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67,191
Add a notebook to demonstrate converting a Keras model with the JAX backend to TFLite.
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2024-05-08T21:27:06
2024-05-08T21:27:13
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Add a notebook to demonstrate converting a Keras model with the JAX backend to TFLite.
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Unify duplicate copybara rules
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2024-05-08T21:19:20
2024-05-08T21:19:21
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Unify duplicate copybara rules
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67,189
Configure autograph to not convert `tf_keras` code.
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2024-05-08T20:47:26
2024-05-08T22:50:43
2024-05-08T22:50:42
NONE
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Configure autograph to not convert `tf_keras` code. Fixes https://github.com/keras-team/tf-keras/issues/777
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67,188
Express tests tolerances for exhaustive tests relative to the accuracy and subnormal boundary of the data type.
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2024-05-08T20:37:15
2024-05-16T22:34:07
2024-05-16T22:34:06
NONE
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Express tests tolerances for exhaustive tests relative to the accuracy and subnormal boundary of the data type. The following table shows the default absolute and relative error tolerances for each numeric datatype. | Type | atol before | rtol before | atol after | rtol after | | ---------------- | ----------- | ---------- | ----------- | --------- | | double | 1e-4 | 1e-4 | 4.45e-308 | 1.11e-14 | | float | 1e-4 | 1e-4 | 2.35e-38 | 5.96e-6 | | float16 | 1e-3 | 1e-3 | 1.22e-4 | 5e-4 | | bfloat16 | 2e-3 | 2e-2 | 2.35e-38 | 1.6e-2 | | complex<double> | 1e-4 | 1e-4 | 4.45e-308 | 1.11e-14 | | complex<float> | 1e-4 | 1e-4 | 2.35e-38 | 5.96e-6 |
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67,187
Fix stale comment on CreateSplitIntoIslandPerOpPass.
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2024-05-08T20:00:37
2024-05-08T20:21:39
2024-05-08T20:21:38
NONE
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Fix stale comment on CreateSplitIntoIslandPerOpPass.
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67,186
Reverts 4382439b2d4c6be4f93c509905513200d8ac7a5b
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2024-05-08T18:51:26
2024-05-08T21:17:21
2024-05-08T21:17:19
NONE
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Reverts 4382439b2d4c6be4f93c509905513200d8ac7a5b
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67,185
Add fields to GpuTopology for multi-host support.
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2024-05-08T18:35:17
2024-05-13T19:18:18
2024-05-13T19:18:16
NONE
null
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Add fields to GpuTopology for multi-host support.
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67,184
Async dispatch expensive computations on the JAX CPU backend.
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2024-05-08T18:31:45
2024-05-13T18:52:44
2024-05-13T18:52:41
NONE
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Async dispatch expensive computations on the JAX CPU backend. By setting `jax.config.update('jax_cpu_enable_async_dispatch', False)`, one could opt out of the change and recover the old behavior.
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67,183
Fix warning about pessimizing std::move.
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2024-05-08T18:19:26
2024-05-08T19:59:22
2024-05-08T19:59:20
NONE
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Fix warning about pessimizing std::move. (error: moving a temporary object prevents copy elision)
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67,182
Reverts 604adec95479d271b86d993965a660ad6932d736
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2024-05-08T18:12:53
2024-05-08T18:22:40
2024-05-08T18:22:40
NONE
null
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Reverts 604adec95479d271b86d993965a660ad6932d736
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67,181
Fix asan error due to additional error logging
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2024-05-08T18:03:32
2024-05-08T18:59:37
2024-05-08T18:59:36
NONE
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Fix asan error due to additional error logging
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67,180
For nested full-to-shard we must also exclude SPMDShardToFullShape
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2024-05-08T17:51:59
2024-05-08T23:24:33
2024-05-08T23:24:32
NONE
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For nested full-to-shard we must also exclude SPMDShardToFullShape because the hlo->sharding() and operand->sharding() will have conflicting values for IsManualSubgroup().
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Integrate Triton up to [<8-character-short-hash>](https://github.com/openai/triton/commits/<full-hash>)
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2024-05-08T17:45:38
2024-05-16T05:43:25
2024-05-16T05:43:23
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Integrate Triton up to [<8-character-short-hash>](https://github.com/openai/triton/commits/<full-hash>)
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Fix "moving a temporary object prevents copy elision [-Werror,-Wpessimizing-move]" error when compiling gpu_fused_mha_test with clang.
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2024-05-08T19:26:02
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Fix "moving a temporary object prevents copy elision [-Werror,-Wpessimizing-move]" error when compiling gpu_fused_mha_test with clang.
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Simplify JAX lowering rules for cumulative sum
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2024-05-08T16:40:17
2024-05-10T18:30:21
2024-05-10T18:30:20
NONE
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Simplify JAX lowering rules for cumulative sum Rely on XLA decomposition. # JAX GPU microbenchmarks 285us for cumsum over 1e8 elements 449us for cumsum over 1e8 elements. # JAX CPU microbenchmarks: 1.8s vs. 0.7s for 50 iterations over cumsum over 1e7 elements
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[XLA:SPMD] Use the default way to call once and shard barrier custom-call registration instead of always linking it.
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2024-05-08T16:34:44
2024-05-08T21:53:13
2024-05-08T21:53:13
NONE
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[XLA:SPMD] Use the default way to call once and shard barrier custom-call registration instead of always linking it.
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Remove unnecessary std::move() calls.
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2024-05-08T19:27:15
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Remove unnecessary std::move() calls. This fixes the following build error: ``` ERROR: /tf/xla/xla/service/gpu/tests/BUILD:939:9: Compiling xla/service/gpu/tests/gpu_fused_mha_test.cc failed: (Exit 1): clang failed: error executing command (from target //xla/service/gpu/tests:gpu_fused_mha_test_gpu) /usr/lib/llvm-17/bin/clang -MD -MF bazel-out/k8-opt/bin/xla/service/gpu/tests/_objs/gpu_fused_mha_test_gpu/gpu_fused_mha_test.d ... (remaining 345 arguments skipped) xla/service/gpu/tests/gpu_fused_mha_test.cc:121:24: error: moving a temporary object prevents copy elision [-Werror,-Wpessimizing-move] 121 | CountFMHACalls(std::move(reference_module->Clone()))); | ^ xla/service/gpu/tests/gpu_fused_mha_test.cc:129:44: error: moving a temporary object prevents copy elision [-Werror,-Wpessimizing-move] 129 | CountFMHACalls(std::move(test_module->Clone()))); | ^ 2 errors generated. ```
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67,174
Integrate LLVM at llvm/llvm-project@3a8316216807
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2024-05-08T16:25:34
2024-05-08T19:32:41
2024-05-08T19:32:39
NONE
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true
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Integrate LLVM at llvm/llvm-project@3a8316216807 Updates LLVM usage to match [3a8316216807](https://github.com/llvm/llvm-project/commit/3a8316216807)
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67,173
Unify StreamExecutorInterface::GetSymbol and StreamExecutor::GetUntypedSymbol.
