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CategoryEncoding layer with output_mode count as part of a model no longer working in TensorFlow 2.16.1
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[ "@Dobiasd,\r\nThank you for reporting the issue. Looks like this is breaking with the combination of tensorflow v2.16.1 & Keras 3.0. I also tried with the multiple options but couldn't get out of the error. I request to check with [keras-team/keras](https://github.com/keras-team/keras/issues) for the better solution. Kindly find the [gist](https://colab.research.google.com/gist/tilakrayal/8575222f1d9b6e99df183256a242bb71/untitled1840.ipynb) for the [reference](https://colab.research.google.com/gist/tilakrayal/85b0271a3899913cc34969233cec647c/untitled1841.ipynb). Thank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Ok, I've submitted the problem to the Keras team: https://github.com/keras-team/keras/issues/19567", "@Dobiasd,\r\nCould you please feel to move this issue to the closed status, as this is fixed at HEAD and mentioned the [same](https://github.com/keras-team/keras/issues/19567) by the developer. Thank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65390\">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/65390\">No</a>\n" ]
2024-04-10T08:44:53
2024-04-23T10:20:17
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With TensorFlow `2.15.1`, the following code works fine: ```python3 import numpy as np from tensorflow.keras.layers import Input, CategoryEncoding from tensorflow.keras.models import Model inputs = [Input((100,))] outputs = [CategoryEncoding(1024, output_mode='count', sparse=False)(inputs[0])] model = Model(inputs=inputs, outputs=outputs) model.compile(loss='mse', optimizer='adam') x = np.random.randint(0, 10, (2, 100)) model.predict(x) ``` But with `2.16.1`, it no longer does: ``` TypeError: Exception encountered when calling CategoryEncoding.call(). Value passed to parameter 'indices' has DataType float32 not in list of allowed values: uint8, int8, int32, int64 ```
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[XLA:GPU] Increase the autotuning measurement error threshold to 4 us.
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[XLA:GPU] Increase the autotuning measurement error threshold to 4 us. We have done soome more measurements that suggest the higher threshold.
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Implement map HLO -> MLIR.
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2024-04-10T08:31:06
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Implement map HLO -> MLIR.
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Fix order of removing dead computations in HloDCE
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2024-04-10T08:07:12
2024-04-10T11:19:08
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Fix order of removing dead computations in HloDCE We used to delete subcomputations that become dead due to removal of its calling computation before deleting the calling computation. This is asking for trouble, e.g. regarding cleanup routines.
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65,381
Incorrect documentation of the hard_sigmoid activation function (TensorFlow 2.16.1)
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[ "Hi @Dobiasd ,\r\n\r\nThanks for reporting. I have replicated the issue with TF2.16v which uses keras3. With Tf2.15v it works fine which uses Keras2 . Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/42b150638df60f701624ad4632a9b32b/65381.ipynb) for same. Will propose a fix for same.", "@Dobiasd , Actually the implementation in Keras3 is inline with `torch` and `jax` whereas Keras2 it is different. Changing the documentation accordingly.", "Hi @Dobiasd ,\r\n\r\nThe proposed PR got merged and it will reflect in documentation probably in next TF version release. ", "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/65381\">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/65381\">No</a>\n" ]
2024-04-10T07:09:30
2024-04-15T08:20:55
2024-04-15T08:20:52
NONE
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The [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/activations/hard_sigmoid) of the `hard_sigmoid` activation function (TensorFlow 2.16.1) [states](https://i.imgur.com/KWQ3Ocp.png): > The hard sigmoid activation is defined as: > > - `0` if `if x < -2.5` > - `1` if `x > 2.5` > - `0.2 * x + 0.5` if `-2.5 <= x <= 2.5` In reality, it's not `-2.5` and `2.5`, but `-3.0` and `3.0` instead. Proof: ```python3 import tensorflow as tf for x in range(-40, 40): print(x/10, tf.keras.activations.hard_sigmoid(x/10).numpy()) ``` ``` -4.0 0.0 -3.9 0.0 -3.8 0.0 -3.7 0.0 -3.6 0.0 -3.5 0.0 -3.4 0.0 -3.3 0.0 -3.2 0.0 -3.1 0.0 -3.0 0.0 -2.9 0.01666665 -2.8 0.033333343 -2.7 0.049999993 -2.6 0.066666685 -2.5 0.083333336 -2.4 0.09999999 -2.3 0.116666675 -2.2 0.13333333 -2.1 0.15000002 -2.0 0.16666667 -1.9 0.18333334 -1.8 0.2 -1.7 0.21666665 -1.6 0.23333333 -1.5 0.25 -1.4 0.26666668 -1.3 0.28333333 -1.2 0.29999998 -1.1 0.31666666 -1.0 0.33333334 -0.9 0.35 -0.8 0.36666667 -0.7 0.38333333 -0.6 0.4 -0.5 0.41666666 -0.4 0.4333333 -0.3 0.45000002 -0.2 0.46666667 -0.1 0.48333335 0.0 0.5 0.1 0.51666665 0.2 0.53333336 0.3 0.55 0.4 0.56666666 0.5 0.5833333 0.6 0.59999996 0.7 0.6166667 0.8 0.6333333 0.9 0.65000004 1.0 0.6666667 1.1 0.68333334 1.2 0.7 1.3 0.7166667 1.4 0.73333335 1.5 0.75 1.6 0.76666665 1.7 0.7833333 1.8 0.8 1.9 0.81666666 2.0 0.8333333 2.1 0.84999996 2.2 0.8666666 2.3 0.8833334 2.4 0.90000004 2.5 0.9166667 2.6 0.93333334 2.7 0.95 2.8 0.9666667 2.9 0.98333335 3.0 1.0 3.1 1.0 3.2 1.0 3.3 1.0 3.4 1.0 3.5 1.0 3.6 1.0 3.7 1.0 3.8 1.0 3.9 1.0 ```
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Add ClearComputations() overload for HloAsyncStart instruction.
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2024-04-10T06:58:19
2024-04-11T06:52:57
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Add ClearComputations() overload for HloAsyncStart instruction. This replaces a previous fix in a better way. Otherwise, we will still have back pointers to instructions which have already been marked for deletion.
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tf.raw_ops.Unbatch aborts with "Check failed: d < dims()"
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[ "[gist](https://colab.research.google.com/drive/195LWjd1yjbF2kSDYexlxay0a9UQHqBO0?usp=sharing)", "@Assoap tf.raw_ops.Unbatch is designed to split a batched tensor along the first dimension (batch dimension) into separate elements. However, a scalar tensor has no dimension to split along. This mismatch in dimensions (d < dims()) leads to the error. Could you please remove the tf.raw_ops.Unbatch call altogether. The scalar tensor already represents a single value. Please let us know if it helps?\r\nThank you!", "@sushreebarsa Thank you so much for your prompt reply. I was just wondering, why didn't the program raise any exceptions, such as ValueError, but instead crashed (core dumped) directly?", "@Assoap Generally this issue occurs when a program encounters a critical error and crashes. The core memory, which holds essential data for the program's execution, is dumped to a file for potential debugging. TensorFlow might not have explicit error handling for every possible scenario within tf.raw_ops.Unbatch. If the internal checks fail due to unexpected input, the program might not have a proper way to recover and raise a user-friendly exception.\r\n\r\nThank you!", "> @Assoap Generally this issue occurs when a program encounters a critical error and crashes. The core memory, which holds essential data for the program's execution, is dumped to a file for potential debugging. TensorFlow might not have explicit error handling for every possible scenario within tf.raw_ops.Unbatch. If the internal checks fail due to unexpected input, the program might not have a proper way to recover and raise a user-friendly exception.\r\n> \r\n> Thank you!\r\n\r\nThat means users don't need to report check fails and they will stay forever then? In that case it will be better to highlight the same in README.md so that users can save their time in reporting such unnecessary (?) issues no ?\r\n\r\nCC: @mihaimaruseac \r\n", "Would be recommended to fix/eliminate the check fails, but they should be treated as bugs, not security issues." ]
2024-04-10T06:56:36
2024-04-29T06:37:47
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### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version tf 2.17 ### Custom code Yes ### OS platform and distribution Linux Ubuntu 20.04.3 LTS ### Mobile device _No response_ ### Python version Python 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? `tf.raw_ops.Unbatch` aborts with "Check failed: d < dims()" ### Standalone code to reproduce the issue ```shell import tensorflow as tf tf.raw_ops.Unbatch(batched_tensor=tf.random.uniform([], dtype=tf.dtypes.float32, maxval=100000000),batch_index=tf.random.uniform([1], dtype=tf.dtypes.int64, minval=-100000000, maxval=1000000000),id=tf.random.uniform([4], dtype=tf.dtypes.int64, minval=-100000000, maxval=1000000000),timeout_micros=30,container="N/J,",shared_name="&o0n7W",) ``` ### Relevant log output ```shell 2024-04-10 06:36:02.435578: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1) Aborted (core dumped) ```
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Remove final specifier from MockLoadedExecutable.
