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[
"Hi @nonenone12135 ,\r\n\r\nReplicated the reported check fail and attached screenshot for reference.\r\n\r\n<img width=\"1502\" alt=\"Screenshot 2024-03-19 at 4 14 58 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/c412edb3-321a-4111-a30c-7d5d63de1492\">\r\n"
] | 2024-03-14T05:48:39 | 2024-04-29T07:37:54 | 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
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.raw_ops.ExperimentalDatasetToTFRecord` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
input_data = tf.data.Dataset.range(10).map(lambda x: tf.strings.as_string(x))
input_data = input_data.batch(2)
filename = tf.constant("output.tfrecord")
compression_type = tf.constant("GZIP")
tf.raw_ops.ExperimentalDatasetToTFRecord(input_dataset=input_data._variant_tensor, filename=filename, compression_type=compression_type)
```
### Relevant log output
```shell
2024-03-14 05:47:55.415453: F tensorflow/core/framework/tensor.cc:852] Check failed: 1 == NumElements() (1 vs. 2)Must have a one element tensor
Aborted (core dumped)
```
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Fix `invalid-enum-load`
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Fix `invalid-enum-load`
type_caster_base.h:1196:61: runtime error: load of value 101, which is not a valid value for type 'const TF_DataType'
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I_kwDOArmXAs6CQ75W
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tf.raw_ops.DatasetToTFRecord: Aborted (core dumped)
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[
"@SuryanarayanaY I was able to replicate the issue [here](https://colab.research.google.com/gist/sushreebarsa/4b75f733badae5b6a8292c394399464b/63689.ipynb). Thank you!"
] | 2024-03-14T05:41:47 | 2024-04-29T07:38:42 | 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
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.raw_ops.DatasetToTFRecord` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
input_data = [[1, 2], [3, 4], [5, 6]]
input_data_strings = [[str(d) for d in inner_list] for inner_list in input_data]
dataset = tf.data.Dataset.from_tensor_slices(input_data_strings)
filename = "output.tfrecord"
tf.raw_ops.DatasetToTFRecord(input_dataset=dataset._variant_tensor, filename=filename, compression_type="")
```
### Relevant log output
```shell
2024-03-14 05:41:32.476705: F tensorflow/core/framework/tensor.cc:852] Check failed: 1 == NumElements() (1 vs. 2)Must have a one element tensor
Aborted (core dumped)
```
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[
"Hi **@nonenone12135** ,\r\n\r\nSorry for the delay, The error message indicates that the rank of the images should be 4. The tf.image.draw_bounding_boxes function expects the input images to have a rank of 4, which means that the shape of the input images should be [batch_size, height, width, channels].\r\n\r\nIn the original code, the reshaped_images variable is created by reshaping the input_images tensor to have a shape of [image_height, image_width, num_channels]. This is incorrect because it removes the batch dimension from the tensor.\r\n\r\nTo fix this, we need to reshape the input_images tensor to have a shape of [batch_size, image_height, image_width, num_channels]. This can be done by adding the batch_size dimension to the beginning of the shape tuple when reshaping the tensor.\r\n```import tensorflow as tf\r\n\r\n# ... (other code remains the same)\r\n\r\nreshaped_images = tf.reshape(input_images, [batch_size, image_height, image_width, num_channels])\r\n\r\noutput_images = tf.image.draw_bounding_boxes(reshaped_images, input_boxes, colors)\r\n\r\n```\r\nI am providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/aee2a698128838e2adfbc7dc941cf084/63688_2-15-v.ipynb) here for your reference.\r\n\r\nThank you!",
"Thank you for your response! Doesn't it need functionality like `OP_REQUIRES` which will raise exceptions instead of aborting? ",
"Hi **@vraebfdsb** ,\r\n- As i expected, It will abort. While i will deep dive and confirm with team and let you know more details.\r\n\r\nThank you!"
] | 2024-03-14T05:39:07 | 2024-06-11T05:06:20 | 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
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.image.draw_bounding_boxes` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
batch_size = 1
image_height = 100
image_width = 100
num_channels = 3
num_boxes = 2
images = np.random.rand(batch_size, image_height, image_width, num_channels)
boxes = np.random.rand(batch_size, num_boxes, 4)
colors = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]) # Define colors for the bounding boxes
input_images = tf.convert_to_tensor(images, dtype=tf.float32)
input_boxes = tf.convert_to_tensor(boxes, dtype=tf.float32)
input_colors = tf.convert_to_tensor(colors, dtype=tf.float32)
reshaped_images = tf.reshape(input_images, [image_height, image_width, num_channels])
output_images = tf.image.draw_bounding_boxes(reshaped_images, input_boxes, colors)
```
### Relevant log output
```shell
2024-03-14 05:38:04.463737: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (3 vs. 3)
Aborted (core dumped)
```
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I_kwDOArmXAs6CQ6kL
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tf.audio.decode_wav: Aborted (core dumped)
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[
"@nonenone12135,\r\nAs mentioned above, the tf.audio.decode_wav is not working for the specific input(most cases when desired_channels is -ve value), with the +ve input it is working without any issue. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/c58f8f57299ad1c4399be0c3615509fa/untitled1764.ipynb). Please allow some time to deep dive into the issue and provide the resolution. Thank you!",
"@vraebfdsb,\r\n\r\nWhen we try to execute the mentioned with num_channels = 2 and the desired_channels will become -3 which is causing the below error.\r\n\r\n```python\r\n INVALID_ARGUMENT: Expected a non-negative size, got -3\r\n```\r\nhttps://www.tensorflow.org/api_docs/python/tf/audio/decode_wav#args\r\ndesired_channels\t-- An optional int. Defaults to -1. Number of sample channels wanted.\r\n\r\nThank you!\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you."
] | 2024-03-14T05:36:18 | 2024-06-12T01:51:02 | 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
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.audio.decode_wav` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
import io
sample_rate = 44100
num_channels = 2
duration_seconds = 5
num_samples = sample_rate * duration_seconds
input_data = np.random.randint(-32768, 32767, size=(num_samples, num_channels)).astype(np.int16)
wav_io = io.BytesIO()
wav_write = tf.audio.encode_wav(input_data, sample_rate)
wav_io.write(wav_write.numpy())
wav_contents = wav_io.getvalue()
desired_channels = -num_channels - 1
decoded_audio = tf.audio.decode_wav(wav_contents, desired_channels=desired_channels)
```
### Relevant log output
```shell
2024-03-14 05:35:10.682129: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Expected a non-negative size, got -3
Aborted (core dumped)
```
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I_kwDOArmXAs6CQ46e
| 63,686 |
During Convert TFlite model from Frozen Graph & labelmap.pbtxt , Getting Error if not isinstance(self._keras_model.call, _def_function.Function): AttributeError: 'str' object has no attribute 'call'?
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[
"Hi @selvakarna,\r\n\r\nFor the Tensorflow Saved Model format, you need to specify `converter=tf.lite.TFLiteConverter.from_saved_model(model_path)` instead of `converter=tf.lite.TFLiteConverter.from_keras_model(r\"\\saved_model.pb\") #model_path` . I tested with sample code. It is working fine. Please refer to the [gist](https://colab.sandbox.google.com/gist/LakshmiKalaKadali/d736991e5efc5a10c7492478d16e4ea8/tflite_63686_frozen-graph.ipynb) and test the code with your `.pb ` and `.pbtxt` files. \r\n\r\nThank You\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.",
"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."