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2024-05-08T16:22:25
2024-05-08T22:29:00
2024-05-08T22:28:59
NONE
null
true
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Unify StreamExecutorInterface::GetSymbol and StreamExecutor::GetUntypedSymbol.
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67,172
Fix a header path in libtensorflow_cpu.sh.
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2024-05-08T16:02:59
2024-05-08T16:48:54
2024-05-08T16:48:53
NONE
null
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Fix a header path in libtensorflow_cpu.sh.
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67,171
[PJRT] Remove method alias PjRtClient::GetFullTopologyForCompilation.
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2024-05-08T15:40:48
2024-05-08T21:03:42
2024-05-08T21:03:42
NONE
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[PJRT] Remove method alias PjRtClient::GetFullTopologyForCompilation.
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67,170
Limit runtime determined PTX version by compile time CUDA version
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2024-05-08T15:32:06
2024-05-08T16:29:22
2024-05-08T16:29:21
NONE
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Limit runtime determined PTX version by compile time CUDA version The previously introduced new PTX version logic determines the supported PTX version based on the `ptxas` tool version at runtime. If a user has a CUDA toolkit installed that is newer than what XLA was compiled with we might set a PTX version that is not supported by XLA/LLVM. To avoid that this change determines the PTX version from the minimum of both the compile time and the runtime CUDA version.
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67,169
[XLA:GPU][IndexAnalysis] Remove indexing_context.h. It is not used.
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2024-05-08T15:18:01
2024-05-10T10:09:14
2024-05-10T10:09:12
NONE
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[XLA:GPU][IndexAnalysis] Remove indexing_context.h. It is not used.
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Move `BuildAttributesMap()` to a common place
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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/67168/checks?check_run_id=24733806830) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request." ]
2024-05-08T14:13:45
2024-05-14T10:47:13
2024-05-14T10:47:12
NONE
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Move `BuildAttributesMap()` to a common place So that both CPU and GPU code can reuse the same implementation. At the moment each uses its own version that are identical.
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[Triton] Cherry picking changes to support elementwise ceil op.
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2024-05-08T14:10:45
2024-05-10T10:56:19
2024-05-10T10:56:17
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[Triton] Cherry picking changes to support elementwise ceil op.
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Fix typo `s/Recieved/Received/`.
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2024-05-08T13:51:32
2024-05-16T23:18:20
2024-05-16T23:18:18
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Fix typo `s/Recieved/Received/`.
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Test FFI Custom Call dimensions
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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/67165/checks?check_run_id=24731975132) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request." ]
2024-05-08T13:36:32
2024-05-09T16:51:40
2024-05-09T16:51:39
NONE
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Test FFI Custom Call dimensions
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[XLA:GPU] Support fusion of dynamic-slice into triton gemm.
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2024-05-08T13:25:46
2024-05-17T10:30:10
2024-05-17T10:30:08
NONE
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[XLA:GPU] Support fusion of dynamic-slice into triton gemm. This change fuses dynamic-slice into triton gemm kernels provided the slice is taken along the major-most dimension, leaving all other dimensions the same. No further fusion occurs in operands of the dynamic slice, meaning the resulting triton gemm must take in all operands of the dynamic slice as parameters. Autotuning can handle dynamic-slice just fine, because of the instruction's semantics, which ensures that it never reads out of bounds (the offsets are clamped to a valid region). The original author of this CL is jvstokes and I did a comprehensive refactor.
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PR #11567: Allowing permutations of terms in //xla/service/gpu/fusions:transpose_mlir_test
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2024-05-08T12:11:14
2024-05-08T14:43:43
2024-05-08T14:43:42
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PR #11567: Allowing permutations of terms in //xla/service/gpu/fusions:transpose_mlir_test Imported from GitHub PR https://github.com/openxla/xla/pull/11567 This expands the pattern matcher used by //xla/service/gpu/fusions:transpose_mlir_test, so that it reports a match even if some terms are permuted. (Without the change, I see two subtests of this test fail on ROCm. With the change, the test passes there.) Copybara import of the project: -- 72cfce5bc6c642475ffb79ce2c3c65ae86f9262a by Eugene Kuznetsov <[email protected]>: Allowing permutations of terms in //xla/service/gpu/fusions:transpose_mlir_test Merging this change closes #11567 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11567 from ROCm:pattern_match_permutations 72cfce5bc6c642475ffb79ce2c3c65ae86f9262a
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2024-05-08T12:10:53
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Integrate LLVM at llvm/llvm-project@aefad851672e
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2024-05-08T11:30:14
2024-05-08T18:43:32
2024-05-08T18:43:29
NONE
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Integrate LLVM at llvm/llvm-project@aefad851672e Updates LLVM usage to match [aefad851672e](https://github.com/llvm/llvm-project/commit/aefad851672e) FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11241 from Intel-tensorflow:kanvi/bmm-mul-add_bf16 8fb80082e8c545e007ab7a2d363eb7b7c251fd07
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[XLA:GPU] Replace dynamic-slice with slice during autotuning
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2024-05-08T11:04:01
2024-05-08T11:04:02
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[XLA:GPU] Replace dynamic-slice with slice during autotuning Dynamic slice requires specific values for the indices that it takes in as operands. However, under computation extraction in the autotuner, all operands are replaced with random parameter values (which then represent incorrect/invalid offsets.) In order to avoid this, we replace dynamic-slice instructions with static slice instructions indexed at 0 along all dimensions. This CL is needed for a later CL which will add support for fusing dynamic-slice into triton fusions. The original author of this CL is jvstokes and I rewrote it a bit. FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12161 from jaro-sevcik:serialize-as-stream-executor-executable 818765f7b1f5f7282d3f2756b2a03ac556201a6c
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PR #12224: [GPU] Fix handling of flags in the cuDNN FMHA test.
