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## @package generator
# Module caffe2.python.docs.generator
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
from caffe2.python import core, workspace
from caffe2.python.docs.formatter import Markdown
OpSchema... |
## @package github
# Module caffe2.python.docs.github
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python.docs.formatter import Markdown
from caffe2.python.docs.generator import OpDocGenerator, DocUploade... |
## @package convnet_benchmarks
# Module caffe2.python.convnet_benchmarks
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
"""
Benchmark for common convnets.
Speed on Titan X, with 10 warmup steps and 10 main steps and w... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.python import core, workspace
import caffe2.pytho... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import unittest
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import cnn, core, workspace, test_util
@unittest.skipIf(not workspace.C.has_mk... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import unittest
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import cnn, core, workspace, test_util
@unittest.skipIf(not workspace.C.has_mk... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.python import core, workspace
import caffe2.pytho... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.python import core, workspace
import caffe2.pytho... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.python import core, workspace
import caffe2.pytho... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import unittest
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import cnn, core, workspace, test_util
@unittest.skipIf(not workspace.C.has_mk... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import unittest
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import cnn, core, workspace, test_util
@unittest.skipIf(not workspace.C.has_mk... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.python import core, workspace
import caffe2.pytho... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import unittest
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import cnn, core, workspace, test_util
@unittest.skipIf(not workspace.C.has_mk... |
## @package lmdb_create_example
# Module caffe2.python.examples.lmdb_create_example
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import numpy as np
import lmdb
from caffe2.proto import caffe2_pb2
fr... |
## @package resnet50_trainer
# Module caffe2.python.examples.resnet50_trainer
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import logging
import numpy as np
import time
import os
from caffe2.python ... |
## @package char_rnn
# Module caffe2.python.examples.char_rnn
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, workspace, model_helper, utils, brew
from caffe2.python.rnn_cell import LSTM... |
## @package helpers
# Module caffe2.python.tutorials.helpers
import numpy as np
import skimage.io
import skimage.transform
import urllib2
def crop_center(img,cropx,cropy):
y,x,c = img.shape
startx = x//2-(cropx//2)
starty = y//2-(cropy//2)
return img[starty:starty+cropy,startx:startx+cropx]
def rescal... |
## @package predictor_py_utils
# Module caffe2.python.predictor.predictor_py_utils
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core
def create_predict_net(predictor_export_meta):
"""... |
## @package predictor_exporter
# Module caffe2.python.predictor.predictor_exporter
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.proto import caffe2_pb2
from caffe2.proto import metanet_pb2
from caffe2.py... |
## @package serde
# Module caffe2.python.predictor.serde
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
def serialize_protobuf_struct(protobuf_struct):
return protobuf_struct.SerializeToString()
def deserialize... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python.test_util import TestCase
from caffe2.python import workspace, brew
from caffe2.python.model_helper import ModelHelper
from caffe2.python.predictor impo... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import tempfile
import unittest
import numpy as np
from caffe2.python import cnn, workspace, core
from caffe2.python.predictor_constants import predictor_constants as pc... |
## @package mobile_exporter
# Module caffe2.python.mobile_exporter
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, utils
from caffe2.proto import caffe2_pb2
def Export(workspace, net, ... |
## @package fc
# Module caffe2.python.helpers.fc
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core
def _FC_or_packed_FC(
model, op_call, blob_in, blob_out, dim_in, dim_out, weight_ini... |
## @package algebra
# Module caffe2.python.helpers.algebra
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
def transpose(model, blob_in, blob_out, use_cudnn=False, **kwargs):
"""Transpose."""
if use_cudnn:
... |
## @package tools
# Module caffe2.python.helpers.tools
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
def image_input(
model, blob_in, blob_out, order="NCHW", use_gpu_transform=False, **kwargs
):
if order == ... |
## @package pooling
# Module caffe2.python.helpers.pooling
## @package fc
# Module caffe2.python.helpers.pooling
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
def max_pool(model, blob_in, blob_out, use_cudnn=False, ... |
## @package arra_helpers
# Module caffe2.python.helpers.array_helpers
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
def concat(model, blobs_in, blob_out, order="NCHW", **kwargs):
"""Depth Concat."""
