Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 400896,7 "name": "AY",8 "username": "Ali_Youssef",9 "avatar_template": "/user_avatar/discuss.pytorch.org/ali_youssef/{size}/58359_2.png",10 "created_at": "2023-05-06T03:17:43.977Z",11 "cooked": "<p>Hello everyone!</p>\n<p>I had a a simple question regarding image patching.</p>\n<p>For exmaple, if we are doign segementation or any other image processing task.</p>\n<ul>\n<li>\n<p>If an image is (256,256) and during training nd testing, we split it to patches of size (32,32), why not just resize the image to (32,32) directly? Do we lose information if we do so?</p>\n</li>\n<li>\n<p>I don’t really understand why apply patches in the first place, does it improve segementation performance or is it purely for computational power expenses?</p>\n</li>\n</ul>\n<p>Thank you!</p>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 2,15 "updated_at": "2023-05-06T03:17:43.977Z",16 "reply_count": 0,17 "reply_to_post_number": null,18 "quote_count": 0,19 "incoming_link_count": 14,20 "reads": 3,21 "readers_count": 2,22 "score": 70.6,23 "yours": false,24 "topic_id": 179269,25 "topic_slug": "image-patching-question",26 "display_username": "AY",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": "",42 "bookmarked": false,43 "actions_summary": [],44 "moderator": false,45 "admin": false,46 "staff": false,47 "user_id": 64862,48 "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/image-patching-question/179269/1",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": 402424,64 "name": "",65 "username": "ptrblck",66 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",67 "created_at": "2023-05-17T07:31:28.183Z",68 "cooked": "<ol>\n<li>Yes, resizing the image from <code>256x256=65536</code> pixels to <code>32x32=1024</code> will lose information.</li>\n<li>Also yes, using the larger input images might cause OOM issues depending on the model and the used GPU. Segmentation results might thus benefit from a patch approach.</li>\n</ol>",69 "post_number": 2,70 "post_type": 1,71 "posts_count": 2,72 "updated_at": "2023-05-17T07:31:28.183Z",73 "reply_count": 0,74 "reply_to_post_number": null,75 "quote_count": 0,76 "incoming_link_count": 0,77 "reads": 3,78 "readers_count": 2,79 "score": 0.6,80 "yours": false,81 "topic_id": 179269,82 "topic_slug": "image-patching-question",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 "read": true,98 "user_title": "",99 "bookmarked": false,100 "actions_summary": [],101 "moderator": true,102 "admin": true,103 "staff": true,104 "user_id": 3534,105 "hidden": false,106 "trust_level": 2,107 "deleted_at": null,108 "user_deleted": 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null,470 "admin": true,471 "moderator": true,472 "trust_level": 2473 },474 {475 "id": 64862,476 "username": "Ali_Youssef",477 "name": "AY",478 "avatar_template": "/user_avatar/discuss.pytorch.org/ali_youssef/{size}/58359_2.png",479 "post_count": 1,480 "primary_group_name": null,481 "flair_name": null,482 "flair_url": null,483 "flair_color": null,484 "flair_bg_color": null,485 "flair_group_id": null,486 "trust_level": 1487 }488 ],489 "created_by": {490 "id": 64862,491 "username": "Ali_Youssef",492 "name": "AY",493 "avatar_template": "/user_avatar/discuss.pytorch.org/ali_youssef/{size}/58359_2.png"494 },495 "last_poster": {496 "id": 3534,497 "username": "ptrblck",498 "name": "",499 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png"500 }501 },502 "bookmarks": []503 },504 {505 "post_stream": {506 "posts": [507 {508 "id": 400727,509 "name": "Adam Wiemerslage",510 "username": "Adamits",511 "avatar_template": "/user_avatar/discuss.pytorch.org/adamits/{size}/4528_2.png",512 "created_at": "2023-05-04T21:15:26.906Z",513 "cooked": "<p>I am getting an error when I try to compute model FLOPs with the torcheval <a href=\"https://github.com/pytorch/torcheval/blob/main/torcheval/tools/module_summary.py\" rel=\"noopener nofollow ugc\">module_summary</a>.</p>\n<p>Specifically, the issue seems to be that <code>module_summary</code> calls the forward function on every submodule of my model (see <a href=\"https://github.com/pytorch/torcheval/blob/main/torcheval/tools/module_summary.py#L258\" rel=\"noopener nofollow ugc\">here</a>), unpacking the provided args and passing them through.</p>\n<p>Since one of my submodules is an <code>nn.LSTM</code> that I pass a <code>PackedSequence</code> to, and a pytorch <code>PackedSequence</code> inherits from a named tuple, this gets unpacked (sorry this term is overloaded, I mean unpacked as an iterable into args to the LSTM forward function), and errors.