python_code stringlengths 0 679k | repo_name stringlengths 9 41 | file_path stringlengths 6 149 |
|---|---|---|
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-... | apex-master | apex/optimizers/fused_sgd.py |
import types
from ..fp16_utils import master_params_to_model_params
from ..multi_tensor_apply import multi_tensor_applier
from ._amp_state import maybe_print
import torch
from ..optimizers import FusedSGD
class AmpOptimizerState(object):
def __init__(self):
pass
def _master_params_to_model_params(self):... | apex-master | apex/amp/_process_optimizer.py |
import torch
# True for post-0.4, when Variables/Tensors merged.
def variable_is_tensor():
v = torch.autograd.Variable()
return isinstance(v, torch.Tensor)
def tensor_is_variable():
x = torch.Tensor()
return type(x) == torch.autograd.Variable
# False for post-0.4
def tensor_is_float_tensor():
x =... | apex-master | apex/amp/compat.py |
import contextlib
import warnings
import sys
import torch
from . import utils
from .opt import OptimWrapper
from .scaler import LossScaler
from ._amp_state import _amp_state, master_params, maybe_print
if torch.distributed.is_available():
from ..parallel.LARC import LARC
# There's no reason to expose the notion... | apex-master | apex/amp/handle.py |
import collections.abc as container_abcs
from types import MethodType
import functools
import sys
import warnings
import numpy as np
import torch
from ._amp_state import _amp_state, warn_or_err
from .handle import disable_casts
from .scaler import LossScaler
from ._process_optimizer import _process_optimizer
from ape... | apex-master | apex/amp/_initialize.py |
import functools
import itertools
import torch
from . import compat, rnn_compat, utils, wrap
from .handle import AmpHandle, NoOpHandle
from .lists import functional_overrides, torch_overrides, tensor_overrides
from ._amp_state import _amp_state
from .frontend import *
_DECORATOR_HANDLE = None
_USER_CAST_REGISTRY = ... | apex-master | apex/amp/amp.py |
from collections import OrderedDict
import torch
from ._initialize import _initialize
from ._amp_state import _amp_state, warn_or_err, maybe_print
class Properties(object):
"""
This class has two purposes: to establish a set of default properties,
and to route setting of these attributes through __setat... | apex-master | apex/amp/frontend.py |
from .amp import init, half_function, float_function, promote_function,\
register_half_function, register_float_function, register_promote_function
from .handle import scale_loss, disable_casts
from .frontend import initialize, state_dict, load_state_dict
from ._amp_state import master_params, _amp_state
| apex-master | apex/amp/__init__.py |
import torch
from ..multi_tensor_apply import multi_tensor_applier
from ._amp_state import _amp_state, master_params, maybe_print
from itertools import product
def scale_check_overflow_python(model_grad, master_grad, scale, check_overflow=False):
# Exception handling for 18.04 compatibility
if check_overflow:
... | apex-master | apex/amp/scaler.py |
VERSION = (0, 1, 0)
__version__ = '.'.join(map(str, VERSION))
| apex-master | apex/amp/__version__.py |
import contextlib
import warnings
from .scaler import LossScaler, master_params
from ._amp_state import maybe_print
import numpy as np
class OptimWrapper(object):
def __init__(self, optimizer, amp_handle, num_loss):
self._optimizer = optimizer
self._amp_handle = amp_handle
self._num_loss ... | apex-master | apex/amp/opt.py |
# This is a "header object" that allows different amp modules to communicate.
# I'm a C++ guy, not a python guy. I decided this approach because it seemed most C++-like.
# But apparently it's ok:
# http://effbot.org/pyfaq/how-do-i-share-global-variables-across-modules.htm
import torch
class AmpState(object):
def... | apex-master | apex/amp/_amp_state.py |
from . import compat
import functools
import itertools
import torch
def is_cuda_enabled():
return torch.version.cuda is not None
def get_cuda_version():
return tuple(int(x) for x in torch.version.cuda.split('.'))
