Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 357991,7 "name": "",8 "username": "Joysn",9 "avatar_template": "/letter_avatar_proxy/v4/letter/j/77aa72/{size}.png",10 "created_at": "2022-07-24T13:04:53.349Z",11 "cooked": "<p>I am developing with torchvision and its infrastructure, like Dataset and Dataloader. During the last months on Win10 there were no issues with that code and how I used it.<br>\nYesterday I moved to a fresh Linux installation and setup the whole env, like CUDA, Python 3.9 and the packages, the requirements.txt created from the venv I used on the Win10 workstation. The GPU memory (2GB) is not sufficient anymore, even when using a batch size of 1 on the dataloaders.<br>\nBut the strangest things happen with the multiprocessing dataloaders. Regardless if I use only one worker or multiples (4). In that code snippet the timeout is reached almost all the time. Only sometimes a record is returned by the call <code>self._data_queue.get(timeout=timeout)</code></p>\n<pre><code class=\"lang-auto\"> def _try_get_data(self, timeout=_utils.MP_STATUS_CHECK_INTERVAL):\n # Tries to fetch data from `self._data_queue` once for a given timeout.\n # This can also be used as inner loop of fetching without timeout, with\n # the sender status as the loop condition.\n #\n # This raises a `RuntimeError` if any worker died expectedly. This error\n # can come from either the SIGCHLD handler in `_utils/signal_handling.py`\n # (only for non-Windows platforms), or the manual check below on errors\n # and timeouts.\n #\n # Returns a 2-tuple:\n # (bool: whether successfully get data, any: data if successful else None)\n try:\n data = self._data_queue.get(timeout=timeout)\n return (True, data)\n except Exception as e:\n # At timeout and error, we manually check whether any worker has\n # failed. Note that this is the only mechanism for Windows to detect\n # worker failures.\n failed_workers = []\n for worker_id, w in enumerate(self._workers):\n if self._workers_status[worker_id] and not w.is_alive():\n failed_workers.append(w)\n self._mark_worker_as_unavailable(worker_id)\n if len(failed_workers) > 0:\n pids_str = ', '.join(str(w.pid) for w in failed_workers)\n raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str)) from e\n if isinstance(e, queue.Empty):\n return (False, None)\n import tempfile\n import errno\n try:\n # Raise an exception if we are this close to the FDs limit.\n # Apparently, trying to open only one file is not a sufficient\n # test.\n # See NOTE [ DataLoader on Linux and open files limit ]\n fds_limit_margin = 10\n fs = [tempfile.NamedTemporaryFile() for i in range(fds_limit_margin)]\n except OSError as e:\n if e.errno == errno.EMFILE:\n raise RuntimeError(\n \"Too many open files. Communication with the\"\n \" workers is no longer possible. Please increase the\"\n \" limit using `ulimit -n` in the shell or change the\"\n \" sharing strategy by calling\"\n \" `torch.multiprocessing.set_sharing_strategy('file_system')`\"\n \" at the beginning of your code\") from None\n raise\n</code></pre>\n<p>I started to debug into the details, and what I see is that the Dataset (a Subset) is properly set in the MultiProcessingDataloaderIter:</p>\n<p>This is how i setup the dataloaders:</p>\n<pre><code class=\"lang-auto\"> # split the dataset in train and test set\n train_size = int(len(dataset) * train_val_split)\n val_size = len(dataset) - train_size\n train_set, val_set = torch.utils.data.random_split(dataset, [train_size, val_size])\n\n # define