Datasets:

12Parker

/

prompt_examples

like 0

celery__celery-2840

You will be provided with a partial code base and an issue statement explaining a problem to resolve. <issue> Message being acknowledged on WorkerLostError when CELERY_ACKS_LATE=True When using celery v3.0.24, with `CELERY_ACKS_LATE = True` , if the OOM killer kills the celery worker, then the worker acknowledges the message. As per [this](https://github.com/celery/celery/commit/e810420c) commit. The `exc_info.internal` comes in as `false`, which means it is not a internal error, due to which the message is acknowledged. The desirable behaviour, in such a case would be to not acknowledge the message (and be able to know, whether its a OOM error), so that some other worker can pick it up. As a workaround, I've commented out the [code](https://github.com/siddharth96/celery/commit/427695d1b23034dadda85fd7a48f7367831be4fa), where celery acknowledges the message, because in such a case, message will be lost. </issue> <code> [start of README.rst] 1 ================================= 2 celery - Distributed Task Queue 3 ================================= 4 5 .. image:: http://cloud.github.com/downloads/celery/celery/celery_128.png 6 7 |build-status| |coverage-status| 8 9 :Version: 3.2.0a1 (Cipater) 10 :Web: http://celeryproject.org/ 11 :Download: http://pypi.python.org/pypi/celery/ 12 :Source: http://github.com/celery/celery/ 13 :Keywords: task queue, job queue, asynchronous, async, rabbitmq, amqp, redis, 14 python, webhooks, queue, distributed 15 16 -- 17 18 What is a Task Queue? 19 ===================== 20 21 Task queues are used as a mechanism to distribute work across threads or 22 machines. 23 24 A task queue's input is a unit of work, called a task, dedicated worker 25 processes then constantly monitor the queue for new work to perform. 26 27 Celery communicates via messages, usually using a broker 28 to mediate between clients and workers. To initiate a task a client puts a 29 message on the queue, the broker then delivers the message to a worker. 30 31 A Celery system can consist of multiple workers and brokers, giving way 32 to high availability and horizontal scaling. 33 34 Celery is a library written in Python, but the protocol can be implemented in 35 any language. So far there's RCelery_ for the Ruby programming language, and a 36 `PHP client`, but language interoperability can also be achieved 37 by using webhooks. 38 39 .. _RCelery: https://github.com/leapfrogonline/rcelery 40 .. _`PHP client`: https://github.com/gjedeer/celery-php 41 .. _`using webhooks`: 42 http://docs.celeryproject.org/en/latest/userguide/remote-tasks.html 43 44 What do I need? 45 =============== 46 47 Celery version 3.0 runs on, 48 49 - Python (2.6, 2.7, 3.3, 3.4) 50 - PyPy (1.8, 1.9) 51 - Jython (2.5, 2.7). 52 53 This is the last version to support Python 2.5, 54 and from Celery 3.1, Python 2.6 or later is required. 55 The last version to support Python 2.4 was Celery series 2.2. 56 57 *Celery* is usually used with a message broker to send and receive messages. 58 The RabbitMQ, Redis transports are feature complete, 59 but there's also experimental support for a myriad of other solutions, including 60 using SQLite for local development. 61 62 *Celery* can run on a single machine, on multiple machines, or even 63 across datacenters. 64 65 Get Started 66 =========== 67 68 If this is the first time you're trying to use Celery, or you are 69 new to Celery 3.0 coming from previous versions then you should read our 70 getting started tutorials: 71 72 - `First steps with Celery`_ 73 74 Tutorial teaching you the bare minimum needed to get started with Celery. 75 76 - `Next steps`_ 77 78 A more complete overview, showing more features. 79 80 .. _`First steps with Celery`: 81 http://docs.celeryproject.org/en/latest/getting-started/first-steps-with-celery.html 82 83 .. _`Next steps`: 84 http://docs.celeryproject.org/en/latest/getting-started/next-steps.html 85 86 Celery is... 87 ========== 88 89 - **Simple** 90 91 Celery is easy to use and maintain, and does *not need configuration files*. 92 93 It has an active, friendly community you can talk to for support, 94 including a `mailing-list`_ and and an IRC channel. 95 96 Here's one of the simplest applications you can make:: 97 98 from celery import Celery 99 100 app = Celery('hello', broker='amqp://guest@localhost//') 101 102 @app.task 103 def hello(): 104 return 'hello world' 105 106 - **Highly Available** 107 108 Workers and clients will automatically retry in the event 109 of connection loss or failure, and some brokers support 110 HA in way of *Master/Master* or *Master/Slave* replication. 111 112 - **Fast** 113 114 A single Celery process can process millions of tasks a minute, 115 with sub-millisecond round-trip latency (using RabbitMQ, 116 py-librabbitmq, and optimized settings). 117 118 - **Flexible** 119 120 Almost every part of *Celery* can be extended or used on its own, 121 Custom pool implementations, serializers, compression schemes, logging, 122 schedulers, consumers, producers, autoscalers, broker transports and much more. 123 124 It supports... 125 ============ 126 127 - **Message Transports** 128 129 - RabbitMQ_, Redis_, 130 - MongoDB_ (experimental), Amazon SQS (experimental), 131 - CouchDB_ (experimental), SQLAlchemy_ (experimental), 132 - Django ORM (experimental), `IronMQ`_ 133 - and more... 134 135 - **Concurrency** 136 137 - Prefork, Eventlet_, gevent_, threads/single threaded 138 139 - **Result Stores** 140 141 - AMQP, Redis 142 - memcached, MongoDB 143 - SQLAlchemy, Django ORM 144 - Apache Cassandra, IronCache 145 146 - **Serialization** 147 148 - *pickle*, *json*, *yaml*, *msgpack*. 149 - *zlib*, *bzip2* compression. 150 - Cryptographic message signing. 151 152 .. _`Eventlet`: http://eventlet.net/ 153 .. _`gevent`: http://gevent.org/ 154 155 .. _RabbitMQ: http://rabbitmq.com 156 .. _Redis: http://redis.io 157 .. _MongoDB: http://mongodb.org 158 .. _Beanstalk: http://kr.github.com/beanstalkd 159 .. _CouchDB: http://couchdb.apache.org 160 .. _SQLAlchemy: http://sqlalchemy.org 161 .. _`IronMQ`: http://iron.io 162 163 Framework Integration 164 ===================== 165 166 Celery is easy to integrate with web frameworks, some of which even have 167 integration packages: 168 169 +--------------------+----------------------------------------------------+ 170 | `Django`_ | not needed | 171 +--------------------+----------------------------------------------------+ 172 | `Pyramid`_ | `pyramid_celery`_ | 173 +--------------------+----------------------------------------------------+ 174 | `Pylons`_ | `celery-pylons`_ | 175 +--------------------+----------------------------------------------------+ 176 | `Flask`_ | not needed | 177 +--------------------+----------------------------------------------------+ 178 | `web2py`_ | `web2py-celery`_ | 179 +--------------------+----------------------------------------------------+ 180 | `Tornado`_ | `tornado-celery`_ | `another tornado-celery`_ | 181 +--------------------+----------------------------------------------------+ 182 183 The integration packages are not strictly necessary, but they can make 184 development easier, and sometimes they add important hooks like closing 185 database connections at ``fork``. 186 187 .. _`Django`: http://djangoproject.com/ 188 .. _`Pylons`: http://www.pylonsproject.org/ 189 .. _`Flask`: http://flask.pocoo.org/ 190 .. _`web2py`: http://web2py.com/ 191 .. _`Bottle`: http://bottlepy.org/ 192 .. _`Pyramid`: http://docs.pylonsproject.org/en/latest/docs/pyramid.html 193 .. _`pyramid_celery`: http://pypi.python.org/pypi/pyramid_celery/ 194 .. _`django-celery`: http://pypi.python.org/pypi/django-celery 195 .. _`celery-pylons`: http://pypi.python.org/pypi/celery-pylons 196 .. _`web2py-celery`: http://code.google.com/p/web2py-celery/ 197 .. _`Tornado`: http://www.tornadoweb.org/ 198 .. _`tornado-celery`: http://github.com/mher/tornado-celery/ 199 .. _`another tornado-celery`: https://github.com/mayflaver/tornado-celery 200 201 .. _celery-documentation: 202 203 Documentation 204 ============= 205 206 The `latest documentation`_ with user guides, tutorials and API reference 207 is hosted at Read The Docs. 208 209 .. _`latest documentation`: http://docs.celeryproject.org/en/latest/ 210 211 .. _celery-installation: 212 213 Installation 214 ============ 215 216 You can install Celery either via the Python Package Index (PyPI) 217 or from source. 218 219 To install using `pip`,:: 220 221 $ pip install -U Celery 222 223 To install using `easy_install`,:: 224 225 $ easy_install -U Celery 226 227 .. _bundles: 228 229 Bundles 230 ------- 231 232 Celery also defines a group of bundles that can be used 233 to install Celery and the dependencies for a given feature. 234 235 You can specify these in your requirements or on the ``pip`` comand-line 236 by using brackets. Multiple bundles can be specified by separating them by 237 commas. 238 :: 239 240 $ pip install "celery[librabbitmq]" 241 242 $ pip install "celery[librabbitmq,redis,auth,msgpack]" 243 244 The following bundles are available: 245 246 Serializers 247 ~~~~~~~~~~~ 248 249 :celery[auth]: 250 for using the auth serializer. 251 252 :celery[msgpack]: 253 for using the msgpack serializer. 254 255 :celery[yaml]: 256 for using the yaml serializer. 257 258 Concurrency 259 ~~~~~~~~~~~ 260 261 :celery[eventlet]: 262 for using the eventlet pool. 263 264 :celery[gevent]: 265 for using the gevent pool. 266 267 :celery[threads]: 268 for using the thread pool. 269 270 Transports and Backends 271 ~~~~~~~~~~~~~~~~~~~~~~~ 272 273 :celery[librabbitmq]: 274 for using the librabbitmq C library. 275 276 :celery[redis]: 277 for using Redis as a message transport or as a result backend. 278 279 :celery[mongodb]: 280 for using MongoDB as a message transport (*experimental*), 281 or as a result backend (*supported*). 