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Imported from GitHub PR https://github.com/openxla/xla/pull/13391
We encountered a new use case where our reset of the `output to operand aliasing` won't be reached when the gemm users > 1 and that additional user does another amax-like ops.
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--Integrate GpuTopology into PjRtTopologyDescription to represent multi-host GPU topologies
--Utilize GlobalTopology to build GpuTopology during PjRt client creation
--Update relevant tests
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Include the unstacker pass in the compiler with a flag.
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the tensorflow-lite-stable target.
Add a test to verify that the list of stable headers included in
the tensorflow-lite-stable target are self-contained.
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"merged_at": "2024-06-... | PR #12975: [XLA:CPU][oneDNN] Fix failures in onednn_matmul_tests
Imported from GitHub PR https://github.com/openxla/xla/pull/12975
This PR fixes failures seen in onednn_matmul_tests
Copybara import of the project:
--
6ff6bd59a20fdabaf450134d6b449348a7a3c2ed by Kanvi Khanna <kanvi.khanna@intel.com>:
Fix failures in ... | {
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"AddressComputation" is confusing, it simply fuses dynamic slice (and dynamic update slice) into other thunks via buffer assignment tricks
Reverts 58a97840542bac9be1aeae91d7ee7c12ee321d71
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Updates LLVM usage to match
[392ca64893dc](https://github.com/llvm/llvm-project/commit/392ca64893dc)
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As of this change, `XLACompatibleSharding` is an alias of `jax.sharding.Sharding` but it will be deprecated in a follow up change.
Why do this?
* All shardings JAX has are XLA Compatible. The reason why `Sharding` was created was to allow non-xla... | {
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This avoids a build error when invoking those macros with multiple
"srcs" with different package names but the same unqualified target
name.
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There few mutex contention points that do not allow to get expected performance improvement. After they are removed this should be a nearly linear (number pf threads) speedup vs the baseline.
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"@2chayma Could you please ensure that you have a TensorFlow version compatible with your Python version ? Please follow this [guide](https://www.tensorflow.org/install/pip) for compatibility. Kindly provide more details on the reported issue?\r\nThank you!"
] | 2024-06-04T17:39:39 | 2024-06-07T07:18:45 | null | NONE | null | null | null | PS C:\Users\HP-MSi> & C:/Users/HP-MSi/AppData/Local/Programs/Python/Python312/python.exe c:/Users/HP-MSi/Desktop/MP2IASER/S4/Tâches/chokri/LSTM1.py
Traceback (most recent call last):
File "C:\Users\HP-MSi\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 70, in ... | {
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"AddressComputation" is confusing, it simply fuses dynamic slice (and dynamic update slice) into other thunks via buffer assignment tricks
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just like we do for nnapi_delegate.
This required also passing `tflite_copts()` in the BUILD file
in order to get the correct definition for `TFL_CAPI_EXPORT`.
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the TFLite Maven package are self-contained.
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} | PR #13289: [XLA:CPU] [oneDNN] Fix accuracy issue with layernorm fusion
Imported from GitHub PR https://github.com/openxla/xla/pull/13289
This PR fixes the accuracy issue seen with F16 ViT model by adding a default value for epsilon. It also adds a slight variation seen for TF layernorm pattern in XLA for F32,BF16,F16... | {
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We have the same logic that finds reduce in a SoftMax fusion duplicated to calculate tile sizes and launch dimensions. It's better to have everything in TritonFusion, so we can also get tile sizes in Cost Model.
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Imported from GitHub PR https://github.com/openxla/xla/pull/12537
This PR fixes :
XLA Issue : https://github.com/openxla/xla/issues/11772
corresponding JAX issue : https://github.com/google/jax/issues/20856
Accuracy fix :
This PR adds s... | {
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Only in-bounds, maskless, 1d transfers are supported. We'll
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FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13265 from openxla:refactor_runner 04cb8447c90d46f171e5a0f0261e81f8690d1ab4
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} | PR #13372: [GPU] Fix autotuner_util_test.
