### Description
update with ONNX 1.16.0 branch according to
https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md
ONNX 1.16.0 release notes:
https://github.com/onnx/onnx/releases/tag/v1.16.0
#### Updated ops for CPU EP:
- DequantizeLinear(21)
- Added int16 and uint16 support + various optimizer tests
- Missing int4 and uint4 support
- Missing block dequantization support
- QuantizeLinear(21)
- Added int16 and uint16 support + various optimizer tests
- Missing int4 and uint4 support
- Missing block quantization support
- Cast(21)
- Missing int4 and uint4 support
- CastLike(21)
- Missing int4 and uint4 support
- ConstantOfShape(21)
- Missing int4 and uint4 support
- Identity(21)
- Missing int4 and uint4 support
- If(21)
- Missing int4 and uint4 support
- Loop(21)
- Missing int4 and uint4 support
- Reshape(21)
- Missing int4 and uint4 support
- Scan(21)
- Missing int4 and uint4 support
- Shape(21)
- Missing int4 and uint4 support
- Size(21)
- Missing int4 and uint4 support
- Flatten(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Pad(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Squeeze(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Transpose(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Unsqueeze(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
#### Unimplemented opset 21 features/ops
- int4 and uint4 data type
- QLinearMatMul(21)
- GroupNormalization(21)
- ai.onnx.ml.TreeEnsemble(5)
### 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. -->
### Disabled tests
#### ORT Training
orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py
- test_ort_custom_ops: Potential shape inference bug for custom ops
#### Python quantization unit tests
test/onnx/python/quantization (shape inference bug)
- test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16
- test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16
- test_op_gemm.py: test_quantize_qop_gemm_s8s8
- test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same
- test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3
- test_op_matmul.py: test_quantize_matmul_u8u8_f16
- test_op_matmul.py: test_quantize_matmul_s8s8_f16
- test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy
- test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile
- test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution
- test_op_relu.py: test_quantize_qop_relu_s8s8
#### ONNX tests
- test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a
maxpool output size bug and added this test. Enable this test when [ORT
PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged.
Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741).
- test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op
ai.onnx.ml.TreeEnsemble
- test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same
- test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same
- test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same
- test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4
yet
- test_cast_INT4_to_INT8_cpu: same
- test_cast_UINT4_to_FLOAT_cpu: same
- test_cast_UINT4_to_UINT8_cpu: same
- test_cast_INT4_to_FLOAT_cuda
- test_cast_INT4_to_INT8_cuda
- test_cast_UINT4_to_FLOAT_cuda
- test_cast_UINT4_to_UINT8_cuda
- test_constantofshape_float_ones_cuda: ConstantOfShape(21) not
implemented for cuda
- test_constantofshape_int_shape_zero_cuda: same
- test_constantofshape_int_zeros_cuda: same
- test_flatten_axis0_cuda: Flatten(21) not implemented for cuda
- test_flatten_axis1_cuda: same
- test_flatten_axis2_cuda: same
- test_flatten_axis3_cuda: same
- test_flatten_default_axis_cuda: same
- test_flatten_negative_axis1_cuda: same
- test_flatten_negative_axis2_cuda: same
- test_flatten_negative_axis3_cuda: same
- test_flatten_negative_axis4_cuda: same
- test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not
implemented in ORT yet
- test_qlinearmatmul_2D_int8_float32_cpu: same
- test_qlinearmatmul_2D_uint8_float16_cpu: same
- test_qlinearmatmul_2D_uint8_float32_cpu: same
- test_qlinearmatmul_3D_int8_float16_cpu: same
- test_qlinearmatmul_3D_int8_float32_cpu: same
- test_qlinearmatmul_3D_uint8_float16_cpu: same
- test_qlinearmatmul_3D_uint8_float32_cpu: same
- test_qlinearmatmul_2D_int8_float16_cuda: same
- test_qlinearmatmul_2D_int8_float32_cuda: same
- test_qlinearmatmul_2D_uint8_float16_cuda: same
- test_qlinearmatmul_2D_uint8_float32_cuda: same
- test_qlinearmatmul_3D_int8_float16_cuda: same
- test_qlinearmatmul_3D_int8_float32_cuda: same
- test_qlinearmatmul_3D_uint8_float16_cuda: same
- test_qlinearmatmul_3D_uint8_float32_cuda: same
- test_size_cuda: Size(21) not implemented for cuda
- test_size_example_cuda: same
- test_dequantizelinear_blocked: Missing implementation for block
dequant for DequantizeLinear(21)
- test_quantizelinear_blocked_asymmetric: Missing implementation for
block quant for QuantizeLinear(21)
- test_quantizelinear_blocked_symmetric: Missing implementation for
block quant for QuantizeLinear(21)
---------
Signed-off-by: liqunfu <liqun.fu@microsoft.com>
Signed-off-by: Ganesan Ramalingam <grama@microsoft.com>
Co-authored-by: Ganesan Ramalingam <grama@microsoft.com>
Co-authored-by: George Wu <jywu@microsoft.com>
Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
Although DML doesn't have a "fast" gelu approximation operator, its
standard GELU operator is still faster than having to combine all the
separate elementwise operators from different ops.
