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65,089
Use absl::Status rather than tsl::Status
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2024-04-04T22:08:03
2024-04-05T22:48:24
2024-04-05T22:48:24
NONE
null
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Use absl::Status rather than tsl::Status
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65,088
Do not add `get-tuple-element(tuple())` if the operand and result share the same sharding, when converting MLIR ReturnOp into HLO ROOT instruction.
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2024-04-04T22:00:37
2024-04-16T17:42:55
2024-04-16T17:42:52
NONE
null
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Do not add `get-tuple-element(tuple())` if the operand and result share the same sharding, when converting MLIR ReturnOp into HLO ROOT instruction.
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PR_kwDOArmXAs5rxFGd
65,087
Import nanobind caster for std::string to avoid casting error.
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2024-04-04T21:57:15
2024-04-04T23:12:50
2024-04-04T23:12:49
NONE
null
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Import nanobind caster for std::string to avoid casting error.
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PR_kwDOArmXAs5rw_t-
65,086
Disabled unsupported cases
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2024-04-04T21:39:16
2024-04-04T23:01:11
2024-04-04T23:01:11
NONE
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Disabled unsupported cases
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65,085
Enable profiler tracing support for threadpools
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2024-04-04T21:25:17
2024-05-21T18:51:33
2024-05-21T18:51:26
NONE
null
true
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Enable profiler tracing support for threadpools FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12896 from openxla:cleanup 32ded36419a4a1b2177a6374846497a8bb458ac9
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PR_kwDOArmXAs5rwzgx
65,084
Upgrade to support and default to clang 18 for the OSS compiler
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2024-04-04T21:04:35
2024-05-16T14:16:05
null
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Upgrade to support and default to clang 18 for the OSS compiler
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65,083
Revert TrivialDce pass
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2024-04-04T21:04:32
2024-04-05T22:00:05
2024-04-05T22:00:04
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Revert TrivialDce pass The need for this was due to a bug in MLIR's GreedyPatternRewriteDriver (https://github.com/llvm/llvm-project/issues/86765) which has been fixed (https://github.com/llvm/llvm-project/pull/86990), so the TrivialDce pass is no longer needed. Reverts 04b31c7d1e349856d4bf7d7758020ce4a36dd21d
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65,082
JUCE LNK2001 - LNK2019: UNRESOLVED EXTERNAL SYMBOL
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[ "@Ikerlandarech Could you please try creating a minimal example project outside of your VST that only includes TensorFlow Lite and JUCE to see if the linker errors persist? Please let us know!\r\nThank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65082\">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/65082\">No</a>\n" ]
2024-04-04T20:40:58
2024-04-10T17:57:53
2024-04-10T17:57:51
NONE
null
null
null
### Issue type Build/Install ### Have you reproduced the bug with TensorFlow Nightly? No ### Source source ### TensorFlow version 2.11 ### Custom code Yes ### OS platform and distribution _No response_ ### Mobile device _No response_ ### Python version _No response_ ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? Hey there and thank you for reading this. I've been trying to work on a VST for my final thesis project which consists of timbre transfer voice to instruments, I've been using JUCE, and all source files are loaded from the Projuicer, the problem I'm facing is those Linker Errors that I think have to do with reading/accessing the Tensorflow files. I have no idea on how to solve it since I tried changing all the "includes" so that filepath were accessible and also tried importing all TF into the Projuicer but then it crashes. The framework that I'm working with is DDSP-VST from the Magenta Team which uses Tensorflow to perform all the ML operations. The original DDSP code framework is implemented in Python with Tensorflow but for DDSP-VST everything is rewritten in C++ using the JUCE framework and Tensorflow Lite. I'm working with the Projuicer and Visual Studio. If anyone can help with this I would really appreciate some help, thank u so very much in advance and have a beautiful day whoever is reading this :) ![Captura de pantalla 2024-03-31 175209](https://github.com/tensorflow/tensorflow/assets/91797318/e9400d3b-b8ed-4c54-8184-1a1ccfbbe75e) ### Standalone code to reproduce the issue ```shell General Problem with headers or accessing the linker files. ``` ### Relevant log output _No response_
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#tf-data Support global shuffling for IndexFlatMap dataset.
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2024-04-10T00:25:09
2024-04-10T00:25:08
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#tf-data Support global shuffling for IndexFlatMap dataset.
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Fix for a bug where the while loop fusible sinking crashes when all of the while loop invariant operands have been propagated.
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2024-04-04T20:35:06
2024-04-04T23:07:00
2024-04-04T23:06:59
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Fix for a bug where the while loop fusible sinking crashes when all of the while loop invariant operands have been propagated.
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Remove obsolete TODOs.
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2024-04-04T20:25:19
2024-04-04T21:24:26
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Remove obsolete TODOs.
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TensorFlow+Lite+in+Play+Services+issue in
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[ "> **System information**\n> - Android Device information (use `adb shell getprop ro.build.fingerprint`\n> if possible):__\n> - TensorFlow Lite in Play Services SDK version (found in `build.gradle`):\n> - Google Play Services version\n> (`Settings` > `Apps` > `Google Play Services` > `App details`):\n> \n> **Standalone code to reproduce the issue**\n> Provide a reproducible test case that is the bare minimum necessary to generate\n> the problem. If possible, please share a link to or attach code demonstrating\n> the problem.\n> \n> **Any other info / logs**\n> Include any logs or source code that would be helpful to diagnose the problem.\n> If including tracebacks, please include the full traceback. Large logs and files\n> should be attached.\n\n", "Hi **@Youniqueli** ,\r\n\r\nWe see that the issue [template]( https://github.com/tensorflow/tensorflow/issues/new/choose) has not been filled, could you please do so as it helps us analyze the issue tf version, steps followed before you ran into this error or stand alone code/colab gist to reproduce the issue faced.\r\n\r\nThank you!\r\n", "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." ]
2024-04-04T20:22:16
2024-04-25T01:48:34
2024-04-25T01:48:34
NONE
null
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**System information** - Android Device information (use `adb shell getprop ro.build.fingerprint` if possible): - TensorFlow Lite in Play Services SDK version (found in `build.gradle`): - Google Play Services version (`Settings` > `Apps` > `Google Play Services` > `App details`): **Standalone code to reproduce the issue** Provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to or attach code demonstrating the problem. **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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First cl of a series of cls to canonicalize all shape layouts to descending layout.
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2024-04-04T19:55:29
2024-04-09T19:38:40
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First cl of a series of cls to canonicalize all shape layouts to descending layout. For now, we only support broadcast operations that appear in the entry computation.
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65,076
Fix error message about fallback call to MLIR bridge.
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2024-04-04T18:59:04
2024-04-05T18:03:46
2024-04-05T18:03:45
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Fix error message about fallback call to MLIR bridge.
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tensorflow-stablehlo_reduce_window-compileFixed
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null
[ "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/65075/checks?check_run_id=23457264164) 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.", "I think i have a CLM with github name : GuoHuanyu and name Huanyu Guo", "Hi @GuoHuanyu Can you please sign CLA. Thank you!", "> Hi @GuoHuanyu Can you please sign CLA. Thank you!\r\n\r\nI have a cla as blow, i have no idea about how to edit it to make check pass\r\n![Screenshot_2024-04-05-12-35-40-27_40deb401b9ffe8e1df2f1cc5ba480b12](https://github.com/tensorflow/tensorflow/assets/13827430/96fd7f01-b5bc-4f82-b164-c03c954655cc)\r\n\r\nThe following contributors were found for this pull request:\r\n\r\n✅ PR Opener: @GuoHuanyu\r\n❌ Author: <Hua*****uo> it should be Huanyu Guo,and i have checked with git log. ", "Hi @GuoHuanyu Can you please make sure to use same GitHub username and email-id associated with it.\r\n", "> Hi @GuoHuanyu Can you please make sure to use same GitHub username and email-id associated with it.\r\n\r\nI tried your suggestion, but I'm still facing issues. Could you help me revert this pull request? Thank you for your assistance.", "> > Hi @GuoHuanyu Can you please make sure to use same GitHub username and email-id associated with it.\r\n> \r\n> I tried your suggestion, but I'm still facing issues. Could you help me revert this pull request? Thank you for your assistance.\r\n\r\nHi @GuoHuanyu Sorry for the delay, please close this PR and create new one. Thank you!" ]
2024-04-04T18:47:47
2024-06-07T17:04:22
null
NONE
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bazel or cmake build tensorflow/lite issues brought by "tensorflow/lite/kernels/stablehlo_reduce_window.cc" It can be fixed by reversing the order of the StridedReduce and ReduceWindowImpl functions. #64376
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Integrate StableHLO at openxla/stablehlo@1bdf7c26
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2024-04-04T18:45:48
2024-04-05T01:06:22
2024-04-05T01:06:21
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Integrate StableHLO at openxla/stablehlo@1bdf7c26
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#tf-data-service Update docstrings for `distributed_save` and `load`.
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2024-04-04T18:37:31
2024-04-04T21:07:36
2024-04-04T21:07:36
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#tf-data-service Update docstrings for `distributed_save` and `load`.
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[XLA:TPU] Support scan loops for parameter input and output streaming in host offloading.
