code
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string | loss_without_docstring
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# If we're already waiting on an/some outstanding disconnects
# make sure we continue to wait for them...
log.debug("%r: close", self)
self._closing = True
# Close down any clients we have
brokerclients, self.clients = self.clients, None
self._close_brokerclients(brokerclients.values())
# clean up other outstanding operations
self.reset_all_metadata()
return self.close_dlist or defer.succeed(None)
|
def close(self)
|
Permanently dispose of the client
- Immediately mark the client as closed, causing current operations to
fail with :exc:`~afkak.common.CancelledError` and future operations to
fail with :exc:`~afkak.common.ClientError`.
- Clear cached metadata.
- Close any connections to Kafka brokers.
:returns:
deferred that fires when all resources have been released
| 12.649477 | 12.638518 | 1.000867 |
topics = tuple(_coerce_topic(t) for t in topics)
log.debug("%r: load_metadata_for_topics(%s)", self, ', '.join(repr(t) for t in topics))
fetch_all_metadata = not topics
# create the request
requestId = self._next_id()
request = KafkaCodec.encode_metadata_request(self._clientIdBytes,
requestId, topics)
# Callbacks for the request deferred...
def _handleMetadataResponse(response):
# Decode the response
brokers, topics = KafkaCodec.decode_metadata_response(response)
log.debug("%r: got metadata brokers=%r topics=%r", self, brokers, topics)
# If we fetched the metadata for all topics, then store away the
# received metadata for diagnostics.
if fetch_all_metadata:
self._brokers = brokers
self._topics = topics
# Iff we were fetching for all topics, and we got at least one
# broker back, then remove brokers when we update our brokers
ok_to_remove = (fetch_all_metadata and len(brokers))
# Take the metadata we got back, update our self.clients, and
# if needed disconnect or connect from/to old/new brokers
self._update_brokers(brokers.values(), remove=ok_to_remove)
# Now loop through all the topics/partitions in the response
# and setup our cache/data-structures
for topic, topic_metadata in topics.items():
_, topic_error, partitions = topic_metadata
self.reset_topic_metadata(topic)
self.topic_errors[topic] = topic_error
if not partitions:
log.warning('No partitions for %s, Err:%d',
topic, topic_error)
continue
self.topic_partitions[topic] = []
for partition, meta in partitions.items():
self.topic_partitions[topic].append(partition)
topic_part = TopicAndPartition(topic, partition)
self.partition_meta[topic_part] = meta
if meta.leader == -1:
log.warning('No leader for topic %s partition %s',
topic, partition)
self.topics_to_brokers[topic_part] = None
else:
self.topics_to_brokers[
topic_part] = brokers[meta.leader]
self.topic_partitions[topic] = sorted(
self.topic_partitions[topic])
return True
def _handleMetadataErr(err):
# This should maybe do more cleanup?
if err.check(t_CancelledError, CancelledError):
# Eat the error
# XXX Shouldn't this return False? The success branch
# returns True.
return None
log.error("Failed to retrieve metadata:%s", err)
raise KafkaUnavailableError(
"Unable to load metadata from configured "
"hosts: {!r}".format(err))
# Send the request, add the handlers
d = self._send_broker_unaware_request(requestId, request)
d.addCallbacks(_handleMetadataResponse, _handleMetadataErr)
return d
|
def load_metadata_for_topics(self, *topics)
|
Discover topic metadata and brokers
Afkak internally calls this method whenever metadata is required.
:param str topics:
Topic names to look up. The resulting metadata includes the list of
topic partitions, brokers owning those partitions, and which
partitions are in sync.
Fetching metadata for a topic may trigger auto-creation if that is
enabled on the Kafka broker.
When no topic name is given metadata for *all* topics is fetched.
This is an expensive operation, but it does not trigger topic
creation.
:returns:
:class:`Deferred` for the completion of the metadata fetch.
This will fire with ``True`` on success, ``None`` on
cancellation, or fail with an exception on error.
On success, topic metadata is available from the attributes of
:class:`KafkaClient`: :data:`~KafkaClient.topic_partitions`,
:data:`~KafkaClient.topics_to_brokers`, etc.
| 4.519485 | 4.418212 | 1.022922 |
group = _coerce_consumer_group(group)
log.debug("%r: load_consumer_metadata_for_group(%r)", self, group)
# If we are already loading the metadata for this group, then
# just return the outstanding deferred
if group in self.coordinator_fetches:
d = defer.Deferred()
self.coordinator_fetches[group][1].append(d)
return d
# No outstanding request, create a new one
requestId = self._next_id()
request = KafkaCodec.encode_consumermetadata_request(
self._clientIdBytes, requestId, group)
# Callbacks for the request deferred...
def _handleConsumerMetadataResponse(response_bytes):
# Decode the response (returns ConsumerMetadataResponse)
response = KafkaCodec.decode_consumermetadata_response(response_bytes)
log.debug("%r: load_consumer_metadata_for_group(%r) -> %r", self, group, response)
if response.error:
raise BrokerResponseError.errnos.get(response.error, UnknownError)(response)
bm = BrokerMetadata(response.node_id, response.host, response.port)
self.consumer_group_to_brokers[group] = bm
self._update_brokers([bm])
return True
def _handleConsumerMetadataErr(err):
log.error("Failed to retrieve consumer metadata for group %r", group,
exc_info=(err.type, err.value, err.getTracebackObject()))
# Clear any stored value for the group's coordinator
self.reset_consumer_group_metadata(group)
# FIXME: This exception should chain from err.
raise ConsumerCoordinatorNotAvailableError(
"Coordinator for group {!r} not available".format(group),
)
def _propagate(result):
[_, ds] = self.coordinator_fetches.pop(group, None)
for d in ds:
d.callback(result)
# Send the request, add the handlers
request_d = self._send_broker_unaware_request(requestId, request)
d = defer.Deferred()
# Save the deferred under the fetches for this group
self.coordinator_fetches[group] = (request_d, [d])
request_d.addCallback(_handleConsumerMetadataResponse)
request_d.addErrback(_handleConsumerMetadataErr)
request_d.addBoth(_propagate)
return d
|
def load_consumer_metadata_for_group(self, group)
|
Determine broker for the consumer metadata for the specified group
Returns a deferred which callbacks with True if the group's coordinator
could be determined, or errbacks with
ConsumerCoordinatorNotAvailableError if not.
Parameters
----------
group:
group name as `str`
| 3.982506 | 3.842242 | 1.036506 |
encoder = partial(
KafkaCodec.encode_produce_request,
acks=acks,
timeout=timeout)
if acks == 0:
decoder = None
else:
decoder = KafkaCodec.decode_produce_response
resps = yield self._send_broker_aware_request(
payloads, encoder, decoder)
returnValue(self._handle_responses(resps, fail_on_error, callback))
|
def send_produce_request(self, payloads=None, acks=1,
timeout=DEFAULT_REPLICAS_ACK_MSECS,
fail_on_error=True, callback=None)
|
Encode and send some ProduceRequests
ProduceRequests will be grouped by (topic, partition) and then
sent to a specific broker. Output is a list of responses in the
same order as the list of payloads specified
Parameters
----------
payloads:
list of ProduceRequest
acks:
How many Kafka broker replicas need to write before
the leader replies with a response
timeout:
How long the server has to receive the acks from the
replicas before returning an error.
fail_on_error:
boolean, should we raise an Exception if we encounter an API error?
callback:
function, instead of returning the ProduceResponse,
first pass it through this function
Return
------
a deferred which callbacks with a list of ProduceResponse
Raises
------
FailedPayloadsError, LeaderUnavailableError, PartitionUnavailableError
| 3.821197 | 4.54659 | 0.840453 |
if (max_wait_time / 1000) > (self.timeout - 0.1):
raise ValueError(
"%r: max_wait_time: %d must be less than client.timeout by "
"at least 100 milliseconds.", self, max_wait_time)
encoder = partial(KafkaCodec.encode_fetch_request,
max_wait_time=max_wait_time,
min_bytes=min_bytes)
# resps is a list of FetchResponse() objects, each of which can hold
# 1-n messages.
resps = yield self._send_broker_aware_request(
payloads, encoder,
KafkaCodec.decode_fetch_response)
returnValue(self._handle_responses(resps, fail_on_error, callback))
|
def send_fetch_request(self, payloads=None, fail_on_error=True,
callback=None,
max_wait_time=DEFAULT_FETCH_SERVER_WAIT_MSECS,
min_bytes=DEFAULT_FETCH_MIN_BYTES)
|
Encode and send a FetchRequest
Payloads are grouped by topic and partition so they can be pipelined
to the same brokers.
Raises
======
FailedPayloadsError, LeaderUnavailableError, PartitionUnavailableError
| 4.641526 | 4.567288 | 1.016254 |
encoder = partial(KafkaCodec.encode_offset_fetch_request,
group=group)
decoder = KafkaCodec.decode_offset_fetch_response
resps = yield self._send_broker_aware_request(
payloads, encoder, decoder, consumer_group=group)
returnValue(self._handle_responses(
resps, fail_on_error, callback, group))
|
def send_offset_fetch_request(self, group, payloads=None,
fail_on_error=True, callback=None)
|
Takes a group (string) and list of OffsetFetchRequest and returns
a list of OffsetFetchResponse objects
| 4.75981 | 4.953605 | 0.960878 |
group = _coerce_consumer_group(group)
encoder = partial(KafkaCodec.encode_offset_commit_request,
group=group, group_generation_id=group_generation_id,
consumer_id=consumer_id)
decoder = KafkaCodec.decode_offset_commit_response
resps = yield self._send_broker_aware_request(
payloads, encoder, decoder, consumer_group=group)
returnValue(self._handle_responses(
resps, fail_on_error, callback, group))
|
def send_offset_commit_request(self, group, payloads=None,
fail_on_error=True, callback=None,
group_generation_id=-1,
consumer_id='')
|
Send a list of OffsetCommitRequests to the Kafka broker for the
given consumer group.
Args:
group (str): The consumer group to which to commit the offsets
payloads ([OffsetCommitRequest]): List of topic, partition, offsets
to commit.
fail_on_error (bool): Whether to raise an exception if a response
from the Kafka broker indicates an error
callback (callable): a function to call with each of the responses
before returning the returned value to the caller.
group_generation_id (int): Must currently always be -1
consumer_id (str): Must currently always be empty string
Returns:
[OffsetCommitResponse]: List of OffsetCommitResponse objects.
Will raise KafkaError for failed requests if fail_on_error is True
| 3.608326 | 4.057764 | 0.88924 |
if self._closing:
raise ClientError("Cannot get broker client for node_id={}: {} has been closed".format(node_id, self))
if node_id not in self.clients:
broker_metadata = self._brokers[node_id]
log.debug("%r: creating client for %s", self, broker_metadata)
self.clients[node_id] = _KafkaBrokerClient(
self.reactor, self._endpoint_factory,
broker_metadata, self.clientId, self._retry_policy,
)
return self.clients[node_id]
|
def _get_brokerclient(self, node_id)
|
Get a broker client.
:param int node_id: Broker node ID
:raises KeyError: for an unknown node ID
:returns: :class:`_KafkaBrokerClient`
| 3.887404 | 3.640951 | 1.067689 |
def _log_close_failure(failure, brokerclient):
log.debug(
'BrokerClient: %s close result: %s: %s', brokerclient,
failure.type.__name__, failure.getErrorMessage())
def _clean_close_dlist(result, close_dlist):
# If there aren't any other outstanding closings going on, then
# close_dlist == self.close_dlist, and we can reset it.
if close_dlist == self.close_dlist:
self.close_dlist = None
if not self.close_dlist:
dList = []
else:
log.debug("%r: _close_brokerclients has nested deferredlist: %r",
self, self.close_dlist)
dList = [self.close_dlist]
for brokerClient in clients:
log.debug("Calling close on: %r", brokerClient)
d = brokerClient.close().addErrback(_log_close_failure, brokerClient)
dList.append(d)
self.close_dlist = DeferredList(dList)
self.close_dlist.addBoth(_clean_close_dlist, self.close_dlist)
|
def _close_brokerclients(self, clients)
|
Close the given broker clients.
:param clients: Iterable of `_KafkaBrokerClient`
| 3.221581 | 3.361144 | 0.958478 |
log.debug("%r: _update_brokers(%r, remove=%r)",
self, brokers, remove)
brokers_by_id = {bm.node_id: bm for bm in brokers}
self._brokers.update(brokers_by_id)
# Update the metadata of broker clients that already exist.
for node_id, broker_meta in brokers_by_id.items():
if node_id not in self.clients:
continue
self.clients[node_id].updateMetadata(broker_meta)
# Remove any clients for brokers which no longer exist.
if remove:
to_close = [
self.clients.pop(node_id)
for node_id in set(self.clients) - set(brokers_by_id)
]
if to_close:
self._close_brokerclients(to_close)
|
def _update_brokers(self, brokers, remove=False)
|
Update `self._brokers` and `self.clients`
Update our self.clients based on brokers in received metadata
Take the received dict of brokers and reconcile it with our current
list of brokers (self.clients). If there is a new one, bring up a new
connection to it, and if remove is True, and any in our current list
aren't in the metadata returned, disconnect from it.
:param brokers: Iterable of `BrokerMetadata`. A client will be created
for every broker given if it doesn't yet exist.
:param bool remove:
Is this metadata for *all* brokers? If so, clients for brokers
which are no longer found in the metadata will be closed.
| 2.842386 | 2.589721 | 1.097564 |
key = TopicAndPartition(topic, partition)
# reload metadata whether the partition is not available
# or has no leader (broker is None)
if self.topics_to_brokers.get(key) is None:
yield self.load_metadata_for_topics(topic)
if key not in self.topics_to_brokers:
raise PartitionUnavailableError("%s not available" % str(key))
returnValue(self.topics_to_brokers[key])
|
def _get_leader_for_partition(self, topic, partition)
|
Returns the leader for a partition or None if the partition exists
but has no leader.
PartitionUnavailableError will be raised if the topic or partition
is not part of the metadata.
| 5.435142 | 4.770844 | 1.139241 |
if self.consumer_group_to_brokers.get(consumer_group) is None:
yield self.load_consumer_metadata_for_group(consumer_group)
returnValue(self.consumer_group_to_brokers.get(consumer_group))
|
def _get_coordinator_for_group(self, consumer_group)
|
Returns the coordinator (broker) for a consumer group
Returns the broker for a given consumer group or
Raises ConsumerCoordinatorNotAvailableError
| 3.899415 | 3.777193 | 1.032358 |
def _timeout_request(broker, requestId):
try:
# FIXME: This should be done by calling .cancel() on the Deferred
# returned by the broker client.
broker.cancelRequest(requestId, reason=RequestTimedOutError(
'Request: {} cancelled due to timeout'.format(requestId)))
except KeyError: # pragma: no cover This should never happen...
log.exception('ERROR: Failed to find key for timed-out '
'request. Broker: %r Req: %d',
broker, requestId)
raise
if self._disconnect_on_timeout:
broker.disconnect()
def _alert_blocked_reactor(timeout, start):
now = self.reactor.seconds()
if now >= (start + timeout):
log.warning('Reactor was starved for %r seconds', now - start)
def _cancel_timeout(result, dc):
if dc.active():
dc.cancel()
return result
# Make the request to the specified broker
log.debug('_mrtb: sending request: %d to broker: %r',
requestId, broker)
d = broker.makeRequest(requestId, request, **kwArgs)
# Set a delayedCall to fire if we don't get a reply in time
dc = self.reactor.callLater(
self.timeout, _timeout_request, broker, requestId)
# Set a delayedCall to complain if the reactor has been blocked
rc = self.reactor.callLater(
(self.timeout * 0.9), _alert_blocked_reactor, self.timeout,
self.reactor.seconds())
# Setup a callback on the request deferred to cancel both callLater
d.addBoth(_cancel_timeout, dc)
d.addBoth(_cancel_timeout, rc)
return d
|
def _make_request_to_broker(self, broker, requestId, request, **kwArgs)
|
Send a request to the specified broker.
| 4.953167 | 4.890821 | 1.012748 |
node_ids = list(self._brokers.keys())
# Randomly shuffle the brokers to distribute the load
random.shuffle(node_ids)
# Prioritize connected brokers
def connected(node_id):
try:
return self.clients[node_id].connected()
except KeyError:
return False
node_ids.sort(reverse=True, key=connected)
for node_id in node_ids:
broker = self._get_brokerclient(node_id)
try:
log.debug('_sbur: sending request %d to broker %r', requestId, broker)
d = self._make_request_to_broker(broker, requestId, request)
resp = yield d
returnValue(resp)
except KafkaError as e:
log.warning((
"Will try next server after request with correlationId=%d"
" failed against server %s:%i. Error: %s"
), requestId, broker.host, broker.port, e)
# The request was not handled, likely because no broker metadata has
# loaded yet (or all broker connections have failed). Fall back to
# boostrapping.
returnValue((yield self._send_bootstrap_request(request)))
|
def _send_broker_unaware_request(self, requestId, request)
|
Attempt to send a broker-agnostic request to one of the known brokers:
1. Try each connected broker (in random order)
2. Try each known but unconnected broker (in random order)
3. Try each of the bootstrap hosts (in random order)
:param bytes request:
The bytes of a Kafka `RequestMessage`_ structure. It must have
a unique (to this connection) correlation ID.
:returns: API response message for *request*
:rtype: Deferred[bytes]
:raises:
`KafkaUnavailableError` when making the request of all known hosts
has failed.
| 5.327105 | 5.042294 | 1.056484 |
hostports = list(self._bootstrap_hosts)
random.shuffle(hostports)
for host, port in hostports:
ep = self._endpoint_factory(self.reactor, host, port)
try:
protocol = yield ep.connect(_bootstrapFactory)
except Exception as e:
log.debug("%s: bootstrap connect to %s:%s -> %s", self, host, port, e)
continue
try:
response = yield protocol.request(request).addTimeout(self.timeout, self.reactor)
except Exception:
log.debug("%s: bootstrap request to %s:%s failed", self, host, port, exc_info=True)
else:
returnValue(response)
finally:
protocol.transport.loseConnection()
raise KafkaUnavailableError("Failed to bootstrap from hosts {}".format(hostports))
|
def _send_bootstrap_request(self, request)
|
Make a request using an ephemeral broker connection
This routine is used to make broker-unaware requests to get the initial
cluster metadata. It cycles through the configured hosts, trying to
connect and send the request to each in turn. This temporary connection
is closed once a response is received.
Note that most Kafka APIs require requests be sent to a specific
broker. This method will only function for broker-agnostic requests
like:
* `Metadata <https://kafka.apache.org/protocol.html#The_Messages_Metadata>`_
* `FindCoordinator <https://kafka.apache.org/protocol.html#The_Messages_FindCoordinator>`_
:param bytes request:
The bytes of a Kafka `RequestMessage`_ structure. It must have
a unique (to this connection) correlation ID.
:returns: API response message for *request*
:rtype: Deferred[bytes]
:raises:
- `KafkaUnavailableError` when making the request of all known hosts
has failed.
- `twisted.internet.defer.TimeoutError` when connecting or making
a request exceeds the timeout.
| 3.35803 | 3.196571 | 1.05051 |
descripter = ExpressionDescriptor(expression, options)
return descripter.get_description(DescriptionTypeEnum.FULL)
|
def get_description(expression, options=None)
|
Generates a human readable string for the Cron Expression
Args:
expression: The cron expression string
options: Options to control the output description
Returns:
The cron expression description
| 10.10568 | 14.089204 | 0.717264 |
try:
if self._parsed is False:
parser = ExpressionParser(self._expression, self._options)
self._expression_parts = parser.parse()
self._parsed = True
choices = {
DescriptionTypeEnum.FULL: self.get_full_description,
DescriptionTypeEnum.TIMEOFDAY: self.get_time_of_day_description,
DescriptionTypeEnum.HOURS: self.get_hours_description,
DescriptionTypeEnum.MINUTES: self.get_minutes_description,
DescriptionTypeEnum.SECONDS: self.get_seconds_description,
DescriptionTypeEnum.DAYOFMONTH: self.get_day_of_month_description,
DescriptionTypeEnum.MONTH: self.get_month_description,
DescriptionTypeEnum.DAYOFWEEK: self.get_day_of_week_description,
DescriptionTypeEnum.YEAR: self.get_year_description,
}
description = choices.get(description_type, self.get_seconds_description)()
except Exception as ex:
if self._options.throw_exception_on_parse_error:
raise
else:
description = str(ex)
return description
|
def get_description(self, description_type=DescriptionTypeEnum.FULL)
|
Generates a human readable string for the Cron Expression
Args:
description_type: Which part(s) of the expression to describe
Returns:
The cron expression description
Raises:
Exception: if throw_exception_on_parse_error is True
| 2.122979 | 1.832478 | 1.158529 |
try:
time_segment = self.get_time_of_day_description()
day_of_month_desc = self.get_day_of_month_description()
month_desc = self.get_month_description()
day_of_week_desc = self.get_day_of_week_description()
year_desc = self.get_year_description()
description = "{0}{1}{2}{3}{4}".format(
time_segment,
day_of_month_desc,
day_of_week_desc,
month_desc,
year_desc)
description = self.transform_verbosity(
description, self._options.verbose)
description = self.transform_case(
description,
self._options.casing_type)
except Exception:
description = _(
"An error occured when generating the expression description. Check the cron expression syntax.")
if self._options.throw_exception_on_parse_error:
raise FormatException(description)
return description
|
def get_full_description(self)
|
Generates the FULL description
Returns:
The FULL description
Raises:
FormatException: if formating fails and throw_exception_on_parse_error is True
| 3.25047 | 2.81008 | 1.156718 |
seconds_expression = self._expression_parts[0]
minute_expression = self._expression_parts[1]
hour_expression = self._expression_parts[2]
description = StringBuilder()
# handle special cases first
if any(exp in minute_expression for exp in self._special_characters) is False and \
any(exp in hour_expression for exp in self._special_characters) is False and \
any(exp in seconds_expression for exp in self._special_characters) is False:
# specific time of day (i.e. 10 14)
description.append(_("At "))
description.append(
self.format_time(
hour_expression,
minute_expression,
seconds_expression))
elif "-" in minute_expression and \
"," not in minute_expression and \
any(exp in hour_expression for exp in self._special_characters) is False:
# minute range in single hour (i.e. 0-10 11)
minute_parts = minute_expression.split('-')
description.append(_("Every minute between {0} and {1}").format(
self.format_time(hour_expression, minute_parts[0]), self.format_time(hour_expression, minute_parts[1])))
elif "," in hour_expression and "-" not in hour_expression and \
any(exp in minute_expression for exp in self._special_characters) is False:
# hours list with single minute (o.e. 30 6,14,16)
hour_parts = hour_expression.split(',')
description.append(_("At"))
for i, hour_part in enumerate(hour_parts):
description.append(" ")
description.append(
self.format_time(hour_part, minute_expression))
if i < (len(hour_parts) - 2):
description.append(",")
if i == len(hour_parts) - 2:
description.append(_(" and"))
else:
# default time description
seconds_description = self.get_seconds_description()
minutes_description = self.get_minutes_description()
hours_description = self.get_hours_description()
description.append(seconds_description)
if description:
description.append(", ")
description.append(minutes_description)
if description:
description.append(", ")
description.append(hours_description)
return str(description)
|
def get_time_of_day_description(self)
|
Generates a description for only the TIMEOFDAY portion of the expression
Returns:
The TIMEOFDAY description
| 2.447194 | 2.469211 | 0.991083 |
return self.get_segment_description(
self._expression_parts[0],
_("every second"),
lambda s: s,
lambda s: _("every {0} seconds").format(s),
lambda s: _("seconds {0} through {1} past the minute"),
lambda s: _("at {0} seconds past the minute")
)
|
def get_seconds_description(self)
|
Generates a description for only the SECONDS portion of the expression
Returns:
The SECONDS description
| 6.297512 | 5.665463 | 1.111562 |
return self.get_segment_description(
self._expression_parts[1],
_("every minute"),
lambda s: s,
lambda s: _("every {0} minutes").format(s),
lambda s: _("minutes {0} through {1} past the hour"),
lambda s: '' if s == "0" else _("at {0} minutes past the hour")
)
|
def get_minutes_description(self)
|
Generates a description for only the MINUTE portion of the expression
Returns:
The MINUTE description
| 6.657679 | 5.897062 | 1.128982 |
expression = self._expression_parts[2]
return self.get_segment_description(
expression,
_("every hour"),
lambda s: self.format_time(s, "0"),
lambda s: _("every {0} hours").format(s),
lambda s: _("between {0} and {1}"),
lambda s: _("at {0}")
)
|
def get_hours_description(self)
|
Generates a description for only the HOUR portion of the expression
Returns:
The HOUR description
| 4.995983 | 5.456813 | 0.91555 |
if self._expression_parts[5] == "*" and self._expression_parts[3] != "*":
