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[
"Hi @dhrupalpatidar1313 ,\r\nI tried to run your code on Colab using TF v2.16.1,nightly and i am not facing any issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/ee0149bcc4d59a5ac5a536692b921364/64787_2-16-1-nightly.ipynb) here for reference.\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64787\">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/64787\">No</a>\n"
] | 2024-03-30T06:30:22 | 2024-04-16T01:47:31 | 2024-04-16T01:47:28 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.2
### Custom code
Yes
### OS platform and distribution
Windows
### Mobile device
_No response_
### Python version
3.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
from tensorflow.python.util import deprecation
ImportError: cannot import name 'deprecation' from 'tensorflow.python.util' (unknown location)
### Standalone code to reproduce the issue
```shell
from tensorflow.python.util import deprecation
ImportError: cannot import name 'deprecation' from 'tensorflow.python.util' (unknown location)
```
### Relevant log output
```shell
from tensorflow.python.util import deprecation
ImportError: cannot import name 'deprecation' from 'tensorflow.python.util' (unknown location)
```
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[xla:gpu][NFC] Remove unused constexprs
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[XLA:Runtime] Moved the nccl_api target to runtime folder.
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[XLA:Runtime] Moved the nccl_api target to runtime folder.
This is part of an effort to move runtime targets to the runtime folder. #5758
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11122 from ROCm:5.7-rccl-hang 2d196b645ccac046309d44f747d42777f00f1c9b
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[pjrt] NFC: Rename HostBufferSemantics::kZeroCopy to kImmutableZeroCopy
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[pjrt] NFC: Rename HostBufferSemantics::kZeroCopy to kImmutableZeroCopy
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Add unbounded dynamism test for AllReduceOp.
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Add unbounded dynamism test for AllReduceOp.
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Make RemoveCollective virtual and return the new custom call instruction
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Make RemoveCollective virtual and return the new custom call instruction
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Add unbounded dynamism test for AllGatherOp.
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Add unbounded dynamism test for AllGatherOp.
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Add Eigen Listener for thread pool schedule and execute events
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Add Eigen Listener for thread pool schedule and execute events
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PR_kwDOArmXAs5rNJJm
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Support RngBitGenerator HloInstruction with single output in the HLO -> MHLO conversion.
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Support RngBitGenerator HloInstruction with single output in the HLO -> MHLO conversion.
For the rng-bit-generator operator, xla HLO instruction can have two kinds of shapes, (1) tuple(output_state, output_data), and (2) output_data. On the contrary, `mhlo::RngBitGeneratorOp` has only one shape, (output_state, output_data). This cl supports RngBitGenerator HloInstruction with single output in the HLO -> MHLO conversion.
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PR_kwDOArmXAs5rNGBe
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Add absl::SourceLocations to xla::ResourceExhaustedError.
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Add absl::SourceLocations to xla::ResourceExhaustedError.
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PR_kwDOArmXAs5rNEc5
| 64,767 |
Reverts 5bbc00214f106863808539104be9fdc6e01af02d
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Reverts 5bbc00214f106863808539104be9fdc6e01af02d
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/63398 from tensorflow:Surya_UnicodeEncode 176048ad39a1d928208b92e1503e2d8d2cd35d28
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PR_kwDOArmXAs5rNCkB
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Remove XlaOutlineEntryFunctions pass
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Remove XlaOutlineEntryFunctions pass
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PR_kwDOArmXAs5rNB3k
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Add a contextmanager to temporarily disable XLA sharding support for ResourceVariables.
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Add a contextmanager to temporarily disable XLA sharding support for ResourceVariables.
