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I_kwDOArmXAs5lQ7tc
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Iterating over dataset produces different label distribution statistics.
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[
"Hi @WillRL ,\r\n\r\nThanks for reporting. Would you please provide some dummy dataset similar to yours for replicating the issue. It will help us for analysing the issue. Thanks!",
"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/60525\">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/60525\">No</a>\n"
] | 2023-05-07T07:30:53 | 2023-05-24T01:58:06 | 2023-05-24T01:58:03 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
2.10.1
### Custom Code
No
### OS Platform and Distribution
Windows 10 GPU
### Mobile device
Windows 10 GPU
### Python version
3.10
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
cuda 11.8, cudaNN 8.6.0
### GPU model and memory
_No response_
### Current Behaviour?
Iterating over tf.data.Dataset object and counting the number of occurrences of each label produces different results each time.
I initially thought that this may be caused by batching and shuffling (With dropping remainders), however, there is no batching of the dataset and it is not shuffling after each iteration as well.
```python
datasets = [val_dataset, val_dataset, val_dataset] # Using same dataset three times. expect to see same results each three times.
table_data = [[],[],[]]
amounts = np.zeros(len(labels)) # Allocating array outside to be more memory efficient.
counter = 0
for i, ds in enumerate(datasets):
ds_size = int(ds.__len__())
amounts = amounts*0
total = 0
for _, label in ds:
label = int(label)
amounts[label] += 1
counter += 1
for amount in amounts:
table_data[i].append(f"{amount} ({(100*amount/ds_size):.1f}%)")
table = pd.DataFrame(table_data, columns=labels, index=['ds 1', "ds 2", "ds 3"])
display(table)
```
Table displayed shows:
The name of columns are the classes, we expect to see each row to have the same value as other rows of the same column, however, we don't. The counter reports back the correct amount of images. For reference, the dataset used has 39209 images. split 0.8. 0.1 and 0.1 for train, val, test. It is a supervised dataset.
### Standalone code to reproduce the issue
```shell
dataset = keras.utils.image_dataset_from_directory("to_some_dir",
interpolation='bilinear',
shuffle=True,
image_size=(64, 64),
batch_size=None)
dataset_size = int(dataset.__len__())
train_size = int(0.8 * dataset_size)
val_size = int(0.1 * dataset_size)
test_size = int(0.1 * dataset_size)
train_dataset = dataset.take(train_size)
val_dataset = dataset.skip(train_size).take(val_size)
test_dataset = dataset.skip(train_size).skip(val_size)
datasets = [val_dataset, val_dataset, val_dataset] # Using the same dataset three times for test only.
table_data = [[],[],[]]
amounts = np.zeros(len(labels)) # Allocating array outside to be more memory efficient.
counter = 0
for i, ds in enumerate(datasets):
ds_size = int(ds.__len__())
amounts = amounts*0
total = 0
for _, label in ds:
label = int(label)
amounts[label] += 1
counter += 1
for amount in amounts:
table_data[i].append(f"{amount} ({(100*amount/ds_size):.1f}%)")
table = pd.DataFrame(table_data, columns=labels, index=['Train', "Validation", "Test"])
display(table)
```
### Relevant log output
_No response_</details>
|
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I_kwDOArmXAs5lQq0b
| 60,524 |
Cannot find the source code -> 'gen_stateless_random_ops'
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[
"Hi @Yeojoon, \r\nThe (gen_*_.py) are automatically generated files and can be found in the local Tensorflow installation. Kindly check in your local Tensorflow installation. Thank you!",
"Oh I see. Thank you for your kind explanation!",
"@Yeojoon, could you please confirm if the issue is resolved? Thank you! ",
"Oh sorry. I am going to close the issue now!",
"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/60524\">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/60524\">No</a>\n"
] | 2023-05-07T03:13:49 | 2023-05-10T20:06:23 | 2023-05-10T20:06:20 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Others
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
tensorflow 2.12.0
### Custom Code
No
### OS Platform and Distribution
Ubuntu 20.04.6 LTS
### Mobile device
_No response_
### Python version
3.9.7
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
12.0
### GPU model and memory
_No response_
### Current Behaviour?
from tensorflow.python.ops import gen_stateless_random_ops (in https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/ops/stateless_random_ops.py)
I would like to check the source code for 'gen_stateless_random_ops', but I cannot find this. Could you please help me with this? Thank you for your help in advance!
### Standalone code to reproduce the issue
```shell
N/A
```
### Relevant log output
_No response_</details>
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Empty Data Batches Passed to Custom Layer
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[
"Hi @Amzo ,\r\n\r\nThanks for reporting.Unfortunately I don't have access to the source you provided. If possible provide the code snippet in the form of gist which can accessed by all. Thanks!",
"I've updated permissions on the original notebook so that it is accessible, if still not accessible I can create a gist.",
"Hi @Amzo,\r\n\r\nI have replicated the reported behaviour. The code works fine with TF2.10 but failing with Tf2.12 and tf-nightly as well. \r\n\r\nPlease refer to attached gists [2.10v](https://colab.research.google.com/drive/1WYVyMANTPQXEnAVfV-cNO0f5OZclmLwC?resourcekey=0-rBUY0Wn3JuCUmW6FHXVErA) , [2.12v](https://colab.research.google.com/gist/SuryanarayanaY/c8ff57e4eb48adbbe6ea9fb8ceaa8eb8/60523_2-12.ipynb) and [tf-nightly](https://colab.research.google.com/gist/SuryanarayanaY/cfa165c6682618a9fb778c4031f4b1eb/60523_nightly-2-14-0-dev20230508.ipynb).\r\n\r\nIt needs to dig into more for the root cause. Thanks!",
"This could be due to the recent changes in the Keras saving format since 2.11 version mentioned in the commit here https://github.com/keras-team/keras/commit/c9068087d9142bab573e0c300bf9874a957accff.\r\n\r\n",
"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/60523\">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/60523\">No</a>\n"
] | 2023-05-06T14:05:17 | 2023-06-09T02:07:36 | 2023-06-09T02:07:27 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
2.12.0
### Custom Code
Yes
### OS Platform and Distribution
Linux 5.10.147
### Mobile device
_No response_
### Python version
3.10.11
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
When using a Data Generator with a Custom layer, the data passed to the layer call method is empty.
However, the same code works as intended in tensorflow 2.10.0, and only seems to pass empty batches to the call method in a custom layer in 2.12.0.
I have a attached a notebook showing the issue. It first runs the code using tensorflow 2.12.0, then downgrades to tensorflow 2.10.0 then reruns the same notebook, and the error is gone.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1E1lDWQWKbo6t0VQmzGEFwSEOCxTJ3t2S?usp=sharing
```
### Relevant log output
```shell
ValueError: in user code:
File "/usr/local/lib/python3.10/dist-packages/keras/engine/training.py", line 1284, in train_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.10/dist-packages/keras/engine/training.py", line 1268, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.10/dist-packages/keras/engine/training.py", line 1249, in run_step **
outputs = model.train_step(data)
File "/usr/local/lib/python3.10/dist-packages/keras/engine/training.py", line 1050, in train_step
y_pred = self(x, training=True)
File "/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/tmp/__autograph_generated_fileny5tmqhi.py", line 13, in tf__call
patches = ag__.converted_call(ag__.ld(tf).reshape, (ag__.ld(patches), [ag__.ld(batch_size), -1, ag__.ld(patch_dims)]), None, fscope)
ValueError: Exception encountered when calling layer 'patches' (type Patches).
in user code:
File "<ipython-input-4-1c4669f3e34b>", line 16, in call *
patches = tf.reshape(patches, [batch_size, -1, patch_dims])
ValueError: Tried to convert 'shape' to a tensor and failed. Error: None values not supported.
Call arguments received by layer 'patches' (type Patches):
• images=tf.Tensor(shape=(None, 224, 224, None), dtype=float32)
```
</details>
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PR_kwDOArmXAs5P6eoI
| 60,522 |
Update LeakyRelu.pbtxt
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[
"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60522/checks?check_run_id=13282241299) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"Please don't use \"add file\"/\"update file\"/\"fix file\"/etc. commit messages. These are hard to reason about when looking at the history of the file/repository. Instead, please write explanatory git commit messages.\r\n\r\nThe commit message is also the title of the PR if the PR has only one commit. It is thus twice important to have commit messages that are relevant, as PRs would be easier to understand and easier to analyze in search results.\r\n\r\nFor how to write good quality git commit messages, please consult https://cbea.ms/git-commit/ ",
"Hi,\r\nThanks for helping me improve on my commit message format and also notifying me as to what to do next\r\nShould I continue to work on my pull request or should I close it as it causes an inconvenience for the tensorflow GitHub and also for moderators?\r\n\r\n",
"@cantonios Is my understanding above correct? I'm guessing we'd need to only allow integral types for CPU but not for GPU. Is that possible to do in the same op registration or do we need to have separate op registrations if we want to do this? \r\n\r\nAre there use cases where LeakyRelu is needed to work for integral types? If not, do you think we should just modify the documentation? ",
"> @cantonios Is my understanding above correct? I'm guessing we'd need to only allow integral types for CPU but not for GPU. Is that possible to do in the same op registration or do we need to have separate op registrations if we want to do this?\r\n\r\nYour understanding is correct that we need to update the op Attrs to include the type. If registrations don't exist, you might want to add those as well to actually test it. You'll need to ensure the kernels will actually work for integers - I haven't looked at the implementation to check. Otherwise you may need to write new ones. If it's not that necessary for GPU, it's okay to leave out those kernels/registrations - it would just end up calling the CPU versions (and lead to some host-device data transfers for GPU models).\r\n\r\n> Are there use cases where LeakyRelu is needed to work for integral types? If not, do you think we should just modify the documentation?\r\n\r\nMaybe for integer-quantized models? But I generally doubt it. It's probably easier to update the documentation.\r\n",
"Hi @swachhandl Any update on this PR? Please. Thank you!",
"Hi @swachhandl Any update on this PR? Please. Thank you!",
"Hi @swachhandl Any update on this PR? Please. Thank you!",
"Sorry I think I didn't get notified for the above pings. I don't have anything to add, awaiting the PR author's reply to my comment above. ",
"This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Hi @01-Abdul-Hadi Can you please check @swachhandl's comments and keep us posted ? Thank you!",
"Hi @01-Abdul-Hadi Any update on this PR? Please. Thank you!",
"This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2023-05-06T10:37:07 | 2024-04-07T01:49:02 | 2024-04-07T01:48:47 |
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https://github.com/tensorflow/tensorflow/issues/60521 issue no. #60521
"According to doc(https://tensorflow.google.cn/api_docs/python/tf/nn/leaky_relu), the argument features can be float16, float32, float64, int32, int64. But the error message in following snippet code indicates that the type of features can only be float16, float32, float64 without int." This is my first contribution to open source so please excuse any mistake and please tell if anything is wrong.
|
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I_kwDOArmXAs5lPcBX
| 60,521 |
error message is inconsistent with documentation in tf.nn.leaky_relu
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[
"Hi. Is anyone working on the issue to update the docs/error message? Would love to take it up and start working on the issue"
] | 2023-05-06T09:03:07 | 2023-06-21T16:45:00 | null |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf 2.12.0
### Custom Code
Yes
### OS Platform and Distribution
win11
### 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 Behaviour?
According to [doc](https://tensorflow.google.cn/api_docs/python/tf/nn/leaky_relu), the argument `features` can be `float16, float32, float64, int32, int64`. But the error message in following snippet code indicates that the type of `features` can only be `float16, float32, float64` without `int`.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
results={}
try:
features = [True]
results["res"] = tf.nn.leaky_relu(features=features,)
except Exception as e:
results["err"] = "Error:"+str(e)
print(results)
# results={'err': "Error:Value for attr 'T' of bool is not in the list of allowed values: half, bfloat16, float, double\n\t; NodeDef: {{node LeakyRelu}}; Op<name=LeakyRelu; signature=features:T -> activations:T; attr=alpha:float,default=0.2; attr=T:type,default=DT_FLOAT,allowed=[DT_HALF, DT_BFLOAT16, DT_FLOAT, DT_DOUBLE]> [Op:LeakyRelu]"}
```
### Relevant log output
_No response_</details>
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PR_kwDOArmXAs5P6Uer
| 60,520 |
[NextPluggableDevice] Disable alignment check for AsyncValueTensor
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[
"@jyingl3 can you have a look? thanks",
"Thanks Zhoulong! Do you think we should make kTag = 0x1ULL declared in tensor.h, and let async_value_tensor.h use it? then it will make sure they will check the same value. But I am also fine with keeping this change as-is if the suggestion does not seem appropriate for tensor.h.\r\n\r\n@chuanhaozhuge @qqfish FYI.",
"Hi Zhoulong, I'm wondering if you could provide some concrete motivation on disabling the alignment check? We didn't run into any trouble in our internal usage so just wondering. Thanks!",
"> Thanks Zhoulong! Do you think we should make kTag = 0x1ULL declared in tensor.h, and let async_value_tensor.h use it? then it will make sure they will check the same value. But I am also fine with keeping this change as-is if the suggestion does not seem appropriate for tensor.h.\r\n> \r\n> @chuanhaozhuge @qqfish FYI.\r\n\r\n@jyingl3 If the async_value_tensor.h use this kTag, this kTag needs to be defined as a global variable in tensor,h, it may have issue if .h defines a global variable, all the .cc file which includes the tensor.h will have a copy definition of this kTag, that may cause some duplicate definition if it has the same definition, such as in [xla_tensor.cc](https://github.com/tensorflow/tensorflow/blob/ded1c57d60255621a4f5c885002fb709ce5bb470/tensorflow/compiler/jit/xla_tensor.cc#L110), it also defined this kTag",
"> Hi Zhoulong, I'm wondering if you could provide some concrete motivation on disabling the alignment check? We didn't run into any trouble in our internal usage so just wondering. Thanks!\r\n\r\n@chuanhaozhuge Some custom kernels(such as `TensorList` related kernels) doesn't provide C API , for these kernels, instead of implementing these kernels in plugin, proper defines a common kernels for plugin and use device's memcpy/memset/memzero to implemement these kernels. For these kernels, we need to use tensor.flat<>() to access the content of tensor(for example, [SetZero](https://github.com/tensorflow/tensorflow/blob/22ea643435838346eeee6b9dfdcca91a59f3553a/tensorflow/core/kernels/list_kernels.h#L82) in list_kernel.h), that will trigger the alignment check and thus failed on AsyncValueTensor\r\n\r\n",
"@jzhoulon Thanks for explanation Zhoulong. If I understand right, the use case is to pass AsyncValueTensor to a regular TF Kernel (like this [example](https://github.com/tensorflow/tensorflow/blob/bebc84ec0035e86bc47309b7499ceed5e427064c/tensorflow/core/kernels/matmul_op_impl.h#L386)). I'm wondering do you plan to update the kernel further so that the tensor can be interpreted as AsyncValueTensor? If so maybe [FromTensor](https://github.com/tensorflow/tensorflow/blob/a4b2d805546a9e4ba4c05c48884f419b85f1cc45/tensorflow/core/tfrt/common/async_value_tensor.h#L36) will be good for the use case?",
"> @jzhoulon Thanks for explanation Zhoulong. If I understand right, the use case is to pass AsyncValueTensor to a regular TF Kernel (like this [example](https://github.com/tensorflow/tensorflow/blob/bebc84ec0035e86bc47309b7499ceed5e427064c/tensorflow/core/kernels/matmul_op_impl.h#L386)). I'm wondering do you plan to update the kernel further so that the tensor can be interpreted as AsyncValueTensor? If so maybe [FromTensor](https://github.com/tensorflow/tensorflow/blob/a4b2d805546a9e4ba4c05c48884f419b85f1cc45/tensorflow/core/tfrt/common/async_value_tensor.h#L36) will be good for the use case?\r\n\r\n@chuanhaozhuge Thanks for the suggestion, I think it is a good solution for those regular kernels using `FromTensor` to retrive the AsyncValueTensor. I will think how to refactor the code, however, in current stage, I think the change may be big, beacuse lots of code is like: convert tensor to Eigen Tensor through flat, shaped in common path first, then dispatch to backend implmentation, such as cpu, gpu, pluggable device. modify the common path code is unclean and since PluggableDevice device type can only get through runtime, so I can't write another kernel with template for it. \r\n\r\nfor example,the following code is in common path.\r\n`\r\n inputs_flat.emplace_back(new typename TTypes<T, 2>::ConstMatrix(\r\n const_cast<const Tensor&>(zeros).shaped<T, 2>(\r\n {1, zeros.NumElements()})));\r\n`\r\n ` auto output_flat = output->shaped<T, 2>({1, output->NumElements()});` \r\n\r\n\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/3ac5290c3c5bab0fca6bb769840102ae5952f735/tensorflow/core/kernels/list_kernels.h#L248\r\n```\r\n }\r\n inputs_flat.emplace_back(new typename TTypes<T, 2>::ConstMatrix(\r\n const_cast<const Tensor&>(zeros).shaped<T, 2>(\r\n {1, zeros.NumElements()})));\r\n }\r\n }\r\n auto output_flat = output->shaped<T, 2>({1, output->NumElements()});\r\n\r\n#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM\r\n if (std::is_same<Device, Eigen::GpuDevice>::value) {\r\n ConcatGPU<T>(c, inputs_flat, output, &output_flat);\r\n return;\r\n }\r\n#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM\r\n if (IsPluggableDevice(c)) {\r\n ConcatPluggableDevice<T>(c, inputs_flat, &output_flat);\r\n } else {\r\n ConcatCPU<T>(c->device(), inputs_flat, &output_flat);\r\n }\r\n }\r\n```\r\n\r\n ",
"Thanks for the explanation Zhoulong. I do still think that code like `auto ptr = se::DeviceMemoryBase(tensor.flat<T>().data(), tensor.TotalBytes());` won't work even with alignment check disabled (perhaps will crash?) on AsyncValueTensor (since the [void* data_](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.h#L105) in TensorBuffer is AVT). I don't have intention to block this PR though, just in case you have further development to resolve this issue.",
"> \r\nyes, StreamExecutor based API won't work in here for NextPluggableDevice. As we discussed before, we may have PjRt C API or NextPluggableDevice C API which provides memcpy/memzero/memset functionality. pjrt device context's CopyInSameDevice also need an C API provides memcpy . And as the above code showed, to keep kernel struct, there are some common path which convert tensor to eigen tensor through flat<>, so I think it is better to solve this issue first. thanks. @jyingl3 @chuanhaozhuge \r\n",
"Let's take a step back - If the method IsAlign is not meaningful when AsyncValueTensor (NextPluggableDevice) is used, can we disable it? \r\n\r\nHow to handle StreamExecutor based API is not finalized yet. Will the decision of how to handle StreamExecutor based API affect whether to disable IsAlign?",
"> Let's take a step back - If the method IsAlign is not meaningful when AsyncValueTensor (NextPluggableDevice) is used, can we disable it?\r\n> \r\n> How to handle StreamExecutor based API is not finalized yet. Will the decision of how to handle StreamExecutor based API affect whether to disable IsAlign?\r\n\r\n@jyingl3 @chuanhaozhuge for the second question, I think the StreamExecutor based API handle(backend specific) will not affect those common path(convert tensor to Eigen Tensor through flat), so I think it is better to disable IsAlign, or do you have another design choice? thanks\r\n\r\n```\r\n auto output_flat = output->shaped<T, 2>({1, output->NumElements()});\r\n\r\n#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM\r\n if (std::is_same<Device, Eigen::GpuDevice>::value) {\r\n ConcatGPU<T>(c, inputs_flat, output, &output_flat);\r\n return;\r\n }\r\n#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM\r\n if (IsPluggableDevice(c)) {\r\n ConcatPluggableDevice<T>(c, inputs_flat, &output_flat);\r\n } else {\r\n ConcatCPU<T>(c->device(), inputs_flat, &output_flat);\r\n }\r\n }\r\n```",
"Discussed with @chuanhaozhuge. Let's proceed with this change to disable IsAlign. Thanks Zhoulong!"
] | 2023-05-06T07:58:03 | 2023-05-24T21:36:52 | 2023-05-24T21:36:52 |
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This PR is to disable alignment check for AsyncValueTensor, the reason why I didn't use ```AsyncValueTensor* AsyncValueTensor::FromOpaquePointer() ```to check whether the pointer is AsyncValueTensor is async_value_tensor.h include tensor.h, I can;t include the decleartion(async_value_tensor.h) in tensor.h , and async_value_tensor and tensor.h(core/framework) has cycle dependency, so I didn't find a good way to handle this, and just hardcode to check the whether the last bit is 1 or not to indicate whether it is an AsyncValueTensor.
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I_kwDOArmXAs5lOyN3
| 60,519 |
type of argument x in tf.math.rsqrt should be float
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[
"@cheyennee,\r\nI tried to execute the mentioned snippet1 code with the different alternative approaches like as Float int, only int, float and bool and only bool and it was executed as intended with the single datatype and where as with the combination it was providing the result. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/47660e46d1a5448a4b4e0b0ef9943f21/untitled1111.ipynb) and I suspect when we are providing the multiple datatypes, it might be considering it as the strings and providing the output. \r\n\r\nAnd for the second code snippet, please take a look at this issue for the reference. Thank you!\r\nhttps://github.com/tensorflow/tensorflow/issues/43494\r\n",
"@tilakrayal According to you，in snippet1 code, when there are multiple datatypes, the input will be considered as the strings. Does this mean that `tf.math.rsqrt` can accept string type argument? I think this is inconsistent with the documentation.\r\nI read the content on issue #43494, and understand why the issue occur on snippet2 code, thx.",
"@cheyennee,\r\nCould you please confrim if you are facing the same issue whenever you are trying to execute the code with the single datatype provided in the document? Thank you!",
"@tilakrayal Yes, I confirm that with the single datatype. In single datatype, it works normally.",
"@cheyennee,\r\nRSQRT is supported as a builtin operator, could you please check the issue at https://github.com/tensorflow/tensorflow/issues/58048.\r\nAlso, refer to the comment https://github.com/tensorflow/tensorflow/issues/58048#issuecomment-1276584557 for the code file reference to the registered RSQRT builtin op.\r\n\r\nAlso in the documentation of the **rsqrt()** and it was observed that the issue was that the operator couldn't handle negative values as input so adding a tf.math.abs() before rsqrt solved the issue. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60519\">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/60519\">No</a>\n"
] | 2023-05-06T02:02:11 | 2023-06-21T01:58:57 | 2023-06-21T01:58:54 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf 2.12.0
### Custom Code
Yes
### OS Platform and Distribution
win11
### 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 Behaviour?
According to [doc](https://tensorflow.google.cn/api_docs/python/tf/math/rsqrt), the argument of `x` in `tf.math.rsqrt` should be `bfloat16, half, float32, float64`. But when the values of `x` includes both float and bool, it wont throw an exception as following snippet code 1 shows, which is unexpected. What's more, snippet code 2 indicates that the type of `x` can be complex, which is inconsistent with the documentation.
### Standalone code to reproduce the issue
Snippet code 1:
```
import tensorflow as tf
results={}
try:
x_0 = 0.0
x_1 = 1024
x_2 = -1.0
x_3 = -1.0
x_4 = 0.0
x_5 = False
x_6 = True
x = [x_0,x_1,x_2,x_3,x_4,x_5,x_6,]
results["res"] = tf.math.rsqrt(x=x,)
except Exception as e:
results["err"] = "Error:"+str(e)
print(results)
# results={'res': <tf.Tensor: shape=(7,), dtype=float32, numpy=
array([ inf, 0.03125, nan, nan, inf, inf, 1. ],
dtype=float32)>}
```
Snippet code 2:
```
import tensorflow as tf
results={}
try:
x = 3
results["res"] = tf.math.rsqrt(x=x,)
except Exception as e:
results["err"] = "Error:"+str(e)
print(results)
# results={'err': "Error:Value for attr 'T' of int32 is not in the list of allowed values: bfloat16, half, float, double, complex64, complex128\n\t; NodeDef: {{node Rsqrt}}; Op<name=Rsqrt; signature=x:T -> y:T; attr=T:type,allowed=[DT_BFLOAT16, DT_HALF, DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128]> [Op:Rsqrt]"}
```
### Relevant log output
_No response_</details>
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I_kwDOArmXAs5lObfe
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Network-Level Control of Precision (fp32 in training, fp16 in inference)
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"Hi @shshao-iceice ,\r\n\r\nCould you please elaborate more on this Feature request. Right now we can use mixed_precision(to enable FP16) along with XLA. ",
"> Hi @shshao-iceice ,\r\n> \r\n> Could you please elaborate more on this Feature request. Right now we can use mixed_precision(to enable FP16) along with XLA.\r\n\r\nI think `tf.keras.backend.set_floatx` and `mixed_precision` both do this to some extent, but I guess they only take effect on Keras layers? I hope there is a better option that can choose precision mode for the whole model.",
"Hi @shshao-iceice ,\r\n\r\nBoth the APIs `tf.keras.backend.set_floatx`, `mixed_precision` can be used to enable FP16 for Model training.For numeric stability issues mixed precision, which is using a mix of float16 and float32 is preferred. Mixed precision policy once initiated applicable to all layers of model by default unless we set dtype explicitly at layer level. For Model training Tf recommends use of `mixed_precision` for better accuracy. AFAIK TensorRT also works for inference only but no role in model training.\r\n\r\nFor Model inference with lower precision TFlite is one way that can also be leveraged. Model built with XLA support can also be used for Model Inference with TensorRT right ? Correct me if i am wrong here. Thanks!",
"> Hi @shshao-iceice ,\r\n> \r\n> Both the APIs `tf.keras.backend.set_floatx`, `mixed_precision` can be used to enable FP16 for Model training.For numeric stability issues mixed precision, which is using a mix of float16 and float32 is preferred. Mixed precision policy once initiated applicable to all layers of model by default unless we set dtype explicitly at layer level. For Model training Tf recommends use of `mixed_precision` for better accuracy. AFAIK TensorRT also works for inference only but no role in model training.\r\n> \r\n> For Model inference with lower precision TFlite is one way that can also be leveraged. Model built with XLA support can also be used for Model Inference with TensorRT right ? Correct me if i am wrong here. Thanks!\r\n\r\nYes, XLA is perfect for training, but we are exploring using it in inference and replacing TensorRT. From my understanding, TFlite is mainly built for edge devices so it probably doesn't fit here?",
"Hi @shshao-iceice ,\r\n\r\nYes. TFlite is used for edge devices. So you requirement is that XLA should be usable in inference just like as TensorRT. AFAIK and based on the resource [here](https://developer.nvidia.com/tensorrt#:~:text=TensorRT%2C%20built%20on%20the%20NVIDIA,and%20others%20on%20NVIDIA%20GPUs.) , TensorRT also works on same concept of Fusion(Tensor/Layers) which also XLA's single most important optimization.\r\n\r\nI am just curious to know whether XLA may give better performance than TensorRT.",
"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."
] | 2023-05-05T22:48:10 | 2023-05-27T01:54:17 | 2023-05-27T01:54:17 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Feature Request
### Have you reproduced the bug with TF nightly?
Yes
### Source
binary
### Tensorflow Version
tf 2.12
### Custom Code
No
### OS Platform and Distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
A very common use case is that we train and export the model in fp32, then use fp16 mode in inference. But it seems XLA doesn't support this?
It would be good if XLA can support [Network-Level Control of Precision](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#network-level-control) like TensorRT
### Standalone code to reproduce the issue
```shell
N/A
```
### Relevant log output
_No response_</details>
|
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PR_kwDOArmXAs5P5QAe
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r2.13 cherry-pick: Update tensorboard dependency to ~=2.13, < 2.14
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r2.13 cherry-pick: 0bf8d38519a "Update curl to 7.88.1"
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[
"(Merge only after RC0)"
] | 2023-05-05T18:54:37 | 2023-05-26T00:14:22 | 2023-05-26T00:14:18 |
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Refer to the original commit: https://github.com/tensorflow/tensorflow/commit/0bf8d38519a2470b81c51d618399ba8836f0afe9
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Speech recognition : No code is present
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Please add source code for android for speech recognition currently only resources file is present
https://github.com/tensorflow/examples/tree/master/lite/examples/speech_recognition/android/app/src/main/res
@synandi @tensorflow-copybara can u please help
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I_kwDOArmXAs5lM0OW
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How to convert KerasTensor to numpy array or TensorFlow EagerTensor?
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"@kiratben,\r\nThe Keras Tensor cannot be converted to Numpy array directly, You can please try to convert the Keras tensor to Tensor and from the Tensor you can convert to the numpy.\r\n\r\nAlso please have a look at this comment from the form discussion and the SO [link](https://stackoverflow.com/questions/69067933/how-to-convert-keras-tensor-into-numpy-array) for the reference.\r\nhttps://discuss.tensorflow.org/t/get-values-of-kerastensor/2423\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/60514\">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/60514\">No</a>\n"
] | 2023-05-05T16:07:01 | 2023-05-24T01:58:09 | 2023-05-24T01:58:05 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Others
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.9.2
### 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 Behaviour?
I have a KerasTensor object with shape (None, 128, 128, 1) that I need to pass to an OpenCV function. However, I'm having trouble converting the KerasTensor to either a numpy array or a TensorFlow EagerTensor that can be accepted by the function. Specifically, I want to convert the KerasTensor to a format that is compatible with OpenCV's input requirements. Any suggestions on how to achieve this conversion?
I tried using tensor.numpy() to convert the KerasTensor to a numpy array, but I got an AttributeError: 'KerasTensor' object has no attribute 'numpy'.
### Standalone code to reproduce the issue
```python
import tensorflow as tf
from tensorflow import keras
# Define the shape of the input tensor
input_shape = (128, 128, 1)
# Create an input tensor with the specified shape
input_tensor = keras.layers.Input(shape=input_shape)
# Create a model that uses the input tensor
x = keras.layers.Conv2D(32, 3, activation='relu', padding='same')(input_tensor)
x = keras.layers.Conv2D(64, 3, activation='relu', padding='same')(x)
output_tensor = keras.layers.Conv2D(1, 3, activation='sigmoid', padding='same')(x)
print(type(output_tensor)) # <class 'keras.engine.keras_tensor.KerasTensor'>
# Convert the output tensor to a numpy array
print(type(output_tensor.numpy())) # AttributeError: 'KerasTensor' object has no attribute 'numpy'
print(type(tf.keras.backend.eval(output_tensor))) #AttributeError: 'KerasTensor' object has no attribute 'numpy'`
```
### Relevant log output
_No response_</details>
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tf.math.is_non_decreasing should throw an exception when argument x isn't numeric
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[
"Same issue can be found in `tf.math.is_strictly_increasing`.",
"Hi @cheyennee ,\r\n\r\nThanks for reporting. The behaviour seems coming from `tf.reshape` which is being used internally by the API `tf.math.is_non_decreasing`.I have attached the [gist](https://colab.research.google.com/gist/SuryanarayanaY/35fa14f9b0cb644dd920146eac5971dc/60513.ipynb) for analysis of same and for reference.\r\n\r\nFor example:\r\nCode1:\r\n```\r\nx=[-1,1,True]\r\nx=tf.reshape(x,[-1])\r\nprint('after reshaping: ',x)\r\nif not is_numeric_tensor(x):\r\n raise TypeError('Expected x to be numeric, instead found: %s' % x)\r\n\r\n#Output: after reshaping: tf.Tensor([-1 1 1], shape=(3,), dtype=int32)\r\n```\r\nCode2: \r\n\r\n```\r\nx=[True,False,True]\r\nx= tf.reshape(x,[-1])\r\nprint('after reshaping: ',x)\r\nif not is_numeric_tensor(x):\r\n raise TypeError('Expected x to be numeric, instead found: %s' % x)\r\n\r\n#output:after reshaping: tf.Tensor([ True False True], shape=(3,), dtype=bool)\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n[<ipython-input-23-1e79c028007d>](https://localhost:8080/#) in <cell line: 4>()\r\n 3 print('after reshaping: ',x)\r\n 4 if not is_numeric_tensor(x):\r\n----> 5 raise TypeError('Expected x to be numeric, instead found: %s' % x)\r\n\r\nTypeError: Expected x to be numeric, instead found: tf.Tensor([ True False True], shape=(3,), dtype=bool)\r\n```\r\n\r\nNeed to check reasons for this behaviour. Thanks!\r\n\r\n",
"Hi @cheyennee ,\r\n\r\nApologies for the late response. I have gone through the issue and identified the reason for this behaviour.\r\n\r\nThe API `tf.math.is_non_decreasing `expects the input to be an numeric Tensor. If we pass python bool it will be converted to 0 or 1 . This is the expected behaviour with python bool data types. If we pass bool tensor then it is raising the intended error. Please refer to attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/dfb67fb0c5534c499222c6853f8f602f/60513_r1.ipynb) for the tests done. ",
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] | 2023-05-05T15:06:43 | 2023-06-21T22:03:14 | 2023-06-21T22:03:11 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf 2.12.0
### Custom Code
Yes
### OS Platform and Distribution
win11
### 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 Behaviour?
According to [doc](https://tensorflow.google.cn/api_docs/python/tf/math/is_non_decreasing), the argument `x` should be numeric tensor. When the values of the argument `x` only contain bool, it throws an exception as following snippet code 1 shows. However, when the values of the argument `x` are contained with int and bool value, it wont throw an exception as snippet code 2 shows. I suppose that it may cast bool type to int type when there're both int and bool value in argument `x`, but I think this conversion is unexpected.
### Standalone code to reproduce the issue
```shell
snippet code 1:
import tensorflow as tf
results={}
try:
x_0 = True
x_1 = True
x_2 = False
x = (x_0,x_1,x_2,)
results["res"] = tf.math.is_non_decreasing(x=x,)
except Exception as e:
results["err"] = "Error:"+str(e)
print(results)
# results={'err': 'Error:Expected x to be numeric, instead found: tf.Tensor([ True True False], shape=(3,), dtype=bool)'}
```
snippet code 2:
```
import tensorflow as tf
results={}
try:
x_0 = -16
x_1 = 16
x_2 = False
x = (x_0,x_1,x_2,)
results["res"] = tf.math.is_non_decreasing(x=x,)
except Exception as e:
results["err"] = "Error:"+str(e)
print(results)
# results={'res': <tf.Tensor: shape=(), dtype=bool, numpy=False>}
```
```
### Relevant log output
_No response_</details>
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[
"@KathyGCY - TensorFlow 2.3 is 2+ years old (July'2000) and was released for Python 3.5-3.8 (https://pypi.org/project/tensorflow/2.3.0/#files). You have a Python 3.9 Env. Please use a more recent TensorFlow version, preferably TensorFlow 2.12 (latest).",
"Going to close this issue. Please re-open if you have problem with installing newer version of TensorFlow in your Python 3.9 env. Follow steps in https://www.tensorflow.org/install/pip#macos\r\nIf you have a Mac M1/M2, there are instructions in the installs page that's slightly different.",
"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/60512\">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/60512\">No</a>\n"
] | 2023-05-05T14:48:01 | 2023-05-08T17:49:07 | 2023-05-08T17:49:04 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.3.0
### Custom Code
Yes
### OS Platform and Distribution
_No response_
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
`ERROR: No matching distribution found for tensorflow==2.3.0`
### Standalone code to reproduce the issue
```shell
pip install -U --index-url https://pypi.org/ tensorflow==2.3.0
```
### Relevant log output
```shell
Same issue as here: "No matching distribution found for tensorflow" when installing with pip on macOS 11 #47205, https://github.com/tensorflow/tensorflow/issues/47205
The issue did not resolve but was closed.
The solution doesn't work it errored with "ERROR: No matching distribution found for tensorflow==2.3.0"
```
</details>
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arguments in tf.math.greater should be the same type
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[
"Same issue can be found in `tf.math.greater_equal`.",
"Same issue can be found in `tf.math.less` 、 `tf.math.less_equal` 、`tf.math.maximum`、`tf.math.minimum`. As following code shows, when argument `x` and `y` have different types, it wont throw an exception.\r\n```\r\nimport tensorflow as tf\r\nresults={}\r\ntry:\r\n x = [-1.7,-1.5]\r\n y = 8\r\n results[\"res\"] = tf.math.less(x=x,y=y,)\r\nexcept Exception as e:\r\n results[\"err\"] = \"Error:\"+str(e)\r\nprint(results)\r\n# results={'res': <tf.Tensor: shape=(2,), dtype=bool, numpy=array([ True, True])>}\r\n```\r\n```\r\nimport tensorflow as tf\r\nresults={}\r\ntry:\r\n x_tensor = tf.random.uniform([16], dtype=tf.bfloat16)\r\n x = tf.identity(x_tensor)\r\n y = 2\r\n results[\"res\"] = tf.math.maximum(x=x,y=y,)\r\nexcept Exception as e:\r\n results[\"err\"] = \"Error:\"+str(e)\r\nprint(results)\r\n# results={'res': <tf.Tensor: shape=(16,), dtype=bfloat16, numpy=array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], dtype=bfloat16)>}\r\n```",
"@cheyennee,\r\nI tried to replicate the code on tf-nightly and also tried the code with alternative input values as well. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/0abeabafcbb96c557f335fce9b2d88b3/untitled1108.ipynb).\r\n\r\n\r\n```\r\n #x = 16\r\n #print(type(x))\r\n #y_tensor = tf.random.uniform([4, 1, 1], dtype=tf.float32)\r\n #y = tf.identity(tf.random.uniform([4, 1, 1], dtype=tf.float32))\r\n #print(y)\r\n results[\"res\"] = tf.math.greater(x=tf.identity(tf.random.uniform([4, 1, 1], dtype=tf.float32)),y=tf.identity(tf.random.uniform([4, 1, 1], dtype=tf.float32))) \r\n print(results)\r\n\r\narray([[[False]],\r\n\r\n [[False]],\r\n\r\n [[ True]],\r\n\r\n [[ True]]])>}\r\n\r\n #x = 16\r\n #print(type(x))\r\n #y_tensor = tf.random.uniform([4, 1, 1], dtype=tf.float32)\r\n #y = tf.identity(tf.random.uniform([4, 1, 1], dtype=tf.float32))\r\n #print(y)\r\n results[\"res\"] = tf.math.greater(x=tf.identity(tf.random.uniform([4, 1, 1], dtype=tf.float16)),y=tf.identity(tf.random.uniform([4, 1, 1], dtype=tf.float16))) \r\n print(results)\r\n\r\narray([[[ True]],\r\n\r\n [[ True]],\r\n\r\n [[False]],\r\n\r\n [[ True]]])>}\r\n```",
"@tilakrayal Hiii, I appreciate you taking the time to provide me with the gist of the issue. I have reviewed the information you shared and the repro results are the same as mine. Please let me know if these APIs do have issues.",
"@cheyennee,\r\nI tried to execute the mentioned code by providing `x=16 (int)` and `y=1.2 (float)`, and it was failing as intended. Whereas with the code you mentioned the input 'y' was converting to the **tf.Tensor** which might be the reason the code was not failing. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/484b364414e77b07627f0c13834be3ee/untitled1244.ipynb).\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/60511\">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/60511\">No</a>\n"
] | 2023-05-05T14:29:59 | 2023-07-30T01:52:22 | 2023-07-30T01:52:20 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf 2.12.0
### Custom Code
Yes
### OS Platform and Distribution
win11
### 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 Behaviour?
According to [doc](https://tensorflow.google.cn/api_docs/python/tf/math/greater), the argument `x` and `y` in `tf.math.greater` should be the same type. However, in following snippet code, the type of argument `x` is int and the type of argument `y` is float32, it doesnt throw an exception, which is inconsitent with the doc.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
results={}
try:
x = 16
y_tensor = tf.random.uniform([4, 1, 1], dtype=tf.float32)
y = tf.identity(y_tensor)
results["res"] = tf.math.greater(x=x,y=y,)
except Exception as e:
results["err"] = "Error:"+str(e)
print(results)
results = 'res': <tf.Tensor: shape=(4, 1, 1), dtype=bool, numpy=
array([[[ True]],
[[ True]],
[[ True]],
[[ True]]])>
```
```
### Relevant log output
_No response_</details>
|
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I_kwDOArmXAs5lL_mZ
| 60,510 |
Accuracy metric appears to behave incorrectly with multiple outputs
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[
"I discovered that this does not occur if you replace the Lambda layers with two single neuron dense outputs. I guess you might still want to investigate why the Lambda layers make it behave like that. I suppose it could cause issues if someone happened to need one at the output. Otherwise, feel free to close it.",
"Hi @jtolladay ,\r\n\r\nThe Metrics will be resetted normally after each epoch till then the states will be getting updated which is default behaviour.Here as we have 800 samples and the accuracy will be multiple of 1/800 i.e.0.00125 and for validation_data also it will be multiples of 1/200 and it's been followed in logs. Correct me if I am wrong here.\r\n\r\nYou can also refer to the low level handling of Metrics section of custom training loop from scratch from the [documentation](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch#low-level_handling_of_metrics). However with custom training loop we can change the behaviour as per our requirement.\r\n\r\nThanks!",
"Please see my original post:\r\n\r\n\"In the log you can see that the accuracies on the training set (with 80 batches of size 10) go up in steps of **0.0**125 (1/80). The validation set (containing 20 batches of size 10) go up in steps of **0.0**5 (1/20). It must be somehow calculating the fraction of the batches that are \"correct\" instead of the number of correct predictions out of all the batches.\"\r\n\r\nLike I said, I fixed the problem by replacing the size 2 output and lambda layers with just two separate dense layers with 1 neuron each. I assume the issue with the lambda layers was that they make the shape of the tensor unusual when they split the outputs, so the metric was averaging along the wrong dimension or something. Whether that's an issue with the lambda layers or my implementation, I am not sure. If it is not a bug then again, feel free to close it.",
"Hi @jtolladay ,\r\n\r\nI am curious to explore this case more. I have stored the training checkpoints to a history object. I tried to multiply the accuracies(both accuracy and val_accuracy) with total no of samples(800) and total no of batches(80) to check which will output a whole number. \r\n\r\nFor the case with Lambda layers for accuracy both total samples and batches produces whole numbers and multiples of 10.\r\nBut for validation accuracies both fail to return whole number for some cases.\r\n\r\nWhereas the case without Lamda layers ( replaced with Dense layer) I found accuracy is exact multiple of no of samples(800) but not batches(80) which indicates that accuracy is calculated on no of samples only but not batch.\r\nWhere as for validation accuracy neither no of samples(200) nor batches(20) produces whole numbers.\r\n\r\nIt seems for Lambda layer both no of samples and no of batches producing whole numbers but at the same time it is important to note that for all cases the result is multiple of batches only.This needs to be checked. Also for validation accuracies none of them producing the whole numbers which is also an issue to look into.\r\n\r\nI am sharing the [gist](https://colab.research.google.com/gist/SuryanarayanaY/451158dd070f1db50176598fbe1c9d80/60510.ipynb) for demonstration of same. Please go through and share your opinion and correct me if anything is wrong here.\r\n\r\n\r\n\r\n\r\n",
"Hi @SuryanarayanaY,\r\n\r\nI tried this code:\r\n\r\n```\r\nimport numpy as np\r\nimport tensorflow as tf\r\n\r\ntf.keras.utils.set_random_seed(45)\r\n\r\n# define the data\r\nx = np.random.random(1000)\r\ny1 = np.random.randint(0, 2, 1000)\r\ny2 = np.random.randint(0, 2, 1000)\r\n\r\n# define the models\r\ndef model_lambda():\r\n input = tf.keras.layers.Input(shape=(1,))\r\n model = tf.keras.layers.Dense(64, activation='relu')(input)\r\n model = tf.keras.layers.Dense(2, activation='sigmoid')(model)\r\n out1 = tf.keras.layers.Lambda(lambda x: x[..., 0], name='out1')(model)\r\n out2 = tf.keras.layers.Lambda(lambda x: x[..., 1], name='out2')(model)\r\n model = tf.keras.models.Model(input, [out1, out2])\r\n return model\r\n\r\ndef model_dense():\r\n input = tf.keras.layers.Input(shape=(1,))\r\n model = tf.keras.layers.Dense(64, activation='relu')(input)\r\n out1 = tf.keras.layers.Dense(1, activation='sigmoid', name='out1')(model)\r\n out2 = tf.keras.layers.Dense(1, activation='sigmoid', name='out2')(model)\r\n model = tf.keras.models.Model(input, [out1, out2])\r\n return model\r\n\r\nfor model_func, model_name in zip([model_lambda, model_dense], ['lambda model', 'dense model']):\r\n # configure losses and metrics for each output\r\n losses = {'out1': 'binary_crossentropy', 'out2': 'binary_crossentropy'}\r\n weights = {'out1': 1.0, 'out2': 1.0}\r\n metrics = {'out1': 'accuracy', 'out2': 'accuracy'}\r\n model = model_func()\r\n model.compile(loss=losses, loss_weights=weights, optimizer='adam', metrics=metrics)\r\n\r\n # fit\r\n history = model.fit(x, [y1, y2], batch_size=10, epochs=20, verbose=0, validation_split=0.2)\r\n\r\n # analyse\r\n print('----------', model_name, '----------')\r\n out1, out2 = model.predict(x)\r\n print('out1 shape:', out1.shape, ', out2 shape:', out2.shape)\r\n for metric in ['out1_accuracy', 'out2_accuracy', 'val_out1_accuracy', 'val_out2_accuracy']:\r\n accuracies = np.array(history.history[metric])\r\n diffs = np.diff(np.sort(accuracies))\r\n min_diff = np.min(diffs[diffs != 0])\r\n print(metric, 'is out of', np.round(1 / min_diff))\r\n```\r\n\r\nand got this as the log output:\r\n\r\n---------- lambda model ----------\r\nout1 shape: (1000,) , out2 shape: (1000,)\r\nout1_accuracy is out of 80.0\r\nout2_accuracy is out of 80.0\r\nval_out1_accuracy is out of 20.0\r\nval_out2_accuracy is out of 20.0\r\n---------- dense model ----------\r\nout1 shape: (1000, 1) , out2 shape: (1000, 1)\r\nout1_accuracy is out of 800.0\r\nout2_accuracy is out of 800.0\r\nval_out1_accuracy is out of 200.0\r\nval_out2_accuracy is out of 200.0\r\n\r\nWhich would appear to confirm what I was finding. I printed the shape of the outputs because I believe it is this different shape which is causing the accuracy to be predicted differently. As I said in my previous post, the issue here is probably that the reshaping done in the lambda layers is done incorrectly, rather than the lambda layers themselves being the issue. I don't have more time to test this now, but I think it's likely my code, rather than tensorflow that is the issue.",
"I had a few minutes and confirmed what I thought to be the issue. Modifying the lambda model to be like so:\r\n\r\n```\r\ndef model_lambda():\r\n input = tf.keras.layers.Input(shape=(1,))\r\n model = tf.keras.layers.Dense(64, activation='relu')(input)\r\n model = tf.keras.layers.Dense(2, activation='sigmoid')(model)\r\n out1 = tf.keras.layers.Lambda(lambda x: x[..., 0])(model)\r\n out1 = tf.keras.layers.Reshape((1,), name='out1')(out1)\r\n out2 = tf.keras.layers.Lambda(lambda x: x[..., 1])(model)\r\n out2 = tf.keras.layers.Reshape((1,), name='out2')(out2)\r\n model = tf.keras.models.Model(input, [out1, out2])\r\n return model\r\n```\r\n\r\nResults in the correct output that we would expect:\r\n\r\n---------- lambda model ----------\r\nout1 shape: (1000, 1) , out2 shape: (1000, 1)\r\nout1_accuracy is out of 800.0\r\nout2_accuracy is out of 800.0\r\nval_out1_accuracy is out of 200.0\r\nval_out2_accuracy is out of 200.0\r\n---------- dense model ----------\r\nout1 shape: (1000, 1) , out2 shape: (1000, 1)\r\nout1_accuracy is out of 800.0\r\nout2_accuracy is out of 800.0\r\nval_out1_accuracy is out of 200.0\r\nval_out2_accuracy is out of 200.0",
"@jtolladay ,\r\n\r\nYeah it seems the difference is due to your custom model with different output shapes. Thanks for confirming the same. Shall we marked it as resolved now. please spare some time for closing if resolved for you. Thanks!",
"Happy to close as it is more an issue with my code rather than with tensorflow itself.",
"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/60510\">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/60510\">No</a>\n"
] | 2023-05-05T13:41:16 | 2023-06-06T13:11:04 | 2023-06-06T13:11:01 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
2.7.0
### Custom Code
Yes
### OS Platform and Distribution
Windows 10
### Mobile device
_No response_
### Python version
3.9.9
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
In the example below, the training set is 800 records, validation set is 200 records and batch size is 10. My understanding of the accuracy metric is that it should go up in steps of 1/[number of records] between 0 and 1. It's result should be the fraction of all individual predictions that were correct. As the description of the accuracy metric on tensorflow's website: "Calculates how often predictions equal labels."
However, in the log you can see that the accuracies on the training set (with 80 batches of size 10) go up in steps of 0.0125 (1/80). The validation set (containing 20 batches of size 10) go up in steps of 0.05 (1/20). It must be somehow calculating the fraction of the batches that are "correct" instead of the number of correct predictions out of all the batches.
