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Testing configuration settings for the eigen threadpool to increase busy-wait spinning.
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Delete chlo.dynamic_reshape and downstream ops
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Delete chlo.dynamic_reshape and downstream ops
The downstream ops are [ms]hlo.{compute_reshape_shape,cstr_reshapable}.
The pattern that goes from tf.reshape to chlo.dynamic_reshape appears to be
dead code, and it is the only use of chlo.dynamic_reshape.
compute_reshape_shape and cstr_reshapable do not seem to be generated anywhere,
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Context: https://github.com/openxla/stablehlo/blob/main/rfcs/20230704-dynamism-101.md#p1
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[xla] Remove xla runtime except cpu_event that will be moved later
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[xla] Remove xla runtime except cpu_event that will be moved later
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Add a flag to space-to-batch
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Add a flag to space-to-batch
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[xla:ffi] NFC: Remove unused runtime::MemrefView conversion
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[xla:cpu] NFC: Remove deprecated XLA:CPU mlir based codegen part #8
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[xla:cpu] NFC: Remove deprecated XLA:CPU mlir based codegen part #8
Reverts changelist 525613555
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PR_kwDOArmXAs5uaOrm
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Reverts 0cf7880d701abe9aad120102380683c4124edbcf
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Reverts 0cf7880d701abe9aad120102380683c4124edbcf
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PR_kwDOArmXAs5uaN4r
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Reverts changelist 443785169
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NONE
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Reverts changelist 443785169
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| 2,276,462,589 |
PR_kwDOArmXAs5uaMug
| 66,881 |
Delete chlo.dynamic_reshape
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NONE
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Delete chlo.dynamic_reshape
Deleting this op is part of the StableHLO Dynamism 101 RFC: https://github.com/openxla/stablehlo/blob/main/rfcs/20230704-dynamism-101.md#-p1-move-tensorflow-specific-operations-out-of-stablehlo
AFAICT, the only remaining use of this op is in the legalization for tf.reshape.
In a future change, I will delete [ms]hlo.compute_reshape_shape and [ms]hlo.cstr_reshapable as well.
I kept the DRR pattern so I could restrict the source pattern to when
the tensor argument is an MHLO_Tensor. However, because the result pattern contains
an op with a region (shape.assuming), I used a NativeCodeCall to implement the
conversion.
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PR_kwDOArmXAs5uaMfs
| 66,880 |
[mhlo] Rip out lmhlo from the bufferization pipeline
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NONE
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[mhlo] Rip out lmhlo from the bufferization pipeline
The bufferization is still used by kernelgen, but the custom call lowering was
only used by XLA:CPU Next and is no longer needed. Unblocks lmhlo removal.
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PR_kwDOArmXAs5uaIsn
| 66,879 |
Use tensorflow::tpu_plugin::RegisterTpuExecuteOp() instead of RegisterTpuExecuteOp() in learning/brain/tfrt/tf_tpu/tpu_registration.cc.
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NONE
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Use tensorflow::tpu_plugin::RegisterTpuExecuteOp() instead of RegisterTpuExecuteOp() in learning/brain/tfrt/tf_tpu/tpu_registration.cc.
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PR_kwDOArmXAs5uaHQU
| 66,878 |
Remove ScopedDeviceMemory constructor that takes a StreamExecutor pointer.
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NONE
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Remove ScopedDeviceMemory constructor that takes a StreamExecutor pointer.
This removes a circular dependency between StreamExecutor and DeviceMemoryAllocator.
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| 2,276,436,741 |
PR_kwDOArmXAs5uaG-q
| 66,877 |
Extract `xla::ShardingPropagation::CanonicalizeLayouts()` as a util function.
|
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NONE
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Extract `xla::ShardingPropagation::CanonicalizeLayouts()` as a util function.
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PR_kwDOArmXAs5uaGAs
| 66,876 |
[xla:cpu] NFC: Remove deprecated XLA:CPU mlir based codegen part #9
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[xla:cpu] NFC: Remove deprecated XLA:CPU mlir based codegen part #9
Reverts changelist 443785169
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[PJRT] Change return type of PjrtDevice::id() to PjRtGlobalDeviceId.
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[PJRT] Change return type of PjrtDevice::id() to PjRtGlobalDeviceId.
Remove PjrtDevice::global_device_id() overload.
