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} | PR #13523: [ROCm ] fixed build due to https://github.com/openxla/xla/commit/e2a86549996ae6ac57f6eae31c1ed9aee4975bab
Imported from GitHub PR https://github.com/openxla/xla/pull/13523
this fixed ROCm build due to https://github.com/openxla/xla/commit/e2a86549996ae6ac57f6eae31c1ed9aee4975bab#diff-1390f748feeae091b1bf422c47df533aba570c7a50a6f3fafda65a5159908cc3R72-R86
@ddunl @xla-rotation
this is already a couple of times, e.g., https://github.com/openxla/xla/pull/13194 , please change related [ir_emitter_triton_rocm.cc](https://github.com/openxla/xla/blob/main/xla/service/gpu/ir_emitter_triton_rocm.cc) next time.
Thanks in advance!
Copybara import of the project:
--
931ddf1d5b87c85c3fe1d825665015882d50ef42 by Chao Chen <cchen104@amd.com>:
fixed build due to https://github.com/openxla/xla/commit/e2a86549996ae6ac57f6eae31c1ed9aee4975bab#diff-1390f748feeae091b1bf422c47df533aba570c7a50a6f3fafda65a5159908cc3R72-R86
Merging this change closes #13523
Reverts changelist 641225077
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13523 from ROCm:ci_hotfit_20240607 931ddf1d5b87c85c3fe1d825665015882d50ef42
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Reverts changelist 641225077
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`eq(k1, k2) -> hash(k1) == hash(k2)` must be true for hashtable usage to be valid.
Reverts changelist 641225077
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} | Make `pxla.shard_arg` batch calls to `xc.copy_array_to_devices_with_sharding`
This CL changes `shard_arg_handlers` to be batched, in that it now receives a list of objects and a list of shardings and returns a list of array. This makes it possible to batch backend calls whenever it's beneficial to do so.
Based on the above, the batched shard arg for arrays leverages the newly added `xla::ifrt::Client::CopyArrays()` (https://github.com/tensorflow/tensorflow/pull/69096) to make bulk copy cheaper in some backend implementations. Since `Client::CopyArrays()` requires batched arrays to have the same set of source/destination devices, `PyArray::BatchedCopyToDeviceWithSharding()` internally groups arrays by their source/destination devices and memory kinds. The grouping is pushed all the way to C++ for performance in case we have lots of arrays.
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FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13523 from ROCm:ci_hotfit_20240607 931ddf1d5b87c85c3fe1d825665015882d50ef42
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[5f1adf0433c6](https://github.com/llvm/llvm-project/commit/5f1adf0433c6)
Reverts changelist 641225077
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Reverts changelist 641225077
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It is currently flaky.
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} | PR #13372: [GPU] Fix autotuner_util_test.
Imported from GitHub PR https://github.com/openxla/xla/pull/13372
Copybara import of the project:
--
99192a7119725e1b80153f8b29a229acb191c4f7 by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Fail on conflicting autotuning results.
--
1af893eaf08a3904562f2d5ad633c4ac1843ce3f by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Fix autotuner_util_test.
Merging this change closes #13372
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13372 from openxla:fix_autotuner_util_test 1af893eaf08a3904562f2d5ad633c4ac1843ce3f
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} | PR #13062: FP8 Windowed Einsums
Imported from GitHub PR https://github.com/openxla/xla/pull/13062
Extends the windowed einsum handler to support collective FP8 GEMMs by shifting the dequantization of dot operands into the body of the while loops of all-gather and reduce-scatter windowed einsums.
Copybara import of the project:
--
3ea415d65e47087bb077fa2b13e34c82a4dae87b by Philipp Hack <phack@nvidia.com>:
Shifts the the dequantization of FP8 operands into the while body of windowed einsums.
Merging this change closes #13062
Reverts changelist 641225077
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13062 from philipphack:u_fp8_windowed_einsum_xla 3ea415d65e47087bb077fa2b13e34c82a4dae87b
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} | [XLA:GPU] Experimental: Add --xla_gpu_per_fusion_autotune_cache_dir option
If the option is set, we will use / maintain a per-fusion autotune cache in the given directory.
The directory must exist.
Cache invalidation has to be handled by the user (e.g. please use an empty directory if you want to start with an empty cache).
XLA version checks must be done by the user (e.g. if you want to cache fusions created with different versions of XLA, please use different directories).
(If the using library already has a version handling mechanism, like JAX, then it shouldn't be difficult for them to create separate directories based on that version (and all the parameters which matter to them).)
Default: no file based cache.
There is minimal support for multiple processes using the same cache - the rename trick is used to avoid writing the same file by multiple processes at the same time or reading incomplete files.
We use SHA256 hashes in the filenames and assume that no collisions occur.
This is a simple implementation to allow people to test it and find good use-cases. If needed we can refine it later.
Considered use case:
People running [multiple] [similar] models [through JAX]. For example there are 2 similar HLOs that we want to run with JAX (using the same "XLA binary") and it would be nice to reuse the autotune results from the first, if some kernels appear in both.
Similarly: Consider the use case of a researcher sitting at a Colab session and making small changes to their model. They should mostly get cache hits!
Reverts changelist 641225077
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} | Fix rank of composition of empty indexing maps.
Also add an integration test for the input_slices emitter that
verifies this behavior.
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} | [XLA:GPU] Add constraints to `SymbolicTileAnalysis`.
Constraints are constructed from merging the constraints of all the
`SymbolicTile`s encountered while constructing the resulting symbolic tiled HLO
computation. If any of the `SymbolicTile`s is unsatisfiable, the construction
of the `SymbolicTileAnalysis` object does not succeed. Likewise, construction
fails if some constraints can not be merged with others.
