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`Segmentation fault` in `tf.raw_ops.SparseFillEmptyRows` when the value of `dense_shape` is too large.
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"@xl1043237213 I was able to replicate the issue [here](https://colab.research.google.com/gist/sushreebarsa/144d2cf3c6f16503d4923b574d9a13ea/-11_tf_raw_ops_sparsefillemptyrows.ipynb). Thank you! "
] | 2024-04-16T05:10:26 | 2024-04-25T07:24:16 | 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
No
### OS platform and distribution
Linux Ubuntu 20.04
### 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?
`Segmentation fault` in `tf.raw_ops.SparseFillEmptyRows` when the value of `dense_shape` is too large.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1-TZcbmGYm72oGehk4PC87LquCXQ5Xn44?usp=sharing
```
### Relevant log output
```shell
segmentation fault (core dumped)
```
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I_kwDOArmXAs6F0X_b
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`Segmentation fault` in `tf.raw_ops.RaggedRange` and `tf.ragged.range` when `starts` or `limits` has large (postive/negative) values.
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[
"Hi **@xl1043237213** ,\r\nI tried to run your code on colab using TF v2.16.1 and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/99177c4e123db04871c0561c7082bd0f/65790_2-16-1-v.ipynb) here for reference.\r\n\r\nThank you!"
] | 2024-04-16T05:05:06 | 2024-04-30T04:55:12 | 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
No
### OS platform and distribution
Linux Ubuntu 20.04
### 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?
`Segmentation fault` in `tf.raw_ops.RaggedRange` and `tf.ragged.range` when `starts` or `limits` has large (postive/negative) values.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1c5J_tSNwLdPmi0TXMaJNNhUb-Ha__dw8?usp=sharing
```
### Relevant log output
```shell
segmentation fault (core dumped)
```
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Attach sharding to `tuple` in mhlo -> hlo conversion when the following two conditions are true.
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Attach sharding to `tuple` in mhlo -> hlo conversion when the following two conditions are true.
1. converting `mhlo::ReturnOp` or `mlir::func::ReturnOp`
2. `num_return_values == 1`
3. `!return_tuple || !is_entry_function`
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[
"@xl1043237213,\r\nThank you for reporting the issue. I was able to reproduce the issue on tensorflow v2.15, v2.16 and tf-nightly. Kindly find the gist of it [here](https://colab.sandbox.google.com/gist/tilakrayal/ffd1aea1441acc9ce8239cba0b6e0ea5/untitled1867.ipynb).\r\n"
] | 2024-04-16T04:43:40 | 2024-04-24T13:12:05 | 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 20.04
### 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?
`Segmentation fault` in `tf.raw_ops.CollectiveGather` when `input` is empty.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1lx42HhXrkx5BPwSxLg-bGuJMMCTEIQyt?usp=sharing
```
### Relevant log output
```shell
segmentation fault (core dumped)
```
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[
"Hi @xl1043237213 ,\r\n\r\nThere has been a PR #63064 in open state which probably address this issue."
] | 2024-04-16T04:35:16 | 2024-04-16T07:33:41 | 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
No
### OS platform and distribution
Linux Ubuntu 20.04
### 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?
When `perm` have negative values, `tf.raw_ops.ConjugateTranspose` triggers `segmentation fault` or `overflow`.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1s1fTdsSESb_nOIuD46fQM1pJBHkcCtZe?usp=sharing
```
### Relevant log output
```shell
segmentation fault (core dumped)
```
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Move the host tensor sharding from IfrtLoadVariable kernel to IfrtCallOp Kernel.
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Move the host tensor sharding from IfrtLoadVariable kernel to IfrtCallOp Kernel.
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Remove unused `this` in lambda capture.
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Create a separate callback for restored tensor propagation and move it out of LoadRestoredTensorAsIfrtLoadedVariable.
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Create a separate callback for restored tensor propagation and move it out of LoadRestoredTensorAsIfrtLoadedVariable.
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[XLA:SPMD] support sharding barrier (2/N).
Add sharding propagation logic for shard barrier.
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[XLA:SPMD] support sharding barrier (0/N).
Fold shard group sharding instruction attribute into operand instead of replacing with an explicit copy.
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Handle gather in LayoutCanonicalization pass.
Currentlu, we only support canonicalization of the output of the gather.
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[XLA:SPMD] support sharding barrier (1/N).
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[XLA:SPMD] support sharding barrier (1/N).
Add XLA sharding attributes to support sharding barrier.
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PR_kwDOArmXAs5svc9D
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Introduce ifrt::Device and ifrt::Memory abstractions.
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Introduce ifrt::Device and ifrt::Memory abstractions.
Previously ifrt::Device and ifrt::Memory were aliases for PJRT objects. But in general IFRT should not require the use of PJRT under the hood, and IFRT should define its own types.
This change introduces the new types and migrates users to refer to them.
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#tf-data Optimizes the parameters only after the first root GetNext is finished.
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#tf-data Optimizes the parameters only after the first root GetNext is finished.
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I_kwDOArmXAs6FzalQ
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ModuleNotFoundError: No module named 'tensorflow.compat'
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[
"@deniseepflo TensorFlow 2.x encourages a different approach. Instead of tensorflow.compat.v1, directly import TensorFlow:\r\n```\r\nimport tensorflow as tf\r\n```\r\n It should work with tensorflow 2.15 and python 3.10.9. \r\nPlease refer to this TensorFlow migration [guide](https://www.tensorflow.org/guide/migrate) for more information.\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/65772\">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/65772\">No</a>\n"
] | 2024-04-16T00:08:13 | 2024-05-01T01:49:26 | 2024-05-01T01:49:23 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.10.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I have tensorflow 2.15 and python 3.10.9 with this error.
import tensorflow.compat.v1 as tf
ModuleNotFoundError: No module named 'tensorflow.compat'
### Standalone code to reproduce the issue
```shell
n
```
### Relevant log output
_No response_
|
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#tf-data Support global shuffling for the memory cache dataset.
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#tf-data Support global shuffling for the memory cache dataset.
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PR_kwDOArmXAs5svPqZ
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Previously, we rely on p2p-schedule-preparation pass to add control dependence
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Previously, we rely on p2p-schedule-preparation pass to add control dependence
to represent how we want to order a Send-Recv chain etc. It turns out that the
dependence from Recv-Done to Send need to be visible to passes, in particular,
to prevent the fusion of the Send-data computation and the computation using
to Recv-Done result.
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I_kwDOArmXAs6FzL5D
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Conversion failure: tfl.batch_matmul "expected 3 but got 2" (regression since 2.14, worked in 2.13)
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[
"hi @gustavla ,\r\n\r\nI have replicated the issue and i got similar results. I am looking into the issue and will get back to you.",
"Hi @gustavla, can you let us know what chip your mac is using? M series? Intel? Thanks for your help.",
"@pkgoogle Apple silicon. Also repros on x86_64 Ubuntu.",
"I was able to reproduce on x86_64 Debian on tf-nightly as well with the exact program above. One difference is I got this error/warning: \r\n\r\n```\r\ntensorflow.lite.python.convert_phase.ConverterError: Variable constant folding is failed. Please consider using enabling `experimental_enable_resource_variables` flag in the TFLite converter object. For example, converter.experimental_enable_resource_variables = True test.py:32:1: error: 'tfl.batch_matmul' op found invalid output rank, expected 3 but got 2\r\n```\r\n\r\nI attempted adding this line to see if it helps:\r\n```py\r\nconverter.experimental_enable_resource_variables = True\r\n```\r\n\r\nIt did not help. @zichuan-wei, can you please take a look? Thanks.",
"Hi @gustavla, if you are able to access a linux system you may be able to resolve your issue by using [AI-Edge-Torch](https://github.com/google-ai-edge/ai-edge-torch), you can find more information here: [googleblog](https://developers.googleblog.com/en/ai-edge-torch-high-performance-inference-of-pytorch-models-on-mobile-devices/).\r\n\r\nI have actually created a simple script for converting your model here:\r\n\r\n```py\r\nimport torch\r\nimport torch.nn as nn\r\nimport ai_edge_torch\r\n\r\n\r\nclass MyMatMul(nn.Module):\r\n def __init__(self):\r\n super().__init__()\r\n\r\n def forward(self, x0, x1):\r\n x0_rank3 = torch.unsqueeze(x0, 1)\r\n out = x0_rank3 @ x1\r\n out = torch.squeeze(out, 1)\r\n return out\r\n\r\n\r\ninput0_shape = (1, 5)\r\ninput1_shape = (1, 5, 7)\r\nmodel = MyMatMul()\r\nsample_inputs = (torch.randn(*input0_shape), torch.randn(*input1_shape))\r\n\r\nedge_model = ai_edge_torch.convert(model.eval(), sample_inputs)\r\nedge_model.export(\"my_mat_mul.tflite\")\r\n```\r\n\r\nIf you want to, you can actually try visualizing the result in [model-explorer](https://github.com/google-ai-edge/model-explorer) as well.\r\n\r\nPlease try them out and let us know if this resolves your issue. If you still need further help, feel free to open a new issue at the respective repo."