] | 2024-03-14T05:30:11 | 2024-04-04T01:47:48 | 2024-04-04T01:47:48 |
NONE
| null | null | null |
### 1. System information
Windows-OS
#### Code
import tensorflow as tf
import numpy as np
model_path ="Model\Path" # Path contain: frozen_inference_graph.pb & labelmap.pbtxt
model = tf.saved_model.load(model_path)
converter=tf.lite.TFLiteConverter.from_keras_model(r"\saved_model.pb") #model_path
converter.optimizations=[tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
tflite_model = converter.convert()
tflite_model_path="converted_.tflite"
with open(tflite_model_path,"wb") as f:
f.write(tflite_model)
### Error
if not isinstance(self._keras_model.call, _def_function.Function): AttributeError: 'str' object has no attribute 'call'?
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I_kwDOArmXAs6CQ1VP
| 63,685 |
tf.transpose lead to program abortion on a normal value while the perm contains -1
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"Hi, I want to have a quick update and draw your attention for this issue.\r\nI recently occurred this issue again and find out that tf.transpose will even lead to program abortion on a very normal tensor that is not empty.\r\n\r\n```\r\nimport tensorflow as tf\r\nimport numpy as np\r\ninput = tf.constant(np.random.rand(2,3))\r\ndim0 = 0\r\ndim1 = -1\r\ntf.transpose(input, perm=[dim1, dim0])\r\n```\r\n\r\nWhen I set the dim1 to -1, which is a valid value for perm, this API will lead to a program abortion!",
"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/ef31e486f62f96d36322731804bb9606/63685.ipynb).\r\nThank you!"
] | 2024-03-14T05:16:01 | 2024-03-22T05:06:43 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### 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?
Hi, the tf.transpose will lead to a program abortion when receiving an empty input and perm is [-1, -2].
The traceback indicate a check fail:
```
tensorflow/core/framework/tensor_shape.cc:356] Check failed: d >= 0 (0 vs. -1)
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
input = tf.constant([[]])
dim0 = -2
dim1 = -1
tf.transpose(input, perm=[dim1, dim0])
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5plVkH
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[XLA] Do not apply kAvoidWaste to async start instructions.
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[XLA] Do not apply kAvoidWaste to async start instructions.
They all have cost 1 and is always preferred. By avoiding them we avoid
releasing preemptively in case the cost model is not that precise.
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PR_kwDOArmXAs5pkxE7
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Propagate effects errors to the results (only if effects are enabled).
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Propagate effects errors to the results (only if effects are enabled).
This will now happen when results of effectful computations are
converted to numpy arrays.
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PR_kwDOArmXAs5pkpor
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Integrate LLVM at llvm/llvm-project@096ee4e16fd6
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Integrate LLVM at llvm/llvm-project@096ee4e16fd6
Updates LLVM usage to match
[096ee4e16fd6](https://github.com/llvm/llvm-project/commit/096ee4e16fd6)
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PR_kwDOArmXAs5pkoIK
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Upgrade Abseil to latest LTS branch (lts_2024_01_16).
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Upgrade Abseil to latest LTS branch (lts_2024_01_16).
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PR_kwDOArmXAs5pkjbh
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Fix a test to support compilation on Apple Silicon
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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/63680/checks?check_run_id=22639224765) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
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Fix a test to support compilation on Apple Silicon
A helper program threw "unsupported CPU" during build. Hence, added Apple Silicon triplet in local_client_aot_test_helper.cc to support it there.
Fixes #7608
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PR_kwDOArmXAs5pkWSE
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Pass ModuleOp directly from pre-calibration to post-calibration
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Pass ModuleOp directly from pre-calibration to post-calibration
This will avoid saving calibrated model as well as loading it.
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PR_kwDOArmXAs5pkWEg
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* `StoreAsLevelDbTable` takes a `tsl::WritableFile file` instead of `const std::string& filename` as a parameter to make it easy to test
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* `StoreAsLevelDbTable` takes a `tsl::WritableFile file` instead of `const std::string& filename` as a parameter to make it easy to test
* `DoStoreAsLevelDbTable` takes a `tsl::WritableFile file` instead of `const std::string& filename` as a parameter to make it easy to test
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PR_kwDOArmXAs5pkTzU
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Fix a typo in FollowReduceStrategy when creating following strategies for reduce ops. When computing communication and memory resharding costs for an operand of the reduce, we were using the shape of the reduce, instead of the shape of the operand.
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Fix a typo in FollowReduceStrategy when creating following strategies for reduce ops. When computing communication and memory resharding costs for an operand of the reduce, we were using the shape of the reduce, instead of the shape of the operand.
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PR_kwDOArmXAs5pkR08
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PR #10374: [GPU] Enable cuDNN GEMM fusions only with cuDNN 9+.
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PR #10374: [GPU] Enable cuDNN GEMM fusions only with cuDNN 9+.
Imported from GitHub PR https://github.com/openxla/xla/pull/10374
Copybara import of the project:
--
2c1b5d653c8280108ce5e3c356475db1f25eee01 by Ilia Sergachev <[email protected]>:
[GPU] Enable cuDNN GEMM fusions only with cuDNN 9+.
Merging this change closes #10374
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10374 from openxla:check_cudnn_version 2c1b5d653c8280108ce5e3c356475db1f25eee01
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PR_kwDOArmXAs5pkPid
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[XLA:SPMD] Fix bug in variadic scatter where shared operands are group-partitioned more than once.
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[XLA:SPMD] Fix bug in variadic scatter where shared operands are group-partitioned more than once.
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PR_kwDOArmXAs5pkPTu
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Integrate StableHLO at openxla/stablehlo@aa69baea
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Integrate StableHLO at openxla/stablehlo@aa69baea
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PR_kwDOArmXAs5pkPIA
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[xla:runtime] Support XLA FFI custom calls as command
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[xla:runtime] Support XLA FFI custom calls as command
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PR_kwDOArmXAs5pkOhW
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[xla:ffi] Automatically infer FFI binding signature from a callable handler
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[xla:ffi] Automatically infer FFI binding signature from a callable handler
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PR_kwDOArmXAs5pkMRn
| 63,671 |
[XLA:MHLO->HLO] Allow partially-set parameter tuple sharding to exist by filling in the missing sharding elements with replicated sharding. (This is what is done for the missing shardings in the result tuple.)
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[XLA:MHLO->HLO] Allow partially-set parameter tuple sharding to exist by filling in the missing sharding elements with replicated sharding. (This is what is done for the missing shardings in the result tuple.)
Before this change, if an element of a tuple parameter did not have a sharding, MHLO->HLO conversion dropped the existing annotations on the parameter. This issue caused the disappearing of the parameter sharding for a model, which then resulted in an OOM.
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Add a knob to WhileLoopConstantSinking to only sink scalar constants.
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Add a knob to WhileLoopConstantSinking to only sink scalar constants.
