[EPLB] Add EPLB support for hunyuan_v1 (#23078)

vllm/model_executor/layers/fused_moe/layer.py

@@ -1508,8 +1508,8 @@ class FusedMoE(CustomOp):
 
         return [
             weight.view(self.local_num_experts, -1) for name, weight in weights
-            if name not in NON_EXPERT_WEIGHTS
-            and not name.startswith("_shared_experts.")
+            if name not in NON_EXPERT_WEIGHTS and weight.shape != torch.Size(
+                []) and not name.startswith("_shared_experts.")
         ]
 
     def set_eplb_state(

vllm/model_executor/models/hunyuan_v1.py

@@ -23,7 +23,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only HunYuan model compatible with HuggingFace weights."""
-from collections.abc import Iterable
+import typing
+from collections.abc import Callable, Iterable
 from typing import Any, Optional, Union
 
 import regex as re
@@ -33,8 +34,8 @@ from transformers import PretrainedConfig
 
 from vllm.attention import Attention, AttentionType
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import CacheConfig, VllmConfig
-from vllm.distributed import (get_pp_group,
+from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
+from vllm.distributed import (get_ep_group, get_pp_group,
                               get_tensor_model_parallel_world_size,
                               tensor_model_parallel_all_reduce)
 from vllm.model_executor.layers.activation import SiluAndMul
@@ -56,7 +57,7 @@ from vllm.model_executor.model_loader.weight_utils import (
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsLoRA, SupportsPP
+from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP
 from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
                     make_layers, maybe_prefix)
 
@@ -355,10 +356,16 @@ class HunYuanSparseMoeBlock(nn.Module):
         quant_config: Optional[QuantizationConfig] = None,
         layer_id: int = -1,
         prefix: str = "",
+        enable_eplb: bool = False,
     ):
         super().__init__()
         self.tp_size = get_tensor_model_parallel_world_size()
 
+        self.ep_group = get_ep_group().device_group
+        self.ep_rank = self.ep_group.rank()
+        self.ep_size = self.ep_group.size()
+        self.n_routed_experts = config.num_experts
+
         if self.tp_size > config.num_experts:
             raise ValueError(
                 f"Tensor parallel size {self.tp_size} is greater than "
@@ -379,8 +386,23 @@ class HunYuanSparseMoeBlock(nn.Module):
                 config.moe_intermediate_size, int) else
                                  config.moe_intermediate_size[layer_id])
 
+        # Load balancing settings.
+        vllm_config = get_current_vllm_config()
+        eplb_config = vllm_config.parallel_config.eplb_config
+        self.enable_eplb = enable_eplb
+
+        self.n_logical_experts = self.n_routed_experts
+        self.n_redundant_experts = eplb_config.num_redundant_experts
+        self.n_physical_experts = (self.n_logical_experts +
+                                   self.n_redundant_experts)
+        self.n_local_physical_experts = self.n_physical_experts // self.ep_size
+        self.physical_expert_start = (self.ep_rank *
+                                      self.n_local_physical_experts)
+        self.physical_expert_end = (self.physical_expert_start +
+                                    self.n_local_physical_experts)
+
         self.experts = FusedMoE(
-            num_experts=config.num_experts,
+            num_experts=self.n_routed_experts,
             top_k=top_k,
             hidden_size=config.hidden_size,
             intermediate_size=intermediate_size,
@@ -388,6 +410,8 @@ class HunYuanSparseMoeBlock(nn.Module):
             renormalize=top_k > 1,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
+            enable_eplb=self.enable_eplb,
+            num_redundant_experts=self.n_redundant_experts,
         )
 
         self.gate = ReplicatedLinear(config.hidden_size,
@@ -446,6 +470,7 @@ class HunYuanDecoderLayer(nn.Module):
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
         layer_id: int = -1,
+        enable_eplb: bool = False,
     ) -> None:
         super().__init__()
         assert layer_id >= 0
@@ -509,6 +534,7 @@ class HunYuanDecoderLayer(nn.Module):
                 quant_config=quant_config,
                 layer_id=layer_id,
                 prefix=f"{prefix}.mlp",
+                enable_eplb=enable_eplb,
             )
         else:
             self.mlp = HunYuanMLP(
@@ -562,6 +588,9 @@ class HunYuanModel(nn.Module):
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
+        eplb_config = vllm_config.parallel_config.eplb_config
+        enable_eplb = vllm_config.parallel_config.enable_eplb
+        self.num_redundant_experts = eplb_config.num_redundant_experts
 
