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[ "TF API is different than PT API. They don't need to have exactly the same signature. Not a bug", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60316\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60316\">No</a>\n" ]

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[ "Not a bug, I'd say. Rules for `NaN` say that any operation involving `NaN` should result in a `NaN`, afaik", "But the input is INF, in this situation the output should be INF as well.\r\nAnd I can get INF output in PyTorch when the input is INF and the operator is Conv2d.", "Does the same behavior occur in TF2.12?", "No, but I can get output -INF in TF2.12 with the same code, I wonder whether this output is correct or not.", "There might be an issue on the ordering of operations, but I am not sure that there is a guarantee on denormalized and special floats.", "TF convolutions call into NVIDIA's cuDNN library, which is producing the NaNs. Perhaps the internal cuDNN algorithm is multiplying some values by 0, which could be producing the NaNs because Inf * 0 = NaN.\r\n\r\nI don't think it's feasible to avoid turning Infs into NaNs in some cases, as algorithms may assume multiplying by zero results in zero, and also we don't have any control over cuDNN's algorithms. So I'll close this issue.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60315\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60315\">No</a>\n" ]

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[ "@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow v2.12 and nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/79d4e1836b23cb219236e63d8ab3443f/untitled1071.ipynb).", "@Janushki , Thanks for reporting the issue. \r\nI was debugging this from my side as well.\r\nMeanwhile, Could you please provide some more details on the mathematical computation of both of the approaches.", "Initial observation: \r\nI was able to compare both the results outside gradient scope for forward computation and the results were identical.\r\nThe issue seems to be happening in the gradient calculation. \r\nBelow is the code I tried to compare both results with a seed and detailed gist can be found [here](https://gist.github.com/sachinprasadhs/a792cadef1ef4f217be127091c7953e6).\r\n```\r\nStep1 \r\nimport tensorflow as tf\r\nimport keras\r\ntf.keras.utils.set_random_seed(42)\r\nx = tf.constant([[0.1,0.90]])\r\ny = tf.constant([[0.0,.0]])\r\nz = tf.keras.Sequential(\r\n [keras.layers.Dense(2,activation = \"linear\", use_bias = False),\r\n keras.layers.Softmax()]\r\n )(x)\r\nprint(z)\r\nz = tf.losses.categorical_crossentropy(y, z)\r\nprint(z)\r\n```\r\n\r\n```\r\ntf.Tensor([[0.22173299 0.778267 ]], shape=(1, 2), dtype=float32)\r\ntf.Tensor([-0.], shape=(1,), dtype=float32)\r\n```\r\n\r\n```\r\nStep2:\r\nimport tensorflow as tf\r\nimport keras\r\ntf.keras.utils.set_random_seed(42)\r\nx = tf.Variable([[0.1,0.90]])\r\ny = tf.Variable([[0.0,.0]])\r\nz = keras.Sequential(\r\n keras.layers.Dense(2, activation = \"softmax\", use_bias = False)\r\n )(x)\r\nprint(z)\r\nz = tf.losses.categorical_crossentropy(y, z)\r\nprint(z)\r\n```\r\n\r\n```\r\ntf.Tensor([[0.22173299 0.778267 ]], shape=(1, 2), dtype=float32)\r\ntf.Tensor([0.], shape=(1,), dtype=float32)\r\n```\r\n", "> @Janushki , Thanks for reporting the issue. I was debugging this from my side as well. Meanwhile, Could you please provide some more details on the mathematical computation of both of the approaches.\r\n\r\nSorry for the late answer, I didnt have time last week. And thanks alot for your effort!\r\n\r\nThe Categorical Crossentropie Loss is defined as:\r\n$$-\\sum_i y_i * \\log(x_i)$$\r\n\r\nif we add the information that $y_i = 0$ this leads to\r\n$$-\\sum_i y_i * \\log(x_i)=-\\sum_i 0 * \\log(x_i)=-\\sum_i 0=0$$\r\nas the loss is constantly zero, the partial derivate for $x_i$ is zero aswell. Actually this should be independent of the pervious activation function.\r\n", "Hi @Janushki ,\r\n\r\nThank you for the report. There was indeed a discrepancy in the results depending on the approach. This is now fixed in `tf-nightly`. https://github.com/keras-team/keras/commit/304bb3d9ab137dd26263381977890f00aac62e75\r\n\r\nSpecifically, this fixes the `Softmax` layer. After this change, the `Softmax` layer produces the same result as the `softmax` activation.\r\n\r\nThanks,\r\nFabien", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60314\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60314\">No</a>\n" ]

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[ "It seems that the crash is happening in the TensorFlow Lite GPU delegate while trying to create an interpreter. One potential solution is to disable the GPU delegate and only use the CPU delegate. You can do this by removing the following line of code:\r\n\r\narduino\r\nCopy code\r\n.addDelegateFactory(new GpuDelegateFactory())\r\nIf the issue persists, you could try updating your TensorFlow Lite in Play Services SDK to the latest version to see if the problem has been resolved in a newer release.\r\n\r\nAdditionally, you can check if there are any compatibility issues between the Samsung a13 device and the TensorFlow Lite GPU delegate by checking the device's specifications and the minimum requirements for the GPU delegate. You may also want to check if any other apps on the device are experiencing similar crashes and try to isolate the issue by running the app on other devices. Finally, you could try reaching out to the TensorFlow community forums for more help and guidance.", "Hi @sandeep5193 \r\n\r\nSorry for delayed response.\r\n\r\nWe can enable the GPU delegate option in the TFlite initialization by\r\n```\r\nTfLite.initialize(context,\r\n TfLiteInitializationOptions.builder()\r\n .setEnableGpuDelegateSupport(true)\r\n .build());\r\n```\r\nbefore adding `.addDelegateFactory(new GpuDelegateFactory())`\r\n\r\nPlease refer [this](https://www.tensorflow.org/lite/android/play_services#gpu_with_interpreter_apis) instructions on using GPU delegate.\r\n \r\nIf the issue still persists, could you please provide a standalone code to reproduce this issue?\r\n\r\nThanks.", "The code I have mentioned is working fine for some time now. All of a\r\nsudden it started crashing on few devices, without any changes in our code. You still think by\r\nchanging initialisation steps you have mentioned will solve the problem?\r\n\r\nOn Mon, 17 Apr, 2023, 5:25 pm pjpratik, ***@***.***> wrote:\r\n\r\n> Hi @sandeep5193 <https://github.com/sandeep5193>\r\n>\r\n> Sorry for delayed response.\r\n>\r\n> We can enable the GPU delegate option in the TFlite initialization by\r\n>\r\n> TfLite.initialize(context,\r\n> TfLiteInitializationOptions.builder()\r\n> .setEnableGpuDelegateSupport(true)\r\n> .build());\r\n>\r\n> before adding .addDelegateFactory(new GpuDelegateFactory())\r\n>\r\n> Please refer this\r\n> <https://www.tensorflow.org/lite/android/play_services#gpu_with_interpreter_apis>\r\n> instructions on using GPU delegate.\r\n>\r\n> If the issue still persists, could you please provide a standalone code to\r\n> reproduce this issue?\r\n>\r\n> Thanks.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/60313#issuecomment-1511200353>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/ABHUPVFGJFMGNNAX76YIALLXBUVTDANCNFSM6AAAAAAW5BL6IQ>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> ***@***.***>\r\n>\r\n", "Hi @sandeep5193 \r\n\r\nWas it working fine with GPU delegate on few cases? \r\n\r\nI was checking if the the GPU delegate is not initialized that maybe causing the issue.\r\n\r\nCan you provide a reproducible code to better understand and investigate the issue.\r\n\r\nThanks.", "hi @pjpratik\r\n\r\nit was working very well in all devices. all of a sudden we started receiving this error in production, for few devices, exact numbers you can read in my first post. so we had to stop it.\r\n\r\nwe are not able to reproduce this in our development devices, but code mentioned in the original post is the point of crash.", "Given that it says [libtensorflowlite_jni_gms_client.so], this is beyond my understanding. @sachinprasadhs can this be assigned to the TFLite in GMS core team?", "Hi, we have identified a compatibility issue between the TFLite runtime module and the GPU delegate module that might be the cause of this crash. The good news is, we are already in the process of rolling out an update that should fix the issue. I will post an update on this bug once the rollout is complete.", "@sheepmaster can I downgrade my tflite version to remedy this in the short term?\r\n", "We have finished the rollout, so devices should pick up the fix automatically. If you still encounter crashes, please let us know (ideally by filing a new bug, so we can distinguish different issues). Thanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60313\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60313\">No</a>\n" ]

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[ "Hi @shkarupa-alex ,\r\n\r\nI am not sure whether this is better comparison of XLA performance or not. I have increased the `repeats` to 1000 and also found similar results. Please refer to attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/ddd84ed3c829b1b5b15206fc4efb59c6/60312-conv-vs-depthwise-speed.ipynb#scrollTo=vtxDK4y79_7B).\r\n\r\n```\r\nstride-1 conv 44.51\r\nstride-1 conv_jit 64.96\r\nstride-1 sepconv 19.95\r\nstride-1 sepconv_jit 17.47\r\nstride-1 dwconv 9.0\r\nstride-1 dwconv_jit 5.89\r\n\r\nstride-2 conv 32.42\r\nstride-2 conv_jit 28.98\r\nstride-2 sepconv 5.18\r\nstride-2 sepconv_jit 6.97\r\nstride-2 dwconv 2.58\r\nstride-2 dwconv_jit 4.29\r\n```\r\n\r\n\r\n", "@shkarupa-alex @SuryanarayanaY Thanks for the bug! Would you be interested in pursuing this further? E.g. when looked under nsys systems profiler, are the kernel names different? A next step would be looking into `conv_algorithm_picker`, enabling logs and checking why in XLA:GPU a different kernel from cuDNN is chosen from TF." ]

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[ "Hi @alankelly Can you please review this PR ? Thank you!", "Hi @alankelly Can you please review this PR ? Thank you!", "Hi @alankelly Can you please review this PR ? Thank you!", "Thanks for spotting this, this is definitely a bug. Since this is only used for benchmarking, it doesn't impact correctness but should nevertheless be fixed" ]

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[ "> Ask discussed offline, all registrations in this PR had caused internal test failures and were reverted before, and the issues haven't been fixed yet. Please guard all device default registrations in this PR with macos ifdefs for now.\r\n\r\nThanks @penpornk , added them to be specific to macos.", "For the record: I had made the requested changes internally before merging the PR.", "> For the record: I had made the requested changes internally before merging the PR.\r\n\r\nThanks Penporn. Much appreciated." ]

