DeepSeek V4.1 · Query RMSNorm

40 layers › 2 groups of 6 layers › 5 reusing layers › Compressed sparse attention › Query compression

Query RMSNorm

learnedself.q_norm(…) · 770

Rescale each position’s features using their root mean square, then apply learned gain.

\begin{aligned}m_2(x)&=\frac{1}{d}\sum_{j=1}^d x_j^2\\y&=\gamma_{{\htmlClass{vl-stream-q}{Q}}}\odot\frac{x}{\sqrt{m_2(x)+\varepsilon}}\\\varepsilon&=10^{-20}\end{aligned}

in: $q^{a}$ [B batch, T positions, 1280 query latent]
out: $c^{Q}$ [B batch, T positions, 1280 query latent]
parameters: 1,280 · × 2 groups × 5 layers = 12,800

Parameters

1,280 parameters per instance.

Exact per-instance accounting
Component	Calculation	Parameters
Learned gain	$1{,}280$	1,280
Per-instance total		1,280

Each normalization instance owns its learned gain vector.

Across 5 independent layers: $1{,}280 \times 5 = 6{,}400$ parameters.

Derived from the configured feature width; the model total matches the checkpoint’s tensors.

Codemodel.py 281–293, 765–789

model.py 281–293

class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.dim = dim
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def forward(self, x: torch.Tensor):
        dtype = x.dtype
        x = x.float()
        var = x.square().mean(-1, keepdim=True)
        x = x * torch.rsqrt(var + self.eps)
        return (self.weight * x).to(dtype)

model.py 765–789

    def forward(self, x: torch.Tensor, start_pos: int):
        bsz, seqlen, _ = x.size()
        freqs_cis = self.freqs_cis[start_pos : start_pos + seqlen]
        rd = self.rope_head_dim

        qr = self.q_norm(self.wq_a(x))
        q = self.wq_b(qr).unflatten(-1, (self.n_local_heads, self.head_dim))
        apply_rotary_emb(q[..., -rd:], freqs_cis)

        kv, topk_idxs = self._window_kv(x, freqs_cis, start_pos)
        if self.compress_ratio:
            compress_kv, compress_idxs = self._compress_kv(x, qr, start_pos, kv.size(1))
            kv = torch.cat([kv, compress_kv], dim=1)
            topk_idxs = torch.cat([topk_idxs, compress_idxs], dim=-1)

        o = sparse_attn(q, kv, self.attn_sink, topk_idxs, self.softmax_scale)
        apply_rotary_emb(o[..., -rd:], freqs_cis, True)

        # wo_a is block-diagonal over groups (each projects only its own heads), hence einsum not
        # Linear. convert.py dequantizes it to bf16; an fp8 grouped GEMM would halve the memory.
        o = o.view(bsz, seqlen, self.n_local_groups, -1)
        wo_a = self.wo_a.weight.view(self.n_local_groups, self.o_lora_rank, -1)
        o = torch.einsum("bsgd,grd->bsgr", o, wo_a)
        x = self.wo_b(o.flatten(2))
        return x

Sources2

Direct evidence

RMSNorm divides by the root mean square over the features, computed in float32, and multiplies by a learned gain.

rmsnorm · deepseek-ai/DeepSeek-V4.1-Flash / inference/model.py
Pinned revision 2cba9e42aa02 ↗ · lines 281–293

Read code excerpt

class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.dim = dim
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def forward(self, x: torch.Tensor):
        dtype = x.dtype
        x = x.float()
        var = x.square().mean(-1, keepdim=True)
        x = x * torch.rsqrt(var + self.eps)
        return (self.weight * x).to(dtype)

Direct evidence

Queries through the latent with their rotary part rotated; window and compressed keys and values joined; sparse attention over the listed positions; the output rotated back, projected per group of heads and then to the model width.

attention-forward · deepseek-ai/DeepSeek-V4.1-Flash / inference/model.py
Pinned revision 2cba9e42aa02 ↗ · lines 765–789

Read code excerpt

    def forward(self, x: torch.Tensor, start_pos: int):
        bsz, seqlen, _ = x.size()
        freqs_cis = self.freqs_cis[start_pos : start_pos + seqlen]
        rd = self.rope_head_dim

        qr = self.q_norm(self.wq_a(x))
        q = self.wq_b(qr).unflatten(-1, (self.n_local_heads, self.head_dim))
        apply_rotary_emb(q[..., -rd:], freqs_cis)

        kv, topk_idxs = self._window_kv(x, freqs_cis, start_pos)
        if self.compress_ratio:
            compress_kv, compress_idxs = self._compress_kv(x, qr, start_pos, kv.size(1))
            kv = torch.cat([kv, compress_kv], dim=1)
            topk_idxs = torch.cat([topk_idxs, compress_idxs], dim=-1)

        o = sparse_attn(q, kv, self.attn_sink, topk_idxs, self.softmax_scale)
        apply_rotary_emb(o[..., -rd:], freqs_cis, True)

        # wo_a is block-diagonal over groups (each projects only its own heads), hence einsum not
        # Linear. convert.py dequantizes it to bf16; an fp8 grouped GEMM would halve the memory.
        o = o.view(bsz, seqlen, self.n_local_groups, -1)
        wo_a = self.wo_a.weight.view(self.n_local_groups, self.o_lora_rank, -1)
        o = torch.einsum("bsgd,grd->bsgr", o, wo_a)
        x = self.wo_b(o.flatten(2))
        return x