282 283 :celery[sqs]: 284 for using Amazon SQS as a message transport (*experimental*). 285 286 :celery[memcache]: 287 for using memcached as a result backend. 288 289 :celery[cassandra]: 290 for using Apache Cassandra as a result backend. 291 292 :celery[couchdb]: 293 for using CouchDB as a message transport (*experimental*). 294 295 :celery[couchbase]: 296 for using CouchBase as a result backend. 297 298 :celery[beanstalk]: 299 for using Beanstalk as a message transport (*experimental*). 300 301 :celery[zookeeper]: 302 for using Zookeeper as a message transport. 303 304 :celery[zeromq]: 305 for using ZeroMQ as a message transport (*experimental*). 306 307 :celery[sqlalchemy]: 308 for using SQLAlchemy as a message transport (*experimental*), 309 or as a result backend (*supported*). 310 311 :celery[pyro]: 312 for using the Pyro4 message transport (*experimental*). 313 314 :celery[slmq]: 315 for using the SoftLayer Message Queue transport (*experimental*). 316 317 .. _celery-installing-from-source: 318 319 Downloading and installing from source 320 -------------------------------------- 321 322 Download the latest version of Celery from 323 http://pypi.python.org/pypi/celery/ 324 325 You can install it by doing the following,:: 326 327 $ tar xvfz celery-0.0.0.tar.gz 328 $ cd celery-0.0.0 329 $ python setup.py build 330 # python setup.py install 331 332 The last command must be executed as a privileged user if 333 you are not currently using a virtualenv. 334 335 .. _celery-installing-from-git: 336 337 Using the development version 338 ----------------------------- 339 340 With pip 341 ~~~~~~~~ 342 343 The Celery development version also requires the development 344 versions of ``kombu``, ``amqp`` and ``billiard``. 345 346 You can install the latest snapshot of these using the following 347 pip commands:: 348 349 $ pip install https://github.com/celery/celery/zipball/master#egg=celery 350 $ pip install https://github.com/celery/billiard/zipball/master#egg=billiard 351 $ pip install https://github.com/celery/py-amqp/zipball/master#egg=amqp 352 $ pip install https://github.com/celery/kombu/zipball/master#egg=kombu 353 354 With git 355 ~~~~~~~~ 356 357 Please the Contributing section. 358 359 .. _getting-help: 360 361 Getting Help 362 ============ 363 364 .. _mailing-list: 365 366 Mailing list 367 ------------ 368 369 For discussions about the usage, development, and future of celery, 370 please join the `celery-users`_ mailing list. 371 372 .. _`celery-users`: http://groups.google.com/group/celery-users/ 373 374 .. _irc-channel: 375 376 IRC 377 --- 378 379 Come chat with us on IRC. The **#celery** channel is located at the `Freenode`_ 380 network. 381 382 .. _`Freenode`: http://freenode.net 383 384 .. _bug-tracker: 385 386 Bug tracker 387 =========== 388 389 If you have any suggestions, bug reports or annoyances please report them 390 to our issue tracker at http://github.com/celery/celery/issues/ 391 392 .. _wiki: 393 394 Wiki 395 ==== 396 397 http://wiki.github.com/celery/celery/ 398 399 400 .. _maintainers: 401 402 Maintainers 403 =========== 404 405 - `@ask`_ (primary maintainer) 406 - `@thedrow`_ 407 - `@chrisgogreen`_ 408 - `@PMickael`_ 409 - `@malinoff`_ 410 - And you? We really need more: https://github.com/celery/celery/issues/2534 411 412 .. _`@ask`: http://github.com/ask 413 .. _`@thedrow`: http://github.com/thedrow 414 .. _`@chrisgogreen`: http://github.com/chrisgogreen 415 .. _`@PMickael`: http://github.com/PMickael 416 .. _`@malinoff`: http://github.com/malinoff 417 418 419 .. _contributing-short: 420 421 Contributing 422 ============ 423 424 Development of `celery` happens at Github: http://github.com/celery/celery 425 426 You are highly encouraged to participate in the development 427 of `celery`. If you don't like Github (for some reason) you're welcome 428 to send regular patches. 429 430 Be sure to also read the `Contributing to Celery`_ section in the 431 documentation. 432 433 .. _`Contributing to Celery`: 434 http://docs.celeryproject.org/en/master/contributing.html 435 436 .. _license: 437 438 License 439 ======= 440 441 This software is licensed under the `New BSD License`. See the ``LICENSE`` 442 file in the top distribution directory for the full license text. 443 444 .. # vim: syntax=rst expandtab tabstop=4 shiftwidth=4 shiftround 445 446 447 .. image:: https://d2weczhvl823v0.cloudfront.net/celery/celery/trend.png 448 :alt: Bitdeli badge 449 :target: https://bitdeli.com/free 450 451 .. |build-status| image:: https://travis-ci.org/celery/celery.svg?branch=master 452 :target: https://travis-ci.org/celery/celery 453 .. |coverage-status| image:: https://coveralls.io/repos/celery/celery/badge.svg 454 :target: https://coveralls.io/r/celery/celery 455 [end of README.rst] [start of celery/app/defaults.py] ... 118 'EAGER_PROPAGATES_EXCEPTIONS': Option(False, type='bool'), 119 'ENABLE_UTC': Option(True, type='bool'), 120 'ENABLE_REMOTE_CONTROL': Option(True, type='bool'), 121 'EVENT_SERIALIZER': Option('json'), 122 'EVENT_QUEUE_EXPIRES': Option(60.0, type='float'), 123 'EVENT_QUEUE_TTL': Option(5.0, type='float'), 124 'IMPORTS': Option((), type='tuple'), 125 'INCLUDE': Option((), type='tuple'), 126 'IGNORE_RESULT': Option(False, type='bool'), 127 'MAX_CACHED_RESULTS': Option(100, type='int'), 128 'MESSAGE_COMPRESSION': Option(type='string'), 129 'MONGODB_BACKEND_SETTINGS': Option(type='dict'), 130 'REDIS_HOST': Option(type='string', **_REDIS_OLD), 131 'REDIS_PORT': Option(type='int', **_REDIS_OLD), 132 'REDIS_DB': Option(type='int', **_REDIS_OLD), 133 'REDIS_PASSWORD': Option(type='string', **_REDIS_OLD), 134 'REDIS_MAX_CONNECTIONS': Option(type='int'), 135 'RESULT_BACKEND': Option(type='string'), 136 'RESULT_DB_SHORT_LIVED_SESSIONS': Option(False, type='bool'), 137 'RESULT_DB_TABLENAMES': Option(type='dict'), 138 'RESULT_DBURI': Option(), ... [end of celery/app/defaults.py] [start of celery/app/task.py] ... 206 #: 207 #: The application default can be overridden using the 208 #: :setting:`CELERY_TRACK_STARTED` setting. 209 track_started = None 210 211 #: When enabled messages for this task will be acknowledged **after** 212 #: the task has been executed, and not *just before* which is the 213 #: default behavior. 214 #: 215 #: Please note that this means the task may be executed twice if the 216 #: worker crashes mid execution (which may be acceptable for some 217 #: applications). 218 #: 219 #: The application default can be overridden with the 220 #: :setting:`CELERY_ACKS_LATE` setting. 221 acks_late = None 222 223 #: Tuple of expected exceptions. 224 #: 225 #: These are errors that are expected in normal operation 226 #: and that should not be regarded as a real error by the worker. ... ... 234 #: Task request stack, the current request will be the topmost. 235 request_stack = None 236 237 #: Some may expect a request to exist even if the task has not been 238 #: called. This should probably be deprecated. 239 _default_request = None 240 241 _exec_options = None 242 243 __bound__ = False 244 245 from_config = ( 246 ('send_error_emails', 'CELERY_SEND_TASK_ERROR_EMAILS'), 247 ('serializer', 'CELERY_TASK_SERIALIZER'), 248 ('rate_limit', 'CELERY_DEFAULT_RATE_LIMIT'), 249 ('track_started', 'CELERY_TRACK_STARTED'), 250 ('acks_late', 'CELERY_ACKS_LATE'), 251 ('ignore_result', 'CELERY_IGNORE_RESULT'), 252 ('store_errors_even_if_ignored', 253 'CELERY_STORE_ERRORS_EVEN_IF_IGNORED'), 254 ) ... [end of celery/app/task.py] [start of celery/worker/request.py] ... 312 if self.task.acks_late: 313 self.acknowledge() 314 315 self.send_event('task-succeeded', result=retval, runtime=runtime) 316 317 def on_retry(self, exc_info): 318 """Handler called if the task should be retried.""" 319 if self.task.acks_late: 320 self.acknowledge() 321 322 self.send_event('task-retried', 323 exception=safe_repr(exc_info.exception.exc), 324 traceback=safe_str(exc_info.traceback)) 325 326 def on_failure(self, exc_info, send_failed_event=True, return_ok=False): 327 """Handler called if the task raised an exception.""" 328 task_ready(self) 329 330 if isinstance(exc_info.exception, MemoryError): 331 raise MemoryError('Process got: %s' % (exc_info.exception,)) 332 elif isinstance(exc_info.exception, Reject): 333 return self.reject(requeue=exc_info.exception.requeue) ... ... 338 339 if isinstance(exc, Retry): 340 return self.on_retry(exc_info) 341 342 # These are special cases where the process would not have had 343 # time to write the result. 344 if self.store_errors: 345 if isinstance(exc, Terminated): 346 self._announce_revoked( 347 'terminated', True, string(exc), False) 348 send_failed_event = False # already sent revoked event 349 elif isinstance(exc, WorkerLostError) or not return_ok: 350 self.task.backend.mark_as_failure( 351 self.id, exc, request=self, 352 ) 353 # (acks_late) acknowledge after result stored. 354 if self.task.acks_late: 355 self.acknowledge() 356 357 if send_failed_event: 358 self.send_event( 359 'task-failed', ... [end of celery/worker/request.py] </code> I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format. <patch> --- a/file.py +++ b/file.py @@ -1,27 +1,35 @@ def euclidean(a, b): - while b: - a, b = b, a % b - return a + if b == 0: + return a + return euclidean(b, a % b) def bresenham(x0, y0, x1, y1): points = [] dx = abs(x1 - x0) dy = abs(y1 - y0) - sx = 1 if x0 < x1 else -1 - sy = 1 if y0 < y1 else -1 - err = dx - dy + x, y = x0, y0 + sx = -1 if x0 > x1 else 1 + sy = -1 if y0 > y1 else 1 - while True: - points.append((x0, y0)) - if x0 == x1 and y0 == y1: - break - e2 = 2 * err - if e2 > -dy: + if dx > dy: + err = dx / 2.0 + while x != x1: + points.append((x, y)) err -= dy - x0 += sx - if e2 < dx: - err += dx - y0 += sy + if err < 0: + y += sy + err += dx + x += sx + else: + err = dy / 2.0 + while y != y1: + points.append((x, y)) + err -= dx + if err < 0: + x += sx + err += dy + y += sy + points.append((x, y)) return points </patch>