training and validation data loaders\n data_loader_train = torch.utils.data.DataLoader(\n train_set, batch_size=config['batch_size'], shuffle=True, num_workers=4,\n collate_fn=utils.collate_fn)\n\n data_loader_val = torch.utils.data.DataLoader(\n val_set, batch_size=config['batch_size'], shuffle=False, num_workers=1,\n collate_fn=utils.collate_fn)\n</code></pre>\n<p>I can observe that from start a timeout is reached three times in a row, and then the first record is returned from the dataset. But then I continue getting timeouts after a view records have been returned. And this pattern continues so that training is not feasible, due to the enormous delays.</p>\n<p>Why is the behavior with Linux different? When I set num_workers to 0, then the blocking is gone.</p>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 1,15 "updated_at": "2022-07-24T13:04:53.349Z",16 "reply_count": 0,17 "reply_to_post_number": null,18 "quote_count": 0,19 "incoming_link_count": 540,20 "reads": 16,21 "readers_count": 15,22 "score": 2703.2,23 "yours": false,24 "topic_id": 157365,25 "topic_slug": "dataloader-hangs-during-training",26 "display_username": "",27 "primary_group_name": null,28 "flair_name": null,29 "flair_url": null,30 "flair_bg_color": null,31 "flair_color": null,32 "flair_group_id": null,33 "badges_granted": [],34 "version": 1,35 "can_edit": false,36 "can_delete": false,37 "can_recover": false,38 "can_see_hidden_post": false,39 "can_wiki": false,40 "read": true,41 "user_title": null,42 "bookmarked": false,43 "actions_summary": [],44 "moderator": false,45 "admin": false,46 "staff": false,47 "user_id": 57335,48 "hidden": false,49 "trust_level": 1,50 "deleted_at": null,51 "user_deleted": 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"hidden": false,379 "can_act": false380 }381 ],382 "chunk_size": 20,383 "bookmarked": false,384 "topic_timer": null,385 "message_bus_last_id": 0,386 "participant_count": 1,387 "show_read_indicator": false,388 "thumbnails": null,389 "slow_mode_enabled_until": null,390 "can_vote": false,391 "vote_count": 0,392 "user_voted": false,393 "discourse_zendesk_plugin_zendesk_id": null,394 "discourse_zendesk_plugin_zendesk_url": "https://your-url.zendesk.com/agent/tickets/",395 "details": {396 "can_edit": false,397 "notification_level": 1,398 "participants": [399 {400 "id": 57335,401 "username": "Joysn",402 "name": "",403 "avatar_template": "/letter_avatar_proxy/v4/letter/j/77aa72/{size}.png",404 "post_count": 1,405 "primary_group_name": null,406 "flair_name": null,407 "flair_url": null,408 "flair_color": null,409 "flair_bg_color": null,410 "flair_group_id": null,411 "trust_level": 1412 }413 ],414 "created_by": {415 "id": 57335,416 "username": "Joysn",417 "name": "",418 "avatar_template": "/letter_avatar_proxy/v4/letter/j/77aa72/{size}.png"419 },420 "last_poster": {421 "id": 57335,422 "username": "Joysn",423 "name": "",424 "avatar_template": "/letter_avatar_proxy/v4/letter/j/77aa72/{size}.png"425 }426 },427 "bookmarks": []428 },429 {430 "post_stream": {431 "posts": [432 {433 "id": 325579,434 "name": "Prerna Khanna",435 "username": "prerna",436 "avatar_template": "/letter_avatar_proxy/v4/letter/p/eada6e/{size}.png",437 "created_at": "2022-01-08T15:16:42.556Z",438 "cooked": "<p>Hi,</p>\n<p>I have a custom PyTorch model, the inputs are of the model as like this:<br>\nmodel(torch.randint(0, 10,(1,1)), torch.rand(1, 51, 2048), torch.randint(0, 10,(1, 1))).