python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
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"""
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
import datetime
import json
import logging
import os
import time
from pathlib import Path
import ... | EXA-1-master | exa/libraries/LAVIS/lavis/runners/runner_base.py |
"""
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
import datetime
import logging
import os
import time
import torch
import torch.distributed as dis... | EXA-1-master | exa/libraries/LAVIS/lavis/runners/runner_iter.py |
from setuptools import setup, find_packages
setup(
name = 'x-transformers',
packages = find_packages(exclude=['examples']),
version = '1.11.0',
license='MIT',
description = 'X-Transformers - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/x-tr... | EXA-1-master | exa/libraries/x-transformers/setup.py |
from math import ceil
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange, pack, unpack
from x_transformers.autoregressive_wrapper import top_p, top_k, eval_decorator
# helper functions
def exists(val):
return val is not None
def divisible_by(numer, denom):
return... | EXA-1-master | exa/libraries/x-transformers/x_transformers/xl_autoregressive_wrapper.py |
from math import ceil
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange, pack, unpack
def exists(val):
return val is not None
def eval_decorator(fn):
def inner(self, *args, **kwargs):
was_training = self.training
self.eval()
out = fn(self, ... | EXA-1-master | exa/libraries/x-transformers/x_transformers/autoregressive_wrapper.py |
import math
from random import random
import torch
from torch import nn, einsum
import torch.nn.functional as F
from functools import partial, wraps
from inspect import isfunction
from collections import namedtuple
from einops import rearrange, repeat, reduce
from einops.layers.torch import Rearrange
from x_transfo... | EXA-1-master | exa/libraries/x-transformers/x_transformers/x_transformers.py |
from x_transformers.x_transformers import XTransformer, Encoder, Decoder, CrossAttender, Attention, TransformerWrapper, ViTransformerWrapper, ContinuousTransformerWrapper
from x_transformers.autoregressive_wrapper import AutoregressiveWrapper
from x_transformers.nonautoregressive_wrapper import NonAutoregressiveWrappe... | EXA-1-master | exa/libraries/x-transformers/x_transformers/__init__.py |
import torch
from torch import nn
import torch.nn.functional as F
def exists(val):
return val is not None
class ContinuousAutoregressiveWrapper(nn.Module):
def __init__(self, net, ignore_index = -100, pad_value = 0):
super().__init__()
self.net = net
self.max_seq_len = net.max_seq_len
... | EXA-1-master | exa/libraries/x-transformers/x_transformers/continuous_autoregressive_wrapper.py |
import math
from random import random
from contextlib import nullcontext
from collections import namedtuple
import torch
import torch.nn.functional as F
from torch import nn
from einops import rearrange, repeat, pack, unpack
from x_transformers.x_transformers import TransformerWrapper
from typing import Optional
# ... | EXA-1-master | exa/libraries/x-transformers/x_transformers/nonautoregressive_wrapper.py |
import tqdm
import torch
import torch.optim as optim
from x_transformers import XTransformer
# constants
NUM_BATCHES = int(1e5)
BATCH_SIZE = 32
LEARNING_RATE = 3e-4
GENERATE_EVERY = 100
NUM_TOKENS = 16 + 2
ENC_SEQ_LEN = 32
DEC_SEQ_LEN = 64 + 1
# helpers
def cycle():
while True:
prefix = torch.ones((BAT... | EXA-1-master | exa/libraries/x-transformers/examples/toy_tasks/enc_dec_copy.py |
from x_transformers import (
TransformerWrapper,
Encoder,
NonAutoregressiveWrapper
)
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(1e8)
B... | EXA-1-master | exa/libraries/x-transformers/examples/enwik8_simple/train_nar.py |
from x_transformers import TransformerWrapper, Decoder
from x_transformers.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
# const... | EXA-1-master | exa/libraries/x-transformers/examples/enwik8_simple/train.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
import sys
import time
import logging
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision.tran... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/train_bss.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
import sys
import time
import logging
import argparse
import numpy as np
from itertools import chain
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as c... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/train_kd.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
import sys
import time
import logging
import argparse
import numpy as np
from itertools import chain
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as c... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/train_ft.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
import sys
import time
import logging
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision.tran... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/train_dml.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
import sys
import time
import logging
import argparse
import numpy as np
from itertools import chain
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as c... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/train_crd.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
import numpy as np
from PIL import Image
import torchvision.datasets as dst
'''
Modified from https://github.com/HobbitLong/RepDistiller/blob/master/dataset/cifar100.py
'''
class CIFAR10IdxSample(dst... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/dataset.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
import shutil
import numpy as np
import torch
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/utils.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
def define_tsnet(name, num_class, cuda=True):
if name == 'resnet20':
net = resnet20(num_class=num_class)
elif name == 'resnet110':
net = resnet110(num_class=num_class)
... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/network.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
import sys
import time
import logging
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision.tran... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/train_base.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
'''
CC with P-order Taylor Expansion of Gaussian RBF kernel
'''
class CC(nn.Module):
'''
Correlation Congruence for Knowledge Di... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/cc.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def conv1x1(in_channels, out_channels):
return nn.Conv2d(in_channels, out_channels,
kernel_size=1, stride=1,
p... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/vid.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
class AB(nn.Module):
'''
Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons
https://arxiv.org/pdf/1811.0... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/ab.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
'''
In the original paper, AFD is one of components of AFDS.
AFDS: Attention Feature Distillation and Selection
AFD: Attention Fea... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/afd.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
class SoftTarget(nn.Module):
'''
Distilling the Knowledge in a Neural Network
https://arxiv.org/pdf/1503.02531.pdf
'''
def __init__(self,... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/st.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
class IRG(nn.Module):
'''
Knowledge Distillation via Instance Relationship Graph
http://openaccess.thecvf.com/content_CVPR_2019/papers/
Li... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/irg.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
# '''
# NST with Polynomial Kernel, where d=2 and c=0
# It can be treated as matching the Gram matrix of two vectorized feature map.
# '''
# c... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/nst.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
class FT(nn.Module):
'''
araphrasing Complex Network: Network Compression via Factor Transfer
http://papers.nips.cc/paper/7541-paraphrasing... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/ft.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
class SP(nn.Module):
'''
Similarity-Preserving Knowledge Distillation
https://arxiv.org/pdf/1907.09682.pdf
'''
def __init__(self):
supe... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/sp.py |
from .logits import Logits
from .st import SoftTarget
from .at import AT
from .fitnet import Hint
from .nst import NST
from .pkt import PKTCosSim
from .fsp import FSP
from .ft import FT
from .dml import DML
from .kdsvd import KDSVD
from .rkd import RKD
from .ab import AB
from .sp import SP
from .sobolev import Sobolev
... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/__init__.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
'''
AT with sum of absolute values with power p
'''
class AT(nn.Module):
'''
Paying More Attention to Attention: Improving the Performance o... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/at.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
'''
From https://github.com/lenscloth/RKD/blob/master/metric/loss.py
'''
class RKD(nn.Module):
'''
Relational Knowledge Distillation
https:... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/rkd.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import grad
'''
LwM is originally an incremental learning method with
classification/distillation/attention distillation l... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/lwm.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd.gradcheck import zero_gradients
'''
Modified by https://github.com/bhheo/BSS_distillation
'''
def reduce_sum(x, keepdim=Tru... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/bss.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
class Logits(nn.Module):
'''
Do Deep Nets Really Need to be Deep?
http://papers.nips.cc/paper/5484-do-deep-nets-really-need-to-be-deep.pdf
... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/logits.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
'''
Adopted from https://github.com/passalis/probabilistic_kt/blob/master/nn/pkt.py
'''
class PKTCosSim(nn.Module):
'''
Learning Deep Repres... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/pkt.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
class FSP(nn.Module):
'''
A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning
http://openacce... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/fsp.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import grad
class Sobolev(nn.Module):
'''
Sobolev Training for Neural Networks
https://arxiv.org/pdf/1706.04859.pdf
K... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/sobolev.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
class Hint(nn.Module):
'''
FitNets: Hints for Thin Deep Nets
https://arxiv.org/pdf/1412.6550.pdf
'''
def __init__(self):
super(Hint, se... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/fitnet.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
'''
DML with only two networks
'''
class DML(nn.Module):
'''
Deep Mutual Learning
https://zpascal.net/cvpr2018/Zhang_Deep_Mutual_Learning_C... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/dml.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
'''
Modified from https://github.com/clovaai/overhaul-distillation/blob/master/CIFAR-100/distiller.py
'''
class OFD(nn.Modu... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/ofd.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
'''
Modified from https://github.com/HobbitLong/RepDistiller/tree/master/crd
'''
class CRD(nn.Module):
'''
Contrastive Represent... | EXA-1-master | exa/libraries/Knowledge-Distillation-Zoo/kd_losses/crd.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import datetime
import distutils.command.clean
import glob
import importlib.util
import json
impo... | EXA-1-master | exa/libraries/xformers/setup.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
from typing import Any, Tuple, TypeVar
import torch
import torch.nn as nn
from pyre_extensions import TypeVarTuple, Unpa... | EXA-1-master | exa/libraries/xformers/stubs/torch_stub_tests.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import subprocess
from pathlib import Path
from typing import Optional
from packaging import version
# TODO: consolidate... | EXA-1-master | exa/libraries/xformers/packaging/compute_wheel_version.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import shutil
import subprocess
from dataclasses import dataclass, field
from pathlib import Pat... | EXA-1-master | exa/libraries/xformers/packaging/build_conda.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
| EXA-1-master | exa/libraries/xformers/experimental/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
from setuptools import setup
setup(
name="ragged_inference",
