### Description
The SequenceMap function-op has a graph-attribute. ORT's
constant-folding optimization may identify constant-expressions inside
the subgraph and promote them to constants, stored as initializers in
the main graph. When it does this, the optimization updates the subgraph
to remove the corresponding nodes.
When we expand a SequenceMap node by inlining its function-expansion, we
need to use this updated subgraph. However, the existing code uses the
original graph-attribute (GraphProto), instead of regenerating it from
the modified subgraph. This results in producing a graph with duplicate
definitions for the constant-folded variable, resulting in an error
during graph-resolve.
This PR fixes this issue (just a single line fix), and adds a test-case
to cover this scenario.
---------
Signed-off-by: Ganesan Ramalingam <grama@microsoft.com>
Co-authored-by: Suryaprakash Shanmugam <suryaprakash.shanmugam@intel.com>
### Description
- Fix check for Softmax with axis attributes not equal to -1. QNN EP
only supports axis values equal to -1 (or rank - 1).
- Explicit error when Reduce* ops have an input with rank > 4 on HTP
backend (unsupported).
- Correctly filter out partitions that only contain a single
QuantizeLinear or DequantizeLinear node.
- Add tests for the above and clean up unnecessary usage of test
description labels.
### Motivation and Context
Make it easier to debug why a model may not be supported.
### Description
Introduce `Float16/BFloat16` support for C# and C++ APIs.
User should be able to perform conversions from `float` to/from
`Float16/BFloat16`, compare values and tests for `NaN, Inifnity, and
whether the number is denormalized.`
### Motivation and Context
User filed issues such as:
https://github.com/microsoft/onnxruntime/issues/14303
0(1) Fix a bug in https://github.com/microsoft/onnxruntime/pull/16560
that UNet shall be set fp16 flag.
(2) Remove wget in requirements since it is no longer needed.
(3) Add benchmark numbers in A100-PCIE-80GB. Note that CUDA EP have
issue to run in batch size 4 so the number is not added.
### Description
- Add hipBLASLt tuning logic in place of default hipBLASLt
implementation;
- add kernel explorer for hipBLASLt.
related operators: Gemm, StridedBatchedGemm, and GemmFastGelu.
Temporarily mark algos that require extra workspace as unsupported.
Will add workspace support in later PR, which will change Gemm Params
def and affect multiple files.
### Description
<!-- Describe your changes. -->
Delete second reference to onnxruntime_api_tests_without_env in the code
coverage commands. One was removed in #16373 and the duplicate wasn't
noticed.
### 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. -->
Fix pipeline.
### Description
fix file size trim for wasm only .min.js
minimal build `ort.wasm.min.js` and `ort.wasm-core.min.js` should
exclude JSEP related source code.
### Description
1. Replacing AMX intrinsics with machine code macros in QGEMM kernel.
2. Removing AMX build flags for GCC in cmake file.
3. Fixing the link time optimization (LTO) issue introduced with asm
.include of an assembly file.
I have moved the AMX instruction macro definitions from
QgemmU8S8KernelAmxCommon.S to the amx_common.h to fix the LTO issue.
Note that I am also pushing the macros defined in
QgemmU8S8KernelAmxCommon.S for future reference.
A special thanks to @laxmansole who helped in the development of the
instruction macro definitions for AMX intrinsics and fixing the LTO
issue.
### Motivation and Context
The additional AMX flag in cmake adds an extra layer of dependency on
GCC version to use the feature.These changes should allow the usage of
the AMX feature with just the CPU ID check.
### Description
- Update existing rocBLAS get_solutions API using
`*_get_solutions_by_type` (supported from ROCm5.6); remove the original
nested TunableOp logic.
- Update kernel_explorer.
This PR mainly optimize ROCm CI test to reduce time and CPU utilization.
- use smaller batch size on strided_batched_gemm/batched_gemm test
- disable cpu training test
- fix test_e2e_padding_elimination Occasional failures on ROCm.