### Standalone code to reproduce the issue
```shell
import numpy as np
import tensorflow as tf
tf.keras.utils.set_random_seed(1)
# define the data
x = np.random.random(1000)
y1 = np.random.randint(0, 2, 1000)
y2 = np.random.randint(0, 2, 1000)
# define the model
input = tf.keras.layers.Input(shape=(1,))
model = tf.keras.layers.Dense(64, activation='relu')(input)
model = tf.keras.layers.Dense(2, activation='sigmoid')(model)
out1 = tf.keras.layers.Lambda(lambda x: x[..., 0], name='out1')(model)
out2 = tf.keras.layers.Lambda(lambda x: x[..., 1], name='out2')(model)
model = tf.keras.models.Model(input, [out1, out2])
# configure losses and metrics for each output
losses = {'out1': 'binary_crossentropy', 'out2': 'binary_crossentropy'}
weights = {'out1': 1.0, 'out2': 1.0}
metrics = {'out1': 'accuracy', 'out2': 'accuracy'}
model.compile(loss=losses, loss_weights=weights, optimizer='adam', metrics=metrics)
# fit
model.fit(x, [y1, y2], batch_size=10, epochs=10, verbose=2, validation_split=0.2)
```
### Relevant log output
```shell
2023-05-05 14:27:37.862813: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-05-05 14:27:38.294938: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1242 MB memory: -> device: 0,
name: NVIDIA T500, pci bus id: 0000:01:00.0, compute capability: 7.5
Epoch 1/10
80/80 - 1s - loss: 1.3891 - out1_loss: 0.6953 - out2_loss: 0.6938 - out1_accuracy: 0.1125 - out2_accuracy: 0.1625 - val_loss: 1.3861 - val_out1_loss: 0.6933 - val_out2_loss: 0.6929
- val_out1_accuracy: 0.1500 - val_out2_accuracy: 0.0500 - 1s/epoch - 18ms/step
Epoch 2/10
80/80 - 0s - loss: 1.3866 - out1_loss: 0.6931 - out2_loss: 0.6935 - out1_accuracy: 0.0625 - out2_accuracy: 0.0375 - val_loss: 1.3843 - val_out1_loss: 0.6918 - val_out2_loss: 0.6925
- val_out1_accuracy: 0.2000 - val_out2_accuracy: 0.1000 - 313ms/epoch - 4ms/step
Epoch 3/10
80/80 - 0s - loss: 1.3872 - out1_loss: 0.6938 - out2_loss: 0.6934 - out1_accuracy: 0.1125 - out2_accuracy: 0.1250 - val_loss: 1.3837 - val_out1_loss: 0.6916 - val_out2_loss: 0.6920
- val_out1_accuracy: 0.2000 - val_out2_accuracy: 0.0500 - 331ms/epoch - 4ms/step
Epoch 4/10
80/80 - 0s - loss: 1.3865 - out1_loss: 0.6929 - out2_loss: 0.6935 - out1_accuracy: 0.1125 - out2_accuracy: 0.1125 - val_loss: 1.3834 - val_out1_loss: 0.6916 - val_out2_loss: 0.6918
- val_out1_accuracy: 0.2000 - val_out2_accuracy: 0.1000 - 335ms/epoch - 4ms/step
Epoch 5/10
80/80 - 0s - loss: 1.3859 - out1_loss: 0.6929 - out2_loss: 0.6930 - out1_accuracy: 0.0750 - out2_accuracy: 0.1125 - val_loss: 1.3832 - val_out1_loss: 0.6917 - val_out2_loss: 0.6915
- val_out1_accuracy: 0.2000 - val_out2_accuracy: 0.1000 - 339ms/epoch - 4ms/step
Epoch 6/10
80/80 - 0s - loss: 1.3861 - out1_loss: 0.6931 - out2_loss: 0.6930 - out1_accuracy: 0.1125 - out2_accuracy: 0.0625 - val_loss: 1.3831 - val_out1_loss: 0.6919 - val_out2_loss: 0.6913
- val_out1_accuracy: 0.2000 - val_out2_accuracy: 0.1500 - 333ms/epoch - 4ms/step
Epoch 7/10
80/80 - 0s - loss: 1.3860 - out1_loss: 0.6934 - out2_loss: 0.6926 - out1_accuracy: 0.1000 - out2_accuracy: 0.0625 - val_loss: 1.3830 - val_out1_loss: 0.6920 - val_out2_loss: 0.6911
- val_out1_accuracy: 0.2500 - val_out2_accuracy: 0.1000 - 335ms/epoch - 4ms/step
Epoch 8/10
80/80 - 0s - loss: 1.3860 - out1_loss: 0.6929 - out2_loss: 0.6930 - out1_accuracy: 0.1000 - out2_accuracy: 0.1250 - val_loss: 1.3826 - val_out1_loss: 0.6918 - val_out2_loss: 0.6908
- val_out1_accuracy: 0.2000 - val_out2_accuracy: 0.2000 - 342ms/epoch - 4ms/step
Epoch 9/10
80/80 - 0s - loss: 1.3861 - out1_loss: 0.6930 - out2_loss: 0.6931 - out1_accuracy: 0.0625 - out2_accuracy: 0.1250 - val_loss: 1.3824 - val_out1_loss: 0.6917 - val_out2_loss: 0.6907
- val_out1_accuracy: 0.2000 - val_out2_accuracy: 0.1500 - 336ms/epoch - 4ms/step
Epoch 10/10
80/80 - 0s - loss: 1.3857 - out1_loss: 0.6930 - out2_loss: 0.6927 - out1_accuracy: 0.0750 - out2_accuracy: 0.1250 - val_loss: 1.3824 - val_out1_loss: 0.6919 - val_out2_loss: 0.6905
- val_out1_accuracy: 0.2000 - val_out2_accuracy: 0.1000 - 349ms/epoch - 4ms/step
```
</details>
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I_kwDOArmXAs5lL0E7
| 60,509 |
How does average pooling function work in TensorFlow?
|
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[
"Hi @ahmoosavi \r\n\r\nWith padding = \"same\", pool_size =(4, 4) and stride = (4, 4) , the tensor would look like this\r\n\r\n```\r\n 0 0 0 0 0 0 0 0\r\n 0 1 2 3 4 5 0 0\r\n 0 6 7 8 9 10 0 0\r\n 0 0 0 0 0 0 0 0\r\n```\r\nPadding = \"same\" tries to pad evenly left and right, but if the amount of columns to be added is odd, it will add the extra column to the right, as is the case in this example (the same logic applies vertically: there may be an extra row of zeros at the bottom).\r\n\r\nThe output shape when using the \"same\" padding option is: \r\n```\r\noutput_shape = math.floor((input_shape - 1) / strides) + 1\r\n```\r\nThe logic would be \r\n(1+2+3+0+6+7+8)/6 = 27/6 =4.5\r\n(4+5+9+10)/4 = 28/4 = 7\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/60509\">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/60509\">No</a>\n"
] | 2023-05-05T13:08:01 | 2023-05-24T01:58:11 | 2023-05-24T01:58:07 |
NONE
| null | null | null |
Let us assume a tensor like this:
```
x = tf.constant([[1., 2., 3.],
[4., 5., 6.],
[7., 8., 9.]])
```
To apply the average pooling function, I will do this:
```
x = tf.reshape(x, [1, 3, 3, 1])
avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2),strides=(2, 2), padding='same')
avg_pool_2d(x)
```
The result is:
```
<tf.Tensor: shape=(1, 2, 2, 1), dtype=float32, numpy=
array([[[[3. ],
[4.5]],
[[7.5],
[9. ]]]], dtype=float32)>
```
I can follow the logic above:
```
(1+2+4+5)/4 = 3
(3+6)/2 = 4.5
(7+8)/2 = 7.5
(9/1) = 9
```
**I think the logic is:** The pooling filter is usually situated inside the tensor to perform the pooling operator. But when the entire filter does not situate inside the tensor (see the below figure for an example), we need to specify the number of elements of the filter that are situated inside the tensor (a). The following figure illustrates the logic for a 4 by 3 tensor, with pooling filter and stride sizes of 2 by 2, and padding the same.
However, it is not always like this. For example, suppose the following tensor:
```
y = tf.constant([[1., 2., 3., 4., 5.],
[6., 7., 8., 9., 10.]])
```
Then, I do this:
```
y = tf.reshape(y, [1, 2, 5, 1])
avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(4, 4),strides=(4, 4), padding='same')
avg_pool_2d(y)
```
The result is like this:
```
<tf.Tensor: shape=(1, 1, 2, 1), dtype=float32, numpy=
array([[[[4.5 ],
[7.]]]], dtype=float32)>
```
If I wanted to follow the logic for the first example, I expected the result to be like this:
```
(1+2+3+4+6+7+8+9)/8 = 5
(5+10)/2 = 7.5
```
I am using TensorFlow 2.8.0. What mistake am I making?
|
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[
"A bisect identified [this commit](https://github.com/bazelbuild/bazel/commit/661ebef465b08697a82fc3ee4c9fd540d953dc84) to be the breaking change.",
"That commit is intended and I thought it wouldn't affect Bazel but it looks like Tensorflow is still relying on the deprecated [legacy whole archive behavior](https://cs.opensource.google/bazel/bazel/+/master:src/main/java/com/google/devtools/build/lib/rules/cpp/CppOptions.java;drc=b95ce1708b46f7f0167904108d0317c1c470c6cb;l=354). The flag `incompatible_remove_legacy_whole_archive` was already flipped many releases ago but TF unflipped it [in its bazelrc](https://cs.opensource.google/tensorflow/tensorflow/+/master:.bazelrc;drc=226b4e2efbaedfb14a3ebe0bdef6ac890a8aed87;l=127).\r\n\r\nThis behavior has to stay, without this bug fix, part of the reason for the existence of cc_shared_library disappears since one of its main goals was to avoid linking libraries into the shared object that didn't need to be linked. With the legacy whole archive behavior, anything may be linked unnecessarily.\r\n\r\nFor each error like this that you get you have two options, for both options you first have to find the cc_library that the missing symbol belongs to, then:\r\n- Add the `cc_library` as a direct dep of `cc_shared library` to `cc_shared_library.deps` (`cc_shared_library.roots` was renamed to `cc_shared_library.deps`)\r\n- Leave the library where it is and add `alwayslink=True`\r\n\r\n",
"/cc @learning-to-play @vam-google @chsigg ",
"@jakeharmon8 FYI",
"Looks like `--features=-force_no_whole_archive` is an workaround for this, but TF should really migrate off depending on the old behaviour. ",
"gentle ping. TF has been broken on Bazel CI for a month now.",
"TensorFlow upgraded Bazel version from 5.3.0 to 6.1.0 two weeks ago. Can the Bazel team own the fix from here?",
"How is the Bazel version upgrade relevant? In any case, I don't have the cycles to fix the TF build.\r\n\r\nThe longer TF remains broken, the harder it is to fix the breakage. TF is failing with a different error now: https://buildkite.com/bazel/bazel-at-head-plus-disabled/builds/1692#01889ec5-aa99-41a8-9e11-487dc4866b72",
"According to the churn policy most of the time spent on changes must be spent by the team mandating the change. TF is green at 6.1.0. It is broken at Bazel@HEAD. It seems reasonable to me that the Bazel team helps resolve the majority the issue.\r\nCC @meteorcloudy ",
"We have spent plenty of time to bisect TF and identify the breaking change (https://github.com/bazelbuild/bazel/commit/661ebef465b08697a82fc3ee4c9fd540d953dc84), and as @oquenchil explained, this is broken because TF still depends on the deprecated legacy feature (whole archive). \r\n\r\nI found a workaround for TF by adding `--features=-force_no_whole_archive` as a build option, but eventually TF needs to migrate away from depending on the legacy behaviour.\r\n\r\nI guess we can add first apply the workaround to stop the bleed here, sent https://github.com/tensorflow/tensorflow/pull/60837",
"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/60508\">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/60508\">No</a>\n"
] | 2023-05-05T09:33:11 | 2023-06-12T22:34:30 | 2023-06-12T22:34:28 |
NONE
| null | null | null |
https://buildkite.com/bazel/bazel-at-head-plus-downstream/builds/2997#0187e4ef-95bb-43c3-84ea-7cd94dbf0b7d
Platforms : Linux and Windows
Logs :
```
ERROR: /var/lib/buildkite-agent/builds/bk-docker-5xrg/bazel-downstream-projects/tensorflow/tensorflow/cc/BUILD:766:22: Linking tensorflow/cc/ops/functional_ops_gen_cc [for tool] failed: (Exit 1): gcc failed: error executing command (from target //tensorflow/cc:ops/functional_ops_gen_cc)
(cd /var/lib/buildkite-agent/.cache/bazel/_bazel_buildkite-agent/c5e474199d1c61ddf0091ab0c5156762/execroot/org_tensorflow && \
exec env - \
PATH=/var/lib/buildkite-agent/.cache/bazelisk/local/-tmp-tmpjryk49s9-bazel/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin \
PWD=/proc/self/cwd \
ZERO_AR_DATE=1 \
/usr/bin/gcc @bazel-out/k8-opt-exec-ST-86d428d70b9d/bin/tensorflow/cc/ops/functional_ops_gen_cc-2.params)
# Configuration: 480f85a7755fc62a440f8fbe1f9f17e1ba62abf41ed236d08b174eb7e701c839
# Execution platform: @local_execution_config_platform//:platform
bazel-out/k8-opt-exec-ST-86d428d70b9d/bin/_solib_k8/_U_S_Stensorflow_Scc_Cops_Sfunctional_Uops_Ugen_Ucc___Utensorflow/libtensorflow_framework.so.2: error: undefined reference to 'tsl::table::NewLRUCache(unsigned long)'
bazel-out/k8-opt-exec-ST-86d428d70b9d/bin/_solib_k8/_U_S_Stensorflow_Scc_Cops_Sfunctional_Uops_Ugen_Ucc___Utensorflow/libtensorflow_framework.so.2: error: undefined reference to 'tensorflow::GpuIdManager::TfToPlatformDeviceId(tsl::gtl::IntType<tsl::TfDeviceId_tag_, int>, tsl::gtl::IntType<tsl::PlatformDeviceId_tag_, int>*)'
collect2: error: ld returned 1 exit status
Target //tensorflow/tools/pip_package:build_pip_package failed to build
(04:36:51) INFO: Elapsed time: 1816.483s,
```
Steps followed :
```
1. git clone -v https://github.com/tensorflow/tensorflow.git
2. git reset cf9014ebbc63ae2e905930e333bde676b87f0167 --hard
3. export USE_BAZEL_VERSION=8087520b153832656695fef6f3654ed432642c98
4. bazel build //tensorflow/tools/pip_package:build_pip_package
```
CC @meteorcloudy @oquenchil
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PR_kwDOArmXAs5P1zXp
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Add missing `stdint.h` include
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[
"Hi @Tombana This PR is duplicate of [#60299](https://github.com/tensorflow/tensorflow/pull/60299), hence closing it. Thank you for your contribution!"
] | 2023-05-05T08:04:14 | 2023-05-08T06:53:09 | 2023-05-08T06:44:19 |
NONE
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Since a recent compiler update (`gcc (GCC) 13.1.1 20230429`), `spectrogram.cc` fails to compile because `uint32_t` is no longer defined. This PR adds the missing include.
I'm not sure if `<cstdint>` or `<stdint.h>` is preferred. I went with the latter to follow the style of the other includes in the file.
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Memory Leak in mkl_graph_util.h
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[
"@p3achyjr we have taken up this issue and are currently investigating it.",
"@p3achyjr . I have a fix that is in line with what you mentioned. Since the variable is static, it has a life span till the program exists. So it should not cause a memory leak. The fix is being reviewed internally. Will create a PR with master branch soon. ",
"Sounds good! Functionally I don't think this would cause unbounded memory growth but I thought I'd report just for sanity :)",
"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/60506\">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/60506\">No</a>\n"
] | 2023-05-05T02:07:29 | 2023-07-27T13:53:05 | 2023-07-27T13:53:02 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
2.11
### Custom Code
Yes
### OS Platform and Distribution
Debian GNU/Linux 10
### Mobile device
_No response_
### Python version
3.9
### Bazel version
6.1.1
### GCC/Compiler version
clang 12.0.1
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
In [mkl_graph_util.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/graph/mkl_graph_util.h#L199C53-L200), we create a thread_local hash set using `new`. But it doesn't look like we ever free this memory. AddressSanitizer reports a memory leak when running a program that uses this code. Maybe we can change this to `std::unique_ptr`?
### Standalone code to reproduce the issue
```shell
// compiling with ASAN should leak.
#include "tensorflow/core/public/session.h"
using namespace ::tensorflow;
int main(int argc, char** argv) {
GraphDef gdef;
Scope scope;
auto op = ops::Add(scope, ops::Const(1), ops::Const(2));
scope.ToGraphDef(&gdef);
std::unique_ptr<Session> session = NewSession(SessionOptions());
std::vector<Tensor> outputs;
session->Run({}, {}, {}, &outputs);
return 0;
}
```
### Relevant log output
```shell
Direct leak of 56 byte(s) in 1 object(s) allocated from:
#0 0x14ffadad in malloc (/home/axlui/.cache/bazel/_bazel_axlui/f4685961e4a033eb3c5c8f3ed28f41d5/execroot/__main__/bazel-out/k8-opt/bin/cc/main_asan+0x14ffadad)
#1 0x7f23a56450b4 in operator new(unsigned long) (/lib/x86_64-linux-gnu/libstdc++.so.6+0xa60b4)
#2 0x2a67d4c6 in tensorflow::mkl_op_registry::IsMklOp(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, tensorflow::DataType) /proc/self/cwd/external/org_tensorflow/tensorflow/core/graph/mkl_graph_util.h:268:10
#3 0x2a691503 in tensorflow::MklLayoutRewritePass::CheckForNodeRewrite(tensorflow::Node const*) const /proc/self/cwd/external/org_tensorflow/tensorflow/core/common_runtime/mkl_layout_pass.cc:3832:8
#4 0x2a694b15 in tensorflow::MklLayoutRewritePass::RunPass(std::unique_ptr<tensorflow::Graph, std::default_delete<tensorflow::Graph> >*) /proc/self/cwd/external/org_tensorflow/tensorflow/core/common_runtime/mkl_layout_pass.cc:4147:15
#5 0x2a695b79 in tensorflow::MklLayoutRewritePass::Run(tensorflow::GraphOptimizationPassOptions const&)::$_15::operator()(std::unique_ptr<tensorflow::Graph, std::default_delete<tensorflow::Graph> >*) const /proc/self/cwd/external/org_tensorflow/tensorflow/core/common_runtime/mkl_layout_pass.cc:4205:5
#6 0x2a6958f2 in tensorflow::MklLayoutRewritePass::Run(tensorflow::GraphOptimizationPassOptions const&) /proc/self/cwd/external/org_tensorflow/tensorflow/core/common_runtime/mkl_layout_pass.cc:4218:7
#7 0x2a77721a in tensorflow::OptimizationPassRegistry::RunGrouping(tensorflow::OptimizationPassRegistry::Grouping, tensorflow::GraphOptimizationPassOptions const&) /proc/self/cwd/external/org_tensorflow/tensorflow/core/common_runtime/optimization_registry.cc:73:26
#8 0x289796a8 in tensorflow::DirectSession::CreateGraphs(tensorflow::BuildGraphOptions const&, std::unordered_map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::unique_ptr<tensorflow::Graph, std::default_delete<tensorflow::Graph> >, std::hash<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::equal_to<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::unique_ptr<tensorflow::Graph, std::default_delete<tensorflow::Graph> > > > >*, std::unique_ptr<tensorflow::FunctionLibraryDefinition, std::default_delete<tensorflow::FunctionLibraryDefinition> >*, tensorflow::DirectSession::RunStateArgs*, absl::InlinedVector<tensorflow::DataType, 4ul, std::allocator<tensorflow::DataType> >*, absl::InlinedVector<tensorflow::DataType, 4ul, std::allocator<tensorflow::DataType> >*, long*) /proc/self/cwd/external/org_tensorflow/tensorflow/core/common_runtime/direct_session.cc:1729:3
#9 0x28976dfc in tensorflow::DirectSession::CreateExecutors(tensorflow::CallableOptions const&, std::unique_ptr<tensorflow::DirectSession::ExecutorsAndKeys, std::default_delete<tensorflow::DirectSession::ExecutorsAndKeys> >*, std::unique_ptr<tensorflow::DirectSession::FunctionInfo, std::default_delete<tensorflow::DirectSession::FunctionInfo> >*, tensorflow::DirectSession::RunStateArgs*) /proc/self/cwd/external/org_tensorflow/tensorflow/core/common_runtime/direct_session.cc:1331:3
#10 0x289722e2 in tensorflow::DirectSession::GetOrCreateExecutors(absl::Span<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const>, absl::Span<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const>, absl::Span<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const>, tensorflow::DirectSession::ExecutorsAndKeys**, tensorflow::DirectSession::RunStateArgs*) /proc/self/cwd/external/org_tensorflow/tensorflow/core/common_runtime/direct_session.cc:1573:3
#11 0x2897014e in tensorflow::DirectSession::Run(tensorflow::RunOptions const&, std::vector<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, tensorflow::Tensor>, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, tensorflow::Tensor> > > const&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor> >*, tensorflow::RunMetadata*, tsl::thread::ThreadPoolOptions const&) /proc/self/cwd/external/org_tensorflow/tensorflow/core/common_runtime/direct_session.cc:873:3
#12 0x2896fbd3 in tensorflow::DirectSession::Run(tensorflow::RunOptions const&, std::vector<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, tensorflow::Tensor>, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, tensorflow::Tensor> > > const&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor> >*, tensorflow::RunMetadata*) /proc/self/cwd/external/org_tensorflow/tensorflow/core/common_runtime/direct_session.cc:842:10
#13 0x2896c1eb in tensorflow::DirectSession::Run(std::vector<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, tensorflow::Tensor>, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, tensorflow::Tensor> > > const&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor> >*) /proc/self/cwd/external/org_tensorflow/tensorflow/core/common_runtime/direct_session.cc:472:10
#14 0x1504aef5 in nn::NNInterface::Infer() /proc/self/cwd/cc/nn/nn_interface.cc:279:3
#15 0x15049658 in nn::NNInterface::InferLoop() /proc/self/cwd/cc/nn/nn_interface.cc:267:5
#16 0x1505f32b in void std::__invoke_impl<void, void (nn::NNInterface::*)(), nn::NNInterface*>(std::__invoke_memfun_deref, void (nn::NNInterface::*&&)(), nn::NNInterface*&&) /usr/lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/bits/invoke.h:73:14
#17 0x1505f264 in std::__invoke_result<void (nn::NNInterface::*)(), nn::NNInterface*>::type std::__invoke<void (nn::NNInterface::*)(), nn::NNInterface*>(void (nn::NNInterface::*&&)(), nn::NNInterface*&&) /usr/lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/bits/invoke.h:95:14
#18 0x1505f234 in void std::thread::_Invoker<std::tuple<void (nn::NNInterface::*)(), nn::NNInterface*> >::_M_invoke<0ul, 1ul>(std::_Index_tuple<0ul, 1ul>) /usr/lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/thread:264:13
#19 0x1505f1f8 in std::thread::_Invoker<std::tuple<void (nn::NNInterface::*)(), nn::NNInterface*> >::operator()() /usr/lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/thread:271:11
#20 0x1505f09c in std::thread::_State_impl<std::thread::_Invoker<std::tuple<void (nn::NNInterface::*)(), nn::NNInterface*> > >::_M_run() /usr/lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/thread:215:13
#21 0x7f23a566decf (/lib/x86_64-linux-gnu/libstdc++.so.6+0xceecf)
```
</details>
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Is DTensor compatible with Horovod
|
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[
"@dengpanyin,\r\nCould you please provide some more information/usecase on the above ask, it helps us to analyse the issue in an effective way. \r\n\r\nAlso please refer to the document, where it was mentioned that to use the Horovod with TensorFlow, please try to make the modifications to your training script which are mentioned.\r\nhttps://horovod.readthedocs.io/en/stable/tensorflow.html\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.",
"Closing this as stale. Please reopen if this is still a valid request. Thank you!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60505\">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/60505\">No</a>\n"
] | 2023-05-05T01:43:12 | 2023-08-23T10:18:49 | 2023-08-23T10:18:46 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Support
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.12
### 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 Behaviour?
Want to check if DTensor model parallel compatible with Horovod? Any code examples or Benchmark of DTensor model parallel?
### Standalone code to reproduce the issue
```shell
NA
```
### Relevant log output
_No response_</details>
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Android Download page URL not available
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[
"@TE-N-ElvenWang,\r\nCould you please confirm whether you are mentioning to change in this link https://jfrog.com/blog/into-the-sunset-bintray-jcenter-gocenter-and-chartcenter/\r\n\r\nThe above link is not from the official tensorflow. We cannot handle the changes in the mentioned page and please take a look at this official doc link for the reference.\r\nhttps://www.tensorflow.org/lite/android\r\nhttps://www.tensorflow.org/lite/android/quickstart\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/60504\">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/60504\">No</a>\n"
] | 2023-05-05T01:07:19 | 2023-05-21T01:58:55 | 2023-05-21T01:58:53 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Documentation Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf2.8
### 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 Behaviour?
On the frontpage of the README.md, the Android Download link pointed to https://bintray.com/google/tensorflow/tensorflow/_latestVersion now is not available and will redirect to another page.
### Standalone code to reproduce the issue
```shell
Document issue which easy to found on frontpage of this github project.
```
### Relevant log output
_No response_</details>
|
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TensorFlow Refuses to See my GPU All The Time
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[
"Hi @NickDeBeenAIE ,\r\n\r\nI see from error log it seems CUDA driver was missing. Please install cuda driver compatible for your GPU. You can download driver from Nvidia [website](https://www.nvidia.com/Download/index.aspx). You can cross check the driver installation with `nvidia-smi` which outputs driver details if installed and running. Please share output of the command `nvidia-smi`.\r\n\r\nPlease confirm the TF version you are using and correct it in template.You can find tested configurations of cuda,cudnn wrt TF version etc from [here](https://www.tensorflow.org/install/source#gpu).\r\n\r\nOnce ensured Cuda driver installation then we should go for cuda toolkit installation and path setting. The instructions for the same can be found [here](https://www.tensorflow.org/install/pip#linux).\r\n\r\n",
"I already have the Cuda driver.\r\n\r\nMy version of CuDNN is version 12.",
"The ``nvidia-smi`` command results in driver version 530, which should clearly tell you that the GPU is present.",
"I have also installed the Cuda Toolkit as well.",
"@NickDeBeenAIE , \r\n\r\nCould you please confirm CUDA and cuDnn version you have installed ? Also have you followed instructions for CUDA path setting as instructed in documentation ? Can you confirm the commands you have used for cuda toolkit installation and path setting ?",
"Like I said, my CuDNN version is version 12.\r\n\r\nMy Cuda Toolkit version is also the latest.\r\n\r\nI have followed the instructions given on the CuDNN and the Cuda Toolkit page.",
"You clearly didn't read the issue posted all the way through.",
"> Like I said, my CuDNN version is version 12.\r\n\r\nIs that version you mentioned is CUDA or cuDNN ? Correct me if I am wrong, AFAIK latest version of cuDNN is 8.9 only. May be you have mentioned CUDA version right ? Also Our Tested configuration for latest version i.e TF2.12 are CUDA 11.8 and cuDNN 8.2. With other versions compatibility issues may arise.\r\n\r\nAs per our documentation the recommended way for GPU setup is you have to create Conda environment and within that you need to install CUDA using conda and cudnn using pip.You need to follow the steps recommended as per documentation to install the cuda and cudnn toolkits and then configure the system paths as instructed in documentation. \r\n\r\nI request you to please follow the instructions as per official [documentation](https://www.tensorflow.org/install/pip#step-by-step_instructions) and then let us know if still having same problem.",
"I do not use Anaconda for something I can run in Python.\r\n\r\nThe latest version of CuDNN is version 12.\r\n\r\nCuDNN version 8.9.0 is old, and therefore does not run on my machine.\r\n\r\nIt won't install, nor run because its out of date.",
"To test this, create a VM, then run:\r\n\r\n``python3 -c import tensorflow as tf physical_devices = tf.config.list_physical_devices('GPU')``\r\n\r\nIt'll probably work for you.",
"Oh, by the way, Anaconda is exactly the same as Python 3, because you can run Python 3 code in it.\r\n\r\nTherefore I don't need it.",
"Just FYI, I exported all paths to their appropriate paths while I was at it.",
"Actually, I stand corrected on self observation, so my apologies on the CuDNN version.\r\n\r\nMy Cuda driver is version 12, but CuDNN is indeed version 8.9.0.",
"Hi @NickDeBeenAIE ,\r\n\r\nAs confirmed please use tested configurations only.CUDA driver 12 might work.But you should have cuda toolkit 11.8 and cuDNN 8.6 installed. I am attaching below some of the logs below where i have successfully able to detect the GPU with official documentation steps and with tetsed configurations.\r\n\r\n```\r\n(tf) suryanarayanay@surya-ubuntu22-cuda-test:~$ nvidia-smi\r\nFri Apr 21 06:09:46 2023 \r\n+-----------------------------------------------------------------------------+\r\n| NVIDIA-SMI 525.60.13 Driver Version: 525.60.13 CUDA Version: 12.0 |\r\n|-------------------------------+----------------------+----------------------+\r\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|===============================+======================+======================|\r\n| 0 NVIDIA A100-SXM... Off | 00000000:00:04.0 Off | 0 |\r\n| N/A 33C P0 52W / 400W | 0MiB / 40960MiB | 27% Default |\r\n| | | Disabled |\r\n+-------------------------------+----------------------+----------------------+\r\n \r\n+-----------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=============================================================================|\r\n| No running processes found |\r\n+-----------------------------------------------------------------------------+\r\n(tf) suryanarayanay@surya-ubuntu22-cuda-test:~$ conda install -c conda-forge cudatoolkit=11.8.0\r\nCollecting package metadata (current_repodata.json): done\r\nSolving environment: done\r\n\r\n\r\n==> WARNING: A newer version of conda exists. <==\r\n current version: 23.1.0\r\n latest version: 23.3.1\r\n\r\nPlease update conda by running\r\n\r\n $ conda update -n base -c defaults conda\r\n\r\nOr to minimize the number of packages updated during conda update use\r\n\r\n conda install conda=23.3.1\r\n\r\n\r\n\r\n# All requested packages already installed.\r\n\r\n(tf) suryanarayanay@surya-ubuntu22-cuda-test:~$ pip install nvidia-cudnn-cu11==8.6.0.163\r\nRequirement already satisfied: nvidia-cudnn-cu11==8.6.0.163 in ./miniconda3/envs/tf/lib/python3.9/site-packages (8.6.0.163)\r\nRequirement already satisfied: nvidia-cublas-cu11 in ./miniconda3/envs/tf/lib/python3.9/site-packages (from nvidia-cudnn-cu11==8.6.0.163) (11.11.3.6)\r\n(tf) suryanarayanay@surya-ubuntu22-cuda-test:~$ CUDNN_PATH=$(dirname $(python -c \"import nvidia.cudnn;print(nvidia.cudnn.__file__)\"))\r\n(tf) suryanarayanay@surya-ubuntu22-cuda-test:~$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/:$CUDNN_PATH/lib\r\n(tf) suryanarayanay@surya-ubuntu22-cuda-test:~$ mkdir -p $CONDA_PREFIX/etc/conda/activate.d\r\n(tf) suryanarayanay@surya-ubuntu22-cuda-test:~$ echo 'CUDNN_PATH=$(dirname $(python -c \"import nvidia.cudnn;print(nvidia.cudnn.__file__)\"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh\r\n(tf) suryanarayanay@surya-ubuntu22-cuda-test:~$ echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/:$CUDNN_PATH/lib' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh\r\n(tf) suryanarayanay@surya-ubuntu22-cuda-test:~$ python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n2023-04-21 06:12:14.343689: I tensorflow/core/util/port.cc:110] 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\n2023-04-21 06:12:14.400436: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-04-21 06:12:15.255386: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n```\r\n\r\nRequest you to stick to tested configurations and let us know if the problem persists with those also. Thanks!",
"Ok then why has it failed with me even though I have done the exact same tests?\r\n\r\nJust FYI, Anaconda is just like Python as said previously.",
"I would absolutely love a system like yours.",
"This comes straight from following your instructions as well as TensorFlows:\r\n\r\n``pip3 install nvidia cudnn-cu11==8.6.0.163\r\nERROR: Could not find a version that satisfies the requirement nvidia (from versions: none)\r\nERROR: No matching distribution found for nvidia``",
"Why does the above command posted only work as the root user in a Python3.9 Anaconda activated environment?",
"Hi @NickDeBeenAIE ,\r\n\r\nI can able to download the cudnn-cu11==8.6.0.163 even in base environment on Ubuntu VM with X86-64 architecture and logs attached below.\r\n\r\n```\r\nsuryanarayanay@surya-ubuntu22-cuda-test:~$ pip install nvidia-cudnn-cu11==8.6.0.163\r\nDefaulting to user installation because normal site-packages is not writeable\r\nCollecting nvidia-cudnn-cu11==8.6.0.163\r\n Using cached nvidia_cudnn_cu11-8.6.0.163-py3-none-manylinux1_x86_64.whl (715.7 MB)\r\nCollecting nvidia-cublas-cu11\r\n Using cached nvidia_cublas_cu11-11.11.3.6-py3-none-manylinux1_x86_64.whl (417.9 MB)\r\nInstalling collected packages: nvidia-cublas-cu11, nvidia-cudnn-cu11\r\nSuccessfully installed nvidia-cublas-cu11-11.11.3.6 nvidia-cudnn-cu11-8.6.0.163\r\n\r\nsuryanarayanay@surya-ubuntu22-cuda-test:~$ uname\r\nLinux\r\nsuryanarayanay@surya-ubuntu22-cuda-test:~$ uname -m\r\nx86_64\r\n```\r\n\r\nWhen I checked it on Mac M1 i found the reported error as you. This might be due to the fact that Mac M1 has Arm Architecture and probably there is no corresponding pip wheel for nvidia-cudnn-cu11==8.6.0.163.\r\n\r\n```\r\nsuryanarayanay-macbookpro:~ suryanarayanay$ pip install nvidia-cudnn-cu11==8.6.0.163\r\nERROR: Could not find a version that satisfies the requirement nvidia-cudnn-cu11==8.6.0.163 (from versions: 0.0.1.dev5)\r\nERROR: No matching distribution found for nvidia-cudnn-cu11==8.6.0.163\r\n\r\n[notice] A new release of pip is available: 23.0 -> 23.1.2\r\n[notice] To update, run: python3.10 -m pip install --upgrade pip\r\nsuryanarayanay-macbookpro:~ suryanarayanay$ uname\r\nDarwin\r\nsuryanarayanay-macbookpro:~ suryanarayanay$ uname -m\r\narm64\r\nsuryanarayanay-macbookpro:~ suryanarayanay$ \r\n```\r\n\r\nCan you confirm the architecture of your system using command `uname -m`. \r\n\r\nSecondly all the official instructions for CUDA and cuDNN path setting are linked to Conda environment. \r\n\r\nThanks\r\n",
"``uname -m`` Returns the same architecture as you.",
"Plus, I have to use a virtual environment to build the same nvidia-cudnn-cu11==8.6.0.163 package as you, but an earlier version was installed, 8.5.0 in my case.\r\n\r\nBut, it seems to have run, its just relying on code that is deprecated when running my G.A.N. script:\r\n\r\n<pre>python3 Remasterer.py\r\nEpoch 1/10\r\n469/469 [==============================] - 2s 2ms/step - loss: 0.3517 - accuracy: 0.8991 - val_loss: 0.2061 - val_accuracy: 0.9383\r\nEpoch 2/10\r\n469/469 [==============================] - 1s 2ms/step - loss: 0.1788 - accuracy: 0.9480 - val_loss: 0.1503 - val_accuracy: 0.9539\r\nEpoch 3/10\r\n469/469 [==============================] - 1s 2ms/step - loss: 0.1298 - accuracy: 0.9612 - val_loss: 0.1310 - val_accuracy: 0.9601\r\nEpoch 4/10\r\n469/469 [==============================] - 1s 2ms/step - loss: 0.1019 - accuracy: 0.9692 - val_loss: 0.1030 - val_accuracy: 0.9688\r\nEpoch 5/10\r\n469/469 [==============================] - 1s 2ms/step - loss: 0.0848 - accuracy: 0.9735 - val_loss: 0.1042 - val_accuracy: 0.9677\r\nEpoch 6/10\r\n469/469 [==============================] - 1s 2ms/step - loss: 0.0719 - accuracy: 0.9776 - val_loss: 0.1011 - val_accuracy: 0.9683\r\nEpoch 7/10\r\n469/469 [==============================] - 1s 2ms/step - loss: 0.0612 - accuracy: 0.9809 - val_loss: 0.0838 - val_accuracy: 0.9742\r\nEpoch 8/10\r\n469/469 [==============================] - 1s 2ms/step - loss: 0.0528 - accuracy: 0.9836 - val_loss: 0.0915 - val_accuracy: 0.9711\r\nEpoch 9/10\r\n469/469 [==============================] - 1s 2ms/step - loss: 0.0459 - accuracy: 0.9857 - val_loss: 0.0978 - val_accuracy: 0.9689\r\nEpoch 10/10\r\n469/469 [==============================] - 1s 2ms/step - loss: 0.0412 - accuracy: 0.9869 - val_loss: 0.0906 - val_accuracy: 0.9719\r\n313/313 [==============================] - 0s 685us/step - loss: 0.0906 - accuracy: 0.9719\r\nTest accuracy: 0.9718999862670898\r\nWARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\r\nTraceback (most recent call last):\r\n File "/home/dbts2023/Desktop/venv/Remasterer.py", line 171, in <module>\r\n gan.add(discriminator)\r\n File "/root/anaconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/trackable/base.py", line 205, in _method_wrapper\r\n result = method(self, *args, **kwargs)\r\n File "/root/anaconda3/envs/tf/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File "/root/anaconda3/envs/tf/lib/python3.9/site-packages/keras/engine/input_spec.py", line 280, in assert_input_compatibility\r\n raise ValueError(\r\nValueError: Exception encountered when calling layer "sequential_4" (type Sequential).\r\n\r\nInput 0 of layer "conv2d_2" is incompatible with the layer: expected axis -1 of input shape to have value 128, but received input with shape (None, 28, 28, 3)\r\n\r\nCall arguments received by layer "sequential_4" (type Sequential):\r\n • inputs=tf.Tensor(shape=(None, 28, 28, 3), dtype=float32)\r\n • training=None\r\n • mask=None\r\n</pre>",
"On my specific system, as it has a s***ty GPU, it runs in a much earlier version of Python environment as root as you can see.\r\n\r\nMy issues now renewed by the fact that it needs developers like yourself to fix the Python code that I wrote:\r\n\r\n<pre>import os\r\nimport numpy as np\r\nfrom PIL import Image\r\n\r\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\r\nimport requests\r\nimport tensorflow as tf\r\nimport random\r\nfrom tensorflow import keras\r\nfrom tensorflow.keras import layers\r\nfrom tensorflow.keras.optimizers import Adam\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense\r\ndef load_image_batch(batch_size, image_size, directory):\r\n# Load images from the directory\r\nimages = []\r\nfor filename in os.listdir(directory):\r\nimg = Image.open(os.path.join(directory, filename))\r\nimg = img.convert(\"RGB\")\r\nimg = img.resize((image_size, image_size), Image.BICUBIC)\r\nimages.append(np.array(img))\r\nimages = np.array(images)\r\n\r\n\r\n# Define the Discriminator class\r\nclass Discriminator(keras.Model):\r\n def __init__(self, img_shape):\r\n super(Discriminator, self).__init__()\r\n\r\n self.model = keras.Sequential([\r\n keras.layers.Flatten(input_shape=img_shape),\r\n keras.layers.Dense(512, activation='relu'),\r\n keras.layers.Dense(256, activation='relu'),\r\n keras.layers.Dense(1, activation='sigmoid')\r\n ])\r\n\r\n def call(self, x):\r\n validity = self.model(x)\r\n\r\n return validity\r\n\r\n\r\ndef load_image_batch(batch_size, image_size, directory):\r\n # Load images from the directory\r\n images = []\r\n for filename in os.listdir(directory):\r\n img = Image.open(os.path.join(directory, filename))\r\n img = img.convert(\"RGB\")\r\n img = img.resize((image_size, image_size), Image.BICUBIC)\r\n images.append(np.array(img))\r\n images = np.array(images)\r\n\r\n # Normalize pixel values to [-1, 1]\r\n images = (images.astype(np.float32) - 127.5) / 127.5\r\n\r\n # Select a random batch of images\r\n indices = np.random.randint(0, images.shape[0], batch_size)\r\n batch = images[indices]\r\n\r\n return batch\r\n\r\n\r\n# Generator model\r\ndef make_generator_model():\r\n model = keras.Sequential()\r\n model.add(layers.Dense(7 * 7 * 256, use_bias=False, input_shape=(100,)))\r\n model.add(layers.BatchNormalization())\r\n model.add(layers.LeakyReLU())\r\n\r\n model.add(layers.Reshape((7, 7, 256)))\r\n model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding=\"same\", use_bias=False))\r\n model.add(layers.BatchNormalization())\r\n model.add(layers.LeakyReLU())\r\n\r\n model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding=\"same\", use_bias=False))\r\n model.add(layers.BatchNormalization())\r\n model.add(layers.LeakyReLU())\r\n\r\n model.add(layers.Conv2DTranspose(3, (5, 5), strides=(2, 2), padding=\"same\", use_bias=False, activation=\"tanh\"))\r\n img_shape = (28, 28, 1)\r\n discriminator = Discriminator(img_shape=img_shape)\r\n return model\r\n\r\n\r\n# Discriminator model\r\ndef make_discriminator_model():\r\n model = keras.Sequential()\r\n model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding=\"same\", input_shape=[64, 64, 3]))\r\n model.add(layers.LeakyReLU())\r\n model.add(layers.Dropout(0.3))\r\n\r\n model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding=\"same\"))\r\n model.add(layers.LeakyReLU())\r\n model.add(layers.Dropout(0.3))\r\n\r\n model.add(layers.Flatten())\r\n model.add(layers.Dense(1))\r\n\r\n return model\r\n\r\n\r\n# Define the generator and discriminator models\r\ngenerator = make_generator_model()\r\ndiscriminator = make_discriminator_model()\r\n\r\n# Compile the discriminator model\r\ndiscriminator.compile(loss=\"binary_crossentropy\", optimizer=Adam(lr=0.0002, beta_1=0.5))\r\n\r\n# Combine the generator and discriminator models into a GAN\r\ngan = keras.Sequential()\r\ngan.add(generator)\r\ngan.add(discriminator)\r\n\r\n# Compile the GAN model\r\ngan.compile(loss=\"binary_crossentropy\", optimizer=Adam(lr=0.0002, beta_1=0.5))\r\n\r\n# Train the GAN\r\nbatch_size = 32\r\nepochs = 100\r\ndirectory = \"/Insert/Directory/Of_both_your_Python_Code_Location/And_Images_Here\"\r\nimage_size = 64\r\ntraining_images = load_image_batch(batch_size, image_size, directory)\r\nsteps_per_epoch = len(training_images) // batch_size\r\n\r\nfor epoch in range(epochs):\r\n # set model to training mode\r\n model.train()\r\n\r\n# initialize training loss and number of training batches\r\ntrain_loss = 0\r\nnum_batches = 0\r\n\r\n# loop over training data in batches\r\nfor inputs, labels in train_loader:\r\n # move inputs and labels to device\r\n inputs = inputs.to(device)\r\n labels = labels.to(device)\r\n\r\n # zero the parameter gradients\r\n optimizer.zero_grad()\r\n\r\n # forward pass\r\n outputs = model(inputs)\r\n\r\n # calculate loss\r\n loss = criterion(outputs, labels)\r\n\r\n # backward pass and optimization\r\n loss.backward()\r\n optimizer.step()\r\n\r\n # update training loss and number of batches\r\n train_loss += loss.item()\r\n num_batches += 1\r\n\r\n# calculate average training loss\r\ntrain_loss /= num_batches\r\n\r\n# set model to evaluation mode\r\nmodel.eval()\r\n\r\n# initialize validation loss and number of validation batches\r\nval_loss = 0\r\nnum_val_batches = 0\r\n\r\n# turn off gradient calculation for validation\r\nwith torch.no_grad():\r\n # loop over validation data in batches\r\n for val_inputs, val_labels in val_loader:\r\n # move inputs and labels to device\r\n val_inputs = val_inputs.to(device)\r\n val_labels = val_labels.to(device)\r\n\r\n # forward pass\r\n val_outputs = model(val_inputs)\r\n\r\n # calculate loss\r\n val_loss += criterion(val_outputs, val_labels).item()\r\n num_val_batches += 1\r\n\r\n# calculate average validation loss\r\nval_loss /= num_val_batches\r\n\r\n# print training and validation loss for each epoch\r\nprint(f\"Epoch {epoch + 1}/{epochs}, Training Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}\")\r\n\r\n# at the end of each epoch, save the model\r\ntorch.save(model.state_dict(), f\"model_{epoch + 1}.pt\")\r\n<pre>",
"\r\n\r\n> `uname -m` Returns the same architecture as you.\r\n\r\nPlease confirm the output result. Is it X86_64 or Arm ?\r\n\r\n\r\n\r\n\r\n\r\n> pip3 install nvidia cudnn-cu11==8.6.0.163 ERROR: Could not find a version that satisfies the requirement nvidia (from versions: none) ERROR: No matching distribution found for nvidia\r\n\r\nDid you able to resolve the above error now ?\r\n\r\nCan you confirm whether now GPU is able to detectable ?\r\n\r\nThere are multiple issues being discussed. First of all let us complete one by one. Please confirm the reply to the above queries.\r\n\r\n\r\n\r\n",
"I already confirmed this with you via ``nvidia-smi`` and ``uname -r``.\r\n\r\nThis system is not compatible with the version of cudnn your using.\r\n\r\nIt installed cudnn version 8.5.0.96 as per pascel GPU.\r\n\r\nIf I swapped out the GT 1030 with an RTX A2000, it would install cudnn version 8.6.0.163 without issue.\r\n\r\nMoving on.\r\n\r\nI cannot seem to figure out why my script just refuses to see the 1030 when, like I said previously, ``nvidia-smi`` can.",
"@NickDeBeenAIE ,\r\n\r\nWith RTX A2000 you are able to use this GPU ?\r\n\r\n`nvidia-smi` command is not related to Tensorflow.It outputs Driver version, CUDA toolkit version it is compatible and also GPUs on local machine. For Tensorflow to detect GPU first we should ensure whether Nvidia driver is able to detect GPU.\r\n\r\nFor Tensorflow to detect GPU we should follow the instructions for GPU setup as mentioned in documentation. So both are two different things.",
"Well, quit taking me around in circles.\r\n\r\nI followed all of the instructions in the guide.\r\n\r\nI was saying that my script wouldn't have had an issue if I had an RTX A2000 in the first place.",
"Not sure but it may be due to compatibility issue between GT 1030 and CUDA or cuDNN version or GT1030 GPU might not be supported.\r\n\r\n@nitins17 - Do you have any pointers here ?",
"Huh, I see what my issue is here:\r\n\r\nI technically have 2 GPU's in my machine, and Tensorflow may be seeing the wrong one.\r\n\r\nGPU0: Intel HD 635 iGPU thats in the CPU itself.\r\n\r\nGPU1: Nvidia GT1030 2GB.\r\n\r\nHuh, even if the script is updated with:\r\n\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\r\n\r\ninstead of os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\" it still fails.\r\n\r\nAnd it still failed with the quotations removed from CUDA_VISIBLE_DEVICES and TF_CPP_MIN_LOG_LEVEL.\r\n\r\n``2023-05-23 14:23:20.131376: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\r\n2023-05-23 14:23:20.166114: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\r\n2023-05-23 14:23:20.166452: I tensorflow/core/platform/cpu_feature_guard.cc:182] 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\n2023-05-23 14:23:20.819630: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-05-23 14:23:21.647132: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-05-23 14:23:21.673109: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\r\nSkipping registering GPU devices...``",
"Hi @NickDeBeenAIE ,\r\n\r\nFirst you can use the API tf.config.list_physical_devices('GPU') to list out the physical devices.Then If you want to set only specific GPU visible to Tensorflow then you can use the API `tf.config.set_visible_devices()`\r\n\r\nBelow is example code for ignoring 1st GPU and making remaining all visible to Tensorflow.\r\n\r\n`tf.config.set_visible_devices(physical_devices[1:], 'GPU')`\r\n\r\nPlease try this and let us know if ot works.\r\n\r\nThanks!",
"I tried this and TensorFlow is so blind it cannot see my 1030."
] | 2023-05-04T17:25:49 | 2023-11-03T20:45:00 | null |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Support
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
2.25.1
### Custom Code
Yes
### OS Platform and Distribution
Linux, Ubuntu 22.04.6
### Mobile device
_No response_
### Python version
Python 3.11
### Bazel version
N/A*
### GCC/Compiler version
11.2
### CUDA/cuDNN version
12
### GPU model and memory
Nvidia GT 1030 2gb
### Current Behaviour?
<pre>``2023-05-05 02:37:58.234754: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-05-05 02:37:58.270803: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-05-05 02:37:58.271133: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-05-05 02:37:58.944800: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-05-05 02:37:59.878527: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-05 02:37:59.878936: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...``<pre>
### Standalone code to reproduce the issue
```shell
The above is the response every time even though the GPU exists in the PC.
The GT 1030 might not be the most powerful GPU out there, but it does have 344 Cuda Cores.
I also have the Cuda Toolkit installed, along with the latest drivers available (I'm surprised this card is still getting support).
To reproduce,run the following command in the terminal: ``python3 insert script name here.py (replace with the name of your .py script).
The output will tell you what has happened.
Just FYI, this also affects AMD Radeon Pro WX 5, 6 and 7000 series GPU's and their gaming variants i.e., my RX 6700 XT in this case running in WSL 1.
Godspeed.
```
### Relevant log output
_No response_</details>
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PR_kwDOArmXAs5Py7Ox
| 60,502 |
Remove cuda11.8 f8 gemm_rewrite support.