] | 2024-04-15T23:12:25 | 2024-06-10T20:36:45 | null |
CONTRIBUTOR
| null | null | null |
### 1. System information
- macOS 14.1.2 / Python 3.10
- `pip install tensorflow-macos==2.16.1 tf-keras==2.16.0 keras==3.2.1`
### 2. Code
```
import tensorflow as tf
import keras
input0_shape = [1, 5]
input1_shape = [1, 5, 7]
output_shape = [1, 1, 7]
tf_input0 = keras.Input(input0_shape[1:], batch_size=1)
tf_input1 = keras.Input(input1_shape[1:], batch_size=1)
class MyMatMul(keras.layers.Layer):
def call(self, tf_input0, tf_input1):
# -> [1, 1, 5]
tf_input0_rank3 = tf.expand_dims(tf_input0, [1])
# [1, 1, 5] x [1, 5, 7] -> [1, 1, 7]
tf_output_rank3 = tf.linalg.matmul(tf_input0_rank3, tf_input1)
# -> [1, 7]
tf_output = tf.squeeze(tf_output_rank3, [1])
return tf_output
tf_output = MyMatMul()(tf_input0, tf_input1)
model = keras.Model(inputs=[tf_input0, tf_input1], outputs=[tf_output])
# Convert the model.
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
# Save the model.
with open('model.tflite', 'wb') as f:
f.write(tflite_model)
```
### 3. Failure after conversion
```
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
W0000 00:00:1713222146.555150 18460241 tf_tfl_flatbuffer_helpers.cc:390] Ignored output_format.
W0000 00:00:1713222146.555506 18460241 tf_tfl_flatbuffer_helpers.cc:393] Ignored drop_control_dependency.
2024-04-15 16:02:26.556328: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: /var/folders/d5/6vzc45z14_79pfg8d_mbpmsr049hy4/T/tmpw802bhj6
2024-04-15 16:02:26.556581: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }
2024-04-15 16:02:26.556589: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: /var/folders/d5/6vzc45z14_79pfg8d_mbpmsr049hy4/T/tmpw802bhj6
2024-04-15 16:02:26.558343: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
2024-04-15 16:02:26.558600: I tensorflow/cc/saved_model/loader.cc:234] Restoring SavedModel bundle.
2024-04-15 16:02:26.582278: I tensorflow/cc/saved_model/loader.cc:218] Running initialization op on SavedModel bundle at path: /var/folders/d5/6vzc45z14_79pfg8d_mbpmsr049hy4/T/tmpw802bhj6
2024-04-15 16:02:26.583903: I tensorflow/cc/saved_model/loader.cc:317] SavedModel load for tags { serve }; Status: success: OK. Took 27577 microseconds.
2024-04-15 16:02:26.602113: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
loc(fused[callsite(callsite(fused["Squeeze:", "functional_1_1/my_mat_mul_1/Squeeze@__inference_serving_default_16"] at fused["PartitionedCall:", "PartitionedCall@__inference_signature_wrapper_serving_default_23"]) at fused["PartitionedCall:", "PartitionedCall"]), callsite(callsite(fused["ExpandDims:", "functional_1_1/my_mat_mul_1/ExpandDims@__inference_serving_default_16"] at fused["PartitionedCall:", "PartitionedCall@__inference_signature_wrapper_serving_default_23"]) at fused["PartitionedCall:", "PartitionedCall"]), callsite(callsite(fused["BatchMatMulV2:", "functional_1_1/my_mat_mul_1/MatMul@__inference_serving_default_16"] at fused["PartitionedCall:", "PartitionedCall@__inference_signature_wrapper_serving_default_23"]) at fused["PartitionedCall:", "PartitionedCall"])]): error: 'tfl.batch_matmul' op found invalid output rank, expected 3 but got 2
```
The key is the final line: `error: 'tfl.batch_matmul' op found invalid output rank, expected 3 but got 2`.
Note that the expand *and* squeeze are **both required for the failure to reproduce**.
### 4. Regression analysis
Replace tensorflow version in the pip installation above:
* 2.13: Works (no conversion error, `model.tflite` produced successfully)
* 2.14: Failure
* 2.15: Failure
* 2.16: Failure
The above error happens across 2.14-2.16.
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Replace `ConstantR1` with templated version `ConstantR1<int32_t>`.
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NONE
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Replace `ConstantR1` with templated version `ConstantR1<int32_t>`.
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| 2,244,785,692 |
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| 65,767 |
Add `MhloDynamicReshape` to XLA client API.
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[] | 2024-04-15T23:10:18 | 2024-05-06T18:40:27 | 2024-05-06T18:40:25 |
NONE
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Add `MhloDynamicReshape` to XLA client API.
This adds a builder method which generates a `custom_call` to generate the MHLO op `dynamic_reshape`. Because of naming conflict with HLO's existing `DynamicReshape` (it's semantically different from `MhloDynamicReshape`), this op is prefixed with "Mhlo". I've also updated the API name for `DynamicBroadcastInDim` to `MhloDynamicBroadcastInDim` to match the names. These are only intended for export to MHLO or StableHLO, and cannot be compiled.
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PR_kwDOArmXAs5svIL0
| 65,766 |
Use `ShapeUtil::HumanString` instead of calling `Shape::ToString` directly in `xla_builder.cc`.
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NONE
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Use `ShapeUtil::HumanString` instead of calling `Shape::ToString` directly in `xla_builder.cc`.
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| 2,244,784,362 |
PR_kwDOArmXAs5svH79
| 65,765 |
Remove unnecessary `xla::` namespace qualifiers.
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Remove unnecessary `xla::` namespace qualifiers.
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PR_kwDOArmXAs5svH73
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Migrate StableHLO to use Properties
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Migrate StableHLO to use Properties
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[tsl] Mark `gtl::InlinedVector` with `ABSL_DEPRECATE_AND_INLINE`
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[tsl] Mark `gtl::InlinedVector` with `ABSL_DEPRECATE_AND_INLINE`
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Remove a check for single user of an operand in convert-memory-placement-to-internal-annotations.
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Remove a check for single user of an operand in convert-memory-placement-to-internal-annotations.
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[XLA:TPU] Refactor TryInputStreaming into a top-down approach, starting from entry computation parameters with host memory space.
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[XLA:TPU] Refactor TryInputStreaming into a top-down approach, starting from entry computation parameters with host memory space.
This refactor allows for a more direct traversal of the graph for streamed parameters, but should not change behavior in any way.
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PR_kwDOArmXAs5svCqD
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Integrate LLVM at llvm/llvm-project@905d2ecbb63f
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Integrate LLVM at llvm/llvm-project@905d2ecbb63f
Updates LLVM usage to match
[905d2ecbb63f](https://github.com/llvm/llvm-project/commit/905d2ecbb63f)
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/65568 from Intel-tensorflow:mraunak/win_config e46de6348d7d86f500ee7ddfcd10989555b1cd38
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PR_kwDOArmXAs5svB8A
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[IFRT] Revise comments for proto messages
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[IFRT] Revise comments for proto messages
Proto messages for IFRT types may be used for various goals other than
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Also set up a linter directive for `DType::Kind` and `DTypeProto::Kind` whose
mirroring is easy to miss.
