合入中科嘉禾mp代码

vllm/config.py

@@ -892,8 +892,8 @@ class ModelConfig:
         optimized_quantization_methods = [
             "fp8", "marlin", "modelopt", "gptq_marlin_24", "gptq_marlin",
             "awq_marlin", "fbgemm_fp8", "compressed-tensors", "experts_int8",
-            "quark", "modelopt_fp4", "bitblas", "gptq_bitblas",
-            "slimquant_w4a8","slimquant_w4a8_marlin"
+            "quark", "modelopt_fp4", "bitblas", "gptq_bitblas", "slimquant_w4a8",
+            "slimquant_w4a8_marlin", "slimquant_compressed_tensors_marlin"
         ]
         if self.quantization is not None:
             self.quantization = cast(me_quant.QuantizationMethods,
@@ -920,7 +920,8 @@ class ModelConfig:
                 "awq_marlin",
                 "ipex",
                 "moe_wna16",
-                "slimquant_w4a8_marlin"
+                "slimquant_w4a8_marlin",
+                "slimquant_compressed_tensors_marlin"
             ]
             quantization_methods = [
                 q for q in supported_quantization if q not in overrides
@@ -1777,7 +1778,7 @@ class LoadConfig:
             self.ignore_patterns = ["original/**/*"]
 
 
-DistributedExecutorBackend = Literal["ray", "mp", "uni", "external_launcher"]
+DistributedExecutorBackend = Literal["ray", "mp", "uni", "external_launcher", "mp_rpc"]
 
 
 @config
@@ -2003,10 +2004,10 @@ class ParallelConfig:
             logger.info("Disabling V1 multiprocessing for external launcher.")
 
         if self.enable_eplb:
-            # if not current_platform.is_cuda():
-            #     raise ValueError(
-            #         "Expert parallelism load balancing is only supported on "
-            #         "CUDA devices now.")
+            if not current_platform.is_cuda():
+                raise ValueError(
+                    "Expert parallelism load balancing is only supported on "
+                    "CUDA devices now.")
             if self.num_redundant_experts < 0:
                 raise ValueError(
                     "num_redundant_experts must be non-negative, but got "
@@ -2068,14 +2069,14 @@ class ParallelConfig:
         from vllm.executor.executor_base import ExecutorBase
         from vllm.platforms import current_platform
         if self.distributed_executor_backend not in (
-                "ray", "mp", "uni",
+                "ray", "mp", "uni", "mp_rpc",
                 "external_launcher", None) and not (isinstance(
                     self.distributed_executor_backend, type) and issubclass(
                         self.distributed_executor_backend, ExecutorBase)):
             raise ValueError(
                 "Unrecognized distributed executor backend "
                 f"{self.distributed_executor_backend}. Supported "
-                "values are 'ray', 'mp' 'uni', 'external_launcher' or"
+                "values are 'ray', 'mp' 'uni', 'external_launcher', 'mp_rpc' or"
                 " custom ExecutorBase subclass.")
         if self.use_ray:
             from vllm.executor import ray_utils
@@ -4755,12 +4756,12 @@ class VllmConfig:
             batch_size_capture_list = []
             if self.model_config is not None and \
                 not self.model_config.enforce_eager:
-                if self.model_config.use_mla and self.scheduler_config.max_num_seqs<=512:
+                if self.model_config.use_mla and self.compilation_config.full_cuda_graph and self.scheduler_config.max_num_seqs<=512:
                     cuda_graph_sizes = [self.scheduler_config.max_num_seqs]
                 else:
                     cuda_graph_sizes = self.scheduler_config.cuda_graph_sizes 
                 if len(cuda_graph_sizes) == 1:
-                    batch_size_capture_list = [1, 2, 4] + [
+                    batch_size_capture_list = [1, 2, 3, 4] + [
                         i for i in range(8, cuda_graph_sizes[0] + 1, 8)
                     ]
                 elif len(cuda_graph_sizes) > 1:

vllm/distributed/device_communicators/shm_broadcast.py

@@ -21,7 +21,7 @@ import vllm.envs as envs
 from vllm.distributed.utils import StatelessProcessGroup, sched_yield
 from vllm.logger import init_logger
 from vllm.utils import (get_ip, get_open_port, get_open_zmq_ipc_path,
-                        is_valid_ipv6_address)
+                        is_valid_ipv6_address, get_loopback_ip)
 
