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PR #11354: Passthrough sharding propagation for host offloading ops
Imported from GitHub PR https://github.com/openxla/xla/pull/11354
To support host memory offloading we propagate sharding on the offloading operations (MoveToDevice and MoveToHost custom calls) so that spmd partitioner does not insert collectives at wrong places.
Copybara import of the project:
--
6a4d73c56e618e921fa32e5e03756e40986a8ad3 by Jaroslav Sevcik <[email protected]>:
Passthrough sharding propagation for host offloading ops
Merging this change closes #11354
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11354 from jaro-sevcik:host-offloading-sharding-propagation 6a4d73c56e618e921fa32e5e03756e40986a8ad3
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Refactoring: Separate the model exporting of calibration and debugging models
Currently, we create the whole-model debugging model by modifying the calibration model. This is no longer suitable as it will be affected by coming changes to prepare the calibration model.
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Denial of Service in tf.raw_ops.Unstage
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"Hi @Zoeeeeey ,\r\n\r\n I have tested the code and observed that the execution hangs at Unstage Op though the inputs seems valid. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/5bc1d69640bd2352ab8dc22509a60999/66174.ipynb) for reference. Needs to look into the issue.\r\n\r\nThanks!"
] | 2024-04-22T02:27:21 | 2024-04-25T07:28:51 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.0
### GPU model and memory
_No response_
### Current behavior?
Hi,
When using `tf.raw_ops.Unstage`, a `Denial of Service` was encountered under normal invocation, with all passed parameters meeting the requirements of the API documentation.
After a long wait, there was still no response.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
tensor = tf.constant([1.0, 2.0, 3.0])
stage_op = tf.raw_ops.Stage(values=[tensor])
print(tensor.dtype) # <dtype: 'float32'>
unstage_op = tf.raw_ops.Unstage(dtypes=[tensor.dtype]) # Hang
with tf.compat.v1.Session() as sess:
print("here")
sess.run(stage_op)
result = sess.run(unstage_op)
print(result) # Expected output: [1. 2. 3.]
```
### Relevant log output
```shell
2024-04-22 02:23:12.166249: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-04-22 02:23:12.166825: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2024-04-22 02:23:12.169923: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2024-04-22 02:23:12.215234: 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 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-22 02:23:13.066132: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-04-22 02:23:13.789282: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
<dtype: 'float32'>
```
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(Roll forward) Refer to `Method` for weight-only quantization
Reverts c712957000de89fa7a1f5fee890946b8cd8c1220
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[tsl:concurrency] Add special handling of Map functors returning StatusOr<U> results
AsyncValueRef<absl::StatusOr<T>> is discouraged and should be replaced with AsyncValueRef<T> because async value is already an container with a absl::Status or a value and Status of Status makes APIs ambiguous.
Add specializations to AsyncValueRef::Map to handle functors returning StatusOr objects.
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[pjrt] Switch TfrtTpuBuffer to PjRtFuture<>::Promise to track definition event
In preparation for disabling absl::Status payloads for AsyncValue convert PjRtClient to PjRtFuture
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bazel rebuild the file not work
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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/66164\">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/66164\">No</a>\n"
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NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
Linux ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I rewrite the /tensorflow_src/tensorflow/python/keras/saving/saved_model/load.py (add the print in def load )
for example:
```
def load(path, compile=True, options=None): # pylint: disable=redefined-builtin
print("load path: ", path)
```
, and go to tensorflow_src/ for running
```
bazel build ---config=cuda --jobs=80 --local_ram_resources=40900 //tensorflow/tools/pip_package:build_pip_package
./bazel-bin/tensorflow/tools/pip_package/build_pip_package /mnt
chown $HOST_PERMS /mnt/tensorflow-2.12.0-cp38-cp38-linux_x86_64.whl
pip uninstall tensorflow -y
pip install /mnt/tensorflow-2.12.0-cp38-cp38-linux_x86_64.whl
```
after rebuild the tensorflow
i run
```
import tensorflow as tf
from tensorflow import keras
mnist = tf.keras.datasets.mnist
def create_model():
model = tf.keras.Sequential([
keras.layers.Dense(512, activation='relu', input_shape=(784,)),
keras.layers.Dropout(0.2),
keras.layers.Dense(10)
])
return model
model = create_model()
print("=============save model================")
model.save('my_model.keras')
print("=============load model================")
new_model = tf.keras.models.load_model('my_model.keras')
```
however I can not see the output "load path: xxxx";
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
from tensorflow import keras
mnist = tf.keras.datasets.mnist
def create_model():
model = tf.keras.Sequential([
keras.layers.Dense(512, activation='relu', input_shape=(784,)),
keras.layers.Dropout(0.2),
keras.layers.Dense(10)
])
return model
model = create_model()
print("=============save model================")
model.save('my_model.keras')
print("=============load model================")
new_model = tf.keras.models.load_model('my_model.keras')
```
### Relevant log output
```shell
2024-04-21 13:51:05.689032: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-21 13:51:05.810219: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-04-21 13:51:05.853864: E tensorflow/tsl/lib/monitoring/collection_registry.cc:81] Cannot register 2 metrics with the same name: /tensorflow/core/bfc_allocator_delay
2024-04-21 13:51:07.227144: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2027] TensorFlow was not built with CUDA kernel binaries compatible with compute capability 8.6. CUDA kernels will be jit-compiled from PTX, which could take 30 minutes or longer.
