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

tests/quantization/test_fp8.py

@@ -20,5 +20,5 @@ def test_load_fp16_model(vllm_runner) -> None:
 
     model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model
     fc1 = model.model.decoder.layers[0].fc1
-    assert isinstance(fc1.linear_method, Fp8LinearMethod)
+    assert isinstance(fc1.quant_method, Fp8LinearMethod)
     assert fc1.weight.dtype == torch.float8_e4m3fn

tests/tensorizer_loader/test_tensorizer.py

@@ -50,10 +50,10 @@ def test_load_with_tensorizer(mock_agent, tensorizer_config):
     mock_agent_instance.deserialize.return_value = MagicMock()
 
     result = load_with_tensorizer(tensorizer_config,
-                                  linear_method=mock_linear_method)
+                                  quant_method=mock_linear_method)
 
     mock_agent.assert_called_once_with(tensorizer_config,
-                                       linear_method=mock_linear_method)
+                                       quant_method=mock_linear_method)
     mock_agent_instance.deserialize.assert_called_once()
     assert result == mock_agent_instance.deserialize.return_value
 

vllm/lora/layers.py

@@ -389,10 +389,9 @@ class ColumnParallelLinearWithLoRA(BaseLayerWithLoRA):
         self.indices = base_indices
         self.indices_len = indices_len
 
-    def apply_weights(self, x: torch.Tensor,
-                      bias: Optional[torch.Tensor]) -> torch.Tensor:
-        output = self.base_layer.linear_method.apply_weights(
-            self.base_layer, x, bias)
+    def apply(self, x: torch.Tensor,
+              bias: Optional[torch.Tensor]) -> torch.Tensor:
+        output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
         _apply_lora(
             x,
             self.lora_a_stacked,
@@ -416,7 +415,7 @@ class ColumnParallelLinearWithLoRA(BaseLayerWithLoRA):
                 if not self.base_layer.skip_bias_add else None)
 
         # Matrix multiply.
-        output_parallel = self.apply_weights(input_, bias)
+        output_parallel = self.apply(input_, bias)
         if self.base_layer.gather_output:
             # All-gather across the partitions.
             output = tensor_model_parallel_all_gather(output_parallel)
@@ -523,10 +522,9 @@ class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
                 index, 0, :lora_b[1].shape[1], :lora_b[1].shape[0]].copy_(
                     lora_b[1].T, non_blocking=True)
 
-    def apply_weights(self, x: torch.Tensor,
-                      bias: Optional[torch.Tensor]) -> torch.Tensor:
-        output = self.base_layer.linear_method.apply_weights(
-            self.base_layer, x, bias)
+    def apply(self, x: torch.Tensor,
+              bias: Optional[torch.Tensor]) -> torch.Tensor:
+        output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
         _apply_lora_packed_nslice(
             x,
             self.lora_a_stacked,
@@ -765,10 +763,9 @@ class MergedQKVParallelLinearWithLora(ColumnParallelLinearWithLoRA):
                 index, 0, :lora_a[2].shape[1], :lora_a[2].shape[0]].copy_(
                     lora_a[2].T, non_blocking=True)
 
-    def apply_weights(self, x: torch.Tensor,
-                      bias: Optional[torch.Tensor]) -> torch.Tensor:
-        output = self.base_layer.linear_method.apply_weights(
-            self.base_layer, x, bias)
+    def apply(self, x: torch.Tensor,
+              bias: Optional[torch.Tensor]) -> torch.Tensor:
+        output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
         _apply_lora_packed_nslice(
             x,
             self.lora_a_stacked,
@@ -862,9 +859,8 @@ class RowParallelLinearWithLoRA(BaseLayerWithLoRA):
         self.indices = base_indices
         self.indices_len = indices_len
 
-    def apply_weights(self, x: torch.Tensor) -> torch.Tensor:
-        output = self.base_layer.linear_method.apply_weights(
-            self.base_layer, x)
+    def apply(self, x: torch.Tensor) -> torch.Tensor:
+        output = self.base_layer.quant_method.apply(self.base_layer, x)
         _apply_lora(
             x,
             self.lora_a_stacked,
@@ -897,7 +893,7 @@ class RowParallelLinearWithLoRA(BaseLayerWithLoRA):
             input_parallel = splitted_input[tp_rank].contiguous()
 
         # Matrix multiply.
-        output_parallel = self.apply_weights(input_parallel)
+        output_parallel = self.apply(input_parallel)
         if self.base_layer.reduce_results and self.base_layer.tp_size > 1:
             output_ = tensor_model_parallel_all_reduce(output_parallel)
         else:

vllm/model_executor/layers/linear.py

@@ -1,9 +1,8 @@
-from abc import ABC, abstractmethod
+from abc import abstractmethod
 from typing import List, Optional
 
 import torch
 import torch.nn.functional as F
-from torch import nn
 from torch.nn.parameter import Parameter
 
 from vllm.distributed import (divide, get_tensor_model_parallel_rank,
@@ -12,6 +11,8 @@ from vllm.distributed import (divide, get_tensor_model_parallel_rank,
                               tensor_model_parallel_all_gather,
                               tensor_model_parallel_all_reduce)
 from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.utils import set_weight_attrs
 
 logger = init_logger(__name__)
@@ -25,7 +26,7 @@ def adjust_marlin_shard(param, shard_size, shard_offset):
     return shard_size * marlin_tile_size, shard_offset * marlin_tile_size
 
 
-class LinearMethodBase(ABC):
+class LinearMethodBase(QuantizeMethodBase):
     """Base class for different (maybe quantized) linear methods."""
 
     @abstractmethod
@@ -50,22 +51,15 @@ class LinearMethodBase(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def apply_weights(self,
-                      layer: torch.nn.Module,
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
         """Apply the weights in layer to the input tensor.
 
         Expects create_weights to have been called before on the layer."""
         raise NotImplementedError
 
-    def process_weights_after_loading(self, layer: nn.Module) -> None:
-        """Process the weight after loading.
-
-        This can be used for example, to transpose weights for computation.
-        """
-        return
-
 
 class UnquantizedLinearMethod(LinearMethodBase):
     """Linear method without quantization.
@@ -92,10 +86,10 @@ class UnquantizedLinearMethod(LinearMethodBase):
         layer.register_parameter("weight", weight)
         set_weight_attrs(weight, extra_weight_attrs)
 
-    def apply_weights(self,
-                      layer: torch.nn.Module,
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
         weight = layer.weight
         if self.separate_bias_add:
             if bias is not None:
@@ -104,8 +98,8 @@ class UnquantizedLinearMethod(LinearMethodBase):
         return F.linear(x, weight, bias)
 
 
-class ReplicatedLinear(torch.nn.Module):
-    """Replicated linear layer.
+class LinearBase(torch.nn.Module):
+    """Base linear layer.
 
