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PR #10341: Use vec.data() instead of &vec[0]
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PR #10341: Use vec.data() instead of &vec[0]
Imported from GitHub PR https://github.com/openxla/xla/pull/10341
I noticed that we use address of the first element of the vector to get pointer of the internal buffer - that is a known style "issue" in CodeChecker / clang-tidy.
_'data' should be used for accessing the data pointer instead of taking the address of the 0-th element_
## readability-container-data-pointer
Finds cases where code could use data() rather than the address of the element at index 0 in a container. This pattern is commonly used to materialize a pointer to the backing data of a container. std::vector and std::string provide a data() accessor to retrieve the data pointer which should be preferred.
This also ensures that in the case that the container is empty, the data pointer access does not perform an errant memory access.
https://clang.llvm.org/extra/clang-tidy/checks/readability/container-data-pointer.html
Copybara import of the project:
--
3f37e3dc17483f4e49fd86add9276183d0b78a65 by Alexander Pivovarov <[email protected]>:
Use vec.data() instead of &vec[0]
Merging this change closes #10341
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10341 from apivovarov:ptr_to_elem_zero 3f37e3dc17483f4e49fd86add9276183d0b78a65
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Fix checkfail in QuantizedConv2D with invalid input shape dimensions
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A checkfail can happen in `raw_ops.QuantizedConv2D` with invalid input shape i.e presence of `0` in input shape dimensions. Repro [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/96418887bc380751c49ce3206d6382df/nightly.ipynb).
Fixes #59927 .
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PR #10334: [ROCM] fixing collective ops tests
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PR #10334: [ROCM] fixing collective ops tests
Imported from GitHub PR https://github.com/openxla/xla/pull/10334
Here I fix collectives ops tests which were not taken care of for quite a some time.
- xla/tests/collective_ops_test_e2e: failed because collectives were made async by default in https://github.com/openxla/xla/commit/79ff5de7601d27e78fc1760b4ca20baded1f9139 while this test checks for sync/async collectives
- xla/tests/collective_ops_test: failed with errors like:
```
INTERNAL: Unsupported instruction opcode: collective-permute
```
because HLO passes were not run for a number of subtests. The point is we need [AsyncCollectiveCreator](https://github.com/ROCm/xla/blob/7ab393fc384651f367fdff4c3b1d5b1c4cbd9342/xla/service/gpu/gpu_compiler.cc#L1103) call in order to rewrite sync collectives into "start" and "done" pairs: e.g., collective-permute gets rewritten into 'collective-permute-start' and 'collective-permute-done'.
@xla-rotation: could you have a look, please ?
Copybara import of the project:
--
4b8bcbc61b49855c2477ab23bba9c59ab81dd62e by Pavel Emeliyanenko <[email protected]>:
fixing collective ops tests
Merging this change closes #10334
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10334 from ROCm:ci_collective_ops_tests_240307 4b8bcbc61b49855c2477ab23bba9c59ab81dd62e
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PR_kwDOArmXAs5pEq9C
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[XLA:GPU] Fix missing dep.
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[XLA:GPU] Fix missing dep.
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PR_kwDOArmXAs5pEchf
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PR_kwDOArmXAs5pEQjr
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PR #10358: Fix use of bitwise and with boolean operands
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PR #10358: Fix use of bitwise and with boolean operands
Imported from GitHub PR https://github.com/openxla/xla/pull/10358
Clang diagnostics reported `use of bitwise '&' with boolean operands` issue
[-Wbitwise-instead-of-logical](https://clang.llvm.org/docs/DiagnosticsReference.html#id82)
Diagnostic text: warning: use of bitwise '&' with boolean operands
Copybara import of the project:
--
3e23fa682693e693eb8373b1e2238e56ac91489b by Alexander Pivovarov <[email protected]>:
Fix use of bitwise and with boolean operands
Merging this change closes #10358
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10358 from apivovarov:bitwise_bool 3e23fa682693e693eb8373b1e2238e56ac91489b
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I_kwDOArmXAs6Br0M0
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prediction change for an image in a batch depending of the size of the batch.
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"@nomadeCodeur,\r\nI tried to execute the mentioned code with the sample images and observed that the code was executed without any issue/error & the output is also intended. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/456ca94783a93daf9bd4be3012d76fb1/untitled1747.ipynb) and also try to execute the code with keras3.0 version. Thank you!",
"@tilakrayal Thank you for your response. If I understand well, the output is as intended because it is really near to zeros (about 10**-8) . It is possible that is a model is not well trained, it deviate a lot from zeros ? I am trying to understand the possible causes of this phenomenon. In my case the deviation from zero was about 0.1 which is significant.\r\n\r\n",
"@nomadeCodeur,\r\nCould you please try to execute the code by passing a single image in multiple iterations as the input. Also **pred_batch** uses the entire batch, while **pred_batch2** uses only the first element. Depending on the model architecture, processing the entire batch might involve internal calculations that affect the final output for each element. This could lead to a slight difference even for the first element compared to using it alone. 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.",
"I tried to test the following. It gives very slight difference. I think there are internal calculations that are not significant for a well trained model. Thank you. \r\nprint(\"start\")\r\n\r\nfirst_image = x_batch[0:1]\r\ntwo_images = tf.concat([first_image, first_image], axis=0)\r\nthree_images = tf.concat([first_image, first_image, first_image], axis=0)\r\n\r\nprint(\"1 => \"); print(model.predict(first_image, verbose=0))\r\nprint(\"2 => \"); print(model.predict(two_images, verbose=0))\r\nprint(\"3 => \"); print(model.predict(three_images, verbose=0))\r\n\r\nThe output is: \r\nstart\r\n1 => \r\n2024-03-22 15:44:55.612396: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8101\r\n2024-03-22 15:44:56.980805: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\r\n[[0.39561632 0.52117676 0.08320692]]\r\n2 => \r\n[[0.39560094 0.52118075 0.08321837]\r\n [0.39560094 0.52118075 0.08321837]]\r\n3 => \r\n[[0.3956172 0.52115935 0.08322345]\r\n [0.3956172 0.52115935 0.08322345]\r\n [0.3956172 0.52115935 0.08322345]]",
"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/63282\">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/63282\">No</a>\n"
] | 2024-03-08T10:33:50 | 2024-03-22T14:49:38 | 2024-03-22T14:49:35 |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.10.1
### Custom code
Yes
### OS platform and distribution
Windows 11
### Mobile device
_No response_
### Python version
3.8.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
cuda 11.2 cuDnn 8.1
### GPU model and memory
NVIDIA GeForce RTX 3070 Ti
### Current behavior?
After training my model I save it using model.save(path_saving_model).
When I reload my model with model = keras.models.load_model(path_saved_model)
I run some predictions
for x_batch, y_GT in batch_generator_i:
print("start");
print("1 => "); print(model.predict(x_batch, verbose=0));
print("2 => "); print(model.predict(x_batch[0:1], verbose=0));
print("end");
Inside one iteration I get the results in the console
start
1 =>
2024-03-08 10:27:03.928564: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8101
2024-03-08 10:27:05.143783: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.
[[8.15018594e-01 6.95814118e-02 1.15400046e-01]
[9.66099262e-01 3.29955555e-02 9.05242225e-04]
[5.14399529e-01 4.07320522e-02 4.44868475e-01]
[4.52679815e-04 9.99545634e-01 1.65109623e-06]]
2 =>
[[0.7397079 0.12610717 0.13418493]]
end
I thought that my second prediction should be [[8.15018594e-01 6.95814118e-02 1.15400046e-01]] instead of [[0.7397079 0.12610717 0.13418493]]
Why it is not the case?
What are the possible reasons for this ?
I am running this in debug mode. I see that x_batch is an EagerTensor.
The model.summary() gives :
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input (InputLayer) [(None, None, None, 3)] 0
resizing_layer (Resizing) (None, 128, 128, 3) 0
tf.__operators__.getitem (S (None, 128, 128, 3) 0
licingOpLambda)
tf.nn.bias_add (TFOpLambda) (None, 128, 128, 3) 0
resnet50 (Functional) (None, 2048) 23587712
output_softmax (Dense) (None, 3) 6147
=================================================================
Total params: 23,593,859
Trainable params: 23,540,739
Non-trainable params: 53,120
_________________________________________________________________
### Standalone code to reproduce the issue
```shell
Above I use my own code base. Below is how I can reproduce. Just have a cat.jpg and elephant.jpg next to your python file.
import numpy as np
import keras
from keras.applications import ResNet50
from keras.preprocessing import image
preprocess_input = keras.applications.resnet.preprocess_input
decode_predictions = keras.applications.resnet.decode_predictions
# Load pre-trained ResNet50 model without top (fully connected layers)
model = ResNet50(weights='imagenet', include_top=True)
# Download an example image for testing
img_path = 'elephant.jpg' # You can change this path to the path of any image you want to use
img2_path = 'cat.jpg'
# Load and preprocess the image
img = keras.utils.load_img(img_path, target_size=(224, 224))
img_array = keras.utils.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = preprocess_input(img_array)
# Load and preprocess the image
img2 = keras.utils.load_img(img2_path, target_size=(224, 224))
img2_array = keras.utils.img_to_array(img)
img2_array = np.expand_dims(img_array, axis=0)
img2_array = preprocess_input(img_array)
batch = np.concatenate((img_array, img2_array), axis=0)
# Make predictions
pred_batch = model.predict(batch)
pred_batch2 = model.predict(batch[0:1])
difference = pred_batch[0]-pred_batch2[0]
print(difference) ## In my understanding there should only have zeros in this vector.
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5pEBib
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PR #10297: [NVIDIA GPU] Unroll all nested loops for double buffering transformer
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PR #10297: [NVIDIA GPU] Unroll all nested loops for double buffering transformer
Imported from GitHub PR https://github.com/openxla/xla/pull/10297
The loop_double_buffer_transformer pass currently only unrolls the loops in the main computation.
There are some cases where the memcpies(which this pass is designed to eliminate) would exist in nested loops.
This pr changes the pass to look at all loops in the module include the nested ones and unroll them.
Copybara import of the project:
--
af8c7b1944d14f5268809c5eb0e71166411ddb19 by TJ <[email protected]>:
Unroll all nested loops for double buffering transformer
Merging this change closes #10297
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10297 from Tixxx:tixxx/enable_double_buffer_nested af8c7b1944d14f5268809c5eb0e71166411ddb19
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PR_kwDOArmXAs5pEA3o
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PR #7854: [NVIDIA SPMD] Add HLO support to run windowed einsum in multiple streams
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PR #7854: [NVIDIA SPMD] Add HLO support to run windowed einsum in multiple streams
Imported from GitHub PR https://github.com/openxla/xla/pull/7854
This PR contains the HLO changes to be able to run windowed einsum in multiple cuda streams.
This adds a debug option to use multi-streaming for einsum, it will unroll the loop when turned on and mark the corresponding dots with different stream ids.
With the unrolled windowed einsum loop and overlapping multiple gemms, we get 2-3% speedup on the gpt-3 175b models.
There are some improvements to be made, major ones are:
1. redundant memcpy for each einsum loop, this can be mitigated by improving loop_double_buffer_transformer to unroll all nested while loops.
2. Some collective-permutes are scheduled after the async gemm, they won't be able to obtain enough SMs to run and are therefore partially exposed, this can be fixed by forcing them to be delayed in the latency hiding scheduler pass.
I'm working on both the optimizations above in separate PRs.
