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Implement hermetic cuda usage across TF projects.
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Implement hermetic cuda usage across TF projects.
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PR #10711: [GPU][NFC] Clarify dependency of cuDNN fusion compilation on stream executor and cuDNN handle.
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PR #10711: [GPU][NFC] Clarify dependency of cuDNN fusion compilation on stream executor and cuDNN handle.
Imported from GitHub PR https://github.com/openxla/xla/pull/10711
LocalCuDnnHandle can now be created only via CudnnAccess which ensures that main cuDNN handle is created first.
DnnGraph methods using cuDNN handle now require DnnSupport&.
Copybara import of the project:
--
44baf4b48b9cedc762a77d3087db0581581a03a7 by Ilia Sergachev <[email protected]>:
[GPU][NFC] Clarify dependency of cuDNN fusion compilation on stream executor and cuDNN handle.
Merging this change closes #10711
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10711 from openxla:cudnn_handle_cleanup 44baf4b48b9cedc762a77d3087db0581581a03a7
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Increase Code Coverage of //third_party/tensorflow/core/tfrt/run_handler_thread_pool/
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Increase Code Coverage of //third_party/tensorflow/core/tfrt/run_handler_thread_pool/
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Support Per-channel quantization for DotGeneral
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Support Per-channel quantization for DotGeneral
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Converting Model to TFLite using Tensorflow 2.16.1 fails - works w/ 2.15.0
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"It seems like you might have a mix of `keras` and `tf.keras` imports in your code, could you please check if your `best_model` is currently built using `tf.keras` or not?",
"@gsexton,\r\nCould you please share a simple standalone code to reproduce the issue? Also, please try to execute the model code with the tf-keras, as the tensorflow v2.16 by default uses keras 3.0 version. Thank you!",
"@tilakrayal The model is built via:\r\n\r\n```python\r\n model = tf.keras.Sequential([\r\n tf.keras.layers.Dense(1024, activation='relu'),\r\n tf.keras.layers.Dense(2048, activation='relu'),\r\n tf.keras.layers.Dense(2048, activation='relu'),\r\n tf.keras.layers.Dense(1, activation='sigmoid')\r\n ])\r\n\r\n model.compile(\r\n loss=tf.keras.losses.binary_crossentropy,\r\n optimizer=tf.keras.optimizers.Adam(learning_rate=0.003),\r\n metrics=[\r\n tf.keras.metrics.BinaryAccuracy(name='accuracy'),\r\n tf.keras.metrics.Precision(name='precision'),\r\n tf.keras.metrics.Recall(name='recall')\r\n ]\r\n )\r\n\r\n history = model.fit(X_train, y_train, epochs=NUM_TRAINING_EPOCHS)\r\n```\r\n\r\nthe convert is:\r\n\r\n```python\r\nconverter = tf.lite.TFLiteConverter.from_keras_model(best_model)\r\n```\r\n\r\nso it seems like this should be OK.\r\n\r\nMy first effort for conversion was:\r\n\r\n```python\r\ntf.saved_model.save(best_model,save_path)\r\n\r\nconverter = tf.lite.TFLiteConverter.from_saved_model(save_path)\r\ntflite_model = converter.convert()\r\n```\r\nThat too gives:\r\n\r\n```\r\nloc(fused[\"ReadVariableOp:\", \"sequential_1_1/dense_4_1/Add/ReadVariableOp@__inference_serving_default_133793\"]): error: missing attribute 'value'\r\nLLVM ERROR: Failed to infer result type(s).\r\nAbort trap: 6\r\n```\r\n\r\n@Aloqeely I'm not sure I follow what to do with this:\r\n\r\n_Also, please try to execute the model code with the tf-keras, as the tensorflow v2.16 by default uses keras 3.0 version. Thank you!_\r\n\r\nI think it is already doing what you suggest. Using tf.keras.\r\n\r\n\r\n",
"Here is a complete simple program and data set that replicates the problem. You can alternate paths by\r\n commenting/uncommenting lines 48-50 of model.\r\n\r\n[support.csv](https://github.com/tensorflow/tensorflow/files/14669723/support.csv)\r\n[support_labels.csv](https://github.com/tensorflow/tensorflow/files/14669729/support_labels.csv)\r\n[model.txt](https://github.com/tensorflow/tensorflow/files/14669764/model.txt)\r\n\r\n\r\n",
"Hi @gsexton,\r\n\r\nI have reproduced the code with TF 2.15 and TF 2.16. With 2.15 it's working fine as expected. As you mentioned, it's getting aborted with TF2.16.1 at TFLite conversion . Right now, TF2.16 has an issue with Keras 3.0. As a workaround install Keras 2 as follows. ```pip install -U tf_keras # Keras 2```\r\n```import os```\r\n```os.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\" ```. Here is the [gist](https://colab.sandbox.google.com/gist/LakshmiKalaKadali/263d55643ef5dec63fa2c17ff2c096ad/tflite_63987_2-16_tfix.ipynb) with TF 2.16 and keras2 workaround. \r\n\r\n\r\nThank You",
"I have tested with the tf_keras installed, and the problem is resolved.",
"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/63987\">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/63987\">No</a>\n",
"I also experienced this bug, and it did not disappear with `tf_keras` installed. I had `tensorflow==2.16.1`, with the below code. I was only able to get it working with `tensorflow==2.15`\r\n\r\n```python\r\n#!/usr/bin/env python3\r\n\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nfrom tensorflow.keras.models import Sequential\r\nfrom tensorflow.keras.layers import Input, DepthwiseConv2D, Conv2D, Flatten, Dense\r\n\r\n# Define a simple model with depthwise convolutions\r\nmodel = Sequential()\r\nmodel.add(Input(shape=(64, 64, 3)))\r\nmodel.add(DepthwiseConv2D(kernel_size=(3, 3), activation=\"relu\"))\r\nmodel.add(Conv2D(64, (1, 1), activation=\"relu\"))\r\nmodel.add(Flatten())\r\nmodel.add(Dense(100, activation=\"relu\"))\r\nmodel.add(Dense(10, activation=\"softmax\"))\r\n\r\n# Compile the model\r\nmodel.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\r\n\r\n\r\n# Define the representative dataset generator\r\ndef representative_dataset_gen():\r\n for _ in range(100):\r\n # Here you should provide a sample from your actual dataset\r\n # For illustration, we'll use random data\r\n # Yield a batch of input data (in this case, a single sample)\r\n yield [tf.random.normal((1, 64, 64, 3))]\r\n\r\n\r\n# Set the TensorFlow Lite converter\r\nconverter = tf.lite.TFLiteConverter.from_keras_model(model)\r\n\r\n# Set the optimization to default for int8 quantization\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\r\n\r\n# Define the representative dataset for quantization\r\nconverter.representative_dataset = representative_dataset_gen\r\n\r\n# Restrict the target spec to int8 for full integer quantization\r\nconverter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\r\n\r\n# Instruct the converter to make the input and output layer as integer\r\nconverter.inference_input_type = tf.int8\r\nconverter.inference_output_type = tf.int8\r\n\r\n# Convert the model\r\ntflite_model = converter.convert()\r\n\r\n# Save the model to a file\r\nwith open(\"model.tflite\", \"wb\") as f:\r\n f.write(tflite_model)\r\n\r\nprint(\"Model has been successfully converted to TFLite and saved as 'model.tflite'.\")\r\n```\r\n\r\nThis crashes with the error:\r\n\r\n```\r\n/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/tensorflow/lite/python/convert.py:964: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway. \r\n warnings.warn( \r\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR \r\nW0000 00:00:1712390503.405253 14212 tf_tfl_flatbuffer_helpers.cc:390] Ignored output_format. \r\nW0000 00:00:1712390503.405271 14212 tf_tfl_flatbuffer_helpers.cc:393] Ignored drop_control_dependency. \r\n2024-04-06 10:01:43.405735: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: /tmp/tmpvw6vrp68 \r\n2024-04-06 10:01:43.406193: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } \r\n2024-04-06 10:01:43.406204: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: /tmp/tmpvw6vrp68 \r\n2024-04-06 10:01:43.409542: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled \r\n2024-04-06 10:01:43.410044: I tensorflow/cc/saved_model/loader.cc:234] Restoring SavedModel bundle. \r\n2024-04-06 10:01:43.469106: I tensorflow/cc/saved_model/loader.cc:218] Running initialization op on SavedModel bundle at path: /tmp/tmpvw6vrp68 \r\n2024-04-06 10:01:43.481939: I tensorflow/cc/saved_model/loader.cc:317] SavedModel load for tags { serve }; Status: success: OK. Took 76207 microseconds. \r\n2024-04-06 