2024-04-21 13:51:07.227457: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-21 13:51:07.230853: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2027] TensorFlow was not built with CUDA kernel binaries compatible with compute capability 8.6. CUDA kernels will be jit-compiled from PTX, which could take 30 minutes or longer.
2024-04-21 13:51:08.993783: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1614] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22278 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:ca:00.0, compute capability: 8.6
=============save model================
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
=============load model================
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.
root@d3e0a845cc18:/test_tensorflow# /usr/bin/python3 /test_tensorflow/test.py
2024-04-21 14:46:48.462440: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-21 14:46:48.581747: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-04-21 14:46:48.624315: E tensorflow/tsl/lib/monitoring/collection_registry.cc:81] Cannot register 2 metrics with the same name: /tensorflow/core/bfc_allocator_delay
2024-04-21 14:46:50.015680: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2027] TensorFlow was not built with CUDA kernel binaries compatible with compute capability 8.6. CUDA kernels will be jit-compiled from PTX, which could take 30 minutes or longer.
2024-04-21 14:46:50.015998: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-21 14:46:50.019436: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2027] TensorFlow was not built with CUDA kernel binaries compatible with compute capability 8.6. CUDA kernels will be jit-compiled from PTX, which could take 30 minutes or longer.
2024-04-21 14:46:51.808631: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1614] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22278 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:ca:00.0, compute capability: 8.6
=============save model================
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
=============load model================
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.
root@d3e0a845cc18:/test_tensorflow# /usr/bin/python3 /test_tensorflow/test.py
2024-04-21 14:50:18.017231: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-21 14:50:18.138431: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-04-21 14:50:18.181418: E tensorflow/tsl/lib/monitoring/collection_registry.cc:81] Cannot register 2 metrics with the same name: /tensorflow/core/bfc_allocator_delay
2024-04-21 14:50:19.563644: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2027] TensorFlow was not built with CUDA kernel binaries compatible with compute capability 8.6. CUDA kernels will be jit-compiled from PTX, which could take 30 minutes or longer.
2024-04-21 14:50:19.563954: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-21 14:50:19.567440: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2027] TensorFlow was not built with CUDA kernel binaries compatible with compute capability 8.6. CUDA kernels will be jit-compiled from PTX, which could take 30 minutes or longer.
2024-04-21 14:50:21.334862: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1614] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22278 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:ca:00.0, compute capability: 8.6
=============save model================
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
=============load model================
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.
```
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[tsl:concurrency] NFC: Add a benchmark for AsyncValuePtr<T>::Map
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[tsl:concurrency] NFC: Add a benchmark for AsyncValuePtr<T>::Map
-----------------------------------------------------------
Benchmark Time CPU Iterations
-----------------------------------------------------------
BM_MapIntToFloat 38.4 ns 38.4 ns 18157872
All of the overheads are in AsyncValueRef<float> reference counting.
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[tsl:concurrency] Add AsyncValueRef<T>::Map() function to transform async values
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[tsl:concurrency] Add AsyncValueRef<T>::Map() function to transform async values
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[tsl:concurrency] Add AsyncValue::Executor to be able to customize where the waiter will execute
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[tsl:concurrency] Add AsyncValue::Executor to be able to customize where the waiter will execute
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Create basic server coverage and model tests.
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NONE
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Create basic server coverage and model tests.
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PR_kwDOArmXAs5tQIne
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[tsl:concurrency] NFC: Clean up AndThen type signature detection
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NONE
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[tsl:concurrency] NFC: Clean up AndThen type signature detection
Rename WaiterT to Waiter to be consistent with google style guide
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Reverts ed7d077f12525083d9e0d429e57de0e9206e1935
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Reverts ed7d077f12525083d9e0d429e57de0e9206e1935
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Automated Code Change
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- Pull mesh from NamedSharding when rewriting manual axes.
