[Misc][Refactor] Generalize linear_method to be quant_method (#4373)

vllm/model_executor/models/deepseek.py

@@ -34,12 +34,13 @@ from vllm.distributed import (get_tensor_model_parallel_rank,
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe import fused_moe
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                ReplicatedLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -56,18 +57,18 @@ class DeepseekMLP(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         reduce_results: bool = True,
     ) -> 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,
+                                           quant_config=quant_config,
                                            reduce_results=reduce_results)
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
@@ -86,7 +87,7 @@ class DeepseekMoE(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -103,7 +104,7 @@ class DeepseekMoE(nn.Module):
             DeepseekMLP(hidden_size=config.hidden_size,
                         intermediate_size=config.moe_intermediate_size,
                         hidden_act=config.hidden_act,
-                        linear_method=linear_method,
+                        quant_config=quant_config,
                         reduce_results=False)
             for idx in range(self.n_routed_experts)
         ])
@@ -112,7 +113,7 @@ class DeepseekMoE(nn.Module):
         self.gate = ReplicatedLinear(config.hidden_size,
                                      self.n_routed_experts,
                                      bias=False,
-                                     linear_method=None)
+                                     quant_config=None)
 
         if config.n_shared_experts is not None:
             intermediate_size = (config.moe_intermediate_size *
@@ -121,7 +122,7 @@ class DeepseekMoE(nn.Module):
                 hidden_size=config.hidden_size,
                 intermediate_size=intermediate_size,
                 hidden_act=config.hidden_act,
-                linear_method=linear_method,
+                quant_config=quant_config,
                 reduce_results=False,
             )
 
@@ -177,7 +178,7 @@ class DeepseekAttention(nn.Module):
         rope_theta: float = 10000,
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 8192,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = hidden_size
@@ -208,14 +209,14 @@ class DeepseekAttention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=False,
-            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(
@@ -251,7 +252,7 @@ class DeepseekDecoderLayer(nn.Module):
         self,
         config: PretrainedConfig,
         layer_idx: int,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -266,18 +267,18 @@ class DeepseekDecoderLayer(nn.Module):
             rope_theta=rope_theta,
             rope_scaling=rope_scaling,
             max_position_embeddings=max_position_embeddings,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         if (config.n_routed_experts is not None
                 and layer_idx >= config.first_k_dense_replace
                 and layer_idx % config.moe_layer_freq == 0):
-            self.mlp = DeepseekMoE(config=config, linear_method=linear_method)
+            self.mlp = DeepseekMoE(config=config, quant_config=quant_config)
         else:
             self.mlp = DeepseekMLP(
                 hidden_size=config.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)
@@ -320,7 +321,7 @@ class DeepseekModel(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.padding_idx = config.pad_token_id
@@ -331,9 +332,7 @@ class DeepseekModel(nn.Module):
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            DeepseekDecoderLayer(config,
-                                 layer_idx,
-                                 linear_method=linear_method)
+            DeepseekDecoderLayer(config, layer_idx, quant_config=quant_config)
             for layer_idx in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -361,12 +360,12 @@ class DeepseekForCausalLM(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = DeepseekModel(config, linear_method)
+        self.quant_config = quant_config
+        self.model = DeepseekModel(config, quant_config)
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()

vllm/model_executor/models/falcon.py

@@ -32,10 +32,11 @@ from vllm.distributed import (get_tensor_model_parallel_rank,
                               tensor_model_parallel_all_reduce)
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -76,7 +77,7 @@ class FalconAttention(nn.Module):
     def __init__(
         self,
         config: FalconConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
 
@@ -115,7 +116,7 @@ class FalconAttention(nn.Module):
             self.total_num_kv_heads,
             bias=config.bias,
             skip_bias_add=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.q_size = self.num_heads * self.head_dim
         self.kv_size = self.num_kv_heads * self.head_dim
@@ -129,7 +130,7 @@ class FalconAttention(nn.Module):
             self.hidden_size,
             bias=config.bias,
             skip_bias_add=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
             reduce_results=self.reduce_row_parallel_results)
 
         self.use_rotary = config.rotary
@@ -192,7 +193,7 @@ class FalconMLP(nn.Module):
     def __init__(
         self,
         config: FalconConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         hidden_size = config.hidden_size
@@ -201,8 +202,8 @@ class FalconMLP(nn.Module):
                                                   4 * hidden_size,
                                                   bias=config.bias,
                                                   skip_bias_add=True,
-                                                  linear_method=linear_method)
-        quant_config = getattr(linear_method, "quant_config", None)
+                                                  quant_config=quant_config)
+        quant_config = getattr(quant_config, "quant_config", None)
         self.act = get_act_fn("gelu", quant_config, 4 * hidden_size)
         self.reduce_row_parallel_results = not (config.new_decoder_architecture
                                                 or config.parallel_attn)
@@ -212,7 +213,7 @@ class FalconMLP(nn.Module):
             bias=config.bias,
             skip_bias_add=True,
             reduce_results=self.reduce_row_parallel_results,
-            linear_method=linear_method)
+            quant_config=quant_config)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         # NOTE(zhuohan): Following huggingface, we do not fuse bias add here.
@@ -229,13 +230,13 @@ class FalconDecoderLayer(nn.Module):
     def __init__(
         self,
         config: FalconConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         hidden_size = config.hidden_size
         self.num_heads = config.num_attention_heads
-        self.self_attention = FalconAttention(config, linear_method)
-        self.mlp = FalconMLP(config, linear_method)
+        self.self_attention = FalconAttention(config, quant_config)
+        self.mlp = FalconMLP(config, quant_config)
         self.config = config
 
         if config.new_decoder_architecture:
@@ -311,7 +312,7 @@ class FalconModel(nn.Module):
     def __init__(
         self,
         config: FalconConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -327,7 +328,7 @@ class FalconModel(nn.Module):
 
         # Transformer blocks
         self.h = nn.ModuleList([
-            FalconDecoderLayer(config, linear_method)
+            FalconDecoderLayer(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
 
