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Anurag1734/cuda-error-resolution-analysis

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1[2  {3    "post_stream": {4      "posts": [5        {6          "id": 429994,7          "name": "Scolpe",8          "username": "Scolpe",9          "avatar_template": "/user_avatar/discuss.pytorch.org/scolpe/{size}/61541_2.png",10          "created_at": "2024-01-12T17:37:21.123Z",11          "cooked": "<p>I am currently trying to understand whether the situation that I’ve encountered is a normal behaviour or a bug. I run experiments that include training models in a simulated distributed environment. Without going into unnecessary details, in each round, clients train on a local trainset, test it against the local test set and report the values. The values are then stored in a csv file together with models that were tested.</p>\n<p>To run a validation check, I fix the seed and load the local model and the local test set. Subsequently, I perform a test evaluation. What bothers me is the fact that the values reported in a csv file (test values recorded during simulation) are not fully aligned with test values that I obtain when checking the simulation validity afterwards.</p>\n<p>This implies that the same model (with the same weights) tested on the same dataset and with fixed seed obtains two different results. As the model stabilizes (for N rounds, we will have N different models), the difference between the value reported in a csv file and one obtained during replication is close to 0. As an example, I am pasting the log below:</p>\n<pre><code class=\"lang-auto\">0: Iteration, Abs. Loss Diff.: 1.0015488862991333, Abs. Acc. Diff.: 0.25\n1: Iteration, Abs. Loss Diff.: 0.0009263801574705965, Abs. Acc. Diff.: 0.0\n2: Iteration, Abs. Loss Diff.: 0.005786736011505145, Abs. Acc. Diff.: 0.0\n3: Iteration, Abs. Loss Diff.: 0.003574820756912178, Abs. Acc. Diff.: 0.0\n4: Iteration, Abs. Loss Diff.: 0.007152392864227308, Abs. Acc. Diff.: 0.0\n5: Iteration, Abs. Loss Diff.: 0.0015836870670318248, Abs. Acc. Diff.: 0.0\n6: Iteration, Abs. Loss Diff.: 0.00476664781570435, Abs. Acc. Diff.: 0.0\n7: Iteration, Abs. Loss Diff.: 0.003446925878524798, Abs. Acc. Diff.: 0.0\n8: Iteration, Abs. Loss Diff.: 0.0017982900142670122, Abs. Acc. Diff.: 0.0\n9: Iteration, Abs. Loss Diff.: 0.0006368839740753529, Abs. Acc. Diff.: 0.0\n10: Iteration, Abs. Loss Diff.: 0.009332650899887107, Abs. Acc. Diff.: 0.0\n11: Iteration, Abs. Loss Diff.: 0.0002723556756972778, Abs. Acc. Diff.: 0.0\n12: Iteration, Abs. Loss Diff.: 0.010622120499610865, Abs. Acc. Diff.: 0.0\n13: Iteration, Abs. Loss Diff.: 0.004144576042890535, Abs. Acc. Diff.: 0.0\n14: Iteration, Abs. Loss Diff.: 0.00525180220603938, Abs. Acc. Diff.: 0.0\n15: Iteration, Abs. Loss Diff.: 0.013058926761150391, Abs. Acc. Diff.: 0.0\n16: Iteration, Abs. Loss Diff.: 0.008403560966253276, Abs. Acc. Diff.: 0.0\n17: Iteration, Abs. Loss Diff.: 0.012890378683805492, Abs. Acc. Diff.: 0.0\n18: Iteration, Abs. Loss Diff.: 0.015538938939571367, Abs. Acc. Diff.: 0.0\n19: Iteration, Abs. Loss Diff.: 0.03375539824366569, Abs. Acc. Diff.: 0.0\n20: Iteration, Abs. Loss Diff.: 0.0018654009699821117, Abs. Acc. Diff.: 0.0\n21: Iteration, Abs. Loss Diff.: 0.008243808336555913, Abs. Acc. Diff.: 0.0\n22: Iteration, Abs. Loss Diff.: 0.00302100986242293, Abs. Acc. Diff.: 0.0\n23: Iteration, Abs. Loss Diff.: 0.004521983098238702, Abs. Acc. Diff.: 0.0\n24: Iteration, Abs. Loss Diff.: 0.008875386621803094, Abs. Acc. Diff.: 0.0\n...\n46: Iteration, Abs. Loss Diff.: 0.046149560796329814, Abs. Acc. Diff.: 0.0\n47: Iteration, Abs. Loss Diff.: 0.0725968092895346, Abs. Acc. Diff.: 0.0\n48: Iteration, Abs. Loss Diff.: 0.03759608950349502, Abs. Acc. Diff.: 0.0\n49: Iteration, Abs. Loss Diff.: 0.05040962719998787, Abs. Acc. Diff.: 0.0\n</code></pre>\n<p>Even though the value is stabilizing, I find this behaviour strange. Can it be due to an inherent randomness of some of the PyTorch components? The full code is much to complex to demonstrate fully, but I am also including my testing function.