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[pjrt] Switch GetReadyFuture to PjRtFuture<> to signal completion event
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[pjrt] Switch GetReadyFuture to PjRtFuture<> to signal completion event
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[pjrt] Switch CopyRawToHost to PjRtFuture<> to signal completion event
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2024-04-10T02:52:04
2024-04-12T07:43:04
2024-04-12T07:43:02
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[pjrt] Switch CopyRawToHost to PjRtFuture<> to signal completion event
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[ifrt] Make xla::ifrt::DeviceList default constructible.
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2024-04-10T02:18:28
2024-04-10T19:55:35
2024-04-10T19:55:32
NONE
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[ifrt] Make xla::ifrt::DeviceList default constructible.
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Remove final specifier from IFRT mock classes.
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2024-04-10T01:38:31
2024-04-10T05:05:31
2024-04-10T05:05:30
NONE
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Remove final specifier from IFRT mock classes.
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[XLA:MSA] Move `BufferIntervalComparator` and its implementations `MemoryBoundednessBufferIntervalComparator` and `DefaultCrossProgramPrefetchBufferIntervalComparator` to `buffer_interval_comparator.h/cc` files under `memory_space_assignment` directory.
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2024-04-10T01:30:37
2024-04-14T23:52:42
2024-04-14T23:52:42
NONE
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[XLA:MSA] Move `BufferIntervalComparator` and its implementations `MemoryBoundednessBufferIntervalComparator` and `DefaultCrossProgramPrefetchBufferIntervalComparator` to `buffer_interval_comparator.h/cc` files under `memory_space_assignment` directory.
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Passes hints between consecutive iterations of lazy memory instantiation.
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2024-04-09T23:56:26
2024-04-10T01:51:15
2024-04-10T01:51:15
NONE
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Passes hints between consecutive iterations of lazy memory instantiation.
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Add tracing to linearization.
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2024-04-09T23:40:19
2024-04-11T18:12:17
2024-04-11T18:12:16
NONE
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Add tracing to linearization.
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Make `xla::TypeToShape` could convert `stablehlo.token` to xla token shape rather than an empty shape.
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2024-04-09T23:23:24
2024-04-15T18:38:38
2024-04-15T18:38:38
NONE
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Make `xla::TypeToShape` could convert `stablehlo.token` to xla token shape rather than an empty shape. Also fixed some headers.
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#tf-data Implement `weighted_flat_map` API that can be used to implement random access compatible dataset mixing.
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2024-04-09T22:53:33
2024-04-11T21:34:27
2024-04-11T21:34:25
NONE
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#tf-data Implement `weighted_flat_map` API that can be used to implement random access compatible dataset mixing.
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65,362
Move tsl/distributed_runtime to xla/tsl/distributed_runtime
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2024-04-09T22:49:46
2024-04-11T23:53:22
2024-04-11T23:53:21
NONE
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Move tsl/distributed_runtime to xla/tsl/distributed_runtime
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65,361
Optimize `BatchedDevicePut` by batching GIL release
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2024-04-09T22:28:18
2024-04-15T19:45:36
2024-04-15T19:45:35
NONE
null
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Optimize `BatchedDevicePut` by batching GIL release The current `BatchedDevicePut` implementation interleaves Python work and C++ IFRT work, causing GIL to be repeatedly released/acquired for a single `jax.device_put()` call. This is particularly problematic for cases where there are a large number of devices. This CL refactors `BatchedDevicePut` into two phases: `DevicePut` performs all Python work inline for formatting/casting the input object and returns a functor that performs C++ work. This allows us to change `BatchedDevicePut` to perform all Python work upfront, leaving all C++ work across all shards to be done in one GIL release.
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65,360
Add `GetOpSharding` and `SetInstructionSharding` in `XlaBuilder`.
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2024-04-09T21:54:27
2024-04-11T04:24:14
2024-04-11T04:24:13
NONE
null
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Add `GetOpSharding` and `SetInstructionSharding` in `XlaBuilder`. They are similar to `GetShape` and `SetInstructionFrontendAttribute`.
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PR_kwDOArmXAs5sLhX8
65,359
Clean up ownership of IfrtServingExecutable.
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2024-04-09T21:43:36
2024-04-10T04:12:03
2024-04-10T04:12:02
NONE
null
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Clean up ownership of IfrtServingExecutable. Instead of using std::shared_ptr, IfrtExecutableRegistry hold the std::unique_ptr to it and guarantee its life time for the model.
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65,358
Fix performance issue in colocate_predecessor_trees pass, remove the use of in_node_mode and Node::IsExit() method.
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2024-04-09T21:43:35
2024-04-09T21:43:35
null
NONE
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Fix performance issue in colocate_predecessor_trees pass, remove the use of in_node_mode and Node::IsExit() method.
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[ifrt] Change Client::GetTopologyForDevices to take a DeviceList instead of a Span of device pointers. This would enable client implementations to make use of the cached hash of the DeviceList.
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2024-04-09T21:42:10
2024-04-10T00:50:58
2024-04-10T00:50:57
NONE
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[ifrt] Change Client::GetTopologyForDevices to take a DeviceList instead of a Span of device pointers. This would enable client implementations to make use of the cached hash of the DeviceList.
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65,356
Register vhlo related pass and other dialects.
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2024-04-09T21:25:49
2024-04-15T20:37:59
2024-04-15T20:37:58
NONE
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Register vhlo related pass and other dialects.
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65,355
[XLA:Python] Delete unused method make_cross_host_receive_buffers.
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2024-04-09T21:21:58
2024-04-10T15:12:26
2024-04-10T15:12:24
NONE
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[XLA:Python] Delete unused method make_cross_host_receive_buffers. This was added as part of a prototype but no users appear to exist any more, and it was never a publicized API. FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11331 from ROCm:rocm_triton_fix_1 a4f80a3f32a10230bfdaa1dadc12f1f385883d8a
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65,354
Integrate LLVM at llvm/llvm-project@e248f0df14e4
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2024-04-09T21:15:14
2024-04-18T10:54:46
2024-04-18T10:54:43
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Integrate LLVM at llvm/llvm-project@e248f0df14e4 Updates LLVM usage to match [e248f0df14e4](https://github.com/llvm/llvm-project/commit/e248f0df14e4)
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65,353
Remove device_ordinal from StreamExecutorInterface::Init method.
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2024-04-09T21:05:45
2024-04-11T00:47:43
2024-04-11T00:47:41
NONE
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Remove device_ordinal from StreamExecutorInterface::Init method. Set it in the constructor of each StreamExecutor implementation that needs it instead.
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Support LLVM code generation for hexagon target in Tensorflow compiler.
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2024-04-09T20:55:19
2024-04-11T16:13:15
2024-04-11T16:13:14
NONE
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Support LLVM code generation for hexagon target in Tensorflow compiler.
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65,351
[pjrt] Switch ToLiteral to PjRtFuture<> to signal completion event
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2024-04-09T20:36:53
2024-04-11T19:51:36
2024-04-11T19:51:35
NONE
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[pjrt] Switch ToLiteral to PjRtFuture<> to signal completion event
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65,350
Add `node_id` to `CoordinationServiceImpl`.
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2024-04-09T20:27:34
2024-04-10T22:26:51
2024-04-10T22:26:49
NONE
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Add `node_id` to `CoordinationServiceImpl`.
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Create IfrtServingCoreSelector wrapper that wraps a tsl::ServiceDeviceSelector.
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2024-04-09T20:25:19
2024-04-11T22:24:34
2024-04-11T22:24:34
NONE
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Create IfrtServingCoreSelector wrapper that wraps a tsl::ServiceDeviceSelector.
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Addressing TF query analysis errors for @local_tsl repository
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2024-04-09T19:51:10
2024-04-09T19:51:10
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Addressing TF query analysis errors for @local_tsl repository
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Added support of C++20 for OSS TF for Linux platform
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2024-04-09T19:28:18
2024-04-09T19:28:18
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Added support of C++20 for OSS TF for Linux platform
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Make StreamExecutorInterface::HostMemoryAllocate return an absl::StatusOr<std::unique_ptr<MemoryAllocation>> in preparation for StreamExecutor inheriting from it.
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2024-04-09T18:54:17
2024-04-10T19:30:10
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Make StreamExecutorInterface::HostMemoryAllocate return an absl::StatusOr<std::unique_ptr<MemoryAllocation>> in preparation for StreamExecutor inheriting from it.