celery/celery

045b52f1450d6d5cc500e0057a4b498250dc5692

Message being acknowledged on WorkerLostError when CELERY_ACKS_LATE=True When using celery v3.0.24, with `CELERY_ACKS_LATE = True` , if the OOM killer kills the celery worker, then the worker acknowledges the message. As per [this](https://github.com/celery/celery/commit/e810420c) commit. The `exc_info.internal` comes in as `false`, which means it is not a internal error, due to which the message is acknowledged. The desirable behaviour, in such a case would be to not acknowledge the message (and be able to know, whether its a OOM error), so that some other worker can pick it up. As a workaround, I've commented out the [code](https://github.com/siddharth96/celery/commit/427695d1b23034dadda85fd7a48f7367831be4fa), where celery acknowledges the message, because in such a case, message will be lost.

This is deliberate as if a task is killed it may mean that the next invocation will also cause the same to happen. If the task is redelivered it may cause a loop where the same conditions occur again and again. Also, sadly you cannot distinguish processes killed by OOM from processes killed by other means, and if an administrator kills -9 a task going amok, you usually don't want that task to be called again. There could be a configuration option for not acking terminated tasks, but I'm not sure how useful that would be. A better solution could be to use `basic_reject(requeue=False)` instead of `basic_ack`, that way you can configure a dead letter queue so that the killed tasks will be sent to a queue for manual inspection. I must say, regardless of the status of this feature request, the documentation is misleading. Specifically, [this FAQ makes it seem that process failures would NOT acknowledge messages](http://celery.readthedocs.org/en/latest/faq.html#faq-acks-late-vs-retry). And [this FAQ boldface states](http://celery.readthedocs.org/en/latest/faq.html#id54) that in the event of a kill signal (9), that acks_late will allow the task to re-run (which again, is patently wrong based on this poorly documented behavior). Nowhere in the docs have I found that if the process _dies_, the message will be acknowledged, regardless of acks_late or not. (for instance, I have a set of 10k+ tasks, and some 1% of tasks wind up acknowledged but incomplete when a WorkerLostError is thrown in connection with the worker, although there are no other errors of any kind in any of my logs related to that task). TL;DR at the least, appropriately document the current state when describing the functionality and limitations of acks_late. A work-around would be helpful -- I'm not sure I understand the solution of using `basic_reject`, although I'll keep looking into it. The docs are referring to killing the worker process with KILL, not the child processes. The term worker will always refer to the worker instance, not the pool processes. The section within about acks_late is probably not very helpful and should be removed