Reverts changelist 443785169
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/61632 from buptzyb:multistream-streammerge 5aabb58431e56868c1f462ec3851785132d4cd47
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[xla:cpu] NFC: Remove deprecated XLA:CPU mlir based codegen part #7
Reverts changelist 525613555
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[XLA] Fix race condition in custom call partitioner registration.
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[XLA] Fix race condition in custom call partitioner registration.
The custom call partitioner registration is not thread-safe. This can lead to a race condition when multiple threads try to register the same partitioner. This CL fixes the race condition by adding a mutex to protect the registration process.
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libhexagon_interface.so for non Android - eLinux platform
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[
"Maybe I should edit the question. I am having a problem running the benchmark with the Hexagon DSP chip. I am using a Debian Linux, and not Android. I can access and run other scripts on the Hexagon DSP, however I am having a lot of problems with Tensorflow. The Hexagon Delegate instructions seem to be out of date and are only for Android, even though it seems that I should be able to compile the necessary files. The Skeleton files: libraries “libhexagon_nn_skel.so”, “libhexagon_nn_skel_v65.so” and “libhexagon_nn_skel_v66.so” are all pre-compiled, so I am stuck with the latest version being v1.20.0.1, and I can't even find that version of Tensorflow.\r\n\r\nThe error I am running into is:\r\n```\r\nWARNING: Failed to fetch Hexagon NN version. This might be because you're using incompatible versions of libhexagon_interface and libhexagon_nn_skel. You must use compatible versions. Refer to Tensorflow Lite Hexagon Delegate Guide.\r\nINFO: Hexagon Delegate is not supported.\r\n```\r\nHow Can I get a libehexagon_interface, libhexagon_nn_skel, and libhexagon_delegate that all work together?\r\n",
"Hi @mmayers88 ,\r\n\r\nFrom what i know, i think the libhexagon_nn_skel.so and other \".so\" files are provided by Qualcomm itself and is a part of snapdragon SDK instead of tensorflow team. Can you please check on Qualcomm forums and let me know if you are able to find them.",
"I've already checked on there. From what I understand the interface and delegate come from you. I have successfully generated a libhexagon_nn_skel.so, but I'm not sure about the other files and generating them. Is it possible to generate the libhexagon_interface.so as was done for other people?",
"Sorry, i misunderstood the question first time, can you please check this [link](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/delegates/hexagon/hexagon_nn) and let me know if this is the one you are looking for .",
"I've tried that. Would I just build the interface with bazel as:\r\n```bazel build --config=elinux_aarch64 -c opt //tensorflow/lite/delegates/hexagon/hexagon_nn:libhexagon_interface.so```\r\n\r\nWould the same be true for building the delegate?:\r\n```bazel build --config=elinux_aarch64 -c opt //tensorflow/lite/delegates/hexagon:hexagon_delegate```",
"Hi. @pkgoogle ,\r\n\r\nCan you please suggest, which one of the above two commands will be able to build the gpu delegate ?",
"Hi @mmayers88, I think you're almost right... I can only find a config option for `linux_aarch64` https://github.com/tensorflow/tensorflow/blob/master/tensorflow/BUILD#L555\r\n\r\nCan you try these and let me know if you run into any issue?\r\n```\r\nbazel build --config=linux_aarch64 -c opt //tensorflow/lite/delegates/hexagon/hexagon_nn:libhexagon_interface.so\r\nbazel build --config=linux_aarch64 -c opt //tensorflow/lite/delegates/hexagon:hexagon_delegate\r\n```",
"I will try that and report back.",
"Reporting back, I get this error:\r\n```ERROR: Config value 'linux_aarch64' is not defined in any .rc file```",
"Hi @mmayers88, apologies, you were actually right it has an e before it:\r\n\r\n```sh\r\nbazel build --config=elinux_aarch64 -c opt //tensorflow/lite/delegates/hexagon/hexagon_nn:libhexagon_interface.so\r\nbazel build --config=elinux_aarch64 -c opt //tensorflow/lite/delegates/hexagon:hexagon_delegate\r\n```\r\n\r\nFor future reference you can find the options under your .bazelrc file in the tensorflow root. I would also run the ./configure script prior to building and answer as appropriately as you can.",
"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/66872\">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/66872\">No</a>\n"
] | 2024-05-02T20:19:14 | 2024-05-28T01:49:19 | 2024-05-28T01:49:16 |
NONE
| null | null | null |
**System information**
- OS Platform and Distribution : Linux Ubuntu 20.04
- Mobile Device: Qualcomm QRB5165/RB5
- TensorFlow installed from Source
- TensorFlow version 2.16.1
- Bazel version (if compiling from source): 6.5.0
- GCC/Compiler version (if compiling from source): aarch64-linux-gnu 11.3.0
**Describe the problem**
Hello.