return m... |
## @package nonlinearity
# Module caffe2.python.helpers.nonlinearity
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core
def prelu(model, blob_in, blob_out, num_channels=1, slope_init=None,... |
## @package train
# Module caffe2.python.helpers.train
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, scope
from caffe2.proto import caffe2_pb2
def _get_weights(model, namescope=None)... |
## @package dropout
# Module caffe2.python.helpers.dropout
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
def dropout(model, blob_in, blob_out, use_cudnn=False, **kwargs):
"""dropout"""
if use_cudnn:
... |
## @package conv
# Module caffe2.python.helpers.conv
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core
def _ConvBase(
model,
is_nd,
blob_in,
blob_out,
dim_in,
dim_... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import contextlib
import copy
import threading
_threadlocal_scope = threading.local()
@contextlib.contextmanager
def arg_scope(single_helper_or_list, **kwargs):
global _threadlocal_scope
if not isinst... |
## @package normalization
# Module caffe2.python.helpers.normalization
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, scope
from caffe2.proto import caffe2_pb2
def lrn(model, blob_in,... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.proto import caffe2_pb2
from caffe2.python import workspace, core, lstm_benchmark, utils
from copy import copy
@utils.debug
def Compare(args):
results = [... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import uuid
from caffe2.distributed.store_ops_test_util import StoreOpsTests
from caffe2.python import core, workspace, dyndep
from caffe2.python.test_util imp... |
## @package store_ops_test_util
# Module caffe2.distributed.store_ops_test_util
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from multiprocessing import Process, Queue
import numpy as np
from caffe2.python import ... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import errno
import os
import tempfile
import shutil
from caffe2.distributed.store_ops_test_util import StoreOpsTests
from caffe2.python import core, workspace, dyndep
f... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
from scipy.sparse import coo_matrix
from hypothesis import given
import hypothesis.strategies as st
from caffe2.python import core
import caffe2.pyth... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
from hypothesis import given
import hypothesis.strategies as st
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util a... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
from scipy.sparse import coo_matrix
from hypothesis import given
import hypothesis.strategies as st
from caffe2.python import core
import caffe2.pyth... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
from scipy.sparse import coo_matrix
from caffe2.python import core, workspace
from caffe2.python.test_util import TestCase
def test_reshape(old_shap... |
## @package convnet_benchmarks
# Module caffe2.experiments.python.convnet_benchmarks
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
"""
Benchmark for common convnets.
(NOTE: Numbers below prior with missing parameter=... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
from hypothesis import given
import hypothesis.strategies as st
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util a... |
## @package SparseTransformer
# Module caffe2.experiments.python.SparseTransformer
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import workspace
import scipy.sparse
class NetDefNode():
def _... |
## @package net_construct_bench
# Module caffe2.experiments.python.net_construct_bench
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import logging
import time
from caffe2.python import workspace, da... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import unittest
import hypothesis.strategies as st
from hypothesis import given, assume, settings
import numpy as np
import time
import os
from caffe2.python import core,... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace, dyndep, test_util
dyndep.InitOpsLibrary('@/caffe2/caffe2/contrib/warpctc:ctc_ops')
workspace.G... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import unittest
import hypothesis.strategies as st
from hypothesis import given, assume
import numpy as np
import time
import os
from caffe2.proto import caffe2_pb2
from ... |
#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from hypothesis import given
import hypothesis.strategies as st
from multiprocessing import Process, Queue
import numpy as np
import os
import pic... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, dyndep
import caffe2.python.hypothesis_test_util as hu
from hypothesis import given
import hypothesis.strategies as st
import numpy as np... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, dyndep
import caffe2.python.hypothesis_test_util as hu
from hypothesis import given
import hypothesis.strategies as st
import numpy as np... |
## @package utils
# Module caffe2.contrib.perf_contbld.utils
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import getpass
import time
from collections import defaultdict
import numpy as np
from caffe2.proto import p... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import unittest
from caffe2.proto import caffe2_pb2
from caffe2.python import core, dyndep, workspace
dyndep.InitOpsLibrary("@/caffe2/caffe2/contrib/prof:cuda_profile_op... |
## @package htrace_to_chrome
# Module caffe2.contrib.prof.htrace_to_chrome
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import json
import re
import sys
display_levels = ["network", "worker", "opera... |
## @package process
# Module doxygen.process
# Script to insert preamble for doxygen and regen API docs
import glob, os, shutil
# Module caffe2...caffe2.python.control_test
def insert(originalfile,first_line,description):
with open(originalfile,'r') as f:
f1 = f.readline()
if(f1.find(first_line)<0... |
## @package diagnose_protobuf
# Module scripts.diagnose_protobuf
"""Diagnoses the current protobuf situation.
