Expert distribution recording without overhead for EPLB (#4957)

docs/backend/native_api.ipynb

@@ -390,7 +390,7 @@
    "outputs": [],
    "source": [
     "expert_record_server_process, port = launch_server_cmd(\n",
-    "    \"python3 -m sglang.launch_server --model-path Qwen/Qwen1.5-MoE-A2.7B --host 0.0.0.0\"\n",
+    "    \"python3 -m sglang.launch_server --model-path Qwen/Qwen1.5-MoE-A2.7B --host 0.0.0.0 --expert-distribution-recorder-mode stat\"\n",
     ")\n",
     "\n",
     "wait_for_server(f\"http://localhost:{port}\")"
@@ -415,19 +415,7 @@
     "print_highlight(response)\n",
     "\n",
     "response = requests.post(f\"http://localhost:{port}/dump_expert_distribution_record\")\n",
-    "print_highlight(response)\n",
+    "print_highlight(response)"
-    "\n",
-    "import glob\n",
-    "\n",
-    "output_file = glob.glob(\"expert_distribution_*.csv\")[0]\n",
-    "with open(output_file, \"r\") as f:\n",
-    "    print_highlight(\"\\n| Layer ID | Expert ID | Count |\")\n",
-    "    print_highlight(\"|----------|-----------|--------|\")\n",
-    "    next(f)\n",
-    "    for i, line in enumerate(f):\n",
-    "        if i < 9:\n",
-    "            layer_id, expert_id, count = line.strip().split(\",\")\n",
-    "            print_highlight(f\"| {layer_id:8} | {expert_id:9} | {count:6} |\")"
    ]
   },
   {

python/sglang/srt/layers/moe/ep_moe/token_dispatcher.py

@@ -1,6 +1,9 @@
 import logging
 
 from sglang.srt.layers.quantization.deep_gemm import _ENABLE_JIT_DEEPGEMM
+from sglang.srt.managers.expert_distribution import (
+    get_global_expert_distribution_recorder,
+)
 from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.utils import DeepEPMode, load_json_config
 
@@ -326,6 +329,13 @@ class _DeepEPDispatcherImplNormal(_DeepEPDispatcherImplBase):
             config=_DeepEPConfig.get_instance().normal_dispatch_config,
         )
 
+        get_global_expert_distribution_recorder().on_deepep_dispatch_normal(
+            num_recv_tokens_per_expert_list,
+            num_tokens_per_rank=num_tokens_per_rank,
+            num_tokens_per_rdma_rank=num_tokens_per_rdma_rank,
+            num_tokens_per_expert=num_tokens_per_expert,
+        )
+
         return (
             recv_x,
             recv_topk_idx,
@@ -489,6 +499,10 @@ class _DeepEPDispatcherImplLowLatency(_DeepEPDispatcherImplBase):
     ):
         hook() if self.return_recv_hook else event.current_stream_wait()
 
+        get_global_expert_distribution_recorder().on_deepep_dispatch_low_latency(
+            masked_m
+        )
+
         reorder_topk_ids = seg_indptr = None
 
         return (

python/sglang/srt/layers/moe/topk.py

@@ -18,7 +18,10 @@ from typing import Callable, Optional
 import torch
 import torch.nn.functional as F
 
-from sglang.srt.managers.expert_distribution import ExpertDistributionRecorder
+from sglang.srt.managers.expert_distribution import (
+    ExpertDistributionRecorder,
+    get_global_expert_distribution_recorder,
+)
 from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.utils import get_compiler_backend, is_cuda, is_hip
 
@@ -31,8 +34,6 @@ if _is_cuda:
 if _is_cuda or _is_hip:
     from sgl_kernel import topk_softmax
 
-expert_distribution_recorder = ExpertDistributionRecorder()
-
 
 def fused_topk_native(
     hidden_states: torch.Tensor,
@@ -353,6 +354,6 @@ def select_experts(
             renormalize=renormalize,
         )
 
-    expert_distribution_recorder.record_new_token(topk_ids)
+    get_global_expert_distribution_recorder().on_select_experts(topk_ids=topk_ids)
 
     return topk_weights, topk_ids

python/sglang/srt/managers/expert_distribution.py

@@ -1,81 +1,620 @@
-import json
+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
 import logging
+import os
 import time
-from collections import defaultdict
+from abc import ABC
-from typing import Dict, List, Tuple
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Dict, List, Literal, Optional, Tuple, Type
 
 import torch
+import torch.distributed
+
+from sglang.srt.managers.expert_location import ExpertLocationMetadata
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.server_args import ServerArgs
+from sglang.srt.utils import Withable
 
 logger = logging.getLogger(__name__)
 
+# --------------------------------------- Entrypoint -----------------------------------------
 
-# global expert distribution recording
+_OutputMode = Literal["file", "object"]
-class ExpertDistributionRecorder:
-    # This class is a singleton class
-    def __new__(cls):
-        if not hasattr(cls, "instance"):
-            cls.instance = super(ExpertDistributionRecorder, cls).__new__(cls)
-        return cls.instance
 
-    def __init__(self):
-        # the length of the dictionary is the number of layers
-        # the length of the list is the number of tokens
-        # the length of the tuple is topk's k value
-        self._expert_distribution_record: Dict[int, List[Tuple[int]]] = defaultdict(
-            list
-        )
-        self._record = False
-        self._current_layer_id = "UNKNOWN"
 
-    def set_current_layer(self, layer_idx):
+class ExpertDistributionRecorder(ABC):
-        self._current_layer_id = layer_idx
+    """Global expert distribution recording"""
 
-    def record_new_token(self, topk_ids):
+    @staticmethod
-        if not self._record:
+    def init_new(
-            return
+        server_args: ServerArgs,
-        topk_ids_list = topk_ids.to("cpu", non_blocking=True).numpy().tolist()
+        expert_location_metadata: "ExpertLocationMetadata",
-        torch.cuda.synchronize()
+        rank: int,
-        for i in topk_ids_list:
+    ):
-            self._expert_distribution_record[self._current_layer_id].append(tuple(i))
+        if server_args.expert_distribution_recorder_mode is not None:
+            return _ExpertDistributionRecorderReal(
+                server_args, expert_location_metadata, rank
+            )
+        else:
+            return _ExpertDistributionRecorderNoop()
 