     Args:
         input_size: input dimension of the linear layer.
@@ -113,17 +107,16 @@ class ReplicatedLinear(torch.nn.Module):
         bias: If true, add bias.
         skip_bias_add: If true, skip adding bias but instead return it.
         params_dtype: Data type for the parameters.
-        linear_method: (Maybe quantized) linear method.
+        quant_config: Quantization configure.
     """
 
     def __init__(
         self,
         input_size: int,
         output_size: int,
-        bias: bool = True,
         skip_bias_add: bool = False,
         params_dtype: Optional[torch.dtype] = None,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
 
@@ -134,12 +127,43 @@ class ReplicatedLinear(torch.nn.Module):
         if params_dtype is None:
             params_dtype = torch.get_default_dtype()
         self.params_dtype = params_dtype
-        if linear_method is None:
-            linear_method = UnquantizedLinearMethod()
-        self.linear_method = linear_method
-        self.linear_method.create_weights(self, self.input_size,
-                                          [self.output_size], self.input_size,
-                                          self.output_size, self.params_dtype)
+        if quant_config is None:
+            self.quant_method = UnquantizedLinearMethod()
+        else:
+            self.quant_method = quant_config.get_quant_method(self)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        raise NotImplementedError
+
+
+class ReplicatedLinear(LinearBase):
+    """Replicated linear layer.
+
+    Args:
+        input_size: input dimension of the linear layer.
+        output_size: output dimension of the linear layer.
+        bias: If true, add bias.
+        skip_bias_add: If true, skip adding bias but instead return it.
+        params_dtype: Data type for the parameters.
+        quant_config: Quantization configure.
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        output_size: int,
+        bias: bool = True,
+        skip_bias_add: bool = False,
+        params_dtype: Optional[torch.dtype] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__(input_size, output_size, skip_bias_add, params_dtype,
+                         quant_config)
+
+        self.quant_method.create_weights(self, self.input_size,
+                                         [self.output_size], self.input_size,
+                                         self.output_size, self.params_dtype)
+
         if bias:
             self.bias = Parameter(
                 torch.empty(self.output_size, dtype=self.params_dtype))
@@ -149,12 +173,12 @@ class ReplicatedLinear(torch.nn.Module):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         bias = self.bias if not self.skip_bias_add else None
-        output = self.linear_method.apply_weights(self, x, bias)
+        output = self.quant_method.apply(self, x, bias)
         output_bias = self.bias if self.skip_bias_add else None
         return output, output_bias
 
 
-class ColumnParallelLinear(torch.nn.Module):
+class ColumnParallelLinear(LinearBase):
     """Linear layer with column parallelism.
 
     The linear layer is defined as Y = XA + b. A is parallelized along
@@ -171,7 +195,7 @@ class ColumnParallelLinear(torch.nn.Module):
                        bias can be fused with other element-wise operations. we
                        skip adding bias but instead return it.
         params_dtype: Data type for the parameters.
-        linear_method: (Maybe quantized) linear method.
+        quant_config: Quantization configure.
         output_sizes: list of output sizes packed into one output, like for QKV
                        the list would be size 3.
     """
@@ -184,34 +208,26 @@ class ColumnParallelLinear(torch.nn.Module):
         gather_output: bool = False,
         skip_bias_add: bool = False,
         params_dtype: Optional[torch.dtype] = None,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         output_sizes: Optional[List[int]] = None,
     ):
-        super().__init__()
+        super().__init__(input_size, output_size, skip_bias_add, params_dtype,
+                         quant_config)
 
-        # Keep input parameters
-        self.input_size = input_size
-        self.output_size = output_size
         self.gather_output = gather_output
+
         # Divide the weight matrix along the last dimension.
         tp_size = get_tensor_model_parallel_world_size()
         self.output_size_per_partition = divide(output_size, tp_size)
-        self.skip_bias_add = skip_bias_add
-        if params_dtype is None:
-            params_dtype = torch.get_default_dtype()
-        self.params_dtype = params_dtype
-        if linear_method is None:
-            linear_method = UnquantizedLinearMethod()
         if output_sizes is None:
             output_sizes = [output_size]
-        self.linear_method = linear_method
-        self.linear_method.create_weights(self,
-                                          self.input_size,
-                                          [x // tp_size for x in output_sizes],
-                                          self.input_size,
-                                          self.output_size,
-                                          self.params_dtype,
-                                          weight_loader=self.weight_loader)
+        self.quant_method.create_weights(self,
+                                         self.input_size,
+                                         [x // tp_size for x in output_sizes],
+                                         self.input_size,
+                                         self.output_size,
+                                         self.params_dtype,
+                                         weight_loader=self.weight_loader)
         if bias:
             self.bias = Parameter(
                 torch.empty(self.output_size_per_partition,
@@ -239,7 +255,7 @@ class ColumnParallelLinear(torch.nn.Module):
         bias = self.bias if not self.skip_bias_add else None
 
         # Matrix multiply.
-        output_parallel = self.linear_method.apply_weights(self, input_, bias)
+        output_parallel = self.quant_method.apply(self, input_, bias)
         if self.gather_output:
             # All-gather across the partitions.
             output = tensor_model_parallel_all_gather(output_parallel)
@@ -267,7 +283,7 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
                        bias can be fused with other element-wise operations. we
                        skip adding bias but instead return it.
         params_dtype: Data type for the parameters.
-        linear_method: (Maybe quantized) linear method.
+        quant_config: Quantization configure.
     """
 
     def __init__(
@@ -278,13 +294,13 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
         gather_output: bool = False,
         skip_bias_add: bool = False,
         params_dtype: Optional[torch.dtype] = None,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         self.output_sizes = output_sizes
         tp_size = get_tensor_model_parallel_world_size()
         assert all(output_size % tp_size == 0 for output_size in output_sizes)
         super().__init__(input_size, sum(output_sizes), bias, gather_output,
-                         skip_bias_add, params_dtype, linear_method,
+                         skip_bias_add, params_dtype, quant_config,
                          self.output_sizes)
 
     def weight_loader(self,
@@ -384,7 +400,7 @@ class QKVParallelLinear(ColumnParallelLinear):
                        bias can be fused with other element-wise operations. we
                        skip adding bias but instead return it.
         params_dtype: Data type for the parameters.
-        linear_method: (Maybe quantized) linear method.
+        quant_config: Quantization configure.
     """
 
     def __init__(
@@ -396,7 +412,7 @@ class QKVParallelLinear(ColumnParallelLinear):
         bias: bool = True,
         skip_bias_add: bool = False,
         params_dtype: Optional[torch.dtype] = None,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         self.hidden_size = hidden_size
         self.head_size = head_size
@@ -424,7 +440,7 @@ class QKVParallelLinear(ColumnParallelLinear):
         ]
 
         super().__init__(input_size, output_size, bias, False, skip_bias_add,
-                         params_dtype, linear_method, output_sizes)
+                         params_dtype, quant_config, output_sizes)
 
     def weight_loader(self,
                       param: Parameter,
@@ -517,7 +533,7 @@ class QKVParallelLinear(ColumnParallelLinear):
         param_data.copy_(loaded_weight)
 
 
-class RowParallelLinear(torch.nn.Module):
+class RowParallelLinear(LinearBase):
     """Linear layer with row parallelism.
 
