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Integrate LLVM at llvm/llvm-project@9fb85b099461
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Integrate LLVM at llvm/llvm-project@9fb85b099461
Updates LLVM usage to match
[9fb85b099461](https://github.com/llvm/llvm-project/commit/9fb85b099461)
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Add a parameter to BuildHloFromGraph to enable the choice to force the use of `_output_shapes` attribute.
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Add a parameter to BuildHloFromGraph to enable the choice to force the use of `_output_shapes` attribute.
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Add a pass to unify the accumulator and the input of the jax.scan loops.
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Add a pass to unify the accumulator and the input of the jax.scan loops.
This pass looks at the loops with accumulator patterns and unifies the
accumulation buffer with the input. The accumulation pattern usually comes
from jax.scan function. This transformation is beneficial in the cases where
the scan loop appears inside a loop body which causes a copy of the
accumulation buffer in the outer body.
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[XLA:GPU] Implement TiledHloInstruction graph in SymbolicTileAnalysis.
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[XLA:GPU] Implement TiledHloInstruction graph in SymbolicTileAnalysis.
SymbolicTileAnalysis create a new tiled HLO node for each unique (HLO instruction, indexing map) pair. Tiled instructions are stored in def-before-use order for easier access.
This representation makes it easier to write codegen, because each instruction knows about it's operands and we can efficiently cache emitter values. Those also allows to do some degree of CSE, but it can be CSEd more for concrete tile sizes.
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Customize Reserved GPU HBM Size by flag in Pathways.
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Customize Reserved GPU HBM Size by flag in Pathways.
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Add support for parameter streaming with while loop by:
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Add support for parameter streaming with while loop by:
- fix up shapes of root instruction of while body and parameter of while condition
- root instruction of while body should use the "on host" operand since it's on host
at loop entry.
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Move `tsl/util` to `xla/tsl/util`
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Move `tsl/util` to `xla/tsl/util`
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Mark alias in tensorflow::profiler to counter parts in tsl::profiler as deprecated.
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Mark alias in tensorflow::profiler to counter parts in tsl::profiler as deprecated.
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[XLA:Runtime] Moved the nccl_collective_broadcast thunk to new folder and removed unused dependencies.Updated the necessary directories pointing to this thunk and removed it from the nccl_collectives_thunks build and removed the nccl_collectives_thunk target as all the remaining thunks have been moved out. #5758
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graphcycles_test: add BM_IsReachableNonConst
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graphcycles_test: add BM_IsReachableNonConst
name time/op
BM_StressTest/2048 489ns ± 5%
BM_StressTest/4096 501ns ± 6%
BM_StressTest/32768 521ns ± 4%
BM_StressTest/262144 575ns ±11%
BM_StressTest/1048576 614ns ± 2%
BM_ContractEdge/1000 129ns ± 2%
BM_ContractEdge/10000 148ns ± 1%
BM_IsReachableNonConst/10 12.2ns ± 7%
BM_IsReachableNonConst/50 19.9ns ±14%
BM_IsReachableNonConst/100 24.7ns ± 7%
BM_IsReachableNonConst/200 26.3ns ± 3%
BM_IsReachableNonConst/1000 37.3ns ± 5%
BM_IsReachableNonConst/30000 66.4ns ± 3%
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PR_kwDOArmXAs5qYvbH
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Print out a warning if an instruction with misaligned sharding annotations.
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Print out a warning if an instruction with misaligned sharding annotations.
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PR_kwDOArmXAs5qYt2E
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Ignore nested tuples, if present, during post processing.
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NONE
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Ignore nested tuples, if present, during post processing.
This is a hack for some cases (specifically observed for the llama and gemma models) where nested tuples as used as inputs/outputs of the kOptimizationBarrier instruction.
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Use the information in allow_spmd_sharding_propagation_to_output and allow_spmd_sharding_propagation_to_parameters to determine what input and output tuple elements we are allowed to modfy the shardings of.
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Use the information in allow_spmd_sharding_propagation_to_output and allow_spmd_sharding_propagation_to_parameters to determine what input and output tuple elements we are allowed to modfy the shardings of.
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Only the DIFFBASE CL is public.
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Update to reflect name change from `TfLiteRegistrationExternal` as `TfLiteOperator`.
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Update to reflect name change from `TfLiteRegistrationExternal` as `TfLiteOperator`.
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Layers.Flatten( ) fails under TF 2.16.1 but not under TF 2.15
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[
"Set the `input_shape` argument of your Dense layer equal to `(sequence_length * raw_data.shape[-1],)`",
"Hi @JuanVargas\r\n\r\nI also encountered a similar error while training an image classification model.\r\nBelow is a list of measures that were effective in suppressing the error in my environment:\r\n\r\n1. Use tf.ensure_shape to explicitly define the shape of tensors in the training and validation dataset.\r\n - Example: https://github.com/tensorflow/tensorflow/issues/57485#issuecomment-1249649166\r\n2. Switch from graph execution to eager execution.\r\n3. Use a reshape layer instead of a flatten layer.\r\n - In your case `tf.keras.Reshape((sequence_length, raw_data.shape[-1]))` or `tf.keras.Reshape((None, sequence_length, raw_data.shape[-1]))`",
"@JuanVargas \r\nIn order to expedite the trouble-shooting process, please provide a complete code snippet to reproduce the issue reported here. 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/64177\">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/64177\">No</a>\n",
"Had the same issue after the TF upgrade. The old code with the new TF gave errors just like you mentioned. \r\nFor me, the minimal solution was to fix the shape.\r\nIn the old TensorFlow `Flatten()(input)` with `input.shape = [None, None, 3, 2]` worked fine. In the new TensorFlow I had to make sure `input.shape=[None, 32, 3, 2]`. NB: the first dimension is batch, the second is the steps in a time series. PS. can't remember if batch was specified or not in the input shape, but the error message made reference to shapes including the batch dimension."