Constraints are now checked to be satisfied by the provided tile parameters
when attempting to extract a `TiledHloComputation` out of
`SymbolicTileAnalysis`. In order to avoid checking constraints too many times,
we allow pinky-promising that the provided tile parameters satisfy the
constraints, to voluntarily bypass the checks.
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} | [XLA] Simplify the comparison computations generated by the `CreateScalarXXXXXComputation` functions.
The new logic is simpler, better tested, and produces simpler output:
E.g. before:
```
compare-greater-than.4 {
p.1.lhs.7 = s32[] parameter(2)
p.1.rhs.8 = s32[] parameter(3)
constant.9 = pred[] constant(true)
broadcast.10 = pred[] broadcast(constant.9), dimensions={}
p.0.lhs.5 = u8[] parameter(0)
p.0.rhs.6 = u8[] parameter(1)
compare.11 = pred[] compare(p.0.lhs.5, p.0.rhs.6), direction=GT
ROOT select.12 = pred[] select(broadcast.10, compare.11, broadcast.10)
}
```
after
```
compare-greater-than.4 {
p.1.lhs.7 = s32[] parameter(2)
p.1.rhs.8 = s32[] parameter(3)
p.0.lhs.5 = u8[] parameter(0)
p.0.rhs.6 = u8[] parameter(1)
ROOT compare.9 = pred[] compare(p.0.lhs.5, p.0.rhs.6), direction=GT
}
```
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} | Integrate LLVM at llvm/llvm-project@5f1adf0433c6
Updates LLVM usage to match
[5f1adf0433c6](https://github.com/llvm/llvm-project/commit/5f1adf0433c6)
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} | Fix float conversions: fp8 and u64.
- fp8 was missing lowerings for cmpf and fptoi
- u64 was using incorrect upper bounds
This fixes unary_ops_test_gpu.
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"Possible workaround. Add these dependecies in the top level `WORKSPACE` file:\r\n\r\n```bazel\r\nhttp_archive(\r\n name = \"llvm_zstd\",\r\n build_file = \"@llvm-raw//utils/bazel/third_party_build:zstd.BUILD\",\r\n sha256 = \"7c42d56fac126929a6a85dbc73ff1db2411d04f104fae9bdea51305663a83fd0\",\r\n strip_prefix = \"zstd-1.5.2\",\r\n url = \"https://github.com/facebook/zstd/releases/download/v1.5.2/zstd-1.5.2.tar.gz\",\r\n)\r\n\r\nhttp_archive(\r\n name = \"llvm_zlib\",\r\n build_file = \"@llvm-raw//utils/bazel/third_party_build:zlib-ng.BUILD\",\r\n sha256 = \"e36bb346c00472a1f9ff2a0a4643e590a254be6379da7cddd9daeb9a7f296731\",\r\n strip_prefix = \"zlib-ng-2.0.7\",\r\n url = \"https://github.com/zlib-ng/zlib-ng/archive/refs/tags/2.0.7.zip\",\r\n)\r\n```\r\n\r\nThe compilation works but then it generates duplicate symbols from `llvm_zstd` and `net_zstd` and fails during the linking:\r\n\r\n```bash\r\nERROR: /tensorflow_src/tensorflow/BUILD:1177:21: Linking tensorflow/libtensorflow_framework.so.2.18.0 [for tool] failed: (Exit 1): clang failed: error executing command (from target //tensorflow:libtensorflow_framework.so.2.18.0) /usr/lib/llvm-17/bin/clang @bazel-out/k8-opt-exec-50AE0418/bin/tensorflow/libtensorflow_framework.so.2.18.0-2.params\r\nld.lld: error: duplicate symbol: FSE_versionNumber\r\n>>> defined at entropy_common.c\r\n>>> bazel-out/k8-opt-exec-50AE0418/bin/external/net_zstd/_objs/zstdlib/entropy_common.pic.o:(FSE_versionNumber)\r\n>>> defined at entropy_common.c\r\n>>> bazel-out/k8-opt-exec-50AE0418/bin/external/llvm_zstd/_objs/zstd/entropy_common.pic.o:(.text.FSE_versionNumber+0x0)\r\n\r\nld.lld: error: duplicate symbol: FSE_isError\r\n>>> defined at entropy_common.c\r\n>>> bazel-out/k8-opt-exec-50AE0418/bin/external/net_zstd/_objs/zstdlib/entropy_common.pic.o:(FSE_isError)\r\n>>> defined at entropy_common.c\r\n>>> bazel-out/k8-opt-exec-50AE0418/bin/external/llvm_zstd/_objs/zstd/entropy_common.pic.o:(.text.FSE_isError+0x0)\r\n\r\nld.lld: error: duplicate symbol: FSE_getErrorName\r\n>>> defined at entropy_common.c\r\n>>> bazel-out/k8-opt-exec-50AE0418/bin/external/net_zstd/_objs/zstdlib/entropy_common.pic.o:(FSE_getErrorName)\r\n>>> defined at entropy_common.c\r\n>>> bazel-out/k8-opt-exec-50AE0418/bin/external/llvm_zstd/_objs/zstd/entropy_common.pic.o:(.text.FSE_getErrorName+0x0)\r\n...\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\n```"
] | 2024-06-07T10:25:21 | 2024-06-11T14:14:15 | null | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
dd0c582
### Custom code
No
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.11.9
### Bazel version
6.5.0
### GCC/compiler version
9.4.0
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Compiling using the docker image `latest-devel` and `devel`.