We use this to prepare the module for urolling while loops.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10631 from Intel-tensorflow:yimei/fix_cpu_bf16_support 9b4f9136f1976b4432e1a60f2deb4a17c150dd3f
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I_kwDOArmXAs6CPeF6
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multiple tflite delegate support?
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"Hi @tensorbuffer ,\r\n\r\nCould please provide any use case/notebook for this specific requirement so that the team can carry out additional analysis. \r\n\r\nThank You",
"Hi, the use case is that we have a tf2 model, after convert to tflite, there's a quantize op at the begining and dequant op at the end. These two nodes can't be run on DSP if use API call, and they are not easily put on XNNPACK, while benchmark_model doesn't have this problem, it can auto assign XNNPACK delegate for those CPU operation.",
"Hi @pkgoogle, \r\n\r\nPlease take a look at this feature request.\r\n\r\nThank You",
"Hi @tensorbuffer, If I'm understanding your request correctly, you wish to be able to have like a tower of delegates where if the situation is such if the top layer is unable to be served by that delegate it goes down to the next delegate and so on? You want this specifically with the c api? Can you provide an example code sample or API call or executable with expected arguments where you expect this to happen?",
"Yes I want the C API to be able to handle that. Basically it should have the same behavior as benchmark_model, since we use that to benchmark latency during model development, afterwards in production we use C API to run the model. Currently benchmark_model can do hexagon and XNNPack delegates (xnnpack for those that hexagon not supported), but this is not the case for C API call, which results in latency increase in production.\r\nI don't have example code, you can extend the current API as you wish.",
"Hi @sirakiin, can you please take a look? Thanks."
] | 2024-03-13T23:32:05 | 2024-04-01T21:57:48 | null |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf2.13
### 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?
Currently if you use C API, you can invoke various delegates like: TfLiteHexagonDelegateCreate() for Hexagon, TfLiteGpuDelegateV2Create() for GPU, TfLiteXNNPackDelegateOptionsDefault() for CPU.
Note you can only use one at a time. For example, if you have model that has an OP not supported by Hexagon, that OP will be run on CPU, without using XNNPack.
However this is not the case with benchmark_model, where it applies XNNPack delegate as default, e.g. you can get both hexagon and xnnpack in one run.
### Standalone code to reproduce the issue
```shell
I would like to see the C API incorporates benchmark_model code, or provide similar API (like create all delegates and pass down to a new API TfLiteDelegateCreate(<delegate>)
```
### Relevant log output
_No response_
|
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Create a generic HostMemorySpace and convert it to TPU specific memory space in the compiler.
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Remove unused StreamExecutor::platform_specific_handle.
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Remove unused StreamExecutor::platform_specific_handle.
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[XLA:Runtime] Moved the nccl_collective_broadcast thunk to new folder and removed unused dependencies.Updated the necessary directories pointing to this thunk and removed it from the nccl_collectives_thunks build. #5758
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[XLA:Runtime] Moved the nccl_collective_broadcast thunk to new folder and removed unused dependencies.Updated the necessary directories pointing to this thunk and removed it from the nccl_collectives_thunks build. #5758
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PR #10474: memcmp is not valid for comparing 4 bit types
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PR #10474: memcmp is not valid for comparing 4 bit types
Imported from GitHub PR https://github.com/openxla/xla/pull/10474
To compare the subshape of a literal that is a 4 bit type it is not valid to compare the memory with memcmp as the upper nibble of each byte may not be initialised and result in a false failure of the comparison. Instead detect this situation and compare each nibble that will hold the 4 bit type.
Copybara import of the project:
--
760978ed02c6f28cb13cc7775b85f9f28b48d977 by Andrew Goodbody <[email protected]>:
memcmp is not valid for comparing 4 bit types
To compare the subshape of a literal that is a 4 bit type
it is not valid to compare the memory with memcmp as the
upper nibble of each byte may not be initialised and result
in a false failure of the comparison. Instead detect this
situation and compare each nibble that will hold the 4 bit
type.
Merging this change closes #10474
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10474 from elfringham:fix_literal 760978ed02c6f28cb13cc7775b85f9f28b48d977
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lite: integrate vhlo in stablehlo pass
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lite: integrate vhlo in stablehlo pass
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PR_kwDOArmXAs5pjy_9
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Use GpuExecutor::gpu_context instead of StreamExecutor::platform_specific_handle in nvptx_compiler.cc.
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Use GpuExecutor::gpu_context instead of StreamExecutor::platform_specific_handle in nvptx_compiler.cc.
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PR_kwDOArmXAs5pjw4i
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[jax] Fix PjRtLayout nanobinds
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[jax] Fix PjRtLayout nanobinds
I meant to include this in a previous change but got mixed up
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PR_kwDOArmXAs5pjwTq
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Move `tsl/util` to `xla/tsl/util`
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Move `tsl/util` to `xla/tsl/util`
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PR_kwDOArmXAs5pjuUO
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Fix event aggregation for events that do not have a group_id.
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Fix event aggregation for events that do not have a group_id.
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PR_kwDOArmXAs5pjs_p
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Remove unused GpuVirtualMemAllocator.
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Remove unused GpuVirtualMemAllocator.
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[mhlo] Add support for uniform_quantize and uniform_dequantize custom calls.
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[mhlo] Add support for uniform_quantize and uniform_dequantize custom calls.
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Linking hell
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#tf-data Support global shuffle for the Tensor dataset.
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#tf-data Support global shuffle for the Tensor dataset.
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I_kwDOArmXAs6COtDd
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How to plugin a PJRT/common runtime device to Tensorflow?
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NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.13.1
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hi all, I am trying to plugin a PJRT device to tensorflow. I saw https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/framework/load_library.py#L160, but that seems to be for the StreamExecutor C API.
I also took a look at https://github.com/tensorflow/tensorflow/blob/6c20138314183635f70dfff454d065f25e833ff1/tensorflow/core/common_runtime/pluggable_device/pluggable_device_plugin_init.cc#L75, which is what I think is what want to call. Is there a python binding for this function or am I supposed to call it directly?
Jax has something like this listed in step 3 here: https://github.com/openxla/xla/blob/b3745173ea3d15306663c8a6f2124da8efea4289/xla/pjrt/c/docs/pjrt_integration_guide.md
Is there something similar for TensorFlow?
Thanks for the help.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf #If I understand correctly a properly plugged in library should be able to be detected on import.
```
### Relevant log output
_No response_
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Define IfrtRestoreVariable op and its mlrt kernel for TFRT + IFRT stack
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Define IfrtRestoreVariable op and its mlrt kernel for TFRT + IFRT stack
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[XLA:GPU][TileAnalysis][NFC] Fix order of function composition in comments.
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[XLA:GPU][TileAnalysis][NFC] Fix order of function composition in comments.
∘ :: (b -> c) -> (a -> b) -> (a -> c)
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PR_kwDOArmXAs5pjSUk
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PR #10504: [GPU] Fix compilation of Triton emitter test.
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PR #10504: [GPU] Fix compilation of Triton emitter test.
Imported from GitHub PR https://github.com/openxla/xla/pull/10504
The test currently does not run in OSS builds. Because of the backend [tags](https://github.com/openxla/xla/blob/f8f416b4116b40d1fa7008086f8b6a76ea05ef31/xla/service/gpu/BUILD#L587) it behaves like this:
```
...