] | 2024-03-21T15:39:41
| 2024-05-23T07:51:27
| 2024-04-11T01:47:56
|
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
TF 2.16.1
### Custom code
No
### OS platform and distribution
Linux Ubuntu 22.04.4 LTS
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA 12.4; CUDNN 8
### GPU model and memory
NVIDIA GeForce RTX 3060 12 GBs
### Current behavior?
Code from pp 289 of Cholllet's book second ed, that uses a Flatten layer, runs OK under TF 2.15 and Keras 2.15. The exact same code fails under TF 2.16.1 when it tries to execute the line to get the history obj. For reference the code is in the section for code of this form and the error msg is also in the box of this form
2024-03-21 11:16:28.427720: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1711034188.704423 18284 service.cc:145] XLA service 0x7ad818006930 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1711034188.704465 18284 service.cc:153] StreamExecutor device (0): NVIDIA GeForce RTX 3060, Compute Capability 8.6
2024-03-21 11:16:28.713034: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at xla_ops.cc:580 : INVALID_ARGUMENT: only one input size may be -1, not both 0 and 1
Stack trace for op definition:
### Standalone code to reproduce the issue
```shell
inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))
x = keras.layers.Flatten()(inputs)
x = keras.layers.Dense(16, activation="relu")(x)
outputs = keras.layers.Dense(1)(x)
model = keras.Model(inputs, outputs)
callbacks = [
keras.callbacks.ModelCheckpoint("jena_dense.keras.x.keras",
save_best_only=True)
]
model.compile(optimizer="rmsprop", loss="mse", metrics=["mae"])
history = model.fit(train_dataset,
epochs=10,
validation_data=val_dataset,
callbacks=callbacks)
```
### Relevant log output
```shell
2024-03-21 11:16:28.427720: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1711034188.704423 18284 service.cc:145] XLA service 0x7ad818006930 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1711034188.704465 18284 service.cc:153] StreamExecutor device (0): NVIDIA GeForce RTX 3060, Compute Capability 8.6
2024-03-21 11:16:28.713034: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at xla_ops.cc:580 : INVALID_ARGUMENT: only one input size may be -1, not both 0 and 1
Stack trace for op definition:
tack trace for op definition:
File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/ipykernel_launcher.py", line 18, in <module>
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/traitlets/config/application.py", line 1075, in launch_instance
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/ipykernel/kernelapp.py", line 739, in start
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/tornado/platform/asyncio.py", line 205, in start
File "/usr/lib/python3.10/asyncio/base_events.py", line 603, in run_forever
File "/usr/lib/python3.10/asyncio/base_events.py", line 1909, in _run_once
File "/usr/lib/python3.10/asyncio/events.py", line 80, in _run
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/ipykernel/kernelbase.py", line 545, in dispatch_queue
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/ipykernel/kernelbase.py", line 534, in process_one
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/ipykernel/kernelbase.py", line 437, in dispatch_shell
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/ipykernel/ipkernel.py", line 359, in execute_request
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/ipykernel/kernelbase.py", line 778, in execute_request
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/ipykernel/ipkernel.py", line 446, in do_execute
File "/tmp/ipykernel_18158/437540976.py", line 29, in wrapper
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/ipykernel/zmqshell.py", line 549, in run_cell
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3075, in run_cell
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3130, in _run_cell
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/IPython/core/async_helpers.py", line 129, in _pseudo_sync_runner
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3334, in run_cell_async
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File "<ipython-input-1-6a9ff972cd60>", line 250, in <module>
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 117, in error_handler
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py", line 325, in fit
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py", line 118, in one_step_on_iterator
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py", line 106, in one_step_on_data
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py", line 57, in train_step
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File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 117, in error_handler
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/ops/operation.py", line 48, in __call__
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 156, in error_handler
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/models/functional.py", line 194, in call
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/ops/function.py", line 151, in _run_through_graph
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/models/functional.py", line 578, in call
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 117, in error_handler
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/layers/layer.py", line 814, in __call__
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 117, in error_handler
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/ops/operation.py", line 48, in __call__
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 156, in error_handler
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/layers/reshaping/flatten.py", line 54, in call
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/ops/numpy.py", line 4507, in reshape
File "/drv3/hm3/code/python/tf.2.16.1/.tf.2.16.1/lib/python3.10/site-packages/keras/src/backend/tensorflow/numpy.py", line 1545, in reshape
[[{{node functional_1_1/flatten_1/Reshape}}]]
tf2xla conversion failed while converting __inference_one_step_on_data_19335[]. Run with TF_DUMP_GRAPH_PREFIX=/path/to/dump/dir and --vmodule=xla_compiler=2 to obtain a dump of the compiled functions.
2024-03-21 11:16:28.713066: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: INVALID_ARGUMENT: only one input size may be -1, not both 0 and 1
```
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PR_kwDOArmXAs5qYM7p
| 64,176
|
Bugfix, nit: do not use designated initializers for aggregate initialization.
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Bugfix, nit: do not use designated initializers for aggregate initialization.
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Always enable XLA spmd
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Always enable XLA spmd
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Delete obsolete block
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Delete obsolete block
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Rename `TfLiteRegistrationExternal` as `TfLiteOperator`.
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Rename `TfLiteRegistrationExternal` as `TfLiteOperator`.
Likewise rename corresponding API functions (all experimental).
Rename (experimental) `TfLiteInterpreterOptionsAddRegistrationExternal` as `TfLiteInterpreterOptionsAddOperator`.
Rename (private) header registration_external.h as operator.h and
rename registration_external.cc as operator.cc.
Also add temporary backwards compatibility aliases that forward the old
names to the new names. These backwards compatibility aliases are needed
even though these names are experimental, because the old names are currently
referenced in MediaPipe, which is in a separate repository, and thus can't be
updated atomically.
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PR_kwDOArmXAs5qX0EC
| 64,172
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Re-enable HoistLayoutConversion pattern and mixed-precision MMA for Ampere.