When I try to compile:
```bash
bazel build --override_repository="llvm-raw=${LLVM_SRC}" -c opt tensorflow/compiler/mlir:tf-opt
```
I get this error:
```bash
ERROR: /root/.cache/bazel/_bazel_root/43801f1e35f242fb634ebbc6079cf6c5/external/llvm-project/llvm/BUILD.bazel:219:11: no such package '@llvm_zlib//': The repository '@llvm_zlib' could not be resolved: Repository '@llvm_zlib' is not defined and referenced by '@llvm-project//llvm:Support'
ERROR: Analysis of target '//tensorflow/compiler/mlir:tf-opt' failed; build aborted:
```
How do I compile it?
### Standalone code to reproduce the issue
```shell
# run docker (latest-devel)
docker run -it -w /tensorflow_src -v $PWD:/mnt -e HOST_PERMS="$(id -u):$(id -g)" tensorflow/tensorflow:latest-devel bash
git pull
cd $HOME
# install dependencies
sudo apt-get update
sudo apt-get install -y build-essential gdb lcov pkg-config \
libbz2-dev libffi-dev libgdbm-dev libgdbm-compat-dev liblzma-dev \
libncurses-dev libreadline-dev libsqlite3-dev libssl-dev \
lzma lzma-dev tk-dev uuid-dev zlib1g-dev patchelf
sudo apt-get install -y python3-dev python3-pip
pip install -U --user pip
# install llvm
wget https://apt.llvm.org/llvm.sh
chmod +x llvm.sh
sudo ./llvm.sh 17
sudo ln -sf /usr/bin/clang-17 /usr/bin/clang
# install local python 3.11
mkdir .python3.11
mkdir src
cd src
wget https://www.python.org/ftp/python/3.11.9/Python-3.11.9.tgz
tar -zxvf Python-3.11.9.tgz
cd Python-3.11.9
make clean
./configure --prefix=$HOME/.python3.11
make
make install
export PATH=$PATH:$HOME/.python3.11/bin
cd $HOME
# install virtualenv and activate
pip3.11 install virtualenv
$HOME/.python3.11/bin/virtualenv --python=$HOME/.python3.11/bin/python3.11 $HOME/.tfdev
source $HOME/.tfdev/bin/activate
# get llvm src
cd $HOME
git clone https://github.com/llvm/llvm-project.git
cd llvm-project
git checkout llvmorg-17.0.6
export LLVM_SRC=$HOME/llvm-project
touch ${LLVM_SRC}/BUILD.bazel ${LLVM_SRC}/WORKSPACE
# compile tensorflow
cd /tensorflow_src
export TF_PYTHON_VERSION=3.11
export PYTHON_BIN_PATH=$(which python)
export PYTHON_LIB_PATH=$HOME/.tfdev/lib/python3.11/site-packages
export TF_NEED_ROCM=0
export TF_NEED_CUDA=0
export TF_NEED_CLANG=1
export CLANG_COMPILER_PATH=$(which clang)
export CC_OPT_FLAGS=-Wno-sign-compare
export TF_SET_ANDROID_WORKSPACE=0
./configure
# build tensorflow mlir tools
bazel build --override_repository="llvm-raw=${LLVM_SRC}" -c opt tensorflow/compiler/mlir:tf-opt
```
### Relevant log output
```shell
INFO: Reading 'startup' options from /tensorflow_src/.bazelrc: --windows_enable_symlinks
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=170
INFO: Reading rc options for 'build' from /tensorflow_src/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from /tensorflow_src/.bazelrc:
'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
INFO: Reading rc options for 'build' from /tensorflow_src/.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=/root/.tfdev/bin/python --action_env PYTHON_LIB_PATH=/root/.tfdev/lib/python3.11/site-packages --python_path=/root/.tfdev/bin/python --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
INFO: Found applicable config definition build:short_logs in file /tensorflow_src/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /tensorflow_src/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:linux in file /tensorflow_src/.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
INFO: Found applicable config definition build:dynamic_kernels in file /tensorflow_src/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
INFO: Repository stablehlo instantiated at:
/tensorflow_src/WORKSPACE:111:14: in <toplevel>
/tensorflow_src/tensorflow/workspace2.bzl:947:28: in workspace
/tensorflow_src/tensorflow/workspace2.bzl:88:14: in _initialize_third_party
/tensorflow_src/third_party/stablehlo/workspace.bzl:11:20: in repo
/tensorflow_src/third_party/repo.bzl:136:21: in tf_http_archive
Repository rule _tf_http_archive defined at:
/tensorflow_src/third_party/repo.bzl:89:35: in <toplevel>
INFO: Repository double_conversion instantiated at:
/tensorflow_src/WORKSPACE:111:14: in <toplevel>
/tensorflow_src/tensorflow/workspace2.bzl:954:21: in workspace
/tensorflow_src/tensorflow/workspace2.bzl:639:20: in _tf_repositories
/tensorflow_src/third_party/repo.bzl:136:21: in tf_http_archive
Repository rule _tf_http_archive defined at:
/tensorflow_src/third_party/repo.bzl:89:35: in <toplevel>
INFO: Repository zlib instantiated at:
/tensorflow_src/WORKSPACE:111:14: in <toplevel>
/tensorflow_src/tensorflow/workspace2.bzl:954:21: in workspace
/tensorflow_src/tensorflow/workspace2.bzl:487:20: in _tf_repositories
/tensorflow_src/third_party/repo.bzl:136:21: in tf_http_archive
Repository rule _tf_http_archive defined at:
/tensorflow_src/third_party/repo.bzl:89:35: in <toplevel>
INFO: Repository flatbuffers instantiated at:
/tensorflow_src/WORKSPACE:111:14: in <toplevel>
/tensorflow_src/tensorflow/workspace2.bzl:947:28: in workspace
/tensorflow_src/tensorflow/workspace2.bzl:70:16: in _initialize_third_party
/tensorflow_src/third_party/flatbuffers/workspace.bzl:14:20: in repo
/tensorflow_src/third_party/repo.bzl:136:21: in tf_http_archive
Repository rule _tf_http_archive defined at:
/tensorflow_src/third_party/repo.bzl:89:35: in <toplevel>
INFO: Repository farmhash_archive instantiated at:
/tensorflow_src/WORKSPACE:111:14: in <toplevel>
/tensorflow_src/tensorflow/workspace2.bzl:947:28: in workspace
/tensorflow_src/tensorflow/workspace2.bzl:69:13: in _initialize_third_party
/tensorflow_src/third_party/farmhash/workspace.bzl:14:20: in repo
