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[XLA:GPU] Implement new Triton Softmax codegen based on symbolic tiles and indexing maps.
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[XLA:GPU] Implement new Triton Softmax codegen based on symbolic tiles and indexing maps.
Symbolic tiles are still under development and can process all instructions. The new codegen is used only when all instruction in the fusion are supported, otherwise we fall back to the old codegen.
Triton Softmax codegen is behind `--xla_gpu_enable_triton_softmax_fusion` flag that is not turned on by default, so this change shouldn't affect any production workloads.
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PR #10788: [ROCM] fixing build brake: disabling cudnn_fusion_compiler for ROCM
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PR #10788: [ROCM] fixing build brake: disabling cudnn_fusion_compiler for ROCM
Imported from GitHub PR https://github.com/openxla/xla/pull/10788
In this PR I disable yet cudnn_fusion_compiler to fix the build brake
These feature is to be enabled later.
@xla-rotation: woud you have a look please ?
Copybara import of the project:
--
7ff1b0094f371188a8cc142eec6e6c77585ae666 by Pavel Emeliyanenko <[email protected]>:
rebased and added virtual function for cudnn compiler pass
Merging this change closes #10788
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10788 from ROCm:ci_build_brake_240321 7ff1b0094f371188a8cc142eec6e6c77585ae666
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"anyone? :( ",
"Hey friend, please wait, I believe good things will come to you.",
"Hi VanBap, can you upload your .h5 and .tflite file? That will help me test this faster.",
"> Hi VanBap, can you upload your .h5 and .tflite file? That will help me test this faster.\r\n\r\nI cant upload model files here because they are >25MB even though I zipped those. \r\nBut they are on my Github: \r\n- model .tflite: https://github.com/VanBap/Adverse_Weather/tree/master/saved_model \r\n- training model .h5: https://github.com/VanBap/Adverse_Weather/blob/master/Detection_model_usingResNet50.ipynb\r\n\r\nBy the way when I try to run model .tflite, I just put a new line code:\r\n\" # Resize Tensor Shape\r\ninterpreter.resize_tensor_input(input_details[0]['index'], (1, 224, 224, 3)) \"\r\nthen it finally ran but I dont really know (how) does it affect the way my model .tflite processing ?!\r\n\r\n",
"Hi @VanBap, in your notebook you do not specify a specific input_shape in the API: https://keras.io/api/applications/resnet/#resnet50-function, Resizing the converted input shape is actually expected: https://www.tensorflow.org/lite/guide/inference#run_inference_with_dynamic_shape_model, so given your parameters, I believe resizing is the correct action. Does that answer your concerns? Thanks.",
"> Hi @VanBap, in your notebook you do not specify a specific input_shape in the API: https://keras.io/api/applications/resnet/#resnet50-function, Resizing the converted input shape is actually expected: https://www.tensorflow.org/lite/guide/inference#run_inference_with_dynamic_shape_model, so given your parameters, I believe resizing is the correct action. Does that answer your concerns? Thanks.\r\n\r\nI really appreciate your help! I am trying your answer and will feed back when its done",
"> Hi @VanBap, in your notebook you do not specify a specific input_shape in the API: https://keras.io/api/applications/resnet/#resnet50-function, Resizing the converted input shape is actually expected: https://www.tensorflow.org/lite/guide/inference#run_inference_with_dynamic_shape_model, so given your parameters, I believe resizing is the correct action. Does that answer your concerns? Thanks.\r\n\r\nHi pkgoogle, I just want to feedback that I have tried your answer and my model ran properly. Thank u so much. I really appreciate it!",
"Hi @VanBap, no worries, if you have no more open items, please feel free to close as completed. Thanks."
] | 2024-03-26T08:24:11 | 2024-04-05T17:54:25 | 2024-04-05T17:54:25 |
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### 1. My topic
- Image Classification to detect 7 different weather conditions deploy on Raspberry Pi4
### 2. Train model .h5 on ggcolab
- I'm using ResNet50 (deep learning) and 1 fully connected layer with Softmax function
- Input: (244, 244, 3) image
- Output: one-hot encoding 7 classes (Ex: [0 0 1 0 0 0 0 0] => Rainy)
- epoch = 20 and accuracy is 90% (code below)
- Then I convert model .h5 to .tflite in order to run on RaspberryPi4
### 3. Problem
- input_details[0]['shape'] gave me input shape: " [1, 1, 1, 3] " which is wrong (correct one is [1, 244, 244, 3] )
- output_details[0]['shape'] gave me output shape: " [1, 7] " which is correct
=> model.tflite run wrong
### How can I fix this bug? ( change input shape of model .tflite to exact input shape )
### Link my code on github: https://github.com/VanBap/Adverse_Weather
Including:
- File train model h5: Adverse_weather_detection_model.ipynb
- File convert model from .h5 -> .tflite: convert.py
- File run test model .tflite: run_tflite_model.py
Thank you so much!
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[MLIR Exporter] Move created `NodeDef` and `AttrValue` protocol buffers into the exported `Graph`.
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WARNING:tensorflow:Your input ran out of data; interrupting training always occuring after 80% of total epochs
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[
"@sushreebarsa I have already tried steps_per_epoch = (len(train_size) // batch_size) - 1. It gives the same error, just slightly later than the run with the calculation as (len(train_size)//batch_size). ",
"@wzhou789 Could you please ensure that your data is shuffled before being fed to the model during training. Shuffling randomizes the order of data points within each batch and across epochs.\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.",
"Hi @wzhou789 ,\r\n\r\nShuffling may not help in repetetion of data. If you are using tf.dataset repeat() function might help.",
"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/64476\">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/64476\">No</a>\n"
] | 2024-03-26T05:48:11 | 2024-04-30T01:47:53 | 2024-04-30T01:47:51 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
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?
While I'm training a tensorflow model, the training keeps gets interrupted at the epoch after completing 80% of the total number of epochs I specified to train for. For example, it will stop training at epoch 81 if I had set epochs to 100, with the warning "WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches".
I calculated steps_per_epoch as len(train_size) // batch_size. I have also tried using (len(train_size)//batch_size)-1. Both of these continue to cause the error specified above. I have also tried to leave out steps_per_epoch, which causes the model to start training with the validation steps per epoch instead.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1zNuEKH-K9pip9UonoOZC_2a1T5KdHFt7?usp=sharing
```
### Relevant log output
```shell
Epoch 300: val_loss did not improve from 1.24497
149/149 [==============================] - 13s 87ms/step - loss: 0.3701 - val_loss: 1.9121
Epoch 301/376
149/149 [==============================] - ETA: 0s - loss: 0.3454
Epoch 301: val_loss did not improve from 1.24497
149/149 [==============================] - 13s 85ms/step - loss: 0.3454 - val_loss: 3.6483
Epoch 302/376
149/149 [==============================] - ETA: 0s - loss: 0.3932
Epoch 302: val_loss did not improve from 1.24497
149/149 [==============================] - 13s 85ms/step - loss: 0.3932 - val_loss: 1.7741
Epoch 303/376
122/149 [=======================>......] - ETA: 1s - loss: 0.3708WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 56024 batches). You may need to use the repeat() function when building your dataset.
```
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Allow optional `stablehlo.broadcast_in_dim` op.
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Allow optional `stablehlo.broadcast_in_dim` op.
When deferring `stablehlo.transpose` of the lhs of `stablehlo.add`, the rhs may have a `constant->broadcast_in_dim` pattern.
