### Description
This PR supports
[DepthToSpace](https://onnx.ai/onnx/operators/onnx__DepthToSpace.html#depthtospace)
operator in webgpu backend.
### Test
We followed the steps described on [this
page](https://gist.github.com/fs-eire/a55b2c7e10a6864b9602c279b8b75dce)
to build, tested with the following commands, and confirmed that it
passed the Model and Op tests that already existed. (Probably, these
test cases were prepared in the past for WebGL backend)
```
~/onnxruntime/js/web>
% npm test -- suite0 -b=webgpu --wasm-number-threads=1 --debug
```
##### NOTE
I want to tell you that the main branch version failed 5 tests for the
resize_upsample_sizes_nearest operator.
Since I didn't touch this issue, those test cases still fail in my
branch as well.
Should I post an issue for this?
### Motivation and Context
Though the DepthToSpace operator plays a crucial role in
super-resolution domains, it was not supported in webgpu backend.
### Description
<!-- Describe your changes. -->
Re-use vector buffers to prevent frequent reallocations.
### 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. -->
Reduce process heap contention.
### Description
The PaddingElimination optimization is enabled when the density of
embedding padding less than 90%. We need to check the density of the
embedding padding to decide whether enable the optimization.
Before this pr, we just check the inputs of graph and correlate one with
the embedding node by iterate graph from the embedding node back to one
graph input.
This is hard to be general because there may be complicated pattern
between graph input and embedding node.
This pr check padding density by the direct input of embedding module
rather than the input of graph at the first graph execution when
exporting onnx graph.
And if the density < 90%, insert a flag PythonOp after the embedding
node as:
```
Embedding
|
PythonOp (func_name:_FlagPaddingElimination) (insert if density < 90%)
|
Following graph
```
When the PaddingElimination is invoked, it check if there is the flag
PythonOp(func_name:_FlagPaddingElimination) after the Embedding node and
if it is, remove it and do the padding elimination optimization.
### Description
make the compilation work on Azure CPU Agent by reduce the parallel
count
### Motivation and Context
The OOM issue mentioned in #20244 was caused the by low
memory/parallel_count.
### Prompt layer-wise when applicable
Give explicit prompts in export failures to users to enable layer-wise
memory optimization if we found the checkpoint function is used.
- Using checkpoint function is a strong indicator that the model is too
large to fit in GPU memory.
- If we don't override the checkpoint function here, mostly ONNX export
will be failed. 1. For old version PyTorch, when handling gradient
checkpoint feature, we just throw an exception. 2. For new version
PyTorch, an export failure happens.
- But both failures did not give users explicitly "HOW" to mitigate.
This PR did that.
``
### 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
It always has been out of memory in training CUDA 12.2 packaging
pipeline
https://dev.azure.com/aiinfra/Lotus/_build?definitionId=1308&_a=summary
since the PR #19910
I tried other CPU agents for example, D64as_v5(256G memory) and
D32as_v4(128G memory and 256 G SSD temp storage), which are still out of
memory like the below image
But it works on T4, though T4 only has 4 vCPUs, 28G memory and 180G temp
storage, and it takes much more time.
### Motivation and Context
Restore CUDA 12.2 training packaging pipeline first.
More time is needed to investigate the root cause
### Other Clues.
These 2 compilation steps take nearly 6 minutes with Cuda 12.2 on T4
And it runs out of memory on CPU machine. @ajindal1
cuda12.2 on T4
```
2024-03-14T05:39:08.7726865Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim32_fp16_sm80.cu.o
2024-03-14T05:45:01.3223393Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim64_bf16_sm80.cu.o
2024-03-14T05:46:07.9218003Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim96_fp16_sm80.cu.o
2024-03-14T05:52:59.2387051Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/group_query_attention_impl.cu.o
```
But they could be finished in about one minute with Cuda 11.8 on CPU
```
cuda11.8 on CPU
2024-04-09T11:34:35.0849836Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim32_fp16_sm80.cu.o
2024-04-09T11:35:53.6648154Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim64_bf16_sm80.cu.o
cuda11.8 on GPU
024-03-13T12:16:33.4102477Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim32_fp16_sm80.cu.o
2024-03-13T12:19:58.8268272Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim64_bf16_sm80.cu.o
```
### Description
<!-- Describe your changes. -->
Re-use pre-computed and pre-allocated buffers for UNICODE conversions.