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PR_kwDOArmXAs5svBbB
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Reverts af3028da043dbb9e9c3a1c830fddc6ae35795d58
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Reverts af3028da043dbb9e9c3a1c830fddc6ae35795d58
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/65568 from Intel-tensorflow:mraunak/win_config e46de6348d7d86f500ee7ddfcd10989555b1cd38
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PR_kwDOArmXAs5su9Cg
| 65,757 |
Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/16ceace318b63a569138261331c66d7809d079de.
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test change to test
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test change to test
Reverts af3028da043dbb9e9c3a1c830fddc6ae35795d58
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PR_kwDOArmXAs5su4KK
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Move ScopedModuleHandle into its own header file and add unit tests.
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Move ScopedModuleHandle into its own header file and add unit tests.
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PR_kwDOArmXAs5suzUy
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Fix a bug in which an absl hashtable is used with inconsistent hash/eq functors.
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NONE
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Fix a bug in which an absl hashtable is used with inconsistent hash/eq functors.
`eq(k1, k2) -> hash(k1) == hash(k2)` must be true for hashtable usage to be valid.
The bug here is that when the hash is layout insensitive, we can't hash the data bytes in their physical order - we must hash them in their logical order.
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PR_kwDOArmXAs5sux0i
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r2.16 cherry-pick: "Reverts changelist 506335783"
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PR_kwDOArmXAs5suuhI
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Log for variableinfo and OpKernelContext combination
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Log for variableinfo and OpKernelContext combination
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/62750 from mattbahr:implement-sampled-addmm-v2 c295a0e649e86304c084b7c9b3c77bdf0d2ed8fb
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PR_kwDOArmXAs5suEgO
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Unpack u64 via vzip variants.
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Unpack u64 via vzip variants.
Although the compiler tends to optimize vget_{low,high} + vcombine to vzip, using vzip directly is more intuitive and the code is much similar to other element sizes.
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PR #11352: [ROCm] Triton in XLA for ROCm - changed ir_emitter_triton_test
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PR #11352: [ROCm] Triton in XLA for ROCm - changed ir_emitter_triton_test
Imported from GitHub PR https://github.com/openxla/xla/pull/11352
Modified the test case to pass on rocm and cuda.
There will be at least one more XLA related PR - to switch on Triton usage for ROCm, but only after PR for adding Triton build files for ROCm on openxla/triton.
Copybara import of the project:
--
dd80a6f44295642efd3ae6af6ffd3e2a3302d36e by Zoran Jovanovic <[email protected]>:
[ROCm] Triton in XLA for ROCm - changed ir_emitter_triton_test to pass on rocm and cuda
Merging this change closes #11352
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11352 from ROCm:rocm_triton_backend_5 dd80a6f44295642efd3ae6af6ffd3e2a3302d36e
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PR_kwDOArmXAs5st9Z-
| 65,749 |
Update Embedded Linux toolchains to GCC 13.2.1
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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/65749/checks?check_run_id=23844857418) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"I'll defer to someone with ARM experience here",
"Hi @nitins17 Can you please review this PR ? Thank you!",
"@nitins17 it's ready for review, however there is an important caveat.\r\n\r\narm-gcc 13.2 ships with 2.38 glibc, while\r\nUbuntu 22.04 is on 2.35 https://launchpad.net/ubuntu/+source/glibc\r\nand Debian Bookworm (current Raspberry Pi OS) is on 2.36 https://packages.debian.org/source/bookworm/glibc\r\nwhich means at least for these two widely used Linux distro versions, the users might not be able to easily use the cross-compiled binaries/libraries.\r\n\r\nI can close it for now. What do you think?",
"Hi @nitins17 Any update on this PR? Please. Thank you!"
] | 2024-04-15T19:25:55 | 2024-06-07T17:06:13 | null |
NONE
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Following up on https://github.com/tensorflow/tensorflow/pull/57585:
The 11.3.1 version of toolchain is affected by issue described here:
https://bugs.linaro.org/show_bug.cgi?id=5825#c34
In a nutshell, it seems that the host architecture has been picked up from that of the build machine when the toolchains was built.
Here is an issue in current repository, which I can reproduce in my setup:
https://github.com/tensorflow/tensorflow/issues/59692
Updating toolchain version to 13.2.1 fixes the issue for me - and it is confirmed in https://bugs.linaro.org/show_bug.cgi?id=5825 that it was indeed fixed in later toolchain versions.
CC @sachinprasadhs
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PR_kwDOArmXAs5st1IH
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Add folders for tfl.maximum/tfl.minimum with +-FLT_MAX arg
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Add folders for tfl.maximum/tfl.minimum with +-FLT_MAX arg
This commit adds folders for the ops `tfl.maximum` and `tfl.minimum` for the case where one of the arguments is `-FLT_MAX` and `FLT_MAX`, respectively.
Reverts 94350b84b5deb780e6d3ae01f8f1a5c7ba71b302
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PR_kwDOArmXAs5st0Ib
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Refactor constructors of memory space impls to take device instead. It doesn't make sense to create a memory space with no device attached. This is mostly true for non-CPU devices.
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Refactor constructors of memory space impls to take device instead. It doesn't make sense to create a memory space with no device attached. This is mostly true for non-CPU devices.
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PR_kwDOArmXAs5stzLN
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PR #11352: [ROCm] Triton in XLA for ROCm - changed ir_emitter_triton_test
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PR #11352: [ROCm] Triton in XLA for ROCm - changed ir_emitter_triton_test
Imported from GitHub PR https://github.com/openxla/xla/pull/11352
Modified the test case to pass on rocm and cuda.
There will be at least one more XLA related PR - to switch on Triton usage for ROCm, but only after PR for adding Triton build files for ROCm on openxla/triton.
Copybara import of the project:
--
dd80a6f44295642efd3ae6af6ffd3e2a3302d36e by Zoran Jovanovic <[email protected]>:
[ROCm] Triton in XLA for ROCm - changed ir_emitter_triton_test to pass on rocm and cuda
Merging this change closes #11352
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11352 from ROCm:rocm_triton_backend_5 dd80a6f44295642efd3ae6af6ffd3e2a3302d36e
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PR_kwDOArmXAs5stpLd
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[IFRT] Add ArraySpec
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[IFRT] Add ArraySpec
`ArraySpec` holds static properties of `Array`. This provides a common and
convenient way of describing an expected or requested static properties of an
input/output array of an operation.
This does not change IFRT Proxy's proto messages as they reflect the IFRT API,
most of which does not use `ArraySpec` yet.