# DOM is specified and DOW is * so to prevent contradiction like "on day 1 of the month, every day"
# we will not specified a DOW description.
return ""
def get_day_name(s):
exp = s
if "#" in s:
exp, useless = s.split("#", 2)
elif "L" in s:
exp = exp.replace("L", '')
return self.number_to_day(int(exp))
def get_format(s):
if "#" in s:
day_of_week_of_month = s[s.find("#") + 1:]
try:
day_of_week_of_month_number = int(day_of_week_of_month)
choices = {
1: _("first"),
2: _("second"),
3: _("third"),
4: _("forth"),
5: _("fifth"),
}
day_of_week_of_month_description = choices.get(day_of_week_of_month_number, '')
except ValueError:
day_of_week_of_month_description = ''
formated = "{}{}{}".format(_(", on the "),
day_of_week_of_month_description, _(" {0} of the month"))
elif "L" in s:
formated = _(", on the last {0} of the month")
else:
formated = _(", only on {0}")
return formated
return self.get_segment_description(
self._expression_parts[5],
_(", every day"),
lambda s: get_day_name(s),
lambda s: _(", every {0} days of the week").format(s),
lambda s: _(", {0} through {1}"),
lambda s: get_format(s)
)
|
def get_day_of_week_description(self)
|
Generates a description for only the DAYOFWEEK portion of the expression
Returns:
The DAYOFWEEK description
| 3.747551 | 3.696458 | 1.013822 |
return self.get_segment_description(
self._expression_parts[4],
'',
lambda s: datetime.date(datetime.date.today().year, int(s), 1).strftime("%B"),
lambda s: _(", every {0} months").format(s),
lambda s: _(", {0} through {1}"),
lambda s: _(", only in {0}")
)
|
def get_month_description(self)
|
Generates a description for only the MONTH portion of the expression
Returns:
The MONTH description
| 6.048262 | 5.959369 | 1.014917 |
expression = self._expression_parts[3]
expression = expression.replace("?", "*")
if expression == "L":
description = _(", on the last day of the month")
elif expression == "LW" or expression == "WL":
description = _(", on the last weekday of the month")
else:
regex = re.compile("(\\d{1,2}W)|(W\\d{1,2})")
if regex.match(expression):
m = regex.match(expression)
day_number = int(m.group().replace("W", ""))
day_string = _("first weekday") if day_number == 1 else _("weekday nearest day {0}").format(
day_number)
description = _(", on the {0} of the month").format(
day_string)
else:
description = self.get_segment_description(
expression,
_(", every day"),
lambda s: s,
lambda s: _(", every day") if s == "1" else _(", every {0} days"),
lambda s: _(", between day {0} and {1} of the month"),
lambda s: _(", on day {0} of the month")
)
return description
|
def get_day_of_month_description(self)
|
Generates a description for only the DAYOFMONTH portion of the expression
Returns:
The DAYOFMONTH description
| 3.281713 | 3.385772 | 0.969266 |
def format_year(s):
regex = re.compile(r"^\d+$")
if regex.match(s):
year_int = int(s)
if year_int < 1900:
return year_int
return datetime.date(year_int, 1, 1).strftime("%Y")
else:
return s
return self.get_segment_description(
self._expression_parts[6],
'',
lambda s: format_year(s),
lambda s: _(", every {0} years").format(s),
lambda s: _(", {0} through {1}"),
lambda s: _(", only in {0}")
)
|
def get_year_description(self)
|
Generates a description for only the YEAR portion of the expression
Returns:
The YEAR description
| 4.000232 | 3.852698 | 1.038294 |
description = None
if expression is None or expression == '':
description = ''
elif expression == "*":
description = all_description
elif any(ext in expression for ext in ['/', '-', ',']) is False:
description = get_description_format(expression).format(
get_single_item_description(expression))
elif "/" in expression:
segments = expression.split('/')
description = get_interval_description_format(
segments[1]).format(get_single_item_description(segments[1]))
# interval contains 'between' piece (i.e. 2-59/3 )
if "-" in segments[0]:
between_segment_description = self.generate_between_segment_description(
segments[0], get_between_description_format, get_single_item_description)
if not between_segment_description.startswith(", "):
description += ", "
description += between_segment_description
elif any(ext in segments[0] for ext in ['*', ',']) is False:
range_item_description = get_description_format(segments[0]).format(
get_single_item_description(segments[0])
)
range_item_description = range_item_description.replace(", ", "")
description += _(", starting {0}").format(range_item_description)
elif "," in expression:
segments = expression.split(',')
description_content = ''
for i, segment in enumerate(segments):
if i > 0 and len(segments) > 2:
description_content += ","
if i < len(segments) - 1:
description_content += " "
if i > 0 and len(segments) > 1 and (i == len(segments) - 1 or len(segments) == 2):
description_content += _(" and ")
if "-" in segment:
between_description = self.generate_between_segment_description(
segment,
lambda s: _(", {0} through {1}"),
get_single_item_description
)
between_description = between_description.replace(", ", "")
description_content += between_description
else:
description_content += get_single_item_description(segment)
description = get_description_format(
expression).format(
description_content)
elif "-" in expression:
description = self.generate_between_segment_description(
expression, get_between_description_format, get_single_item_description)
return description
|
def get_segment_description(
self,
expression,
all_description,
get_single_item_description,
get_interval_description_format,
get_between_description_format,
get_description_format
)
|
Returns segment description
Args:
expression: Segment to descript
all_description: *
get_single_item_description: 1
get_interval_description_format: 1/2
get_between_description_format: 1-2
get_description_format: format get_single_item_description
Returns:
segment description
| 2.411245 | 2.412489 | 0.999484 |
description = ""
between_segments = between_expression.split('-')
between_segment_1_description = get_single_item_description(between_segments[0])
between_segment_2_description = get_single_item_description(between_segments[1])
between_segment_2_description = between_segment_2_description.replace(
":00", ":59")
between_description_format = get_between_description_format(between_expression)
description += between_description_format.format(between_segment_1_description, between_segment_2_description)
return description
|
def generate_between_segment_description(
self,
between_expression,
get_between_description_format,
get_single_item_description
)
|
Generates the between segment description
:param between_expression:
:param get_between_description_format:
:param get_single_item_description:
:return: The between segment description
| 2.059759 | 2.117849 | 0.972571 |
hour = int(hour_expression)
period = ''
if self._options.use_24hour_time_format is False:
period = " PM" if (hour >= 12) else " AM"
if hour > 12:
hour -= 12
minute = str(int(minute_expression)) # !FIXME WUT ???
second = ''
if second_expression is not None and second_expression:
second = "{}{}".format(":", str(int(second_expression)).zfill(2))
return "{0}:{1}{2}{3}".format(str(hour).zfill(2), minute.zfill(2), second, period)
|
def format_time(
self,
hour_expression,
minute_expression,
second_expression=''
)
|
Given time parts, will contruct a formatted time description
Args:
hour_expression: Hours part
minute_expression: Minutes part
second_expression: Seconds part
Returns:
Formatted time description
| 3.520932 | 3.702982 | 0.950837 |
if use_verbose_format is False:
description = description.replace(
_(", every minute"), '')
description = description.replace(_(", every hour"), '')
description = description.replace(_(", every day"), '')
return description
|
def transform_verbosity(self, description, use_verbose_format)
|
Transforms the verbosity of the expression description by stripping verbosity from original description
Args:
description: The description to transform
use_verbose_format: If True, will leave description as it, if False, will strip verbose parts
second_expression: Seconds part
Returns:
The transformed description with proper verbosity
| 4.293999 | 4.824533 | 0.890034 |
if case_type == CasingTypeEnum.Sentence:
description = "{}{}".format(
description[0].upper(),
description[1:])
elif case_type == CasingTypeEnum.Title:
description = description.title()
else:
description = description.lower()
return description
|
def transform_case(self, description, case_type)
|
Transforms the case of the expression description, based on options
Args:
description: The description to transform
case_type: The casing type that controls the output casing
second_expression: Seconds part
Returns:
The transformed description with proper casing
| 3.541681 | 3.030559 | 1.168656 |
return [
calendar.day_name[6],
calendar.day_name[0],
calendar.day_name[1],
calendar.day_name[2],
calendar.day_name[3],
calendar.day_name[4],
calendar.day_name[5]
][day_number]
|
def number_to_day(self, day_number)
|
Returns localized day name by its CRON number
Args:
day_number: Number of a day
Returns:
Day corresponding to day_number
Raises:
IndexError: When day_number is not found
| 1.819823 | 2.083226 | 0.87356 |
# Have we been started already, and not stopped?
if self._start_d is not None:
raise RestartError("Start called on already-started consumer")
# Keep track of state for debugging
self._state = '[started]'
# Create and return a deferred for alerting on errors/stoppage
start_d = self._start_d = Deferred()
# Start a new fetch request, possibly just for the starting offset
self._fetch_offset = start_offset
self._do_fetch()
# Set up the auto-commit timer, if needed
if self.consumer_group and self.auto_commit_every_s:
self._commit_looper = LoopingCall(self._auto_commit)
self._commit_looper.clock = self.client.reactor
self._commit_looper_d = self._commit_looper.start(
self.auto_commit_every_s, now=False)
self._commit_looper_d.addCallbacks(self._commit_timer_stopped,
self._commit_timer_failed)
return start_d
|
def start(self, start_offset)
|
Starts fetching messages from Kafka and delivering them to the
:attr:`.processor` function.
:param int start_offset:
The offset within the partition from which to start fetching.
Special values include: :const:`OFFSET_EARLIEST`,
:const:`OFFSET_LATEST`, and :const:`OFFSET_COMMITTED`. If the
supplied offset is :const:`OFFSET_EARLIEST` or
:const:`OFFSET_LATEST` the :class:`Consumer` will use the
OffsetRequest Kafka API to retrieve the actual offset used for
fetching. In the case :const:`OFFSET_COMMITTED` is used,
`commit_policy` MUST be set on the Consumer, and the Consumer
will use the OffsetFetchRequest Kafka API to retrieve the actual
offset used for fetching.
:returns:
A :class:`~twisted.internet.defer.Deferred` which will resolve
successfully when the consumer is cleanly stopped, or with
a failure if the :class:`Consumer` encounters an error from which
it is unable to recover.
:raises: :exc:`RestartError` if already running.
| 4.843915 | 4.422919 | 1.095185 |
def _handle_shutdown_commit_success(result):
self._shutdown_d, d = None, self._shutdown_d
self.stop()
self._shuttingdown = False # Shutdown complete
d.callback((self._last_processed_offset,
self._last_committed_offset))
def _handle_shutdown_commit_failure(failure):
if failure.check(OperationInProgress):
failure.value.deferred.addCallback(_commit_and_stop)
return
self._shutdown_d, d = None, self._shutdown_d
self.stop()
self._shuttingdown = False # Shutdown complete
d.errback(failure)
def _commit_and_stop(result):
if not self.consumer_group: # No consumer group, no committing
return _handle_shutdown_commit_success(None)
# Need to commit prior to stopping
self.commit().addCallbacks(_handle_shutdown_commit_success,
_handle_shutdown_commit_failure)
# If we're not running, return an failure
if self._start_d is None:
return fail(Failure(
RestopError("Shutdown called on non-running consumer")))
# If we're called multiple times, return a failure
if self._shutdown_d:
return fail(Failure(
RestopError("Shutdown called more than once.")))
# Set our _shuttingdown flag, so our _process_message routine will stop
# feeding new messages to the processor, and fetches won't be retried
self._shuttingdown = True
# Keep track of state for debugging
self._state = '[shutting down]'
# Create a deferred to track the shutdown
self._shutdown_d = d = Deferred()
# Are we waiting for the processor to complete? If so, when it's done,
# commit our offsets and stop.
if self._processor_d:
self._processor_d.addCallback(_commit_and_stop)
else:
# No need to wait for the processor, we can commit and stop now
_commit_and_stop(None)
# return the deferred
return d
|
def shutdown(self)
|
Gracefully shutdown the consumer
Consumer will complete any outstanding processing, commit its current
offsets (if so configured) and stop.
Returns deferred which callbacks with a tuple of:
(last processed offset, last committed offset) if it was able to
successfully commit, or errbacks with the commit failure, if any,
or fail(RestopError) if consumer is not running.
| 4.166119 | 3.672454 | 1.134424 |
if self._start_d is None:
raise RestopError("Stop called on non-running consumer")
self._stopping = True
# Keep track of state for debugging
self._state = '[stopping]'
# Are we waiting for a request to come back?
if self._request_d:
self._request_d.cancel()
# Are we working our way through a block of messages?
if self._msg_block_d:
# Need to add a cancel handler...
_msg_block_d, self._msg_block_d = self._msg_block_d, None
_msg_block_d.addErrback(lambda fail: fail.trap(CancelledError))
_msg_block_d.cancel()
# Are we waiting for the processor to complete?
if self._processor_d:
self._processor_d.cancel()
# Are we waiting to retry a request?
if self._retry_call:
self._retry_call.cancel()
# Are we waiting on a commit request?
if self._commit_ds:
while self._commit_ds:
d = self._commit_ds.pop()
d.cancel()
if self._commit_req:
self._commit_req.cancel()
# Are we waiting to retry a commit?
if self._commit_call:
self._commit_call.cancel()
# Do we have an auto-commit looping call?
if self._commit_looper is not None:
self._commit_looper.stop()
# Done stopping
self._stopping = False
# Keep track of state for debugging
self._state = '[stopped]'
# Clear and possibly callback our start() Deferred
self._start_d, d = None, self._start_d
if not d.called:
d.callback((self._last_processed_offset,
self._last_committed_offset))
# Return the offset of the message we last processed
return self._last_processed_offset
|
def stop(self)
|
Stop the consumer and return offset of last processed message. This
cancels all outstanding operations. Also, if the deferred returned
by `start` hasn't been called, it is called with a tuple consisting
of the last processed offset and the last committed offset.
