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Remove unused StreamExecutorInterface::SynchronousMemcpyDeviceToDevice method and associated unused callers.
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Fix TensorFlow Build on Windows
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"Hi @mihaimaruseac, I changed the code and switched to F-Strings which is supported since Python 3.6"
] | 2024-04-12T22:30:06 | 2024-04-15T22:38:01 | 2024-04-15T22:38:01 |
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This pull request addresses the issue of TensorFlow builds failing on the Windows platform. The specific error encountered was related to the incorrect path being used to detect the Clang installation, resulting in the following error message: "no such target '//:FilesLLVMbinclang.exe': target 'FilesLLVMbinclang.exe' not declared in package ''".
To resolve this issue, the PR fixes the problem by ensuring that spaces are properly escaped in the "Program Files" path. This adjustment allows the build process to accurately detect the Clang installation path
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Support Keras3 model conversion to TFLite.
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Support Keras3 model conversion to TFLite.
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Set metadata and frontend attributes for instructions added in tpu_scatter_gather_normalizer HLO pass.
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[XLA] Clear derived instruction's sharding only if shapes are incompatible.
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[XLA] Clear derived instruction's sharding only if shapes are incompatible.
When AlgebraicSimplifier calls `dot->SetupDerivedInstruction(new_lhs);` in HandleDot, lhs sharding was cleared when dot didn't have a sharding. With this CL, lhs preserves its sharding because the condition for clearing the sharding is narrowed down to only when shapes are incompatible.
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Move #include <stddef.h> out of namespace.
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Move #include <stddef.h> out of namespace.
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[XLA] Add support for two-bit types
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[XLA] Add support for two-bit types
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PR_kwDOArmXAs5siV2t
| 65,561 |
PR #11299: [XLA:CPU] Enable XLA on Windows
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PR #11299: [XLA:CPU] Enable XLA on Windows
Imported from GitHub PR https://github.com/openxla/xla/pull/11299
This PR aims to enable the XLA test cases on the Windows Platform. The changes made:
1. Changed the .bazelrc file to use the correct toolchain and platform
This change will allow the user to successfully run XLA tests on the Windows platform using the Clang compiler using '--config=win_clang' in the bazel command
2. Added conditions to a few test cases to successfully run on the Windows platform
These test cases check the exit/termination status of a process
WIFEXITED is typically supported in POSIX-compliant operating systems like Unix and Linux to check if a process has terminated normally. WEXITSTATUS allows examining the termination status of child processes. However, these macros are not Windows compliant, hence the additional condition block was added to check the exit/termination status of process or child process for the Windows platform
Copybara import of the project:
--
ece9eefa224a6d051bcac089fe2a9a393af16a2b by Raunak <[email protected]>:
Enable XLA Windows
--
347c0326af8f608047f06345cad4dfbb53a52150 by mraunak <[email protected]>:
Update interactive_graphviz_bin_test.cc
--
2d4a3c2bb2ea23f12029583c53087d8739da0319 by mraunak <[email protected]>:
Update xla/tools/interactive_graphviz_bin_test.cc
Co-authored-by: Penporn Koanantakool <[email protected]>
--
90ad8b2730900d7f82b0fb1a83a73ffa2e452e0e by mraunak <[email protected]>:
Update run_hlo_module_bin_test.cc
--
7f31412f6b57e53bd56ba92149b015fcba92b07c by mraunak <[email protected]>:
Update xla/tools/run_hlo_module_bin_test.cc
Co-authored-by: Penporn Koanantakool <[email protected]>
--
4d39e35461f6977f36404a435c68b4809fb51a44 by mraunak <[email protected]>:
Update hlo_expand_test.cc
--
816b9ae0498831b55139c72d627d07f05e51213b by mraunak <[email protected]>:
Update hlo_expand_test.cc
--
a728fff1aca4258602c3d7c78afcc7d38b545b7a by mraunak <[email protected]>:
Update hlo_expand_test.cc
--
ffcb6861becf8de7a5c4e64ef0f19d977475d281 by mraunak <[email protected]>:
Update interactive_graphviz_bin_test.cc
--
f181497793c2c48079d72b545e6efb837f490504 by mraunak <[email protected]>:
Update interactive_graphviz_bin_test.cc
--
f9af75677e4663b3348e9e89376262eaff389ea9 by mraunak <[email protected]>:
Update run_hlo_module_bin_test.cc
Merging this change closes #11299
Reverts 94350b84b5deb780e6d3ae01f8f1a5c7ba71b302
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11299 from Intel-tensorflow:mraunak/xla_windows f9af75677e4663b3348e9e89376262eaff389ea9
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[xla:ifrt] Switch ifrt::Value and ifrt::Array to stateless Future<> futures to communicate events completion
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[xla:ifrt] Switch ifrt::Value and ifrt::Array to stateless Future<> futures to communicate events completion
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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/72b95c9bdabb68dfb64a57da50d87854f0104b96.