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I_kwDOArmXAs6FxfGh
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Fail to build TFLite shared library with GPU delegates
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[
"Hi @dorishyc3, can you please share whether you have an M series or an intel chip for your Mac? Thanks.",
"Hello,\r\nI’m using an M1 mac",
"Hi @dorishyc3, which version of the NDK are you using? Thanks.",
"my ndk version is 26",
"I'm running into a different issue, I tried with ndk version = 26 and 25b and the errors were similar but not exactly the same.\r\n\r\nMy reproduce step (on MacOS M1 and ndk=25b):\r\n```sh\r\ngit clone https://github.com/tensorflow/tensorflow.git tensorflow_src\r\ncd tensorflow_src\r\ngit switch r2.16\r\ngit pull\r\ncd ..\r\nmkdir tflite_build\r\ncd tflite_build\r\ncmake -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DTFLITE_ENABLE_GPU=ON -DBUILD_SHARED_LIBS=ON ../tensorflow_src/tensorflow/lite # This seems to work fine\r\ncmake --build . -j # where it fails\r\n```\r\n\r\nEnd of my output:\r\n```sh\r\n[ 48%] Linking CXX static library libruy_thread_pool.a\r\n[ 48%] Built target ruy_profiler_profiler\r\n[ 48%] Building C object _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/src/qs8-vhswish/gen/qs8-vhswish-scalar-u2.c.o\r\n[ 48%] Building C object 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Linking CXX shared library libabsl_debugging_internal.so\r\n[ 48%] Built target ruy_tune\r\ngmake[1]: *** [CMakeFiles/Makefile2:6772: _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/all] Error 2\r\ngmake[1]: *** Waiting for unfinished jobs....\r\n[ 48%] Building C object _deps/xnnpack-build/CMakeFiles/hardware-config.dir/src/configs/hardware-config.c.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-gemm/gen/f32-gemm-1x8-aarch64-neonfma-cortex-a53.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f16-gemm/gen/f16-gemm-6x16-aarch64-neonfp16arith-cortex-a55r0.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-igemm/gen/f32-igemm-6x8-aarch64-neonfma-cortex-a53.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f16-igemm/gen/f16-igemm-6x16-aarch64-neonfp16arith-cortex-a55r0.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f16-igemm/gen/f16-igemm-6x16-aarch64-neonfp16arith-ld64.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-gemm/gen/f32-gemm-6x8-aarch64-neonfma-cortex-a55.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-igemm/gen/f32-igemm-4x8-aarch64-neonfma-cortex-a55.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f16-gemm/gen/f16-gemm-1x16-aarch64-neonfp16arith-ld64.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-gemm/gen/f32-gemm-6x8-aarch64-neonfma-ld128.cc.o\r\n[ 48%] Building C object _deps/xnnpack-build/CMakeFiles/memory.dir/src/memory.c.o\r\n[ 48%] Built target ruy_block_map\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/jit/aarch64-assembler.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f16-igemm/gen/f16-igemm-4x16-aarch64-neonfp16arith-ld64.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-igemm/gen/f32-igemm-1x8-aarch64-neonfma-cortex-a53.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f16-gemm/gen/f16-gemm-6x16-aarch64-neonfp16arith-ld64.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f16-igemm/gen/f16-igemm-6x16-aarch64-neonfp16arith-cortex-a55.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-igemm/gen/f32-igemm-4x8-aarch64-neonfma-ld128.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-gemm/gen/f32-gemm-6x8-aarch64-neonfma-cortex-a53.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-igemm/gen/f32-igemm-6x8-aarch64-neonfma-cortex-a75.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-igemm/gen/f32-igemm-6x8-aarch64-neonfma-ld128.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-gemm/gen/f32-gemm-4x8-aarch64-neonfma-cortex-a75.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f16-gemm/gen/f16-gemm-4x16-aarch64-neonfp16arith-ld64.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f16-igemm/gen/f16-igemm-1x16-aarch64-neonfp16arith-ld64.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-igemm/gen/f32-igemm-6x8-aarch64-neonfma-cortex-a55.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-gemm/gen/f32-gemm-4x8-aarch64-neonfma-cortex-a55.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f16-gemm/gen/f16-gemm-6x16-aarch64-neonfp16arith-cortex-a75.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f16-gemm/gen/f16-gemm-6x16-aarch64-neonfp16arith-cortex-a55.cc.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-gemm/gen/f32-gemm-4x8-aarch64-neonfma-cortex-a53.cc.o\r\n[ 48%] Building C object _deps/xnnpack-build/CMakeFiles/operator-utils.dir/src/operator-utils.c.o\r\n[ 48%] Building C object _deps/xnnpack-build/CMakeFiles/post-operation.dir/src/operators/post-operation.c.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/jit/aarch32-assembler.cc.o\r\n[ 48%] Building C object _deps/xnnpack-build/CMakeFiles/mutex.dir/src/mutex.c.o\r\n[ 48%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f16-igemm/gen/f16-igemm-6x16-aarch64-neonfp16arith-cortex-a75.cc.o\r\n[ 48%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_pack_avx.dir/pack_avx.cc.o\r\n[ 48%] Built target absl_base\r\n[ 48%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_pack_avx2_fma.dir/pack_avx2_fma.cc.o\r\n[ 49%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/jit/assembler.cc.o\r\n[ 49%] Built target operator-utils\r\n[ 49%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_kernel_avx.dir/kernel_avx.cc.o\r\n[ 49%] Built target absl_debugging_internal\r\n[ 49%] Built target mutex\r\n[ 49%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-gemm/gen/f32-gemm-1x8-aarch64-neonfma-ld64.cc.o\r\n[ 49%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_ctx.dir/ctx.cc.o\r\n[ 49%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_kernel_avx512.dir/kernel_avx512.cc.o\r\n[ 49%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-igemm/gen/f32-igemm-1x8-aarch64-neonfma-cortex-a75.cc.o\r\n[ 49%] Building C object _deps/xnnpack-build/CMakeFiles/operator-run.dir/src/operator-run.c.o\r\n[ 49%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-igemm/gen/f32-igemm-4x8-aarch64-neonfma-cortex-a53.cc.o\r\n[ 49%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_pack_avx512.dir/pack_avx512.cc.o\r\n[ 49%] Linking CXX static library libruy_pack_avx2_fma.a\r\n[ 49%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_kernel_arm.dir/kernel_arm32.cc.o\r\n[ 49%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-gemm/gen/f32-gemm-4x8-aarch64-neonfma-ld128.cc.o\r\n[ 50%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_kernel_avx2_fma.dir/kernel_avx2_fma.cc.o\r\n[ 50%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-gemm/gen/f32-gemm-6x8-aarch64-neonfma-cortex-a75.cc.o\r\n[ 50%] Linking CXX static library libruy_kernel_avx.a\r\n[ 50%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-igemm/gen/f32-igemm-4x8-aarch64-neonfma-cortex-a75.cc.o\r\n[ 50%] Building CXX object _deps/abseil-cpp-build/absl/random/CMakeFiles/absl_random_internal_randen.dir/internal/randen.cc.o\r\n[ 50%] Built target hardware-config\r\n[ 50%] Built target memory\r\n[ 50%] Built target post-operation\r\n[ 50%] Linking CXX static library libruy_pack_avx512.a\r\n[ 50%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_kernel_arm.dir/kernel_arm64.cc.o\r\n[ 50%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_pack_arm.dir/pack_arm.cc.o\r\n[ 50%] Building CXX object _deps/xnnpack-build/CMakeFiles/jit.dir/src/f32-gemm/gen/f32-gemm-1x8-aarch64-neonfma-cortex-a75.cc.o\r\n[ 50%] Linking CXX static library libruy_pack_avx.a\r\n[ 50%] Built target ruy_pack_avx2_fma\r\n[ 50%] Linking CXX static library libruy_kernel_arm.a\r\n[ 50%] Linking CXX static library libruy_ctx.a\r\n[ 50%] Linking CXX static library libruy_kernel_avx512.a\r\n[ 50%] Built target ruy_kernel_avx\r\n[ 50%] Linking CXX static library libruy_kernel_avx2_fma.a\r\n[ 50%] Linking CXX shared library libabsl_random_internal_randen.so\r\n[ 50%] Built target ruy_pack_avx512\r\n[ 50%] Built target operator-run\r\n[ 50%] Built target jit\r\n[ 50%] Linking CXX static library libruy_pack_arm.a\r\n[ 50%] Built target ruy_pack_avx\r\n[ 50%] Built target ruy_kernel_arm\r\n[ 50%] Built target absl_random_internal_randen\r\n[ 50%] Built target ruy_ctx\r\n[ 50%] Built target ruy_kernel_avx2_fma\r\n[ 50%] Built target ruy_kernel_avx512\r\n[ 50%] Built target ruy_pack_arm\r\ngmake: *** [Makefile:136: all] Error 2\r\n```\r\n\r\nHi @terryheo, can you please take a look? Thanks."
] | 2024-04-15T18:27:30 | 2024-04-22T21:40:49 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
MacOS
### Mobile device
Android
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
Clang 17.0.2
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Building TFLite with CMake following [this guide](https://www.tensorflow.org/lite/guide/build_cmake), with the option to build shared lib and GPU delegates fails.
### Standalone code to reproduce the issue
```shell
Follow Step 1-3 in the guide
`cmake -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \
-DANDROID_ABI=arm64-v8a -DTFLITE_ENABLE_GPU=ON -DBUILD_SHARED_LIBS=ON ../tensorflow/lite`
`cmake --build . -j`
```
### Relevant log output
```shell
1 warning generated.
[ 98%] Linking CXX shared library libabsl_flags_usage_internal.so
1 warning generated.