Detailed discussion [here](https://github.com/openxla/xla/discussions/8865)
Copybara import of the project:
--
9674dd75275376c41220da45b04cd9318453e501 by TJ Xu <[email protected]>:
Add option to run windowed einsum in multiple streams
Unroll windowed einsum loop and assign stream id to dots
Add tests
--
39983ab1847849ffc5cd7323c096c6f42c44f3c9 by TJ Xu <[email protected]>:
Added a pass to annotate stream-related attributes to instructions
--
43409a97bcc0473c2415c800064a595cb3d9ea47 by TJ <[email protected]>:
Wrap non-default stream with async instruction
--
c6b98c9c43406d8a2e7efd6c47f0ff3df5a78a1b by TJ <[email protected]>:
Add lhlo lowering for async compute instruction
Add asyncCompute resource in latency hiding scheduler
--
6fb8439198bc667d3b51979d4b6bad5dd39ec743 by TJ <[email protected]>:
Rename async_stream_attribute_wrapper to stream_attribute_async_wrapper
--
c799e1b5811c3bc0d4b3a90580dc36deff474885 by TJ <[email protected]>:
add missing BUILD deps
--
1095524f6bb9da1904ee579a196876bd45e3d4d2 by TJ <[email protected]>:
Added a pass to process windowed einsum after spmd pipeline
Merging this change closes #7854
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/7854 from Tixxx:tixxx/collective_matmul_hlo 1095524f6bb9da1904ee579a196876bd45e3d4d2
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Unable to build tensorflow-lite-select-ops.aar for tf 2.16
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[
"Have you solved this problem?",
"Hi @zerolalala ,\r\nThe problem is not solved",
"Hi @j-dsouza-19,\r\n\r\nI am working on it. Please allow me some time to resolve.\r\n\r\nThank You",
"> Hi @j-dsouza-19,\r\n> \r\n> I am working on it. Please allow me some time to resolve.\r\n> \r\n> Thank You\r\n\r\nThank you @LakshmiKalaKadali \r\n",
"Hi @pkgoogle,\r\n\r\nWhile reproducing the error, I have encountered another error ```File :/home/lakshmikala/tensorflow-2.16.1/./configure.py\", line 536, in get_from_env_or_user_or_default var = get_input(ask_for_var)\r\nFile \"/home/lakshmikala/tensorflow-2.16.1/./configure.py\", line 99, in get_input answer = input(question) # pylint: disable=bad-builtin```\r\nPlease look into the user error.\r\nThank You",
"Hi @j-dsouza-19, can you share the model.tflite file which produced this error? Feel free to make it smaller with only the ops which produced the error. Alternatively, one thing you can try is using the nightly branch from source instead of that zip file. That may resolve your issue.\r\n\r\nThanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"I do have the same issue during building v2.15.0 with select ops. What could be the problem?",
"Hi @vitalii-vorobiov, are you following the same steps as this issue? If not please let us know your reproducible steps. If your steps are different enough than that may be a separate issue. Thanks for your help.",
"@pkgoogle yes I was trying to build it using docker and following the same steps as described in this issue. And also I encountered the same error when I was trying to build locally on my linux machine following the next steps:\r\n1. Clone gtihub repo\r\n2. run ./configure for Android\r\n3. Run bash tensorflow/lite/tools/build_aar.sh --input_models=<my_model> --target_archs=arm64-v8a",
"Hi @vitalii-vorobiov, can you please share your input models (perhaps a reduced one which replicates the issue still)? Thanks.",
"Hi @pkgoogle, here is the model with which I'm trying to build (attached as a file\r\n[transformer_large.tflite.zip](https://github.com/tensorflow/tensorflow/files/14833872/transformer_large.tflite.zip)\r\n). It was created using this repo: https://github.com/PIC4SeR/AcT",
"Hi @vitalii-vorobiov, what error are you reaching? Judging by your replication steps, you are testing against master?\r\n\r\nHere are my results:\r\n```\r\nbazel build -c opt --config=opt --cxxopt=--std=c++17 --fat_apk_cpu=arm64-v8a --define=android_dexmerger_tool=d8_dexmerger --define=android_incremental_dexing_tool=d8_dexbuilder --host_crosstool_top=@bazel_tools//tools/cpp:toolchain //tmp:tensorflow-lite\r\nINFO: Reading 'startup' options from /usr/local/google/home/xxxxxxxxx/git/tensorflow/.bazelrc: --windows_enable_symlinks\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=176\r\nINFO: Reading rc options for 'build' from /usr/local/google/home/xxxxxxxxx/git/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /usr/local/google/home/xxxxxxxxx/git/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /usr/local/google/home/xxxxxxxxx/git/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/usr/local/google/home/xxxxxxxxx/miniconda3/envs/tf_debug_2dot16dot1/bin/python3 --action_env PYTHON_LIB_PATH=/usr/local/google/home/xxxxxxxxx/miniconda3/envs/tf_debug_2dot16dot1/lib/python3.12/site-packages --python_path=/usr/local/google/home/xxxxxxxxx/miniconda3/envs/tf_debug_2dot16dot1/bin/python3 --action_env CLANG_COMPILER_PATH=/usr/lib/llvm-17/bin/clang --repo_env=CC=/usr/lib/llvm-17/bin/clang --repo_env=BAZEL_COMPILER=/usr/lib/llvm-17/bin/clang --copt=-Wno-gnu-offsetof-extensions --action_env ANDROID_NDK_HOME=/usr/local/google/home/xxxxxxxxx/Android/android-ndk-r25c --action_env ANDROID_NDK_VERSION=25 --action_env ANDROID_NDK_API_LEVEL=21 --action_env ANDROID_BUILD_TOOLS_VERSION=34.0.0 --action_env ANDROID_SDK_API_LEVEL=30 --action_env ANDROID_SDK_HOME=/usr/local/google/home/xxxxxxxxx/Android/Sdk\r\nINFO: Found applicable config definition build:short_logs in file /usr/local/google/home/xxxxxxxxx/git/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /usr/local/google/home/xxxxxxxxx/git/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:opt in file /usr/local/google/home/xxxxxxxxx/git/tensorflow/.tf_configure.bazelrc: --copt=-Wno-sign-compare --host_copt=-Wno-sign-compare\r\nINFO: Found applicable config definition build:linux in file /usr/local/google/home/xxxxxxxxx/git/tensorflow/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes\r\nINFO: Found applicable config definition build:dynamic_kernels in file /usr/local/google/home/xxxxxxxxx/git/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS\r\nINFO: Analyzed target //tmp:tensorflow-lite (3 packages loaded, 2413 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /usr/local/google/home/xxxxxxxxx/git/tensorflow/tensorflow/lite/delegates/nnapi/BUILD:14:11: Compiling tensorflow/lite/delegates/nnapi/nnapi_delegate.cc failed: (Exit 1): clang failed: error executing command (from target //tensorflow/lite/delegates/nnapi:nnapi_delegate_no_nnapi_implementation) external/androidndk/toolchains/llvm/prebuilt/linux-x86_64/bin/clang -no-canonical-prefixes '--target=aarch64-linux-android21' -fdiagnostics-color -Wa,--noexecstack -fno-exceptions '-std=gnu++17' ... (remaining 158 arguments skipped)\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:522:7: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n .type = nn_type,\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1529:45: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n ANeuralNetworksOperandType operand_type{.type = nn_type};\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1682:45: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n ANeuralNetworksOperandType operand_type{.type = nn_type};\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1701:45: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n ANeuralNetworksOperandType operand_type{.type = nn_type,\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1743:9: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n .type = nn_type,\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1844:17: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n .channelDim = static_cast<uint32_t>(\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:6915:7: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n .init = [](TfLiteContext* context, const char* buffer,\r\n ^\r\n7 errors generated.\r\nTarget //tmp:tensorflow-lite failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 21.741s, Critical Path: 16.62s\r\nINFO: 836 processes: 59 internal, 777 local.\r\nFAILED: Build did NOT complete successfully\r\n```",
"Hi @pkgoogle, yes I'm testing this on the current master branch. When reproducing my steps I also first encountered the same error as you (error: designated initializers are a C++20 extension).\r\n\r\n`\r\nERROR: /home/chao/Desktop/TFBuild/tensorflow/tensorflow/lite/delegates/nnapi/BUILD:14:11: Compiling tensorflow/lite/delegates/nnapi/nnapi_delegate.cc failed: (Exit 1): clang failed: error executing command (from target //tensorflow/lite/delegates/nnapi:nnapi_delegate_no_nnapi_implementation) external/androidndk/toolchains/llvm/prebuilt/linux-x86_64/bin/clang -no-canonical-prefixes '--target=aarch64-linux-android21' -fdiagnostics-color -Wa,--noexecstack -fno-exceptions '-std=gnu++17' ... (remaining 157 arguments skipped)\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:522:7: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n .type = nn_type,\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1529:45: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n ANeuralNetworksOperandType operand_type{.type = nn_type};\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1682:45: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n ANeuralNetworksOperandType operand_type{.type = nn_type};\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1701:45: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n ANeuralNetworksOperandType operand_type{.type = nn_type,\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1743:9: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n .type = nn_type,\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1844:17: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n .channelDim = static_cast<uint32_t>(\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:6915:7: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n .init = [](TfLiteContext* context, const char* buffer,\r\n ^\r\n7 errors generated.\r\nTarget //tmp:tensorflow-lite failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 94.614s, Critical Path: 24.10s\r\nINFO: 540 processes: 21 internal, 519 local.\r\nFAILED: Build did NOT complete successfully\r\n`\r\n\r\nI fixed this issue just by replacing the C++ initializers syntax as follows:\r\nold: \r\n\r\n`ANeuralNetworksOperandType nn_operand_type{\r\n .type = nn_type,\r\n .dimensionCount = tensor_rank,\r\n .dimensions = tensor_dims,\r\n .scale = scale,\r\n .zeroPoint = zero_point,\r\n };`\r\n\r\nnew:\r\n\r\n`ANeuralNetworksOperandType nn_operand_type{\r\n nn_type,\r\n tensor_rank,\r\n tensor_dims,\r\n scale,\r\n zero_point,\r\n };`\r\n\r\nFor the ./configure action I only selected to configure ./WORKSPACE for Android builds. I've used NDK version 25.08775105, min Android NDK API level 21, Android SDK API level 34, and Android build tools version 34.0.0.\r\n\r\nAfter fixing the issue with C++20 initializers you will be able to reach this error with protobufs while trying to build select-tf-ops:\r\n\r\n`ERROR: /home/chao/Desktop/TFBuild/tensorflow/tmp/BUILD:19:28: Linking tmp/libtensorflowlite_flex_jni.so failed: (Exit 1): clang failed: error executing command (from target //tmp:libtensorflowlite_flex_jni.so) external/androidndk/toolchains/llvm/prebuilt/linux-x86_64/bin/clang @bazel-out/android-arm64-v8a-opt/bin/tmp/libtensorflowlite_flex_jni.so-2.params\r\nld.lld: error: undefined symbol: google::protobuf::Message::DebugString() const\r\n>>> referenced by function_ops.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/0/function_ops.pic.o:(std::__ndk1::__function::__func<tensorflow::$_0, std::__ndk1::allocator<tensorflow::$_0>, absl::lts_20230802::Status (tensorflow::shape_inference::InferenceContext*)>::operator()(tensorflow::shape_inference::InferenceContext*&&))\r\n>>> referenced by function_ops.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/0/function_ops.pic.o:(std::__ndk1::__function::__func<tensorflow::$_0, std::__ndk1::allocator<tensorflow::$_0>, absl::lts_20230802::Status (tensorflow::shape_inference::InferenceContext*)>::operator()(tensorflow::shape_inference::InferenceContext*&&))\r\n>>> referenced by function_ops.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/0/function_ops.pic.o:(std::__ndk1::__function::__func<tensorflow::$_0, std::__ndk1::allocator<tensorflow::$_0>, absl::lts_20230802::Status (tensorflow::shape_inference::InferenceContext*)>::operator()(tensorflow::shape_inference::InferenceContext*&&))\r\n>>> referenced 28 more times\r\n\r\nld.lld: error: undefined symbol: google::protobuf::internal::kGlobalEmptyTable\r\n>>> referenced by partitioned_function_ops.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/partitioned_function_ops.pic.o:(google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue>::Map(google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue> const&))\r\n>>> referenced by partitioned_function_ops.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/partitioned_function_ops.pic.o:(google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue>::Map(google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue> const&))\r\n>>> referenced by optimized_function_graph_info.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/optimized_function_graph_info.pic.o:(google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > >::Map<std::__ndk1::__hash_map_const_iterator<std::__ndk1::__hash_const_iterator<std::__ndk1::__hash_node<std::__ndk1::__hash_value_type<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > >, void*>*> > >(std::__ndk1::__hash_map_const_iterator<std::__ndk1::__hash_const_iterator<std::__ndk1::__hash_node<std::__ndk1::__hash_value_type<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > >, void*>*> > const&, std::__ndk1::__hash_map_const_iterator<std::__ndk1::__hash_const_iterator<std::__ndk1::__hash_node<std::__ndk1::__hash_value_type<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > >, void*>*> > const&))\r\n>>> referenced 207 more times\r\n\r\nld.lld: error: undefined symbol: google::protobuf::Arena::AllocateAlignedWithHookForArray(unsigned long, std::type_info const*)\r\n>>> referenced by partitioned_function_ops.