### 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
<!-- Describe your changes. -->
1. Introduce latest cutlass extension from TRTLLM that gives us cutlass
upgrade(to 3.4) opportunity from MoE side.
2. Fix Windows build issue
3. Add Int4 MoE op and ut
### 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. -->
1.Support Tensor Parallelism in ShardedMoE.
2.Make necessary code changes to support Mixtral MoE.
3.Fix a bug related to using IOBinding in test script.
4.Fix the input size limitation
### 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 and enable few ORTModule Unit Tests
Fix 'test_bert_inputs_with_dynamic_shape' and
'test_bert_result_with_layerwise_recompute' generate Nan loss in ORT
run.
The root cause is, the logic to generatic attention mask test data is
not correct, only 0 or 1 is allowed in the dataset, but we see lots of
other numbers. ( The reason we don't have this using old version of
transformers for example v4.4.2 or 4.16.2 is because they don't contains
such
d3cb28886a,
which increase the scaling to a bigger number, causing a overflow to
inf)
Another improvement during the investigation using convergence tools:
Don't dump the activations during model export phase, otherwise, the
dumped data might contains some PyTorch run's result making us confused
during comparing with stock PyTorch run results.
### 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
DML Implementation for [com.microsoft.DynamicQuantizeMatMul
](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.DynamicQuantizeMatMul)
```
.\onnxruntime_test_all.exe --gtest_filter="*DynamicQuantizeMatMul.*"
Note: Google Test filter = *DynamicQuantizeMatMul.*
[==========] Running 10 tests from 1 test suite.
[----------] Global test environment set-up.
[----------] 10 tests from DynamicQuantizeMatMul
[ RUN ] DynamicQuantizeMatMul.HasZeroPoint_NoBias_test_S8
[ OK ] DynamicQuantizeMatMul.HasZeroPoint_NoBias_test_S8 (635 ms)
[ RUN ] DynamicQuantizeMatMul.HasZeroPoint_NoBias_test_U8
[ OK ] DynamicQuantizeMatMul.HasZeroPoint_NoBias_test_U8 (514 ms)
[ RUN ] DynamicQuantizeMatMul.NoZeroPoint_HasBias_test_S8
[ OK ] DynamicQuantizeMatMul.NoZeroPoint_HasBias_test_S8 (512 ms)
[ RUN ] DynamicQuantizeMatMul.NoZeroPoint_HasBias_test_U8
[ OK ] DynamicQuantizeMatMul.NoZeroPoint_HasBias_test_U8 (505 ms)
[ RUN ] DynamicQuantizeMatMul.NoZeroPoint_NoBias_test_S8
[ OK ] DynamicQuantizeMatMul.NoZeroPoint_NoBias_test_S8 (526 ms)
[ RUN ] DynamicQuantizeMatMul.NoZeroPoint_NoBias_test_U8
[ OK ] DynamicQuantizeMatMul.NoZeroPoint_NoBias_test_U8 (504 ms)
[ RUN ] DynamicQuantizeMatMul.HasZeroPoint_HasBias_test_S8
[ OK ] DynamicQuantizeMatMul.HasZeroPoint_HasBias_test_S8 (512 ms)
[ RUN ] DynamicQuantizeMatMul.HasZeroPoint_HasBias_test_U8
[ OK ] DynamicQuantizeMatMul.HasZeroPoint_HasBias_test_U8 (512 ms)
[ RUN ] DynamicQuantizeMatMul.UInt8_test_with_empty_input
[ OK ] DynamicQuantizeMatMul.UInt8_test_with_empty_input (112 ms)
[ RUN ] DynamicQuantizeMatMul.B_PerColumn_ND
[ OK ] DynamicQuantizeMatMul.B_PerColumn_ND (348 ms)
[----------] 10 tests from DynamicQuantizeMatMul (4685 ms total)
[----------] Global test environment tear-down
[==========] 10 tests from 1 test suite ran. (4686 ms total)
[ PASSED ] 10 tests.