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Split stream_executor_internal.h into separate files defining the three Interface classes, and move them out of the internal namespace in preparation for removing the PIMPL pattern.
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Split stream_executor_internal.h into separate files defining the three Interface classes, and move them out of the internal namespace in preparation for removing the PIMPL pattern.
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Replace `__attribute__((unused))` with `[[maybe_unused]]` to fix [msvc error](https://github.com/google/jax/actions/runs/8630859524/job/23658055502).
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Replace `__attribute__((unused))` with `[[maybe_unused]]` to fix [msvc error](https://github.com/google/jax/actions/runs/8630859524/job/23658055502).
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Integrate LLVM at llvm/llvm-project@20ed5b1f4587
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Integrate LLVM at llvm/llvm-project@20ed5b1f4587
Updates LLVM usage to match
[20ed5b1f4587](https://github.com/llvm/llvm-project/commit/20ed5b1f4587)
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Add unbounded dynamism test for ReduceScatterOp.
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NONE
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Add unbounded dynamism test for ReduceScatterOp.
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PR_kwDOArmXAs5shq6a
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#tf-data Support save/load for index flat map dataset.
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#tf-data Support save/load for index flat map dataset.
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Set metadata and frontend attributes for instructions added in tpu copy normalizer HLO pass.
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Set metadata and frontend attributes for instructions added in tpu copy normalizer HLO pass.
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Update TF dependencies.
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Update TF dependencies.
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PR_kwDOArmXAs5shbCe
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[IFRT] Change {From,To}ShardingProto to Sharding::{From,To}Proto.
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[IFRT] Change {From,To}ShardingProto to Sharding::{From,To}Proto.
`FromShardingProto` and `ToShardingProto` were top-level functions for
`Sharding` (de)serialization. To make the API user-facing (de)serialization
consistent across IFRT types, this change move them into `Sharding`'s
`FromProto` and `ToProto` methods.
This also makes `sharding_serdes.cc` to be linked only for `SerDes`
implementations of basic `Sharding`, similar to other `*_serdes.cc` are used.
The users of `Sharding` (de)serialization only need to include `sharding.h`,
not `sharding_serdes.h`.
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Change defines to local_defines so that the MACRO will not be applied to the dependencies.
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Change defines to local_defines so that the MACRO will not be applied to the dependencies.
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PR_kwDOArmXAs5shXcQ
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Stop accepting single-core TPU graphs in MlirBridgePass
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Stop accepting single-core TPU graphs in MlirBridgePass
This removes the test for JIT compilation of single-core TPU graphs and stop accepting such graphs in MlirBridgePass
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Fix microkernel algorithm typo.
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Fix microkernel algorithm typo.
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IfrtLoadVariable current returns a key to look for loaded variable.
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IfrtLoadVariable current returns a key to look for loaded variable.
This CL adds a second result: restored tensor future.
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Rollback of Use PTX version corresponding to CUDA version
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Rollback of Use PTX version corresponding to CUDA version
Reverts 4ab3351d29d051a28f0b0ea0756c99492360008e
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[xla][gpu] Fixed two problems in p2p-schedule-preparation.
Previously, we record the address of a P2PGroupNode for a complement-group that
forms a cycle with a group. This address can be changed as P2PGroupNode is an
entity of a map. Fixing this problem by recording the channel ID of the
complement-group instead.