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[ "@CaffineAddic,\r\nI tried to execute the mentioned code with the tensorflow v2.12 and it was executed without any issues. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/acc1bef1d752d4e7a5ed36485d49a31f/voxelmorph_tutorial.ipynb) and also looks like the error which you stated was related to tensorflow.\r\n\r\nThe error which was mentioned was discussed in [Tornado issue 2008](https://github.com/tornadoweb/tornado/pull/2008). The current theory is that it only happens when threads are being used incorrectly, but this is not certain.\r\n[Reference](https://stackoverflow.com/questions/44405493/how-to-resolve-buffererror-existing-exports-of-data-object-cannot-be-re-sized). Thank you!", "Thanks for the reply, actually the code works well for 150 epochs with 100 steps per epoch but it stops with this error at any range of epochs from 70 to 200. ", "https://github.com/CaffineAddic/HybridMorph-proof-of-concept-.git\r\n\r\nCan you see if this one works, I used this code to train the models before but now after the update it's failing", "@CaffineAddic,\r\nWhile I was accessing the issue, I was unable to view any code in the above link. Could you please provide the colab gist which helps to analyse the issue in an effective way. Thank you!", "https://github.com/CaffineAddic/HybridMorph-proof-of-concept-/blob/main/HybridMorph_proof%20of%20concept.ipynb", "I am running it on my local machine \r\n", "Could you please provide the model_loc = 'Models/ and csv_loc = 'CSV/' datasets and the models which you are trying to execute the code in the reproducible format. Thank you!", "There is no data-set needed to run this \r\nOne of the data-set is provided by the library other one is generated during the execution.\r\nThose two location are just to store the model weights as .h5 file and the other has the location of the CSV file where it will store errors per step.\r\nJust keep both as random temp folder.\r\nThank you ", "Having the same issue right now while training models, tornado version 6.3.2 and tensorflow version 2.12.0.", "Anyone got any fix??", "@CaffineAddic,\r\nApologies for the delay. We are working on the issue and will update the status here. Thank you!", "Thanks a lot for the reply, Good luck.", "(also seeing this issue)", "@CaffineAddic @akellehe , Could you please try to provide more information on this to debug the root cause of the issue. \r\nAlso, are you facing the similar behavior in other environments as well?", "Yess, over multiple systems configurations, I have tried run it over multiple fresh installs of tf 2.12", "I am getting the same error and stacktrace with a pytorch model with MPS backend from a jupyter notebook. The model continues training, but output stops streaming to jupyter.\r\nI suspect the problem is actually with jupyter and that websocket that allows streaming data from the python backend to the output cell. ", "Did you find any fix for it ??", "Same here. The error just random shows up. Some times after 100 epochs, sometimes at much earlier.\r\n\r\n2023-07-26 17:43:54 [E 00:43:54.737 NotebookApp] Uncaught exception in zmqstream callback\r\n2023-07-26 17:43:54 Traceback (most recent call last):\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 634, in _handle_events\r\n2023-07-26 17:43:54 self._handle_recv()\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 663, in _handle_recv\r\n2023-07-26 17:43:54 self._run_callback(callback, msg)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 584, in _run_callback\r\n2023-07-26 17:43:54 f = callback(*args, **kwargs)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 308, in stream_callback\r\n2023-07-26 17:43:54 return callback(self, msg)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/notebook/services/kernels/handlers.py\", line 572, in _on_zmq_reply\r\n2023-07-26 17:43:54 super()._on_zmq_reply(stream, msg)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/notebook/base/zmqhandlers.py\", line 256, in _on_zmq_reply\r\n2023-07-26 17:43:54 self.write_message(msg, binary=isinstance(msg, bytes))\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/tornado/websocket.py\", line 339, in write_message\r\n2023-07-26 17:43:54 return self.ws_connection.write_message(message, binary=binary)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/tornado/websocket.py\", line 1086, in write_message\r\n2023-07-26 17:43:54 fut = self._write_frame(True, opcode, message, flags=flags)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/tornado/websocket.py\", line 1061, in _write_frame\r\n2023-07-26 17:43:54 return self.stream.write(frame)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/tornado/iostream.py\", line 540, in write\r\n2023-07-26 17:43:54 self._write_buffer.append(data)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/tornado/iostream.py\", line 157, in append\r\n2023-07-26 17:43:54 b += data # type: ignore\r\n2023-07-26 17:43:54 BufferError: Existing exports of data: object cannot be re-sized\r\n2023-07-26 17:43:54 Exception in callback BaseAsyncIOLoop._handle_events(28, 1)\r\n2023-07-26 17:43:54 handle: <Handle BaseAsyncIOLoop._handle_events(28, 1)>\r\n2023-07-26 17:43:54 Traceback (most recent call last):\r\n2023-07-26 17:43:54 File \"/usr/lib/python3.8/asyncio/events.py\", line 81, in _run\r\n2023-07-26 17:43:54 self._context.run(self._callback, *self._args)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/tornado/platform/asyncio.py\", line 206, in _handle_events\r\n2023-07-26 17:43:54 handler_func(fileobj, events)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 634, in _handle_events\r\n2023-07-26 17:43:54 self._handle_recv()\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 663, in _handle_recv\r\n2023-07-26 17:43:54 self._run_callback(callback, msg)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 584, in _run_callback\r\n2023-07-26 17:43:54 f = callback(*args, **kwargs)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 308, in stream_callback\r\n2023-07-26 17:43:54 return callback(self, msg)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/notebook/services/kernels/handlers.py\", line 572, in _on_zmq_reply\r\n2023-07-26 17:43:54 super()._on_zmq_reply(stream, msg)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/notebook/base/zmqhandlers.py\", line 256, in _on_zmq_reply\r\n2023-07-26 17:43:54 self.write_message(msg, binary=isinstance(msg, bytes))\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/tornado/websocket.py\", line 339, in write_message\r\n2023-07-26 17:43:54 return self.ws_connection.write_message(message, binary=binary)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/tornado/websocket.py\", line 1086, in write_message\r\n2023-07-26 17:43:54 fut = self._write_frame(True, opcode, message, flags=flags)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/tornado/websocket.py\", line 1061, in _write_frame\r\n2023-07-26 17:43:54 return self.stream.write(frame)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/tornado/iostream.py\", line 540, in write\r\n2023-07-26 17:43:54 self._write_buffer.append(data)\r\n2023-07-26 17:43:54 File \"/usr/local/lib/python3.8/dist-packages/tornado/iostream.py\", line 157, in append\r\n2023-07-26 17:43:54 b += data # type: ignore\r\n2023-07-26 17:43:54 BufferError: Existing exports of data: object cannot be re-sized", "Some random times I was getting this error training the keras models and the training was being stopped.\r\nSeems like the problem is on threads sync streaming the large output.\r\n\r\nSo, turning off the verbose of **fit()** method worked for me.\r\n**i.e:** model.fit(trainX, trainY, ... , **verbose=0**)\r\n\r\nI guess you can also use **verbose=2** for showing just the final details for each epoch and it will work fine.", "I will test it thank you @tgoMota ", " Uncaught exception in ZMQStream callback\r\n Traceback (most recent call last):\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/zmq/event loop/zmqstream.py\", line 584, in _run_callback\r\n f = callback(*args, **kwargs)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/zmq/event loop/zmqstream.py\", line 308, in stream_callback\r\n return callback(self, msg)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/notebook/ services/kernels/handlers.py\", line 572, in _on_zmq_reply\r\n super()._on_zmq_reply(stream, msg)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/notebook/ base/zmqhandlers.py\", line 256, in _on_zmq_reply\r\n self.write_message(msg, binary=isinstance(msg, bytes))\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/w ebsocket.py\", line 339, in write_message\r\n return self.ws_connection.write_message(message, binary=binary)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/w ebsocket.py\", line 1086, in write_message\r\n fut = self._write_frame(True, opcode, message, flags=flags)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/w ebsocket.py\", line 1061, in _write_frame\r\n return self.stream.write(frame)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/i ostream.py\", line 546, in write\r\n self._handle_write()\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/i ostream.py\", line 976, in _handle_write\r\n self._write_buffer.advance(num_bytes)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/i ostream.py\", line 182, in advance\r\n assert 0 < size <= self._size\r\n AssertionError\r\n[E 16:53:16.352 NotebookApp] Uncaught exception in zmqstream callback\r\n Traceback (most recent call last):\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/zmq/event loop/zmqstream.py\", line 634, in _handle_events\r\n self._handle_recv()\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/zmq/event loop/zmqstream.py\", line 663, in _handle_recv\r\n self._run_callback(callback, msg)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/zmq/event loop/zmqstream.py\", line 584, in _run_callback\r\n f = callback(*args, **kwargs)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/zmq/event loop/zmqstream.py\", line 308, in stream_callback\r\n return callback(self, msg)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/notebook/ services/kernels/handlers.py\", line 572, in _on_zmq_reply\r\n super()._on_zmq_reply(stream, msg)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/notebook/ base/zmqhandlers.py\", line 256, in _on_zmq_reply\r\n self.write_message(msg, binary=isinstance(msg, bytes))\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/w ebsocket.py\", line 339, in write_message\r\n return self.ws_connection.write_message(message, binary=binary)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/w ebsocket.py\", line 1086, in write_message\r\n fut = self._write_frame(True, opcode, message, flags=flags)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/w ebsocket.py\", line 1061, in _write_frame\r\n return self.stream.write(frame)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/i ostream.py\", line 546, in write\r\n self._handle_write()\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/i ostream.py\", line 976, in _handle_write\r\n self._write_buffer.advance(num_bytes)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/i ostream.py\", line 182, in advance\r\n assert 0 < size <= self._size\r\n AssertionError\r\nException in callback BaseAsyncIOLoop._handle_events(33, 1)\r\nhandle: <Handle BaseAsyncIOLoop._handle_events(33, 1)>\r\nTraceback (most recent call last):\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/asyncio/events.py\", line 80 , in _run\r\n self._context.run(self._callback, *self._args)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/platf orm/asyncio.py\", line 206, in _handle_events\r\n handler_func(fileobj, events)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/zmq/eventloop /zmqstream.py\", line 634, in _handle_events\r\n self._handle_recv()\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/zmq/eventloop /zmqstream.py\", line 663, in _handle_recv\r\n self._run_callback(callback, msg)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/zmq/eventloop /zmqstream.py\", line 584, in _run_callback\r\n f = callback(*args, **kwargs)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/zmq/eventloop /zmqstream.py\", line 308, in stream_callback\r\n return callback(self, msg)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/notebook/serv ices/kernels/handlers.py\", line 572, in _on_zmq_reply\r\n super()._on_zmq_reply(stream, msg)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/notebook/base /zmqhandlers.py\", line 256, in _on_zmq_reply\r\n self.write_message(msg, binary=isinstance(msg, bytes))\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/webso cket.py\", line 339, in write_message\r\n return self.ws_connection.write_message(message, binary=binary)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/webso cket.py\", line 1086, in write_message\r\n fut = self._write_frame(True, opcode, message, flags=flags)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/webso cket.py\", line 1061, in _write_frame\r\n return self.stream.write(frame)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/iostr eam.py\", line 546, in write\r\n self._handle_write()\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/iostr eam.py\", line 976, in _handle_write\r\n self._write_buffer.advance(num_bytes)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/iostr eam.py\", line 182, in advance\r\n assert 0 < size <= self._size\r\nAssertionError\r\n\r\n\r\nStill this error is persistent @tgoMota ", "I've encountered the same or a similar issue with TF 2.13.\r\n\r\ndocker run --gpus all --rm -u $(id -u):$(id -g) -p 8888:8888 -p 6006:6006 -v $PWD/:/tf/david_home tensorflow/tensorflow:2.13.0-gpu-jupyter\r\n\r\nIn my case output of the notebook to Firefox has frozen but the training job still seems to be running. \r\nFWIW when I run the same notebook using TF 2.13 with the LambdaStack, this does not happen i.e. output works as expected.\r\n\r\nE 12:40:01.689 NotebookApp] Uncaught exception in ZMQStream callback\r\n Traceback (most recent call last):\r\n File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 584, in _run_callback\r\n f = callback(*args, **kwargs)\r\n File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 308, in stream_callback\r\n return callback(self, msg)\r\n File \"/usr/local/lib/python3.8/dist-packages/notebook/services/kernels/handlers.py\", line 572, in _on_zmq_reply\r\n super()._on_zmq_reply(stream, msg)\r\n File \"/usr/local/lib/python3.8/dist-packages/notebook/base/zmqhandlers.py\", line 256, in _on_zmq_reply\r\n self.write_message(msg, binary=isinstance(msg, bytes))\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/websocket.py\", line 334, in write_message\r\n return self.ws_connection.write_message(message, binary=binary)\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/websocket.py\", line 1081, in write_message\r\n fut = self._write_frame(True, opcode, message, flags=flags)\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/websocket.py\", line 1056, in _write_frame\r\n return self.stream.write(frame)\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/iostream.py\", line 533, in write\r\n self._write_buffer.append(data)\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/iostream.py\", line 157, in append\r\n b += data # type: ignore\r\n BufferError: Existing exports of data: object cannot be re-sized\r\n[E 12:40:01.691 NotebookApp] Uncaught exception in zmqstream callback\r\n Traceback (most recent call last):\r\n File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 634, in _handle_events\r\n self._handle_recv()\r\n File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 663, in _handle_recv\r\n self._run_callback(callback, msg)\r\n File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 584, in _run_callback\r\n f = callback(*args, **kwargs)\r\n File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 308, in stream_callback\r\n return callback(self, msg)\r\n File \"/usr/local/lib/python3.8/dist-packages/notebook/services/kernels/handlers.py\", line 572, in _on_zmq_reply\r\n super()._on_zmq_reply(stream, msg)\r\n File \"/usr/local/lib/python3.8/dist-packages/notebook/base/zmqhandlers.py\", line 256, in _on_zmq_reply\r\n self.write_message(msg, binary=isinstance(msg, bytes))\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/websocket.py\", line 334, in write_message\r\n return self.ws_connection.write_message(message, binary=binary)\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/websocket.py\", line 1081, in write_message\r\n fut = self._write_frame(True, opcode, message, flags=flags)\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/websocket.py\", line 1056, in _write_frame\r\n return self.stream.write(frame)\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/iostream.py\", line 533, in write\r\n self._write_buffer.append(data)\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/iostream.py\", line 157, in append\r\n b += data # type: ignore\r\n BufferError: Existing exports of data: object cannot be re-sized\r\nERROR:asyncio:Exception in callback BaseAsyncIOLoop._handle_events(29, 1)\r\nhandle: <Handle BaseAsyncIOLoop._handle_events(29, 1)>\r\nTraceback (most recent call last):\r\n File \"/usr/lib/python3.8/asyncio/events.py\", line 81, in _run\r\n self._context.run(self._callback, *self._args)\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/platform/asyncio.py\", line 192, in _handle_events\r\n handler_func(fileobj, events)\r\n File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 634, in _handle_events\r\n self._handle_recv()\r\n File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 663, in _handle_recv\r\n self._run_callback(callback, msg)\r\n File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 584, in _run_callback\r\n f = callback(*args, **kwargs)\r\n File \"/usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py\", line 308, in stream_callback\r\n return callback(self, msg)\r\n File \"/usr/local/lib/python3.8/dist-packages/notebook/services/kernels/handlers.py\", line 572, in _on_zmq_reply\r\n super()._on_zmq_reply(stream, msg)\r\n File \"/usr/local/lib/python3.8/dist-packages/notebook/base/zmqhandlers.py\", line 256, in _on_zmq_reply\r\n self.write_message(msg, binary=isinstance(msg, bytes))\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/websocket.py\", line 334, in write_message\r\n return self.ws_connection.write_message(message, binary=binary)\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/websocket.py\", line 1081, in write_message\r\n fut = self._write_frame(True, opcode, message, flags=flags)\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/websocket.py\", line 1056, in _write_frame\r\n return self.stream.write(frame)\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/iostream.py\", line 533, in write\r\n self._write_buffer.append(data)\r\n File \"/usr/local/lib/python3.8/dist-packages/tornado/iostream.py\", line 157, in append\r\n b += data # type: ignore\r\nBufferError: Existing exports of data: object cannot be re-sized\r\n", "as @tgoMota mentioned try keeping \r\n\r\n verbose=2 \r\n\r\nThis worked for me\r\n", "@CaffineAddic , Is this still an issue? if the issue is resolved by changing verbose=0 to verbose=2, could you please close the issue.\r\nAlso, use the latest TensorFlow version to get the latest updates. Thank you!", "Ya essentially allowed me to start training but I cannot comment on the rest of users, also shouldn't training should happen with the default verbose value, tornado errors are still there if you want to I can close this issue, Thank you for your time and support. ", " Exception in callback <bound method WebSocketMixin.send_ping of ZMQChannelsHandler(bccd1408-a4a4-4810-805f-d10d0d4585df)>\r\n Traceback (most recent call last):\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/ioloop.py\", line 921, in _run\r\n val = self.callback()\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/notebook/base/zmqhandlers.py\", line 188, in send_ping\r\n self.ping(b'')\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/websocket.py\", line 445, in ping\r\n self.ws_connection.write_ping(data)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/websocket.py\", line 1101, in write_ping\r\n self._write_frame(True, 0x9, data)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/websocket.py\", line 1061, in _write_frame\r\n return self.stream.write(frame)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/iostream.py\", line 540, in write\r\n self._write_buffer.append(data)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/iostream.py\", line 157, in append\r\n b += data # type: ignore\r\n BufferError: Existing exports of data: object cannot be re-sized\r\n[I 01:36:07.003 NotebookApp] Saving file at /New_BRaTS/Brain_data/HybridMorph_proof of concept.ipynb\r\n[E 01:36:28.774 NotebookApp] Uncaught exception in ZMQStream callback\r\n Traceback (most recent call last):\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/zmq/eventloop/zmqstream.py\", line 584, in _run_callback\r\n f = callback(*args, **kwargs)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/zmq/eventloop/zmqstream.py\", line 308, in stream_callback\r\n return callback(self, msg)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/notebook/services/kernels/handlers.py\", line 572, in _on_zmq_reply\r\n super()._on_zmq_reply(stream, msg)\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/notebook/base/zmqhandlers.py\", line 256, in _on_zmq_reply\r\n self.write_message(msg, binary=isinstance(msg, bytes))\r\n File \"/home/saumya/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/websocket.py\", line 339, in write_message\r\n return self.ws_connection.write_message(message, binary=binary\r\n\r\nSame error with verbose = 2. Any fixes " ]