:raises: :exc:`RestopError` if the :class:`Consumer` is not running.
| 3.602508 | 3.119198 | 1.154947 |
# Can't commit without a consumer_group
if not self.consumer_group:
return fail(Failure(InvalidConsumerGroupError(
"Bad Group_id:{0!r}".format(self.consumer_group))))
# short circuit if we are 'up to date', or haven't processed anything
if ((self._last_processed_offset is None) or
(self._last_processed_offset == self._last_committed_offset)):
return succeed(self._last_committed_offset)
# If we're currently processing a commit we return a failure
# with a deferred we'll fire when the in-progress one completes
if self._commit_ds:
d = Deferred()
self._commit_ds.append(d)
return fail(OperationInProgress(d))
# Ok, we have processed messages since our last commit attempt, and
# we're not currently waiting on a commit request to complete:
# Start a new one
d = Deferred()
self._commit_ds.append(d)
# Send the request
self._send_commit_request()
# Reset the commit_looper here, rather than on success to give
# more stability to the commit interval.
if self._commit_looper is not None:
self._commit_looper.reset()
# return the deferred
return d
|
def commit(self)
|
Commit the offset of the message we last processed if it is different
from what we believe is the last offset committed to Kafka.
.. note::
It is possible to commit a smaller offset than Kafka has stored.
This is by design, so we can reprocess a Kafka message stream if
desired.
On error, will retry according to :attr:`request_retry_max_attempts`
(by default, forever).
If called while a commit operation is in progress, and new messages
have been processed since the last request was sent then the commit
will fail with :exc:`OperationInProgress`. The
:exc:`OperationInProgress` exception wraps
a :class:`~twisted.internet.defer.Deferred` which fires when the
outstanding commit operation completes.
:returns:
A :class:`~twisted.internet.defer.Deferred` which resolves with the
committed offset when the operation has completed. It will resolve
immediately if the current offset and the last committed offset do
not differ.
| 5.812636 | 5.061138 | 1.148484 |
# Check if we are even supposed to do any auto-committing
if (self._stopping or self._shuttingdown or (not self._start_d) or
(self._last_processed_offset is None) or
(not self.consumer_group) or
(by_count and not self.auto_commit_every_n)):
return
# If we're auto_committing because the timer expired, or by count and
# we don't have a record of our last_committed_offset, or we've
# processed enough messages since our last commit, then try to commit
if (not by_count or self._last_committed_offset is None or
(self._last_processed_offset - self._last_committed_offset
) >= self.auto_commit_every_n):
if not self._commit_ds:
commit_d = self.commit()
commit_d.addErrback(self._handle_auto_commit_error)
else:
# We're waiting on the last commit to complete, so add a
# callback to be called when the current request completes
d = Deferred()
d.addCallback(self._retry_auto_commit, by_count)
self._commit_ds.append(d)
|
def _auto_commit(self, by_count=False)
|
Check if we should start a new commit operation and commit
| 4.209057 | 4.185456 | 1.005639 |
# Have we been told to stop or shutdown? Then don't actually retry.
if self._stopping or self._shuttingdown or self._start_d is None:
# Stopping, or stopped already? No more fetching.
return
if self._retry_call is None:
if after is None:
after = self.retry_delay
self.retry_delay = min(self.retry_delay * REQUEST_RETRY_FACTOR,
self.retry_max_delay)
self._fetch_attempt_count += 1
self._retry_call = self.client.reactor.callLater(
after, self._do_fetch)
|
def _retry_fetch(self, after=None)
|
Schedule a delayed :meth:`_do_fetch` call after a failure
:param float after:
The delay in seconds after which to do the retried fetch. If
`None`, our internal :attr:`retry_delay` is used, and adjusted by
:const:`REQUEST_RETRY_FACTOR`.
| 5.378815 | 4.648486 | 1.157111 |
# Got a response, clear our outstanding request deferred
self._request_d = None
# Successful request, reset our retry delay, count, etc
self.retry_delay = self.retry_init_delay
self._fetch_attempt_count = 1
response = response[0]
if hasattr(response, 'offsets'):
# It's a response to an OffsetRequest
self._fetch_offset = response.offsets[0]
else:
# It's a response to an OffsetFetchRequest
# Make sure we got a valid offset back. Kafka uses -1 to indicate
# no committed offset was retrieved
if response.offset == OFFSET_NOT_COMMITTED:
self._fetch_offset = OFFSET_EARLIEST
else:
self._fetch_offset = response.offset + 1
self._last_committed_offset = response.offset
self._do_fetch()
|
def _handle_offset_response(self, response)
|
Handle responses to both OffsetRequest and OffsetFetchRequest, since
they are similar enough.
:param response:
A tuple of a single OffsetFetchResponse or OffsetResponse
| 5.559131 | 5.209544 | 1.067105 |
# outstanding request got errback'd, clear it
self._request_d = None
if self._stopping and failure.check(CancelledError):
# Not really an error
return
# Do we need to abort?
if (self.request_retry_max_attempts != 0 and
self._fetch_attempt_count >= self.request_retry_max_attempts):
log.debug(
"%r: Exhausted attempts: %d fetching offset from kafka: %r",
self, self.request_retry_max_attempts, failure)
self._start_d.errback(failure)
return
# Decide how to log this failure... If we have retried so many times
# we're at the retry_max_delay, then we log at warning every other time
# debug otherwise
if (self.retry_delay < self.retry_max_delay or
0 == (self._fetch_attempt_count % 2)):
log.debug("%r: Failure fetching offset from kafka: %r", self,
failure)
else:
# We've retried until we hit the max delay, log at warn
log.warning("%r: Still failing fetching offset from kafka: %r",
self, failure)
self._retry_fetch()
|
def _handle_offset_error(self, failure)
|
Retry the offset fetch request if appropriate.
Once the :attr:`.retry_delay` reaches our :attr:`.retry_max_delay`, we
log a warning. This should perhaps be extended to abort sooner on
certain errors.
| 5.496176 | 5.023037 | 1.094194 |
# If there's a _commit_call, and it's not active, clear it, it probably
# just called us...
if self._commit_call and not self._commit_call.active():
self._commit_call = None
# Make sure we only have one outstanding commit request at a time
if self._commit_req is not None:
raise OperationInProgress(self._commit_req)
# Handle defaults
if retry_delay is None:
retry_delay = self.retry_init_delay
if attempt is None:
attempt = 1
# Create new OffsetCommitRequest with the latest processed offset
commit_offset = self._last_processed_offset
commit_request = OffsetCommitRequest(
self.topic, self.partition, commit_offset,
TIMESTAMP_INVALID, self.commit_metadata)
log.debug("Committing off=%d grp=%s tpc=%s part=%s req=%r",
self._last_processed_offset, self.consumer_group,
self.topic, self.partition, commit_request)
# Send the request, add our callbacks
self._commit_req = d = self.client.send_offset_commit_request(
self.consumer_group, [commit_request])
d.addBoth(self._clear_commit_req)
d.addCallbacks(
self._update_committed_offset, self._handle_commit_error,
callbackArgs=(commit_offset,),
errbackArgs=(retry_delay, attempt))
|
def _send_commit_request(self, retry_delay=None, attempt=None)
|
Send a commit request with our last_processed_offset
| 3.740995 | 3.561855 | 1.050294 |
# Check if we are stopping and the request was cancelled
if self._stopping and failure.check(CancelledError):
# Not really an error
return self._deliver_commit_result(self._last_committed_offset)
# Check that the failure type is a Kafka error...this could maybe be
# a tighter check to determine whether a retry will succeed...
if not failure.check(KafkaError):
log.error("Unhandleable failure during commit attempt: %r\n\t%r",
failure, failure.getBriefTraceback())
return self._deliver_commit_result(failure)
# Do we need to abort?
if (self.request_retry_max_attempts != 0 and
attempt >= self.request_retry_max_attempts):
log.debug("%r: Exhausted attempts: %d to commit offset: %r",
self, self.request_retry_max_attempts, failure)
return self._deliver_commit_result(failure)
# Check the retry_delay to see if we should log at the higher level
# Using attempts % 2 gets us 1-warn/minute with defaults timings
if retry_delay < self.retry_max_delay or 0 == (attempt % 2):
log.debug("%r: Failure committing offset to kafka: %r", self,
failure)
else:
# We've retried until we hit the max delay, log alternately at warn
log.warning("%r: Still failing committing offset to kafka: %r",
self, failure)
# Schedule a delayed call to retry the commit
retry_delay = min(retry_delay * REQUEST_RETRY_FACTOR,
self.retry_max_delay)
self._commit_call = self.client.reactor.callLater(
retry_delay, self._send_commit_request, retry_delay, attempt + 1)
|
def _handle_commit_error(self, failure, retry_delay, attempt)
|
Retry the commit request, depending on failure type
Depending on the type of the failure, we retry the commit request
with the latest processed offset, or callback/errback self._commit_ds
| 5.260506 | 5.257211 | 1.000627 |
# Check if we're stopping/stopped and the errback of the processor
# deferred is just the cancelling we initiated. If so, we skip
# notifying via the _start_d deferred, as it will be 'callback'd at the
# end of stop()
if not (self._stopping and failure.check(CancelledError)):
if self._start_d: # Make sure we're not already stopped
self._start_d.errback(failure)
|
def _handle_processor_error(self, failure)
|
Handle a failure in the processing of a block of messages
This method is called when the processor func fails while processing
a block of messages. Since we can't know how best to handle a
processor failure, we just :func:`errback` our :func:`start` method's
deferred to let our user know about the failure.
| 11.043701 | 10.882706 | 1.014794 |
# The _request_d deferred has fired, clear it.
self._request_d = None
if failure.check(OffsetOutOfRangeError):
if self.auto_offset_reset is None:
self._start_d.errback(failure)
return
self._fetch_offset = self.auto_offset_reset
if self._stopping and failure.check(CancelledError):
# Not really an error
return
# Do we need to abort?
if (self.request_retry_max_attempts != 0 and
self._fetch_attempt_count >= self.request_retry_max_attempts):
log.debug(
"%r: Exhausted attempts: %d fetching messages from kafka: %r",
self, self.request_retry_max_attempts, failure)
self._start_d.errback(failure)
return
# Decide how to log this failure... If we have retried so many times
# we're at the retry_max_delay, then we log at warning every other time
# debug otherwise
if (self.retry_delay < self.retry_max_delay or
0 == (self._fetch_attempt_count % 2)):
log.debug("%r: Failure fetching messages from kafka: %r", self,
failure)
else:
# We've retried until we hit the max delay, log at warn
log.warning("%r: Still failing fetching messages from kafka: %r",
self, failure)
self._retry_fetch()
|
def _handle_fetch_error(self, failure)
|
A fetch request resulted in an error. Retry after our current delay
When a fetch error occurs, we check to see if the Consumer is being
stopped, and if so just return, trapping the CancelledError. If not, we
check if the Consumer has a non-zero setting for
:attr:`request_retry_max_attempts` and if so and we have reached that limit we
errback() the Consumer's start() deferred with the failure. If not, we
determine whether to log at debug or warning (we log at warning every
other retry after backing off to the max retry delay, resulting in a
warning message approximately once per minute with the default timings)
We then wait our current :attr:`retry_delay`, and retry the fetch. We
also increase our retry_delay by Apery's constant (1.20205) and note
the failed fetch by incrementing :attr:`_fetch_attempt_count`.
NOTE: this may retry forever.
