Instruction:
How would you explain what the following Python 3 function does
def stop(self):
        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

Response: Stops the consumer and returns the offset of the last processed message.


Instruction:
Given the following Python 3 function, write the documentation
def commit(self):
        # 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

Response: Commits the current offset of the message we last processed.


Instruction:
Can you write a function in Python 3 where it
checks if we should start a new commit operation and commit if so retry if necessary

Response:
def _auto_commit(self, by_count=False):
        """Check if we should start a new commit operation and commit"""
        # 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)


Instruction:
Write a Python 3 function for
scheduling a delayed fetch of the resource.

Response:
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`.
        """

        # 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)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _handle_offset_response(self, response):
        # 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()

Response:
Handle responses to OffsetRequest and OffsetFetchRequest since
            they are similar enough.


Instruction:
Create a Python 3 function for
handling an error in fetching the offset from Kafka.

Response:
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.
        """
        # 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()


Instruction:
Can you write a function in Python 3 where it
sends a commit request to the Kafka broker.

Response:
def _send_commit_request(self, retry_delay=None, attempt=None):
        """Send a commit request with our last_processed_offset"""
        # 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))


Instruction:
Can you tell what is the following Python 3 function doing
def _handle_commit_error(self, failure, retry_delay, attempt):
        # 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)

Response: Handle a commit error.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _handle_processor_error(self, failure):
        # 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)

Response:
Handle a failure in processing a block of messages with a specific entry in the a
       .


Instruction:
Can you create a Python 3 function that
handles an error in a fetch request.

Response:
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
        """
        # 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()


Instruction:
How would you explain what the following Python 3 function does
def _handle_fetch_response(self, responses):
        # 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)

Response: The callback handling the successful response from the fetch request.


Instruction:
Here you have a function in Python 3, explain what it does
def _process_messages(self, messages):
        # 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)

Response: Process the given message block and return the result of the processor.


Instruction:
Here you have a function in Python 3, explain what it does
def _do_fetch(self):
        # 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)

Response: Send a fetch request to the Kafka cluster to get the current offset.


Instruction:
Implement a Python 3 function for
handling an error in the commit function in the LoopingCall function.

Response:
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
        """
        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)


Instruction:
Here you have a function in Python 3, explain what it does
def _commit_timer_stopped(self, lCall):
        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

Response: Clean up our looping call...


Instruction:
Write a Python 3 function for
selecting a partition based on the hash of the key.

Response:
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.
        """
        return partitions[(self._hash(key) & 0x7FFFFFFF) % len(partitions)]


Instruction:
Can you create a Python 3 function that
compresses the given data with the Snappy algorithm.

Response:
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`
    """
    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)


Instruction:
Explain what the following Python 3 code does
def _get_video_info(self):
        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

Response: Returns basic information about the video as dictionary.


Instruction:
Write a Python 3 function for
encoding a video to a specified file.

Response:
def encode(self, source_path, target_path, params):  # NOQA: C901
        """
        Encodes a video to a specified file. All encoder specific options
        are passed in using `params`.
        """
        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:
            # 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


Instruction:
How would you explain what the following Python 3 function does
def get_media_info(self, video_path):
        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']),
        }

Response: Returns information about the given video as dict.


Instruction:
Make a summary of the following Python 3 code
def get_thumbnail(self, video_path, at_time=0.5):
        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

Response: Extracts an image of a video and returns its path.


Instruction:
Create a Python 3 function for
converting a given video file into all defined formats.

Response:
def convert_video(fieldfile, force=False):
    """
    Converts a given video file into all defined formats.
    """
    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)


Instruction:
Can you implement a function in Python 3 that
tunes a kernel configuration in the parameter space.