     The linear layer is defined as Y = XA + b. A is parallelized along
@@ -540,7 +556,7 @@ class RowParallelLinear(torch.nn.Module):
                        bias can be fused with other element-wise operations.
                        We skip adding bias but instead return it.
         params_dtype: Data type for the parameters.
-        linear_method: (Maybe quantized) linear method.
+        quant_config: Quantization configure.
     """
 
     def __init__(
@@ -552,32 +568,24 @@ class RowParallelLinear(torch.nn.Module):
         skip_bias_add: bool = False,
         params_dtype: Optional[torch.dtype] = None,
         reduce_results: bool = True,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
-        super().__init__()
-        # Keep input parameters
-        self.input_size = input_size
-        self.output_size = output_size
+        super().__init__(input_size, output_size, skip_bias_add, params_dtype,
+                         quant_config)
+
         self.input_is_parallel = input_is_parallel
         self.reduce_results = reduce_results
-        if params_dtype is None:
-            params_dtype = torch.get_default_dtype()
-        self.params_dtype = params_dtype
 
         # Divide the weight matrix along the last dimension.
         self.tp_size = get_tensor_model_parallel_world_size()
         self.input_size_per_partition = divide(input_size, self.tp_size)
-        self.skip_bias_add = skip_bias_add
-        if linear_method is None:
-            linear_method = UnquantizedLinearMethod()
-        self.linear_method = linear_method
-        self.linear_method.create_weights(self,
-                                          self.input_size_per_partition,
-                                          [self.output_size],
-                                          self.input_size,
-                                          self.output_size,
-                                          self.params_dtype,
-                                          weight_loader=self.weight_loader)
+        self.quant_method.create_weights(self,
+                                         self.input_size_per_partition,
+                                         [self.output_size],
+                                         self.input_size,
+                                         self.output_size,
+                                         self.params_dtype,
+                                         weight_loader=self.weight_loader)
 
         if not reduce_results and (bias and not skip_bias_add):
             raise ValueError("When not reduce the results, adding bias to the "
@@ -616,8 +624,7 @@ class RowParallelLinear(torch.nn.Module):
             input_parallel = splitted_input[tp_rank].contiguous()
 
         # Matrix multiply.
-        output_parallel = self.linear_method.apply_weights(
-            self, input_parallel)
+        output_parallel = self.quant_method.apply(self, input_parallel)
         if self.reduce_results and self.tp_size > 1:
             output_ = tensor_model_parallel_all_reduce(output_parallel)
         else:

vllm/model_executor/layers/quantization/__init__.py

@@ -4,7 +4,7 @@ from vllm.model_executor.layers.quantization.aqlm import AQLMConfig
 from vllm.model_executor.layers.quantization.awq import AWQConfig
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
-from vllm.model_executor.layers.quantization.fp8 import FP8Config
+from vllm.model_executor.layers.quantization.fp8 import Fp8Config
 from vllm.model_executor.layers.quantization.gptq import GPTQConfig
 from vllm.model_executor.layers.quantization.marlin import MarlinConfig
 from vllm.model_executor.layers.quantization.squeezellm import SqueezeLLMConfig
@@ -12,7 +12,7 @@ from vllm.model_executor.layers.quantization.squeezellm import SqueezeLLMConfig
 QUANTIZATION_METHODS = {
     "aqlm": AQLMConfig,
     "awq": AWQConfig,
-    "fp8": FP8Config,
+    "fp8": Fp8Config,
     "gptq": GPTQConfig,
     "squeezellm": SqueezeLLMConfig,
     "marlin": MarlinConfig,

vllm/model_executor/layers/quantization/aqlm.py

@@ -9,10 +9,10 @@ import torch.nn.functional as F
 from torch.nn.parameter import Parameter
 
 from vllm import _custom_ops as ops
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               set_weight_attrs)
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.utils import set_weight_attrs
 
 
 def get_int_dtype(nbits: int) -> torch.dtype:
@@ -207,8 +207,11 @@ class AQLMConfig(QuantizationConfig):
         return cls(in_group_size, nbits_per_codebook, num_code_books,
                    out_group_size)
 
-    def get_linear_method(self) -> "AQLMLinearMethod":
-        return AQLMLinearMethod(self)
+    def get_quant_method(
+            self, layer: torch.nn.Module) -> Optional["AQLMLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return AQLMLinearMethod(self)
+        return None
 
     def get_scaled_act_names(self) -> List[str]:
         return []
@@ -321,7 +324,7 @@ class AQLMLinearMethod(LinearMethodBase):
         layer.register_parameter("scales", scales)
         set_weight_attrs(scales, extra_weight_attrs)
 
-    def apply_weights(
+    def apply(
         self,
         layer: torch.nn.Module,
         x: torch.Tensor,

vllm/model_executor/layers/quantization/awq.py

@@ -4,10 +4,10 @@ import torch
 from torch.nn.parameter import Parameter
 
 from vllm import _custom_ops as ops
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               set_weight_attrs)
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.utils import set_weight_attrs
 
 
 class AWQConfig(QuantizationConfig):
@@ -62,8 +62,11 @@ class AWQConfig(QuantizationConfig):
         zero_point = cls.get_from_keys(config, ["zero_point"])
         return cls(weight_bits, group_size, zero_point)
 
-    def get_linear_method(self) -> "AWQLinearMethod":
-        return AWQLinearMethod(self)
+    def get_quant_method(
+            self, layer: torch.nn.Module) -> Optional["AWQLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return AWQLinearMethod(self)
+        return None
 
     def get_scaled_act_names(self) -> List[str]:
         return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"]
@@ -147,10 +150,10 @@ class AWQLinearMethod(LinearMethodBase):
         layer.register_parameter("scales", scales)
         set_weight_attrs(scales, extra_weight_attrs)
 
-    def apply_weights(self,
-                      layer: torch.nn.Module,
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
         qweight = layer.qweight
         scales = layer.scales
         qzeros = layer.qzeros

vllm/model_executor/layers/quantization/base_config.py

@@ -2,8 +2,33 @@ from abc import ABC, abstractmethod
 from typing import Any, Dict, List
 
 import torch
+from torch import nn
 
-from vllm.model_executor.layers.linear import LinearMethodBase
+
+class QuantizeMethodBase(ABC):
+    """Base class for different quantized methods."""
+
+    @abstractmethod
+    def create_weights(self, layer: torch.nn.Module, *weight_args,
+                       **extra_weight_attrs):
+        """Create weights for a layer.
+
+        The weights will be set as attributes of the layer."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def apply(self, layer: torch.nn.Module, *args, **kwargs) -> torch.Tensor:
+        """Apply the weights in layer to the input tensor.
+
+        Expects create_weights to have been called before on the layer."""
+        raise NotImplementedError
+
+    def process_weights_after_loading(self, layer: nn.Module) -> None:
+        """Process the weight after loading.
+
+        This can be used for example, to transpose weights for computation.
+        """
+        return
 
 
 class QuantizationConfig(ABC):
@@ -51,8 +76,8 @@ class QuantizationConfig(ABC):
                          "quantization config.")
 
     @abstractmethod
-    def get_linear_method(self) -> LinearMethodBase:
-        """Get the linear method to use for the quantized linear layer."""
+    def get_quant_method(self, layer: torch.nn.Module) -> QuantizeMethodBase:
+        """Get the quantize method to use for the quantized layer."""
         raise NotImplementedError
 
     @abstractmethod

vllm/model_executor/layers/quantization/fp8.py

@@ -1,16 +1,17 @@
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional
 
 import torch
 from torch.nn import Module
 from torch.nn.parameter import Parameter
 
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               set_weight_attrs)
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.utils import set_weight_attrs
 
 
-class FP8Config(QuantizationConfig):
+class Fp8Config(QuantizationConfig):
     """Config class for FP8."""
 