def is_fp_tensor(x):
if is_nested(x):
# Fast-fail version of all(is_fp_tensor)
... | apex-master | apex/amp/utils.py |
from . import compat
from . import utils
from ._amp_state import _amp_state
from . import rnn_compat
import functools
import torch
def make_cast_wrapper(orig_fn, cast_fn, handle,
try_caching=False):
@functools.wraps(orig_fn)
def wrapper(*args, **kwargs):
if not handle.is_active(... | apex-master | apex/amp/wrap.py |
from . import utils, wrap
import torch
_VF = torch._C._VariableFunctions
RNN_NAMES = ['rnn_relu', 'rnn_tanh', 'gru', 'lstm']
def _gen_VF_wrapper(name):
def wrapper(*args, **kwargs):
return getattr(_VF, name)(*args, **kwargs)
return wrapper
# Some python magic to generate an object that has the rnn ce... | apex-master | apex/amp/rnn_compat.py |
apex-master | apex/amp/lists/__init__.py | |
import torch
from .. import utils
MODULE = torch
FP16_FUNCS = [
# Low level functions wrapped by torch.nn layers.
# The wrapper layers contain the weights which are then passed in as a parameter
# to these functions.
'conv1d',
'conv2d',
'conv3d',
'conv_transpose1d',
'conv_transpose2d'... | apex-master | apex/amp/lists/torch_overrides.py |
# TODO: think about the following two. They do weird things.
# - torch.nn.utils.clip_grad (but it should always be fp32 anyway)
# - torch.nn.utils.weight_norm
# Notes:
# F.instance_norm uses batch_norm internally. Which correctly handles
# fp16 in/out with fp32 weights. So we shouldn't do anything for
# either of... | apex-master | apex/amp/lists/functional_overrides.py |
from .. import compat
from . import torch_overrides
import importlib
import torch
# if compat.variable_is_tensor() and not compat.tensor_is_variable():
MODULE = torch.Tensor
# else:
# MODULE = torch.autograd.Variable
FP16_FUNCS = compat.filter_attrs(MODULE, [
'__matmul__',
])
FP32_FUNCS = compat.filter_at... | apex-master | apex/amp/lists/tensor_overrides.py |
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import math
def is_iterable(maybe_iterable):
return isinstance(maybe_iterable, list) or isinstance(maybe_iterable, tuple)
def flatten_list(tens_list):
"""
flatten_list
"""
if not is_iterable(... | apex-master | apex/RNN/RNNBackend.py |
import torch
from torch.nn._functions.rnn import LSTMCell, RNNReLUCell, RNNTanhCell, GRUCell
from apex import deprecated_warning
from .RNNBackend import bidirectionalRNN, stackedRNN, RNNCell
from .cells import mLSTMRNNCell, mLSTMCell
def toRNNBackend(inputRNN, num_layers, bidirectional=False, dropout = 0):
"""
... | apex-master | apex/RNN/models.py |
from .models import LSTM, GRU, ReLU, Tanh, mLSTM
__all__ = ['models']
| apex-master | apex/RNN/__init__.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from .RNNBackend import RNNCell
from torch.nn._functions.thnn import rnnFusedPointwise as fusedBackend
import math
class mLSTMRNNCell(RNNCell):
"""
mLSTMRNNCell
"""
def __init__(self, input_size, hidden_size, bias = False, output_... | apex-master | apex/RNN/cells.py |
from .mlp import *
| apex-master | apex/mlp/__init__.py |
from copy import copy
import math
import torch
from torch import nn
from apex._autocast_utils import _cast_if_autocast_enabled
import mlp_cuda
class MlpFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, bias, activation, *args):
output = mlp_cuda.forward(bias, activation, args)
... | apex-master | apex/mlp/mlp.py |
import torch
import torch.distributed as dist
from torch.nn import Parameter
from torch.nn import Module
from apex.parallel import DistributedDataParallel as DDP
import argparse
import os
parser = argparse.ArgumentParser(description='allreduce hook example')
parser.add_argument("--local_rank", default=0, type=int)
ar... | apex-master | tests/distributed/DDP/ddp_race_condition_test.py |
import torch
import argparse
import os
from apex import amp