Allow `GemmSoftmaxGemmPermuteGenericPipeline<T>` to be used in some
cross attention, that opt for rocblas instead of ck if rocblas is
better to the small problem. The improvement is ~20% e2e time reduction
on some test cases for whisper large.
**Note:** This is because ck has some performance issue if the sequence
length is merely 1, and should be improved in the future.
### Description
Use pipeline cache instead of reading data from the image.
### Motivation and Context
1. To reduce the browser dependency of custom image.
2. The onnx node test data is less than 30M and the cache download time
is very short.
### Description
<!-- Describe your changes. -->
As title.
Validation at JS call level in E2E app is not included. Can cover
together in a separate pr.
### 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. -->
Test coverage.
---------
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: rachguo <rachguo@rachguos-Mac-mini.local>
### Description
<!-- Describe your changes. -->
This PR adds support for rotary embeddings in decoder masked
self-attention
### 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: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net>
### Use autograd_inlining for model export
From some versions of PyTorch, there is an issue related to custom
autograd.Function inlining, even though we register custom export
function for the autograd.Function (e.g. when custom autograd function
is enabled).
As an options, PyTorch exporter adds a new flag during export, we can
disable the inline. https://github.com/pytorch/pytorch/pull/104067
Currently the PyTorch change is in nightly built, this PR dynamically
check the torch.onnx.export's signature and decide to use the
`autograd_inlining` when it exists.
### 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
allow creating (u)int64 tensors from either a number array or a bigint
array.
before:
```js
// TypeScript think is good, but actually does not work
// runtime error: Uncaught TypeError: Cannot convert 1 to a BigInt
const myTensor1 = new Tensor('int64', [1, 2, 3, 4], [2, 2]);
// runtime good, but TypeScript thinks myTensor2 is a string tensor
const myTensor2 = new Tensor('int64', [1n, 2n, 3n, 4n], [2, 2]);
```
after:
```js
// both work at runtime and TypeScript populates the correct types
const myTensor1 = new Tensor('int64', [1, 2, 3, 4], [2, 2]);
const myTensor2 = new Tensor('int64', [1n, 2n, 3n, 4n], [2, 2]);
```
### Description
The [ONNX
standard](https://github.com/onnx/onnx/blob/main/docs/Operators.md#type-constraints-181)
permits the `Unique` operator to have `double` input tensor element
type, however this was not supported in onnxruntime. This PR enables
this kernel.
### Motivation and Context
The lack of support for `float64` forces users currently to cast to
`float32` instead. This loss of precision can be severely problematic in
feature engineering pipelines downstream of the `Unique` operator. It
would be good to prevent this by updating ORT to reflect the standard
and support `double` input tensors.
---------
Signed-off-by: Aditya Goel <agoel4512@gmail.com>
### Description
<!-- Describe your changes. -->
Add ort_value.h to session_options.h so OrtValue is defined.
Update a unit test binary to add required include paths. Adding
ort_value.h pulls in more data type headers.
### 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. -->
#16193
### Description
This change reduces the number of calls to globby functions so that it
accelerates the initialization for 'npm test' with suite0/1 tests from
~14sec to <2sec.
Fix Orttraining Linux Lazy Tensor CI
Orttraining Linux Lazy Tensor CI is broken.
The error message is
AttributeError: 'OnnxRegistry' object has no attribute 'register'
### Description
Added Expand operator support.
### 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. -->
Simply add double quotes to prevent there is spaces in the path
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
As if there are spaces in path the bat cannot run, error would occurs.