I'm trying to get hexagon delgate to work on Qualcomm's QRB5165/RB5 platform. This platform is based on Snapdragon 8xx series. But the problem is that this platform works on a Debian Linux. The currently available libhexagon_interface.so doesn't work as it has Android dependencies. I cannot find the source code of libhexagon_interface.so in tensorflow git. Is it possible to get a complied binary of libhexagon_interface.so for aarch64 Linux without Android dependencies?
It seems like this has been done before [#43637](https://github.com/tensorflow/tensorflow/issues/43637) and I would appreciate a binary as well.
Thank you
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[tsl:concurrency] Split Map and TryMap functors into two separate functions
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[tsl:concurrency] Split Map and TryMap functors into two separate functions
Allow users to map AsyncValue into AsyncValue<StatusOr<T>> if they really want it and instead introduce a separate TryMap API that automatically folds returned StatusOr into async value error.
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PR_kwDOArmXAs5uZyFB
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[xla:cpu] NFC: Remove deprecated XLA:CPU mlir based codegen part #5
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[xla:cpu] NFC: Remove deprecated XLA:CPU mlir based codegen part #5
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/62750 from mattbahr:implement-sampled-addmm-v2 c295a0e649e86304c084b7c9b3c77bdf0d2ed8fb
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PR_kwDOArmXAs5uZvhU
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Internal change only.
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NONE
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Internal change only.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/62750 from mattbahr:implement-sampled-addmm-v2 c295a0e649e86304c084b7c9b3c77bdf0d2ed8fb
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PR_kwDOArmXAs5uZuMu
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[XLA:GPU] Use the correct `EXPECT_OK` version in the numerics verifier test.
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NONE
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[XLA:GPU] Use the correct `EXPECT_OK` version in the numerics verifier test.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/62750 from mattbahr:implement-sampled-addmm-v2 c295a0e649e86304c084b7c9b3c77bdf0d2ed8fb
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[XLA:TPU] When finding more host offloading annotations than expected, we now print a warning rather than an error. This is because the excess found annotations are likely no-ops (for example, moving data to the device that is already on the device).
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NONE
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[XLA:TPU] When finding more host offloading annotations than expected, we now print a warning rather than an error. This is because the excess found annotations are likely no-ops (for example, moving data to the device that is already on the device).
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PR_kwDOArmXAs5uZhV4
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[XLA:GPU] Store fusion_roots and fusion_heroes as HloInstructionAdaptor in HloFusionAnalysis.
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NONE
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[XLA:GPU] Store fusion_roots and fusion_heroes as HloInstructionAdaptor in HloFusionAnalysis.
HloFusionAnalysis works on fused and unfused HLO, so it's more logical to instruction adaptors that point to the fusion adaptor.
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PR_kwDOArmXAs5uZPAX
| 66,862 |
Generate mhlo.dynamic_reshape instead of chlo.dynamic_reshape for squeeze
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Generate mhlo.dynamic_reshape instead of chlo.dynamic_reshape for squeeze
The extra logic that chlo.dynamic_reshape implements is not needed for squeeze.
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NFC: Move cpu_event to xla/service/cpu
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NFC: Move cpu_event to xla/service/cpu
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PR_kwDOArmXAs5uY4gN
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Moves all the interval creation logic upstream before the call to Solve().
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PR_kwDOArmXAs5uYyHa
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NFC: Remove LMHLO from mlir-hlo-opt tool
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NFC: Remove LMHLO from mlir-hlo-opt tool
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PR_kwDOArmXAs5uYwzd
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Replace one last use of ScopedDeviceMemory in buffer_comparator_test with DeviceHandle.
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NFC: Remove unused hlo -> lmhlo passes
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NFC: Remove unused hlo -> lmhlo passes
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Integrate LLVM at llvm/llvm-project@306ae14face2
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Integrate LLVM at llvm/llvm-project@306ae14face2
Updates LLVM usage to match
[306ae14face2](https://github.com/llvm/llvm-project/commit/306ae14face2)
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[XLA:GPU] Added more logs in the autotuner to allow for better benchmark debugging during crashes.
