[diffusion] feat: measure warmup memory and layer usage per phase for residency calibration (1/4) (#37916)

Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>

python/sglang/multimodal_gen/configs/pipeline_configs/base.py

@@ -201,6 +201,9 @@ class PipelineConfig:
     native_only_components: ClassVar[tuple[str, ...]] = ()
     task_type: ModelTaskType = ModelTaskType.I2I
     skip_input_image_preprocess: bool = False
+    # False when changing component placement after a calibration request is
+    # known to alter the pipeline's numerical path.
+    supports_auto_residency: bool = True
     # Components that cannot fall back to a native Transformers/Diffusers
     # implementation because their pipeline requires SGLang-specific behavior.
     native_only_components: tuple[str, ...] = ()

python/sglang/multimodal_gen/configs/pipeline_configs/ltx_2.py

@@ -723,3 +723,6 @@ class LTX2PipelineConfig(PipelineConfig):
 @dataclasses.dataclass
 class LTX23PipelineConfig(LTX2PipelineConfig):
     """Configuration overrides for LTX-2.3."""
+
+    # original-mode lora swaps invalidate post-warmup timing calibration
+    supports_auto_residency: bool = False

python/sglang/multimodal_gen/configs/pipeline_configs/qwen_image.py

@@ -755,6 +755,8 @@ class QwenImageEditPlus_2511_PipelineConfig(QwenImageEditPlusPipelineConfig):
 class QwenImageLayeredPipelineConfig(QwenImageEditPipelineConfig):
     resolution: int = 640
     vae_precision: str = "bf16"
+    # promoting the auxiliary components regresses first-request latency
+    supports_auto_residency: bool = False
 
     def get_model_deployment_config(self) -> ModelDeploymentConfig:
         return ModelDeploymentConfig(

python/sglang/multimodal_gen/configs/pipeline_configs/sana_wm.py

@@ -56,6 +56,9 @@ class SanaWMPipelineConfig(PipelineConfig):
     optional 6-DoF camera trajectory, optional Stage-2 LTX-2 refiner)."""
 
     task_type: ModelTaskType = ModelTaskType.TI2V
+    # The current two-stage path is not numerically invariant when its primary
+    # transformer and connectors are promoted after warmup.
+    supports_auto_residency: bool = False
 
     # SanaWMBeforeDenoisingStage._splice_first_frame handles condition-image
     # resize + VAE-encode itself, so bypass the framework's generic TI2V

python/sglang/multimodal_gen/configs/sample/sampling_params.py

@@ -136,6 +136,58 @@ def _sanitize_filename(name: str, replacement: str = "_", max_length: int = 150)
     return ascii_name
 
 
+_SEQUENCE_SHARD_PIPELINE_FAMILIES = ("wan", "helios", "joy", "cosmos3")
+
+
+def resolve_sequence_shard(
+    pipeline_config: Any, enable_sequence_shard: bool | None
+) -> bool:
+    """Whether this pipeline shards the sequence dim instead of aligning frames.
+
+    Shared by ``SamplingParams._adjust_visual_fields`` and the synthetic
+    warmup builder so warmup requests follow the same frame contract as real
+    requests.
+    """
+    pipeline_name_lower = pipeline_config.__class__.__name__.lower()
+    return any(
+        family in pipeline_name_lower for family in _SEQUENCE_SHARD_PIPELINE_FAMILIES
+    ) and (enable_sequence_shard is None or enable_sequence_shard)
+
+
+def align_num_frames_for_num_gpus(
+    num_frames: int,
+    *,
+    num_gpus: int,
+    vae_config: Any,
+    round_down: bool,
+) -> int:
+    """Align the latent frame count to be divisible by ``num_gpus``."""
+    if num_gpus <= 1:
+        return num_frames
+    use_temporal_scaling_frames = vae_config.use_temporal_scaling_frames
+    temporal_scale_factor = vae_config.arch_config.temporal_compression_ratio
+
+    if use_temporal_scaling_frames:
+        orig_latent_num_frames = (num_frames - 1) // temporal_scale_factor + 1
+    else:
+        orig_latent_num_frames = num_frames
+
+    if orig_latent_num_frames % num_gpus == 0:
+        return num_frames
+
+    if round_down:
+        # Ensure we have at least 1 batch per GPU
+        new_latent_num_frames = max(1, (orig_latent_num_frames // num_gpus)) * num_gpus
+    else:
+        new_latent_num_frames = math.ceil(orig_latent_num_frames / num_gpus) * num_gpus
+
+    if use_temporal_scaling_frames:
+        # Convert back to frames, keeping num_frames-1 a multiple of the
+        # temporal scale factor
+        return (new_latent_num_frames - 1) * temporal_scale_factor + 1
+    return new_latent_num_frames
+
+
 class DataType(Enum):
     IMAGE = auto()
     VIDEO = auto()
@@ -792,14 +844,9 @@ class SamplingParams:
                     )
                     logger.warning(error_msg)
 
-        pipeline_name_lower = server_args.pipeline_config.__class__.__name__.lower()
-
-        if (
-            "wan" in pipeline_name_lower
-            or "helios" in pipeline_name_lower
-            or "joy" in pipeline_name_lower
-            or "cosmos3" in pipeline_name_lower
-        ) and (self.enable_sequence_shard is None or self.enable_sequence_shard):
+        if resolve_sequence_shard(
+            server_args.pipeline_config, self.enable_sequence_shard
+        ):
             self.enable_sequence_shard = True
             logger.debug("Automatically enabled enable_sequence_shard")
         else:
@@ -832,43 +879,13 @@ class SamplingParams:
             )
 
             if self.adjust_frames:
-                # Adjust number of frames based on number of GPUs for video task
-                use_temporal_scaling_frames = (
-                    pipeline_config.vae_config.use_temporal_scaling_frames
+                new_num_frames = align_num_frames_for_num_gpus(
+                    self.num_frames,
+                    num_gpus=server_args.num_gpus,
+                    vae_config=pipeline_config.vae_config,
+                    round_down=self.num_frames_round_down,
                 )
-                num_frames = self.num_frames
-                num_gpus = server_args.num_gpus
-                temporal_scale_factor = (
-                    pipeline_config.vae_config.arch_config.temporal_compression_ratio
-                )
-
-                if use_temporal_scaling_frames:
-                    orig_latent_num_frames = (
-                        num_frames - 1
-                    ) // temporal_scale_factor + 1
-                else:
-                    orig_latent_num_frames = num_frames
-
-                if orig_latent_num_frames % server_args.num_gpus != 0:
-                    # Adjust latent frames to be divisible by number of GPUs
-                    if self.num_frames_round_down:
-                        # Ensure we have at least 1 batch per GPU
-                        new_latent_num_frames = (
-                            max(1, (orig_latent_num_frames // num_gpus)) * num_gpus
-                        )
-                    else:
-                        new_latent_num_frames = (
-                            math.ceil(orig_latent_num_frames / num_gpus) * num_gpus
-                        )
-
-                    if use_temporal_scaling_frames:
-                        # Convert back to number of frames, ensuring num_frames-1 is a multiple of temporal_scale_factor
-                        new_num_frames = (
-                            new_latent_num_frames - 1
-                        ) * temporal_scale_factor + 1
-                    else:
-                        new_num_frames = new_latent_num_frames
-
+                if new_num_frames != self.num_frames:
                     logger.info(
                         "Adjusting number of frames from %s to %s based on number of GPUs (%s)",
                         self.num_frames,

python/sglang/multimodal_gen/envs.py

@@ -37,6 +37,7 @@ if TYPE_CHECKING:
     SGLANG_DIFFUSION_TEST_FORCE_HOST_AVAILABLE_GIB: float | None = None
     SGLANG_DIFFUSION_TEST_CAP_DEVICE_MEMORY_GIB: float | None = None
     SGLANG_DIFFUSION_STAGE_LOGGING: bool = False
+    SGLANG_DIFFUSION_DISABLE_AUTO_RESIDENCY: bool = False
     SGLANG_DIFFUSION_MINIMAX_H3_ADALN_GPU_PLANS: int = 64
     SGLANG_DIFFUSION_MINIMAX_H3_ADALN_FP32: bool = False
     SGLANG_DIFFUSION_CFG_GATE_STEP: float = 1.0
@@ -265,6 +266,12 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # If set, sgl_diffusion will enable stage logging, which will print the time
     # taken for each stage
     "SGLANG_DIFFUSION_STAGE_LOGGING": _lazy_bool("SGLANG_DIFFUSION_STAGE_LOGGING"),
+    # Kill-switch for the warmup-calibrated auto residency promotion that runs
+    # under `--performance-mode auto` with server warmup. Set to disable the
+    # promotion without giving up the rest of the auto performance policy.
+    "SGLANG_DIFFUSION_DISABLE_AUTO_RESIDENCY": _lazy_bool(
+        "SGLANG_DIFFUSION_DISABLE_AUTO_RESIDENCY"
+    ),
     # Plan slots in the MiniMax-H3 --minimax-h3-adaln-online GPU slab
     # (9.25 MiB per slot-timestep; 64 x width 4 = 2.31 GiB). A request needs
     # up to num_inference_steps - 1 slots; the default covers the 50-step

python/sglang/multimodal_gen/runtime/distributed/device_communicators/ipc_a2a.py

@@ -140,6 +140,18 @@ class IpcA2AState:
         """Drop mappings that belong to a model-parallel group being replaced."""
         self.__init__()
 
+    def drop_staging(self) -> None:
+        """Release the cached staging buffers (both ranks call this at the same point).
+
+        Staging is keyed by message size and only evicted by count, so a warmup
+        probe at the full serving shape leaves buffers sized for it behind.
+        """
+        if not self.staging:
+            return
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()
+        self.staging.clear()
+
     def _share(self, t, group):
         """Exchange `t` with the peer via torch IPC, re-opening the handle in
         the LOCAL device context (the mapping is only dereferenceable from the

python/sglang/multimodal_gen/runtime/entrypoints/control_requests.py

@@ -0,0 +1,50 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Control-request protocol between the HTTP process and scheduler workers.
+
+These types are cross-process IPC contracts, not utilities: the HTTP side
+constructs them (scheduler_client treats them as ``_CONTROL_REQ_TYPES`` and
+fans them out to every replica) and each scheduler dispatches them through
+``Scheduler.request_handlers``. Keep this module import-light -- both
+processes import it, and the HTTP process must not drag in torch-heavy
+worker modules through it.
+"""
+
+from typing import List, Optional, Union
+
+import msgspec
+
+
+class SetLoraReq(msgspec.Struct):
+    lora_nickname: Union[str, List[str]]
+    lora_path: Optional[Union[str, List[Optional[str]]]] = None
+    target: Union[str, List[str]] = "all"
+    strength: Union[float, List[float]] = 1.0
+    merge_mode: Optional[str] = None
+    lora_alpha: Optional[Union[int, List[Optional[int]]]] = None
+
+
+class MergeLoraWeightsReq(msgspec.Struct):
+    target: str = "all"
+    strength: float = 1.0
+
+
+class UnmergeLoraWeightsReq(msgspec.Struct):
+    target: str = "all"
+
+
+class ListLorasReq(msgspec.Struct):
+    pass
+
+
+class ShutdownReq(msgspec.Struct):
+    pass
+
+
+class ReleaseRealtimeSessionReq(msgspec.Struct):
+    session_id: str
+
+
+class GetDisaggStatsReq(msgspec.Struct):
+    """Request to get disagg pipeline metrics from the scheduler."""
+
+    pass

python/sglang/multimodal_gen/runtime/entrypoints/diffusion_generator.py

@@ -19,13 +19,15 @@ from sglang.multimodal_gen.configs.sample.sampling_params import (
     DataType,
     SamplingParams,
 )
-from sglang.multimodal_gen.runtime.entrypoints.utils import (
-    GenerationResult,
+from sglang.multimodal_gen.runtime.entrypoints.control_requests import (
     ListLorasReq,
     MergeLoraWeightsReq,
     SetLoraReq,
     ShutdownReq,
     UnmergeLoraWeightsReq,
+)
+from sglang.multimodal_gen.runtime.entrypoints.utils import (
+    GenerationResult,
     expand_request_outputs,
     format_lora_message,
     prepare_request,

python/sglang/multimodal_gen/runtime/entrypoints/http_server.py

@@ -264,7 +264,9 @@ async def stats_endpoint(request: Request):
     Returns queue depth, request counts, latency, throughput, etc.
     Sends a GetDisaggStatsReq to the scheduler via ZMQ and returns the result.
     """
-    from sglang.multimodal_gen.runtime.entrypoints.utils import GetDisaggStatsReq
+    from sglang.multimodal_gen.runtime.entrypoints.control_requests import (
+        GetDisaggStatsReq,
+    )
 
     server_args: ServerArgs = request.app.state.server_args
     response: dict = {

python/sglang/multimodal_gen/runtime/entrypoints/openai/common_api.py

@@ -5,11 +5,13 @@ from fastapi import APIRouter, Body, HTTPException
 from pydantic import BaseModel, Field
 
 from sglang.multimodal_gen.registry import get_model_info
-from sglang.multimodal_gen.runtime.entrypoints.utils import (
+from sglang.multimodal_gen.runtime.entrypoints.control_requests import (
     ListLorasReq,
     MergeLoraWeightsReq,
     SetLoraReq,
     UnmergeLoraWeightsReq,
+)
+from sglang.multimodal_gen.runtime.entrypoints.utils import (
     format_lora_message,
 )
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import OutputBatch

python/sglang/multimodal_gen/runtime/entrypoints/openai/realtime/realtime_video_api.py

@@ -8,6 +8,9 @@ from typing import TYPE_CHECKING
 import msgspec.msgpack
 from fastapi import APIRouter, WebSocket, WebSocketDisconnect
 
+from sglang.multimodal_gen.runtime.entrypoints.control_requests import (
+    ReleaseRealtimeSessionReq,
+)
 from sglang.multimodal_gen.runtime.entrypoints.openai.protocol import (
     RealtimeEvent,
     RealtimeVideoGenerationsRequest,
@@ -28,9 +31,6 @@ from sglang.multimodal_gen.runtime.entrypoints.openai.realtime.timer import (
 from sglang.multimodal_gen.runtime.entrypoints.openai.utils import (
     process_generation_batch,
 )
-from sglang.multimodal_gen.runtime.entrypoints.utils import (
-    ReleaseRealtimeSessionReq,
-)
 from sglang.multimodal_gen.runtime.scheduler_client import async_scheduler_client
 from sglang.multimodal_gen.runtime.server_args import get_global_server_args
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger

python/sglang/multimodal_gen/runtime/entrypoints/openai/realtime/registry.py

@@ -4,11 +4,10 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING
 
-from sglang.multimodal_gen.runtime.entrypoints.openai.realtime.realtime_adapter import (
-    BaseRealtimeModelAdapter,
-)
-
 if TYPE_CHECKING:
+    from sglang.multimodal_gen.runtime.entrypoints.openai.realtime.realtime_adapter import (
+        BaseRealtimeModelAdapter,
+    )
     from sglang.multimodal_gen.runtime.server_args import ServerArgs
 
 
@@ -67,3 +66,12 @@ def get_realtime_model_adapter(
         "Realtime video is not supported for pipeline config "
         f"{type(pipeline_config).__name__}; no realtime adapter is registered."
     )
+
+
+def has_realtime_model_adapter(server_args: ServerArgs) -> bool:
+    """Whether the resolved pipeline config has a registered adapter."""
+    _register_builtin_realtime_model_adapters()
+    return any(
+        config_cls in _REALTIME_ADAPTER_REGISTRY
+        for config_cls in type(server_args.pipeline_config).__mro__
+    )

python/sglang/multimodal_gen/runtime/entrypoints/openai/utils.py

@@ -18,12 +18,14 @@ from sglang.multimodal_gen.configs.sample.sampling_params import (
     DataType,
     SamplingParams,
 )
-from sglang.multimodal_gen.runtime.entrypoints.utils import (
+from sglang.multimodal_gen.runtime.entrypoints.control_requests import (
     ListLorasReq,
     MergeLoraWeightsReq,
     SetLoraReq,
     ShutdownReq,
     UnmergeLoraWeightsReq,
+)
+from sglang.multimodal_gen.runtime.entrypoints.utils import (
     format_lora_message,
     save_outputs,
 )

python/sglang/multimodal_gen/runtime/entrypoints/utils.py

@@ -160,49 +160,6 @@ def _close_cached_cuda_video_buffer() -> None:
 atexit.register(_close_cached_cuda_video_buffer)
 
 
-@dataclass
-class SetLoraReq:
-    lora_nickname: Union[str, List[str]]
-    lora_path: Optional[Union[str, List[Optional[str]]]] = None
-    target: Union[str, List[str]] = "all"
-    strength: Union[float, List[float]] = 1.0
-    merge_mode: Optional[str] = None
-    lora_alpha: Optional[Union[int, List[Optional[int]]]] = None
-
-
-@dataclass
-class MergeLoraWeightsReq:
-    target: str = "all"
-    strength: float = 1.0
-
-
-@dataclass
-class UnmergeLoraWeightsReq:
-    target: str = "all"
-
-
-@dataclass
-class ListLorasReq:
-    pass
-
-
-@dataclass
-class ShutdownReq:
-    pass
-
-
-@dataclass
-class ReleaseRealtimeSessionReq:
-    session_id: str
-
-
-@dataclass
-class GetDisaggStatsReq:
-    """Request to get disagg pipeline metrics from the scheduler."""
-
-    pass
-
-
 def format_lora_message(
     lora_nickname: Union[str, List[str]],
     target: Union[str, List[str]],

python/sglang/multimodal_gen/runtime/launch_server.py

@@ -12,8 +12,8 @@ from sglang.multimodal_gen.runtime.disaggregation.orchestrator import (
     DiffusionServer,
 )
 from sglang.multimodal_gen.runtime.disaggregation.roles import RoleType
+from sglang.multimodal_gen.runtime.entrypoints.control_requests import ShutdownReq
 from sglang.multimodal_gen.runtime.entrypoints.http_server import create_app
-from sglang.multimodal_gen.runtime.entrypoints.utils import ShutdownReq
 from sglang.multimodal_gen.runtime.managers.gpu_worker import run_scheduler_process
 from sglang.multimodal_gen.runtime.scheduler_client import SchedulerClient
 from sglang.multimodal_gen.runtime.server_args import (

python/sglang/multimodal_gen/runtime/layers/usp.py

@@ -41,6 +41,20 @@ def _maybe_wait(tensor: torch.Tensor) -> torch.Tensor:
 _A2A_STAGING_BUFFERS: dict[tuple[str, torch.dtype, int], torch.Tensor] = {}
 
 
+def drop_a2a_staging_buffers() -> None:
+    """Release the cached all-to-all staging buffers on this rank.
+
+    The cache only ever grows to the largest message seen, so a warmup probe
+    at the full serving shape leaves buffers sized for it behind; the caller
+    releases them at a point every rank reaches together.
+    """
+    if not _A2A_STAGING_BUFFERS:
+        return
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
+    _A2A_STAGING_BUFFERS.clear()
+
+
 def _a2a_staging_buffer(
     role: str, shape: tuple[int, ...], dtype: torch.dtype, device: torch.device
 ) -> torch.Tensor:

python/sglang/multimodal_gen/runtime/managers/gpu_worker.py

@@ -15,6 +15,8 @@ import numpy as np
 import torch
 from setproctitle import setproctitle
 
+from sglang.multimodal_gen.runtime.warmup_request_builder import lighten_warmup_req
+
 from sglang.multimodal_gen.runtime.utils.logging_utils import (  # isort: skip
     globally_suppress_loggers,
 )
@@ -49,7 +51,17 @@ from sglang.multimodal_gen.runtime.entrypoints.utils import (
     post_process_sample,
     save_outputs,
 )
+from sglang.multimodal_gen.runtime.managers.memory_managers.auto_residency import (
+    DefaultWorkload,
+    WarmupMemoryRecord,
+    estimate_default_workload_peak_bytes,
+    resolve_default_workload,
+)
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
+    peek_global_component_residency_manager,
+)
 from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseUsageTracker,
     configure_layerwise_offload_modules,
 )
 from sglang.multimodal_gen.runtime.managers.memory_managers.memory_occupation_controller import (
@@ -93,14 +105,6 @@ from sglang.srt.utils.network import NetworkAddress
 
 logger = init_logger(__name__)
 
-OFFLOAD_DISABLE_RECOMMENDATION_ORDER = (
-    "vae",
-    "image_encoder",
-    "text_encoder",
-    "text_encoder_2",
-    "transformer",
-)
-
 
 @dataclass
 class _ExpandedOutputParts:
@@ -129,6 +133,64 @@ def _worker_cpu_intra_op_threads(num_gpus: int) -> int | None:
     return max(1, min(16, cpu_count // max(1, num_gpus)))
 
 
+OFFLOAD_DISABLE_RECOMMENDATION_ORDER = (
+    "vae",
+    "image_encoder",
+    "text_encoder",
+    "text_encoder_2",
+    "transformer",
+)
+
+
+PROBE_FIT_MIN_MARGIN_BYTES = 1 << 30
+
+
+def _shape_label(req: Req) -> str:
+    return f"{req.width}x{req.height}x{req.num_frames or 1}f"
+
+
+def fit_auto_residency_probe(
+    req: Req,
+    *,
+    records: list[WarmupMemoryRecord],
+    free_bytes: int,
+    total_bytes: int,
+    server_args: ServerArgs,
+) -> tuple[Req, int | None, int]:
+    """Shrink a full-shape probe until its extrapolated peak fits the memory left.
+
+    The probe measures the default workload under the load-safe placement, so
+    a probe the card cannot hold would only be found out by running out of
+    memory. The bounded warmup that runs before it gives one measurement to
+    extrapolate from; while that extrapolation exceeds free memory minus the
+    reserve, frames go first and then area, the ladder the OOM retry walks.
+    Returns the fitted request, its estimate and the number of shrink steps.
+    """
+    # Only the probe has to fit, so the margin is allocator slack, not the
+    # planner's placement reserve (which held back 4 GiB of a 32 GiB card and
+    # shrank a probe that had 10 GiB to spare).
+    budget = free_bytes - max(PROBE_FIT_MIN_MARGIN_BYTES, total_bytes // 50)
+    # The bounded warmup already ran at the smallest measured shape; a probe
+    # below it measures nothing new and degenerate shapes fail inside models.
+    floor_units = min((record.workload_units() for record in records), default=0)
+    fitted, steps = req, 0
+    while True:
+        units = (
+            max(1, int(fitted.width or 1))
+            * max(1, int(fitted.height or 1))
+            * max(1, int(fitted.num_frames or 1))
+        )
+        estimate = estimate_default_workload_peak_bytes(
+            records=records, target_units=units
+        )
+        if estimate is None or estimate <= budget or units <= floor_units:
+            return fitted, estimate, steps
+        lighter = lighten_warmup_req(server_args, fitted)
+        if lighter is None:
+            return fitted, estimate, steps
+        fitted, steps = lighter, steps + 1
+
+
 class GPUWorker(GPUWorkerPostTrainingMixin):
     """
     A worker that executes the model on a single GPU.
@@ -163,6 +225,10 @@ class GPUWorker(GPUWorkerPostTrainingMixin):
             load_snapshot.peak_allocated_mb if load_snapshot is not None else 0.0
         )
         self._runtime_peak_reserved_mb = 0.0
+        # Warmup probes run the default workload's full shape and may exceed any
+        # serving request; keep their peak out of the runtime figure.
+        self._warmup_peak_reserved_mb = 0.0
+        self._release_warmup_pool_before_serving = False
         self._runtime_peak_allocated_mb = 0.0
         self.sp_group = get_sp_group()
         self.sp_cpu_group = self.sp_group.cpu_group
@@ -173,6 +239,26 @@ class GPUWorker(GPUWorkerPostTrainingMixin):
         self.cfg_cpu_group = self.cfg_group.cpu_group
         self._realtime_sessions = RealtimeSessionCache(max_sessions=1)
         self.memory_occupation: MemoryOccupationController | None = None
+        # per-rank memory measurements of server warmup forwards; consumed by
+        # the auto-residency placement decision before the server turns ready
+        self._auto_residency_warmup_records: list[WarmupMemoryRecord] = []
+        # default workload resolved once for the per-request residency hint
+        self._cached_default_workload: DefaultWorkload | None = None
+        self._cached_default_workload_failed = False
+
+    def _default_workload_for_hint(self) -> DefaultWorkload | None:
+        if (
+            self._cached_default_workload is None
+            and not self._cached_default_workload_failed
+        ):
+            try:
+                self._cached_default_workload = resolve_default_workload(
+                    self.server_args
+                )
+            except Exception:
+                logger.debug("Default workload unresolvable", exc_info=True)
+                self._cached_default_workload_failed = True
+        return self._cached_default_workload
 
     def release_realtime_session(self, session_id: str) -> OutputBatch:
         """release the session of a realtime connection"""
@@ -364,24 +450,37 @@ class GPUWorker(GPUWorkerPostTrainingMixin):
         remaining_gpu_mem_gb = (
             current_platform.get_device_total_memory() / (1024**3) - peak_reserved_gb
         )
-        can_stay_resident = self.get_can_stay_resident_components(remaining_gpu_mem_gb)
+        try:
+            can_stay_resident = self.get_can_stay_resident_components(
+                remaining_gpu_mem_gb
+            )
+        except Exception:
+            # a debug-only hint must never fail a completed request
+            logger.debug("Residency hint unavailable", exc_info=True)
+            can_stay_resident = []
 
         pool_overhead_gb = peak_reserved_gb - peak_allocated_gb
         pool_overhead_pct = (
             pool_overhead_gb / peak_reserved_gb * 100 if peak_reserved_gb else 0.0
         )
 