author="Facebook AI Research",
version="0.0.0"... | EXA-1-master | exa/libraries/xformers/experimental/setup.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from ragged_inference.test_utils import assert_eq, bf16_support
from ragged_inference.triton_... | EXA-1-master | exa/libraries/xformers/experimental/tests/test_triton_v2_matmul.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import time
import pytest
import torch
from ragged_inference.garbage_pad_ragged_acts import RaggedActivations
from ragg... | EXA-1-master | exa/libraries/xformers/experimental/tests/test_seq_kv_cache.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import time
import pytest
import torch
from ragged_inference.test_utils import assert_eq, bf16_support
from ragged_infe... | EXA-1-master | exa/libraries/xformers/experimental/tests/test_triton_v2_qk_dotprod.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import time
import pytest
import torch
from ragged_inference.garbage_pad_ragged_acts import RaggedActivations
from ragg... | EXA-1-master | exa/libraries/xformers/experimental/tests/test_triton_v2_ragged_qk_dotprod.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
from typing import Any, Dict, Tuple
import numpy as np
import torch
_DTYPE_PRECISIONS = {
torch.float16: (1e-3, 1e... | EXA-1-master | exa/libraries/xformers/experimental/ragged_inference/test_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
| EXA-1-master | exa/libraries/xformers/experimental/ragged_inference/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
from typing import List
import numpy as np
import torch
import triton
import triton.language as tl
@triton.jit
def ga... | EXA-1-master | exa/libraries/xformers/experimental/ragged_inference/garbage_pad_ragged_acts.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
from functools import lru_cache
from typing import List, Tuple
import torch
from ragged_inference.garbage_pad_ragged_ac... | EXA-1-master | exa/libraries/xformers/experimental/ragged_inference/seq_kv_cache.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import torch
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, est... | EXA-1-master | exa/libraries/xformers/experimental/ragged_inference/triton_v2_qk_dotprod.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import torch
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, est... | EXA-1-master | exa/libraries/xformers/experimental/ragged_inference/triton_v2_matmul.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass
from typing import List, Optional
import torch
import triton
import triton.language as... | EXA-1-master | exa/libraries/xformers/experimental/ragged_inference/triton_v2_ragged_qk_dotprod.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
import xformers
try:
import timm
from timm.models.vision_transformer import VisionTr... | EXA-1-master | exa/libraries/xformers/tests/test_timm_sparse.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from xformers.components import Activation
from xformers.components.feedforward import FEEDFO... | EXA-1-master | exa/libraries/xformers/tests/test_feedforward.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
import xformers
from xformers.components import MultiHeadDispatch
from xformers.components.at... | EXA-1-master | exa/libraries/xformers/tests/test_triton_blocksparse.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import logging
import pytest
import torch
from torch.cuda.amp.autocast_mode import autocast
import xformers
from xform... | EXA-1-master | exa/libraries/xformers/tests/test_triton_dropout.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import math
import pytest
import torch
from xformers.components.attention import FavorAttention, ScaledDotProduct
from ... | EXA-1-master | exa/libraries/xformers/tests/test_favor.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import logging
import pytest
import torch
from torch.cuda.amp.autocast_mode import autocast
import xformers
try:
... | EXA-1-master | exa/libraries/xformers/tests/test_triton_layernorm.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import torch
from xformers.factory import xFormer, xFormerConfig
from xformers.helpers.hierarchical_configs import (
... | EXA-1-master | exa/libraries/xformers/tests/test_hierarchical_transformer.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import torch
from xformers.components.attention.utils import (
maybe_merge_masks,
reshape_key_padding_mask,
)
... | EXA-1-master | exa/libraries/xformers/tests/test_attention_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import logging
import pytest
import torch
from torch.cuda.amp.autocast_mode import autocast
import xformers
try:
f... | EXA-1-master | exa/libraries/xformers/tests/test_triton_softmax.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
| EXA-1-master | exa/libraries/xformers/tests/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import copy
import functools
import random
from contextlib import nullcontext
from typing import ContextManager, Optional... | EXA-1-master | exa/libraries/xformers/tests/test_swiglu.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import itertools
import pytest
import torch
import xformers.components.attention.attention_patterns as AP
from xformers... | EXA-1-master | exa/libraries/xformers/tests/test_attention_patterns.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from xformers.components.attention import AttentionMask
@pytest.mark.skipif(
not torch... | EXA-1-master | exa/libraries/xformers/tests/test_attention_mask.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from xformers.components import NormalizationType, PreNorm
class Passthrough(torch.nn.Modu... | EXA-1-master | exa/libraries/xformers/tests/test_residual.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from xformers.components import MultiHeadDispatch