INFO: Analyzed target //xla/service/gpu:ir_emitter_triton_test (1 packages loaded, 1 target configured).
INFO: Found 1 test target...
[==========] Running 0 tests from 0 test suites.
[==========] 0 tests from 0 test suites ran. (0 ms total)
[ PASSED ] 0 tests.
Target //xla/service/gpu:ir_emitter_triton_test up-to-date:
bazel-bin/xla/service/gpu/ir_emitter_triton_test
INFO: Elapsed time: 0.930s, Critical Path: 0.10s
INFO: 2 processes: 1 internal, 1 local.
INFO: Build completed successfully, 2 total actions
//xla/service/gpu:ir_emitter_triton_test PASSED in 0.0s
```
If backend tags are changed to `backends=["gpu"]` it starts to compile and fails like this:
```
...
xla/service/gpu/ir_emitter_triton_test.cc:917:3: error: use of undeclared identifier 'EXPECT_OK'
917 | EXPECT_OK(CreateTritonIrAndFileCheck(kHloText, config, EmitMatMul,
| ^
xla/service/gpu/ir_emitter_triton_test.cc:953:3: error: use of undeclared identifier 'EXPECT_OK'
953 | EXPECT_OK(CreateTritonIrAndFileCheck(kHloText, config, EmitMatMul,
| ^
xla/service/gpu/ir_emitter_triton_test.cc:4041:3: error: use of undeclared identifier 'ASSERT_OK'
4041 | ASSERT_OK(
| ^
xla/service/gpu/ir_emitter_triton_test.cc:4086:3: error: use of undeclared identifier 'ASSERT_OK'
4086 | ASSERT_OK(
...
```
I didn't find any uses of EXPECT_OK / ASSERT_OK without TF_ in other tests.
Copybara import of the project:
--
c0c5dcddae22eede40d9c2af7069558f758714bf by Ilia Sergachev <[email protected]>:
[GPU] Fix compilation of Triton emitter test.
Merging this change closes #10504
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10504 from openxla:fix_triton_test c0c5dcddae22eede40d9c2af7069558f758714bf
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PR_kwDOArmXAs5pjPwp
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PR #62782: Set `reshuffle_each_iteration` in `Dataset.shuffle()` directly to `True` to avoid confusion
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NONE
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PR #62782: Set `reshuffle_each_iteration` in `Dataset.shuffle()` directly to `True` to avoid confusion
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/62782
## 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)
Copybara import of the project:
--
ec9557c050b4e627047555f3e171acd731f7fd2b by Haoyu (Daniel) <[email protected]>:
set `reshuffle_each_iteration` directly to `True`
--
efbc98e944890968924a0d8a2867897588f635de by Haoyu (Daniel) <[email protected]>:
add warning in docstring
--
fcf84be0cbf0810ad1443316272c0f0250591627 by Haoyu (Daniel) <[email protected]>:
revise wording
--
134fc245a3f6c9362b04ba8dff3b84e703401549 by Haoyu (Daniel) <[email protected]>:
change default values in API golden files
Merging this change closes #62782
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/62782 from DanielYang59:warn-shuffle 134fc245a3f6c9362b04ba8dff3b84e703401549
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PR_kwDOArmXAs5pjJKs
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Testing adding new attrs.
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Testing adding new attrs.
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PR_kwDOArmXAs5pjCdR
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#shlo_ref Improve typed test parameter name printing.
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#shlo_ref Improve typed test parameter name printing.
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PR_kwDOArmXAs5pjCSx
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#shlo_ref Add `count_leading_zeros` op.
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#shlo_ref Add `count_leading_zeros` op.
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#shlo_ref Add `popcnt` op.
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#shlo_ref Add `popcnt` op.
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_pywrap_tensorflow_internal.so library contains RUNPATH entries that look like C++ symbols
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[
"CC: @learning-to-play "
] | 2024-03-13T19:50:40 | 2024-06-07T16:27:46 | null |
NONE
| null | null | null |
### Issue type
Bug
### TensorFlow version
2.13 - 2.16
### Python version
3.11, 3.12
### Current behavior?
The RUNPATH in the wheel shared objects is weirdly mangled:
```
$ wget https://files.pythonhosted.org/packages/0d/58/d302d19f06d028ad6c2e33c449ef9813058a16dc35c0ca9580740e0dc9ed/tensorflow-2.16.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
$ unzip *.whl
$ objdump -x tensorflow/python/_pywrap_tensorflow_internal.so | grep RUNPATH
RUNPATH $ORIGIN/../../_solib_local/_U_S_Stensorflow_Spython_C_Upywrap_Utensorflow_Uinternal.so_Ucclib___Utensorflow:$ORIGIN/_pywrap_tensorflow_internal.so.runfiles/org_tensorflow/_solib_local/_U_S_Stensorflow_Spython_C_Upywrap_Utensorflow_Uinternal.so_Ucclib___Utensorflow:$ORIGIN/../../_solib_local/_Utensorflow:$ORIGIN/_pywrap_tensorflow_internal.so.runfiles/org_tensorflow/_solib_local/_Utensorflow:$ORIGIN/:$ORIGIN/..:$ORIGIN/../../nvidia/nccl/lib:$ORIGIN/../nvidia/nccl/lib:$ORIGIN/../../tensorflow/tsl/python/lib/core
```
It's not just that one, almost every SO is affected, see:
```
find tensorflow/ -name *.so -exec objdump -x {} \; | grep RUNPATH
```
Under a custom NixOS build, this mess seems to further confuse follow-on patchelf invocations, which is requiring additional hackery to correct. Possibly related to #34991; the objdump in that ticket shows the same kind of RUNPATH.
### Standalone code to reproduce the issue
```shell
Download any wheel from PyPI, examine the `so`s contained within for their RUNPATH values.
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5pjCJj
| 63,645 |
#shlo_ref Add `not` op.
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#shlo_ref Add `not` op.
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PR_kwDOArmXAs5pi92-
| 63,644 |
Parameterize benchmark configuration for performance profiling
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Parameterize benchmark configuration for performance profiling
Parameterize num_tests, num_runs_per_test, and benchmark_command_buffer
for performance profiling. Update benchmark script to passthrough
additional arguments.
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I_kwDOArmXAs6COHxq
| 63,643 |
Incorrect result when using tf.norm to conduct p-norm computation on float16 precision
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[
"@QuantumCoder4 Thank you for raising this issue. Could you please have a look at this [gist](https://colab.research.google.com/gist/sushreebarsa/555614567807ebe2f50e55a5fc1588d5/63643.ipynb) and confirm the results?\r\nThank you!",
"Yes, I confirm the result. I would be appreciated if you could fix it."