memleakdbg:
----- No memory leaks detected -----
```
### Motivation and Context
- CalculateDynamicQuantizeMatMul to replace CPU EP run reference
- Added more FP32 testcases to isolate all input datatype combinations
---------
Co-authored-by: Xiang Zhang <xianz@microsoft.com>
### Fix seed for recomputed Dropout
If Dropout node is recomputed in the backward, we should make sure its
execution is same as the run in the forward.
If we don't set seed attribute, then this cannot be guaranteed.
Add ` export ORTMODULE_MEMORY_OPT_LEVEL=2` to enabled per layer
recompute with compromised recomputable subgraphs.
### Description
Implment IsInf-10,20 for CUDA.
Add FP16 types also on CPU.
### Motivation and Context
Certain models lag in performance due to IsInf not available on CUDA.
### Description
<!-- Describe your changes. -->
1. Support quantized GPTQ weight in huggingface like
[TheBloke/Llama-2-7B-Chat-GPTQ](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GPTQ)
2. Support Act_order for GPTQ
3. Support [HQQ](https://mobiusml.github.io/hqq_blog/) algorithm to
quantize matmul weight and add quant script
### 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
DML Implementation for
[com.microsoft.MatMulIntegerToFloat](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.MatMulIntegerToFloat)
```
.\onnxruntime_test_all.exe --gtest_filter="*MatMulIntegerToFloat.*"
Note: Google Test filter = *MatMulIntegerToFloat.*
[==========] Running 22 tests from 1 test suite.
[----------] Global test environment set-up.
[----------] 22 tests from MatMulIntegerToFloat
[ RUN ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_S8S8
[ OK ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_S8S8 (620 ms)
[ RUN ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_S8S8
[ OK ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_S8S8 (497 ms)
[ RUN ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_S8S8
[ OK ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_S8S8 (488 ms)
[ RUN ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_S8S8
[ OK ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_S8S8 (503 ms)
[ RUN ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_U8U8
[ OK ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_U8U8 (495 ms)
[ RUN ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_U8U8
[ OK ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_U8U8 (488 ms)
[ RUN ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_U8U8
[ OK ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_U8U8 (492 ms)
[ RUN ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_U8X8
[ OK ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_U8X8 (502 ms)
[ RUN ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_S8U8
[ OK ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_S8U8 (452 ms)
[ RUN ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_S8U8
[ OK ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_S8U8 (454 ms)
[ RUN ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_S8U8
[ OK ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_S8U8 (446 ms)
[ RUN ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_S8U8
[ OK ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_S8U8 (508 ms)
[ RUN ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_U8S8
[ OK ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_U8S8 (456 ms)
[ RUN ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_U8S8
[ OK ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_U8S8 (455 ms)
[ RUN ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_U8S8
[ OK ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_U8S8 (447 ms)
[ RUN ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_U8S8
[ OK ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_U8S8 (465 ms)
[ RUN ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_U8U8
[ OK ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_U8U8 (111 ms)
[ RUN ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_U8S8
[ OK ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_U8S8 (115 ms)
[ RUN ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_S8S8
[ OK ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_S8S8 (114 ms)
[ RUN ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_S8U8
[ OK ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_S8U8 (110 ms)
[ RUN ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16
[ OK ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16 (112 ms)
[ RUN ] MatMulIntegerToFloat.MatMulInteger_With_ZeroPoint
[ OK ] MatMulIntegerToFloat.MatMulInteger_With_ZeroPoint (337 ms)
[----------] 22 tests from MatMulIntegerToFloat (8679 ms total)
[----------] Global test environment tear-down
[==========] 22 tests from 1 test suite ran. (8680 ms total)
[ PASSED ] 22 tests.
