python_code stringlengths 0 1.02M | repo_name stringlengths 9 48 | file_path stringlengths 5 114 |
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## Copyright 2021 DeepMind Technologies Limited
#
# 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 agre... | mujoco-main | doc/conf.py |
from setuptools import setup, find_packages
setup(
name = 'muse-maskgit-pytorch',
packages = find_packages(exclude=[]),
version = '0.2.4',
license='MIT',
description = 'MUSE - Text-to-Image Generation via Masked Generative Transformers, in Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.... | muse-maskgit-pytorch-main | setup.py |
import logging
import torch
import transformers
from transformers import T5Tokenizer, T5EncoderModel, T5Config
from beartype import beartype
from typing import List, Union
transformers.logging.set_verbosity_error()
def exists(val):
return val is not None
# config
MAX_LENGTH = 256
DEFAULT_T5_NAME = 'google/t5-... | muse-maskgit-pytorch-main | muse_maskgit_pytorch/t5.py |
import math
from random import random
from functools import partial
import torch
import torch.nn.functional as F
from torch import nn, einsum
import pathlib
from pathlib import Path
import torchvision.transforms as T
from typing import Callable, Optional, List
from einops import rearrange, repeat
from beartype impo... | muse-maskgit-pytorch-main | muse_maskgit_pytorch/muse_maskgit_pytorch.py |
from muse_maskgit_pytorch.vqgan_vae import VQGanVAE
from muse_maskgit_pytorch.muse_maskgit_pytorch import Transformer, MaskGit, Muse, MaskGitTransformer, TokenCritic
from muse_maskgit_pytorch.trainers import VQGanVAETrainer
| muse-maskgit-pytorch-main | muse_maskgit_pytorch/__init__.py |
from functools import wraps
from packaging import version
from collections import namedtuple
import torch
from torch import nn, einsum
import torch.nn.functional as F
from memory_efficient_attention_pytorch.flash_attention import FlashAttentionFunction
# constants
AttentionConfig = namedtuple('AttentionConfig', ['en... | muse-maskgit-pytorch-main | muse_maskgit_pytorch/attend.py |
from math import sqrt
from random import choice
from pathlib import Path
from shutil import rmtree
from functools import partial
from beartype import beartype
import torch
from torch import nn
from torch.optim import Adam
from torch.utils.data import Dataset, DataLoader, random_split
import torchvision.transforms as... | muse-maskgit-pytorch-main | muse_maskgit_pytorch/trainers.py |
from pathlib import Path
import copy
import math
from math import sqrt
from functools import partial, wraps
from vector_quantize_pytorch import VectorQuantize as VQ
import torch
from torch import nn, einsum
import torch.nn.functional as F
from torch.autograd import grad as torch_grad
import torchvision
from einops ... | muse-maskgit-pytorch-main | muse_maskgit_pytorch/vqgan_vae.py |
import sys
from setuptools import setup, find_packages
sys.path[0:0] = ['transganformer']
from version import __version__
setup(
name = 'transganformer',
packages = find_packages(),
entry_points={
'console_scripts': [
'transganformer = transganformer.cli:main',
],
},
version = __version__,
l... | transganformer-main | setup.py |
import random
import torch
import torch.nn.functional as F
def DiffAugment(x, types=[]):
for p in types:
for f in AUGMENT_FNS[p]:
x = f(x)
return x.contiguous()
# """
# Augmentation functions got images as `x`
# where `x` is tensor with this dimensions:
# 0 - count of images
# 1 - channels... | transganformer-main | transganformer/diff_augment.py |
__version__ = '0.0.17'
| transganformer-main | transganformer/version.py |
from transganformer.transganformer import Transganformer, Generator, Discriminator, Trainer, NanException
| transganformer-main | transganformer/__init__.py |
import os
import fire
import random
from retry.api import retry_call
from tqdm import tqdm
from datetime import datetime
from functools import wraps
from transganformer import Trainer, NanException
import torch
import torch.multiprocessing as mp
import torch.distributed as dist
import numpy as np
def exists(val):
... | transganformer-main | transganformer/cli.py |
import os
import json
import multiprocessing
from random import random
import math
from math import log2, floor, sqrt, log, pi
from functools import partial
from contextlib import contextmanager, ExitStack
from pathlib import Path
from shutil import rmtree
import torch
from torch.cuda.amp import autocast, GradScaler
f... | transganformer-main | transganformer/transganformer.py |
from setuptools import setup, find_packages
setup(
name = 'compressive-transformer-pytorch',
packages = find_packages(exclude=['examples']),
version = '0.4.0',
license='MIT',
description = 'Implementation of Compressive Transformer in Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',... | compressive-transformer-pytorch-master | setup.py |
from compressive_transformer_pytorch import CompressiveTransformer
from compressive_transformer_pytorch.autoregressive_wrapper import AutoregressiveWrapper
import random
import tqdm
import gzip
import numpy as np
import torch
import torch.optim as optim
from torch.nn import functional as F
from torch.utils.data import... | compressive-transformer-pytorch-master | examples/enwik8_simple/train.py |
import torch
from torch import nn
import torch.nn.functional as F
from mogrifier import Mogrifier
import math
from collections import namedtuple
from functools import partial
from inspect import isfunction
# structs
Memory = namedtuple('Memory', ['mem', 'compressed_mem'])
# helper functions
def to(t):
return ... | compressive-transformer-pytorch-master | compressive_transformer_pytorch/compressive_transformer_pytorch.py |
import math
from functools import partial
from collections import namedtuple
