Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 405303,7 "name": "Daniel Sabzi",8 "username": "Daniel_Sabzi",9 "avatar_template": "/user_avatar/discuss.pytorch.org/daniel_sabzi/{size}/45800_2.png",10 "created_at": "2023-06-08T09:46:05.872Z",11 "cooked": "<p>Hi everyone,<br>\nI have a transformer model with 10 % dropout at the positional encoding and 20% dropout at both encoder and decoder layers.<br>\nIt works well with this setting in both train and test sections<br>\nWhen I remove the dropout at positional encoding layer or increase it to 15% it still works well in the training section but after 60 epochs or so the encoder starts delivering nan values in test, while training is still working well.</p>\n<p>I have an output with sequence length of 1, and thus a tgt mask [1, 1] and an src mask [1, sequence_length], with no padding mask.</p>\n<p>Any idea what can cause nan values after 60 epochs? or its relationship between positional encoding dropout?</p>\n<p>Thanks</p>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 3,15 "updated_at": "2023-06-08T11:27:13.047Z",16 "reply_count": 0,17 "reply_to_post_number": null,18 "quote_count": 0,19 "incoming_link_count": 337,20 "reads": 12,21 "readers_count": 11,22 "score": 1682.4,23 "yours": false,24 "topic_id": 181717,25 "topic_slug": "after-around-60-epochs-the-encoder-starts-delivering-nan-values-in-transformer-model",26 "display_username": "Daniel Sabzi",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": 4,35 "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": 53564,48 "hidden": false,49 "trust_level": 1,50 "deleted_at": null,51 "user_deleted": false,52 "edit_reason": null,53 "can_view_edit_history": true,54 "wiki": false,55 "post_url": "/t/after-around-60-epochs-the-encoder-starts-delivering-nan-values-in-transformer-model/181717/1",56 "can_accept_answer": false,57 "can_unaccept_answer": false,58 "accepted_answer": false,59 "topic_accepted_answer": null,60 "can_vote": false61 },62 {63 "id": 405371,64 "name": "",65 "username": "ptrblck",66 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",67 "created_at": "2023-06-08T23:27:12.922Z",68 "cooked": "<p>Maybe you could try to use anomaly detection to raise an error if the first invalid gradient is created, which might then help isolating which layer failed first.<br>\nYou could use <a href=\"https://pytorch.org/docs/stable/autograd.html#torch.autograd.detect_anomaly\"><code>torch.autograd.detect_anomaly</code></a> and run the forward/backward pass in this context manager.</p>",69 "post_number": 2,70 "post_type": 1,71 "posts_count": 3,72 "updated_at": "2023-06-08T23:27:12.922Z",73 "reply_count": 1,74 "reply_to_post_number": null,75 "quote_count": 0,76 "incoming_link_count": 3,77 "reads": 12,78 "readers_count": 11,79 "score": 22.4,80 "yours": false,81 "topic_id": 181717,82 "topic_slug": "after-around-60-epochs-the-encoder-starts-delivering-nan-values-in-transformer-model",83 "display_username": "",84 "primary_group_name": null,85 "flair_name": null,86 "flair_url": null,87 "flair_bg_color": null,88 "flair_color": null,89 "flair_group_id": null,90 "badges_granted": [],91 "version": 1,92 "can_edit": false,93 "can_delete": false,94 "can_recover": false,95 "can_see_hidden_post": false,96 "can_wiki": false,97 "link_counts": [98 {99 "url": "https://pytorch.org/docs/stable/autograd.html#torch.autograd.detect_anomaly",100 "internal": false,101 "reflection": false,102 "title": "Automatic differentiation package - torch.autograd — PyTorch 2.0 documentation",103 "clicks": 28104 }105 ],106 "read": true,107 "user_title": "",108 "bookmarked": false,109 "actions_summary": [],110 "moderator": true,111 "admin": true,112 "staff": true,113 "user_id": 3534,114 "hidden": false,115 "trust_level": 2,116 "deleted_at": null,117 "user_deleted": false,118 "edit_reason": null,119 "can_view_edit_history": true,120 "wiki": false,121 "post_url": "/t/after-around-60-epochs-the-encoder-starts-delivering-nan-values-in-transformer-model/181717/2",122 "can_accept_answer": false,123 "can_unaccept_answer": false,124 "accepted_answer": false,125 "topic_accepted_answer": null126 },127 {128 "id": 405451,129 "name": "Daniel Sabzi",130 "username": "Daniel_Sabzi",131 "avatar_template": "/user_avatar/discuss.pytorch.org/daniel_sabzi/{size}/45800_2.png",132 "created_at": "2023-06-09T08:57:43.161Z",133 "cooked": "<p>@ <a href=\"https://discuss.pytorch.org/u/ptrblck\">ptrblck</a><br>\nThanks.