memleakdbg:
----- No memory leaks detected -----
```
### 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. -->
* `CalculateMatMulIntegerToFloat` to replace CPU EP run reference
* Added more FP32 testcases to isolate all input datatype combinations
* Added fixed input to `MatMulIntegerToFloat_FP16*` test cases as for
FP16 test cases.
* onnxruntime/test/testdata/matmul_integer_to_float.py` is capable of
generating FP16 models, but we do not produce any for now
### Follow up fix for Gelu impl
There are two minor comments in
https://github.com/microsoft/onnxruntime/pull/19560.
Fix them in this pull request.
### 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. -->
I've added NHWC GridSample support to the CUDA EP to reduce the number
of layout transforms. Also I've enabled the full set of GridSampleTests
for all EPs. I've also added the GridSample OpSet 16 to the registered
kernels.
### Motivation and Context
This is the first PR is a series of enhancements of the CUDA EP
improving NHWC support to avoid costly layout transforms between NWHC
and NCHW nodes which are layout sensitive. Also testing was quite
rudimentary for the CUDA EP while it was great for the CPU path. I've
regenerated grid_sample_test.cc enabling tests for other platforms as
well. Those tests resurfaced #10607 again which is fixed as well.
### ONNX Gelu Op in Opset 20
Refactor code to support MSDomain Gelu and ONNX Gelu-opset20 Op
1. Move CPU-GELU implmentation from
`onnxruntime/contrib_ops/cpu/activations.h/cc` to
`onnxruntime/core/providers/cpu/tensor/gelu.h/cc`, as the implementation
for approximate attribute to be 'none'.
2. Dumplicate some logic from
`onnxruntime/contrib_ops/cpu/bert/bias_gelu.cc` to
`onnxruntime/core/providers/cpu/tensor/gelu.h/cc`, as the implementation
for approximate attribute to be 'tanh'.
3. Register ONNX domain Gelu CPU kernel from opset 20 in
`onnxruntime/core/providers/cpu/cpu_execution_provider.cc`.
4. Move `onnxruntime/contrib_ops/cuda/bert/fast_gelu_impl.h/cu` to
`onnxruntime/core/providers/cuda/tensor/gelu_impl.h` and
`onnxruntime/core/providers/cuda/tensor/gelu_approximate_impl.cu`
respectively, as the implementation for approximate attribute to be
'tanh'.
5. Implement the logic for approximate attribute to be 'none' in
`onnxruntime/core/providers/cuda/tensor/gelu_impl.cu`.
6. Register ONNX domain Gelu CUDA kernel from opset 20 in
`onnxruntime/core/providers/cuda/cuda_execution_provider.cc`.
7. ROCM ep related changes.
8. Enrich the tests for ONNX domain Gelu in
`onnxruntime/test/providers/cpu/activation/activation_op_test.cc`.
### Description
This PR updates exporting and running the Whisper model with beam search
by adding the following.
- Adds temperature as a graph input to the exported model
- Fixes the token ids by adding them as attributes to
`WhisperBeamSearch`
- Fixes the timestamps test cases so they pass now
- Fixes a bug with invoking `torch.onnx.export`
- Cleans up the Whisper scripts and groups the arguments in
`convert_to_onnx.py`
- Adds a `requirements.txt` file to specify package dependencies
- Adds `whisper-large-v3` to list of pretrained models
- Fixes a bug with missing cross-attention KV cache inputs in the
decoder subgraph
### Motivation and Context
- This is a follow-up to [this
PR](https://github.com/microsoft/onnxruntime/pull/19188).