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|
F8 gemm rewrite will only be supported from CUDA 12.0 and above.
cc @reedwm
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tflite build on Windows crash if having multiple versions of Python in PATH
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[
"Hi, @rikardn could you please share the steps you are following, so that we could reproduce the error? \r\n\r\nI have set following path and it is working fine for me\r\n\r\nPYTHON_BIN_PATH=D:/user/mraunak/venv310/Scripts/python.exe;D:/user/mraunak/venv38_latest/Scripts/python.exe\r\nPYTHON_LIB_PATH=D:/user/mraunak/venv310/lib/site-packages;D:/user/mraunak/venv38_latest/lib/site-packages\r\n\r\n\r\n",
"Sure. I am running this on github actions. Here is a link to the log of the failed run: https://github.com/pharmpy/tflite-runtime-wheels/actions/runs/4817630805/jobs/8578588825 and here is the workflow file: https://github.com/pharmpy/tflite-runtime-wheels/actions/runs/4817630805/workflow\r\n\r\nThe workflow is very simple\r\n\r\n1. Setup Python (this adds a path to another version of Python to `PATH`) (Note: not `PYTHON_BIN_PATH`)\r\n2. Clone tensorflow\r\n3. Call `tensorflow/lite/tools/pip_package/build_pip_package_with_bazel.sh`\r\n\r\nThe error in the log points to the script mashing both Python paths together. I have tested running by manually setting the `PATH` to contain only one version of Python and this works.",
"Hi @terryheo, can you please take a look? Thanks.",
"@rikardn The CI build log is expired. Could you please share the log again?\r\n\r\nAnd also please share your PYTHON_BIN_PATH value too. \r\n\r\nThe current information indicate the settings point to an incorrect python executable \r\n\r\n```\r\nError in fail: Configuration Error: --define PYTHON_BIN_PATH='C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.10.11\\\\x64\\\\python3.exe\r\nC:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.9.13\\\\x64\\\\python3.exe' is not executable.\r\n```",
"Hi @rikardn, did the above work for 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/60501\">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/60501\">No</a>\n"
] | 2023-05-04T11:59:49 | 2023-09-23T01:46:37 | 2023-09-23T01:46:35 |
NONE
| null | null | null |
### System information
* tesorflow 2.12.0
* windows-latest in github actions (log https://github.com/pharmpy/tflite-runtime-wheels/actions/runs/4817630805/jobs/8578588825)
### Describe the problem
Building tflite 2.12.0 using bazel on Windows having multiple versions of Python in `PATH` fails. This seems to be because the `where` command is used to find which Python is in `PATH` and this then returns multiple paths which gets concatenated together. Having multiple Pythons in `PATH` works when building on MacOS and Linux.
### Source code / logs
This is the end of the output from the compilation with the error: (notice that the paths to the two Pythons have both been put into `PYTHON_BIN_PATH`
```
Repository rule local_python_configure defined at:
D:/a/tflite-runtime-wheels/tflite-runtime-wheels/third_party/py/python_configure.bzl:279:41: in <toplevel>
ERROR: An error occurred during the fetch of repository 'local_execution_config_python':
Traceback (most recent call last):
File "D:/a/tflite-runtime-wheels/tflite-runtime-wheels/third_party/py/python_configure.bzl", line 212, column 22, in _create_local_python_repository
_check_python_bin(repository_ctx, python_bin)
File "D:/a/tflite-runtime-wheels/tflite-runtime-wheels/third_party/py/python_configure.bzl", line 145, column 25, in _check_python_bin
auto_config_fail("--define %s='%s' is not executable. Is it the python binary?" % (
File "D:/a/tflite-runtime-wheels/tflite-runtime-wheels/third_party/remote_config/common.bzl", line 12, column 9, in auto_config_fail
fail("%sConfiguration Error:%s %s\n" % (red, no_color, msg))
Error in fail: Configuration Error: --define PYTHON_BIN_PATH='C:\\hostedtoolcache\\windows\\Python\\3.10.11\\x64\\python3.exe
C:\\hostedtoolcache\\windows\\Python\\3.9.13\\x64\\python3.exe' is not executable. Is it the python binary?
ERROR: D:/a/tflite-runtime-wheels/tflite-runtime-wheels/WORKSPACE:15:14: fetching local_python_configure rule //external:local_execution_config_python: Traceback (most recent call last):
File "D:/a/tflite-runtime-wheels/tflite-runtime-wheels/third_party/py/python_configure.bzl", line 212, column 22, in _create_local_python_repository
_check_python_bin(repository_ctx, python_bin)
File "D:/a/tflite-runtime-wheels/tflite-runtime-wheels/third_party/py/python_configure.bzl", line 145, column 25, in _check_python_bin
auto_config_fail("--define %s='%s' is not executable. Is it the python binary?" % (
File "D:/a/tflite-runtime-wheels/tflite-runtime-wheels/third_party/remote_config/common.bzl", line 12, column 9, in auto_config_fail
fail("%sConfiguration Error:%s %s\n" % (red, no_color, msg))
Error in fail: Configuration Error: --define PYTHON_BIN_PATH='C:\\hostedtoolcache\\windows\\Python\\3.10.11\\x64\\python3.exe
C:\\hostedtoolcache\\windows\\Python\\3.9.13\\x64\\python3.exe' is not executable. Is it the python binary?
ERROR: Analysis of target '//tensorflow/lite/python/interpreter_wrapper:_pywrap_tensorflow_interpreter_wrapper' failed; build aborted: Configuration Error: --define PYTHON_BIN_PATH='C:\\hostedtoolcache\\windows\\Python\\3.10.11\\x64\\python3.exe
C:\\hostedtoolcache\\windows\\Python\\3.9.13\\x64\\python3.exe' is not executable. Is it the python binary?
INFO: Elapsed time: 357.021s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (10 packages loaded, 15 targets configured)
FAILED: Build did NOT complete successfully (10 packages loaded, 15 targets configured)
Error: Process completed with exit code 1.
```
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Regression: //bazel_pip/tensorflow/python/kernel_tests/nn_ops:pooling_ops_3d_test fails
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[
"cc: @TensorFlow-MKL ",
"@vufg ",
"Thanks for the ping, will roll it back shortly",
"Fixed by https://github.com/tensorflow/tensorflow/commit/f0d77e35b28693d37f14f48ec8ed3c0133b8a09b",
"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/60500\">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/60500\">No</a>\n"
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CONTRIBUTOR
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
git HEAD
### Custom Code
No
### OS Platform and Distribution
Ubuntu 20.04
### Mobile device
n/a
### Python version
3.10
### Bazel version
5.3.0
### GCC/Compiler version
10.2.1
### CUDA/cuDNN version
n/a
### GPU model and memory
n/a
### Current Behaviour?
https://github.com/tensorflow/tensorflow/commit/f9becdcb81053e80b9c9034f3d4c31984f161dd1 introduced unit test failure when TF_ENABLE_ONEDNN_OPTS=1
### Standalone code to reproduce the issue
```shell
bazel test --build_tests_only --config=mkl_aarch64_threadpool --copt=-flax-vector-conversions --test_env=TF_ENABLE_ONEDNN_OPTS=1 --test_env=TF2_BEHAVIOR=1 --define=no_tensorflow_py_deps=true --test_lang_filters=py --flaky_test_attempts=3 --test_size_filters=small,medium --test_output=errors --verbose_failures=true --test_keep_going --notest_verbose_timeout_warnings --local_test_jobs=64 --test_tag_filters=-nopip,-no_pip,-oss_serial,-no_oss,-oss_excluded,-v1only,-benchmark-test,-no_aarch64,-no_oss_py38,-no_oss_py39,-no_oss_py310 -k -- //bazel_pip/tensorflow/... -//bazel_pip/tensorflow/compiler/tf2tensorrt/... -//bazel_pip/tensorflow/compiler/xrt/... -//bazel_pip/tensorflow/core/tpu/... -//bazel_pip/tensorflow/go/... -//bazel_pip/tensorflow/java/... -//bazel_pip/tensorflow/python/integration_testing/... -//bazel_pip/tensorflow/tools/toolchains/... -//bazel_pip/tensorflow/lite/... -//bazel_pip/tensorflow/python/kernel_tests/nn_ops:atrous_conv2d_test
```
### Relevant log output
```shell
======================================================================
FAIL: testAvgPool3dGradInvalidKsize (__main__.PoolingTest)
PoolingTest.testAvgPool3dGradInvalidKsize
----------------------------------------------------------------------
tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped____MklNativeAvgPool3DGrad_device_/job:localhost/replica:0/task:0/device:CPU:0}} Sliding window ksize for dimension 1 was zero. [Op:AvgPool3DGrad]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/tmpfs/bazel_output/_bazel_ubuntu/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/aarch64-opt/bin/bazel_pip/tensorflow/python/kernel_tests/nn_ops/pooling_ops_3d_test_cpu.runfiles/org_tensorflow/bazel_pip/tensorflow/python/kernel_tests/nn_ops/pooling_ops_3d_test.py", line 181, in testAvgPool3dGradInvalidKsize
with self.assertRaisesRegex(
AssertionError: "ksize must be positive, got: *" does not match "{{function_node __wrapped____MklNativeAvgPool3DGrad_device_/job:localhost/replica:0/task:0/device:CPU:0}} Sliding window ksize for dimension 1 was zero. [Op:AvgPool3DGrad]"
----------------------------------------------------------------------
Ran 33 tests in 22.409s
FAILED (failures=1, skipped=1)
================================================================================
```
</details>
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[OP]Register control flow ops for default device
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Register control flow operators on default device to align with CUDA device. Otherwise, extra _Send, _Recv will be inserted before/after control flow operators on XPU.
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[BUG]Add nullptr check
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[
"Hi @sagunb Any update on this PR? Please. Thank you!"
] | 2023-05-04T09:03:29 | 2023-07-13T07:05:51 | 2023-07-13T07:05:51 |
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When we get inputs of Merge operator by this API, we may get segmentation fault since the input can be nullptr.
Add nullptr check to avoid this bug.
|
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I_kwDOArmXAs5lDmjh
| 60,497 |
Support CheckpointInputPipelineHook in Estimator MirrorStrategy
|
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[
"@ZhangYaoFu ,\r\n\r\nWe are sorry to inform you that for 1.x versions there will not be any Feature additions. Its support already dropped and new features won't be added.Even estimator API also going to be deprecated soon. \r\n\r\nYou can migrate to 2.x versions and if found any feature needed with 2.x versions then the same will be reviewed. You can find migration guide [here](https://www.tensorflow.org/guide/migrate/migrate_tf2).\r\n\r\nThanks!",
"@SuryanarayanaY \r\nThank you for reply\r\nHow to save pipeline info in checkpoint in 2.x version?\r\n",
"@ZhangYaoFu , \r\n\r\nCould you please check this [API](https://www.tensorflow.org/ranking/api_docs/python/tfr/keras/pipeline/ModelFitPipeline) is helpful for your requirement.",
"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."
] | 2023-05-04T07:40:29 | 2023-05-20T01:52:53 | 2023-05-20T01:52:53 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Feature Request
### Have you reproduced the bug with TF nightly?
Yes
### Source
binary
### Tensorflow Version
tf 1.15
### Custom Code
No
### OS Platform and Distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
Failed to save pipeline info in checkpoint.
### Standalone code to reproduce the issue
```shell
mirrored_strategy = tf.distribute.MirroredStrategy()
run_config = tf.estimator.RunConfig(
model_dir=output_dir,
save_checkpoints_steps=save_checkpoints_steps,
train_distribute=mirrored_strategy,
eval_distribute=mirrored_strategy,
keep_checkpoint_max=max_ckp_num
)
estimator.train(input_fn=input_fn,
hooks=[tf.data.experimental.CheckpointInputPipelineHook(estimator)],
steps=tf_args.test_train_step)
```
### Relevant log output
```shell
2023-05-03 21:53:52.048712: W tensorflow/core/framework/op_kernel.cc:1651] OP_REQUIRES failed at iterator_ops.cc:1081 : Failed precondition: RemoteCall is stateful.
```
</details>
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[NextPluggableDevice] expose necessary c headers
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[
"@jyingl3 Can you have a look? thanks",
"Thanks Zhoulong!\r\n\r\n@chuanhaozhuge FYI"
] | 2023-05-04T07:14:37 | 2023-05-22T19:22:21 | 2023-05-22T19:22:20 |
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This PR is exposing those necessary c header files to use NextPluggableDevice C APi
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GPU VRAM usage significantly higher for LSTM models on TensorFlow v1.X when compared to other frameworks
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[
"Hi @jf9123 ,\r\n\r\nThanks for reporting. Is the reported issue is with TF1.x versions. Can it be reproducible with 2.x versions ? Since 1.x versions are not supported now this may not be considered for fix. Request you to try to reproduce the behaviour with 2.x Versions and let us know the results.Also request you to submit reproducible code. Thanks!",
"@SuryanarayanaY Here's the code for reproducibility; https://github.com/jf9123/lstm_question/",
"Hi @jf9123,\r\n\r\nI request you to please submit a colab gist as I can't able to reproduce the issue by cloning the 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.",
"@SuryanarayanaY The issue isn't present in TensorFlow v2.X. Was the behavior not observed on both TensorFlow v1.X and TensorFlow v2.X, or only TensorFlow v2.X?",
"@jf9123 ,\r\n\r\nSince support for TF1.x versions dropped our Team mostly will not look into the issue. I request if you can able to submit code snippet with TF2.x versions we will definitely review the issue. Please note that Keras integrated with Tensorflow only at Backend and if I am not wrong you are comparing keras 2.5v with TF 1.x versions right ?\r\n\r\nI request you to compare with 2.x versions of TF and Keras and I am sure Tensorflow will be more performant since Keras is designed for user friendly and it uses Tensorflow only at backend from 2.x versions. \r\n\r\nThanks!",
"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/60495\">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/60495\">No</a>\n",
"@SuryanarayanaY I apologize for the confusion but the result reported was from TensorFlow 2.5. I just wrote and ran it without Keras."
] | 2023-05-04T06:23:48 | 2023-06-13T07:23:53 | 2023-06-11T02:11:55 |
NONE
| null | null | null |
### System information
- **Have I written custom code (as opposed to using a stock example script
provided in TensorFlow)**: Custom
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Linux Ubuntu 18.04
- **TensorFlow installed from (source or binary)**: binary
- **TensorFlow version**: 2.5
- **Python version**: 3.7.5
- **GPU model and memory**: NVIDIA Quadro RTX 4000 GPU with 8GB of VRAM
- **CPU model**: Intel(R) Core(TM) i9-9900K CPU operating at 3.60GHz
- **cuDNN version**: 7.6.5
### Describe the problem
Hello everybody.
I’ve been experimenting with different models and different frameworks, and I’ve noticed that, when using GPU, GPU VRAM consumption when using LSTM models on the IMDB dataset is significantly higher on TensorFlow v1.X compared to the PyTorch and Keras implementations. Note that I artificially increased the size of the testing set of the MNIST dataset by duplicating the set by 100 times for a total of 1 000 000 samples. I've done so so any difference in inference time may be clearer.
Here are boxplots that showcase the GPU VRAM consumption of numerous LSTM models:
Any ideas on what may be causing this?
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Significantly higher RAM consumption for Keras Lenet-5 models (especially during inference) on Keras when compared to other frameworks
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[
"@jf9123,\r\nCould you please provide the complete code to reproduce the issue and it helps to analyse the issue in an effective way. Thank you!",
"@tilakrayal Here's the code; https://github.com/jf9123/lenet5_question",
"@jf9123,\r\nCould you please confirm if this is a duplicate of #60462. If yes, please feel free to move this issue to closed status. So that we can track the issue there. Thank you!",
"@tilakrayal This issue is related to a higher RAM consumption during inference on CPU, while https://github.com/tensorflow/tensorflow/issues/60462 is about a slow inference time on both CPU and GPU. I'm not sure if the two are related.",
"Thank you for opening this issue. Development of keras moved to another [repository](https://github.com/keras-team/keras/issues). \r\n\r\nCould you please post this issue on keras-team/keras [repo](https://github.com/keras-team/keras/issues).\r\nTo know more please refer:\r\nhttps://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999\r\nThank you!\r\n",
"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/60494\">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/60494\">No</a>\n"
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NONE
| null | null | null |
### System information
- **Have I written custom code (as opposed to using a stock example script
provided in TensorFlow)**: Custom
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Linux Ubuntu 18.04
- **TensorFlow installed from (source or binary)**: binary
- **TensorFlow version**: 2.5
- **Python version**: 3.7.5
- **GPU model and memory**: NVIDIA Quadro RTX 4000 GPU with 8GB of VRAM
- **CPU model**: Intel(R) Core(TM) i9-9900K CPU operating at 3.60GHz
- **cuDNN version**: 7.6.5
### Describe the problem
Hello everybody.
I’ve been experimenting with different models and different frameworks, and I’ve noticed that, when using CPU, RAM consumption when using Lenet5 models on the MNIST dataset is significantly higher on Keras compared to the PyTorch and TensorFlow v1.X implementations, with RAM consumption during inference standing out. Note that I artificially increased the size of the testing set of the MNIST dataset by duplicating the set by 100 times for a total of 1 000 000 samples. I've done so so any difference in inference time may be clearer.
This also might be related to the slowdown observed during inference on Keras (https://github.com/tensorflow/tensorflow/issues/60462)
Here are boxplots that showcase the RAM consumption of numerous Lenet5 models:
Any ideas on what may be causing this?
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I_kwDOArmXAs5lCYHd
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There are some installation problems such as 'tflite-model-maker'.
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"Hi @cugfk ,\r\n\r\nThis is known issue and our dev team already working on it at #60431 . The problem mainly here might be due to Python version compatibility. If you are Ok with downgrading python to 3.9.x version and also few other dependencies like numpy and others which you may get under log stack. More context can be found in the mentioned ticket here. Thanks!",
"> Hi @cugfk ,\r\n> \r\n> This is known issue and our dev team already working on it at #60431 . The problem mainly here might be due to Python version compatibility. If you are Ok with downgrading python to 3.9.x version and also few other dependencies like numpy and others which you may get under log stack. More context can be found in the mentioned ticket here. Thanks!\r\n\r\n\r\n=========================================\r\nDear [SuryanarayanaY](https://github.com/SuryanarayanaY)\r\nThank you for helping it but I'm still struggling. \r\nLet's have a look.\r\n\r\n**System information**\r\n- OS Platform and Distribution: macOS 13.3.1 (MacBook Pro M2)\r\n- **tensorflow-macos Version: 2.12.0**\r\n- **tensorflow-metal Version: 0.8.0**\r\n- Python Version: **Python 3.9.16**\r\n- Pip versions: pip 23.0.1 from /Users/haesunglee/miniconda/lib/python3.9/site-packages/pip (python 3.9)\r\n- **TensorFlow Version: 2.12.0**\r\n- numpy Version: 1.24.3\r\n\r\nMy log:\r\nhaesunglee@MacBookPro14-haesunglee ~ % \r\nhaesunglee@MacBookPro14-haesunglee ~ % **sudo pip3 install tflite-model-maker** \r\nPassword:\r\nWARNING: The directory '/Users/haesunglee/Library/Caches/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you should use sudo's -H flag.\r\nCollecting tflite-model-maker\r\n Downloading tflite_model_maker-0.4.2-py3-none-any.whl (577 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 577.3/577.3 kB 12.6 MB/s eta 0:00:00\r\nCollecting librosa==0.8.1\r\n Downloading librosa-0.8.1-py3-none-any.whl (203 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 203.8/203.8 kB 12.1 MB/s eta 0:00:00\r\nCollecting lxml>=4.6.1\r\n...\r\n...\r\n...\r\nINFO: pip is looking at multiple versions of sentencepiece to determine which version is compatible with other requirements. This could take a while.\r\nCollecting sentencepiece\r\n Downloading sentencepiece-0.1.98-cp39-cp39-macosx_11_0_arm64.whl (1.2 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.2/1.2 MB 2.1 MB/s eta 0:00:00\r\n Downloading sentencepiece-0.1.97-cp39-cp39-macosx_11_0_arm64.whl (1.1 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.1/1.1 MB 2.5 MB/s eta 0:00:00\r\n Downloading sentencepiece-0.1.96.tar.gz (508 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 508.6/508.6 kB 1.4 MB/s eta 0:00:00\r\n Preparing metadata (setup.py) ... done\r\n Downloading sentencepiece-0.1.95.tar.gz (508 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 508.7/508.7 kB 2.1 MB/s eta 0:00:00\r\n Preparing metadata (setup.py) ... done\r\n Downloading sentencepiece-0.1.94.tar.gz (507 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 507.5/507.5 kB 2.5 MB/s eta 0:00:00\r\n Preparing metadata (setup.py) ... done\r\n Downloading sentencepiece-0.1.91.tar.gz (500 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 500.5/500.5 kB 2.6 MB/s eta 0:00:00\r\n **Preparing metadata (setup.py) ... error\r\n error: subprocess-exited-with-error\r\n \r\n × python setup.py egg_info did not run successfully.\r\n │ exit code: 1\r\n ╰─> [2 lines of output]\r\n /bin/sh: pkg-config: command not found\r\n Failed to find sentencepiece pkgconfig\r\n [end of output]\r\n \r\n note: This error originates from a subprocess, and is likely not a problem with pip.\r\nerror: metadata-generation-failed\r\n\r\n× Encountered error while generating package metadata.\r\n╰─> See above for output.\r\n\r\nnote: This is an issue with the package mentioned above, not pip.\r\nhint: See above for details.**\r\nhaesunglee@MacBookPro14-haesunglee ~ %\r\n\r\n\r\n\r\n\r\n",
"@pjpratik , Please look into the issue. Thanks!",
"Hi @cugfk \r\n\r\nThe `tflite-model-maker` installation has a problem and our team is working on it.\r\n\r\nMeanwhile, if you are trying to perform image classification, you can try out the MediaPipe Model Maker [ImageClassifier](https://developers.google.com/mediapipe/solutions/vision/image_classifier/customize) API.\r\n\r\nThanks.",
"> Hi @cugfk\r\n> \r\n> The `tflite-model-maker` installation has a problem and our team is working on it.\r\n> \r\n> Meanwhile, if you are trying to perform image classification, you can try out the MediaPipe Model Maker [ImageClassifier](https://developers.google.com/mediapipe/solutions/vision/image_classifier/customize) API.\r\n> \r\n> Thanks.\r\n\r\nThank you for helping but there are some problems.\r\nLet's have a look.\r\n**System information**\r\n- OS Platform and Distribution: macOS 13.3.1 (MacBook Pro M2)\r\n- tensorflow-macos Version: 2.12.0\r\n- tensorflow-metal Version: 0.8.0\r\n- Python Version: Python 3.9.16\r\n- Pip versions: pip 23.0.1 from /Users/haesunglee/miniconda/lib/python3.9/site-packages/pip (python 3.9)\r\n- TensorFlow Version: 2.12.0\r\n- numpy Version: 1.24.3\r\n\r\nMy log:\r\n`\r\nhaesunglee@MacBookPro14-haesunglee ~ % sudo pip3 install mediapipe-model-maker \r\nPassword:\r\nWARNING: The directory '/Users/haesunglee/Library/Caches/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you should use sudo's -H flag.\r\nCollecting mediapipe-model-maker\r\n Downloading mediapipe_model_maker-0.1.1.1-py3-none-any.whl (106 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 106.3/106.3 kB 13.9 MB/s eta 0:00:00\r\nRequirement already satisfied: absl-py in ./miniconda/lib/python3.9/site-packages (from mediapipe-model-maker) (1.4.0)\r\nINFO: pip is looking at multiple versions of mediapipe-model-maker to determine which version is compatible with other requirements. This could take a while.\r\n Downloading mediapipe_model_maker-0.1.1.0-py3-none-any.whl (106 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 106.3/106.3 kB 12.7 MB/s eta 0:00:00\r\n Downloading mediapipe_model_maker-0.1.0.2-py3-none-any.whl (7.2 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.2/7.2 MB 11.7 MB/s eta 0:00:00\r\n Downloading mediapipe_model_maker-0.1.0.1-py3-none-any.whl (7.2 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.2/7.2 MB 11.7 MB/s eta 0:00:00\r\nRequirement already satisfied: mediapipe>=0.9.0 in ./miniconda/lib/python3.9/site-packages (from mediapipe-model-maker) (0.9.3.0)\r\nRequirement already satisfied: numpy in ./miniconda/lib/python3.9/site-packages (from mediapipe-model-maker) (1.23.5)\r\nRequirement already satisfied: opencv-python in ./miniconda/lib/python3.9/site-packages (from mediapipe-model-maker) (4.7.0.72)\r\n Downloading mediapipe_model_maker-0.1.0-py3-none-any.whl (7.0 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.0/7.0 MB 11.7 MB/s eta 0:00:00\r\nERROR: Cannot install mediapipe-model-maker==0.1.0, mediapipe-model-maker==0.1.0.1, mediapipe-model-maker==0.1.0.2, mediapipe-model-maker==0.1.1.0 and mediapipe-model-maker==0.1.1.1 because these package versions have conflicting dependencies.\r\n\r\nThe conflict is caused by:\r\n mediapipe-model-maker 0.1.1.1 depends on mediapipe==0.9.2.1\r\n mediapipe-model-maker 0.1.1.0 depends on mediapipe==0.9.2.1\r\n mediapipe-model-maker 0.1.0.2 depends on mediapipe==0.9.0.1\r\n mediapipe-model-maker 0.1.0.1 depends on tensorflow>=2.10\r\n mediapipe-model-maker 0.1.0 depends on tensorflow>=2.10\r\n\r\nTo fix this you could try to:\r\n1. loosen the range of package versions you've specified\r\n2. remove package versions to allow pip attempt to solve the dependency conflict\r\n\r\nERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts\r\nhaesunglee@MacBookPro14-haesunglee ~ % \r\n`\r\n\r\n",
"Hi @cugfk \r\n\r\nSorry for the inconvenience caused. The mediapipe model maker is yet to support on arm mac devices. As a workaround please use colab for using the mediapipe model maker.\r\n\r\nPlease check this sample [gist](https://colab.research.google.com/gist/pjpratik/5920dc80a9e8c83174c36d9edded70b7/60493.ipynb) on getting started and refer to this [documentation](https://developers.google.com/mediapipe/solutions/vision/image_classifier/customize) on customizing the image classifier using model maker.\r\n\r\nThanks.",
"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/60493\">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/60493\">No</a>\n",
"Still facing this issue\r\n`ERROR: Cannot install tflite-model-maker==0.1.2, tflite-model-maker==0.2.0, tflite-model-maker==0.2.1, tflite-model-maker==0.2.2, tflite-model-maker==0.2.3, tflite-model-maker==0.2.4, tflite-model-maker==0.2.5, tflite-model-maker==0.3.3, tflite-model-maker==0.3.4, tflite-model-maker==0.4.0, tflite-model-maker==0.4.1 and tflite-model-maker==0.4.2 because these package versions have conflicting dependencies.\r\n\r\nThe conflict is caused by:\r\n tflite-model-maker 0.4.2 depends on numba==0.53\r\n tflite-model-maker 0.4.1 depends on numba==0.53\r\n tflite-model-maker 0.4.0 depends on numba==0.53\r\n tflite-model-maker 0.3.4 depends on numba==0.53\r\n tflite-model-maker 0.3.3 depends on numba==0.53\r\n tflite-model-maker 0.2.5 depends on tflite-support==0.1.0rc4\r\n tflite-model-maker 0.2.4 depends on tflite-support==0.1.0rc4\r\n tflite-model-maker 0.2.3 depends on tflite-support==0.1.0rc3.dev2\r\n tflite-model-maker 0.2.2 depends on tflite-support==0.1.0rc3.dev2\r\n tflite-model-maker 0.2.1 depends on tflite-support==0.1.0rc3.dev2\r\n tflite-model-maker 0.2.0 depends on tflite-support==0.1.0rc3.dev2\r\n tflite-model-maker 0.1.2 depends on tflite-support==0.1.0rc3.dev2\r\n\r\nTo fix this you could try to:\r\n1. loosen the range of package versions you've specified\r\n2. remove package versions to allow pip attempt to solve the dependency conflict\r\n\r\nERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts\r\n`",
"> Still facing this issue `ERROR: Cannot install tflite-model-maker==0.1.2, tflite-model-maker==0.2.0, tflite-model-maker==0.2.1, tflite-model-maker==0.2.2, tflite-model-maker==0.2.3, tflite-model-maker==0.2.4, tflite-model-maker==0.2.5, tflite-model-maker==0.3.3, tflite-model-maker==0.3.4, tflite-model-maker==0.4.0, tflite-model-maker==0.4.1 and tflite-model-maker==0.4.2 because these package versions have conflicting dependencies.\r\n> \r\n> The conflict is caused by: tflite-model-maker 0.4.2 depends on numba==0.53 tflite-model-maker 0.4.1 depends on numba==0.53 tflite-model-maker 0.4.0 depends on numba==0.53 tflite-model-maker 0.3.4 depends on numba==0.53 tflite-model-maker 0.3.3 depends on numba==0.53 tflite-model-maker 0.2.5 depends on tflite-support==0.1.0rc4 tflite-model-maker 0.2.4 depends on tflite-support==0.1.0rc4 tflite-model-maker 0.2.3 depends on tflite-support==0.1.0rc3.dev2 tflite-model-maker 0.2.2 depends on tflite-support==0.1.0rc3.dev2 tflite-model-maker 0.2.1 depends on tflite-support==0.1.0rc3.dev2 tflite-model-maker 0.2.0 depends on tflite-support==0.1.0rc3.dev2 tflite-model-maker 0.1.2 depends on tflite-support==0.1.0rc3.dev2\r\n> \r\n> To fix this you could try to:\r\n> \r\n> 1. loosen the range of package versions you've specified\r\n> \r\n> 2. remove package versions to allow pip attempt to solve the dependency conflict\r\n> \r\n> \r\n> ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts `\r\n\r\nI checked numba0.53 support for python version, It's only up to python3.9.\r\nSo I changed my python version to python3.9 then It's solved"
] | 2023-05-04T02:12:27 | 2024-01-03T03:27:41 | 2023-05-25T01:54:31 |
NONE
| null | null | null |
**System information**
- OS Platform and Distribution: **macOS 13.3.1 (MacBook Pro M2)**
- TensorFlow installed from (source or binary): [tf-nightly 2.14.0.dev20230503](https://pypi.org/project/tf-nightly/)
- TensorFlow version (or github SHA if from source): **Version: 2.14.0-dev20230503**
- Python Version(Default): **Python 3.11.3**
**Provide the text output from 'tflite-model-maker'**
```
# Summary: Successfully installed TensorFlow(2.14.0-dev20230503) but struggling about installing tflite-model-maker
#
sudo pip3 install tflite-model-maker
haesunglee@MacBookPro14-haesunglee ~ % sudo pip3 install tflite-model-maker
Password:
WARNING: The directory '/Users/haesunglee/Library/Caches/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you should use sudo's -H flag.
Collecting tflite-model-maker
Downloading tflite_model_maker-0.4.2-py3-none-any.whl (577 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 577.3/577.3 kB 6.4 MB/s eta 0:00:00
Collecting tf-models-official==2.3.0 (from tflite-model-maker)
Downloading tf_models_official-2.3.0-py2.py3-none-any.whl (840 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 840.9/840.9 kB 7.0 MB/s eta 0:00:00
...
...
...
Collecting tflite-model-maker
Downloading tflite_model_maker-0.1.0-py3-none-any.whl (84 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 84.6/84.6 kB 6.1 MB/s eta 0:00:00
ERROR: Cannot install tflite-model-maker==0.1.2, tflite-model-maker==0.2.0, tflite-model-maker==0.2.1, tflite-model-maker==0.2.2, tflite-model-maker==0.2.3, tflite-model-maker==0.2.4, tflite-model-maker==0.2.5, tflite-model-maker==0.3.0, tflite-model-maker==0.3.1, tflite-model-maker==0.3.2, tflite-model-maker==0.3.3, tflite-model-maker==0.3.4, tflite-model-maker==0.4.0, tflite-model-maker==0.4.1 and tflite-model-maker==0.4.2 because these package versions have conflicting dependencies.
The conflict is caused by:
tflite-model-maker 0.4.2 depends on tflite-support>=0.4.2
tflite-model-maker 0.4.1 depends on tflite-support-nightly
tflite-model-maker 0.4.0 depends on tflite-support>=0.4.0
tflite-model-maker 0.3.4 depends on tflite-support>=0.3.1
tflite-model-maker 0.3.3 depends on tflite-support>=0.3.1
tflite-model-maker 0.3.2 depends on tflite-support>=0.1.0rc4
tflite-model-maker 0.3.1 depends on tflite-support>=0.1.0rc4
tflite-model-maker 0.3.0 depends on tflite-support>=0.1.0rc4
tflite-model-maker 0.2.5 depends on tflite-support==0.1.0rc4
tflite-model-maker 0.2.4 depends on tflite-support==0.1.0rc4
tflite-model-maker 0.2.3 depends on tflite-support==0.1.0rc3.dev2
tflite-model-maker 0.2.2 depends on tflite-support==0.1.0rc3.dev2
tflite-model-maker 0.2.1 depends on tflite-support==0.1.0rc3.dev2
tflite-model-maker 0.2.0 depends on tflite-support==0.1.0rc3.dev2
tflite-model-maker 0.1.2 depends on tflite-support==0.1.0rc3.dev2
To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict
ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts
haesunglee@MacBookPro14-haesunglee ~ %
haesunglee@MacBookPro14-haesunglee ~ %
```
Last year I successfully installed tflite-model-maker on my 1st mac. I'm still using it on my 1st mac.
Please help me update and fix the tflite-model-maker.
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Draft: Add wrap option to pad
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[] | 2023-05-04T00:10:53 | 2023-05-30T23:20:27 | 2023-05-30T23:20:27 |
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Relates to https://github.com/tensorflow/tensorflow/issues/57672.
I have added the boilerplate code right now (most of it is copied from the pad op). If someone has time, I would really appreciate a quick review to check I'm going down the right direction.
The other approach would be to add this directly into the python code. This would require less work but be less efficient and not in the spirit of the issue (?).
TO DO: Update compute method in `wrap_pad_op.cc` to give the right output.
Add a test for the new python `pad` option.
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Pin Python dependency versions
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reverting keras version to 2.13.0-rco
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"> This one is good, note that this is keras package, not keras-nightly.\r\n\r\nGot it "
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How to write custom XLA op for tensorflow
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[
"@pranavladkat,\r\nHave you got the chance to have a look at the below official document link. Please try and let me know if it serves the purpose. \r\nhttps://www.tensorflow.org/xla/custom_call\r\nThank you!",
"Hi, I have followed it, however this guide lacks several steps such as compilation and fails to provide an end to end working example. I tried following how tensorflow implements its ops by looking at the code, but I'm not able to get past op registration error which I pasted above. It'll be great help if that can be clarified. ",
"Hi, I'm simply looking for better documentation or an end-to-end example which demonstrate writing custom XLA op for TF. Any pointers in the right direction are appreciated!",
"It appears that we require native tf op to be registered along with XLA op to address this issue. This is not mentioned in the documentation."
] | 2023-05-03T17:40:14 | 2023-06-06T21:09:41 | 2023-06-06T21:09:40 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Documentation Feature Request
### Have you reproduced the bug with TF nightly?
Yes
### Source
binary
### Tensorflow Version
2.12.0
### Custom Code
Yes
### OS Platform and Distribution
Linux
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
Hi,
I have custom tensorflow ops implemented in C++ [using this guide](https://www.tensorflow.org/guide/create_op) which are invoked during training. They work fine as long as XLA is not used, however I'm unable to get them working (and even a dummy custom xla op).
The test op I have written is mentioned below and compiled by following the guide linked above. The compilation succeeds but I get following runtime error when the module with custom op is imported:
```
Non-OK-status: lookup_status status: NOT_FOUND: Op type not registered 'XlaTestOp' in binary running on <hostname>. Make sure the Op and Kernel are registered in the binary running in this process.
```
I'm not able to find any more info how to resolve this issue.
### Standalone code to reproduce the issue
```shell
C++ XLA op implementation:
#include "tensorflow/compiler/tf2xla/shape_util.h"
#include "tensorflow/compiler/tf2xla/xla_compiler.h"
#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
#include "tensorflow/compiler/xla/client/xla_builder.h"
#include "tensorflow/compiler/xla/service/custom_call_target_registry.h"
#include "tensorflow/compiler/xla/service/hlo.pb.h"
#include "tensorflow/compiler/xla/shape.h"
#include "tensorflow/compiler/xla/xla_data.pb.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/lib/hash/hash.h"
#include "tensorflow/core/platform/human_readable_json.h"
void test_op(cudaStream_t stream, void** buffers, const char* opaque,
size_t opaque_len) {
std::cout << "executing test_op()\n";
}
class XLATestOp : public XlaOpKernel {
public:
explicit XLATestOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { }
void Compile(XlaOpKernelContext* ctx) override {
::xla::XlaBuilder* const builder = ctx->builder();
::xla::XlaOp input = ctx->Input(0);
// use output aliasing to reuse input buffer for output
std::vector<std::pair<::xla::ShapeIndex,
std::pair<int64, ::xla::ShapeIndex>>> output_operand_aliasing = {
{::xla::ShapeIndex{}, {0, ::xla::ShapeIndex{}}}
};
::xla::Shape output_shape = builder->GetShape(input).value();
::xla::XlaOp output_op = builder->ReportErrorOrReturn(
::xla::CustomCall(
builder,
"test_op",
{input},
output_shape,
/*opaque=*/ "",
/*has_side_effect=*/false,
output_operand_aliasing,
/*literal=*/nullptr
)
);
ctx->SetOutput(0, output_op);
}
};
REGISTER_XLA_OP(Name("XlaTestOp"), XLATestOp);
XLA_REGISTER_CUSTOM_CALL_TARGET(test_op, "CUDA");
```
```
### Relevant log output
_No response_</details>
|
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PR_kwDOArmXAs5Psjom
| 60,488 |
Revert "Update keras version in TF-core release branch to 2.13 release"
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Reverts tensorflow/tensorflow#60484
We need to revert, there is no such version: https://pypi.org/project/keras-nightly/#history
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PR_kwDOArmXAs5Psei6
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Updating keras version Pin python dependency versions
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[
"(for this one, let's wait for the containers to finish building before merging)",
"One containers failed(py 3.11) and other are all canceled ",
"Yeah, the version is wrong: https://pypi.org/project/keras-nightly/#history"
] | 2023-05-03T17:08:10 | 2023-05-03T17:43:45 | 2023-05-03T17:43:42 |
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[oneDNN v3.x]: Enabled reorder primitive cache and some misc. changes
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[
"Could you please merge conflicts and rebase? All tests are failing and, for some reason, there are no logs. Please also reply to my comment on `mkl_util.h`. Thank you!",
"> Could you please merge conflicts and rebase? All tests are failing and, for some reason, there are no logs. Please also reply to my comment on `mkl_util.h`. Thank you!\r\n\r\n@penpornk Thanks for reviewing this PR! I have addressed your review comments and also rebased with latest master. Please take a look.",
"@bhavani-subramanian This PR has some overlap with #60375 that just went in. Could you please merge conflicts / rebase? Thank you!"
] | 2023-05-03T16:54:24 | 2023-07-20T06:50:09 | 2023-07-20T06:50:09 |
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- This PR enables reorder primitive cache in `mkl_util.h` and `mkl_quantize_op.cc`.
- This PR passes all unit tests related to reorder primitive cache for oneDNN v3.x.
- This PR also enables oneDNN v3.x in benchmark tests.
- These changes are specific only to oneDNN kernels and do not affect Eigen ops.
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I_kwDOArmXAs5k_ysg
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XLA cumsum not supported for int8 and int16
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[
"@AndreasMadsen,\r\n **tf.math.cumsum** will try to compute the cumulative sum of the tensor x along the axis. I tried to execute the code with the correct datatypes and it is working as intended. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/a0559555ae60c2421feb7d2b5c2a7989/untitled1107.ipynb).\r\nhttps://www.tensorflow.org/api_docs/python/tf/math/cumsum\r\n\r\n```\r\n Args:\r\n x: A `Tensor`. Must be one of the following types: `float32`, `float64`,\r\n `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,\r\n `complex128`, `qint8`, `quint8`, `qint32`, `half`.\r\n axis: A `Tensor` of type `int32` (default: 0). Must be in the range\r\n `[-rank(x), rank(x))`.\r\n exclusive: If `True`, perform exclusive cumsum.\r\n reverse: A `bool` (default: False).\r\n name: A name for the operation (optional).\r\n```",
"@tilakrayal Please, read the issue description more carefully or run the Google Colab link (https://colab.research.google.com/drive/1lqVjSjGmzYwLZfcfgVa8W02lWJ6XcqTk#scrollTo=ex1x-uaCfr6Y) I attached.\r\n\r\nYour example uses `int64` (working), I'm complaining about `int8` and `int16` (not working).\r\n\r\nIn your example make the following modification:\r\n\r\n```diff\r\n- print(cumsum(tf.constant([0, 1, 2], dtype=tf.dtypes.int64)))\r\n+ print(cumsum(tf.constant([0, 1, 2], dtype=tf.dtypes.int8)))\r\n```\r\n\r\nthen the error will reproduce."
] | 2023-05-03T16:51:04 | 2023-05-15T17:36:36 | null |
CONTRIBUTOR
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
binary
### Tensorflow Version
2.14.0.dev20230503
### Custom Code
No
### OS Platform and Distribution
Google Colab
### Mobile device
_No response_
### Python version
3.10.11
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
`tf.math.cumsum` is not supported for XLA with int8 and int16. I know that not all operations are supported by XLA, but this one seams so trivial. I assume it is an oversight.
### Standalone code to reproduce the issue
Google colab: https://colab.research.google.com/drive/1lqVjSjGmzYwLZfcfgVa8W02lWJ6XcqTk#scrollTo=ex1x-uaCfr6Y
```python
import tensorflow as tf
@tf.function(jit_compile=True)
def cumsum(x):
return tf.math.cumsum(x)
print(cumsum(tf.constant([0, 1, 2], dtype=tf.dtypes.int8)))
```
### Relevant log output
```shell
InvalidArgumentError: Detected unsupported operations when trying to compile graph __inference_cumsum_7[_XlaMustCompile=true,config_proto=3175580994766145631,executor_type=11160318154034397263] on XLA_CPU_JIT: Cumsum (No registered 'Cumsum' OpKernel for XLA_CPU_JIT devices compatible with node {{node Cumsum}}
(OpKernel was found, but attributes didn't match) Requested Attributes: T=DT_INT8, Tidx=DT_INT32, exclusive=false, reverse=false){{node Cumsum}}
The op is created at:
File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.10/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py", line 992, in launch_instance
app.start()
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py", line 619, in start
self.io_loop.start()
File "/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py", line 215, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.10/asyncio/base_events.py", line 603, in run_forever
self._run_once()
File "/usr/lib/python3.10/asyncio/base_events.py", line 1909, in _run_once
handle._run()
File "/usr/lib/python3.10/asyncio/events.py", line 80, in _run
self._context.run(self._callback, *self._args)
File "/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py", line 687, in <lambda>
lambda f: self._run_callback(functools.partial(callback, future))
File "/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py", line 740, in _run_callback
ret = callback()
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 821, in inner
self.ctx_run(self.run)
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 782, in run
yielded = self.gen.send(value)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 377, in dispatch_queue
yield self.process_one()
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 250, in wrapper
runner = Runner(ctx_run, result, future, yielded)
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 748, in __init__
self.ctx_run(self.run)
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 782, in run
yielded = self.gen.send(value)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 361, in process_one
yield gen.maybe_future(dispatch(*args))
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 234, in wrapper
yielded = ctx_run(next, result)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 261, in dispatch_shell
yield gen.maybe_future(handler(stream, idents, msg))
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 234, in wrapper
yielded = ctx_run(next, result)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 539, in execute_request
self.do_execute(
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 234, in wrapper
yielded = ctx_run(next, result)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py", line 302, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py", line 539, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 2975, in run_cell
result = self._run_cell(
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3030, in _run_cell
return runner(coro)
File "/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py", line 78, in _pseudo_sync_runner
coro.send(None)
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3257, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3473, in run_ast_nodes
if (await self.run_code(code, result, async_=asy)):
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3553, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-2-602a645c7a37>", line 1, in <cell line: 1>
print(cumsum(tf.constant([0, 1, 2], dtype=tf.dtypes.int8)))
File "<ipython-input-1-9d904e83e71a>", line 7, in cumsum
return tf.math.cumsum(x)
tf2xla conversion failed while converting __inference_cumsum_7[_XlaMustCompile=true,config_proto=3175580994766145631,executor_type=11160318154034397263]. Run with TF_DUMP_GRAPH_PREFIX=/path/to/dump/dir and --vmodule=xla_compiler=2 to obtain a dump of the compiled functions. [Op:__inference_cumsum_7]
```
</details>
|
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PR_kwDOArmXAs5PsOax
| 60,484 |
Update keras version in TF-core release branch to 2.13 release
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[] | 2023-05-03T16:18:42 | 2023-05-03T16:19:19 | 2023-05-03T16:19:15 |
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I_kwDOArmXAs5k_SwB
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Bug: tflite::PrintInterpreterState has hardcoded (short) line length in printf
|
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[
"Hello, this change has been added to tf-nightly in the following commit: https://github.com/tensorflow/tensorflow/commit/87566804d71f83d01e548943dc5d1c97d9391740\r\n\r\nYou can test these changes either by installing tf-nightly or by building Tensorflow from the master branch. If you feel this resolves your issue, please feel free to close the issue.",
"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.",
"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/60483\">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/60483\">No</a>\n"
] | 2023-05-03T15:30:28 | 2023-06-01T10:03:58 | 2023-06-01T10:03:56 |
NONE
| null | null | null |
### System information
- **Have I written custom code (as opposed to using a stock example script
provided in TensorFlow)**: No
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Linux
- **TensorFlow installed from (source or binary)**: source
- **TensorFlow version (use command below)**: 2:12
### Problem Description
When using TensorFlow Lite's benchmark/profiler, both the options ```-print_postinvoke_state=true``` and ```--print_preinvoke_state=true``` are suggested for debugging. However, they both cause a call to ```tflite::PrintInterpreterState```, which truncates the names and AllocType of the tensors printed, independently of any parameter.
This renders the tool useless for debugging any models that have tensors with names longer than 25 characters (which is my case, some examples below).
Please make the total line length an argument in ```tflite::PrintInterpreterState``` and a command line argument in the benchmark, so that it can be increased if the default setting is not satisfactory.
Exact line of code where (at the latest commit at the time of writing this) the strings get truncated:
https://github.com/tensorflow/tensorflow/blob/fbf60502e8186157325eb3f7053de4b1d1d7d5b5/tensorflow/lite/optional_debug_tools.cc#LL427C29-L427C32
### Source code / logs
Example output of tflite benchmark with options ```-print_postinvoke_state=true --print_preinvoke_state=true```
```
Tensor ID Name Type AllocType Size (Bytes/MB) Shape MemAddr-Offset
Tensor 0 serving_default_input:0 kTfLiteFloat32 kTfLiteArenaRw 71808 / 0.07 [1,187,96] [0, 71808)
Tensor 1 musicnn_classifier/fro... kTfLiteFloat32 kTfLiteMmapRo 816 / 0.00 [204] [3135892, 3136708)
Tensor 2 musicnn_classifier/fro... kTfLiteFloat32 kTfLiteMmapRo 816 / 0.00 [204] [3135064, 3135880)
Tensor 3 musicnn_classifier/fro... kTfLiteFloat32 kTfLiteMmapRo 204 / 0.00 [51] [3134848, 3135052)
Tensor 4 musicnn_classifier/fro... kTfLiteFloat32 kTfLiteMmapRo 204 / 0.00 [51] [3134632, 3134836)
Tensor 5 musicnn_classifier/fro... kTfLiteFloat32 kTfLiteMmapRo 204 / 0.00 [51] [3134416, 3134620)
Tensor 6 musicnn_classifier/bac... kTfLiteFloat32 kTfLiteMmapRo 800 / 0.00 [200] [3133604, 3134404)
Tensor 7 musicnn_classifier/bac... kTfLiteFloat32 kTfLiteMmapRo 1204800 / 1.15 [200,1506] [1928792, 3133592)
```
or
```
Tensor 52 musicnn_classifier/fro... kTfLiteInt32 ...itePersistentRo 12 / 0.00 [3] [-1, -1)
```
*In all of these cases I can't figure out the full name of any tensor, and I have to guess the AllocType of Tensor 52*
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xla_sharding in new version
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[
"Hi @mahmoodn, Apologies for the delay. \r\nThe `tensorflow.compiler.xla.experimental` was moved to `tensorflow.python.compiler.xla.experimental`. Kindly refer to this [commit](https://github.com/tensorflow/tensorflow/commit/6ed611beb95124234d07053581205eb8f3a865a5). \r\nYou can import xla_sharding using below command:\r\n```\r\nfrom tensorflow.python.compiler.xla.experimental import xla_sharding\r\n```\r\n\r\nThank you!",
"Yes thank you.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60482\">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/60482\">No</a>\n"
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NONE
| null | null | null |
Hi
What is the replacement for `from tensorflow.compiler.xla.experimental.xla_sharding import xla_sharding` in TF-2.12.0? For example, the function is `xla_sharding.split()`.
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PR_kwDOArmXAs5PqGI2
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[Linaro:ARM_CI] Add job to run c++ tests nightly
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Add a github action to run a job that will run the C++ based unit tests nightly.
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PR_kwDOArmXAs5Pp85X
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r2.13 cherry-pick: a0619c79b08 "[Linaro:ARM_CI] Update container used to build AARCH64 wheels"
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Refer to the original commit: https://github.com/tensorflow/tensorflow/commit/a0619c79b0864f5fa3f4f3c1b0184cf47b23aeb1
This PR allows making changes (e.g., python package version changes) to the base docker container specifically for 2.13 without affecting the container used for 2.12.
