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4453d7475a
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Fix layer construction: match existing API signatures, add RMSNorm impl
- Nvfp4GroupedLinear: (n_local_groups, heads_per_group, head_dim, o_lora_rank)
- mHCLayer: hidden_dim, t_max_sinkhorn (not hidden_size, sinkhorn_iters)
- RMSNorm: PyTorch reference implementation (BF16, cudagraph-safe)
- Verified: all 43 Flash + 61 Pro layers construct cleanly
- All projection shapes validated against architecture spec
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2026-05-21 23:31:58 +00:00 |
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66a89859ed
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Layer dispatch: config, schedule, attention/FFN sub-blocks, TransformerLayer
DSV4Config: frozen dataclass with .flash() / .pro() classmethods.
All architectural constants (dims, heads, MoE params, mHC) in one place.
LayerSchedule: pure-data per-layer-index -> (attn_type, ffn_type, router_mode).
Flash: SWA, SWA, CSA, HCA, CSA, HCA, ... (43 layers)
Pro: HCA, HCA, CSA, HCA, CSA, HCA, ... (61 layers)
Both: first 3 MoE layers = hash routing, rest = dense
validate_schedule() enforces correctness at construction.
AttentionSubBlock: CSA / HCA / SWA variants.
- Low-rank Q projection (q_down -> q_up)
- KV down-projection (varies by attn type: 4h/2h/1h)
- CSA: indexer_q_up + indexer_head_weights
- Grouped output projection (wo_a + wo_b)
- Kernel calls are imports (NotImplementedError until kernel lands)
- No PyTorch fallback paths
FFNSubBlock: MoE + shared expert.
- Router (hash/dense) mode from LayerSpec
- Nvfp4MoE + Nvfp4SharedExpert
TransformerLayer: composition of mHC + norm + attention + FFN.
- Two mHC wrappers (attn + ffn sub-blocks)
- Two RMSNorm (one per sub-block)
- Pure orchestration, no learned params on the layer itself
Tests: schedule construction + validation for both variants.
No forward tests yet (depends on FMHA kernel + KV cache).
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2026-05-21 23:11:09 +00:00 |
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abfe4485f7
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Router: full kernel stack — hash, topk, activation+topk, dense decode/prefill
Step 1: Hash router (hash_router.cu)
- One thread per token, gather from [vocab_size, k] LUT
- Uniform 1/k weights, FP32 output
- 3 MB LUT fits in L2 for repeated decode calls
Step 2: topk_select.cu — general top-k primitive
- Per-thread register min-heap (k=6, compile-time unrolled)
- Shared memory merge: thread 0 merges 64 partial heaps
- Tie-breaking: lower index wins on equal scores
- Reusable by CSA indexer
Step 3: activation_topk.cu — fused sqrt(softplus) + bias + topk + renorm
- Single kernel: all 6 steps of the router math, no intermediate buffers
- Numerically stable softplus: max(x,0) + log1p(exp(-|x|))
- Per-thread heap with unbiased activation co-stored
- Shared memory merge → sort descending → renormalize → store
Step 4: dense_router_decode.py — CuTeDSL fused GEMM kernel (skeleton)
- BF16 GEMM with tcgen05.mma, FP32 accumulator
- Custom epilogue: activation + bias + top-k (structure defined, needs TMA/MMA boilerplate)
- Dispatch: N<=64 uses fused decode, N>64 uses prefill path
Step 5: dense_router_prefill.py — prefill path
- torch.nn.functional.linear for GEMM (DeepGEMM integration deferred)
- Calls activation_topk for fused post-GEMM processing
Step 6: Router class + ops/router.py + test_router.py
- Router: construction-time mode (dense/hash), weight loading, custom_op dispatch
- ops/router.py: torch.library.custom_op wrappers, integer-keyed registry
- test_router.py: spec oracle tests (DO NOT RUN — Carmine is testing Stage C)
Test strategy: each kernel tested against its mathematical spec in FP32.
No reference implementation, no two debug streams. The oracle IS the math.
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2026-05-21 21:54:05 +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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