[EPLB] Support EPLB for Mixtral Model (vllm-project#22842)

Signed-off-by: rouchenzi <[email protected]>
Signed-off-by: rouchenzi <[email protected]>
Co-authored-by: Bowen Wang <[email protected]>

Author: 2 people

vllm/model_executor/models/mixtral.py

@@ -23,7 +23,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only Mixtral model."""
-from collections.abc import Iterable
+import typing
+from collections.abc import Callable, Iterable
 from itertools import islice
 from typing import Optional, Union
 
@@ -33,8 +34,9 @@
 
 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.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (QKVParallelLinear,
@@ -50,8 +52,8 @@
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsLoRA, SupportsPP
-from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
@@ -74,10 +76,32 @@ def __init__(self,
                  quant_config: Optional[QuantizationConfig] = None,
                  tp_size: Optional[int] = None,
                  dp_size: Optional[int] = None,
-                 prefix: str = ""):
+                 prefix: str = "",
+                 enable_eplb: bool = False):
         super().__init__()
         self.hidden_size = hidden_size
 
+        self.ep_group = get_ep_group().device_group
+        self.ep_rank = self.ep_group.rank()
+        self.ep_size = self.ep_group.size()
+
+        # Expert Parallelism Load balancing settings.
+        vllm_config = get_current_vllm_config()
+        parallel_config = vllm_config.parallel_config
+        self.enable_eplb = enable_eplb
+
+        self.n_routed_experts = num_experts
+        self.n_logical_experts = num_experts
+        self.n_redundant_experts = (
+            parallel_config.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)
+
         # Gate always runs at half / full precision for now.
 
         self.gate = ReplicatedLinear(hidden_size,
@@ -97,7 +121,9 @@ def __init__(self,
                                 quant_config=quant_config,
                                 tp_size=tp_size,
                                 dp_size=dp_size,
-                                prefix=f"{prefix}.experts")
+                                prefix=f"{prefix}.experts",
+                                enable_eplb=self.enable_eplb,
+                                num_redundant_experts=self.n_redundant_experts)
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         # NOTE: hidden_states can have either 1D or 2D shape.
@@ -200,6 +226,7 @@ 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
@@ -221,7 +248,8 @@ def __init__(
             hidden_size=config.hidden_size,
             intermediate_size=config.intermediate_size,
             quant_config=quant_config,
-            prefix=f"{prefix}.block_sparse_moe")
+            prefix=f"{prefix}.block_sparse_moe",
+            enable_eplb=enable_eplb)
         self.input_layernorm = RMSNorm(config.hidden_size,
                                        eps=config.rms_norm_eps)
         self.post_attention_layernorm = RMSNorm(config.hidden_size,
@@ -262,6 +290,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
+        parallel_config = vllm_config.parallel_config
 
         self.config = config
         self.quant_config = quant_config
@@ -276,10 +305,18 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
             org_num_embeddings=config.vocab_size,
         )
 
+        self.enable_eplb = parallel_config.enable_eplb
+        self.num_redundant_experts = (
+            parallel_config.eplb_config.num_redundant_experts)
+
         self.start_layer, self.end_layer, self.layers = make_layers(
             config.num_hidden_layers,
             lambda prefix: MixtralDecoderLayer(
-                config, cache_config, quant_config=quant_config, prefix=prefix
+                config,
+                cache_config,
+                quant_config=quant_config,
+                prefix=prefix,
+                enable_eplb=self.enable_eplb,
             ),
             prefix=f"{prefix}.layers")
 
@@ -325,7 +362,8 @@ def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
             ckpt_gate_proj_name="w1",
             ckpt_down_proj_name="w2",
             ckpt_up_proj_name="w3",
-            num_experts=self.config.num_local_experts)
+            num_experts=self.config.num_local_experts,
+            num_redundant_experts=self.num_redundant_experts)
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
@@ -373,26 +411,40 @@ 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)
+
+                    is_expert_weight = True
+                    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
-                    if ((name.endswith(".bias") or name.endswith("_bias"))
-                            and name not in params_dict):
+
+                    if ((name_mapped.endswith(".bias")
+                         or name_mapped.endswith("_bias"))
+                            and name_mapped 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]
+                    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:
+                        continue
                     # Skip loading extra bias for GPTQ models.
                     if ((name.endswith(".bias") or name.endswith("_bias"))
                             and name not in params_dict):
@@ -413,7 +465,8 @@ def load_weights(self, weights: Iterable[tuple[str,
         return loaded_params
 
 
-class MixtralForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+class MixtralForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
+                         MixtureOfExperts):
     fall_back_to_pt_during_load = False
 
     packed_modules_mapping = {
@@ -462,6 +515,67 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.make_empty_intermediate_tensors = (
             self.model.make_empty_intermediate_tensors)
 
+        self.expert_weights = []
+        self.moe_layers: list[FusedMoE] = []
+        example_moe = None
+
+        for layer in self.model.layers:
+            if isinstance(layer, PPMissingLayer):
+                continue
+            assert isinstance(layer, MixtralDecoderLayer)
+            if hasattr(layer, "block_sparse_moe") and isinstance(
+                    layer.block_sparse_moe, MixtralMoE):
+                example_moe = layer.block_sparse_moe
+                self.moe_layers.append(layer.block_sparse_moe.experts)
+
+        self.num_moe_layers = len(self.moe_layers)
+
+        if example_moe is None:
+            raise RuntimeError("No MixtralMoE layer found  in model.layers.")
+
+        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_expert_groups = 1
+        self.num_shared_experts = 0
+
+    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 hasattr(layer, "block_sparse_moe") and isinstance(
+                    layer.block_sparse_moe, MixtralMoE):
+                moe = layer.block_sparse_moe
+                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)