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PR #10782: Offloading 2/3: generate multi-stream async copies using events on GPUs
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PR #10782: Offloading 2/3: generate multi-stream async copies using events on GPUs
Imported from GitHub PR https://github.com/openxla/xla/pull/10782
Emit asynchronous memory copies between hosts and devices using additional streams while the main stream does the computation. The async copies are guarded by RecordEvent() and WaitForEvent() created by the copy-start/copy-done thunks respectively. A hash table is utilized to map copy-start instructions to events. The corresponding events will be waited at copy-done and extracted from the hash table.
Copybara import of the project:
--
a9271ee9b99eb43a0a0806c8dbdfc7753aef4644 by Jane Liu <[email protected]>:
Add the multi-stream implementation for copy-start and copy-done.
Merging this change closes #10782
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10782 from zhenying-liu:async_copy a9271ee9b99eb43a0a0806c8dbdfc7753aef4644
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`tensorflow::RunOptions::RunOptions(void)` symbol missing in built tensorflow.dll (Windows)
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"@advnpzn,\r\nCould you please provide the complete error log of the installation process which helps us to debug the issue in an effective way. Thank you!",
"Hi @tilakrayal \r\n\r\nCould you please specify which logs do you want specifically?\r\nI could provide you the bazel build log for `tensorflow.dll` and the error output I get while compiling/linking a sample program with that `tensorflow.dll`.",
"@advnpzn,\r\nThe logs which are available while trying to build the tensorflow on WIndows would be helpful for us to debug the issue and provide the resolution. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66490\">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/66490\">No</a>\n"
] | 2024-04-26T05:47:00 | 2024-05-23T01:48:55 | 2024-05-23T01:48:49 |
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
Windows 11 23H2 Build 22631.3296
### Mobile device
N/A
### Python version
3.11.2
### Bazel version
6.5.0
### GCC/compiler version
LLVM/clang 17.0.6 & MSVC 19.29.30154
### CUDA/cuDNN version
N/A
### GPU model and memory
N/A
### Current behavior?
I was trying to build the `tensorflow.dll` in Windows to use the C++ API in my code. After successfully building `tensorflow.dll` and trying to use it in the code, I had faced several missing symbols error while linking. But by adding `TF_EXPORT` I was able to remove most of it. But there was still one symbol that was missing, which is `tensorflow::RunOptions::RunOptions(void)`.
As far as I observed this is from a dynamically generated `config.pb.h` from `tensorflow/core/protobuf/config.proto`. What I have tried is to apply `TF_EXPORT` to the generated `config.pb.h` file in relevant places. But as soon as I run the bazel build command for the target, The `config.pb.h` and the `config.pb.cc` files are regenerated, thus whatever changes I made for exporting those symbols invalid. And after the build I still get the same `tensorflow::RunOptions::RunOptions(void)` symbol missing error while linking.
My question is how can I specifically export symbols from dynamically generated stub files from proto files while building ?
I was surprised no one has faced this problem before.
### Standalone code to reproduce the issue
```shell
Build tensorflow with the following command.
`bazelisk.exe build --config=opt --config=win_clang //tensorflow:tensorflow.dll //tensorflow:tensorflow_dll_import_lib //tensorflow:install_headers`
Use the resulting `tensorflow.dll` and `tensorflow.lib` to compile custom code in MSVC.
std::string filename = std::filesystem::path(model).filename().string();
filename.resize(filename.length() - 3);
tensorflow::SavedModelBundle bundle;
tensorflow::SessionOptions session_options;
tensorflow::RunOptions run_options;
run_options.Clear();
tensorflow::Status status = tensorflow::LoadSavedModel(session_options, run_options, model, { }, & bundle);
```
### Relevant log output
```shell
unresolved external symbol "__declspec(dllimport) public: __cdecl tensorflow::RunOptions::RunOptions(void)" (__imp_??0RunOptions@tensorflow@@QEAA@XZ) referenced in function "public: bool __cdecl ModelCollection::Add_v2(class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const &,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > &)" (?Add_v2@ModelCollection@@QEAA_NAEBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@AEAV23@@Z)
```
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Automated Code Change
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| 66,486 |
Register and load all applicable dialects for parallel compilation
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Register and load all applicable dialects for parallel compilation
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| 66,485 |
Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/bdefdf4f5655ff8f19fd401ac16804d8d0db4a4b.
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Use get_compatible_with_portable rather than non_prod directly.
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Use get_compatible_with_portable rather than non_prod directly.
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CL to-
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NONE
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CL to-
1. Fold double transpose in TF dialect to enable an existing prepare_tf pattern that moves a single transpose across a q-dq pair.
2. Fold a trivial reshape into proceeding transpose op to enable existing prepare_tf pattern that moves a single transpose across a q-dq pair.