] | 2024-03-13T19:26:58 | 2024-03-22T04:59:01 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### 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?
tf.norm outputs incorrect result under float16 computation when the ord is large. Here is the code to reproduce:
```
import tensorflow as tf
input = tf.constant(1.751, dtype="float16")
p = 144111111
print(tf.norm(input, p)) # tf.Tensor(1.0, shape=(), dtype=float16)
```
Instead, if I change the dtype to 'float32', tf.norm outputs `inf`. It seems that tf.norm overflows during this computation https://github.com/tensorflow/tensorflow/blob/9ce1d5f2f7e5e8866a55633c182cc2dda1a2d6b8/tensorflow/python/ops/linalg_ops.py#L784
Although fixing this issue by letting tf.norm also outputs `inf` in `float16` (which is consistent with the result in `float32`. I am suggesting another possible fix according to this post https://timvieira.github.io/blog/post/2014/11/10/numerically-stable-p-norms/
Indeed, the original implementation may not be numerical stable, a more stable one is to normalize the input before doing the actual computation. Here is the naive implementation of original tf.norm and the numerical stable one as a possible fix:
```
import tensorflow as tf
input = tf.constant(1.751, dtype="float16")
p = 144111111
def origin_norm(x, p):
result = tf.math.abs(x)
result = tf.math.pow(
tf.reduce_sum(tf.math.pow(result, p), axis=None, keepdims=True),
1.0 / p)
return result
def refined_norm(x, p):
a = np.abs(x).max()
return a * origin_norm(x/a, p)
print(f"The original tf.norm's output is: ", tf.norm(input, p))
print(f"The naive implementation of tf.norm: {origin_norm(input, p)}, the refined norm: {refined_norm(input, p)}")
```
The `refined_norm` will output correct results (i.e., 1.7509765625) not only for `float16` computation but also for `float32` computation.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
input = tf.constant(1.751, dtype="float16")
p = 144111111
def origin_norm(x, p):
result = tf.math.abs(x)
result = tf.math.pow(
tf.reduce_sum(tf.math.pow(result, p), axis=None, keepdims=True),
1.0 / p)
return result
def refined_norm(x, p):
a = np.abs(x).max()
return a * origin_norm(x/a, p)
print(f"The original tf.norm's output is: ", tf.norm(input, p))
print(f"The naive implementation of tf.norm: {origin_norm(input, p)}, the refined norm: {refined_norm(input, p)}")
```
### Relevant log output
_No response_
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Automated Code Change
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[xla:gpu] Add IsContiguousSlice on Shapes instead of HloInstruction
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[xla:gpu] Add IsContiguousSlice on Shapes instead of HloInstruction
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10474 from elfringham:fix_literal 760978ed02c6f28cb13cc7775b85f9f28b48d977
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[xla:gpu][NFC] Expose BufferAllocations::external_allocations_
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[xla:gpu] Add AddressComputationThunk
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[xla:gpu] Add AddressComputationThunk
This thunk wraps the logic to compute dynamic offsets/sizes from dynamic-slice and DUS around some original thunks (e.g. custom call or NCCL thunks)
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10542 from openxla:cudnn_fix_autotuning 69b902f6f977acc7695a32cb228edacfa855646e
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[xla:gpu] Create ExecuteParams from an existing one with updated buffer allocations
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#tf-data-service Merge duplicate method `GetIteratorContextWithIndexMapper`.
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#tf-data-service Merge duplicate method `GetIteratorContextWithIndexMapper`.
Also add a simple class to select the right `IteratorContext` when index
shuffling is needed.
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Treat entry computation parameter that has host memory space as MoveToHost
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Treat entry computation parameter that has host memory space as MoveToHost
annotation in host_offload_legalize.
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[xla] Modify the p2p-schedule-preparation pass to support a new scheme to
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[xla] Modify the p2p-schedule-preparation pass to support a new scheme to
pipeline Send-Recv chains.
The old pipeline scheme only peels off a Recv and rotates the remaining
Recv-done, Send, Send-done inside a while loop.
The new pipeline scheme peels off both the Recv and the Send and rotates
the remaining Recv-done and Send-done inside a while loop.
The new pipeline scheme can also pipeline two Send-Recv groups that form a
cycle. In this case, we peel off all four Recv and Send instructions, and
rotate the remaining four Recv-done and Send-done instructions.
Modify existing tests to use the new pipeline scheme. Add a test for pipelining
two Send-Recv groups that form a cycle.
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PR_kwDOArmXAs5pite3
| 63,634 |
[PJRT:GPU] Don't use XLA transfer manager APIs to perform H2D copies.
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[PJRT:GPU] Don't use XLA transfer manager APIs to perform H2D copies.
This fixes a regression introduced by https://github.com/openxla/xla/commit/36fea6d51809cee927830064e60383d0a1aef041, which had the effect of meaning we were bouncing buffers via pinned memory twice, and performing synchronous chunking. This was a large net slowdown.
However, really our intent here was just to call to perform a H2D transfer and nothing else, so we can just call the StreamExecutor APIs directly.
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[XLA:GPU] Remove dependency to lhlo_ops from hlo_utils
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[XLA:GPU] Remove dependency to lhlo_ops from hlo_utils
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I_kwDOArmXAs6CNwp1
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How can I exit the XLAControlFlowContext when inside a jit_compile tf.function? Exit() function take no effect.
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[
"Hi **@MoFHeka** ,\r\nSorry for the delay, I tried to run your code on colab using tf 2.15, 2.16 and tf-nightly versions faced same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/0d2b7992674f8e23a6f3cfd3e1bbe2f4/63632_2-15-2-16-nightly.ipynb) here for reference. We need to work on it, In few days i will provide a solution for it.\r\n\r\nThank you!",
"Hi **@MoFHeka** ,\r\n- The ctx.Exit() and ctx.Enter() methods are internal and their direct manipulation is generally not advisable due to potential instability and undefined behavior.\r\n- I tried alternative approaches manually. I hope it will use for you. Here i am providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/7d942ef79386b982a402b42218881fb5/63632_nightly-v.ipynb) for your reference.\r\n\r\nThank you!",
"@Venkat6871 I'm afraid this proposal is not suitable for me as I am the developer of tensorflow recommender addon. I wanted to be able to be compatible with keras full graph XLA training without rewriting the new XlaOpKernel code."
] | 2024-03-13T18:29:08 | 2024-06-11T04:58:44 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04
### Python version
3.10
### CUDA/cuDNN version
CUDA12.3/cuDNN9.0
### GPU model and memory
GTX4090 24GiB
### Current behavior?
I have a custom op that is a normal OpKernel unable to be compile by XLA cluster. But unfortunately, when the user use this custom op, they have to wrap it into a tf.funtion. So somehow when the use Keras jit_compile or something else, errors happened.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
@tf.function(jit_compile=True)
def test(a):
b = a + a
ctx = tf.__internal__.get_enclosing_xla_context()
ctx.Exit()
tf.print(b)
ctx.Enter()
return b * b
test(tf.constant(1))
```
### Relevant log output
```shell
2024-03-14 02:28:52.720666: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at xla_ops.cc:296 : INVALID_ARGUMENT: Detected unsupported operations when trying to compile graph __inference_test_9[_XlaMustCompile=true,config_proto=9241198235816212909,executor_type=11160318154034397263] on XLA_GPU_JIT: StringFormat (No registered 'StringFormat' OpKernel for XLA_GPU_JIT devices compatible with node {{node StringFormat}}){{node StringFormat}}
The op is created at:
File "xla_context_test.py", line 12, in <module>
test(tf.constant(1))
File "xla_context_test.py", line 8, in test
tf.print(b)
Traceback (most recent call last):
File "xla_context_test.py", line 12, in <module>
test(tf.constant(1))
File "/usr/local/lib/python3.8/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/python3.8/dist-packages/tensorflow/python/eager/execute.py", line 54, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Detected unsupported operations when trying to compile graph __inference_test_9[_XlaMustCompile=true,config_proto=9241198235816212909,executor_type=11160318154034397263] on XLA_GPU_JIT: StringFormat (No registered 'StringFormat' OpKernel for XLA_GPU_JIT devices compatible with node {{node StringFormat}}){{node StringFormat}}
The op is created at:
File "xla_context_test.py", line 12, in <module>
test(tf.constant(1))
File "xla_context_test.py", line 8, in test
tf.print(b) [Op:__inference_test_9]
```
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PR_kwDOArmXAs5pikcF
| 63,631 |
#tf-data-service Fix a race condition in ParallelTFRecordWriter.