TODO: Possibly make this differentiate based on the failure
| 4.729671 | 3.955642 | 1.195677 |
# Successful fetch, reset our retry delay
self.retry_delay = self.retry_init_delay
self._fetch_attempt_count = 1
# Check to see if we are still processing the last block we fetched...
if self._msg_block_d:
# We are still working through the last block of messages...
# We have to wait until it's done, then process this response
self._msg_block_d.addCallback(
lambda _: self._handle_fetch_response(responses))
return
# No ongoing processing, great, let's get some started.
# Request no longer outstanding, clear the deferred tracker so we
# can refetch
self._request_d = None
messages = []
try:
for resp in responses: # We should really only ever get one...
if resp.partition != self.partition:
log.warning(
"%r: Got response with partition: %r not our own: %r",
self, resp.partition, self.partition)
continue
# resp.messages is a KafkaCodec._decode_message_set_iter
# Note that 'message' here is really an OffsetAndMessage
for message in resp.messages:
# Check for messages included which are from prior to our
# desired offset: can happen due to compressed message sets
if message.offset < self._fetch_offset:
log.debug(
'Skipping message at offset: %d, because its '
'offset is less that our fetch offset: %d.',
message.offset, self._fetch_offset)
continue
# Create a 'SourcedMessage' and add it to the messages list
messages.append(
SourcedMessage(
message=message.message,
offset=message.offset, topic=self.topic,
partition=self.partition))
# Update our notion of from where to fetch.
self._fetch_offset = message.offset + 1
except ConsumerFetchSizeTooSmall:
# A message was too large for us to receive, given our current
# buffer size. Grow it until it works, or we hit our max
# Grow by 16x up to 1MB (could result in 16MB buf), then by 2x
factor = 2
if self.buffer_size <= 2**20:
factor = 16
if self.max_buffer_size is None:
# No limit, increase until we succeed or fail to alloc RAM
self.buffer_size *= factor
elif (self.max_buffer_size is not None and
self.buffer_size < self.max_buffer_size):
# Limited, but currently below it.
self.buffer_size = min(
self.buffer_size * factor, self.max_buffer_size)
else:
# We failed, and are already at our max. Nothing we can do but
# create a Failure and errback() our start() deferred
log.error("Max fetch size %d too small", self.max_buffer_size)
failure = Failure(
ConsumerFetchSizeTooSmall(
"Max buffer size:%d too small for message",
self.max_buffer_size))
self._start_d.errback(failure)
return
log.debug(
"Next message larger than fetch size, increasing "
"to %d (~2x) and retrying", self.buffer_size)
finally:
# If we were able to extract any messages, deliver them to the
# processor now.
if messages:
self._msg_block_d = Deferred()
self._process_messages(messages)
# start another fetch, if needed, but use callLater to avoid recursion
self._retry_fetch(0)
|
def _handle_fetch_response(self, responses)
|
The callback handling the successful response from the fetch request
Delivers the message list to the processor, handles per-message errors
(ConsumerFetchSizeTooSmall), triggers another fetch request
If the processor is still processing the last batch of messages, we
defer this processing until it's done. Otherwise, we start another
fetch request and submit the messages to the processor
| 5.757731 | 5.641209 | 1.020656 |
# Have we been told to shutdown?
if self._shuttingdown:
return
# Do we have any messages to process?
if not messages:
# No, we're done with this block. If we had another fetch result
# waiting, this callback will trigger the processing thereof.
if self._msg_block_d:
_msg_block_d, self._msg_block_d = self._msg_block_d, None
_msg_block_d.callback(True)
return
# Yes, we've got some messages to process.
# Default to processing the entire block...
proc_block_size = sys.maxsize
# Unless our auto commit_policy restricts us to process less
if self.auto_commit_every_n:
proc_block_size = self.auto_commit_every_n
# Divide messages into two lists: one to process now, and remainder
msgs_to_proc = messages[:proc_block_size]
msgs_remainder = messages[proc_block_size:]
# Call our processor callable and handle the possibility it returned
# a deferred...
last_offset = msgs_to_proc[-1].offset
self._processor_d = d = maybeDeferred(self.processor, self, msgs_to_proc)
log.debug('self.processor return: %r, last_offset: %r', d, last_offset)
# Once the processor completes, clear our _processor_d
d.addBoth(self._clear_processor_deferred)
# Record the offset of the last processed message and check autocommit
d.addCallback(self._update_processed_offset, last_offset)
# If we were stopped, cancel the processor deferred. Note, we have to
# do this here, in addition to in stop() because the processor func
# itself could have called stop(), and then when it returned, we re-set
# self._processor_d to the return of maybeDeferred().
if self._stopping or self._start_d is None:
d.cancel()
else:
# Setup to process the rest of our messages
d.addCallback(lambda _: self._process_messages(msgs_remainder))
# Add an error handler
d.addErrback(self._handle_processor_error)
|
def _process_messages(self, messages)
|
Send messages to the `processor` callback to be processed
In the case we have a commit policy, we send messages to the processor
in blocks no bigger than auto_commit_every_n (if set). Otherwise, we
send the entire message block to be processed.
| 5.270091 | 4.93671 | 1.067531 |
# Check for outstanding request.
if self._request_d:
log.debug("_do_fetch: Outstanding request: %r", self._request_d)
return
# Cleanup our _retry_call, if we have one
if self._retry_call is not None:
if self._retry_call.active():
self._retry_call.cancel()
self._retry_call = None
# Do we know our offset yet, or do we need to figure it out?
if (self._fetch_offset == OFFSET_EARLIEST or
self._fetch_offset == OFFSET_LATEST):
# We need to fetch the offset for our topic/partition
offset_request = OffsetRequest(
self.topic, self.partition, self._fetch_offset, 1)
self._request_d = self.client.send_offset_request([offset_request])
self._request_d.addCallbacks(
self._handle_offset_response, self._handle_offset_error)
elif self._fetch_offset == OFFSET_COMMITTED:
# We need to fetch the committed offset for our topic/partition
# Note we use the same callbacks, as the responses are "close
# enough" for our needs here
if not self.consumer_group:
# consumer_group must be set for OFFSET_COMMITTED
failure = Failure(
InvalidConsumerGroupError("Bad Group_id:{0!r}".format(
self.consumer_group)))
self._start_d.errback(failure)
request = OffsetFetchRequest(self.topic, self.partition)
self._request_d = self.client.send_offset_fetch_request(
self.consumer_group, [request])
self._request_d.addCallbacks(
self._handle_offset_response, self._handle_offset_error)
else:
# Create fetch request payload for our partition
request = FetchRequest(
self.topic, self.partition, self._fetch_offset,
self.buffer_size)
# Send request and add handlers for the response
self._request_d = self.client.send_fetch_request(
[request], max_wait_time=self.fetch_max_wait_time,
min_bytes=self.fetch_min_bytes)
# We need a temp for this because if the response is already
# available, _handle_fetch_response() will clear self._request_d
d = self._request_d
d.addCallback(self._handle_fetch_response)
d.addErrback(self._handle_fetch_error)
|
def _do_fetch(self)
|
Send a fetch request if there isn't a request outstanding
Sends a fetch request to the Kafka cluster to get messages at the
current offset. When the response comes back, if there are messages,
it delivers them to the :attr:`processor` callback and initiates
another fetch request. If there is a recoverable error, the fetch is
retried after :attr:`retry_delay`.
In the case of an unrecoverable error, :func:`errback` is called on the
:class:`Deferred` returned by :meth:`start()`.
| 3.034919 | 2.8821 | 1.053024 |
log.warning(
'_commit_timer_failed: uncaught error %r: %s in _auto_commit',
fail, fail.getBriefTraceback())
self._commit_looper_d = self._commit_looper.start(
self.auto_commit_every_s, now=False)
|
def _commit_timer_failed(self, fail)
|
Handle an error in the commit() function
Our commit() function called by the LoopingCall failed. Some error
probably came back from Kafka and _check_error() raised the exception
For now, just log the failure and restart the loop
| 8.353142 | 6.859516 | 1.217745 |
if self._commit_looper is not lCall:
log.warning('_commit_timer_stopped with wrong timer:%s not:%s',
lCall, self._commit_looper)
else:
log.debug('_commit_timer_stopped: %s %s', lCall,
self._commit_looper)
self._commit_looper = None
self._commit_looper_d = None
|
def _commit_timer_stopped(self, lCall)
|
We're shutting down, clean up our looping call...
| 3.216471 | 3.051546 | 1.054046 |
# Ensure byte_array arg is a bytearray
if not isinstance(byte_array, bytearray):
raise TypeError("Type: %r of 'byte_array' arg must be 'bytearray'",
type(byte_array))
length = len(byte_array)
# 'm' and 'r' are mixing constants generated offline.
# They're not really 'magic', they just happen to work well.
m = 0x5bd1e995
r = 24
mod32bits = 0xffffffff
# Initialize the hash to a random value
h = seed ^ length
length4 = length // 4
for i in range(length4):
i4 = i * 4
k = ((byte_array[i4 + 0] & 0xff) + ((byte_array[i4 + 1] & 0xff) << 8) +
((byte_array[i4 + 2] & 0xff) << 16) + ((byte_array[i4 + 3] & 0xff) << 24))
k &= mod32bits
k *= m
k &= mod32bits
k ^= (k % 0x100000000) >> r # k ^= k >>> r
k &= mod32bits
k *= m
k &= mod32bits
h *= m
h &= mod32bits
h ^= k
h &= mod32bits
# Handle the last few bytes of the input array
extra_bytes = length % 4
if extra_bytes == 3:
h ^= (byte_array[(length & ~3) + 2] & 0xff) << 16
h &= mod32bits
if extra_bytes >= 2:
h ^= (byte_array[(length & ~3) + 1] & 0xff) << 8
h &= mod32bits
if extra_bytes >= 1:
h ^= (byte_array[length & ~3] & 0xff)
h &= mod32bits
h *= m
h &= mod32bits
h ^= (h % 0x100000000) >> 13 # h >>> 13;
h &= mod32bits
h *= m
h &= mod32bits
h ^= (h % 0x100000000) >> 15 # h >>> 15;
h &= mod32bits
return h
|
def pure_murmur2(byte_array, seed=0x9747b28c)
|
Pure-python Murmur2 implementation.
Based on java client, see org.apache.kafka.common.utils.Utils.murmur2
https://github.com/apache/kafka/blob/0.8.2/clients/src/main/java/org/apache/kafka/common/utils/Utils.java#L244
Args:
byte_array: bytearray - Raises TypeError otherwise
Returns: MurmurHash2 of byte_array bytearray
Raises: TypeError if byte_array arg is not of type bytearray
| 1.670649 | 1.637852 | 1.020024 |
return partitions[(self._hash(key) & 0x7FFFFFFF) % len(partitions)]
|
def partition(self, key, partitions)
|
Select a partition based on the hash of the key.
:param key: Partition key
:type key: text string or UTF-8 `bytes` or `bytearray`
:param list partitions:
An indexed sequence of partition identifiers.
:returns:
One of the given partition identifiers. The result will be the same
each time the same key and partition list is passed.
| 5.204875 | 5.750541 | 0.90511 |
if not has_snappy(): # FIXME This should be static, not checked every call.
raise NotImplementedError("Snappy codec is not available")
if xerial_compatible:
def _chunker():
for i in range(0, len(payload), xerial_blocksize):
yield payload[i:i+xerial_blocksize]
out = BytesIO()
out.write(_XERIAL_HEADER)
for chunk in _chunker():
block = snappy.compress(chunk)
out.write(struct.pack('!i', len(block)))
out.write(block)
out.seek(0)
return out.read()
else:
return snappy.compress(payload)
|
def snappy_encode(payload, xerial_compatible=False,
xerial_blocksize=32 * 1024)
|
Compress the given data with the Snappy algorithm.
:param bytes payload: Data to compress.
:param bool xerial_compatible:
If set then the stream is broken into length-prefixed blocks in
a fashion compatible with the xerial snappy library.
The format winds up being::
+-------------+------------+--------------+------------+--------------+
| Header | Block1_len | Block1 data | BlockN len | BlockN data |
|-------------+------------+--------------+------------+--------------|
| 16 bytes | BE int32 | snappy bytes | BE int32 | snappy bytes |
+-------------+------------+--------------+------------+--------------+
:param int xerial_blocksize:
Number of bytes per chunk to independently Snappy encode. 32k is the
default in the xerial library.
:returns: Compressed bytes.
:rtype: :class:`bytes`
| 2.89749 | 3.142877 | 0.921923 |
if not hasattr(self, '_info_cache'):
encoding_backend = get_backend()
try:
path = os.path.abspath(self.path)
except AttributeError:
path = os.path.abspath(self.name)
self._info_cache = encoding_backend.get_media_info(path)
return self._info_cache
|
def _get_video_info(self)
|
Returns basic information about the video as dictionary.
| 3.183213 | 2.948523 | 1.079596 |
# any more data?
out = process.stderr.read(10)
if not out:
break
out = out.decode(console_encoding)
output += out
buf += out
try:
line, buf = buf.split('\r', 1)
except ValueError:
continue
try:
time_str = RE_TIMECODE.findall(line)[0]
except IndexError:
continue
# convert progress to percent
time = 0
for part in time_str.split(':'):
time = 60 * time + float(part)
percent = time / total_time
logger.debug('yield {}%'.format(percent))
yield percent
if os.path.getsize(target_path) == 0:
raise exceptions.FFmpegError("File size of generated file is 0")
# wait for process to exit
self._check_returncode(process)
logger.debug(output)
if not output:
raise exceptions.FFmpegError("No output from FFmpeg.")
yield 100
|
def encode(self, source_path, target_path, params): # NOQA: C901
total_time = self.get_media_info(source_path)['duration']
cmds = [self.ffmpeg_path, '-i', source_path]
cmds.extend(self.params)
cmds.extend(params)
cmds.extend([target_path])
process = self._spawn(cmds)
buf = output = ''
# update progress
while True
|
Encodes a video to a specified file. All encoder specific options
are passed in using `params`.
| 3.619004 | 3.726352 | 0.971192 |
cmds = [self.ffprobe_path, '-i', video_path]
cmds.extend(['-print_format', 'json'])
cmds.extend(['-show_format', '-show_streams'])
process = self._spawn(cmds)
stdout, __ = self._check_returncode(process)
media_info = self._parse_media_info(stdout)
return {
'duration': float(media_info['format']['duration']),
'width': int(media_info['video'][0]['width']),
'height': int(media_info['video'][0]['height']),
}
|
def get_media_info(self, video_path)
|
Returns information about the given video as dict.
| 2.497351 | 2.477324 | 1.008084 |
filename = os.path.basename(video_path)
filename, __ = os.path.splitext(filename)
_, image_path = tempfile.mkstemp(suffix='_{}.jpg'.format(filename))
video_duration = self.get_media_info(video_path)['duration']
if at_time > video_duration:
raise exceptions.InvalidTimeError()
thumbnail_time = at_time
cmds = [self.ffmpeg_path, '-i', video_path, '-vframes', '1']
cmds.extend(['-ss', str(thumbnail_time), '-y', image_path])
process = self._spawn(cmds)
self._check_returncode(process)
if not os.path.getsize(image_path):
# we somehow failed to generate thumbnail
os.unlink(image_path)
raise exceptions.InvalidTimeError()
return image_path
|
def get_thumbnail(self, video_path, at_time=0.5)
|
Extracts an image of a video and returns its path.