[ 98%] Built target absl_flags_usage_internal
[ 98%] Building CXX object _deps/abseil-cpp-build/absl/flags/CMakeFiles/absl_flags_usage.dir/usage.cc.o
1 warning generated.
1 warning generated.
1 warning generated.
[ 98%] Linking CXX shared library libabsl_flags_usage.so
1 warning generated.
[ 98%] Built target absl_flags_usage
[ 98%] Building CXX object _deps/abseil-cpp-build/absl/flags/CMakeFiles/absl_flags_parse.dir/parse.cc.o
1 warning generated.
1 warning generated.
1 warning generated.
1 warning generated.
[ 98%] Linking CXX shared library libabsl_flags_parse.so
[ 98%] Built target absl_flags_parse
1 warning generated.
[100%] Linking CXX shared library libtensorflow-lite.so
ld.lld: error: undefined symbol: tflite::gpu::OptionalAndroidHardwareBuffer::OptionalAndroidHardwareBuffer()
>>> referenced by android_hardware_buffer.h:69 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/android_hardware_buffer.h:69)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(TfLiteGpuDelegateV2CreateAsync)
>>> referenced by android_hardware_buffer.h:69 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/android_hardware_buffer.h:69)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::RegisterBuffer(TfLiteOpaqueContext*, TfLiteIoType, TfLiteBackendBuffer const*, TfLiteAttributeMap const*, int))
>>> referenced by android_hardware_buffer.h:69 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/android_hardware_buffer.h:69)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::SetBufferAttributes(TfLiteBackendBuffer const*, TfLiteAttributeMap const*))
>>> referenced 7 more times
ld.lld: error: undefined symbol: tflite::delegates::BackendAsyncKernelInterface::BackendAsyncKernelInterface()
>>> referenced by delegate.cc:772 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:772)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::CreateAsyncRegistration()::$_0::__invoke(TfLiteContext*, char const*, unsigned long))
>>> did you mean: tflite::delegates::BackendAsyncKernelInterface::~BackendAsyncKernelInterface()
>>> defined in: CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o
ld.lld: error: undefined symbol: tflite::delegates::utils::ReadBufferAttrs(TfLiteAttributeMap const*)
>>> referenced by delegate.cc:1034 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1034)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::ReconcileRestrictions(TfLiteOpaqueContext const*, TfLiteOpaqueNode const*, int, TfLiteAttributeMap const*, TfLiteAttributeMap*, TfLiteAttributeMap*) const)
>>> referenced by delegate.cc:892 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:892)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::CheckAttributes(TfLiteAttributeMap const*, tflite::delegates::utils::BufferAttributes&))
ld.lld: error: undefined symbol: tflite::delegates::utils::WriteBufferAttrs(tflite::delegates::utils::BufferAttributes const&, TfLiteAttributeMap*)
>>> referenced by delegate.cc:1036 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1036)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::ReconcileRestrictions(TfLiteOpaqueContext const*, TfLiteOpaqueNode const*, int, TfLiteAttributeMap const*, TfLiteAttributeMap*, TfLiteAttributeMap*) const)
>>> referenced by delegate.cc:1036 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1036)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::ReconcileRestrictions(TfLiteOpaqueContext const*, TfLiteOpaqueNode const*, int, TfLiteAttributeMap const*, TfLiteAttributeMap*, TfLiteAttributeMap*) const)
>>> referenced by delegate.cc:1036 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1036)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::ReconcileRestrictions(TfLiteOpaqueContext const*, TfLiteOpaqueNode const*, int, TfLiteAttributeMap const*, TfLiteAttributeMap*, TfLiteAttributeMap*) const)
>>> referenced 3 more times
ld.lld: error: undefined symbol: tflite::delegates::utils::ReadSyncAttrs(TfLiteAttributeMap const*)
>>> referenced by delegate.cc:1059 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1059)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::ReconcileRestrictions(TfLiteOpaqueContext const*, TfLiteOpaqueNode const*, int, TfLiteAttributeMap const*, TfLiteAttributeMap*, TfLiteAttributeMap*) const)
>>> referenced by delegate.cc:1092 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1092)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::SetAttributes(TfLiteOpaqueContext*, TfLiteOpaqueNode*, int, TfLiteAttributeMap const*))
ld.lld: error: undefined symbol: tflite::delegates::utils::WriteSyncAttrs(tflite::delegates::utils::SyncAttributes const&, TfLiteAttributeMap*)
>>> referenced by delegate.cc:1061 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1061)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::ReconcileRestrictions(TfLiteOpaqueContext const*, TfLiteOpaqueNode const*, int, TfLiteAttributeMap const*, TfLiteAttributeMap*, TfLiteAttributeMap*) const)
>>> referenced by delegate.cc:1061 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1061)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::ReconcileRestrictions(TfLiteOpaqueContext const*, TfLiteOpaqueNode const*, int, TfLiteAttributeMap const*, TfLiteAttributeMap*, TfLiteAttributeMap*) const)
>>> referenced by delegate.cc:1063 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1063)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::ReconcileRestrictions(TfLiteOpaqueContext const*, TfLiteOpaqueNode const*, int, TfLiteAttributeMap const*, TfLiteAttributeMap*, TfLiteAttributeMap*) const)
ld.lld: error: undefined symbol: tflite::gpu::gl::WaitFdGpu(int)
>>> referenced by delegate.cc:1350 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1350)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))
ld.lld: error: undefined symbol: tflite::delegates::utils::WaitForAllFds(absl::lts_20230802::Span<int const>)
>>> referenced by delegate.cc:1353 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1353)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))
ld.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>>*)
>>> referenced by delegate.cc:1358 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1358)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))
ld.lld: error: undefined symbol: tflite::delegates::utils::ConvertToTfLiteStatus(absl::lts_20230802::Status)
>>> referenced by delegate.cc:1358 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1358)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))
>>> referenced by delegate.cc:1369 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1369)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))
>>> referenced by delegate.cc:1370 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1370)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))
>>> referenced 3 more times
ld.lld: error: undefined symbol: tflite::gpu::gl::EglEnvironment::~EglEnvironment()
>>> referenced by unique_ptr.h:65 (/Users/dhe/Library/Android/sdk/ndk/26.2.11394342/toolchains/llvm/prebuilt/darwin-x86_64/sysroot/usr/include/c++/v1/__memory/unique_ptr.h:65)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))
>>> referenced by unique_ptr.h:65 (/Users/dhe/Library/Android/sdk/ndk/26.2.11394342/toolchains/llvm/prebuilt/darwin-x86_64/sysroot/usr/include/c++/v1/__memory/unique_ptr.h:65)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))
ld.lld: error: undefined symbol: tflite::gpu::AsyncBuffer::GetOpenGlBuffer(unsigned int&)
>>> referenced by delegate.cc:1369 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1369)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))
>>> referenced by delegate.cc:1382 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1382)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))
ld.lld: error: undefined symbol: tflite::gpu::gl::CreateFdGpu()
>>> referenced by delegate.cc:1394 (/Users/dhe/Snapchat/Dev/tensorflow/tensorflow/lite/delegates/gpu/delegate.cc:1394)
>>> CMakeFiles/tensorflow-lite.dir/delegates/gpu/delegate.cc.o:(tflite::gpu::(anonymous namespace)::DelegateAsyncKernel::Eval(TfLiteOpaqueContext*, TfLiteOpaqueNode*, TfLiteExecutionTask*))
clang++: error: linker command failed with exit code 1 (use -v to see invocation)
make[2]: *** [libtensorflow-lite.so] Error 1
make[1]: *** [CMakeFiles/tensorflow-lite.dir/all] Error 2
make: *** [all] Error 2
```
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Make StreamExecutor inherit from StreamExecutorInterface, and make all concrete StreamExecutorInterface implementations inherit from StreamExecutor.
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Make StreamExecutor inherit from StreamExecutorInterface, and make all concrete StreamExecutorInterface implementations inherit from StreamExecutor.
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Augments the Memory Term Reducer to merge both primitives *and* groups.
