repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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ELLE | ELLE-main/apex/apex/pyprof/prof/loss.py | from collections import OrderedDict
from .utility import Utility
from .base import OperatorLayerBase
#TODO: Add support for additional loss functions.
class MSELoss(OperatorLayerBase):
def __init__(self, d):
marker = eval(d.argMarker[0])
mod = marker['mod']
op = marker['op']
args = marker['args']
self.ma... | 1,716 | 19.2 | 78 | py |
ELLE | ELLE-main/apex/apex/pyprof/prof/index_slice_join_mutate.py | from collections import OrderedDict
from .utility import Utility
import numpy as np
from .base import OperatorLayerBase
class Cat(OperatorLayerBase):
def __init__(self, d):
marker = eval(d.argMarker[0])
mod = marker['mod']
op = marker['op']
args = marker['args']
self.marker = marker
self.mod_ = mod
se... | 8,004 | 18.059524 | 94 | py |
ELLE | ELLE-main/apex/apex/pyprof/prof/linear.py | from collections import OrderedDict
from .utility import Utility
from .base import OperatorLayerBase
class Linear(OperatorLayerBase):
'''
Notes:
If the bias occurs before the GEMM, then its 1 write (bias expansion).
If the bias occurs after, then its 1 read and 1 write.
bias in bprop is a reduction and hence is ... | 4,426 | 22.42328 | 133 | py |
ELLE | ELLE-main/apex/apex/pyprof/prof/dropout.py | from collections import OrderedDict
from .utility import Utility
from .base import OperatorLayerBase
class Dropout(OperatorLayerBase):
def __init__(self, d):
marker = eval(d.argMarker[0])
mod = marker['mod']
op = marker['op']
args = marker['args']
self.marker = marker
self.mod_ = mod
self.op_ = op
s... | 999 | 18.607843 | 59 | py |
ELLE | ELLE-main/apex/apex/pyprof/prof/conv.py | from collections import OrderedDict
from .utility import Utility
from .base import OperatorLayerBase
class Conv(OperatorLayerBase):
"""
# N = batch size
# C,H,W = input channels, height, width
# K,P,Q = output channels, height, width
# R,S = filter height, width
# g = groups
"""
#todo: refine winograd and FF... | 6,355 | 25.818565 | 292 | py |
ELLE | ELLE-main/apex/apex/pyprof/prof/blas.py | from collections import OrderedDict
from .utility import Utility
from .base import OperatorLayerBase
import numpy as np
TC_GEMMS = ["884gemm", "1688gemm"]
class Addmm(OperatorLayerBase):
def __init__(self, d):
marker = eval(d.argMarker[0])
mod = marker['mod']
op = marker['op']
args = marker['args']
self.... | 6,773 | 18.865103 | 96 | py |
ELLE | ELLE-main/apex/apex/multi_tensor_apply/multi_tensor_apply.py | import torch
class MultiTensorApply(object):
available = False
warned = False
def __init__(self, chunk_size):
try:
import amp_C
MultiTensorApply.available = True
self.chunk_size = chunk_size
except ImportError as err:
MultiTensorApply.availab... | 991 | 31 | 82 | py |
ELLE | ELLE-main/apex/apex/optimizers/fused_adagrad.py | import torch
from apex.multi_tensor_apply import multi_tensor_applier
class FusedAdagrad(torch.optim.Optimizer):
"""Implements Adagrad algorithm.
Currently GPU-only. Requires Apex to be installed via
``pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./``.
This... | 5,231 | 41.885246 | 145 | py |
ELLE | ELLE-main/apex/apex/optimizers/fused_novograd.py | import torch
from apex.multi_tensor_apply import multi_tensor_applier
class FusedNovoGrad(torch.optim.Optimizer):
"""Implements NovoGrad algorithm.
Currently GPU-only. Requires Apex to be installed via
``pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./``.
Th... | 10,116 | 46.947867 | 145 | py |
ELLE | ELLE-main/apex/apex/optimizers/fused_sgd.py | import torch
from torch.optim.optimizer import Optimizer, required
from apex.multi_tensor_apply import multi_tensor_applier
class FusedSGD(Optimizer):
r"""Implements stochastic gradient descent (optionally with momentum).
Currently GPU-only. Requires Apex to be installed via
``pip install -v --no-cache-... | 10,041 | 43.04386 | 145 | py |
ELLE | ELLE-main/apex/apex/optimizers/fused_lamb.py | import torch
from apex.multi_tensor_apply import multi_tensor_applier
class FusedLAMB(torch.optim.Optimizer):
"""Implements LAMB algorithm.
Currently GPU-only. Requires Apex to be installed via
``pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./``.