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PR_kwDOArmXAs5tyXjD
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[xla] Add a test for custom call with output to operand aliasing
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NONE
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[xla] Add a test for custom call with output to operand aliasing
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Mark Hexagon delegate as unsupported
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Mark Hexagon delegate as unsupported
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[XLA:GPU] Remove unnecessary preprocessor directive.
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[XLA:GPU] Remove unnecessary preprocessor directive.
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Fix the number of cores to be based on topolgoy.
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NONE
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Fix the number of cores to be based on topolgoy.
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Add channel_dim in FakeQuantWithMinMaxVarsPerChannelOp
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NONE
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Add channel_dim in FakeQuantWithMinMaxVarsPerChannelOp
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[XLA:GPU][Mlir-based emitters] Add a pattern to fold constants/symbols/dims.
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NONE
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[XLA:GPU][Mlir-based emitters] Add a pattern to fold constants/symbols/dims.
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Update OpenXLA contributing docs
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Update OpenXLA contributing docs
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PR_kwDOArmXAs5tx8Ej
| 66,475 |
Fix optimize_layout target.
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Fix optimize_layout target.
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PR_kwDOArmXAs5tx61P
| 66,474 |
Adds an option to limit the number of iterations performed by the Memory Term Reducer.
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Adds an option to limit the number of iterations performed by the Memory Term Reducer.
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I_kwDOArmXAs6G-ihs
| 66,473 |
Add support for TensorRT 10
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"Hi @plopresti ,\r\n\r\nAFAIK the current TensrRT version that TF tested is 8.X version. 10.X version also will be supported in near future but can't sure of exact timeline.",
"@SuryanarayanaY Can you give me any link to this compatibility matrix between TensorFlow and TensorRT ?",
"Apparently TensorRT support is being removed (?)\r\n\r\nhttps://github.com/tensorflow/tensorflow/pull/69540/files"
] | 2024-04-25T21:38:55 | 2024-06-12T00:19:04 | null |
CONTRIBUTOR
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
nightly
### Custom code
No
### OS platform and distribution
Linux RedHat 9.3
### Mobile device
_No response_
### Python version
3.11
### Bazel version
6.5.0
### GCC/compiler version
13
### CUDA/cuDNN version
12.4.1
### GPU model and memory
_No response_
### Current behavior?
TensorRT 10 GA has been released (https://developer.nvidia.com/tensorrt/download/10x).
It does not have `/usr/include/NvUtils.h` as required by `third_party/tensorrt/tensorrt_configure.bzl`, causing the build to fail.
### Standalone code to reproduce the issue
```shell
env TF_NEED_TENSORRT=1 ./configure
bazel build
```
### Relevant log output
```shell
cp: cannot stat '/usr/include/NvUtils.h': No such file or directory
```
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PR_kwDOArmXAs5txoGG
| 66,472 |
[XLA:GPU] Use raw HloInstruction pointers instead of HloInstructionAdaptor in caches.
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NONE
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[XLA:GPU] Use raw HloInstruction pointers instead of HloInstructionAdaptor in caches.
HloInstructionAdaptor should be an "iterator" to HloFusionAdaptor. Creating an instruction adaptor without fusion adaptor doesn't make sense.
Using raw pointer inside one pass is fine, because instruction are only deleted after the pass. If this becomes a problem at some point, we should used a separate abstraction to compare unique_ids.
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PR_kwDOArmXAs5txflA
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Accounts for the number of buffer elements in the memory usage of the model
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Accounts for the number of buffer elements in the memory usage of the model
Previously, when autotune optimizes the model, it did not accounts for the usage of the actual number of buffered elements. This causes the memory usage to fluctuate and potentially makes tf.data to use RAM more than the ram budget tf.data supposed to respect.
After this change, tf.data's memory usage is expected to be more stable and less likely to consume more than the ram budget.
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PR_kwDOArmXAs5txfIv
| 66,470 |
Fix typo in tflite conversion documentation.
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Fix typo in tflite conversion documentation.
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PR_kwDOArmXAs5txb9m
| 66,469 |
Fix HloAyncStartInstruction::ClassOf
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Fix HloAyncStartInstruction::ClassOf
Ignore verifying computation parameter/result shapes for custom-call async ops
Reverts 838b2f22e36d483be7fd6cccbedd30d46497c7f3
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[XLA:GPU][MLIR-based emitters] Add a pattern to simplify apply_indexing op.
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[XLA:GPU][MLIR-based emitters] Add a pattern to simplify apply_indexing op.
Right now it also removes unused symbols, but later unused dims will be added
as well.
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PR_kwDOArmXAs5txVVR
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Reverts dd491d46f1737d41774e6efeabf694ef59845126
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Add memory allocation timeline in TensorBoard
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Add memory allocation timeline in TensorBoard
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PR_kwDOArmXAs5txFr-
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Some preparation changes for enabling async dispatch on JAX CPU.
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Some preparation changes for enabling async dispatch on JAX CPU.