Response:
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()

    """

    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()


Instruction:
How would you code a function in Python 3 to
return Euclidian distance between self and other Firefly

Response:
def distance_to(self, other):
        """Return Euclidian distance between self and other Firefly"""
        return np.linalg.norm(self.position-other.position)


Instruction:
Write a Python 3 function that can
evaluate cost function and compute intensity at this position

Response:
def compute_intensity(self, _cost_func):
        """Evaluate cost function and compute intensity at this position"""
        self.evaluate(_cost_func)
        self.intensity = 1 / self.time


Instruction:
Write a Python 3 script to
move firefly towards another given beta and alpha values

Response:
def move_towards(self, other, beta, alpha):
        """Move firefly towards another given beta and alpha values"""
        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])


Instruction:
Implement a function in Python 3 to
default verify function based on numpy. allclose

Response:
def _default_verify_function(instance, answer, result_host, atol, verbose):
    """default verify function based on numpy.allclose"""

    #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


Instruction:
Here you have a function in Python 3, explain what it does
def benchmark(self, func, gpu_args, instance, times, verbose):
        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

Response: benchmark the kernel instance


Instruction:
Can you generate the documentation for the following Python 3 function
def check_kernel_output(self, func, gpu_args, instance, answer, atol, verify, verbose):
        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)

Response: runs the kernel once and checks the result against answer


Instruction:
Explain what the following Python 3 code does
def compile_and_benchmark(self, gpu_args, params, kernel_options, tuning_options):

        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

Response: Compile and benchmark a kernel instance based on the kernel strings and parameters


Instruction:
Can you implement a function in Python 3 that
compiles the kernel for this specific instance

Response:
def compile_kernel(self, instance, verbose):
        """compile the kernel for this specific instance"""
        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


Instruction:
Can you create a Python 3 function that
adds constant memory arguments to the most recently compiled module if using CUDA

Response:
def copy_constant_memory_args(self, cmem_args):
        """adds constant memory arguments to the most recently compiled module, if using CUDA"""
        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")


Instruction:
Can you create a Python 3 function that
adds texture memory arguments to the most recently compiled module if using CUDA

Response:
def copy_texture_memory_args(self, texmem_args):
        """adds texture memory arguments to the most recently compiled module, if using CUDA"""
        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")


Instruction:
Explain what the following Python 3 code does
def create_kernel_instance(self, kernel_options, params, verbose):
        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)

Response: create kernel instance from kernel options


Instruction:
Write a Python 3 script for
running a compiled kernel instance on a device

Response:
def run_kernel(self, func, gpu_args, instance):
        """ Run a compiled kernel instance on a device """
        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


Instruction:
Make a summary of the following Python 3 code
def _chunk_list(l, n):
    for i in range(0, len(l), n):
        yield l[i:i + n]

Response: Yield successive n - sized chunks from l.


Instruction:
Explain what the following Python 3 code does
def run(self, parameter_space, kernel_options, tuning_options):
        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, {}

Response: Runs the tuning process on the specified parameter space.


Instruction:
Write a Python 3 function for
building a Noodles workflow by sweeping the parameter space

Response:
def _parameter_sweep(self, parameter_space, kernel_options, device_options, tuning_options):
        """Build a Noodles workflow by sweeping the parameter space"""
        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)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _run_chunk(self, chunk, kernel_options, device_options, tuning_options):

        #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

Response: Benchmark a single kernel instance in the parameter space


Instruction:
Write a Python 3 script for
tuning a random sample of sample_fraction fraction in the parameter space.

Response:
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()

    """

    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


Instruction:
Here you have a function in Python 3, explain what it does
def check_argument_type(dtype, kernel_argument, i):
    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

Response: check if the numpy. dtype matches the type used in the code


Instruction:
Create a Python 3 function for
checking if kernel arguments match host arguments

Response:
def check_argument_list(kernel_name, kernel_string, args):
    """ raise an exception if a kernel arguments do not match host arguments """
    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)


Instruction:
Explain what the following Python 3 code does
def check_tune_params_list(tune_params):
    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!")

