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I_kwDOArmXAs58BpML
| 62,790 |
Mirror Distributed Strategy
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[
"Hi **@hafizuriu** ,\r\nIn order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here and version also.\r\n\r\nThank you!",
"I'm using tensorflow version 2.14",
"Hi @hafizuriu ,\r\nIn order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here.\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."
] | 2024-01-14T16:39:02 | 2024-02-02T01:47:05 | 2024-02-02T01:47:05 |
NONE
| null | null | null |
I'm facing issues when I tried to run model using mirror distributed strategy.
I want to run multiples model one by one in a loop using distributed strategy. First model runs well but is shows error before start the next model.
```
Collective ops is aborted by: Shape mismatch in the collective instance 100. Op at device /job:localhost/replica:0/task:0/device:GPU:2 expected shape [28678440] but another member in the group expected shape [39166184]. This is likely due to different input shapes at different members of the collective op.
The error could be from a previous operation. Restart your program to reset.
[[{{node CollectiveReduceV2_2}}]] [Op:__inference_train_function_51026]
```
Here is my code-
```
strategy = tf.distribute.MirroredStrategy()
# Set up model within scope
with strategy.scope():
model = Create_model()
optimizer = SGD(learning_rate=0.01, weight_decay=1e-4,momentum=0.9)
model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizer, metrics=['sparse_categorical_accuracy','sparse_top_k_categorical_accuracy'])
checkpoint = ModelCheckpoint('Best_model.h5',
monitor='val_loss',
save_best_only=True,
mode='min',
verbose=0)
lr_reduce = ReduceLROnPlateau(
monitor='val_loss',
factor=0.1,
patience=5,
verbose=0,
mode='min',
)
model.fit(train_generator,validation_data=valid_generator,steps_per_epoch=len(train_generator),\
validation_steps=len(valid_generator), epochs = 50,\
callbacks=[checkpoint,lr_reduce], verbose=1
)
```
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I_kwDOArmXAs58AFjB
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Incorrect Reduction Arguments in third_party/xla/xla/experiments/triton_autotuning/matmul_lib.py
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[
"@K-Wu,\r\nCould you please share a reproducible code or the use-case that supports your statement so that the issue can be easily understood? Thank you!",
"Hi @tilakrayal ,\r\n\r\nI don't have a reproducible code as this is not the core part of tensorflow. Besides, it should be fairly easy to tell by reading this argument. Could you please let the owner (maybe @gflegar) briefly check it? \r\n\r\nThank you.\r\n\r\nBest Regards,\r\nKun",
"@K-Wu,\r\nI have raised the PR for the same changes. Could you please take a look at the same PR.\r\nhttps://github.com/tensorflow/tensorflow/pull/62885\r\n\r\nThank you!",
"Hi @tilakrayal, that works. Thank you!",
"@K-Wu,\r\nAs per the comment from the Developer in the PR raised, the requested change should happen in the https://github.com/openxla/xla repository. Could you please try to check with the respective repo as the file belongs to the xla api. 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/62789\">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/62789\">No</a>\n"
] | 2024-01-13T17:14:02 | 2024-03-29T01:47:13 | 2024-03-29T01:47:02 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
latest
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
In https://github.com/tensorflow/tensorflow/blob/master/third_party/xla/xla/experiments/triton_autotuning/matmul_lib.py#L301-L309, should the `row_size` be `int(dims.M)*int(dims.N)` instead?
### Standalone code to reproduce the issue
```shell
No need.
```
### Relevant log output
_No response_
|
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tf.data.Dataset.map() makes unnecessary memory allocations
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"Hi @hrsht ,\r\n\r\nThanks for reporting. The `dataset.map()` method on a dataset indeed returns a new dataset object.But in the attached code snippet during iteration over a `mapped dataset `(even with `ds.as_numpy_iterator`) it seems some mem copies happening as we are using same variable for storing the `dataset` object every time.\r\n\r\nThere seems to be an issue with map() function with dataset object. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/03093a18723135e7e51b20afbf210af3/62788_dataset_map_unnecessary_mem_allocation.ipynb#scrollTo=OtY6ihLwfvNJ) for reference. Needs to dig more for root cause. Thanks!\r\n\r\n",
"Hi @hrsht ,\r\n\r\nIt is intended behaviour. Consider the following code:\r\n\r\n```\r\nds = tf.data.Dataset.range(1)\r\nfor call in range(10):\r\n print('Call Number:',call)\r\n ds = ds.map(get_data)\r\n # iterate ds\r\n```\r\n\r\nWhen we call `map` function on a `dataset` it will return a pipeline `dataset.map()` in first call i.e.` call=0` . Please note that we are copying this again to same dataset (from `ds = ds.map(get_data)`), in second call, i.e when call=1, now ds is not just `tf.data.Dataset.range(1)` but it is `tf.data.Dataset.range(1).map()` .Hence in call=1, we are actually mapping it again now ds will become `ds.map().map()`. When call=2 it will become `ds.map().map().map()` and so on. Hence the problem.\r\n\r\nChanging the code to below will resolve the issue:\r\n\r\n```\r\nds = tf.data.Dataset.range(1)\r\nfor call in range(10):\r\n print('Call Number:',call)\r\n ds1 = ds.map(get_data)\r\n # iterate ds1\r\n```",
"Thanks @SuryanarayanaY for the investigation. \r\n\r\nI don't think you quite understood the issue here. The multiple chaining of `map` calls in my example code was just to highlight the issue of unnecessary memory allocations by exacerbating it with many `map` calls. It is most likely not how one would code in reality. In fact, you can change the size of the tensor referenced by `get_data` to be 8GB and code you referenced last with `ds1` dataset would also OOM with default RAM resource of ~12GB in colab. (See this run: https://colab.research.google.com/drive/1UykDpf0FefrcNyCgPW9-KW-7zCQFbzTg#scrollTo=OtY6ihLwfvNJ&line=17&uniqifier=1)\r\n\r\nI did further investigation on my local machine by printing malloc stats at different steps (I used tcmalloc for this run as the stats it prints are more concise and better than the default malloc). This highlights the issue much better.\r\n\r\nMy code:\r\n```\r\nimport ctypes\r\nimport ctypes.util\r\nimport os\r\nimport sys # To print python version\r\nimport tensorflow as tf\r\n\r\nprint('Python version:', sys.version)\r\nprint('Tensorflow version:', tf.version.VERSION)\r\n\r\ndef _load_malloc_lib():\r\n if 'LD_PRELOAD' in os.environ:\r\n malloc_lib = os.environ['LD_PRELOAD'].split('/')[-1]\r\n else:\r\n # Else find the standard libc library and return\r\n malloc_lib = ctypes.util.find_library('c')\r\n return ctypes.CDLL(malloc_lib)\r\n\r\n_libmalloc = _load_malloc_lib()\r\n\r\ndef print_malloc_stats():\r\n if _libmalloc is not None:\r\n _libmalloc.malloc_stats()\r\n\r\n# Allocate a large tensor. This will take 2GB of RAM.\r\nt = tf.random.uniform((2048, 1024*256))\r\nprint('Malloc stats after tensor initialization:')\r\nprint_malloc_stats()\r\nprint()\r\n\r\ndef get_data(_idx):\r\n return t[0, 0]\r\nprint('Malloc stats after get_data function definition:')\r\nprint_malloc_stats()\r\nprint()\r\n\r\nNUM_MAP_CALLS=1\r\nds = tf.data.Dataset.range(1)\r\nfor _ in range(NUM_MAP_CALLS):\r\n ds = ds.map(get_data)\r\nprint('Malloc stats after dataset initialization:')\r\nprint_malloc_stats()\r\nprint()\r\n\r\nprint('dataset initialized\\n')\r\n\r\nitr = iter(ds)\r\nprint('Malloc stats after iterator setup:')\r\nprint_malloc_stats()\r\nprint()\r\n\r\n_ = next(itr)\r\nprint('Malloc stats after calling next on iterator:')\r\nprint_malloc_stats()\r\nprint()\r\n\r\nprint('done')\r\n```\r\n\r\nHere are the logs from running the above code with tcmalloc using `LD_PRELOAD=<tcmalloc_lib>`\r\n\r\n```\r\nPython version: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]\r\nTensorflow version: 2.15.0\r\n2024-01-26 12:02:45.458639: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 2147483648 exceeds 10% of free system memory.\r\nMalloc stats after tensor initialization:\r\n------------------------------------------------\r\nMALLOC: 2231009920 ( 2127.7 MiB) Bytes in use by application\r\nMALLOC: + 1351680 ( 1.3 MiB) Bytes in page heap freelist\r\nMALLOC: + 1794504 ( 1.7 MiB) Bytes in central cache freelist\r\nMALLOC: + 1254400 ( 1.2 MiB) Bytes in transfer cache freelist\r\nMALLOC: + 1906616 ( 1.8 MiB) Bytes in thread cache freelists\r\nMALLOC: + 4980736 ( 4.8 MiB) Bytes in malloc metadata\r\nMALLOC: ------------\r\nMALLOC: = 2242297856 ( 2138.4 MiB) Actual memory used (physical + swap)\r\nMALLOC: + 475136 ( 0.5 MiB) Bytes released to OS (aka unmapped)\r\nMALLOC: ------------\r\nMALLOC: = 2242772992 ( 2138.9 MiB) Virtual address space used\r\nMALLOC:\r\nMALLOC: 7083 Spans in use\r\nMALLOC: 28 Thread heaps in use\r\nMALLOC: 8192 Tcmalloc page size\r\n------------------------------------------------\r\nCall ReleaseFreeMemory() to release freelist memory to the OS (via madvise()).\r\nBytes released to the OS take up virtual address space but no physical memory.\r\n\r\nMalloc stats after get_data function definition:\r\n------------------------------------------------\r\nMALLOC: 2231009920 ( 2127.7 MiB) Bytes in use by application\r\nMALLOC: + 1351680 ( 1.3 MiB) Bytes in page heap freelist\r\nMALLOC: + 1794504 ( 1.7 MiB) Bytes in central cache freelist\r\nMALLOC: + 1254400 ( 1.2 MiB) Bytes in transfer cache freelist\r\nMALLOC: + 1906616 ( 1.8 MiB) Bytes in thread cache freelists\r\nMALLOC: + 4980736 ( 4.8 MiB) Bytes in malloc metadata\r\nMALLOC: ------------\r\nMALLOC: = 2242297856 ( 2138.4 MiB) Actual memory used (physical + swap)\r\nMALLOC: + 475136 ( 0.5 MiB) Bytes released to OS (aka unmapped)\r\nMALLOC: ------------\r\nMALLOC: = 2242772992 ( 2138.9 MiB) Virtual address space used\r\nMALLOC:\r\nMALLOC: 7083 Spans in use\r\nMALLOC: 28 Thread heaps in use\r\nMALLOC: 8192 Tcmalloc page size\r\n------------------------------------------------\r\nCall ReleaseFreeMemory() to release freelist memory to the OS (via madvise()).\r\nBytes released to the OS take up virtual address space but no physical memory.\r\n\r\nMalloc stats after dataset initialization:\r\n------------------------------------------------\r\nMALLOC: 2231332056 ( 2128.0 MiB) Bytes in use by application\r\nMALLOC: + 983040 ( 0.9 MiB) Bytes in page heap freelist\r\nMALLOC: + 1790136 ( 1.7 MiB) Bytes in central cache freelist\r\nMALLOC: + 1185280 ( 1.1 MiB) Bytes in transfer cache freelist\r\nMALLOC: + 2026608 ( 1.9 MiB) Bytes in thread cache freelists\r\nMALLOC: + 4980736 ( 4.8 MiB) Bytes in malloc metadata\r\nMALLOC: ------------\r\nMALLOC: = 2242297856 ( 2138.4 MiB) Actual memory used (physical + swap)\r\nMALLOC: + 475136 ( 0.5 MiB) Bytes released to OS (aka unmapped)\r\nMALLOC: ------------\r\nMALLOC: = 2242772992 ( 2138.9 MiB) Virtual address space used\r\nMALLOC:\r\nMALLOC: 7104 Spans in use\r\nMALLOC: 28 Thread heaps in use\r\nMALLOC: 8192 Tcmalloc page size\r\n------------------------------------------------\r\nCall ReleaseFreeMemory() to release freelist memory to the OS (via madvise()).\r\nBytes released to the OS take up virtual address space but no physical memory.\r\n\r\ndataset initialized\r\n\r\n2024-01-26 12:02:46.317419: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 2147483648 exceeds 10% of free system memory.\r\nMalloc stats after iterator setup:\r\n------------------------------------------------\r\nMALLOC: 2231803544 ( 2128.4 MiB) Bytes in use by application\r\nMALLOC: + 2148024320 ( 2048.5 MiB) Bytes in page heap freelist\r\nMALLOC: + 1740992 ( 1.7 MiB) Bytes in central cache freelist\r\nMALLOC: + 1188352 ( 1.1 MiB) Bytes in transfer cache freelist\r\nMALLOC: + 2248360 ( 2.1 MiB) Bytes in thread cache freelists\r\nMALLOC: + 7208960 ( 6.9 MiB) Bytes in malloc metadata\r\nMALLOC: ------------\r\nMALLOC: = 4392214528 ( 4188.7 MiB) Actual memory used (physical + swap)\r\nMALLOC: + 270336 ( 0.3 MiB) Bytes released to OS (aka unmapped)\r\nMALLOC: ------------\r\nMALLOC: = 4392484864 ( 4189.0 MiB) Virtual address space used\r\nMALLOC:\r\nMALLOC: 7145 Spans in use\r\nMALLOC: 40 Thread heaps in use\r\nMALLOC: 8192 Tcmalloc page size\r\n------------------------------------------------\r\nCall ReleaseFreeMemory() to release freelist memory to the OS (via madvise()).\r\nBytes released to the OS take up virtual address space but no physical memory.\r\n\r\nMalloc stats after calling next on iterator:\r\n------------------------------------------------\r\nMALLOC: 2231872752 ( 2128.5 MiB) Bytes in use by application\r\nMALLOC: + 2147926016 ( 2048.4 MiB) Bytes in page heap freelist\r\nMALLOC: + 1840152 ( 1.8 MiB) Bytes in central cache freelist\r\nMALLOC: + 1189376 ( 1.1 MiB) Bytes in transfer cache freelist\r\nMALLOC: + 2177272 ( 2.1 MiB) Bytes in thread cache freelists\r\nMALLOC: + 7208960 ( 6.9 MiB) Bytes in malloc metadata\r\nMALLOC: ------------\r\nMALLOC: = 4392214528 ( 4188.7 MiB) Actual memory used (physical + swap)\r\nMALLOC: + 270336 ( 0.3 MiB) Bytes released to OS (aka unmapped)\r\nMALLOC: ------------\r\nMALLOC: = 4392484864 ( 4189.0 MiB) Virtual address space used\r\nMALLOC:\r\nMALLOC: 7154 Spans in use\r\nMALLOC: 42 Thread heaps in use\r\nMALLOC: 8192 Tcmalloc page size\r\n------------------------------------------------\r\nCall ReleaseFreeMemory() to release freelist memory to the OS (via madvise()).\r\nBytes released to the OS take up virtual address space but no physical memory.\r\n\r\ndone\r\n```\r\n\r\nAs you can see from the malloc stats, there is an extra allocation of 2GB when the dataset iterator is initialized using `iter(ds)`. This allocation is freed before returning to the caller, but malloc still holds on to that memory and not yet return it to the OS.\r\n\r\nTo exacerbate the problem here, if you increase the number of chained `map` calls on the dataset, you will see the memory allocations increase by a multiple of 2GB (which is the size of the tensor `t` referenced by `get_data` function).\r\n\r\nThis is not a problem in general when referencing small data from the method used in `map`. But if the data referenced is big, then this may become an issue.\r\n\r\nFWIW, I also see a similar copy of tensor when using `tf.data.Dataset.from_tensors`, where the tensor passed to `from_tensors` is copied. Not sure if it is the same underlying problem.\r\n\r\nI hope this helps clarify the underlying issue. Thanks!",
"I have same problem at 2.12, any solution?🫠🫠",
"@wilsingosti , Could you please put your comment on how chaining of map functions actually accumulating memory with each chaining of map function as it seems each chain call actually creating memory for Input array and it gets accumulating each time. is this intended behaviour?"
] | 2024-01-13T17:07:04 | 2024-02-22T16:08:38 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.15.0
### Custom code
Yes
### OS platform and distribution
Linux
### Mobile device
_No response_
### Python version
3.10.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When `tf.data.Dataset.map` is called with a function which references tensor(s) (or a nested object with tensors), it seems to be making a copy of these tensors. If these tensors are large, this causes large memory allocations which can cause the process to OOM.
Please see the below code snippet which reproduces the issue ([here](https://colab.research.google.com/drive/1UykDpf0FefrcNyCgPW9-KW-7zCQFbzTg#scrollTo=OtY6ihLwfvNJ) is a reference to the colab). I am allocating a tensor which takes 2GB of memory and then referencing it in the `get_data` function. This function is used in `tf.data.Dataset.map` to construct the dataset. I am chaining multiple `map` calls to exacerbate the bug to cause OOM in colab. Each `map` call allocates a new copy of the original tensor referenced by the passed function.
Please note that this is not a memory leak as these copies are subsequently freed and the memory is released back to the mem allocator. However, depending on the allocator and it's settings, the allocator may hold on to the memory for a long time and not release back to the OS, causing a memory bloat for the process in the best case, and an OOM in the worse case.
It is expected that these tensor copies do not happen as there is no functional need.
It is possible that the root cause of this issue is the same as #61344 , in which case feel free to close this issue and track the underlying bug over there.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
print(tf.version.VERSION)
# Depending on the underlying RAM resources,
# increase this value to see OOM. With 10,
# this code should OOM with total RAM resources of 20GB or less.
NUM_MAP_CALLS=10
# Allocate a large tensor. This will take 2GB of RAM.
t = tf.random.uniform((2048, 1024*256))
def get_data(_idx):
return t[0, 0]
ds = tf.data.Dataset.range(1)
for _ in range(NUM_MAP_CALLS):
ds = ds.map(get_data)
next(iter(ds))
```
### Relevant log output
```shell
Jan 13, 2024, 12:04:55 PM WARNING WARNING:root:kernel 0585280c-54b4-4ab5-8a4d-e5502242c92a restarted
Jan 13, 2024, 12:04:55 PM INFO KernelRestarter: restarting kernel (1/5), keep random ports
Jan 13, 2024, 12:04:49 PM WARNING 2024-01-13 17:04:49.124663: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 2147483648 exceeds 10% of free system memory.
Jan 13, 2024, 12:04:44 PM WARNING 2024-01-13 17:04:44.817746: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 2147483648 exceeds 10% of free system memory.
Jan 13, 2024, 12:04:39 PM WARNING 2024-01-13 17:04:39.468439: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 2147483648 exceeds 10% of free system memory.
Jan 13, 2024, 12:04:32 PM WARNING 2024-01-13 17:04:32.994518: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 2147483648 exceeds 10% of free system memory.
Jan 13, 2024, 12:04:28 PM WARNING 2024-01-13 17:04:28.102305: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 2147483648 exceeds 10% of free system memory.
```
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Unable to load the model using vscode
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[
"Make sure to check again that the Google Collab TensorFlow version is the same as your local version. This error often shows up when you try to load a model trained on an older version of TensorFlow into a newer version.",
"> Make sure to check again that the Google Collab TensorFlow version is the same as your local version. This error often shows up when you try to load a model trained on an older version of TensorFlow into a newer version.\r\n\r\n@SunnyWan59 Thanks for your response. I make sure that my machine has `tensorflow version = 2.15.0` and the model which is trained using google colab has exactly same version\r\n<img width=\"419\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/24364597/e1228704-2a1e-4b5c-a776-1e3e7789e23c\">\r\n\r\n<img width=\"389\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/24364597/6a70f7df-7295-41ea-9768-3bd808426205\">\r\n",
"Sir,\r\n\r\nI request you to provide me the complete code and files required direct for VS Code execution so that I can run it and test it in my local machine as the saved model is not available to me to run the code. However, simply from the error logged, I think there is some parameters issue in the model which is causing the model as a whole incompatible between its layers and thus the error.\r\n\r\nThank you.",
"@Praveer1981 If you saved the model please confirm that weights were included using model.save_weights() or a similar method. Please save the model with the same TensorFlow/Keras version you're using in VS Code. \r\nIn order to expedite the trouble-shooting process, please provide a complete code snippet to reproduce the issue reported here. Thank you!",
"@sushreebarsa below is my code ,\r\n\r\nreference :\r\n[Train the model](https://keras.io/examples/vision/image_classification_from_scratch/)\r\n\r\n```\r\nepochs = 25\r\nmodelPath = \"/content/drive/MyDrive/SavedModels/FruitClassification\"\r\ncallbacks = [\r\n ModelCheckpoint(f\"{modelPath}/save_at_{{epoch}}.keras\"),\r\n]\r\nmodel.compile(\r\n optimizer='adam',\r\n loss=SparseCategoricalCrossentropy(from_logits=True),\r\n metrics=['accuracy']\r\n)\r\n\r\nhistory1 = model.fit(\r\n train_ds,\r\n epochs=epochs,\r\n callbacks=callbacks,\r\n validation_data=val_ds,\r\n )\r\n```\r\n\r\nI haven't used **model.save_weights()** \r\n\r\n",
"> [sushreebarsa](/sushreebarsa)\r\n\r\n@sushreebarsa any advice please ?",
"@Praveer1981 Sorry for the late response!\r\nPlease ensure the TensorFlow versions used for saving and loading the model are compatible. Mismatched versions can lead to loading errors.\r\nCould you please make sure that you are using keras3 as the reference you are using is also keras3 based?\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/62787\">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/62787\">No</a>\n"
] | 2024-01-13T07:49:14 | 2024-02-09T01:46:31 | 2024-02-09T01:46:28 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No, I dnt know how to do it
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
windows 11
### Mobile device
_No response_
### Python version
3.11.2
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I am unable to load the model using vscode. It throws below error :
**Error loading the model: Layer 'conv2d' expected 2 variables, but received 0 variables during loading. Expected: ['conv2d/kernel:0', 'conv2d/bias:0']**
Below is my code snippet :
```
epochs = 25
modelPath = "/content/drive/MyDrive/SavedModels/FruitClassification"
callbacks = [
ModelCheckpoint(f"{modelPath}/save_at_{{epoch}}.keras"),
]
model.compile(
optimizer='adam',
loss=SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy']
)
history1 = model.fit(
train_ds,
epochs=epochs,
callbacks=callbacks,
validation_data=val_ds,
)
```
Above is the code that I wrote (**Google Colab**) to generate the model with .keras extension. I downloaded that model in my local machine and I am using below code to load the model:
```
try:
MODEL = tf.keras.models.load_model("./save_at_20.keras")
except ValueError as e:
print("Error loading the model:", e)
Unfortunately, I encountered the below error:
```
**Error loading the model: Layer 'conv2d' expected 2 variables, but received 0 variables during loading. Expected: ['conv2d/kernel:0', 'conv2d/bias:0']**
The TensorFlow version is 2.15.0, both in Google Colab and on my local machine. My machine has Python 3.11.2 installed.
could someone help me on it ?
### Standalone code to reproduce the issue
```shell
epochs = 25
modelPath = "/content/drive/MyDrive/SavedModels/FruitClassification"
callbacks = [
ModelCheckpoint(f"{modelPath}/save_at_{{epoch}}.keras"),
]
model.compile(
optimizer='adam',
loss=SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy']
)
history1 = model.fit(
train_ds,
epochs=epochs,
callbacks=callbacks,
validation_data=val_ds,
)
Above is the code that I wrote(google colab) to generate the model with .keras extension. I downloaded that model in my local machine and I am using below code to load the model:
try:
MODEL = tf.keras.models.load_model("./save_at_20.keras")
except ValueError as e:
print("Error loading the model:", e)
Unfortunately, I encountered the below error:
Error loading the model: Layer 'conv2d' expected 2 variables, but received 0 variables during loading. Expected: ['conv2d/kernel:0', 'conv2d/bias:0']
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs572slH
| 62,786 |
TFLite FP16 with Core ML Delegate gets wrong result and has no speed up
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[
"Hi @TimYao18, I was able to get that original xcode project running... where are you making your code changes? It looks like it's supposed to be \"ModelDataHandler\", the easiest way would be to fork that repo and make your changes so that I can view it best in context. Are you manually loading the image into `data` or populating it from your actual Camera? Thanks for any information you can provide.",
"I revamped the entire ViewController to allow image selection for inference. However, I feel like the main issue arose when I switched the model to fp16.\r\n\r\nHere are the results after replacing the model with [the fp16 model I provided above](https://1drv.ms/u/s!Agxu6N1sjukEgageT2CWn384H4AHJw?e=bW5IIq) in the original Midas app on iPhone 15 Pro Max:\r\n\r\n\r\n",
"Hi @TimYao18, understood your code is probably not the issue but knowing how you are doing it will help me immensely in reproducing the issue. If you can't share it let me know and I'll try to figure out how to change it on my end to just load an arbitrary image, but if you are able to share it, that will expedite the resolution of the issue, thanks!",
"I think I cannot share my whole codes but I can give you some that different from official sample app:\r\n\r\n```swift\r\n\r\n// ViewController.swift\r\n\r\n// To pick up an image\r\nlet imagePicker = UIImagePickerController()\r\nimagePicker.sourceType = .camera\r\npresent(imagePicker, animated: true, completion: nil)\r\n\r\n// Process return images\r\nfunc imagePickerController(\r\n _ picker: UIImagePickerController,\r\n didFinishPickingMediaWithInfo info: [UIImagePickerController.InfoKey: Any]\r\n ) {\r\n picker.dismiss(animated: true, completion: nil)\r\n \r\n if let selectedImage = info[.originalImage] as? UIImage {\r\n let pb = convertUIImageToPixelBuffer(selectedImage)\r\n runModel(on: pb!)\r\n }\r\n}\r\n\r\nfunc convertUIImageToPixelBuffer(_ image: UIImage) -> CVPixelBuffer? {\r\n guard let ciImage = CIImage(image: image) else {\r\n return nil\r\n }\r\n\r\n let context = CIContext()\r\n\r\n var pixelBuffer: CVPixelBuffer?\r\n CVPixelBufferCreate(kCFAllocatorDefault,\r\n Int(ciImage.extent.size.width),\r\n Int(ciImage.extent.size.height),\r\n kCVPixelFormatType_32BGRA,\r\n nil,\r\n &pixelBuffer)\r\n\r\n guard let buffer = pixelBuffer else {\r\n return nil\r\n }\r\n\r\n context.render(ciImage, to: buffer)\r\n\r\n return pixelBuffer\r\n }\r\n\r\n```",
"I'm having trouble using the CoreMLDelegate on the emulator, @yishuangP, can you please take a look? Thanks."
] | 2024-01-12T02:08:44 | 2024-05-29T22:19:41 | null |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution: iOS 17.2 (iPhone 13 Pro)
- TensorFlow library: TensorFlowLiteSwift/CoreML ~> 0.0.1-nightly'
### 2. Code
Please refer to [Tensorflow Lite MiDaS iOS Example](https://github.com/isl-org/MiDaS/tree/master/mobile/ios)
And I found the Core ML delegate inference speed was not increasing, the same as FP32 model.
```swift
var options = Interpreter.Options()
var delegates: [Delegate] = [coreMLDelegate]
var interpreter = try Interpreter(modelPath:"MiDaS_FP16.tflite", options: options, delegates: delegates)
try interpreter.allocateTensors()
inputTensor = try interpreter.input(at: 0)
outputTensor = try interpreter.output(at: 0)
do {
try interpreter.copy(data, toInputAt: 0)
// Run inference by invoking the `Interpreter`.
try interpreter.invoke()
// Get the output `Tensor` to process the inference results.
outputTensor = try interpreter.output(at: 0)
} catch let error {
os_log(
"Failed to invoke the interpreter with error: %s", type: .error,
error.localizedDescription)
return
}
```
### 3. Failure after conversion
- The original image
- Model produces results using Core ML Delegate.
- Model produces results using Metal Delegate.
### 5. (optional) Any other info / logs
[MiDaS Float 16 TFLite model download from my OneDrive](https://1drv.ms/u/s!Agxu6N1sjukEgageT2CWn384H4AHJw?e=bW5IIq)
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ROCm platform installation guide
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[
"@sachinprasadhs When can we expect reaction on this issue? It can affect our team planning, that's why it would be nice to know.",
"@MarkDaoust are you able to speak to this request or know who best to route it to? ",
"> @MarkDaoust are you able to speak to this request or know who best to route it to?\r\n\r\n@MarkDaoust can we get some feedbacks? ",
"That will be my team. \r\n\r\nThese files all come from the docs repo. You can submit changes there. \r\n\r\nhttps://github.com/tensorflow/docs/tree/master/site/en/install\r\n\r\nBut this does sound like a big enough change that we should probably give feedback early. \r\n\r\nAnother layout that could be easier initially would be to add a rocm.md doc to the install section.\r\n\r\n@joefernandez WDYT?"
] | 2024-01-11T17:05:44 | 2024-01-25T23:49:58 | null |
NONE
| null | null | null |
### Issue type
Documentation Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
master branch
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
We tried to write an email: https://github.com/tensorflow/tensorflow/issues/62784
I am currently working on ROCm documentation project as technical writer with @saadrahim and @lisadelaney. The AMD would like to extend the documentation with ROCm platform related information. The main areas, which first we would like to cover:
- install page: https://www.tensorflow.org/install/
- build from source [Linux] page: https://www.tensorflow.org/install/source
- pip install page: https://www.tensorflow.org/install/pip
- Docker install page: https://www.tensorflow.org/install/docker
Our goal is to keep the docs as clear and easy to navigate as is. Adding AMD GPU support to the docs may warrant minor structural changes. We are confident that we can devise a solution that satisfies all parties, but to minimize review bounces and implementation effort:
- Are there generic principles, or principles specific to this document to keep in mind (beside keeping the formatting and grammatical style intact)?
- Are changing these files sufficient or are there others files we have to update to streamline the upstream process?
Some of the things we keep in mind during contribution:
- Platform coverage initially will be narrower with AMD support. (No 1:1 mapping between CUDA/ROCm coverage.)
- Document structure would somehow have to reflect this and be clear.
- The current docs conflate "GPU" support with "CUDA" support.
- The install instructions (and with time other doc parts) would need to be clearer on what is related to offload acceleration and what is specific to vendors.
We already find the following page: https://www.tensorflow.org/community/contribute/docs
### Standalone code to reproduce the issue
```shell
I can't give you standalone code for this.
```
### Relevant log output
_No response_
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| 62,784 |
Tensorflow docs email address is invalid ([email protected])
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[
"Hi @neon60 ,\r\n\r\nI acknowledge the issue. Same issue in [README.md](https://github.com/tensorflow/docs/blob/master/README.md#:~:text=And%20join%20the%20TensorFlow%20documentation%20contributors%20on%20the%20docs%40tensorflow.org%20mailing%20list.) also. Will discuss internally and comeback.\r\n\r\nThanks!\r\n\r\n",
"@neon60 , The Proposed PR got merged. Please verify and close the issue if resolved. Thanks!",
"Yes, these links are correct now. 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/62784\">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/62784\">No</a>\n"
] | 2024-01-11T16:58:46 | 2024-01-25T16:36:53 | 2024-01-25T16:36:49 |
NONE
| null | null | null |
### Issue type
Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
master branch
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The email address for the docs team is invalid:
https://github.com/tensorflow/docs/blob/master/CONTRIBUTING.md?plain=1#L10
### Standalone code to reproduce the issue
```shell
The email address for the docs team is invalid:
https://github.com/tensorflow/docs/blob/master/CONTRIBUTING.md?plain=1#L10
```
### Relevant log output
_No response_
|
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| 2,076,473,981 |
I_kwDOArmXAs57xHp9
| 62,783 |
Potential memory leak with SymbolicTensor
|
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[
"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/9f94bdbe7e415d971172508be6916f44/62783.ipynb). Thank you!",
"Being discussed in Keras. #[19058](https://github.com/keras-team/keras/issues/19058)",
"@SuryanarayanaY I posted a similar thing on Keras as I was not sure if it was a TF problem or a Keras problem",
"Any news @SuryanarayanaY? I just have a research paper waiting on this bugfix >.<"
] | 2024-01-11T12:16:19 | 2024-02-07T08:55:36 | null |
CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15
### Custom code
Yes
### OS platform and distribution
Manjaro Linux
### Mobile device
_No response_
### Python version
3.11.5
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.3.52
### GPU model and memory
_No response_
### Current behavior?
Hello,
I am working on a TinyML NAS framework, i.e. throughout the execution of my code, hundreds if not thousands of models are created and trained. I have come across a problem that has been starving my system of memory after a couple of days of execution. By using `tracemalloc` I have been able to see that the main contributor appears to be the symbolic tensor created when creating a Conv2D layer. Maybe I am missing something basic in terms of garbage collection in my code but over time the demo code will eventually consume all system memory.
I have also tried `tf.keras.backend.clear_session()` and `gc.collect()` but neither help.
Any help would be appreciated.
Cheers
### Standalone code to reproduce the issue
```shell
import gc
import tracemalloc, sys, linecache, os
import numpy as np
import tensorflow as tf
from tensorflow import keras
EPOCHS = 5
BS = 512
TEST_LOOPS = 10000
def start_tracemalloc():
tracemalloc.start()
def display_top(snapshot, key_type="lineno", limit=5):
snapshot = snapshot.filter_traces(
(
tracemalloc.Filter(False, "<frozen importlib._bootstrap>"),
tracemalloc.Filter(False, "<unknown>"),
)
)
top_stats = snapshot.statistics(key_type)
print("Top %s lines" % limit)
for index, stat in enumerate(top_stats[:limit], 1):
frame = stat.traceback[0]
# replace "/path/to/module/file.py" with "module/file.py"
filename = os.sep.join(frame.filename.split(os.sep)[-2:])
print(
"#%s: %s:%s: %.1f KiB" % (index, filename, frame.lineno, stat.size / 1024)
)
line = linecache.getline(frame.filename, frame.lineno).strip()
if line:
print(" %s" % line)
other = top_stats[limit:]
if other:
size = sum(stat.size for stat in other)
print("%s other: %.1f KiB" % (len(other), size / 1024))
total = sum(stat.size for stat in top_stats)
print("Total allocated size: %.1f KiB" % (total / 1024))
def display_snapshot():
snapshot = tracemalloc.take_snapshot()
display_top(snapshot)
def create_model() -> keras.models.Model:
inputs = keras.layers.Input(shape=(28, 28, 1))
x = keras.layers.Conv2D(32, kernel_size=(3, 3), padding="valid")(inputs)
x = keras.layers.MaxPooling2D(pool_size=(2, 2), strides=None)(x)
x = keras.layers.Flatten()(x)
x = keras.layers.Dense(128, activation=tf.nn.relu)(x)
x = keras.layers.Dropout(0.2)(x)
outputs = keras.layers.Dense(10, activation=tf.nn.softmax)(x)
model = keras.models.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
return model
def train_model(model, train_images, train_labels):
model.fit(train_images, train_labels, epochs=EPOCHS, batch_size=BS)
def main() -> int:
mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images.reshape(train_images.shape[0], train_images.shape[1], train_images.shape[2], 1)
train_images = train_images.astype(np.float32) / 255.0
start_tracemalloc()
for i in range(TEST_LOOPS):
tf.keras.backend.clear_session()
gc.collect()
model = create_model()
train_model(model, train_images, train_labels)
display_snapshot()
del model
if __name__ == '__main__':
sys.exit(main())
```
### Relevant log output
```shell
Epoch 1/5
...
Top 5 lines
#1: <frozen abc>:123: 1856.2 KiB
#2: python3.11/linecache.py:137: 606.4 KiB
lines = fp.readlines()
#3: framework/ops.py:245: 197.9 KiB
return pywrap_tf_session.PyTensor.__new__(
#4: <frozen importlib._bootstrap_external>:729: 158.6 KiB
#5: framework/ops.py:1211: 137.3 KiB
self._gradient_function = None
952 other: 1407.6 KiB
Total allocated size: 4364.1 KiB
Epoch 1/5
...
Top 5 lines
#1: <frozen abc>:123: 1846.9 KiB
#2: python3.11/linecache.py:137: 620.8 KiB
lines = fp.readlines()
#3: framework/ops.py:245: 382.3 KiB
return pywrap_tf_session.PyTensor.__new__(
#4: framework/ops.py:1211: 264.9 KiB
self._gradient_function = None
#5: framework/ops.py:1161: 254.7 KiB
self = Operation(c_op, SymbolicTensor)
947 other: 2160.5 KiB
Total allocated size: 5530.0 KiB
Epoch 1/5
...
Top 5 lines
#1: <frozen abc>:123: 1843.9 KiB
#2: python3.11/linecache.py:137: 620.8 KiB
lines = fp.readlines()
#3: framework/ops.py:245: 568.5 KiB
return pywrap_tf_session.PyTensor.__new__(
#4: framework/ops.py:1211: 394.1 KiB
self._gradient_function = None
#5: framework/ops.py:1161: 378.9 KiB
self = Operation(c_op, SymbolicTensor)
949 other: 2702.3 KiB
Total allocated size: 6508.5 KiB
```
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| 2,076,354,641 |
PR_kwDOArmXAs5jynz5
| 62,782 |
Set `reshuffle_each_iteration` in `Dataset.shuffle()` directly to `True` to avoid confusion
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[
"I think I might keep the 3rd task to a separate PR, as I'm still working on a proper way to track if dataset has undergone shuffle(reshuffle_each_iteration=True) before `take` and `skip`. And this PR would not change any behaviour as far as I know. Can you please review? @aaudiber @gbaned ",
"Test is failing because we need to update the golden files with the API change: \r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/fb15a8cad275744f901388f48d2a3ffc56a03224/tensorflow/tools/api/golden/v1/tensorflow.data.-dataset.pbtxt#L190-L193\r\nhttps://github.com/tensorflow/tensorflow/blob/fb15a8cad275744f901388f48d2a3ffc56a03224/tensorflow/tools/api/golden/v2/tensorflow.data.-dataset.pbtxt#L157-L160\r\n\r\nPlease updates the `None`s to `True` there as well",
"Thanks for reviewing and the comments @aaudiber . I'm not quite aware of the API and thanks for the input!",
"Hi @aaudiber. Thanks for reviewing. Also I noticed some tests are failing (ROCm/MacOS CPU and such) though they don't have a `Required` tag. Should I be concerned?",
"This PR has been approved and taged `ready to pull` for over a month now. Is there anything I should do? Thanks! @gbaned @aaudiber "
] | 2024-01-11T11:22:51 | 2024-03-14T01:54:11 | 2024-03-13T20:39:26 |
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## Summary
Set the default `reshuffle_each_iteration` value in `tf.data.Dataset.shuffle` method to `True` directly (previous was `None` but was interpreted to `True`) to raise awareness of possible silent data leakage, see discussion #59279.
## Details (copied and cleaned up from #59279)
The `Dataset.shuffle` method might lead to **dangerously silent** data leakage:
In the [Dataset doc](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) under the `shuffle` method, it reads:
> shuffle(
> buffer_size, seed=None, reshuffle_each_iteration=None, name=None
> )
where the `reshuffle_each_iteration=None` is **super misleading**, as it be easily misinterpreted that reshuffled is off by default (**which is not true at all**).
The `shuffle` method, which called `shuffle_op._shuffle`:
https://github.com/tensorflow/tensorflow/blob/4dacf3f368eb7965e9b5c3bbdd5193986081c3b2/tensorflow/python/data/ops/dataset_ops.py#L1472-L1473
which then called `_ShuffleDataset`:
https://github.com/tensorflow/tensorflow/blob/b756c44e3f3ed52ccb4f05736569b95f4481eea0/tensorflow/python/data/ops/shuffle_op.py#L25-L32
which finally inititate the `_ShuffleDataset` class:
https://github.com/tensorflow/tensorflow/blob/b756c44e3f3ed52ccb4f05736569b95f4481eea0/tensorflow/python/data/ops/shuffle_op.py#L35-L50
has the following dangerous definition:
```
if reshuffle_each_iteration is None:
reshuffle_each_iteration = True
```
As a result, the default `reshuffle_each_iteration = None` would be interpreted to `reshuffle_each_iteration = True` (which is truly unexpected by user).
## TODO list:
- [X] Set the default value of `reshuffle_each_iteration` directly to `True`
- [x] Add warning in docs about possible data leakage related to `reshuffle_each_iteration = True`
~~Issue warning when training/validation datasets are split by using the `shuffle + take/skip` pattern~~ (scheduled for a separate follow-up PR)
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[
"@bergentruckung did you build this from source? if so what flags did you use when building?\r\n",
"Yup, here are the flags that I used:\r\n```\r\n-msse4.2 -mavx2 -mfma -march=sandybridge -mtune=broadwell\r\n```\r\nI believe this is because TF sees that I have better instructions to use, but my build doesn't support it? If that's the case, then I'm good and would there be a way to silence this message?\r\n\r\nAlso, I see this sometimes:\r\n```\r\nIn [1]: import tensorflow \r\n2024-01-15 01:19:37.725211: 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\n```\r\nIs that also a problem? I'm using cuDNN 8.9.5. Should I use the env variable to turn it off, or should I use oneDNN? What are the implications of using oneDNN? In case I'm supposed to turn it off, is there a better way to turn it off, rather than using the env variable (maybe by using a config file)?",
" - `oneDNN custom operations are on` this means that TensorFlow has enabled custom operations from the oneDNN library.\r\n\r\n- `You may see slightly different numerical results due to floating-point round-off errors from different computation orders` this tells you that oneDNN might introduce slight numerical differences in results due to floating-point round-off errors, especially when dealing with different computation orders.\r\n- `To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.` in order to disable the oneDNN custom operations and get consistent numerical results across different computation orders, you can set the TF_ENABLE_ONEDNN_OPTS environment variable to 0 or use do the same in the `Python` interface. \r\n\r\n```python\r\nimport os\r\nos.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'\r\n# import tensorflow with disabled ONEDNN\r\nimport tensorflow\r\n```\r\n",
"Hi **@bergentruckung** ,\r\nSorry for the delay, \"Using TensorFlow backend,\" indicates that TensorFlow is using its default backend for computations.\r\nIf you want to silence these messages or suppress the TensorFlow informational logs, you can adjust the TensorFlow logging level. TensorFlow uses the tf.logging module for logging. For this you need to use\r\n```\r\nimport tensorflow as tf\r\ntf.logging.set_verbosity(tf.logging.ERROR)\r\n```\r\nThis will suppress INFO and WARNING level messages. Place this code at the beginning of your script or notebook to control the logging level when TensorFlow is imported.\r\n\r\nTensorFlow is using oneDNN (formerly known as Intel MKL-DNN) for custom operations, which may lead to slightly different numerical results due to floating-point round-off errors from different computation orders. oneDNN is a highly optimized deep learning library, and TensorFlow uses it for certain operations to enhance performance.\r\n\r\nIf you're not facing any issues with numerical stability or differences in results, you can leave it enabled. However, if you want to turn it off, you can set the environment variable as like this.\r\n```\r\nimport os\r\nos.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'\r\nimport tensorflow as tf\r\n```\r\nthis will disable oneDNN custom operations.\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/62781\">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/62781\">No</a>\n"
] | 2024-01-11T10:48:04 | 2024-02-02T01:47:10 | 2024-02-02T01:47:07 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.13.1
### Custom code
No
### OS platform and distribution
RHEL 8.9
### Mobile device
_No response_
### Python version
3.11.4
### Bazel version
5.4
### GCC/compiler version
10.4
### CUDA/cuDNN version
8.9.5
### GPU model and memory
A100 PCIe
### Current behavior?
Importing tensorflow prints the following:
```
In [1]: import tensorflow as tf
2024-01-11 05:40:05.061907: 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: AVX512F, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Using TensorFlow backend.
```
It was built with support for GPUs and I can assert that from `tf.test.is_built_with_gpu_support()`. Are the above messages problematic? If they're not, what's the way to silence them while importing tensorflow?
### Standalone code to reproduce the issue
```shell
import tensorflow
```
```
### Relevant log output
```shell
In [1]: import tensorflow as tf
2024-01-11 05:40:05.061907: 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: AVX512F, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Using TensorFlow backend.
```
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support POW operator tflm
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[
"I have implemented POW operator locally. It would be great if we could integrate it into the main repo",
"@vigi04,\r\nCould you please refer to the guide on how to port TFLite ops to TFLM is located here:\r\nhttps://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/docs/porting_reference_ops.md\r\n\r\nThank you!\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/62780\">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/62780\">No</a>\n"
] | 2024-01-11T10:33:29 | 2024-01-28T01:48:06 | 2024-01-28T01:48:03 |
NONE
| null | null | null |
POW operator support on tflm
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PR_kwDOArmXAs5jwELw
| 62,779 |
[XLA:TSL] add gil acquire when entering catch region
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[
"The fix looks correct, I'm not sure if the TSL repo can accept PRs from TF.\r\n\r\n@ddunl wdyt?",
"Landed this, so closing now. Thanks for the fix!!"
] | 2024-01-11T02:41:31 | 2024-01-18T00:25:23 | 2024-01-18T00:25:20 |
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If not acquice gil, it will segment fault when calling `py::pring(e.what())`.
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I_kwDOArmXAs57tvOP
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Tensorflow numpy_function causes errors when using tf.shape
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[
"Note: Tensorflow 2.8.4 didn't have this issue. Upgrading to 2.13.1 caused the error.\r\n\r\nAn equivalent code that doesn't produce any error in 2.13.1:\r\n\r\n```\r\nimport tensorflow as tf # 2.13.1\r\nimport tensorflow_datasets as tfds # 4.9.3\r\nfrom functools import partial\r\n\r\n\r\nAUTO = tf.data.experimental.AUTOTUNE\r\n\r\ndataset_name = \"mnist\"\r\ndataset = tfds.load(\r\n dataset_name,\r\n split=\"train[:1%]\"\r\n)\r\n\r\ndataset_len = dataset.reduce(0, lambda x, _: x + 1).numpy()\r\nprint(\"dataset_len\", dataset_len)\r\n\r\nimage_size = [28, 28, 1]\r\nnew_image_size = [14, 2, 28, 1]\r\n\r\n\r\ndef prepare_input(\r\n image, labels,\r\n image_size\r\n):\r\n\r\n image = tf.image.resize(image, image_size)\r\n\r\n split_shape = tf.shape(image)[1] // 2\r\n image_reshape = tf.reshape(\r\n image, [split_shape, 2, image_size[1], 1]\r\n )\r\n\r\n return {\r\n \"image\": image_reshape,\r\n \"label\": labels\r\n }\r\n\r\n\r\npartial_prepare_input = partial(\r\n prepare_input,\r\n image_size=tf.constant(image_size[:2], dtype=tf.int32)\r\n)\r\n\r\n\r\ndataset = dataset.map(\r\n lambda sample: partial_prepare_input(\r\n sample[\"image\"],\r\n sample[\"label\"]\r\n ),\r\n num_parallel_calls=AUTO\r\n)\r\n\r\n\r\nfor x in dataset:\r\n print(x['image'].shape, x['label'].shape)\r\n\r\n```",
"Hi @basnetr ,\r\n\r\nI have tested the attached code snippet it executes fine with Tf2.1.3.1 and Tf2.15 as well. Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/54ff05c4391f698c7cdb17ffa18dd915/62778.ipynb).\r\n\r\nPlease check and confirm. Thanks!",
"@SuryanarayanaY Thanks for testing it in Colab, it does seem to run fine there. However, I still get the error on Linux Ubuntu 20.04.6 LTS. Furthermore, this error seems to appear when running on GPU, code also runs without errors on my environment on CPU with:\r\n```\r\nimport os\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\r\n```",
"Hi @basnetr ,\r\n\r\nI have replicated the behaviour with GPU runtime and attached gist here.The problem arises during iteration of the dataset object with GPU runtime. May need to escalate to SME. Thanks!",
"It seems the behaviour is inconsistent.Repeated runs of same code results sometimes success and sometimes error out.\r\n\r\nThanks!\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.",
"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/62778\">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/62778\">No</a>\n",
"@SuryanarayanaY Yes, but it'd be nice to have always success. Currently it's mostly error.",
"Reopening as it is stalled by mistake.",
"I have a similar error with the following code snippet (with tf v2.15.1 and v2.16.1 on ubuntu 22.04):\r\n```\r\nimport tensorflow as tf\r\n\r\n\r\[email protected]_function(Tout = (tf.float32))\r\ndef read(number):\r\n img = tf.constant(number, tf.float32, shape = (512, 424))\r\n img = tf.image.resize(img[..., tf.newaxis], tf.shape(img) // 2, preserve_aspect_ratio = True)\r\n return img\r\n\r\n\r\ndataset = tf.data.Dataset.from_tensor_slices([\r\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\r\ndataset = dataset.map(read, tf.data.AUTOTUNE)\r\ndataset = dataset.batch(4, False, tf.data.AUTOTUNE).prefetch(tf.data.AUTOTUNE)\r\n\r\nfor img in dataset:\r\n print(img.shape)\r\n```\r\n\r\nExecuting this results in different outputs, e.g.:\r\n[test_output_1.txt](https://github.com/tensorflow/tensorflow/files/15392117/test_output_1.txt)\r\n[test_output_2.txt](https://github.com/tensorflow/tensorflow/files/15392137/test_output_2.txt)\r\n\r\nAdditionally the program never terminates and is apparently stuck waiting on something (0% cpu usage)...\r\nAre these problems maybe symptoms of some race condition somewhere?"
] | 2024-01-11T02:23:23 | 2024-05-21T15:50:57 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.13.1
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04.6 LTS
### Mobile device
_No response_
### Python version
3.9.18
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Raises an error.
Expect: No errors.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf # v2.13.0-17-gf841394b1b7 2.13.1
import tensorflow_datasets as tfds # 4.9.3
from functools import partial
AUTO = tf.data.experimental.AUTOTUNE
dataset_name = "mnist"
dataset = tfds.load(
dataset_name,
split="train[:1%]"
)
dataset_len = dataset.reduce(0, lambda x, _: x + 1).numpy()
print("dataset_len", dataset_len)
image_size = [28, 28, 1]
new_image_size = [14, 2, 28, 1]
def prepare_input(
image, labels,
image_size
):
image = tf.image.resize(image, image_size)
split_shape = tf.shape(image)[1] // 2
image_reshape = tf.reshape(
image, [split_shape, 2, image_size[1], 1]
)
return image_reshape, labels
partial_prepare_input = partial(
prepare_input,
image_size=tf.constant(image_size[:2], dtype=tf.int32)
)
def prepare_input_wrapper(sample):
image = sample["image"]
labels = sample["label"]
labels_shape = labels.shape
image, label = tf.numpy_function(
func=partial_prepare_input,
inp=[image, labels],
Tout=[tf.float32, tf.int64],
name="numpy_function_1"
)
image.set_shape(new_image_size)
label.set_shape(labels_shape)
return {
"image": image,
"label": label
}
dataset = dataset.map(
lambda sample: prepare_input_wrapper(sample),
num_parallel_calls=AUTO
)
for x in dataset:
print(x['image'].shape, x['label'].shape)
```
### Relevant log output
```shell
2024-01-11 02:21:59.538118: 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`.
2024-01-11 02:21:59.575984: 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 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-01-11 02:22:00.346338: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-01-11 02:22:02.022172: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] 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
2024-01-11 02:22:02.058030: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] 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
2024-01-11 02:22:02.061266: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] 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
2024-01-11 02:22:02.064864: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] 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
2024-01-11 02:22:02.067959: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] 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
2024-01-11 02:22:02.070999: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] 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
2024-01-11 02:22:02.820643: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] 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
2024-01-11 02:22:02.821876: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] 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
2024-01-11 02:22:02.822898: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] 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
2024-01-11 02:22:02.823877: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13623 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:1e.0, compute capability: 7.5
dataset_len 600
2024-01-11 02:22:03.323582: W tensorflow/core/framework/op_kernel.cc:1828] OP_REQUIRES failed at strided_slice_op.cc:117 : INVALID_ARGUMENT: slice index 1 of dimension 0 out of bounds.
2024-01-11 02:22:03.323637: W tensorflow/core/framework/op_kernel.cc:1828] OP_REQUIRES failed at strided_slice_op.cc:117 : INVALID_ARGUMENT: Expected begin, end, and strides to be 1D equal size tensors, but got shapes [3], [1], and [1] instead.
2024-01-11 02:22:03.324399: W tensorflow/core/framework/op_kernel.cc:1828] OP_REQUIRES failed at strided_slice_op.cc:117 : INVALID_ARGUMENT: slice index 1 of dimension 0 out of bounds.
2024-01-11 02:22:03.324531: W tensorflow/core/framework/op_kernel.cc:1828] OP_REQUIRES failed at strided_slice_op.cc:117 : INVALID_ARGUMENT: Expected begin, end, and strides to be 1D equal size tensors, but got shapes [3], [1], and [1] instead.
2024-01-11 02:22:03.329642: W tensorflow/core/framework/op_kernel.cc:1816] UNKNOWN: InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} slice index 1 of dimension 0 out of bounds. [Op:StridedSlice] name: strided_slice/
Traceback (most recent call last):
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/ops/script_ops.py", line 268, in __call__
ret = func(*args)
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/autograph/impl/api.py", line 643, in wrapper
return func(*args, **kwargs)
File "/home/ubuntu/automltraining/replicate_error.py", line 27, in prepare_input
split_shape = tf.shape(image)[1] // 2
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/framework/ops.py", line 6656, in raise_from_not_ok_status
raise core._status_to_exception(e) from None # pylint: disable=protected-access
tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} slice index 1 of dimension 0 out of bounds. [Op:StridedSlice] name: strided_slice/
2024-01-11 02:22:03.330620: W tensorflow/core/framework/op_kernel.cc:1816] UNKNOWN: InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} Expected begin, end, and strides to be 1D equal size tensors, but got shapes [3], [1], and [1] instead. [Op:StridedSlice] name: strided_slice/
Traceback (most recent call last):
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/ops/script_ops.py", line 268, in __call__
ret = func(*args)
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/autograph/impl/api.py", line 643, in wrapper
return func(*args, **kwargs)
File "/home/ubuntu/automltraining/replicate_error.py", line 27, in prepare_input
split_shape = tf.shape(image)[1] // 2
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/framework/ops.py", line 6656, in raise_from_not_ok_status
raise core._status_to_exception(e) from None # pylint: disable=protected-access
tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} Expected begin, end, and strides to be 1D equal size tensors, but got shapes [3], [1], and [1] instead. [Op:StridedSlice] name: strided_slice/
2024-01-11 02:22:03.333474: W tensorflow/core/framework/op_kernel.cc:1816] UNKNOWN: InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} Expected begin, end, and strides to be 1D equal size tensors, but got shapes [3], [1], and [1] instead. [Op:StridedSlice] name: strided_slice/
Traceback (most recent call last):
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/ops/script_ops.py", line 268, in __call__
ret = func(*args)
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/autograph/impl/api.py", line 643, in wrapper
return func(*args, **kwargs)
File "/home/ubuntu/automltraining/replicate_error.py", line 27, in prepare_input
split_shape = tf.shape(image)[1] // 2
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/framework/ops.py", line 6656, in raise_from_not_ok_status
raise core._status_to_exception(e) from None # pylint: disable=protected-access
tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} Expected begin, end, and strides to be 1D equal size tensors, but got shapes [3], [1], and [1] instead. [Op:StridedSlice] name: strided_slice/
2024-01-11 02:22:03.337583: W tensorflow/core/framework/op_kernel.cc:1816] UNKNOWN: InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} slice index 1 of dimension 0 out of bounds. [Op:StridedSlice] name: strided_slice/
Traceback (most recent call last):
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/ops/script_ops.py", line 268, in __call__
ret = func(*args)
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/autograph/impl/api.py", line 643, in wrapper
return func(*args, **kwargs)
File "/home/ubuntu/automltraining/replicate_error.py", line 27, in prepare_input
split_shape = tf.shape(image)[1] // 2
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/framework/ops.py", line 6656, in raise_from_not_ok_status
raise core._status_to_exception(e) from None # pylint: disable=protected-access
tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} slice index 1 of dimension 0 out of bounds. [Op:StridedSlice] name: strided_slice/
2024-01-11 02:22:03.337628: W tensorflow/core/framework/op_kernel.cc:1816] UNKNOWN: InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} Expected begin, end, and strides to be 1D equal size tensors, but got shapes [3], [1], and [1] instead. [Op:StridedSlice] name: strided_slice/
Traceback (most recent call last):
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/ops/script_ops.py", line 268, in __call__
ret = func(*args)
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/autograph/impl/api.py", line 643, in wrapper
return func(*args, **kwargs)
File "/home/ubuntu/automltraining/replicate_error.py", line 27, in prepare_input
split_shape = tf.shape(image)[1] // 2
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/framework/ops.py", line 6656, in raise_from_not_ok_status
raise core._status_to_exception(e) from None # pylint: disable=protected-access
tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} Expected begin, end, and strides to be 1D equal size tensors, but got shapes [3], [1], and [1] instead. [Op:StridedSlice] name: strided_slice/
Traceback (most recent call last):
File "/home/ubuntu/automltraining/replicate_error.py", line 69, in <module>
for x in dataset:
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/data/ops/iterator_ops.py", line 814, in __next__
return self._next_internal()
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/data/ops/iterator_ops.py", line 777, in _next_internal
ret = gen_dataset_ops.iterator_get_next(
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/ops/gen_dataset_ops.py", line 3028, in iterator_get_next
_ops.raise_from_not_ok_status(e, name)
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/framework/ops.py", line 6656, in raise_from_not_ok_status
raise core._status_to_exception(e) from None # pylint: disable=protected-access
tensorflow.python.framework.errors_impl.UnknownError: {{function_node __wrapped__IteratorGetNext_output_types_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} slice index 1 of dimension 0 out of bounds. [Op:StridedSlice] name: strided_slice/
Traceback (most recent call last):
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/ops/script_ops.py", line 268, in __call__
ret = func(*args)
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/autograph/impl/api.py", line 643, in wrapper
return func(*args, **kwargs)
File "/home/ubuntu/automltraining/replicate_error.py", line 27, in prepare_input
split_shape = tf.shape(image)[1] // 2
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/python/framework/ops.py", line 6656, in raise_from_not_ok_status
raise core._status_to_exception(e) from None # pylint: disable=protected-access
tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} slice index 1 of dimension 0 out of bounds. [Op:StridedSlice] name: strided_slice/
[[{{node numpy_function_1}}]] [Op:IteratorGetNext] name:
```
|
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| 2,075,084,409 |
I_kwDOArmXAs57r0Z5
| 62,777 |
I can't install tensorflow in the newest python and pip versions
|
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[
"@Jalejosam TensorFlow officially supports Python 3.7-3.11. While it might work with Python 3.12.1, please try using a compatible version if possible. You can create virtual environments to manage multiple Python versions. If you're not sure which version is compatible with your system, try installing a specific version like:\r\n```\r\npip install tensorflow==2.15.0\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/62777\">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/62777\">No</a>\n"
] | 2024-01-10T19:51:27 | 2024-01-27T01:46:18 | 2024-01-27T01:46:16 |
NONE
| null | null | null |
I have python 3.12.1 and pip 23.3.2, but I can't install tensorflow libraries, whit this error message
> pip install tensorflow
ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)
ERROR: No matching distribution found for tensorflow
I investigated, and I found that Tensorflow can't be used in the newest python and pip versions, only for python 3.11 and before.