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Re-enable HoistLayoutConversion pattern and mixed-precision MMA for Ampere.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10965 from trevor-m:tmorris-perstreamcomms-false fed944b06a9fb830a79b874f68732bdd41a9cd75
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PR_kwDOArmXAs5qXxFE
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Simplify indexing maps with mod constraints for symbols/dims
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Simplify indexing maps with mod constraints for symbols/dims
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Enabling XLA in tensorflow 2.16 causes memory leaks
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[
"@NBCBM \r\nPlease do not post low-quality LLM-generated text.",
"Hi **@Di-Is** ,\r\nI tried to run your code on Colab using TF v2.16 and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/ecfc2fbc57f632a152665274bab565d9/64170_2-16-1-v.ipynb) here for reference.\r\n\r\nThank you!",
"@Venkat6871\r\nThank you for your reply!\r\nI am using tensorflow compiled for Nvidia GPU(tensorflow[and-cuda] 2.16.1).\r\nIt seems there is also a difference in the Python version you confirmed.",
"I observed memory leaks in [Google Colab with T4 GPU](https://colab.research.google.com/gist/Di-Is/bcc17f37f8c88639317da9b503d25beb/tf216_memory_leak.ipynb).",
"@Venkat6871, do you have any progress on this issue.\r\n\r\nI'm also affected because of this issue, I tried will all tf versions from 2.11 up to 2.16 and it seems like this happens since tf 2.12. It seems this was not happening for tf 2.11\r\n\r\nthank you",
"I face the exact problem. Using Ubuntu 20.04, NVIDIA RTX3060 & python=3.11\r\n\r\n@Venkat6871, do you have any progress on this issue?",
"This document from NVIDIA on tweaking environment variables for XLA memory could help. We're yet to test this out though and will try to keep this thread updated on any findings: \r\nhttps://docs.nvidia.com/deeplearning/frameworks/tensorflow-user-guide/index.html#xla-best-practices\r\n\r\nBtw, the formation of clusters / Operation fusing / compilation using XLA increases the memory for each different shape of input that the framework comes across. If we keep the shape constant, we've found that after X amount of time, the memory growth stabilizes even with TF 2.15. ",
"I'm also encountering the exact same problem using Ubuntu 20.04 NVIDIA GTX1070 and python 3.10. Would be great to get this fixed since the memory leakage can get pretty astronomical if left unchecked. I've had it grow up to 12gB until it crashed my kernel.",
"Hi @Di-Is ,\r\n\r\nI have replicated the reported memory leak with XLA. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/a63cfe84f71920b5364878c338f22990/64170_tf216_memory_leak.ipynb) for reference. "
] | 2024-03-21T14:13:08
| 2024-04-18T09:14:07
| null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tensorflow 2.16.1
(tensorflow[and-cuda] 2.16.1)
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.3/8.9.7
### GPU model and memory
RTX3060Ti、RTX3060
### Current behavior?
Executing the tf.keras.Model.fit method with XLA enabled will cause a memory leak.
Note that XLA seems to be enabled by default since Tensorflow 2.16.1.
Setting tf.keras.Model.jit_compile to False disables XLA and eliminates the memory leak.
I think that updating to tensorflow 2.16 will cause memory leaks in almost all existing programs.
I think you need to fix this problem as soon as possible or alert people about it in documents such as Release Note or README.
### Standalone code to reproduce the issue
```python
import numpy as np
import tensorflow as tf
import gc
def call(epochs: int):
x = np.arange(-1, 1, 0.01)
y = 0.8 * x + 0.2
model = tf.keras.Sequential([tf.keras.layers.Dense(1, activation=None)])
model.compile("sgd", "mse")
# model.jit_compile = False
model.build(input_shape=(0,1))
model.fit(x, y, epochs=epochs)
del model
epoch_num = 10000
for i in range(epoch_num):
call(1)
tf.keras.backend.clear_session()
gc.collect()
```
### Relevant log output
_No response_
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Fix for tuples inside loops in LayoutAssignment
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Fix for tuples inside loops in LayoutAssignment
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[xla:gpu] Support DUS in DynamicAddressComputationFusion
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[xla:gpu] Support DUS in DynamicAddressComputationFusion
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#shlo_ref Add `multiply` op and binary element-wise test utils.
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#shlo_ref Add `multiply` op and binary element-wise test utils.
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#shlo_ref Small fixes to `test_util.h`
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#shlo_ref Small fixes to `test_util.h`
- Add comments describing the test helpers.
- Rename Distribution to UniformDistribution.
- Make RandomBuffer's distribution configurable.
- Improve IotaBuffer's usability.
- Fix IotaBuffer's behaviour.
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Experimental: Allow users to enable all features of default delegates in python
XNNPack is the only default delegate and this will allow users to benefit from all flag protected features. Other delegates can use this in the future.
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Changes visibility for metrics_proto to public.
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Changes visibility for metrics_proto to public.
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[xla:ffi] Improved fail messages for custom call tests
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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/64161/checks?check_run_id=22932486181) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
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[xla:ffi] Improved fail messages for custom call tests
In case a custom call is not found, the tests now fail gracefully instead of crashing.
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The value of 'num_results_' in XlaCompiledCpuFunction::StaticData was previously unset, so XlaCompiledCpuFunction::num_results() would always return 0.
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The value of 'num_results_' in XlaCompiledCpuFunction::StaticData was previously unset, so XlaCompiledCpuFunction::num_results() would always return 0.
This is an issue with code that relies on the XlaCompiledCpuFunction interface rather than the ProgramShape proto.
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Add a function to determine whether a given op is inside a `Region`.
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Add a function to determine whether a given op is inside a `Region`.
This will be used for filtering target lift patterns in `TableGen` file.
Additionally, this CL does the following:
* Use the `Operation*` than reference-by-value for consistency with MLIR norm.
* Adds additional tests for `IsStablehlOp`.
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Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/3192a484ad8453b5f6f1227b35b91235045569c4.
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[IndexAnalysis] Use mlir::AffineMap instead of IndexingMap for RTVars.
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[IndexAnalysis] Use mlir::AffineMap instead of IndexingMap for RTVars.
If we want to compose the indexing map within RTVar with the indexing map of `hlo` that it is associated with, we can just construct an indexing map using the domain of the IndexingMap to which the RTVar belongs to.
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Implement `Permute`.
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[] | 2024-03-21T12:06:14
| 2024-03-22T00:14:08
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Implement `Permute`.
This change is turning the function originally defined in `fold_constant_transpose.cc` into a library API for more general usages.
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Update lifetime of a PR diagram image
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Fixes #64153
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[XLA] [NFC] Don't use deprecated tsl Status helpers
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[XLA] [NFC] Don't use deprecated tsl Status helpers
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10724 from Tixxx:tixxx/shape_crash 89981b6576a22134831ddfc528fad900129d5058
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I_kwDOArmXAs6DII6Z
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Difficulty viewing "Lifetime of a PR" diagram in CONTRIBUTING.md if you have a dark background
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| 2024-03-22T04:29:05
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CONTRIBUTOR
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In CONTRIBUTING.md, the diagram for lifetime of a PR becomes difficult to see when using github dark mode, this is because the text labels and lines within the diagram are black and lack a background color, making them blend in with the dark background
This is how it looks to me:

Suggested fix:
Add a white background color, for example:

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Unable to run model builder test for Object Detection API
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[
"import tensorflow as tf\r\nfrom tensorflow.keras.layers import BatchNormalization\r\nfrom tensorflow.keras.models import Model\r\nfrom tensorflow.keras.applications import EfficientNetB0\r\n\r\n# Example usage of BatchNormalization\r\ninputs = tf.keras.Input(shape=(32, 32, 3))\r\nx = EfficientNetB0(include_top=False, input_tensor=inputs)\r\nx = BatchNormalization()(x.output)\r\noutputs = tf.keras.layers.Dense(10, activation='softmax')(x)\r\nmodel = Model(inputs=inputs, outputs=outputs)\r\n\r\n# Rest of your code\r\n",
"> import tensorflow as tf\n> from tensorflow.keras.layers import BatchNormalization\n> from tensorflow.keras.models import Model\n> from tensorflow.keras.applications import EfficientNetB0\n> \n> # Example usage of BatchNormalization\n> inputs = tf.keras.Input(shape=(32, 32, 3))\n> x = EfficientNetB0(include_top=False, input_tensor=inputs)\n> x = BatchNormalization()(x.output)\n> outputs = tf.keras.layers.Dense(10, activation='softmax')(x)\n> model = Model(inputs=inputs, outputs=outputs)\n> \n> # Rest of your code\n> \n\nThanks for the response.\nCould you please check the link mentioned above to reproduce the issue?",
"Following this tutorial i also ran into the same problem. \r\nhttps://blog.roboflow.com/train-a-tensorflow2-object-detection-model/\r\n\r\nIt points to this Colab:\r\nhttps://colab.research.google.com/drive/1sLqFKVV94wm-lglFq_0kGo2ciM0kecWD\r\n\r\nAnd after a couple of steps (not Roboflow related part of the setup). It crashes on this step:\r\n\r\n\r\n",
"Hey @mark-nicepants, Thanks for your response. I'll surely try out your suggestion.\r\nHowever, I was able to get some success and will share the link shortly."
] | 2024-03-21T11:10:25
| 2024-04-30T05:42:44
| null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
v2.16.1-0-g5bc9d26649c 2.16.1
### Custom code
No
### OS platform and distribution
Mac OS Sonoma 14.4
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
While running the below script
python3 models/research/object_detection/builders/model_builder_tf2_test.py
I'm getting following error:
Traceback (most recent call last):
File "/Users/smitparmar/Desktop/ML/custom-object-detection/models/research/object_detection/builders/model_builder_tf2_test.py", line 24, in <module>
from object_detection.builders import model_builder
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/object_detection/builders/model_builder.py", line 26, in <module>
from object_detection.builders import hyperparams_builder
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/object_detection/builders/hyperparams_builder.py", line 27, in <module>
from object_detection.core import freezable_sync_batch_norm
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/object_detection/core/freezable_sync_batch_norm.py", line 20, in <module>
class FreezableSyncBatchNorm(tf.keras.layers.experimental.SyncBatchNormalization
AttributeError: module 'keras._tf_keras.keras.layers' has no attribute 'experimental'
### Standalone code to reproduce the issue
```shell
https://drive.google.com/file/d/18z2bj-xRlXT2J6QvplLiYv2uoLei_1bX/view?usp=sharing
```
### Relevant log output
```shell
This output is from google colab and above one is from my local machine Apple MacBook Pro with M1 Pro. (Tensowflow Metal is also installed)
2024-03-21 11:07:15.432006: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-21 11:07:16.927430: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Traceback (most recent call last):
File "/content/models/research/object_detection/builders/model_builder_tf2_test.py", line 24, in <module>
from object_detection.builders import model_builder
File "/usr/local/lib/python3.10/dist-packages/object_detection/builders/model_builder.py", line 26, in <module>
from object_detection.builders import hyperparams_builder
File "/usr/local/lib/python3.10/dist-packages/object_detection/builders/hyperparams_builder.py", line 27, in <module>
from object_detection.core import freezable_sync_batch_norm
File "/usr/local/lib/python3.10/dist-packages/object_detection/core/freezable_sync_batch_norm.py", line 20, in <module>
class FreezableSyncBatchNorm(tf.keras.layers.experimental.SyncBatchNormalization
AttributeError: module 'keras._tf_keras.keras.layers' has no attribute 'experimental'
```
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PR_kwDOArmXAs5qWOO6
| 64,151
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Integrate LLVM at llvm/llvm-project@407937036fa7
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Integrate LLVM at llvm/llvm-project@407937036fa7
Updates LLVM usage to match
[407937036fa7](https://github.com/llvm/llvm-project/commit/407937036fa7)
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PR_kwDOArmXAs5qWMBN
| 64,150
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Remove the `OpSet` alias in `mlir::quant` namespace.
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Remove the `OpSet` alias in `mlir::quant` namespace.
It is defined in the `tensorflow::quantization::` namespace (as part of `QuantizationOptions` proto), but has been unexpectedly exposed under `mlir::quant` due to a `using` directive in `attrs_and_constraints.h`. Remove this exposure and use the one in the `tensorflow::quantization::`.
This change also fixes some include warnings along the way.
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I_kwDOArmXAs6DHwUj
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Negative values returned from tf.math.sobol_sample
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[
"Looks to me like the sobol operation has a couple overflow checks for `skip` and `num_results + skip`, though they don't always get triggered (as in the example given in the issue description)\r\n\r\n```\r\nimport tensorflow as tf\r\n\r\n\r\nint32max = 2147483647\r\n\r\ntestcases = [(1, int32max), (1, int32max+1), (int32max, int32max), (1, int(1e5)), (1, int(1e10)), (1, int(1e15)), (1, int(1e20))]\r\n\r\nfor num_results, skip in testcases:\r\n try:\r\n a = float(tf.math.sobol_sample(1, num_results, skip))\r\n print(f\"computed {a} for num_results={num_results}, skip={skip}\")\r\n except Exception as err:\r\n print(f\"error computing for num_results={num_results}, skip={skip}: {err}\")\r\n```\r\n\r\nConsole output on my laptop:\r\n\r\n```\r\nerror computing for num_results=1, skip=2147483647: num_results+skip must be less than 2147483647 [Op:SobolSample]\r\nerror computing for num_results=1, skip=2147483648: skip must be non-negative [Op:SobolSample]\r\nerror computing for num_results=2147483647, skip=2147483647: num_results+skip must be less than 2147483647 [Op:SobolSample]\r\ncomputed 0.5610733032226562 for num_results=1, skip=100000\r\ncomputed -0.5015935897827148 for num_results=1, skip=10000000000\r\nerror computing for num_results=1, skip=1000000000000000: skip must be non-negative [Op:SobolSample]\r\nerror computing for num_results=1, skip=100000000000000000000: Can't convert Python sequence with out-of-range integer to Tensor.\r\n```",
"[This thread](https://github.com/tensorflow/tensorflow/issues/57514#issuecomment-1251135723) argues signed integer input overflow is not the responsibility of Tensorflow to check unless it causes system crash.\r\n\r\nIt may be worth nonetheless extending the unit tests for those overflow checks that are implemented in https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/sobol_op.cc#L137.\r\n\r\n",
"@nfergu Though I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/f0cd7f1ea8a0456d1a938b454fb5846f/64149.ipynb), could you please try reducing the skip value. Smaller skip values require less complex calculations and might avoid overflow.\r\nThank you!",
"@sushreebarsa Thanks! We already reduced the skip value, as you suggested. We’re happy with this workaround.",
"@nfergu Thank you for the response!\r\nCould you please move this issue to closed status if the workaround works for you?\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/64149\">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/64149\">No</a>\n"
] | 2024-03-21T10:56:25
| 2024-03-29T14:02:21
| 2024-03-29T14:02:18
|
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.15, 2.16-nightly
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Negative values are returned from `tf.math.sobol_sample` with large `skip` values. I would expect all values returned from `tf.math.sobol_sample` to be positive. Perhaps this is an overflow?
