python_code stringlengths 0 1.02M | repo_name stringlengths 9 48 | file_path stringlengths 5 114 |
|---|---|---|
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... | sinkhorn-transformer-master | sinkhorn_transformer/reversible.py |
from sinkhorn_transformer.sinkhorn_transformer import SinkhornTransformer, SinkhornTransformerLM, SinkhornSelfAttention
from sinkhorn_transformer.autoregressive_wrapper import AutoregressiveWrapper
from sinkhorn_transformer.autopadder import Autopadder
| sinkhorn-transformer-master | sinkhorn_transformer/__init__.py |
import math
import torch
from torch import nn
from operator import mul
from math import gcd
import torch.nn.functional as F
from inspect import isfunction
from functools import partial, wraps, reduce
from local_attention import LocalAttention
from axial_positional_embedding import AxialPositionalEmbedding
from product... | sinkhorn-transformer-master | sinkhorn_transformer/sinkhorn_transformer.py |
import os
from copy import deepcopy
import torch
import torch.multiprocessing as mp
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from st_moe_pytorch.st_moe_pytorch import Experts, Expert
from st_moe_pytorch.distributed import all_gather_variable_dim
def setup(rank, wo... | st-moe-pytorch-main | assert.py |
from setuptools import setup, find_packages
setup(
name = 'st-moe-pytorch',
packages = find_packages(exclude=[]),
version = '0.1.1',
license='MIT',
description = 'ST - Mixture of Experts - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
long_description_content_type = 'text/markd... | st-moe-pytorch-main | setup.py |
from st_moe_pytorch.st_moe_pytorch import (
MoE,
SparseMoEBlock
)
| st-moe-pytorch-main | st_moe_pytorch/__init__.py |
import torch
from torch import nn
import torch.nn.functional as F
from torch.autograd import Function
import torch.distributed as dist
from einops import rearrange, pack, unpack
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def divisible_by(num, den):
ret... | st-moe-pytorch-main | st_moe_pytorch/distributed.py |
from functools import partial
from collections import namedtuple
from typing import Optional, Tuple, Union
import torch
from torch.nn import Module, ModuleList
from torch import nn, einsum
import torch.nn.functional as F
from beartype import beartype
from einops import rearrange, repeat, reduce, pack, unpack
from c... | st-moe-pytorch-main | st_moe_pytorch/st_moe_pytorch.py |
from setuptools import setup, find_packages
setup(
name = 'block-recurrent-transformer-pytorch',
packages = find_packages(exclude=[]),
version = '0.4.3',
license='MIT',
description = 'Block Recurrent Transformer - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
long_description_c... | block-recurrent-transformer-pytorch-main | setup.py |
import gzip
import random
import tqdm
import numpy as np
import torch
from torch.optim import Adam
from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset
from accelerate import Accelerator
from block_recurrent_transformer_pytorch import BlockRecurrentTransformer, RecurrentTrainerWrapper... | block-recurrent-transformer-pytorch-main | train.py |
import torch
from packaging import version
if version.parse(torch.__version__) >= version.parse('2.0.0'):
from einops._torch_specific import allow_ops_in_compiled_graph
allow_ops_in_compiled_graph()
from block_recurrent_transformer_pytorch.block_recurrent_transformer_pytorch import BlockRecurrentTransformer, ... | block-recurrent-transformer-pytorch-main | block_recurrent_transformer_pytorch/__init__.py |
import math
from random import random
from functools import wraps, partial
from itertools import zip_longest
from collections import namedtuple, defaultdict
from packaging import version
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat, pack, unpack
from ... | block-recurrent-transformer-pytorch-main | block_recurrent_transformer_pytorch/block_recurrent_transformer_pytorch.py |
from setuptools import setup, find_packages
setup(
name = 'long-short-transformer',
packages = find_packages(),
version = '0.0.5',
license='MIT',
description = 'Long Short Transformer - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/long-shor... | long-short-transformer-main | setup.py |
from long_short_transformer import LongShortTransformer
from long_short_transformer.autoregressive_wrapper import AutoregressiveWrapper
import random
import tqdm
import gzip
import numpy as np
import torch
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
# constants
NUM_BATCHES = int(1e6... | long-short-transformer-main | train.py |
from math import gcd, ceil
import functools
import torch
from torch import nn, einsum
import torch.nn.functional as F
from rotary_embedding_torch import RotaryEmbedding, apply_rotary_emb
from einops import rearrange, repeat
# helpers
def exists(val):
return val is not None
def default(val, d):
return val ... | long-short-transformer-main | long_short_transformer/long_short_transformer.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
... | long-short-transformer-main | long_short_transformer/autoregressive_wrapper.py |
from long_short_transformer.long_short_transformer import LongShortTransformer, LongShortAttention