         self.config = config
         self.quant_config = quant_config
@@ -588,6 +617,7 @@ class HunYuanModel(nn.Module):
                 cache_config=cache_config,
                 quant_config=quant_config,
                 prefix=prefix,
+                enable_eplb=enable_eplb,
             ),
             prefix=f"{prefix}.layers",
         )
@@ -674,6 +704,7 @@ class HunYuanModel(nn.Module):
                 ckpt_down_proj_name="down_proj",
                 ckpt_up_proj_name="up_proj",
                 num_experts=self.config.num_experts,
+                num_redundant_experts=self.num_redundant_experts,
             )
         else:
             return []
@@ -803,25 +834,43 @@ class HunYuanModel(nn.Module):
                 # Skip loading extra bias for GPTQ models.
                 if name.endswith(".bias") and name not in params_dict:
                     continue
+                is_expert_weight = False
                 for mapping in expert_params_mapping:
                     param_name, weight_name, expert_id, shard_id = mapping
                     if weight_name not in name:
                         continue
-                    name = name.replace(weight_name, param_name)
-                    # Skip layers on other devices.
-                    if is_pp_missing_parameter(name, self):
+                    # this is an expert weight and should not be
+                    # attempted to load as other weights later
+                    is_expert_weight = True
+
+                    # Do not modify `name` since the loop may continue here
+                    # Instead, create a new variable
+                    name_mapped = name.replace(weight_name, param_name)
+                    if is_pp_missing_parameter(name_mapped, self):
                         continue
-                    param = params_dict[name]
-                    weight_loader = param.weight_loader
-                    weight_loader(
+                    param = params_dict[name_mapped]
+                    # We should ask the weight loader to return success or not
+                    # here since otherwise we may skip experts with other
+                    # available replicas.
+                    weight_loader = typing.cast(Callable[..., bool],
+                                                param.weight_loader)
+                    success = weight_loader(
                         param,
                         loaded_weight,
-                        name,
+                        name_mapped,
                         shard_id=shard_id,
                         expert_id=expert_id,
+                        return_success=True,
                     )
-                    break
+                    if success:
+                        name = name_mapped
+                        break
                 else:
+                    if is_expert_weight:
+                        # We've checked that this is an expert weight
+                        # However it's not mapped locally to this rank
+                        # So we simply skip it
+                        continue
                     # Remapping the name of FP8 kv-scale.
                     name = maybe_remap_kv_scale_name(name, params_dict)
                     if name is None:
@@ -841,7 +890,7 @@ class HunYuanModel(nn.Module):
         return loaded_params
 
 
-class HunYuanV1Base(nn.Module, SupportsLoRA, SupportsPP):
+class HunYuanV1Base(nn.Module, SupportsLoRA, SupportsPP, MixtureOfExperts):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -883,6 +932,64 @@ class HunYuanV1Base(nn.Module, SupportsLoRA, SupportsPP):
         else:
             self.lm_head = PPMissingLayer()
 
+        # Set MoE hyperparameters
+        self.expert_weights = []
+        self.num_expert_groups = 1
+        self.moe_layers: list[FusedMoE] = []
+        example_layer = None
+        for layer in self.model.layers:
+            if isinstance(layer, PPMissingLayer):
+                continue
+
+            assert isinstance(layer, HunYuanDecoderLayer)
+            if isinstance(layer.mlp, HunYuanSparseMoeBlock):
+                example_layer = layer.mlp
+                self.moe_layers.append(layer.mlp.experts)
+
+        if example_layer is None:
+            raise RuntimeError("No HunYuanMoE layer found in model.layers.")
+
+        self.num_moe_layers = len(self.moe_layers)
+        self.num_logical_experts = example_layer.n_logical_experts
+        self.num_physical_experts = example_layer.n_physical_experts
+        self.num_local_physical_experts = example_layer.n_local_physical_experts
+        self.num_routed_experts = example_layer.n_routed_experts
+        self.num_redundant_experts = example_layer.n_redundant_experts
+
+    def set_eplb_state(
+        self,
+        expert_load_view: torch.Tensor,
+        logical_to_physical_map: torch.Tensor,
+        logical_replica_count: torch.Tensor,
+    ) -> None:
+        for layer_idx, layer in enumerate(self.moe_layers):
+            self.expert_weights.append(layer.get_expert_weights())
+            # Register the expert weights.
+            layer.set_eplb_state(
+                moe_layer_idx=layer_idx,
+                expert_load_view=expert_load_view,
+                logical_to_physical_map=logical_to_physical_map,
+                logical_replica_count=logical_replica_count,
+            )
+
+    def update_physical_experts_metadata(
+        self,
+        num_physical_experts: int,
+        num_local_physical_experts: int,
+    ) -> None:
+        assert self.num_local_physical_experts == num_local_physical_experts
+        self.num_physical_experts = num_physical_experts
+        self.num_local_physical_experts = num_local_physical_experts
+        self.num_redundant_experts = (num_physical_experts -
+                                      self.num_logical_experts)
+        for layer in self.model.layers:
+            if isinstance(layer.mlp, HunYuanSparseMoeBlock):
+                moe = layer.mlp
+                moe.n_local_physical_experts = num_local_physical_experts
+                moe.n_physical_experts = num_physical_experts
+                moe.n_redundant_experts = self.num_redundant_experts
+                moe.experts.update_expert_map()
+
     def forward(
         self,
         input_ids: torch.Tensor,