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
# structs
Return = namedtuple('Return', ['loss', 'aux_loss', 'is_last_batch'])
# helper functions
def top_p(logits, thres = 0.9)... | compressive-transformer-pytorch-master | compressive_transformer_pytorch/autoregressive_wrapper.py |
from compressive_transformer_pytorch.compressive_transformer_pytorch import CompressiveTransformer
from compressive_transformer_pytorch.autoregressive_wrapper import AutoregressiveWrapper | compressive-transformer-pytorch-master | compressive_transformer_pytorch/__init__.py |
from setuptools import setup, find_packages
setup(
name = 'zorro-pytorch',
packages = find_packages(exclude=[]),
version = '0.1.0',
license='MIT',
description = 'Zorro - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
long_description_content_type = 'text/markdown',
url = 'http... | zorro-pytorch-main | setup.py |
from enum import Enum
import functools
from functools import wraps
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat, pack, unpack
from einops.layers.torch import Rearrange
from beartype import beartype
from beartype.typing import Tuple, Optional, Union
f... | zorro-pytorch-main | zorro_pytorch/zorro_pytorch.py |
from zorro_pytorch.zorro_pytorch import Zorro, TokenTypes
| zorro-pytorch-main | zorro_pytorch/__init__.py |
from setuptools import setup, find_packages
setup(
name = 'g-mlp-gpt',
packages = find_packages(),
version = '0.0.15',
license='MIT',
description = 'gMLP - GPT',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/g-mlp-gpt',
keywords = [
'artificial ... | g-mlp-gpt-main | setup.py |
from g_mlp_gpt import gMLPGPT
from g_mlp_gpt.autoregressive_wrapper import AutoregressiveWrapper
import random
import tqdm
import gzip
import numpy as np
import torch
import torch.optim as optim
from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset
# constants
NUM_BATCHES = int(1e5)
B... | g-mlp-gpt-main | train.py |
import torch
from torch import nn
import torch.nn.functional as F
# helper function
def eval_decorator(fn):
def inner(model, *args, **kwargs):
was_training = model.training
model.eval()
out = fn(model, *args, **kwargs)
model.train(was_training)
return out
return inner
... | g-mlp-gpt-main | g_mlp_gpt/autoregressive_wrapper.py |
import torch
import torch.nn as nn
from operator import itemgetter
from torch.autograd.function import Function
from torch.utils.checkpoint import get_device_states, set_device_states
# for routing arguments into the functions of the reversible layer
def route_args(router, args, depth):
routed_args = [(dict(), dic... | g-mlp-gpt-main | g_mlp_gpt/reversible.py |
from g_mlp_gpt.g_mlp_gpt import gMLPGPT
| g-mlp-gpt-main | g_mlp_gpt/__init__.py |
from math import ceil
from functools import partial
from random import randrange
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from g_mlp_gpt.reversible import ReversibleSequence, SequentialSequence
# functions
def exists(val):
return val is not N... | g-mlp-gpt-main | g_mlp_gpt/g_mlp_gpt.py |
from setuptools import setup, find_packages
setup(
name = 'linear_attention_transformer',
packages = find_packages(exclude=['examples']),
version = '0.19.1',
license='MIT',
description = 'Linear Attention Transformer',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.c... | linear-attention-transformer-master | setup.py |
import math
import torch
from torch import nn
import torch.nn.functional as F
from linear_attention_transformer.linear_attention_transformer import LinearAttentionTransformer, LinearAttentionTransformerLM
def find_module(nn_module, type):
for module in nn_module.modules():
if isinstance(module, type):
... | linear-attention-transformer-master | linear_attention_transformer/autopadder.py |
from functools import partial
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
from linear_attention_transformer.autopadder import Autopadder
def top_p(logits, thres = 0.9):
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cum_pro... | linear-attention-transformer-master | linear_attention_transformer/autoregressive_wrapper.py |
import torch
import torch.nn as nn
from operator import itemgetter
from torch.autograd.function import Function
from torch.utils.checkpoint import get_device_states, set_device_states
# for routing arguments into the functions of the reversible layer
def route_args(router, args, depth):
routed_args = [(dict(), dic... | linear-attention-transformer-master | linear_attention_transformer/reversible.py |
import torch
import torch.nn.functional as F
from torch import nn, einsum
import math
from operator import mul
from math import gcd
from collections import namedtuple
from functools import partial, reduce
from local_attention import LocalAttention
from linformer import LinformerSelfAttention
from product_key_memory i... | linear-attention-transformer-master | linear_attention_transformer/linear_attention_transformer.py |
from linear_attention_transformer.linear_attention_transformer import LinearAttentionTransformer, LinearAttentionTransformerLM, LinformerSettings, LinformerContextSettings
from linear_attention_transformer.autoregressive_wrapper import AutoregressiveWrapper
from linear_attention_transformer.images import ImageLinearAtt... | linear-attention-transformer-master | linear_attention_transformer/__init__.py |
import torch
from torch import nn
class ImageLinearAttention(nn.Module):
def __init__(self, chan, chan_out = None, kernel_size = 1, padding = 0, stride = 1, key_dim = 64, value_dim = 64, heads = 8, norm_queries = True):
super().__init__()
self.chan = chan
chan_out = chan if chan_out is None... | linear-attention-transformer-master | linear_attention_transformer/images.py |
import deepspeed
from linear_attention_transformer import LinearAttentionTransformerLM