Allow the pattern `DeferActivationTransposeForAddOp` to handle such a case.
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PR_kwDOArmXAs5qvNl-
| 64,473 |
[xla] hlo_computation: compact instructions' vector on Cleanup()
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[xla] hlo_computation: compact instructions' vector on Cleanup()
tl;dr: this gives a 1.26x compilation time speedup for a large, dense
model in XLA:GPU.
The largest perf leaf seen in profiles of a large, dense model
is related to computing the post order. Surprisingly, it is not
the DFS itself what's most expensive; rather, most of the time is
spent on scanning through HloComputation::Instructions() to identify
DFS roots.
The reason this scan becomes expensive as instructions are removed
is that the vector holding HloInstructionInfo (introduced in
cl/600130708 || https://github.com/openxla/xla/commit/247280ab727)
is not shrunk as it flows through the pipeline, making us having
to walk through many deleted "tombstone" entries. Here is the
histogram of # of tombstones encountered during post order
computations for this model:
```
[ 1 - 1,536,345) ****************************** (1,300,248)
[1,536,345 - 3,072,690) (2)
[3,072,690 - 4,609,034) (364)
[4,609,034 - 6,145,378) (10,443)
```
To ameliorate this, this CL shrinks the vector periodically,
so far only between passes. This is done by running compaction
on the vector during HloComputation::Cleanup(), which is called
after every pass. Note that the removal operation must be stable
because some users depend on it -- I have added comments to
the code about this because it took me some time to realize the
extent of our users' reliance on this property. Fortunately,
I have seen no measurable performance difference between the stable
and unstable ("swap-remove") implementations.
This periodic shrinking of the vector brings the number of seen
tombstones down significantly:
```
[ 1 - 327,699) ****************************** (937,541)
[ 327,699 - 655,396) (308)
[ 655,396 - 983,094) (0)
[ 983,094 - 1,310,792) (1)
```
Note: we could further improve compaction by calling Cleanup()
from some passes, instead of just between passes. However, that
would not yield a significant gain; at least for this model,
scanning the instructions' vector now takes ~1% of total time
(vs. ~17% before).
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PR_kwDOArmXAs5qu9EX
| 64,472 |
Don't reset changed_ to false every time PerformSpaceToBatchOnConvolution is called.
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Don't reset changed_ to false every time PerformSpaceToBatchOnConvolution is called.
For example after PropagateOnUsers is called, new broadcast instructions are added so changed_ should be true. This makes it possible for the hlo module to be saved after this pass has rewrote or added new hlo instructions (module pertaining to the pass is only saved if there are changes).
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MultiWorkerMirrorStrategy Metrics Incorrectly Aggregating
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[
"Hi **@akrupien** ,\r\n\r\nI am providing an example of how you can structure your code to use MultiWorkerMirroredStrategy along with saving checkpoints and using callbacks. This example assumes you have a working model training pipeline and focuses on the tensorflow configuration, strategy setup, and saving checkpoints. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/71d3d3e16bd7392797fb22b77e2c5bfa/untitled140.ipynb) for reference.\r\n\r\nThank you!\r\n\r\n",
"Hi @Venkat6871,\r\n\r\nThank you very much for your response and your example! I have changed my code to match your structure, so I only build and compile my model within strategy.scope(). Synchronous training between my machines is still working which is great. I am still having the issue with my Dice Coefficients/Metrics. I'll attach a snippet of the training output here so you have an example. \r\n\r\nEpoch 1/400\r\n28/28 [==============================] - ETA: 0s - loss: 3.3312 - dice_coef: 0.0686 \r\nEpoch 1: val_loss improved from inf to 1.63522, saving model to /home/path/model1.h5\r\n28/28 [==============================] - 184s 4s/step - loss: 1.6656 - dice_coef: 0.0343 - val_loss: 1.6352 - val_dice_coef: 0.0303\r\n\r\nEpoch 2/400\r\n28/28 [==============================] - ETA: 0s - loss: 3.1451 - dice_coef: 0.1314 \r\nEpoch 2: val_loss improved from 1.63522 to 1.57489, saving model to /home/path/model1.h5\r\n28/28 [==============================] - 23s 833ms/step - loss: 1.5726 - dice_coef: 0.0657 - val_loss: 1.5749 - val_dice_coef: 0.0431\r\n\r\nEpoch 3/400\r\n28/28 [==============================] - ETA: 0s - loss: 3.0354 - dice_coef: 0.1781 \r\nEpoch 3: val_loss improved from 1.57489 to 1.53716, saving model to /home/path/model1.h5\r\n28/28 [==============================] - 23s 828ms/step - loss: 1.5177 - dice_coef: 0.0890 - val_loss: 1.5372 - val_dice_coef: 0.0577\r\n\r\nEpoch 4/400\r\n28/28 [==============================] - ETA: 0s - loss: 2.9451 - dice_coef: 0.2227 \r\nEpoch 4: val_loss improved from 1.53716 to 1.51450, saving model to /home/path/model1.h5\r\n28/28 [==============================] - 23s 831ms/step - loss: 1.4726 - dice_coef: 0.1114 - val_loss: 1.5145 - val_dice_coef: 0.0577\r\n\r\nEpoch 5/400\r\n28/28 [==============================] - ETA: 0s - loss: 2.8993 - dice_coef: 0.2236 \r\nEpoch 5: val_loss improved from 1.51450 to 1.49228, saving model to /home/path/model1.h5\r\n28/28 [==============================] - 23s 829ms/step - loss: 1.4496 - dice_coef: 0.1118 - val_loss: 1.4923 - val_dice_coef: 0.0577\r\n\r\nEpoch 6/400\r\n28/28 [==============================] - ETA: 0s - loss: 2.8554 - dice_coef: 0.2237 \r\nEpoch 6: val_loss improved from 1.49228 to 1.47076, saving model to /home/path/model1.h5\r\n28/28 [==============================] - 23s 834ms/step - loss: 1.4277 - dice_coef: 0.1119 - val_loss: 1.4708 - val_dice_coef: 0.0577 \r\n\r\nYou'll notice my dice coefficients are being divided by 2, I believe this is because I am using two machines.\r\n\r\nIt appears as though it is summing my dice's and losses from each Machine, and showing these values throughout the steps of the epoch, and then when I save it averages the dices and losses between the two machines. (I believe it is summing because of the loss values, my typical loss on a single machine after the first epoch is ~1.6, so a loss of 3.3 only seems achievable to me by summing the losses from each machine). If I turn off the checkpoint saving, it seems to average the dices and losses on the last step of the epoch. I would appreciate some clarification on if this is what is happening. Tensorflow describes NCCL or Ring All reduce to sum the variables between machines, they do not say whether variables get averaged back out. I'd expect it to, but they don't seem to say explicitly anywhere. I am also confused as to why they would show me the summed values during training rather than the averaged value, it seems as though it is training throughout the epoch on an accumulated dice and loss, rather than an average loss and dice between the machines. It would be correct in my mind to train throughout the epoch on the average between workers rather than the sum? Otherwise your loss is artificially high?\r\n\r\nThank you again,\r\n@akrupien",
"Any Ideas?",
"Hi **@warmbasket** ,\r\n- It is working for with example code, In order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. \r\n\r\nThank you!"