Make sure that a group and its complement-group are both annotated with
a stream for pipelining.
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Update batching queue full error code
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Update batching queue full error code
Currently the batching queue returns Unavailable when it is full.
Update it to ResourceExhausted to make this error more clear.
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[pjrt] Switch all remaining PjRtClient APIs to stateless PjRtFuture<>
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[pjrt] Switch all remaining PjRtClient APIs to stateless PjRtFuture<>
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PR_kwDOArmXAs5sg4DB
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r2.16 cherry-pick: aaa5de3fb7a "Reverts changelist 506335783"
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Refer to the original commit: https://github.com/tensorflow/tensorflow/commit/aaa5de3fb7ac296a6ac8a1a4c185a58f7e1e5d4c
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Move `tsl/concurrency` to `xla/tsl/concurrency` and update users
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Move `tsl/concurrency` to `xla/tsl/concurrency` and update users
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PR_kwDOArmXAs5sgqYI
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[XLA:SPMD] Add DUS to aggressiveness 0.
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[XLA:SPMD] Add DUS to aggressiveness 0.
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PR_kwDOArmXAs5sgmHd
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Reverts 942c887143df82fefbacfb73ff338f4957f9886c
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PR_kwDOArmXAs5sgkke
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[oneDNN] Remove MKL blocked format test code
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[
"@ezhulenev I noticed that \"AMD ROCSm\" and \"Windows Bazel check do not pass.\r\n\r\nHowever, PR change should not cause the problem, as NativeFormatEnabled currently always returns \"true\" value. \r\nLater on, I will create another PR to get rid of \"NativeFormatEnabled\" methods and clean up related caller code. \r\n",
"@cantonios @ezhulenev Thanks a lot for the approval!"
] | 2024-04-12T16:12:43 | 2024-04-23T19:53:09 | 2024-04-23T19:53:09 |
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Remove MKL blocked format test code.
MKL blocked format is not supported any more.
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Update the XNNPack version.
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Update the XNNPack version.
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[XLA] Fix issue in MHLO->HLO where a constant with less than 32 elements wouldn't have kept the sharding when being converted to HLO scalar constant + broadcast.
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[XLA] Fix issue in MHLO->HLO where a constant with less than 32 elements wouldn't have kept the sharding when being converted to HLO scalar constant + broadcast.
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Transpose weights for hybrid quantized convolution
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Transpose weights for hybrid quantized convolution
Factored out function for matching input and kernel and function for transposing the weight values to share implementation between SRQ and weight-only convolution.
Reverts 4a59d5ca94c692254e0badb98b5e1d025ae4631d
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[XLA:GPU] Remove the unused `stream` argument of `AutotunerUtil::CreateRedzoneAllocator`.
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[XLA:GPU] Remove the unused `stream` argument of `AutotunerUtil::CreateRedzoneAllocator`.
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PR_kwDOArmXAs5sfld9
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[XLA:GPU] Stop passing `stream` to methods of `GpuConvAlgorithmPicker`.
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[XLA:GPU] Stop passing `stream` to methods of `GpuConvAlgorithmPicker`.
The `stream` always comes from the `config_` field. Passing it around, as opposed to directly reading it from the field is confusing as it gives the wrong impression that it may be different than what is stored in the `config_`.
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Pass a flag whether we are in code emission phase.
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Pass a flag whether we are in code emission phase.
When trying to bisect failing fusions with the new mlir emitters, we have the flags
xla_gpu_max_mlir_kernels and xla_gpu_skip_mlir_kernels. However the function
GetFusionEmitter() is also called during fusion passes. It seems we have some
non-determinism regarding how often it is called during fusion passes, so it is not
easy to determine what value should be used for xla_gpu_skip_mlir_kernels to skip
all the calls during fusion passes. Instead, better only start counting in the
code emission phase.
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Transpose weights for hybrid quantized convolution
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Transpose weights for hybrid quantized convolution
Factored out function for matching input and kernel and function for transposing the weight values to share implementation between SRQ and weight-only convolution.