# FOR DISTRIBUTED: (can also use torch.nn.parallel.DistributedDataParallel instead)
from apex.parallel import DistributedDataParallel
parser = argparse.ArgumentParser()
# FOR DISTRIBUTED: Parse for the local_rank argument, which will be supplied
# automatica... | apex-master | tests/distributed/amp_master_params/amp_master_params.py |
import torch
model_params_rank0 = torch.load("rank0model.pth",
map_location = lambda storage, loc: storage.cuda(0))
model_params_rank1 = torch.load("rank1model.pth",
map_location = lambda storage, loc: storage.cuda(0))
master_params_rank0 = torch.load("rank0m... | apex-master | tests/distributed/amp_master_params/compare.py |
import torch
import apex
model = apex.parallel.SyncBatchNorm(4).cuda()
model.weight.data.uniform_()
model.bias.data.uniform_()
data = torch.rand((8,4)).cuda()
model_ref = torch.nn.BatchNorm1d(4).cuda()
model_ref.load_state_dict(model.state_dict())
data_ref = data.clone()
output = model(data)
output_ref = model_ref(d... | apex-master | tests/distributed/synced_batchnorm/test_batchnorm1d.py |
import torch
import numpy as np
import apex
import syncbn
import os
import argparse
import torch.optim as optim
def compare(desc, inp1, inp2, error):
a = inp1.clone().detach().cpu().numpy()
b = inp2.clone().detach().cpu().numpy()
close = np.allclose(a,b, error, error)
if not close:
print(desc, ... | apex-master | tests/distributed/synced_batchnorm/two_gpu_unit_test.py |
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel as DDP
from apex.parallel import SyncBatchNorm as ApexSyncBatchNorm
import argparse
import os
import numpy as np
var_batch = 16
def compare(desc, inp1, inp2, error= 1e-5):
a = inp1.clone().detach().cpu().numpy()
b = inp2... | apex-master | tests/distributed/synced_batchnorm/two_gpu_test_different_batch_size.py |
import torch
import numpy as np
import apex
if True:
print("using setup tools")
import syncbn
else:
print("using jit")
from torch.utils.cpp_extension import load
syncbn = load(name='syncbn', sources=['../../csrc/syncbn.cpp', '../../csrc/welford.cu'])
def compare(desc, inp1, inp2, error):
a = in... | apex-master | tests/distributed/synced_batchnorm/single_gpu_unit_test.py |
import torch
import numpy as np
import apex
import syncbn
import os
import argparse
import torch.optim as optim
def compare(desc, inp1, inp2, error):
a = inp1.clone().detach().cpu().numpy()
b = inp2.clone().detach().cpu().numpy()
close = np.allclose(a,b, error, error)
if not close:
print(desc, ... | apex-master | tests/distributed/synced_batchnorm/test_groups.py |
import torch
import numpy as np
import apex
def compare(desc, inp1, inp2, error):
a = inp1.clone().detach().cpu().numpy()
b = inp2.clone().detach().cpu().numpy()
close = np.allclose(a,b, error, error)
if not close:
print(desc, close)
z = a - b
index = (np.abs(z) >= error + error... | apex-master | tests/distributed/synced_batchnorm/python_single_gpu_unit_test.py |
"""L0 Tests Runner.
How to run this script?
1. Run all the tests: `python /path/to/apex/tests/L0/run_test.py` If you want an xml report,
pass `--xml-report`, i.e. `python /path/to/apex/tests/L0/run_test.py --xml-report` and
the file is created in `/path/to/apex/tests/L0`.
2. Run one of the tests (e.g. fused l... | apex-master | tests/L0/run_test.py |
import torch
from apex.normalization import FusedLayerNorm
from apex.normalization import FusedRMSNorm
from apex.normalization import MixedFusedLayerNorm
from apex.normalization import MixedFusedRMSNorm
from torch.testing._internal import common_utils
from torch.testing._internal.common_device_type import instantiate_... | apex-master | tests/L0/run_fused_layer_norm/test_fused_layer_norm.py |
import unittest
import os
import torch
from torch.optim import Optimizer
import apex
from apex.multi_tensor_apply import multi_tensor_applier
from itertools import product
class RefLAMB(Optimizer):
r"""Implements Lamb algorithm.
