Compat with latest VLLM 0.4.2 main + fork.number rename + Flashinfer 0.0.4 (#380)

Co-authored-by: ZX <zx@lbx.dev>
Co-authored-by: ZhouXingg <165115237+ZhouXingg@users.noreply.github.com>

python/sglang/srt/models/qwen2.py

@@ -1,27 +1,28 @@
 # Adapted from llama2.py
 # Modify details for the adaptation of Qwen2 model.
 """Inference-only Qwen2 model compatible with HuggingFace weights."""
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, Optional, Tuple
 
 import torch
 from torch import nn
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
-    LinearMethodBase,
     MergedColumnParallelLinear,
     QKVParallelLinear,
     RowParallelLinear,
 )
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.parallel_utils.parallel_state import (
+from vllm.distributed import (
     get_tensor_model_parallel_world_size,
 )
-from vllm.model_executor.weight_utils import (
+from sglang.srt.weight_utils import (
     default_weight_loader,
     hf_model_weights_iterator,
 )
@@ -39,17 +40,17 @@ class Qwen2MLP(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.gate_up_proj = MergedColumnParallelLinear(
             hidden_size,
             [intermediate_size] * 2,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.down_proj = RowParallelLinear(
-            intermediate_size, hidden_size, bias=False, linear_method=linear_method
+            intermediate_size, hidden_size, bias=False, quant_config=quant_config,
         )
         if hidden_act != "silu":
             raise ValueError(
@@ -75,7 +76,7 @@ class Qwen2Attention(nn.Module):
         rope_theta: float = 1000000,
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 32768,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = hidden_size
@@ -106,13 +107,13 @@ class Qwen2Attention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.o_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         self.rotary_emb = get_rope(
@@ -149,7 +150,7 @@ class Qwen2DecoderLayer(nn.Module):
         self,
         config: Qwen2Config,
         layer_id: int = 0,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -164,13 +165,13 @@ class Qwen2DecoderLayer(nn.Module):
             rope_theta=rope_theta,
             rope_scaling=rope_scaling,
             max_position_embeddings=max_position_embeddings,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.mlp = Qwen2MLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.post_attention_layernorm = RMSNorm(
@@ -206,7 +207,7 @@ class Qwen2Model(nn.Module):
     def __init__(
         self,
         config: Qwen2Config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
@@ -218,7 +219,7 @@ class Qwen2Model(nn.Module):
         )
         self.layers = nn.ModuleList(
             [
-                Qwen2DecoderLayer(config, i, linear_method)
+                Qwen2DecoderLayer(config, i, quant_config=quant_config)
                 for i in range(config.num_hidden_layers)
             ]
         )
@@ -252,12 +253,12 @@ class Qwen2ForCausalLM(nn.Module):
     def __init__(
         self,
         config: Qwen2Config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = Qwen2Model(config, linear_method)
+        self.quant_config = quant_config
+        self.model = Qwen2Model(config, quant_config=quant_config)
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config)