[Bugfix] Remove ModelSlim-"M4 Quantization". (#4589)

The M4 quantization method in ModelSlim adds bias to model weights that
originally do not have a linear bias. PR #4235 supported PD-MIX
quantization and M4 quantization, adding bias to `w8a8.py` and
`w8a8_dynamic.py`, and implementing adaptations in `ops/linear.py` to
prevent it from being reset to `None` by
`self.register_parameter("bias", None)`. However, this modification
introduced an issue where the bias was still being reset to `None` in
certain scenarios, causing errors during service startup. Therefore,
support for M4 quantization is temporarily being reverted in this PR.
___
- vLLM version: v0.11.2

Signed-off-by: SlightwindSec <slightwindsec@gmail.com>

vllm_ascend/ops/linear.py

@@ -277,20 +277,18 @@ class AscendRowParallelLinear(RowParallelLinear):
             weight_loader=(
                 self.weight_loader_v2 if self.quant_method.__class__.__name__
                 in WEIGHT_LOADER_V2_SUPPORTED else self.weight_loader))
-        bias_initialized_by_quant = ("bias" in self._parameters
-                                     and self._parameters["bias"] is not None)
         if not reduce_results and (bias and not skip_bias_add):
             raise ValueError("When not reduce the results, adding bias to the "
                              "results can lead to incorrect results")
 
-        if bias and not bias_initialized_by_quant:
+        if bias:
             self.bias = Parameter(
                 torch.empty(self.output_size, dtype=params_dtype))
             set_weight_attrs(self.bias, {
                 "output_dim": 0,
                 "weight_loader": self.weight_loader,
             })
-        elif not bias and not bias_initialized_by_quant:
+        else:
             self.register_parameter("bias", None)
 
         if self.custom_op is not None:
@@ -368,9 +366,7 @@ class AscendColumnParallelLinear(ColumnParallelLinear):
             weight_loader=(
                 self.weight_loader_v2 if self.quant_method.__class__.__name__
                 in WEIGHT_LOADER_V2_SUPPORTED else self.weight_loader))
-        bias_initialized_by_quant = ("bias" in self._parameters
-                                     and self._parameters["bias"] is not None)
-        if bias and not bias_initialized_by_quant:
+        if bias:
             self.bias = Parameter(
                 torch.empty(self.output_size_per_partition,
                             dtype=params_dtype))
@@ -378,7 +374,7 @@ class AscendColumnParallelLinear(ColumnParallelLinear):
                 "output_dim": 0,
                 "weight_loader": self.weight_loader,
             })
-        elif not bias and not bias_initialized_by_quant:
+        else:
             self.register_parameter("bias", None)
 
         if self.custom_op is not None:
@@ -449,16 +445,14 @@ class AscendReplicatedLinear(ReplicatedLinear):
                                          self.params_dtype,
                                          weight_loader=self.weight_loader)
 
-        bias_initialized_by_quant = ("bias" in self._parameters
-                                     and self._parameters["bias"] is not None)
-        if bias and not bias_initialized_by_quant:
+        if bias:
             self.bias = Parameter(
                 torch.empty(self.output_size, dtype=self.params_dtype))
             set_weight_attrs(self.bias, {
                 "output_dim": 0,
                 "weight_loader": self.weight_loader,
             })
-        elif not bias and not bias_initialized_by_quant:
+        else:
             self.register_parameter("bias", None)
 
         if self.custom_op is not None:

vllm_ascend/quantization/w8a8.py

@@ -87,7 +87,6 @@ class AscendW8A8LinearMethod:
         params_dict["weight_offset"] = torch.empty(output_size,
                                                    1,
                                                    dtype=params_dtype)
-        params_dict["bias"] = torch.zeros(output_size, dtype=torch.float32)
         return params_dict
 
     def get_pergroup_param(self,
@@ -199,13 +198,7 @@ class AscendW8A8LinearMethod:
                 layer.weight.data, ACL_FORMAT_FRACTAL_NZ)
         layer.weight_scale.data = torch.flatten(layer.weight_scale.data)
         layer.weight_offset.data = torch.flatten(layer.weight_offset.data)
-        layer.bias.data = layer.bias.data.to(layer.weight_scale.data.dtype)
-
-        try:
-            ascend_quant_method = getattr(layer, "ascend_quant_method")
-        except AttributeError:
-            ascend_quant_method = ""
-
+        ascend_quant_method = getattr(layer, "ascend_quant_method", "")
         if ascend_quant_method == COMPRESSED_TENSORS_METHOD:
             deq_scale = layer.input_scale.data * layer.weight_scale.data
             layer.deq_scale = torch.nn.Parameter(deq_scale,

vllm_ascend/quantization/w8a8_dynamic.py

@@ -60,7 +60,6 @@ class AscendW8A8DynamicLinearMethod:
         params_dict["weight_offset"] = torch.empty(output_size,
                                                    1,
                                                    dtype=params_dtype)
-        params_dict["bias"] = torch.zeros(output_size, dtype=torch.float32)
         return params_dict
 
     def get_pergroup_param(self,
@@ -98,7 +97,6 @@ class AscendW8A8DynamicLinearMethod:
         layer.weight_scale.data = layer.weight_scale.data.flatten()
         layer.weight_scale_fp32 = layer.weight_scale.data.to(torch.float32)
         layer.weight_offset.data = layer.weight_offset.data.flatten()
-        layer.bias.data = layer.bias.data.to(layer.weight_scale.data.dtype)
 
 
 class AscendW8A8DynamicFusedMoEMethod: