* graph tools update
* cuda kernel update
* operator spec update and implementation update
* greed search bug fix on wrong assumption for cross/self attention
input length
* avoid use of "" name in value info when loading graph which
historically in many model
### Description
<!-- Describe your changes. -->
Add dml registration for bitwise and, or, xor and not added in opset 18.
### 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: Linnea May <linneamay@microsoft.com>
### Description
In some scenarios, the triton written kernels are more performant than
CK or other handwritten kernels, so we implement a framework that
onnxruntime can use these triton written kernels.
This PR is to integrate triton into ort, so that ort can use kernels
that written and compiled by triton.
The main change focus on two part:
1. a build part to compile triton written kernel and combine these
kernels into libonnxruntime_providers_rocm.so
2. a loader and launcher in c++, for loading and launch triton written
kernels.
#### Build
To compile triton written kernel, add a script
`tools/ci_build/compile_triton.py`. This script will dynamic load all
kernel files, compile them, and generate `triton_kernel_infos.a` and
`triton_kernel_infos.h`.
`triton_kernel_infos.a` contains all compiled kernel instructions, this
file will be combined into libonnxruntime_providers_rocm.so, using
--whole-archive flag.
`triton_kernel_infos.h` defines a const array that contains all the
metadata for each compiled kernel. These metadata will be used for load
and launch. So this header file is included by 'triton_kernel.cu' which
defines load and launch functions.
Add a build flag in build.py and CMakeList.txt, when building rocm
provider, it will call triton_kernel build command, and generate all
necessary files.
#### C++ Load and Launch
On c++ part, we implement load and launch functions in triton_kernel.cu
and triton_kernel.h.
These two files located in `providers/cuda`, and when compiling rocm,
they will be hipified. so this part supports both cuda and rocm. But
currently we only call triton kernel in rocm.
We also implement a softmax triton op for example. Because there will
generate many kernels for different input shape of softmax, we use
TunableOp to select the best one.
### 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. -->
Register Split18 for DirectML
Split13 was previously implemented. Split18 adds a new attribute called
"num_outputs" that must be used mutually exclusively with the "split"
input.
The "num_outputs" attribute wil split the tensor evenly (and handles odd
uneven splits). To implement, the DML split tensor just needs to be
overridden in the presence of the num_output attribute.
---------
Co-authored-by: Dwayne Robinson <dwayner@microsoft.com>
### Description
This PR enables Whisper's multitask format and allows a user to use
Whisper for multiple tasks (e.g. transcription, translation) and for
multilingual purposes (e.g. English, Spanish). This PR also removes
`attention_mask` as a required input for Whisper with beam search.
### Usage
Here is an example of how you can use Whisper for English transcription.
```
import numpy as np
import onnxruntime as ort
from datasets import load_dataset
from transformers import AutoConfig, AutoProcessor
model = "openai/whisper-tiny"
config = AutoConfig.from_pretrained(model)
processor = AutoProcessor.from_pretrained(model)
forced_decoder_ids = processor.get_decoder_prompt_ids(language="english", task="transcribe")
# forced_decoder_ids is of the format [(1, 50259), (2, 50359), (3, 50363)] and needs to be
# of the format [50258, 50259, 50359, 50363] where 50258 is the start token id
forced_decoder_ids = [config.decoder_start_token_id] + list(map(lambda token: token[1], forced_decoder_ids))
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
input_features = processor(ds[0]["audio"]["array"], return_tensors="np").input_features
inputs = {
"input_features": np.float32(input_features),
"max_length": np.array([26], dtype=np.int32),
"min_length": np.array([1], dtype=np.int32),
"num_beams": np.array([2], dtype=np.int32),
"num_return_sequences": np.array([1], dtype=np.int32),
"length_penalty": np.array([1.0], dtype=np.float32),
"repetition_penalty": np.array([1.0], dtype=np.float32),
"decoder_input_ids": np.array([forced_decoder_ids], dtype=np.int32),
}
sess = ort.InferenceSession("whisper-tiny_beamsearch.onnx", providers=["CPUExecutionProvider"])
outputs = sess.run(None, inputs)
# Print tokens and decoded output
print(outputs[0][0][0])
print(processor.decode(outputs[0][0][0]))
```
If you don't want to provide specific decoder input ids or you want
Whisper to predict the output language and task, you can set
`forced_decoder_ids = [config.decoder_start_token_id]` instead.