What can I do in order to solve it?
|
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I_kwDOArmXAs57qHru
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How do I confirm if tensorflow uses AVX512 instructions?
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[
"Hi **@bergentruckung**,\r\nSorry for the delay, To confirm if tensorflow is using AVX512 instructions,\r\nCheck TensorFlow Build Configuration:\r\n```\r\nimport tensorflow as tf\r\nprint(tf.sysconfig.get_build_info())\r\n```\r\nCheck Runtime CPU Support:\r\n```\r\nimport tensorflow as tf\r\nprint(tf.config.experimental.list_physical_devices('CPU'))\r\n```\r\nCheck CPUID Instruction:\r\n```\r\nimport tensorflow as tf\r\nprint(tf.experimental.cpuinfo.get_info())\r\n```\r\nThe compiler flags you provided during the build (-msse4.2 -mavx2 -mfma -march=sandybridge -mtune=broadwell) suggest that you've enabled SSE4.2, AVX2, and FMA instructions, but AVX512 is not explicitly enabled. Consider rebuilding TensorFlow with an additional flag like -march=skylake-avx512 to enable AVX512 instructions.\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/62776\">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/62776\">No</a>\n"
] | 2024-01-10T15:37:49 | 2024-02-01T01:48:16 | 2024-02-01T01:48:13 |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.13
### Custom code
Yes
### OS platform and distribution
RedHat Enterprise Linux 8.9
### Mobile device
_No response_
### Python version
3.11.4
### Bazel version
5.4
### GCC/compiler version
10.3
### CUDA/cuDNN version
CUDA 12.2 and cuDNN 8.9.5
### GPU model and memory
A100 80GB PCIe
### Current behavior?
What's the way to assert that the TF that I built from source uses AVX512 instructions? These are the compiler optimizations that I provided during the build:
```
-msse4.2 -mavx2 -mfma -march=sandybridge -mtune=broadwell
```
I don't know if these correspond to the minimum compiler optimizations or the maximum.
### Standalone code to reproduce the issue
```shell
-
```
### Relevant log output
_No response_
|
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[RNN] Keras LSTM converted to "While" OPs with hidden states manipulation - TFLite
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[
"Hi @Doomski99, the UnidirectionalSequenceLSTM op may not allow multiple outputs, the body subgraph of the While op might effectively calculate the same thing as the UnidirectionalSequenceLSTM op. You said this op fails to run on your target platform ... what's your target platform? and what's the error? is there a log or any error message?",
"Hello and thank you for your reply @pkgoogle .\r\n\r\n> the body subgraph of the While op might effectively calculate the same thing as the UnidirectionalSequenceLSTM op\r\n\r\nI understand but can you confirm that the performance is the same between the two operators? Could it be that the UnidirectionalSequenceLSTM op has a better optimized implementation?\r\n\r\n> what's your target platform? and what's the error? is there a log or any error message?\r\n\r\nCadence's HiFi 5 DSP. I'm using a simulation environment. The model with While stops after launching with a \"HALTED\" message unlike the second model which runs perfectly.\r\n\r\n> UnidirectionalSequenceLSTM op may not allow multiple outputs\r\n\r\nCan I get a confirmation? ",
"Hi @Doomski99,\r\n\r\nI was able to create, load, and run both models on colab: https://colab.sandbox.google.com/gist/pkgoogle/7cd581019a15c5db26d9071e3f968294/62775.ipynb .... do you have any limitations or controls in using your simulated environment? Is there any way to relax those constraints? Just judging from the message and my results, I think that message was produced by your environment rather from TFLite (I would've expected an exception if it was thrown from TFLite)\r\n\r\n> I understand but can you confirm that the performance is the same between the two operators? Could it be that the UnidirectionalSequenceLSTM op has a better optimized implementation?\r\n\r\nYou can try (maybe in a general linux system first) to benchmark them: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark\r\n\r\n> Can I get a confirmation?\r\n\r\nI can't confirm, here's the code: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/unidirectional_sequence_lstm.cc It probably depends on what that while body subgraph actually is, i.e. the Op might actually be inside there.\r\n\r\n",
"Thank you for your help @pkgoogle !\r\n\r\nBefore digging through my DSP simulation environment, I've successfully set up the benchmark tool you linked in WSL2 and ran it through my models with the following command:\r\n\r\n`bazel-bin/tensorflow/lite/tools/benchmark/benchmark_model --graph=1x_LSTM_hidden_64_float32.tflite --num_threads=1 --enable_op_profiling=true`.\r\n\r\nThe one with \"UnidirectionalSequenceLstm\" is consistently 35% faster. Here's the output of both benchmarks:\r\n\r\nFirst (UnidirectionalSequenceLstm) model:\r\n\r\n```\r\nbazel-bin/tensorflow/lite/tools/benchmark/benchmark_model --graph=1x_LSTM_64_float32.tflite --num_threads=1 --enable_op_profiling=true\r\nINFO: STARTING!\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Num threads: [1]\r\nINFO: Graph: [1x_LSTM_64_float32.tflite]\r\nINFO: Enable op profiling: [1]\r\nINFO: #threads used for CPU inference: [1]\r\nINFO: Loaded model 1x_LSTM_64_float32.tflite\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nINFO: The input model file size (MB): 0.330964\r\nINFO: Initialized session in 0.63ms.\r\nINFO: Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.\r\n^[[AINFO: count=132 first=3678 curr=3790 min=3270 max=5473 avg=3751.66 std=334\r\n\r\nINFO: Running benchmark for at least 50 iterations and at least 1 seconds but terminate if exceeding 150 seconds.\r\nINFO: count=261 first=3937 curr=3959 min=3190 max=6333 avg=3790.92 std=358\r\n\r\nINFO: Inference timings in us: Init: 630, First inference: 3678, Warmup (avg): 3751.66, Inference (avg): 3790.92\r\nINFO: Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.\r\nINFO: Memory footprint delta from the start of the tool (MB): init=5.16016 overall=7.46484\r\nINFO: Profiling Info for Benchmark Initialization:\r\n============================== Run Order ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n ModifyGraphWithDelegate 0.104 0.104 83.871% 83.871% 0.000 1 ModifyGraphWithDelegate/0\r\n AllocateTensors 0.020 0.020 16.129% 100.000% 0.000 1 AllocateTensors/0\r\n\r\n============================== Top by Computation Time ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n ModifyGraphWithDelegate 0.104 0.104 83.871% 83.871% 0.000 1 ModifyGraphWithDelegate/0\r\n AllocateTensors 0.020 0.020 16.129% 100.000% 0.000 1 AllocateTensors/0\r\n\r\nNumber of nodes executed: 2\r\n============================== Summary by node type ==============================\r\n [Node type] [count] [avg ms] [avg %] [cdf %] [mem KB] [times called]\r\n ModifyGraphWithDelegate 1 0.104 83.871% 83.871% 0.000 1\r\n AllocateTensors 1 0.020 16.129% 100.000% 0.000 1\r\n\r\nTimings (microseconds): count=1 curr=124\r\nMemory (bytes): count=0\r\n2 nodes observed\r\n\r\n\r\n\r\nINFO: Operator-wise Profiling Info for Regular Benchmark Runs:\r\n============================== Run Order ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n RANDOM_UNIFORM 0.003 0.003 0.083% 0.083% 0.000 1 [simple_model/random_uniform/RandomUniform]:0\r\n RANDOM_UNIFORM 0.003 0.003 0.070% 0.153% 0.000 1 [simple_model/random_uniform_1/RandomUniform]:1\r\n UNIDIRECTIONAL_SEQUENCE_LSTM 3.912 3.769 99.648% 99.801% 0.000 1 [tfl.unidirectional_sequence_lstm]:2\r\n Fully Connected (NC, F32) GEMM 0.007 0.008 0.199% 100.000% 0.000 1 Delegate/Fully Connected (NC, F32) GEMM:0\r\n\r\n============================== Top by Computation Time ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n UNIDIRECTIONAL_SEQUENCE_LSTM 3.912 3.769 99.648% 99.648% 0.000 1 [tfl.unidirectional_sequence_lstm]:2\r\n Fully Connected (NC, F32) GEMM 0.007 0.008 0.199% 99.847% 0.000 1 Delegate/Fully Connected (NC, F32) GEMM:0\r\n RANDOM_UNIFORM 0.003 0.003 0.083% 99.930% 0.000 1 [simple_model/random_uniform/RandomUniform]:0\r\n RANDOM_UNIFORM 0.003 0.003 0.070% 100.000% 0.000 1 [simple_model/random_uniform_1/RandomUniform]:1\r\n\r\nNumber of nodes executed: 4\r\n============================== Summary by node type ==============================\r\n [Node type] [count] [avg ms] [avg %] [cdf %] [mem KB] [times called]\r\n UNIDIRECTIONAL_SEQUENCE_LSTM 1 3.768 99.683% 99.683% 0.000 1\r\n Fully Connected (NC, F32) GEMM 1 0.007 0.185% 99.868% 0.000 1\r\n RANDOM_UNIFORM 2 0.005 0.132% 100.000% 0.000 2\r\n\r\nTimings (microseconds): count=261 first=3925 curr=3943 min=3186 max=6312 avg=3781.84 std=355\r\nMemory (bytes): count=0\r\n4 nodes observed\r\n```\r\nSecond (While) model:\r\n\r\n```\r\nbazel-bin/tensorflow/lite/tools/benchmark/benchmark_model --graph=1x_LSTM_hidden_64_float32.tflite --num_threads=1 --enable_op_profiling=true\r\nINFO: STARTING!\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Num threads: [1]\r\nINFO: Graph: [1x_LSTM_hidden_64_float32.tflite]\r\nINFO: Enable op profiling: [1]\r\nINFO: #threads used for CPU inference: [1]\r\nINFO: Loaded model 1x_LSTM_hidden_64_float32.tflite\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nWARNING: Attempting to use a delegate that only supports static-sized tensors with a graph that has dynamic-sized tensors (tensor#10 is a dynamic-sized tensor).\r\nINFO: The input model file size (MB): 0.514668\r\nINFO: Initialized session in 0.609ms.\r\nINFO: Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.\r\nINFO: count=107 first=5292 curr=4749 min=4243 max=5357 avg=4657.9 std=246\r\n\r\nINFO: Running benchmark for at least 50 iterations and at least 1 seconds but terminate if exceeding 150 seconds.\r\nINFO: Warning: Dropping ProfileBuffer event.\r\nINFO: count=191 first=4983 curr=5046 min=4295 max=6180 avg=4918.73 std=320\r\n\r\nINFO: Inference timings in us: Init: 609, First inference: 5292, Warmup (avg): 4657.9, Inference (avg): 4918.73\r\nINFO: Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.\r\nINFO: Memory footprint delta from the start of the tool (MB): init=4.94141 overall=10.2656\r\nINFO: Profiling Info for Benchmark Initialization:\r\nPrimary graph profile:\r\n============================== Run Order ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n AllocateTensors 0.018 0.018 19.355% 19.355% 0.000 1 AllocateTensors/0\r\n ModifyGraphWithDelegate 0.075 0.075 80.645% 100.000% 0.000 1 ModifyGraphWithDelegate/0\r\n\r\n============================== Top by Computation Time ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n ModifyGraphWithDelegate 0.075 0.075 80.645% 80.645% 0.000 1 ModifyGraphWithDelegate/0\r\n AllocateTensors 0.018 0.018 19.355% 100.000% 0.000 1 AllocateTensors/0\r\n\r\nNumber of nodes executed: 2\r\n============================== Summary by node type ==============================\r\n [Node type] [count] [avg ms] [avg %] [cdf %] [mem KB] [times called]\r\n ModifyGraphWithDelegate 1 0.075 80.645% 80.645% 0.000 1\r\n AllocateTensors 1 0.018 19.355% 100.000% 0.000 1\r\n\r\nTimings (microseconds): count=1 curr=93\r\nMemory (bytes): count=0\r\n2 nodes observed\r\n\r\nSubgraph (index: 1) profile:\r\n============================== Run Order ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n AllocateTensors 0.007 0.007 100.000% 100.000% 0.000 1 AllocateTensors/1\r\n\r\n============================== Top by Computation Time ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n AllocateTensors 0.007 0.007 100.000% 100.000% 0.000 1 AllocateTensors/1\r\n\r\nNumber of nodes executed: 1\r\n============================== Summary by node type ==============================\r\n [Node type] [count] [avg ms] [avg %] [cdf %] [mem KB] [times called]\r\n AllocateTensors 1 0.007 100.000% 100.000% 0.000 1\r\n\r\nSubgraph (index: 1) Timings (microseconds): count=1 curr=7\r\nMemory (bytes): count=0\r\n1 nodes observed\r\n\r\nSubgraph (index: 2) profile:\r\n============================== Run Order ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n AllocateTensors 0.025 0.016 100.000% 100.000% 0.000 2 AllocateTensors/2\r\n\r\n============================== Top by Computation Time ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n AllocateTensors 0.025 0.016 100.000% 100.000% 0.000 2 AllocateTensors/2\r\n\r\nNumber of nodes executed: 1\r\n============================== Summary by node type ==============================\r\n [Node type] [count] [avg ms] [avg %] [cdf %] [mem KB] [times called]\r\n AllocateTensors 1 0.032 100.000% 100.000% 0.000 2\r\n\r\nSubgraph (index: 2) Timings (microseconds): count=1 curr=32\r\nMemory (bytes): count=0\r\n1 nodes observed\r\n\r\n\r\n\r\nINFO: Operator-wise Profiling Info for Regular Benchmark Runs:\r\nPrimary graph profile:\r\n============================== Run Order ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n RANDOM_UNIFORM 0.003 0.003 0.067% 0.067% 0.000 1 [simple_model/random_uniform/RandomUniform]:0\r\n TRANSPOSE 0.027 0.031 0.628% 0.695% 0.000 1 [transpose]:1\r\n WHILE 4.931 4.863 99.305% 100.000% 0.000 1 [while, while1, while2, while3, while4, Unknown]:2\r\n\r\n============================== Top by Computation Time ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n WHILE 4.931 4.863 99.305% 99.305% 0.000 1 [while, while1, while2, while3, while4, Unknown]:2\r\n TRANSPOSE 0.027 0.031 0.628% 99.933% 0.000 1 [transpose]:1\r\n RANDOM_UNIFORM 0.003 0.003 0.067% 100.000% 0.000 1 [simple_model/random_uniform/RandomUniform]:0\r\n\r\nNumber of nodes executed: 3\r\n============================== Summary by node type ==============================\r\n [Node type] [count] [avg ms] [avg %] [cdf %] [mem KB] [times called]\r\n WHILE 1 4.862 99.326% 99.326% 0.000 1\r\n TRANSPOSE 1 0.030 0.613% 99.939% 0.000 1\r\n RANDOM_UNIFORM 1 0.003 0.061% 100.000% 0.000 1\r\n\r\nTimings (microseconds): count=191 first=4961 curr=5027 min=4281 max=6154 avg=4896.75 std=315\r\nMemory (bytes): count=0\r\n3 nodes observed\r\n\r\nSubgraph (index: 1) profile:\r\n============================== Run Order ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n LESS 0.000 0.000 61.756% 61.756% 0.000 37 [simple_model/dense/Tensordot/MatMul1]:0\r\n AllocateTensors 0.003 0.004 38.244% 100.000% 0.000 1 AllocateTensors/1\r\n\r\n============================== Top by Computation Time ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n AllocateTensors 0.003 0.004 38.244% 38.244% 0.000 1 AllocateTensors/1\r\n LESS 0.000 0.000 61.756% 100.000% 0.000 37 [simple_model/dense/Tensordot/MatMul1]:0\r\n\r\nNumber of nodes executed: 2\r\n============================== Summary by node type ==============================\r\n [Node type] [count] [avg ms] [avg %] [cdf %] [mem KB] [times called]\r\n LESS 1 0.005 62.500% 62.500% 0.000 37\r\n AllocateTensors 1 0.003 37.500% 100.000% 0.000 1\r\n\r\nSubgraph (index: 1) Timings (microseconds): count=191 first=9 curr=7 min=3 max=61 avg=9.24084 std=5\r\nMemory (bytes): count=0\r\n2 nodes observed\r\n\r\nSubgraph (index: 2) profile:\r\n============================== Run Order ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n AllocateTensors 0.024 0.010 0.263% 0.263% 0.000 1 AllocateTensors/2\r\n ADD 0.001 0.000 0.137% 0.400% 0.000 37 [transpose_1]:0 FULLY_CONNECTED 0.018 0.017 16.809% 17.208% 0.000 37 [StatefulPartitionedCall:0]:1\r\n GATHER 0.001 0.001 0.785% 17.993% 0.000 37 [StatefulPartitionedCall:1]:2\r\n FULLY_CONNECTED 0.091 0.068 65.937% 83.930% 0.000 37 [Unknown]:3\r\n ADD 0.001 0.001 0.650% 84.580% 0.000 37 [Unknown]:4\r\n ADD 0.001 0.001 0.623% 85.203% 0.000 37 [Unknown]:5\r\n SPLIT 0.001 0.001 0.639% 85.841% 0.000 37 [Unknown, Unknown, Unknown, Unknown]:6\r\n LOGISTIC 0.001 0.001 0.545% 86.386% 0.000 36 [Unknown]:7\r\n LOGISTIC 0.001 0.000 0.397% 86.783% 0.000 36 [Unknown]:8\r\n MUL 0.001 0.000 0.416% 87.199% 0.000 36 [Unknown]:9\r\n LOGISTIC 0.001 0.000 0.419% 87.618% 0.000 36 [Unknown]:10\r\n ADD 0.000 0.000 0.147% 87.765% 0.000 36 [Unknown]:24\r\n CONCATENATION 0.003 0.004 3.537% 91.302% 0.000 36 [Unknown]:23\r\n RESHAPE 0.000 0.000 0.046% 91.348% 0.000 36 [Unknown]:22\r\n SLICE 0.008 0.004 3.847% 95.195% 0.000 36 [Unknown]:21\r\n TANH 0.000 0.000 0.401% 95.596% 0.000 36 [Unknown]:11\r\n MUL 0.001 0.000 0.265% 95.861% 0.000 36 [Unknown]:12\r\n ADD 0.000 0.000 0.221% 96.082% 0.000 36 [Unknown]:13\r\n TANH 0.000 0.000 0.360% 96.442% 0.000 36 [Unknown]:14\r\n MUL 0.001 0.000 0.261% 96.703% 0.000 36 [Unknown]:15\r\n RESHAPE 0.000 0.000 0.059% 96.762% 0.000 36 [Unknown]:16\r\n CONCATENATION 0.001 0.000 0.282% 97.044% 0.000 36 [Unknown]:17\r\n SLICE 0.001 0.003 2.774% 99.818% 0.000 36 [Unknown]:18\r\n RESHAPE 0.000 0.000 0.049% 99.867% 0.000 36 [Unknown]:19\r\n CONCATENATION 0.000 0.000 0.133% 100.000% 0.000 36 [Unknown]:20\r\n\r\n============================== Top by Computation Time ==============================\r\n [node type] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name]\r\n FULLY_CONNECTED 0.091 0.068 65.937% 65.937% 0.000 37 [Unknown]:3\r\n FULLY_CONNECTED 0.018 0.017 16.809% 82.745% 0.000 37 [StatefulPartitionedCall:0]:1\r\n AllocateTensors 0.024 0.010 0.263% 83.008% 0.000 1 AllocateTensors/2\r\n SLICE 0.008 0.004 3.847% 86.855% 0.000 36 [Unknown]:21\r\n CONCATENATION 0.003 0.004 3.537% 90.393% 0.000 36 [Unknown]:23\r\n SLICE 0.001 0.003 2.774% 93.167% 0.000 36 [Unknown]:18\r\n GATHER 0.001 0.001 0.785% 93.951% 0.000 37 [StatefulPartitionedCall:1]:2\r\n ADD 0.001 0.001 0.650% 94.601% 0.000 37 [Unknown]:4\r\n SPLIT 0.001 0.001 0.639% 95.240% 0.000 37 [Unknown, Unknown, Unknown, Unknown]:6\r\n ADD 0.001 0.001 0.623% 95.863% 0.000 37 [Unknown]:5\r\n\r\nNumber of nodes executed: 26\r\n============================== Summary by node type ==============================\r\n [Node type] [count] [avg ms] [avg %] [cdf %] [mem KB] [times called]\r\n FULLY_CONNECTED 2 3.157 83.035% 83.035% 0.000 74\r\n SLICE 2 0.251 6.602% 89.637% 0.000 72\r\n CONCATENATION 3 0.150 3.945% 93.582% 0.000 108\r\n ADD 5 0.065 1.710% 95.292% 0.000 183\r\n LOGISTIC 3 0.050 1.315% 96.607% 0.000 108\r\n MUL 3 0.034 0.894% 97.501% 0.000 108\r\n GATHER 1 0.029 0.763% 98.264% 0.000 37\r\n TANH 2 0.028 0.736% 99.001% 0.000 72\r\n SPLIT 1 0.024 0.631% 99.632% 0.000 37\r\n AllocateTensors 1 0.010 0.263% 99.895% 0.000 1\r\n RESHAPE 3 0.004 0.105% 100.000% 0.000 108\r\n\r\nSubgraph (index: 2) Timings (microseconds): count=191 first=3863 curr=3956 min=3340 max=4844 avg=3816.78 std=256\r\nMemory (bytes): count=0\r\n26 nodes observed\r\n```\r\n\r\nIt's interesting that we can see the operations happening inside the While loop through this tool. \r\n\r\n> It probably depends on what that while body subgraph actually is, i.e. the Op might actually be inside there.\r\n\r\nI guess the Op isn't there after all. It seems that it's splitting the LSTM operation. What I find interesting is that, in the second model, the \"subgraph index = 2\" that is mentioned inside the While loop is averaging at 3.8ms, which is close to that of the UnidirectionalSequenceLSTM, but the actual while loop is averaging at 4.8ms, more than the \"subgraph index = 2\". Not sure if that's relevant or not.\r\n\r\nAnyways, I've also tried another LSTM model of mine that is dynamically quantized, and I'm getting a 40% speedup. I don't think we can justify the speedup for simply not retaining the hidden_states, right?\r\n\r\nPerformance is critical for my task, how do you think we should proceed? I'll be waiting for your input.\r\n",
"So the converter is in some sense an arbitrary program optimizer, since TF is Turing complete, it probably doesn't handle all cases to complete optimum, there probably exists a conversion which is more optimal (or maybe we need to change the source code/that op to more output those hidden states natively rather than the deconstruction/reconstruction you saw). This will likely be a hard problem to solve generally and you say it's critical for your application. We will get to it eventually but we have to consider all issues and how many issues/users each potential change can help with and their relative impact as well -- so it might take a while to get to this one. That being said, since it is impactful for you and now that you are touching the innards a bit you can try your best to see if you can introduce a PR for this yourself.\r\n\r\nLet's change this into a feature/performance issue and I can direct you a little bit on what to look at but you'll likely have to do a lot of digging for yourself. Learn some MLIR https://mlir.llvm.org/ (A very general compiler framework ... a compiler translates higher level code to lower level code, but some of the same abstractions can be used to .. side translate/optimize such as TF -> TFL, optimization can be viewed as lowering to a representation more efficient for your specific configuration/hardware)\r\n\r\nBuild from source with debug info, especially the tflite_convert CLT: https://www.tensorflow.org/lite/models/convert/convert_models#command_line_tool, use lldb/gdb with it and dig into how your program gets transformed and see how we can do the optimization better\r\n\r\nThis README is probably useful: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/lite/README.md\r\n\r\nSome of the binaries here might be useful:\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/lite/BUILD\r\n\r\nHi @majiddadashi, can you please take a look? Thanks.",
"Hi @Doomski99, can you try it this way and see if it satisfies your performance requirements?\r\n\r\n```py\r\nimport torch\r\nfrom torch import nn\r\nimport ai_edge_torch\r\n\r\n\r\nclass SimpleModel(nn.Module):\r\n def __init__(self, input_size, hidden_size):\r\n super().__init__()\r\n \r\n self.lstm = nn.LSTM(input_size, hidden_size)\r\n self.d1 = nn.Linear(hidden_size, 1)\r\n\r\n def forward(self, x):\r\n \r\n x, (h0, c0) = self.lstm(x)\r\n x = self.d1(x)\r\n \r\n return x, torch.stack([h0, c0])\r\n\r\n\r\nmodel = SimpleModel(256, 64)\r\nsample_inputs = (torch.randn(16, 43, 256),)\r\n\r\nedge_model = ai_edge_torch.convert(model.eval(), sample_inputs)\r\nedge_model.export(\"simple_model.tflite\")\r\n```\r\n\r\nYou can find more information here: [AI-Edge-Torch](https://github.com/google-ai-edge/ai-edge-torch) and [googleblog](https://developers.googleblog.com/en/ai-edge-torch-high-performance-inference-of-pytorch-models-on-mobile-devices/)",
"Hello @pkgoogle . Interesting library, thanks for sharing! I tested it and it seems to unroll the LSTM instead of using a loop (while) which is not quite what I was looking for. My goal was to be able to have the \"UnidirectionalSequenceLSTM\" Operator instead.\r\n\r\n"
] | 2024-01-10T10:14:31 | 2024-06-12T09:04:24 | null |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 11
- TensorFlow installation (pip package or built from source): pip
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.15.0
### 2. Code
```
import numpy as np
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.layers import Dense, LSTM
model_name = "1x_LSTM_64_float32"
input_length = 256
class SimpleModel(Model):
def __init__(self, input_shape, hidden_size):
super().__init__()
self.lstm = LSTM(hidden_size, return_sequences = True, return_state=True, input_shape = [-1, input_shape] )
self.d1 = Dense(1, input_shape = [-1, hidden_size])
def call(self, x):
x, h0, c0 = self.lstm(x)
x = self.d1(x)
return x#, tf.stack([h0, c0])
model = SimpleModel(input_length, 64)
out, states = model(tf.random.uniform([16,43,256]))
print(np.mean(out))
model_path = f"{model_name}.tf"
run_model = tf.function(lambda x: model(x))
BATCH_SIZE = 16
STEPS = 43
INPUT_SIZE = 256
concrete_func = run_model.get_concrete_function(
tf.TensorSpec([BATCH_SIZE, STEPS, INPUT_SIZE], tf.float32))
model.save(model_path, save_format = 'tf', signatures=concrete_func)
converter = tf.lite.TFLiteConverter.from_saved_model(model_path)
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS,
]
tflite_model = converter.convert()
open(f"{model_name}.tflite", "wb").write(tflite_model)
```
The previous model produces the following graph:
With a simple hidden states retention by modifying the following code:
```
class SimpleModel(Model):
def __init__(self, input_shape, hidden_size):
super().__init__()
self.lstm = LSTM(hidden_size, return_sequences = True, return_state=True, input_shape = [-1, input_shape] )
self.d1 = Dense(1, input_shape = [-1, hidden_size])
def call(self, x):
x, h0, c0 = self.lstm(x)
x = self.d1(x)
return x, tf.stack([h0, c0])
model = SimpleModel(input_length, 64)
out, states = model(tf.random.uniform([16,43,256]))
```
I get the following graph:
### 3. Failure after conversion
The While OPs fail to run on my target platform unlike the UnidirectionalSequenceLstm OP.
I can only assume that the 'UnidirectionalSequenceLstm' OP doesn't support returning the hidden states? Or am I doing something wrong? My end goal is to feed initial states to the LSTM and retain the returned ones. And I want optimized LSTM Operators. Do I need to write my own conversion logic?
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I_kwDOArmXAs57nNwd
| 62,774 |
The inference result of converted TFLite model is wrong on mobile
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[
"I run the tf.lite.experimental.Analyzer, and the TensorFlow Lite model result as below for your reference:\r\n```bash\r\n---------------------------------------------------------------\r\nYour TFLite model has '1' signature_def(s).\r\n\r\nSignature#0 key: 'serving_default'\r\n- Subgraph: Subgraph#0\r\n- Inputs: \r\n 'arg_0' : T#0\r\n- Outputs: \r\n '797' : T#801\r\n\r\n---------------------------------------------------------------\r\n Model size: 66529376 bytes\r\n Non-data buffer size: 104044 bytes (00.16 %)\r\n Total data buffer size: 66425332 bytes (99.84 %)\r\n (Zero value buffers): 21280 bytes (00.03 %)\r\n\r\n* Buffers of TFLite model are mostly used for constant tensors.\r\n And zero value buffers are buffers filled with zeros.\r\n Non-data buffers area are used to store operators, subgraphs and etc.\r\n You can find more details from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema.fbs\r\n```",
"I found that the input shape (1, 3,256,256) in TensorFlow Lite is different from the MiDaS provided model (1,256,256,3)\r\n\r\nI use the 'onnx2tf' that it automatically transposes (N,C,H,W) to (N,H,W,C) So I will close this issue."
] | 2024-01-10T08:47:32 | 2024-01-11T14:01:36 | 2024-01-11T14:01:35 |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution: iOS / Android
### 2. Code
I download the [MiDaS ONNX model](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-small.onnx) and then run below python codes:
1. I fix the onnx model's key problem ( the model input tensor name = '0' ==> KeyError: '0')
```python
import onnx
from onnx import helper
onnx_model_path = "model-small.onnx"
onnx_model = onnx.load(onnx_model_path)
# Define a mapping from old names to new names
name_map = {"0": "arg_0"}
# Initialize a list to hold the new inputs
new_inputs = []
# Iterate over the inputs and change their names if needed
for inp in onnx_model.graph.input:
if inp.name in name_map:
# Create a new ValueInfoProto with the new name
new_inp = helper.make_tensor_value_info(name_map[inp.name],
inp.type.tensor_type.elem_type,
[dim.dim_value for dim in inp.type.tensor_type.shape.dim])
new_inputs.append(new_inp)
else:
new_inputs.append(inp)
# Clear the old inputs and add the new ones
onnx_model.graph.ClearField("input")
onnx_model.graph.input.extend(new_inputs)
# Go through all nodes in the model and replace the old input name with the new one
for node in onnx_model.graph.node:
for i, input_name in enumerate(node.input):
if input_name in name_map:
node.input[i] = name_map[input_name]
# Save the renamed ONNX model
onnx_model_path = "model-small-fix.onnx"
onnx.save(onnx_model, onnx_model_path)
```
2. Convert it into TensorFlow saved model format (the result of the TF model is confirmed OK):
```python
import onnx
from onnx_tf.backend import prepare
model_path = "model-small-fix.onnx"
output_path = "modified_model_2"
onnx_model = onnx.load(model_path) # load onnx model
tf_rep = prepare(onnx_model) # prepare tf representation
tf_rep.export_graph(output_path) # export the model
```
3. Convert the TensorFlow saved model into TFLite:
```python
import tensorflow as tf
import io
path = 'modified_model_2'
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir=path)
tf_lite_model = converter.convert()
open('model_1.tflite', 'wb').write(tf_lite_model)
```
4. Evaluate in python
```python
import tensorflow as tf
import cv2
import numpy as np
import os
input_image_path = "input/COCO_val2014_000000000761.jpg"
tflite_model_path = "/model_fp16-3.tflite"
interpreter = tf.lite.Interpreter(model_path=tflite_model_path)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
input_index = interpreter.get_input_details()[0]['index']
output_index = interpreter.get_output_details()[0]['index']
# read image
input_image = cv2.imread(input_image_path)
org_shape = input_image.shape
input_image = cv2.resize(input_image, (256, 256))
input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
input_image = input_image.astype(np.float32) / 255.0
input_image = np.expand_dims(input_image, axis=0)
input_image = np.transpose(input_image, [0, 3, 1, 2])
interpreter.set_tensor(input_index, input_image)
# get result
interpreter.invoke()
output_image = interpreter.get_tensor(output_index)
output_image = output_image[0, :, :] # remove channel
normalized_output = (output_image - np.min(output_image)) / (np.max(output_image) - np.min(output_image))
normalized_output = (normalized_output * 255).astype(np.uint8)
normalized_output = cv2.resize(normalized_output, (org_shape[1], org_shape[0]))
# get file name
file_name = os.path.basename(input_image_path)
output_file_name = f"output/{file_name}"
# write image
cv2.imwrite(output_file_name, normalized_output)
print(f"file {file_name} copy to {output_file_name} ")
```
5. Replace the model in iOS project (model_opt.tflite) and run on iOS, the code is unchanged from [MiDaS iOS](https://github.com/isl-org/MiDaS/tree/master/mobile/ios)
```swift
private func inference(from data: Data) {
// Copy the initialized `Data` to the input `Tensor`.
do {
try interpreter.copy(data, toInputAt: 0)
// Run inference by invoking the `Interpreter`.
try interpreter.invoke()
// Get the output `Tensor` to process the inference results.
outputTensor = try interpreter.output(at: 0)
} catch let error {
os_log(
"Failed to invoke the interpreter with error: %s", type: .error,
error.localizedDescription)
return
}
}
```
### 3. Failure after conversion
The Model runs in **iOS** with wrong result:
The Model runs in **Python** with correct result:
The right result from TensorFlow model during above conversion:
The original input:
### 5. (optional) Any other info / logs
The log when converting from TensorFlow to TensorFlow Lite:
```bash
python convert_tf2tflite_savedmodel.py
2024-01-10 14:26:37.071501: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:378] Ignored output_format.
2024-01-10 14:26:37.071524: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:381] Ignored drop_control_dependency.
2024-01-10 14:26:37.072913: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: /modified_model_2
2024-01-10 14:26:37.089615: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }
2024-01-10 14:26:37.089642: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: /modified_model_2
2024-01-10 14:26:37.110969: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
2024-01-10 14:26:37.118290: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle.
2024-01-10 14:26:37.263435: I tensorflow/cc/saved_model/loader.cc:217] Running initialization op on SavedModel bundle at path: /modified_model_2
2024-01-10 14:26:37.389643: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 316731 microseconds.
2024-01-10 14:26:37.545668: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
Summary on the non-converted ops:
---------------------------------
* Accepted dialects: tfl, builtin, func
* Non-Converted Ops: 293, Total Ops 804, % non-converted = 36.44 %
* 293 ARITH ops
- arith.constant: 293 occurrences (f32: 281, i32: 12)
(f32: 99)
(f32: 73)
(f32: 24)
(f32: 73)
(f32: 45)
(f32: 7)
(f32: 1)
(f32: 5)
(f32: 1)
(f32: 180)
2024-01-10 14:26:38.047603: I tensorflow/compiler/mlir/lite/flatbuffer_export.cc:2989] Estimated count of arithmetic ops: 9.246 G ops, equivalently 4.623 G MACs
```
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| 2,073,636,377 |
I_kwDOArmXAs57mS4Z
| 62,773 |
Once Model Trained Does Not Empty The GPU Space, it uses so much of much GPU Space. Using M1 Pro. Installed the tensorflow using the TensorFlow Plugin Metal Developers
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[
"@NiharJani2002 Could you use tf.memory.total_allocated_memory() to check the total amount of GPU memory allocated by your TensorFlow session. Please ensure you're using the latest versions of TensorFlow and tensorflow-metal plugin as they might have included memory optimization updates. Kindly check for known issues and workarounds related to memory usage on the Apple Developer Forum (https://developer.apple.com/metal/tensorflow-plugin/).\r\nIn order to expedite the trouble-shooting process, please provide a complete code snippet to reproduce the issue reported here.\r\nThank you!\r\n",
"The issue has been solved. I have seen blogs to solve it",
"@NiharJani2002 Thank you for the confirmation!\r\nCould you please move this issue to closed status if it is resolved?\r\nThank you!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62773\">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/62773\">No</a>\n"
] | 2024-01-10T05:47:34 | 2024-01-19T04:38:23 | 2024-01-19T04:38:20 |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
Mac OS Sononoma 14.2 and M1 Pro
### Mobile device
_No response_
### Python version
3.9.18
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Utilises The Entire Gpu and does not empty when the model is completely trained. If I again train the model, it just add up the space in my gpu.
### Standalone code to reproduce the issue
```shell
Training Dataset of Brain Tumour segmentation .nii files, convert it into numpy and train unet on it. Size of Unet 4 encoder and 4 decoder.
```
### Relevant log output
```shell
Suggestion Given By The Mac to close the code, occupies most of the spaces In RAM.
RAM - 16gb
```
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I_kwDOArmXAs57l3px
| 62,772 |
customized model combined with keras model inputshape error
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[
"@MrWangg1992 Could you please provide the entire code? It appears that, during the model training, the `MobilenetV3small` layer receives an input whose last axis has singular dimensionality, while this layer expects input with a 3-dimensional last axis. There might be an issue with how the `CoordinateChannel` layer adds spatial information.\r\n",
"> @MrWangg1992 Could you please provide the entire code? It appears that, during the model training, the `MobilenetV3small` layer receives an input whose last axis has singular dimensionality, while this layer expects input with a 3-dimensional last axis. There might be an issue with how the `CoordinateChannel` layer adds spatial information.\r\n\r\n@aditya02shah \r\nHi, from the error message, it said something regarding the dimensions. My original input image is a (B,224,224,1) then i send it to a coordinate channel change it to (B,224,224,3) then send it to `MobilenetV3small`. This works well when in tf 2.3/2.4. but after I updated to tf 2.14/2.15 it threw up this error. As I mentioned, if I directly run model(input), it works fine, but after `model.compile()` ,`model.fit()`. it will give that error message.\r\nI think i already provided the full code regarding how the model was built. the compile and fit part is not that necessary, andy random `loss` `dataloader` is fine.\r\n\r\n```\r\nimport tensorflow as tf\r\nfrom tensorflow.keras.layers import *\r\nfrom tensorflow.keras.regularizers import l2\r\nimport tensorflow as tf\r\nfrom tensorflow.keras import backend as K\r\nimport numpy as np\r\nimport tensorflow.keras.layers as layers\r\nimport math\r\n\r\n\r\nclass CoordinateChannel(Layer):\r\n\r\n def __init__(self, rank,\r\n use_radius=False,\r\n data_format=None,\r\n **kwargs):\r\n super(CoordinateChannel, self).__init__(**kwargs)\r\n\r\n if data_format not in [None, 'channels_first', 'channels_last']:\r\n raise ValueError('`data_format` must be either \"channels_last\", \"channels_first\" '\r\n 'or None.')\r\n\r\n self.rank = rank\r\n self.use_radius = use_radius\r\n self.data_format = K.image_data_format() if data_format is None else data_format\r\n self.axis = 1 if K.image_data_format() == 'channels_first' else -1\r\n\r\n self.input_spec = InputSpec(min_ndim=2)\r\n self.supports_masking = True\r\n\r\n def build(self, input_shape):\r\n assert len(input_shape) >= 2\r\n input_dim = input_shape[self.axis]\r\n\r\n self.input_spec = InputSpec(min_ndim=self.rank + 2,\r\n axes={self.axis: input_dim})\r\n self.built = True\r\n\r\n def call(self, inputs, training=None, mask=None):\r\n input_shape = K.shape(inputs)\r\n\r\n if self.rank == 1:\r\n input_shape = [input_shape[i] for i in range(3)]\r\n batch_shape, dim, channels = input_shape\r\n\r\n xx_range = K.tile(K.expand_dims(K.arange(0, dim, dtype='float32'), axis=0),\r\n K.stack([batch_shape, 1]))\r\n xx_range = K.expand_dims(xx_range, axis=-1)\r\n\r\n xx_channels = K.cast(xx_range, K.floatx())\r\n xx_channels = xx_channels / K.cast(dim - 1, K.floatx())\r\n xx_channels = (xx_channels * 2) - 1.\r\n\r\n outputs = K.concatenate([inputs, xx_channels], axis=-1)\r\n\r\n if self.rank == 2:\r\n if self.data_format == 'channels_first':\r\n inputs = K.permute_dimensions(inputs, [0, 2, 3, 1])\r\n input_shape = K.shape(inputs)\r\n\r\n input_shape = [input_shape[i] for i in range(4)]\r\n batch_shape, dim1, dim2, channels = input_shape\r\n\r\n xx_ones = tf.ones(K.stack([batch_shape, dim2]), dtype='float32')\r\n xx_ones = K.expand_dims(xx_ones, axis=-1)\r\n\r\n xx_range = K.tile(K.expand_dims(K.arange(0, dim1, dtype='float32'), axis=0),\r\n K.stack([batch_shape, 1]))\r\n xx_range = K.expand_dims(xx_range, axis=1)\r\n xx_channels = K.batch_dot(xx_ones, xx_range, axes=[2, 1])\r\n xx_channels = K.expand_dims(xx_channels, axis=-1)\r\n xx_channels = K.permute_dimensions(xx_channels, [0, 2, 1, 3])\r\n\r\n yy_ones = tf.ones(K.stack([batch_shape, dim1]), dtype='float32')\r\n yy_ones = K.expand_dims(yy_ones, axis=1)\r\n\r\n yy_range = K.tile(K.expand_dims(K.arange(0, dim2, dtype='float32'), axis=0),\r\n K.stack([batch_shape, 1]))\r\n yy_range = K.expand_dims(yy_range, axis=-1)\r\n\r\n yy_channels = K.batch_dot(yy_range, yy_ones, axes=[2, 1])\r\n yy_channels = K.expand_dims(yy_channels, axis=-1)\r\n yy_channels = K.permute_dimensions(yy_channels, [0, 2, 1, 3])\r\n\r\n xx_channels = K.cast(xx_channels, K.floatx())\r\n xx_channels = xx_channels / K.cast(dim1 - 1, K.floatx())\r\n xx_channels = (xx_channels * 2) - 1.\r\n\r\n yy_channels = K.cast(yy_channels, K.floatx())\r\n yy_channels = yy_channels / K.cast(dim2 - 1, K.floatx())\r\n yy_channels = (yy_channels * 2) - 1.\r\n\r\n # import pdb;pdb.set_trace()\r\n outputs = K.concatenate([inputs, xx_channels, yy_channels], axis=-1)\r\n # outputs = K.concatenate([inputs, tf.cast(xx_channels, dtype=tf.float16), tf.cast(yy_channels, dtype=tf.float16)], axis=-1)\r\n\r\n if self.use_radius:\r\n rr = K.sqrt(K.square(xx_channels - 0.5) +\r\n K.square(yy_channels - 0.5))\r\n outputs = K.concatenate([outputs, rr], axis=-1)\r\n\r\n if self.data_format == 'channels_first':\r\n outputs = K.permute_dimensions(outputs, [0, 3, 1, 2])\r\n\r\n if self.rank == 3:\r\n if self.data_format == 'channels_first':\r\n inputs = K.permute_dimensions(inputs, [0, 2, 3, 4, 1])\r\n input_shape = K.shape(inputs)\r\n\r\n input_shape = [input_shape[i] for i in range(5)]\r\n batch_shape, dim1, dim2, dim3, channels = input_shape\r\n\r\n xx_ones = tf.ones(K.stack([batch_shape, dim3]), dtype='float32')\r\n xx_ones = K.expand_dims(xx_ones, axis=-1)\r\n\r\n xx_range = K.tile(K.expand_dims(K.arange(0, dim2, dtype='float32'), axis=0),\r\n K.stack([batch_shape, 1]))\r\n xx_range = K.expand_dims(xx_range, axis=1)\r\n\r\n xx_channels = K.batch_dot(xx_ones, xx_range, axes=[2, 1])\r\n xx_channels = K.expand_dims(xx_channels, axis=-1)\r\n xx_channels = K.permute_dimensions(xx_channels, [0, 2, 1, 3])\r\n\r\n xx_channels = K.expand_dims(xx_channels, axis=1)\r\n xx_channels = K.tile(xx_channels,\r\n [1, dim1, 1, 1, 1])\r\n\r\n yy_ones = tf.ones(K.stack([batch_shape, dim2]), dtype='float32')\r\n yy_ones = K.expand_dims(yy_ones, axis=1)\r\n\r\n yy_range = K.tile(K.expand_dims(K.arange(0, dim3, dtype='float32'), axis=0),\r\n K.stack([batch_shape, 1]))\r\n yy_range = K.expand_dims(yy_range, axis=-1)\r\n\r\n yy_channels = K.batch_dot(yy_range, yy_ones, axes=[2, 1])\r\n yy_channels = K.expand_dims(yy_channels, axis=-1)\r\n yy_channels = K.permute_dimensions(yy_channels, [0, 2, 1, 3])\r\n\r\n yy_channels = K.expand_dims(yy_channels, axis=1)\r\n yy_channels = K.tile(yy_channels,\r\n [1, dim1, 1, 1, 1])\r\n\r\n zz_range = K.tile(K.expand_dims(K.arange(0, dim1, dtype='float32'), axis=0),\r\n K.stack([batch_shape, 1]))\r\n zz_range = K.expand_dims(zz_range, axis=-1)\r\n zz_range = K.expand_dims(zz_range, axis=-1)\r\n\r\n zz_channels = K.tile(zz_range,\r\n [1, 1, dim2, dim3])\r\n zz_channels = K.expand_dims(zz_channels, axis=-1)\r\n\r\n xx_channels = K.cast(xx_channels, K.floatx())\r\n xx_channels = xx_channels / K.cast(dim2 - 1, K.floatx())\r\n xx_channels = xx_channels * 2 - 1.\r\n\r\n yy_channels = K.cast(yy_channels, K.floatx())\r\n yy_channels = yy_channels / K.cast(dim3 - 1, K.floatx())\r\n yy_channels = yy_channels * 2 - 1.\r\n\r\n zz_channels = K.cast(zz_channels, K.floatx())\r\n zz_channels = zz_channels / K.cast(dim1 - 1, K.floatx())\r\n zz_channels = zz_channels * 2 - 1.\r\n\r\n outputs = K.concatenate([inputs, zz_channels, xx_channels, yy_channels],\r\n axis=-1)\r\n\r\n if self.data_format == 'channels_first':\r\n outputs = K.permute_dimensions(outputs, [0, 4, 1, 2, 3])\r\n\r\n return outputs\r\n\r\n def compute_output_shape(self, input_shape):\r\n assert input_shape and len(input_shape) >= 2\r\n assert input_shape[self.axis]\r\n\r\n if self.use_radius and self.rank == 2:\r\n channel_count = 3\r\n else:\r\n channel_count = self.rank\r\n\r\n output_shape = list(input_shape)\r\n output_shape[self.axis] = input_shape[self.axis] + channel_count\r\n return tuple(output_shape)\r\n\r\n def get_config(self):\r\n config = {\r\n 'rank': self.rank,\r\n 'use_radius': self.use_radius,\r\n 'data_format': self.data_format\r\n }\r\n base_config = super(CoordinateChannel, self).get_config()\r\n return dict(list(base_config.items()) + list(config.items()))\r\n\r\ndef binary_focal_loss(gamma=2., alpha=.25, amplifier=1.):\r\n def binary_focal_loss_fixed(y_true, y_pred):\r\n pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))\r\n pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))\r\n\r\n epsilon = K.epsilon()\r\n # clip to prevent NaN's and Inf's\r\n pt_1 = K.clip(pt_1, epsilon, 1. - epsilon)\r\n pt_0 = K.clip(pt_0, epsilon, 1. - epsilon)\r\n return (-K.mean(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1), keepdims=True) \\\r\n - K.mean((1 - alpha) * K.pow(pt_0, gamma) * K.log(1. - pt_0), keepdims=True)) * amplifier\r\n\r\n return binary_focal_loss_fixed\r\n\r\nbackbone = tf.keras.applications.MobileNetV3Small(\r\n input_shape=(224,224,3),\r\n alpha=1.0,\r\n minimalistic=False,\r\n include_top=True,\r\n weights='imagenet',\r\n input_tensor=None,\r\n classes=1000,\r\n pooling=None,\r\n dropout_rate=0.2,\r\n classifier_activation='softmax',\r\n include_preprocessing=True\r\n)\r\n\r\n\r\ninputs = Input(shape=[224, 224] + [1])\r\nx = CoordinateChannel(2)(inputs)\r\nbackbone_out = backbone(x)\r\n\r\nmodel = Model(inputs=inputs outputs=backbone_out)\r\n\r\nlr = 0.001\r\nopt = Adam(lr)\r\nmodel.compile(optimizer=opt,loss=binary_focal_loss(gamma=5., alpha=0.5),metrics=[binary_focal_loss(gamma=5., alpha=0.5)])\r\n\r\nmodel.fit(\r\n train_dataset,\r\n steps_per_epoch=train_steps,\r\n validation_data=val_dataset,\r\n validation_steps=val_steps,\r\n epochs=5,\r\n initial_epoch=0,\r\n workers=0\r\n) # this part will throw error\r\n\r\n\r\n\r\nip = tf.random.normal((1, 224, 224, 1))\r\nmodel(IP) # this works well \r\n\r\n```",
"@MrWangg1992, the code appears to be functioning with a random dataloader and loss that I tested. I recommend examining your specific dataloader implementation and how it interacts with the `CoordinateChannel` Layer. The issue might be related to the data input or preprocessing. Hope this helps!\r\n[Gist](https://colab.research.google.com/drive/1LLrHA4b9JTvq3KJ3MhnbKB2Zs0zo91xi#scrollTo=UKurgsCWvkjj)\r\n",
"> @MrWangg1992, the code appears to be functioning with a random dataloader and loss that I tested. I recommend examining your specific dataloader implementation and how it interacts with the `CoordinateChannel` Layer. The issue might be related to the data input or preprocessing. Hope this helps! [Gist](https://colab.research.google.com/drive/1LLrHA4b9JTvq3KJ3MhnbKB2Zs0zo91xi#scrollTo=UKurgsCWvkjj)\r\n\r\nHi @aditya02shah \r\na quick question does, tf support a800 gpu? i think this is the root cause",
"@MrWangg1992 To verify TensorFlow support for the A800 GPU, check out the documentation at these links: <br/>1.[NVIDIA CUDA GPUs](https://developer.nvidia.com/cuda-gpus) <br/> 2.[Tensorflow GPU Hardware Requirements](https://www.tensorflow.org/install/pip#hardware_requirements)",
"> @MrWangg1992 To verify TensorFlow support for the A800 GPU, check out the documentation at these links: 1.[NVIDIA CUDA GPUs](https://developer.nvidia.com/cuda-gpus) 2.[Tensorflow GPU Hardware Requirements](https://www.tensorflow.org/install/pip#hardware_requirements)\r\n\r\n@aditya02shah \r\ni didn't see a800 listed, would you mind check it on your end? i checked on my end and it doesn't work\r\n\r\n",
"@aditya02shah \r\nI also tried something else not working as well.\r\n\r\n",
"@MrWangg1992 Could you please refer to this [TF Forum Cuda Issue](https://discuss.tensorflow.org/t/tensorflow-install-error/20491) to see if it resolves your TensorFlow CUDA issue.",
"Hi @MrWangg1992 ,\r\n\r\nCould you please confirm did you installed `tensorflow[and-cuda]` package ? This will install all the cuda and cudnn packages required for GPU setup.For earlier versions TF<=2.13 it is manual and now its taken care by TF itself.\r\n\r\nI would recommend you to try with new environment by installing `tensorflow[and-cuda]` and check whether GPU is recognisable.\r\n\r\nIs the input_shape error resolved yet ?\r\n\r\nPlease submit a minimal code snippet for reproduction. For support type issue please post at [tensorflow-forum](https://discuss.tensorflow.org/).",
"> Hi @MrWangg1992 ,\r\n> \r\n> Could you please confirm did you installed `tensorflow[and-cuda]` package ? This will install all the cuda and cudnn packages required for GPU setup.For earlier versions TF<=2.13 it is manual and now its taken care by TF itself.\r\n> \r\n> I would recommend you to try with new environment by installing `tensorflow[and-cuda]` and check whether GPU is recognisable.\r\n> \r\n> Is the input_shape error resolved yet ?\r\n> \r\n> Please submit a minimal code snippet for reproduction. For support type issue please post at [tensorflow-forum](https://discuss.tensorflow.org/).\r\n\r\nHi @SuryanarayanaY \r\nThe gpu can be recognized now, but the shape error, didn't resolve, i used the same code, and everything works fine under tf 2.4/2.3, but I tried on tf 2.11/2.14/2.15, it throws different errors regarding the shape. I used the same processing methods above. i have no idea what changes inside, but it blocked me for a very long time.",
"Hi @MrWangg1992 ,\r\n\r\nCould you please submit minimal code snippet to reproduce the issue ?\r\n\r\nAttached code is a large one and incomplete. If not a minimal code snippet please execute the code in google colab and submit the gist.",
"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/62772\">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/62772\">No</a>\n"
] | 2024-01-10T03:40:40 | 2024-02-07T01:46:28 | 2024-02-07T01:46:24 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15
### Custom code
Yes
### OS platform and distribution
linunx ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
model.fit(
File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/tmp/__autograph_generated_filegq94afq9.py", line 15, in tf__train_function
retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
^^^^^
ValueError: in user code:
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py", line 1401, in train_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py", line 1384, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py", line 1373, in run_step **
outputs = model.train_step(data)
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py", line 1150, in train_step
y_pred = self(x, training=True)
File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/input_spec.py", line 280, in assert_input_compatibility
raise ValueError(
ValueError: Exception encountered when calling layer 'MobilenetV3small' (type Functional).
Input 0 of layer "Conv0" is incompatible with the layer: expected axis -1 of input shape to have value 3, but received input with shape (None, None, None, 1)
Call arguments received by layer 'MobilenetV3small' (type Functional):
• inputs=tf.Tensor(shape=(None, None, None, 1), dtype=float32)
• training=True
• mask=None
### Standalone code to reproduce the issue
```shell
class CoordinateChannel(Layer):
""" Adds Coordinate Channels to the input tensor.
# Arguments
rank: An integer, the rank of the input data-uniform,
e.g. "2" for 2D convolution.
use_radius: Boolean flag to determine whether the
radius coordinate should be added for 2D rank
inputs or not.
data_format: A string,
one of `"channels_last"` or `"channels_first"`.
The ordering of the dimensions in the inputs.
`"channels_last"` corresponds to inputs with shape
`(batch, ..., channels)` while `"channels_first"` corresponds to
inputs with shape `(batch, channels, ...)`.
It defaults to the `image_data_format` value found in your
Keras config file at `~/.keras/keras.json`.
If you never set it, then it will be "channels_last".
# Input shape
ND tensor with shape:
`(samples, channels, *)`
if `data_format` is `"channels_first"`
or ND tensor with shape:
`(samples, *, channels)`
if `data_format` is `"channels_last"`.
# Output shape
ND tensor with shape:
`(samples, channels + 2, *)`
if `data_format` is `"channels_first"`
or 5D tensor with shape:
`(samples, *, channels + 2)`
if `data_format` is `"channels_last"`.
# References:
- [An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution](https://arxiv.org/abs/1807.03247)
"""
def __init__(self, rank,
use_radius=False,
data_format=None,
**kwargs):
super(CoordinateChannel, self).__init__(**kwargs)
if data_format not in [None, 'channels_first', 'channels_last']:
raise ValueError('`data_format` must be either "channels_last", "channels_first" '
'or None.')
self.rank = rank
self.use_radius = use_radius
self.data_format = K.image_data_format() if data_format is None else data_format
self.axis = 1 if K.image_data_format() == 'channels_first' else -1
self.input_spec = InputSpec(min_ndim=2)
self.supports_masking = True
def build(self, input_shape):
assert len(input_shape) >= 2
input_dim = input_shape[self.axis]
self.input_spec = InputSpec(min_ndim=self.rank + 2,
axes={self.axis: input_dim})
self.built = True
def call(self, inputs, training=None, mask=None):
input_shape = K.shape(inputs)
if self.rank == 1:
input_shape = [input_shape[i] for i in range(3)]
batch_shape, dim, channels = input_shape
xx_range = K.tile(K.expand_dims(K.arange(0, dim, dtype='float32'), axis=0),
K.stack([batch_shape, 1]))
xx_range = K.expand_dims(xx_range, axis=-1)
xx_channels = K.cast(xx_range, K.floatx())
xx_channels = xx_channels / K.cast(dim - 1, K.floatx())
xx_channels = (xx_channels * 2) - 1.
outputs = K.concatenate([inputs, xx_channels], axis=-1)
if self.rank == 2:
if self.data_format == 'channels_first':
inputs = K.permute_dimensions(inputs, [0, 2, 3, 1])
input_shape = K.shape(inputs)
input_shape = [input_shape[i] for i in range(4)]
batch_shape, dim1, dim2, channels = input_shape
xx_ones = tf.ones(K.stack([batch_shape, dim2]), dtype='float32')
xx_ones = K.expand_dims(xx_ones, axis=-1)
xx_range = K.tile(K.expand_dims(K.arange(0, dim1, dtype='float32'), axis=0),
K.stack([batch_shape, 1]))
xx_range = K.expand_dims(xx_range, axis=1)
xx_channels = K.batch_dot(xx_ones, xx_range, axes=[2, 1])
xx_channels = K.expand_dims(xx_channels, axis=-1)
xx_channels = K.permute_dimensions(xx_channels, [0, 2, 1, 3])
yy_ones = tf.ones(K.stack([batch_shape, dim1]), dtype='float32')
yy_ones = K.expand_dims(yy_ones, axis=1)
yy_range = K.tile(K.expand_dims(K.arange(0, dim2, dtype='float32'), axis=0),
K.stack([batch_shape, 1]))
yy_range = K.expand_dims(yy_range, axis=-1)
yy_channels = K.batch_dot(yy_range, yy_ones, axes=[2, 1])
yy_channels = K.expand_dims(yy_channels, axis=-1)
yy_channels = K.permute_dimensions(yy_channels, [0, 2, 1, 3])
xx_channels = K.cast(xx_channels, K.floatx())
xx_channels = xx_channels / K.cast(dim1 - 1, K.floatx())
xx_channels = (xx_channels * 2) - 1.
yy_channels = K.cast(yy_channels, K.floatx())
yy_channels = yy_channels / K.cast(dim2 - 1, K.floatx())
yy_channels = (yy_channels * 2) - 1.
