Merge branch 'compressed-tensors-moe-wna16' of https://github.com/neuralmagic/vllm

Author: mgoin

vllm/model_executor/layers/fused_moe/layer.py

@@ -473,6 +473,7 @@ def __init__(
 
         assert intermediate_size % self.tp_size == 0
         self.intermediate_size_per_partition = intermediate_size // self.tp_size
+        self.params_dtype = params_dtype
         self.reduce_results = reduce_results
         self.renormalize = renormalize
         self.use_grouped_topk = use_grouped_topk
@@ -512,7 +513,9 @@ def __init__(
         }
         # need full intermediate size pre-sharding for WNA16 act order
         if (self.quant_method.__class__.__name__
-                in ("GPTQMarlinMoEMethod", "CompressedTensorsWNA16MoEMethod")):
+                in ("GPTQMarlinMoEMethod",
+                    "CompressedTensorsWNA16MarlinMoEMethod",
+                    "CompressedTensorsWNA16MoEMethod")):
             moe_quant_params["intermediate_size_full"] = intermediate_size
 
         self.quant_method.create_weights(layer=self, **moe_quant_params)
@@ -648,9 +651,10 @@ def weight_loader(self, param: torch.nn.Parameter,
         # compressed-tensors checkpoints with packed weights are stored flipped
         # TODO (mgoin): check self.quant_method.quant_config.quant_format
         # against known CompressionFormat enum values that have this quality
-        loaded_weight = loaded_weight.t().contiguous() if (
-            self.quant_method.__class__.__name__
-            == "CompressedTensorsWNA16MoEMethod") else loaded_weight
+        if self.quant_method.__class__.__name__ in (
+                "CompressedTensorsWNA16MarlinMoEMethod",
+                "CompressedTensorsWNA16MoEMethod"):
+            loaded_weight = loaded_weight.t().contiguous()
 
         if shard_id not in ("w1", "w2", "w3"):
             raise ValueError(f"shard_id must be ['w1','w2','w3'] but "

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

@@ -96,8 +96,7 @@ def get_quant_method(
         if isinstance(layer, Attention):
             return CompressedTensorsKVCacheMethod(self)
         if isinstance(layer, FusedMoE):
-            return CompressedTensorsMoEMethod.get_moe_method(
-                self, layer.activation, layer.expert_map)
+            return CompressedTensorsMoEMethod.get_moe_method(self, layer)
         return None
 
     @classmethod

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

@@ -30,9 +30,11 @@ class GPTQMarlinState(Enum):
 
 
 __all__ = [
-    "CompressedTensorsMoEMethod", "CompressedTensorsW8A8Fp8MoEMethod",
+    "CompressedTensorsMoEMethod",
+    "CompressedTensorsW8A8Fp8MoEMethod",
     "CompressedTensorsW8A8Fp8MoECutlassMethod",
-    "CompressedTensorsWNA16MoEMethod"
+    "CompressedTensorsWNA16MarlinMoEMethod",
+    "CompressedTensorsWNA16MoEMethod",
 ]
 
 
@@ -41,8 +43,7 @@ class CompressedTensorsMoEMethod(FusedMoEMethodBase):
     @staticmethod
     def get_moe_method(
         quant_config: "CompressedTensorsConfig",  # type: ignore # noqa E501
-        activation: str,
-        expert_map: Optional[torch.Tensor],
+        layer: torch.nn.Module,
     ) -> "CompressedTensorsMoEMethod":
         # TODO: @dsikka: refactor this to use schemes as other kernels
         # are supported + check if the layer is being ignored.
@@ -51,9 +52,20 @@ def get_moe_method(
             "input_activations")
 
         if quant_config._is_wNa16_group_channel(weight_quant, input_quant):
-            return CompressedTensorsWNA16MoEMethod(quant_config)
+            # Prefer to use the non-marlin kernel when:
+            # 1. Many experts (MarlinMoE gives poor performance when >= 16)
+            # 2. Non-FP16 dtype (MarlinMoE only supports FP16)
+            # 3. Actorder is not dynamic (g_idx is unsupported)
+            # 4. Scaled are grouped (channelwise is unsupported)
+            if ((layer.local_num_experts >= 16
+                 or layer.params_dtype != torch.float16)
+                    and weight_quant.actorder != "group"
+                    and weight_quant.strategy == "group"):
+                return CompressedTensorsWNA16MoEMethod(quant_config)
+            else:
+                return CompressedTensorsWNA16MarlinMoEMethod(quant_config)
         elif (quant_config._is_fp8_w8a8_sm90(weight_quant, input_quant)
-              and activation == "silu" and expert_map is None):
+              and layer.activation == "silu" and layer.expert_map is None):
             return CompressedTensorsW8A8Fp8MoECutlassMethod(quant_config)
         elif quant_config._is_fp8_w8a8(weight_quant, input_quant):
             return CompressedTensorsW8A8Fp8MoEMethod(quant_config)
@@ -482,7 +494,7 @@ def apply(
         )
 