To prevent too much parallelism for non-parallel computations, we add a enqueue event to make sure next computation won't be enqueued until last one is done per user thread.
In `~PyArray_Storage()`, we now release the Python GIL then destroy the underlying buffer to prevent deadlock caused by interactions between argument donations and host callbacks on CPU backend.
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PR_kwDOArmXAs5txFen
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Integrate LLVM at llvm/llvm-project@76a3be7c766b
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Integrate LLVM at llvm/llvm-project@76a3be7c766b
Updates LLVM usage to match
[76a3be7c766b](https://github.com/llvm/llvm-project/commit/76a3be7c766b)
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#tf-data Implement `Cardinality` for flat_map_dataset.
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#tf-data Implement `Cardinality` for flat_map_dataset.
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Fix the cuda deps issue
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Fix the cuda deps issue
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[pjrt] Add const& qualified Await() to fix broken tests
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[pjrt] Add const& qualified Await() to fix broken tests
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Fuse redundant RHS TFL_TransposeOp into TFL_BatchMatMulOp if rhs is constant quantized tensor of rank-2.
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[pjrt] Add OnReady overload for unique (move-only) futures
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[pjrt] Add OnReady overload for unique (move-only) futures
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[pjrt] Add OnReady overload for unique (move-only) futures
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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/8b26d736c7e05a4a4a2fdb099c882c7c0010c8ae.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10638 from jaro-sevcik:host-allocation-custom-call 0f78007475921031549c8555ad56ed8903efe6cb
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[pjrt] Add OnReady overload for unique (move-only) futures
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Update stableHLO temporary.patch
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NONE
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PR #11345: [XLA:GPU] Relax row reduction vectorization restriction
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PR #11345: [XLA:GPU] Relax row reduction vectorization restriction
Imported from GitHub PR https://github.com/openxla/xla/pull/11345
Row reduction did not enable vectorization if num_threads_x <= WarpSize() previously due to half or more of the threads don't do anything. But for current tiling implementation, it is not right. Assume row reduction operand shape is [1024, 256] and num_threads_x is 32 (because of 32 * 16 > 256), each thread can handle 8 scalar or 4 vec<T, 2>.
It is better to judge whether (num_threads_x * vector_size) is larger than the reduced minor dimension.
Copybara import of the project:
--
4fe28d16b52728773a82b6cd31dd3314857ebd21 by Zhou, Lingzhi <[email protected]>:
Relax row reduction vectorization restriction. Below hlo can improve
from 0.030ms to 0.026ms on A100 40GB:
HloModule reduce_sum
region_0.5 {
Arg_1.7.0 = f32[] parameter(1)
Arg_0.6.0 = f32[] parameter(0)
ROOT add.4.0 = f32[] add(Arg_0.6.0, Arg_1.7.0)
}
ENTRY main.14 {
Arg_0.1.0 = bf16[65536,256]{1,0} parameter(0)
convert.8.1 = f32[65536,256]{1,0} convert(Arg_0.1.0)
constant_3_1 = f32[] constant(0)
ROOT reduce.9.1 = f32[65536]{0} reduce(convert.8.1, constant_3_1), dimensions={1}, to_apply=region_0.5
}
--
90e14e2cd2966ac093677536ce2452e4c073541c by Zhou, Lingzhi <[email protected]>:
analysis memory coalescing correctly for vectorization load
--
aa0a3fb62f8877b919ca906291a19440da9ff631 by Zhou, Lingzhi <[email protected]>:
match symbol with range
Merging this change closes #11345
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11345 from lingzhi98:lingzhi/row_reduction_vectorization aa0a3fb62f8877b919ca906291a19440da9ff631
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PR_kwDOArmXAs5twMsL
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Use `if_cuda` instead of `if_cuda_is_configured` in `//third_party/tensorflow/tsl/profiler/lib/BUILD` when including cuda deps.
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Use `if_cuda` instead of `if_cuda_is_configured` in `//third_party/tensorflow/tsl/profiler/lib/BUILD` when including cuda deps.
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PR_kwDOArmXAs5twJgo
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PR #11757: [GPU] Enable workspace support in cuDNN GEMM fusions.
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PR #11757: [GPU] Enable workspace support in cuDNN GEMM fusions.
Imported from GitHub PR https://github.com/openxla/xla/pull/11757
Copybara import of the project:
--
a2b4e3ec5f508e64301307b7eb39d7c4c7009023 by Ilia Sergachev <[email protected]>:
[GPU] Enable workspace support in cuDNN GEMM fusions.
Merging this change closes #11757
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11757 from openxla:cudnn_workspace a2b4e3ec5f508e64301307b7eb39d7c4c7009023
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PR_kwDOArmXAs5twHHQ
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PR #10638: Handle no-op custom call in the emitter
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PR #10638: Handle no-op custom call in the emitter
Imported from GitHub PR https://github.com/openxla/xla/pull/10638
The AllocateBuffer custom call (a.k.a. `kNopCustomCallTarget`) is no-op because the runtime allocates the buffer. Let us handle AllocateBuffer by emitting nothing and returning.
