Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 312726,7 "name": "",8 "username": "Olivier-CR",9 "avatar_template": "/letter_avatar_proxy/v4/letter/o/c5a1d2/{size}.png",10 "created_at": "2021-10-20T20:08:02.474Z",11 "cooked": "<p>Hi,</p>\n<p>I’m trying to launch a train.py DDP script to run over a 4-GPU machine.<br>\ni’m using the launch.py tool described <a href=\"https://github.com/pytorch/examples/blob/master/distributed/ddp/README.md\" rel=\"noopener nofollow ugc\">here</a>, (this experience is quite ugly btw, I which there was a clean PyTorch class to do that!) that is supposed to set local_rank properly in each process: <em>“–local_rank: This is passed in via launch.py”</em> as the documentation says.</p>\n<pre><code class=\"lang-auto\">python /home/ec2-user/anaconda3/envs/pytorch_latest_p37/lib/python3.7/site-packages/torch/distributed/launch.py \\\n --nnode=1 \\\n --node_rank=0 \\\n --nproc_per_node=4 \\\n train.py \\\n --gpu-count 4 \\\n --dataset . \\\n --cache tmp \\\n --height 604 \\\n --width 960 \\\n --checkpoint-dir . \\\n --batch 10 \\\n --workers 24 \\\n --log-freq 20 \\\n --prefetch 2 \\\n --bucket $bucket \\\n --eval-size 10 \\\n --iterations 20 \\\n --class-list a2d2_images/camera_lidar_semantic/class_list.json\n</code></pre>\n<p>However, in each of my processes local_rank = -1 (default value). What is wrong? how to get local_ranks each distinct?</p>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 2,15 "updated_at": "2021-10-20T20:08:02.474Z",16 "reply_count": 0,17 "reply_to_post_number": null,18 "quote_count": 0,19 "incoming_link_count": 37,20 "reads": 8,21 "readers_count": 7,22 "score": 186.6,23 "yours": false,24 "topic_id": 134726,25 "topic_slug": "launch-py-tool-doesnt-set-local-rank-properly",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 "link_counts": [41 {42 "url": "https://github.com/pytorch/examples/blob/master/distributed/ddp/README.md",43 "internal": false,44 "reflection": false,45 "title": "examples/README.md at master · pytorch/examples · GitHub",46 "clicks": 247 }48 ],49 "read": true,50 "user_title": null,51 "bookmarked": false,52 "actions_summary": [],53 "moderator": false,54 "admin": false,55 "staff": false,56 "user_id": 49756,57 "hidden": false,58 "trust_level": 2,59 "deleted_at": null,60 "user_deleted": false,61 "edit_reason": null,62 "can_view_edit_history": true,63 "wiki": false,64 "post_url": "/t/launch-py-tool-doesnt-set-local-rank-properly/134726/1",65 "can_accept_answer": false,66 "can_unaccept_answer": false,67 "accepted_answer": false,68 "topic_accepted_answer": null,69 "can_vote": false70 },71 {72 "id": 313437,73 "name": "Can Balioglu",74 "username": "cbalioglu",75 "avatar_template": "/user_avatar/discuss.pytorch.org/cbalioglu/{size}/33187_2.png",76 "created_at": "2021-10-25T16:59:24.829Z",77 "cooked": "<p>cc <a class=\"mention\" href=\"/u/kiuk_chung\">@Kiuk_Chung</a> <a class=\"mention\" href=\"/u/aivanou\">@aivanou</a></p>",78 "post_number": 2,79 "post_type": 1,80 "posts_count": 2,81 "updated_at": "2021-10-25T16:59:24.829Z",82 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"id": 49756,511 "username": "Olivier-CR",512 "name": "",513 "avatar_template": "/letter_avatar_proxy/v4/letter/o/c5a1d2/{size}.png"514 },515 "last_poster": {516 "id": 40834,517 "username": "cbalioglu",518 "name": "Can Balioglu",519 "avatar_template": "/user_avatar/discuss.pytorch.org/cbalioglu/{size}/33187_2.png"520 },521 "links": [522 {523 "url": "https://github.com/pytorch/examples/blob/master/distributed/ddp/README.md",524 "title": "examples/README.md at master · pytorch/examples · GitHub",525 "internal": false,526 "attachment": false,527 "reflection": false,528 "clicks": 2,529 "user_id": 49756,530 "domain": "github.com",531 "root_domain": "github.com"532 }533 ]534 },535 "bookmarks": []536 },537 {538 "post_stream": {539 "posts": [540 {541 "id": 313348,542 "name": "tsly123",543 "username": "tsly123",544 "avatar_template": "/user_avatar/discuss.pytorch.org/tsly123/{size}/30231_2.png",545 "created_at": "2021-10-25T03:19:50.274Z",546 "cooked": "<p>Hi everyone,</p>\n<p>I want to fine-tune a pre-trained network with same data as it was trained.