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Eliminate call to StreamExecutor::implementation() now that StreamExecutor inherits from StreamExecutorInterface.
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PR_kwDOArmXAs5tPStJ
| 66,123 |
Tool to convert TF SavedModel to StableHLO
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Tool to convert TF SavedModel to StableHLO
Here is the signature of the provide API:
```c++
// Converts a TensorFlow model (either from a SavedModel or an MLIR module) to a
// StableHLO MLIR module.
//
// Args:
// input_path: The path to the input TensorFlow SavedModel or MLIR module.
// context: The MLIR context to use for parsing or creating the MLIR module.
// exported_model_signatures: A comma-separated list of exported model
// signatures (functions) to convert.
// tag_names: A comma-separated list of tag names used for loading SavedModel.
// input_arg_shapes_str: A string representation of input argument shapes.
// Shapes for different tensors are separated by ':', and dimension sizes for
// the same tensor are separated by ','. For example,
// 'input-arg-shapes=1,2::1,?' expresses input arguments with shapes [1,2],
// [] and [1,?].
// is_input_mlir_module: If true, `input_path` is treated as an MLIR
// module instead of a SavedModel.
//
// Returns:
// An absl::StatusOr containing the converted StableHLO MLIR module on
// success, or an absl::Status with an error message on failure.
absl::StatusOr<OwningOpRef<ModuleOp>> TfToStablehlo(
absl::string_view input_path, MLIRContext* context,
absl::string_view exported_model_signatures, absl::string_view tag_names,
absl::string_view input_arg_shapes_str, bool is_input_mlir_module = false);
```
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PR_kwDOArmXAs5tPOi5
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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/cba970ca884bbfcce18a020a0afb32dd1e6581a0.
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PR_kwDOArmXAs5tPIaO
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Integrate LLVM at llvm/llvm-project@5bcf31ebfad8
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Integrate LLVM at llvm/llvm-project@5bcf31ebfad8
Updates LLVM usage to match
[5bcf31ebfad8](https://github.com/llvm/llvm-project/commit/5bcf31ebfad8)
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PR_kwDOArmXAs5tPC4d
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PR #11422: [NVIDIA GPU] Remove control knobs for each individual async collective and use the global xla_gpu_disable_async_collectives
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PR #11422: [NVIDIA GPU] Remove control knobs for each individual async collective and use the global xla_gpu_disable_async_collectives
Imported from GitHub PR https://github.com/openxla/xla/pull/11422
Currently we have 1 global flag(xla_gpu_enable_async_collectives) to control whether we want to asynchronize collectives or not. This flag overrides all other control knobs for each collective. The usage of it is confusing, instead we introduce a new flag xla_gpu_disable_async_collectives which will consolidate all the async flags we have now. We remove xla_gpu_enable_async_collectives and all other individual control knobs.
Sample usage:
xla_gpu_disable_async_collectives=allreduce,reducescatter
disables async allreduce and reducescatter
By default it's empty which indicates enabling async for all collectives.
Copybara import of the project:
--
afff139cb742662801c49052b70a8f234bb280e4 by TJ Xu <[email protected]>:
Remove control knobs for each individual async collective and use the
global xla_gpu_enable_async_collectives
--
e1385c5b1f82aee1d3ea98e5d453c3e60fc5fa8a by TJ Xu <[email protected]>:
Consolidate all flags into one
--
72020bef1368f514760cbfd6630c22d2348121c9 by TJ Xu <[email protected]>:
Change description of xla_gpu_disable_async_collectives
Merging this change closes #11422
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11422 from Tixxx:tixxx/flag_clean_up 72020bef1368f514760cbfd6630c22d2348121c9
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PR_kwDOArmXAs5tPCQm
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Remove test use of StreamExecutor::GetAllocator.
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Remove test use of StreamExecutor::GetAllocator.
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PR_kwDOArmXAs5tO76-
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Moves the 'Solver took' time logic to include construction of the solver & model.
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Moves the 'Solver took' time logic to include construction of the solver & model.
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Adds GQA support for SDPA op.