So with a simple double quotes can fix these problems
- If it fixes an open issue, please link to the issue here. -->
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Log ORTModule initialization overhead
When profiling some model for example
```
torchrun --nproc_per_node=1 examples/onnxruntime/training/language-modeling/run_mlm.py --model_name_or_path microsoft/deberta-v3-large --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --num_train_epochs 10 --per_device_train_batch_size 1 --per_device_eval_batch_size 1 --do_train --overwrite_output_dir --output_dir ./outputs/ --seed 1137 --fp16 --report_to none --optim adamw_ort_fused --max_steps 200 --logging_steps 1 --use_module_with_loss
{'train_runtime': 303.8711, 'train_samples_per_second': 0.658, 'train_steps_per_second': 0.658, 'train_loss': 6.569518616199494, 'epoch': 0.09}
100%|200/200 [05:03<00:00, 1.52s/it]
***** train metrics *****
epoch = 0.09
train_loss = 6.5695
train_runtime = 0:05:03.87
train_samples = 2223
train_samples_per_second = 0.658
train_steps_per_second = 0.658
```
The end to end time is 303s (train_runtime=0:05:03.87), but the
ORTModule first step initialization (including export, graph build, etc)
takes about 255s, so when we compare the end to end time for a baseline
ORT with an improved version of ORT, there is no perf gains, since the
x% gains over (303-255) is diluted out among the overall 303s. This is
misleading!
So this PR outputs the ORTModule initialization overhead in the output,
then we can manually compute the real compte time and get the perf
gains.
If the log level is >= WARNING, then only the total end to end time +
export time is logged, otherwise, more details of break down is logged:
### Description
Add support for Op InstanceNormalization and GroupNormalization via MeanVarianceNormalization.
### Motivation and Context
Enable more models like Olive'ified SD unet to run on WebNN EP.
### Give user warnings if nondeterministic kernels got called when
Deterministic flag is set
When we do accuracy investigation (for example training convergence
issue debug), usually we will set `use_deterministic_compute ` to be
true.
```
SessionOptions sess_options;
sess_options.use_deterministic_compute = true;
```
While in recent investigation, it is found GatherElementsGrad kernel
(who used atomic add) generate non-deterministic results, making a
deberta model ouput pretty different loss curve every time we run it
even we fix the seed, remove the dropout ratio, and set
use_deterministic_compute to be true. It turned out to be an expected
problem if we do the add in different order by cuda threads. The order
cannot be guaranteed.
So this PR will give warnings when users set `use_deterministic_compute
`, but some kernels don't have determinstic kernel impl, has to run with
non-determinstic impls. This would at least let users know the results
is not determinstic though that flag is set to be True.
Only print the message once in case it floods training logs.
### Description
This PR includes documentation updates, providing step-by-step
instructions on how to implement the ModuleWithLoss wrapper in a
different codebase.
The documentation outlines the necessary code changes and offers
customization options based on specific requirements.
---------
Co-authored-by: Adam Louly <adamlouly@microsoft.com@orttrainingdev9.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
### Description
Add Stable Diffusion Text2Image pipelines of TensorRT EP and CUDA EP.
They can automatically export and optimize ONNX model, and create
ONNXRuntime session to use TensorRT EP or CUDA execution provider.
Add support for benchmarking TensorRT.
Add support of cuda graph. The feature is only supported in nightly
package right now.
Engine/Provider to test | command line
---- | ---
CUDA EP | `python benchmark.py -v 1.5`
CUDA EP with cuda graph | `python benchmark.py -v 1.5
--enable_cuda_graph`
TensorRT EP | `python benchmark.py -v 1.5 -r tensorrt`
TensorRT EP with cuda graph | `python benchmark.py -v 1.5 -r tensorrt
--enable_cuda_graph`
TensorRT | `python benchmark.py -v 1.5 -e tensorrt`
Add benchmark numbers of T4 GPU using CUDA 11.7, cuDNN 8.5, PyTorch
1.13.1+cu11.7, TensorRT 8.6.1, onnxruntime-gpu 1.15.1 (or
ort-nightly-gpu 1.16 for cuda graph).
TODO: add benchmark numbers of A100-80GB
### 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. -->
kernel explorer has lots of tests and need numpy to verify the results
of GPU kernels, it will make CPU utilization very high. This PR use
`cupy ` to replace `numpy` to do compute on GPU to reduce CPU
utilization.
set `KERNEL_EXPLORER_TEST_USE_CUPY=1` to enable cupy.