+        residency_hint = (
+            f" Components that can remain on GPU: {can_stay_resident}. "
+            "Make it explicit with --component-residency <name>=resident; "
+            "--performance-mode auto with server warmup applies safe "
+            "adjustments automatically."
+            if can_stay_resident
+            else ""
+        )
         logger.debug(
             "GPU memory: peak=%.2f GB, allocated=%.2f GB, pool=%.2f GB (%.1f%%), "
-            "headroom=%.2f GB. Components that can remain on GPU: %s. "
-            "Adjust --cpu-offload-components or --layerwise-offload-components "
-            "to change residency.",
+            "headroom=%.2f GB.%s",
             peak_reserved_gb,
             peak_allocated_gb,
             pool_overhead_gb,
             pool_overhead_pct,
             remaining_gpu_mem_gb,
-            can_stay_resident,
+            residency_hint,
         )
 
     def execute_forward(
@@ -409,6 +508,8 @@ class GPUWorker(GPUWorkerPostTrainingMixin):
             return self._execute_forward_batch(batch)
 
         req = batch[0]
+        if req.is_warmup and req.extra.get("auto_residency_full_shape_probe"):
+            self._fit_auto_residency_probe(req)
         return self._execute_forward_common(
             req,
             forward_fn=lambda: self.pipeline.forward(req, self.server_args),
@@ -502,9 +603,57 @@ class GPUWorker(GPUWorkerPostTrainingMixin):
         """
         output_batch = None
         forward_failed = False
+        # Prewarm reqs (is_warmup=False) run a different offload layout and
+        # must not contaminate the calibration records. Pipelines that cannot
+        # apply a residency plan also skip the temporary per-layer hooks.
+        measure_server_warmup = (
+            req.is_warmup
+            and bool(req.extra.get("server_based_warmup"))
+            and self.server_args.pipeline_config.supports_auto_residency
+            and current_platform.is_cuda()
+        )
+        warmup_workload = (
+            (
+                int(req.width or 0),
+                int(req.height or 0),
+                int(req.num_frames or 1),
+                max(1, int(req.num_inference_steps or 1)),
+            )
+            if measure_server_warmup
+            else None
+        )
+        warmup_baseline_allocated_bytes = 0
+        layerwise_usage_tracker: LayerwiseUsageTracker | None = None
+        layerwise_layer_uses_by_stage: dict[
+            str, dict[str, dict[str, tuple[int, ...]]]
+        ] = {}
         try:
+            if measure_server_warmup:
+                # Drop the previous request's allocator pool so each probe
+                # starts from the same placement and can return released
+                # component storage before its allocated peak is measured.
+                torch.get_device_module().empty_cache()
+            self._release_warmup_pool(req)
             if not current_platform.is_cpu() and not current_platform.is_mps():
                 torch.get_device_module().reset_peak_memory_stats()
+            if measure_server_warmup:
+                warmup_baseline_allocated_bytes = (
+                    torch.get_device_module().memory_allocated()
+                )
+                if (
+                    self.server_args.performance_mode == "auto"
+                    and self.pipeline is not None
+                ):
+                    layerwise_usage_tracker = LayerwiseUsageTracker(
+                        self.pipeline.modules,
+                        stage_name_provider=(
+                            lambda: (
+                                req.metrics.active_stage_name
+                                if req.metrics is not None
+                                else None
+                            )
+                        ),
+                    )
 
             start_time = (
                 execution_start_time
@@ -559,7 +708,7 @@ class GPUWorker(GPUWorkerPostTrainingMixin):
             req_label = req.request_id[:8] if req.request_id else "unnamed"
             with maybe_record_function(f"SAVE_OUTPUTS {req_label}"):
                 self._materialize_output_transport(output_batch, req, save_output_paths)
-            self._record_output_peak_memory(output_batch)
+            self._record_output_peak_memory(output_batch, is_warmup=req.is_warmup)
 
             collect_perf = (
                 req.perf_dump_path is not None or envs.SGLANG_DIFFUSION_STAGE_LOGGING
@@ -615,12 +764,115 @@ class GPUWorker(GPUWorkerPostTrainingMixin):
             if output_batch is None:
                 output_batch = OutputBatch()
             output_batch.error = f"Error executing {error_context}: {e}"
-            self._record_output_peak_memory(output_batch)
+            self._record_output_peak_memory(output_batch, is_warmup=req.is_warmup)
             # clean cache if OOM
             if not current_platform.is_cpu():
                 torch.get_device_module().empty_cache()
+        finally:
+            # also runs on the propagate_forward_errors re-raise: a warmup
+            # forward that never completed must still leave a failed record,
+            # or the estimator would plan from the remaining partial data
+            if measure_server_warmup:
+                assert warmup_workload is not None
+                if layerwise_usage_tracker is not None:
+                    (
+                        layerwise_layer_uses,
+                        layerwise_layer_uses_by_stage,
+                    ) = layerwise_usage_tracker.finish_with_stages()
+                else:
+                    layerwise_layer_uses = {}
+                self._record_server_warmup_memory(
+                    req=req,
+                    workload=warmup_workload,
+                    baseline_allocated_bytes=warmup_baseline_allocated_bytes,
+                    succeeded=output_batch is not None and output_batch.error is None,
+                    layerwise_layer_uses=layerwise_layer_uses,
+                    layerwise_layer_uses_by_stage=layerwise_layer_uses_by_stage,
+                )
         return output_batch
 
+    def _record_server_warmup_memory(
+        self,
+        *,
+        req: Req,
+        workload: tuple[int, int, int, int],
+        baseline_allocated_bytes: int,
+        succeeded: bool,
+        layerwise_layer_uses: dict[str, dict[str, tuple[int, ...]]] | None = None,
+        layerwise_layer_uses_by_stage: (
+            dict[str, dict[str, dict[str, tuple[int, ...]]]] | None
+        ) = None,
+    ) -> None:
+        phase_allocated_peaks: dict[str, int] = {}
+        phase_components: dict[str, tuple[str, ...]] = {}
+        phase_used_components: dict[str, tuple[str, ...]] = {}
+        phase_full_weight_transition_components: dict[str, tuple[str, ...]] = {}
+        untracked_active_components: tuple[str, ...] = ()
+        residency_manager = peek_global_component_residency_manager()
+        if residency_manager is not None:
+            for phase_name, peak in residency_manager.take_warmup_phase_peaks().items():
+                phase_allocated_peaks[phase_name] = peak.allocated_bytes
+                phase_components[phase_name] = peak.active_components
+                phase_used_components[phase_name] = peak.used_components
+                phase_full_weight_transition_components[phase_name] = (
+                    peak.full_weight_transition_components
+                )
+            untracked_active_components = residency_manager.current_device_components()
+        request_allocated_peak = max(
+            int(torch.get_device_module().max_memory_allocated()),
+            max(phase_allocated_peaks.values(), default=0),
+        )
+        if request_allocated_peak > max(phase_allocated_peaks.values(), default=0):
+            # Work after the residency-managed stage timeline (for example,
+            # output materialization) must remain a placement constraint.
+            # A reserved-only increase is not a second live placement. Record
+            # it separately so post-placement validation can still require
+            # allocator headroom without charging cache to candidate deltas.
+            phase_allocated_peaks["request:untracked"] = request_allocated_peak
+            phase_components["request:untracked"] = untracked_active_components
+            phase_used_components["request:untracked"] = ()
+            phase_full_weight_transition_components["request:untracked"] = ()
+        metrics = req.metrics
+        width, height, num_frames, num_inference_steps = workload
+        self._auto_residency_warmup_records.append(
+            WarmupMemoryRecord(
+                width=width,
+                height=height,
+                num_frames=num_frames,
+                baseline_allocated_bytes=int(baseline_allocated_bytes),
+                peak_allocated_bytes=request_allocated_peak,
+                succeeded=succeeded,
+                peak_reserved_bytes=int(
+                    torch.get_device_module().max_memory_reserved()
+                ),
+                phase_peak_allocated_bytes=phase_allocated_peaks,
+                phase_active_components=phase_components,
+                phase_used_components=phase_used_components,
+                phase_full_weight_transition_components=(
+                    phase_full_weight_transition_components
+                ),
+                layerwise_layer_uses=layerwise_layer_uses or {},
+                layerwise_layer_uses_by_stage=layerwise_layer_uses_by_stage or {},
+                num_inference_steps=num_inference_steps,
+                total_duration_ms=(
+                    float(metrics.total_duration_ms) if metrics is not None else 0.0
+                ),
+                stage_duration_ms=(dict(metrics.stages) if metrics is not None else {}),
+                step_duration_ms=(tuple(metrics.steps) if metrics is not None else ()),
+                step_duration_ms_by_stage=(
+                    {
+                        stage_name: tuple(durations)
+                        for stage_name, durations in metrics.steps_by_stage.items()
+                    }
+                    if metrics is not None
+                    else {}
+                ),
+                stage_iterations=(
+                    dict(metrics.stage_iterations) if metrics is not None else {}
+                ),
+            )
+        )
+
     def _materialize_output_transport(
         self,
         output_batch: OutputBatch,
@@ -733,16 +985,100 @@ class GPUWorker(GPUWorkerPostTrainingMixin):
         )
         return np.asarray(materialized.frames)
 
-    def _record_output_peak_memory(self, output_batch: OutputBatch) -> None:
+    def _fit_auto_residency_probe(self, req: Req) -> None:
+        """Size the full-shape probe to what the card has left, on every rank alike."""
+        records = [r for r in self._auto_residency_warmup_records if r.succeeded]
+        if not records or not current_platform.is_cuda():
+            return
+        device = current_platform.get_device(self.local_rank)
+        free_bytes = int(
+            current_platform.get_available_gpu_memory(empty_cache=True) * (1 << 30)
+        )
+        total_bytes = int(torch.cuda.get_device_properties(device).total_memory)
+        _, _, steps = fit_auto_residency_probe(
+            req,
+            records=records,
+            free_bytes=free_bytes,
+            total_bytes=total_bytes,
+            server_args=self.server_args,
+        )
+        requested_units = (
+            max(1, int(req.width or 1))
+            * max(1, int(req.height or 1))
+            * max(1, int(req.num_frames or 1))
+        )
+        estimate = estimate_default_workload_peak_bytes(
+            records=records, target_units=requested_units
+        )
+        # Ranks see different free memory and hold different records; the
+        # forward must run one shape everywhere, so the most cautious rank wins.
+        agreed = torch.tensor([steps], dtype=torch.int64, device=device)
+        agreed = get_replica_group().all_reduce(
+            agreed, op=torch.distributed.ReduceOp.MAX
+        )
+        steps = int(agreed.item())
+        if steps == 0:
+            return
+        fitted = req
+        for _ in range(steps):
+            lighter = lighten_warmup_req(self.server_args, fitted)
+            if lighter is None:
+                break
+            fitted = lighter
+        if self.is_output_rank:
+            logger.warning(
+                "Auto residency probe %s would not fit: extrapolated peak %.1f GiB "
+                "against %.1f GiB free; probing at %s instead",
+                _shape_label(req),
+                (estimate or 0) / (1 << 30),
+                free_bytes / (1 << 30),
+                _shape_label(fitted),
+            )
+        req.sampling_params = fitted.sampling_params
+
+    def _release_warmup_pool(self, req: Req) -> None:
+        """Drop what the full-shape probe left behind before the next request.
+
+        The probe runs a shape serving may never see. Its cached allocator
+        blocks would become the floor of every runtime peak measurement, and
+        the all-to-all staging buffers it created (IPC and Ulysses) stay
+        allocated at its message size. The request after the probe (the
+        bounded re-warm) regrows all of them at a serving-sized shape.
+        """
+        if req.is_warmup and req.extra.get("auto_residency_full_shape_probe"):
+            self._release_warmup_pool_before_serving = True
+            return
+        if not self._release_warmup_pool_before_serving:
+            return
+        self._release_warmup_pool_before_serving = False
+        if current_platform.is_cpu() or current_platform.is_mps():
+            return
+        from sglang.multimodal_gen.runtime.distributed.device_communicators.ipc_a2a import (
+            IPC_A2A,
+        )
+        from sglang.multimodal_gen.runtime.layers.usp import drop_a2a_staging_buffers
+
+        IPC_A2A.drop_staging()
+        drop_a2a_staging_buffers()
+        torch.get_device_module().empty_cache()
+
+    def _record_output_peak_memory(
+        self, output_batch: OutputBatch, *, is_warmup: bool = False
+    ) -> None:
         if current_platform.is_cpu():
             return
         snapshot = capture_memory_snapshot()
-        self._runtime_peak_reserved_mb = max(
-            self._runtime_peak_reserved_mb, snapshot.peak_reserved_mb
-        )
-        self._runtime_peak_allocated_mb = max(
-            self._runtime_peak_allocated_mb, snapshot.peak_allocated_mb
-        )
+        if is_warmup:
+            self._warmup_peak_reserved_mb = max(
+                self._warmup_peak_reserved_mb, snapshot.peak_reserved_mb
+            )
+        else:
+            self._runtime_peak_reserved_mb = max(
+                self._runtime_peak_reserved_mb, snapshot.peak_reserved_mb
+            )
+            self._runtime_peak_allocated_mb = max(
+                self._runtime_peak_allocated_mb, snapshot.peak_allocated_mb
+            )
         if self.is_output_rank:
             output_batch.peak_memory_mb = snapshot.peak_reserved_mb
 
@@ -755,6 +1091,7 @@ class GPUWorker(GPUWorkerPostTrainingMixin):
             [
                 self._load_peak_reserved_mb,
                 self._runtime_peak_reserved_mb,
+                self._warmup_peak_reserved_mb,
                 self._load_peak_allocated_mb,
                 self._runtime_peak_allocated_mb,
             ],
@@ -769,6 +1106,7 @@ class GPUWorker(GPUWorkerPostTrainingMixin):
         (
             load_peak_mb,
             runtime_peak_mb,
+            warmup_peak_mb,
             load_peak_allocated_mb,
             runtime_peak_allocated_mb,
         ) = peaks.tolist()
@@ -789,6 +1127,9 @@ class GPUWorker(GPUWorkerPostTrainingMixin):
                     peak_allocated_mb=runtime_peak_allocated_mb,
                 ),
             )
+            metrics.record_memory_snapshot(
+                "warmup_peak", replace(snapshot, peak_reserved_mb=warmup_peak_mb)
+            )
 
     def _forward_group(self, batch: list[Req]) -> OutputBatch:
         assert self.pipeline is not None