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65,345
Ignore tiling in layout comparison between parameter shape and computation layout shape.
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2024-04-09T18:47:49
2024-04-11T22:29:58
2024-04-11T22:29:58
NONE
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Ignore tiling in layout comparison between parameter shape and computation layout shape.
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65,344
Integrate LLVM at llvm/llvm-project@1e6ce5e284f5
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2024-04-09T18:45:56
2024-04-10T19:28:24
2024-04-10T19:28:22
NONE
null
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Integrate LLVM at llvm/llvm-project@1e6ce5e284f5 Updates LLVM usage to match [1e6ce5e284f5](https://github.com/llvm/llvm-project/commit/1e6ce5e284f5)
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[pjrt] Switch AddChunk to PjRtFuture<> from PjRtFuture<Status> to signal completion
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2024-04-09T18:42:36
2024-04-11T16:30:14
2024-04-11T16:30:13
NONE
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[pjrt] Switch AddChunk to PjRtFuture<> from PjRtFuture<Status> to signal completion
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65,342
Remove outdated iOS example app.
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2024-04-09T18:37:05
2024-04-10T09:16:55
2024-04-10T09:16:55
NONE
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Remove outdated iOS example app.
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Downgrade rendezvous abortion logging.
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2024-04-09T18:14:57
2024-04-09T23:10:04
2024-04-09T23:10:04
NONE
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Downgrade rendezvous abortion logging. This produces a confusing warning when iterating over a `tf.data.Dataset`. For example, see the current state of https://www.tensorflow.org/tutorials/keras/text_classification#load_the_dataset (sorry for the nonfutureproof link; I can't figure out how to link to a specific version of this tutorial that includes the output), where this warning shows up in the output of the iteration over `raw_train_ds.take(1)`.
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[xla:ffi] Fix test broken by xla:ffi API changes
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2024-04-09T18:03:09
2024-04-10T03:08:27
2024-04-10T03:08:26
NONE
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[xla:ffi] Fix test broken by xla:ffi API changes
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Change StreamExecutor::HostMemoryAllocate to return a std::unique_ptr<MemoryAllocation> rather than a std::unique_ptr<HostMemoryAllocation>.
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2024-04-09T18:00:20
2024-04-10T18:44:50
2024-04-10T18:44:49
NONE
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Change StreamExecutor::HostMemoryAllocate to return a std::unique_ptr<MemoryAllocation> rather than a std::unique_ptr<HostMemoryAllocation>.
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65,338
Allow setting operand_shapes_with_layout on custom call instruction.
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2024-04-09T17:49:05
2024-05-10T02:59:49
2024-05-10T02:59:46
NONE
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Allow setting operand_shapes_with_layout on custom call instruction.
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65,337
Adding null pointer checks.
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2024-04-09T17:45:03
2024-04-09T23:43:12
2024-04-09T23:43:11
NONE
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Adding null pointer checks.
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65,336
[IFRT] Add `DeserializeProgramOptions`
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2024-04-09T17:29:48
2024-04-10T03:52:12
2024-04-10T03:52:11
NONE
null
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[IFRT] Add `DeserializeProgramOptions` `DeserializeProgramOptions` is used when deserializing `Program`. It now takes `lookup_device` that allows `Program` deserialization to reconstruct any `Device`-related objects (e.g., `DeviceList`, `Sharding`). Traditionally, a program encoded device ids without IFRT serialization support -- e.g., `device_assignment` in `xla::ExecutableBuildOptions` included in `xla::CompileOptions` or `devices` in IFRT IR. Similar for sharding representations. They do not need to look up devices when deserializing `Program`, but some stack needs to check the device ids and shardings eventually. Having `lookup_device` in `DeserializeProgramOptions` would make this validation done at the deserialization time and applied to `Program` subclasses automaticallly as long as they use IFRT `Sharding` objects in the subclasses. Also move `Program` out of `ifrt/compiler.h` as a program stands by itself, and is not dependent on `Compiler`.
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65,335
Use DenseElements to serialize/deserialize dense arrays
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2024-04-09T17:24:59
2024-04-09T18:47:42
2024-04-09T18:47:41
NONE
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Use DenseElements to serialize/deserialize dense arrays This avoids asan issues, makes the code a lot cleaner, and avoids depending on implementation details of how the data is laid out.
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65,334
[xla:ffi] Add XLA_FFI_Api access to C API header
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2024-04-09T17:07:11
2024-04-09T18:03:33
2024-04-09T18:03:33
NONE
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[xla:ffi] Add XLA_FFI_Api access to C API header
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65,333
[XLA] don't optimize bitcast(broadcast) if the bitcast does not have teh same
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2024-04-09T17:02:18
2024-04-09T18:40:50
2024-04-09T18:40:49
NONE
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[XLA] don't optimize bitcast(broadcast) if the bitcast does not have teh same input and output types.
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65,332
Remove deprecated usePropertiesForAttributes.
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2024-04-09T16:43:24
2024-04-09T17:22:09
2024-04-09T17:22:09
NONE
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Remove deprecated usePropertiesForAttributes.
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65,331
tf.keras.utils.plot_model doesn't work
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null
[ "Hi **@Inquisitive-ME** ,\r\nSorry for the delay, I tried to run your code on colab using TF v2.15 works fine, 2.16.1 and nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/0e870200a228011b561e9b5b5e510a0e/65331_2-15-2-16-nightly.ipynb) here for reference.\r\n\r\nThank you!", "Hi **@Inquisitive-ME** ,\r\n- I think this was resolved in recent tf-nightly. Please check the [gist](https://colab.research.google.com/gist/Venkat6871/56ddc977ca65f0fbe31bc24d97a68fa4/65331_tf-nightly-v.ipynb) here.\r\n\r\nThank you!" ]
2024-04-09T16:39:55
2024-06-11T15:18:54
null
NONE
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### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? No ### Source binary ### TensorFlow version 2.16.1 ### Custom code No ### OS platform and distribution Linux Ubuntu 22.04 ### Mobile device _No response_ ### Python version 3.10 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? It fails to plot the model. This works like many things in tensorflow 2.15 but is broken in tensorflow 2.16.1 "dot" with args ['-Tpng', '/tmp/tmp48b95hyk'] returned code: -6 stdout, stderr: b'' b"failed at node 11921[1]\ndot: maze.c:313: chkSgraph: Assertion `np->cells[1]' failed.\n" ### Standalone code to reproduce the issue ```shell import tensorflow as tf model = tf.keras.applications.EfficientNetV2B0() tf.keras.utils.plot_model(model, to_file="EfficientNetV2B0.png", show_shapes=True, show_layer_names=True) ``` ### Relevant log output ```shell "dot" with args ['-Tpng', '/tmp/tmp48b95hyk'] returned code: -6 stdout, stderr: b'' b"failed at node 11921[1]\ndot: maze.c:313: chkSgraph: Assertion `np->cells[1]' failed.\n" ```
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[pjrt] Switch DonateWithControlDependency to PjRtFuture<> to pass dependencies
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2024-04-09T16:27:03
2024-04-10T00:44:31
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[pjrt] Switch DonateWithControlDependency to PjRtFuture<> to pass dependencies
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Avoid unnecessary HLO proto copy.
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Avoid unnecessary HLO proto copy.
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Rename mlir_import_options to saved_model_import_options.
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2024-04-09T15:32:14
2024-05-14T17:58:19
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Rename mlir_import_options to saved_model_import_options.
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Add `load()` statements for the Bazel builtin top-level java symbols
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2024-04-11T08:11:39
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Add `load()` statements for the Bazel builtin top-level java symbols Loads are being added in preparation for moving the symbols out of Bazel and into `rules_java`.