Protocol buffer needs to be properly installed for Caffe2 to work, and
sometimes it is rather tricky. Specifically, we will need to have a
consistent version between C++ and python simultaneously. This is a
co... |
## @package get_python_cmake_flags
# Module scripts.get_python_cmake_flags
##############################################################################
# Use this script to find your preferred python installation.
##############################################################################
#
# You can use the follo... |
import torch
from setuptools import setup, find_packages
import subprocess
import sys
if not torch.cuda.is_available():
print("\nWarning: Torch did not find available GPUs on this system.\n",
"If your intention is to cross-compile, this is not an error.\n")
print("torch.__version__ = ", torch.__versio... |
# May help avoid undefined symbol errors https://pytorch.org/cppdocs/notes/faq.html#undefined-symbol-errors-from-pytorch-aten
import torch
from . import parallel
from . import amp
from . import fp16_utils
# For optimizers and normalization there is no Python fallback.
# Absence of cuda backend is a hard error.
# I wo... |
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn import functional as F
import syncbn
from .optimized_sync_batchnorm_kernel import SyncBatchnormFunction
class SyncBatchNorm(_BatchNorm):
"""
synchronized batch normalization module extented from `torch.nn.BatchNormNd`
with the a... |
import torch
from torch.autograd.function import Function
from apex.parallel import ReduceOp
class SyncBatchnormFunction(Function):
@staticmethod
def forward(ctx, input, weight, bias, running_mean, running_variance, eps, process_group, world_size):
torch.cuda.nvtx.range_push("sync_BN_fw")
# ... |
import torch
if hasattr(torch.distributed, 'ReduceOp'):
ReduceOp = torch.distributed.ReduceOp
elif hasattr(torch.distributed, 'reduce_op'):
ReduceOp = torch.distributed.reduce_op
else:
ReduceOp = torch.distributed.deprecated.reduce_op
from .distributed import DistributedDataParallel, Reducer
# This is tri... |
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn import functional as F
from .sync_batchnorm_kernel import SyncBatchnormFunction
from apex.parallel import ReduceOp
class SyncBatchNorm(_BatchNorm):
"""
synchronized batch normalization module extented from ``torch.nn.BatchNormNd``
... |
import torch
import torch.distributed as dist
from torch.nn.modules import Module
from torch.autograd import Variable
from collections import OrderedDict
from itertools import chain
import copy
import importlib
from ..multi_tensor_apply import multi_tensor_applier
imported_flatten_impl = False
def import_flatten_impl... |
import torch
from torch.autograd.function import Function
import syncbn
from apex.parallel import ReduceOp
class SyncBatchnormFunction(Function):
@staticmethod
def forward(ctx, input, weight, bias, running_mean, running_variance, eps, track_running_stats = True, momentum = 1.0, process_group = None, channel_... |
import torch
from torch import nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
class LARC(object):
"""
:class:`LARC` is a pytorch implementation of both the scaling and clipping variants of LARC,
in which the ratio between gradient and parameter magnitudes is used to calcula... |
import torch
import sys
import subprocess
def docstring_hack():
"""
Multiproc file which will launch a set of processes locally for multi-gpu
usage: python -m apex.parallel.multiproc main.py ...