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
float(tf.math.sobol_sample(
1,
1,
skip=int(1e10),
))
```
### Relevant log output
```shell
-0.5015935897827148
```
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PR_kwDOArmXAs5qWJje
| 64,148
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Rename `DequantizeOpQuantizePerChannel` to `DequantizeOpQuantizePerAxis`.
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Rename `DequantizeOpQuantizePerChannel` to `DequantizeOpQuantizePerAxis`.
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PR_kwDOArmXAs5qWIrM
| 64,147
|
#shlo_ref Move helpers that are shared between unary and binary element-wise tests to test_util.
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#shlo_ref Move helpers that are shared between unary and binary element-wise tests to test_util.
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#shlo_ref Improve the `RandomBuffer` test helper.
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#shlo_ref Improve the `RandomBuffer` test helper.
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#shlo_ref Add a helper for binary elementwise ops.
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Fix silent overflow in tf.range
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"What if the value of `delta =1` ? It still same problem as earlier it seems.",
"Sorry, I don't have enough time to work on this at the moment.\r\nIf anyone wants to take over feel free to do that, otherwise I might come back to this later."
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Fixes #64081
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[XLA:GPU] Move helper to set target tile sizes with default offsets and strides.
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[XLA:GPU] Move helper to set target tile sizes with default offsets and strides.
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Make EmitReduce use indexing maps
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Make EmitReduce use indexing maps
All index calculation are now performed by the new indexing maps
which makes the implementation of `EmitReduce` quite a bit simpler.
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Migrate `UniformSupport*` and `FakeQuantSupport*` components from `lite` directory.
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* Uses namespaces rather than `using-directives`.
* Cleans up header imports.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10685 from ROCm:ci_rocm_profiler_code_refactor 9ada3c5629c208de530cf88beb934f401c8d99be
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[xla:gpu][NFC] Simplify HloPredicateIsOp
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[xla:gpu][NFC] Simplify HloPredicateIsOp
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Pass ModuleOp directly from pre-calibration to post-calibration
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Pass ModuleOp directly from pre-calibration to post-calibration
This will avoid saving calibrated model as well as loading it.
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Add version checks to FindCudaExecutable
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Add version checks to FindCudaExecutable
Currently we look for ptxas and nvlink in a few different places on the host machine, then we choose the first found binary without taking its version into account. If the chosen binary doesn't fulfill our version requirements we will later fail even if there was a suitable ptxas or nvlink in the search path in the first place.
This change makes it take the version of each binary into account when going through the search path. Unsuitable binaries will be discarded right away and the search continues until we are out of locations to check.
This should help with host environments that have multiple CUDA toolkits installed and should make ptxas and nvlink selection more robust.
The concreate changes:
1. `FindCudaExecutable` now also takes a minimum version and a list of forbidden (think buggy) versions that are supposed to be skipped.
2. `WarnIfBadPtxAsVersion` has been removed. It was checking for ptxas < 11.1 which is way older than our minimum supported version of 11.8 and was not doing anything given the check described in #3.
3. There was another version check for `ptxas` in `NVPTXCompiler::ChooseLinkingMethod` which was checking for `version(ptxas)` < 11.8. This has also been removed/replace by the version check described in #4.
4. Version checking for `ptxas` and `nvlink` has been consolidated into 2 methods `FindPtxAsExectuable` and `FindNvLinkExecutable`. These methods hard code the current minimum version (and the list of excluded versions) of each tool in one place. It's still not great but at least less spaghetti-like.
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Fix a bug where `PjRtStreamExecutorBuffer::CopyRawToHostFuture` causes a crash in GPUs
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Fix a bug where `PjRtStreamExecutorBuffer::CopyRawToHostFuture` causes a crash in GPUs
The current implementation of `PjRtStreamExecutorBuffer::CopyRawToHostFuture` calls `CopyRawToHost()` inside the `dst` future's OnReady callback. Since `PjRtFuture` invokes the callbacks inline in the context where the corresponding promise is set, this becomes a problem when the `dst` is fulfilled in the context of device buffer ready events, e.g., `stream_executor::gpu::GpuExecutor::InternalHostCallback()`.
This CL fixes the aforementioned issue by adding a trampoline thread before calling `CopyRawToHost()`, thus ensuring that `CopyRawToHost()` is called outside any callback context. The added test in `se_gpu_pjrt_client_test.cc` fails without this trampoline.
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Integrate Triton up to [92ac0aad](https://github.com/openai/triton/commits/92ac0aade352d7be626e8c608331ddebfe5f6fb2)
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Integrate Triton up to [92ac0aad](https://github.com/openai/triton/commits/92ac0aade352d7be626e8c608331ddebfe5f6fb2)
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Add new methods in backend async kernel interface. Reflect to the design of go/async-io-coherency
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Add new methods in backend async kernel interface. Reflect to the design of go/async-io-coherency
New methods will set / retrieve the attributes based on the buffers.
Reverts 42883cb09d1a8155824ce4ed044794c0dffdd19f
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PR_kwDOArmXAs5qUo_x
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Reland of GpuTimer: improve kernel execution time measurement accuracy
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Reland of GpuTimer: improve kernel execution time measurement accuracy
Some production workloads show drastically increased latency. The log shows many instance of this message:
```
Delay kernel timed out: measured time has sub-optimal accuracy.