2015-10-06T05:34:34Z

<patch> <patch> diff --git a/celery/app/defaults.py b/celery/app/defaults.py --- a/celery/app/defaults.py +++ b/celery/app/defaults.py @@ -132,6 +132,7 @@ def __repr__(self): 'REDIS_DB': Option(type='int', **_REDIS_OLD), 'REDIS_PASSWORD': Option(type='string', **_REDIS_OLD), 'REDIS_MAX_CONNECTIONS': Option(type='int'), + 'REJECT_ON_WORKER_LOST': Option(type='bool'), 'RESULT_BACKEND': Option(type='string'), 'RESULT_DB_SHORT_LIVED_SESSIONS': Option(False, type='bool'), 'RESULT_DB_TABLENAMES': Option(type='dict'), diff --git a/celery/app/task.py b/celery/app/task.py --- a/celery/app/task.py +++ b/celery/app/task.py @@ -220,6 +220,12 @@ class Task(object): #: :setting:`CELERY_ACKS_LATE` setting. acks_late = None + #: When CELERY_ACKS_LATE is set to True, the default behavior to + #: handle worker crash is to acknowledge the message. Setting + #: this to true allows the message to be rejected and requeued so + #: it will be executed again by another worker. + reject_on_worker_lost = None + #: Tuple of expected exceptions. #: #: These are errors that are expected in normal operation @@ -248,6 +254,7 @@ class Task(object): ('rate_limit', 'CELERY_DEFAULT_RATE_LIMIT'), ('track_started', 'CELERY_TRACK_STARTED'), ('acks_late', 'CELERY_ACKS_LATE'), + ('reject_on_worker_lost', 'CELERY_REJECT_ON_WORKER_LOST'), ('ignore_result', 'CELERY_IGNORE_RESULT'), ('store_errors_even_if_ignored', 'CELERY_STORE_ERRORS_EVEN_IF_IGNORED'), diff --git a/celery/worker/request.py b/celery/worker/request.py --- a/celery/worker/request.py +++ b/celery/worker/request.py @@ -326,7 +326,6 @@ def on_retry(self, exc_info): def on_failure(self, exc_info, send_failed_event=True, return_ok=False): """Handler called if the task raised an exception.""" task_ready(self) - if isinstance(exc_info.exception, MemoryError): raise MemoryError('Process got: %s' % (exc_info.exception,)) elif isinstance(exc_info.exception, Reject): @@ -352,7 +351,13 @@ def on_failure(self, exc_info, send_failed_event=True, return_ok=False): ) # (acks_late) acknowledge after result stored. if self.task.acks_late: - self.acknowledge() + reject_and_requeue = (self.task.reject_on_worker_lost and + isinstance(exc, WorkerLostError) and + self.delivery_info.get('redelivered', False) is False) + if reject_and_requeue: + self.reject(requeue=True) + else: + self.acknowledge() if send_failed_event: self.send_event( </patch> </s> </patch>

diff --git a/celery/tests/worker/test_request.py b/celery/tests/worker/test_request.py --- a/celery/tests/worker/test_request.py +++ b/celery/tests/worker/test_request.py @@ -325,6 +325,20 @@ def test_on_failure_Reject_rejects_with_requeue(self): req_logger, req.connection_errors, True, ) + def test_on_failure_WrokerLostError_rejects_with_requeue(self): + einfo = None + try: + raise WorkerLostError() + except: + einfo = ExceptionInfo(internal=True) + req = self.get_request(self.add.s(2, 2)) + req.task.acks_late = True + req.task.reject_on_worker_lost = True + req.delivery_info['redelivered'] = False + req.on_failure(einfo) + req.on_reject.assert_called_with(req_logger, + req.connection_errors, True) + def test_tzlocal_is_cached(self): req = self.get_request(self.add.s(2, 2)) req._tzlocal = 'foo'