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Repository rule _tf_http_archive defined at:
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ERROR: /root/.cache/bazel/_bazel_root/43801f1e35f242fb634ebbc6079cf6c5/external/llvm-project/llvm/BUILD.bazel:219:11: no such package '@llvm_zlib//': The repository '@llvm_zlib' could not be resolved: Repository '@llvm_zlib' is not defined and referenced by '@llvm-project//llvm:Support'
ERROR: Analysis of target '//tensorflow/compiler/mlir:tf-opt' failed; build aborted:
INFO: Elapsed time: 41.334s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (253 packages loaded, 3347 targets configured)
currently loading: tensorflow/compiler/mlir/lite/schema
Fetching repository @eigen_archive; starting
Fetching https://storage.googleapis.com/.../github.com/google/flatbuffers/archive/e6463926479bd6b330cbcf673f7e917803fd5831.tar.gz; 1.7 MiB (76.0%)
Fetching https://storage.googleapis.com/mirror.tensorflow.org/github.com/openxla/stablehlo/archive/14e2323f0ee3d308c1384fdb806dc6d0c98b16ca.zip; 1.4 MiB (8.2%)
Fetching https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/double-conversion/archive/v3.2.0.tar.gz
Fetching https://storage.googleapis.com/mirror.tensorflow.org/zlib.net/fossils/zlib-1.2.13.tar.gz
Fetching /root/.cache/bazel/_bazel_root/43801f1e35f242fb634ebbc6079cf6c5/external/eigen_archive; Extracting eigen-c1d637433e3b3f9012b226c2c9125c494b470ae6.tar.gz
Fetching https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/farmhash/archive/0d859a811870d10f53a594927d0d0b97573ad06d.tar.gz
Fetching https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/re2/archive/03da4fc0857c285e3a26782f6bc8931c4c950df4.tar.gz ... (10 fetches)
```
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} | [XLA:GPU] Change the error message in Triton compatibility test when not running on NVIDIA GPUs.
The previous error was a bit misleading when a `CudaComputeCapability` could
not be found.
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See google/jax#21628 and google/jax#21637.
Reverts changelist 641225077
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} | Don't attempt to vectorize complex loads.
In principle we could do this, but we need some fixes to
our vector size heuristics first. Right now, we generate
loops of size 4, which is too much (1024 bytes per warp).
But I'm not sure it's actually helpful in any case.
This fixes the `error: 'arith.constant' op requires attribute
'value'` errors.
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"Sorry, I was in a wrong Conda env and using TF 2.15.1\r\n\r\nIt seems to have been implemented with keras 3 and TF 2.16.1\r\n"
] | 2024-06-07T09:02:14 | 2024-06-07T12:31:06 | 2024-06-07T10:43:14 | NONE | null | null | null | ### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The Model class implements the stop_training attribute, but no stop_evaluating one.
In fit, there is a correct action taken to stop training when stop_training is set to True, in a callback for instance:
```python
for epoch, iterator in data_handler.enumerate_epochs():
self.reset_metrics()
callbacks.on_epoch_begin(epoch)
with data_handler.catch_stop_iteration():
for step in data_handler.steps():
with trace.Trace(
'train',
epoch_num=epoch,
step_num=step,
batch_size=batch_size,
_r=1):
callbacks.on_train_batch_begin(step)
tmp_logs = self.train_function(iterator)
if data_handler.should_sync:
context.async_wait()
logs = tmp_logs # No error, now safe to assign to logs.
end_step = step + data_handler.step_increment
callbacks.on_train_batch_end(end_step, logs)
if self.stop_training:
break
```
and validation:
```python
# Run validation.
if validation_data and self._should_eval(epoch, validation_freq):
# Create data_handler for evaluation and cache it.
if getattr(self, '_eval_data_handler', None) is None:
self._eval_data_handler = data_adapter.get_data_handler(
x=val_x,
y=val_y,
sample_weight=val_sample_weight,
batch_size=validation_batch_size or batch_size,
steps_per_epoch=validation_steps,
initial_epoch=0,
epochs=1,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
model=self,
steps_per_execution=self._steps_per_execution)
val_logs = self.evaluate(
x=val_x,
y=val_y,
sample_weight=val_sample_weight,
batch_size=validation_batch_size or batch_size,
steps=validation_steps,
callbacks=callbacks,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
return_dict=True,
_use_cached_eval_dataset=True)
val_logs = {'val_' + name: val for name, val in val_logs.items()}
epoch_logs.update(val_logs)
callbacks.on_epoch_end(epoch, epoch_logs)
training_logs = epoch_logs
if self.stop_training:
break
```
The evaluate method doesn't include this behaviour:
```python
for _, iterator in data_handler.enumerate_epochs(): # Single epoch.
self.reset_metrics()
with data_handler.catch_stop_iteration():
for step in data_handler.steps():
with trace.Trace('test', step_num=step, _r=1):
callbacks.on_test_batch_begin(step)
tmp_logs = self.test_function(iterator)
if data_handler.should_sync:
context.async_wait()
logs = tmp_logs # No error, now safe to assign to logs.
end_step = step + data_handler.step_increment
callbacks.on_test_batch_end(end_step, logs)
logs = tf_utils.sync_to_numpy_or_python_type(logs)
callbacks.on_test_end(logs=logs)
```
The current way to stop evaluation in a callback is to force raise a ```StopIteration()``` error as it will trigger the ```catch_stop_iteration()``` method. But you can only do it after step 1, otherwise the logs will be empty. This is clearly not clean.
