[Collage] CollagePartition pass #12086
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@mbaret here's the big one! |
See https://github.com/apache/tvm-rfcs/blob/main/rfcs/0062-collage.md. This adds the main CollagePartition pass, which: 1. Inspects all the targets in the CompilationConfig and builds PartitionSpecs describing how to generate speculative CandidatePartitions for them. 2. Runs the above rules on the model to collect all the candidates. 3. Eliminates candidates whose target contradicts any constraints already imposed by, eg, device planning. 4. Eagerly estimates the cost of each candidate. 5. Performs a shortest path search to chose an 'optimal' set of candidate partitions so as to minimize estimated model latency, such that every sub-expression node is contained in exactly one candidate partition. 6. Coalesces adjacent optimal candidates which ended up on the same target. 7. Rewrites the model according to the chosen optimal partitioning. As for the existing partition_for_<external codegen name> methods, the result of CollagePartition can then be built using regular TVM. Very special thanks to @mbaret for authoring test_pass_collage_partition.py. Logic to prune the candidates after step 3 will be in a follow up PR since it deserves its own testing. A demonstration driver will also come as a follow up.
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xinetzone
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* [Collage] CollagePartition pass See https://github.com/apache/tvm-rfcs/blob/main/rfcs/0062-collage.md. This adds the main CollagePartition pass, which: 1. Inspects all the targets in the CompilationConfig and builds PartitionSpecs describing how to generate speculative CandidatePartitions for them. 2. Runs the above rules on the model to collect all the candidates. 3. Eliminates candidates whose target contradicts any constraints already imposed by, eg, device planning. 4. Eagerly estimates the cost of each candidate. 5. Performs a shortest path search to chose an 'optimal' set of candidate partitions so as to minimize estimated model latency, such that every sub-expression node is contained in exactly one candidate partition. 6. Coalesces adjacent optimal candidates which ended up on the same target. 7. Rewrites the model according to the chosen optimal partitioning. As for the existing partition_for_<external codegen name> methods, the result of CollagePartition can then be built using regular TVM. Very special thanks to @mbaret for authoring test_pass_collage_partition.py. Logic to prune the candidates after step 3 will be in a follow up PR since it deserves its own testing. A demonstration driver will also come as a follow up. * - lints * - more lints * - use the _ffi_api properly
mikeseven
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* [Collage] CollagePartition pass See https://github.com/apache/tvm-rfcs/blob/main/rfcs/0062-collage.md. This adds the main CollagePartition pass, which: 1. Inspects all the targets in the CompilationConfig and builds PartitionSpecs describing how to generate speculative CandidatePartitions for them. 2. Runs the above rules on the model to collect all the candidates. 3. Eliminates candidates whose target contradicts any constraints already imposed by, eg, device planning. 4. Eagerly estimates the cost of each candidate. 5. Performs a shortest path search to chose an 'optimal' set of candidate partitions so as to minimize estimated model latency, such that every sub-expression node is contained in exactly one candidate partition. 6. Coalesces adjacent optimal candidates which ended up on the same target. 7. Rewrites the model according to the chosen optimal partitioning. As for the existing partition_for_<external codegen name> methods, the result of CollagePartition can then be built using regular TVM. Very special thanks to @mbaret for authoring test_pass_collage_partition.py. Logic to prune the candidates after step 3 will be in a follow up PR since it deserves its own testing. A demonstration driver will also come as a follow up. * - lints * - more lints * - use the _ffi_api properly
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See https://github.com/apache/tvm-rfcs/blob/main/rfcs/0062-collage.md.
This adds the main CollagePartition pass, which:
PartitionSpecs describing how to generate speculative CandidatePartitions
for them.
imposed by, eg, device planning.
partitions so as to minimize estimated model latency, such that every sub-expression
node is contained in exactly one candidate partition.
As for the existing partition_for_ methods, the result of
CollagePartition can then be built using regular TVM.
Very special thanks to @mbaret for authoring test_pass_collage_partition.py.
Logic to prune the candidates after step 3 will be in a follow up PR since it
deserves its own testing. A demonstration driver will also come as a follow up.