     @classmethod
@@ -33,11 +34,14 @@ class FP8Config(QuantizationConfig):
         return []
 
     @classmethod
-    def from_config(cls, config: Dict[str, Any]) -> "FP8Config":
+    def from_config(cls, config: Dict[str, Any]) -> "Fp8Config":
         return cls()
 
-    def get_linear_method(self) -> "Fp8LinearMethod":
-        return Fp8LinearMethod(self)
+    def get_quant_method(
+            self, layer: torch.nn.Module) -> Optional["QuantizeMethodBase"]:
+        if isinstance(layer, LinearBase):
+            return Fp8LinearMethod(self)
+        return None
 
     def get_scaled_act_names(self) -> List[str]:
         return []
@@ -57,7 +61,7 @@ class Fp8LinearMethod(LinearMethodBase):
         quant_config: The quantization config.
     """
 
-    def __init__(self, quant_config: FP8Config):
+    def __init__(self, quant_config: Fp8Config):
         self.quant_config = quant_config
 
     def create_weights(
@@ -86,24 +90,24 @@ class Fp8LinearMethod(LinearMethodBase):
         layer.register_parameter("weight_scaling_factor", w_scale)
 
     def process_weights_after_loading(self, layer: Module) -> None:
-        # Although the linear_method is propagated to all layers,
+        # Although the quant_method is propagated to all layers,
         # only linear layers invoke "create_weights". So we check
         # whether "weight_scaling_facor" is registered to determine
         # whether the layer is a linear layer that requires quantization.
         if not hasattr(layer, "weight_scaling_factor"):
             return
 
-        qweight, weight_scale = per_tensor_quantize(layer.weight)
+        qweight, weight_scale = ops.scaled_fp8_quant(layer.weight)
         # torch._scaled_mm requires column-major in the second
         # input (weight), so we transpose the quantized weight.
         layer.weight = Parameter(qweight.t(), requires_grad=False)
         layer.weight_scaling_factor.data.copy_(weight_scale)
 
-    def apply_weights(self,
-                      layer: torch.nn.Module,
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-        qinput, x_scale = per_tensor_quantize(x)
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        qinput, x_scale = ops.scaled_fp8_quant(x)
         output, _ = torch._scaled_mm(
             qinput,
             layer.weight,
@@ -113,27 +117,3 @@ class Fp8LinearMethod(LinearMethodBase):
             bias=bias,
         )
         return output
-
-
-def per_tensor_quantize(tensor: torch.Tensor) -> Tuple[torch.Tensor, float]:
-    """Quantize a tensor using per-tensor static scaling factor.
-
-    Args:
-        tensor: The input tensor.
-    """
-    finfo = torch.finfo(torch.float8_e4m3fn)
-    # Calculate the scale as dtype max divided by absmax.
-    # Since .abs() creates a new tensor, we use aminmax to get
-    # the min and max first and then calculate the absmax.
-    min_val, max_val = tensor.aminmax()
-    amax = min_val.abs().max(max_val.abs())
-    scale = finfo.max / amax.clamp(min=1e-12)
-    # scale and clamp the tensor to bring it to
-    # the representative range of float8 data type
-    # (as default cast is unsaturated)
-    qweight = (tensor * scale).clamp(min=finfo.min, max=finfo.max)
-    # Return both float8 data and the inverse scale (as float),
-    # as both required as inputs to torch._scaled_mm
-    qweight = qweight.to(torch.float8_e4m3fn)
-    scale = scale.float().reciprocal()
-    return qweight, scale

vllm/model_executor/layers/quantization/gptq.py

@@ -7,10 +7,10 @@ import torch
 from torch.nn.parameter import Parameter
 
 from vllm import _custom_ops as ops
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               set_weight_attrs)
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.utils import set_weight_attrs
 
 
 class GPTQConfig(QuantizationConfig):
@@ -63,8 +63,11 @@ class GPTQConfig(QuantizationConfig):
         desc_act = cls.get_from_keys(config, ["desc_act"])
         return cls(weight_bits, group_size, desc_act)
 
-    def get_linear_method(self) -> "GPTQLinearMethod":
-        return GPTQLinearMethod(self)
+    def get_quant_method(
+            self, layer: torch.nn.Module) -> Optional["GPTQLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return GPTQLinearMethod(self)
+        return None
 
     def get_scaled_act_names(self) -> List[str]:
         return []
@@ -194,10 +197,10 @@ class GPTQLinearMethod(LinearMethodBase):
 
         layer.exllama_state = exllama_state
 
-    def apply_weights(self,
-                      layer: torch.nn.Module,
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
         qweight = layer.qweight
         out_shape = x.shape[:-1] + (qweight.shape[-1], )
         reshaped_x = x.reshape(-1, x.shape[-1])

vllm/model_executor/layers/quantization/marlin.py

@@ -4,10 +4,10 @@ import torch
 from torch.nn.parameter import Parameter
 
 from vllm import _custom_ops as ops
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               set_weight_attrs)
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.utils import set_weight_attrs
 
 
 class MarlinConfig(QuantizationConfig):
@@ -72,8 +72,11 @@ class MarlinConfig(QuantizationConfig):
         group_size = cls.get_from_keys(config, ["group_size"])
         return cls(group_size)
 
-    def get_linear_method(self) -> "MarlinLinearMethod":
-        return MarlinLinearMethod(self)
+    def get_quant_method(
+            self, layer: torch.nn.Module) -> Optional["MarlinLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return MarlinLinearMethod(self)
+        return None
 
     def get_scaled_act_names(self) -> List[str]:
         return []
@@ -197,7 +200,7 @@ class MarlinLinearMethod(LinearMethodBase):
         layer.register_parameter("workspace", workspace)
         set_weight_attrs(workspace, extra_weight_attrs)
 
-    def apply_weights(
+    def apply(
         self,
         layer: torch.nn.Module,
         x: torch.Tensor,

vllm/model_executor/layers/quantization/squeezellm.py

@@ -4,10 +4,10 @@ import torch
 from torch.nn.parameter import Parameter
 
 from vllm import _custom_ops as ops
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               set_weight_attrs)
+from vllm.model_executor.layers.linear import LinearBase
 from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.utils import set_weight_attrs
 from vllm.utils import is_hip
 
 
@@ -51,14 +51,18 @@ class SqueezeLLMConfig(QuantizationConfig):
         weight_bits = cls.get_from_keys(config, ["wbits"])
         return cls(weight_bits)
 
-    def get_linear_method(self) -> "SqueezeLLMLinearMethod":
-        return SqueezeLLMLinearMethod(self)
+    def get_quant_method(
+            self,
+            layer: torch.nn.Module) -> Optional["SqueezeLLMLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return SqueezeLLMLinearMethod(self)
+        return
 
     def get_scaled_act_names(self) -> List[str]:
         return []
 
 
-class SqueezeLLMLinearMethod(LinearMethodBase):
+class SqueezeLLMLinearMethod(QuantizeMethodBase):
     """Linear method for SqueezeLLM.
 
     Args:
@@ -112,10 +116,10 @@ class SqueezeLLMLinearMethod(LinearMethodBase):
         layer.register_parameter("lookup_table", lookup_table)
         set_weight_attrs(lookup_table, extra_weight_attrs)
 
-    def apply_weights(self,
-                      layer: torch.nn.Module,
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
         qweight = layer.qweight
         lookup_table = layer.lookup_table
         out_shape = x.shape[:-1] + (qweight.shape[-1], )

vllm/model_executor/model_loader/loader.py

@@ -3,8 +3,7 @@ import copy
 import glob
 import os
 from abc import ABC, abstractmethod
-from typing import (TYPE_CHECKING, Any, Dict, Generator, List, Optional, Tuple,
-                    Type)
+from typing import Any, Dict, Generator, List, Optional, Tuple, Type
 
 import torch
 from torch import nn
@@ -13,6 +12,8 @@ from vllm.config import (VLLM_USE_MODELSCOPE, DeviceConfig, LoadConfig,
                          LoadFormat, LoRAConfig, ModelConfig, ParallelConfig,
                          SchedulerConfig, VisionLanguageConfig)
 from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.model_loader.tensorizer import (
     TensorizerConfig, is_vllm_serialized_tensorizer, load_with_tensorizer,
     tensorizer_weights_iterator)
@@ -24,9 +25,6 @@ from vllm.model_executor.model_loader.weight_utils import (
     pt_weights_iterator, safetensors_weights_iterator)
 from vllm.model_executor.models.llava import LlavaForConditionalGeneration
 
