[Qwen] Add fp8 checkpoint support for qwen3-next. (#25079)

Signed-off-by: Tao He <linzhu.ht@alibaba-inc.com>

vllm/model_executor/models/qwen3_next.py

@@ -30,7 +30,6 @@ from vllm.model_executor.layers.layernorm import (
     GemmaRMSNorm as Qwen3NextRMSNorm)
 # yapf: enable
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                ReplicatedLinear,
                                                RowParallelLinear)
@@ -254,12 +253,20 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
         # projection of the input hidden states
         self.projection_size_qkvz = self.key_dim * 2 + self.value_dim * 2
         self.projection_size_ba = self.num_v_heads * 2
-        self.in_proj = MergedColumnParallelLinear(
+        self.in_proj_qkvz = ColumnParallelLinear(
             input_size=self.hidden_size,
-            output_sizes=[self.projection_size_qkvz, self.projection_size_ba],
+            output_size=self.projection_size_qkvz,
             bias=False,
             quant_config=quant_config,
-            prefix=f"{prefix}.in_proj",
+            prefix=f"{prefix}.in_proj_qkvz",
+        )
+        # ba_proj doesn't support blockwise fp8 quantization.
+        self.in_proj_ba = ColumnParallelLinear(
+            input_size=self.hidden_size,
+            output_size=self.projection_size_ba,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.in_proj_ba",
         )
 
         query_key_settings = (self.key_dim, 0, False)
@@ -420,19 +427,14 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
         ssm_state = self_kv_cache[1]
         num_actual_tokens = attn_metadata.num_actual_tokens
         num_accepted_tokens = attn_metadata.num_accepted_tokens
-
-        # 1. Set up dimensions for reshapes later
-        projected_states, _ = self.in_proj(hidden_states[:num_actual_tokens])
         if spec_token_masks is not None:
             spec_token_masks = spec_token_masks[:num_actual_tokens]
-        projected_states_qkvz, projected_states_ba = torch.split(
-            projected_states,
-            [
-                self.projection_size_qkvz // self.tp_size,
-                self.projection_size_ba // self.tp_size
-            ],
-            dim=-1,
-        )
+
+        # 1. Set up dimensions for reshapes later
+        projected_states_qkvz, _ = self.in_proj_qkvz(
+            hidden_states[:num_actual_tokens])
+        projected_states_ba, _ = self.in_proj_ba(
+            hidden_states[:num_actual_tokens])
         query, key, value, z, b, a = self.fix_query_key_value_ordering(
             projected_states_qkvz, projected_states_ba)
         query, key, value = map(lambda x: rearrange(x, 'l p d -> l (p d)'),
@@ -976,8 +978,6 @@ class Qwen3NextModel(nn.Module):
             ("qkv_proj", "v_proj", "v"),
             ("gate_up_proj", "gate_proj", 0),
             ("gate_up_proj", "up_proj", 1),
-            ("in_proj", "in_proj_qkvz", 0),
-            ("in_proj", "in_proj_ba", 1),
         ]
 
         params_dict = dict(self.named_parameters())
@@ -1055,7 +1055,6 @@ class Qwen3NextForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
             "v_proj",
         ],
         "gate_up_proj": ["gate_proj", "up_proj"],
-        "in_proj": ["in_proj_qkvz", "in_proj_ba"],
     }
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):

vllm/model_executor/models/qwen3_next_mtp.py

@@ -63,7 +63,9 @@ class Qwen3NextMultiTokenPredictor(nn.Module):
                                        self.config.hidden_size,
                                        gather_output=True,
                                        bias=False,
-                                       return_bias=False)
+                                       return_bias=False,
+                                       quant_config=quant_config,
+                                       prefix=f'{prefix}.fc')
 
         self.layers = torch.nn.ModuleList(
             Qwen3NextDecoderLayer(
@@ -72,7 +74,7 @@ class Qwen3NextMultiTokenPredictor(nn.Module):
                 model_config=model_config,
                 cache_config=cache_config,
                 quant_config=quant_config,
-                prefix=f'{prefix}.layers.{self.mtp_start_layer_idx + idx}',
+                prefix=f'{prefix}.layers.{idx}',
             ) for idx in range(self.num_mtp_layers))
 
         self.make_empty_intermediate_tensors = (
@@ -233,7 +235,7 @@ class Qwen3NextMTP(nn.Module, SupportsPP):
         self.config = config
         self.model = Qwen3NextMultiTokenPredictor(vllm_config=vllm_config,
                                                   prefix=maybe_prefix(
-                                                      prefix, "model"))
+                                                      prefix, "mtp"))
         self.unpadded_vocab_size = config.vocab_size
         self.lm_head = ParallelLMHead(self.unpadded_vocab_size,
                                       config.hidden_size,