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I_kwDOArmXAs6EFcKk
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tensorflow not utilizing gpu memory and stating limit is 137gb
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[
"@ripjohnbrown1859,\r\nCould you please provide the steps you have followed to install the tensorflow 2.16 and also provide the CUDA, cudNN, Bazel, Environment where you are trying which helps us to analyse the issue in an effective way. Thank you!",
"i managed to fix this specific problem by processing the data on the cpu and under 'with tf.device('/CPU:0'):' and training under 'with strategy'. now i have a problem where every other epoch reports a bunch of rendezvous errors, skips abunch of data, and gives a val accuracy of 1 and an increasingly high loss. also my epochs are reporting greater than 1 accuracy. Should i open a new issue?",
"@ripjohnbrown1859,\r\nGlad the GPU issue was resolved. For the val loss, If the validation loss (error) is going to increase so means overfitting. You must set the number of epochs as high as possible and avoid the overfitting and terminate training based on the error rates. . As long as it keeps dropping training should continue. Till model start to converge at nth epochs. Indeed it should converge quite well to a low val_loss.\r\n\r\nAlso please take a look at this references.\r\nhttps://discuss.tensorflow.org/t/why-does-my-validation-loss-increase-but-validation-accuracy-perfectly-matches-training-accuracy/4283\r\n\r\n\r\n- Reduce your learning rate to a very small number like 0.001 or even 0.0001.\r\n- Provide more data.\r\n- Set Dropout rates to a number like 0.2. Keep them uniform across the network.\r\n- Try decreasing the batch size.\r\n- Using appropriate optimizer: You may need to experiment a bit on this. Use different optimizers on the same network, and select an optimizer which gives you the least loss.",
"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/64764\">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/64764\">No</a>\n"
] | 2024-03-29T21:34:04 | 2024-04-27T01:47:31 | 2024-04-27T01:47:28 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
wsl ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
8.6.0
### GPU model and memory
sli titan x maxwell
### Current behavior?
i have 2 titan x maxwells and am trying to run a CNN on my machine in wsl2, however when I try to run it i get the attached error, which seems to indicate tensorflow is not using any gpu memory and then throwing an error. it runs out of memory while converting the training input tensor
### Standalone code to reproduce the issue
```shell
__name__ == '__main__':
parent_directory = 'physionet.org/files/ecg-arrhythmia/1.0.0/WFDBRecords'
input_dataset, output_dataset = get_data(parent_directory)
print('padding sequences')
output_dataset = pad_sequences(output_dataset, maxlen=64, padding = "post")
print(len(input_dataset))
#output_dataset = to_categorical(output_dataset, num_classes = 64)
training_input_dataset = input_dataset[:40000]
training_output_dataset = output_dataset[:40000]
val_input_dataset = input_dataset[40000:]
val_output_dataset = output_dataset[40000:]
#dataset = process_dataset(dataset)
print(output_dataset[1])
print('converting training input data to tensor')
training_input_data = tf.convert_to_tensor(training_input_dataset,dtype=tf.float32)
print('converting training output data to tensor')
training_output_data = tf.convert_to_tensor(training_output_dataset,dtype=tf.float32)
print('converting validation input data to tensor')
val_input_data = tf.convert_to_tensor(val_input_dataset,dtype=tf.float32)
print('converting val;idation output data to tensor')
val_output_data = tf.convert_to_tensor(val_output_dataset,dtype=tf.float32)
training_input_data = np.expand_dims(training_input_data, axis=-1)
val_input_data = np.expand_dims(val_input_data, axis=-1)
training_output_data = np.expand_dims(training_output_data, axis=-1)
val_output_data = np.expand_dims(val_output_data, axis=-1)
#training_output_data = np.expand_dims(training_output_data, axis=-1)
#val_output_data = np.expand_dims(val_output_data, axis=-1)
print(training_output_data.shape)
print(training_input_data.shape)
#training_output_data = tf.reshape(training_output_data, 32, 5000, 12)
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(64, (1, 1), activation='relu', input_shape=( 12, 5000, 1)), # Adjust input shape based on your data
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(128, (1, 1), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
#tf.keras.layers.Conv2D(128, (1, 1), activation='relu'),
#tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Flatten(),
#tf.keras.layers.Dense(64, activation='relu'),
#tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(64) # Number of classes
])
print(model.summary())
model.compile(optimizer='adam', loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
# Train the model
num_epochs = 10
model.fit(training_input_data, training_output_data, batch_size=32, epochs=num_epochs, validation_data=(val_input_data, val_output_data))
test_loss, test_acc = model.evaluate(val_input_data, val_output_data, verbose=2)
print('\nTest accuracy:', test_acc)
# export NVIDIA_DIR=$(dirname $(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)")))
```
### Relevant log output
```shell
2024-03-29 16:39:36.771397: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:1629] failed to alloc 2147483648 bytes on host: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2024-03-29 16:39:36.771454: W external/local_xla/xla/stream_executor/integrations/device_host_allocator.h:51] could not allocate pinned host memory of size: 2147483648
2024-03-29 16:39:37.984270: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:1629] failed to alloc 1932735232 bytes on host: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2024-03-29 16:39:37.984317: W external/local_xla/xla/stream_executor/integrations/device_host_allocator.h:51] could not allocate pinned host memory of size: 1932735232
2024-03-29 16:39:39.200352: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:1629] failed to alloc 2147483648 bytes on host: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2024-03-29 16:39:39.200399: W external/local_xla/xla/stream_executor/integrations/device_host_allocator.h:51] could not allocate pinned host memory of size: 2147483648
