### Description
reducemax/min have been updated in onnx(20). implement it in ort
### Motivation and Context
this is for ort1.17.0 release
---------
Signed-off-by: Liqun Fu <liqfu@microsoft.com>
### Description
dft is updated in opset20. implement it in ort
### Motivation and Context
this is for ort 1.17.0 release
Fixes#17723
---------
Signed-off-by: Liqun Fu <liqfu@microsoft.com>
### Allow layer-wise recompute
Early, we need users/developers to specify the subgraphs to recompute,
now we introduced a more user-friendly way to enable recompute for all
detected stashed activation recomputation subgraphs. This scarifies
getting the best configs while makes it easier to support user
requirements when they switches from PyTorch per-layer gradient
checkpoint to ORTModule.
`ORTMODULE_MEMORY_OPT_LEVEL` is introduced to control the usage, by
default, it is 0, e.g. `USER_SPECIFIED`, all subgraphs definedin
`ORTMODULE_MEMORY_OPT_CONFIG` will be recomputed. So this is compatible
to existing recompute usage in ORTModule integrated models.
Using `ORTMODULE_MEMORY_OPT_LEVEL=1`, we will enable all recompute plans
detected, so those configs in `ORTMODULE_MEMORY_OPT_CONFIG` will not be
respected any more.
Add Unit Tests using 3 layer blooms.
https://github.com/microsoft/onnxruntime/blob/pengwa/add_aggresive_recompute/docs/Memory_Optimizer.md
Fix a bug that can't create context binary if the model has inputs/outputs with different data type
### Description
Update EPContext op schema to unblock nodes with different data type among inputs & outputs
### Skip module clone for preparing large model export
For LLAMA2 13B, when running with Lora, DeepSpeed stage2 on 8 GPUs . It
failed during preparing outputs which will be used for
torch.onnx.export. The reason, we deep copy all the params including
both big sizes of frozen weights, + a little bit of Lora trainable
weight.
This PR will firstly check whether the GPU memmory is enough for a
cloned module, if not, skip the copy.
Copying the module is to guarantee the fw path run may change the
weight, while this case should be rare. But for now, Not-Able-To-Run is
worse than Runnable-with-A-little-bit-different-initial-weight,
especially for large models.
This PR:
- Remove unused arguments from generated triton code,
- Remove unnecessary mask for symbolic shape case from generated triton
code.
- Add doc for usage of ORTMODULE_TRITON_CONFIG_FILE.
### Description
<!-- Describe your changes. -->
Add bfloat16 support for `MatMulBnb4` contrib op. This is useful for
QLoRA fine-tuning.
- On GPUs with SM80+ (A100, etc), it uses the native cuda bfloat16
dtype, `nv_bfloat16`. On other GPUs, it uses the onnxruntime `BFloat16`
type which uses float for compute.
- I have validated the op in a llama2-7b training scenario. The losses
match pytorch training and the training throughput is better.
- Cannot add a bfloat16 case in the op unit test since casting BFloat16
to and from float multiple times during the test causes the required
tolerances to be unachievable.
The custom autograd function exporter in onnxruntime-training is updated
to support the latest version of bitsandbytes. They changed how the
`quant_state` is stored.
### 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. -->
Enable QLoRA fine-tuning with bfloat16.
### Description
<!-- Describe your changes. -->
change RotaryEmbeddings op implementation, add support for 4D input
tensor that is with shape of [batch, num_heads, seq_len, head_size].
### 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. -->
Current RotaryEmbedding op only support 3d input tensor with shape
[batch, seq_len, hidden_size]
For llamav2 model, when using FusionRotaryEmbeddings to only fuse
RotaryEmbeddings op, there will be a transpose operation for query and
key, and then the input tensor of RotaryEmbeddings becomes 4D [batch,
num_heads, seq_len, head_size].
This scenario can't be supported by current RotaryEmbeddings
implementation. So it needs to support 4D input tensor.