If the requested thumbnail is not within the duration of the video
an `InvalidTimeError` is thrown.
| 2.745295 | 2.626843 | 1.045093 |
# get instance
Model = apps.get_model(app_label=app_label, model_name=model_name)
instance = Model.objects.get(pk=object_pk)
# search for `VideoFields`
fields = instance._meta.fields
for field in fields:
if isinstance(field, VideoField):
if not getattr(instance, field.name):
# ignore empty fields
continue
# trigger conversion
fieldfile = getattr(instance, field.name)
convert_video(fieldfile)
|
def convert_all_videos(app_label, model_name, object_pk)
|
Automatically converts all videos of a given instance.
| 3.115634 | 3.040208 | 1.02481 |
instance = fieldfile.instance
field = fieldfile.field
filename = os.path.basename(fieldfile.path)
source_path = fieldfile.path
encoding_backend = get_backend()
for options in settings.VIDEO_ENCODING_FORMATS[encoding_backend.name]:
video_format, created = Format.objects.get_or_create(
object_id=instance.pk,
content_type=ContentType.objects.get_for_model(instance),
field_name=field.name, format=options['name'])
# do not reencode if not requested
if video_format.file and not force:
continue
else:
# set progress to 0
video_format.reset_progress()
# TODO do not upscale videos
_, target_path = tempfile.mkstemp(
suffix='_{name}.{extension}'.format(**options))
try:
encoding = encoding_backend.encode(
source_path, target_path, options['params'])
while encoding:
try:
progress = next(encoding)
except StopIteration:
break
video_format.update_progress(progress)
except VideoEncodingError:
# TODO handle with more care
video_format.delete()
os.remove(target_path)
continue
# save encoded file
video_format.file.save(
'{filename}_{name}.{extension}'.format(filename=filename,
**options),
File(open(target_path, mode='rb')))
video_format.update_progress(100) # now we are ready
# remove temporary file
os.remove(target_path)
|
def convert_video(fieldfile, force=False)
|
Converts a given video file into all defined formats.
| 3.570161 | 3.603588 | 0.990724 |
results = []
cache = {}
#scale variables in x because PSO works with velocities to visit different configurations
tuning_options["scaling"] = True
#using this instead of get_bounds because scaling is used
bounds, _, _ = get_bounds_x0_eps(tuning_options)
args = (kernel_options, tuning_options, runner, results, cache)
num_particles = 20
maxiter = 100
#parameters needed by the Firefly Algorithm
B0 = 1.0
gamma = 1.0
alpha = 0.20
best_time_global = 1e20
best_position_global = []
# init particle swarm
swarm = []
for i in range(0, num_particles):
swarm.append(Firefly(bounds, args))
# compute initial intensities
for j in range(num_particles):
swarm[j].compute_intensity(_cost_func)
for c in range(maxiter):
if tuning_options.verbose:
print("start iteration ", c, "best time global", best_time_global)
# compare all to all and compute attractiveness
for i in range(num_particles):
for j in range(num_particles):
if swarm[i].intensity < swarm[j].intensity:
dist = swarm[i].distance_to(swarm[j])
beta = B0 * np.exp(-gamma * dist * dist)
swarm[i].move_towards(swarm[j], beta, alpha)
swarm[i].compute_intensity(_cost_func)
# update global best if needed, actually only used for printing
if swarm[i].time <= best_time_global:
best_position_global = swarm[i].position
best_time_global = swarm[i].time
swarm.sort(key=lambda x: x.time)
if tuning_options.verbose:
print('Final result:')
print(best_position_global)
print(best_time_global)
return results, runner.dev.get_environment()
|
def tune(runner, kernel_options, device_options, tuning_options)
|
Find the best performing kernel configuration in the parameter space
:params runner: A runner from kernel_tuner.runners
:type runner: kernel_tuner.runner
:param kernel_options: A dictionary with all options for the kernel.
:type kernel_options: dict
:param device_options: A dictionary with all options for the device
on which the kernel should be tuned.
:type device_options: dict
:param tuning_options: A dictionary with all options regarding the tuning
process.
:type tuning_options: dict
:returns: A list of dictionaries for executed kernel configurations and their
execution times. And a dictionary that contains a information
about the hardware/software environment on which the tuning took place.
:rtype: list(dict()), dict()
| 4.580303 | 4.503734 | 1.017001 |
return np.linalg.norm(self.position-other.position)
|
def distance_to(self, other)
|
Return Euclidian distance between self and other Firefly
| 4.445734 | 3.354374 | 1.325354 |
self.evaluate(_cost_func)
self.intensity = 1 / self.time
|
def compute_intensity(self, _cost_func)
|
Evaluate cost function and compute intensity at this position
| 8.875723 | 7.301169 | 1.215658 |
self.position += beta * (other.position - self.position)
self.position += alpha * (np.random.uniform(-0.5, 0.5, len(self.position)))
self.position = np.minimum(self.position, [b[1] for b in self.bounds])
self.position = np.maximum(self.position, [b[0] for b in self.bounds])
|
def move_towards(self, other, beta, alpha)
|
Move firefly towards another given beta and alpha values
| 2.293157 | 2.062778 | 1.111684 |
#first check if the length is the same
if len(instance.arguments) != len(answer):
raise TypeError("The length of argument list and provided results do not match.")
#for each element in the argument list, check if the types match
for i, arg in enumerate(instance.arguments):
if answer[i] is not None: #skip None elements in the answer list
if isinstance(answer[i], numpy.ndarray) and isinstance(arg, numpy.ndarray):
if answer[i].dtype != arg.dtype:
raise TypeError("Element " + str(i)
+ " of the expected results list is not of the same dtype as the kernel output: "
+ str(answer[i].dtype) + " != " + str(arg.dtype) + ".")
if answer[i].size != arg.size:
raise TypeError("Element " + str(i)
+ " of the expected results list has a size different from "
+ "the kernel argument: "
+ str(answer[i].size) + " != " + str(arg.size) + ".")
elif isinstance(answer[i], numpy.number) and isinstance(arg, numpy.number):
if answer[i].dtype != arg.dtype:
raise TypeError("Element " + str(i)
+ " of the expected results list is not the same as the kernel output: "
+ str(answer[i].dtype) + " != " + str(arg.dtype) + ".")
else:
#either answer[i] and argument have different types or answer[i] is not a numpy type
if not isinstance(answer[i], numpy.ndarray) or not isinstance(answer[i], numpy.number):
raise TypeError("Element " + str(i)
+ " of expected results list is not a numpy array or numpy scalar.")
else:
raise TypeError("Element " + str(i)
+ " of expected results list and kernel arguments have different types.")
def _ravel(a):
if hasattr(a, 'ravel') and len(a.shape) > 1:
return a.ravel()
return a
def _flatten(a):
if hasattr(a, 'flatten'):
return a.flatten()
return a
correct = True
for i, arg in enumerate(instance.arguments):
expected = answer[i]
if expected is not None:
result = _ravel(result_host[i])
expected = _flatten(expected)
output_test = numpy.allclose(expected, result, atol=atol)
if not output_test and verbose:
print("Error: " + util.get_config_string(instance.params) + " detected during correctness check")
print("this error occured when checking value of the %oth kernel argument" % (i,))
print("Printing kernel output and expected result, set verbose=False to suppress this debug print")
numpy.set_printoptions(edgeitems=50)
print("Kernel output:")
print(result)
print("Expected:")
print(expected)
correct = correct and output_test
if not correct:
logging.debug('correctness check has found a correctness issue')
raise Exception("Error: " + util.get_config_string(instance.params) + " failed correctness check")
return correct
|
def _default_verify_function(instance, answer, result_host, atol, verbose)
|
default verify function based on numpy.allclose
| 2.809583 | 2.775992 | 1.0121 |
logging.debug('benchmark ' + instance.name)
logging.debug('thread block dimensions x,y,z=%d,%d,%d', *instance.threads)
logging.debug('grid dimensions x,y,z=%d,%d,%d', *instance.grid)
time = None
try:
time = self.dev.benchmark(func, gpu_args, instance.threads, instance.grid, times)
except Exception as e:
#some launches may fail because too many registers are required
#to run the kernel given the current thread block size
#the desired behavior is to simply skip over this configuration
#and proceed to try the next one
skippable_exceptions = ["too many resources requested for launch", "OUT_OF_RESOURCES", "INVALID_WORK_GROUP_SIZE"]
if any([skip_str in str(e) for skip_str in skippable_exceptions]):
logging.debug('benchmark fails due to runtime failure too many resources required')
if verbose:
print("skipping config", instance.name, "reason: too many resources requested for launch")
else:
logging.debug('benchmark encountered runtime failure: ' + str(e))
print("Error while benchmarking:", instance.name)
raise e
return time
|
def benchmark(self, func, gpu_args, instance, times, verbose)
|
benchmark the kernel instance
| 5.339433 | 5.213839 | 1.024089 |
logging.debug('check_kernel_output')
#if not using custom verify function, check if the length is the same
if not verify and len(instance.arguments) != len(answer):
raise TypeError("The length of argument list and provided results do not match.")