2024-03-29 16:39:50.425728: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:1629] failed to alloc 2147483648 bytes on host: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2024-03-29 16:39:50.425788: W external/local_xla/xla/stream_executor/integrations/device_host_allocator.h:51] could not allocate pinned host memory of size: 2147483648
2024-03-29 16:39:51.809074: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:1629] failed to alloc 2147483648 bytes on host: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2024-03-29 16:39:51.809138: W external/local_xla/xla/stream_executor/integrations/device_host_allocator.h:51] could not allocate pinned host memory of size: 2147483648
2024-03-29 16:39:51.809162: W external/local_tsl/tsl/framework/bfc_allocator.cc:487] Allocator (gpu_host_bfc) ran out of memory trying to allocate 1.77GiB (rounded to 1896960000)requested by op _EagerConst
If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation.
Current allocation summary follows.
Current allocation summary follows.
2024-03-29 16:39:51.809173: I external/local_tsl/tsl/framework/bfc_allocator.cc:1044] BFCAllocator dump for gpu_host_bfc
2024-03-29 16:39:51.809200: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (256): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809228: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (512): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809236: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (1024): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809242: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (2048): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809247: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (4096): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809252: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (8192): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809257: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (16384): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809264: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (32768): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809287: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (65536): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809297: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (131072): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809320: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (262144): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809329: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (524288): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809352: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (1048576): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809360: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (2097152): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809365: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (4194304): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809371: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (8388608): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809376: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (16777216): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809381: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (33554432): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809386: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (67108864): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809391: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (134217728): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809397: I external/local_tsl/tsl/framework/bfc_allocator.cc:1051] Bin (268435456): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-03-29 16:39:51.809404: I external/local_tsl/tsl/framework/bfc_allocator.cc:1067] Bin for 1.77GiB was 256.00MiB, Chunk State:
2024-03-29 16:39:51.809426: I external/local_tsl/tsl/framework/bfc_allocator.cc:1105] Summary of in-use Chunks by size:
2024-03-29 16:39:51.809436: I external/local_tsl/tsl/framework/bfc_allocator.cc:1112] Sum Total of in-use chunks: 0B
2024-03-29 16:39:51.809442: I external/local_tsl/tsl/framework/bfc_allocator.cc:1114] Total bytes in pool: 0 memory_limit_: 137438953472 available bytes: 137438953472 curr_region_allocation_bytes_: 2147483648
2024-03-29 16:39:51.809449: I external/local_tsl/tsl/framework/bfc_allocator.cc:1119] Stats:
Limit: 137438953472
InUse: 0
MaxInUse: 0
NumAllocs: 0
MaxAllocSize: 0
Reserved: 0
PeakReserved: 0
LargestFreeBlock: 0
2024-03-29 16:39:51.809472: W external/local_tsl/tsl/framework/bfc_allocator.cc:499] <allocator contains no memory>
Segmentation fault
```
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Add unbounded dynamism test for DynamicSliceOp.
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Allow MaybeSavedModelDirectory to read via custom env.
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Migrate the SerDes logic for basic IFRT API object types from IFRT Proxy to IFRT
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Migrate the SerDes logic for basic IFRT API object types from IFRT Proxy to IFRT
This CL migrates the (de)serialization logic from IFRT Proxy to IFRT for basic
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Add unbounded dynamism test for SortOp.
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Add unbounded dynamism test for SortOp.
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Add unbounded dynamism test for ReverseOp.
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Add unbounded dynamism test for ReverseOp.
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Add unbounded dynamism test for TriangularSolveOp.
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Add unbounded dynamism test for TriangularSolveOp.
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Implementation of get / retrieve buffer attributes methods in GPU async kernel.
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Implementation of get / retrieve buffer attributes methods in GPU async kernel.