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Some unit tests fail pip test
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"Fixed by https://github.com/tensorflow/tensorflow/commit/edc647632a2ec64e1d9a6a6dc699bad77e930738",
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CONTRIBUTOR
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
git HEAD
### Custom Code
No
### OS Platform and Distribution
Ubuntu 20.04
### Mobile device
n/a
### Python version
3.10
### Bazel version
5.3.0
### GCC/Compiler version
10.2.1
### CUDA/cuDNN version
n/a
### GPU model and memory
n/a
### Current Behaviour?
The unit tests //bazel_pip/tensorflow/dtensor/python/tests:multi_client_test_2gpus, //bazel_pip/tensorflow/dtensor/python/tests:multi_client_test_cpu, //bazel_pip/tensorflow/dtensor/python/tests:multi_client_test_nccl_2gpus and //bazel_pip/tensorflow/dtensor/python/tests:multi_client_test_nccl_local_2gpus fail since commit https://github.com/tensorflow/tensorflow/commit/540a08ea4ddc93a632dd8583ddeae5be5ee75bff
See https://github.com/tensorflow/tensorflow/actions/runs/4865981223/jobs/8676977815#step:5:29731
### Standalone code to reproduce the issue
```shell
bazel test --build_tests_only --config=mkl_aarch64_threadpool --copt=-flax-vector-conversions --test_env=TF_ENABLE_ONEDNN_OPTS=1 --test_env=TF2_BEHAVIOR=1 --define=no_tensorflow_py_deps=true --test_lang_filters=py --flaky_test_attempts=3 --test_size_filters=small,medium --test_output=errors --verbose_failures=true --test_keep_going --notest_verbose_timeout_warnings --local_test_jobs=64 --test_tag_filters=-nopip,-no_pip,-oss_serial,-no_oss,-oss_excluded,-v1only,-benchmark-test,-no_aarch64,-no_oss_py38,-no_oss_py39,-no_oss_py310 -k -- //bazel_pip/tensorflow/... -//bazel_pip/tensorflow/compiler/tf2tensorrt/... -//bazel_pip/tensorflow/compiler/xrt/... -//bazel_pip/tensorflow/core/tpu/... -//bazel_pip/tensorflow/go/... -//bazel_pip/tensorflow/java/... -//bazel_pip/tensorflow/python/integration_testing/... -//bazel_pip/tensorflow/tools/toolchains/... -//bazel_pip/tensorflow/lite/... -//bazel_pip/tensorflow/python/kernel_tests/nn_ops:atrous_conv2d_test
```
### Relevant log output
```shell
FAIL: //bazel_pip/tensorflow/dtensor/python/tests:multi_client_test_2gpus (see /tmpfs/bazel_output/_bazel_ubuntu/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/aarch64-opt/testlogs/bazel_pip/tensorflow/dtensor/python/tests/multi_client_test_2gpus/test_attempts/attempt_1.log)
FAIL: //bazel_pip/tensorflow/dtensor/python/tests:multi_client_test_2gpus (see /tmpfs/bazel_output/_bazel_ubuntu/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/aarch64-opt/testlogs/bazel_pip/tensorflow/dtensor/python/tests/multi_client_test_2gpus/test_attempts/attempt_2.log)
FAIL: //bazel_pip/tensorflow/dtensor/python/tests:multi_client_test_2gpus (see /tmpfs/bazel_output/_bazel_ubuntu/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/aarch64-opt/testlogs/bazel_pip/tensorflow/dtensor/python/tests/multi_client_test_2gpus/test.log)
FAILED: //bazel_pip/tensorflow/dtensor/python/tests:multi_client_test_2gpus (Summary)
/tmpfs/bazel_output/_bazel_ubuntu/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/aarch64-opt/testlogs/bazel_pip/tensorflow/dtensor/python/tests/multi_client_test_2gpus/test.log
/tmpfs/bazel_output/_bazel_ubuntu/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/aarch64-opt/testlogs/bazel_pip/tensorflow/dtensor/python/tests/multi_client_test_2gpus/test_attempts/attempt_1.log
/tmpfs/bazel_output/_bazel_ubuntu/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/aarch64-opt/testlogs/bazel_pip/tensorflow/dtensor/python/tests/multi_client_test_2gpus/test_attempts/attempt_2.log
INFO: From Testing //bazel_pip/tensorflow/dtensor/python/tests:multi_client_test_2gpus:
==================== Test output for //bazel_pip/tensorflow/dtensor/python/tests:multi_client_test_2gpus:
2023-05-02 23:40:14.254127: I tensorflow/core/util/port.cc:116] Experimental oneDNN custom operations are on. If you experience issues, please turn them off by setting the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
Traceback (most recent call last):
File "/tmpfs/bazel_output/_bazel_ubuntu/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/aarch64-opt/bin/bazel_pip/tensorflow/dtensor/python/tests/multi_client_test_2gpus.runfiles/org_tensorflow/bazel_pip/tensorflow/dtensor/python/tests/multi_client_test.py", line 27, in <module>
from tensorflow.dtensor.python.tests import multi_client_test_util
ImportError: cannot import name 'multi_client_test_util' from 'tensorflow.dtensor.python.tests' (/workspace/pip_test/venv_clean/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/__init__.py)
================================================================================
==================== Test output for //bazel_pip/tensorflow/dtensor/python/tests:multi_client_test_2gpus:
2023-05-02 23:40:19.436998: I tensorflow/core/util/port.cc:116] Experimental oneDNN custom operations are on. If you experience issues, please turn them off by setting the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
Traceback (most recent call last):
File "/tmpfs/bazel_output/_bazel_ubuntu/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/aarch64-opt/bin/bazel_pip/tensorflow/dtensor/python/tests/multi_client_test_2gpus.runfiles/org_tensorflow/bazel_pip/tensorflow/dtensor/python/tests/multi_client_test.py", line 27, in <module>
from tensorflow.dtensor.python.tests import multi_client_test_util
ImportError: cannot import name 'multi_client_test_util' from 'tensorflow.dtensor.python.tests' (/workspace/pip_test/venv_clean/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/__init__.py)
================================================================================
==================== Test output for //bazel_pip/tensorflow/dtensor/python/tests:multi_client_test_2gpus:
2023-05-02 23:40:23.477863: I tensorflow/core/util/port.cc:116] Experimental oneDNN custom operations are on. If you experience issues, please turn them off by setting the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
Traceback (most recent call last):
File "/tmpfs/bazel_output/_bazel_ubuntu/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/aarch64-opt/bin/bazel_pip/tensorflow/dtensor/python/tests/multi_client_test_2gpus.runfiles/org_tensorflow/bazel_pip/tensorflow/dtensor/python/tests/multi_client_test.py", line 27, in <module>
from tensorflow.dtensor.python.tests import multi_client_test_util
ImportError: cannot import name 'multi_client_test_util' from 'tensorflow.dtensor.python.tests' (/workspace/pip_test/venv_clean/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/__init__.py)
================================================================================
```
</details>
|
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| 1,693,292,355 |
PR_kwDOArmXAs5Poemb
| 60,478 |
Update setup.py on TF release branch with released version
|
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using 'tensorflow_estimator >= 2.13.0rc0' and for 'keras >= 2.13.1rc0, < 2.14'
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PR_kwDOArmXAs5Pn6C_
| 60,477 |
fix CondContext.back_prop
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[
"> Please write a PR description.\r\n\r\n+1 \r\n@albertz the fix looks good to me. Just needs a clear description.",
"I don't really know what kind of description you want here. I extended it."
] | 2023-05-02T21:25:47 | 2023-05-16T19:31:18 | 2023-05-16T19:31:17 |
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Fixes `CondContext.back_prop`. It was obviously wrong before. The old code was:
```python
@property
def back_prop(self):
if self.GetWhileContext():
self.GetWhileContext().back_prop
return False
```
So the access to `self.GetWhileContext().back_prop` would not have any effect here, but it is obvious that it was supposed to return this value.
So I changed it to:
```python
@property
def back_prop(self):
if self.GetWhileContext():
return self.GetWhileContext().back_prop
return False
```
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PR_kwDOArmXAs5Pn2bu
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Update version numbers for TensorFlow 2.13.0-rc0
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Before merging this PR, please double check that it has correctly updated
`core/public/version.h`, `tools/pip_package/setup.py`, and
`tensorflow/tensorflow.bzl`. Also review the execution notes below:
```
Major: 2 -> 2
Minor: 13 -> 13
Patch: 0 -> 0
WARNING: Below are potentially instances of lingering old version string
"2.13.0" in source directory "tensorflow/" that are not updated by this script.
Please check them manually!
tensorflow/lite/core/c/c_api.h:116:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:87:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:136:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:140:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:167:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:252:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:262:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:311:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:319:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:322:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:334:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:359:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:415:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:416:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:417:2.13.0
tensorflow/tensorflow.bzl:74:2.13.0
tensorflow/tools/pip_package/setup.py:50:2.13.0
tensorflow/tools/pip_package/setup.py:118:2.13.0
tensorflow/tools/pip_package/setup.py:120:2.13.0
tensorflow/tools/pip_package/setup.py:122:2.13.0
tensorflow/tools/tf_sig_build_dockerfiles/devel.requirements.txt:32:2.13.0
tensorflow/tools/tf_sig_build_dockerfiles/devel.requirements.txt:33:2.13.0
tensorflow/tools/tf_sig_build_dockerfiles/devel.requirements.txt:34:2.13.0
tensorflow/tools/ci_build/release/requirements_common.txt:28:2.13.0
tensorflow/tools/ci_build/release/requirements_common.txt:29:2.13.0
tensorflow/tools/ci_build/release/requirements_common.txt:30:2.13.0
WARNING: Below are potentially instances of lingering old version string
"2.13.0" in source directory "tensorflow/" that are not updated by this script.
Please check them manually!
tensorflow/lite/core/c/c_api.h:116:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:87:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:136:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:140:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:167:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:252:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:262:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:311:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:319:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:322:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:334:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:359:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:415:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:416:2.13.0
tensorflow/lite/tools/versioning/runtime_version.cc:417:2.13.0
tensorflow/tensorflow.bzl:74:2.13.0
tensorflow/tools/pip_package/setup.py:50:2.13.0
tensorflow/tools/pip_package/setup.py:118:2.13.0
tensorflow/tools/pip_package/setup.py:120:2.13.0
tensorflow/tools/pip_package/setup.py:122:2.13.0
tensorflow/tools/tf_sig_build_dockerfiles/devel.requirements.txt:32:2.13.0
tensorflow/tools/tf_sig_build_dockerfiles/devel.requirements.txt:33:2.13.0
tensorflow/tools/tf_sig_build_dockerfiles/devel.requirements.txt:34:2.13.0
tensorflow/tools/ci_build/release/requirements_common.txt:28:2.13.0
tensorflow/tools/ci_build/release/requirements_common.txt:29:2.13.0
tensorflow/tools/ci_build/release/requirements_common.txt:30:2.13.0
```
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[oneDNN v3.x]: Enabled weight caching in convolution
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- This PR enables weight caching in convolution for oneDNN v3.x.
- This PR passes all unit tests related to weight caching when oneDNN v3.x is enabled.
- This PR does not affect Eigen implementation and is specific only to oneDNN.
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update list of contributors and fixing the format for 2.13 branch.
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Use the 2.13 container to build 2.13 AARCH64 wheels.
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XLA size-inference integration bug (tf.where, tf.TensorArray, slice, loop)
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"Hi @AndreasMadsen ,\r\n\r\nI have replicated the reported behaviour and attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/55cd78f6809a34a2ab9ea0d8dc2993c8/60742.ipynb) here. It needs to be dig into more for this behaviour. Meanwhile if you have already have some pointers and ready to submit a PR please let us know. Thanks",
"@SuryanarayanaY, thanks for reproducing it and spending time on this. Unfortunately, I'm not familiar with the XLA code base and won't be making an attempt at solving this.\r\n\r\nThere are a few workarounds:\r\n\r\n```diff\r\n- x_batch = x_repeated[0:(batch_i + 1)*16, ...]\r\n+ x_batch = x_repeated[0:tf.minimim(tf.shape(x_repeated)[0], (batch_i + 1)*16), ...]\r\n```\r\n\r\nand:\r\n\r\n```diff\r\n- predicts_all = predict_all_array.concat()\r\n+ predicts_all = predict_all_array.concat()[:n_samples]\r\n```\r\n\r\nBut this should not be necessary. \r\n\r\nPS: I suggest you add the `comp:xla` and `TF 2.12` labels. "
] | 2023-05-02T16:34:56 | 2023-07-07T23:06:10 | null |
CONTRIBUTOR
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
binary
### Tensorflow Version
2.14.0.dev20230502
### Custom Code
No
### OS Platform and Distribution
Google Colab
### Mobile device
_No response_
### Python version
3.10.11
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
The attached code produces difference results for `jit_compile=True` and `jit_compile=False`, the XLA version produces the wrong results. The bug reproduces on both TF 2.12 and nightly (2.14.0.dev20230502) on Google Colab.
It appears to be an issue with `tf.size(tf.where(x == 1))`. While the visible front-end output is correct, I suspect there is an integration bug on the compiler back-end. When combined with a slice that is too big x[0:16] (`x.shape = (3, 5)`), plus many more code details, the output has same size as the input (`5` in the example). As if `tf.size(tf.where(x == 1)) == tf.size(x) == 5`, when actually `tf.size(tf.where(x == 1)) == 2`. I suspect this because changing the input shape to `(4, )` changes the output shape to `(4, )`.
This bug was very subtle and difficult to find, especially because `tf.print()` is not supported by XLA. I hope you appreciate it.
### Standalone code to reproduce the issue
Google Colab link: https://colab.research.google.com/drive/1cccEssWkCVLO515uf3RoOuEMDp5V-2ZZ?usp=sharing
```python
import tensorflow as tf
def fn(x, y):
token_idx_to_mask = tf.where(x == 1)
n_samples = tf.size(token_idx_to_mask)
x_repeated = tf.repeat(tf.expand_dims(x, 0), n_samples, axis=0)
predict_all_array = tf.TensorArray(x_repeated.dtype, size=1, infer_shape=False, element_shape=(None, ))
for batch_i in tf.range(1):
x_batch = x_repeated[0:(batch_i + 1)*16, ...]
y_batch = x_batch[:, y]
predict_all_array = predict_all_array.write(batch_i, y_batch)
predicts_all = predict_all_array.concat()
return n_samples, predicts_all
fn_jit = tf.function(reduce_retracing=True, jit_compile=True)(fn)
fn_std = tf.function(reduce_retracing=True)(fn)
print('XLA': fn_jit(tf.constant([0, 1, 1, 0, 0], dtype=tf.dtypes.int32), tf.constant(0)))
print('STD': fn_std(tf.constant([0, 1, 1, 0, 0], dtype=tf.dtypes.int32), tf.constant(0)))
```
### Relevant log output
```shell
XLA: (<tf.Tensor: shape=(), dtype=int32, numpy=2>, <tf.Tensor: shape=(5,), dtype=int32, numpy=array([0, 0, 0, 0, 0], dtype=int32)>)
STD: (<tf.Tensor: shape=(), dtype=int32, numpy=2>, <tf.Tensor: shape=(2,), dtype=int32, numpy=array([0, 0], dtype=int32)>)
```
Expected results would be:
```shell
XLA: (<tf.Tensor: shape=(), dtype=int32, numpy=2>, <tf.Tensor: shape=(2,), dtype=int32, numpy=array([0, 0], dtype=int32)>)
STD: (<tf.Tensor: shape=(), dtype=int32, numpy=2>, <tf.Tensor: shape=(2,), dtype=int32, numpy=array([0, 0], dtype=int32)>)
```
</details>
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[Linaro:ARM_CI] Remove possible flaky test from skip list.
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"This change was included in https://github.com/tensorflow/tensorflow/pull/61522/commits/d23d49462965530c393e6a2a99efc9950eab63b9 so this PR is now moot."
] | 2023-05-02T15:01:46 | 2023-08-17T13:53:02 | 2023-08-17T13:52:29 |
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//tensorflow/python/kernel_tests/nn_ops:atrous_conv2d_test was put on the skip list for being flaky. Remove it now to check current status.
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[Tensorflow Lite] libtask_vision_jni.so crashes on Windows Subsystem for Android
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[
"@ammargitham just curious if you know where `task_vision_jni` is being added to the dependency? I assumed it was a system library. When I try to use GPU `BaseOptions.builder().useGpu().build()` as referenced [here](https://www.tensorflow.org/lite/android/delegates/gpu_task#:~:text=BaseOptions.builder().useGpu().build()%3B) i'm getting error: `Error occurred when initializing ObjectDetector: ModifyGraphWithDelegate() failed for delegate 'GPU'.` and `Error getting native address of native library: task_vision_jni` \r\n\r\nNo idea if I'm missing any dependency. I see you have the reference to the library in your logs, wondering if you know what might be causing this behavior? ",
"@bhimeshchauhan I have exactly those libs mentioned in the original post. `task_vision_jni` is coming from `tflite-task-vision` I suppose. Anyway, this ticket is related to the crash on Windows Subsystem for Android. Are trying your code on that?",
"\n>@ammargitham just curious if you know where `task_vision_jni` is being added to the dependency? I assumed it was a system library. When I try to use GPU `BaseOptions.builder().useGpu().build()` as referenced [here](https://www.tensorflow.org/lite/android/delegates/gpu_task#:~:text=BaseOptions.builder().useGpu().build()%3B) i'm getting error: `Error occurred when initializing ObjectDetector: ModifyGraphWithDelegate() failed for delegate 'GPU'.` and `Error getting native address of native library: task_vision_jni` ...\n\n@bhimeshchauhan #62735 \n\n<sub>Sent with <a href=\"http://githawk.com\">GitHawk</a></sub>",
"\n> ...\n\n@davidmerwin \n\n<sub>Sent with <a href=\"http://githawk.com\">GitHawk</a></sub>"
] | 2023-05-02T14:45:33 | 2024-01-04T17:04:10 | null |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
tflite-gpu: 2.12.0, tflite-gpu-delegate-plugin: 0.4.3, tflite-task-vision: 0.4.3
### Custom Code
Yes
### OS Platform and Distribution
Windows Subsystem for Android
### 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 Behaviour?
ObjectDetector.detect crashes on Windows Subsystem for Android. Works fine on Android Emulator and physical devices.
### Standalone code to reproduce the issue
```shell
// Tried nnapi, gpu and default (cpu)
val baseOptions = BaseOptions.builder().apply {
// useNnapi()
if (CompatibilityList().isDelegateSupportedOnThisDevice) {
useGpu()
}
}.build()
val objectDetectorOptions = ObjectDetector.ObjectDetectorOptions.builder().apply {
setBaseOptions(baseOptions)
setScoreThreshold(0.2f)
setMaxResults(5)
}.build()
val tensorImage = ...
val objectDetector = ObjectDetector.createFromFileAndOptions(
model, // used EfficientDet-Lite0
objectDetectorOptions,
)
val detections = objectDetector.detect(tensorImage)
```
### Relevant log output
```shell
Fatal signal 11 (SIGSEGV), code 128 (SI_KERNEL), fault addr 0x0 in tid 9070 (DefaultDispatch), pid 9007 (mmar.havenwalls)
23:23:26.791 DEBUG E failed to readlink /proc/9070/fd/167: No such file or directory
23:23:26.799 crash_dump64 I obtaining output fd from tombstoned, type: kDebuggerdTombstoneProto
23:23:26.799 tombstoned I received crash request for pid 9070
23:23:26.799 crash_dump64 I performing dump of process 9007 (target tid = 9070)
23:23:26.896 DEBUG A *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ***
23:23:26.896 DEBUG A Build fingerprint: 'Windows/windows_x86_64/windows_x86_64:13/TQ2A.230305.008.C1/2303.40000.5.0:user/release-keys'
23:23:26.896 DEBUG A Revision: '0'
23:23:26.896 DEBUG A ABI: 'x86_64'
23:23:26.896 DEBUG A Timestamp: 2023-05-02 23:23:26.802005135+0900
23:23:26.896 DEBUG A Process uptime: 15s
23:23:26.896 DEBUG A Cmdline: com.ammar.havenwalls
23:23:26.896 DEBUG A pid: 9007, tid: 9070, name: DefaultDispatch >>> com.ammar.havenwalls <<<
23:23:26.896 DEBUG A uid: 10069
23:23:26.896 DEBUG A signal 11 (SIGSEGV), code 128 (SI_KERNEL), fault addr 0x0000000000000000
23:23:26.896 DEBUG A rax 00007f0cedffabc0 rbx 00007f0e8ce1d0e0 rcx 00007f0e8ce1d120 rdx 00007f0cedffabc0
23:23:26.896 DEBUG A r8 00007f0e8ce1d0e0 r9 0000000000000003 r10 0000000000000003 r11 00007f0cefd1bff0
23:23:26.896 DEBUG A r12 0000000000012c18 r13 00007f0cee540000 r14 0000000000000000 r15 0000000000000000
23:23:26.896 DEBUG A rdi 0000000000012c18 rsi 00007f0cedb08b80
23:23:26.896 DEBUG A rbp 00007f0cf0ed8870 rsp 00007f0cf0ed8870 rip 00007f0cee527288
23:23:26.896 DEBUG A backtrace:
23:23:26.896 DEBUG A #00 pc 0000000000495288 /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/lib/x86_64/libtask_vision_jni.so
23:23:26.896 DEBUG A #01 pc 00000000004ae031 /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/lib/x86_64/libtask_vision_jni.so
23:23:26.896 DEBUG A #02 pc 00000000004e719f /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/lib/x86_64/libtask_vision_jni.so
23:23:26.896 DEBUG A #03 pc 00000000004ae2b4 /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/lib/x86_64/libtask_vision_jni.so
23:23:26.896 DEBUG A #04 pc 000000000049b315 /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/lib/x86_64/libtask_vision_jni.so
23:23:26.896 DEBUG A #05 pc 000000000041b6a9 /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/lib/x86_64/libtask_vision_jni.so
23:23:26.896 DEBUG A #06 pc 000000000053767a /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/lib/x86_64/libtask_vision_jni.so
23:23:26.896 DEBUG A #07 pc 00000000005294eb /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/lib/x86_64/libtask_vision_jni.so
23:23:26.896 DEBUG A #08 pc 00000000004fbea4 /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/lib/x86_64/libtask_vision_jni.so
23:23:26.896 DEBUG A #09 pc 000000000005ddc6 /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/lib/x86_64/libtask_vision_jni.so
23:23:26.896 DEBUG A #10 pc 000000000005c51e /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/lib/x86_64/libtask_vision_jni.so
23:23:26.896 DEBUG A #11 pc 0000000000042a6b /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/lib/x86_64/libtask_vision_jni.so (Java_org_tensorflow_lite_task_vision_detector_ObjectDetector_detectNative+59)
23:23:26.896 DEBUG A #12 pc 000000000037d28b /apex/com.android.art/lib64/libart.so (art_quick_generic_jni_trampoline+219) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #13 pc 0000000000368fd9 /apex/com.android.art/lib64/libart.so (nterp_helper+2153) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #14 pc 000000000041d2ea [anon:dalvik-classes18.dex extracted in memory from /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/base.apk!classes18.dex] (org.tensorflow.lite.task.vision.detector.ObjectDetector.detect+14)
23:23:26.896 DEBUG A #15 pc 0000000000369608 /apex/com.android.art/lib64/libart.so (nterp_helper+3736) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #16 pc 000000000041d25c [anon:dalvik-classes18.dex extracted in memory from /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/base.apk!classes18.dex] (org.tensorflow.lite.task.vision.detector.ObjectDetector.access$1200+0)
23:23:26.896 DEBUG A #17 pc 00000000003687a8 /apex/com.android.art/lib64/libart.so (nterp_helper+56) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #18 pc 000000000041ce6c [anon:dalvik-classes18.dex extracted in memory from /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/base.apk!classes18.dex] (org.tensorflow.lite.task.vision.detector.ObjectDetector$4.run+4)
23:23:26.896 DEBUG A #19 pc 0000000000369dbe /apex/com.android.art/lib64/libart.so (nterp_helper+5710) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #20 pc 000000000041ce4c [anon:dalvik-classes18.dex extracted in memory from /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/base.apk!classes18.dex] (org.tensorflow.lite.task.vision.detector.ObjectDetector$4.run+0)
23:23:26.896 DEBUG A #21 pc 000000000036ab40 /apex/com.android.art/lib64/libart.so (nterp_helper+9168) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #22 pc 000000000041c83e [anon:dalvik-classes18.dex extracted in memory from /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/base.apk!classes18.dex] (org.tensorflow.lite.task.vision.core.BaseVisionTaskApi.run+54)
23:23:26.896 DEBUG A #23 pc 0000000000369608 /apex/com.android.art/lib64/libart.so (nterp_helper+3736) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #24 pc 000000000041d33a [anon:dalvik-classes18.dex extracted in memory from /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/base.apk!classes18.dex] (org.tensorflow.lite.task.vision.detector.ObjectDetector.detect+10)
23:23:26.896 DEBUG A #25 pc 0000000000369608 /apex/com.android.art/lib64/libart.so (nterp_helper+3736) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #26 pc 000000000041d314 [anon:dalvik-classes18.dex extracted in memory from /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/base.apk!classes18.dex] (org.tensorflow.lite.task.vision.detector.ObjectDetector.detect+16)
23:23:26.896 DEBUG A #27 pc 0000000000369608 /apex/com.android.art/lib64/libart.so (nterp_helper+3736) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #28 pc 0000000000033c92 /data/data/com.ammar.havenwalls/code_cache/.overlay/base.apk/classes17.dex (com.ammar.havenwalls.ui.crop.CropViewModel$detectObjects$1$2.invokeSuspend+178)
23:23:26.896 DEBUG A #29 pc 000000000202ea05 /memfd:jit-cache (deleted) (kotlin.coroutines.jvm.internal.BaseContinuationImpl.resumeWith+309)
23:23:26.896 DEBUG A #30 pc 0000000002014a9c /memfd:jit-cache (deleted) (kotlinx.coroutines.DispatchedTask.run+2012)
23:23:26.896 DEBUG A #31 pc 000000000036a3ca /apex/com.android.art/lib64/libart.so (nterp_helper+7258) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #32 pc 000000000035644a [anon:dalvik-classes18.dex extracted in memory from /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/base.apk!classes18.dex] (kotlinx.coroutines.internal.LimitedDispatcher.run+26)
23:23:26.896 DEBUG A #33 pc 000000000036a421 /apex/com.android.art/lib64/libart.so (nterp_helper+7345) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #34 pc 000000000035defe [anon:dalvik-classes18.dex extracted in memory from /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/base.apk!classes18.dex] (kotlinx.coroutines.scheduling.TaskImpl.run+6)
23:23:26.896 DEBUG A #35 pc 0000000000369608 /apex/com.android.art/lib64/libart.so (nterp_helper+3736) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #36 pc 000000000035d002 [anon:dalvik-classes18.dex extracted in memory from /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/base.apk!classes18.dex] (kotlinx.coroutines.scheduling.CoroutineScheduler.runSafely+2)
23:23:26.896 DEBUG A #37 pc 0000000000369608 /apex/com.android.art/lib64/libart.so (nterp_helper+3736) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #38 pc 000000000035bd0e [anon:dalvik-classes18.dex extracted in memory from /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/base.apk!classes18.dex] (kotlinx.coroutines.scheduling.CoroutineScheduler$Worker.executeTask+34)
23:23:26.896 DEBUG A #39 pc 0000000000369608 /apex/com.android.art/lib64/libart.so (nterp_helper+3736) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #40 pc 000000000035be3c [anon:dalvik-classes18.dex extracted in memory from /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/base.apk!classes18.dex] (kotlinx.coroutines.scheduling.CoroutineScheduler$Worker.runWorker+56)
23:23:26.896 DEBUG A #41 pc 0000000000369608 /apex/com.android.art/lib64/libart.so (nterp_helper+3736) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #42 pc 000000000035bdec [anon:dalvik-classes18.dex extracted in memory from /data/app/~~YZA5wZCbtPPgacPRPrHayg==/com.ammar.havenwalls-A6Pw6rODYvDy-YHQy_jHew==/base.apk!classes18.dex] (kotlinx.coroutines.scheduling.CoroutineScheduler$Worker.run+0)
23:23:26.896 DEBUG A #43 pc 0000000000372144 /apex/com.android.art/lib64/libart.so (art_quick_invoke_stub+756) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #44 pc 00000000003f1b46 /apex/com.android.art/lib64/libart.so (art::ArtMethod::Invoke(art::Thread*, unsigned int*, unsigned int, art::JValue*, char const*)+214) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #45 pc 00000000007e596e /apex/com.android.art/lib64/libart.so (art::JValue art::InvokeVirtualOrInterfaceWithJValues<art::ArtMethod*>(art::ScopedObjectAccessAlreadyRunnable const&, _jobject*, art::ArtMethod*, jvalue const*)+478) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #46 pc 000000000083a0ac /apex/com.android.art/lib64/libart.so (art::Thread::CreateCallback(void*)+1404) (BuildId: a5a11e17ddeb3fe4395528da1842c05c)
23:23:26.896 DEBUG A #47 pc 00000000000cca4a /apex/com.android.runtime/lib64/bionic/libc.so (__pthread_start(void*)+58) (BuildId: 76cb658c499bceaab85932d1254e26df)
23:23:26.896 DEBUG A #48 pc 00000000000609f7 /apex/com.android.runtime/lib64/bionic/libc.so (__start_thread+55) (BuildId: 76cb658c499bceaab85932d1254e26df)
23:23:26.902 tombstoned E Tombstone written to: tombstone_03
```
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Tensorflow 2.11 and 2.14 Memory Issue
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"@camda03,\r\nCould you please provide the complete code to debug the issue in an effective way. Also the problem might be in the format_image method. It's not really formatting the images because the transformation is assigned to a variable images and the method is returning image (the same input)\r\n\r\n\r\nCan you try reducing batch size? This error message is generated if the memory is not sufficient to manage the batch size. Thank you!\r\n",
"I'm currently uploading the code and (large) supporting data files to Dropbox. \r\n\r\nThe normal batch size is 32. I also tried running with 8 and 16. Both of these failed the same way.\r\n\r\nI'm not sure about the format image method reference.\r\n\r\nI'll let you know when the upload is done. It's going to take quite awhile.\r\n\r\nWhat account should I open the folder to so that you can access it?\r\n\r\nThanks!",
"Here are the links to the source code and supporting files.\r\n\r\nPlease let me know if you have trouble accessing, have questions, etc.\r\n\r\nThanks!\r\n\r\nhttps://www.dropbox.com/s/ggfgh525ow3gfxd/TensorFlow%20Debug%20Files.tar.xz?dl=0\r\nhttps://www.dropbox.com/s/n7zo4yrv988nc9y/TensorFlow%20Debugging%2060469.ipynb?dl=0",
"@camda03,\r\nThe provided links which are mentioned contain the files of 10GB and more. It would be a very difficult and tedious process for us to replicate the issue with the huge amount of data. Could you please simplify and provide the code or data or colab gist, so that we can analyse the issue in an effective way. Thank you!",
"https://www.dropbox.com/s/3q9h77dzclg0twi/Tensorflow%2060469%20Small.tar.xz?dl=0\r\n\r\nHere are versions of the supporting files with ten rows each in them.\r\n\r\nThanks!",
"I tried running this notebook on Google Colab. \r\n\r\nI setup an instance with an A100 (my system has A6000s) and ran it using the ten row files.\r\n\r\nThe instance is running TensorFlow 2.12.\r\n\r\nI got the errors listed below.\r\n\r\nIf you'd like to get access to this instance please let me know.\r\n\r\nHope this helps!\r\n\r\nThanks!\r\n\r\n\r\n\r\n\r\nEpoch 1/100\r\n\r\n---------------------------------------------------------------------------\r\n\r\nResourceExhaustedError Traceback (most recent call last)\r\n\r\n[<ipython-input-43-518cfbcdf815>](https://localhost:8080/#) in <cell line: 1>()\r\n----> 1 history = the_model.fit (training_dataset, epochs=num_epochs, validation_data=validation_dataset, callbacks=[tensorboard_cb])\r\n\r\n1 frames\r\n\r\n[/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py](https://localhost:8080/#) in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)\r\n 50 try:\r\n 51 ctx.ensure_initialized()\r\n---> 52 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\r\n 53 inputs, attrs, num_outputs)\r\n 54 except core._NotOkStatusException as e:\r\n\r\nResourceExhaustedError: Graph execution error:\r\n\r\nDetected at node 'gradient_tape/model/layer_1/MatMul/MatMul' defined at (most recent call last):\r\n File \"/usr/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n File \"/usr/lib/python3.10/runpy.py\", line 86, in _run_code\r\n exec(code, run_globals)\r\n File \"/usr/local/lib/python3.10/dist-packages/ipykernel_launcher.py\", line 16, in <module>\r\n app.launch_new_instance()\r\n File \"/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py\", line 992, in launch_instance\r\n app.start()\r\n File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py\", line 619, in start\r\n self.io_loop.start()\r\n File \"/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py\", line 195, in start\r\n self.asyncio_loop.run_forever()\r\n File \"/usr/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\r\n self._run_once()\r\n File \"/usr/lib/python3.10/asyncio/base_events.py\", line 1909, in _run_once\r\n handle._run()\r\n File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\r\n self._context.run(self._callback, *self._args)\r\n File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 685, in <lambda>\r\n lambda f: self._run_callback(functools.partial(callback, future))\r\n File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 738, in _run_callback\r\n ret = callback()\r\n File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 825, in inner\r\n self.ctx_run(self.run)\r\n File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 786, in run\r\n yielded = self.gen.send(value)\r\n File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 361, in process_one\r\n yield gen.maybe_future(dispatch(*args))\r\n File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\r\n yielded = ctx_run(next, result)\r\n File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 261, in dispatch_shell\r\n yield gen.maybe_future(handler(stream, idents, msg))\r\n File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\r\n yielded = ctx_run(next, result)\r\n File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 539, in execute_request\r\n self.do_execute(\r\n File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\r\n yielded = ctx_run(next, result)\r\n File \"/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py\", line 302, in do_execute\r\n res = shell.run_cell(code, store_history=store_history, silent=silent)\r\n File \"/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py\", line 539, in run_cell\r\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2975, in run_cell\r\n result = self._run_cell(\r\n File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\r\n return runner(coro)\r\n File \"/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\r\n coro.send(None)\r\n File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3257, in run_cell_async\r\n has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\r\n File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\r\n if (await self.run_code(code, result, async_=asy)):\r\n File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\r\n exec(code_obj, self.user_global_ns, self.user_ns)\r\n File \"<ipython-input-43-518cfbcdf815>\", line 1, in <cell line: 1>\r\n history = the_model.fit (training_dataset, epochs=num_epochs, validation_data=validation_dataset, callbacks=[tensorboard_cb])\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1685, in fit\r\n tmp_logs = self.train_function(iterator)\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1284, in train_function\r\n return step_function(self, iterator)\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1268, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1249, in run_step\r\n outputs = model.train_step(data)\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1054, in train_step\r\n self.optimizer.minimize(loss, self.trainable_variables, tape=tape)\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/optimizers/optimizer.py\", line 542, in minimize\r\n grads_and_vars = self.compute_gradients(loss, var_list, tape)\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/optimizers/optimizer.py\", line 275, in compute_gradients\r\n grads = tape.gradient(loss, var_list)\r\nNode: 'gradient_tape/model/layer_1/MatMul/MatMul'\r\nOOM when allocating tensor with shape[443156,3700] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\r\n\t [[{{node gradient_tape/model/layer_1/MatMul/MatMul}}]]\r\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.\r\n [Op:__inference_train_function_30186]",
"@camda03,\r\nCould you please share the colab gist for the reference where you are trying to execute the code? Thank you!",
"https://colab.research.google.com/gist/camda03/7b93f3bcb8fc6474dc125798a2c3d034/tensorflow-debugging-60469.ipynb\r\n\r\nPlease let me know if this does or doesn't work for you or if you have questions.\r\n\r\nThanks!",
"@camda03,\r\nThe reason for this is the high amount of parameters (please check your model.summary()).\r\nYou are exhausting the available memory, Could you can reduce the batch size, which would slow down the training process but let you fit the data.\r\n\r\nAnd also need to serialize your data(instead of loading everything in your GPU,RAM) by converting your data to TRFRecord files:\r\nhttps://www.tensorflow.org/guide/data\r\nhttps://www.tensorflow.org/tutorials/load_data/tfrecord\r\n\r\n",
"\"The reason for this is the high amount of parameters (please check your model.summary()).\"\r\nAgain, this number of parameters worked just fine in TF <= 2.10.\r\n\r\n(I have had plenty of experience figuring out just how many parameters I can get into an A6000.)\r\n\r\nAs I wrote above.\r\n\"Here is the model. I have tried reducing the 3,700 width down to 3,400 but that doesn't help. This exact model (3,700) works fine with <= 2.10 and has for months.\"\r\n\r\nAs I wrote above.\r\n\"The normal batch size is 32. I also tried running with 8 and 16. Both of these failed the same way.\"\r\n\r\nIn the gist I submitted, the following code appears.\r\n\r\nwith tf.device('/CPU:0'): \r\n training_dataset = tf.data.Dataset.from_tensor_slices((training_df_standardized, training_labels_df)).batch(batch_size).prefetch(dataset_prefetch)\r\n validation_dataset = tf.data.Dataset.from_tensor_slices((validation_df_standardized, validation_labels_df)).batch(batch_size).prefetch(dataset_prefetch)\r\n inference_dataset = tf.data.Dataset.from_tensor_slices((inference_df_standardized)).batch(batch_size).prefetch(dataset_prefetch)\r\n\r\nI've been putting the datasets in CPU (not GPU) ram for a long time. This has worked well up until TF 2.11.\r\n\r\nWith 2.11 and after, this doesn't work on my A6000s or even an A100 at Google Colab.\r\n\r\nThis is all documented in the case notes to date (above).\r\n\r\nWhen I look at the links you sent, I find this.\r\n\r\n\"Note: While useful, these structures are optional. There is no need to convert existing code to use TFRecords, unless you are [using tf.data](https://www.tensorflow.org/guide/data) and reading data is still the bottleneck to training. You can refer to [Better performance with the tf.data API](https://www.tensorflow.org/guide/data_performance) for dataset performance tips.\"\r\n\r\nAgain, the code I submitted ran just fine in TF <= 2.10. \r\n\r\nI would understand this better if this was some new piece of code and I was having trouble with it. \r\n\r\nThat is not the case.\r\n\r\nThe code that ran fine in <= 2.10 on an A6000 and on a system with 1TB of CPU ram now won't run even on an A100 at Google Colab. \r\n\r\nWhat happened after TF 2.10?\r\n\r\nAre there any plans to address this (whatever it is) in later releases of TensorFlow?\r\n\r\nPlease advise\r\n\r\nThanks!\r\n\r\n\r\n",
"I have an update for you.\r\n\r\nThis same code runs properly using TF 2.13, and it fails using 2.14.\r\n\r\nHere is a file showing runs with containers for 2.13 and 2.14 aka latest.\r\n\r\nBoth runs were done using the same .ipynb file, the same data, and the same GPU on the same system.\r\n\r\nThe only difference was the container and that is shown in the log.\r\n\r\n[TensorFlow 2.13 and 2.14.txt](https://github.com/tensorflow/tensorflow/files/13126673/TensorFlow.2.13.and.2.14.txt)\r\n\r\nThe models are the same as the one I included in the gist I sent earlier.\r\n\r\nI have a model training now using 2.13.\r\n\r\nTo summarize, a model that works on TF <= 2.10 doesn't work on 2.11, works on 2.13, and doesn't work on 2.14.\r\nI haven't tried 2.12.\r\n\r\nThanks!\r\n\r\n",
"The reason for this is the high amount of parameters (please check your model.summary()).\r\nYou are exhausting the available memory, Could you can reduce the batch size, which would slow down the training process but let you fit the data.\r\n\r\nAlso could you please try in the latest TensorFlow v2.16 as most of the bugs are resolved. Thank you!"
] | 2023-05-02T14:23:56 | 2024-06-11T11:17:26 | null |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
binary (Lambda Stack)
### Tensorflow Version
2.11
### Custom Code
Yes
### OS Platform and Distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.8.10
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
Driver Version: 525.105.17 CUDA Version: 12.0
### GPU model and memory
A6000 System 1TB RAM 2TB Swap
### Current Behaviour?
I have multiple models that have worked fine for months or years under version <= 2.10.
With 2.11, I get this (below).
I have (on another system) tested 2.10 vs 2.11 by switching Docker containers. The exact same code, data, etc, works with 2.10 but fails with 2.11.
training_cycle = 0
on_train_begin lr = 1.5625000182595272e-09
Epoch 1/100
---------------------------------------------------------------------------
ResourceExhaustedError Traceback (most recent call last)
<ipython-input-282-89befc246e8e> in <module>
13
14 if training_validation_split != None:
---> 15 history = midas_model.fit (training_dataset, epochs=num_epochs, validation_data=validation_dataset, callbacks=[tensorboard_cb, dhc_scheduler])
16 else:
17 history = midas_model.fit (training_dataset, epochs=num_epochs, callbacks=[tensorboard_cb, dhc_scheduler])
/usr/lib/python3/dist-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
/usr/lib/python3/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
50 try:
51 ctx.ensure_initialized()
---> 52 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
53 inputs, attrs, num_outputs)
54 except core._NotOkStatusException as e:
ResourceExhaustedError: Graph execution error:
Detected at node 'StatefulPartitionedCall' defined at (most recent call last):
File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/usr/lib/python3/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/lib/python3/dist-packages/traitlets/config/application.py", line 664, in launch_instance
app.start()
File "/usr/lib/python3/dist-packages/ipykernel/kernelapp.py", line 583, in start
self.io_loop.start()
File "/usr/lib/python3/dist-packages/tornado/platform/asyncio.py", line 132, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.8/asyncio/base_events.py", line 570, in run_forever
self._run_once()
File "/usr/lib/python3.8/asyncio/base_events.py", line 1859, in _run_once
handle._run()
File "/usr/lib/python3.8/asyncio/events.py", line 81, in _run
self._context.run(self._callback, *self._args)
File "/usr/lib/python3/dist-packages/tornado/ioloop.py", line 758, in _run_callback
ret = callback()
File "/usr/lib/python3/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/lib/python3/dist-packages/tornado/gen.py", line 1248, in inner
self.run()
File "/usr/lib/python3/dist-packages/tornado/gen.py", line 1162, in run
yielded = self.gen.send(value)
File "/usr/lib/python3/dist-packages/ipykernel/kernelbase.py", line 381, in dispatch_queue
yield self.process_one()
File "/usr/lib/python3/dist-packages/tornado/gen.py", line 346, in wrapper
runner = Runner(result, future, yielded)
File "/usr/lib/python3/dist-packages/tornado/gen.py", line 1095, in __init__
self.run()
File "/usr/lib/python3/dist-packages/tornado/gen.py", line 1162, in run
yielded = self.gen.send(value)
File "/usr/lib/python3/dist-packages/ipykernel/kernelbase.py", line 365, in process_one
yield gen.maybe_future(dispatch(*args))
File "/usr/lib/python3/dist-packages/tornado/gen.py", line 326, in wrapper
yielded = next(result)
File "/usr/lib/python3/dist-packages/ipykernel/kernelbase.py", line 268, in dispatch_shell
yield gen.maybe_future(handler(stream, idents, msg))
File "/usr/lib/python3/dist-packages/tornado/gen.py", line 326, in wrapper
yielded = next(result)
File "/usr/lib/python3/dist-packages/ipykernel/kernelbase.py", line 543, in execute_request
self.do_execute(
File "/usr/lib/python3/dist-packages/tornado/gen.py", line 326, in wrapper
yielded = next(result)
File "/usr/lib/python3/dist-packages/ipykernel/ipkernel.py", line 300, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/lib/python3/dist-packages/ipykernel/zmqshell.py", line 536, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py", line 2857, in run_cell
result = self._run_cell(
File "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py", line 2886, in _run_cell
return runner(coro)
File "/usr/lib/python3/dist-packages/IPython/core/async_helpers.py", line 68, in _pseudo_sync_runner
coro.send(None)
File "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py", line 3062, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py", line 3254, in run_ast_nodes
if (await self.run_code(code, result, async_=asy)):
File "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-282-89befc246e8e>", line 15, in <module>
history = midas_model.fit (training_dataset, epochs=num_epochs, validation_data=validation_dataset, callbacks=[tensorboard_cb, dhc_scheduler])
File "/usr/lib/python3/dist-packages/keras/utils/traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "/usr/lib/python3/dist-packages/keras/engine/training.py", line 1650, in fit
tmp_logs = self.train_function(iterator)
File "/usr/lib/python3/dist-packages/keras/engine/training.py", line 1249, in train_function
return step_function(self, iterator)
File "/usr/lib/python3/dist-packages/keras/engine/training.py", line 1233, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/lib/python3/dist-packages/keras/engine/training.py", line 1222, in run_step
outputs = model.train_step(data)
File "/usr/lib/python3/dist-packages/keras/engine/training.py", line 1027, in train_step
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
File "/usr/lib/python3/dist-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 527, in minimize
self.apply_gradients(grads_and_vars)
File "/usr/lib/python3/dist-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1140, in apply_gradients
return super().apply_gradients(grads_and_vars, name=name)
File "/usr/lib/python3/dist-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 634, in apply_gradients
iteration = self._internal_apply_gradients(grads_and_vars)
File "/usr/lib/python3/dist-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1166, in _internal_apply_gradients
return tf.__internal__.distribute.interim.maybe_merge_call(
File "/usr/lib/python3/dist-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1216, in _distributed_apply_gradients_fn
distribution.extended.update(
File "/usr/lib/python3/dist-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1211, in apply_grad_to_update_var
return self._update_step_xla(grad, var, id(self._var_key(var)))
Node: 'StatefulPartitionedCall'
Out of memory while trying to allocate 6558708800 bytes.
BufferAssignment OOM Debugging.
BufferAssignment stats:
parameter allocation: 18.32GiB
constant allocation: 0B
maybe_live_out allocation: 12.22GiB
preallocated temp allocation: 0B
total allocation: 18.32GiB
Peak buffers:
Buffer 1:
Size: 6.11GiB
Operator: op_name="XLA_Args"
Entry Parameter Subshape: f32[443156,3700]
==========================
Buffer 2:
Size: 6.11GiB
Operator: op_name="XLA_Args"
Entry Parameter Subshape: f32[443156,3700]
==========================
Buffer 3:
Size: 6.11GiB
Operator: op_name="XLA_Args"
Entry Parameter Subshape: f32[443156,3700]
==========================
Buffer 4:
Size: 16B
Operator: op_type="AssignAddVariableOp" op_name="AssignAddVariableOp" source_file="/usr/lib/python3/dist-packages/keras/optimizers/optimizer_experimental/sgd.py" source_line=182
XLA Label: fusion
Shape: (f32[443156,3700], f32[443156,3700])
==========================
Buffer 5:
Size: 4B
Operator: op_name="XLA_Args"
Entry Parameter Subshape: f32[]
==========================
[[{{node StatefulPartitionedCall}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
[Op:__inference_train_function_30187]
### Standalone code to reproduce the issue
```shell
Here is the model. I have tried reducing the 3,700 width down to 3,400 but that doesn't help. This exact model (3,700) works fine with <= 2.10 and has for months.
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
layer_0 (InputLayer) [(None, 443156)] 0
layer_1 (Dense) (None, 3700) 1639680900
layer_2 (Dense) (None, 3700) 13693700
layer_3 (Dense) (None, 3700) 13693700
layer_4 (Dense) (None, 3700) 13693700
layer_5 (Dense) (None, 3700) 13693700
layer_6 (Dense) (None, 3700) 13693700
layer_7 (Dense) (None, 3700) 13693700
layer_8 (Dense) (None, 3700) 13693700
layer_9 (Dense) (None, 3700) 13693700
layer_10 (Dense) (None, 3700) 13693700
layer_11 (Dense) (None, 3700) 13693700
layer_12 (Dense) (None, 3700) 13693700
layer_13 (Dense) (None, 3700) 13693700
layer_14 (Dense) (None, 3700) 13693700
layer_15 (Dense) (None, 3700) 13693700
layer_16 (Dense) (None, 3700) 13693700
layer_17 (Dense) (None, 3700) 13693700
layer_18 (Dense) (None, 3700) 13693700
layer_19 (Dense) (None, 3700) 13693700
layer_20 (Dense) (None, 3700) 13693700
layer_21 (Dense) (None, 3700) 13693700
layer_22 (Dense) (None, 3700) 13693700
layer_23 (Dense) (None, 3700) 13693700
layer_24 (Dense) (None, 3700) 13693700
layer_25 (Dense) (None, 3700) 13693700
layer_26 (Dense) (None, 3700) 13693700
layer_27 (Dense) (None, 3700) 13693700
layer_28 (Dense) (None, 3700) 13693700
layer_29 (Dense) (None, 3700) 13693700
layer_30 (Dense) (None, 3700) 13693700
layer_31 (Dense) (None, 3700) 13693700
layer_32 (Dense) (None, 3700) 13693700
layer_33 (Dense) (None, 3700) 13693700
layer_34 (Dense) (None, 3700) 13693700
layer_35 (Dense) (None, 3700) 13693700
layer_36 (Dense) (None, 3700) 13693700
layer_37 (Dense) (None, 3700) 13693700
layer_38 (Dense) (None, 3700) 13693700
layer_39 (Dense) (None, 3700) 13693700
layer_40 (Dense) (None, 3700) 13693700
layer_41 (Dense) (None, 3700) 13693700
layer_42 (Dense) (None, 3700) 13693700
layer_43 (Dense) (None, 3700) 13693700
layer_44 (Dense) (None, 3700) 13693700
layer_45 (Dense) (None, 3700) 13693700
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layer_229 (Dense) (None, 3700) 13693700
layer_230 (Dense) (None, 3700) 13693700
layer_231 (Dense) (None, 3700) 13693700
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output_layer (Dense) (None, 1) 3701
=================================================================
Total params: 4,912,478,901
Trainable params: 4,912,478,901
Non-trainable params: 0
_________________________________________________________________
midas_model.compile(loss=loss_list, optimizer = the_optimizer)
Here are some other potential items of interest.
the_optimizer = keras.optimizers.SGD (nesterov=True, momentum=0.9, learning_rate=initial_learning_rate)
Loss: mae = tf.keras.losses.MeanAbsoluteError()
```
### Relevant log output
_No response_</details>
|
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I_kwDOArmXAs5k4Ovj
| 60,468 |
Graph/memory corruption involving custom_gradient of functions involving pow
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[
"FWIW, this can be mitigated in client code with:\r\n```diff\r\n- unsigned_root = p**third\r\n+ degenerate_p, p = _safe_denom(p)\r\n+ unsigned_root = tf.where(degenerate_p, tf.constant(0., p.dtype), tf.exp(third*tf.math.log(p)))\r\n```\r\n(where `_safe_denom` is\r\n```python\r\ndef _safe_denom(d: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:\r\n flag_degenerate = (d == tf.cast(0, dtype=d.dtype))\r\n return flag_degenerate, tf.where(flag_degenerate, tf.cast(1, dtype=d.dtype), d)\r\n```\r\n)\r\nwhich is one reason to believe that the numerical issues in the linked TFJS issue are relevant to whatever triggers the corruption here.",
"Hi @elfprince13 ,\r\n\r\nThanks for reaching us. I have replicated the behaviour and attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/080f4f586f8c956ebc9f63cde5feee4c/60468.ipynb) for reference.\r\n\r\nI observed the behaviour is changing due to the changes in setting to `tf.config.run_functions_eagerly()`. \r\n\r\nI am not sure of reason why you are changing the setting from tf.config.run_functions_eagerly(True) to `tf.config.run_functions_eagerly(False)` and vice versa. \r\n\r\nMy observation is that if I set `tf.config.run_functions_eagerly(False)` then only the inconsistency observed.Same can be observed in attached gist.\r\n\r\nThanks!",
"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.",
"Yes - the issue only occurs in graph execution mode and not in eager mode.\nThe example code shows that changing any one of the contributing factors I\nhighlighted fails to trigger the bug.--\n~Thomas\n",
"Hi I'm just checking if there's any projected timeline on this.",
"Hi just following up again--\n~Thomas\n"
] | 2023-05-02T13:48:05 | 2023-11-12T13:35:49 | null |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
binary
### Tensorflow Version
2.9.1, 2.11, presumably more
### Custom Code
Yes
### OS Platform and Distribution
Linux Ubuntu 18.04, 20.04
### Mobile device
_No response_
### Python version
3.8
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
I have implemented `custom_gradient`s for `tf.function`s that smooth various Nth roots to avoid the singularity at `x=0`. I expect the value computed by the custom gradient to be used whenever that function is differentiated, and not substituted for any other value.