10:01:43.496698: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable. \r\nloc(fused[\"ReadVariableOp:\", callsite(\"sequential_1/conv2d_1/Reshape/ReadVariableOp@__inference_serving_default_132\"(\"/home/wheest/Dropbox/home/proj/n64-dnn/tflite_model_gen/weenet.py\":49:1) at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3\r\n.11/site-packages/tensorflow/lite/python/lite.py\":1175:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/tensorflow/lite/python/lite.py\":1129:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/tenso\r\nrflow/lite/python/lite.py\":1636:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/tensorflow/lite/python/lite.py\":1614:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/tensorflow/lite/python/conve\r\nrt_phase.py\":205:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/tensorflow/lite/python/lite.py\":1537:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/backend/tensorflow/layer.py\":58:1\r\n at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/backend/tensorflow/layer.py\":120:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\":117:1 at callsite(\"\r\n/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/layers/layer.py\":814:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\":117:1 at callsite(\"/home/wheest/.virtualenvs/n64\r\n/lib/python3.11/site-packages/keras/src/ops/operation.py\":48:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\":156:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packa\r\nges/keras/src/models/sequential.py\":202:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/models/functional.py\":194:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/ops/functio\r\nn.py\":151:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/models/functional.py\":578:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\":117:1 at callsi\r\nte(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/layers/layer.py\":814:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\":117:1 at callsite(\"/home/wheest/.virtualenvs\r\n/n64/lib/python3.11/site-packages/keras/src/ops/operation.py\":48:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\":156:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-p\r\nackages/keras/src/layers/convolutional/base_conv.py\":233:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/ops/numpy.py\":4507:1 at callsite(\"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/b\r\nackend/tensorflow/numpy.py\":1545:1 at \"/home/wheest/.virtualenvs/n64/lib/python3.11/site-packages/keras/src/backend/tensorflow/core.py\":64:1))))))))))))))))))))))))))]): error: missing attribute 'value'\r\nLLVM ERROR: Failed to infer result type(s). \r\nAborted (core dumped) \r\n```",
"Is there any chance we can fix this issue at the root cause? We found the same problem when saving a keras model with `tf.saved_model.save(model)` and then converting the SavedModel to TFLite.",
"I have the same problem; In TF2.16 converting a .keras model to .tflite gives `LLVM ERROR: Failed to infer result type(s).`.\r\nLoading the TF 2.16 model with version 2.15 (using `load_model`) also doesn't work.",
"Same issue.",
"Same issue here. Try: \r\n\r\npip install -U tf_keras # Keras 2\r\nimport os\r\nos.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"\r\n\r\nbut didn't work.\r\n\r\nSaving to tflite...\r\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\r\nW0000 00:00:1716173986.478554 526596 tf_tfl_flatbuffer_helpers.cc:390] Ignored output_format.\r\nW0000 00:00:1716173986.479258 526596 tf_tfl_flatbuffer_helpers.cc:393] Ignored drop_control_dependency.\r\n2024-05-19 21:59:46.482609: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: models_saved/EURUSD-OTC-L\r\n2024-05-19 21:59:46.483692: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }\r\n2024-05-19 21:59:46.483699: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: models_saved/EURUSD-OTC-L\r\n2024-05-19 21:59:46.491139: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled\r\n2024-05-19 21:59:46.492726: I tensorflow/cc/saved_model/loader.cc:234] Restoring SavedModel bundle.\r\n2024-05-19 21:59:46.520993: I tensorflow/cc/saved_model/loader.cc:218] Running initialization op on SavedModel bundle at path: models_saved/EURUSD-OTC-L\r\n2024-05-19 21:59:46.526127: I tensorflow/cc/saved_model/loader.cc:317] SavedModel load for tags { serve }; Status: success: OK. Took 43529 microseconds.\r\n2024-05-19 21:59:46.560649: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\r\nloc(fused[\"ReadVariableOp:\", \"sequential_2_1/conv1d_2_1/Reshape/ReadVariableOp@__inference_serving_default_601338\"]): error: missing attribute 'value'\r\nLLVM ERROR: Failed to infer result type(s).\r\n",
"> Same issue here. Try:\r\n> \r\n> pip install -U tf_keras # Keras 2 import os os.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"\r\n> \r\n> but didn't work.\r\n> \r\n> Saving to tflite... WARNING: All log messages before absl::InitializeLog() is called are written to STDERR W0000 00:00:1716173986.478554 526596 tf_tfl_flatbuffer_helpers.cc:390] Ignored output_format. W0000 00:00:1716173986.479258 526596 tf_tfl_flatbuffer_helpers.cc:393] Ignored drop_control_dependency. 2024-05-19 21:59:46.482609: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: models_saved/EURUSD-OTC-L 2024-05-19 21:59:46.483692: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2024-05-19 21:59:46.483699: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: models_saved/EURUSD-OTC-L 2024-05-19 21:59:46.491139: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled 2024-05-19 21:59:46.492726: I tensorflow/cc/saved_model/loader.cc:234] Restoring SavedModel bundle. 2024-05-19 21:59:46.520993: I tensorflow/cc/saved_model/loader.cc:218] Running initialization op on SavedModel bundle at path: models_saved/EURUSD-OTC-L 2024-05-19 21:59:46.526127: I tensorflow/cc/saved_model/loader.cc:317] SavedModel load for tags { serve }; Status: success: OK. Took 43529 microseconds. 2024-05-19 21:59:46.560649: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable. loc(fused[\"ReadVariableOp:\", \"sequential_2_1/conv1d_2_1/Reshape/ReadVariableOp@__inference_serving_default_601338\"]): error: missing attribute 'value' LLVM ERROR: Failed to infer result type(s).\r\n\r\nHave you made sure to set the env variable before importing the tflow module? this fixed it for me",
"```\r\nW0000 00:00:1717755679.073267 6961451 tf_tfl_flatbuffer_helpers.cc:390] Ignored output_format.\r\nW0000 00:00:1717755679.074262 6961451 tf_tfl_flatbuffer_helpers.cc:393] Ignored drop_control_dependency.\r\n2024-06-07 12:21:19.076353: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: saved_model/my_model_two.keras\r\n2024-06-07 12:21:19.091621: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }\r\n2024-06-07 12:21:19.091641: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: saved_model/my_model_two.keras\r\n2024-06-07 12:21:19.386137: I tensorflow/cc/saved_model/loader.cc:234] Restoring SavedModel bundle.\r\n2024-06-07 12:21:20.424521: I tensorflow/cc/saved_model/loader.cc:218] Running initialization op on SavedModel bundle at path: saved_model/my_model_two.keras\r\n2024-06-07 12:21:20.694508: I tensorflow/cc/saved_model/loader.cc:317] SavedModel load for tags { serve }; Status: success: OK.\r\n```\r\n-----\r\n```\r\nConverterError Traceback (most recent call last)\r\nCell In[23], line 3\r\n 1 # Convert the SavedModel to TensorFlow Lite model\r\n 2 converter = tf.lite.TFLiteConverter.from_saved_model(\"saved_model/my_model_two.keras\")\r\n----> 3 tflite_model = converter.convert()\r\n```\r\n\r\n\r\nDo you have an example how to do it? @filipang "
] | 2024-03-19T20:28:55 | 2024-06-07T10:38:10 | 2024-03-21T15:10:51 |
NONE
| null | null | null |
### 1. System information
- OpenSUSE Leap 15.5 AMD64 - Python 3.11
- MacOS 14.4 Sonoma - Python 3.12
- TensorFlow installation: pip package
- TensorFlow library: 2.16.1
### 2. Code
```
#converter = tf.lite.TFLiteConverter.from_saved_model(save_path)
converter = tf.lite.TFLiteConverter.from_keras_model(best_model)
# This is supposed to work. I copied it from the tf website, but
# it blows up.
#
print(f"Calling converter.convert()")
tflite_model = converter.convert()
print("Writing output")
with open(os.path.join(output_directory, 'model.tflite'), 'wb') as f:
f.write(tflite_model)
```
### 3. Failure after conversion
```
WARNING: All log messages before absl::InitializeLog() is called are written to STDERRW0000 00:00:1710168763.106515 29073 tf_tfl_flatbuffer_helpers.cc:390] Ignored output_format.W0000 00:00:1710168763.107037 29073 tf_tfl_flatbuffer_helpers.cc:393] Ignored drop_control_dependency.
2024-03-11 08:52:43.107570: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: tftest/trained_model
{{2024-03-11 08:52:43.107869: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }}}
2024-03-11 08:52:43.107881: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: tftest/trained_model2024-03-11 08:52:43.110991: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
2024-03-11 08:52:43.111327: I tensorflow/cc/saved_model/loader.cc:234] Restoring SavedModel bundle.2024-03-11 08:52:43.134688: I tensorflow/cc/saved_model/loader.cc:218] Running initialization op on SavedModel bundle at path: tftest/trained_model
2024-03-11 08:52:43.138356: I tensorflow/cc/saved_model/loader.cc:317] SavedModel load for tags { serve }; Status: success: OK. Took 30788 microseconds.