] | 2024-03-26T03:23:53 | 2024-06-11T05:32:24 | null |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hi,
I'm training a Model on Multiple Machines using MultiWorkerMirroredStrategy. I took my working Model from one machine, added it to another machine, and added the necessary Tf_Configs above the rest of my code. I declare MultiWorkerMirroredStrategy() and my training waits to commence until I run the code on the second Machine and it joins with the first.
I didn't find tensorflow's description of this very clear, but synchronization by step and by epoch did not commence unless I declared With Strategy.scope():.
Regardless, everything is working as hoped, except my Dice Coefficient is being halved on saving or the last epoch. By step and by epoch my Dice Values are synchronized, but regardless when I save them, they are getting halved, if I remove saving, they still get halved on the last step of the Epoch.
My first thought is that the all reduce algorithm is not summing correctly? Or is dividing by the number of machines when it shouldn't be? I know there's the Global Batch Size, but I'm not sure where in my code I could use this to correct the issue.
I know my dice equation isn't incorrect, has never had an issue before. Is the standard dice equation. Declaring the equation in strategy.scope() doesn't help.
Any suggestions or help would be greatly appreciated!
Thank you -
### Standalone code to reproduce the issue
```shell
tf config
miltiworkermirrorstrategy (using nccl or ring the issue happens)
with strategy.scope()
The rest to train and save your model with checkpoints and callbacks
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5qutjt
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Rename 'hybrid' to 'weight-only'
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Rename 'hybrid' to 'weight-only'
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PR_kwDOArmXAs5qunS9
| 64,469 |
Speed up DetermineHloInstructionIsReplicated.
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Speed up DetermineHloInstructionIsReplicated.
A reasonable chunk of time gets used in resizing hash sets after insertions. We can instead resize these hashsets before insertions.
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Promote TfLiteOperator functions from experimental to mostly stable.
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Promote TfLiteOperator functions from experimental to mostly stable.
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PR_kwDOArmXAs5qulCH
| 64,467 |
Pass in metadata of the consumer instruction when adding a new broadcast instruction.
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Pass in metadata of the consumer instruction when adding a new broadcast instruction.
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PR_kwDOArmXAs5quPX-
| 64,466 |
Remove dead values before shape refinement
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Remove dead values before shape refinement
Some programs have a lot of dead values, causing shape refinement to fail to
converge. Indeed, even if there is no shape refinement to do, greedy pattern
rewrites keep iterating as long as the IR changes, which includes removing dead
values.
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PR_kwDOArmXAs5quPMc
| 64,465 |
PR #10878: Disable Fusion of FP8 Matrix Bias
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PR #10878: Disable Fusion of FP8 Matrix Bias
Imported from GitHub PR https://github.com/openxla/xla/pull/10878
Disables the fusion of a matrix bias into a GEMM Custom Call when the bias has an FP8 data type.
Copybara import of the project:
--
83d29227b698059b56bed4d84ef44c0967a003fe by Philipp Hack <[email protected]>:
Disable FP8 matrix bias fusion.
Merging this change closes #10878
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10878 from philipphack:u_fp8_disable_bias_xla 83d29227b698059b56bed4d84ef44c0967a003fe
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PR_kwDOArmXAs5quNOZ
| 64,464 |
#tf-data Set `warm_start` to false when global shuffling is enabled.
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#tf-data Set `warm_start` to false when global shuffling is enabled.
When `warm_start` is enabled, some ops may use the wrong IteratorContext
to prefetch elements. For example:
https://github.com/tensorflow/tensorflow/blob/29561af231863afb3b6b89e3aa8a6a550c2b7bb0/tensorflow/core/kernels/data/prefetch_dataset_op.cc#L197-L199
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PR_kwDOArmXAs5quLPW
| 64,463 |
Change tf.IfrtLoadVariableOp to receive tensor future from
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Change tf.IfrtLoadVariableOp to receive tensor future from
IfrtRestoreTensorRegistry instead of from resource manager.
Correspondingly, IfrtLoadVariableRegistry is changed to
store a future of array.
This enables tfrt+ifrt to restore tensor asynchronously
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PR_kwDOArmXAs5quKjJ
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Create a util function `BuildGetTupleElementsForTupleResults` with proper processing on the shardings.
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Create a util function `BuildGetTupleElementsForTupleResults` with proper processing on the shardings.
**Issue 1.**
The following code snippet is widely used in `mlir_hlo_to_hlo.cc` when exporting to a XlaOp if the op has multiple results, such as RngBitGeneratorOp and OptimizationBarrierOp. We create a util function to replace the repeating code snippets.
```
for (auto [index, result] : llvm::enumerate(op.getResults())) {
value_map[result] = xla::GetTupleElement(tuple, index);
}
```
**Issue 2.**
When we build `xla::GetTupleElement` in the code snippet, we inherit the tuple sharding (if any) and assign the tuple sharding to the `xla::GetTupleElement`. This triggers a mismatching error on shape and sharding in XlaBuilder. This cl uses `XlaScopedShardingAssignment` to process it appropriately.
Enhance the test cases accordingly.
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If two calls to "ProductOfElementaryHouseholderReflectors" are in the same module with the same shape of the first operand, but different shapes of tau, one will end up expanded to the wrong computation.
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If two calls to "ProductOfElementaryHouseholderReflectors" are in the same module with the same shape of the first operand, but different shapes of tau, one will end up expanded to the wrong computation.
Append tau shape to the computation name when its there to avoid conflicts.
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PR_kwDOArmXAs5quE3p
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Remove CHECKs from op_profile_builder.cc
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Remove CHECKs from op_profile_builder.cc
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10835 from openxla:cudnn_pointwise_select 90c7996c704aeb1edde40243c2770654e9aca446
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PR_kwDOArmXAs5quEy4
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PR #10892: Fix some clang-tidy reported issues in xla/service
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PR #10892: Fix some clang-tidy reported issues in xla/service
Imported from GitHub PR https://github.com/openxla/xla/pull/10892
Fixes:
1. `~BufferAllocation() {}` - There is no needed to define empty dtor explicitly. Also, if dtor is explicitly defined we also need to define copy ctor and copy operator=. The right solution here is to not define dtor explicitly.
2. `std::move(target_config)` - `target_config` is const ref - it is not possible to move/modify const refs. std::move can be removed since it is not doing anything here.
3. `mutable_dot_config()->insert(MapPair)` - better to use `try_emplace(K&&, V&&)` directly because it accepts K&& and V&& params, where `insert` requires to create temp `MapPair` object. Internally `insert` uses `try_emplace`.
4. `const std::vector<bool> require_broadcast` - remove const from this param because `require_broadcast` will be moved in this function
5. add `std::move` to `set_auto_spmd_partitioning_mesh_shape` and `set_auto_spmd_partitioning_mesh_ids` to make them similar and consistent with other setter(std::vector) functions in `HloModuleConfig` class.
6. update callers of HloModuleConfig setter(std::vector) functions to use std::move or rvalue to avoid vector copy.
7. `chunk_(std::move(chunk))` - remove move because struct Chunk is trivially-copyable type. So, move have the same price as copy for such types.
8. `additional_sort_data_.insert(pair)` - replaced with `try_emplace(K&&, V&&)` to remove `std::make_pair`. Remove move from `std::move(sort_data)` - sort_data type AdditionalSortData is trivially-copyable type.
9. `std::move(*buffer_assignment_proto_after_opt)` - remove move because `buffer_assignment_proto_after_opt` is `const BufferAssignmentProto*` - it can not be moved/modified. move does nothing here.