This versi... | 9,910 | 44.884259 | 145 | py |
ELLE | ELLE-main/apex/apex/optimizers/fused_adam.py | import torch
from apex.multi_tensor_apply import multi_tensor_applier
class FusedAdam(torch.optim.Optimizer):
"""Implements Adam algorithm.
Currently GPU-only. Requires Apex to be installed via
``pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./``.
This versi... | 7,661 | 43.289017 | 151 | py |
ELLE | ELLE-main/apex/apex/contrib/sparsity/asp.py | import types
import torch
from .sparse_masklib import create_mask
torchvision_imported=True
try:
import torchvision
except ImportError:
print("[ASP][Warning] torchvision cannot be imported.")
torchvision_imported=False
def eligible_modules(model, whitelist_layer_types, allowed_layer_names, disallowed_laye... | 11,740 | 52.857798 | 193 | py |
ELLE | ELLE-main/apex/apex/contrib/sparsity/sparse_masklib.py | import sys
import torch
import numpy as np
import collections
from itertools import permutations
""" compute density (helper fn to compute % NNZs in a tensor) """
def fill(x):
return float(x.nonzero().size(0))/torch.numel(x)
""" reshape matrix into m-dimensional vectors: (h,w) -> (hw/m, m) """
def reshape_1d(mat... | 7,291 | 38.416216 | 103 | py |
ELLE | ELLE-main/apex/apex/contrib/sparsity/test/checkpointing_test_reference.py | from collections import OrderedDict
import torch
from apex.optimizers import FusedAdam
from apex.contrib.sparsity import ASP
#
# Reference run for checkpointing test (part1 + part2)
#
def build_model(args):
od = OrderedDict()
for i in range(args.num_layers):
if i == 0:
od['linear_layer_%d... | 3,177 | 31.762887 | 125 | py |
ELLE | ELLE-main/apex/apex/contrib/sparsity/test/toy_problem.py | from collections import OrderedDict
import torch
from apex.optimizers import FusedAdam
from apex.contrib.sparsity import ASP
def build_model(args):
od = OrderedDict()
for i in range(args.num_layers):
if i == 0:
od['linear_layer_%d' % (i+1)] = torch.nn.Linear(args.input_features, args.hidde... | 3,217 | 35.568182 | 104 | py |
ELLE | ELLE-main/apex/apex/contrib/sparsity/test/checkpointing_test_part2.py | from collections import OrderedDict
import torch
from apex.optimizers import FusedAdam
from apex.contrib.sparsity import ASP
def build_model(args):
od = OrderedDict()
for i in range(args.num_layers):
if i == 0:
od['linear_layer_%d' % (i+1)] = torch.nn.Linear(args.input_features, args.hidde... | 3,131 | 38.15 | 151 | py |
ELLE | ELLE-main/apex/apex/contrib/sparsity/test/checkpointing_test_part1.py | from collections import OrderedDict
import torch
from apex.optimizers import FusedAdam
from apex.contrib.sparsity import ASP
def build_model(args):
od = OrderedDict()
for i in range(args.num_layers):
if i == 0:
od['linear_layer_%d' % (i+1)] = torch.nn.Linear(args.input_features, args.hidde... | 3,353 | 34.305263 | 151 | py |
ELLE | ELLE-main/apex/apex/contrib/transducer/transducer.py | import torch
import transducer_loss_cuda
import transducer_joint_cuda
class TransducerJoint(torch.nn.Module):
"""Transducer joint
Detail of this loss function can be found in: Sequence Transduction with Recurrent Neural
Networks
Arguments:
pack_output (bool, optional): whether to pack the out... | 8,143 | 49.271605 | 101 | py |
ELLE | ELLE-main/apex/apex/contrib/groupbn/batch_norm.py | import torch
import numpy as np
from torch.nn.modules.batchnorm import _BatchNorm
import bnp
class bn_NHWC_impl(torch.autograd.Function):
@staticmethod
def forward(ctx, x, s, b, rm, riv, mini_m, mini_riv, ret_cta, mom, epsilon, fuse_relu, is_train, bn_group, my_data, pair_data, magic, pair_data2, pair_data3, ... | 11,208 | 48.597345 | 229 | py |
ELLE | ELLE-main/apex/apex/contrib/groupbn/__init__.py | try:
import torch
import bnp
from .batch_norm import BatchNorm2d_NHWC
del torch
del bnp
del batch_norm
except ImportError as err:
print("apex was installed without --bnp flag, contrib.groupbn is not available")
| 239 | 23 | 84 | py |
ELLE | ELLE-main/apex/apex/contrib/examples/multihead_attn/func_test_multihead_attn.py | import torch
import torch.nn.functional as F
import argparse
from apex.contrib.multihead_attn import SelfMultiheadAttn
from apex.contrib.multihead_attn import EncdecMultiheadAttn