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[XLA:GPU] Added more logs in the autotuner to allow for better benchmark debugging during crashes.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12031 from Amir-19:gemm_fusion_log e65340a856101401e9ee56b4ac258cef9c10c517
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PR_kwDOArmXAs5uYcb8
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[xla:cpu] NFC: Remove deprecated XLA:CPU mlir based codegen part #6
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[xla:cpu] NFC: Remove deprecated XLA:CPU mlir based codegen part #6
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PR #12041: [XLA:CPU][oneDNN] Gelu Exact patterns and tests
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PR #12041: [XLA:CPU][oneDNN] Gelu Exact patterns and tests
Imported from GitHub PR https://github.com/openxla/xla/pull/12041
This PR adds support for Matmul fusion with Gelu-Exact pattern and tests.
Copybara import of the project:
--
899b5619e7dc2edca4ea169f6fddf41e2a4a54e7 by Kanvi Khanna <[email protected]>:
Add Gelu-exact pattern and tests
--
bb912bf4de8471883b1938c61aa5b58245cd5a6f by Kanvi Khanna <[email protected]>:
Address review comments
Merging this change closes #12041
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12041 from Intel-tensorflow:kanvi/gelu-erf bb912bf4de8471883b1938c61aa5b58245cd5a6f
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PR_kwDOArmXAs5uYRY2
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PR #12046: Use HloPredicateTrue and HloPredicateFalse
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PR #12046: Use HloPredicateTrue and HloPredicateFalse
Imported from GitHub PR https://github.com/openxla/xla/pull/12046
`xla/utils.h` defines the following functions
```
inline bool HloPredicateTrue(const HloInstruction*) { return true; }
inline bool HloPredicateFalse(const HloInstruction*) { return false; }
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We can use them instead of inline lambdas
```
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[](const HloInstruction*) { return false; }
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Copybara import of the project:
--
723c5a9525a5cf3d540b57601c31373e94992673 by Alexander Pivovarov <[email protected]>:
Use HloPredicateTrue and HloPredicateFalse
Merging this change closes #12046
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12046 from apivovarov:hlo_pred 723c5a9525a5cf3d540b57601c31373e94992673
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I_kwDOArmXAs6HpnG9
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Dataset sharding warning
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[
"@andremfreitas Could you please provide a standalone code to replicate the issue reported here as it would help us to analyze the issue?\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66851\">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/66851\">No</a>\n"
] | 2024-05-02T15:32:01 | 2024-05-24T01:49:18 | 2024-05-24T01:49:12 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS
### 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
A100 40GB
### Current behavior?
Hi,
I am using a mirrored strategy for gpu parallelisation. This is how I prepare my dataset:
```
data = tf.data.Dataset.from_tensor_slices(gt_reshaped).batch(batch_size = batch_size).shuffle(buffer_size = n_ics)
dist_dataset = strategy.experimental_distribute_dataset(data)
```
Which is resulting in the warning below (see relevant log output).
This (closed) issue mentions something similar: [#42146](https://github.com/tensorflow/tensorflow/issues/42146). I would expect this warning not to appear in tf2.16 anymore. I am not trying to do data sharding of a a file or files so this warning shouldn't be thrown in my opinion (and it's annoying). But please let me know if it makes sense to be thrown in a case like this.
Thanks,
Andre
### Standalone code to reproduce the issue
```shell
Cannot produce a MWE at the moment.
```
### Relevant log output
```shell
2024-05-02 17:21:33.678068: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:766] AUTO sharding policy will apply DATA sharding policy as it failed to apply FILE sharding policy because of the following reason: Found an unshardable source dataset: name: "TensorSliceDataset/_1"
op: "TensorSliceDataset"
input: "Placeholder/_0"
attr {
key: "Toutput_types"
value {
list {
type: DT_COMPLEX128
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attr {
key: "is_files"
value {
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value {
s: "\n\024TensorSliceDataset:0"
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attr {
key: "output_shapes"
value {
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shape {
dim {
size: 10
}
dim {
size: 2
}
}
}
}
}
```
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[XLA:GPU] Make symbolic tiles use a single indexing map under the hood
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[XLA:GPU] Make symbolic tiles use a single indexing map under the hood
For now, offset_map(), size_map(), and stride_map() return AffineMaps in the existing format, but in a later cl, we'll change them to return IndexingMaps.
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[XLA:GPU][MLIR-Based emitters] Use xla_gpu.apply_indexing instead of affine.apply.