Copybara import of the project:
--
6ad7c399e05182a543e50ed14f34b99a281155ce by Jaroslav Sevcik <[email protected]>:
Emit no-op (AllocateBuffer custom-call)
--
0f78007475921031549c8555ad56ed8903efe6cb by Jaroslav Sevcik <[email protected]>:
Renamed allocate-buffer to nop in the test
Merging this change closes #10638
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10638 from jaro-sevcik:host-allocation-custom-call 0f78007475921031549c8555ad56ed8903efe6cb
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PR_kwDOArmXAs5tv36j
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iwyu: missing includes in xla
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"Hi @selfisekai Can you please review this PR? Thank you!",
"rebased on top of current master, lgtm"
] | 2024-04-25T16:19:38 | 2024-06-07T17:09:05 | null |
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Add `Subgraph::ResizeInputTensor` overloads that don't require allocating a vector before.
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Add `Subgraph::ResizeInputTensor` overloads that don't require allocating a vector before.
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Populates Auto Sharding solver requests with intervals instead of liveness matrices.
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Populates Auto Sharding solver requests with intervals instead of liveness matrices.
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Clean-up kernels build file.
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NONE
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Clean-up kernels build file.
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PR_kwDOArmXAs5tvs5-
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Fixed proto2 to google::protobuf conversion.
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Fixed proto2 to google::protobuf conversion.
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PR_kwDOArmXAs5tvIJO
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Reverts f1bac5e7917a4542707c2e175abc5929d4beed3e
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Reverts f1bac5e7917a4542707c2e175abc5929d4beed3e
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Updates the solver to support interval-based memory representations.
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Updates the solver to support interval-based memory representations.
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PR_kwDOArmXAs5tuzhU
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Reverts 35a836fd2066b76ec900689416999455c99524ec
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Reverts 35a836fd2066b76ec900689416999455c99524ec
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Check op count and quantization_method in QuantizeSingularOpPattern
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Check op count and quantization_method in QuantizeSingularOpPattern
Currently, it only checks whether the function body contains the given op, therefore the matching becomes inaccurate. For example, convolution with bias addition can be matched to pattern to quantize add op.
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Add a MultiOutputFusion test case for CommonElementwiseUtilization().
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Add a MultiOutputFusion test case for CommonElementwiseUtilization().
This is to document that the function also works as expected if called by
FusionUsesParameterElementwiseFromRoot() with a MultiOutputFusion.
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"@andremfreitas The warnings you're seeing indicate that your two jit-compiled functions, likely containing some floating-point division (`__cuda_sm20_div_rn_f64_full)`, are running out of available registers on your GPU. Could you please provide a standalone code to replicate the issue reported here?\r\nThank you!\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66434\">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/66434\">No</a>\n",
"I am having the same or a similar issue. I am also using a single GPU, and also not turning on jit compilation in my `train_step`, and with `model.compile(jit_compile=False)`\r\n\r\n`\r\nI0000 00:00:1717163798.560575 211396 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_112', 8 bytes spill stores, 8 bytes spill loads\r\n\r\nI0000 00:00:1717163798.631148 211415 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_88', 4 bytes spill stores, 4 bytes spill loads\r\n\r\nI0000 00:00:1717163798.845685 211466 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_73', 104 bytes spill stores, 96 bytes spill loads\r\n\r\nI0000 00:00:1717163798.845731 211485 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_5', 72 bytes spill stores, 72 bytes spill loads\r\n\r\nI0000 00:00:1717163798.944683 211412 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_3', 16 bytes spill stores, 16 bytes spill loads\r\n`\r\n\r\nI am confused because reducing batch size to 1 doesn't change anything and neither does reducing the number of my largest model blocks. Do you have any idea what this could be due to ? \r\n\r\n"
] | 2024-04-25T09:50:59 | 2024-05-31T14:05:17 | 2024-05-23T01:48:51 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I have a function that two functions that are jit compiled. I call them in my training loop function setp (which is not jit compiled, simply in graph mode). With these two functions jit compiled I get a registers spilled warning as follows:
`2024-04-24 12:58:52.550355: I tensorflow/stream_executor/gpu/asm_compiler.cc:323] ptxas warning : Registers are spilled to local memory in function '__cuda_sm20_div_rn_f64_full', 8 bytes spill stores, 8 bytes spill loads
ptxas warning : Registers are spilled to local memory in function '__cuda_sm20_div_rn_f64_full', 8 bytes spill stores, 8 bytes spill loads`
This makes the training much slower.
Without jit compiling those two functions I don't get this warning.