@@ -359,12 +360,12 @@ class FalconForCausalLM(nn.Module):
     def __init__(
         self,
         config: FalconConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.transformer = FalconModel(config, linear_method)
+        self.quant_config = quant_config
+        self.transformer = FalconModel(config, quant_config)
         self.lm_head_weight = self.transformer.word_embeddings.weight
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()

vllm/model_executor/models/gemma.py

@@ -27,11 +27,12 @@ from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import GeluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -77,17 +78,17 @@ class GemmaMLP(nn.Module):
         intermediate_size: int,
         hidden_act: Optional[str] = None,
         hidden_activation: Optional[str] = None,
-        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)
+                                           quant_config=quant_config)
         self.act_fn = _get_gemma_act_fn(hidden_act, hidden_activation)
 
     def forward(self, x):
@@ -106,7 +107,7 @@ class GemmaAttention(nn.Module):
                  head_dim: int,
                  max_position_embeddings: int = 8192,
                  rope_theta: float = 10000,
-                 linear_method: Optional[LinearMethodBase] = None) -> None:
+                 quant_config: Optional[QuantizationConfig] = None) -> None:
         super().__init__()
         self.hidden_size = hidden_size
         tp_size = get_tensor_model_parallel_world_size()
@@ -135,13 +136,13 @@ class GemmaAttention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=False,
-            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(
@@ -176,7 +177,7 @@ class GemmaDecoderLayer(nn.Module):
     def __init__(
         self,
         config: GemmaConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -187,14 +188,14 @@ class GemmaDecoderLayer(nn.Module):
             head_dim=config.head_dim,
             max_position_embeddings=config.max_position_embeddings,
             rope_theta=config.rope_theta,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.mlp = GemmaMLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
             hidden_activation=getattr(config, "hidden_activation", None),
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.input_layernorm = RMSNorm(config.hidden_size,
                                        eps=config.rms_norm_eps)
@@ -235,7 +236,7 @@ class GemmaModel(nn.Module):
     def __init__(
         self,
         config: GemmaConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
@@ -245,7 +246,7 @@ class GemmaModel(nn.Module):
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            GemmaDecoderLayer(config, linear_method)
+            GemmaDecoderLayer(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -308,14 +309,14 @@ class GemmaForCausalLM(nn.Module):
     def __init__(
         self,
         config: GemmaConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         del lora_config  # Unused.
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = GemmaModel(config, linear_method)
+        self.quant_config = quant_config
+        self.model = GemmaModel(config, quant_config)
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()
 

vllm/model_executor/models/gpt2.py

@@ -27,10 +27,11 @@ from vllm.attention import Attention, AttentionMetadata
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
@@ -44,7 +45,7 @@ class GPT2Attention(nn.Module):
     def __init__(
         self,
         config: GPT2Config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -61,13 +62,13 @@ class GPT2Attention(nn.Module):
             self.head_dim,
             total_num_heads,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.c_proj = RowParallelLinear(
             self.hidden_size,
             self.hidden_size,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.attn = Attention(self.num_heads, self.head_dim, scale=self.scale)
 
@@ -90,7 +91,7 @@ class GPT2MLP(nn.Module):
         self,
         intermediate_size: int,
         config: GPT2Config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         hidden_size = config.hidden_size
@@ -98,15 +99,15 @@ class GPT2MLP(nn.Module):
             hidden_size,
             intermediate_size,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.c_proj = RowParallelLinear(
             intermediate_size,
             hidden_size,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
-        quant_config = getattr(linear_method, "quant_config", None)
+        quant_config = getattr(quant_config, "quant_config", None)
         self.act = get_act_fn(config.activation_function, quant_config,
                               intermediate_size)
 
@@ -122,7 +123,7 @@ class GPT2Block(nn.Module):
     def __init__(
         self,
         config: GPT2Config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         hidden_size = config.hidden_size
@@ -130,9 +131,9 @@ class GPT2Block(nn.Module):
                      hidden_size)
 
         self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.attn = GPT2Attention(config, linear_method)
+        self.attn = GPT2Attention(config, quant_config)
         self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.mlp = GPT2MLP(inner_dim, config, linear_method)
+        self.mlp = GPT2MLP(inner_dim, config, quant_config)
 
     def forward(
         self,
@@ -163,7 +164,7 @@ class GPT2Model(nn.Module):
     def __init__(
         self,
         config: GPT2Config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -174,7 +175,7 @@ class GPT2Model(nn.Module):
         self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
         self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
         self.h = nn.ModuleList([
-            GPT2Block(config, linear_method)
+            GPT2Block(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
@@ -203,12 +204,12 @@ class GPT2LMHeadModel(nn.Module):
     def __init__(
         self,
         config: GPT2Config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.transformer = GPT2Model(config, linear_method)
+        self.quant_config = quant_config
+        self.transformer = GPT2Model(config, quant_config)
         self.lm_head_weight = self.transformer.wte.weight
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()

vllm/model_executor/models/gpt_bigcode.py

@@ -28,10 +28,11 @@ from vllm.attention import Attention, AttentionMetadata
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
@@ -45,7 +46,7 @@ class GPTBigCodeAttention(nn.Module):
     def __init__(
         self,
         config: GPTBigCodeConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -72,14 +73,14 @@ class GPTBigCodeAttention(nn.Module):
             total_num_heads,
             total_num_kv_heads,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         self.c_proj = RowParallelLinear(
             self.hidden_size,
             self.hidden_size,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.attn = Attention(self.num_heads,
                               self.head_dim,
@@ -111,7 +112,7 @@ class GPTBigMLP(nn.Module):
         self,
         intermediate_size: int,
         config: GPTBigCodeConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         hidden_size = config.hidden_size
@@ -119,15 +120,15 @@ class GPTBigMLP(nn.Module):
             hidden_size,
             intermediate_size,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.c_proj = RowParallelLinear(
             intermediate_size,
             hidden_size,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
-        quant_config = getattr(linear_method, "quant_config", None)
+        quant_config = getattr(quant_config, "quant_config", None)
         self.act = get_act_fn(config.activation_function, quant_config,
                               intermediate_size)
 