10. return std::move(executable)` - move is not needed in return. It breaks RVO/NRVO compiler optimization.
11. `run_options_(std::move(run_options))` - `std::move` is removed because run_options type ExecutableRunOptions is trivially-copyable type. move has no effect here.
Copybara import of the project:
--
0ad7bde68fea25b626ef1e2ac99ca007151f8b0b by Alexander Pivovarov <[email protected]>:
Fix some clang-tidy reported issues in xla/service
Merging this change closes #10892
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10892 from apivovarov:bugs_server_server 0ad7bde68fea25b626ef1e2ac99ca007151f8b0b
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PR_kwDOArmXAs5qt6ss
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Updating FlatBuffers Internals
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Updating FlatBuffers Internals
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Ensure that the module we consume has no unused computations. This can causes issues as we clone modules to support try_multiple_mesh_shapes, and cloning an HLO module removes dead computations leading to mismatches.
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Ensure that the module we consume has no unused computations. This can causes issues as we clone modules to support try_multiple_mesh_shapes, and cloning an HLO module removes dead computations leading to mismatches.
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#tf-data Add a test for global shuffling `list_files` dataset.
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#tf-data Add a test for global shuffling `list_files` dataset.
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[XLA:Runtime] Moved the thunk target to the runtime folder.
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[XLA:Runtime] Moved the thunk target to the runtime folder.
Updated the necessary directories pointing to the thunk target.
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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.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10835 from openxla:cudnn_pointwise_select 90c7996c704aeb1edde40243c2770654e9aca446
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PR_kwDOArmXAs5qtyh4
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PR #10925: Include FP8 GEMMs in IsCublasGemm
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PR #10925: Include FP8 GEMMs in IsCublasGemm
Imported from GitHub PR https://github.com/openxla/xla/pull/10925
Adds FP8 GEMM Custom Calls to the set of ops considered in function IsCublasGemm.
Copybara import of the project:
--
8449c5ec4a1349fe54f211a2dbe4a0deb78a240b by Philipp Hack <[email protected]>:
Include FP8 GEMMs in function IsCublasGemm.
--
f09bcb8b208c6a74567093af505b26436bd3df9c by Philipp Hack <[email protected]>:
Include FP8 GEMMs in function IsCublasGemm.
Merging this change closes #10925
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10925 from philipphack:u_fp8_iscublas_xla f09bcb8b208c6a74567093af505b26436bd3df9c
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PR #10925: Include FP8 GEMMs in IsCublasGemm
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PR #10925: Include FP8 GEMMs in IsCublasGemm
Imported from GitHub PR https://github.com/openxla/xla/pull/10925
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Copybara import of the project:
--
8449c5ec4a1349fe54f211a2dbe4a0deb78a240b by Philipp Hack <[email protected]>:
Include FP8 GEMMs in function IsCublasGemm.
--
f09bcb8b208c6a74567093af505b26436bd3df9c by Philipp Hack <[email protected]>:
Include FP8 GEMMs in function IsCublasGemm.
Merging this change closes #10925
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10925 from philipphack:u_fp8_iscublas_xla f09bcb8b208c6a74567093af505b26436bd3df9c
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PR_kwDOArmXAs5qtveD
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Update ANDROID_NDK_API_LEVEL default in configure.py
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NONE
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Update ANDROID_NDK_API_LEVEL default in configure.py
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PR_kwDOArmXAs5qtvdk
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Add a few missing enums on XLA custom call version `API_VERSION_TYPED_FFI`.
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Add a few missing enums on XLA custom call version `API_VERSION_TYPED_FFI`.
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[xla:gpu][NFC] Add `update` accessor to HloDynamicUpdateSliceInstruction API
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[xla:gpu][NFC] Add `update` accessor to HloDynamicUpdateSliceInstruction API
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Integrate LLVM at llvm/llvm-project@e6f63a942a45
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Integrate LLVM at llvm/llvm-project@e6f63a942a45
Updates LLVM usage to match
[e6f63a942a45](https://github.com/llvm/llvm-project/commit/e6f63a942a45)
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Remove unnecessary const qualifiers from function definitions.
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Remove unnecessary const qualifiers from function definitions.
`absl::Span<const T>` is already a read-only interface to the elements of its container, so the const here is redundant.
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PR_kwDOArmXAs5qs_BS
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Enable SparseCore threads in TpuLayoutAssignment
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Enable SparseCore threads in TpuLayoutAssignment
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PR_kwDOArmXAs5qs8bc
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Remove unused ConvertFunctionToBef.
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Remove unused ConvertFunctionToBef.
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[xla:ffi] Pass device allocator and device ordinal to FFI execution context
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I_kwDOArmXAs6DhVSK
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TF 2.16 Incorrect model with TextVectorization in SavedModel format contains float input type instead of string type
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[
"@rkazants,\r\nThank you for reporting the issue. Looks like this issue is more related to Keras3.0. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/bcface2dd3959545e755c62f2ee439ce/untitled1786.ipynb). Could you please create a new issue on [keras-team/keras](https://github.com/keras-team/keras/issues/) for the quick resolution. Thank you!",
"> @rkazants, Thank you for reporting the issue. Looks like this issue is more related to Keras3.0. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/bcface2dd3959545e755c62f2ee439ce/untitled1786.ipynb). Could you please create a new issue on [keras-team/keras](https://github.com/keras-team/keras/issues/) for the quick resolution. Thank you!\r\n\r\nFilled here: https://github.com/keras-team/keras/issues/19396",
"@rkazants,\r\nCould you please feel free to move this issue to closed status, since it is already being tracked there. Thank you!\r\n",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64443\">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/64443\">No</a>\n",
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NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Model with TextVectorization layer after saving into SavedModel format using tf.saved_model.save contains float32 input instead of string type
### Standalone code to reproduce the issue
```shell
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, TextVectorization
from tensorflow.keras.models import Model
vocabulary = ['hello', 'there', 'random', 'vocab', 'check']
length = 64
text_input = Input(dtype=tf.string, shape=(1,), name='text_input')
vectorize_layer = TextVectorization(vocabulary=vocabulary, output_mode='int', name='vectorization_layer',
output_sequence_length=length)(text_input)
model = Model(inputs={'text_input': text_input}, outputs=[vectorize_layer])
input_data = np.array([['hello here and there']], dtype=str)
model({'text_input': input_data})
tf.saved_model.save(model, 'my_model')
loaded_model = tf.keras.layers.TFSMLayer('my_model', call_endpoint="serving_default")
loaded_model(input_data)
```
### Relevant log output
```shell
ValueError: could not convert string to float: 'hello here and there'
```
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[tsl] Mark `tsl::NullTerminatedMessage()` with `ABSL_DEPRECATE_AND_INLINE`
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Use the sharding of the appropriate tuple element rather than that of the entire tuple when computing input_shardings for get-tuple-element HLO ops with user sharding annotations.
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Use the sharding of the appropriate tuple element rather than that of the entire tuple when computing input_shardings for get-tuple-element HLO ops with user sharding annotations.
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[tsl] Implement `tsl::NullTerminatedMessage()` in terms of `absl::StatusMessageAsCStr()`
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[tsl] Implement `tsl::NullTerminatedMessage()` in terms of `absl::StatusMessageAsCStr()`
* This allows TF to outsource correct aquisition of a null-terminated C-string of the status message to the Abseil library itself
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Log checkpoint hashing error.