<br>\nI am trying to speed benchmark this model with Android, but I am unable to understand how I should use the “input_dims” and “input_type” parameters as this model involves a mix of float and integer types.</p>\n<p>Thanks!</p>",439 "post_number": 1,440 "post_type": 1,441 "posts_count": 4,442 "updated_at": "2022-01-08T15:16:42.556Z",443 "reply_count": 0,444 "reply_to_post_number": null,445 "quote_count": 0,446 "incoming_link_count": 114,447 "reads": 15,448 "readers_count": 14,449 "score": 573.0,450 "yours": false,451 "topic_id": 141120,452 "topic_slug": "input-dims-and-input-type-for-speed-benchmarking-on-android",453 "display_username": "Prerna Khanna",454 "primary_group_name": null,455 "flair_name": null,456 "flair_url": null,457 "flair_bg_color": null,458 "flair_color": null,459 "flair_group_id": null,460 "badges_granted": [],461 "version": 1,462 "can_edit": false,463 "can_delete": false,464 "can_recover": false,465 "can_see_hidden_post": false,466 "can_wiki": false,467 "read": true,468 "user_title": null,469 "bookmarked": false,470 "actions_summary": [],471 "moderator": false,472 "admin": false,473 "staff": false,474 "user_id": 52200,475 "hidden": false,476 "trust_level": 1,477 "deleted_at": null,478 "user_deleted": false,479 "edit_reason": null,480 "can_view_edit_history": true,481 "wiki": false,482 "post_url": "/t/input-dims-and-input-type-for-speed-benchmarking-on-android/141120/1",483 "can_accept_answer": false,484 "can_unaccept_answer": false,485 "accepted_answer": false,486 "topic_accepted_answer": null,487 "can_vote": false488 },489 {490 "id": 325647,491 "name": "Prerna Khanna",492 "username": "prerna",493 "avatar_template": "/letter_avatar_proxy/v4/letter/p/eada6e/{size}.png",494 "created_at": "2022-01-09T10:38:34.294Z",495 "cooked": "<p>An update:<br>\nI checked the speed_benchmark_torch.cc file. It specifies “If multiple input needed, use semicolon to separate the dimension of different tensors.”</p>\n<p>Therefore, I tried: --input_dims=“1,1;1,51,2048;1,1” --input_type=“int64;float;int64”.</p>\n<p>This results in error:<br>\nterminating with uncaught exception of type c10::Error: [enforce fail at speed_benchmark_torch.cc:111] input_dims_list.size() == input_type_list.size(). 1 vs 0. Input dims and type should have the same number of items.</p>\n<p>(no backtrace available)</p>\n<p>Aborted</p>",496 "post_number": 2,497 "post_type": 1,498 "posts_count": 4,499 "updated_at": "2022-01-09T10:38:57.020Z",500 "reply_count": 0,501 "reply_to_post_number": null,502 "quote_count": 0,503 "incoming_link_count": 3,504 "reads": 15,505 "readers_count": 14,506 "score": 18.0,507 "yours": false,508 "topic_id": 141120,509 "topic_slug": "input-dims-and-input-type-for-speed-benchmarking-on-android",510 "display_username": "Prerna Khanna",511 "primary_group_name": null,512 "flair_name": null,513 "flair_url": null,514 "flair_bg_color": null,515 "flair_color": null,516 "flair_group_id": null,517 "badges_granted": [],518 "version": 1,519 "can_edit": false,520 "can_delete": false,521 "can_recover": false,522 "can_see_hidden_post": false,523 "can_wiki": false,524 "read": true,525 "user_title": null,526 "bookmarked": false,527 "actions_summary": [],528 "moderator": false,529 "admin": false,530 "staff": false,531 "user_id": 52200,532 "hidden": false,533 "trust_level": 1,534 "deleted_at": null,535 "user_deleted": false,536 "edit_reason": null,537 "can_view_edit_history": true,538 "wiki": false,539 "post_url": "/t/input-dims-and-input-type-for-speed-benchmarking-on-android/141120/2",540 "can_accept_answer": false,541 "can_unaccept_answer": false,542 "accepted_answer": false,543 "topic_accepted_answer": null544 },545 {546 "id": 327213,547 "name": "Akshit Khurana",548 "username": "axitkhurana",549 "avatar_template": "/user_avatar/discuss.pytorch.org/axitkhurana/{size}/38375_2.png",550 "created_at": "2022-01-18T22:04:18.455Z",551 "cooked": "<p>Hi <a class=\"mention\" href=\"/u/prerna\">@prerna</a>, what’s the full command you are running?