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#tf-data-service Fix a race condition in ParallelTFRecordWriter.
`CreateUniqueFileName` uses`NowMicros()` to generate random file
names. Try adding a random number to increase randomness.
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PR_kwDOArmXAs5picYS
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GPU Load Tracker in Pathways.
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PR_kwDOArmXAs5piUG2
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Fix Build Failure on Windows
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"Hi @mraunak Seems auto-merge not happened but the changes are merged into the master branch now. Hence closing this PR. Thank you for your contribution!\r\n\r\n"
] | 2024-03-13T17:43:37 | 2024-03-15T05:34:18 | 2024-03-15T05:34:17 |
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This PR aims to resolve the TensorFlow CPU build failure on the Windows platform. The build target to build TensorFlow has been changed from build_pip_package to wheel. (https://github.com/tensorflow/tensorflow/commit/40a00a61fc9c85e4af61178026b6ed3d5bb2a90f)
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#tf-data Test symbolic checkpoints in BatchGlobalShuffleCheckpointTest.
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#tf-data Test symbolic checkpoints in BatchGlobalShuffleCheckpointTest.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9319 from jaro-sevcik:no-cublas-or-triton-for-small-matmuls 690bb0e1d8b5894f64134d68a031d387399c638f
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Always use std::array.
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Always use std::array.
Deprecate usage of Eigen::array custom implementation.
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Add a metric to track MLRT interpreter vs BEF usage
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Add a metric to track MLRT interpreter vs BEF usage
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#shlo_ref Fix cosine test build.
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#shlo_ref Fix cosine test build.
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Add native bfloat support for GPUs based on https://github.com/openxla/xla/pull/9246
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Add native bfloat support for GPUs based on https://github.com/openxla/xla/pull/9246
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Fix simplification of divisions containing negative multipliers.
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Fix simplification of divisions containing negative multipliers.
The GCD logic is only correct for positive multipliers.
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[XLA:GPU] Run ir_emitter_triton_test.cc as a part of CI.
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[XLA:GPU] Run ir_emitter_triton_test.cc as a part of CI.
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[XLA:GPU] Strengthen abs/rel error in softmax triton emitter.
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[XLA:GPU] Strengthen abs/rel error in softmax triton emitter.
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[XLA:GPU] Replace TF_RET_CHECKs with return Status.
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[Linaro:ARM_CI] Fix typo PACAKGES to PACKAGES
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This is causing some unit test failures with Python aborting
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How to solve this issue - ImportError: cannot import name 'estimator' from 'tensorflow.compat.v1' (/root/.local/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v1/__init__.py)
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[
"Tried reinstalling tensorflow-estimator but still results are same.",
"@Rajcr2,\r\nCould you please provide the standalone code to reproduce the issue which helps to debug the same. Thank you!",
"Getting the same error when using TensorFlow ^2.15.0 and tensorflow-hub ^0.15.0 on a Mlflow Docker container with Python 3.10\r\n```bash\r\n from tensorflow_hub import load\r\n File \"/usr/local/lib/python3.10/site-packages/tensorflow_hub/__init__.py\", line 90, in <module>\r\n from tensorflow_hub.estimator import LatestModuleExporter\r\n File \"/usr/local/lib/python3.10/site-packages/tensorflow_hub/estimator.py\", line 21, in <module>\r\n from tensorflow.compat.v1 import estimator as tf_estimator\r\nImportError: cannot import name 'estimator' from 'tensorflow.compat.v1' (/usr/local/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v1/__init__.py)\r\n```",
"@amrabed,\r\nCould you please try to create the new issue from [here](https://github.com/tensorflow/tensorflow/issues/new/choose) providing the complete standalone code which helps to debug the issue. \r\nThank you!",
"For my case, it was incompatible versions of TF and TF-hub. TF got upgraded to `2.16.1`, while TF-hub stayed at `0.15.0`. I fixed the issue by pinning my TF version to `2.16.1` and TF-hub version to `0.16.1`\r\n\r\nHere is the code I used for your reference:\r\n```python\r\nfrom tensorflow_hub import load # this line caused the error before\r\nencode = load(\"https://tfhub.dev/google/universal-sentence-encoder/4\")\r\nencode([\"whatever\"])\r\n```\r\nI hope that helps you or the OP\r\n",
"This solution worked for me!\r\n\r\nRecently, Google Colab updated its TensorFlow version from 2.15.0 to 2.16.1. Try reinstalling TensorFlow using the following commands:\r\n\r\n`!pip uninstall tensorflow --y`\r\n`!pip install tensorflow==2.15.0`\r\n\r\nthen restart runtime when asked in colab. You should be able to import the correct version of TensorFlow.",
"@amrabed\r\nFor the tensorflow v2.15, you can directly run the command from tensorflow_hub import load for the tf-hub. But whereas for the latest stable tensorflow v2.16.1, please try to run **pip install tensorflow-hub** for the tensorflow-hub 0.16.1. \r\nhttps://pypi.org/project/tensorflow-hub/#description\r\nI tried to execute and was able to import. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/923b68ffec7b9bf23d8c347a9aaa4d6c/untitled1767.ipynb).\r\n\r\nAlso there is an open issue in the tensorflow/tf-hub repo for the same issue. https://github.com/tensorflow/hub/issues/901\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/63618\">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/63618\">No</a>\n",
"I fixed the issue by changing my TF version to 2.16.1 and TF-hub version to 0.16.1"
] | 2024-03-13T14:21:14 | 2024-05-06T18:06:49 | 2024-04-02T01:48:14 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### 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?
How to solve this issue - ImportError: cannot import name 'estimator' from 'tensorflow.compat.v1' (/root/.local/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v1/__init__.py)
anyone ?
### Standalone code to reproduce the issue
```shell
2024-03-13 14:13:23.953994: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-13 14:13:25.304466: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Traceback (most recent call last):
File "/content/drive/MyDrive/tf_CrimeDetection/workspace/CCTV_New/model_main_tf2.py", line 31, in <module>
from object_detection import model_lib_v2
File "/usr/local/lib/python3.10/dist-packages/object_detection/model_lib_v2.py", line 30, in <module>
from object_detection import inputs
File "/usr/local/lib/python3.10/dist-packages/object_detection/inputs.py", line 24, in <module>
from tensorflow.compat.v1 import estimator as tf_estimator
ImportError: cannot import name 'estimator' from 'tensorflow.compat.v1' (/root/.local/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v1/__init__.py)
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5pgt9R
| 63,617 |
Explicitly disallow sparse dots in elemental emitter and cublas
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Explicitly disallow sparse dots in elemental emitter and cublas
Also fix assertions in passes that verify number of dot operands (sparse dot has a 3rd metadata operand).