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Add host offloading computation based on PjRt executable
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Internal infrastructure change
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Internal infrastructure change
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Integrate LLVM at llvm/llvm-project@37b7207651b4
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Integrate LLVM at llvm/llvm-project@37b7207651b4
Updates LLVM usage to match
[37b7207651b4](https://github.com/llvm/llvm-project/commit/37b7207651b4)
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I_kwDOArmXAs6Fw__K
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`Assertion failure` in `tf.raw_ops.RFFT` and `tf.signal.rfft` when the value of `fft_length` is 0.
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[
"Hi @xl1043237213 ,\r\n\r\nI have replicated the reported behaviour with tf-nightly and attached screenshot below.\r\n\r\n<img width=\"1489\" alt=\"Screenshot 2024-04-16 at 1 13 40 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/edc318e7-d700-458d-8594-75f25316911d\">\r\n"
] | 2024-04-15T17:12:56 | 2024-04-23T07:14:58 | 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
No
### OS platform and distribution
Linux Ubuntu 20.04
### 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?
`Assertion failure` in `tf.raw_ops.RFFT` and `tf.signal.rfft` when the value of `fft_length` is 0.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1RYmVMgilwfLU-H1weXOkBvwNj-JUhhyK?usp=sharing
```
### Relevant log output
```shell
Assertion failure
axis length mismatch
```
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I_kwDOArmXAs6Fw_Q6
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`Assertion failure` in `tf.raw_ops.RFFT2D` and `tf.signal.rfft2d` when the values of `fft_length` are 0.
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[
"@SuryanarayanaY I was able to replicate this issue [here](https://colab.research.google.com/gist/sushreebarsa/fc8e046b29c3093edefe27f6bfca54d5/-6_tf_raw_ops_rfft2d.ipynb).\r\nThank you! "
] | 2024-04-15T17:11:05 | 2024-04-25T07:35:16 | null |
NONE
| 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
### OS platform and distribution
Linux Ubuntu 20.04
### 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?
`Assertion failure` in `tf.raw_ops.RFFT2D` and `tf.signal.rfft2d` when the values of `fft_length` are 0.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1oXMVvT3ghxI4KF1kMd1_QRbvA1468oUb?usp=sharing
```
### Relevant log output
```shell
Assertion failure
axis length mismatch
```
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PR_kwDOArmXAs5stGmv
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Pass in GraphDef + CallableOptions instead of MetaGraphDef to TFRT's ModelContext.
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Pass in GraphDef + CallableOptions instead of MetaGraphDef to TFRT's ModelContext.
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I_kwDOArmXAs6Fw6QK
| 65,733 |
Some crash errors in `tf.raw_ops.RFFT3D` and `tf.signal.rfft3d`
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"Hi **@xl1043237213** ,\r\n\r\nI tried to run your code on Colab using TF v2.16.1 and faced the same issue. Please find the [gist ](https://colab.sandbox.google.com/gist/Venkat6871/d80d736c33b98d229b60c1ace9b2f732/65733_2-16-1-v.ipynb) here for reference.\r\nThank you!"
] | 2024-04-15T16:58:23 | 2024-04-30T04:57:04 | 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
No
### OS platform and distribution
Linux Ubuntu 20.04
### 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?
When the values of `fft_length` is too large, `tf.raw_ops.RFFT3D` triggers `Overflow`.
When there are zero values in `fft_length`, `tf.raw_ops.RFFT3D` triggers `Assertion failure`.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1qi4GrSy8wDPrLwab94ZyOY4WOdIByXuE?usp=sharing
```
### Relevant log output
```shell
Non-OK-status: RecomputeNumElements() status: INVALID_ARGUMENT: Shape [2147483548,2147483548,3] results in overflow when computing number of elements
or
Assertion failure
axis length mismatch
```
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I_kwDOArmXAs6FwhuT
| 65,732 |
`Segmentation fault` in `tf.raw_ops.LoadAndRemapMatrix` when the value of `num_cols` is too large while `col_remapping` is empty.
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[
"@xl1043237213,\r\nI tried to execute the mentioned code and observed that the issue/error is occuring whenever the input size of num_columns is >=12 digit number. With the <11 digit number as input, the code was executed without any issue and the output is also as expected. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/0aa4f64ba81b34c445d1031cf882ac7a/untitled1856.ipynb) and also observed that the input value for the `num_rows, num_cols and max_rows_in_memory` should be **int**.\r\n\r\nArgs\r\nnum_rows:\tAn int that is >= 0. Number of rows (length of the 1st dimension) in the output matrix.\r\nnum_cols:\tAn int that is >= 1. Number of columns (length of the 2nd dimension) in the output matrix.\r\nmax_rows_in_memory:\tAn optional int. Defaults to -1. \r\n\r\nThank you!",
"@tilakrayal , The reportee knows that and it was mentioned in the title itself. Users can try with specially crafted inputs to check the behaviour of the APIs and expect meaningful exception. Please note that these are specially crafted inputs and they have spent lot of time to check for which inputs the API is failing.\r\n\r\ncc: @mihaimaruseac ",
"@tilakrayal , once again, please be more careful in what canned reply gets sent"
] | 2024-04-15T16:19:28 | 2024-04-18T14:37:50 | 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
No
### OS platform and distribution
Linux Ubuntu 20.04
### 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?
When the value of `num_cols` is too large while `col_remapping` is empty, `tf.raw_ops.LoadAndRemapMatrix` triggers `Segmentation fault`.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1Eba1FTUMG_as_2FtrSUcAt44Z-LWPX4Z?usp=sharing
```
### Relevant log output
```shell
segmentation fault (core dumped)
```
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I_kwDOArmXAs6FwaGG
| 65,731 |
`Check failed` in `tf.raw_ops.LoadAndRemapMatrix` when the rank of `ckpt_path` is not equal to the rank of `old_tensor_name`.
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[
"Hi @xl1043237213 ,\r\n\r\nI have replicated the reported behaviour with tf-nightly and attached screenshot below. [Gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/b36eca39f8e238780c23b51eee4cfc22/copy-of-65731.ipynb)\r\n\r\n<img width=\"1485\" alt=\"Screenshot 2024-04-16 at 1 19 47 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/681bbe8d-b851-4ab9-a62f-21535f3cc6a8\">\r\n"
] | 2024-04-15T16:04:05 | 2024-04-18T07:37:34 | 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
No
### OS platform and distribution
Linux Ubuntu 20.04
### 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?
When the rank of `ckpt_path` is not equal to the rank of `old_tensor_name`, `tf.raw_ops.LoadAndRemapMatrix` triggers `Check fail` error.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1Eba1FTUMG_as_2FtrSUcAt44Z-LWPX4Z?usp=sharing
```
### Relevant log output
```shell
Check failed: NDIMS == dims() (1 vs. 0)Asking for tensor of 1 dimensions from a tensor of 0 dimensions
```
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PR_kwDOArmXAs5ssj_w
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[XLA:GPU][IndexAnalysis] Skip adding constraints with AffineConstantExpr.
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[XLA:GPU][IndexAnalysis] Skip adding constraints with AffineConstantExpr.
If we add a constraint "some_constant, interval" and `some_constant` belongs to
the interval, then we can just skip adding it.
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I_kwDOArmXAs6FwLI_
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`Check failed` in `tf.raw_ops.Batch` when the value of `num_batch_threads` is too large.
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[
"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/71f54090ad4e738d724cede5423bb1a5/-6_tf_raw_ops_batch.ipynb).\r\nThank you!"
] | 2024-04-15T15:35:53 | 2024-04-25T07:31:44 | 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
No
### OS platform and distribution
Linux Ubuntu 20.04
### 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?
When the value of `num_batch_threads` is too large, `tf.raw_ops.Batch` triggers `Check failed` error.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1F4NMwgDO2QY2NV6GYPVbVuRyCSsB7ZBZ?usp=sharing
```
### Relevant log output
```shell
Check failed: ret == 0 (11 vs. 0)Thread batch_threads_ creation via pthread_create() failed.
```
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I_kwDOArmXAs6Fv6Hp
| 65,728 |
`Check Failed` in `tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel` and `tf.quantization.fake_quant_with_min_max_vars_per_channel` when the input of `inputs` is scalar.
