GLM-5 · Shared expert · OpenModelAtlas

in: $x_{\mathrm{ffn}}$ [B batch, T positions, 6144 d]
out: $y^{\mathrm{shared}}$ [B batch, T positions, 6144 d]
parameters: 37,748,736 · × 75 MoE blocks = 2,831,155,200

Parameters

37,748,736 parameters per instance, across its learned components

Exact per-instance accounting
Component	Calculation	Parameters
Gate projection	$6{,}144 \times 2{,}048$	12,582,912
Up projection	$6{,}144 \times 2{,}048$	12,582,912
Down projection	$2{,}048 \times 6{,}144$	12,582,912
Per-instance total		37,748,736

Across 75 independent MoE blocks: $37{,}748{,}736 \times 75 = 2{,}831{,}155{,}200$ parameters.

Derived from the pinned configuration and transformers implementation, and equal to the count of the checkpoint’s tensors read from its safetensors headers, less the multi-token-prediction layer the implementation does not load. The total includes the routers’ 19,200 correction-bias values, which the implementation holds as buffers.

Explanation

Its 2,048 hidden features are moe_intermediate_size × n_shared_experts (modeling_glm_moe_dsa.py 572–574).

Codemodeling_glm_moe_dsa.py 465–583

modeling_glm_moe_dsa.py 562–574

class GlmMoeDsaMoE(nn.Module):
    """
    A mixed expert module containing shared experts.
    """

    def __init__(self, config: GlmMoeDsaConfig):
        super().__init__()
        self.config = config
        self.experts = GlmMoeDsaExperts(config)
        self.gate = GlmMoeDsaTopkRouter(config)
        self.shared_experts = GlmMoeDsaMLP(
            config=config, intermediate_size=config.moe_intermediate_size * config.n_shared_experts
        )

modeling_glm_moe_dsa.py 535–559

    def forward(
        self,
        hidden_states: torch.Tensor,
        top_k_index: torch.Tensor,
        top_k_weights: torch.Tensor,
    ) -> torch.Tensor:
        final_hidden_states = torch.zeros_like(hidden_states)
        with torch.no_grad():
            expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts + 1)
            expert_mask = expert_mask.permute(2, 1, 0)
            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()

        for expert_idx in expert_hit:
            expert_idx = expert_idx[0]
            if expert_idx == self.num_experts:
                continue
            top_k_pos, token_idx = torch.where(expert_mask[expert_idx])
            current_state = hidden_states[token_idx]
            gate, up = nn.functional.linear(current_state, self.gate_up_proj[expert_idx]).chunk(2, dim=-1)
            current_hidden_states = self.act_fn(gate) * up
            current_hidden_states = nn.functional.linear(current_hidden_states, self.down_proj[expert_idx])
            current_hidden_states = current_hidden_states * top_k_weights[token_idx, top_k_pos, None]
            final_hidden_states.index_add_(0, token_idx, current_hidden_states.to(final_hidden_states.dtype))

        return final_hidden_states

modeling_glm_moe_dsa.py 576–583

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        residuals = hidden_states
        orig_shape = hidden_states.shape
        _, topk_weights, topk_indices = self.gate(hidden_states)
        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
        hidden_states = self.experts(hidden_states, topk_indices, topk_weights).view(*orig_shape)
        hidden_states = hidden_states + self.shared_experts(residuals)
        return hidden_states

modeling_glm_moe_dsa.py 465–478

class GlmMoeDsaMLP(nn.Module):
    def __init__(self, config, intermediate_size=None):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size if intermediate_size is None else intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
        return down_proj

Sources3

Direct evidence

The MoE layer holds the routed experts, the router and one shared expert MLP of n_shared_experts × moe_intermediate_size features.

moe-init · huggingface/transformers / modeling_glm_moe_dsa.py
Pinned revision 5e4d6304de55 ↗ · lines 562–574

Read code excerpt

class GlmMoeDsaMoE(nn.Module):
    """
    A mixed expert module containing shared experts.
    """

    def __init__(self, config: GlmMoeDsaConfig):
        super().__init__()
        self.config = config
        self.experts = GlmMoeDsaExperts(config)
        self.gate = GlmMoeDsaTopkRouter(config)
        self.shared_experts = GlmMoeDsaMLP(
            config=config, intermediate_size=config.moe_intermediate_size * config.n_shared_experts
        )

Direct evidence

Each token’s output is the router-weighted sum of its selected experts plus the shared expert applied to every token.

moe-forward · huggingface/transformers / modeling_glm_moe_dsa.py
Pinned revision 5e4d6304de55 ↗ · lines 535–583

Read code excerpt

    def forward(
        self,
        hidden_states: torch.Tensor,
        top_k_index: torch.Tensor,
        top_k_weights: torch.Tensor,
    ) -> torch.Tensor:
        final_hidden_states = torch.zeros_like(hidden_states)
        with torch.no_grad():
            expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts + 1)
            expert_mask = expert_mask.permute(2, 1, 0)
            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()

        for expert_idx in expert_hit:
            expert_idx = expert_idx[0]
            if expert_idx == self.num_experts:
                continue
            top_k_pos, token_idx = torch.where(expert_mask[expert_idx])
            current_state = hidden_states[token_idx]
            gate, up = nn.functional.linear(current_state, self.gate_up_proj[expert_idx]).chunk(2, dim=-1)
            current_hidden_states = self.act_fn(gate) * up
            current_hidden_states = nn.functional.linear(current_hidden_states, self.down_proj[expert_idx])
            current_hidden_states = current_hidden_states * top_k_weights[token_idx, top_k_pos, None]
            final_hidden_states.index_add_(0, token_idx, current_hidden_states.to(final_hidden_states.dtype))

        return final_hidden_states

… lines 560–575 omitted …

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        residuals = hidden_states
        orig_shape = hidden_states.shape
        _, topk_weights, topk_indices = self.gate(hidden_states)
        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
        hidden_states = self.experts(hidden_states, topk_indices, topk_weights).view(*orig_shape)
        hidden_states = hidden_states + self.shared_experts(residuals)
        return hidden_states

Direct evidence

The dense feed-forward network is a SwiGLU MLP, down(silu(gate x) ⊙ up x), computed in the input’s precision.

mlp · huggingface/transformers / modeling_glm_moe_dsa.py
Pinned revision 5e4d6304de55 ↗ · lines 465–478

Read code excerpt

class GlmMoeDsaMLP(nn.Module):
    def __init__(self, config, intermediate_size=None):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size if intermediate_size is None else intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
        return down_proj