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PR_kwDOArmXAs5pgmab
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[XLA:GPU] Improve testing of algorithm support
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[XLA:GPU] Improve testing of algorithm support
- Test that ALG_DOT_BF16_BF16_F32_X3 and ALG_DOT_BF16_BF16_F32_X6 gets dispatched to Triton.
- Add small tests ([2, 16] x [16, 2] -> [2, 2] in addition to [32, 32] x [32, 32] -> [32, 32]). This makes sure that we also rewrite small matmuls where needed because of the special algorithm requested.
- Change IrEmitterTritonTests to also test ALG_DOT_BF16_BF16_F32_X3 and ALG_DOT_BF16_BF16_F32_X6 support using the new algorithm parameter, not just the debug flag.
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PR_kwDOArmXAs5pgfLq
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Don't depend on test_main from test_utils.
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Don't depend on test_main from test_utils.
The latter is supposed to be a library.
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PR_kwDOArmXAs5pgWIt
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[XLA:GPU][IndexAnalysis] Rename Range to Interval to cause less confusion.
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[XLA:GPU][IndexAnalysis] Rename Range to Interval to cause less confusion.
Soon, RTSymbol and RangeSymbol will be introduced to distinguish between symbols with multiple values and runtime symbols.
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PR_kwDOArmXAs5pgSpZ
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Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/4a00a7e5f823c9d5308a3ef68fe61b2534ca002d.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/63565 from rascani:fix_oss_build b31e37d51f0afb610cad8e7ca4228bfd969ac300
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PR_kwDOArmXAs5pgJfL
| 63,612 |
Never return nullptr from RewriteSumIf.
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Never return nullptr from RewriteSumIf.
Currently, each caller checks for nullptr and then uses 0 instead.
That's kind of pointless and dangerous.
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| 63,611 |
Add new scope for sparsity metadata in TritonFusionAnalysis
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Add new scope for sparsity metadata in TritonFusionAnalysis
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/63565 from rascani:fix_oss_build b31e37d51f0afb610cad8e7ca4228bfd969ac300
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PR_kwDOArmXAs5pgC7W
| 63,610 |
Fix transpose indexing maps.
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Fix transpose indexing maps.
They currently don't have the correct bounds. Drive-by fix: use the correct
loop symbol in MLIR transpose.
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I_kwDOArmXAs6CKzXx
| 63,609 |
tfds.load does not download data to my directory
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[
"Hi @j622amilah ,\r\n\r\nNeed more context on this to verify whether this is environmental compatibilities of kaggle. Could you please check the code on Google colab and submit the gist to verify.\r\n\r\nThanks!",
"If tfds.load from the TensorFlow Datasets (TFDS) library is not downloading data to your directory as expected, you can try troubleshooting the issue using the following steps:\r\n\r\n1) Check Internet Connection: Ensure that your internet connection is working properly. TFDS downloads datasets from the internet, so a stable internet connection is necessary.\r\n\r\n2) Update TensorFlow and TensorFlow Datasets: Make sure you have the latest versions of TensorFlow and TensorFlow Datasets installed. You can upgrade them using pip:\r\npip install --upgrade tensorflow tensorflow-datasets\r\n\r\n3) Specify Data Directory: By default, TFDS stores downloaded datasets in the directory specified by the ~/tensorflow_datasets environment variable. You can override this default behavior by specifying the data_dir parameter in tfds.load. For example:\r\n\r\nimport tensorflow_datasets as tfds\r\n\r\n# Specify custom data directory\r\ntfds.load('dataset_name', data_dir='/path/to/custom/directory')\r\n\r\n4) Check Permissions: Ensure that you have the necessary permissions to write to the directory where you want to save the datasets.\r\n\r\n5) Check Firewall and Proxy Settings: If you are behind a firewall or using a proxy server, ensure that they are configured correctly to allow downloads from the TFDS servers.\r\n\r\n6) Try Different Dataset: If the issue persists with a specific dataset, try downloading a different dataset to see if the problem is dataset-specific or more general.\r\n\r\nI hope all these helps\r\n",
"Thank you both for your very helpful responses! I was missing the data_dir path specification for the tfds.load function, I went to the tfds.load description page but it was not apparent that I should do this. I can see both the downloads folder and the dataset_name folder under specified data_dir path. Thank you again for your help. I will make this comment as closed. All the best! ",
"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/63609\">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/63609\">No</a>\n"
] | 2024-03-13T12:20:49 | 2024-03-15T10:24:57 | 2024-03-15T10:24:53 |
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
No
### OS platform and distribution
Ubuntu 20.04.6 LTS
### 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 tried to use several datasets from Tensorflow using tfds.load, and sometimes the data downloads to my directory and sometimes the data DOES NOT download to my directory; I am using Kaggle. When the data does not download to my directory, I can still use the data, it is distributing and I am contacting to understand why I can use the data without it downloading to my PC.
An example command is below:
ds_train = tfds.load('bccd', split='train', shuffle_files=True)
### Standalone code to reproduce the issue
```shell
The data from tfds datasets does not download to my Kaggle directory.
```
### Relevant log output
_No response_
|
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Clean up QuantizationSpec fields that are not in use
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Clean up QuantizationSpec fields that are not in use
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I_kwDOArmXAs6CKo-M
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Convert Tensorflow model to TFLite Model light weight converting
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"Please give the issue a title that is relevant. This helps with searching for similar issues",
"@selvakarna \r\nIn order to expedite the trouble-shooting process here, could you please fill the issue [template](https://github.com/tensorflow/tensorflow/issues/new/choose) and kindly give more description on the issue reported. Thank you!",
"I Try to convert Tensorflow model [Frozen_Graph.pb & label.pbtxt ] Into TFLite Model light weight converting. ",
"@selvakarna Could you use the model path as follows;\r\n```\r\nconverter = tf.lite.TFLiteConverter.from_saved_model(model_path) \r\n```\r\nPlease ensure you're loading the TFLite model correctly using `tf.lite.Interpreter`.\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."
] | 2024-03-13T12:01:05 | 2024-04-04T01:47:50 | 2024-04-04T01:47:49 |
NONE
| null | null | null |
### 1. System information
- Windows
-Tensorflow2
### 2. Code
import tensorflow as tf
import numpy as np
model_path ="Model\Path"
model = tf.saved_model.load(model_path)
converter=tf.lite.TFLiteConverter.from_keras_model(r"\saved_model.pb") #model_path
converter.optimizations=[tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
tflite_model = converter.convert()
tflite_model = converter.convert()
tflite_model_path="converted_.tflite"
with open(tflite_model_path,"wb") as f:
f.write(tflite_model)
#### Option A: Reference colab notebooks
```
### 3. Failure after conversion
f not isinstance(self._keras_model.call, _def_function.Function):
AttributeError: 'str' object has no attribute 'call'
### 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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PR_kwDOArmXAs5pfqsA
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Integrate LLVM at llvm/llvm-project@e371ada409b2
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Integrate LLVM at llvm/llvm-project@e371ada409b2
Updates LLVM usage to match
[e371ada409b2](https://github.com/llvm/llvm-project/commit/e371ada409b2)
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PR_kwDOArmXAs5pfc0a
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[XLA:CPU] While emitting IR, don't ignore layout of constants when checking for duplicates.