It has been proposed in `Large Batch Optimization for Deep Learning: Training BE... | apex-master | tests/L0/run_optimizers/test_lamb.py |
apex-master | tests/L0/run_optimizers/__init__.py | |
import copy
import math
import random
import unittest
import torch
import torch.nn.functional as F
from torch import nn
try:
import apex
except ImportError as e:
HAS_APEX = False
else:
HAS_APEX = True
class Model(torch.nn.Module):
def __init__(self):
super(Model, self).__init__()
sel... | apex-master | tests/L0/run_optimizers/test_adam.py |
from itertools import product
import random
import unittest
import torch
import apex
class TestFusedOptimizer(unittest.TestCase):
def setUp(self, max_abs_diff=1e-3, max_rel_diff=1, iters=7):
self.max_abs_diff = max_abs_diff
self.max_rel_diff = max_rel_diff
self.iters = iters
torc... | apex-master | tests/L0/run_optimizers/test_fused_optimizer.py |
import torch
from torch.optim import Optimizer
import math
import apex
import unittest
from test_fused_optimizer import TestFusedOptimizer
from itertools import product
class Novograd(Optimizer):
"""
Implements Novograd algorithm.
Args:
params (iterable): iterable of parameters to optimize or dic... | apex-master | tests/L0/run_optimizers/test_fused_novograd.py |
import logging
import unittest
import torch
from torch.testing._internal import common_utils
logging.getLogger("torch").setLevel(logging.WARNING)
from apex.transformer import parallel_state
from apex.transformer.pipeline_parallel import p2p_communication
from apex.transformer.testing.distributed_test_base import Ncc... | apex-master | tests/L0/run_transformer/test_p2p_comm.py |
import subprocess
import os
from apex.transformer.testing.commons import TEST_SUCCESS_MESSAGE
def run_gpt(cmd):
args = list(cmd.split(" "))
p = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
outs, errs = p.communicate()
outs = list(str((outs).decode("utf-8")).splitlines())
... | apex-master | tests/L0/run_transformer/gpt_scaling_test.py |
from typing import Tuple, List
import torch
import unittest
from apex.transformer import parallel_state
from apex.transformer.pipeline_parallel.utils import get_num_microbatches
from apex.transformer.pipeline_parallel.schedules.common import (
_get_params_for_weight_decay_optimization, build_model
)
from apex.tra... | apex-master | tests/L0/run_transformer/test_dynamic_batchsize.py |
"""Test for fused softmax functions.
Ref: https://github.com/NVIDIA/Megatron-LM/blob/40becfc96c4144985458ac0e0fae45dbb111fbd2/megatron/fused_kernels/tests/test_fused_kernels.py
""" # NOQA
import itertools
import torch
from torch.testing._internal import common_utils
from apex.transformer import AttnMaskType
from ap... | apex-master | tests/L0/run_transformer/test_fused_softmax.py |
import logging
from typing import Tuple
import torch
import torch.nn.functional as F
from torch.testing._internal import common_utils
logging.getLogger("torch").setLevel(logging.WARNING)
from apex.transformer import parallel_state
from apex.transformer import tensor_parallel
from apex.transformer.tensor_parallel imp... | apex-master | tests/L0/run_transformer/test_cross_entropy.py |
from functools import partial
from typing import List
import time
import torch
import unittest
from apex.transformer._ucc_util import HAS_UCC
from apex.transformer import parallel_state
from apex.transformer.enums import ModelType
from apex.transformer.tensor_parallel import model_parallel_cuda_manual_seed
from apex... | apex-master | tests/L0/run_transformer/test_gpt_minimal.py |
import torch
from torch.testing._internal import common_utils
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from apex.transformer.pipeline_parallel.utils import _split_batch_into_microbatch as split_batch_into_microbatch
class MyIterableDataset(Dataset):
def __init__(self, start, e... | apex-master | tests/L0/run_transformer/test_batch_sampler.py |
import logging
import unittest
import typing
import torch
import torch.nn as nn
from torch.testing._internal import common_utils
from apex.transformer import parallel_state
from apex.transformer.tensor_parallel import layers
from apex.transformer.testing.commons import set_random_seed
from apex.transformer.testing.di... | apex-master | tests/L0/run_transformer/test_layers.py |
apex-master | tests/L0/run_transformer/__init__.py | |
import logging
from typing import List, Optional
from torch.testing._internal import common_utils
logging.getLogger("torch").setLevel(logging.WARNING)
from apex.transformer import parallel_state
from apex.transformer.pipeline_parallel.utils import (
_reconfigure_microbatch_calculator,
get_micro_batch_size,
... | apex-master | tests/L0/run_transformer/test_microbatches.py |
import logging
import torch.testing
from torch.testing._internal import common_utils
logging.getLogger("torch").setLevel(logging.WARNING)
from apex.transformer import parallel_state
from apex.transformer.tensor_parallel import data as data_utils
from apex.transformer.testing.distributed_test_base import NcclDistribu... | apex-master | tests/L0/run_transformer/test_data.py |
import torch
import unittest
from apex.transformer.testing import global_vars
from apex.transformer.testing.standalone_bert import bert_model_provider
