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b9f15c250f
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Stage E: head-packed MQA/GQA, batch dim, custom_op, integration API
- production.py: head-packed M dimension for MQA/GQA (q_per_kv*T rows
in single launch per KV group, eliminating redundant K/V TMA loads)
- production.py: batch dimension support (outer Python loop)
- production.py: warmup_attention_kernels() for pre-compilation
- production.py: dsv4_attention_per_head() for exact per-head sink bias
- __init__.py: sparse_fmha_with_swa, dense_fmha_with_swa, swa_only_fmha
integration functions bridging AttentionSubBlock → production FMHA
- custom_ops.py: dsv4::sparse_fmha_with_swa custom_op registration
- test_production.py: comprehensive tests (MHA/MQA/GQA, head-packed vs
per-head parity, multi-segment KV, SWA+causal+sink, batch, edge cases)
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2026-05-27 15:15:03 +00:00 |
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3fb3c925af
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Restructure: cutedsl/ -> dsv4/ with proper layering
- Split bridge.py -> ops/quantize.py, ops/layouts.py, ops/gemm_runner.py
- Renamed classes: CuTeDSLNvfp4Linear -> Nvfp4Linear, etc.
- Moved kernel code to dsv4/kernels/ (gemm, attention, compressor, decode, cuda)
- Moved PyTorch bridges to dsv4/ops/
- Moved nn.Module layers to dsv4layers/
- Moved reference implementations to dsv4/reference/
- Moved vendored CUTLASS code to vendored/
- Archived ~190 debug tests to tests/archive/
- Kept ~15 canonical tests in tests/unit/
- Updated all import paths
- Added stubs for future components (model/, cache/, loader/)
- Updated pyproject.toml: dsv4-inference package name
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2026-05-21 17:30:44 +00:00 |
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