</p>\n<p>example:</p>\n<pre><code class=\"lang-auto\"> embedded = self.embeddings(source)\n # Packs embedded source symbols into a PackedSequence.\n packed = nn.utils.rnn.pack_padded_sequence(\n embedded, lengths, batch_first=True, enforce_sorted=False\n )\n # -> B x seq_len x encoder_dim, (h0, c0).\n packed_outs, (H, C) = self.encoder(packed)\n encoded, _ = nn.utils.rnn.pad_packed_sequence(\n packed_outs,\n batch_first=True,\n padding_value=self.pad_idx,\n total_length=None,\n )\n</code></pre>\n<p>Causes <code>TypeError: forward() takes from 2 to 3 positional arguments but 5 were given</code> while profiling FLOPs.</p>\n<p>Of course, I can simply pass <code>packed</code> inside an iterable, which solves the issue when profiling:</p>\n<pre><code class=\"lang-auto\">packed_outs, (H, C) = self.encoder((packed,))\n</code></pre>\n<p>but then the actual forward pass errors when I am not profiling with torch eval, since it expects a <code>PackedSequence</code>: <code>AttributeError: 'tuple' object has no attribute 'dim'</code></p>\n<p>This seems like a really silly issue that someone else might have run into. I am probably being dense but is there a simple solution to this?</p>",514 "post_number": 1,515 "post_type": 1,516 "posts_count": 2,517 "updated_at": "2023-05-04T21:15:26.906Z",518 "reply_count": 0,519 "reply_to_post_number": null,520 "quote_count": 0,521 "incoming_link_count": 33,522 "reads": 5,523 "readers_count": 4,524 "score": 166.0,525 "yours": false,526 "topic_id": 179164,527 "topic_slug": "unable-to-use-torcheval-module-summary-with-packed-sequences-for-lstm",528 "display_username": "Adam Wiemerslage",529 "primary_group_name": null,530 "flair_name": null,531 "flair_url": null,532 "flair_bg_color": null,533 "flair_color": null,534 "flair_group_id": null,535 "badges_granted": [],536 "version": 1,537 "can_edit": false,538 "can_delete": false,539 "can_recover": false,540 "can_see_hidden_post": false,541 "can_wiki": false,542 "link_counts": [543 {544 "url": "https://github.com/pytorch/torcheval/blob/main/torcheval/tools/module_summary.py",545 "internal": false,546 "reflection": false,547 "title": "torcheval/module_summary.py at main · pytorch/torcheval · GitHub",548 "clicks": 2549 },550 {551 "url": "https://github.com/pytorch/torcheval/blob/main/torcheval/tools/module_summary.py#L258",552 "internal": false,553 "reflection": false,554 "title": "torcheval/module_summary.py at main · pytorch/torcheval · GitHub",555 "clicks": 1556 }557 ],558 "read": true,559 "user_title": null,560 "bookmarked": false,561 "actions_summary": [],562 "moderator": false,563 "admin": false,564 "staff": false,565 "user_id": 7992,566 "hidden": false,567 "trust_level": 2,568 "deleted_at": null,569 "user_deleted": false,570 "edit_reason": null,571 "can_view_edit_history": true,572 "wiki": false,573 "post_url": "/t/unable-to-use-torcheval-module-summary-with-packed-sequences-for-lstm/179164/1",574 "can_accept_answer": false,575 "can_unaccept_answer": false,576 "accepted_answer": false,577 "topic_accepted_answer": true,578 "can_vote": false579 },580 {581 "id": 402419,582 "name": "",583 "username": "ptrblck",584 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",585 "created_at": "2023-05-17T07:25:02.517Z",586 "cooked": "<p>I would recommend posting this issue in the <code>torcheval</code> GitHub repository to track and fix it, as it’s still in its alpha stage.</p>",587 "post_number": 2,588 "post_type": 1,589 "posts_count": 2,590 "updated_at": "2023-05-17T07:25:02.517Z",591 "reply_count": 0,592 "reply_to_post_number": null,593 "quote_count": 0,594 "incoming_link_count": 1,595 "reads": 3,596 "readers_count": 2,597 "score": 20.6,598 "yours": false,599 "topic_id": 179164,600 "topic_slug": "unable-to-use-torcheval-module-summary-with-packed-sequences-for-lstm",601 "display_username": "",602 "primary_group_name": null,603 "flair_name": null,604 "flair_url": null,605 "flair_bg_color": null,606 "flair_color": null,607 "flair_group_id": null,608 "badges_granted": [],609 "version": 1,610 "can_edit": false,611 "can_delete": false,612 "can_recover": 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"internal": false,1061 "attachment": false,1062 "reflection": false,1063 "clicks": 1,1064 "user_id": 7992,1065 "domain": "github.com",1066 "root_domain": "github.com"1067 }1068 ]1069 },1070 "bookmarks": []1071 },1072 {1073 "post_stream": {1074 "posts": [1075 {1076 "id": 400962,1077 "name": "",1078 "username": "Abinas",1079 "avatar_template": "/user_avatar/discuss.pytorch.org/abinas/{size}/60272_2.png",1080 "created_at": "2023-05-07T01:33:31.732Z",1081 "cooked": "<p>Hi! everyone<br>\nI am currently working on a problem where i have 3 trained regression model, where each model gives output of (1,300) length sequence. things i need to do is combine the output from the regression model at certain ratio a<em>x + b</em>y + c*z = final output (1,300). I want to train the ratio a,b,c. I don’t want the gradient to back propagate to the trained model only the ratio must be updated. suggest me how can i achieve this.</p>",1082 "post_number": 1,1083 "post_type": 1,1084 "posts_count": 4,1085 "updated_at": "2023-05-07T01:33:31.732Z",1086 "reply_count": 0,1087 "reply_to_post_number": null,1088 "quote_count": 0,1089 "incoming_link_count": 10,1090 "reads": 4,1091 "readers_count": 3,1092 "score": 50.8,1093 "yours": false,1094 "topic_id": 179311,1095 "topic_slug": "merging-3-regression-model-at-certain-ratio",1096 "display_username": "",1097 "primary_group_name": null,1098 "flair_name": null,1099 "flair_url": null,1100 "flair_bg_color": null,1101 "flair_color": null,1102 "flair_group_id": null,1103 "badges_granted": [],1104 "version": 1,1105 "can_edit": false,1106 "can_delete": false,1107 "can_recover": false,1108 "can_see_hidden_post": false,1109 "can_wiki": false,1110 "read": true,1111 "user_title": "",1112 "bookmarked": false,1113 "actions_summary": [],1114 "moderator": false,1115 "admin": false,1116 "staff": false,1117 "user_id": 65944,1118 "hidden": false,1119 "trust_level": 1,1120 "deleted_at": null,1121 "user_deleted": false,1122 "edit_reason": null,1123 "can_view_edit_history": true,1124 "wiki": false,1125 "post_url": "/t/merging-3-regression-model-at-certain-ratio/179311/1",1126 "can_accept_answer": false,1127 "can_unaccept_answer": false,1128 "accepted_answer": false,1129 "topic_accepted_answer": null,1130 "can_vote": false1131 },1132 {1133 "id": 400974,1134 "name": "",1135 "username": "ptrblck",1136 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",1137 "created_at": "2023-05-07T05:25:20.981Z",1138 "cooked": "<p>I’d you want to keep the models frozen and only train the scaling factors, initialize them as <code>nn.Parameters</code>, pass them to an optimizer, and update them in each iteration.</p>",1139 "post_number": 2,1140 "post_type": 1,1141 "posts_count": 4,1142 "updated_at": "2023-05-07T05:25:20.981Z",1143 "reply_count": 0,1144 "reply_to_post_number": null,1145 "quote_count": 0,1146 "incoming_link_count": 2,1147 "reads": 3,1148 "readers_count": 2,1149 "score": 10.6,1150 "yours": false,1151 "topic_id": 179311,1152 "topic_slug": "merging-3-regression-model-at-certain-ratio",1153 "display_username": "",1154 "primary_group_name": null,1155 "flair_name": null,1156 "flair_url": null,1157 "flair_bg_color": null,1158 "flair_color": null,1159 "flair_group_id": null,1160 "badges_granted": [],1161 "version": 1,1162 "can_edit": false,1163 "can_delete": false,1164 "can_recover": false,1165 "can_see_hidden_post": false,1166 "can_wiki": false,1167 "read": true,1168 "user_title": "",1169 "bookmarked": false,1170 "actions_summary": [],1171 "moderator": true,1172 "admin": true,1173 "staff": true,1174 "user_id": 3534,1175 "hidden": false,1176 "trust_level": 2,1177 "deleted_at": null,1178 "user_deleted": false,1179 "edit_reason": null,1180 "can_view_edit_history": true,1181 "wiki": false,1182 "post_url": "/t/merging-3-regression-model-at-certain-ratio/179311/2",1183 "can_accept_answer": false,1184 "can_unaccept_answer": false,1185 "accepted_answer": false,1186 "topic_accepted_answer": null1187 },1188 {1189 "id": 400983,1190 "name": "",1191 "username": "Abinas",1192 "avatar_template": "/user_avatar/discuss.pytorch.org/abinas/{size}/60272_2.png",1193 "created_at": "2023-05-07T06:14:58.879Z",1194 "cooked": "<p>did you mean like this ?</p>\n<pre><code class=\"lang-auto\">class combined_model(nn.Module):\n def __init__(self):\n super(combined_model,self).__init__()\n self.a = nn.Parameter(torch.tensor(1.0))\n self.b = nn.Parameter(torch.tensor(1.0))\n self.c = nn.Parameter(torch.tensor(1.0))\n\n def forward(self,x1,x2,x3):\n return self.a*x1+self.b*x2+self.c*x3\n</code></pre>",1195 "post_number": 3,1196 "post_type": 1,1197 "posts_count": 4,1198 "updated_at": "2023-05-07T06:14:58.879Z",1199 "reply_count": 1,1200 "reply_to_post_number": null,