- The incorrect token ids in the timestamps processor were first noticed
during [this PR
review](https://github.com/microsoft/onnxruntime/pull/17500#discussion_r1333520007).
When they were originally added in [this
PR](https://github.com/microsoft/onnxruntime/pull/15853), the offsets
were previously constant across the Whisper model sizes. When comparing
the new `whisper-large-v3` variant, the English-only variants (e.g.
`whisper-tiny.en`), and the original variants (e.g. `whisper-tiny`),
both the values and the offsets differ. Therefore, it is easier to set
the token ids as attributes to `WhisperBeamSearch` when exporting to
ensure the right values are used in the timestamps processor.
- The Hugging Face API for returning timestamps and the expected outputs
from the PyTorch model have both changed.
- The fix for `torch.onnx.export` is a follow-up to [this PR
review](https://github.com/microsoft/onnxruntime/pull/17179#issuecomment-1683001470).
- The argument grouping is a follow-up to [this PR
review](https://github.com/microsoft/onnxruntime/pull/17500#discussion_r1333521721).
- Specific package versions are needed to run the Whisper scripts and
the `requirements.txt` file ensures that these versions are installed.
- The `whisper-large-v3` variant is released and should be in the list
of official pretrained models.
- After the changes from [this
PR](https://github.com/microsoft/onnxruntime/pull/17316), the exported
model is not loading in an ORT inference session because the
cross-attention KV cache inputs are missing in the decoder subgraph.
### Description
Sqrt does not have BF16 support yet. Adding that with this 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. -->
### Description
<!-- Describe your changes. -->
Adds bfloat16 as a supported dtype for SimplifiedLayerNormFusion which
will provide speedup for Llama-v2 on A100 using bfloat16 numerical
format.
_layernorm_optimized_training.onnx exported in bfloat16 vs. float16:_
### Repro Instructions
```python
from torch import nn
from onnxruntime.training.ortmodule import ORTModule, DebugOptions, LogLevel
import torch
dtype = torch.bfloat16
# dtype = torch.float16
class Net(nn.Module):
def __init__(self):
super().__init__()
self.fc = nn.Linear(784, 10, dtype=dtype)
self.layernorm = nn.LayerNorm([784], dtype=dtype)
def forward(self, x):
x = x.view(x.shape[0], -1)
x = self.layernorm(x)
x = self.fc(x)
return x
model = Net()
model = ORTModule(model, DebugOptions(save_onnx=True, onnx_prefix='layernorm', log_level=LogLevel.INFO))
model.to("cuda")
images = torch.randn((8, 28, 28), dtype=dtype).to("cuda")
output = model(images)
```
### 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. -->
ONNX Runtime integration with Llama-v2 family of LLMs.
---------
Co-authored-by: Prathik Rao <prathikrao@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
`ScatterElements` in opset 18 has been around for a while. However, the
highest opset supporting `ScatterElements` in ORT is 13. This PR
implement this op in CUDA EP by replacing `assignment` in the current
CDUA kernel with `atomic reduction` (e.g., atomic add, atomic max). A
series of fundamental atomic functions (e.g., atomic max for int8_t and
half) are implemented in `common.cuh`; the implementation is general
enough to cover old CUDA and new CUDA versions.
- The core changes are in `cuda/atomic/common.cuh` with very detailed
documentation including `bit-wise operation's visualization`. They are
also copied to `rocm/atomic/common.cuh` to support AMD GPU.
- `/cuda/tensor/gather_elements_impl.cu` contains small changes to call
the new atomic functions to support new `reduction` behavior in new
`ScatterElements`.
- New `ScatterElements` are defined in `rocm_execution_provider.cc` and
`cuda_execution_provider.cc`.
### Description
Implement Pad-18 for Cuda.
### Motivation and Context
Latest models converted by Dynamo fall back on CPU for Pad with
performance degradation.