-    def reset(self):
+    @contextmanager
-        """Reset the expert distribution recorder."""
+    def with_current_layer(self, layer_idx):
-        logger.info("Resetting expert distribution record...")
+        yield
-        self._record = False
+
-        self._expert_distribution_record.clear()
+    @contextmanager
-        self._current_layer_id = "UNKNOWN"
+    def with_debug_name(self, debug_name):
+        yield
+
+    @contextmanager
+    def with_forward_pass(self, forward_pass_id: int, forward_batch: ForwardBatch):
+        yield
+
+    def on_select_experts(self, topk_ids: torch.Tensor):
+        pass
+
+    def on_deepep_dispatch_normal(
+        self,
+        local_physical_count_of_layer: List[int],
+        num_tokens_per_rank,
+        num_tokens_per_rdma_rank,
+        num_tokens_per_expert,
+    ):
+        pass
+
+    def on_deepep_dispatch_low_latency(
+        self, local_physical_count_of_layer: torch.Tensor
+    ):
+        pass
 
     def start_record(self):
-        """Start recording the expert distribution. Reset the recorder and set the recording flag to True."""
+        self._on_not_implemented()
-        if self._record == True:
+
+    def stop_record(self):
+        self._on_not_implemented()
+
+    def dump_record(self, output_mode: _OutputMode = "file"):
+        self._on_not_implemented()
+
+    def _on_not_implemented(self):
+        raise Exception(
+            "Please set ServerArgs.expert_distribution_recorder_mode to use ExpertDistributionRecorder."
+        )
+
+
+class _ExpertDistributionRecorderNoop(ExpertDistributionRecorder):
+    pass
+
+
+class _ExpertDistributionRecorderReal(ExpertDistributionRecorder):
+    def __init__(
+        self,
+        server_args: ServerArgs,
+        expert_location_metadata: "ExpertLocationMetadata",
+        rank: int,
+    ):
+        self._server_args = server_args
+        self._expert_location_metadata = expert_location_metadata
+
+        self._recording = False
+        self._current_forward_pass_id = Withable()
+        self._current_layer_idx = Withable()
+        self._current_debug_name = Withable()
+        self._accumulator = _Accumulator.init_new(
+            server_args, expert_location_metadata, rank
+        )
+        self._single_pass_gatherers = {
+            k: _SinglePassGatherer.init_new(server_args, expert_location_metadata, rank)
+            for k in self._accumulator.get_single_pass_gatherer_keys()
+        }
+
+    def with_current_layer(self, layer_idx):
+        return self._current_layer_idx.with_value(layer_idx)
+
+    def with_debug_name(self, debug_name):
+        return self._current_debug_name.with_value(debug_name)
+
+    @contextmanager
+    def with_forward_pass(self, forward_pass_id: int, forward_batch: ForwardBatch):
+        with self._current_forward_pass_id.with_value(forward_pass_id):
+            self._on_forward_pass_start(forward_batch)
+            try:
+                yield
+            finally:
+                self._on_forward_pass_end(forward_pass_id)
+
+    def _on_forward_pass_start(self, forward_batch: ForwardBatch):
+        if not self._recording:
+            return
+        for gatherer_key, gatherer in self._single_pass_gatherers.items():
+            gatherer.reset()
+            gatherer.on_forward_pass_start(forward_batch)
+
+    def _on_forward_pass_end(self, forward_pass_id: int):
+        if not self._recording:
+            return
+        for gatherer_key, gatherer in self._single_pass_gatherers.items():
+            single_pass_data = gatherer.collect()
+            self._accumulator.append(forward_pass_id, gatherer_key, single_pass_data)
+
+    def on_select_experts(self, topk_ids: torch.Tensor):
+        self._on_hook("on_select_experts", topk_ids=topk_ids)
+
+    def on_deepep_dispatch_normal(
+        self,
+        local_physical_count_of_layer: List[int],
+        num_tokens_per_rank,
+        num_tokens_per_rdma_rank,
+        num_tokens_per_expert,
+    ):
+        self._on_hook(
+            "on_deepep_dispatch_normal",
+            local_physical_count_of_layer=local_physical_count_of_layer,
+            num_tokens_per_rank=num_tokens_per_rank,
+            num_tokens_per_rdma_rank=num_tokens_per_rdma_rank,
+            num_tokens_per_expert=num_tokens_per_expert,
+        )
+
+    def on_deepep_dispatch_low_latency(
+        self, local_physical_count_of_layer: torch.Tensor
+    ):
+        self._on_hook(
+            "on_deepep_dispatch_low_latency",
+            local_physical_count_of_layer=local_physical_count_of_layer,
+        )
+
+    def _on_hook(self, hook_name: str, **kwargs):
+        if not (self._recording or torch.cuda.is_current_stream_capturing()):
+            return
+        gatherer = self._single_pass_gatherers[
+            self._accumulator.get_single_pass_gatherer_key(
+                self._current_debug_name.value
+            )
+        ]
+        getattr(gatherer, hook_name)(layer_idx=self._current_layer_idx.value, **kwargs)
+
+    def _reset(self):
+        """Reset the expert distribution recorder."""
+        logger.info("Resetting ExpertDistributionRecorder...")
+        assert (
+            self._current_layer_idx.value is None
+        ), f"{self._current_layer_idx.value=}"
+        for gatherer in self._single_pass_gatherers.values():
+            gatherer.reset()
+        self._accumulator.reset()
+
+    def start_record(self):
+        """Start recording the expert distribution."""
+        if self._recording:
             logger.warning(
                 "SGLang server is already recording expert ids. Did you forget to dump the expert ids recorded so far by sending requests to the `/stop_expert_distribution_record` and `/dump_expert_distribution_record` endpoints?"
             )
-        self.reset()
+        self._reset()
-        self._record = True
+        self._recording = True
 
     def stop_record(self):
-        """Stop recording the expert distribution. Set the recording flag to False."""
+        """Stop recording the expert distribution."""
-        if self._record == False:
+        if not self._recording:
             logger.warning(
                 "SGLang server has not been recording expert ids. Did you forget to start recording by sending request to the `/start_expert_distribution_record` endpoint?"
             )
-        self._record = False
+        self._recording = False
 