Imported from GitHub PR https://github.com/openxla/xla/pull/13372
Copybara import of the project:
--
88982aceca5dcd81c8b99f3a5a9d8e46a5ab843f by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Fix autotuner_util_test.
Merging this change closes #13372
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Imported from GitHub PR https://github.com/openxla/xla/pull/13241
Copybara import of the project:
--
99192a7119725e1b80153f8b29a229acb191c4f7 by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Fail on conflicting autotuning results.
Merging this change closes... | {
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d687c6f117ba11d98653288fb0c1cb8cebd61ed6 by Ilia Sergachev <isergachev@nvidia.com>:
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Imported from GitHub PR https://github.com/openxla/xla/pull/13301
This PR fixes a bug reported for JAX (https://github.com/openxla/xla/issues/13054)
Copybara import of the project:
--
47d5bde8eab607d0fe9b60c6fd82d95365c8169f by mdfaijul <md.faijul.am... | {
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Updates LLVM usage to match
[392ca64893dc](https://github.com/llvm/llvm-project/commit/392ca64893dc)
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} | PR #13106: Fix a bug in AsyncAllocator memory pools access setting.
Imported from GitHub PR https://github.com/openxla/xla/pull/13106
The original code uses the wrong parameter `i` for `cuDeviceCanAccessPeer()`, which can cause undefined behavior when `(*all_ids_)[i] != platform_device_id`, for example, virtual devic... | {
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FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13058 from openxla:aportnoy/fix-ffi-docs-buffer 5db8e9ecb4becd69cab0381c5142058697afadb5
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Running these tests seems to cause OOB shape accesses.
Filed https://github.com/openxla/xla/issues/13361 to track work on re-enabling
them.
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We should not try to run them on MacOS CPU.
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FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13253 from lingzhi98:lingzhi/fix_inplace_fusion d2e7f3565c8476f77c74004c6aceb1a29eb951fc
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Imported from GitHub PR https://github.com/openxla/xla/pull/12892
This patch adds the `collective-permute-valid-iteration-annotator` pass.
If a while loop is known to be a pipelined while loop, has a known trip count and increments with step=1, then this pa... | {
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Imported from GitHub PR https://github.com/openxla/xla/pull/13253
There exist bug when making fusion decision for inplace fusion. Current implementation does not consider non-elementwise ops in consumer and will lead to inplace conflict in some specific case.... | {
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"Hi **@PhyllisJi** ,\r\n- Sorry for the dealy, Please post this issue on [keras-team/keras repo.](https://github.com/keras-team/keras/issues)\r\nTo know more see;\r\n[https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999](https://discuss.tensorflow.org/t/ke... | 2024-06-04T04:38:01 | 2024-06-10T05:55:46 | null | NONE | null | null | null | ### Issue type
Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.14.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
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"@PhyllisJi,\r\nAFAIK the argument Groups should A positive int specifying the number of groups in which the input is split along the channel axis. Each group is convolved separately with `filters // groups` filters. The output is the concatenation of all the `groups` results along the channel axis.\r\n\r\nAlso th... | 2024-06-04T04:26:00 | 2024-06-05T06:00:39 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.14.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
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"followers_url": "https:... | null | [] | 2024-06-04T02:40:48 | 2024-06-10T12:26:00 | null | NONE | null | null | null | ### Issue type
Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.14.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### B... | {
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This saves a copy when the output is nested in a proto on an arena.
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} | Introduce `Client::CopyArrays()` for batched device-to-device copy
This CL introduces an IFRT client API that transfers a list of IFRT arrays to a set of new devices and memory kind.
The API is intentionally designed to impose a constraint that all arrays in a single call must share the same destination devices and m... | {
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} | Rename TfLiteOperatorCreateWithData to TfLiteOperatorCreate.
And drop the old TfLiteOperatorCreate function.
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