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PR #11307: [ROCm] Upstreaming old changes
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2024-04-09T14:46:26
2024-04-10T11:25:27
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PR #11307: [ROCm] Upstreaming old changes Imported from GitHub PR https://github.com/openxla/xla/pull/11307 This PR upstreams changes from [#2467](https://github.com/ROCm/tensorflow-upstream/pull/2467/commits/c3d966e559ffd13f6a473d1b57e606314a456ce8) and also upstreams changes to `gpu_layout_assignment.cc` introduced [here](https://github.com/ROCm/tensorflow-upstream/commit/b34b1eb35c095a16208356913a956256cc419e99) and [here](https://github.com/ROCm/tensorflow-upstream/commit/dfb7dd0b2f7e6c6bbcd3f1dbc9e299be9554b4a7). Copybara import of the project: -- ceaa8b0ab3dfda7ad93a7c57f5c26e6b31365a80 by mmakevic <[email protected]>: adding env variable to control rocm_dnn workspace limit -- b7661ef811dfff68e03b4fe4306c1f63ffd1e2ee by mmakevic <[email protected]>: Changes to use NHWC for FP16 ops on XLA path for ROCm 5.0 and MI100/200 Fix double = -- cfeb2dec629eac32d5afc8256f2c684bbe81fe75 by mmakevic <[email protected]>: Check if rocm or cuda are used during runtime Merging this change closes #11307 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11307 from ROCm:ci_upstream_old_changes cfeb2dec629eac32d5afc8256f2c684bbe81fe75
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Ensemble provides either correct or (the same) incorrect inference results
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[ "@joeyearsley,\r\nPlease, share colab link or simple standalone code with supporting files to reproduce the issue in our environment.It helps us in localizing the issue faster. Thank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65325\">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/65325\">No</a>\n" ]
2024-04-09T14:18:11
2024-04-15T15:38:28
2024-04-15T15:38:26
CONTRIBUTOR
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### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version 2.14 ### 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? I have an ensemble; I run the models sequentially. It's flaky in the results it returns; sometimes, one model gives the correct result of 0.37. Other times, it gives the incorrect result of 0.30 (never any other value). I've tried turning on deterministic ops, but I still have this flaky issue; everything else stays the same. I've tested on an old TitanX, T4 & A100 - this test is always flaky. I've tried debugging via the VLOG, but nothing jumps out; the only difference I can spot is for some reason, different cudnn Conv Ops might be chosen (not 100% from the logs). Are there other suggestions on how to debug this issue? - I understand there might be decimal point changes but this swing is huge. ### Standalone code to reproduce the issue ```shell I can't share the models. ``` ### Relevant log output ```shell - ```
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PR #11307: [ROCm] Upstreaming old changes
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2024-04-09T14:13:27
2024-04-17T08:16:44
2024-04-17T08:16:42
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PR #11307: [ROCm] Upstreaming old changes Imported from GitHub PR https://github.com/openxla/xla/pull/11307 This PR upstreams changes from [#2467](https://github.com/ROCm/tensorflow-upstream/pull/2467/commits/c3d966e559ffd13f6a473d1b57e606314a456ce8) and also upstreams changes to `gpu_layout_assignment.cc` introduced [here](https://github.com/ROCm/tensorflow-upstream/commit/b34b1eb35c095a16208356913a956256cc419e99) and [here](https://github.com/ROCm/tensorflow-upstream/commit/dfb7dd0b2f7e6c6bbcd3f1dbc9e299be9554b4a7). Copybara import of the project: -- ceaa8b0ab3dfda7ad93a7c57f5c26e6b31365a80 by mmakevic <[email protected]>: adding env variable to control rocm_dnn workspace limit -- b7661ef811dfff68e03b4fe4306c1f63ffd1e2ee by mmakevic <[email protected]>: Changes to use NHWC for FP16 ops on XLA path for ROCm 5.0 and MI100/200 Fix double = -- cfeb2dec629eac32d5afc8256f2c684bbe81fe75 by mmakevic <[email protected]>: Check if rocm or cuda are used during runtime Merging this change closes #11307 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11307 from ROCm:ci_upstream_old_changes cfeb2dec629eac32d5afc8256f2c684bbe81fe75
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PR #11205: [ROCm] Fix link failure for rocm targets
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2024-04-09T13:26:41
2024-04-09T15:58:19
2024-04-09T15:58:18
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PR #11205: [ROCm] Fix link failure for rocm targets Imported from GitHub PR https://github.com/openxla/xla/pull/11205 Copybara import of the project: -- 91df7b60a4aac6da70165948a39805d0d3921067 by Harsha HS <[email protected]>: [ROCm] Fix build break due to 4c8a74bb5 -- 85cd084e52340cc2069ab5c4c57c210e466846a1 by Harsha HS <[email protected]>: [ROCm] Fix link failure for rocm targets -- 8ee9b5e90d4e1a3ca7734bbe1ef73140e4d879dd by Harsha H S <[email protected]>: Update xla/stream_executor/gpu/gpu_timer.cc with review comment Co-authored-by: Penporn Koanantakool <[email protected]> Merging this change closes #11205 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11205 from ROCm:ci_link_break_20240404 8ee9b5e90d4e1a3ca7734bbe1ef73140e4d879dd
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2024-04-09T12:32:47
2024-04-09T21:02:19
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2024-04-10T10:01:35
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2024-04-09T11:19:09
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2024-04-09T10:58:34
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[DOCS] Missing complex input for Round op
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[ "Hi @LucaTamSapienza ,\r\n\r\nThanks for reporting. IMO, complex data types not supported for this Op as per below code. \r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/a787405cf0adab1793b8f01c32d222ec469c8818/tensorflow/core/kernels/cwise_ops.h#L513\r\nMay need to update the same wrt documentation. Also I am not sure for what case rounding is applicable to int32, int_64. Though they are not raising any error but returning same result. \r\n\r\nWill propose some modifications and let's hear from dev team." ]
2024-04-09T10:54:25
2024-04-29T06:16:48
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NONE
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### Issue type Documentation Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version 2.15.0 ### Custom code No ### OS platform and distribution MacOS Sonoma ### 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? While testing the `Round` operation, I noticed an inconsistency with the [documentation](https://www.tensorflow.org/api_docs/python/tf/raw_ops/Round). On the official page, it's described that a complex tensor can be passed as input. However, when I try to replicate this in my environment, it doesn't work as expected. I have to apply the `Round` operation separately to the real and imaginary parts of the tensor to achieve the desired result. ### Standalone code to reproduce the issue ```shell import tensorflow as tf x = tf.constant([1+2j, 3+4j]) rounded = tf.raw_ops.Round(x=x) ``` ### Relevant log output ```shell Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/lucatam/Documents/OpenSourceProject/openvino/opvenv/lib/python3.9/site-packages/tensorflow/python/util/tf_export.py", line 403, in wrapper return f(**kwargs) File "/Users/lucatam/Documents/OpenSourceProject/openvino/opvenv/lib/python3.9/site-packages/tensorflow/python/ops/gen_math_ops.py", line 8770, in round _ops.raise_from_not_ok_status(e, name) File "/Users/lucatam/Documents/OpenSourceProject/openvino/opvenv/lib/python3.9/site-packages/tensorflow/python/framework/ops.py", line 5883, in raise_from_not_ok_status raise core._status_to_exception(e) from None # pylint: disable=protected-access tensorflow.python.framework.errors_impl.NotFoundError: Could not find device for node: {{node Round}} = Round[T=DT_COMPLEX128] All kernels registered for op Round: device='XLA_CPU_JIT'; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT16, DT_INT8, DT_COMPLEX64, DT_INT64, DT_BFLOAT16, DT_COMPLEX128, DT_HALF] device='CPU'; T in [DT_HALF] device='CPU'; T in [DT_FLOAT] device='CPU'; T in [DT_DOUBLE] device='CPU'; T in [DT_BFLOAT16] device='CPU'; T in [DT_INT32] device='CPU'; T in [DT_INT64] [Op:Round] name: ```
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[Android x86_64] [TFLite] `Fatal signal 11 (SIGSEGV), code 128 (SI_KERNEL), fault addr 0x0` crash from `libtensorflowlite.so`
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2024-04-09T10:47:23
2024-04-09T12:22:59
2024-04-09T12:22:55
NONE
