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[ "I would appreciate any working solution to this, as long i can run my model on Select Ops based on a TensorFlowLiteSelectTFOps library selectively built based on the tflite model itself. Thanks a lot in advance! :)", "Hi @tanpengshi, it looks like the system is unable to properly link the google protobuf library ... can you ensure you are following all the directions here correctly: https://www.tensorflow.org/lite/guide/build_ios#using_local_swift_or_objective-c_apis\r\n\r\nparticularly I see that your path does not point to the TF root directory\r\n```\r\npod 'TensorFlowLiteSwift', :path => '../../local-podspecs/TensorFlowLiteSwift.podspec'\r\n```\r\n\r\nWhere as the source says:\r\n```\r\npod 'TensorFlowLiteSwift', :path => '<your_tensorflow_root_dir>'\r\n```\r\n\r\nLet me know if that works. Thanks.", "I have tried the above and I am still getting the same error. In addition I did :\r\n\r\n```\r\n pod 'TensorFlowLiteSwift'\r\n pod 'TensorFlowLiteSelectTfOps', '~> 0.0.1-nightly'\r\n```\r\n\r\nThen replace i replaced the TensorFlowLiteSelectTfOps.framework with my selectively build TensorFlowLiteSelectTfOps.framework, and I still get the same error when building! I suspect the error comes inherently from the selectively built framework. Are you able to replicate the error I get?\r\n\r\nThank you!", "Hi @tanpengshi, are you willing to share your model(s) that are used to make your selective build? I'm wondering if any particular ops are causing the issue. Perhaps you can just make a dummy model which includes at least one of all the ops you are using and share that.", "[tflite_CNN_LSTM_dummy.zip](https://github.com/user-attachments/files/15507829/tflite_CNN_LSTM_dummy.zip)\r\n\r\nHi @pkgoogle, here is the dummy model that contains all the ops!\r\n", "Hi @tanpengshi, I attempted to reproduce it with your model... I'm actually failing on the build the select framework step -- considering you were able to build the Select framework, can you share the steps that you use to build it?\r\n\r\nI did this:\r\n```\r\ngit switch r2.14 # to match your TF version\r\n./configure\r\n<Said yes to configure for iOS>\r\n<copied your model file over>\r\nbash tensorflow/lite/ios/build_frameworks.sh --input_models=tflite_CNN_LSTM_dummy.tflite --target_archs=x86_64,armv7,arm64\r\n```\r\n\r\nmy output:\r\n```\r\nINFO: Analyzed target //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework (473 packages loaded, 39899 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /Users/xxxxxxxxx/git/tensorflow/tensorflow/BUILD:1153:21: declared output 'tensorflow/libtensorflow_framework.2.dylib' was not created by genrule. This is probably because the genrule actually didn't create this output, or because the output was a directory and the genrule was run remotely (note that only the contents of declared file outputs are copied from genrules run remotely)\r\nERROR: /Users/xxxxxxxxx/git/tensorflow/tensorflow/BUILD:1153:21: Executing genrule //tensorflow:libtensorflow_framework.2.dylib_sym [for tool] failed: not all outputs were created or valid\r\nrealpath: illegal option -- -\r\nusage: realpath [-q] [path ...]\r\nTarget //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 2868.450s, Critical Path: 470.36s\r\nINFO: 7940 processes: 1075 internal, 6865 local.\r\nFAILED: Build did NOT complete successfully\r\n```", "Hi @pkgoogle I made a mistake, the tensorflow version should be r2.16 instead of r.2.14. When I used r2.16, I can successfully build the model, while r2.14 produced similar error.", "Hmm I'm now currently running into a different issue... which python version are you using? I'm using an M1 and Xcode = 15.0.1. Any other potential environmental difference you can possibly think of?