[EPLB] Support ernie4.5-moe

vllm/model_executor/models/ernie45_moe.py

@@ -33,8 +33,12 @@
 
 from vllm.attention import Attention
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import CacheConfig, VllmConfig
-from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+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,
+)
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe import SharedFusedMoE
@@ -58,7 +62,7 @@
 )
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsLoRA, SupportsPP
+from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP
 from .utils import (
     AutoWeightsLoader,
     PPMissingLayer,
@@ -118,12 +122,34 @@ def __init__(
         config: PretrainedConfig,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
+        enable_eplb: bool = False,
     ):
         super().__init__()
 
         layer_idx = extract_layer_index(prefix)
         self.layer_idx = layer_idx
         self.tp_size = get_tensor_model_parallel_world_size()
+
+        self.moe_num_shared_experts = getattr(config, "moe_num_shared_experts", None)
+        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: int = config.moe_num_experts
+        self.n_shared_experts: int = self.moe_num_shared_experts
+
+        # Load balancing settings.
+        vllm_config = get_current_vllm_config()
+        parallel_config = vllm_config.parallel_config
+        self.enable_eplb = enable_eplb
+
+        self.n_redundant_experts = parallel_config.num_redundant_experts
+        self.n_logical_experts = self.n_routed_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.has_shared_experts = getattr(config, "moe_num_shared_experts", 0) > 0
 
         if self.tp_size > config.moe_num_experts:
@@ -171,6 +197,8 @@ def __init__(
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
             e_score_correction_bias=self.gate.e_score_correction_bias,
+            enable_eplb=self.enable_eplb,
+            num_redundant_experts=self.n_redundant_experts,
         )
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -298,6 +326,7 @@ def __init__(
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
+        enable_eplb: bool = False,
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -338,7 +367,10 @@ def __init__(
             and layer_idx <= moe_layer_end_index
         ):
             self.mlp = Ernie4_5_MoeMoE(
-                config=config, quant_config=quant_config, prefix=f"{prefix}.mlp"
+                config=config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+                enable_eplb=enable_eplb,
             )
         else:
             self.mlp = Ernie4_5_MoeMLP(
@@ -393,6 +425,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
         self.config = config
+        parallel_config = vllm_config.parallel_config
+        enable_eplb = parallel_config.enable_eplb
+        self.num_redundant_experts = parallel_config.num_redundant_experts
 
         if get_pp_group().is_first_rank:
             self.embed_tokens = VocabParallelEmbedding(
@@ -411,6 +446,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
                 cache_config=cache_config,
                 quant_config=quant_config,
                 prefix=prefix,
+                enable_eplb=enable_eplb,
             ),
             prefix=f"{prefix}.layers",
         )
@@ -465,6 +501,7 @@ def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
             ckpt_down_proj_name="down_proj",
             ckpt_up_proj_name="up_proj",
             num_experts=self.config.moe_num_experts,
+            num_redundant_experts=self.num_redundant_experts,
         )
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
@@ -513,15 +550,22 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
+                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)
+                    # Anyway, 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)
                     # Skip layers on other devices.
-                    if is_pp_missing_parameter(name, self):
+                    if is_pp_missing_parameter(name_mapped, self):
                         continue
 
                     # Skip loading extra bias for GPTQ models.
@@ -541,6 +585,12 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     )
                     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
+
                     # Skip loading extra bias for GPTQ models.
                     if (
                         name.endswith(".bias") or name.endswith("_bias")
@@ -563,7 +613,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         return loaded_params
 
 
-class Ernie4_5_MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
+class Ernie4_5_MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA, MixtureOfExperts):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -605,6 +655,81 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
             self.model.make_empty_intermediate_tensors
         )
 
+        self.expert_weights = []
+
+        # Set MoE hyperparameters
+        moe_layers_indices = [
+            i
+            for i in range(config.num_hidden_layers)
+            if (
+                i >= config.moe_layer_start_index
+                and i <= config.moe_layer_end_index
+                and (i + 1) % config.moe_layer_interval == 0
+            )
+        ]
+        self.num_moe_layers = len(moe_layers_indices)
+        self.num_expert_groups = 1
+
+        self.moe_layers: list[SharedFusedMoE] = []
+        example_moe = None
+        for layer in self.model.layers:
+            if isinstance(layer, PPMissingLayer):
+                continue
+
+            assert isinstance(layer, Ernie4_5_MoeDecoderLayer)
+            if isinstance(layer.mlp, Ernie4_5_MoeMoE):
+                example_moe = layer.mlp
+                self.moe_layers.append(layer.mlp.experts)
+
+        if example_moe is None:
+            logger.warning("No Ernie4_5_MoeMoE layer found in model.layers.")
+            self.num_logical_experts = 0
+            self.num_physical_experts = 0
+            self.num_local_physical_experts = 0
+            self.num_routed_experts = 0
+            self.num_shared_experts = 0
+            self.num_redundant_experts = 0
+        else:
+            self.num_logical_experts = example_moe.n_logical_experts
+            self.num_physical_experts = example_moe.n_physical_experts
+            self.num_local_physical_experts = example_moe.n_local_physical_experts
+            self.num_routed_experts = example_moe.n_routed_experts
+            self.num_shared_experts = example_moe.n_shared_experts
+            self.num_redundant_experts = example_moe.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):
+            # Register the expert weights.
+            self.expert_weights.append(layer.get_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, Ernie4_5_MoeMoE):
+                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 get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.model.get_input_embeddings(input_ids)