# import pdb;pdb.set_trace()
outputs = K.concatenate([inputs, xx_channels, yy_channels], axis=-1)
# outputs = K.concatenate([inputs, tf.cast(xx_channels, dtype=tf.float16), tf.cast(yy_channels, dtype=tf.float16)], axis=-1)
if self.use_radius:
rr = K.sqrt(K.square(xx_channels - 0.5) +
K.square(yy_channels - 0.5))
outputs = K.concatenate([outputs, rr], axis=-1)
if self.data_format == 'channels_first':
outputs = K.permute_dimensions(outputs, [0, 3, 1, 2])
if self.rank == 3:
if self.data_format == 'channels_first':
inputs = K.permute_dimensions(inputs, [0, 2, 3, 4, 1])
input_shape = K.shape(inputs)
input_shape = [input_shape[i] for i in range(5)]
batch_shape, dim1, dim2, dim3, channels = input_shape
xx_ones = tf.ones(K.stack([batch_shape, dim3]), dtype='float32')
xx_ones = K.expand_dims(xx_ones, axis=-1)
xx_range = K.tile(K.expand_dims(K.arange(0, dim2, dtype='float32'), axis=0),
K.stack([batch_shape, 1]))
xx_range = K.expand_dims(xx_range, axis=1)
xx_channels = K.batch_dot(xx_ones, xx_range, axes=[2, 1])
xx_channels = K.expand_dims(xx_channels, axis=-1)
xx_channels = K.permute_dimensions(xx_channels, [0, 2, 1, 3])
xx_channels = K.expand_dims(xx_channels, axis=1)
xx_channels = K.tile(xx_channels,
[1, dim1, 1, 1, 1])
yy_ones = tf.ones(K.stack([batch_shape, dim2]), dtype='float32')
yy_ones = K.expand_dims(yy_ones, axis=1)
yy_range = K.tile(K.expand_dims(K.arange(0, dim3, dtype='float32'), axis=0),
K.stack([batch_shape, 1]))
yy_range = K.expand_dims(yy_range, axis=-1)
yy_channels = K.batch_dot(yy_range, yy_ones, axes=[2, 1])
yy_channels = K.expand_dims(yy_channels, axis=-1)
yy_channels = K.permute_dimensions(yy_channels, [0, 2, 1, 3])
yy_channels = K.expand_dims(yy_channels, axis=1)
yy_channels = K.tile(yy_channels,
[1, dim1, 1, 1, 1])
zz_range = K.tile(K.expand_dims(K.arange(0, dim1, dtype='float32'), axis=0),
K.stack([batch_shape, 1]))
zz_range = K.expand_dims(zz_range, axis=-1)
zz_range = K.expand_dims(zz_range, axis=-1)
zz_channels = K.tile(zz_range,
[1, 1, dim2, dim3])
zz_channels = K.expand_dims(zz_channels, axis=-1)
xx_channels = K.cast(xx_channels, K.floatx())
xx_channels = xx_channels / K.cast(dim2 - 1, K.floatx())
xx_channels = xx_channels * 2 - 1.
yy_channels = K.cast(yy_channels, K.floatx())
yy_channels = yy_channels / K.cast(dim3 - 1, K.floatx())
yy_channels = yy_channels * 2 - 1.
zz_channels = K.cast(zz_channels, K.floatx())
zz_channels = zz_channels / K.cast(dim1 - 1, K.floatx())
zz_channels = zz_channels * 2 - 1.
outputs = K.concatenate([inputs, zz_channels, xx_channels, yy_channels],
axis=-1)
if self.data_format == 'channels_first':
outputs = K.permute_dimensions(outputs, [0, 4, 1, 2, 3])
return outputs
def compute_output_shape(self, input_shape):
assert input_shape and len(input_shape) >= 2
assert input_shape[self.axis]
if self.use_radius and self.rank == 2:
channel_count = 3
else:
channel_count = self.rank
output_shape = list(input_shape)
output_shape[self.axis] = input_shape[self.axis] + channel_count
return tuple(output_shape)
def get_config(self):
config = {
'rank': self.rank,
'use_radius': self.use_radius,
'data_format': self.data_format
}
base_config = super(CoordinateChannel, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
backbone = tf.keras.applications.MobileNetV3Small(
input_shape=(224,224,3),
alpha=1.0,
minimalistic=False,
include_top=True,
weights='imagenet',
input_tensor=None,
classes=1000,
pooling=None,
dropout_rate=0.2,
classifier_activation='softmax',
include_preprocessing=True
)
inputs = Input(shape=[224, 224] + [1])
x = CoordinateChannel(2)(inputs)
backbone_out = backbone(x)
model = Model(inputs=inputs outputs=backbone_out)
ip = tf.random.normal((1, 224, 224, 1))
model(IP) # this works well
but when i used for training, model. fir will throw the shape error
```
### Relevant log output
_No response_
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I_kwDOArmXAs57lrwz
| 62,771 |
RuntimeError: Op type not registered 'CreateRangeEncoder' in binary running
|
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[
"@ZH-1225,\r\nIn the above mentioned you are trying to use the model `\"./bls2017\"`. Could you please share the dependencies to reproduce the issue in an effective way. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/aca383c518106cc72f3e1a2f42d969a2/untitled1652.ipynb). Thank you!",
"> @ZH-1225， 上面提到你正在尝试使用该模型`\"./bls2017\"`。您能否分享依赖项以有效地重现该问题。[请在这里](https://colab.research.google.com/gist/tilakrayal/aca383c518106cc72f3e1a2f42d969a2/untitled1652.ipynb)找到它的要点。谢谢你！\r\nI Add a line :\r\nconverter.target_spec.supported_ops = [\r\n tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS\r\n]\r\nsolved the problem thank you!\r\n",
"@ZH-1225,\r\nGlad the issue was resolved. Could you please feel free to move this issue to the closed status. 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/62771\">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/62771\">No</a>\n"
] | 2024-01-10T02:34:58 | 2024-01-12T07:26:46 | 2024-01-12T07:26:44 |
NONE
| null | null | null |
### Issue type
Others
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf2.14.0
### Custom code
Yes
### OS platform and distribution
windows x64
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I do not know what caused the Op type not registered 'CreateRangeEncoder'
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
# 加载 SavedModel
saved_model_path = "./bls2017"
loaded_model = tf.saved_model.load(saved_model_path)
# 创建 TFLite 转换器
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)
# 转换模型为 TensorFlow Lite 格式
tflite_model = converter.convert()
# 保存 TensorFlow Lite 模型
with open("converted_model.tflite", "wb") as f:
f.write(tflite_model)
```
### Relevant log output
```shell
2024-01-10 10:33:25.218158: 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: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Traceback (most recent call last):
File "E:\Pycharm_File\Video_Fuse\venv\lib\site-packages\tensorflow\python\framework\ops.py", line 3022, in op_def_for_type
return self._op_def_cache[type]
KeyError: 'CreateRangeEncoder'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "E:\Pycharm_File\Video_Fuse\学习tensorflow\savemodel_to_lite\init.py", line 5, in <module>
loaded_model = tf.saved_model.load(saved_model_path)
File "E:\Pycharm_File\Video_Fuse\venv\lib\site-packages\tensorflow\python\saved_model\load.py", line 900, in load
result = load_partial(export_dir, None, tags, options)["root"]
File "E:\Pycharm_File\Video_Fuse\venv\lib\site-packages\tensorflow\python\saved_model\load.py", line 1031, in load_partial
loader = Loader(object_graph_proto, saved_model_proto, export_dir,
File "E:\Pycharm_File\Video_Fuse\venv\lib\site-packages\tensorflow\python\saved_model\load.py", line 161, in __init__
function_deserialization.load_function_def_library(
File "E:\Pycharm_File\Video_Fuse\venv\lib\site-packages\tensorflow\python\saved_model\function_deserialization.py", line 456, in load_function_def_library
func_graph = function_def_lib.function_def_to_graph(
File "E:\Pycharm_File\Video_Fuse\venv\lib\site-packages\tensorflow\python\framework\function_def_to_graph.py", line 91, in function_def_to_graph
graph_def, nested_to_flat_tensor_name = function_def_to_graph_def(
File "E:\Pycharm_File\Video_Fuse\venv\lib\site-packages\tensorflow\python\framework\function_def_to_graph.py", line 330, in function_def_to_graph_def
op_def = default_graph.op_def_for_type(node_def.op) # pylint: disable=protected-access
File "E:\Pycharm_File\Video_Fuse\venv\lib\site-packages\tensorflow\python\framework\ops.py", line 3025, in op_def_for_type
self._op_def_for_type(type)
RuntimeError: Op type not registered 'CreateRangeEncoder' in binary running on PC-20230214ZYNL. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib (e.g. `tf.contrib.resampler`), accessing should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.
进程已结束,退出代码1
```
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I_kwDOArmXAs57k-vW
| 62,770 |
Messages at import time should go to the Python logger rather than stderr
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[
"Hi @JakeSummers ,\r\n\r\nThis might be intentional as it will give the users required info/warnings during import which may be useful for some users.As you know already this may be silenced by setting the environment variable `TF_CPP_MIN_LOG_LEVEL`. Also there are some logs generating from C++ backend passed to stderr directly.",
"@SuryanarayanaY \r\n\r\n> This might be intentional as it will give the users required info/warnings during import which may be useful for some users\r\n\r\nThis is totally useful and I **do not want to silence these messages**. \r\n\r\nThat being said, sending them directly to `stderr` is not best practice for Python packages. Sending things through the logger is the standard behaviour. \r\n\r\nThis allows them messages to get redirected to the right place. Two examples of when `writing to stderr` doesn't work well: \r\n\r\n1. Because these logs are getting send to `stderr`, they are raising alerts in our monitoring system. I probably need to redirect them to stdout to prevent this from happening but would prefer to not need to do this for all stderr messages. \r\n1. In some of my projects, we [explicitly forward logs to to gcp-logging ](https://cloud.google.com/logging/docs/reference/libraries#write_request_logs). Since theses logs do not flow through the logger, these logs are lost. \r\n\r\n",
"I thought as a workaround, I would be able to do something like this: \r\n\r\n```python\r\n with redirect_stderr(sys.stdout):\r\n import tensorflow as tf\r\n```\r\n\r\nBut it appears that doesn't work. See stackoverflow question: https://stackoverflow.com/questions/77796003/how-to-redirect-tensorflow-import-errors-from-stderr-to-stdout",
"This is the best workaround that I have found so far: \r\n\r\n```python\r\n# NOTE this must be run prior to any other import tensorflow\r\n\r\nfrom contextlib import redirect_stderr\r\nfrom wurlitzer import sys_pipes\r\nwith redirect_stderr(sys.stdout):\r\n with sys_pipes():\r\n import tensorflow\r\n```"
] | 2024-01-09T22:45:29 | 2024-04-26T19:10:01 | null |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### TensorFlow version
v2.15.0-rc1-8-g6887368d6d4 2.15.0
### OS platform and distribution
Mac Intel 14.1.1
### Python version
3.10.12
### Current behavior?
When I import tensorflow, it prints the following to `stderr`:
```
[Jan-09 17:37]$ poetry run python -c "import tensorflow as tf; print('stdout here')" > stdout.txt
2024-01-09 17:38:41.470429: 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.
[Jan-09 17:38]$ cat stdout.txt
stdout here
```
Even if I configure my Python logger before importing tensorflow the results still go to stderr.
This is a problem for me because these messages show up in my logging system as errors. When I query my logs, it looks like the app is unhealthy.
I know that I could silence messages using:
```
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
import tensorflow as tf
```
[Source](https://stackoverflow.com/a/76797348/251589)
But I would prefer to keep these messages.
Possible solutions:
Ideal solution: Use the python logger.
Less ideal solution: Allow me to redirect these logs to stdout so they do not show up as errors.
### Standalone code to reproduce the issue
```shell
Import tensorflow :)
```
### Relevant log output
_No response_
### Related issues and threads
* Similar discussion for Keras: https://github.com/keras-team/keras/issues/1406#issuecomment-410437449
* Similar feature request: https://github.com/tensorflow/tensorflow/issues/57553#issuecomment-1247641242
* https://stackoverflow.com/questions/35911252/disable-tensorflow-debugging-information
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TFlite minimal example failing on latest tensorflow repo
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[
"Hi , @lcerman! This often occurs due to missing libraries or incorrect linking configuration. The errors specifically mention register.cc, which is a file related to registering built-in operators in TensorFlow Lite. This suggests that there might be problems with the TensorFlow Lite library or its configuration.\r\nPlease let us know the exact TF version you are using ?\r\nThank you!",
"Hello, I have used TensorFlow git master branch (as suggested in the minimal example instructions).\r\n\r\nFor the first Linux PC, with Ubuntu 20.04 the hash of the last commit is fd725e2ec9d50107129e67851c25425c16fede2d.\r\n\r\nI have then tried it later at another Linux PC with Ubuntu 20.04, the revision is also from yesterday, but little bit newer: 46438b8dd7c59769b068a640addca5cb43f01a57\r\n\r\nI have now not access to the Windows PC with WSL and Ubuntu 22.04, which exhibited exactly the same error. But I have made the checkout in approximately the same time as for the first Linux PC. I could tell the exact hash later today or tomorrow.\r\n\r\nIf you need more details, please, let me know.",
"Hello again, we have finally discovered the cause of this error after some hours of CMake debugging. Its the way how the list of the kernel source files is populated in the [CMakeLists.txt](https://github.com/tensorflow/tensorflow/blob/4c6d9fbd92975b6567628feb0a7bc5a711b2146b/tensorflow/lite/CMakeLists.txt#L532C1-L535C2) in combination with the parent directory name I have picked for my project. Note how the files that should be excluded from build are defined there:\r\n\r\n```\r\npopulate_tflite_source_vars(\"kernels\"\r\n TFLITE_KERNEL_SRCS\r\n FILTER \"(.*_test_util_internal|test_.*|.*_ops_wrapper)\\\\.(cc|h)\"\r\n)\r\n```\r\n\r\nI have put my project under `test_tlite_cpp` directory, which caused the second regex in the disjunction - the `test_.*` - to trigger exclusion of all kernel files from the build.\r\n\r\nSteps to reproduce:\r\n\r\n1. `mkdir test_build_breaker`,\r\n2. `cd test_build_breaker`,\r\n3. follow the instructions from [the minimal example](https://github.com/tensorflow/tensorflow/blob/4c6d9fbd92975b6567628feb0a7bc5a711b2146b/tensorflow/lite/examples/minimal/README.md).\r\n\r\nIt was a nasty one... I had tried the build on several systems - Linux machines with Ubuntu 20.04, Windows machine with Ubuntu 22.04 @ WSL2, cross-compiled that under a Windows using a toolchain for ARM target. Always with the same result. To my bad, I had always used the same name for the parent project directory.",
"Hi @Icerman,\r\n\r\nI have reproduced the issue on linux machine with Ubuntu 20.04 and 22.04 with [the minimal example](https://github.com/tensorflow/tensorflow/blob/4c6d9fbd92975b6567628feb0a7bc5a711b2146b/tensorflow/lite/examples/minimal/README.md). Please recheck once. The build is successful without errors. Here is the screenshot of the build. \r\n. \r\n\r\nThank You",
"Hello, it looks like you have missed the detailed description I had provided above: https://github.com/tensorflow/tensorflow/issues/62769#issuecomment-1885292459 and did the build in directory named `~/minimal_build`, which would not trigger the error. By using a directory name that starts with `test_*` (note that the git copy of the tensforflow must be in that directory too) we were able to reproduce this issue on multiple systems -- different Ubuntu versions, native, WSL, cross-copilation with toolchain for ARM...\r\n\r\nPlease,\r\n- read the description provided in https://github.com/tensorflow/tensorflow/issues/62769#issuecomment-1885292459, we did also identified the source of the error, its quite obvious once you look at it...\r\n- try to reproduce the error using the steps given in https://github.com/tensorflow/tensorflow/issues/62769#issuecomment-1885292459\r\n\r\n",
"Hi @icerman, \r\n\r\nSorry for my misunderstanding. Yes, exactly you are correct. As you mentioned in the above comment, the directory starts with test_* is included, has a constraint as it is intended. Thank you for your valuable observation. Let me know if you have any other queries. \r\n\r\nThank You",
"Hello, \r\n\r\nso, were you able to reproduce it?",
"Hi @lcerman ,\r\n\r\nI have reproduced on Ubuntu 22.04. Here is the screenshot\r\n. \r\n@pkgoogle, Please look into the issue.\r\n\r\nThank You\r\n",
"Hi @lcerman, including \"test_\" as the start of your directory name will cause conflicts because it is essentially a reserved nomenclature for us to handle our unit test infrastructure for the repo, please use a different name. Thanks.",
"I don't think that defining such requirement on the environment outside of your project tree is a good design... Is that documented somewhere? At least, as you are not going to fix it, wouldn't be nice to leave some notice in the minimal example README.md? This is a nasty trap, we had spent lot of time in team of several people until we found whats going on...",
"Hi @terryheo, is there a way we can redefine [CMakeLists.txt](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/CMakeLists.txt) to not exclude all these files if the parent directory of our source directory hits one of these filters? Thanks for looking into this.",
"It's generally not advisable to use globing in cmakelists.txt at all, as there are many potential problems stemming from this. Even cmake documentation advises against, albeit for (yet another) different reason: https://cmake.org/cmake/help/latest/command/file.html#glob:\r\n\r\n\"We do not recommend using GLOB to collect a list of source files from your source tree. If no CMakeLists.txt file changes when a source is added or removed then the generated build system cannot know when to ask CMake to regenerate. The CONFIGURE_DEPENDS flag may not work reliably on all generators, or if a new generator is added in the future that cannot support it, projects using it will be stuck. Even if CONFIGURE_DEPENDS works reliably, there is still a cost to perform the check on every rebuild.\"\r\n\r\nPlease consider listing the files explicitly.",
"Tell me about a phenomenon, I found this issue because I encountered this problem\r\n\r\n`collect2: error: ld returned 1 exit status\r\ngmake[2]: *** [CMakeFiles/minimal.dir/build.make:185: minimal] Error 1\r\ngmake[1]: *** [CMakeFiles/Makefile2:1369: CMakeFiles/minimal.dir/all] Error 2\r\ngmake: *** [Makefile:136: all] Error 2`\r\n\r\nI installed Ubuntu 22.04.1 LTS in WSL, and because I am not familiar with Linux systems, I initially executed the code for the smallest example in the/home folder，https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/minimal，The above error has occurred.\r\n\r\nAfter I made the changes according to Lcerman's method and re executed, the number of errors increased.\r\nLater, I saw that LakshmiKalaKadali executed successfully and looked at his directory. I wondered if the directory being executed was incorrect,~/minimal_build actually mean /home/user/minimal_build,Then,I downloaded the file and re exectuted it in the ~$directory,The downloaded file ultimately go to the /home/user folder and executed successfully.\r\n\r\nI hope this case can help later generations.\r\n\r\n\r\n"
] | 2024-01-09T18:02:52 | 2024-05-31T05:27:17 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
the git master branch
### Custom code
No
### OS platform and distribution
Ubuntu 22.04 @ WSL2, Ubuntu 20.04 native
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When I try to follow the minimal example (exactly, step by step) -- https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/minimal I get the exactly same error as in this closed ticket: https://github.com/tensorflow/tensorflow/issues/59537
I got the same error on two systems, WSL with Ubuntu 22.04 and Linux system with Ubuntu 20.04.
## Error on WSL with Ubuntu 22.04
```
[100%] Linking CXX executable minimal
/usr/bin/ld: tensorflow-lite/libtensorflow-lite.a(register.cc.o): in function `tflite::ops::builtin::BuiltinOpResolver::BuiltinOpResolver()':
register.cc:(.text+0x99): undefined reference to `tflite::ops::builtin::Register_ABS()'
/usr/bin/ld: register.cc:(.text+0xbd): undefined reference to `tflite::ops::builtin::Register_HARD_SWISH()'
/usr/bin/ld: register.cc:(.text+0xdb): undefined reference to `tflite::ops::builtin::Register_RELU()'
/usr/bin/ld: register.cc:(.text+0xff): undefined reference to `tflite::ops::builtin::Register_RELU_N1_TO_1()'
/usr/bin/ld: register.cc:(.text+0x11d): undefined reference to `tflite::ops::builtin::Register_RELU_0_TO_1()'
/usr/bin/ld: register.cc:(.text+0x13b): undefined reference to `tflite::ops::builtin::Register_RELU6()'
/usr/bin/ld: register.cc:(.text+0x15f): undefined reference to `tflite::ops::builtin::Register_TANH()'
/usr/bin/ld: register.cc:(.text+0x183): undefined reference to `tflite::ops::builtin::Register_LOGISTIC()'
/usr/bin/ld: register.cc:(.text+0x1a7): undefined reference to `tflite::ops::builtin::Register_AVERAGE_POOL_2D()'
/usr/bin/ld: register.cc:(.text+0x1cb): undefined reference to `tflite::ops::builtin::Register_MAX_POOL_2D()'
/usr/bin/ld: register.cc:(.text+0x1ef): undefined reference to `tflite::ops::builtin::Register_L2_POOL_2D()'
/usr/bin/ld: register.cc:(.text+0x20d): undefined reference to `tflite::ops::builtin::Register_CONV_2D()'
/usr/bin/ld: register.cc:(.text+0x231): undefined reference to `tflite::ops::builtin::Register_DEPTHWISE_CONV_2D()'
/usr/bin/ld: register.cc:(.text+0x255): undefined reference to `tflite::ops::builtin::Register_SVDF()'
/usr/bin/ld: register.cc:(.text+0x279): undefined reference to `tflite::ops::builtin::Register_RNN()'
/usr/bin/ld: register.cc:(.text+0x29d): undefined reference to `tflite::ops::builtin::Register_BIDIRECTIONAL_SEQUENCE_RNN()'
/usr/bin/ld: register.cc:(.text+0x2c1): undefined reference to `tflite::ops::builtin::Register_UNIDIRECTIONAL_SEQUENCE_RNN()'
/usr/bin/ld: register.cc:(.text+0x2e5): undefined reference to `tflite::ops::builtin::Register_EMBEDDING_LOOKUP()'
/usr/bin/ld: register.cc:(.text+0x309): undefined reference to `tflite::ops::builtin::Register_EMBEDDING_LOOKUP_SPARSE()'
/usr/bin/ld: register.cc:(.text+0x327): undefined reference to `tflite::ops::builtin::Register_FULLY_CONNECTED()'
/usr/bin/ld: register.cc:(.text+0x34b): undefined reference to `tflite::ops::builtin::Register_LSH_PROJECTION()'
/usr/bin/ld: register.cc:(.text+0x369): undefined reference to `tflite::ops::builtin::Register_HASHTABLE_LOOKUP()'
/usr/bin/ld: register.cc:(.text+0x387): undefined reference to `tflite::ops::builtin::Register_SOFTMAX()'
/usr/bin/ld: register.cc:(.text+0x3ab): undefined reference to `tflite::ops::builtin::Register_CONCATENATION()'
/usr/bin/ld: register.cc:(.text+0x3cf): undefined reference to `tflite::ops::builtin::Register_ADD()'
/usr/bin/ld: register.cc:(.text+0x3f3): undefined reference to `tflite::ops::builtin::Register_SPACE_TO_BATCH_ND()'
/usr/bin/ld: register.cc:(.text+0x417): undefined reference to `tflite::ops::builtin::Register_BATCH_TO_SPACE_ND()'
/usr/bin/ld: register.cc:(.text+0x43b): undefined reference to `tflite::ops::builtin::Register_MUL()'
/usr/bin/ld: register.cc:(.text+0x45f): undefined reference to `tflite::ops::builtin::Register_L2_NORMALIZATION()'
/usr/bin/ld: register.cc:(.text+0x483): undefined reference to `tflite::ops::builtin::Register_LOCAL_RESPONSE_NORMALIZATION()'
/usr/bin/ld: register.cc:(.text+0x4a1): undefined reference to `tflite::ops::builtin::Register_LSTM()'
/usr/bin/ld: register.cc:(.text+0x4c5): undefined reference to `tflite::ops::builtin::Register_BIDIRECTIONAL_SEQUENCE_LSTM()'
/usr/bin/ld: register.cc:(.text+0x4e9): undefined reference to `tflite::ops::builtin::Register_UNIDIRECTIONAL_SEQUENCE_LSTM()'
/usr/bin/ld: register.cc:(.text+0x50d): undefined reference to `tflite::ops::builtin::Register_PAD()'
/usr/bin/ld: register.cc:(.text+0x531): undefined reference to `tflite::ops::builtin::Register_PADV2()'
/usr/bin/ld: register.cc:(.text+0x555): undefined reference to `tflite::ops::builtin::Register_RESHAPE()'
/usr/bin/ld: register.cc:(.text+0x573): undefined reference to `tflite::ops::builtin::Register_RESIZE_BILINEAR()'
/usr/bin/ld: register.cc:(.text+0x597): undefined reference to `tflite::ops::builtin::Register_RESIZE_NEAREST_NEIGHBOR()'
/usr/bin/ld: register.cc:(.text+0x5bb): undefined reference to `tflite::ops::builtin::Register_SKIP_GRAM()'
/usr/bin/ld: register.cc:(.text+0x5d9): undefined reference to `tflite::ops::builtin::Register_SPACE_TO_DEPTH()'
/usr/bin/ld: register.cc:(.text+0x5fd): undefined reference to `tflite::ops::builtin::Register_DEPTH_TO_SPACE()'
/usr/bin/ld: register.cc:(.text+0x621): undefined reference to `tflite::ops::builtin::Register_GATHER()'
/usr/bin/ld: register.cc:(.text+0x645): undefined reference to `tflite::ops::builtin::Register_TRANSPOSE()'
/usr/bin/ld: register.cc:(.text+0x669): undefined reference to `tflite::ops::builtin::Register_MEAN()'
/usr/bin/ld: register.cc:(.text+0x68d): undefined reference to `tflite::ops::builtin::Register_DIV()'
/usr/bin/ld: register.cc:(.text+0x6b1): undefined reference to `tflite::ops::builtin::Register_SUB()'
/usr/bin/ld: register.cc:(.text+0x6d5): undefined reference to `tflite::ops::builtin::Register_SPLIT()'
/usr/bin/ld: register.cc:(.text+0x6f9): undefined reference to `tflite::ops::builtin::Register_SPLIT_V()'
/usr/bin/ld: register.cc:(.text+0x71d): undefined reference to `tflite::ops::builtin::Register_SQUEEZE()'
/usr/bin/ld: register.cc:(.text+0x741): undefined reference to `tflite::ops::builtin::Register_STRIDED_SLICE()'
/usr/bin/ld: register.cc:(.text+0x765): undefined reference to `tflite::ops::builtin::Register_EXP()'
/usr/bin/ld: register.cc:(.text+0x789): undefined reference to `tflite::ops::builtin::Register_TOPK_V2()'
/usr/bin/ld: register.cc:(.text+0x7ad): undefined reference to `tflite::ops::builtin::Register_LOG()'
/usr/bin/ld: register.cc:(.text+0x7d1): undefined reference to `tflite::ops::builtin::Register_LOG_SOFTMAX()'
/usr/bin/ld: register.cc:(.text+0x7f5): undefined reference to `tflite::ops::builtin::Register_CAST()'
/usr/bin/ld: register.cc:(.text+0x819): undefined reference to `tflite::ops::builtin::Register_DEQUANTIZE()'
/usr/bin/ld: register.cc:(.text+0x83d): undefined reference to `tflite::ops::builtin::Register_PRELU()'
/usr/bin/ld: register.cc:(.text+0x85b): undefined reference to `tflite::ops::builtin::Register_MAXIMUM()'
/usr/bin/ld: register.cc:(.text+0x87f): undefined reference to `tflite::ops::builtin::Register_MINIMUM()'
/usr/bin/ld: register.cc:(.text+0x8a3): undefined reference to `tflite::ops::builtin::Register_ARG_MAX()'
/usr/bin/ld: register.cc:(.text+0x8c7): undefined reference to `tflite::ops::builtin::Register_ARG_MIN()'
/usr/bin/ld: register.cc:(.text+0x8eb): undefined reference to `tflite::ops::builtin::Register_GREATER()'
/usr/bin/ld: register.cc:(.text+0x90f): undefined reference to `tflite::ops::builtin::Register_GREATER_EQUAL()'
/usr/bin/ld: register.cc:(.text+0x933): undefined reference to `tflite::ops::builtin::Register_LESS()'
/usr/bin/ld: register.cc:(.text+0x957): undefined reference to `tflite::ops::builtin::Register_LESS_EQUAL()'
/usr/bin/ld: register.cc:(.text+0x97b): undefined reference to `tflite::ops::builtin::Register_FLOOR()'
/usr/bin/ld: register.cc:(.text+0x999): undefined reference to `tflite::ops::builtin::Register_CEIL()'
/usr/bin/ld: register.cc:(.text+0x9b7): undefined reference to `tflite::ops::builtin::Register_ROUND()'
/usr/bin/ld: register.cc:(.text+0x9d5): undefined reference to `tflite::ops::builtin::Register_NEG()'
/usr/bin/ld: register.cc:(.text+0x9f3): undefined reference to `tflite::ops::builtin::Register_SELECT()'
/usr/bin/ld: register.cc:(.text+0xa17): undefined reference to `tflite::ops::builtin::Register_SELECT_V2()'
/usr/bin/ld: register.cc:(.text+0xa3b): undefined reference to `tflite::ops::builtin::Register_SLICE()'
/usr/bin/ld: register.cc:(.text+0xa5f): undefined reference to `tflite::ops::builtin::Register_SIN()'
/usr/bin/ld: register.cc:(.text+0xa7d): undefined reference to `tflite::ops::builtin::Register_COS()'
/usr/bin/ld: register.cc:(.text+0xa9b): undefined reference to `tflite::ops::builtin::Register_TRANSPOSE_CONV()'
/usr/bin/ld: register.cc:(.text+0xabf): undefined reference to `tflite::ops::builtin::Register_TILE()'
/usr/bin/ld: register.cc:(.text+0xae3): undefined reference to `tflite::ops::builtin::Register_SUM()'
/usr/bin/ld: register.cc:(.text+0xb07): undefined reference to `tflite::ops::builtin::Register_REDUCE_PROD()'
/usr/bin/ld: register.cc:(.text+0xb2b): undefined reference to `tflite::ops::builtin::Register_REDUCE_MAX()'
/usr/bin/ld: register.cc:(.text+0xb4f): undefined reference to `tflite::ops::builtin::Register_REDUCE_MIN()'
/usr/bin/ld: register.cc:(.text+0xb73): undefined reference to `tflite::ops::builtin::Register_REDUCE_ANY()'
/usr/bin/ld: register.cc:(.text+0xb91): undefined reference to `tflite::ops::builtin::Register_REDUCE_ALL()'
/usr/bin/ld: register.cc:(.text+0xbaf): undefined reference to `tflite::ops::builtin::Register_EXPAND_DIMS()'
/usr/bin/ld: register.cc:(.text+0xbcd): undefined reference to `tflite::ops::builtin::Register_SPARSE_TO_DENSE()'
/usr/bin/ld: register.cc:(.text+0xbf1): undefined reference to `tflite::ops::builtin::Register_EQUAL()'
/usr/bin/ld: register.cc:(.text+0xc15): undefined reference to `tflite::ops::builtin::Register_NOT_EQUAL()'
/usr/bin/ld: register.cc:(.text+0xc39): undefined reference to `tflite::ops::builtin::Register_SQRT()'
/usr/bin/ld: register.cc:(.text+0xc57): undefined reference to `tflite::ops::builtin::Register_RSQRT()'
/usr/bin/ld: register.cc:(.text+0xc7b): undefined reference to `tflite::ops::builtin::Register_SHAPE()'
/usr/bin/ld: register.cc:(.text+0xc99): undefined reference to `tflite::ops::builtin::Register_RANK()'
/usr/bin/ld: register.cc:(.text+0xcb7): undefined reference to `tflite::ops::builtin::Register_POW()'
/usr/bin/ld: register.cc:(.text+0xcd5): undefined reference to `tflite::ops::builtin::Register_FAKE_QUANT()'
/usr/bin/ld: register.cc:(.text+0xcf9): undefined reference to `tflite::ops::builtin::Register_PACK()'
/usr/bin/ld: register.cc:(.text+0xd1d): undefined reference to `tflite::ops::builtin::Register_ONE_HOT()'
/usr/bin/ld: register.cc:(.text+0xd3b): undefined reference to `tflite::ops::builtin::Register_LOGICAL_OR()'
/usr/bin/ld: register.cc:(.text+0xd59): undefined reference to `tflite::ops::builtin::Register_LOGICAL_AND()'
/usr/bin/ld: register.cc:(.text+0xd77): undefined reference to `tflite::ops::builtin::Register_LOGICAL_NOT()'
/usr/bin/ld: register.cc:(.text+0xd95): undefined reference to `tflite::ops::builtin::Register_UNPACK()'
/usr/bin/ld: register.cc:(.text+0xdb9): undefined reference to `tflite::ops::builtin::Register_FLOOR_DIV()'
/usr/bin/ld: register.cc:(.text+0xddd): undefined reference to `tflite::ops::builtin::Register_SQUARE()'
/usr/bin/ld: register.cc:(.text+0xdfb): undefined reference to `tflite::ops::builtin::Register_ZEROS_LIKE()'
/usr/bin/ld: register.cc:(.text+0xe19): undefined reference to `tflite::ops::builtin::Register_FLOOR_MOD()'
/usr/bin/ld: register.cc:(.text+0xe3d): undefined reference to `tflite::ops::builtin::Register_RANGE()'
/usr/bin/ld: register.cc:(.text+0xe61): undefined reference to `tflite::ops::builtin::Register_LEAKY_RELU()'
/usr/bin/ld: register.cc:(.text+0xe85): undefined reference to `tflite::ops::builtin::Register_SQUARED_DIFFERENCE()'
/usr/bin/ld: register.cc:(.text+0xea9): undefined reference to `tflite::ops::builtin::Register_FILL()'
/usr/bin/ld: register.cc:(.text+0xecd): undefined reference to `tflite::ops::builtin::Register_MIRROR_PAD()'
/usr/bin/ld: register.cc:(.text+0xef1): undefined reference to `tflite::ops::builtin::Register_UNIQUE()'
/usr/bin/ld: register.cc:(.text+0xf0f): undefined reference to `tflite::ops::builtin::Register_REVERSE_V2()'
/usr/bin/ld: register.cc:(.text+0xf33): undefined reference to `tflite::ops::builtin::Register_ADD_N()'
/usr/bin/ld: register.cc:(.text+0xf51): undefined reference to `tflite::ops::builtin::Register_GATHER_ND()'
/usr/bin/ld: register.cc:(.text+0xf75): undefined reference to `tflite::ops::builtin::Register_WHERE()'
/usr/bin/ld: register.cc:(.text+0xf99): undefined reference to `tflite::ops::builtin::Register_ELU()'
/usr/bin/ld: register.cc:(.text+0xfb7): undefined reference to `tflite::ops::builtin::Register_REVERSE_SEQUENCE()'
/usr/bin/ld: register.cc:(.text+0xfd5): undefined reference to `tflite::ops::builtin::Register_MATRIX_DIAG()'
/usr/bin/ld: register.cc:(.text+0xff3): undefined reference to `tflite::ops::builtin::Register_QUANTIZE()'
/usr/bin/ld: register.cc:(.text+0x1017): undefined reference to `tflite::ops::builtin::Register_MATRIX_SET_DIAG()'
/usr/bin/ld: register.cc:(.text+0x1035): undefined reference to `tflite::ops::builtin::Register_IF()'
/usr/bin/ld: register.cc:(.text+0x1053): undefined reference to `tflite::ops::builtin::Register_WHILE()'
/usr/bin/ld: register.cc:(.text+0x1071): undefined reference to `tflite::ops::builtin::Register_NON_MAX_SUPPRESSION_V4()'
/usr/bin/ld: register.cc:(.text+0x108f): undefined reference to `tflite::ops::builtin::Register_NON_MAX_SUPPRESSION_V5()'
/usr/bin/ld: register.cc:(.text+0x10ad): undefined reference to `tflite::ops::builtin::Register_SCATTER_ND()'
/usr/bin/ld: register.cc:(.text+0x10cb): undefined reference to `tflite::ops::builtin::Register_DENSIFY()'
/usr/bin/ld: register.cc:(.text+0x10e9): undefined reference to `tflite::ops::builtin::Register_SEGMENT_SUM()'
/usr/bin/ld: register.cc:(.text+0x1107): undefined reference to `tflite::ops::builtin::Register_BATCH_MATMUL()'
/usr/bin/ld: register.cc:(.text+0x112b): undefined reference to `tflite::ops::builtin::Register_CUMSUM()'
/usr/bin/ld: register.cc:(.text+0x1149): undefined reference to `tflite::ops::builtin::Register_BROADCAST_TO()'
/usr/bin/ld: register.cc:(.text+0x116d): undefined reference to `tflite::ops::builtin::Register_CALL_ONCE()'
/usr/bin/ld: register.cc:(.text+0x118b): undefined reference to `tflite::ops::builtin::Register_RFFT2D()'
/usr/bin/ld: register.cc:(.text+0x11a9): undefined reference to `tflite::ops::builtin::Register_CONV_3D()'
/usr/bin/ld: register.cc:(.text+0x11c7): undefined reference to `tflite::ops::builtin::Register_IMAG()'
/usr/bin/ld: register.cc:(.text+0x11e5): undefined reference to `tflite::ops::builtin::Register_REAL()'
/usr/bin/ld: register.cc:(.text+0x1203): undefined reference to `tflite::ops::builtin::Register_COMPLEX_ABS()'
/usr/bin/ld: register.cc:(.text+0x1221): undefined reference to `tflite::ops::builtin::Register_BROADCAST_ARGS()'
/usr/bin/ld: register.cc:(.text+0x123f): undefined reference to `tflite::ops::builtin::Register_HASHTABLE()'
/usr/bin/ld: register.cc:(.text+0x125d): undefined reference to `tflite::ops::builtin::Register_HASHTABLE_FIND()'
/usr/bin/ld: register.cc:(.text+0x127b): undefined reference to `tflite::ops::builtin::Register_HASHTABLE_IMPORT()'
/usr/bin/ld: register.cc:(.text+0x1299): undefined reference to `tflite::ops::builtin::Register_HASHTABLE_SIZE()'
/usr/bin/ld: register.cc:(.text+0x12b7): undefined reference to `tflite::ops::builtin::Register_CONV_3D_TRANSPOSE()'
/usr/bin/ld: register.cc:(.text+0x12d5): undefined reference to `tflite::ops::builtin::Register_VAR_HANDLE()'
/usr/bin/ld: register.cc:(.text+0x12f3): undefined reference to `tflite::ops::builtin::Register_READ_VARIABLE()'
/usr/bin/ld: register.cc:(.text+0x1311): undefined reference to `tflite::ops::builtin::Register_ASSIGN_VARIABLE()'
/usr/bin/ld: register.cc:(.text+0x132f): undefined reference to `tflite::ops::builtin::Register_MULTINOMIAL()'
/usr/bin/ld: register.cc:(.text+0x134d): undefined reference to `tflite::ops::builtin::Register_RANDOM_STANDARD_NORMAL()'
/usr/bin/ld: register.cc:(.text+0x136b): undefined reference to `tflite::ops::builtin::Register_BUCKETIZE()'
/usr/bin/ld: register.cc:(.text+0x1389): undefined reference to `tflite::ops::builtin::Register_RANDOM_UNIFORM()'
/usr/bin/ld: register.cc:(.text+0x13a7): undefined reference to `tflite::ops::builtin::Register_GELU()'
/usr/bin/ld: register.cc:(.text+0x13cb): undefined reference to `tflite::ops::builtin::Register_DYNAMIC_UPDATE_SLICE()'
/usr/bin/ld: register.cc:(.text+0x13e9): undefined reference to `tflite::ops::builtin::Register_UNSORTED_SEGMENT_PROD()'
/usr/bin/ld: register.cc:(.text+0x1407): undefined reference to `tflite::ops::builtin::Register_UNSORTED_SEGMENT_MAX()'
/usr/bin/ld: register.cc:(.text+0x1425): undefined reference to `tflite::ops::builtin::Register_UNSORTED_SEGMENT_MIN()'
/usr/bin/ld: register.cc:(.text+0x1443): undefined reference to `tflite::ops::builtin::Register_UNSORTED_SEGMENT_SUM()'
/usr/bin/ld: register.cc:(.text+0x1461): undefined reference to `tflite::ops::builtin::Register_ATAN2()'
/usr/bin/ld: register.cc:(.text+0x147f): undefined reference to `tflite::ops::builtin::Register_SIGN()'
/usr/bin/ld: register.cc:(.text+0x14a3): undefined reference to `tflite::ops::builtin::Register_BITCAST()'
/usr/bin/ld: register.cc:(.text+0x14c1): undefined reference to `tflite::ops::builtin::Register_BITWISE_XOR()'
/usr/bin/ld: register.cc:(.text+0x14df): undefined reference to `tflite::ops::builtin::Register_RIGHT_SHIFT()'
/usr/bin/ld: register.cc:(.text+0x14fd): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_SCATTER()'
/usr/bin/ld: register.cc:(.text+0x151b): undefined reference to `tflite::ops::builtin::Register_DILATE()'
/usr/bin/ld: register.cc:(.text+0x1539): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_RNG_BIT_GENERATOR()'
/usr/bin/ld: register.cc:(.text+0x1557): undefined reference to `tflite::ops::builtin::Register_REDUCE_WINDOW()'
/usr/bin/ld: register.cc:(.text+0x1575): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_REDUCE_WINDOW()'
/usr/bin/ld: register.cc:(.text+0x1593): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_GATHER()'
/usr/bin/ld: register.cc:(.text+0x15b1): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_ADD()'
/usr/bin/ld: register.cc:(.text+0x15cf): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_MULTIPLY()'
/usr/bin/ld: register.cc:(.text+0x15ed): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_MAXIMUM()'
/usr/bin/ld: register.cc:(.text+0x160b): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_MINIMUM()'
/usr/bin/ld: register.cc:(.text+0x1629): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_PAD()'
/usr/bin/ld: register.cc:(.text+0x1647): undefined reference to `tflite::ops::custom::Register_NUMERIC_VERIFY()'
/usr/bin/ld: register.cc:(.text+0x166a): undefined reference to `tflite::ops::custom::Register_MFCC()'
/usr/bin/ld: register.cc:(.text+0x168d): undefined reference to `tflite::ops::custom::Register_AUDIO_SPECTROGRAM()'
/usr/bin/ld: register.cc:(.text+0x16b0): undefined reference to `tflite::ops::custom::Register_DETECTION_POSTPROCESS()'
/usr/bin/ld: tensorflow-lite/libtensorflow-lite.a(xnnpack_delegate.cc.o): in function `tflite::xnnpack::(anonymous namespace)::Delegate::Delegate(TfLiteXNNPackDelegateOptions const*, xnn_workspace*, TfLiteContext*)':
xnnpack_delegate.cc:(.text+0x1368): undefined reference to `tflite::CpuBackendContext::GetFromContext(TfLiteContext*)'
/usr/bin/ld: xnnpack_delegate.cc:(.text+0x1370): undefined reference to `tflite::CpuBackendContext::get_xnnpack_threadpool()'
collect2: error: ld returned 1 exit status
gmake[2]: *** [CMakeFiles/minimal.dir/build.make:185: minimal] Error 1
gmake[1]: *** [CMakeFiles/Makefile2:1362: CMakeFiles/minimal.dir/all] Error 2
gmake: *** [Makefile:136: all] Error 2
```
## Error on Linux system with Ubuntu 20.04
```
[100%] Linking CXX executable minimal
/usr/bin/ld: tensorflow-lite/libtensorflow-lite.a(register.cc.o): in function `tflite::ops::builtin::BuiltinOpResolver::BuiltinOpResolver()':
register.cc:(.text+0x1e5): undefined reference to `tflite::ops::builtin::Register_ABS()'
/usr/bin/ld: register.cc:(.text+0x205): undefined reference to `tflite::ops::builtin::Register_HARD_SWISH()'
/usr/bin/ld: register.cc:(.text+0x21f): undefined reference to `tflite::ops::builtin::Register_RELU()'
/usr/bin/ld: register.cc:(.text+0x23f): undefined reference to `tflite::ops::builtin::Register_RELU_N1_TO_1()'
/usr/bin/ld: register.cc:(.text+0x259): undefined reference to `tflite::ops::builtin::Register_RELU_0_TO_1()'
/usr/bin/ld: register.cc:(.text+0x273): undefined reference to `tflite::ops::builtin::Register_RELU6()'
/usr/bin/ld: register.cc:(.text+0x293): undefined reference to `tflite::ops::builtin::Register_TANH()'
/usr/bin/ld: register.cc:(.text+0x2b3): undefined reference to `tflite::ops::builtin::Register_LOGISTIC()'
/usr/bin/ld: register.cc:(.text+0x2d3): undefined reference to `tflite::ops::builtin::Register_AVERAGE_POOL_2D()'
/usr/bin/ld: register.cc:(.text+0x2f3): undefined reference to `tflite::ops::builtin::Register_MAX_POOL_2D()'
/usr/bin/ld: register.cc:(.text+0x313): undefined reference to `tflite::ops::builtin::Register_L2_POOL_2D()'
/usr/bin/ld: register.cc:(.text+0x32d): undefined reference to `tflite::ops::builtin::Register_CONV_2D()'
/usr/bin/ld: register.cc:(.text+0x34d): undefined reference to `tflite::ops::builtin::Register_DEPTHWISE_CONV_2D()'
/usr/bin/ld: register.cc:(.text+0x36d): undefined reference to `tflite::ops::builtin::Register_SVDF()'
/usr/bin/ld: register.cc:(.text+0x38d): undefined reference to `tflite::ops::builtin::Register_RNN()'
/usr/bin/ld: register.cc:(.text+0x3ad): undefined reference to `tflite::ops::builtin::Register_BIDIRECTIONAL_SEQUENCE_RNN()'
/usr/bin/ld: register.cc:(.text+0x3cd): undefined reference to `tflite::ops::builtin::Register_UNIDIRECTIONAL_SEQUENCE_RNN()'
/usr/bin/ld: register.cc:(.text+0x3ed): undefined reference to `tflite::ops::builtin::Register_EMBEDDING_LOOKUP()'
/usr/bin/ld: register.cc:(.text+0x40d): undefined reference to `tflite::ops::builtin::Register_EMBEDDING_LOOKUP_SPARSE()'
/usr/bin/ld: register.cc:(.text+0x427): undefined reference to `tflite::ops::builtin::Register_FULLY_CONNECTED()'
/usr/bin/ld: register.cc:(.text+0x447): undefined reference to `tflite::ops::builtin::Register_LSH_PROJECTION()'
/usr/bin/ld: register.cc:(.text+0x461): undefined reference to `tflite::ops::builtin::Register_HASHTABLE_LOOKUP()'
/usr/bin/ld: register.cc:(.text+0x47b): undefined reference to `tflite::ops::builtin::Register_SOFTMAX()'
/usr/bin/ld: register.cc:(.text+0x49b): undefined reference to `tflite::ops::builtin::Register_CONCATENATION()'
/usr/bin/ld: register.cc:(.text+0x4bb): undefined reference to `tflite::ops::builtin::Register_ADD()'
/usr/bin/ld: register.cc:(.text+0x4d8): undefined reference to `tflite::ops::builtin::Register_SPACE_TO_BATCH_ND()'
/usr/bin/ld: register.cc:(.text+0x4f8): undefined reference to `tflite::ops::builtin::Register_BATCH_TO_SPACE_ND()'
/usr/bin/ld: register.cc:(.text+0x518): undefined reference to `tflite::ops::builtin::Register_MUL()'
/usr/bin/ld: register.cc:(.text+0x538): undefined reference to `tflite::ops::builtin::Register_L2_NORMALIZATION()'
/usr/bin/ld: register.cc:(.text+0x558): undefined reference to `tflite::ops::builtin::Register_LOCAL_RESPONSE_NORMALIZATION()'
/usr/bin/ld: register.cc:(.text+0x572): undefined reference to `tflite::ops::builtin::Register_LSTM()'
/usr/bin/ld: register.cc:(.text+0x592): undefined reference to `tflite::ops::builtin::Register_BIDIRECTIONAL_SEQUENCE_LSTM()'
/usr/bin/ld: register.cc:(.text+0x5b2): undefined reference to `tflite::ops::builtin::Register_UNIDIRECTIONAL_SEQUENCE_LSTM()'
/usr/bin/ld: register.cc:(.text+0x5d2): undefined reference to `tflite::ops::builtin::Register_PAD()'
/usr/bin/ld: register.cc:(.text+0x5f2): undefined reference to `tflite::ops::builtin::Register_PADV2()'
/usr/bin/ld: register.cc:(.text+0x612): undefined reference to `tflite::ops::builtin::Register_RESHAPE()'
/usr/bin/ld: register.cc:(.text+0x62c): undefined reference to `tflite::ops::builtin::Register_RESIZE_BILINEAR()'
/usr/bin/ld: register.cc:(.text+0x64c): undefined reference to `tflite::ops::builtin::Register_RESIZE_NEAREST_NEIGHBOR()'
/usr/bin/ld: register.cc:(.text+0x66c): undefined reference to `tflite::ops::builtin::Register_SKIP_GRAM()'
/usr/bin/ld: register.cc:(.text+0x686): undefined reference to `tflite::ops::builtin::Register_SPACE_TO_DEPTH()'
/usr/bin/ld: register.cc:(.text+0x6a6): undefined reference to `tflite::ops::builtin::Register_DEPTH_TO_SPACE()'
/usr/bin/ld: register.cc:(.text+0x6c6): undefined reference to `tflite::ops::builtin::Register_GATHER()'
/usr/bin/ld: register.cc:(.text+0x6e6): undefined reference to `tflite::ops::builtin::Register_TRANSPOSE()'
/usr/bin/ld: register.cc:(.text+0x706): undefined reference to `tflite::ops::builtin::Register_MEAN()'
/usr/bin/ld: register.cc:(.text+0x726): undefined reference to `tflite::ops::builtin::Register_DIV()'
/usr/bin/ld: register.cc:(.text+0x746): undefined reference to `tflite::ops::builtin::Register_SUB()'
/usr/bin/ld: register.cc:(.text+0x766): undefined reference to `tflite::ops::builtin::Register_SPLIT()'
/usr/bin/ld: register.cc:(.text+0x786): undefined reference to `tflite::ops::builtin::Register_SPLIT_V()'
/usr/bin/ld: register.cc:(.text+0x7a6): undefined reference to `tflite::ops::builtin::Register_SQUEEZE()'
/usr/bin/ld: register.cc:(.text+0x7c6): undefined reference to `tflite::ops::builtin::Register_STRIDED_SLICE()'
/usr/bin/ld: register.cc:(.text+0x7e6): undefined reference to `tflite::ops::builtin::Register_EXP()'
/usr/bin/ld: register.cc:(.text+0x806): undefined reference to `tflite::ops::builtin::Register_TOPK_V2()'
/usr/bin/ld: register.cc:(.text+0x826): undefined reference to `tflite::ops::builtin::Register_LOG()'
/usr/bin/ld: register.cc:(.text+0x846): undefined reference to `tflite::ops::builtin::Register_LOG_SOFTMAX()'
/usr/bin/ld: register.cc:(.text+0x866): undefined reference to `tflite::ops::builtin::Register_CAST()'
/usr/bin/ld: register.cc:(.text+0x886): undefined reference to `tflite::ops::builtin::Register_DEQUANTIZE()'
/usr/bin/ld: register.cc:(.text+0x8a6): undefined reference to `tflite::ops::builtin::Register_PRELU()'
/usr/bin/ld: register.cc:(.text+0x8c0): undefined reference to `tflite::ops::builtin::Register_MAXIMUM()'
/usr/bin/ld: register.cc:(.text+0x8e0): undefined reference to `tflite::ops::builtin::Register_MINIMUM()'
/usr/bin/ld: register.cc:(.text+0x900): undefined reference to `tflite::ops::builtin::Register_ARG_MAX()'
/usr/bin/ld: register.cc:(.text+0x920): undefined reference to `tflite::ops::builtin::Register_ARG_MIN()'
/usr/bin/ld: register.cc:(.text+0x940): undefined reference to `tflite::ops::builtin::Register_GREATER()'
/usr/bin/ld: register.cc:(.text+0x960): undefined reference to `tflite::ops::builtin::Register_GREATER_EQUAL()'
/usr/bin/ld: register.cc:(.text+0x980): undefined reference to `tflite::ops::builtin::Register_LESS()'
/usr/bin/ld: register.cc:(.text+0x9a0): undefined reference to `tflite::ops::builtin::Register_LESS_EQUAL()'
/usr/bin/ld: register.cc:(.text+0x9c0): undefined reference to `tflite::ops::builtin::Register_FLOOR()'
/usr/bin/ld: register.cc:(.text+0x9da): undefined reference to `tflite::ops::builtin::Register_CEIL()'
/usr/bin/ld: register.cc:(.text+0x9f4): undefined reference to `tflite::ops::builtin::Register_ROUND()'
/usr/bin/ld: register.cc:(.text+0xa0e): undefined reference to `tflite::ops::builtin::Register_NEG()'
/usr/bin/ld: register.cc:(.text+0xa28): undefined reference to `tflite::ops::builtin::Register_SELECT()'
/usr/bin/ld: register.cc:(.text+0xa48): undefined reference to `tflite::ops::builtin::Register_SELECT_V2()'
/usr/bin/ld: register.cc:(.text+0xa68): undefined reference to `tflite::ops::builtin::Register_SLICE()'
/usr/bin/ld: register.cc:(.text+0xa88): undefined reference to `tflite::ops::builtin::Register_SIN()'
/usr/bin/ld: register.cc:(.text+0xaa2): undefined reference to `tflite::ops::builtin::Register_COS()'
/usr/bin/ld: register.cc:(.text+0xabc): undefined reference to `tflite::ops::builtin::Register_TRANSPOSE_CONV()'
/usr/bin/ld: register.cc:(.text+0xadc): undefined reference to `tflite::ops::builtin::Register_TILE()'
/usr/bin/ld: register.cc:(.text+0xafc): undefined reference to `tflite::ops::builtin::Register_SUM()'
/usr/bin/ld: register.cc:(.text+0xb1c): undefined reference to `tflite::ops::builtin::Register_REDUCE_PROD()'
/usr/bin/ld: register.cc:(.text+0xb3c): undefined reference to `tflite::ops::builtin::Register_REDUCE_MAX()'
/usr/bin/ld: register.cc:(.text+0xb5c): undefined reference to `tflite::ops::builtin::Register_REDUCE_MIN()'
/usr/bin/ld: register.cc:(.text+0xb7c): undefined reference to `tflite::ops::builtin::Register_REDUCE_ANY()'
/usr/bin/ld: register.cc:(.text+0xb96): undefined reference to `tflite::ops::builtin::Register_REDUCE_ALL()'
/usr/bin/ld: register.cc:(.text+0xbb0): undefined reference to `tflite::ops::builtin::Register_EXPAND_DIMS()'
/usr/bin/ld: register.cc:(.text+0xbca): undefined reference to `tflite::ops::builtin::Register_SPARSE_TO_DENSE()'
/usr/bin/ld: register.cc:(.text+0xbea): undefined reference to `tflite::ops::builtin::Register_EQUAL()'
/usr/bin/ld: register.cc:(.text+0xc0a): undefined reference to `tflite::ops::builtin::Register_NOT_EQUAL()'
/usr/bin/ld: register.cc:(.text+0xc2a): undefined reference to `tflite::ops::builtin::Register_SQRT()'
/usr/bin/ld: register.cc:(.text+0xc44): undefined reference to `tflite::ops::builtin::Register_RSQRT()'
/usr/bin/ld: register.cc:(.text+0xc64): undefined reference to `tflite::ops::builtin::Register_SHAPE()'
/usr/bin/ld: register.cc:(.text+0xc7e): undefined reference to `tflite::ops::builtin::Register_RANK()'
/usr/bin/ld: register.cc:(.text+0xc98): undefined reference to `tflite::ops::builtin::Register_POW()'
/usr/bin/ld: register.cc:(.text+0xcb2): undefined reference to `tflite::ops::builtin::Register_FAKE_QUANT()'
/usr/bin/ld: register.cc:(.text+0xcd2): undefined reference to `tflite::ops::builtin::Register_PACK()'
/usr/bin/ld: register.cc:(.text+0xcf2): undefined reference to `tflite::ops::builtin::Register_ONE_HOT()'
/usr/bin/ld: register.cc:(.text+0xd0c): undefined reference to `tflite::ops::builtin::Register_LOGICAL_OR()'
/usr/bin/ld: register.cc:(.text+0xd26): undefined reference to `tflite::ops::builtin::Register_LOGICAL_AND()'
/usr/bin/ld: register.cc:(.text+0xd40): undefined reference to `tflite::ops::builtin::Register_LOGICAL_NOT()'
/usr/bin/ld: register.cc:(.text+0xd5a): undefined reference to `tflite::ops::builtin::Register_UNPACK()'
/usr/bin/ld: register.cc:(.text+0xd7a): undefined reference to `tflite::ops::builtin::Register_FLOOR_DIV()'
/usr/bin/ld: register.cc:(.text+0xd9a): undefined reference to `tflite::ops::builtin::Register_SQUARE()'
/usr/bin/ld: register.cc:(.text+0xdb4): undefined reference to `tflite::ops::builtin::Register_ZEROS_LIKE()'
/usr/bin/ld: register.cc:(.text+0xdce): undefined reference to `tflite::ops::builtin::Register_FLOOR_MOD()'
/usr/bin/ld: register.cc:(.text+0xdee): undefined reference to `tflite::ops::builtin::Register_RANGE()'
/usr/bin/ld: register.cc:(.text+0xe0e): undefined reference to `tflite::ops::builtin::Register_LEAKY_RELU()'
/usr/bin/ld: register.cc:(.text+0xe2e): undefined reference to `tflite::ops::builtin::Register_SQUARED_DIFFERENCE()'
/usr/bin/ld: register.cc:(.text+0xe4e): undefined reference to `tflite::ops::builtin::Register_FILL()'
/usr/bin/ld: register.cc:(.text+0xe6e): undefined reference to `tflite::ops::builtin::Register_MIRROR_PAD()'
/usr/bin/ld: register.cc:(.text+0xe8e): undefined reference to `tflite::ops::builtin::Register_UNIQUE()'
/usr/bin/ld: register.cc:(.text+0xea8): undefined reference to `tflite::ops::builtin::Register_REVERSE_V2()'
/usr/bin/ld: register.cc:(.text+0xec8): undefined reference to `tflite::ops::builtin::Register_ADD_N()'
/usr/bin/ld: register.cc:(.text+0xee2): undefined reference to `tflite::ops::builtin::Register_GATHER_ND()'
/usr/bin/ld: register.cc:(.text+0xf02): undefined reference to `tflite::ops::builtin::Register_WHERE()'
/usr/bin/ld: register.cc:(.text+0xf22): undefined reference to `tflite::ops::builtin::Register_ELU()'
/usr/bin/ld: register.cc:(.text+0xf3c): undefined reference to `tflite::ops::builtin::Register_REVERSE_SEQUENCE()'
/usr/bin/ld: register.cc:(.text+0xf56): undefined reference to `tflite::ops::builtin::Register_MATRIX_DIAG()'
/usr/bin/ld: register.cc:(.text+0xf70): undefined reference to `tflite::ops::builtin::Register_QUANTIZE()'
/usr/bin/ld: register.cc:(.text+0xf90): undefined reference to `tflite::ops::builtin::Register_MATRIX_SET_DIAG()'
/usr/bin/ld: register.cc:(.text+0xfaa): undefined reference to `tflite::ops::builtin::Register_IF()'
/usr/bin/ld: register.cc:(.text+0xfc4): undefined reference to `tflite::ops::builtin::Register_WHILE()'
/usr/bin/ld: register.cc:(.text+0xfde): undefined reference to `tflite::ops::builtin::Register_NON_MAX_SUPPRESSION_V4()'
/usr/bin/ld: register.cc:(.text+0xff8): undefined reference to `tflite::ops::builtin::Register_NON_MAX_SUPPRESSION_V5()'
/usr/bin/ld: register.cc:(.text+0x1012): undefined reference to `tflite::ops::builtin::Register_SCATTER_ND()'
/usr/bin/ld: register.cc:(.text+0x102c): undefined reference to `tflite::ops::builtin::Register_DENSIFY()'
/usr/bin/ld: register.cc:(.text+0x1046): undefined reference to `tflite::ops::builtin::Register_SEGMENT_SUM()'
/usr/bin/ld: register.cc:(.text+0x1060): undefined reference to `tflite::ops::builtin::Register_BATCH_MATMUL()'
/usr/bin/ld: register.cc:(.text+0x1080): undefined reference to `tflite::ops::builtin::Register_CUMSUM()'
/usr/bin/ld: register.cc:(.text+0x109a): undefined reference to `tflite::ops::builtin::Register_BROADCAST_TO()'
/usr/bin/ld: register.cc:(.text+0x10ba): undefined reference to `tflite::ops::builtin::Register_CALL_ONCE()'
/usr/bin/ld: register.cc:(.text+0x10d4): undefined reference to `tflite::ops::builtin::Register_RFFT2D()'
/usr/bin/ld: register.cc:(.text+0x10ee): undefined reference to `tflite::ops::builtin::Register_CONV_3D()'
/usr/bin/ld: register.cc:(.text+0x1108): undefined reference to `tflite::ops::builtin::Register_IMAG()'
/usr/bin/ld: register.cc:(.text+0x1122): undefined reference to `tflite::ops::builtin::Register_REAL()'
/usr/bin/ld: register.cc:(.text+0x113c): undefined reference to `tflite::ops::builtin::Register_COMPLEX_ABS()'
/usr/bin/ld: register.cc:(.text+0x1156): undefined reference to `tflite::ops::builtin::Register_BROADCAST_ARGS()'
/usr/bin/ld: register.cc:(.text+0x1170): undefined reference to `tflite::ops::builtin::Register_HASHTABLE()'
/usr/bin/ld: register.cc:(.text+0x118a): undefined reference to `tflite::ops::builtin::Register_HASHTABLE_FIND()'
/usr/bin/ld: register.cc:(.text+0x11a4): undefined reference to `tflite::ops::builtin::Register_HASHTABLE_IMPORT()'
/usr/bin/ld: register.cc:(.text+0x11be): undefined reference to `tflite::ops::builtin::Register_HASHTABLE_SIZE()'
/usr/bin/ld: register.cc:(.text+0x11d8): undefined reference to `tflite::ops::builtin::Register_CONV_3D_TRANSPOSE()'
/usr/bin/ld: register.cc:(.text+0x11f2): undefined reference to `tflite::ops::builtin::Register_VAR_HANDLE()'
/usr/bin/ld: register.cc:(.text+0x120c): undefined reference to `tflite::ops::builtin::Register_READ_VARIABLE()'
/usr/bin/ld: register.cc:(.text+0x1226): undefined reference to `tflite::ops::builtin::Register_ASSIGN_VARIABLE()'
/usr/bin/ld: register.cc:(.text+0x1240): undefined reference to `tflite::ops::builtin::Register_MULTINOMIAL()'
/usr/bin/ld: register.cc:(.text+0x125a): undefined reference to `tflite::ops::builtin::Register_RANDOM_STANDARD_NORMAL()'
/usr/bin/ld: register.cc:(.text+0x1274): undefined reference to `tflite::ops::builtin::Register_BUCKETIZE()'
/usr/bin/ld: register.cc:(.text+0x128e): undefined reference to `tflite::ops::builtin::Register_RANDOM_UNIFORM()'
/usr/bin/ld: register.cc:(.text+0x12a8): undefined reference to `tflite::ops::builtin::Register_GELU()'
/usr/bin/ld: register.cc:(.text+0x12c8): undefined reference to `tflite::ops::builtin::Register_DYNAMIC_UPDATE_SLICE()'
/usr/bin/ld: register.cc:(.text+0x12e2): undefined reference to `tflite::ops::builtin::Register_UNSORTED_SEGMENT_PROD()'
/usr/bin/ld: register.cc:(.text+0x12fc): undefined reference to `tflite::ops::builtin::Register_UNSORTED_SEGMENT_MAX()'
/usr/bin/ld: register.cc:(.text+0x1316): undefined reference to `tflite::ops::builtin::Register_UNSORTED_SEGMENT_MIN()'
/usr/bin/ld: register.cc:(.text+0x1330): undefined reference to `tflite::ops::builtin::Register_UNSORTED_SEGMENT_SUM()'
/usr/bin/ld: register.cc:(.text+0x134a): undefined reference to `tflite::ops::builtin::Register_ATAN2()'
/usr/bin/ld: register.cc:(.text+0x1364): undefined reference to `tflite::ops::builtin::Register_SIGN()'
/usr/bin/ld: register.cc:(.text+0x1384): undefined reference to `tflite::ops::builtin::Register_BITCAST()'
/usr/bin/ld: register.cc:(.text+0x139e): undefined reference to `tflite::ops::builtin::Register_BITWISE_XOR()'
/usr/bin/ld: register.cc:(.text+0x13b8): undefined reference to `tflite::ops::builtin::Register_RIGHT_SHIFT()'
/usr/bin/ld: register.cc:(.text+0x13d2): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_SCATTER()'
/usr/bin/ld: register.cc:(.text+0x13ec): undefined reference to `tflite::ops::builtin::Register_DILATE()'
/usr/bin/ld: register.cc:(.text+0x1406): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_RNG_BIT_GENERATOR()'
/usr/bin/ld: register.cc:(.text+0x1420): undefined reference to `tflite::ops::builtin::Register_REDUCE_WINDOW()'
/usr/bin/ld: register.cc:(.text+0x143a): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_REDUCE_WINDOW()'
/usr/bin/ld: register.cc:(.text+0x1454): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_GATHER()'
/usr/bin/ld: register.cc:(.text+0x146e): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_ADD()'
/usr/bin/ld: register.cc:(.text+0x1488): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_MULTIPLY()'
/usr/bin/ld: register.cc:(.text+0x14a2): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_MAXIMUM()'
/usr/bin/ld: register.cc:(.text+0x14bc): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_MINIMUM()'
/usr/bin/ld: register.cc:(.text+0x14d6): undefined reference to `tflite::ops::builtin::Register_STABLEHLO_PAD()'
/usr/bin/ld: register.cc:(.text+0x14f0): undefined reference to `tflite::ops::custom::Register_NUMERIC_VERIFY()'
/usr/bin/ld: register.cc:(.text+0x150c): undefined reference to `tflite::ops::custom::Register_MFCC()'
/usr/bin/ld: register.cc:(.text+0x1528): undefined reference to `tflite::ops::custom::Register_AUDIO_SPECTROGRAM()'
/usr/bin/ld: register.cc:(.text+0x1544): undefined reference to `tflite::ops::custom::Register_DETECTION_POSTPROCESS()'
/usr/bin/ld: tensorflow-lite/libtensorflow-lite.a(xnnpack_delegate.cc.o): in function `TfLiteXNNPackDelegateCreateWithThreadpool':
xnnpack_delegate.cc:(.text+0xab0f): undefined reference to `tflite::CpuBackendContext::GetFromContext(TfLiteContext*)'
/usr/bin/ld: xnnpack_delegate.cc:(.text+0xab17): undefined reference to `tflite::CpuBackendContext::get_xnnpack_threadpool()'
collect2: error: ld returned 1 exit status
make[2]: *** [CMakeFiles/minimal.dir/build.make:172: minimal] Error 1
make[1]: *** [CMakeFiles/Makefile2:1511: CMakeFiles/minimal.dir/all] Error 2
make: *** [Makefile:130: all] Error 2
```
### Standalone code to reproduce the issue
```shell
Just follow the steps in: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/minimal
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5jmSVP
| 62,768 |
Fixing a small typo
|
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[] | 2024-01-09T16:40:14 | 2024-01-10T18:36:46 | 2024-01-10T18:36:45 |
CONTRIBUTOR
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There is a small typo in the documentation of tf.data.Dataset.shuffle(): which is fixed now. Please have a look and do the needful. Thank you!