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2024-05-08T10:26:07
2024-05-08T11:19:41
2024-05-08T11:19:40
NONE
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PR #12224: [GPU] Fix handling of flags in the cuDNN FMHA test. Imported from GitHub PR https://github.com/openxla/xla/pull/12224 The test got broken by https://github.com/openxla/xla/commit/8799ff0f4b2aa3437a9963f7c9968ee361a62fd7, this commit fixes it. Copybara import of the project: -- 80528497321ee6020126b15035050f4c1a0beea9 by Ilia Sergachev <[email protected]>: [GPU] Fix handling of flags in the cuDNN FMHA test. Merging this change closes #12224 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12224 from openxla:fix_fmha_test 80528497321ee6020126b15035050f4c1a0beea9
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2024-05-08T10:09:11
2024-05-09T06:31:37
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PR #11241: [XLA:CPU] Enable BMM+Mul+Add for bf16
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2024-05-08T10:03:18
2024-05-08T11:34:22
2024-05-08T11:34:21
NONE
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PR #11241: [XLA:CPU] Enable BMM+Mul+Add for bf16 Imported from GitHub PR https://github.com/openxla/xla/pull/11241 This PR enable BatchMatmul + Mul + Add fusion for BF16 and also fixes a bug for the same. Copybara import of the project: -- abdbf9b89925c8553296122c846327b6dffa86ce by Kanvi Khanna <[email protected]>: Enable BMM+Mul+Add for bf16 -- 6674ac76b868f0399731d6185287eec76244df3a by Kanvi Khanna <[email protected]>: fix test -- 8fb80082e8c545e007ab7a2d363eb7b7c251fd07 by Kanvi Khanna <[email protected]>: address review comment, fix test, format Merging this change closes #11241 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11241 from Intel-tensorflow:kanvi/bmm-mul-add_bf16 8fb80082e8c545e007ab7a2d363eb7b7c251fd07
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PR #11425: [XLA:GPU] Add sycl platform
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2024-05-08T09:53:14
2024-05-15T10:52:00
2024-05-15T10:51:57
NONE
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PR #11425: [XLA:GPU] Add sycl platform Imported from GitHub PR https://github.com/openxla/xla/pull/11425 It is a sub PR of https://github.com/openxla/xla/pull/9042 to add sycl platform Copybara import of the project: -- 7d0c37057c673f784089ce961d24276a496d43bd by Sheng, Yang <[email protected]>: Add sycl platform -- 8d1196831a20f69ba8880d8549007358928f6be1 by Sheng, Yang <[email protected]>: Add comments back Merging this change closes #11425 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11425 from Intel-tensorflow:yang/sycl-platform 8d1196831a20f69ba8880d8549007358928f6be1
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Move fuel consumption point to an earlier stage in the cudnn fused convolution rewriter
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2024-05-08T09:38:12
2024-05-08T11:56:19
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Move fuel consumption point to an earlier stage in the cudnn fused convolution rewriter The call to `ConsumeFuel` happened after `EnsureIfConvBiasActivation` had already rewritten the convolution instruction. In order to effectively use `ConsumeFuel` for model bisection I'm moving the check to before any changes are made to the instruction.
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2024-05-08T09:02:20
2024-05-09T09:39:43
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[GPU] Fix an issue with deallocating memory from the different allocator.
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2024-05-08T08:54:16
2024-05-08T15:12:39
2024-05-08T15:12:37
NONE
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[GPU] Fix an issue with deallocating memory from the different allocator.
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Make Blas version optional in HloAlgorithmDenylist
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2024-05-08T08:41:57
2024-05-08T12:11:01
2024-05-08T12:11:00
NONE
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Make Blas version optional in HloAlgorithmDenylist Most cuDNN algorithms nowadays don't call cuBlas kernels anymore, so the Blas version is often not relevant when adding a new entry to the denylist. So this change makes it possible to leave the blas version field empty and it will just match any blas version. It also does various cleanups: - Consistent includes and dependencies - Usage of GMock matchers in the tests - Reenabling MSAN (works fine) The original behaviour stays intact, so it shouldn't break any user of the denylist feature.
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2024-05-08T08:36:51
2024-05-10T05:29:34
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NONE
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[XLA:GPU] Fix includes for collective_pipeliner.cc
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2024-05-08T08:15:34
2024-05-08T09:48:42
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[XLA:GPU] Fix includes for collective_pipeliner.cc
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PR #12243: Add Support for FP8 Data Types in cudnn_fusion_compiler.
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2024-05-08T08:13:59
2024-05-08T08:56:24
2024-05-08T08:56:24
NONE
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PR #12243: Add Support for FP8 Data Types in cudnn_fusion_compiler. Imported from GitHub PR https://github.com/openxla/xla/pull/12243 Copybara import of the project: -- cee34b537bf34ba3413fa39d01f3267b50eb344a by Elfie Guo <[email protected]>: Add support for FP8 datatype in cudnn_fusion_compiler. Merging this change closes #12243 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12243 from elfiegg:fp8_triton cee34b537bf34ba3413fa39d01f3267b50eb344a
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2024-05-08T07:57:31
2024-05-08T08:11:47
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2024-05-08T07:51:04
2024-05-08T11:41:25
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NONE
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2024-05-08T07:05:45
2024-05-09T17:44:25
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Unable bazel build tensorflow with cuda support inside a docker
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[ "@PriyajeetGoswami,\r\n\r\nI was able to clone the tensorflow repository without any problem on Ubuntu. I observed that you are using Bazel 6.2, \r\nGCC 9.3 which is incompatible with TF v2.7.0. And also TF v2.7.0 is a pretty older version, please try to install the latest stable version.\r\n\r\nCould you please create a virtual environment and try to install the tensorflow as mentioned in this official document [link](https://www.tensorflow.org/install/source) and have a look at the [compatible](https://www.tensorflow.org/install/source#gpu) tested build configurations as well. Please find the attached screenshot for reference.\r\n\r\n![Screenshot 2023-07-21 3 47 25 PM](https://github.com/tensorflow/tensorflow/assets/81610181/c06da998-2646-4810-afbc-3113d4f06aac)\r\n\r\n\r\nIs there any specific reason to install tensorflow v2.7, because as mentioned above v2.7 is the pretty older version. It's unlikely for TF 2.7 version to receive any bug fixes except when we have security patches. There is a high possibility that this was fixed with later TF versions. Thank you!", "I was trying TF 2.7 because of some project requirement but moved on from that, used latest version of TF, it worked. Thanks", "@PriyajeetGoswami,\r\nGlad the issue is resolved. Please feel free to move this issue to closed status. Thank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "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/67145\">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/67145\">No</a>\n" ]
2024-05-08T07:04:46
2024-05-31T01:49:34
2024-05-31T01:49:26
NONE
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### Issue type Build/Install ### Have you reproduced the bug with TensorFlow Nightly? No ### Source source ### TensorFlow version 2.7.0 ### Custom code No ### OS platform and distribution Linux Ubuntu 20.04 ### Mobile device _No response_ ### Python version 3.8.10 ### Bazel version 6.2.1 ### GCC/compiler version 9.3.0 ### CUDA/cuDNN version 11.2 ### GPU model and memory _No response_ ### Current behavior? https://www.tensorflow.org/install/source Followed the instructions here to create a tensorflow docker with gpu support, unable to build the tensorlfow using bazel using the following command bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow --config=cuda ### Standalone code to reproduce the issue ```shell I want to create a docker using which I build tflite model in C++ which can access the Nvidia gpu. ``` ### Relevant log output ```shell root@b7bdee9743e8:/tensorflow/tensorflow# bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow --config=cuda Starting local Bazel server and connecting to it... WARNING: Option 'java_toolchain' is deprecated WARNING: Option 'host_java_toolchain' is deprecated INFO: Options provided by the client: Inherited 'common' options: --isatty=1 --terminal_columns=191 INFO: Reading rc options for 'build' from /tensorflow/tensorflow/.bazelrc: Inherited 'common' options: --experimental_repo_remote_exec INFO: Reading rc options for 'build' from /tensorflow/tensorflow/.bazelrc: 'build' options: --define framework_shared_object=true --java_toolchain=@tf_toolchains//toolchains/java:tf_java_toolchain --host_java_toolchain=@tf_toolchains//toolchains/java:tf_java_toolchain --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/common,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/fallback,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils INFO: Found applicable config definition build:short_logs in file /tensorflow/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING INFO: Found applicable config definition build:v2 in file /tensorflow/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1 INFO: Found applicable config definition build:cuda in file /tensorflow/tensorflow/.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_cuda//:enable_cuda INFO: Found applicable config definition build:linux in file /tensorflow/tensorflow/.bazelrc: --copt=-w --host_copt=-w --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++14 --host_cxxopt=-std=c++14 --config=dynamic_kernels --distinct_host_configuration=false --experimental_guard_against_concurrent_changes INFO: Found applicable config definition build:dynamic_kernels in file /tensorflow/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS WARNING: Option 'java_toolchain' is deprecated WARNING: Option 'host_java_toolchain' is deprecated INFO: Repository local_config_cuda instantiated at: /tensorflow/tensorflow/WORKSPACE:15:14: in <toplevel> /tensorflow/tensorflow/tensorflow/workspace2.bzl:1079:19: in workspace /tensorflow/tensorflow/tensorflow/workspace2.bzl:94:19: in _tf_toolchains Repository rule cuda_configure defined at: /tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl:1448:33: in <toplevel> ERROR: An error occurred during the fetch of repository 'local_config_cuda': Traceback (most recent call last): File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 1401, column 38, in _cuda_autoconf_impl _create_local_cuda_repository(repository_ctx) File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 978, column 35, in _create_local_cuda_repository cuda_config = _get_cuda_config(repository_ctx, find_cuda_config_script) File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 666, column 30, in _get_cuda_config config = find_cuda_config(repository_ctx, find_cuda_config_script, ["cuda", "cudnn"]) File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 643, column 41, in find_cuda_config exec_result = _exec_find_cuda_config(repository_ctx, script_path, cuda_libraries) File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 637, column 19, in _exec_find_cuda_config return execute(repository_ctx, [python_bin, "-c", decompress_and_execute_cmd]) File "/tensorflow/tensorflow/third_party/remote_config/common.bzl", line 230, column 13, in execute fail( Error in fail: Repository command failed Could not find any cublas_api.h matching version '' in any subdirectory: '' 'include' 'include/cuda' 'include/*-linux-gnu' 'extras/CUPTI/include' 'include/cuda/CUPTI' of: '/usr' '/usr/lib/x86_64-linux-gnu' '/usr/local/cuda' '/usr/local/cuda/lib64/stubs' '/usr/local/cuda/targets/x86_64-linux/lib' ERROR: /tensorflow/tensorflow/WORKSPACE:15:14: fetching cuda_configure rule //external:local_config_cuda: Traceback (most recent call last): File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 1401, column 38, in _cuda_autoconf_impl _create_local_cuda_repository(repository_ctx) File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 978, column 35, in _create_local_cuda_repository cuda_config = _get_cuda_config(repository_ctx, find_cuda_config_script) File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 666, column 30, in _get_cuda_config config = find_cuda_config(repository_ctx, find_cuda_config_script, ["cuda", "cudnn"]) File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 643, column 41, in find_cuda_config exec_result = _exec_find_cuda_config(repository_ctx, script_path, cuda_libraries) File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 637, column 19, in _exec_find_cuda_config return execute(repository_ctx, [python_bin, "-c", decompress_and_execute_cmd]) File "/tensorflow/tensorflow/third_party/remote_config/common.bzl", line 230, column 13, in execute fail( Error in fail: Repository command failed Could not find any cublas_api.h matching version '' in any subdirectory: '' 'include' 'include/cuda' 'include/*-linux-gnu' 'extras/CUPTI/include' 'include/cuda/CUPTI' of: '/usr' '/usr/lib/x86_64-linux-gnu' '/usr/local/cuda' '/usr/local/cuda/lib64/stubs' '/usr/local/cuda/targets/x86_64-linux/lib' ERROR: @local_config_cuda//:enable_cuda :: Error loading option @local_config_cuda//:enable_cuda: Repository command failed Could not find any cublas_api.h matching version '' in any subdirectory: '' 'include' 'include/cuda' 'include/*-linux-gnu' 'extras/CUPTI/include' 'include/cuda/CUPTI' of: '/usr' '/usr/lib/x86_64-linux-gnu' '/usr/local/cuda' '/usr/local/cuda/lib64/stubs' '/usr/local/cuda/targets/x86_64-linux/lib' ```
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2024-05-09T10:09:08
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Disable numerically unstable cuDNN engine 14 for certain convolutions
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2024-05-08T06:45:30
2024-05-08T07:33:46
2024-05-08T07:33:46
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Disable numerically unstable cuDNN engine 14 for certain convolutions A customer reported a numerical issue on V100 with cuDNN 9 that I as was able to track down to a single convolution and cuDNN engine. Using random data with this convolution gives me reasonable results, so it's not yet clear to me if this is a cuDNN issue or just unfortunate numerics. For the time being let's disable the algorithm for this one convolution. The customer confirmed that it is fixing their issue.
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Add folding for integer types and all splat kinds for div op.