python/sglang/multimodal_gen/runtime/managers/memory_managers/auto_residency.py

@@ -0,0 +1,690 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Warmup-calibrated automatic component residency placement.
+
+Under ``--performance-mode auto`` with server warmup, each rank measures the
+peak GPU memory of bounded synthetic warmup requests and a low-step probe at
+the complete default serving shape, then selects a complete serving placement
+for every eligible component under the measured memory constraints.
+
+When no full-shape measurement is available, the fallback estimate splits the
+measured peak into persistent weights and workload-scaled activations. Scaling
+the whole peak would multiply resident weights by the video frame/area cap
+ratio (~16x for Wan-class defaults) and residency adjustment would never trigger.
+
+The planner targets the model default workload only (default resolution,
+default frames, batch=1). Larger shapes, batches, or multi-image inputs need
+explicit ``--component-residency``.
+
+Loading and serving are deliberately separate placement states. The existing
+auto policy provides the initial state; when that state can complete loading
+and calibration, this module optimizes the long-lived serving state and
+validates the transition with a post-placement warmup. It does not force a
+single placement to serve two different lifecycle objectives.
+"""
+
+from __future__ import annotations
+
+import statistics
+from typing import TYPE_CHECKING, Iterable, Mapping
+
+import msgspec
+
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_residency import (
+    COMPONENT_OFFLOAD,
+    LAYERWISE_OFFLOAD,
+    RESIDENT,
+)
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload_components import (
+    is_dit_component_name,
+)
+from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
+
+if TYPE_CHECKING:
+    from sglang.multimodal_gen.runtime.server_args import ServerArgs
+
+logger = init_logger(__name__)
+
+GIB_BYTES = 1024**3
+
+# Activation memory rarely scales perfectly linearly with workload units;
+# pad the extrapolated activation part before checking the budget.
+ACTIVATION_EXTRAPOLATION_MARGIN = 1.2
+# A target-shape measurement plus the mandatory post-placement warmup justifies
+# a tighter reserve than an extrapolated estimate. Both retain an absolute
+# floor for allocator slack, shape variance, and CUDA graph or compile pools.
+MEASURED_VRAM_RESERVE_FRACTION = 0.05
+EXTRAPOLATED_VRAM_RESERVE_FRACTION = 0.10
+MIN_VRAM_RESERVE_BYTES = 4 * GIB_BYTES
+# The absolute floor is sized for datacenter cards, where either fraction can
+# dominate. On a 12 GiB card a flat 4 GiB would fence off a third of the device,
+# so cap the floor as a share of what is actually there.
+MAX_VRAM_RESERVE_FRACTION = 0.20
+
+# A feasible placement is not automatically useful. Predictions inside this
+# interval are treated as latency-equivalent. The joint optimizer then avoids
+# changing strategy, minimizes device memory, preserves the faster estimate,
+# and finally minimizes HostPin. The raw estimate is already an upper bound:
+# transfer time is capped by the measured request.
+ESTIMATED_PINNED_H2D_BYTES_PER_SECOND = 24 * GIB_BYTES
+MIN_LATENCY_EQUIVALENCE_NS = 50_000_000
+MAX_LATENCY_EQUIVALENCE_NS = 100_000_000
+LATENCY_EQUIVALENCE_FRACTION = 0.01
+# The transfer model ranks feasible placements; the mandatory warmup is the
+# authority on whether a selected placement actually helped. Allow normal
+# measurement noise, but undo a round whose calibrated request is materially
+# slower than the original layout.
+POST_ADJUSTMENT_REGRESSION_FRACTION = 0.05
+
+PLACEMENT_STATUS_SKIPPED = "skipped"
+PLACEMENT_STATUS_ADJUSTED = "adjusted"
+PLACEMENT_STATUS_VALIDATED = "validated"
+PLACEMENT_STATUS_ROLLED_BACK = "rolled_back"
+PLACEMENT_STATUS_ROLLBACK_FAILED = "rollback_failed"
+
+
+def describe_error(error: BaseException) -> str:
+    """Never-empty error text (str(AssertionError()) is "" and would be
+    dropped by any truthiness filter)."""
+    text = str(error)
+    return f"{type(error).__name__}: {text}" if text else type(error).__name__
+
+
+class WarmupMemoryRecord(msgspec.Struct, frozen=True):
+    """Per-rank memory measurement of one server warmup forward."""
+
+    width: int
+    height: int
+    num_frames: int
+    baseline_allocated_bytes: int
+    peak_allocated_bytes: int
+    succeeded: bool
+    peak_reserved_bytes: int = 0
+    phase_peak_allocated_bytes: dict[str, int] = {}
+    phase_active_components: dict[str, tuple[str, ...]] = {}
+    phase_used_components: dict[str, tuple[str, ...]] = {}
+    phase_full_weight_transition_components: dict[str, tuple[str, ...]] = {}
+    layerwise_layer_uses: dict[str, dict[str, tuple[int, ...]]] = {}
+    layerwise_layer_uses_by_stage: dict[str, dict[str, dict[str, tuple[int, ...]]]] = {}
+    num_inference_steps: int = 1
+    total_duration_ms: float = 0.0
+    stage_duration_ms: dict[str, float] = {}
+    step_duration_ms: tuple[float, ...] = ()
+    step_duration_ms_by_stage: dict[str, tuple[float, ...]] = {}
+    stage_iterations: dict[str, tuple[int, int]] = {}
+
+    def workload_units(self) -> int:
+        return max(1, self.width) * max(1, self.height) * max(1, self.num_frames)
+
+
+class ResidencyTarget(msgspec.Struct, frozen=True):
+    """One complete target state for an auto-managed component."""
+
+    component_name: str
+    residency_mode: str
+    target_resident_weight_bytes: int
+    # Estimated per-request host-to-device traffic this target removes.
+    h2d_bytes_per_request: int
+    # Layerwise candidates jointly choose stage-scoped GPU residency and host
+    # pinning. None is used by ordinary component placement.
+    target_layerwise_resident_layers: tuple[int, ...] | None = None
+    target_layerwise_pinned_layers: tuple[tuple[int, ...], ...] | None = None
+    pinned_host_delta_bytes: int = 0
+    host_unpin_scratch_bytes: int = 0
+    host_pin_scratch_bytes: int = 0
+    host_materialize_scratch_bytes: int = 0
+    # Signed device-memory delta while applying the placement before the
+    # validation warmup. Layerwise -> resident materializes every managed
+    # layer immediately; a demotion can release those bytes first and fund a
+    # later materialization in the same transaction.
+    device_transition_delta_bytes: int = 0
+    permanent_residency: bool = False
+    # Device-memory delta relative to the measured placement. A component
+    # already loaded for its own phase has a different delta from phases where
+    # it is absent; keeping both avoids adding the same weights twice.
+    active_device_delta_bytes: int = 0
+    # Delta when the component is already present because of async prefetch,
+    # but is not the semantic owner of this phase.
+    present_device_delta_bytes: int = 0
+    inactive_device_delta_bytes: int = 0
+    # None preserves the historical derived target for hand-built callers:
+    # partial layerwise targets remain layerwise, every other option is
+    # resident. Generated complete-state frontiers set this explicitly.
+    target_residency_mode: str | None = None
+    current_placement: bool = False
+    target_device_weight_bytes: int = 0
+    target_pinned_host_bytes: int = 0
+
+    def target_mode(self) -> str:
+        if self.target_residency_mode is not None:
+            return self.target_residency_mode
+        if (
+            self.target_layerwise_resident_layers is not None
+            and not self.permanent_residency
+        ):
+            return LAYERWISE_OFFLOAD
+        return RESIDENT
+
+    def option_key(self) -> str:
+        target_mode = self.target_mode()
+        if target_mode == COMPONENT_OFFLOAD:
+            return f"{self.component_name}:component-offload"
+        if self.target_layerwise_resident_layers is None:
+            return f"{self.component_name}:resident"
+        layer_counts = ",".join(
+            str(count) for count in self.target_layerwise_resident_layers
+        )
+        pinned = "|".join(
+            ",".join(str(index) for index in indices) or "-"
+            for indices in self.target_layerwise_pinned_layers or ()
+        )
+        permanence = "permanent" if self.permanent_residency else "stage"
+        return f"{self.component_name}:{permanence}:layers={layer_counts}:pins={pinned}"
+
+
+class DefaultWorkload(msgspec.Struct, frozen=True):
+    """The model-default request shape the planner is calibrated for."""
+
+    width: int | None
+    height: int | None
+    num_frames: int
+    num_inference_steps: int
+
+    def workload_units(self) -> int | None:
+        if self.width is None or self.height is None:
+            return None
+        return max(1, self.width) * max(1, self.height) * max(1, self.num_frames)
+
+    def describe(self) -> str:
+        if self.width is None or self.height is None:
+            return "model-default"
+        return f"{self.width}x{self.height}x{self.num_frames}f"
+
+
+class RankResidencyReport(msgspec.Struct, frozen=True):
+    """One rank's inputs to the replica-wide placement decision."""
+
+    rank: int
+    budget_bytes: int
+    estimated_peak_bytes: int | None
+    target_workload_measured: bool = False
+    observed_reserved_bytes: int = 0
+    estimated_peak_bytes_by_phase: dict[str, int] = {}
+    active_components_by_phase: dict[str, tuple[str, ...]] = {}
+    used_components_by_phase: dict[str, tuple[str, ...]] = {}
+    full_weight_transition_components_by_phase: dict[str, tuple[str, ...]] = {}
+    current_device_weight_bytes_by_component: dict[str, int] = {}
+    node_rank: int = 0
+    pinned_host_bytes: int = 0
+    host_pin_capacity_bytes: int = 0
+    host_transition_headroom_bytes: int = 0
+    device_transition_allocated_bytes: int = 0
+    estimated_request_duration_ns: int = 0
+    measured_request_duration_ns: int = 0
+    candidate_latency_savings_ns: dict[str, int] = {}
+    candidates: list[ResidencyTarget] = []
+    skip_reason: str | None = None
+
+
+def resolve_default_workload(server_args: ServerArgs) -> DefaultWorkload:
+    """Resolve the default request shape the planner is optimized for."""
+    from sglang.multimodal_gen.runtime.warmup_request_builder import (
+        get_model_sampling_defaults,
+        resolve_default_workload_shape,
+    )
+
+    defaults = get_model_sampling_defaults(server_args)
+    width, height, num_frames = resolve_default_workload_shape(server_args, defaults)
+    return DefaultWorkload(
+        width=width,
+        height=height,
+        num_frames=num_frames,
+        num_inference_steps=defaults.num_inference_steps or 1,
+    )
+
+
+def resolve_measured_default_workload(
+    workload: DefaultWorkload, records: Iterable[WarmupMemoryRecord]
+) -> DefaultWorkload:
+    """Fill an implicit default resolution from the executed warmup.
+
+    Image-edit pipelines can derive their output size from the input image, so
+    the sampling defaults legitimately omit width and height. The warmup record
+    is captured after input validation and therefore contains the effective
+    serving shape. Keep the model-default frame count because video warmup may
+    intentionally cap frames before measurement.
+    """
+    if workload.workload_units() is not None:
+        return workload
+    measured = [
+        record
+        for record in records
+        if record.succeeded and record.width > 0 and record.height > 0
+    ]
+    if not measured:
+        return workload
+    representative = max(measured, key=lambda record: record.width * record.height)
+    return DefaultWorkload(
+        width=representative.width,
+        height=representative.height,
+        num_frames=workload.num_frames,
+        num_inference_steps=workload.num_inference_steps,
+    )
+
+
+def estimate_layerwise_layer_uses(
+    *,
+    records: Iterable[WarmupMemoryRecord],
+    target_units: int | None,
+    target_num_inference_steps: int,
+) -> dict[str, dict[str, tuple[int, ...]]]:
+    """Estimate per-request layer calls from the same calibration forward.
+
+    A full-shape memory probe deliberately runs only a few denoise steps.
+    Stage-attributed calls use that stage's measured and target iteration
+    counts, so independent shape, paint, refiner, and chunk loops are not all
+    multiplied by one request-wide ratio. Legacy records retain the repeated
+    DiT-layer heuristic.
+    """
+    successful = [record for record in records if record.succeeded]
+    if target_units is not None:
+        covering = [
+            record for record in successful if record.workload_units() >= target_units
+        ]
+        if covering:
+            successful = covering
+
+    estimated: dict[str, dict[str, list[int]]] = {}
+    for record in successful:
+        source_steps = max(1, record.num_inference_steps)
+        component_stages = _component_stages(record)
+        repeated_stages = _repeated_stages(record, component_stages)
+        for component_name, groups in record.layerwise_layer_uses.items():
+            component = estimated.setdefault(component_name, {})
+            for layer_name, counts in groups.items():
+                target = component.setdefault(layer_name, [0] * len(counts))
+                if len(target) != len(counts):
+                    continue
+                stage_counts = [
+                    (
+                        stage_name,
+                        stage_components[component_name][layer_name],
+                    )
+                    for stage_name, stage_components in (
+                        record.layerwise_layer_uses_by_stage.items()
+                    )
+                    if component_name in stage_components
+                    and layer_name in stage_components[component_name]
+                    and len(stage_components[component_name][layer_name]) == len(counts)
+                ]
+                for layer_index, count in enumerate(counts):
+                    if stage_counts:
+                        measured_by_stage = sum(
+                            per_layer_counts[layer_index]
+                            for _, per_layer_counts in stage_counts
+                        )
+                        scaled = max(0, count - measured_by_stage)
+                        for stage_name, per_layer_counts in stage_counts:
+                            measured_iterations, target_iterations = _stage_iterations(
+                                record,
+                                stage_name,
+                                repeated_stages=repeated_stages,
+                                target_num_inference_steps=(target_num_inference_steps),
+                            )
+                            stage_count = per_layer_counts[layer_index]
+                            if stage_count <= 1:
+                                scaled += stage_count
+                            else:
+                                scaled += (
+                                    stage_count * target_iterations
+                                    + measured_iterations
+                                    - 1
+                                ) // measured_iterations
+                    else:
+                        scaled = count
+                        component_is_repeated = is_dit_component_name(
+                            component_name
+                        ) or any(
+                            stage_name in repeated_stages
+                            for stage_name in component_stages.get(component_name, ())
+                        )
+                        if (
+                            component_is_repeated
+                            and count > 1
+                            and target_num_inference_steps > source_steps
+                        ):
+                            scaled = (
+                                count * target_num_inference_steps + source_steps - 1
+                            ) // source_steps
+                    target[layer_index] = max(target[layer_index], scaled)
+    return {
+        component_name: {
+            layer_name: tuple(counts) for layer_name, counts in groups.items()
+        }
+        for component_name, groups in estimated.items()
+    }
+
+
+def _component_stages(
+    record: WarmupMemoryRecord,
+    *,
+    timed_stage_names: set[str] | None = None,
+) -> dict[str, set[str]]:
+    component_stages: dict[str, set[str]] = {}
+    phase_components = record.phase_used_components or record.phase_active_components
+    for phase_name, components in phase_components.items():
+        fields = phase_name.split(":", 2)
+        if len(fields) < 2 or not fields[0].isdigit():
+            continue
+        stage_name = fields[1]
+        if timed_stage_names is not None and stage_name not in timed_stage_names:
+            continue
+        for component_name in components:
+            component_stages.setdefault(component_name, set()).add(stage_name)
+    return component_stages
+
+
+def _repeated_stages(
+    record: WarmupMemoryRecord,
+    component_stages: Mapping[str, set[str]],
+) -> set[str]:
+    stages = {
+        stage_name
+        for component_name, stage_names in component_stages.items()
+        if is_dit_component_name(component_name)
+        for stage_name in stage_names
+    }
+    stages.update(record.stage_iterations)
+    stages.update(
+        stage_name
+        for stage_name in set(record.stage_duration_ms).union(
+            *(stage_names for stage_names in component_stages.values())
+        )
+        if stage_name.endswith("DenoisingStage")
+        and not stage_name.endswith("BeforeDenoisingStage")
+    )
+    return stages
+
+
+def _stage_iterations(
+    record: WarmupMemoryRecord,
+    stage_name: str,
+    *,
+    repeated_stages: set[str],
+    target_num_inference_steps: int,
+) -> tuple[int, int]:
+    explicit = record.stage_iterations.get(stage_name)
+    if explicit is not None:
+        return max(1, explicit[0]), max(1, explicit[1])
+    measured = max(1, record.num_inference_steps)
+    target = (
+        max(1, target_num_inference_steps)
+        if stage_name in repeated_stages
+        else measured
+    )
+    return measured, target
+
+
+def estimate_default_workload_timing(
+    *,
+    records: Iterable[WarmupMemoryRecord],
+    target_units: int | None,
+    target_num_inference_steps: int,
+) -> tuple[int, dict[str, int], dict[str, tuple[str, ...]]]:
+    """Estimate full-request and stage durations from the warmup workload.
+
+    Repeated stages scale by their own measured/default iteration counts. The
+    full-shape probe intentionally executes only a few steps, so using its raw
+    total would make every one-shot encoder transfer look important relative
+    to a long video request.
+    """
+    successful = [record for record in records if record.succeeded]
+    if not successful:
+        return 0, {}, {}
+    if target_units is not None:
+        at_target = [
+            record for record in successful if record.workload_units() >= target_units
+        ]
+        if at_target:
+            successful = at_target
+    representative = max(
+        successful,
+        key=lambda record: (
+            record.workload_units(),
+            record.total_duration_ms,
+        ),
+    )
+    if representative.total_duration_ms <= 0 or not representative.stage_duration_ms:
+        return 0, {}, {}
+
+    component_stages = _component_stages(
+        representative,
+        timed_stage_names=set(representative.stage_duration_ms),
+    )
+    repeated_stages = _repeated_stages(representative, component_stages)
+
+    stage_duration_ns: dict[str, int] = {}
+    for stage_name, duration_ms in representative.stage_duration_ms.items():
+        measured_iterations, target_iterations = _stage_iterations(
+            representative,
+            stage_name,
+            repeated_stages=repeated_stages,
+            target_num_inference_steps=target_num_inference_steps,
+        )
+        step_durations = representative.step_duration_ms_by_stage.get(stage_name, ())
+        if not step_durations and len(repeated_stages) == 1:
+            step_durations = representative.step_duration_ms
+        if stage_name in repeated_stages and step_durations:
+            steady_steps = (
+                step_durations[1:] if len(step_durations) > 1 else step_durations
+            )
+            non_step_ms = max(0.0, duration_ms - sum(step_durations))
+            target_duration_ms = non_step_ms + (
+                statistics.median(steady_steps) * target_iterations
+            )
+        else:
+            target_duration_ms = duration_ms * target_iterations / measured_iterations
+        stage_duration_ns[stage_name] = max(0, int(target_duration_ms * 1_000_000))
+
+    measured_stage_ms = sum(representative.stage_duration_ms.values())
+    untracked_ms = max(0.0, representative.total_duration_ms - measured_stage_ms)
+    request_duration_ns = sum(stage_duration_ns.values()) + int(
+        untracked_ms * 1_000_000
+    )
+
+    return (
+        request_duration_ns,
+        stage_duration_ns,
+        {
+            component_name: tuple(sorted(stage_names))
+            for component_name, stage_names in component_stages.items()
+        },
+    )
+
+
+def estimate_default_workload_peak_bytes(
+    *,
+    records: Iterable[WarmupMemoryRecord],
+    target_units: int | None,
+    constant_weight_bytes: int = 0,
+) -> int | None:
+    """Extrapolate live warmup memory to the default workload.
+
+    Real measurements use allocated bytes. Cached allocator blocks are
+    reclaimable and are covered by the explicit VRAM reserve; treating them as
+    live memory would charge the same storage again when adding resident
+    weights.
+
+    Preference order:
+    1. A measurement at or above the target workload bounds the peak directly.
+    2. Two distinct measured workload sizes fit ``peak = constant + slope *
+       units``; only the fitted linear part is extrapolated (and padded).
+       Under offload the pre-forward baseline is nearly empty, so a
+       single-point split cannot separate constant costs (streamed weights,
+       attention workspace, tiled VAE decode) from workload-linear
+       activations -- measured on Wan2.1-14B, single-point scaling
+       overestimated a ~30 GiB peak as ~183 GiB.
+    3. One usable size: scale everything above the pre-forward allocated
+       baseline (conservative; may block adjustment but never over-allocates).
+
+    Returns None when the estimate cannot be trusted: no successful records,
+    the target workload is unknown (an unknown target would silently equate the
+    area/frame-capped warmup peak with the real serving peak), or a probe at or
+    below the target ran out of memory. That last case is not missing data but
+    a measurement: the card could not hold the target as it is already
+    configured, so making more weights resident can only make it worse. A probe
+    that failed strictly above the target says nothing about the target and is
+    dropped instead.
+    """
+    records = list(records)
+    if target_units is None:
+        return None
+    failed_units = [
+        record.workload_units() for record in records if not record.succeeded
+    ]
+    if any(units <= target_units for units in failed_units):
+        return None
+    records = [record for record in records if record.succeeded]
+    if not records:
+        return None
+
+    peak_by_units: dict[int, int] = {}
+    for record in records:
+        units = record.workload_units()
+        peak = record.peak_allocated_bytes
+        peak_by_units[units] = max(peak_by_units.get(units, 0), peak)
+
+    covering_peaks = [
+        peak for units, peak in peak_by_units.items() if units >= target_units
+    ]
+    if covering_peaks:
+        return max(covering_peaks)
+
+    if len(peak_by_units) >= 2:
+        # fit on the two largest sizes: closest to the target, best local slope
+        (large_units, large_peak), (small_units, small_peak) = sorted(
+            peak_by_units.items(), reverse=True
+        )[:2]
+        slope = (large_peak - small_peak) / (large_units - small_units)
+        if slope >= 0:
+            constant = max(
+                large_peak - slope * large_units,
+                min(constant_weight_bytes, large_peak),
+            )
+            return int(
+                constant + slope * target_units * ACTIVATION_EXTRAPOLATION_MARGIN
+            )
+        # negative slope is measurement noise; fall through to the
+        # conservative single-point estimate
+
+    estimates = []
+    for record in records:
+        peak = record.peak_allocated_bytes
+        baseline = min(
+            max(record.baseline_allocated_bytes, constant_weight_bytes), peak
+        )
+        activation = peak - baseline
+        ratio = target_units / record.workload_units()
+        estimates.append(
+            baseline + int(activation * ratio * ACTIVATION_EXTRAPOLATION_MARGIN)
+        )
+    return max(estimates)
+
+
+def estimate_workload_phase_peaks(
+    *,
+    records: Iterable[WarmupMemoryRecord],
+    target_units: int | None,
+    component_weight_bytes: Mapping[str, int],
+) -> tuple[
+    dict[str, int],
+    dict[str, tuple[str, ...]],
+    dict[str, tuple[str, ...]],
+    dict[str, tuple[str, ...]],
+]:
+    """Estimate each measured execution phase at the target workload.
+
+    A component already active in a phase is part of that phase's measured
+    peak. Keeping a component resident therefore adds no weight bytes to its
+    own component-offload phase, while it adds the full footprint to phases
+    where the component was absent. Measurements with different active layouts
+    remain separate constraints; combining one layout's peak with another
+    layout's component set would describe a state that never occurred.
+    """
+    successful = [record for record in records if record.succeeded]
+    if target_units is not None:
+        covering = [
+            record for record in successful if record.workload_units() >= target_units
+        ]
+        if covering:
+            successful = covering
+
+    grouped: dict[
+        tuple[str, tuple[str, ...], tuple[str, ...], tuple[str, ...]],
+        list[WarmupMemoryRecord],
+    ] = {}
+    for record in successful:
+        used_by_phase = record.phase_used_components or record.phase_active_components
+        for phase_name in record.phase_peak_allocated_bytes:
+            active = tuple(sorted(record.phase_active_components.get(phase_name, ())))
+            used = tuple(sorted(used_by_phase.get(phase_name, ())))
+            full_weight_transitions = tuple(
+                sorted(
+                    record.phase_full_weight_transition_components.get(phase_name, ())
+                )
+            )
+            grouped.setdefault(
+                (phase_name, active, used, full_weight_transitions), []
+            ).append(record)
+
+    layouts_per_phase: dict[str, int] = {}
+    for phase_name, _, _, _ in grouped:
+        layouts_per_phase[phase_name] = layouts_per_phase.get(phase_name, 0) + 1
+
+    estimated_peaks: dict[str, int] = {}
+    active_components: dict[str, tuple[str, ...]] = {}
+    used_components: dict[str, tuple[str, ...]] = {}
+    full_weight_transition_components: dict[str, tuple[str, ...]] = {}
+    layout_indices: dict[str, int] = {}
+    for (
+        phase_name,
+        active,
+        used,
+        full_weight_transitions,
+    ), phase_records in sorted(grouped.items()):
+        output_name = phase_name
+        if layouts_per_phase[phase_name] > 1:
+            index = layout_indices.get(phase_name, 0)
+            layout_indices[phase_name] = index + 1
+            output_name = f"{phase_name}:layout:{index}"
+        weight_floor = sum(component_weight_bytes.get(name, 0) for name in active)
+        phase_measurements = [
+            WarmupMemoryRecord(
+                width=record.width,
+                height=record.height,
+                num_frames=record.num_frames,
+                baseline_allocated_bytes=min(
+                    record.baseline_allocated_bytes,
+                    record.phase_peak_allocated_bytes[phase_name],
+                ),
+                peak_allocated_bytes=record.phase_peak_allocated_bytes[phase_name],
+                succeeded=True,
+            )
+            for record in phase_records
+        ]
+        estimate = estimate_default_workload_peak_bytes(
+            records=phase_measurements,
+            target_units=target_units,
+            constant_weight_bytes=weight_floor,
+        )
+        if estimate is None:
+            continue
+        estimated_peaks[output_name] = estimate
+        active_components[output_name] = active
+        used_components[output_name] = used
+        full_weight_transition_components[output_name] = full_weight_transitions
+    return (
+        estimated_peaks,
+        active_components,
+        used_components,
+        full_weight_transition_components,
+    )

python/sglang/multimodal_gen/runtime/managers/memory_managers/component_manager.py

@@ -1,4 +1,4 @@
-from collections.abc import Iterator
+from collections.abc import Iterable, Iterator
 from contextlib import contextmanager
 from dataclasses import dataclass
 from typing import Mapping, MutableMapping, Protocol, Sequence
@@ -58,6 +58,14 @@ class ResidencyState:
     batch_is_warmup: bool = False
 
 
+@dataclass(frozen=True, slots=True)
+class WarmupPhasePeak:
+    active_components: tuple[str, ...]
+    allocated_bytes: int
+    used_components: tuple[str, ...] = ()
+    full_weight_transition_components: tuple[str, ...] = ()
+
+
 class ResidencyBatch(Protocol):
     is_warmup: bool
 
@@ -129,6 +137,13 @@ class ComponentResidencyManager:
         ] = {}
         self._uses_seen: dict[str, ComponentUse] = {}
         self._modules_seen: dict[str, nn.Module] = {}
+        self._track_warmup_memory = False
+        self._warmup_phase_key: str | None = None
+        self._warmup_phase_components: tuple[str, ...] = ()
+        self._warmup_phase_used_components: tuple[str, ...] = ()
+        self._warmup_phase_full_weight_transition_components: tuple[str, ...] = ()
+        self._warmup_phase_peaks: dict[str, WarmupPhasePeak] = {}
+        self._completed_warmup_phase_peaks: dict[str, WarmupPhasePeak] = {}
 
     def refresh_pipeline(self, pipeline: ComponentResidencyPipeline) -> None:
         custom_strategies = dict(pipeline.component_residency_strategies)
@@ -178,6 +193,24 @@ class ComponentResidencyManager:
         self._ordered_uses = tuple(
             use for uses in self._stage_uses_by_index for use in uses
         )
+        self._track_warmup_memory = (
+            self.state.batch_is_warmup
+            and self.server_args.pipeline_config.supports_auto_residency
+            and current_platform.is_cuda()
+            and torch.get_device_module().is_available()
+        )
+        self._warmup_phase_key = None
+        self._warmup_phase_components = ()
+        self._warmup_phase_used_components = ()
+        self._warmup_phase_full_weight_transition_components = ()
+        self._warmup_phase_peaks = {}
+        self._completed_warmup_phase_peaks = {}
+        if self._track_warmup_memory:
+            # GPUWorker reset the request peak before entering the pipeline.
+            # Start the first interval without another reset so preprocessing
+            # before stage 0 remains part of the placement constraints.
+            self._warmup_phase_key = "request:before-stage"
+            self._warmup_phase_components = self._warmup_active_components()
         self._validate_explicit_nonresident_components()
 
     def _validate_explicit_nonresident_components(self) -> None:
@@ -224,6 +257,49 @@ class ComponentResidencyManager:
         self.state.stage_index = stage_index
         self.state.stage_name = self.stage_name(stage)
         self.state.next_stage_name = self._next_stage_name(stage_index)
+        if self._track_warmup_memory:
+            self._begin_warmup_phase(
+                key=f"{stage_index}:{self.state.stage_name}:setup",
+                components=self._warmup_active_components(),
+                used_components=(),
+            )
+
+    @contextmanager
+    def full_weight_transition(self, component_names: Iterable[str]) -> Iterator[None]:
+        """Measure request logic that temporarily materializes complete weights."""
+        names = tuple(sorted(set(component_names)))
+        if not self._track_warmup_memory or not names:
+            yield
+            return
+        previous_phase = (
+            self._warmup_phase_key,
+            self._warmup_phase_components,
+            self._warmup_phase_used_components,
+            self._warmup_phase_full_weight_transition_components,
+        )
+        self._begin_warmup_phase(
+            key=(
+                f"{self.state.stage_index}:{self.state.stage_name}:"
+                f"full-weight-transition:{','.join(names)}"
+            ),
+            components=self._warmup_active_components(),
+            used_components=(),
+            full_weight_transition_components=names,
+        )
+        try:
+            yield
+        finally:
+            previous_key, components, used_components, transitions = previous_phase
+            if previous_key is None:
+                self._record_warmup_phase_peak()
+                self._warmup_phase_key = None
+            else:
+                self._begin_warmup_phase(
+                    key=previous_key,
+                    components=components,
+                    used_components=used_components,
+                    full_weight_transition_components=transitions,
+                )
 
     def begin_stage(self) -> None:
         """Prepare a stage that declares one uninterrupted component use."""
@@ -233,6 +309,7 @@ class ComponentResidencyManager:
 
     def end_stage(self) -> None:
         """Close the component interval owned by the current stage."""
+        self._record_warmup_phase_peak()
         if self._active_use is None:
             return
         if self._active_use.stage_name != self.state.stage_name:
@@ -263,7 +340,13 @@ class ComponentResidencyManager:
                 self._active_use_module is not None
                 and active_module is not self._active_use_module
             )
+            requires_prepare = active_module is not None and (
+                self._active_use_module is None
+                or module_changed
+                or use.target_dtype != previous_use.target_dtype
+            )
             if module_changed:
+                self._begin_warmup_transition(previous_use, None)
                 self._disable_active_nvtx()
                 self._finish_use(
                     previous_use,
@@ -271,18 +354,19 @@ class ComponentResidencyManager:
                     keep_on_warmup=False,
                     force=True,
                 )
-            if active_module is not None and (
-                self._active_use_module is None
-                or module_changed
-                or use.target_dtype != previous_use.target_dtype
-            ):
+                self._begin_warmup_transition(None, use)
+            elif requires_prepare:
+                self._begin_warmup_transition(previous_use, use)
+            if requires_prepare:
                 active_module = self._prepare_forward_use(use, module=active_module)
+                self._begin_warmup_use(use)
                 self._active_use = use
                 self._active_use_module = active_module
                 self.state.current_use = use
             self._enable_nvtx_for_use(use, active_module)
             return
         if self._active_use is not None:
+            self._begin_warmup_transition(self._active_use, None)
             self._disable_active_nvtx()
             self._finish_use(
                 self._active_use,
@@ -292,8 +376,10 @@ class ComponentResidencyManager:
             self._active_use = None
             self._active_use_module = None
             self.state.current_use = None
+        self._begin_warmup_transition(None, use)
         self._mark_current_use(use)
         module = self._prepare_forward_use(use, module=module)
+        self._begin_warmup_use(use)
         self._active_use = use
         self._active_use_module = module
         self._enable_nvtx_for_use(use, module)
@@ -303,6 +389,7 @@ class ComponentResidencyManager:
         """End one sequential component use interval."""
         if self._active_use is None or not self._same_use(self._active_use, use):
             return
+        self._begin_warmup_transition(self._active_use, None)
         self._disable_active_nvtx()
         self._finish_use(
             self._active_use,
@@ -316,6 +403,7 @@ class ComponentResidencyManager:
         self._active_use = None
         self._active_use_module = None
         self.state.current_use = None
+        self._begin_warmup_between_uses()
         self._prefetch_next_memory_intensive_use()
 