1.0

NVIDIA__NeMo-473

You will be provided with a partial code base and an issue statement explaining a problem to resolve. <issue> Jasper Encoder Export failed The export of Jasper Encoder is failing. I am using the core API [deployment_export](https://nvidia.github.io/NeMo/api-docs/nemo.html#nemo.core.neural_factory.NeuralModuleFactory.deployment_export) like in the script: https://github.com/NVIDIA/NeMo/blob/403238f82d26879ba5fca53fbf75b3cdc70fb49b/scripts/export_jasper_to_onnx.py#L92 I believe the issue (as shown below) is that the` input_example` provided does not match the `output_example`. ``` /opt/conda/lib/python3.6/site-packages/torch/jit/__init__.py:1023: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error: Not within tolerance rtol=1e-05 atol=1e-05 at input[0, 870, 67] (0.6547648906707764 vs. 0.6546438932418823) and 812 other locations (0.00%) check_tolerance, _force_outplace, True, _module_class) [NeMo E 2020-02-23 19:10:07 actions:1023] module export failed for JasperEncoder with exception number of output names provided (2) exceeded number of outputs (1) ``` **What is the correct `input_example` and `output_example` to export JasperEncoder?** The full output can be seen here: ``` adrianaf@2a520c7abb1e:/tmp/NeMo$ ! python /tmp/NeMo/scripts/export_jasper_to_onnx.py --config /raid/datasets/asr/data/config_files/WSJ-test_acoustic_quartznet15x5.yaml --nn_encoder /home/adrianaf/projects/nemo_asr_app/models/quartznet15x5/JasperEncoder-STEP-247400.pt --nn_decoder /home/adrianaf/projects/nemo_asr_app/models/quartznet15x5/JasperDecoderForCTC-STEP-247400.pt --onnx_encoder /raid/datasets/asr/data/models/ONNX/pre-trained_encoder.onnx --onnx_decoder /raid/datasets/asr/data/models/ONNX/pre-trained_decoder.onnx /opt/conda/lib/python3.6/site-packages/torchvision/io/_video_opt.py:17: UserWarning: video reader based on ffmpeg c++ ops not available warnings.warn("video reader based on ffmpeg c++ ops not available") /tmp/NeMo/nemo/collections/asr/audio_preprocessing.py:48: UserWarning: Could not import torchaudio. Some features might not work. warnings.warn('Could not import torchaudio. Some features might not work.') [NeMo I 2020-02-23 19:09:42 export_jasper_to_onnx:48] Loading config file... [NeMo I 2020-02-23 19:09:42 export_jasper_to_onnx:52] Determining model shape... [NeMo I 2020-02-23 19:09:42 export_jasper_to_onnx:60] Num encoder input features: 64 [NeMo I 2020-02-23 19:09:42 export_jasper_to_onnx:61] Num decoder input features: 1024 [NeMo W 2020-02-23 19:09:42 deprecated:68] Function ``_get_trainer`` is deprecated. It is going to be removed in the future version. [NeMo I 2020-02-23 19:09:42 export_jasper_to_onnx:65] Initializing models... [NeMo I 2020-02-23 19:09:45 export_jasper_to_onnx:76] Loading checkpoints... [NeMo I 2020-02-23 19:09:45 export_jasper_to_onnx:91] Exporting encoder... [NeMo W 2020-02-23 19:09:45 neural_factory:627] Turned off 170 masked convolutions [NeMo I 2020-02-23 19:09:45 actions:937] Module is JasperEncoder. We are removing input and output length ports since they are not needed for deployment [NeMo W 2020-02-23 19:09:46 deprecated:68] Function ``local_parameters`` is deprecated. It is going to be removed in the 0.11 version. /opt/conda/lib/python3.6/site-packages/torch/jit/__init__.py:1023: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error: Not within tolerance rtol=1e-05 atol=1e-05 at input[0, 870, 67] (0.6547648906707764 vs. 0.6546438932418823) and 812 other locations (0.00%) check_tolerance, _force_outplace, True, _module_class) [NeMo E 2020-02-23 19:10:07 actions:1023] module export failed for JasperEncoder with exception number of output names provided (2) exceeded number of outputs (1) [NeMo I 2020-02-23 19:10:07 export_jasper_to_onnx:98] Exporting decoder... graph(%encoder_output : Float(1, 1024, 128), %1 : Float(29, 1024, 1), %2 : Float(29)): %3 : Float(1, 29, 128) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%encoder_output, %1, %2), scope: JasperDecoderForCTC/Sequential[decoder_layers]/Conv1d[0] # /opt/conda/lib/python3.6/site-packages/torch/nn/modules/conv.py:202:0 %4 : Float(1, 128, 29) = onnx::Transpose[perm=[0, 2, 1]](%3), scope: JasperDecoderForCTC # /tmp/NeMo/nemo/collections/asr/jasper.py:235:0 %output : Float(1, 128, 29) = onnx::LogSoftmax[axis=2](%4), scope: JasperDecoderForCTC # /opt/conda/lib/python3.6/site-packages/torch/nn/functional.py:1317:0 return (%output) /opt/conda/lib/python3.6/site-packages/torch/onnx/utils.py:774: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input encoder_output 'Automatically generated names will be applied to each dynamic axes of input {}'.format(key)) /opt/conda/lib/python3.6/site-packages/torch/onnx/utils.py:774: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input output 'Automatically generated names will be applied to each dynamic axes of input {}'.format(key)) [NeMo I 2020-02-23 19:10:07 export_jasper_to_onnx:105] Export completed successfully. ``` </issue> <code> [start of README.rst] 1 .. image:: http://www.repostatus.org/badges/latest/active.svg 2 :target: http://www.repostatus.org/#active 3 :alt: Project Status: Active – The project has reached a stable, usable state and is being actively developed. 4 5 .. image:: https://img.shields.io/badge/documentation-github.io-blue.svg 6 :target: https://nvidia.github.io/NeMo/ 7 :alt: NeMo documentation on GitHub pages 8 9 .. image:: https://img.shields.io/badge/License-Apache%202.0-brightgreen.svg 10 :target: https://github.com/NVIDIA/NeMo/blob/master/LICENSE 11 :alt: NeMo core license and license for collections in this repo 12 13 .. image:: https://img.shields.io/lgtm/grade/python/g/NVIDIA/NeMo.svg?logo=lgtm&logoWidth=18 14 :target: https://lgtm.com/projects/g/NVIDIA/NeMo/context:python 15 :alt: Language grade: Python 16 17 .. image:: https://img.shields.io/lgtm/alerts/g/NVIDIA/NeMo.svg?logo=lgtm&logoWidth=18 18 :target: https://lgtm.com/projects/g/NVIDIA/NeMo/alerts/ 19 :alt: Total alerts 20 21 .. image:: https://img.shields.io/badge/code%20style-black-000000.svg 22 :target: https://github.com/psf/black 23 :alt: Code style: black 24 25 26 27 NVIDIA Neural Modules: NeMo 28 =========================== 29 30 NeMo is a toolkit for defining and building `Conversational AI <https://developer.nvidia.com/conversational-ai#started>`_ applications. 31 32 Goal of the NeMo toolkit is to make it possible for researchers to easily compose complex neural network architectures for conversational AI using reusable components. Built for speed, NeMo can utilize NVIDIA's Tensor Cores and scale out training to multiple GPUs and multiple nodes. 33 34 **Neural Modules** are conceptual blocks of neural networks that take *typed* inputs and produce *typed* outputs. Such modules typically represent data layers, encoders, decoders, language models, loss functions, or methods of combining activations. 35 36 The toolkit comes with extendable collections of pre-built modules for automatic speech recognition (ASR), natural language processing (NLP) and text synthesis (TTS). 37 38 **Introduction** 39 40 * Watch `this video <https://nvidia.github.io/NeMo/>`_ for a quick walk-through. 41 42 * Documentation (latest released version): https://nvidia.github.io/NeMo/ 43 44 * Read NVIDIA `Developer Blog for example applications <https://devblogs.nvidia.com/how-to-build-domain-specific-automatic-speech-recognition-models-on-gpus/>`_ 45 46 * Read NVIDIA `Developer Blog for Quartznet ASR model <https://devblogs.nvidia.com/develop-smaller-speech-recognition-models-with-nvidias-nemo-framework/>`_ 47 48 * Recommended version to install is **0.9.0** via ``pip install nemo-toolkit`` 49 50 * Recommended NVIDIA `NGC NeMo Toolkit container <https://ngc.nvidia.com/catalog/containers/nvidia:nemo>`_ 51 52 * Pretrained models are available on NVIDIA `NGC Model repository <https://ngc.nvidia.com/catalog/models?orderBy=modifiedDESC&query=nemo&quickFilter=models&filters=>`_ 53 54 55 Getting started 56 ~~~~~~~~~~~~~~~ 57 58 THE LATEST STABLE VERSION OF NeMo is **0.9.0** (Available via PIP). 59 60 **Requirements** 61 62 1) Python 3.6 or 3.7 63 2) PyTorch 1.4.* with GPU support 64 3) (optional, for best performance) NVIDIA APEX. Install from here: https://github.com/NVIDIA/apex 65 66 **NeMo Docker Container** 67 NVIDIA `NGC NeMo Toolkit container <https://ngc.nvidia.com/catalog/containers/nvidia:nemo>`_ is now available. 