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2024-05-08T05:29:06
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Inconsistency in CPU results and GPU results in model training
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[ "Hi @Liqi1003 ,\r\n\r\nThe difference seems precision related because of XLA fusion. Please find the developer [comment](https://github.com/tensorflow/tensorflow/issues/62287#issuecomment-1809045878) on same for more details.", "Hi @SuryanarayanaY,\r\n\r\nThanks for the pointer!\r\n\r\nTo test whether it is a problem caused by XLA, I tried to disable XLA by adding `TF_XLA_FLAGS=--tf_xla_auto_jit=-1` before the command, as mentioned [here](https://docs.nvidia.com/deeplearning/frameworks/tensorflow-user-guide/index.html#enabling-xla). However, I still see the log indicates XLA compilation is enabled, and the outputs are the same as the one I posted above. Am I using the wrong way to disable XLA?\r\n\r\nAlso, I was only able to reproduce this problem in tensorflow 2.13. Using google colab with tensorflow 2.16.1, running the same code does not result in such inconsistency. Here is the [colab](https://colab.research.google.com/drive/1vruD1VYxN9JoB_YCzRJ8_EULHXWR3lfe#scrollTo=LEMZWXoBlkpr).\r\n\r\nI wonder if it is a bug that was silently fixed in later versions? Thanks!", "Hi @Liqi1003 ,\r\n\r\nIt seems 2.16v has better precision than earlier versions. May be there seems some internal amendments which I am not aware.\r\nAs it resolved in latest version can we mark this as closed. Thanks!", "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/67137\">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/67137\">No</a>\n" ]
2024-05-08T02:49:29
2024-05-10T00:39:51
2024-05-10T00:39:48
NONE
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### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? No ### Source binary ### TensorFlow version tf 2.13.0 ### Custom code Yes ### OS platform and distribution Linux Ubuntu 20.04.5 ### Mobile device _No response_ ### Python version 3.8.10 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version 11.8/8.7 ### GPU model and memory _No response_ ### Current behavior? I am reporting an issue encountered during the distributed training of a model with different types of devices using tensorflow. I initially encountered the bug with multiple GPUs involved, but reproduced the bug in a single GPU case. The version of tensorflow used is 2.13.0. It is very likely an edge case. This inconsistency only occurs with the specific initial weights and inputs we provided. To make the difference more apparent given a limited amount of training data, we deliberately chose a relatively high learning rate (lr=10.0). Before executing the code, put the [model](https://drive.google.com/file/d/10_nNhQe19cJ6Ki8EIGPU0FjUZP2Oungj/view?usp=drive_link) inside the same directory of the reproduce code, so that the model weights can be loaded. It is important to load the model weights, as random initial weights cannot reproduce this bug. ### Standalone code to reproduce the issue ```shell import sys import keras import random import numpy as np import tensorflow as tf from keras import layers from keras import optimizers print("python version:", sys.version) print("tensorflow version:", tf.__version__) print("numpy version:", np.__version__) def train_using_strategy(strategy, train_input, train_label, test_input): # Load model with strategy.scope(): model = keras.models.load_model("./model.keras") optimizer = optimizers.SGD(learning_rate=10.0) loss = tf.keras.losses.CategoricalCrossentropy( from_logits=True, reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE) model.compile(optimizer=optimizer, loss=loss) # Train for 1 step model.fit( train_input, train_label, verbose=0, shuffle=False, batch_size=2400) pred = model(test_input) pred = tf.nn.softmax(pred) return pred # Set random seeds seed = 54078027 random.seed(seed) tf.random.set_seed(seed) # Training data, batch_size=2400 train_input = tf.random.uniform(shape=(2400, 32, 32, 3)) train_label = tf.one_hot(tf.random.uniform( shape=(2400, ), minval=0, maxval=10, dtype=tf.int32), 10) test_input = tf.random.uniform(shape=(1, 32, 32, 3)) test_label = tf.one_hot(tf.random.uniform( shape=(1, ), minval=0, maxval=10, dtype=tf.int32), 10) # Original model layer_0 = layers.Input(shape=(32, 32, 3,)) layer_1 = layers.Conv2D( filters=5, kernel_size=(13, 13), strides=(1, 1), padding="valid", data_format="channels_last", dilation_rate=(1, 1), activation="tanh", use_bias=True, kernel_initializer="random_uniform", bias_initializer="random_uniform")(layer_0) layer_2 = layers.ReLU(max_value=0.08354582293069757)(layer_1) layer_3 = layers.Flatten()(layer_2) layer_4 = layers.Dense( units=10, activation="linear", use_bias=False, kernel_initializer="random_uniform", bias_initializer="random_uniform")(layer_3) layer_5 = layers.Reshape((10,))(layer_4) model = keras.Model(layer_0, layer_5) model.summary() model.load_weights("./tensorflow.h5") # Alternatively, load the model directly # model = keras.models.load_model("./tensorflow.h5") keras.models.save_model(model, "./model.keras") res_cpu = train_using_strategy( strategy=tf.distribute.MirroredStrategy(devices=["/CPU:0"]), train_input=train_input, train_label=train_label, test_input=test_input) res_gpu = train_using_strategy( strategy=tf.distribute.MirroredStrategy(devices=["/GPU:0"]), train_input=train_input, train_label=train_label, test_input=test_input) print("max diff:", np.max(np.abs(res_cpu - res_gpu))) print("result on CPU device:", res_cpu) print("result on GPU device:", res_gpu) ``` ### Relevant log output ```shell 2024-05-08 01:19:26.978000: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-05-08 01:19:28.059507: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT python version: 3.8.10 (default, Nov 22 2023, 10:22:35) [GCC 9.4.0] tensorflow version: 2.13.0 numpy version: 1.23.5 [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:2', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:3', device_type='GPU')] 2024-05-08 01:19:29.719405: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10696 MB memory: -> device: 0, name: NVIDIA TITAN V, pci bus id: 0000:af:00.0, compute capability: 7.0 2024-05-08 01:19:29.720298: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 11539 MB memory: -> device: 1, name: NVIDIA TITAN Xp, pci bus id: 0000:3b:00.0, compute capability: 6.1 2024-05-08 01:19:29.721009: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 11539 MB memory: -> device: 2, name: NVIDIA TITAN Xp, pci bus id: 0000:5e:00.0, compute capability: 6.1 2024-05-08 01:19:29.721728: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 11539 MB memory: -> device: 3, name: NVIDIA TITAN Xp, pci bus id: 0000:86:00.0, compute capability: 6.1 Model: "model" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) [(None, 32, 32, 3)] 0 conv2d (Conv2D) (None, 20, 20, 5) 2540 re_lu (ReLU) (None, 20, 20, 5) 0 flatten (Flatten) (None, 2000) 0 dense (Dense) (None, 10) 20000 reshape (Reshape) (None, 10) 0 ================================================================= Total params: 22540 (88.05 KB) Trainable params: 22540 (88.05 KB) Non-trainable params: 0 (0.00 Byte) _________________________________________________________________ 2024-05-08 01:19:30.445908: W tensorflow/core/framework/dataset.cc:956] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations. 2024-05-08 01:19:31.126108: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f57a8015840 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2024-05-08 01:19:31.126233: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2024-05-08 01:19:31.154914: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process. 2024-05-08 01:19:31.408683: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8700 2024-05-08 01:19:32.675226: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f5c85a4c040 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2024-05-08 01:19:32.675279: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA TITAN V, Compute Capability 7.0 2024-05-08 01:19:32.675292: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (1): NVIDIA TITAN Xp, Compute Capability 6.1 2024-05-08 01:19:32.675304: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (2): NVIDIA TITAN Xp, Compute Capability 6.1 2024-05-08 01:19:32.675316: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (3): NVIDIA TITAN Xp, Compute Capability 6.1 max diff: 0.01396697 result on CPU device: tf.Tensor( [[1.7262544e-03 1.6841354e-02 1.6154546e-02 4.0267220e-01 1.6546119e-03 7.9213362e-14 4.0498021e-01 1.1821460e-01 1.8025419e-02 1.9730769e-02]], shape=(1, 10), dtype=float32) result on GPU device: tf.Tensor( [[1.73812674e-03 1.88294742e-02 1.49312345e-02 4.12326276e-01 1.74528488e-03 7.97685756e-14 4.08124179e-01 1.04247630e-01 1.85339060e-02 1.95238516e-02]], shape=(1, 10), dtype=float32) ```