### Motivation and Context
As seen in the figure below from the [OpenAI Whisper
paper](https://cdn.openai.com/papers/whisper.pdf), Whisper can be used
for multiple tasks and languages.
### Description
<!-- Describe your changes. -->
V100, b_4_s_128, max_output_len=64, beam=4
before:
t5_small: 101.28ms
t5_base: 200.07ms
after:
t5_small: 87.65ms
t5_base: 174.44ms
### 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>
Register CPU OptionalGetElement, OptionalHasElement on DirectML
Graphs with OptionalGetElement and OptionalHasElement should work in a
DML graph without extra memcpy operation on and off the GPU.
CopyCpuTensor is swapped with DataTransferManager.CopyTensor() to make
the CPU operator usable by other providers.
---------
Co-authored-by: Dwayne Robinson <dwayner@microsoft.com>
### Description
<!-- Describe your changes. -->
Add registration for DML RoiAlign-16 and tests for new
coordinate_transform_mode attribute. PR
[7354](https://github.com/microsoft/onnxruntime/pull/7354) is still open
to fix the CPU EP version, which is why there are skipped tests right
now. That will be completed separately so that, for now, we can
officially support opset16 with the next release.
### 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: Linnea May <linneamay@microsoft.com>
Co-authored-by: Dwayne Robinson <dwayner@microsoft.com>
### Description
- Update DML version to 1.11.0
- Disable Gemm+Softmax fusion
### 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
1. Update VERSION_NUMBER for preparing the upcoming release. This PR's
commit will not be included in the 1.15 release branch
2. Delete package/rpm/onnxruntime.spec since it was not used in past
years.
### Motivation and Context
Preparing the release.
Fixed
[AB#15311](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15311)
### Description
<!-- Describe your changes. -->
Add registration for DML reduce functions in opset 18.
### 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: Linnea May <linneamay@microsoft.com>
### Description
This PR changes an EmbedLayerNormalization node's mask index output to
be an optional output if a mask input is not provided.
### Motivation and Context
The documentation for EmbedLayerNormalization states
```
The last input mask is optional. If mask is provided, mask index (that is position of first 0 in mask, or number of words) will be calculated.
```
However, if the mask input is not provided, the mask index output is
still calculated and required.
### Description
The PR adds VPU support to OpenVINO Execution Provider
Bug fixes for GPU, CPU.
Changes to OpenVINO Backend in Serialized Model API for faster First
Inference Latency.
Deprecation to HDDL-VADM and MYRIAD, removed code
Support OpenVINO 2023.0
Dynamic Shapes Support for iGPU
### Motivation and Context
- VPU is an upcoming hardware that can provide AI Acceleration for
Client Systems through OpenVINO
- If it fixes an open issue, please link to the issue here. -->
---------
Signed-off-by: MaajidKhan <n.maajid.khan@intel.com>
Co-authored-by: Suryaprakash Shanmugam <suryaprakash.shanmugam@intel.com>
Co-authored-by: MaajidKhan <n.maajid.khan@intel.com>
Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com>
### Description
This PR contains fusion-level and kernel-level optimizations for
[OpenAI's Whisper](https://github.com/openai/whisper).