-    def dump_record(self):
+    def dump_record(self, output_mode: _OutputMode = "file"):
-        """Dump the expert distribution record to a file. Reset the recorder after dumping."""
+        """Dump the expert distribution record and reset the recorder after dumping."""
-        results = {}
+        output = self._accumulator.dump(output_mode=output_mode)
-        for layer_idx, layer_record in self._expert_distribution_record.items():
+        self._reset()
-            results[layer_idx] = defaultdict(int)
+        return output
-            for token_record in layer_record:
+
-                for expert_idx in token_record:
+
-                    results[layer_idx][expert_idx] += 1
+_global_expert_distribution_recorder: Optional[ExpertDistributionRecorder] = (
-        with open(
+    _ExpertDistributionRecorderNoop()
-            f"expert_distribution_rank{torch.distributed.get_rank()}_timestamp{time.time()}.csv",
+)
-            "w",
+
-        ) as fd:
+
-            fd.write("layer_id,expert_id,count\n")
+def get_global_expert_distribution_recorder():
-            for layer_idx, layer_results in results.items():
+    return _global_expert_distribution_recorder
-                for expert_idx, count in layer_results.items():
+
-                    fd.write(f"{layer_idx},{expert_idx},{count}\n")
+
-        self.reset()
+def set_global_expert_distribution_recorder(value):
+    global _global_expert_distribution_recorder
+    _global_expert_distribution_recorder = value
+
+
+# --------------------------------------- SinglePassGatherer -----------------------------------------
+
+
+class _SinglePassGatherer(ABC):
+    @staticmethod
+    def init_new(
+        server_args: ServerArgs,
+        expert_location_metadata: "ExpertLocationMetadata",
+        rank: int,
+    ) -> "_SinglePassGatherer":
+        if server_args.expert_distribution_recorder_mode == "per_token":
+            return _DetailSinglePassGatherer(
+                server_args, expert_location_metadata, rank
+            )
+        if server_args.enable_deepep_moe:
+            if server_args.deepep_mode == "normal":
+                return _DeepepNormalSinglePassGatherer(expert_location_metadata, rank)
+            elif server_args.deepep_mode == "low_latency":
+                return _DeepepLowLatencySinglePassGatherer(
+                    expert_location_metadata, rank
+                )
+            else:
+                raise NotImplementedError
+        return _SelectExpertsSinglePassGatherer(expert_location_metadata, rank)
+
+    def __init__(self, expert_location_metadata: "ExpertLocationMetadata", rank: int):
+        self._expert_location_metadata = expert_location_metadata
+        self._rank = rank
+
+    def on_forward_pass_start(self, forward_batch: ForwardBatch):
+        pass
+
+    def on_select_experts(self, layer_idx: int, topk_ids: torch.Tensor):
+        pass
+
+    def on_deepep_dispatch_normal(
+        self,
+        layer_idx: int,
+        local_physical_count_of_layer: List[int],
+        num_tokens_per_rank,
+        num_tokens_per_rdma_rank,
+        num_tokens_per_expert,
+    ):
+        pass
+
+    def on_deepep_dispatch_low_latency(
+        self, layer_idx: int, local_physical_count_of_layer: torch.Tensor
+    ):
+        pass
+
+    def reset(self):
+        raise NotImplementedError
+
+    def collect(self) -> Dict:
+        raise NotImplementedError
+
+
+class _LayerBasedSinglePassGatherer(_SinglePassGatherer):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._objects_of_layer = {}
+
+    def _on_layer_data(self, layer_idx: int, objects: List[int]):
+        assert 0 <= layer_idx < self._expert_location_metadata.num_layers
+        if layer_idx in self._objects_of_layer:
+            self._objects_of_layer[layer_idx] = _list_sum(
+                self._objects_of_layer[layer_idx], objects
+            )
+        else:
+            self._objects_of_layer[layer_idx] = objects
+
+    def reset(self):
+        self._objects_of_layer.clear()
+
+    def _collect_objects(self, pad_len: int) -> torch.Tensor:
+        data = [
+            self._objects_of_layer.get(layer_index) or ([0] * pad_len)
+            for layer_index in range(self._expert_location_metadata.num_layers)
+        ]
+        return torch.tensor(data)
+
+
+def _list_sum(a: List, b: List) -> List:
+    return [x + y for x, y in zip(a, b, strict=True)]
+
+
+class _SelectExpertsSinglePassGatherer(_LayerBasedSinglePassGatherer):
+    # pretty slow, but we will use the DeepEP Gatherer in production
+    def on_select_experts(self, layer_idx: int, topk_ids: torch.Tensor):
+        topk_ids_list = topk_ids.to("cpu", non_blocking=True).numpy().tolist()
+        torch.cuda.synchronize()
+
+        global_physical_count = [
+            0
+        ] * self._expert_location_metadata.num_physical_experts
+        for token_record in topk_ids_list:
+            for global_physical_expert_idx in token_record:
+                global_physical_count[global_physical_expert_idx] += 1
+
+        self._on_layer_data(layer_idx, global_physical_count)
+
+    def collect(self) -> Dict:
+        global_physical_count = super()._collect_objects(
+            pad_len=self._expert_location_metadata.num_physical_experts
+        )
+        return dict(global_physical_count=global_physical_count)
+
+
+class _DeepepNormalSinglePassGatherer(_LayerBasedSinglePassGatherer):
+    def on_deepep_dispatch_normal(
+        self,
+        layer_idx: int,
+        local_physical_count_of_layer: List[int],
+        num_tokens_per_rank,
+        num_tokens_per_rdma_rank,
+        num_tokens_per_expert,
+    ):
+        assert isinstance(local_physical_count_of_layer, list)
+        self._on_layer_data(layer_idx, local_physical_count_of_layer)
+
+    def collect(self) -> Dict:
+        local_physical_count = super()._collect_objects(
+            pad_len=self._expert_location_metadata.num_local_physical_experts
+        )
+        global_physical_count = _convert_local_to_global_physical_count(
+            local_physical_count,
+            rank=self._rank,
+            num_local_physical_experts=self._expert_location_metadata.num_local_physical_experts,
+            num_physical_experts=self._expert_location_metadata.num_physical_experts,
+        )
+        return dict(global_physical_count=global_physical_count)
+
+
+class _DeepepLowLatencySinglePassGatherer(_SinglePassGatherer):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._data = torch.zeros(
+            (
+                self._expert_location_metadata.num_layers,
+                self._expert_location_metadata.num_local_physical_experts,
+            ),
+            dtype=torch.int,
+            device="cuda",
+        )
+
+    def on_deepep_dispatch_low_latency(
+        self, layer_idx: int, local_physical_count_of_layer: torch.Tensor
+    ):
+        # Most naive implementation, can optimize later
+        self._data[layer_idx, :] += local_physical_count_of_layer
+
+    def reset(self):
+        self._data[...] = 0
+
+    def collect(self) -> Dict:
+        # Can optimize if bottleneck
+        global_physical_count = _convert_local_to_global_physical_count(
+            self._data,
+            rank=self._rank,
+            num_local_physical_experts=self._expert_location_metadata.num_local_physical_experts,
+            num_physical_experts=self._expert_location_metadata.num_physical_experts,