@@ -143,7 +144,7 @@ class GPTBigCodeBlock(nn.Module):
     def __init__(
         self,
         config: GPTBigCodeConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         hidden_size = config.hidden_size
@@ -151,9 +152,9 @@ class GPTBigCodeBlock(nn.Module):
                      hidden_size)
 
         self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.attn = GPTBigCodeAttention(config, linear_method)
+        self.attn = GPTBigCodeAttention(config, quant_config)
         self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.mlp = GPTBigMLP(inner_dim, config, linear_method)
+        self.mlp = GPTBigMLP(inner_dim, config, quant_config)
 
     def forward(
         self,
@@ -184,7 +185,7 @@ class GPTBigCodeModel(nn.Module):
     def __init__(
         self,
         config: GPTBigCodeConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -195,7 +196,7 @@ class GPTBigCodeModel(nn.Module):
         self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
         self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
         self.h = nn.ModuleList([
-            GPTBigCodeBlock(config, linear_method)
+            GPTBigCodeBlock(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
@@ -224,12 +225,12 @@ class GPTBigCodeForCausalLM(nn.Module):
     def __init__(
         self,
         config: GPTBigCodeConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.transformer = GPTBigCodeModel(config, linear_method)
+        self.quant_config = quant_config
+        self.transformer = GPTBigCodeModel(config, quant_config)
         self.lm_head_weight = self.transformer.wte.weight
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()

vllm/model_executor/models/gpt_j.py

@@ -26,10 +26,11 @@ from vllm.attention import Attention, AttentionMetadata
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -44,7 +45,7 @@ class GPTJAttention(nn.Module):
     def __init__(
         self,
         config: GPTJConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.total_num_heads = config.num_attention_heads
@@ -56,13 +57,13 @@ class GPTJAttention(nn.Module):
             self.head_size,
             self.total_num_heads,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.out_proj = RowParallelLinear(
             config.hidden_size,
             config.hidden_size,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         tp_world_size = get_tensor_model_parallel_world_size()
@@ -105,21 +106,21 @@ class GPTJMLP(nn.Module):
         self,
         intermediate_size: int,
         config: GPTJConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         hidden_size = config.n_embd
         self.fc_in = ColumnParallelLinear(
             hidden_size,
             intermediate_size,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.fc_out = RowParallelLinear(
             intermediate_size,
             hidden_size,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
-        quant_config = getattr(linear_method, "quant_config", None)
+        quant_config = getattr(quant_config, "quant_config", None)
         self.act = get_act_fn(config.activation_function, quant_config,
                               intermediate_size)
 
@@ -135,14 +136,14 @@ class GPTJBlock(nn.Module):
     def __init__(
         self,
         config: GPTJConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         inner_dim = (4 * config.n_embd
                      if config.n_inner is None else config.n_inner)
         self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
-        self.attn = GPTJAttention(config, linear_method)
-        self.mlp = GPTJMLP(inner_dim, config, linear_method)
+        self.attn = GPTJAttention(config, quant_config)
+        self.mlp = GPTJMLP(inner_dim, config, quant_config)
 
     def forward(
         self,
@@ -169,7 +170,7 @@ class GPTJModel(nn.Module):
     def __init__(
         self,
         config: GPTJConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -179,7 +180,7 @@ class GPTJModel(nn.Module):
             self.embed_dim,
         )
         self.h = nn.ModuleList(
-            [GPTJBlock(config, linear_method) for _ in range(config.n_layer)])
+            [GPTJBlock(config, quant_config) for _ in range(config.n_layer)])
         self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
 
     def forward(
@@ -207,13 +208,13 @@ class GPTJForCausalLM(nn.Module):
     def __init__(
         self,
         config: GPTJConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
+        self.quant_config = quant_config
         assert not config.tie_word_embeddings
-        self.transformer = GPTJModel(config, linear_method)
+        self.transformer = GPTJModel(config, quant_config)
         self.lm_head = ParallelLMHead(
             config.vocab_size,
             config.n_embd,

vllm/model_executor/models/gpt_neox.py

@@ -26,10 +26,11 @@ from vllm.attention import Attention, AttentionMetadata
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -44,7 +45,7 @@ class GPTNeoXAttention(nn.Module):
     def __init__(
         self,
         config: GPTNeoXConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.total_num_heads = config.num_attention_heads
@@ -63,13 +64,13 @@ class GPTNeoXAttention(nn.Module):
             self.head_size,
             self.total_num_heads,
             bias=self.bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.dense = RowParallelLinear(
             config.hidden_size,
             config.hidden_size,
             bias=self.bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         scaling = self.head_size**-0.5
         rotary_dim = int(self.head_size * config.rotary_pct)
@@ -105,20 +106,20 @@ class GPTNeoXMLP(nn.Module):
     def __init__(
         self,
         config: GPTNeoXConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.dense_h_to_4h = ColumnParallelLinear(
             config.hidden_size,
             config.intermediate_size,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.dense_4h_to_h = RowParallelLinear(
             config.intermediate_size,
             config.hidden_size,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
-        quant_config = getattr(linear_method, "quant_config", None)
+        quant_config = getattr(quant_config, "quant_config", None)
         self.act = get_act_fn(config.hidden_act, quant_config,
                               config.intermediate_size)
 
@@ -134,7 +135,7 @@ class GPTNeoXLayer(nn.Module):
     def __init__(
         self,
         config: GPTNeoXConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.use_parallel_residual = config.use_parallel_residual
@@ -142,8 +143,8 @@ class GPTNeoXLayer(nn.Module):
                                             eps=config.layer_norm_eps)
         self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
                                                      eps=config.layer_norm_eps)
-        self.attention = GPTNeoXAttention(config, linear_method)
-        self.mlp = GPTNeoXMLP(config, linear_method)
+        self.attention = GPTNeoXAttention(config, quant_config)
+        self.mlp = GPTNeoXMLP(config, quant_config)
 
     def forward(
         self,
@@ -182,7 +183,7 @@ class GPTNeoXModel(nn.Module):
     def __init__(
         self,
         config: GPTNeoXConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -192,7 +193,7 @@ class GPTNeoXModel(nn.Module):
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            GPTNeoXLayer(config, linear_method)
+            GPTNeoXLayer(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.final_layer_norm = nn.LayerNorm(config.hidden_size,
@@ -223,12 +224,12 @@ class GPTNeoXForCausalLM(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.gpt_neox = GPTNeoXModel(config, linear_method)
+        self.quant_config = quant_config
+        self.gpt_neox = GPTNeoXModel(config, quant_config)
         self.embed_out = ParallelLMHead(
             config.vocab_size,
             config.hidden_size,