     @contextmanager
@@ -378,6 +466,7 @@ class ComponentResidencyManager:
         if self._active_use is None:
             return
         active_use = self._active_use
+        self._begin_warmup_transition(active_use, None)
         self._disable_active_nvtx()
         self._finish_use(
             active_use,
@@ -387,6 +476,7 @@ class ComponentResidencyManager:
         self._active_use = None
         self._active_use_module = None
         self.state.current_use = None
+        self._begin_warmup_between_uses()
         if prefetch_next:
             self._prefetch_next_memory_intensive_use()
 
@@ -483,8 +573,11 @@ class ComponentResidencyManager:
 
         self._uses_seen[use.component_name] = use
         self._modules_seen[use.component_name] = module
+        self._begin_warmup_prefetch(use)
         if strategy.prefetch_for_use(module, use, self.state):
             self._prefetched_use_keys.add(self._use_key(use))
+        else:
+            self._begin_warmup_between_uses()
 
     def _finish_use(
         self,
@@ -534,11 +627,154 @@ class ComponentResidencyManager:
                 not self._is_single_dit_component(component_name) or keep_single_dit
             )
             strategy = self.strategy_for(component_name, module)
+            if self._track_warmup_memory:
+                will_prepare = self.state.batch_is_warmup and preferred
+                self._begin_warmup_phase(
+                    key=f"request:cleanup:{component_name}",
+                    components=self._warmup_active_components(
+                        (use,) if will_prepare else ()
+                    ),
+                    used_components=(component_name,) if will_prepare else (),
+                )
             was_on_supported_device = self._module_on_supported_device(module)
             strategy.finish_request(module, use, self.state, preferred=preferred)
             self._empty_cache_after_large_release(
                 use, strategy, module, was_on_supported_device
             )
+        if self._track_warmup_memory:
+            self._record_warmup_phase_peak()
+            self._warmup_phase_peaks["idle"] = WarmupPhasePeak(
+                active_components=self._warmup_active_components(),
+                allocated_bytes=int(torch.get_device_module().memory_allocated()),
+                used_components=(),
+            )
+            self._completed_warmup_phase_peaks = dict(self._warmup_phase_peaks)
+        self._track_warmup_memory = False
+
+    def _begin_warmup_phase(
+        self,
+        *,
+        key: str,
+        components: tuple[str, ...],
+        used_components: tuple[str, ...],
+        full_weight_transition_components: tuple[str, ...] = (),
+    ) -> None:
+        if not self._track_warmup_memory:
+            return
+        self._record_warmup_phase_peak()
+        self._warmup_phase_key = key
+        self._warmup_phase_components = tuple(sorted(set(components)))
+        self._warmup_phase_used_components = tuple(sorted(set(used_components)))
+        self._warmup_phase_full_weight_transition_components = tuple(
+            sorted(set(full_weight_transition_components))
+        )
+        torch.get_device_module().reset_peak_memory_stats()
+
+    def _begin_warmup_transition(
+        self, previous: ComponentUse | None, upcoming: ComponentUse | None
+    ) -> None:
+        if not self._track_warmup_memory:
+            return
+        previous_name = previous.component_name if previous is not None else "idle"
+        upcoming_name = upcoming.component_name if upcoming is not None else "idle"
+        self._begin_warmup_phase(
+            key=(
+                f"{self.state.stage_index}:{self.state.stage_name}:transition:"
+                f"{previous_name}->{upcoming_name}"
+            ),
+            components=self._warmup_active_components(
+                tuple(use for use in (previous, upcoming) if use is not None)
+            ),
+            used_components=tuple(
+                use.component_name for use in (previous, upcoming) if use is not None
+            ),
+        )
+
+    def _begin_warmup_use(self, use: ComponentUse) -> None:
+        phase = use.phase or use.component_name
+        self._begin_warmup_phase(
+            key=f"{self.state.stage_index}:{self.state.stage_name}:use:{phase}",
+            components=self._warmup_active_components((use,)),
+            used_components=(use.component_name,),
+        )
+
+    def _begin_warmup_between_uses(self) -> None:
+        self._begin_warmup_phase(
+            key=f"{self.state.stage_index}:{self.state.stage_name}:between",
+            components=self._warmup_active_components(),
+            used_components=(),
+        )
+
+    def _begin_warmup_prefetch(self, use: ComponentUse) -> None:
+        phase = use.phase or use.component_name
+        self._begin_warmup_phase(
+            key=f"{self.state.stage_index}:{self.state.stage_name}:prefetch:{phase}",
+            components=self._warmup_active_components((use,)),
+            used_components=(use.component_name,),
+        )
+
+    def _warmup_active_components(
+        self, active_uses: Sequence[ComponentUse] = ()
+    ) -> tuple[str, ...]:
+        components = {use.component_name for use in active_uses}
+        for component_name, module in self.pipeline.modules.items():
+            if not isinstance(module, nn.Module):
+                continue
+            if is_layerwise_offloaded_module(module):
+                continue
+            if self._module_on_supported_device(module):
+                components.add(component_name)
+        return tuple(sorted(components))
+
+    def _record_warmup_phase_peak(self) -> None:
+        if not self._track_warmup_memory or self._warmup_phase_key is None:
+            return
+        peak = WarmupPhasePeak(
+            active_components=self._warmup_phase_components,
+            allocated_bytes=int(torch.get_device_module().max_memory_allocated()),
+            used_components=self._warmup_phase_used_components,
+            full_weight_transition_components=(
+                self._warmup_phase_full_weight_transition_components
+            ),
+        )
+        previous = self._warmup_phase_peaks.get(self._warmup_phase_key)
+        if previous is None:
+            self._warmup_phase_peaks[self._warmup_phase_key] = peak
+        else:
+            self._warmup_phase_peaks[self._warmup_phase_key] = WarmupPhasePeak(
+                active_components=tuple(
+                    sorted(
+                        set(previous.active_components) & set(peak.active_components)
+                    )
+                ),
+                allocated_bytes=max(previous.allocated_bytes, peak.allocated_bytes),
+                used_components=tuple(
+                    sorted(set(previous.used_components) & set(peak.used_components))
+                ),
+                full_weight_transition_components=tuple(
+                    sorted(
+                        set(previous.full_weight_transition_components)
+                        & set(peak.full_weight_transition_components)
+                    )
+                ),
+            )
+
+    def take_warmup_phase_peaks(
+        self,
+    ) -> dict[str, WarmupPhasePeak]:
+        """Return and clear the most recently completed warmup phase peaks."""
+        peaks = self._completed_warmup_phase_peaks
+        self._completed_warmup_phase_peaks = {}
+        return peaks
+
+    def current_device_components(self) -> tuple[str, ...]:
+        """Components whose complete module is currently on the device.
+
+        Dormant layerwise-managed modules are excluded because only their
+        resident window is present. Active layerwise uses are attributed by
+        the managed phase timeline instead.
+        """
+        return self._warmup_active_components()
 
     def stage_name(self, stage: ComponentResidencyStage) -> str:
         return self._stage_names_by_id.get(id(stage), stage.__class__.__name__)
@@ -703,6 +939,11 @@ class ComponentResidencyManager:
 _GLOBAL_COMPONENT_RESIDENCY_MANAGER: ComponentResidencyManager | None = None
 
 
+def peek_global_component_residency_manager() -> ComponentResidencyManager | None:
+    """Return the process-global manager without creating one."""
+    return _GLOBAL_COMPONENT_RESIDENCY_MANAGER
+
+
 def get_global_component_residency_manager(
     pipeline: ComponentResidencyPipeline,
     server_args: ServerArgs,

python/sglang/multimodal_gen/runtime/managers/memory_managers/layerwise_offload.py

@@ -5,7 +5,15 @@ import threading
 from collections.abc import Mapping, Sequence
 from contextlib import nullcontext
 from time import perf_counter
-from typing import Any, Dict, List, Optional, Set, Tuple
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Set,
+    Tuple,
+)
 
 import torch
 from torch.distributed.tensor import DTensor
@@ -2205,3 +2213,99 @@ def configure_layerwise_offload_modules(
     elif warn_missing:
         logger.debug("No selected pipeline component enabled layerwise offload")
     return configured_component_names
+
+
+class LayerwiseUsageTracker:
+    """Temporary per-layer call counters for one calibration request.
+
+    Managers cannot provide this information when layerwise offload has not
+    been configured yet or was disabled by a resident placement. Observe the
+    declared layer groups directly and remove every hook before returning, so
+    ordinary serving requests pay no counter or hook-dispatch cost.
+    """
+
+    def __init__(
+        self,
+        modules: Mapping[str, object],
+        *,
+        stage_name_provider: Callable[[], str | None] | None = None,
+    ) -> None:
+        self._handles: list[Any] = []
+        self._counts: dict[str, dict[str, list[int]]] = {}
+        self._counts_by_stage: dict[str, dict[str, dict[str, list[int]]]] = {}
+        self._stage_name_provider = stage_name_provider
+        for component_name, module in modules.items():
+            if not isinstance(module, LayerwiseOffloadableModuleMixin):
+                continue
+            named_modules = dict(module.named_modules())
+            component_counts: dict[str, list[int]] = {}
+            for layer_name in module.layer_names:
+                layers = named_modules.get(layer_name)
+                if not isinstance(layers, (torch.nn.ModuleList, torch.nn.Sequential)):
+                    continue
+                counts = [0] * len(layers)
+                if not counts:
+                    continue
+                component_counts[layer_name] = counts
+                for layer_index, layer in enumerate(layers):
+
+                    def record_use(
+                        _module,
+                        _inputs,
+                        *,
+                        target_counts=counts,
+                        target_index=layer_index,
+                        target_component_name=component_name,
+                        target_layer_name=layer_name,
+                        target_layer_count=len(layers),
+                    ) -> None:
+                        target_counts[target_index] += 1
+                        if self._stage_name_provider is None:
+                            return
+                        stage_name = self._stage_name_provider()
+                        if stage_name is None:
+                            return
+                        stage_counts = self._counts_by_stage.setdefault(stage_name, {})
+                        component_stage_counts = stage_counts.setdefault(
+                            target_component_name, {}
+                        )
+                        layer_stage_counts = component_stage_counts.setdefault(
+                            target_layer_name, [0] * target_layer_count
+                        )
+                        layer_stage_counts[target_index] += 1
+
+                    self._handles.append(layer.register_forward_pre_hook(record_use))
+            if component_counts:
+                self._counts[component_name] = component_counts
+
+    def finish(self) -> dict[str, dict[str, tuple[int, ...]]]:
+        counts, _ = self.finish_with_stages()
+        return counts
+
+    def finish_with_stages(
+        self,
+    ) -> tuple[
+        dict[str, dict[str, tuple[int, ...]]],
+        dict[str, dict[str, dict[str, tuple[int, ...]]]],
+    ]:
+        for handle in self._handles:
+            handle.remove()
+        self._handles.clear()
+        counts = {
+            component_name: {
+                layer_name: tuple(counts)
+                for layer_name, counts in component_counts.items()
+            }
+            for component_name, component_counts in self._counts.items()
+        }
+        counts_by_stage = {
+            stage_name: {
+                component_name: {
+                    layer_name: tuple(counts)
+                    for layer_name, counts in component_counts.items()
+                }
+                for component_name, component_counts in stage_counts.items()
+            }
+            for stage_name, stage_counts in self._counts_by_stage.items()
+        }
+        return counts, counts_by_stage

python/sglang/multimodal_gen/runtime/managers/scheduler.py

@@ -16,15 +16,7 @@ from sglang.multimodal_gen.runtime.disaggregation.roles import RoleType
 from sglang.multimodal_gen.runtime.disaggregation.scheduler_mixin import (
     SchedulerDisaggMixin,
 )
-from sglang.multimodal_gen.runtime.entrypoints.post_training.io_struct import (
-    GetWeightsChecksumReqInput,
-    ReleaseMemoryOccupationReqInput,
-    ResumeMemoryOccupationReqInput,
-    UpdateWeightFromDiskReqInput,
-    UpdateWeightFromTensorCheckerReqInput,
-    UpdateWeightFromTensorReqInput,
-)
-from sglang.multimodal_gen.runtime.entrypoints.utils import (
+from sglang.multimodal_gen.runtime.entrypoints.control_requests import (
     GetDisaggStatsReq,
     ListLorasReq,
     MergeLoraWeightsReq,
@@ -33,6 +25,14 @@ from sglang.multimodal_gen.runtime.entrypoints.utils import (
     ShutdownReq,
     UnmergeLoraWeightsReq,
 )
+from sglang.multimodal_gen.runtime.entrypoints.post_training.io_struct import (
+    GetWeightsChecksumReqInput,
+    ReleaseMemoryOccupationReqInput,
+    ResumeMemoryOccupationReqInput,
+    UpdateWeightFromDiskReqInput,
+    UpdateWeightFromTensorCheckerReqInput,
+    UpdateWeightFromTensorReqInput,
+)
 from sglang.multimodal_gen.runtime.ipc_array import (
     is_local_endpoint,
     spill_large_arrays_to_file_refs,

python/sglang/multimodal_gen/runtime/pipelines_core/schedule_batch.py

@@ -341,6 +341,7 @@ class Req:
         self.suppress_logs = True
         self.metrics.suppress_stage_breakdown = True
         self.extra["cache_dit_num_inference_steps"] = self.num_inference_steps
+        self.extra["warmup_target_num_inference_steps"] = self.num_inference_steps
         self.num_inference_steps = warmup_steps
 
     def copy_as_warmup(self, warmup_steps: int = 1) -> Req:
@@ -348,6 +349,34 @@ class Req:
         req.set_as_warmup(warmup_steps)
         return req
 
+    def record_stage_iterations(
+        self,
+        measured_iterations: int,
+        target_iterations: int | None = None,
+    ) -> None:
+        """Record a stage loop against its full default-request work.
+
+        Most stages declare the count as a formula of the step count
+        (``PipelineStage.default_workload_iterations``) and never call this.
+        It is for loops whose length is only known inside them (chunked or
+        block-wise schedules); ``target_iterations`` defaults to scaling the
+        measured count from the probe's steps to the default workload's.
+        """
+        if not self.is_warmup or self.metrics is None:
+            return
+        measured = max(1, int(measured_iterations))
+        if target_iterations is None:
+            measured_request_steps = max(1, int(self.num_inference_steps))
+            target_request_steps = int(
+                self.extra.get(
+                    "warmup_target_num_inference_steps", measured_request_steps
+                )
+            )
+            target_iterations = (
+                measured * max(1, target_request_steps) + measured_request_steps - 1
+            ) // measured_request_steps
+        self.metrics.record_stage_iterations(measured, target_iterations)
+
     def validate(self):
         """Initialize dependent fields after dataclass initialization."""
         if getattr(self.sampling_params, "data_type", None) == DataType.ACTION:

python/sglang/multimodal_gen/runtime/pipelines_core/stages/base.py

@@ -55,6 +55,31 @@ class StageVerificationError(Exception):
     pass
 
 
+def record_default_workload_iterations(stage, batch) -> None:
+    """Record how often a stage's repeated unit ran, once per warmup request.
+
+    A stage whose loop length is a formula of the request's step count
+    declares it in ``default_workload_iterations``; the same formula at the
+    default workload's step count is the target. A stage that recorded
+    explicitly inside its loop (a count only known there) is left alone.
+    """
+    metrics = batch.metrics
+    if metrics is None or metrics.active_stage_name is None:
+        return
+    if metrics.active_stage_name in metrics.stage_iterations:
+        return
+    measured = stage.default_workload_iterations(batch, int(batch.num_inference_steps))
+    if measured is None:
+        return
+    target_steps = int(
+        batch.extra.get("warmup_target_num_inference_steps", batch.num_inference_steps)
+    )
+    target = stage.default_workload_iterations(batch, target_steps)
+    metrics.record_stage_iterations(
+        max(1, int(measured)), max(1, int(measured if target is None else target))
+    )
+
+
 class PipelineStage(StageDedupMixin, ABC):
     """
     Abstract base class for all pipeline stages.
@@ -163,6 +188,14 @@ class PipelineStage(StageDedupMixin, ABC):
     def set_profile_stage_name(self, stage_name: str) -> None:
         self._profile_stage_name = stage_name
 
+    def default_workload_iterations(
+        self, batch: Req, num_inference_steps: int
+    ) -> int | None:
+        """How many times this stage's repeated unit runs for a request with
+        ``num_inference_steps`` steps; ``None`` means the stage is not repeated
+        (or records its count itself with ``batch.record_stage_iterations``)."""
+        return None
+
     def _component_stage_name(self, stage_name: str | None = None) -> str:
         return stage_name or self._registered_stage_name or self.__class__.__name__
 
@@ -383,7 +416,14 @@ class PipelineStage(StageDedupMixin, ABC):
 
         # Execute the actual stage logic with unified profiling.
         previous_batch_is_warmup = self._current_batch_is_warmup
+        metrics = batch.metrics
+        warmup_metrics = metrics if batch.is_warmup else None
+        previous_active_stage = (
+            warmup_metrics.active_stage_name if warmup_metrics is not None else None
+        )
         self._current_batch_is_warmup = batch.is_warmup
+        if warmup_metrics is not None:
+            warmup_metrics.active_stage_name = self._component_stage_name()
         try:
             with StageProfiler(
                 stage_name,
@@ -394,7 +434,11 @@ class PipelineStage(StageDedupMixin, ABC):
                 perf_dump_path_provided=batch.perf_dump_path is not None,
             ):
                 result = self.forward(batch, server_args)
+                if warmup_metrics is not None:
+                    record_default_workload_iterations(self, batch)
         finally:
+            if warmup_metrics is not None:
+                warmup_metrics.active_stage_name = previous_active_stage
             self._current_batch_is_warmup = previous_batch_is_warmup
             self._current_use_nvtx = False
 

python/sglang/multimodal_gen/runtime/pipelines_core/stages/causal_denoising.py

@@ -145,6 +145,12 @@ class CausalDMDRealtimeCacheContext:
 
 
 class CausalDMDDenoisingStage(DenoisingStage):
+    def default_workload_iterations(
+        self, batch: Req, num_inference_steps: int
+    ) -> int | None:
+        # blocks x fixed DMD steps, known only once the block sizes are laid out
+        return None
+
     """
     Denoising stage for causal diffusion.
     """
@@ -218,9 +224,8 @@ class CausalDMDDenoisingStage(DenoisingStage):
                 (scheduler.timesteps.cpu(), torch.tensor([0], dtype=torch.float32))
             )
             timesteps = scheduler_timesteps[1000 - timesteps]
-        timesteps = timesteps.to(device)
         logger.info("Using timesteps: %s", timesteps)
-        return timesteps
+        return timesteps.to(device)
 
     def _prepare_causal_dmd_image_kwargs(
         self,
@@ -1254,6 +1259,9 @@ class CausalDMDDenoisingStage(DenoisingStage):
             block_sizes = [1] + [self.num_frames_per_block] * num_blocks
             start_index = 0
 
+        total_iterations = len(block_sizes) * len(timesteps)
+        batch.record_stage_iterations(total_iterations, total_iterations)
+
         def prepare_context_input(current_latents):
             return current_latents
 

python/sglang/multimodal_gen/runtime/pipelines_core/stages/denoising.py

@@ -334,6 +334,11 @@ class DenoisingStage(PipelineStage, RolloutDenoisingMixin):
     def role_affinity(self):
         return RoleType.DENOISER
 
+    def default_workload_iterations(
+        self, batch: Req, num_inference_steps: int
+    ) -> int | None:
+        return num_inference_steps
+
     def __init__(
         self, transformer, scheduler, pipeline=None, transformer_2=None, vae=None
     ) -> None:

python/sglang/multimodal_gen/runtime/pipelines_core/stages/denoising_dmd.py

@@ -25,6 +25,12 @@ logger = init_logger(__name__)
 
 
 class DmdDenoisingStage(DenoisingStage):
+    def default_workload_iterations(
+        self, batch: Req, num_inference_steps: int
+    ) -> int | None:
+        # a fixed distilled schedule: the same count at any requested step count
+        return len(self.server_args.pipeline_config.dmd_denoising_steps)
+
     """
     Denoising stage for DMD.
     """

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/cosmos3.py

@@ -1063,6 +1063,11 @@ class Cosmos3TimestepPreparationStage(PipelineStage):
 
 
 class Cosmos3DenoisingStage(PipelineStage, RolloutDenoisingMixin):
+    def default_workload_iterations(
+        self, batch: Req, num_inference_steps: int
+    ) -> int | None:
+        return num_inference_steps
+
     """Cosmos3 denoise loop, including CFG and the parallelism modes.
 
