = k_pos_emb[:sequence_end, :, :, :]
rotary_pos_emb = (q_pos_emb, k_pos_emb)
return key, value, rotary_pos_emb
@abstractmethod
def get_query_key_value_tensors(self, hidden_states, key_value_states):
"""
This method needs to be implemented based on whether the derived class
is "self-attn" or "cross-attn".
"""
def forward(
self,
hidden_states,
attention_mask,
key_value_states=None,
inference_params=None,
rotary_pos_emb=None,
):
# hidden_states: [sq, b, h]
# For self attention we just duplicate the rotary_pos_emb if it isn't already
if rotary_pos_emb is not None and not isinstance(rotary_pos_emb, tuple):
rotary_pos_emb = (rotary_pos_emb,) * 2
# =====================
# Query, Key, and Value
# =====================
# Get the query, key and value tensors based on the type of attention -
# self or cross attn.
query, key, value = self.get_query_key_value_tensors(hidden_states, key_value_states)
# ===================================================
# Adjust key, value, and rotary_pos_emb for inference
# ===================================================
key, value, rotary_pos_emb = self._adjust_key_value_for_inference(
inference_params, key, value, rotary_pos_emb
)
# ================================================
# relative positional embedding (rotary embedding)
# ================================================
if rotary_pos_emb is not None:
q_pos_emb, k_pos_emb = rotary_pos_emb
query = apply_rotary_pos_emb(query, q_pos_emb)
key = apply_rotary_pos_emb(key, k_pos_emb)
# TODO, can apply positional embedding to value_layer so it has
# absolute positional embedding.
# otherwise, only relative positional embedding takes effect
# value_layer = apply_rotary_pos_emb(value_layer, k_pos_emb)
# ==================================
# core attention computation
# ==================================
# expand the key_layer and value_layer [sk, b, ng, hn] -> [sk, b, np, hn]
# This is a noop for normal attention where ng == np. When using group query attention this
# creates a view that has the keys and values virtually repeated along their dimension to
# match the number of queries.
key = key.repeat_interleave(
self.num_attention_heads_per_partition // self.num_query_groups_per_partition, dim=2
)
value = value.repeat_interleave(
self.num_attention_heads_per_partition // self.num_query_groups_per_partition, dim=2
)
if self.checkpoint_dot_product_attention:
core_attn_out = self._checkpointed_attention_forward(query, key, value, attention_mask)
else:
core_attn_out = self.dot_product_attention(query, key, value, attention_mask)
# =================
# Output. [sq, b, h]
# =================
output, bias = self.linear_proj(core_attn_out)
return output, bias
class SelfAttention(Attention):
"""Self-attention layer class
Self-attention layer takes input with size [s, b, h]
and returns output of the same size.
"""
def __init__(
self, config: TransformerConfig, layer_number: int = 1, attn_mask_type=AttnMaskType.padding
):
super().__init__(config=config, layer_number=layer_number, attn_mask_type=attn_mask_type)
self.linear_qkv = TELayerNormColumnParallelLinear(
self.config.hidden_size,
self.query_projection_size + 2 * self.kv_projection_size,
config=self.config,
init_method=self.config.init_method,
bias=self.config.add_bias_linear,
skip_bias_add=False,
)
def get_query_key_value_tensors(self, hidden_states, key_value_states=None):
"""
Derives `query`, `key` and `value` tensors from `hidden_states`.
"""
# Attention heads [sq, b, h] --> [sq, b, ng * (np/ng + 2) * hn)]
mixed_qkv, _ = self.linear_qkv(hidden_states)
# [sq, b, hp] --> [sq, b, ng, (np/ng + 2) * hn]
new_tensor_shape = mixed_qkv.size()[:-1] + (
self.num_query_groups_per_partition,
(
(self.num_attention_heads_per_partition // self.num_query_groups_per_partition + 2)
* self.hidden_size_per_attention_head
),
)
mixed_qkv = mixed_qkv.view(*new_tensor_shape)
# [sq, b, ng, (np/ng + 2) * hn] --> [sq, b, ng, np/ng * hn], [sq, b, ng, hn], [sq, b, ng, hn]
(query, key, value) = torch.split(
mixed_qkv,
[
(
self.num_attention_heads_per_partition
// self.num_query_groups_per_partition
* self.hidden_size_per_attention_head
),
self.hidden_size_per_attention_head,
self.hidden_size_per_attention_head,
],
dim=3,
)
# [sq, b, ng, np/ng * hn] -> [sq, b, np, hn]
query = query.reshape(query.size(0), query.size(1), -1, self.hidden_size_per_attention_head)
return query, key, value
class CrossAttention(Attention):
"""Cross-attention layer class
Cross-attention layer takes input with size [s, b, h] and context with size
[s, b, h] and returns output of the same size.
"""
def __init__(
self, config: TransformerConfig, layer_number: int = 1, attn_mask_type=AttnMaskType.padding
):
super().__init__(config=config, layer_number=layer_number, attn_mask_type=attn_mask_type)
if self.config.num_query_groups != self.config.num_attention_heads:
raise ValueError(
f"Group query attention is not currently supported in cross attention."
)
assert self.query_projection_size == self.kv_projection_size
self.linear_q = TELayerNormColumnParallelLinear(
self.config.hidden_size,
self.query_projection_size,
config=self.config,
init_method=self.config.init_method,
bias=self.config.add_bias_linear,
skip_bias_add=False,
)
self.linear_kv = TELayerNormColumnParallelLinear(
self.config.hidden_size,
2 * self.kv_projection_size,
config=self.config,
init_method=self.config.init_method,
bias=self.config.add_bias_linear,
skip_bias_add=False,
)
def get_query_key_value_tensors(self, hidden_states, key_value_states):
"""
Derives `query` tensor from `hidden_states`, and `key`/`value` tensors
from `key_value_states`.
"""
# Attention heads [sk, b, h] --> [sk, b, (np * 2 * hn)]
mixed_kv, _ = self.linear_kv(key_value_states)
# [sk, b, (np * 2 * hn)] --> [sk, b, np, 2 * hn]
new_tensor_shape = mixed_kv.size()[:-1] + (
self.num_attention_heads_per_partition,
2 * self.hidden_size_per_attention_head,
)
mixed_kv = mixed_kv.view(*new_tensor_shape)
# [sk, b, np, 2 * hn] --> 2 [sk, b, np, hn]
(key, value) = tensor_parallel.split_tensor_along_last_dim(mixed_kv, 2)
# Attention head [sq, b, h] --> [sq, b, hp]
query, _ = self.linear_q(hidden_states)
# [sq, b, hp] --> [sq, b, np, hn]
new_tensor_shape = query.size()[:-1] + (
self.num_attention_heads_per_partition,
self.hidden_size_per_attention_head,
)
query = query.view(*new_tensor_shape)
return query, key, value
|
Megatron-LM-master
|
megatron/core/transformer/attention.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import enum
# can we get rid of this?
# it's being used in pipeline schedules
class ModelType(enum.Enum):
encoder_or_decoder = 1
encoder_and_decoder = 2
# class LayerType(enum.Enum):
# encoder = 1
# decoder = 2
class AttnType(enum.Enum):
self_attn = 1
cross_attn = 2
class AttnMaskType(enum.Enum):
padding = 1
causal = 2
|
Megatron-LM-master
|
megatron/core/transformer/enums.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
from .transformer_config import TransformerConfig
|
Megatron-LM-master
|
megatron/core/transformer/__init__.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import re
import torch
from megatron.core import parallel_state
from megatron.core.dist_checkpointing.mapping import ShardedObject, ShardedTensor
from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
from megatron.core.transformer.attention import SelfAttention
from megatron.core.transformer.custom_layers.transformer_engine import TENorm
from megatron.core.transformer.enums import AttnMaskType
from megatron.core.transformer.identity_op import IdentityOp
from megatron.core.transformer.mlp import MLP
from megatron.core.transformer.module import MegatronModule
from megatron.core.transformer.transformer_config import TransformerConfig
from megatron.core.utils import make_viewless_tensor
class TransformerLayer(MegatronModule):
"""A single transformer layer.
Transformer layer takes input with size [s, b, h] and returns an
output of the same size.
"""
def __init__(
self,
config: TransformerConfig,
layer_number: int = 1,
self_attn_mask_type=AttnMaskType.padding,
):
super().__init__(config=config)
self.config: TransformerConfig = config
self.layer_number = layer_number + self._get_layer_offset()
self.self_attn_mask_type = self_attn_mask_type
# Layernorm on the input data.
# TODO: add pytorch only layernorm
self.input_layernorm = IdentityOp(
config=self.config,
hidden_size=self.config.hidden_size,
eps=self.config.layernorm_epsilon,
persist_layer_norm=self.config.persist_layer_norm,
sequence_parallel=self.config.sequence_parallel,
zero_centered_gamma=self.config.layernorm_zero_centered_gamma,
normalization=self.config.normalization,
)
# Self attention.
self.self_attention = SelfAttention(
config=self.config, layer_number=layer_number, attn_mask_type=self_attn_mask_type,
)
# Layernorm on the attention output
self.post_self_attn_layernorm = IdentityOp(
config=self.config,
hidden_size=self.config.hidden_size,
eps=self.config.layernorm_epsilon,
persist_layer_norm=self.config.persist_layer_norm,
sequence_parallel=self.config.sequence_parallel,
zero_centered_gamma=self.config.layernorm_zero_centered_gamma,
normalization=self.config.normalization,
)
# MLP
self.mlp = MLP(config=self.config)
# @jcasper how should we handle nvfuser?
# Set bias+dropout+add fusion grad_enable execution handler.
# TORCH_MAJOR = int(torch.__version__.split('.')[0])
# TORCH_MINOR = int(torch.__version__.split('.')[1])
# use_nvfuser = TORCH_MAJOR > 1 or (TORCH_MAJOR == 1 and TORCH_MINOR >= 10)
# self.bias_dropout_add_exec_handler = nullcontext if use_nvfuser else torch.enable_grad
self.bias_dropout_add_exec_handler = torch.enable_grad
def _get_layer_offset(self):
pipeline_rank = parallel_state.get_pipeline_model_parallel_rank()
num_layers_per_pipeline_rank = (
self.config.num_layers // parallel_state.get_pipeline_model_parallel_world_size()
)
if parallel_state.get_virtual_pipeline_model_parallel_world_size() is not None:
vp_rank = parallel_state.get_virtual_pipeline_model_parallel_rank()
vp_size = parallel_state.get_virtual_pipeline_model_parallel_world_size()
total_num_layers = self.config.num_layers
num_layers_per_virtual_rank = num_layers_per_pipeline_rank // vp_size
total_virtual_chunks = total_num_layers // vp_size
offset = vp_rank * total_virtual_chunks + (pipeline_rank * num_layers_per_virtual_rank)
else:
# Each stage gets a contiguous set of layers.
if parallel_state.get_pipeline_model_parallel_world_size() > 1:
offset = pipeline_rank * num_layers_per_pipeline_rank
else:
offset = 0
return offset
def forward(
self,
hidden_states,
attention_mask,
encoder_output=None,
enc_dec_attn_mask=None,
inference_params=None,
rotary_pos_emb=None,
):
# hidden_states: [s, b, h]
# Layer norm at the beginning of the transformer layer.
layernorm_output = self.input_layernorm(hidden_states)
# Self attention.
attention_output_with_bias = self.self_attention(
layernorm_output,
attention_mask,
inference_params=inference_params,
rotary_pos_emb=rotary_pos_emb,
)
# Residual connection.
if self.config.apply_residual_connection_post_layernorm:
residual = layernorm_output
else:
residual = hidden_states
bias_dropout_add_func = get_bias_dropout_add(self.training, self.config.bias_dropout_fusion)
# bias_dropout_add fusion returning fp32 instead of bf16
with self.bias_dropout_add_exec_handler():
layernorm_input = bias_dropout_add_func(
attention_output_with_bias, residual, self.config.hidden_dropout
)
# Layer norm post the self attention.
layernorm_output = self.post_self_attn_layernorm(layernorm_input)
# MLP.
mlp_output_with_bias = self.mlp(layernorm_output)
# Second residual connection.
if self.config.apply_residual_connection_post_layernorm:
residual = layernorm_output
else:
residual = layernorm_input
with self.bias_dropout_add_exec_handler():
output = bias_dropout_add_func(
mlp_output_with_bias, residual, self.config.hidden_dropout
)
# Jit compiled function creates 'view' tensor. This tensor
# potentially gets saved in the MPU checkpoint function context,
# which rejects view tensors. While making a viewless tensor here
# won't result in memory savings (like the data loader, or
# p2p_communication), it serves to document the origin of this
# 'view' tensor.
output = make_viewless_tensor(
inp=output, requires_grad=output.requires_grad, keep_graph=True
)
return output
def sharded_state_dict(self, prefix=''):
# state_dict = self.state_dict(prefix=prefix, keep_vars=True)
state_dict = self.state_dict(keep_vars=True)
tensor_parallel_layers_axis_map = {
'self_attention.linear_qkv.weight': 0,
'self_attention.linear_qkv.bias': 0,
'self_attention.linear_proj.weight': 1,
'mlp.linear_fc1.weight': 0,
'mlp.linear_fc1.bias': 0,
'mlp.linear_fc2.weight': 1,
}
offset = self._get_layer_offset()
num_layers = self.config.num_layers
sharded_state_dict = {}
for layer_name in state_dict.keys():
tensor = state_dict[layer_name]
global_layer_offset = self.layer_number - 1 # self.layer_number starts at 1
layer_key = f'{prefix}{global_layer_offset - offset}.{layer_name}' # module list index in TransformerBlock
sharded_offsets = [(0, global_layer_offset, num_layers)] # PP sharding
if layer_name in tensor_parallel_layers_axis_map:
tp_axis = tensor_parallel_layers_axis_map[layer_name]
# TP sharding
sharded_offsets.append(
[
tp_axis + 1, # +1 for PP dimension
parallel_state.get_tensor_model_parallel_rank(),
parallel_state.get_tensor_model_parallel_world_size(),
]
)
replica_id = parallel_state.get_data_parallel_rank()
else:
replica_id = (
parallel_state.get_data_parallel_rank()
* parallel_state.get_data_parallel_world_size()
+ parallel_state.get_tensor_model_parallel_rank()
)
if layer_name.endswith('._extra_state'):
sharded_state_dict[layer_key] = ShardedObject(
f'{prefix}{layer_name}',
tensor,
(num_layers,),
(global_layer_offset,),
replica_id,
)
else:
sharded_state_dict[layer_key] = ShardedTensor.from_rank_offsets(
f'{prefix}{layer_name}',
tensor,
*sharded_offsets,
replica_id=replica_id,
prepend_axis_num=1, # for PP sharding
)
return sharded_state_dict
|
Megatron-LM-master
|
megatron/core/transformer/transformer_layer.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import torch
import torch.nn.functional as F
from megatron.core import tensor_parallel
from megatron.core.fusions.fused_bias_gelu import bias_gelu_impl
from megatron.core.transformer.custom_layers.transformer_engine import (
TELayerNormColumnParallelLinear,
TERowParallelLinear,
)
from megatron.core.transformer.module import MegatronModule
from megatron.core.transformer.transformer_config import TransformerConfig
class MLP(MegatronModule):
"""
MLP will take the input with h hidden state, project it to 4*h
hidden dimension, perform nonlinear transformation, and project the
state back into h hidden dimension.
Returns an output and a bias to be added to the output.
If config.add_bias_linear is False, the bias returned is None.
We use the following notation:
h: hidden size
p: number of tensor model parallel partitions
b: batch size
s: sequence length
"""
def __init__(self, config: TransformerConfig):
super().__init__(config=config)
self.config: TransformerConfig = config
# If this is a gated linear unit we double the output width, see https://arxiv.org/pdf/2002.05202.pdf
ffn_hidden_size = self.config.ffn_hidden_size
if self.config.gated_linear_unit:
ffn_hidden_size *= 2
self.linear_fc1 = TELayerNormColumnParallelLinear(
self.config.hidden_size,
ffn_hidden_size,
config=self.config,
init_method=self.config.init_method,
bias=self.config.add_bias_linear,
skip_bias_add=True,
)
if self.config.gated_linear_unit:
def glu(x):
x = torch.chunk(x, 2, dim=-1)
return self.config.activation_func(x[0]) * x[1]
self.activation_func = glu
else:
self.activation_func = self.config.activation_func
self.linear_fc2 = TERowParallelLinear(
self.config.ffn_hidden_size,
self.config.hidden_size,
config=self.config,
init_method=self.config.output_layer_init_method,
bias=self.config.add_bias_linear,
skip_bias_add=True,
)
def forward(self, hidden_states):
# [s, b, 4 * h/p]
intermediate_parallel, bias_parallel = self.linear_fc1(hidden_states)
if self.config.bias_gelu_fusion:
assert self.config.add_bias_linear is True
assert self.activation_func == F.gelu
intermediate_parallel = bias_gelu_impl(intermediate_parallel, bias_parallel)
else:
if bias_parallel is not None:
intermediate_parallel = intermediate_parallel + bias_parallel
intermediate_parallel = self.activation_func(intermediate_parallel)
# [s, b, h]
output, output_bias = self.linear_fc2(intermediate_parallel)
return output, output_bias
|
Megatron-LM-master
|
megatron/core/transformer/mlp.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import math
import torch
from torch import Tensor
from megatron.core import parallel_state, tensor_parallel
from megatron.core.fusions.fused_softmax import FusedScaleMaskSoftmax
from megatron.core.transformer.enums import AttnMaskType
from megatron.core.transformer.module import MegatronModule
from megatron.core.transformer.transformer_config import TransformerConfig
from megatron.core.transformer.utils import attention_mask_func
from megatron.core.utils import divide
class DotProductAttention(MegatronModule):
"""
Region where selective activation recomputation is applied.
This region is memory intensive but less compute intensive which
makes activation checkpointing more efficient for LLMs (20B+).
See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details.
We use the following notation:
h: hidden size
n: number of attention heads
p: number of tensor model parallel partitions
b: batch size
s: sequence length
"""
def __init__(
self, config: TransformerConfig, layer_number: int = 1, attn_mask_type=AttnMaskType.padding
):
super().__init__(config=config)
self.config: TransformerConfig = config
self.layer_number = max(1, layer_number)
self.attn_mask_type = attn_mask_type
projection_size = self.config.kv_channels * config.num_attention_heads
# Per attention head and per partition values.
world_size = parallel_state.get_tensor_model_parallel_world_size()
self.hidden_size_per_partition = divide(projection_size, world_size)
self.hidden_size_per_attention_head = divide(projection_size, config.num_attention_heads)
self.num_attention_heads_per_partition = divide(config.num_attention_heads, world_size)
coeff = None
self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
if self.config.apply_query_key_layer_scaling:
coeff = self.layer_number
self.norm_factor *= coeff
self.scale_mask_softmax = FusedScaleMaskSoftmax(
input_in_fp16=self.config.fp16,
input_in_bf16=self.config.bf16,
attn_mask_type=self.attn_mask_type,
scaled_masked_softmax_fusion=self.config.masked_softmax_fusion,
mask_func=attention_mask_func,
softmax_in_fp32=self.config.attention_softmax_in_fp32,
scale=coeff,
)
# Dropout. Note that for a single iteration, this layer will generate
# different outputs on different number of parallel partitions but
# on average it should not be partition dependent.
self.attention_dropout = torch.nn.Dropout(self.config.attention_dropout)
def forward(
self, query_layer: Tensor, key_layer: Tensor, value_layer: Tensor, attention_mask: Tensor
):
# ===================================
# Raw attention scores. [b, n/p, s, s]
# ===================================
# [b, np, sq, sk]
output_size = (
query_layer.size(1),
query_layer.size(2),
query_layer.size(0),
key_layer.size(0),
)
# [sq, b, np, hn] -> [sq, b * np, hn]
# This will be a simple view when doing normal attention, but in group query attention
# the key and value tensors are repeated to match the queries so you can't use simple strides
# to extract the queries.
query_layer = query_layer.reshape(output_size[2], output_size[0] * output_size[1], -1)
# [sk, b, np, hn] -> [sk, b * np, hn]
key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1)
# preallocting input tensor: [b * np, sq, sk]
matmul_input_buffer = parallel_state.get_global_memory_buffer().get_tensor(
(output_size[0] * output_size[1], output_size[2], output_size[3]),
query_layer.dtype,
"mpu",
)
# Raw attention scores. [b * np, sq, sk]
matmul_result = torch.baddbmm(
matmul_input_buffer,
query_layer.transpose(0, 1), # [b * np, sq, hn]
key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk]
beta=0.0,
alpha=(1.0 / self.norm_factor),
)
# change view to [b, np, sq, sk]
attention_scores = matmul_result.view(*output_size)
# ===========================
# Attention probs and dropout
# ===========================
# attention scores and attention mask [b, np, sq, sk]
attention_probs: Tensor = self.scale_mask_softmax(attention_scores, attention_mask)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
if not self.config.sequence_parallel:
with tensor_parallel.get_cuda_rng_tracker().fork():
attention_probs = self.attention_dropout(attention_probs)
else:
attention_probs = self.attention_dropout(attention_probs)
# =========================
# Context layer. [sq, b, hp]
# =========================
# value_layer -> context layer.
# [sk, b, np, hn] --> [b, np, sq, hn]
# context layer shape: [b, np, sq, hn]
output_size = (
value_layer.size(1),
value_layer.size(2),
query_layer.size(0),
value_layer.size(3),
)
# change view [sk, b * np, hn]
value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1)
# change view [b * np, sq, sk]
attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
# matmul: [b * np, sq, hn]
context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))
# change view [b, np, sq, hn]
context_layer = context_layer.view(*output_size)
# [b, np, sq, hn] --> [sq, b, np, hn]
context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
# [sq, b, np, hn] --> [sq, b, hp]
new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
context_layer = context_layer.view(*new_context_layer_shape)
return context_layer
|
Megatron-LM-master
|
megatron/core/transformer/dot_product_attention.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Utilities for transformer layers."""
import torch
from megatron import get_args
def attention_mask_func(attention_scores, attention_mask):
attention_scores.masked_fill_(attention_mask, -10000.0)
return attention_scores
def get_linear_layer(rows, columns, init_method):
"""Simple linear layer with weight initialization."""
layer = torch.nn.Linear(rows, columns)
if get_args().perform_initialization:
init_method(layer.weight)
with torch.no_grad():
layer.bias.zero_()
return layer
@torch.jit.script
def gelu_impl(x):
"""OpenAI's gelu implementation."""
return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x * (1.0 + 0.044715 * x * x)))
def openai_gelu(x):
return gelu_impl(x)
# This is actually Python equivalent of torch.nn.functional.gelu(), also with type hints for ONNX exporter
@torch.jit.script
def erf_gelu(x):
return (
x * 0.5 * (torch.erf(x / 1.41421).to(dtype=x.dtype) + torch.ones_like(x).to(dtype=x.dtype))
)
|
Megatron-LM-master
|
megatron/core/transformer/utils.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import torch
class IdentityOp(torch.nn.Module):
"""
This is a placeholder for IdentityOp (NoOp)
"""
def __init__(self, *args, **kwargs):
super(IdentityOp, self).__init__()
def forward(self, x, *args, **kwargs):
return x
|
Megatron-LM-master
|
megatron/core/transformer/identity_op.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
"""Megatron Module"""
import torch
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from megatron.core import parallel_state, tensor_parallel
from megatron.core.transformer.transformer_config import TransformerConfig
_FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor)
_HALF_TYPES = (torch.HalfTensor, torch.cuda.HalfTensor)
_BF16_TYPES = (torch.BFloat16Tensor, torch.cuda.BFloat16Tensor)
def param_is_not_shared(param):
return not hasattr(param, 'shared') or not param.shared
class MegatronModule(torch.nn.Module):
"""Megatron specific extensions of torch Module with support
for pipelining."""
# def __init__(self, config: TransformerConfig, share_word_embeddings=True):
def __init__(self, config: TransformerConfig):
super().__init__()
self.config = config
def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False):
"""Use this function to override the state dict for
saving checkpoints.
"""
return self.state_dict(prefix=prefix, keep_vars=keep_vars)
def sharded_state_dict(self, prefix=''):
""" Override sharded_state_dict when using distributed checkpointing.
keep_vars must always be set to True so that optimizer states
can be sharded.
"""
return self.state_dict(prefix=prefix, keep_vars=True)
def conversion_helper(val, conversion):
"""Apply conversion to val. Recursively apply conversion if `val`
#is a nested tuple/list structure."""
if not isinstance(val, (tuple, list)):
return conversion(val)
rtn = [conversion_helper(v, conversion) for v in val]
if isinstance(val, tuple):
rtn = tuple(rtn)
return rtn
def fp32_to_float16(val, float16_convertor):
"""Convert fp32 `val` to fp16/bf16"""
def half_conversion(val):
val_typecheck = val
if isinstance(val_typecheck, (Parameter, Variable)):
val_typecheck = val.data
if isinstance(val_typecheck, _FLOAT_TYPES):
val = float16_convertor(val)
return val
return conversion_helper(val, half_conversion)
def float16_to_fp32(val):
"""Convert fp16/bf16 `val` to fp32"""
def float_conversion(val):
val_typecheck = val
if isinstance(val_typecheck, (Parameter, Variable)):
val_typecheck = val.data
if isinstance(val_typecheck, (_BF16_TYPES, _HALF_TYPES)):
val = val.float()
return val
return conversion_helper(val, float_conversion)
class Float16Module(MegatronModule):
def __init__(self, config: TransformerConfig, module: torch.nn.Module):
super(Float16Module, self).__init__(config)
self.config = config
self.fp16 = config.fp16
self.bf16 = config.bf16
if self.fp16:
self.add_module('module', module.half())
def float16_convertor(val):
return val.half()
elif self.bf16:
self.add_module('module', module.bfloat16())
def float16_convertor(val):
return val.bfloat16()
else:
raise Exception('Either config.fp16 or config.bf16 should be True.')
self.float16_convertor = float16_convertor
def set_input_tensor(self, input_tensor):
return self.module.set_input_tensor(input_tensor)
def forward(self, *inputs, **kwargs):
if parallel_state.is_pipeline_first_stage():
inputs = fp32_to_float16(inputs, self.float16_convertor)
outputs = self.module(*inputs, **kwargs)
if parallel_state.is_pipeline_last_stage():
outputs = float16_to_fp32(outputs)
return outputs
def state_dict(self, destination=None, prefix='', keep_vars=False):
return self.module.state_dict(destination=destination, prefix=prefix, keep_vars=keep_vars)
def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False):
""" Retrieve state_dict from the module being wrapped."""
return self.module.state_dict_for_save_checkpoint(prefix=prefix, keep_vars=keep_vars)
def sharded_state_dict(self, prefix=''):
""" Retrieve state_dict from the module being wrapped.
When using distributed checkpointing, keep_vars must always be set to True.
"""
return self.module.sharded_state_dict(prefix=prefix, keep_vars=True)
def load_state_dict(self, state_dict, strict=True):
self.module.load_state_dict(state_dict, strict=strict)
|
Megatron-LM-master
|
megatron/core/transformer/module.py
|
from importlib.metadata import version
from typing import Callable
import torch
import transformer_engine as te
from pkg_resources import packaging
from megatron.core.parallel_state import get_tensor_model_parallel_group
from megatron.core.tensor_parallel import get_cuda_rng_tracker
from megatron.core.transformer.enums import AttnMaskType
from megatron.core.transformer.transformer_config import TransformerConfig
def _get_extra_te_kwargs(config: TransformerConfig):
extra_transformer_engine_kwargs = {}
from importlib.metadata import version
from pkg_resources import packaging
te_version = packaging.version.Version(version("transformer-engine"))
if te_version >= packaging.version.Version("0.12.0"):
if config.use_cpu_initialization:
extra_transformer_engine_kwargs["device"] = 'cpu'
else:
extra_transformer_engine_kwargs["device"] = torch.cuda.current_device()
return extra_transformer_engine_kwargs
class TENorm:
"""
A conditional wrapper to initialize an instance of Transformer-Engine's
`LayerNorm` or `RMSNorm` based on input
"""
def __new__(
cls,
config: TransformerConfig,
hidden_size: int,
eps: float = 1e-5,
sequence_parallel: bool = False,
normalization="LayerNorm",
**kwargs
):
if normalization == "LayerNorm":
instance = te.pytorch.LayerNorm(
hidden_size=hidden_size,
eps=eps,
sequence_parallel=sequence_parallel,
**_get_extra_te_kwargs(config),
)
elif normalization == "RMSNorm":
assert hasattr(
te.pytorch, "RMSNorm"
), "Transformer-Engine >= v0.11 required to use this feature"
instance = te.pytorch.RMSNorm(
hidden_size=hidden_size,
eps=eps,
sequence_parallel=sequence_parallel,
**_get_extra_te_kwargs(config),
)
else:
raise Exception('Only LayerNorm and RMSNorm are curently supported')
return instance
class TELinear(te.pytorch.Linear):
"""
Wrapper for the Transformer-Engine's `Linear` layer.
Note that if Megatron's parallel_state has not been initialized
yet, the tp_group passed to TE will be None and must be set later
via set_tensor_parallel_group().