</p>",552 "post_number": 3,553 "post_type": 1,554 "posts_count": 4,555 "updated_at": "2022-01-18T22:04:18.455Z",556 "reply_count": 0,557 "reply_to_post_number": null,558 "quote_count": 0,559 "incoming_link_count": 3,560 "reads": 12,561 "readers_count": 11,562 "score": 17.4,563 "yours": false,564 "topic_id": 141120,565 "topic_slug": "input-dims-and-input-type-for-speed-benchmarking-on-android",566 "display_username": "Akshit Khurana",567 "primary_group_name": null,568 "flair_name": null,569 "flair_url": null,570 "flair_bg_color": null,571 "flair_color": null,572 "flair_group_id": null,573 "badges_granted": [],574 "version": 1,575 "can_edit": false,576 "can_delete": false,577 "can_recover": false,578 "can_see_hidden_post": false,579 "can_wiki": false,580 "read": true,581 "user_title": null,582 "bookmarked": false,583 "actions_summary": [],584 "moderator": false,585 "admin": false,586 "staff": false,587 "user_id": 45448,588 "hidden": false,589 "trust_level": 1,590 "deleted_at": null,591 "user_deleted": false,592 "edit_reason": null,593 "can_view_edit_history": true,594 "wiki": false,595 "post_url": "/t/input-dims-and-input-type-for-speed-benchmarking-on-android/141120/3",596 "can_accept_answer": false,597 "can_unaccept_answer": false,598 "accepted_answer": false,599 "topic_accepted_answer": null600 },601 {602 "id": 357985,603 "name": "Timofey Pushkin",604 "username": "TimPushkin",605 "avatar_template": "/user_avatar/discuss.pytorch.org/timpushkin/{size}/51734_2.png",606 "created_at": "2022-07-24T10:31:29.459Z",607 "cooked": "<p>I had the same problem. The solution is to put an escape character before <code>;</code>, so the parameters should look like <code>--input_dims=“1,2\\;1,2,3\\;5,6,7” --input_type=“float\\;float\\;uint8_t”</code> for example. Also, the script will require you to specify the memory format: <code>--input_memory_format=\"contiguous_format\\;contiguous_format\\;contiguous_format\"</code>, for example (<code>contiguous_format</code> is the default value).</p>",608 "post_number": 4,609 "post_type": 1,610 "posts_count": 4,611 "updated_at": "2022-07-24T10:49:03.640Z",612 "reply_count": 0,613 "reply_to_post_number": null,614 "quote_count": 0,615 "incoming_link_count": 0,616 "reads": 8,617 "readers_count": 7,618 "score": 1.6,619 "yours": false,620 "topic_id": 141120,621 "topic_slug": "input-dims-and-input-type-for-speed-benchmarking-on-android",622 "display_username": "Timofey Pushkin",623 "primary_group_name": null,624 "flair_name": null,625 "flair_url": null,626 "flair_bg_color": null,627 "flair_color": null,628 "flair_group_id": null,629 "badges_granted": [],630 "version": 2,631 "can_edit": false,632 "can_delete": false,633 "can_recover": false,634 "can_see_hidden_post": false,635 "can_wiki": false,636 "read": true,637 "user_title": null,638 "bookmarked": false,639 "actions_summary": [],640 "moderator": false,641 "admin": false,642 "staff": false,643 "user_id": 57931,644 "hidden": false,645 "trust_level": 1,646 "deleted_at": null,647 "user_deleted": false,648 "edit_reason": null,649 "can_view_edit_history": true,650 "wiki": false,651 "post_url": "/t/input-dims-and-input-type-for-speed-benchmarking-on-android/141120/4",652 "can_accept_answer": false,653 "can_unaccept_answer": false,654 "accepted_answer": false,655 "topic_accepted_answer": null656 }657 ],658 "stream": [659 325579,660 325647,661 327213,662 357985663 ]664 },665 "timeline_lookup": [666 [667 1,668 1386669 ],670 [671 2,672 1385673 ],674 [675 3,676 1376677 ],678 [679 4,680 1189681 ]682 ],683 "suggested_topics": [684 {685 "fancy_title": "Pytorch on Android App Open Source Code",686 "id": 212345,687 "title": "Pytorch on Android App Open Source Code",688 "slug": "pytorch-on-android-app-open-source-code",689 "posts_count": 4,690 "reply_count": 2,691 "highest_post_number": 4,692 "image_url": null,693 "created_at": "2024-10-31T05:01:09.173Z",694 "last_posted_at": "2024-11-01T05:45:54.334Z",695 "bumped": true,696 "bumped_at": "2024-11-01T05:45:54.334Z",697 "archetype": "regular",698 "unseen": false,699 "pinned": false,700 "unpinned": null,701 "visible": true,702 "closed": false,703 "archived": false,704 "bookmarked": null,705 "liked": null,706 "tags_descriptions": {},707 "like_count": 0,708 "views": 415,709 "category_id": 18,710 "featured_link": null,711 "has_accepted_answer": false,712 "posters": [713 {714 "extras": null,715 "description": "Original Poster",716 "user": {717 "id": 80605,718 "username": "hasan10242048",719 "name": "Hasan",720 "avatar_template": "/letter_avatar_proxy/v4/letter/h/db5fbb/{size}.png",721 "trust_level": 0722 }723 },724 {725 "extras": "latest",726 "description": "Most Recent Poster",727 "user": {728 "id": 78822,729 "username": "Mert_Oguzhan",730 "name": "Mert Oğuzhan",731 "avatar_template": "/user_avatar/discuss.pytorch.org/mert_oguzhan/{size}/72669_2.png",732 "trust_level": 1733 }734 }735 ]736 },737 {738 "fancy_title": "Xcode got error “Error loading model: Following ops cannot be found: [aten::scaled_dot_product_attention]. 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question about how to get the output of the hidden layer after each epoch because when I tried to do it under the callback, the last result will be covered by the newer one, hence I can only get the final result of the training process. Could you give me some idea about it?</p>",1154 "post_number": 1,1155 "post_type": 1,1156 "posts_count": 5,1157 "updated_at": "2022-07-23T19:14:58.415Z",1158 "reply_count": 0,1159 "reply_to_post_number": null,1160 "quote_count": 0,1161 "incoming_link_count": 34,1162 "reads": 5,1163 "readers_count": 4,1164 "score": 171.0,1165 "yours": false,1166 "topic_id": 157339,1167 "topic_slug": "how-to-save-the-hidden-layer-output-after-each-epoch",1168 "display_username": "Aaron Wang",1169 "primary_group_name": null,1170 "flair_name": null,1171 "flair_url": null,1172 "flair_bg_color": null,1173 "flair_color": null,1174 "flair_group_id": null,1175 "badges_granted": [],1176 "version": 1,1177 "can_edit": false,1178 "can_delete": false,1179 "can_recover": false,1180 "can_see_hidden_post": false,1181 "can_wiki": false,1182 "read": true,1183 "user_title": null,1184 "bookmarked": false,1185 "actions_summary": [],1186 "moderator": false,1187 "admin": false,1188 "staff": false,1189 "user_id": 57593,1190 "hidden": false,1191 "trust_level": 1,1192 "deleted_at": null,1193 "user_deleted": false,1194 "edit_reason": null,1195 "can_view_edit_history": true,1196 "wiki": false,1197 "post_url": "/t/how-to-save-the-hidden-layer-output-after-each-epoch/157339/1",1198 "can_accept_answer": false,1199 "can_unaccept_answer": false,1200 "accepted_answer": false,