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Use actual PTX target version for CUDA compilation
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Use actual PTX target version for CUDA compilation
Provides two code paths: either use the configured CUDA version, or the runtime version of ptxas (this might be different in some configurations).
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Convert TFlite buffer created using TF1 to TF2 TFlite buffer
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[
"Did you try using, the Tflite converter tool to convert the old to the new format? \n\nFrom what I've been seeing floating around google it's possible to convert Tflite files to adhere to the new scheme. ",
"Hi @mrtpk ,\r\n\r\nCan you please try using the tflite converter tool and just use different names for the input and output model and let me know if it works for you ? \r\n\r\nAlternatively if you still have the original tf1 model , you can convert it to a tf2 model using existing tools and then convert the tf2 model to tflite which will adhere to the new schema.",
"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."
] | 2024-04-23T09:35:52 | 2024-05-15T01:49:49 | 2024-05-15T01:49:48 |
NONE
| null | null | null |
Hi Team,
Context:
I have a bunch of TFlite files that were created using [schema.fbs](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema.fbs) before [`version` was added into the schema](https://github.com/tensorflow/tensorflow/blob/20cb0d184f27341418c77ce3432ce46376040426/tensorflow/lite/schema/schema.fbs#L1612). The latest tflite buffers generated using TF2 has `version` and other newly added fields.
Question:
Is there a way to convert tflite buffers to new tflite buffers that adhere to the new schema? Any pointer is very much appreciated. Thanks.
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Automated Code Change
Reverts 7edfea8c24f42045c75d329ff3d498c7dd6e34b9
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Make the id of CustomAggregator op deterministic
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NONE
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Make the id of CustomAggregator op deterministic
The id is set as:
`<function name>_<arg or result>_<arg/result index>_<calibration_method>`
The critical point here is to make the composite function name deterministic. It is done by making sorting the functions by their names.
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PR_kwDOArmXAs5tcfya
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Reverts 7edfea8c24f42045c75d329ff3d498c7dd6e34b9
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NONE
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Reverts 7edfea8c24f42045c75d329ff3d498c7dd6e34b9
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11435 from jaro-sevcik:host-offloading-spmd-partitioning a6d01010f3b468de02ebee8a0c59f9e28ea4297d
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PR_kwDOArmXAs5tcWea
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Reverts 04aa434c0e5a011e980c9b42f4da489834b054de
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NONE
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Reverts 04aa434c0e5a011e980c9b42f4da489834b054de
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Integrate LLVM at llvm/llvm-project@9ba6961ce05b
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NONE
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Integrate LLVM at llvm/llvm-project@9ba6961ce05b
Updates LLVM usage to match
[9ba6961ce05b](https://github.com/llvm/llvm-project/commit/9ba6961ce05b)
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Automated Code Change
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Support per-channel weight-only quantization and enable by default
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Support per-channel weight-only quantization and enable by default
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Automated Code Change
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Automated Code Change
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PR #11435: Spmd partitioning for host memory offloading ops
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PR #11435: Spmd partitioning for host memory offloading ops
Imported from GitHub PR https://github.com/openxla/xla/pull/11435
This partitions MoveToHost and MoveToDevice custom calls in a way that
tries to avoid resharding on the host. That is, MoveToHost reshards input,
MoveToDevice reshards output.
Copybara import of the project:
--
a6d01010f3b468de02ebee8a0c59f9e28ea4297d by Jaroslav Sevcik <[email protected]>:
Partition host offloading ops
Merging this change closes #11435
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11435 from jaro-sevcik:host-offloading-spmd-partitioning a6d01010f3b468de02ebee8a0c59f9e28ea4297d
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[pjrt] Switch TfrtTpuBuffer to PjRtFuture<>::Promise to track definition event
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[pjrt] Switch TfrtTpuBuffer to PjRtFuture<>::Promise to track definition event
In preparation for disabling absl::Status payloads for AsyncValue convert PjRtClient to PjRtFuture
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which version of keras is used by latest kws_streaming
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"@andrewmabc Could you refer to [this](https://github.com/google-research/google-research/blob/master/kws_streaming/README.md) for more information on kws_streaming. \r\nThank you!\r\n\r\n",
"Thanks for your reply. I have read the webpage of 'Streaming Aware neural network models'.\r\n\r\nI just want to run jupyter sample code of 'Quick onboarding with toy demo'. I think I should install correct packages. But I can't get the reason why utils.to_streaming_inference() can't return in 02_inference.ipynb.\r\n",
"@andrewmabc Could you try with Keras 3 as tf_keras will be legacy Keras?\r\nThank you!",
"I use keras 3 and don't install tf_keras. 00_check_data.ipynb report error when executing fourth cell.\r\n\r\nThe fourth cell is :\r\n\r\nimport tensorflow as tf\r\nimport tensorflow.compat.v1 as tf1\r\nimport logging\r\nfrom kws_streaming.models import model_flags\r\nfrom kws_streaming.models import models\r\nfrom kws_streaming.layers.modes import Modes\r\nfrom kws_streaming.train import test\r\nfrom kws_streaming.models import utils\r\nfrom kws_streaming.data import input_data\r\nfrom kws_streaming.data import input_data_utils as du\r\nfrom kws_streaming.models import model_params\r\n\r\nReported error message :\r\n\r\n\r\nimport tensorflow as tf\r\nimport tensorflow.compat.v1 as tf1\r\nimport logging\r\nfrom kws_streaming.models import model_flags\r\nfrom kws_streaming.models import models\r\nfrom kws_streaming.layers.modes import Modes\r\nfrom kws_streaming.train import test\r\nfrom kws_streaming.models import utils\r\nfrom kws_streaming.data import input_data\r\nfrom kws_streaming.data import input_data_utils as du\r\nfrom kws_streaming.models import model_params\r\n2024-04-25 07:03:24.703360: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\nWARNING:tensorflow:Your environment has TF_USE_LEGACY_KERAS set to True, but you do not have the tf_keras package installed. You must install it in order to use the legacy tf.keras. Install it via: `pip install tf_keras`\r\n---------------------------------------------------------------------------\r\nImportError Traceback (most recent call last)\r\nCell In[4], line 5\r\n 3 import logging\r\n 4 from kws_streaming.models import model_flags\r\n----> 5 from kws_streaming.models import models\r\n 6 from kws_streaming.layers.modes import Modes\r\n 7 from kws_streaming.train import test\r\n\r\nFile /export/Intel_ssd/debug_kws/colab/./google-research/kws_streaming/models/models.py:17\r\n 1 # coding=utf-8\r\n 2 # Copyright 2024 The Google Research Authors.\r\n 3 #\r\n (...)\r\n 13 # See the License for the specific language governing permissions and\r\n 14 # limitations under the License.\r\n 16 \"\"\"Supported models.\"\"\"\r\n---> 17 import kws_streaming.models.att_mh_rnn as att_mh_rnn\r\n 18 import kws_streaming.models.att_rnn as att_rnn\r\n 19 import kws_streaming.models.bc_resnet as bc_resnet\r\n\r\nFile /export/Intel_ssd/debug_kws/colab/./google-research/kws_streaming/models/att_mh_rnn.py:18\r\n 16 \"\"\"BiRNN model with multihead attention.\"\"\"\r\n 17 from kws_streaming.layers import modes\r\n---> 18 from kws_streaming.layers import speech_features\r\n 19 from kws_streaming.layers.compat import tf\r\n 20 import kws_streaming.models.model_utils as utils\r\n\r\nFile /export/Intel_ssd/debug_kws/colab/./google-research/kws_streaming/layers/speech_features.py:26\r\n 24 from kws_streaming.layers import random_shift\r\n 25 from kws_streaming.layers import random_stretch_squeeze\r\n---> 26 from kws_streaming.layers import spectrogram_augment\r\n 27 from kws_streaming.layers import spectrogram_cutout\r\n 28 from kws_streaming.layers import windowing\r\n\r\nFile /export/Intel_ssd/debug_kws/colab/./google-research/kws_streaming/layers/spectrogram_augment.py:20\r\n 17 from typing import Any, Dict\r\n 19 import tensorflow as tf\r\n---> 20 import tensorflow_model_optimization as tfmot\r\n 22 from kws_streaming.layers.compat import tf\r\n 23 from tensorflow.python.ops import array_ops # pylint: disable=g-direct-tensorflow-import\r\n\r\nFile /export/Intel_ssd/debug_kws/colab/venv3/lib/python3.11/site-packages/tensorflow_model_optimization/__init__.py:86\r\n 82 # To ensure users only access the expected public API, the API structure is\r\n 83 # created in the `api` directory. Import all api modules.\r\n 84 from tensorflow_model_optimization.python.core import version\r\n---> 86 from tensorflow_model_optimization.python.core.api import clustering\r\n 87 from tensorflow_model_optimization.python.core.api import experimental\r\n 88 from tensorflow_model_optimization.python.core.api import quantization\r\n\r\nFile /export/Intel_ssd/debug_kws/colab/venv3/lib/python3.11/site-packages/tensorflow_model_optimization/python/core/api/__init__.py:16\r\n 1 # Copyright 2021 The TensorFlow Authors. All Rights Reserved.\r\n 2 #\r\n 3 # Licensed under the Apache License, Version 2.0 (the \"License\");\r\n (...)\r\n 13 # limitations under the License.\r\n 14 # ==============================================================================\r\n 15 \"\"\"Import API modules for Tensorflow Model Optimization.\"\"\"\r\n---> 16 from tensorflow_model_optimization.python.core.api import clustering\r\n 17 from tensorflow_model_optimization.python.core.api import experimental\r\n 18 from tensorflow_model_optimization.python.core.api import quantization\r\n\r\nFile /export/Intel_ssd/debug_kws/colab/venv3/lib/python3.11/site-packages/tensorflow_model_optimization/python/core/api/clustering/__init__.py:16\r\n 1 # Copyright 2020 The TensorFlow Authors. All Rights Reserved.\r\n 2 #\r\n 3 # Licensed under the Apache License, Version 2.0 (the \"License\");\r\n (...)\r\n 13 # limitations under the License.