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PR_kwDOArmXAs5rMUhv
| 64,755 |
Reverts 3a2cd8887ed96de6abbd26e46844b959aa42e481
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Reverts 3a2cd8887ed96de6abbd26e46844b959aa42e481
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PR_kwDOArmXAs5rMT-R
| 64,754 |
Hoist async copies when start_after is -1
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NONE
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Hoist async copies when start_after is -1
Previously, the counter for `MemorySpaceAssignment::FixSchedule` started at `0` and would check for async copies scheduled for before the counter and after (but only the exact value). We have some async copies that set their `start_after` value to `-1` meaning we would skip inserting them at the earliest point and then catch that they weren't inserted by their `start_before` time and insert them then. This would lead to a few async copy operations where `*-start` would be scheduled immediately before their corresponding `*-done` operation, leading to none of the latency being hidden.
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PR_kwDOArmXAs5rMOwf
| 64,753 |
Add missing description 'END' markers to definitions of *Xla*ND ops.
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NONE
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Add missing description 'END' markers to definitions of *Xla*ND ops.
In the case of AssignVariableXlaConcatND, also remove the (broken) reference to "output", which this op doesn't have.
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PR_kwDOArmXAs5rMNU0
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Add fp8 test for h100
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Add fp8 test for h100
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PR_kwDOArmXAs5rMK4f
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[xla:gpu][NFC] Simplify `collect_slice_info`
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[xla:gpu][NFC] Simplify `collect_slice_info`
Reverts 42883cb09d1a8155824ce4ed044794c0dffdd19f
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PR_kwDOArmXAs5rMH34
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Use `bytes` proto field type for string values of `xla::PjRtValueType` to `bytes`
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Use `bytes` proto field type for string values of `xla::PjRtValueType` to `bytes`
`xla::PjRtValueType` is defined in C++, where its `std::string` value can
contain any string (not necessarily UTF-8). Protobuf verison 3 requires a
`string` field to contain UTF-8, so it is more suitable to use `bytes` to
express this value.
(Note that the string value of `xla::PjRtValueType` would be often consumed by
Python, where nanobind would convert `std::string` into Python `str` with UTF-8
decoding. However, this is what some users of `xla::PjRtValueType` choose to
do; this is not sufficient enough to constrain the string to be UTF-8 only in
C++ APIs.)
This is a preemptive change; there is no known problem of using a `string`
field previously.
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PR_kwDOArmXAs5rMDtP
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Change include order in `ml_dtypes.cc` to prevent errors.
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Change include order in `ml_dtypes.cc` to prevent errors.
Trying to prevent `error: "Using deprecated NumPy API, disable it with #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION"`
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PR_kwDOArmXAs5rL4jJ
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Modify the matrix class to keep track of both memory and communication resharding costs for a given edge as part of one matrix object.
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Modify the matrix class to keep track of both memory and communication resharding costs for a given edge as part of one matrix object.
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PR_kwDOArmXAs5rLoqR
| 64,747 |
Integrate LLVM at llvm/llvm-project@80aa52d8c5a8
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Integrate LLVM at llvm/llvm-project@80aa52d8c5a8
Updates LLVM usage to match
[80aa52d8c5a8](https://github.com/llvm/llvm-project/commit/80aa52d8c5a8)
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PR_kwDOArmXAs5rLh92
| 64,746 |
Deduplicate inferred mesh shapes when try_multiple_mesh_shapes=true.
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Deduplicate inferred mesh shapes when try_multiple_mesh_shapes=true.
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PR_kwDOArmXAs5rLgm7
| 64,745 |
[PJRT:CPU] Replace references to pjrt/tfrt_cpu_pjrt_client with pjrt/cpu/cpu_client.h.
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[PJRT:CPU] Replace references to pjrt/tfrt_cpu_pjrt_client with pjrt/cpu/cpu_client.h.
The two are aliases and the former is a forwarding header pointing to the latter.
Cleanup only, no functional changes.
Reverts 5bbc00214f106863808539104be9fdc6e01af02d
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[PJRT:CPU] Replace references to pjrt/tfrt_cpu_pjrt_client with pjrt/cpu/cpu_client.h.
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[PJRT:CPU] Replace references to pjrt/tfrt_cpu_pjrt_client with pjrt/cpu/cpu_client.h.