<br>\nI need help in setting some element of conv2d layer weight (and bias too) to 0 for each training iteration and others are trained as normal. My goal is to have 0 outputs where set as 0.</p>\n<p>For example, my layer has shape<br>\n<code>conv1.weight.shape # torch.Size([32, 64, 7, 7])</code><br>\nand I have a list of unit that need to be set to 0 <code>zeroed_units = [1, 3, 5, 7, 9]</code><br>\nand I would like it to have the <code>tensor of conv1.weight at index [1, 3, 5, 7, 9] = 0</code>, i.e. <code>torch([32,zeroed_units,7,7] = 0)</code></p>\n<ul>\n<li>I have thought about setting these units 0 and set requires_grad=False. However, according to google, requires_grad only allow for entire tensor layer not part of it.</li>\n<li>Another way is to apply a non-trainable mask buffer of 1’s and set 0 where needed after this conv2d and does not touch the conv2d layer. This method could give me the desired output. However, when training in this way, the conv2d layer weight distribution would have the same distribution as before. The number might change but the distribution is not changed much. I would like to see the effect of different weight’s distribution on the final results when setting units to 0.</li>\n</ul>\n<p>Thank you.</p>",547 "post_number": 1,548 "post_type": 1,549 "posts_count": 2,550 "updated_at": "2021-10-25T03:19:50.274Z",551 "reply_count": 0,552 "reply_to_post_number": null,553 "quote_count": 0,554 "incoming_link_count": 823,555 "reads": 13,556 "readers_count": 12,557 "score": 4112.6,558 "yours": false,559 "topic_id": 135019,560 "topic_slug": "setting-some-elements-of-layer-parameters-to-0-for-each-training-iteration",561 "display_username": "tsly123",562 "primary_group_name": null,563 "flair_name": null,564 "flair_url": null,565 "flair_bg_color": null,566 "flair_color": null,567 "flair_group_id": null,568 "badges_granted": [],569 "version": 1,570 "can_edit": false,571 "can_delete": false,572 "can_recover": false,573 "can_see_hidden_post": false,574 "can_wiki": false,575 "read": true,576 "user_title": null,577 "bookmarked": false,578 "actions_summary": [],579 "moderator": false,580 "admin": false,581 "staff": false,582 "user_id": 20148,583 "hidden": false,584 "trust_level": 1,585 "deleted_at": null,586 "user_deleted": false,587 "edit_reason": null,588 "can_view_edit_history": true,589 "wiki": false,590 "post_url": "/t/setting-some-elements-of-layer-parameters-to-0-for-each-training-iteration/135019/1",591 "can_accept_answer": false,592 "can_unaccept_answer": false,593 "accepted_answer": false,594 "topic_accepted_answer": null,595 "can_vote": false596 },597 {598 "id": 313436,599 "name": "tsly123",600 "username": "tsly123",601 "avatar_template": "/user_avatar/discuss.pytorch.org/tsly123/{size}/30231_2.png",602 "created_at": "2021-10-25T16:38:33.426Z",603 "cooked": "<p>With a bit of luck, I found some answers that are close to what I want to do.</p>\n<p><a href=\"https://discuss.pytorch.org/t/can-detach-work-for-parts-of-the-layer-weights/38064\">Can.detach() work for parts of the layer weights?</a><br>\nand<br>\n<a href=\"https://discuss.pytorch.org/t/update-only-sub-elements-of-weights/29101\">Update only sub-elements of weights</a></p>\n<p>It seems that these are not the optimal solution in terms of saving resources since it uses the gradient mask and sets the gradient to 0 where needed. However, this seems to be the only possible solution for me right now.