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### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source source ### TensorFlow version tf 2.16.1 ### Custom code Yes ### OS platform and distribution Ubuntu Host Build Machine ### Mobile device Android 11 X86_64 ### Python version _No response_ ### Bazel version 6.5.0 ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? I was testing https://github.com/nyadla-sys/whisper.tflite / https://github.com/nyadla-sys/whisper.tflite/tree/main/whisper_android on Android x86_64 Initially I was able to run it without any issues on real x86_64 Android device. It was correctly running the inference and returned back with transcribed text. But after about 30 mins of using it, it just mysteriously started crashing without much visibility from the logs ### Standalone code to reproduce the issue ```shell Steps performed to build x86_64 Android 1) Clone tensorflow repo 2) Build with bazel using `bazel build -c opt //tensorflow/lite:libtensorflowlite.so --linkopt="-s" --cpu=x86_64 --config=android_x86_64 --fat_apk_cpu=x86_64` 3) Load https://github.com/nyadla-sys/whisper.tflite/tree/main/whisper_android in Android Studio 4) Copied generated `libtensorflowlite.so` and replaced it @ `cpp/tf-lite-api/generated-libs/x86_64` 5) Updated tensorflow headers along with absl and flatbuffers by copying it from bazel-tensorflow_src/tensorflow/lite, bazel-tensorflow_src/external/flatbuffers, bazel-tensorflow_src/external/com_google_absl @ `cpp/tf-lite-api/include` and `cpp/tf-lite-api/tensorflow_src/tensorflow/lite` 6) Run ``` ### Relevant log output ```shell 03:28:23.831 A Fatal signal 11 (SIGSEGV), code 128 (SI_KERNEL), fault addr 0x0 in tid 18464 (Thread-3), pid 17926 (m.whispertflite) 03:28:23.846 A pid: 17926, tid: 18464, name: Thread-3 >>> com.whispertflite <<< 03:28:23.902 A #00 pc 0000000000beac5a /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/base.apk!libtensorflowlite.so (offset 0x70f000) (BuildId: 8687099524ac9c38f7c004e514867227) 03:28:23.902 A #01 pc 0000000000959178 /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/base.apk!libtensorflowlite.so (offset 0x70f000) (BuildId: 8687099524ac9c38f7c004e514867227) 03:28:23.902 A #02 pc 0000000000bec1b2 /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/base.apk!libtensorflowlite.so (offset 0x70f000) (BuildId: 8687099524ac9c38f7c004e514867227) 03:28:23.902 A #03 pc 0000000000959b62 /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/base.apk!libtensorflowlite.so (offset 0x70f000) (BuildId: 8687099524ac9c38f7c004e514867227) 03:28:23.902 A #04 pc 000000000091f02b /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/base.apk!libtensorflowlite.so (offset 0x70f000) (BuildId: 8687099524ac9c38f7c004e514867227) 03:28:23.902 A #05 pc 000000000043e5df /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/base.apk!libtensorflowlite.so (offset 0x70f000) (BuildId: 8687099524ac9c38f7c004e514867227) 03:28:23.902 A #06 pc 0000000000433cd8 /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/base.apk!libtensorflowlite.so (offset 0x70f000) (BuildId: 8687099524ac9c38f7c004e514867227) 03:28:23.902 A #07 pc 0000000000c5b3de /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/base.apk!libtensorflowlite.so (offset 0x70f000) (tflite::Subgraph::OpInvoke(TfLiteRegistration const&, TfLiteNode*)+270) (BuildId: 8687099524ac9c38f7c004e514867227) 03:28:23.902 A #08 pc 0000000000c5c097 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(tflite::impl::Interpreter::Invoke()+114) (BuildId: 8687099524ac9c38f7c004e514867227) 03:28:23.902 A #16 pc 0000000000066482 /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/base.apk!libaudioEngine.so (offset 0x430000) (TFLiteEngine::transcribeBuffer(std::__ndk1::vector<float, std::__ndk1::allocator<float> >)+498) (BuildId: 61305ae1e4739848602842cf100b2e63d86d9044) 03:28:23.902 A #17 pc 00000000000667bc /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/base.apk!libaudioEngine.so (offset 0x430000) (TFLiteEngine::transcribeFile(char const*)+236) (BuildId: 61305ae1e4739848602842cf100b2e63d86d9044) 03:28:23.902 A #18 pc 0000000000068f02 /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/base.apk!libaudioEngine.so (offset 0x430000) (Java_com_whispertflite_engine_WhisperEngineNative_transcribeFile+66) (BuildId: 61305ae1e4739848602842cf100b2e63d86d9044) 03:28:23.902 A #19 pc 000000000002fc5b /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/oat/x86_64/base.odex (art_jni_trampoline+203) 03:28:23.902 A #21 pc 00000000008067e0 /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/oat/x86_64/base.vdex (com.whispertflite.engine.WhisperEngineNative.transcribeFile+4) 03:28:23.902 A #23 pc 00000000008065ee /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/oat/x86_64/base.vdex (com.whispertflite.asr.Whisper.threadFunction+126) 03:28:23.902 A #25 pc 0000000000806344 /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/oat/x86_64/base.vdex (com.whispertflite.asr.Whisper.lambda$start$0$com-whispertflite-asr-Whisper+12) 03:28:23.902 A #27 pc 00000000008061d0 /data/app/~~jUSctKS5U4J9GxcVh9Mxsw==/com.whispertflite-jOl8yuCGBpVIrjyiqNnSfw==/oat/x86_64/base.vdex (com.whispertflite.asr.Whisper$$ExternalSyntheticLambda0.run+4) 03:28:23.985 E channel 'ddeecf4 com.whispertflite/com.whispertflite.MainActivity (server)' ~ Channel is unrecoverably broken and will be disposed! ```
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2024-04-09T10:42:11
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2024-04-09T10:41:52
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2024-04-09T10:00:29
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Revert "Sum and Mean only support F32"
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null
[ "This was over-zealous, mean does in fact support quantization, sum does not however. This was triggered when enabling support for models with dynamic tensors.\r\n\r\nI will revert the mean change internally as it will undoubtedly cause some tests to fail. Should land in a few days. ", "Thanks for the fast reply, I wasn't aware that sum doesn't yet support quantization.", "Support for generic reduction (any axis combination supported) for f32 and signed quantized is being worked on and should land in the next few weeks." ]
2024-04-09T09:51:57
2024-04-10T03:25:14
2024-04-09T10:34:26
CONTRIBUTOR
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This PR reverts commit 990b87a2d40a236517b758e27c850679f0a87213. 990b87a2d40a236517b758e27c850679f0a87213 removed support for integer quantized sum and mean ops. As far as I can tell XNNPACK correctly supports quantized versions of these ops but maybe @alankelly has additional context on why this was removed 990b87a2d40a236517b758e27c850679f0a87213.
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2024-04-09T09:42:22
2024-04-09T19:39:37
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Complex dtype input for keras layer in tf2.16+
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null
[ "Hi **@refraction-ray** ,\r\n\r\nSorry for the delay, I tried to run your code on Colab using TF v2.15, 2.16.1 and faced the same issue. Please find the [gist ](https://colab.sandbox.google.com/gist/Venkat6871/ff152f2a9a9ccea82ee442fccaac4677/65306-2-15-2-16-1-nightly.ipynb)here for reference.\r\n\r\nThank you!", "Hi @refraction-ray ,\r\n\r\nIn Keras3 complex dtypes not yet included in supported dtypes. Not sure of the reason for same or is it just missed adding.\r\n\r\nYou can found the allowed input dtypes of Keras3 from [here](https://github.com/keras-team/keras/blob/master/keras/src/backend/common/dtypes.py#L26).\r\n\r\nIf you have bandwidth you can add complex dtypes to allowed dtypes locally and test with different backends like torch and jax and update us. Thanks!", "hi @refraction-ray , I am having the exact same problem. Could you find a temporary solution for this? ", "@myigitavci , my current solution is to fix the tf version <= 2.15" ]
2024-04-09T09:25:59
2024-05-06T02:27:36
null
CONTRIBUTOR
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### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? No ### Source binary ### TensorFlow version tf2.16.1 ### Custom code Yes ### OS platform and distribution colab ### Mobile device _No response_ ### Python version colab default ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? The following code works well for tf2.15 but fails tf2.16.1 with the introduction of keras3, ``` import tensorflow as tf print(tf.__version__) class MyDenseLayer(tf.keras.layers.Layer): def __init__(self, num_outputs): super(MyDenseLayer, self).__init__() self.num_outputs = num_outputs def build(self, input_shape): self.kernel = self.add_weight(shape=[int(input_shape[-1]), self.num_outputs], ) def call(self, inputs): kernel = tf.cast(self.kernel, tf.complex64) return tf.matmul(inputs, kernel) layer = MyDenseLayer(10) layer(tf.zeros([10, 5], dtype=tf.complex64)) ``` For short, tf2.16.1 with newly introduced keras 3 seems not supporting the input tensor for a layer in complex dtype ### Standalone code to reproduce the issue ```shell import tensorflow as tf print(tf.__version__) class MyDenseLayer(tf.keras.layers.Layer): def __init__(self, num_outputs): super(MyDenseLayer, self).__init__() self.num_outputs = num_outputs def build(self, input_shape): self.kernel = self.add_weight(shape=[int(input_shape[-1]), self.num_outputs], ) def call(self, inputs): kernel = tf.cast(self.kernel, tf.complex64) return tf.matmul(inputs, kernel) layer = MyDenseLayer(10) layer(tf.zeros([10, 5], dtype=tf.complex64)) ``` ### Relevant log output ```shell [usr/local/lib/python3.10/dist-packages/keras/src/backend/common/variables.py](https://localhost:8080/#) in standardize_dtype(dtype) 428 429 if dtype not in dtypes.ALLOWED_DTYPES: --> 430 raise ValueError(f"Invalid dtype: {dtype}") 431 return dtype 432 ValueError: Invalid dtype: complex64 ```
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PR #11182: [NVIDIA GPU] Optimize collective permute using memcpy
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2024-04-09T09:18:11
2024-04-10T14:47:29
2024-04-10T14:47:29
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PR #11182: [NVIDIA GPU] Optimize collective permute using memcpy Imported from GitHub PR https://github.com/openxla/xla/pull/11182 When the source and target pair of a collective permute instruction are located within the same node, we will try to use cudaMemCpyPeerToPeer to send data instead of invoking nccl send and recv kernels. Because memcpy will use the copy engine on the GPU instead of launching a kernel, copy engine can fully saturate the bandwidth without occupying any SM. Since memcpyP2p needs the destination pointer of the other device, we first communicate the pointer values with peer using nccl calls and use the pointer to invoke memcpy. Some perf comparison: sending a tensor of this size bf16[512,24576]{1,0}: Using collective-permute: 445 us using memcpy: 100us Copybara import of the project: -- 73a2568520d411eb6f62b6383a3841cb0f8edf22 by TJ <[email protected]>: optimize collective permute using memcpy -- 1dd896f544852fa898e937af8c0b94e075db3ec2 by TJ <[email protected]>: Remove nccl calls and use Async value -- a2fbc7d29d3d8106753f36e2a477e0889da6cb58 by TJ <[email protected]>: wrap async map and mutex with a class Merging this change closes #11182 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11182 from Tixxx:tixxx/optimize_cp a2fbc7d29d3d8106753f36e2a477e0889da6cb58
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Support arbitrary indexing for epilogue tuple elements.