2024-03-11 08:52:43.158539: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
loc(fused["ReadVariableOp:", "sequential_1/dense_1/add/ReadVariableOp@__inference_serving_default_124025"]):
error: missing attribute 'value'LLVM ERROR: Failed to infer result type(s).Abort trap: 6
```
### 5. (optional) Any other info / logs
This issue was reported on #62610 originally.
I've tested this on Linux AMD 64, and on Mac OS 14.4 ARM and the same error occurs.
Per the comment from @adamantivm, I tried using Tensorflow 2.15.0 on Linux AMD64, and it worked as expected.
I tried setting the env variable for MLIR_CRASH... and running, but no output was found.
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PR_kwDOArmXAs5qI53C
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Make TaskQueue keep track of the start time of each task
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PR #10687: [CPU] Add SimplifyFPConversions only if xla_allow_excess_precision
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PR #10687: [CPU] Add SimplifyFPConversions only if xla_allow_excess_precision
Imported from GitHub PR https://github.com/openxla/xla/pull/10687
Several weeks ago it was a change which enables "simplify-fp-conversions" pass in cpu_compiler.cc for intel cpus unconditionally.
[PR-8402](https://github.com/openxla/xla/pull/8402) - [XLA:CPU] [oneDNN] Enable Dot op (MatMul) in BF16 Type
I noticed the following issue with having "simplify-fp-conversions" pass in cpu_compiler.cc enabled unconditionally.
My model uses bf16 operators (e.g. convolution). I want to jit compile and run it on CPU preserving intermediate bf16 accuracy.
Cpu compiler uses`float-normalization-bf16` pass which converts bf16 convolution to f32_convolution + convert_to_bf16 + convert_to_f32. (because typical cpu does not support bf16 computation)
Cpu compiler (on XEON) also uses `simplify-fp-conversions` pass which simplifies `f32_convolution + convert_to_bf16 + convert_to_f32` to just `f32_convolution`.
As the result - the whole model was converted to f32 precision internally and conversion to bf16 happens only at the very end.
In some cases we want to execute bf16 model on CPU but get results with accuracy similar to the case when it is executed on bf16 hardware.
To control the accuracy we can use debug_option `xla_allow_excess_precision`
By default it is true - hence, `simplify-fp-conversions` pass is enabled.
If we need to emulate bf16 computation on intel cpu we can set `XLA_FLAGS="--xla_allow_excess_precision=false"` - in this case `simplify-fp-conversions` will not be added to cpu_compiler pipeline. f32 ops results will be converted to bf16 immediately. This will preserve bf16 accuracy internally.
[gpu_compiler.cc](https://github.com/openxla/xla/blob/main/xla/service/gpu/gpu_compiler.cc#L1359) already enables `SimplifyFPConversions` pass only if `debug_options.xla_allow_excess_precision()` is true.
Copybara import of the project:
--
796dc83ef34455e53b83c02dc68cd6d71306e654 by Alexander Pivovarov <[email protected]>:
[CPU] Add SimplifyFPConversions only if xla_allow_excess_precision
Merging this change closes #10687
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10687 from apivovarov:fix_cpu_SimplifyFPConversions 796dc83ef34455e53b83c02dc68cd6d71306e654
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Update keras-nightly version in TensorFlow requirements.
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Update keras-nightly version in TensorFlow requirements.
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[xla:gpu][NFC] Use llvm::zip instead of unsigned index
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[xla:gpu][NFC] Use llvm::zip instead of unsigned index
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Add a parameter to BuildHloFromGraph to enable the choice to force the use of `_output_shapes` attribute.
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Add a parameter to BuildHloFromGraph to enable the choice to force the use of `_output_shapes` attribute.
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lite: enable VHLO serialization for TFLite path
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lite: enable VHLO serialization for TFLite path
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Supports PjRt CPU array converting py array when the CPU PjRt arrays have non-default layouts.
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Supports PjRt CPU array converting py array when the CPU PjRt arrays have non-default layouts.
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PR #10704: [ROCm] Make DynLoadShim static struct in header to avoid redefinition of sym…
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PR #10704: [ROCm] Make DynLoadShim static struct in header to avoid redefinition of sym…
Imported from GitHub PR https://github.com/openxla/xla/pull/10704
…bols
Copybara import of the project:
--
b407a7c7ed136c37189a2a907c50b14d0dac1230 by Harsha HS <[email protected]>:
Make DynLoadShim static struct in header to avoid redefinition of symbols
Merging this change closes #10704
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10704 from ROCm:ci_dynLoad_20240319 b407a7c7ed136c37189a2a907c50b14d0dac1230
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[XLA:Python] Fix breakage in get_client_memory_stats()
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[XLA:Python] Fix breakage in get_client_memory_stats()
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| 63,977 |
Remove TSL configs now that TSL is tested via XLA
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Remove TSL configs now that TSL is tested via XLA
Later the Windows and MacOS configs should be revived under XLA
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Generalize int4 to other sizes
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Generalize int4 to other sizes
No functional change is intended, just a refactoring.
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PR_kwDOArmXAs5qH4SK
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Update TFRT dependency to use revision
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NONE
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/1741a4616ea64a43b4d58a43e5fbc32e382afeaf.
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PR_kwDOArmXAs5qH3v9
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Add test coverage for attr_util.cc
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NONE
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Add test coverage for attr_util.cc
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PR_kwDOArmXAs5qHrlD
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[xla:gpu] Handle arguments passed in arbitrary order to AddressComputationThunk
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NONE
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[xla:gpu] Handle arguments passed in arbitrary order to AddressComputationThunk
Passing the buffers in correct order to embedded thunk when AddressComputationThunk itself is getting buffers in arbitrary order.
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PR_kwDOArmXAs5qHfZm
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[XLA:GPU][IndexAnalysis] Add indexing map for DynamicSlice.
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[XLA:GPU][IndexAnalysis] Add indexing map for DynamicSlice.
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PR_kwDOArmXAs5qHcIq
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[xla:gpu] Emission of DynamicAddressComputationFusion
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[xla:gpu] Emission of DynamicAddressComputationFusion
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10684 from openxla:cudnn_fusion_more_op_types 210f8aaae3206264379c9f1ff5f70f24ffca4a14
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#shlo_ref Add `sign` op.
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#shlo_ref Add `sign` op.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/63954 from elfringham:fix_312_setuptools da09611b50c51fc7654e139a01ceceb4174fce89
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[xla:gpu] Do not take into account original slice offset when computing offsets in AddressComputationThunk
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[xla:gpu] Do not take into account original slice offset when computing offsets in AddressComputationThunk
The original slice offset is already taken into account in `BufferAllocations::GetDeviceAddress`.
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PR_kwDOArmXAs5qHWCi
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[xla:gpu] Handle arbitrary number of arguments passed to AddressComputationThunk
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[xla:gpu] Handle arbitrary number of arguments passed to AddressComputationThunk
Passing the correct buffers to embedded thunk when AddressComputationThunk itself is getting an arbitrary number of buffers.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/63954 from elfringham:fix_312_setuptools da09611b50c51fc7654e139a01ceceb4174fce89
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PR #10684: [GPU] Support more elementwise operations and boolean data type in cuDNN GEMM fusions.
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NONE
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PR #10684: [GPU] Support more elementwise operations and boolean data type in cuDNN GEMM fusions.
Imported from GitHub PR https://github.com/openxla/xla/pull/10684
Copybara import of the project:
--
210f8aaae3206264379c9f1ff5f70f24ffca4a14 by Ilia Sergachev <[email protected]>:
[GPU] Support more elementwise operations and data types in cuDNN GEMM fusions.
Merging this change closes #10684
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10684 from openxla:cudnn_fusion_more_op_types 210f8aaae3206264379c9f1ff5f70f24ffca4a14
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PR_kwDOArmXAs5qHLW-
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Pass XNN_FLAG_REDUCE_DIMS to global sum pooling and global average pooling
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Pass XNN_FLAG_REDUCE_DIMS to global sum pooling and global average pooling
Remove deprecated flag in graph builder
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PR_kwDOArmXAs5qHIf-
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Integrate StableHLO at openxla/stablehlo@b27ef13c
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Integrate StableHLO at openxla/stablehlo@b27ef13c
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PR_kwDOArmXAs5qHEZy
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[XLA:GPU] Avoid adding CudnnNormRewriter to the compilation pipeline if it is disabled.
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[XLA:GPU] Avoid adding CudnnNormRewriter to the compilation pipeline if it is disabled.
This pass previously performed expensive matching work at even when it was disabled
and would not rewrite anything.