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Updates the solver & evaluator to handle requests that contain pre-populated reduced groups.
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Updates the solver & evaluator to handle requests that contain pre-populated reduced groups.
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PR_kwDOArmXAs5uXYWF
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Fix custom call legalization `mhlo` -> `stablehlo`
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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/66847/checks?check_run_id=24513679185) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
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Fix custom call legalization `mhlo` -> `stablehlo`
This change ports the fix for legalization custom call from mhlo to stablehlo. This fix existed in the `standard` path (`matchAndRewrite` function), this change applies it in the path where custom call has public features not yet supported in stablehlo (`rewriteMhloOpAsCustomCall` function). It allows to legalize typed FFI custom call.
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[XLA:GPU] When running run_hlo_module in isolation mode, do not stop on errors.
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[XLA:GPU] When running run_hlo_module in isolation mode, do not stop on errors.
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PR_kwDOArmXAs5uXOFm
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PR_kwDOArmXAs5uXDdu
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Integrate LLVM at llvm/llvm-project@dcbf0fcd0d55
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Integrate LLVM at llvm/llvm-project@dcbf0fcd0d55
Updates LLVM usage to match
[dcbf0fcd0d55](https://github.com/llvm/llvm-project/commit/dcbf0fcd0d55)
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[XLA:GPU][MLIR-based emitters] Add a pattern to fold constant operands for apply_indexing op.
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[XLA:GPU][MLIR-based emitters] Add a pattern to fold constant operands for apply_indexing op.
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PR_kwDOArmXAs5uWyWi
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Integrate LLVM at llvm/llvm-project@dcbf0fcd0d55
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Integrate LLVM at llvm/llvm-project@dcbf0fcd0d55
Updates LLVM usage to match
[dcbf0fcd0d55](https://github.com/llvm/llvm-project/commit/dcbf0fcd0d55)
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[XLA:GPU][MLIR-based emitters] Remove unused results from apply_indexing op.
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Fix reductions with side outputs.
- The current epilogue logic doesn't work for all fusions
- The output indices have to be computed based on a reduction root
- Side outputs have to be written at the end of the loop body
Verified with JAX test suite.
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[
"Hi **@RSRcoder001** ,\r\n\r\n- Thanks for reporting issue i also observed the same, We will check with developers and provide the resolution.\r\n\r\nThank you!",
"Hi **@RSRcoder001** ,\r\n\r\n- Could you please check with latest version?. it might be fix with latest version. i am adding **[commit](https://github.com/tensorflow/tensorflow/commit/66681ea34bee93fa359486458f92ceea864cc6de)** , [#61964](https://github.com/tensorflow/tensorflow/issues/61964) for here for your reference.\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66835\">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/66835\">No</a>\n"
] | 2024-05-02T10:37:13 | 2024-05-23T01:48:49 | 2024-05-23T01:48:46 |
NONE
| null | null | null |
ref Windows CPU link: https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.15.0.zip
The zip file for the C api seems to be missing files e.g. tf_buffer.h.
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I_kwDOArmXAs6HnEd0
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Buffer size mismatch in tensorflow/lite/kernels/stablehlo_pad.cc
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"Will wait for PR review to see how that goes."
] | 2024-05-02T10:31:51 | 2024-05-17T20:29:33 | null |
CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Pointers `this->edge_pad_low_`, `this->edge_pad_high_`, `this->interior_pad_` reference memory locations of size 48 bytes as they point to arrays of int64_t of `kMaxDims` elements, where `kMaxDims == 6`:
https://github.com/tensorflow/tensorflow/blob/bd1c3bf880a103425cfa5dd49ab3d1bf84857d13/tensorflow/lite/kernels/stablehlo_pad.cc#L36
https://github.com/tensorflow/tensorflow/blob/bd1c3bf880a103425cfa5dd49ab3d1bf84857d13/tensorflow/lite/kernels/stablehlo_pad.cc#L221-L224
These pointers are passed as parameters to function 'memcpy' with a size parameter `TFLITE_STABLEHLO_PAD_PARAMS_MAX_DIMENSION_COUNT` * 8 that is always equal to 64 bytes:
https://github.com/tensorflow/tensorflow/blob/bd1c3bf880a103425cfa5dd49ab3d1bf84857d13/tensorflow/lite/kernels/stablehlo_pad.cc#L98-L108
https://github.com/tensorflow/tensorflow/blob/b19a54c0d9739f9c548222d625c1413628b3a43f/tensorflow/lite/core/c/builtin_op_data.h#L38
This can lead to a buffer overflow.