\r\n 14 # ==============================================================================\r\n 15 \"\"\"Module containing code for clustering.\"\"\"\r\n---> 16 from tensorflow_model_optimization.python.core.api.clustering import keras\r\n\r\nFile /export/Intel_ssd/debug_kws/colab/venv3/lib/python3.11/site-packages/tensorflow_model_optimization/python/core/api/clustering/keras/__init__.py:19\r\n 16 # pylint: disable=g-bad-import-order\r\n 17 from tensorflow_model_optimization.python.core.clustering.keras import experimental\r\n---> 19 from tensorflow_model_optimization.python.core.clustering.keras.cluster import cluster_scope\r\n 20 from tensorflow_model_optimization.python.core.clustering.keras.cluster import cluster_weights\r\n 21 from tensorflow_model_optimization.python.core.clustering.keras.cluster import strip_clustering\r\n\r\nFile /export/Intel_ssd/debug_kws/colab/venv3/lib/python3.11/site-packages/tensorflow_model_optimization/python/core/clustering/keras/cluster.py:22\r\n 19 import tensorflow as tf\r\n 21 from tensorflow_model_optimization.python.core.clustering.keras import cluster_config\r\n---> 22 from tensorflow_model_optimization.python.core.clustering.keras import cluster_wrapper\r\n 23 from tensorflow_model_optimization.python.core.clustering.keras import clustering_centroids\r\n 24 from tensorflow_model_optimization.python.core.keras.compat import keras\r\n\r\nFile /export/Intel_ssd/debug_kws/colab/venv3/lib/python3.11/site-packages/tensorflow_model_optimization/python/core/clustering/keras/cluster_wrapper.py:23\r\n 21 from tensorflow_model_optimization.python.core.clustering.keras import cluster_config\r\n 22 from tensorflow_model_optimization.python.core.clustering.keras import clusterable_layer\r\n---> 23 from tensorflow_model_optimization.python.core.clustering.keras import clustering_centroids\r\n 24 from tensorflow_model_optimization.python.core.clustering.keras import clustering_registry\r\n 25 from tensorflow_model_optimization.python.core.keras.compat import keras\r\n\r\nFile /export/Intel_ssd/debug_kws/colab/venv3/lib/python3.11/site-packages/tensorflow_model_optimization/python/core/clustering/keras/clustering_centroids.py:22\r\n 20 from tensorflow.python.ops import clustering_ops\r\n 21 from tensorflow_model_optimization.python.core.clustering.keras import cluster_config\r\n---> 22 from tensorflow_model_optimization.python.core.keras.compat import keras\r\n 25 k = keras.backend\r\n 26 CentroidInitialization = cluster_config.CentroidInitialization\r\n\r\nFile /export/Intel_ssd/debug_kws/colab/venv3/lib/python3.11/site-packages/tensorflow_model_optimization/python/core/keras/compat.py:41\r\n 37 keras_internal = tf.keras\r\n 38 return keras_internal\r\n---> 41 keras = _get_keras_instance()\r\n 43 def assign(ref, value, name=None):\r\n 44 if hasattr(tf, 'assign'):\r\n\r\nFile /export/Intel_ssd/debug_kws/colab/venv3/lib/python3.11/site-packages/tensorflow_model_optimization/python/core/keras/compat.py:33, in _get_keras_instance()\r\n 30 os.environ['TF_USE_LEGACY_KERAS'] = '1'\r\n 32 # Use Keras 2.\r\n---> 33 version_fn = getattr(tf.keras, 'version', None)\r\n 34 if version_fn and version_fn().startswith('3.'):\r\n 35 import tf_keras as keras_internal # pylint: disable=g-import-not-at-top,unused-import\r\n\r\nFile /export/Intel_ssd/debug_kws/colab/venv3/lib/python3.11/site-packages/tensorflow/python/util/lazy_loader.py:181, in KerasLazyLoader.__getattr__(self, item)\r\n 179 return super(types.ModuleType, self).__getattribute__(item)\r\n 180 if not self._tfll_initialized:\r\n--> 181 self._initialize()\r\n 182 if self._tfll_keras_version == \"keras_3\":\r\n 183 if (\r\n 184 self._tfll_mode == \"v1\"\r\n 185 and not self._tfll_submodule\r\n 186 and item.startswith(\"compat.v1.\")\r\n 187 ):\r\n\r\nFile /export/Intel_ssd/debug_kws/colab/venv3/lib/python3.11/site-packages/tensorflow/python/util/lazy_loader.py:173, in KerasLazyLoader._initialize(self)\r\n 169 super().__init__(\r\n 170 self._tfll_name, self._tfll_parent_module_globals, package_name\r\n 171 )\r\n 172 else:\r\n--> 173 raise ImportError( # pylint: disable=raise-missing-from\r\n 174 \"Keras cannot be imported. Check that it is installed.\"\r\n 175 )\r\n\r\nImportError: Keras cannot be imported. Check that it is installed.\r\n\r\n\r\n\r\nI notice some tf_keras functions are used in latest kws_streaming, such as tf.keras.backend.learning_phase(). backend.learning_phase() can't be found in keras 3.\r\n\r\n",
"Hi @andrewmabc ,\r\n\r\nI have checked the [requirements.txt](https://github.com/google-research/google-research/blob/master/kws_streaming/requirements.txt#L3) file from kws_streaming and it is using `tf_nightly==2.3.0.dev20200515` version which is quiet older. Hence it will not work with Keras3. Enabling tf_keras (i.e.Keras2) might work.\r\n\r\nWe are not supporting kws_training repo here. If you have any issues on same please report at concern repo. Thanks!",
"what/where is concern repo?\r\n\r\nFrom the issue(https://github.com/google-research/google-research/issues/783, rybakov said, 'use tf-nightly. kws_streaming is always in sync with the latest version of tf-nightly,' But in the file of requirements.txt, the version of tf_nightly is too old.\r\n",
"> what/where is concern repo?\r\n> \r\n> From the issue([google-research/google-research#783](https://github.com/google-research/google-research/issues/783), rybakov said, 'use tf-nightly. kws_streaming is always in sync with the latest version of tf-nightly,' But in the file of requirements.txt, the version of tf_nightly is too old.\r\n\r\nI think you need to raise an issue there itself. The attached issue is quiet older and may be current issue is fresh. Kindly make a note that if other libraries has dependency on TF or keras and its not working then it should be addressed there itself. May be they will communicate internally or do required changes. \r\n\r\nI suggest you raise an issue there and let hear the reply from there.",
"Thanks for your reply. But I still don't understand what is concern repo. How can I raise an issue on the concern repo?",
"> what/where is concern repo?\r\n> \r\n> From the issue([google-research/google-research#783](https://github.com/google-research/google-research/issues/783), rybakov said, 'use tf-nightly. kws_streaming is always in sync with the latest version of tf-nightly,' But in the file of requirements.txt, the version of tf_nightly is too old.\r\n\r\nPlease note that kws_streaming is not a dependency of keras.May be it is using keras but not vice versa. You may please raise the issue in google-research repo and let's hear from there.",
"Thanks for your reply. I raise the same issue in google-research repo.(https://github.com/google-research/google-research/issues/2051)",
"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/66267\">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/66267\">No</a>\n"
] | 2024-04-23T07:22:19 | 2024-05-06T03:02:10 | 2024-05-06T03:02:08 |
NONE
| null | null | null |
I'm trying to run kws_streaming's 00_check_data.ipynb/01_train.ipynb/02_inference.ipynb.
docker image tensorflow/tensorflow:nightly-gpu-jupyter is used to run the demo. And the following packages are installed via pip:
tf-model-optimization-nightly
tf-keras-nightly
tfa-nightly
scipy
pydot
graphviz
The following package is install via apt-get:
apt-get install graphviz
tf._keras_internal.utils.control_flow_util.smart_cond is replaced by tensorflow.python.keras.utils.control_flow_util.smart_cond.
tf._keras_internal.models._clone_layers_and_model_config is replaced by tensorflow.python.keras.models._clone_layers_and_model_config.
tf._keras_internal.models._clone_layer is replaced by tensorflow.python.keras.models._clone_layer.
tf._keras_internal.engine.functional.reconstruct_from_config is replaced by tensorflow.python.keras.engine.functional.reconstruct_from_config.
If TF_USE_LEGACY_KERAS=1, 01_train.ipynb can be run successfully. But the following calling is always running infinitely, i.e. don't stop. Why?
model_non_stream = utils.to_streaming_inference(model_non_stream_batch, flags, Modes.NON_STREAM_INFERENCE)
For the latest kws_streaming, tf.keras.backend.learning_phase() is called in several python files. It seems tf_keras should be used instead of keras 3.
If TF_USE_LEGACY_KERAS=0, 01_train.ipynb/02_inference.ipynb report error.
which version is used, keras 3 or tf_keras?
kws_streaming @rybakov
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PR #11631: Cleanup passes names
Imported from GitHub PR https://github.com/openxla/xla/pull/11631
I found couple passes with simple `absl::string_view name()` implementation which were defined in cc files.
All other passes define `name()` in header file. (Unless name() impl requires if/switch block)
This PR makes simple `absl::string_view name()` implementation consistent across all passes.
Copybara import of the project:
--
d53f3da013b68002f1cca9370a5618051f819cf5 by Alexander Pivovarov <[email protected]>:
Cleanup passes names
Merging this change closes #11631
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11631 from apivovarov:cleanup_passes_names d53f3da013b68002f1cca9370a5618051f819cf5
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PR_kwDOArmXAs5tbhxv
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Unify token lowering in JAX to always use `stablehlo.token`.
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NONE
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Unify token lowering in JAX to always use `stablehlo.token`.
Right now, in JAX native execution, tokens are lowered to dummy `bool[0]`, but when exporting, we use `stablehlo.token`.
To make it work, we need to make changes across XLA/PjRt/IFRT, the main change is to allow `token` parameters in compiler and runtime.
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PR_kwDOArmXAs5tbgcP
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PR #11649: [GPU] Rename and better describe flags related to GEMM fusions.
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PR #11649: [GPU] Rename and better describe flags related to GEMM fusions.
Imported from GitHub PR https://github.com/openxla/xla/pull/11649
Rename one flag for consistency because fusions can use different backends.
Also clarify descriptions of other 2 related flags.
Copybara import of the project:
--
e82637723b3252b9218ded171b026ff4f860e835 by Ilia Sergachev <[email protected]>:
[GPU] Rename and better describe flags related to GEMM fusions.
Rename one flag for consistency because fusions can use different backends.
Also clarify descriptions of other 2 related flags.
Merging this change closes #11649
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11649 from openxla:rename_dump_flag e82637723b3252b9218ded171b026ff4f860e835
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PR #11631: Cleanup passes names
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PR #11631: Cleanup passes names
Imported from GitHub PR https://github.com/openxla/xla/pull/11631
I found couple passes with simple `absl::string_view name()` implementation which were defined in cc files.