     The UND pathway runs once and its K/V is cached per cache_key (``cond`` /

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/helios_denoising.py

@@ -776,5 +776,9 @@ class HeliosChunkedDenoisingStage(PipelineStage):
         # separately to avoid temporal artifacts at chunk boundaries.
         batch.latent_chunks = chunk_latents_list
         batch.latents = history_latents[:, :, -total_generated_latent_frames:]
+        batch.record_stage_iterations(
+            global_step_offset,
+            global_step_offset if is_enable_stage2 else None,
+        )
 
         return batch

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/lingbot_world/lingbot_world_causal_denoising.py

@@ -691,6 +691,7 @@ class LingBotWorldCausalDMDDenoisingStage(CausalDMDDenoisingStage):
             "Ensure ImageVAEEncodingStage runs before this stage."
         )
         ctx = self._prepare_causal_dmd_forward_context(batch, server_args)
+        batch.record_stage_iterations(len(ctx.timesteps), len(ctx.timesteps))
         latents = ctx.latents
         cache_ctx = self._prepare_realtime_causal_caches(batch, server_args, ctx)
 

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/longlive2.py

@@ -421,6 +421,9 @@ class LongLive2CausalDenoisingStage(CausalDMDDenoisingStage):
             num_blocks = (t - 1) // self.num_frames_per_block
             block_sizes = [1] + [self.num_frames_per_block] * num_blocks
 
+        total_iterations = len(block_sizes) * len(timesteps)
+        batch.record_stage_iterations(total_iterations, total_iterations)
+
         start_index = 0
         self._validate_block_prompt_count(batch, block_sizes)
 

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/ltx_2/denoising_av.py

@@ -108,6 +108,12 @@ class LTX2AVDenoisingStage(LTX2DenoisingStage):
 
 
 class LTX2RefinementStage(LTX2AVDenoisingStage):
+    def default_workload_iterations(
+        self, batch: Req, num_inference_steps: int
+    ) -> int | None:
+        # the refiner runs its distilled sigma schedule regardless of the request's steps
+        return max(1, len(self.distilled_sigmas) - 1)
+
     """Stage-2 refinement wrapper that re-noises distilled LTX latents once."""
 
     def __init__(

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/minimax_h3/stages/denoising.py

@@ -443,6 +443,12 @@ def _precompute_rope_cache(
 
 
 class MiniMaxH3DenoisingStage(DenoisingStage):
+    def default_workload_iterations(
+        self, batch: Req, num_inference_steps: int
+    ) -> int | None:
+        # one denoise per sigma interval: steps - 1
+        return max(1, num_inference_steps - 1)
+
     def __init__(self, transformer, pipeline=None) -> None:
         super().__init__(
             transformer=transformer,

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/mova.py

@@ -152,6 +152,11 @@ class MOVATimestepPreparationStage(PipelineStage):
 
 
 class MOVADenoisingStage(PipelineStage):
+    def default_workload_iterations(
+        self, batch: Req, num_inference_steps: int
+    ) -> int | None:
+        return num_inference_steps
+
     """Run MOVA dual-tower denoising loop."""
 
     def __init__(self, video_dit, video_dit_2, audio_dit, dual_tower_bridge, scheduler):

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/sana_wm/refiner.py

@@ -725,6 +725,8 @@ class SanaWMLTX2RefinerStage(PipelineStage):
             return batch
 
         batch_size = int(batch.latents.shape[0])
+        total_iterations = batch_size * (len(STAGE_2_DISTILLED_SIGMA_VALUES) - 1)
+        batch.record_stage_iterations(total_iterations, total_iterations)
         prompts = self._prompts_for_batch(batch, batch_size)
         fps = float(getattr(batch, "fps", 16) or 16)
 

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/sana_wm/streaming.py

@@ -630,6 +630,8 @@ class SanaWMStreamingDenoisingStage(CausalDMDDenoisingStage):
                 len(explicit_sigmas),
                 do_cfg,
             )
+            total_iterations = num_chunks * len(explicit_sigmas)
+            batch.record_stage_iterations(total_iterations, total_iterations)
 
             for chunk_idx in self.progress_bar(range(num_chunks), batch=batch):
                 chunk_kv, sink_num = self._accumulate_kv_cache(

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/sana_wm/streaming_refiner.py

@@ -671,6 +671,8 @@ class SanaWMStreamingRefinerStage(SanaWMLTX2RefinerStage):
             )
             return batch
         n_blocks = math.ceil(n_active / self.block_size)
+        total_iterations = n_blocks * (len(STAGE_2_DISTILLED_SIGMA_VALUES) - 1)
+        batch.record_stage_iterations(total_iterations, total_iterations)
         self.log_info(
             "SANA-WM streaming refiner: latent=%s, sink=%d, block=%d, blocks=%d, kv_max=%d, seed=%d",
             tuple(latents.shape),

python/sglang/multimodal_gen/runtime/scheduler_client.py

@@ -7,6 +7,13 @@ from typing import Any, Optional
 import zmq
 import zmq.asyncio
 
+from sglang.multimodal_gen.runtime.entrypoints.control_requests import (
+    ListLorasReq,
+    MergeLoraWeightsReq,
+    SetLoraReq,
+    ShutdownReq,
+    UnmergeLoraWeightsReq,
+)
 from sglang.multimodal_gen.runtime.entrypoints.post_training.io_struct import (
     GetWeightsChecksumReqInput,
     ReleaseMemoryOccupationReqInput,
@@ -15,13 +22,6 @@ from sglang.multimodal_gen.runtime.entrypoints.post_training.io_struct import (
     UpdateWeightFromTensorCheckerReqInput,
     UpdateWeightFromTensorReqInput,
 )
-from sglang.multimodal_gen.runtime.entrypoints.utils import (
-    ListLorasReq,
-    MergeLoraWeightsReq,
-    SetLoraReq,
-    ShutdownReq,
-    UnmergeLoraWeightsReq,
-)
 from sglang.multimodal_gen.runtime.ipc_array import materialize_file_refs
 from sglang.multimodal_gen.runtime.pipelines_core import Req
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import OutputBatch

python/sglang/multimodal_gen/runtime/server_args/auto_tune.py

@@ -10,6 +10,10 @@ from sglang.multimodal_gen import envs
 from sglang.multimodal_gen.configs.pipeline_configs.model_deployment_config import (
     ModelDeploymentConfig,
 )
+from sglang.multimodal_gen.runtime.disaggregation.roles import RoleType
+from sglang.multimodal_gen.runtime.entrypoints.openai.realtime.registry import (
+    has_realtime_model_adapter,
+)
 from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload_components import (
     LAYERWISE_OFFLOAD_ALL_COMPONENTS,
     LAYERWISE_OFFLOAD_DIT_GROUP,
@@ -26,6 +30,49 @@ if TYPE_CHECKING:
 
 logger = init_logger(__name__)
 
+
+def auto_residency_args_skip_reason(server_args: ServerArgs) -> str | None:
+    """Return why args cannot use warmup-calibrated residency."""
+    if envs.SGLANG_DIFFUSION_DISABLE_AUTO_RESIDENCY:
+        return "disabled via SGLANG_DIFFUSION_DISABLE_AUTO_RESIDENCY"
+    if server_args.performance_mode != "auto":
+        return f"performance_mode={server_args.performance_mode}"
+    if (
+        server_args.pipeline_class_name == "LTX2TwoStagePipeline"
+        and server_args.ltx2_two_stage_device_mode is None
+    ):
+        return "legacy LTX-2 two-stage placement"
+    if server_args.ltx2_two_stage_device_mode == "original":
+        return "LTX-2 original two-stage placement"
+    if (
+        server_args.warmup_mode != "server"
+        or server_args.disagg_role != RoleType.MONOLITHIC
+    ):
+        return "no synthetic server warmup to calibrate from"
+    task_type = server_args.pipeline_config.task_type
+    if not (task_type.is_visual_gen() or task_type.is_mesh_gen()):
+        return "no synthetic server warmup to calibrate from"
+    if not server_args.pipeline_config.supports_auto_residency:
+        return "pipeline does not support post-warmup residency changes"
+    if has_realtime_model_adapter(server_args):
+        return "realtime serving has no representative synthetic warmup"
+    if server_args.backend == "diffusers":
+        return "diffusers backend"
+    if server_args.enable_breakable_cuda_graph:
+        return "breakable CUDA graph captures during warmup"
+    if server_args.enable_torch_compile:
+        # Compile warmup temporarily evicts resident auxiliaries and may
+        # layerwise-offload the DiT, so its peak is not a serving peak.
+        return "torch.compile warmup uses a stripped memory layout"
+    if envs.SGLANG_CACHE_DIT_ENABLED:
+        return "cache-dit enabled"
+    if server_args.batching_max_size > 1:
+        return "dynamic batching enabled"
+    if not current_platform.is_cuda():
+        return "requires CUDA"
+    return None
+
+
 PERFORMANCE_MODES = ("manual", "auto", "speed", "memory")
 
 DEFAULT_LAYERWISE_COMPONENT_ARG_NAMES = (

python/sglang/multimodal_gen/runtime/server_args/server_args.py

@@ -454,6 +454,10 @@ class ServerArgs(DisaggServerArgsMixin):
     warmup_resolutions: list[str] = None
     warmup_num_frames: int | None = None
     warmup_steps: int = 1
+    # JSON overrides for the representative request shape used by synthetic
+    # warmup and automatic residency planning. Execution remains bounded by
+    # warmup_steps and the server warmup frame/area caps.
+    warmup_sampling_params: dict[str, Any] | str | None = None
 
     disable_autocast: bool | None = None
 
@@ -2384,6 +2388,18 @@ class ServerArgs(DisaggServerArgsMixin):
             default=ServerArgs.warmup_steps,
             help="The number of warmup steps to perform for each resolution.",
         )
+        parser.add_argument(
+            "--warmup-sampling-params",
+            type=str,
+            default=ServerArgs.warmup_sampling_params,
+            help=(
+                "JSON object overriding model sampling defaults for synthetic "
+                "warmup and auto residency planning, for example "
+                '\'{"width":832,"height":480,"num_frames":9,'
+                '"num_inference_steps":4}\'. Warmup still applies its '
+                "bounded execution caps."
+            ),
+        )
         # component residency and legacy offload controls
         parser.add_argument(
             "--component-residency",

python/sglang/multimodal_gen/runtime/server_warmup.py

@@ -7,15 +7,22 @@ from typing import Any, Awaitable, Callable
 
 from tqdm.auto import tqdm
 
+from sglang.multimodal_gen.runtime.entrypoints.openai.realtime.registry import (
+    has_realtime_model_adapter,
+)
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import (
     OutputBatch,
     Req,
 )
 from sglang.multimodal_gen.runtime.server_args import ServerArgs
+from sglang.multimodal_gen.runtime.server_args.auto_tune import (
+    auto_residency_args_skip_reason,
+)
 from sglang.multimodal_gen.runtime.utils.image_io import save_base64_image_to_path
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 from sglang.multimodal_gen.runtime.warmup_request_builder import (
     build_warmup_reqs,
+    lighten_warmup_req,
     should_include_warmup_image,
     supports_synthetic_warmup,
 )
@@ -74,15 +81,7 @@ def should_run_server_warmup(server_args: ServerArgs) -> bool:
 
 def is_realtime_serving(server_args: ServerArgs) -> bool:
     """Synthetic warmup has no realtime session state."""
-    try:
-        from sglang.multimodal_gen.runtime.entrypoints.openai.realtime.registry import (
-            get_realtime_model_adapter,
-        )
-
-        get_realtime_model_adapter(server_args)
-        return True
-    except Exception:
-        return False
+    return has_realtime_model_adapter(server_args)
 
 
 def should_run_synthetic_server_warmup(server_args: ServerArgs) -> bool:
@@ -101,11 +100,69 @@ def should_run_explicit_client_warmup(server_args: ServerArgs) -> bool:
     )
 
 
+def auto_residency_skip_reason(server_args: ServerArgs) -> str | None:
+    """Final gate for warmup-calibrated residency placement.
+
+    Only rules out paths the planner was not designed for; the workers
+    re-check per component (explicit placement, FSDP modules, custom
+    strategies, missing sizes) and per measurement.
+    """
+    args_reason = auto_residency_args_skip_reason(server_args)
+    if args_reason is not None:
+        return args_reason
+    if not should_run_synthetic_server_warmup(server_args):
+        return "no synthetic server warmup to calibrate from"
+    return None
+
+
+# Enough to clear a probe that overshot the card, few enough that a failure
+# which is not about probe size gives up quickly instead of walking the
+# workload down to nothing.
+MAX_WARMUP_DEGRADE_ATTEMPTS = 3
+
+
+_OUT_OF_MEMORY_MARKERS = (
+    "out of memory",
+    "outofmemory",
+    "cudaerrormemoryallocation",
+    "cublas_status_alloc_failed",
+    "cannot allocate memory",
+    "unable to allocate",
+)
+
+
+def _is_out_of_memory(error: Any) -> bool:
+    text = str(error).lower()
+    return any(marker in text for marker in _OUT_OF_MEMORY_MARKERS)
+
+
+def _degrade_after_oom(server_args: ServerArgs, req: Req) -> Req | None:
+    """Next warmup probe to try after `req` ran the card out of memory.
+
+    Only memory failures are worth retrying smaller; anything else fails the
+    same way at every size and should surface instead of being shrunk away.
+    """
+    lighter = lighten_warmup_req(server_args, req)
+    if lighter is None:
+        return None
+    logger.warning(
+        "%s ran out of memory; retrying warmup at %s",
+        format_warmup_req(req),
+        format_warmup_req(lighter),
+    )
+    return lighter
+
+
 def format_warmup_req(req_or_group: Any) -> str:
     req = get_first_generation_req(req_or_group)
-    prefix = (
-        "server warmup req" if is_server_based_warmup(req_or_group) else "warmup req"
-    )
+    if req is not None and req.extra.get("auto_residency_full_shape_probe"):
+        prefix = "auto residency probe"
+    else:
+        prefix = (
+            "server warmup req"
+            if is_server_based_warmup(req_or_group)
+            else "warmup req"
+        )
     if req is None:
         return prefix
 
@@ -131,6 +188,8 @@ def build_client_warmup_reqs(
     server_args: ServerArgs,
     *,
     warmup_input_path: str | None = None,
+    rewarm: bool = False,
+    step_limit: int | None = None,
 ) -> list[Req]:
     warmup_reqs = build_warmup_reqs(
         server_args,
@@ -143,6 +202,12 @@ def build_client_warmup_reqs(
     for req in warmup_reqs:
         if req.is_warmup:
             req.extra["warmup_total"] = warmup_total
+            if step_limit is not None:
+                req.num_inference_steps = min(req.num_inference_steps, step_limit)
+        if rewarm:
+            # a repeat pass after an auto-residency change: keep it out of
+            # the scheduler's warmup progress accounting (already at N/N)
+            req.extra["server_warmup_rewarm"] = True
     return warmup_reqs
 
 
@@ -151,6 +216,8 @@ async def run_async_client_warmup(
     forward: Callable[[Req], Awaitable[OutputBatch]],
     *,
     fail_open: bool = False,
+    rewarm: bool = False,
+    step_limit: int | None = None,
 ) -> None:
     try:
         warmup_input_path = None
@@ -158,9 +225,20 @@ async def run_async_client_warmup(
             warmup_input_path = prepare_warmup_image_path(server_args)
 
         for req in build_client_warmup_reqs(
-            server_args, warmup_input_path=warmup_input_path
+            server_args,
+            warmup_input_path=warmup_input_path,
+            rewarm=rewarm,
+            step_limit=step_limit,
         ):
             response = await forward(req)
+            for _ in range(MAX_WARMUP_DEGRADE_ATTEMPTS):
+                if response.error is None or not _is_out_of_memory(response.error):
+                    break
+                lighter = _degrade_after_oom(server_args, req)
+                if lighter is None:
+                    break
+                req = lighter
+                response = await forward(req)
             if response.error is not None:
                 raise RuntimeError(response.error)
     except Exception:
@@ -184,6 +262,14 @@ def run_sync_client_warmup(
         server_args, warmup_input_path=warmup_input_path
     ):
         response = forward(req)
+        for _ in range(MAX_WARMUP_DEGRADE_ATTEMPTS):
+            if response.error is None or not _is_out_of_memory(response.error):
+                break
+            lighter = _degrade_after_oom(server_args, req)
+            if lighter is None:
+                break
+            req = lighter
+            response = forward(req)
         if response.error is not None:
             raise RuntimeError(response.error)
 
@@ -275,6 +361,18 @@ class SchedulerWarmupMixin:
         if not is_warmup:
             return
 
+        req = get_first_generation_req(req_or_group)
+        if req is not None and req.extra.get("server_warmup_rewarm"):
+            # auto-residency re-warm passes repeat already-counted requests;
+            # advancing the bar again would log N+1/N in CI
+            if output_batch.error is not None:
+                logger.warning(
+                    "%s processing failed: %s",
+                    self._format_warmup_req(req_or_group),
+                    output_batch.error,
+                )
+            return
+
         server_based_warmup = is_server_based_warmup(req_or_group)
         self._warmup_processed += 1
         self._advance_warmup_progress_bar(req_or_group, output_batch)

python/sglang/multimodal_gen/runtime/utils/perf_logger.py

@@ -58,6 +58,9 @@ class RequestMetrics:
         self.request_id = request_id
         self.stages: Dict[str, float] = {}
         self.steps: list[float] = []
+        self.steps_by_stage: Dict[str, list[float]] = {}
+        self.stage_iterations: Dict[str, tuple[int, int]] = {}
+        self.active_stage_name: str | None = None
         self.total_duration_ms: float = 0.0
         self.suppress_stage_breakdown: bool = False
         # memory tracking: {checkpoint_name: MemorySnapshot}
@@ -77,7 +80,26 @@ class RequestMetrics:
         """Records the duration of a denoising step in execution order."""
         if self.suppress_stage_breakdown:
             return
-        self.steps.append(duration_s * 1000)
+        duration_ms = duration_s * 1000
+        self.steps.append(duration_ms)
+        if self.active_stage_name is not None:
+            self.steps_by_stage.setdefault(self.active_stage_name, []).append(
+                duration_ms
+            )
+
+    def record_stage_iterations(
+        self, measured_iterations: int, target_iterations: int
+    ) -> None:
+        """Record calibration and default-workload iterations for this stage."""
+        if self.suppress_stage_breakdown or self.active_stage_name is None:
+            return
+        measured = max(1, int(measured_iterations))
+        target = max(1, int(target_iterations))
+        previous = self.stage_iterations.get(self.active_stage_name, (0, 0))
+        self.stage_iterations[self.active_stage_name] = (
+            previous[0] + measured,
+            previous[1] + target,
+        )
 
     def record_memory_snapshot(self, checkpoint_name: str, snapshot: MemorySnapshot):
         if self.suppress_stage_breakdown:

python/sglang/multimodal_gen/runtime/warmup_request_builder.py

@@ -6,6 +6,12 @@ first real request, without copying user traffic. It starts from the model's
 sampling defaults, then keeps startup bounded by choosing common low-cost
 resolution/frame buckets and trimming the denoising step count.
 
+When warmup-calibrated auto residency is active, warmup uses the full default
+serving shape but keeps the trimmed step count. Memory depends on the
+activation shape rather than the number of repeated denoising steps, so this
+directly measures placement headroom without running a full generation or an
+extra stateful pipeline request.
+
 Image models may run a tiny second step because first/last step paths often
 initialize different kernels or scheduler state. Video models cap frames and
 steps to keep startup bounded. Explicit warmup resolutions share this builder;
@@ -13,17 +19,26 @@ callers send them through the scheduler client so warmup exercises the same
 request transport path as real generation.
 """
 
+import json
 from copy import copy
+from dataclasses import fields, replace
 from typing import Any
 
 from sglang.multimodal_gen.configs.pipeline_configs.base import ModelTaskType
-from sglang.multimodal_gen.configs.sample.sampling_params import SamplingParams
+from sglang.multimodal_gen.configs.sample.sampling_params import (
+    SamplingParams,
+    align_num_frames_for_num_gpus,
+    resolve_sequence_shard,
+)
 from sglang.multimodal_gen.registry import get_pipeline_config_classes
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import Req
 from sglang.multimodal_gen.runtime.server_args import (
     ServerArgs,
     is_ltx2_two_stage_pipeline_name,
 )
+from sglang.multimodal_gen.runtime.server_args.auto_tune import (
+    auto_residency_args_skip_reason,
+)
 from sglang.multimodal_gen.runtime.utils.common import parse_size
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 
@@ -42,6 +57,11 @@ SERVER_WARMUP_MAX_VIDEO_FRAMES = 17
 SERVER_WARMUP_LTX2_TWO_STAGE_MAX_VIDEO_FRAMES = 25
 SERVER_WARMUP_IMAGE_STEPS = 2
 SERVER_WARMUP_VIDEO_STEPS = 2
+# Two-step schedulers can have one compile-heavy step and one lower-order
+# boundary step, leaving no representative steady-state timing sample. Auto
+# residency extrapolates this timing to the default request, so collect four
+# steps while retaining the shorter warmup for every non-planning path.
+AUTO_RESIDENCY_TIMING_STEPS = 4
 
 
 def get_model_sampling_defaults(server_args: ServerArgs) -> SamplingParams:
@@ -50,13 +70,44 @@ def get_model_sampling_defaults(server_args: ServerArgs) -> SamplingParams:
         config_classes = get_pipeline_config_classes(pipeline_class_name)
         if config_classes is not None:
             _, sampling_params_cls = config_classes
-            return sampling_params_cls()
+            defaults = sampling_params_cls()
+            return _apply_warmup_sampling_overrides(server_args, defaults)
 
-    return SamplingParams.from_pretrained(
+    defaults = SamplingParams.from_pretrained(
         server_args.model_path,
         backend=server_args.backend,
         model_id=server_args.model_id,
     )
+    return _apply_warmup_sampling_overrides(server_args, defaults)
+
+
+def _apply_warmup_sampling_overrides(
+    server_args: ServerArgs, defaults: SamplingParams
+) -> SamplingParams:
+    value = server_args.warmup_sampling_params
+    if value is None:
+        return defaults
+    if isinstance(value, str):
+        try:
+            value = json.loads(value)
+        except json.JSONDecodeError as error:
+            raise ValueError(
+                "--warmup-sampling-params must be a JSON object"
+            ) from error
+    if not isinstance(value, dict):
+        raise ValueError("--warmup-sampling-params must be a JSON object")
+    field_names = {item.name for item in fields(defaults)}
+    unknown = value.keys() - field_names
+    if unknown:
+        raise ValueError(
+            f"invalid --warmup-sampling-params fields: {', '.join(sorted(unknown))}"
+        )
+    updated = copy(defaults)
+    for name, field_value in value.items():
+        # Some model contracts intentionally expose fixed dataclass fields
+        # with init=False; a warmup workload still needs to mirror the request.
+        object.__setattr__(updated, name, field_value)
+    return updated
 
 
 def _resolve_default_warmup_resolution(
@@ -221,6 +272,72 @@ def _fit_resolution_to_area(
     )
 
 
+def _halve_num_frames(server_args: ServerArgs, num_frames: int) -> int:
+    """Halve the latent frame count, keeping the model's frame arithmetic."""
+    if num_frames <= 1:
+        return num_frames
+    ratio = (
+        server_args.pipeline_config.vae_config.arch_config.temporal_compression_ratio
+    )
+    if not ratio or ratio <= 1:
+        return max(1, num_frames // 2)
+    latent_frames = (num_frames - 1) // ratio + 1
+    # round up: halving 5 latent frames should land on 3 (9 frames), not 2 (5)
+    return ((latent_frames + 1) // 2 - 1) * ratio + 1
+
+
+def _lighter_valid_num_frames(server_args: ServerArgs, num_frames: int) -> int:
+    """Largest frame count at or below half that the model's frame contract accepts.
+
+    ``adjust_num_frames`` rounds up (LongLive2 maps 17 frames to 29), so the
+    halved count is walked down until it is a fixed point of the contract.
+    """
+    halved = _halve_num_frames(server_args, num_frames)
+    adjust = getattr(server_args.pipeline_config, "adjust_num_frames", None)
+    for candidate in range(halved, 0, -1):
+        adjusted = adjust(candidate) if callable(adjust) else candidate
+        if not isinstance(adjusted, int) or isinstance(adjusted, bool):
+            adjusted = candidate
+        if adjusted == candidate:
+            return candidate if candidate < num_frames else num_frames
+    return num_frames
+
+
+def lighten_warmup_req(server_args: ServerArgs, req: Req) -> Req | None:
+    """Roughly halve a warmup probe, or None once it cannot shrink further.
+
+    Warmup peak memory tracks width * height * num_frames, so a card that could
+    not hold the full probe usually holds half of it while still walking the
+    same code path. Frames go first: they drive video activation size, and
+    cutting them leaves the spatial kernels at their serving shape.
+    """
+    params = req.sampling_params
+    if params is None:
+        return None
+
+    num_frames = params.num_frames or 1
+    lighter_frames = _lighter_valid_num_frames(server_args, num_frames)
+    if lighter_frames < num_frames:
+        return _replace_warmup_workload(req, num_frames=lighter_frames)
+
+    width = params.width
+    height = params.height
+    if not width or not height:
+        return None
+    alignment = _warmup_resolution_alignment(server_args)
+    lighter = _fit_resolution_to_area(width, height, width * height // 2, alignment)
+    # below the alignment floor the fit rounds back up; only take a real cut
+    if lighter[0] * lighter[1] >= width * height:
+        return None
+    return _replace_warmup_workload(req, width=lighter[0], height=lighter[1])
+
+
+def _replace_warmup_workload(req: Req, **changes: int) -> Req:
+    lighter = copy(req)
+    lighter.sampling_params = replace(req.sampling_params, **changes)
+    return lighter
+
+
 def _is_resolution_aligned(resolution: tuple[int, int], alignment: int) -> bool:
     width, height = resolution
     return width % alignment == 0 and height % alignment == 0
@@ -268,7 +385,87 @@ def _resolve_warmup_num_frames(
         )
         warmup_num_frames = min(num_frames, frame_budget)
 