Also, the ```catch_stop_iteration()``` except text is only related to the training. A more general text would help to debug:
```python
@contextlib.contextmanager
def catch_stop_iteration(self):
"""Catches errors when an iterator runs out of data."""
try:
yield
self.sync()
except (StopIteration, errors.OutOfRangeError):
if self._inferred_steps is None:
self._inferred_steps = self._current_step
else:
self._insufficient_data = True
total_epochs = self._epochs - self._initial_epoch
logging.warning(
"Your input ran out of data; interrupting training. "
"Make sure that your dataset or generator can generate at "
"least `steps_per_epoch * epochs` batches (in this case, "
"{} batches). You may need to use the repeat() function "
"when building your dataset.".format(total_epochs *
self._inferred_steps))
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
model = tf.keras.Sequential(
[
tf.keras.layers.Dense(2, activation="relu", name="layer1"),
]
)
print(model.stop_training)
print(model.stop_evaluating)
```
### Relevant log output
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} | [XLA] [NFC] 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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A known empty map with rank 0 is different from a known
empty map with rank N.
This fixes bitcast_convert_test_gpu and probably some others.
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Updates LLVM usage to match
[d843c02aba5d](https://github.com/llvm/llvm-project/commit/d843c02aba5d)
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See the added test for an explanation of the problem. This fixes gpu_index_test.
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Reverts changelist 641225077
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13523 from ROCm:ci_hotfit_20240607 931ddf1d5b87c85c3fe1d825665015882d50ef42
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"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/69345/checks?check_run_id=25917262557) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"@gbaned , The AMD ROCm -- Community CI Build keeps failing. Can you please let me know if I am authorized to look into this? If yes, I will proceed and will try to fix it. If not, could you please help me?\r\n\r\nThanks!"
] | 2024-06-06T22:35:11 | 2024-06-07T22:31:52 | null | NONE | null | false | {
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} | Hello @Venkat6871 , @eggie5
Fixed the #69284 issue.
I believe we just have to make sure to pass the inputs and the be consistent with the datatypes that we are using.
The vital observation that I was able to make is the indices list and the relationship between var and accum parameters, and the relationship between grad and indices.
I mentioned the obseravations clearly in the file where I believe we could include that in the website - [https://www.tensorflow.org/api_docs/python/tf/raw_ops/ResourceSparseApplyAdagrad](url), so that user's will have a better understanding of the usage of ResourceSparseApplyAdagrad
```
#Considered some example values for testing
var = tf.Variable([1.0, 2.2, 0.3, 6.3, 5.2], dtype=tf.complex64)
accum = tf.Variable([0.1, 0.2, 0.3, 1.1, 9.1], dtype=tf.complex64)
accum2 = tf.Variable([0, 1.2, -3.3], dtype=tf.complex64)
lr = tf.Variable(0.01, dtype=tf.complex64)
grad = tf.constant([1.1, 5.2, 7.3, 9.2], dtype=tf.complex64)
momentum = 0.9
#Actual values to be used
var = tf.Variable([1.0, 2.0, 3.0, 4.0, 5.0], dtype=tf.complex64)
accum = tf.Variable([0.1, 0.2, 0.3, 0.4, 0.5], dtype=tf.complex64)
accum2 = tf.Variable([0, 1.2, -3.3], dtype=tf.complex64)
lr = tf.Variable(0.01, dtype=tf.complex64)
grad = tf.constant([0.1, 0.2, 0.3, 0.4], dtype=tf.complex64)
#Executed Successfully
tf.raw_ops.ResourceSparseApplyAdagrad(
var=var.handle,
accum=accum.handle,
lr=lr,
grad=grad,
# indices=tf.constant([1, 2, 3, 4]),
indices=tf.constant([0, 1, 2, 3]),
# use_locking=False,
)
```
I believe the same rules apply to ResourceSparseApplyAdagradDA and ResourceSparseApplyAdagradV2.
Please correct me if I am wrong.
Thanks!
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Reverts changelist 641225077
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Reverts changelist 641225077
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Updates LLVM usage to match
[7476c20c481c](https://github.com/llvm/llvm-project/commit/7476c20c481c)
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"@ShoumitroC Could you try to use keras.layers.Lambda layer that allows you to define a custom function using a Python lambda expression that can operate on KerasTensors within the model building process?\r\nPlease upgrade to the latest TF version and if the issue still persists then kindly provide the standalone code to replicate this issue?\r\nThank you!"
] | 2024-06-06T21:16:12 | 2024-06-10T09:24:31 | null | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
Mac OS x86-64
### Mobile device
Macbook
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I'm working on an image processing project using InsightFace and OpenNSFW2. I'm encountering an error with TensorFlow and Keras integration. When I try to use tf.pad on a tensor, I get the following error:
ValueError: A KerasTensor cannot be used as input to a TensorFlow function. A KerasTensor is a symbolic placeholder for a shape and dtype, used when constructing Keras Functional models or Keras Functions. You can only use it as input to a Keras layer or a Keras operation (from the namespaces `keras.layers` and `keras.operations`). You are likely doing something like:
### Standalone code to reproduce the issue
```shell
ValueError: A KerasTensor cannot be used as input to a TensorFlow function. A KerasTensor is a symbolic placeholder for a shape and dtype, used when constructing Keras Functional models or Keras Functions. You can only use it as input to a Keras layer or a Keras operation (from the namespaces `keras.layers` and `keras.operations`). You are likely doing something like:
x = Input(...)
