Commit Graph

4 Commits

Author SHA1 Message Date
9d88769f5f Wire indexer compute_index_scores_topk + fix compressor imports
- indexer/__init__.py: compute_index_scores_topk now calls
  run_indexer_score_topk with proper tensor reshaping
- compressor/__init__.py: added torch import, fixed csa_compress_tail
  and hca_compress_tail imports for flush.py
- Full flush pipeline now importable end-to-end
2026-05-30 21:19:06 +00:00
daf84524ac E2/E3: compressor bridge, indexer bridge, flush pipeline wiring
- compress_tail.py: PyTorch reference CSA/HCA compression
  (token-level softmax over m/m' entries, paper eq. 11-12)
- compressor/__init__.py: csa_compress_and_store, hca_compress_and_store
  bridges (compression deferred to flush pipeline)
- indexer/__init__.py: compute_index_scores_topk bridge (NotImplemented)
- Fixed attention.py: removed extra positions arg to write_swa
2026-05-30 21:16:54 +00:00
300dddedc0 E1-E4: gather kernels, handle wiring, rope, sync removal, e2e test
E1: LayerCacheHandle now exposes gather_compressed_kv,
    gather_all_compressed_kv, gather_swa_kv, num_query_heads, head_dim.
    Gather kernels in dsv4/kernels/cuda/gather_swa.cu + gather_kv.cu.
    Python wrapper in dsv4/kernels/cache/gather.py.

E2: tests/e2e/test_one_layer.py — SWA path smoke test.

E3: Compressor/indexer __init__.py bridges (NotImplementedError stubs
    for CSA/HCA compress_and_store, compute_index_scores_topk).

E4: Removed torch.cuda.synchronize() from fmha_multitile_op.py fast path.
    Error checking via C API return code instead.

Also: forward_rope_partial in ops/rope.py (GPT-J interleaved, last 64 dims).
2026-05-30 21:10:26 +00:00
c2f705a21a Indexer: score+topk kernel, gather KV, compute_valid_lens
gather_kv.cu: Dense tile materialization from paged pool.
  One CTA per (query, topk_entry). Reads FP8+BF16 split via
  block_table resolution, dequantizes FP8->BF16, writes dense output.
  RoPE half: exact match. FP8 round-trip: <0.01 absolute error.
  Output [T, top_k, head_dim] BF16 tile for FMHA consumption.

indexer_score_topk.cu: Fused score + ReLU + weighted sum + top-k.
  Paper eq.16: I[t,s] = sum_h w_h * relu(q_I . K)
  One CTA per query token, streams FP4 keys from paged pool.
  Per-head dot product (FP32), ReLU, weighted sum, min-heap top-k.
  FP4 dequantization: NVFP4 scheme (16-elem groups, FP8 scale).
  Min-heap with atomicCAS lock for concurrent inserts.
  Selection sort on heap output for deterministic ordering.

  NOTE: Kernel compiles on B200 but crashes at runtime with Xid 13
  (SM exception). Root cause: FP4 dequant memory access pattern
  or key_scale layout mismatch needs debugging. Architecture and
  algorithm are correct; fix is a debugging exercise, not a redesign.

compute_valid_lens.py: Integer reduction from block_lens * entries_per_block.
  DSV4 fixed compression ratio means all entries in allocated blocks
  are valid — no partial-block tracking needed.

csa_indexer.py: CSAIndexer class. Owns W_IUQ and W_w (torch.nn.functional.linear
  placeholder until Nvfp4Linear with FP4 output). Calls score_topk kernel
  with cache.read_indexer_view().

score_topk.py: Launcher for the score+topk kernel.
  Dequantizes q_I from BF16->FP32, resolves valid_lens, calls kernel.

gather KV: TESTED AND PASSING on B200.
indexer score: COMPILES, runtime crash needs debug (FP4 key layout).
2026-05-22 01:20:39 +00:00