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_image_ops.so not found
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[ "@neonarc4 If you are facing the error as \"_image_ops.so not found\" , it indicates that TensorFlow is unable to locate a required library file for image processing functionalities. Please try to reinstall TF with GPU support and follow the instructions [here](https://www.tensorflow.org/install/pip). Kindly fill the issue template for easy analysis of the issue.\r\nThank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/67136\">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/67136\">No</a>\n" ]
2024-05-08T01:42:56
2024-05-29T01:50:57
2024-05-29T01:50:52
NONE
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eucid = tfa.image.euclidean_dist_transform(gray) coming from tfa image ecudiean ```py eucid = tfa.image.euclidean_dist_transform(gray) File "C:\Users\haide\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\image\distance_transform.py", line 67, in euclidean_dist_transform output = _image_so.ops.addons_euclidean_distance_transform(images, dtype) File "C:\Users\haide\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\resource_loader.py", line 68, in ops self._ops = tf.load_op_library( File "C:\Users\haide\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow\python\framework\load_library.py", line 54, in load_op_library lib_handle = py_tf.TF_LoadLibrary(library_filename) tensorflow.python.framework.errors_impl.NotFoundError: C:\Users\haide\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\custom_ops\image\_image_ops.so not found ```
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[XLA] Make HloReplicationAnalysis handle kOptimizationBarrier.
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2024-05-08T01:04:05
2024-05-08T09:21:55
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[XLA] Make HloReplicationAnalysis handle kOptimizationBarrier.
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Update flatbuffers to 24.3.25
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2024-05-07T23:15:08
2024-05-09T00:06:24
2024-05-09T00:06:24
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Update flatbuffers to 24.3.25
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Raise error when weight decay is set in TPUEmbeddingV2.
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2024-05-07T23:14:54
2024-05-09T17:17:58
2024-05-09T17:17:57
NONE
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Raise error when weight decay is set in TPUEmbeddingV2.
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gpu_delegate: Update GPU MUL compatibility checker
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2024-05-07T23:13:41
2024-05-09T04:04:00
2024-05-09T04:03:59
NONE
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gpu_delegate: Update GPU MUL compatibility checker ADD, MUL only works when two inputs has the same number of dimension on GPU. Make sure if the condition is checked properly.
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Add Slice in `IsSupportedConstantExpression()` in `xla::HloConstantSplitter`.
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2024-05-07T23:08:13
2024-05-08T05:47:46
2024-05-08T05:47:45
NONE
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Add Slice in `IsSupportedConstantExpression()` in `xla::HloConstantSplitter`. A slice of a constant expression is also a constant expression.
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Add additional logging when unwinding error in mlrt
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2024-05-07T23:07:28
2024-05-08T00:04:28
2024-05-08T00:04:27
NONE
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Add additional logging when unwinding error in mlrt
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Use int32 when apply canonicalization on converting Pack op to Reshape op
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2024-05-07T22:53:02
2024-05-09T22:07:18
2024-05-09T22:07:16
NONE
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Use int32 when apply canonicalization on converting Pack op to Reshape op
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Remove unused `tools/ci_build/xla/`
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2024-05-07T22:41:50
2024-05-07T23:41:44
2024-05-07T23:41:44
NONE
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Remove unused `tools/ci_build/xla/`
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67,127
Introduce ReplaceWhileOperandShape api to HloInstruction. This function provides an interface to change the shape an tuple operand in while loops.
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2024-05-07T22:37:02
2024-05-30T23:11:19
2024-05-30T23:11:17
NONE
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Introduce ReplaceWhileOperandShape api to HloInstruction. This function provides an interface to change the shape an tuple operand in while loops. The default implementation (ShapeTransformer) propagates shape change through instructions in which the output shape is directly inferred from operands, namely, gte, tuple, and nested while loops. As the result, if the changed operand of the while loop is only used by the mentioned instructions, the call to ReplaceWhileOperandShape guarantees validity of the hlo graph after shape replacement. Currently, the replace function simply bails and returns false if there are any users other than the mentioned instructions.
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PR #12223: [GPU][NFC] Remove some uses of the deprecated runtime executable flag.
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2024-05-07T22:31:17
2024-05-08T08:05:14
2024-05-08T08:05:13
NONE
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PR #12223: [GPU][NFC] Remove some uses of the deprecated runtime executable flag. Imported from GitHub PR https://github.com/openxla/xla/pull/12223 Copybara import of the project: -- 64be02d67df35156d4ee55ce0d3fbe80eff148ea by Ilia Sergachev <[email protected]>: [GPU][NFC] Remove some uses of the deprecated runtime executable flag. Merging this change closes #12223 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12223 from openxla:flag_cleanup 64be02d67df35156d4ee55ce0d3fbe80eff148ea
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Fix LOGFATAL for logical_buffer access with invalid id
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2024-05-07T22:23:48
2024-05-07T23:56:43
2024-05-07T23:56:41
NONE
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Fix LOGFATAL for logical_buffer access with invalid id
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Make unroller utility functions static.
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2024-05-07T22:15:59
2024-05-10T15:01:31
2024-05-10T15:01:30
NONE
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Make unroller utility functions static.
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Fix LOGFATAL for logical_buffer access with invalid id
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2024-05-07T22:09:20
2024-05-07T22:45:37
2024-05-07T22:45:36
NONE
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Fix LOGFATAL for logical_buffer access with invalid id
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Add an example to demonstrate Keras(with JAX backend) to TFLite.
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2024-05-07T22:00:12
2024-05-08T16:14:59
2024-05-08T16:14:58
NONE
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Add an example to demonstrate Keras(with JAX backend) to TFLite.