 VLLM_RINGBUFFER_WARNING_INTERVAL = envs.VLLM_RINGBUFFER_WARNING_INTERVAL
 
@@ -255,7 +255,7 @@ class MessageQueue:
             # for remote readers, we will:
             # create a publish-subscribe socket to communicate large data
             if not connect_ip:
-                connect_ip = get_ip()
+                connect_ip = get_loopback_ip()
             self.remote_socket = context.socket(XPUB)
             self.remote_socket.setsockopt(XPUB_VERBOSE, True)
             remote_subscribe_port = get_open_port()

vllm/distributed/parallel_state.py

@@ -948,12 +948,6 @@ def init_distributed_environment(
                 "Fallback Gloo backend is not available.")
             backend = "gloo"
         # this backend is used for WORLD
-
-        parallel_config = config.parallel_config
-        data_parallel_size = parallel_config.data_parallel_size
-        use_mori_ep = envs.VLLM_ALL2ALL_BACKEND == 'mori' and data_parallel_size > 1 and parallel_config.enable_expert_parallel
-        if use_mori_ep:
-            backend="cpu:gloo,cuda:nccl"
         torch.distributed.init_process_group(
             backend=backend,
             init_method=distributed_init_method,
@@ -1044,7 +1038,7 @@ def initialize_model_parallel(
     _TP = init_model_parallel_group(group_ranks,
                                     get_world_group().local_rank,
                                     backend,
-                                    use_message_queue_broadcaster=True,
+                                    use_message_queue_broadcaster=False,
                                     group_name="tp")
 
     # Build the pipeline model-parallel groups.

vllm/engine/arg_utils.py

@@ -1499,7 +1499,7 @@ class EngineArgs:
         if (self.pipeline_parallel_size > 1
                 and self.distributed_executor_backend
                 not in (ParallelConfig.distributed_executor_backend, "ray",
-                        "mp", "external_launcher")):
+                        "mp", "external_launcher", "mp_rpc")):
             name = "Pipeline Parallelism without Ray distributed executor " \
                     "or multiprocessing executor or external launcher"
             _raise_or_fallback(feature_name=name, recommend_to_remove=False)

vllm/envs.py

@@ -175,7 +175,10 @@ if TYPE_CHECKING:
     USE_FUSED_SILU_MUL_QUANT: bool = False
     VLLM_P2P_ASYNC: bool = False
     VLLM_P2P_BUF_TOKENS: int = 30000
-    VLLM_ENABLE_MOE_GROUP_GEMM: bool = False
+    VLLM_SCHED_ENABLE_MINIMAL_INJECTION: bool = False
+    VLLM_USE_PD_SPLIT: bool = False
+    VLLM_LOOPBACK_IP: str = ""
+    VLLM_MP_RPC_READY_BASE_PORT: int = 28888
 
 def get_default_cache_root():
     return os.getenv(
@@ -945,7 +948,6 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # - "pplx": use pplx kernels
     # - "deepep_high_throughput", use deepep high-throughput kernels
     # - "deepep_low_latency", use deepep low-latency kernels
-    # - "mori", use mori kernels
     "VLLM_ALL2ALL_BACKEND":
     lambda: os.getenv("VLLM_ALL2ALL_BACKEND", "naive"),
 
