support both weight loading methods

vllm/model_executor/__init__.py

@@ -1,6 +1,6 @@
+from vllm.model_executor.parameter import *
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.model_executor.utils import set_random_seed
-from vllm.model_executor.parameter import *
 
 __all__ = [
     "SamplingMetadata",

vllm/model_executor/layers/quantization/compressed_tensors/__init__.py

@@ -0,0 +1 @@
+from .compressed_tensors import CompressedTensorsLinearMethod  # noqa: F401

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -15,6 +15,8 @@
     CompressionFormat, QuantizationArgs, QuantizationStrategy,
     find_first_name_or_class_match)
 
+__all__ = ["CompressedTensorsLinearMethod"]
+
 
 class CompressedTensorsConfig(QuantizationConfig):
 

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a16_24.py

@@ -2,12 +2,13 @@
 
 import torch
 from torch.nn import Parameter
+
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
 from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
     GPTQ_MARLIN_24_MAX_PARALLEL, GPTQ_MARLIN_24_MIN_THREAD_N)
-from vllm.model_executor.parameter import vLLMParameter, PackedvLLMParameter
+from vllm.model_executor.parameter import PackedvLLMParameter, vLLMParameter
 
 __all__ = ["CompressedTensorsW4A16Sparse24"]
 W4A16SPARSE24_SUPPORTED_BITS = [4]
@@ -65,7 +66,8 @@ def create_weights(self, layer: torch.nn.Module, input_size: int,
             output_dim=1,
             input_dim=input_dim,
             use_col_loading=True,
-            use_row_loading=True if input_dim is not None else False,
+            use_row_loading=True  #noqa: SIM210
+            if input_dim is not None else False,
             weight_loader=weight_loader)
 
         weight_shape = vLLMParameter(data=torch.empty(2, dtype=torch.int64),

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py

@@ -6,7 +6,8 @@
     CompressedTensorsScheme)
 from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
     QuantizationStrategy)
-from vllm.model_executor.parameter import vLLMParameter, ScalerToArrayvLLMParameter
+from vllm.model_executor.parameter import (ScalerToArrayvLLMParameter,
+                                           vLLMParameter)
 
 
 class CompressedTensorsW8A8(CompressedTensorsScheme):

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py

@@ -2,13 +2,14 @@
 
 import torch
 from torch.nn import Parameter
+
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
 from vllm.model_executor.layers.quantization.gptq_marlin import (
     GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N, GPTQMarlinState,
     marlin_permute_scales)
-from vllm.model_executor.parameter import vLLMParameter, PackedvLLMParameter
+from vllm.model_executor.parameter import PackedvLLMParameter, vLLMParameter
 
 __all__ = ["CompressedTensorsWNA16"]
 WNA16_SUPPORTED_BITS = [4, 8]
@@ -68,7 +69,8 @@ def create_weights(self, layer: torch.nn.Module, input_size: int,
             input_dim=weight_scale_dim,
             output_dim=0,
             use_col_loading=True,
-            use_row_loading=True if weight_scale_dim is not None else False,
+            use_row_loading=True  # noqa: SIM210
+            if weight_scale_dim is not None else False,
             weight_loader=weight_loader,
             data=torch.empty(
                 output_size_per_partition,

vllm/model_executor/parameter.py

@@ -1,6 +1,8 @@
-from torch.nn import Parameter
-from typing import Optional, Callable, List, Tuple, Union
+from typing import Callable, List, Optional, Tuple, Union
+
 import torch
+from torch.nn import Parameter
+
 from vllm.logger import init_logger
 
 __all__ = [