onnxruntime/cmake/external
liqun Fu cd7112f800
Integration with ONNX 1.16.0 (#19745)
### 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>
2024-04-12 09:46:49 -07:00
..
emsdk@4e2496141e update to emsdk-3.1.51 (#18844) 2024-01-12 16:04:33 -08:00
git.Win32.2.41.03.patch Fix ability to use patch on Windows CI machines (#18356) 2023-11-11 07:32:14 +10:00
libprotobuf-mutator@7a2ed51a6b
onnx@990217f043 Integration with ONNX 1.16.0 (#19745) 2024-04-12 09:46:49 -07:00
abseil-cpp.cmake Update abseil to a release tag and register neural_speed (#19255) 2024-01-24 14:37:39 -08:00
abseil-cpp.natvis Update abseil to a release tag and register neural_speed (#19255) 2024-01-24 14:37:39 -08:00
composable_kernel.cmake [ROCm] Update CK version (#17628) 2023-11-13 15:43:38 -08:00
cutlass.cmake Remove USE_CUTLASS flag (#19271) 2024-01-25 16:57:58 -08:00
dml.cmake Enable generic feature level devices in DML EP (#20114) 2024-03-29 14:37:30 -07:00
dnnl.cmake Update oneDNN to v3.0.1 in order to support gcc 13 (#19344) 2024-02-01 15:39:03 -08:00
eigen.cmake Fix ability to use patch on Windows CI machines (#18356) 2023-11-11 07:32:14 +10:00
extensions.cmake Update C/C++ dependencies: abseil, date, nsync, googletest, wil, mp11, cpuinfo and safeint (#15470) 2023-09-08 13:35:04 -07:00
find_snpe.cmake Improve dependency management (#13523) 2022-12-01 09:51:59 -08:00
FindNumPy.cmake
helper_functions.cmake Improve cache hit rate in windows build (#15538) 2023-04-18 09:31:35 -07:00
ipp-crypto.cmake
mimalloc.cmake Improve dependency management (#13523) 2022-12-01 09:51:59 -08:00
neural_speed.cmake turn on neural_speed by default (#19627) 2024-03-20 12:49:58 -07:00
onnx_minimal.cmake Fix some build issues on MacOS with Xcode 14.3. (#15878) 2023-06-07 12:07:11 -07:00
onnx_protobuf.natvis Fix visualization issues with Attribute/Tensor protos (#17188) 2023-08-16 13:56:51 -07:00
onnxruntime_external_deps.cmake CMake: support reading dependency zip files from a local mirror (#20005) 2024-03-21 17:58:59 -07:00
protobuf_function.cmake Fix some build issues on MacOS with Xcode 14.3. (#15878) 2023-06-07 12:07:11 -07:00
pybind11.cmake Improve dependency management (#13523) 2022-12-01 09:51:59 -08:00
pyxir.cmake
tvm.cmake [TVM EP] Support zero copying TVM EP output tensor to ONNX Runtime output tensor (#12593) 2023-02-08 10:02:20 -08:00
wil.cmake Rework WIL dependency retrieval/usage (#17130) 2023-08-15 09:11:46 -07:00
xnnpack.cmake Enable RISC-V 64-bit Cross-Compiling Support for ONNX Runtime on Linux (#19238) 2024-01-24 16:27:05 -08:00