All other passes define `name()` in header file. (Unless name() impl requires if/switch block)
This PR makes simple `absl::string_view name()` implementation consistent across all passes.
Copybara import of the project:
--
d53f3da013b68002f1cca9370a5618051f819cf5 by Alexander Pivovarov <[email protected]>:
Cleanup passes names
Merging this change closes #11631
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11631 from apivovarov:cleanup_passes_names d53f3da013b68002f1cca9370a5618051f819cf5
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PR_kwDOArmXAs5tbfLq
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[pjrt] Remove PjRtFuture<>::Promise::SetError() in favor of Set()
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[] | 2024-04-23T05:51:17 | 2024-04-23T22:25:49 | 2024-04-23T22:25:46 |
NONE
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[pjrt] Remove PjRtFuture<>::Promise::SetError() in favor of Set()
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[pjrt] NFC: Remove a workaround for unsafe PjRtFuture<>::Await() call as it's no longer needed
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NONE
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[pjrt] NFC: Remove a workaround for unsafe PjRtFuture<>::Await() call as it's no longer needed
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[pjrt] Add static_assert to disable PjRtFuture<absl::Status>
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[pjrt] Make sure that PjRtFuture<> is copyable and moveable
Explicitly add copy and move constructors/assignment to make sure that stateless
future stays copyable and moveable.
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Skip parallel mapfn optimization if the tensorlist cannot be found in the current function block.
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Skip parallel mapfn optimization if the tensorlist cannot be found in the current function block.
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Add get_device_ordinal to cuda plugin so that CUDA dependency can be removed from py_array (jaxlib).
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Add get_device_ordinal to cuda plugin so that CUDA dependency can be removed from py_array (jaxlib).
py_array still has CUDA dependency as a fallback to keep jaxlib[cuda] working before the migration to CUDA plugin.
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Tensorflow Building from Source Code
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[
"Hi **@Nicky9319** ,\r\n- Ensure that you have visual studio installed on your system with the necessary components for building tensorflow. This typically includes the ` desktop development with C++` workload and the `MSVC v142 - VS 2019 C++ x64/x86 build tools` component.\r\n- Make sure that the visual studio environment variables are set correctly. You can do this by opening a developer command prompt for visual studio and running the `vcvarsall.bat` script for your target architecture (x64 or x86) and platform (Debug or Release).\r\n- Try to clean the Bazel cache by running this command.\r\n```\r\nbazel clean --expunge\r\n```\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66238\">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/66238\">No</a>\n"
] | 2024-04-23T02:13:14 | 2024-05-09T01:48:41 | 2024-05-09T01:48:38 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.9
### Custom code
No
### OS platform and distribution
Windows 11
### Mobile device
_No response_
### Python version
3.10.0
### Bazel version
5.0.0
### GCC/compiler version
9.2.0
### CUDA/cuDNN version
11.2 , 8.1
### GPU model and memory
_No response_
### Current behavior?
I am trying to build Tensorflow from source because i want to run tensorflow using c++ and i am trying to download the GPU version
So I ran ./Configure command, and choose the option to enable GPU Support
I ran the bazel command given on the tensorflow Build From source on windows
https://www.tensorflow.org/install/source_windows
and ran into the following error, What does it imply and how can i Solve it
### Standalone code to reproduce the issue
```shell
ERROR: An error occurred during the fetch of repository 'local_config_cuda':
Traceback (most recent call last):
File "D:/tensorflow/tensorflow-2.9.0/third_party/gpus/cuda_configure.bzl", line 1401, column 38, in _cuda_autoconf_impl
_create_local_cuda_repository(repository_ctx)
File "D:/tensorflow/tensorflow-2.9.0/third_party/gpus/cuda_configure.bzl", line 1179, column 17, in _create_local_cuda_repository
cc = find_cc(repository_ctx)
File "D:/tensorflow/tensorflow-2.9.0/third_party/gpus/cuda_configure.bzl", line 216, column 34, in find_cc
return _get_msvc_compiler(repository_ctx)
File "D:/tensorflow/tensorflow-2.9.0/third_party/gpus/cuda_configure.bzl", line 133, column 26, in _get_msvc_compiler
return find_msvc_tool(repository_ctx, vc_path, "cl.exe").replace("\", "/")
File "C:/users/paart/_bazel_paart/3pmtytha/external/bazel_tools/tools/cpp/windows_cc_configure.bzl", line 447, column 27, in find_msvc_tool
if _is_vs_2017_or_2019(repository_ctx, vc_path) or _is_msbuildtools(vc_path):
File "C:/users/paart/_bazel_paart/3pmtytha/external/bazel_tools/tools/cpp/windows_cc_configure.bzl", line 265, column 72, in _is_vs_2017_or_2019
vc_path_contents = [d.basename.lower() for d in repository_ctx.path(vc_path).readdir()]
Error in path: in call to path(), parameter 'path' got value of type 'NoneType', want 'string, Label, or path'
ERROR: D:/tensorflow/tensorflow-2.9.0/WORKSPACE:15:14: fetching cuda_configure rule //external:local_config_cuda: Traceback (most recent call last):
File "D:/tensorflow/tensorflow-2.9.0/third_party/gpus/cuda_configure.bzl", line 1401, column 38, in _cuda_autoconf_impl
_create_local_cuda_repository(repository_ctx)
File "D:/tensorflow/tensorflow-2.9.0/third_party/gpus/cuda_configure.bzl", line 1179, column 17, in _create_local_cuda_repository
cc = find_cc(repository_ctx)
File "D:/tensorflow/tensorflow-2.9.0/third_party/gpus/cuda_configure.bzl", line 216, column 34, in find_cc
return _get_msvc_compiler(repository_ctx)
File "D:/tensorflow/tensorflow-2.9.0/third_party/gpus/cuda_configure.bzl", line 133, column 26, in _get_msvc_compiler
return find_msvc_tool(repository_ctx, vc_path, "cl.exe").replace("\", "/")
File "C:/users/paart/_bazel_paart/3pmtytha/external/bazel_tools/tools/cpp/windows_cc_configure.bzl", line 447, column 27, in find_msvc_tool
if _is_vs_2017_or_2019(repository_ctx, vc_path) or _is_msbuildtools(vc_path):
File "C:/users/paart/_bazel_paart/3pmtytha/external/bazel_tools/tools/cpp/windows_cc_configure.bzl", line 265, column 72, in _is_vs_2017_or_2019
vc_path_contents = [d.basename.lower() for d in repository_ctx.path(vc_path).readdir()]
Error in path: in call to path(), parameter 'path' got value of type 'NoneType', want 'string, Label, or path'
INFO: Found applicable config definition build:cuda in file d:\tensorflow\tensorflow-2.9.0.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_cuda//:enable_cuda
ERROR: @local_config_cuda//:enable_cuda :: Error loading option @local_config_cuda//:enable_cuda: in call to path(), parameter 'path' got value of type 'NoneType', want 'string, Label, or path'
```
### Relevant log output
_No response_
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Add support for nested tuples in MutableBorrowingLiteral
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Add support for nested tuples in MutableBorrowingLiteral
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#tf-data Add a test for deeply nested `map_func`.
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#tf-data Add a test for deeply nested `map_func`.
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Integrate LLVM at llvm/llvm-project@59bf49a63261
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Integrate LLVM at llvm/llvm-project@59bf49a63261
Updates LLVM usage to match
[59bf49a63261](https://github.com/llvm/llvm-project/commit/59bf49a63261)
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PR #11761: [GPU] Support nccl comm splitting in multiprocess mode
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PR #11761: [GPU] Support nccl comm splitting in multiprocess mode
Imported from GitHub PR https://github.com/openxla/xla/pull/11761
Currently `--xla_gpu_enable_nccl_comm_splitting` is a no-op unless using single process mode. This PR allows it to work in multiprocess mode by removing the IsLocal check and fixing the key in the rank -> comm map which was causing the following error:
```
7: Communicator for rank 1 not found in a NCCL clique devices=[3,7]; stream=0
5: E0403 15:37:24.104311 3871038 pjrt_stream_executor_client.cc:2809] Execution of replica 0 failed: INTERNAL: Communicator for rank 1 not found in a NCCL clique devices=[1,5]; stream=0
6: E0403 15:37:24.104477 3870182 pjrt_stream_executor_client.cc:2809] Execution of replica 0 failed: INTERNAL: Communicator for rank 1 not found in a NCCL clique devices=[2,6]; stream=0
4: E0403 15:37:24.105872 3871021 pjrt_stream_executor_client.cc:2809] Execution of replica 0 failed: INTERNAL: Communicator for rank 1 not found in a NCCL clique devices=[0,4]; stream=0
```
Copybara import of the project:
--
c77ed08f77e252a5068a23d02555ffa765913d42 by Trevor Morris <[email protected]>:
Enable nccl comm split for multiprocess mode
Merging this change closes #11761
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11761 from trevor-m:tmorris-multiprocess-split c77ed08f77e252a5068a23d02555ffa765913d42
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| 66,233 |
Add num_bins and calibration_data_dir to CalibrationOptions
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NONE
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Add num_bins and calibration_data_dir to CalibrationOptions
The initial_num_bins is no-longer used, thus, being removed.
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PR #11405: [gpu] Add address computation fusion for RS->DUS pattern
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PR #11405: [gpu] Add address computation fusion for RS->DUS pattern
Imported from GitHub PR https://github.com/openxla/xla/pull/11405
This patch recognizes a reduce-scatter operation that is used by a dynamic-update-slice operation. It then fuses this operation to an address-computation fusion.
For example, the following pattern is extracted to a fusion:
```
reduce-scatter = f16[64,128] reduce-scatter(%p0), channel_id=64, replica_groups={{0,1}}, use_global_device_ids=true, dimensions={0}, to_apply=add
dynamic-update-slice = f16[128,128] dynamic-update-slice(%p1, reduce-scatter, %p3, constant.718)
```
Copybara import of the project:
--
2c26d292ac1cbe8742b773228caf4a21d8bb6d36 by Shraiysh Vaishay <[email protected]>:
[gpu] Add address computation fusion for RS->DUS pattern
Merging this change closes #11405
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11405 from shraiysh:address_computation 2c26d292ac1cbe8742b773228caf4a21d8bb6d36
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PR_kwDOArmXAs5taPe3
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XProf GPU: Enable CUPTI per-thread activity buffer for better overhead.
