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# under the License.
# coding: utf-8
# pylint: disable=
"""Parallelization utility optimizer."""
__all__ = ['split_data', 'split_and_load', 'clip_global_norm',
'check_sha1', 'download']
import os
import hashlib
import warnings
try:
import requests
except ImportError:
class requests_failed_to_import(object):
pass
requests = requests_failed_to_import
import numpy as np
from .. import ndarray
[docs]def split_data(data, num_slice, batch_axis=0, even_split=True):
"""Splits an NDArray into `num_slice` slices along `batch_axis`.
Usually used for data parallelism where each slices is sent
to one device (i.e. GPU).
Parameters
----------
data : NDArray
A batch of data.
num_slice : int
Number of desired slices.
batch_axis : int, default 0
The axis along which to slice.
even_split : bool, default True
Whether to force all slices to have the same number of elements.
If `True`, an error will be raised when `num_slice` does not evenly
divide `data.shape[batch_axis]`.
Returns
-------
list of NDArray
Return value is a list even if `num_slice` is 1.
"""
size = data.shape[batch_axis]
if size < num_slice:
raise ValueError(
"Too many slices for data with shape %s. Arguments are " \
"num_slice=%d and batch_axis=%d."%(str(data.shape), num_slice, batch_axis))
if even_split and size % num_slice != 0:
raise ValueError(
"data with shape %s cannot be evenly split into %d slices along axis %d. " \
"Use a batch size that's multiple of %d or set even_split=False to allow " \
"uneven partitioning of data."%(
str(data.shape), num_slice, batch_axis, num_slice))
step = size // num_slice
if batch_axis == 0:
slices = [data[i*step:(i+1)*step] if i < num_slice - 1 else data[i*step:size]
for i in range(num_slice)]
elif even_split:
slices = ndarray.split(data, num_outputs=num_slice, axis=batch_axis)
else:
slices = [ndarray.slice_axis(data, batch_axis, i*step, (i+1)*step)
if i < num_slice - 1 else
ndarray.slice_axis(data, batch_axis, i*step, size)
for i in range(num_slice)]
return slices
[docs]def split_and_load(data, ctx_list, batch_axis=0, even_split=True):
"""Splits an NDArray into `len(ctx_list)` slices along `batch_axis` and loads
each slice to one context in `ctx_list`.
Parameters
----------
data : NDArray
A batch of data.
ctx_list : list of Context
A list of Contexts.
batch_axis : int, default 0
The axis along which to slice.
even_split : bool, default True
Whether to force all slices to have the same number of elements.
Returns
-------
list of NDArray
Each corresponds to a context in `ctx_list`.
"""
if not isinstance(data, ndarray.NDArray):
data = ndarray.array(data, ctx=ctx_list[0])
if len(ctx_list) == 1:
return [data.as_in_context(ctx_list[0])]
slices = split_data(data, len(ctx_list), batch_axis, even_split)
return [i.as_in_context(ctx) for i, ctx in zip(slices, ctx_list)]
[docs]def clip_global_norm(arrays, max_norm):
"""Rescales NDArrays so that the sum of their 2-norm is smaller than `max_norm`.
"""
assert len(arrays) > 0
ctx = arrays[0].context
total_norm = ndarray.add_n(*[ndarray.dot(x, x).as_in_context(ctx)
for x in (arr.reshape((-1,)) for arr in arrays)])
total_norm = ndarray.sqrt(total_norm).asscalar()
if not np.isfinite(total_norm):
warnings.warn(UserWarning('nan or inf is detected. Clipping results will be undefined.'),
stacklevel=2)
scale = max_norm / (total_norm + 1e-8)
if scale < 1.0:
for arr in arrays:
arr *= scale
return total_norm
def _indent(s_, numSpaces):
"""Indent string
"""
s = s_.split('\n')
if len(s) == 1:
return s_
first = s.pop(0)
s = [first] + [(numSpaces * ' ') + line for line in s]
s = '\n'.join(s)
return s
[docs]def check_sha1(filename, sha1_hash):
"""Check whether the sha1 hash of the file content matches the expected hash.
Parameters
----------
filename : str
Path to the file.
sha1_hash : str
Expected sha1 hash in hexadecimal digits.
Returns
-------
bool
Whether the file content matches the expected hash.
"""
sha1 = hashlib.sha1()
with open(filename, 'rb') as f:
while True:
data = f.read(1048576)
if not data:
break
sha1.update(data)
return sha1.hexdigest() == sha1_hash
[docs]def download(url, path=None, overwrite=False, sha1_hash=None):
"""Download an given URL
Parameters
----------
url : str
URL to download
path : str, optional
Destination path to store downloaded file. By default stores to the
current directory with same name as in url.
overwrite : bool, optional
Whether to overwrite destination file if already exists.
sha1_hash : str, optional
Expected sha1 hash in hexadecimal digits. Will ignore existing file when hash is specified
but doesn't match.
Returns
-------
str
The file path of the downloaded file.
"""
if path is None:
fname = url.split('/')[-1]
else:
path = os.path.expanduser(path)
if os.path.isdir(path):
fname = os.path.join(path, url.split('/')[-1])
else:
fname = path
if overwrite or not os.path.exists(fname) or (sha1_hash and not check_sha1(fname, sha1_hash)):
dirname = os.path.dirname(os.path.abspath(os.path.expanduser(fname)))
if not os.path.exists(dirname):
os.makedirs(dirname)
print('Downloading %s from %s...'%(fname, url))
r = requests.get(url, stream=True)
if r.status_code != 200:
raise RuntimeError("Failed downloading url %s"%url)
with open(fname, 'wb') as f:
for chunk in r.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
if sha1_hash and not check_sha1(fname, sha1_hash):
raise UserWarning('File {} is downloaded but the content hash does not match. ' \
'The repo may be outdated or download may be incomplete. ' \
'If the "repo_url" is overridden, consider switching to ' \
'the default repo.'.format(fname))
return fname
def _get_repo_url():
"""Return the base URL for Gluon dataset and model repository."""
default_repo = 'https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/'
repo_url = os.environ.get('MXNET_GLUON_REPO', default_repo)
if repo_url[-1] != '/':
repo_url = repo_url+'/'
return repo_url
def _get_repo_file_url(namespace, filename):
"""Return the URL for hosted file in Gluon repository.
Parameters
----------
namespace : str
Namespace of the file.
filename : str
Name of the file
"""
return '{base_url}{namespace}/{filename}'.format(base_url=_get_repo_url(),
namespace=namespace,
filename=filename)
def _brief_print_list(lst, limit=7):
"""Print at most `limit` elements of list."""
if len(lst) > limit:
return _brief_print_list(lst[:limit//2], limit) + ', ..., ' + \
_brief_print_list(lst[-limit//2:], limit)
return ', '.join(["'%s'"%str(i) for i in lst])