It's worth noting that in [tensorflow/lite/core/api/flatbuffer_conversions_test.cc](https://github.com/tensorflow/tensorflow/blob/b19a54c0d9739f9c548222d625c1413628b3a43f/tensorflow/lite/core/api/flatbuffer_conversions_test.cc)
var `kMaxDims` is explicitly assigned the value of the constant `TFLITE_STABLEHLO_PAD_PARAMS_MAX_DIMENSION_COUNT`:
https://github.com/tensorflow/tensorflow/blob/b19a54c0d9739f9c548222d625c1413628b3a43f/tensorflow/lite/core/api/flatbuffer_conversions_test.cc#L737-L738
### Standalone code to reproduce the issue
```shell
Bug was found by Svace static analysis tool.
```
### Relevant log output
_No response_
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Automated Code Change
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PR_kwDOArmXAs5uVn0J
| 66,832 |
Lower index to 64 bits if 64 bit indices are used in HLO.
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Lower index to 64 bits if 64 bit indices are used in HLO.
Currently, we only look at shapes to determine the bit width
of `index`. We have to also look at index operands.
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Add a test case to document how HloCSE works for multi-output fusions.
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Add a test case to document how HloCSE works for multi-output fusions.
Currently it would create a duplicate tuple element. We might want to change
this in the future.
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[XLA:GPU] Add an optional compiler pass that verifies Triton fusions.
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[XLA:GPU] Add an optional compiler pass that verifies Triton fusions.
The pass uses existing autotuning logic to compile and run two versions of all `FusionInstructions` with a Triton backend:
- The original instruction with the Triton Backend
- A modified instruction without a backend. This one will be compiled by the regular emitters.
The outs of the two versions are compared and the pass fails if there is a mismatch.
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[XLA:GPU] Cast bf16 matmuls to fp32 on pre-Ampere GPUs
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[XLA:GPU] Cast bf16 matmuls to fp32 on pre-Ampere GPUs
bf16 matmuls exist on pre-Ampere, but are exceedingly (>4x) slow.
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[XLA:GPU][MLIR-based emitters] Remove unused dimensions in xla_gpu.indexing_op.
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[StablehloShapeRefinement] Skip constant folding of convert operations with dynamic shapes.
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[StablehloShapeRefinement] Skip constant folding of convert operations with dynamic shapes.
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Do not deduplicate roots in FindRoots() method.
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NONE
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Do not deduplicate roots in FindRoots() method.
We should not filter out duplicate roots here, as then it becomes harder to map
from the returned roots to the tuple elements. This is mostly a theoretical
concern, as it is unlikely that we will have duplicate roots.
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| 66,819 |
Add a utility for finding HLO use chains
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Add a utility for finding HLO use chains
This is needed for proper epilogue indexing computations, e.g.
in fusions like this:
```
reduce
/ \
broadcast log
| |
neg bitcast
\ /
ROOT
```
The current assumption in `ComputeEpilogueInputToOutputIndexing`
that we can just take the first user is incorrect here - the
reduce is both part of the side output's computation and the hero
of the fusion. Fusions like this make absolutely no sense, but they
exist.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/61773 from Intel-tensorflow:kanvi/remove_stopgradient dcc39ce50742700042f8a89b9a48dce320057ffe
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Rollback
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NONE
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Rollback
Reverts 00efc614ef17c12ef8c45c4505fc8871ac2befd2
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Reverts 08fdea60bee843ec228eacef8bb37fc62b87108f
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Reverts 08fdea60bee843ec228eacef8bb37fc62b87108f
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PR_kwDOArmXAs5uUAl0
| 66,816 |
Enable saving quantization report to file.
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Enable saving quantization report to file.
Impelements `SaveQuantizationReportInstrumentation` to allow analyzing and saving quantization report to file after `QuantizeCompositeFunctionsPass`.
Adds a new config field in `QuantizationConfig` for the users to provide the path to file to save the quantization report.
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PR_kwDOArmXAs5uT41J
| 66,815 |
Add TFExecutorGraphPruningPass to TF dialect to executor export pipeline
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Add TFExecutorGraphPruningPass to TF dialect to executor export pipeline
This is to prune out unused ops/nodes in the graph. This doesn't affect correctness but in some cases will drastically reduce memory/network usage and improve performance depending on the number and type of dead nodes that are pruned.
