GitLab

Ref Unique Operator only

Add min and max reduction modes
Implement duplicate index handling
Add corresponding reference, onnx, and verify tests.

Simplify what we parse the ONNX operator ConstantOfShape into a literal for a static shape

Introduce dilations attribute to pooling operators reference implementation.

Converts dynamic reshapes with constant output dimensions to a static reshape instruction
Intended to be used after split_single_dyn_dim pass and after find_static_dimensions_of makes shape calculations doable at compile-time.

Handles all 4 Fp8 dtypes listed here : https://onnx.ai/onnx/technical/float8.html
Follows saturation/clipping logic from table there as well : https://onnx.ai/onnx/technical/float8.html#cast
Only adding fp8e4m3fnuz in MIGraphX IR for now.

Reworked the simplify_qdq pass to support:

Per-axis quantization (ie. allow 1D scales and zero points)
Allow broadcast and transpose ops between dq and quant_op

Since ONNX opset version 14, layout attribute has been introduced to LSTM operator which allows two predefined layouts for input and output shapes.  Add corresponding reference, onnx, and verify tests.

Remove contiguous from simplify algebra passes for reshape op
Remove contiguous from fuse_pointwise for reshape op

Removes numpy dependency from test_gpu.py test and re-enables Python tests for Python 3.6.

The inaccuracy was caused by ONNX round requires nearest integer rounding for halway (0.5) cases.
std::round rounds away from zero, thus giving wrong results with halfway cases.
Replaced std::round with std::nearbyint which uses the correct rounding by default.

Fixes an issue that comes up for variable input slice with steps set manually in ONNX to default 1's.

Simplifies dimensions_of instructions to a literal when possible.
Intended to be used after the split_single_dyn_dim pass.

Disable python tests on Python 3.6 temporarily till we remove numpy dependencies from the tests using them.
Different versions of Python are compatible with different versions of numpy, so we need to remove numpy dependency.

Since opset version 18, the Split operator allows splitting into unevenly sized outputs when the split input is not present, and a num_outputs attribute has been introduced.