Some of the added optimizations include:
- Pruning of duplicate/unnecessary inputs and outputs
- Fusion support for Whisper models with or without these inputs/outputs
(e.g. with these inputs/outputs if exporting with an older official
Optimum version, without these inputs/outputs if exporting with Optimum
from source)
- Attention fusions
- For Whisper's encoder and decoder
- Modified symbolic shape inference for present output when no past
input exists (for decoder)
- Multi-head attention fusions
- For Whisper's decoder and decoder with past
- Packed MatMul for the 3 MatMuls excluded in multi-head attention
fusion
- Attention kernel changes
- CPU:
- Different Q and KV sequence lengths
- Parallel memset for large sequence lengths
- Convert broadcast add after MatMul of Q and K (add_qk) to element-wise
add
- Separate present key-value output into present key and present value
(for multi-head attention spec)
- CUDA:
- Use memory efficient attention compute kernel with present state (for
decoder)
- Multi-head attention kernel changes
- CPU:
- Introduction of multi-head attention CPU kernel (previously did not
exist)
- Use AddBiasReshape instead of AddBiasTranspose when sequence length =
1 (for decoder with past)
- Different Q, K, V input shapes
- Pass past key and past value directly as key and value
- CUDA:
- Use memory efficient attention compute kernel with past and/or present
state (for decoder with past)
### Usage
To use the optimizations, run the ORT transformer optimizer script as
follows:
```
$ cd onnxruntime/onnxruntime/python/tools/transformers/
$ python3 optimizer.py --input <filename>.onnx --output <filename>.onnx --model_type bart --num_heads <number of attention heads, depends on the size of the whisper model used> --hidden_size <attention hidden size, depends on the size of the whisper model used> --use_external_data_format --use_multi_head_attention
```
Once optimized, here's an example of how to run Whisper with [Hugging
Face's Optimum](https://github.com/huggingface/optimum):
```
from transformers.onnx.utils import get_preprocessor
from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
from optimum.pipelines import pipeline as ort_pipeline
import whisper # Installed from OpenAI's repo - setup instructions at https://github.com/openai/whisper/
directory = './whisper_opt' # Where the optimized ONNX models are located
model_name = 'openai/whisper-tiny'
device = 'cpu'
# Get pipeline
processor = get_preprocessor(model_name)
model = ORTModelForSpeechSeq2Seq.from_pretrained(
directory,
use_io_binding=(device == 'cuda'),
provider='CPUExecutionProvider',
).to(device)
pipe = ort_pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
device=(-1 if device == 'cpu' else 0),
)
# Load audio file and run pipeline
audio = whisper.load_audio('tests/jfk.flac')
audio = whisper.pad_or_trim(audio)
outputs = pipe([audio])
print(outputs)
```
Note: In order to use these changes with Optimum, it is recommended to
use Optimum from source to have the following changes:
- https://github.com/huggingface/optimum/pull/872
- https://github.com/huggingface/optimum/pull/920
### Motivation and Context
This PR helps the following issues:
- https://github.com/microsoft/onnxruntime/issues/15100
- https://github.com/microsoft/onnxruntime/issues/15235
- https://github.com/huggingface/optimum/issues/869 (work in progress)
This PR can be used with the other currently merged Whisper PRs:
- https://github.com/microsoft/onnxruntime/pull/15247
- https://github.com/microsoft/onnxruntime/pull/15339
- https://github.com/microsoft/onnxruntime/pull/15362
- https://github.com/microsoft/onnxruntime/pull/15365
- https://github.com/microsoft/onnxruntime/pull/15427
This PR uses changes from the following merged PRs:
- https://github.com/microsoft/onnxruntime/pull/14198
- https://github.com/microsoft/onnxruntime/pull/14146
- https://github.com/microsoft/onnxruntime/pull/14201
- https://github.com/microsoft/onnxruntime/pull/14928 (this introduced
the new multi-head attention spec)
### Description
Bump ruff version in CI and fixed new lint errors.
- This change enables the flake8-implicit-str-concat rules which helps
detect unintended string concatenations:
https://beta.ruff.rs/docs/rules/#flake8-implicit-str-concat-isc
- Update gitignore to include common python files that we want to
exclude.