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XProf GPU: Enable CUPTI per-thread activity buffer for better overhead.
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PR_kwDOArmXAs5taIRI
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Disable already failing tests on TPU
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Disable already failing tests on TPU
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PR_kwDOArmXAs5taBeM
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[XLA:GPU] Refactor HloFusionAdaptor to a flat structure.
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[XLA:GPU] Refactor HloFusionAdaptor to a flat structure.
There is a need for HloInstructionAdaptor to know it's parent HloFusionAdaptor to be able to distinguish fusion instruction for operands and users. Current nested structure of HloFusionAdaptor prevents us from having a convenient single parent pointer.
This CL add `parent` field to HloInstructionAdaptor, but doesn't set or use it consistently yet.
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Bugfix for the transform api.
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Bugfix for the transform api.
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PR #11643: Fix unexpected resursive call in ksl for loop
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"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/66226/checks?check_run_id=24123724965) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
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PR #11643: Fix unexpected resursive call in ksl for loop
Imported from GitHub PR https://github.com/openxla/xla/pull/11643
ksl does not have this func: ForWithStatus(string_view, llvm::Value*, llvm::Value*, llvm::Value, bool, const std::function<Status(llvm::Value*, bool)>&), only has ForWithStatus(string_view, llvm::Value*, llvm::Value*, llvm::Value, const std::function<Status(llvm::Value*, bool)>&) which is the expected func call. So if use peel first iteration, will call ForWithStatus(string_view, llvm::Value*, llvm::Value*, llvm::Value, bool, const std::function<Status(llvm::Value*, llvm::Value*)>&) recursively (std::function<Status(llvm::Value*, bool)> will cast to std::function<Status(llvm::Value*, llvm::Value*)> implicitly on my running enviroment), which leads stack smash.
Copybara import of the project:
--
daa71e5687726f30cc8d0da6419e576d4ddedff8 by Zhou, Lingzhi <[email protected]>:
fix unexpected resursive call
--
d67a61ea83d8204c034f6eadb98bebc7fecbcd27 by Zhou, Lingzhi <[email protected]>:
remove deadcode
--
7e1c236da49cf4012cac372db5c7760876563461 by Zhou, Lingzhi <[email protected]>:
fix build error
--
e5ac16d665d88b8b8d1258b1e0e4ebfd94813547 by Zhou, Lingzhi <[email protected]>:
fix filecheck error
Merging this change closes #11643
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11643 from lingzhi98:lingzhi/fix_ksl_for e5ac16d665d88b8b8d1258b1e0e4ebfd94813547
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Updated linux_arm64 Dockerfile Ubuntu from 20.04 to 22.04 (LTS) to support C++20 in TensorFlow.
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Updated linux_arm64 Dockerfile Ubuntu from 20.04 to 22.04 (LTS) to support C++20 in TensorFlow.
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PR_kwDOArmXAs5tZJ30
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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/2b13a98c79d17027f474ed545740fe2b39547dbd.
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PR_kwDOArmXAs5tYtOM
| 66,223 |
Allow XlaHostComputeOp to the allowed list in mark_ops_for_outside_compilation.cc
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Allow XlaHostComputeOp to the allowed list in mark_ops_for_outside_compilation.cc
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| 2,257,124,352 |
PR_kwDOArmXAs5tYehU
| 66,222 |
Replaces the lazy constraint instantiation logic with memory term reduction.
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Replaces the lazy constraint instantiation logic with memory term reduction.
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PR_kwDOArmXAs5tYcoU
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PR #11306: [XLA:GPU] Fix not unique name issue in sanitize constant pass
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PR #11306: [XLA:GPU] Fix not unique name issue in sanitize constant pass
Imported from GitHub PR https://github.com/openxla/xla/pull/11306
Fix `Instruction name is not unique` error reported by JAX UT [`ShardMapTest.test_matmul_reduce_scatter`](https://github.com/google/jax/blob/jaxlib-v0.4.24/tests/shard_map_test.py#L149-L162) in XLA:GPU.
### Background
Error message:
```
!ContainsKey(instruction_names, instruction->name()) Instruction name is not unique: param_1
```
This error is reported after `PrepareHloModuleForIrEmitting()`. The non-unique name `param_1` is generated from 2 different passes `GpuSanitizeConstantNames` and `FusionWrapper`. The related HLO changes are as follows:
1. Original HLO, there's no `param_1` and only got `param_0` in `async_computation`:
```ll
%main.19_spmd (param: s32[4,4], param.1: s32[4,8]) -> s32[2,8] {
%param = s32[4,4]{1,0} parameter(0), sharding={devices=[2,2]<=[4]}, metadata={op_name="jit(fwd)/jit(main)/shard_map[mesh=Mesh(\'x\': 2, \'y\': 2) in_names=({0: (\'x\',), 1: (\'y\',)}, {0: (\'x\',)}) out_names=({0: (\'x\', \'y\')},) check_rep=True rewrite=True auto=frozenset()]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=161}
%param.1 = s32[4,8]{1,0} parameter(1), sharding={devices=[2,1,2]<=[4] last_tile_dim_replicate}, metadata={op_name="jit(fwd)/jit(main)/shard_map[mesh=Mesh(\'x\': 2, \'y\': 2) in_names=({0: (\'x\',), 1: (\'y\',)}, {0: (\'x\',)}) out_names=({0: (\'x\', \'y\')},) check_rep=True rewrite=True auto=frozenset()]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=161}
%dot.1 = s32[4,8]{1,0} dot(s32[4,4]{1,0} %param, s32[4,8]{1,0} %param.1), lhs_contracting_dims={1}, rhs_contracting_dims={0}, metadata={op_name="jit(fwd)/jit(main)/jit(shmap_body)/dot_general[dimension_numbers=(((1,), (0,)), ((), ())) precision=None preferred_element_type=int32]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=158}
%reduce-scatter-start = ((s32[4,8]{1,0}), s32[2,8]{1,0}) reduce-scatter-start(s32[4,8]{1,0} %dot.1), channel_id=1, replica_groups={{0,1},{2,3}}, use_global_device_ids=true, dimensions={0}, to_apply=%region_0.7, metadata={op_name="jit(fwd)/jit(main)/jit(shmap_body)/reduce_scatter[axis_name=y scatter_dimension=0 axis_index_groups=None axis_size=2 tiled=True]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=159}, backend_config={"operation_queue_id":"0","wait_on_operation_queues":[],"collective_backend_config":{"is_sync":false,"no_parallel_custom_call":false}}
ROOT %reduce-scatter-done = s32[2,8]{1,0} reduce-scatter-done(((s32[4,8]{1,0}), s32[2,8]{1,0}) %reduce-scatter-start), metadata={op_name="jit(fwd)/jit(main)/jit(shmap_body)/reduce_scatter[axis_name=y scatter_dimension=0 axis_index_groups=None axis_size=2 tiled=True]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=159}
}
%async_computation (param_0: s32[4,8]) -> s32[2,8] {
%param_0 = s32[4,8]{1,0} parameter(0)
ROOT %reduce-scatter.2 = s32[2,8]{1,0} reduce-scatter(s32[4,8]{1,0} %param_0), channel_id=1, replica_groups={{0,1},{2,3}}, use_global_device_ids=true, dimensions={0}, to_apply=%region_0.7, metadata={op_name="jit(fwd)/jit(main)/jit(shmap_body)/reduce_scatter[axis_name=y scatter_dimension=0 axis_index_groups=None axis_size=2 tiled=True]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=159}
}
```
2. `param_0` was changed to `param_1` after `GpuSanitizeConstantNames` pass:
```ll
%main.19_spmd (param: s32[4,4], param.1: s32[4,8]) -> s32[2,8] {
%param = s32[4,4]{1,0} parameter(0), sharding={devices=[2,2]<=[4]}, metadata={op_name="jit(fwd)/jit(main)/shard_map[mesh=Mesh(\'x\': 2, \'y\': 2) in_names=({0: (\'x\',), 1: (\'y\',)}, {0: (\'x\',)}) out_names=({0: (\'x\', \'y\')},) check_rep=True rewrite=True auto=frozenset()]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=161}
%param.1 = s32[4,8]{1,0} parameter(1), sharding={devices=[2,1,2]<=[4] last_tile_dim_replicate}, metadata={op_name="jit(fwd)/jit(main)/shard_map[mesh=Mesh(\'x\': 2, \'y\': 2) in_names=({0: (\'x\',), 1: (\'y\',)}, {0: (\'x\',)}) out_names=({0: (\'x\', \'y\')},) check_rep=True rewrite=True auto=frozenset()]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=161}
%dot.1 = s32[4,8]{1,0} dot(s32[4,4]{1,0} %param, s32[4,8]{1,0} %param.1), lhs_contracting_dims={1}, rhs_contracting_dims={0}, metadata={op_name="jit(fwd)/jit(main)/jit(shmap_body)/dot_general[dimension_numbers=(((1,), (0,)), ((), ())) precision=None preferred_element_type=int32]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=158}
%reduce-scatter-start = ((s32[4,8]{1,0}), s32[2,8]{1,0}) reduce-scatter-start(s32[4,8]{1,0} %dot.1), channel_id=1, replica_groups={{0,1},{2,3}}, use_global_device_ids=true, dimensions={0}, to_apply=%region_0.7, metadata={op_name="jit(fwd)/jit(main)/jit(shmap_body)/reduce_scatter[axis_name=y scatter_dimension=0 axis_index_groups=None axis_size=2 tiled=True]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=159}, backend_config={"operation_queue_id":"0","wait_on_operation_queues":[],"collective_backend_config":{"is_sync":false,"no_parallel_custom_call":false}}
ROOT %reduce-scatter-done = s32[2,8]{1,0} reduce-scatter-done(((s32[4,8]{1,0}), s32[2,8]{1,0}) %reduce-scatter-start), metadata={op_name="jit(fwd)/jit(main)/jit(shmap_body)/reduce_scatter[axis_name=y scatter_dimension=0 axis_index_groups=None axis_size=2 tiled=True]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=159}
}
%async_computation (param_1: s32[4,8]) -> s32[2,8] {
%param_1 = s32[4,8]{1,0} parameter(0)
ROOT %reduce-scatter.2 = s32[2,8]{1,0} reduce-scatter(s32[4,8]{1,0} %param_1), channel_id=1, replica_groups={{0,1},{2,3}}, use_global_device_ids=true, dimensions={0}, to_apply=%region_0.7, metadata={op_name="jit(fwd)/jit(main)/jit(shmap_body)/reduce_scatter[axis_name=y scatter_dimension=0 axis_index_groups=None axis_size=2 tiled=True]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=159}
}
```
3. Another `param_1` was generated after `FusionWrapper` pass:
```ll
%async_computation (param_1: s32[4,8]) -> s32[2,8] {