vllm/model_executor/models/internlm2.py

@@ -9,11 +9,12 @@ from vllm.attention import Attention, AttentionMetadata
 from vllm.distributed import get_tensor_model_parallel_world_size
 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,
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -30,17 +31,17 @@ class InternLM2MLP(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.w2 = RowParallelLinear(intermediate_size,
                                     hidden_size,
                                     bias=False,
-                                    linear_method=linear_method)
+                                    quant_config=quant_config)
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -63,7 +64,7 @@ class InternLM2Attention(nn.Module):
         rope_theta: float = 10000,
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 8192,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = hidden_size
@@ -94,13 +95,13 @@ class InternLM2Attention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.wo = 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(
@@ -135,7 +136,7 @@ class InternLMDecoderLayer(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -150,13 +151,13 @@ class InternLMDecoderLayer(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.feed_forward = InternLM2MLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.attention_norm = RMSNorm(config.hidden_size,
                                       eps=config.rms_norm_eps)
@@ -195,7 +196,7 @@ class InternLM2Model(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
@@ -206,7 +207,7 @@ class InternLM2Model(nn.Module):
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            InternLMDecoderLayer(config, linear_method)
+            InternLMDecoderLayer(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -238,12 +239,12 @@ class InternLM2ForCausalLM(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = InternLM2Model(config, linear_method)
+        self.quant_config = quant_config
+        self.model = InternLM2Model(config, quant_config)
         self.output = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()

vllm/model_executor/models/jais.py

@@ -29,10 +29,11 @@ from vllm.attention import Attention, AttentionMetadata
 from vllm.distributed import (get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size)
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
@@ -68,7 +69,7 @@ class JAISAttention(nn.Module):
     def __init__(
         self,
         config: JAISConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -88,13 +89,13 @@ class JAISAttention(nn.Module):
             self.head_dim,
             total_num_heads,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.c_proj = RowParallelLinear(
             self.hidden_size,
             self.hidden_size,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         tp_rank = get_tensor_model_parallel_rank()
@@ -128,7 +129,7 @@ class JAISMLP(nn.Module):
         self,
         intermediate_size: int,
         config: JAISConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         hidden_size = config.hidden_size
@@ -137,19 +138,19 @@ class JAISMLP(nn.Module):
             hidden_size,
             intermediate_size,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.c_fc2 = (ColumnParallelLinear(
             hidden_size,
             intermediate_size,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         ) if self.swiglu else None)
         self.c_proj = RowParallelLinear(
             intermediate_size,
             hidden_size,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         self.act = SwiGLUActivation()
@@ -169,7 +170,7 @@ class JAISBlock(nn.Module):
     def __init__(
         self,
         config: JAISConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         hidden_size = config.hidden_size
@@ -177,9 +178,9 @@ class JAISBlock(nn.Module):
                      hidden_size)
 
         self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.attn = JAISAttention(config, linear_method)
+        self.attn = JAISAttention(config, quant_config)
         self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.mlp = JAISMLP(inner_dim, config, linear_method)
+        self.mlp = JAISMLP(inner_dim, config, quant_config)
 
     def forward(
         self,
@@ -210,7 +211,7 @@ class JAISModel(nn.Module):
     def __init__(
         self,
         config: JAISConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -227,7 +228,7 @@ class JAISModel(nn.Module):
         else:
             self.embeddings_scale = config.mup_embeddings_scale
         self.h = nn.ModuleList([
-            JAISBlock(config, linear_method)
+            JAISBlock(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
@@ -261,12 +262,12 @@ class JAISLMHeadModel(nn.Module):
     def __init__(
         self,
         config: JAISConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.transformer = JAISModel(config, linear_method)
+        self.quant_config = quant_config
+        self.transformer = JAISModel(config, quant_config)
         self.lm_head_weight = self.transformer.wte.weight
         if hasattr(config, "width_scale"):
             self.output_logits_scale = config.width_scale

vllm/model_executor/models/llama.py

@@ -33,11 +33,12 @@ from vllm.distributed import (get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size)
 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,
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -56,17 +57,17 @@ class LlamaMLP(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QKVParallelLinear] = 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)
+                                           quant_config=quant_config)
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -89,7 +90,7 @@ class LlamaAttention(nn.Module):
         rope_theta: float = 10000,
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 8192,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         bias: bool = False,
         sliding_window: Optional[int] = None,
     ) -> None:
@@ -131,13 +132,13 @@ class LlamaAttention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.o_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         self.rotary_emb = get_rope(
@@ -174,7 +175,7 @@ class LlamaDecoderLayer(nn.Module):
     def __init__(
         self,
         config: LlamaConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -199,7 +200,7 @@ class LlamaDecoderLayer(nn.Module):
             rope_theta=rope_theta,
             rope_scaling=rope_scaling,
             max_position_embeddings=max_position_embeddings,
-            linear_method=linear_method,
+            quant_config=quant_config,
             bias=attention_bias,
             sliding_window=sliding_window,
         )
@@ -207,7 +208,7 @@ class LlamaDecoderLayer(nn.Module):
             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)
@@ -248,7 +249,7 @@ class LlamaModel(nn.Module):
     def __init__(
         self,
         config: LlamaConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         super().__init__()
@@ -264,7 +265,7 @@ class LlamaModel(nn.Module):
             org_num_embeddings=config.vocab_size,
         )
         self.layers = nn.ModuleList([
-            LlamaDecoderLayer(config, linear_method)
+            LlamaDecoderLayer(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -329,13 +330,12 @@ class LlamaForCausalLM(nn.Module):
     def __init__(
         self,
         config: LlamaConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = LlamaModel(config, linear_method, lora_config=lora_config)
+        self.model = LlamaModel(config, quant_config, lora_config=lora_config)
         self.unpadded_vocab_size = config.vocab_size
         if lora_config:
             self.unpadded_vocab_size += lora_config.lora_extra_vocab_size

vllm/model_executor/models/llava.py

@@ -9,8 +9,9 @@ from transformers import CLIPVisionModel, LlavaConfig
 from vllm.attention import AttentionMetadata
 from vllm.config import VisionLanguageConfig
 from vllm.model_executor.layers.activation import get_act_fn
-from vllm.model_executor.layers.linear import LinearMethodBase
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
@@ -61,7 +62,7 @@ class LlavaForConditionalGeneration(nn.Module):
     def __init__(self,
                  config: "LlavaConfig",
                  vision_language_config: VisionLanguageConfig,
-                 linear_method: Optional["LinearMethodBase"] = None) -> None:
+                 quant_config: Optional["QuantizationConfig"] = None) -> None:
         super().__init__()
         self.config = config
 
@@ -83,8 +84,8 @@ class LlavaForConditionalGeneration(nn.Module):
             text_hidden_size=config.text_config.hidden_size,
             projector_hidden_act=config.projector_hidden_act)
 
