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self.gate dtype update for GLM-4.5 #22203

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Merged
merged 5 commits into from
Aug 5, 2025
Merged

self.gate dtype update for GLM-4.5 #22203

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e2b1ab9
fuse fp32
zRzRzRzRzRzRzR Aug 3, 2025
54b2e8b
Merge branch 'vllm-project:main' into glm-45
zRzRzRzRzRzRzR Aug 4, 2025
e124e40
Update glm4_moe.py
zRzRzRzRzRzRzR Aug 4, 2025
04c219f
Merge branch 'vllm-project:main' into glm-45
zRzRzRzRzRzRzR Aug 4, 2025
b1c9528
rename
zRzRzRzRzRzRzR Aug 4, 2025
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3 changes: 2 additions & 1 deletion vllm/model_executor/models/glm4_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -123,6 +123,7 @@ def __init__(
config.n_routed_experts,
bias=False,
quant_config=None,
params_dtype=torch.float32,
prefix=f"{prefix}.gate")

self.gate.e_score_correction_bias = nn.Parameter(
Expand Down Expand Up @@ -180,7 +181,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:

if self.n_shared_experts is not None:
shared_output = self.shared_experts(hidden_states)
router_logits, _ = self.gate(hidden_states)
router_logits, _ = self.gate(hidden_states.to(dtype=torch.float32))
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high

The explicit cast hidden_states.to(dtype=torch.float32) is redundant and introduces an unnecessary memory copy, which can negatively impact performance.

Since self.gate.weight is already of dtype=torch.float32 (due to the change in __init__), torch.nn.functional.linear (which is called internally by ColumnParallelLinear) will automatically perform the matrix multiplication in float32 by upcasting the hidden_states tensor. This implicit type promotion is more efficient than an explicit cast.

Removing the explicit cast will rely on this standard PyTorch behavior and avoid the overhead, while still achieving the goal of performing the gate computation in float32.

Suggested change
router_logits, _ = self.gate(hidden_states.to(dtype=torch.float32))
router_logits, _ = self.gate(hidden_states)

final_hidden_states = self.experts(
hidden_states=hidden_states,
router_logits=router_logits) * self.routed_scaling_factor
Expand Down
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