Response: raise an exception if a tune parameter has a forbidden name


Instruction:
Write a Python 3 function for
checking whether a specific instance meets the search space restrictions

Response:
def check_restrictions(restrictions, element, keys, verbose):
    """ check whether a specific instance meets the search space restrictions """
    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


Instruction:
Can you implement a function in Python 3 that
attempts to detect language from the kernel_string

Response:
def detect_language(lang, kernel_source):
    """attempt to detect language from the kernel_string if not specified"""
    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


Instruction:
Implement a function in Python 3 to
return a compact string representation of a dictionary

Response:
def get_config_string(params, units=None):
    """ return a compact string representation of a dictionary """
    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


Instruction:
How would you implement a function in Python 3 that
computes grid dims based on problem sizes and listed grid divisors

Response:
def get_grid_dimensions(current_problem_size, params, grid_div, block_size_names):
    """compute grid dims based on problem sizes and listed grid divisors"""
    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))


Instruction:
Can you generate a brief explanation for the following Python 3 code
def get_kernel_string(kernel_source, params=None):
    #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

Response: retrieve the kernel source and return as a string


Instruction:
Create a Python 3 function to
compute current problem size

Response:
def get_problem_size(problem_size, params):
    """compute current problem size"""
    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


Instruction:
Can you generate the documentation for the following Python 3 function
def get_temp_filename(suffix=None):
    file = tempfile.mkstemp(suffix=suffix or "", prefix="temp_", dir=os.getcwd()) # or "" for Python 2 compatibility
    os.close(file[0])
    return file[1]

Response: returns a string in the form of temp_X where X is a large integer


Instruction:
How would you implement a function in Python 3 that
threads block size from tuning params currently using convention

Response:
def get_thread_block_dimensions(params, block_size_names=None):
    """thread block size from tuning params, currently using convention"""
    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))


Instruction:
How would you explain what the following Python 3 function does
def looks_like_a_filename(kernel_source):
    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

Response: detect whether kernel_source is a filename


Instruction:
How would you implement a function in Python 3 that
prepares the kernel string for compilation

Response:
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

    """
    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


Instruction:
Can you generate a brief explanation for the following Python 3 code
def prepare_list_of_files(kernel_name, kernel_file_list, params, grid, threads, block_size_names):
    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

Response: This function takes a list of files and creates a temporary file with the appropriate preprocessor statements.


Instruction:
Explain what the following Python 3 code does
def read_file(filename):
    if os.path.isfile(filename):
        with open(filename, 'r') as f:
            return f.read()

Response: read the contents of the file named filename


Instruction:
Explain what the following Python 3 code does
def replace_param_occurrences(string, params):
    for k, v in params.items():
        string = string.replace(k, str(v))
    return string

Response: replace occurrences of the tuning params with their current value


Instruction:
Here you have a function in Python 3, explain what it does
def setup_block_and_grid(problem_size, grid_div, params, block_size_names=None):
    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

Response: compute problem size thread block and grid dimensions for this kernel


Instruction:
Can you write a function in Python 3 where it
dumps the contents of string to a file called filename

Response:
def write_file(filename, string):
    """dump the contents of string to a file called filename"""
    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"))


Instruction:
Create a Python 3 function to
ready argument list to be passed to the kernel allocates gpu mem

Response:
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, ... )
        """
        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


Instruction:
Here you have a function in Python 3, explain what it does
def compile(self, kernel_name, kernel_string):
        prg = cl.Program(self.ctx, kernel_string).build(options=self.compiler_options)
        func = getattr(prg, kernel_name)
        return func

Response: call the OpenCL compiler to compile the kernel return the device function


Instruction:
How would you implement a function in Python 3 that
runs the kernel and measures time repeatedly returns average time

Response:
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
        """
        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)


Instruction:
Implement a function in Python 3 to
run the OpenCL kernel passed as func

Response:
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)
        """
        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()


Instruction:
Create a Python 3 function for
setting the memory in allocation to the value in value

Response:
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

        """
        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)


Instruction:
Write a Python 3 function that can
perform a device to host memory copy

Response:
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
        """
        if isinstance(src, cl.Buffer):
            cl.enqueue_copy(self.queue, dest, src)