### Description
Implement preliminary version of local (sliding window) attention.
Currently only supported by Flash Attention (sm >= 80, Linux). Currently
only supports sliding attention with a large cached kv.
### Motivation and Context
This change enables to run Mistral and other models which use sliding
window attention.
### Description
<!-- Describe your changes. -->
1. Introduce MoE CUDA op to ORT based on FT implementation.
2. Upgrade cutlass to 3.1.0 to avoid some build failures on Windows.
Remove patch file for cutlass 3.0.0.
3. Sharded MoE implementation will come with another PR
limitation: __CUDA_ARCH__ >= 700
### 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. -->
Registers BFloat16 datatype as valid input type for CUDA Neg Kernel.
### 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>
### Tune logging experience a bit
After last time we update the ORTModule log experience, we found few
issues:
1. `INFO` level output too many things, including PyTorch exporter
verbose logs (tracing graphs) on every ranks. On this level, we only
want to
- Output a little bit more information to Users than `WARNING` level,
for example the memory recomputation recommendations or other
not-fully-ready features.
- Output a little bit more information for a quick diagnostic, collected
on rank-0 only.
2. ONNX Runtime logging filter during graph build, session init
sometimes will hide the issues (for example segement fault), there is no
useful information in `WARNING`/`INFO` for users to report to us. This
is not good!
3. Some of our devs like using `pdb` to debug Python code, but if we add
`import pdb; pdb.set_trace()` in models' code might hang when they use
`INFO` or `WARNING`, where exporter happens and all output got
redirected due to log filtering. The only workaround is to switch to
VERBOSE, which output toooooooooooo many logs.
The corresponding changes proposed here are:
1. For `INFO` logging,
- We only logs rank-0.
- We restricted the ORT backend logging level to be WARNING in this
case, because ORT backend code output way too many logs that should be
under verbose, while we cannot guarantee we can get them cleaned up
immediately once they are added.
- We output the PyTorch exporter verbose log (including tracing graph),
which is useful for a quick diagnostic when an issue happens.
2. Remove all logging filtering on ORT backend, then the segment fault
issue details will not be hidden once it happens again.
3. Introduced a `DEVINFO` logging,
- Log logs on all ranks
- Log ORT backend logging level INFO
- PyTorch exporter logging filtering are all turned OFF (to unblock the
pdb debugging).
4. Currently, to use Memory Optimizer, need use DEVINFO (which will
output ORT backend INFO log). So update memory optimizer document to
reflect this. https://github.com/microsoft/onnxruntime/pull/17481 will
update the requirement back to INFO for show memory optimization infos.
You can check
https://github.com/microsoft/onnxruntime/blob/pengwa/devinfo_level/docs/ORTModule_Training_Guidelines.md#log-level-explanations
for a better view of different log levels.
This PR also extract some changes from a bigger one
https://github.com/microsoft/onnxruntime/pull/17481, to reduce its
complexity for review.
### 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: mindest <30493312+mindest@users.noreply.github.com>
### Description
GQA now only works with Flash Attention with Attention Mask input,
allowing for batched input. Note: This PR Disables Memory Efficient
Attention, only allowing Flash Attention kernel to be used.
### Motivation and Context
Allows GQA to work with batched input.
---------
Co-authored-by: Yufeng Li <liyufeng1987@gmail.com>
This is a graph implementation of RotaryEmbedding since there's no time
to add it to DML before 1.16.2, but it eventually should move into
DirectML since we're bandwidth-bound.
### 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>
### Optimize 4bit Qlora training
Extent existing `MatmulBnb4bit` to its usage in training scenarios.
The PR includes following changes:
1. Add special `torch.autograd.Function` export logic for
`bitsandbytes.autograd._functions.MatMul4Bit` that is preferred before
common PythonOp exporter.
2. Add `training_mode` optional attribute for op `MatmulBnb4bit`, which
help skip some inference specific logic in implementation.