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2024-04-04T18:24:31
2024-04-22T03:10:23
2024-04-22T03:10:22
NONE
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[XLA:TPU] Support scan loops for parameter input and output streaming in host offloading. Currently, parameter input and output streaming use HloAliasAnalysis to correlate MoveToDevice calls with their corresponding input buffers. However, this can break down in scan loops, in which a dynamic slice creates the buffer which is offloaded. This prevents the AliasAnalysis from operating on the right buffer and finding the entry parameter. This change adds a function to TryParameterStreaming which traces up the call graph to potentially find a dynamic slice, and if so performs alias analysis on the input to that dynamic slice. For TryOutputStreaming, we trace down the call graph to find a dynamic update slice and perform alias analysis on that buffer instead.
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Add SourceLocation information to xla::InvalidArgument.
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2024-04-04T18:24:22
2024-04-05T23:58:28
2024-04-05T23:58:27
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Add SourceLocation information to xla::InvalidArgument.
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Move `tsl/BUILD`, `tsl.bzl`, and `tsl.default.bzl` to XLA
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2024-04-04T18:05:50
2024-04-09T18:16:00
2024-04-09T18:15:58
NONE
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Move `tsl/BUILD`, `tsl.bzl`, and `tsl.default.bzl` to XLA
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[xla:gpu] Fix the dimension order propagation for bitcast
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2024-04-04T17:58:28
2024-04-05T07:03:36
2024-04-05T07:03:33
NONE
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[xla:gpu] Fix the dimension order propagation for bitcast If bitcast's destination contains a trivial dimension, it will create an unnecessary trivial fragment after propagation. For example: %p = f32[5,1] parameter(0) %bitcast = f32[5] bitcast(%p) When propagating dimension order from bitcast to p, it creates a fragment of size 1 for %p.
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PR #10503: Fix log1p inaccuracies on complex inputs with large absolute values.
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2024-04-04T17:52:07
2024-04-05T09:44:54
2024-04-05T09:44:51
NONE
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PR #10503: Fix log1p inaccuracies on complex inputs with large absolute values. Imported from GitHub PR https://github.com/openxla/xla/pull/10503 As in the title. Tests and improvement reports are in https://github.com/google/jax/pull/20144. Accuracy tests are enabled in https://github.com/google/jax/pull/20436 Copybara import of the project: -- 2b8f2539d6bf364c0a97f65e186430c5eb3ed07b by Pearu Peterson <[email protected]>: Fix log1p inaccuracies on complex inputs with large absolute values. -- d35cef4f5fa09482c49edfee709e86c5ca29adde by Pearu Peterson <[email protected]>: Add tests to complex Log1p Merging this change closes #10503 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10503 from pearu:pearu/log1p d35cef4f5fa09482c49edfee709e86c5ca29adde
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Integrate LLVM at llvm/llvm-project@e0e615efac52
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2024-04-04T17:29:41
2024-04-10T23:05:50
2024-04-10T23:05:47
NONE
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Integrate LLVM at llvm/llvm-project@e0e615efac52 Updates LLVM usage to match [e0e615efac52](https://github.com/llvm/llvm-project/commit/e0e615efac52)
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presubmits: Add a presubmit for CHECK and related macros.
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2024-04-04T17:23:47
2024-04-04T20:48:07
2024-04-04T20:48:06
NONE
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presubmits: Add a presubmit for CHECK and related macros. Generally it's a better idea to find a way to report the error rather than to crash the process. When XLA is used in a server, crashes expose the server to potential queries of death when unexpected data or API calls occur. Removing existing crashes is a longer-term fixit-type effort, but at least we can prevent new intentional crashes from slipping in.
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Fix msan error introduced in https://github.com/openxla/xla/commit/7b4b2754f96309dd76d0ecbea12c2e9b5533f74d.
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2024-04-04T16:50:53
2024-04-04T17:31:22
2024-04-04T17:31:21
NONE
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Fix msan error introduced in https://github.com/openxla/xla/commit/7b4b2754f96309dd76d0ecbea12c2e9b5533f74d. The commit above makes accessing args.attributes unconditionally when creating a PJRT C API client.
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Integrate LLVM at llvm/llvm-project@e0e615efac52
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2024-04-04T16:05:23
2024-04-05T09:16:39
2024-04-05T09:16:39
NONE
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Integrate LLVM at llvm/llvm-project@e0e615efac52 Updates LLVM usage to match [e0e615efac52](https://github.com/llvm/llvm-project/commit/e0e615efac52)
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Insert symmetric quantization parameters for weights
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2024-04-04T14:59:56
2024-04-08T03:08:49
2024-04-08T03:08:47
NONE
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Insert symmetric quantization parameters for weights
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[XLA:GPU] Move code to compute block id to tile offset indexing map to SymbolicTileAnalysis.
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2024-04-04T14:55:00
2024-04-05T16:26:52
2024-04-05T16:26:52
NONE
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[XLA:GPU] Move code to compute block id to tile offset indexing map to SymbolicTileAnalysis. The block id to tile offset map is relevant for both Triton Emitter and Cost Model, so SymbolicTileAnalysis looks like a better place to have it ATM.
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65,061
Update triton compiler passes to support sparse dot operation.
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2024-04-04T14:18:08
2024-04-05T12:57:44
2024-04-05T12:57:44
NONE
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Update triton compiler passes to support sparse dot operation. 1) TritonToTritonGPU: adds initial tensor encoding; 2) AccelerateMatmul: adds layout conversions; 3) ReduceDataDuplication: forces loading metadata into shared memory; 4) MatmulLoopPipeline: enables async loading of the metadata; 5) FenceInsertion: adds fences (Hopper only).
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65,060
Introduce a new dot operation with a sparse operand (2:4) and its lowering to Nvidia-specific PTX instructions ("mma.sp" or "wgmma.sp").
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2024-04-04T14:16:41
2024-04-05T11:19:15
2024-04-05T11:19:14
NONE
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Introduce a new dot operation with a sparse operand (2:4) and its lowering to Nvidia-specific PTX instructions ("mma.sp" or "wgmma.sp").
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65,059
Add support for sparse dot (wgmma.sp) to NVGPU triton dialect.
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2024-04-04T14:15:37
2024-04-05T10:31:49
2024-04-05T10:31:48
NONE
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Add support for sparse dot (wgmma.sp) to NVGPU triton dialect.
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65,058
Add virtual method `IsInlineableCallOp` to `CallInliner` to allow subclasses to change which call instructions to inline. And clean up `#include`s in `call_inliner.cc`.
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2024-04-04T14:03:17
2024-04-12T14:35:54
2024-04-12T14:35:53
NONE
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Add virtual method `IsInlineableCallOp` to `CallInliner` to allow subclasses to change which call instructions to inline. And clean up `#include`s in `call_inliner.cc`.
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Reverts 9a5c06c9382417ba65bd8e79ef0de9ba093f9086
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2024-04-04T13:46:33
2024-04-04T13:58:13
2024-04-04T13:58:12
NONE
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Reverts 9a5c06c9382417ba65bd8e79ef0de9ba093f9086
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65,056
load_model no longer works with Dot layer in TensorFlow 2.16
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null
[ "@Dobiasd,\r\nYes, As you mentioned it was executed with the error on **tensorflow [v2.16](https://colab.research.google.com/gist/tilakrayal/d42cdec73677d9016f79ca69d54cd825/untitled1815.ipynb) & keras 3.1.1**. On tensorflow **v2.15** it was executed without any error. And now it got fixed on **tf-nightly(2.17.0-dev20240403) and keras(3.2.0.dev2024040303)** which will be available in the upcoming releases.\r\nKindly find the attached [gist](https://colab.research.google.com/gist/tilakrayal/eada8b34ec84ce783f8e3fc69c403254/untitled1813.ipynb) for the reference.\r\n\r\nAlso there is PR raised for the similar issue in Keras repo which was merged recently.\r\nhttps://github.com/keras-team/keras/pull/19362\r\n\r\nThank you!", "Ok, perfect, thank you. 👍", "@Dobiasd,\r\nAs this issue is resolved in tf-nightly, Could you please feel free to move this issue to closed status. Thank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65056\">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/65056\">No</a>\n" ]
2024-04-04T13:40:20
2024-04-04T16:47:05
2024-04-04T16:47:02
NONE
null
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With TensorFlow 2.15.1, the following code works fine: ```python3 from tensorflow.keras.layers import Input, Dot from tensorflow.keras.models import Model, load_model input_1 = Input(shape=(2, 3, 4)) input_2 = Input(shape=(2, 3, 4)) x = Dot(axes=(1, 1))([input_1, input_2]) model = Model(inputs=[input_1, input_2], outputs=[x]) model.compile() model.save('model.keras') load_model('model.keras') ``` However, with TensorFlow 2.16.1, `load_model` throws an exception: ``` ValueError: A `Dot` layer should be called on a list of 2 inputs. Received: input_shape=[[None, 2, 3, 4], [None, 2, 3, 4]] ```
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65,055
Disable a test that is failing on H100
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2024-04-04T13:35:45
2024-04-04T15:43:14
2024-04-04T15:43:12
NONE
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Disable a test that is failing on H100 Reverts 9a5c06c9382417ba65bd8e79ef0de9ba093f9086
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2,225,489,651
PR_kwDOArmXAs5rtO_e
65,054
Bump the operands+outputs threshold to allow larger fusions.
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2024-04-04T13:15:58
2024-04-04T15:01:44
2024-04-04T15:01:42
NONE
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Bump the operands+outputs threshold to allow larger fusions.
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65,053
Add sharding to added tuple for single func result
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2024-04-04T13:13:03
2024-04-05T09:44:44
2024-04-05T09:44:41
NONE
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Add sharding to added tuple for single func result
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65,052
PR #10649: [ROCm] Triton in XLA for ROCm - ir_emitter_triton related changes.
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2024-04-04T13:00:06
2024-04-05T16:58:06
2024-04-05T16:58:05
NONE
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PR #10649: [ROCm] Triton in XLA for ROCm - ir_emitter_triton related changes. Imported from GitHub PR https://github.com/openxla/xla/pull/10649 Second commit of the series for enabling Triton in XLA for ROCm. Copybara import of the project: -- 23d442f83c731cd86131bcd1d91c4e3d7cc42468 by Zoran Jovanovic <[email protected]>: [ROCm] Triton in XLA for ROCm - ir_emitter_triton related changes. Merging this change closes #10649 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10649 from ROCm:rocm_triton_backend_3 23d442f83c731cd86131bcd1d91c4e3d7cc42468
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65,051
how to use tfLite c_api
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null
[ "I didn't find the problem, but I used Bazel as an alternative.\r\n\r\nClone the TensorFlow Lite repository.\r\nBuild the library using this command: `bazel build -c opt //tensorflow/lite/c:libtensorflowlite_c.so`.\r\nCopy **libtensorflowlite_c.so** to your folder from `bazel-bin/tensorflow/lite/c/`.\r\nCompile: `gcc -o hello_tf hello_tf.c -I. -Llib/tensorflow/lite -ltensorflowlite_c`.\r\nCopy and paste your dynamic library to **/usr/lib**: `sudo cp lib/tensorflow/lite/libtensorflowlite_c.so /usr/lib`.\r\nAlmost done: `./hello_tf`." ]
2024-04-04T12:41:57
2024-04-08T09:53:40
2024-04-08T09:53:40
NONE
null
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**System information** - OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 22.04.4 LTS - TensorFlow installed from (source or binary): - TensorFlow version (or github SHA if from source): **Provide the text output from tflite_convert** ``` cant find information of how to build the lite c_api ``` **Standalone code to reproduce the issue** Provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to Colab/Jupyter/any notebook. Also, please include a link to a GraphDef or the model if possible. **Any other info / logs** I used the tensorflow [documentation ](https://www.tensorflow.org/lite/guide/build_cmake)of how to build the library with c_make but the terminal crush and close automatically in the step 5 whitout any error msg
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Rename `quantization_config`'s `QuantizationSpec` field name for disambiguation.
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2024-04-04T12:01:59
2024-04-08T01:48:48
2024-04-08T01:48:46
NONE
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Rename `quantization_config`'s `QuantizationSpec` field name for disambiguation.
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PR #11139: [GPU] Enable cuDNN integer math mode only with v9.1+.
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2024-04-04T09:49:11
2024-04-04T11:26:06
2024-04-04T11:26:05
NONE
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PR #11139: [GPU] Enable cuDNN integer math mode only with v9.1+. Imported from GitHub PR https://github.com/openxla/xla/pull/11139 Copybara import of the project: -- 8b346d0c6bc03b58ddca79e345e9b57b6af05cb2 by Ilia Sergachev <[email protected]>: [GPU] Enable cuDNN integer math mode only with v9.1+. Merging this change closes #11139 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11139 from openxla:cudnn_integer_math 8b346d0c6bc03b58ddca79e345e9b57b6af05cb2
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Capture exceptions raised from `PyFunctionLibrary` functions for debuggability.
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2024-04-04T08:46:07
2024-04-09T06:33:38
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Capture exceptions raised from `PyFunctionLibrary` functions for debuggability. Before this change, when there is an error raised inside a `PyFunctionLibrary` method, the program would crash with a long stack trace, something like the following: ``` <super-long c++ stack trace with no meaningful information> libc++abi: terminating due to uncaught exception of type pybind11::error_already_set: ValueError: Failed to run graph for post-training quantization calibration. ``` The information in the raised exception is lost in this case, hindering debuggability. This change instead returns regular value `None` back to the c++ layer to indicate error, and prints the exception chain from the python side. It prints out the exact error message one would see when an exception is raised from regular python script. The return values of `PyFunctionLibrary` methods are all changed to `std::optional<T>`, where `T` is the successful return value and `std::nullopt` indicates error. To avoid conflict btw erroneous `None`, methods that do not essentially return a value now returns a dummy `True` to indicate successful run.
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Integrate LLVM at llvm/llvm-project@c511c90680ee
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Integrate LLVM at llvm/llvm-project@c511c90680ee Updates LLVM usage to match [c511c90680ee](https://github.com/llvm/llvm-project/commit/c511c90680ee)
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Internal CI configuration to run tests on H100
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Internal CI configuration to run tests on H100 Do some required BUILD file adjustments
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Unpack per-channel hybrid quantized MHLO ops to float ops
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Unpack per-channel hybrid quantized MHLO ops to float ops
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[ "@soheil-asgari Could you verify if you have a compatible Nvidia GPU by running nvidia-smi in your terminal. If there's no output, you might not have an Nvidia GPU or the drivers might not be installed?\r\nThank you!", "It is horrendous how hard it is to get this working on Windows.", "> It is horrendous how hard it is to get this working on Windows.\n\nHello @Retalak \nPlease note that Tensorflow 2.10 was the last version that support GPU on Windows. If you want to use Tensorflow-gpu on Windows, you need to install Tensorflow in WSL2. ", "I had the similar issue with Tensorflow GPU, but the wheel install from this [wheel](https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-2.15.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl) solved the issue.\n\nMake sure your nvidia-smi running properly and your cuda version is compatible with Tensorflow 2.15 (Default 2.16 version is yet to be supported).\n\nYou can also search compatible wheel file [here](https://www.tensorflow.org/install/pip)!", "```\r\nPython 3.9.19 (main, Mar 21 2024, 17:11:28) \r\n[GCC 11.2.0] :: Anaconda, Inc. on linux\r\nNVIDIA-SMI 550.54.10 Driver Version: 551.61 CUDA Version: 12.4\r\nSkipping registering GPU devices...\r\n[name: \"/device:CPU:0\"\r\ndevice_type: \"CPU\"\r\nmemory_limit: 268435456\r\nlocality {\r\n}\r\nincarnation: 6427527073389244107\r\nxla_global_id: -1\r\n]\r\nTF Version\r\n2.16.1\r\n```\r\nOn WSL2 Windows 11 23H2 4090 laptop version. Followed `pip install tensorflow[and-cuda]` no luck", "I eventually got it working. Part of the problem with the documentation IMO is that it does not clearly state what is required on the host vs the guest machine for WSL2. I have the CUDA Toolkit 12.4 installed on my Windows host, not sure if that is necessary but I'm not messing with it now that I got it working. I did not install anything on the guest before running the above install command, it installed the needed versions of the toolkit and CUDNN (I also did a fresh install of my WSL2 - Ubuntu LTS 22.04.3 - to clear anything I had done previously). Another issue is there are a lot of error messages that are considered \"normal\" and may throw off new users. Here is the test command result on my WSL2 guest with it working (I think):\r\n\r\n```\r\nretalak@**-********:~$ python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n2024-04-08 16:07:56.717457: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-04-08 16:07:57.579692: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2024-04-08 16:07:58.688620: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-04-08 16:07:58.852701: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-04-08 16:07:58.852827: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n```\r\n\r\nI also had to add this to the end of ~/.bashrc on my WSL2 guest (replace USER_NAME and make sure to exit WSL2 terminal and start a new one after saving and before running the test command):\r\n\r\n```\r\nexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/USER_NAME/.local/bin;\r\nexport NVIDIA_DIR=$(dirname $(dirname $(python3 -c \"import nvidia.cudnn;print(nvidia.cudnn.__file__)\")))\r\nexport LD_LIBRARY_PATH=$(echo ${NVIDIA_DIR}/*/lib/ | sed -r 's/\\s+/:/g')${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}\r\n```\r\n\r\nNote I am not using miniconda, so this may be different if you are trying to do this in a miniconda environment.", "@soheil-asgari Could you please let us know any update on this 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 is still an issue. Documentation not states to set environment variable required for GPU like mentioned by @Retalak. If i remember correctly tensorflow used to have this documented earlier versions. \r\n", "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/65035\">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/65035\">No</a>\n" ]
2024-04-04T07:29:20
2024-04-30T01:47:50
2024-04-30T01:47:47
NONE
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### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? No ### Source source ### TensorFlow version 2.16 ### Custom code Yes ### OS platform and distribution ubuntu ### Mobile device _No response_ ### Python version 3.11 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory nvidia gtx 1650 ### Current behavior? I did all the installation steps step by step but still I can't use the GPU and this is really bothering me. ### Standalone code to reproduce the issue ```shell 2024-04-04 10:56:25.510348: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices... [] ``` ### Relevant log output ```shell 2024-04-04 10:56:25.510348: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices... [] ```
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65,034
Make the CustomAggregator op stateless
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2024-04-04T05:03:35
2024-04-05T09:09:44
2024-04-05T09:09:44
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Make the CustomAggregator op stateless This cl make the CustomAggregator output a fixed size histogram, thus make it stateless. Histograms are aggregated with the first bin width so the behavior is kept the same as existing implementation.
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When performing inference, the error 'DNN library not found.' occurs intermittently.
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[ "Hi **@kimbyungeun** ,\r\n\r\n- Sorry for the delay, Check your environment variables related to CUDA and cuDNN. Set LD_LIBRARY_PATH to include the directories containing the CUDA and cuDNN libraries. \r\n- Run a script to check if tensorflow can detect your GPU and if it's using the correct CUDA/cuDNN versions.\r\n```\r\nfrom tensorflow.python.client import device_lib\r\nprint(device_lib.list_local_devices())\r\n```\r\n- Clearing the tensorflow cache can resolve intermittent issues. You can do this by deleting the '~/.keras' directory.\r\n- If the issue persists, consider reinstalling TensorFlow and its dependencies from scratch. Use a virtual environment to isolate the installation.\r\n\r\nThank you!\r\n\r\n", "Hi @Venkat6871 \r\n\r\nI am planning to proceed with the following two tasks:\r\n\r\n* Set the LD_LIBRARY_PATH environment variable in a Docker container.\r\n* Downgrade CUDA version from 11.3.0 to 11.2.2.\r\n * https://github.com/googlecolab/colabtools/issues/2600#issuecomment-1061312120\r\n\r\nAdditionally, I have a question:\r\n* Between CUDA 11.3.1 and CUDA 11.2.2, which direction would be better? Please recommend.\r\n\r\nThank you!", "Hi @kimbyungeun ,\r\n- As per [documentation](https://www.tensorflow.org/install/source#gpu). For TF 2.11 compatible version is CUDA 11.2.2. So i can say this compatible version for smooth installation.\r\n\r\nThank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65033\">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/65033\">No</a>\n", "The base image has been changed from `nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04` to `nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04`, and so far the same error has not occurred. It is considered to be resolved.\r\n\r\nhttps://hub.docker.com/layers/nvidia/cuda/11.3.1-cudnn8-devel-ubuntu20.04/images/sha256-278b6f43436029c8fa7b4905970457156774178b99509aa753b08547fb721cd9?context=explore\r\n\r\nhttps://hub.docker.com/layers/nvidia/cuda/11.2.2-cudnn8-devel-ubuntu20.04/images/sha256-7d4850083e83853f757014e9fb852934907317a4830013591ec71480181232e7?context=explore\r\n\r\n@Venkat6871 👍 " ]
2024-04-04T04:15:05
2024-05-28T04:15:36
2024-05-22T07:44:11
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### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? No ### Source binary ### TensorFlow version TF 2.11 ### Custom code Yes ### OS platform and distribution Ubuntu 20.04 LTS 5.15.0-46-generic ### Mobile device _No response_ ### Python version 3.8.10 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version 11.3.0 / 8.2.0.53 ### GPU model and memory NVIDIA A16 ### Current behavior? When attempting inference, the error 'DNN library is not found.' occurs, leading to inference failure. This error does not occur every time but happens randomly, and sometimes it does not occur at all. ### Standalone code to reproduce the issue ```shell os.environ["CUDA_VISIBLE_DEVICES"] = self.gpu_no self.model = tf.keras.models.load_model(self.model_file_path) size = tf_model.get_model_image_size(self.model_type) img = tf.keras.preprocessing.image.load_img(image_file, target_size=(size, size)) img_array = tf.keras.preprocessing.image.img_to_array(img) img_array = tf.expand_dims(img_array, 0) predictions = self.model.predict(img_array) ``` ### Relevant log output ```shell Exception: Graph execution error: Detected at node 'efficientnetb6/efficientnetb6_backbone/efficientnetb6_m1/stem_conv/Conv2D' defined at (most recent call last): File "/home/user/inference.py", line 170, in predict_multi_step predictions = self.model.predict(img_array) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 561, in __call__ return super().__call__(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py", line 1132, in __call__ outputs = call_fn(inputs, *args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 96, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py", line 511, in call return self._run_internal_graph(inputs, training=training, mask=mask) File "/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py", line 668, in _run_internal_graph outputs = node.layer(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 561, in __call__ return super().__call__(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py", line 1132, in __call__ outputs = call_fn(inputs, *args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 96, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py", line 511, in call return self._run_internal_graph(inputs, training=training, mask=mask) File "/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py", line 668, in _run_internal_graph outputs = node.layer(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 561, in __call__ return super().__call__(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py", line 1132, in __call__ outputs = call_fn(inputs, *args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 96, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py", line 511, in call return self._run_internal_graph(inputs, training=training, mask=mask) File "/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py", line 668, in _run_internal_graph outputs = node.layer(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py", line 1132, in __call__ outputs = call_fn(inputs, *args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 96, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/layers/convolutional/base_conv.py", line 283, in call outputs = self.convolution_op(inputs, self.kernel) File "/usr/local/lib/python3.8/dist-packages/keras/layers/convolutional/base_conv.py", line 255, in convolution_op return tf.nn.convolution( Node: 'efficientnetb6/efficientnetb6_backbone/efficientnetb6_m1/stem_conv/Conv2D' Detected at node 'efficientnetb6/efficientnetb6_backbone/efficientnetb6_m1/stem_conv/Conv2D' defined at (most recent call last): File "/home/user/inference.py", line 170, in predict_multi_step predictions = self.model.predict(img_array) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 561, in __call__ return super().__call__(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py", line 1132, in __call__ outputs = call_fn(inputs, *args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 96, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py", line 511, in call return self._run_internal_graph(inputs, training=training, mask=mask) File "/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py", line 668, in _run_internal_graph outputs = node.layer(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 561, in __call__ return super().__call__(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py", line 1132, in __call__ outputs = call_fn(inputs, *args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 96, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py", line 511, in call return self._run_internal_graph(inputs, training=training, mask=mask) File "/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py", line 668, in _run_internal_graph outputs = node.layer(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 561, in __call__ return super().__call__(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py", line 1132, in __call__ outputs = call_fn(inputs, *args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 96, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py", line 511, in call return self._run_internal_graph(inputs, training=training, mask=mask) File "/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py", line 668, in _run_internal_graph outputs = node.layer(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py", line 1132, in __call__ outputs = call_fn(inputs, *args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 96, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/keras/layers/convolutional/base_conv.py", line 283, in call outputs = self.convolution_op(inputs, self.kernel) File "/usr/local/lib/python3.8/dist-packages/keras/layers/convolutional/base_conv.py", line 255, in convolution_op return tf.nn.convolution( Node: 'efficientnetb6/efficientnetb6_backbone/efficientnetb6_m1/stem_conv/Conv2D' 2 root error(s) found. (0) UNIMPLEMENTED: DNN library is not found. [[{{node efficientnetb6/efficientnetb6_backbone/efficientnetb6_m1/stem_conv/Conv2D}}]] [[Func/efficientnetb6/efficientnetb6_head/conditional_multi_head_v6/while/body/_1/efficientnetb6/efficientnetb6_head/conditional_multi_head_v6/while/StatefulPartitionedCall/StatefulPartitionedCall/output_control_node/_983/_177]] (1) UNIMPLEMENTED: DNN library is not found. [[{{node efficientnetb6/efficientnetb6_backbone/efficientnetb6_m1/stem_conv/Conv2D}}]] 0 successful operations. ```
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PR #10503: Fix log1p inaccuracies on complex inputs with large absolute values.
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2024-04-04T03:25:45
2024-04-04T19:41:50
2024-04-04T19:41:50
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PR #10503: Fix log1p inaccuracies on complex inputs with large absolute values. Imported from GitHub PR https://github.com/openxla/xla/pull/10503 As in the title. Tests and improvement reports are in https://github.com/google/jax/pull/20144. Accuracy tests are enabled in https://github.com/google/jax/pull/20436 Copybara import of the project: -- 2b8f2539d6bf364c0a97f65e186430c5eb3ed07b by Pearu Peterson <[email protected]>: Fix log1p inaccuracies on complex inputs with large absolute values. -- d35cef4f5fa09482c49edfee709e86c5ca29adde by Pearu Peterson <[email protected]>: Add tests to complex Log1p Merging this change closes #10503 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10503 from pearu:pearu/log1p d35cef4f5fa09482c49edfee709e86c5ca29adde
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#tf-data Implement index_flat_map dataset.
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2024-04-04T01:48:11
2024-04-08T23:01:18
2024-04-08T23:01:18
NONE
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#tf-data Implement index_flat_map dataset. This is similar to flat_map, but supports global shuffling. In addition to a `map_func`, the user needs to provide an `index_map_fn`. Given an index in the flattened dataset, the `index_map_fn` returns a (element index, offset) tuple that represents the index of the element in the unflattened dataset, and the offset in the unflattened element.
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65,030
Add unbounded dynamism test for CollectivePermuteOp.
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2024-04-04T01:24:16
2024-04-10T22:13:20
2024-04-10T22:13:19
NONE
null
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Add unbounded dynamism test for CollectivePermuteOp.
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65,029
Update SharedBatchScheduler to use the mixed priority batching policy
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2024-04-03T23:54:30
2024-04-04T00:49:18
2024-04-04T00:49:18
NONE
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Update SharedBatchScheduler to use the mixed priority batching policy
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65,028
[XLA] Correctly check if an instruction is a fusion during removal
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2024-04-03T23:39:48
2024-04-15T17:03:05
2024-04-15T17:03:02
NONE
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[XLA] Correctly check if an instruction is a fusion during removal IsFused() refers to instructions within the fusion, not the fusion instruction iteslf. Use a helper to make this detection clearer.
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65,027
Slightly increase test tolerance for comparison with golden outputs in LSTM test.
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2024-04-03T22:49:28
2024-04-05T21:09:39
2024-04-05T21:09:39
NONE
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Slightly increase test tolerance for comparison with golden outputs in LSTM test.
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65,026
Add IFRT pass that verifies if all !ifrt.arrays have sharding specified.
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2024-04-03T22:28:55
2024-04-03T23:14:30
2024-04-03T23:14:29
NONE
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Add IFRT pass that verifies if all !ifrt.arrays have sharding specified.
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Reverts 4242a023fa1a1c44d7e8871eaf835ae836ee70d0
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2024-04-03T21:56:17
2024-04-04T16:42:25
2024-04-04T16:42:24
NONE
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Reverts 4242a023fa1a1c44d7e8871eaf835ae836ee70d0
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65,024
Better disable mechanism for tensor cores for 8-bit-or-less dot with F32.
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2024-04-03T21:38:08
2024-04-04T15:22:08
2024-04-04T15:22:08
NONE
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Better disable mechanism for tensor cores for 8-bit-or-less dot with F32.
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65,023
Remove add_default_attributes param as it is not being used by clients.
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2024-04-03T21:23:01
2024-04-03T21:56:55
2024-04-03T21:56:54
NONE
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Remove add_default_attributes param as it is not being used by clients.
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65,022
Remove fallback call to MLIR bridge.
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2024-04-03T21:20:47
2024-04-03T23:42:09
2024-04-03T23:42:08
NONE
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Remove fallback call to MLIR bridge.
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PR_kwDOArmXAs5rnzdf
65,021
Make common host memory spaces sharable across backends.
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2024-04-03T21:08:00
2024-04-05T22:22:24
2024-04-05T22:22:23
NONE
null
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Make common host memory spaces sharable across backends.
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65,020
Adds support for using custom op SDPA via XNNPACK.
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2024-04-03T21:07:05
2024-04-12T01:43:08
2024-04-12T01:43:07
NONE
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Adds support for using custom op SDPA via XNNPACK.
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65,019
Remove the constraint that tokens cannot be passed as entry parameters in `HloVerifier`. Also fixed some headers.
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2024-04-03T20:49:12
2024-04-03T23:34:43
2024-04-03T23:34:42
NONE
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Remove the constraint that tokens cannot be passed as entry parameters in `HloVerifier`. Also fixed some headers.
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65,018
tf.linalg.cross convert to tflite
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[ "@MinaBabahaji-ML,\r\nCould you please provide a simple standalone code to reproduce the reported behavior and would help us to investigate further. Thank you!\r\n", "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/65018\">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/65018\">No</a>\n" ]
2024-04-03T20:36:40
2024-04-20T01:47:20
2024-04-20T01:47:17
NONE
null
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### 1. System information - Macos - TensorFlow installation 2.11 - Pip package ### 2. Problem summary: I have a deep learning model with `tf.linalg.cross` operation. I am having problem with converting it to tflite version. Is there a possibility of supporting this operation?
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[JAX] Rebuild CUDA 12.1 image with newer ml_dtypes and numpy.
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2024-04-03T20:33:10
2024-04-03T21:36:48
2024-04-03T21:36:47
NONE
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[JAX] Rebuild CUDA 12.1 image with newer ml_dtypes and numpy.
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65,016
Add SourceLocation information to xla::Unimplemented errors.
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Add SourceLocation information to xla::Unimplemented errors.
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65,015
Set metadata and frontend attributes for instructions added/modified in algebraic simplifier HLO pass.
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2024-04-03T19:55:30
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Set metadata and frontend attributes for instructions added/modified in algebraic simplifier HLO pass.
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[XLA:Runtime] Moved the nccl_clique target to runtime folder.
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2024-04-03T19:43:07
2024-04-04T21:18:48
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[XLA:Runtime] Moved the nccl_clique target to runtime folder. This is part of an effort to move runtime targets to the runtime folder. #5758
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Add flag to guard TF2/Min ICI weight optimization
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Add flag to guard TF2/Min ICI weight optimization
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error: missing attribute 'value` when converting
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[ "Hi @savvn001, can you upload the preconverted model? Also if you have any code which constructed/produced the model that will likely be helpful as well.", "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.", "Have this same issue in 2.16.1 and I just tried and no issue in 2.14.1.\r\n\r\nI reproduced the issue by trying to convert a Keras model DenseNet121.\r\n\r\n```\r\nmodel = tf.keras.applications.DenseNet121 (\r\n include_top = True,\r\n weights = None,\r\n classes = 3,\r\n classifier_activation = \"softmax\"\r\n)\r\n```\r\n\r\nThen just the normal converter:\r\n\r\n```\r\n cvt = tf.lite.TFLiteConverter.from_keras_model(model)\r\n cvt.convert()\r\n```\r\n\r\nDidn't matter if I got the model from the saved_model dir or just directly from the Keras model. 2.16.1 fails with the error either way for me." ]
2024-04-03T19:15:34
2024-05-21T18:19:31
2024-04-19T01:47:40
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### 1. System information mac OS 14.4.1 (23E224) M2 Python3 3.10.11 pip tensorflow 2.16.1 ### 2. Code Simple conversion of model ``` # Load the saved TensorFlow model converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path) # path to the SavedModel directory converter.optimizations = [tf.lite.Optimize.DEFAULT] tflite_model = converter.convert() ``` ### 3. Failure after conversion The ` error: missing attribute 'value'` error ``` 2024-04-03 20:06:56.393074: E tensorflow/core/framework/node_def_util.cc:676] NodeDef mentions attribute debug_name which is not in the op definition: Op<name=VarHandleOp; signature= -> resource:resource; attr=container:string,default=""; attr=shared_name:string,default=""; attr=dtype:type; attr=shape:shape; attr=allowed_devices:list(string),default=[]; is_stateful=true> This may be expected if your graph generating binary is newer than this binary. Unknown attributes will be ignored. NodeDef: {{node sequential_4/dense_19/bias}} 2024-04-03 20:06:56.421553: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:364] Ignored output_format. 2024-04-03 20:06:56.421569: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:367] Ignored drop_control_dependency. 2024-04-03 20:06:56.422061: I tensorflow/cc/saved_model/reader.cc:45] Reading SavedModel from: /Users/Me/etc/model 2024-04-03 20:06:56.423458: I tensorflow/cc/saved_model/reader.cc:91] Reading meta graph with tags { serve } 2024-04-03 20:06:56.423473: I tensorflow/cc/saved_model/reader.cc:132] Reading SavedModel debug info (if present) from: /Users/Me/etc/model 2024-04-03 20:06:56.426253: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:375] MLIR V1 optimization pass is not enabled 2024-04-03 20:06:56.428912: I tensorflow/cc/saved_model/loader.cc:231] Restoring SavedModel bundle. 2024-04-03 20:06:56.491848: E tensorflow/core/framework/node_def_util.cc:676] NodeDef mentions attribute debug_name which is not in the op definition: Op<name=VarHandleOp; signature= -> resource:resource; attr=container:string,default=""; attr=shared_name:string,default=""; attr=dtype:type; attr=shape:shape; attr=allowed_devices:list(string),default=[]; is_stateful=true> This may be expected if your graph generating binary is newer than this binary. Unknown attributes will be ignored. NodeDef: {{node Variable}} 2024-04-03 20:06:56.501951: I tensorflow/cc/saved_model/loader.cc:215] Running initialization op on SavedModel bundle at path: /Users/Me/etc/model 2024-04-03 20:06:56.523353: I tensorflow/cc/saved_model/loader.cc:314] SavedModel load for tags { serve }; Status: success: OK. Took 101287 microseconds. 2024-04-03 20:06:56.540596: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable. loc(fused["ReadVariableOp:", "sequential_4_1/conv1d_24_1/Reshape/ReadVariableOp@__inference_serving_default_142497"]): error: missing attribute 'value' LLVM ERROR: Failed to infer result type(s). ``` ### 5. (optional) Any other info / logs Have tried creating fresh venvs, downgrading back to TF 2.13 and I still get same error. Other issues with this same error have had success downgrading to 2.15.0 but am facing the same issue. Pip list output: ``` absl-py 2.1.0 astunparse 1.6.3 cachetools 5.3.3 certifi 2024.2.2 charset-normalizer 3.3.2 flatbuffers 24.3.25 gast 0.5.4 google-auth 2.29.0 google-auth-oauthlib 1.2.0 google-pasta 0.2.0 grpcio 1.62.1 h5py 3.10.0 idna 3.6 keras 2.15.0 libclang 18.1.1 Markdown 3.6 MarkupSafe 2.1.5 ml-dtypes 0.2.0 numpy 1.26.4 oauthlib 3.2.2 opt-einsum 3.3.0 packaging 24.0 pip 24.0 protobuf 4.25.3 pyasn1 0.6.0 pyasn1_modules 0.4.0 requests 2.31.0 requests-oauthlib 2.0.0 rsa 4.9 setuptools 65.5.0 six 1.16.0 tensorboard 2.15.2 tensorboard-data-server 0.7.2 tensorflow 2.15.0 tensorflow-estimator 2.15.0 tensorflow-io-gcs-filesystem 0.36.0 tensorflow-macos 2.15.0 termcolor 2.4.0 typing_extensions 4.10.0 urllib3 2.2.1 Werkzeug 3.0.2 wheel 0.43.0 wrapt 1.14.1 ```
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Setup boiler plate (entry point, test infra, pass header and td gen) for shlo converter.