68 69 * Pull the docker: ``docker pull nvcr.io/nvidia/nemo:v0.9`` 70 * Run: ``docker run --runtime=nvidia -it --rm -v <nemo_github_folder>:/NeMo --shm-size=8g -p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/nemo:v0.9`` 71 72 If you are using the NVIDIA `NGC PyTorch container <https://ngc.nvidia.com/catalog/containers/nvidia:pytorch>`_ follow these instructions 73 74 * Pull the docker: ``docker pull nvcr.io/nvidia/pytorch:20.01-py3`` 75 * Run: ``docker run --runtime=nvidia -it --rm -v <nemo_github_folder>:/NeMo --shm-size=8g -p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/pytorch:20.01-py3`` 76 * ``apt-get update && apt-get install -y libsndfile1`` 77 * ``pip install nemo_toolkit`` NeMo core 78 * ``pip install nemo_asr`` NeMo ASR (Speech Recognition) collection 79 * ``pip install nemo_nlp`` NeMo NLP (Natural Language Processing) collection 80 * ``pip install nemo_tts`` NeMo TTS (Speech Synthesis) collection 81 82 See `examples/start_here` to get started with the simplest example. The folder `examples` contains several examples to get you started with various tasks in NLP and ASR. 83 84 **Tutorials** 85 86 * `Speech recognition <https://nvidia.github.io/NeMo/asr/intro.html>`_ 87 * `Natural language processing <https://nvidia.github.io/NeMo/nlp/intro.html>`_ 88 * `Speech Synthesis <https://nvidia.github.io/NeMo/tts/intro.html>`_ 89 90 91 DEVELOPMENT 92 ~~~~~~~~~~~ 93 If you'd like to use master branch and/or develop NeMo you can run "reinstall.sh" script. 94 95 `Documentation (master branch) <http://nemo-master-docs.s3-website.us-east-2.amazonaws.com/>`_. 96 97 **Installing From Github** 98 99 If you prefer to use NeMo's latest development version (from GitHub) follow the steps below: 100 101 1) Clone the repository ``git clone https://github.com/NVIDIA/NeMo.git`` 102 2) Go to NeMo folder and re-install the toolkit with collections: 103 104 .. code-block:: bash 105 106 ./reinstall.sh 107 108 **Style tests** 109 110 .. code-block:: bash 111 112 python setup.py style # Checks overall project code style and output issues with diff. 113 python setup.py style --fix # Tries to fix error in-place. 114 python setup.py style --scope=tests # Operates within certain scope (dir of file). 115 116 **Unittests** 117 118 This command runs unittests: 119 120 .. code-block:: bash 121 122 ./reinstall.sh 123 python pytest tests 124 125 126 Citation 127 ~~~~~~~~ 128 129 If you are using NeMo please cite the following publication 130 131 .. code-block:: tex 132 133 @misc{nemo2019, 134 title={NeMo: a toolkit for building AI applications using Neural Modules}, 135 author={Oleksii Kuchaiev and Jason Li and Huyen Nguyen and Oleksii Hrinchuk and Ryan Leary and Boris Ginsburg and Samuel Kriman and Stanislav Beliaev and Vitaly Lavrukhin and Jack Cook and Patrice Castonguay and Mariya Popova and Jocelyn Huang and Jonathan M. Cohen}, 136 year={2019}, 137 eprint={1909.09577}, 138 archivePrefix={arXiv}, 139 primaryClass={cs.LG} 140 } 141 142 [end of README.rst] [start of nemo/core/neural_modules.py] ... 379 def input_ports(self) -> Optional[Dict[str, NeuralType]]: 380 """Returns definitions of module input ports 381 382 Returns: 383 A (dict) of module's input ports names to NeuralTypes mapping 384 """ 385 386 @property 387 @abstractmethod 388 def output_ports(self) -> Optional[Dict[str, NeuralType]]: 389 """Returns definitions of module output ports 390 391 Returns: 392 A (dict) of module's output ports names to NeuralTypes mapping 393 """ 394 395 @property 396 def disabled_deployment_input_ports(self) -> Optional[Set[str]]: 397 """Returns names of input ports that will not be included in an export 398 399 Returns: 400 A (set) of module's input port names that are not exportable ... ... 388 def output_ports(self) -> Optional[Dict[str, NeuralType]]: 389 """Returns definitions of module output ports 390 391 Returns: 392 A (dict) of module's output ports names to NeuralTypes mapping 393 """ 394 395 @property 396 def disabled_deployment_input_ports(self) -> Optional[Set[str]]: 397 """Returns names of input ports that will not be included in an export 398 399 Returns: 400 A (set) of module's input port names that are not exportable 401 """ 402 return set([]) 403 404 @property 405 def disabled_deployment_output_ports(self) -> Optional[Set[str]]: 406 """Returns names of output ports that will not be included in an export 407 408 Returns: 409 A (set) of module's output port names that are not exportable ... ... 396 def disabled_deployment_input_ports(self) -> Optional[Set[str]]: 397 """Returns names of input ports that will not be included in an export 398 399 Returns: 400 A (set) of module's input port names that are not exportable 401 """ 402 return set([]) 403 404 @property 405 def disabled_deployment_output_ports(self) -> Optional[Set[str]]: 406 """Returns names of output ports that will not be included in an export 407 408 Returns: 409 A (set) of module's output port names that are not exportable 410 """ 411 return set([]) 412 413 def prepare_for_deployment(self) -> None: 414 """Patch the module if required to prepare for deployment 415 416 """ 417 return ... [end of nemo/core/neural_modules.py] [start of nemo/backends/pytorch/actions.py] ... 923 @staticmethod 924 def __module_export(module, output, d_format: DeploymentFormat, input_example=None, output_example=None): 925 # Check if output already exists 926 destination = Path(output) 927 if destination.exists(): 928 raise FileExistsError(f"Destination {output} already exists. " f"Aborting export.") 929 930 input_names = list(module.input_ports.keys()) 931 output_names = list(module.output_ports.keys()) 932 dynamic_axes = defaultdict(list) 933 934 def __extract_dynamic_axes(port_name: str, ntype: NeuralType, dynamic_axes: defaultdict): 935 if ntype.axes: 936 for ind, axis in enumerate(ntype.axes): 937 if axis.kind == AxisKind.Batch or axis.kind == AxisKind.Time: 938 dynamic_axes[port_name].append(ind) 939 940 # This is a hack for Jasper to Jarvis export -- need re-design for this 941 inputs_to_drop = set() 942 outputs_to_drop = set() 943 if type(module).__name__ == "JasperEncoder": 944 logging.info( 945 "Module is JasperEncoder. We are removing input and output length ports since they are not needed for " 946 "deployment" 947 ) 948 inputs_to_drop.add("length") 949 outputs_to_drop.add("encoded_lengths") 950 951 # for input_ports 952 for port_name, ntype in module.input_ports.items(): 953 if port_name in inputs_to_drop: 954 input_names.remove(port_name) 955 continue 956 __extract_dynamic_axes(port_name, ntype, dynamic_axes) 957 # for output_ports 958 for port_name, ntype in module.output_ports.items(): 959 if port_name in outputs_to_drop: 960 output_names.remove(port_name) 961 continue 962 __extract_dynamic_axes(port_name, ntype, dynamic_axes) 963 ... [end of nemo/backends/pytorch/actions.py] [start of nemo/collections/asr/jasper.py] ... 104 } 105 106 @property 107 @add_port_docs() 108 def output_ports(self): 109 """Returns definitions of module output ports. 110 """ 111 return { 112 # "outputs": NeuralType( 113 # {0: AxisType(BatchTag), 1: AxisType(EncodedRepresentationTag), 2: AxisType(ProcessedTimeTag),} 114 # ), 115 # "encoded_lengths": NeuralType({0: AxisType(BatchTag)}), 116 "outputs": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), 117 "encoded_lengths": NeuralType(tuple('B'), LengthsType()), 118 } 119 120 @property 121 def disabled_deployment_input_ports(self): 122 return set(["length"]) 123 124 @property 125 def disabled_deployment_output_ports(self): 126 return set(["encoded_lengths"]) 127 128 def prepare_for_deployment(self): 129 m_count = 0 130 for m in self.modules(): 131 if type(m).__name__ == "MaskedConv1d": 132 m.use_mask = False ... [end of nemo/collections/asr/jasper.py] [start of nemo/core/neural_factory.py] ... 596 raise TypeError(f"All callbacks passed to the eval action must" f"be inherited from EvaluatorCallback") 597 self.train( 598 tensors_to_optimize=None, optimizer='sgd', callbacks=callbacks, optimization_params={'num_epochs': 1}, 599 ) 600 601 def deployment_export( 602 self, module, output: str, d_format: DeploymentFormat, input_example=None, output_example=None 603 ): 604 """Exports Neural Module instance for deployment. 605 606 Args: 607 module: neural module to export 608 output (str): where export results should be saved 609 d_format (DeploymentFormat): which deployment format to use 610 input_example: sometimes tracing will require input examples 611 output_example: Should match inference on input_example 612 """ 613 module.prepare_for_deployment() 614 615 return self._trainer.deployment_export( 616 module=module, 617 output=output, ... [end of nemo/core/neural_factory.py] </code> I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format. <patch> --- a/file.py +++ b/file.py @@ -1,27 +1,35 @@ def euclidean(a, b): - while b: - a, b = b, a % b - return a + if b == 0: + return a + return euclidean(b, a % b) def bresenham(x0, y0, x1, y1): points = [] dx = abs(x1 - x0) dy = abs(y1 - y0) - sx = 1 if x0 < x1 else -1 - sy = 1 if y0 < y1 else -1 - err = dx - dy + x, y = x0, y0 + sx = -1 if x0 > x1 else 1 + sy = -1 if y0 > y1 else 1 - while True: - points.append((x0, y0)) - if x0 == x1 and y0 == y1: - break - e2 = 2 * err - if e2 > -dy: + if dx > dy: + err = dx / 2.0 + while x != x1: + points.append((x, y)) err -= dy - x0 += sx - if e2 < dx: - err += dx - y0 += sy + if err < 0: + y += sy + err += dx + x += sx + else: + err = dy / 2.0 + while y != y1: + points.append((x, y)) + err -= dx + if err < 0: + x += sx + err += dy + y += sy + points.append((x, y)) return points </patch>