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[XLA:GPU] Remove unused function.
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[XLA:GPU] Remove unused function.
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Cannot convert model to tflite
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[
"Hi @mdomaradzki-cosmose ,\r\nSorry for the delay, @LakshmiKalaKadali the issue Was able to reproduce the issue on Colab using TF v2.15 , 2.16 , and TF-nightly ,Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/ac03e60bbd0a1d868f77a7a8b34f819c/65529_2-15-2-16-v.ipynb) here for reference.\r\n\r\nThank you!",
"Thanks for your response",
"Hi @mdomaradzki-cosmose,\r\n\r\nThe error is due to Keras 3. As a workaround install Keras 2 as follows. \r\n``` \r\n pip install -U tf_keras # Keras 2\r\n import os\r\n os.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\" \r\n```\r\n\r\nHere is the [gist](https://colab.sandbox.google.com/gist/LakshmiKalaKadali/c08717846629c71a93eb6b3d7f6be640/tflite_65529_2-16_conversion-issue.ipynb#scrollTo=bzHILp1jNTUo) with TF 2.16 and keras2 workaround. The TFLite model is successfully converted.\r\n\r\nThank You",
"Thank you, It works!\r\n",
"Hi @mdomaradzki-cosmose,\r\n\r\nGlad to know, the issue is resolved. Please feel free to close the issue.\r\n\r\nThank You"
] | 2024-04-12T12:02:13 | 2024-04-22T09:29:11 | 2024-04-22T09:29:11 |
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### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 22.04
- TensorFlow installation (pip package or built from source): pip
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.16.1
### 2. Code
```
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import tensorflow as tf
x, y = make_classification(random_state=42)
train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.2, stratify=y)
print(train_x.shape, test_x.shape, train_y.shape, test_y.shape)
model = tf.keras.models.Sequential([
tf.keras.Input(shape=(20,)),
tf.keras.layers.Dense(units=2, activation='softmax')
])
model.compile(optimizer=tf.keras.optimizers.SGD(), loss=tf.keras.losses.SparseCategoricalCrossentropy(), metrics=['accuracy'])
model.fit(x=train_x, y=train_y, epochs=50)
model.evaluate(test_x, test_y)
model.save("model.h5")
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
with open('model.tflite', 'wb') as f:
f.write(tflite_model)
```
### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:
I tried to convert the model to tflite using two options. Using the above code and from terminal.
When I do this from code I get 'INFO:tensorflow:Assets written to: /tmp/tmph7onx9_d/assets' and then the code never ends (at least during two hours it didn't finish).
When I try to do this from terminal it crushes with following message:
```
Traceback (most recent call last):
File "/home/xxx/miniconda3/envs/exp/bin/tflite_convert", line 8, in <module>
sys.exit(main())
File "/home/xxx/miniconda3/envs/exp/lib/python3.10/site-packages/tensorflow/lite/python/tflite_convert.py", line 692, in main
app.run(main=run_main, argv=sys.argv[:1])
File "/home/xxx/miniconda3/envs/exp/lib/python3.10/site-packages/absl/app.py", line 308, in run
_run_main(main, args)
File "/home/xxx/miniconda3/envs/exp/lib/python3.10/site-packages/absl/app.py", line 254, in _run_main
sys.exit(main(argv))
File "/home/xxx/miniconda3/envs/exp/lib/python3.10/site-packages/tensorflow/lite/python/tflite_convert.py", line 675, in run_main
_convert_tf2_model(tflite_flags)
File "/home/xxx/miniconda3/envs/exp/lib/python3.10/site-packages/tensorflow/lite/python/tflite_convert.py", line 281, in _convert_tf2_model
model = keras_deps.get_load_model_function()(flags.keras_model_file)
TypeError: 'NoneType' object is not callable
```
### 4. (optional) RNN conversion support
If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title.