#zero GPU memory for output arguments
for i, arg in enumerate(instance.arguments):
if verify or answer[i] is not None:
if isinstance(arg, numpy.ndarray):
self.dev.memcpy_htod(gpu_args[i], arg)
#run the kernel
check = self.run_kernel(func, gpu_args, instance)
if not check:
return True #runtime failure occured that should be ignored, skip correctness check
#retrieve gpu results to host memory
result_host = []
for i, arg in enumerate(instance.arguments):
if verify or answer[i] is not None:
if isinstance(arg, numpy.ndarray):
result_host.append(numpy.zeros_like(arg))
self.dev.memcpy_dtoh(result_host[-1], gpu_args[i])
else:
result_host.append(None)
#if the user has specified a custom verify function, then call it, else use default based on numpy allclose
if verify:
try:
return verify(answer, result_host, atol=atol)
except TypeError:
return verify(answer, result_host)
else:
return _default_verify_function(instance, answer, result_host, atol, verbose)
|
def check_kernel_output(self, func, gpu_args, instance, answer, atol, verify, verbose)
|
runs the kernel once and checks the result against answer
| 3.698081 | 3.678184 | 1.00541 |
instance_string = util.get_instance_string(params)
logging.debug('compile_and_benchmark ' + instance_string)
mem_usage = round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024.0, 1)
logging.debug('Memory usage : %2.2f MB', mem_usage)
verbose = tuning_options.verbose
instance = self.create_kernel_instance(kernel_options, params, verbose)
if instance is None:
return None
try:
#compile the kernel
func = self.compile_kernel(instance, verbose)
if func is None:
return None
#add constant memory arguments to compiled module
if kernel_options.cmem_args is not None:
self.dev.copy_constant_memory_args(kernel_options.cmem_args)
#add texture memory arguments to compiled module
if kernel_options.texmem_args is not None:
self.dev.copy_texture_memory_args(kernel_options.texmem_args)
#test kernel for correctness and benchmark
if tuning_options.answer is not None:
self.check_kernel_output(func, gpu_args, instance, tuning_options.answer, tuning_options.atol, tuning_options.verify, verbose)
#benchmark
time = self.benchmark(func, gpu_args, instance, tuning_options.times, verbose)
except Exception as e:
#dump kernel_string to temp file
temp_filename = util.get_temp_filename(suffix=".c")
util.write_file(temp_filename, instance.kernel_string)
print("Error while compiling or benchmarking, see source files: " + temp_filename + " ".join(instance.temp_files.values()))
raise e
#clean up any temporary files, if no error occured
for v in instance.temp_files.values():
util.delete_temp_file(v)
return time
|
def compile_and_benchmark(self, gpu_args, params, kernel_options, tuning_options)
|
Compile and benchmark a kernel instance based on kernel strings and parameters
| 3.504588 | 3.438639 | 1.019179 |
logging.debug('compile_kernel ' + instance.name)
#compile kernel_string into device func
func = None
try:
func = self.dev.compile(instance.name, instance.kernel_string)
except Exception as e:
#compiles may fail because certain kernel configurations use too
#much shared memory for example, the desired behavior is to simply
#skip over this configuration and try the next one
if "uses too much shared data" in str(e):
logging.debug('compile_kernel failed due to kernel using too much shared memory')
if verbose:
print("skipping config", instance.name, "reason: too much shared memory used")
else:
logging.debug('compile_kernel failed due to error: ' + str(e))
print("Error while compiling:", instance.name)
raise e
return func
|
def compile_kernel(self, instance, verbose)
|
compile the kernel for this specific instance
| 5.941555 | 5.941059 | 1.000083 |
if self.lang == "CUDA":
self.dev.copy_constant_memory_args(cmem_args)
else:
raise Exception("Error cannot copy constant memory arguments when language is not CUDA")
|
def copy_constant_memory_args(self, cmem_args)
|
adds constant memory arguments to the most recently compiled module, if using CUDA
| 4.772903 | 3.904177 | 1.222512 |
if self.lang == "CUDA":
self.dev.copy_texture_memory_args(texmem_args)
else:
raise Exception("Error cannot copy texture memory arguments when language is not CUDA")
|
def copy_texture_memory_args(self, texmem_args)
|
adds texture memory arguments to the most recently compiled module, if using CUDA
| 4.884994 | 3.916668 | 1.247232 |
instance_string = util.get_instance_string(params)
grid_div = (kernel_options.grid_div_x, kernel_options.grid_div_y, kernel_options.grid_div_z)
#insert default block_size_names if needed
if not kernel_options.block_size_names:
kernel_options.block_size_names = util.default_block_size_names
#setup thread block and grid dimensions
threads, grid = util.setup_block_and_grid(kernel_options.problem_size, grid_div, params, kernel_options.block_size_names)
if numpy.prod(threads) > self.dev.max_threads:
if verbose:
print("skipping config", instance_string, "reason: too many threads per block")
return None
#obtain the kernel_string and prepare additional files, if any
temp_files = dict()
kernel_source = kernel_options.kernel_string
if not isinstance(kernel_source, list):
kernel_source = [kernel_source]
name, kernel_string, temp_files = util.prepare_list_of_files(kernel_options.kernel_name, kernel_source, params, grid, threads, kernel_options.block_size_names)
#collect everything we know about this instance and return it
return KernelInstance(name, kernel_string, temp_files, threads, grid, params, kernel_options.arguments)
|
def create_kernel_instance(self, kernel_options, params, verbose)
|
create kernel instance from kernel source, parameters, problem size, grid divisors, and so on
| 4.014205 | 3.824275 | 1.049664 |
logging.debug('run_kernel %s', instance.name)
logging.debug('thread block dims (%d, %d, %d)', *instance.threads)
logging.debug('grid dims (%d, %d, %d)', *instance.grid)
try:
self.dev.run_kernel(func, gpu_args, instance.threads, instance.grid)
except Exception as e:
if "too many resources requested for launch" in str(e) or "OUT_OF_RESOURCES" in str(e):
logging.debug('ignoring runtime failure due to too many resources required')
return False
else:
logging.debug('encountered unexpected runtime failure: ' + str(e))
raise e
return True
|
def run_kernel(self, func, gpu_args, instance)
|
Run a compiled kernel instance on a device
| 3.532151 | 3.488238 | 1.012589 |
for i in range(0, len(l), n):
yield l[i:i + n]
|
def _chunk_list(l, n)
|
Yield successive n-sized chunks from l.
| 2.003627 | 1.847176 | 1.084697 |
workflow = self._parameter_sweep(parameter_space, kernel_options, self.device_options,
tuning_options)
if tuning_options.verbose:
with NCDisplay(_error_filter) as display:
answer = run_parallel_with_display(workflow, self.max_threads, display)
else:
answer = run_parallel(workflow, self.max_threads)
if answer is None:
print("Tuning did not return any results, did an error occur?")
return None
# Filter out None times
result = []
for chunk in answer:
result += [d for d in chunk if d['time']]
return result, {}
|
def run(self, parameter_space, kernel_options, tuning_options)
|
Tune all instances in parameter_space using a multiple threads
:param parameter_space: The parameter space as an iterable.
:type parameter_space: iterable
:param kernel_options: A dictionary with all options for the kernel.
:type kernel_options: kernel_tuner.interface.Options
:param tuning_options: A dictionary with all options regarding the tuning
process.
:type tuning_options: kernel_tuner.interface.Options
:returns: A list of dictionaries for executed kernel configurations and their
execution times. And a dictionary that contains a information
about the hardware/software environment on which the tuning took place.
:rtype: list(dict()), dict()
| 6.757653 | 7.053584 | 0.958045 |
results = []
#randomize parameter space to do pseudo load balancing
parameter_space = list(parameter_space)
random.shuffle(parameter_space)
#split parameter space into chunks
work_per_thread = int(numpy.ceil(len(parameter_space) / float(self.max_threads)))
chunks = _chunk_list(parameter_space, work_per_thread)
for chunk in chunks:
chunked_result = self._run_chunk(chunk, kernel_options, device_options, tuning_options)
results.append(lift(chunked_result))
return gather(*results)
|
def _parameter_sweep(self, parameter_space, kernel_options, device_options, tuning_options)
|
Build a Noodles workflow by sweeping the parameter space
| 4.297132 | 4.054715 | 1.059787 |
#detect language and create high-level device interface
self.dev = DeviceInterface(kernel_options.kernel_string, iterations=tuning_options.iterations, **device_options)
#move data to the GPU
gpu_args = self.dev.ready_argument_list(kernel_options.arguments)
results = []
for element in chunk:
params = dict(OrderedDict(zip(tuning_options.tune_params.keys(), element)))
try:
time = self.dev.compile_and_benchmark(gpu_args, params, kernel_options, tuning_options)
params['time'] = time
results.append(params)
except Exception:
params['time'] = None
results.append(params)
return results
|
def _run_chunk(self, chunk, kernel_options, device_options, tuning_options)
|
Benchmark a single kernel instance in the parameter space
| 5.653329 | 5.453789 | 1.036588 |
tune_params = tuning_options.tune_params
#compute cartesian product of all tunable parameters
parameter_space = itertools.product(*tune_params.values())
#check for search space restrictions
if tuning_options.restrictions is not None:
parameter_space = filter(lambda p: util.check_restrictions(tuning_options.restrictions, p,
tune_params.keys(),
tuning_options.verbose),
parameter_space)
#reduce parameter space to a random sample using sample_fraction
parameter_space = numpy.array(list(parameter_space))
size = len(parameter_space)
fraction = int(numpy.ceil(size * float(tuning_options.sample_fraction)))
sample_indices = numpy.random.choice(range(size), size=fraction, replace=False)
parameter_space = parameter_space[sample_indices]
#call the runner
results, env = runner.run(parameter_space, kernel_options, tuning_options)
return results, env
|
def tune(runner, kernel_options, device_options, tuning_options)
|
Tune a random sample of sample_fraction fraction in the parameter space
:params runner: A runner from kernel_tuner.runners
:type runner: kernel_tuner.runner
:param kernel_options: A dictionary with all options for the kernel.
:type kernel_options: kernel_tuner.interface.Options
:param device_options: A dictionary with all options for the device
on which the kernel should be tuned.
:type device_options: kernel_tuner.interface.Options
:param tuning_options: A dictionary with all options regarding the tuning
process.
:type tuning_options: kernel_tuner.interface.Options
:returns: A list of dictionaries for executed kernel configurations and their
execution times. And a dictionary that contains a information
about the hardware/software environment on which the tuning took place.
:rtype: list(dict()), dict()
| 3.278509 | 3.143378 | 1.042989 |
types_map = {"uint8": ["uchar", "unsigned char", "uint8_t"],
"int8": ["char", "int8_t"],
"uint16": ["ushort", "unsigned short", "uint16_t"],
"int16": ["short", "int16_t"],
"uint32": ["uint", "unsigned int", "uint32_t"],
"int32": ["int", "int32_t"], #discrepancy between OpenCL and C here, long may be 32bits in C
"uint64": ["ulong", "unsigned long", "uint64_t"],
"int64": ["long", "int64_t"],
"float16": ["half"],
"float32": ["float"],
"float64": ["double"]}
if dtype in types_map:
return any([substr in kernel_argument for substr in types_map[dtype]])
else:
return False
|
def check_argument_type(dtype, kernel_argument, i)
|
check if the numpy.dtype matches the type used in the code
| 2.14612 | 2.135399 | 1.005021 |
kernel_arguments = list()
collected_errors = list()
for iterator in re.finditer(kernel_name + "[ \n\t]*" + "\(", kernel_string):
kernel_start = iterator.end()
kernel_end = kernel_string.find(")", kernel_start)
if kernel_start != 0:
kernel_arguments.append(kernel_string[kernel_start:kernel_end].split(","))
for arguments_set, arguments in enumerate(kernel_arguments):
collected_errors.append(list())
if len(arguments) != len(args):
collected_errors[arguments_set].append("Kernel and host argument lists do not match in size.")
continue
for (i, arg) in enumerate(args):
kernel_argument = arguments[i]
if not isinstance(arg, (numpy.ndarray, numpy.generic)):
raise TypeError("Argument at position " + str(i) + " of type: " + str(type(arg)) + " should be of type numpy.ndarray or numpy scalar")
correct = True
if isinstance(arg, numpy.ndarray) and not "*" in kernel_argument:
correct = False #array is passed to non-pointer kernel argument
if correct and check_argument_type(str(arg.dtype), kernel_argument, i):
continue
collected_errors[arguments_set].append("Argument at position " + str(i) + " of dtype: " + str(arg.dtype) +
" does not match " + kernel_argument + ".")
if not collected_errors[arguments_set]:
# We assume that if there is a possible list of arguments that matches with the provided one
# it is the right one
return
for errors in collected_errors:
warnings.warn(errors[0], UserWarning)
|
def check_argument_list(kernel_name, kernel_string, args)
|
raise an exception if a kernel arguments do not match host arguments
| 3.620862 | 3.498674 | 1.034924 |
forbidden_names = ("grid_size_x", "grid_size_y", "grid_size_z")
forbidden_name_substr = ("time", "times")
for name, param in tune_params.items():
if name in forbidden_names:
raise ValueError("Tune parameter " + name + " with value " + str(param) + " has a forbidden name!")
for forbidden_substr in forbidden_name_substr:
if forbidden_substr in name:
raise ValueError("Tune parameter " + name + " with value " + str(param) + " has a forbidden name: not allowed to use " + forbidden_substr + " in tune parameter names!")
|
def check_tune_params_list(tune_params)
|
raise an exception if a tune parameter has a forbidden name
| 2.85054 | 2.532871 | 1.125418 |
params = OrderedDict(zip(keys, element))
for restrict in restrictions:
if not eval(replace_param_occurrences(restrict, params)):
if verbose:
print("skipping config", get_instance_string(params), "reason: config fails restriction")
return False
return True
|
def check_restrictions(restrictions, element, keys, verbose)
|
check whether a specific instance meets the search space restrictions
| 7.684898 | 6.61769 | 1.161266 |
if lang is None:
if callable(kernel_source):
raise TypeError("Please specify language when using a code generator function")
kernel_string = get_kernel_string(kernel_source)
if "__global__" in kernel_string:
lang = "CUDA"
elif "__kernel" in kernel_string:
lang = "OpenCL"
else:
lang = "C"
return lang
|
def detect_language(lang, kernel_source)
|
attempt to detect language from the kernel_string if not specified
| 3.71569 | 3.302067 | 1.125262 |
compact_str_items = []
# first make a list of compact strings for each parameter
for k, v in params.items():
unit = ""
if isinstance(units, dict): #check if not None not enough, units could be mocked which causes errors
unit = units.get(k, "")
compact_str_items.append(k + "=" + str(v) + unit)
# and finally join them
compact_str = ", ".join(compact_str_items)
return compact_str
|
def get_config_string(params, units=None)
|
return a compact string representation of a dictionary
| 5.590233 | 5.289203 | 1.056914 |
def get_dimension_divisor(divisor_list, default, params):
if divisor_list is None:
if default in params:
divisor_list = [default]
else:
return 1
return numpy.prod([int(eval(replace_param_occurrences(s, params))) for s in divisor_list])
divisors = [get_dimension_divisor(d, block_size_names[i], params) for i, d in enumerate(grid_div)]
return tuple(int(numpy.ceil(float(current_problem_size[i]) / float(d))) for i, d in enumerate(divisors))
|
def get_grid_dimensions(current_problem_size, params, grid_div, block_size_names)
|
compute grid dims based on problem sizes and listed grid divisors
| 3.119802 | 2.979399 | 1.047125 |
#logging.debug('get_kernel_string called with %s', str(kernel_source))
logging.debug('get_kernel_string called')
kernel_string = None
if callable(kernel_source):
kernel_string = kernel_source(params)
elif isinstance(kernel_source, str):
if looks_like_a_filename(kernel_source):
kernel_string = read_file(kernel_source) or kernel_source
else:
kernel_string = kernel_source
else:
raise TypeError("Error kernel_source is not a string nor a callable function")
return kernel_string
|
def get_kernel_string(kernel_source, params=None)
|
retrieve the kernel source and return as a string
This function processes the passed kernel_source argument, which could be
a function, a string with a filename, or just a string with code already.