\r\n\r\n```sh\r\nERROR: /Users/xxxxxxxx/git/tensorflow/tensorflow/python/BUILD:650:24: Linking tensorflow/python/_pywrap_tensorflow_internal.so [for tool] failed: (Exit 1): cc_wrapper.sh failed: error executing command (from target //tensorflow/python:_pywrap_tensorflow_internal.so) external/local_config_cc/cc_wrapper.sh @bazel-out/darwin_arm64-opt-exec-50AE0418-ST-c5da4e9d584e/bin/tensorflow/python/_pywrap_tensorflow_internal.so-2.params\r\nld: building exports trie: duplicate symbol '__ZN40TableStruct_tsl_2fprotobuf_2fdnn_2eproto7offsetsE'\r\nclang: error: linker command failed with exit code 1 (use -v to see invocation)\r\nError in child process '/usr/bin/xcrun'. 1\r\nTarget //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 4574.330s, Critical Path: 373.41s\r\nINFO: 14670 processes: 407 internal, 14263 local.\r\n```", "My apologies again, if you use TensorFlow r2.16, you will run into the issue later on in Xcode:\r\n\r\n[](https://github.com/tensorflow/tensorflow/issues/63952)\r\n\r\nThe TensorFlow version should instead be r2.9, with the .frameworks bazel installed with Python 3.9 installed with Anaconda with M3 and Xcode 15.3" ]

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[ "Hi @ShabbirMarfatiya ,\r\n\r\nI will replicate your issue and will get back to you.", "Hey @sawantkumar,\r\n\r\nThanks for looking into this issue. If you need any more details, feel free to let me know.", "Hi @sawantkumar,\r\n\r\nHave you happened to find anything related to my issue that might be helpful?", "Hi @ShabbirMarfatiya ,\r\n\r\nI am in the process of replicating your issue. Meanwhile did you try out with the latest tensorflow version , 2.16.1 ?", "Hi @sawantkumar,\r\n\r\nNo, I didn't try that, but I'll try and let you know the outcome.", "Hi @ShabbirMarfatiya ,\r\n\r\nI see that you have already filed a bug report for this, right? Anyway , just in case can you also also provide all the other assets like model file and dataset. ", "Hi @sawantkumar,\r\n\r\nI have uploaded all necessary folders and files to GDrive. Here's the link for you: https://drive.google.com/drive/folders/1Ov1qVyreDWq0q3VQbAq0BIvrxRSB4dRs?usp=sharing\r\n\r\nIf you need any more details, feel free to let me know. ", "Hi @ShabbirMarfatiya ,\r\n\r\nI am getting a lot of protobuf library issues. It will take me some time to resolve and replicate your issue. I will let you know once i am done. Thank you ", "Hi @ShabbirMarfatiya , we're wondering if you may be able to resolve your issue by using [AI-Edge-Torch](https://github.com/google-ai-edge/ai-edge-torch), you can find more information here: [googleblog](https://developers.googleblog.com/en/ai-edge-torch-high-performance-inference-of-pytorch-models-on-mobile-devices/).\r\n\r\nI have actually created a simple script for converting a mobilenet model here:\r\n\r\n```py\r\nimport torch\r\nimport torchvision\r\nimport ai_edge_torch\r\n\r\nmobilenet_model = torchvision.models.mobilenet_v3_small()\r\nsample_inputs = (torch.randn(1, 3, 224, 224),)\r\n\r\nedge_model = ai_edge_torch.convert(mobilenet_model.eval(), sample_inputs)\r\nedge_model.export(\"mobilenet_v3_small.tflite\")\r\n```\r\n\r\nIf you want to, you can actually try visualizing the result in [model-explorer](https://github.com/google-ai-edge/model-explorer) as well.\r\n\r\nPlease try them out and let us know if this resolves your issue. If you still need further help, feel free to open a new issue at the respective repo." ]

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[ "@vaibhavAIJKV GPU support on native-Windows is only available for 2.10 or earlier versions, starting in TF 2.11, CUDA build is not supported for Windows. For using TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2 or use tensorflow-cpu with TensorFlow-DirectML-Plugin.\r\nPlease refer to this [doc](https://www.tensorflow.org/install/source_windows) for further information.\r\nThank you!", "> @vaibhavAIJKV GPU support on native-Windows is only available for 2.10 or earlier versions, starting in TF 2.11, CUDA build is not supported for Windows. For using TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2 or use tensorflow-cpu with TensorFlow-DirectML-Plugin. Please refer to this [doc](https://www.tensorflow.org/install/source_windows) for further information. Thank you!\r\n\r\nHi\r\nAs mentioned I am using TensorFlow-DirectML-Plugin, but let me go through the document to see if installing other dependencies resolve this issue", "@vaibhavAIJKV Sure, kindly let us know?\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/67768\">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/67768\">No</a>\n" ]