"""
def __init__(
self,
input_size: int,
output_size: int,
config: TransformerConfig,
parallel_mode: str,
init_method: Callable,
*,
bias: bool = True,
skip_bias_add: bool = False,
**kwargs
):
self.config = config
# TE returns a zero length Tensor when bias=False and
# return_bias=True, but we prefer None. So in that case we
# tell TE to not return the bias, and return None
# ourselves. This way our forward always returns two values
# and we don't have to deal with the zero length Tensor.
self.te_return_bias = skip_bias_add and bias
super().__init__(
in_features=input_size,
out_features=output_size,
sequence_parallel=self.config.sequence_parallel,
fuse_wgrad_accumulation=self.config.gradient_accumulation_fusion,
tp_group=get_tensor_model_parallel_group(check_initialized=False),
tp_size=self.config.tensor_model_parallel_size,
get_rng_state_tracker=get_cuda_rng_tracker,
init_method=init_method,
params_dtype=self.config.params_dtype,
parallel_mode=parallel_mode,
bias=bias,
return_bias=self.te_return_bias,
**_get_extra_te_kwargs(config),
**kwargs,
)
def forward(self, x):
out = super().forward(x)
# TE only returns a tuple when return_bias is True, otherwise
# it returns a single Tensor, we always want to return two
# values regardless of the arguments.
if self.te_return_bias:
return out
return out, None
class TELayerNormColumnParallelLinear(te.pytorch.LayerNormLinear):
"""
Wrapper for the Transformer-Engine's `LayerNormLinear` layer that combines
layernorm and linear layers
"""
def __init__(
self,
input_size: int,
output_size: int,
config: TransformerConfig,
init_method: Callable,
bias: bool,
skip_bias_add: bool,
**kwargs
):
self.config = config
# TE returns a zero length Tensor when bias=False and
# return_bias=True, but we prefer None. So in that case we
# tell TE to not return the bias, and return None
# ourselves. This way our forward always returns two values
# and we don't have to deal with the zero length Tensor.
self.te_return_bias = skip_bias_add and bias
# Only Transformer-Engine version >= 0.11.0 supports `RMSNorm`
te_version = packaging.version.Version(version("transformer-engine"))
if te_version >= packaging.version.Version("0.11.0"):
kwargs["normalization"] = self.config.normalization
super().__init__(
in_features=input_size,
out_features=output_size,
bias=bias,
sequence_parallel=self.config.sequence_parallel,
fuse_wgrad_accumulation=self.config.gradient_accumulation_fusion,
tp_group=get_tensor_model_parallel_group(check_initialized=False),
tp_size=self.config.tensor_model_parallel_size,
get_rng_state_tracker=get_cuda_rng_tracker,
init_method=init_method,
params_dtype=self.config.params_dtype,
parallel_mode="column",
return_bias=self.te_return_bias,
**_get_extra_te_kwargs(config),
**kwargs,
)
def forward(self, x):
out = super().forward(x)
# TE only returns a tuple when return_bias is True, otherwise
# it returns a single Tensor, we always want to return two
# values regardless of the arguments.
if self.te_return_bias:
return out
return out, None
class TEColumnParallelLinear(TELinear):
"""
Wrapper for the Transformer-Engine's `Linear` layer but specialized similar
to megatron's `ColumnParallelLinear` layer.
"""
def __init__(self, input_size: int, output_size: int, config: TransformerConfig, **kwargs):
self.config = config
super().__init__(
input_size=input_size,
output_size=output_size,
config=self.config,
parallel_mode="column",
**kwargs,
)
class TERowParallelLinear(TELinear):
"""
Wrapper for the Transformer-Engine's `Linear` layer but specialized similar
to megatron's `RowParallelLinear` layer.
"""
def __init__(self, input_size: int, output_size: int, config: TransformerConfig, **kwargs):
self.config = config
super().__init__(
input_size=input_size,
output_size=output_size,
config=self.config,
parallel_mode="row",
**kwargs,
)
class TEDotProductAttention(te.pytorch.DotProductAttention):
"""
Wrapper for the Transformer-Engine's `DotProductAttention` layer that also
has "flash attention" enabled.
Note that if Megatron's parallel_state has not been initialized
yet, the tp_group passed to TE will be None and must be set later
via set_tensor_parallel_group().
"""
def __init__(
self,
config: TransformerConfig,
layer_number: int = 1,
attn_mask_type: AttnMaskType = AttnMaskType.padding,
**kwargs
):
self.config = config
super().__init__(
num_attention_heads=self.config.num_attention_heads,
kv_channels=self.config.kv_channels,
attention_dropout=self.config.attention_dropout,
layer_number=layer_number,
attn_mask_type=attn_mask_type.name,
sequence_parallel=self.config.sequence_parallel,
tp_size=self.config.tensor_model_parallel_size,
get_rng_state_tracker=get_cuda_rng_tracker,
tp_group=get_tensor_model_parallel_group(check_initialized=False),
**kwargs,
)
|
Megatron-LM-master
|
megatron/core/transformer/custom_layers/transformer_engine.py
|
from .schedules import get_forward_backward_func
|
Megatron-LM-master
|
megatron/core/pipeline_parallel/__init__.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import contextlib
from typing import Callable, Iterator, List, Optional, Union
import torch
from torch.autograd.variable import Variable
from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
from megatron import core
from megatron.core import parallel_state
from megatron.core.enums import ModelType
from megatron.core.pipeline_parallel import p2p_communication
from megatron.core.utils import get_attr_wrapped_model, get_model_config, get_model_type
# Types
Shape = Union[List[int], torch.Size]
def get_forward_backward_func():
"""Retrieves the appropriate forward_backward function given the
configuration of parallel_state.
Returns a function that will perform all of the forward and
backward passes of the model given the pipeline model parallel
world size and virtual pipeline model parallel world size in the
global parallel_state.
Note that if using sequence parallelism, the sequence length component of
the tensor shape is updated to original_sequence_length /
tensor_model_parallel_world_size.
The function returned takes the following arguments:
forward_step_func (required): A function that takes a data
iterator and a model as its arguments and return the model's
forward output and the loss function. The loss function should
take one torch.Tensor and return a torch.Tensor of loss and a
dictionary of string -> torch.Tensor.
A third argument, checkpoint_activations_microbatch, indicates
that the activations for this microbatch should be
checkpointed. A None value for this argument indicates that
the default from the configuration should be used. This is
used when the
num_microbatches_with_partial_activation_checkpoints is used.
For example:
def loss_func(loss_mask, output_tensor):
losses = output_tensor.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])
return loss, {'lm loss': averaged_loss[0]}
def forward_step(data_iterator, model):
data, loss_mask = next(data_iterator)
output = model(data)
return output, partial(loss_func, loss_mask)
forward_backward_func(forward_step_func=forward_step, ...)
data_iterator (required): an iterator over the data, will be
passed as is to forward_step_func. Expected to be a list of
iterators in the case of interleaved pipeline parallelism.
model (required): the actual model. Expected to be a list of modules in the case of interleaved
pipeline parallelism. Must be a (potentially wrapped) megatron.core.models.MegatronModule.
num_microbatches (int, required):
The number of microbatches to go through
seq_length (int, required): Sequence length of the current global batch. If this is a dual-stack
transformer, this is the encoder's sequence length. This is ignored if variable_seq_lengths
in the config is True. Otherwise, each microbatch in the current global batch size must use
this sequence length.
micro_batch_size (int, required): The number of sequences in a microbatch.
decoder_seq_length (int, optional): The sequence length for the decoder in a dual-stack
transformer. This is ignored for a single-stack transformer.
forward_only (optional, default = False): Perform only the forward step
collect_non_loss_data (optional, bool, default=False): TODO
"""
pipeline_model_parallel_size = parallel_state.get_pipeline_model_parallel_world_size()
if pipeline_model_parallel_size > 1:
if parallel_state.get_virtual_pipeline_model_parallel_world_size() is not None:
forward_backward_func = forward_backward_pipelining_with_interleaving
else:
forward_backward_func = forward_backward_pipelining_without_interleaving
else:
forward_backward_func = forward_backward_no_pipelining
return forward_backward_func
def deallocate_output_tensor(out, deallocate_pipeline_outputs=False):
'''Pseudo-deallocate (i.e., set to scalar) the output tensor's '.data' field.
This method should be called right after the output tensor has been
sent to the next pipeline stage. At this point, the output tensor is
only useful for its '.grad_fn' field, and not its '.data'.
'''
if (out is None) or (not deallocate_pipeline_outputs):
return
assert isinstance(out, torch.Tensor), "expected Tensor, found %s." % type(out).__name__
assert out._base is None, "counter-productive to free a view of another tensor."
out.data = torch.empty((1,), device=out.device, dtype=out.dtype,)
def custom_backward(output, grad_output):
'''Directly call C++ autograd engine.
To make the 'deallocate_output_tensor' (above) optimization work, the C++
autograd engine must be called directly, bypassing Pytorch's
torch.autograd.backward. Pytorch's 'backward' checks that the output and
grad have the same shape, while C++'s 'backward' does not.
'''
assert output.numel() == 1, "output should be pseudo-'freed' in schedule, to optimize memory"
assert isinstance(output, torch.Tensor), "output == '%s'." % type(output).__name__
assert isinstance(grad_output, (torch.Tensor, type(None))), (
"grad_output == '%s'." % type(grad_output).__name__
)
# Handle scalar output
if grad_output is None:
assert output.numel() == 1, "implicit grad requires scalar output."
grad_output = torch.ones_like(output, memory_format=torch.preserve_format,)
# Call c++ engine [ see torch/csrc/autograd/python_engine.cpp ]
Variable._execution_engine.run_backward(
tensors=(output,),
grad_tensors=(grad_output,),
keep_graph=False,
create_graph=False,
inputs=tuple(),
allow_unreachable=True,
accumulate_grad=True,
)
def forward_step(
forward_step_func,
data_iterator,
model,
num_microbatches,
input_tensor,
forward_data_store,
config,
collect_non_loss_data=False,
checkpoint_activations_microbatch=None,
):
"""Forward step for passed-in model.
If first stage, input tensor is obtained from data_iterator, otherwise
passed-in input_tensor is used.
Returns output tensor."""
if config.timers is not None:
config.timers('forward-compute', log_level=2).start()
unwrap_output_tensor = False
if not isinstance(input_tensor, list):
input_tensor = [input_tensor]
unwrap_output_tensor = True
set_input_tensor = get_attr_wrapped_model(model, "set_input_tensor")
set_input_tensor(input_tensor)
if config.enable_autocast:
context_manager = torch.autocast("cuda", dtype=config.autocast_dtype)
else:
context_manager = contextlib.nullcontext()
with context_manager:
if checkpoint_activations_microbatch is None:
output_tensor, loss_func = forward_step_func(data_iterator, model)
else:
output_tensor, loss_func = forward_step_func(
data_iterator, model, checkpoint_activations_microbatch
)
if parallel_state.is_pipeline_last_stage():
if not collect_non_loss_data:
output_tensor = loss_func(output_tensor)
loss, loss_reduced = output_tensor
output_tensor = loss / num_microbatches
forward_data_store.append(loss_reduced)
else:
data = loss_func(output_tensor, non_loss_data=True)
forward_data_store.append(data)
if config.timers is not None:
config.timers('forward-compute').stop()
# If T5 model (or other model with encoder and decoder)
# and in decoder stack, then send encoder_hidden_state
# downstream as well.
model_type = get_model_type(model)
if (
parallel_state.is_pipeline_stage_after_split()
and model_type == ModelType.encoder_and_decoder
):
return [output_tensor, input_tensor[-1]]
if unwrap_output_tensor:
return output_tensor
return [output_tensor]
def backward_step(input_tensor, output_tensor, output_tensor_grad, model_type, config):
"""Backward step through passed-in output tensor.
If last stage, output_tensor_grad is None, otherwise gradient of loss
with respect to stage's output tensor.
Returns gradient of loss with respect to input tensor (None if first
stage)."""
# NOTE: This code currently can handle at most one skip connection. It
# needs to be modified slightly to support arbitrary numbers of skip
# connections.
if config.timers is not None:
config.timers('backward-compute', log_level=2).start()
# Retain the grad on the input_tensor.
unwrap_input_tensor_grad = False
if not isinstance(input_tensor, list):
input_tensor = [input_tensor]
unwrap_input_tensor_grad = True
for x in input_tensor:
if x is not None:
x.retain_grad()
if not isinstance(output_tensor, list):
output_tensor = [output_tensor]
if not isinstance(output_tensor_grad, list):
output_tensor_grad = [output_tensor_grad]
# Backward pass.
if output_tensor_grad[0] is None and config.grad_scale_func is not None:
output_tensor[0] = config.grad_scale_func(output_tensor[0])
if config.deallocate_pipeline_outputs:
custom_backward(output_tensor[0], output_tensor_grad[0])
else:
torch.autograd.backward(output_tensor[0], grad_tensors=output_tensor_grad[0])
# Collect the grad of the input_tensor.
input_tensor_grad = [None]
if input_tensor is not None:
input_tensor_grad = []
for x in input_tensor:
if x is None:
input_tensor_grad.append(None)
else:
input_tensor_grad.append(x.grad)
# Handle single skip connection if it exists (encoder_hidden_state in
# model with encoder and decoder).
if (
parallel_state.get_pipeline_model_parallel_world_size() > 1
and parallel_state.is_pipeline_stage_after_split()
and model_type == ModelType.encoder_and_decoder
):
if output_tensor_grad[1] is not None:
input_tensor_grad[-1].add_(output_tensor_grad[1])
if unwrap_input_tensor_grad:
input_tensor_grad = input_tensor_grad[0]
if config.timers is not None:
config.timers('backward-compute').stop()
return input_tensor_grad
def forward_backward_no_pipelining(
*,
forward_step_func,
data_iterator: Union[Iterator, List[Iterator]],
model: Union[torch.nn.Module, List[torch.nn.Module]],
num_microbatches: int,
seq_length: int, # unused
micro_batch_size: int, # unused
decoder_seq_length: int = None, # unused
forward_only: bool = False,
collect_non_loss_data: bool = False,
):
"""Run forward and backward passes with no pipeline parallelism
(no inter-stage communication).
Returns dictionary with losses.
See get_forward_backward_func() for argument details
"""
if isinstance(model, list):
assert len(model) == 1, "non-pipeline-parallel schedule does not support model chunking"
model = model[0]
if isinstance(data_iterator, list):
assert (
len(data_iterator) == 1
), "non-pipeline-parallel schedule does not support model chunking"
data_iterator = data_iterator[0]
config = get_model_config(model)
no_sync_func = config.no_sync_func
if no_sync_func is None and isinstance(model, torchDDP):
no_sync_func = model.no_sync
if no_sync_func is None:
no_sync_func = contextlib.nullcontext
model_type = get_model_type(model)
forward_data_store = []
input_tensor, output_tensor_grad = None, None
with no_sync_func():
for i in range(num_microbatches - 1):
output_tensor = forward_step(
forward_step_func,
data_iterator,
model,
num_microbatches,
input_tensor,
forward_data_store,
config,
collect_non_loss_data,
)
if not forward_only:
backward_step(input_tensor, output_tensor, output_tensor_grad, model_type, config)
# Run computation for last microbatch out of context handler (want to
# synchronize gradients).
output_tensor = forward_step(
forward_step_func,
data_iterator,
model,
num_microbatches,
input_tensor,
forward_data_store,
config,
collect_non_loss_data,
)
if not forward_only:
backward_step(input_tensor, output_tensor, output_tensor_grad, model_type, config)
return forward_data_store
def forward_backward_pipelining_with_interleaving(
*,
forward_step_func,
data_iterator: Union[Iterator, List[Iterator]],
model: Union[torch.nn.Module, List[torch.nn.Module]],
num_microbatches: int,
seq_length: int,
micro_batch_size: int,
decoder_seq_length: int = None,
forward_only: bool = False,
collect_non_loss_data: bool = False,
):
"""Run interleaved 1F1B schedule (model split into model chunks), with
communication between pipeline stages as needed.
Returns dictionary with losses if the last stage, empty dict otherwise."""
assert isinstance(model, list), "interleaved pipeline parallelism expected model chunking"
assert all(isinstance(chunk, torch.nn.Module) for chunk in model), "invalid model chunking"
assert isinstance(
data_iterator, list
), "interleaved pipeline parallelism expected each model chunk to have a data iterator"
config = get_model_config(model[0])
if config.overlap_p2p_comm and config.batch_p2p_comm:
raise ValueError("Can not use both overlap_p2p_comm and batch_p2p_comm")
# Disable async grad reductions
no_sync_func = config.no_sync_func
if no_sync_func is None and all(isinstance(chunk, torchDDP) for chunk in model):
def multi_no_sync():
stack = contextlib.ExitStack()
for chunk in model:
stack.enter_context(chunk.no_sync())
return stack
no_sync_func = multi_no_sync
if no_sync_func is None:
no_sync_func = contextlib.nullcontext
no_sync_context = None
def disable_grad_sync():
"""Disable asynchronous grad reductions"""
nonlocal no_sync_context
if no_sync_context is None:
no_sync_context = no_sync_func()
no_sync_context.__enter__()
def enable_grad_sync():
"""Enable asynchronous grad reductions"""
nonlocal no_sync_context
if no_sync_context is not None:
no_sync_context.__exit__(None, None, None)
no_sync_context = None
disable_grad_sync()
# Model chunk IDs with synchronized grads
synchronized_model_chunks = set()
input_tensors = [[] for _ in range(len(model))]
output_tensors = [[] for _ in range(len(model))]
forward_data_store = []
if not forward_only:
output_tensor_grads = [[] for _ in range(len(model))]
pipeline_parallel_size = parallel_state.get_pipeline_model_parallel_world_size()
pipeline_parallel_rank = parallel_state.get_pipeline_model_parallel_rank()
if num_microbatches % pipeline_parallel_size != 0:
msg = f'number of microbatches ({num_microbatches}) is not divisible by '
msg += f'pipeline-model-parallel-size ({pipeline_parallel_size}) '
msg += 'when using interleaved schedule'
raise RuntimeError(msg)
model_type = get_model_type(model[0])
if model_type == ModelType.encoder_and_decoder:
raise RuntimeError("Interleaving is not supported with an encoder and decoder model.")
if decoder_seq_length is not None and decoder_seq_length != seq_length:
raise RuntimeError(
"Interleaving is not supported with a different decoder sequence length."
)
tensor_shape = [seq_length, micro_batch_size, config.hidden_size]
if config.sequence_parallel:
tensor_shape[0] = tensor_shape[0] // parallel_state.get_tensor_model_parallel_world_size()
# Compute number of warmup and remaining microbatches.
num_model_chunks = len(model)
total_num_microbatches = num_microbatches * num_model_chunks
all_warmup_microbatches = False
if forward_only:
num_warmup_microbatches = total_num_microbatches
else:
# Run all forward passes and then all backward passes if number of
# microbatches is just the number of pipeline stages.
# Otherwise, perform (num_model_chunks-1)*pipeline_parallel_size on
# all workers, followed by more microbatches after depending on
# stage ID (more forward passes for earlier stages, later stages can
# immediately start with 1F1B).
if num_microbatches == pipeline_parallel_size:
num_warmup_microbatches = total_num_microbatches
all_warmup_microbatches = True
else:
num_warmup_microbatches = (pipeline_parallel_size - pipeline_parallel_rank - 1) * 2
num_warmup_microbatches += (num_model_chunks - 1) * pipeline_parallel_size
num_warmup_microbatches = min(num_warmup_microbatches, total_num_microbatches)
num_microbatches_remaining = total_num_microbatches - num_warmup_microbatches
# Checkpoint the activations of partial Transformer layers in a number of micro-batches
# within the maximum outstanding micro-batch backpropagations.
# Micro-batches with the ids less than 'num_microbatches_with_partial_activation_checkpoints'
# checkpoint partial Transformer layers (or skip checkpointing) and
# the rest of micro-batches within a window of micro-batches checkpoint
# all Transformer layers. The window of micro-batches is set by the maximum
# outstanding backpropagations and becomes smaller at later pipeline stages.
# Please refer the appendix C in https://arxiv.org/pdf/2205.05198.pdf
max_outstanding_backprops = None
if config.num_microbatches_with_partial_activation_checkpoints is not None:
max_outstanding_backprops = num_warmup_microbatches + 1
# Synchronize params for first two model chunks
if config.param_sync_func is not None:
config.param_sync_func(model[0].parameters())
config.param_sync_func(model[1].parameters())
def get_model_chunk_id(microbatch_id, forward):
"""Helper method to get the model chunk ID given the iteration number."""
microbatch_id_in_group = microbatch_id % (pipeline_parallel_size * num_model_chunks)
model_chunk_id = microbatch_id_in_group // pipeline_parallel_size
if not forward:
model_chunk_id = num_model_chunks - model_chunk_id - 1
return model_chunk_id
def is_first_microbatch_for_model_chunk(microbatch_id: int) -> bool:
"""Check if an iteration is the first for a model chunk."""
microbatch_group_size = pipeline_parallel_size * num_model_chunks
num_microbatch_groups = total_num_microbatches // microbatch_group_size
microbatch_group_id = microbatch_id // microbatch_group_size
microbatch_id_in_group = microbatch_id % microbatch_group_size
if microbatch_group_id == 0:
return microbatch_id_in_group % pipeline_parallel_size == 0
else:
return False
def is_last_microbatch_for_model_chunk(microbatch_id: int) -> bool:
"""Check if an iteration is the last for a model chunk."""
microbatch_group_size = pipeline_parallel_size * num_model_chunks
num_microbatch_groups = total_num_microbatches // microbatch_group_size
microbatch_group_id = microbatch_id // microbatch_group_size
microbatch_id_in_group = microbatch_id % microbatch_group_size
if microbatch_group_id == num_microbatch_groups - 1:
return microbatch_id_in_group % pipeline_parallel_size == pipeline_parallel_size - 1
else:
return False
def forward_step_helper(microbatch_id, checkpoint_activations_microbatch):
"""Helper method to run forward step with model split into chunks
(run set_virtual_pipeline_model_parallel_rank() before calling
forward_step())."""
model_chunk_id = get_model_chunk_id(microbatch_id, forward=True)
parallel_state.set_virtual_pipeline_model_parallel_rank(model_chunk_id)
# launch param synchronization for next model chunk
# Note: Asynchronous communication tends to slow down compute.
# To reduce idling from mismatched microbatch times, we launch
# asynchronous communication at the same time across the
# pipeline-parallel group.
if config.param_sync_func is not None:
param_sync_microbatch_id = microbatch_id + pipeline_parallel_rank
if (
param_sync_microbatch_id < total_num_microbatches
and is_first_microbatch_for_model_chunk(param_sync_microbatch_id)
):
param_sync_chunk_id = get_model_chunk_id(param_sync_microbatch_id, forward=True) + 1
if 1 < param_sync_chunk_id < num_model_chunks:
config.param_sync_func(model[param_sync_chunk_id].parameters())
# forward step
if parallel_state.is_pipeline_first_stage():
if len(input_tensors[model_chunk_id]) == len(output_tensors[model_chunk_id]):
input_tensors[model_chunk_id].append(None)
input_tensor = input_tensors[model_chunk_id][-1]
output_tensor = forward_step(
forward_step_func,
data_iterator[model_chunk_id],
model[model_chunk_id],
num_microbatches,
input_tensor,
forward_data_store,
config,
collect_non_loss_data,
checkpoint_activations_microbatch,
)
output_tensors[model_chunk_id].append(output_tensor)
# if forward-only, no need to save tensors for a backward pass
if forward_only:
input_tensors[model_chunk_id].pop()
output_tensors[model_chunk_id].pop()
return output_tensor
def backward_step_helper(microbatch_id):
"""Helper method to run backward step with model split into chunks
(run set_virtual_pipeline_model_parallel_rank() before calling
backward_step())."""
model_chunk_id = get_model_chunk_id(microbatch_id, forward=False)
parallel_state.set_virtual_pipeline_model_parallel_rank(model_chunk_id)
# launch grad synchronization (default)
if config.grad_sync_func is None and is_last_microbatch_for_model_chunk(microbatch_id):
enable_grad_sync()
synchronized_model_chunks.add(model_chunk_id)
if parallel_state.is_pipeline_last_stage():
if len(output_tensor_grads[model_chunk_id]) == 0:
output_tensor_grads[model_chunk_id].append(None)
input_tensor = input_tensors[model_chunk_id].pop(0)
output_tensor = output_tensors[model_chunk_id].pop(0)
output_tensor_grad = output_tensor_grads[model_chunk_id].pop(0)
input_tensor_grad = backward_step(
input_tensor, output_tensor, output_tensor_grad, model_type, config
)
# launch grad synchronization (custom grad sync)
# Note: Asynchronous communication tends to slow down compute.
# To reduce idling from mismatched microbatch times, we launch
# asynchronous communication at the same time across the
# pipeline-parallel group.
if config.grad_sync_func is not None:
grad_sync_microbatch_id = microbatch_id - pipeline_parallel_rank
if grad_sync_microbatch_id >= 0 and is_last_microbatch_for_model_chunk(
grad_sync_microbatch_id
):
grad_sync_chunk_id = get_model_chunk_id(grad_sync_microbatch_id, forward=False)
enable_grad_sync()
config.grad_sync_func(model[grad_sync_chunk_id].parameters())
synchronized_model_chunks.add(grad_sync_chunk_id)
disable_grad_sync()
return input_tensor_grad
# Run warmup forward passes.
parallel_state.set_virtual_pipeline_model_parallel_rank(0)
input_tensors[0].append(p2p_communication.recv_forward(tensor_shape, config))
fwd_wait_handles = None
bwd_wait_handles = None
for k in range(num_warmup_microbatches):
if fwd_wait_handles is not None:
for req in fwd_wait_handles:
req.wait()
# Decide to checkpoint all layers' activations of the current micro-batch
if max_outstanding_backprops is not None:
checkpoint_activations_microbatch = (
k % max_outstanding_backprops
>= config.num_microbatches_with_partial_activation_checkpoints
)
else:
checkpoint_activations_microbatch = None
output_tensor = forward_step_helper(k, checkpoint_activations_microbatch)
# Determine if tensor should be received from previous stage.
next_forward_model_chunk_id = get_model_chunk_id(k + 1, forward=True)
recv_prev = True
if parallel_state.is_pipeline_first_stage(ignore_virtual=True):
if next_forward_model_chunk_id == 0:
recv_prev = False
if k == (total_num_microbatches - 1):
recv_prev = False
# Don't send tensor downstream if on last stage.
if parallel_state.is_pipeline_last_stage():
output_tensor = None
# Send and receive tensors as appropriate (send tensors computed
# in this iteration; receive tensors for next iteration).
if not config.overlap_p2p_comm:
if (
k == (num_warmup_microbatches - 1)
and not forward_only
and not all_warmup_microbatches
):
input_tensor_grad = None
recv_next = True
if parallel_state.is_pipeline_last_stage(ignore_virtual=True):
recv_next = False
(
input_tensor,
output_tensor_grad,
) = p2p_communication.send_forward_backward_recv_forward_backward(
output_tensor,
input_tensor_grad,
recv_prev=recv_prev,
recv_next=recv_next,
tensor_shape=tensor_shape,
config=config,
)
output_tensor_grads[num_model_chunks - 1].append(output_tensor_grad)
else:
input_tensor = p2p_communication.send_forward_recv_forward(
output_tensor, recv_prev=recv_prev, tensor_shape=tensor_shape, config=config
)
input_tensors[next_forward_model_chunk_id].append(input_tensor)
else:
input_tensor, fwd_wait_handles = p2p_communication.send_forward_recv_forward(
output_tensor,
recv_prev=recv_prev,
tensor_shape=tensor_shape,
config=config,
overlap_p2p_comm=True,
)
if (
k == (num_warmup_microbatches - 1)
and not forward_only
and not all_warmup_microbatches
):
input_tensor_grad = None
recv_next = True
if parallel_state.is_pipeline_last_stage(ignore_virtual=True):
recv_next = False
(
output_tensor_grad,
bwd_wait_handles,
) = p2p_communication.send_backward_recv_backward(
input_tensor_grad,
recv_next=recv_next,
tensor_shape=tensor_shape,
config=config,
overlap_p2p_comm=True,
)
output_tensor_grads[num_model_chunks - 1].append(output_tensor_grad)
input_tensors[next_forward_model_chunk_id].append(input_tensor)
deallocate_output_tensor(output_tensor, config.deallocate_pipeline_outputs)
# Run 1F1B in steady state.
for k in range(num_microbatches_remaining):
# Forward pass.
forward_k = k + num_warmup_microbatches
# Decide to checkpoint all layers' activations of the current micro-batch
if max_outstanding_backprops is not None:
checkpoint_activations_microbatch = (
forward_k % max_outstanding_backprops
>= config.num_microbatches_with_partial_activation_checkpoints
)
else:
checkpoint_activations_microbatch = None
if config.overlap_p2p_comm:
if fwd_wait_handles is not None:
for req in fwd_wait_handles:
req.wait()
deallocate_output_tensor(output_tensor, config.deallocate_pipeline_outputs)
output_tensor = forward_step_helper(forward_k, checkpoint_activations_microbatch)
# Determine if current stage has anything to send in either direction,
# otherwise set tensor to None.
forward_model_chunk_id = get_model_chunk_id(forward_k, forward=True)
parallel_state.set_virtual_pipeline_model_parallel_rank(forward_model_chunk_id)
# Last virtual stage no activation tensor to send
if parallel_state.is_pipeline_last_stage():
output_tensor = None
# Determine if peers are sending, and where in data structure to put
# received tensors.
recv_prev = True
if parallel_state.is_pipeline_first_stage(ignore_virtual=True):
# First stage is ahead of last stage by (pipeline_parallel_size - 1).
next_forward_model_chunk_id = get_model_chunk_id(
forward_k - (pipeline_parallel_size - 1), forward=True
)
if next_forward_model_chunk_id == (num_model_chunks - 1):
recv_prev = False
next_forward_model_chunk_id += 1
else:
next_forward_model_chunk_id = get_model_chunk_id(forward_k + 1, forward=True)
# If last iteration, don't receive; we already received one extra
# before the start of the for loop.
if k == (num_microbatches_remaining - 1):
recv_prev = False
# Send activation tensor to the next stage and receive activation tensor from the
# previous stage
input_tensor, fwd_wait_handles = p2p_communication.send_forward_recv_forward(
output_tensor,
recv_prev=recv_prev,
tensor_shape=tensor_shape,
config=config,
overlap_p2p_comm=True,
)
# assert fwd_wait_handles is not None
if bwd_wait_handles is not None:
for req in bwd_wait_handles:
req.wait()
# Backward pass.
backward_k = k
input_tensor_grad = backward_step_helper(backward_k)
backward_model_chunk_id = get_model_chunk_id(backward_k, forward=False)
parallel_state.set_virtual_pipeline_model_parallel_rank(backward_model_chunk_id)
# First virtual stage no activation gradient tensor to send
if parallel_state.is_pipeline_first_stage():
input_tensor_grad = None
# Determine if the current virtual stage has an activation gradient tensor to receive
recv_next = True
if parallel_state.is_pipeline_last_stage(ignore_virtual=True):
# Last stage is ahead of first stage by (pipeline_parallel_size - 1).
next_backward_model_chunk_id = get_model_chunk_id(
backward_k - (pipeline_parallel_size - 1), forward=False
)
if next_backward_model_chunk_id == 0:
recv_next = False
next_backward_model_chunk_id -= 1
else:
next_backward_model_chunk_id = get_model_chunk_id(backward_k + 1, forward=False)
output_tensor_grad, bwd_wait_handles = p2p_communication.send_backward_recv_backward(
input_tensor_grad,
recv_next=recv_next,
tensor_shape=tensor_shape,
config=config,
overlap_p2p_comm=True,
)
else: # no p2p overlap
output_tensor = forward_step_helper(forward_k, checkpoint_activations_microbatch)
# Backward pass.
backward_k = k
input_tensor_grad = backward_step_helper(backward_k)
