biondizzle
4453d7475a
- 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
31 lines
1.1 KiB
Python
31 lines
1.1 KiB
Python
"""RMSNorm — PyTorch reference implementation.
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Swap to fused kernel (CuTeDSL) in Phase 6. API won't change.
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"""
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import torch
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class RMSNorm:
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"""Root Mean Square Layer Normalization.
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y = x / sqrt(mean(x^2) + eps) * weight
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CUDA-graph-compatible: weight is a buffer, no CPU syncs.
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"""
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def __init__(self, hidden_size: int, eps: float = 1e-6, device: str = "cuda"):
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self.hidden_size = hidden_size
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self.eps = eps
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self.device = device
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self.weight: torch.Tensor | None = None # (hidden_size,) FP32, set by load_weights
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def load_weights(self, weight: torch.Tensor) -> None:
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assert weight.shape == (self.hidden_size,), f"weight shape {weight.shape} != ({self.hidden_size},)"
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self.weight = weight.to(device=self.device, dtype=torch.float32)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""x: (T, hidden_size) BF16 -> (T, hidden_size) BF16"""
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x_f = x.float()
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rms = x_f.pow(2).mean(dim=-1, keepdim=True).add(self.eps).rsqrt()
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return (x_f * rms * self.weight).to(torch.bfloat16)
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