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PR_kwDOArmXAs5uTvZT
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[XLA:MSA] Avoid sorting colocations.
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[XLA:MSA] Avoid sorting colocations.
Maintaining a map from AliasedOffset* to an index in a vector lets us avoid having to sort by just constructing a colocation_vector which is stable in iteration order.
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PR #12005: [XLA:GPU] Fix cuDNN flash attention unit test failure with cuDNN 9.0
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PR #12005: [XLA:GPU] Fix cuDNN flash attention unit test failure with cuDNN 9.0
Imported from GitHub PR https://github.com/openxla/xla/pull/12005
* Use `build_plans` instead of `build_plan_at_index` for flash attention as cuDNN 9.0 could have more than 1 plan and some plans cant be built.
Copybara import of the project:
--
5379425cce6606435c84219eee3d4ca4f98fc5ab by cjkkkk <[email protected]>:
use build_plans as cudnn 9 has more than 1 plan
Merging this change closes #12005
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12005 from Cjkkkk:cudnn_9_0_fa_fix 5379425cce6606435c84219eee3d4ca4f98fc5ab
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PR #11348: [XLA:GPU] Enable Async nccl collective operators in command buffer
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PR #11348: [XLA:GPU] Enable Async nccl collective operators in command buffer
Imported from GitHub PR https://github.com/openxla/xla/pull/11348
This PR enables async collective operators (AllReduceCmd, ReduceScatterCmd, AllGatherCmd, CollectiveBroadcastCmd) in command buffers.
The idea is to translate collective-start thunk to command buffer nccl operator with different execution scope id, and insert a barrier from the original execution scope to the nccl execution scope. on collective-done thunk, it translate into command buffer Barrier command that does synchronization from nccl execution scope to main execution scope.
Copybara import of the project:
--
7678c2306b2f4b477e5a120a61e933d49ba5d14e by Shawn Wang <[email protected]>:
add command buffer scope support for async instructions
use type safe NcclStreamId
fix format
fix unittest failure
fix unittest error
fix test failures
--
0d30d92343b3383c6d97c308199493b331023140 by Shawn Wang <[email protected]>:
fix parameter name
--
0170d4b2b9d9db3302187b0d7d1dd1fa27c089b5 by Shawn Wang <[email protected]>:
add header file
--
0cb9fdb1e5f24a84a6a6d1ed72a0f1270d91e977 by Shawn Wang <[email protected]>:
fix build file
--
321c31204a119ecb92e2bb8489aa6661c8b1cf15 by Shawn Wang <[email protected]>:
fix stream id assignment
--
15bd4871f5dfd23bd8564a5e710b4dce7cf8100b by Shawn Wang <[email protected]>:
fix c-style error
--
dfe36017756e07362fed49a2430d72d394aadb4e by Shawn Wang <[email protected]>:
add dep
Merging this change closes #11348
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11348 from shawnwang18:shawnw/add_async_command_buffer dfe36017756e07362fed49a2430d72d394aadb4e
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PR_kwDOArmXAs5uTNe3
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[PJRT] Remove the deprecated PjRtClient::LookupDevices() overload that takes a raw int.
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[PJRT] Remove the deprecated PjRtClient::LookupDevices() overload that takes a raw int.
Update users to use the PjRtGlobalDeviceId overloads (or to use different APIs).
Fixes a TODO, no functional changes intended. However, the change is in preparation for allowing int64_t sized device IDs, so fixing up users of `int` device IDs is a preparatory step.
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Update friends. Nothing public here.
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Update friends. Nothing public here.
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[XLA:GPU] Enable strength-reduction for dots that are not supported by GemmFusion
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[XLA:GPU] Enable strength-reduction for dots that are not supported by GemmFusion
If dot is not supported by Triton, we should do strength-reduction on it, so that it can be handled by fusion pipeline later.
This is NFC because GpuAlgebraicSimplifer is not used yet.
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PR_kwDOArmXAs5uS__r
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[IFRT] Implement RemapArrays API
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[IFRT] Implement RemapArrays API
We introduce `ifrt::Client::RemapArrays()`
* It takes `N` input arrays, returns `M` output arrays, performing is a
shard-level scatter-gather. It is a generalization of existing shard-level
APIs: `Client::AssembleArrayFromSingleDeviceArrays()`,
`Array::DisassembleIntoSingleDeviceArrays()`, and
`Array::FullyReplicatedShard()`.
* It is metadata-only operation.