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/partitioned_function_ops.pic.o:(google::protobuf::Arena::AllocateAlignedWithHookForArray(unsigned long, unsigned long, std::type_info const*))\r\n>>> referenced by partitioned_function_ops.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/partitioned_function_ops.pic.o:(google::protobuf::Arena::AllocateAlignedWithHookForArray(unsigned long, unsigned long, std::type_info const*))\r\n>>> referenced by kernel.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tensorflow/lite/delegates/flex/_objs/delegate_only_runtime/kernel.pic.o:(std::__ndk1::pair<google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue>::InnerMap::iterator_base<google::protobuf::MapPair<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue> >, bool> google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue>::InnerMap::TryEmplaceInternal<char const (&) [25]>(char const (&) [25]))\r\n\r\nld.lld: error: undefined symbol: google::protobuf::internal::ThreadSafeArena::AddCleanup(void*, void (*)(void*))\r\n>>> referenced by partitioned_function_ops.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/partitioned_function_ops.pic.o:(void google::protobuf::Arena::RegisterDestructorInternal<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > >(std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >*, google::protobuf::Arena*, std::__ndk1::integral_constant<bool, false>))\r\n>>> referenced by kernel.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tensorflow/lite/delegates/flex/_objs/delegate_only_runtime/kernel.pic.o:(std::__ndk1::pair<google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue>::InnerMap::iterator_base<google::protobuf::MapPair<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue> >, bool> google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue>::InnerMap::TryEmplaceInternal<char const (&) [25]>(char const (&) [25]))\r\n>>> referenced by [feature.pb.cc](http://feature.pb.cc/)\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tensorflow/core/example/_objs/example_protos_cc_impl/feature.pb.pic.o:(tensorflow::Features::Features(google::protobuf::Arena*, bool))\r\n>>> referenced 71 more times\r\n\r\nld.lld: error: undefined symbol: google::protobuf::Arena::AllocateAlignedWithCleanup(unsigned long, std::type_info const*)\r\n>>> referenced by partitioned_function_ops.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/partitioned_function_ops.pic.o:(google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue>::InnerMap::TreeConvert(unsigned long))\r\n>>> referenced by feature_util.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/feature_util.pic.o:(google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::FeatureList>::InnerMap::TreeConvert(unsigned long))\r\n>>> referenced by function.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/0/function.pic.o:(google::protobuf::internal::StringTypeHandler::New(google::protobuf::Arena*, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >&&))\r\n>>> referenced 59 more times\r\n\r\nld.lld: error: undefined symbol: google::protobuf::internal::RepeatedPtrFieldBase::AddOutOfLineHelper(void*)\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedPtrField<tensorflow::SavedSliceMeta>::TypeHandler::Type* google::protobuf::internal::RepeatedPtrFieldBase::Add<google::protobuf::RepeatedPtrField<tensorflow::SavedSliceMeta>::TypeHandler>(google::protobuf::RepeatedPtrField<tensorflow::SavedSliceMeta>::TypeHandler::Type const*))\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedPtrField<tensorflow::TensorSliceProto>::TypeHandler::Type* google::protobuf::internal::RepeatedPtrFieldBase::Add<google::protobuf::RepeatedPtrField<tensorflow::TensorSliceProto>::TypeHandler>(google::protobuf::RepeatedPtrField<tensorflow::TensorSliceProto>::TypeHandler::Type const*))\r\n>>> referenced by compression_utils.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(google::protobuf::RepeatedPtrField<tensorflow::data::CompressedComponentMetadata>::TypeHandler::Type* google::protobuf::internal::RepeatedPtrFieldBase::Add<google::protobuf::RepeatedPtrField<tensorflow::data::CompressedComponentMetadata>::TypeHandler>(google::protobuf::RepeatedPtrField<tensorflow::data::CompressedComponentMetadata>::TypeHandler::Type const*))\r\n>>> referenced 85 more times\r\n\r\nld.lld: error: undefined symbol: google::protobuf::RepeatedField<unsigned long>::Reserve(int)\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<unsigned long>::Add<unsigned long const*>(unsigned long const*, unsigned long const*))\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedField<unsigned long>::FastAdderImpl<0, true>::Add(unsigned long))\r\n\r\nld.lld: error: undefined symbol: google::protobuf::RepeatedField<long>::Reserve(int)\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<long>::Add<long const*>(long const*, long const*))\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedField<long>::FastAdderImpl<0, true>::Add(long))\r\n\r\nld.lld: error: undefined symbol: google::protobuf::RepeatedField<unsigned int>::Reserve(int)\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<unsigned int>::Add<unsigned int const*>(unsigned int const*, unsigned int const*))\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedField<unsigned int>::FastAdderImpl<0, true>::Add(unsigned int))\r\n\r\nld.lld: error: undefined symbol: google::protobuf::RepeatedField<int>::Reserve(int)\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<int>::Add<unsigned short const*>(unsigned short const*, unsigned short const*))\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedField<int>::FastAdderImpl<0, true>::Add(int))\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<int>::Add<short const*>(short const*, short const*))\r\n>>> referenced 5 more times\r\n\r\nld.lld: error: undefined symbol: google::protobuf::RepeatedField<float>::Reserve(int)\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<float>::Add<float const*>(float const*, float const*))\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedField<float>::FastAdderImpl<0, true>::Add(float))\r\n\r\nld.lld: error: undefined symbol: google::protobuf::RepeatedField<double>::Reserve(int)\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<double>::Add<double const*>(double const*, double const*))\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedField<double>::FastAdderImpl<0, true>::Add(double))\r\n\r\nld.lld: error: undefined symbol: google::protobuf::RepeatedField<bool>::Reserve(int)\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<bool>::Add<bool const*>(bool const*, bool const*))\r\n>>> referenced by save_restore_tensor.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedField<bool>::FastAdderImpl<0, true>::Add(bool))\r\n\r\nld.lld: error: undefined symbol: google::protobuf::MessageLite::AppendToString(std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >*) const\r\n>>> referenced by tensor_list.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/tensor_list.pic.o:(tensorflow::TensorList::Encode(tensorflow::VariantTensorData*) const)\r\n>>> referenced by tensor_coding.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/tensor_coding.pic.o:(tensorflow::port::StringListEncoderImpl::Append(google::protobuf::MessageLite const&))\r\n\r\nld.lld: error: undefined symbol: google::protobuf::MessageLite::ParseFromString(std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > const&)\r\n>>> referenced by tensor_list.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/tensor_list.pic.o:(tensorflow::TensorList::Decode(tensorflow::VariantTensorData const&))\r\n>>> referenced by compression_utils.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(tensorflow::data::UncompressElement(tensorflow::data::CompressedElement const&, std::__ndk1::vector<tensorflow::Tensor, std::__ndk1::allocator<tensorflow::Tensor> >*))\r\n>>> referenced by compression_utils.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(bool tensorflow::DecodeVariantImpl<tensorflow::data::CompressedElement>(tensorflow::VariantTensorData, tensorflow::TypeResolver<tensorflow::data::CompressedElement, false, false, true>, tensorflow::data::CompressedElement*))\r\n>>> referenced 8 more times\r\n\r\nld.lld: error: undefined symbol: google::protobuf::MessageLite::SerializeToArray(void*, int) const\r\n>>> referenced by compression_utils.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(tensorflow::data::CompressElement(std::__ndk1::vector<tensorflow::Tensor, std::__ndk1::allocator<tensorflow::Tensor> > const&, tensorflow::data::CompressedElement*))\r\n\r\nld.lld: error: undefined symbol: google::protobuf::RepeatedField<unsigned long>::Add(unsigned long const&)\r\n>>> referenced by compression_utils.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(tensorflow::data::CompressedComponentMetadata::_internal_add_uncompressed_bytes(unsigned long))\r\n>>> referenced by [tensor.pb_text.cc](http://tensor.pb_text.cc/)\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/tensor.pb_text.pic.o:(tensorflow::TensorProto::_internal_add_uint64_val(unsigned long))\r\n>>> referenced by graph_debug_info_builder.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/graph_debug_info_builder.pic.o:(tensorflow::GraphDebugInfo_StackTrace::_internal_add_frame_id(unsigned long))\r\n>>> referenced 1 more times\r\n\r\nld.lld: error: undefined symbol: google::protobuf::internal::ArenaStringPtr::Mutable(google::protobuf::Arena*)\r\n>>> referenced by compression_utils.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(tensorflow::data::CompressedElement::_internal_mutable_data())\r\n>>> referenced by dataset_utils.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/dataset_utils.pic.o:(tensorflow::NodeDef::_internal_mutable_device())\r\n>>> referenced by [attr_value.pb_text.cc](http://attr_value.pb_text.cc/)\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/attr_value.pb_text.pic.o:(tensorflow::AttrValue::_internal_mutable_s())\r\n>>> referenced 64 more times\r\n\r\nld.lld: error: undefined symbol: google::protobuf::Message::GetTypeName() const\r\n>>> referenced by compression_utils.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(tensorflow::Variant::Value<tensorflow::data::CompressedElement>::TypeName() const)\r\n>>> referenced by compression_utils.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(void tensorflow::EncodeVariant<tensorflow::data::CompressedElement>(tensorflow::data::CompressedElement const&, tensorflow::VariantTensorData*))\r\n>>> referenced by compression_utils.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(void tensorflow::EncodeVariant<tensorflow::data::CompressedElement>(tensorflow::data::CompressedElement const&, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >*))\r\n>>> referenced 287 more times\r\n\r\nld.lld: error: undefined symbol: google::protobuf::MessageLite::SerializeToString(std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >*) const\r\n>>> referenced by compression_utils.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(void tensorflow::EncodeVariantImpl<tensorflow::data::CompressedElement>(tensorflow::data::CompressedElement const&, tensorflow::TypeResolver<tensorflow::data::CompressedElement, false, false, true>, tensorflow::VariantTensorData*))\r\n>>> referenced by variant.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/variant.pic.o:(void tensorflow::EncodeVariant<tensorflow::VariantTensorDataProto>(tensorflow::VariantTensorDataProto const&, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >*))\r\n>>> referenced by variant_tensor_data.cc\r\n>>> bazel-out/android-arm64-v8a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/variant_tensor_data.pic.o:(tensorflow::VariantTensorData::SerializeToString(std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >*))\r\n>>> referenced 3 more times\r\n\r\nld.lld: error: too many errors emitted, stopping now (use -error-limit=0 to see all errors)\r\nclang: error: linker command failed with exit code 1 (use -v to see invocation)\r\nTarget //tmp:tensorflow-lite-select-tf-ops failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 8650.876s, Critical Path: 460.98s\r\nINFO: 15523 processes: 346 internal, 15177 local.\r\nFAILED: Build did NOT complete successfully`",
"Hi @vitalii-vorobiov, we probably need to fix both but changing the source might make things unstable, we need to at least fix the original issue so I will say we will focus on that first (Though if you want to make a PR on your own we definitely won't stop you either)\r\n\r\nSummary of reproduce steps:\r\n```sh\r\ngit clone https://github.com/tensorflow/tensorflow.git\r\ncd tensorflow\r\n./configure\r\n```\r\n> For the ./configure action I only selected to configure ./WORKSPACE for Android builds. I've used NDK version 25.08775105, min Android NDK API level 21, Android SDK API level 34, and Android build tools version 34.0.0.\r\n\r\ninput model: [transformer_large.tflite.zip](https://github.com/tensorflow/tensorflow/files/14833872/transformer_large.tflite.zip)\r\n```sh\r\nbash tensorflow/lite/tools/build_aar.sh --input_models=<path/to/model> --target_archs=arm64-v8a\r\n```\r\nproduces the above issues\r\n\r\nHi @terryheo, can you please take a look? Thanks.",
"@pkgoogle thanks for looking into the issue. By the way, do you know what stable version (branch, commit hash) I can use in order to build select-tf-ops for both Android and IOS without any errors or issues?",
"Hi @vitalii-vorobiov, I would try with r2.15 and go backwards until you have something stable... there may be some weird interactions with iOS and xcode if you go too far back though."