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[ "the tflite model is convert from https://tfhub.dev/captain-pool/esrgan-tf2/1", "@OwenHongJinmu \r\nCould you please elaborate more and provide detailed steps to replicate the issue reported here ?\r\n\r\nThank you !", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Hi @OwenHongJinmu \r\n\r\nThe TFLite model converted from the TF model might loose some accuracy because of quantization.\r\n\r\nYou can keep the float model by turning off the optimization by `converter.optimizations =[]` while converting the model to TFLite which can preserve better accuracy.\r\n\r\nThanks.\r\n\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60308\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60308\">No</a>\n" ]

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[ "@penpornk Thank you reviewing this PR! I have addressed your review comments. Please take a look." ]

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[ "@reedwm Sorry for informing late. We decided not to cast F32->BF16 directly by XLA since it breaks what we previously did. Instead, our proposal is to only fuse the following pattern:\r\ny_fp32 = matmul(a, b) + broadcast(cast(cast(bias, bf16), fp32)) ---> y_fp32 = cublas-custom-call(a, b, cast(bias, bf16))\r\n\r\n", "Should I not merge this PR then? Why does casting F32->BF16 break what you previously did? It seems weird to require the user to cast the bias to bf16 then back to f32.", "Yes, please don't merge this PR. User will not normally do that. Casting the bias to bf16 then back to f32 is only for FP8 purpose." ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60305/checks?check_run_id=12704844397) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Hi @VijayR19 Can you please sign CLA. Thank you!", "> Hi @VijayR19 Can you please sign CLA. Thank you!\r\n\r\nHi, I have already done that but I still see that missing CLA. Very strange!" ]

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[ "Please don't use \"add file\"/\"update file\"/\"fix file\"/etc. commit messages. These are hard to reason about when looking at the history of the file/repository. Instead, please write explanatory git commit messages.\r\n\r\nThe commit message is also the title of the PR if the PR has only one commit. It is thus twice important to have commit messages that are relevant, as PRs would be easier to understand and easier to analyze in search results.\r\n\r\nFor how to write good quality git commit messages, please consult https://cbea.ms/git-commit/ \r\n\r\nCC @yishuangP ", "Thanks for the tip! I'll use a more meaningful commit message next time" ]

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[]

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[ "Rolled forward again at https://github.com/tensorflow/tensorflow/commit/641501d1c773fe2b8527f63052ee3ae4f05c01a2" ]

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[ "[proposed Label] comp:lite-tosa, seems I don't have the access to config the labels.\r\n\r\n", "Hi @Jerry-Ge,\r\n\r\nYes, currently it is true that `tf.IfOp -> tfl.IfOp` only happens at export time. Would you mind sharing the tflite model in question as well as how it was generated?", "Thanks @LukeBoyer, I pushed the dummy model I used for testing here: https://github.com/Jerry-Ge/tfl_models/tree/main\r\n\r\nIt's generated by using the `tf.cond()` operator and the `tf.lite.TFLiteConverter.from_concrete_functions` \r\n\r\nThen you ran the flatbuffer_translate code provided from above, you will get the `test_tflite.preopt.mlir` output I got. \r\n\r\nLet me know if you need more details. \r\n\r\n\r\n\r\n", "Thanks @Jerry-Ge. We are prioritizing fixing this issue and having the if op be legalized appropriately. I think 1-2 weeks is when the fix will come.", "> \r\n\r\nHi Luke, any updates here? Tks ", "Hi @Jerry-Ge, we're still working on this. It's taking longer than expected since a lot of tests expect `tf.If` and the op is not entirely equivalent to `tfl.If` so it's not a trivial find/replace change. We also have internal downstream dependents which will need to be updated.\r\n\r\nCan you help provide additional detail and context about your need for this? Just trying to understand if it's a nuisance, blocker, or something in between. Thanks!", "Thanks for the update! Don't worry and I can wait : )", "Hi @arfaian, any updates on this issue? If it's done, then we can close it? ", "If nobody plans to fix this, I will close this ticket by the end of this week. ", "Nobody is responding to this issue. Close this ticket for now. " ]

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[ "[proposed Label] comp:lite-tosa, seems I don't have the access to config the labels. ", "Hi @Jerry-Ge, few questions:\r\n* how can I access the model in question; `relu0To1/test_relu0To1_13x21x3_qu8/model.tflite`?\r\n* What do we mean exactly by \"generate relu_0_to_1\" op? Is this op present in the flatbuffer but not present in output mlir via the translate tool?\r\n * If so, would you mind sharing how this tflite model was created?\r\n", "> Hi @Jerry-Ge, few questions:\r\n> \r\n> * how can I access the model in question; `relu0To1/test_relu0To1_13x21x3_qu8/model.tflite`?\r\n> * What do we mean exactly by \"generate relu_0_to_1\" op? Is this op present in the flatbuffer but not present in output mlir via the translate tool?\r\n> \r\n> * If so, would you mind sharing how this tflite model was created?\r\n\r\nHi Luke, thanks for following up with this. \r\n- That's a very dummy model created by using `tf.math.minimum(1.0, tf.math.maximum(0.0, a))` . I can see a relu with minimum there but ideally we should have relu0To1 right? \r\n Link to the model file: https://github.com/Jerry-Ge/tfl_models/blob/main/model_relu0To1.tflite\r\n<img width=\"382\" alt=\"Screenshot 2023-05-19 at 1 01 21 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/24451859/8eba1ac9-d85c-4a77-99c9-06515ece88e0\">\r\n\r\n- Yea, I am currently using the `tf.lite.TFLiteConverter.from_concrete_functions` to generate the tflite models. ", "Ok I see the issue. While there is an op def for `relu_0_to_1` there is no logic to write them into the graph.\r\n\r\nI can add some logic to rewrite patterns such as the following:\r\n```\r\n %cst0 = arith.constant dense<0.0> : tensor<f32>\r\n %0 = \"tfl.maximum\"(%arg0, %cst0) : (tensor<2x2xf32>, tensor<f32>) -> tensor<2x2xf32>\r\n %cst1 = arith.constant dense<1.0> : tensor<f32>\r\n %1 = \"tfl.minimum\"(%0, %cst1) : (tensor<2x2xf32>, tensor<f32>) -> tensor<2x2xf32>\r\n```\r\nto \r\n```\r\n%0 = \"tfl.relu_0_to_1\"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>\r\n```\r\n\r\nI'll keep this thread posted with status of change. Also worth noting that the min/maxes commute so either ordering should be rewritten.", "Hi @Jerry-Ge, this should be all set with https://github.com/tensorflow/tensorflow/commit/0a2aab71fedfc92ce99b484a87e926bdfcbec1d9", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60300\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60300\">No</a>\n", "> Hi @Jerry-Ge, this should be all set with [0a2aab7](https://github.com/tensorflow/tensorflow/commit/0a2aab71fedfc92ce99b484a87e926bdfcbec1d9)\r\n\r\nYes, this is what I want and Thanks so much Luke!" ]

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[ "Hi @alankelly Can you please review this PR ? Thank you!" ]

[]

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[ "Working on a rollforward", "Rolled forward at https://github.com/tensorflow/tensorflow/commit/da439b7d05584f93e745511d66f6d74a58b54b34" ]

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[ "Let's try with this fix, but if test fail let's do the real larger change that handles this better (https://github.com/tensorflow/tensorflow/pull/60296#discussion_r1163413390)" ]

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[ "@TimofeyVO,\r\nI was facing a different issue while executing the mentioned code. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/0d6564af601c83c9fdab62cb4b30855a/untitled1068.ipynb) and provide required dependencies which helps to reproduce the issue. \r\n\r\nWhen you give the validation data as a variable it throws the Graph execution error. (for instance when you try to write \"model.predict([[a,b]]))\". But when you give an integer or float (model.predict([[17,2]])) it works and execution will happen.\r\n We can bypassed the problem by writing **\"model.predict([[float(a),float(b)]])\"** (We can also use the related type instead of float) Also there are similar issues that are still with the developer and they are working on the same.\r\nhttps://github.com/keras-team/tf-keras/issues/66\r\nhttps://github.com/keras-team/tf-keras/issues/639\r\n\r\nThank you!", "> \r\nIf I understood your remark correctly, then what\r\n1 file - mit_data_processing.py\r\n2 file - train_face_id.py\r\n3 file - test_face_id.py\r\n\r\nIn this screenshot, the file name should not be\r\n\r\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60294\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60294\">No</a>\n" ]

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[]

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[ "@penpornk Hi Penporn, I noticed that there were failures related internal CI and AMD ROCm build. But I am not sure if this PR caused them. The old should NOT be executed in recent years after we moved from MKL ML to MKL DNN (around 2017 or 18). \r\nPlease help to check. Thanks!\r\n\r\nMy next step is to clean up related bazel configuration (to get rid of \"if_mkl_ml\" staff). -GZ", "@gzmkl I haven't seen the latest ROCm results on this PR. (It's still running at the time I'm writing this comment.) But ROCm has been having infra failure lately. (It failed in many other PRs.) So it's likely unrelated to this PR. I'll take a look again after it's done running. :)", "@penpornk Please help to check the latest ROCm results on this PR. I could not find anything with the \"Details\" link. THANKS", "This PR is replaced by the new one (hopefully no more conflicts after oneDNN 3.x integration)\r\n\r\nhttps://github.com/tensorflow/tensorflow/pull/61471\r\n\r\nThus close this one\r\n" ]

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[ "@edwardyehuang \r\nCould you please provide reproducible code or Colab gist to replicate the issue reported here ?\r\n\r\nThank you !", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60291\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60291\">No</a>\n" ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60290/checks?check_run_id=12660438542) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Please don't use \"add file\"/\"update file\"/\"fix file\"/etc. commit messages. These are hard to reason about when looking at the history of the file/repository. Instead, please write explanatory git commit messages.\r\n\r\nThe commit message is also the title of the PR if the PR has only one commit. It is thus twice important to have commit messages that are relevant, as PRs would be easier to understand and easier to analyze in search results.\r\n\r\nFor how to write good quality git commit messages, please consult https://cbea.ms/git-commit/ ", "Hi @AspireVenom Can you please check @mihaimaruseac's comments and keep us posted ? Thank you!", "PR title changed, commit message not. But let's take it as it is and future commits would follow guidelines." ]

