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PR_kwDOArmXAs5p-Y6W
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Lower tf.IfrtRestoreVariableOp to tf_mlrt.IfrtRestoreVariableOp
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Lower tf.IfrtRestoreVariableOp to tf_mlrt.IfrtRestoreVariableOp
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PR_kwDOArmXAs5p-Xnp
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PR #10510: [ROCm] Triton in XLA for ROCm - gemm rewriter triton related changes.
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PR #10510: [ROCm] Triton in XLA for ROCm - gemm rewriter triton related changes.
Imported from GitHub PR https://github.com/openxla/xla/pull/10510
First commit of the series for enabling Triton in XLA for ROCm .
Copybara import of the project:
--
832d7253db1f8972862252f034b216d1eed1da29 by Zoran Jovanovic <[email protected]>:
[ROCm] Triton in XLA for ROCm - gemm rewriter triton related changes.
Merging this change closes #10510
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10510 from ROCm:rocm_triton_backend 832d7253db1f8972862252f034b216d1eed1da29
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Only mark ops converted by pattern at illegal
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NONE
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Only mark ops converted by pattern at illegal
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PR_kwDOArmXAs5p-R6w
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Created `FinalizeTPUEmbeddingV2` to output `embedding_partitions` and `hbm_buffers_config`, and created V2 Ops for BC XLA Ops which accept `embedding_partitions`, `hbm_buffers_config` and the serialization of `TpuTopologyArgsProto`.
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NONE
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PR_kwDOArmXAs5p-OMQ
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PR #10562: Add missing warmup run when autotuning.
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PR #10562: Add missing warmup run when autotuning.
Imported from GitHub PR https://github.com/openxla/xla/pull/10562
The `xla/service/gpu:gemm_algorithm_picker_test` test, run on V100, was hitting the delay kernel timeout because of this.
See #9757 for explanation of why the best practice is to execute a warmup run **without the GpuTimer active**.
Copybara import of the project:
--
b4ccf2928ee45ec9139db003378095b948bb73d5 by Olli Lupton <[email protected]>:
Add missing warmup run when autotuning.
The xla/service/gpu:gemm_algorithm_picker_test test, run on V100, was
hitting the delay kernel timeout because of this.
Merging this change closes #10562
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10562 from olupton:missing-warmup b4ccf2928ee45ec9139db003378095b948bb73d5
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PR_kwDOArmXAs5p-Fwa
| 63,886 |
PR #10612: [GPU] cuDNN GEMM fusions: enable noncontracting dimension transformations.
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PR #10612: [GPU] cuDNN GEMM fusions: enable noncontracting dimension transformations.
Imported from GitHub PR https://github.com/openxla/xla/pull/10612
This kind of transformations is already in use by the Triton GEMM backend for a while.
Copybara import of the project:
--
51eaaf6c8b722ef7c3273825d0585371cd55da26 by Ilia Sergachev <[email protected]>:
[GPU] Support broadcasts in cuDNN GEMM fusions.
--
40aed83572cd3f09fa8530b6c130bf84be226593 by Ilia Sergachev <[email protected]>:
[XLA:GPU] Enable noncontracting to batch dimension transformation in cuDNN GEMM fusions.
This transformation is already in use by the Triton GEMM backend for a while.
Merging this change closes #10612
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10612 from openxla:cudnn_gemm_split_noncontracting 40aed83572cd3f09fa8530b6c130bf84be226593
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Add support for 'tflite_data' parameter to the 'cc_test_with_tflite' rule.
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Add support for 'tflite_data' parameter to the 'cc_test_with_tflite' rule.
This is for tests that have run-time dependencies on dynamically loaded library targets that contain code that works with both regular TF Lite and TFLite in
Play Services.
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Add conditional data dependency on the shared library used for
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Add conditional data dependency on the shared library used for
TF Lite in Play Services.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/63954 from elfringham:fix_312_setuptools da09611b50c51fc7654e139a01ceceb4174fce89
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#shlo_ref Add std compatible member functions to `Shape`.
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#shlo_ref Add std compatible member functions to `Shape`.
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PR_kwDOArmXAs5p99FJ
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#shlo_ref Add typedefs for the tensor (element) type variant.
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#shlo_ref Add typedefs for the tensor (element) type variant.
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PR #10626: [XLA:CPU][oneDNN] Add a BF16 pattern for Softmax
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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/63881/checks?check_run_id=22794441560) 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 #10626: [XLA:CPU][oneDNN] Add a BF16 pattern for Softmax
Imported from GitHub PR https://github.com/openxla/xla/pull/10626
This PR:
1. Adds a pattern for BF16 softmax.
2. Adds a test to verify rewrite and execution result.
3. Skips BF16 matmul / softmax tests on machines that do not support BF16
Copybara import of the project:
--
f0370a18eed1f2b47d8dc15470b970029726b0e8 by Akhil Goel <[email protected]>:
Add Softmax BF16 pattern and test
--
62af424e4e74ddb7f64584aa3b05be761db956dc by Akhil Goel <[email protected]>:
Declare convert_instr identifier
--
d030100ea87923582aa64cdcf0fac1464051288c by Akhil Goel <[email protected]>:
Fix formatting
Merging this change closes #10626
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10626 from Intel-tensorflow:akhil/bf16_softmax d030100ea87923582aa64cdcf0fac1464051288c
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[XLA:GPU] Create prototype class for tiling propagation using symbolic tiles.
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[XLA:GPU] Create prototype class for tiling propagation using symbolic tiles.
SymbolicTileAnalysis allows computing symbolic tiles for all the instructions
in a given computation. All the symbolic tiles contained in an instance of
SymbolicTileAnalysis are parameterized by the chosen output tile (i.e. the
tile at the root instruction).
A given instruction may be tiled in several different ways in order to produce
the same output tile. In order to make sure all relevant tilings of an
instruction are captured, SymbolicTileAnalysis stores a symbolic tile for
each path from the root instruction of the computation to the relevant
instruction.
The class also allows evaluating the value of the concrete tiles for each
(instruction, path) pair once the offset, size, and stride parameters of the
output tile have been discovered.
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PR_kwDOArmXAs5p96WP
| 63,879 |
Align is_finite op parameters with other ops
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Align is_finite op parameters with other ops
This change no longer caches the input and output tensors in the IsFiniteOp data structure, similar to the unary elementwise ops. This aligns with the guidance in the README.
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PR_kwDOArmXAs5p95Bq
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Replaces XNN_FLAG_KEEP_DIM --> XNN_FLAG_REDUCE_DIM to maintain existing behaviour
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Replaces XNN_FLAG_KEEP_DIM --> XNN_FLAG_REDUCE_DIM to maintain existing behaviour
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PR_kwDOArmXAs5p93XK
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Fix clang-tidy warnings on status_matcher.h
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Fix clang-tidy warnings on status_matcher.h
The status_matcher.h file was producing a few clang-tidy warnings: the include guards weren't matching correct style and some internal IWYU warnings.
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PR #10635: Fix build error from PR 10497
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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
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Verify that input and output tensors have the same quantization parameters
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Verify that input and output tensors have the same quantization parameters
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PR #9873: [ROCm] Don't use CUDA PTX for ROCM in ComputationIdCmd
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PR #9873: [ROCm] Don't use CUDA PTX for ROCM in ComputationIdCmd
Imported from GitHub PR https://github.com/openxla/xla/pull/9873
Copybara import of the project:
--
818077159230e06ce8e94b3c556d1d68fa125b09 by Dragan Mladjenovic <[email protected]>:
[ROCm] Don't use CUDA PTX for ROCM in ComputationIdCmd
Merging this change closes #9873
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9873 from ROCm:comp_id 818077159230e06ce8e94b3c556d1d68fa125b09
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Fix build breakage for //tensorflow/lite/delegates/flex:util_test.
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Fix build breakage for //tensorflow/lite/delegates/flex:util_test.
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PR_kwDOArmXAs5p9eXm
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[xla:hlo] Do not add processed instructions to DFS stack
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PR_kwDOArmXAs5p9dtn
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[xla:hlo] NFC: Convert VisitState to enum class
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[xla:hlo] NFC: Convert VisitState to enum class
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Per-channel int4 quantization support for FullyConnected in XNNPack delegate
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Per-channel int4 quantization support for FullyConnected in XNNPack delegate
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PR_kwDOArmXAs5p9LD9
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Test 2 for check file set presubmit
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Insert a task to the low priority task queue when the criticality is one of the fixed list of low priority criticalities and support padding the high priority batch with the unbatched tasks given via the ProcessBatchCallBack.
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"> ```python\r\n> model = Sequential([\r\n> data_augmentation,\r\n> # layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),\r\n> # layers.Conv2D(16, 3, padding='same', activation=activation1),\r\n> # layers.MaxPooling2D(),\r\n> layers.Conv2D(32, 3, padding='same', activation=activation1),\r\n> layers.MaxPooling2D(),\r\n> layers.Conv2D(48, 3, padding='same', activation=activation2),\r\n> layers.MaxPooling2D(),\r\n> layers.Conv2D(64, 3, padding='same', activation=activation1),\r\n> layers.MaxPooling2D(),\r\n> layers.Dropout(0.15),\r\n> layers.Flatten(),\r\n> layers.Dense(128, activation=activation2),\r\n> layers.Dense(num_classes)\r\n> ])\r\n\r\n`Sequential` here might be imported from something other than `tf.keras`. Please take a look at #39001 ",
"Hi @schwefeljm,\r\n\r\nI am trying to reproduce the code meanwhile I encountered some other error. Is it `args = argparser(). Please provide complete reproducible code/link to debug the issue. \r\n\r\nThank You",
"> Hi @schwefeljm,\r\n> \r\n> I am trying to reproduce the code meanwhile I encountered some other error. Is it `args = argparser(). Please provide complete reproducible code/link to debug the issue.\r\n> \r\n> Thank You\r\nHi @LakshmiKalaKadali ,\r\n\r\nI updated to code to remove the depency on 'argsparser()'\r\n\r\nThe dataset I used is from: https://www.kaggle.com/datasets/gpiosenka/100-bird-species Though, I expect it work on any dataset.\r\n\r\nJason \r\n",
"> > ```python\r\n> > model = Sequential([\r\n> > data_augmentation,\r\n> > # layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),\r\n> > # layers.Conv2D(16, 3, padding='same', activation=activation1),\r\n> > # layers.MaxPooling2D(),\r\n> > layers.Conv2D(32, 3, padding='same', activation=activation1),\r\n> > layers.MaxPooling2D(),\r\n> > layers.Conv2D(48, 3, padding='same', activation=activation2),\r\n> > layers.MaxPooling2D(),\r\n> > layers.Conv2D(64, 3, padding='same', activation=activation1),\r\n> > layers.MaxPooling2D(),\r\n> > layers.Dropout(0.15),\r\n> > layers.Flatten(),\r\n> > layers.Dense(128, activation=activation2),\r\n> > layers.Dense(num_classes)\r\n> > ])\r\n> > ```\r\n> \r\n> `Sequential` here might be imported from something other than `tf.keras`. Please take a look at #39001\r\n@Aloqeely \r\n\r\nI went through and forced 'tf.keras.models.Sequential' and it had no effect. Thank you for the suggestions, though.\r\n\r\nJason",
"I was able to replicate on tf-nightly as well as 2.16.1. [gist](https://colab.sandbox.google.com/gist/pkgoogle/6a5062597daa1a59a15a97d275136d4b/63867.ipynb), I reduced the reproducible sample to what mattered i.e. the training process is actually irrelevant. This appears to happen when using Keras with the TFLite converter in 2.16.1 onward.\r\n\r\nHi @haozha111, can you please take a look?",
"I'm experiencing the same error here\r\n```\r\nconverter = tf.lite.TFLiteConverter.from_keras_model(model)\r\ntfmodel = converter.convert()\r\nopen (\"mobilenetv2_finetuned.tflite\" , \"wb\") .write(tfmodel)\r\n```\r\n\r\n Is it gonna help if I downgrade to an older version of Tensorflow?\r\n\r\n",
"> I'm experiencing the same error here\r\n> \r\n> ```\r\n> converter = tf.lite.TFLiteConverter.from_keras_model(model)\r\n> tfmodel = converter.convert()\r\n> open (\"mobilenetv2_finetuned.tflite\" , \"wb\") .write(tfmodel)\r\n> ```\r\n> \r\n> Is it gonna help if I downgrade to an older version of Tensorflow?\r\n\r\n@Takudzwamz \r\n\r\nFor me it did.",
"Hi @Takudzwamz, it seems like 2.15 does not exhibit this behavior currently.",
"@pkgoogle I downgraded to 2.15 and it worked, thanks.",
"Referencing legacy Keras worked for me: https://blog.tensorflow.org/2024/03/whats-new-in-tensorflow-216.html",
"As @Lerxp stated, using Legacy Keras is the preferred workaround for now:\r\n\r\n```\r\nimport os;os.environ[\"TF_USE_LEGACY_KERAS\"]=\"1\"\r\n```",
"I'm having the same issue, but Legacy Keras isn't working either. ",
"Hi @gusbernard, do you have a notebook or code snippet to share? Additionally have you tried using 2.15 for now? Thanks for any information you can provide.",
"I met the same problem when I using tensorflow2.6.1 in python3.12.3, I tried to downgrade tensorflow to 2.15.0, while it not support python3.12.3. I am trying to downgrade python now...",
"I have the same issue when trying to convert a keras model to TFLite exactly like in the [docs](https://www.tensorflow.org/lite/models/convert/convert_models#convert_a_keras_model_).\r\n```\r\nPython 3.12.3\r\nKeras 3.3.3\r\nTensorflow 2.16.1\r\n```",
"I used model.export() to deal with the problem.\r\n```\r\n# Export the keras model to a saved model format\r\nmodel.export(\"saved_model\")\r\n\r\n# Convert the saved model to TensorFlow Lite\r\nconverter = tf.lite.TFLiteConverter.from_saved_model(\"saved_model\")\r\ntflite_model = converter.convert()\r\n\r\n# Save the TensorFlow Lite model to a file\r\nwith open(\"model.tflite\", \"wb\") as f:\r\n f.write(tflite_model)\r\n```",
"Hi @schwefeljm, have you heard of [AI-Edge-Torch](https://github.com/google-ai-edge/ai-edge-torch)?, you can find more information here: [googleblog](https://developers.googleblog.com/en/ai-edge-torch-high-performance-inference-of-pytorch-models-on-mobile-devices/). You will not run into this issue if you go this route.\r\n\r\nI have created a script for converting your model here:\r\n\r\n```py\r\nimport torch\r\nimport torch.nn as nn\r\nimport ai_edge_torch\r\n\r\n\r\nclass CustomCVModel(nn.Module):\r\n def __init__(self, num_classes, img_height, img_width, activation1=nn.SiLU, activation2=nn.ELU):\r\n super().__init__()\r\n \r\n self.feature_extractor = nn.Sequential(\r\n nn.Conv2d(3, 32, kernel_size=3, padding=\"same\"),\r\n activation1(),\r\n nn.MaxPool2d(2),\r\n nn.Conv2d(32, 48, kernel_size=3, padding=\"same\"),\r\n activation2(),\r\n nn.MaxPool2d(2),\r\n nn.Conv2d(48, 64, kernel_size=3, padding=\"same\"),\r\n activation1(),\r\n nn.MaxPool2d(2),\r\n nn.Dropout(0.15),\r\n nn.Flatten(),\r\n )\r\n\r\n self.classifier = nn.Sequential(\r\n nn.Linear(64 * (img_height // 8) * (img_width // 8), 128), \r\n activation2(),\r\n nn.Linear(128, num_classes)\r\n )\r\n\r\n def forward(self, x):\r\n x = self.feature_extractor(x)\r\n x = self.classifier(x)\r\n return x\r\n\r\n\r\nimg_height = 128\r\nimg_width = 128\r\nmodel = CustomCVModel(1000, img_height, img_width, nn.SiLU, nn.ELU)\r\nsample_inputs = (torch.randn(1, 3, img_height, img_width),)\r\n\r\nedge_model = ai_edge_torch.convert(model.eval(), sample_inputs)\r\nedge_model.export(\"custom_cv_model.tflite\")\r\n```\r\n\r\nYou will still need to modify your training code but I have tested this and the conversion does work w/o issue.\r\n\r\nIf you want to, you can actually try visualizing the result in [model-explorer](https://github.com/google-ai-edge/model-explorer) as well.\r\n\r\nPlease try them out and let us know if this resolves your issue. If you still need further help, feel free to open a new issue at the respective repo."