This contributes to
https://github.com/microsoft/onnx-rewriter/issues/126
### Description
These changes add rotary embedding and packed qkv input to gqa. As of
now, the changes are only supported with Flash-Attention (SM >= 80) but
should soon be supported with Memory Efficient Attention as well.
### Motivation and Context
With the fusion of rotary embedding into this Attention op, we hope to
observe some perf gain. The packed QKV should also provide some perf
gain in the context of certain models, like Llama2, that would benefit
from running ops on the fused QKV matrix, rather than the separate Q, K,
and V.
---------
Co-authored-by: Yufeng Li <liyufeng1987@gmail.com>
### Description
<!-- Describe your changes. -->
Add `temperature` as an input to WhisperBeamSearch op and initialize
correctly in parameter setup.
### Motivation and Context
Currently, temperature is included as an attribute to the BeamSearch op,
which doesn't let the model act dynamically in a single inference
session. By including this variable as an input, the temperature value
can be altered in any inference call (important for 1P teams)
---------
Co-authored-by: Peter McAughan <petermca@microsoft.com>
Co-authored-by: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
Co-authored-by: Kunal Vaishnavi <kvaishnavi@microsoft.com>
### Description
<!-- Describe your changes. -->
Register DML operators for opset 19.
- Cast19
- Castlike19
- Constant19
- Equal19
- Identity19
- QuantizeLinear19
- DequantizeLinear19
- Reshape19
- Shape19
- 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. -->
---------
Co-authored-by: linnealovespie <linneamay@microsoft.com>
### Description
<!-- Describe your changes. -->
1. support causal mask in MHA cpu
2. support custom rotary_dim in rotary_emb
3. add bf16 for rotary_emb
4. fix a bug in attention rotary
### 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. -->
Bump up version to 1.18.0 since the release branch has been cut.
### 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: rachguo <rachguo@rachguos-Mini.attlocal.net>
### Description
<!-- Describe your changes. -->
Implements LabelEncoder as per `ai.onnx.ml` opset 4 for the upcoming
ONNX 1.15 release. ~~This currently depends on a new ONNX release
candidate and so is marked as draft in the meantime.~~
### Motivation and Context
Closes https://github.com/microsoft/onnxruntime/issues/17602
When the TRT engine cache (precompiled engine) is present, it doesn't
make sense to go over the processes of model verification, model
optimization, TRT EP's GetCapability(), TRT EP's model proto
reconstruction, calling TRT parser and engine compilation.
This PR makes TRT EP skip those processes and directly load the engine
to perform inference.
The feature request:
https://github.com/microsoft/onnxruntime/issues/18072
Features:
- Replace original model with TRT engine wrapped ONNX model. It can save
a lot of time as mentioned above.
- How to get TRT engine wrapped ONNX model?
1. Set `trt_dump_ep_context_model` provider option to "true" and run the
inference. You will find the "xxx_wrapper.onnx" at the engine cache
path. (The same logic of generating engine cache)
2. Use gen_trt_engine_wrapper_onnx_model.py
- Three provider options are added,
`trt_dump_ep_context_model`: Enable dump wrapped onnx model by TRT EP
`trt_ep_context_embed_mode`: Add embed_mode as attribute. 0 means engine
cache path, 1 means engine binary data.
`trt_ep_context_compute_capability_enable`: Add hardware_arch as
attribute. When running the model, TRT EP will check consistency between
model's hardware_arch and GPU's compute capability.
- When the engine cache path is given in the wrapped model, TRT EP will
first search for the engine file using the path (relative to model
path), if it can't find it, it will change to use the path as it is
(depends on user, could be relative to working dir or absolute path)
Note:
1. This PR includes the change of
https://github.com/microsoft/onnxruntime/pull/17751
Constraints:
1. The whole model should be fully supported by TRT.
4. Users need to make sure the engine is built with min/max/opt
optimization profiles that large enough to cover the range of all
inputs. TRT EP will simply fail and won't rebuild the engine if the
input shape is out of range during runtime.
### 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
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.