3. Add `transB` optional attribute, which is by default be 1; setting it
to be 0 is needed by backward usage.
Changing from `PythonOp` to this `MatmulBnb4bit` brings roughly ~2.9%
throughput gains. The reason is:
`bitsandbytes.autograd._functions.MatMul4Bit` has logic
`ctx.save_for_backward`, which would need an additional copy in
PythonOp, otherwise, the tensor might be released by ORT, while backward
op still references it.
Removing the clones also reduce the peak memory consumptions because
`bitsandbytes.autograd._functions.MatMul4Bit` saved tensors that are not
needed in backward compute.
Implement Cutlass Memory Efficient Attention Kernel into Group Query
Attention Operator.
### Motivation and Context
Before this change, Group Query Attention Operator was supported only by
Flash-Attention. While this is the most efficient kernel for the
operation, it only supports sm >= 80. Cutlass Memory Efficient Attention
Kernel supports sm >= 53, allowing us to support a broader range of GPU
hardware.
### Description
Integration to OpenVINO 2023.1
### Motivation and Context
- Alignment with latest OpenVINO Version.
- Device name change from VPUX to NPU and Remove from supported list
until official public support is available.
---------
Co-authored-by: Sahar Fatima <sfatima.3001@gmail.com>
Co-authored-by: Saurabh Kale <saurabh1.kale@intel.com>
Co-authored-by: Suryaprakash Shanmugam <suryaprakash.shanmugam@intel.com>
Co-authored-by: sfatimar <sahar.fatima@intel.com>
* 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
Improve the QNN context binary cache feature to reduce the memory
overhead and initialization time overhead.
Instead of dumping a Qnn context binary file with metadata as header, we
dump a Onnx format file with metadata inside Onnx node.
### Motivation and Context
reduce the memory overhead and initialization time overhead
### 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>
### Description
Updated a couple of old links in the technical documentation that where
pointing to files present prior to the migration to
https://onnxruntime.ai/docs.
### Introduce ZeROOffloadSubscriber for ORTModule
As part of the work: integrate ORTModule with DeepSpeed stage3, this PR
mainly focus on moving original PyTorch-based (leveraging hooks) param
partition/offload implementation to ORTModule compatible implementation.
Changes include:
1. Refactor `SubscriberBase`/`SubcriberManager` to support
pre-forward/post_forward hooks.
2. Implement new `ZeROOffloadSubscriber` by re-using DeepSpeed hook
function as much as possible. Since all hook functions are defined in
`DeepSpeedZeRoOffload._register_hooks_recursively` and
`DeepSpeedZeRoOffload.setup_zero_stage3_hooks`, and the good thing is,
the closure is not complex, all hooks are referencing the owning
`DeepSpeedZeRoOffload` instance, so we can create new hook function with
`FunctionType` by binding the owning `DeepSpeedZeRoOffload` instance,
then call the new created function in subscriber's
`pre_forward_module_apply_impl` and `post_forward_module_apply_impl`
interfaces.
3. Monkey patch `DeepSpeedZeRoOffload.setup_zero_stage3_hooks` to
register the `ZeROOffloadSubscriber` for the model, then we don't need
change any code on the DeepSpeed repo (at least so far).
4. Fix the ATen embedding custom symbolic exporter function by
tolerating weights size be (0) (changed by DeepSpeed zero stage 3).
UT will be added once stage3 is fully supported.
### 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. -->
This PR fixes broken hyperlinks in the documentation that should lead
users to Jupyter notebooks. Currently, the hyperlinks are not working as
intended. The PR resolves this issue by updating the hyperlinks to
correctly direct users to the Jupyter notebooks.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve? -->
It fixes broken hyperlinks leading to the Jupyter notebooks.
### Description
Remove the onnxruntime-extensions submodule since it now was used via
cmake FetchContent
### Motivation and Context
The submodule relies on an outdated version of the extensions, and the
build instructions should be updated to eliminate any confusion.