] | 2024-03-18T14:34:37 | 2024-06-11T18:58:09 | null |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
Debian 13 - Trixie
- TensorFlow installation (pip package or built from source):
pip
- TensorFlow library (version, if pip package or github SHA, if built from source):
2.16.0-rc0 & 2.16.1
I am brand new to TensorFlow and welcome any suggestions.
I tested the exact same code on 2.15.0.post1 and 2.16.x. It runs on 15 and errors out on 16
### 2. Code
Provide code to help us reproduce your issues using one of the following options:
#### Option B: Paste your code here or provide a link to a custom end-to-end colab
##
```python
def main():
# Removed dependance on argsparser()
# args = argsparer()
# batch_size = args.batchSize # 32
batch_size = 32
# img_height = args.height # 128
img_height = 128
# img_width = args.width # 128
img_width = 128
activation1 = 'swish'
activation2 = 'elu'
optimizer = tf.keras.optimizers.AdamW(learning_rate=0.0005)
# epochs = args.epochs # 1
epochs = 1
train_data_dir = pathlib.Path("{}/{}/".format(args.inputDir, args.trainingSubDir)).with_suffix('')
val_data_dir = pathlib.Path("birds/birds/valid/".format(args.inputDir, args.validationSubDir)).with_suffix('')
# assume all files in training directory are readable image files.
image_count = len(list(train_data_dir.glob('*/*.*')))
AUTOTUNE = tf.data.AUTOTUNE
train_ds = tf.keras.utils.image_dataset_from_directory(
train_data_dir,
seed=random.randint(1, 10000),
image_size=(img_height, img_width),
batch_size=batch_size)
val_ds = tf.keras.utils.image_dataset_from_directory(
val_data_dir,
# validation_split=0.1,
# subset="validation",
seed=random.randint(1, 10000),
image_size=(img_height, img_width),
batch_size=batch_size)
num_classes = len(train_ds.class_names)
print("Training directory '{}' contains {} images in {} categories.".format(train_data_dir, image_count, num_classes))
normalization_layer = layers.Rescaling(1. / 255)
train_ds_norm = train_ds.map(lambda x, y: (normalization_layer(x), y),
num_parallel_calls=tf.data.AUTOTUNE,
deterministic=False)
val_ds_norm = val_ds.map(lambda x, y: (normalization_layer(x), y),
num_parallel_calls=tf.data.AUTOTUNE,
deterministic=False)
# train_ds = train_ds.shuffle(1000).prefetch(buffer_size=AUTOTUNE)
# val_ds = val_ds.prefetch(buffer_size=AUTOTUNE)
data_augmentation = tf.keras.Sequential(
[
layers.RandomFlip("horizontal",
input_shape=(img_height,
img_width,
3)),
layers.RandomRotation(0.1),
layers.RandomZoom(0.1),
]
)
model = Sequential([
data_augmentation,
# layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
# layers.Conv2D(16, 3, padding='same', activation=activation1),
# layers.MaxPooling2D(),
layers.Conv2D(32, 3, padding='same', activation=activation1),
layers.MaxPooling2D(),
layers.Conv2D(48, 3, padding='same', activation=activation2),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation=activation1),
layers.MaxPooling2D(),
layers.Dropout(0.15),
layers.Flatten(),
layers.Dense(128, activation=activation2),
layers.Dense(num_classes)
])
model.compile(optimizer=optimizer,
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
history = model.fit(
train_ds_norm,
validation_data=val_ds_norm,
epochs=epochs)
# Convert the model.
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.
]
tflite_model = converter.convert()
# Save the model.
with open('model.tflite', 'wb') as f:
f.write(tflite_model)
```
(You can paste links or attach files by dragging & dropping them below)
- Include code to invoke the TFLite Converter Python API and the errors.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
### 3. Failure after conversion
Conversion completely fails with "AttributeError: 'Sequential' object has no attribute '_get_save_spec'. Did you mean: '_set_save_spec'?" See below for complete log and traceback.
I have had to revert to 2.15.0.post1 to get model to convert and save as TFLite.
### 5. (optional) Any other info / logs
```python
024-03-18 09:24:25.859843: 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`.
2024-03-18 09:24:25.884237: 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 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-18 09:24:26.564689: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.569165: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.569272: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.570638: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.570740: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.570825: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.616558: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.616674: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.616763: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.616839: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 11308 MB memory: -> device: 0, name: NVIDIA RTX A4000, pci bus id: 0000:01:00.0, compute capability: 8.6
Found 84635 files belonging to 525 classes.
Found 2625 files belonging to 525 classes.
Training directory 'birds/birds/train' contains 84635 images in 525 categories.
/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/keras/src/layers/preprocessing/tf_data_layer.py:19: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
super().__init__(**kwargs)
2024-03-18 09:24:29.337903: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:961] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inStatefulPartitionedCall/sequential_1_1/dropout_1/stateless_dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer
2024-03-18 09:24:29.937251: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8907
2645/2645 ━━━━━━━━━━━━━━━━━━━━ 46s 17ms/step - accuracy: 0.0066 - loss: 6.1631 - val_accuracy: 0.0579 - val_loss: 5.2888
Traceback (most recent call last):
File "/home/jschwefel/repositories/aiml/TensorFlow/Test001/imageclassifier.py", line 129, in <module>
main()
File "/home/jschwefel/repositories/aiml/TensorFlow/Test001/imageclassifier.py", line 122, in main
tflite_model = converter.convert()
^^^^^^^^^^^^^^^^^^^
File "/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 1175, in wrapper
return self._convert_and_export_metrics(convert_func, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 1129, in _convert_and_export_metrics
result = convert_func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 1641, in convert
self._freeze_keras_model()
File "/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/tensorflow/lite/python/convert_phase.py", line 215, in wrapper
raise error from None # Re-throws the exception.
^^^^^^^^^^^^^^^^^^^^^
File "/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/tensorflow/lite/python/convert_phase.py", line 205, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 1582, in _freeze_keras_model
input_signature = _model_input_signature(
^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/tensorflow/lite/python/tflite_keras_util.py", line 84, in model_input_signature
input_specs = model._get_save_spec( # pylint: disable=protected-access
^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Sequential' object has no attribute '_get_save_spec'. Did you mean: '_set_save_spec'?
```
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PR_kwDOArmXAs5p84Lf
| 63,866 |
Integrate LLVM at llvm/llvm-project@0d313ee39387
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Integrate LLVM at llvm/llvm-project@0d313ee39387
Updates LLVM usage to match
[0d313ee39387](https://github.com/llvm/llvm-project/commit/0d313ee39387)
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| 2,192,368,373 |
PR_kwDOArmXAs5p8ywa
| 63,865 |
Safely cast float to int32
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Safely cast float to int32
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PR_kwDOArmXAs5p8rLz
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Address some ClangTidy warnings.
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Address some ClangTidy warnings.
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PR_kwDOArmXAs5p8p7L
| 63,863 |
[XLA:GPU] Add IndexingContext to store MLIRContext* and RTVars registry.
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[XLA:GPU] Add IndexingContext to store MLIRContext* and RTVars registry.
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PR_kwDOArmXAs5p8cAD
| 63,862 |
Fix MOF transpose fusions.
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Fix MOF transpose fusions.
The current code attempts to evaluate the epilogue for each transpose, but it
needs to be evaluated once for all transposes together.
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PR_kwDOArmXAs5p8bXB
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Rollback of GpuTimer: improve kernel execution time measurement accuracy
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Rollback of GpuTimer: improve kernel execution time measurement accuracy
This breaks the gemm algorithm picker test on V100
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I_kwDOArmXAs6CqE9q
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Conv3D Operation Error on Windows platform
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"Hi @AGFACBNNR ,\r\n\r\nIs this issue is specific for Windows OS? I have tested on colab(Linux) with TF package and tensorflow-intel package which are working fine as per attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/613a61b4dee3431d20b3ca9046560fb6/63860.ipynb).\r\n\r\n",
"Yes, I can only reproduce it on Windows OS.\r\nI also found that this issue cannot be reproduced on colab, which confused me for a whole day. Therefore, I tested it on another Winodws OS machine and a Linux OS machine and checked the difference in package lists. Finally I guess the reason is hidden in tensorflow-intel>=2.15.0, which can not be installed on colab. \r\n\r\nSorry for missing the version info."
] | 2024-03-18T12:26:37 | 2024-03-19T06:15:09 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf-2.15.0, tf-2.16.0, tf-2.16.1
### Custom code
Yes
### OS platform and distribution
Windows 10
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I found an output shape inconsistent in Conv3D layer:
```
from tensorflow.keras.layers import Conv3D
import numpy as np
x=np.random.rand(3,5,5,5,4)
l=Conv3D(5,[2,2,3],[1,1,1],'valid','channels_first',[2,2,2],1)
print(l(x).shape)
print(l.compute_output_shape(x.shape))
```
The output is:
```
(3, 5, 5, 5, 4)
(3, 5, 3, 3, 0)
```
However, I failed to reproduce it on google colab. After checking the package and re-running it on a Linux machine and another Windows machine, I found this bug may come from the package 'tensorflow-intel', which exists only on Windows platform.
Could you please check it?
### Standalone code to reproduce the issue
```shell
from tensorflow.keras.layers import Conv3D
import numpy as np
x=np.random.rand(3,5,5,5,4)
l=Conv3D(5,[2,2,3],[1,1,1],'valid','channels_first',[2,2,2],1)
print(l(x).shape)
print(l.compute_output_shape(x.shape))
```
```
### Relevant log output
```shell
(3, 5, 5, 5, 4)
(3, 5, 3, 3, 0)
```
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PR_kwDOArmXAs5p7MNJ
| 63,859 |
Avoid hard-coding the list of different sanitizer build types.
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Avoid hard-coding the list of different sanitizer build types.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/63954 from elfringham:fix_312_setuptools da09611b50c51fc7654e139a01ceceb4174fce89
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PR_kwDOArmXAs5p5qDr
| 63,858 |
Internal change only
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Remove LLVM patch to clang test that is causing internal tool failures.
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"You will have to install `tensorflow-metal` to add support for GPU on Apple silicons. [More info](https://developer.apple.com/metal/tensorflow-plugin/)",
"I did. Please look at the notebooks where I show exactly what was installed.\r\n\r\nhttps://github.com/msusol/jupyter-notebook-on-macos/blob/main/notebooks/TensorFlow216.ipynb",
"Hi @msusol ,\r\n\r\nAs a temporary fix you can use Keras2 backend. Since TF2.16 defaults to Keras3 backend it uses Keras3 by default with tf.keras. You can switch to Keras2 backend by installing `tf_keras` package and setting `os.environ['TF_USE_LEGACY_KERAS'] = '1'`. Meanwhile team will look into the issue with Keras3 vs Mac compatibility.\r\n",
"> Hi @msusol ,\r\n> \r\n> As a temporary fix you can use Keras2 backend. Since TF2.16 defaults to Keras3 backend it uses Keras3 by default with tf.keras. You can switch to Keras2 backend by installing `tf_keras` package and setting `os.environ['TF_USE_LEGACY_KERAS'] = '1'`. Meanwhile team will look into the issue with Keras3 vs Mac compatibility.\r\n\r\nVerified that this change does restore GPU access to the kernel:\r\nhttps://github.com/msusol/jupyter-notebook-on-macos/commit/7688a9fd2494807e5bb161c4f721ac82bada5c80\r\n\r\nWaiting for the resolution to remove the legacy flag."
] | 2024-03-17T23:24:14 | 2024-03-29T20:17:35 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
v2.16.1-0-g5bc9d26649c 2.16.1
### Custom code
Yes
### OS platform and distribution
Mac OS 14.3.1 Apple M3 Pro
### 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?
I can not get the [basic example](https://developer.apple.com/metal/tensorflow-plugin/) showing use of GPU to work on MacOS w/ TF 2.16.1.
However, It performs as expected in TF2.15.0
### Standalone code to reproduce the issue
I have prepared 2 notebooks to show the behavior clearly, each using their own virtual environments.