 
-class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
+class CompressedTensorsWNA16MarlinMoEMethod(CompressedTensorsMoEMethod):
 
     def __init__(
             self,
@@ -823,3 +835,215 @@ def apply(
             sort_indices2=layer.w2_g_idx_sort_indices,
             num_bits=self.num_bits,
             is_k_full=self.is_k_full)
+
+
+class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
+
+    def __init__(
+            self,
+            quant_config: "CompressedTensorsConfig"  # type: ignore # noqa E501
+    ):
+        self.quant_config = quant_config
+        # TODO: @dsikka: refactor this to use schemes as other kernels
+        # are supported + check if the layer is being ignored.
+        config = self.quant_config.target_scheme_map["Linear"].get("weights")
+        self.num_bits = config.num_bits
+        self.packed_factor = 32 // config.num_bits
+        self.strategy = config.strategy
+        # channelwise is not supported by this kernel
+        assert config.strategy == "group"
+        self.group_size = config.group_size
+        # grouped actorder isn't supported by this kernel
+        assert config.actorder != "group"
+        assert config.symmetric, (
+            "Only symmetric quantization is supported for MoE")
+
+        if not (self.quant_config.quant_format
+                == CompressionFormat.pack_quantized.value
+                and self.num_bits in WNA16_SUPPORTED_BITS):
+            raise ValueError("For Fused MoE layers, only ",
+                             f"{CompressionFormat.pack_quantized.value} ",
+                             "is supported for the following bits: ",
+                             f"{WNA16_SUPPORTED_BITS}")
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size_per_partition: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        # Will transpose the loaded weight along the
+        # intermediate and hidden dim sizes. Will
+        # shard for TP along the transposed dims
+        extra_weight_attrs.update({
+            "is_transposed": True,
+            "quant_method": self.strategy
+        })
+        w13_weight = torch.nn.Parameter(torch.empty(
+            num_experts,
+            hidden_size // self.packed_factor,
+            2 * intermediate_size_per_partition,
+            dtype=torch.int32),
+                                        requires_grad=False)
+        layer.register_parameter("w13_weight_packed", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w2_weight = torch.nn.Parameter(torch.empty(
+            num_experts,
+            intermediate_size_per_partition // self.packed_factor,
+            hidden_size,
+            dtype=torch.int32),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight_packed", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        w2_scales_size = intermediate_size_per_partition
+
+        if self.strategy == "channel":
+            num_groups_w2 = num_groups_w13 = 1
+            self.group_size = -1
+        else:
+            num_groups_w2 = w2_scales_size // self.group_size
+            num_groups_w13 = hidden_size // self.group_size
+
+        w13_scale = torch.nn.Parameter(torch.ones(
+            num_experts,
+            num_groups_w13,
+            2 * intermediate_size_per_partition,
+            dtype=params_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w13_weight_scale", w13_scale)
+        set_weight_attrs(w13_scale, extra_weight_attrs)
+
+        w2_scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                 num_groups_w2,
+                                                 hidden_size,
+                                                 dtype=params_dtype),
+                                      requires_grad=False)
+        layer.register_parameter("w2_weight_scale", w2_scale)
+        set_weight_attrs(w2_scale, extra_weight_attrs)
+        set_weight_attrs(w2_scale, {"load_full_w2": False})
+
+        w2_weight_shape = torch.nn.Parameter(torch.empty(num_experts, 2),
+                                             requires_grad=False)
+        layer.register_parameter("w2_weight_shape", w2_weight_shape)
+        set_weight_attrs(w2_weight_shape, extra_weight_attrs)
+        w13_weight_shape = torch.nn.Parameter(torch.empty(num_experts, 2),
+                                              requires_grad=False)
+
+        layer.register_parameter("w13_weight_shape", w13_weight_shape)
+        set_weight_attrs(w13_weight_shape, extra_weight_attrs)
+
+        w13_g_idx = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_weight_g_idx", w13_g_idx)
+        set_weight_attrs(w13_g_idx, extra_weight_attrs)
+
+        w2_g_idx = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                intermediate_size_per_partition,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_weight_g_idx", w2_g_idx)
+        set_weight_attrs(w2_g_idx, extra_weight_attrs)
+
+        w13_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_g_idx_sort_indices",
+                                 w13_g_idx_sort_indices)
+        set_weight_attrs(w13_g_idx_sort_indices, extra_weight_attrs)
+
+        w2_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                intermediate_size_per_partition,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_g_idx_sort_indices",
+                                 w2_g_idx_sort_indices)
+        set_weight_attrs(w2_g_idx_sort_indices, extra_weight_attrs)
+
+        layer.a13_scale = None
+        layer.a2_scale = None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # Reconfigure packed weights and scales to match moe_wna16 format
+        layer.w13_weight_packed = torch.nn.Parameter(
+            layer.w13_weight_packed.transpose(1, 2).contiguous().view(
+                torch.uint8),
+            requires_grad=False)
+        layer.w2_weight_packed = torch.nn.Parameter(
+            layer.w2_weight_packed.transpose(1,
+                                             2).contiguous().view(torch.uint8),
+            requires_grad=False)
+        layer.w13_weight_scale = torch.nn.Parameter(
+            layer.w13_weight_scale.transpose(1, 2).contiguous(),
+            requires_grad=False)
+        layer.w2_weight_scale = torch.nn.Parameter(
+            layer.w2_weight_scale.transpose(1, 2).contiguous(),
+            requires_grad=False)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+    ) -> torch.Tensor:
+        from vllm.model_executor.layers.fused_moe import fused_experts
+        assert activation == "silu", "Only SiLU activation is supported."
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            e_score_correction_bias=e_score_correction_bias)
+
+        return fused_experts(
+            x,
+            layer.w13_weight_packed,
+            layer.w2_weight_packed,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=True,
+            use_int4_w4a16=self.num_bits == 4,
+            use_int8_w8a16=self.num_bits == 8,
+            global_num_experts=global_num_experts,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            expert_map=expert_map,
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
+            w1_zp=None,
+            w2_zp=None,
+            block_shape=[0, self.group_size])