### Motivation and Context
Code quality
### 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
Adding 'Add' functionality to FP16 Conv operator. It takes a tensor that
has the same shape of the output tensor, and add it to the result
tensor.
### Motivation and Context
Needed to run Resnet 50
### Description
Adjust various code paths to allow Whisper model to function with
BeamSearch op.
Approach: Add a new kModelType enum value in IGenerationParameters as
so:
#### Old: 0 = GPT2, 1 = T5
#### New: 0 = GPT2, 1 = T5, 2 = Whisper
When the user assigns this attribute value to 2, various shape and type
checks are changed to accommodate Whisper inputs.
### Motivation and Context
BeamSearch is currently designed to function with BERT-based models with
inputs as vocab tokens, and needs changes to function with Whisper
inputs (3-D float values processed from audio data).
---------
Co-authored-by: Peter McAughan <petermca@microsoft.com>
### Description
<!-- Describe your changes. -->
Add a tool to convert fused BERT like model to packing mode
### 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
`lintrunner` is a linter runner successfully used by pytorch, onnx and
onnx-script. It provides a uniform experience running linters locally
and in CI. It supports all major dev systems: Windows, Linux and MacOs.
The checks are enforced by the `Python format` workflow.
This PR adopts `lintrunner` to onnxruntime and fixed ~2000 flake8 errors
in Python code. `lintrunner` now runs all required python lints
including `ruff`(replacing `flake8`), `black` and `isort`. Future lints
like `clang-format` can be added.
Most errors are auto-fixed by `ruff` and the fixes should be considered
robust.
Lints that are more complicated to fix are applied `# noqa` for now and
should be fixed in follow up PRs.
### Notable changes
1. This PR **removed some suboptimal patterns**:
- `not xxx in` -> `xxx not in` membership checks
- bare excepts (`except:` -> `except Exception`)
- unused imports
The follow up PR will remove:
- `import *`
- mutable values as default in function definitions (`def func(a=[])`)
- more unused imports
- unused local variables
2. Use `ruff` to replace `flake8`. `ruff` is much (40x) faster than
flake8 and is more robust. We are using it successfully in onnx and
onnx-script. It also supports auto-fixing many flake8 errors.
3. Removed the legacy flake8 ci flow and updated docs.
4. The added workflow supports SARIF code scanning reports on github,
example snapshot:
5. Removed `onnxruntime-python-checks-ci-pipeline` as redundant
### 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. -->
Unified linting experience in CI and local.
Replacing https://github.com/microsoft/onnxruntime/pull/14306
---------
Signed-off-by: Justin Chu <justinchu@microsoft.com>
### Description
<!-- Describe your changes. -->
As synced offline, rename this op and will create another op for mha
that supports both self and cross 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>
### Description
<!-- Describe your changes. -->
1. upgrade cutlass to 3.0 that containing attn_bias support.
2. extend Attention/MHA to use memory efficient attention when
rel_pos_bias with [1, num_head, s, s*] and 1d mask with [2 * batch_size
+ 1] are present.
new mask format introduction:
MASK_1D_KEY_SEQ_LEN_START,
[3 * batch_size + 2] with [key_len[0], ..., key_len[batch_size - 1],
query_start[0], ..., query_start[batch_size - 1], query_end[batch_size -
1], key_start[0], ..., key_start[batch_size - 1], key_end[batch_size -
1]]
e.g
2D mask with [[1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 0]] converts to this
1D mask is [3, 5, 0, 6, 12, 0, 6, 12]
### 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. -->
It potentially benefits tnlrv6 and t5(encoder)
---------
Co-authored-by: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net>
Co-authored-by: Kunal Vaishnavi <kvaishnavi@microsoft.com>
Co-authored-by: Kunal Vaishnavi <kvaishnavi@microsoft.com@orttrainingdev7.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