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Log checkpoint hashing error.
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PR_kwDOArmXAs5qsKWD
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[XLA:GPU] Implement new Triton Softmax codegen based on symbolic tiles and indexing maps.
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[XLA:GPU] Implement new Triton Softmax codegen based on symbolic tiles and indexing maps.
Symbolic tiles are still under development and can process all instructions. The new codegen is used only when all instruction in the fusion are supported, otherwise we fall back to the old codegen.
Triton Softmax codegen is behind `--xla_gpu_enable_triton_softmax_fusion` flag that is not turned on by default, so this change shouldn't affect any production workloads.
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[xla:ffi] Add support for passing array attributes
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[xla:ffi] Add support for passing array attributes
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[XLA:GPU][IndexAnalysis] Implement fusion of indexing maps with RTVars.
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[XLA:GPU][IndexAnalysis] Implement fusion of indexing maps with RTVars.
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[xla:ffi] Add support for array attributes to custom call thunk
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Change PReLU operation
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"Hi @pmasousa It looks like your PR relates to the Keras component. Please submit it to the github.com/keras-team/keras repository instead. Thankyou.\r\n@fchollet, @qlzh727"
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fix #63823
Not sure if I can use "if"s here but the original TF implementation isn't correct for all the possible cases, like the one in the issue. This implementation is used in many other libraries although it is slower.
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Reduce memory usage of convert_control_to_data_outputs.
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Reduce memory usage of convert_control_to_data_outputs.
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To ensure that C++20 is fully supported in TensorFlow,
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To ensure that C++20 is fully supported in TensorFlow,
Ubuntu image used in TF SIG Build Dockerfile upgraded from 20.04 to 22.04 (LTS).
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PR_kwDOArmXAs5qrp_p
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[XLA:GPU] Run canonicalizer and CSE after creating Triton module.
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PR_kwDOArmXAs5qrnwl
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Integrate LLVM at llvm/llvm-project@e6f63a942a45
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Integrate LLVM at llvm/llvm-project@e6f63a942a45
Updates LLVM usage to match
[e6f63a942a45](https://github.com/llvm/llvm-project/commit/e6f63a942a45)
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PR_kwDOArmXAs5qrjBV
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Fix build errors and warnings when building TF with oneDNN v2.x.
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Fix build errors and warnings when building TF with oneDNN v2.x.
- Add missing ENABLE_ONEDNN_V3 guards for oneDNN sparse matmul op since the CSR primitive is only available in oneDNN v3+.
- Add missing header includes and remove unused ones.
- Add missing build dependencies.
- Also fix the years in the copyright header. They were changed in PR #63030. The years shouldn't be changed when the files are updated.
Example error message:
```
In file included from tensorflow/core/kernels/mkl/mkl_batch_matmul_helper.h:25:
tensorflow/core/kernels/mkl/mkl_matmul_ops_common.h:927:38: error: no member named 'csr' in 'dnnl::memory::desc'
927 | const auto tmp = memory::desc::csr(
| ~~~~~~~~~~~~~~^
1 error generated.
```
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Avoid out-of-range crashes in hlo_proto_to_memory_visualization_utils.cc
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Avoid out-of-range crashes in hlo_proto_to_memory_visualization_utils.cc
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I_kwDOArmXAs6DfmY9
| 64,419 |
I am encountering an error: Error with computation = RQAComputation.create(settings, verbose=True) when I use HPC. However, I do not encounter any error when I use my personal laptop for the same code and installing the same package : from pyrqa.computation import RQAComputation
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[
"Hi @NOORLEICESTER ,\r\n\r\nCould you please provide more details.If this query is related to TFRanking then I think you should report same at Ranking repo.",
"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/64419\">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/64419\">No</a>\n",
"@SuryanarayanaY \r\n**The error:**\r\nTraceback (most recent call last):\r\n File \"/home/n/nnq2/PhD_env/RPs/RPs.py\", line 139, in <module>\r\n computation = RQAComputation.create(settings,\r\n File \"/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/computation.py\", line 98, in create\r\n return RQAEngine(settings,\r\n File \"/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/variants/rqa/radius/engine.py\", line 87, in __init__\r\n self.__initialise()\r\n File \"/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/variants/rqa/radius/engine.py\", line 93, in __initialise\r\n self.validate_opencl()\r\n File \"/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/variants/engine.py\", line 80, in validate_opencl\r\n self.opencl = OpenCL(verbose=self.verbose)\r\n File \"/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/opencl.py\", line 77, in __init__\r\n self.__initialise()\r\n File \"/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/opencl.py\", line 91, in __initialise\r\n self.create_environment()\r\n File \"/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/opencl.py\", line 277, in create_environment\r\n self.set_platform()\r\n File \"/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/opencl.py\", line 386, in set_platform\r\n platforms = cl.get_platforms()\r\npyopencl._cl.LogicError: clGetPlatformIDs failed: PLATFORM_NOT_FOUND_KHR\r\n\r\n**following is the code and the error is in line : computation = RQAComputation.create(settings, ....**"
] | 2024-03-25T15:57:09 | 2024-04-10T14:37:27 | 2024-04-10T01:47:37 |
NONE
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PR_kwDOArmXAs5qrJ8J
| 64,418 |
Add temporary backwards compatibility aliases that forward the old
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Add temporary backwards compatibility aliases that forward the old
names (TfLiteRegistrationExternal etc.) to the new names (TfLiteOperator etc.).
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_kwDOArmXAs5qrCLk
| 64,417 |
[xla:ffi] Change scalar attributes ABI and annotate error handling branches with XLA_FFI_PREDICT_FALSE
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[xla:ffi] Change scalar attributes ABI and annotate error handling branches with XLA_FFI_PREDICT_FALSE
In preparation for adding dense array attributes change scalar attributes ABI for consistency
Small regression in decoding attributes because of one extra branch.