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Cache begin/end iterators in min/max extraction loop
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Cache begin/end iterators in min/max extraction loop
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tf.function deadlock with multiple multiprocess/threading
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"Hi **@jonas-eschle** ,\r\nSorry for the delay, I tried to run your code on colab using TF v2.16.1, nightly and faced the same issue. Please find the [gist ](https://colab.sandbox.google.com/gist/Venkat6871/fe1981c41176af276d2630a676f64b36/66115_2-16-1-nightly-v.ipynb)here for reference.\r\n\r\nThank you!"
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CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.16
### Custom code
Yes
### Current behavior?
TensorFlow gets stuck when using multiprocessing/threading more than once.
I've observed it in more complicated situations with only once multithreading, but the following is a reproducible, standalone example that illustrates the point.
The code works correctly if either the `tf.function` decorator is removed _or_ if xla compilation `jit_compile` is enabled (!).
### Standalone code to reproduce the issue
```shell
import multiprocessing as mp # can also be another module for multiprocess/threading
import random
import tensorflow as tf
# if we use jit_compile=True, it will work, magically
# it also works if there is no decorator at all, i.e. only eager mode
@tf.function(jit_compile=False)
def testjit(x):
return tf.math.reduce_sum(x)
def make_zdata(_=None):
print('Making data')
# just to make sure that we recompile the function (different shapes)
rnd = tf.random.uniform([random.randint(100, 10000)], -1, 1)
zdata = testjit(rnd)
print('Made data')
return zdata
with mp.Pool(1) as executor:
executor.map(make_zdata, [1])
executor.terminate()
# if we run this, it will fail (if jit_compile=False)
with mp.Pool(1) as executor:
executor.map(make_zdata, [1]) # here, the code will be stuck
executor.terminate()
```
### Relevant log output
will be, approximately:
```
Making data
Made data
Making data
```
and then it's stuck. Otherwise, if using `jit_compile=True` or not using the tf.function decorator at all, another `Made data` will be printed
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Add support for nested tuples in BorrowingLiteral
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Add support for nested tuples in BorrowingLiteral
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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/fc58ce0031ace5ccbcb8c9c1fb390fc22e88996c.
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Internal only change.
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[IFRT] Introduce PjRtCompatibleDevice and PjRtCompatibleMemory classes.
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[IFRT] Introduce PjRtCompatibleDevice and PjRtCompatibleMemory classes.
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Adds default move constructors to `tensorflow::XlaCompiledFunction`.
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Adds default move constructors to `tensorflow::XlaCompiledFunction`.
This class already deletes the copy constructors, but does not enable the move constructors. As written today, users of `tensorflow::XlaCompiledFunction` must wrap their instances in a `std::unique_ptr` (or similar) in order to move this instance. That solution would add indirection and proliferate null checks that is not needed after this chance.
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#tf-data Adds random access support to the zip dataset
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#tf-data Adds random access support to the zip dataset
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Update eigh_expander python example to a working one, with a little test that reconstructs the input to prove that the eigh works
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[XLA:GPU][MLIR Emitters] Lower atomic_rmw to direct atomic ops when possible.
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[XLA:GPU][MLIR Emitters] Lower atomic_rmw to direct atomic ops when possible.
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Fix the CPU full failure. Initialize the data in default constructor.
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Fix the CPU full failure. Initialize the data in default constructor.
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Add a ShapeTree::Map with StatusOr result.
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Add a ShapeTree::Map with StatusOr result.
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Add a ShapeTree::Map with StatusOr result.
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Add a ShapeTree::Map with StatusOr result.
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Add transform to TSL's common.bara.sky to fix llvm include paths
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Internal tooling changes.
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Teach TSL's common.bara.sky about llvm/mlir headers
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NONE
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Teach TSL's common.bara.sky about llvm/mlir headers
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PR_kwDOArmXAs5tNKLC
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Integrate LLVM at llvm/llvm-project@82c320ca5974
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NONE
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Integrate LLVM at llvm/llvm-project@82c320ca5974
Updates LLVM usage to match
[82c320ca5974](https://github.com/llvm/llvm-project/commit/82c320ca5974)
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| 66,099 |
Create and cache a DeviceMemoryAllocator in GemmFusionAutotuner rather than the one in StreamExecutor as an intermediate step in eliminating a circular dependency between the two classes.
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NONE
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Create and cache a DeviceMemoryAllocator in GemmFusionAutotuner rather than the one in StreamExecutor as an intermediate step in eliminating a circular dependency between the two classes.
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PR_kwDOArmXAs5tND7l
| 66,098 |
Don't call StreamExecutor::GetAllocator in AutotuneConfig as an intermediate step to eliminate a circular dependency with DeviceMemoryAllocator.