### 5. (optional) Any other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.
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PR_kwDOArmXAs5sewBm
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Fix sparse matmul implementation on Hopper
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Fix sparse matmul implementation on Hopper
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11458 from ROCm:build_fix_11042024 b253cbac2da4515ff4437b820f37a2b097a287c1
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PR #11458: [ROCm] Build fixes
Imported from GitHub PR https://github.com/openxla/xla/pull/11458
Contains fix for compile failure after e21311e52a0be80042d4c807847ec17919f2f954
Contains fix for link failure after 596745eca6b9fb1553ee71b63c71e92527f1d7f4
Copybara import of the project:
--
b253cbac2da4515ff4437b820f37a2b097a287c1 by Dragan Mladjenovic <[email protected]>:
[ROCm] Build fixes
Contains fix for compile failure after e21311e52a0be80042d4c807847ec17919f2f954
Contains fix for link failure after 596745eca6b9fb1553ee71b63c71e92527f1d7f4
Merging this change closes #11458
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11458 from ROCm:build_fix_11042024 b253cbac2da4515ff4437b820f37a2b097a287c1
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Support unsigned scalar constants in new mlir-based emitters.
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"@saad-koukous,\r\nCould you please share colab link or simple standalone code with supporting files to reproduce the issue in our environment. It helps us in localizing and debugging the issue faster. Thank you!",
"@tilakrayal,\r\nThank you for your response. However, I can't share the .tflite model and data. As I mentioned before, the expected input dimensions are 1x3x40x180. When I use the model in Python, I provide input with these dimensions. But when using the C API, it indicates that the dimensions are 1x3x1x1. Below is a snippet of my C code.\r\n```\r\n#include <stdio.h>\r\n#include <stdlib.h>\r\n#include <string.h>\r\n#include <tensorflow/lite/c/c_api.h>\r\nint main() {\r\n // Load the TFLite model\r\n const char* model_path = \"model.tflite\";\r\n TfLiteModel* model = TfLiteModelCreateFromFile(model_path);\r\n if (!model) {\r\n printf(\"Failed to load the model.\\n\");\r\n return 1;\r\n }\r\n // Create an interpreter\r\n TfLiteInterpreterOptions* options = TfLiteInterpreterOptionsCreate();\r\n TfLiteInterpreter* interpreter = TfLiteInterpreterCreate(model, options);\r\n TfLiteInterpreterOptionsDelete(options);\r\n if (!interpreter) {\r\n printf(\"Failed to create the interpreter.\\n\");\r\n return 1;\r\n }\r\n // Allocate input tensors\r\n TfLiteInterpreterAllocateTensors(interpreter);\r\n // Get the input tensor\r\n TfLiteTensor* input_tensor = TfLiteInterpreterGetInputTensor(interpreter, 0);\r\n int num_dims = TfLiteTensorNumDims(input_tensor);\r\n int dim1 = TfLiteTensorDim(input_tensor, 0);\r\n int dim2 = TfLiteTensorDim(input_tensor, 1);\r\n int dim3 = TfLiteTensorDim(input_tensor, 2);\r\n int dim4 = TfLiteTensorDim(input_tensor, 4);\r\n printf(\"Input tensor:\\n\");\r\n printf(\"Number of dimensions: %i\\n\", num_dims);\r\n printf(\"Dimensions: %i x %i x %i x %i\\n\", dim1, dim2, dim3, dim4);\r\n if (TfLiteTensorType(input_tensor) != kTfLiteFloat32) {\r\n printf(\"Unexpected data type in the input tensor.\\n\");\r\n return 1;\r\n}\r\n\r\n // Set the input data\r\n\r\n // the input data is a 3D array of shape (3, 40, 180)\r\n\r\n // Allocate memory for the input data\r\n\r\n printf(\"stacked_input_data[0]: %f\\n\", stacked_input_data[0]);\r\n\r\n TfLiteStatus copy_status = TfLiteTensorCopyFromBuffer(input_tensor, stacked_input_data,3 * 40 * 180 * sizeof(float) );\r\n\r\n if (copy_status != kTfLiteOk) {\r\n\r\n printf(\"Failed to copy input data to tensor.\\n\");\r\n\r\n return 1;\r\n\r\n }\r\n\r\n printf(\"data copied\\n\");\r\n\r\n if (interpreter == NULL) {\r\n\r\n printf(\"Interpreter is null.