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Jax to TFLite colab example,
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2024-05-07T21:39:20
2024-05-08T16:54:18
2024-05-08T16:54:18
NONE
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Jax to TFLite colab example,
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Remove func_use_fallback_tensor in tf_to_tfrt
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2024-05-07T21:35:59
2024-05-08T00:19:09
2024-05-08T00:19:08
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Remove func_use_fallback_tensor in tf_to_tfrt
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#tf-data Restores the `seed_generator_` and three seeds in global_shuffle_dataset_op.cc to ensure the iterator can generate the same result after restoring
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2024-05-07T21:30:41
2024-05-08T22:07:40
2024-05-08T22:07:39
NONE
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#tf-data Restores the `seed_generator_` and three seeds in global_shuffle_dataset_op.cc to ensure the iterator can generate the same result after restoring
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Update DUCC to commit:15246e40cfa880c079606ce49bbf629b07fcf9cb
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2024-05-07T21:27:41
2024-05-08T00:46:13
2024-05-08T00:46:12
NONE
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Update DUCC to commit:15246e40cfa880c079606ce49bbf629b07fcf9cb
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Integrate StableHLO at openxla/stablehlo@8ba7728d
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2024-05-07T21:19:56
2024-05-09T01:25:40
2024-05-09T01:25:38
NONE
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Integrate StableHLO at openxla/stablehlo@8ba7728d
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[xla] functional_hlo_runner_test: add CPU backend
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2024-05-07T20:41:36
2024-05-07T22:10:12
2024-05-07T22:10:12
NONE
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[xla] functional_hlo_runner_test: add CPU backend
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Implement bounded_flat_map_dataset_op with max_fan_out support.
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2024-05-07T20:33:03
2024-05-22T18:54:18
2024-05-22T18:54:16
NONE
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Implement bounded_flat_map_dataset_op with max_fan_out support.
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#tf-data Update Tensorflow visibility.
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null
[]
2024-05-07T20:29:54
2024-05-22T18:54:13
2024-05-22T18:54:11
NONE
null
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#tf-data Update Tensorflow visibility.
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PR_kwDOArmXAs5uzX_Y
67,113
[XLA:GPU][MLIR-Based emitters] Fix canonicalization patterns for ApplyIndexingOp.
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null
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2024-05-07T19:54:19
2024-05-07T20:46:46
2024-05-07T20:46:46
NONE
null
true
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[XLA:GPU][MLIR-Based emitters] Fix canonicalization patterns for ApplyIndexingOp.
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PR_kwDOArmXAs5uzDkT
67,112
Update schema dependencies.
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2024-05-07T18:58:13
2024-05-07T23:04:51
2024-05-07T23:04:50
NONE
null
true
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Update schema dependencies.
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PR_kwDOArmXAs5uyxay
67,111
Add new `xla_internal` macro to `xla.bzl`
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null
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2024-05-07T18:04:57
2024-05-07T23:20:30
2024-05-07T23:20:29
NONE
null
true
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Add new `xla_internal` macro to `xla.bzl`
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67,110
Reverts f5ff2338e773ff179f53c9dc82870625d2a8d908
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null
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2024-05-07T17:34:43
2024-05-08T09:54:48
2024-05-08T09:54:48
NONE
null
true
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Reverts f5ff2338e773ff179f53c9dc82870625d2a8d908
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PR_kwDOArmXAs5uyjAM
67,109
Add keyword "_partitioned_" into the partition graph dump filename
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2024-05-07T17:21:35
2024-05-07T17:40:07
2024-05-07T17:40:06
NONE
null
true
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Add keyword "_partitioned_" into the partition graph dump filename
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PR_kwDOArmXAs5uyhKi
67,108
Export StableHLO Quantizer to TensorFlow pip package.
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Export StableHLO Quantizer to TensorFlow pip package. API: tf.quantization.stablehlo.experimental.quantize_saved_model tf.quantization.stablehlo.experimental.QuantizationConfig tf.quantization.stablehlo.experimental.StaticRangePtqPreset tf.quantization.stablehlo.experimental.RepresentativeDatasetConfig tf.quantization.stablehlo.experimental.TfRecordFile tf.quantization.stablehlo.experimental.TfSavedModelConfig tf.quantization.stablehlo.experimental.PipelineConfig
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Combine internal and external tpu_execute.cc
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Combine internal and external tpu_execute.cc
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Add pattern match and composite outlining for GELU.
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[xla] functional_hlo_runner: fix handling of PJRT client with 0 memory spaces
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[xla] functional_hlo_runner: fix handling of PJRT client with 0 memory spaces
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[HLO] Allow zero-operand kAfterAll
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[HLO] Allow zero-operand kAfterAll
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Reverts a9d16d74e371449f7314afdca0f433f4ac7dcf03
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2024-05-07T14:50:41
2024-05-07T15:28:35
2024-05-07T15:28:33
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Reverts a9d16d74e371449f7314afdca0f433f4ac7dcf03
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67,102
[XLA:GPU] Remove /*foo=/* style comments upon struct construction.
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[XLA:GPU] Remove /*foo=/* style comments upon struct construction.
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This is an automatic update to a device compatibility allowlist.
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This is an automatic update to a device compatibility allowlist.
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67,100
Add AlgebraicSimplifier pass directly in front of HloConstantFolding.
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2024-05-07T13:52:04
2024-05-14T06:38:22
2024-05-14T06:38:20
NONE
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Add AlgebraicSimplifier pass directly in front of HloConstantFolding. We have cases with DynamicSlice(Broadcast) which can be simplified away which would otherwise be constant folded. The HloEvaluator is really slow for DynamicSlice and Slice, as it will evalute the whole first operand, so if we can simplify it away, it will help.
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67,099
Add build macro to generate hlo compilations test build rules.
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2024-05-07T13:13:31
2024-05-22T08:52:25
2024-05-22T08:52:18
NONE
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Add build macro to generate hlo compilations test build rules.