-    return server_args.pipeline_config.adjust_num_frames(warmup_num_frames)
+    return _apply_warmup_frame_contract(
+        server_args, sampling_defaults, num_frames=warmup_num_frames
+    )
+
+
+def _apply_warmup_frame_contract(
+    server_args: ServerArgs, sampling_defaults: SamplingParams, *, num_frames: int
+) -> int:
+    """Re-apply the real-request frame contract to a capped warmup frame count.
+
+    Warmup requests skip ``SamplingParams._adjust``, so the cap must run the
+    model frame contract itself -- without this, e.g. LongLive2's capped 17
+    frames map to 5 latent frames (not divisible by its 8-frame causal block)
+    and every server warmup fails silently under fail-open. Pipelines that
+    align frames to ``num_gpus`` (rather than sharding the sequence dim) get
+    the same latent alignment real requests get.
+    """
+    num_frames = server_args.pipeline_config.adjust_num_frames(num_frames)
+    if (
+        sampling_defaults.adjust_frames
+        and not resolve_sequence_shard(
+            server_args.pipeline_config, sampling_defaults.enable_sequence_shard
+        )
+        and server_args.num_gpus > 1
+    ):
+        num_frames = align_num_frames_for_num_gpus(
+            num_frames,
+            num_gpus=server_args.num_gpus,
+            vae_config=server_args.pipeline_config.vae_config,
+            round_down=sampling_defaults.num_frames_round_down,
+        )
+    return num_frames
+
+
+def resolve_default_workload_shape(
+    server_args: ServerArgs,
+    sampling_defaults: SamplingParams,
+) -> tuple[int | None, int | None, int]:
+    """Resolve the serving shape used by warmup-based memory planning."""
+    width = sampling_defaults.width
+    height = sampling_defaults.height
+    if (width is None or height is None) and sampling_defaults.supported_resolutions:
+        width, height = max(
+            sampling_defaults.supported_resolutions,
+            key=lambda size: size[0] * size[1],
+        )
+    num_frames = sampling_defaults.num_frames or 1
+    if num_frames > 1:
+        num_frames = _apply_warmup_frame_contract(
+            server_args, sampling_defaults, num_frames=num_frames
+        )
+    return width, height, num_frames
+
+
+def _resolve_auto_residency_warmup_shape(
+    server_args: ServerArgs,
+    sampling_defaults: SamplingParams,
+    *,
+    warmup_shape: tuple[int, int, int | None],
+    server_based_warmup: bool,
+) -> tuple[int, int, int] | None:
+    """Return the full serving shape when bounded warmup is smaller.
+
+    The probe still runs only the bounded warmup step count. Denoising steps
+    repeat the same activation shape, so one full-shape forward measures the
+    placement constraints directly without paying for a full generation or
+    extrapolating a small-shape allocator peak.
+    """
+    if not server_based_warmup:
+        return None
+    if auto_residency_args_skip_reason(server_args) is not None:
+        return None
+    width, height, num_frames = resolve_default_workload_shape(
+        server_args, sampling_defaults
+    )
+    if width is None or height is None:
+        return None
+    target = (width, height, num_frames)
+    if target == warmup_shape:
+        return None
+    return target
 
 
 def _effective_cfg_scale(sampling_defaults: SamplingParams) -> float | None:
@@ -381,11 +578,42 @@ def build_warmup_reqs(
         sampling_defaults,
         server_based_warmup=server_based_warmup,
     )
+    auto_residency_warmup_shape = (
+        _resolve_auto_residency_warmup_shape(
+            server_args,
+            sampling_defaults,
+            warmup_shape=(width, height, warmup_num_frames),
+            server_based_warmup=server_based_warmup,
+        )
+        if warmup_resolutions is None
+        else None
+    )
+    collect_auto_residency_metrics = (
+        warmup_resolutions is None
+        and server_based_warmup
+        and auto_residency_args_skip_reason(server_args) is None
+    )
+    if collect_auto_residency_metrics and sampling_defaults.num_inference_steps:
+        warmup_steps = min(
+            int(sampling_defaults.num_inference_steps),
+            max(warmup_steps, AUTO_RESIDENCY_TIMING_STEPS),
+        )
+    shapes = [
+        (width, height, warmup_num_frames, False) for width, height in resolutions
+    ]
+    if auto_residency_warmup_shape is not None:
+        # The bounded warmup runs first: its measurement lets the worker size
+        # the full-shape probe to the memory the card actually has left. The
+        # bounded shape then runs once more so the allocator pool and kernel
+        # caches serving starts from are shaped by a serving-sized request,
+        # not by the probe (the worker drops the probe's pool before it).
+        shapes.append((*auto_residency_warmup_shape, True))
+        shapes.append(shapes[0])
 
     # build warmup reqs
     warmup_reqs = []
     include_warmup_image = should_include_warmup_image(server_args, server_based_warmup)
-    for width, height in resolutions:
+    for width, height, num_frames, is_probe in shapes:
         req_kwargs = dict(
             data_type=task_type.data_type(),
             width=width,
@@ -399,7 +627,7 @@ def build_warmup_reqs(
             guidance_scale_2=sampling_defaults.guidance_scale_2,
             true_cfg_scale=sampling_defaults.true_cfg_scale,
             num_inference_steps=sampling_defaults.num_inference_steps,
-            num_frames=warmup_num_frames,
+            num_frames=num_frames,
         )
         if include_warmup_image:
             if warmup_input_path is None:
@@ -407,7 +635,13 @@ def build_warmup_reqs(
                     "Warmup image path is required for image-input model"
                 )
             req_kwargs["prompt"] = DEFAULT_PLACEHOLDER_PROMPT
-            req_kwargs["image_path"] = [warmup_input_path]
+            default_image_path = sampling_defaults.image_path
+            image_count = (
+                len(default_image_path)
+                if isinstance(default_image_path, (list, tuple))
+                else 1
+            )
+            req_kwargs["image_path"] = [warmup_input_path] * max(1, image_count)
         if server_args.enable_cfg_parallel:
             if not req_kwargs.get("negative_prompt"):
                 req_kwargs["negative_prompt"] = DEFAULT_PLACEHOLDER_PROMPT
@@ -441,6 +675,13 @@ def build_warmup_reqs(
                 req.extra["return_warmup_result"] = True
             if server_based_warmup:
                 req.extra["server_based_warmup"] = True
+            if collect_auto_residency_metrics:
+                # Stage timers already synchronize around warmup stages. Keep
+                # their values for residency planning instead of discarding
+                # the measurements after paying that cost.
+                req.metrics.suppress_stage_breakdown = False
+            if is_probe:
+                req.extra["auto_residency_full_shape_probe"] = True
             warmup_reqs.append(req)
 
     return warmup_reqs

python/sglang/multimodal_gen/test/server/perf_baselines/h100.json

@@ -1568,6 +1568,7 @@
             "expected_median_denoise_ms": 1010.9,
             "load_peak_vram_mb": 27320.0,
             "runtime_peak_vram_mb": 35926.0,
+            "warmup_peak_vram_mb": 56748.0,
             "load_peak_allocated_mb": 26241.0,
             "runtime_peak_allocated_mb": 31922.0,
             "estimated_full_test_time_s": 77.0
@@ -1701,6 +1702,7 @@
             "expected_median_denoise_ms": 656.68,
             "load_peak_vram_mb": 35350.0,
             "runtime_peak_vram_mb": 42674.0,
+            "warmup_peak_vram_mb": 45782.0,
             "load_peak_allocated_mb": 34033.0,
             "runtime_peak_allocated_mb": 38569.0,
             "estimated_full_test_time_s": 243.0
@@ -1833,6 +1835,7 @@
             "expected_median_denoise_ms": 2020.71,
             "load_peak_vram_mb": 6440.0,
             "runtime_peak_vram_mb": 21524.0,
+            "warmup_peak_vram_mb": 26902.0,
             "load_peak_allocated_mb": 6175.0,
             "runtime_peak_allocated_mb": 15472.0,
             "estimated_full_test_time_s": 204.3
@@ -2812,7 +2815,7 @@
             "expected_avg_denoise_ms": 477.49,
             "expected_median_denoise_ms": 56.74,
             "load_peak_vram_mb": 34022.0,
-            "runtime_peak_vram_mb": 61190.0,
+            "runtime_peak_vram_mb": 62706.0,
             "load_peak_allocated_mb": 33866.0,
             "runtime_peak_allocated_mb": 57075.0,
             "estimated_full_test_time_s": 153.1

python/sglang/multimodal_gen/test/server/test_server_common.py

@@ -465,6 +465,7 @@ class DiffusionServerBase:
                         summary,
                         expected_load_peak_vram_mb,
                         expected_runtime_peak_vram_mb,
+                        scenario.warmup_peak_vram_mb,
                         expected_load_peak_allocated_mb=scenario.load_peak_allocated_mb,
                         expected_runtime_peak_allocated_mb=(
                             scenario.runtime_peak_allocated_mb
@@ -539,10 +540,12 @@ class DiffusionServerBase:
 
         if os.environ.get("SGLANG_GEN_BASELINE", "0") == "1":
             logger.info(
-                "%s realtime peak VRAM baseline: load=%.0fMiB, runtime=%.0fMiB",
+                "%s realtime peak VRAM baseline: load=%.0fMiB, runtime=%.0fMiB, "
+                "warmup=%.0fMiB",
                 case.id,
                 summary.load_peak_vram_mb,
                 summary.runtime_peak_vram_mb,
+                summary.warmup_peak_vram_mb,
             )
             return
 
@@ -559,6 +562,7 @@ class DiffusionServerBase:
                 summary,
                 scenario.load_peak_vram_mb,
                 scenario.runtime_peak_vram_mb,
+                scenario.warmup_peak_vram_mb,
                 expected_load_peak_allocated_mb=scenario.load_peak_allocated_mb,
                 expected_runtime_peak_allocated_mb=scenario.runtime_peak_allocated_mb,
             )
@@ -579,6 +583,7 @@ class DiffusionServerBase:
             "median_denoise_ms": summary.median_denoise_ms,
             "load_peak_vram_mb": summary.load_peak_vram_mb,
             "runtime_peak_vram_mb": summary.runtime_peak_vram_mb,
+            "warmup_peak_vram_mb": summary.warmup_peak_vram_mb,
             "load_peak_allocated_mb": summary.load_peak_allocated_mb,
             "runtime_peak_allocated_mb": summary.runtime_peak_allocated_mb,
             "stage_metrics": summary.stage_metrics,
@@ -680,6 +685,7 @@ class DiffusionServerBase:
                 {
                     "load_peak_vram_mb": round(summary.load_peak_vram_mb, 2),
                     "runtime_peak_vram_mb": round(summary.runtime_peak_vram_mb, 2),
+                    "warmup_peak_vram_mb": round(summary.warmup_peak_vram_mb, 2),
                     "load_peak_allocated_mb": round(summary.load_peak_allocated_mb, 2),
                     "runtime_peak_allocated_mb": round(
                         summary.runtime_peak_allocated_mb, 2

python/sglang/multimodal_gen/test/server/test_server_utils.py

@@ -405,7 +405,7 @@ class ServerManager:
             "--log-level=debug",
         ]
         if self.extra_args.strip():
-            command.extend(self.extra_args.strip().split())
+            command.extend(shlex.split(self.extra_args))
         access_log_exclude_flag = "--uvicorn-access-log-exclude-prefixes"
         if not any(arg.startswith(access_log_exclude_flag) for arg in command):
             command.extend(["--uvicorn-access-log-exclude-prefixes", "/health"])
@@ -590,6 +590,7 @@ class PerformanceValidator:
         summary: PerformanceSummary,
         expected_load_peak_vram_mb: float,
         expected_runtime_peak_vram_mb: float,
+        expected_warmup_peak_vram_mb: float | None = None,
         expected_load_peak_allocated_mb: float | None = None,
         expected_runtime_peak_allocated_mb: float | None = None,
     ) -> None:
@@ -611,6 +612,16 @@ class PerformanceValidator:
             expected_allocated=expected_runtime_peak_allocated_mb,
             tolerance=self.tolerances.runtime_peak_vram,
         )
+        # the full-shape warmup probe keeps its own budget, separate from serving
+        if expected_warmup_peak_vram_mb is not None and summary.warmup_peak_vram_mb > 0:
+            self._assert_le(
+                "Warmup Peak VRAM",
+                summary.warmup_peak_vram_mb,
+                expected_warmup_peak_vram_mb,
+                self.tolerances.runtime_peak_vram,
+                min_abs_tolerance=128.0,
+                unit=" MiB",
+            )
 
     def _assert_peak_vram(
         self,

python/sglang/multimodal_gen/test/server/testcase_configs.py

@@ -117,6 +117,9 @@ class ScenarioConfig:
     estimated_full_test_time_s: float | None = None
     load_peak_vram_mb: float | None = None
     runtime_peak_vram_mb: float | None = None
+    # Peak of the warmup calibration probe (the default workload's full shape
+    # under the load-safe placement); None skips the check until a baseline exists.
+    warmup_peak_vram_mb: float | None = None
     # Allocated peaks; when present they are the enforced VRAM figure and the
     # reserved peaks above are reported only (reserved tracks pool history).
     load_peak_allocated_mb: float | None = None
@@ -140,6 +143,7 @@ class ScenarioConfig:
             estimated_full_test_time_s=optional_float("estimated_full_test_time_s"),
             load_peak_vram_mb=optional_float("load_peak_vram_mb"),
             runtime_peak_vram_mb=optional_float("runtime_peak_vram_mb"),
+            warmup_peak_vram_mb=optional_float("warmup_peak_vram_mb"),
             load_peak_allocated_mb=optional_float("load_peak_allocated_mb"),
             runtime_peak_allocated_mb=optional_float("runtime_peak_allocated_mb"),
             load_peak_host_anon_mb=optional_float("load_peak_host_anon_mb"),
@@ -451,6 +455,7 @@ class PerformanceSummary:
     all_denoise_steps: dict[int, float]
     load_peak_vram_mb: float = 0.0
     runtime_peak_vram_mb: float = 0.0
+    warmup_peak_vram_mb: float = 0.0
     load_peak_allocated_mb: float = 0.0
     runtime_peak_allocated_mb: float = 0.0
     load_peak_host_anon_mb: float = 0.0
@@ -490,6 +495,9 @@ class PerformanceSummary:
         runtime_peak_vram_mb = float(
             record.memory_snapshots.get("runtime_peak", {}).get("peak_reserved_mb", 0.0)
         )
+        warmup_peak_vram_mb = float(
+            record.memory_snapshots.get("warmup_peak", {}).get("peak_reserved_mb", 0.0)
+        )
         load_peak_allocated_mb = float(
             record.memory_snapshots.get("load_peak", {}).get("peak_allocated_mb", 0.0)
         )
@@ -517,6 +525,7 @@ class PerformanceSummary:
             all_denoise_steps=per_step,
             load_peak_vram_mb=load_peak_vram_mb,
             runtime_peak_vram_mb=runtime_peak_vram_mb,
+            warmup_peak_vram_mb=warmup_peak_vram_mb,
             load_peak_allocated_mb=load_peak_allocated_mb,
             runtime_peak_allocated_mb=runtime_peak_allocated_mb,
             load_peak_host_anon_mb=load_peak_host_anon_mb,

python/sglang/multimodal_gen/test/unit/realtime/test_realtime_runtime.py

@@ -35,6 +35,7 @@ from sglang.multimodal_gen.runtime.entrypoints.openai.realtime.realtime_output_a
 )
 from sglang.multimodal_gen.runtime.entrypoints.openai.realtime.registry import (
     get_realtime_model_adapter,
+    has_realtime_model_adapter,
 )
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import OutputBatch
 from sglang.multimodal_gen.runtime.pipelines_core.stages.model_specific_stages.lingbot_world.lingbot_world_causal_denoising import (
@@ -976,6 +977,7 @@ def test_realtime_input_validation_reuses_generator_across_chunks():
 def test_realtime_registry_resolves_lingbot_adapter():
     server_args = SimpleNamespace(pipeline_config=LingBotWorldCausalDMDConfig())
 
+    assert has_realtime_model_adapter(server_args)
     adapter = get_realtime_model_adapter(server_args)
 
     assert isinstance(adapter, lingbot_realtime.LingBotWorldRealtimeAdapter)

