DeepSeek-V3.2-Exp · Up features

61 transformer blocks › 58 MoE blocks › Mixture of experts › 256 routed experts

Up features

tensorself.w3(x)

The parallel feature expansion.

shape: $u$ [B batch, T positions, 2048 expert d_ff]

Sources

Contextual evidence from Up projection

Each routed expert is its own SwiGLU MLP of moe_inter_dim hidden features.

expert · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 730–744

Read code excerpt

        self.w1 = Linear(dim, inter_dim)
        self.w2 = Linear(inter_dim, dim)
        self.w3 = Linear(dim, inter_dim)

… lines 733–743 omitted …

        return self.w2((F.silu(self.w1(x).float()) * self.w3(x).float()).type_as(x))

Contextual evidence from Up projection

Linear layers have no bias unless asked. FP8 weights carry one float32 scale per 128 × 128 block; the scales are quantization metadata and are excluded from parameter counts.

linear · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 15–193

Read code excerpt

block_size = 128

… lines 16–178 omitted …

    def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = nn.Parameter(torch.empty(out_features, in_features, dtype=dtype or Linear.dtype))
        if self.weight.element_size() == 1:
            scale_out_features = (out_features + block_size - 1) // block_size
            scale_in_features = (in_features + block_size - 1) // block_size
            self.weight.scale = self.scale = nn.Parameter(torch.empty(scale_out_features, scale_in_features, dtype=torch.float32))
        else:
            self.register_parameter("scale", None)
        if bias:
            self.bias = nn.Parameter(torch.empty(out_features))
        else:
            self.register_parameter("bias", None)