parser = argparse.ArgumentParser(description='Multihead Attention Standalone Test')
parser.add_argument('--seq-length', default=64, type=in... | 5,740 | 51.669725 | 164 | py |
ELLE | ELLE-main/apex/apex/contrib/examples/multihead_attn/perf_test_multihead_attn.py | import torch
import torch.nn.functional as F
import argparse
from apex.contrib.multihead_attn import SelfMultiheadAttn
from apex.contrib.multihead_attn import EncdecMultiheadAttn
parser = argparse.ArgumentParser(description='Multihead Attention Standalone Test')
parser.add_argument('--seq-length', default=64, type=in... | 6,163 | 52.137931 | 157 | py |
ELLE | ELLE-main/apex/apex/contrib/test/test_label_smoothing.py | import torch
from apex.contrib import xentropy as label_smoothing
import unittest
import warnings
import random
import numpy as np
import time
def label_smoothing_raw(x, target, padding_idx, smoothing):
logprobs = torch.nn.functional.log_softmax(x, dim=-1, dtype=torch.float32)
non_pad_mask = (target != paddi... | 4,800 | 36.217054 | 85 | py |
ELLE | ELLE-main/apex/apex/contrib/test/transducer/test_transducer_joint.py | import torch
import unittest
from apex.contrib.transducer import TransducerJoint
import transducer_ref
class TransducerJointTest(unittest.TestCase):
def setUp(self, seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def gen_input(self, for_vector_kernel):
self.B = 4
... | 4,367 | 39.82243 | 100 | py |
ELLE | ELLE-main/apex/apex/contrib/test/transducer/transducer_ref.py | import torch
import numpy as np
import pdb
def transducer_loss_reference(x, label, f_len, y_len, blank_idx, loss_grad):
def log_sum_exp(a, b):
if (a >= b):
return a + torch.log(1 + torch.exp(b-a))
else:
return b + torch.log(1 + torch.exp(a-b))
def forward_alpha(x, label... | 4,341 | 41.15534 | 104 | py |
ELLE | ELLE-main/apex/apex/contrib/test/transducer/test_transducer_loss.py | import torch
import unittest
from apex.contrib.transducer import TransducerLoss
import transducer_ref
class TransducerLossTest(unittest.TestCase):
def setUp(self, seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def gen_input(self, scalar_t):
self.B = 5
T_mi... | 5,899 | 47.760331 | 100 | py |
ELLE | ELLE-main/apex/apex/contrib/test/multihead_attn/test_encdec_multihead_attn_norm_add.py | import torch
import unittest
from apex.contrib.multihead_attn import EncdecMultiheadAttn
class EncdecMultiheadAttnNormAddTest(unittest.TestCase):
def setUp(self, seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
self.seq_length = 80
self.sequences = 10
... | 3,875 | 48.692308 | 110 | py |
ELLE | ELLE-main/apex/apex/contrib/test/multihead_attn/test_fast_self_multihead_attn_bias.py | import torch
import unittest
from apex.contrib.multihead_attn import SelfMultiheadAttn
class SelfMultiheadAttnTest(unittest.TestCase):
def setUp(self, seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
self.seq_length = 80
self.sequences = 10
self.h... | 3,668 | 46.038462 | 108 | py |
ELLE | ELLE-main/apex/apex/contrib/test/multihead_attn/test_self_multihead_attn.py | import torch
import unittest
from apex.contrib.multihead_attn import SelfMultiheadAttn
class SelfMultiheadAttnTest(unittest.TestCase):
def setUp(self, seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
self.seq_length = 80
self.sequences = 10
self.h... | 6,569 | 49.152672 | 147 | py |
ELLE | ELLE-main/apex/apex/contrib/test/multihead_attn/test_encdec_multihead_attn.py | import torch
import unittest
from apex.contrib.multihead_attn import EncdecMultiheadAttn
class EncdecMultiheadAttnTest(unittest.TestCase):
def setUp(self, seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
self.seq_length = 80
self.sequences = 10
se... | 7,429 | 53.233577 | 152 | py |
ELLE | ELLE-main/apex/apex/contrib/test/multihead_attn/test_self_multihead_attn_norm_add.py | import torch
import unittest
from apex.contrib.multihead_attn import SelfMultiheadAttn
class SelfMultiheadAttnNormAddTest(unittest.TestCase):
def setUp(self, seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
self.seq_length = 80
self.sequences = 10
... | 3,305 | 44.287671 | 108 | py |
ELLE | ELLE-main/apex/apex/contrib/test/multihead_attn/test_mha_fused_softmax.py | import torch
