### Description
<!-- Describe your changes. -->
Adds bfloat16 as a valid input parameter type for where node for ONNX
opset 16+.
### 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. -->
Enabling `meta-llama/Llama-2-70b` to be finetuned with ONNX Runtime
training.
---------
Co-authored-by: Prathik Rao <prathikrao@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
* Add a new operator SkipGroupNorm to support skip and bias inputs.
* Update GroupNorm kernel to support number of channels used in SD XLrefiner.
* Add epsilon in kernel
* Add parity and performance test script
* Remove many limitations including max batch size, max number of groups, c % cPerBlock ==0 etc.
### Motivation and Context
Update GroupNorm to support SD XL Refiner and beyond.
### Description
Add support for Gemm with float 8 as a contrib op.
---------
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev9.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
### Description
Opset 18 apply the "axes as input" change from ReduceSum to all the
other reduce ops. Our cuda kernel actually support it, but we didn't
enable it for opset18. This PR update the reduce ops' kernel
registration to enable the "axes as input" behavior for opset18.
As part of the fix, I also simplify the reduce op kernel registration
part. ORT doesn't require the kernel definition need to be exactly the
same as onnx op definition. For our case, which we share the same kernel
for all the reduce ops (from version 1 to version 18), we don't need to
maintain different version of kernel definitions. we can simplify it by
just using a single kernel definition for multiple versions. Although
for some cases, we might register more types for legacy versions, but it
is harmless. Framework is using schema to validate the graph, not kernel
definition.
---------
Co-authored-by: Cheng Tang <chenta@a100.crj0ad2y1kku1j4yxl4sj10o4e.gx.internal.cloudapp.net>
Co-authored-by: Cheng Tang <chenta@microsoft.com>
### Description
Add a contrib op MatMulBnb4 (FP4 and NF4) and related toolchain to
support quantization on weight.
This PR adds:
- schema for contrib op MatMulBnb4 which can support FP4 (4-bit floating
point) and NF4 (4-bit NormalFloat) quantization on weight.
- a naive implementation for MatMulBnb4 on CPU and GPU, i.e.,
implemented like MatMul(A, Dequantize(B)).
- a special implementation for GemV for MatMulBnb4 and related benchmark
tool.
- tool to quantize model to FP4 or NF4.
### Description
<!-- Describe your changes. -->
Add a contrib op MatMulNBits and related toolchain to support
quantization on weight. This PR only adds support for 4bits. It:
- add schema for contrib op MatMulNBits which can support 1-7 bits
quantization on weight.
- a naive implementation for 4bits MatMulNBits on CPU and GPU, i.e.,
implemented like MatMul(A, Dequantize(B)).
- a special implementation for GemV for 4bits MatMulNBits and related
benchmark tool
- tool to quantization model with 4bits.
Next:
- add general and more efficient kernels for 4bits MatMulNBits on CPU
and GPU
Support cross qk in beam search for whisper model and related features
Make whisper exporting tools support cross qk and some related features,
* extra_decoding_ids
* no_speech_prob
Implement DTW kernel, unfold tensor kernel with unit test Several fix
related with multiple session running parallel, like:
* guard multihead_attention, fused_fp16_runner_
* some memory allocation with stream awareness
* add use_ep_level_unified_stream option
### Description
this is for ORT 1.17.0 - make ORT to use ONNX release 1.15.0 branch. Eventually will update to the release tag once ONNX 1.15.0 is released
### Motivation and Context
Prepare for ORT 1.17.0 release. People can start work on new and updated ONNX ops in ORT.
---------
Signed-off-by: Liqun Fu <liqfu@microsoft.com>
Being able to leverage I/O binding for DML and registering `If` for the
DML EP allows us to avoid copying the past/present key/values back and
forth between the CPU and the GPU after every token.
This gives us a 25% performance increase for Dolly V2 with 128 tokens on
an RTX 4090.
### 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>
This fixes the type lists used to register DML kernels for Microsoft
domain QuantizeLinear and DequantizeLinear. These previously did not
include FP16 and incorrectly used the same type list for both operators.
The new type lists are the same as opset 19 ONNX which aren't
implemented yet in the DML EP.
### Description
The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ
as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA
API to cast float/half to float8 if CUDA>=11.8, a custom implementation
if CUDA<11.8.
* It implements, Cast, QuantizeLinear, DequantizeLinear for all types on
CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA.
* It extends the supported types for control flow operator, Shape,
Reshape, Identity, If, Loop, Scan, Reshape
* It implements Equal(19).
* Cast, QuantizeLinear, DequantizeLinear operators now support a
parameter `saturate` only valid for float 8 types. It is true by
default. In that case, any value out of range is converted into the
maximum float 8 value. If false, it is infinite.
* QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA
(and ROCm by extension), scale = 1D tensor with one scale per channel
### Motivation and Context
Supports latest onnx version.
Fixes
[AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395)
---------
Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
### Description
<!-- Describe your changes. -->
Pad18 adds the `axes` input, which is used to indicate what axes the
padding values should be applied to. Add logic to manipulate paddings
into DML padding operator inputs.
### 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>
* 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>
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.
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
<!-- 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 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
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>