1. GPU working [TensorFlow215.ipynb](https://github.com/msusol/jupyter-notebook-on-macos/blob/main/notebooks/TensorFlow215.ipynb)
2. Unexpected [TensorFlow216.ipynb](https://github.com/msusol/jupyter-notebook-on-macos/blob/main/notebooks/TensorFlow216.ipynb)
### Relevant log output
_No response_
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[
"Hi **@AaronEggert** ,\r\nSorry for the delay, This error typically occurs when the input shape of the loaded model does not match the expected input shape of the layers.\r\nHere i am providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/7bef42319504f21bd20690f05dbebaac/63853_2-16-1.ipynb) with updated code for your reference. Please go through it once.\r\n\r\nThank you!\r\n",
"Thank you for your help. \r\nWhen I define the input size while loading the model, I get the same Error.\r\n\r\ninput_shape = (224, 224, 3)\r\nmodel = tf.keras.models.load_model('model-v1.keras', compile=False, custom_objects={'input_shape': input_shape})\r\n\r\nTraceback (most recent call last):\r\n File \"C:\\Users\\Aaron-Desktop\\OneDrive\\Dokumente\\Meine Projekte\\Deep Learning\\Hund erkennung\\code\\train.py\", line 64, in <module>\r\n n_model = tf.keras.models.load_model('model-v1.keras', compile=False, custom_objects={'input_shape': input_shape})\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"C:\\Users\\Aaron-Desktop\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\saving\\saving_api.py\", line 176, in load_model\r\n return saving_lib.load_model(\r\n ^^^^^^^^^^^^^^^^^^^^^^\r\n File \"C:\\Users\\Aaron-Desktop\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\saving\\saving_lib.py\", line 155, in load_model\r\n model = deserialize_keras_object(\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"C:\\Users\\Aaron-Desktop\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\saving\\serialization_lib.py\", line 711, in deserialize_keras_object\r\n instance = cls.from_config(inner_config)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"C:\\Users\\Aaron-Desktop\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\models\\sequential.py\", line 336, in from_config\r\n model.add(layer)\r\n File \"C:\\Users\\Aaron-Desktop\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\models\\sequential.py\", line 117, in add\r\n self._maybe_rebuild()\r\n File \"C:\\Users\\Aaron-Desktop\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\models\\sequential.py\", line 136, in _maybe_rebuild\r\n self.build(input_shape)\r\n File \"C:\\Users\\Aaron-Desktop\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\layers\\layer.py\", line 224, in build_wrapper\r\n original_build_method(*args, **kwargs)\r\n File \"C:\\Users\\Aaron-Desktop\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\models\\sequential.py\", line 177, in build\r\n x = layer(x)\r\n ^^^^^^^^\r\n File \"C:\\Users\\Aaron-Desktop\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 123, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File \"C:\\Users\\Aaron-Desktop\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\layers\\input_spec.py\", line 156, in assert_input_compatibility\r\n raise ValueError(\r\nValueError: Layer 'dense' expected 1 input(s). Received 2 instead.",
"I have the exact same error. Although the platform is different (MacBook Pro w/ M3), the TF version is the same (2.16.1). My models are also really similar to the ones described here (CNN Backbone + GlobalAveragePooling2D + Dense). The proposed fix also did not seem to work properly.\r\n\r\nDo we have any updates on this issue?",
"I found a solution to that:\nYou need to define the input_size on loading the model\n\nThis Code whould help you:\n```\ninput_shape = (224, 224, 3)\nmodel = tf.keras.models.load_model('model.keras', compile=False, custom_objects={'input_shape': input_shape})\n```",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63853\">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/63853\">No</a>\n",
"Well, it doesn't seem to work properly. Am I missing something?\r\n\r\n```\r\n>>> tf.keras.models.load_model('model.keras', compile=False, custom_objects={'input_shape': (48, 48, 3)})\r\n...\r\nValueError: Layer 'dense' expected 1 input(s). Received 2 instead.\r\n```",
"I am having the shape issue on a Windows 10 machine with TF 2.16.1 and similar model. The proposed solution yields the same ValueError as before. ",
"Have exactly the same issue on Mac. Setting the input shape does't help",
"same here with tf 2.16.1， I've also tried tf2.15 with keras 3.0&3.1.1 but with no luck. My trained and saved .keras model （trained with 2.16.1）is not able to load with keras.saving.load_model()\r\n\r\n\r\n```\r\nbase_model = keras.applications.MobileNetV2(input_shape=(224,224,3),include_top=False,weights=\"imagenet\")\r\nmodel = keras.Sequential([\r\n base_model,\r\n keras.layers.GlobalAveragePooling2D(),\r\n keras.layers.Dropout(rate=0.5),\r\n keras.layers.Dense(8,activation='softmax'),\r\n])\r\n\r\nmodel.compile(...)\r\nmodel.fit(...)\r\n\r\nmodel.summary() #OK\r\n\r\nmodel.save(\"test_mobilenetv2_save.keras\")\r\n\r\nmodel_2 = keras.saving.load_model(\"test_mobilenetv2_save.keras\") #Error\r\nmodel_2 = keras.saving.load_model(\"test_mobilenetv2_save.keras\",compile=False, custom_objects={'input_shape': input_shape}) #Error\r\n\r\nmodel_2.summary() \r\n```\r\n\r\n\r\na walkround is to save the trained model with .weights.h5 and build model , load weights(but you need to retrain the model and save weights only which is sometimes not acceptable)\r\n\r\n`model.save_weights(\"mobilenetv2_weights_only.weights.h5\")`\r\n\r\nthen\r\n```\r\n\r\nbase_model2 = keras.applications.MobileNetV2(input_shape=(224,224,3),include_top=False,weights=\"imagenet\")\r\n\r\nbase_model2.trainable = True\r\n\r\nmodel2 = keras.Sequential([\r\n base_model2,\r\n keras.layers.GlobalAveragePooling2D(),\r\n keras.layers.Dropout(rate=0.5),\r\n keras.layers.Dense(8,activation='softmax'),\r\n])\r\n\r\nmodel2.compile(\r\n loss=loss,\r\n # optimizer=keras.optimizers.SGD(learning_rate=0.01),\r\n optimizer = optimizer,\r\n metrics=[\r\n metrics.CategoricalAccuracy(),\r\n metrics.TopKCategoricalAccuracy(k=3),\r\n ]\r\n)\r\n\r\nmodel2.summary()\r\n\r\nmodel2.predict(np.ones((32,224,224,3))) #must build model first or you not able to load weights\r\n\r\nmodel2.load_weights(\"mobilenetv2_weights_only.weights.h5\", skip_mismatch=False)\r\n```",
"@squallssck I think that we reached the same workaround. The only thing working for now is saving the weights and loading them to the model:\r\n\r\n```\r\nmodel = build_model()\r\nmodel.build(input_shape=(None, *INPUT_SHAPE))\r\nmodel.load_weights('model.weights.h5')\r\n```\r\n\r\nNot the optimal solution, but it works.",
"Any update on this issue?",
"есть ли фид бек по тому когда эта ошибка появилась , потому что раньше все работало отлично и никаких проблем не наблюдалось",
"Version 2.15.0.post1 is working fine for *.keras extensions. The model should also be trained on this version. It's working fine on 2.15.0.post1",
"Thanks @91Abdullah. It worked for me!",
"I am also having the shape issue on a Ubuntu 20.4 machine with TF 2.16.1 \r\nthis how i trained my model, here is when i load my model happens\r\n```\r\nimport pathlib\r\nimport pickle\r\nimport tensorflow as tf\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nfrom keras.preprocessing.image import ImageDataGenerator\r\n\r\n'''\r\nimport os\r\nfor dirname, _, filenames in os.walk('/kaggle/input'):\r\n for filename in filenames:\r\n print(os.path.join(dirname, filename))\r\n'''\r\nformat_1 = '/kaggle/input/flower-dataset/train'\r\nformat_2 = '/kaggle/input/flower-dataset/valid'\r\ntrain_dir = pathlib.Path(format_1)\r\nvalid_dir = pathlib.Path(format_2)\r\ntrain_count = len(list(train_dir.glob('*/*.jpg')))\r\nvalid_count = len(list(valid_dir.glob('*/*.jpg')))\r\n\r\nprint(train_count)\r\nprint(valid_count)\r\n\r\nCLASS_NAMES = np.array([item.name for item in train_dir.glob('*') if item.is_dir()])\r\n\r\nimage_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255)\r\nBATCH_SIZE = 32\r\nIMG_HEIGHT = 224\r\nIMG_WIDTH = 224\r\ntrain_data_gen = image_generator.flow_from_directory(directory=str(train_dir),\r\n batch_size=BATCH_SIZE,\r\n target_size=(IMG_HEIGHT, IMG_WIDTH),\r\n shuffle=True,\r\n classes=list(CLASS_NAMES))\r\nvalid_data_gen = image_generator.flow_from_directory(directory=str(valid_dir),\r\n batch_size=BATCH_SIZE,\r\n target_size=(IMG_HEIGHT, IMG_WIDTH),\r\n shuffle=True,\r\n classes=list(CLASS_NAMES))\r\nprint(CLASS_NAMES)\r\nResNet50 = tf.keras.applications.resnet_v2.ResNet50V2(include_top=False, weights='imagenet', input_shape=(224, 224, 3))\r\nResNet50.trainable = True\r\nmy_net = tf.keras.models.Sequential()\r\nmy_net.add(ResNet50)\r\nmy_net.add(tf.keras.layers.GlobalAveragePooling2D())\r\nmy_net.add(tf.keras.layers.Dense(100, activation='softmax'))\r\nmy_net.summary()\r\nmy_net.compile(optimizer=tf.keras.optimizers.Adamax(0.001),\r\n loss='categorical_crossentropy',\r\n metrics=['accuracy'])\r\nepoch_steps = train_count // BATCH_SIZE\r\nval_steps = valid_count // BATCH_SIZE\r\nmy_net.fit_generator(\r\n train_data_gen,\r\n steps_per_epoch=epoch_steps,\r\n epochs=5,\r\n validation_data=valid_data_gen,\r\n validation_steps=val_steps\r\n)\r\nmy_net.save('flower.h5')\r\n```\r\nhere is error\r\n```\r\nTraceback (most recent call last):\r\n File \"/root/keras-flask-deploy-webapp/app.py\", line 42, in <module>\r\n model = tf.keras.models.load_model(MODEL_PATH, compile=False, custom_objects={'input_shape': input_shape})\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py\", line 183, in load_model\r\n return legacy_h5_format.load_model_from_hdf5(filepath)\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/src/legacy/saving/legacy_h5_format.py\", line 133, in load_model_from_hdf5\r\n model = saving_utils.model_from_config(\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/src/legacy/saving/saving_utils.py\", line 85, in model_from_config\r\n return serialization.deserialize_keras_object(\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/src/legacy/saving/serialization.py\", line 495, in deserialize_keras_object\r\n deserialized_obj = cls.from_config(\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py\", line 335, in from_config\r\n model.add(layer)\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py\", line 116, in add\r\n self._maybe_rebuild()\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py\", line 135, in _maybe_rebuild\r\n self.build(input_shape)\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/src/layers/layer.py\", line 223, in build_wrapper\r\n original_build_method(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py\", line 176, in build\r\n x = layer(x)\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 122, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/src/layers/input_spec.py\", line 156, in assert_input_compatibility\r\n raise ValueError(\r\nValueError: Layer 'dense' expected 1 input(s). Received 2 instead.\r\n```",
"Also having the same issue. Windows 10 machine with TF 2.16.1. Here is how I trained my model:\r\n```\r\nimport tensorflow as tf\r\nfrom tensorflow.keras.applications import EfficientNetV2S\r\n\r\nV2S_model = EfficientNetV2S(weights='imagenet',\r\n include_top=False,\r\n input_shape=(224, 224, 3))\r\n\r\nfor layer in V2S_model.layers:\r\n layer.trainable = False\r\nfrom tensorflow.keras import layers\r\n\r\nimage_preprocess = tf.keras.Sequential([\r\n tf.keras.Input((None,None,3)),\r\n layers.Resizing(224,224, crop_to_aspect_ratio = True),\r\n layers.RandomFlip(\"horizontal_and_vertical\"),\r\n layers.RandomRotation(0.2)\r\n], name = \"image_aug\")\r\n\r\ntransfer_model = tf.keras.Sequential([\r\n tf.keras.Input((None,None,3)),\r\n image_preprocess,\r\n V2S_model,\r\n layers.GlobalAveragePooling2D(),\r\n layers.Dense(512, activation='relu'),\r\n layers.Dropout(0.2),\r\n layers.Dense(1, activation = 'sigmoid')\r\n])\r\n\r\ntransfer_model.summary()\r\n\r\nmetrics = [tf.keras.metrics.BinaryAccuracy(threshold=0.5, name='accuracy'),\r\n tf.keras.metrics.AUC(),\r\n tf.keras.metrics.TruePositives(),\r\n tf.keras.metrics.TrueNegatives(),\r\n tf.keras.metrics.FalsePositives(),\r\n tf.keras.metrics.FalseNegatives()\r\n ]\r\n\r\ntransfer_model.compile(loss=tf.keras.losses.BinaryCrossentropy(),\r\n optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\r\n metrics=metrics)\r\n\r\nBATCH_SIZE = 16\r\nIMAGE_SIZE = (224,224)\r\nSEED = 42\r\n\r\n# This sets up a training and validation set from our ../data/ directory\r\ntrain_dataset = tf.keras.utils.image_dataset_from_directory(\r\n '../data/',\r\n class_names = ['not_niblet','niblet'],\r\n color_mode='rgb',\r\n batch_size=BATCH_SIZE,\r\n image_size=IMAGE_SIZE,\r\n shuffle=True,\r\n validation_split=0.2,\r\n subset='training',\r\n seed=SEED)\r\n\r\n# This is the validation set. Notice `shuffle = FALSE` and `subset = validation`\r\nval_dataset = tf.keras.utils.image_dataset_from_directory(\r\n '../data/',\r\n class_names = ['not_niblet','niblet'],\r\n color_mode='rgb',\r\n batch_size=BATCH_SIZE,\r\n image_size=IMAGE_SIZE,\r\n shuffle=True,\r\n validation_split=0.2,\r\n subset='validation',\r\n seed=SEED)\r\n\r\nfrom tensorflow.keras.callbacks import EarlyStopping\r\n\r\nes = EarlyStopping(patience=15, monitor='val_loss')\r\n\r\nhistory = transfer_model.fit(train_dataset, epochs=100,\r\n validation_data=val_dataset,\r\n callbacks=[es])\r\n\r\ntransfer_model.save('../models/transfer_model_gt_2024_04_23.keras')\r\n\r\ntransfer_model_loaded = tf.keras.models.load_model('../models/transfer_model_gt_2024_04_23.keras')\r\n```\r\n\r\nand here is my error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\nCell In[16], line 2\r\n 1 input_shape = (None,None,3)\r\n----> 2 transfer_model_loaded = tf.keras.models.load_model('../models/transfer_model_gt_2024_04_23.keras', compile=False, custom_objects={'input_shape': input_shape})\r\n\r\nFile [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\saving\\saving_api.py:176](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/saving/saving_api.py#line=175), in load_model(filepath, custom_objects, compile, safe_mode)\r\n 173 is_keras_zip = True\r\n 175 if is_keras_zip:\r\n--> 176 return saving_lib.load_model(\r\n 177 filepath,\r\n 178 custom_objects=custom_objects,\r\n 179 compile=compile,\r\n 180 safe_mode=safe_mode,\r\n 181 )\r\n 182 if str(filepath).endswith((\".h5\", \".hdf5\")):\r\n 183 return legacy_h5_format.load_model_from_hdf5(filepath)\r\n\r\nFile [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\saving\\saving_lib.py:152](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/saving/saving_lib.py#line=151), in load_model(filepath, custom_objects, compile, safe_mode)\r\n 147 raise ValueError(\r\n 148 \"Invalid filename: expected a `.keras` extension. \"\r\n 149 f\"Received: filepath={filepath}\"\r\n 150 )\r\n 151 with open(filepath, \"rb\") as f:\r\n--> 152 return _load_model_from_fileobj(\r\n 153 f, custom_objects, compile, safe_mode\r\n 154 )\r\n\r\nFile [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\saving\\saving_lib.py:170](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/saving/saving_lib.py#line=169), in _load_model_from_fileobj(fileobj, custom_objects, compile, safe_mode)\r\n 168 # Construct the model from the configuration file in the archive.\r\n 169 with ObjectSharingScope():\r\n--> 170 model = deserialize_keras_object(\r\n 171 config_dict, custom_objects, safe_mode=safe_mode\r\n 172 )\r\n 174 all_filenames = zf.namelist()\r\n 175 if _VARS_FNAME + \".h5\" in all_filenames:\r\n\r\nFile [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\saving\\serialization_lib.py:711](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/saving/serialization_lib.py#line=710), in deserialize_keras_object(config, custom_objects, safe_mode, **kwargs)\r\n 709 with custom_obj_scope, safe_mode_scope:\r\n 710 try:\r\n--> 711 instance = cls.from_config(inner_config)\r\n 712 except TypeError as e:\r\n 713 raise TypeError(\r\n 714 f\"{cls} could not be deserialized properly. Please\"\r\n 715 \" ensure that components that are Python object\"\r\n (...)\r\n 719 f\"\\n\\nconfig={config}[.\\n\\nException](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/n/nException) encountered: {e}\"\r\n 720 )\r\n\r\nFile [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\models\\sequential.py:339](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/models/sequential.py#line=338), in Sequential.from_config(cls, config, custom_objects)\r\n 334 else:\r\n 335 layer = serialization_lib.deserialize_keras_object(\r\n 336 layer_config,\r\n 337 custom_objects=custom_objects,\r\n 338 )\r\n--> 339 model.add(layer)\r\n 340 if (\r\n 341 not model._functional\r\n 342 and build_input_shape\r\n 343 and isinstance(build_input_shape, (tuple, list))\r\n 344 ):\r\n 345 model.build(build_input_shape)\r\n\r\nFile [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\models\\sequential.py:120](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/models/sequential.py#line=119), in Sequential.add(self, layer, rebuild)\r\n 118 self._layers.append(layer)\r\n 119 if rebuild:\r\n--> 120 self._maybe_rebuild()\r\n 121 else:\r\n 122 self.built = False\r\n\r\nFile [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\models\\sequential.py:139](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/models/sequential.py#line=138), in Sequential._maybe_rebuild(self)\r\n 137 if isinstance(self._layers[0], InputLayer) and len(self._layers) > 1:\r\n 138 input_shape = self._layers[0].batch_shape\r\n--> 139 self.build(input_shape)\r\n\r\nFile [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\layers\\layer.py:222](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/layers/layer.py#line=221), in Layer.__new__.<locals>.build_wrapper(*args, **kwargs)\r\n 219 @wraps(original_build_method)\r\n 220 def build_wrapper(*args, **kwargs):\r\n 221 with obj._open_name_scope():\r\n--> 222 original_build_method(*args, **kwargs)\r\n 223 # Record build config.\r\n 224 signature = inspect.signature(original_build_method)\r\n\r\nFile [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\models\\sequential.py:180](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/models/sequential.py#line=179), in Sequential.build(self, input_shape)\r\n 178 for layer in self._layers[1:]:\r\n 179 try:\r\n--> 180 x = layer(x)\r\n 181 except NotImplementedError:\r\n 182 # Can happen if shape inference is not implemented.\r\n 183 # TODO: consider reverting inbound nodes on layers processed.\r\n 184 return\r\n\r\nFile [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/utils/traceback_utils.py#line=121), in filter_traceback.<locals>.error_handler(*args, **kwargs)\r\n 119 filtered_tb = _process_traceback_frames(e.__traceback__)\r\n 120 # To get the full stack trace, call:\r\n 121 # `keras.config.disable_traceback_filtering()`\r\n--> 122 raise e.with_traceback(filtered_tb) from None\r\n 123 finally:\r\n 124 del filtered_tb\r\n\r\nFile [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\layers\\input_spec.py:156](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/layers/input_spec.py#line=155), in assert_input_compatibility(input_spec, inputs, layer_name)\r\n 154 inputs = tree.flatten(inputs)\r\n 155 if len(input_spec) != len(inputs):\r\n--> 156 raise ValueError(\r\n 157 f\"Layer '{layer_name}' expected {len(input_spec)} input(s). \"\r\n 158 f\"Received {len(inputs)} instead.\"\r\n 159 )\r\n 160 for x in inputs:\r\n 161 # Having a shape/dtype is the only commonality of the various\r\n 162 # tensor-like objects that may be passed. The most common kind of\r\n 163 # invalid type we are guarding for is a Layer instance (Functional API),\r\n 164 # which does not have a `shape` attribute.\r\n 165 if not hasattr(x, \"shape\"):\r\n\r\nValueError: Layer 'dense' expected 1 input(s). Received 2 instead.\r\n```",
