### Support inplace update for PythonOp/Grad
This PR is based on another PR
https://github.com/microsoft/onnxruntime/pull/17685's branch, to make it
easier to review.
With PR: PR https://github.com/microsoft/onnxruntime/pull/17685, By
default all PythonOp inputs/outputs are assumed to not be inplaced, if
during run, we found some inplace update happens (by checking output
data address with all inputs data address), we add clone before set it
as PythonOp/Grad's outputs. In this case, results are correct, but
implicit copies overheads are introduced.
This PR allow users to define output input reuse map, to let ORT know
how to do the reuse map, avoid such unnecessary copies.
### Description
<!-- Describe your changes. -->
Split out the more basic changes from #15552 for easier review.
Re-organize to clarify the structure
- Separate out generic base functionality from ORT specific components
- pass in handlers for internal ORT ops to Optimize
- Split out layout transformation from transpose optimization
- Separate out level 1 transpose optimizer
- Cleanup some naming to try and clarify things like an optimizer vs.
general optimization code
Most of the changes are from this movement of code.
Two implementation changes:
- the extended handlers are queried first in GetHandler
- allows the extended handlers to override the default behaviour for an
ONNX operator
- simplify the Optimize function to remove OptimizerMode.
- `can_modify_node` is used instead of `mode` and
`ignore_assigned_nodes` and a long description of the current usage is
added. I don't _think_ that changes the current behavior and hopefully
clarifies what happens and when, and makes the base transpose optimizer
implementation more generic.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Create a cleaner separation to support adding EP specific logic next to
cleanly handle where an EP has additional layout sensitive behaviour
required (e.g. it's Resize implementation only handles one layout).
🛠️ __Changes in this pull request:__
This pull request introduces two significant changes to the project:
- Changing on device training checkpoint format: The current
implementation stores the on device training checkpoint as a sequence of
tensors in multiple files inside a checkpoint folder, which can be
inefficient in terms of storage and performance. In this PR, I have
modified the checkpoint format to utilize the flatbuffer table to save
the checkpoint to a single file, providing a more compact and efficient
representation. The changes around this are twofold:
- Add the checkpoint flatbuffer schema that will generate the necessary
checkpoint source files.
- Update the checkpoint saving and loading functionality to use the new
format.
- Adding support for onnxruntime minimal build: To support scenarios
where binary size is a constraint, I made changes to ensure that the
training build can work well with the minimal build.
🔍 __Open Issues:__
- In order to extract the optimizer type, the existing implementation
re-loaded the onnx optimizer model and parsed it. This is no longer
possible, since the model format can either be onnx or ort. One idea is
to do the same for ort format optimizer model. This needs some
investigation.
- Changes to the offline tooling to generate ort format training
artifacts.
- End-to-end training example showcasing the use of the minimal training
build.
- Add support for export model for inferencing in a minimal build.
**Description**:
Adds support for cmake find_package.
**Motivation and Context**
As mentioned in issue #7150 onnxruntime doesn't have support for CMake
find_package, this PR adds that and also adds the CMake package version
file. Now anyone can link onnxruntime like this:
```cmake
find_package(onnxruntime)
add_executable(test Source.cpp)
target_link_libraries(test PRIVATE onnxruntime::onnxruntime)
```
this also simplifies #3124
### Optimize SCE loss compute
Compute optimization based on label data sparsity:
- Insert ShrunkenGather before SCELoss node, to filter out invalid
labels for compute.
- Support ShrunkenGather upstream.
- Added test for the above.
- Added flag to enable label sparsity optimization with env var, by
default disabled now. Will enable after comprehensive benchmarking
later.