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[ "@DLumi \r\nThis issue seems to be Keras issue. Please post this issue on [keras-team/keras repo.](https://github.com/keras-team/keras/issues) as Keras development is fully moving to [github.com/keras-team/keras](http://github.com/keras-team/keras). All issues and PRs related to keras will be addressed in that repo.\r\nTo know more see this TF forum discussion ; \r\n[https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999](https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999)\r\nThank you!", "I really don't see how it is the Keras issue, since it seems like a manifestation of default behavior for calling `tf.function().get_concrete_function()`. If you don't specify tensor names in provided list of TensorSpecs, the algorithm would just stick to defaults (x for inputs; Identity for outputs), but if you do name your tensors, the `x` would be replaced with whatever you provided. Sadly that's not the case for outputs that get named `Identity`, since the algorithm slaps a fresh copy of default Identity operation on top of whatever there was before, and you simply don't have an option to provide a spec for this, as outputs seem to be tracked and named automatically. Thus you have no ability of preserving your Keras output names. \r\nI will repost this in Keras repo as you suggested, but I'll probably get redirected back here pretty quickly. ", "Hi @DLumi,\r\n\r\nAs the issue is already on Keras repo can we close it here so that it can be better tracked at single repo. Thanks!", "> Hi @DLumi,\r\n> \r\n> As the issue is already on Keras repo can we close it here so that it can be better tracked at single repo. Thanks!\r\n\r\nHonestly, I'd keep both issues open in case there are some tweaks required from the TF team. Unless the Keras team explicitly says they will handle this themselves, that is. \r\nBut I understand your wish to reduce issues piling up, so feel free to close this if that's really needed. ", "@SuryanarayanaY as expected, I got bounced back to you guys.\r\nhttps://github.com/keras-team/tf-keras/issues/219" ]

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[ "Hi @davidkflau, Thank you for raising the issue!\r\nWe were able to replicate the issue in Colab using Tensorflow 2.12 and TPU. Please find the gist [here](https://colab.sandbox.google.com/gist/synandi/f1de44f03fd28314691b7d9ee3512600/60288_tpu.ipynb). It seems like we have to dig deep into the issue. We'll update here soon. Thank you!", "Hi @davidkflau ,\r\n\r\nAt present not all Ops are executable with TPUs. The iter operation might be one of them. Also, from the error log:\r\n```\r\nExecuting non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.\r\n```\r\n\r\nIt seems iter operation is not supported on TPUs. Please find the TPU supported Ops list [here](https://cloud.google.com/tpu/docs/tensorflow-ops) for reference. \r\n\r\nThank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60288\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60288\">No</a>\n" ]

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[ "If you read the tensorflow documentation for tfp.stats.histogram(https://www.tensorflow.org/probability/api_docs/python/tfp/stats/histogram), upon reading the description of 'axis', it says : \"Optional 0-D or 1-D integer Tensor with constant values.\" which means that tensorflow expects the axis to be passed as a tensor to generate the required results.\r\n\r\nYou can try passing the axis as a tensor, something like this : axis = tf.constant(0, 1)\r\n", "@joaozenobio,\r\nLooks like this issue is more related to tensorflow propability repo. Could you please raise the issue in the respective repo from here for further assistance. \r\nhttps://github.com/tensorflow/probability/issues\r\nThank you!\r\n", "@ayushsh314, I've tried using this code now:\r\n```\r\nimport tensorflow_probability as tfp\r\nimport tensorflow as tf\r\nx = tf.constant([[1, 2, 3], [1, 2, 3.]])\r\nw = tf.constant([[1, 1, 1], [1, 1, 1.]])\r\ne = tf.constant([0, 2.5, 10])\r\naxis = tf.constant([0, 1])\r\ntfp.stats.histogram(x=x, edges=e, axis=axis, weights=w)\r\n```\r\n\r\nIt gives me the same error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/opt/.pycharm_helpers/pydev/pydevconsole.py\", line 364, in runcode\r\n coro = func()\r\n File \"<input>\", line 1, in <module>\r\n File \"/usr/local/lib/python3.8/dist-packages/tensorflow_probability/python/stats/quantiles.py\", line 393, in histogram\r\n raise ValueError('Number of dimensions of `x` and `weights` must '\r\nValueError: Number of dimensions of `x` and `weights` must coincide. Found: x has 2, weights has 1\r\n```\r\n", "@tilakrayal, I will raise a new issue there and make a reference to this one, thanks.", "@joaozenobio,\r\nPlease feel free to move this issue to closed status, as it has been tracked in the respective repo. Thank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60287\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60287\">No</a>\n" ]

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[ "The test code is as follows:\r\nTEST_F(GpuKernelTilingTest, module_0615_entry_bitcast_2) { \r\n const char* testcase = R\"( \r\n ¦ HloModule m, is_scheduled=true \r\n ¦ ENTRY out_of_fusion { \r\n ¦ ¦ p0 = f32[1]{0} parameter(0) \r\n ¦ ¦ ROOT res = f32[] bitcast(f32[1]{0} p0) \r\n ¦ } \r\n )\"; \r\n auto module = ParseAndReturnVerifiedModule(testcase).value(); \r\n EXPECT_TRUE(RunAndCompareNoHloPasses(std::move(module), ErrorSpec(1e-5))); \r\n } \r\n ", "Due to the entry parameter not being an alias for the output, the input parameter memory was freed before memcpying its data to host.", "Is it possible to repro with pre-optimizations HLO? Note that `bitcast` does not exist in input HLO, it only appears after optimizations." ]

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[ "hi i am new to opensource , can you tell explain a little bit so that i can look into it ", "https://www.deepmind.com/blog/trfl is not valid.\r\nthis is an external link in https://www.tensorflow.org/resources/libraries-extensions page.\r\nhttps://www.deepmind.com/blog/trfl is link of \"TRFL \" text.", "Can I work on this issue", "Can you please say the file location where the error occurs", "Hi @jntdst \r\nThank you for reporting the issue! We will update here soon.", "Hi @synandi Can you help me i can solve this problem. I Try to find but i cant able to find. As per @jntdst Version 2.8 now current version is 2.12.0. May be in the update it's resolved.", "Hello، I filled Tensorflow version by mistake. I mean the current version.\nBecause of, GitHub didn't allow me to send a report without a version, and\nI also used the Github default version. I still see the link on the page.\ndon't you see?\n\nOn Tue, Apr 11, 2023, 10:28 PM Kannan N ***@***.***> wrote:\n\n> Hi @synandi <https://github.com/synandi> Can you help me i can solve this\n> problem. I Try to find but i cant able to find. As per @jntdst\n> <https://github.com/jntdst> Version 2.8 now current version is 2.12.0.\n> May be in the update it's resolved.\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/tensorflow/tensorflow/issues/60285#issuecomment-1503850500>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AB5S6JI75JRJXYOTDWIQXOTXAWLVHANCNFSM6AAAAAAWYTKQ5A>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n", "@jntdst can you assisn me the issue I will check and try to solve the issue ", "@jntdst can you tell me more details i have more experience in working with tensorflow and keras", "@jntdst, Apologies for the delay. We have raised a PR internally, this issue will be closed once the PR is merged. Thank you! ", "Hi, \r\n\r\nThanks for reporting the issue. \r\n\r\nThe link has been updated in the https://www.tensorflow.org/resources/libraries-extensions\r\nNow TFRL will redirect to github repository https://github.com/deepmind/trfl/.\r\nPlease check and feel free to close the issue. Thanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60285\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60285\">No</a>\n" ]

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[ "Also, is there any corresponding C API for `GetHandleShapeAndType` and `SetHandleShapeAndType` in `python_api.h`?" ]

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[ "the following are the two log files:\r\n[err - 副本.txt](https://github.com/tensorflow/tensorflow/files/11187225/err.-.txt)\r\n[stdout-23850.txt](https://github.com/tensorflow/tensorflow/files/11187248/stdout-23850.txt)\r\n", "@SuryanarayanaY,\r\nI was able to reproduce the issue on tensorflow v2.12. Kindly find the reference below.\r\n```\r\n# Execution platform: @local_execution_config_platform//:platform\r\nERROR: C:/tensorflow/tensorflow/BUILD:1309:11: Linking tensorflow/tensorflow_cc.dll failed: (Exit 1120): link.exe failed: error executing command \r\n \r\n```", "@mraunak , Could you please take a look into the issue. Thanks!", "Hi, @yisir323, sorry for the delayed response. Could you please run the above command again on the latest commit and let us know the issue you are facing?\r\n", "Hi all, any chance there's been some progress on this issue ?", "please check this issue https://github.com/tensorflow/tensorflow/issues/61226\r\nSeems similar to this one. \r\nSeems tensorflow_cc target not supported in windows in some tensorflow versions" ]

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[ "Hi @samveen \r\n\r\nAs per the TFLite [documentation](https://www.tensorflow.org/lite/guide/build_cmake_arm#build_for_raspberry_pi_zero_armv6), the support for XNNPACK is disabled for ARMv6 since there is no NEON support. \r\n\r\nHave you observed the same behaviour with `-mfpu=vfpv2` as suggested by [README for xxnpack](https://github.com/google/XNNPACK#supported-architectures)?\r\n\r\nThanks.", "As can be seen in the log output, no matter what the user supplied values of `-march` and `-mfpu` are, the build integration of tflite with XNNPACK will add `-march=armv8.2-a+dotprod -mfpu=neon-fp-armv8` to the build flags for XNNPACK. \r\n\r\nFrom the [XNNPACK readme](https://github.com/google/XNNPACK#supported-architectures), it's clear that there is a subset of XNNPACK that is usable for Raspberry Pi Zero/ Zero W). However, what is not clear is that can tflite use just that subset of XNNPACK when it's being built for the Zero/Zero W.\r\n\r\nI'll try and build XNNPACK on the Pi Zero as per [the instructions](https://github.com/google/XNNPACK#raspberry-pi) and get back to you on the build state.\r\n", "@pjpratik\r\n- I've opened an issue against XNNPACK for more details (google/XNNPACK#4636) but there's been no response there\r\n- I've managed to native build a version of XNNPACK on the ZeroW (upwards of 24 hours to build) with the following:\r\n```bash ./scripts/build-local.sh -DXNNPACK_ENABLE_ARM_DOTPROD:BOOL=OFF```\r\n- One of the DOTPROD kernels has Neon assembly instruction which causes the build to fail, this the need for `XNNPACK_ENABLE_ARM_DOTPROD:BOOL=OFF`\r\n- The script runs tests against the build, but hasn't completed the 3rd test for `FP32 MobileNet v3 Large` even after 24 hours of running, which leads me believe that it might be stuck instead of taking time (the expected time on the Zero/Zero W is less that for `FP32 MobileNet v2 1.0X` which completes in less than 3 seconds)\r\n- In light of the above sticking point, I'm hoping I get some response from the XNNPACK team, even a 'no longer possible' one, which should give some clarity.", "@samveen Thanks for the information.\r\n\r\n@sachinprasadhs Could you please look into this issue?\r\n\r\nThanks.", "@pjpratik @sachinprasadhs I've created an issue against XNNPACK with a lot more details with regards to RPi0 builds - google/XNNPACK#4701 , giving the issues I've faced, the build process I followed and details of my native build environment. Hopefully that should give more insight into the underlying issues." ]

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[ "@SuryanarayanaY \r\nI was able to reproduce the issue on Mac M1 using tf 2.11. Please find the below screenshot for reference:\r\n<img width=\"565\" alt=\"Screenshot 2023-04-12 at 3 12 54 PM\" src=\"https://user-images.githubusercontent.com/111861663/231423500-0dd0eafd-f081-4000-9d01-34a7190b2539.png\">\r\nI was able to execute the code without error on colab using tf2.11 and tf2.12. Please find the gist of [tf2.11](https://colab.research.google.com/gist/tiruk007/c1366ad9bf92608d71a884b46bfc58a0/untitled187.ipynb) and [tf2.12](https://colab.research.google.com/gist/tiruk007/069354340b8af5dfd3f5751b263c23de/untitled188.ipynb) for reference.\r\n\r\nThank you !\r\n", "@AlbertoSinigaglia ,\r\n\r\nI have explicitly tried the code with `Colab` on CPU and GPU by using tf.device() and it works fine for both.Reference [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/b260d9fb9990274e679cab1de774685d/60281.ipynb).\r\n\r\nWhen tried the code on Mac by with `tf.device('CPU')` the code works fine and the problem raises when used `tf.device('GPU')`. Please refer the attached logs below.\r\n\r\n[60281_mac_logs.txt](https://github.com/tensorflow/tensorflow/files/11210566/60281_mac_logs.txt)\r\n\r\n\r\nThis seems to be issue specific to `tensorflow-macos` which is maintained by Apple it self. Request you to file an issue there also and mostly should be addressed by them.Efforts from our side also will be in place to collaborate with Apple team to resolve the issue.\r\n\r\nThanks!", "> @AlbertoSinigaglia ,\r\n> \r\n> I have explicitly tried the code with `Colab` on CPU and GPU by using tf.device() and it works fine for both.Reference [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/b260d9fb9990274e679cab1de774685d/60281.ipynb).\r\n> \r\n> When tried the code on Mac by with `tf.device('CPU')` the code works fine and the problem raises when used `tf.device('GPU')`. Please refer the attached logs below.\r\n> \r\n> [60281_mac_logs.txt](https://github.com/tensorflow/tensorflow/files/11210566/60281_mac_logs.txt)\r\n> \r\n> This seems to be issue specific to `tensorflow-macos` which is maintained by Apple it self. Request you to file an issue there also and mostly should be addressed by them.Efforts from our side also will be in place to collaborate with Apple team to resolve the issue.\r\n> \r\n> Thanks!\r\n\r\nWill do that, even though I have already many pending requests to Apple, with no answer since months...", "@nitins17 , Could you please have a look and confirm whether any pointers here?\r\n\r\nCC: @learning-to-play ", "Hi,\r\n\r\nThe issue seems to be resolved with the latest Tensorflow version.\r\n\r\nYou can now install Tensorflow package on M1 from 2.13 using pip install Tensorflow.\r\n\r\nBelow is the command to install Tensorflow package, once it is installed, code mentioned in the issue runs without any error.\r\n\r\n!pip install tensorflow==2.13rc1", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60281\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60281\">No</a>\n" ]