\\n\");\r\n\r\n return 1;\r\n\r\n }\r\n // Free the memory for the stacked input data\r\n\r\n free(stacked_input_data);\r\n\r\n TfLiteStatus invoke_status = TfLiteInterpreterInvoke(interpreter);\r\n\r\n if (invoke_status != kTfLiteOk) {\r\n\r\n printf(\"Failed to invoke interpreter.\\n\");\r\n\r\n return 1;\r\n\r\n }\r\n // Get the output tensor\r\n\r\n const TfLiteTensor* output_tensor = TfLiteInterpreterGetOutputTensor(interpreter, 0);\r\n\r\n // Process the output data as needed\r\n\r\n // Assuming the output is a float array called 'output_data'\r\n\r\n float* output_data = (float*)malloc(40 * 180 * sizeof(float));\r\n\r\n if (!output_data) {\r\n printf(\"Failed to allocate memory for the output data.\\n\");\r\n free(output_data);\r\n return 1;\r\n }\r\n TfLiteTensorCopyToBuffer(output_tensor, output_data,40 * 180 * sizeof(float));\r\n // Clean up\r\n free(output_data);\r\n TfLiteInterpreterDelete(interpreter);\r\n TfLiteModelDelete(model);\r\n return 0;\r\n}\r\n```",
"Hi @saad-koukous, can you please show us a colab/code for how the model is produced in Python? usually you see this when those dimensions are dynamic in Python but were not explicitly set in some way but C++ doesn't have the right information to infer the actual dimensions. Explicitly setting the dimensions in Python or Resizing them in C++: https://www.tensorflow.org/lite/guide/inference#run_inference_with_dynamic_shape_model will likely resolve your issue. Let us know if that works. Thanks!",
"@pkgoogle Thank you so much! I discovered that the model's input has dynamic dimensions, so I regenerated a TFLite model with fixed input dimensions.",
"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/65515\">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/65515\">No</a>\n"
] | 2024-04-12T07:46:02 | 2024-04-17T07:26:28 | 2024-04-17T07:26:24 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.17.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04.4 LTS
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
bazel 6.5.0
### GCC/compiler version
gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I expect an input tensor dimension of 1 x 3 x 40 x 180, but when running found dimension 1x3x1x1
```
int num_dims = TfLiteTensorNumDims(input_tensor);
int dim1 = TfLiteTensorDim(input_tensor, 0);
int dim2 = TfLiteTensorDim(input_tensor, 1);
int dim3 = TfLiteTensorDim(input_tensor, 2);
int dim4 = TfLiteTensorDim(input_tensor, 4);
```
I need help to understand the problem, and i need clarification to how to use `TfLiteTensorCopyFromBuffer` for my dimension case
### Standalone code to reproduce the issue
```shell
...
TfLiteStatus copy_status = TfLiteTensorCopyFromBuffer(input_tensor, stacked_input_data,3 * 40 * 180 * sizeof(float) );
if (copy_status != kTfLiteOk) {
printf("Failed to copy input data to tensor.\n");
return 1;
}
```
### Relevant log output
```shell
Input tensor:
Number of dimensions: 4
Dimensions: 1 x 3 x 1 x 1
input_data[0]: 0.937447
Failed to copy input data to tensor
```
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Enable properties for dtensor dialect.
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(1/N) Remove StableHLO Quantizer quantize pass dependency to preset.
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(1/N) Remove StableHLO Quantizer quantize pass dependency to preset.
Preset flags should be replaced by respective `QuantizationSpecs` and should not be referred in later passes after `ExpandPresets`. This CL does the following:
* Removes `*Preset` after `ExpandPresets`, making sure later passes do not reference to `Preset` but via `QuantizationSpecs`.
* Removes StableHLO Quantizer's dependency on explicit `enable_full_int_quantization` preset flag in quantization passes.
Follow-up cl's will completely remove `StaticRangePtqPreset` dependency in quantize passes.