from apex.transformer.pipeline_parallel.schedules.common import (
_get_params_for_weight_decay_optimization, build_model
)
from apex.transformer.pipeline_parallel.sc... | apex-master | tests/L0/run_transformer/test_bert_minimal.py |
import logging
import torch
from torch.testing._internal import common_utils
logging.getLogger("torch").setLevel(logging.WARNING)
from apex.transformer import parallel_state
from apex.transformer.tensor_parallel import utils
from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
logging.... | apex-master | tests/L0/run_transformer/test_transformer_utils.py |
import logging
import os
from torch.testing._internal import common_utils
logging.getLogger("torch").setLevel(logging.WARNING)
from apex.transformer import parallel_state
from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
from apex.transformer.testing.distributed_test_base import UccD... | apex-master | tests/L0/run_transformer/test_parallel_state.py |
import contextlib
import logging
import itertools
import os
from datetime import datetime
from packaging.version import parse, Version
import re
from typing import Optional, Tuple, List
import unittest
import torch
from torch.testing._internal import common_utils
from apex._autocast_utils import _get_autocast_dtypes
... | apex-master | tests/L0/run_transformer/test_pipeline_parallel_fwd_bwd.py |
import logging
import torch
from torch.testing._internal import common_utils
from apex.transformer import parallel_state
from apex.transformer.tensor_parallel import mappings
from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
from apex.transformer.testing.distributed_test_base import U... | apex-master | tests/L0/run_transformer/test_mapping.py |
import logging
import torch
from torch.testing._internal import common_utils
logging.getLogger("torch").setLevel(logging.WARNING)
from apex.transformer import parallel_state
from apex.transformer import tensor_parallel
from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
from apex.trans... | apex-master | tests/L0/run_transformer/test_random.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
from apex.amp import _amp_state
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Parameter
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
class MyModel(to... | apex-master | tests/L0/run_amp/test_multiple_models_optimizers_losses.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
import torch
from torch import nn
import torch.nn.functional as F
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
try:
import amp_C
from amp_C import multi_tensor_l2norm
from apex.multi_t... | apex-master | tests/L0/run_amp/test_multi_tensor_l2norm.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
from apex.amp import _amp_state
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Parameter
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
try:
import a... | apex-master | tests/L0/run_amp/test_fused_sgd.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
from apex.amp import _amp_state
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Parameter
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
class MyModel(to... | apex-master | tests/L0/run_amp/test_add_param_group.py |
apex-master | tests/L0/run_amp/__init__.py | |
import unittest
import itertools as it
from apex import amp
import torch
from torch import nn
import torch.nn.functional as F
from utils import common_init, HALF, FLOAT, DTYPES
class TestPromotion(unittest.TestCase):
def setUp(self):
self.handle = amp.init(enabled=True)
common_init(self)
de... | apex-master | tests/L0/run_amp/test_promotion.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
import torch
from torch import nn
import torch.nn.functional as F
from math import floor
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
try:
import amp_C
from amp_C import multi_tensor_axp... | apex-master | tests/L0/run_amp/test_multi_tensor_axpby.py |
import torch
HALF = 'torch.cuda.HalfTensor'
FLOAT = 'torch.cuda.FloatTensor'
DTYPES = [torch.half, torch.float]
ALWAYS_HALF = {torch.float: HALF,
torch.half: HALF}
ALWAYS_FLOAT = {torch.float: FLOAT,
torch.half: FLOAT}
MATCH_INPUT = {torch.float: FLOAT,
torch.half: HALF}... | apex-master | tests/L0/run_amp/utils.py |
import unittest
from apex import amp
import random
import torch
from torch import nn
from utils import common_init, HALF
class TestRnnCells(unittest.TestCase):
def setUp(self):
self.handle = amp.init(enabled=True)
common_init(self)
def tearDown(self):
self.handle._deactivate()
d... | apex-master | tests/L0/run_amp/test_rnn.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
import torch
from torch import nn
import torch.nn.functional as F
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
try:
import amp_C
from amp_C import multi_tensor_scale
from apex.multi_t... | apex-master | tests/L0/run_amp/test_multi_tensor_scale.py |
import unittest
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from apex import amp
from utils import common_init, FLOAT
class MyModel(torch.nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 3, 1, 1)
... | apex-master | tests/L0/run_amp/test_checkpointing.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
