a644350589
Tacotron2+Waveglow/PyT * AMP support * Data preprocessing for Tacotron 2 training * Fixed dropouts on LSTMCells SSD/PyT * script and notebook for inference * AMP support * README update * updates to examples/* BERT/PyT * initial release GNMT/PyT * Default container updated to NGC PyTorch 19.05-py3 * Mixed precision training implemented using APEX AMP * Added inference throughput and latency results on NVIDIA Tesla V100 16G * Added option to run inference on user-provided raw input text from command line NCF/PyT * Updated performance tables. * Default container changed to PyTorch 19.06-py3. * Caching validation negatives between runs Transformer/PyT * new README * jit support added UNet Medical/TF * inference example scripts added * inference benchmark measuring latency added * TRT/TF-TRT support added * README updated GNMT/TF * Performance improvements Small updates (mostly README) for other models.
81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) 2017-present, Facebook, Inc.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the LICENSE file in
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# the root directory of this source tree. An additional grant of patent rights
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# can be found in the PATENTS file in the same directory.
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#
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#-------------------------------------------------------------------------
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#
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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from setuptools import setup, find_packages, Extension
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from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CppExtension
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import sys
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if sys.version_info < (3,):
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sys.exit('Sorry, Python3 is required for fairseq.')
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with open('README.md') as f:
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readme = f.read()
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with open('LICENSE') as f:
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license = f.read()
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with open('requirements.txt') as f:
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reqs = f.read()
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bleu = Extension(
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'fairseq.libbleu',
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sources=[
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'fairseq/clib/libbleu/libbleu.cpp',
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'fairseq/clib/libbleu/module.cpp',
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],
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extra_compile_args=['-std=c++11'],
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)
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strided_batched_gemm = CUDAExtension(
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name='strided_batched_gemm',
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sources=['fairseq/modules/strided_batched_gemm/strided_batched_gemm.cpp', 'fairseq/modules/strided_batched_gemm/strided_batched_gemm_cuda.cu'],
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extra_compile_args={
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'cxx': ['-O2',],
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'nvcc':['--gpu-architecture=compute_70','--gpu-code=sm_70','-O3','-I./cutlass/','-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__']
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}
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)
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batch_utils = CppExtension(
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name='fairseq.data.batch_C',
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sources=['fairseq/data/csrc/make_batches.cpp'],
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extra_compile_args={
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'cxx': ['-O2',],
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}
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)
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setup(
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name='fairseq',
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version='0.5.0',
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description='Facebook AI Research Sequence-to-Sequence Toolkit',
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long_description=readme,
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license=license,
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install_requires=reqs.strip().split('\n'),
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packages=find_packages(),
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ext_modules=[bleu, strided_batched_gemm, batch_utils],
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cmdclass={
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'build_ext': BuildExtension
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},
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test_suite='tests',
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)
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