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Commit cb00a196 authored by Jared Casper's avatar Jared Casper
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Merge branch 'main' into t5_pipeline_parallelism

parents 38a774e9 5ab64637
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Showing with 74 additions and 68 deletions
megatron/training.py
View file @ cb00a196
......@@ -193,7 +193,7 @@ def update_train_iters(args):
print_rank_0('setting training iterations to {}'.format(args.train_iters))
def get_model(model_provider_func, model_type):
def get_model(model_provider_func, model_type, wrap_with_ddp=True):
"""Build the model."""
args = get_args()
args.model_type = model_type
......@@ -272,22 +272,24 @@ def get_model(model_provider_func, model_type):
if args.fp16 or args.bf16:
model = [Float16Module(model_module, args) for model_module in model]
if args.DDP_impl == 'torch':
i = torch.cuda.current_device()
model = [torchDDP(model_module, device_ids=[i], output_device=i,
process_group=mpu.get_data_parallel_group())
for model_module in model]
return model
if wrap_with_ddp:
if args.DDP_impl == 'torch':
i = torch.cuda.current_device()
model = [torchDDP(model_module, device_ids=[i], output_device=i,
process_group=mpu.get_data_parallel_group())
for model_module in model]
if args.DDP_impl == 'local':
model = [LocalDDP(model_module,
args.accumulate_allreduce_grads_in_fp32,
args.use_contiguous_buffers_in_ddp)
for model_module in model]
return model
elif args.DDP_impl == 'local':
model = [LocalDDP(model_module,
args.accumulate_allreduce_grads_in_fp32,
args.use_contiguous_buffers_in_local_ddp)
for model_module in model]
else:
raise NotImplementedError('Unknown DDP implementation specified: '
'{}. Exiting.'.format(args.DDP_impl))
raise NotImplementedError('Unknown DDP implementation specified: {}. '
'Exiting.'.format(args.DDP_impl))
return model
def get_learning_rate_scheduler(optimizer):
......@@ -380,11 +382,10 @@ def train_step(forward_step_func, data_iterator,
timers = get_timers()
# Set grad to zero.
if args.DDP_impl == 'local' and args.use_contiguous_buffers_in_ddp:
if args.DDP_impl == 'local' and args.use_contiguous_buffers_in_local_ddp:
for partition in model:
partition.zero_grad_buffer()
else:
optimizer.zero_grad()
optimizer.zero_grad()
forward_backward_func = get_forward_backward_func()
losses_reduced = forward_backward_func(
......@@ -392,7 +393,7 @@ def train_step(forward_step_func, data_iterator,
optimizer, timers, forward_only=False)
# Empty unused memory
if args.empty_unused_memory_each_iter >= 1:
if args.empty_unused_memory_level >= 1:
torch.cuda.empty_cache()
# All-reduce if needed.
......@@ -443,7 +444,7 @@ def train_step(forward_step_func, data_iterator,
skipped_iter = 1
# Empty unused memory
if args.empty_unused_memory_each_iter >= 2:
if args.empty_unused_memory_level >= 2:
torch.cuda.empty_cache()
if mpu.is_pipeline_last_stage(ignore_virtual=True):
......@@ -755,7 +756,7 @@ def evaluate(forward_step_func, data_iterator, model, verbose=False):
timers=None, forward_only=True)
# Empty unused memory
if args.empty_unused_memory_each_iter >= 1:
if args.empty_unused_memory_level >= 1:
torch.cuda.empty_cache()
if mpu.is_pipeline_last_stage(ignore_virtual=True):
......
tasks/zeroshot_gpt/evaluate.py
View file @ cb00a196
......@@ -205,7 +205,7 @@ def main():
args.task))
# Set up model and load checkpoint.
model = get_model(get_model_provider(eval_metric))
model = get_model(get_model_provider(eval_metric), wrap_with_ddp=False)
if args.load is not None:
_ = load_checkpoint(model, None, None)
......
tools/generate_samples_gpt.py → tools/run_text_generation_server.py
View file @ cb00a196
......@@ -14,37 +14,31 @@
# limitations under the License.
"""Sample Generate GPT"""
import os
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
os.path.pardir)))
import socket
from megatron import get_args
from megatron import print_rank_0
from megatron import get_tokenizer
from megatron import mpu
from megatron.checkpointing import load_checkpoint
from megatron.initialize import initialize_megatron
from megatron.model import GPTModel
from megatron.training import get_model
from megatron.text_generation_utils import generate_and_write_samples_unconditional
from megatron.text_generation_utils import generate_samples_input_from_file
from megatron.text_generation_utils import generate_samples_interactive
from megatron.text_generation_server import MegatronServer
from megatron.text_generation_utils import generate
import torch
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)
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,
......@@ -57,26 +51,10 @@ def add_text_generate_args(parser):
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')
group.add_argument("--recompute", action='store_true',
help='During generation recompute all attention '
'instead of using previously computed keys/values.')
return parser
def main():
"""Main program."""
if __name__ == "__main__":
initialize_megatron(extra_args_provider=add_text_generate_args,
args_defaults={'tokenizer_type': 'GPT2BPETokenizer',
'no_load_rng': True,
......@@ -86,27 +64,20 @@ def main():
if args.num_layers_per_virtual_pipeline_stage is not None:
print("Interleaved pipeline schedule is not yet supported for text generation.")
exit()
# Set up model and load checkpoint.
model = get_model(model_provider)
# Set up model and load checkpoint
model = get_model(model_provider, wrap_with_ddp=False)
if args.load is not None:
_ = load_checkpoint(model, None, None)
assert len(model) == 1, "Above condition should have caught this"
model = model[0]
# Generate samples.
if args.num_samples == 0:
args.micro_batch_size = 1
if args.sample_input_file != None:
generate_samples_input_from_file(model)
else:
generate_samples_interactive(model)
else:
generate_and_write_samples_unconditional(model)
if __name__ == "__main__":
main()
if mpu.is_pipeline_first_stage() and mpu.get_tensor_model_parallel_rank() == 0:
server = MegatronServer(model)
server.run("0.0.0.0")
while True:
choice = torch.cuda.LongTensor(1)
torch.distributed.broadcast(choice, 0)
if choice[0].item() == 0:
generate(model)
tools/text_generation_cli.py 0 → 100644
View file @ cb00a196
# coding=utf-8
# Copyright (c) 2020, 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.
import json
import sys
import urllib2
class PutRequest(urllib2.Request):
'''class to handling putting with urllib2'''
def get_method(self, *args, **kwargs):
return 'PUT'
if __name__ == "__main__":
url = sys.argv[1]
while True:
sentence = raw_input("Enter prompt: ")
max_len = int(input("Enter number tokens output: "))
data = json.dumps({"sentences": [sentence], "max_len":max_len})
req = PutRequest(url, data, {'Content-Type': 'application/json'})
response = urllib2.urlopen(req)
resp_sentences = json.load(response)
print("Megatron Response: ")
print(resp_sentences["sentences"][0])
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