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[ "@Yang-Yuan Thanks for reporting an issue!\r\nCould you please elaborate the issue here by stating the example if you are using any. Thank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60280\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60280\">No</a>\n" ]

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[]

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[ "@SuryanarayanaY \r\nI was able to reproduce the issue on Colab using tf 2.12 and tf-nightly. Please find the gist of [2.12](https://colab.research.google.com/gist/tiruk007/d6c87adf711369429ae38ead62a7d322/untitled182.ipynb) and [tf-nightly](https://colab.research.google.com/gist/tiruk007/9f02ac71d52e640f864f0736c59fed97/tf-nightly.ipynb#scrollTo=Az7CQKKwobxM) for reference.\r\n\r\nThank you !", "Hi @shijy16 ,\r\n\r\nThe issue got resolved now with tf-nightly(2.14.0-dev20230514) and now TF is successfully able to raise the intended Error.\r\nPlease refer to attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/11f2a25b393d2532bf781b08b3849ff6/60278.ipynb).\r\n\r\nPlease check and confirm and let us know if we can close the issue as it resolved now.\r\n\r\nThanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60278\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60278\">No</a>\n" ]

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[ "@sachinprasadhs \r\nI was able to reproduce the issue on Colab using TF v2.11 and tf-nightly. Please find the gist of [2.12](https://colab.research.google.com/gist/tiruk007/f1f72fe0b887bddd0598f90dabab5da2/untitled185.ipynb) and [tf-nightly]( https://colab.research.google.com/gist/tiruk007/bd93c0da07e5ae4f8221d5664f718f53/untitled186.ipynb)for reference.\r\n\r\nThank you !", "This should not be a real issue. The check failure is an overflow due to \r\n\r\n```py\r\n superdiag = tf.random.uniform([5, 13, 5, 0, 6291163412844499803, 7], dtype=tf.float64, minval=-1024, maxval=1024)\r\n```\r\n\r\nThis is similar to writing the overflow itself, it won't show up in a real model.", "Hi @shijy16 ,\r\n\r\nThis has been fixed. tested with TF2.16v and found raising an exception rather than check fail. Refer [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/30733cd9b19b8509065f590c1c747e45/60277_nightly_success.ipynb).", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60277\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60277\">No</a>\n" ]

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[ "@shijy16 This ticket is related to vulnerabilities. Please consult https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md . Please refer to [this](https://github.com/tensorflow/tensorflow/issues/60197#issuecomment-1496337927) comment as well. Thank you!\r\n\r\n", "@sushreebarsa \r\n\r\nHi, thank you. I will report this issue to OSS VRP.\r\n\r\nBut I am not very clear about the boundary between vulnerability and bug. As stated in the security.md:\r\n````\r\nIf an assertion failure only leads to program termination and no other exploits, we will no longer consider assertion failures (e.g., CHECK-fails) as vulnerabilities.\r\n````\r\nThe check-fails are not regarded as vulnerabilities. Why is this one related to vulnerabilities? ", "@sushreebarsa \r\nHi, OSS VRP has replied that this issue is not severe enough to be tracked as a security bug.", "Hi @shijy16 ,\r\n\r\nThis has been fixed at tf-nightly as per [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/fd19bf2247cf811eb947cb2f4ac80cce/60276.ipynb). Now it's raising exception.\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60276\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60276\">No</a>\n" ]

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[ "@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow v2.12 and nightly. Kindly find the gist and the screen shot for the reference.\r\n\r\n```\r\n(tf) tilakrayal@tilak-cpu-instance:~$ python3 mkjfgkkll.py\r\n2023-04-10 09:16:56.384974: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\r\n2023-04-10 09:16:56.956235: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\r\n2023-04-10 09:16:56.959832: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-04-10 09:16:59.274496: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-04-10 09:17:02.144480: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Encountered overflow when multiplying 13 with 8440370290997831992, result: -1\r\nAborted\r\n```", "Hi @shijy16 \r\nThis has been fixed. tested with TF2.16v and found raising an exception rather than check fail.Please refer [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/dcc83ff235a447e466eac66a9310966d/60275_nightly_success.ipynb).\r\nThanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60275\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60275\">No</a>\n" ]

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[ " The gradient computation on GPU is faster for large matrices but not for large batch dimensions when the submatrices are small. In this case it might be faster to use the cpu \r\n\r\n> `", "@SuryanarayanaY \r\nI was able to reproduce the issue on Colab using tf 2.12 and tf-nightly. Please find the gist of [2.12](https://colab.research.google.com/gist/tiruk007/a8ac5acfde8ceff7c0b28fc5e8d1941f/untitled181.ipynb) and [tf-nightly](https://colab.research.google.com/gist/tiruk007/28ba594db7ef2ef77fab1ab2521fa754/untitled182.ipynb) for reference.\r\n\r\nThank you!", "Hi @shijy16 ,\r\n\r\nThe issue got resolved now with tf-nightly(2.14.0-dev20230514) and now TF is successfully able to raise the intended Error.\r\nPlease refer to attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/b238a78a3b1e85af4cb11013966b39ba/60274.ipynb).\r\n\r\nPlease check and confirm and let us know if we can close the issue as it resolved now.\r\n\r\nThanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60274\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60274\">No</a>\n" ]

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[ "Hi @shijy16, thank you for reporting the issue!\r\nI was able to replicate the issue using tf-nightly(2.13.0-dev20230406). Please find the screenshot below.\r\n\r\n Thank you!", "@shijy16,\r\nTsplits has to be one of the dtype from tf.dtypes.\r\n\r\n`Tsplits | An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int64.`\r\n\r\nI tried to execute the mentioned code on tf-nightly with alternative work-around and it was executed without any fail or error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/4a38cf2173412697737e43c6140000cb/untitled1679.ipynb). Thank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60273\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60273\">No</a>\n" ]

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[ "More details:\r\n\r\nI found no evidence yet this is a heat throttling issue, 'cos after the huge drop in GPU utilization, other processes will overtake using the GPU.\r\nI wonder what's going on? Is there any profiling tips I can do to help investigate. I am aware I can \"fix\" this by doing batch inferences. (or just use CPU as some may recommend for non batch inference). But seeing this GPU utilization decay is still unsettling, since this can potentially happen for a training session (which is far longer).\r\n\r\nSo I would like to know the root cause. I have experience in TF in colab (and GCP linux), and I don't quite remember seeing this sort of behavior. I understood M2 Max is new.", "I found out this has something to do with the variation in length of input tokens from one inference to the next. It doesn't seem to like receiving lengths that vary greatly, maybe this causes some sort of weird fragmentation in GPU memory?? Here's the code that only extract IMDB sentences that has >512 tokens. And it is able to sustain GPU utilization, with ~30it/s.\r\n\r\n```\r\nfrom transformers import AutoTokenizer, TFDistilBertForSequenceClassification\r\n\r\ntokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-cased\")\r\nmodel = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-cased')\r\n\r\nfrom datasets import load_dataset\r\nimdb = load_dataset('imdb')\r\n\r\nprint('starting collecting sentences with tokens >= 512')\r\nsentences = [sentence for sentence in imdb['train']['text'] if tokenizer(sentence, truncation=True, return_tensors='tf')['input_ids'].shape[-1] >= 512]\r\nprint('finished collecting sentences with tokens >= 512')\r\n\r\nfor k, sentence in tqdm(enumerate(sentences)):\r\n inputs = tokenizer(sentence, truncation=True, return_tensors='tf')\r\n\r\n output = model(inputs).logits\r\n pred = np.argmax(output.numpy(), axis=1)\r\n \r\n if k % 100 == 0:\r\n print(f\"len(input_ids): {inputs['input_ids'].shape[-1]}\")\r\n\r\n``` \r\n\r\nprint\r\n\r\n```\r\n7it [00:00, 31.12it/s]\r\nlen(input_ids): 512\r\n107it [00:03, 32.38it/s]\r\nlen(input_ids): 512\r\n...\r\n...\r\n3804it [02:00, 31.85it/s]\r\nlen(input_ids): 512\r\n3904it [02:03, 32.50it/s]\r\nlen(input_ids): 512\r\n3946it [02:04, 31.70it/s]\r\n```\r\n\r\nAny ideas, is there a serious bug lurking behind? If you have instructions/tips on how to debug, I would be eager to try.", "Hi @kechan ,\r\n\r\nI just tried the code on Linux environment just to cross check the behaviour but got an error. Please refer to attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/0954c3cd6b8bc90db9decfd171bdfffe/60271.ipynb) and confirm whether the attached code is missing anything ? \r\n\r\nSince you are using tensorflow-macos with metal plugin request you to please post the issue in Apple forum also. \r\n\r\nThanks!", "@SuryanarayanaY Thanks for looking into it. \r\n\r\nI went to Colab notebook. That error I believed is due to incorrect import of tqdm. It should be:\r\n\r\n`from tqdm import tqdm`\r\n\r\nI will modify my code snippet to include the pip and imports from your notebook\r\n\r\nI changed it my own copy of colab notebook, and it worked. Please bear in mind this issue is not reproducible on Colab with GPU acceleration. It happens only with metal on apple silicon. \r\n\r\nAnd yes, here is the issue I logged on their forum:\r\n\r\nhttps://developer.apple.com/forums/thread/727890?answerId=750313022#750313022\r\n\r\nI am aware it may be a bit complicated to triage in lot of cases where more than one group is involved. However, if you can provide me tips, I can try debug this as far as I could, and cross-post whatever I find over with Apple. \r\n\r\nWhile I understood the problem, and know how to work around it, I felt that the Apple GPU is not fulfilling its potential, since aligning every input length to the max_len (to ensure data uniformity) may not memory-efficient.", "Hi @kechan,\r\n\r\nI got a query here. I gone through the code and In the code I can't see anywhere you have used tensorflow or keras. Is that Transformers using tensorflow at backend ? Is that the reason you posted issue here ? I am not sure whether the issue needs to be addressed here ? Please confirm the dependency with Tensorflow/keras. Thanks!\r\n", "@SuryanarayanaY This is the transformers module from HuggingFace. It sort of a higher level framework that sits atop both Tensorflow and Pytorch. The code snippet given is backed by TF, as indicated by the model name “ TFDistilBertForSequenceClassification”. While this code is only for inference, I can also reproduce this issue during training, (i.e. calling model.fit)", "@kechan ,\r\n\r\nThanks for the confirmation.You mean that `TFDistilBertForSequenceClassification` is completely built upon Tensorflow as backend right? \r\n\r\nAFAIK, This is the problem with only Mac M2 as you confirmed its fine on Colab/Linux, this needs to be addressed by Apple and I see you have already logged a ticket there. I can see some response there and hope it will be addressed\r\n\r\nIf the problem also observed with Linux/colab then we might also have a look into it.I see this is not the case here.\r\n", "@SuryanarayanaY This issue is specific to Apple M2 Max with Metal (GPU). And yes, TFDistil**** means it is backed by tensorflow in the backend. When I get the chance, I will attempt to build a case where huggingface is not involved, and see if it can reproduce. ", "@kechan ,\r\nThanks for confirmation. But we are not supporting `tensorflow-macos` and it is not same as `tensorflow`. If you can reproduce the behaviour with only `tensorflow` involved then we will definitely have a look into the issue. Thanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60271\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60271\">No</a>\n" ]