%param_1 = s32[4,8]{1,0} parameter(0)
ROOT %reduce-scatter.2 = s32[2,8]{1,0} reduce-scatter(s32[4,8]{1,0} %param_1), channel_id=1, replica_groups={{0,2},{1,3},{4,6},{5,7}}, use_global_device_ids=true, dimensions={0}, to_apply=%region_0.7, metadata={op_name="jit(fwd)/jit(main)/jit(shmap_body)/reduce_scatter[axis_name=y scatter_dimension=0 axis_index_groups=None axis_size=2 tiled=True]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=159}
}
%wrapped_dot_computation (param_0.1: s32[4,4], param_1: s32[4,8]) -> s32[4,8] {
%param_0.1 = s32[4,4]{1,0} parameter(0)
%param_1 = s32[4,8]{1,0} parameter(1)
ROOT %dot.2 = s32[4,8]{1,0} dot(s32[4,4]{1,0} %param_0.1, s32[4,8]{1,0} %param_1), lhs_contracting_dims={1}, rhs_contracting_dims={0}, metadata={op_name="jit(fwd)/jit(main)/jit(shmap_body)/dot_general[dimension_numbers=(((1,), (0,)), ((), ())) precision=None preferred_element_type=int32]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=158}
}
%main.19_spmd (param: s32[4,4], param.1: s32[4,8]) -> s32[2,8] {
%param = s32[4,4]{1,0} parameter(0), sharding={devices=[2,2]<=[4]}, metadata={op_name="jit(fwd)/jit(main)/shard_map[mesh=Mesh(\'x\': 2, \'y\': 2) in_names=({0: (\'x\',), 1: (\'y\',)}, {0: (\'x\',)}) out_names=({0: (\'x\', \'y\')},) check_rep=True rewrite=True auto=frozenset()]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=161}
%param.1 = s32[4,8]{1,0} parameter(1), sharding={devices=[2,1,2]<=[4] last_tile_dim_replicate}, metadata={op_name="jit(fwd)/jit(main)/shard_map[mesh=Mesh(\'x\': 2, \'y\': 2) in_names=({0: (\'x\',), 1: (\'y\',)}, {0: (\'x\',)}) out_names=({0: (\'x\', \'y\')},) check_rep=True rewrite=True auto=frozenset()]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=161}
%dot.1 = s32[4,8]{1,0} dot(s32[4,4]{1,0} %param, s32[4,8]{1,0} %param.1), lhs_contracting_dims={1}, rhs_contracting_dims={0}, metadata={op_name="jit(fwd)/jit(main)/jit(shmap_body)/dot_general[dimension_numbers=(((1,), (0,)), ((), ())) precision=None preferred_element_type=int32]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=158}
%reduce-scatter-start = ((s32[4,8]{1,0}), s32[2,8]{1,0}) reduce-scatter-start(s32[4,8]{1,0} %dot.1), channel_id=1, replica_groups={{0,1},{2,3}}, use_global_device_ids=true, dimensions={0}, to_apply=%region_0.7, metadata={op_name="jit(fwd)/jit(main)/jit(shmap_body)/reduce_scatter[axis_name=y scatter_dimension=0 axis_index_groups=None axis_size=2 tiled=True]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=159}, backend_config={"operation_queue_id":"0","wait_on_operation_queues":[],"collective_backend_config":{"is_sync":false,"no_parallel_custom_call":false}}
ROOT %reduce-scatter-done = s32[2,8]{1,0} reduce-scatter-done(((s32[4,8]{1,0}), s32[2,8]{1,0}) %reduce-scatter-start), metadata={op_name="jit(fwd)/jit(main)/jit(shmap_body)/reduce_scatter[axis_name=y scatter_dimension=0 axis_index_groups=None axis_size=2 tiled=True]" source_file="/home/sdp/tenglu/intel-jax/tests/shard_map_test.py" source_line=159}
}
```
### Root cause
`GpuSanitizeConstantNames` runs before `FusionWrapper` and may change HLO instruction name by a [local name uniquer](https://github.com/openxla/xla/blob/main/xla/service/gpu/gpu_sanitize_constant_names.cc#L37). There're 2 issues here:
* The original HLO instruction name may be changed unexpectedly even though [the pass does not intend to do so](https://github.com/openxla/xla/blob/main/xla/service/gpu/gpu_sanitize_constant_names.cc#L50-L51).
* The global name uniquer of HLO module isn't aware of this change and may wrongly assign the same name to another HLO instruction in other passes, e.g. `FusionWrapper`.
`param_0` is changed to `param_1` by `GpuSanitizeConstantNames` unexpectedly.
### Solution
Only record HLO instruction names in the local name uniquer of `GpuSanitizeConstantNames` and do not change the original names. It exactly follows the pass design.
Copybara import of the project:
--
c39bc256957db681cfe1706fb716986da89edf3a by Lu Teng <[email protected]>:
Fix not unique name issue in sanitize constant pass.
Merging this change closes #11306
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11306 from Intel-tensorflow:tenglu/fix_unique_name c39bc256957db681cfe1706fb716986da89edf3a
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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/28da0975d11f4efd9167d63d4dcce04ec60db5d6.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11714 from dimvar:device-info-tostring 15d36e3dcde6637f91a28e56658cdde9a81eaae3
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PR_kwDOArmXAs5tYTKK
| 66,219 |
Call IsCollective when looking for collectives with channel id.
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Call IsCollective when looking for collectives with channel id.
This change unifies the place we check if an instruction is collective or not.
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PR_kwDOArmXAs5tYQ7N
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PR #11644: [XLA:GPU] move cublasLt creation to thunk initialization.
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PR #11644: [XLA:GPU] move cublasLt creation to thunk initialization.
Imported from GitHub PR https://github.com/openxla/xla/pull/11644
To avoid cublasLt and command buffer deadlock, move cublasLt creation to thunk initialization.
Copybara import of the project:
--
1d9b36f31fa8dbfce6f628065e946b3219148757 by Shawn Wang <[email protected]>:
move cublasLt creation to thunk initialize
Merging this change closes #11644
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11644 from shawnwang18:shawnw/move_cublaslt_creation_to_thunk_init 1d9b36f31fa8dbfce6f628065e946b3219148757
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Conda Release supporting Windows + Python 3.11
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[
"@dcompiled,\r\nThank you for reporting the issue. We are also trying to replicate the issue in our environment. Please allow sometime to reproduce and get back to you the same. Thank you!",
"@dcompiled,\r\nI tried to create the conda environment with python 3.11 on Windows machine, and haven't faced any issue/error. Could you please try using the below command & activate the created environment and then install the tensorflow.\r\n\r\n``` python\r\nconda create --name tf python=3.11\r\n```\r\n\r\nKindly find the screenshot for the reference.\r\n\r\n\r\n\r\n\r\n",
"I am trying to do this using miniconda which uses conda-forge as it source. There is no current version of tensorflow in conda-forge and this is probably the root cause. Are there any plans to release a copy of tensorflow to conda-forge on windows?",
"@dcompiled,\r\n\r\nAfter creating Conda environment please use pip to install TF package. For GPU package please use pip install tensorflow[and-cuda] that will install all the required compatible cuda/cudnn packages. Please ensure compatible cuda driver is installed manually. \r\n\r\nAlso **Conda-forge** is a community led collection of recipes, build infrastructure and distributions for the conda package manager which the tensorflow doesn't have any type of control on. If the issue is with the conda-forge, please try to check with the respective team for the quick response. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66217\">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/66217\">No</a>\n"
] | 2024-04-22T17:06:42 | 2024-05-23T01:49:01 | 2024-05-23T01:48:52 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
latest
### Custom code
Yes
### OS platform and distribution
Windows 10
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Unable to build a conda environment with tensorflow on python 3.11 on windows but works fine on Linux. I am aware I can use pip to install this but prefer not to. Any plans to address this?
### Standalone code to reproduce the issue
```shell
(base) C:\Users\Alex>conda create -n tf python=3.11 tensorflow
```
### Relevant log output
```shell
(base) C:\Users\Alex>conda create -n tf python=3.11 tensorflow
Collecting package metadata (current_repodata.json): done
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: -
Found conflicts! Looking for incompatible packages.
This can take several minutes. Press CTRL-C to abort.
failed
UnsatisfiableError: The following specifications were found to be incompatible with each other:
Output in format: Requested package -> Available versions
Package python conflicts for:
tensorflow -> absl-py[version='>=0.1.6'] -> python[version='2.7.*|>=2.7,<2.8.0a0|>=3.8,<3.9.0a0|>=3|>=3.6|>=3.7|>=3.9,<3.10.0a0|>=3.12,<3.13.0a0|>=3.10,<3.11.0a0|>=3.11,<3.12.0a0|>=3.4|>=3.12.0rc3,<3.13.0a0|3.4.*|>=3.8|>=3.5|>=3.2|>=3.2,<3.10']
python=3.11
tensorflow -> python[version='3.10.*|3.5.*|3.6.*|>=3.5,<3.6.0a0|>=3.6,<3.7.0a0|>=3.7,<3.8.0a0|3.9.*|3.8.*|3.7.*']
```
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PR #11714: Add ToString methods that make it easier to debug DeviceDescription issues.
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PR #11714: Add ToString methods that make it easier to debug DeviceDescription issues.
Imported from GitHub PR https://github.com/openxla/xla/pull/11714
Add ToString methods that make it easier to debug DeviceDescription issues.
Copybara import of the project:
--
15d36e3dcde6637f91a28e56658cdde9a81eaae3 by Dimitris Vardoulakis <[email protected]>:
Add ToString methods that make it easier to debug DeviceDescription issues.
Merging this change closes #11714
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11714 from dimvar:device-info-tostring 15d36e3dcde6637f91a28e56658cdde9a81eaae3
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Compile Error: Undeclared Identifier ‘tsl’ in libtensorflow
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[] | 2024-04-22T16:45:29 | 2024-04-23T05:34:33 | null |
NONE
| null | null | null |
### Issue type
Others
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
WSL2 on Windows 11
### Mobile device
_No response_
### Python version
3.7.12
### Bazel version
_No response_
### GCC/compiler version
clang++-16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
While compiling a test program using libtensorflow, I encountered an issue. The problem seems to be related to an error: use of undeclared identifier 'tsl'.