"""
pass
argslist = list(sys.argv)[1:]
world_size = torch.cuda.device_count()
if '--world-size' in arg... |
import math
import torch
import numbers
from torch.nn.parameter import Parameter
from torch.nn import init
from torch.nn import functional as F
import importlib
class FusedLayerNormAffineFunction(torch.autograd.Function):
def __init__(self, normalized_shape, eps=1e-6):
global fused_layer_norm_cuda
fused_laye... |
from .fused_layer_norm import FusedLayerNorm
|
from .fp16util import (
BN_convert_float,
network_to_half,
prep_param_lists,
model_grads_to_master_grads,
master_params_to_model_params,
tofp16,
to_python_float,
clip_grad_norm,
convert_module,
convert_network,
FP16Model,
)
from .fp16_optimizer import FP16_Optimizer
from .lo... |
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
class tofp16(nn.Module):
"""
Utility module that implements::
def forward(self, input):
return input.half()
"""
def __init__(self):
... |
import torch
from torch import nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
from ..amp._amp_state import _amp_state, maybe_print
from ..amp.scaler import LossScaler
from ..multi_tensor_apply import multi_tensor... |
import torch
# item() is a recent addition, so this helps with backward compatibility.
def to_python_float(t):
if hasattr(t, 'item'):
return t.item()
else:
return t[0]
class LossScaler:
"""
Class that manages a static loss scale. This class is intended to interact with
:class:`FP1... |
from .multi_tensor_apply import MultiTensorApply
multi_tensor_applier = MultiTensorApply(2048*32)
|
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... |
import types
import torch
import importlib
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: A Method for Stochastic Optimization`_.
Arguments:
... |
from .fused_adam import FusedAdam
from .fp16_optimizer import FP16_Optimizer
|
import torch
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
class FP16_Optimizer(object):
"""
:class:`FP16_Optimizer` A cutdown version of apex.fp16_utils.FP16_Optimizer.
Designed only to wrap apex.optimizers.FusedAdam.
Refer to apex.fp16_utils documents for more information.... |
from .weight_norm import WeightNorm
from .reparameterization import Reparameterization
def apply_weight_norm(module, name='', dim=0, hook_child=True):
"""
Applies weight normalization to a parameter in the given module.
If no parameter is provided, applies weight normalization to all
parameters in mode... |
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... |
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... |
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
class AmpOptimizerState(object):
def __init__(self):
pass
def lazy_init_with_master_weights(self):
stash = self._amp_stash
... |
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 =... |
import contextlib
import warnings
import torch
from . import utils
from .opt import OptimWrapper
from .scaler import LossScaler
from ._amp_state import _amp_state, master_params, maybe_print
from ..fp16_utils import FP16_Optimizer as FP16_Optimizer_general
from ..optimizers import FP16_Optimizer as FP16_Optimizer_for_... |
import torch
from torch._six import string_classes
import functools
import numpy as np
import warnings
from ._amp_state import _amp_state, warn_or_err, container_abcs
from .handle import disable_casts
from .scaler import LossScaler
from ._process_optimizer import _process_optimizer
from apex.fp16_utils import convert_n... |
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 *
import functools
import itertools
import torch
_DECORATOR_HANDLE = None
_USER_CAST_REGISTRY = ... |
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 __setattr__ so that (in theory)
they can ... |
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
from ._amp_state import master_params, _amp_state
|
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:
... |
VERSION = (0, 1, 0)
__version__ = '.'.join(map(str, VERSION))
|
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 ... |
# 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 os
import torch
TORCH_MAJOR = int(to... |
from . import compat
import functools
import itertools
import torch
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)
for y in x:
if not is_fp_tensor(y):
... |
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(... |
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... |
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'... |
# 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... |
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 = [
'__matmul__',
]
FP32_FUNCS = [
'__ipow__',
'__pow__',
'__rpow_... |
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