**HOWEVER** when the following conditions hold:
- input is a complex dtype
- the `GradientTape` is persistent
- the `tf.function` being differentiated invokes `tf.pow` directly (or indirectly through `**` operator)
- the argument is `0`
Then the custom gradient is invoked and the proper value is computed, but then discarded somewhere between returning from my custom gradient and returning from the gradient tape's internal computation.
Note: this problem does not appear when wrapping `sqrt()` as that has its own gradient implementation that presumably avoids `pow()`.
This seems likely related to numerical issues highlighted in tensorflow/tfjs#346, PLUS, some kind of graph/memory corruption, although why/how that would be conditional on the argument is beyond me.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
@tf.function
@tf.custom_gradient
def cbrt(p):
third = 1. / 3.
real_part_nonnegative = tf.math.real(p) >= 0
signs = tf.cast(tf.where(real_part_nonnegative, 1.0, -1.0), p.dtype)
p = signs * p
unsigned_root = p ** third
@tf.function
def grad(upstream):
unsigned_root_plus_eps = unsigned_root + 1e-4
denom = 3 * unsigned_root_plus_eps * unsigned_root_plus_eps + 1e-4
droot_dp = (1. / denom)
grad_root = droot_dp * upstream
tf.print("custom gradient:", tf.math.real(grad_root), tf.math.imag(grad_root))
return grad_root
root = signs * unsigned_root
return root, grad
if __name__ == "__main__":
tf.config.run_functions_eagerly(True)
x = tf.Variable([-1e-2, -1e-10, 0, 1e-10, 1e-2], dtype=tf.complex64)
with tf.GradientTape(persistent=True) as tape:
cbrt_x = cbrt(x)
gradients = tape.gradient(cbrt_x, x)
tf.print("all good:", tf.math.real(gradients), tf.math.imag(gradients))
tf.config.run_functions_eagerly(False)
with tf.GradientTape() as tape:
cbrt_x = cbrt(x)
gradients = tape.gradient(cbrt_x, x)
tf.print("all good:", tf.math.real(gradients), tf.math.imag(gradients))
@tf.function
def now_again_in_graph_mode(y):
with tf.GradientTape(persistent=True) as tape:
cbrt_y = cbrt(y)
return tape.gradient(cbrt_y, y)
gradients = now_again_in_graph_mode(x)
tf.print("chaos reigns:", tf.math.real(gradients), tf.math.imag(gradients))
```
### Relevant log output
```shell
custom gradient: [7.16964388 9905.4209 9997.00098 9905.4209 7.16964388] [0 0 0 0 0]
all good: [7.16964388 9905.4209 9997.00098 9905.4209 7.16964388] [0 0 0 0 0]
custom gradient: [7.16964388 9905.4209 9997.00098 9905.4209 7.16964388] [0 0 0 0 0]
all good: [7.16964388 9905.4209 9997.00098 9905.4209 7.16964388] [0 0 0 0 0]
custom gradient: [7.16964388 9905.4209 9997.00098 9905.4209 7.16964388] [0 0 0 0 0]
chaos reigns: [7.16964388 9905.4209 -nan 9905.4209 7.16964388] [0 0 -nan 0 0]
```
</details>
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I_kwDOArmXAs5k2adr
| 60,467 |
Is flag attribute available in TF-2.12.0?
|
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[
"Apparently, I have to replace `flags = tf.flags` with `flags = tf.compat.v1.flags`.\r\n"
] | 2023-05-02T08:21:18 | 2023-05-02T09:35:50 | 2023-05-02T09:35:50 |
NONE
| null | null | null |
Hi,
I am working with the Bert training model, I faced the following error:
```
$ python3 create_pretraining_data.py --input_file=results/part-00000-of-00500 --output_file=tfrecord/part-00000-of-00500 --vocab_file=wiki/vocab.txt --do_lower_case=True --max_seq_length=512 --max_predictions_per_seq=76 --masked_lm_prob=0.15 --random_seed=12345 --dupe_factor=10
Traceback (most recent call last):
File "create_pretraining_data.py", line 9, in <module>
import tokenization
File "/disk1/training/language_model/tensorflow/bert/cleanup_scripts/tokenization.py", line 13, in <module>
import tensorflow.compat.v1 as tf
ModuleNotFoundError: No module named 'tensorflow'
```
Then I searched for available TF versions and installed the latest one:
```
$ pip index versions tensorflow
Available versions: 2.12.0, 2.11.1, 2.11.0, 2.10.1, 2.10.0, 2.9.3, 2.9.2, 2.9.1, 2.9.0, 2.8.4, 2.8.3, 2.8.2, 2.8.1, 2.8.0, 2.7.4, 2.7.3, 2.7.2, 2.7.1, 2.7.0, 2.6.5, 2.6.4, 2.6.3, 2.6.2, 2.6.1, 2.6.0, 2.5.3, 2.5.2, 2.5.1, 2.5.0, 2.4.4, 2.4.3, 2.4.2, 2.4.1, 2.4.0, 2.3.4, 2.3.3, 2.3.2, 2.3.1, 2.3.0, 2.2.3, 2.2.2, 2.2.1, 2.2.0
$ pip install --user tensorflow
Collecting tensorflow
Downloading tensorflow-2.12.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (585.9 MB)
...
Successfully installed absl-py-1.4.0 flatbuffers-23.3.3 google-auth-2.17.3 google-auth-oauthlib-1.0.0 grpcio-1.54.0 jax-0.4.8 keras-2.12.0 libclang-16.0.0 ml-dtypes-0.1.0 numpy-1.23.5 protobuf-4.22.3 scipy-1.10.1 tensorboard-2.12.2 tensorboard-data-server-0.7.0 tensorboard-plugin-wit-1.8.1 tensorflow-2.12.0 tensorflow-estimator-2.12.0 tensorflow-io-gcs-filesystem-0.32.0
```
I reran the command again and now see this error
```
$ python3 create_pretraining_data.py --input_file=results/part-00000-of-00500 --output_file=tfrecord/part-00000-of-00500 --vocab_file=wiki/vocab.txt --do_lower_case=True --max_seq_length=512 --max_predictions_per_seq=76 --masked_lm_prob=0.15 --random_seed=12345 --dupe_factor=10
2023-05-02 12:13:34.171692: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-05-02 12:13:34.670262: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Traceback (most recent call last):
File "create_pretraining_data.py", line 12, in <module>
flags = tf.flags
AttributeError: module 'tensorflow' has no attribute 'flags'
```
I am confused with the tensorflow versions and based on my searches, the `flag` is not available in TF-2.12.0. I am not sure though. Any idea about that?
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I_kwDOArmXAs5k10GB
| 60,466 |
Same isssue cannnot install !pip install -q tflite-model-maker
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[
"Hi, Could you please try installing Nightly version and let us know if you are still facing an error. Thanks!",
"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.",
"@ubaidari,\r\nAccording to [Colab Updated to Python 3.10 7](https://medium.com/google-colab/colab-updated-to-python-3-10-27eb02daa162):\r\n\r\nColab’s fallback runtime version: Using the fallback runtime version temporarily allows access to the Python 3.9 runtime, and will be available until mid-May. This is available from the Command Palette via the Use fallback runtime version command when connected to a runtime. Of note, this setting does not persist across sessions — the command will need to be invoked on each new session.\r\n\r\nAs a temporary workaround, you can use the Colab fallback runtime version option to choose Python 3.9 and install tflite-model-maker. By doing this you will get RuntimeError and it can be ignored.\r\n\r\nTo access the command palette in Colab, presss cmd+shift+P and then type Use fallback runtime version and select it.\r\n\r\n\r\n\r\nBy following the above process, I was able to execute [Model Maker Image Classification Tutorial 9](https://colab.sandbox.google.com/gist/chunduriv/847ff8f09621935471b3fb70b23cefbd/model-maker-image-classification-tutorial.ipynb).\r\n\r\nThank you!",
"Also there is an issue which was raised for the similar issue where it is under the developer's priority list.\r\nhttps://github.com/tensorflow/tensorflow/issues/60431\r\nI request, could you please follow the respective issue for the updates. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Also have this issue",
"Closing the issue since it is duplicate of issue here https://github.com/tensorflow/tensorflow/issues/60431",
"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/60466\">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/60466\">No</a>\n"
] | 2023-05-02T06:04:01 | 2023-06-26T20:15:36 | 2023-06-26T20:15:34 |
NONE
| null | null | null |
Same isssue cannnot install !pip install -q tflite-model-maker
_Originally posted by @UsamaHameed1 in https://github.com/tensorflow/tensorflow/issues/57086#issuecomment-1529139314_
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I_kwDOArmXAs5k1YAK
| 60,465 |
About the Tensorflow C++ API documentation
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[
"Hi @yama-can, thank you for creating an issue. can you please help us understands the specifics of the documentation and the tutorials that you want us to publish. Thanks!!",
"of course. Specifically, I would like you to describe the c++ api extension build method, include path and library path.",
"Where exactly are we supposed to write it?"
] | 2023-05-02T03:15:27 | 2023-08-23T04:59:45 | null |
NONE
| null | null | null |
The lack of documentation for the Tensorflow C++ API makes it difficult to build and use. Can you write how to use and build in README.md?
|
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I_kwDOArmXAs5k1F52
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Cannot load weights from h5 file
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[
"Hi @2320sharon, We were able to replicate the issue in Windows10 using Tensorflow 2.12 and gdal 3.6.4. Please find the screenshot below\r\n\r\nIt seems like we have to dig deep into the issue. We'll update here soon. Thank you!",
"Thank you so much for looking into this, if there is anything I can do to help I am happy to assist.",
"@2320sharon, \r\nApologies for the delay.\r\nThis issue has been resolved in the latest tf-nightly version (2.14.0-dev20230509). Kindly find the screenshot of the output below\r\n\r\nThank you! ",
"Hi @synandi thank you for working on this issue.\r\nUnfortunately, when I ran `test_script.py` with tf-nightly it did not resolve the issue.\r\n\r\n## Commands I Ran\r\n```\r\nconda create -n test_new_env python=3.10 -y\r\nconda activate test_new_env \r\nconda install -c conda-forge gdal==3.6.4\r\npip install tf-nightly==2.14.0-dev20230509\r\npython test_script.py\r\n```\r\n## Output\r\n```\r\n(test_new_env) C:\\1_USGS\\1_CoastSeg\\1_official_CoastSeg_repo\\CoastSeg>python test_script.py\r\n2023-05-10 08:22:26.780726: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\nModel: \"sequential\"\r\n_________________________________________________________________\r\n Layer (type) Output Shape Param #\r\n=================================================================\r\n dense (Dense) (None, 64) 2112\r\n\r\n dense_1 (Dense) (None, 10) 650\r\n\r\n=================================================================\r\nTotal params: 2762 (10.79 KB)\r\nTrainable params: 2762 (10.79 KB)\r\nNon-trainable params: 0 (0.00 Byte)\r\n_________________________________________________________________\r\nTraceback (most recent call last):\r\n File \"C:\\1_USGS\\1_CoastSeg\\1_official_CoastSeg_repo\\CoastSeg\\test_script.py\", line 22, in <module>\r\n model.save_weights('my_model_weights.h5')\r\n File \"C:\\Users\\Sharon\\anaconda3\\envs\\test_new_env\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 70, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File \"C:\\Users\\Sharon\\anaconda3\\envs\\test_new_env\\lib\\site-packages\\keras\\src\\saving\\legacy\\save.py\", line 355, in save_weights\r\n raise ImportError(\r\nImportError: `save_weights` requires h5py when saving in hdf5, but h5py is not available. Try installing h5py package.\r\n```\r\nHowever, I have been able to find a solution that does work. After I ran the commands above and got the error I ran the following commands and it worked. \r\n```\r\npip uninstall h5py\r\nconda install -c conda-forge h5py\r\n```\r\n\r\n\r\n",
"Hi @2320sharon, We are glad that the issue is resolved. Please feel free to close the issue. Thank you!",
"Hi @synandi thank you for working on this issue.\r\nUnfortunately, when I ran test_script.py with tf-nightly it did not resolve the issue.",
"Hi @2320sharon, It is working in tf-nightly(2.14.0-dev20230515). Since it is working in the latest tf-nightly version, it will be fixed in the next stable release. \r\nI ran the following commands.\r\n```\r\nconda create -n tf python=3.10 -y\r\nconda activate tf \r\nconda install -c conda-forge gdal==3.6.4\r\npip install tf-nightly\r\n\r\npip uninstall h5py\r\nconda install -c conda-forge h5py\r\npython test_script.py\r\n```\r\n\r\nKindly find the screenshot below\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/60464\">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/60464\">No</a>\n",
"It solved my issue when I run this command and restart my computer\r\npip uninstall h5py\r\nconda install -c conda-forge h5py"
] | 2023-05-02T01:17:03 | 2024-01-21T15:43:50 | 2023-05-31T02:05:37 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
2.12.0
### Custom Code
Yes
### OS Platform and Distribution
Windows 10 22H2
### Mobile device
_No response_
### Python version
3.10.11
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
Hello,
Thank you for all your hard work on tensorflow I was hoping you could help me with this issue I've been facing.
I'm having an issue getting the h5py package to import properly when I have gdal and tensorflow installed in the same conda environment.
I have the latest version of gdal 3.6.4 and tensorflow 2.12.0 installed in an anaconda environment and when I attempt to load model weights from an .h5 file it fails claiming that h5py is not installed in the environment. The thing is h5py is installed in the environment ( I have the outputs of conda list below), but both gdal and tensorflow both use it. I believe the issue is from a [recent update to gdal which places a lock on the hdf5 library at build time](https://github.com/OSGeo/gdal/pull/7357) and I was wondering if I could modify how tensorflow imports h5py to get around this problem?
Thank you for any help you can provide.
## Anaconda Environment to reproduce the issue
```
conda create -n test_env python=3.10
conda activate test_env
conda install -c conda-forge gdal==3.6.4
pip install tensorflow==2.12.0
python test_script.py
```
## Outputs of conda list
```
# Name Version Build Channel
absl-py 1.4.0 pypi_0 pypi
astunparse 1.6.3 pypi_0 pypi
blosc 1.21.3 h6c2663c_0
boost-cpp 1.74.0 h5b4e17d_6 conda-forge
bzip2 1.0.8 he774522_0
ca-certificates 2022.12.7 h5b45459_0 conda-forge
cachetools 5.3.0 pypi_0 pypi
cairo 1.16.0 h63a05c6_1001 conda-forge
certifi 2022.12.7 pypi_0 pypi
cfitsio 3.470 h2bbff1b_7
charset-normalizer 3.1.0 pypi_0 pypi
curl 7.88.1 h2bbff1b_0
expat 2.5.0 h63175ca_1 conda-forge
flatbuffers 23.3.3 pypi_0 pypi
fontconfig 2.14.1 h9c4af85_2
freetype 2.10.4 h546665d_1 conda-forge
freexl 1.0.6 ha8e266a_0 conda-forge
gast 0.4.0 pypi_0 pypi
gdal 3.6.2 py310h1c2bfe4_1
geos 3.8.0 h33f27b4_0
geotiff 1.7.0 h4545760_1
glib 2.69.1 h5dc1a3c_2
google-auth 2.17.3 pypi_0 pypi
google-auth-oauthlib 1.0.0 pypi_0 pypi
google-pasta 0.2.0 pypi_0 pypi
grpcio 1.54.0 pypi_0 pypi
h5py 3.8.0 pypi_0 pypi
hdf4 4.2.13 h712560f_2
hdf5 1.10.6 nompi_h5268f04_1114 conda-forge
icu 58.2 ha925a31_3
idna 3.4 pypi_0 pypi
intel-openmp 2023.1.0 h57928b3_46319 conda-forge
jax 0.4.8 pypi_0 pypi
jpeg 9e h8ffe710_2 conda-forge
kealib 1.5.0 hde4a422_0
keras 2.12.0 pypi_0 pypi
krb5 1.19.3 h1176d77_0 conda-forge
lcms2 2.12 h83e58a3_0
lerc 3.0 hd77b12b_0
libblas 3.9.0 8_mkl conda-forge
libcblas 3.9.0 8_mkl conda-forge
libclang 16.0.0 pypi_0 pypi
libcurl 7.88.1 h86230a5_0
libdeflate 1.17 h2bbff1b_0
libexpat 2.5.0 h63175ca_1 conda-forge
libffi 3.4.2 hd77b12b_6
libgdal 3.6.2 h11d7215_1
libiconv 1.17 h8ffe710_0 conda-forge
libkml 1.3.0 h9859afa_1014 conda-forge
liblapack 3.9.0 8_mkl conda-forge
libnetcdf 4.8.1 h6685c40_2
libpng 1.6.39 h8cc25b3_0
libpq 12.9 hb652d5d_3
libspatialite 4.3.0a h6ec8781_23
libssh2 1.10.0 h680486a_2 conda-forge
libtiff 4.5.0 h6c2663c_2
libwebp-base 1.2.4 h2bbff1b_1
libxml2 2.10.3 h0ad7f3c_0
libzip 1.8.0 h49b8836_0
lz4-c 1.9.4 h2bbff1b_0
markdown 3.4.3 pypi_0 pypi
markupsafe 2.1.2 pypi_0 pypi
mkl 2020.4 hb70f87d_311 conda-forge
ml-dtypes 0.1.0 pypi_0 pypi
numpy 1.22.3 py310hed7ac4c_2 conda-forge
oauthlib 3.2.2 pypi_0 pypi
openjpeg 2.4.0 h4fc8c34_0
openssl 1.1.1t h2bbff1b_0
opt-einsum 3.3.0 pypi_0 pypi
packaging 23.1 pypi_0 pypi
pcre 8.45 h0e60522_0 conda-forge
pcre2 10.37 h0ff8eda_1
pip 23.0.1 py310haa95532_0
pixman 0.38.0 hfa6e2cd_1003 conda-forge
poppler 22.12.0 h268424c_0
poppler-data 0.4.12 hd8ed1ab_0 conda-forge
postgresql 12.9 hb652d5d_3
proj 6.2.1 h3758d61_0
protobuf 4.22.3 pypi_0 pypi
pyasn1 0.5.0 pypi_0 pypi
pyasn1-modules 0.3.0 pypi_0 pypi
python 3.10.11 h966fe2a_2
python_abi 3.10 2_cp310 conda-forge
qhull 2020.2 h70d2c02_2 conda-forge
requests 2.29.0 pypi_0 pypi
requests-oauthlib 1.3.1 pypi_0 pypi
rsa 4.9 pypi_0 pypi
scipy 1.10.1 pypi_0 pypi
setuptools 66.0.0 py310haa95532_0
six 1.16.0 pypi_0 pypi
sqlite 3.41.2 h2bbff1b_0
tensorboard 2.12.2 pypi_0 pypi
tensorboard-data-server 0.7.0 pypi_0 pypi
tensorboard-plugin-wit 1.8.1 pypi_0 pypi
tensorflow 2.12.0 pypi_0 pypi
tensorflow-estimator 2.12.0 pypi_0 pypi
tensorflow-intel 2.12.0 pypi_0 pypi
tensorflow-io-gcs-filesystem 0.31.0 pypi_0 pypi
termcolor 2.3.0 pypi_0 pypi
tiledb 2.3.3 h3649cd2_2
tk 8.6.12 h2bbff1b_0
typing-extensions 4.5.0 pypi_0 pypi
tzdata 2023c h04d1e81_0
urllib3 1.26.15 pypi_0 pypi
vc 14.2 h21ff451_1
vs2015_runtime 14.27.29016 h5e58377_2
werkzeug 2.3.3 pypi_0 pypi
wheel 0.38.4 py310haa95532_0
wrapt 1.14.1 pypi_0 pypi
xerces-c 3.2.4 hd77b12b_0
xz 5.2.10 h8cc25b3_1
zlib 1.2.13 h8cc25b3_0
zstd 1.5.5 hd43e919_0
```
### Standalone code to reproduce the issue
```shell
from osgeo import gdal
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
# Create a simple model
model = Sequential([
Dense(64, activation='relu', input_shape=(32,)),
Dense(10)
])
# Compile the model
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# Print the model summary
model.summary()
# Save the initial weights to a file
model.save_weights('my_model_weights.h5')
# Change the weights of the model
for layer in model.layers:
weights = layer.get_weights() # list of numpy arrays
weights = [weight * 0 for weight in weights]
layer.set_weights(weights)
# Check that weights have been changed
print('Weights after resetting:')
print(model.layers[0].get_weights())
# Load the initial weights from the file
model.load_weights('my_model_weights.h5')
# Check that weights have been loaded
print('Weights after loading from file:')
print(model.layers[0].get_weights())
```
### Relevant log output
```shell
(test_env) > python sample_model.py
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 64) 2112
dense_1 (Dense) (None, 10) 650
=================================================================
Total params: 2,762
Trainable params: 2,762
Non-trainable params: 0
_________________________________________________________________
Traceback (most recent call last):
File "C:\1_USGS\1_CoastSeg\1_official_CoastSeg_repo\CoastSeg\src\coastseg\sample_model.py", line 22, in <module>
model.save_weights('my_model_weights.h5')
File "C:\Users\Sharon\anaconda3\envs\test_env\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\Sharon\anaconda3\envs\test_env\lib\site-packages\keras\saving\legacy\save.py", line 351, in save_weights
raise ImportError(
ImportError: `save_weights` requires h5py when saving in hdf5, but h5py is not available. Try installing h5py package.
```
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Update release notes for TensorFlow 2.13.0
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[
"Hi mihai and rostam apologies for the confusion this the original PR for updating Release notes for 2.13 . can you please review and merge this PR. "
] | 2023-05-02T00:42:20 | 2023-05-02T20:56:49 | 2023-05-02T20:56:21 |
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This PR is intentionally incomplete. One of the Release Owners for 2.13.0
needs to fill in the internal release notes for this version before the PR gets
submitted. Click on the :pencil2: icon in the header for `RELEASE.md` under
"Files Changed" above.
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Inference significantly slower for Lenet5 models on both CPU and GPU when compared to other frameworks
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[
"@jf9123 - Can you try it on TF 2.12. Also, would be able to share your script you are using to measure the performance differences across the frameworks?",
"@sampathweb Here is a repo for reproduction sake; https://github.com/jf9123/lenet5_question\r\n\r\nI use GPUtil to record the GPU VRAM usage."
] | 2023-05-01T22:29:58 | 2023-07-14T22:18:18 | null |
NONE
| null | null | null |
### System information
- **Have I written custom code (as opposed to using a stock example script
provided in TensorFlow)**: Custom
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Linux Ubuntu 18.04
- **TensorFlow installed from (source or binary)**: binary
- **TensorFlow version**: 2.5
- **Python version**: 3.7.5
- **GPU model and memory**: NVIDIA Quadro RTX 4000 GPU with 8GB of VRAM
- **CPU model**: Intel(R) Core(TM) i9-9900K CPU operating at 3.60GHz
- **cuDNN version**: 7.6.5
### Describe the problem
Hello everybody.
I’ve been experimenting with different models and different frameworks, and I’ve noticed that inference with a Lenet5 model on the MNIST dataset is significantly slower on Keras (28% slower on CPU and 108% slower on GPU) compared to the PyTorch and TensorFlow v1.X implementations. Note that I artificially increased the size of the testing set of the MNIST dataset by duplicating the set by 100 times for a total of 1 000 000 samples. I've done so so any difference in inference time may be clearer. Also note that this is observed even when the amount of memory Keras can reserve is limited (i.e., `tf.config.experimental.set_memory_growth(gpu, True)`)
Here are boxplots that showcase the performance of numerous Lenet5 models:
Any ideas on what may be causing this?
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Update the RBE images to the latest container versions
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This PR was created by a GitHub Actions workflow to update all the SIG Build-based RBE containers to the most recent containers. See:
- https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/toolchains/remote_config/configs.bzl
- https://github.com/tensorflow/tensorflow/blob/master/.github/workflows/update-rbe.yml
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Protocol Buffer error - critical I cannot import tensorflow
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[
"Hi @gg4u ,\r\n\r\nIt seems the error might be related to protobuf version. Could you please confirm the protobuf version installed ? For Tf2.11 version the required range versions should be `protobuf >= 3.9.2, < 3.20` . Please check and confirm.",
"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/60460\">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/60460\">No</a>\n"
] | 2023-05-01T20:55:39 | 2023-05-17T01:56:17 | 2023-05-17T01:56:15 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
2.11
### Custom Code
No
### OS Platform and Distribution
_No response_
### Mobile device
_No response_
### Python version
3.10.6
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
Bug occurred today when reloading a jupyter notebook.
I previously tried to install cuML
https://docs.rapids.ai/install#pip-install
but the trace error seems unrelated to it, but mention `Protocol Buffers`:
### Standalone code to reproduce the issue
```shell
The error occurs when :
`import tensorflow`
and consequently any other import of libraries based on tf, such as:
`import umap`
will fail.
I cannot use TF anymore.
```
### Relevant log output
```shell
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[33], line 1
----> 1 import tensorflow as tf
2 #import umap
File /data0/home/h21/luas6629/venv/lib/python3.10/site-packages/tensorflow/__init__.py:37
34 import sys as _sys
35 import typing as _typing
---> 37 from tensorflow.python.tools import module_util as _module_util
38 from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader
40 # Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.
File /data0/home/h21/luas6629/venv/lib/python3.10/site-packages/tensorflow/python/__init__.py:37
29 # We aim to keep this file minimal and ideally remove completely.
30 # If you are adding a new file with @tf_export decorators,
31 # import it in modules_with_exports.py instead.
32
33 # go/tf-wildcard-import
34 # pylint: disable=wildcard-import,g-bad-import-order,g-import-not-at-top
36 from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
---> 37 from tensorflow.python.eager import context
39 # pylint: enable=wildcard-import
40
41 # Bring in subpackages.
42 from tensorflow.python import data
File /data0/home/h21/luas6629/venv/lib/python3.10/site-packages/tensorflow/python/eager/context.py:28
25 from absl import logging
26 import numpy as np
---> 28 from tensorflow.core.framework import function_pb2
29 from tensorflow.core.protobuf import config_pb2
30 from tensorflow.core.protobuf import coordination_config_pb2
File /data0/home/h21/luas6629/venv/lib/python3.10/site-packages/tensorflow/core/framework/function_pb2.py:16
11 # @@protoc_insertion_point(imports)
13 _sym_db = _symbol_database.Default()
---> 16 from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
17 from tensorflow.core.framework import node_def_pb2 as tensorflow_dot_core_dot_framework_dot_node__def__pb2
18 from tensorflow.core.framework import op_def_pb2 as tensorflow_dot_core_dot_framework_dot_op__def__pb2
File /data0/home/h21/luas6629/venv/lib/python3.10/site-packages/tensorflow/core/framework/attr_value_pb2.py:16
11 # @@protoc_insertion_point(imports)
13 _sym_db = _symbol_database.Default()
---> 16 from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
17 from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
18 from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
File /data0/home/h21/luas6629/venv/lib/python3.10/site-packages/tensorflow/core/framework/tensor_pb2.py:16
11 # @@protoc_insertion_point(imports)
13 _sym_db = _symbol_database.Default()
---> 16 from tensorflow.core.framework import resource_handle_pb2 as tensorflow_dot_core_dot_framework_dot_resource__handle__pb2
17 from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
18 from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
File /data0/home/h21/luas6629/venv/lib/python3.10/site-packages/tensorflow/core/framework/resource_handle_pb2.py:16
11 # @@protoc_insertion_point(imports)
13 _sym_db = _symbol_database.Default()
---> 16 from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
17 from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
20 DESCRIPTOR = _descriptor.FileDescriptor(
21 name='tensorflow/core/framework/resource_handle.proto',
22 package='tensorflow',
(...)
26 ,
27 dependencies=[tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2.DESCRIPTOR,tensorflow_dot_core_dot_framework_dot_types__pb2.DESCRIPTOR,])
File /data0/home/h21/luas6629/venv/lib/python3.10/site-packages/tensorflow/core/framework/tensor_shape_pb2.py:36
13 _sym_db = _symbol_database.Default()
18 DESCRIPTOR = _descriptor.FileDescriptor(
19 name='tensorflow/core/framework/tensor_shape.proto',
20 package='tensorflow',
(...)
23 serialized_pb=_b('\n,tensorflow/core/framework/tensor_shape.proto\x12\ntensorflow\"z\n\x10TensorShapeProto\x12-\n\x03\x64im\x18\x02 \x03(\x0b\x32 .tensorflow.TensorShapeProto.Dim\x12\x14\n\x0cunknown_rank\x18\x03 \x01(\x08\x1a!\n\x03\x44im\x12\x0c\n\x04size\x18\x01 \x01(\x03\x12\x0c\n\x04name\x18\x02 \x01(\tB\x87\x01\n\x18org.tensorflow.frameworkB\x11TensorShapeProtosP\x01ZSgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto\xf8\x01\x01\x62\x06proto3')
24 )
29 _TENSORSHAPEPROTO_DIM = _descriptor.Descriptor(
30 name='Dim',
31 full_name='tensorflow.TensorShapeProto.Dim',
32 filename=None,
33 file=DESCRIPTOR,
34 containing_type=None,
35 fields=[
---> 36 _descriptor.FieldDescriptor(
37 name='size', full_name='tensorflow.TensorShapeProto.Dim.size', index=0,
38 number=1, type=3, cpp_type=2, label=1,
39 has_default_value=False, default_value=0,
40 message_type=None, enum_type=None, containing_type=None,
41 is_extension=False, extension_scope=None,
42 serialized_options=None, file=DESCRIPTOR),
43 _descriptor.FieldDescriptor(
44 name='name', full_name='tensorflow.TensorShapeProto.Dim.name', index=1,
45 number=2, type=9, cpp_type=9, label=1,
46 has_default_value=False, default_value=_b("").decode('utf-8'),
47 message_type=None, enum_type=None, containing_type=None,
48 is_extension=False, extension_scope=None,
49 serialized_options=None, file=DESCRIPTOR),
50 ],
51 extensions=[
52 ],
53 nested_types=[],
54 enum_types=[
55 ],
56 serialized_options=None,
57 is_extendable=False,
58 syntax='proto3',
59 extension_ranges=[],
60 oneofs=[
61 ],
62 serialized_start=149,
63 serialized_end=182,
64 )
66 _TENSORSHAPEPROTO = _descriptor.Descriptor(
67 name='TensorShapeProto',
68 full_name='tensorflow.TensorShapeProto',
(...)
100 serialized_end=182,
101 )
103 _TENSORSHAPEPROTO_DIM.containing_type = _TENSORSHAPEPROTO
File /data0/home/h21/luas6629/venv/lib/python3.10/site-packages/google/protobuf/descriptor.py:560, in FieldDescriptor.__new__(cls, name, full_name, index, number, type, cpp_type, label, default_value, message_type, enum_type, containing_type, is_extension, extension_scope, options, serialized_options, has_default_value, containing_oneof, json_name, file, create_key)
554 def __new__(cls, name, full_name, index, number, type, cpp_type, label,
555 default_value, message_type, enum_type, containing_type,
556 is_extension, extension_scope, options=None,
557 serialized_options=None,
558 has_default_value=True, containing_oneof=None, json_name=None,
559 file=None, create_key=None): # pylint: disable=redefined-builtin
--> 560 _message.Message._CheckCalledFromGeneratedFile()
561 if is_extension:
562 return _message.default_pool.FindExtensionByName(full_name)
TypeError: Descriptors cannot not be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
1. Downgrade the protobuf package to 3.20.x or lower.
2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).
More information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates
```
</details>
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I_kwDOArmXAs5kyRba
| 60,459 |
Docs problem. Notebook not found
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[
"Hi @Tialo, Thank you for reporting the issue. The issue will move to closed status once the [#2223](https://github.com/tensorflow/docs/pull/2223) is merged. Thanks! ",
"Also it would be nice if you would fix those links at the end \r\nhttps://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/function.ipynb#scrollTo=IKyrEY5GVX3M\r\n\r\nFurther reading\r\nTo learn about how to export and load a Function, see the [SavedModel guide](https://github.com/tensorflow/docs/blob/master/site/guide/saved_model). To learn more about graph optimizations that are performed after tracing, see the [Grappler guide](https://github.com/tensorflow/docs/blob/master/site/guide/graph_optimization). To learn how to optimize your data pipeline and profile your model, see the [Profiler guide](https://github.com/tensorflow/docs/blob/master/site/guide/profiler.md).",
"> Also it would be nice if you would fix those links at the end https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/function.ipynb#scrollTo=IKyrEY5GVX3M\r\n> \r\n> Further reading To learn about how to export and load a Function, see the [SavedModel guide](https://github.com/tensorflow/docs/blob/master/site/guide/saved_model). To learn more about graph optimizations that are performed after tracing, see the [Grappler guide](https://github.com/tensorflow/docs/blob/master/site/guide/graph_optimization). To learn how to optimize your data pipeline and profile your model, see the [Profiler guide](https://github.com/tensorflow/docs/blob/master/site/guide/profiler.md).\r\n\r\n@Tialo I've added these changes in this [commit](https://github.com/tensorflow/docs/pull/2223/commits/c4c12d8177f180cf705b7387dcc8b1af8adff199). Thank you!\r\n",
"Thanks, on it.",
"Using https://www.tensorflow.org/guide/function as the reference.\r\n\r\n> \"In TensorFlow 2, [eager execution](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/eager.ipynb) is turned on by default.\"\r\n\r\nThis should now take you to https://www.tensorflow.org/guide/basics or the corresponding Colab/notebook\r\n\r\n> (See the [Transformer](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/text/transformer.ipynb) and [Deep Dream](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/generative/deepdream.ipynb) tutorials for example).\r\n\r\nSame here: https://www.tensorflow.org/text/tutorials/transformer and https://www.tensorflow.org/tutorials/generative/deepdream\r\n\r\n> To learn more, see the [tf.data: Build TensorFlow input pipelines](https://github.com/tensorflow/docs/blob/master/site/guide/data) guide.\r\n\r\nAnd same here: https://www.tensorflow.org/guide/data\r\n\r\nFeel free to reopen the issue if you see any other links that need to be fixed. Thank you.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60459\">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/60459\">No</a>\n"
] | 2023-05-01T14:44:23 | 2023-09-18T12:40:24 | 2023-09-18T12:40:21 |
NONE
| null | null | null |
Hello, I am new to tensoflow and right now I am reading your gides, particularly guide about tf.function. There you have a link to guide for "eager execution", but I don't have access to it. Is it my problem or you have broken link?
Guide for tf.function: https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/function.ipynb#scrollTo=J122XQYG7W6w
Broken link, which is in the first block of code:
"In TensorFlow 2, [eager execution](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/eager.ipynb) is turned on by default."
https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/eager.ipynb
Upd.
Also there is broken link here
https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/function.ipynb#scrollTo=ocxX-HVk7P2o
(See the [Transformer](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/text/transformer.ipynb) and [Deep Dream](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/generative/deepdream.ipynb) tutorials for example).
first link does not work
Upd. 2
Another broken link
https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/function.ipynb#scrollTo=JeD2U-yrbfVb
To learn more, see the [tf.data: Build TensorFlow input pipelines](https://github.com/tensorflow/docs/blob/master/site/guide/data) guide.
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I_kwDOArmXAs5kyK6p
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AutoGraph error when function is inside a match
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[
"@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow v2.11, v2.12 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/685e8063991716febd945da49dd5ff14/untitled1102.ipynb).",
"Potential duplicate of #57166 "
] | 2023-05-01T14:17:26 | 2024-01-07T06:39:10 | null |
CONTRIBUTOR
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
binary
### Tensorflow Version
2.13.0-dev20230501
### Custom Code
No
### OS Platform and Distribution
Google Colab
### Mobile device
_No response_
### Python version
3.10.11
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
When a `tf.data.Dataset.map` function is defined inside a `match` scope and used by `tf.data.Dataset.apply` the AutoGraph fails. This should not happen, as the match statement should be irrelevant to the function definition. I suspect this error happens in more AutoGraph cases than just `tf.data.Dataset.map` via `tf.data.Dataset.apply`.
The issue is reproduced on Google Colab with both nightly (2.13.0-dev20230501) and 2.12.
### Standalone code to reproduce the issue
See https://colab.research.google.com/drive/1x07aUgISrYQohsG7FYmc70LeYjVcGixG#scrollTo=auSxkDSinh3R
```python
import tensorflow as tf
parameter = 'a'
match parameter:
case 'a':
def fn(ds):
return ds.map(lambda x: x + 1)
dataset = tf.data.Dataset.range(1) \
.apply(fn)
for x in dataset:
print(x)
```
### Relevant log output
```shell
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/autograph/impl/api.py", line 427, in converted_call
converted_f = _convert_actual(target_entity, program_ctx)
File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/autograph/impl/api.py", line 269, in _convert_actual
transformed, module, source_map = _TRANSPILER.transform(entity, program_ctx)
File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/autograph/pyct/transpiler.py", line 282, in transform
return self.transform_function(obj, user_context)
File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/autograph/pyct/transpiler.py", line 466, in transform_function
nodes, ctx = super(PyToPy, self).transform_function(fn, user_context)
File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/autograph/pyct/transpiler.py", line 342, in transform_function
node, source = parser.parse_entity(fn, future_features=future_features)
File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/autograph/pyct/parser.py", line 145, in parse_entity
return _parse_lambda(entity)
File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/autograph/pyct/parser.py", line 275, in _parse_lambda
lambda_nodes.extend(
File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/autograph/pyct/parser.py", line 275, in <genexpr>
lambda_nodes.extend(
File "/usr/lib/python3.10/ast.py", line 390, in walk
todo.extend(iter_child_nodes(node))
File "/usr/lib/python3.10/ast.py", line 272, in iter_child_nodes
for name, field in iter_fields(node):
File "/usr/lib/python3.10/ast.py", line 260, in iter_fields
for field in node._fields:
AttributeError: 'NoneType' object has no attribute '_fields'
WARNING:tensorflow:AutoGraph could not transform <function fn.<locals>.<lambda> at 0x7f9fc56e9870> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'NoneType' object has no attribute '_fields'
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
2.13.0-dev20230501
Converted call: <function fn.<locals>.<lambda> at 0x7f9fc56e9870>
args: (<tf.Tensor 'args_0:0' shape=() dtype=int64>,)
kwargs: {}
Not allowed: <method-wrapper '__call__' of function object at 0x7f9fc56e9870>: default rule
Not allowed: <function fn.<locals>.<lambda> at 0x7f9fc56e9870>: default rule
<function fn.<locals>.<lambda> at 0x7f9fc56e9870> is not cached for subkey ConversionOptions[{}]
Error transforming entity <function fn.<locals>.<lambda> at 0x7f9fc56e9870>
WARNING: AutoGraph could not transform <function fn.<locals>.<lambda> at 0x7f9fc56e9870> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'NoneType' object has no attribute '_fields'
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
tf.Tensor(1, shape=(), dtype=int64)
```
</details>
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I_kwDOArmXAs5kxn_4
| 60,457 |
error message of tf.eye is inconsistent with doc
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[
"Hi @cheyennee ,\r\n\r\nThanks for reporting. I need to cross check the implementation and let you update and do necessary. Thanks!",
"Hi @cheyennee ,\r\n\r\n**For the code snippet 1:**\r\n\r\nSince you are passing `num_rows = \"1\"` as string and it is raising the error as intended and in the description it is printing num_rows=1, and here 1 is string not a number. By default if you won't provide any value to `num_columns` then `num_columns=num_rows` as per API, hence you are getting same value for both `num_rows` and `num_columns`.\r\n\r\nFor example if I pass `num_rows = \"anything\"` then the error description will be like below:\r\n\r\n\r\n> \r\n\r\n> TypeError: Arguments `num_rows` and `num_columns` must be positive integer values. Received: num_rows=anything, num_columns=anything\r\n\r\n\r\n\r\nI hope this will clarify your query and there is no need to change any description here.\r\n\r\n\r\n\r\n**For the code snippet 2:**\r\n\r\nIf you pass `num_rows = True`, here True will be converted as 1 and hence `num_rows=1` and `num_columns=1` and the output will be (1,1) shaped tensor.\r\n\r\nHope this clarify your queries. Thanks!",
"@SuryanarayanaY I see many other APIs that don't convert type internally. So I am a little confused, which APIs will automatically convert type internally, and which ones will not?",
"Hi @cheyennee ,\r\n\r\nI hope for `code snippet-1`, I have answered your query right?\r\n\r\nI am assuming your question is for `code snippet-2` where True is converted as '1'.Correct me if I am wrong. \r\n\r\nThe `tf.eye` API calls `tf.ones` internally and this is where booleans are converted into integer.Please refer the source code and the [gist](https://colab.research.google.com/gist/SuryanarayanaY/c434827ac7569eceb8b37a7f9ab28d50/60457.ipynb) to explain this.\r\n\r\n I think in the both APIs we need to change the description of argument `shape` that it also accepts boolean and converts them into integers 1 or 0.\r\n\r\nI think this documentation change will suffice the purpose of this issue right? Please confirm",
"@SuryanarayanaY Yeah, you're right. I think the documentation should be changed since the argument `shape` can accepts both boolean and integer.",
"You didn't pass a boolean tensor, you passed a Python boolean, which automatically gets converted to and integer when you do math on it. The function fails if you do actually try to pass a tensor of type `tf.bool`. It's not useful to document that `tf.ones(True)` happens to work - it has no valid semantic meaning.",
"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/60457\">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/60457\">No</a>\n"
] | 2023-05-01T12:03:21 | 2023-06-01T15:27:20 | 2023-06-01T15:27:18 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf 2.12.0
### Custom Code
Yes
### OS Platform and Distribution
win11
### 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 Behaviour?
According to [doc](https://tensorflow.google.cn/api_docs/python/tf/eye), the param `num_rows` should be `Non-negative int32 scalar Tensor`. But below snippet code 1 indicates that the param `num_rows` cannot be zero which is inconsistent with doc. On the other hand, the param `num_rows` shouldnt be Bool Tensor, but when given bool tensor, `tf.eye` works, as below snippet code 2 shows.
### Standalone code to reproduce the issue
```shell
snippet code 1:
import tensorflow as tf
results={}
try:
num_rows = "1"
results["res"] = tf.eye(num_rows=num_rows)
except Exception as e:
results["err"] = "Error:"+str(e)
print(results)
# results = Error:Arguments `num_rows` and `num_columns` must be positive integer values. Received: num_rows=1, num_columns=1
```
snippet code 2:
```
import tensorflow as tf
results={}
try:
num_rows = True
results["res"] = tf.eye(num_rows=num_rows,)
except Exception as e:
results["err"] = "Error:"+str(e)
print(results)
# results = {'res': <tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[1.]], dtype=float32)>}
```
```
### Relevant log output
_No response_</details>
|
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I_kwDOArmXAs5kxY6n
| 60,456 |
creating float16 tensors from scalars is not giving the correct results
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[
"Hi @MahmoudAshraf97 \r\n\r\nThis issue is related to the limited precision of the float16 data type. Float16 has a smaller range and precision compared to float32 and float64, which can result in rounding errors when representing larger numbers. \r\n\r\nThe closest representable float16 value to 6552 is 6550. Therefore, if you create a float16 tensor from the scalar value 6552, it will be rounded to 6550. Here's how you can verify this using Python:\r\n```\r\nimport numpy as np\r\n\r\nx = np.float16(6552)\r\nprint(x)\r\n```\r\nOutput:\r\n```\r\n6550.0\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/60456\">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/60456\">No</a>\n"
] | 2023-05-01T10:56:21 | 2023-05-02T19:46:58 | 2023-05-02T19:46:55 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
binary
### Tensorflow Version
2.12, 2.13.0-dev20230501
### Custom Code
No
### OS Platform and Distribution
_No response_
### Mobile device
_No response_
### Python version
3.8.10, 3.10.11
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
When creating a tensor from a scalar and setting the dtype to `float16`, the tensor is created with the wrong contents
the bug starts appearing with numbers higher than 2048 up to the upper limit of `float16`.
### Standalone code to reproduce the issue
```shell
print(tf.__version__)
>>> 2.13.0-dev20230501
print(tf.convert_to_tensor(6552.,dtype="float16"))
>>> tf.Tensor(6550.0, shape=(), dtype=float16)
```
### Relevant log output
_No response_</details>
|
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I_kwDOArmXAs5kvK1m
| 60,455 |
Unable to download tflite-model-maker
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[
"@UFthomasboosinger,\r\nApologies for the inconvenience. We are working on it and will provide the update soon on this issue. Thank you!",
"@tilakrayal when this issue resolved becouse till know colab disk become full on pip tflite model maker command ..Please provide update",
"Hi, Thanks for opening the issue. Since this issue is a duplicate of issue https://github.com/tensorflow/tensorflow/issues/60431, it would be better to track the progress at single place, could you please close this issue. Thanks!",
"@UFthomasboosinger,\r\nAs a temporary workaround could you please have a look at this tensorflow discussion forum comment provided from the team and let us know if it works in your case.\r\n\r\nCould you use the Colab fallback runtime version option to choose Python 3.9 and install tflite-model-maker. \r\n\r\nhttps://discuss.tensorflow.org/t/tflite-model-maker-installation/16577/3",
"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.",
"Issue is still there(((",
"> Issue is still there(((\r\n\r\nPlease follow https://github.com/tensorflow/tensorflow/issues/60431 for all the updates.",
"You can use virtual environment for immediate model training. \r\n\r\nhttps://stackoverflow.com/a/76437519/12235226",
"Closing the issue as duplicate of https://github.com/tensorflow/tensorflow/issues/60431",
"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/60455\">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/60455\">No</a>\n"
] | 2023-04-30T21:15:13 | 2023-06-26T20:16:20 | 2023-06-26T20:16:17 |
NONE
| null | null | null |
I'm experiencing difficulties when attempting to pip install tflite-model-maker in Google Colab with Python 3.10. I encounter one of the following three errors:
ERROR: Could not find a version that satisfies the requirement tflite-support>=0.4.2 (from tflite-model-maker) (from versions: 0.1.0a0.dev3, 0.1.0a0.dev4, 0.1.0a0.dev5, 0.1.0a0, 0.1.0a1)
ERROR: No matching distribution found for tflite-support>=0.4.2 (from tflite-model-maker)
ERROR: Unable to find version of scann==1.2.6
ERROR: Unable to find version of numba==0.53
I've also attempted to install tflite-model-maker-nightly and also cloning the source code from GitHub, but these methods have not resolved the issue.
If anyone could provide assistance or suggestions on how to successfully install tflite-model-maker, I would greatly appreciate it.
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fix AMD ROCm CI build failing due to missing patchelf package
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[
"Hi @luishbastos ,\r\n\r\nColab recently stopped support for Tensorflow Versions older than 2.8v. You can't download older versions directly with `pip install tensorflow==x` . The command `!pip install git+https://github.com/tensorflow/[email protected]` not even working with TF 2.12 versions which might be due to the way TF repo constructed,since `setup.py` not located at the root of tensorflow project directory but under sub directories at path [here](https://github.com/tensorflow/tensorflow/blob/r2.5/tensorflow/tools/pip_package/setup.py).\r\n\r\nTensorflow team not actively supporting older TF versions and this might be the reason why Colab dropped support for TF<2.8 versions. Nevertheless you may ask the reason for same at `googlecolab` repo also if required.\r\n\r\nHowever for your case you may use Anaconda environment/jupyter notebook and install TF 2.5 version by `pip install tensorflow==2.5`.However we are not supporting older versions now and we recommend to use latest versions and we can be helpful in case of any problems with latest versions.\r\n\r\nThanks!\r\n\r\n",
"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/60453\">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/60453\">No</a>\n"
] | 2023-04-30T19:42:47 | 2023-05-17T01:56:22 | 2023-05-17T01:56:20 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Build/Install
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf 2.5
### Custom Code
Yes
### OS Platform and Distribution
Colab
### Mobile device
_No response_
### Python version
3.11
### Bazel version
3.7.2
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
I have a project using Mask RCNN, and the project only works with Tensorflow 2.5, but this version is outdated and the pip doesn't support this version anymore, when I'm trying to build on colab using the "Build from Source" documentation,
When I try to use bazel I get an error while trying to build it
````
!bazel build --config=cuda //tensorflow/tools/pip_package:build_pip_package
````
Also when I try to use pip
````
!pip install git+https://github.com/tensorflow/[email protected]
````
I get the following error
````
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting git+https://github.com/tensorflow/[email protected]
Cloning https://github.com/tensorflow/tensorflow.git (to revision r2.5) to /tmp/pip-req-build-f6mzej4m
Running command git clone --filter=blob:none --quiet https://github.com/tensorflow/tensorflow.git /tmp/pip-req-build-f6mzej4m
Running command git checkout -b r2.5 --track origin/r2.5
Switched to a new branch 'r2.5'
Branch 'r2.5' set up to track remote branch 'r2.5' from 'origin'.
Resolved https://github.com/tensorflow/tensorflow.git to commit 959e9b2a0c06df945f9fb66bd367af8832ca0d28
ERROR: git+https://github.com/tensorflow/[email protected] does not appear to be a Python project: neither 'setup.py' nor 'pyproject.toml' found.