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[XLA:Python] Use PyType_FromSpec to build heap types, rather than manually building Python heap types.
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[XLA:Python] Use PyType_FromSpec to build heap types, rather than manually building Python heap types.
Cleanup; no functional changes intended.
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Integrate LLVM at llvm/llvm-project@930f21c6bc0f
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Integrate LLVM at llvm/llvm-project@930f21c6bc0f
Updates LLVM usage to match
[930f21c6bc0f](https://github.com/llvm/llvm-project/commit/930f21c6bc0f)
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Hoist async copies when start_after is -1
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Hoist async copies when start_after is -1
Previously, the counter for `MemorySpaceAssignment::FixSchedule` started at `0` and would check for async copies scheduled for before the counter and after (but only the exact value). We have some async copies that set their `start_after` value to `-1` meaning we would skip inserting them at the earliest point and then catch that they weren't inserted by their `start_before` time and insert them then. This would lead to a few async copy operations where `*-start` would be scheduled immediately before their corresponding `*-done` operation, leading to none of the latency being hidden.
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PR #10631: [XLA:CPU][oneDNN] Minor fix in CPU bfloat16 support
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PR #10631: [XLA:CPU][oneDNN] Minor fix in CPU bfloat16 support
Imported from GitHub PR https://github.com/openxla/xla/pull/10631
Align the usage of oneDNN bf16 cpu float normalization with the same guarding condition as for oneDNN rewrites in compilation step, i.e., only when oneDNN is enabled and in non-AOT mode.
Copybara import of the project:
--
8166205b6491dd53dcd65e3898b4b9edfa641517 by Yimei Sun <[email protected]>:
[XLA:CPU][oneDNN] Minor fix in CPU bfloat16 support
--
9b4f9136f1976b4432e1a60f2deb4a17c150dd3f by Yimei Sun <[email protected]>:
Move CpuFloatSupport reference under guard condition
Merging this change closes #10631
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10631 from Intel-tensorflow:yimei/fix_cpu_bf16_support 9b4f9136f1976b4432e1a60f2deb4a17c150dd3f
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Typos are fixed in quantization_debugger.ipynb
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Typos and Grammatical errors are fixed
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Create a workspace per subgraph to prevent race condition
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[XLA:GPU][IndexAnalysis] Add DimVar, RangeVar and RTVar to IndexingMap.
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[XLA:GPU][IndexAnalysis] Add DimVar, RangeVar and RTVar to IndexingMap.
RTVar is a new type of symbol/variable, that is associated with a runtime value of an HLO instruction, e.g. it can be used to model DUS, gather, etc.
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PR #9688: [XLA:CPU] Add F16 support for oneDNN matmul
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PR #9688: [XLA:CPU] Add F16 support for oneDNN matmul
Imported from GitHub PR https://github.com/openxla/xla/pull/9688
This PR enables F16 support for dot operation when onednn is enabled and adds tests. In the existing pipeline, F16 dot is converted to FP32 through the change-op-data-type pass. With the changes in this PR, if onednn is enabled and the conditions for rewriting dot to onednn matmul are met then F16 dot is not upcast to FP32.
Copybara import of the project:
--
190e446d920fb82732054383c81545c528d6d9d9 by Kanvi Khanna <[email protected]>:
Add F16 support for matmul
--
f99f2bb30febc5c818c28c379232151085deebd6 by Kanvi Khanna <[email protected]>:
fix formatting
--
aef8cb1685539a5e1c4765262b8ed5f537613d58 by Kanvi Khanna <[email protected]>:
fix for failing test
--
72dd379ce16eab6838ef568b20ab15e0e0ea0449 by Kanvi Khanna <[email protected]>:
Add check in ChangeOpDataType pass for F16 support as a separate pass for F16 support is not needed; undo changes for BMM;
Merging this change closes #9688
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9688 from Intel-tensorflow:kanvi/f16_matmul 72dd379ce16eab6838ef568b20ab15e0e0ea0449
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PR #10211: [XLA:CPU][oneDNN] Update oneDNN LayerNorm and Softmax
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PR #10211: [XLA:CPU][oneDNN] Update oneDNN LayerNorm and Softmax
Imported from GitHub PR https://github.com/openxla/xla/pull/10211
This PR addresses following :
1) Add support for FP16 to oneDNN softmax pattern matching
2) Adds another pattern for oneDNN layernorm based on keras layernorm operation
3) Fixes previous oneDNN layernorm pattern, as it was focused on flax layernorm pattern, which recently wen through an update while moment calculation
4) Adds FP16 support to oneDNN layernorm pattern matching.
Copybara import of the project:
--
9eff5f704d99a2a594226e6a9cc1db72a67c8de8 by Sachin Muradi <[email protected]>:
Update LN and Softmax
--
62497479066d9d4a9017ad13ab2389c4795b37db by Sachin Muradi <[email protected]>:
Address review comments
Merging this change closes #10211
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10211 from Intel-tensorflow:onednn-update-ln 62497479066d9d4a9017ad13ab2389c4795b37db
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PR_kwDOArmXAs5qFQni
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[Linaro:ARM_CI] Fix Python 3.12 ARM_CD builds
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Remove the setting of SETUPTOOLS_USE_DISTUTILS environment variable as it is no longer needed and breaks Python 3.12 package installation in some cases.
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PR_kwDOArmXAs5qFQEg
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Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/59be786cfd0fa9945bce36b9b2918d4b1753c426.
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I_kwDOArmXAs6C0Krj
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Selectively build TFLite frameworks failed！
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[
"Hi @chenyingchao, can you share \"migan_8bit_a3.tflite\" or where I can download it? Thanks!",
"[Uploading migan_8bit_new.tflite.zip…]()\r\n@pkgoogle ",
"Hi @chenyingchao, it appears that link just links back to this issue, perhaps that is not the right link? Thanks for your help.",
"[migan_8bit_0.tflite.zip](https://github.com/tensorflow/tensorflow/files/14784018/migan_8bit_0.tflite.zip)\r\n@pkgoogle please try this.",
