[QUANT] Add GPTQModel Dynamic Quantization + lm_head Quantization (#3790)

Signed-off-by: ZX-ModelCloud <zx@modelcloud.ai>
Co-authored-by: ZX-ModelCloud <zx@modelcloud.ai>

python/sglang/srt/models/minicpm.py

@@ -37,6 +37,7 @@ from sglang.srt.layers.vocab_parallel_embedding import (
 )
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.utils import add_prefix
 
 
 class MiniCPMMLP(nn.Module):
@@ -46,6 +47,7 @@ class MiniCPMMLP(nn.Module):
         intermediate_size: int,
         hidden_act: str,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.gate_up_proj = MergedColumnParallelLinear(
@@ -53,12 +55,14 @@ class MiniCPMMLP(nn.Module):
             [intermediate_size] * 2,
             bias=False,
             quant_config=quant_config,
+            prefix=add_prefix("gate_up_proj", prefix),
         )
         self.down_proj = RowParallelLinear(
             intermediate_size,
             hidden_size,
             bias=False,
             quant_config=quant_config,
+            prefix=add_prefix("down_proj", prefix),
         )
         if hidden_act != "silu":
             raise ValueError(
@@ -85,6 +89,7 @@ class MiniCPMAttention(nn.Module):
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 8192,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.hidden_size = hidden_size
@@ -116,12 +121,14 @@ class MiniCPMAttention(nn.Module):
             self.total_num_kv_heads,
             bias=False,
             quant_config=quant_config,
+            prefix=add_prefix("qkv_proj", prefix),
         )
         self.o_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=False,
             quant_config=quant_config,
+            prefix=add_prefix("o_proj", prefix),
         )
 
         self.rotary_emb = get_rope(
@@ -139,6 +146,7 @@ class MiniCPMAttention(nn.Module):
             self.scaling,
             num_kv_heads=self.num_kv_heads,
             layer_id=layer_id,
+            prefix=add_prefix("attn", prefix),
         )
 
     def forward(
@@ -164,6 +172,7 @@ class MiniCPMDecoderLayer(nn.Module):
         config,
         layer_id: int = 0,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.config = config
@@ -180,12 +189,14 @@ class MiniCPMDecoderLayer(nn.Module):
             rope_scaling=rope_scaling,
             max_position_embeddings=max_position_embeddings,
             quant_config=quant_config,
+            prefix=add_prefix("self_attn", prefix),
         )
         self.mlp = MiniCPMMLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
             quant_config=quant_config,
+            prefix=add_prefix("mlp", prefix),
         )
         self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.post_attention_layernorm = RMSNorm(
@@ -227,6 +238,7 @@ class MiniCPMModel(nn.Module):
         self,
         config,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.config = config
@@ -236,10 +248,16 @@ class MiniCPMModel(nn.Module):
             self.vocab_size,
             config.hidden_size,
             org_num_embeddings=config.vocab_size,
+            prefix=add_prefix("embed_tokens", prefix),
         )
         self.layers = nn.ModuleList(
             [
-                MiniCPMDecoderLayer(config, i, quant_config=quant_config)
+                MiniCPMDecoderLayer(
+                    config,
+                    i,
+                    quant_config=quant_config,
+                    prefix=add_prefix(f"layers.{i}", prefix),
+                )
                 for i in range(config.num_hidden_layers)
             ]
         )
@@ -275,19 +293,23 @@ class MiniCPMForCausalLM(nn.Module):
         self,
         config,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.config = config
 
         self.num_experts = getattr(self.config, "num_experts", 0)
         self.quant_config = quant_config
-        self.model = MiniCPMModel(config, quant_config=quant_config)
+        self.model = MiniCPMModel(
+            config, quant_config=quant_config, prefix=add_prefix("model", prefix)
+        )
         # self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         if not self.config.tie_word_embeddings:
             self.lm_head = ParallelLMHead(
                 config.vocab_size,
                 config.hidden_size,
                 org_num_embeddings=config.vocab_size,
+                prefix=add_prefix("lm_head", prefix),
             )
 
         self.scale_width = self.config.hidden_size / self.config.dim_model_base