Instruction:
Here you have a function in Python 3, explain what it does
def tune(runner, kernel_options, device_options, tuning_options):

    results = []
    cache = {}

    tuning_options["scaling"] = False
    #build a bounds array as needed for the optimizer
    bounds = get_bounds(tuning_options.tune_params)

    args = (kernel_options, tuning_options, runner, results, cache)

    #call the differential evolution optimizer
    opt_result = differential_evolution(_cost_func, bounds, args, maxiter=1,
                                        polish=False, disp=tuning_options.verbose)

    if tuning_options.verbose:
        print(opt_result.message)

    return results, runner.dev.get_environment()

Response: Tune the kernel in the parameter space.


Instruction:
How would you explain what the following Python 3 function does
def tune(runner, kernel_options, device_options, tuning_options):

    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

    best_time_global = 1e20
    best_position_global = []

    # init particle swarm
    swarm = []
    for i in range(0, num_particles):
        swarm.append(Particle(bounds, args))

    for i in range(maxiter):
        if tuning_options.verbose:
            print("start iteration ", i, "best time global", best_time_global)

        # evaluate particle positions
        for j in range(num_particles):
            swarm[j].evaluate(_cost_func)

            # update global best if needed
            if swarm[j].time <= best_time_global:
                best_position_global = swarm[j].position
                best_time_global = swarm[j].time

        # update particle velocities and positions
        for j in range(0, num_particles):
            swarm[j].update_velocity(best_position_global)
            swarm[j].update_position(bounds)

    if tuning_options.verbose:
        print('Final result:')
        print(best_position_global)
        print(best_time_global)

    return results, runner.dev.get_environment()

Response: Tune a kernel configuration in the parameter space.


Instruction:
Can you write a function in Python 3 where it
tunes the kernel configuration in the parameter space.

Response:
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: 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()

    """

    dna_size = len(tuning_options.tune_params.keys())
    pop_size = 20
    generations = 100
    tuning_options["scaling"] = False

    tune_params = tuning_options.tune_params

    population = random_population(dna_size, pop_size, tune_params)

    best_time = 1e20
    all_results = []
    cache = {}

    for generation in range(generations):
        if tuning_options.verbose:
            print("Generation %d, best_time %f" % (generation, best_time))

        #determine fitness of population members
        weighted_population = []
        for dna in population:
            time = _cost_func(dna, kernel_options, tuning_options, runner, all_results, cache)
            weighted_population.append((dna, time))
        population = []

        #'best_time' is used only for printing
        if tuning_options.verbose and all_results:
            best_time = min(all_results, key=lambda x: x["time"])["time"]

        #population is sorted such that better configs have higher chance of reproducing
        weighted_population.sort(key=lambda x: x[1])

        #crossover and mutate
        for _ in range(pop_size//2):
            ind1 = weighted_choice(weighted_population)
            ind2 = weighted_choice(weighted_population)

            ind1, ind2 = crossover(ind1, ind2)

            population.append(mutate(ind1, dna_size, tune_params))
            population.append(mutate(ind2, dna_size, tune_params))

    return all_results, runner.dev.get_environment()


Instruction:
Write a Python 3 function for
creating a random population of DNA and other attributes

Response:
def random_population(dna_size, pop_size, tune_params):
    """create a random population"""
    population = []
    for _ in range(pop_size):
        dna = []
        for i in range(dna_size):
            dna.append(random_val(i, tune_params))
        population.append(dna)
    return population


Instruction:
Write a Python 3 function that can
return a random value for a parameter

Response:
def random_val(index, tune_params):
    """return a random value for a parameter"""
    key = list(tune_params.keys())[index]
    return random.choice(tune_params[key])


Instruction:
Implement a Python 3 function for
mutating DNA with 1 / mutation_chance chance

Response:
def mutate(dna, dna_size, tune_params):
    """Mutate DNA with 1/mutation_chance chance"""
    dna_out = []
    mutation_chance = 10
    for i in range(dna_size):
        if int(random.random()*mutation_chance) == 1:
            dna_out.append(random_val(i, tune_params))
        else:
            dna_out.append(dna[i])
    return dna_out