python/sglang/multimodal_gen/test/unit/test_auto_residency.py

@@ -0,0 +1,921 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Unit tests for warmup-calibrated auto residency adjustment."""
+
+from types import SimpleNamespace
+
+import pytest
+
+from sglang.multimodal_gen.configs.pipeline_configs.base import ModelTaskType
+from sglang.multimodal_gen.configs.pipeline_configs.longlive2 import LongLive2T2VConfig
+from sglang.multimodal_gen.runtime.managers.memory_managers.auto_residency import (
+    ACTIVATION_EXTRAPOLATION_MARGIN,
+    GIB_BYTES,
+    DefaultWorkload,
+    WarmupMemoryRecord,
+    estimate_default_workload_peak_bytes,
+    estimate_default_workload_timing,
+    estimate_layerwise_layer_uses,
+    estimate_workload_phase_peaks,
+    resolve_measured_default_workload,
+)
+from sglang.multimodal_gen.runtime.warmup_request_builder import (
+    SERVER_WARMUP_MAX_VIDEO_FRAMES,
+    _resolve_auto_residency_warmup_shape,
+    _resolve_warmup_num_frames,
+)
+
+
+def _record(
+    *,
+    width=832,
+    height=480,
+    num_frames=17,
+    baseline_gib=10,
+    peak_gib=12,
+    peak_reserved_gib=0,
+    succeeded=True,
+    num_inference_steps=1,
+    total_duration_ms=0.0,
+    stage_duration_ms=None,
+    step_duration_ms=(),
+    step_duration_ms_by_stage=None,
+    stage_iterations=None,
+    phase_active_components=None,
+    phase_used_components=None,
+    phase_full_weight_transition_components=None,
+    layerwise_layer_uses=None,
+    layerwise_layer_uses_by_stage=None,
+) -> WarmupMemoryRecord:
+    return WarmupMemoryRecord(
+        width=width,
+        height=height,
+        num_frames=num_frames,
+        baseline_allocated_bytes=baseline_gib * GIB_BYTES,
+        peak_allocated_bytes=peak_gib * GIB_BYTES,
+        succeeded=succeeded,
+        peak_reserved_bytes=peak_reserved_gib * GIB_BYTES,
+        num_inference_steps=num_inference_steps,
+        total_duration_ms=total_duration_ms,
+        stage_duration_ms=stage_duration_ms or {},
+        step_duration_ms=step_duration_ms,
+        step_duration_ms_by_stage=step_duration_ms_by_stage or {},
+        stage_iterations=stage_iterations or {},
+        phase_active_components=phase_active_components or {},
+        phase_used_components=phase_used_components or {},
+        phase_full_weight_transition_components=(
+            phase_full_weight_transition_components or {}
+        ),
+        layerwise_layer_uses=layerwise_layer_uses or {},
+        layerwise_layer_uses_by_stage=layerwise_layer_uses_by_stage or {},
+    )
+
+
+class TestEstimateDefaultWorkloadTiming:
+    def test_scales_only_denoising_with_target_steps(self):
+        record = _record(
+            num_inference_steps=2,
+            total_duration_ms=1_200,
+            stage_duration_ms={
+                "TextEncodingStage": 100,
+                "DenoisingStage": 1_000,
+            },
+            phase_active_components={
+                "0:TextEncodingStage:use:text_encoder": ("text_encoder",),
+                "1:DenoisingStage:use:transformer": ("transformer",),
+            },
+        )
+
+        request_ns, stage_ns, component_stages = estimate_default_workload_timing(
+            records=[record],
+            target_units=record.workload_units(),
+            target_num_inference_steps=40,
+        )
+
+        assert stage_ns == {
+            "TextEncodingStage": 100_000_000,
+            "DenoisingStage": 20_000_000_000,
+        }
+        assert request_ns == 20_200_000_000
+        assert component_stages == {
+            "text_encoder": ("TextEncodingStage",),
+            "transformer": ("DenoisingStage",),
+        }
+
+    def test_uses_steady_step_instead_of_scaling_first_step_setup(self):
+        record = _record(
+            num_inference_steps=2,
+            total_duration_ms=720,
+            stage_duration_ms={
+                "TextEncodingStage": 100,
+                "DenoisingStage": 620,
+            },
+            step_duration_ms=(500, 100),
+            phase_active_components={
+                "0:TextEncodingStage:use:text_encoder": ("text_encoder",),
+                "1:DenoisingStage:use:transformer": ("transformer",),
+            },
+        )
+
+        request_ns, stage_ns, _ = estimate_default_workload_timing(
+            records=[record],
+            target_units=record.workload_units(),
+            target_num_inference_steps=10,
+        )
+
+        assert stage_ns["DenoisingStage"] == 1_020_000_000
+        assert request_ns == 1_120_000_000
+
+    def test_scales_standard_and_nonstandard_denoising_stages_together(self):
+        record = _record(
+            num_inference_steps=2,
+            total_duration_ms=1_200,
+            stage_duration_ms={
+                "TextEncodingStage": 100,
+                "DenoisingStage": 500,
+                "CustomDenoisingStage": 600,
+            },
+            phase_active_components={
+                "0:TextEncodingStage:use:text_encoder": ("text_encoder",),
+                "1:DenoisingStage:use:transformer": ("transformer",),
+                "2:CustomDenoisingStage:use:custom_refiner": ("custom_refiner",),
+            },
+        )
+
+        request_ns, stage_ns, _ = estimate_default_workload_timing(
+            records=[record],
+            target_units=record.workload_units(),
+            target_num_inference_steps=10,
+        )
+
+        assert stage_ns == {
+            "TextEncodingStage": 100_000_000,
+            "DenoisingStage": 2_500_000_000,
+            "CustomDenoisingStage": 3_000_000_000,
+        }
+        assert request_ns == 5_600_000_000
+
+    def test_uses_each_stage_iteration_target(self):
+        record = _record(
+            num_inference_steps=4,
+            total_duration_ms=1_100,
+            stage_duration_ms={
+                "ShapeDenoisingStage": 500,
+                "PaintStage": 600,
+            },
+            step_duration_ms_by_stage={
+                "ShapeDenoisingStage": (200, 100, 100, 100),
+            },
+            stage_iterations={
+                "ShapeDenoisingStage": (4, 50),
+                "PaintStage": (4, 30),
+            },
+            phase_active_components={
+                "0:ShapeDenoisingStage:use:hy3dshape_model": ("hy3dshape_model",),
+                "1:PaintStage:use:paint_transformer": ("paint_transformer",),
+            },
+        )
+
+        request_ns, stage_ns, _ = estimate_default_workload_timing(
+            records=[record],
+            target_units=record.workload_units(),
+            target_num_inference_steps=50,
+        )
+
+        assert stage_ns == {
+            "ShapeDenoisingStage": 5_000_000_000,
+            "PaintStage": 4_500_000_000,
+        }
+        assert request_ns == 9_500_000_000
+
+
+class TestEstimateLayerwiseLayerUses:
+    def test_scales_repeated_groups_but_not_one_shot_groups(self):
+        record = _record(
+            num_inference_steps=2,
+            layerwise_layer_uses={
+                "transformer": {
+                    "token_refiner.blocks": (1, 1),
+                    "blocks": (2, 2, 2),
+                },
+                "vae": {
+                    "encoder.down_blocks": (0, 0),
+                    "decoder.up_blocks": (1, 1),
+                },
+            },
+        )
+
+        uses = estimate_layerwise_layer_uses(
+            records=[record],
+            target_units=record.workload_units(),
+            target_num_inference_steps=10,
+        )
+
+        assert uses["transformer"] == {
+            "token_refiner.blocks": (1, 1),
+            "blocks": (10, 10, 10),
+        }
+        assert uses["vae"] == {
+            "encoder.down_blocks": (0, 0),
+            "decoder.up_blocks": (1, 1),
+        }
+
+    def test_stage_role_scales_nonstandard_denoiser_but_not_encoder_repeats(self):
+        record = _record(
+            num_inference_steps=2,
+            phase_used_components={
+                "0:TextEncodingStage:use:custom_encoder": ("custom_encoder",),
+                "1:CustomDenoisingStage:use:custom_refiner": ("custom_refiner",),
+            },
+            layerwise_layer_uses={
+                "custom_encoder": {"layers": (2, 2)},
+                "custom_refiner": {"blocks": (2, 2)},
+            },
+        )
+
+        uses = estimate_layerwise_layer_uses(
+            records=[record],
+            target_units=record.workload_units(),
+            target_num_inference_steps=10,
+        )
+
+        assert uses["custom_encoder"]["layers"] == (2, 2)
+        assert uses["custom_refiner"]["blocks"] == (10, 10)
+
+    def test_scales_each_stage_calls_with_its_own_iteration_target(self):
+        record = _record(
+            num_inference_steps=4,
+            phase_used_components={
+                "0:ShapeStage:use:transformer": ("transformer",),
+                "1:PaintStage:use:transformer": ("transformer",),
+            },
+            stage_iterations={
+                "ShapeStage": (4, 50),
+                "PaintStage": (4, 30),
+            },
+            layerwise_layer_uses={
+                "transformer": {"blocks": (9, 9), "one_shot": (2, 2)}
+            },
+            layerwise_layer_uses_by_stage={
+                "ShapeStage": {"transformer": {"blocks": (4, 4), "one_shot": (1, 1)}},
+                "PaintStage": {"transformer": {"blocks": (4, 4), "one_shot": (1, 1)}},
+            },
+        )
+
+        uses = estimate_layerwise_layer_uses(
+            records=[record],
+            target_units=record.workload_units(),
+            target_num_inference_steps=50,
+        )
+
+        # one untracked one-shot call + 50 shape calls + 30 paint calls
+        assert uses["transformer"]["blocks"] == (81, 81)
+        assert uses["transformer"]["one_shot"] == (2, 2)
+
+
+class TestEstimateDefaultWorkloadPeak:
+    def test_same_shape_uses_measured_peak(self):
+        record = _record()
+        estimate = estimate_default_workload_peak_bytes(
+            records=[record], target_units=record.workload_units()
+        )
+        assert estimate == record.peak_allocated_bytes
+
+    def test_unknown_target_disables_estimation(self):
+        # An unknown target would silently equate the capped warmup peak with
+        # the serving peak and promote with no margin at all.
+        record = _record()
+        estimate = estimate_default_workload_peak_bytes(
+            records=[record], target_units=None
+        )
+        assert estimate is None
+
+    def test_single_capped_record_scales_only_the_activation_part(self):
+        # Warmup capped to 832x480x17; Wan-class default is 704x1280x121.
+        record = _record()
+        target_units = 704 * 1280 * 121
+        ratio = target_units / record.workload_units()
+        assert ratio > 10  # the cap ratio this formula exists for
+
+        estimate = estimate_default_workload_peak_bytes(
+            records=[record], target_units=target_units
+        )
+        activation = record.peak_allocated_bytes - record.baseline_allocated_bytes
+        expected = record.baseline_allocated_bytes + int(
+            activation * ratio * ACTIVATION_EXTRAPOLATION_MARGIN
+        )
+        assert estimate == expected
+        # Scaling the whole peak would inflate the estimate by the resident
+        # weights times the cap ratio and adjustment would never trigger.
+        naive = int(record.peak_allocated_bytes * ratio)
+        assert estimate < naive
+
+    def test_two_point_fit_separates_constant_from_linear(self):
+        # Two calibration sizes let the estimator measure the slope instead
+        # of assuming everything above the baseline scales. Under offload the
+        # baseline is nearly empty, so the single-point formula would scale
+        # the whole peak (~x13 here); the fit extrapolates only the measured
+        # linear part.
+        small = _record(num_frames=9, baseline_gib=1, peak_gib=14)
+        large = _record(num_frames=17, baseline_gib=1, peak_gib=16)
+        target_units = 1280 * 720 * 81
+
+        estimate = estimate_default_workload_peak_bytes(
+            records=[small, large], target_units=target_units
+        )
+        slope = (large.peak_allocated_bytes - small.peak_allocated_bytes) / (
+            large.workload_units() - small.workload_units()
+        )
+        constant = large.peak_allocated_bytes - slope * large.workload_units()
+        expected = int(
+            constant + slope * target_units * ACTIVATION_EXTRAPOLATION_MARGIN
+        )
+        assert estimate == expected
+        single_point = estimate_default_workload_peak_bytes(
+            records=[large], target_units=target_units
+        )
+        assert estimate < single_point
+
+    def test_negative_slope_falls_back_to_single_point_formula(self):
+        small = _record(num_frames=9, peak_gib=16)
+        large = _record(num_frames=17, peak_gib=14)
+        target_units = 1280 * 720 * 81
+        estimate = estimate_default_workload_peak_bytes(
+            records=[small, large], target_units=target_units
+        )
+        fallback = max(
+            estimate_default_workload_peak_bytes(
+                records=[record], target_units=target_units
+            )
+            for record in (small, large)
+        )
+        assert estimate == fallback
+
+    def test_weight_floor_prevents_scaling_constant_component_memory(self):
+        small = WarmupMemoryRecord(
+            width=832,
+            height=480,
+            num_frames=9,
+            baseline_allocated_bytes=GIB_BYTES,
+            peak_allocated_bytes=int(29.5 * GIB_BYTES),
+            succeeded=True,
+        )
+        large = WarmupMemoryRecord(
+            width=832,
+            height=480,
+            num_frames=17,
+            baseline_allocated_bytes=GIB_BYTES,
+            peak_allocated_bytes=int(29.4 * GIB_BYTES),
+            succeeded=True,
+        )
+        target_units = 1024 * 1024 * 81
+
+        estimate = estimate_default_workload_peak_bytes(
+            records=[small, large],
+            target_units=target_units,
+            constant_weight_bytes=int(29.4 * GIB_BYTES),
+        )
+        fallback_without_weights = estimate_default_workload_peak_bytes(
+            records=[small, large], target_units=target_units
+        )
+
+        assert estimate < 50 * GIB_BYTES
+        assert estimate < fallback_without_weights
+
+    def test_covering_measurement_bounds_the_target(self):
+        capped = _record(num_frames=17, peak_gib=12)
+        full = _record(width=1280, height=720, num_frames=81, peak_gib=30)
+        estimate = estimate_default_workload_peak_bytes(
+            records=[capped, full], target_units=1280 * 720 * 81
+        )
+        assert estimate == full.peak_allocated_bytes
+
+    def test_multiple_records_take_the_max(self):
+        low = _record(peak_gib=12)
+        high = _record(peak_gib=20)
+        estimate = estimate_default_workload_peak_bytes(
+            records=[low, high], target_units=low.workload_units()
+        )
+        assert estimate == high.peak_allocated_bytes
+
+    def test_failure_at_the_target_size_disables_estimation(self):
+        good = _record(num_frames=9, peak_gib=8)
+        failed = _record(num_frames=17, succeeded=False)
+        assert (
+            estimate_default_workload_peak_bytes(
+                records=[good, failed], target_units=failed.workload_units()
+            )
+            is None
+        )
+
+    def test_failure_below_the_target_size_disables_estimation(self):
+        good = _record(num_frames=9, peak_gib=8)
+        failed = _record(num_frames=17, succeeded=False)
+        assert (
+            estimate_default_workload_peak_bytes(
+                records=[good, failed], target_units=failed.workload_units() * 2
+            )
+            is None
+        )
+
+    def test_failure_above_the_target_size_is_dropped(self):
+        good = _record(num_frames=9, peak_gib=8)
+        failed = _record(num_frames=81, succeeded=False)
+        estimate = estimate_default_workload_peak_bytes(
+            records=[good, failed], target_units=good.workload_units()
+        )
+        assert estimate == good.peak_allocated_bytes
+
+    def test_no_records_disables_estimation(self):
+        assert (
+            estimate_default_workload_peak_bytes(records=[], target_units=None) is None
+        )
+
+    def test_phase_estimation_preserves_active_component_membership(self):
+        small = _record(num_frames=9, peak_gib=30)
+        large = _record(num_frames=17, peak_gib=32)
+        small = WarmupMemoryRecord(
+            width=small.width,
+            height=small.height,
+            num_frames=small.num_frames,
+            baseline_allocated_bytes=small.baseline_allocated_bytes,
+            peak_allocated_bytes=small.peak_allocated_bytes,
+            succeeded=small.succeeded,
+            phase_peak_allocated_bytes={"denoise": 30 * GIB_BYTES},
+            phase_active_components={"denoise": ("transformer",)},
+        )
+        large = WarmupMemoryRecord(
+            width=large.width,
+            height=large.height,
+            num_frames=large.num_frames,
+            baseline_allocated_bytes=large.baseline_allocated_bytes,
+            peak_allocated_bytes=large.peak_allocated_bytes,
+            succeeded=large.succeeded,
+            phase_peak_allocated_bytes={"denoise": 32 * GIB_BYTES},
+            phase_active_components={"denoise": ("transformer",)},
+        )
+
+        peaks, active, used, _ = estimate_workload_phase_peaks(
+            records=[small, large],
+            target_units=832 * 480 * 81,
+            component_weight_bytes={"transformer": 28 * GIB_BYTES},
+        )
+
+        assert peaks["denoise"] >= 32 * GIB_BYTES
+        assert active == {"denoise": ("transformer",)}
+        assert used == active
+
+    def test_phase_estimation_uses_allocated_peak(self):
+        record = WarmupMemoryRecord(
+            width=1024,
+            height=1024,
+            num_frames=1,
+            baseline_allocated_bytes=5 * GIB_BYTES,
+            peak_allocated_bytes=12 * GIB_BYTES,
+            succeeded=True,
+            phase_peak_allocated_bytes={"denoise": 11 * GIB_BYTES},
+            phase_active_components={"denoise": ("transformer",)},
+        )
+
+        peaks, _, _, _ = estimate_workload_phase_peaks(
+            records=[record],
+            target_units=record.workload_units(),
+            component_weight_bytes={"transformer": 10 * GIB_BYTES},
+        )
+
+        assert peaks["denoise"] == 11 * GIB_BYTES
+
+    def test_phase_estimation_preserves_full_weight_transition_components(self):
+        record = WarmupMemoryRecord(
+            width=1024,
+            height=1024,
+            num_frames=1,
+            baseline_allocated_bytes=2 * GIB_BYTES,
+            peak_allocated_bytes=4 * GIB_BYTES,
+            succeeded=True,
+            phase_peak_allocated_bytes={"lora_switch": 4 * GIB_BYTES},
+            phase_full_weight_transition_components={"lora_switch": ("transformer",)},
+        )
+
+        peaks, _, _, transitions = estimate_workload_phase_peaks(
+            records=[record],
+            target_units=record.workload_units(),
+            component_weight_bytes={"transformer": 2 * GIB_BYTES},
+        )
+
+        assert peaks == {"lora_switch": 4 * GIB_BYTES}
+        assert transitions == {"lora_switch": ("transformer",)}
+
+    def test_phase_estimation_prefers_target_layout_over_smaller_warmup(self):
+        small = WarmupMemoryRecord(
+            width=256,
+            height=256,
+            num_frames=9,
+            baseline_allocated_bytes=2 * GIB_BYTES,
+            peak_allocated_bytes=30 * GIB_BYTES,
+            succeeded=True,
+            phase_peak_allocated_bytes={"denoise": 30 * GIB_BYTES},
+            phase_active_components={"denoise": ()},
+        )
+        target = WarmupMemoryRecord(
+            width=768,
+            height=512,
+            num_frames=25,
+            baseline_allocated_bytes=2 * GIB_BYTES,
+            peak_allocated_bytes=48 * GIB_BYTES,
+            succeeded=True,
+            phase_peak_allocated_bytes={"denoise": 48 * GIB_BYTES},
+            phase_active_components={"denoise": ("transformer",)},
+        )
+
+        peaks, active, used, _ = estimate_workload_phase_peaks(
+            records=[small, target],
+            target_units=target.workload_units(),
+            component_weight_bytes={"transformer": 40 * GIB_BYTES},
+        )
+
+        assert peaks == {"denoise": 48 * GIB_BYTES}
+        assert active == {"denoise": ("transformer",)}
+        assert used == active
+
+    def test_phase_estimation_keeps_distinct_active_layouts_separate(self):
+        transformer_phase = WarmupMemoryRecord(
+            width=768,
+            height=512,
+            num_frames=25,
+            baseline_allocated_bytes=2 * GIB_BYTES,
+            peak_allocated_bytes=48 * GIB_BYTES,
+            succeeded=True,
+            phase_peak_allocated_bytes={"denoise": 48 * GIB_BYTES},
+            phase_active_components={"denoise": ("transformer",)},
+        )
+        encoder_phase = WarmupMemoryRecord(
+            width=768,
+            height=512,
+            num_frames=25,
+            baseline_allocated_bytes=2 * GIB_BYTES,
+            peak_allocated_bytes=20 * GIB_BYTES,
+            succeeded=True,
+            phase_peak_allocated_bytes={"denoise": 20 * GIB_BYTES},
+            phase_active_components={"denoise": ("text_encoder",)},
+        )
+
+        peaks, active, used, _ = estimate_workload_phase_peaks(
+            records=[transformer_phase, encoder_phase],
+            target_units=transformer_phase.workload_units(),
+            component_weight_bytes={
+                "transformer": 40 * GIB_BYTES,
+                "text_encoder": 10 * GIB_BYTES,
+            },
+        )
+
+        assert peaks == {
+            "denoise:layout:0": 20 * GIB_BYTES,
+            "denoise:layout:1": 48 * GIB_BYTES,
+        }
+        assert active == {
+            "denoise:layout:0": ("text_encoder",),
+            "denoise:layout:1": ("transformer",),
+        }
+        assert used == active
+
+
+class TestResolveMeasuredDefaultWorkload:
+    def test_uses_effective_warmup_resolution_for_implicit_image_size(self):
+        workload = DefaultWorkload(
+            width=None,
+            height=None,
+            num_frames=1,
+            num_inference_steps=40,
+        )
+
+        resolved = resolve_measured_default_workload(
+            workload,
+            [
+                _record(width=512, height=512),
+                _record(width=1024, height=1024),
+            ],
+        )
+
+        assert resolved == DefaultWorkload(
+            width=1024,
+            height=1024,
+            num_frames=1,
+            num_inference_steps=40,
+        )
+
+    def test_keeps_default_frames_when_warmup_caps_video(self):
+        workload = DefaultWorkload(
+            width=None,
+            height=None,
+            num_frames=81,
+            num_inference_steps=30,
+        )
+
+        resolved = resolve_measured_default_workload(
+            workload, [_record(width=832, height=480, num_frames=17)]
+        )
+
+        assert resolved.num_frames == 81
+
+    def test_does_not_replace_explicit_default_shape(self):
+        workload = DefaultWorkload(
+            width=1280,
+            height=720,
+            num_frames=81,
+            num_inference_steps=30,
+        )
+
+        assert (
+            resolve_measured_default_workload(
+                workload, [_record(width=512, height=512)]
+            )
+            is workload
+        )
+
+
+class TestWarmupFrameAdjustment:
+    def _server_args(self, *, bcg: bool = False, num_gpus: int = 1) -> SimpleNamespace:
+        return SimpleNamespace(
+            pipeline_config=LongLive2T2VConfig(),
+            pipeline_class_name=None,
+            enable_breakable_cuda_graph=bcg,
+            num_gpus=num_gpus,
+        )
+
+    def _defaults(self) -> SimpleNamespace:
+        return SimpleNamespace(
+            num_frames=61,
+            adjust_frames=True,
+            enable_sequence_shard=None,
+            num_frames_round_down=False,
+        )
+
+    def test_capped_frames_keep_the_model_frame_contract(self):
+        # LongLive2 default 61 frames is capped to 17, whose 5 latent frames
+        # break the 8-frame causal block; the builder must re-align to 29.
+        assert SERVER_WARMUP_MAX_VIDEO_FRAMES == 17
+        num_frames = _resolve_warmup_num_frames(
+            self._server_args(), self._defaults(), server_based_warmup=True
+        )
+        assert num_frames == 29
+
+    def test_bcg_keeps_full_serving_frames(self):
+        num_frames = _resolve_warmup_num_frames(
+            self._server_args(bcg=True), self._defaults(), server_based_warmup=True
+        )
+        assert num_frames == 61
+
+    def test_non_server_warmup_keeps_default_frames(self):
+        num_frames = _resolve_warmup_num_frames(
+            self._server_args(), self._defaults(), server_based_warmup=False
+        )
+        assert num_frames == 61
+
+    def test_capped_frames_get_the_gpu_alignment_real_requests_get(self):
+        # a frame-aligning pipeline (no sequence shard) on multiple GPUs:
+        # 17 frames -> 5 latent frames -> ceil to 6 latents on 2 GPUs -> 21
+        args = SimpleNamespace(
+            pipeline_config=SimpleNamespace(
+                task_type=ModelTaskType.T2V,
+                adjust_num_frames=lambda n: n,
+                vae_config=SimpleNamespace(
+                    use_temporal_scaling_frames=True,
+                    arch_config=SimpleNamespace(temporal_compression_ratio=4),
+                ),
+            ),
+            pipeline_class_name=None,
+            enable_breakable_cuda_graph=False,
+            num_gpus=2,
+        )
+        defaults = SimpleNamespace(
+            num_frames=81,
+            adjust_frames=True,
+            enable_sequence_shard=None,
+            num_frames_round_down=False,
+        )
+        num_frames = _resolve_warmup_num_frames(
+            args, defaults, server_based_warmup=True
+        )
+        assert num_frames == 21
+
+
+class TestAutoResidencyWarmupShape:
+    def _patch_gate(self, monkeypatch, reason: str | None = None) -> None:
+        monkeypatch.setattr(
+            "sglang.multimodal_gen.runtime.warmup_request_builder.auto_residency_args_skip_reason",
+            lambda _args: reason,
+        )
+
+    def _wan_like_args(self) -> SimpleNamespace:
+        return SimpleNamespace(
+            pipeline_config=SimpleNamespace(
+                task_type=ModelTaskType.T2V,
+                adjust_num_frames=lambda n: n,
+            ),
+            num_gpus=1,
+        )
+
+    def _defaults(
+        self,
+        num_frames: int,
+        *,
+        width: int | None = 1280,
+        height: int | None = 720,
+        supported_resolutions=None,
+    ) -> SimpleNamespace:
+        return SimpleNamespace(
+            width=width,
+            height=height,
+            num_frames=num_frames,
+            supported_resolutions=supported_resolutions,
+            adjust_frames=True,
+            enable_sequence_shard=None,
+            num_frames_round_down=False,
+        )
+
+    def test_capped_video_gets_a_full_shape_probe(self, monkeypatch):
+        self._patch_gate(monkeypatch)
+        probe = _resolve_auto_residency_warmup_shape(
+            self._wan_like_args(),
+            self._defaults(81),
+            warmup_shape=(832, 480, 17),
+            server_based_warmup=True,
+        )
+        assert probe == (1280, 720, 81)
+
+    def test_matching_warmup_needs_no_probe(self, monkeypatch):
+        self._patch_gate(monkeypatch)
+        probe = _resolve_auto_residency_warmup_shape(
+            self._wan_like_args(),
+            self._defaults(17, width=832, height=480),
+            warmup_shape=(832, 480, 17),
+            server_based_warmup=True,
+        )
+        assert probe is None
+
+    def test_unknown_target_resolution_skips_probe(self, monkeypatch):
+        self._patch_gate(monkeypatch)
+        probe = _resolve_auto_residency_warmup_shape(
+            self._wan_like_args(),
+            self._defaults(81, width=None, height=None),
+            warmup_shape=(832, 480, 17),
+            server_based_warmup=True,
+        )
+        assert probe is None
+
+    def test_skip_gate_disables_probe(self, monkeypatch):
+        # Full-shape calibration must share the adjustment's own gate (kill
+        # switch, quantized, manual, ...).
+        self._patch_gate(monkeypatch, reason="performance_mode=manual")
+        probe = _resolve_auto_residency_warmup_shape(
+            self._wan_like_args(),
+            self._defaults(81),
+            warmup_shape=(832, 480, 17),
+            server_based_warmup=True,
+        )
+        assert probe is None
+
+    def test_supported_resolution_fills_missing_target_size(self, monkeypatch):
+        self._patch_gate(monkeypatch)
+        probe = _resolve_auto_residency_warmup_shape(
+            self._wan_like_args(),
+            self._defaults(
+                81,
+                width=None,
+                height=None,
+                supported_resolutions=[(832, 480), (1024, 1024)],
+            ),
+            warmup_shape=(832, 480, 17),
+            server_based_warmup=True,
+        )
+        assert probe == (1024, 1024, 81)
+
+
+class TestAutoResidencySkipReason:
+    def _base_args(self, **overrides) -> SimpleNamespace:
+        args = SimpleNamespace(
+            performance_mode="auto",
+            warmup_mode="server",
+            warmup_resolutions=None,
+            disagg_role="monolithic",
+            backend="sglang",
+            enable_breakable_cuda_graph=False,
+            enable_torch_compile=False,
+            batching_max_size=1,
+            dp_size=1,
+            ulysses_degree=1,
+            use_fsdp_inference=False,
+            quantization=None,
+            component_quantizations={},
+            transformer_weights_path=None,
+            nunchaku_config=None,
+            direct_gpu_weight_loading=False,
+            ltx2_two_stage_device_mode=None,
+            pipeline_class_name=None,
+            pipeline_config=SimpleNamespace(
+                task_type=ModelTaskType.T2V,
+                supports_auto_residency=True,
+            ),
+        )
+        for key, value in overrides.items():
+            setattr(args, key, value)
+        return args
+
+    def _skip_reason(self, args):
+        from sglang.multimodal_gen.runtime.server_warmup import (
+            auto_residency_skip_reason,
+        )
+
+        return auto_residency_skip_reason(args)
+
+    def test_env_kill_switch(self, monkeypatch):
+        monkeypatch.setenv("SGLANG_DIFFUSION_DISABLE_AUTO_RESIDENCY", "1")
+        reason = self._skip_reason(self._base_args())
+        assert (
+            reason is not None and "SGLANG_DIFFUSION_DISABLE_AUTO_RESIDENCY" in reason
+        )
+
+    def test_manual_performance_mode(self, monkeypatch):
+        monkeypatch.delenv("SGLANG_DIFFUSION_DISABLE_AUTO_RESIDENCY", raising=False)
+        reason = self._skip_reason(self._base_args(performance_mode="manual"))
+        assert reason == "performance_mode=manual"
+
+    @pytest.mark.parametrize(
+        "overrides, expected_fragment",
+        [
+            ({"warmup_mode": "request"}, "server warmup"),
+            ({"disagg_role": "denoiser"}, "server warmup"),
+            ({"backend": "diffusers"}, "diffusers"),
+            (
+                {"ltx2_two_stage_device_mode": "original"},
+                "LTX-2 original two-stage placement",
+            ),
+            (
+                {"pipeline_class_name": "LTX2TwoStagePipeline"},
+                "legacy LTX-2 two-stage placement",
+            ),
+            ({"enable_breakable_cuda_graph": True}, "CUDA graph"),
+            # compile warmup strips the memory layout (layerwise DiT +
+            # resident aux components on CPU): its peaks are not serving peaks
+            ({"enable_torch_compile": True}, "stripped memory layout"),
+            ({"batching_max_size": 4}, "batching"),
+            (
+                {
+                    "pipeline_config": SimpleNamespace(
+                        task_type=ModelTaskType.T2V,
+                        supports_auto_residency=False,
+                    )
+                },
+                "post-warmup residency changes",
+            ),
+        ],
+    )
+    def test_excluded_paths(self, monkeypatch, overrides, expected_fragment):
+        monkeypatch.delenv("SGLANG_DIFFUSION_DISABLE_AUTO_RESIDENCY", raising=False)
+        monkeypatch.delenv("SGLANG_CACHE_DIT_ENABLED", raising=False)
+        reason = self._skip_reason(self._base_args(**overrides))
+        assert reason is not None and expected_fragment in reason
+
+    @pytest.mark.parametrize(
+        "overrides",
+        [
+            {"quantization": "fp8"},
+            {"component_quantizations": {"image_encoder": "fp8"}},
+            {"transformer_weights_path": "/x.safetensors"},
+            {"direct_gpu_weight_loading": True},
+        ],
+    )
+    def test_fixed_loading_paths_still_calibrate_other_components(
+        self, monkeypatch, overrides
+    ):
+        monkeypatch.delenv("SGLANG_DIFFUSION_DISABLE_AUTO_RESIDENCY", raising=False)
+        monkeypatch.delenv("SGLANG_CACHE_DIT_ENABLED", raising=False)
+        reason = self._skip_reason(self._base_args(**overrides))
+        assert reason is None or reason == "requires CUDA"
+
+    def test_cache_dit_excluded(self, monkeypatch):
+        monkeypatch.delenv("SGLANG_DIFFUSION_DISABLE_AUTO_RESIDENCY", raising=False)
+        monkeypatch.setenv("SGLANG_CACHE_DIT_ENABLED", "true")
+        reason = self._skip_reason(self._base_args())
+        assert reason is not None and "cache-dit" in reason
+
+    @pytest.mark.parametrize(
+        "overrides",
+        [
+            {"dp_size": 2},
+            {"ulysses_degree": 2},
+            {"use_fsdp_inference": True},
+        ],
+    )
+    def test_parallel_paths_reach_platform_gate(self, monkeypatch, overrides):
+        monkeypatch.delenv("SGLANG_DIFFUSION_DISABLE_AUTO_RESIDENCY", raising=False)
+        monkeypatch.delenv("SGLANG_CACHE_DIT_ENABLED", raising=False)
+        reason = self._skip_reason(self._base_args(**overrides))
+        assert reason is None or reason == "requires CUDA"
+
+    def test_eligible_path_reaches_platform_gate(self, monkeypatch):
+        monkeypatch.delenv("SGLANG_DIFFUSION_DISABLE_AUTO_RESIDENCY", raising=False)
+        monkeypatch.delenv("SGLANG_CACHE_DIT_ENABLED", raising=False)
+        reason = self._skip_reason(self._base_args())
+        # on a CUDA host everything passes; CPU CI stops at the platform gate
+        assert reason is None or reason == "requires CUDA"

