Fix Neureka#188
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…te conv layers - in parser, weights are contrained to have 3 dimensions. That is correct for PW only. DW and Dense require 4 dimensions. The fix allows weights with 4 dimensions for DW and Dense. - in tiler, again only 3-dim weights for PW is supported. Add support for 4-dim weights for DW and Dense. - in test-runner, arguments for neureka are ignored. Add support for `enable-3x3` and `neureka-wmem` arguments.
…the bias is coherent with requantshift add vector
The flag avoid that aliases of input/output (such as a no-op reshape view) is deallocated
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Current implementation of Neureka has a few bugs that need to be fixed. This PR wants to fix those bugs and provide some test to check the functionality. Most changes are related to the tiler for Dense and DW Convolutions.
Added
NeurekaNCHWtoNHWCDwConvPasswhich apply the weight layout depending on the engine.Changed
NeurekaReshapePointwiseConvolutionPass_requantized_gemm_to_pw_fun, add a guard onmulandaddshapes to check if they are consistent with PW output channels.Fixed
_createIOBindings, set_live = Trueon network input and output buffers. They are externally allocated and effectively reserved for the whole inference (mirroringConstantBuffer, which already hardcodes_live = True). This makeshas_live_aliases()correctly see them as live, so any buffer aliasing a network I/O tensor is no longer deallocated while the I/O tensor is still in use.has_live_aliases():visited = set(self.name)built a set of the name's characters instead of{self.name}PR Merge Checklist
develcommit and pointing todevel.CHANGELOG.mdfile has been updated.