</p>\n<pre><code class=\"lang-auto\">def test_loop(net: torch.nn,\n              testdata = torch.utils.data.DataLoader):\n    net.to(device)\n    net.eval()\n    criterion = nn.CrossEntropyLoss()\n    test_loss = 0\n    correct = 0\n    total = 0\n    y_pred = []\n    y_true = []\n    losses = []\n    \n    with torch.no_grad():\n        for _, dic in enumerate(testdata):\n            inputs = dic['image']\n            targets = dic['label']\n            inputs, targets = inputs.to(device), targets.to(device)\n            outputs = net(inputs)\n            \n            ######################\n            outputs = outputs.cpu()\n            targets = targets.cpu()\n            #######################\n            \n            total += targets.size(0)\n            test_loss = criterion(outputs, targets)\n            losses.append(test_loss)\n            pred = outputs.argmax(dim=1, keepdim=True)\n            correct += pred.eq(targets.view_as(pred)).sum().item()\n            y_pred.append(pred)\n            y_true.append(targets)\n    \n    test_loss = np.mean(losses)\n    accuracy = correct / total\n    \n    \n    y_true = [item.item() for sublist in y_true for item in sublist]\n    y_pred = [item.item() for sublist in y_pred for item in sublist]\n\n...\n    \n    return {\n        'test_loss': test_loss,\n        'accuracy': accuracy,\n...\n        'false_positive_rate': false_positive_rate\n    }\n    \n</code></pre>",12          "post_number": 1,13          "post_type": 1,14          "posts_count": 3,15          "updated_at": "2024-01-12T17:37:21.123Z",16          "reply_count": 0,17          "reply_to_post_number": null,18          "quote_count": 0,19          "incoming_link_count": 35,20          "reads": 6,21          "readers_count": 5,22          "score": 171.2,23          "yours": false,24          "topic_id": 195330,25          "topic_slug": "different-test-results-with-a-fixed-weights-and-fixed-seed",26          "display_username": "Scolpe",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": 67235,48          "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/different-test-results-with-a-fixed-weights-and-fixed-seed/195330/1",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": 430000,64          "name": "",65          "username": "ptrblck",66          "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",67          "created_at": "2024-01-12T18:57:41.983Z",68          "cooked": "<p>I don’t see if and where you’ve enabled deterministic algorithms as described in the <a href=\"https://pytorch.org/docs/stable/notes/randomness.html\">Reproducibility docs</a>. Did you check the docs and followed them?</p>",69          "post_number": 2,70          "post_type": 1,71          "posts_count": 3,72          "updated_at": "2024-01-12T18:57:41.983Z",73          "reply_count": 1,74          "reply_to_post_number": null,75          "quote_count": 0,76          "incoming_link_count": 2,77          "reads": 6,78          "readers_count": 5,79          "score": 31.2,80          "yours": false,81          "topic_id": 195330,82          "topic_slug": "different-test-results-with-a-fixed-weights-and-fixed-seed",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/notes/randomness.html",100              "internal": false,101              "reflection": false,102              "title": "Reproducibility — PyTorch 2.1 documentation",103              "clicks": 4104            }105          ],106          "read": true,107          "user_title": "",108          "bookmarked": false,109          "actions_summary": [110            {111              "id": 2,112              "count": 1113            }114          ],115          "moderator": true,116          "admin": true,117          "staff": true,118          "user_id": 3534,119          "hidden": false,120          "trust_level": 2,121          "deleted_at": null,122          "user_deleted": false,123          "edit_reason": null,124          "can_view_edit_history": true,125          "wiki": false,126          "post_url": "/t/different-test-results-with-a-fixed-weights-and-fixed-seed/195330/2",127          "can_accept_answer": false,128          "can_unaccept_answer": false,129          "accepted_answer": false,130          "topic_accepted_answer": null131        },132        {133          "id": 430075,134          "name": "Scolpe",135          "username": "Scolpe",136          "avatar_template": "/user_avatar/discuss.pytorch.org/scolpe/{size}/61541_2.png",137          "created_at": "2024-01-13T12:05:46.232Z",138          "cooked": "<p>Yes, I’ve first followed the documents on reproductibility.</p>\n<p>I am fixing seeds and enabling deterministic algorithms in the main script from which I am running the simulation. The script opens with imports and the following lines:</p>\n<pre><code class=\"lang-auto\">random.seed(42)\nnp.random.seed(42)\ntorch.cuda.manual_seed(42)\ntorch.cuda.manual_seed_all(42)\ntorch.use_deterministic_algorithms(True)\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\n</code></pre>\n<p>Then I call <code> CUBLAS_WORKSPACE_CONFIG=:16:8 python script.py args</code>. The script calls other libraries and modules and runs a full simulation cycle.