] | 2024-03-08T09:52:22 | 2024-04-03T21:44:43 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
TF 2.16.1
### Custom code
No
### OS platform and distribution
Ubuntu 20.04
### Mobile device
Android
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Getting build errors when building tensorflow lite 2.16.
```
ERROR: /host_dir/tensorflow-2.16.1/tmp/BUILD:19:28: Linking tmp/libtensorflowlite_flex_jni.so failed: (Exit 1): clang failed: error executing command (from target //tmp:libtensorflowlite_flex_jni.so) external/androidndk/toolchains/llvm/prebuilt/linux-x86_64/bin/clang @bazel-out/android-armeabi-v7a-opt/bin/tmp/libtensorflowlite_flex_jni.so-2.params
ld.lld: error: undefined symbol: google::protobuf::Message::DebugString() const
>>> referenced by function_ops.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/0/function_ops.pic.o:(std::__ndk1::__function::__func<tensorflow::$_0, std::__ndk1::allocator<tensorflow::$_0>, absl::lts_20230802::Status (tensorflow::shape_inference::InferenceContext*)>::operator()(tensorflow::shape_inference::InferenceContext*&&))
>>> referenced by function_ops.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/0/function_ops.pic.o:(std::__ndk1::__function::__func<tensorflow::$_0, std::__ndk1::allocator<tensorflow::$_0>, absl::lts_20230802::Status (tensorflow::shape_inference::InferenceContext*)>::operator()(tensorflow::shape_inference::InferenceContext*&&))
>>> referenced by function_ops.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/0/function_ops.pic.o:(std::__ndk1::__function::__func<tensorflow::$_0, std::__ndk1::allocator<tensorflow::$_0>, absl::lts_20230802::Status (tensorflow::shape_inference::InferenceContext*)>::operator()(tensorflow::shape_inference::InferenceContext*&&))
>>> referenced 36 more times
ld.lld: error: undefined symbol: google::protobuf::internal::kGlobalEmptyTable
>>> referenced by partitioned_function_ops.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/partitioned_function_ops.pic.o:(google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue>::Map(google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue> const&))
>>> referenced by optimized_function_graph_info.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/optimized_function_graph_info.pic.o:(google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > >::Map<std::__ndk1::__hash_map_const_iterator<std::__ndk1::__hash_const_iterator<std::__ndk1::__hash_node<std::__ndk1::__hash_value_type<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > >, void*>*> > >(std::__ndk1::__hash_map_const_iterator<std::__ndk1::__hash_const_iterator<std::__ndk1::__hash_node<std::__ndk1::__hash_value_type<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > >, void*>*> > const&, std::__ndk1::__hash_map_const_iterator<std::__ndk1::__hash_const_iterator<std::__ndk1::__hash_node<std::__ndk1::__hash_value_type<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > >, void*>*> > const&))
>>> referenced by optimized_function_graph_info.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/optimized_function_graph_info.pic.o:(google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > >::Map(google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > > const&))
>>> referenced 125 more times
ld.lld: error: undefined symbol: google::protobuf::Arena::AllocateAlignedWithHookForArray(unsigned int, std::type_info const*)
>>> referenced by partitioned_function_ops.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/partitioned_function_ops.pic.o:(google::protobuf::Arena::AllocateAlignedWithHookForArray(unsigned int, unsigned int, std::type_info const*))
>>> referenced by partitioned_function_ops.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/partitioned_function_ops.pic.o:(google::protobuf::Arena::AllocateAlignedWithHookForArray(unsigned int, unsigned int, std::type_info const*))
>>> referenced by kernel.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tensorflow/lite/delegates/flex/_objs/delegate_only_runtime/kernel.pic.o:(std::__ndk1::pair<google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue>::InnerMap::iterator_base<google::protobuf::MapPair<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue> >, bool> google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue>::InnerMap::TryEmplaceInternal<char const (&) [25]>(char const (&) [25]))
ld.lld: error: undefined symbol: google::protobuf::internal::ThreadSafeArena::AddCleanup(void*, void (*)(void*))
>>> referenced by partitioned_function_ops.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/partitioned_function_ops.pic.o:(void google::protobuf::Arena::RegisterDestructorInternal<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > >(std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >*, google::protobuf::Arena*, std::__ndk1::integral_constant<bool, false>))
>>> referenced by kernel.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tensorflow/lite/delegates/flex/_objs/delegate_only_runtime/kernel.pic.o:(std::__ndk1::pair<google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue>::InnerMap::iterator_base<google::protobuf::MapPair<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue> >, bool> google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue>::InnerMap::TryEmplaceInternal<char const (&) [25]>(char const (&) [25]))
>>> referenced by feature.pb.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tensorflow/core/example/_objs/example_protos_cc_impl/feature.pb.pic.o:(tensorflow::Features::Features(google::protobuf::Arena*, bool))
>>> referenced 71 more times
ld.lld: error: undefined symbol: google::protobuf::Arena::AllocateAlignedWithCleanup(unsigned int, std::type_info const*)
>>> referenced by partitioned_function_ops.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/partitioned_function_ops.pic.o:(google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::AttrValue>::InnerMap::TreeConvert(unsigned int))
>>> referenced by feature_util.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/feature_util.pic.o:(google::protobuf::Map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, tensorflow::Feature>::InnerMap::TreeConvert(unsigned int))
>>> referenced by function.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/0/function.pic.o:(google::protobuf::internal::StringTypeHandler::New(google::protobuf::Arena*, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >&&))
>>> referenced 59 more times
ld.lld: error: undefined symbol: google::protobuf::internal::RepeatedPtrFieldBase::AddOutOfLineHelper(void*)
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedPtrField<tensorflow::SavedSliceMeta>::TypeHandler::Type* google::protobuf::internal::RepeatedPtrFieldBase::Add<google::protobuf::RepeatedPtrField<tensorflow::SavedSliceMeta>::TypeHandler>(google::protobuf::RepeatedPtrField<tensorflow::SavedSliceMeta>::TypeHandler::Type const*))
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedPtrField<tensorflow::TensorSliceProto>::TypeHandler::Type* google::protobuf::internal::RepeatedPtrFieldBase::Add<google::protobuf::RepeatedPtrField<tensorflow::TensorSliceProto>::TypeHandler>(google::protobuf::RepeatedPtrField<tensorflow::TensorSliceProto>::TypeHandler::Type const*))
>>> referenced by compression_utils.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(google::protobuf::RepeatedPtrField<tensorflow::data::CompressedComponentMetadata>::TypeHandler::Type* google::protobuf::internal::RepeatedPtrFieldBase::Add<google::protobuf::RepeatedPtrField<tensorflow::data::CompressedComponentMetadata>::TypeHandler>(google::protobuf::RepeatedPtrField<tensorflow::data::CompressedComponentMetadata>::TypeHandler::Type const*))
>>> referenced 85 more times
ld.lld: error: undefined symbol: google::protobuf::RepeatedField<unsigned long long>::Reserve(int)
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<unsigned long long>::Add<unsigned long long const*>(unsigned long long const*, unsigned long long const*))
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedField<unsigned long long>::FastAdderImpl<0, true>::Add(unsigned long long))
ld.lld: error: undefined symbol: google::protobuf::RepeatedField<long long>::Reserve(int)
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<long long>::Add<long long const*>(long long const*, long long const*))
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedField<long long>::FastAdderImpl<0, true>::Add(long long))
ld.lld: error: undefined symbol: google::protobuf::RepeatedField<unsigned int>::Reserve(int)
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<unsigned int>::Add<unsigned int const*>(unsigned int const*, unsigned int const*))
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedField<unsigned int>::FastAdderImpl<0, true>::Add(unsigned int))
ld.lld: error: undefined symbol: google::protobuf::RepeatedField<int>::Reserve(int)
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<int>::Add<unsigned short const*>(unsigned short const*, unsigned short const*))
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedField<int>::FastAdderImpl<0, true>::Add(int))
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<int>::Add<short const*>(short const*, short const*))
>>> referenced 8 more times
ld.lld: error: undefined symbol: google::protobuf::RepeatedField<float>::Reserve(int)
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<float>::Add<float const*>(float const*, float const*))
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedField<float>::FastAdderImpl<0, true>::Add(float))
ld.lld: error: undefined symbol: google::protobuf::RepeatedField<double>::Reserve(int)
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<double>::Add<double const*>(double const*, double const*))
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedField<double>::FastAdderImpl<0, true>::Add(double))
ld.lld: error: undefined symbol: google::protobuf::RepeatedField<bool>::Reserve(int)
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(void google::protobuf::RepeatedField<bool>::Add<bool const*>(bool const*, bool const*))
>>> referenced by save_restore_tensor.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/save_restore_tensor.pic.o:(google::protobuf::RepeatedField<bool>::FastAdderImpl<0, true>::Add(bool))
ld.lld: error: undefined symbol: google::protobuf::MessageLite::AppendToString(std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >*) const
>>> referenced by tensor_list.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/tensor_list.pic.o:(tensorflow::TensorList::Encode(tensorflow::VariantTensorData*) const)
>>> referenced by tensor_coding.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/tensor_coding.pic.o:(tensorflow::port::StringListEncoderImpl::Append(google::protobuf::MessageLite const&))
ld.lld: error: undefined symbol: google::protobuf::MessageLite::ParseFromString(std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > const&)
>>> referenced by tensor_list.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/tensor_list.pic.o:(tensorflow::TensorList::Decode(tensorflow::VariantTensorData const&))
>>> referenced by compression_utils.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(tensorflow::data::UncompressElement(tensorflow::data::CompressedElement const&, std::__ndk1::vector<tensorflow::Tensor, std::__ndk1::allocator<tensorflow::Tensor> >*))
>>> referenced by compression_utils.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(bool tensorflow::DecodeVariantImpl<tensorflow::data::CompressedElement>(tensorflow::VariantTensorData, tensorflow::TypeResolver<tensorflow::data::CompressedElement, false, false, true>, tensorflow::data::CompressedElement*))
>>> referenced 11 more times
ld.lld: error: undefined symbol: google::protobuf::MessageLite::SerializeToArray(void*, int) const
>>> referenced by compression_utils.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(tensorflow::data::CompressElement(std::__ndk1::vector<tensorflow::Tensor, std::__ndk1::allocator<tensorflow::Tensor> > const&, tensorflow::data::CompressedElement*))
ld.lld: error: undefined symbol: google::protobuf::RepeatedField<unsigned long long>::Add(unsigned long long const&)
>>> referenced by compression_utils.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(tensorflow::data::CompressedComponentMetadata::_internal_add_uncompressed_bytes(unsigned long long))
>>> referenced by tensor.pb_text.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/tensor.pb_text.pic.o:(tensorflow::TensorProto::_internal_add_uint64_val(unsigned long long))
>>> referenced by graph_debug_info_builder.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/graph_debug_info_builder.pic.o:(tensorflow::GraphDebugInfo_StackTrace::_internal_add_frame_id(unsigned long long))
>>> referenced 1 more times
ld.lld: error: undefined symbol: google::protobuf::internal::ArenaStringPtr::Mutable(google::protobuf::Arena*)
>>> referenced by compression_utils.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(tensorflow::data::CompressedElement::_internal_mutable_data())
>>> referenced by dataset_utils.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/dataset_utils.pic.o:(tensorflow::NodeDef::_internal_mutable_device())
>>> referenced by attr_value.pb_text.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/attr_value.pb_text.pic.o:(tensorflow::AttrValue::_internal_mutable_s())
>>> referenced 66 more times
ld.lld: error: undefined symbol: google::protobuf::Message::GetTypeName() const
>>> referenced by compression_utils.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(tensorflow::Variant::Value<tensorflow::data::CompressedElement>::TypeName() const)
>>> referenced by compression_utils.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(void tensorflow::EncodeVariant<tensorflow::data::CompressedElement>(tensorflow::data::CompressedElement const&, tensorflow::VariantTensorData*))
>>> referenced by compression_utils.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(void tensorflow::EncodeVariant<tensorflow::data::CompressedElement>(tensorflow::data::CompressedElement const&, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >*))
>>> referenced 290 more times
ld.lld: error: undefined symbol: google::protobuf::MessageLite::SerializeToString(std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >*) const
>>> referenced by compression_utils.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tmp/_objs/custom_tensorflowlite_flex_flex_delegate_tensorflow_lib/compression_utils.pic.o:(void tensorflow::EncodeVariantImpl<tensorflow::data::CompressedElement>(tensorflow::data::CompressedElement const&, tensorflow::TypeResolver<tensorflow::data::CompressedElement, false, false, true>, tensorflow::VariantTensorData*))
>>> referenced by variant.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/variant.pic.o:(.text._ZN10tensorflow13EncodeVariantINS_22VariantTensorDataProtoEEEvRKT_PNSt6__ndk112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEE+0x0)
>>> referenced by variant_tensor_data.cc
>>> bazel-out/android-armeabi-v7a-opt/bin/tensorflow/core/_objs/portable_tensorflow_lib_lite/variant_tensor_data.pic.o:(tensorflow::VariantTensorData::SerializeToString(std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >*))
>>> referenced 3 more times
ld.lld: error: too many errors emitted, stopping now (use -error-limit=0 to see all errors)
clang: error: linker command failed with exit code 1 (use -v to see invocation)
Target //tmp:tensorflow-lite-select-tf-ops failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 1652.863s, Critical Path: 470.96s
INFO: 17918 processes: 1284 internal, 16634 local.