| long-short-transformer-main | long_short_transformer/__init__.py |
from setuptools import setup, find_packages
setup(
name = 'scattering-transform',
packages = find_packages(),
version = '0.0.7',
license='MIT',
description = 'Scattering Transform module from the paper Scattering Compositional Learner',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url =... | scattering-compositional-learner-master | setup.py |
from scattering_transform.scattering_transform import SCL, ScatteringTransform, SCLTrainingWrapper
| scattering-compositional-learner-master | scattering_transform/__init__.py |
import torch
from torch import nn
import torch.nn.functional as F
# helper functions
def default(val, default_val):
return val if val is not None else default_val
def expand_dim(t, dim, k):
t = t.unsqueeze(dim)
expand_shape = [-1] * len(t.shape)
expand_shape[dim] = k
return t.expand(*expand_shape... | scattering-compositional-learner-master | scattering_transform/scattering_transform.py |
from dotenv import load_dotenv
# set path to cache in .env and unset the next comment
# load_dotenv()
from enformer_pytorch import Enformer
from tf_bind_transformer import AdapterModel, Trainer
# instantiate enformer or load pretrained
enformer = Enformer.from_hparams(
dim = 768,
depth = 4,
heads = 8,
... | tf-bind-transformer-main | finetune_binary_pred.py |
import click
from tqdm import tqdm
from pathlib import Path
from Bio import SeqIO
from tf_bind_transformer.protein_utils import get_protein_embedder
@click.command()
@click.option('--model-name', default = 'protalbert', help = 'Protein model name')
@click.option('--fasta-folder', help = 'Path to factor fastas', requir... | tf-bind-transformer-main | precache_proteins.py |
from setuptools import setup, find_packages
setup(
name = 'tf-bind-transformer',
packages = find_packages(exclude=[]),
version = '0.0.118',
license='MIT',
description = 'Transformer for Transcription Factor Binding',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com... | tf-bind-transformer-main | setup.py |
from dotenv import load_dotenv
# set path to cache in .env and unset the next comment
# load_dotenv()
from enformer_pytorch import Enformer
from tf_bind_transformer import AdapterModel, BigWigTrainer
# training constants
BATCH_SIZE = 1
GRAD_ACCUM_STEPS = 8
LEARNING_RATE = 1e-4 # Deepmind used 1e-4 for fine-tuning... | tf-bind-transformer-main | finetune_track.py |
import torch
from torch import nn
from tf_bind_transformer.optimizer import get_optimizer
from tf_bind_transformer.data_bigwig import BigWigDataset, BigWigTracksOnlyDataset, get_bigwig_dataloader, get_bigwig_tracks_dataloader
from enformer_pytorch.modeling_enformer import poisson_loss, pearson_corr_coef
def exists(val... | tf-bind-transformer-main | tf_bind_transformer/training_utils_bigwig.py |
import torch
from torch import nn
from einops import rearrange
from torch import einsum
from bidirectional_cross_attention import BidirectionalCrossAttention
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
# classes
def FeedForward(dim, mult = 4, dropout = 0.):
... | tf-bind-transformer-main | tf_bind_transformer/attention.py |
# for fetching transcription factor sequences
GENE_IDENTIFIER_MAP = {
'RXR': 'RXRA'
}
NAMES_WITH_HYPHENS = {
'NKX3-1',
'NKX2-1',
'NKX2-5',
'SS18-SSX'
}
def parse_gene_name(name):
if '-' not in name or name in NAMES_WITH_HYPHENS:
name = GENE_IDENTIFIER_MAP.get(name, name)
if '... | tf-bind-transformer-main | tf_bind_transformer/gene_utils.py |
import torch
import os
import logging
from transformers import AutoTokenizer, AutoModelForMaskedLM, logging
from tf_bind_transformer.cache_utils import cache_fn, run_once
logging.set_verbosity_error()
def exists(val):
return val is not None
def map_values(fn, dictionary):
return {k: fn(v) for k, v in diction... | tf-bind-transformer-main | tf_bind_transformer/context_utils.py |
from tf_bind_transformer.tf_bind_transformer import AdapterModel
from tf_bind_transformer.training_utils import Trainer
from tf_bind_transformer.training_utils_bigwig import BigWigTrainer
| tf-bind-transformer-main | tf_bind_transformer/__init__.py |
import torch
import os
import re
from pathlib import Path
from functools import partial
import esm
from torch.nn.utils.rnn import pad_sequence
from transformers import AlbertTokenizer, AutoModelForMaskedLM, logging
from tf_bind_transformer.cache_utils import cache_fn, run_once, md5_hash_fn
def exists(val):
return ... | tf-bind-transformer-main | tf_bind_transformer/protein_utils.py |
import copy
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from functools import wraps
from einops import rearrange, reduce, repeat
from einops.layers.torch import Rearrange, Reduce
from contextlib import contextmanager
from enformer_pytorch import Enformer
from enformer_pytorc... | tf-bind-transformer-main | tf_bind_transformer/tf_bind_transformer.py |
import os
from shutil import rmtree
import torch
import hashlib
from functools import wraps
from pathlib import Path
def exists(val):
return val is not None
# constants
CACHE_PATH = Path(os.getenv('TF_BIND_CACHE_PATH', os.path.expanduser('~/.cache.tf.bind.transformer')))
CACHE_PATH.mkdir(exist_ok = True, parents... | tf-bind-transformer-main | tf_bind_transformer/cache_utils.py |
import torch
from torch import nn
from tf_bind_transformer.optimizer import get_optimizer
from tf_bind_transformer.data import read_bed, collate_dl_outputs, get_dataloader, remap_df_add_experiment_target_cell
from tf_bind_transformer.data import RemapAllPeakDataset, NegativePeakDataset, ScopedNegativePeakDataset
def e... | tf-bind-transformer-main | tf_bind_transformer/training_utils.py |
from pathlib import Path
import polars as pl
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from tf_bind_transformer.data import FactorProteinDataset, ContextDataset, cast_list, filter_df_by_tfactor_fastas
from tf_bind_transformer.data import pl_isin, pl_notin, fetch_experiments_inde... | tf-bind-transformer-main | tf_bind_transformer/data_bigwig.py |
from torch.optim import AdamW
def separate_weight_decayable_params(params):
no_wd_params = set([param for param in params if param.ndim < 2])
wd_params = set(params) - no_wd_params
return wd_params, no_wd_params
def get_optimizer(params, lr = 3e-4, wd = 1e-1, filter_by_requires_grad = False):
if filte... | tf-bind-transformer-main | tf_bind_transformer/optimizer.py |
from Bio import SeqIO
from random import choice, randrange
from pathlib import Path
import functools
import polars as pl
from collections import defaultdict
import os
import json
import shutil
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from tf_bind_tr... | tf-bind-transformer-main | tf_bind_transformer/data.py |
import polars as pl
from pathlib import Path
from tf_bind_transformer.data import read_bed, save_bed
def generate_separate_exp_target_cell_beds(
remap_file,
*,
output_folder = './negative-peaks-per-target',
exp_target_cell_type_col = 'column_4'
):
output_folder = Path(output_folder)
output_fold... | tf-bind-transformer-main | scripts/remap_to_separate_exp_target_cell_beds.py |
import json
import tqdm
import requests
NCBI_TAX_ID = dict(
human = 9606,
mouse = 10090
)
SPECIES = 'human'
API_URL = 'https://remap.univ-amu.fr/api/v1/'
def get_json(url, params = dict()):
headers = dict(Accept = 'application/json')
resp = requests.get(url, params = params, headers = headers)
re... | tf-bind-transformer-main | scripts/download_experiments.py |
#/usr/bin/python
import polars as pl
import numpy as np
from pathlib import Path
import sys
NEGATIVE_PEAK_PATH = sys.argv[1]
NUMROWS = int(sys.argv[2])
ID_COLUMN = 'column_6'
df = pl.read_csv(NEGATIVE_PEAK_PATH, sep = '\t', has_headers = False)
np_array = df.get_column(ID_COLUMN).to_numpy()
to_save = np.full((NUMRO... | tf-bind-transformer-main | scripts/negative_peak_to_bool_npy.py |
import requests
from pathlib import Path
import click
import polars as pl
from tqdm import tqdm
from tf_bind_transformer.gene_utils import parse_gene_name
from tf_bind_transformer.data import read_bed
# constants
UNIPROT_URL = 'http://www.uniprot.org'
DEFAULT_REMAP_PATH = dict(
HUMAN = './remap2022_crm_macs2_hg3... | tf-bind-transformer-main | scripts/fetch_factor_fastas.py |
from setuptools import setup, find_packages
setup(
name = 'vector_quantize_pytorch',
packages = find_packages(),
version = '1.7.1',
license='MIT',
description = 'Vector Quantization - Pytorch',
long_description_content_type = 'text/markdown',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',... | vector-quantize-pytorch-master | setup.py |
import torch
from torch import nn, einsum
import torch.nn.functional as F
from vector_quantize_pytorch.vector_quantize_pytorch import VectorQuantize
from einops import rearrange, repeat, pack, unpack
def exists(val):
return val is not None
class RandomProjectionQuantizer(nn.Module):
""" https://arxiv.org/abs... | vector-quantize-pytorch-master | vector_quantize_pytorch/random_projection_quantizer.py |
from vector_quantize_pytorch.vector_quantize_pytorch import VectorQuantize
from vector_quantize_pytorch.residual_vq import ResidualVQ, GroupedResidualVQ
from vector_quantize_pytorch.random_projection_quantizer import RandomProjectionQuantizer | vector-quantize-pytorch-master | vector_quantize_pytorch/__init__.py |
from functools import partial
import torch
from torch import nn, einsum
import torch.nn.functional as F
import torch.distributed as distributed
from torch.optim import Optimizer
from torch.cuda.amp import autocast
from einops import rearrange, repeat, reduce, pack, unpack
from typing import Callable
def exists(val)... | vector-quantize-pytorch-master | vector_quantize_pytorch/vector_quantize_pytorch.py |
from math import ceil
from functools import partial
from itertools import zip_longest
from random import randrange
import torch
from torch import nn
import torch.nn.functional as F
from vector_quantize_pytorch.vector_quantize_pytorch import VectorQuantize
from einops import rearrange, repeat, pack, unpack
# helper f... | vector-quantize-pytorch-master | vector_quantize_pytorch/residual_vq.py |
# FashionMnist VQ experiment with various settings.
# From https://github.com/minyoungg/vqtorch/blob/main/examples/autoencoder.py
from tqdm.auto import trange
import torch
