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[None][fix] Ensure that the W4A8 custom input scale remains aligned across all ranks #7614

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Merged
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Sep 16, 2025
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[None][fix] Ensure that the W4A8 custom input scale remains aligned across all ranks #7614

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8 changes: 6 additions & 2 deletions tensorrt_llm/_torch/modules/fused_moe/quantization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1144,16 +1144,20 @@ def load_quant_scales(self, module: torch.nn.Module, weights: Dict):
weight_scale_name = "weight_scale"

assert (len(module.interleave) == 2)

# Ensure that the input_scale remains aligned across all ranks for W4A8 custom.
input_scale_expert_ids = module.initial_local_expert_ids if not w4a8_custom else range(
module.num_experts)
# fc31 scales
all_w3_input_scales = [
load_weight_shard(weights[f"{expert_id}.w3.input_scale"],
device=self.device)
for expert_id in module.initial_local_expert_ids
for expert_id in input_scale_expert_ids
]
all_w1_input_scales = [
load_weight_shard(weights[f"{expert_id}.w1.input_scale"],
device=self.device)
for expert_id in module.initial_local_expert_ids
for expert_id in input_scale_expert_ids
]
all_w3_w1_input_scales_max = torch.max(
torch.stack(all_w3_input_scales),
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