[CUBLAS] Add cuBLAS as a Relay partitioning target (BYOC)#10820
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mbrookhart merged 2 commits intoapache:mainfrom Mar 30, 2022
Merged
[CUBLAS] Add cuBLAS as a Relay partitioning target (BYOC)#10820mbrookhart merged 2 commits intoapache:mainfrom
mbrookhart merged 2 commits intoapache:mainfrom
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This PR adds a partitioning pass for cuBLAS so that supported Relay patterns can be offloaded to cuBLAS. This initial commit only adds offloading support for nn.matmul. Although cuBLAS is already enabled in TVM by using strategy selection in TE, by exposing it explicitly as a Relay partitioning target we can more precisely describe how to execute a model in Relay. This is desirable particularly in the Collage effort to improve multi-backend graph partitioning.
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Quite nice I think, thanks. It will be fun to see this used for complex patterns.
When we add the second+ examples I think we can consider:
- removing per-op boilerplate, since placeholder construction, create_schedule and build will always be the same
- avoiding the tvm.build entry point since I believe the support for schedules & tensors is now considered anachronistic. Perhaps a tvm.build_te(op, placeholders, target, name.
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Thanks @mbs-octoml, I've refactored both the 'lower funcs' and tests to extract all the boilerplate into reusable code. I've also switched to first using create_prim_func, then I call tvm.build on the resulting TIR. |
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thanks @mbaret LGTM |
mbrookhart
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Mar 30, 2022
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Thanks @mbaret @mbs-octoml @mikepapadim |
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* [CUBLAS] Add cuBLAS as a Relay partitioning target (BYOC) This PR adds a partitioning pass for cuBLAS so that supported Relay patterns can be offloaded to cuBLAS. This initial commit only adds offloading support for nn.matmul. Although cuBLAS is already enabled in TVM by using strategy selection in TE, by exposing it explicitly as a Relay partitioning target we can more precisely describe how to execute a model in Relay. This is desirable particularly in the Collage effort to improve multi-backend graph partitioning. * Refactor to remove boilerplate
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This PR adds a partitioning pass for cuBLAS so that supported Relay patterns can be offloaded to cuBLAS. This initial commit only adds offloading support for nn.matmul.
Although cuBLAS is already enabled in TVM by using strategy selection in TE, by exposing it explicitly as a Relay partitioning target we can more precisely describe how to execute a model in Relay. This is desirable particularly in the Collage effort to improve multi-backend graph partitioning.