[Feature][EPLB] Add eplb support for Qwen2

vllm/model_executor/models/qwen2_moe.py

@@ -24,8 +24,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only Qwen2MoE model compatible with HuggingFace weights."""
+import typing
 from collections.abc import Iterable
-from typing import Any, Optional, Union
+from typing import Any, Callable, Optional, Union
 
 import torch
 import torch.nn.functional as F
@@ -34,8 +35,9 @@
 
 from vllm.attention import Attention
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import CacheConfig, VllmConfig
+from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.distributed.parallel_state import get_ep_group
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe import FusedMoE
@@ -50,6 +52,7 @@
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.deepseek_v2 import MixtureOfExperts
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
@@ -101,23 +104,47 @@ def __init__(
         config: PretrainedConfig,
         quant_config: Optional[QuantizationConfig] = None,
         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 "
                 f"the number of experts {config.num_experts}.")
 
-        self.experts = FusedMoE(num_experts=config.num_experts,
+        # Load balancing settings.
+        vllm_config = get_current_vllm_config()
+        parallel_config = vllm_config.parallel_config
+        self.enable_eplb = enable_eplb
+
+        self.n_logical_experts = self.n_routed_experts
+        self.n_redundant_experts = parallel_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=self.n_routed_experts,
                                 top_k=config.num_experts_per_tok,
                                 hidden_size=config.hidden_size,
                                 intermediate_size=config.moe_intermediate_size,
                                 reduce_results=False,
                                 renormalize=config.norm_topk_prob,
                                 quant_config=quant_config,
-                                prefix=f"{prefix}.experts")
+                                prefix=f"{prefix}.experts",
+                                enable_eplb=self.enable_eplb,
+                                num_redundant_experts=self.n_redundant_experts)
 
         self.gate = ReplicatedLinear(config.hidden_size,
                                      config.num_experts,
@@ -260,6 +287,7 @@ 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
@@ -293,7 +321,8 @@ def __init__(
             (layer_idx + 1) % config.decoder_sparse_step == 0):
             self.mlp = Qwen2MoeSparseMoeBlock(config=config,
                                               quant_config=quant_config,
-                                              prefix=f"{prefix}.mlp")
+                                              prefix=f"{prefix}.mlp",
+                                              enable_eplb=enable_eplb)
         else:
             self.mlp = Qwen2MoeMLP(
                 hidden_size=config.hidden_size,
@@ -340,7 +369,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         config = vllm_config.model_config.hf_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
-
+        enable_eplb = vllm_config.parallel_config.enable_eplb
+        self.num_redundant_experts = vllm_config.parallel_config.\
+            num_redundant_experts
         self.vocab_size = config.vocab_size
         self.config = config
 
@@ -353,7 +384,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
             lambda prefix: Qwen2MoeDecoderLayer(config=config,
                                                 cache_config=cache_config,
                                                 quant_config=quant_config,
-                                                prefix=prefix),
+                                                prefix=prefix,
+                                                enable_eplb=enable_eplb),
             prefix=f"{prefix}.layers",
         )
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -391,15 +423,6 @@ def forward(
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
-    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        # Params for weights, fp8 weight scales, fp8 activation scales
-        # (param_name, weight_name, expert_id, shard_id)
-        return FusedMoE.make_expert_params_mapping(
-            ckpt_gate_proj_name="gate_proj",
-            ckpt_down_proj_name="down_proj",
-            ckpt_up_proj_name="up_proj",
-            num_experts=self.config.num_experts)
-
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
         stacked_params_mapping = [
@@ -411,9 +434,17 @@ def load_weights(self, weights: Iterable[tuple[str,
             ("gate_up_proj", "up_proj", 1),
         ]
 
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            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)
+
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
-        expert_params_mapping = self.get_expert_mapping()
         for name, loaded_weight in weights:
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 # Skip non-stacked layers and experts (experts handled below).
@@ -443,29 +474,47 @@ def load_weights(self, weights: Iterable[tuple[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)
-                    if "layers.13.mlp.experts.w2_weight" in name:
-                        pass
-                    # Skip layers on other devices.
-                    if is_pp_missing_parameter(name, self):
+                    # 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)
+
+                    if is_pp_missing_parameter(name_mapped, self):
                         continue
                     # Skip loading extra bias for GPTQ models.
-                    if ((name.endswith(".bias") or name.endswith("_bias"))
+                    if ((name_mapped.endswith(".bias")
+                         or name_mapped.endswith("_bias"))
                             and name not in params_dict):
                         continue
-                    param = params_dict[name]
-                    weight_loader = param.weight_loader
-                    weight_loader(param,
-                                  loaded_weight,
-                                  name,
-                                  shard_id=shard_id,
-                                  expert_id=expert_id)
-                    break
+                    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_mapped,
+                                            shard_id=shard_id,
+                                            expert_id=expert_id,
+                                            return_success=True)
+                    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
                     # Skip loading extra bias for GPTQ models.
                     if ((name.endswith(".bias") or name.endswith("_bias"))
                             and name not in params_dict):
@@ -494,20 +543,9 @@ def load_weights(self, weights: Iterable[tuple[str,
         return loaded_params
 
 
-class Qwen2MoeForCausalLM(nn.Module, SupportsPP):
+class Qwen2MoeForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
 
     fall_back_to_pt_during_load = False
-    packed_modules_mapping = {
-        "qkv_proj": [
-            "q_proj",
-            "k_proj",
-            "v_proj",
-        ],
-        "gate_up_proj": [
-            "gate_proj",
-            "up_proj",
-        ],
-    }
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
@@ -526,6 +564,34 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.make_empty_intermediate_tensors = (
             self.model.make_empty_intermediate_tensors)
 
+        # Initialize EPLB-related attributes
+        self.expert_weights = []
+
+        # Set MoE hyperparameters
+        self.moe_layers: list[FusedMoE] = []
+        for layer in self.model.layers:
+            assert isinstance(layer, Qwen2MoeDecoderLayer)
+            if isinstance(layer.mlp, Qwen2MoeSparseMoeBlock):
+                self.moe_layers.append(layer.mlp.experts)
+
+        self.num_moe_layers = len(self.moe_layers)
+        self.num_expert_groups = 1
+
+        example_moe = None
+        for layer_idx in range(config.num_hidden_layers):
+            layer = self.model.layers[layer_idx]
+            if isinstance(layer.mlp, Qwen2MoeSparseMoeBlock):
+                example_moe = layer.mlp
+                break
+        assert example_moe is not None
+
+        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_redundant_experts = example_moe.n_redundant_experts
+        self.num_shared_experts = 0
+
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.model.get_input_embeddings(input_ids)
 
@@ -554,5 +620,26 @@ def load_weights(self, weights: Iterable[tuple[str,
         loader = AutoWeightsLoader(self)
         return loader.load_weights(weights)
 
-    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        return self.model.get_expert_mapping()
+    def set_eplb_state(
+        self,
+        expert_load_view: torch.Tensor,
+        logical_to_physical_map: torch.Tensor,
+        logical_replica_count: torch.Tensor,
+    ) -> None:
+        """
+        Register the EPLB state in the MoE model.
+
+        Args:
+            expert_load_view: A view of the expert load metrics tensor.
+            logical_to_physical_map: Mapping from logical to physical experts.
+            logical_replica_count: Count of replicas for each logical expert.
+        """
+        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,
+            )