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Update gate.py

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Atream 2025-03-19 16:14:54 +08:00 • committed by GitHub
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1 changed files with 11 additions and 9 deletions
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ktransformers/operators/gate.py
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@ -132,6 +132,7 @@ def grouped_topk(hidden_states: torch.Tensor,
renormalize: bool,
num_expert_group: int = 0,
topk_group: int = 0,
routed_scaling_factor: float = 1.0,
scoring_func: str = "sigmoid",
e_score_correction_bias: Optional[torch.Tensor] = None):
@ -163,8 +164,8 @@ def grouped_topk(hidden_states: torch.Tensor,
score_mask = group_mask.unsqueeze(-1).expand(
num_token, num_expert_group,
scores.shape[-1] // num_expert_group).reshape(num_token, -1) # [n, e]
tmp_scores = scores.masked_fill(~score_mask.bool(),
float("-inf")) # [n, e]
tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0)
#float("-inf")) # [n, e]
if e_score_correction_bias is not None:
topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=False)[1]
@ -176,9 +177,10 @@ def grouped_topk(hidden_states: torch.Tensor,
dim=-1,
sorted=False)
if renormalize:
topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
if topk > 1 and renormalize:
denominator = topk_weights.sum(dim=-1, keepdim=True) + 1e-20
topk_weights = topk_weights / denominator
topk_weights = topk_weights * routed_scaling_factor # must multiply the scaling factor
return topk_ids.to(torch.long), topk_weights.to(torch.float32)
class KMoEGateDeepSeekV3(BaseInjectedModule, KMoEGateBase):
@ -204,6 +206,7 @@ class KMoEGateDeepSeekV3(BaseInjectedModule, KMoEGateBase):
self.is_windows = os.name == 'nt'
self.use_quant = use_quant
if not self.is_windows and use_quant:
print("injecting gate_linear")
self.gate_linear = nn.Linear(self.gating_dim, self.n_routed_experts, device=generate_device)
self.gate_linear = KTransformersLinear(key + ".ffn_gate_inp",
gguf_loader, config, self.gate_linear, #orig_module
@ -219,14 +222,13 @@ class KMoEGateDeepSeekV3(BaseInjectedModule, KMoEGateBase):
### compute gating score
hidden_states = hidden_states.view(-1, h)
if self.use_quant:
logits = self.gate_linear.forward(logits)
logits = self.gate_linear.forward(hidden_states)
else:
logits = F.linear(
hidden_states.type(torch.float32), self.weight.type(torch.float32), None
)
return grouped_topk(hidden_states, logits, self.top_k, self.norm_topk_prob,
self.n_group, self.topk_group, "sigmoid", self.e_score_correction_bias)
return grouped_topk(hidden_states, logits, self.top_k, self.norm_topk_prob, self.n_group,
self.topk_group, self.routed_scaling_factor, "sigmoid", self.e_score_correction_bias)
def load(self, w: dict | nn.Parameter | tuple | None = None, device: str|None = None):
if device is None: device = self.device