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]  ]

[migraphx-bot]

Test	Batch	Rate new 59e5f0	Rate old 23ac3a	Diff	Compare
torchvision-resnet50	64	3,235.59	3,237.23	-0.05%	✅
torchvision-resnet50_fp16	64	6,874.33	6,866.95	0.11%	✅
torchvision-densenet121	32	2,439.60	2,437.09	0.10%	✅
torchvision-densenet121_fp16	32	4,185.99	4,197.72	-0.28%	✅
torchvision-inceptionv3	32	1,613.72	1,613.72	0.00%	✅
torchvision-inceptionv3_fp16	32	2,677.82	2,675.59	0.08%	✅
cadene-inceptionv4	16	750.82	750.82	0.00%	✅
cadene-resnext64x4	16	810.13	810.13	0.00%	✅
slim-mobilenet	64	6,663.41	6,661.70	0.03%	✅
slim-nasnetalarge	64	196.91	196.87	0.02%	✅
slim-resnet50v2	64	3,433.94	3,431.78	0.06%	✅
bert-mrpc-onnx	8	1,136.36	1,134.75	0.14%	✅
bert-mrpc-tf	1	500.00	495.05	1.00%	✅
pytorch-examples-wlang-gru	1	470.41	469.71	0.15%	✅
pytorch-examples-wlang-lstm	1	444.98	443.98	0.23%	✅
torchvision-resnet50_1	1	800.00	800.00	0.00%	✅
cadene-dpn92_1	1	429.88	431.70	-0.42%	✅
cadene-resnext101_1	1	390.62	389.11	0.39%	✅
onnx-taau-downsample	1	396.72	371.30	6.85%	🔆
dlrm-criteoterabyte	1	31.79	31.80	-0.02%	✅
dlrm-criteoterabyte_fp16	1	51.06	50.95	0.21%	✅
agentmodel	1	8,840.40	8,751.74	1.01%	✅
unet_fp16	2	58.36	58.29	0.12%	✅
resnet50v1_fp16	1	1,052.63	1,052.63	0.00%	✅
resnet50v1_int8	1	787.40	800.00	-1.57%	✅
bert_base_cased_fp16	64	1,159.63	1,159.63	0.00%	✅
bert_large_uncased_fp16	32	360.20	360.16	0.01%	✅
bert_large_fp16	1	215.98	216.92	-0.43%	✅
distilgpt2_fp16	16	2,188.78	2,188.78	0.00%	✅
yolov5s	1	521.33	537.05	-2.93%	✅
tinyllama	1	49.09	49.09	0.00%	✅
vicuna-fastchat	1	43.61	43.61	0.02%	✅
whisper-tiny-encoder	1	436.68	436.68	0.00%	✅
whisper-tiny-decoder	1	448.43	450.45	-0.45%	✅

Check results before merge 🔆

[migraphx-bot]

     ✅ bert-mrpc-onnx: PASSED: MIGraphX meets tolerance

     ✅ bert-mrpc-tf: PASSED: MIGraphX meets tolerance

     ✅ pytorch-examples-wlang-gru: PASSED: MIGraphX meets tolerance

     ✅ pytorch-examples-wlang-lstm: PASSED: MIGraphX meets tolerance

     ✅ torchvision-resnet50_1: PASSED: MIGraphX meets tolerance

     ✅ cadene-dpn92_1: PASSED: MIGraphX meets tolerance

     ✅ cadene-resnext101_1: PASSED: MIGraphX meets tolerance

     ✅ dlrm-criteoterabyte: PASSED: MIGraphX meets tolerance

     ✅ agentmodel: PASSED: MIGraphX meets tolerance

     ✅ unet: PASSED: MIGraphX meets tolerance

     ✅ resnet50v1: PASSED: MIGraphX meets tolerance

     ✅ bert_base_cased_fp16: PASSED: MIGraphX meets tolerance

🔴bert_large_uncased_fp16: FAILED: MIGraphX is not within tolerance - check verbose output

     ✅ bert_large: PASSED: MIGraphX meets tolerance

     ✅ yolov5s: PASSED: MIGraphX meets tolerance

     ✅ tinyllama: PASSED: MIGraphX meets tolerance

     ✅ vicuna-fastchat: PASSED: MIGraphX meets tolerance

     ✅ whisper-tiny-encoder: PASSED: MIGraphX meets tolerance

     ✅ whisper-tiny-decoder: PASSED: MIGraphX meets tolerance

     ✅ distilgpt2_fp16: PASSED: MIGraphX meets tolerance