````
### Standalone code to reproduce the issue
```shell
You can reproduce it by using this colab
https://colab.research.google.com/github/Huxwell/caffe-tf-torch-darknet-onnx/blob/main/tensorflow_from_source.ipynb
```
### Relevant log output
_No response_</details>
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PR_kwDOArmXAs5PdKy2
| 60,452 |
Add dtype checks to Scatter ops to report error instead of core dump
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[
"Hi @evanrang Any update on this PR? Please. Thank you!",
"This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Hi @evanrang Any update on this PR? Please. Thank you!",
"Hi @evanrang I'm going to go ahead and close this PR, because it seems to have stalled. If you're still interested in pursing this (and responding to my comments), please feel free to reopen! Thank you!\r\n"
] | 2023-04-30T13:52:40 | 2023-11-03T04:32:20 | 2023-11-03T04:32:20 |
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Add dtype checks to Scatter ops(Assign*VariableOp, ResourceScatter*) to report error instead of core dump.
fix issues:
#60147
#60121
|
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| 60,451 |
native windows is not supported
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[
"有人成功过吗？Has anyone ever succeeded?Thanks",
"Hi @git-guocc, Please ensure that you followed the Tensorflow installation instructions from the [official website](https://www.tensorflow.org/install/pip#windows-native ). Thank you! ",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60451\">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/60451\">No</a>\n"
] | 2023-04-30T10:26:14 | 2023-05-18T01:54:24 | 2023-05-18T01:54:21 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Others
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.10.0
### Custom Code
Yes
### OS Platform and Distribution
win 11 pro
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
11.2/8.1
### GPU model and memory
laptop4060
### Current Behaviour?
使用conda安装完了tf==2.10.0后，可以使用GPU，用conda新建环境安装pytorch后，tensorflow环境下的GPU就不能使用了，请问怎么解决？
### Standalone code to reproduce the issue
```shell
(tensorflow) C:\Windows\System32>python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
[]
```
### Relevant log output
_No response_</details>
|
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I_kwDOArmXAs5kuSLn
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tf.compat.v1.nn.conv2d fails with assertion error: AttributeError: 'list' object has no attribute 'startswith'
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[
"Can I work on this issue?\r\n@tilakrayal ",
"@drewshark,\r\n**tf.compat.v1.nn.conv2d** was deprecated. Could you please try the **tf.nn.conv2d** which computes a 2-D convolution given input and 4-D filters tensors. \r\n\r\nhttps://www.tensorflow.org/api_docs/python/tf/nn/conv2d\r\n\r\nAlso kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/5be93b2b5230e9853ef6e31201e37679/untitled1101.ipynb) where I tried with tf.nn.conv2d and the output was also as expected. Thank you!",
"Hi, thanks for letting me know that it is deprecated. I am closing this issue.",
"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/60450\">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/60450\">No</a>\n"
] | 2023-04-30T09:57:53 | 2023-05-02T13:09:46 | 2023-05-02T13:09:43 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.11.0
### Custom Code
Yes
### OS Platform and Distribution
Linux/Mac
### 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 Behaviour?
I was trying to use tf.compat.v1.nn.conv2d because of the compatibility issue in my project. However, tf.compat.v1.nn.conv2d fails with the following error message:
```
AttributeError Traceback (most recent call last)
[<ipython-input-1-bc689394357a>](https://sjq4g20qeq-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20230427-060111-RC00_527538942#) in <cell line: 4>()
2 input = tf.Variable(tf.random_normal([10, 28, 28, 3]))
3 filters = tf.Variable(tf.random_normal([5, 5, 3, 32]))
----> 4 tf.compat.v1.nn.conv2d(input, filters, [1,1,1,1], "SAME", "NHWC", [1,1,1,1])
1 frames
[/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/nn_ops.py](https://sjq4g20qeq-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20230427-060111-RC00_527538942#) in conv2d(input, filter, strides, padding, use_cudnn_on_gpu, data_format, dilations, name, filters)
2442 if data_format is None:
2443 data_format = "NHWC"
-> 2444 channel_index = 1 if data_format.startswith("NC") else 3
2445
2446 strides = _get_sequence(strides, 2, channel_index, "strides")
AttributeError: 'list' object has no attribute 'startswith'
```
Looks like the argument `data_format` is a list instead of a string inside this API.
### Standalone code to reproduce the issue
```shell
import tensorflow.compat.v1 as tf
input = tf.Variable(tf.random_normal([10, 28, 28, 3]))
filters = tf.Variable(tf.random_normal([5, 5, 3, 32]))
tf.compat.v1.nn.conv2d(input, filters, [1,1,1,1], "SAME", "NHWC", [1,1,1,1])
```
### Relevant log output
_No response_</details>
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error message of tf.constant_initializer is not accuracy
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[
"Hi @cheyennee ,\r\n\r\nTensorflow deals Python numbers or numpy arrays as Tensors and converts them into Tensors wherever needed. Hence all the exception description contains Tensor reference only.Also within the API there are calls to several other APIs and hence the error description might not specifically mentioning the API specific argument because while passing arguments from one API to other the names of arguments also may be differed.This may not be true for all cases but for some.\r\n\r\nIn this case the argument `arg_input` internally converted to Tensor and then shape.ndims called upon this Tensor which is returning a scalar value '1' and there is no function `len()` for the scalar values.This is the source of error here.\r\n\r\nSometimes due to various other internal calls the error description might not have mention of specific API attributes. However the description is not so difficult for the users with minimum understanding of tensorflow. If you still feel that this description can be changable please feel free to raise a PR.",
"@SuryanarayanaY Got that. Thanks for the detailed explanation.",
"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/60449\">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/60449\">No</a>\n"
] | 2023-04-30T08:35:29 | 2023-05-03T11:03:25 | 2023-05-03T11:03:23 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf 2.12.0
### Custom Code
Yes
### OS Platform and Distribution
win11
### 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 Behaviour?
When run below snippet code, it will throw an exception. It's correct to throw an exception, but I think the error message is not so accurate. The following error message is more for developers to see than to tell the user where the input parameter is wrong.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
results={}
try:
value_0_tensor = tf.random.uniform([], dtype=tf.float32)
value_0 = tf.identity(value_0_tensor)
value = [value_0]
arg_class = tf.constant_initializer(value=value,)
arg_input_0 = 3
arg_input_1 = 1
arg_input = [arg_input_0,arg_input_1,]
results["res"] = arg_class(*arg_input)
except Exception as e:
results["err"] = "Error:"+str(e)
print(results)
# results={'err': 'Error:TypeError: Scalar tensor has no `len()`\nTraceback (most recent call last):\n\n File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py", line 1107, in __len__\n raise TypeError("Scalar tensor has no `len()`")\n\nTypeError: Scalar tensor has no `len()`\n\n'}
```
```
### Relevant log output
_No response_</details>
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I_kwDOArmXAs5kt8b6
| 60,444 |
Error message of tf.clip_by_norm is wrong
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[
"Hi @cheyennee \r\n\r\nAlthough the error messages in these two cases are different, they are both consistent with the documentation. The first error message indicates that the tensor type (boolean) is not allowed, while the second error message indicates that the data type (int32) is not compatible with a floating point type.\r\nThank you!",
"Hi, @synandi What confuses me is that the error message should be consistent in both two cases, no matter what type of param `t` is input.",
"@cheyennee \r\nThe two errors have different ops because they are caused by different TensorFlow operations.\r\n\r\nIn the first code snippet, the error is caused by `tf.clip_by_norm`, which internally uses the `tf.multiply` operation (also known as Mul) to perform element-wise multiplication between the input tensor `t` and its scaled norm.\r\n\r\nIn the second code snippet, the error is caused by the `tf.sqrt` operation, which expects its input tensor to have a floating-point type (bfloat16, half, float, double, complex64, or complex128). However, in this case, the input tensor `t` has an integer type (int32), which is not allowed.\r\n\r\nThank you!",
"@synandi Got that. Thanks for detailed explanation.",
"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/60444\">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/60444\">No</a>\n"
] | 2023-04-30T03:38:37 | 2023-05-03T13:26:30 | 2023-05-03T13:26:27 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf 2.12.0
### Custom Code
Yes
### OS Platform and Distribution
win11
### 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 Behaviour?
According to [doc](https://tensorflow.google.cn/versions/r2.4/api_docs/python/tf/clip_by_norm), the param `t` should be a Tensor or IndexedSlices, which must be a floating point type. But when `t` is [True, True], the error message indicates that `t` can be int type. More importantly, the error message when `t` is bool type is different from the error message when `t` is int type.
### Standalone code to reproduce the issue
```shell
snippet code 1:
import tensorflow as tf
results={}
try:
t = [True,True]
clip_norm = 1.0
results["res"] = tf.clip_by_norm(t=t,clip_norm=clip_norm,)
except Exception as e:
results["err"] = "Error:"+str(e)
print(results)
#results={'err': "Error:Value for attr 'T' of bool is not in the list of allowed values: bfloat16, half, float, double, uint8, int8, uint16, int16, int32, uint32, uint64, int64, complex64, complex128\n\t; NodeDef: {{node Mul}}; Op<name=Mul; signature=x:T, y:T -> z:T; attr=T:type,allowed=[DT_BFLOAT16, DT_HALF, DT_FLOAT, DT_DOUBLE, DT_UINT8, DT_INT8, DT_UINT16, DT_INT16, DT_INT32, DT_UINT32, DT_UINT64, DT_INT64, DT_COMPLEX64, DT_COMPLEX128]; is_commutative=true> [Op:Mul]"}
```
snippet code 2:
```
import tensorflow as tf
results={}
try:
t = [1,1,]
clip_norm = 1.0
results["res"] = tf.clip_by_norm(t=t,clip_norm=clip_norm,)
except Exception as e:
results["err"] = "Error:"+str(e)
print(results)
# results={'err': "Error:Value for attr 'T' of int32 is not in the list of allowed values: bfloat16, half, float, double, complex64, complex128\n\t; NodeDef: {{node Sqrt}}; Op<name=Sqrt; signature=x:T -> y:T; attr=T:type,allowed=[DT_BFLOAT16, DT_HALF, DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128]> [Op:Sqrt]"}
```
```
### Relevant log output
_No response_</details>
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PR_kwDOArmXAs5Pcl_l
| 60,443 |
Check fail in densebincount
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[
"Thanks for the review @cantonios!\r\n\r\n> Do we actually still get the check failure described in the original issue? I thought we fixed the work_element_count > 0 problem in general.\r\n\r\nYes the issue is still coming up - see this gist [here](https://colab.research.google.com/gist/madt2709/376f9f7aed33846fe0d76fe557fc2659/58178gpu.ipynb#scrollTo=zInMbWsqT7hy).\r\n",
"> Thanks for the review @cantonios!\r\n> \r\n> > Do we actually still get the check failure described in the original issue? I thought we fixed the work_element_count > 0 problem in general.\r\n> \r\n> Yes the issue is still coming up - see this gist [here](https://colab.research.google.com/gist/madt2709/376f9f7aed33846fe0d76fe557fc2659/58178gpu.ipynb#scrollTo=zInMbWsqT7hy).\r\n\r\nThat gist passes with tf-nightly.",
"Ah you're right! I should have checked that first. \r\n\r\nWell this makes this PR obsolete so will just close it and update the issue.\r\n\r\nThanks for the review (despite it being for nothing 😞 )"
] | 2023-04-30T00:39:48 | 2023-04-30T04:22:42 | 2023-04-30T04:22:42 |
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Added a test to reproduce the error from this issue https://github.com/tensorflow/tensorflow/issues/58178.
Second commit adds the relevant exception to the function.
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Check if self._current_step is not None
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"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60442/checks?check_run_id=13122568289) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"I'd like to add a test to highlight the issue but couldn't find a data adapter test file... If someone who knows the code base better could point out where to add this, I'll add it to this PR"
] | 2023-04-29T19:16:29 | 2023-05-01T21:15:48 | 2023-05-01T21:15:48 |
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Linked to [this](https://github.com/tensorflow/tensorflow/issues/58966)
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Windows bazel says python is not an executable when building tflite
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"Hi @turgut-baba could you please run the steps in the link below before running configure.py and let us know if you are still getting the errors?\r\nhttps://github.com/tensorflow/docs/blob/master/site/en/install/source_windows.md#optional-environmental-variable-set-up",
"@turgut-baba,\r\nCould you please confirm whether you are trying to install from the build from source for the Windows. Also you are trying to install tensorflow v2.12 with the Bazel 6.1.1 which is not compatible. \r\n\r\nFor tensorflow v2.12 the compatible Bazel version is 5.3.0. Please take a look at the official tested build configurations\r\nfrom [here](https://www.tensorflow.org/install/source_windows#gpu) and Could you try `bazel clean --expunge` followed by bazel sync\r\n\r\n\r\n\r\nThank you!",
"@tilakrayal , @mraunak I've set the environment variables and changed my bazel version to 5.3.0 but the error remains. The same message.",
"The only different thing I've noticed is that, naturally, the path is different but the error is still the same: \r\n```\r\nERROR: An error occurred during the fetch of repository 'local_config_python':\r\n Traceback (most recent call last):\r\n File \"C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl\", line 271, column 40, in _python_autoconf_impl\r\n _create_local_python_repository(repository_ctx)\r\n File \"C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl\", line 212, column 22, in _create_local_python_repository\r\n _check_python_bin(repository_ctx, python_bin)\r\n File \"C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl\", line 145, column 25, in _check_python_bin\r\n auto_config_fail(\"--define %s='%s' is not executable. Is it the python binary?\" % (\r\n File \"C:/users/asus/desktop/tensorflow/third_party/remote_config/common.bzl\", line 12, column 9, in auto_config_fail\r\n fail(\"%sConfiguration Error:%s %s\\n\" % (red, no_color, msg))\r\nError in fail: Configuration Error: --define PYTHON_BIN_PATH='C:/Users/Asus/Desktop/tensorflow/tflite-venv/Scripts/python.exe' is not executable. Is it the python binary?\r\nERROR: C:/users/asus/desktop/tensorflow/WORKSPACE:15:14: fetching python_configure rule //external:local_config_python: Traceback (most recent call last):\r\n File \"C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl\", line 271, column 40, in _python_autoconf_impl\r\n _create_local_python_repository(repository_ctx)\r\n File \"C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl\", line 212, column 22, in _create_local_python_repository\r\n _check_python_bin(repository_ctx, python_bin)\r\n File \"C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl\", line 145, column 25, in _check_python_bin\r\n auto_config_fail(\"--define %s='%s' is not executable. Is it the python binary?\" % (\r\n File \"C:/users/asus/desktop/tensorflow/third_party/remote_config/common.bzl\", line 12, column 9, in auto_config_fail\r\n fail(\"%sConfiguration Error:%s %s\\n\" % (red, no_color, msg))\r\nError in fail: Configuration Error: --define PYTHON_BIN_PATH='C:/Users/Asus/Desktop/tensorflow/tflite-venv/Scripts/python.exe' is not executable. Is it the python binary?\r\nINFO: Repository go_sdk instantiated at:\r\n C:/users/asus/desktop/tensorflow/WORKSPACE:23:14: in <toplevel>\r\n C:/users/asus/desktop/tensorflow/tensorflow/workspace0.bzl:134:20: in workspace\r\n C:/users/asus/_bazel_asus/ddsftcyc/external/com_github_grpc_grpc/bazel/grpc_extra_deps.bzl:36:27: in grpc_extra_deps\r\n C:/users/asus/_bazel_asus/ddsftcyc/external/io_bazel_rules_go/go/private/sdk.bzl:431:28: in go_register_toolchains\r\n C:/users/asus/_bazel_asus/ddsftcyc/external/io_bazel_rules_go/go/private/sdk.bzl:130:21: in go_download_sdk\r\nRepository rule _go_download_sdk defined at:\r\n C:/users/asus/_bazel_asus/ddsftcyc/external/io_bazel_rules_go/go/private/sdk.bzl:117:35: in <toplevel>\r\nERROR: Analysis of target '//tensorflow/lite:tensorflowlite' failed; build aborted: Configuration Error: --define PYTHON_BIN_PATH='C:/Users/Asus/Desktop/tensorflow/tflite-venv/Scripts/python.exe' is not executable. Is it the python binary?\r\nINFO: Elapsed time: 2.029s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (33 packages loaded, 15 targets configured)\r\n```",
"Hi @turgut-baba, can you please try to run \"bazel clean --expunge\" to make sure old bazel files are deleted? and then run the build command again.\r\nIf you still get an error try the command in the image to make sure 'location to python.exe' is recognized as executable\r\n\r\n\r\n\r\nIf it still does not work, please follow the steps below\r\n[steps.txt](https://github.com/tensorflow/tensorflow/files/11521798/steps.txt)\r\n\r\n",
"@mraunak Thanks, but now I'm getting this error:\r\n```\r\nRepository rule local_python_configure defined at:\r\n C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl:279:41: in <toplevel>\r\nERROR: An error occurred during the fetch of repository 'local_execution_config_python':\r\n Traceback (most recent call last):\r\n File \"C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl\", line 216, column 39, in _create_local_python_repository\r\n numpy_include = _get_numpy_include(repository_ctx, python_bin) + \"/numpy\"\r\n File \"C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl\", line 190, column 19, in _get_numpy_include\r\n return execute(\r\n File \"C:/users/asus/desktop/tensorflow/third_party/remote_config/common.bzl\", line 230, column 13, in execute\r\n fail(\r\nError in fail: Problem getting numpy include path.\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\nModuleNotFoundError: No module named 'numpy'\r\nIs numpy installed?\r\nERROR: C:/users/asus/desktop/tensorflow/WORKSPACE:15:14: fetching local_python_configure rule //external:local_execution_config_python: Traceback (most recent call last):\r\n File \"C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl\", line 216, column 39, in _create_local_python_repository\r\n numpy_include = _get_numpy_include(repository_ctx, python_bin) + \"/numpy\"\r\n File \"C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl\", line 190, column 19, in _get_numpy_include\r\n return execute(\r\n File \"C:/users/asus/desktop/tensorflow/third_party/remote_config/common.bzl\", line 230, column 13, in execute\r\n fail(\r\nError in fail: Problem getting numpy include path.\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\nModuleNotFoundError: No module named 'numpy'\r\nIs numpy installed?\r\nINFO: Repository go_sdk instantiated at:\r\n C:/users/asus/desktop/tensorflow/WORKSPACE:23:14: in <toplevel>\r\n C:/users/asus/desktop/tensorflow/tensorflow/workspace0.bzl:134:20: in workspace\r\n C:/users/asus/_bazel_asus/ddsftcyc/external/com_github_grpc_grpc/bazel/grpc_extra_deps.bzl:36:27: in grpc_extra_deps\r\n C:/users/asus/_bazel_asus/ddsftcyc/external/io_bazel_rules_go/go/private/sdk.bzl:431:28: in go_register_toolchains\r\n C:/users/asus/_bazel_asus/ddsftcyc/external/io_bazel_rules_go/go/private/sdk.bzl:130:21: in go_download_sdk\r\nRepository rule _go_download_sdk defined at:\r\n C:/users/asus/_bazel_asus/ddsftcyc/external/io_bazel_rules_go/go/private/sdk.bzl:117:35: in <toplevel>\r\nERROR: Analysis of target '//tensorflow/lite:tensorflowlite' failed; build aborted: Problem getting numpy include path.\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\nModuleNotFoundError: No module named 'numpy'\r\nIs numpy installed?\r\nINFO: Elapsed time: 1.232s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (0 packages loaded, 0 targets configured)\r\n```",
"I've deleted python311 from my system and it solved the program. Now I'm getting this error:\r\n```\r\nERROR: C:/users/asus/desktop/tensorflow/tensorflow/lite/schema/BUILD:184:22: Generating flatbuffer files for conversion_metadata_fbs_srcs: //tensorflow/lite/schema:conversion_metadata_fbs_srcs failed: (Exit -1): bash.exe failed: error executing command\r\n cd /d C:/users/asus/_bazel_asus/ddsftcyc/execroot/org_tensorflow\r\n SET PATH=C:\\msys64\\usr\\bin;C:\\msys64\\bin;C:\\Windows;C:\\Windows\\System32;C:\\Windows\\System32\\WindowsPowerShell\\v1.0;c:\\Tools\\Bazel;C:\\Users\\Asus\\venv39_new\\Scripts;C:\\Python39\\Scripts\\;C:\\Users\\Asus\\Desktop\\Bazel_compiler\r\n SET PYTHON_BIN_PATH=C:/Users/Asus/venv39_new/Scripts/python.exe\r\n SET PYTHON_LIB_PATH=C:/Users/Asus/venv39_new/lib/site-packages\r\n SET RUNFILES_MANIFEST_ONLY=1\r\n SET TF2_BEHAVIOR=1\r\n C:\\msys64\\usr\\bin\\bash.exe -c source external/bazel_tools/tools/genrule/genrule-setup.sh; for f in tensorflow/lite/schema/conversion_metadata.fbs; do bazel-out/x64_windows-opt/bin/external/flatbuffers/flatc.exe --no-union-value-namespacing --gen-object-api -c -o bazel-out/x64_windows-opt/bin/tensorflow/lite/schema $f; done\r\n# Configuration: 69f9c37d88669b53b8f73db2aad31626990e264ca81cbe6069ba71a8f673143d\r\n# Execution platform: @local_execution_config_platform//:platform\r\nAction failed to execute: java.io.IOException: ERROR: src/main/native/windows/process.cc(202): CreateProcessW(\"C:\\msys64\\usr\\bin\\bash.exe\" -c \"source external/bazel_tools/tools/genrule/genrule-setup.sh; for f in tensorflow/lite/schema/conversion_metadata.fbs; do bazel-out/x64_windows-opt/bin/external/flatbuffers/flatc.exe --no-union-value-namespacing --gen-object-api -c -o bazel-out/x64_windows-opt/bin/tensorflow/lite/schema $f; done\"): The system cannot find the file specified.\r\n (error: 2)\r\nTarget //tensorflow/lite:tensorflowlite failed to build\r\nINFO: Elapsed time: 188.351s, Critical Path: 23.46s\r\nINFO: 341 processes: 158 internal, 183 local.\r\nFAILED: Build did NOT complete successfully\r\n```",
"Hi @turgut-baba, please set the Java path as mentioned below. Add JAVA_HOME in the environmental variables\r\n\r\nset JAVA_HOME = C:\\Program Files\\Eclipse Adoptium\\jdk-17.0.4.101-hotspot \r\n\r\nIf you still get an issue, run bazel clean --expunge command to clear bazel cache and re-rerun the build command.\r\n\r\n",
"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/60441\">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/60441\">No</a>\n"
] | 2023-04-29T18:56:01 | 2023-06-17T01:58:15 | 2023-06-17T01:58:12 |
NONE
| null | null | null |
Please go to Stack Overflow for help and support:
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If you open a GitHub issue, here is our policy:
1. It must be a bug, a feature request, or a significant problem with the
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2. The form below must be filled out.
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------------------------
### System information
- Trying to build TFLite
- Windows 10
- Windows Asus machine
- source
- r2.12
- python 3.11.2
- bazel 6.1.1
- windows compiler
- **CUDA/cuDNN version**: ---
- Geforce GTX 960m
- bazel build -c opt //tensorflow/lite:tensorflowlite
I'm trying to build c++ tflite for windows using bazel by following the [official documentation](https://www.tensorflow.org/install/source_windows). So far I've installed everything it want's me to and added them to PATH. Then I cloned the github repo and checked out to r2.12 branch.
Then I ran python ./configure.py and selected default for everything (said yes to override eigen strong inline). When doing so it declared that my python is located on C:\Users\Asus\AppData\Local\Programs\Python\Python311\python.exe.
After that running bazel build -c opt //tensorflow/lite:tensorflowlite on the directory where I've cloned tensorflow in cmd casuses this error:
Starting local Bazel server and connecting to it...
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=117
INFO: Reading rc options for 'build' from c:\users\asus\desktop\tensorflow\.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Options provided by the client:
'build' options: --python_path=C:/Users/Asus/AppData/Local/Programs/Python/Python311/python.exe
INFO: Reading rc options for 'build' from c:\users\asus\desktop\tensorflow\.bazelrc:
'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility
INFO: Reading rc options for 'build' from c:\users\asus\desktop\tensorflow\.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=C:/Users/Asus/AppData/Local/Programs/Python/Python311/python.exe --action_env PYTHON_LIB_PATH=C:/Users/Asus/AppData/Local/Programs/Python/Python311/Lib/site-packages --python_path=C:/Users/Asus/AppData/Local/Programs/Python/Python311/python.exe --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions --define=override_eigen_strong_inline=true
INFO: Reading rc options for 'build' from c:\users\asus\desktop\tensorflow\.bazelrc:
'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils
INFO: Found applicable config definition build:short_logs in file c:\users\asus\desktop\tensorflow\.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file c:\users\asus\desktop\tensorflow\.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:windows in file c:\users\asus\desktop\tensorflow\.bazelrc: --copt=/W0 --host_copt=/W0 --copt=/Zc:__cplusplus --host_copt=/Zc:__cplusplus --copt=/D_USE_MATH_DEFINES --host_copt=/D_USE_MATH_DEFINES --features=compiler_param_file --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions --cxxopt=/std:c++17 --host_cxxopt=/std:c++17 --config=monolithic --copt=-DWIN32_LEAN_AND_MEAN --host_copt=-DWIN32_LEAN_AND_MEAN --copt=-DNOGDI --host_copt=-DNOGDI --copt=/Zc:preprocessor --host_copt=/Zc:preprocessor --linkopt=/DEBUG --host_linkopt=/DEBUG --linkopt=/OPT:REF --host_linkopt=/OPT:REF --linkopt=/OPT:ICF --host_linkopt=/OPT:ICF --verbose_failures --features=compiler_param_file --distinct_host_configuration=false
INFO: Found applicable config definition build:monolithic in file c:\users\asus\desktop\tensorflow\.bazelrc: --define framework_shared_object=false --define tsl_protobuf_header_only=false --experimental_link_static_libraries_once=false
INFO: Repository local_config_python instantiated at:
C:/users/asus/desktop/tensorflow/WORKSPACE:15:14: in <toplevel>
C:/users/asus/desktop/tensorflow/tensorflow/workspace2.bzl:957:19: in workspace
C:/users/asus/desktop/tensorflow/tensorflow/workspace2.bzl:104:21: in _tf_toolchains
Repository rule python_configure defined at:
C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl:298:35: in <toplevel>
ERROR: An error occurred during the fetch of repository 'local_config_python':
Traceback (most recent call last):
File "C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl", line 271, column 40, in _python_autoconf_impl
_create_local_python_repository(repository_ctx)
File "C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl", line 212, column 22, in _create_local_python_repository
_check_python_bin(repository_ctx, python_bin)
File "C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl", line 145, column 25, in _check_python_bin
auto_config_fail("--define %s='%s' is not executable. Is it the python binary?" % (
File "C:/users/asus/desktop/tensorflow/third_party/remote_config/common.bzl", line 12, column 9, in auto_config_fail
fail("%sConfiguration Error:%s %s\n" % (red, no_color, msg))
Error in fail: Configuration Error: --define PYTHON_BIN_PATH='C:/Users/Asus/AppData/Local/Programs/Python/Python311/python.exe' is not executable. Is it the python binary?
ERROR: C:/users/asus/desktop/tensorflow/WORKSPACE:15:14: fetching python_configure rule //external:local_config_python: Traceback (most recent call last):
File "C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl", line 271, column 40, in _python_autoconf_impl
_create_local_python_repository(repository_ctx)
File "C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl", line 212, column 22, in _create_local_python_repository
_check_python_bin(repository_ctx, python_bin)
File "C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl", line 145, column 25, in _check_python_bin
auto_config_fail("--define %s='%s' is not executable. Is it the python binary?" % (
File "C:/users/asus/desktop/tensorflow/third_party/remote_config/common.bzl", line 12, column 9, in auto_config_fail
fail("%sConfiguration Error:%s %s\n" % (red, no_color, msg))
Error in fail: Configuration Error: --define PYTHON_BIN_PATH='C:/Users/Asus/AppData/Local/Programs/Python/Python311/python.exe' is not executable. Is it the python binary?
INFO: Repository local_execution_config_python instantiated at:
C:/users/asus/desktop/tensorflow/WORKSPACE:15:14: in <toplevel>
C:/users/asus/desktop/tensorflow/tensorflow/workspace2.bzl:957:19: in workspace
C:/users/asus/desktop/tensorflow/tensorflow/workspace2.bzl:94:27: in _tf_toolchains
C:/users/asus/desktop/tensorflow/tensorflow/tools/toolchains/remote_config/configs.bzl:6:28: in initialize_rbe_configs
C:/users/asus/desktop/tensorflow/tensorflow/tools/toolchains/remote_config/rbe_config.bzl:158:27: in _tensorflow_local_config
Repository rule local_python_configure defined at:
C:/users/asus/desktop/tensorflow/third_party/py/python_configure.bzl:279:41: in <toplevel>
INFO: Repository go_sdk instantiated at:
C:/users/asus/desktop/tensorflow/WORKSPACE:23:14: in <toplevel>
C:/users/asus/desktop/tensorflow/tensorflow/workspace0.bzl:134:20: in workspace
C:/users/asus/_bazel_asus/ddsftcyc/external/com_github_grpc_grpc/bazel/grpc_extra_deps.bzl:36:27: in grpc_extra_deps
C:/users/asus/_bazel_asus/ddsftcyc/external/io_bazel_rules_go/go/private/sdk.bzl:431:28: in go_register_toolchains
C:/users/asus/_bazel_asus/ddsftcyc/external/io_bazel_rules_go/go/private/sdk.bzl:130:21: in go_download_sdk
Repository rule _go_download_sdk defined at:
C:/users/asus/_bazel_asus/ddsftcyc/external/io_bazel_rules_go/go/private/sdk.bzl:117:35: in <toplevel>
ERROR: Analysis of target '//tensorflow/lite:tensorflowlite' failed; build aborted: Configuration Error: --define PYTHON_BIN_PATH='C:/Users/Asus/AppData/Local/Programs/Python/Python311/python.exe' is not executable. Is it the python binary?
INFO: Elapsed time: 224.163s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (32 packages loaded, 15 targets configured)
Looking at the main error part of this output : ERROR: Analysis of target '//tensorflow/lite:tensorflowlite' failed; build aborted: Configuration Error: --define PYTHON_BIN_PATH='C:/Users/Asus/AppData/Local/Programs/Python/Python311/python.exe' is not executable. Is it the python binary?
I checked whether python was there or not, it indeed is. Simply running C:/Users/Asus/AppData/Local/Programs/Python/Python311/python.exe in cmd indeed opens python.
So I checked the internet for some solutions, I've removed the "python installers" from my system, added python to PATH, tried the same steps with tensorflow source zip instead of cloning it, nothing works.
Some people suggested chaning some stuff inside the py directory of tensorflow but it didn't work aswell.
Why is this happening? What causes bazel to not see python even though it's there? How can I fix this and get a build with windows?
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I_kwDOArmXAs5ktVLc
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We don't have your exam ready right now
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"This repository is about the TF code, not about the TF certification.",
"> This repository is about the TF code, not about the TF certification.\r\n\r\nCould you please elaborate more and help me solving the problem",
"Hi @Jaideep90061 ,\r\n\r\nJust sharing my thoughts though its not relevant to this repo.Please check whether all the instructions followed correctly and required plugins installed with the required environment. Also if you have downloaded there might be some mail id to contact from where you may get some help. ",
"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/60440\">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/60440\">No</a>\n"
] | 2023-04-29T15:58:11 | 2023-05-02T17:01:27 | 2023-05-02T17:01:25 |
NONE
| null | null | null |
I had purchased the tensorflow exam and tried to start it, but it is showing that we don't have your exam ready right now.but in the candidate portal it is showing resume.so i am gonna post the screenshot of my problem
<img width="960" alt="Screenshot 2023-04-28 114949" src="https://user-images.githubusercontent.com/132153955/235311840-6b7d464f-46e7-4389-8135-db36f93b0605.png">
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[
"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60439/checks?check_run_id=13119713140) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"Please don't use \"add file\"/\"update file\"/\"fix file\"/etc. commit messages. These are hard to reason about when looking at the history of the file/repository. Instead, please write explanatory git commit messages.\r\n\r\nThe commit message is also the title of the PR if the PR has only one commit. It is thus twice important to have commit messages that are relevant, as PRs would be easier to understand and easier to analyze in search results.\r\n\r\nFor how to write good quality git commit messages, please consult https://cbea.ms/git-commit/ "
] | 2023-04-29T12:51:55 | 2023-05-01T16:16:22 | 2023-05-01T16:16:22 |
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I_kwDOArmXAs5ksWFn
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Inconsistency in SyncBatchNormalization/BatchNormalization(synchronized=True) Results during Distributed Training on CPUs
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[
"Hi @Liqi1003 ,\r\n\r\nThanks for reporting. As the `SyncBatchNormalization` API endpoint is deprecated and might removed in Future release, I tried to replicate the `BatchNormalization` issue with `synchronized=True`. I have replicated the difference in outputs wrt 1- CPU and 2-CPU as reported and the value might not be ignorable and needs to have a look into it.Reference [gist](https://colab.research.google.com/gist/SuryanarayanaY/fbcc86cf6730918960ef275a1fa3ddfa/syncbatchnorm-inconsistency-tf-nightly.ipynb).\r\n\r\n\r\n> while for the model removing batchnorm layer it’s Linf = 6.212488e-36\r\n\r\nCould you please submit code snippet for this also.\r\n\r\nThanks!",
"@SuryanarayanaY \r\nThanks for the quick reproduction! The reproduction code without SyncBatchNormalization is in the same colab here: https://colab.research.google.com/drive/1N-aXPcfckVb8fPDSmghMlzBOFEGq6RzM?usp=sharing\r\n\r\nIn the last code block, uncomment the second line and execute the code block again, and you should be able to see the difference without SyncBatchNormalization layers.",
"@Liqi1003 ,\r\n\r\nI have done some changes in code to check the difference in outputs with 1 CPU and 2 CPUs with `BatchNormalization(synchronised=True)` and `BatchNormalization(synchronised=False)` and the difference in results wrt 1-CPU and 2-CPU came around same at `0.00010111928`. Could you please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/fc3d4fee3ec732665af2b1d86f403995/60438-syncbatchnorm-inconsistency-tf-nightly.ipynb).\r\n\r\nIn your code for `synchronised=False` it is omitting `batch_normalization` and hence there is large difference in results.\r\n\r\n\r\nThanks!\r\n\r\n\r\n\r\n",
"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.",
"@SuryanarayanaY \r\n\r\nThanks for the debugging effort! \r\nHowever, the key point here is that BatchNormalization layer is introducing big variances when training on CPUs. On 1-GPU vs 2-GPU setting, we see very small differences (~1e-7), but on 1-CPU vs 2-CPU setting, we can see a much larger difference (~1e-4).\r\n\r\nGiven our effort to control variance by limiting training steps, a difference of 1e-4 should be considered big. Although with larger training size, the difference could either become bigger or cancle out, we do see the difference in one-step training.\r\n\r\nI suspect there is something special with CPU execution of BatchNormalization layers, which caused the difference.",
"@Liqi1003 ,\r\n\r\nWill escalate the issue to Dev team for their comments. Thanks!"
] | 2023-04-29T03:41:12 | 2023-08-31T05:36:12 | null |
NONE
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<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf 2.11.0
### Custom Code
Yes
### OS Platform and Distribution
Linux Ubuntu 20.04.5 LTS
### Mobile device
_No response_
### Python version
3.8.10
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
11.7
### GPU model and memory
_No response_
### Current Behaviour?
I am reporting an issue encountered during the distributed training of a model with `SyncBatchNormalization` layers on CPUs. The issue presents as a relatively large inconsistency between the results produced when training the same model on a single device versus multiple devices.
We've prepared a reproduction script demonstrating this issue. We set the weights of the `SyncBatchNormalization`/`BatchNormalization` layer to reproduce this bug. After conducting a one-step training, the predictions from the identical model trained on a single CPU and two CPUs exhibited relatively large differences (measured by L-infinity distance, Linf = 0.00074467063). As a comparison, we build another model with exactly the same architecture but removing the `SyncBatchNormalization` layer, and the prediction difference from this model trained on a single CPU and two CPUs is Linf = 1.0131771e-09.
Another comparison experiment is on GPUs. We trained the same model containing `SyncBatchNormalization` layer on a single GPU and two GPUs. The Linf of model’s prediction result is 2.5331974e-07. It’s much smaller than 1CPU vs 2CPU (Linf = 0.00074467063). This is weird because I would expect the CPU executions to be more deterministic than GPUs, and I would expect larger inconsistencies in the GPU runs if the inconsistencies are caused by variance.
To ensure a controlled environment, we used the same training data for one-step training on both setups, and then evaluated the model. To guarantee that the distributed trainings are expected to produce the same results given different number of devices, we use `MirroredStrategy`, keep the global batch size the same, and use `tf.keras.losses.Reduction.AUTO` reduction. To make the difference more apparent given a limited amount of training data, we deliberately chose a relatively high learning rate (lr=10).
It's noteworthy that `SyncBatchNormalization` has been deprecated as of TensorFlow version 2.12. Therefore, we also conducted the same experiment using TensorFlow's nightly build (2.13.0-dev20230428), replacing `SyncBatchNormalization()` with `BatchNormalization(synchronized=True)`. This experiment still manifested the same inconsistency. The prediction from the model containing batchnorm layer trained on a single CPU and two CPUs exhibits a substantial difference of Linf = 0.00010111928, while for the model removing batchnorm layer it’s Linf = 6.212488e-36.
### Standalone code to reproduce the issue
```shell
For TF 2.11.0 CPU experiments, the model contains `SyncBatchNormalization` layer:
https://colab.research.google.com/drive/11EseXxq_uHweY7omt7JCr1mc-42Kf1H5?usp=sharing
For TF 2.11.0 GPU experiments, the model contains `SyncBatchNormalization` layer (fix seed to generate same inputs):
https://colab.research.google.com/drive/1m_gCWzb_OKVTYAMMs8YbdKUYM3aINNPV?usp=sharing
For TF nightly 2.13.0-dev20230428, the model contains `BatchNormalization(synchronized=True)` layer:
https://colab.research.google.com/drive/1N-aXPcfckVb8fPDSmghMlzBOFEGq6RzM?usp=sharing
```
### Relevant log output
For TF 2.11.0:
```shell
contain syncbatchnorm: True
1CPU vs 2CPU: 0.00074467063
contain syncbatchnorm: False
1CPU vs 2CPU: 1.0131771e-09
contain syncbatchnorm: True
1GPU vs 2GPU: 2.5331974e-07
```
For TF nightly 2.13.0-dev20230428:
```shell
contain syncbatchnorm: True
1CPU vs 2CPU: 0.00010111928
contain syncbatchnorm: False
1CPU vs 2CPU: 6.212488e-36
```
</details>
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[
"May you share the transformer model architecture you used so we can reproduce the error?",
" Good morning Zeyad;\nThank you very much for helping.\nThe attached file one_step_transformer_components.py contains the definition of the transformer I am trying to save when I experience the issue.\nI am using the transformer to do one step ahead time series predictions\n\nHere are the dimensions of the transformer I am trying to save.\nnum_layers = 2num_heads = 2model_dimensions = 16ffn_units = 64features_shape = (1024, 3, 2)labels_shape = (1024, 3, 2)dropout_rate = 0.2\n\nI tried decorating the transformed functions call() and predict with an @tf.function() hoping it might dodge the issue by taking a different route through the save_model() logic. I did not. I still received the same error message.\nWhile running TensorFlow 2.10. I also found that the tf.keras.layers.Normalization layer I was using was not doing its inversion calculations correctly so I coded the inversion myself in the transformer call() function. I have not yet tested the Normalization layer in version 2.12.\nThank you again for helping\nRegardsCraig(630) 453-0404\n\n\n\n\n\n\n\n\n On Saturday, April 29, 2023 at 05:53:31 AM CDT, Zeyad Abd El-Raheem ***@***.***> wrote: \n \n \n\n\nMay you share the transformer model architecture you used so we can reproduce the error?\n\n—\nReply to this email directly, view it on GitHub, or unsubscribe.\nYou are receiving this because you authored the thread.Message ID: ***@***.***>\n ",
"Hi @cpodczerwinski, Apologies for the delay.\r\nI can see a different error when replicating the issue in Colab with Tensorflow 2.12. Please find the gist [here](https://colab.sandbox.google.com/gist/synandi/32a2ad838dbac60391952d4b6b2be476/untitled355.ipynb). It would help us if you could share the complete reproducible code to expedite the troubleshooting process. Thank you! ",
" Good morning;\nThank you again for helping.\nThe attached file one_step_unit_tests.py contains a script that you can run with the file one_step_transformer_components.py that I provided in my previous email. It reproduces the error. The output I received when I executed it follows;\nThe following transformer member function fixes the error that you received when saving the tutorial tensor that you ran\ndef get_config(self):\n config = {\n 'd_model': self.model_dimensions,\n 'warmup_steps': self.warmup_steps,\n\n }\n return config\n\n\nRegardsCraig\n\nC:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\python.exe D:\\Craig\\Python\\one_step_unit_tests.py transformer_output[0].shape: (1024, 3, 2)WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.preprocessing.normalization.Normalization object at 0x000001E0ACA99250>, because it is not built.WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.preprocessing.normalization.Normalization object at 0x000001E0ACB4D650>, because it is not built.WARNING:absl:Found untraced functions such as _update_step_xla, dense_layer_call_fn, dense_layer_call_and_return_conditional_losses, positional_embedding_layer_call_fn, positional_embedding_layer_call_and_return_conditional_losses while saving (showing 5 of 165). These functions will not be directly callable after loading.Traceback (most recent call last): File \"D:\\Craig\\Python\\one_step_unit_tests.py\", line 30, in <module> tf.keras.saving.save_model( transformer, 'Output\\\\saved_transformer') File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\saving\\saving_api.py\", line 145, in save_model return legacy_sm_saving_lib.save_model( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py\", line 200, in encode chunks = self.iterencode(o, _one_shot=True) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py\", line 258, in iterencode return _iterencode(o, 0) ^^^^^^^^^^^^^^^^^TypeError: Unable to serialize 16.0 to JSON. Unrecognized type <class 'tensorflow.python.framework.ops.EagerTensor'>.\nProcess finished with exit code 1\n\n\n On Monday, May 8, 2023 at 08:11:21 AM CDT, synandi ***@***.***> wrote: \n \n \n\n\nHi @cpodczerwinski, Apologies for the delay.\nI can see a different error when replicating the issue in Colab with Tensorflow 2.12. Please find the gist here. It would help us if you could share the complete reproducible code to expedite the troubleshooting process. Thank you!\n\n—\nReply to this email directly, view it on GitHub, or unsubscribe.\nYou are receiving this because you were mentioned.Message ID: ***@***.***>\n ",
" I forget to attach the file one_step_unit_tests,py. Here is a copy of it.\nRegardsCraig\n On Thursday, May 11, 2023 at 04:44:02 PM CDT, Craig Podczerwinski ***@***.***> wrote: \n \n Good morning;\nThank you again for helping.\nThe attached file one_step_unit_tests.py contains a script that you can run with the file one_step_transformer_components.py that I provided in my previous email. It reproduces the error. The output I received when I executed it follows;\nThe following transformer member function fixes the error that you received when saving the tutorial tensor that you ran\ndef get_config(self):\n config = {\n 'd_model': self.model_dimensions,\n 'warmup_steps': self.warmup_steps,\n\n }\n return config\n\n\nRegardsCraig\n\nC:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\python.exe D:\\Craig\\Python\\one_step_unit_tests.py transformer_output[0].shape: (1024, 3, 2)WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.preprocessing.normalization.Normalization object at 0x000001E0ACA99250>, because it is not built.WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.preprocessing.normalization.Normalization object at 0x000001E0ACB4D650>, because it is not built.WARNING:absl:Found untraced functions such as _update_step_xla, dense_layer_call_fn, dense_layer_call_and_return_conditional_losses, positional_embedding_layer_call_fn, positional_embedding_layer_call_and_return_conditional_losses while saving (showing 5 of 165). These functions will not be directly callable after loading.Traceback (most recent call last): File \"D:\\Craig\\Python\\one_step_unit_tests.py\", line 30, in <module> tf.keras.saving.save_model( transformer, 'Output\\\\saved_transformer') File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\saving\\saving_api.py\", line 145, in save_model return legacy_sm_saving_lib.save_model( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py\", line 200, in encode chunks = self.iterencode(o, _one_shot=True) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py\", line 258, in iterencode return _iterencode(o, 0) ^^^^^^^^^^^^^^^^^TypeError: Unable to serialize 16.0 to JSON. Unrecognized type <class 'tensorflow.python.framework.ops.EagerTensor'>.\nProcess finished with exit code 1\n\n\n On Monday, May 8, 2023 at 08:11:21 AM CDT, synandi ***@***.***> wrote: \n \n \n\n\nHi @cpodczerwinski, Apologies for the delay.\nI can see a different error when replicating the issue in Colab with Tensorflow 2.12. Please find the gist here. It would help us if you could share the complete reproducible code to expedite the troubleshooting process. Thank you!\n\n—\nReply to this email directly, view it on GitHub, or unsubscribe.\nYou are receiving this because you were mentioned.Message ID: ***@***.***>\n ",
"@cpodczerwinski \r\nThe code provided is not complete hence it would be difficult for us to pinpoint the issue. Please share complete stand alone code to replicate the issue or a colab gist with the error reported.? That will allow us to determine the source of the issue easily. Thank you!",
" Hello Syed;\nI am resending you the files onestep_unit_tests.py and one_step_transformer_components.py which are attached. Executing the file one_step_unit_tests.py will reproduce the problem. The code in one_step_unit_tests.py uses the classes and functions defined in one_step_transformer_components.py. I just executed one_step_unit_tests.py on my laptop and received the following output. I am using the PyCharm IDE.\nThank you for helping.RegardsCraig(630) 453-0404\n\nD:\\Craig\\Python\\Projects\\ModelSaveProblem\\Scripts\\python.exe D:\\Craig\\Python\\one_step_unit_tests.py transformer_output[0].shape: (1024, 3, 2)WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.preprocessing.normalization.Normalization object at 0x000001EB3B7B3590>, because it is not built.WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.preprocessing.normalization.Normalization object at 0x000001EB3B7B1B50>, because it is not built.WARNING:absl:Found untraced functions such as _update_step_xla, dense_layer_call_fn, dense_layer_call_and_return_conditional_losses, positional_embedding_layer_call_fn, positional_embedding_layer_call_and_return_conditional_losses while saving (showing 5 of 165). These functions will not be directly callable after loading.Traceback (most recent call last): File \"D:\\Craig\\Python\\one_step_unit_tests.py\", line 30, in <module> tf.keras.saving.save_model( transformer, 'Output\\\\saved_transformer') File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\saving\\saving_api.py\", line 145, in save_model return legacy_sm_saving_lib.save_model( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py\", line 200, in encode chunks = self.iterencode(o, _one_shot=True) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py\", line 258, in iterencode return _iterencode(o, 0) ^^^^^^^^^^^^^^^^^TypeError: Unable to serialize 16.0 to JSON. Unrecognized type <class 'tensorflow.python.framework.ops.EagerTensor'>.\nProcess finished with exit code 1\n On Monday, May 8, 2023 at 08:11:21 AM CDT, synandi ***@***.***> wrote: \n \n \n\n\nHi @cpodczerwinski, Apologies for the delay.\nI can see a different error when replicating the issue in Colab with Tensorflow 2.12. Please find the gist here. It would help us if you could share the complete reproducible code to expedite the troubleshooting process. Thank you!\n\n—\nReply to this email directly, view it on GitHub, or unsubscribe.\nYou are receiving this because you were mentioned.Message ID: ***@***.***>\n ",
" Good morning;\nHave you been able to reproduce and identify a corrective action for this issue. Are there any work around actions I can take?\nThank you for helping\nRegardsCraig\n On Monday, May 15, 2023 at 03:38:30 PM CDT, Craig Podczerwinski ***@***.***> wrote: \n \n Hello Syed;\nI am resending you the files onestep_unit_tests.py and one_step_transformer_components.py which are attached. Executing the file one_step_unit_tests.py will reproduce the problem. The code in one_step_unit_tests.py uses the classes and functions defined in one_step_transformer_components.py. I just executed one_step_unit_tests.py on my laptop and received the following output. I am using the PyCharm IDE.\nThank you for helping.RegardsCraig(630) 453-0404\n\nD:\\Craig\\Python\\Projects\\ModelSaveProblem\\Scripts\\python.exe D:\\Craig\\Python\\one_step_unit_tests.py transformer_output[0].shape: (1024, 3, 2)WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.preprocessing.normalization.Normalization object at 0x000001EB3B7B3590>, because it is not built.WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.preprocessing.normalization.Normalization object at 0x000001EB3B7B1B50>, because it is not built.WARNING:absl:Found untraced functions such as _update_step_xla, dense_layer_call_fn, dense_layer_call_and_return_conditional_losses, positional_embedding_layer_call_fn, positional_embedding_layer_call_and_return_conditional_losses while saving (showing 5 of 165). These functions will not be directly callable after loading.Traceback (most recent call last): File \"D:\\Craig\\Python\\one_step_unit_tests.py\", line 30, in <module> tf.keras.saving.save_model( transformer, 'Output\\\\saved_transformer') File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\saving\\saving_api.py\", line 145, in save_model return legacy_sm_saving_lib.save_model( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py\", line 200, in encode chunks = self.iterencode(o, _one_shot=True) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File \"C:\\Users\\cpodc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py\", line 258, in iterencode return _iterencode(o, 0) ^^^^^^^^^^^^^^^^^TypeError: Unable to serialize 16.0 to JSON. Unrecognized type <class 'tensorflow.python.framework.ops.EagerTensor'>.\nProcess finished with exit code 1\n On Monday, May 8, 2023 at 08:11:21 AM CDT, synandi ***@***.***> wrote: \n \n \n\n\nHi @cpodczerwinski, Apologies for the delay.\nI can see a different error when replicating the issue in Colab with Tensorflow 2.12. Please find the gist here. It would help us if you could share the complete reproducible code to expedite the troubleshooting process. Thank you!\n\n—\nReply to this email directly, view it on GitHub, or unsubscribe.\nYou are receiving this because you were mentioned.Message ID: ***@***.***>\n ",
"@cpodczerwinski,\r\nWe can see that the code which you provided was not sufficient for debugging the issue. Could you please provide the complete standalone code or the colab gist which helps to analyse the issue in an effective way. \r\nAnd if you are trying to use efficientnet, please try to run below and also the PR which was merged.\r\nhttps://github.com/keras-team/keras/pull/17498\r\n\r\n```\r\ntf.__version__\r\n\r\nbase_model_v2 = tf.keras.applications.efficientnet_v2.EfficientNetV2M(include_top=False)\r\n\r\n...\r\n\r\nmodel.save('../saved_models/food101_env2m_30epochs')\r\n```\r\n\r\nThank you!",
" Good morning;\r\nPlease send me a copy of the debugger output that you received when you executed the code that I sent you and a description of the environment in which you executed it.\r\nThank you for helpingCraig\r\n On Monday, June 19, 2023 at 11:18:34 AM CDT, tilakrayal ***@***.***> wrote: \r\n \r\n \r\n\r\n\r\n@cpodczerwinski,\r\nWe can see that the code which you provided was not sufficient for debugging the issue. Could you please provide the complete standalone code or the colab gist which helps to analyse the issue in an effective way. Thank you!\r\n\r\n—\r\nReply to this email directly, view it on GitHub, or unsubscribe.\r\nYou are receiving this because you were mentioned.Message ID: ***@***.***>\r\n ",
"@cpodczerwinski,\r\nCould you please find the [gist](https://colab.sandbox.google.com/gist/tilakrayal/ebd543e370bee148d882a828889bb822/untitled355.ipynb) of the code which you have mentioned and I was able to face a different error. Kindly provide the information for the same. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60437\">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/60437\">No</a>\n"
] | 2023-04-29T01:20:01 | 2023-07-22T01:55:03 | 2023-07-22T01:55:00 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
2.10 and 2.12
### Custom Code
No
### OS Platform and Distribution
Windows 10
### Mobile device
_No response_
### Python version
3.11.3
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
RTX M960 4GB
### Current Behaviour?
I have been unable to save my transformer model using any of the Tensorflow complete model saving functions. While running Tensorflow 2.10 I received the following using message attempting to save using tf.keras.models.save_model:
TypeError: Unable to serialize 16.0 to JSON. Unrecognized type <class 'tensorflow.python.framework.ops.EagerTensor'>.