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[
"Hi **@xl1043237213** ,\r\n\r\nI don't have access to open your colab code. Could you please provide proper link or access to present link.\r\n\r\nThank you!",
"I'm sorry, I've changed the access permission now. @Venkat6871 ",
"Hi **@xl1043237213** ,\r\n\r\nI tried to run your code on colab using TF v2.16.1, nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/cf6cd93274c851b32722e3c4a5200516/65728_2-16-1-nighly-v.ipynb) here for reference.\r\n\r\nThank you!"
] | 2024-04-15T15:09:23 | 2024-04-30T04:50:31 | 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
No
### OS platform and distribution
Linux Ubuntu 20.04
### 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?
`Check Failed` in `tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel` and `tf.quantization.fake_quant_with_min_max_vars_per_channel` when the input of `inputs` is scalar, which causes the program to crash. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1ydn7hiGhj5xFypCmJPZ410gmboo0Edji#scrollTo=NfIGXCwpS1Dr
```
### Relevant log output
```shell
Check failed: d >= 0 (0 vs. -1)
```
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I_kwDOArmXAs6Fvvht
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`Overflow` in `tf.raw_ops.SerializeManySparse` when there are too large values in `sparse_shape`.
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"@xl1043237213,\r\nI tried it on a VM with Ubuntu 22.04 OS and executed the code multiple times which executed without crash in multiple runs and outputs intended error.\r\n\r\n\r\nAnd also observed that the issue/error is occuring whenever the input size is **>=8 digit number. With the <7 digit number** as input, the code was executed without any issue and the output is also as expected. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/31966921c979a2b7880bb899786fcfe5/untitled1855.ipynb).\r\n\r\nThank you!\r\n",
"Thanks for your response.\r\nI will keep tracking the issue and waiting for the root cause of the bug. I think it's an unexpected execution behavior and hoping that TensorFlow will fix the bug soon."
] | 2024-04-15T14:50:33 | 2024-04-18T13:55:11 | 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
No
### OS platform and distribution
Linux Ubuntu 20.04
### 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?
When there are too large values in `sparse_shape` while `sparse_indices` and `sparse_values` are empty, ``tf.raw_ops.SerializeManySparse`` triggers overflow error.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1itdHdvRiR28lfLLBay-2sUuKSX_IzBPC?usp=sharing
```
### Relevant log output
```shell
Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Encountered overflow when multiplying 7777777777777777777 with 3, result: -1
```
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PR_kwDOArmXAs5sr_9E
| 65,726 |
Fix check fail in crop_and_resize_op
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The Op `CropAndResize` checkfails with an input where `boxes` and `box_ind` both are `1D` and `empty` tensors. This is due to missing validation for this particular case which skips the check of boxes for Rank-2.
The check skips from this line which checks for empty tensors but skips rank checking.
https://github.com/tensorflow/tensorflow/blob/7aac92419d6cb25c8dcd6e4bcfe2f234efaff168/tensorflow/core/kernels/image/crop_and_resize_op.cc#L62-L64
May Fix #65629
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[XLA:GPU][CoalescingAnalysis] Speed-up computation of coalescing.
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[XLA:GPU][CoalescingAnalysis] Speed-up computation of coalescing.
Reverts 8a2d31612fbafc7019bb15ad0a5752178cce745c
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I_kwDOArmXAs6Fvp0K
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Some errors in `tf.raw_ops.SparseTensorDenseMatMul` when there are negative or too large values in `a_shape`.
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[
"Hi @xl1043237213 ,\r\n\r\nI have replicated the reported behaviour with tf-nightly and attached screenshot below and [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/a9af875775f8a6340340a44297ea262a/65724.ipynb) for reference.\r\n",
"<img width=\"1502\" alt=\"Screenshot 2024-04-18 at 10 54 57 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/edf9132f-ec8c-447a-8fea-a94e143c7bd2\">\r\n\r\n\r\n<img width=\"1504\" alt=\"Screenshot 2024-04-18 at 10 53 50 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/d5c80efa-c146-42be-ae20-da749a1a2347\">\r\n"
] | 2024-04-15T14:40:25 | 2024-04-23T07:16:18 | null |
NONE
| 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
### OS platform and distribution
Linux Ubuntu 20.04
### 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?
When there are negative values in `a_shape`, `tf.raw_ops.SparseTensorDenseMatMul` triggers `INVALID_ARGUMENT` error. When there are too large values in `a_shape`, `tf.raw_ops.SparseTensorDenseMatMul` triggers `overflow` error.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1_dmg1zT1TuYnmPgtlUWSDq8Gg4eeyq0b?usp=sharing
```
### Relevant log output
```shell
Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Expected shape dimensions to be non-negative, got -1
or
Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Encountered overflow when multiplying 9223372036854775804 with 3, result: -1
```
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Fix IncludeCleaner errors.
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Support complex constants in mlir-based fusion emitters.
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I_kwDOArmXAs6FupTH
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`Check failed` in `f.raw_ops.CropAndResizeGradBoxes` when `boxes` and `box_indices` are empty.
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"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/6bdde8791c65728b29558d3fdde9afa9/-6_tf_raw_ops_cropandresizegradboxes.ipynb). Thank you!"
] | 2024-04-15T12:48:08 | 2024-04-25T07:32:31 | 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
No
### OS platform and distribution
Linux Ubuntu 20.04
### 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?
`Check failed` in `f.raw_ops.CropAndResizeGradBoxes` when `boxes` and `box_indices` are empty, which causes the program to crash. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1jt4YeBZkcM_o_tqxDsIKePHoeFZ-CdCS?usp=sharing
```
### Relevant log output
```shell
Check failed: NDIMS == dims() (1 vs. 2)Asking for tensor of 1 dimensions from a tensor of 2 dimensions
```
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PR_kwDOArmXAs5srADC
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Relax expectation for log(complex) where abs(complex) = max_float.
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Relax expectation for log(complex) where abs(complex) = max_float.
Both 88.7228394 and inf should be valid results for the real part.
Note that inf is already returned for log(complex * (1+epsilon)).
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I_kwDOArmXAs6FudDn
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build TensorFlow in windows
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[
"Hi **@swoiow** ,\r\n- Ensure there are no residual files causing conflicts, For cleaning your build directory Use this following command.\r\n```\r\nbazel clean --expunge\r\n```\r\n- Make sure you are using the latest versions of Bazel and TensorFlow Text. Outdated versions can sometimes lead to compatibility issues and build errors.\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/65719\">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/65719\">No</a>\n",
"> Hi **@swoiow** ,\r\n> \r\n> * Ensure there are no residual files causing conflicts, For cleaning your build directory Use this following command.\r\n> \r\n> ```\r\n> bazel clean --expunge\r\n> ```\r\n> \r\n> * Make sure you are using the latest versions of Bazel and TensorFlow Text. Outdated versions can sometimes lead to compatibility issues and build errors.\r\n> \r\n> Thank you!\r\n\r\ni just tested with `bazel clean --expunge`, it looks not good for me with same error\r\n\r\n"
] | 2024-04-15T12:24:37 | 2024-05-08T23:15:12 | 2024-05-03T01:48:41 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
unknow
### Custom code
No
### OS platform and distribution
windows
### 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?
i just want to build tensorflow-text and fork the code used github action to do this. but failed to build with error `cl : Command line error D8022 : cannot open 'bazel-out/x64_windows-opt/bin/external/org_tensorflow/tensorflow/compiler/mlir/quantization/tensorflow/calibrator/_objs/calibration_statistics_proto_cc_impl/calibration_statistics.pb.obj.params'`
Any help with me?
### Standalone code to reproduce the issue
the source code in [fork](https://github.com/swoiow/text)
### Relevant log output
_No response_
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[XLA:GPU] Print the correct error message when dumping llvm ir in trtion.
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[XLA:GPU] Print the correct error message when dumping llvm ir in trtion.
If `log_stream.emplace` returns error, we destruct `log_stream` and keep trying to use it later. Changed to fail earlier on and print a corresponding error message.