+        )
+        return dict(global_physical_count=global_physical_count)
+
+
+def _convert_local_to_global_physical_count(
+    local_physical_count: torch.Tensor,
+    rank: int,
+    num_local_physical_experts: int,
+    num_physical_experts: int,
+) -> torch.Tensor:
+    dtype = local_physical_count.dtype
+    device = local_physical_count.device
+    num_layers, _ = local_physical_count.shape
+
+    ans = torch.zeros((num_layers, num_physical_experts), dtype=dtype, device=device)
+    ans[
+        :, num_local_physical_experts * rank : num_local_physical_experts * (rank + 1)
+    ] = local_physical_count
+    return ans
+
+
+# --------------------------------------- Accumulator -----------------------------------------
+
+_SINGLE_PASS_GATHERER_KEY_PRIMARY = "primary"
+
+
+class _Accumulator(ABC):
+    @staticmethod
+    def init_new(
+        server_args: ServerArgs,
+        expert_location_metadata: "ExpertLocationMetadata",
+        rank: int,
+    ) -> "_Accumulator":
+        return _Accumulator.get_class(server_args)(
+            server_args, expert_location_metadata, rank
+        )
+
+    @staticmethod
+    def get_class(server_args: ServerArgs) -> Type["_Accumulator"]:
+        return {
+            "stat": _StatAccumulator,
+            # TODO pr-chain: enable this later
+            # "per_pass": _DetailAccumulator,
+            # "per_token": _DetailAccumulator,
+        }[server_args.expert_distribution_recorder_mode]
+
+    def __init__(
+        self,
+        server_args: ServerArgs,
+        expert_location_metadata: "ExpertLocationMetadata",
+        rank: int,
+    ):
+        self._server_args = server_args
+        self._expert_location_metadata = expert_location_metadata
+        self._rank = rank
+
+    def get_single_pass_gatherer_keys(self):
+        return [_SINGLE_PASS_GATHERER_KEY_PRIMARY]
+
+    def get_single_pass_gatherer_key(self, debug_name: Optional[str]):
+        return _SINGLE_PASS_GATHERER_KEY_PRIMARY
+
+    def append(
+        self,
+        forward_pass_id: int,
+        gatherer_key: str,
+        single_pass_data: Dict,
+    ):
+        pass
+
+    def reset(self):
+        pass
+
+    def dump(self, output_mode: _OutputMode):
+        pass
+
+
+class _StatAccumulator(_Accumulator):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._global_physical_count_of_buffered_step = _Buffer.init_new(
+            item_shape=(
+                self._expert_location_metadata.num_layers,
+                # Cannot use local_physical_count to support select_experts
+                self._expert_location_metadata.num_physical_experts,
+            ),
+            buffer_size=self._server_args.expert_distribution_recorder_buffer_size,
+            dtype=torch.int32,
+            device=self._server_args.device,
+        )
+
+    def append(
+        self,
+        forward_pass_id: int,
+        gatherer_key: str,
+        single_pass_data: Dict,
+    ):
+        super().append(forward_pass_id, gatherer_key, single_pass_data)
+        # Can optimize if overhead here is large
+        self._global_physical_count_of_buffered_step.append(
+            single_pass_data["global_physical_count"]
+        )
+
+    def reset(self):
+        super().reset()
+        self._global_physical_count_of_buffered_step.reset()
+
+    def dump(self, output_mode: _OutputMode):
+        logical_count_of_buffered_step = _convert_global_physical_count_to_logical_count(
+            self._global_physical_count_of_buffered_step.get_all(),
+            num_layers=self._expert_location_metadata.num_layers,
+            num_logical_experts=self._expert_location_metadata.num_logical_experts,
+            physical_to_logical_map=self._expert_location_metadata.physical_to_logical_map,
+        )
+        torch.distributed.all_reduce(
+            logical_count_of_buffered_step, op=torch.distributed.ReduceOp.SUM
+        )
+        output = dict(
+            rank=self._rank,
+            logical_count=logical_count_of_buffered_step,
+        )
+
+        if output_mode == "file":
+            if self._rank == 0:
+                _dump_to_file(f"expert_distribution_recorder_{time.time()}.pt", output)
+        elif output_mode == "object":
+            return output
+        else:
+            raise NotImplementedError
+
+
+def _dump_to_file(name, data):
+    save_dir = Path(os.environ.get("SGLANG_EXPERT_DISTRIBUTION_RECORDER_DIR", "/tmp"))
+    path_output = save_dir / name
+    logger.info(f"Write expert distribution to {path_output}")
+    if not save_dir.exists():
+        save_dir.mkdir(parents=True, exist_ok=True)
+    torch.save(data, str(path_output))
+
+
+class _Buffer:
+    @staticmethod
+    def init_new(item_shape: Tuple, buffer_size: int, dtype, device):
+        if buffer_size < 0:
+            return _InfiniteBuffer(item_shape, dtype=dtype, device=device)
+        else:
+            return _CircularBuffer(item_shape, buffer_size, dtype=dtype, device=device)
+
+    def append(self, value: torch.Tensor):
+        raise NotImplementedError
+
+    def get_all(self) -> torch.Tensor:
+        raise NotImplementedError
+
+    def reset(self):
+        raise NotImplementedError
+
+
+class _CircularBuffer(_Buffer):
+    def __init__(self, item_shape: Tuple, buffer_size: int, dtype, device):
+        self._buffer = torch.zeros(
+            (buffer_size, *item_shape), dtype=dtype, device=device
+        )
+        self._curr_index = 0
+
+    def append(self, value: torch.Tensor):
+        self._buffer[self._curr_index] = value
+        self._curr_index = (self._curr_index + 1) % len(self._buffer)
+
+    def get_all(self) -> torch.Tensor:
+        return self._buffer
+
+    def reset(self):
+        self._buffer[...] = 0
+
+
+class _InfiniteBuffer(_Buffer):
+    def __init__(self, item_shape: Tuple, dtype, device):
+        self._item_shape = item_shape
+        self._buffer = torch.zeros((128, *item_shape), dtype=dtype, device=device)
+        self._size = 0
+
+    def append(self, value: torch.Tensor):
+        curr_buffer_size = len(self._buffer)
+        dtype = self._buffer.dtype
+        device = self._buffer.device
+
+        if self._size == curr_buffer_size:
+            new_buffer = torch.zeros(
+                (2 * curr_buffer_size, *self._item_shape), dtype=dtype, device=device
+            )
+            new_buffer[:curr_buffer_size] = self._buffer
+            self._buffer = new_buffer
+
+        self._buffer[self._size] = value
+        self._size += 1
+
+    def get_all(self) -> torch.Tensor:
+        return self._buffer[: self._size]
+
+    def reset(self):
+        self._buffer[...] = 0
+        self._size = 0
+
+
+def _convert_global_physical_count_to_logical_count(
+    # (whatever, num_layers, num_physical_experts)
+    global_physical_count: torch.Tensor,
+    num_layers: int,
+    num_logical_experts: int,
+    physical_to_logical_map: torch.Tensor,
+):
+    dim_extra, _, _ = global_physical_count.shape
+    dtype = global_physical_count.dtype
+    device = global_physical_count.device
+    logical_count = torch.zeros(
+        (dim_extra, num_layers, num_logical_experts), dtype=dtype, device=device
+    )
+    logical_count.scatter_add_(
+        dim=2,
+        index=physical_to_logical_map.unsqueeze(0).expand(dim_extra, -1, -1),
+        src=global_physical_count,
+    )
+    return logical_count

python/sglang/srt/managers/expert_location.py

@@ -0,0 +1,273 @@
+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+import json
+import logging
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional
+
+import torch
+import torch.distributed
+import torch.nn.functional as F
+
+from sglang.srt.configs.model_config import ModelConfig
+from sglang.srt.model_loader import get_model_architecture
+from sglang.srt.server_args import ServerArgs
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ExpertLocationMetadata:
+    physical_to_logical_map: torch.Tensor  # (layers, num_physical_experts)
+    logical_to_all_physical_map: torch.Tensor  # (layers, num_logical_experts, X)
+    logical_to_all_physical_map_num_valid: torch.Tensor  # (layers, num_logical_experts)
+
+    # -------------------------------- properties ------------------------------------
+
+    @property
+    def num_layers(self) -> int:
+        return self.physical_to_logical_map.shape[0]
+
+    @property
+    def num_physical_experts(self) -> int:
+        return self.physical_to_logical_map.shape[1]
+
+    @property
+    def num_local_physical_experts(self) -> int:
+        ans, remainder = divmod(self.num_physical_experts, self.ep_size)
+        assert remainder == 0
+        return ans
+
+    @property
+    def num_logical_experts(self) -> int:
+        return self.logical_to_all_physical_map.shape[1]
+
+    @property
+    def ep_size(self):
+        # TODO change when EP size != world size
+        return torch.distributed.get_world_size()
+
+    def __post_init__(self):
+        num_layers_0, num_physical_experts_0 = self.physical_to_logical_map.shape
+        num_layers_1, num_logical_experts_0, num_physical_experts_1 = (
+            self.logical_to_all_physical_map.shape
+        )
+        num_layers_2, num_logical_experts_1 = (
+            self.logical_to_all_physical_map_num_valid.shape
+        )
+        # TODO pr-chain: enable this later
+        # assert num_layers_0 == num_layers_1 == num_layers_2 == num_layers_3
+        # assert num_logical_experts_0 == num_logical_experts_1 == num_logical_experts_2
+        assert num_physical_experts_0 == num_physical_experts_1
+
+    # -------------------------------- construction ------------------------------------
+
+    @staticmethod
+    def init_trivial(server_args: ServerArgs, model_config: ModelConfig):
+        """Trivial location - logical expert i corresponds to physical expert i"""
+        common = ExpertLocationMetadata._init_common(server_args, model_config)
+        num_physical_experts = common["num_physical_experts"]
+        model_config_for_expert_location = common["model_config_for_expert_location"]
+        num_layers = model_config_for_expert_location.num_layers
+        num_logical_experts = model_config_for_expert_location.num_logical_experts
+
+        physical_to_logical_map = (
+            torch.arange(0, num_physical_experts).repeat(num_layers, 1)
+            % num_logical_experts
+        )
+
+        return ExpertLocationMetadata.init_by_mapping(
+            server_args,
+            model_config,
+            physical_to_logical_map=physical_to_logical_map,
+        )
+
+    @staticmethod
+    def init_by_mapping(
+        server_args: ServerArgs,
+        model_config: ModelConfig,
+        physical_to_logical_map,
+    ):
+        if not isinstance(physical_to_logical_map, torch.Tensor):
+            physical_to_logical_map = torch.tensor(physical_to_logical_map)
+        physical_to_logical_map = physical_to_logical_map.to(server_args.device)
+
+        common = ExpertLocationMetadata._init_common(server_args, model_config)
+        model_config_for_expert_location = common["model_config_for_expert_location"]
+        logical_to_all_physical_map = _compute_logical_to_all_physical_map(
+            physical_to_logical_map,
+            num_logical_experts=model_config_for_expert_location.num_logical_experts,
+        )
+
+        return ExpertLocationMetadata._init_raw(
+            ep_size=common["ep_size"],
+            physical_to_logical_map=physical_to_logical_map,
+            logical_to_all_physical_map=logical_to_all_physical_map,
+        )
+
+    @staticmethod
+    def _init_common(server_args: ServerArgs, model_config: ModelConfig):
+        model_config_for_expert_location = (
+            ModelConfigForExpertLocation.from_model_config(model_config)
+        )
+
+        num_physical_experts = (
+            model_config_for_expert_location.num_logical_experts
+            # TODO pr-chain: enable this later
+            # + server_args.ep_num_redundant_experts
+        )
+        ep_size = server_args.ep_size
+        assert num_physical_experts % ep_size == 0
+        num_local_physical_experts = num_physical_experts // ep_size
+
+        return dict(
+            model_config_for_expert_location=model_config_for_expert_location,
+            num_physical_experts=num_physical_experts,
+            num_local_physical_experts=num_local_physical_experts,
+            ep_size=ep_size,
+        )
+
+    @staticmethod
+    def _init_raw(
+        ep_size: int,
+        physical_to_logical_map: torch.Tensor,
+        logical_to_all_physical_map: torch.Tensor,
+    ):
+        _, num_physical_experts = physical_to_logical_map.shape
+
+        logical_to_all_physical_map_padded = F.pad(
+            logical_to_all_physical_map,
+            (0, num_physical_experts - logical_to_all_physical_map.shape[-1]),
+            value=-1,
+        )
+
+        logical_to_all_physical_map_num_valid = torch.count_nonzero(
+            logical_to_all_physical_map != -1, dim=-1
+        )
+
+        return ExpertLocationMetadata(
+            physical_to_logical_map=physical_to_logical_map,
+            logical_to_all_physical_map=logical_to_all_physical_map_padded,
+            logical_to_all_physical_map_num_valid=logical_to_all_physical_map_num_valid,
+        )
+
+
+_global_expert_location_metadata: Optional[ExpertLocationMetadata] = None
+
+
+def get_global_expert_location_metadata():
+    return _global_expert_location_metadata
+
+
+def set_global_expert_location_metadata(value):
+    global _global_expert_location_metadata
+    assert _global_expert_location_metadata is None
+    _global_expert_location_metadata = value
+
+
+def _compute_logical_to_all_physical_map(
+    physical_to_logical_map: torch.Tensor, num_logical_experts: int
+):
+    # This is rarely called, so we use for loops for maximum clarity
+
+    num_layers, num_physical_experts = physical_to_logical_map.shape
+
+    logical_to_all_physical_map = [
+        [[] for _ in range(num_logical_experts)] for _ in range(num_layers)
+    ]
+    for layer_id in range(num_layers):
+        for physical_expert_id in range(num_physical_experts):
+            logical_expert_id = physical_to_logical_map[
+                layer_id, physical_expert_id
+            ].item()
+            logical_to_all_physical_map[layer_id][logical_expert_id].append(
+                physical_expert_id
+            )
+
+    logical_to_all_physical_map = _pad_nested_array(
+        logical_to_all_physical_map, pad_value=-1
+    )
+
+    return torch.tensor(
+        logical_to_all_physical_map, device=physical_to_logical_map.device
+    )
+
+
+def _pad_nested_array(arr, pad_value):
+    max_len = max(len(inner) for outer in arr for inner in outer)
+    padded = [
+        [inner + [pad_value] * (max_len - len(inner)) for inner in outer]
+        for outer in arr
+    ]
+    return padded
+
+
+@dataclass
+class ModelConfigForExpertLocation:
+    num_layers: int
+    num_logical_experts: int
+    num_groups: Optional[int] = None
+
+    @staticmethod
+    def init_dummy():
+        return ModelConfigForExpertLocation(num_layers=1, num_logical_experts=1)
+
+    @staticmethod
+    def from_model_config(model_config: ModelConfig):
+        model_class, _ = get_model_architecture(model_config)
+        if hasattr(model_class, "get_model_config_for_expert_location"):
+            return model_class.get_model_config_for_expert_location(
+                model_config.hf_config
+            )
+        else:
+            return ModelConfigForExpertLocation.init_dummy()
+
+
+def compute_initial_expert_location_metadata(
+    server_args: ServerArgs, model_config: ModelConfig
+) -> ExpertLocationMetadata:
+    data = server_args.init_expert_location
+    if data == "trivial":
+        logger.info("init_expert_location from trivial")
+        return ExpertLocationMetadata.init_trivial(server_args, model_config)
+
+    # TODO unify with the utils function
+    if data.endswith(".pt"):
+        data_dict = torch.load(data, weights_only=True)
+    elif data.endswith(".json"):
+        data_dict = json.loads(Path(data).read_text())
+    else:
+        data_dict = json.loads(data)
+
+    if "physical_to_logical_map" in data_dict:
+        logger.info(
+            "init_expert_location from init_by_mapping using ServerArgs.init_expert_location"
+        )
+        return ExpertLocationMetadata.init_by_mapping(
+            server_args, model_config, **data_dict
+        )
+    elif "logical_count" in data_dict:
+        # TODO pr-chain: enable this later
+        raise NotImplementedError
+        # logger.info(
+        #     "init_expert_location from init_by_eplb using ServerArgs.init_expert_location"
+        # )
+        # return ExpertLocationMetadata.init_by_eplb(
+        #     server_args, model_config, logical_count=data_dict["logical_count"]
+        # )
+    else:
+        raise NotImplementedError(
+            f"Unknown init_expert_location format ({list(data_dict.keys())=})"
+        )

python/sglang/srt/managers/scheduler.py

@@ -59,7 +59,10 @@ from sglang.srt.hf_transformers_utils import (
 )
 from sglang.srt.layers.dp_attention import compute_dp_attention_world_info
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
-from sglang.srt.managers.expert_distribution import ExpertDistributionRecorder
+from sglang.srt.managers.expert_distribution import (
+    ExpertDistributionRecorder,
+    get_global_expert_distribution_recorder,
+)
 from sglang.srt.managers.io_struct import (
     AbortReq,
     CloseSessionReqInput,
@@ -142,8 +145,6 @@ from sglang.srt.utils import (
 )
 from sglang.utils import TypeBasedDispatcher, get_exception_traceback
 
-expert_distribution_recorder = ExpertDistributionRecorder()
-
 logger = logging.getLogger(__name__)
 
 # Test retract decode for debugging purposes
@@ -2162,11 +2163,11 @@ class Scheduler(
 
     def expert_distribution_handle(self, recv_req: ExpertDistributionReq):
         if recv_req == ExpertDistributionReq.START_RECORD:
-            expert_distribution_recorder.start_record()
+            get_global_expert_distribution_recorder().start_record()
         elif recv_req == ExpertDistributionReq.STOP_RECORD:
-            expert_distribution_recorder.stop_record()
+            get_global_expert_distribution_recorder().stop_record()
         elif recv_req == ExpertDistributionReq.DUMP_RECORD:
-            expert_distribution_recorder.dump_record()
+            get_global_expert_distribution_recorder().dump_record()
         else:
             raise ValueError("Unrecognized ExpertDistributionReq value")
         return ExpertDistributionReqOutput()

python/sglang/srt/model_executor/model_runner.py

@@ -52,6 +52,16 @@ from sglang.srt.layers.quantization.deep_gemm import (
 from sglang.srt.layers.sampler import Sampler
 from sglang.srt.layers.torchao_utils import apply_torchao_config_to_model
 from sglang.srt.lora.lora_manager import LoRAManager
+from sglang.srt.managers.expert_distribution import (
+    ExpertDistributionRecorder,
+    get_global_expert_distribution_recorder,
+    set_global_expert_distribution_recorder,
+)
+from sglang.srt.managers.expert_location import (
+    compute_initial_expert_location_metadata,
+    get_global_expert_location_metadata,
+    set_global_expert_location_metadata,
+)
 from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.mem_cache.memory_pool import (
     DoubleSparseTokenToKVPool,
@@ -161,6 +171,8 @@ class ModelRunner:
         self.use_mla_backend = self.model_config.attention_arch == AttentionArch.MLA
         self.attention_chunk_size = model_config.attention_chunk_size
 
+        self.forward_pass_id = 0
+
         # Model-specific adjustment
         self.model_specific_adjustment()
 
@@ -219,6 +231,25 @@ class ModelRunner:
             enable=self.server_args.enable_memory_saver
         )
 
+        if not self.is_draft_worker:
+            set_global_expert_location_metadata(
+                compute_initial_expert_location_metadata(server_args, self.model_config)
+            )
+            if self.tp_rank == 0 and get_bool_env_var(
+                "SGLANG_LOG_EXPERT_LOCATION_METADATA"
+            ):
+                logger.info(
+                    f"Initial expert_location_metadata: {get_global_expert_location_metadata().debug_str()}"
+                )
+
+            set_global_expert_distribution_recorder(
+                ExpertDistributionRecorder.init_new(
+                    server_args,
+                    get_global_expert_location_metadata(),
+                    rank=self.tp_rank,
+                )
+            )
+
         # Load the model
         self.sampler = Sampler()
         self.load_model()
@@ -1093,6 +1124,22 @@ class ModelRunner:
         forward_batch: ForwardBatch,
         skip_attn_backend_init: bool = False,
         pp_proxy_tensors: Optional[PPProxyTensors] = None,
+    ) -> Tuple[Union[LogitsProcessorOutput, PPProxyTensors], bool]:
+        self.forward_pass_id += 1
+
+        with get_global_expert_distribution_recorder().with_forward_pass(
+            self.forward_pass_id,
+            forward_batch,
+        ):
+            return self._forward_raw(
+                forward_batch, skip_attn_backend_init, pp_proxy_tensors
+            )
+
+    def _forward_raw(
+        self,
+        forward_batch: ForwardBatch,
+        skip_attn_backend_init: bool,
+        pp_proxy_tensors: Optional[PPProxyTensors],
     ) -> Tuple[Union[LogitsProcessorOutput, PPProxyTensors], bool]:
         can_run_cuda_graph = bool(
             forward_batch.forward_mode.is_cuda_graph()

python/sglang/srt/models/deepseek_v2.py

@@ -77,7 +77,11 @@ from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from sglang.srt.managers.expert_distribution import ExpertDistributionRecorder
+from sglang.srt.managers.expert_distribution import (
+    ExpertDistributionRecorder,
+    get_global_expert_distribution_recorder,
+)
+from sglang.srt.managers.expert_location import ModelConfigForExpertLocation
 from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
 from sglang.srt.model_loader.weight_utils import default_weight_loader
@@ -109,8 +113,6 @@ if _is_hip:
         decode_attention_fwd_grouped_rope,
     )
 
-expert_distribution_recorder = ExpertDistributionRecorder()
-
 logger = logging.getLogger(__name__)
 
 
@@ -302,6 +304,7 @@ class DeepseekV2MoE(nn.Module):
     def forward(
         self, hidden_states: torch.Tensor, forward_batch: Optional[ForwardBatch] = None
     ) -> torch.Tensor:
+        forward_mode = forward_batch.forward_mode
         if (not self._enable_deepep_moe) or is_non_idle_and_non_empty(
             forward_mode, hidden_states
         ):
@@ -1278,7 +1281,7 @@ class DeepseekV2DecoderLayer(nn.Module):
             )
 
         # Fully Connected
-        hidden_states = self.mlp(hidden_states)
+        hidden_states = self.mlp(hidden_states, forward_batch)
 
         # TODO(ch-wan): use reduce-scatter in MLP to avoid this scatter
         # Scatter
@@ -1422,11 +1425,11 @@ class DeepseekV2Model(nn.Module):
 
         residual = None
         for i in range(len(self.layers)):
-            expert_distribution_recorder.set_current_layer(i)
+            with get_global_expert_distribution_recorder().with_current_layer(i):
-            layer = self.layers[i]
+                layer = self.layers[i]
-            hidden_states, residual = layer(
+                hidden_states, residual = layer(
-                positions, hidden_states, forward_batch, residual, zero_allocator
+                    positions, hidden_states, forward_batch, residual, zero_allocator
-            )
+                )
         if not forward_batch.forward_mode.is_idle():
             if residual is None:
                 hidden_states = self.norm(hidden_states)
@@ -1872,6 +1875,14 @@ class DeepseekV2ForCausalLM(nn.Module):
         torch.cuda.empty_cache()
         torch.cuda.synchronize()
 
+    @classmethod
+    def get_model_config_for_expert_location(cls, config):
+        return ModelConfigForExpertLocation(
+            num_layers=config.num_hidden_layers,
+            num_logical_experts=config.n_routed_experts,
+            num_groups=config.n_group,
+        )
+
 
 class DeepseekV3ForCausalLM(DeepseekV2ForCausalLM):
     pass

python/sglang/srt/models/qwen2_moe.py

@@ -59,14 +59,16 @@ from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from sglang.srt.managers.expert_distribution import ExpertDistributionRecorder
+from sglang.srt.managers.expert_distribution import (
+    ExpertDistributionRecorder,
+    get_global_expert_distribution_recorder,
+)
+from sglang.srt.managers.expert_location import ModelConfigForExpertLocation
 from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
 from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.utils import add_prefix, make_layers
 
-expert_distribution_recorder = ExpertDistributionRecorder()
-
 logger = logging.getLogger(__name__)
 
 
@@ -591,11 +593,11 @@ class Qwen2MoeModel(nn.Module):
             residual = pp_proxy_tensors["residual"]
 
         for i in range(self.start_layer, self.end_layer):
-            expert_distribution_recorder.set_current_layer(i)
+            with get_global_expert_distribution_recorder().with_current_layer(i):
-            layer = self.layers[i]
+                layer = self.layers[i]
-            hidden_states, residual = layer(
+                hidden_states, residual = layer(
-                positions, hidden_states, forward_batch, residual
+                    positions, hidden_states, forward_batch, residual
-            )
+                )
         if not self.pp_group.is_last_rank:
             return PPProxyTensors(
                 {
@@ -752,5 +754,13 @@ class Qwen2MoeForCausalLM(nn.Module):
                     else:
                         logger.warning(f"Parameter {name} not found in params_dict")
 
+    @classmethod
+    def get_model_config_for_expert_location(cls, config):
+        return ModelConfigForExpertLocation(
+            num_layers=config.num_hidden_layers,
+            num_logical_experts=config.num_experts,
+            num_groups=None,
+        )
+
 
 EntryClass = Qwen2MoeForCausalLM

python/sglang/srt/server_args.py

@@ -170,6 +170,11 @@ class ServerArgs:
     enable_ep_moe: bool = False
     enable_deepep_moe: bool = False
     deepep_mode: Optional[Literal["auto", "normal", "low_latency"]] = "auto"
+    init_expert_location: str = "trivial"
+    expert_distribution_recorder_mode: Optional[
+        Literal["stat", "per_pass", "per_token"]
+    ] = None
+    expert_distribution_recorder_buffer_size: Optional[int] = None
     deepep_config: Optional[str] = None
     enable_torch_compile: bool = False
     torch_compile_max_bs: int = 32
@@ -361,6 +366,15 @@ class ServerArgs:
                 "Pipeline parallelism is incompatible with overlap schedule."
             )
 
+        if self.expert_distribution_recorder_buffer_size is None:
+            # TODO pr-chain: enable this later
+            # if (x := self.eplb_rebalance_num_iterations) is not None:
+            #     self.expert_distribution_recorder_buffer_size = x
+            if False:
+                pass
+            elif self.expert_distribution_recorder_mode is not None:
+                self.expert_distribution_recorder_buffer_size = 1000
+
         # Speculative Decoding
         if self.speculative_algorithm == "NEXTN":
             # NEXTN shares the same implementation of EAGLE
@@ -1257,6 +1271,24 @@ class ServerArgs:
             default="auto",
             help="Select the mode when enable DeepEP MoE, could be `normal`, `low_latency` or `auto`. Default is `auto`, which means `low_latency` for decode batch and `normal` for prefill batch.",
         )
+        parser.add_argument(
+            "--init-expert-location",
+            type=str,
+            default=ServerArgs.init_expert_location,
+            help="Initial location of EP experts.",
+        )
+        parser.add_argument(
+            "--expert-distribution-recorder-mode",
+            type=str,
+            default=ServerArgs.expert_distribution_recorder_mode,
+            help="Mode of expert distribution recorder.",
+        )
+        parser.add_argument(
+            "--expert-distribution-recorder-buffer-size",
+            type=int,
+            default=ServerArgs.expert_distribution_recorder_buffer_size,
+            help="Circular buffer size of expert distribution recorder. Set to -1 to denote infinite buffer.",
+        )
         parser.add_argument(
             "--deepep-config",
             type=str,

python/sglang/srt/utils.py

@@ -46,7 +46,19 @@ from importlib.util import find_spec
 from io import BytesIO
 from multiprocessing.reduction import ForkingPickler
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Protocol, Set, Tuple, Union
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Generic,
+    List,
+    Optional,
+    Protocol,
+    Set,
+    Tuple,
+    TypeVar,
+    Union,
+)
 
 import numpy as np
 import psutil
@@ -2126,3 +2138,25 @@ def load_json_config(data: str):
 
