support v1 loader for machete (#3999)

fastdeploy/model_executor/layers/quantization/weight_only.py

@@ -226,12 +226,19 @@ class WeightOnlyLinearMethod(QuantMethodBase):
                 quant_attrs,
             )
         else:
-            # The scale shape should be equal to the output dim of weight using Per-Channel Quantization.
+            if isinstance(self, MacheteWeightOnlyLinearMethod):
-            weight_scale_shape = [layer.weight_shape[1]]
+                weight_scale_shape = [1, layer.weight_shape[1]]
-            layer.weight_shape.reverse()
+                if self.quant_config.name() == "wint4":
-            if self.quant_config.name() == "wint4":
+                    layer.weight_shape[0] //= 8
-                layer.weight_shape[0] //= 2
+                layer.weight_dtype = "int32"
-            layer.weight_dtype = "int8"
+            else:
+                # The scale shape should be equal to the output dim of weight using Per-Channel Quantization.
+                weight_scale_shape = [layer.weight_shape[1]]
+                layer.weight_shape.reverse()
+                if self.quant_config.name() == "wint4":
+                    layer.weight_shape[0] //= 2
+                layer.weight_dtype = "int8"
+
             layer.weight = layer.create_parameter(
                 shape=layer.weight_shape,
                 dtype=layer.weight_dtype,
@@ -267,17 +274,28 @@ class WeightOnlyLinearMethod(QuantMethodBase):
     def process_weights_after_loading(self, layer) -> None:
         if not self.quant_config.is_checkpoint_bf16:
             return
-        quanted_weight_tensor, weight_scale_tensor = weight_quantize(
+        if isinstance(self, MacheteWeightOnlyLinearMethod):
-            layer.weight,
+            from fastdeploy.model_executor.layers.quantization.ops import (
-            algo=self.quant_config.algo,
+                machete_quantize_and_pack,
-            arch=self.quant_config.weight_only_linear_arch,
+            )
-        )
+
+            quanted_weight_tensor, weight_scale_tensor = machete_quantize_and_pack(
+                w=layer.weight,
+                atype=layer._dtype,
+                quant_type="uint4b8",
+            )
+        else:
+            quanted_weight_tensor, weight_scale_tensor = weight_quantize(
+                layer.weight,
+                algo=self.quant_config.algo,
+                arch=self.quant_config.weight_only_linear_arch,
+            )
 
         free_tensor(layer.weight)
 
         layer.weight = layer.create_parameter(
             shape=quanted_weight_tensor.shape,
-            dtype="int8",
+            dtype="int8" if not isinstance(self, MacheteWeightOnlyLinearMethod) else "int32",
             is_bias=False,
             default_initializer=paddle.nn.initializer.Constant(0),
         )
@@ -368,32 +386,6 @@ class MacheteWeightOnlyLinearMethod(WeightOnlyLinearMethod):
     ) -> None:
         super().__init__(quant_config)
 
-    def create_weights(self, layer, **extra_weight_attrs):
-
-        assert layer.bias is None, "Machete weight only linear method does not support bias."
-        assert self.quant_config.name() == "wint4", "Machete weight only linear method only supports wint4."
-
-        # The scale shape should be equal to the output dim of weight using Per-Channel Quantization.
-        weight_scale_shape = [1, layer.weight_shape[1]]
-
-        # layer.weight_shape.reverse()
-        if self.quant_config.name() == "wint4":
-            layer.weight_shape[0] //= 8
-        layer.weight_dtype = "int32"
-
-        layer.weight = layer.create_parameter(
-            shape=layer.weight_shape,
-            dtype=layer.weight_dtype,
-            is_bias=False,
-            default_initializer=paddle.nn.initializer.Constant(0),
-        )
-
-        layer.weight_scale = layer.create_parameter(
-            shape=weight_scale_shape,
-            dtype=layer._dtype,
-            is_bias=False,
-        )
-
     def process_prequanted_weights(self, layer, state_dict) -> None:
         pass
 
@@ -412,7 +404,6 @@ class MacheteWeightOnlyLinearMethod(WeightOnlyLinearMethod):
 
     def apply(self, layer, x):
         assert layer.bias is None, "Machete weight only linear method does not support bias."
-        assert self.quant_config.name() == "wint4", "Machete weight only linear method only supports wint4."
         from fastdeploy.model_executor.layers.quantization.ops import machete_wint_mm
 
         linear_out = machete_wint_mm(

tests/model_loader/test_common_model.py

@@ -72,6 +72,7 @@ model_param_map = {
                     "FD_ATTENTION_BACKEND": "MLA_ATTN",
                     "FLAGS_mla_use_tensorcore": "1",
                     "FLAGS_flash_attn_version": "3",
+                    "FD_USE_MACHETE": "1",
                 },
             },
         ],