"Thanks @chenyingchao, I was able to replicate with the same results:\r\n\r\nOn MacOS:\r\n```\r\nbash tensorflow/lite/ios/build_frameworks.sh --input_models=migan_8bit_0.tflite --target_archs=x86_64,armv7,arm64\r\nStarting local Bazel server and connecting to it...\r\nINFO: Reading 'startup' options from /Users/xxxxxx/git/tensorflow/.bazelrc: --windows_enable_symlinks\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=192\r\nINFO: Reading rc options for 'build' from /Users/xxxxxx/git/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/xxxxxx/git/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /Users/xxxxxx/git/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/Users/xxxxxx/miniforge3/envs/python3dot9/bin/python3 --action_env PYTHON_LIB_PATH=/Users/xxxxxx/miniforge3/envs/python3dot9/lib/python3.9/site-packages --python_path=/Users/xxxxxx/miniforge3/envs/python3dot9/bin/python3 --action_env ANDROID_NDK_HOME=/Users/xxxxxx/Library/Android/sdk/ndk/21.4.7075529 --action_env ANDROID_NDK_API_LEVEL=26 --action_env ANDROID_BUILD_TOOLS_VERSION=34.0.0 --action_env ANDROID_SDK_API_LEVEL=34 --action_env ANDROID_SDK_HOME=/Users/xxxxxx/library/Android/Sdk\r\nINFO: Found applicable config definition build:short_logs in file /Users/xxxxxx/git/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/xxxxxx/git/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:ios in file /Users/xxxxxx/git/tensorflow/.bazelrc: --apple_platform_type=ios --apple_bitcode=embedded --copt=-fembed-bitcode --copt=-Wno-c++11-narrowing --noenable_platform_specific_config --copt=-w --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --define=with_xla_support=false\r\nINFO: Analyzed target //tensorflow/lite/ios/tmp:TensorFlowLiteC_framework (150 packages loaded, 7433 targets configured).\r\nINFO: Found 1 target...\r\nTarget //tensorflow/lite/ios/tmp:TensorFlowLiteC_framework up-to-date:\r\n bazel-bin/tensorflow/lite/ios/tmp/TensorFlowLiteC_framework.zip\r\nINFO: Elapsed time: 214.692s, Critical Path: 24.21s\r\nINFO: 2032 processes: 366 internal, 1666 local.\r\nINFO: Build completed successfully, 2032 total actions\r\nINFO: Reading 'startup' options from /Users/xxxxxx/git/tensorflow/.bazelrc: --windows_enable_symlinks\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=192\r\nINFO: Reading rc options for 'build' from /Users/xxxxxx/git/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/xxxxxx/git/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /Users/xxxxxx/git/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/Users/xxxxxx/miniforge3/envs/python3dot9/bin/python3 --action_env PYTHON_LIB_PATH=/Users/xxxxxx/miniforge3/envs/python3dot9/lib/python3.9/site-packages --python_path=/Users/xxxxxx/miniforge3/envs/python3dot9/bin/python3 --action_env ANDROID_NDK_HOME=/Users/xxxxxx/Library/Android/sdk/ndk/21.4.7075529 --action_env ANDROID_NDK_API_LEVEL=26 --action_env ANDROID_BUILD_TOOLS_VERSION=34.0.0 --action_env ANDROID_SDK_API_LEVEL=34 --action_env ANDROID_SDK_HOME=/Users/xxxxxx/library/Android/Sdk\r\nINFO: Found applicable config definition build:short_logs in file /Users/xxxxxx/git/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/xxxxxx/git/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:monolithic in file /Users/xxxxxx/git/tensorflow/.bazelrc: --define framework_shared_object=false --define tsl_protobuf_header_only=false --experimental_link_static_libraries_once=false\r\nINFO: Found applicable config definition build:macos in file /Users/xxxxxx/git/tensorflow/.bazelrc: --apple_platform_type=macos --copt=-DGRPC_BAZEL_BUILD --features=archive_param_file --copt=-w --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=no_tfrt\r\nINFO: Found applicable config definition build:no_tfrt in file /Users/xxxxxx/git/tensorflow/.bazelrc: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/ir,tensorflow/compiler/mlir/tfrt/ir/mlrt,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ifrt,tensorflow/compiler/mlir/tfrt/tests/mlrt,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/compiler/mlir/tfrt/transforms/mlrt,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/runtime_fallback/test,tensorflow/core/runtime_fallback/test/gpu,tensorflow/core/runtime_fallback/test/saved_model,tensorflow/core/runtime_fallback/test/testdata,tensorflow/core/tfrt/stubs,tensorflow/core/tfrt/tfrt_session,tensorflow/core/tfrt/mlrt,tensorflow/core/tfrt/mlrt/attribute,tensorflow/core/tfrt/mlrt/kernel,tensorflow/core/tfrt/mlrt/bytecode,tensorflow/core/tfrt/mlrt/interpreter,tensorflow/compiler/mlir/tfrt/translate/mlrt,tensorflow/compiler/mlir/tfrt/translate/mlrt/testdata,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils,tensorflow/core/tfrt/utils/debug,tensorflow/core/tfrt/saved_model/python,tensorflow/core/tfrt/graph_executor/python,tensorflow/core/tfrt/saved_model/utils\r\nINFO: Build options --apple_bitcode, --apple_platform_type, --copt, and 2 more have changed, discarding analysis cache.\r\nINFO: Analyzed target //tensorflow/lite/tools:list_flex_ops_no_kernel_main (9 packages loaded, 1402 targets configured).\r\nINFO: Found 1 target...\r\nTarget //tensorflow/lite/tools:list_flex_ops_no_kernel_main up-to-date:\r\n bazel-bin/tensorflow/lite/tools/list_flex_ops_no_kernel_main\r\nINFO: Elapsed time: 22.719s, Critical Path: 9.28s\r\nINFO: 206 processes: 7 internal, 199 local.\r\nINFO: Build completed successfully, 206 total actions\r\nOutput can be found here:\r\nls: /Users/xxxxxx/git/tensorflow/bazel-out/applebin_ios-ios_sim_arm64-opt-ST-7b90815cfaca/bin/tensorflow/lite/ios/tmp/TensorFlowLiteC_framework.zip: No such file or directory\r\n```\r\nSimilarly opening the .zip file is as you described, something seems wrong/off.\r\n\r\nHi @yishuangP, can you please take a look? Thanks.",
"@chenyingchao i encountered exactly the same error as you! \r\n\r\n\"'builtin_ops.h' file not found\"\r\n\r\nHowever，after I have selectively built the libraries with Tensorflow r2.9, the error goes away\r\n"
] | 2024-03-19T11:30:31 | 2024-05-24T01:52:48 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
13.5
### Mobile device
_No response_
### Python version
3.12.2
### Bazel version
6.5.0
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
bash tensorflow/lite/ios/build_frameworks.sh \
--input_models=migan_8bit_a3.tflite \
--target_archs=x86_64,armv7,arm64
There were no errors after executing the script, but no such file or directory occurred。
ls: /Users/xxx/Desktop/tensorflow-2.16.1/bazel-out/applebin_ios-ios_sim_arm64-opt-ST-7b90815cfaca/bin/tensorflow/lite/ios/tmp/TensorFlowLiteC_framework.zip: No such file or directory
<img width="1342" alt="image" src="https://github.com/tensorflow/tensorflow/assets/20237703/46de32ea-2a05-4cfe-a22a-845e804aba2b">
but，I found the framework in the “/Users/xxx/Desktop/tensorflow-2.16.1/bazel-out/applebin_ios-ios_sim_arm64-opt-ST-7b90815cfaca/bin/tensorflow/lite/ios/tmp/TensorFlowLiteC_framework.zip” path
I opened the framework and found that its files seemed to be incorrect, Only five files.
last，when I build， Xcode show error，not found "builtin_ops. h"
### Standalone code to reproduce the issue
```shell
bash tensorflow/lite/ios/build_frameworks.sh \
--input_models=migan_8bit_a3.tflite \
--target_archs=x86_64,armv7,arm64
```
### Relevant log output
```shell
ls: /Users/xxx/Desktop/tensorflow-2.16.1/bazel-out/applebin_ios-ios_sim_arm64-opt-ST-7b90815cfaca/bin/tensorflow/lite/ios/tmp/TensorFlowLiteC_framework.zip: No such file or directory
when I build Xcode，it show error，not found "builtin_ops. h"
```
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PR_kwDOArmXAs5qExWK
| 63,951 |
Integrate LLVM at llvm/llvm-project@fadc38efed81
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Integrate LLVM at llvm/llvm-project@fadc38efed81
Updates LLVM usage to match
[fadc38efed81](https://github.com/llvm/llvm-project/commit/fadc38efed81)
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PR_kwDOArmXAs5qEo9q
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Bounding box coordinates terminology updated in detection_postprocess.cc
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closes: [#63815](https://github.com/tensorflow/tensorflow/issues/63815)
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#shlo_ref Add `sine` op.
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#shlo_ref Add `sine` op.