-if TYPE_CHECKING:
-    from vllm.model_executor.layers.linear import LinearMethodBase
-
 _VISION_MODEL_CLASSES = [
     LlavaForConditionalGeneration,
 ]
@@ -34,11 +32,10 @@ _VISION_MODEL_CLASSES = [
 logger = init_logger(__name__)
 
 
-def _get_linear_method(
+def _get_quantization_config(
         model_config: ModelConfig,
-        load_config: LoadConfig) -> Optional["LinearMethodBase"]:
-    """Get the (maybe quantized) linear method."""
-    linear_method = None
+        load_config: LoadConfig) -> Optional[QuantizationConfig]:
+    """Get the quantization config."""
     if model_config.quantization is not None:
         quant_config = get_quant_config(model_config, load_config)
         capability = torch.cuda.get_device_capability()
@@ -55,9 +52,8 @@ def _get_linear_method(
                 f"{model_config.dtype} is not supported for quantization "
                 f"method {model_config.quantization}. Supported dtypes: "
                 f"{supported_dtypes}")
-
-        linear_method = quant_config.get_linear_method()
-    return linear_method
+        return quant_config
+    return None
 
 
 def _get_model_initialization_kwargs(
@@ -85,10 +81,10 @@ def _initialize_model(
         vision_language_config: Optional[VisionLanguageConfig]) -> nn.Module:
     """Initialize a model with the given configurations."""
     model_class = get_model_architecture(model_config)[0]
-    linear_method = _get_linear_method(model_config, load_config)
+    quant_config = _get_quantization_config(model_config, load_config)
 
     return model_class(config=model_config.hf_config,
-                       linear_method=linear_method,
+                       quant_config=quant_config,
                        **_get_model_initialization_kwargs(
                            model_class, lora_config, vision_language_config))
 
@@ -229,9 +225,11 @@ class DefaultModelLoader(BaseModelLoader):
                                                "fall_back_to_pt_during_load",
                                                True)), )
             for _, module in model.named_modules():
-                linear_method = getattr(module, "linear_method", None)
-                if linear_method is not None:
-                    linear_method.process_weights_after_loading(module)
+                quant_method = getattr(module, "quant_method", None)
+                if quant_method is not None:
+                    quant_method.process_weights_after_loading(module)
+                # FIXME: Remove this after Mixtral is updated
+                # to use quant_method.
                 if hasattr(module, "process_weights_after_loading"):
                     module.process_weights_after_loading()
         return model.eval()
@@ -314,11 +312,11 @@ class TensorizerLoader(BaseModelLoader):
         with set_default_torch_dtype(model_config.dtype):
             with torch.device(device_config.device):
                 model_class = get_model_architecture(model_config)[0]
-                linear_method = _get_linear_method(model_config,
-                                                   self.load_config)
+                quant_config = _get_quantization_config(
+                    model_config, self.load_config)
                 extra_kwargs = _get_model_initialization_kwargs(
                     model_class, lora_config, vision_language_config)
-                extra_kwargs["linear_method"] = linear_method
+                extra_kwargs["quant_config"] = quant_config
 
                 tensorizer_config = copy.copy(self.tensorizer_config)
                 tensorizer_config.model_class = model_class

vllm/model_executor/model_loader/tensorizer.py

@@ -13,7 +13,8 @@ from transformers import PretrainedConfig
 
 from vllm.config import ModelConfig, ParallelConfig
 from vllm.logger import init_logger
-from vllm.model_executor.layers.linear import LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
 
@@ -251,7 +252,7 @@ class TensorizerAgent:
     """
 
     def __init__(self, tensorizer_config: TensorizerConfig,
-                 linear_method: LinearMethodBase, **extra_kwargs):
+                 quant_config: QuantizationConfig, **extra_kwargs):
         if tensorizer_load_fail is not None:
             raise ImportError(
                 "Tensorizer is not installed. Please install tensorizer "
@@ -262,10 +263,10 @@ class TensorizerAgent:
         self.tensorizer_args = (
             self.tensorizer_config._construct_tensorizer_args())
         self.extra_kwargs = extra_kwargs
-        if extra_kwargs.get("linear_method", None) is not None:
-            self.linear_method = extra_kwargs["linear_method"]
+        if extra_kwargs.get("quant_config", None) is not None:
+            self.quant_config = extra_kwargs["quant_config"]
         else:
-            self.linear_method = linear_method
+            self.quant_config = quant_config
         self.model = self._init_model()
 
     def _init_model(self):
@@ -274,7 +275,7 @@ class TensorizerAgent:
         with no_init_or_tensor():
             return self.tensorizer_config.model_class(
                 config=model_args,
-                linear_method=self.linear_method,
+                quant_config=self.quant_config,
                 **self.extra_kwargs)
 
     def _resize_lora_embeddings(self):

vllm/model_executor/models/baichuan.py

@@ -31,11 +31,12 @@ 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 (
@@ -77,17 +78,17 @@ class BaiChuanMLP(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str,
-        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.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.")
@@ -110,7 +111,7 @@ class BaiChuanAttention(nn.Module):
         position_embedding: str,
         rope_theta: float = 10000,
         max_position_embeddings: int = 8192,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.hidden_size = hidden_size
@@ -132,13 +133,13 @@ class BaiChuanAttention(nn.Module):
             self.total_num_heads,
             self.total_num_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,
         )
         # Create the alibi slopes and slice them.
         if self.postion_embedding == "ALIBI":
@@ -184,7 +185,7 @@ class BaiChuanDecoderLayer(nn.Module):
     def __init__(self,
                  config: PretrainedConfig,
                  position_embedding: str,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.hidden_size = config.hidden_size
         rope_theta = getattr(config, "rope_theta", 10000)
@@ -196,13 +197,13 @@ class BaiChuanDecoderLayer(nn.Module):
             position_embedding=position_embedding,
             rope_theta=rope_theta,
             max_position_embeddings=max_position_embeddings,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.mlp = BaiChuanMLP(
             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)
@@ -243,7 +244,7 @@ class BaiChuanModel(nn.Module):
     def __init__(self,
                  config: PretrainedConfig,
                  position_embedding: str,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.config = config
         self.padding_idx = config.pad_token_id
@@ -254,7 +255,7 @@ class BaiChuanModel(nn.Module):
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            BaiChuanDecoderLayer(config, position_embedding, linear_method)
+            BaiChuanDecoderLayer(config, position_embedding, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -303,13 +304,13 @@ class BaiChuanBaseForCausalLM(nn.Module):
         self,
         config,
         position_embedding: str,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = BaiChuanModel(config, position_embedding, linear_method)
+        self.quant_config = quant_config
+        self.model = BaiChuanModel(config, position_embedding, quant_config)
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()
@@ -388,13 +389,13 @@ class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ):
         if config.hidden_size == 4096:  # baichuan2 7b
-            super().__init__(config, "ROPE", linear_method, lora_config)
+            super().__init__(config, "ROPE", quant_config, lora_config)
         else:  # baichuan 13b, baichuan2 13b
-            super().__init__(config, "ALIBI", linear_method, lora_config)
+            super().__init__(config, "ALIBI", quant_config, lora_config)
 
 
 class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
@@ -403,7 +404,7 @@ class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ):
-        super().__init__(config, "ROPE", linear_method, lora_config)
+        super().__init__(config, "ROPE", quant_config, lora_config)