"@gmtanner-cord try to use version 2.15 it should work as expected. ",
"i have solved this problem by lower my tensorflow version to 2.12.0, maybe u can have a try\r\n\r\n\r\n\r\n\r\n------------------ 原始邮件 ------------------\r\n发件人: ***@***.***>; \r\n发送时间: 2024年4月24日(星期三) 上午10:10\r\n收件人: ***@***.***>; \r\n抄送: ***@***.***>; ***@***.***>; \r\n主题: Re: [tensorflow/tensorflow] "ValueError: Layer 'dense' expected 1 input(s). Received 2 instead" on tf.keras.models.load_model (Issue #63853)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \r\nAlso having the same issue. Windows 10 machine with TF 2.16.1. Here is how I trained my model:\r\n import tensorflow as tf from tensorflow.keras.applications import EfficientNetV2S V2S_model = EfficientNetV2S(weights='imagenet', include_top=False, input_shape=(224, 224, 3)) for layer in V2S_model.layers: layer.trainable = False from tensorflow.keras import layers image_preprocess = tf.keras.Sequential([ tf.keras.Input((None,None,3)), layers.Resizing(224,224, crop_to_aspect_ratio = True), layers.RandomFlip(\"horizontal_and_vertical\"), layers.RandomRotation(0.2) ], name = \"image_aug\") transfer_model = tf.keras.Sequential([ tf.keras.Input((None,None,3)), image_preprocess, V2S_model, layers.GlobalAveragePooling2D(), layers.Dense(512, activation='relu'), layers.Dropout(0.2), layers.Dense(1, activation = 'sigmoid') ]) transfer_model.summary() metrics = [tf.keras.metrics.BinaryAccuracy(threshold=0.5, name='accuracy'), tf.keras.metrics.AUC(), tf.keras.metrics.TruePositives(), tf.keras.metrics.TrueNegatives(), tf.keras.metrics.FalsePositives(), tf.keras.metrics.FalseNegatives() ] transfer_model.compile(loss=tf.keras.losses.BinaryCrossentropy(), optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), metrics=metrics) BATCH_SIZE = 16 IMAGE_SIZE = (224,224) SEED = 42 # This sets up a training and validation set from our ../data/ directory train_dataset = tf.keras.utils.image_dataset_from_directory( '../data/', class_names = ['not_niblet','niblet'], color_mode='rgb', batch_size=BATCH_SIZE, image_size=IMAGE_SIZE, shuffle=True, validation_split=0.2, subset='training', seed=SEED) # This is the validation set. Notice `shuffle = FALSE` and `subset = validation` val_dataset = tf.keras.utils.image_dataset_from_directory( '../data/', class_names = ['not_niblet','niblet'], color_mode='rgb', batch_size=BATCH_SIZE, image_size=IMAGE_SIZE, shuffle=True, validation_split=0.2, subset='validation', seed=SEED) from tensorflow.keras.callbacks import EarlyStopping es = EarlyStopping(patience=15, monitor='val_loss') history = transfer_model.fit(train_dataset, epochs=100, validation_data=val_dataset, callbacks=[es]) transfer_model.save('../models/transfer_model_gt_2024_04_23.keras') transfer_model_loaded = tf.keras.models.load_model('../models/transfer_model_gt_2024_04_23.keras') \r\nand here is my error:\r\n --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[16], line 2 1 input_shape = (None,None,3) ----> 2 transfer_model_loaded = tf.keras.models.load_model('../models/transfer_model_gt_2024_04_23.keras', compile=False, custom_objects={'input_shape': input_shape}) File [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\saving\\saving_api.py:176](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/saving/saving_api.py#line=175), in load_model(filepath, custom_objects, compile, safe_mode) 173 is_keras_zip = True 175 if is_keras_zip: --> 176 return saving_lib.load_model( 177 filepath, 178 custom_objects=custom_objects, 179 compile=compile, 180 safe_mode=safe_mode, 181 ) 182 if str(filepath).endswith((\".h5\", \".hdf5\")): 183 return legacy_h5_format.load_model_from_hdf5(filepath) File [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\saving\\saving_lib.py:152](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/saving/saving_lib.py#line=151), in load_model(filepath, custom_objects, compile, safe_mode) 147 raise ValueError( 148 \"Invalid filename: expected a `.keras` extension. \" 149 f\"Received: filepath={filepath}\" 150 ) 151 with open(filepath, \"rb\") as f: --> 152 return _load_model_from_fileobj( 153 f, custom_objects, compile, safe_mode 154 ) File [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\saving\\saving_lib.py:170](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/saving/saving_lib.py#line=169), in _load_model_from_fileobj(fileobj, custom_objects, compile, safe_mode) 168 # Construct the model from the configuration file in the archive. 169 with ObjectSharingScope(): --> 170 model = deserialize_keras_object( 171 config_dict, custom_objects, safe_mode=safe_mode 172 ) 174 all_filenames = zf.namelist() 175 if _VARS_FNAME + \".h5\" in all_filenames: File [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\saving\\serialization_lib.py:711](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/saving/serialization_lib.py#line=710), in deserialize_keras_object(config, custom_objects, safe_mode, **kwargs) 709 with custom_obj_scope, safe_mode_scope: 710 try: --> 711 instance = cls.from_config(inner_config) 712 except TypeError as e: 713 raise TypeError( 714 f\"{cls} could not be deserialized properly. Please\" 715 \" ensure that components that are Python object\" (...) 719 f\"\\n\\nconfig={config}[.\\n\\nException](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/n/nException) encountered: {e}\" 720 ) File [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\models\\sequential.py:339](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/models/sequential.py#line=338), in Sequential.from_config(cls, config, custom_objects) 334 else: 335 layer = serialization_lib.deserialize_keras_object( 336 layer_config, 337 custom_objects=custom_objects, 338 ) --> 339 model.add(layer) 340 if ( 341 not model._functional 342 and build_input_shape 343 and isinstance(build_input_shape, (tuple, list)) 344 ): 345 model.build(build_input_shape) File [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\models\\sequential.py:120](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/models/sequential.py#line=119), in Sequential.add(self, layer, rebuild) 118 self._layers.append(layer) 119 if rebuild: --> 120 self._maybe_rebuild() 121 else: 122 self.built = False File [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\models\\sequential.py:139](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/models/sequential.py#line=138), in Sequential._maybe_rebuild(self) 137 if isinstance(self._layers[0], InputLayer) and len(self._layers) > 1: 138 input_shape = self._layers[0].batch_shape --> 139 self.build(input_shape) File [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\layers\\layer.py:222](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/layers/layer.py#line=221), in Layer.__new__.<locals>.build_wrapper(*args, **kwargs) 219 @wraps(original_build_method) 220 def build_wrapper(*args, **kwargs): 221 with obj._open_name_scope(): --> 222 original_build_method(*args, **kwargs) 223 # Record build config. 224 signature = inspect.signature(original_build_method) File [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\models\\sequential.py:180](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/models/sequential.py#line=179), in Sequential.build(self, input_shape) 178 for layer in self._layers[1:]: 179 try: --> 180 x = layer(x) 181 except NotImplementedError: 182 # Can happen if shape inference is not implemented. 183 # TODO: consider reverting inbound nodes on layers processed. 184 return File [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/utils/traceback_utils.py#line=121), in filter_traceback.<locals>.error_handler(*args, **kwargs) 119 filtered_tb = _process_traceback_frames(e.__traceback__) 120 # To get the full stack trace, call: 121 # `keras.config.disable_traceback_filtering()` --> 122 raise e.with_traceback(filtered_tb) from None 123 finally: 124 del filtered_tb File [~\\anaconda3\\envs\\CS380_final\\Lib\\site-packages\\keras\\src\\layers\\input_spec.py:156](http://localhost:8888/lab/tree/Documents/GitHub/CSC380/scripts/~/anaconda3/envs/CS380_final/Lib/site-packages/keras/src/layers/input_spec.py#line=155), in assert_input_compatibility(input_spec, inputs, layer_name) 154 inputs = tree.flatten(inputs) 155 if len(input_spec) != len(inputs): --> 156 raise ValueError( 157 f\"Layer '{layer_name}' expected {len(input_spec)} input(s). \" 158 f\"Received {len(inputs)} instead.\" 159 ) 160 for x in inputs: 161 # Having a shape/dtype is the only commonality of the various 162 # tensor-like objects that may be passed. The most common kind of 163 # invalid type we are guarding for is a Layer instance (Functional API), 164 # which does not have a `shape` attribute. 165 if not hasattr(x, \"shape\"): ValueError: Layer 'dense' expected 1 input(s). Received 2 instead. \r\n—\r\nReply to this email directly, view it on GitHub, or unsubscribe.\r\nYou are receiving this because you commented.Message ID: ***@***.***>",
"> الإصدار 2.15.0.post1 يعمل بشكل جيد مع امتدادات *.keras. يجب أيضًا تدريب النموذج على هذا الإصدار. إنه يعمل بشكل جيد على 2.15.0.post1\r\n\r\nThank you it wored ."
] | 2024-03-17T20:17:06 | 2024-05-23T08:11:15 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
v2.16.0-rc0-18-g5bc9d26649c 2.16.1
### Custom code
Yes
### OS platform and distribution
Windows 11 (KB5035853)
### Mobile device
_No response_
### Python version
3.11.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I want to load a saved model directly after training. But when it executes tf.keras.models.load_model it says `ValueError: Layer 'dense' expected 1 input(s). Received 2 instead.`
### Standalone code to reproduce the issue
```shell
base_model = tf.keras.applications.ResNet50(include_top=False, weights='imagenet')
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
output_layer = tf.keras.layers.Dense(1)
model = tf.keras.Sequential([
base_model,
global_average_layer,
output_layer
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
history = model.fit(train_dataset,
epochs=epochs,
validation_data=val_dataset)
loss, accuracy = model.evaluate(val_dataset)
# Save the model
model.save('model-v1.keras')
model.summary()
n_model = tf.keras.models.load_model('model-v1.keras')
n_model.summary()
```
### Relevant log output
```shell
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type) ┃ Output Shape ┃ Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ resnet50 (Functional) │ (None, 12, 12, 2048) │ 23,587,712 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ global_average_pooling2d │ (None, 2048) │ 0 │
│ (GlobalAveragePooling2D) │ │ │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense (Dense) │ (None, 1) │ 2,049 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 70,663,045 (269.56 MB)
Trainable params: 23,536,641 (89.79 MB)
Non-trainable params: 53,120 (207.50 KB)
Optimizer params: 47,073,284 (179.57 MB)
Traceback (most recent call last):
File "C:\Users\Aaron-Desktop\OneDrive\Dokumente\Meine Projekte\Deep Learning\Hund erkennung\code\train.py", line 63, in <module>
n_model = tf.keras.models.load_model('model-v1.keras')
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\saving\saving_api.py", line 176, in load_model
return saving_lib.load_model(
^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\saving\saving_lib.py", line 155, in load_model
model = deserialize_keras_object(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\saving\serialization_lib.py", line 711, in deserialize_keras_object
instance = cls.from_config(inner_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\models\sequential.py", line 336, in from_config
model.add(layer)
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\models\sequential.py", line 117, in add
self._maybe_rebuild()
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\models\sequential.py", line 136, in _maybe_rebuild
self.build(input_shape)
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\layers\layer.py", line 224, in build_wrapper
original_build_method(*args, **kwargs)
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\models\sequential.py", line 177, in build
x = layer(x)
^^^^^^^^
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\utils\traceback_utils.py", line 123, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\Aaron-Desktop\AppData\Roaming\Python\Python311\site-packages\keras\src\layers\input_spec.py", line 156, in assert_input_compatibility
raise ValueError(
ValueError: Layer 'dense' expected 1 input(s). Received 2 instead.
```
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I_kwDOArmXAs6ClS8b
| 63,852 |
tf.linalg.svd([inf, -inf]) throws internal error logs to the console, and generates inconsistent results on different dtypes.
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[
"@QuantumCoder4,\r\nLooks like this is a duplicate of the issue [#61955](https://github.com/tensorflow/tensorflow/issues/61955). Could you please close this issue, since it is already being tracked there? Thank you!\r\n",
"Thanks for reply. I have checked the issue #61955. Although the problem seems to be not exactly the same, I will be happy if this issue can also be tracked there.",
"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/63852\">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/63852\">No</a>\n"
] | 2024-03-17T18:58:12 | 2024-03-18T13:13:14 | 2024-03-18T13:13:10 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15.0
### Custom code
Yes
### OS platform and distribution
Linux-5.14.0-362.18.1.el9_3.x86_64-x86_64-with-glibc2.34
### Mobile device
AlmaLinux 9
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
An internal error log was thrown to the console.
Also, we found that API generates inconsistent results on different input dtypes.
The same error log has appeared in #56204, but I am not sure whether we are talking about the same problem.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
input = tf.constant([[np.inf, -np.inf]], dtype=tf.complex64)
out = tf.linalg.svd(input, full_matrices=False, compute_uv=False) # 0
input = tf.constant([[np.inf, -np.inf]], dtype=tf.float64)
out = tf.linalg.svd(input, full_matrices=False, compute_uv=False) # nan
```
### Relevant log output
```shell
2024-03-18 02:28:14.593059: E ./tensorflow/core/kernels/linalg/svd_op_impl.h:110] Eigen::BDCSVD failed with error code 3
```
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PR_kwDOArmXAs5p3fwb
| 63,851 |
Update README.md Badges
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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/63851/checks?check_run_id=22758093053) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"Hi @eforce-tech Can you please sign CLA. Thank you!"
] | 2024-03-17T18:16:40 | 2024-03-22T22:05:27 | 2024-03-18T13:32:02 |
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Update the README.md badges style and add icons where able. Creates a more modern look.
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update cpuinfo
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[
"Hi, I'm sorry for the ping here, but I was wondering if there're any estimated date for this to be merged? I understand that the risc-v architecture is not in the top tier of support, but as this should be a fairly small PR and would help others to build Tensorflow Lite for risc-v devices. :)",
"Also I'll happy to bump this to the latest commit ([3c8b153](https://github.com/pytorch/cpuinfo/tree/3c8b1533ac03dd6531ab6e7b9245d488f13a82a5)) of `pytorch/cpuinfo` if you'd like me to do so. 😃",
"> Hi, I'm sorry for the ping here, but I was wondering if there're any estimated date for this to be merged? I understand that the risc-v architecture is not in the top tier of support, but as this should be a fairly small PR and would help others to build Tensorflow Lite for risc-v devices. :)\r\n\r\nHi @cocoa-xu Sorry for the delay, we are working on this PR internally. Thank you for your contribution! ",
"> > Hi, I'm sorry for the ping here, but I was wondering if there're any estimated date for this to be merged? I understand that the risc-v architecture is not in the top tier of support, but as this should be a fairly small PR and would help others to build Tensorflow Lite for risc-v devices. :)\r\n> \r\n> Hi @cocoa-xu Sorry for the delay, we are working on this PR internally. Thank you for your contribution!\r\n\r\nGot it! Thanks for your kind reply 😃"
] | 2024-03-17T13:55:25 | 2024-06-05T08:15:13 | null |
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Update cpuinfo so that TensorFlow builds for riscv64 again, as PR https://github.com/pytorch/cpuinfo/pull/204 broke it for including the `<sys/hwprobe.h>` header, which only exists in the RISC-V Android NDK. It was fixed in https://github.com/pytorch/cpuinfo/pull/215.