Make sure we do not introduce unnecessary intermediate `std::string`
instances.
Create a Utf8Generic converter for use with non-Windows platforms.
### 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. -->
This reduces heap contention in P1 customer.
### Optimize constant sharing perf
by avoiding [renaming for the first name we detect a constant pattern.
Currently every time we start run ConstantSharing, for each initializer,
we find its pattern does not exist, then we create a new NodeArg with a
unique name. Then later if other initializer share the same pattern,
they will be replaced by the NodeArg.
The problem is: once there is no real constant sharing cases, we still
modify the graph for each initializer. This is not needed.
### 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
<!-- Describe your changes. -->
There is a problem in relu_quantizelinear transformer that causes wrong
results. The purpose of this PR is to solve this problem.
### 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. -->
This does not take into account the situation where Q's zeropoint is
tensor(int16), tensor(uint16), so when this happens, an error will
occur.
How to verify：
```python
import onnx
import onnxruntime as ort
import numpy as np
model_name = 'relu_quantize_testcase.onnx'
model = onnx.load(model_name)
ort_input0 = np.random.rand((1, 64, 64, 128),np.float32)
# infer with GraphOptimizationLevel=0
so = ort.SessionOptions()
so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
ort_session = ort.InferenceSession(
model_name,
providers=["CPUExecutionProvider"],
sess_options=so
)
outputs = [x.name for x in ort_session.get_outputs()]
ort_outs_mod = ort_session.run(outputs, { 'generator/conv2d_input/conv2d/Conv2D:0': ort_input0} )
del ort_session
# infer with GraphOptimizationLevel=default
model_orig = onnx.load(model_name)
ort_session_orig = ort.InferenceSession(model_orig.SerializeToString())
outputs_orig = [x.name for x in ort_session_orig.get_outputs()]
ort_outs_orig = ort_session_orig.run(outputs_orig, { 'generator/conv2d_input/conv2d/Conv2D:0': ort_input0} )
# diff
print(np.linalg.norm(ort_outs_mod[0].astype(np.float32) - ort_outs_orig[0].astype(np.float32)))
del ort_session_orig
```
[relu_quantize_testcase.zip](https://github.com/microsoft/onnxruntime/files/14848160/relu_quantize_testcase.zip)
---------
Co-authored-by: genmingz <genming.zhong@amd.com>
### Support more ops for recompute
To cover Mistral model, and support padding elimination ops.
### 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
I recently opened a PR in hf transformers repo to fix an issue on the
indexing part.
https://github.com/huggingface/transformers/issues/29857
onnx exporter was failing because of the tolist() conversion so we had
to remove it.
I found out that the code was also a part of our codebase so this PR is
to keep the code consistent.
### Description
<!-- Describe your changes. -->
### 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
<!-- Describe your changes. -->
### 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
Update QNN python packages to use QNN SDK version 2.19.2.
### Motivation and Context
Our CI builds already use QNN SDK version 2.19.2. We should make sure
the ort-nightly-qnn python packages are also built with the same QNN SDK
version.
Improve the script to add Q, DQ nodes around EPContext node so that the wrapper model use float data as inputs and outputs. User don't need to quantize or dequantize the data in their application
### Description
Add onnxruntime/test/run_benchmark.py helper script to repeat benchmark
runs until a target coefficient of variance is reached. It works with
[Google Benchmark](https://github.com/google/benchmark) programs like
`onnxruntime_mlas_benchmark`.