### Standalone code to reproduce the issue
```c++
#include <tensorflow/core/platform/env.h>
#include <tensorflow/core/public/session.h>
#include <iostream>
using namespace std;
using namespace tensorflow;
int main(){
Session *session;
Status status = NewSession(SessionOptions(), &session);
if(!status.ok()){
cout << status.ToString() << "\n";
return 1;
}
cout << "Session successfully created.\n";
}
```
```make
cmake_minimum_required(VERSION 3.12)
set(MY_PROJECT "tf")
set(MY_EXECUTABLE "hello_tf")
# set(CMAKE_CXX_COMPILER "/usr/bin/x86_64-linux-gnu-g++-11")
project("tf"
VERSION 0.1.0
LANGUAGES CXX C
DESCRIPTION "tf"
)
add_executable(${MY_EXECUTABLE})
set_target_properties(${MY_EXECUTABLE}
PROPERTIES
CXX_STANDARD 17
CXX_STANDARD_REQUIRED ON
CXX_EXTENSIONS OFF
C_STANDARD 11
C_STANDARD_REQUIRED ON
)
find_package(absl CONFIG REQUIRED)
find_package(protobuf CONFIG REQUIRED)
# TensorFlow include directory
include_directories("\\home\\mrjb\\tensorflow\\tf_env\\include")
target_include_directories(${MY_EXECUTABLE} PRIVATE "include" ${STB_INCLUDE_DIRS})
file(GLOB MY_SOURCE CONFIGURE_DEPENDS
"src/*.cc"
"src/*.c" # Add .c files to be compiled
)
target_sources(${MY_EXECUTABLE} PRIVATE ${MY_SOURCE})
# TensorFlow library directory
link_directories("\\home\\mrjb\\tensorflow\\tf_env\\lib")
link_directories("\\home\\mrjb\\miniconda3\\envs\\main\\lib\\python3.7\\site-packages\\tensorflow_io_gcs_filesystem")
# Link TensorFlow libraries
target_link_libraries(${MY_EXECUTABLE} PRIVATE
absl::any absl::log absl::base absl::bits
protobuf::libprotoc protobuf::libprotobuf protobuf::libprotobuf-lite
tensorflow
tensorflow_framework
tsl
)
target_compile_options(${MY_EXECUTABLE} PRIVATE "-fpermissive")
```
### Relevant log output
```shell
/usr/bin/cmake --build /mnt/c/Users/gonec/Documents/C++/tf/build/Debug --config Debug --target all --
[1/2 0% :: 0.000] Re-checking globbed directories...
[1/2 50% :: 2.171] Building CXX object CMakeFiles/hello_tf.dir/src/main.cc.o
FAILED: CMakeFiles/hello_tf.dir/src/main.cc.o
/usr/bin/clang++-16 -I/home/mrjb/tensorflow/tf_env/include -I/mnt/c/Users/gonec/Documents/C++/tf/include -isystem /home/mrjb/vcpkg/installed/x64-linux/include -g -fpermissive -std=c++17 -MD -MT CMakeFiles/hello_tf.dir/src/main.cc.o -MF CMakeFiles/hello_tf.dir/src/main.cc.o.d -o CMakeFiles/hello_tf.dir/src/main.cc.o -c /mnt/c/Users/gonec/Documents/C++/tf/src/main.cc
In file included from /mnt/c/Users/gonec/Documents/C++/tf/src/main.cc:1:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/env.h:26:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/env_time.h:20:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:19:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/bfloat16.h:20:
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/byte_order.h:24:32: error: use of undeclared identifier 'tsl'
constexpr bool kLittleEndian = tsl::port::kLittleEndian;
^
In file included from /mnt/c/Users/gonec/Documents/C++/tf/src/main.cc:1:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/env.h:26:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/env_time.h:20:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:19:
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/bfloat16.h:25:9: error: use of undeclared identifier 'tsl'
typedef tsl::bfloat16 bfloat16;
^
In file included from /mnt/c/Users/gonec/Documents/C++/tf/src/main.cc:1:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/env.h:26:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/env_time.h:20:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:21:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/tstring.h:21:
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/stringpiece.h:33:21: error: use of undeclared identifier 'tsl'
using StringPiece = tsl::StringPiece;
^
In file included from /mnt/c/Users/gonec/Documents/C++/tf/src/main.cc:1:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/env.h:26:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/env_time.h:20:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:21:
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/tstring.h:26:17: error: use of undeclared identifier 'tsl'
using tstring = tsl::tstring;
^
In file included from /mnt/c/Users/gonec/Documents/C++/tf/src/main.cc:1:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/env.h:26:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/env_time.h:20:
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:27:7: error: use of undeclared identifier 'tsl'
using tsl::string;
^
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:29:7: error: use of undeclared identifier 'tsl'
using tsl::uint16;
^
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:30:7: error: use of undeclared identifier 'tsl'
using tsl::uint32;
^
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:31:7: error: use of undeclared identifier 'tsl'
using tsl::uint4;
^
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:32:7: error: use of undeclared identifier 'tsl'
using tsl::uint64;
^
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:33:7: error: use of undeclared identifier 'tsl'
using tsl::uint8;
^
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:35:7: error: use of undeclared identifier 'tsl'
using tsl::int16;
^
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:36:7: error: use of undeclared identifier 'tsl'
using tsl::int32;
^
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:37:7: error: use of undeclared identifier 'tsl'
using tsl::int4;
^
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:38:7: error: use of undeclared identifier 'tsl'
using tsl::int64;
^
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:39:7: error: use of undeclared identifier 'tsl'
using tsl::int8;
^
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:41:7: error: use of undeclared identifier 'tsl'
using tsl::float8_e4m3fn;
^
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:42:7: error: use of undeclared identifier 'tsl'
using tsl::float8_e5m2;
^
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:44:14: error: unknown type name 'uint8'; did you mean 'uint'?
static const uint8 kuint8max = tsl::kuint8max;
^~~~~
uint
/usr/include/x86_64-linux-gnu/sys/types.h:150:22: note: 'uint' declared here
typedef unsigned int uint;
^
In file included from /mnt/c/Users/gonec/Documents/C++/tf/src/main.cc:1:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/env.h:26:
In file included from /home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/env_time.h:20:
/home/mrjb/tensorflow/tf_env/include/tensorflow/core/platform/types.h:44:32: error: use of undeclared identifier 'tsl'
static const uint8 kuint8max = tsl::kuint8max;
^
fatal error: too many errors emitted, stopping now [-ferror-limit=]
20 errors generated.
ninja: build stopped: subcommand failed.
```
|
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PR_kwDOArmXAs5tX5V6
| 66,214 |
PR #11721: [XLA:CPU] Add MpiCollectives to the the .pyi stubs
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NONE
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PR #11721: [XLA:CPU] Add MpiCollectives to the the .pyi stubs
Imported from GitHub PR https://github.com/openxla/xla/pull/11721
we forgot this in https://github.com/openxla/xla/pull/7849.
Copybara import of the project:
--
3924cc0fbbb63e9503f38a59aede3b8e817b17fa by Clemens Giuliani <[email protected]>:
[XLA:CPU] add missing type annotations for the mpi collectives
Merging this change closes #11721
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11721 from inailuig:mpicollectives_pytype 3924cc0fbbb63e9503f38a59aede3b8e817b17fa
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Add new toolchains for Windows. (TEST)
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Add new toolchains for Windows. (TEST)
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I_kwDOArmXAs6GgyIq
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tensorflow 1.x and 2.x produce different results during inferencing based on the same parameters
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[
"@jamesYu365 Could you have a look at the TensorFlow Compatibility [Checker](https://www.tensorflow.org/guide/versions) to identify deprecated or removed operations from your TensorFlow 1.x model. The tool suggests alternative operations for [migration](https://www.tensorflow.org/guide/migrate). Thank you!\r\n",
"Hello, thank you for your prompt response. Following your instructions, I added compat.v1 when importing the TensorFlow-related modules:\r\n```\r\nimport tensorflow.compat.v1 as tf\r\nfrom tensorflow.compat.v1.keras.models import Sequential\r\nfrom tensorflow.compat.v1.keras.layers import LSTM, Dense\r\n```\r\nThe result now is the same between two versions. \r\nHowever, I'm still curious why this happened. Since the LSTM model involves only basic arithmetic operations, I wonder why TensorFlow 1 and TensorFlow 2 give different results.",
"@jamesYu365 Using compat.v1 likely mitigated the issue by forcing TF2 to use a more TF1-like behavior, potentially including non-deterministic behavior. This might have made the results from both versions more comparable.\r\nThank you!",
"Thank you for your prompt response again. I'm still puzzled by the non-deterministic behavior you mentioned, considering that all the parameters and inputs are fixed. Where did this non-deterministic behavior come from? Could you please elaborate more on it? Perhaps you could provide some links related to this question, as I'm new to using TensorFlow. Thanks."
] | 2024-04-22T14:48:54 | 2024-04-29T05:37:15 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 1.15 and tf 2.13
### Custom code
Yes
### OS platform and distribution
Windows
### Mobile device
_No response_
### Python version
3.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
None
### GPU model and memory
None
### Current behavior?
I trained a LSTM in Tensorflow1.15, then load the saved model parameters on Tensorflow2.13, the prediction is significantly different from Tensorflow1.15.