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Adding TensorFlow Hub KerasLayer to Sequential Model Raises ValueError
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[
"After investigating the code, I found a potential cause of the issue in `tensorflow_hub/keras_layer.py` lines 26-31:\r\n```python\r\n# Use Keras 2.\r\nversion_fn = getattr(tf.keras, \"version\", None)\r\nif version_fn and version_fn().startswith(\"3.\"):\r\n import tf_keras as keras\r\nelse:\r\n keras = tf.keras\r\n```\r\nDepending on the Keras version, the module might either import `tf_keras` or directly use `tf.keras`, the former causes the `isinstance(layer, Layer)` check in `Sequential.add` to return `False` for `hub.KerasLayer`, even though it inherits from `keras.layers.Layer`",
"Hi @ruddyscent ,\r\n\r\nTFHub has dependency on `tf_keras` package (i.e Keras2) as per the setup.py of TFHub.\r\n\r\nhttps://github.com/tensorflow/hub/blob/ff72e25fef44bd67d5c14fb5328aa44e303e3404/tensorflow_hub/pip_package/setup.py#L31\r\n\r\nSince TF2.16 comes with Keras3 the problem arises. As a workaround, you can install tf_keras package and set environment variable TF_USE_LEGACY_KERAS=1 to ensure Keras2 will be used with tf.keras. ",
"> After investigating the code, I found a potential cause of the issue in `tensorflow_hub/keras_layer.py` lines 26-31:\r\n> \r\n> ```python\r\n> # Use Keras 2.\r\n> version_fn = getattr(tf.keras, \"version\", None)\r\n> if version_fn and version_fn().startswith(\"3.\"):\r\n> import tf_keras as keras\r\n> else:\r\n> keras = tf.keras\r\n> ```\r\n> \r\n> Depending on the Keras version, the module might either import `tf_keras` or directly use `tf.keras`, the latter causes the `isinstance(layer, Layer)` check in `Sequential.add` to return `False` for `hub.KerasLayer`, even though it inherits from `keras.layers.Layer`\r\n\r\nHi, When I tried with TF2.16v on Colab environment the error stack seems generated from the _ensure_keras_2_importable() function from `tensorflow_hub/__init__.py` as per attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/5585bf7974198ca45bacdb9e84db91d4/63849_tf2-16.ipynb).",
"> ```python\r\n> # Use Keras 2.\r\n> version_fn = getattr(tf.keras, \"version\", None)\r\n> if version_fn and version_fn().startswith(\"3.\"):\r\n> import tf_keras as keras\r\n> else:\r\n> keras = tf.keras\r\n> ```\r\n\r\nEven this code seems confusing to me. If version_fn results in Keras3 then it tries to import tf_keras to use Keras2 else it assumes Keras2 is alreday there(i.e TF<=2.15v) hence it takes keras=tf.keras. \r\n\r\nBut `version_fn = getattr(tf.keras, \"version\", None)` returns None making this to use tf.keras (i.e Keras3) which ic not compatible with TF_Hub.\r\n",
"> > ```python\r\n> > # Use Keras 2.\r\n> > version_fn = getattr(tf.keras, \"version\", None)\r\n> > if version_fn and version_fn().startswith(\"3.\"):\r\n> > import tf_keras as keras\r\n> > else:\r\n> > keras = tf.keras\r\n> > ```\r\n> \r\n> Even this code seems confusing to me. If version_fn results in Keras3 then it tries to import tf_keras to use Keras2 else it assumes Keras2 is alreday there(i.e TF<=2.15v) hence it takes keras=tf.keras.\r\n> \r\n> But `version_fn = getattr(tf.keras, \"version\", None)` returns None making this to use tf.keras (i.e Keras3) which ic not compatible with TF_Hub.\r\n\r\nActually, `version_fn` is not None, that code works as intended, which is to use Keras 2\r\n\r\n`tf.keras.Sequential` is on Keras 3. and `hub.KerasLayer` is on Keras 2, so I believe due to the version difference, it causes `isinstance(layer, Layer)` to fail and return False",
"> ```python\r\n> # Use Keras 2.\r\n> version_fn = getattr(tf.keras, \"version\", None)\r\n> if version_fn and version_fn().startswith(\"3.\"):\r\n> import tf_keras as keras\r\n> ```\r\n\r\nFrom the code above if version_fn is not None and if it starts with \"3\" (which means Keras 3 found) in that case it is importing tf_keras package as keras. Please note that tf_keras package is for Keras2. This indicates TFHub supports only Keras2 package.\r\n\r\nThe else part it assumes that version_fn doesn't starts with \"3\" which case it assumes Keras2 installed with TF package and it marks keras=tf.keras. But for any case if version_fn becomes None and if Keras3 installed with TF then tf.keras will become Keras3 which might a problem. \r\n\r\n@Aloqeely , Could you please import TF2.16 and confirm what will be the version_fn output ?",
"Here you go\r\n```python\r\nimport tensorflow as tf\r\n\r\nversion_fn = getattr(tf.keras, \"version\", None)\r\nprint(\"TF Version: \" + tf.__version__)\r\nprint(\"TF Keras Version: \" + version_fn())\r\n```\r\nOutput:\r\n```\r\nTF Version: 2.16.1\r\nTF Keras Version: 3.0.5\r\n```\r\n",
"> Hi @ruddyscent ,\r\n> \r\n> TFHub has dependency on `tf_keras` package (i.e Keras2) as per the setup.py of TFHub.\r\n> \r\n> https://github.com/tensorflow/hub/blob/ff72e25fef44bd67d5c14fb5328aa44e303e3404/tensorflow_hub/pip_package/setup.py#L31\r\n> \r\n> Since TF2.16 comes with Keras3 the problem arises. As a workaround, you can install tf_keras package and set environment variable TF_USE_LEGACY_KERAS=1 to ensure Keras2 will be used with tf.keras.\r\n\r\nIf this workaround fixes ruddyscent's problem, then I think we should mark this issue as resolved, because the problem is tensorflow hub not supporting Keras 3, so it is not relevant to this repository.",
"> 3.0.5\r\n\r\nThis means when tf.keras version is 3.x then Hub suggesting to import tf_keras as keras which is a Keras2 package. For that we should install with tf_keras package using `pip install tf_keras`. But from the error log the package is having keras/src which seems to be from Keras3 package for me.",
"> This means when tf.keras version is 3.x then Hub suggesting to import tf_keras as keras which is a Keras2 package. For that we should install with tf_keras package using `pip install tf_keras`. But from the error log the package is having keras/src which seems to be from Keras3 package for me.\r\n\r\nYep, hub does that.\r\nThe error log is caused by `tf.keras.Sequential` (Keras 3, hence the error is produced from keras/src) after it received a `hub.KerasLayer` (Keras 2)",
"win10, no gpu, no colab\r\n\r\ntensorflow_hub를 사용하시려면 다음과 같이 해보세요\r\nimport tensorflow as tf\r\nimport tensorflow_hub as hub\r\nimport tf_keras as keras \"\"\"tensorflow_hub 설치시 자동으로 설치됩니다.\"\r\n\r\n...\r\n...\r\nmodel = keras.Sequential([ \"\"\"\"tf.keras --> keras\"\"\"\r\n hub,KerasLayer(url, input_shape=(size, size, 3)\r\n...\r\n...\r\nimgfile = keras.utils.get_file('image.jpg', 'https://..........') \"\"\"\"tf.keras --> keras\"\"\"\r\n작동이 잘됩니다.\r\n",
"Thank you, @sj-yoon, for your assistance. \r\n\r\nThe solution provided by @SuryanarayanaY works well for my situation.",
"I had the same issue, and I could fix it with the solution provided by @SuryanarayanaY. Thanks \r\n\r\nOthers can check my notebook for steps to fix it at this [page](https://www.kaggle.com/code/alishohadaee/dl-transfer-learning-on-images)."
] | 2024-03-17T11:08:55 | 2024-05-03T18:35:13 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
Ubuntu 22.04.3 LTS
### Mobile device
_No response_
### Python version
Python 3.11.0rc1
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.3.0
### GPU model and memory
_No response_
### Current behavior?
I can execute the following code without any issues in TensorFlow 2.15.0 and TensorFlow Hub 1.16.1. However, when I upgrade the TensorFlow version to 2.16.0 or above, I encounter an error stating that `KerasLayer` cannot be added to the Sequential model.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import tensorflow_hub as hub
image_size = 224
URL = "https://tfhub.dev/tensorflow/efficientnet/b0/feature-vector/1"
model = tf.keras.Sequential([
hub.KerasLayer(URL, input_shape=(image_size, image_size, 3))
])
```
### Relevant log output
```shell
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[29], line 1
----> 1 model = tf.keras.Sequential([
2 feature_extractor,
3 tf.keras.layers.Dense(2, activation = 'softmax')
4 ])
6 model.build([None, image_size, image_size, 3])
7 model.summary()
File /usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py:70, in Sequential.__init__(self, layers, trainable, name)
68 if layers:
69 for layer in layers:
---> 70 self.add(layer, rebuild=False)
71 self._maybe_rebuild()
File /usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py:92, in Sequential.add(self, layer, rebuild)
90 layer = origin_layer
91 if not isinstance(layer, Layer):
---> 92 raise ValueError(
93 "Only instances of `keras.Layer` can be "
94 f"added to a Sequential model. Received: {layer} "
95 f"(of type {type(layer)})"
96 )
97 if not self._is_layer_name_unique(layer):
...
101 "the name of a layer in this model. Update the `name` argument "
102 "to pass a unique name."
103 )
ValueError: Only instances of `keras.Layer` can be added to a Sequential model. Received: <tensorflow_hub.keras_layer.KerasLayer object at 0x7a4ac7e30f40> (of type <class 'tensorflow_hub.keras_layer.KerasLayer'>)
```
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I_kwDOArmXAs6CkQ6D
| 63,848 |
tf.math.angle(nan) returns inconsistent results on dtype float64 / complex128.
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[
"import tensorflow as tf\r\nimport numpy as np\r\n\r\ndef angle_with_nan_handling(input_tensor):\r\n # Check if input_tensor contains NaN values\r\n contains_nan = tf.reduce_any(tf.math.is_nan(input_tensor))\r\n \r\n # If input_tensor contains NaN values, return NaN\r\n if contains_nan:\r\n return tf.fill(tf.shape(input_tensor), tf.constant(np.nan, dtype=tf.float64))\r\n else:\r\n # Calculate angle for non-NaN values\r\n return tf.math.angle(input_tensor)\r\n\r\n# Test with float64 input\r\ninput_float64 = tf.constant([np.nan], dtype=tf.float64)\r\nout_float64 = angle_with_nan_handling(input_float64)\r\nprint(\"Output for float64 input:\", out_float64.numpy()) # Output: [nan]\r\n\r\n# Test with complex128 input\r\ninput_complex128 = tf.constant([np.nan], dtype=tf.complex128)\r\nout_complex128 = angle_with_nan_handling(input_complex128)\r\nprint(\"Output for complex128 input:\", out_complex128.numpy()) # Output: [nan]\r\n",
"@SuryanarayanaY I was able to replicate the issue [here](https://colab.research.google.com/gist/sushreebarsa/641b0babdb1ca568e2c70ac1ee7b83e3/63848.ipynb).\r\nThank you!"
] | 2024-03-17T08:26:25 | 2024-03-22T05:25:27 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15.0
### Custom code
Yes
### OS platform and distribution
Linux-5.14.0-362.18.1.el9_3.x86_64-x86_64-with-glibc2.34
### Mobile device
AlmaLinux 9
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
tf.math.angle gives inconsistent outputs on NaN value: if tensor dtype=complex128 then NaN, while for dtype=float64 it is 0.
Although doc states that for any real input the output will be zero, I would like to list some points to argue.
1. It does not make sense for ending up with a regular value for calculating NaN.
2. The nature of NaN should not change, regardless of the dtype of the tensor it's staying in.
3. Such behavior may let NaN error to escape, causing trouble in debugging.
Expected behavior: the result is NaN when input is NaN.
### Standalone code to reproduce the issue
```shell
input = tf.constant([np.nan], dtype=tf.float64)
out = tf.math.angle(input) # tf.Tensor([0.], shape=(1,), dtype=float64)
input = tf.constant([np.nan], dtype=tf.complex128)
out = tf.math.angle(input) # tf.Tensor([nan], shape=(1,), dtype=float64)
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5p2kVf
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Implement deferring `stablehlo.transpose` ops attached to activations of `stablehlo.add`.
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[] | 2024-03-17T06:29:30 | 2024-03-20T13:33:08 | 2024-03-20T13:33:08 |
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Implement deferring `stablehlo.transpose` ops attached to activations of `stablehlo.add`.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10626 from Intel-tensorflow:akhil/bf16_softmax d030100ea87923582aa64cdcf0fac1464051288c
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I_kwDOArmXAs6CjxqN
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GPU libraries not found in Google Colab Pro
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[
"Hi **@KJGithub2021** ,\r\nSorry for the delay, I will give some solutions to your issue.\r\n1. First Thing you need to change runtime type 'CPU' to 'T4-GPU'.\r\n2. After that you need to install 'CUDA' version by using below code.\r\n```\r\n!pip install tensorflow[and-cuda]\r\n```\r\nAfter these 2 steps gpu will work on your colab. Here i am providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/b2c2512012bae61806d0914222cecce6/63846_gpu_.ipynb) for your reference.\r\n\r\nThank you!\r\n",
"Thanks for the response. My issue was resolved, however GPU is not available as a runtime now. Although I just purchased 100 compute units to make sure of its availability but still I get the attached message! whats going on ??\r\n\r\nAnd my purchased compute units will slowly go down the drain even if u-used. You guys are stealing my money. I am a colab pro user with 100 compute units and I cant still use a T4-GPU !!\r\n\r\n",
"Hi **@KJGithub2021** ,\r\n\r\nThanks for your response. As you mentioned issue got resolved. Please feel free to close the issue. \r\nAnd regarding your queries you can post on TF forum to get the answers as on github we provide solution on issues only.\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/63846\">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/63846\">No</a>\n"
] | 2024-03-17T01:02:03 | 2024-04-17T01:47:57 | 2024-04-17T01:47:48 |
NONE
| null | null | null |
Hi,
I am a colab pro user and have never faced the following issue before today after enabling a GPU, although the required tensorflow and related libraries (tensorflow-gpu) are already installed in colab pro using pip install:
2024-03-17 00:42:56.263947: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] 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...
I face the same issue while using T4-GPU on free account as well.
Can you please help ?
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Fix readability issues in `quantization_driver.h/cc`.
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Fix readability issues in `quantization_driver.h/cc`.
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[xla:hlo] Do not compute channel dependencies when building DFS reachability
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[xla:hlo] Do not compute channel dependencies when building DFS reachability
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Use ranged for-loop instead of while in `QuantizationDriver`.
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Use ranged for-loop instead of while in `QuantizationDriver`.