from apex.amp import _amp_state
import torch
from torch import nn
import torch.nn.functional as F
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
def get_reference_grad(i, w, ops):
# Creati... | apex-master | tests/L0/run_amp/test_cache.py |
import unittest
import torch
from torch import nn
from torch.nn import Parameter
from apex import amp
from apex.parallel.LARC import LARC
from utils import common_init
class MyModel(torch.nn.Module):
def __init__(self, unique):
super(MyModel, self).__init__()
self.weight0 = Parameter(
... | apex-master | tests/L0/run_amp/test_larc.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
import torch
from torch import nn
import torch.nn.functional as F
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
def run_layer_test(test_case, fns, expected, input_shape, test_backward=True):
... | apex-master | tests/L0/run_amp/test_basic_casts.py |
import unittest
import torch
import torch.nn as nn
from apex.fp16_utils import FP16Model
class DummyBlock(nn.Module):
def __init__(self):
super(DummyBlock, self).__init__()
self.conv = nn.Conv2d(10, 10, 2)
self.bn = nn.BatchNorm2d(10, affine=True)
def forward(self, x):
retu... | apex-master | tests/L0/run_fp16util/test_fp16util.py |
apex-master | tests/L0/run_fp16util/__init__.py | |
import unittest
import torch
import apex
from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
def init_model_and_optimizer():
model = torch.nn.Linear(1, 1, bias=False).cuda()
optimizer = torch.optim.SGD(model.parameters(), 1.0)
return model, optimizer
@unittest.skipUnless... | apex-master | tests/L0/run_deprecated/test_deprecated_warning.py |
"""Tests for c++ MLP"""
from itertools import product
from time import time
import torch
from torch import nn
from torch.testing._internal import common_utils
from torch.testing._internal.common_device_type import instantiate_device_type_tests
from torch.testing._internal.common_device_type import onlyCUDA
from apex.... | apex-master | tests/L0/run_mlp/test_mlp.py |
import os
import logging
import itertools
from typing import Optional, Tuple, List
import unittest
import torch
from torch.testing._internal import common_utils
from torch.testing._internal import common_cuda
from torch.testing._internal import common_distributed
from apex._autocast_utils import _get_autocast_dtypes
... | apex-master | tests/L1/transformer/pipeline_parallel_fwd_bwd_ucc_async.py |
import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvi... | apex-master | tests/L1/common/main_amp.py |
import argparse
import torch
parser = argparse.ArgumentParser(description='Compare')
parser.add_argument('--opt-level', type=str)
parser.add_argument('--keep-batchnorm-fp32', type=str, default=None)
parser.add_argument('--loss-scale', type=str, default=None)
parser.add_argument('--fused-adam', action='store_true')
par... | apex-master | tests/L1/common/compare.py |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# PyTorch documentation build configuration file, created by
# sphinx-quickstart on Fri Dec 23 13:31:47 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# au... | apex-master | docs/source/conf.py |
from __future__ import print_function
import argparse
import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchv... | apex-master | examples/dcgan/main_amp.py |
import torch
import argparse
import os
from apex import amp
# FOR DISTRIBUTED: (can also use torch.nn.parallel.DistributedDataParallel instead)
from apex.parallel import DistributedDataParallel
parser = argparse.ArgumentParser()
# FOR DISTRIBUTED: Parse for the local_rank argument, which will be supplied
# automatica... | apex-master | examples/simple/distributed/distributed_data_parallel.py |
import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvi... | apex-master | examples/imagenet/main_amp.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | setup.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/constants.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/__init__.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/metrics/__init__.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/metrics/general/mse.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/metrics/general/reduction.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/metrics/general/histogram.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/metrics/general/__init__.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/metrics/general/ensemble_metrics.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/metrics/general/wasserstein.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/metrics/general/crps.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/metrics/general/entropy.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/metrics/general/calibration.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/metrics/climate/efi.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/metrics/climate/reduction.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/metrics/climate/__init__.py |
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 requi... | modulus-main | modulus/metrics/climate/acc.py |
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