### Standalone code to reproduce the issue
```shell
https://drive.google.com/file/d/1spHn4waDE3vNQzslA7AD06E0XQ1i-qL_/view?usp=sharing
```
### Relevant log output
_No response_
|
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Tensforflow-cpu 2.12.0 suddenly stopped working with Python 3.7-slim
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[
"Hi **@gsamaras** ,\r\n\r\n- Could you please update tensorflow to a newer version that is compatible with Python 3.7-slim. Newer versions of tensorflow often include bug fixes, performance improvements, and compatibility updates.\r\n- Here i providing [link](https://www.tensorflow.org/install/source) for your reference. please go through it once for checking compatible. And let us know if the issue still persists?\r\n\r\nThank you!",
"Hi @Venkat6871, if I update Tensorflow to let's say 2.16.1, then I get myriads of compatibility/runtime issues, so for now I want to postpone upgrading Tensorflow please.\r\n\r\nI was able to use Tensorflow 2.12.0 with Python3.7-slim a month ago or so, why I cannot do this now please? I am looking for an explanation, so that I can weigh my options.",
"Hi **@gsamaras** ,\r\n- It might have worked at that time, but now it won't work due to compatibility issues. For TensorFlow 2.12.0 we need to use python version \"3.8-3.11\", only then it will work. Here i am providing [link](https://screenshot.googleplex.com/BPjTucLeCL6pNcF) for your reference. \r\n\r\n\r\nThank you!",
"@Venkat6871 thank you for getting back to me. I am *sure* that TF 2.12.0 was working perfectly with Python 3.7-slim a month ago or so.\r\n\r\nThe link you provide redirects me to an SSO link for google.com I think, was that your intention? \r\n\r\nI want to understand why it was working before and now it does not. Could it be that the Python dependency for TF 2.12.0 changed? If so, is there a link or similar that shows that change/edit history please?",
"Hi **@gsamaras** ,\r\n \r\n- As my observation there is no changes in TF 2.12.0, It might be changed in Python 3.7-slim. That's Why it works may be a month ago. But there is no chance to work because of compatibility issues. \r\n- I just provided a screenshot belongs to compatibility versions for your reference. TensorFlow 2.12.0 we need to use python version \"3.8-3.11\".\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66211\">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/66211\">No</a>\n"
] | 2024-04-22T14:34:20 | 2024-05-15T01:49:54 | 2024-05-15T01:49:51 |
NONE
| null | null | null |
I have a Dockerfile that was working fine until recently. I made a software update in my Oracle VM VirtualBox Ubuntu 20.04.3 LTS x86_64, and after that the build would fail, due to version incompatibility. If update the version of tensforflow-cpu or Python, then a chain reaction happens with all the other packages I use, and like a Hydra head, build and/or runtime errors spawn continuously.
I see here https://pypi.org/project/tensorflow-cpu/2.12.0/ that tensorflow-cpu 1.12.0 has Python 3.9 as minimum Python version. However, I am pretty sure I was able to have everything perfectly work with Python 3.7-slim (slim may does a difference). Did something change in the dependencies of tensorflow-cpu 1.12.0? Could it be that the Python dependency changed recently?
Dockerfile:
```
# Build stage
FROM python:3.7-slim AS build
WORKDIR /todo
ENV LANG C.UTF-8
ENV LC_ALL C.UTF-8
ENV PATH="/root/miniconda3/bin:${PATH}"
ARG PATH="/root/miniconda3/bin:${PATH}"
RUN apt-get update
RUN apt-get update && apt-get install -y wget && rm -rf /var/lib/apt/lists/*
RUN wget \
https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
&& mkdir /root/.conda \
&& bash Miniconda3-latest-Linux-x86_64.sh -b \
&& rm -f Miniconda3-latest-Linux-x86_64.sh
RUN conda --version
RUN apt-get --purge autoremove -y wget
RUN python -m pip install --no-cache-dir --no-deps tensorflow-cpu==2.12.0 # <--- ERROR HERE
# Runtime stage
FROM python:3.7-slim
WORKDIR /todo
RUN apt-get update && apt-get install -y curl && rm -rf /var/lib/apt/lists/*
COPY --from=build /root/miniconda3 /root/miniconda3
COPY requirements.txt requirements.txt
COPY get_predict_m_data.py get_predict_m_data.py
ENV PATH="/root/miniconda3/bin:${PATH}"
ENV PATH="/usr/local/lib/:${PATH}"
RUN pip3 install --no-cache-dir -r requirements.txt
ENV PORT 5556
EXPOSE 5556
RUN rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
CMD ["python", "-u", "get_predict_m_data.py"]
```
Output with Error:
```
gsamaras@lv74744332234a:~/Code$ sudo docker build --no-cache -t predict_api .
[sudo] password for gsamaras:
[+] Building 36.1s (14/23) docker:default
=> [internal] load build definition from Dockerfile 0.0s
=> => transferring dockerfile: 1.68kB 0.0s
=> [internal] load metadata for docker.io/library/python:3.7-slim-buster 3.4s
=> [auth] library/python:pull token for registry-1.docker.io 0.0s
=> [internal] load .dockerignore 0.0s
=> => transferring context: 2B 0.0s
=> [build 1/8] FROM docker.io/library/python:3.7-slim-buster@sha256:9bd2bfc822a533f99cbe6b1311d5bf0ff136f776ebac9b985407829f17278935 0.0s
=> [internal] load build context 0.0s
=> => transferring context: 533B 0.0s
=> CACHED [build 2/8] WORKDIR /todo 0.0s
=> [build 3/8] RUN apt-get update 6.5s
=> CANCELED [stage-1 3/12] RUN apt-get update && apt-get install -y curl && rm -rf /var/lib/apt/lists/* 32.7s
=> [build 4/8] RUN apt-get update && apt-get install -y wget && rm -rf /var/lib/apt/lists/* 2.9s
=> [build 5/8] RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && mkdir /root/.conda && bash Miniconda3-latest-Linux-x86_64.sh -b && rm -f Miniconda3-latest-Linux- 20.8s
=> [build 6/8] RUN conda --version 0.5s
=> [build 7/8] RUN apt-get --purge autoremove -y wget 0.5s
=> ERROR [build 8/8] RUN python -m pip install --no-cache-dir --no-deps tensorflow-cpu==2.12.0 1.4s
------
> [build 8/8] RUN python -m pip install --no-cache-dir --no-deps tensorflow-cpu==2.12.0:
1.094 ERROR: Could not find a version that satisfies the requirement tensorflow-cpu==2.12.0 (from versions: 2.16.0rc0, 2.16.1)
1.094 ERROR: No matching distribution found for tensorflow-cpu==2.12.0
------
Dockerfile:22
--------------------
20 | RUN apt-get --purge autoremove -y wget
21 |
22 | >>> RUN python -m pip install --no-cache-dir --no-deps tensorflow-cpu==2.12.0
23 |
24 | # Runtime stage
--------------------
ERROR: failed to solve: process "/bin/sh -c python -m pip install --no-cache-dir --no-deps tensorflow-cpu==2.12.0" did not complete successfully: exit code: 1
```
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Fix support for sparse dots in algebraic simplifier, add more tests.
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Fix support for sparse dots in algebraic simplifier, add more tests.
All the uses of `CreateDot` and `MakeDotHlo` should either support sparse dots (i.e. pass down "sparsity" and "sparse_meta" arguments), or skip sparse dots. Failure to do so would result in compilation errors when sparse dots are encountered.
Some support was introduced previously, but not all the uses were covered, and some of the tests were missing.
Detailed list of changes:
1) Skip the dots where both operands are transposed - as removing the transposes requires swapping the operands, and sparse dot implementation only supports sparsity on the LHS operand (nVidia hardware restriction).
2) Skip the optimization for sparse dot of concat, gather, and transpose/reshape - those operate only on constants, so have limited applicability.
3) Add support for sparse dots for eliminating reduction of batch dimensions.
4) Add previously missing tests for associative reordering of sparse dots.
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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/8120c877848a8ea7fb39a7228df442a6cccaf683.
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[xla:ffi] Add test cases for nested tuple to CPU custom call tests
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[xla:ffi] Add test cases for nested tuple to CPU custom call tests
Add test cases for handling nested tuples (given both as inputs and outputs) to CPU custom call typed FFI API tests.
Implements #10056.
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Enable saving quantization report to file from `QuantizationReport`.
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Enable saving quantization report to file from `QuantizationReport`.
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PR #11568: Improve NVTX ranges emitted during compilation
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PR #11568: Improve NVTX ranges emitted during compilation
Imported from GitHub PR https://github.com/openxla/xla/pull/11568
New ranges:
- XlaBufferAssignment
- XlaCompileCudnnFusion
- XlaCompileGpuAsm
- XlaCreateGpuExecutable
- XlaDumpHloModule
- XlaDumpLlvmIr
- XlaEmitGpuAsm
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- XlaMemoryScheduler
- XlaOptimizeLlvmIr
Tweaked XlaCompile, XlaCompileBackend and XlaPass to improve consistency. To allow more explicit range names, the `llvm::Module` is now set to the corresponding HLO module name, where previously it was an empty string.
Because part of #9896 was reverted, `GOOGLE_CUDA` needs to be defined in more translation units to make the NVTX ranges appear. This is done in a separate PR, https://github.com/openxla/xla/pull/11611, on the request of @cheshire.
Copybara import of the project:
--
998bebdee84498eb5ec2df97b9ba94b84d8162cb by Olli Lupton <[email protected]>:
Improve NVTX ranges emitted during compilation
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- XlaCompileCudnnFusion
- XlaCompileGpuAsm
- XlaCreateGpuExecutable
- XlaDumpHloModule
- XlaDumpLlvmIr
- XlaEmitGpuAsm
- XlaEmitLlvmIr
- XlaMemoryScheduler
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set to the corresponding HLO module name, where previously it was an
empty string.
Because part of openxla#9896 was reverted, additional changes to define the
GOOGLE_CUDA macro are needed in order for the new ranges to appear.
Merging this change closes #11568
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11568 from olupton:nvtx-compilation-annotations 998bebdee84498eb5ec2df97b9ba94b84d8162cb
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PR #11568: Improve NVTX ranges emitted during compilation
Imported from GitHub PR https://github.com/openxla/xla/pull/11568
New ranges:
- XlaBufferAssignment
- XlaCompileCudnnFusion
- XlaCompileGpuAsm
- XlaCreateGpuExecutable
- XlaDumpHloModule
- XlaDumpLlvmIr
- XlaEmitGpuAsm
- XlaEmitLlvmIr
- XlaMemoryScheduler
- XlaOptimizeLlvmIr
Tweaked XlaCompile, XlaCompileBackend and XlaPass to improve consistency. To allow more explicit range names, the `llvm::Module` is now set to the corresponding HLO module name, where previously it was an empty string.
Because part of #9896 was reverted, `GOOGLE_CUDA` needs to be defined in more translation units to make the NVTX ranges appear. This is done in a separate PR, https://github.com/openxla/xla/pull/11611, on the request of @cheshire.
Copybara import of the project:
--
998bebdee84498eb5ec2df97b9ba94b84d8162cb by Olli Lupton <[email protected]>:
Improve NVTX ranges emitted during compilation
New ranges:
- XlaBufferAssignment
- XlaCompileCudnnFusion
- XlaCompileGpuAsm
- XlaCreateGpuExecutable
- XlaDumpHloModule
- XlaDumpLlvmIr
- XlaEmitGpuAsm
- XlaEmitLlvmIr
- XlaMemoryScheduler
- XlaOptimizeLlvmIr
Tweaked XlaCompile, XlaCompileBackend and XlaPass to improve
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set to the corresponding HLO module name, where previously it was an
empty string.