My problem is similar to the one in [this forum thread](https://discuss.tensorflow.org/t/xla-jit-compile-causing-memory-leak/22657). In this thread some user redirects to this open issue [`#56423`](https://github.com/tensorflow/tensorflow/issues/56423). However this one relates this problem to the distribution strategy. In my case I am just using one GPU. In this issue someone, mentioned that if they performed the optimizer step outside of the jit function it worked. In my case, as I said above, my training step function is not jit compiled and so the optimizer step is already not inside a jit compiled function.
### Standalone code to reproduce the issue
```shell
Unfortunately, I cannot build a MWE at the moment.
```
### Relevant log output
_No response_
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PR #11592: Add a TritonFusionAnalysis util to query an op's scope.
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PR #11592: Add a TritonFusionAnalysis util to query an op's scope.
Imported from GitHub PR https://github.com/openxla/xla/pull/11592
Add a TritonFusionAnalysis util to query an op's scope.
This will be used in cuDNN compiler for deciding an op's dimensions in the followup PR.
Copybara import of the project:
--
33d4a59ebf970689a4c05c1847152c9a6aab632f by Elfie Guo <[email protected]>:
Add a TritonFusionAnalysis util to query an op's scope.
Merging this change closes #11592
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11592 from elfiegg:cudnn_scalar_constants 33d4a59ebf970689a4c05c1847152c9a6aab632f
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PR #11717: [XLA:GPU] Default to flash attention and remove mask input in cuDNN
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PR #11717: [XLA:GPU] Default to flash attention and remove mask input in cuDNN
Imported from GitHub PR https://github.com/openxla/xla/pull/11717
* Default to flash attention as it is high performant and maintained actively by cuDNN, remove old fused attn.
* Remove lowering to fused attn in rewriter
* Remove cudnn graph generation
* Remove mask input as it is only doing multiply instead of masking with -inf, give incorrect results. Also cuDNN does not support this anymore, mask should be combined with bias. This is follow up on https://github.com/openxla/xla/pull/11444.
* Remove mask logic in rewriter
* Remove mask buffer/descriptor in thunk
* Remove bmm1-bmm2 pattern as it is not support by flash attention. Modified related rewriter test to use bmm1-softmax - bmm2.
Current pattern:
bmm1 - (scale) - (bias) - softmax - (dropout) - bmm2.
Copybara import of the project:
--
552b4a3387c6d5b2b5adcf31b6f44cc858387b23 by cjkkkk <[email protected]>:
remove fused attn
--
13b683bf923e6fe344f879f913ae6ce41334eeb2 by cjkkkk <[email protected]>:
remove mask and bmm1-bmm2 pattern
--
9e843dd66c8f7d51d239b433e1b9bc329afee90d by cjkkkk <[email protected]>:
rm unused vari
--
1104df540e9196b34d9e61e679d88260151728d5 by cjkkkk <[email protected]>:
remove fused attn cudnnv version check and update flash attn cudnn version check
--
b020cb8e91d8f7b834645ff815c38c4798174857 by cjkkkk <[email protected]>:
remove mask related cudnnfmhakind&descriptor&buffer
--
ff1952faa460eecfca62660a5c34ea6fa3c2dfd4 by cjkkkk <[email protected]>:
rename hlo_string to shorter name
Merging this change closes #11717
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11717 from Cjkkkk:remove_fused_attn_and_mask ff1952faa460eecfca62660a5c34ea6fa3c2dfd4
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functional_hlo_runner: read entry_computation_layout from HloModule
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functional_hlo_runner: read entry_computation_layout from HloModule
Instead of reading it from CompilationOptions; the compiled HloModule
already contains this information.
While at it, simplify the argument_memory_space lambda -- it is
reasonable to expect parameter tuples to be flattened.
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functional_hlo_runner: force untuple_result if any output leaf buffer is in host memory space
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functional_hlo_runner: force untuple_result if any output leaf buffer is in host memory space
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Add back TPU device check in MlirBridgePass::GetPassState()
This unblocks some graphs that target TPU yet without replication.
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Tensorflow compatibility with pyinstaller
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[
"Hi **@Bhavi-cd** ,\r\n\r\n- Ensure that the version of PyTorch used by Ultralytics (indirectly through YOLO) is compatible with the other components in your environment. If possible, try downgrading or upgrading PyTorch to a version that is known to work well with YOLOv8-obb and your other dependencies.\r\n- Verify if the version of Ultralytics (8.2.2) you are using is compatible with the specific version of YOLOv8-obb you have trained. Sometimes, newer or older versions of object detection frameworks like YOLO may not work seamlessly with the corresponding versions of their supporting libraries. And et us know if the issue still persists?\r\n\r\n\r\nThank you!",
"Hi @Venkat6871 \r\nI have tried downgrading the version, also when I run the .py file it is running perfectly but when I convert it to .exe these problem occur."
] | 2024-04-25T05:07:08 | 2024-04-29T04:06:00 | null |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
Windows
### Mobile device
_No response_
### Python version
3.11.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I have encountered a problem while running my custom trained YOLOv8-obb model for object detection using an executable (.exe) file generated with PyInstaller. The model loads properly, but when attempting to perform inference, the code halts without providing any output.