No shard from the input can be used twice (no replication). No shard will have
its location changed (no transfer). However, some shards from the input may be
not used in any of the output arrays and be discarded.
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PR_kwDOArmXAs5uS78F
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[xla:gpu] ExecutionCounters for Send/Recv thunks should be per StreamExecutor
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[xla:gpu] ExecutionCounters for Send/Recv thunks should be per StreamExecutor
and RunId.
This fixes the problem that the runtime optimization to make Send/Recv
conditional no-op only works for the first run of a module.
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Remove XLA:CPU runtime support from tf2xla
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[xla:cpu] NFC: Remove deprecated XLA:CPU mlir based codegen part #4
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[xla:cpu] NFC: Remove deprecated XLA:CPU mlir based codegen part #2
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Adds the skeleton for `BasicStringArray` - a simple `ifrt::Array` implementation that wraps a local (or host) string buffer.
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Adds the skeleton for `BasicStringArray` - a simple `ifrt::Array` implementation that wraps a local (or host) string buffer.
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PR_kwDOArmXAs5uSph7
| 66,801 |
[Multi-host GPU Test]Add num_slices, num_hosts_per_slice, and num_devices_per_host to GpuTopology.
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[Multi-host GPU Test]Add num_slices, num_hosts_per_slice, and num_devices_per_host to GpuTopology.
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PR_kwDOArmXAs5uSm9r
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Add virtual StreamExecutorInterface::GetPlatform method.
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Add virtual StreamExecutorInterface::GetPlatform method.
This will eventually replace StreamExecutor::platform which collides with TpuExecutorInterface::platform method if StreamExecutor::platform was made virtual.
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PR_kwDOArmXAs5uSjrx
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Integrate LLVM at llvm/llvm-project@a7b968a57834
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Integrate LLVM at llvm/llvm-project@a7b968a57834
Updates LLVM usage to match
[a7b968a57834](https://github.com/llvm/llvm-project/commit/a7b968a57834)
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PR_kwDOArmXAs5uShF5
| 66,798 |
[XLA] Enlarge size of integers in xla::DeviceAssignment to 64-bits.
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[XLA] Enlarge size of integers in xla::DeviceAssignment to 64-bits.
In PJRT I want to use structured device IDs that pack both a node ID and a local device ID into a single device ID. A 32-bit space is uncomfortably small to be doing this.
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PR_kwDOArmXAs5uSc3p
| 66,797 |
Move the eligible intervening Bitcast between AR and DS ahead of AR to enable RS fusion.
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Move the eligible intervening Bitcast between AR and DS ahead of AR to enable RS fusion.
The parsing codes for ReduceScatter fusion does not allow an intervening Bitcast between AR and DS. This cl detects such pattern and moves the Bitcast before AR and the Bitcast converts the original AR input to the target shape. This is done only when the Bitcast shape matches DS' shape, namely eligible for RS fusion.
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PR_kwDOArmXAs5uSXJi
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hlo_runner_pjrt: support parameter and output streaming
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[] | 2024-05-01T20:46:13 | 2024-05-02T21:41:55 | 2024-05-02T21:41:54 |
NONE
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hlo_runner_pjrt: support parameter and output streaming
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[xla:cpu] NFC: Remove deprecated XLA:CPU mlir based codegen part #1
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[] | 2024-05-01T19:45:46 | 2024-05-01T22:45:43 | 2024-05-01T22:45:41 |
NONE
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[xla:cpu] NFC: Remove deprecated XLA:CPU mlir based codegen part #1
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Update Absl Library
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[] | 2024-05-01T19:07:52 | 2024-05-19T03:34:51 | null |
NONE
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Update Absl Library
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PR_kwDOArmXAs5uRsET
| 66,793 |
Implement Freeze() in IfrtServingExecutable to release mlir module
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[] | 2024-05-01T18:34:44 | 2024-05-06T21:42:38 | 2024-05-06T21:42:36 |
NONE
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Implement Freeze() in IfrtServingExecutable to release mlir module
to saved memory and prevent further compilation of the program.
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PR_kwDOArmXAs5uRqQa
| 66,792 |
Disable BES uploads and set BEP uploads to minimal for Mac RBE cross-compile builds
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[] | 2024-05-01T18:28:18 | 2024-05-02T19:34:30 | 2024-05-02T19:34:29 |
NONE
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Disable BES uploads and set BEP uploads to minimal for Mac RBE cross-compile builds
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