[Feat] Unquantized linear nz support (#2619)

### What this PR does / why we need it?
Currently, when executing to the Linear layer of the model in
vLLM-Ascend, the weights input format is ND in unquantized case and
skipped ascend case, which is slower than FRACTAL_NZ.
This PR supplements the execution logic for Linear layer. When
VLLM_ASCEND_ENABLE_MLP_OPTIMIZE=1 and CANN version is 8.3, the weights
of the Linear layer will be converted to FRACTAL_NZ, in both unquantized
case and skipped ascend case.

- vLLM version: main
- vLLM main:
267c80d31f

Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>

vllm_ascend/ops/linear.py

@@ -36,12 +36,36 @@ from vllm.model_executor.utils import set_weight_attrs
 
 from vllm_ascend.distributed.parallel_state import (get_mlp_tp_group,
                                                     get_otp_group)
-from vllm_ascend.utils import (dense_optim_enable, matmul_allreduce_enable,
-                               mlp_tp_enable, oproj_tp_enable)
+from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ, dense_optim_enable,
+                               matmul_allreduce_enable, mlp_tp_enable,
+                               oproj_tp_enable)
 
 _HCOMM_INFO = None
 
 
+class AscendUnquantizedLinearMethod(UnquantizedLinearMethod):
+    """Linear method without quantization."""
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        super().process_weights_after_loading(layer)
+        if torch.version.cann.startswith("8.3"):
+            layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
+            layer.weight.data = torch_npu.npu_format_cast(
+                layer.weight.data, ACL_FORMAT_FRACTAL_NZ)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        if torch.version.cann.startswith("8.3"):
+            if bias is None:
+                return torch.matmul(x, layer.weight)
+            else:
+                return torch.matmul(x, layer.weight) + bias
+        else:
+            return torch.nn.functional.linear(x, layer.weight, bias)
+
+
 class AscendColumnParallelLinear(ColumnParallelLinear):
     """Linear layer with column parallelism.
 
@@ -617,7 +641,7 @@ class AscendLinearBase(LinearBase):
         self.prefix = prefix
         if quant_config is None:
             self.quant_method: Optional[
-                QuantizeMethodBase] = UnquantizedLinearMethod()
+                QuantizeMethodBase] = AscendUnquantizedLinearMethod()
         else:
             self.quant_method = quant_config.get_quant_method(self,
                                                               prefix=prefix)

vllm_ascend/quantization/quant_config.py

@@ -23,8 +23,7 @@ from vllm.distributed import get_tensor_model_parallel_rank
 from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
                                                   FusedMoeWeightScaleSupported)
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
-                                               RowParallelLinear,
-                                               UnquantizedLinearMethod)
+                                               RowParallelLinear)
 from vllm.model_executor.layers.quantization import \
     register_quantization_config
 from vllm.model_executor.layers.quantization.base_config import (
@@ -38,6 +37,7 @@ from vllm.model_executor.utils import set_weight_attrs
 from vllm_ascend.distributed.parallel_state import (get_mlp_tp_group,
                                                     get_otp_group)
 from vllm_ascend.ops.fused_moe import AscendUnquantizedFusedMoEMethod
+from vllm_ascend.ops.linear import AscendUnquantizedLinearMethod
 from vllm_ascend.utils import (ASCEND_QUANTIZATION_METHOD, mlp_tp_enable,
                                oproj_tp_enable)
 
@@ -92,7 +92,7 @@ class AscendQuantConfig(QuantizationConfig):
         if isinstance(layer, LinearBase):
             if self.is_layer_skipped_ascend(prefix,
                                             self.packed_modules_mapping):
-                return UnquantizedLinearMethod()
+                return AscendUnquantizedLinearMethod()
             return AscendLinearMethod(self, prefix,
                                       self.packed_modules_mapping)
         elif isinstance(layer, Attention) and \