Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 306039,7 "name": "Maxwell Albert",8 "username": "Maxwell_Albert",9 "avatar_template": "/user_avatar/discuss.pytorch.org/maxwell_albert/{size}/31003_2.png",10 "created_at": "2021-09-10T04:34:19.648Z",11 "cooked": "<p>This question is quite complicated. I give an example.</p>\n<pre><code class=\"lang-python\">class model(nn.Module)\n def __init__()\n self.l1 = nn.linear(10,10)\n self.l2 = nn.linear(10,10)\n def forward(x)\n y1 = self.l1(x)\n y2 = self.l2(x)\n y3 = y1 + y2\n return y2 + y2 + y3*y1\n</code></pre>\n<p>In this example, I could use forward_hook functions to trace two linear layers and their parameters.fn is hook function.\\</p>\n<pre><code class=\"lang-auto\">m.register_forward_hook(fn)\n</code></pre>\n<p>However, <code>y3</code> is not counted as a parameter and the macs of <code>y2 + y2 + y3*y1</code> is not counted in macs, too.<br>\nHow can I solve this?<br>\nWhile “macs” is a way of measuring layers’ complexity. For example, y1 *(y2 + y3) is one macs, if y1, y2, y3 are floats.</p>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 1,15 "updated_at": "2021-09-10T04:34:19.648Z",16 "reply_count": 0,17 "reply_to_post_number": null,18 "quote_count": 0,19 "incoming_link_count": 228,20 "reads": 7,21 "readers_count": 6,22 "score": 1141.4,23 "yours": false,24 "topic_id": 131580,25 "topic_slug": "how-to-count-macs-and-parameters-during-forwarding-in-models-or-layers-by-pytorch",26 "display_username": "Maxwell Albert",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": 48895,48 "hidden": 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"/user_avatar/discuss.pytorch.org/maxwell_albert/{size}/31003_2.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": 2412 }413 ],414 "created_by": {415 "id": 48895,416 "username": "Maxwell_Albert",417 "name": "Maxwell Albert",418 "avatar_template": "/user_avatar/discuss.pytorch.org/maxwell_albert/{size}/31003_2.png"419 },420 "last_poster": {421 "id": 48895,422 "username": "Maxwell_Albert",423 "name": "Maxwell Albert",424 "avatar_template": "/user_avatar/discuss.pytorch.org/maxwell_albert/{size}/31003_2.png"425 }426 },427 "bookmarks": []428 },429 {430 "post_stream": {431 "posts": [432 {433 "id": 306038,434 "name": "Lynx Commando",435 "username": "Lynx_Commando",436 "avatar_template": "/user_avatar/discuss.pytorch.org/lynx_commando/{size}/14537_2.png",437 "created_at": "2021-09-10T04:15:01.618Z",438 "cooked": "<p>Is it safe to share a module among different cuda streams during training?<br>\nMore specifically, will module instance ``a’’ suffer from race condition in the following code?</p>\n<pre><code class=\"lang-auto\">form torch import nn\nimport torch\n\nclass A(nn.module):\n ...\nclass B(nn.module):\n ...\nclass C(nn.module):\n ...\n\nclass AB:\n def __init__(this,a,b):\n this.a=a;\n this.b=b;\n this.stream=torch.cuda.Stream();\n def involke(x):\n with torch.cuda.stream(this.stream):\n r=this.b(this.a(x));\n def wait():\n this.stream.synchronize();\n\n\nclass AC:\n def __init__(this,a,c):\n this.a=a;\n this.c=c;\n this.stream=torch.cuda.Stream();\n def involke(x):\n with torch.cuda.stream(this.stream):\n r=this.c(this.a(x));\n def wait():\n this.stream.synchronize();\n\n\ndef main():\n a,b,c=A(),B(),C()\n fab=AB(a,b);\n fac=AC(a,c);\n ...\n x=loaddata();\n y_ab=fab.involke(x);\n y_ac=fac.involke(x);\n fab.wait();\n fac.wait();\n ...\n \n</code></pre>",439 "post_number": 1,440 "post_type": 1,441 "posts_count": 1,442 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