import unittest
import torch.nn.functional as F
from apex.contrib.multihead_attn import fast_mask_softmax_dropout_func
class FusedSoftmaxTest(unittest.TestCase):
def setUp(self, seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
self.seq_length = 80
... | 1,800 | 40.883721 | 111 | py |
ELLE | ELLE-main/apex/apex/contrib/test/layer_norm/test_fast_layer_norm.py | import torch
import unittest
import numpy as np
import torch.nn.functional as F
from apex.contrib.layer_norm import FastLayerNorm
import fast_layer_norm as fln
class GPUTimer:
def __init__(self, stream):
self.start_ = torch.cuda.Event(enable_timing=True)
self.stop_ = torch.cuda.Event(enable_tim... | 4,960 | 30.00625 | 116 | py |
ELLE | ELLE-main/apex/apex/contrib/xentropy/softmax_xentropy.py | import torch
import xentropy_cuda
class SoftmaxCrossEntropyLoss(torch.autograd.Function):
@staticmethod
def forward(ctx, logits, labels, smoothing=0.0, padding_idx=0, half_to_float=False):
losses, max_log_sum_exp = xentropy_cuda.forward(
logits, labels, smoothing, half_to_float)
los... | 1,023 | 34.310345 | 88 | py |
ELLE | ELLE-main/apex/apex/contrib/xentropy/__init__.py | try:
import torch
import xentropy_cuda
from .softmax_xentropy import SoftmaxCrossEntropyLoss
del torch
del xentropy_cuda
del softmax_xentropy
except ImportError as err:
print("apex was installed without --xentropy flag, contrib.xentropy is not available")
| 284 | 27.5 | 90 | py |
ELLE | ELLE-main/apex/apex/contrib/multihead_attn/fast_encdec_multihead_attn_func.py | import torch
import fast_encdec_multihead_attn
class FastEncdecAttnFunc(torch.autograd.Function):
@staticmethod
def forward(ctx, use_time_mask, is_training, heads, inputs_q, inputs_kv, input_weights_q, input_weights_kv, output_weights, pad_mask, dropout_prob):
heads_t = torch.tensor([heads])
... | 5,447 | 60.213483 | 152 | py |
ELLE | ELLE-main/apex/apex/contrib/multihead_attn/fast_self_multihead_attn_norm_add_func.py | import torch
import fast_self_multihead_attn_norm_add
class FastSelfAttnNormAddFunc(torch.autograd.Function):
@staticmethod
def forward(ctx, use_time_mask, is_training, heads, inputs, lyr_nrm_gamma_weights, lyr_nrm_beta_weights, input_weights, output_weights, pad_mask, dropout_prob):
heads_t = ... | 6,704 | 61.663551 | 164 | py |
ELLE | ELLE-main/apex/apex/contrib/multihead_attn/fast_self_multihead_attn_func.py | import torch
import fast_self_multihead_attn
import fast_self_multihead_attn_bias
import fast_self_multihead_attn_bias_additive_mask
class FastSelfAttnFunc(torch.autograd.Function) :
@staticmethod
def forward(ctx, use_time_mask, is_training, heads, inputs, input_weights, output_weights, input_biases, output_bi... | 13,483 | 67.446701 | 163 | py |
ELLE | ELLE-main/apex/apex/contrib/multihead_attn/fast_encdec_multihead_attn_norm_add_func.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import torch
import fast_encdec_mu... | 8,251 | 61.992366 | 197 | py |
ELLE | ELLE-main/apex/apex/contrib/multihead_attn/self_multihead_attn.py | import math
import torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
from .self_multihead_attn_func import self_attn_func
from .fast_self_multihead_attn_func import fast_self_attn_func
from .fast_self_multihead_attn_norm_add_func import fast_self_attn_nor... | 9,054 | 49.586592 | 301 | py |
ELLE | ELLE-main/apex/apex/contrib/multihead_attn/encdec_multihead_attn_func.py | import torch
import torch.nn.functional as F
class EncdecAttnFunc(torch.autograd.Function):
@staticmethod
def forward(ctx, use_time_mask, is_training, heads, scale, inputs_q, inputs_kv,
input_weights_q, input_weights_kv, output_weights,
input_biases_q, input_biases_kv, output_b... | 17,587 | 64.3829 | 178 | py |
ELLE | ELLE-main/apex/apex/contrib/multihead_attn/mask_softmax_dropout_func.py | import torch
import fast_mask_softmax_dropout
import fast_additive_mask_softmax_dropout
class MaskSoftmaxDropout(torch.autograd.Function) :
@staticmethod
def forward(ctx, is_training, heads, inputs, pad_mask, mask_additive, dropout_prob):
heads_t = torch.tensor([heads])
dropout_prob_t =... | 4,603 | 55.146341 | 91 | py |