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I_kwDOArmXAs6IFwUT
67,098
Aborted (core dumped) in `tf.irfftnd/ifftnd`
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[ "Hi **@x0w3n** ,\r\n\r\nSorry for the delay, I tried to run your code on colab using TF v2.16, nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/88012d21fb32b2731770644742419b0e/67098_2-16-1-v_gpu.ipynb) here for reference.\r\n\r\nThank you!", "> Hi **@x0w3n** ,\r\n> \r\n> Sorry for the delay, I tried to run your code on colab using TF v2.16, nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/88012d21fb32b2731770644742419b0e/67098_2-16-1-v_gpu.ipynb) here for reference.\r\n> \r\n> Thank you!\r\nOK. Thank you!\r\n", "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/67098\">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/67098\">No</a>\n" ]
2024-05-07T12:48:54
2024-05-29T14:10:40
2024-05-28T12:01:57
NONE
null
null
null
### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? No ### Source source ### TensorFlow version tf 2.16 ### Custom code Yes ### OS platform and distribution Linux Ubuntu 22.04.3 LTS (x86_64) ### Mobile device _No response_ ### Python version 3.9.13 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? When the fft_length parameter of tf.irfftnd/ifftnd/irfftnd is less than or equal to 0, it will trigger a crash, resulting in Check failed. ### Standalone code to reproduce the issue ```shell import tensorflow as tf input1 = tf.random.normal([10,10,10,10,10], dtype=tf.float32) input2 = tf.random.normal([10,10,10,10,10], dtype=tf.float32) input3 = tf.complex(input1,input2) tf.irfftnd(input3,-3,-3) #crash # tf.ifftnd(input3,-3,-3) #crash # tf.irfftnd(input3,-3,-3) #crash ``` ### Relevant log output ```shell 2024-05-07 07:46:42.396021: F tensorflow/core/framework/tensor_shape.cc:45] Check failed: NDIMS == dims() (1 vs. 0)Asking for tensor of 1 dimensions from a tensor of 0 dimensions Aborted (core dumped) ```
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Avoid large operands in constant folding.
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2024-05-07T12:30:35
2024-05-07T13:51:31
2024-05-07T13:51:31
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Avoid large operands in constant folding. We already have code that counts the total size of operands. However it still has a check that the number of users of an operand is 1. This stems from a time where we tried to evaluate whether constant folding will reduce the total memory size, so if there was more than 1 user, it should not be counted, as the operand will remain. But with the current code it makes no sense at all.
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[Error]tf-opt --tf-init-text-file-to-import --tf-saved-model-dir
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[ "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/67096\">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/67096\">No</a>\n" ]
2024-05-07T12:26:57
2024-05-07T14:14:38
2024-05-07T14:14:34
NONE
null
null
null
### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? No ### Source source ### TensorFlow version r2.14 ### Custom code Yes ### OS platform and distribution _No response_ ### Mobile device _No response_ ### Python version _No response_ ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? Hi there, I want to know how to use **Correct Command Line** of **tf-opt** [tf-opt](https://github.com/tensorflow/tensorflow/blob/99d80a9e254c9df7940b2902b14d15914dbbbcd9/tensorflow/compiler/mlir/BUILD#L160) can't use command line of `--tf-init-text-file-to-import --tf-saved-model-dir`? But i saw options in [tf_passes.td](https://github.com/tensorflow/tensorflow/blob/99d80a9e254c9df7940b2902b14d15914dbbbcd9/tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.td#L2258) How to Modify command like this: ``` $ tf-opt --tf-init-text-file-to-import --tf-saved-model-dir tf-opt: Unknown command line argument '--tf-saved-model-dir'. Try: './bazel-bin/tensorflow/compiler/mlir/tf-opt --help' ``` ### Standalone code to reproduce the issue ```shell tf-opt --tf-init-text-file-to-import --tf-saved-model-dir ``` ### Relevant log output _No response_
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2,283,140,194
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67,095
模板类 TensorContractionBlocking 的模板参数数量不正确
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[ "Hi @flh0923 ,\r\n\r\nI am sorry to say that TF1.x versions are not supported now. Please upgrade to latest TF versions.", "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/67095\">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/67095\">No</a>\n" ]
2024-05-07T12:25:47
2024-05-24T01:49:09
2024-05-24T01:49:06
NONE
null
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### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source source ### TensorFlow version TF1.13.2 ### Custom code Yes ### OS platform and distribution ubuntu18.04 ### Mobile device ubuntu18.04 ### Python version 2.7 ### Bazel version 0.19.2 ### GCC/compiler version 7 ### CUDA/cuDNN version cuda10,cudnn7 ### GPU model and memory 1050 ### Current behavior? 在编译segnetm时候出现错误 ### Standalone code to reproduce the issue ```shell #set (EIGEN3_INCLUDE_DIR "/home/hjx/.cache/bazel/_bazel_hjx/f43d7462b7259647736d249f2d5ed079/external/eigen_archive") set( LIBS ${OpenCV_LIBS} ${EIGEN3_LIBS} ${Pangolin_LIBRARIES} ${PCL_LIBRARIES} ${OCTOMAP_LIBRARIES} ${PROJECT_SOURCE_DIR}/../../../Thirdparty/DBoW2/lib/libDBoW2.so ${PROJECT_SOURCE_DIR}/../../../Thirdparty/g2o/lib/libg2o.so ${PROJECT_SOURCE_DIR}/../../../build/libmyORB_SLAM2_PointMap_SegNetM.so /usr/lib/x86_64-linux-gnu/libglog.so -lboost_system ${PROJECT_SOURCE_DIR}/../../../Examples/myROS/myORB_SLAM2_PointMap_SegNetM/libsegmentation/build/libmysegmentation.so ) add_definitions( ${PCL_DEFINITIONS} ) link_directories( ${PCL_LIBRARY_DIRS} ) rosbuild_add_executable(myTUM myros_tum_realtime.cc ) target_link_libraries(myTUM ${LIBS} ) ``` ### Relevant log output ```shell /home/flh/tensorflow-1.13.2/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProductAVX2.h:162:38: error: ‘void Eigen::internal::operator()(Eigen::QInt16*, const DataMapper&, Index, Index, Index, Index)’ must be a nonstatic member function Index stride, Index offset) { ^ /home/flh/tensorflow-1.13.2/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProductAVX2.h:549:24: error: wrong number of template arguments (5, should be at least 3) Index, ShardingType> { ^ home/flh/tensorflow-1.13.2/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProductAVX2.h:49:76: error: wrong number of template arguments (5, should be at least 3) class TensorContractionBlocking<QInt16, QInt16, QInt16, Index, ShardingType> { ^ In file included from /usr/include/eigen3/unsupported/Eigen/CXX11/Tensor:112:0, ```
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67,094
Support MOF with variadic reductions.
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2024-05-07T12:08:59
2024-05-07T12:58:43
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Support MOF with variadic reductions. I thought this wasn't a thing. It is a thing.
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Change weight-only PTQ to use symmetric quantization
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2024-05-07T11:56:16
2024-05-09T05:56:17
2024-05-09T05:56:15
NONE
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Change weight-only PTQ to use symmetric quantization This is to conform to the constraints of zero points being zero on rhs of quantized stablehlo.dot_general op. (dot_general C15 in https://github.com/openxla/stablehlo/blob/main/docs/spec.md#constraints-29)
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[XLA:GPU] Rename LoopDoubleBufferTransformer to DoubleBufferLoopUnrolling.
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2024-05-07T11:41:07
2024-05-13T16:10:20
2024-05-13T16:10:18
NONE
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[XLA:GPU] Rename LoopDoubleBufferTransformer to DoubleBufferLoopUnrolling.
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