- Extract common logic into test_optimizer_utils.h/cc from
core/optimizer/compute_optimzier_test.cc, then the common functions can
be shared by both core/optimizer/compute_optimzier_test.cc and
orttraining/core/optimizer/compute_optimzier_test.cc
- Extract common logic into shared_utils.h/cc: `GetONNXOpSetVersion` and
`Create1DInitializerFromVector`
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
### Description
Enable creating dedicated build for on device training. With this PR we
can build a lean binary for on device training using flag
--enable_training_apis. This binary includes only the essentials like
training ops, optimizers etc and NOT features like Aten fallback,
strided tensors, gradient builders etc . This binary also removes all
the deprecated components like training::TrainingSession and OrtTrainer
etc
### Motivation and Context
This enables our partners to create a lean binary for on device
training.
### Optimize computation orders
In `Roberta/Electra`, when `ClassificationHead` is used, there is
slicing operation on features on sequence_length dimensions, then loss
calculations only depend on this sliced data. This is a slicing at axis
1. Before slicing the shape is [batch, sequence_length, hidden], after
slicing, it becomes [batch , hidden_stage]
We had opportunities to bring this slicing earlier as much as possible,
by passing through simple elementwise ops (like Add/Div), or
Layernorm/Softmax(if their reduce axis is after the slicing axis), or
even MatMul's the left operand (if only it did not affect the last
dims).
For operators like Reshape/Transpose, it is special since they have
either data specified (after slicing we need update), or they have perm
specified, which requires the input rank remain unchanged. So for those
kinds of operators, we can remain the original rank, but just leave the
sliced dim to be 1, after the compute completed, we do a Squeeze.
```
class RobertaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
```
src\transformers\models\roberta\modeling_roberta.py
src\transformers\models\electra\modeling_electra.py
#### Benchmark
A simple benchmark shows Robeta training latency dropped from 208ms ~
199ms. 4.5+% reduction.
More comprehensive tests are on the way.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
### Description
Use target name for flatbuffers.
Add version range for flatbuffers. It is similar to #13870
### Motivation and Context
To fix a build error:
```
CMake Error at onnxruntime_graph.cmake:88 (add_dependencies):
The dependency target "flatbuffers" of target "onnxruntime_graph" does not
exist.
Call Stack (most recent call first):
CMakeLists.txt:1490 (include)
```
It happens when flatbuffers library is already installed. For example,
on Ubuntu people may get it from apt-get. But, the one provided by
Ubuntu 20.04 is not compatible with our code. The one in Ubuntu 22.04
works fine.
## Description
1. Convert some git submodules to cmake external projects
2. Update nsync from
[1.23.0](https://github.com/google/nsync/releases/tag/1.23.0) to
[1.25.0](https://github.com/google/nsync/releases/tag/1.25.0)
3. Update re2 from 2021-06-01 to 2022-06-01
4. Update wil from an old commit to 1.0.220914.1 tag
5. Update gtest to a newer commit so that it can optionally leverage
absl/re2 for parsing command line flags.
The following git submodules are deleted:
1. FP16
2. safeint
3. XNNPACK
4. cxxopts
5. dlpack
7. flatbuffers
8. googlebenchmark
9. json
10. mimalloc
11. mp11
12. pthreadpool
More will come.
## Motivation and Context
There are 3 ways of integrating 3rd party C/C++ libraries into ONNX
Runtime:
1. Install them to a system location, then use cmake's find_package
module to locate them.
2. Use git submodules
6. Use cmake's external projects(externalproject_add).
At first when this project was just started, we considered both option 2
and option 3. We preferred option 2 because:
1. It's easier to handle authentication. At first this project was not
open source, and it had some other non-public dependencies. If we use
git submodule, ADO will handle authentication smoothly. Otherwise we
need to manually pass tokens around and be very careful on not exposing
them in build logs.
2. At that time, cmake fetched dependencies after "cmake" finished
generating vcprojects/makefiles. So it was very difficult to make cflags
consistent. Since cmake 3.11, it has a new command: FetchContent, which
fetches dependencies when it generates vcprojects/makefiles just before
add_subdirectories, so the parent project's variables/settings can be
easily passed to the child projects.
