biondizzle
fb243a4133
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.
28 lines
1.0 KiB
Python
28 lines
1.0 KiB
Python
# tests/unit/test_dense_router.py
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import torch
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from dsv4.layers.router import Router
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def test_dense_router_matches_spec(N=64, H=4096, E=256, k=6):
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X = torch.randn(N, H, dtype=torch.bfloat16, device='cuda')
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W = torch.randn(H, E, dtype=torch.bfloat16, device='cuda')
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bias = torch.randn(E, dtype=torch.float32, device='cuda') * 0.01
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scaling = 2.5
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# Oracle: directly compute the spec, in one expression, in FP32.
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# This is not "a PyTorch reference implementation" — it's the math.
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logits = (X.float() @ W.float())
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act = torch.sqrt(torch.nn.functional.softplus(logits))
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score = act + bias
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ids = score.topk(k, dim=-1).indices
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w = act.gather(-1, ids)
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w = w / w.sum(-1, keepdim=True) * scaling
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# Kernel under test:
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router = Router(H, E, k, scaling, mode='dense')
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router.W_gate.copy_(W)
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router.e_bias.copy_(bias)
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out_w, out_ids = router(X, layer_idx=5)
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assert (out_ids == ids).all() # ids must be exact match
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torch.testing.assert_close(out_w, w, atol=1e-4, rtol=1e-3)
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