@@ -1093,7 +1095,6 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_USE_FLASH_ATTN_PA":
     lambda: (os.environ.get("VLLM_USE_FLASH_ATTN_PA", "True").lower() in
              ("true", "1")),
-    
     # vLLM will use apex for rmsnorm
     "VLLM_USE_APEX_RN":
     lambda: (os.environ.get("VLLM_USE_APEX_RN", "False").lower() in
@@ -1134,29 +1135,31 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "USE_FUSED_RMS_QUANT": 
     lambda: (os.getenv('USE_FUSED_RMS_QUANT', '0').lower() in
              ("true", "1")),
-
-    # vllm will use lightop's moe_sum fusion operator for deepseek
-    "VLLM_USE_DEEPSEEK_MOE_SUM_MUL_ADD":
-    lambda: (os.getenv('VLLM_USE_DEEPSEEK_MOE_SUM_MUL_ADD', 'True').lower() in
-             ("true", "1")),
-
     # vllm will use silu_mul_quant fused op 
     "USE_FUSED_SILU_MUL_QUANT": 
     lambda: (os.getenv('USE_FUSED_SILU_MUL_QUANT', '0').lower() in
              ("true", "1")),
-
     # vllm pd separation will be used async
     "VLLM_P2P_ASYNC":
     lambda: bool(int(os.getenv("VLLM_P2P_ASYNC", "0"))),
-    
     # pd separation p2p async buf tokens
     "VLLM_P2P_BUF_TOKENS":
     lambda: int(os.getenv("VLLM_P2P_BUF_TOKENS", "30000")),
-
-    # pd separation p2p async buf tokens
-    "VLLM_ENABLE_MOE_GROUP_GEMM":
-        lambda: (os.environ.get("VLLM_ENABLE_MOE_GROUP_GEMM", "False").lower() in
+    # vllm will enable minimal injection for pipeline parallel scheduling
+    "VLLM_SCHED_ENABLE_MINIMAL_INJECTION":
+        lambda: (os.getenv("VLLM_SCHED_ENABLE_MINIMAL_INJECTION", "0").lower() in
                  ("true", "1")),
+    # vLLM will split prefill and decode, not mix up
+    "VLLM_USE_PD_SPLIT":
+        lambda: (os.environ.get("VLLM_USE_PD_SPLIT", "True").lower() in
+                 ("true", "1")), 
+    # Used to force set up loopback IP
+    "VLLM_LOOPBACK_IP":
+    lambda: os.getenv("VLLM_LOOPBACK_IP", ""),
+
+    # Used to get READY_BASE_PORT in multiproc_rpc_executor
+    "VLLM_MP_RPC_READY_BASE_PORT":
+    lambda: int(os.getenv("VLLM_MP_RPC_READY_BASE_PORT", "28888")),
 }
 
 # --8<-- [end:env-vars-definition]

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_marlin.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import TYPE_CHECKING, Any, Literal, Optional, cast
+
+import torch
+from compressed_tensors.config import SparsityCompressionConfig
+from compressed_tensors.quantization import QuantizationArgs
+
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.linear import LinearBase
+
+from vllm.model_executor.layers.vocab_parallel_embedding import UnquantizedEmbeddingMethod
+from vllm.model_executor.layers.quantization import QuantizationMethods
+
+
+from vllm.model_executor.layers.quantization.base_config import (  # noqa: E501
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import (
+    CompressedTensorsConfig, CompressedTensorsLinearMethod, CompressedTensorsKVCacheMethod)
+from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe_marlin import (
+    CompressedTensorsMarlinMoEMethod)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    should_ignore_layer)
+from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
+
+import os
+from vllm import _custom_ops as ops
+
+if TYPE_CHECKING:
+    from vllm.model_executor.models.utils import WeightsMapper
+
+logger = init_logger(__name__)
+
+__all__ = ["CompressedTensorsLinearMethod"]
+
+SPARSITY_CONFIG_NAME: Literal["sparsity_config"] = "sparsity_config"
+QUANTIZATION_SCHEME_MAP_TYPE = dict[str, Optional[dict[str, QuantizationArgs]]]
+
+
+class SlimQuantCompressedTensorsMarlinConfig(CompressedTensorsConfig):
+    def __init__(
+        self,
+        target_scheme_map: dict[str, Any],
+        ignore: list[str],
+        quant_format: str,
+        sparsity_scheme_map: dict[str, SparsityCompressionConfig],
+        sparsity_ignore_list: list[str],
+        kv_cache_scheme: Optional[dict[str, Any]] = None,
+        config: Optional[dict[str, Any]] = None,
+    ):
+        super().__init__(
+            target_scheme_map,
+            ignore,
+            quant_format,
+            sparsity_scheme_map,
+            sparsity_ignore_list,
+            kv_cache_scheme,
+            config
+        )
+    @classmethod
+    def override_quantization_method(
+            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
+        if hf_quant_cfg.get("quant_method") == "compressed-tensors" \
+                and user_quant == "slimquant_marlin":
+            return cls.get_name()
+        return None
+    @classmethod
+    def get_name(cls) -> QuantizationMethods:
+        return "slimquant_compressed_tensors_marlin"
+
+    def get_quant_method(
+            self,
+            layer: torch.nn.Module,
+            prefix: str,
+    ) -> Optional["QuantizeMethodBase"]:
+        from vllm.attention.layer import Attention  # Avoid circular import
+
+        # Check if the layer is skipped for quantization.
+
+        if should_ignore_layer(prefix,
+                               ignore=self.ignore,
+                               fused_mapping=self.packed_modules_mapping):
+            return UnquantizedEmbeddingMethod()#UnquantizedLinearMethod()
+        if isinstance(layer, LinearBase):
+            scheme = self.get_scheme(layer=layer, layer_name=prefix)
+            if scheme is None:
+                return UnquantizedEmbeddingMethod()#UnquantizedLinearMethod()
+            layer.scheme = scheme
+            return CompressedTensorsLinearMethod(self)
+        if isinstance(layer, Attention):
+            return CompressedTensorsKVCacheMethod(self)
+        if isinstance(layer, FusedMoE):
+            return CompressedTensorsMarlinMoEMethod.get_moe_method(self, layer)
+        return None
\ No newline at end of file