Instruction:
Given the following Python 3 function, write the documentation
def tune(runner, kernel_options, device_options, tuning_options):

    results = []
    cache = {}

    method = tuning_options.method

    #scale variables in x to make 'eps' relevant for multiple variables
    tuning_options["scaling"] = True

    bounds, x0, _ = get_bounds_x0_eps(tuning_options)
    kwargs = setup_method_arguments(method, bounds)
    options = setup_method_options(method, tuning_options)

    #not all methods support 'disp' option
    if not method in ['TNC']:
        options['disp'] = tuning_options.verbose

    args = (kernel_options, tuning_options, runner, results, cache)

    opt_result = scipy.optimize.minimize(_cost_func, x0, args=args, method=method, options=options, **kwargs)

    if tuning_options.verbose:
        print(opt_result.message)

    return results, runner.dev.get_environment()

Response: Tune the kernel in the parameter space.


Instruction:
Can you generate the documentation for the following Python 3 function
def _cost_func(x, kernel_options, tuning_options, runner, results, cache):

    error_time = 1e20
    logging.debug('_cost_func called')
    logging.debug('x: ' + str(x))

    x_key = ",".join([str(i) for i in x])
    if x_key in cache:
        return cache[x_key]

    #snap values in x to nearest actual value for each parameter unscale x if needed
    if tuning_options.scaling:
        params = unscale_and_snap_to_nearest(x, tuning_options.tune_params, tuning_options.eps)
    else:
        params = snap_to_nearest_config(x, tuning_options.tune_params)

    logging.debug('params ' + str(params))

    x_int = ",".join([str(i) for i in params])
    if x_int in cache:
        return cache[x_int]

    #check if this is a legal (non-restricted) parameter instance
    if tuning_options.restrictions:
        legal = util.check_restrictions(tuning_options.restrictions, params, tuning_options.tune_params.keys(), tuning_options.verbose)
        if not legal:
            cache[x_int] = error_time
            cache[x_key] = error_time
            return error_time

    #compile and benchmark this instance
    res, _ = runner.run([params], kernel_options, tuning_options)

    #append to tuning results
    if res:
        results.append(res[0])
        cache[x_int] = res[0]['time']
        cache[x_key] = res[0]['time']
        return res[0]['time']

    cache[x_int] = error_time
    cache[x_key] = error_time
    return error_time

Response: This function is used by minimize. It is used by minimize.


Instruction:
How would you code a function in Python 3 to
compute bounds x0 and eps

Response:
def get_bounds_x0_eps(tuning_options):
    """compute bounds, x0 (the initial guess), and eps"""
    values = tuning_options.tune_params.values()

    if tuning_options.scaling:
        #bounds = [(0, 1) for _ in values]
        #x0 = [0.5 for _ in bounds]
        eps = numpy.amin([1.0/len(v) for v in values])

        #reducing interval from [0, 1] to [0, eps*len(v)]
        bounds = [(0, eps*len(v)) for v in values]
        x0 = [0.5*eps*len(v) for v in values]
    else:
        bounds = get_bounds(tuning_options.tune_params)
        x0 = [(min_v+max_v)/2.0 for (min_v, max_v) in bounds]
        eps = 1e9
        for v_list in values:
            vals = numpy.sort(v_list)
            eps = min(eps, numpy.amin(numpy.gradient(vals)))

    tuning_options["eps"] = eps
    logging.debug('get_bounds_x0_eps called')
    logging.debug('bounds ' + str(bounds))
    logging.debug('x0 ' + str(x0))
    logging.debug('eps ' + str(eps))

    return bounds, x0, eps


Instruction:
Can you generate the documentation for the following Python 3 function
def get_bounds(tune_params):
    bounds = []
    for values in tune_params.values():
        sorted_values = numpy.sort(values)
        bounds.append((sorted_values[0], sorted_values[-1]))
    return bounds