...
tf_fn(x) # Invalid.
I've tried wrapping tf.pad in a custom layer as suggested, but I'm still getting the error. Here's the relevant code snippet:
class MyLayer(Layer):
def call(self, x):
return tf.pad(x, [[0, 0], [3, 3], [3, 3], [0, 0]], "CONSTANT")
# Usage
x = MyLayer()(x)
Could someone please help me understand what I'm doing wrong and how to fix it?
Thank you!
```
### Relevant log output
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Updates LLVM usage to match
[79393124ff74](https://github.com/llvm/llvm-project/commit/79393124ff74)
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} | Fix a bug where treedef.flatten_up_to(...) was overly permissive for None treedefs.
For example, tree_map(..., None, [2, 3]) did not raise an error, but None is a container and only leaves can be considered tree prefixes in this case.
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} | [XLA:GPU][NFC] Remove `analysis_` member from `SymbolicTileAnalysisTest` class.
The state is not particularly necessary and adds indirection to the tests,
which is not desirable.
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This is required for cross-host device transfer. Devices between different hosts need a way to communicate, and we will use kv_store for that.
Update the signature of relevant APIs and pybind to create a ifrt::PjRtClient.
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"isn't this because the tensor is too big for the ram ? if you reduce the size to 20000, 20000 it should output without the resource exhaustion error.",
"> isn't this because the tensor is too big for the ram ? if you reduce the size to 20000, 20000 it should output without the resource exhaustion error.\r\n\r\n\r\n\r\n> isn't this because the tensor is too big for the ram ? if you reduce the size to 20000, 20000 it should output without the resource exhaustion error.\r\n\r\nIf the size parameter is reduced to 20000, the code will not cause a crash; however, we recommend that the Tensorflow throws a runtime error when the requested memory exceeds the limit instead of continuing to request memory and triggering a crash.",
"Hi @x0w3n ,\r\n- I was able to reproduce the issue on Colab using TF v2.15 and TF-nightly ,Please find the [gist](https://colab.research.google.com/gist/Venkat6871/bc3456879f297ec177eab055a633b67f/69322_2-15-nightly-v.ipynb) here for reference.\r\n\r\nThank you!"
] | 2024-06-06T14:30:10 | 2024-06-11T04:55:47 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
TensorFlow Nightly
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### 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?
On specific input, these APIs will output "The session crashed because it took up all available RAM." The affected APIs are listed below:
1. tf.raw_ops.ResizeBilinear
2. tf.raw_ops.ResizeBicubic
3. tf.raw_ops.ResizeArea
4. tf.raw_ops.ResizeNearestNeighbor
5. tf.raw_ops.ResizeNearestNeighborGrad
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1a6B_lYMfENTbO4ifQMLYe8Hq6CHcc6hP?usp=sharing
```
### Relevant log output
```shell
Timestamp Level Message
Jun 6, 2024, 6:57:43 PM WARNING WARNING:root:kernel d0444564-3fb8-4d42-ac83-1338e6842d5a restarted
Jun 6, 2024, 6:57:43 PM INFO KernelRestarter: restarting kernel (1/5), keep random ports
Jun 6, 2024, 6:57:25 PM WARNING 2024-06-06 10:57:25.421101: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 51536461872 exceeds 10% of free system memory.
Jun 6, 2024, 6:57:21 PM WARNING 2024-06-06 10:57:21.507228: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Jun 6, 2024, 6:57:17 PM WARNING To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Jun 6, 2024, 6:57:17 PM WARNING 2024-06-06 10:57:17.995922: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
Jun 6, 2024, 6:56:13 PM WARNING WARNING:root:kernel d0444564-3fb8-4d42-ac83-1338e6842d5a restarted
Jun 6, 2024, 6:56:13 PM INFO KernelRestarter: restarting kernel (1/5), keep random ports
Jun 6, 2024, 6:55:56 PM WARNING 2024-06-06 10:55:56.764126: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 103072923744 exceeds 10% of free system memory.
Jun 6, 2024, 6:55:52 PM WARNING 2024-06-06 10:55:52.277507: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
```
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"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/69319/checks?check_run_id=25895001605) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
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It loads the kernel made of a pointer to function on host machine.
This is useful for loading kernel compiled together with xla by regular cpp compiler.
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`(a mod b) * x` worked, but `(a * x) mod b` didn't.
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} | Fix i4 transfer_read/transfer_write lowerings.
LLVM and XLA pack i4s in opposite orders, so we have to permute the
vector elements.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13461 from knightXun:transpose-typo e7c6c23be44648237a31d17bf2574c4f26510889
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} | PR #11425: [XLA:GPU] Add sycl platform
Imported from GitHub PR https://github.com/openxla/xla/pull/11425
It is a sub PR of https://github.com/openxla/xla/pull/9042 to add sycl platform
Copybara import of the project:
--
7d0c37057c673f784089ce961d24276a496d43bd by Sheng, Yang <yang.sheng@intel.com>:
Add sycl platform
--
8d1196831a20f69ba8880d8549007358928f6be1 by Sheng, Yang <yang.sheng@intel.com>:
Add comments back
Merging this change closes #11425
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11425 from Intel-tensorflow:yang/sycl-platform 8d1196831a20f69ba8880d8549007358928f6be1
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} | [XLA] [NFC] Remove redundant options from multihost_hlo_runner
Whether SPMD is used is inferrable from number of partitions, having both
num_partitions and use_spmd specified is redundant.
An extra flag to skip hlo passes is also redundant, since
--xla_disbale_hlo_passes could be just used.