There may be a missing warmup execution, please investigate in Nsight Systems.
```
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Introduce hermetic CUDA in Google ML projects.
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Introduce hermetic CUDA in Google ML projects.
Instead of having pre-installed NVIDIA CUDA and CUDNN libraries and setting environment variables pointing to the installation locations, Bazel should automatically download CUDA and CUDNN distributives in the cache and use them during build and test phases.
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PR #10653: [ROCM] fixing build brake and temporarily disable delayed kernel for rocm
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PR #10653: [ROCM] fixing build brake and temporarily disable delayed kernel for rocm
Imported from GitHub PR https://github.com/openxla/xla/pull/10653
This PR fixes the ongoing build brake after https://github.com/openxla/xla/commit/28cfdcd27859e79cf10bd92779bc29f6404c79a0
and also I temporarily disable delayed kernel for ROCM since it significantly increases running times (to be investigated).
@xla-rotation: would you please have a look ?
Copybara import of the project:
--
015e67e4abe8e5a58ac017c3d2c6693d547626b7 by Pavel Emeliyanenko <pavel.emeliyanenko@amd.com>:
fixing build brake and temporarily disable delayed kernel for rocm
Merging this change closes #10653
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10653 from ROCm:ci_build_brake_fix_240318 015e67e4abe8e5a58ac017c3d2c6693d547626b7
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Add a pass to remove unused restore op
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Add a pass to remove unused restore op
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Also verify whether instructions are allowed to change layouts.
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Also verify whether instructions are allowed to change layouts.
If a test provides a instruction_can_change_layout_func, we should also use it
for the HloVerifier created inside the VerifiedHloModule.
Doing this exposed a bug in algebraic_simplifier where a simplification didn't
check whether it is running in layout sensitive mode (since it doesn't handle
layouts in any way, it should not be used in layout sensitive mode).
Also adjust HloVerifier to be not as strict regarding tiling and memory space
to make a test pass.
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Add a capacity check when scheduling a low priority task
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Add a capacity check when scheduling a low priority task
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Integrate CHLO->StableHLO lowerings in XLA
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LossScaleOptimizer#get_scaled_loss() does not exist
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[
"Hi **@keisen** ,\r\n\r\nI tried to run your code on colab using TF v2.15, 2.16 and nightly versions, and faced the same issue. Please find the [gist ](https://colab.sandbox.google.com/gist/Venkat6871/0b7dc2e7d692a41f7859030e0b0cd169/64124_2-15-2-16-nightly-v.ipynb)here for reference.\r\n\r\nThank you!",
"Hi @Venkat6871 \r\n\r\nThank you for reproducing this issue. I believe it's the same as I faced.\r\nMy OSS library has been using this API, so I want your team to fix it asap...\r\nWhen do you expect it to be fixed?\r\n\r\nThanks!",
"Hi @Venkat6871 \r\nIs there any progress?",
"@Venkat6871 \r\n\r\nThe same issue:\r\n scaled_loss = self.optimizer.get_scaled_loss(loss)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nAttributeError: 'LossScaleOptimizer' object has no attribute 'get_scaled_loss'\r\n\r\nBut it's here defined: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/mixed_precision/loss_scale_optimizer.py#L524\r\n",
"@keisen @Venkat6871 \r\nPlease look at this solution:\r\nhttps://github.com/keras-team/keras/issues/19479",
"@markub3327 \r\n\r\nThank you so much for letting us know that!\r\nI'll close this issue 👍 \r\n\r\nThanks!",
"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/64124\">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/64124\">No</a>\n"
] | 2024-03-21T06:20:46
| 2024-04-15T09:52:21
| 2024-04-15T09:52:17
|
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf2.16+
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04 on Windows10 WSL
### Mobile device
_No response_
### Python version
3.10.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
As this title says, `LossScaleOptimizer#get_scaled_loss()` method does NOT exist.
Until tf2.15, the method definitely existed.
Where is it now? or How do we access it?
[https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision/LossScaleOptimizer](https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision/LossScaleOptimizer?_gl=1*1kcgzin*_up*MQ..*_ga*MTU5OTYzMDg2MS4xNzExMDAwMDg0*_ga_W0YLR4190T*MTcxMTAwMDA4My4xLjAuMTcxMTAwMDA4My4wLjAuMA..#get_scaled_loss)
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
optimizer = tf.optimizers.Adam()
wrapped_optimizer = tf.keras.mixed_precision.LossScaleOptimizer(optimizer)
print(f'get_scaled_loss() exists: {"get_scaled_loss" in dir(wrapped_optimizer)}')
```
### Relevant log output
```shell
get_scaled_loss() exists: False
```
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Placeholder. Will remove after the base cl is submitted.
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Add CompositeOp to CustomOp conversion
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Add CompositeOp to CustomOp conversion
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Add two arguments in OpExpanderPass: `preserve_sharding` and `relay_control_dependency`.
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Add two arguments in OpExpanderPass: `preserve_sharding` and `relay_control_dependency`.
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PR_kwDOArmXAs5qTuPr
| 64,120
|
[NFC] Spelling fix.
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[NFC] Spelling fix.
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PR_kwDOArmXAs5qTsGx
| 64,119
|
Avoid loading then saving reprensentative dataset again
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Avoid loading then saving reprensentative dataset again
If the representative dataset is in QuantizationOptions, currently, we first load it then save it again. This is not efficient, just use it directly.
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PR_kwDOArmXAs5qTsDL
| 64,118
|
Enable sparsecore layout assignment
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Enable sparsecore layout assignment
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I_kwDOArmXAs6DFAPC
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Issue with file reading when using TPU enabled colab machine
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[
"@arnava13,\r\nCould you please try loading the file from local file when using TPU - read them as a normal python file.read() not with tf.io.\r\n\r\nAlso please have a look at this issue which is similar https://stackoverflow.com/questions/62870656/file-system-scheme-local-not-implemented-in-google-colab-tpu \r\n\r\nhttps://github.com/tensorflow/models/issues/8265#issuecomment-610535054\r\n\r\nThank you!",
"Thank you. Reverting to numpy I/O.",
"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/64117\">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/64117\">No</a>\n"
] | 2024-03-21T05:03:44
| 2024-03-22T23:36:33
| 2024-03-22T23:36:30
|
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
Google Colab
### Mobile device
_No response_
### Python version
Python 3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I'm encountering an UnimplementedError related to the file system scheme '[local]' not being implemented when attempting to read files from the local filesystem in a Google Colab environment while using TPUs. The error occurs when trying to execute tf.io.read_file(file_path) within a data generator class, even when explicitly setting tf.device('/cpu:0') around the file reading operation.