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2024-04-09T09:17:04
2024-04-09T11:33:34
2024-04-09T11:33:33
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Support arbitrary indexing for epilogue tuple elements. The roots of a tuple can all have different indexing, something that wasn't considered in the original design. I'm not happy with the implementation, but it's the best I could come up with before we switch to tensor lowerings.
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Automated Code Change
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65,302
Tensorflow TF ops - CPU Factory not registered
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[ "@BasilZinsli,\r\nCould you please share the colab link or simple standalone code with supporting files to reproduce the issue in our environment and the tensorflow version you are using. It helps us in localizing the issue faster. Thank you!", "@tilakrayal \r\nThank you for your fast response! I work with @BasilZinsli on this project. Hereafter you can find the whole project as zip:\r\n\r\nhttps://drive.proton.me/urls/GPQ76SS1J4#WKIT4NEkhHBH\r\n\r\nNote that the folder \"PA_Emotion_Detection_Python_Model\" contains a legacy project which the current project was built upon. The actual code is in \"Voiceswap\". Spleeter.swift is the class of interest. We converted the 2stems spleeter model into tflite and want to run it on iOS. \r\n\r\nThank you in advance!", "@tilakrayal and to reproduce the error, just hit the play button on the app after building it. \r\n- currently the app is using the selfmade build of TFOPS via bazel as mentioned above.\r\n- runing the pod installed version for TFOPS also throws an error which also should be solved in my opinion.\r\n\r\nthanks for your help! \r\nBest Regards", "Hi @fabiancannaheim, @BasilZinsli, I tried building your project but I run into this issue:\r\n\r\n```\r\nOne of the paths in DEVELOPMENT_ASSET_PATHS does not exist: /path/to/App/AudioPlayer/Prevew Content\r\n```\r\n\r\nI checked that path from where I unzipped the archive and that path does indeed not exist, should I remove this from Development Assets? Are the development assets in some other location in the archive? Let me know how you think I should best proceed. Thanks.", "Hi @pkgoogle \r\nThanks for answering. I guess the preview does not work without the simulater anyways, it is becuase of the Pods.\r\nYou can remove it I guess, or create the folder it asks for. But the preview does not need to work to reproduce the problem i guess.\r\n\r\nLet me know if I can help further.\r\nThanks again\r\n\r\n", "@pkgoogle oh and very important, please use the AppV1 branch. Totally forgot to tell you", "Hi @BasilZinsli, Now it's complaining about these 2 models missing (2stems and 4stems):\r\n\r\n![image](https://github.com/tensorflow/tensorflow/assets/132095473/239af99f-216c-4729-afda-ca295329de89)\r\n\r\nI'm not understanding what you mean by the AppV1 branch, is that a subdirectory in the uploaded zip or are you referring to a repo? If it is a repo, can you point me to the repo?\r\n", "Hey @pkgoogle \r\n\r\nSorry i made a mistake mistake in my thinking. It is a git repo but we send you the essential part of it as ZIP. So no branches for you. \r\n\r\nI see the mistake, we had the models saved in a different folder, i will send them here:\r\nhttps://drive.google.com/file/d/1nWW3TIvbg_f4usNddwwglwYSOqDbIoQx/view?usp=sharing\r\n\r\nMake sure to drag them into the voiceswap and hit copy items so the files a correctly linked into the xcode project.\r\n\r\nThank you!\r\n\r\n\r\n", "Hi @BasilZinsli, so I followed your instructions and I was actually able to run the app and hit play on Emulator. I did do this so that it could run on emulator:\r\n\r\n![image](https://github.com/tensorflow/tensorflow/assets/132095473/0fd027b1-48cb-4641-a1cf-6e9b43af4275)\r\n\r\nConsole output (I hit play a couple of times):\r\n\r\n```\r\n2024-04-12 13:36:46.278163-0700 App[17567:1256634] [SwiftUI] Reading a Namespace property outside View.body. This will result in identifiers that never match any other identifier.\r\n2024-04-12 13:36:47.379210-0700 App[17567:1257886] [plugin] AddInstanceForFactory: No factory registered for id <CFUUID 0x600000720540> F8BB1C28-BAE8-11D6-9C31-00039315CD46\r\n2024-04-12 13:36:47.553785-0700 App[17567:1257964] [AMCP] 38411 HALC_ProxyIOContext.cpp:1329 HALC_ProxyIOContext::IOWorkLoop: skipping cycle due to overload\r\n2024-04-12 13:36:47.568176-0700 App[17567:1256634] Initialized TensorFlow Lite runtime.\r\nINFO: Initialized TensorFlow Lite runtime.\r\n2024-04-12 13:36:48.260436-0700 App[17567:1257964] [AMCP] 38411 HALC_ProxyIOContext.cpp:1329 HALC_ProxyIOContext::IOWorkLoop: skipping cycle due to overload\r\n0.0\r\n0.1546936\r\n[0.0, 0.1546936, 0.06842053, 0.02947998, 0.12677014, 0.21240246, 0.20684814, 0.15496826, 0.22085571, 0.23931885, 0.34414673, 0.39282238, 0.26080322, 0.35849, 0.43966675, 0.36972058, 0.32791138, 0.40118408, 0.4326172, 0.2223512, 0.29861462, 0.40655518, 0.23242188, 0.16989136, 0.16012585, 0.20785522, 0.1984253, 0.14593518, 0.09185791, 0.05581665, 0.14602661, 0.14328015, 0.051300168, -0.026397824, 0.0019836426, 0.07058716, 0.08828735, -0.14208972, -0.2237854, 0.03527832, -0.022949219, -0.15008545, -0.19873047, -0.22698975, -0.20520008, -0.12213135, -0.045654297, -0.21685791, -0.29281628, -0.18814099, -0.13778675, -0.04638672, -0.0071105957, 0.041412354, 0.17886353, 0.11175537, 0.058380127, 0.1972351, 0.2889099, 0.38058472, 0.414734, 0.27072144, 0.3463745, 0.5189514, 0.44750965, 0.32089233, 0.2972107, 0.41915894, 0.6077881, 0.47155774, 0.24908447, 0.4510498, 0.48709106, 0.33963013, 0.29187024, 0.31436157, 0.21914673, 0.09072876, 0.24047852, 0.22073364, 0.18740833, 0.29693604, 0.10269165, 0.034851074, 0.13198853, 0.013793945, 0.13748169, 0.24127197, 0.14208984, 0.12869263, 0.13903809, 0.101379395, 0.071624875, 0.2015382, 0.23123157, 0.023101807, 0.069610596, 0.110687256, 0.019653201, 0.090026855]\r\nTEST1\r\n[1, 2, 44100]\r\nInput details: Tensor(name: \"waveform\", dataType: App.Tensor.DataType.float32, shape: App.Tensor.Shape(rank: 2, dimensions: [1, 2]), data: 8 bytes, quantizationParameters: nil)\r\nTEST2\r\nTEST3\r\nTEST4\r\nTensorFlow Lite Error: Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter. Make sure you apply/link the Flex delegate before inference. For the Android, it can be resolved by adding \"org.tensorflow:tensorflow-lite-select-tf-ops\" dependency. See instructions: https://www.tensorflow.org/lite/guide/ops_select\r\nTensorFlow Lite Error: Node number 41 (FlexTranspose) failed to prepare.\r\nError processing Spleeter: Must call allocateTensors().\r\nFirst Chunk played.\r\n<AVAudioPCMBuffer@0x6000007ed600: 176400/176400 bytes> 0\r\nTimer tick - Elapsed Time: 0.0010600090026855469 seconds\r\nAll chunks have been played.\r\n2024-04-12 13:36:51.025764-0700 App[17567:1257878] [SwiftUI] Publishing changes from background threads is not allowed; make sure to publish values from the main thread (via operators like receive(on:)) on model updates.