GitLab

Add dynamic shape support to slice operator.

First draft of this feature doesn't support ops slicing non-fixed, dynamic axes. Resulting shape in such cases is not guaranteed.* Also, onnx parsing doesn't support any arguments other than "axes".

dyn shape support for Where operator.  Includes shape test, ref_ops test, onx_test. 

Allows migraphx to inline the IF operator when we run into an IF that can be evaluated at compile time, thus avoiding us injecting IF and just inserting the instructions directly.

supports upper and lower trilu
not able to support dynamic k values
not able to support negative k values

Adds support for parsing dynamic ONNX gemm bias input C

Extends ONNX Gemm parser to handle dynamic input shapes
Limits ONNX Gemm parsing to 2D input tensors for A and B inputs
As per the ONNX specifications
Changed Gemm ONNX tests to 2D input versions
Add onnx_verify tests for Gemm
Parsing ONNX Gemm links to more than one operator, checking that it produces the correct result

Extends reshape to handle the case of a single non-fixed dynamic_dimension

Extends pad operator to handle dynamic input shapes
Only handles computing the shape for adding constant padding to a dynamic shape
- adds the padding to the min, max, and opt values (unless opt is 0, where it keeps it 0)
- does not handle reflect padding with dynamic shapes

Extends parse_matmul.hpp to handle dynamic input shapes
Does not support broadcasting of the outer dimensions for dynamic shapes at this time

Fixes ONNX parsing of convolution to handle dynamic broadcasting of bias input

Implements dynamic shapes in reduce_op and all its child operator classes (reduce_max etc.)

Extends the pooling operators for dynamic shape inputs

AveragePooling
GlobalAveragePooling
MaxPooling
GlobalMaxPooling
LpNormPooling
GlobalLpNormPooling
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Extends ref transpose operator for dynamic shapes
Make dynamic tests more consistent naming

Updated Multibroadcast op to have a two input version for dynamic shapes
Current dynamic shape broadcasting logic
dynamic_dimensions must be the same or one of them is {1, 1, 0} or {1, 1, 1}
Works for dyn-dyn, dyn-static, and static-static shape combinations
Changed common.cpp for multibroadcasting for binary ops with dynamic shapes
Extended binary.hpp for dynamic shapes to test the new common.cpp stuff

Two input version of the broadcast operator to handle dynamic shapes
Added comments to describe the versions of the broadcast operator
Dynamic broadcast only handles broadcasting a static 1D shape tensor into the other input shape

Newer split moves the split attribute to an input. In this case we check the
number of input args then.

It doesn't look like this functionality is used. Probably was a thing in
ONNX that became deprecated when they adopted NumPy-like broadcasting.

Upgraded Dockerfiles and fixed tidy issues to make Ubuntu 20.04 and ROCm 5.3.0 the default 

Allows for rank 2 tensors into batchnorm.  Specifically when spatial dimensions are all 1 and removed

Removes use_dynamic_same_auto_pad
Change padding_mode to be used for dynamic padding
Move compute_padded_shape to pad_calc.cpp as it will be used in other dynamic padding cases
Fix same_lower compute_padded_shape bug and add a test.

This reverts commit cb0da1cb.

For dynamic shape handling

Rewrites the BatchNormalization ONNX operator into other MIGX operators
- Added handling of 1D input tensor case (edge case in ONNX spec)
Removes the spatial and per_activation functionality (not in the ONNX spec)
- Did not remove the batch_norm_inference related code as the TensorFlow parser still uses it
- Can remove that code when the TF version is updated

Weird bug with ref padding shape
still need to change parse_convolution