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe_marlin.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import enum
+from enum import Enum
+from typing import Callable, Optional
+from math import prod
+
+import torch
+from compressed_tensors.quantization import (QuantizationStrategy)
+
+import vllm.envs as envs
+from vllm.logger import init_logger
+from vllm.config import get_current_vllm_config
+from vllm.distributed import get_tensor_model_parallel_world_size, get_ep_group, get_dp_group
+from torch.nn.parameter import Parameter
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE, FusedMoEActivationFormat, FusedMoEMethodBase,
+    FusedMoEConfig, FusedMoeWeightScaleSupported,
+    FusedMoEPermuteExpertsUnpermute, FusedMoEPrepareAndFinalize,)
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.model_executor.layers.fused_moe.utils import _resize_cache
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import(
+    get_w8a8_int8_marlin_weights, w8a8_nt_kpack2_marlin_weight)
+
+try:
+    from lightop import m_grouped_w8a8_gemm_nt_masked, fuse_silu_mul_quant_ep
+    from lmslim.layers.fused_moe.fuse_moe_int8_marlin import fused_experts_impl_int8_marlin
+except Exception:
+    print("INFO: Please install lmslim if you want to infer the quantitative model of moe.\n")
+
+logger = init_logger(__name__)
+
+__all__ = [
+    "CompressedTensorsW8A8Int8MarlinMoEMethod",
+]
+
+
+class CompressedTensorsMarlinMoEMethod(FusedMoEMethodBase):
+    @staticmethod
+    def get_moe_method(
+        quant_config: "SlimQuantCompressedTensorsMarlinConfig",  # type: ignore # noqa E501
+        layer: torch.nn.Module,
+    ) -> "CompressedTensorsMarlinMoEMethod":
+
+        # are supported + check if the layer is being ignored.
+        weight_quant = quant_config.target_scheme_map["Linear"].get("weights")
+        input_quant = quant_config.target_scheme_map["Linear"].get(
+            "input_activations")
+        if quant_config._is_dynamic_token_w8a8(weight_quant, input_quant):
+            return CompressedTensorsW8A8Int8MarlinMoEMethod(quant_config)
+        else:
+            raise RuntimeError(
+                f"Slimquant_marlin does not support the FusedMoe scheme: {weight_quant}, {input_quant}")
+
+class CompressedTensorsW8A8Int8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod):
+    def __init__(
+            self,
+            quant_config: "CompressedTensorsMarlinConfig"  # type: ignore # noqa E501
+    ):
+        self.quant_config = quant_config
+        self.weight_quant = self.quant_config.target_scheme_map["Linear"].get(
+            "weights")
+        self.input_quant = self.quant_config.target_scheme_map["Linear"].get(
+            "input_activations")
+
+        per_channel = (
+            self.weight_quant.strategy == QuantizationStrategy.CHANNEL
+            and self.input_quant.strategy == QuantizationStrategy.TOKEN)
+        if not per_channel:
+            raise ValueError(
+                "For INT8 Fused MoE layers, we require channelwise, "
+                "dynamic per token quantization. Found "
+                f"{self.weight_quant}, {self.input_quant}")
+
+        self.static_input_scales = not self.input_quant.dynamic
+        if self.static_input_scales:
+            raise ValueError(
+                "For INT8 Fused MoE layers, we require channelwise, "
+                "dynamic per token quantization. Found static input scales.")
+        
+        self.fused_experts = self.fused_moe_forward
+        vllm_config = get_current_vllm_config()
+        parallel_config = vllm_config.parallel_config
+        dp_size = get_dp_group().world_size
+        self.use_deepep = dp_size > 1 and parallel_config.enable_expert_parallel and \
+            (envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput" or \
+             envs.VLLM_ALL2ALL_BACKEND == "deepep_low_latency")
+        
+        if self.use_deepep:
+            all2all_manager = get_ep_group().device_communicator.all2all_manager
+            assert all2all_manager is not None
+            self.num_dispatchers = all2all_manager.world_size
+
+        
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size_per_partition: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        if self.use_deepep:
+            self.N = 2 * intermediate_size_per_partition
+            self.K = hidden_size
+
+        params_dtype = torch.int8
+
+        # WEIGHTS
+        w13_weight = torch.nn.Parameter(torch.empty(
+            num_experts,
+            2 * intermediate_size_per_partition,
+            hidden_size,
+            dtype=params_dtype),
+                                        requires_grad=False)
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w2_weight = torch.nn.Parameter(torch.empty(
+            num_experts,
+            hidden_size,
+            intermediate_size_per_partition,
+            dtype=params_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        # WEIGHT_SCALES
+        assert self.weight_quant.strategy == QuantizationStrategy.CHANNEL
+        w13_weight_scale = torch.nn.Parameter(torch.ones(
+            num_experts,