Response: create a bounds array from the tunable parameters


Instruction:
Here you have a function in Python 3, explain what it does
def setup_method_options(method, tuning_options):
    kwargs = {}

    #pass size of parameter space as max iterations to methods that support it
    #it seems not all methods iterpret this value in the same manner
    maxiter = numpy.prod([len(v) for v in tuning_options.tune_params.values()])
    kwargs['maxiter'] = maxiter
    if method in ["Nelder-Mead", "Powell"]:
        kwargs['maxfev'] = maxiter
    elif method == "L-BFGS-B":
        kwargs['maxfun'] = maxiter

    #pass eps to methods that support it
    if method in ["CG", "BFGS", "L-BFGS-B", "TNC", "SLSQP"]:
        kwargs['eps'] = tuning_options.eps
    elif method == "COBYLA":
        kwargs['rhobeg'] = tuning_options.eps

    return kwargs

Response: prepare method specific options


Instruction:
Given the following Python 3 function, write the documentation
def snap_to_nearest_config(x, tune_params):
    params = []
    for i, k in enumerate(tune_params.keys()):
        values = numpy.array(tune_params[k])
        idx = numpy.abs(values-x[i]).argmin()
        params.append(int(values[idx]))
    return params

Response: helper function that for each param selects the closest actual value


Instruction:
Can you tell what is the following Python 3 function doing
def unscale_and_snap_to_nearest(x, tune_params, eps):
    x_u = [i for i in x]
    for i, v in enumerate(tune_params.values()):
        #create an evenly spaced linear space to map [0,1]-interval
        #to actual values, giving each value an equal chance
        #pad = 0.5/len(v)  #use when interval is [0,1]
        pad = 0.5*eps      #use when interval is [0, eps*len(v)]
        linspace = numpy.linspace(pad, (eps*len(v))-pad, len(v))

        #snap value to nearest point in space, store index
        idx = numpy.abs(linspace-x[i]).argmin()

        #safeguard that should not be needed
        idx = min(max(idx, 0), len(v)-1)

        #use index into array of actual values
        x_u[i] = v[idx]
    return x_u

Response: helper function that unscale a variable to the nearest config


Instruction:
Write a Python 3 script for
running the kernel tuning process for the entire parameter space.

Response:
def run(self, parameter_space, kernel_options, tuning_options):
        """ Iterate through the entire parameter space using a single Python process

        :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.iterface.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()

        """
        logging.debug('sequential runner started for ' + kernel_options.kernel_name)

        results = []

        #iterate over parameter space
        for element in parameter_space:
            params = OrderedDict(zip(tuning_options.tune_params.keys(), element))

            time = self.dev.compile_and_benchmark(self.gpu_args, params, kernel_options, tuning_options)

            if time is None:
                logging.debug('received time is None, kernel configuration was skipped silently due to compile or runtime failure')
                continue

            #print and append to results
            params['time'] = time
            output_string = get_config_string(params, self.units)
            logging.debug(output_string)
            if not self.quiet:
                print(output_string)
            results.append(params)

        return results, self.dev.get_environment()


Instruction:
Can you generate the documentation for the following Python 3 function
def tune(runner, kernel_options, device_options, tuning_options):

    results = []
    cache = {}

    method = tuning_options.method

    #scale variables in x to make 'eps' relevant for multiple variables
    tuning_options["scaling"] = True

    bounds, x0, eps = get_bounds_x0_eps(tuning_options)

    kwargs = setup_method_arguments(method, bounds)
    options = setup_method_options(method, tuning_options)
    kwargs['options'] = options

    args = (kernel_options, tuning_options, runner, results, cache)

    minimizer_kwargs = dict(**kwargs)
    minimizer_kwargs["method"] = method
    minimizer_kwargs["args"] = args

    opt_result = scipy.optimize.basinhopping(_cost_func, x0, stepsize=eps, minimizer_kwargs=minimizer_kwargs, disp=tuning_options.verbose)

    if tuning_options.verbose:
        print(opt_result.message)

    return results, runner.dev.get_environment()

Response: Tune the kernel configuration in the parameter space.