This issue arises despite attempts to ensure file operations are directed to execute on the CPU within a TPU environment. The expected behavior is for TensorFlow to successfully read the local file when specified to do so on the CPU, even in a TPU context, without encountering a file system scheme error.
Full repository: https://github.com/arnava13/SeniorHonoursProject
(Training run in training_colab.ipynb on a colab machine)
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
class DataGenerator(tf.compat.v2.keras.utils.Sequence):
def __init__(self, data_root, norm_data_name):
self.data_root = data_root
self.norm_data_path = tf.io.gfile.join(self.data_root, norm_data_name)
# Reading and processing the file
self.all_ks = self.read_file(self.norm_data_path, column_indices=column_indices, dtype=dtype)
self.all_ks = tf.cast(self.all_ks, dtype=tf.float32)
self.original_k_len = tf.cast(tf.size(self.all_ks).numpy(), tf.int32)
@tf.function
def read_file(self, file_path, *, column_indices=None, dtype=tf.float32):
with tf.device('/cpu:0'):
file_content = tf.io.read_file(file_path)
# Process file content
file_content = tf.strings.regex_replace(file_content, "\r\n", "\n")
file_content = tf.strings.regex_replace(file_content, "\r", "\n")
lines = tf.strings.split([file_content], '\n').values
lines = tf.cond(tf.equal(lines[-1], ""), lambda: lines[:-1], lambda: lines)
def extract_columns(line):
# Normalize spaces
line = tf.strings.regex_replace(line, "\s+", " ")
line = tf.strings.strip(line) # Strip whitespace
columns = tf.strings.split([line], ' ').values
if column_indices is not None:
selected_columns = tf.gather(columns, column_indices)
else:
selected_columns = columns
return tf.strings.to_number(selected_columns, out_type=dtype)
columns_values = tf.map_fn(extract_columns, lines, fn_output_signature=dtype)
if isinstance(columns_values, tf.RaggedTensor):
columns_values = columns_values.to_tensor()
return columns_values
tpu_resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu_resolver)
tf.tpu.experimental.initialize_tpu_system(tpu_resolver)
strategy = tf.distribute.TPUStrategy(tpu_resolver)
FLAGS.TPU = True
('/data)
if FLAGS.TPU:
with tf.device('/cpu:0'):
training_generator = DataGenerator('/data', 'planck.txt')
```
### Relevant log output
```shell
err: File "/content/SeniorHonoursProject/BaCoN-II/data_generator.py", line 824, in create_generators
err: training_generator = DataGenerator(partition['train'], labels, labels_dict, data_root = FLAGS.DIR, save_indexes=False, seed = seed, strategy=strategy, **params)
err: File "/content/SeniorHonoursProject/BaCoN-II/data_generator.py", line 170, in __init__
err: self.original_k_len = tf.cast(tf.size(self.all_ks).numpy(), tf.int32)
err: File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
err: raise e.with_traceback(filtered_tb) from None
err: File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/array_ops.py", line 861, in size_internal
err: num_elements = np.prod(input._shape_tuple(), dtype=np_out_type) # pylint: disable=protected-access
err: tensorflow.python.framework.errors_impl.UnimplementedError: {{function_node __inference_read_file_213}} File system scheme '[local]' not implemented (file: 'data/ds_ee2_train_1k_equal_examples_rands/planck_ee2.txt')
err: [[{{node ReadFile}}]]
err: Exception ignored in atexit callback: <function async_wait at 0x7de5be890670>
err: Traceback (most recent call last):
err: File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/context.py", line 2833, in async_wait
err: context().sync_executors()
err: File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/context.py", line 749, in sync_executors
err: pywrap_tfe.TFE_ContextSyncExecutors(self._context_handle)
err: tensorflow.python.framework.errors_impl.UnimplementedError: {{function_node __inference_read_file_213}} File system scheme '[local]' not implemented (file: 'data/ds_ee2_train_1k_equal_examples_rands/planck_ee2.txt')
err: [[{{node ReadFile}}]]
```
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PR_kwDOArmXAs5qTiTh
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Migrate direct callers of options().packed() to is_packed() helper.
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Migrate direct callers of options().packed() to is_packed() helper.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/62817 from Intel-tensorflow:gaurides/amp_fp16 9780d75ad68275bf30b81c7cf02688fb87e5f1e2
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[PJRT][IFRT] Update PJRT, IFRT, and Py executable getters to return PjRtLayouts
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[PJRT][IFRT] Update PJRT, IFRT, and Py executable getters to return PjRtLayouts
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Remove legacy post-training quantization functions
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Remove legacy post-training quantization functions
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Apply the sharding on RngBitGeneratorOp itself, instead of its tuple elements.
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Apply the sharding on RngBitGeneratorOp itself, instead of its tuple elements.
When exporting RngBitGeneratorOp, whose output shape is tuple of (output_state, output), we apply the sharding on the op itself. We also create GetTupleElement, which should not share the same sharding with RngBitGeneratorOp.
Add a test case that fails before this cl and succeeds with this cl.
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[xla][gpu] Preserve a frontend attribute for runtime optimization when
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[xla][gpu] Preserve a frontend attribute for runtime optimization when
decomposing a CollectivePermute with a cycle.
The frontend attribute annotates a range of runtime execution instances where
each source-target pair is communicating data that affects the outcome of the
application. As such, when we split the source-target pairs for the decomposed
CollectivePermute, we also need to split the corresponding bounds represented
by the frontend attribute.
Modify two existing tests.
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Supports PjRt CPU array converting py array when the CPU PjRt arrays have non-default layouts.
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Supports PjRt CPU array converting py array when the CPU PjRt arrays have non-default layouts.
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Add int4 support to most remaining ops on CPU/GPU.
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Add int4 support to most remaining ops on CPU/GPU.
Now almost all ops support int4, while before, only convert and a few others did. A few ops still do not work properly with int4, and I will add support for these ops soon. The unsupported ops are: Scatter, Clz, Popcnt, ShiftRightLogical, Convolution.
FloatNormalization is used to convert most int4 ops to an int8 version. The name "FloatNormalization" is now somewhat misleading as it applies to int4 as well as float-types, so I will change this in a future CL.