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Don't call StreamExecutor::GetAllocator in AutotuneConfig as an intermediate step to eliminate a circular dependency with DeviceMemoryAllocator.
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[IFRT] Don't populate the pjrt_device() field of PJRT-IFRT devices for non-addressable devices.
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NONE
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[IFRT] Don't populate the pjrt_device() field of PJRT-IFRT devices for non-addressable devices.
Change in preparation for removing knowledge of non-addressable devices from PJRT in many cases. In the future, only IFRT should know about devices outside the current process. This change removes any access to those PJRT devices via IFRT.
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Create a DeviceMemoryAllocator in HloRunner as a step in resolving circular dependencies between DeviceMemoryAllocator and StreamExecutor.
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Create a DeviceMemoryAllocator in HloRunner as a step in resolving circular dependencies between DeviceMemoryAllocator and StreamExecutor.
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PR_kwDOArmXAs5tM1_j
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Use the cached allocator pointer rather than looking it up again when creating XlaScopedShapedBuffers and ScopedDeviceMemories.
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Use the cached allocator pointer rather than looking it up again when creating XlaScopedShapedBuffers and ScopedDeviceMemories.
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PR #11299: [XLA:CPU] Enable XLA on Windows
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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/66094/checks?check_run_id=24035780523) 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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PR #11299: [XLA:CPU] Enable XLA on Windows
Imported from GitHub PR https://github.com/openxla/xla/pull/11299
This PR aims to enable the XLA test cases on the Windows Platform. The changes made:
1. Changed the .bazelrc file to use the correct toolchain and platform
This change will allow the user to successfully run XLA tests on the Windows platform using the Clang compiler using '--config=win_clang' in the bazel command
2. Added conditions to a few test cases to successfully run on the Windows platform
These test cases check the exit/termination status of a process
WIFEXITED is typically supported in POSIX-compliant operating systems like Unix and Linux to check if a process has terminated normally. WEXITSTATUS allows examining the termination status of child processes. However, these macros are not Windows compliant, hence the additional condition block was added to check the exit/termination status of process or child process for the Windows platform
Copybara import of the project:
--
ece9eefa224a6d051bcac089fe2a9a393af16a2b by Raunak <[email protected]>:
Enable XLA Windows
--
347c0326af8f608047f06345cad4dfbb53a52150 by mraunak <[email protected]>:
Update interactive_graphviz_bin_test.cc
--
2d4a3c2bb2ea23f12029583c53087d8739da0319 by mraunak <[email protected]>:
Update xla/tools/interactive_graphviz_bin_test.cc
Co-authored-by: Penporn Koanantakool <[email protected]>
--
90ad8b2730900d7f82b0fb1a83a73ffa2e452e0e by mraunak <[email protected]>:
Update run_hlo_module_bin_test.cc
--
7f31412f6b57e53bd56ba92149b015fcba92b07c by mraunak <[email protected]>:
Update xla/tools/run_hlo_module_bin_test.cc
Co-authored-by: Penporn Koanantakool <[email protected]>
--
4d39e35461f6977f36404a435c68b4809fb51a44 by mraunak <[email protected]>:
Update hlo_expand_test.cc
--
816b9ae0498831b55139c72d627d07f05e51213b by mraunak <[email protected]>:
Update hlo_expand_test.cc
--
a728fff1aca4258602c3d7c78afcc7d38b545b7a by mraunak <[email protected]>:
Update hlo_expand_test.cc
--
ffcb6861becf8de7a5c4e64ef0f19d977475d281 by mraunak <[email protected]>:
Update interactive_graphviz_bin_test.cc
--
f181497793c2c48079d72b545e6efb837f490504 by mraunak <[email protected]>:
Update interactive_graphviz_bin_test.cc
--
f9af75677e4663b3348e9e89376262eaff389ea9 by mraunak <[email protected]>:
Update run_hlo_module_bin_test.cc
Merging this change closes #11299
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11299 from Intel-tensorflow:mraunak/xla_windows f9af75677e4663b3348e9e89376262eaff389ea9
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PR_kwDOArmXAs5tMsqo
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Create a DeviceMemoryAllocator in HloTestBase rather than use the one in the StreamExecutor.
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NONE
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Create a DeviceMemoryAllocator in HloTestBase rather than use the one in the StreamExecutor.
This is a step in reducing circular dependencies between StreamExecutor and DeviceMemoryAllocator.
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PR_kwDOArmXAs5tMpHk
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Notebooks should run end-to-end without stopping for configmation.
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Notebooks should run end-to-end without stopping for configmation.
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