Merge branch 'main' into checkpoint_util

requirements.txt

-pybind11
-torch
-six
-regex
-numpy

setup.py

-# 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.
-
-"""Setup for pip package."""
-
-import os
-import sys
-import setuptools
-
-if sys.version_info < (3,):
-    raise Exception("Python 2 is not supported by Megatron.")
-
-from megatron.package_info import (
-    __description__,
-    __contact_names__,
-    __url__,
-    __download_url__,
-    __keywords__,
-    __license__,
-    __package_name__,
-    __version__,
-)
-
-with open("README.md", "r") as fh:
-    long_description = fh.read()
-
-###############################################################################
-#                             Dependency Loading                              #
-# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #
-
-
-def req_file(filename):
-    with open(filename) as f:
-        content = f.readlines()
-    return [x.strip() for x in content]
-
-
-install_requires = req_file("requirements.txt")
-
-setuptools.setup(
-    name=__package_name__,
-    # Versions should comply with PEP440.  For a discussion on single-sourcing
-    # the version across setup.py and the project code, see
-    # https://packaging.python.org/en/latest/single_source_version.html
-    version=__version__,
-    description=__description__,
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    # The project's main homepage.
-    url=__url__,
-    author=__contact_names__,
-    maintainer=__contact_names__,
-    # The licence under which the project is released
-    license=__license__,
-    classifiers=[
-        'Intended Audience :: Developers',
-        'Intended Audience :: Science/Research',
-        'Intended Audience :: Information Technology',
-        # Indicate what your project relates to
-        'Topic :: Scientific/Engineering :: Artificial Intelligence',
-        'Topic :: Software Development :: Libraries :: Python Modules',
-        # Supported python versions
-        'Programming Language :: Python :: 3.6',
-        'Programming Language :: Python :: 3.7',
-        'Programming Language :: Python :: 3.8',
-        # Additional Setting
-        'Environment :: Console',
-        'Natural Language :: English',
-        'Operating System :: OS Independent',
-    ],
-    python_requires='>=3.6',
-    packages=setuptools.find_packages(),
-    install_requires=install_requires,
-    # Add in any packaged data.
-    include_package_data=True,
-    zip_safe=False,
-    # PyPI package information.
-    keywords=__keywords__
-)

tasks/zeroshot_gpt/evaluate.py

@@ -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

-# 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.
-
-"""Sample Generate GPT"""
-
-import os
-import sys
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
-                                             os.path.pardir)))
-
-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
-
-
-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')
-    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."""
-
-    initialize_megatron(extra_args_provider=add_text_generate_args,
-                        args_defaults={'tokenizer_type': 'GPT2BPETokenizer',
-                                       'no_load_rng': True,
-                                       'no_load_optim': True})
-
-    args = get_args()
-    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)
-
-    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()

tools/run_text_generation_server.py

@@ -27,7 +27,7 @@ from megatron.initialize import initialize_megatron
 from megatron.model import GPTModel
 from megatron.training import get_model
 from megatron.text_generation_server import MegatronServer
-from megatron.text_generation_utils import generate
+from megatron.text_generation import generate_and_post_process
 import torch
 
 def model_provider(pre_process=True, post_process=True):
@@ -43,8 +43,6 @@ def add_text_generate_args(parser):
 
     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,
@@ -65,7 +63,7 @@ if __name__ == "__main__":
         print("Interleaved pipeline schedule is not yet supported for text generation.")
         exit()
     # Set up model and load checkpoint
-    model = get_model(model_provider)
+    model = get_model(model_provider, wrap_with_ddp=False)
 
     if args.load is not None:
         _ = load_checkpoint(model, None, None)
@@ -78,8 +76,6 @@ if __name__ == "__main__":
 
     while True:
         choice = torch.cuda.LongTensor(1)
-        torch.distributed.broadcast(choice,
-                                    mpu.get_tensor_model_parallel_src_rank(),
-                                    group=mpu.get_tensor_model_parallel_group())
+        torch.distributed.broadcast(choice, 0)
         if choice[0].item() == 0:
-            generate(model)
+            generate_and_post_process(model)

tools/text_generation_cli.py

@@ -25,10 +25,10 @@ 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})
+        tokens_to_generate = int(input("Enter number of tokens to generate: "))
+        data = json.dumps({"prompts": [sentence], "tokens_to_generate":tokens_to_generate})
         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])
+        print(resp_sentences["text"][0])