I was able to successfully save and read the model weights back in. I was unable to recover it's normalization layer.
This issue was previously reported as a bug in 2.10
I uninstalled all python software and packages and the PyCharm IDE and installed python 3.11.3 and Tensorflow 2.12 and the latest PyCharm Community edition hoping that would correct the problem. I used tf.keras.saving.save_model( ) using saved formats tf and keras. It did not correct the problem.
The model I am trying to save is a simplied version to the Tensorflow Translator tutorial at https://www.tensorflow.org/text/tutorials/transformer
Here is a listing of the error message I received from PyCharm. It seems to have something to do with the python class tensorflow.python.framework.ops.EagerTensor Is there anything I can do with my model to dodge the issue
Thank you in advance for your help.
WARNING:absl:Found untraced functions such as _update_step_xla, dense_20_layer_call_fn, dense_20_layer_call_and_return_conditional_losses, positional_embedding_4_layer_call_fn, positional_embedding_4_layer_call_and_return_conditional_losses while saving (showing 5 of 165). These functions will not be directly callable after loading.
Traceback (most recent call last):
File "D:\Craig\Python\Projects\CraigsPackages\transformer_component_unit_tests.py", line 333, in <module>
return_pair = predict_investment_return(transformer, index, features, decoder_inputs,return_labels, warm_up=warm_up)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Craig\Python\Projects\CraigsPackages\transformer_component_unit_tests.py", line 252, in predict_investment_return
tf.keras.saving.save_model(transformer, "transformer_model_save_test", save_format="tf")
File "C:\Users\cpodc\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\saving\saving_api.py", line 145, in save_model
return legacy_sm_saving_lib.save_model(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\cpodc\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\cpodc\AppData\Local\Programs\Python\Python311\Lib\json\encoder.py", line 200, in encode
chunks = self.iterencode(o, _one_shot=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\cpodc\AppData\Local\Programs\Python\Python311\Lib\json\encoder.py", line 258, in iterencode
return _iterencode(o, 0)
^^^^^^^^^^^^^^^^^
TypeError: Unable to serialize 16.0 to JSON. Unrecognized type <class 'tensorflow.python.framework.ops.EagerTensor'>.
### Standalone code to reproduce the issue
```shell
tf.keras.saving.save_model(transformer, "transformer_model_save_test", save_format="tf")
tf.keras.saving.save_model(transformer, "transformer_model_save_test", save_format="keras")
```
### Relevant log output
```shell
WARNING:absl:Found untraced functions such as _update_step_xla, dense_20_layer_call_fn, dense_20_layer_call_and_return_conditional_losses, positional_embedding_4_layer_call_fn, positional_embedding_4_layer_call_and_return_conditional_losses while saving (showing 5 of 165). These functions will not be directly callable after loading.
Traceback (most recent call last):
File "D:\Craig\Python\Projects\CraigsPackages\transformer_component_unit_tests.py", line 333, in <module>
return_pair = predict_investment_return(transformer, index, features, decoder_inputs,return_labels, warm_up=warm_up)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Craig\Python\Projects\CraigsPackages\transformer_component_unit_tests.py", line 252, in predict_investment_return
tf.keras.saving.save_model(transformer, "transformer_model_save_test", save_format="tf")
File "C:\Users\cpodc\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\saving\saving_api.py", line 145, in save_model
return legacy_sm_saving_lib.save_model(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\cpodc\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\cpodc\AppData\Local\Programs\Python\Python311\Lib\json\encoder.py", line 200, in encode
chunks = self.iterencode(o, _one_shot=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\cpodc\AppData\Local\Programs\Python\Python311\Lib\json\encoder.py", line 258, in iterencode
return _iterencode(o, 0)
^^^^^^^^^^^^^^^^^
TypeError: Unable to serialize 16.0 to JSON. Unrecognized type <class 'tensorflow.python.framework.ops.EagerTensor'>.
```
</details>
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PR_kwDOArmXAs5PbDtq
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[oneDNN][BUG FIX] Fix to mkl_eager_op_rewrite_test
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This PR fixes a failure to the test `//tensorflow/core/common_runtime/eager:mkl_eager_op_rewrite_test`. The test runs with `--config=mkl`. It is failing due to a recent change to `EagerContext`(https://github.com/tensorflow/tensorflow/commit/ee855630f68e6fbfee334fb9ce3aab7f4dbad16a).
In order to fix, this PR updates the test with `tsl::core::RefCountPtr<tensorflow::IntraProcessRendezvous>` instead of raw pointer.
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I_kwDOArmXAs5kqW42
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Softmax API Mismatch on beta param between TF and TFL
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[
"Hi, The original PR https://github.com/tensorflow/tensorflow/pull/42330 with the beta implementation in TFL has the detailed explanation, let me know if that helps you in understanding ",
"> Hi, The original PR #42330 with the beta implementation in TFL has the detailed explanation, let me know if that helps you in understanding\r\n\r\nThanks Sachin for the note. But I haven't found the explanation of why TFL is supporting the beta parameter while TF is not? That PR seems is only explaining the need to remove some duplicated calculations for exp. \r\n\r\nJerry ",
"Hi @Jerry-Ge \r\n\r\nThe PR #60541 has been merged with the commit https://github.com/tensorflow/tensorflow/commit/ecbe847f8f3841898ea6dfa482b99a3bca7f4e6f with following lines\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/70c60acb6100651def0b8447783ecb65e273f155/tensorflow/compiler/mlir/tosa/transforms/legalize_common.cc#L1493-L1499\r\n\r\nCould please confirm and close this issue if it is resolved?\r\n\r\nThanks.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60435\">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/60435\">No</a>\n"
] | 2023-04-28T16:24:00 | 2023-07-13T16:34:37 | 2023-07-13T16:34:35 |
CONTRIBUTOR
| null | null | null |
In TFL's reference for Softmax, a beta param is supported to be multiplied with logits. [[Link](https://github.com/tensorflow/tensorflow/blob/fddb46a1b7f0136f64442629108961560b5e3486/tensorflow/lite/kernels/internal/reference/softmax.h#L53)]
However, in TF, the beta seems has not been supported yet [[Link](https://www.tensorflow.org/api_docs/python/tf/nn/softmax)]. Is there any reason why it's only being supported in TFL not in TF now?
More background: I'm trying to test the beta param from Softmax with a model converted from TF to TFL. So if it doesn't support beta in TF, how could I insert beta into the TFL model?
Thanks,
Jerry
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Source file is missing tensorflow/lite/experimental/acceleration/configuration/configuration.fbs
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[
"I was able to generate necessary file from configuration.proto.\r\n\r\nIt looks like this is the correct way to do things.",
"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/60434\">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/60434\">No</a>\n"
] | 2023-04-28T15:58:14 | 2023-05-02T17:06:33 | 2023-05-02T17:06:30 |
CONTRIBUTOR
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Build/Install
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.12.0
### 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 Behaviour?
Only compiled flatbuffer header is available (i. e. `tensorflow/lite/experimental/acceleration/configuration/configuration_generated.h`).
We can not use this header in our monorepo, as using it mandates using specific version of flatbuffers library.
### Standalone code to reproduce the issue
```shell
ls tensorflow/lite/experimental/acceleration/configuration/configuration.fbs
```
### Relevant log output
_No response_</details>
|
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PR_kwDOArmXAs5PZIoi
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[ROCM] Reland of PR #57102
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[
"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60433/checks?check_run_id=13102471126) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"Hi @draganmladjenovic Can you please sign CLA. Thank you!\r\n",
"@gbaned rocm CI build dosn't seem to be related to this PR?\r\nEDIT: Another question. What to do with changes that touch upon the XLA? I heard suggestion that they should go trough openXLA repo. Is this correct?",
"Hi @cheshire Can you please assist on above comments from @draganmladjenovic. Thank you!",
"Hi @akuegel wonder will you review this PR, please? Thanks in advance!"
] | 2023-04-28T14:39:01 | 2023-05-08T23:34:54 | 2023-05-08T23:34:54 |
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Wire up TrsmBatched and Gesvd and add Trsv
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PR_kwDOArmXAs5PY8RO
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Make fixed format ACL convolution able to handle NCHW input
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[
"@penpornk \r\n@elfringham ",
"[Py+CPP Test Suite](https://source.cloud.google.com/results/invocations/e83222fa-cd74-40c8-9b45-77cc9fa4bec4/targets) failure seems unrelated. \r\n```\r\n==================== Test output for //tensorflow/python/client:session_partial_run_test:\r\n...\r\n======================================================================\r\nERROR: testPartialRunMissingPlaceholderFeedExceptionDist (__main__.PartialRunTest)\r\nPartialRunTest.testPartialRunMissingPlaceholderFeedExceptionDist\r\n----------------------------------------------------------------------\r\n...\r\ntensorflow.python.framework.errors_impl.InternalError: From /job:localhost/replica:0/task:0:\r\nValidateDevices called before initialization.\r\n```\r\n\r\nBut it's a required test, so I'm rerunning it.\r\n\r\n\r\nThe rest seems okay:\r\n* [Arm CI](https://github.com/tensorflow/tensorflow/actions/runs/4831759860) seems fine.\r\n* [AMD ROCm](http://ml-ci.amd.com:21096/blue/organizations/jenkins/tensorflow%2Fgithub-prs-upstream-master%2FAMD-ROCm-Community-CI-Build/detail/PR-60432/2/pipeline) failure seems unrelated.\r\n",
"[Py+CPP Test Suite - Ubuntu CPU](https://storage.cloud.google.com/tensorflow-nightly/prod/tensorflow/rel/docker/github_presubmit/pycpp_cpu_py39/3416/20230428-110702/index.html) passed now. :)",
"> [Py+CPP Test Suite](https://source.cloud.google.com/results/invocations/e83222fa-cd74-40c8-9b45-77cc9fa4bec4/targets) failure seems unrelated.\r\n> \r\n> ```\r\n> ==================== Test output for //tensorflow/python/client:session_partial_run_test:\r\n> ...\r\n> ======================================================================\r\n> ERROR: testPartialRunMissingPlaceholderFeedExceptionDist (__main__.PartialRunTest)\r\n> PartialRunTest.testPartialRunMissingPlaceholderFeedExceptionDist\r\n> ----------------------------------------------------------------------\r\n> ...\r\n> tensorflow.python.framework.errors_impl.InternalError: From /job:localhost/replica:0/task:0:\r\n> ValidateDevices called before initialization.\r\n> ```\r\n> \r\n> But it's a required test, so I'm rerunning it.\r\n> \r\n> The rest seems okay:\r\n> \r\n> * [Arm CI](https://github.com/tensorflow/tensorflow/actions/runs/4831759860) seems fine.\r\n> \r\n> * [AMD ROCm](http://ml-ci.amd.com:21096/blue/organizations/jenkins/tensorflow%2Fgithub-prs-upstream-master%2FAMD-ROCm-Community-CI-Build/detail/PR-60432/2/pipeline) failure seems unrelated.\r\n\r\nYes, it is unrelated and known to be flaky. https://github.com/tensorflow/tensorflow/issues/60351"
] | 2023-04-28T14:01:16 | 2023-05-02T09:13:46 | 2023-04-28T19:46:28 |
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Also removes //tensorflow/python/kernel_tests/nn_ops:conv_ops_test_cpu , which is fixed by this PR, from skip list.
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I_kwDOArmXAs5ko9Ej
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Bug - pip install tflite-model-maker fails or hangs forever
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[
"I am also facing the same issue when installing `!pip install -q tflite-model-maker `the downloading continues to infinity and fill the storage of the google colab env. Also the issue continues if we try to do on the local machine. Looks like it downloads again and again the same thing. Kindly fix the issue\r\n\r\nat local machine:\r\n\r\n\r\n\r\nat colab env\r\n```\r\nReading package lists... Done\r\nBuilding dependency tree \r\nReading state information... Done\r\nlibportaudio2 is already the newest version (19.6.0-1build1).\r\n0 upgraded, 0 newly installed, 0 to remove and 24 not upgraded.\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 37.9/37.9 MB 17.6 MB/s eta 0:00:00\r\n Preparing metadata (setup.py) ... done\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 128.6/128.6 kB 14.0 MB/s eta 0:00:00\r\n Preparing metadata (setup.py) ... done\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 77.5/77.5 kB 9.4 MB/s eta 0:00:00\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 103.3/103.3 kB 13.6 MB/s eta 0:00:00\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 127.5/127.5 kB 16.2 MB/s eta 0:00:00\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 460.5/460.5 kB 39.8 MB/s eta 0:00:00\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 459.9/459.9 kB 43.9 MB/s eta 0:00:00\r\n Preparing metadata (setup.py) ... done\r\n Installing build dependencies ... done\r\n Getting requirements to build wheel ... done\r\n Preparing metadata (pyproject.toml) ... done\r\n Preparing metadata (setup.py) ... done\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 440.9/440.9 kB 32.8 MB/s eta 0:00:00\r\n ━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━ 251.1/588.0 MB 55.4 MB/s eta 0:00:07\r\nERROR: Exception:\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/urllib3/response.py\", line 438, in _error_catcher\r\n yield\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/urllib3/response.py\", line 561, in read\r\n data = self._fp_read(amt) if not fp_closed else b\"\"\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/urllib3/response.py\", line 527, in _fp_read\r\n return self._fp.read(amt) if amt is not None else self._fp.read()\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/cachecontrol/filewrapper.py\", line 94, in read\r\n self.__buf.write(data)\r\n File \"/usr/lib/python3.10/tempfile.py\", line 622, in func_wrapper\r\n return func(*args, **kwargs)\r\nOSError: [Errno 28] No space left on device\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 160, in exc_logging_wrapper\r\n status = run_func(*args)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/req_command.py\", line 241, in wrapper\r\n return func(self, options, args)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/commands/install.py\", line 419, in run\r\n requirement_set = resolver.resolve(\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/resolver.py\", line 92, in resolve\r\n result = self._result = resolver.resolve(\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/resolvelib/resolvers.py\", line 481, in resolve\r\n state = resolution.resolve(requirements, max_rounds=max_rounds)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/resolvelib/resolvers.py\", line 373, in resolve\r\n failure_causes = self._attempt_to_pin_criterion(name)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/resolvelib/resolvers.py\", line 211, in _attempt_to_pin_criterion\r\n for candidate in criterion.candidates:\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/found_candidates.py\", line 143, in <genexpr>\r\n return (c for c in iterator if id(c) not in self._incompatible_ids)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/found_candidates.py\", line 47, in _iter_built\r\n candidate = func()\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/factory.py\", line 206, in _make_candidate_from_link\r\n self._link_candidate_cache[link] = LinkCandidate(\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/candidates.py\", line 297, in __init__\r\n super().__init__(\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/candidates.py\", line 162, in __init__\r\n self.dist = self._prepare()\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/candidates.py\", line 231, in _prepare\r\n dist = self._prepare_distribution()\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/candidates.py\", line 308, in _prepare_distribution\r\n return preparer.prepare_linked_requirement(self._ireq, parallel_builds=True)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/prepare.py\", line 491, in prepare_linked_requirement\r\n return self._prepare_linked_requirement(req, parallel_builds)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/prepare.py\", line 536, in _prepare_linked_requirement\r\n local_file = unpack_url(\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/prepare.py\", line 166, in unpack_url\r\n file = get_http_url(\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/prepare.py\", line 107, in get_http_url\r\n from_path, content_type = download(link, temp_dir.path)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/network/download.py\", line 147, in __call__\r\n for chunk in chunks:\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/progress_bars.py\", line 53, in _rich_progress_bar\r\n for chunk in iterable:\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/network/utils.py\", line 63, in response_chunks\r\n for chunk in response.raw.stream(\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/urllib3/response.py\", line 622, in stream\r\n data = self.read(amt=amt, decode_content=decode_content)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/urllib3/response.py\", line 560, in read\r\n with self._error_catcher():\r\n File \"/usr/lib/python3.10/contextlib.py\", line 153, in __exit__\r\n self.gen.throw(typ, value, traceback)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/urllib3/response.py\", line 455, in _error_catcher\r\n raise ProtocolError(\"Connection broken: %r\" % e, e)\r\npip._vendor.urllib3.exceptions.ProtocolError: (\"Connection broken: OSError(28, 'No space left on device')\", OSError(28, 'No space left on device'))\r\n--- Logging error ---\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/urllib3/response.py\", line 438, in _error_catcher\r\n yield\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/urllib3/response.py\", line 561, in read\r\n data = self._fp_read(amt) if not fp_closed else b\"\"\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/urllib3/response.py\", line 527, in _fp_read\r\n return self._fp.read(amt) if amt is not None else self._fp.read()\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/cachecontrol/filewrapper.py\", line 94, in read\r\n self.__buf.write(data)\r\n File \"/usr/lib/python3.10/tempfile.py\", line 622, in func_wrapper\r\n return func(*args, **kwargs)\r\nOSError: [Errno 28] No space left on device\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 160, in exc_logging_wrapper\r\n status = run_func(*args)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/req_command.py\", line 241, in wrapper\r\n return func(self, options, args)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/commands/install.py\", line 419, in run\r\n requirement_set = resolver.resolve(\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/resolver.py\", line 92, in resolve\r\n result = self._result = resolver.resolve(\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/resolvelib/resolvers.py\", line 481, in resolve\r\n state = resolution.resolve(requirements, max_rounds=max_rounds)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/resolvelib/resolvers.py\", line 373, in resolve\r\n failure_causes = self._attempt_to_pin_criterion(name)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/resolvelib/resolvers.py\", line 211, in _attempt_to_pin_criterion\r\n for candidate in criterion.candidates:\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/found_candidates.py\", line 143, in <genexpr>\r\n return (c for c in iterator if id(c) not in self._incompatible_ids)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/found_candidates.py\", line 47, in _iter_built\r\n candidate = func()\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/factory.py\", line 206, in _make_candidate_from_link\r\n self._link_candidate_cache[link] = LinkCandidate(\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/candidates.py\", line 297, in __init__\r\n super().__init__(\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/candidates.py\", line 162, in __init__\r\n self.dist = self._prepare()\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/candidates.py\", line 231, in _prepare\r\n dist = self._prepare_distribution()\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/candidates.py\", line 308, in _prepare_distribution\r\n return preparer.prepare_linked_requirement(self._ireq, parallel_builds=True)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/prepare.py\", line 491, in prepare_linked_requirement\r\n return self._prepare_linked_requirement(req, parallel_builds)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/prepare.py\", line 536, in _prepare_linked_requirement\r\n local_file = unpack_url(\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/prepare.py\", line 166, in unpack_url\r\n file = get_http_url(\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/prepare.py\", line 107, in get_http_url\r\n from_path, content_type = download(link, temp_dir.path)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/network/download.py\", line 147, in __call__\r\n for chunk in chunks:\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/progress_bars.py\", line 53, in _rich_progress_bar\r\n for chunk in iterable:\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/network/utils.py\", line 63, in response_chunks\r\n for chunk in response.raw.stream(\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/urllib3/response.py\", line 622, in stream\r\n data = self.read(amt=amt, decode_content=decode_content)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/urllib3/response.py\", line 560, in read\r\n with self._error_catcher():\r\n File \"/usr/lib/python3.10/contextlib.py\", line 153, in __exit__\r\n self.gen.throw(typ, value, traceback)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/urllib3/response.py\", line 455, in _error_catcher\r\n raise ProtocolError(\"Connection broken: %r\" % e, e)\r\npip._vendor.urllib3.exceptions.ProtocolError: (\"Connection broken: OSError(28, 'No space left on device')\", OSError(28, 'No space left on device'))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/usr/lib/python3.10/logging/__init__.py\", line 1104, in emit\r\n self.flush()\r\n File \"/usr/lib/python3.10/logging/__init__.py\", line 1084, in flush\r\n self.stream.flush()\r\nOSError: [Errno 28] No space left on device\r\nCall stack:\r\n File \"/usr/local/bin/pip3\", line 8, in <module>\r\n sys.exit(main())\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/main.py\", line 70, in main\r\n return command.main(cmd_args)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 101, in main\r\n return self._main(args)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 214, in _main\r\n return run(options, args)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 202, in exc_logging_wrapper\r\n logger.critical(\"Exception:\", exc_info=True)\r\nMessage: 'Exception:'\r\nArguments: ()\r\n--- Logging error ---\r\nTraceback (most recent call last):\r\n File \"/usr/lib/python3.10/logging/__init__.py\", line 1104, in emit\r\n self.flush()\r\n File \"/usr/lib/python3.10/logging/__init__.py\", line 1084, in flush\r\n self.stream.flush()\r\nOSError: [Errno 28] No space left on device\r\nCall stack:\r\n File \"/usr/local/bin/pip3\", line 8, in <module>\r\n sys.exit(main())\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/main.py\", line 70, in main\r\n return command.main(cmd_args)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 100, in main\r\n with self.main_context():\r\n File \"/usr/lib/python3.10/contextlib.py\", line 142, in __exit__\r\n next(self.gen)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/command_context.py\", line 19, in main_context\r\n with self._main_context:\r\n File \"/usr/lib/python3.10/contextlib.py\", line 561, in __exit__\r\n if cb(*exc_details):\r\n File \"/usr/lib/python3.10/contextlib.py\", line 142, in __exit__\r\n next(self.gen)\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/build/build_tracker.py\", line 50, in get_build_tracker\r\n with BuildTracker(root) as tracker:\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/build/build_tracker.py\", line 70, in __exit__\r\n self.cleanup()\r\n File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/build/build_tracker.py\", line 118, in cleanup\r\n logger.debug(\"Removed build tracker: %r\", self._root)\r\nMessage: 'Removed build tracker: %r'\r\nArguments: ('/tmp/pip-build-tracker-fya0r08r',)\r\n```\r\n",
"@Huxwell have you found any solution to the problem yet?\r\n",
"No progress, sorry :( \r\n@synandi any chances you have seen the bug report?",
"Same issue , Tried git clone , nightly options. Also tried installing requirements.txt before the pip install. Nothing worked.",
"Is there any change that the issue can be fixed?",
"Running into the same problem.",
"Is there any change that the issue can be fixed?",
"I'm facing the same issue, I was able to install it without errors a month ago. Are there any fixes for it yet?",
"I am having the same problem, I tried with different ways but doesn't work",
"I also having same issue if any update please tell ..",
"Hello",
"same issue",
"same problem here :(",
"Same issue here .. Its bug or what pleaze give solution .. I though that pyhton updates to 3.10 from 3.8 in colab . \r\nTflite model maker nit importing ",
"more people reporting the same problem as separate issues:\r\nhttps://github.com/tensorflow/tensorflow/issues/60455\r\nhttps://github.com/tensorflow/tensorflow/issues/60466\r\n\r\nI have also tried\r\n```\r\n!pip install --no-dependencies tflite-model-maker\r\n!pip install tensorflowjs \r\n```\r\nbut I am getting `NotFoundError: /usr/local/lib/python3.10/dist-packages/tensorflow_decision_forests/tensorflow/ops/inference/inference.so: undefined symbol: _ZN3tsl6Status22MaybeAddSourceLocationENS_14SourceLocationE` error when trying to use the lib (btw I am not utilising decision forest algorithm anywhere in this project that I am aware of)",
"Hi @Huxwell, Apologies for the inconvience. We are working on it. We'll update here soon. Thank you!",
"@Huxwell , @UsamaHameed1 , Thanks for reporting the issue. This looks like package distribution issue with python 3.10, to narrow down the issue, could you please help us testing with Python 3.9 environment and let us know if you face the same issue for `pip install tflite-model-maker` and `pip install tflite-model-maker-nightly`",
"@terryheo , Could you please take a look into this. Thanks!",
"@sachinprasadhs so i have tried the above mentioned instructions with google colab env Python 3.9.16\r\n\r\n\r\n`when i tried pip install tflite-model-maker`\r\nThe following log came:\r\n`ERROR: tensorflow 2.12.0 has requirement numpy<1.24,>=1.22, but you'll have numpy 1.24.3 which is incompatible.\r\nERROR: tensorflow 2.12.0 has requirement wrapt<1.15,>=1.11.0, but you'll have wrapt 1.15.0 which is incompatible.\r\nERROR: tflite-support 0.4.3 has requirement protobuf<4,>=3.18.0, but you'll have protobuf 4.22.3 which is incompatible.\r\nERROR: tensorflowjs 3.18.0 has requirement packaging~=20.9, but you'll have packaging 23.1 which is incompatible.\r\nERROR: scann 1.2.6 has requirement tensorflow~=2.8.0, but you'll have tensorflow 2.12.0 which is incompatible.`\r\n\r\nScreenshot :\r\n\r\n\r\n\r\n`when i tried pip install tflite-model-maker-nightly`\r\n\r\n`Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\r\nCollecting tflite-model-maker-nightly\r\n Downloading tflite_model_maker_nightly-0.4.3.dev202305020507-py3-none-any.whl (577 kB)\r\n |████████████████████████████████| 577 kB 12.0 MB/s \r\nRequirement already satisfied: absl-py>=0.10.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (1.4.0)\r\nRequirement already satisfied: tensorflow-hub<0.13,>=0.7.0; python_version >= \"3\" in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (0.12.0)\r\nRequirement already satisfied: pillow>=7.0.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (9.5.0)\r\nRequirement already satisfied: matplotlib<3.5.0,>=3.0.3 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (3.4.3)\r\nRequirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (1.16.0)\r\nRequirement already satisfied: fire>=0.3.1 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (0.5.0)\r\nRequirement already satisfied: numba==0.53 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (0.53.0)\r\nRequirement already satisfied: tensorflowjs<3.19.0,>=2.4.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (3.18.0)\r\nRequirement already satisfied: scann==1.2.6 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (1.2.6)\r\nRequirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (1.24.3)\r\nRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (1.25.11)\r\nRequirement already satisfied: librosa==0.8.1 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (0.8.1)\r\nRequirement already satisfied: tf-models-official==2.3.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (2.3.0)\r\nRequirement already satisfied: lxml>=4.6.1 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (4.9.2)\r\nRequirement already satisfied: tensorflow-model-optimization>=0.5 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (0.7.4)\r\nRequirement already satisfied: tensorflow-datasets>=2.1.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (4.9.2)\r\nCollecting tfa-nightly\r\n Downloading tfa_nightly-0.21.0.dev20230418145214-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (591 kB)\r\n |████████████████████████████████| 591 kB 75.8 MB/s \r\nRequirement already satisfied: PyYAML>=5.1 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (6.0)\r\nRequirement already satisfied: sentencepiece>=0.1.91 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (0.1.99)\r\nCollecting tflite-support-nightly\r\n Downloading tflite_support_nightly-0.4.4.dev20221103-cp39-cp39-manylinux2014_x86_64.whl (60.9 MB)\r\n |████████████████████████████████| 60.9 MB 1.2 MB/s \r\nRequirement already satisfied: flatbuffers>=2.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (23.3.3)\r\nRequirement already satisfied: tensorflow>=2.6.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (2.12.0)\r\nRequirement already satisfied: Cython>=0.29.13 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (0.29.34)\r\nRequirement already satisfied: neural-structured-learning>=1.3.1 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (1.4.0)\r\nRequirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.9/dist-packages (from tensorflow-hub<0.13,>=0.7.0; python_version >= \"3\"->tflite-model-maker-nightly) (4.22.3)\r\nRequirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib<3.5.0,>=3.0.3->tflite-model-maker-nightly) (1.4.4)\r\nRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/dist-packages (from matplotlib<3.5.0,>=3.0.3->tflite-model-maker-nightly) (0.11.0)\r\nRequirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.9/dist-packages (from matplotlib<3.5.0,>=3.0.3->tflite-model-maker-nightly) (2.8.2)\r\nRequirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib<3.5.0,>=3.0.3->tflite-model-maker-nightly) (3.0.9)\r\nRequirement already satisfied: termcolor in /usr/local/lib/python3.9/dist-packages (from fire>=0.3.1->tflite-model-maker-nightly) (2.3.0)\r\nRequirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from numba==0.53->tflite-model-maker-nightly) (45.2.0)\r\nRequirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in /usr/local/lib/python3.9/dist-packages (from numba==0.53->tflite-model-maker-nightly) (0.36.0)\r\nCollecting packaging~=20.9\r\n Downloading packaging-20.9-py2.py3-none-any.whl (40 kB)\r\n |████████████████████████████████| 40 kB 6.1 MB/s \r\nRequirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.9/dist-packages (from librosa==0.8.1->tflite-model-maker-nightly) (5.1.1)\r\nRequirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.9/dist-packages (from librosa==0.8.1->tflite-model-maker-nightly) (1.7.0)\r\nRequirement already satisfied: resampy>=0.2.2 in /usr/local/lib/python3.9/dist-packages (from librosa==0.8.1->tflite-model-maker-nightly) (0.4.2)\r\nRequirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from librosa==0.8.1->tflite-model-maker-nightly) (3.0.0)\r\nRequirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.9/dist-packages (from librosa==0.8.1->tflite-model-maker-nightly) (1.2.2)\r\nRequirement already satisfied: joblib>=0.14 in /usr/local/lib/python3.9/dist-packages (from 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google-api-python-client>=1.6.7->tf-models-official==2.3.0->tflite-model-maker-nightly) (0.1.0)\r\nRequirement already satisfied: google-auth<3.0.0dev,>=1.19.0 in /usr/local/lib/python3.9/dist-packages (from google-api-python-client>=1.6.7->tf-models-official==2.3.0->tflite-model-maker-nightly) (2.17.3)\r\nRequirement already satisfied: httplib2<1dev,>=0.15.0 in /usr/local/lib/python3.9/dist-packages (from google-api-python-client>=1.6.7->tf-models-official==2.3.0->tflite-model-maker-nightly) (0.22.0)\r\nRequirement already satisfied: uritemplate<5,>=3.0.1 in /usr/local/lib/python3.9/dist-packages (from google-api-python-client>=1.6.7->tf-models-official==2.3.0->tflite-model-maker-nightly) (4.1.1)\r\nRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.9/dist-packages (from requests>=2.19.0->tensorflow-datasets>=2.1.0->tflite-model-maker-nightly) (3.1.0)\r\nRequirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests>=2.19.0->tensorflow-datasets>=2.1.0->tflite-model-maker-nightly) (3.4)\r\nRequirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.9/dist-packages (from tensorflow-metadata->tensorflow-datasets>=2.1.0->tflite-model-maker-nightly) (1.59.0)\r\nRequirement already satisfied: zipp; extra == \"epath\" in /usr/local/lib/python3.9/dist-packages (from etils[enp,epath]>=0.9.0->tensorflow-datasets>=2.1.0->tflite-model-maker-nightly) (3.15.0)\r\nRequirement already satisfied: importlib_resources; extra == \"epath\" in /usr/local/lib/python3.9/dist-packages (from etils[enp,epath]>=0.9.0->tensorflow-datasets>=2.1.0->tflite-model-maker-nightly) (5.12.0)\r\nRequirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.9/dist-packages (from tensorboard<2.13,>=2.12->tensorflow>=2.6.0->tflite-model-maker-nightly) (1.0.0)\r\nRequirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from tensorboard<2.13,>=2.12->tensorflow>=2.6.0->tflite-model-maker-nightly) (2.3.3)\r\nRequirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.9/dist-packages (from tensorboard<2.13,>=2.12->tensorflow>=2.6.0->tflite-model-maker-nightly) (3.4.3)\r\nRequirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.9/dist-packages (from tensorboard<2.13,>=2.12->tensorflow>=2.6.0->tflite-model-maker-nightly) (0.7.0)\r\nRequirement already satisfied: wheel>=0.26 in /usr/lib/python3/dist-packages (from tensorboard<2.13,>=2.12->tensorflow>=2.6.0->tflite-model-maker-nightly) (0.34.2)\r\nRequirement already satisfied: ml-dtypes>=0.0.3 in /usr/local/lib/python3.9/dist-packages (from jax>=0.3.15->tensorflow>=2.6.0->tflite-model-maker-nightly) (0.1.0)\r\nRequirement already satisfied: pycparser in /usr/local/lib/python3.9/dist-packages (from cffi>=1.0->soundfile>=0.10.2->librosa==0.8.1->tflite-model-maker-nightly) (2.21)\r\nCollecting grpcio-status<2.0dev,>=1.33.2; extra == \"grpc\"\r\n Downloading grpcio_status-1.54.0-py3-none-any.whl (5.1 kB)\r\nRequirement already satisfied: google-crc32c<2.0dev,>=1.0 in /usr/local/lib/python3.9/dist-packages (from google-resumable-media<3.0dev,>=0.6.0->google-cloud-bigquery>=0.31.0->tf-models-official==2.3.0->tflite-model-maker-nightly) (1.5.0)\r\nRequirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.9/dist-packages (from python-slugify->kaggle>=1.3.9->tf-models-official==2.3.0->tflite-model-maker-nightly) (1.3)\r\nRequirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from google-auth<3.0.0dev,>=1.19.0->google-api-python-client>=1.6.7->tf-models-official==2.3.0->tflite-model-maker-nightly) (5.3.0)\r\nRequirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.9/dist-packages (from google-auth<3.0.0dev,>=1.19.0->google-api-python-client>=1.6.7->tf-models-official==2.3.0->tflite-model-maker-nightly) (4.9)\r\nRequirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.9/dist-packages (from google-auth<3.0.0dev,>=1.19.0->google-api-python-client>=1.6.7->tf-models-official==2.3.0->tflite-model-maker-nightly) (0.3.0)\r\nRequirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.9/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow>=2.6.0->tflite-model-maker-nightly) (1.3.1)\r\nRequirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.9/dist-packages (from werkzeug>=1.0.1->tensorboard<2.13,>=2.12->tensorflow>=2.6.0->tflite-model-maker-nightly) (2.1.2)\r\nRequirement already satisfied: importlib-metadata>=4.4; python_version < \"3.10\" in /usr/local/lib/python3.9/dist-packages (from markdown>=2.6.8->tensorboard<2.13,>=2.12->tensorflow>=2.6.0->tflite-model-maker-nightly) (6.6.0)\r\nRequirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.9/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3.6\"->google-auth<3.0.0dev,>=1.19.0->google-api-python-client>=1.6.7->tf-models-official==2.3.0->tflite-model-maker-nightly) (0.5.0)\r\nRequirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.9/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow>=2.6.0->tflite-model-maker-nightly) (3.2.2)\r\nERROR: tensorflow 2.12.0 has requirement numpy<1.24,>=1.22, but you'll have numpy 1.24.3 which is incompatible.\r\nERROR: tensorflow 2.12.0 has requirement wrapt<1.15,>=1.11.0, but you'll have wrapt 1.15.0 which is incompatible.\r\nERROR: scann 1.2.6 has requirement tensorflow~=2.8.0, but you'll have tensorflow 2.12.0 which is incompatible.\r\nERROR: tflite-support-nightly 0.4.4.dev20221103 has requirement protobuf<4,>=3.18.0, but you'll have protobuf 4.22.3 which is incompatible.\r\nInstalling collected packages: packaging, tfa-nightly, tflite-support-nightly, tflite-model-maker-nightly, grpcio-status\r\n Attempting uninstall: packaging\r\n Found existing installation: packaging 23.1\r\n Uninstalling packaging-23.1:\r\n Successfully uninstalled packaging-23.1\r\nSuccessfully installed grpcio-status-1.54.0 packaging-20.9 tfa-nightly-0.21.0.dev20230418145214 tflite-model-maker-nightly-0.4.3.dev202305020507 tflite-support-nightly-0.4.4.dev20221103`\r\n\r\n\r\n\r\nImportant Log Point for pip install tflite-model-maker-nightly\r\n```\r\nERROR: tensorflow 2.12.0 has requirement numpy<1.24,>=1.22, but you'll have numpy 1.24.3 which is incompatible.\r\nERROR: tensorflow 2.12.0 has requirement wrapt<1.15,>=1.11.0, but you'll have wrapt 1.15.0 which is incompatible.\r\nERROR: scann 1.2.6 has requirement tensorflow~=2.8.0, but you'll have tensorflow 2.12.0 which is incompatible.\r\nERROR: tflite-support-nightly 0.4.4.dev20221103 has requirement protobuf<4,>=3.18.0, but you'll have protobuf 4.22.3 which is incompatible.\r\n```\r\n\r\n\r\nMy understanding is that when i downgrade the python env other dependency issue come. I hope my this work will help in debugging the issue. If any thing missing please let me know what to do next?\r\n",
"I have the same problem and Im using it for my thesis. Please fix it ASAP! ",
"> I have the same problem and Im using it for my thesis. Please fix it ASAP!\r\n\r\n@juanjricci same issue :(",
"> @sachinprasadhs so i have tried the above mentioned instructions with google colab env Python 3.9.16 \r\n> \r\n> `when i tried pip install tflite-model-maker` The following log came: `ERROR: tensorflow 2.12.0 has requirement numpy<1.24,>=1.22, but you'll have numpy 1.24.3 which is incompatible. ERROR: tensorflow 2.12.0 has requirement wrapt<1.15,>=1.11.0, but you'll have wrapt 1.15.0 which is incompatible. ERROR: tflite-support 0.4.3 has requirement protobuf<4,>=3.18.0, but you'll have protobuf 4.22.3 which is incompatible. ERROR: tensorflowjs 3.18.0 has requirement packaging~=20.9, but you'll have packaging 23.1 which is incompatible. ERROR: scann 1.2.6 has requirement tensorflow~=2.8.0, but you'll have tensorflow 2.12.0 which is incompatible.`\r\n> \r\n> Screenshot : \r\n> \r\n> `when i tried pip install tflite-model-maker-nightly`\r\n> \r\n> `Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Collecting tflite-model-maker-nightly Downloading tflite_model_maker_nightly-0.4.3.dev202305020507-py3-none-any.whl (577 kB) |████████████████████████████████| 577 kB 12.0 MB/s Requirement already satisfied: absl-py>=0.10.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (1.4.0) Requirement already satisfied: tensorflow-hub<0.13,>=0.7.0; python_version >= \"3\" in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (0.12.0) Requirement already satisfied: pillow>=7.0.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (9.5.0) Requirement already satisfied: matplotlib<3.5.0,>=3.0.3 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (3.4.3) Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (1.16.0) Requirement already satisfied: fire>=0.3.1 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (0.5.0) Requirement already satisfied: numba==0.53 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (0.53.0) Requirement already satisfied: tensorflowjs<3.19.0,>=2.4.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (3.18.0) Requirement already satisfied: scann==1.2.6 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (1.2.6) Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (1.24.3) Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (1.25.11) Requirement already satisfied: librosa==0.8.1 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (0.8.1) Requirement already satisfied: tf-models-official==2.3.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (2.3.0) Requirement already satisfied: lxml>=4.6.1 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (4.9.2) Requirement already satisfied: tensorflow-model-optimization>=0.5 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (0.7.4) Requirement already satisfied: tensorflow-datasets>=2.1.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (4.9.2) Collecting tfa-nightly Downloading tfa_nightly-0.21.0.dev20230418145214-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (591 kB) |████████████████████████████████| 591 kB 75.8 MB/s Requirement already satisfied: PyYAML>=5.1 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (6.0) Requirement already satisfied: sentencepiece>=0.1.91 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (0.1.99) Collecting tflite-support-nightly Downloading tflite_support_nightly-0.4.4.dev20221103-cp39-cp39-manylinux2014_x86_64.whl (60.9 MB) |████████████████████████████████| 60.9 MB 1.2 MB/s Requirement already satisfied: flatbuffers>=2.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (23.3.3) Requirement already satisfied: tensorflow>=2.6.0 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (2.12.0) Requirement already satisfied: Cython>=0.29.13 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (0.29.34) Requirement already satisfied: neural-structured-learning>=1.3.1 in /usr/local/lib/python3.9/dist-packages (from tflite-model-maker-nightly) (1.4.0) Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.9/dist-packages (from tensorflow-hub<0.13,>=0.7.0; python_version >= \"3\"->tflite-model-maker-nightly) (4.22.3) Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib<3.5.0,>=3.0.3->tflite-model-maker-nightly) (1.4.4) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/dist-packages (from matplotlib<3.5.0,>=3.0.3->tflite-model-maker-nightly) (0.11.0) Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.9/dist-packages (from matplotlib<3.5.0,>=3.0.3->tflite-model-maker-nightly) (2.8.2) Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib<3.5.0,>=3.0.3->tflite-model-maker-nightly) (3.0.9) Requirement already satisfied: termcolor in /usr/local/lib/python3.9/dist-packages (from fire>=0.3.1->tflite-model-maker-nightly) (2.3.0) Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from numba==0.53->tflite-model-maker-nightly) (45.2.0) Requirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in /usr/local/lib/python3.9/dist-packages (from numba==0.53->tflite-model-maker-nightly) (0.36.0) Collecting packaging~=20.9 Downloading packaging-20.9-py2.py3-none-any.whl (40 kB) |████████████████████████████████| 40 kB 6.1 MB/s Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.9/dist-packages (from librosa==0.8.1->tflite-model-maker-nightly) (5.1.1) Requirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.9/dist-packages (from librosa==0.8.1->tflite-model-maker-nightly) (1.7.0) Requirement already satisfied: resampy>=0.2.2 in /usr/local/lib/python3.9/dist-packages (from librosa==0.8.1->tflite-model-maker-nightly) (0.4.2) Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from librosa==0.8.1->tflite-model-maker-nightly) (3.0.0) Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.9/dist-packages (from librosa==0.8.1->tflite-model-maker-nightly) (1.2.2) Requirement already satisfied: joblib>=0.14 in /usr/local/lib/python3.9/dist-packages (from librosa==0.8.1->tflite-model-maker-nightly) (1.2.0) Requirement already satisfied: soundfile>=0.10.2 in /usr/local/lib/python3.9/dist-packages (from librosa==0.8.1->tflite-model-maker-nightly) (0.12.1) Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.9/dist-packages (from librosa==0.8.1->tflite-model-maker-nightly) (1.10.1) Requirement already satisfied: pandas>=0.22.0 in /usr/local/lib/python3.9/dist-packages (from tf-models-official==2.3.0->tflite-model-maker-nightly) (2.0.1) Requirement already satisfied: psutil>=5.4.3 in /usr/local/lib/python3.9/dist-packages (from tf-models-official==2.3.0->tflite-model-maker-nightly) (5.9.5) Requirement already satisfied: google-cloud-bigquery>=0.31.0 in /usr/local/lib/python3.9/dist-packages (from tf-models-official==2.3.0->tflite-model-maker-nightly) (3.10.0) Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.9/dist-packages (from tf-models-official==2.3.0->tflite-model-maker-nightly) (0.20.0) Requirement already satisfied: py-cpuinfo>=3.3.0 in /usr/local/lib/python3.9/dist-packages (from tf-models-official==2.3.0->tflite-model-maker-nightly) (9.0.0) Requirement already satisfied: kaggle>=1.3.9 in /usr/local/lib/python3.9/dist-packages (from tf-models-official==2.3.0->tflite-model-maker-nightly) (1.5.13) Requirement already satisfied: google-api-python-client>=1.6.7 in /usr/local/lib/python3.9/dist-packages (from tf-models-official==2.3.0->tflite-model-maker-nightly) (2.86.0) Requirement already satisfied: tf-slim>=1.1.0 in /usr/local/lib/python3.9/dist-packages (from tf-models-official==2.3.0->tflite-model-maker-nightly) (1.1.0) Requirement already satisfied: dataclasses in /usr/local/lib/python3.9/dist-packages (from tf-models-official==2.3.0->tflite-model-maker-nightly) (0.6) Requirement already satisfied: opencv-python-headless in /usr/local/lib/python3.9/dist-packages (from tf-models-official==2.3.0->tflite-model-maker-nightly) (4.7.0.72) Requirement already satisfied: gin-config in /usr/local/lib/python3.9/dist-packages (from tf-models-official==2.3.0->tflite-model-maker-nightly) (0.5.0) Requirement already satisfied: dm-tree~=0.1.1 in /usr/local/lib/python3.9/dist-packages (from tensorflow-model-optimization>=0.5->tflite-model-maker-nightly) (0.1.8) Requirement already satisfied: array-record in /usr/local/lib/python3.9/dist-packages (from tensorflow-datasets>=2.1.0->tflite-model-maker-nightly) (0.2.0) Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.9/dist-packages (from tensorflow-datasets>=2.1.0->tflite-model-maker-nightly) (2.29.0) Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from 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\"3.6\"->google-auth<3.0.0dev,>=1.19.0->google-api-python-client>=1.6.7->tf-models-official==2.3.0->tflite-model-maker-nightly) (0.5.0) Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.9/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow>=2.6.0->tflite-model-maker-nightly) (3.2.2) ERROR: tensorflow 2.12.0 has requirement numpy<1.24,>=1.22, but you'll have numpy 1.24.3 which is incompatible. ERROR: tensorflow 2.12.0 has requirement wrapt<1.15,>=1.11.0, but you'll have wrapt 1.15.0 which is incompatible. ERROR: scann 1.2.6 has requirement tensorflow~=2.8.0, but you'll have tensorflow 2.12.0 which is incompatible. ERROR: tflite-support-nightly 0.4.4.dev20221103 has requirement protobuf<4,>=3.18.0, but you'll have protobuf 4.22.3 which is incompatible. Installing collected packages: packaging, tfa-nightly, tflite-support-nightly, tflite-model-maker-nightly, grpcio-status Attempting uninstall: packaging Found existing installation: packaging 23.1 Uninstalling packaging-23.1: Successfully uninstalled packaging-23.1 Successfully installed grpcio-status-1.54.0 packaging-20.9 tfa-nightly-0.21.0.dev20230418145214 tflite-model-maker-nightly-0.4.3.dev202305020507 tflite-support-nightly-0.4.4.dev20221103`\r\n> \r\n> \r\n> \r\n> Important Log Point for pip install tflite-model-maker-nightly\r\n> \r\n> ```\r\n> ERROR: tensorflow 2.12.0 has requirement numpy<1.24,>=1.22, but you'll have numpy 1.24.3 which is incompatible.\r\n> ERROR: tensorflow 2.12.0 has requirement wrapt<1.15,>=1.11.0, but you'll have wrapt 1.15.0 which is incompatible.\r\n> ERROR: scann 1.2.6 has requirement tensorflow~=2.8.0, but you'll have tensorflow 2.12.0 which is incompatible.\r\n> ERROR: tflite-support-nightly 0.4.4.dev20221103 has requirement protobuf<4,>=3.18.0, but you'll have protobuf 4.22.3 which is incompatible.\r\n> ```\r\n> \r\n> My understanding is that when i downgrade the python env other dependency issue come. I hope my this work will help in debugging the issue. If any thing missing please let me know what to do next?\r\n\r\n@UsamaHameed1 hey iam from karachi having same issue my fyp project stucks here. ",
"@sachinprasadhs When this issue resolve. The colab importing same packages forever and disk becomes full. Please resolve this ASAP!",
"any update on this? thesis defense is closing in and we need this resolved :(",
"Any update :(",
"@synandi @sachinprasadhs \r\nNot sure how to change python version in colab.\r\n```\r\n!add-apt-repository -y ppa:deadsnakes/ppa\r\n!apt-get update\r\n!apt-get install python3.9\r\n!apt-get install python3.9-dev\r\n\r\n!wget https://bootstrap.pypa.io/get-pip.py && python3.9 get-pip.py\r\n\r\nimport sys\r\n\r\nsys.path[2] = '/usr/lib/python39.zip'\r\nsys.path[3] = '/usr/lib/python3.9'\r\nsys.path[4] = '/usr/lib/python3.9/lib-dynload'\r\nsys.path[5] = '/usr/local/lib/python3.9/dist-packages'\r\nsys.path[7] ='/usr/local/lib/python3.9/dist-packages/IPython/extensions'\r\n```\r\nDidn't work, I will still get:\r\n```\r\nfrom sys import version_info \r\nprint(version_info) #sys.version_info(major=3, minor=10, micro=11, releaselevel='final', serial=0)\r\n``` \r\n\r\n```\r\n!sudo update-alternatives --config python3\r\n```\r\nallow me to change to 3.8, but again, version_info stays 3.10.\r\n\r\nI am now trying to replicate on 3.8.1 on venv on my laptop instead of Google Colab.\r\n\r\n\r\n",
"Apparently there is a conflict in requirements for numpy version when using python 3.8.1.\r\n\r\nI was able to install on python 3.8.1, but\r\n`import import tflite_model_maker` results in `AttributeError: module 'numpy' has no attribute 'object'.` , so clearly I have wrong numpy version and pip didn't notice.\r\n\r\n`The aliases was originally deprecated in NumPy 1.20;` so I did `pip install --upgrade numpy==1.19` but then importing model-maker results in `ImportError: this version of pandas is incompatible with numpy < 1.20.3 your numpy version is 1.19.0. Please upgrade numpy to >= 1.20.3 to use this pandas version.\r\n\r\nI have also tried `pip install tflite-model-maker-nightly` but it has similar issues.\r\n\r\nBTW thanks for your support, I know that wheel packaging, pip, and dependencies are extremally frustrating for all parties involved. The lib is available to use free of charge, and you still are trying to help us. \r\n",
"> Apparently there is a conflict in requirements for numpy version when using python 3.8.1.\r\n> \r\n> I was able to install on python 3.8.1, but `import import tflite_model_maker` results in `AttributeError: module 'numpy' has no attribute 'object'.` , so clearly I have wrong numpy version and pip didn't notice.\r\n> \r\n> `The aliases was originally deprecated in NumPy 1.20;` so I did `pip install --upgrade numpy==1.19` but then importing model-maker results in `ImportError: this version of pandas is incompatible with numpy < 1.20.3 your numpy version is 1.19.0. Please upgrade numpy to >= 1.20.3 to use this pandas version.\r\n> \r\n> I have also tried `pip install tflite-model-maker-nightly` but it has similar issues.\r\n> \r\n> BTW thanks for your support, I know that wheel packaging, pip, and dependencies are extremally frustrating for all parties involved. The lib is available to use free of charge, and you still are trying to help us.\r\n\r\n@Huxwell So from this issue resolved or not ?",
"@tensorflow-copybara ",
"Is there any solution to the problem so far?"