The two are aliases and the former is a forwarding header pointing to the latter.
Cleanup only, no functional changes.
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[xla:gpu] Unify GEMM emission for (Dynamic)AddressComputationFusion emitters
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[xla:gpu] Unify GEMM emission for (Dynamic)AddressComputationFusion emitters
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[xla:gpu] Add a version of `HloPredicateIsOp` for `HloInstructionAdaptor`
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[xla:gpu] Add a version of `HloPredicateIsOp` for `HloInstructionAdaptor`
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[xla:gpu][NFC] Simplify `collect_slice_info`
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[xla:gpu][NFC] Simplify `collect_slice_info`
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[PJRT:CPU] Fix thread-pool stack sizes to 2MiB.
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[PJRT:CPU] Fix thread-pool stack sizes to 2MiB.
The default thread pool size is too small on Mac OS.
An older version of this runtime based on StreamExecutor set a 2MiB stack size as well, but that change was most likely lost during the TFRT rewrite.
Fixes https://github.com/google/jax/issues/20428
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Enhanced zeta readability based on the article
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Enhanced zeta readability based on the article
Changes based on the Hurwitz Zeta algorithm from the article linked in the comments.
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Split hybrid quantized dot-like StableHLO ops into TFLite dequantize and float op
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Split hybrid quantized dot-like StableHLO ops into TFLite dequantize and float op
Hybrid quantized op has semantics for weight-only quantization within StableHLO, so it should be splitted into dequantize and float op for legalization towards TFLite.
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Split hybrid quantized dot-like StableHLO ops into TFLite dequantize and float op
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Split hybrid quantized dot-like StableHLO ops into TFLite dequantize and float op
Hybrid quantized op has semantics for weight-only quantization within StableHLO, so it should be splitted into dequantize and float op for legalization towards TFLite.
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Add machine attributes to topology
(so Jax persistent cache won't reuse code from incompatible machines)
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[XLA:AlgebraicSimplifier] Simplify the following gather patterns:
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[XLA:AlgebraicSimplifier] Simplify the following gather patterns:
1. gather(pad(...)) -> pad(gather(...))
2. gather(reshape(pad(...))) -> pad(gather(reshape(...)))
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PR #7849: [XLA:CPU] Add support for cross-process collectives using mpi.
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PR #7849: [XLA:CPU] Add support for cross-process collectives using mpi.
Imported from GitHub PR https://github.com/openxla/xla/pull/7849
Mpi collectives as proposed in https://github.com/google/jax/issues/11182?notification_referrer_id=NT_kwDOAG8zGbIzODQ5MDcxMzM0OjcyODc1Nzc#issuecomment-1851591135.
I only implemented the inter-process communication and this does not yet support more than 1 threads per process. Adding support for multiple threads/devices per process in the future seems quite a bit more involved if one wanted to do it properly.
For MPI I am building and linking against https://github.com/eschnett/MPItrampoline, which dlopens the (wrapped) mpi library at runtime. To wrap and load the desired mpi library one needs compile https://github.com/eschnett/MPIwrapper and set `MPITRAMPOLINE_LIB=/path/to/libmpiwrapper.so`.
@hawkinsp
Copybara import of the project:
--
b74bbb909d902bd30523f943a7c15f2c754cf98a by Clemens Giuliani <[email protected]>:
add mpi collectives
--
23508eb46848464f6711dd8f3f91830ea1adb16d by Clemens Giuliani <[email protected]>:
add explicit Init and Finalize methods and export them to python
--
bbe5840b8eb56a306a66ed03d701fd8976e01491 by Clemens Giuliani <[email protected]>:
add comment
--
38d156282ecc89509f4b21d80db1a37cb290437a by Clemens Giuliani <[email protected]>:
fix windows build
--
201f7238f166197ede5cf5d4d70e117a91eddcd7 by Clemens Giuliani <[email protected]>:
fmt
--
2784869df650c1c123c346401db2f67cb153b03e by Clemens Giuliani <[email protected]>:
bump xla_extension_version
Merging this change closes #7849
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/7849 from inailuig:mpi_collectives 2784869df650c1c123c346401db2f67cb153b03e
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Open source TfXlaHostCallback implementation
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Open source TfXlaHostCallback implementation
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Rollback https://github.com/openxla/xla/commit/0ab2be0b5a575da3206d2c2f92b85e6346708405.