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[ "I am experiencing the same issue when implementing my own transformer encoder decoder. So far, i am still missing positional encoding and some masking layers, i don't know whether those would affect it in any way. \r\n\r\nHere is my code, showing the same warning with tf-nightly, and python 3.11.9\r\n\r\n\r\n import keras\r\n import numpy as np\r\n \r\n def possitionalEmbedding(input_dim, output_dim): # TODO\r\n return keras.layers.Embedding(input_dim=input_dim, output_dim=output_dim)\r\n \r\n def model_func(encoder_vocab_len, decoder_vocab_len, encoder_maxlen, decoder_maxlen, params):\r\n num_heads, key_dim, d_v, d_ff, d_model, n = params\r\n \r\n encoder_input = keras.Input(shape=(None,))\r\n decoder_input = keras.Input(shape=(None,))\r\n \r\n # encoder part\r\n embedded = possitionalEmbedding(encoder_vocab_len, d_model)(encoder_input) # todo possitional embedding\r\n embedded = keras.layers.Dropout(0.1)(embedded)\r\n \r\n encoded = embedded\r\n for i in range(n):\r\n attended_encoded = keras.layers.MultiHeadAttention(num_heads,\r\n key_dim,\r\n dropout=0.1,\r\n use_bias=True,\r\n output_shape=(d_model,))(encoded, encoded, encoded) # todo padding_mask\r\n attended_encoded_d = keras.layers.Dropout(0.1)(attended_encoded)\r\n add = encoded + attended_encoded_d\r\n normalised = keras.layers.LayerNormalization()(add)\r\n fed_f = keras.layers.Dense(d_ff)(normalised) # feed forward 1 part\r\n fed_ff = keras.layers.Dense(d_model)(keras.activations.relu(fed_f)) # feed forward 2 part\r\n fed_ff_d = keras.layers.Dropout(0.1)(fed_ff)\r\n \r\n add2 = normalised + fed_ff_d\r\n normalised2 = keras.layers.LayerNormalization()(add2)\r\n \r\n encoded = normalised2 # and the loop is repeated\r\n \r\n encoder_output = encoded # output from encoder\r\n \r\n # decoder part\r\n de_embed = possitionalEmbedding(decoder_vocab_len, d_model)(decoder_input)\r\n de_embed = keras.layers.Dropout(0.1)(de_embed)\r\n \r\n for i in range(n):\r\n self_attention = (keras.layers.MultiHeadAttention(num_heads,\r\n key_dim,\r\n dropout=0.1,\r\n use_bias=True,\r\n output_shape=(d_model,))\r\n (de_embed, de_embed, de_embed))\r\n self_attention_d = keras.layers.Dropout(0.1)(self_attention)\r\n add = de_embed + self_attention_d\r\n normalised1 = keras.layers.LayerNormalization()(add)\r\n cross_attention = (keras.layers.MultiHeadAttention(num_heads,\r\n key_dim,\r\n dropout=0.1,\r\n use_bias=True,\r\n output_shape=(d_model,))\r\n (normalised1, encoder_output,encoder_output))\r\n cross_attention_d = keras.layers.Dropout(0.1)(cross_attention)\r\n \r\n add2 = normalised1 + cross_attention_d\r\n normalised2 = keras.layers.LayerNormalization()(add2)\r\n \r\n fed_f = keras.layers.Dense(d_ff)(normalised2) # feed forward 1 part\r\n fed_ff = keras.layers.Dense(d_model)(keras.activations.relu(fed_f)) # feed forward 2 part\r\n fed_ff_d = keras.layers.Dropout(0.1)(fed_ff)\r\n \r\n add3 = normalised2 + fed_ff_d\r\n normalised3 = keras.layers.LayerNormalization()(add3)\r\n \r\n de_embed = normalised3\r\n \r\n decoder_dense_output = keras.layers.Dense(decoder_vocab_len, activation='softmax', name='decoder_output')(de_embed)\r\n \r\n return keras.Model(inputs=[encoder_input, decoder_input], outputs=decoder_dense_output)\r\n \r\n if __name__ == '__main__':\r\n params = (8, 64, 64, 256, 512, 6)\r\n model = model_func(10000, 10000, 100, 100, params)\r\n model.summary()\r\n \r\n # Generate random input data with appropriate shapes\r\n encoder_input_data = np.random.randint(0, 10000, (2, 1)) # (batch_size, sequence_length)\r\n decoder_input_data = np.random.randint(0, 10000, (2, 4)) # (batch_size, sequence_length)\r\n \r\n # Call the model with the random input data\r\n output = model.call([encoder_input_data, decoder_input_data], training=False)\r\n \r\n # Print the shape of the output\r\n print(f'Output shape: {output.shape}')\r\n\r\nUserWarning: You are using a softmax over axis 3 of a tensor of shape (2, 8, 1, 1). This axis has size 1. The softmax operation will always return the value 1, which is likely not what you intended. Did you mean to use a sigmoid instead?