NVIDIA/NeMo

ba4616f1f011d599de87f0cb3315605e715d402a

Jasper Encoder Export failed The export of Jasper Encoder is failing. I am using the core API [deployment_export](https://nvidia.github.io/NeMo/api-docs/nemo.html#nemo.core.neural_factory.NeuralModuleFactory.deployment_export) like in the script: https://github.com/NVIDIA/NeMo/blob/403238f82d26879ba5fca53fbf75b3cdc70fb49b/scripts/export_jasper_to_onnx.py#L92 I believe the issue (as shown below) is that the` input_example` provided does not match the `output_example`. ``` /opt/conda/lib/python3.6/site-packages/torch/jit/__init__.py:1023: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error: Not within tolerance rtol=1e-05 atol=1e-05 at input[0, 870, 67] (0.6547648906707764 vs. 0.6546438932418823) and 812 other locations (0.00%) check_tolerance, _force_outplace, True, _module_class) [NeMo E 2020-02-23 19:10:07 actions:1023] module export failed for JasperEncoder with exception number of output names provided (2) exceeded number of outputs (1) ``` **What is the correct `input_example` and `output_example` to export JasperEncoder?** The full output can be seen here: ``` adrianaf@2a520c7abb1e:/tmp/NeMo$ ! python /tmp/NeMo/scripts/export_jasper_to_onnx.py --config /raid/datasets/asr/data/config_files/WSJ-test_acoustic_quartznet15x5.yaml --nn_encoder /home/adrianaf/projects/nemo_asr_app/models/quartznet15x5/JasperEncoder-STEP-247400.pt --nn_decoder /home/adrianaf/projects/nemo_asr_app/models/quartznet15x5/JasperDecoderForCTC-STEP-247400.pt --onnx_encoder /raid/datasets/asr/data/models/ONNX/pre-trained_encoder.onnx --onnx_decoder /raid/datasets/asr/data/models/ONNX/pre-trained_decoder.onnx /opt/conda/lib/python3.6/site-packages/torchvision/io/_video_opt.py:17: UserWarning: video reader based on ffmpeg c++ ops not available warnings.warn("video reader based on ffmpeg c++ ops not available") /tmp/NeMo/nemo/collections/asr/audio_preprocessing.py:48: UserWarning: Could not import torchaudio. Some features might not work. warnings.warn('Could not import torchaudio. Some features might not work.') [NeMo I 2020-02-23 19:09:42 export_jasper_to_onnx:48] Loading config file... [NeMo I 2020-02-23 19:09:42 export_jasper_to_onnx:52] Determining model shape... [NeMo I 2020-02-23 19:09:42 export_jasper_to_onnx:60] Num encoder input features: 64 [NeMo I 2020-02-23 19:09:42 export_jasper_to_onnx:61] Num decoder input features: 1024 [NeMo W 2020-02-23 19:09:42 deprecated:68] Function ``_get_trainer`` is deprecated. It is going to be removed in the future version. [NeMo I 2020-02-23 19:09:42 export_jasper_to_onnx:65] Initializing models... [NeMo I 2020-02-23 19:09:45 export_jasper_to_onnx:76] Loading checkpoints... [NeMo I 2020-02-23 19:09:45 export_jasper_to_onnx:91] Exporting encoder... [NeMo W 2020-02-23 19:09:45 neural_factory:627] Turned off 170 masked convolutions [NeMo I 2020-02-23 19:09:45 actions:937] Module is JasperEncoder. We are removing input and output length ports since they are not needed for deployment [NeMo W 2020-02-23 19:09:46 deprecated:68] Function ``local_parameters`` is deprecated. It is going to be removed in the 0.11 version. /opt/conda/lib/python3.6/site-packages/torch/jit/__init__.py:1023: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error: Not within tolerance rtol=1e-05 atol=1e-05 at input[0, 870, 67] (0.6547648906707764 vs. 0.6546438932418823) and 812 other locations (0.00%) check_tolerance, _force_outplace, True, _module_class) [NeMo E 2020-02-23 19:10:07 actions:1023] module export failed for JasperEncoder with exception number of output names provided (2) exceeded number of outputs (1) [NeMo I 2020-02-23 19:10:07 export_jasper_to_onnx:98] Exporting decoder... graph(%encoder_output : Float(1, 1024, 128), %1 : Float(29, 1024, 1), %2 : Float(29)): %3 : Float(1, 29, 128) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%encoder_output, %1, %2), scope: JasperDecoderForCTC/Sequential[decoder_layers]/Conv1d[0] # /opt/conda/lib/python3.6/site-packages/torch/nn/modules/conv.py:202:0 %4 : Float(1, 128, 29) = onnx::Transpose[perm=[0, 2, 1]](%3), scope: JasperDecoderForCTC # /tmp/NeMo/nemo/collections/asr/jasper.py:235:0 %output : Float(1, 128, 29) = onnx::LogSoftmax[axis=2](%4), scope: JasperDecoderForCTC # /opt/conda/lib/python3.6/site-packages/torch/nn/functional.py:1317:0 return (%output) /opt/conda/lib/python3.6/site-packages/torch/onnx/utils.py:774: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input encoder_output 'Automatically generated names will be applied to each dynamic axes of input {}'.format(key)) /opt/conda/lib/python3.6/site-packages/torch/onnx/utils.py:774: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input output 'Automatically generated names will be applied to each dynamic axes of input {}'.format(key)) [NeMo I 2020-02-23 19:10:07 export_jasper_to_onnx:105] Export completed successfully. ```

2020-03-10T03:03:23Z

<patch> <patch> diff --git a/nemo/backends/pytorch/actions.py b/nemo/backends/pytorch/actions.py --- a/nemo/backends/pytorch/actions.py +++ b/nemo/backends/pytorch/actions.py @@ -937,26 +937,16 @@ def __extract_dynamic_axes(port_name: str, ntype: NeuralType, dynamic_axes: defa if axis.kind == AxisKind.Batch or axis.kind == AxisKind.Time: dynamic_axes[port_name].append(ind) - # This is a hack for Jasper to Jarvis export -- need re-design for this - inputs_to_drop = set() - outputs_to_drop = set() - if type(module).__name__ == "JasperEncoder": - logging.info( - "Module is JasperEncoder. We are removing input and output length ports since they are not needed for " - "deployment" - ) - inputs_to_drop.add("length") - outputs_to_drop.add("encoded_lengths") - + # extract dynamic axes and remove unnecessary inputs/outputs # for input_ports for port_name, ntype in module.input_ports.items(): - if port_name in inputs_to_drop: + if port_name in module._disabled_deployment_input_ports: input_names.remove(port_name) continue __extract_dynamic_axes(port_name, ntype, dynamic_axes) # for output_ports for port_name, ntype in module.output_ports.items(): - if port_name in outputs_to_drop: + if port_name in module._disabled_deployment_output_ports: output_names.remove(port_name) continue __extract_dynamic_axes(port_name, ntype, dynamic_axes) diff --git a/nemo/collections/asr/jasper.py b/nemo/collections/asr/jasper.py --- a/nemo/collections/asr/jasper.py +++ b/nemo/collections/asr/jasper.py @@ -118,14 +118,14 @@ def output_ports(self): } @property - def disabled_deployment_input_ports(self): + def _disabled_deployment_input_ports(self): return set(["length"]) @property - def disabled_deployment_output_ports(self): + def _disabled_deployment_output_ports(self): return set(["encoded_lengths"]) - def prepare_for_deployment(self): + def _prepare_for_deployment(self): m_count = 0 for m in self.modules(): if type(m).__name__ == "MaskedConv1d": diff --git a/nemo/core/neural_factory.py b/nemo/core/neural_factory.py --- a/nemo/core/neural_factory.py +++ b/nemo/core/neural_factory.py @@ -610,7 +610,7 @@ def deployment_export( input_example: sometimes tracing will require input examples output_example: Should match inference on input_example """ - module.prepare_for_deployment() + module._prepare_for_deployment() return self._trainer.deployment_export( module=module, diff --git a/nemo/core/neural_modules.py b/nemo/core/neural_modules.py --- a/nemo/core/neural_modules.py +++ b/nemo/core/neural_modules.py @@ -393,7 +393,7 @@ def output_ports(self) -> Optional[Dict[str, NeuralType]]: """ @property - def disabled_deployment_input_ports(self) -> Optional[Set[str]]: + def _disabled_deployment_input_ports(self) -> Optional[Set[str]]: """Returns names of input ports that will not be included in an export Returns: @@ -402,7 +402,7 @@ def disabled_deployment_input_ports(self) -> Optional[Set[str]]: return set([]) @property - def disabled_deployment_output_ports(self) -> Optional[Set[str]]: + def _disabled_deployment_output_ports(self) -> Optional[Set[str]]: """Returns names of output ports that will not be included in an export Returns: @@ -410,7 +410,7 @@ def disabled_deployment_output_ports(self) -> Optional[Set[str]]: """ return set([]) - def prepare_for_deployment(self) -> None: + def _prepare_for_deployment(self) -> None: """Patch the module if required to prepare for deployment """ </patch> </s> </patch>