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[XLA:CPU] While emitting IR, don't ignore layout of constants when checking for duplicates.
Fixes https://github.com/openxla/xla/issues/8471.
The issue only reproduced in ≈3% of runs, because the hash used for the `flat_hash_map` does include layout, but the equality check didn't. So the issue only showed up when two layout happened to be in the same hashmap bucket.
The Literal's `operator==` intentionally doesn't compare layouts (according to the function's comment).
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Added the core_count attribute to GPU devices
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Added the core_count attribute to GPU devices
The attribute corresponds to the number of SMs for an NVidia device or the
number of compute units for an AMD device.
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[XLA:Python] Also wrap CompiledMemoryStats while migrating to nanobind.
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"Hi **@Supriob9** ,\r\n\r\nSorry for the delay, Experiment with different learning rates. Instead of directly setting it to 0.08, try a range of values and monitor the impact on loss.\r\nIncrease the variety and intensity of data augmentation techniques. This can help the model generalize better and reduce overfitting, potentially reducing regularization loss.\r\nExperiment with different base feature extractors or backbone architectures. You can try variations of MobileNet, EfficientNet, or other architectures to see if they improve performance.\r\nAdjust the hyperparameters of the feature extractor, such as depth multiplier and minimum depth, to find optimal settings for your dataset.\r\n\r\nThank you!\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63602\">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/63602\">No</a>\n"
] | 2024-03-13T11:04:23 | 2024-04-04T01:47:53 | 2024-04-04T01:47:50 |
NONE
| null | null | null |
### Issue type
Performance
### 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
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8, 8.1
### GPU model and memory
Nvidia Quadro p3200
### Current behavior?
"Loss/regularization_loss' is high during the training of a object detection model with "ssd_mobilenet_v2_320x320_coco17_tpu-8".
How can I reduce the loss. Specially the regularization loss and classification_loss are high. I have tried reducing the learning rate to .08 and increasing the batch size to 24. I have added my configurations here.
### Standalone code to reproduce the issue
```shell
# SSD with Mobilenet v2
# Trained on COCO17, initialized from Imagenet classification checkpoint
# Train on TPU-8
#
# Achieves 22.2 mAP on COCO17 Val
model {
ssd {
inplace_batchnorm_update: true
freeze_batchnorm: false
num_classes: 3
box_coder {
faster_rcnn_box_coder {
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
encode_background_as_zeros: true
anchor_generator {
ssd_anchor_generator {
num_layers: 6
min_scale: 0.2
max_scale: 0.95
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
aspect_ratios: 3.0
aspect_ratios: 0.3333
}
}
image_resizer {
fixed_shape_resizer {
height: 300
width: 300
}
}
box_predictor {
convolutional_box_predictor {
min_depth: 0
max_depth: 0
num_layers_before_predictor: 0
use_dropout: false
dropout_keep_probability: 0.8
kernel_size: 1
box_code_size: 4
apply_sigmoid_to_scores: false
class_prediction_bias_init: -4.6
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
train: true,
scale: true,
center: true,
decay: 0.97,
epsilon: 0.001,
}
}
}
}
feature_extractor {
type: 'ssd_mobilenet_v2_keras'
min_depth: 16
depth_multiplier: 1.0
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
train: true,
scale: true,
center: true,
decay: 0.97,
epsilon: 0.001,
}
}
override_base_feature_extractor_hyperparams: true
}
loss {
classification_loss {
weighted_sigmoid_focal {
alpha: 0.75,
gamma: 2.0
}
}
localization_loss {
weighted_smooth_l1 {
delta: 1.0
}
}
classification_weight: 1.0
localization_weight: 1.0
}
normalize_loss_by_num_matches: true
normalize_loc_loss_by_codesize: true
post_processing {
batch_non_max_suppression {
score_threshold: 1e-8
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
}
}
train_config: {
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "ssd_mobilenet_v2_320x320_coco17_tpu-8/checkpoint/ckpt-0"
fine_tune_checkpoint_type: "detection"
batch_size: 24
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
num_steps: 50000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
ssd_random_crop {
}
}
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: .08
total_steps: 50000
warmup_learning_rate: 0.02666
warmup_steps: 5000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
}
train_input_reader: {
label_map_path: "labelmap.pbtxt"
tf_record_input_reader {
input_path: "train.record"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
}
eval_input_reader: {
label_map_path: "labelmap.pbtxt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "test.record"
}
}
```
### Relevant log output
```shell
I0313 12:02:59.305537 3000 model_lib_v2.py:705] Step 1300 per-step time 2.458s
INFO:tensorflow:{'Loss/classification_loss': 66547.22,
'Loss/localization_loss': 3.502803,
'Loss/regularization_loss': 2212851.2,
'Loss/total_loss': 2279402.0,
'learning_rate': 0.0405284}
I0313 12:02:59.344294 3000 model_lib_v2.py:708] {'Loss/classification_loss': 66547.22,
'Loss/localization_loss': 3.502803,
'Loss/regularization_loss': 2212851.2,
'Loss/total_loss': 2279402.0,
'learning_rate': 0.0405284}
```
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Fix bug in reshape simplification.
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Fix bug in reshape simplification.
I forgot to check that the `x`es are actually the same.
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Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/96055ce827bb1c0eb5f167bd9b34e125b695bd37.
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Add the options to require complete AOT autotuning results
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Add the options to require complete AOT autotuning results
This cl adds an XLA flag `--require_complete_aot_autotune_results` that requires complete AOT autotuning results inside XLA, so that a `NotFound` will be raised when there is cache miss and JIT autotuning is needed. This can help users detect JIT autotuning happening inside XLA.
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PR_kwDOArmXAs5pexg4
| 63,597 |
Remove stale bugs from quantization_lib.
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Remove stale bugs from quantization_lib.
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PR_kwDOArmXAs5pemF0
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Automated Code Change
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Automated Code Change
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Integrate LLVM at llvm/llvm-project@a38b7a432d3c
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Integrate LLVM at llvm/llvm-project@a38b7a432d3c
Updates LLVM usage to match
[a38b7a432d3c](https://github.com/llvm/llvm-project/commit/a38b7a432d3c)
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Integrate LLVM at llvm/llvm-project@a38b7a432d3c
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[a38b7a432d3c](https://github.com/llvm/llvm-project/commit/a38b7a432d3c)
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Insert optimization_barrier to the second operand of hybrid dot-like ops
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[] | 2024-03-13T07:51:21 | 2024-03-14T04:37:45 | 2024-03-14T04:37:45 |
NONE
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Insert optimization_barrier to the second operand of hybrid dot-like ops
This is needed to prevent constant folding of dequantize + quantized weights.
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Support per-channel quantization of dot_general layers.
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NONE
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Support per-channel quantization of dot_general layers.
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