Steps to Reproduce:
Compile YOLOv8-obb model with custom training data.
Generate an executable file using PyInstaller.
Run the executable file on a machine with compatible dependencies.
Attempt object detection using the executable.
### Standalone code to reproduce the issue
```shell
##current behavior
After running the executable, I expect the object detection process to proceed smoothly and provide the desired output, identifying objects in the input images or videos.
## Actual Behavior
Upon running the executable, the model loads successfully but fails to perform inference. The code halts without providing any output, leaving the object detection process incomplete.
`PyInstaller :6.6.0`
`Ultralytics: 8.2.2`
I have tried running the code directly without PyInstaller to ensure it's not an issue with the model or code itself.
I have checked for any missing dependencies or compatibility issues between PyInstaller and the YOLOv8-obb model. I have tried running the code directly without PyInstaller to ensure it's not an issue with the model or code itself.
I have checked for any missing dependencies or compatibility issues between PyInstaller and the YOLOv8-obb model.
```
### Relevant log output
```shell
import logging
from logging.handlers import RotatingFileHandler
import time
from ultralytics import YOLO
import cv2
# Configure logging with rotation
log_formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
file_handler = RotatingFileHandler(r'log_files\test_log_test.log', maxBytes=1024 * 1024 * 5, backupCount=5) # Rotate every 5MB, keep 5 backups
file_handler.setFormatter(log_formatter)
logger = logging.getLogger('test_log_test')
logger.setLevel(logging.INFO)
logger.addHandler(file_handler)
cnt = 0
while True:
cnt+=1
try:
logger.info(cnt)
logger.info("service test working")
time.sleep(10)
model = YOLO(r"weights\best(100epoch).pt")
model.fuse()
logger.info("yolo done")
img_path = r"test_image.jpg"
img = cv2.imread(img_path)
logger.info("reading image")
cv2.imwrite(r"test_image_output.jpg",img)
logger.info("writing image")
results = model.predict(img_path)
logger.info("prediction doness")
except Exception as e:
# Log error
logger.error(f"Error occurred : {e}")
# Logs
```
124851 WARNING: Failed to collect submodules for 'keras.src.backend.torch' because importing 'keras.src.backend.torch' raised: AttributeError: module 'torch' has no attribute 'float8_e4m3fn'
```
```
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PR_kwDOArmXAs5tqq0R
| 66,413 |
Support shape transpose in `hlo_sharding_util::ReshapeSharding`.
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[] | 2024-04-25T02:00:38 | 2024-04-26T19:19:58 | 2024-04-26T19:19:58 |
NONE
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Support shape transpose in `hlo_sharding_util::ReshapeSharding`.
Before this cl, `hlo_sharding_util::ReshapeSharding` can handle the cases where source and target shapes can be transformed to each other by merging and splitting dimension sizes. It returns `std::nullopt` if transpose is needed between source and target shapes.
This cl extracts the `gcd(source_sharding_tile_size, target_shape)` when `source_shape % source_sharding_tile_size == 0` in the major dimensions. We also skip the source_dim if `source_sharding_tile_size` is 1. An example is shown below.
```
input_shape: [6, 2, 5]
output_shape: [4, 3, 5]
input_sharding: {devices=[2, 1, 5]<=[10]}
output_sharding: {devices=[2, 1, 5]<=[10]}
```
Reverts e30abe681fdcced8ba3d19685293a3e6fc18e7b7
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PR_kwDOArmXAs5tqUv7
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internal code change
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internal code change
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PR_kwDOArmXAs5tqUG5
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Make CachedExecutableBundle movable only to avoid repeated copy and
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Make CachedExecutableBundle movable only to avoid repeated copy and
allows us to clarify ownership of IfrtExecutable and TfHostCallbacks
using std::unique_ptr instead of using std::shared_ptr
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I_kwDOArmXAs6G2PI6
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Profiler does not Seem to Output Timesteps in xplane.pb - "No step marker observed and hence the step time is unknown" from Tensorboard
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[
"@stellarpower \r\nThank you for reporting the issue. We are also trying to replicate the issue in our environment. Please allow sometime to reproduce and get back to you the same. Thank you!",
"Thanks! I can try to push a container image if it helps, let me know if that's be useful. Or I guess I can try a colab notebook too.",
"As a side note, is there an out-of-the-box way to create a colab notebook using a tensorflow nightly build? If not, given that's one of the main requests I see asked, before people ping some gists back and forth, I think that would be useful to have to support creating MREs. I assume I can pip install within the notebook, but I guess that having a template to work from would help save some time on both sides."