name old cpu/op new cpu/op delta
BM_BufferBaseArgX1 9.51ns ± 5% 9.10ns ± 5% -4.29% (p=0.000 n=20+18)
BM_BufferBaseArgX4 17.4ns ± 7% 16.3ns ± 7% -6.15% (p=0.000 n=19+20)
BM_BufferArgX1 10.8ns ±11% 11.4ns ±15% +5.86% (p=0.010 n=20+20)
BM_BufferArgX4 19.3ns ± 8% 18.5ns ± 4% -3.99% (p=0.000 n=20+20)
BM_TupleOfI32Attrs 56.5ns ± 3% 60.9ns ± 3% +7.90% (p=0.000 n=20+20)
BM_BufferArgX1 regression looks like noise
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PR_kwDOArmXAs5qq3Tc
| 64,416 |
Delete the redundant compilation_cache_test
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NONE
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Delete the redundant compilation_cache_test
|
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I_kwDOArmXAs6DfAqZ
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Saving a model defined by model subclassing can not be saved
|
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[
"@PowerToThePeople111 Please refer to the TensorFlow documentation for the latest recommendations on saving subclassed models: https://www.tensorflow.org/tutorials/keras/save_and_load and kindly provide the access to your notebook.\r\nThank you! ",
"Hi sushreebarsa,\r\n\r\nthank you for your reply. You should now be able to access the notebook. Also I put the whole code neccessary below the link to it.\r\n\r\nI read the documentation again and was lead by the following parts:\r\n\r\n\r\n> You can switch to the SavedModel format by:\r\n> \r\n> Passing save_format='tf' to save()\r\n> Passing a filename without an extension\r\n> \r\n> ...\r\n> \r\n> *Custom objects (for example, subclassed models or layers) require special attention when saving and loading. Refer to the Saving custom objects section below.\r\n> \r\n> ...\r\n> \r\n\r\nSo for me it seemed that the above is basically everything i need to do since later on in the \"Custom objects\"-section you can read at the very top:\r\n\r\n> If you are using the SavedModel format, you can skip this section. \r\n\r\nSo actually I should be able to store the model by using `save()` with `save_format 'tf'` and providing a filename without ending, which is what I did:\r\nNeither `dp2.save(\"delteme\", save_format=\"tf\") ` nor `dp2.save(\"delteme\") ` did the trick for me and result in the error message i posted in my initial statement.\r\n\r\nDid I miss something here?\r\n\r\n\r\n",
"Hi @PowerToThePeople111 ,\r\n\r\nI don't think the problem in saving is actually coming from model subclassing. Most probably, it comes from passing `df` as argument to the `call` method.\r\n\r\nBy slightly changing your code to pass a list instead of a Pandas dataframe, the model can be saved.\r\n\r\n```@tf.keras.utils.register_keras_serializable(\"DataPrep2\")\r\nclass DataPrep2(tf.keras.Model):\r\n def __init__(self):\r\n super().__init__()\r\n self.layer1 = tf.keras.layers.StringLookup(output_mode='int', vocabulary=[\"a\", \"b\"])\r\n self.layer2 = tf.keras.layers.Discretization(bin_boundaries=[0,1,2,3,4], output_mode='int')\r\n\r\n\r\n def call(self, x):\r\n return {\r\n \"conv1\": self.layer1(x[0]),\r\n \"conv2\": self.layer2(x[1])\r\n }\r\ndp2 = DataPrep2()\r\ndp2([df[\"var1\"], df[\"var2\"]])\r\ndp2.save(\"delteme\")\r\n```",
"Thank you @karimsr4,\r\n\r\nthat solution works. :) ",
"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/64415\">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/64415\">No</a>\n"
] | 2024-03-25T14:50:28 | 2024-03-28T08:02:40 | 2024-03-28T08:02:36 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.15.1
### Custom code
No
### OS platform and distribution
Mac 14.2.1
### 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?
Hey all,
_Remark:_
I am using simple CPU computations and installed tf using pip (so i did not build it myself). Therefor, I did not provide any Cuda version.
_The problem I am facing:_
I am currently trying to save a model which was created by subclassing which throws me an error. (see below)
This is the code for the definition of the model ...
1. first using function API
2. secondly using model subclassing
The first one runs fine, the second throws an error during the last step where I try to save the model. As you can see, I called the model with the data before saving it (just as the error message suggests), but that still does not help. It seems to me that this is a bug, but maybe I have also missed something important.
```
df = pd.DataFrame({"var1": ["a", "b", "c"], "var2": [1, 2, 3]})
inp_var1 = tf.keras.layers.Input(shape=(1,))
inp_var2 = tf.keras.layers.Input(shape=(1,))
output1 = tf.keras.layers.StringLookup(output_mode='int', vocabulary=["a", "b"])(inp_var1)
output2 = tf.keras.layers.Discretization(bin_boundaries=[0,1,2,3,4], output_mode='int')(inp_var2)
dp = tf.keras.Model([inp_var1, inp_var2],[output1, output2])
converted = dp([df["var1"],df["var2"]])
for row in converted:
print(row)
dp.save("delteme1") #this works
class DataPrep2(tf.keras.Model):
def __init__(self):
super().__init__()
self.layer1 = tf.keras.layers.StringLookup(output_mode='int', vocabulary=["a", "b"])
self.layer2 = tf.keras.layers.Discretization(bin_boundaries=[0,1,2,3,4], output_mode='int')
def call(self, x):
return {
"conv1": self.layer1(x["var1"]),
"conv2": self.layer2(x["var2"])
}
dp2 = DataPrep2()
dp2(df)
dp2.save("delteme") #this does not
# same result with this line btw: dp2.save("delteme", save_format="tf")
```
Thanks for looking into this in advance.
best,
### Standalone code to reproduce the issue
The code is in a notebook here: https://colab.research.google.com/drive/1oaGNP902-MsN8o5OvY_FeH7pedeev8M_?usp=sharing
The code inline here:
```
import tensorflow as tf
import pandas as pd
df = pd.DataFrame({"var1": ["a", "b", "c"], "var2": [1, 2, 3]})
inp_var1 = tf.keras.layers.Input(shape=(1,))
inp_var2 = tf.keras.layers.Input(shape=(1,))
output1 = tf.keras.layers.StringLookup(output_mode='int', vocabulary=["a", "b"])(inp_var1)
output2 = tf.keras.layers.Discretization(bin_boundaries=[0,1,2,3,4], output_mode='int')(inp_var2)
dp = tf.keras.Model([inp_var1, inp_var2],[output1, output2])
converted = dp([df["var1"],df["var2"]])
for row in converted:
print(row)
dp.save("delteme1") #this works
@tf.keras.utils.register_keras_serializable("DataPrep2")
class DataPrep2(tf.keras.Model):
def __init__(self):
super().__init__()
self.layer1 = tf.keras.layers.StringLookup(output_mode='int', vocabulary=["a", "b"])
self.layer2 = tf.keras.layers.Discretization(bin_boundaries=[0,1,2,3,4], output_mode='int')
def call(self, x):
return {
"conv1": self.layer1(x["var1"]),
"conv2": self.layer2(x["var2"])
}
dp2 = DataPrep2()
dp2(df)
dp2.save("delteme") #this does not
# same result with this line btw: dp2.save("delteme", save_format="tf")
```
### Relevant log output
```shell
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-ece1b8e07810> in <cell line: 32>()
30 dp2 = DataPrep2()
31 dp2(df)
---> 32 dp2.save("delteme") #this does not
1 frames
/usr/local/lib/python3.10/dist-packages/keras/src/saving/legacy/saving_utils.py in raise_model_input_error(model)
95 # If the model is not a `Sequential`, it is intended to be a subclassed
96 # model.
---> 97 raise ValueError(
98 f"Model {model} cannot be saved either because the input shape is not "
99 "available or because the forward pass of the model is not defined."
ValueError: Model <__main__.DataPrep2 object at 0x7cfd49e6f8b0> cannot be saved either because the input shape is not available or because the forward pass of the model is not defined.To define a forward pass, please override `Model.call()`. To specify an input shape, either call `build(input_shape)` directly, or call the model on actual data using `Model()`, `Model.fit()`, or `Model.predict()`. If you have a custom training step, please make sure to invoke the forward pass in train step through `Model.__call__`, i.e. `model(inputs)`, as opposed to `model.call()`.
```
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PR_kwDOArmXAs5qqcYG
| 64,414 |
Remove angerson from workflow reviewers
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Remove angerson from workflow reviewers
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PR_kwDOArmXAs5qqans
| 64,413 |
Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/968eb3e5b0aa2e20301a41af9bb14a48dd1aee40.
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PR_kwDOArmXAs5qqVVQ
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Do not produce negative strides when building symbolic maps
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Do not produce negative strides when building symbolic maps
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Integrate LLVM at llvm/llvm-project@7ac7d418ac2b
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Integrate LLVM at llvm/llvm-project@7ac7d418ac2b
Updates LLVM usage to match
[7ac7d418ac2b](https://github.com/llvm/llvm-project/commit/7ac7d418ac2b)
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Automated Code Change
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PR_kwDOArmXAs5qpOyh
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Add deferring transposes of the input of `stablehlo.maximum` to output.