+            2 * intermediate_size_per_partition,
+            1,
+            dtype=torch.float32),
+                                              requires_grad=False)
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+        w2_weight_scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                        hidden_size,
+                                                        1,
+                                                        dtype=torch.float32),
+                                             requires_grad=False)
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+        # Add PER-CHANNEL quantization for FusedMoE.weight_loader.
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value})
+        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+        set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+
+        # INPUT_SCALES
+        assert not self.static_input_scales
+        layer.w13_input_scale = None
+        layer.w2_input_scale = None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        w1_marlin_list = []
+        for ii in range(layer.w13_weight.shape[0]):
+            if not self.use_deepep:
+                w1_marlin_in = get_w8a8_int8_marlin_weights(layer.w13_weight[ii])
+            else:
+                w1_marlin_in = w8a8_nt_kpack2_marlin_weight(layer.w13_weight[ii])
+            w1_marlin_list.append(w1_marlin_in)
+
+        w1_marlin = torch.stack(w1_marlin_list, dim=0)
+
+        del w1_marlin_list
+        w2_marlin_list = []
+        for ii in range(layer.w2_weight.shape[0]):
+            if not self.use_deepep:
+                w2_marlin_in = get_w8a8_int8_marlin_weights(layer.w2_weight[ii])
+            else:
+                w2_marlin_in = w8a8_nt_kpack2_marlin_weight(layer.w2_weight[ii])
+            w2_marlin_list.append(w2_marlin_in)
+
+        w2_marlin = torch.stack(w2_marlin_list, dim=0)
+        layer.w13_weight = Parameter(w1_marlin, requires_grad=False)
+        layer.w2_weight = Parameter(w2_marlin, requires_grad=False)
+
+    def groupgemm_workspace_shapes(self,
+                                   a: torch.Tensor,
+                                   aq: torch.Tensor,
+                                   M: int,
+                                   N: int,
+                                   K: int,
+                                   topk: int,
+                                   global_num_experts: int,
+                                   local_num_experts: int,):
+        assert a.dim() == 2
+        # FIXME (varun): We should be able to dispatch only from the leader
+        # DP ranks in the case of TP > 1. At the moment, all the Ranks
+        # end up sending their tokens. This needs to be fixed.
+        num_dispatchers = self.num_dispatchers
+        num_experts = local_num_experts
+        max_num_tokens = a.size(
+            0) if self.max_num_tokens_per_rank is None else self.max_num_tokens_per_rank
+        workspace13 = (num_experts, max_num_tokens * num_dispatchers,
+                       max(K, N))
+        workspace2 = (num_experts, max_num_tokens * num_dispatchers, (N // 2))
+        output = (num_experts, max_num_tokens * num_dispatchers, K)
+        return (workspace13, workspace2, output, a.dtype)
+    
+    def w8a8_groupgemm_forward(self,
+        x: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_ids: torch.Tensor,
+        topk_weights: torch.Tensor,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        w1_scale: Optional[torch.Tensor] = None,
+        w2_scale: Optional[torch.Tensor] = None,
+        a1_scale: Optional[torch.Tensor] = None,
+        a2_scale: Optional[torch.Tensor] = None,
+        expert_num_tokens: Optional[torch.Tensor] = None,
+        use_nn_moe: Optional[bool] = False,
+        routed_scaling_factor: Optional[float] = None,
+        shared_output: Optional[torch.Tensor] = None,
+        q_x: Optional[torch.Tensor] = None,
+        **_  ):
+            
+            import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+            local_num_experts = w1.size(0)
+
+            E, max_num_tokens, _, _, top_k = mk._moe_problem_size(
+                q_x, w1, w2, topk_ids)
+            
+            N, K = self.N, self.K
+            (workspace13_shape, workspace2_shape, fused_out_shape,
+                 workspace_dtype) = self.groupgemm_workspace_shapes(
+                     x, q_x, max_num_tokens, N, K, top_k, global_num_experts,
+                     local_num_experts)
+            workspace13 = torch.empty(prod(workspace13_shape),
+                                      device=x.device,
+                                      dtype=workspace_dtype)
+            workspace1 = _resize_cache(workspace13, (E, max_num_tokens, N))
+            fused_out = _resize_cache(workspace13, fused_out_shape)
+
+            # (from deepgemm docs) : A value hint (which is a value on CPU)
+            # for the M expectation of each batch, correctly setting this value
+            # may lead to better performance.
+            expected_m = max_num_tokens
+
+            m_grouped_w8a8_gemm_nt_masked((q_x, a1_scale), 