-        self.linear_method = linear_method
-        self.language_model = LlamaModel(config.text_config, linear_method)
+        self.quant_config = quant_config
+        self.language_model = LlamaModel(config.text_config, quant_config)
         self.unpadded_vocab_size = config.text_config.vocab_size
         self.lm_head = ParallelLMHead(
             self.unpadded_vocab_size,

vllm/model_executor/models/minicpm.py

@@ -35,12 +35,13 @@ from vllm.distributed import (get_tensor_model_parallel_rank,
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe import fused_moe
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                ReplicatedLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -84,7 +85,7 @@ class MiniCPMMoE(nn.Module):
                                      self.num_total_experts,
                                      bias=False,
                                      params_dtype=self.params_dtype,
-                                     linear_method=None)
+                                     quant_config=None)
 
         self.ws = nn.Parameter(
             torch.empty(self.num_total_experts,
@@ -147,17 +148,17 @@ class MiniCPMMLP(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)
+                                           quant_config=quant_config)
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -180,7 +181,7 @@ class MiniCPMAttention(nn.Module):
         rope_theta: float = 10000,
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 8192,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = hidden_size
@@ -211,13 +212,13 @@ class MiniCPMAttention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=False,
-            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(
@@ -258,7 +259,7 @@ class MiniCPMDecoderLayer(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
@@ -274,7 +275,7 @@ class MiniCPMDecoderLayer(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.num_experts = getattr(self.config, "num_experts", 0)
         if self.num_experts == 0:
@@ -282,7 +283,7 @@ class MiniCPMDecoderLayer(nn.Module):
                 hidden_size=self.hidden_size,
                 intermediate_size=config.intermediate_size,
                 hidden_act=config.hidden_act,
-                linear_method=linear_method,
+                quant_config=quant_config,
             )
         else:
             self.mlp = MiniCPMMoE(num_experts=config.num_experts,
@@ -329,7 +330,7 @@ class MiniCPMModel(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         super().__init__()
@@ -345,7 +346,7 @@ class MiniCPMModel(nn.Module):
             org_num_embeddings=config.vocab_size,
         )
         self.layers = nn.ModuleList([
-            MiniCPMDecoderLayer(config, linear_method)
+            MiniCPMDecoderLayer(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -412,15 +413,15 @@ class MiniCPMForCausalLM(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
         self.num_experts = getattr(self.config, "num_experts", 0)
-        self.linear_method = linear_method
+        self.quant_config = quant_config
         self.model = MiniCPMModel(config,
-                                  linear_method,
+                                  quant_config,
                                   lora_config=lora_config)
         unpadded_vocab_size = config.vocab_size
         if lora_config:

vllm/model_executor/models/mixtral.py

@@ -27,6 +27,7 @@ import torch
 from torch import nn
 from transformers import MixtralConfig
 
+from vllm import _custom_ops as ops
 from vllm.attention import Attention, AttentionMetadata
 from vllm.config import LoRAConfig
 from vllm.distributed import (get_tensor_model_parallel_rank,
@@ -34,13 +35,13 @@ from vllm.distributed import (get_tensor_model_parallel_rank,
                               tensor_model_parallel_all_reduce)
 from vllm.model_executor.layers.fused_moe import fused_moe
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               QKVParallelLinear,
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
                                                ReplicatedLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.quantization.fp8 import (Fp8LinearMethod,
-                                                         per_tensor_quantize)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.quantization.fp8 import Fp8Config
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -69,7 +70,7 @@ class MixtralMoE(nn.Module):
         intermediate_size: int,
         params_dtype: Optional[torch.dtype] = None,
         tp_size: Optional[int] = None,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.tp_size = tp_size or get_tensor_model_parallel_world_size()
@@ -79,7 +80,7 @@ class MixtralMoE(nn.Module):
         self.intermediate_size = intermediate_size // self.tp_size
         # FIXME(pcmoritz): Make this more general to support different
         # quantization schemes
-        self.use_fp8 = isinstance(linear_method, Fp8LinearMethod)
+        self.use_fp8 = isinstance(quant_config, Fp8Config)
 
         if params_dtype is None:
             params_dtype = torch.get_default_dtype()
@@ -89,7 +90,7 @@ class MixtralMoE(nn.Module):
                                      self.num_total_experts,
                                      bias=False,
                                      params_dtype=self.params_dtype,
-                                     linear_method=None)
+                                     quant_config=None)
 
         self.ws = nn.Parameter(
             torch.empty(self.num_total_experts,
@@ -140,10 +141,10 @@ class MixtralMoE(nn.Module):
             ws = torch.empty_like(self.ws.data, dtype=torch.float8_e4m3fn)
             w2s = torch.empty_like(self.w2s.data, dtype=torch.float8_e4m3fn)
             for expert in range(self.num_total_experts):
-                ws[expert, :, :], self.ws_scale[expert] = per_tensor_quantize(
+                ws[expert, :, :], self.ws_scale[expert] = ops.scaled_fp8_quant(
                     self.ws.data[expert, :, :])
                 w2s[expert, :, :], self.w2s_scale[
-                    expert] = per_tensor_quantize(self.w2s.data[expert, :, :])
+                    expert] = ops.scaled_fp8_quant(self.w2s.data[expert, :, :])
             self.ws = nn.Parameter(ws, requires_grad=False)
             self.w2s = nn.Parameter(w2s, requires_grad=False)
 
@@ -178,7 +179,7 @@ class MixtralAttention(nn.Module):
                  num_kv_heads: int,
                  max_position: int = 4096 * 32,
                  rope_theta: float = 10000,
-                 linear_method: Optional[LinearMethodBase] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
                  sliding_window: Optional[int] = None) -> None:
         super().__init__()
         self.hidden_size = hidden_size
@@ -203,12 +204,12 @@ class MixtralAttention(nn.Module):
         self.rope_theta = rope_theta
         self.sliding_window = sliding_window
 