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I_kwDOArmXAs6CzTDd
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Tensorflow WSL GPU CUDA recognition issue RTX3090
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[
"@jeanswiegers If TF is not recognizing GPU, then could you please verify the build compatibility by running the following in your WSL environment;\r\n```\r\nimport tensorflow as tf\r\nprint(tf.test.is_built_with_cuda())\r\n\r\n```\r\nThis should output True. If it's False, you might need to reinstall TensorFlow with GPU support. For more information on WSL with GPU support please refer to https://www.tensorflow.org/install/pip. The TensorFlow version needs to be compatible with your CUDA version.\r\n\r\nThank you!",
"> @jeanswiegers If TF is not recognizing GPU, then could you please verify the build compatibility by running the following in your WSL environment;\r\n> \r\n> ```\r\n> import tensorflow as tf\r\n> print(tf.test.is_built_with_cuda())\r\n> ```\r\n> \r\n> This should output True. If it's False, you might need to reinstall TensorFlow with GPU support. For more information on WSL with GPU support please refer to https://www.tensorflow.org/install/pip. The TensorFlow version needs to be compatible with your CUDA version.\r\n> \r\n> Thank you!\r\n\r\nHi Sushreebarsa, thanks for your help. It does return True in my environment.",
"@jeanswiegers Thank you for your quick response!\r\nSimply restarting your WSL instance (`wsl --shutdown`) or your entire computer can resolve environment variable issues. Could you please try this once. If the issue continues then, please use nvidia-smi within your WSL terminal to confirm your RTX3090 is recognized by the NVIDIA drivers. If not, there might be an issue with the driver installation itself.\r\nThank you!",
"i have restarted pc and ubuntu multiple times.\r\n\r\n```\r\n+-----------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 550.60.01 Driver Version: 551.76 CUDA Version: 12.4 |\r\n|-----------------------------------------+------------------------+----------------------+\r\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|=========================================+========================+======================|\r\n| 0 NVIDIA GeForce RTX 3090 On | 00000000:01:00.0 On | N/A |\r\n| 30% 54C P0 121W / 350W | 1642MiB / 24576MiB | 4% Default |\r\n| | | N/A |\r\n+-----------------------------------------+------------------------+----------------------+\r\n\r\n+-----------------------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=========================================================================================|\r\n| No running processes found |\r\n+-----------------------------------------------------------------------------------------+\r\n```",
"I managed to get it working by installing the latest supported CUDA version (12.3) Ubuntu runfile stated on tensorflows website. \r\n\r\nOnly running\r\n`pip install tensorflow[with-cuda]` doesn't work.\r\n",
"Working",
"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/63948\">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/63948\">No</a>\n",
"@jeanswiegers Glad it worked fine for you.\r\nThank you!",
"# Almost final and automated fix below\r\n\r\n* Where I found the resolution\r\n * [TF 2.16.1 Fails to work with GPUs](https://github.com/tensorflow/tensorflow/issues/63362)\r\n * Solution proposed by \"sh-shahrokhi\", improved by \"ChristofKaufmann\"\r\n * See specially [Comment by COntributor](https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1990133275)\r\n * Related Issues\r\n * [GPU not detected on WSL2](https://github.com/tensorflow/tensorflow/issues/63341), where I have post some comments\r\n * [Tensorflow WSL GPU CUDA recognition issue RTX3090](https://github.com/tensorflow/tensorflow/issues/63948)\r\n * [Once gain: tf.2.16.1 fails to recognize GPUs](https://github.com/keras-team/keras/issues/19276)\r\n * Other mention on social media\r\n * [Reddit issue](https://www.reddit.com/r/bashonubuntuonwindows/comments/1bb0xqg/your_kernel_may_have_been_built_without_numa/)\r\n\r\n* Exact solution\r\n * Temporary fix (after activating environment in which Tensorflow 2.16.1 is installed)\r\n <pre>\r\n export NVIDIA_DIR=$(dirname $(dirname $(python -c \"import nvidia.cudnn;print(nvidia.cudnn.__file__)\")))\r\n export LD_LIBRARY_PATH=$(echo ${NVIDIA_DIR}/*/lib/ | sed -r 's/\\s+/:/g')${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}\r\n </pre>\r\n * Automating the variable set/unset process with Anaconda (one-time setup)\r\n * Activate your environement in which TF 2.16.1 is installed\r\n * Two files to be created in \"anaconda3/envs/<ENV_NAME>/etc/conda\"\r\n * anaconda3/envs/<ENV_NAME>/etc/conda/activate.d/env_vars.sh\r\n <pre>\r\n #!/bin/sh\r\n export NVIDIA_DIR=$(dirname $(dirname $(python -c \"import nvidia.cudnn;print(nvidia.cudnn.__file__)\")))\r\n export LD_LIBRARY_PATH=$(echo ${NVIDIA_DIR}/*/lib/ | sed -r 's/\\s+/:/g')${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}\r\n </pre>\r\n * anaconda3/envs/<ENV_NAME>/etc/conda/deactivate.d/env_vars.sh\r\n <pre>\r\n #!/bin/sh\r\n unset NVIDIA_DIR\r\n unset LD_LIBRARY_PATH\r\n </pre>\r\n * Official documentation to do this via [conda.io](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#macos-and-linux)\r\n * Stack-overflow question where I got this [Set environment vars when activating conda env](https://stackoverflow.com/questions/31598963/how-to-set-specific-environment-variables-when-activating-conda-environment)\r\n\r\n\r\n* What else helped me\r\n * [How to list all variables names and their current values?](https://askubuntu.com/questions/275965/how-to-list-all-variables-names-and-their-current-values)\r\n * [Conda documenation for \"env_vars.sh\" for activate/deactivate](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#macos-and-linux)\r\n"
] | 2024-03-19T09:45:13 | 2024-04-10T06:08:54 | 2024-03-20T17:20:42 |
NONE
| null | null | null |
[tf_env.txt](https://github.com/tensorflow/tensorflow/files/14648691/tf_env.txt)
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04.6
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
_No response_
### GCC/compiler version
11.2.0
### CUDA/cuDNN version
8.6
### GPU model and memory
NVIDIA GeForce RTX 3090
### Current behavior?
I installed Tensorflow using gpu install guide for WSL as follows:
wsl --install
sudo apt update
sudo apt upgrade
sudo apt install python3-pip
cd Desktop
Download anaconda linux
https://www.anaconda.com/download#downloads
bash Anaconda3-2024.02-1-Linux-x86_64.sh
~/anaconda3/bin/conda init bash
~/anaconda3/bin/conda init zsh
conda create --name tf-gpu python==3.11
conda activate tf-gpu
pip install --upgrade pip
pip install tensorflow[and-cuda]
Pytorch is working perfectly in WSL with Cuda enabled. Tensorflow is not recognizing my GPU.
**nvidia-smi output:**
```
Tue Mar 19 11:19:07 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.60.01 Driver Version: 551.76 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 3090 On | 00000000:01:00.0 On | N/A |
| 30% 54C P0 121W / 350W | 1642MiB / 24576MiB | 4% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
```
**nvcc --version output:**
```
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Sep_21_10:33:58_PDT_2022
Cuda compilation tools, release 11.8, V11.8.89
Build cuda_11.8.r11.8/compiler.31833905_0
```
**python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" output:**
```
2024-03-19 11:29:26.228449: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-03-19 11:29:26.250017: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-19 11:29:26.585563: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-03-19 11:29:26.832574: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-19 11:29:26.844141: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
```
**/usr/local/cuda-11.8/extras/demo_suite/deviceQuery output:**
```
/usr/local/cuda-11.8/extras/demo_suite/deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "NVIDIA GeForce RTX 3090"
CUDA Driver Version / Runtime Version 12.4 / 11.8
CUDA Capability Major/Minor version number: 8.6
Total amount of global memory: 24576 MBytes (25769279488 bytes)
(82) Multiprocessors, (128) CUDA Cores/MP: 10496 CUDA Cores
GPU Max Clock rate: 1695 MHz (1.70 GHz)
Memory Clock rate: 9751 Mhz
Memory Bus Width: 384-bit
L2 Cache Size: 6291456 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 1536
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 1 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: No
Device PCI Domain ID / Bus ID / location ID: 0 / 1 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 12.4, CUDA Runtime Version = 11.8, NumDevs = 1, Device0 = NVIDIA GeForce RTX 3090
Result = PASS
```
ENVs set:
```
export CUDA_HOME=/usr/local/cuda
export PATH=$CUDA_HOME/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH
```
### Standalone code to reproduce the issue
```shell
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
```
### Relevant log output
```shell
2024-03-19 11:29:26.228449: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-03-19 11:29:26.250017: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-19 11:29:26.585563: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-03-19 11:29:26.832574: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-19 11:29:26.844141: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
[]
```
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#tf-data Fix global shuffling for shard dataset.
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#tf-data Fix global shuffling for shard dataset.
The issue is similar to batching without dropping remainders:
The input dataset may return `end_of_sequence` in the middle
of its output since the index is shuffled.
For example, without fix, when shuffling
```
range(10).shard(3, 0)
```
The range dataset could return `end_of_sequence` after producing
9.
This CL fixes the index mapper for `shard` so no skipping is
needed for global shuffling.
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Expand presets to `QuantizationConfig` to populate `_quantization_method` for each quantizable unit.
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Expand presets to `QuantizationConfig` to populate `_quantization_method` for each quantizable unit.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10626 from Intel-tensorflow:akhil/bf16_softmax d030100ea87923582aa64cdcf0fac1464051288c
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Migrate direct callers of options().packed() to is_packed() helper.
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PR_kwDOArmXAs5qCrBh
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Admits partially replicated strategies for broadcast ops.
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Admits partially replicated strategies for broadcast ops.
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[XLA:Runtime] Moved the nccl_collective_broadcast thunk to new folder and removed unused dependencies.Updated the necessary directories pointing to this thunk and removed it from the nccl_collectives_thunks build and removed the nccl_collectives_thunk target as all the remaining thunks have been moved out. #5758
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[OptimizeFunctionGraph] Prune the function library to reachable functions in the post-optimized graph.
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Adds an overload `FunctionLibraryDefinition::ReachableDefinitions(const Graph&)` that enables capturing the reachable definitions from a `tensorflow::Graph` (and not just a protobuf-based graph).
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Implement expanding presets from `QuantizationConfig`.
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Implement expanding presets from `QuantizationConfig`.
`ExpandPresets` transfers quantization presets and populates other fields in `QuantizationConfig`.
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I_kwDOArmXAs6Cw9DU
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Aborted (core dumped) in tf.raw_ops.SparseMatrixZeros
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[
"Hi **@Zoeeeeey** ,\r\n\r\nI tried to run your code on colab using tf 2.16.1 and tf-nightly versions faced same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/424d0904418c5a1cd7fcc2a8892a1025/63937_2-16-1-nightly-v.ipynb) here for reference.\r\n\r\nThank you!",
"Hi, thank you for your reply. Indeed, there are crashes happening for unclear reasons, and I believe my inputs are all in compliance with the requirements. Is this an existing bug?\r\n Additionally, I would like to ask, does the code hint `Aborted (core dumped)` indicate that there is a bug in the current version? @Venkat6871"
] | 2024-03-19T02:26:44 | 2024-04-01T05:10:09 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.13
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.8
### Bazel version
_No response_
### GCC/compiler version
7.5.0
### CUDA/cuDNN version
12.0
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.raw_ops.SparseMatrixZeros` encounters "Aborted (core dumped)".