from linear_attention_transformer.autoregressive_wrapper import AutoregressiveWrapper
import argparse
import random
import tqdm
import gzip
import numpy as np
import torch
import torch.optim as optim
from torch.nn import functional... | linear-attention-transformer-master | examples/enwik8_deepspeed/train.py |
import tqdm
import torch
import torch.optim as optim
from linear_attention_transformer import LinearAttentionTransformerLM
from linear_attention_transformer.autoregressive_wrapper import AutoregressiveWrapper
# constants
NUM_BATCHES = int(1e5)
BATCH_SIZE = 16
LEARNING_RATE = 1e-4
GENERATE_EVERY = 100
NUM_TOKENS = 1... | linear-attention-transformer-master | examples/toy_tasks/copy_task.py |
from linear_attention_transformer import LinearAttentionTransformerLM
from linear_attention_transformer.autoregressive_wrapper import AutoregressiveWrapper
from product_key_memory import fetch_optimizer_parameters
import random
import tqdm
import gzip
import numpy as np
import torch
import torch.optim as optim
from to... | linear-attention-transformer-master | examples/enwik8_simple/train.py |
"""Install Mesh TensorFlow."""
from setuptools import find_packages
from setuptools import setup
setup(
name='mesh-tensorflow',
version='0.1.18',
description='Mesh TensorFlow',
author='Google Inc.',
author_email='no-reply@google.com',
url='http://github.com/tensorflow/mesh',
license='Apach... | mesh-master | setup.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/optimize.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/layers_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/test_utils.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/utils_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/ops_with_redefined_builtins.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/simd_mesh_impl.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/__init__.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/tpu_variables.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/beam_search.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/ops.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/utils.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/import_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/ops_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/layers.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/placement_mesh_impl.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/simd_mesh_impl_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/test_utils_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/experimental/data_aug_lib.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/experimental/unet.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/experimental/model_executor.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/experimental/__init__.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/experimental/input_reader.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/experimental/data_aug_lib_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/experimental/input_reader_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/experimental/offline_data_aug.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/memory_layers_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/attention.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/utils_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/adaptive_softmax.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/fixup_layers.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/adaptive_softmax_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/funnel_transformer.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/__init__.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/universal_transformer.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/t2t_vocabulary.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/dataset_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/evolved_transformer.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/dataset.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/utils.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/transformer.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/transformer_layers_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/learning_rate_schedules_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/funnel_transformer_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/moe.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/main.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/vocab_embeddings_test.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/vocab_embeddings.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/vocabulary.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/learning_rate_schedules.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/memory_layers.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/transformer_layers.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/transformer/gin/__init__.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/bert/optimization.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/bert/run_squad.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/bert/__init__.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/bert/tokenization.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/bert/run_pretraining.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/bert/run_classifier.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/bert/bert.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/auto_mtf/graph_interface.py |
# coding=utf-8
# Copyright 2021 The Mesh TensorFlow 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 applicab... | mesh-master | mesh_tensorflow/auto_mtf/scheduler_test.py |
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