\r\n warnings.warn(\r\nUserWarning: You are using a softmax over axis 3 of a tensor of shape (2, 8, 4, 1). This axis has size 1. The softmax operation will always return the value 1, which is likely not what you intended. Did you mean to use a sigmoid instead?\r\n warnings.warn(\r\n \r\nOutput shape: (2, 4, 10000)\r\n\r\n", "Hi **@sp00N221** ,\r\n- Sorry for the delay, Can you please check with recent TF compatibility versions? I tried with TF2.16.1 and I cannot reproduce the error. \r\nPlease check the screenshot\r\n here. Thanks!", "Hey,\r\n\r\nThank you for taking the time to review my issue. I've had nothing but problems with my task over the past few days. I had a combination of a TransformerBlock and LSTM layers. Coupled with Optuna, it was probably just too many variables and possibilities, causing the model to become unstable. I have now switched to this task:\r\n\r\n\r\ndef objective(trial, features, target):\r\n n_estimators = trial.suggest_int('n_estimators', 50, 300)\r\n max_depth = trial.suggest_int('max_depth', 3, 15)\r\n learning_rate = trial.suggest_float('learning_rate', 0.01, 0.3, log=True)\r\n subsample = trial.suggest_float('subsample', 0.5, 1.0)\r\n colsample_bytree = trial.suggest_float('colsample_bytree', 0.5, 1.0)\r\n gamma = trial.suggest_float('gamma', 0, 5)\r\n min_child_weight = trial.suggest_int('min_child_weight', 1, 10)\r\n reg_lambda = trial.suggest_float('lambda', 1e-8, 10.0, log=True)\r\n reg_alpha = trial.suggest_float('alpha', 1e-8, 10.0, log=True)\r\n\r\n model = XGBClassifier(\r\n n_estimators=n_estimators, max_depth=max_depth, learning_rate=learning_rate,\r\n subsample=subsample, colsample_bytree=colsample_bytree, gamma=gamma,\r\n min_child_weight=min_child_weight, reg_lambda=reg_lambda, reg_alpha=reg_alpha,\r\n random_state=42\r\n )\r\n\r\n x_train, x_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\r\n\r\n numeric_features = x_train.columns\r\n preprocessor = ColumnTransformer(\r\n transformers=[\r\n ('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='mean')), ('scaler', StandardScaler())]),\r\n numeric_features)\r\n ])\r\n\r\n x_train = preprocessor.fit_transform(x_train)\r\n x_test = preprocessor.transform(x_test)\r\n\r\n model.set_params(early_stopping_rounds=10, eval_metric='logloss')\r\n model.fit(x_train, y_train, eval_set=[(x_test, y_test)], verbose=False)\r\n\r\n predictions = model.predict(x_test)\r\n accuracy = accuracy_score(y_test, predictions)\r\n\r\n return accuracy\r\n \r\n \r\nWith this, I have no problems.\r\nHave a nice day!", "Hi **@sp00N221** ,\r\n- Could you please confirm if this issue is resolved for you? Please feel free to close the issue.\r\n\r\nThank you!\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." ]

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[ "Hi @thereallee, are you using an emulator or a real phone? The steps to integrate Select ops with iOS are different depending on that, so I want to understand your situation.", "Hi - I see essentially the same error when building for device and simulator. See attached.\r\n\r\nSimulator build:\r\n\r\n\r\n\r\n\r\nDevice build:\r\n\r\n\r\n\r\n", "Hi @thereallee, I'm not sure if the Pod build is the right way for iOS emulator so let's stick with the Device build for now. How did you fix the \"-force_load\" issue? Have you tried getting it to work w/ the image Classification app like the bottom of this section suggested? https://www.tensorflow.org/lite/guide/ops_select#ios Thanks for your help. If you can zip and share your xcode project, that'll be easier to check. (Perhaps just a copy of the classification app with your attempted changes)" ]

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