# Send output_tensor and input_tensor_grad, receive input_tensor
# and output_tensor_grad.
# Determine if current stage has anything to send in either direction,
# otherwise set tensor to None.
forward_model_chunk_id = get_model_chunk_id(forward_k, forward=True)
parallel_state.set_virtual_pipeline_model_parallel_rank(forward_model_chunk_id)
if parallel_state.is_pipeline_last_stage():
output_tensor = None
backward_model_chunk_id = get_model_chunk_id(backward_k, forward=False)
parallel_state.set_virtual_pipeline_model_parallel_rank(backward_model_chunk_id)
if parallel_state.is_pipeline_first_stage():
input_tensor_grad = None
# Determine if peers are sending, and where in data structure to put
# received tensors.
recv_prev = True
if parallel_state.is_pipeline_first_stage(ignore_virtual=True):
# First stage is ahead of last stage by (pipeline_parallel_size - 1).
next_forward_model_chunk_id = get_model_chunk_id(
forward_k - (pipeline_parallel_size - 1), forward=True
)
if next_forward_model_chunk_id == (num_model_chunks - 1):
recv_prev = False
next_forward_model_chunk_id += 1
else:
next_forward_model_chunk_id = get_model_chunk_id(forward_k + 1, forward=True)
recv_next = True
if parallel_state.is_pipeline_last_stage(ignore_virtual=True):
# Last stage is ahead of first stage by (pipeline_parallel_size - 1).
next_backward_model_chunk_id = get_model_chunk_id(
backward_k - (pipeline_parallel_size - 1), forward=False
)
if next_backward_model_chunk_id == 0:
recv_next = False
next_backward_model_chunk_id -= 1
else:
next_backward_model_chunk_id = get_model_chunk_id(backward_k + 1, forward=False)
# If last iteration, don't receive; we already received one extra
# before the start of the for loop.
if k == (num_microbatches_remaining - 1):
recv_prev = False
# Communicate tensors.
(
input_tensor,
output_tensor_grad,
) = p2p_communication.send_forward_backward_recv_forward_backward(
output_tensor,
input_tensor_grad,
recv_prev=recv_prev,
recv_next=recv_next,
tensor_shape=tensor_shape,
config=config,
)
deallocate_output_tensor(output_tensor, config.deallocate_pipeline_outputs)
# Put input_tensor and output_tensor_grad in data structures in the
# right location.
if recv_prev:
input_tensors[next_forward_model_chunk_id].append(input_tensor)
if recv_next:
output_tensor_grads[next_backward_model_chunk_id].append(output_tensor_grad)
deallocate_output_tensor(output_tensor, config.deallocate_pipeline_outputs)
# Run cooldown backward passes (flush out pipeline).
if not forward_only:
if config.overlap_p2p_comm and bwd_wait_handles is not None:
for wait_handle in bwd_wait_handles:
wait_handle.wait()
if all_warmup_microbatches:
output_tensor_grads[num_model_chunks - 1].append(
p2p_communication.recv_backward(tensor_shape, config=config)
)
for k in range(num_microbatches_remaining, total_num_microbatches):
input_tensor_grad = backward_step_helper(k)
next_backward_model_chunk_id = get_model_chunk_id(k + 1, forward=False)
recv_next = True
if parallel_state.is_pipeline_last_stage(ignore_virtual=True):
if next_backward_model_chunk_id == (num_model_chunks - 1):
recv_next = False
if k == (total_num_microbatches - 1):
recv_next = False
output_tensor_grads[next_backward_model_chunk_id].append(
p2p_communication.send_backward_recv_backward(
input_tensor_grad, recv_next=recv_next, tensor_shape=tensor_shape, config=config
)
)
# Launch any remaining grad reductions
enable_grad_sync()
if config.grad_sync_func is not None:
params = []
for model_chunk_id in range(num_model_chunks):
if model_chunk_id not in synchronized_model_chunks:
params.extend(model[model_chunk_id].parameters())
synchronized_model_chunks.add(model_chunk_id)
if params:
config.grad_sync_func(params)
return forward_data_store
def get_tensor_shapes(
*,
rank: int,
model_type: ModelType,
seq_length: int,
micro_batch_size: int,
decoder_seq_length: int,
config,
):
# Determine right tensor sizes (based on position of rank with respect to split
# rank) and model size.
# Send two tensors if model is T5 and rank is in decoder stage:
# first tensor is decoder (pre-transpose),
# second tensor is encoder (post-transpose).
# If model is T5 and rank is at the boundary:
# send one tensor (post-transpose from encoder).
# Otherwise, send one tensor (pre-transpose).
tensor_shapes = []
if config.sequence_parallel:
seq_length = seq_length // parallel_state.get_tensor_model_parallel_world_size()
if model_type == ModelType.encoder_and_decoder:
decoder_seq_length = (
decoder_seq_length // parallel_state.get_tensor_model_parallel_world_size()
)
if model_type == ModelType.encoder_and_decoder:
if parallel_state.is_pipeline_stage_before_split(rank):
tensor_shapes.append((seq_length, micro_batch_size, config.hidden_size))
else:
tensor_shapes.append((decoder_seq_length, micro_batch_size, config.hidden_size))
tensor_shapes.append((seq_length, micro_batch_size, config.hidden_size))
else:
tensor_shapes.append((seq_length, micro_batch_size, config.hidden_size))
return tensor_shapes
def recv_forward(tensor_shapes, config):
input_tensors = []
for tensor_shape in tensor_shapes:
if tensor_shape is None:
input_tensors.append(None)
else:
input_tensors.append(p2p_communication.recv_forward(tensor_shape, config))
return input_tensors
def recv_backward(tensor_shapes, config):
output_tensor_grads = []
for tensor_shape in tensor_shapes:
if tensor_shape is None:
output_tensor_grads.append(None)
else:
output_tensor_grads.append(p2p_communication.recv_backward(tensor_shape, config))
return output_tensor_grads
def send_forward(output_tensors, tensor_shapes, config):
if not isinstance(output_tensors, list):
output_tensors = [output_tensors]
for (output_tensor, tensor_shape) in zip(output_tensors, tensor_shapes):
if tensor_shape is None:
continue
p2p_communication.send_forward(output_tensor, config)
def send_backward(input_tensor_grads, tensor_shapes, config):
if not isinstance(input_tensor_grads, list):
input_tensor_grads = [input_tensor_grads]
for (input_tensor_grad, tensor_shape) in zip(input_tensor_grads, tensor_shapes):
if tensor_shape is None:
continue
p2p_communication.send_backward(input_tensor_grad, config)
def send_forward_recv_backward(output_tensors, tensor_shapes, config):
if not isinstance(output_tensors, list):
output_tensors = [output_tensors]
output_tensor_grads = []
for (output_tensor, tensor_shape) in zip(output_tensors, tensor_shapes):
if tensor_shape is None:
output_tensor_grads.append(None)
continue
output_tensor_grad = p2p_communication.send_forward_recv_backward(
output_tensor, tensor_shape, config
)
output_tensor_grads.append(output_tensor_grad)
return output_tensor_grads
def send_backward_recv_forward(input_tensor_grads, tensor_shapes, config):
if not isinstance(input_tensor_grads, list):
input_tensor_grads = [input_tensor_grads]
input_tensors = []
for (input_tensor_grad, tensor_shape) in zip(input_tensor_grads, tensor_shapes):
if tensor_shape is None:
input_tensors.append(None)
continue
input_tensor = p2p_communication.send_backward_recv_forward(
input_tensor_grad, tensor_shape, config
)
input_tensors.append(input_tensor)
return input_tensors
def forward_backward_pipelining_without_interleaving(
*,
forward_step_func,
data_iterator: Union[Iterator, List[Iterator]],
model: Union[torch.nn.Module, List[torch.nn.Module]],
num_microbatches: int,
seq_length: int,
micro_batch_size: int,
decoder_seq_length: int = None,
forward_only: bool = False,
collect_non_loss_data: bool = False,
):
"""Run non-interleaved 1F1B schedule, with communication between pipeline
stages.
Returns dictionary with losses if the last stage, empty dict otherwise."""
if isinstance(model, list):
assert (
len(model) == 1
), "non-interleaved pipeline parallelism does not support model chunking"
model = model[0]
if isinstance(data_iterator, list):
assert (
len(data_iterator) == 1
), "non-pipeline-parallel schedule does not support model chunking"
data_iterator = data_iterator[0]
config = get_model_config(model)
if config.overlap_p2p_comm:
raise ValueError(
"Non-interleaved pipeline parallelism does not support overlapping p2p communication"
)
# Disable async grad reductions
no_sync_func = config.no_sync_func
if no_sync_func is None and isinstance(model, torchDDP):
no_sync_func = model.no_sync
if no_sync_func is None:
no_sync_func = contextlib.nullcontext
no_sync_context = None
def disable_grad_sync():
"""Disable asynchronous grad reductions"""
nonlocal no_sync_context
if no_sync_context is None:
no_sync_context = no_sync_func()
no_sync_context.__enter__()
def enable_grad_sync():
"""Enable asynchronous grad reductions"""
nonlocal no_sync_context
if no_sync_context is not None:
no_sync_context.__exit__(None, None, None)
no_sync_context = None
disable_grad_sync()
# Compute number of warmup microbatches.
num_warmup_microbatches = (
parallel_state.get_pipeline_model_parallel_world_size()
- parallel_state.get_pipeline_model_parallel_rank()
- 1
)
num_warmup_microbatches = min(num_warmup_microbatches, num_microbatches)
num_microbatches_remaining = num_microbatches - num_warmup_microbatches
# Checkpoint the activations of partial Transformer layers in a number of micro-batches
# within the maximum outstanding micro-batch backpropagations.
# Micro-batches with the ids less than 'num_microbatches_with_partial_activation_checkpoints'
# checkpoint partial Transformer layers (or skip checkpointing) and
# the rest of micro-batches within a window of micro-batches checkpoint
# all Transformer layers. The window of micro-batches is set by the maximum
# outstanding backpropagations and becomes smaller at later pipeline stages.
# Please refer the appendix C in https://arxiv.org/pdf/2205.05198.pdf
max_outstanding_backprops = None
if config.num_microbatches_with_partial_activation_checkpoints is not None:
max_outstanding_backprops = num_warmup_microbatches + 1
model_type = get_model_type(model)
rank = parallel_state.get_pipeline_model_parallel_rank()
recv_tensor_shapes = get_tensor_shapes(
rank=rank - 1,
model_type=model_type,
seq_length=seq_length,
micro_batch_size=micro_batch_size,
decoder_seq_length=decoder_seq_length,
config=config,
)
send_tensor_shapes = get_tensor_shapes(
rank=rank,
model_type=model_type,
seq_length=seq_length,
micro_batch_size=micro_batch_size,
decoder_seq_length=decoder_seq_length,
config=config,
)
# Input, output tensors only need to be saved when doing backward passes
input_tensors = None
output_tensors = None
if not forward_only:
input_tensors = []
output_tensors = []
forward_data_store = []
# Run warmup forward passes.
for i in range(num_warmup_microbatches):
# Decide to checkpoint all layers' activations of the current micro-batch
if max_outstanding_backprops is not None:
checkpoint_activations_microbatch = (
i % max_outstanding_backprops
>= config.num_microbatches_with_partial_activation_checkpoints
)
else:
checkpoint_activations_microbatch = None
input_tensor = recv_forward(recv_tensor_shapes, config)
output_tensor = forward_step(
forward_step_func,
data_iterator,
model,
num_microbatches,
input_tensor,
forward_data_store,
config,
collect_non_loss_data,
checkpoint_activations_microbatch,
)
send_forward(output_tensor, send_tensor_shapes, config)
if not forward_only:
input_tensors.append(input_tensor)
output_tensors.append(output_tensor)
deallocate_output_tensor(output_tensor[0], config.deallocate_pipeline_outputs)
# Before running 1F1B, need to receive first forward tensor.
# If all microbatches are run in warmup / cooldown phase, then no need to
# receive this tensor here.
if num_microbatches_remaining > 0:
input_tensor = recv_forward(recv_tensor_shapes, config)
# Run 1F1B in steady state.
for i in range(num_microbatches_remaining):
last_iteration = i == (num_microbatches_remaining - 1)
# Decide to checkpoint all layers' activations of the current micro-batch
if max_outstanding_backprops is not None:
checkpoint_activations_microbatch = (
(i + num_warmup_microbatches) % max_outstanding_backprops
) >= config.num_microbatches_with_partial_activation_checkpoints
else:
checkpoint_activations_microbatch = None
output_tensor = forward_step(
forward_step_func,
data_iterator,
model,
num_microbatches,
input_tensor,
forward_data_store,
config,
collect_non_loss_data,
checkpoint_activations_microbatch,
)
if forward_only:
send_forward(output_tensor, send_tensor_shapes, config)
if not last_iteration:
input_tensor = recv_forward(recv_tensor_shapes, config)
else:
output_tensor_grad = send_forward_recv_backward(
output_tensor, send_tensor_shapes, config
)
# Add input_tensor and output_tensor to end of list.
input_tensors.append(input_tensor)
output_tensors.append(output_tensor)
deallocate_output_tensor(output_tensor[0], config.deallocate_pipeline_outputs)
# Pop input_tensor and output_tensor from the start of the list for
# the backward pass.
input_tensor = input_tensors.pop(0)
output_tensor = output_tensors.pop(0)
input_tensor_grad = backward_step(
input_tensor, output_tensor, output_tensor_grad, model_type, config
)
if last_iteration:
input_tensor = None
send_backward(input_tensor_grad, recv_tensor_shapes, config)
else:
input_tensor = send_backward_recv_forward(
input_tensor_grad, recv_tensor_shapes, config
)
# Run cooldown backward passes.
if not forward_only:
for i in range(num_warmup_microbatches):
# Enable async grad reduction in the last backward pass
# Note: If grad sync function is provided, only enable
# async grad reduction in first pipeline stage. Other
# pipeline stages do grad reduction during pipeline
# bubble.
if i == num_warmup_microbatches - 1:
if config.grad_sync_func is None or rank == 0:
enable_grad_sync()
input_tensor = input_tensors.pop(0)
output_tensor = output_tensors.pop(0)
output_tensor_grad = recv_backward(send_tensor_shapes, config)
input_tensor_grad = backward_step(
input_tensor, output_tensor, output_tensor_grad, model_type, config
)
send_backward(input_tensor_grad, recv_tensor_shapes, config)
# Launch any remaining grad reductions
if no_sync_context is not None:
enable_grad_sync()
if config.grad_sync_func is not None:
config.grad_sync_func(model.parameters())
return forward_data_store
|
Megatron-LM-master
|
megatron/core/pipeline_parallel/schedules.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import operator
from functools import reduce
from typing import Callable, List, Optional, Tuple, Union
import torch
from megatron import core
from megatron.core import ModelParallelConfig
from megatron.core.parallel_state import (
get_pipeline_model_parallel_group,
get_pipeline_model_parallel_next_rank,
get_pipeline_model_parallel_prev_rank,
get_pipeline_model_parallel_rank,
)
# Types
Shape = Union[List[int], torch.Size]
def _communicate_shapes(tensor_send_next, tensor_send_prev, recv_prev, recv_next, config):
"""Communicate tensor shapes between stages. Used to communicate
tensor shapes before the actual tensor communication happens.
This is required when the sequence lengths across micro batches
are not uniform.
Takes the following arguments:
tensor_send_next: tensor to send to next rank (no tensor sent if
set to None).
tensor_send_prev: tensor to send to prev rank (no tensor sent if
set to None).
recv_prev: boolean for whether tensor should be received from
previous rank.
recv_next: boolean for whether tensor should be received from
next rank.
Returns:
(recv_prev_shape, recv_next_shape)
"""
recv_prev_shape_tensor = None
recv_next_shape_tensor = None
send_prev_shape_tensor = None
send_next_shape_tensor = None
if recv_prev:
recv_prev_shape_tensor = torch.empty(
(3), device=torch.cuda.current_device(), dtype=torch.int64
)
if recv_next:
recv_next_shape_tensor = torch.empty(
(3), device=torch.cuda.current_device(), dtype=torch.int64
)
if tensor_send_prev is not None:
send_prev_shape_tensor = torch.tensor(
tensor_send_prev.size(), device=torch.cuda.current_device(), dtype=torch.int64
)
if tensor_send_next is not None:
send_next_shape_tensor = torch.tensor(
tensor_send_next.size(), device=torch.cuda.current_device(), dtype=torch.int64
)
if config.use_ring_exchange_p2p:
torch.distributed.ring_exchange(
tensor_send_prev=send_prev_shape_tensor,
tensor_recv_prev=recv_prev_shape_tensor,
tensor_send_next=send_next_shape_tensor,
tensor_recv_next=recv_next_shape_tensor,
group=get_pipeline_model_parallel_group(),
)
else:
ops = []
if send_prev_shape_tensor is not None:
send_prev_op = torch.distributed.P2POp(
torch.distributed.isend,
send_prev_shape_tensor,
get_pipeline_model_parallel_prev_rank(),
)
ops.append(send_prev_op)
if recv_prev_shape_tensor is not None:
recv_prev_op = torch.distributed.P2POp(
torch.distributed.irecv,
recv_prev_shape_tensor,
get_pipeline_model_parallel_prev_rank(),
)
ops.append(recv_prev_op)
if send_next_shape_tensor is not None:
send_next_op = torch.distributed.P2POp(
torch.distributed.isend,
send_next_shape_tensor,
get_pipeline_model_parallel_next_rank(),
)
ops.append(send_next_op)
if recv_next_shape_tensor is not None:
recv_next_op = torch.distributed.P2POp(
torch.distributed.irecv,
recv_next_shape_tensor,
get_pipeline_model_parallel_next_rank(),
)
ops.append(recv_next_op)
if len(ops) > 0:
reqs = torch.distributed.batch_isend_irecv(ops)
for req in reqs:
req.wait()
# To protect against race condition when using batch_isend_irecv().
# should take this out once the bug with batch_isend_irecv is resolved.
torch.cuda.synchronize()
recv_prev_shape = [0, 0, 0]
if recv_prev_shape_tensor is not None:
recv_prev_shape = recv_prev_shape_tensor.tolist()
recv_next_shape = [0, 0, 0]
if recv_next_shape_tensor is not None:
recv_next_shape = recv_next_shape_tensor.tolist()
return recv_prev_shape, recv_next_shape
def _batched_p2p_ops(
*,
tensor_send_prev: Optional[torch.Tensor],
tensor_recv_prev: Optional[torch.Tensor],
tensor_send_next: Optional[torch.Tensor],
tensor_recv_next: Optional[torch.Tensor],
group: torch.distributed.ProcessGroup
):
ops = []
if tensor_send_prev is not None:
send_prev_op = torch.distributed.P2POp(
torch.distributed.isend,
tensor_send_prev,
get_pipeline_model_parallel_prev_rank(),
group,
)
ops.append(send_prev_op)
if tensor_recv_prev is not None:
recv_prev_op = torch.distributed.P2POp(
torch.distributed.irecv,
tensor_recv_prev,
get_pipeline_model_parallel_prev_rank(),
group,
)
ops.append(recv_prev_op)
if tensor_send_next is not None:
send_next_op = torch.distributed.P2POp(
torch.distributed.isend,
tensor_send_next,
get_pipeline_model_parallel_next_rank(),
group,
)
ops.append(send_next_op)
if tensor_recv_next is not None:
recv_next_op = torch.distributed.P2POp(
torch.distributed.irecv,
tensor_recv_next,
get_pipeline_model_parallel_next_rank(),
group,
)
ops.append(recv_next_op)
if len(ops) > 0:
reqs = torch.distributed.batch_isend_irecv(ops)
else:
reqs = []
return reqs
def _p2p_ops(
*,
tensor_send_prev: Optional[torch.Tensor],
tensor_recv_prev: Optional[torch.Tensor],
tensor_send_next: Optional[torch.Tensor],
tensor_recv_next: Optional[torch.Tensor],
group: torch.distributed.ProcessGroup
):
reqs = []
rank = get_pipeline_model_parallel_rank()
if get_pipeline_model_parallel_rank() % 2 == 0:
if tensor_send_next is not None:
send_next_req = torch.distributed.isend(
tensor=tensor_send_next, dst=get_pipeline_model_parallel_next_rank(), group=group,
)
reqs.append(send_next_req)
if tensor_recv_prev is not None:
recv_prev_req = torch.distributed.irecv(
tensor=tensor_recv_prev, src=get_pipeline_model_parallel_prev_rank(), group=group,
)
reqs.append(recv_prev_req)
if tensor_send_prev is not None:
send_prev_req = torch.distributed.isend(
tensor=tensor_send_prev, dst=get_pipeline_model_parallel_prev_rank(), group=group,
)
reqs.append(send_prev_req)
if tensor_recv_next is not None:
recv_next_req = torch.distributed.irecv(
tensor=tensor_recv_next, src=get_pipeline_model_parallel_next_rank(), group=group,
)
reqs.append(recv_next_req)
else:
if tensor_recv_prev is not None:
recv_prev_req = torch.distributed.irecv(
tensor=tensor_recv_prev, src=get_pipeline_model_parallel_prev_rank(), group=group,
)
reqs.append(recv_prev_req)
if tensor_send_next is not None:
send_next_req = torch.distributed.isend(
tensor=tensor_send_next, dst=get_pipeline_model_parallel_next_rank(), group=group,
)
reqs.append(send_next_req)
if tensor_recv_next is not None:
recv_next_req = torch.distributed.irecv(
tensor=tensor_recv_next, src=get_pipeline_model_parallel_next_rank(), group=group,
)
reqs.append(recv_next_req)
if tensor_send_prev is not None:
send_prev_req = torch.distributed.isend(
tensor=tensor_send_prev, dst=get_pipeline_model_parallel_prev_rank(), group=group,
)
reqs.append(send_prev_req)
return reqs
def _communicate(
*,
tensor_send_next: Optional[torch.Tensor],
tensor_send_prev: Optional[torch.Tensor],
recv_prev: bool,
recv_next: bool,
tensor_shape: Shape,
config: ModelParallelConfig,
wait_on_reqs: bool = True
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Communicate tensors between stages. Used as helper method in other
communication methods that are used in megatron/schedules.py.
Arguments:
tensor_send_next (torch.Tensor, optional):
Tensor to send to next rank (no tensor sent if None)
tensor_send_prev (torch.Tensor, optional):
Tensor to send to prev rank (no tensor sent if None)
recv_prev (boolean, required):
whether tensor should be received from previous rank.
recv_next (boolean, required):
whether tensor should be received from next rank.
tensor_shape (List[int] or torch.Size, required):
shape of tensor to receive (this method assumes that all
tensors sent and received in a single function call are
the same shape).
wait_on_reqs (boolean, optional, default=False):
For non-batched p2p communication, wait on each request
before returning.
Returns:
tuple containing
- tensor_recv_prev: torch.Tensor if recv_prev is True, None otherwise.
- tensor_recv_next: torch.Tensor if recv_next is True, None otherwise.
"""
# Create placeholder tensors for receive in forward and backward directions
# if needed.
tensor_recv_prev = None
tensor_recv_next = None
if not config.variable_seq_lengths:
recv_prev_shape = tensor_shape
recv_next_shape = tensor_shape
else:
recv_prev_shape, recv_next_shape = _communicate_shapes(
tensor_send_next, tensor_send_prev, recv_prev, recv_next, config
)
if recv_prev:
if config.pipeline_dtype is None:
raise RuntimeError("pipeline_dtype must be provided if recv_prev is True")
if tensor_shape is None:
raise RuntimeError(
"tensor_shape must be specified if recv_prev is True. "
"Common tensor_shape is (seq_length, micro_batch_size, hidden_size)"
)
tensor_recv_prev = torch.empty(
recv_prev_shape,
requires_grad=True,
device=torch.cuda.current_device(),
dtype=config.pipeline_dtype,
)
if recv_next:
if config.pipeline_dtype is None:
raise RuntimeError("dtype must be provided if recv_next is True")
if tensor_shape is None:
raise RuntimeError(
"tensor_shape must be specified if recv_next is True. "
"Common tensor_shape is (seq_length, micro_batch_size, hidden_size)"
)
tensor_recv_next = torch.empty(
recv_next_shape,
requires_grad=True,
device=torch.cuda.current_device(),
dtype=config.pipeline_dtype,
)
# Send tensors in both the forward and backward directions as appropriate.
if config.use_ring_exchange_p2p:
def _ring_exchange_wrapper(**kwargs):
torch.distributed.ring_exchange(**kwargs)
return []
p2p_func = _ring_exchange_wrapper
elif config.batch_p2p_comm:
assert wait_on_reqs
p2p_func = _batched_p2p_ops
else:
p2p_func = _p2p_ops
reqs = p2p_func(
tensor_send_prev=tensor_send_prev,
tensor_recv_prev=tensor_recv_prev,
tensor_send_next=tensor_send_next,
tensor_recv_next=tensor_recv_next,
group=get_pipeline_model_parallel_group(),
)
if wait_on_reqs and len(reqs) > 0:
for req in reqs:
req.wait()
reqs = None
if config.batch_p2p_comm and config.batch_p2p_sync:
# To protect against race condition when using batch_isend_irecv().
# User should assert that we have a modern enough PyTorch to not need this
torch.cuda.synchronize()
return tensor_recv_prev, tensor_recv_next, reqs
def recv_forward(tensor_shape: Shape, config: ModelParallelConfig) -> torch.Tensor:
""" Receive tensor from previous rank in pipeline (forward receive).
See _communicate for argument details.
"""
if core.parallel_state.is_pipeline_first_stage():
input_tensor = None
else:
if config.timers is not None:
config.timers('forward-recv', log_level=2).start()
input_tensor, _, _ = _communicate(
tensor_send_next=None,
tensor_send_prev=None,
recv_prev=True,
recv_next=False,
tensor_shape=tensor_shape,
config=config,
)
if config.timers is not None:
config.timers('forward-recv').stop()
return input_tensor
def recv_backward(tensor_shape: Shape, config: ModelParallelConfig) -> torch.Tensor:
"""Receive tensor from next rank in pipeline (backward receive).
See _communicate for argument details.
"""
if core.parallel_state.is_pipeline_last_stage():
output_tensor_grad = None
else:
if config.timers is not None:
config.timers('backward-recv', log_level=2).start()
_, output_tensor_grad, _ = _communicate(
tensor_send_next=None,
tensor_send_prev=None,
recv_prev=False,
recv_next=True,
tensor_shape=tensor_shape,
config=config,
)
if config.timers is not None:
config.timers('backward-recv').stop()
return output_tensor_grad
def send_forward(output_tensor: torch.Tensor, config: ModelParallelConfig) -> None:
"""Send tensor to next rank in pipeline (forward send).
See _communicate for argument details.
"""
if not core.parallel_state.is_pipeline_last_stage():
if config.timers is not None:
config.timers('forward-send', log_level=2).start()
_communicate(
tensor_send_next=output_tensor,
tensor_send_prev=None,
recv_prev=False,
recv_next=False,
tensor_shape=None,
config=config,
)
if config.timers is not None:
config.timers('forward-send').stop()
def send_backward(input_tensor_grad: torch.Tensor, config: ModelParallelConfig) -> None:
"""Send tensor to previous rank in pipeline (backward send).
See _communicate for argument details.
"""
if not core.parallel_state.is_pipeline_first_stage():
if config.timers is not None:
config.timers('backward-send', log_level=2).start()
_communicate(
tensor_send_next=None,
tensor_send_prev=input_tensor_grad,
recv_prev=False,
recv_next=False,
tensor_shape=None,
config=config,
)
if config.timers is not None:
config.timers('backward-send').stop()
def send_forward_recv_backward(
output_tensor: torch.Tensor, tensor_shape: Shape, config: ModelParallelConfig
) -> torch.Tensor:
"""Batched send and recv with next rank in pipeline.
See _communicate for argument details.
"""
if core.parallel_state.is_pipeline_last_stage():
output_tensor_grad = None
else:
if config.timers is not None:
config.timers('forward-send-backward-recv', log_level=2).start()
_, output_tensor_grad, _ = _communicate(
tensor_send_next=output_tensor,
tensor_send_prev=None,
recv_prev=False,
recv_next=True,
tensor_shape=tensor_shape,
config=config,
)
if config.timers is not None:
config.timers('forward-send-backward-recv').stop()
return output_tensor_grad
def send_backward_recv_forward(
input_tensor_grad: torch.Tensor, tensor_shape: Shape, config: ModelParallelConfig
) -> torch.Tensor:
"""Batched send and recv with previous rank in pipeline.
See _communicate for argument details.
"""
if core.parallel_state.is_pipeline_first_stage():
input_tensor = None
else:
if config.timers is not None:
config.timers('backward-send-forward-recv', log_level=2).start()
input_tensor, _, _ = _communicate(
tensor_send_next=None,
tensor_send_prev=input_tensor_grad,
recv_prev=True,
recv_next=False,
tensor_shape=tensor_shape,
config=config,
)
if config.timers is not None:
config.timers('backward-send-forward-recv').stop()
return input_tensor
def send_forward_recv_forward(
output_tensor: torch.Tensor,
recv_prev: bool,
tensor_shape: Shape,
config: ModelParallelConfig,
overlap_p2p_comm: bool = False,
) -> torch.Tensor:
"""Batched recv from previous rank and send to next rank in pipeline.
See _communicate for argument details.
"""
if config.timers is not None:
config.timers('forward-send-forward-recv', log_level=2).start()
input_tensor, _, wait_handles = _communicate(
tensor_send_next=output_tensor,
tensor_send_prev=None,
recv_prev=recv_prev,
recv_next=False,
tensor_shape=tensor_shape,
wait_on_reqs=(not overlap_p2p_comm),
config=config,
)
if config.timers is not None:
config.timers('forward-send-forward-recv').stop()
if overlap_p2p_comm:
return input_tensor, wait_handles
return input_tensor
def send_backward_recv_backward(
input_tensor_grad: torch.Tensor,
recv_next: bool,
tensor_shape: Shape,
config: ModelParallelConfig,
overlap_p2p_comm: bool = False,
) -> torch.Tensor:
"""Batched recv from next rank and send to previous rank in pipeline.
See _communicate for argument details.