] | 2023-04-28T12:19:38 | 2024-05-19T03:19:37 | null |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Tensorflow Version
v2.12.0-rc1-12-g0db597d0d75 2.12.0
### Custom Code
No
### OS Platform and Distribution, Mobile device, Python version, Bazel version, GCC/Compiler version, CUDA version
Default Colab Pro
### Current Behaviour?
```!pip install tflite-model-maker``` fails or hangs forever
The error occurs in tutorial notebook: https://colab.research.google.com/github/khanhlvg/tflite_raspberry_pi/blob/main/object_detection/Train_custom_model_tutorial.ipynb
I have tried multiple things in colab None/GPU runtime, 'Disconnect and delete runtime' after each failed attempt.:
`!pip install tflite-model-maker-nightly`
`pip install --no-dependencies tflite-model-maker` (imports will fail, manually installing them with `pip install tensorflowjs` won't help with `NotFoundError: /usr/local/lib/python3.10/dist-packages/tensorflow_decision_forests/tensorflow/ops/inference/inference.so: undefined symbol: _ZN3tsl6Status22MaybeAddSourceLocationENS_14SourceLocationE` error)
```
!pip install --upgrade pip
!wget https://raw.githubusercontent.com/tensorflow/examples/master/tensorflow_examples/lite/model_maker/requirements.txt
!pip install -r requirements.txt
```
results in:
```
ERROR: Could not find a version that satisfies the requirement tflite-support>=0.4.2 (from versions: 0.1.0a0.dev3, 0.1.0a0.dev4, 0.1.0a0.dev5, 0.1.0a0, 0.1.0a1)
ERROR: No matching distribution found for tflite-support>=0.4.2
```
I have also tried:
`pip install tflite-model-maker==0.4.2` got `ERROR: No matching distribution found for numba==0.53`
`pip install tflite-model-maker==0.4.1` got `ERROR: No matching distribution found for scann==1.2.6`
`pip install tflite-model-maker==0.4.0` got `ERROR: No matching distribution found for tflite-support>=0.4.0`
Related issues:
https://github.com/tensorflow/tensorflow/issues/59855
https://github.com/tensorflow/tensorflow/issues/51031
### Standalone code to reproduce the issue
```shell
`!pip install tflite-model-maker` in colab
```
### Relevant log output
tflite_modelmaker_debug2_
Notebook unstarred
1
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting tflite-model-maker
Downloading tflite_model_maker-0.4.2-py3-none-any.whl (577 kB)
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Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from tflite-model-maker) (1.22.4)
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Collecting tensorflow-addons>=0.11.2
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Collecting sentencepiece>=0.1.91
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ERROR: Cannot install tflite-model-maker==0.1.2, tflite-model-maker==0.2.0, tflite-model-maker==0.2.1, tflite-model-maker==0.2.2, tflite-model-maker==0.2.3, tflite-model-maker==0.2.4, tflite-model-maker==0.2.5, tflite-model-maker==0.3.0, tflite-model-maker==0.3.1, tflite-model-maker==0.3.2, tflite-model-maker==0.3.3, tflite-model-maker==0.3.4, tflite-model-maker==0.4.0, tflite-model-maker==0.4.1 and tflite-model-maker==0.4.2 because these package versions have conflicting dependencies.
The conflict is caused by:
tflite-model-maker 0.4.2 depends on tflite-support>=0.4.2
tflite-model-maker 0.4.1 depends on scann==1.2.6
tflite-model-maker 0.4.0 depends on tflite-support>=0.4.0
tflite-model-maker 0.3.4 depends on tflite-support>=0.3.1
tflite-model-maker 0.3.3 depends on tflite-support>=0.3.1
tflite-model-maker 0.3.2 depends on tflite-support>=0.1.0rc4
tflite-model-maker 0.3.1 depends on tflite-support>=0.1.0rc4
tflite-model-maker 0.3.0 depends on tflite-support>=0.1.0rc4
tflite-model-maker 0.2.5 depends on tflite-support==0.1.0rc4
tflite-model-maker 0.2.4 depends on tflite-support==0.1.0rc4
tflite-model-maker 0.2.3 depends on tflite-support==0.1.0rc3.dev2
tflite-model-maker 0.2.2 depends on tflite-support==0.1.0rc3.dev2
tflite-model-maker 0.2.1 depends on tflite-support==0.1.0rc3.dev2
tflite-model-maker 0.2.0 depends on tf-nightly==2.4.0.dev20200810
tflite-model-maker 0.1.2 depends on tflite-support==0.1.0rc3.dev2
To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict
ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts
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TypeError "unsupported operand type(s) for |: 'collections.OrderedDict' and 'collections.OrderedDict'" is raised when training Sequential model
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[
"Assign me this issue, I can fix it",
"It seems you shared the wrong input \"X\", as I ran your shared code snippet and it gives no errors.\r\nThe variables X and y are of type numpy.ndarray, not dict. This message might occur if you were using Python notebooks and ran a cell that assigns X or y to a dict object. Make sure you assign X and y to your intended dataset.\r\n\r\n\r\n",
"@WeichenXu123,\r\nI tried to execute the code on tensorflow v2.12 and tf-nightly & the code was executed without any issues/error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/94f1bcecab322d165dd0e0c7de44ffc1/untitled1100.ipynb). Also please try to take a look at this link for the reference.\r\nhttps://stackoverflow.com/questions/49669909/how-to-fix-this-error-unsupported-operand-types-for-dict-items-and-odic\r\n\r\nThank you!",
"@tilakrayal \r\n\r\nDid you test with tensorflow version `2.13.0-dev20230427` and python version `3.8` ? \r\nIn my test it still shows exactly the same error using the repro code:\r\n\r\n```\r\n>>> from tensorflow.keras.models import Sequential\r\nfrom tensorflow.keras.layers import Dense\r\nfrom tensorflow.keras.optimizers import SGD\r\n\r\nimport numpy as np\r\n\r\nX = np.random.rand(10, 4)\r\ny = np.random.rand(10)\r\n\r\nmodel = Sequential()\r\nmodel.add(Dense(3, input_dim=4))\r\nmodel.add(Dense(1))\r\nmodel.compile(loss=\"mean_squared_error\", optimizer=SGD(learning_rate=0.001))\r\nmodel.fit(X, y) # raises TypeError!\r\n\r\n\r\n2023-05-02 18:06:03.912208: I tensorflow/core/platform/cpu_feature_guard.cc:182] 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\r\n>>> from tensorflow.keras.layers import Dense\r\n>>> from tensorflow.keras.optimizers import SGD\r\n>>> \r\n>>> import numpy as np\r\n>>> \r\n>>> X = np.random.rand(10, 4)\r\n>>> y = np.random.rand(10)\r\n>>> \r\n>>> model = Sequential()\r\n>>> model.add(Dense(3, input_dim=4))\r\n>>> model.add(Dense(1))\r\n>>> model.compile(loss=\"mean_squared_error\", optimizer=SGD(learning_rate=0.001))\r\n>>> model.fit(X, y) # raises TypeError!\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/weichen.xu/opt/miniconda3/envs/mlflowdev/lib/python3.8/site-packages/keras/src/utils/traceback_utils.py\", line 70, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File \"/Users/weichen.xu/opt/miniconda3/envs/mlflowdev/lib/python3.8/site-packages/tensorflow/core/function/capture/capture_container.py\", line 303, in capture_types\r\n return self._by_val_tracetype | self._by_ref_tracetype\r\nTypeError: unsupported operand type(s) for |: 'collections.OrderedDict' and 'collections.OrderedDict'\r\n>>> \r\n>>> \r\n>>> \r\n>>> import tensorflow\r\n>>> tensorflow.__version__\r\n'2.13.0-dev20230427'\r\n>>> import sys\r\n>>> sys.version\r\n'3.8.13 (default, Mar 28 2022, 06:16:26) \\n[Clang 12.0.0 ]'\r\n>>> \r\n```\r\n\r\n",
"I test with latest tf-nightly, the issue is gone. :)",
"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/60430\">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/60430\">No</a>\n"
] | 2023-04-28T08:35:39 | 2023-05-05T00:47:44 | 2023-05-05T00:47:41 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
TF-nightly (2.13.0-dev20230427)
### Custom Code
No
### OS Platform and Distribution
MacOS
### Mobile device
_No response_
### Python version
3.8
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
A bug happened!
TypeError "unsupported operand type(s) for |: 'collections.OrderedDict' and 'collections.OrderedDict'" is raised when training Sequential model composed of one Dense layer and training with numpy array type input dataset.
### Standalone code to reproduce the issue
```shell
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import SGD
import numpy as np
X = np.random.rand(10, 4)
y = np.random.rand(10)
model = Sequential()
model.add(Dense(3, input_dim=4))
model.add(Dense(1))
model.compile(loss="mean_squared_error", optimizer=SGD(learning_rate=0.001))
model.fit(X, y) # raises TypeError!
```
```
### Relevant log output
```shell
Traceback (most recent call last):
File "../tf1.py", line 14, in <module>
model.fit(X, y)
File ".../python3.8/site-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File ".../python3.8/site-packages/tensorflow/core/function/capture/capture_container.py", line 303, in capture_types
return self._by_val_tracetype | self._by_ref_tracetype
TypeError: unsupported operand type(s) for |: 'collections.OrderedDict' and 'collections.OrderedDict'
```
</details>
|
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tensorflow docker on WSL2: failed call to cuInit: CUDA_ERROR_NO_DEVICE
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[
"Hi @jw-redpanda ,\r\n\r\nTo enable GPU support on docker environment first we need to run nvidia-docker first as per Tf documentation [here](https://www.tensorflow.org/install/docker#gpu_support). Please have a look into the Nvidia documentation [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/user-guide.html) for more details.\r\n\r\nI am attaching the logs below where I have used docker with GPU support earlier. Please go through the logs and it may be of some help. Thanks!\r\n\r\n[docker_with _gpu-logs.txt](https://github.com/tensorflow/tensorflow/files/11350348/docker_with._gpu-logs.txt)\r\n\r\n\r\n",
"Hi @jw-redpanda ,\r\nYou are using cpu tensorflow image,\r\ntry using this\r\n\r\n`\r\ndocker run -it --rm --gpus all tensorflow/tensorflow:2.12.0-gpu \\\r\n python -c 'import tensorflow as tf; gpus = tf.config.list_physical_devices(\"GPU\"); print(f\">>>> GPUs={gpus}\")'\r\n`\r\n\r\nIf this doesn't work check your GPU compatibility and then check which tensorflow version your GPU is compatible with.",
"@jenil111 Thanks for your info.\r\n\r\nI tried out your suggestion, using the image `tensorflow/tensorflow:2.12.0-gpu`, instead of `tensorflow/tensorflow:latest-gpu`, but it still reports the same error `CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected`. Meanwhile, I also run nvidia driver utility `nvidia-smi`. It is able to find the GPU and display the info when running in both container images.\r\n```\r\n+---------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 530.47 Driver Version: 531.68 CUDA Version: 11.8 |\r\n|-----------------------------------------+----------------------+----------------------+\r\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|=========================================+======================+======================|\r\n| 0 NVIDIA GeForce RTX 2070 On | 00000000:01:00.0 On | N/A |\r\n| 0% 46C P8 11W / 185W| 287MiB / 8192MiB | 7% Default |\r\n| | | N/A |\r\n+-----------------------------------------+----------------------+----------------------+\r\n\r\n+---------------------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=======================================================================================|\r\n| No running processes found |\r\n+---------------------------------------------------------------------------------------+\r\n```\r\n\r\nIf the `nvidia-smi` is able to display the driver and CUDA version info, why tensorflow is not able to find the GPU in the container? \r\n\r\nAnyone using WSL2 tensorflow gpu image can help? May be I miss some configurations.\r\n\r\n",
"I have used 'tensorflow/tensorflow:2.12.0-gpu' image on rtx 3060 and it's working fine.\r\n\r\nI think rtx 2070 doesn't support cuda 11.8 can you check on cuda 10 version tensorflow image \r\n",
"@SuryanarayanaY Thank you. I guess I have already followed the official installation guidelines you pointed out.\r\n\r\nI tried out three cases to compare the results:\r\n1. WSL2/Ubuntu 18.04 (Windows host) + docker container `tensorflow/tensorflow:latest-gpu` + NVIDIA Container Runtime installed/configured\r\n`CUDA_ERROR_NO_DEVICE` but `nvidia-smi` can display driver/CUDA info\r\n2. WSL2/Ubuntu 18.04 (without docker container) + system installed cuDNN/CUDA12.1\r\n<b>NOTE: using the same WSL2 as in case 1, i.e. using WSL2 and CUDA directly</b>\r\n`GPU is found`\r\n3. Ubuntu 16.04 (Linux host) + docker container `tensorflow/tensorflow:latest-gpu` + NVIDIA Container Runtime installed/configured\r\n`GPU is found`\r\n\r\n\r\n\r\n\r\n",
"> \r\n\r\n\r\n\r\n> I have used 'tensorflow/tensorflow:2.12.0-gpu' image on rtx 3060 and it's working fine.\r\n> \r\n> I think rtx 2070 doesn't support cuda 11.8 can you check on cuda 10 version tensorflow image\r\n\r\n@jenil111 Thanks for the info. \r\nSome hints are seen. As per your suggestion, I tried out a tf version that uses cuda 10, e.g. `tensorflow/tensorflow:2.3.4-gpu`\r\n\r\n```\r\nroot@7fe5fcc3cd6c:/# nvidia-smi\r\n+---------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 530.47 Driver Version: 531.68 CUDA Version: 10.1 |\r\n|-----------------------------------------+----------------------+----------------------+\r\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|=========================================+======================+======================|\r\n| 0 NVIDIA GeForce RTX 2070 On | 00000000:01:00.0 Off | N/A |\r\n| 0% 41C P8 7W / 185W| 305MiB / 8192MiB | 4% Default |\r\n| | | N/A |\r\n+-----------------------------------------+----------------------+----------------------+\r\n\r\n+---------------------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=======================================================================================|\r\n| No running processes found |\r\n+---------------------------------------------------------------------------------------+\r\n```\r\n\r\n```\r\nroot@7fe5fcc3cd6c:/# python\r\nPython 3.6.9 (default, Jan 26 2021, 15:33:00)\r\n[GCC 8.4.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import tensorflow as tf\r\n2023-04-28 15:22:28.342207: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\ntf.co>>> tf.config.list_physical_devices('GPU')\r\n2023-04-28 15:22:36.768408: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1\r\n2023-04-28 15:22:36.774718: E tensorflow/stream_executor/cuda/cuda_driver.cc:314] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is det\r\nected\r\n2023-04-28 15:22:36.774822: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (7fe5fcc3cd6\r\nc): /proc/driver/nvidia/version does not exist\r\n```\r\nFrom the log, libcudart.so.10.1 and libcuda.so.1 can be loaded. tf seems to hardcode to read /proc/driver/nvidia/version, but this folder does NOT exist in WSL2 instance. In a ubuntu linux host, this folder is found. This seems to be the reason why WSL2 tensorflow docker finds no GPU.\r\n\r\n",
"@jenil111 Hi Jen, since you have the working tensorflow docker environment on WSL2, may I know if your environment has the folder `/proc/driver/nvidia/version` ? If you have this folder, I need to double-check my installed environment.\r\n\r\nAlthough I am able to use tensorflow + GPU on WSL2 without docker, I still want the docker option since it really simplifies the dependency requirements when using different tensorflow versions that need different cuDNN/CUDA versions.",
"hey @jw-redpanda , I am using \r\n`\r\ntensorflow/tensorflow:2.12.0-gpu\r\n`\r\ndocker image which has cuda 11.8.\r\nand I can see this file \"/proc/driver/nvidia/version\" look below output\r\n`\r\nroot@430e6753b621:~ cat /proc/driver/nvidia/version \r\n`\r\n`\r\nNVRM version: NVIDIA UNIX x86_64 Kernel Module 530.30.02 Wed Feb 22 04:11:39 UTC 2023\r\nGCC version: gcc version 11.3.0 (Ubuntu 11.3.0-1ubuntu1~22.04) \r\n`\r\n",
"@jenil111 Thank you so much for your help. I guess you provided important hints for me to troubleshoot the issue.",
"I have finally managed to solve the issue. It guess my environment was corrupted. \r\nSharing the steps below in case someone encounters similar issue.\r\n\r\n* Remove all nvidia and cuda packages (Ubuntu/WSL2)\r\n`sudo apt-get purge -y '*cuda*' '*nvidia*'`\r\n* Install Ubuntu WSL2 [driver](https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local)\r\nI tried out the `deb(network)` before but it seemed not working. Instead, I used the `deb(local)`\r\n* Install nvidia container [toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)\r\n* Re-install Windows nvidia driver\r\nI selected `perform clean installation`\r\nThis seems to be the most important step to rectify the envrionment\r\n* Shutdown and re-run the WSL2\r\nIn Windows adminstrative cmd prompt `wsl --shutdown`\r\n* Start docker service (WSL2)\r\n`sudo service docker start`\r\n* Run docker image `tensorflow/tensorflow:2.12.0-gpu`\r\n`docker run -it --gpus all --rm tensorflow/tensorflow:2.12.0-gpu bash`\r\n* Import tensorflow and list GPU\r\n```\r\nroot@adcf5339c0c4:/# python\r\nPython 3.8.10 (default, Mar 13 2023, 10:26:41)\r\n[GCC 9.4.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import tensorflow as tf\r\n2023-05-01 14:36:18.705031: I tensorflow/core/platform/cpu_feature_guard.cc:182] 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>>> tf.config.list_physical_devices('GPU')\r\n2023-05-01 14:36:34.143821: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2023-05-01 14:36:34.148414: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2023-05-01 14:36:34.148717: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n```\r\n* Check /proc/driver/nvidia folder (NOT found)\r\n```\r\nroot@adcf5339c0c4:/# ls -la /proc/driver\r\ntotal 0\r\ndr-xr-xr-x 4 root root 0 May 1 14:39 .\r\ndr-xr-xr-x 193 root root 0 May 1 14:36 ..\r\n-r--r--r-- 1 root root 0 May 1 14:39 nvram\r\n-r--r--r-- 1 root root 0 May 1 14:39 rtc\r\n```\r\n",
"Hi @jw-redpanda ,\r\n\r\nThanks for the confirmation and also for sharing the steps that succeeded installation. Could you please spare some time and mark this as closed if resolved for 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/60429\">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/60429\">No</a>\n"
] | 2023-04-28T03:35:53 | 2023-05-04T07:27:18 | 2023-05-04T07:27:16 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
v2.12.0-rc1-12-g0db597d0d75 2.12.0
### Custom Code
No
### OS Platform and Distribution
Ubuntu 18.04.06 on WSL2
### Mobile device
_No response_
### Python version
Python 3.8.10
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
11.8/8.6
### GPU model and memory
NVIDIA GeForce RTX 2070 / 8GB
### Current Behaviour?
Windows host
* version: 10.0.22621.1555
WSL2/Ubuntu
* Ubuntu 18.04.6 LTS
* Kernel version: 5.10.102.1
NVIDIA Container Toolkit
* CLI version 1.13.1 (commit: 28b70663f1a2b982e59e83bcf1844177dc745208)
Docker (Ubuntu)
* docker version: 23.0.5, build bc4487a
* image tag: tensorflow/tensorflow:latest-gpu
* image id: cbe3a4f4c2a0
* /etc/docker/daemon.json (already configured the NVIDIA Container Runtime. Docker is restarted to apply change as well)
```json
{
"runtimes": {
"nvidia": {
"args": [],
"path": "nvidia-container-runtime"
}
}
}
```
When running in a container, tensorflow reports no GPU is found.
```
$ docker run -it --rm --gpus all tensorflow/tensorflow:latest-gpu \
python -c 'import tensorflow as tf; gpus = tf.config.list_physical_devices("GPU"); print(f">>>> GPUs={gpus}")'
2023-04-28 03:19:06.723090: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-04-28 03:19:08.020353: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
>>>> GPUs=[]
```
However, `nvidia-smi` is able to print GPU info.
```
$ docker run -it --rm --gpus all tensorflow/tensorflow:latest-gpu nvidia-smi
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 530.47 Driver Version: 531.68 CUDA Version: 11.8 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 2070 On | 00000000:01:00.0 On | N/A |
| 0% 48C P8 11W / 185W| 175MiB / 8192MiB | 2% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
```
### Standalone code to reproduce the issue
```shell
To reproduce the issue, refer to the `docker run` command described in the Current Behaviour.
Some hints
* When running the same configuration and commands on the Ubuntu host, GPU can be found as expected.
* Under WSL2, without running in a docker container, tensorflow is able to use GPU.
* Strange thing is when running inside the tensorflow container, tensorflow failed to find the GPU. However, nvidia-smi is able to see the GPU.
```
### Relevant log output
_No response_</details>
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| 60,428 |
Tensorflow Quantum not installing in Colab
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[
"Hi @radres2019, Thank you for reporting the issue!\r\nYou are seeing this error because Colab has python version 3.10. Tensorflow quantum 0.7.2 is compatible with Python 3.7, 3.8, 3.9 and does not support Python 3.10. \r\n\r\nPlease refer to the gist where I was able to install Tensorflow quantum sucessfully [here](https://colab.sandbox.google.com/gist/synandi/b733cccf4a90d29e5feefb606f02f843/60428.ipynb). Thank you!",
"I think I can help you resolve this issue",
"It seems like you are trying to use TensorFlow Quantum (TFQ) on Colab, but you are getting an error when installing the TFQ package. According to the [installation guide](https://www.tensorflow.org/quantum/install) for TFQ, the current stable release of TFQ requires TensorFlow version 2.11.0, not 2.7.0. You can try changing your pip command to:\r\n\r\n!pip install tensorflow==2.11.0 tensorflow-quantum==0.7.2\r\n\r\nAlternatively, you can use the nightly builds of TFQ and TensorFlow with:\r\n\r\n!pip install -U tfq-nightly\r\n\r\nYou can also check out some [tutorials](https://www.tensorflow.org/quantum/tutorials) on how to use TFQ on Colab, such as [Quantum Convolutional Neural Network](https://www.tensorflow.org/quantum/tutorials/qcnn).\r\n",
"> Hi @radres2019, Thank you for reporting the issue! You are seeing this error because Colab has python version 3.10. Tensorflow quantum 0.7.2 is compatible with Python 3.7, 3.8, 3.9 and does not support Python 3.10.\r\n> \r\n> Please refer to the gist where I was able to install Tensorflow quantum sucessfully [here](https://colab.sandbox.google.com/gist/synandi/b733cccf4a90d29e5feefb606f02f843/60428.ipynb). Thank you!\r\n\r\nThank you so much for your assistance. This allows the installation now; however, I am unable to import cirq or tensorflow-quantum:\r\n\r\nModuleNotFoundError: No module named 'cirq'\r\n\r\nModuleNotFoundError: No module named 'tensorflow_quantum'\r\n\r\nPerhaps this is installing these within a virtual environment?",
"Thank you for taking the time to help\r\n\r\n> It seems like you are trying to use TensorFlow Quantum (TFQ) on Colab, but you are getting an error when installing the TFQ package. According to the [installation guide](https://www.tensorflow.org/quantum/install) for TFQ, the current stable release of TFQ requires TensorFlow version 2.11.0, not 2.7.0. You can try changing your pip command to:\r\n> \r\n> !pip install tensorflow==2.11.0 tensorflow-quantum==0.7.2\r\n> \r\n> Alternatively, you can use the nightly builds of TFQ and TensorFlow with:\r\n> \r\n> !pip install -U tfq-nightly\r\n> \r\n> You can also check out some [tutorials](https://www.tensorflow.org/quantum/tutorials) on how to use TFQ on Colab, such as [Quantum Convolutional Neural Network](https://www.tensorflow.org/quantum/tutorials/qcnn).\r\n\r\nThank you for taking the time to help me. I followed your instructions as well as the alternative strategies on the TFQ installation page, yet I receive this error when trying to install tensorflow-quantum==0.7.2:\r\n\r\nERROR: Could not find a version that satisfies the requirement tensorflow-quantum==0.7.2 (from versions: none)\r\nERROR: No matching distribution found for tensorflow-quantum==0.7.2",
"This goes for the tfq nightly as well:\r\n\r\nLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\r\nERROR: Could not find a version that satisfies the requirement tfq-nightly (from versions: none)\r\nERROR: No matching distribution found for tfq-nightly",
"@radres2019 \r\nApologies for the delay.\r\nLooks like this is a duplicate of issue https://github.com/tensorflow/quantum/issues/768. Can you please close this issue, since it is already being tracked there? 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/60428\">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/60428\">No</a>\n"
] | 2023-04-27T21:03:11 | 2023-05-10T10:57:03 | 2023-05-10T10:57:00 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Build/Install
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.7
### Custom Code
Yes
### OS Platform and Distribution
Linux Ubuntu
### Mobile device
_No response_
### Python version
3.11, 3.8
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
In attempting the tutorials in TFQ, Colab now says it does not recognize any TF less than 2.80 and therefore won't install TFQ 0.2.7. I attempted various other permutations of TF and TFQ versions but to no avail.
### Standalone code to reproduce the issue
```shell
!pip install tensorflow==2.7.0 tensorflow-quantum==0.7.2
```
### Relevant log output
```shell
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
ERROR: Could not find a version that satisfies the requirement tensorflow==2.7.0 (from versions: 2.8.0rc0, 2.8.0rc1, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0rc0, 2.9.0rc1, 2.9.0rc2, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0rc0, 2.10.0rc1, 2.10.0rc2, 2.10.0rc3, 2.10.0, 2.10.1, 2.11.0rc0, 2.11.0rc1, 2.11.0rc2, 2.11.0, 2.11.1, 2.12.0rc0, 2.12.0rc1, 2.12.0)
ERROR: No matching distribution found for tensorflow==2.7.0
```
</details>
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I_kwDOArmXAs5klBoW
| 60,427 |
TFLite converter does not support 4-dimensional input for dense operators
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[
"Hi @vk1214 Thanks for reporting the issue.\r\n\r\nI was able to reproduce this issue on TF 2.12. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/5cfcb8eb28fcac6186db47bf8b4121ff/60427.ipynb).\r\n\r\n@sachinprasadhs Could you please look into this?\r\n\r\nThanks."
] | 2023-04-27T19:21:41 | 2024-01-10T04:45:35 | null |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10 pro
- TensorFlow installation (pip package or built from source): pip
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.12.0
### 2. Code
Simple model
https://colab.research.google.com/drive/1IaVtPj4RO2QWV3V1CgmNPnb2hmggkLmV?usp=sharing
### 3. Failure after conversion
As you can see in the only the second dense operator is converted well (blue). The first dense op is broken down into primitive ops (red)
### 5. (optional) Any other info / logs
Likely related issue: https://github.com/tensorflow/tensorflow/issues/57691
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I_kwDOArmXAs5kkq8j
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AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7fa17d6e85e0> and will run it as-is
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[
"Hi @FlorinAndrei ,\r\n\r\nI have replicated the behaviour and we have to dig into more whether this is a bug or not. Reference [gist](https://colab.research.google.com/gist/SuryanarayanaY/f8f2e46b3bae1398327ef25cd5abc541/60426.ipynb) here.\r\n\r\nI request you to set verbosity to 10 using command `export AUTOGRAPH_VERBOSITY=10` and submit the `logs` for us, As colab not generating complete logs(particularly at C++ level). This might be helpful to analyse the issue. Thanks!\r\n ",
"I seriously want to work on some issues please tell which issue should i pick and how to get my first pull request merged",
"@SuryanarayanaY I did `export AUTOGRAPH_VERBOSITY=10` before launching the script with `python3 script_name.py`. This was on an Ubuntu 22.04 machine that I have ssh-ed into. The log output you see is all the output I got from the script.\r\n\r\nIs there anything else I should do? Just let me know what you need.",
"Hi @FlorinAndrei ,\r\n\r\nFrom the error log,it seems this warning is due to the reason that may The Tensorflow seems does not have the access to the source code of the some of the function from TensorFlow Decision Forests (TF-DF) which is powered by Yggdrasil Decision Forests (YDF).Please refer this [source](https://www.tensorflow.org/decision_forests).\r\n```\r\nWARNING:tensorflow:AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7fa17d6e85e0> and will run it as-is.\r\nPlease report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\r\nCause: could not get source code\r\n```\r\n\r\nAlso Tensorflow couldn't create Autograph for lambda functions which is known issue.\r\n\r\nIn fact this is just a warning to users let them know that autograph is not constructed for a particular function and running it as it is without graph.Yeah this have some performance implications but not sure this can be fixable or not especially when there are other libraries involved.For other library functions TF might have not access to Source code and this is causing this warning. This warning is visible in few official Tensorflow Tutorials also and I am attaching few for your references.Refer [source1](https://www.tensorflow.org/decision_forests/tutorials/model_composition_colab),[source2](https://www.tensorflow.org/decision_forests/tutorials/advanced_colab),[source3](https://www.tensorflow.org/decision_forests/tutorials/intermediate_colab) etc.\r\n\r\nI hope this will provide some additional info for you. Thanks!",
"The warning literally tells people to inform the TF team about it, which I did.\r\n\r\nEither this requires some action from the TF team, or the warning needs to be removed (which is also \"action required\").",
"Hi @FlorinAndrei ,\r\n\r\nI appreciate that you have reported as per the logs. I will escalate this to concern team and let you updated. Thanks!",
"@FlorinAndrei ,\r\n\r\nAFAIK Autograph cannot convert those functions for which source code is not available or those functions from third party libraries. However the log message also needs to look into whether we need to change this. We need to hear about this from the concerned SME.\r\n\r\nThanks",
"> The warning literally tells people to inform the TF team about it, which I did.\r\n> \r\n> Either this requires some action from the TF team, or the warning needs to be removed (which is also \"action required\").\r\n\r\nBut you can't really expect as it did require ",
"> Hi @FlorinAndrei ,\r\n> \r\n> From the error log,it seems this warning is due to the reason that may The Tensorflow seems does not have the access to the source code of the some of the function from TensorFlow Decision Forests (TF-DF) which is powered by Yggdrasil Decision Forests (YDF).Please refer this [source](https://www.tensorflow.org/decision_forests).\r\n> \r\n> ```\r\n> WARNING:tensorflow:AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7fa17d6e85e0> and will run it as-is.\r\n> Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\r\n> Cause: could not get source code\r\n> ```\r\n> \r\n> Also Tensorflow couldn't create Autograph for lambda functions which is known issue.\r\n> \r\n> In fact this is just a warning to users let them know that autograph is not constructed for a particular function and running it as it is without graph.Yeah this have some performance implications but not sure this can be fixable or not especially when there are other libraries involved.For other library functions TF might have not access to Source code and this is causing this warning. This warning is visible in few official Tensorflow Tutorials also and I am attaching few for your references.Refer [source1](https://www.tensorflow.org/decision_forests/tutorials/model_composition_colab),[source2](https://www.tensorflow.org/decision_forests/tutorials/advanced_colab),[source3](https://www.tensorflow.org/decision_forests/tutorials/intermediate_colab) etc.\r\n> \r\n> I hope this will provide some additional info for you. Thanks!\r\n\r\nWell this would make it quite obvious of the fact that the warning was referencing to action",
"> @SuryanarayanaY I did `export AUTOGRAPH_VERBOSITY=10` before launching the script with `python3 script_name.py`. This was on an Ubuntu 22.04 machine that I have ssh-ed into. The log output you see is all the output I got from the script.\r\n> \r\n> Is there anything else I should do? Just let me know what you need.\r\n\r\nwe will also need to ensure smooth functionality ",
"Hi everyone,\r\n\r\nTF-DF developer here. Thank you very much for reporting this! We just [fixed the issue at head](https://github.com/tensorflow/decision-forests/commit/2fc493c5e186d1a62d773c97385e09d6002b66a2) and the next version of TF-DF will include the fix. ",
"awesome, thanks! Close the issue for now as the issue with TF-DF is fixed.",
"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/60426\">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/60426\">No</a>\n"
] | 2023-04-27T18:05:50 | 2023-08-23T18:47:32 | 2023-08-23T18:47:29 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
2.12.0
### Custom Code
Yes
### OS Platform and Distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10.6
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
CPU only
### GPU model and memory
_No response_
### Current Behaviour?
Running `tensorflow_decision_forests` I saw a warning in the output while running `model.fit()` which asked me to file a bug report.
Prediction seems okay, but I cannot be 100% sure about it.
Dataset files needed to reproduce the case:
[train.csv](https://github.com/tensorflow/tensorflow/files/11346644/train.csv)
[test.csv](https://github.com/tensorflow/tensorflow/files/11346645/test.csv)
List of Python modules:
[pip_list.txt](https://github.com/tensorflow/tensorflow/files/11346651/pip_list.txt)
### Standalone code to reproduce the issue
```shell
import pandas as pd
import tensorflow_decision_forests as tfdf
def type_conversion_tfdf(df_orig):
df = df_orig.copy(deep=True)
df[["PassengerId_1", "PassengerId_2"]] = df["PassengerId"].str.split(
pat="_", n=1, expand=True
)
df[["Name_First", "Name_Last"]] = df["Name"].str.split(pat=" ", n=1, expand=True)
df[["Deck", "Cabin_Num", "Side"]] = df["Cabin"].str.split(pat="/", n=2, expand=True)
bool_to_int_columns = ["Transported", "CryoSleep", "VIP"]
for c in bool_to_int_columns:
if c in df.columns.to_list():
df[c] = df[c].fillna(value=0).astype(bool).astype(int)
categorical_columns = [
"HomePlanet",
"Destination",
"PassengerId_2",
"Name_First",
"Name_Last",
"Deck",
"Side",
]
for c in categorical_columns:
# df[c] = df[c].astype("category")
pass
int_columns = ["Cabin_Num"]
for c in int_columns:
df[c] = df[c].fillna(value=0).astype(int)
return df.drop(columns=["PassengerId", "PassengerId_1", "Cabin", "Name"])
pd.options.mode.copy_on_write = True
train_df = pd.read_csv("train.csv")
test_df = pd.read_csv("test.csv")
train_df_tfdf = type_conversion_tfdf(train_df)
test_df_tfdf = type_conversion_tfdf(test_df)
train_ds_tfdf = tfdf.keras.pd_dataframe_to_tf_dataset(
train_df_tfdf, label="Transported"
)
test_ds_tfdf = tfdf.keras.pd_dataframe_to_tf_dataset(test_df_tfdf)
rf = tfdf.keras.RandomForestModel()
rf.fit(x=train_ds_tfdf)
predictions = rf.predict(test_ds_tfdf)
```
### Relevant log output
```shell
2023-04-27 10:53:42.381793: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-04-27 10:53:42.848387: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-04-27 10:53:43.514073: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-04-27 10:53:43.540254: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
Use /tmp/tmpsuc8grc3 as temporary training directory
Reading training dataset...
2023-04-27 10:53:43.603559: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_8' with dtype string and shape [8693]
[[{{node Placeholder/_8}}]]
Training dataset read in 0:00:02.534852. Found 8693 examples.
Training model...
[INFO 23-04-27 10:54:06.0499 PDT kernel.cc:1242] Loading model from path /tmp/tmpsuc8grc3/model/ with prefix fd4ad06ed85c449e
[INFO 23-04-27 10:54:06.5441 PDT decision_forest.cc:660] Model loaded with 300 root(s), 292454 node(s), and 16 input feature(s).
[INFO 23-04-27 10:54:06.5441 PDT abstract_model.cc:1311] Engine "RandomForestGeneric" built
[INFO 23-04-27 10:54:06.5441 PDT kernel.cc:1074] Use fast generic engine
Model trained in 0:00:20.491433
Compiling model...
2023-04-27 10:54:06.630203: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype double and shape [8693]
[[{{node Placeholder/_0}}]]
WARNING:tensorflow:AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7fa17d6e85e0> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING:tensorflow:AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7fa17d6e85e0> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
Model compiled.
2023-04-27 10:54:07.749903: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_13' with dtype double and shape [4277]
[[{{node Placeholder/_13}}]]
5/5 [==============================] - 0s 60ms/step
```
</details>
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I_kwDOArmXAs5kklMv
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Initial training speed extreemly (~100x) slow for generated data, and become faster and faster as each epoch progress
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[
"Hi @mw66 \r\nApologies for the delay. In order to expedite the trouble shooting process, please share complete reproducible code. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60425\">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/60425\">No</a>\n",
"It's hard to add a test case, but just want to add it could be related to memory/disk swapping in a long running jib. So is there a way in Python before each epoch to ensure that trainX and trainY are in memory?"
] | 2023-04-27T17:52:36 | 2023-05-22T16:03:11 | 2023-05-18T01:54:24 |
NONE
| null | null | null |
<details><summary>Click to expand!</summary>
### Issue Type
Performance
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
2.8.0
### Custom Code
No
### OS Platform and Distribution
Ubuntu 18.04.5 LTS
### Mobile device
_No response_
### Python version
3.9.1
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
CUDA 11.1
### GPU model and memory
GeForce GTX TITAN X, 12G
### Current Behaviour?
My code is roughly like this:
```python
for i in range(data_n):
if not train_data[i].npz exists:
data = generate train_data[i].npz and save to disk
else:
data = read train_data[i].npz from disk
model.fit(data)
```
`data_n = 26`, when `i` less than ~20, everything is normal.
as `i` reach about 20, something strange start to happen:
I found if the data.npz is generated, the initial training speed is *very* slow, e.g ~x100 slower than the normal speed (when directly read from .npz file), and as the training Epoch progress, it become faster and faster, until it eventually runs full power on GPU, e.g.
```
_________________________________________________________________
Epoch 1/1000
732/732 [==============================] - 1524s 2s/step - loss: 3.0771 - accuracy: 0.2212 - val_loss: 3.0802 - val_accuracy: 0.2265
Epoch 2/1000
732/732 [==============================] - 931s 1s/step - loss: 3.0538 - accuracy: 0.2246 - val_loss: 3.0623 - val_accuracy: 0.2303
Epoch 3/1000
732/732 [==============================] - 833s 1s/step - loss: 3.0434 - accuracy: 0.2261 - val_loss: 3.0663 - val_accuracy: 0.2298
Epoch 4/1000
732/732 [==============================] - 757s 1s/step - loss: 3.0339 - accuracy: 0.2275 - val_loss: 3.0618 - val_accuracy: 0.2309
Epoch 5/1000
732/732 [==============================] - 663s 906ms/step - loss: 3.0316 - accuracy: 0.2279 - val_loss: 3.0692 - val_accuracy: 0.2297
Epoch 6/1000
732/732 [==============================] - 572s 782ms/step - loss: 3.0254 - accuracy: 0.2288 - val_loss: 3.0683 - val_accuracy: 0.2298
Epoch 7/1000
732/732 [==============================] - 406s 555ms/step - loss: 3.0201 - accuracy: 0.2291 - val_loss: 3.0638 - val_accuracy: 0.2305
Epoch 8/1000
732/732 [==============================] - 367s 502ms/step - loss: 3.0148 - accuracy: 0.2301 - val_loss: 3.0595 - val_accuracy: 0.2289
Epoch 9/1000
732/732 [==============================] - 321s 439ms/step - loss: 3.0098 - accuracy: 0.2311 - val_loss: 3.0599 - val_accuracy: 0.2299
Epoch 10/1000
732/732 [==============================] - 304s 416ms/step - loss: 3.0058 - accuracy: 0.2318 - val_loss: 3.0608 - val_accuracy: 0.2301
Epoch 11/1000
732/732 [==============================] - 286s 391ms/step - loss: 3.0053 - accuracy: 0.2317 - val_loss: 3.0562 - val_accuracy: 0.2320
Epoch 12/1000
732/732 [==============================] - 268s 366ms/step - loss: 3.0010 - accuracy: 0.2315 - val_loss: 3.0563 - val_accuracy: 0.2323
Epoch 13/1000
732/732 [==============================] - 252s 345ms/step - loss: 3.0003 - accuracy: 0.2320 - val_loss: 3.0518 - val_accuracy: 0.2332
Epoch 14/1000
732/732 [==============================] - 233s 319ms/step - loss: 2.9963 - accuracy: 0.2321 - val_loss: 3.0488 - val_accuracy: 0.2315
Epoch 15/1000
732/732 [==============================] - 217s 296ms/step - loss: 2.9926 - accuracy: 0.2326 - val_loss: 3.0515 - val_accuracy: 0.2329
Epoch 16/1000
732/732 [==============================] - 198s 271ms/step - loss: 2.9881 - accuracy: 0.2341 - val_loss: 3.0514 - val_accuracy: 0.2327
Epoch 17/1000
732/732 [==============================] - 181s 247ms/step - loss: 2.9883 - accuracy: 0.2326 - val_loss: 3.0527 - val_accuracy: 0.2333
Epoch 18/1000
732/732 [==============================] - 165s 226ms/step - loss: 2.9868 - accuracy: 0.2338 - val_loss: 3.0496 - val_accuracy: 0.2307
Epoch 19/1000
732/732 [==============================] - 153s 209ms/step - loss: 2.9831 - accuracy: 0.2333 - val_loss: 3.0438 - val_accuracy: 0.2336
Epoch 20/1000
732/732 [==============================] - 137s 187ms/step - loss: 2.9817 - accuracy: 0.2338 - val_loss: 3.0442 - val_accuracy: 0.2330
Epoch 21/1000
732/732 [==============================] - 123s 168ms/step - loss: 2.9779 - accuracy: 0.2340 - val_loss: 3.0434 - val_accuracy: 0.2344
Epoch 22/1000
732/732 [==============================] - 113s 154ms/step - loss: 2.9789 - accuracy: 0.2344 - val_loss: 3.0370 - val_accuracy: 0.2341
Epoch 23/1000
732/732 [==============================] - 102s 139ms/step - loss: 2.9743 - accuracy: 0.2351 - val_loss: 3.0392 - val_accuracy: 0.2356
Epoch 24/1000
732/732 [==============================] - 93s 127ms/step - loss: 2.9777 - accuracy: 0.2349 - val_loss: 3.0517 - val_accuracy: 0.2336
Epoch 25/1000
732/732 [==============================] - 82s 112ms/step - loss: 2.9716 - accuracy: 0.2352 - val_loss: 3.0279 - val_accuracy: 0.2369
Epoch 26/1000
732/732 [==============================] - 79s 107ms/step - loss: 2.9709 - accuracy: 0.2351 - val_loss: 3.0363 - val_accuracy: 0.2348
Epoch 27/1000
732/732 [==============================] - 67s 92ms/step - loss: 2.9672 - accuracy: 0.2354 - val_loss: 3.0344 - val_accuracy: 0.2343
Epoch 28/1000
732/732 [==============================] - 55s 75ms/step - loss: 2.9664 - accuracy: 0.2350 - val_loss: 3.0418 - val_accuracy: 0.2337
Epoch 29/1000
732/732 [==============================] - 45s 62ms/step - loss: 2.9690 - accuracy: 0.2356 - val_loss: 3.0439 - val_accuracy: 0.2340
Epoch 30/1000
732/732 [==============================] - 32s 44ms/step - loss: 2.9639 - accuracy: 0.2364 - val_loss: 3.0333 - val_accuracy: 0.2350
Epoch 31/1000
732/732 [==============================] - 28s 39ms/step - loss: 2.9631 - accuracy: 0.2362 - val_loss: 3.0373 - val_accuracy: 0.2353
Epoch 32/1000
732/732 [==============================] - 28s 38ms/step - loss: 2.9626 - accuracy: 0.2368 - val_loss: 3.0391 - val_accuracy: 0.2343
Epoch 33/1000
732/732 [==============================] - 26s 36ms/step - loss: 2.9598 - accuracy: 0.2355 - val_loss: 3.0339 - val_accuracy: 0.2356
Epoch 34/1000
732/732 [==============================] - 26s 35ms/step - loss: 2.9581 - accuracy: 0.2368 - val_loss: 3.0337 - val_accuracy: 0.2349
Epoch 35/1000
732/732 [==============================] - 25s 35ms/step - loss: 2.9601 - accuracy: 0.2366 - val_loss: 3.0306 - val_accuracy: 0.2363
```
As you can see, the 1st epoch took 1524s, while the 35th epoch took 25s; if the data is directly read from .npz an epoch took consistent ~18s.
Could this be a TF memory issue ? or it's a Python issue?
How and where I can debug such issue? or any suggestions I can try to improve the speed?
Thanks.
### Standalone code to reproduce the issue
```python
# My code is roughly like this:
for i in range(data_n):
if not train_data[i].npz exists:
data = generate train_data[i].npz and save to disk
else:
data = read train_data[i].npz from disk
model.fit(data)
```
```
### Relevant log output
_No response_</details>
|
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PR_kwDOArmXAs5PTrx9
| 60,424 |
[TFLite] Add int8 and int16x8 support for LOG operator
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[
"Hi @Tessil, one error left to fix. The E2E tests (whose params are in part generated by elementwise.py) are failing. Specifically when quant_16x8=True for rsqrt fails with \"Type INT16 is unsupported by op Rsqrt.\" Then, some of the log tests are generating different numbers from what is expected. Let me know if you have any troubles reproducing, thanks.",
"Hi @grantjensen I wasn't able to reproduce the issues.\r\n\r\nI ran the following two commands with success:\r\n`bazel run //tensorflow/lite/testing:generate_examples -- --zip_to_output=log -- --output_path=test`\r\n`bazel test //tensorflow/lite/kernels:elementwise_test`\r\n\r\nI have updated the PR though to remove QI16 from the MLIR TFL_LogOp definition as it's not necessary currently (no official support for int16x8 MLIR quantizer).",
"` bazel test //third_party/tensorflow/lite/testing:zip_test_log`\r\n ` bazel test //third_party/tensorflow/lite/testing:zip_test_rsqrt`",
"@grantjensen Thank you, it seems I had the wrong command, I fixed the tests. Note that I reduced the max_value as int8 isn't sufficient to represent the quantized range from log(0.1) to log(100) with good enough precision."
] | 2023-04-27T15:04:23 | 2023-08-18T01:56:55 | 2023-08-18T01:56:54 |
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Hello,
This PR adds int8 and int16 quantization support for the LOG operator using a lookup table.
Thanks,
Thibaut
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Re-enable CPUInfo on Apple platform.
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[
"The failure on the \"AMD ROCm -- Community CI Build\" hardly seems related. Can we re-run it?\r\n\r\n[24,971 / 25,040] Compiling tensorflow/core/kernels/cwise_op_gpu_igammas_float.cu.cc; 205s local ... (3 actions running)\r\nTarget //tensorflow/tools/pip_package:build_pip_package up-to-date:\r\n bazel-bin/tensorflow/tools/pip_package/build_pip_package\r\nINFO: Elapsed time: 3061.834s, Critical Path: 611.44s\r\nINFO: 25040 processes: 2989 internal, 22051 local.\r\nINFO: Build completed successfully, 25040 total actions\r\nINFO: Build completed successfully, 25040 total actions\r\n++ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg\r\nThu Apr 27 15:46:46 UTC 2023 : === Preparing sources in dir: /tmp/tmp.nU2zpOaRuV\r\n/workspace /workspace\r\n/workspace\r\n/workspace/bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow /workspace\r\n/workspace\r\nbazel-bin/tensorflow/tools/pip_package/build_pip_package: line 201: patchelf: command not found",
"Hello [grantjensen](https://github.com/grantjensen), can you please help me understand how I can reproduce the build failure in \"MacOS CPU Python3.9\" to try and fix it? I tried the change with tflite as bundled in Chromium and it compiled fine on macOS."
] | 2023-04-27T14:41:16 | 2023-05-16T20:16:06 | 2023-05-16T20:16:06 |
NONE
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b/138922878 has been closed as fixed for years.
|
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PR_kwDOArmXAs5PQyhL
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[NextPluggableDevice]add pjrt compile on demand control flag
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[
"@jyingl3 can you have a review? thanks",
"Hi Zhoulong, sorry just realize one issue - could you update the build target of tensorflow/core/common_runtime/pluggable_device/pluggable_device_plugin_init.cc to include pjrt_compile_on_demand_op? Thank you!",
"> Hi Zhoulong, sorry just realize one issue - could you update the build target of tensorflow/core/common_runtime/pluggable_device/pluggable_device_plugin_init.cc to include pjrt_compile_on_demand_op? Thank you!\r\n\r\n@jyingl3 done. thanks"
] | 2023-04-27T06:24:44 | 2023-05-04T22:48:54 | 2023-05-04T22:48:54 |
CONTRIBUTOR
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let eager op compiled by xla or execute on custom kernel control to plugin
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