Fixes #62745
|
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I_kwDOArmXAs57gQoc
| 62,767 |
Inconsistencies in Results for `tf.keras.metrics` and `tf.keras.losses`
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[
"Hi **@markub3327** ,\r\nI was able to reproduce the issue on Colab using TF v2.15 and nightly. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/8d77ba53d1a1663516393435c04959c3/62767_2-15-_nighlty-v.ipynb) for your reference.\r\n\r\nThank you!"
] | 2024-01-09T10:13:42 | 2024-04-24T06:35:08 | null |
CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04
### Mobile device
Ubuntu 22.04
### Python version
3.9.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The current behavior highlights an inconsistency between the outcomes obtained using `tf.keras.metrics.LogCoshError()` and `tf.keras.losses.LogCosh()`. The discrepancy arises when utilizing the `sample_weight=weights` argument. The objective is to compute a weighted mean during both the training and testing phases.
### Standalone code to reproduce the issue
```shell
true_values = tf.constant([[3.5, 8.2], [1.8, 6.7]])
predicted_values = tf.constant([[5.1, 7.9], [10.4, 3.2]])
weights = tf.constant([0.9, 0.4])
logcosh_metric = tf.keras.metrics.LogCoshError()
logcosh_metric.update_state(true_values, predicted_values)
print("LogCosh without weights:", logcosh_metric.result().numpy())
logcosh_metric = tf.keras.metrics.LogCoshError()
logcosh_metric.update_state(true_values, predicted_values, sample_weight=weights)
print("LogCosh with weights:", logcosh_metric.result().numpy())
true_labels = tf.constant([[3.5, 8.2], [1.8, 6.7]])
predicted_labels = tf.constant([[5.1, 7.9], [10.4, 3.2]])
weights = tf.constant([0.9, 0.4])
logcosh_loss = tf.keras.losses.LogCosh()
print("LogCosh without weights:", logcosh_loss(true_labels, predicted_labels).numpy())
print("LogCosh with weights:", logcosh_loss(true_labels, predicted_labels, sample_weight=weights).numpy())
```
### Relevant log output
```shell
<tf.Variable 'UnreadVariable' shape=() dtype=float32, numpy=2.0>
LogCosh without weights: 2.926441
<tf.Variable 'UnreadVariable' shape=() dtype=float32, numpy=1.3>
LogCosh with weights: 1.9914919
LogCosh without weights: 2.926441
LogCosh with weights: 1.2944697
```
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I_kwDOArmXAs57fqPY
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How to cross compile tensorflow lite library for xtensa lx6 platform
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[
"Hi @Tamilarasan-C ,\r\nAre you looking for ```esp_xtensa-esp32```? If it is the same, Please refer to the examples for using tflite micro [here](https://www.tensorflow.org/lite/microcontrollers#explore_the_examples). Also tensorflow has [TensorFlow Lite for Microcontrollers Experiments](https://experiments.withgoogle.com/collection/tfliteformicrocontrollers) features work by developers combining Arduino and TensorFlow to examples and tools. \r\n\r\nFor esp32, you can refer to the espressif official examples [here](https://github.com/espressif/tflite-micro-esp-examples/tree/master/examples). \r\n\r\nThank you.\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.",
"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/62766\">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/62766\">No</a>\n"
] | 2024-01-09T08:47:14 | 2024-01-24T09:41:18 | 2024-01-24T09:41:14 |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution: Linux Ubuntu 20.04
- TensorFlow installation: Built from source
- TensorFlow library: Release 2.16.0
### 2. Query:
Hi,
I need to cross compile tensorflow lite library for xtensa lx6 platform, please provide instructions/links on how to do this.
Thanks.
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[
"Hi @soham2k06 ,\r\n\r\nPlease report here only Tensorflow related Bugs. UI related issues may not be considered here as it won't have any effect on this library users. Similar issue #62761 was closed by the Engg team.\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/62765\">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/62765\">No</a>\n"
] | 2024-01-09T06:11:49 | 2024-02-03T01:47:08 | 2024-02-03T01:47:01 |
NONE
| null | null | null |
In section 'Stay up to date' on the home page. two buttons named 'Read the blog' and 'Watch now' has border on hover too and it is also exposing border-radius difference on hover.
I noticed that other buttons on homepage of this theme has border removed on hover.
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I_kwDOArmXAs57e1Vr
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[feedback] language selections component in two places
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[
"Hi, @soham2k06! Generally users expect the language selector to be in a consistent location, usually the navigation bar. Placing it in two different places is inconsistent and makes it harder for users to find. Having the language selector only on the navigation bar keeps the footer uncluttered and focused on other important information or actions. \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/62764\">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/62764\">No</a>\n"
] | 2024-01-09T05:57:20 | 2024-01-24T01:49:47 | 2024-01-24T01:49:44 |
NONE
| null | null | null |
In home page, the website has language selection twice.
It would be more efficient to have the language selection only once on the navigation bar, instead of having it both on the navigation bar and footer.
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I_kwDOArmXAs57e01S
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Unable to generate libtensorflowlite_flex_jni.so
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[
"Hi @pkgoogle\r\n\r\nPlease look into the issue\r\n\r\nThank You",
"Hi @prerna1124, can you ensure you are following all the directions on this page? https://www.tensorflow.org/lite/android/development, specifically please be sure you have these abiFilters: https://www.tensorflow.org/lite/android/development#lite_lib, if you can share your gradle file or if possible, your entire project we can also take a look. Thanks for your help.",
"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.",
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] | 2024-01-09T05:55:19 | 2024-01-28T01:48:09 | 2024-01-28T01:48:06 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.10.0
### 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 behavior?
I am working with soong build system. I have created a text based classification model and saved it as ".tflite" with select_tf_ops. Now, I am generating a ".apk" file using Android.bp only which is responsible for doing my classification task. I can push it on my target and it should classify. But I am getting below error.
--------- beginning of crash
10-23 11:33:25.970 4445 4445 E AndroidRuntime: FATAL EXCEPTION: main
10-23 11:33:25.970 4445 4445 E AndroidRuntime: Process: com.test.classifier, PID: 4445
10-23 11:33:25.970 4445 4445 E AndroidRuntime: java.lang.UnsatisfiedLinkError: dlopen failed: library "libtensorflowlite_flex_jni.so" not found
10-23 11:33:25.970 4445 4445 E AndroidRuntime: at java.lang.Runtime.loadLibrary0(Runtime.java:1077)
10-23 11:33:25.970 4445 4445 E AndroidRuntime: at java.lang.Runtime.loadLibrary0(Runtime.java:998)
10-23 11:33:25.970 4445 4445 E AndroidRuntime: at java.lang.System.loadLibrary(System.java:1661)
10-23 11:33:25.970 4445 4445 E AndroidRuntime: at org.tensorflow.lite.flex.FlexDelegate.<clinit>(FlexDelegate.java:61)
10-23 11:33:25.970 4445 4445 E AndroidRuntime: at java.lang.Class.classForName(Native Method)
10-23 11:33:25.970 4445 4445 E AndroidRuntime: at java.lang.Class.forName(Class.java:454)
10-23 11:33:25.970 4445 4445 E AndroidRuntime: at java.lang.Class.forName(Class.java:379)
10-23 11:33:25.970 4445 4445 E AndroidRuntime: at org.tensorflow.lite.NativeInterpreterWrapper.maybeCreateFlexDelegate(NativeInterpreterWrapper.java:548)
10-23 11:33:25.970 4445 4445 E AndroidRuntime: at org.tensorflow.lite.NativeInterpreterWrapper.applyDelegates(NativeInterpreterWrapper.java:510)
10-23 11:33:25.970 4445 4445 E AndroidRuntime: at org.tensorflow.lite.NativeInterpreterWrapper.init(NativeInterpreterWrapper.java:88)
10-23 11:33:25.970 4445 4445 E AndroidRuntime: at org.tensorflow.lite.NativeInterpreterWrapper.<init>(NativeInterpreterWrapper.java:66)
10-23 11:33:25.970 4445 4445 E AndroidRuntime: at org.tensorflow.lite.NativeInterpreterWrapperExperimental.<init>(NativeInterpreterWrapperExperimental.java:44)
10-23 11:33:25.970 4445 4445 E AndroidRuntime: at org.tensorflow.lite.Interpreter.<init>(Interpreter.java:228)
10-23 11:33:25.970 4445 4445 E AndroidRuntime: at org.tensorflow.lite.Interpreter.<init>(Interpreter.java:212)
### Standalone code to reproduce the issue
```shell
Added below code in /tensorflow/lite/java/Android.bp
srcs: [
":tflite_flex_delegate_java",
":tflite_nnapi_delegate_java",
"src/main/java/org/tensorflow/lite/annotations/*.java",
"src/main/java/org/tensorflow/lite/*.java",
]
Used below code for saving the model:
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.allow_custom_ops=True
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.
]
```
### Relevant log output
_No response_
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[
"@soham2k06,\r\nCould you please try to clear the cache and try to reload the same page and also try in another browser & let us know if the same situation is happening. 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/62762\">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/62762\">No</a>\n"
] | 2024-01-09T05:50:48 | 2024-01-25T01:50:23 | 2024-01-25T01:50:20 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0.post1
### Custom code
Yes
### OS platform and distribution
macOs
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
If we scroll all the way down on the home page, click the logo to go back to the top, and then reload the page, it should start back at the top, but instead, it stays stuck at the bottom (even if we are already in the top).
https://github.com/tensorflow/tensorflow/assets/118199354/48e51429-0585-4381-bf44-9a00c0fa010b
### Standalone code to reproduce the issue
```shell
1. Navigate to the home page of the website.
2. Scroll till hero section is not visible.
3. Click the logo.
4. Reload the page (or navigate to other links of the site and go back).
5. Scroll position will be at your previous scroll position when you clicked logo.
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs57evu6
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[bug] button 'view releases' has loading issue
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[
"Hi @soham2k06 ,\r\n\r\nI cross checked and acknowledged that there seems some delay in fetching the tab `TF 2.15 released.View releases`.\r\n\r\nCC: @MarkDaoust , Any comments on this ?\r\n",
"Hi, \r\n\r\nWhere might I be able to find the source code for the home page in tensorflow/docs?",
"I see the delay too, but this is not something we can easily fix.",
"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/62761\">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/62761\">No</a>\n"
] | 2024-01-09T05:30:48 | 2024-01-16T18:13:49 | 2024-01-16T18:13:45 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0.post1
### Custom code
Yes
### OS platform and distribution
macOs
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
There is some issue on loading button 'View releases' in home page.
It seems that the button is relying in some state coming from fetched data.
That's why it is being shown after some time.
https://github.com/tensorflow/tensorflow/assets/118199354/0211ff74-032d-4493-93b8-722dc3b4d029
### Standalone code to reproduce the issue
```shell
1. Search tensorflow in browser
2. Navigate to the website (https://www.tensorflow.org/)
3. In the very first page. you will be able to see the bug.
Note: I have not signed in.
```
### Relevant log output
_No response_
|
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[
"Can you verify if both `pydot` and `graphviz` are installed on your system and included in the system PATH for `subprocess` to execute?\r\n\r\n```python\r\nYou must install pydot (`pip install pydot`) \"\r\n 454 \"and install graphviz \"\r\n 455 \"(see instructions at https://graphviz.gitlab.io/download/) \"\r\n 456 \"for plot_model to work.\"\r\n```\r\n\r\npip install pydot graphviz",
"They are installed but I didn't set up a PATH. What do I put in path?",
"installing from https://graphviz.gitlab.io/download/ made it work!",
"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/62760\">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/62760\">No</a>\n",
"Great it helped 👍🏿 @ninjaguardian "
] | 2024-01-09T02:21:05 | 2024-01-09T18:02:37 | 2024-01-09T15:46:02 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
Windows 11
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
running: plot_model(model=model9,to_file='file.png',show_shapes=True, show_layer_names=True)
getting:
FileNotFoundError: [WinError 2] The system cannot find the file specified
AttributeError: module 'pydot' has no attribute 'InvocationException'
### Standalone code to reproduce the issue
```shell
import pydot
import graphviz
from tensorflow.keras.utils import plot_model
...make a model (the model isn't the problem. it works with other things fine)
plot_model(model=model9,to_file='file.png',show_shapes=True, show_layer_names=True)
```
### Relevant log output
```shell
FileNotFoundError Traceback (most recent call last)
File c:\Users\carte\AppData\Local\Programs\Python\Python311\Lib\site-packages\pydot\core.py:1753, in Dot.create(self, prog, format, encoding)
1752 try:
-> 1753 stdout_data, stderr_data, process = call_graphviz(
1754 program=prog,
1755 arguments=arguments,
1756 working_dir=tmp_dir,
1757 )
1758 except OSError as e:
File c:\Users\carte\AppData\Local\Programs\Python\Python311\Lib\site-packages\pydot\core.py:133, in call_graphviz(program, arguments, working_dir, **kwargs)
131 program_with_args = [program] + arguments
--> 133 process = subprocess.Popen(
134 program_with_args,
135 env=env,
136 cwd=working_dir,
137 shell=False,
138 stderr=subprocess.PIPE,
139 stdout=subprocess.PIPE,
140 **kwargs,
141 )
142 stdout_data, stderr_data = process.communicate()
File c:\Users\carte\AppData\Local\Programs\Python\Python311\Lib\subprocess.py:1026, in Popen.__init__(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask, pipesize, process_group)
1023 self.stderr = io.TextIOWrapper(self.stderr,
1024 encoding=encoding, errors=errors)
-> 1026 self._execute_child(args, executable, preexec_fn, close_fds,
1027 pass_fds, cwd, env,
1028 startupinfo, creationflags, shell,
1029 p2cread, p2cwrite,
1030 c2pread, c2pwrite,
1031 errread, errwrite,
1032 restore_signals,
1033 gid, gids, uid, umask,
1034 start_new_session, process_group)
1035 except:
1036 # Cleanup if the child failed starting.
File c:\Users\carte\AppData\Local\Programs\Python\Python311\Lib\subprocess.py:1538, in Popen._execute_child(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_gid, unused_gids, unused_uid, unused_umask, unused_start_new_session, unused_process_group)
1537 try:
-> 1538 hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
1539 # no special security
1540 None, None,
1541 int(not close_fds),
1542 creationflags,
1543 env,
1544 cwd,
1545 startupinfo)
1546 finally:
1547 # Child is launched. Close the parent's copy of those pipe
1548 # handles that only the child should have open. You need
(...)
1551 # pipe will not close when the child process exits and the
1552 # ReadFile will hang.
FileNotFoundError: [WinError 2] The system cannot find the file specified
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
File c:\Users\carte\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\utils\vis_utils.py:57, in check_graphviz()
54 try:
55 # Attempt to create an image of a blank graph
56 # to check the pydot/graphviz installation.
---> 57 pydot.Dot.create(pydot.Dot())
58 return True
File c:\Users\carte\AppData\Local\Programs\Python\Python311\Lib\site-packages\pydot\core.py:1762, in Dot.create(self, prog, format, encoding)
1761 args[1] = '"{prog}" not found in path.'.format(prog=prog)
-> 1762 raise OSError(*args)
1763 else:
FileNotFoundError: [WinError 2] "dot" not found in path.
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
Cell In[22], line 203
201 elif DotOrPng.lower() == 'png':
202 print("graphing")
--> 203 print(plot_model(model=model9,show_shapes=True, show_layer_names=True))
204 print("graphed")
205 if command.lower() == 'exit':
File c:\Users\carte\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\utils\vis_utils.py:451, in plot_model(model, to_file, show_shapes, show_dtype, show_layer_names, rankdir, expand_nested, dpi, layer_range, show_layer_activations, show_trainable)
444 if not model.built:
445 raise ValueError(
446 "This model has not yet been built. "
447 "Build the model first by calling `build()` or by calling "
448 "the model on a batch of data."
449 )
--> 451 if not check_graphviz():
452 message = (
453 "You must install pydot (`pip install pydot`) "
454 "and install graphviz "
455 "(see instructions at https://graphviz.gitlab.io/download/) "
456 "for plot_model to work."
457 )
458 if "IPython.core.magics.namespace" in sys.modules:
459 # We don't raise an exception here in order to avoid crashing
460 # notebook tests where graphviz is not available.
File c:\Users\carte\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\utils\vis_utils.py:59, in check_graphviz()
57 pydot.Dot.create(pydot.Dot())
58 return True
---> 59 except (OSError, pydot.InvocationException):
60 return False
AttributeError: module 'pydot' has no attribute 'InvocationException'
```
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The available server factories are: [ GRPC_SERVER ] error, while passing grpc+verbs protocol!
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[
"Hi **@Maziyar-Na** ,\r\nI was able to reproduce the issue on Colab using TF v2.15. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/bb14bec5ff894b60f6b701406d06659f/62759_2-15-v.ipynb) for your reference.\r\n\r\nThank you!",
"Thanks @Venkat6871 !\r\nIs there any solution? I want to run TF 2.15 using verbs api.",
"Hi @Maziyar-Na ,\r\n\r\nIMO, verbs support not available in 2.x versions. There are few issues attached [here](https://github.com/tensorflow/tensorflow/issues?q=is%3Aissue+is%3Aopen+verb) for more details.",
"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/62759\">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/62759\">No</a>\n"
] | 2024-01-08T23:19:35 | 2024-04-04T01:48:00 | 2024-04-04T01:47:58 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15
### Custom code
Yes
### OS platform and distribution
Linux Ubntu 22.04
### Mobile device
N/A
### Python version
3.10
### Bazel version
6.1.0
### GCC/compiler version
clang-16
### CUDA/cuDNN version
N/A
### GPU model and memory
N/A
### Current behavior?
I am trying to run a distributed training code using multi-worker mirrored strategy and I want the code to use grpc+verbs on a RoCE-capable network card. I have built the tensorflow from the source and since the "verbs" option is not there anymore, I've added the following lines in the .bazelrc file in tensorflow root direcory:
build:verbs --define=with_verbs_support=true
And passed --config=verbs and It's compiled successfully. Now when I want to use the grpc+verbs api in the code like the following (based on [https://github.com/tensorflow/tensorflow/issues/37622]) :
worker_index = 0 # For instance
os.environ['TF_CONFIG'] = json.dumps({
'cluster': {
'worker': ["192.168.6.6:12345", "192.168.6.7:12345", "192.168.6.8:123>
},
'task': {'type': 'worker', 'index': worker_index},
'rpc_layer':'grpc+verbs'
})
it gives me error:
tensorflow.python.framework.errors_impl.NotFoundError: No server factory registered for the given ServerDef
(The available server factories are: [ GRPC_SERVER ])
Could you please help me figure out how to run distributed training using multi-worker mirrored strategy on tensorflow using the verbs api ?
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import keras
import os
import json
def get_compiled_model():
# Make a simple 2-layer densely-connected neural network.
inputs = keras.Input(shape=(784,))
x = keras.layers.Dense(256, activation="relu")(inputs)
x = keras.layers.Dense(256, activation="relu")(x)
outputs = keras.layers.Dense(10)(x)
model = keras.Model(inputs, outputs)
model.compile(
optimizer=keras.optimizers.Adam(),
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
return model
def get_dataset():
batch_size = 32
num_val_samples = 10000
# Return the MNIST dataset in the form of a `tf.data.Dataset`.
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
# Preprocess the data (these are Numpy arrays)
x_train = x_train.reshape(-1, 784).astype("float32") / 255
x_test = x_test.reshape(-1, 784).astype("float32") / 255
y_train = y_train.astype("float32")
y_test = y_test.astype("float32")
# Reserve num_val_samples samples for validation
x_val = x_train[-num_val_samples:]
y_val = y_train[-num_val_samples:]
x_train = x_train[:-num_val_samples]
y_train = y_train[:-num_val_samples]
return (
tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size),
tf.data.Dataset.from_tensor_slices((x_val, y_val)).batch(batch_size),
tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch_size),
)
# Set TF_CONFIG
worker_index = 0 # For instance
os.environ['TF_CONFIG'] = json.dumps({
'cluster': {
'worker': ["192.168.6.6:12345", "192.168.6.7:12345", "192.168.6.8:12345"]
},
'task': {'type': 'worker', 'index': worker_index},
'rpc_layer':'grpc+verbs'
})
# Create a MirroredStrategy.
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
print("Number of devices: {}".format(strategy.num_replicas_in_sync))
# Open a strategy scope.
with strategy.scope():
# Everything that creates variables should be under the strategy scope.
# In general this is only model construction & `compile()`.
model = get_compiled_model()
# Train the model on all available devices.
train_dataset, val_dataset, test_dataset = get_dataset()
model.fit(train_dataset, epochs=2, validation_data=val_dataset)
# Test the model on all available devices.
model.evaluate(test_dataset)
```
### Relevant log output
```shell
2024-01-08 15:35:36.165997: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
WARNING:tensorflow:From /users/maziyar/dnn.py:57: _CollectiveAllReduceStrategyExperimental.__init__ (from tensorflow.python.distribute.collective_all_reduce_strategy) is deprecated and will be removed in a future version.
Instructions for updating:
use distribute.MultiWorkerMirroredStrategy instead
2024-01-08 15:35:37.225240: E tensorflow/core/common_runtime/eager/context_distributed_manager.cc:783] No server factory registered for the given ServerDef: cluster {
job {
name: "worker"
tasks {
key: 0
value: "192.168.6.6:12345"
}
tasks {
key: 1
value: "192.168.6.7:12345"
}
tasks {
key: 2
value: "192.168.6.8:12345"
}
}
}
job_name: "worker"
default_session_config {
device_count {
key: "CPU"
value: 1
}
device_count {
key: "GPU"
value: 0
}
device_filters: "/job:worker/task:0"
gpu_options {
experimental {
}
}
allow_soft_placement: true
graph_options {
rewrite_options {
scoped_allocator_optimization: ON
scoped_allocator_opts {
enable_op: "CollectiveReduce"
}
}
}
experimental {
collective_group_leader: "/job:worker/replica:0/task:0"
coordination_config {
service_type: "standalone"
service_leader: "/job:worker/replica:0/task:0"
enable_health_check: true
coordinated_job_list {
name: "worker"
num_tasks: 3
}
}
}
}
protocol: "grpc+verbs"
The available server factories are: [ GRPC_SERVER ]
Traceback (most recent call last):
File "/users/maziyar/dnn.py", line 57, in <module>
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
File "/users/maziyar/.local/lib/python3.10/site-packages/tensorflow/python/util/deprecation.py", line 383, in new_func
return func(*args, **kwargs)
File "/users/maziyar/.local/lib/python3.10/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py", line 255, in __init__
self).__init__(cluster_resolver, communication_options)
File "/users/maziyar/.local/lib/python3.10/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py", line 186, in __init__
CollectiveAllReduceExtended(
File "/users/maziyar/.local/lib/python3.10/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py", line 339, in __init__
self._initialize_strategy(self._cluster_resolver, devices=devices)
File "/users/maziyar/.local/lib/python3.10/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py", line 358, in _initialize_strategy
self._initialize_multi_worker(cluster_resolver)
File "/users/maziyar/.local/lib/python3.10/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py", line 530, in _initialize_multi_worker
context.context().ensure_initialized()
File "/users/maziyar/.local/lib/python3.10/site-packages/tensorflow/python/eager/context.py", line 619, in ensure_initialized
pywrap_tfe.TFE_EnableCollectiveOps(context_handle, server_def_str)
tensorflow.python.framework.errors_impl.NotFoundError: No server factory registered for the given ServerDef: cluster {
job {
name: "worker"
tasks {
key: 0
value: "192.168.6.6:12345"
}
tasks {
key: 1
value: "192.168.6.7:12345"
}
tasks {
key: 2
value: "192.168.6.8:12345"
}
}
}
job_name: "worker"
default_session_config {
device_count {
key: "CPU"
value: 1
}
device_count {
key: "GPU"
value: 0
}
device_filters: "/job:worker/task:0"
gpu_options {
experimental {
}
}
allow_soft_placement: true
graph_options {
rewrite_options {
scoped_allocator_optimization: ON
scoped_allocator_opts {
enable_op: "CollectiveReduce"
}
}
}
experimental {
collective_group_leader: "/job:worker/replica:0/task:0"
coordination_config {
service_type: "standalone"
service_leader: "/job:worker/replica:0/task:0"
enable_health_check: true
coordinated_job_list {
name: "worker"
num_tasks: 3
}
}
}
}
protocol: "grpc+verbs"
The available server factories are: [ GRPC_SERVER ]
```
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ERROR: tensorflow-2.15.0-cp310-cp310-macosx_11_0_arm64.whl is not a supported wheel on this platform
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[
"Do I have the right clang for installing Tensorflow? Seems I need Clang 16?\r\n\r\nhttps://www.tensorflow.org/install/source#install_clang_recommended_linux_only\r\n\r\nClang is a C/C++/Objective-C compiler that is compiled in C++ based on LLVM. It\r\nis the default compiler to build TensorFlow starting with TensorFlow 2.13. The\r\ncurrent supported version is LLVM/Clang 16.\r\n\r\n```shell\r\n% /usr/bin/clang -v\r\nApple clang version 15.0.0 (clang-1500.1.0.2.5)\r\nTarget: arm64-apple-darwin23.2.0\r\nThread model: posix\r\nInstalledDir: /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin\r\n```\r\n\r\nSeems I need this?\r\ndownload https://github.com/llvm/llvm-project/releases/tag/llvmorg-16.0.0: clang+llvm-16.0.0-arm64-apple-darwin22.0.tar.xz",
"|Version | Python version | Compiler | Build tools |\r\n|--------|----------------|----------|-------------|\r\n|tensorflow-2.15.0 | 3.9-3.11 | Clang 16.0.0 | Bazel 6.1.0 |\r\n\r\nI don't have the right version of Clang:\r\n\r\n```bash\r\n% /usr/bin/clang -v\r\nApple clang version 15.0.0 (clang-1500.1.0.2.5)\r\nTarget: arm64-apple-darwin23.2.0\r\nThread model: posix\r\nInstalledDir: /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin\r\n```\r\n\r\nMacports: https://www.macports.org/\r\n\r\nInstalled macports, in order to install Clang 16.0 easily enough. I avoid ports and brew in general, but this is the one example I'm ok with.\r\n\r\n```bash\r\n~ % source ~/.zprofile # Macports already added PATH\r\n~ % sudo port install clang-16\r\n~ % port contents clang-16\r\n\r\n~ % /opt/local/bin/clang-mp-16 -v\r\nclang version 16.0.6\r\nTarget: arm64-apple-darwin23.2.0\r\nThread model: posix\r\nInstalledDir: /opt/local/libexec/llvm-16/bin\r\n```\r\n\r\nAnd now update PATH for Clang\r\n\r\n```bash\r\n~ % echo \"\" >> ~/.zprofile\r\n~ % echo \"# Clang (v16.0) for Tensorflow v2.15\" >> ~/.zprofile\r\n~ % echo \"PATH=\\\"/opt/local/libexec/llvm-16/bin:\\${PATH}\\\"\" >> ~/.zprofile\r\n~ % echo \"export PATH\" >> ~/.zprofile\r\n~ % source ~/.zprofile\r\n```\r\n\r\nand now we have what is needed.\r\n\r\n```bash\r\n~ % clang -v\r\nclang version 16.0.6\r\nTarget: arm64-apple-darwin23.2.0\r\nThread model: posix\r\nInstalledDir: /opt/local/libexec/llvm-16/bin\r\n```\r\n\r\nAfter getting Clang 16.0 on MacOS 14 when it appears Xcode only provides Clang 15.0, and now everything is working as expected.\r\n\r\n```bash\r\ntensorflow_io-0.35.0-cp311-cp311-macosx_14_0_universal2.whl\r\ntensorflow-2.15.0-cp311-cp311-macosx_14_0_arm64.whl\r\n```\r\n\r\n\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/62758\">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/62758\">No</a>\n"
] | 2024-01-08T16:40:21 | 2024-01-09T04:43:14 | 2024-01-09T04:40:43 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Mac OS 14.2 - Apple M3 Pro
### Mobile device
_No response_
### Python version
3.11
### Bazel version
6.1.0
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
[ERROR: tensorflow-2.15.0-cp310-cp310-macosx_11_0_arm64.whl is not a supported wheel on this platform](https://github.com/bazelbuild/bazel/issues/20779#top)
% python3 -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"
v2.15.0-rc1-8-g6887368d6d4 2.15.0
### Standalone code to reproduce the issue
https://github.com/msusol/jupyter-notebook-on-macos/blob/main/tensorflow.md
Python.org install
https://github.com/msusol/jupyter-notebook-on-macos/blob/main/README.md#install-python
### Relevant log output
_No response_
|
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I_kwDOArmXAs57aYDI
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NCCL + XLA fails for multi-GPU training.
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[
"Hi @cyanic-selkie ,\r\n\r\nCould you please provide complete code snippet required for replication of this problem. Thanks!",
"Sorry for the delay, @SuryanarayanaY, I updated my issue with the full code snippet.",
"Hi @cyanic-selkie ,\r\n\r\nAs you are using TF2.15 version with tf.keras,first you need to import `tf-keras` package using `pip install tf-keras`\r\nand then set environment variable `os.environ[\"TF_USE_LEGACY_KERAS\"]=\"1\"`.\r\n\r\nThen you can import keras from tensorflow or simply use tf.keras.\r\n\r\nCan you try this and comeback with outcome. Thanks!",
"@SuryanarayanaY I did as you said, there is no difference.",
"Hi @cyanic-selkie ,\r\n\r\nCould you please provide minimal code snippet for testing? Thanks!",
"@SuryanarayanaY I already provided the minimum reproducible example in the initial post. Is there something else you need?",
"same bug 'xla.gpu.all_reduce' failed, i found that \"tensorflow.keras.applications.inception_resnet_v2.InceptionResNetV2\" was called before all_reduce, the problem occur, but if i use dense layer as replacement, it work well.... sb. help me plz",
"i guess the former batchnormalize layer called cause the later all_reduce crash as the code above"
] | 2024-01-08T13:54:59 | 2024-03-12T03:08:32 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.2/8.9.4
### GPU model and memory
A100 40GB (20GB MiG)
### Current behavior?
I am trying to run multi-GPU training with an XLA compiled model (simple CNN with a classification head). Without XLA, everything runs fine. With XLA enabled, I get one of two errors in the log, depending on whether I am using 4 GPUs or 2 GPUs. The GPUs are split into 2 MiGs.
I also tried on previous TF/CUDA versions and I get the same result.
### Standalone code to reproduce the issue
```python
import argparse
import json
import os
from typing import Any
import numpy as np
import tensorflow as tf
def get_replica_hostnames():
...
def get_replica_id():
...
def set_multiworker_env_config():
hostnames = get_replica_hostnames()
replica_index = get_replica_id()
os.environ["TF_CONFIG"] = json.dumps(
{
"cluster": {
"worker": hostnames,
},
"task": {"type": "worker", "index": replica_index},
}
)
class Model(tf.keras.models.Model):
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, **kwargs)
self._embedder = tf.keras.Sequential(
[
tf.keras.layers.Conv2D(
filters=8,
kernel_size=3,
padding="same",
activation=tf.keras.activations.relu,
use_bias=False,
),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Conv2D(
filters=8,
kernel_size=3,
padding="same",
activation=tf.keras.activations.relu,
use_bias=False,
),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.MaxPool2D(),
tf.keras.layers.GlobalAveragePooling2D(),
]
)
self._classifier = tf.keras.layers.Dense(550)
def call(self, x: tf.Tensor) -> tf.Tensor:
x = self._embedder(x)
x = self._classifier(x)
x = tf.keras.layers.Activation("linear", dtype="float32")(x)
return x
def create_dummy_dataset(batch_size: int) -> tf.data.Dataset:
X = np.random.rand(batch_size, 384, 640, 1)
y = np.random.randint(550, size=batch_size)
return tf.data.Dataset.from_tensor_slices((X, y)).batch(batch_size, True).repeat()
def train():
set_multiworker_env_config()
strategy = tf.distribute.MultiWorkerMirroredStrategy()
num_replicas = strategy.num_replicas_in_sync
batch_size = 16 * num_replicas
dataset = create_dummy_dataset(batch_size)
dataset = strategy.experimental_distribute_dataset(dataset)
with strategy.scope():
model = Model()
model.compile(
loss=tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True,
),
optimizer=tf.keras.optimizers.Adam(
learning_rate=1e-3,
weight_decay=1e-5,
),
metrics=[
"accuracy",
],
jit_compile=True,
)
model.fit(
dataset,
epochs=10,
steps_per_epoch=100,
)
if __name__ == "__main__":
train()
```
### Relevant log output
```shell
Log output for 4 GPUs:
2024-01-08 12:48:24.103746: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-01-08 12:48:24.103807: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-01-08 12:48:24.104896: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-01-08 12:48:24.111875: 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.
2024-01-08 12:48:24.978525: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-01-08 12:48:27.492019: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 18370 MB memory: -> device: 0, name: NVIDIA A100-SXM4-40GB MIG 3g.20gb, pci bus id: 0000:01:00.0, compute capability: 8.0
2024-01-08 12:48:27.503592: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:worker/replica:0/task:0/device:GPU:0 with 18370 MB memory: -> device: 0, name: NVIDIA A100-SXM4-40GB MIG 3g.20gb, pci bus id: 0000:01:00.0, compute capability: 8.0
2024-01-08 12:48:27.528772: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:457] Started server with target: grpc://gen-svc-4e03d749-0565-47d7-b7ff-63437f0ab5b3:80
2024-01-08 12:48:27.535501: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service.cc:553] /job:worker/replica:0/task:0 has connected to coordination service. Incarnation: 9493928235207696637
2024-01-08 12:48:27.535846: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service_agent.cc:304] Coordination agent has successfully connected.
2024-01-08 12:48:28.425710: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service.cc:553] /job:worker/replica:0/task:1 has connected to coordination service. Incarnation: 17537869834892823189
2024-01-08 12:48:29.509519: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service.cc:553] /job:worker/replica:0/task:2 has connected to coordination service. Incarnation: 11924625857490357420
2024-01-08 12:48:29.766944: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service.cc:553] /job:worker/replica:0/task:3 has connected to coordination service. Incarnation: 8010175117178506894
WARNING:absl:You use TensorFlow DType <dtype: 'string'> in tfds.features This will soon be deprecated in favor of NumPy DTypes. In the meantime it was converted to object.
WARNING:absl:You use TensorFlow DType <dtype: 'int64'> in tfds.features This will soon be deprecated in favor of NumPy DTypes. In the meantime it was converted to int64.
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:563 [0] NCCL INFO Bootstrap : Using eth0:10.233.118.112<0>
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:563 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation
2024-01-08 12:48:32.756935: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.
2024-01-08 12:48:32.756993: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.
2024-01-08 12:48:32.757173: I external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:1883] Profiler found 1 GPUs
2024-01-08 12:48:32.790801: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.
2024-01-08 12:48:32.790934: I external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2017] CUPTI activity buffer flushed
Epoch 1/10
2024-01-08 12:48:37.520453: I external/local_xla/xla/service/service.cc:168] XLA service 0x7fe284006a40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-01-08 12:48:37.520583: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA A100-SXM4-40GB MIG 3g.20gb, Compute Capability 8.0
2024-01-08 12:48:37.680983: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-01-08 12:48:38.693930: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8904
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1704718137.764316 430 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] NCCL INFO cudaDriverVersion 12020
NCCL version 2.16.5+cudaCUDA_MAJOR.CUDA_MINOR
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] NCCL INFO Failed to open libibverbs.so[.1]
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] NCCL INFO NET/Socket : Using [0]eth0:10.233.118.112<0>
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] NCCL INFO Using network Socket
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] external/nccl_archive/src/init.cc:642 NCCL WARN Duplicate GPU detected : rank 0 and rank 2 both on CUDA device 1000
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] NCCL INFO external/nccl_archive/src/init.cc:1100 -> 5
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] NCCL INFO external/nccl_archive/src/init.cc:1173 -> 5
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] NCCL INFO external/nccl_archive/src/init.cc:1209 -> 5
2024-01-08 12:48:58.577448: W external/local_xla/xla/service/gpu/runtime/support.cc:58] Intercepted XLA runtime error:
INTERNAL: external/local_xla/xla/service/gpu/nccl_utils.cc:297: NCCL operation ncclCommInitRank(&comm, nranks, id, rank) failed: invalid usage
2024-01-08 12:48:58.577775: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 16898057275935290807
2024-01-08 12:48:58.577798: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 7386102362502530449
2024-01-08 12:48:58.577844: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 11220456033729140565
Traceback (most recent call last):
File "/kirax_source/train.py", line 346, in <module>
train_tf(args=args, jit_compile=XLA)
File "/kirax_source/train.py", line 246, in train_tf
model.fit(
File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/execute.py", line 53, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tensorflow.python.framework.errors_impl.InternalError: Graph execution error:
Detected at node StatefulPartitionedCall defined at (most recent call last):
File "/usr/lib/python3.11/threading.py", line 1002, in _bootstrap
File "/usr/lib/python3.11/threading.py", line 1045, in _bootstrap_inner
Detected at node StatefulPartitionedCall defined at (most recent call last):
File "/usr/lib/python3.11/threading.py", line 1002, in _bootstrap
File "/usr/lib/python3.11/threading.py", line 1045, in _bootstrap_inner
2 root error(s) found.
(0) INTERNAL: Failed to execute XLA Runtime executable: run time error: custom call 'xla.gpu.all_reduce' failed: external/local_xla/xla/service/gpu/nccl_utils.cc:297: NCCL operation ncclCommInitRank(&comm, nranks, id, rank) failed: invalid usage; current tracing scope: all-reduce-start.4; current profiling annotation: XlaModule:#hlo_module=a_inference_run_step_7562__.4006,program_id=447#.
[[{{node StatefulPartitionedCall}}]]
[[Reshape_3/_22]]
(1) INTERNAL: Failed to execute XLA Runtime executable: run time error: custom call 'xla.gpu.all_reduce' failed: external/local_xla/xla/service/gpu/nccl_utils.cc:297: NCCL operation ncclCommInitRank(&comm, nranks, id, rank) failed: invalid usage; current tracing scope: all-reduce-start.4; current profiling annotation: XlaModule:#hlo_module=a_inference_run_step_7562__.4006,program_id=447#.
[[{{node StatefulPartitionedCall}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_7900]
Log output for 2 GPUs:
2024-01-08 13:32:56.555585: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-01-08 13:32:56.555666: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-01-08 13:32:56.557055: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-01-08 13:32:56.563733: 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.
2024-01-08 13:32:57.475551: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-01-08 13:32:59.868467: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 18370 MB memory: -> device: 0, name: NVIDIA A100-SXM4-40GB MIG 3g.20gb, pci bus id: 0000:41:00.0, compute capability: 8.0
2024-01-08 13:32:59.880625: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:worker/replica:0/task:0/device:GPU:0 with 18370 MB memory: -> device: 0, name: NVIDIA A100-SXM4-40GB MIG 3g.20gb, pci bus id: 0000:41:00.0, compute capability: 8.0
2024-01-08 13:32:59.903354: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:457] Started server with target: grpc://gen-svc-d871b43e-6f9e-4d8f-9faf-d98a734319f3:80
2024-01-08 13:32:59.912608: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service.cc:553] /job:worker/replica:0/task:0 has connected to coordination service. Incarnation: 12924650074147221766
2024-01-08 13:32:59.913045: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service_agent.cc:304] Coordination agent has successfully connected.
2024-01-08 13:33:00.820050: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service.cc:553] /job:worker/replica:0/task:1 has connected to coordination service. Incarnation: 4023732460757352804
WARNING:absl:You use TensorFlow DType <dtype: 'string'> in tfds.features This will soon be deprecated in favor of NumPy DTypes. In the meantime it was converted to object.
WARNING:absl:You use TensorFlow DType <dtype: 'int64'> in tfds.features This will soon be deprecated in favor of NumPy DTypes. In the meantime it was converted to int64.
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:784 [0] NCCL INFO Bootstrap : Using eth0:10.233.118.63<0>
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:784 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation
2024-01-08 13:33:03.552619: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.
2024-01-08 13:33:03.552667: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.
2024-01-08 13:33:03.552814: I external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:1883] Profiler found 1 GPUs
2024-01-08 13:33:03.587433: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.
2024-01-08 13:33:03.587611: I external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2017] CUPTI activity buffer flushed
Epoch 1/10
2024-01-08 13:33:08.149597: I external/local_xla/xla/service/service.cc:168] XLA service 0x7ff338008540 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-01-08 13:33:08.149730: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA A100-SXM4-40GB MIG 3g.20gb, Compute Capability 8.0
2024-01-08 13:33:08.732338: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-01-08 13:33:09.871101: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8904
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1704720809.106117 425 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO cudaDriverVersion 12020
NCCL version 2.16.5+cudaCUDA_MAJOR.CUDA_MINOR
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Failed to open libibverbs.so[.1]
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO NET/Socket : Using [0]eth0:10.233.118.63<0>
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Using network Socket
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] external/nccl_archive/src/misc/nvmlwrap.cc:183 NCCL WARN nvmlDeviceGetHandleByPciBusId() failed: Not Found
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Setting affinity for GPU 0 to ffffffff,00000000,ffffffff
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Channel 00/02 : 0 1
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Channel 01/02 : 0 1
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] -1/-1/-1->0->1
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO P2P Chunksize set to 131072
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1284 [0] NCCL INFO NCCL_SOCKET_NTHREADS set by environment to 8.
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1284 [0] NCCL INFO NET/Socket: Using 8 threads and 1 sockets per thread
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Channel 00/0 : 1[1000] -> 0[41000] [receive] via NET/Socket/0
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1284 [0] NCCL INFO NET/Socket: Using 8 threads and 1 sockets per thread
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Channel 01/0 : 1[1000] -> 0[41000] [receive] via NET/Socket/0
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Channel 00/0 : 0[41000] -> 1[1000] [send] via NET/Socket/0
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Channel 01/0 : 0[41000] -> 1[1000] [send] via NET/Socket/0
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Connected all rings
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Connected all trees
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO threadThresholds 8/8/64 | 16/8/64 | 512 | 512
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO comm 0x7fe61cffcb20 rank 0 nranks 2 cudaDev 0 busId 41000 commId 0xe105507e5746b5a2 - Init COMPLETE
2024-01-08 13:33:29.780117: W external/local_xla/xla/service/gpu/runtime/support.cc:58] Intercepted XLA runtime error:
INTERNAL: There was an error before calling cuModuleGetFunction (101): cudaErrorInvalidDevice : invalid device ordinal
2024-01-08 13:33:29.780331: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 14229809023376429067
2024-01-08 13:33:29.780349: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 10883816187180422187
2024-01-08 13:33:29.780389: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 5243788738829094043
Traceback (most recent call last):
File "/kirax_source/train.py", line 346, in <module>
train_tf(args=args, jit_compile=XLA)
File "/kirax_source/train.py", line 246, in train_tf
model.fit(
File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/execute.py", line 53, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tensorflow.python.framework.errors_impl.InternalError: Graph execution error:
Detected at node StatefulPartitionedCall defined at (most recent call last):
File "/usr/lib/python3.11/threading.py", line 1002, in _bootstrap
File "/usr/lib/python3.11/threading.py", line 1045, in _bootstrap_inner
Detected at node StatefulPartitionedCall defined at (most recent call last):
File "/usr/lib/python3.11/threading.py", line 1002, in _bootstrap
File "/usr/lib/python3.11/threading.py", line 1045, in _bootstrap_inner
2 root error(s) found.
(0) INTERNAL: Failed to execute XLA Runtime executable: run time error: custom call 'xla.gpu.func.launch' failed: There was an error before calling cuModuleGetFunction (101): cudaErrorInvalidDevice : invalid device ordinal; current tracing scope: fusion.274; current profiling annotation: XlaModule:#hlo_module=a_inference_run_step_7562__.4006,program_id=447#.
[[{{node StatefulPartitionedCall}}]]
[[CollectiveReduceV2_1/_17]]
(1) INTERNAL: Failed to execute XLA Runtime executable: run time error: custom call 'xla.gpu.func.launch' failed: There was an error before calling cuModuleGetFunction (101): cudaErrorInvalidDevice : invalid device ordinal; current tracing scope: fusion.274; current profiling annotation: XlaModule:#hlo_module=a_inference_run_step_7562__.4006,program_id=447#.
[[{{node StatefulPartitionedCall}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_7900]
```
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I_kwDOArmXAs57XL4g
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InvalidArgumentError: Cannot convert a Tensor of dtype resource to a NumPy array.
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[
"@suprateembanerjee The issue occurs in `self.vectorization.get_config()`. \r\nIt contains a non-serializable element, namely the `standardize` element : `'standardize': <bound method TextVectorizer.standardize of <__main__.TextVectorizer object at 0x7846c98d39d0>>`. \r\nTry filtering out the non-serializable elements from the config before pickling it, like this:\r\n `serializable_config = {k: v if not callable(v) else None for k, v in config.items()}`. \r\nWorks on [Colab](https://colab.research.google.com/drive/15ptbQqJfaKMUdQsQlca_rXf9WnzM5IDZ#scrollTo=ctmL4l2dmE-C). Hope this helps!",
"@aditya02shah While this does solve the immediate problem, you are not saving the `standardize` method in the pickle file either. Without this information being saved, when the pickle is reloaded for inference, it does not produce the correct vectorization, leading to gibberish translations.\r\n\r\nEdit: The methodology seems sound. The unintended artifacts may have been a result of randomized shuffling of the dataset. Seeding this randomization helped mitigate this issue.",
"@suprateembanerjee Explore saving only the necessary configuration and weights, excluding internal layer states that might not be pickle-friendly. Use `layer.get_config() `and `layer.get_weights()`.\r\nCould you please ensure the custom function is compatible with pickling. If it references external objects or resources, make them picklable as well . Please do consider using a plain Python function or a lambda function within the wrapper class.\r\nIn order to expedite the trouble-shooting process, please provide the complete code snippet to reproduce the issue reported here. Thank you!\r\n",
"@sushreebarsa @aditya02shah Aditya's comment pointed me in the right direction. Here's the working wrapper:\r\n\r\n```\r\[email protected]_keras_serializable(package='custom_layers', name='TextVectorizer')\r\nclass TextVectorizer(layers.Layer):\r\n '''English - Spanish Text Vectorizer'''\r\n\r\n def __init__(self, max_tokens=None, output_mode='int', output_sequence_length=None, standardize='lower_and_strip_punctuation', vocabulary=None, config=None):\r\n super().__init__()\r\n if config:\r\n self.vectorization = layers.TextVectorization.from_config(config)\r\n\r\n else:\r\n self.max_tokens = max_tokens\r\n self.output_mode = output_mode\r\n self.output_sequence_length = output_sequence_length\r\n self.vocabulary = vocabulary\r\n if standardize != 'lower_and_strip_punctuation':\r\n self.vectorization = layers.TextVectorization(max_tokens=self.max_tokens,\r\n output_mode=self.output_mode,\r\n output_sequence_length=self.output_sequence_length,\r\n vocabulary=self.vocabulary,\r\n standardize=self.standardize)\r\n else:\r\n self.vectorization = layers.TextVectorization(max_tokens=self.max_tokens,\r\n output_mode=self.output_mode,\r\n output_sequence_length=self.output_sequence_length,\r\n vocabulary=self.vocabulary)\r\n\r\n\r\n def standardize(self, input_string, preserve=['[', ']'], add=['¿']) -> str:\r\n strip_chars = string.punctuation\r\n for item in add:\r\n strip_chars += item\r\n \r\n for item in preserve:\r\n strip_chars = strip_chars.replace(item, '')\r\n\r\n lowercase = tf.strings.lower(input_string)\r\n output = tf.strings.regex_replace(lowercase, f'[{re.escape(strip_chars)}]', '')\r\n\r\n return output\r\n \r\n def __call__(self, *args, **kwargs):\r\n return self.vectorization.__call__(*args, **kwargs)\r\n \r\n def get_config(self):\r\n return {key: value if not callable(value) else None for key, value in self.vectorization.get_config().items()}\r\n \r\n def from_config(config):\r\n return TextVectorizer(config=config)\r\n \r\n def set_weights(self, weights):\r\n self.vectorization.set_weights(weights)\r\n\r\n def adapt(self, dataset):\r\n self.vectorization.adapt(dataset)\r\n \r\n def get_vocabulary(self):\r\n return self.vectorization.get_vocabulary()\r\n```\r\n\r\nTo adapt and save the vectorization, \r\n\r\n```\r\nvocab_size = 15000\r\nsequence_length = 20\r\n\r\nsource_vectorization = TextVectorizer(max_tokens=vocab_size,\r\n output_mode='int',\r\n output_sequence_length=sequence_length)\r\n\r\ntarget_vectorization = TextVectorizer(max_tokens=vocab_size,\r\n output_mode='int',\r\n output_sequence_length=sequence_length + 1,\r\n standardize='spanish')\r\n\r\ntrain_english_texts = [pair[0] for pair in train_pairs]\r\ntrain_spanish_texts = [pair[1] for pair in train_pairs]\r\nsource_vectorization.adapt(train_english_texts)\r\ntarget_vectorization.adapt(train_spanish_texts)\r\n\r\npickle.dump({'config': source_vectorization.get_config(),\r\n 'weights': source_vectorization.get_weights()}, open('ckpts/english_vectorization.pkl', 'wb'))\r\n\r\npickle.dump({'config': target_vectorization.get_config(),\r\n 'weights': target_vectorization.get_weights()}, open('ckpts/spanish_vectorization.pkl', 'wb'))\r\n```\r\n...and to load it back,\r\n```\r\nvectorization_data = pickle.load(open('ckpts/english_vectorization.pkl', 'rb'))\r\nsource_vectorization = TextVectorizer.from_config(vectorization_data['config'])\r\nsource_vectorization.set_weights(vectorization_data['weights'])\r\n\r\nvectorization_data = pickle.load(open('ckpts/spanish_vectorization.pkl', 'rb'))\r\ntarget_vectorization = TextVectorizer.from_config(vectorization_data['config'])\r\ntarget_vectorization.set_weights(vectorization_data['weights'])\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/62756\">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/62756\">No</a>\n"
] | 2024-01-08T05:05:53 | 2024-01-09T16:55:37 | 2024-01-09T16:55:34 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15
### Custom code
Yes
### OS platform and distribution
MacOS Ventura 13.1
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I am trying to save TextVectorization configurations and weights (adapted from training data) into pickle dumps so that they can be initialized at inference time in the absence of training data. I wrote a wrapper class around keras.Layers.TextVectorization() so that I can have access to a custom standardization function. I expect the pickle dump to successfully get created, but instead, this error pops up when I try to create the dump for the case where I am using the custom standardization function.