"""
if config.timers is not None:
config.timers('backward-send-backward-recv', log_level=2).start()
_, output_tensor_grad, wait_handles = _communicate(
tensor_send_next=None,
tensor_send_prev=input_tensor_grad,
recv_prev=False,
recv_next=recv_next,
tensor_shape=tensor_shape,
wait_on_reqs=(not overlap_p2p_comm),
config=config,
)
if config.timers is not None:
config.timers('backward-send-backward-recv').stop()
if overlap_p2p_comm:
return output_tensor_grad, wait_handles
return output_tensor_grad
def send_forward_backward_recv_forward_backward(
output_tensor: torch.Tensor,
input_tensor_grad: torch.Tensor,
recv_prev: bool,
recv_next: bool,
tensor_shape: Shape,
config: ModelParallelConfig,
) -> torch.Tensor:
"""Batched send and recv with previous and next ranks in pipeline.
See _communicate for argument details.
"""
if config.timers is not None:
config.timers('forward-backward-send-forward-backward-recv', log_level=2).start()
input_tensor, output_tensor_grad, _ = _communicate(
tensor_send_next=output_tensor,
tensor_send_prev=input_tensor_grad,
recv_prev=recv_prev,
recv_next=recv_next,
tensor_shape=tensor_shape,
config=config,
)
if config.timers is not None:
config.timers('forward-backward-send-forward-backward-recv').stop()
return input_tensor, output_tensor_grad
|
Megatron-LM-master
|
megatron/core/pipeline_parallel/p2p_communication.py
|
Megatron-LM-master
|
megatron/core/models/__init__.py
|
|
from .gpt_model import GPTModel
|
Megatron-LM-master
|
megatron/core/models/gpt/__init__.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import logging
from typing import Literal, Optional
import torch
from torch import Tensor
from megatron.core import parallel_state, tensor_parallel
from megatron.core.models.common.rotary_pos_embedding import RotaryEmbedding
from megatron.core.models.gpt.gpt_embedding import GPTEmbedding
from megatron.core.transformer.enums import AttnMaskType, ModelType
from megatron.core.transformer.module import MegatronModule
from megatron.core.transformer.transformer_block import TransformerBlock
from megatron.core.transformer.transformer_config import TransformerConfig
from megatron.core.utils import make_tp_sharded_tensor_for_checkpoint
class GPTModel(MegatronModule):
"""Transformer language model.
Arguments:
config (TransformerConfig): transformer config
vocab_size (int): vocabulary size
max_sequence_length (int): maximum size of sequence. This is used for positional embedding
pre_process (bool): Include embedding layer (used with pipeline parallelism)
post_process (bool): Include an output layer (used with pipeline parallelism)
parallel_output (bool): Do not gather the outputs, keep them split across tensor parallel ranks
share_embeddings_and_output_weights (bool): When True, input embeddings and output logit weights are
shared. Defaults to False.
position_embedding_type (string): Position embedding type. Options ['learned_absolute', 'rope'].
Defaults is 'learned_absolute'.
rotary_percent (float): Percent of rotary dimension to use for rotary position embeddings.
Defaults to 1.0 (100%). Ignored unless position_embedding_type is 'rope'.
seq_len_interpolation_factor (float): scale of linearly interpolating RoPE for longer sequences.
The value must be a float larger than 1.0. Defaults to None.
"""
def __init__(
self,
config: TransformerConfig,
vocab_size: int,
max_sequence_length: int,
pre_process: bool = True,
post_process: bool = True,
fp16_lm_cross_entropy: bool = False,
parallel_output: bool = True,
share_embeddings_and_output_weights: bool = False,
position_embedding_type: Literal['learned_absolute', 'rope'] = 'learned_absolute',
rotary_percent: float = 1.0,
seq_len_interpolation_factor: Optional[float] = None,
):
super(GPTModel, self).__init__(config=config)
self.config: TransformerConfig = config
self.vocab_size = vocab_size
self.max_sequence_length = max_sequence_length
self.pre_process = pre_process
self.post_process = post_process
self.fp16_lm_cross_entropy = fp16_lm_cross_entropy
self.parallel_output = parallel_output
self.share_embeddings_and_output_weights = share_embeddings_and_output_weights
self.position_embedding_type = position_embedding_type
# megatron core pipelining currently depends on model type
# TODO: remove this dependency ?
self.model_type = ModelType.encoder_or_decoder
# Embeddings.
if self.pre_process:
self.embedding = GPTEmbedding(
config=self.config,
vocab_size=self.vocab_size,
max_sequence_length=self.max_sequence_length,
add_position_embedding=(self.position_embedding_type == 'learned_absolute'),
)
# Rotary Position Embeddings
if self.position_embedding_type == 'rope':
rotary_dim = self.config.kv_channels
if rotary_percent < 1.0:
rotary_dim = int(rotary_dim * rotary_percent)
self.rotary_pos_emb = RotaryEmbedding(rotary_dim, seq_len_interpolation_factor)
else:
self.rotary_pos_emb = None
# Transformer.
self.decoder = TransformerBlock(
config=self.config,
self_attn_mask_type=AttnMaskType.causal,
pre_process=self.pre_process,
post_process=self.post_process,
)
# Output
if post_process:
self.output_layer = tensor_parallel.ColumnParallelLinear(
config.hidden_size,
self.vocab_size,
config=config,
init_method=config.init_method,
bias=False,
skip_bias_add=False,
gather_output=not self.parallel_output,
skip_weight_param_allocation=self.pre_process
and self.share_embeddings_and_output_weights,
)
if self.share_embeddings_and_output_weights and (self.pre_process or self.post_process):
self.initialize_last_stage_with_word_embeddings()
def set_input_tensor(self, input_tensor):
""" See megatron.model.transformer.set_input_tensor()"""
# This is usually handled in schedules.py but some inference code still
# gives us non-lists or None
if not isinstance(input_tensor, list):
input_tensor = [input_tensor]
assert len(input_tensor) == 1, 'input_tensor should only be length 1 for gpt'
self.decoder.set_input_tensor(input_tensor[0])
def forward(
self,
input_ids: Tensor,
position_ids: Tensor,
attention_mask: Tensor,
decoder_input: Tensor = None,
labels: Tensor = None,
inference_params=None,
):
# If decoder_input is provided (not None), then input_ids and position_ids are ignored.
# Otherwise, apply embedding layer on input_ids and position_ids to get decoder_input.
# Decoder embedding.
if decoder_input is not None:
pass
elif self.pre_process:
decoder_input = self.embedding(input_ids=input_ids, position_ids=position_ids)
else:
# intermediate stage of pipeline
# decoder will get hidden_states from encoder.input_tensor
decoder_input = None
# Rotary positional embeddings
rotary_pos_emb = None
if self.rotary_pos_emb is not None:
if inference_params is not None:
rotary_seq_len = inference_params.max_sequence_length
else:
if self.decoder.input_tensor is not None:
rotary_seq_len = self.decoder.input_tensor.size(0)
else:
rotary_seq_len = decoder_input.size(0)
# Decoder input is split along sequence dimension, but RoPE is applied in tensor parallel region
if self.config.sequence_parallel:
rotary_seq_len *= self.config.tensor_model_parallel_size
rotary_pos_emb = self.rotary_pos_emb(rotary_seq_len)
# Run decoder.
hidden_states = self.decoder(
hidden_states=decoder_input,
attention_mask=attention_mask,
inference_params=inference_params,
rotary_pos_emb=rotary_pos_emb,
)
if not self.post_process:
return hidden_states
# logits and loss
output_weight = None
if self.share_embeddings_and_output_weights:
output_weight = self.shared_embedding_or_output_weight()
logits, _ = self.output_layer(hidden_states, weight=output_weight)
if labels is None:
# [s b h] => [b s h]
return logits.transpose(0, 1).contiguous()
# [b s] => [s b]
labels = labels.transpose(0, 1).contiguous()
loss = tensor_parallel.vocab_parallel_cross_entropy(logits.float(), labels)
# [s b] => [b, s]
loss = loss.transpose(0, 1).contiguous()
return loss
def shared_embedding_or_output_weight(self):
if self.pre_process:
return self.embedding.word_embeddings.weight
elif self.post_process:
return self.output_layer.weight
return None
def initialize_last_stage_with_word_embeddings(self):
# This function just initializes the word embeddings in the final stage
# when we are using pipeline parallelism and sharing word
# embeddings. Nothing to do if we aren't sharing weights or aren't using
# pipeline parallelism.
if not self.share_embeddings_and_output_weights or (self.pre_process and self.post_process):
return
if self.post_process and not self.pre_process:
assert not parallel_state.is_pipeline_first_stage()
# set word_embeddings weights to 0 here, then copy first
# stage's weights using all_reduce below.
self.output_layer.weight.data.fill_(0)
self.output_layer.weight.shared = True
# Parameters are shared between the word embeddings layers, and the
# heads at the end of the model. In a pipelined setup with more than
# one stage, the initial embedding layer and the head are on different
# workers, so we do the following:
# 1. Create a second copy of word_embeddings on the last stage, with
# initial parameters of 0.0.
# 2. Do an all-reduce between the first and last stage to ensure that
# the two copies of word_embeddings start off with the same
# parameter values.
# 3. In the training loop, before an all-reduce between the grads of
# the two word_embeddings layers to ensure that every applied weight
# update is the same on both stages.
# Ensure that first and last stages have the same initial parameter
# values.
if torch.distributed.is_initialized():
if parallel_state.is_rank_in_embedding_group():
weight = self.shared_embedding_or_output_weight()
torch.distributed.all_reduce(
weight.data, group=parallel_state.get_embedding_group()
)
elif not getattr(GPTModel, "embedding_warning_printed", False):
logging.getLogger(__name__).warning(
"Distributed processes aren't initialized, so the output layer "
"is not initialized with weights from the word embeddings. "
"If you are just manipulating a model this is fine, but "
"this needs to be handled manually. If you are training "
"something is definitely wrong."
)
GPTModel.embedding_warning_printed = True
def sharded_state_dict(self, prefix=''):
sharded_state_dict = {}
if self.pre_process:
embedding_prefix = f'{prefix}embedding.'
embedding_sharded_state_dict = self.embedding.sharded_state_dict(
prefix=embedding_prefix
)
sharded_state_dict.update(embedding_sharded_state_dict)
decoder_prefix = f'{prefix}decoder.'
decoder_sharded_state_dict = self.decoder.sharded_state_dict(prefix=decoder_prefix)
sharded_state_dict.update(decoder_sharded_state_dict)
if self.post_process:
output_layer_prefix = f'{prefix}output_layer.'
output_layer_key = f'{output_layer_prefix}weight'
if self.share_embeddings_and_output_weights:
if not self.pre_process:
# when sharing embeddings with last stage, we need to use the weights from the first stage
# on pipeline first rank, word embeddings are saved to {prefix}embedding.word_embeddings.weight
tensor = self.shared_embedding_or_output_weight()
first_stage_word_emb_key = f'{prefix}embedding.word_embeddings.weight'
dp_rank = parallel_state.get_data_parallel_rank()
dp_size = parallel_state.get_data_parallel_world_size()
last_stage_word_emb_replica_id = (
dp_rank + dp_size
) # copy of first stage embedding
sharded_output_layer_tensor = make_tp_sharded_tensor_for_checkpoint(
tensor=tensor,
key=first_stage_word_emb_key,
replica_id=last_stage_word_emb_replica_id,
allow_shape_mismatch=True,
)
sharded_state_dict[output_layer_key] = sharded_output_layer_tensor
else:
output_layer_state_dict = self.output_layer.state_dict(
prefix=output_layer_prefix, keep_vars=True
)
output_layer_tensor = output_layer_state_dict[output_layer_key]
# independent output layer
sharded_output_layer_tensor = make_tp_sharded_tensor_for_checkpoint(
tensor=output_layer_tensor,
key=output_layer_key,
replica_id=parallel_state.get_data_parallel_rank(),
allow_shape_mismatch=True,
)
sharded_state_dict[output_layer_key] = sharded_output_layer_tensor
return sharded_state_dict
|
Megatron-LM-master
|
megatron/core/models/gpt/gpt_model.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import torch
from megatron.core import tensor_parallel
from megatron.core.transformer.module import MegatronModule
from megatron.core.transformer.transformer_config import TransformerConfig
from megatron.core.utils import (
make_sharded_tensor_for_checkpoint,
make_tp_sharded_tensor_for_checkpoint,
)
class GPTEmbedding(MegatronModule):
"""Language model embeddings.
Arguments:
config (TransformerConfig): config object with all necessary configs for TransformerBlock
vocab_size (int): vocabulary size
max_sequence_length (int): maximum size of sequence. This
is used for positional embedding
add_position_embedding (bool): Add a position embedding.
embedding_dropout_prob float): dropout probability for embeddings
"""
def __init__(
self,
config: TransformerConfig,
vocab_size: int,
max_sequence_length: int,
add_position_embedding: bool,
):
super().__init__(config=config)
self.config: TransformerConfig = config
self.vocab_size: int = vocab_size
self.max_sequence_length: int = max_sequence_length
self.add_position_embedding: bool = add_position_embedding
# Word embeddings (parallel).
self.word_embeddings = tensor_parallel.VocabParallelEmbedding(
num_embeddings=self.vocab_size,
embedding_dim=self.config.hidden_size,
init_method=self.config.init_method,
config=self.config,
)
# Position embedding (serial).
if self.add_position_embedding:
self.position_embeddings = torch.nn.Embedding(
self.max_sequence_length, self.config.hidden_size
)
# Initialize the position embeddings.
if self.config.perform_initialization:
self.config.init_method(self.position_embeddings.weight)
# Embeddings dropout
self.embedding_dropout = torch.nn.Dropout(self.config.hidden_dropout)
def zero_parameters(self):
"""Zero out all parameters in embedding."""
self.word_embeddings.weight.data.fill_(0)
self.word_embeddings.weight.shared = True
self.position_embeddings.weight.data.fill_(0)
self.position_embeddings.weight.shared = True
def forward(self, input_ids, position_ids):
# Embeddings.
word_embeddings = self.word_embeddings(input_ids)
if self.add_position_embedding:
position_embeddings = self.position_embeddings(position_ids)
embeddings = word_embeddings + position_embeddings
else:
embeddings = word_embeddings
# Data format change to avoid explicit tranposes : [b s h] --> [s b h].
embeddings = embeddings.transpose(0, 1).contiguous()
# If the input flag for fp32 residual connection is set, convert for float.
if self.config.fp32_residual_connection:
embeddings = embeddings.float()
# Dropout.
if self.config.sequence_parallel:
embeddings = tensor_parallel.scatter_to_sequence_parallel_region(embeddings)
with tensor_parallel.get_cuda_rng_tracker().fork():
embeddings = self.embedding_dropout(embeddings)
else:
embeddings = self.embedding_dropout(embeddings)
return embeddings
def sharded_state_dict(self, prefix=''):
sharded_state_dict = {}
word_embeddings_prefix = f'{prefix}word_embeddings.'
word_embeddings_state_dict = self.word_embeddings.state_dict(
prefix=word_embeddings_prefix, keep_vars=True
)
sharded_word_embeddings_key = f'{word_embeddings_prefix}weight'
sharded_word_embeddings_tensor = make_tp_sharded_tensor_for_checkpoint(
tensor=word_embeddings_state_dict[sharded_word_embeddings_key],
key=sharded_word_embeddings_key,
allow_shape_mismatch=True,
)
sharded_state_dict[sharded_word_embeddings_key] = sharded_word_embeddings_tensor
if self.add_position_embedding:
position_embeddings_prefix = f'{prefix}position_embeddings.'
position_embeddings_state_dict = self.position_embeddings.state_dict(
prefix=position_embeddings_prefix, keep_vars=True
)
sharded_position_embeddings_key = f'{position_embeddings_prefix}weight'
sharded_position_embeddings_tensor = make_sharded_tensor_for_checkpoint(
tensor=position_embeddings_state_dict[sharded_position_embeddings_key],
key=sharded_position_embeddings_key,
)
sharded_state_dict[sharded_position_embeddings_key] = sharded_position_embeddings_tensor
return sharded_state_dict
|
Megatron-LM-master
|
megatron/core/models/gpt/gpt_embedding.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import importlib.util
import torch
from torch import einsum, nn
__all__ = ['RotaryEmbedding', 'apply_rotary_pos_emb']
class RotaryEmbedding(nn.Module):
def __init__(self, dim, seq_len_interpolation_factor=None):
super().__init__()
self.seq_len_interpolation_factor = seq_len_interpolation_factor
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer('inv_freq', inv_freq, persistent=False)
def forward(self, max_seq_len, offset=0):
seq = torch.arange(max_seq_len, device=self.inv_freq.device) + offset
if self.seq_len_interpolation_factor is not None:
seq = seq.type_as(self.inv_freq)
seq *= 1 / self.seq_len_interpolation_factor
freqs = einsum('i , j -> i j', seq.type_as(self.inv_freq), self.inv_freq)
# first part even vector components, second part odd vector components,
# 2 * dim in dimension size
emb = torch.cat((freqs, freqs), dim=-1)
# emb [seq_length, .., dim]
return emb[:, None, None, :]
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
state_dict.pop(f'{prefix}inv_freq', None)
return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
def _rotate_half(x):
"""
change sign so the last dimension becomes [-odd, +even]
"""
x1, x2 = torch.chunk(x, 2, dim=-1)
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(t, freqs):
"""
input tensor t is of shape [seq_length, ..., dim]
rotary positional embeding tensor freqs is of shape [seq_length, ..., dim]
check https://kexue.fm/archives/8265 for detailed formulas
"""
rot_dim = freqs.shape[-1]
# ideally t_pass is empty so rotary pos embedding is applied to all tensor t
t, t_pass = t[..., :rot_dim], t[..., rot_dim:]
# first part is cosine component
# second part is sine component, need to change signs with _rotate_half method
t = (t * freqs.cos()) + (_rotate_half(t) * freqs.sin())
return torch.cat((t, t_pass), dim=-1)
|
Megatron-LM-master
|
megatron/core/models/common/rotary_pos_embedding.py
|
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
""" Utilities for operating with dicts and lists. """
from collections import defaultdict
from typing import Any, Callable, Iterable, Optional, Tuple, Union
import torch
def extract_matching_values(
x: Union[dict, list], predicate: Callable
) -> Tuple[Union[dict, list], Union[dict, list]]:
""" Return matching and nonmatching values. Keeps hierarchy. """
if isinstance(x, dict):
matching_vals = {}
nonmatching_vals = {}
for k, v in x.items():
if isinstance(v, (list, dict)):
match, nonmatch = extract_matching_values(v, predicate)
if match:
matching_vals[k] = match
if nonmatch:
nonmatching_vals[k] = nonmatch
elif predicate(v):
matching_vals[k] = v
else:
nonmatching_vals[k] = v
else:
assert isinstance(x, list)
matching_vals = []
nonmatching_vals = []
for v in x:
if isinstance(v, (list, dict)) and v:
match, nonmatch = extract_matching_values(v, predicate)
if match:
matching_vals.append(match)
if nonmatch:
nonmatching_vals.append(nonmatch)
elif predicate(v):
matching_vals.append(v)
else:
nonmatching_vals.append(v)
return matching_vals, nonmatching_vals
def diff(x1: Any, x2: Any, prefix: Tuple = ()) -> Tuple[list, list, list]:
mismatch = []
if isinstance(x1, dict) and isinstance(x2, dict):
only_left = [prefix + (k,) for k in x1.keys() - x2.keys()]
only_right = [prefix + (k,) for k in x2.keys() - x1.keys()]
for k in x2.keys() & x1.keys():
_left, _right, _mismatch = diff(x1[k], x2[k], prefix + (k,))
only_left.extend(_left)
only_right.extend(_right)
mismatch.extend(_mismatch)
elif isinstance(x1, list) and isinstance(x2, list):
only_left = list(range(len(x1) - 1, len(x2) - 1, -1))
only_right = list(range(len(x1) - 1, len(x2) - 1, -1))
for i, (v1, v2) in enumerate(zip(x1, x2)):
_left, _right, _mismatch = diff(v1, v2, prefix + (i,))
only_left.extend(_left)
only_right.extend(_right)
mismatch.extend(_mismatch)
else:
only_left = []
only_right = []
if isinstance(x1, torch.Tensor) and isinstance(x2, torch.Tensor):
_is_mismatch = not torch.all(x1 == x2)
else:
try:
_is_mismatch = bool(x1 != x2)
except RuntimeError:
_is_mismatch = True
if _is_mismatch:
mismatch.append((prefix, type(x1), type(x2)))
return only_left, only_right, mismatch
def inspect_keys_types(d: dict, prefix: Tuple = (), indent: int = 4):
print_indent = lambda: print(' ' * indent * len(prefix), end='')
for k, v in d.items():
if isinstance(v, dict):
print_indent()
print(f'> {k}:')
inspect_keys_types(v, prefix + (k,), indent)
else:
print_indent()
if isinstance(v, torch.Tensor):
print(f'> {k}: {type(v)} of shape {v.shape}')
else:
print(f'> {k}: {type(v)}')
def inspect_types(x: Any, prefix: Tuple = (), indent: int = 4):
print_indent = lambda: print(' ' * indent * len(prefix), end='')
if isinstance(x, dict):
print()
for k, v in x.items():
print_indent()
print(f'> {k}: ', end='')
inspect_types(v, prefix + (k,), indent)
elif isinstance(x, list):
print()
for i, v in enumerate(x):
print_indent()
print(f'- {i}: ', end='')
inspect_types(v, prefix + (i,), indent)
else:
if isinstance(x, torch.Tensor):
print(f'Tensor of shape {x.shape}')
else:
try:
x_str = str(x)
except:
x_str = '<no string repr>'
if len(x_str) > 30:
x_str = x_str[:30] + '... (truncated)'
print(f'[{type(x)}]: {x_str}')
def nested_values(x: Union[dict, list]):
x_iter = x.values() if isinstance(x, dict) else x
for v in x_iter:
if isinstance(v, (dict, list)):
yield from nested_values(v)
else:
yield v
def nested_items_iter(x: Union[dict, list]):
x_iter = x.items() if isinstance(x, dict) else enumerate(x)
for k, v in x_iter:
if isinstance(v, (dict, list)):
yield from nested_items_iter(v)
else:
yield x, k, v
def dict_map(f: Callable, d: dict):
for sub_d, k, v in nested_items_iter(d):
sub_d[k] = f(v)
def dict_map_with_key(f: Callable, d: dict):
for sub_d, k, v in nested_items_iter(d):
sub_d[k] = f(k, v)
def dict_list_map_inplace(f: Callable, x: Union[dict, list]):
if isinstance(x, dict):
for k, v in x.items():
x[k] = dict_list_map_inplace(f, v)
elif isinstance(x, list):
x[:] = (dict_list_map_inplace(f, v) for v in x)
else:
return f(x)
return x
def dict_list_map_outplace(f: Callable, x: Union[dict, list]):
if isinstance(x, dict):
return {k: dict_list_map_outplace(f, v) for k, v in x.items()}
elif isinstance(x, list):
return [dict_list_map_outplace(f, v) for v in x]
else:
return f(x)
def merge(x1: dict, x2: dict):
if isinstance(x1, dict) and isinstance(x2, dict):
for k, v2 in x2.items():
if k not in x1:
x1[k] = v2
else:
x1[k] = merge(x1[k], v2)
elif isinstance(x1, list) and isinstance(x2, list):
if len(x1) != len(x2):
raise ValueError('Cannot merge two lists with different lengths')
for i, v2 in enumerate(x2):
x1[i] = merge(x1[i], v2)
else:
raise ValueError(f'Duplicate non-dict and non-list values encountered: `{x1}` and `{x2}`')
return x1
def map_reduce(
xs: Iterable,
key_fn: Callable = lambda x: x,
value_fn: Callable = lambda x: x,
reduce_fn: Callable = lambda x: x,
) -> dict:
res = defaultdict(list)
for x in xs:
res[key_fn(x)].append(value_fn(x))
for k in res:
res[k] = reduce_fn(res[k])
return dict(res)
|
Megatron-LM-master
|
megatron/core/dist_checkpointing/dict_utils.py
|
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
from .core import check_is_distributed_checkpoint
from .mapping import LocalNonpersitentObject, ShardedTensor
from .serialization import load, load_common_state_dict, save
|
Megatron-LM-master
|
megatron/core/dist_checkpointing/__init__.py
|
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
import json
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Optional
CONFIG_FNAME = 'metadata.json'
class CheckpointingException(Exception):
pass
@dataclass
class CheckpointingConfig:
""" Documents backends used in the checkpoint. """
sharded_backend: str
sharded_backend_version: int = 1
common_backend: str = 'torch'
common_backend_version: int = 1
def check_is_distributed_checkpoint(checkpoint_dir):
return maybe_load_config(checkpoint_dir) is not None
def maybe_load_config(checkpoint_dir: str) -> Optional[CheckpointingConfig]:
config_path = Path(checkpoint_dir, CONFIG_FNAME)
if not config_path.exists():
return None
with config_path.open() as f:
config_dict = json.load(f)
return CheckpointingConfig(**config_dict)
def save_config(config: CheckpointingConfig, checkpoint_dir: str):
config_path = Path(checkpoint_dir, CONFIG_FNAME)
with config_path.open('w') as f:
json.dump(asdict(config), f)
|
Megatron-LM-master
|
megatron/core/dist_checkpointing/core.py
|
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
from typing import Tuple
from .dict_utils import dict_list_map_inplace, extract_matching_values
from .mapping import LocalNonpersitentObject, ShardedStateDict, ShardedTensor, StateDict
def extract_sharded_tensors(
sharded_state_dict: ShardedStateDict,
) -> Tuple[ShardedStateDict, StateDict]:
return extract_matching_values(sharded_state_dict, lambda v: isinstance(v, ShardedTensor))
def extract_sharded_tensors_or_nonpersistent(
sharded_state_dict: ShardedStateDict,
) -> Tuple[ShardedStateDict, StateDict]:
return extract_matching_values(
sharded_state_dict, lambda v: isinstance(v, (ShardedTensor, LocalNonpersitentObject))
)
def add_prefix_for_sharding(sharded_state_dict: ShardedStateDict, prefix: str):
def add_prefix(t):
if isinstance(t, ShardedTensor):
t.key = f'{prefix}.{t.key}'
return t
dict_list_map_inplace(add_prefix, sharded_state_dict)
|
Megatron-LM-master
|
megatron/core/dist_checkpointing/utils.py
|
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
""" Optimizer related helpers. """
import logging
from copy import deepcopy
from dataclasses import replace
from itertools import chain
from typing import Dict, Iterable, List
logger = logging.getLogger(__name__)
import torch
from .dict_utils import nested_values
from .mapping import LocalNonpersitentObject, ShardedStateDict, ShardedTensor, StateDict
from .utils import extract_sharded_tensors
def get_optim_param_to_id_map(optim_params_iter: Iterable[torch.nn.Parameter]) -> Dict[int, int]:
param_mappings = {}
for i, param in enumerate(optim_params_iter):
if id(param) not in param_mappings:
param_mappings[id(param)] = i
return param_mappings
def get_param_id_to_sharded_param_map(
model_sharded_state_dict: ShardedStateDict, optim_params_iter: Iterable[torch.nn.Parameter]
) -> Dict[int, ShardedTensor]:
model_sharded_state_dict, _ = extract_sharded_tensors(model_sharded_state_dict)
id_to_sharded_param_map = {}
param_to_id_map = get_optim_param_to_id_map(optim_params_iter)
for ten in nested_values(model_sharded_state_dict):
if id(ten.data) in param_to_id_map:
id_to_sharded_param_map[param_to_id_map[id(ten.data)]] = ten
else:
logger.debug(f'{ten} is not tracked by the optimizer')
if not id_to_sharded_param_map:
logger.warning(
"Sharded parameters mapping is empty. It means tensors in model state dict"
" do not correspond to tensors in optimizer parameters map."
" Make sure to call state_dict with `keep_vars=True`."
)
return id_to_sharded_param_map
def make_sharded_optimizer_tensor(
model_param: ShardedTensor, optim_param: torch.Tensor, prefix: str
) -> ShardedTensor:
assert (
tuple(optim_param.shape) == model_param.local_shape
), f'Optimizer shape ({tuple(optim_param.shape)} does not match model shape ({model_param.local_shape})'
return replace(
model_param, key=f'{prefix}.{model_param.key}', data=optim_param, dtype=optim_param.dtype
)
def optim_state_to_sharding_state(
optim_state_dict: StateDict, id_to_sharded_param_map: Dict[int, ShardedTensor]
):
sharded_state = {}
for param_id, param_state in optim_state_dict['state'].items():
sharded_state[param_id] = {}
for state_key, param in param_state.items():
if param_id in id_to_sharded_param_map:
sharded_state[param_id][state_key] = make_sharded_optimizer_tensor(
id_to_sharded_param_map[param_id], param, prefix=f'optimizer.state.{state_key}'
)
else:
raise ValueError(f'Param id {param_id} does not match any model sharded param')
optim_state_dict['param_groups'] = deepcopy(optim_state_dict['param_groups'])
for group in optim_state_dict['param_groups']:
group['params'] = LocalNonpersitentObject(group['params'])
optim_state_dict['state'] = sharded_state
|
Megatron-LM-master
|
megatron/core/dist_checkpointing/optimizer.py
|
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
""" Core library classes. """
from dataclasses import dataclass, replace
from itertools import chain
from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
import torch
from .core import CheckpointingException
# These type definitions are just hints to differentiate a plain model state
# dict (StateDict) from a state dict with tensors replaced with ShardedTensors
# (ShardedStateDict).
StateDict = Dict[str, Any]
ShardedStateDict = Dict[str, Any]
ReplicaId = Union[int, Tuple[int, ...]]
@dataclass
class ShardedTensor:
"""Represents a mapping between a local tensor and a global tensor.
Global tensor is assumed to consist of many local tensors distributed
between different processes.
Attributes:
key: unique identifier of a global tensor
data: local tensor data. Can be None only for consistency validation
dtype: tensor dtype
local_shape: local tensor shape
global_shape: global tensor shape
global_offset: offset of a local tensor in a global tensor, specified
in number of tensor elements
axis_fragmentations: global tensor fragmentation of each axis
replica_id: indicates given local tensor's replication wrt. local
tensors in different processes
prepend_axis_num: number of axes prepended to the local tensor
to reflect global tensor shape.