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[xla:hlo] Use llvm::BitVector instead of a set when checking reachability
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[xla:hlo] Use llvm::BitVector instead of a set when checking reachability
name old cpu/op new cpu/op delta
BM_HloDfsReachabilityBuild/1 109ns ± 4% 111ns ± 4% ~
BM_HloDfsReachabilityBuild/64 1.71µs ± 6% 1.71µs ± 4% ~
BM_HloDfsReachabilityBuild/128 3.38µs ± 3% 3.43µs ± 3% +1.54%
BM_HloDfsReachabilityBuild/256 6.80µs ± 4% 6.95µs ± 5% +2.25%
BM_HloDfsReachabilityBuild/512 13.8µs ± 4% 14.2µs ± 6% +2.63%
BM_HloDfsReachabilityBuild/4096 155µs ± 4% 157µs ± 4% ~
BM_HloDfsReachabilityBuild/32768 1.42ms ± 5% 1.45ms ± 3% +1.94%
BM_HloDfsReachabilityBuild/262144 32.2ms ± 4% 32.1ms ± 4% ~
BM_HloDfsReachabilityCheck/1 7.37ns ± 3% 7.41ns ± 4% ~
BM_HloDfsReachabilityCheck/64 295ns ± 5% 139ns ± 8% -52.78%
BM_HloDfsReachabilityCheck/128 679ns ± 3% 278ns ± 7% -59.05%
BM_HloDfsReachabilityCheck/256 1.53µs ± 5% 0.61µs ± 6% -60.06%
BM_HloDfsReachabilityCheck/512 3.06µs ± 5% 1.31µs ± 6% -57.27%
BM_HloDfsReachabilityCheck/4096 30.2µs ± 7% 17.9µs ± 4% -40.53%
BM_HloDfsReachabilityCheck/32768 532µs ± 4% 327µs ± 5% -38.52%
BM_HloDfsReachabilityCheck/262144 8.72ms ± 3% 6.66ms ± 4% -23.59%
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[xla:hlo] Optimize hlo_dfs_reachability
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[xla:hlo] Optimize hlo_dfs_reachability
name old cpu/op new cpu/op delta
BM_HloDfsReachabilityBuild/1 87.1ns ± 4% 100.5ns ± 4% +15.40%
BM_HloDfsReachabilityBuild/64 2.04µs ± 3% 1.72µs ± 3% -15.33%
BM_HloDfsReachabilityBuild/128 4.08µs ± 4% 3.44µs ± 2% -15.68%
BM_HloDfsReachabilityBuild/256 8.23µs ± 6% 6.95µs ± 3% -15.57%
BM_HloDfsReachabilityBuild/512 16.4µs ± 3% 14.2µs ± 2% -13.12%
BM_HloDfsReachabilityBuild/4096 174µs ± 3% 157µs ± 4% -9.80%
BM_HloDfsReachabilityBuild/32768 2.40ms ±41% 2.25ms ±43% ~
BM_HloDfsReachabilityBuild/262144 35.5ms ± 9% 33.8ms ± 6% -4.93%
BM_HloDfsReachabilityCheck/1 7.68ns ± 0% 7.67ns ± 1% ~
BM_HloDfsReachabilityCheck/64 670ns ± 4% 303ns ± 4% -54.77%
BM_HloDfsReachabilityCheck/128 1.34µs ± 2% 0.70µs ± 4% -47.76%
BM_HloDfsReachabilityCheck/256 2.76µs ± 4% 1.56µs ± 3% -43.34%
BM_HloDfsReachabilityCheck/512 5.50µs ± 2% 3.17µs ± 4% -42.33%
BM_HloDfsReachabilityCheck/4096 53.2µs ± 5% 31.3µs ± 7% -41.29%
BM_HloDfsReachabilityCheck/32768 1.00ms ± 7% 0.55ms ±11% -45.43%
BM_HloDfsReachabilityCheck/262144 14.6ms ±10% 10.5ms ± 7% -28.04%
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[xla:hlo] NFC: Add benchmark for HloDfsReachability::IsReachable
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[xla:hlo] NFC: Add benchmark for HloDfsReachability::IsReachable
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PR_kwDOArmXAs5pz3N8
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Fix broken GemmRewriteTest.BF16GemmCodeGen on Hopper.
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Fix broken GemmRewriteTest.BF16GemmCodeGen on Hopper.
This was broken by https://github.com/openxla/xla/commit/4e09e73967247dc0226fec1021bf794d80614059.
The issue is that the optimized HLO was changed by having a native BF16 multiply, so the filecheck string had to be changed. Because the multiply is done in BF16 instead of FP32, the tolerance must also be lowered.
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#tf-data Support global shuffle for the skip dataset.
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#tf-data Support global shuffle for the skip dataset.
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PR_kwDOArmXAs5pzzDg
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Force creating XlaSharding ops for optimizer slot variables.
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Force creating XlaSharding ops for optimizer slot variables.
We do a read_value on the slot variables when creating the update op (eg. ResourceApplyAdagrad) for the optimizer to make sure the XlaSharding op is also generated.
We make this a control dependency for the update op so that this appears before it.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10635 from hmonishN:hmonish/fix_build_error d344105a62e5f1749d994c2f1f2a8a76a5880c3d
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PR_kwDOArmXAs5pzoGD
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PR #10628: Exclude FP8 Conversions in DotOperandConverter
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PR #10628: Exclude FP8 Conversions in DotOperandConverter
Imported from GitHub PR https://github.com/openxla/xla/pull/10628
Excludes conversions of dot operands between F8E4M3FN and F8E5M2 types in the DotOperandConverter pass.
Copybara import of the project:
--
037add4423247836b743537f08be563719294c45 by Philipp Hack <[email protected]>:
Excludes conversions of dot operands between FP8 types.
Merging this change closes #10628
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10628 from philipphack:u_fp8_operand_converter_xla 037add4423247836b743537f08be563719294c45
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PR_kwDOArmXAs5pzn_N
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[PJRT:GPU] Don't stage transfers greater than 1GB.
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[PJRT:GPU] Don't stage transfers greater than 1GB.
Allocating pinned memory of that size takes a significant amount of time on GPU.
Fixes https://github.com/google/jax/issues/20209
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TensorFlow 2.16 API docs not available online
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[
"@jonas-eschle,\r\nThank you for reporting this issue. Please allow some duration to discuss with the developer on this and will provide the update on the same issue. Thank you!",
"@jonas-eschle,\r\nThere is a change request for this issue and we are tracking it internally. Once it is resolved, the documentation will be updated to v2.16 and will update the same. Thank you!",
"Thanks a lot for taking care of it!",
"@jonas-eschle,\r\nIf you feel the issue is addressed, Could you please feel free to move this issue to closed status. Thank you!",
"I will check, but at this moment in time, the docs don't show up yet",
"@jonas-eschle,\r\nYeah. As mentioned the issue was informed to the respective team and will update the docs in upcoming days. 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.",
"Hi, we are also interested in the docs for v2.16 API. I am commenting to keep this issue open. ",
"@jonas-eschle, @Cpicon,\r\nWhen I checked the official document, I could able to check the 2.16 api docs now. https://www.tensorflow.org/api_docs/python/tf\r\n\r\nCould you please check and confirm the same. Screenshot for the reference.\r\n\r\n",
"I confirm that the docs are visible now, thanks for the fix! closing",
"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/63834\">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/63834\">No</a>\n"
] | 2024-03-15T23:15:50 | 2024-04-01T17:50:59 | 2024-04-01T17:50:56 |
CONTRIBUTOR
| null | null | null |
### Issue type
Documentation Bug
### TensorFlow version
2.16
The website displaying the 2.16 API of TensorFlow is not available, see [https://www.tensorflow.org/api/r2.16](https://www.tensorflow.org/api/r2.16)
Only the 2.15 appears if clicking through
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[XLA:GPU] Make TensorIterationSpec::IsPhysicallyEquivalent correctly handle trivial dimensions
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[XLA:GPU] Make TensorIterationSpec::IsPhysicallyEquivalent correctly handle trivial dimensions
If a dimension has iteration count 1, it does not affect the physical equivalence to other tensors.
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TPU Connectivity Issue on Google Colab
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[
"Hi @arnava13 ,\r\n\r\nCould you please provide more context and minimal repro code snippet ? Thanks!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63832\">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/63832\">No</a>\n",
"After ensuring training of my models is running properly on CPUs and GPUs, I am getting the following error when training on colab. I have initialised my TPU strategy as usual, and I am running all model related and training operations under strategy.scope(). \r\n`\r\nerr: File \"/content/SeniorHonoursProject/BaCoN-II/train.py\", line 737, in <module>\r\nerr: main()\r\nerr: File \"/content/SeniorHonoursProject/BaCoN-II/train.py\", line 697, in main\r\nerr: model, history = my_train(model, loss,\r\nerr: File \"/content/SeniorHonoursProject/BaCoN-II/train.py\", line 169, in my_train\r\nerr: new_history = model.fit(train_dataset.dataset, epochs=epochs,\r\nerr: File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 70, in error_handler\r\nerr: raise e.with_traceback(filtered_tb) from None\r\nerr: File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\", line 362, in _numpy\r\nerr: raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\nerr: tensorflow.python.framework.errors_impl.InternalError: 8 root error(s) found.\r\nerr: (0) INTERNAL: {{function_node __inference_train_function_11526}} failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused\r\nerr: Additional GRPC error information from remote target /job:localhost/replica:0/task:0/device:CPU:0:\r\nerr: :UNKNOWN:failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused {grpc_status:14, created_time:\"2024-03-22T23:36:04.934268533+00:00\"}\r\nerr: [[{{node MultiDeviceIteratorGetNextFromShard}}]]\r\nerr: Executing non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.\r\nerr: [[RemoteCall]]\r\nerr: [[IteratorGetNextAsOptional]]\r\nerr: [[TPUReplicate/_compile/_9902494219978988908/_4/_384]]\r\nerr: (1) INTERNAL: {{function_node __inference_train_function_11526}} failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused\r\nerr: Additional GRPC error information from remote target /job:localhost/replica:0/task:0/device:CPU:0:\r\nerr: :UNKNOWN:failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused {grpc_status:14, created_time:\"2024-03-22T23:36:04.934268533+00:00\"}\r\nerr: [[{{node MultiDeviceIteratorGetNextFromShard}}]]\r\nerr: Executing non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.\r\nerr: [[RemoteCall]]\r\nerr: [[IteratorGetNextAsOptional]]\r\nerr: [[Pad_3/_250]]\r\nerr: (2) INTERNAL: {{function_node __inference_train_function_11526}} failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused\r\nerr: Additional GRPC error information from remote target /job:localhost/replica:0/task:0/device:CPU:0:\r\nerr: :UNKNOWN:failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused {grpc_status:14, created_time:\"2024-03-22T23:36:04.934268533+00:00\"}\r\nerr: [[{{node MultiDeviceIteratorGetNextFromShard}}]]\r\nerr: Executing non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.\r\nerr: [[RemoteCall]]\r\nerr: [[IteratorGetNextAsOptional]]\r\nerr: [[Pad_15/_466]]\r\nerr: (3) INTERNAL: {{function_node __inference_train_function_11526}} failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused\r\nerr: Additional GRPC error information from remote target /job:localhost/replica:0/task:0/device:CPU:0:\r\nerr: :UNKNOWN:failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused {grpc_status:14, created_time:\"2024-03-22T23:36:04.934268533+00:00\"}\r\nerr: [[{{node MultiDeviceIteratorGetNextFromShard}}] ... [truncated]\r\nerr: Exception ignored in atexit callback: <function async_wait at 0x7ec0f4a74790>\r\nerr: Traceback (most recent call last):\r\nerr: File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/context.py\", line 2833, in async_wait\r\nerr: context().sync_executors()\r\nerr: File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/context.py\", line 749, in sync_executors\r\nerr: pywrap_tfe.TFE_ContextSyncExecutors(self._context_handle)\r\nerr: tensorflow.python.framework.errors_impl.InternalError: 8 root error(s) found.\r\nerr: (0) INTERNAL: {{function_node __inference_train_function_11526}} failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused\r\nerr: Additional GRPC error information from remote target /job:localhost/replica:0/task:0/device:CPU:0:\r\nerr: :UNKNOWN:failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused {grpc_status:14, created_time:\"2024-03-22T23:36:04.934268533+00:00\"}\r\nerr: [[{{node MultiDeviceIteratorGetNextFromShard}}]]\r\nerr: Executing non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.\r\nerr: [[RemoteCall]]\r\nerr: [[IteratorGetNextAsOptional]]\r\nerr: [[TPUReplicate/_compile/_9902494219978988908/_4/_384]]\r\nerr: (1) INTERNAL: {{function_node __inference_train_function_11526}} failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused\r\nerr: Additional GRPC error information from remote target /job:localhost/replica:0/task:0/device:CPU:0:\r\nerr: :UNKNOWN:failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused {grpc_status:14, created_time:\"2024-03-22T23:36:04.934268533+00:00\"}\r\nerr: [[{{node MultiDeviceIteratorGetNextFromShard}}]]\r\nerr: Executing non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.\r\nerr: [[RemoteCall]]\r\nerr: [[IteratorGetNextAsOptional]]\r\nerr: [[Pad_3/_250]]\r\nerr: (2) INTERNAL: {{function_node __inference_train_function_11526}} failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused\r\nerr: Additional GRPC error information from remote target /job:localhost/replica:0/task:0/device:CPU:0:\r\nerr: :UNKNOWN:failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused {grpc_status:14, created_time:\"2024-03-22T23:36:04.934268533+00:00\"}\r\nerr: [[{{node MultiDeviceIteratorGetNextFromShard}}]]\r\nerr: Executing non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.\r\nerr: [[RemoteCall]]\r\nerr: [[IteratorGetNextAsOptional]]\r\nerr: [[Pad_15/_466]]\r\nerr: (3) INTERNAL: {{function_node __inference_train_function_11526}} failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused\r\nerr: Additional GRPC error information from remote target /job:localhost/replica:0/task:0/device:CPU:0:\r\nerr: :UNKNOWN:failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:50188: Failed to connect to remote host: Connection refused {grpc_status:14, created_time:\"2024-03-22T23:36:04.934268533+00:00\"}\r\nerr: [[{{node MultiDeviceIteratorGetNextFromShard}}] ... [truncated]\r\nout: g1D)\r\n`",
"Note also that I have initialised the TPU with the following lines, \r\n\r\n`\r\ntpu_resolver = tf.distribute.cluster_resolver.TPUClusterResolver() # Automatically detects the TPU\r\ntf.config.experimental_connect_to_cluster(tpu_resolver) # Connects to the TPU cluster\r\ntf.tpu.experimental.initialize_tpu_system(tpu_resolver) # Initializes the TPU system\r\nstrategy = tf.distribute.TPUStrategy(tpu_resolver)\r\ntpu_device = tpu_resolver.master() # Retrieves the TPU device URI\r\nprint(\"Running on TPU:\", tpu_device)\r\n`\r\n\r\nand got the following print, indicating that the TPU is connected.\r\n`\r\nRunning on TPU: grpc://10.74.203.82:8470\r\n`\r\n\r\n",
"Fixed. The issue was using a generator function to build datasets in my pipeline. Using tf.data.Dataset.from_tensor_slices instead fixes the issue.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63832\">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/63832\">No</a>\n"
] | 2024-03-15T21:36:43 | 2024-03-24T20:53:52 | 2024-03-24T20:53:49 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Code was designed by someone else, I've just been adapting it to use TPUs and TensorFlow datasets in training. When training in TPU mode I get the attached error.
### Standalone code to reproduce the issue
```shell
https://github.com/arnava13/SeniorHonoursProject/tree/main/BaCoN-II
train.py has the training loop.
data_generator.py has the datagenerators.
Training is run by opening training_colab.ipynb directly on colab's from github option.