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Add deferring transposes of the input of `stablehlo.maximum` to output.
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PR_kwDOArmXAs5qpLv4
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Disable Tensor Cores for 8-bit x F32 dot.
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Disable Tensor Cores for 8-bit x F32 dot.
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PR_kwDOArmXAs5qo-3q
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PR #10835: [GPU] Add support of select operation in cuDNN fusions.
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PR #10835: [GPU] Add support of select operation in cuDNN fusions.
Imported from GitHub PR https://github.com/openxla/xla/pull/10835
Copybara import of the project:
--
90c7996c704aeb1edde40243c2770654e9aca446 by Ilia Sergachev <[email protected]>:
[GPU] Add support of select operation in cuDNN fusions.
Merging this change closes #10835
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10835 from openxla:cudnn_pointwise_select 90c7996c704aeb1edde40243c2770654e9aca446
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PR_kwDOArmXAs5qo7jx
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Integrate LLVM at llvm/llvm-project@b44771f48038
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Integrate LLVM at llvm/llvm-project@b44771f48038
Updates LLVM usage to match
[b44771f48038](https://github.com/llvm/llvm-project/commit/b44771f48038)
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PR_kwDOArmXAs5qo3L9
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[XLA:GPU] Remove unused RTVars when calling RemoveUnusedSymbols.
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[XLA:GPU] Remove unused RTVars when calling RemoveUnusedSymbols.
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PR_kwDOArmXAs5qotPF
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[xla:gpu] Handle OOB start indices in AddressComputationThunk
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[xla:gpu] Handle OOB start indices in AddressComputationThunk
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[XLA] Add the option to flatten function tuple arguments and result when converting HLO to MHLO.
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[XLA] Add the option to flatten function tuple arguments and result when converting HLO to MHLO.
Given that the added option is set to false by default, this change is a no-op for existing uses.
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Add support for symbol rescaling to the indexing map
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Add support for symbol rescaling to the indexing map
Rescaling allows symbols that are constrained by a modulus expression:
```
s_k mod C = [N, N]
```
to be replaced by:
```
s_k -> C * s_k + N
```
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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/b182e75065369d1eea499fe1d08b6c57d03da2ab.
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Integrate LLVM at llvm/llvm-project@b44771f48038
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Integrate LLVM at llvm/llvm-project@b44771f48038
Updates LLVM usage to match
[b44771f48038](https://github.com/llvm/llvm-project/commit/b44771f48038)
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Automated Code Change
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Consistently handle algorithm selection in GemmAlgorithmPicker
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Consistently handle algorithm selection in GemmAlgorithmPicker
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Refer to `_quantization_method` for per-channel quantization of conv layers.
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Refer to `_quantization_method` for per-channel quantization of conv layers.
Currently per-channel quantization is controlled by a global field [`enable_per_channel_quantized_weight`](http://google3/third_party/tensorflow/compiler/mlir/quantization/stablehlo/quantization_config.proto;l=55;rcl=617473690).
This change allows using `Method` instead for determining per-channel quantization for convolution op pattern family.
It allows a consistent, local reasoning for determining per-channel quantization, whereas previously it relied on some global pre-defined configuration such as [`GetStableHloOpQuantSpec`](http://google3/third_party/tensorflow/compiler/mlir/quantization/stablehlo/ops/stablehlo_op_quant_spec.cc;l=66;rcl=615942585). Moreover, it allows granular control of per-channel quantization (per-quantizable-unit), whereas `enable_per_channel_quantized_weight` is essentially a global flag that cannot provide granular control.
After this change, per-channel quantization can be specified by setting the `Granularity` field of `QuantizedType` in `Method`.
For example, a `Method` for per-channel quantization at quantization dimension = 3 for the operand index of 1 is represented as:
```txtpb
static_range_ptq {
input_quantized_types {
key: 1
value {
granularity {dimension_specs {dimension: 3}}
}
}
}
```
Per-channel quantization is then achieved in two places:
* In `PrepareQuantizePass`, where it refers to `OpQuantSpec` (now populated using `Method`) to insert Q/DQ pairs with per-channel quantized types.
* In `QuantizePass`, where it looks up the `Method` to tell the `RewriteGemmStyleOp` to perform per-channel quantization.
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Conv2D computes wrongly in Windows OS
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"Hi **@Shuo-Sun20** ,\r\nI tried to run your code on Colab using TF v2.16.1 and i am not facing any issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/b9af30cf64d9451c5100271495d67fca/64396_2-16-1.ipynb) here for reference.\r\n\r\nThank you!",
"This issue only exists on Windows OS, so on Colab(linux OS) this issue will not show up. \r\nPlease try it on Windows.",
"@Shuo-Sun20 \r\n\r\n\r\n1. \r\nUse following code, I got same result in both linux & windows:\r\n```\r\nfrom keras.layers import Conv2D\r\nimport numpy as np\r\n\r\nx=np.random.rand(1,2,2,1)\r\nl=Conv2D(1,3,(1,1),'valid','channels_last', [1,1],1, 'linear', True)\r\nprint(l(x).shape)\r\nprint(l.compute_output_shape(x.shape))\r\n\r\n(1, 2, 2, 1)\r\n(1, 0, 0, 1)\r\n```\r\n\r\ntensorflow 2.16.1\r\nkeras 3.1.1\r\n\r\n2. The kernel is 3, but the input is <3. It's strange case.\r\nCould you confirm if such input parameters are right?",
"1. You are right, kernel_size > input_size should be an invalid parameter combination, while now Conv2D can generate a reseult without warning. Maybe a checker should exist here?\r\n2. The behavior of Conv2D is diffenrent on Linux and Windows (since I failed to reproduce it on colab), this inconsisdent may need deeper inspection.",
"@Shuo-Sun20 \r\n1. For the checker of abnormal input, could report it as a feature in another issue?\r\n2. I run the case and got same result in both windows and linux.\r\nYou said they were different in your case.\r\nCould you share the whole logs of them?\r\nAnd share the result of 'pip list' in windows and linux.\r\n",