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Rollback https://github.com/openxla/xla/commit/0ab2be0b5a575da3206d2c2f92b85e6346708405.
There is an internal issue with running tests on H100s requiring the change to be rolled back.
Reverts 42883cb09d1a8155824ce4ed044794c0dffdd19f
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tensorflow_model_optimization,quantization.keras.quantize_model raises exception on its documentation example
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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/64721\">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/64721\">No</a>\n",
"I encountered this problem. Where is the solution to the problem?",
"keras.Sequential and tf_keras.Sequential are different classes. They have different signatures.\r\n\r\nRun as:\r\n\r\n```\r\nimport keras\r\nfrom keras import layers\r\nimport tensorflow_model_optimization as tfmot\r\nmodel = tfmot.quantization.keras.quantize_model(\r\n keras.Sequential([\r\n layers.Dense(10, activation='relu', input_shape=(100,)),\r\n layers.Dense(2, activation='sigmoid')\r\n ]))\r\n```\r\nit will not work. But run as:\r\n\r\n```\r\nimport tf_keras\r\nfrom tf_keras import layers\r\nimport tensorflow_model_optimization as tfmot\r\n \r\nmodel = tfmot.quantization.keras.quantize_model(\r\n tf_keras.Sequential([\r\n layers.Dense(10, activation='relu', input_shape=(100,)),\r\n layers.Dense(2, activation='sigmoid')\r\n ]))\r\n```\r\nIt will work.\r\n\r\nkeras.Sequential's signature is ` keras.src.models.sequential.Sequential`\r\n\r\ntf_keras.Sequential's is `tf_keras.src.engine.sequential.Sequential`\r\n\r\n",
"The tensorflow team should probably update the `tensorflow_model_optimization` examples"
] | 2024-03-29T02:47:38 | 2024-04-15T17:06:10 | 2024-03-29T17:33:13 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11.5
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.3
### GPU model and memory
NVIDIA GeForce RTX 4060 8GB
### Current behavior?
The example program in the documentation of tensorflow_model_optimization,quantization.keras.quantize_model does not run successfully
```
import keras
from keras import layers
import tensorflow_model_optimization as tfmot
model = tfmot.quantization.keras.quantize_model(
keras.Sequential([
layers.Dense(10, activation='relu', input_shape=(100,)),
layers.Dense(2, activation='sigmoid')
]))
```
```
Python 3.11.5 (main, Sep 29 2023, 17:18:47) [GCC 13.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import keras
>>> from keras import layers
>>> import tensorflow_model_optimization as tfmot
>>> model = tfmot.quantization.keras.quantize_model(
... keras.Sequential([
... layers.Dense(10, activation='relu', input_shape=(100,)),
... layers.Dense(2, activation='sigmoid')
... ]))
/home/valerio/py3-tf-nightly/lib/python3.11/site-packages/keras/src/layers/core/dense.py:88: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
super().__init__(activity_regularizer=activity_regularizer, **kwargs)
2024-03-28 20:45:09.603570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6086 MB memory: -> device: 0, name: NVIDIA GeForce RTX 4060, pci bus id: 0000:01:00.0, compute capability: 8.9
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/valerio/py3-tf-nightly/lib/python3.11/site-packages/tensorflow_model_optimization/python/core/quantization/keras/quantize.py", line 135, in quantize_model
raise ValueError(
ValueError: `to_quantize` can only either be a keras Sequential or Functional model.
```
### Standalone code to reproduce the issue
```shell
import keras
from keras import layers
import tensorflow_model_optimization as tfmot
model = tfmot.quantization.keras.quantize_model(
keras.Sequential([
layers.Dense(10, activation='relu', input_shape=(100,)),
layers.Dense(2, activation='sigmoid')
]))
```
```
### Relevant log output
```shell
/home/valerio/py3-tf-nightly/lib/python3.11/site-packages/keras/src/layers/core/dense.py:88: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
super().__init__(activity_regularizer=activity_regularizer, **kwargs)
2024-03-28 20:45:09.603570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6086 MB memory: -> device: 0, name: NVIDIA GeForce RTX 4060, pci bus id: 0000:01:00.0, compute capability: 8.9
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/valerio/py3-tf-nightly/lib/python3.11/site-packages/tensorflow_model_optimization/python/core/quantization/keras/quantize.py", line 135, in quantize_model
raise ValueError(
ValueError: `to_quantize` can only either be a keras Sequential or Functional model.