\r\nTimer tick\r\n0.0\r\n0.1546936\r\n[0.0, 0.1546936, 0.06842053, 0.02947998, 0.12677014, 0.21240246, 0.20684814, 0.15496826, 0.22085571, 0.23931885, 0.34414673, 0.39282238, 0.26080322, 0.35849, 0.43966675, 0.36972058, 0.32791138, 0.40118408, 0.4326172, 0.2223512, 0.29861462, 0.40655518, 0.23242188, 0.16989136, 0.16012585, 0.20785522, 0.1984253, 0.14593518, 0.09185791, 0.05581665, 0.14602661, 0.14328015, 0.051300168, -0.026397824, 0.0019836426, 0.07058716, 0.08828735, -0.14208972, -0.2237854, 0.03527832, -0.022949219, -0.15008545, -0.19873047, -0.22698975, -0.20520008, -0.12213135, -0.045654297, -0.21685791, -0.29281628, -0.18814099, -0.13778675, -0.04638672, -0.0071105957, 0.041412354, 0.17886353, 0.11175537, 0.058380127, 0.1972351, 0.2889099, 0.38058472, 0.414734, 0.27072144, 0.3463745, 0.5189514, 0.44750965, 0.32089233, 0.2972107, 0.41915894, 0.6077881, 0.47155774, 0.24908447, 0.4510498, 0.48709106, 0.33963013, 0.29187024, 0.31436157, 0.21914673, 0.09072876, 0.24047852, 0.22073364, 0.18740833, 0.29693604, 0.10269165, 0.034851074, 0.13198853, 0.013793945, 0.13748169, 0.24127197, 0.14208984, 0.12869263, 0.13903809, 0.101379395, 0.071624875, 0.2015382, 0.23123157, 0.023101807, 0.069610596, 0.110687256, 0.019653201, 0.090026855]\r\nTEST1\r\n[1, 2, 44100]\r\nInput details: Tensor(name: \"waveform\", dataType: App.Tensor.DataType.float32, shape: App.Tensor.Shape(rank: 2, dimensions: [44100, 2]), data: 352800 bytes, quantizationParameters: nil)\r\nTEST2\r\nTEST3\r\nTEST4\r\nTensorFlow Lite Error: Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter. Make sure you apply/link the Flex delegate before inference. For the Android, it can be resolved by adding \"org.tensorflow:tensorflow-lite-select-tf-ops\" dependency. See instructions: https://www.tensorflow.org/lite/guide/ops_select\r\nTensorFlow Lite Error: Node number 41 (FlexTranspose) failed to prepare.\r\nError processing Spleeter: Must call allocateTensors().\r\nFirst Chunk played.\r\n<AVAudioPCMBuffer@0x6000007ee1e0: 176400/176400 bytes> 0\r\nTimer tick - Elapsed Time: 0.0001779794692993164 seconds\r\nAll chunks have been played.\r\n2024-04-12 13:37:33.643700-0700 App[17567:1260166] [SwiftUI] Publishing changes from background threads is not allowed; make sure to publish values from the main thread (via operators like receive(on:)) on model updates.\r\nTimer tick\r\n0.0\r\n0.1546936\r\n[0.0, 0.1546936, 0.06842053, 0.02947998, 0.12677014, 0.21240246, 0.20684814, 0.15496826, 0.22085571, 0.23931885, 0.34414673, 0.39282238, 0.26080322, 0.35849, 0.43966675, 0.36972058, 0.32791138, 0.40118408, 0.4326172, 0.2223512, 0.29861462, 0.40655518, 0.23242188, 0.16989136, 0.16012585, 0.20785522, 0.1984253, 0.14593518, 0.09185791, 0.05581665, 0.14602661, 0.14328015, 0.051300168, -0.026397824, 0.0019836426, 0.07058716, 0.08828735, -0.14208972, -0.2237854, 0.03527832, -0.022949219, -0.15008545, -0.19873047, -0.22698975, -0.20520008, -0.12213135, -0.045654297, -0.21685791, -0.29281628, -0.18814099, -0.13778675, -0.04638672, -0.0071105957, 0.041412354, 0.17886353, 0.11175537, 0.058380127, 0.1972351, 0.2889099, 0.38058472, 0.414734, 0.27072144, 0.3463745, 0.5189514, 0.44750965, 0.32089233, 0.2972107, 0.41915894, 0.6077881, 0.47155774, 0.24908447, 0.4510498, 0.48709106, 0.33963013, 0.29187024, 0.31436157, 0.21914673, 0.09072876, 0.24047852, 0.22073364, 0.18740833, 0.29693604, 0.10269165, 0.034851074, 0.13198853, 0.013793945, 0.13748169, 0.24127197, 0.14208984, 0.12869263, 0.13903809, 0.101379395, 0.071624875, 0.2015382, 0.23123157, 0.023101807, 0.069610596, 0.110687256, 0.019653201, 0.090026855]\r\nTEST1\r\n[1, 2, 44100]\r\nInput details: Tensor(name: \"waveform\", dataType: App.Tensor.DataType.float32, shape: App.Tensor.Shape(rank: 2, dimensions: [44100, 2]), data: 352800 bytes, quantizationParameters: nil)\r\nTEST2\r\nTEST3\r\nTEST4\r\nTensorFlow Lite Error: Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter. Make sure you apply/link the Flex delegate before inference. For the Android, it can be resolved by adding \"org.tensorflow:tensorflow-lite-select-tf-ops\" dependency. See instructions: https://www.tensorflow.org/lite/guide/ops_select\r\nTensorFlow Lite Error: Node number 41 (FlexTranspose) failed to prepare.\r\nError processing Spleeter: Must call allocateTensors().\r\nFirst Chunk played.\r\n<AVAudioPCMBuffer@0x6000007e0a20: 176400/176400 bytes> 0\r\nTimer tick - Elapsed Time: 6.198883056640625e-05 seconds\r\nAll chunks have been played.\r\n2024-04-12 13:37:40.196745-0700 App[17567:1257879] [SwiftUI] Publishing changes from background threads is not allowed; make sure to publish values from the main thread (via operators like receive(on:)) on model updates.\r\nTimer tick\r\n0.0\r\n0.1546936\r\n[0.0, 0.1546936, 0.06842053, 0.02947998, 0.12677014, 0.21240246, 0.20684814, 0.15496826, 0.22085571, 0.23931885, 0.34414673, 0.39282238, 0.26080322, 0.35849, 0.43966675, 0.36972058, 0.32791138, 0.40118408, 0.4326172, 0.2223512, 0.29861462, 0.40655518, 0.23242188, 0.16989136, 0.16012585, 0.20785522, 0.1984253, 0.14593518, 0.09185791, 0.05581665, 0.14602661, 0.14328015, 0.051300168, -0.026397824, 0.0019836426, 0.07058716, 0.08828735, -0.14208972, -0.2237854, 0.03527832, -0.022949219, -0.15008545, -0.19873047, -0.22698975, -0.20520008, -0.12213135, -0.045654297, -0.21685791, -0.29281628, -0.18814099, -0.13778675, -0.04638672, -0.0071105957, 0.041412354, 0.17886353, 0.11175537, 0.058380127, 0.1972351, 0.2889099, 0.38058472, 0.414734, 0.27072144, 0.3463745, 0.5189514, 0.44750965, 0.32089233, 0.2972107, 0.41915894, 0.6077881, 0.47155774, 0.24908447, 0.4510498, 0.48709106, 0.33963013, 0.29187024, 0.31436157, 0.21914673, 0.09072876, 0.24047852, 0.22073364, 0.18740833, 0.29693604, 0.10269165, 0.034851074, 0.13198853, 0.013793945, 0.13748169, 0.24127197, 0.14208984, 0.12869263, 0.13903809, 0.101379395, 0.071624875, 0.2015382, 0.23123157, 0.023101807, 0.069610596, 0.110687256, 0.019653201, 0.090026855]\r\nTEST1\r\n[1, 2, 44100]\r\nInput details: Tensor(name: \"waveform\", dataType: App.Tensor.DataType.float32, shape: App.Tensor.Shape(rank: 2, dimensions: [44100, 2]), data: 352800 bytes, quantizationParameters: nil)\r\nTEST2\r\nTEST3\r\nTEST4\r\nTensorFlow Lite Error: Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter. Make sure you apply/link the Flex delegate before inference. For the Android, it can be resolved by adding \"org.tensorflow:tensorflow-lite-select-tf-ops\" dependency. See instructions: https://www.tensorflow.org/lite/guide/ops_select\r\nTensorFlow Lite Error: Node number 41 (FlexTranspose) failed to prepare.\r\nError processing Spleeter: Must call allocateTensors().\r\nFirst Chunk played.\r\n<AVAudioPCMBuffer@0x600000753c40: 176400/176400 bytes> 0\r\nTimer tick - Elapsed Time: 7.200241088867188e-05 seconds\r\nAll chunks have been played.\r\n2024-04-12 13:37:45.869375-0700 App[17567:1260558] [SwiftUI] Publishing changes from background threads is not allowed; make sure to publish values from the main thread (via operators like receive(on:)) on model updates.\r\nTimer tick\r\n```\r\n\r\nThis doesn't look like your error... can you maybe see if you can get this far? Thanks.", "hey @pkgoogle \r\n\r\nthanks for your efforts, i will try that out. But as you can see in errorcode, I guess I was already past that error:\r\n\r\nYour Error:\r\nTensorFlow Lite Error: Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter. Make sure you apply/link the Flex delegate before inference. For the Android, it can be resolved by adding \"org.tensorflow:tensorflow-lite-select-tf-ops\" dependency. See instructions: https://www.tensorflow.org/lite/guide/ops_select\r\nTensorFlow Lite Error: Node number 41 (FlexTranspose) failed to prepare.\r\n\r\nin my Log:\r\n\r\nCreated TensorFlow Lite delegate for select TF ops.\r\n\r\n\r\n\r\nExcluding Arm64 would simply deactivate the build for Phones right?\r\nBecause the problem in my eyes is with the X86 build.\r\n\r\n\r\nBest wishes", "Hi @BasilZinsli, I'm having issues integrating the built TensorFlowLiteSelectTfOps.framework with the project, while I work on that, have you tried using this on a real iPhone? This issue might only occur on emulator.", "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/65302\">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/65302\">No</a>\n" ]