FAILED: Build did NOT complete successfully
```
### Standalone code to reproduce the issue
```shell
- Using docker setup from this tutorial https://www.tensorflow.org/lite/android/lite_build
wget https://github.com/tensorflow/tensorflow/archive/refs/tags/v2.16.1.zip
unzip v2.16.1.zip
cd tensorflow-v2.16.1/tensorflow/lite/tools
docker build . -t tflite-builder -f tflite-android.Dockerfile
cd ../../../
docker run -it -v $PWD:/host_dir tflite-builder bash
sdkmanager \
"build-tools;${ANDROID_BUILD_TOOLS_VERSION}" \
"platform-tools" \
"platforms;android-${ANDROID_API_LEVEL}"
./configure
tensorflow/lite/tools/build_aar.sh \
--input_models=/host_dir/model.tflite \
--target_archs=arm64-v8a,armeabi-v7a
```
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5pD7zx
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Rewrite tfl.batch_matmul with batch_1 constants of ones into reduce_sum, to avoid materializing the constant.
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Rewrite tfl.batch_matmul with batch_1 constants of ones into reduce_sum, to avoid materializing the constant.
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PR #10341: Use vec.data() instead of &vec[0]
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PR #10341: Use vec.data() instead of &vec[0]
Imported from GitHub PR https://github.com/openxla/xla/pull/10341
I noticed that we use address of the first element of the vector to get pointer of the internal buffer - that is a known style "issue" in CodeChecker / clang-tidy.
_'data' should be used for accessing the data pointer instead of taking the address of the 0-th element_
## readability-container-data-pointer
Finds cases where code could use data() rather than the address of the element at index 0 in a container. This pattern is commonly used to materialize a pointer to the backing data of a container. std::vector and std::string provide a data() accessor to retrieve the data pointer which should be preferred.
This also ensures that in the case that the container is empty, the data pointer access does not perform an errant memory access.
https://clang.llvm.org/extra/clang-tidy/checks/readability/container-data-pointer.html
Copybara import of the project:
--
3f37e3dc17483f4e49fd86add9276183d0b78a65 by Alexander Pivovarov <[email protected]>:
Use vec.data() instead of &vec[0]
Merging this change closes #10341
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10341 from apivovarov:ptr_to_elem_zero 3f37e3dc17483f4e49fd86add9276183d0b78a65
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Automated Code Change
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PR_kwDOArmXAs5pD0X2
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PR #10358: Fix use of bitwise and with boolean operands
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PR #10358: Fix use of bitwise and with boolean operands
Imported from GitHub PR https://github.com/openxla/xla/pull/10358
Clang diagnostics reported `use of bitwise '&' with boolean operands` issue
[-Wbitwise-instead-of-logical](https://clang.llvm.org/docs/DiagnosticsReference.html#id82)
Diagnostic text: warning: use of bitwise '&' with boolean operands
Copybara import of the project:
--
3e23fa682693e693eb8373b1e2238e56ac91489b by Alexander Pivovarov <[email protected]>:
Fix use of bitwise and with boolean operands
Merging this change closes #10358
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10358 from apivovarov:bitwise_bool 3e23fa682693e693eb8373b1e2238e56ac91489b
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PR_kwDOArmXAs5pDzA5
| 63,273 |
PR #10290: [GPU][NFC] Unify few methods fetching DNN version info.
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PR #10290: [GPU][NFC] Unify few methods fetching DNN version info.
Imported from GitHub PR https://github.com/openxla/xla/pull/10290
Copybara import of the project:
--
043ef2be61ff3fb39e99824087c4a69c0496c997 by Ilia Sergachev <[email protected]>:
[GPU][NFC] Unify few methods fetching DNN version info.
Merging this change closes #10290
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10290 from openxla:cleanup_dnn_version 043ef2be61ff3fb39e99824087c4a69c0496c997
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PR_kwDOArmXAs5pDvuy
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Integrate LLVM at llvm/llvm-project@3a56b5a27d71
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Integrate LLVM at llvm/llvm-project@3a56b5a27d71
Updates LLVM usage to match
[3a56b5a27d71](https://github.com/llvm/llvm-project/commit/3a56b5a27d71)
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I_kwDOArmXAs6BrAV8
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The result of tf.quantization.fake_quant_with_min_max_args is inconsistent between CPU and GPU
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[
"@sachinprasadhs I was able to replicate the issue reported here in [CPU](https://colab.research.google.com/gist/sushreebarsa/2216e9f37019c5fd21a3e8d88491197f/63271.ipynb) and [GPU](https://colab.research.google.com/gist/sushreebarsa/10de3e64c0048c2b85924488d7a2a969/untitled934.ipynb#scrollTo=ryI-x0hpVuC8). Please find the attached gists. Thank you!"
] | 2024-03-08T08:28:07 | 2024-03-13T07:15:43 | 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
3.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA 12.2
### GPU model and memory
_No response_
### Current behavior?
For the same input, `tf.quantization.fake_quant_with_min_max_args ` produces inconsistent results on CPU and GPU.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
input_data = tf.constant([0.0, 1.0, 2.0, 3.0, 4.0])
with tf.device('cpu:0'):
quantized_input_data = tf.quantization.fake_quant_with_min_max_args(input_data, min=-6.0, max=6.0)
print(quantized_input_data)
with tf.device('gpu:0'):
quantized_input_data = tf.quantization.fake_quant_with_min_max_args(input_data, min=-6.0, max=6.0)
print(quantized_input_data)
```
### Relevant log output
```shell
tf.Tensor([0. 0.9882353 2.0235295 3.0117648 4. ], shape=(5,), dtype=float32)
tf.Tensor([0. 0.9882353 1.9764706 3.0117648 4. ], shape=(5,), dtype=float32)
```
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PR_kwDOArmXAs5pC4Nd
| 63,270 |
Allow scalar pred in select and scalar min/max in clamp.
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Allow scalar pred in select and scalar min/max in clamp.
Semantics for both clamp and select allow restricted form of broadcasting, but it wasn't reflected in code.
For clamp, scalar min and/or max is allowed.
For select, scalar pred is allowed.
Context:
* https://openxla.org/xla/operation_semantics#clamp
* https://openxla.org/xla/operation_semantics#select
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I_kwDOArmXAs6BqIL1
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Tensorflow 2.16.1 wheels are not available on pypi
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NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Wheels for version 2.16.1 are not available on pypi
### Standalone code to reproduce the issue
```shell
$ pip install tensorflow==2.16.1
```
### Relevant log output
_No response_
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[nanobind] Add patch https://github.com/wjakob/nanobind/pull/461 to nanobind build.
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[nanobind] Add patch https://github.com/wjakob/nanobind/pull/461 to nanobind build.
This patch is needed for an upcoming JAX change.
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[XLA:Python] Attempt to fix GCC build failure in nb_absl_span.h
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[XLA:Python] Attempt to fix GCC build failure in nb_absl_span.h
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PR_kwDOArmXAs5pCtXB
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Integrate LLVM at llvm/llvm-project@3a56b5a27d71
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Integrate LLVM at llvm/llvm-project@3a56b5a27d71
Updates LLVM usage to match
[3a56b5a27d71](https://github.com/llvm/llvm-project/commit/3a56b5a27d71)
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Add an interface for backend-specific request payloads
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Add an interface for backend-specific request payloads
`IfrtRequestMetadata` can now carry additional payloads of any type in order to accommodate backend-specific metadata. Payloads are considered optional by default and are unordered, but they must still provide the version compatibility like the rest of the IFRT Proxy protocol.
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PR_kwDOArmXAs5pCnU2
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Lower memory_kind on inputs/outputs to `mhlo.memory_kind` and then translate that to entry_computation_layout attached to HLO.
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Lower memory_kind on inputs/outputs to `mhlo.memory_kind` and then translate that to entry_computation_layout attached to HLO.
This is very similar to how layouts are plumbed from stablehlo to entry_computation_layout.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/63288 from tensorflow:Surya_QuantizedConv2D_checkfail_fix bba7c37c810dc340530444caf069c463ead6efe3
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Rewrite TODO's according to latest syntax.
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Rewrite TODO's according to latest syntax.
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PR_kwDOArmXAs5pCf1P
| 63,261 |
Apply quantization denylisting at `QuantizePass` instead of `LiftQuantizableSpotsAsFunctionsPass`.
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Apply quantization denylisting at `QuantizePass` instead of `LiftQuantizableSpotsAsFunctionsPass`.