ELLE | ELLE-main/apex/apex/contrib/multihead_attn/encdec_multihead_attn.py | import math
import torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
from .encdec_multihead_attn_func import encdec_attn_func
from .fast_encdec_multihead_attn_func import fast_encdec_attn_func
from .fast_encdec_multihead_attn_norm_add_func import fast_enc... | 7,043 | 48.605634 | 129 | py |
ELLE | ELLE-main/apex/apex/contrib/multihead_attn/self_multihead_attn_func.py | import torch
import torch.nn.functional as F
class SelfAttnFunc(torch.autograd.Function):
@staticmethod
def forward(ctx, use_time_mask, is_training, heads, scale, inputs,
input_weights, output_weights,
input_biases, output_biases,
mask, is_additive_mask, dropout_... | 14,741 | 61.466102 | 178 | py |
ELLE | ELLE-main/apex/apex/contrib/layer_norm/layer_norm.py | import torch
from torch.nn import init
import fast_layer_norm
class FastLayerNormFN(torch.autograd.Function):
@staticmethod
def forward(ctx, x, gamma, beta, epsilon):
x = x.contiguous()
gamma = gamma.contiguous()
beta = beta.contiguous()
hidden_size = gamma.numel()
xmat... | 1,490 | 32.133333 | 85 | py |
ELLE | ELLE-main/apex/apex/contrib/optimizers/distributed_fused_adam_v2.py | import math
import torch
import importlib
import amp_C
from apex.multi_tensor_apply import multi_tensor_applier
class DistributedFusedAdamV2(torch.optim.Optimizer):
"""Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via
``python setup.py install --cuda_ext --cpp_ext``.
It ha... | 31,780 | 50.592532 | 282 | py |
ELLE | ELLE-main/apex/apex/contrib/optimizers/distributed_fused_adam.py | import math
import torch
import importlib
import amp_C
from apex.multi_tensor_apply import multi_tensor_applier
import torch.distributed.distributed_c10d as c10d
class DistributedFusedAdam(torch.optim.Optimizer):
"""Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via
``python se... | 34,787 | 53.612245 | 283 | py |
ELLE | ELLE-main/apex/apex/contrib/optimizers/fp16_optimizer.py | import torch
from apex.multi_tensor_apply import multi_tensor_applier
class FP16_Optimizer(object):
"""
:class:`FP16_Optimizer` A cutdown version of apex.fp16_utils.FP16_Optimizer.
Designed only to wrap apex.contrib.optimizers.FusedAdam, FusedSGD.
Refer to apex.fp16_utils documents for more information... | 10,448 | 41.82377 | 126 | py |
ELLE | ELLE-main/apex/apex/contrib/optimizers/distributed_fused_adam_v3.py | import math
import torch
import importlib
import amp_C
from apex.multi_tensor_apply import multi_tensor_applier
class DistributedFusedAdamV3(torch.optim.Optimizer):
"""Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via
``python setup.py install --cuda_ext --cpp_ext``.
It ha... | 15,709 | 47.190184 | 244 | py |
ELLE | ELLE-main/apex/apex/contrib/optimizers/fused_sgd.py | import types
import torch
from torch.optim.optimizer import Optimizer, required
from apex.multi_tensor_apply import multi_tensor_applier
class FusedSGD(Optimizer):
r"""Implements stochastic gradient descent (optionally with momentum).
This version of fused SGD implements 2 fusions.
* Fusion of the SGD ... | 9,468 | 43.665094 | 145 | py |
ELLE | ELLE-main/apex/apex/contrib/optimizers/fused_lamb.py | import torch
import importlib
import math
from apex.multi_tensor_apply import multi_tensor_applier
class FusedLAMB(torch.optim.Optimizer):
"""Implements LAMB algorithm.
Currently GPU-only. Requires Apex to be installed via
``pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cu... | 9,408 | 44.019139 | 145 | py |
ELLE | ELLE-main/apex/apex/contrib/optimizers/fused_adam.py | import types
import torch
import importlib
from apex.multi_tensor_apply import multi_tensor_applier
class FusedAdam(torch.optim.Optimizer):
"""Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via
``python setup.py install --cuda_ext --cpp_ext``.
It has been proposed in `Adam:... | 9,284 | 43.855072 | 145 | py |
ELLE | ELLE-main/apex/apex/contrib/optimizers/distributed_fused_lamb.py | import math
import torch
import importlib
import amp_C
from apex.multi_tensor_apply import multi_tensor_applier
import torch.distributed.distributed_c10d as c10d
class DistributedFusedLAMB(torch.optim.Optimizer):
"""Implements LAMB algorithm.