<br>\nWhen I am analyzing the results in the jupyter notebook, I am using the same commands:</p>\n<pre><code class=\"lang-auto\">random.seed(42)\nnp.random.seed(42)\ntorch.cuda.manual_seed(42)\ntorch.cuda.manual_seed_all(42)\ntorch.use_deterministic_algorithms(True)\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\n</code></pre>\n<p>The only thing that I am not fixing is the seed for the testloader. However, given that the datasets are already partitioned into training/testing data…my guess is that it should not make much difference (?). But maybe I am wrong on this one.</p>",139          "post_number": 3,140          "post_type": 1,141          "posts_count": 3,142          "updated_at": "2024-01-13T12:05:46.232Z",143          "reply_count": 0,144          "reply_to_post_number": 2,145          "quote_count": 0,146          "incoming_link_count": 4,147          "reads": 5,148          "readers_count": 4,149          "score": 21.0,150          "yours": false,151          "topic_id": 195330,152          "topic_slug": "different-test-results-with-a-fixed-weights-and-fixed-seed",153          "display_username": "Scolpe",154          "primary_group_name": null,155          "flair_name": null,156          "flair_url": null,157          "flair_bg_color": null,158          "flair_color": null,159          "flair_group_id": null,160          "badges_granted": [],161          "version": 1,162          "can_edit": false,163          "can_delete": false,164          "can_recover": false,165          "can_see_hidden_post": 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"created_at": "2024-01-12T14:40:20.562Z",618          "cooked": "<p>Hi please i need help i have a discriminator architecture of</p>\n<pre><code class=\"lang-auto\">class Discriminator(nn.Module):\n    def __init__(self):\n        super(Discriminator, self).__init__()\n\n        self.main = nn.Sequential(\n            nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1),\n            nn.LeakyReLU(0.2),\n            nn.Dropout(0.25),\n            nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1),\n            nn.ZeroPad2d((0, 1, 0, 1)),\n            nn.BatchNorm2d(32, momentum=0.82),\n            nn.LeakyReLU(0.25),\n            nn.Dropout(0.25),\n            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),\n            nn.BatchNorm2d(64, momentum=0.82),\n            nn.LeakyReLU(0.2),\n            nn.Dropout(0.25),\n            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),\n            nn.BatchNorm2d(128, momentum=0.82),\n            nn.LeakyReLU(0.25),\n            nn.Dropout(0.25),\n            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),\n            nn.BatchNorm2d(256, momentum=0.8),\n            nn.LeakyReLU(0.25),\n            nn.Dropout(0.25),\n            nn.Flatten(),\n            nn.Linear(256 * 4 * 4, 1),  \n            #nn.Linear(32, 73984),  \n            nn.Sigmoid()\n        )\n\n    def forward(self, x):\n        return self.main(x)\n</code></pre>\n<p>the nn.linear output is</p>\n<pre><code class=\"lang-auto\"> Linear(in_features=4096, out_features=1, bias=True)\n</code></pre>\n<p>i am trying to change the nn.linear to in_features=32, out_features=73984 from my architecture please help</p>",619          "post_number": 1,620          "post_type": 1,621          "posts_count": 5,622          "updated_at": "2024-01-12T14:40:20.562Z",623          "reply_count": 0,624          "reply_to_post_number": null,625          "quote_count": 0,626          "incoming_link_count": 20,627          "reads": 7,628          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       "deleted_at": null,658          "user_deleted": false,659          "edit_reason": null,660          "can_view_edit_history": true,661          "wiki": false,662          "post_url": "/t/runtimeerror-mat1-and-mat2-shapes-cannot-be-multiplied-32x73984-and-4096x1/195323/1",663          "can_accept_answer": false,664          "can_unaccept_answer": false,665          "accepted_answer": false,666          "topic_accepted_answer": null,667          "can_vote": false668        },669        {670          "id": 429983,671          "name": "",672          "username": "smth",673          "avatar_template": "/user_avatar/discuss.pytorch.org/smth/{size}/13_2.png",674          "created_at": "2024-01-12T15:12:05.483Z",675          "cooked": "<p>change this line</p>\n<pre><code class=\"lang-auto\">nn.Linear(256 * 4 * 4, 1),  \n</code></pre>\n<p>to:</p>\n<pre><code class=\"lang-auto\">nn.Linear(256 * 4 * 4, 32),  \n</code></pre>\n<p>And then uncomment the line <code>#nn.Linear(32, 73984),  </code></p>\n<p>That’s it, you’ll be all set</p>",676          "post_number": 2,677          "post_type": 1,678          "posts_count": 5,679          "updated_at": "2024-01-12T15:12:05.483Z",680          "reply_count": 1,681          "reply_to_post_number": null,682          "quote_count": 0,683          "incoming_link_count": 1,684          "reads": 7,685          "readers_count": 6,686          "score": 11.4,687          "yours": false,688          "topic_id": 195323,689          "topic_slug": "runtimeerror-mat1-and-mat2-shapes-cannot-be-multiplied-32x73984-and-4096x1",690          "display_username": "",691          "primary_group_name": null,692          "flair_name": null,693          "flair_url": null,694          "flair_bg_color": null,695          "flair_color": null,696          "flair_group_id": null,697          "badges_granted": [],698          "version": 1,699          "can_edit": false,700          "can_delete": false,701          "can_recover": false,702          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"avatar_template": "/letter_avatar_proxy/v4/letter/b/439d5e/{size}.png",731          "created_at": "2024-01-12T18:04:42.097Z",732          "cooked": "<p>yes i have done that but this is the error</p>\n<pre><code class=\"lang-auto\">RuntimeError: mat1 and mat2 shapes cannot be multiplied (32x278784 and 32x278784)\n\n</code></pre>",733          "post_number": 3,734          "post_type": 1,735          "posts_count": 5,736          "updated_at": "2024-01-12T18:04:42.097Z",737          "reply_count": 1,738          "reply_to_post_number": 2,739          "quote_count": 0,740          "incoming_link_count": 1,741          "reads": 6,742          "readers_count": 5,743          "score": 11.2,744          "yours": false,745          "topic_id": 195323,746          "topic_slug": "runtimeerror-mat1-and-mat2-shapes-cannot-be-multiplied-32x73984-and-4096x1",747          "display_username": "bashir",748          "primary_group_name": null,749          "flair_name": null,750          "flair_url": 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true,781          "wiki": false,782          "post_url": "/t/runtimeerror-mat1-and-mat2-shapes-cannot-be-multiplied-32x73984-and-4096x1/195323/3",783          "can_accept_answer": false,784          "can_unaccept_answer": false,785          "accepted_answer": false,786          "topic_accepted_answer": null787        },788        {789          "id": 430001,790          "name": "",791          "username": "smth",792          "avatar_template": "/user_avatar/discuss.pytorch.org/smth/{size}/13_2.png",793          "created_at": "2024-01-12T18:57:45.355Z",794          "cooked": "<p>you must’ve not done what I mentioned. that error message would come if you put the numbers in the wrong order.</p>",795          "post_number": 4,796          "post_type": 1,797          "posts_count": 5,798          "updated_at": "2024-01-12T18:57:45.355Z",799          "reply_count": 0,800          "reply_to_post_number": 3,801          "quote_count": 0,802          "incoming_link_count": 0,803          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"2024-01-13T10:10:28.189Z",857          "cooked": "<p>yes i understand what you are saying but it will give the same error</p>\n<pre><code class=\"lang-auto\">RuntimeError: mat1 and mat2 shapes cannot be multiplied (32x73984 and 4096x3)\n</code></pre>\n<pre><code class=\"lang-auto\">(16): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n    (17): BatchNorm2d(256, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n    (18): LeakyReLU(negative_slope=0.25)\n    (19): Dropout(p=0.25, inplace=False)\n    (20): Flatten(start_dim=1, end_dim=-1)\n    (21): Linear(in_features=4096, out_features=3, bias=True)\n    (22): Linear(in_features=32, out_features=73984, bias=True)\n    (23): Sigmoid()\n  )\n)\n</code></pre>",858          "post_number": 5,859          "post_type": 1,860          "posts_count": 5,861          "updated_at": "2024-01-13T10:10:28.189Z",862          "reply_count": 0,863          "reply_to_post_number": null,864          "quote_count": 0,865   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