```
### Relevant log output
```shell
err: Traceback (most recent call last):
err: File "/content/SeniorHonoursProject/BaCoN-II/train.py", line 763, in <module>
err: main()
err: File "/content/SeniorHonoursProject/BaCoN-II/train.py", line 733, in main
err: model, history = my_train(model, optimizer, loss,
err: File "/content/SeniorHonoursProject/BaCoN-II/train.py", line 168, in my_train
err: for IDs, x_batch_train, y_batch_train in train_generator.dataset:
err: File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/distribute/input_lib.py", line 264, in __next__
err: return self.get_next()
err: File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/distribute/input_lib.py", line 331, in get_next
err: num_replicas_with_values = _calculate_replicas_with_values(
err: File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/distribute/input_lib.py", line 196, in _calculate_replicas_with_values
err: client_has_values = math_ops.reduce_sum(worker_has_values, keepdims=True)
err: File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/weak_tensor_ops.py", line 88, in wrapper
err: return op(*args, **kwargs)
err: File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
err: raise e.with_traceback(filtered_tb) from None
err: File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py", line 498, in shape
err: self._tensor_shape = tensor_shape.TensorShape(self._shape_tuple())
err: tensorflow.python.framework.errors_impl.InternalError: failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:36768: Failed to connect to remote host: Connection refused
err: Additional GRPC error information from remote target /job:localhost/replica:0/task:0/device:CPU:0:
err: :UNKNOWN:failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:36768: Failed to connect to remote host: Connection refused {created_time:"2024-03-15T21:29:31.939700993+00:00", grpc_status:14}
err: [[{{node MultiDeviceIteratorGetNextFromShard}}]]
err: Executing non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.
err: [[RemoteCall]]
err: Exception ignored in atexit callback: <function async_wait at 0x7b4d88d84670>
err: Traceback (most recent call last):
err: File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/context.py", line 2833, in async_wait
err: context().sync_executors()
err: File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/context.py", line 749, in sync_executors
err: pywrap_tfe.TFE_ContextSyncExecutors(self._context_handle)
err: tensorflow.python.framework.errors_impl.InternalError: failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:36768: Failed to connect to remote host: Connection refused
err: Additional GRPC error information from remote target /job:localhost/replica:0/task:0/device:CPU:0:
err: :UNKNOWN:failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:36768: Failed to connect to remote host: Connection refused {created_time:"2024-03-15T21:29:31.939700993+00:00", grpc_status:14}
err: [[{{node MultiDeviceIteratorGetNextFromShard}}]]
err: Executing non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.
err: [[RemoteCall]]
err: 2024-03-15 21:29:31.946346: W tensorflow/core/distributed_runtime/eager/remote_tensor_handle_data.cc:77] Unable to destroy remote tensor handles. If you are running a tf.function, it usually indicates some op in the graph gets an error: failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:36768: Failed to connect to remote host: Connection refused
err: Additional GRPC error information from remote target /job:localhost/replica:0/task:0/device:CPU:0:
err: :UNKNOWN:failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:36768: Failed to connect to remote host: Connection refused {created_time:"2024-03-15T21:29:31.939700993+00:00", grpc_status:14}
err: [[{{node MultiDeviceIteratorGetNextFromShard}}]]
err: Executing non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.
err: [[RemoteCall]]
err: 2024-03-15 21:29:32.185465: W ./tensorflow/core/distributed_runtime/eager/destroy_tensor_handle_node.h:59] Ignoring an error encountered when deleting remote tensors handles: INVALID_ARGUMENT: Unable to find the relevant tensor remote_handle: Op ID: 2124, Output num: 0
err: Additional GRPC error information from remote target /job:worker/replica:0/task:0 while calling /tensorflow.eager.EagerService/Enqueue:
err: :{"created":"@1710538172.185353260","description":"Error received from peer ipv4:10.120.183.98:8470","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Unable to find the relevant tensor remote_handle: Op ID: 2124, Output num: 0","grpc_status":3} [type.googleapis.com/tensorflow.core.platform.ErrorSourceProto='\x08\x05']
```
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#tf-data Ramp up `map_fusion` experiment back to 50% job-level.
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#tf-data Ramp up `map_fusion` experiment back to 50% job-level.
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#tf-data Support global shuffle for the take dataset.
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#tf-data Support global shuffle for the take dataset.
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Implement pass to convert custom calls to composites
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Add empty dep to XLA in TSL's cc_library to avoid https://github.com/bazelbuild/bazel/issues/21519
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I_kwDOArmXAs6CgBED
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load_model is failing to load a model created by a save. It reports an IndexError
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[
"@RoswellMiller Please make sure you're using the correct loading method based on how the model was saved?\r\nKindly ensure you have used the same version for model building and saving. \r\nIn order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here.\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/63827\">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/63827\">No</a>\n"
] | 2024-03-15T20:38:45 | 2024-04-05T01:47:23 | 2024-04-05T01:47:18 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
Windows 11 Pro
### Mobile device
_No response_
### Python version
3.12.2
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
A file call au01.keras created by a save of an autoencode model will not load using load_model. It fails with an IndexError list index out of range
### Standalone code to reproduce the issue
```shell
I tried to send you the file but the system won't accept filetype .keras
```
### Relevant log output
```shell
vscode ➜ /workspaces/Prj000-GDL2ndEd (master) $ cd /workspaces/Prj000-GDL2ndEd ; /usr/bin/env /usr/local/bin/python /home/vscode/.vscode-server/extensions/ms-python.debugpy-2024.2.0-linux-x64/bundled/libs/debugpy/adapter/../../debugpy/launcher 37803 -- /workspaces/Prj000-GDL2ndEd/autoenc_01.py
2024-03-15 20:01:19.114748: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2024-03-15 20:01:19.121734: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2024-03-15 20:01:19.182123: 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 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-15 20:01:23.498352: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
TensorFlow version 2.16.1
Loading autoencode ...
list index out of range
Models not found
```
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PR_kwDOArmXAs5py8ek
| 63,826 |
#tf-data For `ParallelMap`, the default value of `deterministic` argument is `True`. Map fusion graph rewrite does not merge nodes with default `deterministic` argument and one with `deterministic=True`. In addition, in the graph, the attribute can have a "default" value. This CL merges `ParallelMaps` with the same `deterministic` values accounting for default values.
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#tf-data For `ParallelMap`, the default value of `deterministic` argument is `True`. Map fusion graph rewrite does not merge nodes with default `deterministic` argument and one with `deterministic=True`. In addition, in the graph, the attribute can have a "default" value. This CL merges `ParallelMaps` with the same `deterministic` values accounting for default values.
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PR_kwDOArmXAs5pyz51
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Move deps inside of `if_cuda_is_configured` where possible
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Move deps inside of `if_cuda_is_configured` where possible
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PR_kwDOArmXAs5pyy9l
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Add CompositeOp to CustomOp conversion
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I_kwDOArmXAs6Cfjbm
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tf.keras.layers.PReLU outputs NaN on positive input
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[
"Hi @maybeLee, I tried to reproduce the issue, from the given constraint/dummy code, and i am able to get the error output as \r\n\r\n\r\nand after spending some time over your issue, got to know, this issue may be arised due to mismatch in the package version.",
"HI @AkashKumarSingh11032001 \r\nMy previous code is working for tensorflow 2.15.0 and it seems that this API does not support the `weights` parameter in 2.16.0.\r\nHere is the adjusted code for 2.16.0:\r\n\r\n```\r\nimport numpy as np\r\nimport tensorflow as tf\r\ninput = tf.constant(1.1234, dtype='float32')\r\nout = tf.keras.layers.PReLU(alpha_initializer=tf.keras.initializers.Constant(np.inf))(input)\r\nprint(out)\r\n```",
"Hi **@maybeLee** ,\r\n\r\nI tried to run your code on colab using tf 2.15, 2.16 and tf-nightly versions faced same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/b66ec0d29b22e32c1e7910b09688bf56/63823_2-15-2-16-nightly.ipynb) here for reference. We need to work on it, In few days i will provide a solution for it.\r\n\r\nThank you!",
"Hi **@maybeLee** ,\r\n- Could you please take a look at the comment from the developer in the raised PR? Also the pr got closed and try to contribute for the same.\r\n\r\nThank you!"
] | 2024-03-15T19:13:42 | 2024-06-11T05:17:33 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Following the documentation, https://www.tensorflow.org/api_docs/python/tf/keras/layers/PReLU, tf.keras.layers.PReLU should output the input if the input > 0 regardless of the weight. However, it outputs NaN when weight is infinity although input > 0.
I think the issue is located here:
https://github.com/tensorflow/tensorflow/blob/90d97b99797892f3d571f27076e4fcdfb1bde059/tensorflow/python/keras/layers/advanced_activations.py#L164
, where the calculation `inf*0` happens.
According to the documentation, I think it would be better to directly return input if input > 0.
Also, I quickly checked the PyTorch's implementation and I find that pytorch use the following code to do prelu:
```
(input > scalar_t(0)) ? input : weight * input;
```
https://github.com/pytorch/pytorch/blob/3bc2bb678150e02bfa2552b82572d771a892d0c3/aten/src/ATen/native/cpu/Activation.cpp#L1380
### Standalone code to reproduce the issue
```shell
import numpy as np
import tensorflow as tf
input = tf.constant(1.1234, dtype='float32')
weight = tf.constant(np.random.randn(1)*np.inf, dtype='float32')
out = tf.keras.layers.PReLU(weights=weight)(input)
print(out)
```
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5pycc_
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Integrate LLVM at llvm/llvm-project@a4ca07f13b56
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Integrate LLVM at llvm/llvm-project@a4ca07f13b56
Updates LLVM usage to match
[a4ca07f13b56](https://github.com/llvm/llvm-project/commit/a4ca07f13b56)
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Add the options to require complete AOT autotuning results
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Add the options to require complete AOT autotuning results
This cl adds an XLA flag `--require_complete_aot_autotune_results` that requires complete AOT autotuning results inside XLA, so that a `NotFound` will be raised when there is cache miss and JIT autotuning is needed. This can help users detect JIT autotuning happening inside XLA.
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1. Fix flaky test after recently enabling the modelling of resharding memory costs by default in auto-sharding.
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1. Fix flaky test after recently enabling the modelling of resharding memory costs by default in auto-sharding.
2. Also check shapes of parameters in the test instead of sharding annotations as sharding annotations may not be preserved through the compilation.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10562 from olupton:missing-warmup b4ccf2928ee45ec9139db003378095b948bb73d5
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Fix OSS build
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Fix OSS build
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PR_kwDOArmXAs5px_A0
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PR #10565: [GPU] Support broadcasts in cuDNN GEMM fusions.
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PR #10565: [GPU] Support broadcasts in cuDNN GEMM fusions.
Imported from GitHub PR https://github.com/openxla/xla/pull/10565
Stacked PR.
Copybara import of the project:
--
51eaaf6c8b722ef7c3273825d0585371cd55da26 by Ilia Sergachev <[email protected]>:
[GPU] Support broadcasts in cuDNN GEMM fusions.
Merging this change closes #10565
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10565 from openxla:cudnn_gemm_broadcasts 51eaaf6c8b722ef7c3273825d0585371cd55da26
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PR_kwDOArmXAs5pxu5m
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[xla:gpu] Add collective-permute-cycle-decomposer to pre-fusion collective
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[xla:gpu] Add collective-permute-cycle-decomposer to pre-fusion collective
optimization passes.
Change the pass to process collective-permute instead of
collective-permute-start as the pass should be invoked before fusion so that
the generated compare-and-select code can be fused.
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PR_kwDOArmXAs5pxa-j
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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/9c80654b96f0fcf47cb636500f47edbe1fbc555e.
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I_kwDOArmXAs6CeML_
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Comment in detection_postprocess.cc is incorrect and misleading.
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[
"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/63815\">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/63815\">No</a>\n",
"Hi @xwin,\r\n\r\nPlease track the PR [#63950](https://github.com/tensorflow/tensorflow/pull/63950)\r\n\r\nThank You",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63815\">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/63815\">No</a>\n"
] | 2024-03-15T15:56:28 | 2024-03-22T06:16:03 | 2024-03-22T06:16:00 |
NONE
| null | null | null |
The comment pointed by the link below is incorrect
https://github.com/tensorflow/tensorflow/blob/82b21d203296ce5640631aba39c8fb64c02508fa/tensorflow/lite/kernels/detection_postprocess.cc#L58
The comment should state
```
// BoxCorner represents the upper left corner (xmin, ymin) and
// the lower right corner (xmax, ymax).
```
While this is not a functional bug it can case some wasted time and is easy to fix
For the reference this link https://www.tensorflow.org/lite/examples/object_detection/overview#output_signature in tensorflow documentation has a correct description.
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PR_kwDOArmXAs5pxTpV
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PR #10612: [GPU] cuDNN GEMM fusions: enable noncontracting dimension transformations.
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PR #10612: [GPU] cuDNN GEMM fusions: enable noncontracting dimension transformations.
Imported from GitHub PR https://github.com/openxla/xla/pull/10612
This kind of transformations is already in use by the Triton GEMM backend for a while.
Copybara import of the project:
--
51eaaf6c8b722ef7c3273825d0585371cd55da26 by Ilia Sergachev <[email protected]>:
[GPU] Support broadcasts in cuDNN GEMM fusions.
--
40aed83572cd3f09fa8530b6c130bf84be226593 by Ilia Sergachev <[email protected]>:
[XLA:GPU] Enable noncontracting to batch dimension transformation in cuDNN GEMM fusions.
This transformation is already in use by the Triton GEMM backend for a while.
Merging this change closes #10612
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10612 from openxla:cudnn_gemm_split_noncontracting 40aed83572cd3f09fa8530b6c130bf84be226593
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PR_kwDOArmXAs5pxOLn
| 63,813 |
[XLA:Python] Reland change removing CompiledFunction.
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[XLA:Python] Reland change removing CompiledFunction.
This class is unused and does nothing.
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PR_kwDOArmXAs5pxHJa
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[xla:gpu] Change the method to invoke collective-pipeliner for pipelining
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[xla:gpu] Change the method to invoke collective-pipeliner for pipelining
Send-Recv chains.
This also change the way we pipeline Send-Recv chains and allow the pipeling of
two Send-Recv chains that form a cycle in source-target pairs.
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I_kwDOArmXAs6Cd10v
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Predict crashes if last batch is smaller then batch_size
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[
"Hi @AdnanAvdagic ,\r\n\r\nCould you please submit minimal reproducible code snippet for this. I tried with a dummy model with an input size that is not a multiple of batch_size but couldn't get any issue.Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/231d7d802afc70c568611129ae40b7f2/63811_sample_model-tf2-16v.ipynb) for reference.",
"```python\r\nbackbone = keras_cv.models.YOLOV8Backbone.from_preset(\r\n 'yolo_v8_l_backbone',\r\n input_shape=[512,512, 3],\r\n )\r\n\r\nmodel = keras_cv.models.YOLOV8Detector(\r\n num_classes=1,\r\n bounding_box_format='xyxy',\r\n backbone=backbone,\r\n fpn_depth=3,\r\n )\r\n\r\nmodel.prediction_decoder = keras_cv.layers.NonMaxSuppression(\r\n bounding_box_format='xyxy',\r\n from_logits=True,\r\n iou_threshold=0.35,\r\n confidence_threshold=0.53,\r\n max_detections=50,\r\n )\r\n\r\ndef prepare_image(self: Self, input_image_path: Path) -> np.ndarray:\r\n try:\r\n input_image = cv2.imread(str(input_image_path))\r\n # input_image = cv2.bilateralFilter(input_image, 9, 75, 75)\r\n if self.color_mode == \"rgb\":\r\n input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)\r\n\r\n else:\r\n input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)\r\n\r\n return input_image\r\ntiles_paths = ['01.jpg', '02.jpg', ...]\r\n\r\nimage_patches = np.array(\r\n [\r\n prepare_image(input_image_path=tile_path)\r\n for tile_path in tiles_paths\r\n ],\r\n)\r\ny_pred = model.predict(\r\n image_patches,\r\n batch_size=8,\r\n)\r\n```\r\n\r\nHere we get a crash if the number of image_patches is not divisible with the batch_size it will crash at the last batch which is less than the batch_size",
"Hi @AdnanAvdagic ,\r\n\r\nSince this issue seems specific to `keras_cv` model. Could you please file an issue at [keras_cv](https://github.com/keras-team/keras-cv/issues) repo,since all `keras_cv` issues are actively tracked there?",
"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/63811\">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/63811\">No</a>\n"
] | 2024-03-15T15:29:18 | 2024-04-19T01:48:05 | 2024-04-19T01:47:56 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 23.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA 12.4 cuDNN 8.9
### GPU model and memory
RTX 4090
### Current behavior?
When sending in a numpy array which is not exactly divisible into the batch size, ex length of numpy array is 100 and batch size is 8 the inferencing crashes.