### Standalone code to reproduce the issue
```shell
@keras.utils.register_keras_serializable(package='custom_layers', name='TextVectorizer')
class TextVectorizer(layers.Layer):
'''English - Spanish Text Vectorizer'''
def __init__(self, max_tokens=None, output_mode='int', output_sequence_length=None, standardize='lower_and_strip_punctuation', vocabulary=None):
super().__init__()
self.max_tokens = max_tokens
self.output_mode = output_mode
self.output_sequence_length = output_sequence_length
self.vocabulary = vocabulary
if standardize != 'lower_and_strip_punctuation':
self.vectorization = layers.TextVectorization(max_tokens=self.max_tokens,
output_mode=self.output_mode,
output_sequence_length=self.output_sequence_length,
vocabulary=self.vocabulary,
standardize=self.standardize)
else:
self.vectorization = layers.TextVectorization(max_tokens=self.max_tokens,
output_mode=self.output_mode,
output_sequence_length=self.output_sequence_length,
vocabulary=self.vocabulary)
def standardize(self, input_string, preserve=['[', ']'], add=['¿']) -> str:
strip_chars = string.punctuation
for item in add:
strip_chars += item
for item in preserve:
strip_chars = strip_chars.replace(item, '')
lowercase = tf.strings.lower(input_string)
output = tf.strings.regex_replace(lowercase, f'[{re.escape(strip_chars)}]', '')
return output
def get_config(self):
return self.vectorization.get_config()
def adapt(self, dataset):
self.vectorization.adapt(dataset)
def get_vocabulary(self):
return self.vectorization.get_vocabulary()
vocab_size = 15000
sequence_length = 20
source_vectorization = TextVectorizer(max_tokens=vocab_size,
output_mode='int',
output_sequence_length=sequence_length)
target_vectorization = TextVectorizer(max_tokens=vocab_size,
output_mode='int',
output_sequence_length=sequence_length + 1,
standardize='spanish')
train_english_texts = [pair[0] for pair in train_pairs]
train_spanish_texts = [pair[1] for pair in train_pairs]
source_vectorization.adapt(train_english_texts)
target_vectorization.adapt(train_spanish_texts)
pickle.dump({'config': target_vectorization.get_config(),
'weights': target_vectorization.get_weights()}
, open('ckpts/spanish_vectorization.pkl', 'wb'))
```
### Relevant log output
```shell
InvalidArgumentError Traceback (most recent call last)
/Users/suprateembanerjee/Library/Mobile Documents/com~apple~CloudDocs/Python Projects/DL With Python/C11.ipynb Cell 79 line 1
----> 1 pickle.dump({'config': target_vectorization.get_config(),
2 'weights': target_vectorization.get_weights()}
3 , open('ckpts/spanish_vectorization.pkl', 'wb'))
File ~/miniforge3/envs/tensorflow/lib/python3.11/site-packages/tensorflow/python/framework/ops.py:314, in _EagerTensorBase.__reduce__(self)
313 def __reduce__(self):
--> 314 return convert_to_tensor, (self._numpy(),)
File ~/miniforge3/envs/tensorflow/lib/python3.11/site-packages/tensorflow/python/framework/ops.py:362, in _EagerTensorBase._numpy(self)
360 return self._numpy_internal()
361 except core._NotOkStatusException as e: # pylint: disable=protected-access
--> 362 raise core._status_to_exception(e) from None
InvalidArgumentError: Cannot convert a Tensor of dtype resource to a NumPy array.
```
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[
"Hi @ydy1127,\r\n\r\nI reproduced the code with tensorflow 2.15 latest version. It works fine and there is no import error. Refer to the [gist](https://colab.research.google.com/gist/LakshmiKalaKadali/9e3315b445a37a6bf90716c34cb5e0a7/tflite_62755.ipynb). Please upgrade to the latest tensorflow version. If the problem persists, please let us know.\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.",
"Hi LakshmiKalaKadali，\r\n\r\nBased on the information you provided, I recreated the Conda environment with Python 3.10.12 and then installed tensorflow 2.15.0. The problem persists, as shown below.\r\n\r\n````\r\nPython 3.10.12 (main, Jul 5 2023, 18:54:27) [GCC 11.2.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import tensorflow as tf\r\n2024-01-22 11:26:32.545016: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2024-01-22 11:26:32.547237: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\r\n2024-01-22 11:26:32.590044: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2024-01-22 11:26:32.590079: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2024-01-22 11:26:32.591447: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2024-01-22 11:26:32.599425: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\r\n2024-01-22 11:26:32.599712: 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\n2024-01-22 11:26:33.217922: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n>>> import tflite_runtime.interpreter as tflite\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/yuandy/anaconda3/envs/tensorflow/lib/python3.10/site-packages/tflite_runtime/interpreter.py\", line 33, in <module>\r\n from tflite_runtime import _pywrap_tensorflow_interpreter_wrapper as _interpreter_wrapper\r\nImportError: /lib64/libm.so.6: version `GLIBC_2.27' not found (required by /home/yuandy/anaconda3/envs/tensorflow/lib/python3.10/site-packages/tflite_runtime/_pywrap_tensorflow_interpreter_wrapper.so)\r\n````\r\n\r\nFrom the error message, it seems that the system does not have a GPU and the GLIBC library version is too low.\r\nCan you provide a version of the GLIBC library on your system?\r\n\r\nThank You,\r\nDanyang Yuan",
"Hi @ydy1127,\r\n\r\nRegarding the GLIBC_2.27 not found error: The error is due to incompatibility between GLIBC version of TFLite and the version available on your system. First, know the glibc version of your system. Refer this [link](https://iq.opengenus.org/find-glibc-version/) to know the version. If it is a lower version, upgrade glibc version using the commands ```sudo apt-get update``` and ```sudo apt-get install libc6```. Then run ``` import tflite_runtime.interpreter as tflite```. In my old post, i have used glibc 2.35 version.\r\n\r\nUpon observing the error log, if you want to use GPU acceleration, install necessary GPU drivers.\r\n\r\nThank You.\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."
] | 2024-01-08T03:12:17 | 2024-02-08T01:46:30 | 2024-02-08T01:46:30 |
NONE
| null | null | null |
Dear TensorFlow Team,
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): CentOS Linux 7 (Core)
- Python version: Python 3.7.16
- TensorFlow installation (pip package or built from source): tflite-runtime-2.11.0
### 2. Code and issue
tflite-runtime is installed successfully, but an error occurs when the tflite-runtime package is imported. Some of the solutions we have found require root permission, how to solve the problem without root permission
```
python3 -m pip install tflite-runtime
>>> import tflite_runtime.interpreter as tflite
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/yuandy/anaconda3/envs/tflite/lib/python3.7/site-packages/tflite_runtime/interpreter.py", line 33, in <module>
from tflite_runtime import _pywrap_tensorflow_interpreter_wrapper as _interpreter_wrapper
ImportError: /lib64/libm.so.6: version `GLIBC_2.27' not found (required by /home/yuandy/anaconda3/envs/tflite/lib/python3.7/site-packages/tflite_runtime/_pywrap_tensorflow_interpreter_wrapper.so)
```
It would be great if you could let me know.
Best,
Danyang Yuan
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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/62754/checks?check_run_id=20242796157) 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.",
"Why did the ROCm build failed?\r\n",
"Hi @AryanNanda17 This PR is the duplicate of https://github.com/tensorflow/tensorflow/pull/62747, hence closing this. Thank you for your contribution!"
] | 2024-01-07T22:24:40 | 2024-01-08T05:12:33 | 2024-01-08T05:12:31 |
NONE
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Solved #62745 issue.
This line in the documentation of tf.data.Dataset.shuffle() has a missing backtick:
```To shuffle an entire dataset, set `buffer_size=dataset.cardinality(). This
is equivalent to setting ...```
This should have been:-
```To shuffle an entire dataset, set `buffer_size=dataset.cardinality()`. This
is equivalent to setting ...```
This PR does the required changes.
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I_kwDOArmXAs57VmhA
| 62,753 |
benchmark_model mobilenet with GPU wont run
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"@SOLEROM Could you check if you have built TFLite with -DTFLITE_ENABLE_GPU=ON during conversion? Building TFLite without GPU support will prevent GPU usage. Please ensure your GPU is properly configured and recognized by TensorFlow. You can check in the TensorFlow shell with tf.config.list_physical_devices('GPU').\r\nPlease let us know! \r\nThank you!",
"(1) our TFLite app benchmark_model was indeed build with -DTFLITE_ENABLE_GPU=ON ;\r\n\r\n(2) the python tflite (from pip3 install tensorflow-io tensorflow) dont see any GPU; \r\n>>> tf.config.list_physical_devices()\r\n[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]\r\n\r\nChecked with hello world opencl kernel example to make sure the hw is working.\r\n\r\nIs there any configs to tflite to set the GPU? or any on the coral DEV maybe?\r\nThank you!",
"@SOLEROM TFLite leverages \"delegates\" to offload computations to hardware accelerators like GPUs.\r\nYou'll need to add the appropriate GPU delegate library to your project, depending on your platform (Android, iOS, etc.). Please refer to this [doc](https://www.tensorflow.org/lite/performance/gpu) for more details. Thank you!",
"just to make sure: is compiling the app with -DTFLITE_ENABLE_GPU=ON in cmake cmd wont create the \"deleage\" for the GPU?\r\nrunning that app log that **GPU delegate created and loaded** :\r\n\r\n```\r\nroot@silly-finch:/# ./benchmark_model --use_gpu=true --graph=mobilenet_v1_1.0_224_float.tflite \r\n...\r\n\r\n\r\nINFO: Use gpu: [1]\r\nINFO: Loaded model mobilenet_v1_1.0_224_float.tflite\r\nINFO: Created TensorFlow Lite delegate for GPU.\r\nINFO: GPU delegate created. <<<<========\r\n\r\n....\r\n\r\n\r\n```",
"@SOLEROM Compiling the app with -DTFLITE_ENABLE_GPU=ON in the cmake command won't create the delegate for the GPU. While enabling GPU support builds the necessary libraries for GPU execution, it doesn't automatically inject the delegate into your model.\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."
] | 2024-01-07T19:36:48 | 2024-02-09T01:46:30 | 2024-02-09T01:46:30 |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution = Linux Ubuntu) on coral dev
- TensorFlow installation= built from source):
- TensorFlow library= if built from source):
### 2. code
benchmark_model from tflite build from source using
-DTFLITE_ENABLE_GPU=ON in cmake cmd
-DCL_DELEGATE_NO_GL in ARMCC_FLAGS
imx-gpu-viv installed on coral
clinfo show opencl1.2 and the device
but running the benchmark_model with use gpu wont work:
```
root@silly-finch:/# ./benchmark_model --use_gpu=true --graph=mobilenet_v1_1.0_224_float.tflite
INFO: STARTING!
INFO: Log parameter values verbosely: [0]
INFO: Graph: [mobilenet_v1_1.0_224_float.tflite]
INFO: Use gpu: [1]
INFO: Loaded model mobilenet_v1_1.0_224_float.tflite
INFO: Created TensorFlow Lite delegate for GPU.
INFO: GPU delegate created.
ERROR: Failed to build program executable - Build program failure(97:0) : error : conversion between different vector types not allowed
(98:0) : error : conversion between different vector types not allowed
(99:0) : error : conversion between different vector types not allowed
```
will be glad for any tips...
thanks;
|
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PR_kwDOArmXAs5jZl3l
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Create appleflow
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"Hi @SuskyOG It is a spam PR, hence closing this. Thank you."
] | 2024-01-07T06:44:36 | 2024-01-08T05:35:50 | 2024-01-08T05:35:49 |
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PR_kwDOArmXAs5jZkfs
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Added a missing Backtick
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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/62751/checks?check_run_id=20232937572) 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 @UdityaRaj11 This PR is the duplicate of [#PR62747](https://github.com/tensorflow/tensorflow/pull/62747), hence closing this. Thank you for your contribution! "
] | 2024-01-07T06:07:26 | 2024-01-08T05:09:00 | 2024-01-08T05:08:59 |
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Solved Issue #62745 - Error in markdown based comment.
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Implement sampled addmm
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"Hi @mattbahr Can you please check @cantonios's comments and keep us posted ? Thank you!",
"@gbaned absolutely!\r\n@cantonios thanks for the comments! I’m going to be busy traveling for a bit but I’ll be able to get the requested changes made sometime next week",
"😭😭😭😭😭\r\n\r\nOn Sat, Jan 27, 2024, 1:39 AM Matt Bahr ***@***.***> wrote:\r\n\r\n> ***@***.**** commented on this pull request.\r\n> ------------------------------\r\n>\r\n> In tensorflow/python/ops/math_ops.py\r\n> <https://github.com/tensorflow/tensorflow/pull/62750#discussion_r1468364264>\r\n> :\r\n>\r\n> > + mat1 = ops.convert_to_tensor(mat1)\r\n> + if not isinstance(mat2, tensor_lib.Tensor):\r\n> + mat2 = ops.convert_to_tensor(mat2)\r\n> +\r\n> + if values.dtype != output_type:\r\n> + values = cast(values, output_type)\r\n> + if mat1.dtype != output_type:\r\n> + mat1 = cast(mat1, output_type)\r\n> + if mat2.dtype != output_type:\r\n> + mat2 = cast(mat2, output_type)\r\n> +\r\n> + dense_rows = mat1.shape[-2]\r\n> + dense_cols = mat2.shape[-1]\r\n> +\r\n> + # TODO(mattbahr): use dense_shape to validate the shapes of mat1 and mat2\r\n> + dense_shape = constant_op.constant([dense_rows, dense_cols])\r\n>\r\n> Importing check_ops introduces a circular dependency. I tried using a\r\n> regular assert, but I kept getting AssertionErrors when I run my unit\r\n> tests.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/pull/62750#discussion_r1468364264>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/A5CIWVH2QNOI4J7IY4FRHPTYQSOJTAVCNFSM6AAAAABBQB7C26VHI2DSMVQWIX3LMV43YUDVNRWFEZLROVSXG5CSMV3GSZLXHMYTQNBWHA4TINBQGY>\r\n> .\r\n> You are receiving this because you are subscribed to this thread.Message\r\n> ID: ***@***.***>\r\n>\r\n",
"@cantonios When you get the chance, you mind taking a look at my solution for the dense shape validation? Appreciate it!",
"> The doc test is failing with:\r\n> \r\n> ValueError: Tensor conversion requested dtype float32 for Tensor with dtype int32: <tf.Tensor: shape=(2, 3), dtype=int32, numpy= array([[1, 2, 3], [4, 5, 6]], dtype=int32)>\r\n> \r\n> Please double-check your examples all run.\r\n\r\nHmm, my unit tests are all passing when I run on my system, but I can replicate the error if I run the following:\r\n```\r\n>>> indices = tf.constant([[0, 0],[1,1]])\r\n>>> indices = tf.constant([[0, 0],[1, 1]])\r\n>>> values = tf.constant([0.5, 0.3], dtype=tf.float32)\r\n>>> dense_shape = tf.constant([2, 2])\r\n>>> mat1 = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3], dtype=tf.int32)\r\n>>> mat2 = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2], dtype=tf.float32)\r\n>>> ind, res, ds = tf.sparse.sampled_addmm(indices, values, dense_shape, mat1, mat2, output_type=tf.float32)\r\n```\r\n@cantonios I suppose we could add a validation at the start where if `values`, `mat1`, and `mat2` are tensors, there types match `output_type`? The supported data types I had listed in the python function doc were `bfloat16`, `float16`, `float32`, and `float64`. I only set up the unit tests to run on those types.",
"I mean to just fix the pydoc that you added - the examples in your documentation don't run. In one of those examples, you implicitly create an integer tensor.",
"Ah, derp thanks lol will do",
"@cantonios I think we should be good. Recopied over the outputs and just modified to make pylint happy about line lengths. Sorry about that, and thanks for your help! Definitely still learning the project.",
"Ok, now I understand what you meant by the doc test. I’m away for the weekend, but I’ll get this resolved Sunday night",
"The doc test is passing now",
"@cantonios I made a slight change for the API compatibility. When you get the chance, you mind taking another quick look at this? Thanks!",
"Hi @cantonios Can you please review this PR ? Thank you!",
"@gbaned @cantonios Is there anything I can do on my end to resolve the failing `feedback/copybara` check? I'm not sure what's causing it to fail.",
"> @gbaned @cantonios Is there anything I can do on my end to resolve the failing `feedback/copybara` check? I'm not sure what's causing it to fail.\r\n\r\nIt is the API compatibility test, since this PR adds a new API entry point. See https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/api/tests"
] | 2024-01-07T05:18:47 | 2024-05-03T13:45:29 | 2024-05-02T20:18:35 |
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Relates to #56311
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fixed multiple spelling errors
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wording fixed
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resolved issue: Error in markdown based comment #62745
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Line 1447 line in the documentation of tf.data.Dataset.shuffle():
To shuffle an entire dataset, set `buffer_size=dataset.cardinality(). This
has a missing backtick (`).
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tensorflow-macos==2.15.0 still requesting ml-dtypes (~=0.2.0) instead of 0.3.1
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[
"Hi @mathpluscode ,\r\n\r\nThe requirements updated for master branch which is having `ml-dtypes~= 0.3.1` now. \r\n\r\nThis is an issue with Tf2.15v with tensorflow package also apart from tensorflow-macos. In setup.py `ml-dtypes~= 0.2.0` but for all 3 `requirements_lock_3_x.txt` it's still showing `ml-dtypes~= 0.3.1` . Need to check on this. Thanks!",
"CC: @nitins17 , @learning-to-play , For your review please.",
"Any updates on a resolution to this issue? cc @nitins17 @learning-to-play ",
"We are having the same problem on Linux. This issue is especially problematic as it affects Jax and related packages which demand a newer ml-dypes version.",
"If I'm understanding correctly, this appears to already be fixed on `master`:\r\n- https://github.com/tensorflow/tensorflow/blob/v2.15.0/tensorflow/tools/pip_package/setup.py#L92 \r\n - `'ml_dtypes ~= 0.2.0',`\r\n- https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py#L93\r\n - `'ml_dtypes ~= 0.3.1',`\r\n\r\nSo it seems like the operative questions are: \r\n1. Am I understanding correctly? \r\n2. When will 2.15.1 be released?",
"Thanks for letting us know! #63020 updates the ml-dtypes version to 0.3.1. It should be included in the upcoming patch release for TF 2.15. \r\n\r\n> When will 2.15.1 be released?\r\n\r\nI do not know the exact date but believe it should be out by end of March. Adding @learning-to-play to confirm. ",
"@jakeBass TF 2.15 patch release is planned to be released before the end of March.\r\n\r\n@nitins17 Thank you for cherry picking the fix to the r2.15 branch."
] | 2024-01-06T16:29:41 | 2024-02-22T01:36:44 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.15.0
### Custom code
No
### OS platform and distribution
MacOS 14.1.2
### Mobile device
Mac
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Currently, we could not install the following packages together
- tensorflow-macos==2.15.0
- tensorflow-metal==1.1.0
- jax==0.4.23
- jaxlib==0.4.23
- orbax-checkpoint==0.4.8
```
The conflict is caused by:
tensorflow-macos 2.15.0 depends on ml-dtypes~=0.2.0
jax 0.4.23 depends on ml-dtypes>=0.2.0
jaxlib 0.4.23 depends on ml-dtypes>=0.2.0
tensorstore 0.1.52 depends on ml-dtypes>=0.3.1
tensorflow-macos 2.15.0 depends on ml-dtypes~=0.2.0
jax 0.4.23 depends on ml-dtypes>=0.2.0
jaxlib 0.4.23 depends on ml-dtypes>=0.2.0
tensorstore 0.1.51 depends on ml-dtypes>=0.3.1
```
From the [requirement](https://github.com/tensorflow/tensorflow/blob/master/requirements_lock_3_9.txt#L353), it seems that it should require `ml-dtypes==0.3.1`.
But if running
```
curl -s https://pypi.org/pypi/tensorflow/2.15.0/json | jq '.info.requires_dist' | grep 'ml-dtypes'
```
We will get `"ml-dtypes (~=0.2.0)",`.
Note, it does work without `orbax-checkpoint` which is now recommended by [Flax](https://flax.readthedocs.io/en/latest/guides/training_techniques/use_checkpointing.html) for checkpointing.
### Standalone code to reproduce the issue
```shell
name: debug
channels:
- defaults
dependencies:
- python=3.9
- pip=23.3.1
- pip:
- tensorflow-macos==2.15.0
- tensorflow-metal==1.1.0
- jax==0.4.23
- jaxlib==0.4.23
- orbax-checkpoint==0.4.8
Please put this into `environment_mac_m1_debug.yml` then execute `conda env create -f environment_mac_m1_debug.yml`.
```
### Relevant log output
```shell
conda env create -f environment_mac_m1_debug.yml ok base py
Channels:
- defaults
- conda-forge
Platform: osx-arm64
Collecting package metadata (repodata.json): done
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 23.3.1
latest version: 23.11.0
Please update conda by running
$ conda update -n base -c conda-forge conda
Downloading and Extracting Packages
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
Installing pip dependencies: \ Ran pip subprocess with arguments:
['/Users/user/miniforge3/envs/debug/bin/python', '-m', 'pip', 'install', '-U', '-r', '/Users/user/condaenv.1tnhkmib.requirements.txt', '--exists-action=b']
Pip subprocess output:
Collecting tensorflow-macos==2.15.0 (from -r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Using cached tensorflow_macos-2.15.0-cp39-cp39-macosx_12_0_arm64.whl.metadata (4.2 kB)
Collecting tensorflow-metal==1.1.0 (from -r /Users/user/condaenv.1tnhkmib.requirements.txt (line 2))
Downloading tensorflow_metal-1.1.0-cp39-cp39-macosx_12_0_arm64.whl.metadata (1.2 kB)
Collecting jax==0.4.23 (from -r /Users/user/condaenv.1tnhkmib.requirements.txt (line 3))
Using cached jax-0.4.23-py3-none-any.whl.metadata (24 kB)
Collecting jaxlib==0.4.23 (from -r /Users/user/condaenv.1tnhkmib.requirements.txt (line 4))
Using cached jaxlib-0.4.23-cp39-cp39-macosx_11_0_arm64.whl.metadata (2.1 kB)
Collecting orbax-checkpoint==0.4.8 (from -r /Users/user/condaenv.1tnhkmib.requirements.txt (line 5))
Using cached orbax_checkpoint-0.4.8-py3-none-any.whl.metadata (1.7 kB)
Collecting tensorflow-datasets==4.9.3 (from -r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Downloading tensorflow_datasets-4.9.3-py3-none-any.whl.metadata (9.3 kB)
Collecting absl-py>=1.0.0 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading absl_py-2.0.0-py3-none-any.whl.metadata (2.3 kB)
Collecting astunparse>=1.6.0 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading astunparse-1.6.3-py2.py3-none-any.whl (12 kB)
Collecting flatbuffers>=23.5.26 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading flatbuffers-23.5.26-py2.py3-none-any.whl.metadata (850 bytes)
Collecting gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading gast-0.5.4-py3-none-any.whl (19 kB)
Collecting google-pasta>=0.1.1 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading google_pasta-0.2.0-py3-none-any.whl (57 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 57.5/57.5 kB 45.2 kB/s eta 0:00:00
Collecting h5py>=2.9.0 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading h5py-3.10.0-cp39-cp39-macosx_11_0_arm64.whl.metadata (2.5 kB)
Collecting libclang>=13.0.0 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading libclang-16.0.6-py2.py3-none-macosx_11_0_arm64.whl.metadata (5.2 kB)
Collecting ml-dtypes~=0.2.0 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading ml_dtypes-0.2.0-cp39-cp39-macosx_10_9_universal2.whl.metadata (20 kB)
Collecting numpy<2.0.0,>=1.23.5 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading numpy-1.26.3-cp39-cp39-macosx_11_0_arm64.whl.metadata (61 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 61.2/61.2 kB 60.0 kB/s eta 0:00:00
Collecting opt-einsum>=2.3.2 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Using cached opt_einsum-3.3.0-py3-none-any.whl (65 kB)
Collecting packaging (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Using cached packaging-23.2-py3-none-any.whl.metadata (3.2 kB)
Collecting protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading protobuf-4.25.1-cp37-abi3-macosx_10_9_universal2.whl.metadata (541 bytes)
Requirement already satisfied: setuptools in /Users/user/miniforge3/envs/debug/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1)) (68.2.2)
Collecting six>=1.12.0 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting termcolor>=1.1.0 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading termcolor-2.4.0-py3-none-any.whl.metadata (6.1 kB)
Collecting typing-extensions>=3.6.6 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Using cached typing_extensions-4.9.0-py3-none-any.whl.metadata (3.0 kB)
Collecting wrapt<1.15,>=1.11.0 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading wrapt-1.14.1-cp39-cp39-macosx_11_0_arm64.whl (35 kB)
Collecting tensorflow-io-gcs-filesystem>=0.23.1 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading tensorflow_io_gcs_filesystem-0.34.0-cp39-cp39-macosx_12_0_arm64.whl.metadata (14 kB)
Collecting grpcio<2.0,>=1.24.3 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading grpcio-1.60.0-cp39-cp39-macosx_10_10_universal2.whl.metadata (4.0 kB)
Collecting tensorboard<2.16,>=2.15 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Using cached tensorboard-2.15.1-py3-none-any.whl.metadata (1.7 kB)
Collecting tensorflow-estimator<2.16,>=2.15.0 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Using cached tensorflow_estimator-2.15.0-py2.py3-none-any.whl.metadata (1.3 kB)
Collecting keras<2.16,>=2.15.0 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Using cached keras-2.15.0-py3-none-any.whl.metadata (2.4 kB)
Requirement already satisfied: wheel~=0.35 in /Users/user/miniforge3/envs/debug/lib/python3.9/site-packages (from tensorflow-metal==1.1.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 2)) (0.41.2)
Collecting scipy>=1.9 (from jax==0.4.23->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 3))
Downloading scipy-1.11.4-cp39-cp39-macosx_12_0_arm64.whl.metadata (60 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 60.4/60.4 kB 21.7 kB/s eta 0:00:00
Collecting importlib-metadata>=4.6 (from jax==0.4.23->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 3))
Downloading importlib_metadata-7.0.1-py3-none-any.whl.metadata (4.9 kB)
Collecting etils[epath,epy] (from orbax-checkpoint==0.4.8->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 5))
Downloading etils-1.5.2-py3-none-any.whl.metadata (6.3 kB)
Collecting msgpack (from orbax-checkpoint==0.4.8->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 5))
Downloading msgpack-1.0.7-cp39-cp39-macosx_11_0_arm64.whl.metadata (9.1 kB)
Collecting pyyaml (from orbax-checkpoint==0.4.8->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 5))
Downloading PyYAML-6.0.1-cp39-cp39-macosx_11_0_arm64.whl.metadata (2.1 kB)
Collecting tensorstore>=0.1.51 (from orbax-checkpoint==0.4.8->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 5))
Using cached tensorstore-0.1.52-cp39-cp39-macosx_11_0_arm64.whl.metadata (3.0 kB)
Collecting nest_asyncio (from orbax-checkpoint==0.4.8->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 5))
Downloading nest_asyncio-1.5.8-py3-none-any.whl.metadata (2.8 kB)
Collecting array-record (from tensorflow-datasets==4.9.3->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Downloading array_record-0.4.1-py39-none-any.whl.metadata (503 bytes)
Collecting click (from tensorflow-datasets==4.9.3->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Using cached click-8.1.7-py3-none-any.whl.metadata (3.0 kB)
Collecting dm-tree (from tensorflow-datasets==4.9.3->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Downloading dm_tree-0.1.8-cp39-cp39-macosx_11_0_arm64.whl (110 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 110.8/110.8 kB 28.6 kB/s eta 0:00:00
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Using cached promise-2.3-py3-none-any.whl
Collecting psutil (from tensorflow-datasets==4.9.3->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Downloading psutil-5.9.7-cp38-abi3-macosx_11_0_arm64.whl.metadata (21 kB)
Collecting requests>=2.19.0 (from tensorflow-datasets==4.9.3->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Using cached requests-2.31.0-py3-none-any.whl.metadata (4.6 kB)
Collecting tensorflow-metadata (from tensorflow-datasets==4.9.3->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Downloading tensorflow_metadata-1.14.0-py3-none-any.whl.metadata (2.1 kB)
Collecting toml (from tensorflow-datasets==4.9.3->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Downloading toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting tqdm (from tensorflow-datasets==4.9.3->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Downloading tqdm-4.66.1-py3-none-any.whl.metadata (57 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 57.6/57.6 kB 40.7 kB/s eta 0:00:00
Collecting fsspec (from etils[enp,epath,etree]>=0.9.0->tensorflow-datasets==4.9.3->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Downloading fsspec-2023.12.2-py3-none-any.whl.metadata (6.8 kB)
Collecting importlib_resources (from etils[enp,epath,etree]>=0.9.0->tensorflow-datasets==4.9.3->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Downloading importlib_resources-6.1.1-py3-none-any.whl.metadata (4.1 kB)
Collecting zipp (from etils[enp,epath,etree]>=0.9.0->tensorflow-datasets==4.9.3->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Downloading zipp-3.17.0-py3-none-any.whl.metadata (3.7 kB)
Collecting charset-normalizer<4,>=2 (from requests>=2.19.0->tensorflow-datasets==4.9.3->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Downloading charset_normalizer-3.3.2-cp39-cp39-macosx_11_0_arm64.whl.metadata (33 kB)
Collecting idna<4,>=2.5 (from requests>=2.19.0->tensorflow-datasets==4.9.3->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Downloading idna-3.6-py3-none-any.whl.metadata (9.9 kB)
Collecting urllib3<3,>=1.21.1 (from requests>=2.19.0->tensorflow-datasets==4.9.3->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Downloading urllib3-2.1.0-py3-none-any.whl.metadata (6.4 kB)
Collecting certifi>=2017.4.17 (from requests>=2.19.0->tensorflow-datasets==4.9.3->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 6))
Downloading certifi-2023.11.17-py3-none-any.whl.metadata (2.2 kB)
Collecting google-auth<3,>=1.6.3 (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading google_auth-2.26.1-py2.py3-none-any.whl.metadata (4.7 kB)
Collecting google-auth-oauthlib<2,>=0.5 (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading google_auth_oauthlib-1.2.0-py2.py3-none-any.whl.metadata (2.7 kB)
Collecting markdown>=2.6.8 (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading Markdown-3.5.1-py3-none-any.whl.metadata (7.1 kB)
Collecting protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 (from tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading protobuf-4.23.4-cp37-abi3-macosx_10_9_universal2.whl.metadata (540 bytes)
Collecting tensorboard-data-server<0.8.0,>=0.7.0 (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading tensorboard_data_server-0.7.2-py3-none-any.whl.metadata (1.1 kB)
Collecting werkzeug>=1.0.1 (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1))
Downloading werkzeug-3.0.1-py3-none-any.whl.metadata (4.1 kB)
INFO: pip is looking at multiple versions of tensorstore to determine which version is compatible with other requirements. This could take a while.
Collecting tensorstore>=0.1.51 (from orbax-checkpoint==0.4.8->-r /Users/user/condaenv.1tnhkmib.requirements.txt (line 5))
Using cached tensorstore-0.1.51-cp39-cp39-macosx_11_0_arm64.whl.metadata (3.0 kB)
The conflict is caused by:
tensorflow-macos 2.15.0 depends on ml-dtypes~=0.2.0
jax 0.4.23 depends on ml-dtypes>=0.2.0
jaxlib 0.4.23 depends on ml-dtypes>=0.2.0
tensorstore 0.1.52 depends on ml-dtypes>=0.3.1
tensorflow-macos 2.15.0 depends on ml-dtypes~=0.2.0
jax 0.4.23 depends on ml-dtypes>=0.2.0
jaxlib 0.4.23 depends on ml-dtypes>=0.2.0
tensorstore 0.1.51 depends on ml-dtypes>=0.3.1
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
Pip subprocess error:
ERROR: Cannot install -r /Users/user/condaenv.1tnhkmib.requirements.txt (line 1), -r /Users/user/condaenv.1tnhkmib.requirements.txt (line 3), -r /Users/user/condaenv.1tnhkmib.requirements.txt (line 4) and orbax-checkpoint because these package versions have conflicting dependencies.
ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts
failed
CondaEnvException: Pip failed
```
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I_kwDOArmXAs57TIQ9
| 62,745 |
Error in markdown based comment
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[
"Hii @rohan843 and @sushreebarsa, I've started my open source contribution by solving this issue, my PR #62751. Looking forward to solving more issues. \r\n",
"ERROR: /home/jenkins/workspace/ROCm-Community-CI-Build_PR-62754/bazel-ci_build-cache/.cache/bazel/_bazel_jenkins/eab0d61a99b6696edb3d2aff87b585e8/external/XNNPACK/BUILD.bazel:1861:19: Compiling src/amalgam/gen/avxvnni.c failed: (Exit 1): crosstool_wrapper_driver_is_not_gcc failed: error executing command (from target @XNNPACK//:avxvnni_prod_microkernels) \r\n\r\n (cd /home/jenkins/workspace/ROCm-Community-CI-Build_PR-62754/bazel-ci_build-cache/.cache/bazel/_bazel_jenkins/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow && \\\r\n\r\n exec env - \\\r\n\r\n LD_LIBRARY_PATH='' \\\r\n\r\n PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin \\\r\n\r\n PWD=/proc/self/cwd \\\r\n\r\n PYTHON_BIN_PATH=/usr/bin/python3 \\\r\n\r\n PYTHON_LIB_PATH=/usr/lib/python3/dist-packages \\\r\n\r\n ROCM_PATH=/opt/rocm-5.3.0 \\\r\n\r\n TF2_BEHAVIOR=1 \\\r\n\r\n external/local_config_rocm/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG -ffunction-sections -fdata-sections -MD -MF bazel-out/k8-opt/bin/external/XNNPACK/_objs/avxvnni_prod_microkernels/avxvnni.pic.d '-frandom-seed=bazel-out/k8-opt/bin/external/XNNPACK/_objs/avxvnni_prod_microkernels/avxvnni.pic.o' -fPIC '-DBAZEL_CURRENT_REPOSITORY=\"XNNPACK\"' -iquote external/XNNPACK -iquote bazel-out/k8-opt/bin/external/XNNPACK -isystem external/XNNPACK/include -isystem bazel-out/k8-opt/bin/external/XNNPACK/include -isystem external/XNNPACK/src -isystem bazel-out/k8-opt/bin/external/XNNPACK/src -Wno-all -Wno-extra -Wno-deprecated -Wno-deprecated-declarations -Wno-ignored-attributes -Wno-array-bounds -Wunused-result '-Werror=unused-result' -Wswitch '-Werror=switch' '-Wno-error=unused-but-set-variable' -DAUTOLOAD_DYNAMIC_KERNELS -Iinclude -Isrc -mavx2 -mavxvnni '-std=c99' -O2 -fno-canonical-system-headers -Wno-builtin-macro-redefined '-D__DATE__=\"redacted\"' '-D__TIMESTAMP__=\"redacted\"' '-D__TIME__=\"redacted\"' '-DTENSORFLOW_USE_ROCM=1' -D__HIP_PLATFORM_AMD__ -DEIGEN_USE_HIP -no-canonical-prefixes -fno-canonical-system-headers -c external/XNNPACK/src/amalgam/gen/avxvnni.c -o bazel-out/k8-opt/bin/external/XNNPACK/_objs/avxvnni_prod_microkernels/avxvnni.pic.o)\r\n\r\n# Configuration: c1ff79497c6beaeff02baebfade101eb9d79226e84d7eb2351a32bd41a926e41\r\n\r\n# Execution platform: @local_execution_config_platform//:platform\r\n\r\ngcc: error: unrecognized command line option '-mavxvnni'; did you mean '-mavx512vnni'?\r\n\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\n\r\nINFO: Elapsed time: 56.332s, Critical Path: 12.79s\r\n\r\nINFO: 2608 processes: 1653 internal, 955 local.\r\n\r\nFAILED: Build did NOT complete successfully",
"Why did build of AMD ROCm failed?\r\nAny idea @sushreebarsa ",
"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/62745\">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/62745\">No</a>\n"
] | 2024-01-06T13:06:43 | 2024-01-10T18:36:47 | 2024-01-10T18:36:44 |
NONE
| null | null | null |
[This](https://github.com/tensorflow/tensorflow/blob/4dacf3f368eb7965e9b5c3bbdd5193986081c3b2/tensorflow/python/data/ops/dataset_ops.py#L1440) line in the documentation of `tf.data.Dataset.shuffle()`:
```md
To shuffle an entire dataset, set `buffer_size=dataset.cardinality(). This
```
has a missing backtick (`). I believe that the line instead may have been:
```md
To shuffle an entire dataset, set `buffer_size=dataset.cardinality()`. This
```
This is causing the docs to be shown as:
<kbd></kbd>
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PR_kwDOArmXAs5jX91r
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Fix some typos
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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/62744/checks?check_run_id=20221029831) 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 @GoodDaisy Can you please sign CLA. Thank you!",
"@gbaned I found that my github's email was not the same as google email...Is there any way to resolve it?",
"You'll have to sign the CLA with the noreply email too.",
"> You'll have to sign the CLA with the noreply email too.\r\n\r\nCould you tell me how to sign the CLA with the github noreply email? Thank you very much.",
"> > You'll have to sign the CLA with the noreply email too.\r\n> \r\n> Could you tell me how to sign the CLA with the github noreply email? Thank you very much.\r\n\r\nSee https://cla.developers.google.com/about for full details\r\n\r\nReading it, I think this applies (but please read the full page to make sure):\r\n\r\n> Your contribution (commit) must be associated with at least one of:\r\n>\r\n> * The primary email on your Google Account associated with the signed CLA.\r\n> * An Alternate email on your Google Account associated with the signed CLA. See and modify your aternate emails at https://myaccount.google.com/alternateemail.\r\n> * The GitHub username associated with the signed CLA.\r\n\r\n",
"When I added, it failed and showed that [email protected] was not a valid email address...",
"I think in this case, you'll have to use your real email :( (a fake one created just for this and attached to your github profile should also work).",
"Hi @GoodDaisy Any update on this PR? Please. Thank you!",
"I am sorry that I have to close it since I can't solve the CLA. Thank you for your all helps."
] | 2024-01-06T09:57:58 | 2024-01-28T03:33:54 | 2024-01-28T03:33:48 |
NONE
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Tensorflow 2.x: call model inference using C/C++ API from inputs, allocated in GPU memory
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"I am also interested in running a TF model with inputs and outputs allocated on the GPU in the C API.",
"This feature would be very useful.",
"Unfortunately, this doesn't seem to be very easy if there has been no response for a month"
] | 2024-01-05T23:35:09 | 2024-02-19T13:50:39 | null |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
v2.13.0-rc2-7-g1cb1a030a62
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
A100 80Gb
### Current behavior?
I’d like to use TensorFlow 2.13 in scientific simulation code, written in C++, via [C-API](https://www.tensorflow.org/install/lang_c). The code runs simulation on GPU, so all necessary input data for the model are already placed on GPU too.
I need to prepare input for the model, that contains multiple TF_Tensors.
My question is: Is it possible to control, where to place TF_Tensor? Can I make it point to existing on-GPU array to avoid CPU-to-GPU memory transfer?
If wrapping TF tensor around existing data is not possible, would that be possible to copy memory within GPU?
I found [TF_AllocatorAttributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/tf_tensor.h#L33) struct, that contains placement flag and it is used, for example, in [TF_AllocateTemp](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/kernels.h#L503), but this function requires also TF_OpKernelContext ctx. Unfortunately it is not clear for me where to take it and is it safe at all to use it in C-API
### Standalone code to reproduce the issue
```shell
-
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5jWUZ5
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Add release-ready envs and new grouped upload configuration
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[
"Ok -- how about this? I thoroughly tidied everything up. Now, every job config will be a combination of multiple env files. I updated the instructions to indicate this. Running a TensorFlow job locally is now possible in a one-liner, like:\r\n\r\n```\r\nTFCI=py311,linux_x86,multicache ci/official/wheel.sh\r\n```\r\n\r\nI'm pretty sure this still works just fine for all of our special cases, which can be handled with either conditionals in the code (like with the MacOS Python 3.11 differences) or extra envs to add to the CI jobs. \r\n\r\nNote: All the CI jobs will fail because the way of specifying TFCI= has changed. After review, I'll have to import this PR manually and update our CI jobs in the same CL to reflect this new method. I think I already handled the merge conflicts to pull in the latest job state (I'll add Nitin for internal review once the CL gets created)."
] | 2024-01-05T20:48:12 | 2024-01-11T19:40:52 | 2024-01-11T19:40:52 |
CONTRIBUTOR
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This is another big cleanup of envs:
- Rewrote TFCI variable settings as combinations of other TFCI variables
- Created "upload.sh" and new `TFCI_ARTIFACT_...` variables.
- Every script execution in a pipeline (i.e. all of the day's platform-specific tf-nightly jobs) will be given a pipeline-specific parameter such that every build artifact for a pipeline's jobs will go to one GCS bucket.
- At the end of the pipeline, `upload.sh` will fetch every artifact from the current pipeline and uploads them to PyPI and TensorFlow's public GCS bucket.
- I'm planning for the layout to be `gs://tensorflow/(nightly|release)/VERSION/`, with `gs://tensorflow/nightly/latest` pointing to the most recent nightly and `gs://tensorflow/release/latest` for the latest release. One-folder-per-version is a huge simplification of the way packages are uploaded today, and should help keep upload-maintenance simple.
- Added `ci_version` envs which can expose TF version strings, sourced from the canonical sources, to the scripts if explicitly sourced. Currently only used by upload.sh, but may be useful for more later.
- [x] I still need to fix any.sh, bisect.sh, and a couple other places that referenced the old TFCI method
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[
"Hi, @PeterPirog! There are a few ways to check the existing images; such as exploring the TensorFlow Docker Hub: https://www.tensorflow.org/install/docker: https://www.tensorflow.org/install/docker\r\nPlease check the tags like devel-gpu-jupyter or nightly-devel-gpu-jupyter that might have updated configurations.\r\nKindly monitor TensorFlow release notes for new images: https://github.com/tensorflow/tensorflow/releases: https://github.com/tensorflow/tensorflow/releases.\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/62741\">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/62741\">No</a>\n",
"I confronted the same problem. the cuda and cudnn in devel-gpu image is 11.2 and 8.1. \r\nAccordding to https://www.tensorflow.org/install/source#tested_build_configurations:\r\n```\r\ntensorflow-2.15.0\t3.9-3.11\tClang 16.0.0\tBazel 6.1.0\t8.9\t12.2\r\ntensorflow-2.14.0\t3.9-3.11\tClang 16.0.0\tBazel 6.1.0\t8.7\t11.8\r\ntensorflow-2.13.0\t3.8-3.11\tClang 16.0.0\tBazel 5.3.0\t8.6\t11.8\r\ntensorflow-2.12.0\t3.8-3.11\tGCC 9.3.1\tBazel 5.3.0\t8.6\t11.8\r\ntensorflow-2.11.0\t3.7-3.10\tGCC 9.3.1\tBazel 5.3.0\t8.1\t11.2\r\n```\r\n\r\nit seems this image is for building tensorflow-2.11.0. But when I search https://hub.docker.com/r/tensorflow/tensorflow/tags/?page=1&name=devel-gpu&ordering=last_updated. the latest update is one year ago. "
] | 2024-01-05T14:14:04 | 2024-02-14T03:33:16 | 2024-01-24T01:49:48 |
NONE
| null | null | null |
Is possible to add new version of docker image tensorflow:latest-devel-gpu ?
Current version has CUDA 11.2 python 3.8 and can't support build from source version 2.15.0.
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I_kwDOArmXAs57OOY7
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M1 macOS-arm64, for golang Install TensorFlow for C,missing arm64 libtensorflow
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[
"Somebody help me",
"@Codeprh,\r\nCould you please provide the complete error log you faced while installing which helps us to debug the issue in an effective way. 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.",
"@Codeprh You probably need to add `-L/opt/homebrew/lib` for linking if you installed via homebrew. Or something like shown here. https://apple.stackexchange.com/questions/414622/installing-a-c-c-library-with-homebrew-on-m1-macs\r\n\r\nSomehow if is finding the equivalent to `-I/opt/homebrew/include` otherwise it shouldn't even compile.",
"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/62740\">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/62740\">No</a>\n"
] | 2024-01-05T13:08:53 | 2024-02-14T01:47:17 | 2024-02-14T01:47:14 |
NONE
| null | null | null |
Please make sure that this is a build/installation issue. As per our [GitHub Policy](https://github.com/tensorflow/tensorflow/blob/master/ISSUES.md), we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:build_template
System information
OS Platform and Distribution : MacBook Pro(Apple M1 Pro) macOS Monterey 12.5
Describe the problem
M1 macOS-arm64, for golang Install TensorFlow for C,missing libtensorflow-cpu-darwin-arm64.tar.gz
I followed the link: [https://www.tensorflow.org/install/lang_c#macos](https://www.tensorflow.org/install/lang_c#macos) to build TensorFlow golang api, but unfortunately I found that libtensorflow for macOS arm64 is missing.
If you can provide support for arm64, I would really appreciate it. Apple users will definitely like TensorFlow more and more.
Provide the exact sequence of commands / steps that you executed before running into the problem
```
$ gcc hello_tf.c -ltensorflow -o hello_tf
ld: warning: ignoring file /usr/local/lib/libtensorflow.dylib, building for macOS-arm64 but attempting to link with file built for macOS-x86_64
Undefined symbols for architecture arm64:
"_TF_Version", referenced from:
_main in hello_tf-bf3ad1.o
ld: symbol(s) not found for architecture arm64
clang: error: linker command failed with exit code 1 (use -v to see invocation)
```
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I_kwDOArmXAs57NoSw
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Tensorflow C API builds for Mac M1
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[
" @0110G There are no officially released pre-built TensorFlow C API binaries for Mac M1 on the TensorFlow website.\r\nIt's possible that official binaries will be provided in the future. Please stay updated on the TensorFlow [release](https://github.com/tensorflow/tensorflow/releases) notes and GitHub repository for updates.\r\nIf you're comfortable with building software from source, you can refer to the official TensorFlow documentation for detailed instructions: https://www.tensorflow.org/install/source.\r\nFor M1 Compatibility please ensure you follow the correct steps for building on Apple Silicon (ARM64) architecture.\r\nThank you!\r\n",
"> @0110G There are no officially released pre-built TensorFlow C API binaries for Mac M1 on the TensorFlow website. It's possible that official binaries will be provided in the future. Please stay updated on the TensorFlow [release](https://github.com/tensorflow/tensorflow/releases) notes and GitHub repository for updates. If you're comfortable with building software from source, you can refer to the official TensorFlow documentation for detailed instructions: https://www.tensorflow.org/install/source. For M1 Compatibility please ensure you follow the correct steps for building on Apple Silicon (ARM64) architecture. Thank you!\r\n\r\nAre there any specific documentation regarding the steps for building on ARM64 (bazel config flags, C compiler versions etc.?) ",
"@0110G Though there isn't any specific official documentation for building TensorFlow on ARM64 using Bazel. \r\nFor C compiler versions make sure that you check the C compiler you use should be compatible with the ARM64 architecture.You can use the clang compiler that comes with the Android NDK.\r\nAlternatively, you can use a different C compiler, such as GCC, as long as it is compatible with ARM64.\r\nFor using the correct flags, and troubleshooting common problems. please refer https://github.com/topics/tensorflow-android.\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/62739\">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/62739\">No</a>\n"
] | 2024-01-05T11:19:38 | 2024-02-09T01:46:35 | 2024-02-09T01:46:32 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
MacOS 12.5 (Monterey)
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I have written a Prediction Service based on Tensorflow C API (found https://www.tensorflow.org/install/lang_c) which allows me to fetch predictions of pre trained models in realtime. Recently, I have upgraded to M1 Mac and found that there is no support for c api on this. I have tried many different solutions like:
1. https://gist.github.com/lnshi/eb3dea05d99daba5c932bbc786cc3701
2. https://stackoverflow.com/questions/72114748/installing-c-api-for-tensorflow-on-macbook-m1
3. https://stackoverflow.com/questions/65953780/immediate-runtime-error-with-tensorflow-c-api-on-m1-mac?rq=4
Can someone help me by
1. Providing compiled binary for Mac M1 for version 2.13.0 just like the ones provided here: https://www.tensorflow.org/install/lang_c
OR
2. Providing a clear set of instructions to build tf for M1
### Standalone code to reproduce the issue
```shell
Following instructions on https://www.tensorflow.org/install/lang_c
#include <stdio.h>
#include <tensorflow/c/c_api.h>
int main() {
printf("Hello from TensorFlow C library version %s\n", TF_Version());
return 0;
}
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs57LiNz
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[TensorFlow Lite Delegates] iOS run laggy on first inference
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[
"Hi **@TimYao18** ,\r\nYou could consider pre-warming the model by running a dummy inference or setting up the interpreter without any actual input data before the actual inferences start. This could help to ensure that the initial setup costs are incurred before the critical path where low-latency is crucial.\r\n```\r\n// Initialize interpreter and allocate tensors without actual input data\r\ndelegates = [MetalDelegate()]\r\ninterpreter = try Interpreter(modelPath: modelPath, options: options, delegates: delegates)\r\ntry interpreter.allocateTensors()\r\n\r\n// Run a dummy inference to perform any one-time setup\r\ntry interpreter.invoke()\r\n\r\n// Actual inference loop\r\nfor inferenceIndex in 1...numInferences {\r\n do {\r\n // Provide input data for the inference\r\n try interpreter.copy(data, toInputAt: 0)\r\n\r\n // Run the inference\r\n try interpreter.invoke()\r\n\r\n // Get the output `Tensor` to process the inference results.\r\n outputTensor = try interpreter.output(at: 0)\r\n \r\n // Process the output as needed\r\n\r\n } catch let error {\r\n os_log(\r\n \"Failed to invoke the interpreter with error: %s\", type: .error,\r\n error.localizedDescription)\r\n return\r\n }\r\n}\r\n```\r\nThis modification involves initializing the interpreter and allocating tensors without any actual input data before the inference loop. Then, a dummy inference is performed to handle any one-time setup. After that, the actual inferences are run in the loop. This approach can help ensure a more consistent latency across all inferences.\r\n\r\nThank you!",
"Is this behavior considered as a system behavior on iOS?\r\nI want to make sure the issue doesn't be with the delegate. \r\nIf I just want to inference single time, ex image classification, I think it will get a little bit strange that I have to do a pre-warming inference.",
"Hi @TimYao18, most complex systems have some sort of initial setup costs, In this case it mostly looks GPU specific ... is there a use case where this is critical?",
"I just found this phenomenon during testing, for example, performing inference again after a minute also becomes slightly slow. But since it's considered as initial setup costs, I can only take it as a system limitation. 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/62738\">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/62738\">No</a>\n"
] | 2024-01-05T02:13:02 | 2024-01-09T02:52:41 | 2024-01-09T02:52:38 |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
'TensorFlowLiteSwift', '~> 0.0.1-nightly'
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
iPhone 15 Pro MAX
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The first inference should have nearly the same latency at other inferences.
### Standalone code to reproduce the issue
```shell
https://github.com/isl-org/MiDaS/tree/master/mobile/ios
delegates = [MetalDelegate()]
interpreter = try Interpreter(modelPath: modelPath, options: options, delegates: delegates)
try interpreter.allocateTensors()
inputTensor = try interpreter.input(at: 0)
outputTensor = try interpreter.output(at: 0)
do {
try interpreter.copy(data, toInputAt: 0)
try interpreter.invoke()
// Get the output `Tensor` to process the inference results.
outputTensor = try interpreter.output(at: 0)
} catch let error {
os_log(
"Failed to invoke the interpreter with error: %s", type: .error,
error.localizedDescription)
return
}
```
### Relevant log output
```shell
inference index,CPU,GPU,ANE,
1,83.08,123.78,20.00,
2,66.86,35.96,28.76,
3,67.70,19.67,24.16,
4,78.89,19.11,24.29,
5,69.42,19.15,24.85,
6,69.58,19.35,24.16,
7,68.47,19.29,26.29,
8,69.42,19.86,24.36,
9,69.24,19.49,24.91,
10,73.53,20.40,24.03,
11,70.91,19.29,24.62,
12,70.52,18.94,24.71,
13,71.66,19.35,24.30,
14,70.40,19.15,24.09,
15,71.73,19.37,23.74,
16,72.06,20.41,24.79,
17,70.98,19.28,23.81,
18,71.97,19.52,25.02,
19,72.19,20.14,23.95,
20,71.55,19.28,24.05,
21,73.17,19.00,24.26,
22,73.02,19.47,24.35,
23,71.86,20.24,23.90,
24,73.81,20.58,24.00,
25,72.03,19.51,24.55,
26,72.37,20.75,24.30,
27,73.91,19.23,23.61,
28,72.73,19.16,23.46,
29,72.86,19.36,24.23,
30,73.44,19.52,24.02,
31,72.76,20.09,24.99,
32,73.49,19.51,24.24,
33,76.49,20.70,23.92,
34,73.78,19.53,23.98,
35,73.45,19.01,23.92,
36,74.01,19.53,24.03,
37,74.03,20.48,23.95,
38,73.22,19.78,24.13,
39,74.21,20.78,23.45,
40,74.64,19.48,24.41,
41,73.77,19.36,23.42,
42,75.36,19.85,23.76,
43,75.76,20.30,23.87,
44,74.97,19.84,24.27,
45,74.44,19.83,23.75,
46,76.28,20.96,24.07,
47,76.44,19.36,23.70,
48,75.56,19.42,24.21,
49,74.88,20.82,24.74,
50,76.20,20.10,24.32,
51,76.29,19.62,23.08,
52,75.45,20.93,23.53,
53,75.03,19.33,23.05,
54,76.22,19.32,23.25,
55,76.57,19.69,23.67,
56,76.05,20.14,24.51,
57,75.14,19.58,23.07,
58,76.09,20.45,23.58,
59,76.01,19.37,23.57,
60,76.39,19.26,24.53,
61,76.18,19.21,23.38,
62,75.79,19.60,23.24,
63,76.43,19.75,23.20,
64,77.32,19.63,23.84,
65,77.05,19.45,22.99,
66,76.45,19.90,23.32,
67,76.25,20.06,23.38,
68,76.99,20.11,23.55,
69,77.57,20.86,23.28,
70,77.42,19.61,23.37,
71,76.84,19.92,24.12,
72,76.49,21.05,23.25,
73,76.61,20.25,23.84,
74,78.08,20.95,23.44,
75,77.66,19.91,23.47,
76,76.42,19.52,23.76,
77,77.00,19.92,23.38,
78,77.56,19.62,23.69,
79,78.40,20.31,23.65,
80,78.56,19.67,23.30,
81,77.59,19.21,24.01,
82,77.83,20.21,24.67,
83,77.07,19.80,21.67,
84,77.55,20.08,28.61,
85,77.73,19.68,31.18,
86,78.05,19.10,31.83,
87,78.54,19.70,30.31,
88,77.76,20.37,29.35,
89,77.24,19.58,27.23,
90,77.72,19.92,28.56,
91,77.66,19.38,24.71,
92,78.45,20.20,24.32,
93,79.30,20.01,25.32,
94,78.52,19.99,24.10,
95,76.68,19.76,23.12,
96,77.93,19.42,22.87,
97,77.74,20.54,22.79,
98,77.39,19.82,22.46,
99,78.75,20.26,21.99,
100,79.28,19.93,22.60,
101,78.99,19.94,20.58,
102,79.06,20.11,21.44,
103,78.65,20.09,21.25,
104,78.57,19.77,21.29,
105,77.44,19.84,20.99
```
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I_kwDOArmXAs57KAYk
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Quantized TFLite fails to load after conversion.