<br>\nI tried this, but as I said, the training section works just fine and it is only in the test section that I see “nan” results, even in the test the first 60 epochs work well. and after that it starts to give \"nan results for loss.<br>\nanyways, I ran it and it did not pick anything.<br>\ndo you have any idea what can be the source of such error?</p>",134 "post_number": 3,135 "post_type": 1,136 "posts_count": 3,137 "updated_at": "2023-06-09T08:57:43.161Z",138 "reply_count": 0,139 "reply_to_post_number": 2,140 "quote_count": 0,141 "incoming_link_count": 2,142 "reads": 12,143 "readers_count": 11,144 "score": 12.4,145 "yours": false,146 "topic_id": 181717,147 "topic_slug": "after-around-60-epochs-the-encoder-starts-delivering-nan-values-in-transformer-model",148 "display_username": "Daniel Sabzi",149 "primary_group_name": null,150 "flair_name": null,151 "flair_url": null,152 "flair_bg_color": null,153 "flair_color": null,154 "flair_group_id": null,155 "badges_granted": [],156 "version": 1,157 "can_edit": false,158 "can_delete": false,159 "can_recover": false,160 "can_see_hidden_post": false,161 "can_wiki": false,162 "read": true,163 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"bookmarks": []601 },602 {603 "post_stream": {604 "posts": [605 {606 "id": 405117,607 "name": "Miguel Campos",608 "username": "Miguel_Campos",609 "avatar_template": "/user_avatar/discuss.pytorch.org/miguel_campos/{size}/14538_2.png",610 "created_at": "2023-06-07T09:44:08.692Z",611 "cooked": "<p>Hello all.</p>\n<p>I have begun to learn about Quantization with “dynamic quantization” as a first try.<br>\nAccording to several tutorials like this or this, it catches my attention that <code>torch.nn.Relu</code> is used in <a href=\"https://pytorch.org/tutorials/intermediate/dynamic_quantization_bert_tutorial.html#apply-the-dynamic-quantization\" rel=\"noopener nofollow ugc\">this example from the official tutorials</a> in the qconfig_spec argument of the function quantize_dynamic:</p>\n<blockquote>\n<p>quantized_model = torch.quantization.quantize_dynamic(<br>\nmodel, {torch.nn.Linear}, dtype=torch.qint8<br>\n)</p>\n</blockquote>\n<p>The tutorial says: “We specify that we want the torch.nn.Linear modules in our model to be quantized”. But to me, it is obvious that you want them to be converted to int8. You probably want everything you can convert to int8 converted.</p>\n<p>With this already being said, I would like to know if it is necessary to specify the Linear module.</p>",612 "post_number": 1,613 "post_type": 1,614 "posts_count": 3,615 "updated_at": "2023-06-07T09:46:36.342Z",616 "reply_count": 0,617 "reply_to_post_number": null,618 "quote_count": 0,619 "incoming_link_count": 243,620 "reads": 12,621 "readers_count": 11,622 "score": 1217.4,623 "yours": false,624 "topic_id": 181607,625 "topic_slug": "what-is-the-sense-of-parameter-qconfig-spec-in-quantize-dynamic",626 "display_username": "Miguel Campos",627 "primary_group_name": null,628 "flair_name": null,629 "flair_url": null,630 "flair_bg_color": null,631 "flair_color": null,632 "flair_group_id": null,633 "badges_granted": [],634 "version": 1,635 "can_edit": false,636 "can_delete": false,637 "can_recover": false,638 "can_see_hidden_post": false,639 "can_wiki": false,640 "link_counts": [641 {642 "url": "https://pytorch.org/tutorials/intermediate/dynamic_quantization_bert_tutorial.html#apply-the-dynamic-quantization",643 "internal": false,644 "reflection": false,645 "title": "(beta) Dynamic Quantization on BERT — PyTorch Tutorials 2.0.1+cu117 documentation",646 "clicks": 3647 }648 ],649 "read": true,650 "user_title": null,651 "bookmarked": false,652 "actions_summary": [],653 "moderator": false,654 "admin": false,655 "staff": false,656 "user_id": 22220,657 "hidden": false,658 "trust_level": 2,659 "deleted_at": null,660 "user_deleted": false,661 "edit_reason": null,662 "can_view_edit_history": true,663 "wiki": false,664 "post_url": "/t/what-is-the-sense-of-parameter-qconfig-spec-in-quantize-dynamic/181607/1",665 "can_accept_answer": false,666 "can_unaccept_answer": false,667 "accepted_answer": false,668 "topic_accepted_answer": true,669 "can_vote": false670 },671 {672 "id": 405383,673 "name": "Jesse Cai",674 "username": "jcaip",675 "avatar_template": "/user_avatar/discuss.pytorch.org/jcaip/{size}/53908_2.png",676 "created_at": "2023-06-09T00:17:26.320Z",677 "cooked": "<p>HI <a class=\"mention\" href=\"/u/miguel_campos\">@Miguel_Campos</a></p>\n<p>Are you talking about <code>nn.Linear</code>? I don’t see <code>nn.Relu</code> anywhere in the tutorial you linked. You need to specify the Linear.