And when the project went on, we had some new concerns:
1. As we started to have more and more EPs and build configs, the number
of submodules grew quickly. For more developers, most ORT submodules are
not relevant to them. They shouldn't need to download all of them.
2. It is impossible to let two different build configs use two different
versions of the same dependency. For example, right now we have protobuf
3.18.3 in the submodules. Then every EP must use the same version.
Whenever we have a need to upgrade protobuf, we need to coordinate
across the whole team and many external developers. I can't manage it
anymore.
3. Some projects want to manage the dependencies in a different way,
either because of their preference or because of compliance
requirements. For example, some Microsoft teams want to use vcpkg, but
we don't want to force every user of onnxruntime using vcpkg.
7. Someone wants to dynamically link to protobuf, but our build script
only does static link.
8. Hard to handle security vulnerabilities. For example, whenever
protobuf has a security patch, we have a lot of things to do. But if we
allowed people to build ORT with a different version of protobuf without
changing ORT"s source code, the customer who build ORT from source will
be able to act on such things in a quicker way. They will not need to
wait ORT having a patch release.
9. Every time we do a release, github will also publish a source file
zip file and a source file tarball for us. But they are not usable,
because they miss submodules.
### New features
After this change, users will be able to:
1. Build the dependencies in the way they want, then install them to
somewhere(for example, /usr or a temp folder).
2. Or download the dependencies by using cmake commands from these
dependencies official website
3. Similar to the above, but use your private mirrors to migrate supply
chain risks.
4. Use different versions of the dependencies, as long as our source
code is compatible with them. For example, you may use you can't use
protobuf 3.20.x as they need code changes in ONNX Runtime.
6. Only download the things the current build needs.
10. Avoid building external dependencies again and again in every build.
### Breaking change
The onnxruntime_PREFER_SYSTEM_LIB build option is removed you could think from now
it is default ON. If you don't like the new behavior, you can set FETCHCONTENT_TRY_FIND_PACKAGE_MODE to NEVER.
Besides, for who relied on the onnxruntime_PREFER_SYSTEM_LIB build
option, please be aware that this PR will change find_package calls from
Module mode to Config mode. For example, in the past if you have
installed protobuf from apt-get from ubuntu 20.04's official repo,
find_package can find it and use it. But after this PR, it won't. This
is because that protobuf version provided by Ubuntu 20.04 is too old to
support the "config mode". It can be resolved by getting a newer version
of protobuf from somewhere.
* Add layout transformer for NNAPI
* plus merge fixes
* plus some more merge fixes
* test fixes
* comments + cleanup
* plus updates
* post merge changes
* enable layout transformer in extended minimal build
* plus more comments
* more tests + fix CI
* plus updates per review
* more updates per review
* fix file name
* fix qdq tests
* plus more updates
* plus updates
* typo fix
* fix qdq selection in 2nd optimization pass
* fix typo
* fix a test
* update dependency structure for layout transformer
* plus updates
* more updates
* plus change
* more updates to fix linker error in minimal build
* remove unnecessary headers
Update QDQ propagation transformer to insert new QDQ nodes instead of moving the existing one. This creates a more consistent `DQ -> op -> Q` pattern for other components to recognize.
Upgrade this transformer to a basic level optimization as it yields a valid ONNX graph.
* add qdqgroup as input for NodeUnit
* minor update
* hookup nnapi_ep
* minor update
* update compiler setting
* Add a simple UT
* Pipeline change to add build minimal extended with NNAPI for Android
* move GetAllNodeUnits to node_unit.h, add UT for NodeUnits, minor updates
* minor updates
* address CR comments
Co-authored-by: gwang0000 <62914304+gwang0000@users.noreply.github.com>
* Add new transformer that can split node selection from node modification to allow just the modifications to be applied at runtime in a minimal build. This is the first step of a few to enable a QDQ model to be optimized for the NNAPI EP and/or the CPU EP at runtime in a mobile scenario.