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[
"Hi @basnetr,\r\n\r\nI have reproduced the code with tensorflow latest version 2.15. All the 3 models are working fine. Please update tensorflow to 2.15. Refer the [gist.](https://colab.research.google.com/gist/LakshmiKalaKadali/3f6ef5cc570c0c4276a945f815c82f63/tflite_62737.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/62737\">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/62737\">No</a>\n"
] | 2024-01-04T19:30:32 | 2024-01-28T01:48:11 | 2024-01-28T01:48:07 |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution: Linux Ubuntu 20.04.5 LTS
- TensorFlow installation: pip package
- TensorFlow library: 2.8.4
### 2. Code
Code to reproduce issue:
```
import numpy as np # 1.26.2
import tensorflow as tf # 2.8.4
from tensorflow import keras # 2.8.0
from pathlib import Path
class ImplicitA(keras.layers.Layer):
def __init__(self, mean, std, name, **kwargs):
super(ImplicitA, self).__init__(name=name, **kwargs)
self.mean = mean
self.std = std
def build(self, input_shape):
self.impa = self.add_weight(
name=self.name,
shape=(1, 1, 1, input_shape[-1]),
initializer=keras.initializers.RandomNormal(
mean=self.mean, stddev=self.std
),
trainable=True
)
def call(self, x):
return tf.cast(x, self.impa.dtype) + self.impa
def get_config(self):
config = super(ImplicitA, self).get_config()
config.update(
{'mean': self.mean, 'std': self.std}
)
return config
class ImplicitM(keras.layers.Layer):
def __init__(self, mean, std, name, **kwargs):
super(ImplicitM, self).__init__(name=name, **kwargs)
self.mean = mean
self.std = std
def build(self, input_shape):
self.impm = self.add_weight(
name=self.name,
shape=(1, 1, 1, input_shape[-1]),
initializer=keras.initializers.RandomNormal(
mean=self.mean, stddev=self.std
),
trainable=True
)
def call(self, x):
return tf.cast(x, self.impm.dtype) * self.impm
def get_config(self):
config = super(ImplicitM, self).get_config()
config.update(
{'mean': self.mean, 'std': self.std}
)
return config
def model1():
inputs = keras.Input(shape=(32, 32, 4))
x = ImplicitA(mean=0.0, std=0.02, name='impa')(inputs)
x = keras.layers.Conv2D(
filters=8, kernel_size=1, strides=1, name='conv'
)(x)
x = ImplicitM(mean=0.0, std=0.02, name='impm')(x)
return keras.Model(inputs=inputs, outputs=x)
def model2():
inputs = keras.Input(shape=(32, 32, 4))
x = ImplicitA(mean=0.0, std=0.02, name='impa')(inputs)
x = keras.layers.Conv2D(
filters=8, kernel_size=1, strides=1, name='conv'
)(x)
return keras.Model(inputs=inputs, outputs=x)
def model3():
inputs = keras.Input(shape=(32, 32, 4))
x = ImplicitA(mean=0.0, std=0.02, name='impa')(inputs)
x = ImplicitM(mean=0.0, std=0.02, name='impm')(x)
x = keras.layers.Conv2D(
filters=8, kernel_size=1, strides=1, name='conv'
)(x)
return keras.Model(inputs=inputs, outputs=x)
if __name__ == "__main__":
model = model1()
model.summary()
model_path = str(Path(__file__).parent / "temp_model.h5")
model.save(model_path)
# load model
model = keras.models.load_model(
model_path,
custom_objects={
"ImplicitA": ImplicitA,
"ImplicitM": ImplicitM
}
)
# convert to tflite
in_sh = model.input.shape
def datagen():
yield [np.ones((1, in_sh[1], in_sh[2], in_sh[3])).astype(np.float32)]
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.representative_dataset = datagen
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
tflite_model = converter.convert()
tflite_model_path = str(Path(__file__).parent / "temp_model.tflite")
with open(tflite_model_path, 'wb') as f:
f.write(tflite_model)
interpreter = tf.lite.Interpreter(model_path=tflite_model_path)
```
### 3. Failure after conversion
Conversion is successful but error while loading the converted model using:
`interpreter = tf.lite.Interpreter(model_path=tflite_model_path)`
Note: `model2()` and `model3()` work fine but issue with `model1()`
### 4. Logs
```
Traceback (most recent call last):
File "/home/ubuntu/replicate_quantization_error.py", line 124, in <module>
interpreter = tf.lite.Interpreter(model_path=tflite_model_path)
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/tensorflow/lite/python/interpreter.py", line 456, in __init__
_interpreter_wrapper.CreateWrapperFromFile(
ValueError: num_scales must be 1 for per-layer quantization, or 1 for per-axis quantization, but got 8.Tensor 3 has invalid quantization parameters.
```
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I_kwDOArmXAs57JPkh
| 62,736 |
Need MPMD supporting for GPU to use pipeline parallelism training large scale model
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[
"Hi @MoFHeka ,\r\n\r\nTensorflow supports Data Parallelism only now. Model parallelism is yet to support fully for training. But with help of Dtensors we can achieve both data and model parallelism as per attached [tutorial](https://www.tensorflow.org/tutorials/distribute/dtensor_ml_tutorial).\r\n\r\nAs per my understanding of pipeline parallelism its hybrid approach of data and model parallelism.",
"@SuryanarayanaY But apparently DTensor can't reach pipeline parallelism. Pipeline parallelism was based on sending and receiving collective operator. \r\nAs tensorflow/compiler/xla/hlo/experimental/auto_sharding/auto_sharding.cc show, it seems that ZeRO stage 3 and pipeline parallel were already supported by XLA. ",
"@MoFHeka I always felt like the XLA and Tensorflow/Jax actually support many hidden features but never mention or write the document for it :)).",
"@dathudeptrai I really agree with that. Large projects often lead to difficulties in project management.",
"\r\n\r\n\r\n> As tensorflow/compiler/xla/hlo/experimental/auto_sharding/auto_sharding.cc show, it seems that ZeRO stage 3 and pipeline parallel were already supported by XLA.\r\n\r\nHi @MoFHeka ,\r\n \r\nI doubt it, correct me if I am wrong. I can see from Tf2.14v code [auto_sharding.cc](https://github.com/tensorflow/tensorflow/blob/v2.14.1/tensorflow/compiler/xla/hlo/experimental/auto_sharding/auto_sharding.cc) XLA supports SPMD which is for data parallelism which is supported by TF. Can you point exactly which part you are referring to that you feel that XLA supports Model parallelism or MPMD. This may help us to escalate the issue to SME and get confirmation. Thanks!",
"> > As tensorflow/compiler/xla/hlo/experimental/auto_sharding/auto_sharding.cc show, it seems that ZeRO stage 3 and pipeline parallel were already supported by XLA.\r\n> \r\n> Hi @MoFHeka ,\r\n> \r\n> I doubt it, correct me if I am wrong. I can see from Tf2.14v code [auto_sharding.cc](https://github.com/tensorflow/tensorflow/blob/v2.14.1/tensorflow/compiler/xla/hlo/experimental/auto_sharding/auto_sharding.cc) XLA supports SPMD which is for data parallelism which is supported by TF. Can you point exactly which part you are referring to that you feel that XLA supports Model parallelism or MPMD. This may help us to escalate the issue to SME and get confirmation. Thanks!\r\n\r\n@SuryanarayanaY Please check these comments. It said \"This can result in a strategy similar to **ZeRO stage 3**. NOTE: The combination of this branch with **pipeline parallel** is not tested.\"\r\nhttps://github.com/tensorflow/tensorflow/blob/99d80a9e254c9df7940b2902b14d15914dbbbcd9/tensorflow/compiler/xla/hlo/experimental/auto_sharding/auto_sharding.cc#L3249\r\n\r\nAnd please check here, TPU already support MPMD for a long time. It said \"If any of the inputs/outputs have maximal sharding, then fallback to MPMD. \"\r\nhttps://github.com/tensorflow/tensorflow/blob/d0321579d44b2a313df3389f95d77a480d1a0816/tensorflow/compiler/mlir/tensorflow/transforms/tpu_sharding_identification_pass.cc#L580\r\n",
"@MoFHeka I suggest you use jax instead. SOmething like this or flash-attention, FP8 training, int8 training, ... all available in jax with support from XLA (natively). TF also used XLA but kinda hard to custom. ",
"> @MoFHeka I suggest you use jax instead. SOmething like this or flash-attention, FP8 training, int8 training, ... all available in jax with support from XLA (natively). TF also used XLA but kinda hard to custom.\r\n\r\n@dathudeptrai Unfortunately, JAX also doesn't support many features, such as sequence parallelism. And at the XLA level, even with JAX, many features are actually only supported by TPU. \r\nOne more important thing, I can't train the CTR model with JAX, which lacks too many things.",
"@MoFHeka https://github.com/NVIDIA/TransformerEngine/pull/602",
"> @MoFHeka [NVIDIA/TransformerEngine#602](https://github.com/NVIDIA/TransformerEngine/pull/602)\r\n\r\n@dathudeptrai This really surprised me. I always thought it was difficult to split the segmentation of sequence dimension in JAX sharding process.\r\nBut there is one thing I am not sure about, if I am going to use pipeline parallel training LLM with JAX, should I use a ray engine like alpa or a JAX native one? JAX doesn't seem to have a good software library that supports all accelerations right now.",
"@MoFHeka Yeah. Generally speaking, coding in jax is harder than pytorch and a bit easier than TF. About low level customization, I think jax is better. Performance wise in my experiments showed that jax is better than pytorch :). Even deepspeed + Flash-attention-2 + Pytorch still not as good as jax :)). \r\n\r\nYou can refer some opensource to see how you can custom the paralelism training in jax. https://github.com/alpa-projects/alpa, this I called `Deepspeed for Jax` :). ",
"@dathudeptrai Unfortunately, due to the lack of sequence parallelism, the compute utilization of Alpa is lower than that of Megatron with the same tensor parallelism optimization. Because Alpa use too much device memory when using TP, which leads to a smaller batch size.\r\n\r\nBesides, the CTR models really can't be trained with Jax. The Jax ecosystem of online services, data processing, and other components (such as Keras) is way too far behind TF.",
"@MoFHeka Why not use both TF and Jax at the same time :), you can call jax code in TF code nowadays. Also please check out some advanced attention techniques recently introduced in jax (https://github.com/lhao499/large-sequence-modeling/tree/main). \r\n\r\nI personally think the biggest problem with both TF and Jax is documentations :)).",
"> @MoFHeka Why not use both TF and Jax at the same time :), you can call jax code in TF code nowadays. Also please check out some advanced attention techniques recently introduced in jax (https://github.com/lhao499/large-sequence-modeling/tree/main).\r\n\r\n@dathudeptrai Yes, that's right, the Jax kernel can be used in TF code, although there’s no big difference between Jax kernels and Keras layers with XLA. \r\nBut the problem is that the **pipeline parallelism** capabilities of JAX cannot be used in TF. TF currently lacks pipeline parallelism components.\r\n \r\nEven in recent updates, DTensor is used to support tensor parallelism. But in the training of the **CTR model**, one of the biggest usage scenarios of TF, what is more needed is the ability of pipeline parallelism.",
"@SuryanarayanaY Hi~? Is there any way to implement pipeline training in tensorflow?\r\n'tf.distribute.experimental.rpc.Server' with 'server.register' looks like a good choice, but I'm not sure."
] | 2024-01-04T16:59:35 | 2024-01-19T07:21:09 | null |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### 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 behavior?
Nowadays pipeline parallelism has been implemented in PyTorch for a long time. It's very useful for training a CTR model with Embedding pipeline or training a large language model between two machine.
### Standalone code to reproduce the issue
```shell
Maybe a easy send/recv construction like tpu with xla?
```
### Relevant log output
_No response_
|
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| 2,065,694,028 |
I_kwDOArmXAs57H_1M
| 62,735 |
can't convert checkpoint to tflite model !
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[
"@TiantianZhang Could you please provide standalone code to replicate this issue and also let us know the TF version being used here. Please refer to [this](https://www.tensorflow.org/lite/models/convert) guide for tflite conversion.\r\nThank you!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62735\">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/62735\">No</a>\n",
"> @TiantianZhang Could you please provide standalone code to replicate this issue and also let us know the TF version being used here. Please refer to [this](https://www.tensorflow.org/lite/models/convert) guide for tflite conversion. Thank you!\r\n\r\n@sushreebarsa TF version is TF2.6.0, the checkpoint is generated in the same env. I think the key point is how to convert ckpt to pb. Then, I can convert the pb model to tflite version. I have tried the following scheme but it does't work. \r\nhttps://stackoverflow.com/questions/69311861/tf2-6-valueerror-model-cannot-be-saved-because-the-input-shapes-have-not-been\r\n\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py\r\n\r\n\r\n@sushreebarsa There are the standalone code:\r\n\r\nrun_ckpt_to_tflite_test.py is the main function.\r\n\r\n[standalone_code.zip](https://github.com/tensorflow/tensorflow/files/13839231/standalone_code.zip)\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.",
"I tried to run a instance before the convert. Then, the workflow seems ok!",
"@TiantianZhang Thank you for the confirmation!\r\nCould you please let us know if we can move this issue to closed status?\r\nThank you!",
"> @TiantianZhang Thank you for the confirmation! Could you please let us know if we can move this issue to closed status? Thank you!\r\n\r\nThank you for your help. I have resolved the relevant issue. Although there are some unclear areas, I am currently able to complete the corresponding model format conversion. This issue can be closed. Thank you again!\r\n\r\n\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/62735\">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/62735\">No</a>\n"
] | 2024-01-04T13:48:39 | 2024-01-17T08:18:08 | 2024-01-17T08:18:06 |
NONE
| null | null | null |
model = model_lib.ModelSl(train_args) # get model
checkpoint = tf.train.Checkpoint(model=model) # load weight
checkpoint.restore(latest_ckpt).expect_partial()
input_shape = (128, 128, 1)
model._set_inputs(input_shape)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
errors：
raise TypeError("Invalid input_signature {}; input_signature must be "
TypeError: Invalid input_signature [(None, None, None)]; input_signature must be a possibly nested sequence of TensorSpec objects.
Exception ignored in: <function Pool.__del__ at 0x7f1e64dec0d0>
Traceback (most recent call last):
File "/home/nnn/.conda/envs/tf2.6.0/lib/python3.9/multiprocessing/pool.py", line 268, in __del__
File "/home/nnn/.conda/envs/tf2.6.0/lib/python3.9/multiprocessing/queues.py", line 372, in put
AttributeError: 'NoneType' object has no attribute 'dumps'
if i delete the shape, the errors just as follows:
model = model_lib.ModelSl(train_args) # get model
checkpoint = tf.train.Checkpoint(model=model) # load weight
checkpoint.restore(latest_ckpt).expect_partial()
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
ValueError: Model <models.model.ModelSl object at 0x7f495c9c8af0> cannot be saved because the input shapes have not been set. Usually, input shapes are automatically determined from calling `.fit()` or `.predict()`. To manually set the shapes, call `model.build(input_shape)`.
python-BaseException
Exception ignored in: <function Pool.__del__ at 0x7f49686ad0d0>
Traceback (most recent call last):
File "/home/nnn/.conda/envs/tf2.6.0/lib/python3.9/multiprocessing/pool.py", line 268, in __del__
File "/home/nnn/.conda/envs/tf2.6.0/lib/python3.9/multiprocessing/queues.py", line 372, in put
AttributeError: 'NoneType' object has no attribute 'dumps'
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PR_kwDOArmXAs5jMZDN
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Add a caution note for embedding_lookup for CPU vs GPU behaviour
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The API `embedding_lookup` have different behaviour on CPU vs GPU when there is out of range id passed to the argument `ids`.
On CPU it will raise error but on GPU it will output `0` to corresponding value. This is due to the reason that this API calls `tf.gather` internally where this constraint is mentioned.
I am proposing to add a caution note on this behaviour as requested by the author in #62628 .
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PR_kwDOArmXAs5jMHGw
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Fix AttriburteError when call model.export
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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/62733/checks?check_run_id=20149111647) 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.",
"Check out this pull request on <a href=\"https://app.reviewnb.com/tensorflow/tensorflow/pull/62733\"><img align=\"absmiddle\" alt=\"ReviewNB\" height=\"28\" class=\"BotMessageButtonImage\" src=\"https://raw.githubusercontent.com/ReviewNB/support/master/images/button_reviewnb.png\"/></a> \n\n See visual diffs & provide feedback on Jupyter Notebooks. \n\n---\n\n <i>Powered by <a href='https://www.reviewnb.com/?utm_source=gh'>ReviewNB</a></i>",
"Hi @ziyeqinghan Can you please review this PR ? Thank you!",
"Hi @ziyeqinghan Can you please review this PR ? Thank you!",
"LGTMed the PR."
] | 2024-01-04T05:04:17 | 2024-06-05T08:19:26 | null |
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I_kwDOArmXAs57ES9Q
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Can no longer run XLA lit tests
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[
"@ddunl It seems like perhaps the lit configs need to be updated in order to run the XLA tests via TF? I see a few hardcoded paths like this: https://github.com/tensorflow/tensorflow/blob/fc347ca5597a0a2a58d4f0f344d1210afede2cc5/third_party/xla/xla/glob_lit_test.bzl#L54",
"I see, I think that I probably deleted the transformations that kept this working as these aren't tested on CI anymore from the TF point of view, but I'll try to fix this in the next two weeks or so (I'll be on vacation for a little bit soon so won't get to this as quickly as I normally could).",
"> I see, I think that I probably deleted the transformations that kept this working as these aren't tested on CI anymore from the TF point of view, but I'll try to fix this in the next two weeks or so (I'll be on vacation for a little bit soon so won't get to this as quickly as I normally could).\r\n\r\nThank you!",
"HI @ddunl, wondering if you had a chance to take a look at this issue yet. Thanks!",
"@ddunl I managed to get these working with a combination of:\r\n1. My changes here https://github.com/trevor-m/tensorflow/commit/c2fabfcb0e67df4f269483f61f1a443b853dded7 \r\nLooks like there are a few paths that need to be modified to reflect the TF runfile structure: `MLIR_HLO_TOOLS_DIR` used for lit config template and also `XlaSrcRoot()` used by the tests.\r\nAlso, it looks like some string substitution is going awry in some of the .mlir files during the automated transfer from XLA->TF (copybara?)\r\n2. This commit https://github.com/tensorflow/tensorflow/commit/767225e0d1acdb2ac5f478baba9a158f7c4b5ea0 \r\n\r\n"
] | 2024-01-03T22:23:21 | 2024-02-02T01:40:47 | null |
CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
TF 2.15
### Custom code
No
### Current behavior?
Previously, I could run the XLA unit tests via `bazel test //tensorflow/compiler/xla/...:all`.
However, in TF 2.15 after xla was moved to third_party/xla I am encountering issues.
I updated my command to `bazel test @local_xla//xla/...:all`. While most tests run successfully, it seems there are some hardcoded paths which are preventing the llvm lit tests from running correctly. See the logs below. Probably the lit configs need to be updated?
### Standalone code to reproduce the issue
```shell
Checkout tensorflow. Configure. Run `bazel test @local_xla//xla/...:all`
```
### Relevant log output
```shell
================================================================================
FAIL: @local_xla//xla/mlir/backends/gpu/transforms/tests:gpu_memcpy.mlir.test (see /root/.cache/bazel/_bazel_root/a8fc6d0749b4f3c43761726a36e8ec4c/execroot/org_tensorflow/bazel-out/k8-opt/testlogs/external/local_xla/xla/mlir/backends/gpu/transforms/tests/gpu_memcpy.mlir.test/test.log)
[27,548 / 27,604] 348 / 447 tests, 216 failed; [Sched] Testing @local_xla//xla/mlir_hlo/tests:Dialect/mhlo/hlo-collapse-elementwise-map.mlir.test; 45s ... (55 actions, 2 running)
INFO: From Testing @local_xla//xla/mlir/backends/gpu/transforms/tests:gpu_memcpy.mlir.test:
==================== Test output for @local_xla//xla/mlir/backends/gpu/transforms/tests:gpu_memcpy.mlir.test:
Running test /root/.cache/bazel/_bazel_root/a8fc6d0749b4f3c43761726a36e8ec4c/execroot/org_tensorflow/bazel-out/k8-opt/bin/external/local_xla/xla/mlir/backends/gpu/transforms/tests/gpu_memcpy.mlir.test.runfiles/org_tensorflow/../local_xla/xla/mlir/backends/gpu/transforms/tests/gpu_memcpy.mlir.test xla/gpu_memcpy.mlir --config-prefix=runlit -v on GPU 0
lit.py: /root/.cache/bazel/_bazel_root/a8fc6d0749b4f3c43761726a36e8ec4c/external/llvm-raw/llvm/utils/lit/lit/discovery.py:137: warning: unable to find test suite for 'xla/gpu_memcpy.mlir'
lit.py: /root/.cache/bazel/_bazel_root/a8fc6d0749b4f3c43761726a36e8ec4c/external/llvm-raw/llvm/utils/lit/lit/discovery.py:276: warning: input 'xla/gpu_memcpy.mlir' contained no tests
error: did not discover any tests for provided path(s)
================================================================================
FAIL: @local_xla//xla/mlir_hlo/tests:Dialect/mhlo/hlo-collapse-elementwise-map.mlir.test (see /root/.cache/bazel/_bazel_root/a8fc6d0749b4f3c43761726a36e8ec4c/execroot/org_tensorflow/bazel-out/k8-opt/testlogs/external/local_xla/xla/mlir_hlo/tests/Dialect/mhlo/hlo-collapse-elementwise-map.mlir.test/test.log)
INFO: From Testing @local_xla//xla/mlir_hlo/tests:Dialect/mhlo/hlo-collapse-elementwise-map.mlir.test:
==================== Test output for @local_xla//xla/mlir_hlo/tests:Dialect/mhlo/hlo-collapse-elementwise-map.mlir.test:
Running test /root/.cache/bazel/_bazel_root/a8fc6d0749b4f3c43761726a36e8ec4c/execroot/org_tensorflow/bazel-out/k8-opt/bin/external/local_xla/xla/mlir_hlo/tests/Dialect/mhlo/hlo-collapse-elementwise-map.mlir.test.runfiles/org_tensorflow/../local_xla/xla/mlir_hlo/tests/Dialect/mhlo/hlo-collapse-elementwise-map.mlir.test -v external/local_xla/xla/mlir_hlo/tests/Dialect/mhlo/hlo-collapse-elementwise-map.mlir on GPU 0
lit.py: /root/.cache/bazel/_bazel_root/a8fc6d0749b4f3c43761726a36e8ec4c/external/llvm-raw/llvm/utils/lit/lit/TestingConfig.py:151: fatal: unable to parse config file '/root/.cache/bazel/_bazel_root/a8fc6d0749b4f3c43761726a36e8ec4c/execroot/org_tensorflow/bazel-out/k8-opt/bin/external/local_xla/xla/mlir_hlo/tests/Dialect/mhlo/hlo-collapse-elementwise-map.mlir.test.runfiles/org_tensorflow/external/local_xla/xla/mlir_hlo/tests/lit.site.cfg.py', traceback: Traceback (most recent call last):
File "/root/.cache/bazel/_bazel_root/a8fc6d0749b4f3c43761726a36e8ec4c/external/llvm-raw/llvm/utils/lit/lit/TestingConfig.py", line 139, in load_from_path
exec(compile(data, path, "exec"), cfg_globals, None)
File "/root/.cache/bazel/_bazel_root/a8fc6d0749b4f3c43761726a36e8ec4c/execroot/org_tensorflow/bazel-out/k8-opt/bin/external/local_xla/xla/mlir_hlo/tests/Dialect/mhlo/hlo-collapse-elementwise-map.mlir.test.runfiles/org_tensorflow/external/local_xla/xla/mlir_hlo/tests/lit.site.cfg.py", line 44, in <module>
lit_config.load_config(config, "xla/mlir_hlo/tests/lit.cfg.py")
File "/root/.cache/bazel/_bazel_root/a8fc6d0749b4f3c43761726a36e8ec4c/external/llvm-raw/llvm/utils/lit/lit/LitConfig.py", line 152, in load_config
config.load_from_path(path, self)
File "/root/.cache/bazel/_bazel_root/a8fc6d0749b4f3c43761726a36e8ec4c/external/llvm-raw/llvm/utils/lit/lit/TestingConfig.py", line 126, in load_from_path
f = open(path)
FileNotFoundError: [Errno 2] No such file or directory: 'xla/mlir_hlo/tests/lit.cfg.py'
```
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Implemented SAMPLED_ADDMM
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[
"You might be able to do this with existing ops:\r\n- `tf.gather_nd` to extract the appropriate rows/columns of `mat_a` and `mat_b`.\r\n- `tf.matmul` to do the dot-product\r\n- scale and sum to the sparse input values\r\n\r\nThe benefit of doing it this way is XLA should just work out of the box, and you wouldn't need to support a new op.",
"@cantonios Thanks for the feedback! Will look more into it over the next couple days. Working on a shape function now.",
"> You might be able to do this with existing ops:\r\n> \r\n> * `tf.gather_nd` to extract the appropriate rows/columns of `mat_a` and `mat_b`.\r\n> * `tf.matmul` to do the dot-product\r\n> * scale and sum to the sparse input values\r\n> \r\n> The benefit of doing it this way is XLA should just work out of the box, and you wouldn't need to support a new op.\r\n\r\nIt seems like `tf.gather_nd` will only gather slices horizontally, so I had to transpose `mat_b` to extract the columns. From there instead of `tf.matmul` I just did an element-wise multiplication and then used `tf.math.reduce_sum` to get the dot product. But I think you're right in that we can do this without a new op. I'll see about just implementing it in `math_ops.py`.\r\n\r\n```\r\n>>> import tensorflow as tf\r\n2024-01-05 13:56:56.117075: 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: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n>>> indices = tf.constant([[[0,1],[1,0]],[[0,0],[1,1]]])\r\n2024-01-05 13:57:04.040611: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:962] 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\n2024-01-05 13:57:04.096683: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:962] 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\n2024-01-05 13:57:04.096895: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:962] 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\n2024-01-05 13:57:04.098041: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:962] 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\n2024-01-05 13:57:04.098207: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:962] 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\n2024-01-05 13:57:04.098354: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:962] 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\n2024-01-05 13:57:04.170050: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:962] 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\n2024-01-05 13:57:04.170255: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:962] 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\n2024-01-05 13:57:04.170419: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:962] 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\n2024-01-05 13:57:04.170549: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5734 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3070, pci bus id: 0000:2d:00.0, compute capability: 8.6\r\n>>> values = tf.constant([[0.12,0.0256],[0.54,0.77]])\r\n>>> mat1 = tf.constant([[[0.7,0.094,1.043],[1.4,0.26,6.03]],[[0.842,0.439,0.218],[0.666,0.988,0.407]]])\r\n>>> mat2 = tf.constant([[[0.076,0.99],[1.23,1.04],[0.98,0.326]],[[0.309,0.208],[0.557,0.4681],[0.6,0.742]]])\r\n>>> alpha = 0.43\r\n>>> beta = 0.77\r\n>>> indices_dims = indices.get_shape().ndims\r\n>>> row_indices = tf.slice(indices, [0] * indices_dims, [-1] * (indices_dims - 1) + [1])\r\n>>> col_indices = tf.slice(indices, [0] * (indices_dims - 1) + [1], [-1] * (indices_dims - 1) + [1])\r\n>>> rows = tf.gather_nd(mat1, row_indices, batch_dims=indices_dims-2)\r\n>>> mat2_transpose = tf.transpose(mat2, perm=[0] * (indices_dims - 2) + [indices_dims - 1] + [indices_dims - 2])\r\n>>> cols = tf.gather_nd(mat2_transpose, col_indices, batch_dims=indices_dims - 2)\r\n>>> product = rows * cols\r\n>>> dot = tf.reduce_sum(product, axis=-1)\r\n>>> res = alpha * dot + beta * values\r\n>>> print(res)\r\ntf.Tensor(\r\n[[0.57863456 2.7440202 ]\r\n [0.68906546 0.981192 ]], shape=(2, 2), dtype=float32)\r\n```"
] | 2024-01-03T20:42:16 | 2024-01-06T15:27:40 | 2024-01-05T22:45:29 |
CONTRIBUTOR
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Relates to #56311
I have the operation implemented for cpu/gpu, but not for tpu/xla. If someone has time, I'd appreciate a quick review to make sure I have the right idea as well as any advice for where to go from here. Currently I only have the op registered for the float type.
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I_kwDOArmXAs57DQ4b
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Failure in building MLIR dialects and utilities from source
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"@wonjeon Could you try cleaning the build directory with `cmake --build . --target clean` and then rebuilding with `cmake --build `, please make sure that you have installed all compatible configurations correctly with the latest build. Thank you! ",
"Do you have any resolution for bazel? I don't use cmake for this process.",
"Hi @wonjeon,\r\n\r\nThe build failure might be due to StrCat function in ```graph_execution_options.cc```. A recent commit [afc843c](https://github.com/tensorflow/tensorflow/commit/afc843c3838f8896ebabb3ebd3c9252e252cbebf) related to this function in github repo is updated. Could you please try with this commit for your usecase and let us know the status.\r\n\r\nThank You.\r\n",
"Hi @LakshmiKalaKadali ,\r\n\r\nTried the latest code including the commit that you mentioned, but it still doesn't compile the code correctly. I'm using clang 14.0.0-1ubuntu1.1 and python 3.10.12.\r\n\r\n```\r\n$ bazel build -j 24 tensorflow/compiler/mlir/...\r\n...\r\n\r\nERROR: /home/wonjeo01/tensorflow/tensorflow/compiler/mlir/tfrt/transforms/ifrt/BUILD:88:11: Compiling tensorflow/compiler/mlir/tfrt/transforms/ifrt/tf2hlo.cc failed: (Exit 1): clang failed: error executing command (from target //tensorflow/compiler/mlir/tfrt/transforms/ifrt:tf2hlo) /usr/lib/llvm-14/bin/clang -U_FORTIFY_SOURCE -fstack-protector -Wall -Wthread-safety -Wself-assign -Wunused-but-set-parameter -Wno-free-nonheap-object -fcolor-diagnostics -fno-omit-frame-pointer -g0 ... (remaining 486 arguments skipped)\r\nIn file included from tensorflow/compiler/mlir/tfrt/transforms/ifrt/tf2hlo.cc:24:\r\nexternal/com_google_absl/absl/log/log.h:199:9: warning: 'LOG' macro redefined [-Wmacro-redefined]\r\n#define LOG(severity) ABSL_LOG_INTERNAL_LOG_IMPL(_##severity)\r\n ^\r\nexternal/local_tsl/tsl/platform/default/logging.h:165:9: note: previous definition is here\r\n#define LOG(severity) _TF_LOG_##severity\r\n ^\r\nIn file included from tensorflow/compiler/mlir/tfrt/transforms/ifrt/tf2hlo.cc:24:\r\nexternal/com_google_absl/absl/log/log.h:237:9: warning: 'LOG_EVERY_N' macro redefined [-Wmacro-redefined]\r\n#define LOG_EVERY_N(severity, n) \\\r\n ^\r\nexternal/local_tsl/tsl/platform/default/logging.h:278:9: note: previous definition is here\r\n#define LOG_EVERY_N(severity, n) \\\r\n ^\r\nIn file included from tensorflow/compiler/mlir/tfrt/transforms/ifrt/tf2hlo.cc:24:\r\nexternal/com_google_absl/absl/log/log.h:245:9: warning: 'LOG_FIRST_N' macro redefined [-Wmacro-redefined]\r\n#define LOG_FIRST_N(severity, n) \\\r\n ^\r\nexternal/local_tsl/tsl/platform/default/logging.h:284:9: note: previous definition is here\r\n#define LOG_FIRST_N(severity, n) \\\r\n ^\r\nIn file included from tensorflow/compiler/mlir/tfrt/transforms/ifrt/tf2hlo.cc:24:\r\nexternal/com_google_absl/absl/log/log.h:253:9: warning: 'LOG_EVERY_POW_2' macro redefined [-Wmacro-redefined]\r\n#define LOG_EVERY_POW_2(severity) \\\r\n ^\r\nexternal/local_tsl/tsl/platform/default/logging.h:290:9: note: previous definition is here\r\n#define LOG_EVERY_POW_2(severity) \\\r\n ^\r\nIn file included from tensorflow/compiler/mlir/tfrt/transforms/ifrt/tf2hlo.cc:24:\r\nexternal/com_google_absl/absl/log/log.h:265:9: warning: 'LOG_EVERY_N_SEC' macro redefined [-Wmacro-redefined]\r\n#define LOG_EVERY_N_SEC(severity, n_seconds) \\\r\n ^\r\nexternal/local_tsl/tsl/platform/default/logging.h:300:9: note: previous definition is here\r\n#define LOG_EVERY_N_SEC(severity, n_seconds) \\\r\n ^\r\ntensorflow/compiler/mlir/tfrt/transforms/ifrt/tf2hlo.cc:83:35: error: no viable conversion from '::llvm::StringRef' to 'const std::string' (aka 'const basic_string<char>')\r\n if (!metadata.ParseFromString(metadata_attr.getValue())) {\r\n ^~~~~~~~~~~~~~~~~~~~~~~~\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/basic_string.h:456:7: note: candidate constructor not viable: no known conversion from '::llvm::StringRef' to 'const std::basic_string<char> &' for 1st argument\r\n basic_string(const basic_string& __str)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/basic_string.h:533:7: note: candidate constructor template not viable: no known conversion from '::llvm::StringRef' to 'const char *' for 1st argument\r\n basic_string(const _CharT* __s, const _Alloc& __a = _Alloc())\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/basic_string.h:565:7: note: candidate constructor not viable: no known conversion from '::llvm::StringRef' to 'std::basic_string<char> &&' for 1st argument\r\n basic_string(basic_string&& __str) noexcept\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/basic_string.h:592:7: note: candidate constructor not viable: no known conversion from '::llvm::StringRef' to 'initializer_list<char>' for 1st argument\r\n basic_string(initializer_list<_CharT> __l, const _Alloc& __a = _Alloc())\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/basic_string.h:448:7: note: explicit constructor is not a candidate\r\n basic_string(const _Alloc& __a) _GLIBCXX_NOEXCEPT\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/basic_string.h:664:2: note: explicit constructor is not a candidate\r\n basic_string(const _Tp& __t, const _Alloc& __a = _Alloc())\r\n ^\r\nexternal/llvm-project/llvm/include/llvm/ADT/StringRef.h:248:15: note: candidate function\r\n constexpr operator std::string_view() const {\r\n ^\r\nexternal/com_google_protobuf/src/google/protobuf/message_lite.h:280:74: note: passing argument to parameter 'data' here\r\n PROTOBUF_ATTRIBUTE_REINITIALIZES bool ParseFromString(ConstStringParam data);\r\n ^\r\ntensorflow/compiler/mlir/tfrt/transforms/ifrt/tf2hlo.cc:94:13: error: no viable conversion from '::llvm::StringRef' to 'const std::string' (aka 'const basic_string<char>')\r\n metadata_text_attr.getValue(), &metadata)) {\r\n ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/basic_string.h:456:7: note: candidate constructor not viable: no known conversion from '::llvm::StringRef' to 'const std::basic_string<char> &' for 1st argument\r\n basic_string(const basic_string& __str)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/basic_string.h:533:7: note: candidate constructor template not viable: no known conversion from '::llvm::StringRef' to 'const char *' for 1st argument\r\n basic_string(const _CharT* __s, const _Alloc& __a = _Alloc())\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/basic_string.h:565:7: note: candidate constructor not viable: no known conversion from '::llvm::StringRef' to 'std::basic_string<char> &&' for 1st argument\r\n basic_string(basic_string&& __str) noexcept\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/basic_string.h:592:7: note: candidate constructor not viable: no known conversion from '::llvm::StringRef' to 'initializer_list<char>' for 1st argument\r\n basic_string(initializer_list<_CharT> __l, const _Alloc& __a = _Alloc())\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/basic_string.h:448:7: note: explicit constructor is not a candidate\r\n basic_string(const _Alloc& __a) _GLIBCXX_NOEXCEPT\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/basic_string.h:664:2: note: explicit constructor is not a candidate\r\n basic_string(const _Tp& __t, const _Alloc& __a = _Alloc())\r\n ^\r\nexternal/llvm-project/llvm/include/llvm/ADT/StringRef.h:248:15: note: candidate function\r\n constexpr operator std::string_view() const {\r\n ^\r\nexternal/com_google_protobuf/src/google/protobuf/text_format.h:479:48: note: passing argument to parameter 'input' here\r\n static bool ParseFromString(ConstStringParam input, Message* output);\r\n ^\r\ntensorflow/compiler/mlir/tfrt/transforms/ifrt/tf2hlo.cc:104:62: error: invalid operands to binary expression ('tsl::internal::LogMessage' and 'tensorflow::tpu::TPUCompileMetadataProto')\r\n VLOG(3) << \"TpuCompileMetadata before shape is populated \" << metadata;\r\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ^ ~~~~~~~~\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/cstddef:126:5: note: candidate function template not viable: no known conversion from 'tsl::internal::LogMessage' to 'std::byte' for 1st argument\r\n operator<<(byte __b, _IntegerType __shift) noexcept\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/system_error:279:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'const std::error_code' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, const error_code& __e)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:518:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'char' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __out, char __c)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:524:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'char' for 2nd argument\r\n operator<<(basic_ostream<char, _Traits>& __out, char __c)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:530:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'signed char' for 2nd argument\r\n operator<<(basic_ostream<char, _Traits>& __out, signed char __c)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:535:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'unsigned char' for 2nd argument\r\n operator<<(basic_ostream<char, _Traits>& __out, unsigned char __c)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:611:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'const char *' for 2nd argument\r\n operator<<(basic_ostream<char, _Traits>& __out, const char* __s)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:624:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'const signed char *' for 2nd argument\r\n operator<<(basic_ostream<char, _Traits>& __out, const signed char* __s)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:629:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'const unsigned char *' for 2nd argument\r\n operator<<(basic_ostream<char, _Traits>& __out, const unsigned char* __s)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/ostream.tcc:321:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'const char *' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __out, const char* __s)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:79:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::_Resetiosflags' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Resetiosflags __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:109:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::_Setiosflags' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Setiosflags __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:143:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::_Setbase' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Setbase __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:208:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::_Setprecision' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Setprecision __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:238:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::_Setw' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Setw __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1020:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'const std::bernoulli_distribution' for 2nd argument\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/thread:92:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'thread::id' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __out, thread::id __id)\r\n ^\r\n./tensorflow/core/framework/tensor_shape.h:452:22: note: candidate function not viable: expects an lvalue for 1st argument\r\ninline std::ostream& operator<<(std::ostream& os, const TensorShape& ts) {\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:513:5: note: candidate template ignored: deduced conflicting types for parameter '_CharT' ('char' vs. 'tensorflow::tpu::TPUCompileMetadataProto')\r\n operator<<(basic_ostream<_CharT, _Traits>& __out, _CharT __c)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/string_view:667:5: note: candidate template ignored: could not match 'basic_string_view<_CharT, _Traits>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/basic_string.h:6531:5: note: candidate template ignored: could not match 'basic_string<_CharT, _Traits, _Alloc>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:594:5: note: candidate template ignored: could not match 'const _CharT *' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(basic_ostream<_CharT, _Traits>& __out, const _CharT* __s)\r\n ^\r\n./tensorflow/core/framework/tensor_shape.h:346:15: note: candidate template ignored: could not match 'TensorShapeBase<Shape>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\nstd::ostream& operator<<(std::ostream& os, const TensorShapeBase<Shape>& tsb) {\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:750:5: note: candidate template ignored: substitution failure [with _Ostream = tsl::internal::LogMessage, _Tp = tensorflow::tpu::TPUCompileMetadataProto]: invalid operands to binary expression ('tsl::internal::LogMessage' and 'const tensorflow::tpu::TPUCompileMetadataProto')\r\n operator<<(_Ostream&& __os, const _Tp& __x)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/shared_ptr.h:70:5: note: candidate template ignored: could not match '__shared_ptr<_Tp, _Lp>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_Ch, _Tr>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:178:5: note: candidate template ignored: could not match '_Setfill<_CharT>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Setfill<_CharT> __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:311:5: note: candidate template ignored: could not match '_Put_money<_MoneyT>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Put_money<_MoneyT> __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:363:5: note: candidate template ignored: could not match '_Put_time<_CharT>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Put_time<_CharT> __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/complex:555:5: note: candidate template ignored: could not match 'complex<_Tp>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, const complex<_Tp>& __x)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bitset:1540:5: note: candidate template ignored: could not match 'bitset<_Nb>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.h:1309:5: note: candidate template ignored: could not match 'std::independent_bits_engine<_RandomNumberEngine, __w, _UIntType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:154:5: note: candidate template ignored: could not match 'linear_congruential_engine<_UIntType, __a, __c, __m>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:474:5: note: candidate template ignored: could not match 'mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:628:5: note: candidate template ignored: could not match 'subtract_with_carry_engine<_UIntType, __w, __s, __r>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:696:5: note: candidate template ignored: could not match 'discard_block_engine<_RandomNumberEngine, __p, __r>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:857:5: note: candidate template ignored: could not match 'shuffle_order_engine<_RandomNumberEngine, __k>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:901:5: note: candidate template ignored: could not match 'uniform_int_distribution<_IntType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:959:5: note: candidate template ignored: could not match 'uniform_real_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1101:5: note: candidate template ignored: could not match 'geometric_distribution<_IntType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1215:5: note: candidate template ignored: could not match 'negative_binomial_distribution<_IntType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1425:5: note: candidate template ignored: could not match 'poisson_distribution<_IntType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1691:5: note: candidate template ignored: could not match 'binomial_distribution<_IntType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1753:5: note: candidate template ignored: could not match 'exponential_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1919:5: note: candidate template ignored: could not match 'normal_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1986:5: note: candidate template ignored: could not match 'lognormal_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2058:5: note: candidate template ignored: could not match 'chi_squared_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2144:5: note: candidate template ignored: could not match 'cauchy_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2219:5: note: candidate template ignored: could not match 'fisher_f_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2293:5: note: candidate template ignored: could not match 'student_t_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2462:5: note: candidate template ignored: could not match 'gamma_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2538:5: note: candidate template ignored: could not match 'weibull_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2612:5: note: candidate template ignored: could not match 'extreme_value_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2748:5: note: candidate template ignored: could not match 'discrete_distribution<_IntType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2970:5: note: candidate template ignored: could not match 'piecewise_constant_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:3176:5: note: candidate template ignored: could not match 'piecewise_linear_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:108:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::basic_ostream<char>::__ostream_type &(*)(std::basic_ostream<char>::__ostream_type &)' (aka 'basic_ostream<char, std::char_traits<char>> &(*)(basic_ostream<char, std::char_traits<char>> &)') for 1st argument\r\n operator<<(__ostream_type& (*__pf)(__ostream_type&))\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:117:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::basic_ostream<char>::__ios_type &(*)(std::basic_ostream<char>::__ios_type &)' (aka 'basic_ios<char, std::char_traits<char>> &(*)(basic_ios<char, std::char_traits<char>> &)') for 1st argument\r\n operator<<(__ios_type& (*__pf)(__ios_type&))\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:127:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::ios_base &(*)(std::ios_base &)' for 1st argument\r\n operator<<(ios_base& (*__pf) (ios_base&))\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:166:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'long' for 1st argument\r\n operator<<(long __n)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:170:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'unsigned long' for 1st argument\r\n operator<<(unsigned long __n)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:174:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'bool' for 1st argument\r\n operator<<(bool __n)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:178:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'short' for 1st argument\r\n operator<<(short __n);\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:181:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'unsigned short' for 1st argument\r\n operator<<(unsigned short __n)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:189:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'int' for 1st argument\r\n operator<<(int __n);\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:192:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'unsigned int' for 1st argument\r\n operator<<(unsigned int __n)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:201:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'long long' for 1st argument\r\n operator<<(long long __n)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:205:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'unsigned long long' for 1st argument\r\n operator<<(unsigned long long __n)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:220:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'double' for 1st argument\r\n operator<<(double __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:224:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'float' for 1st argument\r\n operator<<(float __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:232:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'long double' for 1st argument\r\n operator<<(long double __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:245:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'const void *' for 1st argument; take the address of the argument with &\r\n operator<<(const void* __p)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:250:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::nullptr_t' for 1st argument\r\n operator<<(nullptr_t)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:283:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::basic_ostream<char>::__streambuf_type *' (aka 'basic_streambuf<char, std::char_traits<char>> *') for 1st argument\r\n operator<<(__streambuf_type* __sb);\r\n ^\r\ntensorflow/compiler/mlir/tfrt/transforms/ifrt/tf2hlo.cc:191:39: error: invalid operands to binary expression ('tsl::internal::LogMessage' and 'tensorflow::tpu::TPUCompileMetadataProto')\r\n VLOG(1) << \"Compilation metadata: \" << compile_metadata;\r\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ^ ~~~~~~~~~~~~~~~~\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/cstddef:126:5: note: candidate function template not viable: no known conversion from 'tsl::internal::LogMessage' to 'std::byte' for 1st argument\r\n operator<<(byte __b, _IntegerType __shift) noexcept\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/system_error:279:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'const std::error_code' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, const error_code& __e)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:518:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'char' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __out, char __c)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:524:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'char' for 2nd argument\r\n operator<<(basic_ostream<char, _Traits>& __out, char __c)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:530:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'signed char' for 2nd argument\r\n operator<<(basic_ostream<char, _Traits>& __out, signed char __c)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:535:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'unsigned char' for 2nd argument\r\n operator<<(basic_ostream<char, _Traits>& __out, unsigned char __c)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:611:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'const char *' for 2nd argument\r\n operator<<(basic_ostream<char, _Traits>& __out, const char* __s)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:624:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'const signed char *' for 2nd argument\r\n operator<<(basic_ostream<char, _Traits>& __out, const signed char* __s)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:629:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'const unsigned char *' for 2nd argument\r\n operator<<(basic_ostream<char, _Traits>& __out, const unsigned char* __s)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/ostream.tcc:321:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'const char *' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __out, const char* __s)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:79:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::_Resetiosflags' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Resetiosflags __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:109:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::_Setiosflags' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Setiosflags __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:143:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::_Setbase' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Setbase __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:208:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::_Setprecision' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Setprecision __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:238:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::_Setw' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Setw __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1020:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'const std::bernoulli_distribution' for 2nd argument\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/thread:92:5: note: candidate function template not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'thread::id' for 2nd argument\r\n operator<<(basic_ostream<_CharT, _Traits>& __out, thread::id __id)\r\n ^\r\n./tensorflow/core/framework/tensor_shape.h:452:22: note: candidate function not viable: expects an lvalue for 1st argument\r\ninline std::ostream& operator<<(std::ostream& os, const TensorShape& ts) {\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:513:5: note: candidate template ignored: deduced conflicting types for parameter '_CharT' ('char' vs. 'tensorflow::tpu::TPUCompileMetadataProto')\r\n operator<<(basic_ostream<_CharT, _Traits>& __out, _CharT __c)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/string_view:667:5: note: candidate template ignored: could not match 'basic_string_view<_CharT, _Traits>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/basic_string.h:6531:5: note: candidate template ignored: could not match 'basic_string<_CharT, _Traits, _Alloc>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:594:5: note: candidate template ignored: could not match 'const _CharT *' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(basic_ostream<_CharT, _Traits>& __out, const _CharT* __s)\r\n ^\r\n./tensorflow/core/framework/tensor_shape.h:346:15: note: candidate template ignored: could not match 'TensorShapeBase<Shape>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\nstd::ostream& operator<<(std::ostream& os, const TensorShapeBase<Shape>& tsb) {\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:750:5: note: candidate template ignored: substitution failure [with _Ostream = tsl::internal::LogMessage, _Tp = tensorflow::tpu::TPUCompileMetadataProto]: invalid operands to binary expression ('tsl::internal::LogMessage' and 'const tensorflow::tpu::TPUCompileMetadataProto')\r\n operator<<(_Ostream&& __os, const _Tp& __x)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/shared_ptr.h:70:5: note: candidate template ignored: could not match '__shared_ptr<_Tp, _Lp>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_Ch, _Tr>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:178:5: note: candidate template ignored: could not match '_Setfill<_CharT>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Setfill<_CharT> __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:311:5: note: candidate template ignored: could not match '_Put_money<_MoneyT>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Put_money<_MoneyT> __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/iomanip:363:5: note: candidate template ignored: could not match '_Put_time<_CharT>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, _Put_time<_CharT> __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/complex:555:5: note: candidate template ignored: could not match 'complex<_Tp>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(basic_ostream<_CharT, _Traits>& __os, const complex<_Tp>& __x)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bitset:1540:5: note: candidate template ignored: could not match 'bitset<_Nb>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.h:1309:5: note: candidate template ignored: could not match 'std::independent_bits_engine<_RandomNumberEngine, __w, _UIntType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:154:5: note: candidate template ignored: could not match 'linear_congruential_engine<_UIntType, __a, __c, __m>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:474:5: note: candidate template ignored: could not match 'mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:628:5: note: candidate template ignored: could not match 'subtract_with_carry_engine<_UIntType, __w, __s, __r>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:696:5: note: candidate template ignored: could not match 'discard_block_engine<_RandomNumberEngine, __p, __r>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:857:5: note: candidate template ignored: could not match 'shuffle_order_engine<_RandomNumberEngine, __k>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:901:5: note: candidate template ignored: could not match 'uniform_int_distribution<_IntType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:959:5: note: candidate template ignored: could not match 'uniform_real_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1101:5: note: candidate template ignored: could not match 'geometric_distribution<_IntType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1215:5: note: candidate template ignored: could not match 'negative_binomial_distribution<_IntType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1425:5: note: candidate template ignored: could not match 'poisson_distribution<_IntType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1691:5: note: candidate template ignored: could not match 'binomial_distribution<_IntType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1753:5: note: candidate template ignored: could not match 'exponential_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1919:5: note: candidate template ignored: could not match 'normal_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:1986:5: note: candidate template ignored: could not match 'lognormal_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2058:5: note: candidate template ignored: could not match 'chi_squared_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2144:5: note: candidate template ignored: could not match 'cauchy_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2219:5: note: candidate template ignored: could not match 'fisher_f_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2293:5: note: candidate template ignored: could not match 'student_t_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2462:5: note: candidate template ignored: could not match 'gamma_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2538:5: note: candidate template ignored: could not match 'weibull_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2612:5: note: candidate template ignored: could not match 'extreme_value_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2748:5: note: candidate template ignored: could not match 'discrete_distribution<_IntType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:2970:5: note: candidate template ignored: could not match 'piecewise_constant_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/random.tcc:3176:5: note: candidate template ignored: could not match 'piecewise_linear_distribution<_RealType>' against 'tensorflow::tpu::TPUCompileMetadataProto'\r\n operator<<(std::basic_ostream<_CharT, _Traits>& __os,\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:108:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::basic_ostream<char>::__ostream_type &(*)(std::basic_ostream<char>::__ostream_type &)' (aka 'basic_ostream<char, std::char_traits<char>> &(*)(basic_ostream<char, std::char_traits<char>> &)') for 1st argument\r\n operator<<(__ostream_type& (*__pf)(__ostream_type&))\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:117:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::basic_ostream<char>::__ios_type &(*)(std::basic_ostream<char>::__ios_type &)' (aka 'basic_ios<char, std::char_traits<char>> &(*)(basic_ios<char, std::char_traits<char>> &)') for 1st argument\r\n operator<<(__ios_type& (*__pf)(__ios_type&))\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:127:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::ios_base &(*)(std::ios_base &)' for 1st argument\r\n operator<<(ios_base& (*__pf) (ios_base&))\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:166:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'long' for 1st argument\r\n operator<<(long __n)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:170:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'unsigned long' for 1st argument\r\n operator<<(unsigned long __n)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:174:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'bool' for 1st argument\r\n operator<<(bool __n)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:178:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'short' for 1st argument\r\n operator<<(short __n);\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:181:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'unsigned short' for 1st argument\r\n operator<<(unsigned short __n)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:189:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'int' for 1st argument\r\n operator<<(int __n);\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:192:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'unsigned int' for 1st argument\r\n operator<<(unsigned int __n)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:201:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'long long' for 1st argument\r\n operator<<(long long __n)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:205:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'unsigned long long' for 1st argument\r\n operator<<(unsigned long long __n)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:220:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'double' for 1st argument\r\n operator<<(double __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:224:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'float' for 1st argument\r\n operator<<(float __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:232:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'long double' for 1st argument\r\n operator<<(long double __f)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:245:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'const void *' for 1st argument; take the address of the argument with &\r\n operator<<(const void* __p)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:250:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::nullptr_t' for 1st argument\r\n operator<<(nullptr_t)\r\n ^\r\n/usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/ostream:283:7: note: candidate function not viable: no known conversion from 'tensorflow::tpu::TPUCompileMetadataProto' to 'std::basic_ostream<char>::__streambuf_type *' (aka 'basic_streambuf<char, std::char_traits<char>> *') for 1st argument\r\n operator<<(__streambuf_type* __sb);\r\n ^\r\n5 warnings and 4 errors generated.\r\n```",
"Hi @wonjeon, can you ensure your system is updated to a tested build configuration for your system: https://www.tensorflow.org/install/source#tested_build_configurations specifically it seems your Clang is out of date (we tested against 16.0). Also what does ubuntu1.1 mean? Reason being I don't usually see Ubuntu versions that aren't 22.XX LTS, 20.XX LTS, 18.XX LTS etc. Thanks for your help.",
"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/62730\">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/62730\">No</a>\n"
] | 2024-01-03T18:13:22 | 2024-02-08T01:46:35 | 2024-02-08T01:46:32 |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 22.04.3 LTS
- TensorFlow installation (pip package or built from source): build from source
- TensorFlow library (version, if pip package or github SHA, if built from source): commit id 2454fa808a6
### 2. Code
Provide code to help us reproduce your issues using one of the following options:
#### Option A: Reference colab notebooks
1) Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.
2) Reference [TensorFlow Lite Model Colab](https://colab.research.google.com/gist/ymodak/0dfeb28255e189c5c48d9093f296e9a8/tensorflow-lite-debugger-colab.ipynb): Demonstrate how to convert your TF model to a TF Lite model (with quantization, if used) and run TFLite Inference (if possible).
```
(You can paste links or attach files by dragging & dropping them below)
- Provide links to your updated versions of the above two colab notebooks.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```
#### Option B: Paste your code here or provide a link to a custom end-to-end colab
```
$ bazel build -j tensorflow/compiler/mlir/...
...
ERROR: /home/.../tensorflow/tensorflow/core/tfrt/graph_executor/BUILD:27:11: Compiling tensorflow/core/tfrt/graph_executor/graph_execution_options.cc failed: (Exit 1): clang failed: error executing command (from target //tensorflow/core/tfrt/graph_executor:graph_execution_options) /usr/lib/llvm-14/bin/clang -U_FORTIFY_SOURCE -fstack-protector -Wall -Wthread-safety -Wself-assign -Wunused-but-set-parameter -Wno-free-nonheap-object -fcolor-diagnostics -fno-omit-frame-pointer -g0 ... (remaining 241 arguments skipped)
In file included from tensorflow/core/tfrt/graph_executor/graph_execution_options.cc:19:
external/com_google_absl/absl/log/log.h:199:9: warning: 'LOG' macro redefined [-Wmacro-redefined]
#define LOG(severity) ABSL_LOG_INTERNAL_LOG_IMPL(_##severity)
^
external/local_tsl/tsl/platform/default/logging.h:165:9: note: previous definition is here
#define LOG(severity) _TF_LOG_##severity
^
In file included from tensorflow/core/tfrt/graph_executor/graph_execution_options.cc:19:
external/com_google_absl/absl/log/log.h:237:9: warning: 'LOG_EVERY_N' macro redefined [-Wmacro-redefined]
#define LOG_EVERY_N(severity, n) \
^
external/local_tsl/tsl/platform/default/logging.h:278:9: note: previous definition is here
#define LOG_EVERY_N(severity, n) \
^
In file included from tensorflow/core/tfrt/graph_executor/graph_execution_options.cc:19:
external/com_google_absl/absl/log/log.h:245:9: warning: 'LOG_FIRST_N' macro redefined [-Wmacro-redefined]
#define LOG_FIRST_N(severity, n) \
^
external/local_tsl/tsl/platform/default/logging.h:284:9: note: previous definition is here
#define LOG_FIRST_N(severity, n) \
^
In file included from tensorflow/core/tfrt/graph_executor/graph_execution_options.cc:19:
external/com_google_absl/absl/log/log.h:253:9: warning: 'LOG_EVERY_POW_2' macro redefined [-Wmacro-redefined]
#define LOG_EVERY_POW_2(severity) \
^
external/local_tsl/tsl/platform/default/logging.h:290:9: note: previous definition is here
#define LOG_EVERY_POW_2(severity) \
^
In file included from tensorflow/core/tfrt/graph_executor/graph_execution_options.cc:19:
external/com_google_absl/absl/log/log.h:265:9: warning: 'LOG_EVERY_N_SEC' macro redefined [-Wmacro-redefined]
#define LOG_EVERY_N_SEC(severity, n_seconds) \
^
external/local_tsl/tsl/platform/default/logging.h:300:9: note: previous definition is here
#define LOG_EVERY_N_SEC(severity, n_seconds) \
^
tensorflow/core/tfrt/graph_executor/graph_execution_options.cc:131:16: error: no matching function for call to 'StrCat'
<< absl::StrCat(options.model_metadata)
^~~~~~~~~~~~
external/com_google_absl/absl/strings/str_cat.h:451:41: note: candidate function not viable: no known conversion from 'const tensorflow::SessionMetadata' to 'const absl::AlphaNum' for 1st argument
ABSL_MUST_USE_RESULT inline std::string StrCat(const AlphaNum& a) {
^
external/com_google_absl/absl/strings/str_cat.h:449:41: note: candidate function not viable: requires 0 arguments, but 1 was provided
ABSL_MUST_USE_RESULT inline std::string StrCat() { return std::string(); }
^
external/com_google_absl/absl/strings/str_cat.h:455:34: note: candidate function not viable: requires 2 arguments, but 1 was provided
ABSL_MUST_USE_RESULT std::string StrCat(const AlphaNum& a, const AlphaNum& b);
^
external/com_google_absl/absl/strings/str_cat.h:456:34: note: candidate function not viable: requires 3 arguments, but 1 was provided
ABSL_MUST_USE_RESULT std::string StrCat(const AlphaNum& a, const AlphaNum& b,
^
external/com_google_absl/absl/strings/str_cat.h:458:34: note: candidate function not viable: requires 4 arguments, but 1 was provided
ABSL_MUST_USE_RESULT std::string StrCat(const AlphaNum& a, const AlphaNum& b,
^
external/com_google_absl/absl/strings/str_cat.h:463:41: note: candidate function template not viable: requires at least 5 arguments, but 1 was provided
ABSL_MUST_USE_RESULT inline std::string StrCat(
^
5 warnings and 1 error generated.
INFO: Elapsed time: 27.129s, Critical Path: 24.43s
INFO: 1098 processes: 701 internal, 397 local.
FAILED: Build did NOT complete successfully
```
### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:
- Model produces wrong results and/or has lesser accuracy.
- Model produces correct results, but it is slower than expected.
### 4. (optional) RNN conversion support
If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title.