vllm/model_executor/models/bloom.py

@@ -28,10 +28,11 @@ 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)
@@ -70,7 +71,7 @@ class BloomAttention(nn.Module):
     def __init__(
         self,
         config: BloomConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -87,13 +88,13 @@ class BloomAttention(nn.Module):
             self.head_dim,
             self.total_num_heads,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.dense = RowParallelLinear(
             self.hidden_size,
             self.hidden_size,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         # Create the alibi slopes and slice them.
@@ -129,21 +130,21 @@ class BloomMLP(nn.Module):
     def __init__(
         self,
         config: BloomConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         hidden_size = config.hidden_size
         self.dense_h_to_4h = ColumnParallelLinear(
             hidden_size,
             4 * 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.gelu_impl = get_act_fn("gelu", quant_config, 4 * hidden_size)
         self.dense_4h_to_h = RowParallelLinear(
             4 * hidden_size,
             hidden_size,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -158,17 +159,17 @@ class BloomBlock(nn.Module):
     def __init__(
         self,
         config: BloomConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         hidden_size = config.hidden_size
 
         self.input_layernorm = nn.LayerNorm(hidden_size,
                                             eps=config.layer_norm_epsilon)
-        self.self_attention = BloomAttention(config, linear_method)
+        self.self_attention = BloomAttention(config, quant_config)
         self.post_attention_layernorm = nn.LayerNorm(
             hidden_size, eps=config.layer_norm_epsilon)
-        self.mlp = BloomMLP(config, linear_method)
+        self.mlp = BloomMLP(config, quant_config)
         self.apply_residual_connection_post_layernorm = (
             config.apply_residual_connection_post_layernorm)
 
@@ -214,7 +215,7 @@ class BloomModel(nn.Module):
     def __init__(
         self,
         config: BloomConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.embed_dim = config.hidden_size
@@ -229,7 +230,7 @@ class BloomModel(nn.Module):
 
         # Transformer blocks
         self.h = nn.ModuleList([
-            BloomBlock(config, linear_method)
+            BloomBlock(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
 
@@ -262,12 +263,12 @@ class BloomForCausalLM(nn.Module):
     def __init__(
         self,
         config: BloomConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.transformer = BloomModel(config, linear_method)
+        self.quant_config = quant_config
+        self.transformer = BloomModel(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/chatglm.py

@@ -13,11 +13,12 @@ from vllm.config import LoRAConfig
 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 (
@@ -33,7 +34,7 @@ class GLMAttention(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -65,13 +66,13 @@ class GLMAttention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=config.add_bias_linear or config.add_qkv_bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.dense = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             config.hidden_size,
             bias=config.add_bias_linear,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         # https://huggingface.co/THUDM/chatglm3-6b-32k/blob/e210410255278dd9d74463cf396ba559c0ef801c/modeling_chatglm.py#L141
@@ -123,7 +124,7 @@ class GLMMLP(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
 
@@ -134,7 +135,7 @@ class GLMMLP(nn.Module):
             config.hidden_size,
             [config.ffn_hidden_size] * 2,
             bias=config.add_bias_linear,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         self.activation_func = SiluAndMul()
@@ -144,7 +145,7 @@ class GLMMLP(nn.Module):
             config.ffn_hidden_size,
             config.hidden_size,
             bias=config.add_bias_linear,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
     def forward(self, hidden_states):
@@ -166,7 +167,7 @@ class GLMBlock(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.apply_residual_connection_post_layernorm = (
@@ -180,7 +181,7 @@ class GLMBlock(nn.Module):
                                                eps=config.layernorm_epsilon)
 
         # Self attention.
-        self.self_attention = GLMAttention(config, linear_method)
+        self.self_attention = GLMAttention(config, quant_config)
         self.hidden_dropout = config.hidden_dropout
 
         # Layernorm on the attention output
@@ -188,7 +189,7 @@ class GLMBlock(nn.Module):
             config.hidden_size, eps=config.layernorm_epsilon)
 
         # MLP
-        self.mlp = GLMMLP(config, linear_method)
+        self.mlp = GLMMLP(config, quant_config)
 
     def forward(
         self,
@@ -236,7 +237,7 @@ class GLMTransformer(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.post_layer_norm = config.post_layer_norm
@@ -246,7 +247,7 @@ class GLMTransformer(nn.Module):
 
         # Transformer layers.
         self.layers = nn.ModuleList(
-            [GLMBlock(config, linear_method) for i in range(self.num_layers)])
+            [GLMBlock(config, quant_config) for i in range(self.num_layers)])
 
         if self.post_layer_norm:
             layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
@@ -281,7 +282,7 @@ class ChatGLMModel(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
 
@@ -291,7 +292,7 @@ class ChatGLMModel(nn.Module):
         self.num_layers = config.num_layers
         self.multi_query_group_num = config.multi_query_group_num
         self.kv_channels = config.kv_channels
-        self.encoder = GLMTransformer(config, linear_method)
+        self.encoder = GLMTransformer(config, quant_config)
 
         self.output_layer = ParallelLMHead(config.padded_vocab_size,
                                            config.hidden_size)
@@ -333,13 +334,13 @@ class ChatGLMForCausalLM(nn.Module):
     def __init__(
         self,
         config: ChatGLMConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ):
         super().__init__()
         self.config: ChatGLMConfig = config
-        self.linear_method = linear_method
-        self.transformer = ChatGLMModel(config, linear_method)
+        self.quant_config = quant_config
+        self.transformer = ChatGLMModel(config, quant_config)
         self.lm_head_weight = self.transformer.output_layer.weight
         self.logits_processor = LogitsProcessor(config.padded_vocab_size)
         self.sampler = Sampler()

vllm/model_executor/models/commandr.py

@@ -32,11 +32,12 @@ 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.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 (
@@ -91,7 +92,7 @@ class CohereMLP(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -101,13 +102,13 @@ class CohereMLP(nn.Module):
             self.hidden_size,
             [self.intermediate_size] * 2,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.down_proj = RowParallelLinear(
             self.intermediate_size,
             self.hidden_size,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.act_fn = SiluAndMul()
 
@@ -123,7 +124,7 @@ class CohereAttention(nn.Module):
     def __init__(
         self,
         config: CohereConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         tp_size = get_tensor_model_parallel_world_size()
@@ -158,13 +159,13 @@ class CohereAttention(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,
             self.hidden_size,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.rotary_emb = get_rope(
             self.head_dim,
@@ -218,13 +219,13 @@ class CohereDecoderLayer(nn.Module):
 
     def __init__(self,
                  config: CohereConfig,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.hidden_size = config.hidden_size
 
-        self.self_attn = CohereAttention(config, linear_method=linear_method)
+        self.self_attn = CohereAttention(config, quant_config=quant_config)
 
-        self.mlp = CohereMLP(config, linear_method=linear_method)
+        self.mlp = CohereMLP(config, quant_config=quant_config)
         self.input_layernorm = LayerNorm(param_shape=(config.hidden_size),
                                          eps=config.layer_norm_eps)
 
@@ -257,7 +258,7 @@ class CohereModel(nn.Module):
     def __init__(
         self,
         config: CohereConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -265,7 +266,7 @@ class CohereModel(nn.Module):
         self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
                                                    config.hidden_size)
         self.layers = nn.ModuleList([
-            CohereDecoderLayer(config, linear_method=linear_method)
+            CohereDecoderLayer(config, quant_config=quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = LayerNorm(param_shape=(config.hidden_size),
@@ -298,14 +299,14 @@ class CohereForCausalLM(nn.Module):
     def __init__(
         self,
         config: CohereConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
+        self.quant_config = quant_config
         self.logits_processor = LogitsProcessor(config.vocab_size,
                                                 scale=config.logit_scale)
-        self.model = CohereModel(config, linear_method)
+        self.model = CohereModel(config, quant_config)
         self.sampler = Sampler()
 
     @torch.no_grad()

vllm/model_executor/models/dbrx.py

@@ -9,11 +9,12 @@ 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.fused_moe import fused_moe
-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 (
@@ -44,7 +45,7 @@ class DbrxRouter(nn.Module):
             self.num_total_experts,
             bias=False,
             params_dtype=params_dtype,
-            linear_method=None,
+            quant_config=None,
         )
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -63,7 +64,7 @@ class DbrxExperts(nn.Module):
     def __init__(
         self,
         config: DbrxConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         params_dtype: Optional[torch.dtype] = None,
     ):
         super().__init__()
@@ -165,7 +166,7 @@ class DbrxAttention(nn.Module):
     def __init__(
         self,
         config: DbrxConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.d_model = config.d_model
@@ -183,13 +184,13 @@ class DbrxAttention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.out_proj = RowParallelLinear(
             self.d_model,
             self.d_model,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.rotary_emb = get_rope(
             self.head_dim,
@@ -244,11 +245,11 @@ class DbrxFusedNormAttention(nn.Module):
     def __init__(
         self,
         config: DbrxConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.d_model = config.d_model
-        self.attn = DbrxAttention(config, linear_method)
+        self.attn = DbrxAttention(config, quant_config)
         self.norm_1 = nn.LayerNorm(self.d_model)
         self.norm_2 = nn.LayerNorm(self.d_model)
 
@@ -278,11 +279,11 @@ class DbrxBlock(nn.Module):
     def __init__(
         self,
         config: DbrxConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
-        self.norm_attn_norm = DbrxFusedNormAttention(config, linear_method)
-        self.ffn = DbrxExperts(config, linear_method)
+        self.norm_attn_norm = DbrxFusedNormAttention(config, quant_config)
+        self.ffn = DbrxExperts(config, quant_config)
 
     def forward(
         self,
@@ -307,7 +308,7 @@ class DbrxModel(nn.Module):
     def __init__(
         self,
         config: DbrxConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.wte = VocabParallelEmbedding(
@@ -315,7 +316,7 @@ class DbrxModel(nn.Module):
             config.d_model,
         )
         self.blocks = nn.ModuleList(
-            [DbrxBlock(config, linear_method) for _ in range(config.n_layers)])
+            [DbrxBlock(config, quant_config) for _ in range(config.n_layers)])
         self.norm_f = nn.LayerNorm(config.d_model, eps=1e-5)
         for module in self.modules():
             if hasattr(module, "bias") and isinstance(module.bias,
@@ -348,13 +349,13 @@ class DbrxForCausalLM(nn.Module):
     def __init__(
         self,
         config: DbrxConfig,
-        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.unpadded_vocab_size = config.vocab_size
-        self.transformer = DbrxModel(config, linear_method)
+        self.transformer = DbrxModel(config, quant_config)
         self.lm_head = ParallelLMHead(
             config.vocab_size,
             config.d_model,

vllm/model_executor/models/decilm.py

@@ -29,7 +29,8 @@ import torch
 from transformers import PretrainedConfig
 
 from vllm.config import LoRAConfig
-from vllm.model_executor.layers.linear import LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.llama import LlamaForCausalLM
 
@@ -55,13 +56,13 @@ class DeciLMForCausalLM(LlamaForCausalLM):
     def __init__(
         self,
         config: Optional[PretrainedConfig] = None,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         config.num_key_value_heads = max(config.num_key_value_heads_per_layer)
         delattr(config, "num_key_value_heads_per_layer")
         super().__init__(config=config,
-                         linear_method=linear_method,
+                         quant_config=quant_config,
                          lora_config=lora_config)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

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()

vllm/model_executor/models/qwen2.py

@@ -33,11 +33,12 @@ from vllm.config import LoRAConfig
 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 (
@@ -54,17 +55,17 @@ class Qwen2MLP(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.gate_up_proj = MergedColumnParallelLinear(
             hidden_size, [intermediate_size] * 2,
             bias=False,
-            linear_method=linear_method)
+            quant_config=quant_config)
         self.down_proj = RowParallelLinear(intermediate_size,
                                            hidden_size,
                                            bias=False,
-                                           linear_method=linear_method)
+                                           quant_config=quant_config)
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -86,7 +87,7 @@ class Qwen2Attention(nn.Module):
                  max_position: int = 4096 * 32,
                  rope_theta: float = 10000,
                  use_sliding_window: bool = False,
-                 linear_method: Optional[LinearMethodBase] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
                  sliding_window: Optional[int] = None) -> None:
         super().__init__()
         self.hidden_size = hidden_size
@@ -117,13 +118,13 @@ class Qwen2Attention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.o_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         self.rotary_emb = get_rope(
@@ -159,7 +160,7 @@ class Qwen2DecoderLayer(nn.Module):
         self,
         config: Qwen2Config,
         layer_idx: int,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -174,13 +175,13 @@ class Qwen2DecoderLayer(nn.Module):
             num_kv_heads=config.num_key_value_heads,
             rope_theta=rope_theta,
             use_sliding_window=use_sliding_window,
-            linear_method=linear_method,
+            quant_config=quant_config,
             sliding_window=config.sliding_window)
         self.mlp = Qwen2MLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.input_layernorm = RMSNorm(config.hidden_size,
                                        eps=config.rms_norm_eps)
@@ -221,7 +222,7 @@ class Qwen2Model(nn.Module):
     def __init__(
         self,
         config: Qwen2Config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
@@ -233,7 +234,7 @@ class Qwen2Model(nn.Module):
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            Qwen2DecoderLayer(config, layer_idx, linear_method)
+            Qwen2DecoderLayer(config, layer_idx, quant_config)
             for layer_idx in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -286,14 +287,14 @@ class Qwen2ForCausalLM(nn.Module):
     def __init__(
         self,
         config: Qwen2Config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         del lora_config
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = Qwen2Model(config, linear_method)
+        self.quant_config = quant_config
+        self.model = Qwen2Model(config, quant_config)
 
         if config.tie_word_embeddings:
             self.lm_head_weight = self.model.embed_tokens.weight