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Integrate LLVM at llvm/llvm-project@37b7207651b4
Updates LLVM usage to match
[37b7207651b4](https://github.com/llvm/llvm-project/commit/37b7207651b4)
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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/65710/checks?check_run_id=23819570777) 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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[xla:ffi] Adapt CPU custom call tests to changes in typed FFI API
Handle Result<T> and Ret tags.
Change expected status codes and messages for tests expecting failure.
Fix FfiHandleBufferBaseDouble test.
Implements #10060.
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How to build the tensorflow origin code fast when I modify the code
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"@YuMJie,\r\nWhile WHL files offer faster installation, building from source gives the direct access to the code. If you choose this way, consider using a tool like Bazel which streamlines the build process for large projects like TensorFlow.\r\n\r\nAlso try to create a virtual environment to isolate your development dependencies and avoid conflicts with other projects. This allows you to quickly switch between different TensorFlow versions or experiment with custom libraries.\r\n \r\nIf you build the tensorflow from source, you can modify TensorFlow's behavior for specific use cases, Integrate the TensorFlow with custom libraries or frameworks and You can step through the code line by line using a debugger to identify the root cause of problems. 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/65709\">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/65709\">No</a>\n"
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NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### 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
_No response_
### Current behavior?
I want to learn how TensorFlow is implemented and participate in the development of TensorFlow, but as far as I know, if you install it in the form of WHL through Build, you will spend a lot of time on Build, and there is any way to modify its code faster and verify that the modified code works
### Standalone code to reproduce the issue
```shell
./configure # answer prompts or use defaults
bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
./bazel-bin/tensorflow/tools/pip_package/build_pip_package /mnt # create package
chown $HOST_PERMS /mnt/tensorflow-version-tags.whl
```
### Relevant log output
_No response_
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Include a simple quantization report after `QuantizeCompositeFunctionsPass`.
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Include a simple quantization report after `QuantizeCompositeFunctionsPass`.
Implements a method `QuantizationReport::ToString` that returns a human-readable report that contains the textproto representation of `QuantizationResults`.
After `QuantizeCompositeFunctionsPass` it simply prints this string to stdout.
The printed report would look something like this:
```
===== Quantization Report =====
results {
quantizable_unit {
name: "composite_dot_general_fn_2"
}
}
===== Quantization Report End =====
```
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Create an explicit `QuantizationPreset` with flat preset options.
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Create an explicit `QuantizationPreset` with flat preset options.
`QuantizationPreset` is the primary quantization configuration for the StableHLO Quantizer.
This message will cover most general quantization use cases, which can be overwritten with
`QuantizationConfig` (i.e. for selective quantization).
Follow up cl's will migrate the `QuantizationConfig` > `preset` usage from existing pipelines.
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Use payload in jit compiler Error
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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/65704/checks?check_run_id=23815081063) 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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Use payload in jit compiler Error
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Introduces a 'Memory Term Reducer' that merges terms appearing together in many memory constraints.
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Introduces a 'Memory Term Reducer' that merges terms appearing together in many memory constraints.
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Fix checkfail in MatrixSetDiagV3
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Currently `MatrixSetDiagV3` Op will lead to check fail due to lack of rank validation of input wrt diagonal.
The diagonal rank should be either same as input or one less than the input rank. But there is no check for this.
Might fix #65673
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PR #11449: [XLA:GPU] replace is_causal_mask with enum mask_type to support more cudnn mask generation
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PR #11449: [XLA:GPU] replace is_causal_mask with enum mask_type to support more cudnn mask generation
Imported from GitHub PR https://github.com/openxla/xla/pull/11449
* replace is_causal_mask in backend_config with enum mask_type.
* cudnn also supports padding mask and alibi mask.
Copybara import of the project:
--
5780ffd6622978c0a173d90cc562d32f354bfbfd by cjkkkk <[email protected]>:
replace is_causal_mask with mask_type to support more mask
--
73555dba8f8cc1319cf75ed9cf8fe49ad88948bd by cjkkkk <[email protected]>:
add newline
--
2fed993035a2e5c17c990b60b7268ea9dbaeb7e5 by cjkkkk <[email protected]>:
add is_causal_mask back in backend_config
Merging this change closes #11449
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11449 from Cjkkkk:cudnn_mask_type 2fed993035a2e5c17c990b60b7268ea9dbaeb7e5
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Pass a flag whether we are in code emission phase.
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Pass a flag whether we are in code emission phase.
When trying to bisect failing fusions with the new mlir emitters, we have the flags
xla_gpu_max_mlir_kernels and xla_gpu_skip_mlir_kernels. However the function
GetFusionEmitter() is also called during fusion passes. It seems we have some
non-determinism regarding how often it is called during fusion passes, so it is not
easy to determine what value should be used for xla_gpu_skip_mlir_kernels to skip
all the calls during fusion passes. Instead, better only start counting in the
code emission phase.
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TensorFlow 2.16 is not found in Maven Central
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[
"TF Lite 2.16.1 is available on Maven Central now.\r\nSee <https://mvnrepository.com/artifact/org.tensorflow/tensorflow-lite/2.16.1>.",
"> TF Lite 2.16.1 is available on Maven Central now. See https://mvnrepository.com/artifact/org.tensorflow/tensorflow-lite/2.16.1.\r\n\r\n@fergushenderson Thanks! We have confirmed that TF Lite 2.16.1 is already available on Maven Central.",
"Hi @fergushenderson ,\r\n\r\nIf the issue is fixed can you please close the issue .",
"Yes, the issue is fixed.\r\n\r\nHowever, I can't close the issue because I don't have the appropriate access/permission.\r\n(Although I am a Google employee, I am not a member of the TF Lite team, nor am I a member of the TensorFlow team.)",
"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/65694\">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/65694\">No</a>\n"
] | 2024-04-15T05:53:49 | 2024-06-08T01:49:26 | 2024-06-08T01:49:23 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.16
### 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 TensorFlow 2.16 is not found in Maven Central, also blocks the issue https://github.com/tensorflow/tensorflow/issues/61951
### Standalone code to reproduce the issue
```shell
implementation ("org.tensorflow:tensorflow-lite:2.16.1")
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5soKp2
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PR #11478: [XLA:GPU] add guards for flash attention graph with cuDNN >= 8.9.4
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PR #11478: [XLA:GPU] add guards for flash attention graph with cuDNN >= 8.9.4
Imported from GitHub PR https://github.com/openxla/xla/pull/11478
* Build XLA with cuDNN 8.6 will cause compilation error because flash attention graph will use `CudnnfMHAUid` which is only defined with cuDNN > 8.8.
* Add a guard for flash attention graph with at least cuDNN 8.9.4. So the current logic would be only compile FMHA with cuDNN > 8.8 and only compile flash attention with cuDNN > 8.9.4.
Copybara import of the project:
--
a1aa585f4e6ce42c7486336549447151cd5f7690 by cjkkkk <[email protected]>:
add guards for flash attention graph with at least 8.9.4
Merging this change closes #11478
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11478 from Cjkkkk:fix_cudnn_8_6 a1aa585f4e6ce42c7486336549447151cd5f7690
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PR_kwDOArmXAs5soKga
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PR #11449: [XLA:GPU] replace is_causal_mask with enum mask_type to support more cudnn mask generation
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[] | 2024-04-15T05:49:22 | 2024-04-22T09:48:34 | 2024-04-22T09:48:31 |
NONE
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PR #11449: [XLA:GPU] replace is_causal_mask with enum mask_type to support more cudnn mask generation
Imported from GitHub PR https://github.com/openxla/xla/pull/11449
* replace is_causal_mask in backend_config with enum mask_type.
* cudnn also supports padding mask and alibi mask.
Copybara import of the project:
--
5780ffd6622978c0a173d90cc562d32f354bfbfd by cjkkkk <[email protected]>:
replace is_causal_mask with mask_type to support more mask
--
73555dba8f8cc1319cf75ed9cf8fe49ad88948bd by cjkkkk <[email protected]>:
add newline
--
2fed993035a2e5c17c990b60b7268ea9dbaeb7e5 by cjkkkk <[email protected]>:
add is_causal_mask back in backend_config
Merging this change closes #11449
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11449 from Cjkkkk:cudnn_mask_type 2fed993035a2e5c17c990b60b7268ea9dbaeb7e5
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