Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 385466,7 "name": "Vivek Khandelwal",8 "username": "vivek_khandelwal",9 "avatar_template": "/user_avatar/discuss.pytorch.org/vivek_khandelwal/{size}/56905_2.png",10 "created_at": "2023-01-31T13:50:23.744Z",11 "cooked": "<p>Hi all, I’m working on compiling a model using Torchdynamo + <a href=\"https://github.com/llvm/torch-mlir\" rel=\"noopener nofollow ugc\">Torch-MLIR</a>, and running it via <a href=\"https://github.com/iree-org/iree/\" rel=\"noopener nofollow ugc\">IREE</a>. The issue I’m facing is that the fx graph that is getting generated contains nearly 1000 function args (all tensors). Because of such a large number of function args, it results in a segmentation fault during IREE runtime. The reason is that the system heap gets exhausted when allocating such a large number of tensors (which are large in size). FYI, these tensors are the weights of the model. I have two questions here:<br>\n1.) Can we make any changes to the fx graph such that these function args are replaced by a single arg which would be a list of tensors and all the usages of these function args with the list accesses?<br>\n2.) Is there any other way we can handle this type of instance via torchdynamo?</p>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 3,15 "updated_at": "2023-01-31T13:51:04.412Z",16 "reply_count": 0,17 "reply_to_post_number": null,18 "quote_count": 0,19 "incoming_link_count": 203,20 "reads": 16,21 "readers_count": 15,22 "score": 1018.2,23 "yours": false,24 "topic_id": 171469,25 "topic_slug": "torchdynamo-too-many-function-args-in-a-fx-graph",26 "display_username": "Vivek Khandelwal",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/iree-org/iree/",43 "internal": false,44 "reflection": false,45 "title": "GitHub - iree-org/iree: 👻",46 "clicks": 947 },48 {49 "url": "https://github.com/llvm/torch-mlir",50 "internal": false,51 "reflection": false,52 "title": "GitHub - llvm/torch-mlir: The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.",53 "clicks": 554 }55 ],56 "read": true,57 "user_title": null,58 "bookmarked": false,59 "actions_summary": [],60 "moderator": false,61 "admin": false,62 "staff": false,63 "user_id": 62952,64 "hidden": false,65 "trust_level": 0,66 "deleted_at": null,67 "user_deleted": false,68 "edit_reason": null,69 "can_view_edit_history": true,70 "wiki": false,71 "post_url": "/t/torchdynamo-too-many-function-args-in-a-fx-graph/171469/1",72 "can_accept_answer": false,73 "can_unaccept_answer": false,74 "accepted_answer": false,75 "topic_accepted_answer": null,76 "can_vote": false77 },78 {79 "id": 393443,80 "name": "Mark Saroufim",81 "username": "marksaroufim",82 "avatar_template": "/user_avatar/discuss.pytorch.org/marksaroufim/{size}/36747_2.png",83 "created_at": "2023-03-22T00:45:57.420Z",84 "cooked": "<p>cc <a class=\"mention\" href=\"/u/sherlocknomad\">@SherlockNoMad</a> who might know</p>",85 "post_number": 2,86 "post_type": 1,87 "posts_count": 3,88 "updated_at": "2023-03-22T00:45:57.420Z",89 "reply_count": 0,90 "reply_to_post_number": null,91 "quote_count": 0,92 "incoming_link_count": 0,93 "reads": 11,94 "readers_count": 10,95 "score": 2.2,96 "yours": false,97 "topic_id": 171469,98 "topic_slug": "torchdynamo-too-many-function-args-in-a-fx-graph",99 "display_username": "Mark Saroufim",100 "primary_group_name": null,101 "flair_name": null,102 "flair_url": null,103 "flair_bg_color": null,104 "flair_color": null,105 "flair_group_id": null,106 "badges_granted": [],107 "version": 1,108 "can_edit": false,109 "can_delete": false,110 "can_recover": false,111 "can_see_hidden_post": false,112 "can_wiki": false,113 "read": true,114 "user_title": "Regular",115 "title_is_group": false,116 "bookmarked": false,117 "actions_summary": [],118 "moderator": false,119 "admin": false,120 "staff": false,121 "user_id": 43941,122 "hidden": false,123 "trust_level": 3,124 "deleted_at": null,125 "user_deleted": false,126 "edit_reason": null,127 "can_view_edit_history": true,128 "wiki": false,129 "post_url": "/t/torchdynamo-too-many-function-args-in-a-fx-graph/171469/2",130 "can_accept_answer": false,131 "can_unaccept_answer": false,132 "accepted_answer": false,133 "topic_accepted_answer": null134 },135 {136 "id": 393446,137 "name": "Sherlock",138 "username": "SherlockNoMad",139 "avatar_template": "/user_avatar/discuss.pytorch.org/sherlocknomad/{size}/44866_2.png",140 "created_at": "2023-03-22T01:28:01.785Z",141 "cooked": "<p>You are getting a FX graph produced by AOTAutograd, which lifts parameters/buffers as graph inputs. You may ask why we wanna do this? This is because we want to produce a stateless graph, which will be easier to work with for most backends.</p>\n<p>As a work around, you may want to write a pass that fold inputs back into graph as its internal state.</p>",142 "post_number": 3,143 "post_type": 1,144 "posts_count": 3,145 "updated_at": "2023-03-22T01:28:01.785Z",146 "reply_count": 0,147 "reply_to_post_number": null,148 "quote_count": 0,149 "incoming_link_count": 2,150 "reads": 10,151 "readers_count": 9,152 "score": 12.0,153 "yours": false,154 "topic_id": 171469,155 "topic_slug": "torchdynamo-too-many-function-args-in-a-fx-graph",156 "display_username": "Sherlock",157 "primary_group_name": null,158 "flair_name": null,159 "flair_url": null,160 "flair_bg_color": null,161 "flair_color": null,162 "flair_group_id": null,163 "badges_granted": [],164 "version": 1,165 "can_edit": false,166 "can_delete": false,167 "can_recover": false,168 "can_see_hidden_post": false,169 "can_wiki": false,170 "read": true,171 "user_title": 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1437 }438 }439 ]440 }441 ],442 "tags_descriptions": {},443 "fancy_title": "[TorchDynamo] Too many function args in a fx graph",444 "id": 171469,445 "title": "[TorchDynamo] Too many function args in a fx graph",446 "posts_count": 3,447 "created_at": "2023-01-31T13:50:23.628Z",448 "views": 847,449 "reply_count": 0,450 "like_count": 0,451 "last_posted_at": "2023-03-22T01:28:01.785Z",452 "visible": true,453 "closed": false,454 "archived": false,455 "has_summary": false,456 "archetype": "regular",457 "slug": "torchdynamo-too-many-function-args-in-a-fx-graph",458 "category_id": 39,459 "word_count": 230,460 "deleted_at": null,461 "user_id": 62952,462 "featured_link": null,463 "pinned_globally": false,464 "pinned_at": null,465 "pinned_until": null,466 "image_url": null,467 "slow_mode_seconds": 0,468 "draft": null,469 "draft_key": "topic_171469",470 "draft_sequence": null,471 "unpinned": null,472 "pinned": false,473 "current_post_number": 1,474 "highest_post_number": 3,475 "deleted_by": 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"/user_avatar/discuss.pytorch.org/marksaroufim/{size}/36747_2.png",524 "post_count": 1,525 "primary_group_name": null,526 "flair_name": null,527 "flair_url": null,528 "flair_color": null,529 "flair_bg_color": null,530 "flair_group_id": null,531 "trust_level": 3532 },533 {534 "id": 51520,535 "username": "SherlockNoMad",536 "name": "Sherlock",537 "avatar_template": "/user_avatar/discuss.pytorch.org/sherlocknomad/{size}/44866_2.png",538 "post_count": 1,539 "primary_group_name": null,540 "flair_name": null,541 "flair_url": null,542 "flair_color": null,543 "flair_bg_color": null,544 "flair_group_id": null,545 "trust_level": 1546 },547 {548 "id": 62952,549 "username": "vivek_khandelwal",550 "name": "Vivek Khandelwal",551 "avatar_template": "/user_avatar/discuss.pytorch.org/vivek_khandelwal/{size}/56905_2.png",552 "post_count": 1,553 "primary_group_name": null,554 "flair_name": null,555 "flair_url": null,556 "flair_color": null,557 "flair_bg_color": null,558 "flair_group_id": null,559 "trust_level": 0560 }561 ],562 "created_by": {563 "id": 62952,564 "username": "vivek_khandelwal",565 "name": "Vivek Khandelwal",566 "avatar_template": "/user_avatar/discuss.pytorch.org/vivek_khandelwal/{size}/56905_2.png"567 },568 "last_poster": {569 "id": 51520,570 "username": "SherlockNoMad",571 "name": "Sherlock",572 "avatar_template": "/user_avatar/discuss.pytorch.org/sherlocknomad/{size}/44866_2.png"573 },574 "links": [575 {576 "url": "https://github.com/iree-org/iree/",577 "title": "GitHub - iree-org/iree: 👻",578 "internal": false,579 "attachment": false,580 "reflection": false,581 "clicks": 9,582 "user_id": 62952,583 "domain": "github.com",584 "root_domain": "github.com"585 },586 {587 "url": "https://github.com/llvm/torch-mlir",588 "title": "GitHub - llvm/torch-mlir: The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.",589 "internal": false,590 "attachment": false,591 "reflection": false,592 "clicks": 5,593 "user_id": 62952,594 "domain": "github.com",595 "root_domain": "github.com"596 }597 ]598 },599 "bookmarks": []600 },601 {602 "post_stream": {603 "posts": [604 {605 "id": 383566,606 "name": "jay thakur",607 "username": "jay_thakur",608 "avatar_template": "/user_avatar/discuss.pytorch.org/jay_thakur/{size}/23949_2.png",609 "created_at": "2023-01-18T08:20:03.103Z",610 "cooked": "<p>I see that there is warning when run a model using torch.compile(model) way</p>\n<p>Warning:<br>\n2128922-2023-01-18 10:11:40:751 INFO [logging.py:121] — Step 1: torchdynamo start tracing forward<br>\n2128922-2023-01-18 10:11:40:756 INFO [logging.py:121] — Step 1: torchdynamo done tracing forward (RETURN_VALUE)<br>\n2128922-2023-01-18 10:11:40:757 INFO [logging.py:121] — Step 2: calling compiler function debug_wrapper<br>\n2128922-2023-01-18 10:11:40:884 INFO [logging.py:121] — Step 3: torchinductor compiling FORWARDS graph 0<br>\n2128922-2023-01-18 10:11:40:887 WARNING [graph.py:280] — Creating implicit fallback for:<br>\ntarget: aten._adaptive_avg_pool3d.default<br>\nargs[0]: TensorBox(StorageBox(<br>\nInputBuffer(name=‘primals_1’, layout=FixedLayout(‘cpu’, torch.float32, size=[8, 2048, 7, 7], stride=[100352, 49, 7, 1]))<br>\n))<br>\nargs[1]: [32, 7, 7]<br>\n2128922-2023-01-18 10:11:40:894 WARNING [ir.py:2942] — Using FallbackKernel: torch.ops.aten._adaptive_avg_pool3d.default</p>\n<p>can someone help me to understand above the reason of fallback? is it running in eager mode?<br>\nwhat can be done to execute in compile mode?</p>",611 "post_number": 1,612 "post_type": 1,613 "posts_count": 2,614 "updated_at": "2023-01-18T08:20:03.103Z",615 "reply_count": 0,616 "reply_to_post_number": null,617 "quote_count": 0,618 "incoming_link_count": 145,619 "reads": 12,620 "readers_count": 11,621 "score": 717.2,622 "yours": false,623 "topic_id": 170517,624 "topic_slug": "pytorch-2-0-compile-warning-meaning",625 "display_username": "jay thakur",626 "primary_group_name": null,627 "flair_name": null,628 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"created_at": "2023-03-22T00:48:26.321Z",667 "cooked": "<p>Yes fallback means it’s falling back to eager mode, you can add a decomposition for it so that’s no longer the case - it’s a good first issue <a href=\"https://github.com/pytorch/pytorch/issues/93757\" class=\"inline-onebox\" rel=\"noopener nofollow ugc\">TorchInductor missing ops tracker · Issue #93757 · pytorch/pytorch · GitHub</a></p>",668 "post_number": 2,669 "post_type": 1,670 "posts_count": 2,671 "updated_at": "2023-03-22T00:48:26.321Z",672 "reply_count": 0,673 "reply_to_post_number": null,674 "quote_count": 0,675 "incoming_link_count": 3,676 "reads": 8,677 "readers_count": 7,678 "score": 16.4,679 "yours": false,680 "topic_id": 170517,681 "topic_slug": "pytorch-2-0-compile-warning-meaning",682 "display_username": "Mark Saroufim",683 "primary_group_name": null,684 "flair_name": null,685 "flair_url": null,686 "flair_bg_color": null,687 "flair_color": null,688 "flair_group_id": null,689 "badges_granted": [],690 "version": 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{1121 "url": "https://github.com/pytorch/pytorch/issues/93757",1122 "title": "TorchInductor missing ops tracker · Issue #93757 · pytorch/pytorch · GitHub",1123 "internal": false,1124 "attachment": false,1125 "reflection": false,1126 "clicks": 24,1127 "user_id": 43941,1128 "domain": "github.com",1129 "root_domain": "github.com"1130 }1131 ]1132 },1133 "bookmarks": []1134 },1135 {1136 "post_stream": {1137 "posts": [1138 {1139 "id": 388776,1140 "name": "Daniel Wood",1141 "username": "aifartist",1142 "avatar_template": "/user_avatar/discuss.pytorch.org/aifartist/{size}/57106_2.png",1143 "created_at": "2023-02-21T19:31:02.832Z",1144 "cooked": "<p>I’m trying to leverage torch.compile(model) in an existing largish application called AUTOMATIC1111(stable difusion inference). When run with a compiled model I get:<br>\n<code>RuntimeError: Triton Error [CUDA]: an illegal memory access was encountered</code><br>\nThrough instrumention I’ve found that it appears to create on the fly PY benchmark programs that are run to determine the best way to do something. The function generated in my /tmp dir is:</p>\n<pre><code class=\"lang-auto\">@triton.jit\ndef triton_mm(in_ptr0, arg_A, arg_B, out_ptr0):\n <body deleted for brevity>\n tl.store(out_ptr0 + (xindex + tl.zeros(mask.shape, tl.int32)), tmp1, mask)\n</code></pre>\n<p>My guess is that this last line of the function is writting outside the bounds of the output variable. If I try to print the 5 var’s used in tl.store() I get:</p>\n<pre><code class=\"lang-auto\">out_ptr0 pointer<fp16>[constexpr[1]]\nxindex int32[constexpr[32],constexpr[32]]\nmask.shape [constexpr[32], constexpr[32]]\ntmp1 fp32[constexpr[32],constexpr[32]]\nmask int1[constexpr[32],constexpr[32]]\n</code></pre>\n<p>I’m still new to python. What are these? I want to see the actual values so I can see if the index offset is crazy. What was printed look like types instead of values. I tried to print “out_ptr0[0]” and got a syntax error. Is it an array?<br>\nNOTE:<br>\nprint(f\"xxx = {xxx}\") doesn’t work giving an<br>\n<code> NotImplementedError: Unsupported node: JoinedStr</code><br>\nerror as if this is some restricted python that can only print simple strings and values. Also print(type(out_ptr0)) doesn’t work.</p>",1145 "post_number": 1,1146 "post_type": 1,1147 "posts_count": 10,1148 "updated_at": "2023-02-21T21:24:19.402Z",1149 "reply_count": 0,1150 "reply_to_post_number": null,1151 "quote_count": 0,1152 "incoming_link_count": 3145,1153 "reads": 65,1154 "readers_count": 64,1155 "score": 15662.8,1156 "yours": false,1157 "topic_id": 173168,1158 "topic_slug": "triton-error-cuda-an-illegal-memory-access-was-encountered",1159 "display_username": "Daniel Wood",1160 "primary_group_name": null,1161 "flair_name": null,1162 "flair_url": null,1163 "flair_bg_color": null,1164 "flair_color": null,1165 "flair_group_id": null,1166 "badges_granted": [],1167 "version": 3,1168 "can_edit": false,1169 "can_delete": false,1170 "can_recover": false,1171 "can_see_hidden_post": false,1172 "can_wiki": false,1173 "read": true,1174 "user_title": null,1175 "bookmarked": false,1176 "actions_summary": [],1177 "moderator": false,1178 "admin": false,1179 "staff": false,1180 "user_id": 63148,1181 "hidden": false,1182 "trust_level": 1,1183 "deleted_at": null,1184 "user_deleted": false,1185 "edit_reason": null,1186 "can_view_edit_history": true,1187 "wiki": false,1188 "post_url": "/t/triton-error-cuda-an-illegal-memory-access-was-encountered/173168/1",1189 "can_accept_answer": false,1190 "can_unaccept_answer": false,1191 "accepted_answer": false,1192 "topic_accepted_answer": null,1193 "can_vote": false1194 },1195 {1196 "id": 388832,1197 "name": "Daniel Wood",1198 "username": "aifartist",1199 "avatar_template": "/user_avatar/discuss.pytorch.org/aifartist/{size}/57106_2.png",1200 "created_at": "2023-02-22T02:34:06.597Z",