</p>\n<p>Sometimes you have more than one type of module you want to quantize, for example in the case of LSTM + Linear, seen here: <a href=\"https://pytorch.org/tutorials/recipes/recipes/dynamic_quantization.html\" class=\"inline-onebox\" rel=\"noopener nofollow ugc\">Dynamic Quantization — PyTorch Tutorials 2.0.1+cu117 documentation</a></p>\n<p>Different layers of a network have a different effect on the accuracy so thats why you may want some quantized and some not.</p>",678 "post_number": 2,679 "post_type": 1,680 "posts_count": 3,681 "updated_at": "2023-06-09T00:17:26.320Z",682 "reply_count": 0,683 "reply_to_post_number": null,684 "quote_count": 0,685 "incoming_link_count": 5,686 "reads": 11,687 "readers_count": 10,688 "score": 27.2,689 "yours": false,690 "topic_id": 181607,691 "topic_slug": "what-is-the-sense-of-parameter-qconfig-spec-in-quantize-dynamic",692 "display_username": "Jesse Cai",693 "primary_group_name": null,694 "flair_name": null,695 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"can_unaccept_answer": false,733 "accepted_answer": true,734 "topic_accepted_answer": true735 },736 {737 "id": 405448,738 "name": "Miguel Campos",739 "username": "Miguel_Campos",740 "avatar_template": "/user_avatar/discuss.pytorch.org/miguel_campos/{size}/14538_2.png",741 "created_at": "2023-06-09T08:56:24.080Z",742 "cooked": "<p>Sorry my bad, I mean nn.Linear.</p>\n<p>Ok I see, thanks for your answer</p>",743 "post_number": 3,744 "post_type": 1,745 "posts_count": 3,746 "updated_at": "2023-06-09T08:56:24.080Z",747 "reply_count": 0,748 "reply_to_post_number": null,749 "quote_count": 0,750 "incoming_link_count": 2,751 "reads": 10,752 "readers_count": 9,753 "score": 12.0,754 "yours": false,755 "topic_id": 181607,756 "topic_slug": "what-is-the-sense-of-parameter-qconfig-spec-in-quantize-dynamic",757 "display_username": "Miguel Campos",758 "primary_group_name": null,759 "flair_name": null,760 "flair_url": null,761 "flair_bg_color": null,762 "flair_color": null,763 "flair_group_id": null,764 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I don’t see nn.Relu anywhere in the tutorial you linked. You need to specify the Linear. \nSometimes you have more than one type of module you want to quantize, for example in the case of LSTM + Linear, seen here: <a href=\"https://pytorch.org/tutorials/recipes/recipes/dynamic_quantization.html\" class=\"inline-onebox\" rel=\"noopener nofollow ugc\">Dynamic Quantization — PyTorch Tuto…</a>"1142 },1143 "can_vote": false,1144 "vote_count": 0,1145 "user_voted": false,1146 "discourse_zendesk_plugin_zendesk_id": null,1147 "discourse_zendesk_plugin_zendesk_url": "https://your-url.zendesk.com/agent/tickets/",1148 "details": {1149 "can_edit": false,1150 "notification_level": 1,1151 "participants": [1152 {1153 "id": 22220,1154 "username": "Miguel_Campos",1155 "name": "Miguel Campos",1156 "avatar_template": "/user_avatar/discuss.pytorch.org/miguel_campos/{size}/14538_2.png",1157 "post_count": 2,1158 "primary_group_name": null,1159 "flair_name": null,1160 "flair_url": null,1161 "flair_color": null,1162 "flair_bg_color": null,1163 "flair_group_id": null,1164 "trust_level": 21165 },1166 {1167 "id": 60005,1168 "username": "jcaip",1169 "name": "Jesse Cai",1170 "avatar_template": "/user_avatar/discuss.pytorch.org/jcaip/{size}/53908_2.png",1171 "post_count": 1,1172 "primary_group_name": null,1173 "flair_name": null,1174 "flair_url": null,1175 "flair_color": null,1176 "flair_bg_color": null,1177 "flair_group_id": null,1178 "trust_level": 21179 }1180 ],1181 "created_by": {1182 "id": 22220,1183 "username": "Miguel_Campos",1184 "name": "Miguel Campos",1185 "avatar_template": "/user_avatar/discuss.pytorch.org/miguel_campos/{size}/14538_2.png"1186 },1187 "last_poster": {1188 "id": 22220,1189 "username": "Miguel_Campos",1190 "name": "Miguel Campos",1191 "avatar_template": "/user_avatar/discuss.pytorch.org/miguel_campos/{size}/14538_2.png"1192 },1193 "links": [1194 {1195 "url": "https://pytorch.org/tutorials/recipes/recipes/dynamic_quantization.html",1196 "title": "Dynamic Quantization — PyTorch Tutorials 2.0.1+cu117 documentation",1197 "internal": false,1198 "attachment": false,1199 "reflection": false,1200 "clicks": 34,