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} | PR #13461: typo: fix transpse
Imported from GitHub PR https://github.com/openxla/xla/pull/13461
Copybara import of the project:
--
e7c6c23be44648237a31d17bf2574c4f26510889 by knightXun <1004815462@qq.com>:
typo: fix transpse
Merging this change closes #13461
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"You might want to look into Quantization-Aware Training",
"I think no, because I train Pytorch models and then convert them to onnx and tflite",
"Hi @DaraOrange , this issue may be resolved by following a different work flow.\r\n\r\nHave you tried using [AI-Edge-Torch](https://github.com/google-ai-edge/ai-edge-torch)?, you can find more information here: [googleblog](https://developers.googleblog.com/en/ai-edge-torch-high-performance-inference-of-pytorch-models-on-mobile-devices/).\r\n\r\nI have actually created a simple script for converting a CV model here:\r\n\r\n```\r\nimport torch\r\nimport torchvision\r\nimport ai_edge_torch\r\n\r\nmobilenet_model = torchvision.models.mobilenet_v3_small()\r\nsample_inputs = (torch.randn(1, 3, 224, 224),)\r\n\r\nedge_model = ai_edge_torch.convert(mobilenet_model.eval(), sample_inputs)\r\nedge_model.export(\"mobilenet_v3_small.tflite\")\r\n```\r\nIf you want to, you can actually try visualizing the result in [model-explorer](https://github.com/google-ai-edge/model-explorer) as well.\r\n\r\nPlease try them out and let us know if this resolves your issue. If you still need further help, feel free to open a new issue at the respective repo.",
"I have no problems with conversion, but results after quantization are poor. Does this tool support quantization? ",
"Hm, I see that it just wraps tflite quantizer and don't provide more complicated methods of quantization"
] | 2024-06-06T11:18:18 | 2024-06-11T09:42:32 | null | NONE | null | null | null | Hi! I'm trying to quantize MobileNetV3 with tflite, but int8-model performs very poor. I think, it is because of linear quantization, which is too simple method not appropriate for any weights distributions. What else can I try? Are you going to support logarithmic scale for quantization in the future? | {
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"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/69310/checks?check_run_id=25887708721) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
] | 2024-06-06T11:00:56 | 2024-06-06T11:49:58 | 2024-06-06T11:49:57 | NONE | null | true | {
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} | PR #13408: Fix mlir_hlo tests on Windows
Imported from GitHub PR https://github.com/openxla/xla/pull/13408
This PR aims to fix the mlir_hlo tests which are designed to test the implementation of the HLO (High-Level Optimizer) for MLIR (Multi-Level Intermediate Representation).
**Error**
mlir_hlo tests were failing on the Windows platform with an error shown below:
INFO: From Testing //xla/mlir_hlo/tests:Dialect/mhlo/verifier_while_op.mlir.test:
==================== Test output for //xla/mlir_hlo/tests:Dialect/mhlo/verifier_while_op.mlir.test:
lit.py: C:\Users\mraunak\AppData\Local\Temp\Bazel.runfiles_8ccro7s1\runfiles\llvm-project\llvm\utils\lit\lit\llvm\config.py:57: note: using lit tools: C:\Program Files\Git\usr\bin
lit.py: C:\Users\mraunak\AppData\Local\Temp\Bazel.runfiles_8ccro7s1\runfiles\llvm-project\llvm\utils\lit\lit\llvm\subst.py:133: **fatal**: **Did not find FileCheck in external/llvm-project/llvm**
**Solution**
Tweaked the PATH to correctly find the tools required for executing LIT tests on the Windows platform such as FileCheck.
Copybara import of the project:
--
b71b35ffac522acd8989d3ea79e038a4e30b7493 by Raunak <mayank.kumar.raunak@intel.com>:
Fix mlir_hlo tests on Windows
Merging this change closes #13408
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13408 from Intel-tensorflow:mraunak/mlir_xla b71b35ffac522acd8989d3ea79e038a4e30b7493
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} | [XLA:GPU] Minor improvements to the CUB sort test.
- Add a comment
- Verify that the rewrite is actually happening and CUB sort is indeed used. I got bitten by a passing test with a temporary change that didn't actually use CUB.
- Make the test size medium. Otherwise it timed out quite a lot for me.
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} | Add a readme.md file for mlir_interpreter.
I just coincidentally noticed this is missing.
