[Distributed] Add enable_expert_parallel arg (vllm-project#14305)

Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com>
Signed-off-by: Louis Ulmer <ulmerlouis@gmail.com>

Author: tlrmchlsmth

examples/offline_inference/data_parallel.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # usage:
-# VLLM_TEST_ENABLE_EP=1 VLLM_USE_V1=1 \
-#   python examples/offline_inference/data_parallel.py
+# VLLM_USE_V1=1 python examples/offline_inference/data_parallel.py
 # we need to have a launcher to create multiple data parallel
 # ranks. And each rank will create a vLLM instance to process its own prompts.
 import os
@@ -55,7 +54,8 @@ def main(dp_size, dp_rank, dp_master_ip, dp_master_port, GPUs_per_dp_rank):
     # Create an LLM.
     llm = LLM(model="ibm-research/PowerMoE-3b",
               tensor_parallel_size=GPUs_per_dp_rank,
-              enforce_eager=True)
+              enforce_eager=True,
+              enable_expert_parallel=True)
     outputs = llm.generate(prompts, sampling_params)
     # Print the outputs.
     for output in outputs:

vllm/config.py

@@ -754,7 +754,7 @@ def verify_with_parallel_config(
                 " must be divisible by tensor parallel size "
                 f"({tensor_parallel_size}).")
 
-        if envs.VLLM_TEST_ENABLE_EP:
+        if parallel_config.enable_expert_parallel:
             self._verify_with_expert_parallelism()
 
         pipeline_parallel_size = parallel_config.pipeline_parallel_size
@@ -1334,6 +1334,7 @@ class ParallelConfig:
     # IP of the data parallel master.
     data_parallel_master_ip: str = "127.0.0.1"
     data_parallel_master_port: int = 29500  # Port of the data parallel master.
+    enable_expert_parallel: bool = False  # Use EP instead of TP for MoE layers.
 
     # Maximum number of multiple batches
     # when load model sequentially. To avoid RAM OOM when using tensor

vllm/engine/arg_utils.py

@@ -114,6 +114,7 @@ class EngineArgs:
     # number of P/D disaggregation (or other disaggregation) workers
     pipeline_parallel_size: int = 1
     tensor_parallel_size: int = 1
+    enable_expert_parallel: bool = False
     max_parallel_loading_workers: Optional[int] = None
     block_size: Optional[int] = None
     enable_prefix_caching: Optional[bool] = None
@@ -440,6 +441,11 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
                             type=int,
                             default=EngineArgs.tensor_parallel_size,
                             help='Number of tensor parallel replicas.')
+        parser.add_argument(
+            '--enable-expert-parallel',
+            action='store_true',
+            help='Use expert parallelism instead of tensor parallelism '
+            'for MoE layers.')
         parser.add_argument(
             '--max-parallel-loading-workers',
             type=int,
@@ -1207,6 +1213,7 @@ def create_engine_config(self,
         parallel_config = ParallelConfig(
             pipeline_parallel_size=self.pipeline_parallel_size,
             tensor_parallel_size=self.tensor_parallel_size,
+            enable_expert_parallel=self.enable_expert_parallel,
             max_parallel_loading_workers=self.max_parallel_loading_workers,
             disable_custom_all_reduce=self.disable_custom_all_reduce,
             tokenizer_pool_config=TokenizerPoolConfig.create_config(

vllm/envs.py

@@ -86,7 +86,6 @@
     VLLM_MLA_PERFORM_MATRIX_ABSORPTION: bool = True
     VLLM_MLA_DISABLE_REQUANTIZATION: bool = False
     VLLM_MLA_CUDA_MEM_ALIGN_KV_CACHE: bool = True
-    VLLM_TEST_ENABLE_EP: bool = False
     VLLM_ENABLE_MOE_ALIGN_BLOCK_SIZE_TRITON: bool = False
     VLLM_RAY_PER_WORKER_GPUS: float = 1.0
     VLLM_RAY_BUNDLE_INDICES: str = ""
@@ -579,12 +578,6 @@ def maybe_convert_int(value: Optional[str]) -> Optional[int]:
     lambda: bool(int(os.getenv("VLLM_ENABLE_MOE_ALIGN_BLOCK_SIZE_TRITON", "0"))
                  ),
 
-    # If set, vLLM will use the experimental expert parallel implementation on
-    # the FusedMoE layer, using tensor parallelism size as expert parallelism
-    # size.
-    "VLLM_TEST_ENABLE_EP":
-    lambda: bool(int(os.getenv("VLLM_TEST_ENABLE_EP", "0"))),
-
     # Number of GPUs per worker in Ray, if it is set to be a fraction,
     # it allows ray to schedule multiple actors on a single GPU,
     # so that users can colocate other actors on the same GPUs as vLLM.

vllm/model_executor/layers/fused_moe/layer.py

@@ -7,7 +7,6 @@
 import torch
 from torch.nn.parameter import UninitializedParameter
 
-import vllm.envs as envs
 from vllm.config import get_current_vllm_config
 from vllm.distributed import (get_dp_group, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size,
@@ -342,14 +341,6 @@ def __init__(
         if params_dtype is None:
             params_dtype = torch.get_default_dtype()
 
-        # For smuggling this layer into the fused moe custom op
-        compilation_config = get_current_vllm_config().compilation_config
-        if prefix in compilation_config.static_forward_context:
-            raise ValueError("Duplicate layer name: {}".format(prefix))
-        compilation_config.static_forward_context[prefix] = self
-        self.layer_name = prefix
-        self.use_direct_call = not envs.VLLM_TEST_ENABLE_EP
-
         # Note: here we guard against accessing the TP and DP groups when
         # uninitialized (this happens when testing)
         self.tp_size = (tp_size if tp_size is not None else
@@ -361,7 +352,21 @@ def __init__(
                         if self.dp_size == 1 else get_dp_group().rank_in_group)
         self.global_num_experts = num_experts
 
-        if envs.VLLM_TEST_ENABLE_EP:
+        # Use expert parallelism instead of tensor parallelism?
+        vllm_config = get_current_vllm_config()
+        use_ep = (vllm_config.parallel_config.enable_expert_parallel
+                  and self.tp_size > 1)
+
+        # For smuggling this layer into the fused moe custom op
+        self.use_direct_call = self.dp_size == 1
+        if self.use_direct_call:
+            compilation_config = vllm_config.compilation_config
+            if prefix in compilation_config.static_forward_context:
+                raise ValueError("Duplicate layer name: {}".format(prefix))
+            compilation_config.static_forward_context[prefix] = self
+            self.layer_name = prefix
+
+        if use_ep:
             # Set TP size to 1 to adjust for EP and adjust EP size and rank
             # for DP attention.
             self.ep_rank = tp_rank + self.tp_size * self.dp_rank