The behavior is similar to unsqueezing the local tensor.
allow_shape_mismatch: if True, during loading, the global shape of a
stored tensor does not have to match the expected global shape.
Useful for representing tensors with flexible shape, e.g. padded.
flattened_range: specifies a slice that should be applied to a flattened
tensor with `local_shape` in order to get the tensor stored as `data`
"""
key: str
data: Optional[torch.Tensor]
dtype: torch.dtype
local_shape: Tuple[int, ...]
global_shape: Tuple[int, ...]
global_offset: Tuple[int, ...]
axis_fragmentations: Optional[Tuple[int, ...]]
replica_id: ReplicaId = 0
prepend_axis_num: int = 0
allow_shape_mismatch: bool = False
flattened_range: Optional[slice] = None
def global_slice(self) -> Tuple[Union[int, slice], ...]:
assert len(self.global_offset) == len(self.local_shape) + self.prepend_axis_num
return tuple(
chain(
(off for off in self.global_offset[: self.prepend_axis_num]),
(
slice(off, off + sh)
for off, sh in zip(
self.global_offset[self.prepend_axis_num :], self.local_shape
)
),
)
)
def global_coordinates(self) -> Tuple[np.ndarray, ...]:
if self.flattened_range is None:
raise CheckpointingException(
f'`global_coordinates` is undefined for'
f' {self.__class__.__name__} without `flattened_range`'
)
local_coords = self.local_coordinates()
assert len(local_coords) + self.prepend_axis_num == len(self.global_offset), (
len(local_coords),
self,
)
global_coords = tuple(
c + off
for c, off in zip((0,) * self.prepend_axis_num + local_coords, self.global_offset)
)
return global_coords
def local_coordinates(self) -> Tuple[np.ndarray, ...]:
if self.flattened_range is None:
raise CheckpointingException(
f'`local_coordinates` is undefined for'
f' {self.__class__.__name__} without `flattened_range`'
)
# TODO: np.unravel_index?
mask = np.zeros(np.product(self.local_shape), dtype=bool)
mask[self.flattened_range] = True
return np.nonzero(mask.reshape(self.local_shape))
def max_allowed_chunks(self) -> Tuple[int, ...]:
chunks = []
for axis_sh, axis_fragm in zip(self.global_shape, self.axis_fragmentations):
if not self.allow_shape_mismatch and axis_sh % axis_fragm != 0:
raise CheckpointingException(
f'Axis shape ({axis_sh}) not divisible' f' by axis fragmentation ({axis_fragm}'
)
axis_chunk_size = axis_sh // axis_fragm
chunks.append(axis_chunk_size)
return tuple(chunks)
def without_data(self):
return replace(self, data=None)
@classmethod
def from_rank_offsets(
cls,
key: str,
data: torch.Tensor,
*rank_offsets: Tuple[int, int, int],
replica_id: ReplicaId = 0,
prepend_axis_num: int = 0,
allow_shape_mismatch: bool = False,
):
"""Allows to construct the ShardedTensor given offset specified in process ranks.
Arguments:
key: unique key
data: local tensor data
rank_offsets: each tuple (axis, axis_rank_offset, axis_fragm)
says that if global tensor is divided into `axis_fragm`
fragment along `axis` axis, then local tensor data
corresponds to the `axis_rank_offset` chunk.
replica_id: see ShardedTensor
prepend_axis_num: see ShardedTensor
allow_shape_mismatch: see ShardedTensor
"""
global_offset = [0] * (data.ndim + prepend_axis_num)
global_shape = ([1] * prepend_axis_num) + list(data.shape)
axis_fragmentations = [1] * (data.ndim + prepend_axis_num)
_seen_axis = set()
for axis, axis_rank_offset, axis_fragm in rank_offsets:
assert axis >= 0 and axis_rank_offset >= 0 and axis_fragm >= 0, (
axis,
axis_rank_offset,
axis_fragm,
)
assert (
axis_rank_offset < axis_fragm
), 'Rank offset must be lower than axis fragmentation'
if axis in _seen_axis:
raise CheckpointingException('Duplicated axis specified')
_seen_axis.add(axis)
local_axis_shape = 1 if axis < prepend_axis_num else data.shape[axis - prepend_axis_num]
global_shape[axis] = axis_fragm * local_axis_shape
global_offset[axis] = axis_rank_offset * local_axis_shape
axis_fragmentations[axis] = axis_fragm
return cls(
key,
data,
data.dtype,
tuple(data.shape),
tuple(global_shape),
tuple(global_offset),
tuple(axis_fragmentations),
replica_id,
prepend_axis_num,
allow_shape_mismatch,
)
def __str__(self):
return f'{self.__class__.__name__}(key=\'{self.key}\')'
def is_main_replica(replica_id):
if isinstance(replica_id, int):
return replica_id == 0
return all(r == 0 for r in replica_id)
class LocalNonpersitentObject:
"""Object that should not be stored in a checkpoint, but restored locally.
Wrapping any object inside the state dict with LocalNonpersitentObject
will result in:
- during saving, this object will *not* be stored in the checkpoint
- during loading, a local version of this object will be placed in a state dict
"""
def __init__(self, obj):
self.obj = obj
def unwrap(self):
return self.obj
@dataclass
class ShardedObject:
"""Represents a mapping between a local object and a global object.
Global object is assumed to consist of many local objects distributed
between different processes.
NOTE: Contrary to ShardedTensor, it's impossible to change global object
sharding. Conceptually, ShardedObject is a fully-sharded ShardedTensor
with atomic arbitrary typed elements.
Attributes:
key: unique identifier of a global tensor
data: local object data. Can be None only for consistency validation
global_shape: global object shape
global_offset: offset of a local object in a global object, specified
in number of shards
replica_id: indicates local object replication wrt. local
objects in different processes
"""
key: str
data: object
global_shape: Tuple[int, ...]
global_offset: Tuple[int, ...]
replica_id: ReplicaId = 0
def without_data(self):
return replace(self, data=None)
@property
def unique_key(self):
return f'{self.key}/shard_{".".join(map(str, self.global_offset))}_{".".join(map(str, self.global_shape))}'
def __str__(self):
return f'{self.__class__.__name__}(key=\'{self.key}\')'
|
Megatron-LM-master
|
megatron/core/dist_checkpointing/mapping.py
|
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
import logging
import os
from collections import Counter, defaultdict
from itertools import chain
from pathlib import Path
from typing import Iterable, List, Tuple, Union
import numpy as np
import torch
from .core import CheckpointingConfig, maybe_load_config, save_config
from .dict_utils import (
dict_list_map_inplace,
diff,
extract_matching_values,
map_reduce,
merge,
nested_values,
)
from .mapping import (
CheckpointingException,
ShardedObject,
ShardedStateDict,
ShardedTensor,
StateDict,
is_main_replica,
)
from .strategies.base import (
LoadCommonStrategy,
LoadShardedStrategy,
SaveCommonStrategy,
SaveShardedStrategy,
StrategyAction,
get_default_strategy,
)
from .utils import extract_sharded_tensors, extract_sharded_tensors_or_nonpersistent
COMMON_STATE_FNAME = 'common.pt'
logger = logging.getLogger(__name__)
def load(
sharded_state_dict: ShardedStateDict,
checkpoint_dir: str,
sharded_strategy: Union[LoadShardedStrategy, None] = None,
common_strategy: Union[LoadCommonStrategy, None] = None,
) -> StateDict:
"""Loading entrypoint.
Arguments:
sharded_state_dict: state dict of the existing model populated with
ShardedTensors. Used as a mapping to determine which parts of
global tensors stored in the checkpoint should be loaded.
checkpoint_dir: directory with the checkpoint
sharded_strategy: configures loading behavior for sharded tensors
common_strategy: configures loading behavior for common data
"""
if common_strategy is not None:
raise NotImplementedError('The only supported common strategy is torch')
checkpoint_dir = Path(checkpoint_dir)
common_state_dict = load_common_state_dict(checkpoint_dir)
if not sharded_state_dict:
return common_state_dict
sharded_objects, sharded_state_dict = load_sharded_objects(sharded_state_dict, checkpoint_dir)
merge(common_state_dict, sharded_objects)
saved_config = maybe_load_config(checkpoint_dir)
if saved_config is None:
raise CheckpointingException(f'{checkpoint_dir} is not a distributed checkpoint')
sharded_state_dict, _ = extract_sharded_tensors_or_nonpersistent(sharded_state_dict)
sharded_state_dict, nonpersistent_state_dict = extract_sharded_tensors(sharded_state_dict)
dict_list_map_inplace(lambda o: o.unwrap(), nonpersistent_state_dict)
merge(common_state_dict, nonpersistent_state_dict)
validate_sharding_integrity(nested_values(sharded_state_dict))
if sharded_strategy is None:
sharded_strategy = get_default_strategy(
StrategyAction.LOAD_SHARDED,
saved_config.sharded_backend,
saved_config.sharded_backend_version,
)
else:
# TODO: implement consistency checks here
pass
loaded_state_dict = sharded_strategy.load(sharded_state_dict, checkpoint_dir)
merge(common_state_dict, loaded_state_dict)
return common_state_dict
# TODO: implement it as common torch strategy
def load_common_state_dict(checkpoint_dir: Path):
return torch.load(Path(checkpoint_dir) / COMMON_STATE_FNAME, map_location='cpu')
def load_sharded_objects(sharded_state_dict: ShardedStateDict, checkpoint_dir: Path):
sharded_objects, sharded_state_dict = extract_matching_values(
sharded_state_dict, lambda v: isinstance(v, ShardedObject)
)
def load_sharded_object(sh_obj: ShardedObject):
sh_obj.data = None
load_path = (checkpoint_dir / sh_obj.unique_key).with_suffix('.pt')
loaded_obj = torch.load(load_path)
return loaded_obj
return dict_list_map_inplace(load_sharded_object, sharded_objects), sharded_state_dict
def save(
sharded_state_dict: ShardedStateDict,
checkpoint_dir: str,
sharded_strategy: Union[SaveShardedStrategy, None] = None,
common_strategy: Union[SaveCommonStrategy, None] = None,
):
"""Saving entrypoint.
Extracts ShardedTensors from the given state dict. Rank 0 saves the
"regular" part of the checkpoint to common torch file.
The ShardedTensors are saved according to a strategy specified by the
config.
Arguments:
sharded_state_dict: state dict of the populated with
ShardedTensors. Used as a mapping to determine how local tensors
should be saved as global tensors in the checkpoint.
checkpoint_dir: directory to save the checkpoint to
sharded_strategy: configures sharded tensors saving behavior and backend
common_strategy: configures common data saving behavior and backend
"""
checkpoint_dir = Path(checkpoint_dir)
if torch.distributed.get_rank() == 0:
if not checkpoint_dir.exists():
raise CheckpointingException(
f'Checkpoint destination directory does not exist: {checkpoint_dir}'
)
if next(checkpoint_dir.iterdir(), None) is not None:
raise CheckpointingException(
f'Checkpoint destination directory ({checkpoint_dir}) is not empty'
)
if common_strategy is not None:
raise NotImplementedError('The only supported common strategy is torch')
if sharded_strategy is None:
sharded_strategy = get_default_strategy(StrategyAction.SAVE_SHARDED, 'zarr', 1)
sharded_state_dict, state_dict = extract_sharded_tensors_or_nonpersistent(sharded_state_dict)
sharded_state_dict, _ = extract_sharded_tensors(sharded_state_dict)
sharded_tensors = list(nested_values(sharded_state_dict))
validate_sharding_integrity(sharded_tensors)
_save_common_dict(state_dict, checkpoint_dir, True)
sharded_strategy.save(sharded_tensors, checkpoint_dir)
save_config(
CheckpointingConfig(sharded_strategy.backend, sharded_strategy.version), checkpoint_dir
)
# TODO: implement it as common torch strategy
def _save_common_dict(
state_dict: StateDict, checkpoint_dir: Path, validate_consistency: bool = False
):
common_state_dict = _extract_and_save_sharded_objects(
state_dict, checkpoint_dir, validate_consistency
)
if torch.distributed.get_rank() == 0:
torch.save(common_state_dict, checkpoint_dir / COMMON_STATE_FNAME)
if validate_consistency:
# TODO: implement checking consistency with rank 0 common dict on other ranks
pass
# torch.distributed.barrier()
# if not torch.distributed.get_rank() == 0:
# rank_0_state_dict = torch.load(checkpoint_dir / COMMON_STATE_FNAME)
# print(diff(common_state_dict, rank_0_state_dict))
def _extract_and_save_sharded_objects(
state_dict: StateDict, checkpoint_dir: Path, validate_consistency: bool = False
):
sharded_objects, state_dict = extract_matching_values(
state_dict, lambda v: isinstance(v, ShardedObject)
)
sharded_objects = list(nested_values(sharded_objects))
if validate_consistency:
validate_objects_sharding_integrity(sharded_objects)
for sh_obj in sharded_objects:
if is_main_replica(sh_obj.replica_id):
save_path = (checkpoint_dir / sh_obj.unique_key).with_suffix('.pt')
os.makedirs(save_path.parent, exist_ok=True)
torch.save(sh_obj.data, save_path)
return state_dict
def validate_sharding_integrity(sharded_tensors: Iterable[ShardedTensor]):
sharding = [ten.without_data() for ten in sharded_tensors]
all_sharding = [None] * torch.distributed.get_world_size()
torch.distributed.all_gather_object(all_sharding, sharding)
if torch.distributed.get_rank() != 0:
return
key_shardings = defaultdict(list)
for rank, rank_shardings in enumerate(all_sharding):
for sharding in rank_shardings:
key_shardings[sharding.key].append((rank, sharding))
for key, shardings in key_shardings.items():
_validate_sharding_for_key(shardings)
def _validate_sharding_for_key(rank_sharding: List[Tuple[int, ShardedTensor]]):
global_shape = rank_sharding[0][1].global_shape
local_shape = rank_sharding[0][1].local_shape
dtype = rank_sharding[0][1].dtype
has_flattened_range = rank_sharding[0][1].flattened_range is not None
for rank, sharding in rank_sharding:
assert sharding.dtype == dtype, (sharding.dtype, dtype)
assert sharding.global_shape == global_shape, (sharding.global_shape, global_shape)
assert sharding.local_shape == local_shape, (sharding.local_shape, local_shape)
assert (sharding.flattened_range is not None) == has_flattened_range, (
(sharding.flattened_range is not None),
has_flattened_range,
)
shard_access_cnt = _compute_shards_access(rank_sharding)
if has_flattened_range:
map_reduce(
rank_sharding,
lambda x: x[1].global_offset,
lambda x: x[1],
_validate_sharding_for_key_flattened,
)
else:
if not torch.all(shard_access_cnt == 1):
logger.error(f'Invalid access pattern for {rank_sharding[0][1]}: {shard_access_cnt}')
raise CheckpointingException(f'Invalid access pattern for {rank_sharding[0][1]}')
def _compute_shards_access(rank_sharding):
def chunk_offset(sharding):
assert len(sharding.global_offset) == len(sharding.local_shape) + sharding.prepend_axis_num
return tuple(
chain(
(off for off in sharding.global_offset[: sharding.prepend_axis_num]),
(
off // sh
for off, sh in zip(
sharding.global_offset[sharding.prepend_axis_num :], sharding.local_shape
)
),
)
)
shard_access_cnt = torch.zeros(
rank_sharding[0][1].axis_fragmentations, dtype=torch.int, device='cpu'
)
for rank, sharding in rank_sharding:
if is_main_replica(sharding.replica_id):
shard_access_cnt[chunk_offset(sharding)] += 1
# TODO: consider validating different replicas too
return shard_access_cnt
def _validate_sharding_for_key_flattened(tensors_by_shard):
all_slices = []
local_shape = tensors_by_shard[0].local_shape
for sharding in tensors_by_shard:
assert sharding.local_shape == local_shape
sharding: ShardedTensor
if not is_main_replica(sharding.replica_id):
# TODO: this checks only saving (and loading replica_id=0) consistency
continue
all_slices.append((sharding.flattened_range.start, sharding.flattened_range.stop))
starts, stops = map(np.asarray, zip(*sorted(all_slices)))
if (
starts[0] != 0
or stops[-1] != np.product(local_shape)
or not np.all(starts[1:] == stops[:-1])
):
logger.error(
f'Flattened ranges dont cover the whole shard {tensors_by_shard[0]}. Ranges: {(starts, stops)}'
)
raise CheckpointingException(
f'Flattened ranges dont cover the whole shard {tensors_by_shard[0]}'
)
def validate_objects_sharding_integrity(sharded_objects: List[ShardedObject]):
""" Ensure uniqueness of saved objects. """
local_sh_objs = [sh_obj.without_data() for sh_obj in sharded_objects]
all_sh_objs = [None] * torch.distributed.get_world_size()
torch.distributed.all_gather_object(all_sh_objs, local_sh_objs)
if torch.distributed.get_rank() != 0:
return
unique_keys = [
sh_obj.unique_key
for sh_obj in chain.from_iterable(all_sh_objs)
if is_main_replica(sh_obj.replica_id)
]
if len(unique_keys) != len(set(unique_keys)):
duplicates = {k: cnt for k, cnt in Counter(unique_keys).items() if cnt > 1}
logger.error(f'Duplicate ShardedObject keys and counts: {duplicates}')
raise CheckpointingException(f'Duplicate ShardedObject keys: {list(duplicates.keys())}')
|
Megatron-LM-master
|
megatron/core/dist_checkpointing/serialization.py
|
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
""" Various loading and saving strategies """
import logging
logger = logging.getLogger(__name__)
try:
import tensorstore
import zarr
from .tensorstore import _import_trigger
from .zarr import _import_trigger
except ImportError:
logger.warning('Zarr-based strategies will not be registered because of missing packages')
|
Megatron-LM-master
|
megatron/core/dist_checkpointing/strategies/__init__.py
|
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
""" 2-stage checkpoint loading. """
import os
import time
from collections import defaultdict
from dataclasses import dataclass
from functools import partial, wraps
from itertools import chain
from logging import DEBUG, INFO, StreamHandler, getLogger
from operator import attrgetter, itemgetter
from pathlib import Path
from typing import Iterable, List, NamedTuple, Optional, Tuple, Union
import torch
from ..dict_utils import dict_list_map_inplace, map_reduce, nested_values
from ..mapping import ShardedStateDict, ShardedTensor, StateDict
from .base import LoadShardedStrategy
from .tensorstore import _load_from_array
from .zarr import flatten_range
_import_trigger = None
timers = defaultdict(list)
logger = getLogger(__name__)
def timed(verbose=True):
def timed_dec(fn):
name = fn.__name__
@wraps(fn)
def wrapped(*args, **kwargs):
if verbose:
logger.debug(f'{name} init')
start = time.time()
ret = fn(*args, **kwargs)
took = time.time() - start
if verbose:
logger.debug(f'{name} took {took}s')
timers[name].append(took)
return ret
return wrapped
return timed_dec
@dataclass
class _ShardedTensorMetadata:
global_rank: int
sharded_tensor_no_data: ShardedTensor
dist_group_rank: Tuple[int] # id of distributed group
dist_group_ranks: Tuple[int] # id of distributed group
data_size: Optional[int] = None # bytes
def sharded_tensor_chunk_id(sharded_tensor: ShardedTensor):
return (
sharded_tensor.key,
sharded_tensor.global_offset,
)
class TwoStageDataParallelLoadShardedStrategy(LoadShardedStrategy):
""" Loads one checkpoint replica from storage and broadcasts to other nodes.
This strategy loads checkpoint from storage on minimal set of nodes
and distributes the checkpoint to other nodes with torch.distributed.
Loading is performed with tensorstore.
Steps:
0. (optional) create Gloo distributed groups
1. Exchange ShardedTensors metadata between all nodes
2. Align needed tensors within DP groups
3. For each globally unique tensor:
a) on one of the ranks load it from storage to CPU and move to CUDA
b) allocate CUDA tensor on other ranks
c) broadcast within DP group
d) copy tensor content to the model param location
e) free tensor buffers from a) and b)
Notes:
1. Loading and broadcasting is done sequentially to avoid both host and device OOMs
2. There is a lot of overlap potential between all three steps done for each tensor:
a) loading from storage to numpy
b) moving CPU tensors to CUDA
c) broadcast
"""
def __init__(self, data_parallel_group, cpu_transfer=True):
super().__init__()
self.cpu_transfer = cpu_transfer
self.data_parallel_group_orig = data_parallel_group
self.data_parallel_group = None if cpu_transfer else data_parallel_group
self.dp_group_ranks = tuple(
sorted(torch.distributed.get_process_group_ranks(data_parallel_group))
)
self.dp_group_rank = torch.distributed.get_rank(self.data_parallel_group_orig)
self.global_rank = torch.distributed.get_rank()
def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path):
self.maybe_init_gloo_group()
all_tensors_sorted = self._build_load_plan(sharded_state_dict)
self._exchange_loaded_tensors(all_tensors_sorted, sharded_state_dict, checkpoint_dir)
self.summarize_load_times()
return sharded_state_dict
def summarize_load_times(self):
torch.distributed.barrier()
logger.info('Checkpoint loading finished. Summary:')
for key, times in sorted(timers.items()):
times_sum = sum(times)
max_times = torch.tensor([times_sum], device='cuda')
avg_times = torch.tensor([times_sum], device='cuda')
torch.distributed.all_reduce(max_times, op=torch.distributed.ReduceOp.MAX)
torch.distributed.all_reduce(avg_times, op=torch.distributed.ReduceOp.SUM)
avg_times /= torch.distributed.get_world_size()
if torch.distributed.get_rank() == 0:
logger.info(f'{key}: max {max_times[0]}, avg {avg_times[0]}')
@timed(verbose=False)
def load_tensor_from_storage(self, checkpoint_dir, ten_meta: _ShardedTensorMetadata):
logger.debug(f'_load_from_array({ten_meta.sharded_tensor_no_data.key}) init')
ret = _load_from_array(
ten_meta.sharded_tensor_no_data,
checkpoint_dir,
load_directly_on_device=False,
apply_flattened_range=False,
)
logger.debug(f'_load_from_array({ten_meta.sharded_tensor_no_data.key}) DONE')
return ret
@timed()
def maybe_init_gloo_group(self):
if not self.cpu_transfer:
return
all_groups = [None] * torch.distributed.get_world_size()
torch.distributed.all_gather_object(all_groups, self.dp_group_ranks)
all_groups = set(tuple(sorted(gr)) for gr in all_groups)
for group_ranks in sorted(all_groups):
gloo_pg = torch.distributed.new_group(ranks=group_ranks, backend='gloo')
if self.global_rank in group_ranks:
self.data_parallel_group = gloo_pg
assert self.dp_group_rank == torch.distributed.get_rank(self.data_parallel_group)
def check_backend_compatibility(self, loaded_version):
pass # TODO
def check_version_compatibility(self, loaded_version):
pass # TODO
@timed()
def _build_load_plan(
self, sharded_state_dict: ShardedStateDict
) -> List[_ShardedTensorMetadata]:
local_meta = [
_ShardedTensorMetadata(
self.global_rank,
sharded_ten.without_data(),
self.dp_group_rank,
self.dp_group_ranks,
)
for sharded_ten in nested_values(sharded_state_dict)
]
all_meta = [None] * torch.distributed.get_world_size(group=self.data_parallel_group)
torch.distributed.all_gather_object(all_meta, local_meta, group=self.data_parallel_group)
all_meta = list(chain.from_iterable(all_meta))
all_tensors_sorted = self.deduplicate_chunks(all_meta)
return all_tensors_sorted
@timed()
def deduplicate_chunks(self, ten_metas: List[_ShardedTensorMetadata]):
""" Group tensors by chunk and then pick the tensor with the lowest rank.
NOTE: with proper loading overlap, loading from randomized ranks
(instead of the smallest one) could be beneficial here.