Currently GPU-only. Requires Apex to be installed via
``... | 39,051 | 53.771388 | 283 | py |
ELLE | ELLE-main/apex/apex/reparameterization/reparameterization.py | import torch
from torch.nn.parameter import Parameter
import sys
class Reparameterization(object):
"""
Class interface for performing weight reparameterizations
Arguments:
name (str): name of weight parameter
dim (int): dimension over which to compute the norm
module (nn.Module): par... | 6,291 | 40.394737 | 127 | py |
ELLE | ELLE-main/apex/apex/reparameterization/__init__.py | from .weight_norm import WeightNorm
from .reparameterization import Reparameterization
def apply_weight_norm(module, name='', dim=0, hook_child=True):
r"""
Applies weight normalization to a parameter in the given module.
If no parameter is provided, applies weight normalization to all
parameters in mod... | 5,374 | 40.992188 | 106 | py |
ELLE | ELLE-main/apex/apex/reparameterization/weight_norm.py | import torch
from torch.nn.parameter import Parameter
from ..fp16_utils import Fused_Weight_Norm
import time
from .reparameterization import Reparameterization
def _norm(p, dim):
"""Computes the norm over all dimensions except dim"""
if dim is None:
return p.norm()
elif dim == 0:
output_si... | 3,203 | 39.556962 | 84 | py |
ELLE | ELLE-main/apex/apex/mlp/mlp.py | from copy import copy
import math
import torch
from torch import nn
import mlp_cuda
from .. import amp
class MlpFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, bias, activation, *args):
output = mlp_cuda.forward(bias, activation, args)
ctx.save_for_backward(*args)
ctx.... | 2,614 | 31.6875 | 115 | py |
ELLE | ELLE-main/downstream/convert_roberta_to_hf_batch.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# 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... | 8,381 | 45.309392 | 112 | py |
ELLE | ELLE-main/downstream/convert_roberta_to_hf.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# 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... | 8,169 | 44.388889 | 117 | py |
ELLE | ELLE-main/downstream/scripts/mlm_study.py | from typing import Tuple, List, Dict
from transformers import AutoModelWithLMHead, AutoTokenizer, PreTrainedTokenizer, PreTrainedModel, PreTrainedTokenizer
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
from torch.nn.utils.rnn import pad_sequence
import numpy as np
import torch
from... | 9,100 | 41.528037 | 161 | py |
ELLE | ELLE-main/downstream/scripts/run_language_modeling.py | ### THIS FILE IS COPIED FROM THE HUGGINGFACE REPOSITORY FOR CONVENIENCE.
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not u... | 34,477 | 42.643038 | 165 | py |
ELLE | ELLE-main/downstream/scripts/tapt_selection/run_vampire.py | """
The ``predict`` subcommand allows you to make bulk JSON-to-JSON
or dataset to JSON predictions using a trained model and its
:class:`~allennlp.service.predictors.predictor.Predictor` wrapper.
.. code-block:: bash
$ allennlp predict --help
usage: allennlp predict [-h] [--output-file OUTPUT_FILE]
... | 10,674 | 38.83209 | 115 | py |
ELLE | ELLE-main/downstream/scripts/tapt_selection/query_index.py | import faiss
import torch
import glob
import argparse
from tqdm import tqdm, trange
import json
from itertools import islice
import numpy as np
import logging
from torch.utils.data import Dataset, DataLoader
import os
from tempfile import mkdtemp
import re
import pandas as pd
logging.basicConfig(level=logging.INFO)
l... | 10,014 | 37.079848 | 128 | py |
ELLE | ELLE-main/downstream/scripts/tapt_selection/build_index.py | import faiss
import torch
import glob
import argparse
from tqdm import tqdm, trange
import simplejson as json
from itertools import islice
import numpy as np
import logging
from torch.utils.data import Dataset, DataLoader
import os
from tempfile import mkdtemp
import re
logging.basicConfig(level=logging.INFO)
logger ... | 6,641 | 35.295082 | 113 | py |
ELLE | ELLE-main/downstream/scripts/tapt_selection/convert_pytorch_to_memmap.py | import glob
from tqdm import tqdm
import numpy as np
import torch
import sys
from numpy.lib.format import open_memmap
import os
if __name__ == '__main__':
input_dir = sys.argv[1]
dirs = glob.glob(input_dir)
for file_ in tqdm(dirs):