### 5. (optional) Any other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.
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I_kwDOArmXAs57DEJN
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Missing support for type `tf.float64` from c++ tflite inference
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[
"Hi @pkgoogle,\r\n\r\nI have reproduced the code with some basic functions using python. The main observations here are that with float32 for few random inputs the precision lost and for few preserves. For flaot64, the precision preserves for all random inputs. The code throws a value error upon adding ```converter.inference_input_type = tf.float64 ,\r\nconverter.inference_output_type = tf.float64``` lines. Might require feature support for float64. Please find the [gist](https://colab.research.google.com/gist/LakshmiKalaKadali/e296117af1c35dd222ef1fe87908cae5/-62729.ipynb).\r\n\r\nThank You\r\n",
"Hi @SanjayMarreddi, I don't believe we support float64 for tflite ... generally speaking, mobile & edge operates with memory and latency constraints so we generally try to do the opposite -- try to support lower precision models and quantization. @miaout17 will we support this in the future? Thanks.",
"Hi @pkgoogle, Thanks a lot for the response. Yeah, I can understand that. \r\n\r\nThe reason I was looking to use `tflite` format model is to reduce some latency overhead for the inference of the `tensorflow` models from Java and C++. And also high precision is needed.\r\n\r\nDo you / @miaout17 have any idea on whether `tf.float64` would be supported anytime soon in the future?\r\n\r\nAny other suggestions are also highly appreciated. Thanks!\r\n\r\n\r\n"
] | 2024-01-03T17:32:40 | 2024-01-06T19:50:42 | null |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 22.04
- TensorFlow installation (pip package or built from source): pip
### 2. Details
- I have a small tensorflow model created with pure tensor operations. I am converting the python model into the `.tflite` format.
- Then I am doing the inference on the model from [python](https://www.tensorflow.org/lite/guide/signatures#python) and [c++](https://www.tensorflow.org/lite/guide/signatures#c) using the sample codes linked from documentation.
- Basic model architecture:
```
class Model(tf.Module):
@tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.float32)]) # ---> Line 2
def solve(self, x):
...
...
return y
# Save the model in the usual way:
model = Model()
SAVED_MODEL_PATH = 'updated_model'
tf.saved_model.save(
model, SAVED_MODEL_PATH,
signatures={
'solve': model.solve.get_concrete_function()
})
# The way I am converting the model:
converter = tf.lite.TFLiteConverter.from_saved_model(SAVED_MODEL_PATH)
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS
]
tflite_model = converter.convert()
with open('model.tflite', 'wb') as f:
f.write(tflite_model)
```
### 3. There are 3 specific cases I tested:
1. With the above code model structure and conversion, both C++ inference and python inference are working. But both are giving the values with less precision ( 2-3 decimal places ).
2. As I need more precision, I have changed the line `2` above to use `tf.float64`. The conversion is successful.
- Then, the python inference works again, **but with values of high precision, 6-8 decimal places. ( The same is desired in C++)**
- But the C++ inference gives Segmentation fault.
3. In addition to the test 2 change, I have added the below 2 lines specifying the type before converting:
```
converter = tf.lite.TFLiteConverter.from_saved_model(SAVED_MODEL_PATH)
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS
]
converter.inference_input_type = tf.float64 # Added lines
converter.inference_output_type = tf.float64
tflite_model = converter.convert()
```
The error it gives DURING CONVERSION:
```
Traceback (most recent call last):
File "/home/smarreddi/dev/analytics/price_estimator/fairpriceaggregator/model_generator/save_and_convert.py", line 66, in <module>
tflite_model = converter.convert()
File "/home/smarreddi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/lite/python/lite.py", line 1065, in wrapper
return self._convert_and_export_metrics(convert_func, *args, **kwargs)
File "/home/smarreddi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/lite/python/lite.py", line 1042, in _convert_and_export_metrics
result = convert_func(self, *args, **kwargs)
File "/home/smarreddi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/lite/python/lite.py", line 1390, in convert
return self._convert_from_saved_model(graph_def)
File "/home/smarreddi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/lite/python/lite.py", line 1247, in _convert_from_saved_model
self._validate_inference_input_output_types(quant_mode)
File "/home/smarreddi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/lite/python/lite.py", line 1103, in _validate_inference_input_output_types
raise ValueError(
ValueError: The inference_input_type and inference_output_type must be tf.float32.
```
I would like to understand why the type `tf.float32` is only allowed in the c++ inference path. I would like to attain the high precision like the python inference in the C++ as well ( like test case 2 ).
Let me know your thoughts on the same @tflite-support-robot, community
Thanks!
|
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I_kwDOArmXAs57Ahf_
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tf.gather_nd implementation mismatch with documentation
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[
"@edwardyehuang,\r\nThank you for the issue. Could you please share a reproducible code that supports your statement so that the issue can be easily debugged? 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/62728\">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/62728\">No</a>\n",
"Hi @edwardyehuang ,\r\n\r\nIt seems you are missing this part of code below.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/4dacf3f368eb7965e9b5c3bbdd5193986081c3b2/tensorflow/python/ops/array_ops.py#L5656\r\n\r\nThe above piece of code converts scalar Tensor passed to `batch_dims` (Acceptable argument for `tf.gather_nd`) into a python integer.Please refer [gist](https://colab.research.google.com/gist/SuryanarayanaY/75f5be6f00758aed7622aa2d6b91a264/62278.ipynb). As tensor is converted to integer in subsequent steps validation done for integer only.\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/62728\">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/62728\">No</a>\n"
] | 2024-01-03T10:37:14 | 2024-02-14T01:47:21 | 2024-02-14T01:47:15 |
CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Ubuntu 22.04
### Current behavior?
There seems to be a mismatch between the implementation of tf.gather_nd and the documentation.
In documentation, the `batch_dims` can be a tensor.
https://github.com/tensorflow/tensorflow/blob/4dacf3f368eb7965e9b5c3bbdd5193986081c3b2/tensorflow/python/ops/array_ops.py#L5650-L5652
But in the implementation below, only the Python integer is supported.
https://github.com/tensorflow/tensorflow/blob/4dacf3f368eb7965e9b5c3bbdd5193986081c3b2/tensorflow/python/ops/array_ops.py#L5657-L5665
https://github.com/tensorflow/tensorflow/blob/4dacf3f368eb7965e9b5c3bbdd5193986081c3b2/tensorflow/python/ops/array_ops.py#L5684-L5687
Enabling or disabling the autograph does not work.
|
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Error reported when training model using tf 2.x
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[
"Hi @panhu ,\r\n\r\nIt seems you are passing a Keras3 Tensor to tensorflow functions which won't work and raises the above mentioned error.\r\n\r\nA KerasTensor is a symbolic placeholder for a shape and dtype, used when constructing Keras Functional models or Keras Functions. You can only use it as input to a Keras layer or a Keras operation (from the namespaces `keras.layers` and `keras.operations`).\r\n\r\nFor more details please refer documentation [here](https://github.com/keras-team/keras/blob/master/keras/backend/common/keras_tensor.py).",
"Thanks,My tf is 2.13, but there is still an error when using \"tensor.numpy()\"",
"`numpy()` method can be called on eager mode (i.e eager tensors) but not on graph mode.",
"What should I do when my model has multiple outputs and a custom loss function. I don't seem to be writing correctly：\r\n\r\n enh_spec = Lambda(self.mk_mask)([real,imag,output_mask])\r\n \r\n self.enh_real, self.enh_imag = enh_spec[0],enh_spec[1]\r\n \r\n enh_frame = Lambda(self.ifftLayer,arguments = {'mode':'real_imag'})(enh_spec)\r\n enh_frame = enh_frame * self.win\r\n enh_time = Lambda(self.overlapAddLayer, name = 'enhanced_time')(enh_frame)\r\n \r\n self.model = Model(time_dat,[enh_time,enh_spec])\r\n\r\nloss function:\r\n def lossFunction(y_true,y_pred):\r\n \r\n loss = tf.squeeze(self.cost_function(y_pred[0],y_true[0])) \r\n mag_loss = tf.log(self.spectrum_loss(y_pred[1],y_true[1]) + 1e-8)\r\n # calculate mean over batches\r\n loss = tf.reduce_mean(loss)\r\n return loss + mag_loss\r\n def spectrum_loss(self,y_pred,y_true):\r\n \r\n enh_real = y_pred[:,0]\r\n enh_imag = y_pred[:,1]\r\n enh_mag = tf.sqrt(enh_real**2 + enh_imag**2 + 1e-8)\r\n \r\n true_real,true_imag = self.stftLayer(y_true[1], mode='real_imag')\r\n true_mag = tf.sqrt(true_real**2 + true_imag**2 + 1e-8)\r\n \r\n loss_real = tf.reduce_mean((enh_real - true_real)**2,)\r\n loss_imag = tf.reduce_mean((enh_imag - true_imag)**2,)\r\n loss_mag = tf.reduce_mean((enh_mag - true_mag)**2,) \r\n \r\n return loss_real + loss_imag + loss_mag\r\n\r\nHow should I solve this problem。",
"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/62727\">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/62727\">No</a>\n"
] | 2024-01-03T09:14:49 | 2024-01-16T03:41:12 | 2024-01-16T03:41:09 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.13
### Custom code
Yes
### OS platform and distribution
linux
### 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 behavior?
Error reported when training model using tf 2.x:
TypeError: You are passing KerasTensor(type_spec=TensorSpec(shape=(), dtype=tf.float32, name=None), name='Placeholder:0', description="created by layer 'tf.cast_2'"), an intermediate Keras symbolic input/output, to a TF API that does not allow registering custom dispatchers, such as tf.cond, tf.function, gradient tapes, or tf.map_fn. Keras Functional model construction only supports TF API calls that do support dispatching, such as tf.math.add or tf.reshape. Other APIs cannot be called directly on symbolic Kerasinputs/outputs. You can work around this limitation by putting the operation in a custom Keras layer call and calling that layer on this symbolic input/output.
### Standalone code to reproduce the issue
```shell
What should I do if I don't want to use “import tensorflow.compat.v1 as tf”
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs56-0MW
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Problem in my code due to `tf.shape` and `Tensor.shape`. `tf.shape` and `Tensor.shape`, both are not working
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[
"@VachanVY Could you use tf.shape to access dynamic shapes within computations and reshape tensors as needed, ensuring compatibility with layers and operations.\r\nPlease consider using functions like tf.reshape or tf.expand_dims as well and let us know?\r\nThank you!",
">Could you use tf.shape to access dynamic shapes within computations\r\n\r\nI've used both `tf.shape` and `Tensor.shape`, but getting error as mentioned in the issue\r\n\r\n>reshape tensors as needed, ensuring compatibility with layers and operations.\r\n\r\nBut I don't want to reshape the tensors, should I just do it to ensure compatibility with layers and operations as you said?\r\nThat means it's a bug right?\r\nAnd I'll do as you said and share the details.",
"@sushreebarsa If you don't mind could you please check the notebook [DETR](https://www.kaggle.com/code/vachanvy/detr-object-detection) once, it's difficult to follow up like this.\r\nThank You.",
"Hi @VachanVY ,\r\n\r\nWe won't debug user code particularly when its a long notebook due to nour bandwidth issues. I request you to submit a minimal code snippet that can reproduce the error so that it can be fixed or debugged. For support issues you can post the same at [tensorflow-forum](https://discuss.tensorflow.org/) or stackoverflow.",
"Individual components work, but only while training there's a problem, so the entire code is required to reproduce the issue.",
"Hi @VachanVY ,\r\n\r\nCan you please import the code to google colab, execute it and then submit a colab gist here ? ",
"@SuryanarayanaY But I've provided a Kaggle notebook link.",
"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/62726\">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/62726\">No</a>\n"
] | 2024-01-03T05:57:02 | 2024-01-29T15:08:29 | 2024-01-29T15:08:25 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I've coded the DETR object detection pipeline from scratch in Tensorflow.
I've tested all the individual components in the pipeline and it works. But when I start training it on my dataset (in `tf.data.Dataset` form) I get an error
This mostly due to the behaviour of `Tensor.shape` and `tf.shape`. `Tensor.shape` returns `None` in it's shape and `tf.shape` returns something like `Tensor("Shape_2:0", shape=(1,), dtype=int32)` which is not the shape of the tensor
Please help. Thank you.
### Standalone code to reproduce the issue
[Kaggle Notebook to reproduce error](https://www.kaggle.com/code/vachanvy/detr-object-detection?scriptVersionId=157371411)
Make a copy of the notebook to reproduce this issue.
### Relevant log output
```shell
ValueError Traceback (most recent call last)
Cell In[33], line 3
1 for epoch in range(1, DETR_ARGS.epochs + 1):
2 print(f"Epoch {epoch}/{DETR_ARGS.epochs}")
----> 3 loss = train_step(train_ds)
4 print(f"Loss at Epoch {epoch} : {loss}\n")
5 model.save_weights(f'detr_weights_epoch{epoch}.keras')
File /opt/conda/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py:153, in filter_traceback.<locals>.error_handler(*args, **kwargs)
151 except Exception as e:
152 filtered_tb = _process_traceback_frames(e.__traceback__)
--> 153 raise e.with_traceback(filtered_tb) from None
154 finally:
155 del filtered_tb
File /tmp/__autograph_generated_fileg7gvd3up.py:36, in outer_factory.<locals>.inner_factory.<locals>.tf__train_step(train_ds)
34 grads = ag__.Undefined('grads')
35 loss = ag__.Undefined('loss')
---> 36 ag__.for_stmt(ag__.converted_call(ag__.ld(enumerate), (ag__.ld(train_ds),), None, fscope), None, loop_body, get_state, set_state, (), {'iterate_names': '(step, (x_train, y_train))'})
37 try:
38 do_return = True
File /tmp/__autograph_generated_fileg7gvd3up.py:22, in outer_factory.<locals>.inner_factory.<locals>.tf__train_step.<locals>.loop_body(itr)
20 with ag__.ld(tf).GradientTape() as tape:
21 y_pred = ag__.converted_call(ag__.ld(model), (ag__.ld(x_train),), dict(training=True), fscope)
---> 22 y_pred = ag__.converted_call(ag__.ld(matcher), (ag__.ld(y_train), ag__.ld(y_pred)), None, fscope)
23 loss = ag__.converted_call(ag__.ld(loss_fn), (ag__.ld(y_train), ag__.ld(y_pred)), None, fscope)
24 grads = ag__.converted_call(ag__.ld(tape).gradient, (ag__.ld(loss), ag__.ld(model).trainable_weights), None, fscope)
File /tmp/__autograph_generated_fileweg7gf52.py:12, in outer_factory.<locals>.inner_factory.<locals>.tf____call__(self, y, y_hat)
10 (class_true, bbox_true) = ag__.ld(y)
11 (class_prob, bbox_pred) = ag__.ld(y_hat)
---> 12 (class_prob, bbox_pred) = ag__.converted_call(ag__.ld(Matcher).match, (ag__.ld(class_true), ag__.ld(bbox_true), ag__.ld(class_prob), ag__.ld(bbox_pred)), None, fscope)
13 try:
14 do_return = True
File /tmp/__autograph_generated_fileuj8_9ikk.py:11, in outer_factory.<locals>.inner_factory.<locals>.tf__match(class_true, bbox_true, class_prob, bbox_pred)
9 retval_ = ag__.UndefinedReturnValue()
10 (bbox_true, bbox_pred) = (ag__.converted_call(ag__.ld(swap_xy), (ag__.converted_call(ag__.ld(xywh_to_xyxy), (ag__.ld(bbox_true),), None, fscope),), None, fscope), ag__.converted_call(ag__.ld(swap_xy), (ag__.converted_call(ag__.ld(xywh_to_xyxy), (ag__.ld(bbox_pred),), None, fscope),), None, fscope))
---> 11 C = ag__.converted_call(ag__.ld(Matcher).batched_cost_matrix, (ag__.ld(class_true), ag__.ld(bbox_true), ag__.ld(class_prob), ag__.ld(bbox_pred)), None, fscope)
12 idx = ag__.converted_call(ag__.ld(tf).stack, ([ag__.converted_call(ag__.ld(linear_sum_assignment), (ag__.ld(C)[ag__.ld(i)],), None, fscope)[1] for i in ag__.converted_call(ag__.ld(range), (ag__.ld(C).shape[0],), None, fscope)],), None, fscope)
13 class_prob = ag__.converted_call(ag__.ld(tf).gather, (ag__.ld(class_prob), ag__.ld(idx)), dict(batch_dims=1), fscope)
File /tmp/__autograph_generated_filet5rvjc4z.py:20, in outer_factory.<locals>.inner_factory.<locals>.tf__batched_cost_matrix(class_true, bbox_true, class_prob, bbox_pred)
18 try:
19 do_return = True
---> 20 retval_ = ag__.converted_call(ag__.ld(tf).map_fn, (ag__.autograph_artifact(lambda B: ag__.converted_call(ag__.ld(Matcher).compute_cost_matrix, (ag__.ld(class_true)[ag__.ld(B)], ag__.ld(class_prob)[ag__.ld(B)], ag__.ld(bbox_true)[ag__.ld(B)], ag__.ld(bbox_pred)[ag__.ld(B)]), None, fscope)), ag__.converted_call(ag__.ld(tf).range, (ag__.converted_call(ag__.ld(tf).shape, (ag__.ld(class_true),), None, fscope)[0],), None, fscope)), dict(fn_output_signature=ag__.ld(tf).float32), fscope)
21 except:
22 do_return = False
File /tmp/__autograph_generated_filet5rvjc4z.py:20, in outer_factory.<locals>.inner_factory.<locals>.tf__batched_cost_matrix.<locals>.<lambda>(B)
18 try:
19 do_return = True
---> 20 retval_ = ag__.converted_call(ag__.ld(tf).map_fn, (ag__.autograph_artifact(lambda B: ag__.converted_call(ag__.ld(Matcher).compute_cost_matrix, (ag__.ld(class_true)[ag__.ld(B)], ag__.ld(class_prob)[ag__.ld(B)], ag__.ld(bbox_true)[ag__.ld(B)], ag__.ld(bbox_pred)[ag__.ld(B)]), None, fscope)), ag__.converted_call(ag__.ld(tf).range, (ag__.converted_call(ag__.ld(tf).shape, (ag__.ld(class_true),), None, fscope)[0],), None, fscope)), dict(fn_output_signature=ag__.ld(tf).float32), fscope)
21 except:
22 do_return = False
File /tmp/__autograph_generated_filejl_v0o33.py:11, in outer_factory.<locals>.inner_factory.<locals>.tf__compute_cost_matrix(class_true, class_prob, bbox_true, bbox_pred)
9 do_return = False
10 retval_ = ag__.UndefinedReturnValue()
---> 11 N = ag__.converted_call(ag__.ld(tf).shape, (ag__.ld(class_true),), None, fscope)[0]
12 cost_i = ag__.autograph_artifact(lambda i: ag__.converted_call(ag__.ld(tf).map_fn, (ag__.autograph_artifact(lambda j: ag__.converted_call(ag__.ld(Matcher).L_match, (ag__.ld(class_true)[ag__.ld(i)], ag__.ld(class_prob)[ag__.ld(j), ag__.converted_call(ag__.ld(int), (ag__.ld(class_true)[ag__.ld(i)],), None, fscope)], ag__.ld(bbox_true)[ag__.ld(i)], ag__.ld(bbox_pred)[ag__.ld(j)]), None, fscope)), ag__.converted_call(ag__.ld(tf).range, (ag__.ld(N),), None, fscope)), dict(fn_output_signature=ag__.ld(tf).float32), fscope))
13 try:
ValueError: in user code:
File "/tmp/ipykernel_42/4115406382.py", line 7, in train_step *
y_pred = matcher(y_train, y_pred)
File "/tmp/ipykernel_42/968499204.py", line 64, in __call__ *
class_prob, bbox_pred = Matcher.match(class_true, bbox_true, class_prob, bbox_pred)
File "/tmp/ipykernel_42/968499204.py", line 53, in match *
C = Matcher.batched_cost_matrix(class_true, bbox_true, class_prob, bbox_pred)
File "/tmp/ipykernel_42/968499204.py", line 46, in batched_cost_matrix *
tf.range(tf.shape(class_true)[0]), fn_output_signature=tf.float32
File "/tmp/ipykernel_42/968499204.py", line 22, in compute_cost_matrix *
N = tf.shape(class_true)[0]
ValueError: slice index 0 of dimension 0 out of bounds. for '{{node map/while/strided_slice_4}} = StridedSlice[Index=DT_INT32, T=DT_INT32, begin_mask=0, ellipsis_mask=0, end_mask=0, new_axis_mask=0, shrink_axis_mask=1](map/while/Shape, map/while/strided_slice_4/stack, map/while/strided_slice_4/stack_1, map/while/strided_slice_4/stack_2)' with input shapes: [0], [1], [1], [1] and with computed input tensors: input[1] = <0>, input[2] = <1>, input[3] = <1>.
```
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PR_kwDOArmXAs5jGDth
| 62,725 |
Updating a typo in doc
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[
"Hi @sushreebarsa It looks like your PR relates to the Keras component. Please submit it to the github.com/keras-team/keras repository instead. Thankyou.\r\n@fchollet, @qlzh727"
] | 2024-01-03T04:20:49 | 2024-02-08T19:59:40 | 2024-01-03T05:16:56 |
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Updated the line from if (y_pred_rank - y_true_rank != 1) or y_pred_shape[-1] == 1: to
if (y_pred_rank - y_true_rank == 1) or y_pred_shape[-1] == 1:
as the current behavior will squeeze the y_pred tensor even when it’s the same rank and shape as the y_pred tensor.
Fixes #62718
Please have a look at this and do the needful. Thank you!
|
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PR_kwDOArmXAs5jF4mI
| 62,724 |
[AMD-ZENDNN] Remove Zen layout pass from TF Proper
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[
"/cc @penpornk ",
"Hello @penpornk and @gbaned ,\r\n\r\nCan you please review this PR ? \r\n\r\nThank you!"
] | 2024-01-03T02:51:14 | 2024-01-11T22:55:39 | 2024-01-11T22:55:38 |
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We have introduced Graph module with layout pass in plugin.
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I_kwDOArmXAs56-S47
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tf 2.15.0.post1 complains about tf.keras.optimizers.Adam to be not trackable
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[
"Hi **@Binjian** ,\r\nI was able to reproduce this issue on Colab using TF v2.15 and TF-nightly. Please find the [gist](https://colab.research.google.com/gist/Venkat6871/43f392f1cd7ab2c28cca41dceceafee8/62723_2-15-v.ipynb) here for reference.\r\n\r\nThank you!",
"@Venkat6871 ,\r\n\r\nthank you for the response! Based on your label \"keras\", I was able to track down an issue in keras back in 2021:\r\n\r\nhttps://github.com/keras-team/keras/issues/14776\r\n\r\nIt seems to relate to the way Keras implemented its trackable interface of optimizers. I couldn't find the documentation for the typing of the trackable interface though.",
"Hello, this bug has been fixed in Keras 3.1.1. Optimizers are now trackable. TensorFlow 2.16 uses the latest version of Keras. So if you use TensorFlow 2.16, you should find that the bug is fixed.",
"Hi **@Binjian** ,\r\nCould you close this issue as it is fixed in latest version of TensorFlow. Please feel free to reopen the issue if you still have a concern. Here i am providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/1ee849fceebd976190d4396573799479/62723_2-16-1-v.ipynb) for your reference.\r\n\r\nThank you!",
"Many thanks for the reminder. My bad. ",
"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/62723\">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/62723\">No</a>\n"
] | 2024-01-03T02:05:19 | 2024-04-24T06:57:28 | 2024-04-24T06:57:25 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
v2.15.0-2-g0b15fdfcb3f 2.15.0
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
Linux Ubuntu 22.04
### Python version
3.11.7
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.2.140
### GPU model and memory
NVidia 3080 with 10240MB
### Current behavior?
1. define a model and a Keras Adam optimizer
```python
import tensorflow as tf
import keras
inputs = keras.Input(shape=(37,))
x = keras.layers.Dense(32, activation="relu")(inputs)
outputs = keras.layers.Dense(5, activation="softmax")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
optimizer = tf.keras.optimizers.Adam(0.001)
```
2. create a checkpoint
```python
ckpt = tf.train.Checkpoint(
step=tf.Variable(1, name="step"),
optimizer=optimizer,
net=model,
)
```
3. Output error
```python
ValueError: `Checkpoint` was expecting optimizer to be a trackable object (an object derived from `Trackable`), got <keras.src.optimizers.adam.Adam object at 0x7f836446dad0>. If you believe this object should be trackable (i.e. it is part of the TensorFlow Python API and manages state), please open an issue.
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import keras
inputs = keras.Input(shape=(37,))
x = keras.layers.Dense(32, activation="relu")(inputs)
outputs = keras.layers.Dense(5, activation="softmax")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
optimizer = keras.optimizers.Adam(0.001)
ckpt_actor = tf.train.Checkpoint(
step=tf.Variable(1, name="step"),
optimizer=optimizer,
net=model
)
```
### Relevant log output
```shell
{
"name": "ValueError",
"message": "`Checkpoint` was expecting optimizer to be a trackable object (an object derived from `Trackable`), got <keras.src.optimizers.adam.Adam object at 0x7f836446dad0>. If you believe this object should be trackable (i.e. it is part of the TensorFlow Python API and manages state), please open an issue.",
"stack": "---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[10], line 1
----> 1 ckpt_actor = tf.train.Checkpoint(
2 step=tf.Variable(1, name=\"step\"),
3 optimizer=optimizer,
4 net=model
5 )
File /dpt/.pyenv/versions/miniconda3-3.11-23.11.0-2/envs/meos-nbd/lib/python3.11/site-packages/tensorflow/python/checkpoint/checkpoint.py:2200, in Checkpoint.__init__(self, root, **kwargs)
2198 if isinstance(converted_v, weakref.ref):
2199 converted_v = converted_v()
-> 2200 _assert_trackable(converted_v, k)
2202 if root:
2203 # Make sure that root doesn't already have dependencies with these names
2204 child = trackable_root._lookup_dependency(k)
File /dpt/.pyenv/versions/miniconda3-3.11-23.11.0-2/envs/meos-nbd/lib/python3.11/site-packages/tensorflow/python/checkpoint/checkpoint.py:1548, in _assert_trackable(obj, name)
1545 def _assert_trackable(obj, name):
1546 if not isinstance(
1547 obj, (base.Trackable, def_function.Function)):
-> 1548 raise ValueError(
1549 f\"`Checkpoint` was expecting {name} to be a trackable object (an \"
1550 f\"object derived from `Trackable`), got {obj}. If you believe this \"
1551 \"object should be trackable (i.e. it is part of the \"
1552 \"TensorFlow Python API and manages state), please open an issue.\")
ValueError: `Checkpoint` was expecting optimizer to be a trackable object (an object derived from `Trackable`), got <keras.src.optimizers.adam.Adam object at 0x7f836446dad0>. If you believe this object should be trackable (i.e. it is part of the TensorFlow Python API and manages state), please open an issue."
}
```
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I_kwDOArmXAs569UlG
| 62,722 |
pad to fixed BATCH_SIZE in tf.data.Dataset?
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[
"@pure-rgb,\r\nCould you please share a reproducible code that supports your statement so that the issue can be easily debugged? 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/62722\">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/62722\">No</a>\n"
] | 2024-01-02T20:15:18 | 2024-01-19T01:49:20 | 2024-01-19T01:49:17 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I have a dataset with 11 samples. And when I choose the `BATCH_SIZE` be 2, the following code will have errors:
dataset = tf.contrib.data.TFRecordDataset(filenames)
dataset = dataset.map(parser)
if shuffle:
dataset = dataset.shuffle(buffer_size=128)
dataset = dataset.batch(batch_size)
dataset = dataset.repeat(count=1)
The problem lies in `dataset = dataset.batch(batch_size)`, when the `Dataset` looped into the last batch, the remaining count of samples is just 1, so is there any way to pick randomly one from the previous visited samples and generate the last batch?
The problem lies in `dataset = dataset.batch(batch_size)`, when the `Dataset` looped into the last batch, the remaining count of samples is just 1, so is there any way to pick randomly one from the previous visited samples and generate the last batch?
```
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Updated copyright year to 2024
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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/62721/checks?check_run_id=20094281049) 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."
] | 2024-01-02T14:22:08 | 2024-01-02T16:04:39 | 2024-01-02T16:04:36 |
NONE
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Improved documentation for `script_ops.py`
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Carefully read python """docstrings""" inside `tensorflow/python/ops/script_ops.py` and fixed grammar and spelling mistakes, without editing code snippets and other implicit references to source code.
I was unable to run CPU tests due to this issue #62645.
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ability to crete mask from points file
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[
"Hi @SigireddyBalasai ,\r\n\r\nI am unable to get your query. Could you please elaborate ?",
"i know it is present in other libraries but i am asking in tf\r\n",
"Basically I am doing a image segmentation project and I need to create a\r\npolygon mask from points file and there are quite a methods in other\r\nmodules I want to see if tensor flow has such a method if not I am\r\nsuggesting to implement is hope this is clear\r\n\r\nOn Tue, Jan 2, 2024, 4:24 PM Surya ***@***.***> wrote:\r\n\r\n> Hi @SigireddyBalasai <https://github.com/SigireddyBalasai> ,\r\n>\r\n> I am unable to get your query. Could you please elaborate ?\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/62719#issuecomment-1873875804>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AYHQ36NKDHVEJM35OK6ZGELYMPROZAVCNFSM6AAAAABBJ3VEQCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQNZTHA3TKOBQGQ>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"@SuryanarayanaY ",
"Hi @SigireddyBalasai ,\r\n\r\nLet me put my understandings here.\r\n\r\n> i want to know if there is a way to get a label from bbox coordinates\r\n \r\nFor generating labels from bounding boxes you might need a backbone model that can able to detect the objects in the bounding box.You may refer this [example](https://keras.io/examples/vision/sam/) for this.\r\n\r\n\r\n\r\n\r\n> Basically I am doing a image segmentation project and I need to create a polygon mask from points file and there are quite a methods in other modules I want to see if tensor flow has such a method if not I am suggesting to implement is hope this is clear\r\n\r\nIf you want to generate a bbox you may use the [draw_bounding_boxes](https://www.tensorflow.org/api_docs/python/tf/image/draw_bounding_boxes) API.\r\n\r\nIf you want to generate a mask for the bbox,as per my understanding its not pretty straight forward with a single method/API.You can refer this [tutorial](https://www.tensorflow.org/tfmodels/vision/instance_segmentation) for an implementation.\r\n\r\nAs you are referring there are methods from other libraries, could you please refer some examples so that it can be better understood and can provide a appropriate response.\r\n\r\nThanks!\r\n\r\n",
"i am not intending to get a bbox label but it was a label with n points as boundaries @SuryanarayanaY ",
"@SuryanarayanaY can you make that available under any contributor welcome ",
"Hi @SigireddyBalasai ,\r\n\r\nCan you add some references of other libraries for better understanding. Would you like to contribute to this feature?",
"Hi @SigireddyBalasai ,\r\n\r\nI am not 100% getting the requirement. Add a sample demo or equivalent API from other libraries(if any) so that we can take a call on that. 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."
] | 2024-01-02T10:48:14 | 2024-02-14T01:47:17 | 2024-02-14T01:47:17 |
NONE
| null | null | null |
### Issue type
Documentation Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
latest
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
i want to know if there is a way to get a label from bbox coordinates
### Standalone code to reproduce the issue
```shell
i dont have one
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs566VRf
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Critical typo: squeeze the y_pred tensor even when it’s the same rank and shape as the y_pred tensor should be == instead of !=
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[
"@rodriguesk Thank you for reporting an issue here.\r\nCould you please elaborate the issue you are facing here ?\r\nI was able to run the file successfully, please find the [gist](https://colab.research.google.com/gist/sushreebarsa/e3ede62d9cc1bb882d0464499ebdb158/62718.ipynb) here!\r\nThank you!",
"The issue is that the code is written wrong. read the code comments and what it’s supposed to do (only squeeze when the ranks differ by exactly 1 and not squeeze for situations of equal rank). Yet the code’s if statements will squeeze whenever the ranks are equal, which is wrong.On Jan 2, 2024, at 1:09 AM, sushreebarsa ***@***.***> wrote:External Email\r\n@rodriguesk Thank you for reporting an issue here.\r\nCould you please elaborate the issue you are facing here ?\r\nI was able to run the file successfully, please find the gist here!\r\nThank you!\r\n\r\n—Reply to this email directly, view it on GitHub, or unsubscribe.You are receiving this because you were mentioned.Message ID: ***@***.***>",
"@rodriguesk I have created a fix internally, once the changes are verified this issue will be resolved.\r\nThank you!",
"it looks like the PR you made was closed as this is a fix needs to be made in the keras repo? What's the plan?",
"@sushreebarsa I made a PR for the appropriate tf-keras repo. https://github.com/keras-team/tf-keras/pull/719\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/62718\">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/62718\">No</a>\n"
] | 2024-01-02T08:52:31 | 2024-01-15T22:22:14 | 2024-01-15T22:22:10 |
NONE
| null | null | null |
https://github.com/tensorflow/tensorflow/blob/a32a1c9123a7c4fb926c1cdc3d99188a171cdfe5/tensorflow/python/keras/utils/losses_utils.py#L191
should be:
if (y_pred_rank - y_true_rank == 1) or y_pred_shape[-1] == 1:
Current behavior will squeeze the y_pred tensor even when it’s the same rank and shape as the y_pred tensor.
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I_kwDOArmXAs565zle
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Are the TFLite examples preventing color information from being passed along for inference?
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[
"Hi @pkgoogle,\r\nPlease look into the issue.\r\nThank You",
"Hi @titanium-cranium, yes, I believe you are correct -- However the inference preprocessing should (generally) match the training preprocessing i.e. the model would have been trained on this format, For your own app, you should edit this to match your model's training format. Does this answer your question?",
"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/62717\">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/62717\">No</a>\n"
] | 2024-01-02T05:59:13 | 2024-01-20T01:48:58 | 2024-01-20T01:48:55 |
NONE
| null | null | null |
I've recently noticed in the Object Detection example, that the CameraFragment.kt code includes this line:
https://github.com/tensorflow/examples/blob/fff4bcda7201645a1efaea4534403daf5fc03d42/lite/examples/object_detection/android_play_services/app/src/main/java/org/tensorflow/lite/examples/objectdetection/fragments/CameraFragment.kt#L276
```
private fun detectObjects(image: ImageProxy) {
// Copy out RGB bits to the shared bitmap buffer
image.use { bitmapBuffer.copyPixelsFromBuffer(image.planes[0].buffer) } <-------
val imageRotation = image.imageInfo.rotationDegrees
// Pass Bitmap and rotation to the object detector helper for processing and detection
objectDetectorHelper.detect(bitmapBuffer, imageRotation)
}
```
IIUC, then this is extracting only the grayscale out of the YUV image being passed from the camera and passing that to ObjectDetector. Won't that destroy any sort of color based inference in the models?
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I_kwDOArmXAs565zT-
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Are the TFLite examples preventing color information from being passed along for inference?
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[
"@titanium-cranium,\r\nLooks like this is a duplicate of issue [#62717](https://github.com/tensorflow/tensorflow/issues/62717). Could you please close this issue, since it is already being tracked there? Thank you!\r\n",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62716\">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/62716\">No</a>\n",
"Duplicated in error. "
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NONE
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PR_kwDOArmXAs5jBGYj
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Update resource_base.h
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[] | 2024-01-01T19:31:16 | 2024-02-08T19:59:39 | 2024-01-02T00:56:51 |
CONTRIBUTOR
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For testing a fix attempt only, not ready for merge
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PR_kwDOArmXAs5i-_Zm
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Fixed multiple typos
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Fixed multiple typos (english)
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Update log check lib
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[] | 2023-12-30T23:30:43 | 2024-01-02T06:49:29 | 2023-12-31T04:42:34 |
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For internal testing only
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Tensorflow 2.13.1 not detecting GPU on Jetson Orin, not built with CUDA
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[
"Hi @Paul-Mick ,\r\n\r\nPlease find the installation instructions for Tf2.13v from the history of [pip.md ](https://github.com/tensorflow/docs/blob/513d92d69cacbe9f2bda19e96045e6b836c93545/site/en/install/pip.md)file.\r\n\r\nIn Tf2.13v you need to install CUDA toolkit manually using the instructions in the attached link.\r\n\r\n",
"Thanks for the reply.\r\n\r\nThe instructions in the pip.md file did not resolve my issue.\r\n\r\nLuckily I found the solution elsewhere: the [NVIDIA Jetson Framework Installation Page](https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html)\r\n\r\nApparently there is a special version of Tensorflow for Jetson devices, the newest of which is 2.14.0+nv23.11.\r\n\r\nInstalling this with `pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v60dp tensorflow==2.14.0+nv23.11` has Tensorflow recognizing my GPU properly.\r\n\r\nJust wanted to post this incase others have the same issue.",
"@Paul-Mick ,\r\n\r\nTensorflow officially provides installation instructions for Ubuntu only. Your [comment](https://github.com/tensorflow/tensorflow/issues/62711#issuecomment-1875936782) can help others who uses the Jetson device. Just curious to mention that in above comment you installed TF2.14v which you are not intending to earlier?",
"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/62711\">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/62711\">No</a>\n",
"Issue fixed",
"@SuryanarayanaY \r\n\r\nYou are correct, I did end up installing Tensorflow 2.14 which I was not intending to. I had run into a weird issue on my AGX Orin where the ethernet adapter would go down every once in a while, and I was unable able to fix it directly.\r\n\r\nI ended up re-flashing the Orin to JetPack 6.0DP, which supports CUDA 12.2 and cuDNN 8.9. When looking into installing Tensorflow on Jetson devices deeper I found [NVIDIA's index of Tensorflow builds for Jetson](https://developer.download.nvidia.com/compute/redist/jp), and decided to use the newest of those, which is 2.14.0+nv23.11.\r\n\r\nI did not want to upgrade Tensorflow originally because it would have required completely restarting my project, but since that was going to happen anyhow when re-flashing my Orin upgrading became less troublesome. And using the version of Tensorflow built for Jetson fixed my original issue."
] | 2023-12-30T17:58:54 | 2024-01-05T22:23:54 | 2024-01-05T17:28:24 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.13.1
### Custom code
No
### OS platform and distribution
Ubuntu 20.04
### Mobile device
NVIDIA Jetson AGX Orin
### Python version
3.8
### Bazel version
NA
### GCC/compiler version
NA
### CUDA/cuDNN version
CUDA 11.8/cuDNN 8.6.0.163
### GPU model and memory
NA
### Current behavior?
I am working on an object detection project and am ready to train my model, but Tensorflow does not recognize my GPU. Looking into it further, I found that my Tensorflow had been without GPU or CUDA support, just XLA support.
I had installed Tensorflow with pip.
I know that Tensorflow 2.13.1 does not have the "and-cuda" extra, so how can I specify through pip installation that Tensorflow be installed with GPU and CUDA support?
P.S. I want to avoid switching to Tensorflow 2.14.* because I am using python 3.8 which Tensorflow 2.13.1 is the last supporting version of.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
print(tf.config.list_logical_devices())
print(tf.config.list_physical_devices())
print(tf.test.is_built_with_cuda())
print(tf.test.is_built_with_gpu_support())
print(tf.test.is_built_with_rocm())
print(tf.test.is_built_with_xla())
print(tf.version.GIT_VERSION)
print(tf.version.VERSION)
```
### Relevant log output
```shell
[LogicalDevice(name='/device:CPU:0', device_type='CPU')]
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]
False
False
False
True
v2.13.1-0-gf841394b
2.13.1
```
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I_kwDOArmXAs561GEV
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OOM when create a TensorShape {0, 16,16}
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[
"@xuesu Could you consider using dynamic shapes (e.g., tf.TensorShape(None, 16, 16)) if the zero-sized dimension is expected during model execution?\r\nThank you!",
"I couldn't control the real shape here, but of course, I can add a 0-check. However, I think there should be an assertion or a warning instead of just OOM."
] | 2023-12-30T12:51:12 | 2024-01-03T22:50:03 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.14.1
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu22.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
clang-16
### CUDA/cuDNN version
11.8;8.7.0
### GPU model and memory
_No response_
### Current behavior?
If I create a normal shape like {2,5,8} then nothing weird will happen.
If I create a shape like {0, 16,16} then no warning will be output, the program will stuck for several seconds, then OOM or Segmentation fault (core dumped).
### Standalone code to reproduce the issue
```shell
#include "tensorflow/cc/client/client_session.h"
#include "tensorflow/cc/framework/grad_op_registry.h"
#include "tensorflow/cc/framework/gradient_checker.h"
#include "tensorflow/cc/framework/gradients.h"
#include "tensorflow/cc/framework/testutil.h"
#include "tensorflow/cc/ops/math_ops.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/lib/random/random.h"
#include "tensorflow/tsl/platform/status.h"
using namespace tensorflow;
using namespace tensorflow::ops;
int main(){
TensorShape shape({0, 16, 16});
// printf("shape: %d, %d, %d\n", shape.dim_sizes()[0], shape.dim_sizes()[1], shape.dim_sizes()[2]);
}
```
```
### Relevant log output
```shell
with libfuzzer:
INFO: Seed: 1375624859 [1590/1851]
INFO: Loaded 3 modules (2749475 inline 8-bit counters): 139385 [0x7f275352b8a0, 0x7f275354d919), 2607128 [0x7f276dda0300, 0x7f276e01cb18), 2962 [0x55a5f8763978, 0x55a5f876450a),
INFO: Loaded 3 PC tables (2749475 PCs): 139385 [0x7f275354d920,0x7f275376e0b0), 2607128 [0x7f276e01cb18,0x7f27707e4c98), 2962 [0x55a5f8764510,0x55a5f876fe30),
INFO: -max_len is not provided; libFuzzer will not generate inputs larger than 4096 bytes
INFO: A corpus is not provided, starting from an empty corpus
#2 INITED cov: 2 ft: 2 corp: 1/1b exec/s: 0 rss: 301Mb
shape: 0, 16, 16
2023-12-30 09:27:54.078978: 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: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
==751782== ERROR: libFuzzer: out-of-memory (used: 2049Mb; limit: 2048Mb)
To change the out-of-memory limit use -rss_limit_mb=<N>
MS: 4 InsertRepeatedBytes-CrossOver-InsertByte-InsertRepeatedBytes-; base unit: adc83b19e793491b1c6ea0fd8b46cd9f32e592fc
0xa,0xff,0xff,0xff,0x2c,0xff,0xff,0xff,0xa,0xff,0xff,0xff,0xff,
\012\377\377\377,\377\377\377\012\377\377\377\377
artifact_prefix='./'; Test unit written to ./oom-ff3224e0c400438358258acad47e95d83a32a8bc
Base64: Cv///yz///8K/////w==
SUMMARY: libFuzzer: out-of-memory
```
standalone:
```
Segmentation fault (core dumped)
```
```
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After converting the model to TFLite, there is a significant loss in accuracy.
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[
"My models are all basic convolutional、GRU and deconvolution layers。",
"Hi @panhu,\r\n\r\nPlease make sure that the model layers, activation functions, padding and other parameters are configured properly and also check if the model consists of any unsupported ops. Also run the code with latest tensorflow 2.15 version. If the problem still persists, please share reproducible code for further help. \r\nThank You\r\n"
] | 2023-12-30T10:55:39 | 2024-01-03T04:10:02 | 2024-01-03T03:47:07 |
NONE
| null | null | null |
### 1. System information
- Linux Ubuntu 16.04
- TensorFlow 2
### 2. Code
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
with open('model.tflite', 'wb') as f:
f.write(tflite_model)
#### Option A: Reference colab notebooks
Hello, may I ask why there is a significant decrease in accuracy when using tflite for inference after converting the h5 model to tflite? I did not use quantification.
Thanks!
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"@Fergibson,\r\nCould you please provide the details about what platform you are using (operating system, architecture). Also include your TensorFlow version if you are having any issue on the tensorflow side. Thank you!",
"Closing this as stale. Please reopen if this is still a valid request. Thank you!"
] | 2023-12-30T09:20:05 | 2024-02-01T06:51:36 | 2024-02-01T06:51:36 |
NONE
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| 2,059,030,726 |
I_kwDOArmXAs56ulDG
| 62,707 |
summarize_graph building failed
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"@oldtimerzhy Please ensure you're in the correct conda environment with TensorFlow installed. Verify Bazel installation and version compatibility with your TensorFlow version. Reinstalling Bazel or using a different version might help. Clear any potential issues by running bazel clean before rebuilding. Please let us know if it helps? \r\nThank you!\r\n",
"Hi @sushreebarsa, thanks for your replying and guidence. If no people meet the same issue, I think the source code is OK and seem the problems come from the environment. The related version of bazel and tensorflow I used are shown as the description above. I am on the master branch of tensorflow, so I think maybe I should checkout to some previous versions but not so far from now. I will try again. Thanks~",
"@oldtimerzhy Thank you for the confirmation!\r\nIf you are not facing the source code related issue then please check the environment compatibility as well.\r\nPlease let us know if we can move this issue to closed status for now.\r\nThank you!",
"@sushreebarsa \r\nI will try to fix the problems by checking environment. Please close this issus. Thank you.",
"@oldtimerzhy Thank you for the confirmation. Closing the issue for 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/62707\">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/62707\">No</a>\n"
] | 2023-12-29T03:31:59 | 2024-01-03T15:59:35 | 2024-01-03T15:59:33 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
master
### Custom code
Yes
### OS platform and distribution
Ubuntu 18.04
### Mobile device
_No response_
### Python version
3.7
### Bazel version
7.0.0
### GCC/compiler version
gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I want to figure out the inputs and outputs from a ckpt model.
When I building summarize_graph with using `bazel build tensorflow/tools/graph_transforms:summarize_graph` in conda without compile the whole tf repository. I found the error as following. I have changed the environment of conda, same error existed. PTAL~
### Standalone code to reproduce the issue
### Relevant log output
```shell
/usr/include/c++/7/type_traits:878:48: error: constructor required before non-static data member for 'stream_executor::CommandBuffer::Deleter::owned' has been parsed
template<typename _Tp, typename = decltype(_Tp())>
^~~~~
Target //tensorflow/tools/graph_transforms:summarize_graph failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 64.002s, Critical Path: 46.63s
INFO: 2034 processes: 66 internal, 1968 local.
FAILED: Build did NOT complete successfully
```
_No response_
|
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Update visibility for tensorflow/core/tfrt/gpu/kernal
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For internal testing only, not ready for merge
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TFLite CMake suggestion: option `TFLITE_ENABLE_GLES3` and changes for OpenGL ES Delegate build
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[
"Can't believe the month has passed 😱 Will be glad if I can get an opinion. ",
"Hi @zichuan-wei Can you please review this PR ? Thank you!",
"Update: the following error was generated when set `TFLITE_ENABLE_GLES3=OFF`, and when `TFLITE_ENABLE_GLES3=ON` everything is fine.\r\n\r\nHi @luncliff, thanks for your patch. I have tried your patch on Mac, with NDK 26. And I got the following error:\r\n\r\n```\r\nld.lld: error: undefined symbol: tflite::gpu::gl::GetOpenGlErrors()\r\n>>> referenced by async_buffers.cc\r\n>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/async_buffers.cc.o:(tflite::gpu::AsyncBuffer::MapAHardwareBufferToGlBuffer())\r\n[ 55%] Building C object _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/src/qs8-gemm/gen/qs8-gemm-4x16c4-minmax-rndnu-neon-mlal-ld1r.c.o\r\n\r\nld.lld: error: undefined symbol: eglGetProcAddress\r\n>>> referenced by async_buffers.cc\r\n>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/async_buffers.cc.o:(tflite::gpu::AsyncBuffer::MapAHardwareBufferToGlBuffer())\r\n>>> referenced by async_buffers.cc\r\n>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/async_buffers.cc.o:(tflite::gpu::AsyncBuffer::MapAHardwareBufferToGlBuffer())\r\n\r\nld.lld: error: undefined symbol: glGenBuffers\r\n>>> referenced by async_buffers.cc\r\n>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/async_buffers.cc.o:(tflite::gpu::AsyncBuffer::AllocateOpenGlBuffer())\r\n\r\nld.lld: error: undefined symbol: glBindBuffer\r\n>>> referenced by async_buffers.cc\r\n>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/async_buffers.cc.o:(tflite::gpu::AsyncBuffer::AllocateOpenGlBuffer())\r\n>>> referenced by async_buffers.cc\r\n>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/async_buffers.cc.o:(tflite::gpu::AsyncBuffer::AllocateOpenGlBuffer())\r\n\r\nld.lld: error: undefined symbol: glBufferData\r\n>>> referenced by async_buffers.cc\r\n>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/async_buffers.cc.o:(tflite::gpu::AsyncBuffer::AllocateOpenGlBuffer())\r\n\r\nld.lld: error: undefined symbol: tflite::gpu::gl::WaitFdGpu(int)\r\n>>> referenced by delegate.cc\r\n>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))\r\n\r\nld.lld: error: undefined symbol: tflite::gpu::gl::EglEnvironment::NewEglEnvironment(std::__ndk1::unique_ptr<tflite::gpu::gl::EglEnvironment, std::__ndk1::default_delete<tflite::gpu::gl::EglEnvironment>>*)\r\n>>> referenced by delegate.cc\r\n>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))\r\n\r\nld.lld: error: undefined symbol: tflite::gpu::gl::EglEnvironment::~EglEnvironment()\r\n>>> referenced by delegate.cc\r\n>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))\r\n>>> referenced by delegate.cc\r\n>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))\r\n\r\nld.lld: error: undefined symbol: tflite::gpu::gl::CreateFdGpu()\r\n>>> referenced by delegate.cc\r\n>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))\r\n```\r\n\r\nLooks like I can not get the gl `.so` file. Can you give some advice to fix the issue?",
"I think linkage with libGLES.so and compile macro has some error.\r\nI will try with NDK 26 on my device within days.\r\n\r\nMaybe additional commits will be appended",
"@luncliff Thanks for your reply. It's my mistake, the error happens when `TFLITE_ENABLE_GLES3=OFF`. And when I set `TFLITE_ENABLE_GLES3=ON`, everything is ok. You saved me.",
"Hi @zichuan-wei Can you please review this PR ? Thank you!",
"Hi @zichuan-wei Can you please review this PR ? Thank you!",
"If I have to rebase with the master, please let me know @zichuan-wei "
] | 2023-12-28T18:52:38 | 2024-06-08T02:16:42 | null |
NONE
| null | false |
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Hi, I'm maintaining [vcpkg](https://github.com/microsoft/vcpkg) registry for TensorFlow Lite.
I was patching CMakeLists.txt files to support multiple platforms, but the difference is getting bigger since 2.11
Will be glad if I can submit those changes upstream.
## Changes
* Create CMake build option `TFLITE_ENABLE_GLES3`
* Use `find_package(OpenGL)` to use `OpenGL::GLES3` https://cmake.org/cmake/help/latest/module/FindOpenGL.html
* Use `TFLITE_TARGET_PRIVATE_DEFINITIONS` for ease of macro search
### Android(EGL, OpenGL)
* Include related sources under `"${TFLITE_SOURCE_DIR}/delegates/gpu/cl"`
### Flatbuffers
* Run `flatc` on `${TFLITE_SOURCE_DIR}/delegates/gpu/gl/*.fbs`
## Example
The PR contains CMakePresets for Android.
After Git clone,
```ps1
cmake --preset arm64-android -S tensorflow/lite -DFLATBUFFERS_FLATC_EXECUTABLE:FILEPATH="...\flatc.exe"
```
```ps1
cmake --build cmake_build
```
## References
May collide with the following PR
* #61381
In-use patch files.
* https://github.com/luncliff/vcpkg-registry/pull/131
* https://github.com/luncliff/vcpkg-registry/pull/117
|
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I_kwDOArmXAs56tO4I
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how to implement movinet video action classification model in tensorflow c++ lite
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"@ranjith502 Could you try to use the tf.lite.Interpreter class to load the MoViNet model first then preprocess video frames into tensors of expected shape. After that pass tensors to the interpreter for inference and extract class probabilities or labels from output tensors. \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."
] | 2023-12-28T17:39:41 | 2024-01-17T01:49:31 | 2024-01-17T01:49:30 |
NONE
| null | null | null |
I appreciate the tutorial on MoViNet available at the TensorFlow models GitHub repository. Your contributions have been very helpful. I've experimented with the MoViNet model using custom data. However, during my search, I couldn't find any implementation of MoViNet in TensorFlow Lite for C++ for video action classification. Could you provide any references or guidance on this?
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TensorFlow在尝试将数据从CPU复制到GPU时遇到了问题
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"MirroredStrategy策略默认使用NCCL进行通讯，NCCL是不支持Windows的。你可以尝试使用tf.distribute.MirroredStrategy(cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())代替。\r\n当然，如果你不需要使用多卡并行计算，你可以直接去掉strategy = tf.distribute.MirroredStrategy()相关的代码。\r\n",
"Hi **@starxinyi** ,\r\n\r\nCheck GPU Availability:\r\nEnsure that your machine has a GPU available, and TensorFlow correctly recognizes the GPU. You can use the following code to verify:\r\n```\r\nfrom tensorflow.python.client import device_lib\r\nprint(device_lib.list_local_devices())\r\n```\r\nMake sure you can see the list of GPU devices without errors.\r\nMemory Settings:\r\nIn your code, you've already set GPU memory growth, but sometimes it might be necessary to explicitly set GPU memory allocation. You can try the following code:\r\n```\r\ngpus = tf.config.experimental.list_physical_devices('GPU')\r\nif gpus:\r\n try:\r\n for gpu in gpus:\r\n tf.config.experimental.set_memory_growth(gpu, True)\r\n logical_gpus = tf.config.experimental.list_logical_devices('GPU')\r\n print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\r\n except RuntimeError as e:\r\n print(e)\r\n```\r\nThis ensures that TensorFlow allocates only the required GPU memory.\r\nTensorFlow Version Compatibility:\r\nEnsure that your TensorFlow version is compatible with your GPU drivers. Sometimes, specific TensorFlow and GPU driver versions need to be matched for stability.\r\n\r\nThank you!",
"> 你好**@starxinyi**,\r\n> \r\n> 检查 GPU 可用性： 确保您的机器有可用的 GPU，并且 TensorFlow 可以正确识别 GPU。您可以使用以下代码来验证：\r\n> \r\n> ```\r\n> from tensorflow.python.client import device_lib\r\n> print(device_lib.list_local_devices())\r\n> ```\r\n> \r\n> 确保您可以看到 GPU 设备列表且没有错误。 内存设置： 在代码中，您已经设置了 GPU 内存增长，但有时可能需要显式设置 GPU 内存分配。您可以尝试以下代码：\r\n> \r\n> ```\r\n> gpus = tf.config.experimental.list_physical_devices('GPU')\r\n> if gpus:\r\n> try:\r\n> for gpu in gpus:\r\n> tf.config.experimental.set_memory_growth(gpu, True)\r\n> logical_gpus = tf.config.experimental.list_logical_devices('GPU')\r\n> print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\r\n> except RuntimeError as e:\r\n> print(e)\r\n> ```\r\n> \r\n> 这可确保 TensorFlow 仅分配所需的 GPU 内存。 TensorFlow 版本兼容性： 确保您的 TensorFlow 版本与 GPU 驱动程序兼容。有时，需要匹配特定的 TensorFlow 和 GPU 驱动程序版本以确保稳定性。\r\n> \r\n> 谢谢你！\r\n\r\n 我有可用的 GPU，你给的这个代码我本身也有，也确保TensorFlow 版本与 GPU 驱动程序兼容了",
"> MirroredStrategy策略默认使用NCCL进行通讯，NCCL是不支持Windows的。你可以尝试使用tf.distribute.MirroredStrategy(cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())代替。 当然，如果你不需要使用多卡资源计算，你可以直接去掉strategy = tf.distribute.MirroredStrategy()相关的代码。\r\n\r\n我按照您说的进行修改，得到的还是这个问题InternalError: Failed copying input tensor from /job:localhost/replica:0/task:0/device:CPU:0 to /job:localhost/replica:0/task:0/device:GPU:0 in order to run _EagerConst: Dst tensor is not initialized.无法解决",
"您好，请问问题解决了吗?我也遇到了相同的问题",
"> TensorFlow Version Compatibility:\r\n\r\n您好 请问解决了吗 我的代码也遇到了这个问题 而且在报错以前GPU会突然从百分之十左右飙升到100%的峰值，然后报错，我是nividia的T1000 4GB"
] | 2023-12-28T12:20:44 | 2024-04-18T07:28:40 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.10
### Custom code
Yes
### OS platform and distribution
Window 10
### Mobile device
_No response_
### Python version
3
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA11.2和cuDNN8.1
### GPU model and memory
NVIDIA Tesla V100-SXM2-32GB
### Current behavior?
这是我RNN模型一运行代码运行的错误：InternalError: Failed copying input tensor from /job:localhost/replica:0/task:0/device:CPU:0 to /job:localhost/replica:0/task:0/device:GPU:0 in order to run _EagerConst: Dst tensor is not initialized,
对于网上提出的解决方案1.GPU内存不足，我试过换成4个显卡的，还是出现同样的错误。2. TensorFlow版本问题：我现在的版本是2.10，但是我试过更换为2.4，2.5，2.6都是不行。3.减小你的批量大小（batch size），我试过改到batch size=2，也是同样的错误，4.我的TensorFlow和我的CUDA,cuDNN都是匹配的。5.关键是我这个模型的代码在CPU下运行是非常良好的，只是速度慢。改为GPU运行是为了提高速度，但是在GPU上一运行就出现这个错误。
### Standalone code to reproduce the issue
```shell
import numpy as np
import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
except RuntimeError as e:
print(e)
# from tensorflow.keras import mixed_precision
from keras import backend as K
from tensorflow.keras import layers, models
from tensorflow.keras.regularizers import l2
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from keras.utils import np_utils
import os
# policy = mixed_precision.Policy('mixed_float16')
# mixed_precision.set_global_policy(policy)
# 加载数据
data_folder = "RamanLib.npy"
X_train_list = []
y_train_list = []
X_test_list = []
y_test_list = []
for mineral_class in os.listdir(data_folder):
class_folder = os.path.join(data_folder, mineral_class)
for fold in range(5): # 假设你有5折数据
X_train = np.load(os.path.join(class_folder, f"fold{fold}_train_data.npy"))
X_test = np.load(os.path.join(class_folder, f"fold{fold}_test_data.npy"))
# 增加一个维度以满足RNN的输入要求
X_train = X_train[:, :, np.newaxis]
X_test = X_test[:, :, np.newaxis]
y_train = [mineral_class] * len(X_train)
y_test = [mineral_class] * len(X_test)
X_train_list.append(X_train)
y_train_list.extend(y_train)
X_test_list.append(X_test)
y_test_list.extend(y_test)
X_train = np.vstack(X_train_list) # 将列表转换为numpy数组
y_train = np.array(y_train_list)
X_test = np.vstack(X_test_list)
y_test = np.array(y_test_list)
# 对标签进行编码
encoder = LabelEncoder()
encoder.fit(np.concatenate((y_train, y_test)))
y_train = np_utils.to_categorical(encoder.transform(y_train))
y_test = np_utils.to_categorical(encoder.transform(y_test))
def create_model(neurons=128, l2_weight=0.001, dropout_rate=0, learning_rate=0.001):
model = models.Sequential()
model.add(layers.Bidirectional(layers.LSTM(neurons, input_shape=X_train.shape[1:], return_sequences=True,
kernel_regularizer=l2(l2_weight))))
model.add(layers.Dropout(dropout_rate))
model.add(layers.Bidirectional(layers.LSTM(neurons, return_sequences=True, kernel_regularizer=l2(l2_weight))))
model.add(layers.Dropout(dropout_rate))
model.add(layers.Bidirectional(layers.LSTM(neurons, return_sequences=True, kernel_regularizer=l2(l2_weight))))
model.add(layers.Dropout(dropout_rate))
model.add(layers.Bidirectional(layers.LSTM(neurons, kernel_regularizer=l2(l2_weight))))
model.add(layers.Dropout(dropout_rate))
model.add(layers.Dense(y_train.shape[1]))
model.add(layers.Activation('softmax'))
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
model.compile(optimizer=optimizer,
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
# 创建并编译模型
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
model = create_model(learning_rate=0.001)
# 训练模型
history = model.fit(X_train, y_train,
validation_data=(X_test, y_test),
epochs=50,
batch_size=32)
# 评估模型
train_loss, train_acc = model.evaluate(X_train, y_train)
print(f"Training Accuracy: {train_acc}")#训练完成后，使用测试集进行评价
test_loss, test_acc = model.evaluate(X_test, y_test)#查看模型在训练集上的损失和精度值
print(f"Test Accuracy: {test_acc}")
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5i3ET-
| 62,702 |
Update .bazelrc
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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/62702/checks?check_run_id=19996127640) 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 @prasannarajezzzy 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/\r\n\r\nAlso, Can you please sign CLA?\r\n\r\nThank you for your contribution!\r\n"
] | 2023-12-28T04:17:56 | 2023-12-28T18:40:03 | 2023-12-28T18:40:03 |
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Model conversion crashed when feed data during quantization
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[
"Hi @pkgoogle,\r\n\r\nI have reproduced the issue. It's getting crashed at the time of conversion. Here is the [gist](https://colab.research.google.com/gist/LakshmiKalaKadali/00d0b47c4bd47b490531314cab18a6bc/-62701.ipynb). The sampling rate recommendation in the error log is fixed by setting ```sample_rate= 16000``` and passed to the processor.\r\n\r\nThank You",
"I was able to reproduce with the same gist, @abattery, can you please take a look? Thanks.",
"Hi all, is there anything can do for helping the progress?\r\nThank you a lot.",
"Hi @houcheng, thanks for your interest. It probably depends on your familiarity, if you're comfortable with C++ & Bazel please review these BUILD files: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/lite/BUILD, https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/BUILD. The binaries here effectively use the same codepaths as when you do converter.convert, you'll have to figure out how to use them properly for your use case, then you can use lldb/gdb with these binaries to try and debug this.\r\n\r\nAlternatively you can research how to attach lldb/gdb to a python process (the one that gets launched when you do `python your_script.py`) and set a breakpoint somewhere above `tensorflow/lite/kernels/reduce.cc:445`, and then dig into why that error is being thrown.\r\n\r\nBoth of these require good C++ knowledge, so if you don't have that, you'll have to learn at least the basics first.",
"Hi @pkgoogle,\r\nThank you for kindly reply my questions. I've tried the bazel in my ubuntu before and it can compile okay by the default build script and works well. I think maybe I include too much code into the model code and there is some non primities python code caused the problem. I think I'll either reduce the features of the model or using the gdb breakpoints you mentioned for finding the exact problem, later.\r\n",
"@houcheng, np, you may also want to directly use the converter CLT: https://www.tensorflow.org/lite/models/convert/convert_models#command_line_tool_, this is essentially an entry point into what converter.convert does but it skips the python wrappers."