] | 2024-04-24T23:48:35 | 2024-05-16T09:09:45 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15.0 (cuda120py39hb94c71b_3 from conda-forge)
### Custom code
No
### OS platform and distribution
Ubuntu Jammy in podman Container
### Mobile device
_No response_
### Python version
3.9.18
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.4 (cuda-cupti 12.4.127 @ h59595ed_1 from conda-forge)
### GPU model and memory
RTX 3090, 24GiB
### Current behavior?
I am in the process of writing a [custom loss function]( https://github.com/stellarpower/Soft-DTW/tree/stellapower.AddFeature.BetterParallelisation ), and trying to profile it to see where resources are currently used.
I have installed newer CUDA drives, the latest release of Tensorflow (2.15), the CUDA PTI libraries, and other dependencies needed for the Tensorboard profiler plugin.
I can run an example with the profiler and get what looks like reasonable data. With my own code, I get a warning back from `_pywrap_profiler.xspace_to_tools_data()` that no timesteps are contained in the file, and thus, some of the useful profiling information is absent/unusable. I cut back the example and found that if I use the MSE loss, the profile is complete; if I change to my own loss function, the timesteps are no longer output.
Given that the message is coming back from the [core profiler library]( https://github.com/tensorflow/tensorflow/blob/v2.15.0/tensorflow/core/profiler/utils/diagnostics.cc#L62 ), and is contained within the encoded protocol buffers, I believe that this is an issue with the profiler and the main library, rather than the tensorboard utility or the profiling plugin.
The loss function is reasonably complicated, so I suspected at first the large files might be an issue. However, when reducing down to the toy example above, whilst there's a difference, the overhead in the files makes this difference much closer:
I previously had warning s that the processor has dropped frames due to insufficient buffer space, but with this trivially small data size, those are gone.
### Standalone code to reproduce the issue
```shell
#!/usr/bin/env python
import os, sys, pprint, numpy
import tensorflow as tf
ScriptDirectory = os.path.dirname(os.path.realpath(__file__))
# https://github.com/stellarpower/Soft-DTW/tree/stellapower.AddFeature.BetterParallelisation
sys.path.insert(0, f"/path/to/Soft-DTW")
from softdtwkeras.SDTWLoss import SDTWLoss
#############################################################
from tensorflow.keras import layers
from tensorflow import keras
from softdtwkeras.SDTWLoss import SDTWLoss
from datetime import datetime
## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Epochs = 128
ProfilingTensorboardLogsDirectory = f"{ ScriptDirectory }/BugLogs/{ datetime.now().strftime('%Y-%m-%d_%H.%M.%S') }"
tboard_callback = tf.keras.callbacks.TensorBoard(
log_dir = ProfilingTensorboardLogsDirectory,
histogram_freq = 1,
profile_batch = (32, 96) # Should be Middle 50%
)
## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Very small toy data, in case the file size is an issue.
x_train = numpy.zeros((1, 1, 1))
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(batch_input_shape=(1, 1, 1), name='layers_flatten'),
])
model.compile(
optimizer = 'adam',
# This runs okay:
# loss = 'mse',
# This results in no timestep info in the xplane file.
loss = SDTWLoss(gamma=0.5, BatchSize = 1)
)
model.fit(
x = x_train,
y = x_train, # << trivial identity function
epochs = Epochs,
callbacks = [tboard_callback]
)
```
### Relevant log output
```shell
2024-04-24 23:32:23.290829: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-04-24 23:32:23.290868: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-04-24 23:32:23.291486: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-04-24 23:32:23.296288: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-24 23:32:26.851127: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.
2024-04-24 23:32:26.851153: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.
2024-04-24 23:32:26.857074: I external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:1883] Profiler found 1 GPUs
2024-04-24 23:32:26.861348: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.
2024-04-24 23:32:26.861419: I external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2017] CUPTI activity buffer flushed
2024-04-24 23:32:26.871326: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-24 23:32:26.903533: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-24 23:32:26.903741: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-24 23:32:26.906062: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-24 23:32:26.906217: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-24 23:32:26.906350: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-24 23:32:26.982483: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-24 23:32:26.982679: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-24 23:32:26.982827: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-24 23:32:26.982929: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22199 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:01:00.0, compute capability: 8.6
Epoch 1/128
1/1 [==============================] - 2s 2s/step - loss: 0.0000e+00
...
Epoch 31/128
1/1 [==============================] - 0s 6ms/step - loss: 0.0000e+00
Epoch 32/128
2024-04-24 23:32:30.249189: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.
2024-04-24 23:32:30.249218: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.
1/1 [==============================] - 0s 15ms/step - loss: 0.0000e+00
Epoch 33/128
1/1 [==============================] - 0s 7ms/step - loss: 0.0000e+00
...
Epoch 95/128
1/1 [==============================] - 0s 7ms/step - loss: 0.0000e+00
Epoch 96/128
2024-04-24 23:32:30.746769: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:70] Profiler session collecting data.