"""
ten_metas = map_reduce(
ten_metas,
key_fn=lambda meta: sharded_tensor_chunk_id(meta.sharded_tensor_no_data),
reduce_fn=partial(min, key=attrgetter('dist_group_rank')),
)
all_metas_sorted = list(map(itemgetter(1), sorted(ten_metas.items())))
return all_metas_sorted
@timed()
def _exchange_loaded_tensors(
self, ten_metas: List[_ShardedTensorMetadata], sharded_state_dict, checkpoint_dir
):
logger.debug(f'_exchange_loaded_tensors, num ten_metas: {len(ten_metas)}')
for ten_meta in ten_metas:
src_rank = torch.distributed.get_global_rank(
self.data_parallel_group, ten_meta.dist_group_rank
)
if self.dp_group_rank == ten_meta.dist_group_rank:
exchange_tensor = self.load_tensor_from_storage(checkpoint_dir, ten_meta)
if not self.cpu_transfer:
exchange_tensor = exchange_tensor.cuda()
else:
# TODO: for non-flattened ranges we could reuse the buffer from the start here
exchange_tensor = torch.empty(
ten_meta.sharded_tensor_no_data.local_shape,
device='cpu' if self.cpu_transfer else 'cuda',
dtype=ten_meta.sharded_tensor_no_data.dtype,
)
logger.debug(
f'exchange {ten_meta.sharded_tensor_no_data.key}, {exchange_tensor.shape}({exchange_tensor.numel()}), broadcast({src_rank} -> {self.dp_group_ranks})'
)
torch.distributed.broadcast(
exchange_tensor, group=self.data_parallel_group, src=src_rank
)
self._distribute_data_to_state_dict(ten_meta, exchange_tensor, sharded_state_dict)
logger.debug(f'exchange {ten_meta.sharded_tensor_no_data.key} done')
# free buffer memory
exchange_tensor = None
@timed(verbose=False)
def _distribute_data_to_state_dict(
self,
ten_meta: _ShardedTensorMetadata,
loaded_ten: torch.Tensor,
sharded_state_dict: ShardedStateDict,
):
tensor_key = sharded_tensor_chunk_id(ten_meta.sharded_tensor_no_data)
def _fill_in_data(t: Union[ShardedTensor, torch.Tensor]):
if not isinstance(t, ShardedTensor) or sharded_tensor_chunk_id(t) != tensor_key:
# already filled-in or key not matching
return t
sharded_tensor: ShardedTensor = t
x = loaded_ten
if sharded_tensor.flattened_range is not None:
x = flatten_range(sharded_tensor, x)
# Reuse existing buffer
sharded_tensor.data.data.copy_(x)
return sharded_tensor.data
dict_list_map_inplace(_fill_in_data, sharded_state_dict)
|
Megatron-LM-master
|
megatron/core/dist_checkpointing/strategies/two_stage.py
|
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
""" Strategies using Zarr as an underlying format. """
import os
from functools import partial
from pathlib import Path
from typing import List
import numpy as np
import torch
import zarr
from ..core import CheckpointingException
from ..dict_utils import dict_list_map_inplace
from ..mapping import ShardedStateDict, ShardedTensor, is_main_replica
from .base import LoadShardedStrategy, SaveShardedStrategy, StrategyAction, default_strategies
numpy_to_torch_dtype_dict = {
np.bool_: torch.bool,
np.uint8: torch.uint8,
np.int8: torch.int8,
np.int16: torch.int16,
np.int32: torch.int32,
np.int64: torch.int64,
np.float16: torch.float16,
np.float32: torch.float32,
np.float64: torch.float64,
np.complex64: torch.complex64,
np.complex128: torch.complex128,
}
torch_to_numpy_dtype_dict = {v: k for k, v in numpy_to_torch_dtype_dict.items()}
try:
import tensorstore
HAS_BFLOAT16 = True
numpy_to_torch_dtype_dict[np.dtype('bfloat16')] = torch.bfloat16
torch_to_numpy_dtype_dict[torch.bfloat16] = np.dtype('bfloat16')
except ImportError:
HAS_BFLOAT16 = False
_import_trigger = None
class ZarrSaveShardedStrategy(SaveShardedStrategy):
def save(self, sharded_tensors: List[ShardedTensor], checkpoint_dir: Path):
arrays = _create_or_open_zarr_arrays(sharded_tensors, checkpoint_dir)
for ten, arr in zip(sharded_tensors, arrays):
_save_to_existing_array(ten, arr)
torch.distributed.barrier()
def _create_or_open_zarr_arrays(
sharded_tensors: List[ShardedTensor], checkpoint_dir: Path
) -> List[zarr.Array]:
arrays = []
for ten in sharded_tensors:
if _should_create_array(ten):
_create_zarr_array(ten, checkpoint_dir)
# TODO: maybe reuse the opened arrays
torch.distributed.barrier()
for ten in sharded_tensors:
# if is_main_replica(ten.replica_id) and set(ten.global_offset) == {0}:
# continue
open_kwargs = {}
if ten.flattened_range is not None:
open_kwargs['synchronizer'] = zarr.ProcessSynchronizer(
str(checkpoint_dir / f'{ten.key}.sync')
)
arr = zarr.open(checkpoint_dir / ten.key, 'r+', **open_kwargs)
arrays.append(arr)
return arrays
def _should_create_array(ten: ShardedTensor):
return (
is_main_replica(ten.replica_id)
and set(ten.global_offset) == {0}
and (ten.flattened_range is None or ten.flattened_range.start == 0)
)
def _save_to_existing_array(sharded_tensor: ShardedTensor, arr: zarr.Array):
if not is_main_replica(sharded_tensor.replica_id):
return
x = sharded_tensor.data
x = x.detach().cpu()
torch.cuda.synchronize()
if x.dtype == torch.bfloat16:
x = x.float()
x = x.numpy()
x = x.astype('bfloat16')
else:
x = x.numpy()
if sharded_tensor.flattened_range is None:
arr[sharded_tensor.global_slice()] = x
else:
arr.set_coordinate_selection(sharded_tensor.global_coordinates(), x)
def _create_zarr_array(sharded_tensor: ShardedTensor, checkpoint_dir: Path):
np_dtype = torch_to_numpy_dtype_dict[sharded_tensor.dtype]
try:
arr = zarr.create(
sharded_tensor.global_shape,
dtype=np_dtype,
store=checkpoint_dir / sharded_tensor.key,
chunks=sharded_tensor.max_allowed_chunks(),
compressor=None,
fill_value=None,
write_empty_chunks=True,
)
except zarr.errors.ContainsArrayError as e:
raise CheckpointingException(
f'Array {checkpoint_dir / sharded_tensor.key} already exists'
) from e
if HAS_BFLOAT16 and np_dtype == np.dtype('bfloat16'):
arr._dtype = np_dtype
zarray = arr.store['.zarray']
arr.store['.zarray'] = zarray.replace(b'<V2', b'bfloat16')
return arr
class ZarrLoadShardedStrategy(LoadShardedStrategy):
def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path):
dict_list_map_inplace(
partial(_load_from_array, checkpoint_dir=checkpoint_dir), sharded_state_dict
)
return sharded_state_dict
def check_backend_compatibility(self, loaded_version):
pass # TODO
def check_version_compatibility(self, loaded_version):
pass # TODO
def _load_from_array(sharded_tensor: ShardedTensor, checkpoint_dir: Path):
assert isinstance(sharded_tensor, ShardedTensor), type(sharded_tensor)
try:
arr = zarr.open(checkpoint_dir / sharded_tensor.key, 'r')
except zarr.errors.PathNotFoundError as e:
raise CheckpointingException(
f'Array {checkpoint_dir / sharded_tensor.key} not found'
) from e
if not sharded_tensor.allow_shape_mismatch and sharded_tensor.global_shape != arr.shape:
_msg = (
f'Global shape mismatch for loaded ({arr.shape})'
f' and expected ({sharded_tensor.global_shape}) tensor'
f' for key {sharded_tensor.key}'
)
raise CheckpointingException(_msg)
x = arr[sharded_tensor.global_slice()] # flattened tensors loading is delayed
return postprocess_numpy_array(x, sharded_tensor)
def postprocess_numpy_array(loaded_array, sharded_tensor, apply_flattened_range=True):
x = loaded_array
if HAS_BFLOAT16 and x.dtype == np.dtype('bfloat16'):
x = x.astype(np.dtype('float32'))
x = torch.from_numpy(x)
x = x.bfloat16()
else:
x = torch.from_numpy(x)
# TODO: consider some other consistency checks
if x.shape != sharded_tensor.local_shape:
if sharded_tensor.allow_shape_mismatch:
x = pad_to_expected_shape(x, sharded_tensor)
else:
_msg = (
f'Local shape mismatch for loaded ({x.shape})'
f' and expected ({sharded_tensor.local_shape}) tensor'
f' for key {sharded_tensor.key}'
)
raise CheckpointingException(_msg)
if apply_flattened_range and sharded_tensor.flattened_range is not None:
x = flatten_range(sharded_tensor, x)
# TODO: consider cuda() tensors support
return x
def flatten_range(sharded_tensor, x):
return x.flatten()[sharded_tensor.flattened_range]
def pad_to_expected_shape(x: torch.Tensor, expected_sharded_ten: ShardedTensor):
pad_args = []
assert len(x.shape) == len(expected_sharded_ten.local_shape)
# Reversed iteration order because F.pad expects so
for x_sh, exp_sh, axis_fragm in reversed(
list(
zip(x.shape, expected_sharded_ten.local_shape, expected_sharded_ten.axis_fragmentations)
)
):
if x_sh == exp_sh:
pad_args.extend((0, 0))
elif x_sh > exp_sh:
assert (
False
), f'Expected shape ({exp_sh}) smaller than actual ({x_sh}) for {repr(expected_sharded_ten)}'
else:
pad_args.extend((0, exp_sh - x_sh))
# TODO: behavior control with envvar is for testing purposes only, remove it
if not int(os.environ.get('DIST_CKPT_PAD_REPLICATE', 0)):
return torch.nn.functional.pad(x, pad_args)
# unsqueeze and squeeze to get shapes supported by cudnn
print(f'Replicating last row for {expected_sharded_ten.key}')
if x.dtype == torch.bfloat16:
return (
torch.nn.functional.pad(x.float().unsqueeze(0), pad_args, mode='replicate')
.squeeze(0)
.bfloat16()
)
return torch.nn.functional.pad(x.unsqueeze(0), pad_args, mode='replicate').squeeze(0)
# default_strategies[StrategyAction.LOAD_SHARDED.value][('zarr', 1)] = ZarrLoadShardedStrategy()
default_strategies[StrategyAction.SAVE_SHARDED.value][('zarr', 1)] = ZarrSaveShardedStrategy(
'zarr', 1
)
|
Megatron-LM-master
|
megatron/core/dist_checkpointing/strategies/zarr.py
|
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
from abc import ABC, abstractmethod
from collections import defaultdict
from enum import Enum
from pathlib import Path
from typing import Dict, List, Optional
from ..mapping import CheckpointingException, ShardedStateDict, ShardedTensor, StateDict
class StrategyAction(Enum):
LOAD_COMMON = 'load_common'
LOAD_SHARDED = 'load_sharded'
SAVE_COMMON = 'save_common'
SAVE_SHARDED = 'save_sharded'
default_strategies = defaultdict(dict)
def get_default_strategy(action: StrategyAction, backend: str, version: int):
try:
return default_strategies[action.value][(backend, version)]
except KeyError as e:
raise CheckpointingException(
f'Cannot find default strategy for: {(action, backend, version)}'
) from e
class LoadStrategyBase(ABC):
@abstractmethod
def check_backend_compatibility(self, loaded_version):
raise NotImplementedError
@abstractmethod
def check_version_compatibility(self, loaded_version):
raise NotImplementedError
class SaveStrategyBase(ABC):
def __init__(self, backend: str, version: int):
self.backend = backend
self.version = version
class LoadCommonStrategy(LoadStrategyBase):
@abstractmethod
def load(self, checkpoint_dir: Path):
raise NotImplementedError
class LoadShardedStrategy(LoadStrategyBase):
@abstractmethod
def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path):
raise NotImplementedError
class SaveCommonStrategy(SaveStrategyBase):
@abstractmethod
def save(self, common_state_dict: StateDict, checkpoint_dir: Path):
raise NotImplementedError
class SaveShardedStrategy(SaveStrategyBase):
@abstractmethod
def save(self, sharded_tensors: List[ShardedTensor], checkpoint_dir: Path):
raise NotImplementedError
|
Megatron-LM-master
|
megatron/core/dist_checkpointing/strategies/base.py
|
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
""" Strategies using TensorStore to load and save Zarr arrays. """
from functools import partial
from itertools import starmap
from pathlib import Path
import tensorstore as ts
import torch
from ..core import CheckpointingException
from ..dict_utils import dict_list_map_inplace
from ..mapping import ShardedStateDict, ShardedTensor
from .base import LoadShardedStrategy, StrategyAction, default_strategies
from .zarr import postprocess_numpy_array
_import_trigger = None
class TensorStoreLoadShardedStrategy(LoadShardedStrategy):
def __init__(self, load_directly_on_device: bool = False):
super().__init__()
self.load_directly_on_device = load_directly_on_device
def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path):
if torch.distributed.get_rank() == 0:
print(f'Loading distributed checkpoint with {self.__class__.__name__}')
if self.load_directly_on_device:
print(f'Loading distributed checkpoint directly on the GPU')
load_fn = partial(
_load_from_array,
checkpoint_dir=checkpoint_dir,
load_directly_on_device=self.load_directly_on_device,
)
dict_list_map_inplace(load_fn, sharded_state_dict)
return sharded_state_dict
def check_backend_compatibility(self, loaded_version):
pass # TODO
def check_version_compatibility(self, loaded_version):
pass # TODO
def merge_global_slice_with_shape(global_slice, actual_shape, key):
def _merge_slice(dim_slice, dim_size):
if isinstance(dim_slice, slice):
assert (
dim_slice.start < dim_size
), f'Got empty slice for ShardedTensor {key} ({dim_slice}, {dim_size})'
if dim_slice.stop > dim_size:
dim_slice = slice(dim_slice.start, dim_size, dim_slice.step)
return dim_slice
assert len(global_slice) == len(actual_shape), (global_slice, actual_shape, key)
return tuple(starmap(_merge_slice, zip(global_slice, actual_shape)))
def _load_from_array(
sharded_tensor: ShardedTensor,
checkpoint_dir: Path,
load_directly_on_device: bool = False,
apply_flattened_range: bool = True,
):
x = _load_regular_chunk(sharded_tensor, checkpoint_dir)
ten = postprocess_numpy_array(x, sharded_tensor, apply_flattened_range)
if load_directly_on_device:
sharded_tensor.data.data.copy_(ten)
return sharded_tensor.data
else:
return ten
def _load_regular_chunk(sharded_tensor: ShardedTensor, checkpoint_dir: Path):
assert isinstance(sharded_tensor, ShardedTensor), type(sharded_tensor)
spec = {'driver': 'zarr', 'metadata_key': '.zarray', 'kvstore': {}}
spec['kvstore'] = {
'driver': 'file',
'path': str(checkpoint_dir / sharded_tensor.key),
}
try:
arr = ts.open(ts.Spec(spec), open=True).result()
except Exception as e:
raise CheckpointingException(
f'Array {checkpoint_dir / sharded_tensor.key} could not be loaded. Error: {e}'
) from e
if sharded_tensor.global_shape == arr.shape:
x = (
arr[sharded_tensor.global_slice()].read().result()
) # flattened tensors loading is delayed
elif sharded_tensor.allow_shape_mismatch:
global_slice = merge_global_slice_with_shape(
sharded_tensor.global_slice(), arr.shape, sharded_tensor.key
)
x = arr[global_slice].read().result() # flattened tensors loading is delayed
else:
_msg = (
f'Global shape mismatch for loaded ({arr.shape})'
f' and expected ({sharded_tensor.global_shape}) tensor'
f' for key {sharded_tensor.key}'
)
raise CheckpointingException(_msg)
return x
default_strategies[StrategyAction.LOAD_SHARDED.value][
('zarr', 1)
] = TensorStoreLoadShardedStrategy()
|
Megatron-LM-master
|
megatron/core/dist_checkpointing/strategies/tensorstore.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Gradient clipping."""
import os
import torch
from torch import inf
from apex.multi_tensor_apply import multi_tensor_applier
import amp_C
from megatron.model.module import param_is_not_shared
from megatron.core.tensor_parallel import param_is_not_tensor_parallel_duplicate
def clip_grad_norm_fp32(parameters, grads_for_norm,
max_norm, check_for_nan_in_grad,
norm_type=2, model_parallel_group=None):
"""Clips gradient norm of an iterable of parameters whose gradients
are in fp32.
This is adapted from torch.nn.utils.clip_grad.clip_grad_norm_ and
added functionality to handle model parallel parameters. Note that
the gradients are modified in place.
Arguments:
parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
single Tensor that will have gradients normalized
grads_for_norm (Iterable[Tensor]): an iterable of Tensors or a single
Tensor that will be used for calculating the grad norm.
max_norm (float or int): max norm of the gradients.
check_for_nan_in_grad (bool): check if gradients have a NaN.
norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
infinity norm.
model_parallel_group (group): given the nature of the distributed
optimizer, this is passed as an argument.
Returns:
Total norm of the parameters (viewed as a single vector).
"""
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
if isinstance(grads_for_norm, torch.Tensor):
grads_for_norm = [grads_for_norm]
# Grads.
grads = []
for param in parameters:
if param.grad is not None:
assert param.grad.type() == 'torch.cuda.FloatTensor'
grads.append(param.grad.detach())
# Norm parameters.
max_norm = float(max_norm)
norm_type = float(norm_type)
total_norm = 0.0
# Calculate norm.
if norm_type == inf:
total_norm = max(grad.abs().max() for grad in grads_for_norm)
total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
# Take max across all model-parallel GPUs.
torch.distributed.all_reduce(total_norm_cuda,
op=torch.distributed.ReduceOp.MAX,
group=model_parallel_group)
total_norm = total_norm_cuda[0].item()
else:
if norm_type == 2.0:
dummy_overflow_buf = torch.cuda.IntTensor([0])
# Use apex's multi-tensor applier for efficiency reasons.
# Multi-tensor applier takes a function and a list of list
# and performs the operation on that list all in one kernel.
if grads_for_norm:
grad_norm, _ = multi_tensor_applier(
amp_C.multi_tensor_l2norm,
dummy_overflow_buf,
[grads_for_norm],
False # no per-parameter norm
)
else:
grad_norm = torch.cuda.FloatTensor([0])
# Since we will be summing across data parallel groups,
# we need the pow(norm-type).
total_norm = grad_norm ** norm_type
else:
for grad in grads_for_norm:
grad_norm = torch.norm(grad, norm_type)
total_norm += grad_norm ** norm_type
# Check individual rank grad norms are not NaN
# prior to model-parallel all-reduce.
if check_for_nan_in_grad:
global_rank = torch.distributed.get_rank()
assert not total_norm.isnan(), (
f'Rank {global_rank}: found NaN in local grad norm in '
f'backwards pass. Device: {torch.cuda.current_device()}, '
f'node: {os.uname()[1]}'
)
# Sum across all model-parallel GPUs.
torch.distributed.all_reduce(total_norm,
op=torch.distributed.ReduceOp.SUM,
group=model_parallel_group)
total_norm = total_norm.item() ** (1.0 / norm_type)
# Scale.
clip_coeff = max_norm / (total_norm + 1.0e-6)
if clip_coeff < 1.0:
dummy_overflow_buf = torch.cuda.IntTensor([0])
multi_tensor_applier(amp_C.multi_tensor_scale,
dummy_overflow_buf,
[grads, grads],
clip_coeff)
return total_norm
def count_zeros_fp32(parameters, model_parallel_group):
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
# Filter parameters based on:
# - grad should not be none
# - parameter should not be shared
# - should not be a replica due to tensor model parallelism
total_num_zeros = torch.cuda.FloatTensor([0.0])
for param in parameters:
grad_not_none = param.grad is not None
is_not_shared = param_is_not_shared(param)
is_not_tp_duplicate = param_is_not_tensor_parallel_duplicate(param)
if grad_not_none and is_not_shared and is_not_tp_duplicate:
grad = param.grad.detach()
num_zeros = grad.numel() - torch.count_nonzero(grad)
total_num_zeros = num_zeros + total_num_zeros
# Sum across all model-parallel GPUs.
torch.distributed.all_reduce(total_num_zeros,
op=torch.distributed.ReduceOp.SUM,
group=model_parallel_group)
total_num_zeros = total_num_zeros.item()
return total_num_zeros
|
Megatron-LM-master
|
megatron/optimizer/clip_grads.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Megatron grad scaler."""
from abc import ABC
from abc import abstractmethod
import torch
class MegatronGradScaler(ABC):
def __init__(self, initial_scale):
"""Initialize scale value with the input initial scale."""
assert initial_scale > 0.0
self._scale = torch.cuda.FloatTensor([initial_scale])
@property
def scale(self):
return self._scale
@property
def inv_scale(self):
return self._scale.double().reciprocal().float()
@abstractmethod
def update(self, found_inf):
pass
@abstractmethod
def state_dict(self):
pass
@abstractmethod
def load_state_dict(self, state_dict):
pass
class ConstantGradScaler(MegatronGradScaler):
def update(self, found_inf):
pass
def state_dict(self):
return dict()
def load_state_dict(self, state_dict):
pass
class DynamicGradScaler(MegatronGradScaler):
def __init__(self, initial_scale, min_scale,
growth_factor, backoff_factor,
growth_interval, hysteresis):
""""Grad scaler with dynamic scale that gets adjusted
during training."""
super(DynamicGradScaler, self).__init__(initial_scale)
# Lower bound on the scale.
assert min_scale > 0.0
assert min_scale <= initial_scale
self.min_scale = torch.cuda.FloatTensor([min_scale])
# Growth and backoff factors for the scale.
assert growth_factor > 1.0
self.growth_factor = torch.cuda.FloatTensor([growth_factor])
assert backoff_factor < 1.0
assert backoff_factor > 0.0
self.backoff_factor = torch.cuda.FloatTensor([backoff_factor])
# Interval over which if we don't see any inf/nan,
# we will scale the grad scale by the growth factor.
assert growth_interval > 0
self.growth_interval = growth_interval
# Number of inf/nans we should see before scaling down
# the grad scale by the backoff factor.
assert hysteresis > 0
self.hysteresis = hysteresis
# Trackers.
self._growth_tracker = 0
self._hysteresis_tracker = self.hysteresis
def update(self, found_inf):
# If we have an inf/nan, growth tracker is set to 0
# and hysterisis tracker is reduced by 1.
if found_inf:
self._growth_tracker = 0
self._hysteresis_tracker -= 1
# Now if we are out of hysteresis count, scale down the loss.
if self._hysteresis_tracker <= 0:
self._scale = torch.max(self._scale * self.backoff_factor,
self.min_scale)
else:
# If there is no nan/inf, increment the growth tracker.
self._growth_tracker += 1
# If we have had enough consequitive intervals with no nan/inf:
if self._growth_tracker == self.growth_interval:
# Reset the tracker and hysteresis trackers,
self._growth_tracker = 0
self._hysteresis_tracker = self.hysteresis
# and scale up the loss scale.
self._scale = self._scale * self.growth_factor
def state_dict(self):
state_dict = {}
state_dict['scale'] = self._scale
state_dict['growth_tracker'] = self._growth_tracker
state_dict['hysteresis_tracker'] = self._hysteresis_tracker
return state_dict
def load_state_dict(self, state_dict):
self._scale = state_dict['scale'].cuda(torch.cuda.current_device())
self._growth_tracker = state_dict['growth_tracker']
self._hysteresis_tracker = state_dict['hysteresis_tracker']
|
Megatron-LM-master
|
megatron/optimizer/grad_scaler.py
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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
from apex.optimizers import FusedAdam as Adam
from apex.optimizers import FusedSGD as SGD
from megatron import get_args
from .distrib_optimizer import DistributedOptimizer
from .grad_scaler import ConstantGradScaler, DynamicGradScaler
from .optimizer import Float16OptimizerWithFloat16Params, FP32Optimizer
def get_param_groups(modules,
no_weight_decay_cond,
scale_lr_cond,
lr_mult):
"""creates param groups based on weight decay condition (regularized vs non regularized)
and learning rate scale condition (args.lr vs lr_mult * args.lr)
scale_lr_cond is used during finetuning where head of the network requires a scaled
version of the base learning rate.
"""
wd_no_scale_lr = []
wd_scale_lr = []
no_wd_no_scale_lr = []
no_wd_scale_lr = []
for module in modules:
for name, param in module.named_parameters():
if not param.requires_grad:
continue
if no_weight_decay_cond is not None:
no_wd = no_weight_decay_cond(name, param)
else:
# do not regularize biases nor Norm parameters
no_wd = name.endswith(".bias") or len(param.shape) == 1
if scale_lr_cond is not None:
scale_lr = scale_lr_cond(name, param)
else:
scale_lr = False
if not no_wd and not scale_lr:
wd_no_scale_lr.append(param)
elif not no_wd and scale_lr:
wd_scale_lr.append(param)
elif no_wd and not scale_lr:
no_wd_no_scale_lr.append(param)
else:
no_wd_scale_lr.append(param)
param_groups = []
if len(wd_no_scale_lr):
param_groups.append({'params': wd_no_scale_lr, 'wd_mult': 1.0, 'lr_mult': 1.0})
if len(wd_scale_lr):
param_groups.append({'params': wd_scale_lr, 'wd_mult': 1.0, 'lr_mult': lr_mult})
if len(no_wd_no_scale_lr):
param_groups.append({'params': no_wd_no_scale_lr, 'wd_mult': 0.0, 'lr_mult': 1.0})
if len(no_wd_scale_lr):
param_groups.append({'params': no_wd_scale_lr, 'wd_mult': 0.0, 'lr_mult': lr_mult})
return param_groups
def get_megatron_optimizer(model,
no_weight_decay_cond=None,
scale_lr_cond=None,
lr_mult=1.0):
args = get_args()
# Base optimizer.
param_groups = get_param_groups(model,
no_weight_decay_cond,
scale_lr_cond,
lr_mult)
if args.optimizer == 'adam':
optimizer = Adam(param_groups,
lr=args.lr,
weight_decay=args.weight_decay,
betas=(args.adam_beta1, args.adam_beta2),
eps=args.adam_eps)
elif args.optimizer == 'sgd':
optimizer = SGD(param_groups,
lr=args.lr,
weight_decay=args.weight_decay,
momentum=args.sgd_momentum)
else:
raise Exception('{} optimizer is not supported.'.format(
args.optimizer))
# Determine whether the params have main-grad field.
params_have_main_grad = True
# Mixed precision optimizer.
# - Note: both the Float16Optimizer and the DistributedOptimizer inherit
# from the MixedPrecisionOptimizer, which manages any optimizer where
# the model params and main params are distinct.
if args.fp16 or args.bf16 or args.use_distributed_optimizer:
# Grad scaler:
# if loss-scale is provided, instantiate the constant scaler.
# if we are using fp16 and loss-scale is not present, use a
# dynamic scaler.
# otherwise we are running in bf16 with no loss-scale so
# leave it as None.
grad_scaler = None
# Constant loss scale.
if args.loss_scale:
grad_scaler = ConstantGradScaler(args.loss_scale)
# Dynamic loss scale.
else:
if args.fp16:
grad_scaler = DynamicGradScaler(
initial_scale=args.initial_loss_scale,
min_scale=args.min_loss_scale,
growth_factor=2.0,
backoff_factor=0.5,
growth_interval=args.loss_scale_window,
hysteresis=args.hysteresis)
# Megatron optimizer.
opt_ty = DistributedOptimizer \
if args.use_distributed_optimizer else \
Float16OptimizerWithFloat16Params
return opt_ty(optimizer,
args.clip_grad,
args.log_num_zeros_in_grad,
args.check_for_nan_in_loss_and_grad,
params_have_main_grad,
args.fp16,
args.bf16,
args.params_dtype,
grad_scaler,
model)
# FP32.
return FP32Optimizer(optimizer, args.clip_grad,
args.log_num_zeros_in_grad,
args.check_for_nan_in_loss_and_grad,
params_have_main_grad,
model)
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Megatron-LM-master
|
megatron/optimizer/__init__.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
"""Megatron distributed optimizer."""
from apex.optimizers import FusedAdam as Adam
import math
import torch
from megatron import get_args
from megatron import get_timers
from megatron import print_rank_0
from megatron.core import mpu, tensor_parallel
from megatron.model.module import param_is_not_shared
from .optimizer import MixedPrecisionOptimizer, _zero_grad_group_helper
class Range:
"""
A range represents a start and end points for indexing a shard
from a full tensor.
"""
def __init__(self, start, end):
self.start = start
self.end = end
self.size = end - start
def normalize(self, start = 0):
return Range(start, start + self.size)
def __str__(self):
return "%d,%d [%d]" % (self.start, self.end, self.size)
def __len__(self):
return self.end - self.start
class DistributedOptimizer(MixedPrecisionOptimizer):
"""Distributed optimizer, for all data types (fp16, bf16, and fp32).
Arguments:
optimizer: base optimizer such as Adam or SGD
clip_grad: clip gradeints with this global L2 norm. Note
that clipping is ignored if clip_grad == 0
log_num_zeros_in_grad: return number of zeros in the gradients.
check_for_nan_in_grad: check if gradients have a NaN.
params_have_main_grad: flag indicating if parameters have
a `main_grad` field. If this is set, we are assuming
that the model parameters are store in the `main_grad`
field instead of the typical `grad` field. This happens
for the DDP cases where there is a continuous buffer
holding the gradients. For example for bfloat16, we want
to do gradient accumulation and all-reduces in float32
and as a result we store those gradients in the main_grad.
Note that main grad is not necessarily in float32.
fp16: if true, the model is running in fp16.
bf16: if true, the model is running in bfloat16.
grad_scaler: used for scaling gradients. Note that this can be
None. This case happens when `bf16 = True` and we don't
use any loss scale. Note that for `bf16 = True`, we can have
a constnat gradient scaler. Also for `bf16 = False`, we
always require a grad scaler.
models: list of models (i.e., the virtual pipelining models). This
is used by the distributed optimizer for mapping parameters.
"""
@classmethod
def build_model_gbuf_param_range_map(cls, model, dtype, gbuf_world_range):
"""
Build mapping from param reference to grad buffer shard ranges.
This method builds a mapping from parameter references to grad
buffer shard ranges, specific to each data-parallel (DP) rank's
set of 'owned' parameters. Each grad buffer (padded to be an even
multiple of DP-world-size) is conceptually divided into DP-world-size
contiguous regions, where each DP rank 'owns' a contiguous regions.
Ownership in this sense means DP rank is responsible for reducing
the relevant subset of grads, and updating the relevant subset of
params.
This conceptual partitioning of the grad buffer does NOT respect
parameter boundaries, and as such it is assumed that each created
range references a shard (or subset) of the full parameter. It is
easiest to think of each DP rank as operating (i.e., reducing,
gathering) purely on views into the grad buffer, for all model-to-
main & main-to-model operations.
This method creates three ranges:
- The param's range within the entire grad buffer (i.e., world index).
- The param's range within the DP rank's local view of the grad buffer.
- The param's range within itself (i.e., its shard).
"""
# Param range map.
param_world_index_map = model.grad_buffer_param_index_map[dtype]
param_range_map = {}
for param, param_world_indexes in param_world_index_map.items():
# Param range.
param_world_start, param_world_end = param_world_indexes
param_local_start = max(
0,
param_world_start - gbuf_world_range.start)
param_local_end = min(
gbuf_world_range.size,
param_world_end - gbuf_world_range.start)
# Add param, if within local gbuf range.
if param_local_end > param_local_start:
param_local_range = Range(param_local_start, param_local_end)
param_world_range = param_local_range.normalize(
param_local_start + gbuf_world_range.start)
sub_param_start = max(0, gbuf_world_range.start-param_world_start)
sub_param_range = param_local_range.normalize(sub_param_start)
param_range_map[param] = {
"gbuf_world" : param_world_range,
"gbuf_local" : param_local_range,
"param" : sub_param_range,
}
return param_range_map
@classmethod
def build_model_gbuf_range(cls, model, dtype):
"""
Build mapping between params and their grad buffers.
This method does the initial setup for the method above. This setup
includes determining the shard ranges into the DDP's grad buffer for
each data-parallel (DP) rank. Each DP rank keeps range info for
all other DP ranks, for the purpose of creating args for
reduce-scatter and all-gather.
"""
data_parallel_rank = mpu.get_data_parallel_rank()
data_parallel_world_size = mpu.get_data_parallel_world_size()
# Grad buffer range.
grad_buffer = model.grad_buffers[dtype]
gbuf_size = grad_buffer.numel
max_gbuf_range_size = int(math.ceil(gbuf_size / data_parallel_world_size))
# All world ranges. (i.e., across all data parallel ranks)
gbuf_world_all_ranges = []
for r in range(data_parallel_world_size):
gbuf_world_start = r * max_gbuf_range_size
gbuf_world_end = min(gbuf_size, gbuf_world_start+max_gbuf_range_size)
gbuf_world_range = Range(gbuf_world_start, gbuf_world_end)
gbuf_world_all_ranges.append(gbuf_world_range)
# Local DP's ranges.
gbuf_world_range = gbuf_world_all_ranges[data_parallel_rank]
gbuf_local_range = gbuf_world_range.normalize()
# Get each param's ranges.
param_range_map = cls.build_model_gbuf_param_range_map(model,
dtype,
gbuf_world_range)
# Group into dict.
data = {
"local" : gbuf_local_range,
"world" : gbuf_world_range,
"world_all" : gbuf_world_all_ranges,
"param_map" : param_range_map,
"max_range_size" : max_gbuf_range_size,
}
return data
@classmethod
def build_model_gbuf_range_map(cls, model):
"""
Create param-to-grad-buffer mappings, for grad buffer data types
within a specific virtual model.
"""
return {
dtype : cls.build_model_gbuf_range(model, dtype)
for dtype in model.grad_buffers
}
@classmethod
def build_model_param_gbuf_map(cls, model_gbuf_ranges):
"""
Create a reverse of the model_gbuf_ranges, for referencing in
opposite direction.
"""
param_gbuf_map = {}
for model_index, model_gbuf_range_map in enumerate(model_gbuf_ranges):
for dtype, gbuf_range_map in model_gbuf_range_map.items():
for param, param_range_map in gbuf_range_map["param_map"].items():
param_gbuf_map[param] = (model_index, dtype)
return param_gbuf_map
@classmethod
def build_optimizer_group_ranges(cls, param_groups, model_gbuf_ranges):
"""
Create optimizer groups.
Given the set of parameter shard ranges that are owned by the current
data-parallel (DP) rank, gather the set of parameters that will be
used (in the method below) to create the current DP's optimizer
groups.
"""
num_groups = len(param_groups)
# Param group map.
# World param group map.
# - Store a mapping of <model_parameter:group_index> for all parameters
# across all DP ranks. This is necessary because it is our first
# cross reference between the DDP mappings and the optimizer group
# parameters. This mapping only for use in the next step of building
# the local mapping over this DP rank's parameters.
world_param_group_map = {}
for group_index, group in enumerate(param_groups):
for param in group["params"]:
assert param.requires_grad
world_param_group_map[param] = group_index
# Optimizer group ranges & param-group mapping.
# - Build a mapping from groups to their contained parameters, and also
# from parameters to their containing group index and order within
# the group. The group index and order are particularly important for
# saving and loading checkpoints.
local_param_group_map = {}
group_ranges = [ {"params": []} for _ in param_groups ]
for model_gbuf_range_map in model_gbuf_ranges:
for dtype, gbuf_range_map in model_gbuf_range_map.items():
for param in gbuf_range_map["param_map"]:
group_index = world_param_group_map[param]
group_range = group_ranges[group_index]
group_range["params"].append(param)
local_param_group_map[param] = \
(group_index, len(group_range["params"]) - 1)
# Squeeze zero-size group ranges.
for group_index, group_range in enumerate(group_ranges):
group_range["orig_group"] = param_groups[group_index]
group_range["orig_group_idx"] = param_groups[group_index]
return local_param_group_map, group_ranges
@classmethod
def build_model_and_main_param_groups(cls,
model_gbuf_ranges,
param_gbuf_map,
opt_group_ranges):
"""
Create main parameter groups needed for the optimizer step.
These groups encompass both: 1) groups used by this class, for
reducing/gather, and 2) groups used by the inner optimizer for the
parameter update. Given that the conceptual grad buffer partitioning
(created in earlier method) doesn't respect parameter boundaries,
the optimizer operates on shards of the model parameters, rather than
the full parameters.