Instead of removing quantization related attributes from `LiftQuantizableSpotsAsFunctionsPass` when a denylisted quantizable unit is found,
apply denylisting at the point of quantization within `QuantizePass`.
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PR_kwDOArmXAs5pCct6
| 63,260 |
[XLA] Reuse utilities for type conversion between XLA and MLIR across XLA-related dialects
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[XLA] Reuse utilities for type conversion between XLA and MLIR across XLA-related dialects
This CL moves the type conversion utilities from the MHLO dialect to `tensorflow/compiler/xla/mlir/utils/type_util.cc`, where they can be reused across other XLA-related MLIR dialects.
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PR_kwDOArmXAs5pCcjR
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Add serialization passes during export instead of pre/post-calibration
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Add serialization passes during export instead of pre/post-calibration
When we pass the ModuleOp directly from pre-calibration to post-calibration, the serialization in pre-calibration should be avoided. In fact, serialization is more suitable to be in the export steps.
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I_kwDOArmXAs6BpqlN
| 63,258 |
2.16.1 Tflite CMake compiling error
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[
"Hi @zihaomu, can you try following these instructions? https://www.tensorflow.org/lite/guide/build_ios, let us know if that works for you.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63258\">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/63258\">No</a>\n"
] | 2024-03-08T02:58:33 | 2024-03-27T01:47:18 | 2024-03-27T01:47:14 |
NONE
| null | null | null |
**System information**
- OS Platform and Distribution : MacOS 14.1.2
- TensorFlow installed from (source or binary): source
- TensorFlow version : 2.16.1
- NDK version: 26.1.1090912
**Provide the text output from tflite_convert**
```
/Users/mzh/work/github/tensorflow_archive/tensorflow_216/tensorflow/lite/delegates/gpu/gl_delegate.cc:53:10: fatal error: 'tensorflow/lite/delegates/gpu/gl/metadata_generated.h' file not found
#include "tensorflow/lite/delegates/gpu/gl/metadata_generated.h"
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
1 error generated.
make[2]: *** [CMakeFiles/tensorflow-lite.dir/delegates/gpu/gl_delegate.cc.o] Error 1
make[1]: *** [CMakeFiles/tensorflow-lite.dir/all] Error 2
```
looks like the `tensorflow/tensorflow/lite/delegates/gpu/gl/metadata.fbs` is not be compiled to `.h` and `.cpp` file.
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Implement `GetQuantizationMethod` that retrieves `Method` from `XlaCallModuleOp`.
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Implement `GetQuantizationMethod` that retrieves `Method` from `XlaCallModuleOp`.
A utility function that will be useful when retrieving `Method` object from `XlaCallModuleOp`s.
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Update version numbers for TensorFlow 2.15.1 in setup.py
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Automation missed this file in #63118 probably due to "post1" in the old version.
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Public changes are in the diffbase.
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Add method GetUniqueValuePtr() in HloValue and HloValueSet.
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Add method GetUniqueValuePtr() in HloValue and HloValueSet.
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Fix Fusion of fully_connected + And
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Fix Fusion of fully_connected + And
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PR #10350: [ROCm] fixed CudaCubinInMemory build error from https://github.com/openxla/xla/pull/9045
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PR #10350: [ROCm] fixed CudaCubinInMemory build error from https://github.com/openxla/xla/pull/9045
Imported from GitHub PR https://github.com/openxla/xla/pull/10350
This PR fixed a build error from https://github.com/openxla/xla/pull/9045
@xla-rotation @ddunl
Copybara import of the project:
--
e233082e980f1d2cf74c96084472e32fa238fca9 by Chao Chen <[email protected]>:
fixed CudaCubinInMemory for https://github.com/openxla/xla/pull/9045
Merging this change closes #10350
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10350 from ROCm:fixed_CudaCubinInMemory e233082e980f1d2cf74c96084472e32fa238fca9
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Add mlir::mhlo::createShapeLegalizeToHloPass() in the stablehlo->tflite conversion path to convert shape ops to valid HLOs/types (i32).
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Add mlir::mhlo::createShapeLegalizeToHloPass() in the stablehlo->tflite conversion path to convert shape ops to valid HLOs/types (i32).
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[XLA:GPU] Make CublasRequiresPadding work on vectors
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[XLA:GPU] Make CublasRequiresPadding work on vectors
The function expects each operand to the dot instruction has exactly 2 non-batch dimension. We should make it accept operands with only 1 non-batch dimension.
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[IFRT] Introduce ifrt::Array::layout()
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[IFRT] Introduce ifrt::Array::layout()
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Add new methods in backend async kernel interface. Reflect to the design of go/async-io-coherency
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Add new methods in backend async kernel interface. Reflect to the design of go/async-io-coherency
New methods will set / retrieve the attributes based on the buffers.
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PR_kwDOArmXAs5pB643
| 63,247 |
#tf-data RepeatDataset index_mapper handles out-of-range element_positions..
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#tf-data RepeatDataset index_mapper handles out-of-range element_positions..
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PR_kwDOArmXAs5pB4YB
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#tf-data makes data_service_client.cc nonblocking when getting elements from workers
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#tf-data makes data_service_client.cc nonblocking when getting elements from workers
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PR_kwDOArmXAs5pB4He
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[PJRT][IFRT] Update PJRT, IFRT, and Py executable getters to return PjRtLayouts
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[PJRT][IFRT] Update PJRT, IFRT, and Py executable getters to return PjRtLayouts
Also updates the PjRtLayout binding to use nanobind, so we're not
mixing nanobind methods with pybind return types.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/63954 from elfringham:fix_312_setuptools da09611b50c51fc7654e139a01ceceb4174fce89
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PR_kwDOArmXAs5pB3au
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[PJRT] Add hashing, equality checking, and pickling to PjRtLayout.
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[PJRT] Add hashing, equality checking, and pickling to PjRtLayout.
This could really use some tests. However, we don't have a good way to
produce PjRtLayout objects in xla_client_test yet, so I'm giving up
for now.
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PR_kwDOArmXAs5pB1gd
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Integrate LLVM at llvm/llvm-project@a213df5d3895
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Integrate LLVM at llvm/llvm-project@a213df5d3895
Updates LLVM usage to match
[a213df5d3895](https://github.com/llvm/llvm-project/commit/a213df5d3895)
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PR_kwDOArmXAs5pB09E
| 63,242 |
PR #9531: Fp8 matmul support on AMD MI300
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PR #9531: Fp8 matmul support on AMD MI300
Imported from GitHub PR https://github.com/openxla/xla/pull/9531
This PR enables support of fp8 matmul (with fp8e4m3fnuz and fp8e5m2fnuz) on MI300.
It is based off this previous PR to fix build break on ROCm (which is still open): https://github.com/openxla/xla/pull/9367
Some of the GEMM pattern rewrites were changed to accommodate current limitations in hipblasLt:
- hipblasLt currently does not support fp8 matmul with fp8 output. Therefore, it is rewritten to custom call of fp8 matmul with fp32 output, and HLO instructions are used to convert fp32 output to fp8.
- hipblasLt currently does not support fp8 matmul with bf16 output. Therefore, it is rewritten to custom call of fp8 matmul with fp32 output, and HLO instructions are used to convert fp32 output to bf16.
Copybara import of the project:
--
a4423f9136072f090494baff91bd87d3ab4d4069 by Wen Chen <[email protected]>:
[ROCM] Initial support of fp8 Matmul via hipBlasLt.
[ROCM] Code refactoring of initial hipBlasLt fp8 Matmul support
- Clean up unnecessary code, particularly regarding output types of fp8
- Override methods in ParameterizedFp8GemmRewriteTest to replace the
patterns for CUDA and ROCm respectively for HLO checks.
- Explicitly set c_scale and d_scale to nullptr as hipblasLt currently
does not support them.
Merging this change closes #9531
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9531 from ROCm:ci_fp8_gemm_support a4423f9136072f090494baff91bd87d3ab4d4069
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Add mlir::mhlo::createShapeLegalizeToHloPass() in the stablehlo->tflite conversion path to convert shape ops to valid HLOs/types (i32).
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Add mlir::mhlo::createShapeLegalizeToHloPass() in the stablehlo->tflite conversion path to convert shape ops to valid HLOs/types (i32).
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PR_kwDOArmXAs5pBvqt
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Use HostCPU instead of lexicographically first device for client device
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Use HostCPU instead of lexicographically first device for client device
The existing code happens to work when the only devices are CPUs and GPUs because "C" comes before "G", but if we add other devices with certain names it will pick the wrong device (e.g. imagine adding a new device called "APU").
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PR_kwDOArmXAs5pBuoY
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Delete seemingly unused shape_component_analysis pass
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Delete seemingly unused shape_component_analysis pass
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PR_kwDOArmXAs5pBq96
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Add a TODO to explain why MHLO_Tensor does not alias HLO_Tensor
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Add a TODO to explain why MHLO_Tensor does not alias HLO_Tensor
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PR_kwDOArmXAs5pBoe7
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Remove support for PjRtNamedValue without bool type.
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Remove support for PjRtNamedValue without bool type.
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PR_kwDOArmXAs5pBhKQ
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Fix a bug in which an absl hashtable is used with inconsistent hash/eq functors.
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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.
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PR_kwDOArmXAs5pBgZ9
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Update KV cache custom op for persistent memory.
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Update KV cache custom op for persistent memory.
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PR_kwDOArmXAs5pBcYl
| 63,234 |
Add invoke loop into GPU delegate for TFLite GPU benchmarking to exclude CPU-GPU memory copy & sync time from total latency.
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Add invoke loop into GPU delegate for TFLite GPU benchmarking to exclude CPU-GPU memory copy & sync time from total latency.
This is to be used only by TFLite benchmarker and only enabled by "--copt=-DTFLITE_GPU_ENABLE_INVOKE_LOOP" flag.
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PR_kwDOArmXAs5pBTmM
| 63,233 |
Add TFRT metric for device execution latency.
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Add TFRT metric for device execution latency.
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PR_kwDOArmXAs5pBTLT
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Remove nobuild_tests_only for XLA ARM64
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Remove nobuild_tests_only for XLA ARM64
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PR_kwDOArmXAs5pBRTv
| 63,231 |
Don't legalize back to VHLO FuncOp during ODML->StableHLO
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Don't legalize back to VHLO FuncOp during ODML->StableHLO
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| 2,174,858,150 |
PR_kwDOArmXAs5pBQYj
| 63,230 |
Create streams using StreamExecutor::CreateStream rather than creating them and calling Initialize.
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NONE
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Create streams using StreamExecutor::CreateStream rather than creating them and calling Initialize.
CreateStream does both.
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PR_kwDOArmXAs5pBLSb
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[MSA] Refactor options out of memory_space_assignment.
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[MSA] Refactor options out of memory_space_assignment.
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PR_kwDOArmXAs5pBLQ4
| 63,228 |
Allow setting XLA sharding annotations on TF2 ResourceVariables.
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Allow setting XLA sharding annotations on TF2 ResourceVariables.
xla_sharding API calls on ResourceVariables simply store the sharding proto on the ResourceVariable python object. A new XlaSharding op is created whenever a ReadVariable op is created if the ResourceVariable has its _xla_sharding field set.
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PR_kwDOArmXAs5pBKRy
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#tensorflow uses logging.error instead of warning.warn in tf.InteractiveSession to redirect users to use tf.Session in production.
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#tensorflow uses logging.error instead of warning.warn in tf.InteractiveSession to redirect users to use tf.Session in production.
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Move `tsl/c` to `xla/tsl/c` and update users
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Move `tsl/c` to `xla/tsl/c` and update users
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[xla:gpu] Support a new method to pipeline Send-Recv chains.
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[xla:gpu] Support a new method to pipeline Send-Recv chains.