if ".emb" not in file_ and ".id" not in file_:
x = torc... | 791 | 33.434783 | 117 | py |
ELLE | ELLE-main/downstream/dont_stop_pretraining/pnn_roberta.py | import argparse
import pathlib
import os
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.models.roberta.hub_interface import RobertaHubInterface
from fairseq.models.pnn_roberta import PNN_Roberta
from fairseq.tasks.continual_KI import Continual_KI
from fai... | 7,530 | 42.034286 | 136 | py |
ELLE | ELLE-main/downstream/dont_stop_pretraining/modules/seq2vec_encoders/cls_pooler.py | from typing import Union
from overrides import overrides
import torch
import torch.nn
from pytorch_pretrained_bert import BertModel
from allennlp.modules.seq2vec_encoders.seq2vec_encoder import Seq2VecEncoder
@Seq2VecEncoder.register("cls_pooler")
class CLSPooler(Seq2VecEncoder):
"""
The pooling layer at t... | 1,903 | 35.615385 | 123 | py |
ELLE | ELLE-main/downstream/dont_stop_pretraining/training/ft_checkpointer.py | from typing import Union, Dict, Any, List, Tuple
import logging
import os
import re
import shutil
import time
import torch
from allennlp.common.registrable import Registrable
from allennlp.nn import util as nn_util
from allennlp.training.checkpointer import Checkpointer
logger = logging.getLogger(__name__)
@Checkpo... | 7,836 | 49.237179 | 115 | py |
ELLE | ELLE-main/downstream/dont_stop_pretraining/models/basic_classifier_with_f1.py | from typing import Dict, Optional
from overrides import overrides
import torch
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.modules import Seq2SeqEncoder, Seq2VecEncoder, TextFieldEmbedder, FeedForward
from allennlp.nn import InitializerApplicator, RegularizerApplicator
f... | 7,368 | 39.712707 | 121 | py |
ELLE | ELLE-main/downstream/mlm_study/huggingface_study/mlm.py | from typing import Tuple, List
from transformers import AutoModelWithLMHead, AutoTokenizer, PreTrainedTokenizer
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
from torch.nn.utils.rnn import pad_sequence
import numpy as np
import torch
from tqdm import tqdm
import random
import argpa... | 5,944 | 43.699248 | 161 | py |
ELLE | ELLE-main/downstream/mlm_study/fairseq_study/validate_modified.py | #!/usr/bin/env python3 -u
#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import sys
import torch
from fairseq import checkpoint_utils, distributed... | 3,838 | 30.991667 | 88 | py |
ELLE | ELLE-main/downstream/mlm_study/fairseq_study/convert_hf_to_fairseq.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# 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... | 7,266 | 47.771812 | 196 | py |
ELLE | ELLE-main/fairseq_ELLE/setup.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
from setuptools import setup, find_packages, Extension
import sys
if sys.version_info < (3, 5):
sys.exi... | 4,357 | 25.736196 | 92 | py |
ELLE | ELLE-main/fairseq_ELLE/generate.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Translate pre-processed data with a trained model.
"""
import torch
from fairseq import bleu, checkpoint_utils,... | 8,179 | 39.098039 | 110 | py |
ELLE | ELLE-main/fairseq_ELLE/hubconf.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import functools
from fairseq.hub_utils import BPEHubInterface as bpe # noqa
from fairseq.hub_utils import TokenizerHubInterface as tokenize... | 1,432 | 28.244898 | 78 | py |
ELLE | ELLE-main/fairseq_ELLE/validate.py | #!/usr/bin/env python3 -u
#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from fairseq import checkpoint_utils, options, progress_bar, utils
def mai... | 3,163 | 30.64 | 88 | py |
ELLE | ELLE-main/fairseq_ELLE/eval_lm.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Evaluate the perplexity of a trained language model.
"""
import numpy as np
import torch
from fairseq import c... | 8,132 | 34.671053 | 118 | py |
ELLE | ELLE-main/fairseq_ELLE/interactive.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Translate raw text with a trained model. Batches data on-the-fly.
"""
from collections import namedtuple
import ... | 6,445 | 32.05641 | 103 | py |
ELLE | ELLE-main/fairseq_ELLE/train.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Train a new model on one or across multiple GPUs.
