Comments (5)
call for review: @naoyam @csarofeen @mmigdal-nv @drzejan2
from fuser.
The multi-device runtime by @samnordmann seems quite relevant. He's extending the overall design. Maybe we should also consider finer-grained task parallelism like warp specialization.
from fuser.
In case of matmul and use of TensorCores, the horizontal group with tasks will effectively mean partitioning of input tensors into sub-regions and within each of such tasks will be a vertical group with two tasks:
- handling data loading (TMA)
- process data with TensorCore
?
Or this is too low level, and for now by task we mean a higher level operation, not low level memory managment?
from fuser.
The multi-device runtime by @samnordmann seems quite relevant. He's extending the overall design. Maybe we should also consider finer-grained task parallelism like warp specialization.
I would be happy to discuss about it. I'll put here some design docs when it will be ready
from fuser.
In case of matmul and use of TensorCores, the horizontal group with tasks will effectively mean partitioning of input tensors into sub-regions and within each of such tasks will be a vertical group with two tasks:
- handling data loading (TMA)
- process data with TensorCore
?Or this is too low level, and for now by task we mean a higher level operation, not low level memory managment?
You are right, it is to parallelize TMA and tensor core.
from fuser.
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 7
- Evaluation for rFactored reductions is not supported. HOT 1
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from fuser.