+                                  (w1, w1_scale),
+                                    workspace1,
+                                    expert_num_tokens, 
+                                    expected_m,
+                                    )
+
+            assert expert_num_tokens is not None
+
+            a2q, a2q_scale = fuse_silu_mul_quant_ep(workspace1, expert_num_tokens)
+            m_grouped_w8a8_gemm_nt_masked((a2q, a2q_scale),
+                                          (w2, w2_scale),
+                                          fused_out,
+                                          expert_num_tokens,
+                                          expected_m)
+
+            return fused_out
+    
+
+    def fused_moe_forward(self,
+        x: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_ids: torch.Tensor,
+        topk_weights: torch.Tensor,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        w1_scale: Optional[torch.Tensor] = None,
+        w2_scale: Optional[torch.Tensor] = None,
+        a1_scale: Optional[torch.Tensor] = None,
+        a2_scale: Optional[torch.Tensor] = None,
+        expert_num_tokens: Optional[torch.Tensor] = None,
+        use_nn_moe: Optional[bool] = False,
+        routed_scaling_factor: Optional[float] = None,
+        shared_output: Optional[torch.Tensor] = None,
+        **_  ):
+            return fused_experts_impl_int8_marlin(
+                hidden_states=x,
+                w1=w1,
+                w2=w2,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                inplace=True,
+                activation=activation,
+                apply_router_weight_on_input=apply_router_weight_on_input,
+                use_int8_w8a8=True,
+                per_channel_quant=True,
+                global_num_experts=global_num_experts,
+                expert_map=expert_map,
+                w1_scale=w1_scale,
+                w2_scale=w2_scale,
+                a1_scale=a1_scale,
+                a2_scale=a2_scale,
+                use_nn_moe=False,
+                shared_output=shared_output,
+                routed_scaling_factor=routed_scaling_factor)
+
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        enable_eplb: bool = False,
+        use_nn_moe: Optional[bool] = False,
+        routed_scaling_factor: Optional[float] = None,
+        use_fused_gate: Optional[bool] = False,
+        expert_load_view: Optional[torch.Tensor] = None,
+        logical_to_physical_map: Optional[torch.Tensor] = None,
+        logical_replica_count: Optional[torch.Tensor] = None,
+        shared_output: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if enable_eplb:
+            raise NotImplementedError(
+                "EPLB not supported for "
+                "`CompressedTensorsW8A8Int8MoEMethod` yet.")
+
+
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            routed_scaling_factor=routed_scaling_factor,
+            use_fused_gate=use_fused_gate,
+            e_score_correction_bias=e_score_correction_bias,
+            indices_type=torch.int64 if self.use_deepep else None,)
+    
+        return self.fused_experts(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=True,
+            activation=activation,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            global_num_experts=global_num_experts,
+            expert_map=expert_map,
+            w1_scale=(layer.w13_weight_scale),
+            w2_scale=(layer.w2_weight_scale),
+            a1_scale=layer.w13_input_scale,
+            a2_scale=layer.w2_input_scale,
+            use_nn_moe=use_nn_moe,
+            shared_output=shared_output,
+            routed_scaling_factor=routed_scaling_factor,
+        )
+    
+    def select_gemm_impl(
+        self,
+        prepare_finalize: FusedMoEPrepareAndFinalize,
+        moe: FusedMoEConfig,
+    ) -> FusedMoEPermuteExpertsUnpermute:
+        from vllm.model_executor.layers.fused_moe import (
+            TritonOrGroupGemmExperts)
+
+        if (prepare_finalize.activation_format ==
+                FusedMoEActivationFormat.BatchedExperts):
+            max_num_tokens_per_rank = (
+                prepare_finalize.max_num_tokens_per_rank())
+            assert max_num_tokens_per_rank is not None
+            self.max_num_tokens_per_rank = max_num_tokens_per_rank
+            logger.debug(
+                "TritonOrGroupGemmExperts(%s): "
+                "max_tokens_per_rank=%s, block_size=%s, per_act_token=%s",
+                self.__class__.__name__, max_num_tokens_per_rank,
+                None, True)
+            return TritonOrGroupGemmExperts(
+                use_int8_w8a8=True,
+                per_act_token_quant=True,
+                fused_experts=self.w8a8_groupgemm_forward
+            )
+        else:
+            logger.debug(
+                "TritonOrGroupGemmExperts(%s): block_size=%s, per_act_token=%s",
+                self.__class__.__name__, None,
+                False)
+            
+            return TritonOrGroupGemmExperts(
+                fused_experts=self.fused_moe_forward
+            )
\ No newline at end of file