### Motivation and Context
Sometimes there is variability in benchmark run results. This automates
the repeated running needed to get results that are stable enough.
### Description
<!-- Describe your changes. -->
Re-order so that we don't get two messages for the one node.
Currently the batched matmul 'not supported' message will appear for 2D
input which is valid, which can be confusing to understand.
Change the order so we only check if batched matmul can be used when the
input ranks are > 3, as that is one of the requirements.
c311d1faf5/onnxruntime/core/providers/nnapi/nnapi_builtin/builders/op_builder_helpers.cc (L257-L264)
### Description
This adjusts the path used in the nuget script for dnnl to the new
location of the file.
There isn't a CI pipeline for this as far as I can tell, and I can't
easily confirm this change works on master, so please check.
### Motivation and Context
It is currently not possible to build onednn nuget packages. It's
possible that the correct action would be to move the file not fix this
path, but I'm not familiar enough with the repository layout.
---------
Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
### Description
<!-- Describe your changes. -->
### 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
For C++ standards >= 20, use `std::chrono::operator<<` in place of
`date::operator<<` to fix ambiguous operator compile error.
### Motivation and Context
The external dependency HowardHinnant/date has a conflict with
std::chrono for >=C++20.
Solves #20137
### How to run it locally
1. conda install ninja
2. "C:\Program Files\Microsoft Visual
Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" x64
3. python.exe {ort_repo}\tools\ci_build\build.py --config RelWithDebInfo
--build_dir {ort_repo}\build_cuda --skip_submodule_sync --build_csharp
--update --parallel --cmake_generator "Ninja" --build_shared_lib
--enable_onnx_tests --enable_pybind --build_java --build_nodejs
--use_cuda "--cuda_home=C:\Program Files\NVIDIA GPU Computing
Toolkit\CUDA\v11.8" --enable_cuda_profiling --cmake_extra_defines
CMAKE_CUDA_ARCHITECTURES=60
4. cd build_cuda\RelWithDebInfo
5. cmake --build . j16
### Motivation and Context
In packaging pipelines, we often come across a random issue that the
building with CUDA on Windows takes too much time.
Although it has been reduced much by moving the building to the CPU
machine.
We're planning to build with Ninja instead of msbuild in Packaging
pipelines, thus, nvcc can run parallelly.
It's the first step to support it locally.
Adds an example to demonstrate the export of openai whipser
implemenation with batch_size > 1 and addition of prompts for each audio
snippet.
Also handles the scenario for when prompts are not of the same size. For
example if our prompt ids are [p1_id_1, p1_id_2] and [p2_id_1], the
final decoder_input_ids will look as such after padding:
`[prev_token, p1_id_1, p1_id_2, start_token, lang_token,
transcribe_token]
[prev_token, p2_id_1, PAD_TOKEN, start_token, lang_token,
transcribe_token]`
---------
Co-authored-by: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
### Description
<!-- Describe your changes. -->
See #19921 Just to address one comment:
https://github.com/microsoft/onnxruntime/pull/19921#discussion_r1543398640
since this is an external branch. need to open another pull request for
this.
### 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. -->
---------
Co-authored-by: Sai Kishan Pampana <sai.kishan.pampana@intel.com>
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Jian Chen <cjian@microsoft.com>
### Description
<!-- Describe your changes. -->
### 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. -->
Certain transformers slow down session loading time while providing no
runtime perf benefits.
Allow clients to exclude them.
Previous [PR
](https://github.com/microsoft/onnxruntime/pull/19663)changes msvc
compiler warning level from set_msvc_c_cpp_compiler_warning_level(3) to
set_msvc_c_cpp_compiler_warning_level(4) when using CUDA EP (it also
applies to TRT EP).
Some warnings still need to be addressed in TRT EP code.