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PR #11444: [XLA:GPU] disable mask in cuDNN attention
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PR #11444: [XLA:GPU] disable mask in cuDNN attention
Imported from GitHub PR https://github.com/openxla/xla/pull/11444
1. cuDNN attention mask is not doing masking with -inf but multiply which is not correct. Hence disable patterns with mask.
2. Follow up PR to clean up the remaining mask related logic.
Copybara import of the project:
--
acf95b6cc7e1084026eaf87c0119ba3801ba8f8c by cjkkkk <[email protected]>:
disable mask
Merging this change closes #11444
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11444 from Cjkkkk:remove_mask acf95b6cc7e1084026eaf87c0119ba3801ba8f8c
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Reland Use PTX version corresponding to CUDA version
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Reland Use PTX version corresponding to CUDA version
Reverts 5b632707d126791a78d151340c98ef09e10dc189
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Adjust ptxas search path priorities
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Adjust ptxas search path priorities
When looking for ptxas and other NVIDIA binary tools we go through a list of potential locations until we find a suitable version of ptxas.
Changing this list or its order has been deemed difficult in the past because there are many users out there that rely on the fact that this list happens to choose the right binary for them.
So this is making a careful change that is only affecting Google environments and makes no change to any OSS build of TF, JAX or XLA.
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Add prettier printing for constant operations to make it easier to debug differences is non-constant operations, even if constants are different
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Add prettier printing for constant operations to make it easier to debug differences is non-constant operations, even if constants are different
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Bump idna from 3.6 to 3.7
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Bumps [idna](https://github.com/kjd/idna) from 3.6 to 3.7.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/kjd/idna/releases">idna's releases</a>.</em></p>
<blockquote>
<h2>v3.7</h2>
<h2>What's Changed</h2>
<ul>
<li>Fix issue where specially crafted inputs to encode() could take exceptionally long amount of time to process. [CVE-2024-3651]</li>
</ul>
<p>Thanks to Guido Vranken for reporting the issue.</p>
<p><strong>Full Changelog</strong>: <a href="https://github.com/kjd/idna/compare/v3.6...v3.7">https://github.com/kjd/idna/compare/v3.6...v3.7</a></p>
</blockquote>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/kjd/idna/blob/master/HISTORY.rst">idna's changelog</a>.</em></p>
<blockquote>
<p>3.7 (2024-04-11)
++++++++++++++++</p>
<ul>
<li>Fix issue where specially crafted inputs to encode() could
take exceptionally long amount of time to process. [CVE-2024-3651]</li>
</ul>
<p>Thanks to Guido Vranken for reporting the issue.</p>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/kjd/idna/commit/1d365e17e10d72d0b7876316fc7b9ca0eebdd38d"><code>1d365e1</code></a> Release v3.7</li>
<li><a href="https://github.com/kjd/idna/commit/c1b3154939907fab67c5754346afaebe165ce8e6"><code>c1b3154</code></a> Merge pull request <a href="https://redirect.github.com/kjd/idna/issues/172">#172</a> from kjd/optimize-contextj</li>
<li><a href="https://github.com/kjd/idna/commit/0394ec76ff022813e770ba1fd89658790ea35623"><code>0394ec7</code></a> Merge branch 'master' into optimize-contextj</li>
<li><a href="https://github.com/kjd/idna/commit/cd58a23173d2b0a40b95ee680baf3e59e8d33966"><code>cd58a23</code></a> Merge pull request <a href="https://redirect.github.com/kjd/idna/issues/152">#152</a> from elliotwutingfeng/dev</li>
<li><a href="https://github.com/kjd/idna/commit/5beb28b9dd77912c0dd656d8b0fdba3eb80222e7"><code>5beb28b</code></a> More efficient resolution of joiner contexts</li>
<li><a href="https://github.com/kjd/idna/commit/1b121483ed04d9576a1291758f537e1318cddc8b"><code>1b12148</code></a> Update ossf/scorecard-action to v2.3.1</li>
<li><a href="https://github.com/kjd/idna/commit/d516b874c3388047934938a500c7488d52c4e067"><code>d516b87</code></a> Update Github actions/checkout to v4</li>
<li><a href="https://github.com/kjd/idna/commit/c095c75943413c75ebf8ac74179757031b7f80b7"><code>c095c75</code></a> Merge branch 'master' into dev</li>
<li><a href="https://github.com/kjd/idna/commit/60a0a4cb61ec6834d74306bd8a1fa46daac94c98"><code>60a0a4c</code></a> Fix typo in GitHub Actions workflow key</li>
<li><a href="https://github.com/kjd/idna/commit/5918a0ef8034379c2e409ae93ee11d24295bb201"><code>5918a0e</code></a> Merge branch 'master' into dev</li>
<li>Additional commits viewable in <a href="https://github.com/kjd/idna/compare/v3.6...v3.7">compare view</a></li>
</ul>
</details>
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Fix a typo
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Fix a typo
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Create an OSS version of StableHLO Quantizer demonstration for ODML.