If kernel_source is a function, the function is called with instance
parameters in 'params' as the only argument.
If kernel_source looks like filename, the file is read in, but if
the file does not exist, it is assumed that the string is not a filename
after all.
:param kernel_source: One of the sources for the kernel, could be a
function that generates the kernel code, a string containing a filename
that points to the kernel source, or just a string that contains the code.
:type kernel_source: string or callable
:param params: Dictionary containing the tunable parameters for this specific
kernel instance, only needed when kernel_source is a generator.
:type param: dict
:returns: A string containing the kernel code.
:rtype: string
| 2.856332 | 2.751482 | 1.038107 |
if isinstance(problem_size, (str, int, numpy.integer)):
problem_size = (problem_size, )
current_problem_size = [1, 1, 1]
for i, s in enumerate(problem_size):
if isinstance(s, str):
current_problem_size[i] = int(eval(replace_param_occurrences(s, params)))
elif isinstance(s, (int, numpy.integer)):
current_problem_size[i] = s
else:
raise TypeError("Error: problem_size should only contain strings or integers")
return current_problem_size
|
def get_problem_size(problem_size, params)
|
compute current problem size
| 2.645934 | 2.508815 | 1.054655 |
file = tempfile.mkstemp(suffix=suffix or "", prefix="temp_", dir=os.getcwd()) # or "" for Python 2 compatibility
os.close(file[0])
return file[1]
|
def get_temp_filename(suffix=None)
|
return a string in the form of temp_X, where X is a large integer
| 4.897072 | 4.757533 | 1.02933 |
if not block_size_names:
block_size_names = default_block_size_names
block_size_x = params.get(block_size_names[0], 256)
block_size_y = params.get(block_size_names[1], 1)
block_size_z = params.get(block_size_names[2], 1)
return (int(block_size_x), int(block_size_y), int(block_size_z))
|
def get_thread_block_dimensions(params, block_size_names=None)
|
thread block size from tuning params, currently using convention
| 1.688576 | 1.660776 | 1.016739 |
logging.debug('looks_like_a_filename called')
result = False
if isinstance(kernel_source, str):
result = True
#test if not too long
if len(kernel_source) > 250:
result = False
#test if not contains special characters
for c in "();{}\\":
if c in kernel_source:
result = False
#just a safeguard for stuff that looks like code
for s in ["__global__ ", "__kernel ", "void ", "float "]:
if s in kernel_source:
result = False
#string must contain substring ".c", ".opencl", or ".F"
result = result and any([s in kernel_source for s in (".c", ".opencl", ".F")])
logging.debug('kernel_source is a filename: %s' % str(result))
return result
|
def looks_like_a_filename(kernel_source)
|
attempt to detect whether source code or a filename was passed
| 4.222158 | 4.13382 | 1.02137 |
logging.debug('prepare_kernel_string called for %s', kernel_name)
grid_dim_names = ["grid_size_x", "grid_size_y", "grid_size_z"]
for i, g in enumerate(grid):
kernel_string = "#define " + grid_dim_names[i] + " " + str(g) + "\n" + kernel_string
for i, g in enumerate(threads):
kernel_string = "#define " + block_size_names[i] + " " + str(g) + "\n" + kernel_string
for k, v in params.items():
if k not in block_size_names:
kernel_string = "#define " + k + " " + str(v) + "\n" + kernel_string
name = kernel_name
#name = kernel_name + "_" + get_instance_string(params)
#kernel_string = kernel_string.replace(kernel_name, name)
return name, kernel_string
|
def prepare_kernel_string(kernel_name, kernel_string, params, grid, threads, block_size_names)
|
prepare kernel string for compilation
Prepends the kernel with a series of C preprocessor defines specific
to this kernel instance:
* the thread block dimensions
* the grid dimensions
* tunable parameters
Additionally the name of kernel is replace with an instance specific name. This
is done to prevent that the kernel compilation could be skipped by PyCUDA and/or PyOpenCL,
which may use caching to save compilation time. This feature could lead to strange bugs
in the source code if the name of the kernel is also used for other stuff.
:param kernel_name: Name of the kernel.
:type kernel_name: string
:param kernel_string: One of the source files of the kernel as a string containing code.
:type kernel_string: string
:param params: A dictionary containing the tunable parameters specific to this instance.
:type params: dict
:param grid: A tuple with the grid dimensions for this specific instance.
:type grid: tuple(x,y,z)
:param threads: A tuple with the thread block dimensions for this specific instance.
:type threads: tuple(x,y,z)
:param block_size_names: A tuple with the names of the thread block dimensions used
in the code. By default this is ["block_size_x", ...], but the user
may supply different names if they prefer.
:type block_size_names: tuple(string)
:returns: A string containing the source code made specific to this kernel instance.
:rtype: string
| 2.140274 | 2.085354 | 1.026336 |
temp_files = dict()
kernel_string = get_kernel_string(kernel_file_list[0], params)
name, kernel_string = prepare_kernel_string(kernel_name, kernel_string, params, grid, threads, block_size_names)
if len(kernel_file_list) > 1:
for f in kernel_file_list[1:]:
#generate temp filename with the same extension
temp_file = get_temp_filename(suffix="." + f.split(".")[-1])
temp_files[f] = temp_file
#add preprocessor statements to the additional file
_, temp_file_string = prepare_kernel_string(kernel_name, get_kernel_string(f, params), params, grid, threads, block_size_names)
write_file(temp_file, temp_file_string)
#replace occurences of the additional file's name in the first kernel_string with the name of the temp file
kernel_string = kernel_string.replace(f, temp_file)
return name, kernel_string, temp_files
|
def prepare_list_of_files(kernel_name, kernel_file_list, params, grid, threads, block_size_names)
|
prepare the kernel string along with any additional files
The first file in the list is allowed to include or read in the others
The files beyond the first are considered additional files that may also contain tunable parameters
For each file beyond the first this function creates a temporary file with
preprocessors statements inserted. Occurences of the original filenames in the
first file are replaced with their temporary counterparts.
:param kernel_file_list: A list of filenames. The first file in the list is
allowed to read or include the other files in the list. All files may
will have access to the tunable parameters.
:type kernel_file_list: list(string)
:param params: A dictionary with the tunable parameters for this particular
instance.
:type params: dict()
:param grid: The grid dimensions for this instance. The grid dimensions are
also inserted into the code as if they are tunable parameters for
convenience.
:type grid: tuple()
| 2.778987 | 2.630539 | 1.056432 |
if os.path.isfile(filename):
with open(filename, 'r') as f:
return f.read()
|
def read_file(filename)
|
return the contents of the file named filename or None if file not found
| 2.394426 | 2.059848 | 1.162428 |
for k, v in params.items():
string = string.replace(k, str(v))
return string
|
def replace_param_occurrences(string, params)
|
replace occurrences of the tuning params with their current value
| 2.501508 | 2.256708 | 1.108477 |
threads = get_thread_block_dimensions(params, block_size_names)
current_problem_size = get_problem_size(problem_size, params)
grid = get_grid_dimensions(current_problem_size, params, grid_div, block_size_names)
return threads, grid
|
def setup_block_and_grid(problem_size, grid_div, params, block_size_names=None)
|
compute problem size, thread block and grid dimensions for this kernel
| 3.293582 | 2.874445 | 1.145815 |
import sys
#ugly fix, hopefully we can find a better one
if sys.version_info[0] >= 3:
with open(filename, 'w', encoding="utf-8") as f:
f.write(string)
else:
with open(filename, 'w') as f:
f.write(string.encode("utf-8"))
|
def write_file(filename, string)
|
dump the contents of string to a file called filename
| 2.721849 | 2.680082 | 1.015584 |
gpu_args = []
for arg in arguments:
# if arg i is a numpy array copy to device
if isinstance(arg, numpy.ndarray):
gpu_args.append(cl.Buffer(self.ctx, self.mf.READ_WRITE | self.mf.COPY_HOST_PTR, hostbuf=arg))
else: # if not an array, just pass argument along
gpu_args.append(arg)
return gpu_args
|
def ready_argument_list(self, arguments)
|
ready argument list to be passed to the kernel, allocates gpu mem
:param arguments: List of arguments to be passed to the kernel.
The order should match the argument list on the OpenCL kernel.
Allowed values are numpy.ndarray, and/or numpy.int32, numpy.float32, and so on.
:type arguments: list(numpy objects)
:returns: A list of arguments that can be passed to an OpenCL kernel.
:rtype: list( pyopencl.Buffer, numpy.int32, ... )
| 3.539785 | 3.317511 | 1.067 |
prg = cl.Program(self.ctx, kernel_string).build(options=self.compiler_options)
func = getattr(prg, kernel_name)
return func
|
def compile(self, kernel_name, kernel_string)
|
call the OpenCL compiler to compile the kernel, return the device function
:param kernel_name: The name of the kernel to be compiled, used to lookup the
function after compilation.
:type kernel_name: string
:param kernel_string: The OpenCL kernel code that contains the function `kernel_name`
:type kernel_string: string
:returns: An OpenCL kernel that can be called directly.
:rtype: pyopencl.Kernel
| 2.864553 | 3.720062 | 0.770028 |
global_size = (grid[0]*threads[0], grid[1]*threads[1], grid[2]*threads[2])
local_size = threads
time = []
for _ in range(self.iterations):
event = func(self.queue, global_size, local_size, *gpu_args)
event.wait()
time.append((event.profile.end - event.profile.start)*1e-6)
time = sorted(time)
if times:
return time
else:
if self.iterations > 4:
return numpy.mean(time[1:-1])
else:
return numpy.mean(time)
|
def benchmark(self, func, gpu_args, threads, grid, times)
|
runs the kernel and measures time repeatedly, returns average time
Runs the kernel and measures kernel execution time repeatedly, number of
iterations is set during the creation of OpenCLFunctions. Benchmark returns
a robust average, from all measurements the fastest and slowest runs are
discarded and the rest is included in the returned average. The reason for
this is to be robust against initialization artifacts and other exceptional
cases.
:param func: A PyOpenCL kernel compiled for this specific kernel configuration
:type func: pyopencl.Kernel
:param gpu_args: A list of arguments to the kernel, order should match the
order in the code. Allowed values are either variables in global memory
or single values passed by value.
:type gpu_args: list( pyopencl.Buffer, numpy.int32, ...)
:param threads: A tuple listing the number of work items in each dimension of
the work group.
:type threads: tuple(int, int, int)
:param grid: A tuple listing the number of work groups in each dimension
of the NDRange.
:type grid: tuple(int, int)
:param times: Return the execution time of all iterations.
:type times: bool
:returns: All execution times, if times=True, or a robust average for the
kernel execution time.
:rtype: float
| 2.851524 | 2.476219 | 1.151564 |
global_size = (grid[0]*threads[0], grid[1]*threads[1], grid[2]*threads[2])
local_size = threads
event = func(self.queue, global_size, local_size, *gpu_args)
event.wait()
|
def run_kernel(self, func, gpu_args, threads, grid)
|
runs the OpenCL kernel passed as 'func'
:param func: An OpenCL Kernel
:type func: pyopencl.Kernel
:param gpu_args: A list of arguments to the kernel, order should match the
order in the code. Allowed values are either variables in global memory
or single values passed by value.
:type gpu_args: list( pyopencl.Buffer, numpy.int32, ...)
:param threads: A tuple listing the number of work items in each dimension of
the work group.
:type threads: tuple(int, int, int)
:param grid: A tuple listing the number of work groups in each dimension
of the NDRange.
:type grid: tuple(int, int)
| 2.799035 | 2.957211 | 0.946512 |
if isinstance(buffer, cl.Buffer):
try:
cl.enqueue_fill_buffer(self.queue, buffer, numpy.uint32(value), 0, size)
except AttributeError:
src=numpy.zeros(size, dtype='uint8')+numpy.uint8(value)
cl.enqueue_copy(self.queue, buffer, src)
|
def memset(self, buffer, value, size)
|
set the memory in allocation to the value in value
:param allocation: An OpenCL Buffer to fill
:type allocation: pyopencl.Buffer
:param value: The value to set the memory to
:type value: a single 32-bit int
:param size: The size of to the allocation unit in bytes
:type size: int
| 3.042678 | 3.532301 | 0.861387 |
if isinstance(src, cl.Buffer):
cl.enqueue_copy(self.queue, dest, src)
|
def memcpy_dtoh(self, dest, src)
|
perform a device to host memory copy
:param dest: A numpy array in host memory to store the data
:type dest: numpy.ndarray
:param src: An OpenCL Buffer to copy data from
:type src: pyopencl.Buffer
| 3.720823 | 4.606407 | 0.807749 |
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