"""
import collections
import math
import random
import os
imp... | 27,316 | 41.816614 | 216 | py |
ELLE | ELLE-main/fairseq_ELLE/examples/roberta/commonsense_qa/commonsense_qa_task.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
import numpy as np
import torch
from fairseq.data import (
data_utils,
Dictionary,
encoders,
IdDataset... | 5,921 | 32.84 | 103 | py |
ELLE | ELLE-main/fairseq_ELLE/examples/roberta/wsc/wsc_task.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
import tempfile
import numpy as np
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq... | 13,149 | 33.973404 | 103 | py |
ELLE | ELLE-main/fairseq_ELLE/examples/roberta/wsc/wsc_criterion.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.data import encoders
from fairseq.criterions... | 6,022 | 35.065868 | 88 | py |
ELLE | ELLE-main/fairseq_ELLE/scripts/average_checkpoints.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import collections
import torch
import os
import re
def average_checkpoints(inputs):
"""Loads che... | 5,292 | 36.539007 | 134 | py |
ELLE | ELLE-main/fairseq_ELLE/scripts/wav2vec_featurize.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Helper script to pre-compute embeddings for a wav2letter++ dataset
"""
import argparse
import glob
import os
from ... | 7,102 | 28.970464 | 135 | py |
ELLE | ELLE-main/fairseq_ELLE/tests/test_train.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import contextlib
from io import StringIO
import unittest
from unittest.mock import MagicMock, patch
import torch
from fairseq import data, ... | 4,691 | 35.092308 | 94 | py |
ELLE | ELLE-main/fairseq_ELLE/tests/test_average_checkpoints.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import collections
import os
import tempfile
import unittest
import shutil
import numpy as np
import torch
from torch import nn
from script... | 4,494 | 30.215278 | 80 | py |
ELLE | ELLE-main/fairseq_ELLE/tests/test_sequence_scorer.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import unittest
import torch
from fairseq.sequence_scorer import SequenceScorer
import tests.utils as test_utils
class Te... | 3,949 | 33.051724 | 75 | py |
ELLE | ELLE-main/fairseq_ELLE/tests/test_memory_efficient_fp16.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import unittest
import torch
from fairseq.optim.adam import FairseqAdam
from fairseq.optim.fp16_optimizer import MemoryEffic... | 1,787 | 28.311475 | 69 | py |
ELLE | ELLE-main/fairseq_ELLE/tests/test_multihead_attention.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import unittest
from fairseq.modules.multihead_attention import MultiheadAttention
class TestMultiheadAttention(unittest.TestCa... | 1,904 | 30.229508 | 80 | py |
ELLE | ELLE-main/fairseq_ELLE/tests/utils.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import torch
from fairseq import utils
from fairseq.data import Dictionary
from fairseq.data.language_pair_dataset import col... | 7,442 | 30.67234 | 101 | py |
ELLE | ELLE-main/fairseq_ELLE/tests/test_binaries.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import contextlib
from io import StringIO
import os
import random
import sys
import tempfile
import unittest
import torch
from fairseq impor... | 31,257 | 40.183136 | 115 | py |
ELLE | ELLE-main/fairseq_ELLE/tests/test_concat_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from fairseq.data import LanguagePairDataset, TokenBlockDataset
from fairseq.data.concat_dataset import ConcatDa... | 1,943 | 28.907692 | 66 | py |
ELLE | ELLE-main/fairseq_ELLE/tests/test_noising.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from typing import Dict, List
import tests.utils as test_utils
import torch
from fairseq import utils
from fairseq.data impor... | 19,779 | 36.533207 | 87 | py |
ELLE | ELLE-main/fairseq_ELLE/tests/test_sparse_multihead_attention.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import unittest
from fairseq.modules.sparse_multihead_attention import SparseMultiheadAttention
class TestSparseMultiheadAttent... | 2,545 | 50.959184 | 114 | py |
ELLE | ELLE-main/fairseq_ELLE/tests/test_backtranslation_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from fairseq.data import (
BacktranslationDataset,
LanguagePairDataset,
TransformEosDataset,
)
from... | 4,032 | 33.470085 | 90 | py |
ELLE | ELLE-main/fairseq_ELLE/tests/test_sequence_generator.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import unittest
import torch
from fairseq.sequence_generator import SequenceGenerator
import tests.utils as test_utils
cl... | 14,876 | 38.884718 | 96 | py |
ELLE | ELLE-main/fairseq_ELLE/tests/test_label_smoothing.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import copy
import unittest
import torch
from fairseq.criterions.cross_entropy import CrossEntropyCriterion
from fairseq.cri... | 4,139 | 40.4 | 101 | py |
ELLE | ELLE-main/fairseq_ELLE/tests/test_convtbc.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import unittest
from fairseq.modules import ConvTBC
import torch.nn as nn
class TestConvTBC(unittest.TestCase):
def test_c... | 1,679 | 33.285714 | 102 | py |
ELLE | ELLE-main/fairseq_ELLE/tests/test_token_block_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from fairseq.data import TokenBlockDataset
import tests.utils as test_utils
class TestTokenBlockDataset(unit... | 2,970 | 36.607595 | 89 | py |
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