All int4 arrays must have the element_size_in_bits layout field set to 4. Many passes previously created new instructions but did not set element_size_in_bits, which causes problems whens expanding int4 support to all ops. For each pass, this was solved in one of two ways:
1. Some passes which have this problem call the SubByteNormalization after the pass runs, which properly sets element_size_in_bits on all instructions.
2. Other passes call UpdateLayout(Shape*), a base method of HloModulePass which previously did nothing. Now it sets element_size_in_bits properly on the shape since all backends except the interpreter pack int4 arrays. UpdateLayout is already called by several passes on newly-added instructions. This change adds more calls to UpdateLayout.
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PR_kwDOArmXAs5qTNaj
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Replace hard-failing CHECKs in triton emitter with TF_RET_CHECK, TF_RETURN_IF_ERROR, and the like.
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Replace hard-failing CHECKs in triton emitter with TF_RET_CHECK, TF_RETURN_IF_ERROR, and the like.
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Add unbounded dynamism test for for Atan2Op.
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Add missing includes and fix typo.
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Add missing includes and fix typo.
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Add unbounded dynamism test for BitcastConvertOp.
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Change flat_hash_map to vector to have deterministic order while unrolling.
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#tf-data Do not skip in the global shuffling mode.
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#tf-data Do not skip in the global shuffling mode.
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Update `Shape::Equals()` to have proper default comparison for unbounded shapes.
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Update `Shape::Equals()` to have proper default comparison for unbounded shapes.
Previously, the default `Shape::Equals()` used `ShapeUtil::SameDimensions(lhs, rhs)` to check whether the two shapes are equal. However, the implementation of `ShapeUtil::SameDimensions()` was updated such that when one the dimension of one shape is unbounded, it compares equal for each dimension.
The default behavior should be that unbounded size compared to any size other than unbounded size should compare not equal.
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Changes visibility for metrics_proto to public.
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TEST 1
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PR_kwDOArmXAs5qSr6F
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[XLA:GPU] Add a Gemv rewriter pass to convert gemv to gemm with a trivial dimension
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[XLA:GPU] Add a Gemv rewriter pass to convert gemv to gemm with a trivial dimension
We need this pass since GemmFusion only accepts gemms. We should run this pass before GemmFusion. After GemmFusion, we should use AlgebraicSimplifier to remove trivial dimension from gemms that are not fused by GemmFusion.
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PR_kwDOArmXAs5qSmzq
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Create new PjRt GPU client with remote devices after coordination service agent is available
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Create new PjRt GPU client with remote devices after coordination service agent is available
This does not modify the previous PjRt GPU client. The test environment case where multiple threads are used to simulate multiple workers is supported.
For the PjRt GPU MultiWorkerMirroredStrategy (MWMS) case where there are multiple workers each with a GPU or GPUs, the symptom of a client without this fix is a error message like the following where the number at the end is the ID of the first remote device, e.g. 8 when there are eight local GPUs with IDs 0..7. (Another example is 1 when there is one local GPU with ID 0.)
`INVALID_ARGUMENT: No matching device found for device_id 8`
Note that while the primary purpose of `BaseGPUDeviceFactory::CreateDevices` is to do one-time initialization, it is often called multiple times. In the typical production MWMS case, it is called both when creating a TF Context and when creating GRPC servers when enabling collectives. In the unit test added by this CL (which uses two GPUs), it is first called when a TF Context is created when starting up the test environment (with both GPUS) and then two worker TF processes are created which both call it twice during MWMS startup (each with the one GPU assigned to the process). Other test cases have different patterns.
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PR_kwDOArmXAs5qSmHU
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Ignore nested tuples, if present, during post processing.
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Ignore nested tuples, if present, during post processing.
This is a hack for some cases (specifically observed for the llama and gemma models) where nested tuples as used as inputs/outputs of the kOptimizationBarrier instruction.
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PR_kwDOArmXAs5qSfeE
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[PJRT C API] Plumb plugin attributes from plugin to JAX python.
|
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"login": "copybara-service[bot]",
"id": 56741989,
"node_id": "MDM6Qm90NTY3NDE5ODk=",
"avatar_url": "https://avatars.githubusercontent.com/in/44061?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/copybara-service%5Bbot%5D",
"html_url": "https://github.com/apps/copybara-service",
"followers_url": "https://api.github.com/users/copybara-service%5Bbot%5D/followers",
"following_url": "https://api.github.com/users/copybara-service%5Bbot%5D/following{/other_user}",
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"starred_url": "https://api.github.com/users/copybara-service%5Bbot%5D/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/copybara-service%5Bbot%5D/subscriptions",
"organizations_url": "https://api.github.com/users/copybara-service%5Bbot%5D/orgs",
"repos_url": "https://api.github.com/users/copybara-service%5Bbot%5D/repos",
"events_url": "https://api.github.com/users/copybara-service%5Bbot%5D/events{/privacy}",
"received_events_url": "https://api.github.com/users/copybara-service%5Bbot%5D/received_events",
"type": "Bot",
"site_admin": false
}
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[] |
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[] | 2024-03-20T23:31:44
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NONE
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PR_kwDOArmXAs5qScb6
| 64,093
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Migrate `UniformSupport*` and `FakeQuantSupport*` components from `lite` directory.
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"id": 56741989,
"node_id": "MDM6Qm90NTY3NDE5ODk=",
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"following_url": "https://api.github.com/users/copybara-service%5Bbot%5D/following{/other_user}",
"gists_url": "https://api.github.com/users/copybara-service%5Bbot%5D/gists{/gist_id}",
"starred_url": "https://api.github.com/users/copybara-service%5Bbot%5D/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/copybara-service%5Bbot%5D/subscriptions",
"organizations_url": "https://api.github.com/users/copybara-service%5Bbot%5D/orgs",
"repos_url": "https://api.github.com/users/copybara-service%5Bbot%5D/repos",
"events_url": "https://api.github.com/users/copybara-service%5Bbot%5D/events{/privacy}",
"received_events_url": "https://api.github.com/users/copybara-service%5Bbot%5D/received_events",
"type": "Bot",
"site_admin": false
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[] | 2024-03-20T23:18:13
| 2024-04-10T19:14:09
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NONE
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Migrate `UniformSupport*` and `FakeQuantSupport*` components from `lite` directory.
* Uses namespaces rather than `using-directives`.
* Cleans up header imports.
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