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"@ian-huu One workaround could be to specify the input names during saving using the tf.saved_model.save API with the input argument. This ensures that all your model's inputs are included in the serving_default signature.\r\nThank you!",
"> @ian-huu One workaround could be to specify the input names during saving using the tf.saved_model.save API with the input argument. This ensures that all your model's inputs are included in the serving_default signature. Thank you!\r\n\r\nDo you mean to do it this way? It seems to be working.\r\n```python\r\nclass Model3(keras.Model):\r\n def __init__(self):\r\n super().__init__()\r\n\r\n self.fc1 = keras.layers.Dense(8)\r\n self.fc2 = keras.layers.Dense(1)\r\n\r\n def build(self, input_shape):\r\n self.fc1.build((None, sum([shape[1] for shape in input_shape])))\r\n self.fc2.build((None, 8))\r\n\r\n @tf.function\r\n def call(self, inputs, **kwargs):\r\n concat_inputs = keras.ops.concatenate(inputs, axis=1)\r\n out1 = self.fc1(concat_inputs)\r\n out2 = self.fc2(out1)\r\n return out2\r\n\r\n\r\nmodel3 = Model3()\r\nmodel3.build([(None, 8), (None, 8)])\r\ntf.saved_model.save(\r\n model3,\r\n f\"{path}/model3\",\r\n signatures=model3.call.get_concrete_function(\r\n (\r\n tf.TensorSpec(shape=(None, 8), dtype=tf.float32),\r\n tf.TensorSpec(shape=(None, 8), dtype=tf.float32),\r\n )\r\n ),\r\n)\r\n\r\nloaded3 = tf.saved_model.load(f\"{path}/model3\")\r\nprint(f\"loaded3: {loaded3.signatures}\")\r\n\r\n# got:\r\n# loaded3: _SignatureMap({'serving_default': <ConcreteFunction (*, inputs: TensorSpec(shape=(None, 8), dtype=tf.float32, name='inputs'), inputs_1: TensorSpec(shape=(None, 8), dtype=tf.float32, name='inputs_1')) -> Dict[['output_0', TensorSpec(shape=(None, 1), dtype=tf.float32, name='output_0')]] at 0x7F2CB1FF4190>})\r\n```\r\n\r\nHowever, it seems there is another problem. The loaded SavedModel does not have attributes like `trainable_variables` and `__call__` anymore.\r\n\r\n```python\r\n# for all loaded1, loaded2, loaded3\r\nprint(loaded1.trainable_variables)\r\n# got: AttributeError: '_UserObject' object has no attribute 'trainable_variables'.\r\n\r\nprint(loaded1.__call__)\r\n# got: AttributeError: '_UserObject' object has no attribute '__call__'.\r\n```\r\n\r\nAnd if I directly call model3.call, an error occurs, even though I manage to obtain the result.\r\n```python\r\nprint(loaded3.call((tf.zeros((1, 8)), tf.zeros((1, 8)))))\r\n\r\n```\r\ngot:\r\n```\r\ntf.Tensor([[0.]], shape=(1, 1), dtype=float32)\r\nException ignored in: <function AtomicFunction.__del__ at 0x72f6cf3a7760>\r\nTraceback (most recent call last):\r\n File \"***/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py\", line 291, in __del__\r\nTypeError: 'NoneType' object is not subscriptable\r\n```\r\n"
] | 2024-06-06T09:43:20 | 2024-06-11T06:55:53 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.1
### Mobile device
_No response_
### Python version
3.10.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The code below illustrates that when a Keras model has multiple inputs, the tf.saved_model does not function correctly, resulting in an empty signatures field.
### Standalone code to reproduce the issue
```shell
import tempfile
import keras
import tensorflow as tf
path = tempfile.mkdtemp()
model1 = keras.models.Sequential([keras.layers.Dense(8), keras.layers.Dense(1)])
model1.build((None, 16))
tf.saved_model.save(model1, f"{path}/model1")
loaded1 = tf.saved_model.load(f"{path}/model1")
print(f"loaded1: {loaded1.signatures}")
class Model2(keras.Model):
def __init__(self):
super().__init__()
self.fc1 = keras.layers.Dense(8)
self.fc2 = keras.layers.Dense(1)
def call(self, inputs, **kwargs):
out1 = self.fc1(inputs)
out2 = self.fc2(out1)
return out2
model2 = Model2()
model2.build((None, 16))
tf.saved_model.save(model2, f"{path}/model2")
loaded2 = tf.saved_model.load(f"{path}/model2")
print(f"loaded2: {loaded2.signatures}")
class Model3(keras.Model):
def __init__(self):
super().__init__()
self.fc1 = keras.layers.Dense(8)
self.fc2 = keras.layers.Dense(1)
def build(self, input_shape):
self.fc1.build((None, sum([shape[1] for shape in input_shape])))
self.fc2.build((None, 8))
def call(self, inputs, **kwargs):
concat_inputs = keras.ops.concatenate(inputs, axis=1)
out1 = self.fc1(concat_inputs)
out2 = self.fc2(out1)
return out2
model3 = Model3()
model3.build([(None, 8), (None, 8)])
tf.saved_model.save(model3, f"{path}/model3")
loaded3 = tf.saved_model.load(f"{path}/model3")
print(f"loaded3: {loaded3.signatures}")
```
### Relevant log output
```shell
loaded1: _SignatureMap({'serving_default': <ConcreteFunction (*, inputs: TensorSpec(shape=(None, 16), dtype=tf.float32, name='inputs')) -> Dict[['output_0', TensorSpec(shape=(None, 1), dtype=tf.float32, name='output_0')]] at 0x7A4E8429C8E0>})
python3.10/site-packages/keras/src/layers/layer.py:361: UserWarning: `build()` was called on layer 'model2', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.
warnings.warn(
loaded2: _SignatureMap({'serving_default': <ConcreteFunction (*, inputs: TensorSpec(shape=(None, 16), dtype=tf.float32, name='inputs')) -> Dict[['output_0', TensorSpec(shape=(None, 1), dtype=tf.float32, name='output_0')]] at 0x7A4E841D4A00>})
loaded3: _SignatureMap({})
```
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} | PR #13258: Give GPU compiler class access to PJRT key-value store.
Imported from GitHub PR https://github.com/openxla/xla/pull/13258
Copybara import of the project:
--
735291df0028b9d3a1a73a7db8fe5104910b4ca5 by Ilia Sergachev <isergachev@nvidia.com>:
Give GPU compiler class access to PJRT key-value store.
Merging this change closes #13258
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13258 from openxla:kv 735291df0028b9d3a1a73a7db8fe5104910b4ca5
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} | Integrate LLVM at llvm/llvm-project@55d2fffdae55
Updates LLVM usage to match
[55d2fffdae55](https://github.com/llvm/llvm-project/commit/55d2fffdae55)
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} | Fix transfer_read and transfer_write with i1 vectors.
LLVM's vector<i1> is packed, but we need vector<u8> semantics.
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} | Rewrite gpu_index_test to don't be a change detector test.
Also get rid of unnecessary HloModuleConfigs. The method from the base class
already adds those. Fix one test that had invalid layouts.
Remove one TENSORFLOW_USE_ROCM guard that most likely isn't doing anything
(we don't define it in the build target).
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