GitLab

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

Introduce dilations attribute to pooling operators reference implementation.

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.

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.

Currently, we see MLIR partition candidates recieving non-standard shape due to not fusing in squeeze, flatten and unsqueeze ops. These ops could be canonicalized to reshape without introducing additional ops as long as MLIR backend is concerned.

* Fix scatter operator for nonstandard shapes

remove standard() shape check for scatter inputs.

* Add nostandard input tests for scatter

---------
Co-authored-by: Chris Austen <causten@users.noreply.github.com>

MIGraphX verification by default uses normalized RMS error as the basis for the verification.  This change adds some logic to allow migraphx to do "np.allclose" type of elementwise verification using atol and rtol.

Commit also includes changes to consistently pass "gold" or "expected" results as the second argument for "verify_range()" calls.  Default RMS tolerance inside driver is set to 0.001 which IMO is high for FP32 compared to what we had earlier. Need better defaults

* Improve performance of pointwise/reduction kernels when using NHWC layouts

* Format

* Add nhwc test

* Format

* Remove inline namespace

* Add reduce test

Throwing on these calls catches dynamic shape errors earlier rather than having to backpedal from a bad call

Renames deconvolution -> convolution_backwards to be more consistent with the literature
Note: this is not the cross-correlation operator (which is the adjoint of convolution). This is technically a standard convolution operator combined with an upsampling operator rather than a downsampling operator.
Adds unit tests for the padding, strides, dilations, and other op attributes.
Throws on auto_pad attribute since it has not been implemented
Previously it read the attribute and set it but then did nothing with it
Extended for dynamic shapes
Does not support using asymmetric padding (padding_L != padding_R) and output_shape with dynamic shapes.

Changes to the way Pooling operation calculates pooling when there's padding. Old code would clip off any padding values before computing; for instance if an Average pooling window contained 0 1 2 where the 0 is padding, the result was 1.5 instead of 1.0. See Issue 1766

Add a CI job to test CK
Add MIGRAPHX_TUNE_CK env variable to only do tuning for CK
Continue tuning even when there is invalid configs
Fix a bug with parallel compilation not using all available threads
Add additional test for gemms using half types
Removed int32 as supported type since it doesnt pass our test suite

Fixes #1852  Fixes #1847

By converting to fp32 : fp16 3d-unet model accuracy comes out the same as FP32 accuracy.

By using reduce_sum method on Fp16 : accuracy comes out ~0.9% lower compared to fp32 while keeping entire model in fp16.

Use std::numeric_limits::min/max() functions plus the appropriate value to encode -inf/inf 

* use half hip functions to compute max and min
* add verify test for min and max

When multiplying either the input or output across the K dimensions then the multiple can be applied to the constant which can then be folded with propagate_const.

This fixes #1700

Converts can be inserted when the scales and input differ in the onnx file(we are already doing this implicit conversion in the ref implementation). This will also improve the compile-time of quantizelinear.hpp since we can remove the nested visit method.

This will replace conv(x+a, w) with conv(x, w) + conv(a, w) where a is a constant so conv(a, w) can be replaced with a constant.