"1. I'll report it in another issue.\r\n2. I run the following code in both windows and linux:\r\n```\r\nfrom keras.layers import Conv2D\r\nimport numpy as np\r\n\r\nx=np.random.rand(1,2,2,1)\r\nl=Conv2D(1,3,(1,1),'valid','channels_last', [1,1],1, 'linear', True)\r\nprint(l(x).shape)\r\nprint(l.compute_output_shape(x.shape))\r\n```\r\n\r\nIn windows the result is\r\n```\r\n2024-04-07 16:35:31.739978: 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`.\r\n2024-04-07 16:35:32.406322: 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`.\r\n2024-04-07 16:35:33.580266: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n(1, 2, 2, 1)\r\n(1, 0, 0, 1)\r\n```\r\n\r\nThe pip list in windows:\r\n```\r\nabsl-py==2.1.0\r\nastunparse==1.6.3\r\ncertifi==2024.2.2\r\ncharset-normalizer==3.3.2\r\nflatbuffers==24.3.25\r\ngast==0.5.4\r\ngoogle-pasta==0.2.0\r\ngrpcio==1.62.1\r\nh5py==3.10.0\r\nidna==3.6\r\nimportlib_metadata==7.1.0\r\nkeras==3.1.1\r\nlibclang==18.1.1\r\nMarkdown==3.6\r\nmarkdown-it-py==3.0.0\r\nMarkupSafe==2.1.5\r\nmdurl==0.1.2\r\nml-dtypes==0.3.2\r\nnamex==0.0.7\r\nnumpy==1.26.4\r\nopt-einsum==3.3.0\r\noptree==0.11.0\r\npackaging==24.0\r\nprotobuf==4.25.3\r\nPygments==2.17.2\r\nrequests==2.31.0\r\nrich==13.7.1\r\nsix==1.16.0\r\ntensorboard==2.16.2\r\ntensorboard-data-server==0.7.2\r\ntensorflow==2.16.1\r\ntensorflow-intel==2.16.1\r\ntensorflow-io-gcs-filesystem==0.31.0\r\ntermcolor==2.4.0\r\ntyping_extensions==4.11.0\r\nurllib3==2.2.1\r\nWerkzeug==3.0.2\r\nwrapt==1.16.0\r\nzipp==3.18.1\r\n```\r\n\r\n\r\nwhile in this linux [colab](https://colab.research.google.com/drive/1OVe3gBmZGeog36-sDGMxT7j6D-Qs29Ap?usp=sharing), the result is:\r\n```\r\n(1, 0, 0, 1)\r\n(1, 0, 0, 1)\r\n```\r\n\r\nI failed to install tensorflow-intel 2.16.1 in colab(linux), so I just installed tensorflow and keras using regular pip command. \r\nYou can freely edit the code in the [colab](https://colab.research.google.com/drive/1OVe3gBmZGeog36-sDGMxT7j6D-Qs29Ap?usp=sharing) and install packages.\r\n\r\nThe pip list is a little different since colab pre installs many packages used in Deep Learning. The list is too long to show in this comment. You can find it yourself with the shared [link](https://colab.research.google.com/drive/1OVe3gBmZGeog36-sDGMxT7j6D-Qs29Ap?usp=sharing).",
"@Shuo-Sun20 \r\nIn windows, my result is same as yours.\r\nIn linux, when enable oneDNN path in TF by TF_ENABLE_ONEDNN_OPTS=1, the result is same as windows.\r\nIf TF_ENABLE_ONEDNN_OPTS=0, the result is same as yours (different with windows).\r\n\r\nI think it's the different between oneDNN code and Eigen code of TF.\r\n\r\nBut the following code can't work in colab env you provide.\r\nI guess the TF in colab doesn't support oneDNN code path.\r\nMaybe you could try in local/another linux.\r\n\r\n```\r\nimport os\r\nos.environ['TF_ENABLE_ONEDNN_OPTS']='1'\r\n\r\nfrom keras.layers import Conv2D\r\nimport numpy as np\r\n\r\nx=np.random.rand(1,2,2,1)\r\nl=Conv2D(1,3,(1,1),'valid','channels_last', [1,1],1, 'linear', True)\r\nprint(l(x).shape)\r\nprint(l.compute_output_shape(x.shape))\r\n```\r\n\r\n"
] | 2024-03-25T07:25:12 | 2024-04-09T08:26:26 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Windows 10
### 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?
On Windows OS, Conv2D generates a wrong output in some cases, while performs correctly on some others.
This error does not occur on Linux OS, even with the same code.
An wrong execution example:

You can tell that the result `l(x)` has a wrong shape.
I notice that an exisiting issue #63860 points out the similar error in Conv3D. I guess Conv2D and Conv3D have similar problem since they have the same parent class BaseConv.
### Standalone code to reproduce the issue
```shell
#This test case works fine on linux OS, while goes wrongly on Windows.
from keras.layers import Conv2D
import numpy as np
x=np.random.rand(1,2,2,1)
print(l(x).shape)
print(l.compute_output_shape(x.shape))
```
### Relevant log output
```shell
TensorShape([1, 2, 2, 1])
(1, 0, 0, 1)
```
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ValueError occurred when building cascaded model with loaded sub-model
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[
"Hi, @tilakrayal \r\n\r\nI've tested on co-lab whether this problem can be reproduced with TensorFlow Nightly.\r\nThen the error occurred on there, so i updated the description of this issue above 👆 \r\n\r\nThanks!",
"@keisen,\r\nBy default Tensorflow v2.16 uses keras v3.0 which might be the reason for the error you are facing. As the work-around, Could you please try to install keras 2 where the code was executed without any issue/error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/59285ac9a54836f764cc271f6f4ebde4/untitled1778.ipynb).\r\n\r\n```python\r\n!pip install -U tf_keras # Keras 2\r\nimport os\r\nos.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"\r\n```\r\n\r\nThank you!",
"@tilakrayal , Thank you for the quick response!\r\n\r\n> As the work-around, Could you please try to install keras 2 where the code was executed without any issue/error.\r\n\r\nUnfortunately, I can't choose the way you suggested. so i hope it will be fixed asap...\r\nPlease let me know another way or when you expect it to be done.\r\n\r\nThanks!",
"@keisen,\r\nAs mentioned above, it is the workaround for the temporary usage. Since this request is specific to Keras 3.0, could you please close here and open a new issue in Keras repo https://github.com/keras-team/keras/issues for quick resolution. \r\nThank you!\r\n",
"@tilakrayal \r\n\r\nYeah, I thought you might say so. So I did it already lol\r\nhttps://github.com/keras-team/keras/issues/19374\r\n\r\nThank you for your help.\r\nRegards!",
"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/64393\">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/64393\">No</a>\n"
] | 2024-03-25T07:05:52 | 2024-03-27T09:53:58 | 2024-03-27T09:53:55 |
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
No
### OS platform and distribution
Ubuntu on Windows 10 WSL2
### 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?
Hi, tensorflow team!
I'm facing a error when build cascaded model with sub-model loaded from a file.
It's okay if the sub-model is NOT loaded from file, just created on memory.
This behavior appeared form tf 2.16+. Until tf 2.15-, it didn't exist definitely.
Please improve this issue or let me know how to work around....
Thanks!
### Standalone code to reproduce the issue
```shell
https://gist.github.com/keisen/4703d6508a950ff6a35d84da73e335b8
```
### Relevant log output
```shell
ValueError Traceback (most recent call last)
<ipython-input-7-2fefc420998f> in <cell line: 1>()
----> 1 model = create_cascaded_model(loaded_child_model)
2 model.summary()
<ipython-input-5-e10dc8c731da> in create_cascaded_model(_child_model)
3 x = tf.keras.layers.Conv2D(3, 3, padding='same', activation='relu', name='conv_1')(inputs)
4 x = _child_model(x)
----> 5 x = tf.keras.layers.Dense(1, name='dense_2')(x)
6 return tf.keras.models.Model(inputs=inputs, outputs=x)
/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py in error_handler(*args, **kwargs)
120 # To get the full stack trace, call:
121 # `keras.config.disable_traceback_filtering()`
--> 122 raise e.with_traceback(filtered_tb) from None
123 finally:
124 del filtered_tb
/usr/local/lib/python3.10/dist-packages/keras/src/backend/common/variables.py in standardize_dtype(dtype)
431
432 if dtype not in ALLOWED_DTYPES:
--> 433 raise ValueError(f"Invalid dtype: {dtype}")
434 return dtype
435
ValueError: Invalid dtype: tuple
```
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