2024-04-09T09:06:42
2024-05-01T01:49:37
2024-05-01T01:49:33
NONE
null
null
null
**System information** -iMac Retina 5K, 27-inch, 2017 -OS 13.5.2 **Provide the text output from tflite_convert** ``` 2024-04-09 10:54:09.109505+0200 App[91449:6577901] Initialized TensorFlow Lite runtime. INFO: Initialized TensorFlow Lite runtime. 2024-04-09 10:54:09.110104+0200 App[91449:6577901] Created TensorFlow Lite delegate for select TF ops. INFO: Created TensorFlow Lite delegate for select TF ops. TensorFlow Lite Error: Failed to initialize TensorFlow context: CPU Factory not registered. Did you link in threadpool_device? TensorFlow Lite Error: Restored original execution plan after delegate application failure. Failed to create the interpreter: Failed to create the interpreter. Failed to create Spleeter instance. ``` **Standalone code to reproduce the issue** Let me know if you want to recieve the xcode project as ZIP, it is a big project Iphone function has not been tested yet. Issue is on simulater **Any other info / logs** We tried many things so far found in other posts. Bazel build tfops framwork to include x86 support: bazel build -c opt --config=ios_x86_64 \ //tensorflow/lite/ios:TensorFlowLiteSelectTfOps_framework Linker Flags: $(inherited) -ObjC -l"c++" -framework TensorFlowLite -framework TensorFlowLiteSelectTfOps -u _TF_AcquireFlexDelegate this flag: -u _TF_AcquireFlexDelegate eliminated the error that flex delegate could have been found but now with own TF ops Framwork and this flag this error persists. Without own build (using nightly build PODs) not even TF ops could been found. Thanks for any help
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PR #11249: [XLA:GPU] Update cuDNN flash attention backward to use new cuDNN frontend (>1.0)
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2024-04-09T09:05:33
2024-04-11T10:44:30
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PR #11249: [XLA:GPU] Update cuDNN flash attention backward to use new cuDNN frontend (>1.0) Imported from GitHub PR https://github.com/openxla/xla/pull/11249 * Replace complicated node by node flash attention cuDNN graph construction with simple cudnn sdpa call provided in new cuDNN frontend (>1.0). * Remove softmax_sum/dq_accum tensors from the custom call outputs since it is not required anymore in new cuDNN frontend. * Add a pass to query workspace from cuDNN graph instead of manual calculation as workspace size is subject to change. Copybara import of the project: -- 2a1ee169fb2c12fbbd4e791ff406b3272490cf09 by cjkkkk <[email protected]>: init -- e84388a1859ef26acad7ba68cebedf546098d99b by cjkkkk <[email protected]>: fix stat datatype to be fp32 -- 3a0b75ccfa88791d5cc437a9cc86333849d5fee3 by cjkkkk <[email protected]>: use temp value for workspace & remove softmax buffer/dq_accum -- 77e6db53f4f4d0b5344e3492da21e871ceb0bb0b by cjkkkk <[email protected]>: add workspace upperbound -- 11dd85db2ebea5cbd12b7108bdc995d8f8f5f1ba by cjkkkk <[email protected]>: use tighter upperbound -- 2f245303893f0aef5dc48557f8254d16ae373d9d by cjkkkk <[email protected]>: remove unused param -- 5e648ae07c743e95955f23fbd1c4fa50a13be5de by cjkkkk <[email protected]>: add pass to fix workspace size -- 368e51879c10266a3ecc200c105afc37dcba544c by cjkkkk <[email protected]>: remove comments -- 2fb7b3854f57c80e0c2e7805349ff5b637ba2007 by cjkkkk <[email protected]>: rm header order change -- d0cb184a32a5489f17ca5a20cd11901544e88ba1 by cjkkkk <[email protected]>: move def close to use and remove comments -- f09eb59328d2416374c31c5336744231b67954cd by cjkkkk <[email protected]>: move use to def -- 1c56f1fe670cfc43cc1bfcbc16ed75cf9ce5357f by cjkkkk <[email protected]>: add missing dependency in BUILD -- efd8ff45b5cf13e0ae820c03150068a6412204d0 by cjkkkk <[email protected]>: pass in correct descriptor to activation Merging this change closes #11249 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11249 from Cjkkkk:flash-attn-backward efd8ff45b5cf13e0ae820c03150068a6412204d0
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2024-04-09T08:58:11
2024-04-09T18:32:52
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We integrate tensorflow movenet with our webapp, and it stopped working (it worked two weeks ago) with the following error message, what can be the possible reasons and how to fix it?
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[ "We're facing the same issue. An urgent response would be highly appreciated as our code is live", "Got the same problem.\r\n\r\nDownload your model here: \r\nhttps://www.kaggle.com/models/google/movenet/tfJs/singlepose-lightning\r\n\r\nAnd provide it from your own server\r\n\r\nMake sure you select the right platform and type (tensorflow.js and single lightning in my case). \r\n\r\n![image](https://github.com/tensorflow/tensorflow/assets/5720176/602a0893-a84d-4cf7-83eb-f368f7f8aee2)\r\n\r\n" ]
2024-04-09T08:52:32
2024-04-10T06:53:51
null
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### Issue type Others ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source source ### TensorFlow version 0.0.6 ### 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? Access to fetch at 'https://www.kaggle.com/models/google/movenet/frameworks/tfJs/variations/singlepose-lightning/versions/4/model.json?tfjs-format=file&tfhub-redirect=true' (redirected from 'https://tfhub.dev/google/tfjs-model/movenet/singlepose/lightning/4/model.json?tfjs-format=file') from origin 'https://ourwebsite.com' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. If an opaque response serves your needs, set the request's mode to 'no-cors' to fetch the resource with CORS disabled. ### Standalone code to reproduce the issue ```shell It was working normally a few days ago, but today I found that it cannot be used. ``` ### Relevant log output _No response_
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2024-04-09T08:48:16
2024-04-12T05:10:17
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[xla:ffi] Improve error messages in custom call tests
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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/65296/checks?check_run_id=23602844642) 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." ]
2024-04-09T08:40:30
2024-04-09T11:39:54
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[xla:ffi] Improve error messages in custom call tests Improve error messages in typed FFI custom call tests: print error message along with error code. Implements #10060.
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Check the number of bias elements matches the number of output channels instead of the number of dims.
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Check the number of bias elements matches the number of output channels instead of the number of dims. Remove unnecessary input shape checks.
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[XLA:LatencyHidingScheduler] Don't allocate buffer if it is first defined by an entry computation parameter.
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[XLA:LatencyHidingScheduler] Don't allocate buffer if it is first defined by an entry computation parameter.
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