[Quant][Perf] Use moe_wna16 kernel by default for MoEs with many expe…

…rts (#13236)

Signed-off-by: mgoin <[email protected]>

tests/weight_loading/test_weight_loading.py

@@ -12,7 +12,7 @@
                             "robertgshaw2/zephyr-7b-beta-channelwise-gptq")
 REVISION = os.environ.get("REVISION", "main")
 QUANTIZATION = os.environ.get("QUANTIZATION", "gptq_marlin")
-MIN_CAPABILITY = os.environ.get("MIN_CAPABILITY", "89")
+MIN_CAPABILITY = os.environ.get("MIN_CAPABILITY", "80")
 
 
 @pytest.mark.skipif(

vllm/model_executor/layers/quantization/awq_marlin.py

@@ -17,6 +17,7 @@
                                                          is_layer_skipped_awq)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.quantization.moe_wna16 import MoeWNA16Config
 from vllm.model_executor.layers.quantization.utils import replace_parameter
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     apply_awq_marlin_linear, awq_to_marlin_zero_points, check_marlin_supported,
@@ -134,7 +135,12 @@ def get_quant_method(self, layer: torch.nn.Module,
                     self.full_config).get_quant_method(layer, prefix)
             return AWQMarlinLinearMethod(self)
         elif isinstance(layer, FusedMoE):
-            return AWQMoEMethod(self)
+            if layer.num_experts > 32:
+                # For MoEs with many experts the moe_wna16 kernel is faster
+                return MoeWNA16Config.from_config(
+                    self.full_config).get_quant_method(layer, prefix)
+            else:
+                return AWQMoEMethod(self)
         return None
 
     @classmethod

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -10,20 +10,18 @@
 from vllm.model_executor.layers.fused_moe.layer import (
     FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
 from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               UnquantizedLinearMethod,
                                                set_weight_attrs)
 from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+    QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.kernels.mixed_precision import (
     MPLinearLayerConfig, choose_mp_linear_kernel)
+from vllm.model_executor.layers.quantization.moe_wna16 import MoeWNA16Config
 from vllm.model_executor.layers.quantization.utils import replace_parameter
 from vllm.model_executor.layers.quantization.utils.gptq_utils import (
     get_linear_quant_method)
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     check_marlin_supported, marlin_moe_permute_scales,
     marlin_repeat_scales_on_all_ranks, verify_marlin_supported)
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    UnquantizedEmbeddingMethod)
 from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
                                            GroupQuantScaleParameter,
                                            PackedColumnParameter,
@@ -44,15 +42,10 @@ class GPTQMarlinConfig(QuantizationConfig):
         (8, True): scalar_types.uint8b128,
     }
 
-    def __init__(
-        self,
-        weight_bits: int,
-        group_size: int,
-        desc_act: bool,
-        is_sym: bool,
-        lm_head_quantized: bool,
-        dynamic: Dict[str, Dict[str, Union[int, bool]]],
-    ) -> None:
+    def __init__(self, weight_bits: int, group_size: int, desc_act: bool,
+                 is_sym: bool, lm_head_quantized: bool,
+                 dynamic: Dict[str, Dict[str, Union[int, bool]]],
+                 full_config: Dict[str, Any]) -> None:
         if desc_act and group_size == -1:
             # In this case, act_order == True is the same as act_order == False
             # (since we have only one group per output channel)
@@ -90,6 +83,7 @@ def __init__(
         self.group_size = group_size
         self.desc_act = desc_act
         self.lm_head_quantized = lm_head_quantized
+        self.full_config = full_config
 
         if (weight_bits, is_sym) not in self.TYPE_MAP:
             raise ValueError("Unsupported quantization config: "
@@ -132,7 +126,7 @@ def from_config(cls, config: Dict[str, Any]) -> "GPTQMarlinConfig":
         lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
                                                  default=False)
         return cls(weight_bits, group_size, desc_act, is_sym,
-                   lm_head_quantized, dynamic)
+                   lm_head_quantized, dynamic, config)
 
     @classmethod
     def override_quantization_method(cls, hf_quant_cfg,
@@ -155,12 +149,15 @@ def override_quantization_method(cls, hf_quant_cfg,
                         " faster inference")
         return None
 
-    def get_quant_method(
-        self, layer: torch.nn.Module, prefix: str
-    ) -> Optional[Union["GPTQMarlinLinearMethod", "GPTQMarlinMoEMethod",
-                        UnquantizedLinearMethod, UnquantizedEmbeddingMethod]]:
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
         if isinstance(layer, FusedMoE):
-            return GPTQMarlinMoEMethod(self)
+            if layer.num_experts > 32:
+                # For MoEs with many experts the moe_wna16 kernel is faster
+                return MoeWNA16Config.from_config(
+                    self.full_config).get_quant_method(layer, prefix)
+            else:
+                return GPTQMarlinMoEMethod(self)
         return get_linear_quant_method(self, layer, prefix,
                                        GPTQMarlinLinearMethod)
 

vllm/model_executor/layers/quantization/moe_wna16.py

@@ -9,13 +9,8 @@
     FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
 from vllm.model_executor.layers.linear import (LinearBase,
                                                UnquantizedLinearMethod)
-from vllm.model_executor.layers.quantization.awq import AWQConfig
-from vllm.model_executor.layers.quantization.awq_marlin import AWQMarlinConfig
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
-from vllm.model_executor.layers.quantization.gptq import GPTQConfig
-from vllm.model_executor.layers.quantization.gptq_marlin import (
-    GPTQMarlinConfig)
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     check_marlin_supports_layer)
 from vllm.model_executor.utils import set_weight_attrs
@@ -37,6 +32,12 @@ def __init__(self, linear_quant_method: str, weight_bits: int,
         self.linear_quant_method = linear_quant_method
         self.full_config = full_config
         self.use_marlin = False
+        # Avoid circular import
+        from vllm.model_executor.layers.quantization.awq import AWQConfig
+        from vllm.model_executor.layers.quantization.awq_marlin import (
+            AWQMarlinConfig)
+        from vllm.model_executor.layers.quantization.gptq_marlin import (
+            GPTQMarlinConfig)
         if self.linear_quant_method == "gptq":
             self.use_marlin = GPTQMarlinConfig.is_gptq_marlin_compatible(
                 full_config)
@@ -115,6 +116,8 @@ def is_moe_wna16_compatible(cls, quant_config: Dict[str, Any]):
         capability_tuple = current_platform.get_device_capability()
         device_capability = (-1 if capability_tuple is None else
                              capability_tuple.to_int())
+        # Avoid circular import
+        from vllm.model_executor.layers.quantization.awq import AWQConfig
         awq_min_capability = AWQConfig.get_min_capability()
 
         gptq_compatible = quant_method == "gptq" and \
@@ -129,6 +132,13 @@ def get_quant_method(self, layer: torch.nn.Module,
         if is_layer_skipped_quant(prefix, self.modules_to_not_convert):
             return UnquantizedLinearMethod()
         elif isinstance(layer, LinearBase):
+            # Avoid circular import
+            from vllm.model_executor.layers.quantization.awq import AWQConfig
+            from vllm.model_executor.layers.quantization.awq_marlin import (
+                AWQMarlinConfig)
+            from vllm.model_executor.layers.quantization.gptq import GPTQConfig
+            from vllm.model_executor.layers.quantization.gptq_marlin import (
+                GPTQMarlinConfig)
             if self.linear_quant_method == "gptq":
                 if self.use_marlin:
                     return GPTQMarlinConfig.from_config(