However, the parameters `dense_shape` and `type` meet the requirements in the API documentation. (https://tensorflow.google.cn/api_docs/python/tf/raw_ops/SparseMatrixZeros)
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
dense_shape=tf.constant(value=np.random.randint(0,100,size=(1, 1)), shape=(1, 1), dtype=tf.int64)
type=tf.float64
name=None
tf.raw_ops.SparseMatrixZeros(dense_shape=dense_shape, type=type, name=name)
```
### Relevant log output
```shell
2024-03-19 02:18:57.223847: F tensorflow/core/framework/tensor_shape.cc:45] Check failed: NDIMS == dims() (1 vs. 2)Asking for tensor of 1 dimensions from a tensor of 2 dimensions
Aborted (core dumped)
```
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PR_kwDOArmXAs5qB7aT
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When possible, use the device ids provided by the user instead of defaulting to the iota order.
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PR_kwDOArmXAs5qB4Xg
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Convert stablehlo.dynamic_slice to tfl.slice
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Convert stablehlo.dynamic_slice to tfl.slice
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PR_kwDOArmXAs5qBzrN
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Fixes `SAME` padding calculation to produce float results for correct `std::ceil` input.
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Fixes `SAME` padding calculation to produce float results for correct `std::ceil` input.
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PR #10261: [ROCm] ConvBfloat16Support HLO pass for AMDGPU Compiler
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PR #10261: [ROCm] ConvBfloat16Support HLO pass for AMDGPU Compiler
Imported from GitHub PR https://github.com/openxla/xla/pull/10261
Copybara import of the project:
--
0568134a7d3108f1c29794f536f3acbfe238dff1 by Pavel Emeliyanenko <[email protected]>:
added ConvBfloat16Support HLO pass
--
3106f99a8d75e1d891053f3a2b3ee1a46c29f5db by Harsha HS <[email protected]>:
fix typo
Merging this change closes #10261
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10261 from ROCm:ci_convbf16norm_20240305 37d32141d043c4ee647d74e47257080a5ef1c0ca
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Integrate LLVM at llvm/llvm-project@fadc38efed81
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Integrate LLVM at llvm/llvm-project@fadc38efed81
Updates LLVM usage to match
[fadc38efed81](https://github.com/llvm/llvm-project/commit/fadc38efed81)
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Cache the executor pointer in the StreamPool at construction time. Otherwise there could potentially be streams from difference executors cached in the same pool.
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Cache the executor pointer in the StreamPool at construction time. Otherwise there could potentially be streams from difference executors cached in the same pool.
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PR_kwDOArmXAs5qBXMC
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Implement basic `QuantizationReport`.
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Implement basic `QuantizationReport`.
This is a minimally working version of `QuantizationReport` where a user may add a single `QuantizationResult` manually.
In future revisions, it will be able to parse `QuantizationResult`s from `ModuleOp` and populate internal data automatically.
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PR_kwDOArmXAs5qBWy-
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Fix the shared_batch_scheduler_test to avoid using the designated initializer to fix the windows build failure
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Fix the shared_batch_scheduler_test to avoid using the designated initializer to fix the windows build failure
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[xla:cpu] Remove lmhlo dependency
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[xla:cpu] Remove lmhlo dependency
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PR_kwDOArmXAs5qBTb-
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[xla:gpu] Handle arguments passed in arbitrary order to AddressComputationThunk
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[xla:gpu] Handle arguments passed in arbitrary order to AddressComputationThunk
Passing the buffers in correct order to embedded thunk when AddressComputationThunk itself is getting buffers in arbitrary order.
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Update to reflect name change from `TfLiteRegistrationExternal` as `TfLiteOperator`.
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Update to reflect name change from `TfLiteRegistrationExternal` as `TfLiteOperator`.
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Only the DIFFBASE CL is public.
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Integrate Triton up to [61be8e79](https://github.com/openai/triton/commit/61be8e792f206366f5ab2fe66d8984e97acc7324)
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Integrate Triton up to [61be8e79](https://github.com/openai/triton/commit/61be8e792f206366f5ab2fe66d8984e97acc7324)
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Testing testing testing
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Testing testing testing
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[xla:ffi] Add XLA_FFI_Handler_Traits to capture properties of an FFI handler
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[xla:ffi] Add XLA_FFI_Handler_Traits to capture properties of an FFI handler
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Rename `fold_constant_transpose_pass.cc`->`fold_constant_transpose.cc`.
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Rename `fold_constant_transpose_pass.cc`->`fold_constant_transpose.cc`.
Conventionally the `_pass` prefix isn't used.
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PR_kwDOArmXAs5qAtd9
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#tf-data Set the iterator prefix and `DebugString` of `GlobalShuffleDataset` to `GlobalShuffle` to be consistent with other datasets.
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#tf-data Set the iterator prefix and `DebugString` of `GlobalShuffleDataset` to `GlobalShuffle` to be consistent with other datasets.
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#tf-data Re-use `GlobalShuffleIterator` for the Range dataset.
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#tf-data Re-use `GlobalShuffleIterator` for the Range dataset.
Also fix an issue with global shuffling when not dropping remainders:
The original range_op implementation did not check out-of-range errors
after getting the shuffled index. If the parent iterator produces an
out-of-range index, it may return an out-of-range element.
Without fix, shuffling
```
range(10).batch(3, drop_remainder=False)
```
could return [[3, 4, 5], [9, 10, 11], [0, 1, 2], [6]]
which should be [[3, 4, 5], [9], [0, 1, 2], [6, 7, 8]]
However, this may be confusing to the users, so we'll not support
global shuffling with `drop_remainder=False` for now.
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Cleanup: `lift_as_function_call.h/cc`.
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Cleanup: `lift_as_function_call.h/cc`.
* Remove unnecessary `llvm::` and `mlir::`.
* Use `ArrayRef`s where applicable.
* Use pass-by-reference where applicable (e.g. OpBuilder).
* Use pass-by-value where applicable (e.g. Value).
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Temporarily disables failing shared_batch_scheduler_test on Windows.
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Temporarily disables failing shared_batch_scheduler_test on Windows.
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Rolling back to unbreak Windows build.
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Rolling back to unbreak Windows build.
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[XLA:Python] Improve error checking for the return value of the to_iterable function of custom pytree nodes.
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[XLA:Python] Improve error checking for the return value of the to_iterable function of custom pytree nodes.
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[XLA:GPU] Add test for triton custom call.
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[XLA:GPU] Add test for triton custom call.
Triton custom calls allow the caller to express their own triton kernels using the MLIR triton dialect (tt). This adds a test that the passed in tt ir is correctly lowered to llvm ir.
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Add software prefetchers to help reduce cache-misses
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Add software prefetchers to help reduce cache-misses
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[xla:gpu] No need to distinguish operand vs. result slices for AddressComputationThunk
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[xla:gpu] No need to distinguish operand vs. result slices for AddressComputationThunk
Distinguishing between operand and result is only required when creating the embedded thunk, during `ExecuteOnStream` all we need is the list of buffers.
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[xla:gpu] Dynamic offsets must be read one by one
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[xla:gpu] Dynamic offsets must be read one by one
When creating address computation thunk, we need to load dynamic offsets from device to host one by one, as dynamic-slice and DUS ops have each offset defined by a separate runtime value.
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PR #10642: Make configure.py command visible
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PR #10642: Make configure.py command visible
Imported from GitHub PR https://github.com/openxla/xla/pull/10642
Copybara import of the project:
--
d5df8a14b8837c0980a8641a83b9d6d9e33577cc by prrathi <[email protected]>:
Make configure.py command visible
Merging this change closes #10642
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PR_kwDOArmXAs5p_miy
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Add a field to StreamZ metric /tensorflow/core/tf_mlir_bridge_first_phase_count
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Add a field to StreamZ metric /tensorflow/core/tf_mlir_bridge_first_phase_count
Add a filed for the type of TF2XLA Phase 1 Bridge , i.e. Replicated Bridge vs. Non-replicated Bridge.
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[XLA:GPU] Refactor code that sets priorities into a function.
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NONE
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[XLA:GPU] Refactor code that sets priorities into a function.
This change lookups and erases a few more elements from `reverse_map_`, but overall it shouldn't make a dent in the compile time.
With this change, we check if priority is negative before inserting into the queue, not after. This also doesn't affect compile time, but arguably makes the code easier to understand.
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Make mock_nccl_utils.cc compile.
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Make mock_nccl_utils.cc compile.
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Deduplicate BuildAttributesMap code.