-        if isinstance(linear_method, Fp8LinearMethod):
+        if isinstance(quant_config, Fp8Config):
             print_warning_once(
                 "For Mixtral FP8 quantization, we currently do not quantize "
                 "the attention layers until their FP8 performance is improved."
             )
-            linear_method = None
+            quant_config = None
 
         self.qkv_proj = QKVParallelLinear(
             hidden_size,
@@ -216,13 +217,13 @@ class MixtralAttention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=False,
-            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(
             self.head_dim,
@@ -259,7 +260,7 @@ class MixtralDecoderLayer(nn.Module):
     def __init__(
         self,
         config: MixtralConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -272,13 +273,13 @@ class MixtralDecoderLayer(nn.Module):
             num_kv_heads=config.num_key_value_heads,
             rope_theta=rope_theta,
             sliding_window=config.sliding_window,
-            linear_method=linear_method)
+            quant_config=quant_config)
         self.block_sparse_moe = MixtralMoE(
             num_experts=config.num_local_experts,
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.intermediate_size,
-            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(config.hidden_size,
@@ -318,7 +319,7 @@ class MixtralModel(nn.Module):
     def __init__(
         self,
         config: MixtralConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         super().__init__()
@@ -334,7 +335,7 @@ class MixtralModel(nn.Module):
             org_num_embeddings=config.vocab_size,
         )
         self.layers = nn.ModuleList([
-            MixtralDecoderLayer(config, linear_method=linear_method)
+            MixtralDecoderLayer(config, quant_config=quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -384,14 +385,13 @@ class MixtralForCausalLM(nn.Module):
     def __init__(
         self,
         config: MixtralConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
         self.model = MixtralModel(config,
-                                  linear_method,
+                                  quant_config,
                                   lora_config=lora_config)
         self.unpadded_vocab_size = config.vocab_size
         if lora_config:

vllm/model_executor/models/mixtral_quant.py

@@ -34,11 +34,12 @@ from vllm.distributed import (get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size,
                               tensor_model_parallel_all_reduce)
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               QKVParallelLinear,
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
                                                ReplicatedLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -55,7 +56,7 @@ class MixtralMLP(nn.Module):
         num_experts: int,
         hidden_size: int,
         intermediate_size: int,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.num_experts = num_experts
@@ -65,15 +66,15 @@ class MixtralMLP(nn.Module):
         self.w1 = ReplicatedLinear(self.hidden_dim,
                                    self.ffn_dim,
                                    bias=False,
-                                   linear_method=linear_method)
+                                   quant_config=quant_config)
         self.w2 = ReplicatedLinear(self.ffn_dim,
                                    self.hidden_dim,
                                    bias=False,
-                                   linear_method=linear_method)
+                                   quant_config=quant_config)
         self.w3 = ReplicatedLinear(self.hidden_dim,
                                    self.ffn_dim,
                                    bias=False,
-                                   linear_method=linear_method)
+                                   quant_config=quant_config)
 
         # TODO: Use vllm's SiluAndMul
         self.act_fn = nn.SiLU()
@@ -92,7 +93,7 @@ class MixtralMoE(nn.Module):
     def __init__(
         self,
         config: MixtralConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -115,14 +116,14 @@ class MixtralMoE(nn.Module):
             MixtralMLP(self.num_total_experts,
                        config.hidden_size,
                        config.intermediate_size,
-                       linear_method=linear_method)
+                       quant_config=quant_config)
             if idx in self.expert_indicies else None
             for idx in range(self.num_total_experts)
         ])
         self.gate = ReplicatedLinear(config.hidden_size,
                                      self.num_total_experts,
                                      bias=False,
-                                     linear_method=None)
+                                     quant_config=None)
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         num_tokens, hidden_dim = hidden_states.shape
@@ -162,7 +163,7 @@ class MixtralAttention(nn.Module):
                  num_kv_heads: int,
                  max_position: int = 4096 * 32,
                  rope_theta: float = 10000,
-                 linear_method: Optional[LinearMethodBase] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
                  sliding_window: Optional[int] = None) -> None:
         super().__init__()
         self.hidden_size = hidden_size
@@ -193,13 +194,13 @@ class MixtralAttention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=False,
-            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(
             self.head_dim,
@@ -236,7 +237,7 @@ class MixtralDecoderLayer(nn.Module):
     def __init__(
         self,
         config: MixtralConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -249,9 +250,9 @@ class MixtralDecoderLayer(nn.Module):
             num_kv_heads=config.num_key_value_heads,
             rope_theta=rope_theta,
             sliding_window=config.sliding_window,
-            linear_method=linear_method)
+            quant_config=quant_config)
         self.block_sparse_moe = MixtralMoE(config=config,
-                                           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(config.hidden_size,
@@ -291,7 +292,7 @@ class MixtralModel(nn.Module):
     def __init__(
         self,
         config: MixtralConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.padding_idx = config.pad_token_id
@@ -302,7 +303,7 @@ class MixtralModel(nn.Module):
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            MixtralDecoderLayer(config, linear_method=linear_method)
+            MixtralDecoderLayer(config, quant_config=quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -331,12 +332,12 @@ class MixtralForCausalLM(nn.Module):
     def __init__(
         self,
         config: MixtralConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = MixtralModel(config, linear_method)
+        self.quant_config = quant_config
+        self.model = MixtralModel(config, quant_config)
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()

vllm/model_executor/models/mpt.py

@@ -11,10 +11,11 @@ from vllm.distributed import (get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size)
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
@@ -42,7 +43,7 @@ class MPTAttention(nn.Module):
     def __init__(
         self,
         config: MPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.d_model = config.d_model
@@ -65,7 +66,7 @@ class MPTAttention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=not config.no_bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         if self.qk_ln:
             self.q_ln = nn.LayerNorm(self.d_model)
@@ -74,7 +75,7 @@ class MPTAttention(nn.Module):
             self.d_model,
             self.d_model,
             bias=not config.no_bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         tp_world_size = get_tensor_model_parallel_world_size()
@@ -133,7 +134,7 @@ class MPTMLP(nn.Module):
     def __init__(
         self,
         config: MPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         hidden_size = config.d_model
@@ -143,15 +144,15 @@ class MPTMLP(nn.Module):
             hidden_size,
             intermediate_size,
             bias=not config.no_bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
-        quant_config = getattr(linear_method, "quant_config", None)
+        quant_config = getattr(quant_config, "quant_config", None)
         self.act = get_act_fn("gelu", quant_config, intermediate_size)
         self.down_proj = RowParallelLinear(
             intermediate_size,
             hidden_size,
             bias=not config.no_bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -166,14 +167,14 @@ class MPTBlock(nn.Module):
     def __init__(
         self,
         config: MPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         hidden_size = config.d_model
         self.norm_1 = nn.LayerNorm(hidden_size)
-        self.attn = MPTAttention(config, linear_method)
+        self.attn = MPTAttention(config, quant_config)
         self.norm_2 = nn.LayerNorm(hidden_size)
-        self.ffn = MPTMLP(config, linear_method)
+        self.ffn = MPTMLP(config, quant_config)
 