# Automatically test all the registered at... | EXA-1-master | exa/libraries/xformers/tests/test_compositional_attention.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from torch import nn
from xformers import _is_triton_available
from xformers.components.atten... | EXA-1-master | exa/libraries/xformers/tests/test_core_attention.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import logging
import pytest
import torch
from torch.cuda.amp.autocast_mode import autocast
import xformers
from xforme... | EXA-1-master | exa/libraries/xformers/tests/test_triton_fused_linear.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import random
import pytest
import torch
import xformers.ops as xops
from .utils import assert_allclose
cuda_only = p... | EXA-1-master | exa/libraries/xformers/tests/test_indexing.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from xformers.components import PatchEmbeddingConfig, build_patch_embedding
from xformers.com... | EXA-1-master | exa/libraries/xformers/tests/test_embedding.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
# needed to register custom ops
import xformers # noqa: F401
import xformers.components.atte... | EXA-1-master | exa/libraries/xformers/tests/test_custom_ops.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from torch.utils._python_dispatch import TorchDispatchMode, _get_current_dispatch_mode
import... | EXA-1-master | exa/libraries/xformers/tests/test_profiler.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import math
import random
from typing import List, Optional, Sequence, Tuple, Type, TypeVar
import pytest
import torch
f... | EXA-1-master | exa/libraries/xformers/tests/test_mem_eff_attention.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
# Automatically fetch all registered attentions and Feedforwards
from xformers.components imp... | EXA-1-master | exa/libraries/xformers/tests/test_block_factory.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import torch
def assert_allclose(
out: torch.Tensor,
ref: torch.Tensor,
msg: str = "failed",
atol: floa... | EXA-1-master | exa/libraries/xformers/tests/utils.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from xformers.components.attention import maybe_sparsify
from xformers.components.attention._... | EXA-1-master | exa/libraries/xformers/tests/test_sparsecs.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
# needed to register custom ops
import xformers # noqa: F401
from xformers.ops import masked... | EXA-1-master | exa/libraries/xformers/tests/test_sparse_tensors.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
# CREDITS: Initially suggested by Jason Ramapuram, see
# https://github.com/facebookresearch/xformers/issues/203
import ... | EXA-1-master | exa/libraries/xformers/tests/test_pickling.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
from xformers.components.positional_embedding import RotaryEmbedding
from xformers.components... | EXA-1-master | exa/libraries/xformers/tests/test_rotary_embeddings.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Tuple
import pytest
import torch
from xformers.components import (
InputProjection,
... | EXA-1-master | exa/libraries/xformers/tests/test_attentions.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import torch
from xformers.components.attention import GlobalAttention, ScaledDotProduct
def test_global_attention():
... | EXA-1-master | exa/libraries/xformers/tests/test_global_attention.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
from hydra.core.config_store import ConfigStore
from xformers.factory.hydra_helper import import_xformer_config_schema
... | EXA-1-master | exa/libraries/xformers/tests/test_hydra_helper.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import random
import pytest
import torch
import xformers.ops
@pytest.mark.parametrize("contiguous", [True, False])
@p... | EXA-1-master | exa/libraries/xformers/tests/test_unbind.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import random
import pytest
import torch
from xformers.components.attention import OrthoFormerAttention, ScaledDotProduc... | EXA-1-master | exa/libraries/xformers/tests/test_ortho_attention.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import random
import pytest
import torch
from xformers import _is_triton_available
if _is_triton_available():
from... | EXA-1-master | exa/libraries/xformers/tests/test_pytorch_transformer_parity.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import torch
import xformers
SHAPES = [
(384, 128),
(8 * 384, 128),
(34, 128),
(16, 128)... | EXA-1-master | exa/libraries/xformers/tests/test_triton_basics.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
from contextlib import nullcontext
import pytest
import torch
import xformers.factory.weight_init as xformers_weight_in... | EXA-1-master | exa/libraries/xformers/tests/test_model_factory.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import random
import pytest
import torch
from xformers.components.attention import NystromAttention, ScaledDotProduct
fr... | EXA-1-master | exa/libraries/xformers/tests/test_nystrom_attention.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import random
import pytest
import torch
from xformers.factory.model_factory import xFormer, xFormerConfig
BATCH = 2
S... | EXA-1-master | exa/libraries/xformers/tests/test_reversible.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
# type: ignore
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the... | EXA-1-master | exa/libraries/xformers/docs/source/conf.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import pytorch_lightning as pl
import torch
from pl_bolts.datamodules import CIFAR10DataModule
from torch import nn
from... | EXA-1-master | exa/libraries/xformers/examples/cifar_MetaFormer.py |
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