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[CK] CK Tile improvements and fixes for depthwise merged convolutions forward (#4873) ## Motivation Performance benchmarks showed that old CK's depthwise merged convolutions are much faster than CK Tile's ones. ## Technical Details After investigation it showed up that the requirement that A/CVectorload is a multiple of gemm's rightmost dimension is too strict in case of processing multiple groups, because if tensor is in NHWGC/NHWGK format, then if C/K is equal to 1, we can use vectorloads on the G dimension, which is added by this PR. Filter5x5 specialization was also added, because some models are using it, it's similar to 3x3, the only difference is the window size. This addition was needed, because of the differences of tensor descriptor transformations betweeen CK and CK Tile. In old CK the case of grouped depthwise 5x5 convs was supported via Default specialization, but in CK Tile that case was not working properly. ## Test Plan Performance was tested by our internal test suite, which contains several DL models. ## Test Result Tests results showed significant performance uplift for depthwise(3x3, 5x5) cases