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Deduplicate BuildAttributesMap code.
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[OptimizeFunctionGraph] Prune the function library to reachable functions in the post-optimized graph.
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[OptimizeFunctionGraph] Prune the function library to reachable functions in the post-optimized graph.
Adds an overload `FunctionLibraryDefinition::ReachableDefinitions(const Graph&)` that enables capturing the reachable definitions from a `tensorflow::Graph` (and not just a protobuf-based graph).
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[XLA:Python] Migrate remaining python bindings to nanobind, and delete unused pybind11 code.
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[XLA:Python] Migrate remaining python bindings to nanobind, and delete unused pybind11 code.
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Enable sparsecore layout assignment
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Enable sparsecore layout assignment
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PR_kwDOArmXAs5p_LhX
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[kernelgen] Don't try bufferizing TensorDialect
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[kernelgen] Don't try bufferizing TensorDialect
This currently doesn't do anything because we
don't load the BufferizableOpInterface. But if
someone does we have a miscompile on our hands.
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Upgrade to support and default to clang 18 for the OSS compiler
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Upgrade to support and default to clang 18 for the OSS compiler
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PR_kwDOArmXAs5p-3qs
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Fix conversions between ShardingParam and HloSharding.
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Fix conversions between ShardingParam and HloSharding.
This change ensures that conversions work correctly for meshes with more than 2 axis, and adds additional tests for conversions between HloSharding to ShardingParam, ShardingParam to OpSharding, and ShardingParam to HloSharding.
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PR_kwDOArmXAs5p-xvE
| 63,899 |
Use the appropriate operand sharding when computing resharding costs for HLO ops when a new ShardingStrategy is generated for n op based on a user annotation.
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Use the appropriate operand sharding when computing resharding costs for HLO ops when a new ShardingStrategy is generated for n op based on a user annotation.
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PR_kwDOArmXAs5p-tmb
| 63,898 |
Add absl::SourceLocations to xla::ResourceExhaustedError.
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Add absl::SourceLocations to xla::ResourceExhaustedError.
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PR_kwDOArmXAs5p-qRv
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Call DropAllControlDeps before removing an instruction from the computation during RematerializeInstructions, or RemoveInstruction will fail.
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Call DropAllControlDeps before removing an instruction from the computation during RematerializeInstructions, or RemoveInstruction will fail.
Before this change, hlo-opt was failing with this stack:
INTERNAL: RET_CHECK failure (third_party/tensorflow/compiler/xla/hlo/ir/hlo_computation.cc:426) ignore_safety_check || IsSafelyRemovable(instruction) cannot remove instruction: %all-reduce-start.285 = (s32[8,64]{1,0}, s32[], s32[], s32[], s32[], /*index=5*/s32[], s32[], s32[], s32[], s32[], /*index=10*/s32[], s32[], s32[], s32[], s32[], /*index=15*/s32[], s32[], s32[], s32[], s32[]) all-reduce-start(s32[8,64]{1,0} %input_scatter_fusion.16, s32[] %convert.81230.0.remat, s32[] %copy.13646, s32[] %copy.13647, s32[] %copy.13648, /*index=5*/s32[] %copy.13649, s32[] %copy.13650, s32[] %copy.13651, s32[] %copy.13652, s32[] %copy.13653, /*index=10*/s32[] %copy.13654, s32[] %copy.13655, s32[] %copy.13656, s32[] %copy.13657, s32[] %copy.13658, /*index=15*/s32[] %copy.13659, s32[] %copy.13660, s32[] %copy.13661, s32[] %copy.13662, s32[] %copy.13663), channel_id=711, replica_groups={{0,4,8,12,16,20,24,28,32,36,40,44,48,52,56,60,64,68,72,76,80,84,88,92,96,100,104,108,112,116,120,124},{1,5,9,13,17,21,25,29,33,37,41,45,49,53,57,61,65,69,73,77,81,85,89,93,97,101,105,109,113,117,121,125},{2,6,10,14,18,22,26,30,34,38,42,46,50,54,58,62,66,70,74,78,82,86,90,94,98,102,106,110,114,118,122,126},{3,7,11,15,19,23,27,31,35,39,43,47,51,55,59,63,67,71,75,79,83,87,91,95,99,103,107,111,115,119,123,127}}, use_global_device_ids=true, to_apply=%region_597.10276.clone.1, control-predecessors={%copy.14013, %copy.14014, %copy.14015, %copy.14016, %copy.14017, %copy.14018, %copy.14019, %copy.14020, %copy.14021, %copy.14022, %copy.14023, %copy.14024, %copy.14025, %copy.14026, %copy.14027, %copy.14028, %copy.14029, %copy.14030, %copy.14031, %copy.14032, %copy.14033, %copy.14034, %copy.14035, %copy.14036, %copy.14037, %copy.14038, %copy.14039, %copy.14040, %copy.14041, %copy.14042}, backend_config={"operation_queue_id":"0","wait_on_operation_queues":[],"collective_backend_config":{"is_sync":false,"no_parallel_custom_call":false}}
=== Source Location Trace: ===
third_party/tensorflow/compiler/xla/status_macros.cc:80
third_party/tensorflow/compiler/xla/service/hlo_rematerialization.cc:2165
third_party/tensorflow/compiler/xla/service/hlo_rematerialization.cc:2564
third_party/tensorflow/compiler/xla/service/hlo_rematerialization.cc:2708
third_party/tensorflow/compiler/xla/service/hlo_rematerialization.cc:2767
third_party/tensorflow/compiler/xla/service/hlo_rematerialization.cc:2905
third_party/tensorflow/compiler/xla/service/hlo_pass_pipeline.h:140
third_party/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc:185
third_party/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc:2183
third_party/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc:1923
third_party/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc:2012
third_party/tensorflow/compiler/xla/tools/hlo_opt/opt_lib.cc:155
third_party/tensorflow/compiler/xla/tools/hlo_opt/opt_lib.cc:108
third_party/tensorflow/compiler/xla/tools/hlo_opt/gpu_opt.cc:80
third_party/tensorflow/compiler/xla/tools/hlo_opt/opt_main.cc:166
third_party/tensorflow/compiler/xla/tools/hlo_opt/opt_main.cc:177
After this change, hlo-opt fails with an expected failure due to no JAX:
UNIMPLEMENTED: No registered implementation for custom call to cu_threefry2x32 for platform CUDA
=== Source Location Trace: ===
third_party/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc:1391
third_party/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc:3023
third_party/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc:2574
third_party/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc:2015
third_party/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc:3023
third_party/tensorflow/compiler/xla/service/gpu/compile_module_to_llvm_ir.cc:205
third_party/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc:1949
third_party/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc:2028
third_party/tensorflow/compiler/xla/tools/hlo_opt/opt_lib.cc:155
third_party/tensorflow/compiler/xla/tools/hlo_opt/opt_lib.cc:108
third_party/tensorflow/compiler/xla/tools/hlo_opt/gpu_opt.cc:80
third_party/tensorflow/compiler/xla/tools/hlo_opt/opt_main.cc:166
third_party/tensorflow/compiler/xla/tools/hlo_opt/opt_main.cc:177
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PR_kwDOArmXAs5p-k8n
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Delete the redundant compilation_cache_test
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Delete the redundant compilation_cache_test
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PR_kwDOArmXAs5p-iUG
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Add tensor shape check for ADD & MUL.
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NONE
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Add tensor shape check for ADD & MUL.
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PR_kwDOArmXAs5p-fr2
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PR #10635: Fix build error from PR 10497
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NONE
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PR #10635: Fix build error from PR 10497
Imported from GitHub PR https://github.com/openxla/xla/pull/10635
Added changes to fix build error that were encountered after merging changes from PR: https://github.com/openxla/xla/pull/10497
Used ::tsl::testing::StatusIs instead of ::testing::status::StatusIs
Copybara import of the project:
--
d344105a62e5f1749d994c2f1f2a8a76a5880c3d by hmonishN <[email protected]>:
Adding changes to use tsl::testing:StatusIs
Merging this change closes #10635
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10635 from hmonishN:hmonish/fix_build_error d344105a62e5f1749d994c2f1f2a8a76a5880c3d
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PR_kwDOArmXAs5p-Zxh
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GPU Load tracker in TFRT.
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GPU Load tracker in TFRT.
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PR_kwDOArmXAs5p-Zdd
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Cherry-pick https://github.com/llvm/llvm-project/commit/daa350c1995015daac552548c34b87220f21156d into TF
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[] | 2024-03-18T17:25:52 | 2024-03-18T18:22:59 | 2024-03-18T18:22:59 |
NONE
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Cherry-pick https://github.com/llvm/llvm-project/commit/daa350c1995015daac552548c34b87220f21156d into TF
This lets MSVC compile MLIR again
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