Because part of openxla#9896 was reverted, additional changes to define the
GOOGLE_CUDA macro are needed in order for the new ranges to appear.
Merging this change closes #11568
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11568 from olupton:nvtx-compilation-annotations 998bebdee84498eb5ec2df97b9ba94b84d8162cb
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"@nmdesai22,\r\nCould you please provide the steps you are following to install the tensorflow and also provide the complete error log which helps to debug the issue in an effective way. Thank you!",
"- I am using bazel (v5.4.1) to download tensorflow and build the project.\r\n- I declare TF version in a WORKSPACE file of the project.\r\n```\r\nhttp_archive(\r\n name = \"org_tensorflow\",\r\n strip_prefix = \"tensorflow-2.16.1\",\r\n sha256 = \"54c976f828182f85e10e03840dd3b0504109f57760a498075574e35e9aa983fe\",\r\n urls = [\r\n \"https://github.com/tensorflow/tensorflow/archive/v2.16.1.zip\"\r\n ],\r\n)\r\n\r\n````\r\nThen , give below command to buiild project\r\n```\r\nbazel build --subcommands --verbose_failures --config android_arm64 -c opt ronn/ronn_wrapper:ronn_wrapper\r\n```\r\n\r\nI get below Log on the terminal\r\n```\r\nDEBUG: /home/user.dave/.cache/bazel/_bazel_user.dave/930ed5452c6d7a47881f9fd479f92e3f/external/org_tensorflow/third_party/repo.bzl:132:14:\r\nWarning: skipping import of repository 'com_google_protobuf' because it already exists.\r\nDEBUG: /home/user.dave/.cache/bazel/_bazel_user.dave/930ed5452c6d7a47881f9fd479f92e3f/external/org_tensorflow/third_party/repo.bzl:132:14:\r\nWarning: skipping import of repository 'zlib' because it already exists.\r\nERROR: /home/user.dave/workspace/GIT_REPO/Google_io/tflite_ronn_project/ronn/ronn_wrapper/BUILD:34:11: While resolving toolchains for target //ronn/ronn_wrapper:ronn_wrapper: invalid registered toolchain '@local_execution_config_python//:py_toolchain': error loading package '@local_execution_config_python//': Unable to find package for @python//:defs.bzl: The repository '@python' could not be resolved: Repository '@python' is not defined.\r\nERROR: Analysis of target '//ronn/ronn_wrapper:ronn_wrapper' failed; build aborted:\r\nINFO: Elapsed time: 0.112s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (0 packages loaded, 0 targets configured)\r\n currently loading: @local_execution_config_python//\r\n Fetching @local_jdk; fetching\r\n+ rm -rf /tmp/tmp.JBqcj6caTS\r\n```",
"**Update** -\r\n\r\nAs a Temporary fix, I added python definitions in my Project's _{project}/WORKSPACE_ file, and it worked. and Project can be build succesfully\r\n\r\n\r\nI added below additional changes in WORKSPACE. \r\n\r\n```\r\n# bazel_skylib and rules_python are from @org_tensorflow/WORKSPACE.\r\n# Remove them once https://github.com/tensorflow/tensorflow/issues/66203 is fixed.\r\nhttp_archive(\r\n name = \"bazel_skylib\",\r\n sha256 = \"74d544d96f4a5bb630d465ca8bbcfe231e3594e5aae57e1edbf17a6eb3ca2506\",\r\n urls = [\r\n \"https://storage.googleapis.com/mirror.tensorflow.org/github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz\",\r\n \"https://github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz\",\r\n ],\r\n)\r\n\r\nhttp_archive(\r\n name = \"rules_python\",\r\n sha256 = \"9d04041ac92a0985e344235f5d946f71ac543f1b1565f2cdbc9a2aaee8adf55b\",\r\n strip_prefix = \"rules_python-0.26.0\",\r\n url = \"https://github.com/bazelbuild/rules_python/releases/download/0.26.0/rules_python-0.26.0.tar.gz\",\r\n)\r\n\r\n# Copied from `@org_tensorflow//:WORKSPACE`.\r\n#remove these definitions once https://github.com/tensorflow/tensorflow/issues/66203 is fixed.\r\nload(\r\n \"@rules_python//python:repositories.bzl\",\r\n \"py_repositories\",\r\n \"python_register_toolchains\",\r\n)\r\npy_repositories()\r\n\r\nload(\r\n \"@org_tensorflow//tensorflow/tools/toolchains/python:python_repo.bzl\",\r\n \"python_repository\",\r\n)\r\npython_repository(name = \"python_version_repo\")\r\nload(\"@python_version_repo//:py_version.bzl\", \"HERMETIC_PYTHON_VERSION\")\r\n\r\n# Toolchain setup here is to please the TF workspace scripts,\r\n# and we do not use this Python version to build pip packages.\r\npython_register_toolchains(\r\n name = \"python\",\r\n ignore_root_user_error = True,\r\n python_version = HERMETIC_PYTHON_VERSION,\r\n)\r\n```",
"@nmdesai22,\r\nCould you please try to run **bazel clean --expunge** to make sure old bazel files are deleted? and then run the build command again.\r\nIf you still get an error try the command in the image to make sure 'location to python.exe' is recognized as executable\r\n\r\n\r\n\r\nIf it still does not work, please try to follow the steps below\r\n\r\n<details><summary>Click to expand!</summary>\r\nStep1: Install Python 3.10 in C:\\\r\n\r\nStep2: Create and activate Virtual env\r\nC:\\python39\\python.exe -m venv venv39_new\r\nD:\\user\\venv39_new\\scripts\\activate ==>\r\n(venv39_new) D:\\user\\\r\n\r\n\r\nStep3: git clone https://github.com/tensorflow/tensorflow.git==>(venv39_new) D:\\user\\tensorflow>\r\n\r\nStep4: Set up all the paths and files\r\n\r\nset BAZEL_SH=C:\\msys64\\usr\\bin\\bash.exe\r\n\r\nset BAZEL_VS=C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\BuildTools\r\n\r\nset BAZEL_VC=C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\BuildTools\\VC\r\n\r\nset PATH=c:\\Tools\\Bazel;D:\\user\\venv39_new\\Scripts;C:\\Program Files\\Python39\\Scripts\\\r\n\r\nset PYTHON_BIN_PATH=D:/user/venv39_new/Scripts/python.exe\r\nset PYTHON_LIB_PATH=D:/user/venv39_new/lib/site-packages\r\nset PYTHON_DIRECTORY=D:/user/venv39_new/Scripts/\r\n\r\n\r\n\r\nThank you!",
"I am using Linux ENV to build the project. I did try all three suggestions, but get the same error"
] | 2024-04-22T09:53:27 | 2024-05-08T03:45:13 | 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
Linux Ubuntu 22.04.1
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
5.4.1
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
while building a tool with tensorflow v2.16.1, it gives build error as "The repository '@python' could not be resolved: Repository '@python' is not defined."
### Standalone code to reproduce the issue
```shell
It seems issue is due to Hermatic Python from tensroflow 2.14.0 onwards.
This commit creates the issue - https://github.com/tensorflow/tensorflow/commit/e85860e8382a460a0dd8547a536e5eaaf9096a9f
Reproduction env - TBD
```
### Relevant log output
```shell
ERROR: /home/ronn/ronn_wrapper/BUILD:34:11: While resolving toolchains for target //ronn/ronn_wrapper:ronn_wrapper: invalid registered toolchain '@local_execution_config_python//:py_toolchain': error loading package '@local_execution_config_python//': Unable to find package for @python//:defs.bzl: The repository '@python' could not be resolved: Repository '@python' is not defined.
ERROR: Analysis of target '//ronn/ronn_wrapper:ronn_wrapper' failed; build aborted:
INFO: Elapsed time: 3.515s
```
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/711b66495de2cb3538b7c3c95e4a98f90e2640bf.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11611 from olupton:nvtx-annotation-bazel-changes 80e8f28422aa1accfd4b972839b5f8ae1b7ff74b
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Switch quantization dialect to use properties.
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PR #11611: Define GOOGLE_CUDA to turn on #11568
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PR #11611: Define GOOGLE_CUDA to turn on #11568
Imported from GitHub PR https://github.com/openxla/xla/pull/11611
The new NVTX annotations added in #11568 will not appear unless GOOGLE_CUDA is defined. The part of #9896 that was supposed to avoid this was rolled back in c4b031bf9522685f998ecbd12570f29b391495ec.
cc: @cheshire
Copybara import of the project:
--
80e8f28422aa1accfd4b972839b5f8ae1b7ff74b by Olli Lupton <[email protected]>:
Define GOOGLE_CUDA to turn on #11568
The new NVTX annotations added in #11568 will not appear unless
GOOGLE_CUDA is defined. The part of #9896 that was supposed to avoid
this was rolled back in c4b031bf9522685f998ecbd12570f29b391495ec.
Merging this change closes #11611
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11611 from olupton:nvtx-annotation-bazel-changes 80e8f28422aa1accfd4b972839b5f8ae1b7ff74b
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[TF] Forward "__dict__" attribute request to the wrapped object of a `_TupleWrapper`
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"followers_url": "https://api.github.com/users/copybara-service%5Bbot%5D/followers",
"following_url": "https://api.github.com/users/copybara-service%5Bbot%5D/following{/other_user}",
"gists_url": "https://api.github.com/users/copybara-service%5Bbot%5D/gists{/gist_id}",
"starred_url": "https://api.github.com/users/copybara-service%5Bbot%5D/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/copybara-service%5Bbot%5D/subscriptions",
"organizations_url": "https://api.github.com/users/copybara-service%5Bbot%5D/orgs",
"repos_url": "https://api.github.com/users/copybara-service%5Bbot%5D/repos",
"events_url": "https://api.github.com/users/copybara-service%5Bbot%5D/events{/privacy}",
"received_events_url": "https://api.github.com/users/copybara-service%5Bbot%5D/received_events",
"type": "Bot",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[] | 2024-04-22T07:30:23 | 2024-04-23T09:35:20 | 2024-04-23T09:35:17 |
NONE
| null | true |
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}
|
Automated Code Change
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{
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https://api.github.com/repos/tensorflow/tensorflow/issues/66192/timeline
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