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Setup boiler plate (entry point, test infra, pass header and td gen) for shlo converter.
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Fix signature of the GetActivationName utility function that is used in the remapper implementation
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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/65010/checks?check_run_id=23408975021) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Hi @ezhulenev Can you please review this PR ? Thank you!", "Hi @ezhulenev Can you please review this PR ? Thank you!" ]
2024-04-03T18:49:34
2024-06-07T16:57:49
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Here we can pass the input string by const ref in order to avoid the cost of copying the string.
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XProf GPU: Using Per-Thread callback api data for CUPTI collector for better overhead.
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2024-04-03T18:36:54
2024-06-10T18:32:19
2024-06-10T18:32:06
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XProf GPU: Using Per-Thread callback api data for CUPTI collector for better overhead.
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Integrate LLVM at llvm/llvm-project@9df19ce40281
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2024-04-03T18:00:31
2024-04-04T00:07:53
2024-04-04T00:07:52
NONE
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Integrate LLVM at llvm/llvm-project@9df19ce40281 Updates LLVM usage to match [9df19ce40281](https://github.com/llvm/llvm-project/commit/9df19ce40281)
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Add an error log of the Combined bridge failures if the MLIR bridge passed.
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2024-04-03T17:55:45
2024-04-05T22:39:15
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Add an error log of the Combined bridge failures if the MLIR bridge passed.
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[XLA:GPU] Add TiledHloInstruction.
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2024-04-03T17:40:17
2024-04-04T18:14:41
2024-04-04T18:14:41
NONE
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[XLA:GPU] Add TiledHloInstruction. A graph of TiledHloInstruction represents an HLO graph with associated concrete tiles sizes. In the following changes I'll add code to build the graph from SymbolicTiledHloInstruction and use the tiled graph for Cost Model and Triton codegen.
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Rollforward with fix.
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2024-04-03T17:39:10
2024-04-03T18:52:47
2024-04-03T18:52:46
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Rollforward with fix. Reverts 8323e79751693db8e5dfa6c8493a945b310d5a7a
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Assign `layer_index` and `num_layers` as we build KV cache from composite op.
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2024-04-03T17:22:51
2024-04-03T23:06:37
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Assign `layer_index` and `num_layers` as we build KV cache from composite op.
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s/DFATAL/WARNING/ in ResolveShapeIndex.
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2024-04-03T17:12:08
2024-04-03T18:29:35
2024-04-03T18:29:32
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illegal hardware instruction
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null
[ "@FiReTiTi If DeepCell requires a specific TensorFlow version, could you try to uninstall the current one (if installed) and install the compatible version using pip in the Py38 environment:\r\n```\r\nconda activate Py38\r\npip uninstall tensorflow # Uninstall if already installed\r\npip install tensorflow==<compatible_version> # Replace with the correct version\r\n\r\n```\r\nThen try to import tensorflow;\r\n```\r\nimport tensorflow as tf\r\n```\r\nPlease let us know the outcome?\r\nThank you!", "thank you for your answer.\r\nThe required version is ~2.8.0, and by default the installed version was 2.8.4\r\nI uninstalled it and reinstalled it, same error.\r\n I tried tensorflow==2.8.3, 2.9, and 2.9.3 still the same error.", "@FiReTiTi If there is a specific requirement to use TF v2.8 which is an older and not actively supported version, then please raise post this issue in TF [forum](https://discuss.tensorflow.org/) where there is a larger community to help?\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/65002\">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/65002\">No</a>\n", "@sushreebarsa I tried to update tensorflow to the most recent version, same problem:\r\n\r\n [~] ᐅ python -X faulthandler\r\n Python 3.9.16 (main, May 15 2023, 18:51:40) \r\n [Clang 14.0.6 ] :: Anaconda, Inc. on darwin\r\n Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n >>> import tensorflow\r\n Fatal Python error: Illegal instruction\r\n\r\n Current thread 0x0000000204b51240 (most recent call first):\r\n File \"<frozen importlib._bootstrap>\", line 228 in _call_with_frames_removed\r\n File \"<frozen importlib._bootstrap_external>\", line 1173 in create_module\r\n File \"<frozen importlib._bootstrap>\", line 565 in module_from_spec\r\n File \"<frozen importlib._bootstrap>\", line 666 in _load_unlocked\r\n File \"<frozen importlib._bootstrap>\", line 986 in _find_and_load_unlocked\r\n File \"<frozen importlib._bootstrap>\", line 1007 in _find_and_load\r\n File \"<frozen importlib._bootstrap>\", line 228 in _call_with_frames_removed\r\n File \"<frozen importlib._bootstrap>\", line 1058 in _handle_fromlist\r\n File \"/Users/XXX/miniconda3/envs/Py39dev/lib/python3.9/site-packages/tensorflow/python/tf2.py\", line 21 in <module>\r\n File \"<frozen importlib._bootstrap>\", line 228 in _call_with_frames_removed\r\n File \"<frozen importlib._bootstrap_external>\", line 850 in exec_module\r\n File \"<frozen importlib._bootstrap>\", line 680 in _load_unlocked\r\n File \"<frozen importlib._bootstrap>\", line 986 in _find_and_load_unlocked\r\n File \"<frozen importlib._bootstrap>\", line 1007 in _find_and_load\r\n File \"<frozen importlib._bootstrap>\", line 228 in _call_with_frames_removed\r\n File \"<frozen importlib._bootstrap>\", line 1058 in _handle_fromlist\r\n File \"/Users/XXX/miniconda3/envs/Py39dev/lib/python3.9/site-packages/tensorflow/__init__.py\", line 42 in <module>\r\n File \"<frozen importlib._bootstrap>\", line 228 in _call_with_frames_removed\r\n File \"<frozen importlib._bootstrap_external>\", line 850 in exec_module\r\n File \"<frozen importlib._bootstrap>\", line 680 in _load_unlocked\r\n File \"<frozen importlib._bootstrap>\", line 986 in _find_and_load_unlocked\r\n File \"<frozen importlib._bootstrap>\", line 1007 in _find_and_load\r\n File \"<stdin>\", line 1 in <module>\r\n [1] 98773 illegal hardware instruction python -X faulthandler\r\n\r\nI tried on both Python 3.8 and 3.9\r\n", "@FiReTiTi Thanks for your response!\r\nReopening the issue as it still persists. Thank you!", "I had to install tensorflow for python 3.7.16 because it was used by another library, and I got the exact same issue.\r\nIt seems that I cannot run any tensorflow for any version of python.\r\n\r\nI'm on a Mac M2. Is there something specific that I have to do when installing it?", "@FiReTiTi TensorFlow doesn't officially support installing Apple Silicon (M1 and M2) for Python versions below 3.8.\r\n\r\n", "I tried on both 3.8 and 3.9, this was just another test. So the problem is likely somewhere else." ]
2024-04-03T17:03:51
2024-05-15T06:43:08
null
NONE
null
null
null
### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? No ### Source binary ### TensorFlow version 2.8.4 ### Custom code No ### OS platform and distribution MacOS 14.4.1 ### Mobile device MacBook Pro M2 ### Python version 3.8.18 (but same problem on 3.8.19 and 3.9.16) ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? I installed TensorFlow indirectly as it was installed as a dependence of deepcell 0.12.6. I simply tried to run "import tensorflow as tf" I got the error "illegal hardware instruction" ### Standalone code to reproduce the issue ```shell $ conda create -n Py38 python==3.8.18 $ pip install deepcell==0.12.6 $ python -X faulthandler >>> import tensorflow as tf ``` ### Relevant log output ```shell $ python -X faulthandler Python 3.8.18 (default, Sep 11 2023, 08:17:33) [Clang 14.0.6 ] :: Anaconda, Inc. on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf Fatal Python error: Illegal instruction Current thread 0x0000000200c89240 (most recent call first): File "/Users/XXX/miniconda3/envs/Py38/lib/python3.8/ctypes/__init__.py", line 373 in __init__ File "/Users/XXX/miniconda3/envs/Py38/lib/python3.8/site-packages/tensorflow/python/platform/self_check.py", line 65 in preload_check File "/Users/XXX/miniconda3/envs/Py38/lib/python3.8/site-packages/tensorflow/python/pywrap_tensorflow.py", line 24 in <module> File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed File "<frozen importlib._bootstrap_external>", line 843 in exec_module File "<frozen importlib._bootstrap>", line 671 in _load_unlocked File "<frozen importlib._bootstrap>", line 975 in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 991 in _find_and_load File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed File "<frozen importlib._bootstrap>", line 1042 in _handle_fromlist File "/Users/XXX/miniconda3/envs/Py38/lib/python3.8/site-packages/tensorflow/python/__init__.py", line 36 in <module> File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed File "<frozen importlib._bootstrap_external>", line 843 in exec_module File "<frozen importlib._bootstrap>", line 671 in _load_unlocked File "<frozen importlib._bootstrap>", line 975 in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 991 in _find_and_load File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed File "<frozen importlib._bootstrap>", line 961 in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 991 in _find_and_load File "/Users/XXX/miniconda3/envs/Py38/lib/python3.8/site-packages/tensorflow/__init__.py", line 37 in <module> File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed File "<frozen importlib._bootstrap_external>", line 843 in exec_module File "<frozen importlib._bootstrap>", line 671 in _load_unlocked File "<frozen importlib._bootstrap>", line 975 in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 991 in _find_and_load File "<stdin>", line 1 in <module> [1] 43523 illegal hardware instruction python -X faulthandler ```
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Disable colocate_predecessor_trees pass
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2024-04-03T16:44:55
2024-04-03T17:43:56
2024-04-03T17:43:55
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Disable colocate_predecessor_trees pass
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[ReplicaGroupV2] Create CollectiveDeviceList.
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2024-04-03T16:39:35
2024-04-22T17:30:30
2024-04-22T17:30:28
NONE
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[ReplicaGroupV2] Create CollectiveDeviceList. As part of an effort the speed up compile time, there have been some explorations on different ways to represent replica groups. In order to support these new representations, we need to migrate from directly passing around lists of replica groups (list of list of device). This creates a CollectiveDeviceList class whose purpose is to abstract away the details of replica group representation from usage. We later intend to expand this class to support compact representations.
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Add attribute interface for IFRT IR sharding.
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2024-04-03T16:39:15
2024-04-03T18:17:22
2024-04-03T18:17:21
NONE
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Add attribute interface for IFRT IR sharding. This change modifies the `ifrt.array` type, which previously directly used `ShardingParam` as a parameter to instead use a newly introduced `IfrtShardingAttrInterface`. By doing so, we can support multiple types of shardings in IFRT IR.
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Implement convolution via indexing maps.
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2024-04-04T08:07:36
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Implement convolution via indexing maps. Reusing the dot emitter code, which is (almost) the same as the convolution code. Note that adding the tests uncovered an issue with convolution indexing analysis - it was using an incorrect divisor when `feature_group_count` attribute is used.
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Add missing deps.
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2024-04-03T16:04:37
2024-04-04T21:13:11
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Add missing deps.
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#shlo_ref Use 4 bit integer wrapper in ops implementation.
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2024-04-03T14:39:26
2024-04-05T15:35:59
2024-04-05T15:35:59
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#shlo_ref Use 4 bit integer wrapper in ops implementation.
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Use NHWC for Ampere TF32 convolutions.
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2024-04-03T14:30:49
2024-05-02T12:23:29
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Use NHWC for Ampere TF32 convolutions.
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GPU delegate linker errors when building TFLite 2.16.1 for Android with CMake
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[ "Hi @eltimen,\r\n\r\nEnabling GPU support requires additional libraries and dependencies related to OpenCL. This might be the reason for \r\n the error ` tflite::gpu::gl and tflite::gpu::OptionalAndroidHardwareBuffer`. Please try to add the following to the Android manifest in order to detect GPU delegate.\r\n```\r\n<uses-library android:name=\"libOpenCL.so\"\r\n android:required=\"false\"/>\r\n\r\n<uses-library android:name=\"libOpenCL-pixel.so\"\r\n android:required=\"false\"/>\r\n```\r\nAlso Verify that the NDK version (r25b) is compatible with the TensorFlow Lite version you are trying to build.\r\n\r\nThank You", "Hi @LakshmiKalaKadali,\r\n\r\nI'm not using Gradle or Android Studio. I just tried to build `libtensorflow-lite.so` for Android using CMake following the [documentation](https://www.tensorflow.org/lite/guide/build_cmake#specifics_of_android_cross-compilation) and I got the linker errors above.\r\n\r\nI tried to build it with Bazel as mentioned [here](https://www.tensorflow.org/lite/android/lite_build#set_up_build_environment_without_docker) and build was successful. According to [this page](https://www.tensorflow.org/lite/android/lite_build#configure_workspace_and_bazelrc) NDK r25b should be compatible with this TFLite version. \r\n\r\nAccording to the documentation, TensorFlow Lite supports both ways to build it for Android: with CMake and with Bazel. Looks like CMake building is broken with enabled GPU delegate in this version.", "This looks like a duplicate of https://github.com/tensorflow/tensorflow/issues/65744, investigating.", "Hi @eltimen, I was able to build fine with these steps:\r\n\r\n```\r\ngit clone https://github.com/tensorflow/tensorflow.git tensorflow_src\r\ncd tensorflow_src\r\ngit switch r2.16\r\ncd ..\r\nmkdir tflite_build\r\ncd tflite_build\r\ncmake -S ../tensorflow_src/tensorflow/lite -B build -D CMAKE_BUILD_TYPE=Release -D CMAKE_SYSTEM_PROCESSOR=aarch64 -D BUILD_SHARED_LIBS=ON -D TFLITE_ENABLE_GPU=ON -D CMAKE_SYSTEM_NAME=Linux -D CMAKE_SYSTEM_VERSION=29 -D ANDROID_PLATFORM=29 -D CMAKE_ANDROID_ARCH_ABI=arm64-v8a -D ANDROID_ABI=arm64-v8a -D CMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake\r\n```\r\n\r\nPlease note $ANDROID_NDK is the path to my NDK, which is 25b.\r\n\r\nI am building on MacOS, you didn't specify an OS so that could be an issue.\r\n\r\nCan you please let us know what OS you are using? Additionally please review my reproduce steps and let me know if I did something different than what you are doing? Thanks.", "Hi @pkgoogle, I use Ubuntu 22.04.\r\nI'm building TFLite from the tag `v2.16.1`, but you are building it from the branch `r2.16`. \r\nCan you try to reproduce this using the [release 2.16.1 version](https://github.com/tensorflow/tensorflow/releases/tag/v2.16.1)?", "Hi @eltimen, I am using a Debian system, and went to that tag, but am using NDK=r25c, I am still not reproducing the error:\r\n\r\n```\r\ngit clone https://github.com/tensorflow/tensorflow.git tensorflow_src\r\ncd tensorflow_src\r\ngit checkout v2.16.1\r\ncd ..\r\nmkdir tflite_build\r\ncd tflite_build\r\ncmake -S ../tensorflow_src/tensorflow/lite -B build -D CMAKE_BUILD_TYPE=Release -D CMAKE_SYSTEM_PROCESSOR=aarch64 -D BUILD_SHARED_LIBS=ON -D TFLITE_ENABLE_GPU=ON -D CMAKE_SYSTEM_NAME=Linux -D CMAKE_SYSTEM_VERSION=29 -D ANDROID_PLATFORM=29 -D CMAKE_ANDROID_ARCH_ABI=arm64-v8a -D ANDROID_ABI=arm64-v8a -D CMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake\r\n```\r\n\r\nIt looks like my Clang is different (14.0.7) probably from the different r25c.\r\n\r\nCan you try with a fresh repo, perhaps fresh NDK, and ensure you follow the same steps exactly? Let me know if somehow that resolves your issue.", "Hello @pkgoogle,\r\nI followed your step exactly, the error does not come from the generating the build file but from actual building with `cmake --build . -j`, can you verify this is the correct command to build?", "Also just double checking, when building with CMake, are the steps in [this guide](https://www.tensorflow.org/lite/android/delegates/gpu_native) still needed to build GPU delegate?", "Hi @dorishyc3, I believe you are in the wrong thread, please follow your issue in that thread instead so we don't confuse the issues (Same error but different OS right now), to answer your question -- that is the correct command if you are following these directions: https://www.tensorflow.org/lite/guide/build_cmake. That guide is following directions with Bazel, I don't recommend you mix build systems unless you truly understand both and what they are doing. For `cmake` I would follow this: https://www.tensorflow.org/lite/guide/build_cmake#opencl_gpu_delegate. If you want to try Bazel or see if you can complete your workflow with it instead, please feel free to do so.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Hi, @pkgoogle. Sorry for the delay.\r\n\r\nI tried building the TFLite exactly following your steps using NDK r25c, but I'm still reproducing this error. My CMake version is 3.27.7", "Hi @eltimen,\r\n\r\nIt seems we have been miscommunicating slightly :). The original problem didn't include the `cmake --build . -j` step, so I wasn't doing that -- as seen in my previous comment. After doing that step, I was able to reproduce. To recompile to ensure there is no further miscommunication the reproduce steps are as follows:\r\n\r\n```\r\ngit clone https://github.com/tensorflow/tensorflow.git tensorflow_src\r\ncd tensorflow_src\r\ngit checkout v2.16.1\r\ncd ..\r\nmkdir tflite_build\r\ncd tflite_build\r\ncmake -S ../tensorflow_src/tensorflow/lite -B build -D CMAKE_BUILD_TYPE=Release -D CMAKE_SYSTEM_PROCESSOR=aarch64 -D BUILD_SHARED_LIBS=ON -D TFLITE_ENABLE_GPU=ON -D CMAKE_SYSTEM_NAME=Linux -D CMAKE_SYSTEM_VERSION=29 -D ANDROID_PLATFORM=29 -D CMAKE_ANDROID_ARCH_ABI=arm64-v8a -D ANDROID_ABI=arm64-v8a -D CMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake\r\ncd build\r\ncmake --build . -j\r\n```\r\n\r\nHi @terryheo, can you please take a look? Thanks." ]
2024-04-03T13:48:41
2024-05-01T17:58:09
null
NONE
null
null
null