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[ "@arivero \r\n\r\nRunning a large language model on a cluster of GPUs can be achieved with TensorFlow's distribution strategies. Since TensorFlow 2.x provides a much easier interface to work with, I'll outline a high-level approach for you to follow using TensorFlow 2.x, Keras, and Horovod.\r\n\r\nInstall the necessary packages:\r\nInstall TensorFlow 2.x.\r\nInstall Horovod, a distributed deep learning framework, which works well with TensorFlow, Keras, and other DL frameworks.\r\nSet up the cluster:\r\nFor an in-house cluster of GPUs, make sure all devices are connected to the same network and have the necessary GPU drivers and software installed (CUDA, cuDNN, etc.).\r\nFor a cloud-based cluster, use the provider's GPU-based instances and set up a virtual private network (VPN) between the instances.\r\nConfigure TensorFlow and Horovod:\r\nImport TensorFlow, Keras, and Horovod.\r\nInitialize Horovod by calling horovod.tensorflow.keras.init().\r\nPrepare your model:\r\nLoad your large language model using TensorFlow or Keras, e.g., tf.keras.models.load_model('path/to/model').\r\nWrap the model's optimizer with horovod.DistributedOptimizer(optimizer).\r\nCompile the model with the wrapped optimizer and appropriate loss and metric functions.\r\nConfigure the training and evaluation steps:\r\nSet up your dataset and data pipeline.\r\nWrap your dataset with tf.data.experimental.parallel_interleave() or tf.data.experimental.CsvDataset.from_generator() to distribute the data across multiple GPUs.\r\nDefine a custom training loop or use the built-in model.fit() and model.evaluate() functions. With Horovod, the training and evaluation steps will be automatically distributed across the available GPUs.\r\nRun the training and evaluation:\r\nUse horovodrun to launch your script on all the available devices. For example, horovodrun -np 4 -H localhost:4 python your_script.py will run your script on 4 GPUs.\r\nThis high-level approach should provide a starting point for running a large language model on a cluster of GPUs using TensorFlow 2.x, Keras, and Horovod. You may need to fine-tune the configuration and code to adapt to your specific setup and requirements.", "@Adesoji1 just to be sure can you confirm that if I use a model such that **it does not fit in one GPU** but its graph can be put across four GPUs, then horovod allows me to run inference? I can not see in the documentation any decent sample of model parallelism, not even GPUs in the same machine, and we are speaking of multi-node clusters.\r\n\r\nEven if it does, I still think that a tensorflow only tutorial would be a good thing, as distributing graphs was one of the design goals of tensorflow, so it could be good to exhibit this capability.\r\n\r\n", "Hi @arivero ,\r\n\r\nFor training on multiple works you can go through the attached tutorials [here](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras) and [here](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_ctl).All these uses data parallelism concept.\r\n\r\nHowever if you are looking for model parallelism right now Tensorflow not supporting model parallelism.Please refer to the source [here](https://www.tensorflow.org/api_docs/python/tf/distribute#:~:text=Data%20parallelism%20is,in%20the%20future).Here Model parallelism refers to splitting the Model on to different Ops and placing them on to different devices/workers and executing the Ops in Parallel. Right now Tensorflow supports data Parallelism i.e. splitting the data into different devices/workers.\r\n\r\nIf I am still missing something here please let me know. Thanks!\r\n", "Thanks, yes, I was looking for model parallelism, I see that it is not supported for training, but I was hoping that some support for inference only, when the graph is already know and trained. There is some legacy information around where it is suggested to use submodels and tf.device() contexts, to deploy manually, but it is mainly for tensorflow 1.x and not sure if it really works across servers.\r\n\r\nWhat I was looking for was a recipe such as:\r\n- build the model.\r\n- from the model, obtain the graph.\r\n- for each node, launch a server for all the devices it manages, or allows allocation.\r\n- for the graph, loop manually across, using tf.device() or something similar to allocate each operation to a device.\r\n- or, alternatively, give to tf a list of all the target devices and let it to allocate as it needs.\r\n- execute." ]

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[ "@WenTheProgrammer \r\nSorry for the late reply, Beginning with Tensorflow 2.11, support for GPU on native windows has changed. You will need to install TensorFlow in WSL2 or install tensorflow-cpu within windows machines or try the TensorFlow-DirectML-Plugin.\r\nhttps://www.tensorflow.org/install/pip#windows-native\r\nhttps://www.tensorflow.org/install/pip#windows-wsl2\r\n\r\nGoing forward, Tensorflow support will be developed and maintained by Tensorflow Official build collaborators (Intel, AWS, ARM, linaro etc.). For more details please take a look at this [link].(https://blog.tensorflow.org/2022/09/announcing-tensorflow-official-build-collaborators.html)\r\n\r\nThank you !", "Simply following the instructions in this link worked for me!\r\nhttps://www.tensorflow.org/install/pip#windows-native", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60269\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60269\">No</a>\n" ]

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[ "Hi, In [this TensorFlow blog](https://blog.tensorflow.org/2022/09/whats-new-in-tensorflow-210.html) please see section \"Expanded GPU support on Windows\". Also please see [TensorFlow install page](https://www.tensorflow.org/install/pip) and [this page](https://learn.microsoft.com/en-us/windows/ai/directml/gpu-tensorflow-plugin) for more info. Please feel free to sign up to the mailing list [email protected] to be notified of the most recent updates.\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60268\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60268\">No</a>\n" ]

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[ "In addition to this, isn't it a bug that setting the `class_weight` parameter in `model.fit` does not apply those class weights to the `validation_data`?\r\n\r\nFor example, this code:\r\n\r\n\r\n```py\r\nmodel.fit(\r\n X_trn, \r\n y_trn,\r\n # NOTE: Validation loss is *incorrect* because it doesn't take into account the class weights\r\n validation_data=(X_val, y_val),\r\n class_weight=calculate_weights_for_my_imbalanced_classes(y_trn),\r\n)\r\n```\r\n\r\nresults in `loss` values around 0.001, but `val_loss` values of around 4.0, because I have one very dominant class (~99% of the observations) and 49 classes in the remaining 1%.\r\n\r\nShouldn't the `class_weight` dictionary be applied to the `val_loss` calculation if `validation_data` is provided?", "@sushreebarsa is this a known issue? or is there any solution planned?", "Hello @beyarkay! I was able to run the code successfully on colab using TF v2.12, please find the [gist](https://colab.research.google.com/gist/sushreebarsa/9aa8f3c9c8b6e7b0ecb705f364ac6d1a/60267.ipynb) here and try to upgrade the TF version to latest. Thank you!", "I am unable to update to the latest version of TF because of [this macos/metal bug](https://developer.apple.com/forums/thread/722389?740707022). Is there any fix that I can use besides updating? (since it seems like apple isn't going to be fixing the issue super soon)", "@beyarkay Thank you for the response!\r\n@SuryanarayanaY Could you please have a look at this issue?\r\nThank you!", "Hi @beyarkay ,\r\n\r\nIf you want to use latest tensorflow-macos with metal plugin, you need to use legacy optimizer. The constraint you have mentioned for updating to new version is due to the optimizer issue.Suppose if you are using `keras.optimizers.Adam(1e-5)` replace it with `tf.keras.optimizers.legacy.Adam(1e-5)` and it should work fine. \r\n\r\nPlease try this and let us know of still having any problem. Thanks!\r\n", "Thanks! That works perfectly. Are there any differences between `keras.optimizers.Adam` and `tf.keras.optimizers.legacy.Adam` that I should know about which might cause me issues in the future?", "Okay nevermind, it's not working now for some reason. I'll paste the code I'm using (although I literally only used the legacy version of Adam) but it's having the same error. I've tried restarting my notebook, but no luck.\r\n\r\n\r\n<details>\r\n<summary>Code</summary>\r\n\r\n```py\r\nimport numpy as np\r\nimport keras\r\nimport tensorflow as tf\r\nimport sklearn.model_selection\r\n\r\nX = np.random.randint(low=300, high=900, size=(215699, 30, 30))\r\ny = np.random.randint(low=0, high=51, size=(215699,))\r\nX_trn, X_val, y_trn, y_val = sklearn.model_selection.train_test_split(X, y)\r\n\r\nm = tf.keras.Sequential(\r\n [\r\n keras.layers.Input(shape=X.shape[1:]),\r\n keras.layers.Flatten(),\r\n keras.layers.Dense(units=64, activation=\"relu\",),\r\n keras.layers.Dense(units=64, activation=\"relu\",),\r\n keras.layers.Dense(len(np.unique(y))),\r\n ]\r\n)\r\nm.compile(\r\n optimizer=tf.keras.optimizers.legacy.Adam(1e-5),\r\n loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\r\n)\r\n\r\nsample_weights_val = np.ones(y_val.shape)\r\n\r\nhistory = m.fit(\r\n X_trn,\r\n y_trn,\r\n validation_data=(X_val, y_val, sample_weights_val),\r\n batch_size=128,\r\n epochs=5,\r\n)\r\n```\r\n\r\n</details>\r\n\r\nAnd the output is:\r\n\r\n```\r\nEpoch 1/5\r\n 12/1264 [..............................] - ETA: 5s - loss: 844.2266 \r\n\r\n2023-04-14 11:53:32.963873: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\r\n\r\n1256/1264 [============================>.] - ETA: 0s - loss: 125.0102\r\n```", "@beyarkay ,\r\n\r\nI have replicated the issue with tensorflow-macos==2.11 version and its still an issue with 2.11 version also. The code hangs as reported and i have to interrupt it manually after around 2 minutes.For reference I have attached the logs below.\r\n\r\n```\r\n(tf-metal) suryanarayanay-macbookpro:~ suryanarayanay$ python 60267.py\r\nMetal device set to: Apple M1 Pro\r\n\r\nsystemMemory: 16.00 GB\r\nmaxCacheSize: 5.33 GB\r\n\r\n2023-04-14 19:14:12.631493: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\r\n2023-04-14 19:14:12.631513: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\r\n2023-04-14 19:14:13.425947: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\r\nEpoch 1/5\r\n2023-04-14 19:14:13.569687: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\r\n1264/1264 [==============================] - ETA: 0s - loss: 111.5523^CTraceback (most recent call last):\r\n File \"/Users/suryanarayanay/60267.py\", line 35, in <module>\r\n history = m.fit(\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/keras/engine/training.py\", line 1680, in fit\r\n self._eval_data_handler = data_adapter.get_data_handler(\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/keras/engine/data_adapter.py\", line 1579, in get_data_handler\r\n return DataHandler(*args, **kwargs)\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/keras/engine/data_adapter.py\", line 1259, in __init__\r\n self._adapter = adapter_cls(\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/keras/engine/data_adapter.py\", line 251, in __init__\r\n (sample_weights, _, _) = training_utils.handle_partial_sample_weights(\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/keras/engine/training_utils.py\", line 78, in handle_partial_sample_weights\r\n partial_sample_weight = any_sample_weight and any(\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/keras/engine/training_utils.py\", line 78, in <genexpr>\r\n partial_sample_weight = any_sample_weight and any(\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 7327, in __next__\r\n result = self._tensor[self._index]\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py\", line 150, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/tensorflow/python/util/dispatch.py\", line 1176, in op_dispatch_handler\r\n return dispatch_target(*args, **kwargs)\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/tensorflow/python/ops/array_ops.py\", line 1096, in _slice_helper\r\n return strided_slice(\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py\", line 150, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/tensorflow/python/util/dispatch.py\", line 1176, in op_dispatch_handler\r\n return dispatch_target(*args, **kwargs)\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/tensorflow/python/ops/array_ops.py\", line 1269, in strided_slice\r\n op = gen_array_ops.strided_slice(\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 10672, in strided_slice\r\n _result = pywrap_tfe.TFE_Py_FastPathExecute(\r\nKeyboardInterrupt\r\n\r\n(tf-metal) suryanarayanay-macbookpro:~ suryanarayanay$ python3 -c \"import tensorflow as tf; print(tf.__version__)\"\r\n2.11.0\r\n(tf-metal) suryanarayanay-macbookpro:~ suryanarayanay$ \r\n```\r\n\r\nI have also tested with tensorflow-macos 2.12 versions and the same behaviour observed there also.Attched logs below for reference.\r\n[60267_logs(macos-2.12).txt](https://github.com/tensorflow/tensorflow/files/11233613/60267_logs.macos-2.12.txt)\r\n", "Thanks for looking at it! Is there any hope for a fix?", "@nitins17 , Do you have any pointers ? The same code works fine with tensorflow==2.12 on colab but fails with tensorflow-macos. \r\n\r\ncc: @learning-to-play " ]

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[ "@dwzdtx,\r\n**TF.gradients** : \r\n`tf.gradients` is only valid in a graph context. In particular, it is valid in the context of a `tf.function` wrapper, where code is executing as a graph.\r\n\r\n```\r\[email protected]\r\ndef example():\r\n a = tf.constant(0.)\r\n b = 2 * a\r\n return tf.gradients(a + b, [a, b], stop_gradients=[a, b])\r\nexample()\r\n```\r\n**tf.GradientTape** :\r\n\r\nTensorFlow provides the **tf.GradientTape** API for automatic differentiation. TensorFlow \"records\" relevant operations executed inside the context of a `tf.GradientTape` onto a \"tape\". TensorFlow then uses that tape to compute the gradients of a \"recorded\" computation using reverse mode differentiation. `tf.GradientTape not really required tf.function wrapper. It automatically runs in Graph mode.`\r\n\r\n```\r\nx = tf.constant(3.0)\r\nwith tf.GradientTape() as g:\r\n g.watch(x)\r\n y = x * x\r\ndy_dx = g.gradient(y, x)\r\nprint(dy_dx)\r\n```\r\n\r\nAlso please provide the complete code and the tensorflow version you are using which helps to debug the issue. Thank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60266\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60266\">No</a>\n" ]

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[ "Oh, and I have already tried some workarounds, like creating a CUDNN_INSTALL_PATH environment variable and enablind the Developer mode, like suggested in this post : https://discuss.tensorflow.org/t/error-building-tensorflow-2-8-in-windows-10/7984", "The error message you are seeing suggests that there is an issue with the LLVM project in your TensorFlow build.\r\n\r\nHere are some possible steps you can try to resolve the error:\r\n\r\nCheck your LLVM version: TensorFlow 2.10 requires LLVM 9.0, which you can download from the LLVM website. Make sure to download the correct version of LLVM that matches your system.\r\n\r\nCheck your Bazel version: Make sure that you have installed the correct version of Bazel that matches the TensorFlow version you are trying to build. You can find the required Bazel version in the TensorFlow documentation.\r\n\r\nClean the Bazel cache: Try cleaning the Bazel cache by running \"bazel clean\" in your TensorFlow source directory. This will clear any cached build artifacts that may be causing issues.\r\n\r\nRebuild the LLVM project: Try rebuilding the LLVM project by running \"bazel build //external:llvm-project\" in your TensorFlow source directory. This will rebuild the LLVM project from scratch.\r\n\r\nRebuild TensorFlow: Once you have rebuilt the LLVM project, try rebuilding TensorFlow by running \"bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package\" in your TensorFlow source directory. This will create a new TensorFlow pip package with GPU support.", "Thank you very much for your answer.\r\n\r\nI'm using Bazel 5.1.0, which is the correct version that should be used with TF 2.10.\r\nI installed LLVM 9.0 on my system, however it does not seem to change anything (LLVM install folder has been added to the Windows Path) as it still tries to fetch the llvm project.\r\nFinally, i tried building the llvm project manually with the bazel command and had this error:\r\n\r\nERROR: D:/tensorflow/tensorflow/WORKSPACE:15:14: in llvm_configure rule //external:llvm-project: Found reference to a workspace rule in a context where a build rule was expected; probably a reference to a target in that external repository, properly specified as @reponame//path/to/package:target, should have been specified by the requesting rule.\r\n\r\nA bazel clean did not help.", "I have some updates. After some further manual cleaning another error came out:\r\n\r\nERROR: An error occurred during the fetch of repository 'envoy_api':\r\n Traceback (most recent call last):\r\n File \"D:/tensorflow/bazel-out/zfk46uyn/external/bazel_tools/tools/build_defs/repo/http.bzl\", line 100, column 45, in _http_archive_impl\r\n download_info = ctx.download_and_extract(\r\nError in download_and_extract: java.io.IOException: Error downloading [https://github.com/envoyproxy/data-plane-api/archive/c83ed7ea9eb5fb3b93d1ad52b59750f1958b8bde.tar.gz] to D:/tensorflow/bazel-out/zfk46uyn/external/envoy_api/temp17372839701998073658/c83ed7ea9eb5fb3b93d1ad52b59750f1958b8bde.tar.gz: Unknown host: codeload.github.com\r\n\r\nSo it seems I have troubles fetching the envoy_api project now... In general I also have a lot of warnings stating that files were not found when trying to download some projects:\r\n\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/tensorflow/runtime/archive/6ca793b5d862ef6c50f242d77a811f06cce9b60a.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/0538e5431afdb1fa05bdcedf70ee502ccfcd112a.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://github.com/envoyproxy/data-plane-api/archive/c83ed7ea9eb5fb3b93d1ad52b59750f1958b8bde.tar.gz failed: class com.google.devtools.build.lib.bazel.repository.downloader.UnrecoverableHttpException Unknown host: codeload.github.com", "You can find the download link for the c83ed7ea9eb5fb3b93d1ad52b59750f1958b8bde.tar.gz file in the error message. Download the file and extract it to the D:/tensorflow/bazel-out/zfk46uyn/external/envoy_api directory.\r\n\r\nOnce the file is in the correct directory, try running the build command again and see if the issue is resolved.\r\nI'm afraid if this does not work, it may either be a network issue (your ISP forbids you from visiting the urls) or a bazel issue.", "So, I am progressing a little bit. Basically I had troubles creating symlinks under my account for some reason. I started from the beginning from a fresh Windows account on my machine and this solved it.\r\n\r\nHowever, I am still running into issues when linking the project:\r\n\r\nERROR: D:/tensorflow/tensorflow/tensorflow/BUILD:1034:21: Linking tensorflow/libtensorflow_framework.so.2.10.1 failed: (Exit 1189): link.exe failed: error executing command\r\n cd /d D:/tensorflow/bazel-out/zfk46uyn/execroot/org_tensorflow\r\n SET INCLUDE=C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\VC\\Tools\\MSVC\\14.34.31933\\include;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\VC\\Tools\\MSVC\\14.34.31933\\ATLMFC\\include;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\VC\\Auxiliary\\VS\\include;C:\\Program Files (x86)\\Windows Kits\\10\\include\\10.0.22000.0\\ucrt;C:\\Program Files (x86)\\Windows Kits\\10\\\\include\\10.0.22000.0\\\\um;C:\\Program Files (x86)\\Windows Kits\\10\\\\include\\10.0.22000.0\\\\shared;C:\\Program Files (x86)\\Windows Kits\\10\\\\include\\10.0.22000.0\\\\winrt;C:\\Program Files (x86)\\Windows Kits\\10\\\\include\\10.0.22000.0\\\\cppwinrt;C:\\Program Files (x86)\\Windows Kits\\NETFXSDK\\4.8\\include\\um\r\n SET LIB=C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\VC\\Tools\\MSVC\\14.34.31933\\ATLMFC\\lib\\x64;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\VC\\Tools\\MSVC\\14.34.31933\\lib\\x64;C:\\Program Files (x86)\\Windows Kits\\NETFXSDK\\4.8\\lib\\um\\x64;C:\\Program Files (x86)\\Windows Kits\\10\\lib\\10.0.22000.0\\ucrt\\x64;C:\\Program Files (x86)\\Windows Kits\\10\\\\lib\\10.0.22000.0\\\\um\\x64\r\n SET PATH=C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\VC\\Tools\\MSVC\\14.34.31933\\bin\\HostX64\\x64;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\Common7\\IDE\\VC\\VCPackages;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\Common7\\IDE\\CommonExtensions\\Microsoft\\TestWindow;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\Common7\\IDE\\CommonExtensions\\Microsoft\\TeamFoundation\\Team Explorer;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\MSBuild\\Current\\bin\\Roslyn;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\Team Tools\\Performance Tools\\x64;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\Team Tools\\Performance Tools;C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Common\\VSPerfCollectionTools\\vs2019\\\\x64;C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Common\\VSPerfCollectionTools\\vs2019\\;C:\\Program Files (x86)\\Microsoft SDKs\\Windows\\v10.0A\\bin\\NETFX 4.8 Tools\\x64\\;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\Common7\\IDE\\CommonExtensions\\Microsoft\\FSharp\\Tools;C:\\Program Files (x86)\\Windows Kits\\10\\bin\\10.0.22000.0\\\\x64;C:\\Program Files (x86)\\Windows Kits\\10\\bin\\\\x64;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\\\MSBuild\\Current\\Bin\\amd64;C:\\Windows\\Microsoft.NET\\Framework64\\v4.0.30319;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\Common7\\IDE\\;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\Common7\\Tools\\;;C:\\Windows\\system32;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\Common7\\IDE\\CommonExtensions\\Microsoft\\CMake\\CMake\\bin;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\Common7\\IDE\\CommonExtensions\\Microsoft\\CMake\\Ninja;C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\Common7\\IDE\\VC\\Linux\\bin\\ConnectionManagerExe\r\n SET PWD=/proc/self/cwd\r\n SET RUNFILES_MANIFEST_ONLY=1\r\n SET TEMP=C:\\Users\\AURELI~1\\AppData\\Local\\Temp\r\n SET TMP=C:\\Users\\AURELI~1\\AppData\\Local\\Temp\r\n C:\\Program Files\\Microsoft Visual Studio\\2022\\Professional\\VC\\Tools\\MSVC\\14.34.31933\\bin\\HostX64\\x64\\link.exe @bazel-out/x64_windows-opt-exec-50AE0418/bin/tensorflow/libtensorflow_framework.so.2.10.1-2.params\r\n# Configuration: 235f51f52e7e1d2ff49596414691042bdab9491882365c09175bc46cf2ee6814\r\n# Execution platform: @local_execution_config_platform//:platform\r\nLINK : warning LNK4044: option '/lm' non reconnue ; ignorée\r\nLINK : fatal error LNK1189: limite de 65535 objets dépassée pour la bibliothèque\r\nTarget //tensorflow:tensorflow_cc.dll failed to build\r\n\r\nWhat I understood from the last LINK error (it is in french, it means \"Link error 1189 library limit of 65535 objects exceeded\") is that MSVC is trying to create a dll or a lib with too many objects in it. As I understood it is a MSVC limitation, however it is strange I did not encounter this error before, since I previously built the 2.4 TF version without any of this type.\r\n\r\nSo, is there a means to overcome this limitation (seems unlikely), or to prevent TF from putting too many objects in the binaries? For example by choosing which objects to exclude from the compilation (I certainly do not need all that exists in the TensorFlow release)?", "@Rayndell ,\r\n\r\nCould you please cross check with the tested configuration for TF2.10 versions (CUDA=11.2 and cuDNN=8.1) and let us know if the build has any problem. We can't guarantee that CUDA 11.8 works with Tf2.10v or not. But if the build fails with CUDA-11.2 then definitely we will have a look .\r\n\r\nThanks!", "Thanks for the response. Actually I succeeded by downgrading to the 2.7 version (haven't tested 2.8 and 2.9) and using Python 3.9. The built went well with CUDA 11.8. I am not sure if I will have the time to re-test the 2.10 build with lower versions of CUDA though.", "@Rayndell ,\r\n\r\nHigher CUDA versions may also work if taken care of backward compatibility.Since we have tested configurations and also documented them we recommend users to follow same and we guarantee that they work and if not will be taken care by us.\r\n\r\nIn case your purpose resolved can we mark it as closed ? Please spare some time to close the issue and in future if have any problems please feel free to open a ticket. Thanks!\r\n\r\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60264\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60264\">No</a>\n" ]

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[ "The error message you are seeing suggests that Bazel is unable to locate the Python executable specified in the PATH environment variable.\r\n\r\nHere are a few things you can try to fix the issue:\r\n\r\n1. Verify that the Python executable is in your PATH: Make sure that the directory containing the Python executable is actually included in your PATH environment variable. You can verify this by running echo %PATH% in the Command Prompt and checking whether the directory containing the Python executable is listed. Next, Use the full path to the Python executable: Instead of relying on the PATH environment variable, you can try specifying the full path to the Python executable in the configure.py script. For example, you can try running python configure.py --interpreter=C:/Users/Asus/AppData/Local/Programs/Python/Python311/python.exe.\r\n\r\n2.Try using a different version of Python: It's possible that the version of Python you are using is not compatible with TensorFlow. You can try using a different version of Python (e.g., Python 3.7) and see if that resolves the issue and check your Bazel version: Make sure that you are using the correct version of Bazel that is compatible with TensorFlow 2.12. You can check the Bazel version requirements in the TensorFlow documentation.\r\n\r\nIf all fails, then try using a pre-built TensorFlow package: If you are still having issues building TensorFlow from source, you can try using a pre-built TensorFlow package instead. You can download pre-built TensorFlow packages for Windows from the TensorFlow website.", "Hi @TurgutBababalim, As per the official documentation, Tensorflow 2.12 is compatible with Bazel 5.3.0. Please try again with the supported versions. Kindly refer to the [tested build configurations](https://www.tensorflow.org/install/source_windows#cpu). Thank you! ", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60263\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60263\">No</a>\n" ]

