[DNNL][Relay extern-schedule] DNNL Conv2D Kernel enable by assigning "-libs=mkldnn" #11571
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@comaniac @AndrewZhaoLuo @junrushao1994 @masahi could you pls help review and have some suggestions on how to organized the file to USE_MKLDNN not only for cblas ? should the test case added in test_dnnl.py? |
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Will take a look early next week. |
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@yangulei thanks for your suggesttion ! : ) and I've added the check for registed external function in test case, seems it works~ |
| out_shape, | ||
| [src, weights], | ||
| lambda ins, outs: tvm.tir.call_packed( | ||
| "tvm.contrib.mkldnn.conv2d", |
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The name is a bit confusing...so does the use of libraries. We now have USE_MKLDNN (for cblas with matmul/dense) and USE_DNNL (for DNNL/OneDNN with matmal/dense/conv2d). AFAIK, MKL-DNN can be covered by DNNL, so should we deprecate MKL-DNN and use DNNL for both cases (e.g., -libs and BYOC)?
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I agree with that should be a unified name of DNNL, we already have USE_DNNL_CODEGEN and USE_MKLDNN for BYOC and -libs，I suggest to have USE_DNNL_LIBS for -libs and USE_DNNL_CODEGEN for BYOC, and change 'mkldnn' to 'dnnl' in codes.
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I don't think we really need to have 2 flags? It seems fine to enable both libs and BYOC when USE_DNNL is ON.
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Yep, that would be more concise, 💯
I'll try and commit later.
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@comaniac hi，I change the cmake config flags and the symbol |
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The current scope seems good to me. You could either make this PR upstream compatible and rebase #11638, or rebase this PR after 11638 has merged. I'm fine with either way. |
Seems #11638 was suggested to support |
…"-libs=mkldnn" (apache#11571) * enable oneDNN conv op by using -libs=mkldnn * add channel last format support and let oneDNN chose blocked format. * remove unnecessary changes * reformat 3 files * reformat 1 file * change the argument name * change the argument name * rename the arguments * fix cpp lint issue * fix cpp lint issue * fix cpp lint issue * clang reformated * adjust .py import for testing * function existence check in test
This PR mainly about mapping oneDNN OP implementation in X86 Relay Op Strategy. we've observed that nn.dense kernel that could be dispatched to DNNL by assigning "-libs=mkldnn" and there is also conv2d kernel implemented in runtime/contrib/dnnl.
so we mapping it in X86 Relay Op Strategy and optimized the kernel implementation to let DNNL choose blocked format according to different input shape, as performance-profiling example discribed in oneDNN doc.
Here is the details:
We are trying to enable more DNNL kernels including different format and datatypes this way.