     def forward(
         self,
@@ -201,7 +202,7 @@ class MPTModel(nn.Module):
     def __init__(
         self,
         config: MPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         assert config.embedding_fraction == 1.0
@@ -212,7 +213,7 @@ class MPTModel(nn.Module):
             config.d_model,
         )
         self.blocks = nn.ModuleList(
-            [MPTBlock(config, linear_method) for _ in range(config.n_layers)])
+            [MPTBlock(config, quant_config) for _ in range(config.n_layers)])
         self.norm_f = nn.LayerNorm(config.d_model)
         if config.no_bias:
             for module in self.modules():
@@ -246,14 +247,14 @@ class MPTForCausalLM(nn.Module):
     def __init__(
         self,
         config: MPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
         assert config.tie_word_embeddings
-        self.linear_method = linear_method
+        self.quant_config = quant_config
 
-        self.transformer = MPTModel(config, linear_method)
+        self.transformer = MPTModel(config, quant_config)
         self.lm_head_weight = self.transformer.wte.weight
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()

vllm/model_executor/models/olmo.py

@@ -30,11 +30,12 @@ from transformers import OlmoConfig
 from vllm.attention import Attention, AttentionMetadata
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -54,7 +55,7 @@ class OlmoAttention(nn.Module):
     def __init__(
         self,
         config: OlmoConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -79,7 +80,7 @@ class OlmoAttention(nn.Module):
             self.head_dim,
             self.total_num_heads,
             bias=config.attention_bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         # Rotary embeddings.
@@ -99,7 +100,7 @@ class OlmoAttention(nn.Module):
             self.hidden_size,
             self.hidden_size,
             bias=config.attention_bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
     def forward(
@@ -129,7 +130,7 @@ class OlmoMLP(nn.Module):
     def __init__(
         self,
         config: OlmoConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -141,7 +142,7 @@ class OlmoMLP(nn.Module):
             self.hidden_size,
             [self.intermediate_size] * 2,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         # Activation function.
@@ -152,7 +153,7 @@ class OlmoMLP(nn.Module):
             self.intermediate_size,
             self.hidden_size,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
     def forward(
@@ -174,13 +175,13 @@ class OlmoDecoderLayer(nn.Module):
 
     def __init__(self,
                  config: OlmoConfig,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         # Attention block.
-        self.self_attn = OlmoAttention(config, linear_method)
+        self.self_attn = OlmoAttention(config, quant_config)
 
         # MLP block.
-        self.mlp = OlmoMLP(config, linear_method)
+        self.mlp = OlmoMLP(config, quant_config)
 
         # LayerNorm
         self.input_layernorm = nn.LayerNorm(config.hidden_size,
@@ -216,14 +217,14 @@ class OlmoModel(nn.Module):
 
     def __init__(self,
                  config: OlmoConfig,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.config = config
 
         self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
                                                    config.hidden_size)
         self.layers = nn.ModuleList([
-            OlmoDecoderLayer(config, linear_method)
+            OlmoDecoderLayer(config, quant_config)
             for layer_idx in range(config.num_hidden_layers)
         ])
         self.norm = nn.LayerNorm(config.hidden_size,
@@ -270,11 +271,10 @@ class OlmoForCausalLM(nn.Module):
 
     def __init__(self,
                  config: OlmoConfig,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = OlmoModel(config, linear_method)
+        self.model = OlmoModel(config, quant_config)
         if config.tie_word_embeddings:
             self.lm_head_weight = self.model.embed_tokens.weight
         else:

vllm/model_executor/models/opt.py

@@ -27,11 +27,12 @@ from vllm.attention import Attention, AttentionMetadata
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
                                                QKVParallelLinear,
                                                ReplicatedLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
@@ -60,7 +61,7 @@ class OPTAttention(nn.Module):
         embed_dim: int,
         num_heads: int,
         bias: bool = True,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.embed_dim = embed_dim
@@ -77,13 +78,13 @@ class OPTAttention(nn.Module):
             self.head_dim,
             total_num_heads,
             bias=bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.out_proj = RowParallelLinear(
             embed_dim,
             embed_dim,
             bias=bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.attn = Attention(self.num_heads,
                               self.head_dim,
@@ -107,7 +108,7 @@ class OPTDecoderLayer(nn.Module):
     def __init__(
         self,
         config: OPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -116,7 +117,7 @@ class OPTDecoderLayer(nn.Module):
             embed_dim=self.embed_dim,
             num_heads=config.num_attention_heads,
             bias=config.enable_bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.do_layer_norm_before = config.do_layer_norm_before
 
@@ -127,16 +128,16 @@ class OPTDecoderLayer(nn.Module):
             self.embed_dim,
             config.ffn_dim,
             bias=config.enable_bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
-        quant_config = getattr(linear_method, "quant_config", None)
+        quant_config = getattr(quant_config, "quant_config", None)
         self.activation_fn = get_act_fn(config.activation_function,
                                         quant_config, config.ffn_dim)
         self.fc2 = RowParallelLinear(
             config.ffn_dim,
             self.embed_dim,
             bias=config.enable_bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.final_layer_norm = nn.LayerNorm(
             self.embed_dim,
@@ -181,7 +182,7 @@ class OPTDecoder(nn.Module):
     def __init__(
         self,
         config: OPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -202,7 +203,7 @@ class OPTDecoder(nn.Module):
             self.project_out = ReplicatedLinear(config.hidden_size,
                                                 config.word_embed_proj_dim,
                                                 bias=False,
-                                                linear_method=linear_method)
+                                                quant_config=quant_config)
         else:
             self.project_out = None
 
@@ -210,7 +211,7 @@ class OPTDecoder(nn.Module):
             self.project_in = ReplicatedLinear(config.word_embed_proj_dim,
                                                config.hidden_size,
                                                bias=False,
-                                               linear_method=linear_method)
+                                               quant_config=quant_config)
         else:
             self.project_in = None
 
@@ -226,7 +227,7 @@ class OPTDecoder(nn.Module):
             self.final_layer_norm = None
 
         self.layers = nn.ModuleList([
-            OPTDecoderLayer(config, linear_method)
+            OPTDecoderLayer(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
 