diff --git a/tests/unit/core/test_deploy_export.py b/tests/unit/core/test_deploy_export.py --- a/tests/unit/core/test_deploy_export.py +++ b/tests/unit/core/test_deploy_export.py @@ -46,9 +46,11 @@ import nemo.collections.nlp.nm.trainables.common.token_classification_nm from nemo import logging +TRT_ONNX_DISABLED = False + # Check if the required libraries and runtimes are installed. +# Only initialize GPU after this runner is activated. try: - # Only initialize GPU after this runner is activated. import pycuda.autoinit # This import causes pycuda to automatically manage CUDA context creation and cleanup. @@ -63,16 +65,17 @@ ) from .tensorrt_runner import TensorRTRunnerV2 except: - # Skip tests. - pytestmark = pytest.mark.skip + TRT_ONNX_DISABLED = True @pytest.mark.usefixtures("neural_factory") class TestDeployExport(TestCase): - def setUp(self): - logging.setLevel(logging.WARNING) - device = nemo.core.DeviceType.GPU - self.nf = nemo.core.NeuralModuleFactory(backend=nemo.core.Backend.PyTorch, placement=device) + # def setUp(self): + # super().setUp() + + # logging.setLevel(logging.WARNING) + # device = nemo.core.DeviceType.GPU + # self.nf = nemo.core.NeuralModuleFactory(backend=nemo.core.Backend.PyTorch, placement=device) def __test_export_route(self, module, out_name, mode, input_example=None): out = Path(out_name) @@ -112,7 +115,13 @@ def __test_export_route(self, module, out_name, mode, input_example=None): loader_cache = DataLoaderCache(data_loader) profile_shapes = OrderedDict() names = list(module.input_ports) + list(module.output_ports) - + names = list( + filter( + lambda x: x + not in (module._disabled_deployment_input_ports | module._disabled_deployment_output_ports), + names, + ) + ) if isinstance(input_example, tuple): si = [tuple(input_example[i].shape) for i in range(len(input_example))] elif isinstance(input_example, OrderedDict): @@ -152,7 +161,7 @@ def __test_export_route(self, module, out_name, mode, input_example=None): input_names = list(input_metadata.keys()) for i in range(len(input_names)): input_name = input_names[i] - if input_name in module.disabled_deployment_input_ports: + if input_name in module._disabled_deployment_input_ports: continue inputs[input_name] = ( input_example[input_name].cpu().numpy() @@ -209,8 +218,8 @@ def __test_export_route(self, module, out_name, mode, input_example=None): ort_inputs = ort_session.get_inputs() for i in range(len(input_names)): input_name = input_names[i] - if input_name in module.disabled_deployment_input_ports: - input_name = ort_inputs[i].name + if input_name in module._disabled_deployment_input_ports: + continue inputs[input_name] = ( input_example[input_name].cpu().numpy() if isinstance(input_example, OrderedDict) @@ -263,9 +272,10 @@ def __test_export_route(self, module, out_name, mode, input_example=None): def __test_export_route_all(self, module, out_name, input_example=None): if input_example is not None: - self.__test_export_route( - module, out_name + '.trt.onnx', nemo.core.DeploymentFormat.TRTONNX, input_example=input_example - ) + if not TRT_ONNX_DISABLED: + self.__test_export_route( + module, out_name + '.trt.onnx', nemo.core.DeploymentFormat.TRTONNX, input_example=input_example + ) self.__test_export_route(module, out_name + '.onnx', nemo.core.DeploymentFormat.ONNX, input_example) self.__test_export_route(module, out_name + '.pt', nemo.core.DeploymentFormat.PYTORCH, input_example) self.__test_export_route(module, out_name + '.ts', nemo.core.DeploymentFormat.TORCHSCRIPT, input_example) @@ -323,9 +333,7 @@ def test_jasper_encoder(self): ) self.__test_export_route_all( - module=jasper_encoder, - out_name="jasper_encoder", - input_example=(torch.randn(16, 64, 256).cuda(), torch.randn(256).cuda()), + module=jasper_encoder, out_name="jasper_encoder", input_example=torch.randn(16, 64, 256).cuda(), ) @pytest.mark.unit @@ -343,7 +351,5 @@ def test_quartz_encoder(self): ) self.__test_export_route_all( - module=jasper_encoder, - out_name="quartz_encoder", - input_example=(torch.randn(16, 64, 256).cuda(), torch.randint(20, (16,)).cuda()), + module=jasper_encoder, out_name="quartz_encoder", input_example=torch.randn(16, 64, 256).cuda(), )

1.0

NVIDIA__NeMo-3632

"You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED)

NVIDIA/NeMo

022f0292aecbc98d591d49423d5045235394f793

"./reinstall.sh crashes due to not being able to uninstall llvmlite\nStarting off of `nemo:1.5.1` co(...TRUNCATED)

2022-02-09T05:12:31Z

"<patch>\n<patch>\ndiff --git a/nemo_text_processing/text_normalization/__init__.py b/nemo_text_proc(...TRUNCATED)

"diff --git a/tests/nemo_text_processing/es/data_text_normalization/test_cases_cardinal.txt b/tests/(...TRUNCATED)

1.0

NVIDIA__NeMo-7582

"You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED)

NVIDIA/NeMo

8a892b86186dbdf61803d75570cb5c58471e9dda

"Ubuntu 22.04 Python 3.11 [asr]: multiple errors `dataclasses ValueError: mutable default * for fiel(...TRUNCATED)

"Seems to be a similar to #7002\nInteresting. The fix is easy but needs to be applied to basically e(...TRUNCATED)

2023-09-30T01:26:50Z

"<patch>\n<patch>\ndiff --git a/examples/asr/experimental/k2/align_speech_parallel.py b/examples/asr(...TRUNCATED)

"diff --git a/tests/collections/asr/test_text_to_text_dataset.py b/tests/collections/asr/test_text_t(...TRUNCATED)

1.0

NVIDIA__NeMo-7616

"You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED)

NVIDIA/NeMo

15db83ec4a65e649d83b61d7a4a58d911586e853

"Ubuntu 22.04 Python 3.11 [asr]: multiple errors `dataclasses ValueError: mutable default * for fiel(...TRUNCATED)

"Seems to be a similar to #7002\nInteresting. The fix is easy but needs to be applied to basically e(...TRUNCATED)

2023-10-03T19:14:38Z

"<patch>\n<patch>\ndiff --git a/examples/asr/experimental/k2/align_speech_parallel.py b/examples/asr(...TRUNCATED)

"diff --git a/tests/collections/asr/test_text_to_text_dataset.py b/tests/collections/asr/test_text_t(...TRUNCATED)

1.0

slackapi__python-slack-events-api-71

"You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED)

slackapi/python-slack-events-api

0c0ce604b502508622fb14c278a0d64841fa32e3

"Passing Flask app proxy as server\nHi Guys,\r\n\r\nI have an app factory on my setup and the app ob(...TRUNCATED)

2020-06-12T06:58:10Z

"<patch>\n<patch>\ndiff --git a/example/current_app/main.py b/example/current_app/main.py\nnew file (...TRUNCATED)

"diff --git a/example/current_app/test_module/__init__.py b/example/current_app/test_module/__init__(...TRUNCATED)

1.0

celery__celery-2598

"You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED)

celery/celery

6592ff64b6b024a4b68abcc53b151888fdf0dee3

"CELERY_RESULT_SERIALIZER = 'json' breaks Exception marshaling\nSetting `CELERY_RESULT_SERIALIZER = (...TRUNCATED)

This is biting me as well. Any news?

2015-04-29T14:52:17Z

"<patch>\n<patch>\ndiff --git a/celery/backends/amqp.py b/celery/backends/amqp.py\n--- a/celery/back(...TRUNCATED)

"diff --git a/celery/tests/backends/test_amqp.py b/celery/tests/backends/test_amqp.py\n--- a/celery/(...TRUNCATED)

1.0

celery__celery-2840

"You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED)

celery/celery

045b52f1450d6d5cc500e0057a4b498250dc5692

"Message being acknowledged on WorkerLostError when CELERY_ACKS_LATE=True\nWhen using celery v3.0.24(...TRUNCATED)

"This is deliberate as if a task is killed it may mean that the next invocation will also cause the (...TRUNCATED)

2015-10-06T05:34:34Z

"<patch>\n<patch>\ndiff --git a/celery/app/defaults.py b/celery/app/defaults.py\n--- a/celery/app/de(...TRUNCATED)

"diff --git a/celery/tests/worker/test_request.py b/celery/tests/worker/test_request.py\n--- a/celer(...TRUNCATED)

1.0

NVIDIA__NeMo-473

"You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED)

NVIDIA/NeMo

ba4616f1f011d599de87f0cb3315605e715d402a

"Jasper Encoder Export failed\nThe export of Jasper Encoder is failing. I am using the core API [dep(...TRUNCATED)

2020-03-10T03:03:23Z

"<patch>\n<patch>\ndiff --git a/nemo/backends/pytorch/actions.py b/nemo/backends/pytorch/actions.py\(...TRUNCATED)

"diff --git a/tests/unit/core/test_deploy_export.py b/tests/unit/core/test_deploy_export.py\n--- a/t(...TRUNCATED)

1.0

NVIDIA__NeMo-3632

"You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED)

NVIDIA/NeMo

022f0292aecbc98d591d49423d5045235394f793

"./reinstall.sh crashes due to not being able to uninstall llvmlite\nStarting off of `nemo:1.5.1` co(...TRUNCATED)

2022-02-09T05:12:31Z

"<patch>\n<patch>\ndiff --git a/nemo_text_processing/text_normalization/__init__.py b/nemo_text_proc(...TRUNCATED)

"diff --git a/tests/nemo_text_processing/es/data_text_normalization/test_cases_cardinal.txt b/tests/(...TRUNCATED)

1.0