Add generic and QDQ specific helpers for selection and modification.
Replace existing QDQ optimizers with optimizer based on new approach.
1. Update manylinux build scripts. This will add [PEP600](https://www.python.org/dev/peps/pep-0600/)(manylinux2 tags) support. numpy has adopted this new feature, we should do the same. The old build script files were copied from https://github.com/pypa/manylinux, but they has been deleted and replaced in the upstream repo. The manylinux repo doesn't have a manylinux2014 branch anymore. So I'm removing the obsolete code, sync the files with the latest master.
2. Update GPU CUDA version from 11.0 to 11.1(after a discussion with PMs).
3. Delete tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cuda10_2. (Merged the content to tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cuda11)
4. Modernize the cmake code of how to locate python devel files. It was suggested in https://github.com/onnx/onnx/pull/1631 .
5. Remove `onnxruntime_MSVC_STATIC_RUNTIME` and `onnxruntime_GCC_STATIC_CPP_RUNTIME` build options. Now cmake has builtin support for it. Starting from cmake 3.15, we can use `CMAKE_MSVC_RUNTIME_LIBRARY` cmake variable to choose which MSVC runtime library we want to use.
6. Update Ubuntu docker images that used in our CI build from Ubuntu 18.04 to Ubuntu 20.04.
7. Update GCC version in CUDA 11.1 pipelines from 8.x to 9.3.1
8. Split Linux GPU CI pipeline to two jobs: build the code on a CPU machine then run the tests on another GPU machines. In the past we didn't test our python packages. We only tested the pre-packed files. So we didn't catch the rpath issue in CI build.
9. Add a CentOS machine pool and test our Linux GPU build on real CentOS machines.
10. Rework ARM64 Linux GPU python packaging pipeline. Previously it uses cross-compiling therefore we must static link to C Runtime. But now have pluggable EP API and it doesn't support static link. So I changed to use qemu emulation instead. Now the build is 10x slower than before. But it is more extensible.
* Initial set of changes to start disabling code in the minimal build. Breaking changes into multiple PRs so they're more easily reviewed. Focus on InferenceSession, Model and Graph here. SessionState will be next.
Needs to be integrated with de/serialization code before being testable so changes are all off by default.
Changes are limited to
- #ifdef'ing out code
- moving some things around so there are fewer #ifdef statements
- moving definition of some one-line methods into the header so we don't need to #ifdef out in a .cc as well
- exclude some things in the cmake setup
* Update session state and a few other places.
The core code builds if ORT_MINIMAL_BUILD is specified.
1. Enable warning "4503" # Decorated name length exceeded.
2. Enable warning "4146" # unary minus operator applied to unsigned type.
3. Enable float64 support for the Softmax operator
4. Enable compliance checks for Windows x86 32bits build
5. Use TryBatchParallelFor to replace some fallback code in mlas pooling.cc
6. Fix Android CI pipeline.
Remove gsl subodule and replace with a local copy of gsl-lite
Refactor for onnxruntime::make_unique
gsl::span size and index are now size_t
Remove lambda auto argument type detection.
Remove constexpr from fail_fast in gsl due to Linux not being happy.
Comment out std::stream support due to MacOS std lib broken.
Move make_unique into include/core/common so it is accessible for server builds.
Relax requirements for onnxruntime/test/providers/cpu/ml/write_scores_test.cc
due to x86 build.
Add ONNXRUNTIME_ROOT to Server Lib includes so gsl is recognized
* Create a project for graph optimizer.
Move optimizer related code to the folder optimizer.
* Fix build failures.
* rebase and fix build failures.
* fix build failure.
* fix build failure with cuda path.
* fix python build failure.
* Move two transformers(memcpy and insert_cast) from framework to optimizer.
* rebase.
* SessionState should not depend on optimizer.
