Add INT8 support MTP NextN function (#3911)

python/sglang/srt/models/deepseek_nextn.py

@@ -30,6 +30,9 @@ from sglang.srt.layers.quantization.fp8_utils import (
     block_quant_to_tensor_quant,
     normalize_e4m3fn_to_e4m3fnuz,
 )
+from sglang.srt.layers.quantization.int8_utils import (
+    block_dequant as int8_block_dequant,
+)
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,
@@ -291,6 +294,23 @@ class DeepseekV3ForCausalLMNextN(DeepseekV3ForCausalLM):
                         weight, weight_scale, weight_block_size
                     )
                     self_attn.w_scale = scale
+            if w.dtype == torch.int8:
+                if hasattr(self.quant_config, "weight_block_size"):
+                    # block-wise int8 need it
+                    weight_block_size = self.quant_config.weight_block_size
+                    if weight_block_size is not None:
+                        assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
+                        weight = w
+                        weight_scale = self_attn.kv_b_proj.weight_scale_inv
+                        w = int8_block_dequant(
+                            weight, weight_scale, weight_block_size
+                        ).to(torch.bfloat16)
+                else:
+                    # channel-wise int8 need it
+                    assert hasattr(self_attn.kv_b_proj, "weight_scale")
+                    w = w.to(torch.bfloat16) * self_attn.kv_b_proj.weight_scale.to(
+                        torch.bfloat16
+                    )
             w_kc, w_vc = w.unflatten(
                 0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
             ).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)