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[ "Seems we have encountered similar problems. @stefanbucur Can you find `events.out.tfevents.*` in the directory where `plugins` is located?", "The Tf profile is working well for me.\r\nBut I am not able to see any loss values in TensorBoard.\r\n", "> \r\n\r\nYes, here is the full directory contents:\r\n\r\n```\r\n$ ls -R logs/\r\nlogs/:\r\nfit\r\n\r\nlogs/fit:\r\n20230417-090906\r\n\r\nlogs/fit/20230417-090906:\r\nplugins train\r\n\r\nlogs/fit/20230417-090906/plugins:\r\nprofile\r\n\r\nlogs/fit/20230417-090906/plugins/profile:\r\n2023_04_17_09_09_10\r\n\r\nlogs/fit/20230417-090906/plugins/profile/2023_04_17_09_09_10:\r\nsaturn.xplane.pb\r\n\r\nlogs/fit/20230417-090906/train:\r\nevents.out.tfevents.1681736947.saturn.73263.0.v2\r\n```", "I must have done something right, now it works fine.", "Update: I was able to get it working, *under certain conditions*.\r\n\r\nIf I do a `$ tensorboard --logdir logs/` command, per the instructions in [the tutorial](https://www.tensorflow.org/tensorboard/tensorboard_profiling_keras), profiling will not work.\r\n\r\nIf instead I run `tensorboard` on the specific subdir that contains the profile (`$ tensorboard --logdir logs/fit/20230417-090906/`), then the profile will show up, but **only after I refresh the browser window once**. Once I refresh the browser window, and I access the profile tab, `*.hlo_proto.pb` files start showing up alongside the original `*.xplane.pb` profile.\r\n\r\nNote that refreshing the window won't fix it in the original case (opening the top-level `logs/` dir).\r\n\r\nJust a guess, but perhaps there is a regression in how tensorboard traverses the logs directory tree?" ]

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[ "According to the log output, there appears to be a warning relating to the Eigen library in the TensorFlow source. The warning notice specifically states that a function called check_is_representable() is deprecated.\r\n\r\nThis warning is most likely generated by the Eigen library being used with a later version of the compiler, which causes the warning.\r\n\r\nWhile this warning should have no effect on TensorFlow's operation, it's always a good idea to avoid utilizing deprecated functions in your code. One method is to upgrade to a more recent version of the library that does not use deprecated functions.\r\n\r\nYou can also attempt silencing the warning with a compiler flag, although this is generally not recommended because it may hide the warning.", "Hi @samveen did the above response answer your question?", "@pkgoogle No, that did not help at all. \r\n\r\nI have made no changes to the codebase, and this warning occurs. The comment is suggesting that I modify the tensorflow build system for the lite build, which defeats the purpose of reporting this issue. This probably ties in with build failures for RasPi 0/1 as per thew build status section of the README. \r\n\r\nIt would be great to know the last commit ID that allowed this to build successfully, as I have not been able to get that information from digging into the commit history.", "Hi @samveen, Are you actually able to compile despite the warning? Generally speaking, warnings will take a long time to get to as they are lower priority. Which README section are you referring to? Thanks for any additional information you can provide.", "@pkgoogle \r\n\r\n Please look at the table listed here: \r\nhttps://github.com/tensorflow/tensorflow#official-builds\r\n\r\nAs for the build, if you give me a revision or git tag, I'll run a build for it and give you the results (though it will be slow, as it'll be a native build)", "Hi @samveen, can you try with nightly? It seems the official build is blocked by a malformed docker issue rather than the actual build.", "OK. I will run a native 32-bit build for tf lite on my raspberry Pi Zero W and let you know the result. (The build will take quite a bit or time to complete).", "Progress report:\r\n- Instructions source https://www.tensorflow.org/lite/guide/build_cmake\r\n- Source details:\r\n ```\r\n samveen@facez:~/tensorflow_src $ git remote -v\r\n origin https://github.com/tensorflow/tensorflow.git (fetch)\r\n origin https://github.com/tensorflow/tensorflow.git (push)\r\n samveen@facez:~/tensorflow_src $ git describe --tags\r\n v2.12.1\r\n ```\r\n- CMake command and options:\r\n ```\r\n cmake ../tensorflow_src/tensorflow/lite\r\n ```\r\n- Build command:\r\n ```\r\n ~/tflite_build $ cmake --build . -j 1 -t label_image\r\n ```\r\n- End result: Error with build due to missing options for libatomic in `examples/label_image/CMakeLists.txt`\r\n\r\n(snipped)\r\n```\r\n/usr/bin/ld: eigen_support.cc:(.text._ZN14EigenForTFLite15ThreadPoolTemplINS_20StlThreadEnvironmentEE11WaitForWorkEPNS_10EventCount6WaiterEPNS1_4TaskE[_ZN14EigenForTFLite15ThreadPoolTemplINS_20StlThreadEnvironmentEE11WaitForWorkEPNS_10EventCount6WaiterEPNS1_4TaskE]+0x778): undefined reference to `__atomic_load_8'\r\n/usr/bin/ld: eigen_support.cc:(.text._ZN14EigenForTFLite15ThreadPoolTemplINS_20StlThreadEnvironmentEE11WaitForWorkEPNS_10EventCount6WaiterEPNS1_4TaskE[_ZN14EigenForTFLite15ThreadPoolTemplINS_20StlThreadEnvironmentEE11WaitForWorkEPNS_10EventCount6WaiterEPNS1_4TaskE]+0x7fc): undefined reference to `__atomic_compare_exchange_8'\r\n/usr/bin/ld: eigen_support.cc:(.text._ZN14EigenForTFLite15ThreadPoolTemplINS_20StlThreadEnvironmentEE11WaitForWorkEPNS_10EventCount6WaiterEPNS1_4TaskE[_ZN14EigenForTFLite15ThreadPoolTemplINS_20StlThreadEnvironmentEE11WaitForWorkEPNS_10EventCount6WaiterEPNS1_4TaskE]+0x860): undefined reference to `__atomic_load_8'\r\n/usr/bin/ld: eigen_support.cc:(.text._ZN14EigenForTFLite15ThreadPoolTemplINS_20StlThreadEnvironmentEE11WaitForWorkEPNS_10EventCount6WaiterEPNS1_4TaskE[_ZN14EigenForTFLite15ThreadPoolTemplINS_20StlThreadEnvironmentEE11WaitForWorkEPNS_10EventCount6WaiterEPNS1_4TaskE]+0x8c4): undefined reference to `__atomic_load_8'\r\ncollect2: error: ld returned 1 exit status\r\ngmake[3]: *** [examples/label_image/CMakeFiles/label_image.dir/build.make:370: examples/label_image/label_image] Error 1\r\ngmake[2]: *** [CMakeFiles/Makefile2:6608: examples/label_image/CMakeFiles/label_image.dir/all] Error 2\r\ngmake[1]: *** [CMakeFiles/Makefile2:6615: examples/label_image/CMakeFiles/label_image.dir/rule] Error 2\r\n```\r\n\r\nI am modifying the CMake config and restarting the process.", "I've managed to get target `benchmark_model` build. However, using the examples as listed in https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/benchmark using the model files from https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip gives me the following output:\r\n\r\n```\r\nsamveen@piz:~/tflite_build $ ./tools/benchmark/benchmark_model --graph=../tf/tensorflow_inception_graph.pb --input_layer=\"input:0\" --input_layer_shape=\"1,224,224,3\" --input_layer_type=\"float\" --output_layer=\"output:0\"\r\nSTARTING!\r\nUnconsumed cmdline flags: --input_layer_type=float --output_layer=output:0\r\nLog parameter values verbosely: [0]\r\nGraph: [../tf/tensorflow_inception_graph.pb]\r\nInput layers: [input:0]\r\nInput shapes: [1,224,224,3]\r\nERROR: The model is not a valid Flatbuffer buffer\r\nFailed to load model ../tf/tensorflow_inception_graph.pb\r\nBenchmarking failed```", "After fixing the error due to missing linkage against `libatomic` for the image_label target, the build fails as below:\r\n\r\n```\r\n[100%] Built target tensorflow-lite\r\n[100%] Linking CXX executable label_image\r\n/usr/bin/ld: CMakeFiles/label_image.dir/__/__/tools/evaluation/utils.cc.o: in function `tflite::evaluation::CreateXNNPACKDelegate(TfLiteXNNPackDelegateOptions const*)':\r\nutils.cc:(.text+0x1b04): undefined reference to `TfLiteXnnpackDelegatePluginCApi'\r\ncollect2: error: ld returned 1 exit status\r\ngmake[3]: *** [examples/label_image/CMakeFiles/label_image.dir/build.make:370: examples/label_image/label_image] Error 1\r\ngmake[2]: *** [CMakeFiles/Makefile2:6608: examples/label_image/CMakeFiles/label_image.dir/all] Error 2\r\ngmake[1]: *** [CMakeFiles/Makefile2:6615: examples/label_image/CMakeFiles/label_image.dir/rule] Error 2\r\n```", "I was able to replicate with linux (debian) on the nightly branch with the instructions from https://www.tensorflow.org/lite/guide/build_cmake\r\n\r\nspecifically:\r\n```sh\r\ngit clone https://github.com/tensorflow/tensorflow.git tensorflow_src\r\ncd tensorflow_src\r\ngit switch nightly\r\ngit pull\r\ncd ..\r\nmkdir tflite_build\r\ncd tflite_build\r\ncmake ../tensorflow_src/tensorflow/lite\r\ncmake --build . -j\r\ncmake --build . -j -t benchmark_model #builds properly\r\ncmake --build . -j -t label_image #this fails\r\n```\r\n\r\nError:\r\n```sh\r\n[100%] Building CXX object examples/label_image/CMakeFiles/label_image.dir/__/__/profiling/profile_summary_formatter.cc.o\r\n[100%] Building CXX object examples/label_image/CMakeFiles/label_image.dir/__/__/profiling/memory_info.cc.o\r\n[100%] Building CXX object examples/label_image/CMakeFiles/label_image.dir/__/__/tools/evaluation/utils.cc.o\r\n[100%] Building CXX object examples/label_image/CMakeFiles/label_image.dir/__/__/profiling/profile_summarizer.cc.o\r\n[100%] Building CXX object examples/label_image/CMakeFiles/label_image.dir/__/__/tools/tool_params.cc.o\r\n[100%] Building CXX object examples/label_image/CMakeFiles/label_image.dir/__/__/tools/delegates/xnnpack_delegate_provider.cc.o\r\n[100%] Linking CXX executable label_image\r\n/usr/bin/ld: CMakeFiles/label_image.dir/__/__/tools/evaluation/utils.cc.o: in function `tflite::evaluation::CreateXNNPACKDelegate(TfLiteXNNPackDelegateOptions const*)':\r\nutils.cc:(.text+0x2f4f): undefined reference to `TfLiteXnnpackDelegatePluginCApi'\r\ncollect2: error: ld returned 1 exit status\r\ngmake[3]: *** [examples/label_image/CMakeFiles/label_image.dir/build.make:375: examples/label_image/label_image] Error 1\r\ngmake[2]: *** [CMakeFiles/Makefile2:7494: examples/label_image/CMakeFiles/label_image.dir/all] Error 2\r\ngmake[1]: *** [CMakeFiles/Makefile2:7501: examples/label_image/CMakeFiles/label_image.dir/rule] Error 2\r\ngmake: *** [Makefile:2522: label_image] Error 2\r\n```\r\n\r\nHi @terryheo, can you please take a look? Thanks.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60261\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60261\">No</a>\n" ]

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[]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60259/checks?check_run_id=12571837686) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "This has not been merged in the 2.12.1 release. Not sure why, but it should. ", "It did not land on master and was not cherrypicked. Too late now for 2.12.x series since @learning-to-play and team decided to only patch the last release, so now only 2.13 will get patches.", "@feranick Please see if [TF patching guidelines](https://github.com/tensorflow/tensorflow#patching-guidelines) can provide a solution for your case.", "I have a solution to my case. I found it, reported here, created a pull request, that was reviewed and accepted for release. But then it was neither landed on master nor cherrypicked. As a result, the problem remains for whoever is trying to compile 2.12.x on MacOS. Patching guidelines are just that, a guideline for a specific patch. Since my patch has not been cherrypicked, suggesting the use of the patching guidelines becomes pointless and irrelevant.\r\n\r\nSo this (resolved) bug report is the only place where one can find a solution, and manually apply the patch. I would expect more, at the very least list it in the \"known issues\" so there is some guidance. Again, I am fine as I found a solution myself, but frankly going to the trouble to push it upstream through the official channels only to find that it's considered irrelevant, doesn't really inspire any future effort on my part to contribute patches. \r\n\r\nBesides 2.13.0 is out now and does not suffer from this. \r\n\r\nThank you for your help." ]

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[ "Hi @yishuangP Can you please review this PR ? Thank you!", "Hi @yishuangP Can you please review this PR ? Thank you!", "Hi @yishuangP Can you please review this PR ? Thank you!", "Hi @JunyoungLim Can you please review this PR ? Thank you!", "Hi @JunyoungLim Can you please review this PR ? Thank you!" ]

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[ "Hi @gbaned \r\nIs there something I should check to make the merge progress?", "> Hi @gbaned Is there something I should check to make the merge progress?\r\n\r\nHi @aflaischer Can you please take a look on below error. Thank you!\r\n\r\nclang: error: unable to execute command: Segmentation fault: 11\r\nclang: error: clang frontend command failed due to signal (use -v to see invocation)", "Hi @gbaned \r\n\r\nFrom which job is it coming? it seems I don't have access to feedback/copybara\r\nwhat is the command to run?\r\n\r\nIn any case it seems like an environment issue no? if the clang command is segfaulting \r\n\r\nShould I rebase my changes perhaps?\r\n", "Hi @yishuangP Can you please review this PR ? Thank you!", "Hi @yishuangP Can you please review this PR ? Thank you!", "Hi @aflaischer Can you please resolve conflicts? Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further." ]