### Description
<!-- Describe your changes. -->
### 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
The dml_provider_factory header file can't be used in C programs as it
defines C++ inline operators. This PR rearranges that header file so
that it looks like valid C when used from C, and also makes a couple of
small modifications to the Java code so it correctly binds to the DML EP
at build time.
I'm having some difficulty testing it as I think it's pulling in the old
version of DirectML on my computer and I can't figure out what the
library loading path is in Java to make it look at the recent version I
downloaded. So the test I added fails with:
```
InferenceTest > testDirectML() FAILED
ai.onnxruntime.OrtException: Error code - ORT_RUNTIME_EXCEPTION - message: Exception during initialization: <path-to-ort>\onnxruntime\core\providers\dml\DmlExecutionProvider\src\AbiCustomRegistry.cpp(518)\onnxruntime.dll!00007FFF74819333: (caller: 00007FFF74793509) Exception(3) tid(4f58) 80070057 The parameter is incorrect.
at app//ai.onnxruntime.OrtSession.createSession(Native Method)
at app//ai.onnxruntime.OrtSession.<init>(OrtSession.java:74)
at app//ai.onnxruntime.OrtEnvironment.createSession(OrtEnvironment.java:236)
at app//ai.onnxruntime.OrtEnvironment.createSession(OrtEnvironment.java:221)
at app//ai.onnxruntime.InferenceTest.openSessionSqueezeNet(InferenceTest.java:1961)
at app//ai.onnxruntime.InferenceTest.runProvider(InferenceTest.java:665)
at app//ai.onnxruntime.InferenceTest.testDirectML(InferenceTest.java:657)
```
But it does correctly compile, and this error seems very similar to
other issues with the DML provider when it doesn't like a model due to
the loaded library being old. The test is using the squeezenet file
that's been in the repo since 2019. If someone can help me figure out
how to get the right version of DML in the library path I can test it
more on my end. I tried adding the folder with the new version into the
system path, but I'm not very familiar with Windows' library loading
behaviour.
### Motivation and Context
Fixes#19656 to allow use of the DirectML EP from ORT Java.
cc @martinb35
### Description
New flag of `dump_om_model` for **CANN EP**, which defaults to "True".
### Motivation and Context
When building an onnx model with CANN EP, the intermediate **OM(offline
model for Ascend NPU)** is automatically saved. There are some users
don't want to dump OM when resources are limited.
This PR will resovle this situation with `dump_om_model=False`
### Description
See issue 15465: https://github.com/microsoft/onnxruntime/issues/15465
This PR just applies the workaround suggested in the thread that I and
numerous others on the thread have validated to work for them and allows
them to successfully export a PyTorch model with LSTM layers that are
dynamically quantized by ONNX.
### Motivation and Context
It is not possible to successfully export a dynamically quantized LSTM
model that I have trained for use in the onnx runtime without this
change.
Currently, this workaround lives as a local change in my python package
directory, and makes it basically impossible for anyone else at the
place I work at to successfully export the quantized model that I am
exporting.
See issue 15465: https://github.com/microsoft/onnxruntime/issues/15465
Co-authored-by: Dhruv Matani <dhruv.matani@grammarly.com>
### Description
<!-- Describe your changes. -->
The cost computation of ComputeVxAttentionScore is wrong. It should be
sequence_length * v_head_size * total_sequence_length instead of
sequence_length * v_head_size * sequence_length.
The PR also fine-tuned the cost computation.
on my local box with i9 cpu, the performance is same as unfused version,
but it is much faster on an azure vm with 16 threads.
### 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. -->
https://github.com/microsoft/onnxruntime/issues/19924
### Description
Created a new `LabelEncoderFusion` pass. This is useful in model that
result from automatic conversion tools related to data-science:
sometimes the produced model contains consecutive `LabelEncoder`-s.
To merge 2 `LabelEncoder`-s the optimizer propagates the outputs of the
first encoder through the second one.
### Motivation and Context
This enhances the capabilities of the `onnxruntime::optimizer` by fusing
consecutive `LabelEncoder` nodes.