] | 2023-12-27T14:16:18 | 2024-01-16T19:30:49 | null |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution: Ubuntu 22.04
- TensorFlow installation (pip package or built from source): pip
- TensorFlow library (version, if pip package or github SHA, if built from source): install tensorflow==2.15.0
### 2. Code
#### Option B: Paste your code here or provide a link to a custom end-to-end colab
```
import tensorflow as tf
from transformers import WhisperProcessor, WhisperFeatureExtractor, WhisperTokenizer, TFWhisperForConditionalGeneration
import soundfile as sf
import numpy as np
class GenerateModel2(tf.Module):
def __init__(self, model):
super(GenerateModel2, self).__init__()
self.model = model
@tf.function(
input_signature=[
tf.TensorSpec((1, 80, 3000), tf.float32, name="input_ids"),
]
)
def serving(self, input_features):
outputs = self.model.generate(input_features)
return outputs
# Initialize the feature extractor, tokenizer, and processor
feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-tiny.en")
tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny.en", predict_timestamps=True)
processor = WhisperProcessor(feature_extractor, tokenizer)
# Load the Whisper model
model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
generate_model = GenerateModel2(model=model)
def representative_data_gen():
audio_input, sample_rate = sf.read("story-1-second-2.wav")
inputs = processor(audio_input, return_tensors="tf")
yield {"input_ids": inputs.input_features}
converter = tf.lite.TFLiteConverter.from_concrete_functions([generate_model.serving.get_concrete_function()], generate_model)
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS_INT8,
tf.lite.OpsSet.SELECT_TF_OPS
]
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
converter.representative_dataset = representative_data_gen
tflite_model_bin = converter.convert() # <---- crash here
```
### 3. Failure during conversion
* The converter.convert() failed at calibrator.FeedTensor()
* The stacktrace is
* optimize/calibrator.py", line 254, in calibrate
* optimize/calibrator.py", line 152, in _feed_tensors
* RuntimeError: tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.
* ....
* gather index out of boundsNode number 33 (GATHER) failed to invoke.
* Node number 390 (WHILE) failed to invoke.
Seems the process crashed when the calibrator collect data.
### 5. (optional) Any other info / logs
```
Summary on the non-converted ops:
---------------------------------
* Accepted dialects: tfl, builtin, func
* Non-Converted Ops: 317, Total Ops 1095, % non-converted = 28.95 %
* 314 ARITH ops, 3 TF ops
- arith.constant: 314 occurrences (f32: 244, i1: 1, i32: 69)
(i1: 1, i32: 1)
- tf.If: 3 occurrences (f32: 3)
(f32: 114, i32: 8)
(i32: 1)
(f32: 35)
(f32: 1, i32: 1)
(f32: 8)
(f32: 2)
(i32: 1)
(f32: 8, i32: 1)
(i1: 2)
(f32: 85)
(f32: 2)
(f32: 13)
(i1: 2)
(i1: 1)
(i1: 1)
(i1: 1)
(i1: 1)
(f32: 60)
(f32: 94, i32: 7)
(i32: 4)
(f32: 8)
(i1: 1)
(i1: 1)
(f32: 110, i32: 6)
(f32: 30)
(f32: 6, i32: 2)
(f32: 17)
(f32: 30)
(f32: 18, i32: 6)
(f32: 34, i32: 5)
(f32: 25)
(i32: 1)
2023-12-27 22:09:12.621978: I tensorflow/compiler/mlir/lite/flatbuffer_export.cc:2989] Estimated count of arithmetic ops: 30.250 G ops, equivalently 15.125 G MACs
It is strongly recommended to pass the `sampling_rate` argument to this function. Failing to do so can result in silent errors that might be hard to debug.
Traceback (most recent call last):
File "/home/houcheng/workspace/whisper.play/try-forced-ids-quantized-official/try-3-with-gm-conversion-lite.py", line 44, in <module>
tflite_model_bin = converter.convert() # <---- crash here
^^^^^^^^^^^^^^^^^^^
File "/home/houcheng/miniconda3/envs/cuda-transformers/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 2185, in convert
return super(TFLiteConverterV2, self).convert()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/houcheng/miniconda3/envs/cuda-transformers/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 1139, in wrapper
return self._convert_and_export_metrics(convert_func, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/houcheng/miniconda3/envs/cuda-transformers/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 1093, in _convert_and_export_metrics
result = convert_func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/houcheng/miniconda3/envs/cuda-transformers/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 1780, in convert
saved_model_convert_result = self._convert_as_saved_model()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/houcheng/miniconda3/envs/cuda-transformers/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 1760, in _convert_as_saved_model
return self._convert_from_saved_model(graph_def)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/houcheng/miniconda3/envs/cuda-transformers/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 1332, in _convert_from_saved_model
return self._optimize_tflite_model(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/houcheng/miniconda3/envs/cuda-transformers/lib/python3.11/site-packages/tensorflow/lite/python/convert_phase.py", line 215, in wrapper
raise error from None # Re-throws the exception.
^^^^^^^^^^^^^^^^^^^^^
File "/home/houcheng/miniconda3/envs/cuda-transformers/lib/python3.11/site-packages/tensorflow/lite/python/convert_phase.py", line 205, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/houcheng/miniconda3/envs/cuda-transformers/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 1037, in _optimize_tflite_model
model = self._quantize(
^^^^^^^^^^^^^^^
File "/home/houcheng/miniconda3/envs/cuda-transformers/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 735, in _quantize
calibrated = calibrate_quantize.calibrate(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/houcheng/miniconda3/envs/cuda-transformers/lib/python3.11/site-packages/tensorflow/lite/python/convert_phase.py", line 215, in wrapper
raise error from None # Re-throws the exception.
^^^^^^^^^^^^^^^^^^^^^
File "/home/houcheng/miniconda3/envs/cuda-transformers/lib/python3.11/site-packages/tensorflow/lite/python/convert_phase.py", line 205, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/houcheng/miniconda3/envs/cuda-transformers/lib/python3.11/site-packages/tensorflow/lite/python/optimize/calibrator.py", line 254, in calibrate
self._feed_tensors(dataset_gen, resize_input=True)
File "/home/houcheng/miniconda3/envs/cuda-transformers/lib/python3.11/site-packages/tensorflow/lite/python/optimize/calibrator.py", line 152, in _feed_tensors
self._calibrator.FeedTensor(input_array)
RuntimeError: tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.gather index out of boundsNode number 33 (GATHER) failed to invoke.Node number 390 (WHILE) failed to invoke.
(cuda-transformers) ➜ try-forced-ids-quantized-official git:(master) pip list | tensor
```
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| 2,056,945,028 |
PR_kwDOArmXAs5izoMF
| 62,700 |
fix DebugString
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PR_kwDOArmXAs5izjql
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Update graph_execution_options.cc by adding DebugString()
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I_kwDOArmXAs56mXco
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Add operator broadcast error
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[
"@zxros10 Please double-check the conversion command or configuration file for options to control tensor layout preservation. Could you try to consider different conversion tools with NCHW support?\r\nIn order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. Thank 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/62698\">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/62698\">No</a>\n"
] | 2023-12-27T03:46:57 | 2024-01-12T01:49:15 | 2024-01-12T01:49:12 |
NONE
| null | null | null |
### 1. System information
tensorflow 2.15
### 2. Code
#### Option A: Reference colab notebooks
When I convert onnx to tflite:
INFO: 19 / 25
INFO: onnx_op_type: Add onnx_op_name: Add_13
INFO: input_name.1: 20 shape: [384, 4, 144, 144] dtype: float32
INFO: input_name.2: 21 shape: [1, 4, 144, 144] dtype: float32
INFO: output_name.1: 22 shape: [384, 4, 144, 144] dtype: float32
ERROR: The trace log is below.
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/onnx2tf/utils/common_functions.py", line 309, in print_wrapper_func
result = func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/onnx2tf/utils/common_functions.py", line 382, in inverted_operation_enable_disable_wrapper_func
result = func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/onnx2tf/utils/common_functions.py", line 52, in get_replacement_parameter_wrapper_func
func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/onnx2tf/ops/Add.py", line 187, in make_node
correction_process_for_accuracy_errors(
File "/usr/local/lib/python3.10/dist-packages/onnx2tf/utils/common_functions.py", line 5764, in correction_process_for_accuracy_errors
dummy_op = tf_func(
File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/weak_tensor_ops.py", line 142, in wrapper
return op(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/tf_op_layer.py", line 119, in handle
return TFOpLambda(op)(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
ValueError: Exception encountered when calling layer "tf.math.add" (type TFOpLambda).
Dimensions must be equal, but are 4 and 144 for '{{node tf.math.add/Add}} = AddV2[T=DT_FLOAT](Placeholder, tf.math.add/Add/y)' with input shapes: [384,4,144,144], [1,144,144,4].
Call arguments received by layer "tf.math.add" (type TFOpLambda):
• x=tf.Tensor(shape=(384, 4, 144, 144), dtype=float32)
• y=tf.Tensor(shape=(1, 144, 144, 4), dtype=float32)
In onnx model, the add node's two input tensor shape is [384, 4, 144, 144] and [1, 4, 144, 144] . I think the second one can broadcast to [384, 4, 144, 144]. But the tflite convert the second to NHWC, and keep the first NCHW. Is this a bug ?
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I_kwDOArmXAs56lQoS
| 62,697 |
Adding new feature to perform reverse operation of tf.image.extract_patches
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[
"@ash-S26 \r\nYes, TensorFlow doesn't offer a direct inverse function for tf.image.extract_patches, here are several approaches to reconstruct an image or approximate the reverse operation as Gradient-Based Reconstruction:\r\n\r\n1. Use Gradient Tape: Employ tf.GradientTape to compute gradients of a loss function with respect to the extracted patches.\r\n2. Update Patches: Iteratively update the patches to minimize the loss, aiming to reconstruct the original image.\r\n\r\nPlease let us know more on the feature request you are proposing here with its use-case. \r\nThank you!",
"I conceived the idea when combining the patch merging concept from the Swin Transformer paper (https://arxiv.org/pdf/2103.14030.pdf) with the SwinIR Transformer (https://arxiv.org/pdf/2108.10257.pdf).\r\n\r\n\r\n**Background Explanation: -**\r\nThe Swin Transformer is an architecture designed for image classification. It starts with an image, for example, of size (224,224,3), divides it into patches of size (4,4), creating a new dimensional vector of shape (56,56,344). Attention is then calculated within small windows of size 7. In the next stage, patch merging combines adjacent patches (e.g., at indices [0,0], [0,1], [1,0], [1,1]), resulting in an output shape of (56/2,56/2,344*4). The process is repeated, and the final stage includes flattening the layers.\r\n\r\nCurrently, the SwinIR Transformer, designed for image super-resolution, does not utilize the patch merging concept. It maintains the same image size while calculating attention within a single window of size 7 and a small shifted window.\r\n\r\n\r\n**Use-Case (Why this feature came to my mind): -**\r\nThe goal is to integrate the benefits of patch merging into SwinIR, enabling the capture of global-level attention with reduced computation cost, thus enhancing the model. During the implementation, I observed an easy way to reach the last merging step but lacked a tool in TensorFlow to reverse the process (i.e., un-patch the image), placing pixels back in their original positions. This approach results in the next block containing pixels in their original positions but with more context/attention captured over the image. I raised this issue to propose the addition of this feature.\r\n\r\nAlso I am enthusiastic about contributing to the implementation of Swin Transformer and SwinIR Transformer for the official TensorFlow repository. I would appreciate the opportunity to create and contribute this feature. \r\n\r\nPlease let me know if you need additional information on this topic.\r\n\r\nThank you!"
] | 2023-12-26T17:36:13 | 2024-01-03T22:48:06 | null |
NONE
| null | null | null |
Need of feature :-
Will increase use of transformers in Computer Vision task, we need a way to efficient apply attention mechanism for images (unlike vision transformer which calculate attention for entire image). Here we can calculate attention over extracted patches for **purpose of Image Super-resolution, Image Restoration**. Currently there is no feature in Tensorflow to do reverse operation of tf.image.extract_patches, hence I would like to contribute to this feature.
Inspiration for feature :-
Swin Transformer
I would like to contribute for this feature. Could you please assign this task to me.
Thank you
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the cuda version of my laptop is 12.1, but I can not find the correspond tensorflow-gpu
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[
"From this webpage (https://www.tensorflow.org/install/source_windows), I see the following notice:\r\n\r\nI think if you want to leverage a GPU using CUDA 12 on that laptop, you have to try WSL2.",
"Hi **@yoyoengineer**,\r\nIt is clearly there on note, GPU support on native-Windows is only available for 2.10 or earlier versions, starting in TF 2.11, CUDA build is not supported for Windows. For using TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2 or use tensorflow-cpu with TensorFlow-DirectML-Plugin.\r\nCould you go through it once again [link](https://www.tensorflow.org/install/source_windows).\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/62696\">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/62696\">No</a>\n",
"Like 10 minutes\n\n[Infinito “BeenADonSlim” Azul](https://spikenow.com/r/a/?ref=spike-organic-signature&_ts=2f3w4q)\t[2f3w4q]\nJameo Nitti\n\nOn January 12, 2024 at 1:49 GMT, google ml butler bot ***@***.***> wrote:\n\nAre you satisfied with the resolution of your issue?\n[Yes](https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62696)\n[No](https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62696)\n\n—\nReply to this email directly, [view it on GitHub](https://github.com/tensorflow/tensorflow/issues/62696#issuecomment-1888286851), or [unsubscribe](https://github.com/notifications/unsubscribe-auth/A5CIWVE7VVZ2LTN2Y3F4EHTYOCJDDAVCNFSM6AAAAABBDG4LVSVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQOBYGI4DMOBVGE).\nYou are receiving this because you are subscribed to this thread.Message ID: ***@***.***>"
] | 2023-12-26T12:06:24 | 2024-01-12T01:50:18 | 2024-01-12T01:49:14 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
...
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
the cuda version of my laptop is 12.1, but I can not find the correspond tensorflow-gpu version from
https://tensorflow.google.cn/install/source_windows?hl=zh-cn#gpu
### Standalone code to reproduce the issue
```shell
...
```
### Relevant log output
_No response_
|
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Lack of wheel file for tensorflow 1.x versions for aarch64 architecture machines.
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[
"@akashKaitCaastle,\r\nWe see that you are using tf version 1.15, 1.x is not actively supported, please update to 2.x.\r\nI can see the tensorflow pypi wheels are available for aarch64 [tensorflow-aarch64 · PyPI](https://pypi.org/project/tensorflow-aarch64/#files) for tensorflow v2.15. Could you please try by using pip install tensorflow-aarch64. \r\nThank You!",
"Hey, the reason why we require tensorflow 1.x version is that, we are using [tensorrec](https://github.com/jfkirk/tensorrec) which is a recommendation algo based upon tensorflow and it strictly requires versions >1.7 and <1.15\r\nSeems like tensorrec project is not actively supported anymore and we are forced to use TF 1.x versions. Till now we've been using i386 machines for the job, but now we are shifting to aarch64 machines, hence facing this issue. We are trying to get away from tensorrec and use more active algorithms for our usecase, but for now we require TF 1.x wheel file in aarch64.",
"I also just hit this installation problem. While further dev on version 1.15 shouldn't be done, there is a lot of legacy code that requires it, personal projects, third party and even gits on deepmind github that have not been updated. For example https://github.com/google-deepmind/grid-cells\r\n\r\nUbuntu 22.05\r\n\r\n",
"a comment on my comment - even though pip only installs > 2.0, mamba repoquery search tensorflow shows a whole bunch of distributions from - 0.7 to 1.14, as well as 2.0 + \r\nIll have to docker it as it 1.14 as it seems 1.14 requires python 3.6\r\n\r\nstill, keeping at least 1.15 in pypi would be preferred. implementations with it that may be required for reproducibility are everywhere still. ",
"docker pull tensorflow/tensorflow:1.15.0-gpu\r\nits all there. dont deprecate that one please",
"@xvdp is it possible to use this tensorflow wheel file outside of docker container?",
"> @xvdp is it possible to use this tensorflow wheel file outside of docker container?\r\n\r\nThat would be crazy, no? it requires python 3.6 and CUDA 10, I think. If you have the machine set up like that, maybe you can look at the Dockerfile and follow the installation steps. I think it would be a waste of time anyway. Time better spent, figuring out how to install and use docker\r\n"
] | 2023-12-26T11:53:29 | 2024-01-12T09:57:41 | null |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
1.15
### Custom code
Yes
### OS platform and distribution
Amazon Linux 2
### Mobile device
Amazon Linux 2
### Python version
3.7
### Bazel version
0.26.1
### GCC/compiler version
7.3.1
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
As mentioned, there's a lack of aarch64 architecture wheel file for tensorflow 1.x version. I have built v1.15.0 from source after a few patches to make the wheel for aarch64. Would be really great of this community, if they can upload this wheel file to pypi so that it is accessible through "pip install tensorflow==1.15" for my team as well anyone else who would like to use tensorflow 1.x in this architecture. I have attached the patch file which you can use to build the code and generate the wheel file. If possible, please upload that wheel file to the pypi.
[tensorflow1_15_aarch64.patch](https://github.com/tensorflow/tensorflow/files/13771176/tensorflow1_15_aarch64.patch)
The change in setup.py is because, when i install the wheel file generated through "pip install tensorflow.whl -t lib" to my lib folder like this I get only header files in my tensorflow_core folder. Only if i "--upgrade" to the command, then I'm able to get the lib files. To change this behaviour, I changed the path for header files.
### Standalone code to reproduce the issue
```shell
NA
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5ixL0b
| 62,694 |
Fixing an issue with reflect padding in TfLite GPU delegate
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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/62694/checks?check_run_id=19956194708) 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 @mkarpushin-enhancelab Can you please sign CLA. Thank you!",
"Hi @gbaned,\r\n\r\nCLA has been signed and seems to be accepted by the bot.\r\n\r\nThank you.",
"Hello @gbaned and @sirakiin,\r\n\r\nAny update on this?\r\n\r\nThanks,\r\nMax",
"This is test comment Alpha Gamma Zeta",
"Hello @gbaned and @sirakiin,\r\n\r\nWe would love this to be fixed. Could you please take a look?\r\n\r\nThanks,\r\nMax",
"Hi @sirakiin Can you please review this PR ? Thank you!",
"Hello @gbaned and @sirakiin,\r\n\r\nWould it be possible to conduct a quick review of this change?\r\n\r\nThanks.",
"Hi @sirakiin Can you please review this PR ? Thank you!",
"Hi @sirakiin Can you please review this PR ? Thank you!"
] | 2023-12-26T09:07:08 | 2024-06-07T16:40:01 | null |
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This PR fixes a bug in an OpenCL kernel code for reflect padding operation in TfLite GPU delegate.
This issue occurs systematically when the following conditions are met:
* Running inference of a model containing `tf.pad` operation with `mode="REFLECT"`, getting an input tensor with batch dimension
* Using TfLite GPU delegate with OpenCL backend in Android
When these conditions are fulfilled, the following diagnostics message appears in logcat during the interpreter initialization in Android code:
Once linked to the source code, this error message allows to easily identify the root cause: under the conditions above, a variable named `s_b` appears declared twice in the corresponding OpenCL kernel code.
- Its first declaration is appended to the kernel code at [line 71 of padding.cc](https://github.com/tensorflow/tensorflow/blob/04cac016115e031ea988fac42f42f79b6b55602f/tensorflow/lite/delegates/gpu/common/tasks/padding.cc#L71).
- If the input tensor has batch dimension and the padding mode is set to `REFLECT`, **the same variable is further redeclared** [at line 78](https://github.com/tensorflow/tensorflow/blob/04cac016115e031ea988fac42f42f79b6b55602f/tensorflow/lite/delegates/gpu/common/tasks/padding.cc#L78), whine being used at the same line in the right-hand side of the assignment.
We believe, a simple assignment without declaration should take place. This PR brings this change.
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I_kwDOArmXAs56jkB5
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I can't install tensorflow lower than 2.13
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[
"I think this is because you are on an ARM Mac (M1?) and ARM support has been added on 2.13.",
"@unolife TensorFlow 2.13 introduced official pre-built wheels for Apple Silicon Macs, making installation simpler. It might be a reason why you are facing this challenge.\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/62693\">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/62693\">No</a>\n"
] | 2023-12-26T07:54:02 | 2024-01-12T01:49:21 | 2024-01-12T01:49:16 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.13
### Custom code
No
### OS platform and distribution
macOS Ventura 13.5.2
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
If I type pip install tensorflow==2.10.1 in my mac, I get this
'ERROR: Could not find a version that satisfies the requirement tensorflow==2.10.1 (from versions: 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0, 2.13.1, 2.14.0rc0, 2.14.0rc1, 2.14.0, 2.14.1, 2.15.0rc0, 2.15.0rc1, 2.15.0)
ERROR: No matching distribution found for tensorflow==2.10.1'
### Standalone code to reproduce the issue
```shell
pip install tensorflow==2.10.1
```
### Relevant log output
```shell
ERROR: Could not find a version that satisfies the requirement tensorflow==2.10.1 (from versions: 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0, 2.13.1, 2.14.0rc0, 2.14.0rc1, 2.14.0, 2.14.1, 2.15.0rc0, 2.15.0rc1, 2.15.0)
ERROR: No matching distribution found for tensorflow==2.10.1
```
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Reduce TensorFlow Lite binary size(Build custom C/C++ shared libraries on android) For the models with the Select TF ops fail.
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[
"@Qinlong275 Please ensure your models are compatible with the Select TF ops feature. Register any custom ops with the selective build system. Make sure to use Bazel to build an AAR file containing the selective TensorFlow Lite library and include the AAR in your Android project. Please let us know if it works?\r\n\r\nThank you!",
"> @Qinlong275 Please ensure your models are compatible with the Select TF ops feature. Register any custom ops with the selective build system. Make sure to use Bazel to build an AAR file containing the selective TensorFlow Lite library and include the AAR in your Android project. Please let us know if it works?\r\n> \r\n> Thank you!\r\n I just follw the link https://www.tensorflow.org/lite/guide/reduce_binary_size, want to build the reduce libtensorflowlite_flex.so, use these code:\r\nload(\r\n \"@org_tensorflow//tensorflow/lite/delegates/flex:build_def.bzl\",\r\n \"tflite_flex_shared_library\"\r\n)\r\n\r\n# Shared lib target for convenience, pulls in the standard set of TensorFlow\r\n# ops and kernels. The output library name is platform dependent:\r\n# - Linux/Android: `libtensorflowlite_flex.so`\r\n# - Mac: `libtensorflowlite_flex.dylib`\r\n# - Windows: `libtensorflowlite_flex.dll`\r\ntflite_flex_shared_library(\r\n name = \"tensorflowlite_flex\",\r\n models = [\r\n \":FRCRN_model.tflite\",\r\n ],\r\n)\r\n\r\nbazel build -c opt --cxxopt='--std=c++17' \\\r\n --config=android_arm \\\r\n --config=monolithic \\\r\n --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \\\r\n //tmp:tensorflowlite_flex\r\n\r\nAnd my model is transfer with select op use the link https://www.tensorflow.org/lite/guide/ops_select?hl=zh-cn:\r\nimport tensorflow as tf\r\n\r\nconverter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)\r\nconverter.target_spec.supported_ops = [\r\n tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.\r\n tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.\r\n]\r\ntflite_model = converter.convert()\r\nopen(\"converted_model.tflite\", \"wb\").write(tflite_model)\r\n\r\n",
"Hi @Qinlong275, can you share your /tmp/BUILD file? It's created here as part of the instructions: https://www.tensorflow.org/lite/guide/reduce_binary_size#advanced_usages_build_custom_cc_shared_libraries",
"> 你好@Qinlong275，你能分享你的 /tmp/BUILD 文件吗？它是作为说明的一部分在此处创建的： https: [//www.tensorflow.org/lite/guide/reduce_binary_size#advanced_usages_build_custom_cc_shared_libraries](https://www.tensorflow.org/lite/guide/reduce_binary_size#advanced_usages_build_custom_cc_shared_libraries)\r\n[Uploading tmp.zip…]()\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/62692\">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/62692\">No</a>\n"
] | 2023-12-26T02:20:55 | 2024-01-19T01:49:23 | 2024-01-19T01:49:19 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tflite 2.12.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
6.3.2-homebrew
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I follow the document(https://www.tensorflow.org/lite/guide/reduce_binary_size), want to build the reduce android tensorflowlite_flex so. But occur some error.
Repository rule _tf_http_archive defined at:
/Users/qinlong/android/tensorflow/third_party/repo.bzl:89:35: in <toplevel>
INFO: Repository boringssl instantiated at:
/Users/qinlong/android/tensorflow/WORKSPACE:15:14: in <toplevel>
/Users/qinlong/android/tensorflow/tensorflow/workspace2.bzl:938:21: in workspace
/Users/qinlong/android/tensorflow/tensorflow/workspace2.bzl:562:20: in _tf_repositories
/Users/qinlong/android/tensorflow/third_party/repo.bzl:136:21: in tf_http_archive
Repository rule _tf_http_archive defined at:
/Users/qinlong/android/tensorflow/third_party/repo.bzl:89:35: in <toplevel>
ERROR: /private/var/tmp/_bazel_qinlong/4d0b12327583c8b4e37f0824509a394f/external/llvm-project/llvm/BUILD.bazel:154:11: Illegal ambiguous match on configurable attribute "defines" in @llvm-project//llvm:config:
@llvm-project//llvm:macos_arm64
@bazel_tools//src/conditions:darwin
Multiple matches are not allowed unless one is unambiguously more specialized or they resolve to the same value. See https://bazel.build/reference/be/functions#select.
ERROR: Analysis of target '//tmp:tensorflowlite_flex' failed; build aborted:
INFO: Elapsed time: 1.323s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (1 packages loaded, 87 targets con\
figured)
Fetching https://storage.googleapis.com/.../archive/2.1.0.tar.gz
Fetching https://storage.googleapis.com/...sqlite-amalgamation-3390400.zip
Fetching https://storage.googleapis.com/.../cython/archive/3.0.0a10.tar.gz
Fetching https://storage.googleapis.com/.../source/d/dill/dill-0.3.4.zip
Fetching https://storage.googleapis.com/.../refs/tags/131.1.0.tar.gz
Fetching https://storage.googleapis.com/.../download/curl-7.85.0.tar.gz
Fetching https://storage.googleapis.com/...6a0fb8ca9b1bf60c3b283ce8.tar.gz
### Standalone code to reproduce the issue
```shell
bazel build -c opt --cxxopt='--std=c++17' \
--config=android_arm64 \
--config=monolithic \
--host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
//tmp:tensorflowlite_flex
```
### Relevant log output
_No response_
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I_kwDOArmXAs56heeB
| 62,691 |
UnicodeDecodeError when file path of keras model contains an umlaut such as 'ü'
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[
"Hi **@Steffenhir** ,\r\nSorry for the delay, This issue arises because the default encoding used by the file I/O functions in python is 'utf-8', and the path contains characters that cannot be decoded using this encoding.\r\nTo resolve this issue, you can try specifying the encoding explicitly when providing the file path.\r\n```\r\nimport tensorflow as tf\r\n\r\nAI_dir = r'C:/Users/steff/Desktop/background/Background_extraction/ü/weights_G_RGB.keras'\r\nmodel = tf.keras.models.load_model(AI_dir, compile=False)\r\n```\r\n\r\nIn this example, the 'r' prefix before the string (raw string) indicates that the string should be treated as a raw string literal, and this can sometimes help with special characters in file paths.\r\n\r\nThank you!",
"Hi @Venkat6871,\r\nthank you for your help! Unfortunately, putting the 'r' prefix in front of the string does not help and I get the same error. Do you have another idea how I could solve this problem?",
"I get the same error when using a pathlib.path object as an argument",
"Hi @Steffenhir,\r\nI've been looking into issue [#62691](https://github.com/tensorflow/tensorflow/issues/62691) regarding the UnicodeDecodeError with non-UTF-8 characters in file paths. \r\n\r\nAfter reviewing the related code in file_io.py, I believe I have a potential solution that could address this issue. The main idea revolves around modifying the FileIO class to handle file paths with a broader range of character encodings, especially on Windows where UTF-8 is not the default encoding. Here are the key points of the proposed solution:\r\n\r\nAdaptive Default Encoding: Conditionally set the default encoding based on the operating system. For instance, using 'mbcs' for Windows, which is more accommodating for such characters.\r\n\r\nGraceful Handling of Encoding Errors: Implement a fallback mechanism in the _prepare_value method to try alternative encodings if a UnicodeEncodeError is encountered.\r\n\r\nIf this approach seems viable, I'd be keen to work on it. Could you consider assigning me to this task, or would you like to discuss this further?",
"Hi **@Steffenhir**,\r\n\r\nCould you try this once,\r\n````\r\nimport tensorflow as tf\r\n\r\n# File path with non-ASCII characters (e.g., 'ü')\r\nAI_dir = 'C:/Users/steff/Desktop/background/Background_extraction/ü/weights_G_RGB.keras'\r\n\r\n# Load the model with explicit encoding (e.g., 'latin1')\r\nmodel = tf.keras.models.load_model(AI_dir, compile=False, custom_objects=None, options=None, file_content=None, encoding='latin1')\r\n\r\n# Now you can use the loaded model\r\n````\r\nBy adding the encoding='latin1' parameter to the load_model function, you can specify a different encoding that handles non-ASCII characters correctly. In this example, 'latin1' is used, but you may also try other encodings if needed. Additionally, the compile, custom_objects, options, and file_content parameters are included for completeness; you can adjust them based on your specific use case.\r\n\r\nThank you!",
"Hi @Venkat6871,\r\nwhen I specify the encoding with the encoding parameter, that is\r\n```\r\nmodel = tf.keras.models.load_model(AI_dir, compile=False, encoding='latin1')\r\n```\r\nI get the error:\r\n```\r\n2023-12-29 10:25:17,229 MainProcess root ERROR load_model() got an unexpected keyword argument 'encoding'\r\nTraceback (most recent call last):\r\n File \"C:\\Users\\steff\\Desktop\\background\\Background_extraction\\GraXpert\\graxpert\\application\\app.py\", line 141, in on_calculate_request\r\n extract_background(\r\n File \"C:\\Users\\steff\\Desktop\\background\\Background_extraction\\GraXpert\\graxpert\\background_extraction.py\", line 74, in extract_background\r\n model = tf.keras.models.load_model(AI_dir, compile=False, encoding='latin1')\r\n File \"C:\\Users\\steff\\anaconda3\\envs\\graxpert\\lib\\site-packages\\keras\\src\\saving\\saving_api.py\", line 238, in load_model\r\n return legacy_sm_saving_lib.load_model(\r\n File \"C:\\Users\\steff\\anaconda3\\envs\\graxpert\\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\\steff\\anaconda3\\envs\\graxpert\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\nTypeError: load_model() got an unexpected keyword argument 'encoding'\r\n```\r\n\r\nWhen I specify all parameters as in your example, I get the same error for the unexpected keyword 'file_content'.\r\n\r\nI think @printROSHN's proposal makes sense for Windows users and would be nice to have!\r\n",
"With the tensorflow.saved_model.load API I can load models from directories which contain special characters such as german umlauts",
"Hi @Steffenhir ,\r\n\r\nCould you try encoding the the string `AI_dir` first to `utf-8 ` like this below snippet.\r\n\r\n```\r\nAI_dir = \"C:/Users/steff/Desktop/background/Background_extraction/ü/weights_G_RGB.keras\"\r\nAI_dir = AI_dir.encode('utf-8')\r\nmodel = tf.keras.models.load_model(AI_dir)\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.",
"Hey @Steffenhir ,\r\n\r\nIf this issue is still unresolved, I would like to attempt to address it.\r\n\r\nThanks.",
"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/62691\">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/62691\">No</a>\n"
] | 2023-12-25T10:34:25 | 2024-01-27T01:46:22 | 2024-01-27T01:46:20 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.10
### Custom code
Yes
### OS platform and distribution
Windows 10
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hi, when I try to load a keras model with the tf.keras.models.load_model(file_path) method, I get an error when the file path to my .keras model contains an umlaut such as 'ü'
### Standalone code to reproduce the issue
```shell
AI_dir = 'C:/Users/steff/Desktop/background/Background_extraction/ü/weights_G_RGB.keras'
model = tf.keras.models.load_model(AI_dir)
```
### Relevant log output
```shell
2023-12-25 11:28:14,472 MainProcess root ERROR 'utf-8' codec can't decode byte 0xfc in position 60: invalid start byte
Traceback (most recent call last):
File "C:\Users\steff\Desktop\background\Background_extraction\GraXpert\graxpert\application\app.py", line 141, in on_calculate_request
extract_background(
File "C:\Users\steff\Desktop\background\Background_extraction\GraXpert\graxpert\background_extraction.py", line 72, in extract_background
model = tf.keras.models.load_model(AI_dir)
File "C:\Users\steff\anaconda3\envs\graxpert\lib\site-packages\keras\src\saving\saving_api.py", line 238, in load_model
return legacy_sm_saving_lib.load_model(
File "C:\Users\steff\anaconda3\envs\graxpert\lib\site-packages\keras\src\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\steff\anaconda3\envs\graxpert\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 703, in is_directory_v2
return _pywrap_file_io.IsDirectory(compat.path_to_bytes(path))
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xfc in position 60: invalid start byte
```
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I_kwDOArmXAs56gAQ-
| 62,690 |
How to get tf.data autotune metrics?
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[
"Based on the tensorflow documentation, it seems there are different autotune algorithms. \r\nhttps://www.tensorflow.org/api_docs/python/tf/data/experimental/AutotuneAlgorithm\r\n\r\nOpen the **symbols panel** to see the structure in the options.py file below\r\nhttps://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/data/ops/options.py#L540\r\n",
"@franklsf95,\r\n**tf.data** builds a performance model of the input pipeline and runs an optimization algorithm to find a good allocation of its CPU budget across all parameters specified as **AUTOTUNE**. While the input pipeline is running, `tf.data` tracks the time spent in each operation, so that these times can be fed into the optimization algorithm.\r\n\r\nThe [OptimizationOptions](https://www.tensorflow.org/api_docs/python/tf/data/experimental/OptimizationOptions?version=stable) object gives some control over how autotune will behave. Thank you!",
"@tilakrayal I understand the autotune algorithm, but want to see how it works in action. Specifically, for example, the code logs the stopping condition for the autotune algorithm (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/model.cc#L2695), which would help me understand how the algorithm works. My question is where can I find these metrics? They must be logged somewhere."
] | 2023-12-24T20:50:52 | 2024-01-03T22:42:51 | null |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
latest
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I want to better understand the internals of the `tf.data` autotuning algorithm. I found that a lot of metrics are logged in https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/metrics.h and https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/model.cc, but I cannot find in my program runs where these metrics are logged or how I can get programatic access to them.
For example, I want to see how tensorflow models the output latency of my operators, according to Section 3.3.2 of the tf.data paper (https://arxiv.org/abs/2101.12127). Can I somehow get this information from either the Python API or the logs?
cc @tlongeri if you are still working on Tensorflow metrics
### Standalone code to reproduce the issue
```shell
Any tf.data pipeline. I'm using the code from https://www.tensorflow.org/guide/data_performance#reproducing_the_figures
```
### Relevant log output
_No response_
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I_kwDOArmXAs56fQp4
| 62,689 |
Unable to build TensorFlow without TensorRT
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"@torotoki \r\nDuring the configuration stage, explicitly disable TensorRT using the following command:\r\n\r\nBash\r\n```\r\n./configure --no_tensorrt\r\n```\r\nPlease double-check the generated .config file to ensure TensorRT is indeed disabled.\r\nThank you!",
"@sushreebarsa Thank you for your response. But I couldn't find that argument or any related arguments. \r\n```\r\n>>> ./configure --no_tensorrt\r\nusage: configure.py [-h] [--workspace WORKSPACE]\r\nconfigure.py: error: unrecognized arguments: --no_tensorrt\r\n```\r\n\r\nAlso I couldn't find any `.config` file. (You meant `.bazel`?). Anyway, it is too hard to disable TensorRT in `.bazel` because there are so many lines that contains `tensorrt` which are incorporated in many other complicated building commands.\r\n\r\nIn my opinion, this is a bug related to the build code",
"I can confirm of having the same problem, the only difference is I am using CUDA 12.2 and cuDNN 8.9.\r\nUsing either CLang v16 or v17 produces the same issue with tensorrt.\r\nAlso have tried some combinations of editing the Bazel Config file \".tf_configure.bazelrc\" for -no_tensorrt, but still have the same result.\r\n\r\nAnd did try of installing TensorRT without the purpose the using it, to see if the error would then be corrected, but the error remained.",
"Just to add, taking a shot that perhaps the checkout version of 2.15.0 does not contain the Hotfix as indicated from here:\r\nhttps://blog.tensorflow.org/2023/12/tensorflow-215-update-hot-fix-linux-installation-issue.html\r\nAccording to the blog post, to install v2.15.0 is to use \"2.15.0.post1\", which does not appear to be possible with a checkout.",
"Another update, interestingly I was able to compile from the Master (v2.16.0), but only by using GCC, as both versions of CLang v16 and v17 failed. I am however, unable to compile v2.15.0 with CLang or GCC.\r\n\r\nI have to go through the process again as my current system is dirty from attempting to get TensorFlow with GPU to compile. I had installed TensorRT with the intent of tricking v2.15.0 to compile without using TensorRt if that makes since. So I am not sure as if that was needed for v2.16.0 to compile. \r\n\r\nUsed:\r\n- Ubuntu v24.04 with updates/upgrades\r\n- Base OS build of Python v3.10\r\n- Nvidia driver v535\r\n- Cuda v12.2\r\n- cuDNN v8.9.0\r\n\r\nAlso interesting that the finished whl file requires Python v3.11 for use, but not to compile. \r\n\r\nSo from what I gather, TensorFlow v2.15.0 is not possible to compile from source as per documentation. \r\n",
"Hello!\r\nAnother way to build from source is to use python3.10 with TensorFlow v2.14 which seems more stable.\r\n\r\n- check out another release branch\r\n`git checkout r2.14`\r\n- Run the configuration script\r\n`./configure`\r\n- Use **nvcc** instead of Clang to compile cuda.\r\n> Do you want to use Clang to build TensorFlow? [Y/n] **N**\r\n\r\n\r\nContinue with the instructions as described [here](https://www.tensorflow.org/install/source#configure_the_build).\r\n\r\nI hope this helps.",
"I do agree and for a stable version, though that does prevent the use of CUDA v12.\r\nBelow are the steps for building from source for the current version, v2.16. I haven't put v2.16 into rigorous testing to determine its issues or limitations, however.\r\nAlthough I have CLang listed below for how to install, I could only get TensorFlow v2.16 GPU to work with NVCC/GCC (as stated above for v2.14, by TheophileH)\r\n\r\n**INSTALLING TENSORFLOW FOR GPU v2.16.0**\r\n\r\n1. INSTALLATION UBUTUN DESKTOP v24.04.3 LTS\r\nUsed Minimal Installation\r\n< Did not download 3rd Party Applications\r\n< Ubuntu v22.04.3 LTS (Minimal Installation)\r\n< Python3: v3.10.12\r\n< No Nvida, Cuda, GCC, No CLang, No Bazel\r\n\r\n2. PERFORM UPDATES\r\nAfter installing Ubuntu, go to the Software Updater:\r\n< Ubuntu Software > Download from: Main Server\r\n< Click on Close\r\n< Click on Reload\r\nOpen Software Updater:\r\n< Additional Drivers > Select: Using NVIDIA driver metapackage from nvidia-driver-535 (proprietary,tested)\r\n< Click on Apply Changes\r\n< Close\r\nOpen Software Updater:\r\n< Wait for window popup to perform updates, or click on the Nortification error due to Ubuntu Pro not loading, then click on Show Updates\r\n< At the Software Updater, click on Install Now\r\n< Click on Restart Now\r\n< Allow Ubuntu to bootup normally this time\r\n\r\n3. SETUP ENVIRONMENT\r\nOpen Terminal from Menu\r\nTo check for Nvidia Cards:\r\n`lspci | grep -i nvidia`\r\nTo check the Nvidia Version:\r\n`nvidia-smi | grep \"Driver Version\" | awk '{print $6}' | cut -c1-`\r\nOR\r\n`nvidia-smi`\r\nSetup Python, can first check the current version:\r\n`python3 -V`\r\n< Result would be: Python 3.10.12\r\nAdd in Python Repositories and Dependencies\r\n`sudo add-apt-repository ppa:ubuntu-toolchain-r/test`\r\n`sudo add-apt-repository ppa:deadsnakes/ppa`\r\n`sudo apt update -y && sudo apt upgrade -y`\r\n`sudo apt install git python-is-python3 python3-pip python3-dev patchelf -y`\r\nTo Verify:\r\n`python -V`\r\nSet Path:\r\n`sudo nano ~/.bashrc`\r\nAdd at the end of the file:\r\n`export PATH=\"$PATH:/home/sysop/.local/bin\"`\r\nSave and exit\r\nTo apply changes:\r\n`source ~/.bashrc`\r\n\r\n4. INSTALL PYTHON v3.11.7:\r\nTo verify Python 3.11 is available:\r\n`apt list | grep python3.11`\r\nTo install all Python 3.11 modules:\r\n`sudo apt install python3.11-full -y`\r\nVerify Install:\r\n`python3.11 -V`\r\nResult would be: Python 3.11.7\r\nSet Alternative Versions for Python3:\r\n`sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1`\r\n`sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.10 2`\r\nPython v3.10 is automatically set to Auto Mode, but can be edit as Manual:\r\n`sudo update-alternatives --config python3`\r\n< Select v3.10\r\n\r\n5. INSTALL BAZELISK:\r\n`cd Downloads`\r\n`wget https://github.com/bazelbuild/bazelisk/releases/download/v1.19.0/bazelisk-linux-amd64`\r\n`chmod +x bazelisk-linux-amd64`\r\n`sudo mv bazelisk-linux-amd64 /usr/local/bin/bazel`\r\n`sudo nano ~/.bashrc`\r\nAdd at the end:\r\n`export PATH=/usr/local/bin/bazel:$PATH`\r\nAfter closing the editor, type,\r\n`source ~/.bashrc`\r\n\r\n6. INSTALL CLANG v16:\r\n`wget https://apt.llvm.org/llvm.sh`\r\n`chmod +x llvm.sh`\r\n`sudo ./llvm.sh 16`\r\n\r\nOPTIONAL: IF DUAL MONITORS ARE NOT WORKING:\r\n`sudo prime-select nvidia`\r\n`sudo reboot`\r\n\r\n7. PREPARE FOR TENSORFLOW:\r\nOpen Terminal from Menu\r\n`pip install -U --user pip numpy wheel packaging requests opt_einsum`\r\n`pip install -U --user keras_preprocessing --no-deps`\r\n**I have found this trick to work, to get additional dependencies and of the correct versions:**\r\n`pip install tensorflow==2.15.0 --upgrade`\r\n\r\n8. PULL REPOSITORY TENSORFLOW:\r\n`git clone https://github.com/tensorflow/tensorflow.git`\r\n`cd tensorflow/`\r\n**No Checkout**\r\nCheck for CPU Flags:\r\n`grep flags -m1 /proc/cpuinfo | cut -d \":\" -f 2 | tr '[:upper:]' '[:lower:]' | { read FLAGS; OPT=\"-march=native\"; for flag in $FLAGS; do case \"$flag\" in \"sse4_1\" | \"sse4_2\" | \"ssse3\" | \"fma\" | \"cx16\" | \"popcnt\" | \"avx\" | \"avx2\") OPT+=\" -m$flag\";; esac; done; MODOPT=${OPT//_/\\.}; echo \"$MODOPT\"; }`\r\n< Copy the flags, such as:\r\n`-march=native -mssse3 -mcx16 -msse4.1 -msse4.2 -mpopcnt`\r\n\r\n9. NVIDIA CUDA TOOLKIT:\r\nFOR CUDA v12.2:\r\nFirefox: https://developer.nvidia.com/cuda-downloads\r\n< Scroll to bottom, click on \"Archive of Previous CUDA Releases\"\r\n< Click on: CUDA Toolkit 12.2\r\n`cd Downloads`\r\n`wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin`\r\n`sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600`\r\n`wget https://developer.download.nvidia.com/compute/cuda/12.2.0/local_installers/cuda-repo-ubuntu2204-12-2-local_12.2.0-535.54.03-1_amd64.deb`\r\n`sudo dpkg -i cuda-repo-ubuntu2204-12-2-local_12.2.0-535.54.03-1_amd64.deb`\r\n`sudo cp /var/cuda-repo-ubuntu2204-12-2-local/cuda-*-keyring.gpg /usr/share/keyrings/`\r\n`sudo apt update -y && sudo apt upgrade -y`\r\n`sudo apt install cuda -y`\r\n`sudo apt install nvidia-gds -y`\r\n`sudo reboot`\r\n\r\n10. ADD PERSISTENCE:\r\nLog back in, open Terminal and type:\r\n`export PATH=/usr/local/cuda-12.2/bin${PATH:+:${PATH}}`\r\n`export LD_LIBRARY_PATH=/usr/local/cuda-12.2/lib64\\\r\n ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}`\r\n`source ~/.bashrc`\r\n`systemctl status nvidia-persistenced`\r\nVerify version:\r\n`cat /proc/driver/nvidia/version`\r\nCheck Versions:\r\n`nvidia-smi`\r\nOR\r\n`nvcc --version`\r\n\r\n11. INSTALL NVIDIA cuDNN:\r\nFOR cuDNN v8.9.0:\r\n(Requires registration/login)\r\nFirefox: https://developer.nvidia.com/cudnn\r\nClick on: Download cuDNN Library\r\nCheckbox \"I Agree To the Terms of the cuDNN Software License Agreement\"\r\nClick on the Archived cuDNN Releases link\r\nClick on \"Download cuDNN v8.9.0 (April 11th, 2023), for CUDA 12.x\"\r\nClick on \"Local Installer for Ubuntu22.04 x86_64 (Deb)\"\r\nAt Terminal:\r\n`cd Downloads`\r\n`sudo dpkg -i cudnn-local-repo-ubuntu2204-8.9.0.131_1.0-1_amd64.deb`\r\n`sudo cp /var/cudnn-local-repo-*/cudnn-local-*-keyring.gpg /usr/share/keyrings/`\r\n`sudo apt update -y`\r\n`sudo apt install libcudnn8=8.9.0.131-1+cuda12.1`\r\n`sudo apt install libcudnn8-samples=8.9.0.131-1+cuda12.1`\r\n`sudo apt install make libfreeimage3 libfreeimage-dev`\r\nAdd CUDA Paths:\r\n`echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrc`\r\n`echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc`\r\n`source ~/.bashrc`\r\nFor Checking with Nvidia Settings:\r\n`nvidia-smi`\r\n\r\nAs of Now: Ubuntu v22.04.3 LTS (Minimal Installation)\r\n- Python3 v3.10.12\r\n- Python3.11 v3.11.7\r\n- Nvidia v535.129.03\r\n- Cuda v12.2\r\n- GCC v11.4.0\r\n- CLang v16.06\r\n- Bazel Bazelisk\r\n- cuDNN: v8.9.0\r\n\r\n12. CONFIGURE TENSORFLOW v2.16:\r\n```\r\n./configure\r\n- Python Location:\t\tDefault (python3)\r\n- Python Library:\t\tDefault (python3)\r\n- Tensorflow with ROCm:\t\tN\r\n- Tensorflow with CUDA:\t\tY\r\n- Tensorflow with TensorRT:\tN\r\n- CUDA Capabilities: \t\tDefault\r\n- CLang as CUDA Compiler: \tN\r\n- GCC Path: \t\t\tDefault\r\n- Optimization Flags: \t\t-march=native -mssse3 -mcx16 -msse4.1 -msse4.2 -mpopcnt -Wno-gnu-offsetof-extensions\r\n- Android Builds: \t\tN\r\n```\r\nTo edit the Bazel Configuration because of an added use of \"-Wno-gnu-offsetof-extensions\":\r\n`sudo nano .tf_configure.bazelrc`\r\nRemove the first line entry of \"-Wno-gnu-offsetof-extensions\". Leave the remaining two lines in place (towards 2/3rds the way down)\r\n\r\n10. COMPILE TENSORFLOW:\r\nPart 1 - Build the package-builder:\r\n`export TF_PYTHON_VERSION=3.10`\r\n`sudo bazel build --config=opt --jobs=8 //tensorflow/tools/pip_package:build_pip_package`\r\nPart 2 - Build the package:\r\n`sudo ./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg`\r\n\r\n11. INSTALL TENSORFLOW:\r\nRemove the existing Tensorflow v2.15\r\n`cd ..`\r\n`pip uninstall tensorflow`\r\nInstall the Tensorflow v2.16:\r\n`python3.11 -m pip install /tmp/tensorflow_pkg/tensorflow*.whl --force-reinstall`\r\n\r\n11. VERIFY TENSORFLOW:\r\n`python3.11`\r\n`import tensorflow as tf`\r\n`print(\"Number of GPUs Available: \", len(tf.config.list_physical_devices('GPU')))`\r\n\r\nEND",
"Same issue with TF 2.16.1\r\n"
] | 2023-12-24T07:39:04 | 2024-03-22T06:59:22 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
nightly
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
6.1.0
### GCC/compiler version
clang-16
### CUDA/cuDNN version
CUDA 12.0.1/cuDNN 8.8
### GPU model and memory
GTX 1060
### Current behavior?
I followed the [tutorial](https://www.tensorflow.org/install/source) to build the TensorFlow nightly source code, but it aborted and asserted an error related to TensorRT, even though I had configured it to build TensorFlow *without* TensorRT.
Please see the attached Dockerfile for reproduction steps.
Then, I executed:
```
>>> cd tensorflow
>>> ./configure
You have bazel 6.1.0 installed.
Please specify the location of python. [Default is /usr/bin/python3]:
Found possible Python library paths:
/usr/lib/python3/dist-packages
/usr/local/lib/python3.10/dist-packages
Please input the desired Python library path to use. Default is [/usr/lib/python3/dist-packages]
Do you wish to build TensorFlow with ROCm support? [y/N]: N
No ROCm support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.
Do you wish to build TensorFlow with TensorRT support? [y/N]: N
No TensorRT support will be enabled for TensorFlow.
Found CUDA 12.0 in:
/usr/local/cuda-12.0/targets/x86_64-linux/lib
/usr/local/cuda-12.0/targets/x86_64-linux/include
Found cuDNN 8 in:
/usr/lib/x86_64-linux-gnu
/usr/include
Please specify a list of comma-separated CUDA compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. Each capability can be specified as "x.y" or "compute_xy" to include both virtual and binary GPU code, or as "sm_xy" to only include the binary code.
Please note that each additional compute capability significantly increases your build time and binary size, and that TensorFlow only supports compute capabilities >= 3.5 [Default is: 3.5,7.0]:
Do you want to use clang as CUDA compiler? [Y/n]: Y
Clang will be used as CUDA compiler.
Please specify clang path that to be used as host compiler. [Default is /usr/lib/llvm-16/bin/clang]:
You have Clang 16.0.6 installed.
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -Wno-sign-compare]:
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]:
Not configuring the WORKSPACE for Android builds.
Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details.
--config=mkl # Build with MKL support.
--config=mkl_aarch64 # Build with oneDNN and Compute Library for the Arm Architecture (ACL).
--config=monolithic # Config for mostly static monolithic build.
--config=numa # Build with NUMA support.
--config=dynamic_kernels # (Experimental) Build kernels into separate shared objects.
--config=v1 # Build with TensorFlow 1 API instead of TF 2 API.
Preconfigured Bazel build configs to DISABLE default on features:
--config=nogcp # Disable GCP support.
--config=nonccl # Disable NVIDIA NCCL support.
Configuration finished
```
```
>>> bazel build --config=dbg //tensorflow/tools/pip_package:build_pip_package
ERROR: /tensorflow/WORKSPACE:84:14: fetching tensorrt_configure rule //external:local_config_tensorrt: Traceback (most recent call last):
File "/tensorflow/third_party/tensorrt/tensorrt_configure.bzl", line 300, column 38, in _tensorrt_configure_impl
_create_local_tensorrt_repository(repository_ctx)
File "/tensorflow/third_party/tensorrt/tensorrt_configure.bzl", line 159, column 30, in _create_local_tensorrt_repository
config = find_cuda_config(repository_ctx, ["cuda", "tensorrt"])
File "/tensorflow/third_party/gpus/cuda_configure.bzl", line 652, column 26, in find_cuda_config
exec_result = execute(repository_ctx, [python_bin, repository_ctx.attr._find_cuda_config] + cuda_libraries)
File "/tensorflow/third_party/remote_config/common.bzl", line 230, column 13, in execute
fail(
Error in fail: Repository command failed
Could not find any NvInferVersion.h matching version '' in any subdirectory:
''
'include'
'include/cuda'
'include/*-linux-gnu'
'extras/CUPTI/include'
'include/cuda/CUPTI'
'local/cuda/extras/CUPTI/include'
'targets/x86_64-linux/include'
of:
'/lib'
'/lib32'
'/usr'
'/usr/lib/x86_64-linux-gnu'
'/usr/local/cuda'
'/usr/local/cuda/targets/x86_64-linux/lib'
ERROR: Skipping '//tensorflow/tools/pip_package:build_pip_package': no such package '@local_config_tensorrt//': Repository command failed
Could not find any NvInferVersion.h matching version '' in any subdirectory:
''
'include'
'include/cuda'
'include/*-linux-gnu'
'extras/CUPTI/include'
'include/cuda/CUPTI'
'local/cuda/extras/CUPTI/include'
'targets/x86_64-linux/include'
of:
'/lib'
'/lib32'
'/usr'
'/usr/lib/x86_64-linux-gnu'
'/usr/local/cuda'
'/usr/local/cuda/targets/x86_64-linux/lib'
WARNING: Target pattern parsing failed.
ERROR: no such package '@local_config_tensorrt//': Repository command failed
Could not find any NvInferVersion.h matching version '' in any subdirectory:
''
'include'
'include/cuda'
'include/*-linux-gnu'
'extras/CUPTI/include'
'include/cuda/CUPTI'
'local/cuda/extras/CUPTI/include'
'targets/x86_64-linux/include'
of:
'/lib'
'/lib32'
'/usr'
'/usr/lib/x86_64-linux-gnu'
'/usr/local/cuda'
'/usr/local/cuda/targets/x86_64-linux/lib'
INFO: Elapsed time: 0.171s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (0 packages loaded)
currently loading: tensorflow/tools/pip_package
```
In fact, the `NvInferVersion.h` file does not exist, but to my knowledge, `NvInferVersion.h` is supposed to be a header file for TensorRT.
### Standalone code to reproduce the issue
See my Dockerfile that I reproduced the error on this:
https://gist.github.com/torotoki/8f88a4dd932c882018ccc3f38aab2ae7
### Relevant log output
_No response_
|
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