2024-04-24 23:32:30.747796: I external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2017] CUPTI activity buffer flushed
2024-04-24 23:32:30.792759: I external/local_xla/xla/backends/profiler/gpu/cupti_collector.cc:541] GpuTracer has collected 4216 callback api events and 3979 activity events.
2024-04-24 23:32:30.840791: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.
2024-04-24 23:32:30.841246: I external/local_tsl/tsl/profiler/rpc/client/save_profile.cc:144] Collecting XSpace to repository: /BugLogs/2024-04-24_23.32.26/plugins/profile/2024_04_24_23_32_30/the-alchemist.xplane.pb
1/1 [==============================] - 0s 120ms/step - loss: 0.0000e+00
Epoch 97/128
...
```
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Update py_client_gpu config so that it is built when if_google is true.
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Update py_client_gpu config so that it is built when if_google is true.
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[XLA:GPU] Only upload the autotuning results for the module being compiled.
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NONE
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[XLA:GPU] Only upload the autotuning results for the module being compiled.
If multiple uploads occur from the same process, there's no need to upload all
modules every time. At best it's wasteful.
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Allow multiple epilogues.
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[] | 2024-04-24T23:17:06 | 2024-04-24T23:49:38 | 2024-04-24T23:49:38 |
NONE
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Allow multiple epilogues.
Each reduction group requires its own epilogue.
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Update TFRT dependency to use revision
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NONE
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/17c8497777411bc497ebdde64f02b002d7da4889.
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[IFRT] Support portable PjRtLoadedExecutable
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NONE
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[IFRT] Support portable PjRtLoadedExecutable
This change fixes support for portable `PjRtLoadedExecutable` (an executable
that can be compiled once and dynamically loaded onto any device at execution
time). The previous code had device assignment handling that is not compatible
with `TfrtCpuClient`: `xla::ifrt::PjRtLoadedExecutable` expects some device to
be present in the device assignment, while `xla::PjRtLoadedExecutable` expects
no device to be present. This is resolved by making
`xla::ifrt::PjRtLoadedExecutable` allow empty device assignment.
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NFC: Use the free function variants for `dyn_cast/cast/isa/...`.
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NONE
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NFC: Use the free function variants for `dyn_cast/cast/isa/...`.
The member functions in `Type/Attribute/Value/Location/AffineExpr` are deprecated and will go away.
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Added checksum TraceMe for Megascale.
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NONE
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Added checksum TraceMe for Megascale.
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Adding PRED x BF16 test to provide better coverage.
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NONE
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Adding PRED x BF16 test to provide better coverage.
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PR_kwDOArmXAs5tp2y3
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Enabling autotuner logs for the benchmark runner to allow for faster root-cause analysis during benchmark crashes inside Triton.
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NONE
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Enabling autotuner logs for the benchmark runner to allow for faster root-cause analysis during benchmark crashes inside Triton.
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PR_kwDOArmXAs5tpvqG
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temp this will not be submitted.
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PR_kwDOArmXAs5tpvno
| 66,399 |
Support MOF DUS fusions.
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NONE
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Support MOF DUS fusions.
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PR_kwDOArmXAs5tptYw
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Update py_client_gpu config
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NONE
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Update py_client_gpu config
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PR_kwDOArmXAs5tpptW
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Fuse redundant RHS TFL_TransposeOp into TFL_BatchMatMulOp if rhs is constant quantized tensor of rank-2.
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NONE
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Fuse redundant RHS TFL_TransposeOp into TFL_BatchMatMulOp if rhs is constant quantized tensor of rank-2.
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PR_kwDOArmXAs5tpcku
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[pjrt] Clean up PjRtFuture documentation and internal APIs
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NONE
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[pjrt] Clean up PjRtFuture documentation and internal APIs
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11717 from Cjkkkk:remove_fused_attn_and_mask ff1952faa460eecfca62660a5c34ea6fa3c2dfd4
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PR_kwDOArmXAs5tpQNd
| 66,395 |
Reverts f9e60c776338536f2b31049399b5bfced6337977
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NONE
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Reverts f9e60c776338536f2b31049399b5bfced6337977
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PR_kwDOArmXAs5tpCyW
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Remove redundant load of QuantizationDialect
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NONE
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Remove redundant load of QuantizationDialect
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PR_kwDOArmXAs5to73e
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[tsl:concurrency] Disable ambiguous AndThen callback for AsyncValueRef<absl::Status>
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NONE
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[tsl:concurrency] Disable ambiguous AndThen callback for AsyncValueRef<absl::Status>
Reverts 21f6e08a3f39591c1915410f4c256c9456e36d80
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PR_kwDOArmXAs5to5jL
| 66,392 |
Fixes some corner cases when a non-participating task makes the barrier call
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closed
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[] | 2024-04-24T18:55:29 | 2024-04-30T01:46:17 | 2024-04-30T01:46:16 |
NONE
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Fixes some corner cases when a non-participating task makes the barrier call
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