In the function take_along_axis we found that the batch_size axis was None instead of 8 for the last batch
### Standalone code to reproduce the issue
```python
image_patches = np.array([self.prepare_image(tile_path) for tile_path in tiles_paths])
y_pred = model.predict(
image_patches,
batch_size=8
)
```
### Relevant log output
```shell
228/229 ━━━━━━━━━━━━━━━━━━━━ 0s 74ms/step2024-03-15 16:02:37.436169: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at bcast_ops.cc:52 : INVALID_ARGUMENT: Input 0 to node non_max_suppression_1_1/BroadcastArgs with op BroadcastArgs must be a compile-time constant.
XLA compilation requires that operator arguments that represent shapes or dimensions be evaluated to concrete values at compile time. This error means that a shape or dimension argument could not be evaluated at compile time, usually because the value of the argument depends on a parameter to the computation, on a variable, or on a stateful operation such as a random number generator.
Stack trace for op definition:
File "main_inferencer.py", line 231, in <module>
File "utils/exception_utils/inspectors.py", line 317, in wrapper
File "main_inferencer.py", line 130, in main
File "src/e_inference/src/geotif_inferencer.py", line 59, in main
File "src/e_inference/src/geotif_inferencer.py", line 168, in detect_image
File ".venv/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py", line 118, in error_handler
File ".venv/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py", line 513, in predict
File ".venv/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py", line 212, in one_step_on_data_distributed
File ".venv/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py", line 201, in one_step_on_data
File ".venv/lib/python3.11/site-packages/keras_cv/src/models/object_detection/yolo_v8/yolo_v8_detector.py", line 616, in predict_step
File ".venv/lib/python3.11/site-packages/keras_cv/src/models/object_detection/yolo_v8/yolo_v8_detector.py", line 609, in decode_predictions
File ".venv/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py", line 118, in error_handler
File ".venv/lib/python3.11/site-packages/keras/src/layers/layer.py", line 816, in __call__
File ".venv/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py", line 118, in error_handler
File ".venv/lib/python3.11/site-packages/keras/src/ops/operation.py", line 42, in __call__
File ".venv/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py", line 157, in error_handler
File ".venv/lib/python3.11/site-packages/keras_cv/src/layers/object_detection/non_max_suppression.py", line 143, in call
File ".venv/lib/python3.11/site-packages/keras/src/ops/numpy.py", line 4991, in take_along_axis
File ".venv/lib/python3.11/site-packages/keras/src/backend/tensorflow/numpy.py", line 1505, in take_along_axis
```
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PR #10607: [ROCM] fixing missing includes and API change
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PR #10607: [ROCM] fixing missing includes and API change
Imported from GitHub PR https://github.com/openxla/xla/pull/10607
This is a recent build-brake fix due to API changes
@xla-rotation: would you please have a look ?
Copybara import of the project:
--
ad9a9626fb22d05e137f8b8bad9d81efacda352f by Pavel Emeliyanenko <[email protected]>:
fixing missing includes and API change
Merging this change closes #10607
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10607 from ROCm:ci_build_brake_fix_240315 ad9a9626fb22d05e137f8b8bad9d81efacda352f
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Support sparse dots in GemmFusionAutotuner pass
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Support sparse dots in GemmFusionAutotuner pass
1) Add `allow_cublas` flag and set it to false for sparse dots (we cannot run cublas for reference, as it doesn't support sparsity).
2) Make sure the configs that are not supported by the codegen are excluded. Specifically, if there are more threads than metadata values, it'd fail.
3) For deviceless compilations, apply the `ReduceTileSizes` to the default config, as otherwise it produces an incorrect config for sparse dots (too many threads).
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Fix erf approximation.
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Fix erf approximation.
The upstream one is too inaccurate.
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Resolve wrong overload to boolean in createDestOps.
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Resolve wrong overload to boolean in createDestOps.
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Support sparse dots in GemmFusion pass
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Support sparse dots in GemmFusion pass
The codegen will only support this for NVidia GPUs, which have the following restrictions:
- only 2:4 structured sparsity is allowed;
- only the first dot operand may be sparse;
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[xla:gpu][NFC] Add more comments to clarify AddressComputationThunk testing
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[xla:gpu][NFC] Add more comments to clarify AddressComputationThunk testing
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PR_kwDOArmXAs5pvzM5
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Add stub for delay_kernel
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Add stub for delay_kernel
This is part of a temporary workaround for Google-only builds and doesn't affect the general OSS build.
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Fix a missing function argument
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Grammatically updated and expanded the texts for the documentation fo…
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"Hi @Victory-Onumaku Can you please sign CLA. Thank you!"
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Grammatically updated and expanded the texts for the documentation for better understanding to any user.
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PR_kwDOArmXAs5pvbgU
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Remerge after a revert: PR #10037: [NVIDIA XLA] Copy backend config for derived instruction that has same opcode
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NONE
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Remerge after a revert: PR #10037: [NVIDIA XLA] Copy backend config for derived instruction that has same opcode
Imported from GitHub PR https://github.com/openxla/xla/pull/10037
SetupDerivedInstruction copies many properties like frontend_attributes, metadata and etc, but it doesnt transfer over backend configs. If the derived instruction has the same opcode as the current one, the backend config is most likely applicable, this change copies over the backend config in that situation.
Copybara import of the project:
--
0c63d26478dacb9ddb68811c72287243fc60c374 by TJ <[email protected]>:
Copy backend config for derived instruction that has same opcode
Merging this change closes #10037
Merging this change closes #10037
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10037 from Tixxx:tixxx/derived_backend_config 0c63d26478dacb9ddb68811c72287243fc60c374
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PR_kwDOArmXAs5pvMsc
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[XLA:GPU] Add option to return FDO profile as textproto.
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[XLA:GPU] Add option to return FDO profile as textproto.
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PR_kwDOArmXAs5pvL2L
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PR #10247: Add sycl name in se_gpu_pjrt_client
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PR #10247: Add sycl name in se_gpu_pjrt_client
Imported from GitHub PR https://github.com/openxla/xla/pull/10247
It is a sub PR of https://github.com/openxla/xla/pull/9042.
Copybara import of the project:
--
c2f60c318aebfe4e64bf3becfd40d04949c50b41 by Sheng, Yang <[email protected]>:
Add sycl name in se_gpu_pjrt_client
Merging this change closes #10247
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10247 from Intel-tensorflow:yang/pjrt c2f60c318aebfe4e64bf3becfd40d04949c50b41
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Handle element_size_in_bits in constant folding.
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Handle element_size_in_bits in constant folding.
Constant folding runs HloEvaluator, which creates Literals. Before, if element_size_in_bits was nonzero in a constant folded op, an error would occur since Literals do not support element_size_in_bits and so CHECKed it was zero in the constructor. Now Literal will silently set element_size_in_bits to zero in the constructor.
Because the newly created constant-folded constant op derives its Shape from the constant-folded literal, now HloConstantFolding explicitly sets element_size_in_bits on the newly created constant op since the Literal will always have element_size_in_bits set to zero.
This will be needed to support int4 in arbitrary ops on CPUs/GPUs.
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All subgraphs share a mutex. Subgraphs and Delegates are not thread safe.
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PR_kwDOArmXAs5pvCl6
| 63,796 |
PR #10562: Add missing warmup run when autotuning.
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PR #10562: Add missing warmup run when autotuning.
Imported from GitHub PR https://github.com/openxla/xla/pull/10562
The `xla/service/gpu:gemm_algorithm_picker_test` test, run on V100, was hitting the delay kernel timeout because of this.
See #9757 for explanation of why the best practice is to execute a warmup run **without the GpuTimer active**.
Copybara import of the project:
--
b4ccf2928ee45ec9139db003378095b948bb73d5 by Olli Lupton <[email protected]>:
Add missing warmup run when autotuning.
The xla/service/gpu:gemm_algorithm_picker_test test, run on V100, was
hitting the delay kernel timeout because of this.
Merging this change closes #10562
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10562 from olupton:missing-warmup b4ccf2928ee45ec9139db003378095b948bb73d5
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im getting this error in jupyter- Exception encountered when calling Lambda.call() in this line- attention_context = Lambda(lambda x: tf.einsum('ijk,ijl->ikl', x[0], x[1]))([attention_scores, se3])
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[
"It looks like the issue is related to the use of the `Lambda` layer in your code. The error message suggests that there's a mix of tensors and non-tensors in a nested `Lambda` call, specifically related to the `mask` argument.\r\n\r\nThe error might be occurring because the `Lambda` layer is expecting a tensor for its `mask` argument, but it's receiving a list with `None` and a tensor instead. To resolve this, you can try modifying the `Lambda` layer to explicitly handle the `mask` argument. Here's an updated version of the `Lambda` layer that should resolve the issue:\r\n\r\n`attention_context = Lambda(lambda x: tf.einsum('ijk,ijl->ikl', x[0], x[1]), mask=lambda inputs, mask: mask[1])([attention_scores, se3])\r\n`\r\n\r\nIn this modification, the `mask` argument is explicitly handled to pass the mask from the `se3` input to the `Lambda` layer.\r\n\r\nPlease try this modification and see if it resolves the issue. If the error persists, there may be other issues in your code that need to be addressed.",
"@ShahidRRRRRR Please print the shapes of attention_scores and se3 before passing them to the Lambda layer to verify compatibility. If you still face the issue then, could you please ensure to use the latest TF version and share the code in a notebook or colab gist? \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/63795\">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/63795\">No</a>\n"
] | 2024-03-15T11:04:54 | 2024-04-04T01:47:46 | 2024-04-04T01:47:43 |
NONE
| null | null | null |
# Encoder model
inputs1 = Input(shape=(4096,))
fe1 = Dropout(0.5)(inputs1)
fe2 = Dense(256, activation='relu')(fe1)
fe2_projected = RepeatVector(max_caption_length)(fe2)
fe2_projected = Bidirectional(LSTM(256, return_sequences=True))(fe2_projected)
# Sequence feature layers
inputs2 = Input(shape=(max_caption_length,))
se1 = Embedding(vocab_size, 256, mask_zero=True)(inputs2)
se2 = Dropout(0.5)(se1)
se3 = Bidirectional(LSTM(256, return_sequences=True))(se2)
# Apply attention mechanism using Dot product
attention = Dot(axes=[2, 2])([fe2_projected, se3]) # Calculate attention scores
# Softmax attention scores
attention_scores = Activation('softmax')(attention)
# Apply attention scores to sequence embeddings
attention_context = Lambda(lambda x: tf.einsum('ijk,ijl->ikl', x[0], x[1]))([attention_scores, se3])
# Sum the attended sequence embeddings along the time axis
context_vector = tf.reduce_sum(attention_context, axis=1)
# Decoder model
decoder_input = concatenate([context_vector, fe2], axis=-1)
decoder1 = Dense(256, activation='relu')(decoder_input)
outputs = Dense(vocab_size, activation='softmax')(decoder1)
# Create the model
model = Model(inputs=[inputs1, inputs2], outputs=outputs)
model.compile(loss='categorical_crossentropy', optimizer='adam')
# Visualize the model
plot_model(model, show_shapes=True)
im getting this error someone help
Exception encountered when calling Lambda.call().
In a nested call() argument, you cannot mix tensors and non-tensors. Received invalid mixed argument: mask=[None, <KerasTensor shape=(None, 34), dtype=bool, sparse=False, name=keras_tensor_42>]
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PR_kwDOArmXAs5pu4Up
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[xla::ffi] Forked Pointer to xla/ffi/api/ffi.h
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[xla::ffi] Forked Pointer to xla/ffi/api/ffi.h
It is useful in both "internal" and "external" FFI versions.
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PR_kwDOArmXAs5pux-P
| 63,793 |
Rewrite and simplify epilogue handling.
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[] | 2024-03-15T10:47:50 | 2024-03-15T12:06:35 | 2024-03-15T12:06:34 |
NONE
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Rewrite and simplify epilogue handling.
The old approach ("isolated and injected") is confusing and doesn't work.
The new approach is to create a separate subgraph for the epilogue and
emit that as its own function. That way, only the epilogue needs to deal
with injected values.
This fixes most of the miscompiles in JAX. Transpose doesn't handle non-
trivial epilogues yet, but that should be an easy fix, similar to what's
done with reduce.
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I_kwDOArmXAs6CbMQg
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TensorFlow 2.16 / Keras 3 have undocumented breaking API changes
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[
"Sorry, but this is as unhelpful an answer as it gets. Points 1, 2, 4 and 5 are merely repetitions of my issue description, and your conclusion literally prompts me to do what I just did by opening this issue.\r\n\r\nAs for point 3, the all point of my opening this issue is to point out that there is a lack of documentation regarding these changes, which means everyone has to spend time figuring out what is broken or not, and how to adjust to API changes. I managed to do it in my specific case with a few hours' effort, but the parts I use in my software only cover a small part of the entire TensorFlow/Keras API, hence I expect others will not only encounter the same issues as me, but probably more undocumented issues.\r\n\r\nHence my call for a broader migration guide, that would help developers adjust their code to the recent changes with limited effort.",
"(it's an answer generated by an LLM, just spam)",
"The LLM generated spam comment has been removed silently.\r\n\r\nBut for TF 2.16, see https://github.com/tensorflow/tensorflow/issues/63362 for a history of how things got so back"
] | 2024-03-15T10:12:53 | 2024-06-12T13:23:19 | null |
CONTRIBUTOR
| null | null | null |
### Issue type
Documentation Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
With the (documented) switch to Keras 3, TensorFlow 2.16 has introduced undocumented API-breaking changes that derogate to SemVer and results in unexpected code maintenance costs for depending projects.
While I understand the interest of switching to Keras 3 (and acknowledge the possibility to keep using Keras 2 as backend, albeit non-trivial to impose on end-users of a third-party solution that uses TensorFlow as dependency), I would have expected some kind of effort on documenting parts of the API that changed due to the switch. [This Keras issue](https://github.com/keras-team/keras/issues/18467) and [this guide](https://keras.io/guides/migrating_to_keras_3/#tensorflow-variables-tracking) are presented as listing breaking changes, but seem not to be covering everything that changed, especially when calling keras from TensorFlow rather than as a primary endpoint for setting up models (agnostic to the computation backend).
As I put effort in updating custom code that calls TensorFlow / Keras code, I notably noticed that:
- `tf.keras.Model.weights` (and its variants) as well as `tf.keras.optimizers.Optimizer.variables` no longer return a list of `tf.Variable` instances, but instead return a list of `tf.keras.Variable` that themselves wrap `tf.Variable` instances.
- Some losses were dropped / renamed, _e.g._ `tf.keras.losses.deserialize("mse")` no longer works, but was to the best of my knowledge never reported to be deprecated in previous versions.
- `tf.keras.optimizers.Optimizer.variables()` has been removed in favor of `tf.keras.optimizers.Optimizer.variables`.
These changes can be adjusted for, but require a lot of `if hasattr(tf.keras, "version") and tf.keras.version().startswith("3"):` branching to keep supporting not-so-old versions while adding support for newer TF versions, and, most importantly, require some tedious experiments to find out what has changed and how. As such, I categorize his issue as "Documentation Feature Request" as I obviously do not expect changes to be rolled back, but believe that it would be a sensible gesture towards the community to publish a clearer and more exhaustive guide as to what has changed with the introduction of Keras 3, and how to accommodate third-party code that does not want to impose on end-users the choice of a single TF version.
### Standalone code to reproduce the issue
As a single example of what I am talking about:
This works with TensorFlow 2.15:
```python
import tensorflow as tf
optim = tf.keras.Adagrad()
grads = [tf.zeros((32, 1)), tf.zeros((1,))]
optim.build([tf.Variable(tf.zeros_like(g), name=f"{i}") for i, g in enumerate(grads)])
state = {v.name: v.value() for v in optim.variables()}
assert len(state) == 2
```
But it fails for numerous reasons in TensorFlow 2.16.1 (with Keras 3 backend):
- `state = {v.name: v.value() for v in optim.variables()}` should be replaced with `state = {v.path: v.value.value() for v in optim.variables}`...
- ... and in fact this will not work because the names have not been propagated and all accumulator variables have the shame name/path, hence to put them in a dict I have to deterministically set names, _e.g._ based on indices, which drops some information.
### Relevant log output
_No response_
|
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