We are planning to have the compiler to use a new method to pipeline Send-Recv
chains for performance. With the new pipelining approach, we will peel off the
first Recv and Send as well as the last Recv-done and Send-done of a while-body
in a calling computation of the while instruction. In the while-body
computation, we will have a Recv-done and a Send-done ordered before their
corresponding Recv and Send.
Here we are changing the thunk emitter to support this new code pattern.
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Add the low priority task queue to the internal::Queue of the SharedBatchScheduler. Update the internal::Queue to accept the callback function with the additional task vector argument and SharedBatchScheduler::ThreadLogic to pass the tasks from the low priority queue to the callback function.
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Add the low priority task queue to the internal::Queue of the SharedBatchScheduler. Update the internal::Queue to accept the callback function with the additional task vector argument and SharedBatchScheduler::ThreadLogic to pass the tasks from the low priority queue to the callback function.
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PR_kwDOArmXAs5pA_ET
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[XLA:Python] Attempt to fix GCC build failure in nb_absl_span.h
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[XLA:Python] Attempt to fix GCC build failure in nb_absl_span.h
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PR_kwDOArmXAs5pA2FX
| 63,222 |
Move tranpose ops through element wise.
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Move tranpose ops through element wise.
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PR_kwDOArmXAs5pA1fQ
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[XLA:GPU] Reduce the size of `GpuCompiler::OptimizeHloModule` by extracting smaller functions.
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[XLA:GPU] Reduce the size of `GpuCompiler::OptimizeHloModule` by extracting smaller functions.
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PR_kwDOArmXAs5pAoaT
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[xla][gpu] Avoid repeatedly running layout normalization pass
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[xla][gpu] Avoid repeatedly running layout normalization pass
This CL moves layout normalization pass before all fusion passes to avoid running it multiple times in post layout-assignment pipeline
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[xla:gpu] NFC: clean up compile_module_to_llvm_ir
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[xla:gpu] NFC: clean up compile_module_to_llvm_ir
remove unused code and deps
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[XLA:Python] Port sharding and device lists to nanobind.
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[XLA:Python] Port sharding and device lists to nanobind.
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PR_kwDOArmXAs5pAT_i
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Add containers with CUDA 12.1, CUDNN 8.9.
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Add containers with CUDA 12.1, CUDNN 8.9.
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PR_kwDOArmXAs5pANFh
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Replace '--add-rpath' for patchelf with '--print-rpath' and '--set-rpath'. `'--add-rpath' is not available on old Ubuntu images.
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Replace '--add-rpath' for patchelf with '--print-rpath' and '--set-rpath'. `'--add-rpath' is not available on old Ubuntu images.
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PR_kwDOArmXAs5o_9lk
| 63,215 |
Add toggle in eager context for supporting XLA sharding on TF2 ResourceVariables
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Add toggle in eager context for supporting XLA sharding on TF2 ResourceVariables
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PR_kwDOArmXAs5o_39v
| 63,214 |
[xla:gpu] Allow send-done to take a non-send instruction as an operand.
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NONE
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[xla:gpu] Allow send-done to take a non-send instruction as an operand.
This is to support a new method to pipeline two Send-Recv chains that together
form a source-target cycle.
Add a test.
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PR_kwDOArmXAs5o_1f9
| 63,213 |
[FFI] Allow introspecting the set of registered custom calls.
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NONE
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[FFI] Allow introspecting the set of registered custom calls.
The custom call API only has a lookup operation today, not an iteration operation.
Having an iteration option allows inspecting the entire set of enabled calls to, say,
improve error messages or filter provided HLOs to only those that support expected
custom calls.
Both custom call API versions that are currently supported by xla_client are supported.
The proto defines more, but since they aren't actually register-able there isn't anything
we can do yet.
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PR_kwDOArmXAs5o_wW6
| 63,212 |
Make kernel_gen use ptxas instead of libnvptxcompiler
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Make kernel_gen use ptxas instead of libnvptxcompiler
libnvptxcompiler has some multithreading issue where I'm still working on a workaround. For the time being let's use ptxas instead.
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| 2,174,419,793 |
PR_kwDOArmXAs5o_v_U
| 63,211 |
Integrate LLVM at llvm/llvm-project@a213df5d3895
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Integrate LLVM at llvm/llvm-project@a213df5d3895
Updates LLVM usage to match
[a213df5d3895](https://github.com/llvm/llvm-project/commit/a213df5d3895)
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| 2,174,404,074 |
PR_kwDOArmXAs5o_saN
| 63,210 |
[xla][gpu] Implement bf16 3way gemm to compute F32 matmul
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[xla][gpu] Implement bf16 3way gemm to compute F32 matmul
This CL implements bf16 3way gemm in triton codegen. It uses three BF16 muls to compute one F32 mul instead of 6 as in bf16 6way gemm. The experiments show that its precision is about one order of magnitude less than bf16 6way gemm.
|
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I_kwDOArmXAs6Bmn6-
| 63,209 |
jit_compile keeps filling gpu memory to crash with simple dense and small data with official docker image
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[
"Hi @de-mh ,\r\n\r\nI have tested the code on Colab and it works fine as per attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/2f376aa2f5ebe8af3098caa4ac723ef5/63209.ipynb#scrollTo=O-oucVZCfKlL).Please note that Colab has Tesla T4 16GB GPU. Maybe this is related to docker image as the crash is after few epochs. Could you please confirm TF docker image source you have used?\r\n\r\nThanks!",
"Hi @SuryanarayanaY \r\n\r\nThis is the image that I used. It is the latest official image\r\n```tensorflow/tensorflow latest-gpu-jupyter a2cf87758fef 3 months ago 7.32GB```"
] | 2024-03-07T17:32:48 | 2024-03-08T08:25:50 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
v2.15.0-2-g0b15fdfcb3f 2.15.0
### Custom code
No
### OS platform and distribution
Linux Ubuntu 22 (Docker)
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.3
### GPU model and memory
_No response_
### Current behavior?
To narrow down the problem, I simplified everything as much as possible. I used official latest docker image to isolate as much as possible.
This is the GPU stats:
```
Thu Mar 7 17:16:10 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.161.07 Driver Version: 535.161.07 CUDA Version: 12.3 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 Tesla M60 Off | 00000000:00:1E.0 Off | 0 |
| N/A 30C P8 14W / 150W | 0MiB / 7680MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
```
I'm running These few simple lines:
```
import tensorflow as tf
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
with tf.device("CPU"):
train_dataset = tf.data.Dataset.from_tensor_slices((tf.random.uniform((1000_000,400)), tf.random.uniform((1000_000,1)))).batch(4000)
model = tf.keras.Sequential(
[
tf.keras.Input(shape=(400,)),
tf.keras.layers.Dense(400, activation='relu'),
tf.keras.layers.Dense(400, activation='relu'),
tf.keras.layers.Dense(400, activation='relu'),
tf.keras.layers.Dense(400, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid'),
]
)
model.compile(
optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.BinaryCrossentropy(from_logits=False),
jit_compile=True,
)
model.fit(train_dataset,
epochs=50,
verbose=True,
)
```
And the code crashes after 15 epochs because memory is filled. Here is the notebook for full errors detail:
[Break_XLA.ipynb.zip](https://github.com/tensorflow/tensorflow/files/14527818/Break_XLA.1.ipynb.zip)
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
with tf.device("CPU"):
train_dataset = tf.data.Dataset.from_tensor_slices((tf.random.uniform((1000_000,400)), tf.random.uniform((1000_000,1)))).batch(4000)
model = tf.keras.Sequential(
[
tf.keras.Input(shape=(400,)),
tf.keras.layers.Dense(400, activation='relu'),
tf.keras.layers.Dense(400, activation='relu'),
tf.keras.layers.Dense(400, activation='relu'),
tf.keras.layers.Dense(400, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid'),
]
)
model.compile(
optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.BinaryCrossentropy(from_logits=False),
jit_compile=True,
)
model.fit(train_dataset,
epochs=50,
verbose=True,
)
```
### Relevant log output
```shell
Epoch 1/50
2024-03-07 17:28:52.944900: I external/local_xla/xla/service/service.cc:168] XLA service 0x7fd9180c3f10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-03-07 17:28:52.944951: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): Tesla M60, Compute Capability 5.2
2024-03-07 17:28:52.980936: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-03-07 17:28:53.293838: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8906
13/250 [>.............................] - ETA: 3s - loss: 0.7202
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1709832534.134273 948 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
250/250 [==============================] - 5s 11ms/step - loss: 0.6946
Epoch 2/50
250/250 [==============================] - 3s 10ms/step - loss: 0.6932
Epoch 3/50
250/250 [==============================] - 3s 10ms/step - loss: 0.6932
Epoch 4/50
250/250 [==============================] - 3s 10ms/step - loss: 0.6932
Epoch 5/50
250/250 [==============================] - 2s 10ms/step - loss: 0.6932
Epoch 6/50
250/250 [==============================] - 3s 10ms/step - loss: 0.6931
Epoch 7/50
250/250 [==============================] - 3s 10ms/step - loss: 0.6931
Epoch 8/50
250/250 [==============================] - 3s 10ms/step - loss: 0.6931
Epoch 9/50
250/250 [==============================] - 3s 10ms/step - loss: 0.6931
Epoch 10/50
250/250 [==============================] - 2s 10ms/step - loss: 0.6931
Epoch 11/50
250/250 [==============================] - 3s 10ms/step - loss: 0.6931
Epoch 12/50
250/250 [==============================] - 3s 10ms/step - loss: 0.6931
Epoch 13/50
250/250 [==============================] - 3s 10ms/step - loss: 0.6931
Epoch 14/50
250/250 [==============================] - 3s 10ms/step - loss: 0.6931
Epoch 15/50
193/250 [======================>.......] - ETA: 0s - loss: 0.6931
2024-03-07 17:29:31.761561: W external/local_xla/xla/service/gpu/runtime/support.cc:58] Intercepted XLA runtime error:
INTERNAL: the requested functionality is not supported
2024-03-07 17:29:31.761643: W external/local_xla/xla/service/gpu/runtime/support.cc:58] Intercepted XLA runtime error:
INTERNAL: CaptureGpuGraph failed (the requested functionality is not supported; current tracing scope: custom-call): INTERNAL: Failed to end stream capture: CUDA_ERROR_STREAM_CAPTURE_INVALIDATED: operation failed due to a previous error during capture
---------------------------------------------------------------------------
InternalError Traceback (most recent call last)
Cell In[5], line 17
1 model = tf.keras.Sequential(
2 [
3 tf.keras.Input(shape=(400,)),
(...)