```
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PR_kwDOArmXAs5rH0AO
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Mhlo to Hlo converters for dynamic_reshape and dynamic_broadcast_in_dim
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Mhlo to Hlo converters for dynamic_reshape and dynamic_broadcast_in_dim
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PR_kwDOArmXAs5rHrAW
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[xla:gpu][NFC] Use absl::Span more consistenly
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[xla:gpu][NFC] Use absl::Span more consistenly
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PR_kwDOArmXAs5rHkex
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[XLA] Respect min_rank for reduce scatter version of MatchReduceScatter.
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[XLA] Respect min_rank for reduce scatter version of MatchReduceScatter.
We need to honor it.
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Add unbounded dynamism test for RngBitGeneratorOp.
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Add unbounded dynamism tests for RngNormalOp and RngUniformOp.
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Add unbounded dynamism tests for RngNormalOp and RngUniformOp.
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#tf-data makes data_service_client.cc nonblocking when getting elements from workers
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#tf-data makes data_service_client.cc nonblocking when getting elements from workers
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Remove the duplicate device assignment in ifrt_serving_executable.
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Remove the duplicate device assignment in ifrt_serving_executable.
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PR_kwDOArmXAs5rHZb_
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[xla:gpu] Create fake buffer allocations for embedded thunk
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[xla:gpu] Create fake buffer allocations for embedded thunk
This is required in cases where embedded thunk arguments share the same buffer (i.e. they are located at different offsets of the same buffer)
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[tf.data] Add `synchronous` parameter to `map`.
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[tf.data] Add `synchronous` parameter to `map`.
When `synchronous` is set to `True`, the `map` will always run synchronously, even when `options.experimental_optimization.map_parallelization=True`. Setting `synchronous=True` is useful for saving memory, since it buffers one less element than setting `num_parallel_calls=1`.
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PR_kwDOArmXAs5rHUre
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Correctly handle output streaming case where the MoveToHost annotation is the entry computation root
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Correctly handle output streaming case where the MoveToHost annotation is the entry computation root
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PR_kwDOArmXAs5rHNSP
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Add unbounded dynamism test for CholeskyOp.
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Add unbounded dynamism test for CholeskyOp.
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PR_kwDOArmXAs5rHKEb
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Add a Resource for KV Cache buffer storage
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Add a Resource for KV Cache buffer storage
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[tflite] Add the composite lowering logic for hardswish
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[tflite] Add the composite lowering logic for hardswish
This directly lowers an aten hardswish to a tflite hardswish
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[XLA:Python] Making `absl::StatusOr`-casting explicit.
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[XLA:Python] Making `absl::StatusOr`-casting explicit.
This removes the need for Abseil's pybind11 status-casters, and is working towards the goal of porting everything to use nanobind.
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Add unbounded dynamism test for GetTupleElementOp.
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Add unbounded dynamism test for GetTupleElementOp.
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Add unbounded dynamism test for WhileOp.
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Add unbounded dynamism test for WhileOp.
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Add unbounded dynamism test for TupleOp.
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Add unbounded dynamism test for TupleOp.
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Set release_base for all release platforms
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Set release_base for all release platforms
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Try to make everything from `tensorflow/third_party` writable via Copybara
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Add support for LocalDeviceManager in tfrt_session.
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Add support for LocalDeviceManager in tfrt_session.
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Fix an internal breakage.
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Fix an internal breakage.
Reverts 573fc841781f593f40ac75e8f8de204c1162f8f9
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Integrate StableHLO at openxla/stablehlo@271e8634
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Integrate StableHLO at openxla/stablehlo@271e8634
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PR_kwDOArmXAs5rGcSv
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Add missing 'END' to definition of XlaSplitND.
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Add missing 'END' to definition of XlaSplitND.