Instruction:
Can you write a function in Python 3 where it
allocates n context - portable pinned host memory

Response:
def allocate(n, dtype=numpy.float32):
    """ allocate context-portable pinned host memory """
    return drv.pagelocked_empty(int(n), dtype, order='C', mem_flags=drv.host_alloc_flags.PORTABLE)


Instruction:
Can you write a function in Python 3 where it
readies 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 CUDA 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 CUDA kernel. :rtype: list( pycuda.driver.DeviceAllocation, numpy.int32, ... )

Response:
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 CUDA 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 CUDA kernel.
        :rtype: list( pycuda.driver.DeviceAllocation, numpy.int32, ... )
        """
        gpu_args = []
        for arg in arguments:
            # if arg i is a numpy array copy to device
            if isinstance(arg, numpy.ndarray):
                alloc = drv.mem_alloc(arg.nbytes)
                self.allocations.append(alloc)
                gpu_args.append(alloc)
                drv.memcpy_htod(gpu_args[-1], arg)
            else: # if not an array, just pass argument along
                gpu_args.append(arg)
        return gpu_args


Instruction:
How would you explain what the following Python 3 function does
def compile(self, kernel_name, kernel_string):
        try:
            no_extern_c = 'extern "C"' in kernel_string

            compiler_options = ['-Xcompiler=-Wall']
            if self.compiler_options:
                compiler_options += self.compiler_options

            self.current_module = self.source_mod(kernel_string, options=compiler_options + ["-e", kernel_name],
                                             arch=('compute_' + self.cc) if self.cc != "00" else None,
                                             code=('sm_' + self.cc) if self.cc != "00" else None,
                                             cache_dir=False, no_extern_c=no_extern_c)

            func = self.current_module.get_function(kernel_name)
            return func
        except drv.CompileError as e:
            if "uses too much shared data" in e.stderr:
                raise Exception("uses too much shared data")
            else:
                raise e

Response: call the CUDA compiler to compile the kernel return the device function


Instruction:
Write a Python 3 function that can
run the kernel and measures time repeatedly returns average time

Response:
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 CudaFunctions. 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 PyCuda kernel compiled for this specific kernel configuration
        :type func: pycuda.driver.Function

        :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( pycuda.driver.DeviceAllocation, numpy.int32, ...)

        :param threads: A tuple listing the number of threads in each dimension of
            the thread block
        :type threads: tuple(int, int, int)

        :param grid: A tuple listing the number of thread blocks in each dimension
            of the grid
        :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
        """
        start = drv.Event()
        end = drv.Event()
        time = []
        for _ in range(self.iterations):
            self.context.synchronize()
            start.record()
            self.run_kernel(func, gpu_args, threads, grid)
            end.record()
            self.context.synchronize()
            time.append(end.time_since(start))
        time = sorted(time)
        if times:
            return time
        else:
            if self.iterations > 4:
                return numpy.mean(time[1:-1])
            else:
                return numpy.mean(time)


Instruction:
Implement a function in Python 3 to
add constant memory arguments to the most recently compiled module

Response:
def copy_constant_memory_args(self, cmem_args):
        """adds constant memory arguments to the most recently compiled module

        :param cmem_args: A dictionary containing the data to be passed to the
            device constant memory. The format to be used is as follows: A
            string key is used to name the constant memory symbol to which the
            value needs to be copied. Similar to regular arguments, these need
            to be numpy objects, such as numpy.ndarray or numpy.int32, and so on.
        :type cmem_args: dict( string: numpy.ndarray, ... )
        """
        logging.debug('copy_constant_memory_args called')
        logging.debug('current module: ' + str(self.current_module))
        for k, v in cmem_args.items():
            symbol = self.current_module.get_global(k)[0]
            logging.debug('copying to symbol: ' + str(symbol))
            logging.debug('array to be copied: ')
            logging.debug(v.nbytes)
            logging.debug(v.dtype)
            logging.debug(v.flags)
            drv.memcpy_htod(symbol, v)