Comments (2)
naoyam
commented on August 21, 2024
We could also extend this to ops that currently require their inputs to be segment inputs, namely reshape and resize ops.
They are requirements of the schedulers. Having the requirements realized as a preseg pass would mean a tight binding of the preseg pass and each scheduler, which may be error-prone to maintain.
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rdspring1
commented on August 21, 2024
What about presegmenting in some way based on the higher-level torch operations? Our schedulers are loosely mapped to a set of torch operations. Thunder could insert these annotations before running nvfuser.
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Related Issues (20)
- Hopper-only crash: Unsupported loop structure. Two loops are mapped together. HOT 26
- Sharding propagation
- ExpressionEvaluator errors out when running `logical_and` HOT 1
- Add runtime check for circular buffer correctness
- Codegen error: `have dynamic allocations but are placed in local memory.` coming from qkv_split_rope backwards HOT 9
- benchmarking thunder.jit
- MarkAliasPrepare does not preserve shardings
- Performance of innerOuterPersistent scheduler is very sensitive to register usage and spills HOT 3
- Generalize pattern matching of matmul patterns
- SDPAOp and ComputeAtMap HOT 2
- makeInitialSchedulerEntry requires more than the current segment's inputs. HOT 6
- nvFuser confuses sharded dimension size and unsharded. HOT 3
- crash in transformOutputFromAllocationToLogical: frontier.size() != logical.size() HOT 2
- Indexing failure due to self-mapped broadcast domains
- Indexing error in matmul with certain parameters HOT 1
- Allocs must be based on constant integers HOT 7
- Promote reuse over specialization for small reductions
- INTERNAL ASSERT FAILED: Allocations must be based on constant integers for local memory HOT 2
- performance issue on dynamic shaped tensor HOT 8
- Evaluation for rFactored reductions is not supported. HOT 1
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