Layout convolution as NHWC or NCHW only

[pfultz2]

No description provided.

[TedThemistokleous]

Changes look simple and I get the point here.

I think you need to update auto convolution though

[2024-12-20T21:46:54.569Z] [   RUN    ] auto_conv_nchw

[2024-12-20T21:46:54.569Z] [ COMPLETE ] auto_conv_nchw (33.3551ms)

[2024-12-20T21:46:54.569Z] [   RUN    ] auto_conv_nhwc

[2024-12-20T21:46:54.569Z] void auto_conv_nhwc()

[2024-12-20T21:46:54.569Z] /var/jenkins/workspace/AMDMIGraphX_PR-3729/test/layout_convolution.cpp:90:

[2024-12-20T21:46:54.569Z]     FAILED: m1.sort() == m2.sort() [ @0 = @literal{ ... } -> float_type, {16, 3, 3, 8}, {72, 24, 8, 1}

[2024-12-20T21:46:54.569Z] @1 = transpose[permutation={0, 3, 1, 2}](@0) -> float_type, {16, 8, 3, 3}, {72, 1, 24, 8}

[2024-12-20T21:46:54.569Z] @2 = layout[permutation={0, 1, 2, 3}](@1) -> float_type, {16, 8, 3, 3}, {72, 9, 3, 1}

[2024-12-20T21:46:54.570Z] x = @param:x -> float_type, {1, 16, 16, 8}, {2048, 128, 8, 1}

[2024-12-20T21:46:54.570Z] @4 = transpose[permutation={0, 3, 1, 2}](x) -> float_type, {1, 8, 16, 16}, {2048, 1, 128, 8}

[2024-12-20T21:46:54.570Z] @5 = layout[permutation={0, 1, 2, 3}](@4) -> float_type, {1, 8, 16, 16}, {2048, 256, 16, 1}

[2024-12-20T21:46:54.570Z] @6 = convolution[padding={1, 1},stride={2, 2},dilation={1, 1},group=1,padding_mode=0](@5,@2) -> float_type, {1, 16, 8, 8}, {1024, 64, 8, 1}

[2024-12-20T21:46:54.570Z] @7 = relu(@6) -> float_type, {1, 16, 8, 8}, {1024, 64, 8, 1}

[2024-12-20T21:46:54.570Z] @8 = layout[permutation={0, 2, 3, 1}](@7) -> float_type, {1, 16, 8, 8}, {1024, 1, 128, 16}

[2024-12-20T21:46:54.570Z] @9 = @return(@8)

[2024-12-20T21:46:54.570Z]  == @0 = @literal{ ... } -> float_type, {16, 3, 3, 8}, {72, 24, 8, 1}

[2024-12-20T21:46:54.570Z] @1 = transpose[permutation={0, 3, 1, 2}](@0) -> float_type, {16, 8, 3, 3}, {72, 1, 24, 8}

[2024-12-20T21:46:54.570Z] x = @param:x -> float_type, {1, 16, 16, 8}, {2048, 128, 8, 1}

[2024-12-20T21:46:54.570Z] @3 = transpose[permutation={0, 3, 1, 2}](x) -> float_type, {1, 8, 16, 16}, {2048, 1, 128, 8}

[2024-12-20T21:46:54.570Z] @4 = convolution[padding={1, 1},stride={2, 2},dilation={1, 1},group=1,padding_mode=0](@3,@1) -> float_type, {1, 16, 8, 8}, {1024, 1, 128, 16}

[2024-12-20T21:46:54.570Z] @5 = relu(@4) -> float_type, {1, 16, 8, 8}, {1024, 1, 128, 16}

[2024-12-20T21:46:54.570Z] @6 = @return(@5)

[2024-12-20T21:46:54.570Z]  ]