vllm/utils/__init__.py

@@ -70,6 +70,7 @@ import vllm.envs as envs
 from vllm.logger import enable_trace_function_call, init_logger
 import json
 
+
 if TYPE_CHECKING:
     from argparse import Namespace
 
@@ -80,12 +81,11 @@ logger = init_logger(__name__)
 
 # This value is chosen to have a balance between ITL and TTFT. Note it is
 # not optimized for throughput.
-DEFAULT_MAX_NUM_BATCHED_TOKENS = 2048
+DEFAULT_MAX_NUM_BATCHED_TOKENS = 10240
 POOLING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768
 MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS = 5120
 
 GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
-is_kme = any(arch in GPU_ARCH for arch in ["gfx928"])
 SUPPORT_TC = any(arch in GPU_ARCH for arch in ["gfx928", "gfx936"])
 
 def _generate_random_int8(
@@ -630,6 +630,31 @@ def get_ip() -> str:
         stacklevel=2)
     return "0.0.0.0"
 
+def test_loopback_bind(address, family):
+    try:
+        s = socket.socket(family, socket.SOCK_DGRAM)
+        s.bind((address, 0))  # Port 0 = auto assign
+        s.close()
+        return True
+    except OSError:
+        return False
+
+
+def get_loopback_ip() -> str:
+    loopback_ip = envs.VLLM_LOOPBACK_IP
+    if loopback_ip:
+        return loopback_ip
+
+    # VLLM_LOOPBACK_IP is not set, try to get it based on network interface
+
+    if test_loopback_bind("127.0.0.1", socket.AF_INET):
+        return "127.0.0.1"
+    elif test_loopback_bind("::1", socket.AF_INET6):
+        return "::1"
+    else:
+        raise RuntimeError(
+            "Neither 127.0.0.1 nor ::1 are bound to a local interface. "
+            "Set the VLLM_LOOPBACK_IP environment variable explicitly.")
 
 def is_valid_ipv6_address(address: str) -> bool:
     try:

vllm/v1/executor/abstract.py

@@ -44,6 +44,10 @@ class Executor(ExecutorBase):
         elif distributed_executor_backend == "mp":
             from vllm.v1.executor.multiproc_executor import MultiprocExecutor
             executor_class = MultiprocExecutor
+        elif distributed_executor_backend == "mp_rpc":
+            from vllm.v1.executor.multiproc_rpc_executor import (
+                MultiprocRPCExecutor)
+            executor_class = MultiprocRPCExecutor
         elif distributed_executor_backend == "uni":
             executor_class = UniProcExecutor
         elif distributed_executor_backend == "external_launcher":

vllm/v1/executor/multiproc_executor.py

@@ -530,3 +530,4 @@ class WorkerProc:
             if output_rank is None or self.rank == output_rank:
                 self.worker_response_mq.enqueue(
                     (WorkerProc.ResponseStatus.SUCCESS, output))
+