61 transformer blocks › 58 MoE blocks › Sparse latent attention › Query compression
Query latent RMSNorm
×learnedself.q_norm(self.wq_a(x))
Rescale each position’s features using their root mean square, then apply learned gain.
- in
- [B batch, T positions, 1536 q latent]
- out
- [B batch, T positions, 1536 q latent]
- parameters
- 1,536 · × 58 MoE blocks = 89,088
Normalized in float32, multiplied by the gain in float32, then cast back to the input type (model.py 296–301).
Parameters
1,536 parameters per instance.
| Component | Calculation | Parameters |
|---|---|---|
| Learned gain | 1,536 | |
| Per-instance total | 1,536 | |
Each normalization instance owns its learned gain vector.
Across 58 independent MoE blocks: parameters.
Derived from the configured feature width; checkpoint tensors have not been independently counted.
Codemodel.py 280–564
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.dim = dim
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim, dtype=torch.float32))
def forward(self, x: torch.Tensor, residual: Optional[torch.Tensor] = None):
"""
Forward pass for RMSNorm.
Args:
x (torch.Tensor): Input tensor.
Returns:
torch.Tensor: Normalized tensor with the same shape as input.
"""
dtype = x.dtype
if residual is None:
x = x.float()
var = x.pow(2).mean(-1, keepdim=True)
x = x * torch.rsqrt(var + self.eps)
return (self.weight * x).to(dtype)
else:
x = residual = x.float() + residual.float()
var = x.pow(2).mean(-1, keepdim=True)
x = x * torch.rsqrt(var + self.eps)
return (self.weight * x).to(dtype), residual.to(dtype) self.qk_head_dim = args.qk_nope_head_dim + args.qk_rope_head_dim
self.v_head_dim = args.v_head_dim
self.wq_a = Linear(self.dim, self.q_lora_rank)
self.q_norm = RMSNorm(self.q_lora_rank)
self.wq_b = ColumnParallelLinear(self.q_lora_rank, self.n_heads * self.qk_head_dim)
self.wkv_a = Linear(self.dim, self.kv_lora_rank + self.qk_rope_head_dim)
self.kv_norm = RMSNorm(self.kv_lora_rank)
self.wkv_b = ColumnParallelLinear(self.kv_lora_rank, self.n_heads * (self.qk_nope_head_dim + self.v_head_dim))
self.wo = RowParallelLinear(self.n_heads * self.v_head_dim, self.dim)
self.softmax_scale = self.qk_head_dim ** -0.5
self.scale_fmt = args.scale_fmt
if args.max_seq_len > args.original_seq_len:
mscale = 0.1 * args.mscale * math.log(args.rope_factor) + 1.0
self.softmax_scale = self.softmax_scale * mscale * mscale
self.indexer = Indexer(args) qr = self.q_norm(self.wq_a(x))
q = self.wq_b(qr)
q = q.view(bsz, seqlen, self.n_local_heads, self.qk_head_dim)
q_nope, q_pe = torch.split(q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
q_pe = apply_rotary_emb(q_pe, freqs_cis)Sources3
Direct evidence
RMSNorm owns one gain per feature and computes in float32; with a residual argument it first adds the residual, fusing the residual addition into the next normalization.
rmsnorm · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 280–306
Read code excerpt
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.dim = dim
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim, dtype=torch.float32))
def forward(self, x: torch.Tensor, residual: Optional[torch.Tensor] = None):
"""
Forward pass for RMSNorm.
Args:
x (torch.Tensor): Input tensor.
Returns:
torch.Tensor: Normalized tensor with the same shape as input.
"""
dtype = x.dtype
if residual is None:
x = x.float()
var = x.pow(2).mean(-1, keepdim=True)
x = x * torch.rsqrt(var + self.eps)
return (self.weight * x).to(dtype)
else:
x = residual = x.float() + residual.float()
var = x.pow(2).mean(-1, keepdim=True)
x = x * torch.rsqrt(var + self.eps)
return (self.weight * x).to(dtype), residual.to(dtype)Direct evidence
MLA owns a query down-projection, query RMSNorm and up-projection, a joint key–value down-projection that also emits the shared rotary key, a latent RMSNorm, a key–value up-projection and an output projection.
mla-init · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 523–539
Read code excerpt
self.qk_head_dim = args.qk_nope_head_dim + args.qk_rope_head_dim
self.v_head_dim = args.v_head_dim
self.wq_a = Linear(self.dim, self.q_lora_rank)
self.q_norm = RMSNorm(self.q_lora_rank)
self.wq_b = ColumnParallelLinear(self.q_lora_rank, self.n_heads * self.qk_head_dim)
self.wkv_a = Linear(self.dim, self.kv_lora_rank + self.qk_rope_head_dim)
self.kv_norm = RMSNorm(self.kv_lora_rank)
self.wkv_b = ColumnParallelLinear(self.kv_lora_rank, self.n_heads * (self.qk_nope_head_dim + self.v_head_dim))
self.wo = RowParallelLinear(self.n_heads * self.v_head_dim, self.dim)
self.softmax_scale = self.qk_head_dim ** -0.5
self.scale_fmt = args.scale_fmt
if args.max_seq_len > args.original_seq_len:
mscale = 0.1 * args.mscale * math.log(args.rope_factor) + 1.0
self.softmax_scale = self.softmax_scale * mscale * mscale
self.indexer = Indexer(args)Direct evidence
Queries are produced through a low-rank latent, split into a non-rotated and a rotated part, and rotated.
mla-queries · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 560–564
Read code excerpt
qr = self.q_norm(self.wq_a(x))
q = self.wq_b(qr)
q = q.view(bsz, seqlen, self.n_local_heads, self.qk_head_dim)
q_nope, q_pe = torch.split(q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
q_pe = apply_rotary_emb(q_pe, freqs_cis)