"""
# Parameter groups:
# model_float16_groups: original float16 parameters
# model_fp32_groups: original fp32 parameters
# shard_float16_groups: shards of original float16 parameters
# shard_fp32_groups: shards of original fp32 parameters
# shard_fp32_from_float16_groups: fp32 copy of float16 parameters
model_float16_groups = []
model_fp32_groups = []
shard_float16_groups = []
shard_fp32_groups = []
shard_fp32_from_float16_groups = []
# Allocate (or slice) each group's param shard.
for group_index, group_range in enumerate(opt_group_ranges):
# Params of this group.
model_float16_params_this_group = []
model_fp32_params_this_group = []
shard_float16_params_this_group = []
shard_fp32_params_this_group = []
shard_fp32_from_float16_params_this_group = []
model_float16_groups.append(model_float16_params_this_group)
model_fp32_groups.append(model_fp32_params_this_group)
shard_float16_groups.append(shard_float16_params_this_group)
shard_fp32_groups.append(shard_fp32_params_this_group)
shard_fp32_from_float16_groups.append(
shard_fp32_from_float16_params_this_group)
for model_param in group_range["params"]:
assert model_param.requires_grad
model_index, dtype = param_gbuf_map[model_param]
gbuf_range = model_gbuf_ranges[model_index][dtype]
param_range = gbuf_range["param_map"][model_param]["param"]
# fp16, bf16 params.
if model_param.type() in ['torch.cuda.HalfTensor',
'torch.cuda.BFloat16Tensor']:
# Clone model -> main.
shard_model_param = model_param.detach().view(-1) \
[param_range.start:param_range.end]
shard_main_param = shard_model_param.clone().float()
tensor_parallel.copy_tensor_model_parallel_attributes(
shard_model_param, model_param)
tensor_parallel.copy_tensor_model_parallel_attributes(
shard_main_param, model_param)
if hasattr(model_param, 'shared'):
shard_model_param.shared = model_param.shared
shard_main_param.shared = model_param.shared
# Add to group.
model_float16_params_this_group.append(model_param)
shard_float16_params_this_group.append(shard_model_param)
shard_fp32_from_float16_params_this_group.append(shard_main_param)
# fp32 params.
elif model_param.type() == 'torch.cuda.FloatTensor':
shard_model_param = model_param.view(-1) \
[param_range.start:param_range.end]
model_fp32_params_this_group.append(model_param)
shard_fp32_params_this_group.append(shard_model_param)
tensor_parallel.copy_tensor_model_parallel_attributes(
shard_model_param, model_param)
if hasattr(model_param, 'shared'):
shard_model_param.shared = model_param.shared
else:
raise TypeError('Wrapped parameters must be one of '
'torch.cuda.FloatTensor, '
'torch.cuda.HalfTensor, or '
'torch.cuda.BFloat16Tensor. '
'Received {}'.format(model_param.type()))
# Update optimizer's params.
group_range["orig_group"]["params"] = [
*shard_fp32_params_this_group,
*shard_fp32_from_float16_params_this_group,
]
return (
model_float16_groups,
model_fp32_groups,
shard_float16_groups,
shard_fp32_groups,
shard_fp32_from_float16_groups,
)
def __init__(self, optimizer, clip_grad, log_num_zeros_in_grad,
check_for_nan_in_grad, params_have_main_grad, fp16,
bf16, params_dtype, grad_scaler, models):
"""
See top of class definition for argument descriptions.
The steps in this method create the core mapping between DDP grad
buffers, parameters, and parameter shard ranges, that is needed for
converting between model param indexes and main parameter shard
indexes. This method also updates the optimizer parameter groups
with the newly created shards.
"""
super().__init__(
optimizer, clip_grad, log_num_zeros_in_grad,
check_for_nan_in_grad, params_have_main_grad,
fp16, bf16, params_dtype, grad_scaler, models)
assert isinstance(optimizer, Adam), \
"Only Adam currently supported, due to checkpointing requirements."
# Model grad buffer ranges.
self.model_gbuf_ranges = []
for model_index, model in enumerate(self.models):
self.model_gbuf_ranges.append(self.build_model_gbuf_range_map(model))
self.model_param_gbuf_map = \
self.build_model_param_gbuf_map(self.model_gbuf_ranges)
# Optimizer ranges.
self.model_param_group_index_map, self.opt_group_ranges = \
self.build_optimizer_group_ranges(self.optimizer.param_groups,
self.model_gbuf_ranges)
# Allocate main param shards.
(
self.model_float16_groups,
self.model_fp32_groups,
self.shard_float16_groups,
self.shard_fp32_groups,
self.shard_fp32_from_float16_groups,
) = self.build_model_and_main_param_groups(self.model_gbuf_ranges,
self.model_param_gbuf_map,
self.opt_group_ranges)
# Initialize param buffers.
# - These are views on the DDP model's grad buffers, that share
# storage & have their own dtype. This is safe because the param
# dtype size is always <= grad dtype size.
self.param_buffers = []
for model_index, model in enumerate(self.models):
current_param_buffers = {}
for dtype, grad_buffer in model.grad_buffers.items():
# Handle older/newer method for getting untyped storage.
try:
storage = grad_buffer.data.storage()._untyped()
except:
storage = grad_buffer.data.storage().untyped()
# Typed param buffer.
param_buffer = torch.tensor(
storage,
dtype = params_dtype,
device = grad_buffer.data.device)
param_buffer = param_buffer[:grad_buffer.numel_padded]
current_param_buffers[dtype] = param_buffer
self.param_buffers.append(current_param_buffers)
# Update optimizer groups.
# - Also, leverage state_dict() and load_state_dict() to
# recast preexisting per-param state tensors.
self.optimizer.param_groups = \
[ g["orig_group"] for g in self.opt_group_ranges ]
self.optimizer.load_state_dict(self.optimizer.state_dict())
def get_model_param_range_map(self, param):
"""
Given a model param, get the index sub-range of the param that this
data-parallel rank owns.
"""
model_index, dtype = self.model_param_gbuf_map[param]
gbuf_range_map = self.model_gbuf_ranges[model_index][dtype]
param_range_map = gbuf_range_map["param_map"][param]
return param_range_map
def get_model_parallel_group(self):
"""
With the distributed optimizer, the model parallel group is the
entire world.
"""
return None
def state_dict(self):
"""
The state dict contains all non-DP-rank-dependent (i.e., non-parameter-
related) optimizer variables. The returned state dict can be stored in
the standard model/RNG checkpoint file. The parameter and dependent
optimizer state (e.g., exp_avg, exp_avg_sq) are stored in a separate
checkpoint file by calling 'save_parameter_state()'.
"""
state_dict = {}
# Optimizer state (do not store parameter state here).
state_dict['optimizer'] = {
k : v
for k, v in self.optimizer.state_dict().items()
if k != "state"
}
for param_group in state_dict["optimizer"]["param_groups"]:
del param_group["params"]
# Grad scaler state.
if self.grad_scaler:
state_dict['grad_scaler'] = self.grad_scaler.state_dict()
return state_dict
def load_state_dict(self, state_dict):
"""Load the state dict.
As detailed in state_dict(), the state dict contains all non-
parameter-related variables. This method is notably longer than
state_dict(), because the Torch optimizers state has yet to be
allocated at this point, and so we must do a cross referencing between
the optimizers state (and the ordering it expects for parameter state)
and this DP rank's shards. The optimizer at this point does not contain
any tensor dimension information, so we must get these dimensions from
the DP shards mapped during DistributedOptimizer.__init__().
The tensor parameter state is loaded via load_parameter_state(), and
so this method also must populate the loaded state dict with dummy
tensor data (i.e., via torch.empty() below). This will be overwritten
during load_parameter_state().
** Note: Torch optimizer's state structure. **
The Torch optimizer stores its state in two levels. The top level is a
list of groups, where each group contains a list of integer indexes
(corresponding to parameters) that index into a master parameter list
that is shared by all groups. As such, three values are necessary for
maintaining this ordering:
- group_index : The group to which a parameter belongs.
- group_order : The index of a parameter within its group.
- state_order : The index of a parameter within the shared parameter
list.
"""
# Get the Torch optimizer's state dict.
# - This 'inner' optimizer at this point is unallocated, and only
# contains an integer odering of parameters within each group, and
# the ordering of parameters within its flattened parameter state
# list.
inner_state_dict = self.optimizer.state_dict()
state_dict_param_groups = [{
**group,
"params" : list(inner_state_dict["param_groups"][idx]["params"]),
} for idx, group in enumerate(state_dict["optimizer"]["param_groups"])]
# Allocate 'dummy' data for optimizer state (i.e., torch.empty() below)
# - Real data is overwritten during load_parameter_state().
state_dict_state = []
for gbuf_range_maps in self.model_gbuf_ranges:
for gbuf_range_map in gbuf_range_maps.values():
for model_param, param_range_map in \
gbuf_range_map["param_map"].items():
# Get parameter ordering information (see method docstring
# for details).
group_index, group_order = \
self.model_param_group_index_map[model_param]
state_order = inner_state_dict["param_groups"] \
[group_index]["params"][group_order]
# Allocate dummy tensors.
numel = len(param_range_map["gbuf_world"])
init_shard = lambda : torch.empty(
(numel,),
dtype=torch.float32,
device=torch.cuda.current_device())
state_dict_state.append((state_order, {
"exp_avg" : init_shard(),
"exp_avg_sq" : init_shard(),
}))
# Sort by state order (see method docstring for details).
state_dict_state.sort(key = lambda s : s[0])
state_dict_state = {s[0]:s[1] for s in state_dict_state}
# Optimizer.
self.optimizer.load_state_dict({
"state" : state_dict_state,
"param_groups" : state_dict_param_groups,
})
# Grad scaler.
if 'grad_scaler' not in state_dict:
if self.fp16:
print_rank_0('***WARNING*** found an old checkpoint, will not '
'load grad scaler ...')
else:
if self.grad_scaler:
self.grad_scaler.load_state_dict(state_dict['grad_scaler'])
else:
print_rank_0('***WARNING*** fould the grad scaler in the '
'checkpoint but it is None in the class. '
'Skipping loading grad scaler ...')
def save_parameter_state(self, filename):
"""Save parameter state (i.e., parameter & optimizer tensors).
This method performs three steps:
- For each DP rank, copy param & optimizer shards to contiguous CPU
buffers. (e.g., one buffer each for main_param, exp_avg, and
exp_avg_sq).
- Gather contiguous buffers on DP rank 0 and concatenate to world
buffers.
- Save world buffers to disk (i.e., distrib_opt.pt).
"""
# Data parallelism variables.
data_parallel_world_size = mpu.get_data_parallel_world_size()
data_parallel_rank = mpu.get_data_parallel_rank()
data_parallel_group_gloo = mpu.get_data_parallel_group_gloo()
data_parallel_global_ranks = list(mpu._DATA_PARALLEL_GLOBAL_RANKS)
# Collect param states.
state = {}
for model_idx, gbuf_range_maps in enumerate(self.model_gbuf_ranges):
# Iterate grad buffers (by data type).
dtype_state = {}
assert len(gbuf_range_maps) == 1, "single dtype supported, for now."
for dtype, gbuf_range_map in gbuf_range_maps.items():
# Compute local DP contiguous shard's size.
model = self.models[model_idx]
gbuf_world_numel = model.grad_buffers[dtype].numel_padded
gbuf_local_numel = int(gbuf_world_numel/data_parallel_world_size)
local_shards = {key:torch.empty((gbuf_local_numel,),
dtype=torch.float32,
device="cpu")
for key in ("param", "exp_avg", "exp_avg_sq")}
# Build contiguous DP rank shards (for param + optim states).
for model_param, param_range_map in \
gbuf_range_map["param_map"].items():
# Main param & optimizer states.
group_index, group_order = \
self.model_param_group_index_map[model_param]
main_param = self.optimizer.param_groups \
[group_index]["params"][group_order]
optim_state = self.optimizer.state[main_param]
tensors = {
"param" : main_param,
**optim_state,
}
# Copy states into contiguous shard.
gbuf_local_start = param_range_map["gbuf_local"].start
gbuf_local_end = param_range_map["gbuf_local"].end
for key in local_shards:
local_shards[key][gbuf_local_start:gbuf_local_end] \
.data.copy_(tensors[key].detach().cpu())
# Gather contiguous shards on DP rank 0.
world_tensors = {}
for key, send_tensor in local_shards.items():
# Gather tensor list.
if data_parallel_rank == 0:
recv_tensors = [torch.empty((gbuf_local_numel,),
dtype=torch.float32,
device="cpu")
for _ in range(data_parallel_world_size)]
else:
recv_tensors = None
# Gather.
torch.distributed.gather(
send_tensor,
recv_tensors,
data_parallel_global_ranks[0],
data_parallel_group_gloo,
)
# Concatenate.
if data_parallel_rank == 0:
world_tensors[key] = torch.cat(recv_tensors)
# Collect world state.
dtype_state[dtype] = world_tensors
state[model_idx] = dtype_state
# Save param state.
if data_parallel_rank == 0:
torch.save(state, filename)
def load_parameter_state(self, filename):
"""Load parameter state (i.e., parameter & optimizer tensors).
This method performs the reverse of save_parameter_state():
- Load world buffers from disk (i.e., distrib_opt.pt).
- Scatter contiguous buffers from DP rank 0 to each DP rank (each DP
rank receives its relevant subset of the world buffers).
- For each DP rank, copy param & optimizer shards from contiguous CPU
buffers. (e.g., one buffer each for main_param, exp_avg, and
exp_avg_sq).
"""
# Data parallelism variables.
data_parallel_world_size = mpu.get_data_parallel_world_size()
data_parallel_rank = mpu.get_data_parallel_rank()
data_parallel_group_gloo = mpu.get_data_parallel_group_gloo()
data_parallel_global_ranks = list(mpu._DATA_PARALLEL_GLOBAL_RANKS)
# Load on DP rank 0.
if data_parallel_rank == 0:
loaded_state = torch.load(filename)
# Scatter tensors to all DP ranks.
for model_idx, gbuf_range_maps in enumerate(self.model_gbuf_ranges):
for dtype, gbuf_range_map in gbuf_range_maps.items():
# Compute local DP contiguous shard's size.
model = self.models[model_idx]
gbuf_world_numel = model.grad_buffers[dtype].numel_padded
gbuf_local_numel = int(gbuf_world_numel/data_parallel_world_size)
# Contiguous local shards (received from DP rank 0).
local_shards = {key:torch.empty((gbuf_local_numel,),
dtype=torch.float32,
device="cpu")
for key in ("param", "exp_avg", "exp_avg_sq")}
# Scatter local shards from DP rank 0.
for key, recv_tensor in local_shards.items():
# Scatter tensor list.
if data_parallel_rank == 0:
world_tensor = loaded_state[model_idx][dtype][key]
gbuf_start_idxs = \
list(range(0, gbuf_world_numel, gbuf_local_numel))
send_tensors = [world_tensor[i:(i+gbuf_local_numel)]
for i in gbuf_start_idxs]
else:
send_tensors = None
# Scatter.
torch.distributed.scatter(
recv_tensor,
send_tensors,
data_parallel_global_ranks[0],
data_parallel_group_gloo,
)
# Copy local contiguous shards to param/optim shards.
for model_param, param_range_map in \
gbuf_range_map["param_map"].items():
# Main param & optimizer states.
group_index, group_order = \
self.model_param_group_index_map[model_param]
main_param = self.optimizer.param_groups \
[group_index]["params"][group_order]
optim_state = self.optimizer.state[main_param]
tensors = {
"param" : main_param,
**optim_state,
}
# Copy states into contiguous shard.
gbuf_local_start = param_range_map["gbuf_local"].start
gbuf_local_end = param_range_map["gbuf_local"].end
for key in local_shards:
tensors[key].data.copy_(
local_shards[key][gbuf_local_start:gbuf_local_end])
def zero_grad(self, set_to_none=True):
"""
Zero grads.
We only need to zero the model related parameters, i.e.,
model_float16_groups & model_fp32_groups. We additionally zero
the remaining groups as a memory optimization to reduce
fragmentation; in the case of set_to_none==True, the space
used by this field can be safely deallocated at this point.
"""
for groups in (
self.model_float16_groups,
self.model_fp32_groups,
self.shard_float16_groups, # grad empty/unused here?
self.shard_fp32_groups, # throws grad-access warning
self.shard_fp32_from_float16_groups):
for group in groups:
_zero_grad_group_helper(group, set_to_none)
@staticmethod
def get_model_buffer_dp_views(model_buffers):
"""
Get shard views of each of the DDP's param/grad buffers.
In this nested list, the top level is grouped by the virtual model
index and the buffer's data type. The sub-level is a list of
shards of that buffer, where each shard in the list represents
a contiguous view of the buffer, that is owned by a data-parallel
rank. The shard boundary does not respect parameter boundaries, and
so the elements of some parameters are split across data parallel
ranks.
Additionally, return references to the entire buffers, for use
in _reduce_scatter_base and _all_gather_base.
"""
data_parallel_world_size = mpu.get_data_parallel_world_size()
# Buffer views.
view_items = []
for model_index, buffers in enumerate(model_buffers):
for dtype, buf in buffers.items():
assert buf.numel() % data_parallel_world_size == 0
shard_size = int(buf.numel() / data_parallel_world_size)
buf_views = [buf[(r*shard_size):((r+1)*shard_size)]
for r in range(data_parallel_world_size)]
view_items.append((model_index, dtype, buf, buf_views))
return view_items
def get_model_grad_buffer_dp_views(self):
return self.get_model_buffer_dp_views([
{dtype : mem_buffer.data}
for model in self.models
for dtype, mem_buffer in model.grad_buffers.items()])
def get_model_param_buffer_dp_views(self):
return self.get_model_buffer_dp_views(self.param_buffers)
def reduce_model_grads(self, args, timers):
"""
Reduce-scatter model grads.
The DDP's grad buffer is used for the reduce-scatter, and thus no
tensors are dynamically allocated.
Note: this is a different order of reduction, versus the non-
distributed optimizer, which reduces: 1) layernorm grads, 2) all
grads, 3) embedding grads.
"""
# All-reduce layer-norm grads (for sequence parallelism).
timers('layernorm-grads-all-reduce', log_level=1).start(
barrier=args.barrier_with_L1_time)
self.allreduce_layernorm_grads(args)
timers('layernorm-grads-all-reduce').stop()
# All-reduce embedding grads.
timers('embedding-grads-all-reduce', log_level=1).start(
barrier=args.barrier_with_L1_time)
self.allreduce_embedding_grads(args)
timers('embedding-grads-all-reduce').stop()
# Reduce-scatter setup.
timers('grads-reduce-scatter', log_level=1).start(
barrier=args.barrier_with_L1_time)
data_parallel_rank = mpu.get_data_parallel_rank()
data_parallel_world_size = mpu.get_data_parallel_world_size()
data_parallel_group = mpu.get_data_parallel_group()
# Scale grad buffers by '1 / data_parallel_world_size'.
for model in self.models:
for dtype, gbuf in model.grad_buffers.items():
gbuf.data /= data_parallel_world_size
# Reduce-scatter all grads.
gbuf_view_items = self.get_model_grad_buffer_dp_views()
for index, (model_index, dtype, gbuf, gbuf_views) \
in enumerate(gbuf_view_items):
torch.distributed._reduce_scatter_base(
gbuf_views[data_parallel_rank],
gbuf,
group = data_parallel_group,
)
timers('grads-reduce-scatter').stop()
def gather_model_params(self, args, timers):
"""
All-gather updated model params.
The DDP's param buffer is used for the all-gather, and thus no
tensors are dynamically allocated. After the all-gather, the params
can be copied from the param buffer to the param.
"""
timers('params-all-gather', log_level=1).start(
barrier=args.barrier_with_L1_time)
data_parallel_rank = mpu.get_data_parallel_rank()
data_parallel_group = mpu.get_data_parallel_group()
# All-gather updated main params.
# - All param buffer views are guaranteed to have the same num elements
# across all data parallel ranks, due to grad buffer padding that is
# done in distributed.py, and extended to the param buffers. Thus,
# all sub-views will have consistent start/end indexes across data
# parallel ranks.
pbuf_view_items = self.get_model_param_buffer_dp_views()
for index, (model_index, dtype, pbuf, pbuf_views) \
in enumerate(pbuf_view_items):
torch.distributed._all_gather_base(
pbuf,
pbuf_views[data_parallel_rank],
group = data_parallel_group,
)
# Copy from param buffer to each param.
for model_id, model in enumerate(self.models):
for dtype, param_map in model.grad_buffer_param_index_map.items():
for param, (buf_start, buf_end) in param_map.items():
param_buf = self.param_buffers[model_id][dtype]
param_buf_shard = param_buf[buf_start:buf_end]
param.view(-1).detach().copy_(param_buf_shard)
timers('params-all-gather').stop()
def _collect_main_grad_data_for_unscaling(self):
"""
Note: this should be equivalent to the float-16 optimizer's method,
but writtent differently, so the two should be combined.
"""
return [
param.grad.data
for group in self.optimizer.param_groups
for param in group["params"]
]
def _get_model_and_main_params_data_float16(self):
"""
Get aligned list of model and main params.
"""
model_data = []
main_data = []
for model_group, main_group in zip(self.shard_float16_groups,
self.shard_fp32_from_float16_groups):
for model_param, main_param in zip(model_group, main_group):
model_data.append(model_param.data)
main_data.append(main_param.data)
return model_data, main_data
def _copy_model_grads_to_main_grads(self):
"""
Copy model grads to main grads.
Since this step follows a reduce-scatter through the DDP's grad
buffer, this method is responsible for copying the updated grads
from the grad buffer to the main shard's grad field.
"""
# Utility method for copying group grads.
def copy_group_grads(model_groups, shard_main_groups):
for model_group, shard_main_group in zip(model_groups,
shard_main_groups):
for model_param, shard_main_param in zip(model_group,
shard_main_group):
param_range_map = self.get_model_param_range_map(model_param)
param_range = param_range_map["param"]
assert param_range.size == shard_main_param.nelement()
model_grad = model_param.main_grad
shard_model_grad = model_grad.view(-1) \
[param_range.start:param_range.end]
shard_main_param.grad = shard_model_grad.float()
# Copy model groups to shard groups.
copy_group_grads(self.model_float16_groups,
self.shard_fp32_from_float16_groups)
copy_group_grads(self.model_fp32_groups,
self.shard_fp32_groups)
def _copy_main_params_to_model_params(self):
"""
Copy main params to model params.
Since this step is followed by an all-gather through the DDP's grad
buffer, this method is responsible for copying the updated params
from the main shards into the correct position in the grad buffer.
"""
# Utility method for copying group params.
def copy_group_params(shard_main_groups, model_groups):
for shard_main_group, model_group in zip(shard_main_groups,
model_groups):
for shard_main_param, model_param in zip(shard_main_group,
model_group):
param_range_map = self.get_model_param_range_map(model_param)
world_range = param_range_map["gbuf_world"]
assert world_range.size == shard_main_param.nelement()
model_id, dtype = self.model_param_gbuf_map[model_param]
model_param_buffer = self.param_buffers[model_id][dtype]
shard_model_param = model_param_buffer.view(-1) \
[world_range.start:world_range.end]
shard_model_param.data.copy_(shard_main_param)
# Copy shard groups to model groups.
copy_group_params(self.shard_fp32_from_float16_groups,
self.model_float16_groups)
copy_group_params(self.shard_fp32_groups,
self.model_fp32_groups)
def _copy_model_params_to_main_params(self):
"""
Copy model params to main params.
During finetuning, this method is used to reload the main params from
the model params. This copy does not make use of the grad buffer as
an intermediary.
"""
# Utility method for copying group params.
def copy_group_params(model_groups, shard_main_groups):
for model_group, shard_main_group in zip(model_groups,
shard_main_groups):
for model_param, shard_main_param in zip(model_group,
shard_main_group):
param_range_map = self.get_model_param_range_map(model_param)
param_range = param_range_map["param"]
assert param_range.size == shard_main_param.nelement()
shard_model_param = model_param.view(-1) \
[param_range.start:param_range.end]
shard_main_param.data.copy_(shard_model_param)
# Copy model groups to shard groups.
copy_group_params(self.model_float16_groups,
self.shard_fp32_from_float16_groups)
copy_group_params(self.model_fp32_groups,
self.shard_fp32_groups)
|
Megatron-LM-master
|
megatron/optimizer/distrib_optimizer.py
|
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
"""Megatron optimizer."""
from abc import ABC
from abc import abstractmethod
from apex.multi_tensor_apply import multi_tensor_applier
import amp_C
import torch
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
from megatron import get_timers
from megatron import print_rank_0
from megatron.core import mpu, tensor_parallel
from megatron.model import Float16Module
from megatron.model.module import param_is_not_shared
from megatron.utils import unwrap_model
from .clip_grads import clip_grad_norm_fp32, count_zeros_fp32
def _zero_grad_group_helper(group, set_to_none):
"""Zero out the gradient for a group of parameters.
Note: copied from torch.optim.optimizer."""
for param in group:
if param.grad is not None:
if set_to_none:
param.grad = None
else:
if param.grad.grad_fn is not None:
param.grad.detach_()
else:
param.grad.requires_grad_(False)
param.grad.zero_()
def _multi_tensor_copy_this_to_that(this, that, overflow_buf=None):
"""Use multi-tensor-applier to copy values from one list to another.
We don't have a blfoat16 implementation so for now if the overflow_buf
is not provided, we default back to simple loop copy to be compatible
with bfloat16."""
if overflow_buf:
overflow_buf.fill_(0)
# Scaling with factor `1.0` is equivalent to copy.
multi_tensor_applier(amp_C.multi_tensor_scale,
overflow_buf,
[this, that],
1.0)
else:
for this_, that_ in zip(this, that):
that_.copy_(this_)
class MegatronOptimizer(ABC):
def __init__(self, optimizer, clip_grad,
log_num_zeros_in_grad,
check_for_nan_in_grad,
params_have_main_grad,
models):
"""Input optimizer is the base optimizer for example Adam."""
self.optimizer = optimizer
assert self.optimizer, 'no optimizer is provided.'
# Set gradient clipping and logging params.
self.clip_grad = clip_grad
self.log_num_zeros_in_grad = log_num_zeros_in_grad
self.check_for_nan_in_grad = check_for_nan_in_grad
self.params_have_main_grad = params_have_main_grad
# 'models' are retained for access to the contiguous grad buffers.
# (see distributed optimizer)
self.models = models
def get_parameters(self):
params = []
for param_group in self.optimizer.param_groups:
for param in param_group['params']:
params.append(param)
return params
def get_main_grads_for_grad_norm(self):
# Filter parameters based on:
# - grad should not be none
# - parameter should not be shared
# - should not be a replica due to tensor model parallelism
params = self.get_parameters()
grads_for_norm = []
for param in params:
grad = param.grad
grad_not_none = grad is not None
is_not_shared = param_is_not_shared(param)
is_not_tp_duplicate = tensor_parallel.param_is_not_tensor_parallel_duplicate(param)
if grad_not_none and is_not_shared and is_not_tp_duplicate:
grads_for_norm.append(grad)
return grads_for_norm
def get_model_parallel_group(self):
"""Default returned here, but the distributed optimizer overrides this."""
return mpu.get_model_parallel_group()
def clip_grad_norm(self, clip_grad, check_for_nan_in_grad):
params = self.get_parameters()
grads_for_norm = self.get_main_grads_for_grad_norm()
return clip_grad_norm_fp32(
params, grads_for_norm, clip_grad,
check_for_nan_in_grad,
model_parallel_group=self.get_model_parallel_group())
def count_zeros(self):
params = self.get_parameters()
return count_zeros_fp32(params,
model_parallel_group=self.get_model_parallel_group())
@abstractmethod
def zero_grad(self, set_to_none=True):
pass
@abstractmethod
def get_loss_scale(self):
"""The output should be a cuda tensor of size 1."""
pass
def scale_loss(self, loss):
"""Simple scaling."""
return self.get_loss_scale() * loss
@abstractmethod
def reload_model_params(self):
"""Refreshes any internal state from the current model parameters.
Call whenever the parameters are changed outside of the optimizer.
For example, when we load a model from a checkpoint without loading
the optimizer, the model parameters are updated but for fp16 optimizer
with main parameters, the main parameters need to also be updated."""
pass
@abstractmethod
def state_dict(self):
pass
@abstractmethod
def load_state_dict(self, state_dict):
pass
# Promote state so it can be retrieved or set via
# "optimizer_instance.state"
def _get_state(self):
return self.optimizer.state
def _set_state(self, value):
self.optimizer.state = value
state = property(_get_state, _set_state)
# Promote param_groups so it can be retrieved or set via
# "optimizer_instance.param_groups"
# (for example, to adjust the learning rate)
def _get_param_groups(self):
return self.optimizer.param_groups
def _set_param_groups(self, value):
self.optimizer.param_groups = value
param_groups = property(_get_param_groups, _set_param_groups)
@abstractmethod
def step(self, args, timers):
pass
def gather_model_params(self, args, timers):
"""
For the case of a non-distributed-optimizer, there is nothing to
do here.
"""
pass
def allreduce_word_embedding_grads(self, args):
"""
All-reduce word embedding grads.
Reduce grads across first and last stages to ensure that word_embeddings
parameters stay in sync. This should only run for models that support
pipelined model parallelism (BERT and GPT-2).
"""
if mpu.is_rank_in_embedding_group(ignore_virtual=True) and \
mpu.get_pipeline_model_parallel_world_size() > 1:
if mpu.is_pipeline_first_stage(ignore_virtual=True):
unwrapped_model = self.models[0]
elif mpu.is_pipeline_last_stage(ignore_virtual=True):
unwrapped_model = self.models[-1]
else: # We do not support the interleaved schedule for T5 yet.
unwrapped_model = self.models[0]
unwrapped_model = unwrap_model(unwrapped_model)
if unwrapped_model.share_embeddings_and_output_weights:
weight = unwrapped_model.shared_embedding_or_output_weight()
grad = weight.main_grad
torch.distributed.all_reduce(grad, group=mpu.get_embedding_group())
def allreduce_position_embedding_grads(self, args):
"""
All-reduce position_embeddings grad across first (encoder) and
split (decoder) stages to ensure that position embeddings parameters
stay in sync. This should only run for T5 models with pipeline
parallelism.
"""
if mpu.is_rank_in_position_embedding_group() and \
mpu.get_pipeline_model_parallel_world_size() > 1 and \
args.pipeline_model_parallel_split_rank is not None:
unwrapped_model = self.models[0]
unwrapped_model = unwrap_model(unwrapped_model)
grad = unwrapped_model.language_model.embedding.position_embeddings.weight.main_grad
torch.distributed.all_reduce(grad, group=mpu.get_position_embedding_group())
def allreduce_embedding_grads(self, args):
"""All-reduce both word and position embeddings."""
self.allreduce_word_embedding_grads(args)
self.allreduce_position_embedding_grads(args)
def allreduce_layernorm_grads(self, args):
"""All-reduce layernorm grads (for sequence parallelism)."""
# All-reduce layernorm parameters across model parallel nodes
# when sequence parallelism is used
if mpu.get_tensor_model_parallel_world_size() > 1 and \
args.sequence_parallel:
grads = []
for model_module in self.models:
unwrapped_model = unwrap_model(model_module)
for param in unwrapped_model.parameters():
if getattr(param, 'sequence_parallel', False):
grad = param.main_grad
grads.append(grad.data)
coalesced = _flatten_dense_tensors(grads)
torch.distributed.all_reduce(
coalesced, group=mpu.get_tensor_model_parallel_group())
for buf, synced in zip(grads, _unflatten_dense_tensors(
coalesced, grads)):
buf.copy_(synced)
def reduce_model_grads(self, args, timers):
"""All-reduce all grads, and all-reduce embeddings."""
# All-reduce.
timers('grads-all-reduce', log_level=1).start(
barrier=args.barrier_with_L1_time)
for model in self.models:
model.allreduce_gradients()
timers('grads-all-reduce').stop()
# All-reduce layer-norm grads (for sequence parallelism).
timers('layernorm-grads-all-reduce', log_level=1).start(
barrier=args.barrier_with_L1_time)
self.allreduce_layernorm_grads(args)
timers('layernorm-grads-all-reduce').stop()
# All-reduce embedding grads.
timers('embedding-grads-all-reduce', log_level=1).start(
barrier=args.barrier_with_L1_time)
self.allreduce_embedding_grads(args)
timers('embedding-grads-all-reduce').stop()
class MixedPrecisionOptimizer(MegatronOptimizer):
"""Base class for both the float-16 and the distributed optimizer.
Arguments:
optimizer: base optimizer such as Adam or SGD
clip_grad: clip gradeints with this global L2 norm. Note
that clipping is ignored if clip_grad == 0
log_num_zeros_in_grad: return number of zeros in the gradients.
check_for_nan_in_grad: check if gradients have a NaN.
params_have_main_grad: flag indicating if parameters have
a `main_grad` field. If this is set, we are assuming
that the model parameters are store in the `main_grad`
field instead of the typical `grad` field. This happens
for the DDP cases where there is a continuous buffer
holding the gradients. For example for bfloat16, we want
to do gradient accumulation and all-reduces in float32
and as a result we store those gradients in the main_grad.
Note that main grad is not necessarily in float32.
fp16: if true, the model is running in fp16.
bf16: if true, the model is running in bfloat16.
params_dtype: used by distributed optimizer.
grad_scaler: used for scaling gradients. Note that this can be
None. This case happens when `bf16 = True` and we don't
use any loss scale. Note that for `bf16 = True`, we can have
a constnat gradient scaler. Also for `bf16 = False`, we
always require a grad scaler.
models: list of models (i.e., the virtual pipelining models). This
is used by the distributed optimizer for mapping parameters.
"""
def __init__(self, optimizer, clip_grad, log_num_zeros_in_grad,
check_for_nan_in_grad, params_have_main_grad,
fp16, bf16, params_dtype, grad_scaler, models):
super().__init__(
optimizer, clip_grad, log_num_zeros_in_grad,
check_for_nan_in_grad, params_have_main_grad,
models)
self.fp16 = fp16
self.bf16 = bf16
self.params_dtype = params_dtype
self.grad_scaler = grad_scaler
# None grad scaler is only supported for bf16.
if self.grad_scaler is None:
assert not self.fp16, 'fp16 expects a grad scaler.'
# Tensor used to determine if a nan/if has happend.
# Any non-zero value indicates inf/nan.
# Note that we keep this for the cases that grad scaler is none.
# We still record nan/inf if we have a bfloat16 with a grad scaler.
if self.grad_scaler:
self.found_inf = torch.cuda.FloatTensor([0.0])
# Dummy tensor needed for apex multi-apply tensor.
# For bfloat, we don't have multi-tensor apply and for now
# we set it to none so the multi-tensor apply gets ignored.
if bf16:
self._dummy_overflow_buf = None
else:
self._dummy_overflow_buf = torch.cuda.IntTensor([0])
# In case grad scaler is not passed, define the unity scale.
if self.grad_scaler is None:
self._scale_one = torch.cuda.FloatTensor([1.0])
def get_loss_scale(self):
if self.grad_scaler is None:
return self._scale_one
return self.grad_scaler.scale
def reload_model_params(self):
self._copy_model_params_to_main_params()
def _unscale_main_grads_and_check_for_nan(self):
# Collect main grads.
main_grads = self._collect_main_grad_data_for_unscaling()
# Reset found inf.
self.found_inf.fill_(0.0)
# Unscale and set found inf/nan
torch._amp_foreach_non_finite_check_and_unscale_(
main_grads, self.found_inf, self.grad_scaler.inv_scale)
# Update across all model parallel instances.
torch.distributed.all_reduce(self.found_inf,
op=torch.distributed.ReduceOp.MAX,
group=self.get_model_parallel_group())
# Check for nan.
found_inf_flag = (self.found_inf.item() > 0)
return found_inf_flag
@torch.no_grad()
def step(self, args, timers):
# Copy gradients from model params to main params.
timers('optimizer-copy-to-main-grad', log_level=1).start(
barrier=args.barrier_with_L1_time)
self._copy_model_grads_to_main_grads()
timers('optimizer-copy-to-main-grad').stop()
# Do unscale, check for inf, and update grad scaler only for
# the case that grad scaler is provided.
if self.grad_scaler:
# Unscale and check for inf/nan.
timers('optimizer-unscale-and-check-inf', log_level=1).start(
barrier=args.barrier_with_L1_time)
found_inf_flag = self._unscale_main_grads_and_check_for_nan()
timers('optimizer-unscale-and-check-inf').stop()
# We are done with scaling gradients
# so we can update the loss scale.
self.grad_scaler.update(found_inf_flag)
# If we found inf/nan, skip the update.
if found_inf_flag:
return False, None, None
# Clip the main gradients.
timers('optimizer-clip-main-grad', log_level=1).start(
barrier=args.barrier_with_L1_time)
grad_norm = None
if self.clip_grad > 0.0:
grad_norm = self.clip_grad_norm(self.clip_grad,
self.check_for_nan_in_grad)
timers('optimizer-clip-main-grad').stop()
# Count the zeros in the grads.
timers('optimizer-count-zeros', log_level=1).start(
barrier=args.barrier_with_L1_time)
num_zeros_in_grad = self.count_zeros() if \
self.log_num_zeros_in_grad else None
timers('optimizer-count-zeros').stop()
# Step the optimizer.
timers('optimizer-inner-step', log_level=1).start(
barrier=args.barrier_with_L1_time)
self.optimizer.step()
timers('optimizer-inner-step').stop()
# Update params from main params.
timers('optimizer-copy-main-to-model-params', log_level=1).start(
barrier=args.barrier_with_L1_time)
self._copy_main_params_to_model_params()
timers('optimizer-copy-main-to-model-params').stop()
# Successful update.
return True, grad_norm, num_zeros_in_grad
class Float16OptimizerWithFloat16Params(MixedPrecisionOptimizer):
"""Float16 optimizer for fp16 and bf16 data types.
Arguments:
optimizer: base optimizer such as Adam or SGD
clip_grad: clip gradeints with this global L2 norm. Note
that clipping is ignored if clip_grad == 0
log_num_zeros_in_grad: return number of zeros in the gradients.
check_for_nan_in_grad: check if gradients have a NaN.
params_have_main_grad: flag indicating if parameters have
a `main_grad` field. If this is set, we are assuming
that the model parameters are store in the `main_grad`
field instead of the typical `grad` field. This happens
for the DDP cases where there is a continuous buffer
holding the gradients. For example for bfloat16, we want
to do gradient accumulation and all-reduces in float32
and as a result we store those gradients in the main_grad.
Note that main grad is not necessarily in float32.
fp16: if true, the model is running in fp16.
bf16: if true, the model is running in bfloat16.
grad_scaler: used for scaling gradients. Note that this can be
None. This case happens when `bf16 = True` and we don't
use any loss scale. Note that for `bf16 = True`, we can have
a constnat gradient scaler. Also for `bf16 = False`, we
always require a grad scaler.
models: list of models (i.e., the virtual pipelining models). This
is used by the distributed optimizer for mapping parameters.
"""
def __init__(self, optimizer, clip_grad, log_num_zeros_in_grad,
check_for_nan_in_grad, params_have_main_grad, fp16, bf16,
params_dtype, grad_scaler, models):
super().__init__(
optimizer, clip_grad, log_num_zeros_in_grad,
check_for_nan_in_grad, params_have_main_grad,
fp16, bf16, params_dtype, grad_scaler, models)
# ======================
# main parameter stuff
# ======================
# Three groups of parameters:
# float16_groups: original float16 parameters
# fp32_from_float16_groups: fp32 copy of float16 parameters
# fp32_from_fp32_groups: original fp32 parameters
self.float16_groups = []
self.fp32_from_float16_groups = []
self.fp32_from_fp32_groups = []
# For all the groups in the original optimizer:
for param_group in self.optimizer.param_groups:
float16_params_this_group = []
fp32_params_this_group = []
fp32_from_float16_params_this_group = []
# For all the parameters in this group:
for i, param in enumerate(param_group['params']):
if param.requires_grad:
# float16 params:
if param.type() in ['torch.cuda.HalfTensor',
'torch.cuda.BFloat16Tensor']:
float16_params_this_group.append(param)
# Create a copy
main_param = param.detach().clone().float()
# Copy tensor model parallel attributes.
tensor_parallel.copy_tensor_model_parallel_attributes(main_param,
param)
if hasattr(param, 'shared'):
main_param.shared = param.shared
# Replace the optimizer params with the new fp32 copy.
param_group['params'][i] = main_param
fp32_from_float16_params_this_group.append(main_param)
# Reset existing state dict key to the new main param.
if param in self.optimizer.state:
self.optimizer.state[main_param] \
= self.optimizer.state.pop(param)
# fp32 params.
elif param.type() == 'torch.cuda.FloatTensor':
fp32_params_this_group.append(param)
param_group['params'][i] = param
else:
raise TypeError('Wrapped parameters must be one of '
'torch.cuda.FloatTensor, '
'torch.cuda.HalfTensor, or '
'torch.cuda.BFloat16Tensor. '
'Received {}'.format(param.type()))
self.float16_groups.append(float16_params_this_group)
self.fp32_from_float16_groups.append(
fp32_from_float16_params_this_group)
self.fp32_from_fp32_groups.append(fp32_params_this_group)
def zero_grad(self, set_to_none=True):
"""We only need to zero the model related parameters, i.e.,
float16_groups & fp32_from_fp32_groups. We additionally zero
fp32_from_float16_groups as a memory optimization to reduce
fragmentation; in the case of set_to_none==True, the space
used by this field can be safely deallocated at this point."""
for group in self.float16_groups:
_zero_grad_group_helper(group, set_to_none)
for group in self.fp32_from_float16_groups:
_zero_grad_group_helper(group, set_to_none)
for group in self.fp32_from_fp32_groups:
_zero_grad_group_helper(group, set_to_none)
def _collect_main_grad_data_for_unscaling(self):
main_grads = []
# fp32 params from float16 ones.
for main_group in self.fp32_from_float16_groups:
for main_param in main_group:
if main_param.grad is not None:
main_grads.append(main_param.grad.data)
# Append fp32 parameters.
for main_group in self.fp32_from_fp32_groups:
for main_param in main_group:
if main_param.grad is not None:
main_grads.append(main_param.grad.data)
return main_grads
def _get_model_and_main_params_data_float16(self):
model_data = []
main_data = []
for model_group, main_group in zip(self.float16_groups,
self.fp32_from_float16_groups):
for model_param, main_param in zip(model_group, main_group):
model_data.append(model_param.data)
main_data.append(main_param.data)
return model_data, main_data
def _copy_model_grads_to_main_grads(self):
# This only needs to be done for the float16 group.
for model_group, main_group in zip(self.float16_groups,
self.fp32_from_float16_groups):
for model_param, main_param in zip(model_group, main_group):
if self.params_have_main_grad and hasattr(model_param, 'main_grad'):
main_param.grad = model_param.main_grad.float()
else:
if model_param.grad is not None:
main_param.grad = model_param.grad.float()
# Safe to deallocate model's grad/main_grad after copying.
# (If using contiguous buffers, main_grad's memory should
# persist and therefore should not be deallocated.)
model_param.grad = None
# For fp32 grads, we need to reset the grads to main grad.
if self.params_have_main_grad:
for model_group in self.fp32_from_fp32_groups:
for model_param in model_group:
model_param.grad = model_param.main_grad
def _copy_main_params_to_model_params(self):
# Only needed for the float16 params.
model_data, main_data = self._get_model_and_main_params_data_float16()
_multi_tensor_copy_this_to_that(this=main_data, that=model_data,
overflow_buf=self._dummy_overflow_buf)
def _copy_model_params_to_main_params(self):
# Only needed for the float16 params.
model_data, main_data = self._get_model_and_main_params_data_float16()
_multi_tensor_copy_this_to_that(this=model_data, that=main_data,
overflow_buf=self._dummy_overflow_buf)
def state_dict(self):
state_dict = {}
state_dict['optimizer'] = self.optimizer.state_dict()
if self.grad_scaler:
state_dict['grad_scaler'] = self.grad_scaler.state_dict()
state_dict['fp32_from_fp16_params'] = self.fp32_from_float16_groups
return state_dict
def load_state_dict(self, state_dict):
# Optimizer.
optimizer_key = 'optimizer'
if optimizer_key not in state_dict:
optimizer_key = 'optimizer_state_dict'
print_rank_0('***WARNING*** loading optimizer from '
'an old checkpoint ...')
self.optimizer.load_state_dict(state_dict[optimizer_key])
# Grad scaler.
if 'grad_scaler' not in state_dict:
if self.fp16:
print_rank_0('***WARNING*** found an old checkpoint, will not '
'load grad scaler ...')
else:
if self.grad_scaler:
self.grad_scaler.load_state_dict(state_dict['grad_scaler'])
else:
print_rank_0('***WARNING*** fould the grad scaler in the '
'checkpoint but it is None in the class. '
'Skipping loading grad scaler ...')
# Copy data for the main params.
fp32_from_float16_params_key = 'fp32_from_fp16_params'
if fp32_from_float16_params_key not in state_dict:
fp32_from_float16_params_key = 'fp32_from_fp16'
for current_group, saved_group in zip(
self.fp32_from_float16_groups,
state_dict[fp32_from_float16_params_key]):
for current_param, saved_param in zip(current_group, saved_group):
current_param.data.copy_(saved_param.data)
class FP32Optimizer(MegatronOptimizer):
def __init__(self, optimizer, clip_grad,
log_num_zeros_in_grad,
check_for_nan_in_grad,
params_have_main_grad,
models):
super(FP32Optimizer, self).__init__(
optimizer, clip_grad, log_num_zeros_in_grad,
check_for_nan_in_grad, params_have_main_grad,
models)
self._scale = torch.cuda.FloatTensor([1.0])
def zero_grad(self, set_to_none=True):
"""Copied from torch.optim.optimizer"""
for group in self.optimizer.param_groups:
_zero_grad_group_helper(group['params'], set_to_none)
def get_loss_scale(self):
"""FP32 optimizer does not do any scaling."""
return self._scale
@torch.no_grad()
def step(self, args, timers):
"""Clip gradients (if needed) and step the base optimizer.
Always return successful since there is no overflow."""
# Copy main_grads to grads.
timers('optimizer-copy-to-main-grad', log_level=1).start(
barrier=args.barrier_with_L1_time)
if self.params_have_main_grad:
for param_group in self.optimizer.param_groups:
for param in param_group['params']:
param.grad = param.main_grad
timers('optimizer-copy-to-main-grad').stop()
# Clip gradients.
timers('optimizer-clip-main-grad', log_level=1).start(
barrier=args.barrier_with_L1_time)
grad_norm = None
if self.clip_grad > 0.0:
grad_norm = self.clip_grad_norm(self.clip_grad,
self.check_for_nan_in_grad)
timers('optimizer-clip-main-grad').stop()
# count the zeros in the grads
timers('optimizer-count-zeros', log_level=1).start(
barrier=args.barrier_with_L1_time)
num_zeros_in_grad = self.count_zeros() if \
self.log_num_zeros_in_grad else None
timers('optimizer-count-zeros').stop()
# Update parameters.
timers('optimizer-inner-step', log_level=1).start(
barrier=args.barrier_with_L1_time)
self.optimizer.step()
timers('optimizer-inner-step').stop()
# No overflow for FP32 optimizer.
return True, grad_norm, num_zeros_in_grad
def reload_model_params(self):
pass
def state_dict(self):
return self.optimizer.state_dict()
def load_state_dict(self, state_dict):
self.optimizer.load_state_dict(state_dict)
|
Megatron-LM-master
|
megatron/optimizer/optimizer.py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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.
"""Tokenization classes."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
import unicodedata
import six
def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
"""Checks whether the casing config is consistent with the checkpoint name."""
# The casing has to be passed in by the user and there is no explicit check
# as to whether it matches the checkpoint. The casing information probably
# should have been stored in the bert_config.json file, but it's not, so
# we have to heuristically detect it to validate.
if not init_checkpoint:
return
m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)
if m is None:
return
model_name = m.group(1)
lower_models = [
"uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",
"multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"
]
cased_models = [
"cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",
"multi_cased_L-12_H-768_A-12"
]
is_bad_config = False
if model_name in lower_models and not do_lower_case:
is_bad_config = True
actual_flag = "False"
case_name = "lowercased"
opposite_flag = "True"
if model_name in cased_models and do_lower_case:
is_bad_config = True
actual_flag = "True"
case_name = "cased"
opposite_flag = "False"
if is_bad_config:
raise ValueError(
"You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
"However, `%s` seems to be a %s model, so you "
"should pass in `--do_lower_case=%s` so that the fine-tuning matches "
"how the model was pre-training. If this error is wrong, please "
"just comment out this check." % (actual_flag, init_checkpoint,
model_name, case_name, opposite_flag))
def convert_to_unicode(text):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text.decode("utf-8", "ignore")
elif isinstance(text, unicode):
return text
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def printable_text(text):
"""Returns text encoded in a way suitable for print or `tf.logging`."""
# These functions want `str` for both Python2 and Python3, but in one case
# it's a Unicode string and in the other it's a byte string.
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text
elif isinstance(text, unicode):
return text.encode("utf-8")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
index = 0
with open(vocab_file, "r", encoding = "utf-8") as reader:
while True:
token = convert_to_unicode(reader.readline())
if not token:
break
token = token.strip()
vocab[token] = index
index += 1
return vocab
def convert_by_vocab(vocab, items):
"""Converts a sequence of [tokens|ids] using the vocab."""
output = []
for item in items:
output.append(vocab[item])
return output
def convert_tokens_to_ids(vocab, tokens):
return convert_by_vocab(vocab, tokens)
def convert_ids_to_tokens(inv_vocab, ids):
return convert_by_vocab(inv_vocab, ids)
def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a piece of text."""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
class FullTokenizer(object):
"""Runs end-to-end tokenziation."""
def __init__(self, vocab_file, do_lower_case=True):
self.vocab = load_vocab(vocab_file)
self.inv_vocab = {v: k for k, v in self.vocab.items()}
self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
def tokenize(self, text):
split_tokens = []
for token in self.basic_tokenizer.tokenize(text):
for sub_token in self.wordpiece_tokenizer.tokenize(token):
split_tokens.append(sub_token)
return split_tokens
def convert_tokens_to_ids(self, tokens):
return convert_by_vocab(self.vocab, tokens)
def convert_ids_to_tokens(self, ids):
return convert_by_vocab(self.inv_vocab, ids)
@staticmethod
def convert_tokens_to_string(tokens, clean_up_tokenization_spaces=True):
""" Converts a sequence of tokens (string) in a single string. """
def clean_up_tokenization(out_string):
""" Clean up a list of simple English tokenization artifacts
like spaces before punctuations and abreviated forms.
"""
out_string = (
out_string.replace(" .", ".")
.replace(" ?", "?")
.replace(" !", "!")
.replace(" ,", ",")
.replace(" ' ", "'")
.replace(" n't", "n't")
.replace(" 'm", "'m")
.replace(" 's", "'s")
.replace(" 've", "'ve")
.replace(" 're", "'re")
)
return out_string
text = ' '.join(tokens).replace(' ##', '').strip()
if clean_up_tokenization_spaces:
clean_text = clean_up_tokenization(text)
return clean_text
else:
return text
def vocab_size(self):
return len(self.vocab)
class BasicTokenizer(object):
"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
def __init__(self, do_lower_case=True):
"""Constructs a BasicTokenizer.
Args:
do_lower_case: Whether to lower case the input.
"""
self.do_lower_case = do_lower_case
def tokenize(self, text):
"""Tokenizes a piece of text."""
text = convert_to_unicode(text)
text = self._clean_text(text)
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
# characters in the vocabulary because Wikipedia does have some Chinese
# words in the English Wikipedia.).
text = self._tokenize_chinese_chars(text)
orig_tokens = whitespace_tokenize(text)
split_tokens = []
for token in orig_tokens:
if self.do_lower_case:
token = token.lower()
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token))
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output)
def _run_split_on_punc(self, text):
"""Splits punctuation on a piece of text."""
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output)
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if ((cp >= 0x4E00 and cp <= 0x9FFF) or #
(cp >= 0x3400 and cp <= 0x4DBF) or #
(cp >= 0x20000 and cp <= 0x2A6DF) or #
(cp >= 0x2A700 and cp <= 0x2B73F) or #
(cp >= 0x2B740 and cp <= 0x2B81F) or #
(cp >= 0x2B820 and cp <= 0x2CEAF) or
(cp >= 0xF900 and cp <= 0xFAFF) or #
(cp >= 0x2F800 and cp <= 0x2FA1F)): #
return True
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xfffd or _is_control(char):
continue
if _is_whitespace(char):
output.append(" ")
else:
output.append(char)
return "".join(output)
class WordpieceTokenizer(object):
"""Runs WordPiece tokenziation."""
def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""Tokenizes a piece of text into its word pieces.
This uses a greedy longest-match-first algorithm to perform tokenization
using the given vocabulary.
For example:
input = "unaffable"
output = ["un", "##aff", "##able"]
Args:
text: A single token or whitespace separated tokens. This should have
already been passed through `BasicTokenizer.
Returns:
A list of wordpiece tokens.
"""
text = convert_to_unicode(text)
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
while start < len(chars):
end = len(chars)
cur_substr = None
while start < end:
substr = "".join(chars[start:end])
if start > 0:
substr = "##" + substr
if substr in self.vocab:
cur_substr = substr
break
end -= 1
if cur_substr is None:
is_bad = True
break
sub_tokens.append(cur_substr)
start = end
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens
def _is_whitespace(char):
"""Checks whether `chars` is a whitespace character."""
# \t, \n, and \r are technically contorl characters but we treat them
# as whitespace since they are generally considered as such.
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
cat = unicodedata.category(char)
if cat == "Zs":
return True
return False
def _is_control(char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat in ("Cc", "Cf"):
return True
return False
def _is_punctuation(char):
"""Checks whether `chars` is a punctuation character."""
cp = ord(char)
# We treat all non-letter/number ASCII as punctuation.
# Characters such as "^", "$", and "`" are not in the Unicode
# Punctuation class but we treat them as punctuation anyways, for
# consistency.
if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
(cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
return True
cat = unicodedata.category(char)
if cat.startswith("P"):
return True
return False
|
Megatron-LM-master
|
megatron/tokenizer/bert_tokenization.py
|
# coding=utf-8
# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
#
# 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.
"""Tokenization classes for OpenAI GPT."""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import sys
import json
import logging
import os
import regex as re
from io import open
try:
from functools import lru_cache
except ImportError:
# Just a dummy decorator to get the checks to run on python2
# because honestly I don't want to support a byte-level unicode BPE
# tokenizer on python 2 right now.
def lru_cache():
return lambda func: func
logger = logging.getLogger(__name__)
PRETRAINED_VOCAB_ARCHIVE_MAP = {
'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json",
}
PRETRAINED_MERGES_ARCHIVE_MAP = {
'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt",
}
PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
'gpt2': 1024,
}
VOCAB_NAME = 'vocab.json'
MERGES_NAME = 'merges.txt'
SPECIAL_TOKENS_NAME = 'special_tokens.txt'
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a signficant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
_chr = unichr if sys.version_info[0] == 2 else chr
bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + \
list(range(ord("®"), ord("ÿ") + 1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8 + n)
n += 1
cs = [_chr(n) for n in cs]
return dict(zip(bs, cs))
def get_pairs(word):
"""Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
class GPT2Tokenizer(object):
"""
GPT-2 BPE tokenizer. Peculiarities:
- Byte-level BPE
"""
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
"""
Instantiate a PreTrainedBertModel from a pre-trained model file.
Download and cache the pre-trained model file if needed.
"""
if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path]
special_tokens_file = None
else:
vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME)
merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME)
special_tokens_file = os.path.join(pretrained_model_name_or_path, SPECIAL_TOKENS_NAME)
if not os.path.exists(special_tokens_file):
special_tokens_file = None
else:
logger.info("loading special tokens file {}".format(special_tokens_file))
# redirect to the cache, if necessary
try:
from .file_utils import cached_path
resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir)
except EnvironmentError:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find files {} and {} "
"at this path or url.".format(
pretrained_model_name_or_path,
', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
pretrained_model_name_or_path,
vocab_file, merges_file))
return None
if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file:
logger.info("loading vocabulary file {}".format(vocab_file))
logger.info("loading merges file {}".format(merges_file))
else:
logger.info("loading vocabulary file {} from cache at {}".format(
vocab_file, resolved_vocab_file))
logger.info("loading merges file {} from cache at {}".format(
merges_file, resolved_merges_file))
if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
# if we're using a pretrained model, ensure the tokenizer wont index sequences longer
# than the number of positional embeddings
max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
# Instantiate tokenizer.
if special_tokens_file and 'special_tokens' not in kwargs:
special_tokens = open(special_tokens_file, encoding='utf-8').read().split('\n')[:-1]
else:
special_tokens = kwargs.pop('special_tokens', [])
tokenizer = cls(
resolved_vocab_file,
resolved_merges_file,
special_tokens=special_tokens,
*inputs,
**kwargs)
return tokenizer
def __init__(self, vocab_file, merges_file, errors='replace',
special_tokens=None, max_len=None):
self.max_len = max_len if max_len is not None else int(1e12)
self.encoder = json.load(open(vocab_file))
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
bpe_merges = [tuple(merge.split()) for merge in bpe_data]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.cache = {}
# Should haved added re.IGNORECASE so BPE merges can happen for
# capitalized versions of contractions
self.pat = re.compile(
r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
self.special_tokens = {}
self.special_tokens_decoder = {}
self.set_special_tokens(special_tokens)
def __len__(self):
return len(self.encoder) + len(self.special_tokens)
def set_special_tokens(self, special_tokens):
""" Add a list of additional tokens to the encoder.
The additional tokens are indexed starting from the last index of the
current vocabulary in the order of the `special_tokens` list.
"""
if not special_tokens:
self.special_tokens = {}
self.special_tokens_decoder = {}
return
self.special_tokens = dict((tok, len(self.encoder) + i)
for i, tok in enumerate(special_tokens))
self.special_tokens_decoder = {v: k for k, v in self.special_tokens.items()}
logger.info("Special tokens {}".format(self.special_tokens))
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = get_pairs(word)
if not pairs:
return token
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
new_word.extend(word[i:j])
i = j
except BaseException:
new_word.extend(word[i:])
break
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = ' '.join(word)
self.cache[token] = word
return word
def tokenize(self, text):
""" Tokenize a string. """
bpe_tokens = []
for token in re.findall(self.pat, text):
if sys.version_info[0] == 2:
token = ''.join(self.byte_encoder[ord(b)] for b in token)
else:
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
return bpe_tokens
def convert_tokens_to_ids(self, tokens):
""" Converts a sequence of tokens into ids using the vocab. """
ids = []
if isinstance(tokens, str) or (sys.version_info[0] == 2 and isinstance(tokens, unicode)):
if tokens in self.special_tokens:
return self.special_tokens[tokens]
else:
return self.encoder.get(tokens, 0)
for token in tokens:
if token in self.special_tokens:
ids.append(self.special_tokens[token])
else:
ids.append(self.encoder.get(token, 0))
if len(ids) > self.max_len:
logger.warning(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this OpenAI GPT model ({} > {}). Running this"
" sequence through the model will result in indexing errors".format(
len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
"""Converts a sequence of ids in BPE tokens using the vocab."""
tokens = []
for i in ids:
if i in self.special_tokens_decoder:
if not skip_special_tokens:
tokens.append(self.special_tokens_decoder[i])
else:
tokens.append(self.decoder[i])
return tokens
def encode(self, text):
return self.convert_tokens_to_ids(self.tokenize(text))
def decode(self, tokens):
text = ''.join([self.decoder[token] for token in tokens])
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
return text
def save_vocabulary(self, vocab_path):
"""Save the tokenizer vocabulary and merge files to a directory."""
if not os.path.isdir(vocab_path):
logger.error("Vocabulary path ({}) should be a directory".format(vocab_path))
return
vocab_file = os.path.join(vocab_path, VOCAB_NAME)
merge_file = os.path.join(vocab_path, MERGES_NAME)
special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME)
with open(vocab_file, 'w', encoding='utf-8') as f:
f.write(json.dumps(self.encoder, ensure_ascii=False))
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write(u'#version: 0.2\n')
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!".format(merge_file))
index = token_index
writer.write(' '.join(bpe_tokens) + u'\n')
index += 1
index = len(self.encoder)
with open(special_tokens_file, 'w', encoding='utf-8') as writer:
for token, token_index in sorted(self.special_tokens.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning("Saving special tokens vocabulary to {}: BPE indices are not consecutive."
" Please check that the tokenizer is not corrupted!".format(special_tokens_file))
index = token_index
writer.write(token + u'\n')
index += 1
return vocab_file, merge_file, special_tokens_file
|
Megatron-LM-master
|
megatron/tokenizer/gpt2_tokenization.py
|
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
from .tokenizer import build_tokenizer
|
Megatron-LM-master
|
megatron/tokenizer/__init__.py
|
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