import torch.nn as nn
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from vector_quantize_pytorch import Ve... | vector-quantize-pytorch-master | examples/autoencoder.py |
from setuptools import setup, find_packages
setup(
name = 'coco-lm-pytorch',
packages = find_packages(),
version = '0.0.2',
license='MIT',
description = 'COCO - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/coco-lm-pytorch',
keywords = [
... | coco-lm-pytorch-main | setup.py |
from coco_lm_pytorch.coco_lm_pytorch import COCO
| coco-lm-pytorch-main | coco_lm_pytorch/__init__.py |
import math
from functools import reduce
import torch
from torch import nn, einsum
import torch.nn.functional as F
# helpers
def log(t, eps=1e-9):
return torch.log(t + eps)
def norm(t):
return F.normalize(t, p = 2, dim = -1)
def gumbel_noise(t):
noise = torch.zeros_like(t).uniform_(0, 1)
return -lo... | coco-lm-pytorch-main | coco_lm_pytorch/coco_lm_pytorch.py |
from setuptools import setup, find_packages
setup(
name = 'siren-pytorch',
packages = find_packages(),
version = '0.1.7',
license='MIT',
description = 'Implicit Neural Representations with Periodic Activation Functions',
long_description_content_type = 'text/markdown',
author = 'Phil Wang',
author_emai... | siren-pytorch-master | setup.py |
from siren_pytorch.siren_pytorch import Sine, Siren, SirenNet, SirenWrapper
| siren-pytorch-master | siren_pytorch/__init__.py |
import math
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange
# helpers
def exists(val):
return val is not None
def cast_tuple(val, repeat = 1):
return val if isinstance(val, tuple) else ((val,) * repeat)
# sin activation
class Sine(nn.Module):
def __init__(... | siren-pytorch-master | siren_pytorch/siren_pytorch.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | setup.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/random_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/parallel_test.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/fft_test.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/debug_nans_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/lax_control_flow_test.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/jax_to_hlo_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/lax_scipy_test.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/multi_device_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/batching_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/lax_numpy_einsum_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/multibackend_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/stax_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/generated_fun_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/optimizers_test.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/dtypes_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/lax_numpy_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/core_test.py |
# Copyright 2020 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/util_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/masking_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/scipy_stats_test.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/nn_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/linalg_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/lax_numpy_indexing_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/pmap_test.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/scipy_ndimage_test.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/vectorize_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/optix_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/api_test.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/infeed_test.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/xla_bridge_test.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/tree_util_tests.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/loops_test.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/lax_test.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | tests/benchmarks/xla.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | docs/conf.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | jaxlib/version.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | jaxlib/cuda_prng.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | jaxlib/cusolver.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | examples/examples_test.py |
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | examples/differentially_private_sgd.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | examples/mnist_classifier.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | examples/gaussian_process_regression.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | examples/kernel_lsq.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | examples/mnist_classifier_fromscratch.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | examples/datasets.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | examples/__init__.py |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | jax-master | examples/spmd_mnist_classifier_fromscratch.py |
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