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PR #10337: [ROCm] Add CudnnPadForConvolutions and CudnnVectorizeConvolutionsHLO pass to AMDGPU compiler
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PR #10337: [ROCm] Add CudnnPadForConvolutions and CudnnVectorizeConvolutionsHLO pass to AMDGPU compiler
Imported from GitHub PR https://github.com/openxla/xla/pull/10337
Copybara import of the project:
--
ea05b2bdf1eb07d040dc77e6138efea8938ba8ef by Harsha HS <[email protected]>:
Add CudnnPadForConvolutions and CudnnVecotrizeConvolutions HLO pass
--
ae95d4b4dc114104ccdf455a3a194be5fa64a85e by Pavel Emeliyanenko <[email protected]>:
added std visitor to distinguish between gpu archs
--
ce6321ef093781167f931f63930ff0c2a24ed2e4 by Harsha HS <[email protected]>:
Add missing `tf_cuda_tets_tags` to unit tests
--
2b149983980de7c641683ce9e78c431a5a591160 by Harsha HS <[email protected]>:
Define TENSORFLOW_USE_ROCM when ROCm is configured
--
faea34b5db30430bf7f35b6caaf2a4d6f6741401 by Harsha HS <[email protected]>:
Address review comments
--
be14e59717dba77d03a4aae34109a3300a596070 by Harsha HS <[email protected]>:
Refactor and reduce number of `#ifdefs`
Merging this change closes #10337
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10337 from ROCm:ci_pad_vec_conv_20240307 be14e59717dba77d03a4aae34109a3300a596070
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[XLA:Python] Adding `xla::PrimitiveType` <-> `numpy.dtype` conversions to the library for internal debugging tools.
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[XLA:Python] Adding `xla::PrimitiveType` <-> `numpy.dtype` conversions to the library for internal debugging tools.
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PR_kwDOArmXAs5rGLfb
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Disable Compiler ir test for h100 GPU targets
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Disable Compiler ir test for h100 GPU targets
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PR_kwDOArmXAs5rGI27
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Add xla_(cpu|gpu)_jit dependencies to failing test.
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Add xla_(cpu|gpu)_jit dependencies to failing test.
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I_kwDOArmXAs6D9tTg
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TfLite C/C++ shared library via Bazel
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[
"@suyash-narain Could you please refer to the official TensorFlow Lite [documentation](https://www.tensorflow.org/lite/guide/build_cmake) for complete Bazel build and let us know if it helps?\r\nThank you!",
"Hi @sushreebarsa , the documentation link you mentioned points to cmake build, not bazel. How do i build a combined tf .so file containing both c++ and c symbols using bazel?",
"@suyash-narain Bazel doesn't directly support creating a single shared library (.so) file containing both C++ and C symbols. To achieve this you can try creating separate libraries for your C++ and C code and linking them together during your main program's build process. Thank you!",
"thanks",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64693\">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/64693\">No</a>\n"
] | 2024-03-28T19:39:14 | 2024-04-04T16:58:20 | 2024-04-04T16:58:17 |
NONE
| null | null | null |
Hi,
I am trying to build Tensorflowlite v2.14.0 using Bazel and I do obtain libtensorflowlite.so shared library.
I am using Ubuntu OS 22.04
I want to know is it possible for the generated libtensorflowlite.so to contain the c_api symbols as well? Whenever i build tensorflowlite using bazel, i need to specifically build libtensorflowlite.so and libtensorflowlite_c.so to use c_api.
I though libtensorflowlite.so should contain the c_api symbols as well. Is it possible? Is there a way via bazel to include c_api symbols in libtensorflowlite.so instead of generating a separate libtensorflowlite_c.so?
I think the build using CMAKE generates libtensorflowlite.so containing c_api symbols as well. How to do it via bazel?
thanks
|
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Delete populateRankSpecialization*Patterns functions
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Delete populateRankSpecialization*Patterns functions
These were used by KernelGen but are no longer needed.
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Integrate LLVM at llvm/llvm-project@aa2c14de1adc
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NONE
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Integrate LLVM at llvm/llvm-project@aa2c14de1adc
Updates LLVM usage to match
[aa2c14de1adc](https://github.com/llvm/llvm-project/commit/aa2c14de1adc)
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Logs a fatal message if Auto Sharding times out (since we no longer rely on GSPMD as a backup).
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NONE
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Logs a fatal message if Auto Sharding times out (since we no longer rely on GSPMD as a backup).
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10759 from Intel-tensorflow:akhil/gemv 4534fbd7e96a6e45ccc5a501b38b940aa8bd9d38
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[stream_executor:host] Add missing externs to C API header
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NONE
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[stream_executor:host] Add missing externs to C API header
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