### Fusion examples
```
Applying fusion
node1: (a,C) (b,B) (c,A) -> Default: _Unused
node2: (A,1) (B,2) (C,3) -> Default: -1
fused: (a,3) (b,2) (c,1) -> Default: -1
Applying fusion
node1: (a,C) (b,B) (c,A) -> Default: D
node2: (A,a) (B,b) (C,c) (D,d) -> Default: default
fused: (a,c) (b,b) (c,a) -> Default: d
Applying fusion
node1: (a,0) (b,1) (c,2) -> Default: -1
node2: (2,a) (1,b) (0,c) -> Default: default
fused: (a,c) (b,b) (c,a) -> Default: default
Applying fusion
node1: (a,3) (b,2) (c,1) -> Default: -1
node2: (1,a) (2,b) (3,c) -> Default: d
fused: (a,c) (b,b) (c,a) -> Default: d
```
---------
Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com>
### Description
Except [Python-CUDA-Packaging
pipeline](https://dev.azure.com/aiinfra/Lotus/_build?definitionId=1299&_a=summary),
all windows cuda packaging jobs have been running well now.
After comparison, enable_lto isn't added in the pipeline, which might be
one root cause of the random hang.
### 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
This pull request addresses several issues:
- The DML Graph's nodes were not sorted in a topologically ordered
sequence, leading to crashes during deserialization when a child node
preceded its parent node. This PR resolves this issue by implementing a
topological sorting algorithm before serialization.
- During the `RemoveUnconnectedNodes` process:
- we update `intermeidateEdge.FromNodeIndex`. Additionally, we must
update `intermediateEdge.Name` when it includes
`intermediateEdge.FromNodeIndex`, as serialization/deserialization
heavily relies on edge names.
- we also eliminate unused edges. Consequently, we must erase inputs
(now unused) from corresponding maps
`serializedGraphInputIndexToSubgraphInputIndex` and
`serializedGraphLargeConstantNameToSubgraphInputIndex`.
### Motivation and Context
Why is this change required? What problem does it solve?
There are few ONNX Zoo public models which were crashing during
deserialization.
<!-- - - If it fixes an open issue, please link to the issue here. -->
---------
Co-authored-by: Jeff Bloomfield <38966965+jeffbloo@users.noreply.github.com>
### Description
This PR adds flash attention v2 and support for INT4 CUDA benchmarking
in PyTorch.
### Motivation and Context
The [flash attention v2](https://github.com/Dao-AILab/flash-attention)
algorithm helps improve model performance in PyTorch. Support for INT4
CUDA in PyTorch is done through the
[`bitsandbytes`](https://github.com/TimDettmers/bitsandbytes) package.
### Description
<!-- Describe your changes. -->
- update axis value for per_channel quantization of QDQConv
- we should use `axis=1` for ConvTranspose operator.
### 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. -->
- this PR fixes https://github.com/microsoft/onnxruntime/issues/19694,
which I have opened
### Description
<!-- Describe your changes. -->
Previously the expert weights are in row-major. But with the updated
cutlass extension introduced by
https://github.com/microsoft/onnxruntime/pull/20108, weights are stored
in col-major that aligns with Pytorch implementation. This change fixes
the way the tensors are sliced across shards.
### 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 NPUs supporting DXCORE_ADAPTER_ATTRIBUTE_D3D12_GENERIC_ML and
D3D_FEATURE_LEVEL_1_0_GENERIC with DML EP. This also begins ingesting DX
headers through the DirectX-Headers repo.
Note that this includes an update to cgamanifest.json for onnx-tensorrt
which is triggered during re-generation due to a prior changes to
deps.txt.
### 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
<!-- Describe your changes. -->
Add API function GetAliasMap and ReleaseAliasMap in OrtCustomOp
### 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. -->
Add API function GetAliasMap and ReleaseAliasMap in OrtCustomOp