vllm/model_executor/models/qwen2_moe.py

@@ -36,12 +36,13 @@ 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 (
@@ -58,18 +59,18 @@ class Qwen2MoeMLP(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}. "
@@ -88,7 +89,7 @@ class Qwen2MoeSparseMoeBlock(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -105,7 +106,7 @@ class Qwen2MoeSparseMoeBlock(nn.Module):
             Qwen2MoeMLP(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)
         ])
@@ -114,13 +115,13 @@ class Qwen2MoeSparseMoeBlock(nn.Module):
         self.gate = ReplicatedLinear(config.hidden_size,
                                      self.n_routed_experts,
                                      bias=False,
-                                     linear_method=None)
+                                     quant_config=None)
         if config.shared_expert_intermediate_size > 0:
             self.shared_expert = Qwen2MoeMLP(
                 hidden_size=config.hidden_size,
                 intermediate_size=config.shared_expert_intermediate_size,
                 hidden_act=config.hidden_act,
-                linear_method=linear_method,
+                quant_config=quant_config,
                 reduce_results=False,
             )
         else:
@@ -186,7 +187,7 @@ class Qwen2MoeAttention(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
@@ -217,14 +218,14 @@ class Qwen2MoeAttention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         self.o_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         self.rotary_emb = get_rope(
@@ -260,7 +261,7 @@ class Qwen2MoeDecoderLayer(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
@@ -275,18 +276,18 @@ class Qwen2MoeDecoderLayer(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.num_experts is not None
                 and (layer_idx + 1) % config.decoder_sparse_step == 0):
             self.mlp = Qwen2MoeSparseMoeBlock(config=config,
-                                              linear_method=linear_method)
+                                              quant_config=quant_config)
         else:
             self.mlp = Qwen2MoeMLP(
                 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)
@@ -327,7 +328,7 @@ class Qwen2MoeModel(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
@@ -338,9 +339,7 @@ class Qwen2MoeModel(nn.Module):
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            Qwen2MoeDecoderLayer(config,
-                                 layer_idx,
-                                 linear_method=linear_method)
+            Qwen2MoeDecoderLayer(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)
@@ -370,12 +369,12 @@ class Qwen2MoeForCausalLM(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 = Qwen2MoeModel(config, linear_method)
+        self.quant_config = quant_config
+        self.model = Qwen2MoeModel(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/stablelm.py

@@ -28,11 +28,12 @@ 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 (
@@ -46,7 +47,7 @@ class StablelmMLP(nn.Module):
 
     def __init__(self,
                  config: PretrainedConfig,
-                 linear_method: Optional[LinearMethodBase] = None) -> None:
+                 quant_config: Optional[QuantizationConfig] = None) -> None:
         super().__init__()
         self.config = config
         self.hidden_size = config.hidden_size
@@ -54,7 +55,7 @@ class StablelmMLP(nn.Module):
         self.gate_up_proj = MergedColumnParallelLinear(
             config.hidden_size, [config.intermediate_size] * 2,
             bias=False,
-            linear_method=linear_method)
+            quant_config=quant_config)
         self.down_proj = RowParallelLinear(config.intermediate_size,
                                            config.hidden_size,
                                            bias=False)
@@ -71,7 +72,7 @@ class StablelmAttention(nn.Module):
 
     def __init__(self,
                  config: PretrainedConfig,
-                 linear_method: Optional[LinearMethodBase] = None) -> None:
+                 quant_config: Optional[QuantizationConfig] = None) -> None:
         super().__init__()
         self.config = config
         self.hidden_size = config.hidden_size
@@ -109,11 +110,11 @@ class StablelmAttention(nn.Module):
                                           self.total_num_heads,
                                           self.total_num_key_value_heads,
                                           self.qkv_bias,
-                                          linear_method=linear_method)
+                                          quant_config=quant_config)
         self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
                                         self.hidden_size,
                                         bias=False,
-                                        linear_method=linear_method)
+                                        quant_config=quant_config)
         self.rotary_emb = get_rope(
             self.head_dim,
             rotary_dim=self.rotary_ndims,
@@ -145,11 +146,11 @@ class StablelmDecoderLayer(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.self_attn = StablelmAttention(config)
-        self.mlp = StablelmMLP(config, linear_method)
+        self.mlp = StablelmMLP(config, quant_config)
         norm_eps = getattr(config, "norm_eps",
                            getattr(config, "layer_norm_eps", 1e-05))
         self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=norm_eps)
@@ -187,14 +188,14 @@ class StableLMEpochModel(nn.Module):
 
     def __init__(self,
                  config: PretrainedConfig,
-                 linear_method: Optional[LinearMethodBase] = None) -> None:
+                 quant_config: Optional[QuantizationConfig] = None) -> None:
         super().__init__()
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            StablelmDecoderLayer(config, linear_method)
+            StablelmDecoderLayer(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         norm_eps = getattr(config, "norm_eps",
@@ -226,12 +227,12 @@ class StablelmForCausalLM(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 = StableLMEpochModel(config, linear_method)
+        self.quant_config = quant_config
+        self.model = StableLMEpochModel(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/starcoder2.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.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -45,7 +46,7 @@ class Starcoder2Attention(nn.Module):
 
     def __init__(self,
                  config: Starcoder2Config,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.config = config
 
@@ -79,13 +80,13 @@ class Starcoder2Attention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=self.use_bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.o_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             self.hidden_size,
             bias=self.use_bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.rotary_emb = get_rope(
             self.head_dim,
@@ -121,21 +122,21 @@ class Starcoder2MLP(nn.Module):
 
     def __init__(self,
                  config: Starcoder2Config,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.c_fc = ColumnParallelLinear(
             config.hidden_size,
             config.intermediate_size,
             bias=config.use_bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.c_proj = RowParallelLinear(
             config.intermediate_size,
             config.hidden_size,
             bias=config.use_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(config.hidden_act, quant_config,
                               config.intermediate_size)
 
@@ -150,12 +151,11 @@ class Starcoder2DecoderLayer(nn.Module):
 
     def __init__(self,
                  config: Starcoder2Config,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.hidden_size = config.hidden_size
-        self.self_attn = Starcoder2Attention(config,
-                                             linear_method=linear_method)
-        self.mlp = Starcoder2MLP(config, linear_method=linear_method)
+        self.self_attn = Starcoder2Attention(config, quant_config=quant_config)
+        self.mlp = Starcoder2MLP(config, quant_config=quant_config)
         self.input_layernorm = nn.LayerNorm(config.hidden_size,
                                             eps=config.norm_epsilon)
         self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
@@ -192,7 +192,7 @@ class Starcoder2Model(nn.Module):
 
     def __init__(self,
                  config: Starcoder2Config,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.config = config
         self.padding_idx = config.pad_token_id
@@ -202,7 +202,7 @@ class Starcoder2Model(nn.Module):
         self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
                                                    config.hidden_size)
         self.layers = nn.ModuleList([
-            Starcoder2DecoderLayer(config, linear_method=linear_method)
+            Starcoder2DecoderLayer(config, quant_config=quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_epsilon)
@@ -227,10 +227,10 @@ class Starcoder2ForCausalLM(nn.Module):
 
     def __init__(self,
                  config: Starcoder2Config,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.config = config
-        self.model = Starcoder2Model(config, linear_method=linear_method)
+        self.model = Starcoder2Model(config, quant_config=quant_config)
         self.vocab_size = config.vocab_size
         self.unpadded_vocab_size = config.vocab_size
         if config.tie_word_embeddings:

vllm/model_executor/models/xverse.py

@@ -31,11 +31,12 @@ from vllm.config import LoRAConfig
 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 (
@@ -52,17 +53,17 @@ class XverseMLP(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.")
@@ -85,7 +86,7 @@ class XverseAttention(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:
@@ -112,13 +113,13 @@ class XverseAttention(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(
@@ -154,7 +155,7 @@ class XverseDecoderLayer(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
@@ -171,7 +172,7 @@ class XverseDecoderLayer(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=getattr(config, "bias", False),
             sliding_window=sliding_window,
         )
@@ -179,7 +180,7 @@ class XverseDecoderLayer(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)
@@ -220,7 +221,7 @@ class XverseModel(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         super().__init__()
@@ -236,7 +237,7 @@ class XverseModel(nn.Module):
             org_num_embeddings=config.vocab_size,
         )
         self.layers = nn.ModuleList([
-            XverseDecoderLayer(config, linear_method)
+            XverseDecoderLayer(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -294,13 +295,13 @@ class XverseForCausalLM(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config=None,
     ) -> None:
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = XverseModel(config, linear_method)
+        self.quant_config = quant_config
+        self.model = XverseModel(config, quant_config)
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()