</p>",604 "post_number": 2,605 "post_type": 1,606 "posts_count": 2,607 "updated_at": "2021-10-25T16:38:33.426Z",608 "reply_count": 0,609 "reply_to_post_number": null,610 "quote_count": 0,611 "incoming_link_count": 7,612 "reads": 10,613 "readers_count": 9,614 "score": 37.0,615 "yours": false,616 "topic_id": 135019,617 "topic_slug": "setting-some-elements-of-layer-parameters-to-0-for-each-training-iteration",618 "display_username": "tsly123",619 "primary_group_name": null,620 "flair_name": null,621 "flair_url": null,622 "flair_bg_color": null,623 "flair_color": null,624 "flair_group_id": null,625 "badges_granted": [],626 "version": 1,627 "can_edit": false,628 "can_delete": false,629 "can_recover": false,630 "can_see_hidden_post": false,631 "can_wiki": false,632 "link_counts": [633 {634 "url": "https://discuss.pytorch.org/t/update-only-sub-elements-of-weights/29101",635 "internal": true,636 "reflection": false,637 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efficiently in low-level C++ code.</p>\n<p>Thank you!</p>",1064 "post_number": 1,1065 "post_type": 1,1066 "posts_count": 10,1067 "updated_at": "2021-10-14T14:38:33.466Z",1068 "reply_count": 0,1069 "reply_to_post_number": null,1070 "quote_count": 0,1071 "incoming_link_count": 683,1072 "reads": 26,1073 "readers_count": 25,1074 "score": 3415.2,1075 "yours": false,1076 "topic_id": 134237,1077 "topic_slug": "is-there-a-pytorch-low-level-function-to-efficiently-combine-two-tensors-using-a-0-1-mask",1078 "display_username": "",1079 "primary_group_name": null,1080 "flair_name": null,1081 "flair_url": null,1082 "flair_bg_color": null,1083 "flair_color": null,1084 "flair_group_id": null,1085 "badges_granted": [],1086 "version": 1,1087 "can_edit": false,1088 "can_delete": false,1089 "can_recover": false,1090 "can_see_hidden_post": false,1091 "can_wiki": false,1092 "read": true,1093 "user_title": "",1094 "bookmarked": false,1095 "actions_summary": [],1096 "moderator": false,1097 "admin": false,1098 "staff": false,1099 "user_id": 17236,1100 "hidden": false,1101 "trust_level": 2,1102 "deleted_at": null,1103 "user_deleted": false,1104 "edit_reason": null,1105 "can_view_edit_history": true,1106 "wiki": false,1107 "post_url": "/t/is-there-a-pytorch-low-level-function-to-efficiently-combine-two-tensors-using-a-0-1-mask/134237/1",1108 "can_accept_answer": false,1109 "can_unaccept_answer": false,1110 "accepted_answer": false,1111 "topic_accepted_answer": true,1112 "can_vote": false1113 },1114 {1115 "id": 311667,1116 "name": "Oriel Banne",1117 "username": "OrielBanne",1118 "avatar_template": "/user_avatar/discuss.pytorch.org/orielbanne/{size}/23185_2.png",1119 "created_at": "2021-10-14T14:52:05.123Z",1120 "cooked": "<p>it should work the way you wrote it given sizes are the same</p>",1121 "post_number": 2,1122 "post_type": 1,1123 "posts_count": 10,1124 "updated_at": "2021-10-14T14:52:05.123Z",1125 "reply_count": 1,1126 "reply_to_post_number": null,1127 "quote_count": 0,1128 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"/t/is-there-a-pytorch-low-level-function-to-efficiently-combine-two-tensors-using-a-0-1-mask/134237/2",1165 "can_accept_answer": false,1166 "can_unaccept_answer": false,1167 "accepted_answer": false,1168 "topic_accepted_answer": true1169 },1170 {1171 "id": 313234,1172 "name": "",1173 "username": "zfzhang",1174 "avatar_template": "/letter_avatar_proxy/v4/letter/z/ecc23a/{size}.png",1175 "created_at": "2021-10-24T04:15:40.487Z",1176 "cooked": "<p>Thanks! I’m looking for a faster low-level operation.</p>",1177 "post_number": 3,1178 "post_type": 1,1179 "posts_count": 10,1180 "updated_at": "2021-10-24T04:15:40.487Z",1181 "reply_count": 0,1182 "reply_to_post_number": 2,1183 "quote_count": 0,1184 "incoming_link_count": 8,1185 "reads": 23,1186 "readers_count": 22,1187 "score": 44.6,1188 "yours": false,1189 "topic_id": 134237,1190 "topic_slug": "is-there-a-pytorch-low-level-function-to-efficiently-combine-two-tensors-using-a-0-1-mask",1191 "display_username": "",1192 "primary_group_name": null,1193 "flair_name": null,1194 "flair_url": null,1195 "flair_bg_color": null,1196 "flair_color": null,1197 "flair_group_id": null,1198 "badges_granted": [],1199 "version": 1,1200 "can_edit": false,