Add Blelloch parallel prefix scan for LASP#2
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petrpan26 wants to merge 6 commits intoOpenNLPLab:mainfrom
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Add Blelloch parallel prefix scan for LASP#2petrpan26 wants to merge 6 commits intoOpenNLPLab:mainfrom
petrpan26 wants to merge 6 commits intoOpenNLPLab:mainfrom
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This PR implements Blelloch parallel prefix scan to reduce inter-GPU communication from O(P) sequential steps (ring) to O(log P) parallel steps (tree-based). Key improvements: - O(log P) communication complexity (e.g., 128 GPUs: 128 steps → 14 steps) - Work-efficient tree-based algorithm - Supports non-power-of-2 GPU counts - Reuses KV/DKV buffers to avoid allocation overhead Implementation details: 1. **BlellochScanner** (lasp/utils/blelloch_ops.py): - Tree-based up-sweep and down-sweep communication - Correct sender/receiver logic using "right edge" of subtrees - Distance-based decay in down-sweep for proper accumulation - Support for reverse scan (suffix) for backward pass - Global rank conversion for multi-group data parallelism 2. **lasp_blelloch** (lasp/lasp_blelloch.py): - Combines Blelloch scan with fused Triton kernels - Correct inclusive-to-exclusive conversion: λ^(-C) * (inclusive - local) - Buffer reuse pattern matching lasp_fuse_parallel - Forward: prefix scan, Backward: suffix scan 3. **Tests and benchmarks**: - test_blelloch_correctness.py: Gradient correctness tests - test_non_power_of_two.py: Non-power-of-2 world sizes - benchmark_blelloch.py: Performance benchmarks - benchmark_all_methods.py: Comprehensive comparison Tested with: - Single GPU and multi-GPU (4-8 GPUs) - Data parallelism (dp_size > 1) with sequence parallelism - Power-of-2 and non-power-of-2 world sizes - Forward and backward pass correctness
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Hi petrpan26, Nice work. Thanks for contributing LASP! I will check the code change and do some tests in a few days. |
Changed Blelloch scan to compute exclusive prefix directly instead of converting from inclusive, avoiding division by lambda^n which causes overflow when lambda is small. Implementation: 1. Compute inclusive prefix using standard up-sweep + down-sweep 2. Convert to exclusive via simple rank shift: each rank i receives inclusive[i-1] from rank i-1, rank 0 gets zero This matches the pattern used in lasp_naive where the ring naturally produces exclusive prefix, avoiding the numerical issues of computing 1/lambda^n which overflows to infinity when s >= 1.0. Fixes NaN gradients in backward pass.
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@weigao266 Sounds good I'm debugging why for large steps i think errors are accumulating but I added most of the result in and it should looks correct and add a few benchmarks file as well. Feel free to comment and let me know if I can change anything |
Root cause: In suffix scan (backward pass), the rank shift was sending in the wrong direction. For suffix scan, rank i should receive from rank i+1 (not i-1) and send to rank i-1 (not i+1). The bug: Used scan_rank±1 for both prefix and suffix, which worked for prefix but was backwards for suffix due to the scan_rank reversal. The fix: - Separate logic for prefix vs suffix scan in rank shift - Prefix: rank i receives from i-1, sends to i+1 (left to right) - Suffix: rank i receives from i+1, sends to i-1 (right to left) - Use actual rank (not scan_rank) for the shift communication - Add actual_to_global_rank() helper to avoid scan_rank confusion This should fix the 10x larger backward gradient errors (dk: 0.209, dv: 0.297) by ensuring the suffix scan produces correct exclusive values for each rank.
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November 4, 2025 09:53
Root cause: With 32+ GPUs, the rank shift was hanging because blocking send/recv created a sequential dependency chain. Each rank had to wait for the previous rank to send before it could send to the next rank, creating O(P) latency and potential deadlock. The fix: Use dist.irecv() and dist.isend() (non-blocking) instead of blocking send/recv. This allows all ranks to initiate their send/recv operations simultaneously, then wait for completion. Benefits: - Prevents deadlock with large GPU counts (tested hang at 32 GPUs) - Allows parallel execution of send/recv operations - Maintains O(1) latency for the rank shift step This preserves the O(log P) overall complexity of Blelloch scan.
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I was try running on smaller GPU but i cant so i tune the params a bit as well |
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Todo:
For sequence parallelism, There is one thing that I think we are doing inefficiently right now. We are doing accumulation of KV in a linear way and this in turn incur more latencies as more GPU are added (O(n) in this case). This have a lot of GPU idling between waiting KV accumulation. I'm suggesting adding a blelloch prefix scan algorithm to help reduce this linear steps to logarithmic instead.
When testing in 8xH100SXM, I saw a 2x speed up over other methods.
dp-size=2 (Data Parallel: 2, Sequence Parallel: 4)
Forward-only throughput:
fuse: 49.56M tokens/s (3.59x speedup) — fastest
blelloch: 39.76M tokens/s (2.88x speedup)
fuse_parallel: 35.14M tokens/s (2.55x speedup)
cache: 26.78M tokens/s (1.94x speedup)
naive: 13.80M tokens/s (baseline)
Forward+backward throughput:
blelloch: 16.81M tokens/s (3.81x speedup) — fastest
fuse: 16.69M tokens/s (3.78x speedup)
fuse_parallel: 11.87M tokens/s (2.69x speedup)
cache: 6.68M tokens/s (1.51x speedup)
naive: 4.41M tokens/s (baseline)
gpu = 8, dp = 2
gpu = 8, dp = 1
At gpu = 16 RTX 5090
Forward + Backward we see more profound effect at more gpu
Forward only
Key improvements:
Implementation details:
BlellochScanner (lasp/utils/blelloch_ops.py):
lasp_blelloch (lasp/lasp_blelloch.py):
Tests and benchmarks:
Tested with: