Multi-Stage Awake: Support Resume and Pause KV Cache and Weights separately (sgl-project#7099)

Author: hebiao064

python/pyproject.toml

@@ -98,7 +98,7 @@ srt_npu = ["sglang[runtime_common]", "outlines>=0.0.44,<=0.1.11"]
 openai = ["openai>=1.0", "tiktoken"]
 anthropic = ["anthropic>=0.20.0"]
 litellm = ["litellm>=1.0.0"]
-torch_memory_saver = ["torch_memory_saver>=0.0.4"]
+torch_memory_saver = ["torch_memory_saver>=0.0.8"]
 decord = ["decord"]
 test = [
     "accelerate",

python/sglang/srt/constants.py

@@ -0,0 +1,3 @@
+# GPU Memory Types
+GPU_MEMORY_TYPE_KV_CACHE = "kv_cache"
+GPU_MEMORY_TYPE_WEIGHTS = "weights"

python/sglang/srt/disaggregation/decode.py

@@ -31,6 +31,7 @@
 import torch
 from torch.distributed import ProcessGroup
 
+from sglang.srt.constants import GPU_MEMORY_TYPE_KV_CACHE
 from sglang.srt.disaggregation.base import BaseKVManager, BaseKVReceiver, KVPoll
 from sglang.srt.disaggregation.utils import (
     FAKE_BOOTSTRAP_HOST,
@@ -90,7 +91,7 @@ def __init__(
         self.max_context_len = max_context_len
         self.device = device
         self.pre_alloc_size = pre_alloc_size
-        with memory_saver_adapter.region():
+        with memory_saver_adapter.region(tag=GPU_MEMORY_TYPE_KV_CACHE):
             self.req_to_token = torch.zeros(
                 (size + pre_alloc_size, max_context_len),
                 dtype=torch.int32,

python/sglang/srt/entrypoints/engine.py

@@ -479,17 +479,15 @@ def get_weights_by_name(self, name: str, truncate_size: int = 100):
             self.tokenizer_manager.get_weights_by_name(obj, None)
         )
 
-    def release_memory_occupation(self):
-        """Release GPU occupation temporarily."""
-        obj = ReleaseMemoryOccupationReqInput()
+    def release_memory_occupation(self, tags: Optional[List[str]] = None):
+        obj = ReleaseMemoryOccupationReqInput(tags=tags)
         loop = asyncio.get_event_loop()
         return loop.run_until_complete(
             self.tokenizer_manager.release_memory_occupation(obj, None)
         )
 
-    def resume_memory_occupation(self):
-        """Resume GPU occupation."""
-        obj = ResumeMemoryOccupationReqInput()
+    def resume_memory_occupation(self, tags: Optional[List[str]] = None):
+        obj = ResumeMemoryOccupationReqInput(tags=tags)
         loop = asyncio.get_event_loop()
         return loop.run_until_complete(
             self.tokenizer_manager.resume_memory_occupation(obj, None)
@@ -670,11 +668,9 @@ def _launch_subprocesses(
 
     scheduler_procs = []
     if server_args.dp_size == 1:
-        # Launch tensor parallel scheduler processes
         memory_saver_adapter = TorchMemorySaverAdapter.create(
             enable=server_args.enable_memory_saver
         )
-
         scheduler_pipe_readers = []
 
         nnodes_per_tp_group = max(server_args.nnodes // server_args.pp_size, 1)
@@ -710,6 +706,7 @@ def _launch_subprocesses(
                         writer,
                     ),
                 )
+
                 with memory_saver_adapter.configure_subprocess():
                     proc.start()
                 scheduler_procs.append(proc)

python/sglang/srt/managers/io_struct.py

@@ -812,7 +812,9 @@ class GetWeightsByNameReqOutput:
 
 @dataclass
 class ReleaseMemoryOccupationReqInput:
-    pass
+    # Optional tags to identify the memory region, which is primarily used for RL
+    # Currently we only support `weights` and `kv_cache`
+    tags: Optional[List[str]] = None
 
 
 @dataclass
@@ -822,7 +824,9 @@ class ReleaseMemoryOccupationReqOutput:
 
 @dataclass
 class ResumeMemoryOccupationReqInput:
-    pass
+    # Optional tags to identify the memory region, which is primarily used for RL
+    # Currently we only support `weights` and `kv_cache`
+    tags: Optional[List[str]] = None
 
 
 @dataclass

python/sglang/srt/managers/scheduler.py

@@ -36,6 +36,7 @@
 
 from sglang.global_config import global_config
 from sglang.srt.configs.model_config import ModelConfig
+from sglang.srt.constants import GPU_MEMORY_TYPE_KV_CACHE, GPU_MEMORY_TYPE_WEIGHTS
 from sglang.srt.constrained.base_grammar_backend import (
     INVALID_GRAMMAR_OBJ,
     create_grammar_backend,
@@ -450,8 +451,6 @@ def __init__(
         t = threading.Thread(target=self.watchdog_thread, daemon=True)
         t.start()
         self.parent_process = psutil.Process().parent()
-
-        # Init memory saver
         self.memory_saver_adapter = TorchMemorySaverAdapter.create(
             enable=server_args.enable_memory_saver
         )
@@ -2227,23 +2226,40 @@ def get_weights_by_name(self, recv_req: GetWeightsByNameReqInput):
         return GetWeightsByNameReqOutput(parameter)
 
     def release_memory_occupation(self, recv_req: ReleaseMemoryOccupationReqInput):
-        self.memory_saver_adapter.check_validity(
-            caller_name="release_memory_occupation"
-        )
-        self.stashed_model_static_state = _export_static_state(
-            self.tp_worker.worker.model_runner.model
-        )
-        self.memory_saver_adapter.pause()
-        self.flush_cache()
+        tags = recv_req.tags
+        import subprocess
+
+        if tags is None:
+            tags = [GPU_MEMORY_TYPE_WEIGHTS, GPU_MEMORY_TYPE_KV_CACHE]
+
+        if GPU_MEMORY_TYPE_KV_CACHE in tags:
+            self.memory_saver_adapter.pause(GPU_MEMORY_TYPE_KV_CACHE)
+            self.flush_cache()
+
+        if GPU_MEMORY_TYPE_WEIGHTS in tags:
+            self.stashed_model_static_state = _export_static_state(
+                self.tp_worker.worker.model_runner.model
+            )
+            self.memory_saver_adapter.pause(GPU_MEMORY_TYPE_WEIGHTS)
+
         return ReleaseMemoryOccupationReqOutput()
 
     def resume_memory_occupation(self, recv_req: ResumeMemoryOccupationReqInput):
-        self.memory_saver_adapter.check_validity(caller_name="resume_memory_occupation")
-        self.memory_saver_adapter.resume()
-        _import_static_state(
-            self.tp_worker.worker.model_runner.model, self.stashed_model_static_state
-        )
-        del self.stashed_model_static_state
+        tags = recv_req.tags
+        if tags is None or len(tags) == 0:
+            tags = [GPU_MEMORY_TYPE_WEIGHTS, GPU_MEMORY_TYPE_KV_CACHE]
+
+        if GPU_MEMORY_TYPE_WEIGHTS in tags:
+            self.memory_saver_adapter.resume(GPU_MEMORY_TYPE_WEIGHTS)
+            _import_static_state(
+                self.tp_worker.worker.model_runner.model,
+                self.stashed_model_static_state,
+            )
+            del self.stashed_model_static_state
+
+        if GPU_MEMORY_TYPE_KV_CACHE in tags:
+            self.memory_saver_adapter.resume(GPU_MEMORY_TYPE_KV_CACHE)
+
         return ResumeMemoryOccupationReqOutput()
 
     def slow_down(self, recv_req: SlowDownReqInput):

python/sglang/srt/mem_cache/memory_pool.py

@@ -35,6 +35,7 @@
 import triton
 import triton.language as tl
 
+from sglang.srt.constants import GPU_MEMORY_TYPE_KV_CACHE
 from sglang.srt.layers.radix_attention import RadixAttention
 from sglang.srt.utils import debug_timing, get_bool_env_var, is_cuda, next_power_of_2
 
@@ -54,14 +55,15 @@ def __init__(
         device: str,
         enable_memory_saver: bool,
     ):
+
         memory_saver_adapter = TorchMemorySaverAdapter.create(
             enable=enable_memory_saver
         )
 
         self.size = size
         self.max_context_len = max_context_len
         self.device = device
-        with memory_saver_adapter.region():
+        with memory_saver_adapter.region(GPU_MEMORY_TYPE_KV_CACHE):
             self.req_to_token = torch.zeros(
                 (size, max_context_len), dtype=torch.int32, device=device
             )
@@ -292,7 +294,7 @@ def __init__(
         )
 
     def _create_buffers(self):
-        with self.memory_saver_adapter.region():
+        with self.memory_saver_adapter.region(GPU_MEMORY_TYPE_KV_CACHE):
             with (
                 torch.cuda.use_mem_pool(self.custom_mem_pool)
                 if self.enable_custom_mem_pool
@@ -610,7 +612,7 @@ def __init__(
         else:
             self.custom_mem_pool = None
 
-        with self.memory_saver_adapter.region():
+        with self.memory_saver_adapter.region(GPU_MEMORY_TYPE_KV_CACHE):
             with (
                 torch.cuda.use_mem_pool(self.custom_mem_pool)
                 if self.custom_mem_pool
@@ -753,7 +755,7 @@ def __init__(
             end_layer,
         )
 
-        with self.memory_saver_adapter.region():
+        with self.memory_saver_adapter.region(GPU_MEMORY_TYPE_KV_CACHE):
             # [size, head_num, head_dim] for each layer
             self.k_buffer = [
                 torch.zeros(

python/sglang/srt/model_executor/model_runner.py

@@ -30,6 +30,7 @@
 from sglang.srt.configs.device_config import DeviceConfig
 from sglang.srt.configs.load_config import LoadConfig
 from sglang.srt.configs.model_config import AttentionArch, ModelConfig
+from sglang.srt.constants import GPU_MEMORY_TYPE_WEIGHTS
 from sglang.srt.distributed import (
     get_tp_group,
     get_world_group,
@@ -222,6 +223,7 @@ def __init__(
 
     def initialize(self, min_per_gpu_memory: float):
         server_args = self.server_args
+
         self.memory_saver_adapter = TorchMemorySaverAdapter.create(
             enable=self.server_args.enable_memory_saver
         )
@@ -547,7 +549,7 @@ def load_model(self):
         monkey_patch_vllm_parallel_state()
         monkey_patch_isinstance_for_vllm_base_layer()
 
-        with self.memory_saver_adapter.region():
+        with self.memory_saver_adapter.region(GPU_MEMORY_TYPE_WEIGHTS):
             self.model = get_model(
                 model_config=self.model_config,
                 load_config=self.load_config,

python/sglang/srt/torch_memory_saver_adapter.py

@@ -1,11 +1,13 @@
 import logging
+import threading
+import time
 from abc import ABC
-from contextlib import contextmanager
+from contextlib import contextmanager, nullcontext
 
 try:
     import torch_memory_saver
 
-    _primary_memory_saver = torch_memory_saver.TorchMemorySaver()
+    _memory_saver = torch_memory_saver.torch_memory_saver
     import_error = None
 except ImportError as e:
     import_error = e
@@ -38,13 +40,13 @@ def check_validity(self, caller_name):
     def configure_subprocess(self):
         raise NotImplementedError
 
-    def region(self):
+    def region(self, tag: str):
         raise NotImplementedError
 
-    def pause(self):
+    def pause(self, tag: str):
         raise NotImplementedError
 
-    def resume(self):
+    def resume(self, tag: str):
         raise NotImplementedError
 
     @property
@@ -53,21 +55,23 @@ def enabled(self):
 
 
 class _TorchMemorySaverAdapterReal(TorchMemorySaverAdapter):
+    """Adapter for TorchMemorySaver with tag-based control"""
+
     def configure_subprocess(self):
         return torch_memory_saver.configure_subprocess()
 
-    def region(self):
-        return _primary_memory_saver.region()
+    def region(self, tag: str):
+        return _memory_saver.region(tag=tag)
 
-    def pause(self):
-        return _primary_memory_saver.pause()
+    def pause(self, tag: str):
+        return _memory_saver.pause(tag=tag)
 
-    def resume(self):
-        return _primary_memory_saver.resume()
+    def resume(self, tag: str):
+        return _memory_saver.resume(tag=tag)
 
     @property
     def enabled(self):
-        return _primary_memory_saver.enabled
+        return _memory_saver is not None and _memory_saver.enabled
 
 
 class _TorchMemorySaverAdapterNoop(TorchMemorySaverAdapter):
@@ -76,13 +80,13 @@ def configure_subprocess(self):
         yield
 
     @contextmanager
-    def region(self):
+    def region(self, tag: str):
         yield
 
-    def pause(self):
+    def pause(self, tag: str):
         pass
 
-    def resume(self):
+    def resume(self, tag: str):
         pass
 
     @property

python/sglang/test/test_utils.py

@@ -37,6 +37,7 @@
 # General test models
 DEFAULT_MODEL_NAME_FOR_TEST = "meta-llama/Llama-3.1-8B-Instruct"
 DEFAULT_SMALL_MODEL_NAME_FOR_TEST = "meta-llama/Llama-3.2-1B-Instruct"
+DEFAULT_SMALL_MODEL_NAME_FOR_TEST_BASE = "meta-llama/Llama-3.2-1B"
 DEFAULT_MOE_MODEL_NAME_FOR_TEST = "mistralai/Mixtral-8x7B-Instruct-v0.1"
 DEFAULT_SMALL_MOE_MODEL_NAME_FOR_TEST = "Qwen/Qwen1.5-MoE-A2.7B"