 def dispose_tensor(x: torch.Tensor):
     x.set_(torch.empty((0,), device=x.device, dtype=x.dtype))
+
+
+T = TypeVar("T")
+
+
+class Withable(Generic[T]):
+    def __init__(self):
+        self._value: Optional[T] = None
+
+    @property
+    def value(self) -> T:
+        return self._value
+
+    @contextmanager
+    def with_value(self, new_value: T):
+        assert self._value is None
+        self._value = new_value
+        try:
+            yield
+        finally:
+            assert self._value is new_value
+            self._value = None

test/srt/test_expert_distribution.py

@@ -1,9 +1,10 @@
-import csv
-import glob
 import os
+import tempfile
 import unittest
+from pathlib import Path
 
 import requests
+import torch
 
 from sglang.srt.utils import kill_process_tree
 from sglang.test.test_utils import (
@@ -16,108 +17,86 @@ from sglang.test.test_utils import (
 
 
 class TestExpertDistribution(CustomTestCase):
-    def setUp(self):
-        # Clean up any existing expert distribution files before each test
-        for f in glob.glob("expert_distribution_*.csv"):
-            os.remove(f)
-
-    def tearDown(self):
-        # Clean up any expert distribution files after each test
-        for f in glob.glob("expert_distribution_*.csv"):
-            os.remove(f)
-
     def test_expert_distribution_record(self):
+        # TODO: Add tests for DeepEP gatherer (currently our CI cannot run that)
+        for info in [
+            dict(model_path="deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"),
+            dict(model_path="Qwen/Qwen1.5-MoE-A2.7B"),
+            dict(model_path="Qwen/Qwen1.5-MoE-A2.7B", tp_size=2),
+            # TODO enable in next PR
+            # dict(model_path="Qwen/Qwen1.5-MoE-A2.7B", mode="per_pass"),
+            # dict(model_path="Qwen/Qwen1.5-MoE-A2.7B", mode="per_token"),
+        ]:
+            with self.subTest(info=info):
+                self._execute_core(**info)
+
+    def _execute_core(self, model_path: str, mode: str = "stat", tp_size: int = 1):
         """Test expert distribution record endpoints"""
-        process = popen_launch_server(
+        with tempfile.TemporaryDirectory() as tmp_dir:
-            # The feature is only implemented in deepseek_v2.py
+            os.environ["SGLANG_EXPERT_DISTRIBUTION_RECORDER_DIR"] = tmp_dir
-            "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
-            DEFAULT_URL_FOR_TEST,
-            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
-            other_args=[
-                "--trust-remote-code",
-            ],
-        )
 
-        try:
+            process = popen_launch_server(
-            # Start recording
+                model_path,
-            response = requests.post(
+                DEFAULT_URL_FOR_TEST,
-                f"{DEFAULT_URL_FOR_TEST}/start_expert_distribution_record"
+                timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
-            )
+                other_args=[
-            self.assertEqual(response.status_code, 200)
+                    "--trust-remote-code",
-
+                    "--tp-size",
-            # Make some requests to generate expert distribution data
+                    str(tp_size),
-            response = requests.post(
+                    "--expert-distribution-recorder-mode",
-                f"{DEFAULT_URL_FOR_TEST}/generate",
+                    mode,
-                json={
+                    "--disable-cuda-graph",
-                    "text": "The capital of France is",
+                    "--disable-overlap-schedule",
-                    "sampling_params": {
+                ],
-                        "temperature": 0,
-                        "max_new_tokens": 32,
-                    },
-                },
-            )
-            self.assertEqual(response.status_code, 200)
-
-            # Stop recording
-            response = requests.post(
-                f"{DEFAULT_URL_FOR_TEST}/stop_expert_distribution_record"
-            )
-            self.assertEqual(response.status_code, 200)
-
-            # Dump the recorded data
-            response = requests.post(
-                f"{DEFAULT_URL_FOR_TEST}/dump_expert_distribution_record"
-            )
-            self.assertEqual(response.status_code, 200)
-
-            # Verify the dumped file exists and has correct format
-            csv_files = glob.glob("expert_distribution_*.csv")
-            self.assertEqual(
-                len(csv_files),
-                1,
-                f"Expected exactly one expert distribution CSV file {csv_files=}",
             )
 
-            # Check CSV file format
+            try:
-            with open(csv_files[0], "r") as f:
+                # Start recording
-                csv_reader = csv.reader(f)
+                response = requests.post(
-
+                    f"{DEFAULT_URL_FOR_TEST}/start_expert_distribution_record"
-                # Check header
-                header = next(csv_reader)
-                self.assertEqual(
-                    header,
-                    ["layer_id", "expert_id", "count"],
-                    "CSV header should be 'layer_id,expert_id,count'",
                 )
+                self.assertEqual(response.status_code, 200)
+
+                # Make some requests to generate expert distribution data
+                response = requests.post(
+                    f"{DEFAULT_URL_FOR_TEST}/generate",
+                    json={
+                        "text": "The capital of France is",
+                        "sampling_params": {
+                            "temperature": 0,
+                            "max_new_tokens": 32,
+                        },
+                    },
+                )
+                self.assertEqual(response.status_code, 200)
+
+                # Stop recording
+                response = requests.post(
+                    f"{DEFAULT_URL_FOR_TEST}/stop_expert_distribution_record"
+                )
+                self.assertEqual(response.status_code, 200)
+
+                # Dump the recorded data
+                response = requests.post(
+                    f"{DEFAULT_URL_FOR_TEST}/dump_expert_distribution_record"
+                )
+                self.assertEqual(response.status_code, 200)
 
                 # Check data rows
-                rows = list(csv_reader)
+                data = torch.load(
-                self.assertGreater(len(rows), 0, "CSV file should contain data rows")
+                    list(Path(tmp_dir).glob("*.pt"))[0], weights_only=True
+                )
+                print(f"{data=}")
 
-                for row in rows:
+                if mode in ["per_pass", "per_token"]:
-                    # Verify each row has 3 columns
+                    self.assertGreater(len(data), 0, "Should contain data rows")
-                    self.assertEqual(
+                else:
-                        len(row),
+                    logical_count = data["logical_count"]
-                        3,
+                    print(f"{logical_count.sum()=} {logical_count=}")
-                        "Each row should have layer_id, expert_id and count",
+                    self.assertTrue(logical_count.sum() > 0)
-                    )
 
-                    # Verify data types
+            finally:
-                    layer_id, expert_id, count = row
+                kill_process_tree(process.pid)
-                    self.assertTrue(
-                        layer_id.isdigit(),
-                        f"layer_id should be an integer {row=} {rows=}",
-                    )
-                    self.assertTrue(
-                        expert_id.isdigit(),
-                        f"expert_id should be an integer {row=} {rows=}",
-                    )
-                    self.assertTrue(
-                        count.isdigit(), f"count should be an integer {row=} {rows=}"
-                    )
-
-        finally:
-            kill_process_tree(process.pid)
 
 
 if __name__ == "__main__":