### Issue type Build/Install ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source source ### TensorFlow version 2.16.1 ### Custom code Yes ### OS platform and distribution _No response_ ### Mobile device _No response_ ### Python version _No response_ ### Bazel version _No response_ ### GCC/compiler version Clang 14.0.6 (Android NDK r25b) ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? I'm trying to build TFLite 2.16.1 for Android using CMake and Android NDK r25b and got the following linker errors related to GPU delegate. It looks like some source files was not added to CMake configuration files. ``` ld: error: undefined symbol: tflite::gpu::OptionalAndroidHardwareBuffer::OptionalAndroidHardwareBuffer() >>> referenced by android_hardware_buffer.h:69 (tensorflow/lite/delegates/gpu/android_hardware_buffer.h:69) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(TfLiteGpuDelegateV2CreateAsync) >>> referenced by android_hardware_buffer.h:69 (tensorflow/lite/delegates/gpu/android_hardware_buffer.h:69) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::RegisterBuffer(TfLiteOpaqueContext*, TfLiteIoType, TfLiteBackendBuffer const*, TfLiteAttributeMap const*, int)) >>> referenced by android_hardware_buffer.h:69 (tensorflow/lite/delegates/gpu/android_hardware_buffer.h:69) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::RegisterBuffer(TfLiteOpaqueContext*, TfLiteIoType, TfLiteBackendBuffer const*, TfLiteAttributeMap const*, int)) >>> referenced 5 more times ld: error: undefined symbol: tflite::delegates::BackendAsyncKernelInterface::BackendAsyncKernelInterface() >>> referenced by delegate.cc:687 (tensorflow/lite/delegates/gpu/delegate.cc:687) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::CreateAsyncRegistration()::$_2::__invoke(TfLiteContext*, char const*, unsigned long)) >>> did you mean: tflite::delegates::BackendAsyncKernelInterface::~BackendAsyncKernelInterface() >>> defined in: CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o ld: error: undefined symbol: tflite::delegates::utils::ReadBufferAttrs(TfLiteAttributeMap const*) >>> referenced by delegate.cc:1048 (tensorflow/lite/delegates/gpu/delegate.cc:1048) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::RegisterBuffer(TfLiteOpaqueContext*, TfLiteIoType, TfLiteBackendBuffer const*, TfLiteAttributeMap const*, int)) >>> referenced by delegate.cc:911 (tensorflow/lite/delegates/gpu/delegate.cc:911) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::ReconcileRestrictions(TfLiteOpaqueContext const*, TfLiteOpaqueNode const*, int, TfLiteAttributeMap const*, TfLiteAttributeMap*, TfLiteAttributeMap*) const) ld: error: undefined symbol: tflite::delegates::utils::WriteBufferAttrs(tflite::delegates::utils::BufferAttributes const&, TfLiteAttributeMap*) >>> referenced by delegate.cc:913 (tensorflow/lite/delegates/gpu/delegate.cc:913) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::ReconcileRestrictions(TfLiteOpaqueContext const*, TfLiteOpaqueNode const*, int, TfLiteAttributeMap const*, TfLiteAttributeMap*, TfLiteAttributeMap*) const) >>> referenced by delegate.cc:913 (tensorflow/lite/delegates/gpu/delegate.cc:913) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::ReconcileRestrictions(TfLiteOpaqueContext const*, TfLiteOpaqueNode const*, int, TfLiteAttributeMap const*, TfLiteAttributeMap*, TfLiteAttributeMap*) const) >>> referenced by delegate.cc:913 (tensorflow/lite/delegates/gpu/delegate.cc:913) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::ReconcileRestrictions(TfLiteOpaqueContext const*, TfLiteOpaqueNode const*, int, TfLiteAttributeMap const*, TfLiteAttributeMap*, TfLiteAttributeMap*) const) >>> referenced 2 more times ld: error: undefined symbol: tflite::delegates::utils::ReadSyncAttrs(TfLiteAttributeMap const*) >>> referenced by delegate.cc:936 (tensorflow/lite/delegates/gpu/delegate.cc:936) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::ReconcileRestrictions(TfLiteOpaqueContext const*, TfLiteOpaqueNode const*, int, TfLiteAttributeMap const*, TfLiteAttributeMap*, TfLiteAttributeMap*) const) >>> referenced by delegate.cc:969 (tensorflow/lite/delegates/gpu/delegate.cc:969) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::SetAttributes(TfLiteOpaqueContext*, TfLiteOpaqueNode*, int, TfLiteAttributeMap const*)) ld: error: undefined symbol: tflite::delegates::utils::WriteSyncAttrs(tflite::delegates::utils::SyncAttributes const&, TfLiteAttributeMap*) >>> referenced by delegate.cc:938 (tensorflow/lite/delegates/gpu/delegate.cc:938) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::ReconcileRestrictions(TfLiteOpaqueContext const*, TfLiteOpaqueNode const*, int, TfLiteAttributeMap const*, TfLiteAttributeMap*, TfLiteAttributeMap*) const) >>> referenced by delegate.cc:940 (tensorflow/lite/delegates/gpu/delegate.cc:940) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::ReconcileRestrictions(TfLiteOpaqueContext const*, TfLiteOpaqueNode const*, int, TfLiteAttributeMap const*, TfLiteAttributeMap*, TfLiteAttributeMap*) const) ld: error: undefined symbol: tflite::gpu::gl::WaitFdGpu(int) >>> referenced by delegate.cc:1167 (tensorflow/lite/delegates/gpu/delegate.cc:1167) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*)) ld: error: undefined symbol: tflite::delegates::utils::WaitForAllFds(absl::lts_20230802::Span<int const>) >>> referenced by delegate.cc:1170 (tensorflow/lite/delegates/gpu/delegate.cc:1170) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*)) ld: error: undefined symbol: tflite::gpu::gl::EglEnvironment::NewEglEnvironment(std::__ndk1::unique_ptr<tflite::gpu::gl::EglEnvironment, std::__ndk1::default_delete<tflite::gpu::gl::EglEnvironment> >*) >>> referenced by delegate.cc:1175 (tensorflow/lite/delegates/gpu/delegate.cc:1175) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*)) ld: error: undefined symbol: tflite::delegates::utils::ConvertToTfLiteStatus(absl::lts_20230802::Status) >>> referenced by delegate.cc:1175 (tensorflow/lite/delegates/gpu/delegate.cc:1175) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*)) >>> referenced by delegate.cc:1186 (tensorflow/lite/delegates/gpu/delegate.cc:1186) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*)) >>> referenced by delegate.cc:1187 (tensorflow/lite/delegates/gpu/delegate.cc:1187) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*)) >>> referenced 3 more times ld: error: undefined symbol: tflite::gpu::gl::EglEnvironment::~EglEnvironment() >>> referenced by memory:2427 (android-ndk-r25b/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include/c++/v1/memory:2427) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*)) >>> referenced by memory:2427 (android-ndk-r25b/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include/c++/v1/memory:2427) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*)) ld: error: undefined symbol: tflite::gpu::AsyncBuffer::GetOpenGlBuffer(unsigned int&) >>> referenced by delegate.cc:1186 (tensorflow/lite/delegates/gpu/delegate.cc:1186) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*)) >>> referenced by delegate.cc:1199 (tensorflow/lite/delegates/gpu/delegate.cc:1199) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*)) ld: error: undefined symbol: tflite::gpu::gl::CreateFdGpu() >>> referenced by delegate.cc:1211 (tensorflow/lite/delegates/gpu/delegate.cc:1211) >>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*)) clang++: error: linker command failed with exit code 1 (use -v to see invocation) make[2]: *** [CMakeFiles/tensorflow-lite.dir/build.make:6296: libtensorflow-lite.so] Error 1 make[1]: *** [CMakeFiles/Makefile2:1336: CMakeFiles/tensorflow-lite.dir/all] Error 2 make[1]: *** Waiting for unfinished jobs.... ``` ### Standalone code to reproduce the issue ```shell CMake options: cmake -S tensorflow/lite -B build -D CMAKE_BUILD_TYPE=Release -D CMAKE_SYSTEM_PROCESSOR=aarch64 -D BUILD_SHARED_LIBS=ON -D TFLITE_ENABLE_GPU=ON -D CMAKE_SYSTEM_NAME=Linux -D CMAKE_SYSTEM_VERSION=29 -D ANDROID_PLATFORM=29 -D CMAKE_ANDROID_ARCH_ABI=arm64-v8a -D ANDROID_ABI=arm64-v8a -D CMAKE_TOOLCHAIN_FILE=android-ndk-r25b/build/cmake/android.toolchain.cmake ``` ### Relevant log output _No response_
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Add an XNNPACK delegate for quantized `BatchMatMul`.
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2024-04-03T13:32:03
2024-04-08T18:23:55
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Add an XNNPACK delegate for quantized `BatchMatMul`.
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Expose these functions to make testing delegation easier.
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2024-04-03T13:30:10
2024-04-03T16:15:46
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Expose these functions to make testing delegation easier.
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[ifrt_proxy] Added a separate thread pool for host callbacks
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2024-04-03T13:28:53
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[ifrt_proxy] Added a separate thread pool for host callbacks The default thread pool does not have sufficient stack size for reasonable Python callbacks and in fact overflows for google/jax#49178.
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[XLA:GPU] Extract SymbolicTiledHloInstruction into a separate file.
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[XLA:GPU] Extract SymbolicTiledHloInstruction into a separate file.
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