@@ -259,10 +260,10 @@ class OPTModel(nn.Module):
     def __init__(
         self,
         config: OPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
-        self.decoder = OPTDecoder(config, linear_method)
+        self.decoder = OPTDecoder(config, quant_config)
 
     def forward(
         self,
@@ -279,12 +280,12 @@ class OPTForCausalLM(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = OPTModel(config, linear_method)
+        self.quant_config = quant_config
+        self.model = OPTModel(config, quant_config)
         self.lm_head_weight = self.model.decoder.embed_tokens.weight
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()

vllm/model_executor/models/orion.py

@@ -13,11 +13,12 @@ from transformers import PretrainedConfig
 from vllm.attention import Attention, AttentionMetadata
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -34,17 +35,17 @@ class OrionMLP(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)
+                                           quant_config=quant_config)
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -67,7 +68,7 @@ class OrionAttention(nn.Module):
         rope_theta: float = 10000,
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 8192,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = hidden_size
@@ -98,13 +99,13 @@ class OrionAttention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=False,
-            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(
@@ -139,7 +140,7 @@ class OrionDecoderLayer(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -154,13 +155,13 @@ class OrionDecoderLayer(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 = OrionMLP(
             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 = nn.LayerNorm(config.hidden_size,
@@ -201,7 +202,7 @@ class OrionModel(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
@@ -212,7 +213,7 @@ class OrionModel(nn.Module):
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            OrionDecoderLayer(config, linear_method)
+            OrionDecoderLayer(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -244,12 +245,12 @@ class OrionForCausalLM(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = OrionModel(config, linear_method)
+        self.quant_config = quant_config
+        self.model = OrionModel(config, quant_config)
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()

vllm/model_executor/models/phi.py

@@ -45,10 +45,11 @@ from vllm.attention import Attention, AttentionMetadata
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -62,7 +63,7 @@ class PhiAttention(nn.Module):
 
     def __init__(self,
                  config: PretrainedConfig,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.total_num_heads = config.num_attention_heads
         self.hidden_size = config.hidden_size
@@ -80,12 +81,12 @@ class PhiAttention(nn.Module):
             self.head_size,
             self.total_num_heads,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.dense = RowParallelLinear(
             self.hidden_size,
             self.hidden_size,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         scaling = self.head_size**-0.5
@@ -125,7 +126,7 @@ class PhiMLP(nn.Module):
 
     def __init__(self,
                  config: PretrainedConfig,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
 
         n_inner = getattr(config, "n_inner", None)
@@ -134,14 +135,14 @@ class PhiMLP(nn.Module):
         self.fc1 = ColumnParallelLinear(
             config.hidden_size,
             n_inner,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.fc2 = RowParallelLinear(
             n_inner,
             config.hidden_size,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
-        quant_config = getattr(linear_method, "quant_config", None)
+        quant_config = getattr(quant_config, "quant_config", None)
         self.act = get_act_fn(config.hidden_act, quant_config, n_inner)
 
     def forward(self, hidden_states):
@@ -155,12 +156,12 @@ class PhiLayer(nn.Module):
 
     def __init__(self,
                  config: PretrainedConfig,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.input_layernorm = nn.LayerNorm(config.hidden_size,
                                             eps=config.layer_norm_eps)
-        self.self_attn = PhiAttention(config, linear_method)
-        self.mlp = PhiMLP(config, linear_method)
+        self.self_attn = PhiAttention(config, quant_config)
+        self.mlp = PhiMLP(config, quant_config)
 
     def forward(
         self,
@@ -186,14 +187,14 @@ class PhiModel(nn.Module):
 
     def __init__(self,
                  config: PretrainedConfig,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
+        self.quant_config = quant_config
         self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
                                                    config.hidden_size)
         self.layers = nn.ModuleList([
-            PhiLayer(config, linear_method)
+            PhiLayer(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.final_layernorm = nn.LayerNorm(config.hidden_size,
@@ -225,12 +226,12 @@ class PhiForCausalLM(nn.Module):
 
     def __init__(self,
                  config: PretrainedConfig,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
+        self.quant_config = quant_config
 
-        self.model = PhiModel(config, linear_method)
+        self.model = PhiModel(config, quant_config)
 
         self.lm_head = ParallelLMHead(config.vocab_size,
                                       config.hidden_size,

vllm/model_executor/models/qwen.py

@@ -14,11 +14,12 @@ from vllm.attention import Attention, AttentionMetadata
 from vllm.distributed import get_tensor_model_parallel_world_size
 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,
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -35,17 +36,17 @@ class QWenMLP(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str = "silu",
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.gate_up_proj = MergedColumnParallelLinear(
             hidden_size, [intermediate_size] * 2,
             bias=False,
-            linear_method=linear_method)
+            quant_config=quant_config)
         self.c_proj = RowParallelLinear(intermediate_size,
                                         hidden_size,
                                         bias=False,
-                                        linear_method=linear_method)
+                                        quant_config=quant_config)
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -67,7 +68,7 @@ class QWenAttention(nn.Module):
         max_position_embeddings: int,
         rope_theta: float = 10000,
         rope_scaling: Optional[Dict[str, Any]] = None,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.hidden_size = hidden_size
@@ -83,13 +84,13 @@ class QWenAttention(nn.Module):
             self.head_dim,
             self.total_num_heads,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.c_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.scaling = self.head_dim**-0.5
 
@@ -122,7 +123,7 @@ class QWenBlock(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.ln_1 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
@@ -134,13 +135,13 @@ class QWenBlock(nn.Module):
                                   config.max_position_embeddings,
                                   rope_theta=rope_theta,
                                   rope_scaling=rope_scaling,
-                                  linear_method=linear_method)
+                                  quant_config=quant_config)
 
         self.ln_2 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
 
         self.mlp = QWenMLP(config.hidden_size,
                            config.intermediate_size // 2,
-                           linear_method=linear_method)
+                           quant_config=quant_config)
 
     def forward(
         self,
@@ -174,7 +175,7 @@ class QWenModel(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -185,7 +186,7 @@ class QWenModel(nn.Module):
             config.hidden_size,
         )
         self.h = nn.ModuleList([
-            QWenBlock(config, linear_method)
+            QWenBlock(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.ln_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
@@ -217,12 +218,12 @@ class QWenLMHeadModel(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.transformer = QWenModel(config, linear_method)
+        self.quant_config = quant_config
+        self.transformer = QWenModel(config, quant_config)
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()