9 ]
10 )
11 model.compile(
12 optimizer=tf.keras.optimizers.Adam(),
13 loss=tf.keras.losses.BinaryCrossentropy(from_logits=False),
14 jit_compile=True,
15 )
---> 17 model.fit(train_dataset,
18 epochs=50,
19 verbose=True,
20 )
File /usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.__traceback__)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
File /usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/execute.py:53, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
51 try:
52 ctx.ensure_initialized()
---> 53 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
54 inputs, attrs, num_outputs)
55 except core._NotOkStatusException as e:
56 if name is not None:
InternalError: Graph execution error:
Detected at node StatefulPartitionedCall defined at (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "/usr/local/lib/python3.11/dist-packages/ipykernel_launcher.py", line 17, in <module>
File "/usr/local/lib/python3.11/dist-packages/traitlets/config/application.py", line 1077, in launch_instance
File "/usr/local/lib/python3.11/dist-packages/ipykernel/kernelapp.py", line 739, in start
File "/usr/local/lib/python3.11/dist-packages/tornado/platform/asyncio.py", line 205, in start
File "/usr/lib/python3.11/asyncio/base_events.py", line 604, in run_forever
File "/usr/lib/python3.11/asyncio/base_events.py", line 1909, in _run_once
File "/usr/lib/python3.11/asyncio/events.py", line 80, in _run
File "/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py", line 529, in dispatch_queue
File "/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py", line 518, in process_one
File "/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py", line 424, in dispatch_shell
File "/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py", line 766, in execute_request
File "/usr/local/lib/python3.11/dist-packages/ipykernel/ipkernel.py", line 429, in do_execute
File "/usr/local/lib/python3.11/dist-packages/ipykernel/zmqshell.py", line 549, in run_cell
File "/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py", line 3048, in run_cell
File "/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py", line 3103, in _run_cell
File "/usr/local/lib/python3.11/dist-packages/IPython/core/async_helpers.py", line 129, in _pseudo_sync_runner
File "/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py", line 3308, in run_cell_async
File "/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py", line 3490, in run_ast_nodes
File "/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py", line 3550, in run_code
File "/tmp/ipykernel_879/3821501683.py", line 17, in <module>
File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py", line 1807, in fit
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py", line 1401, in train_function
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py", line 1384, in step_function
Failed to execute XLA Runtime executable: run time error: custom call 'xla.gpu.graph.launch' failed: CaptureGpuGraph failed (the requested functionality is not supported; current tracing scope: custom-call): INTERNAL: Failed to end stream capture: CUDA_ERROR_STREAM_CAPTURE_INVALIDATED: operation failed due to a previous error during capture; current profiling annotation: XlaModule:#hlo_module=a_inference_run_step_1288__.701,program_id=132#.
[[{{node StatefulPartitionedCall}}]] [Op:__inference_train_function_1361]
```
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Use fully-qualified namespace in `TF_RETURN_IF_CUDA_ERROR` macros.
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Use fully-qualified namespace in `TF_RETURN_IF_CUDA_ERROR` macros.
To avoid the necessity of user code that uses the macros has to live in the `tensorflow` namespace.
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PR_kwDOArmXAs5o_e-N
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Model additional memory usage that can arise due to tensor reshardings.
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Model additional memory usage that can arise due to tensor reshardings.
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PR_kwDOArmXAs5o_bVe
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Move optimized function graph proto into graph construction to avoid copies.
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Move optimized function graph proto into graph construction to avoid copies.
Previously we were attempting to `std::move()` a `const GraphDef&` which led to the copying overload of `ConvertGraphDefToGraph(..., const GraphDef&, ...)` being called. After this change we properly call the `ConvertGraphDefToGraph(..., GraphDef&&, ...)` overload, which avoids the memory overheads of copying each `NodeDef` in the graph.
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[XLA:Python] Restore support for string inputs to mhlo_to_stablehlo.
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[XLA:Python] Restore support for string inputs to mhlo_to_stablehlo.
The previous change made this function accept only bytes (bytecode). Allow textual MLIR as well.
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PR_kwDOArmXAs5o_J5G
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[Linaro:ARM_CI] Update docker container for AARCH64
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CONTRIBUTOR
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Pick up the updated docker container used for github Actions on AARCH64 to use updated wheel build target
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[Linaro:ARM_CI] Need to use correct Python lib path
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configure will choose the wrong Python lib path so fix that up to use the correct path. This will fix the Python 3.12 build so it can find setuptools.
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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/29cc253f52a0a836885f224ddb3f6c61020e8c4e.
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[xla][gpu] Add an HLO pass to decompose CollectivePermute with a cycle in its
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NONE
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[xla][gpu] Add an HLO pass to decompose CollectivePermute with a cycle in its
source-target pairs.
The HLO pass converts CollectivePermute with all participants forming a cycle
into two CollectivePermute.
We currently limit the transformation to asynchronous CollectivePermute with one
input and without any context data.
Add tests.
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[XLA:GPU] Add algorithm to PrecisionConfig
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[XLA:GPU] Add algorithm to PrecisionConfig
This adds a way to specify the used algorithm more precisely compared to the current operand_precision.
The current change is the first of a list of changes, which will together add support for the algorithm selection.
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[XLA:GPU] Fix non-existing SSA ref.
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[XLA:GPU] Fix non-existing SSA ref.
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PR #9757: GpuTimer: improve kernel execution time measurement accuracy
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PR #9757: GpuTimer: improve kernel execution time measurement accuracy
Imported from GitHub PR https://github.com/openxla/xla/pull/9757
This PR changes how `GpuTimer` measures execution time, which should improve the measurement accuracy.
Quoting my comment in the code:
```c++
// When a timer is created it launches a delay kernel into the given stream and
// queues a start event immediately afterwards. This delay kernel blocks
// execution on the stream until GetElapsedDuration() is called, at which point
// an end event is queued and the delay kernel exits. This allows the device
// execution time of the tasks queued to the stream while the timer is active
// to be measured more accurately.
```
this should improve the accuracy of the measurements that are used to make auto-tuning decisions, especially for small kernels.
There are a couple of edge cases that have special treatment:
- if `CUDA_LAUNCH_BLOCKING=1` then the delay kernel will not achieve anything, so we don't launch it
- if any code in the timed region synchronises the device then the host will wait for the delay kernel to time out before actually launching the kernels to be measured. This will lead to a poor quality measurement, and an error message is printed. This condition can be met if one of the kernels being measured is lazily loaded, as lazy loading can trigger synchronisation. Best practice is to execute a warmup run (without timing enabled) before the timed execution.
Two parts of the autotuning code are updated to follow this best practice.
The `GpuTimer::Create*` signatures that were deprecated in https://github.com/openxla/xla/pull/9841 do not benefit from these accuracy improvements.
cc: @sergachev @nouiz
Copybara import of the project:
--
2b834f7154158ddc7294d45bb5481b8c38efdc61 by Olli Lupton <[email protected]>:
GpuTimer: use delay kernel to improve accuracy
This ensures that all the device operations to be timed are queued to
the relevant stream before any of them are executed, resulting in a more
accurate measurement. This is skipped if CUDA_LAUNCH_BLOCKING=1 or
unified addressing is not available.
In addition, make sure that auto-tuning code paths have warm-up
executions of the kernels being measured. The warm-up executions should
not be inside GpuTimer regions.
With the example HLO:
parameter_0 = bf16[128,12,128]{2,1,0} parameter(0)
parameter_1 = bf16[12,128,128]{2,1,0} parameter(1)
ROOT dot.26 = bf16[12,128,128]{2,1,0} dot(parameter_0, parameter_1),
lhs_batch_dims={1}, lhs_contracting_dims={0}, rhs_batch_dims={0},
rhs_contracting_dims={2}
a cuBLAS kernel was selected with a CUPTI/NSys-measured runtime of
3.9µs and before this change, GpuTimer measured a runtime of 17.5µs.
With this change, GpuTimer measures a runtime of 8.1µs.
With the example HLO:
p0 = bf16[128,1536]{0,1} parameter(0)
p1 = s8[1536,12288]{0,1} parameter(1)
c = bf16[1536,12288]{0,1} convert(p1)
ROOT d = bf16[128,12288]{1,0} dot(p0, c), lhs_contracting_dims={1},
rhs_contracting_dims={0}
five cuDNN plans were auto-tuned, with CUPTI/Nsys-measured runtimes of
{34.9, 18.5, 30.9, 32.4, 31.5}µs. Before this change, GpuTimer measured
runtimes of {76.6, 40.0, 52.5, 55.7, 51.8}µs. With this change, GpuTimer
measures runtimes of {39.6, 23.0, 35.4, 35.1, 35.1}µs.
In summary, with this change, GpuTimer gives results that are much
closer to the CUPTI/Nsys measurements, with a ~uniform offset of ~4µs. A
constant offset doesn't matter for auto-tuning.
--
8f5ce0e7e309a46bf69c369bc4b1da8c26fd1579 by Olli Lupton <[email protected]>:
Address CR
Merging this change closes #9757
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9757 from olupton:gpu-timer-improvement 8f5ce0e7e309a46bf69c369bc4b1da8c26fd1579
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PR_kwDOArmXAs5o-gVk
| 63,197 |
PR #10315: Add move assignment operator to Shape and ProgramShape
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PR #10315: Add move assignment operator to Shape and ProgramShape
Imported from GitHub PR https://github.com/openxla/xla/pull/10315
The attempt to use `Shape` move assignment operator is found in [ShapeUtil::MakeProgramShape](https://github.com/openxla/xla/blob/main/xla/shape_util.cc#L278)
```
Shape result) {
...
ProgramShape program_shape;
...
*program_shape.mutable_result() = std::move(result);
...
```
Because `Shape` move assignment operator is currently not declared, C++ uses copy assignment operator which copies all Shape internal fields from src Shape (`result`) to dest Shape (`program_shape.result_`).
Copybara import of the project:
--
e5ac9c58ee6f9008e19ec345ae1d05a43fe166fc by Alexander Pivovarov <[email protected]>:
Add move assn op to Shape and ProgramShape
Merging this change closes #10315
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10315 from apivovarov:shape_move_ctor e5ac9c58ee6f9008e19ec345ae1d05a43fe166fc
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PR_kwDOArmXAs5o-gTk
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PR #10290: [GPU][NFC] Unify few methods fetching DNN version info.
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PR #10290: [GPU][NFC] Unify few methods fetching DNN version info.
Imported from GitHub PR https://github.com/openxla/xla/pull/10290
Copybara import of the project:
--
043ef2be61ff3fb39e99824087c4a69c0496c997 by Ilia Sergachev <[email protected]>:
[GPU][NFC] Unify few methods fetching DNN version info.
Merging this change closes #10290
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10290 from openxla:cleanup_dnn_version 043ef2be61ff3fb39e99824087c4a69c0496c997
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PR_kwDOArmXAs5o-e5q
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PR #9354: Remove unused legalize_sparse_ops param from MlirToXlaComputation
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NONE
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PR #9354: Remove unused legalize_sparse_ops param from MlirToXlaComputation
Imported from GitHub PR https://github.com/openxla/xla/pull/9354
`xla::ParseMlirModuleStringAndConvertToXlaComputation` uses the same params as `xla::MlirToXlaComputation`.
New param `legalize_sparse_ops` was added to `xla::MlirToXlaComputation` on Sep 14, 2023. But this param is unused now.
1. It was decided to remove `legalize_sparse_ops` param from `xla::MlirToXlaComputation`.
2. `options` param has default value `{}` in `MlirToXlaComputation` decl and it is passed by value. In case func caller uses empty options we can simply omit passing `options` param to remove unnecessary obj copy.
Copybara import of the project:
--
9619fa34d03cf2229d3e4a14bc202f4adbe2d03b by Alexander Pivovarov <[email protected]>:
Remove unused legalize_sparse_ops param from MlirToXlaComputation
Merging this change closes #9354
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9354 from apivovarov:legalize_sparse_ops 9619fa34d03cf2229d3e4a14bc202f4adbe2d03b
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PR_kwDOArmXAs5o-Va5
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Remove updating of the deleted v2/setup.py file in update_version.py.
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[] | 2024-03-07T14:34:38 | 2024-03-07T14:44:28 | 2024-03-07T14:44:28 |
NONE
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Remove updating of the deleted v2/setup.py file in update_version.py.
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PR_kwDOArmXAs5o9_Ff
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Minor oneDNN build fixes. Delete unused variables and add missing build dependencies.
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Minor oneDNN build fixes. Delete unused variables and add missing build dependencies.
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PR_kwDOArmXAs5o9rvG
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Integrate Triton up to [bfb8e413](https://github.com/openai/triton/commits/bfb8e413b075583228c961bdbb65a98dc54d0868)
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Integrate Triton up to [bfb8e413](https://github.com/openai/triton/commits/bfb8e413b075583228c961bdbb65a98dc54d0868)
|
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