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Create an OSS version of StableHLO Quantizer demonstration for ODML.
More changes will follow to demonstrate selective quantization.
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PR_kwDOArmXAs5sbL-g
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Modify DirectPluginOpKernelContext constructor to get more type safety
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Modify DirectPluginOpKernelContext constructor to get more type safety
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PR_kwDOArmXAs5sa7rP
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Add reduce scatter to the list of collectives ops
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Add reduce scatter to the list of collectives ops
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[XLA:MSA] Rename `AlternateMemoryBestFitHeap` to `MsaAlgorithm`.
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NONE
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[XLA:MSA] Rename `AlternateMemoryBestFitHeap` to `MsaAlgorithm`.
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[XLA:MSA] Refactor `MsaAlgorithm` out of `memory_space_assignment.h/cc` files.
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[XLA:MSA] Refactor `MsaAlgorithm` out of `memory_space_assignment.h/cc` files.
1. Move `MsaAlgorithm` to `algorithm.h/cc` files under `memory_space_assignment` directory.
1. Move `AllocationValue`, `AsynchronousCopyOrdering`, `AsynchronousCopyResource`, `AsynchronousCopy` to `algorithm.h/cc`.
1. Change remaining usages of `BufferInterval` and `BufferIntervalCompare` to `MsaBufferInterval` and `MsaBufferIntervalCompare`
1. Move functions related to AllocationSequence in allocation.h/cc
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PR_kwDOArmXAs5sa0ds
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[XLA:MSA] Move SlicedPrefetchStartTimePicker to slice.h/cc files.
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[XLA:MSA] Move SlicedPrefetchStartTimePicker to slice.h/cc files.
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Added Compress and Decompress Megascale graph actions.
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[PJRT:GPU] Don't use XLA transfer manager APIs to perform D2H copies.
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[PJRT:GPU] Don't use XLA transfer manager APIs to perform D2H copies.
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[] | 2024-04-11T23:17:11 | 2024-04-12T21:31:33 | 2024-04-12T21:31:32 |
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Fixes for libtensorflow nightly build breakage
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PR_kwDOArmXAs5sao-1
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Store variable's dtype and shape in `IfrtRestoreTensorRegistry` and store the `IfrtRestoreTensorRegistry` in IfrtServingExecutable for looking up the dtype and shape.
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[] | 2024-04-11T23:08:19 | 2024-04-17T02:26:31 | 2024-04-17T02:26:29 |
NONE
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Store variable's dtype and shape in `IfrtRestoreTensorRegistry` and store the `IfrtRestoreTensorRegistry` in IfrtServingExecutable for looking up the dtype and shape.
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PR_kwDOArmXAs5saoZs
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Reverts 15c15ee35a11361b42aeb49dc53e9034dc54c400
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[] | 2024-04-11T23:05:13 | 2024-04-15T19:35:00 | 2024-04-15T19:35:00 |
NONE
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Reverts 15c15ee35a11361b42aeb49dc53e9034dc54c400
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PR_kwDOArmXAs5sanAI
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#tf-data Ramp down `map_fusion` experiment.
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[] | 2024-04-11T22:57:44 | 2024-04-12T00:09:30 | 2024-04-12T00:09:29 |
NONE
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#tf-data Ramp down `map_fusion` experiment.
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