python/sglang/multimodal_gen/test/unit/test_cfg_parallel_warmup.py

@@ -28,12 +28,13 @@ from sglang.multimodal_gen.configs.pipeline_configs.longlive2 import (
     LongLive2T2VConfig,
 )
 from sglang.multimodal_gen.configs.sample.longlive2 import LongLive2SamplingParams
+from sglang.multimodal_gen.configs.sample.minimax_h3 import MiniMaxH3SamplingParams
 from sglang.multimodal_gen.configs.sample.sampling_params import SamplingParams
-from sglang.multimodal_gen.runtime.entrypoints.diffusion_generator import DiffGenerator
-from sglang.multimodal_gen.runtime.entrypoints.utils import (
+from sglang.multimodal_gen.runtime.entrypoints.control_requests import (
     SetLoraReq,
     UnmergeLoraWeightsReq,
 )
+from sglang.multimodal_gen.runtime.entrypoints.diffusion_generator import DiffGenerator
 from sglang.multimodal_gen.runtime.managers.scheduler import Scheduler
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import (
     OutputBatch,
@@ -53,6 +54,7 @@ from sglang.multimodal_gen.runtime.server_warmup import (
 from sglang.multimodal_gen.runtime.warmup_request_builder import (
     DEFAULT_PLACEHOLDER_PROMPT,
     SERVER_WARMUP_IMAGE_FALLBACK_RESOLUTION,
+    _apply_warmup_sampling_overrides,
     _resolve_warmup_num_frames,
     build_warmup_reqs,
     should_include_warmup_image,
@@ -108,6 +110,51 @@ def _make_validation_server_args(enable_cfg_parallel: bool) -> MagicMock:
 class TestWarmupReqCfgParallel(unittest.TestCase):
     """Warmup request construction and req-based warmup guards."""
 
+    def test_sampling_workload_override_accepts_json(self):
+        defaults = SamplingParams(
+            width=1024,
+            height=1024,
+            num_frames=81,
+            num_inference_steps=35,
+        )
+        server_args = SimpleNamespace(
+            warmup_sampling_params=(
+                '{"width":832,"height":480,"num_frames":9,"num_inference_steps":4}'
+            )
+        )
+
+        overridden = _apply_warmup_sampling_overrides(server_args, defaults)
+
+        self.assertEqual(
+            (
+                overridden.width,
+                overridden.height,
+                overridden.num_frames,
+                overridden.num_inference_steps,
+            ),
+            (832, 480, 9, 4),
+        )
+        self.assertEqual((defaults.width, defaults.height), (1024, 1024))
+
+    def test_sampling_workload_override_rejects_unknown_field(self):
+        server_args = SimpleNamespace(
+            warmup_sampling_params={"not_a_sampling_field": 1}
+        )
+
+        with self.assertRaisesRegex(ValueError, "invalid --warmup-sampling-params"):
+            _apply_warmup_sampling_overrides(server_args, SamplingParams())
+
+    def test_sampling_workload_override_supports_fixed_model_fields(self):
+        defaults = MiniMaxH3SamplingParams()
+        server_args = SimpleNamespace(
+            warmup_sampling_params={"num_frames": 49, "fps": 12}
+        )
+
+        overridden = _apply_warmup_sampling_overrides(server_args, defaults)
+
+        self.assertEqual((overridden.num_frames, overridden.fps), (49, 12))
+        self.assertEqual((defaults.num_frames, defaults.fps), (1, 24))
+
     def test_warmup_req_cfg_parallel_sets_do_cfg(self):
         server_args = _make_bare_scheduler(enable_cfg_parallel=True).server_args
         sampling_defaults = SamplingParams()
@@ -270,6 +317,7 @@ class TestWarmupReqCfgParallel(unittest.TestCase):
         server_args.warmup_steps = 1
         server_args.enable_cfg_parallel = False
         server_args.enable_torch_compile = False
+        server_args.num_gpus = 1
 
         server_args.pipeline_config.task_type = ModelTaskType.T2V
         server_args.pipeline_config.adjust_num_frames.side_effect = lambda value: value
@@ -334,6 +382,68 @@ class TestWarmupReqCfgParallel(unittest.TestCase):
         self.assertTrue(req.extra["return_warmup_result"])
         self.assertTrue(req.extra["server_based_warmup"])
 
+    def test_auto_residency_uses_one_full_serving_shape_probe(self):
+        server_args = SimpleNamespace(
+            warmup_steps=1,
+            enable_cfg_parallel=False,
+            enable_torch_compile=False,
+            enable_breakable_cuda_graph=False,
+            pipeline_class_name=None,
+            num_gpus=1,
+            pipeline_config=SimpleNamespace(
+                task_type=ModelTaskType.T2V,
+                adjust_num_frames=lambda value: value,
+                vae_stride=None,
+                vae_scale_factor=None,
+                vae_config=SimpleNamespace(arch_config=None),
+            ),
+            is_arg_explicitly_set=lambda _name: False,
+        )
+        sampling_defaults = SamplingParams(
+            width=1280,
+            height=720,
+            num_frames=81,
+            num_inference_steps=35,
+            adjust_frames=False,
+            supported_resolutions=[(1280, 720), (832, 480)],
+        )
+        with (
+            patch(
+                "sglang.multimodal_gen.runtime.warmup_request_builder.get_model_sampling_defaults",
+                return_value=sampling_defaults,
+            ),
+            patch(
+                "sglang.multimodal_gen.runtime.warmup_request_builder.auto_residency_args_skip_reason",
+                return_value=None,
+            ),
+        ):
+            reqs = build_warmup_reqs(
+                server_args,
+                warmup_resolutions=None,
+                server_based_warmup=True,
+            )
+
+        # the bounded warmup runs first so the worker can size the probe, and
+        # once more after it so serving starts from a serving-shaped pool
+        self.assertEqual(len(reqs), 3)
+        self.assertFalse(reqs[0].extra.get("auto_residency_full_shape_probe"))
+        self.assertFalse(reqs[2].extra.get("auto_residency_full_shape_probe"))
+        self.assertEqual(
+            (reqs[2].width, reqs[2].height, reqs[2].num_frames),
+            (reqs[0].width, reqs[0].height, reqs[0].num_frames),
+        )
+        self.assertEqual(
+            (reqs[1].width, reqs[1].height, reqs[1].num_frames),
+            (1280, 720, 81),
+        )
+        self.assertTrue(reqs[1].extra["auto_residency_full_shape_probe"])
+        self.assertFalse(reqs[1].metrics.suppress_stage_breakdown)
+        self.assertEqual(reqs[1].num_inference_steps, 4)
+        self.assertIn(
+            "auto residency probe (1280x720x81f, 4/35 steps)",
+            format_warmup_req(reqs[1]),
+        )
+
     def test_server_based_warmup_uses_model_default_resolution(self):
         server_args = MagicMock()
         server_args.warmup_steps = 1
@@ -362,6 +472,7 @@ class TestWarmupReqCfgParallel(unittest.TestCase):
         server_args.warmup_steps = 1
         server_args.enable_cfg_parallel = False
         server_args.enable_torch_compile = False
+        server_args.num_gpus = 1
 
         server_args.pipeline_config.task_type = ModelTaskType.T2V
         server_args.pipeline_config.adjust_num_frames.side_effect = lambda value: value
@@ -474,6 +585,7 @@ class TestWarmupReqCfgParallel(unittest.TestCase):
         server_args.warmup_steps = 1
         server_args.enable_cfg_parallel = False
         server_args.enable_torch_compile = False
+        server_args.num_gpus = 1
 
         server_args.pipeline_config.task_type = ModelTaskType.T2V
         server_args.pipeline_config.adjust_num_frames.side_effect = lambda value: value
@@ -503,6 +615,7 @@ class TestWarmupReqCfgParallel(unittest.TestCase):
             pipeline_config=pipeline_config,
             enable_breakable_cuda_graph=False,
             pipeline_class_name=None,
+            num_gpus=1,
         )
 
         num_frames = _resolve_warmup_num_frames(
@@ -545,6 +658,7 @@ class TestWarmupReqCfgParallel(unittest.TestCase):
         server_args.warmup_steps = 1
         server_args.enable_cfg_parallel = False
         server_args.enable_torch_compile = False
+        server_args.num_gpus = 1
 
         server_args.pipeline_config.task_type = ModelTaskType.T2V
         server_args.pipeline_config.adjust_num_frames.side_effect = lambda value: value
@@ -581,10 +695,15 @@ class TestWarmupReqCfgParallel(unittest.TestCase):
         server_args.warmup_steps = 1
         server_args.enable_cfg_parallel = False
         server_args.enable_torch_compile = False
+        server_args.num_gpus = 1
         server_args.pipeline_class_name = "LTX2TwoStageHQPipeline"
 
         server_args.pipeline_config.task_type = ModelTaskType.T2V
         server_args.pipeline_config.vae_scale_factor = 32
+        server_args.pipeline_config.vae_config = SimpleNamespace(
+            use_temporal_scaling_frames=True,
+            arch_config=SimpleNamespace(temporal_compression_ratio=8),
+        )
         server_args.pipeline_config.adjust_num_frames.return_value = 25
         server_args.num_gpus = 2
 
@@ -725,6 +844,7 @@ class TestWarmupReqCfgParallel(unittest.TestCase):
         server_args.is_arg_explicitly_set.return_value = False
         server_args.pipeline_config = SimpleNamespace(
             task_type=ModelTaskType.I2M,
+            supports_auto_residency=True,
             vae_stride=None,
             vae_scale_factor=None,
             vae_config=None,
@@ -772,6 +892,33 @@ class TestWarmupReqCfgParallel(unittest.TestCase):
 
         self.assertEqual(reqs[0].image_path, ["/tmp/warmup.png"])
 
+    def test_server_based_warmup_keeps_image_input_count(self):
+        server_args = MagicMock()
+        server_args.warmup_steps = 1
+        server_args.enable_cfg_parallel = False
+        server_args.enable_torch_compile = False
+        server_args.pipeline_config.task_type = ModelTaskType.TI2I
+
+        with patch(
+            "sglang.multimodal_gen.runtime.warmup_request_builder.get_model_sampling_defaults",
+            return_value=SamplingParams(
+                width=512,
+                height=512,
+                image_path=["first.png", "second.png"],
+            ),
+        ):
+            reqs = build_warmup_reqs(
+                server_args,
+                warmup_resolutions=None,
+                warmup_input_path="/tmp/warmup.png",
+                server_based_warmup=True,
+            )
+
+        self.assertEqual(
+            reqs[0].image_path,
+            ["/tmp/warmup.png", "/tmp/warmup.png"],
+        )
+
     def test_server_based_warmup_keeps_required_image_input(self):
         server_args = MagicMock()
         server_args.warmup_steps = 1
@@ -797,6 +944,7 @@ class TestWarmupReqCfgParallel(unittest.TestCase):
         server_args.warmup_steps = 1
         server_args.enable_cfg_parallel = False
         server_args.enable_torch_compile = False
+        server_args.num_gpus = 1
         server_args.pipeline_config.task_type = ModelTaskType.TI2V
 
         with patch(

python/sglang/multimodal_gen/test/unit/test_component_residency.py

@@ -8,6 +8,7 @@ from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager im
     ComponentResidencyManager,
     ComponentUse,
     ResidencyState,
+    WarmupPhasePeak,
 )
 from sglang.multimodal_gen.runtime.managers.memory_managers.component_residency import (
     ComponentResidencyError,
@@ -22,9 +23,19 @@ from sglang.multimodal_gen.runtime.pipelines_core.stages.image_encoding import (
 from sglang.multimodal_gen.runtime.pipelines_core.stages.realtime.text_encoding import (
     RealtimeTextEncodingStage,
 )
+from sglang.multimodal_gen.runtime.platforms import current_platform
 from sglang.multimodal_gen.runtime.server_args import ServerArgs
 
 
+def _server_args(*, supports_auto_residency=True):
+    return SimpleNamespace(
+        enable_layerwise_nvtx_marker=False,
+        pipeline_config=SimpleNamespace(
+            supports_auto_residency=supports_auto_residency,
+        ),
+    )
+
+
 def test_component_offload_releases_preferred_component_after_request():
     strategy = ComponentOffloadStrategy()
     strategy.finish_use = Mock()
@@ -143,7 +154,7 @@ def test_group_warmup_state_requires_every_batch_to_be_warmup():
         _stage_name_mapping={},
         component_residency_strategies={},
     )
-    server_args = SimpleNamespace(enable_layerwise_nvtx_marker=False)
+    server_args = _server_args()
     manager = ComponentResidencyManager(pipeline, server_args)
 
     manager.begin_request(
@@ -166,6 +177,246 @@ class _Stage:
         return self.uses
 
 
+def test_warmup_records_use_and_transition_peaks(monkeypatch):
+    device_module = SimpleNamespace(
+        is_available=lambda: True,
+        reset_peak_memory_stats=Mock(),
+        max_memory_allocated=lambda: 7,
+        memory_allocated=lambda: 2,
+    )
+    monkeypatch.setattr(torch, "get_device_module", lambda: device_module)
+    monkeypatch.setattr(current_platform, "is_cuda", lambda: True)
+
+    use = ComponentUse("denoise", "transformer")
+    stage = _Stage(use)
+    module = torch.nn.Linear(2, 2)
+    pipeline = SimpleNamespace(
+        modules={"transformer": module},
+        _stage_name_mapping={"denoise": stage},
+        component_residency_strategies={},
+    )
+    server_args = _server_args()
+    manager = ComponentResidencyManager(pipeline, server_args)
+    manager.strategy_for = Mock(return_value=Mock())
+    manager.refresh_pipeline(pipeline)
+    manager.begin_request([stage], SimpleNamespace(is_warmup=True), server_args)
+
+    manager.before_stage(stage, 0, SimpleNamespace(is_warmup=True), server_args)
+    manager.begin_stage()
+    manager.end_stage()
+    manager.finish_request()
+
+    peaks = manager.take_warmup_phase_peaks()
+    inactive_peak = WarmupPhasePeak((), 7)
+    transformer_peak = WarmupPhasePeak(
+        ("transformer",), 7, used_components=("transformer",)
+    )
+    assert peaks["request:before-stage"] == inactive_peak
+    assert peaks["0:denoise:setup"] == inactive_peak
+    assert peaks["0:denoise:transition:idle->transformer"] == transformer_peak
+    assert peaks["0:denoise:use:transformer"] == transformer_peak
+    assert peaks["0:denoise:transition:transformer->idle"] == transformer_peak
+    assert peaks["0:denoise:between"] == inactive_peak
+    # A non-preferred component is being released during cleanup, so it is no
+    # longer part of the placement that follows this transition.
+    assert peaks["request:cleanup:transformer"] == inactive_peak
+    assert peaks["idle"] == WarmupPhasePeak(
+        active_components=(),
+        allocated_bytes=2,
+    )
+
+
+def test_warmup_skips_memory_tracking_for_unsupported_pipeline(monkeypatch):
+    device_module = SimpleNamespace(
+        is_available=lambda: True,
+        reset_peak_memory_stats=Mock(),
+    )
+    monkeypatch.setattr(torch, "get_device_module", lambda: device_module)
+    monkeypatch.setattr(current_platform, "is_cuda", lambda: True)
+
+    stage = _Stage()
+    pipeline = SimpleNamespace(
+        modules={},
+        _stage_name_mapping={"stage": stage},
+        component_residency_strategies={},
+    )
+    server_args = _server_args(supports_auto_residency=False)
+    manager = ComponentResidencyManager(pipeline, server_args)
+
+    manager.begin_request([stage], SimpleNamespace(is_warmup=True), server_args)
+    manager.before_stage(stage, 0, SimpleNamespace(is_warmup=True), server_args)
+    manager.finish_request()
+
+    assert manager._track_warmup_memory is False
+    assert manager.take_warmup_phase_peaks() == {}
+    device_module.reset_peak_memory_stats.assert_not_called()
+
+
+def test_warmup_records_full_weight_transition_without_preparing(monkeypatch):
+    device_module = SimpleNamespace(
+        is_available=lambda: True,
+        reset_peak_memory_stats=Mock(),
+        max_memory_allocated=lambda: 7,
+        memory_allocated=lambda: 2,
+    )
+    monkeypatch.setattr(torch, "get_device_module", lambda: device_module)
+    monkeypatch.setattr(current_platform, "is_cuda", lambda: True)
+
+    stage = _Stage()
+    module = torch.nn.Linear(2, 2)
+    pipeline = SimpleNamespace(
+        modules={"transformer": module},
+        _stage_name_mapping={"lora_switch": stage},
+        component_residency_strategies={},
+    )
+    server_args = _server_args()
+    manager = ComponentResidencyManager(pipeline, server_args)
+    manager.strategy_for = Mock()
+    manager.refresh_pipeline(pipeline)
+    manager.begin_request([stage], SimpleNamespace(is_warmup=True), server_args)
+
+    manager.before_stage(stage, 0, SimpleNamespace(is_warmup=True), server_args)
+    with manager.full_weight_transition(("transformer",)):
+        pass
+
+    assert manager._warmup_phase_peaks[
+        "0:lora_switch:full-weight-transition:transformer"
+    ] == WarmupPhasePeak(
+        (),
+        7,
+        full_weight_transition_components=("transformer",),
+    )
+    assert manager._warmup_phase_key == "0:lora_switch:setup"
+    manager.strategy_for.assert_not_called()
+
+
+def test_warmup_records_same_component_dtype_prepare_as_transition(monkeypatch):
+    device_module = SimpleNamespace(
+        is_available=lambda: True,
+        reset_peak_memory_stats=Mock(),
+        max_memory_allocated=lambda: 7,
+        memory_allocated=lambda: 2,
+    )
+    monkeypatch.setattr(torch, "get_device_module", lambda: device_module)
+    monkeypatch.setattr(current_platform, "is_cuda", lambda: True)
+
+    first = ComponentUse("stage", "transformer", target_dtype=torch.float16)
+    second = ComponentUse("stage", "transformer", target_dtype=torch.bfloat16)
+    stage = _Stage(first, second)
+    module = torch.nn.Linear(2, 2)
+    pipeline = SimpleNamespace(
+        modules={"transformer": module},
+        _stage_name_mapping={"stage": stage},
+        component_residency_strategies={},
+    )
+    server_args = _server_args()
+    manager = ComponentResidencyManager(pipeline, server_args)
+    strategy = Mock()
+    manager.strategy_for = Mock(return_value=strategy)
+    manager.refresh_pipeline(pipeline)
+    manager.begin_request([stage], SimpleNamespace(is_warmup=True), server_args)
+    manager.before_stage(stage, 0, SimpleNamespace(is_warmup=True), server_args)
+
+    manager.begin_use(first, module=module)
+    manager.begin_use(second, module=module)
+
+    manager._record_warmup_phase_peak()
+    assert manager._warmup_phase_peaks[
+        "0:stage:transition:transformer->transformer"
+    ] == WarmupPhasePeak(("transformer",), 7, used_components=("transformer",))
+    assert strategy.prepare_for_use.call_count == 2
+
+
+def test_warmup_attributes_prefetch_peak_to_prefetched_component(monkeypatch):
+    device_module = SimpleNamespace(
+        is_available=lambda: True,
+        reset_peak_memory_stats=Mock(),
+        max_memory_allocated=lambda: 7,
+        memory_allocated=lambda: 2,
+    )
+    monkeypatch.setattr(torch, "get_device_module", lambda: device_module)
+    monkeypatch.setattr(current_platform, "is_cuda", lambda: True)
+
+    encoder_use = ComponentUse("encode", "text_encoder")
+    transformer_use = ComponentUse("denoise", "transformer", memory_intensive=True)
+    encode_stage = _Stage(encoder_use)
+    denoise_stage = _Stage(transformer_use)
+    modules = {
+        "text_encoder": torch.nn.Linear(2, 2),
+        "transformer": torch.nn.Linear(2, 2),
+    }
+    pipeline = SimpleNamespace(
+        modules=modules,
+        _stage_name_mapping={
+            "encode": encode_stage,
+            "denoise": denoise_stage,
+        },
+        component_residency_strategies={},
+    )
+    server_args = _server_args()
+    manager = ComponentResidencyManager(pipeline, server_args)
+    strategy = Mock()
+    strategy.prefetch_for_use.return_value = True
+    manager.strategy_for = Mock(return_value=strategy)
+    manager.refresh_pipeline(pipeline)
+    manager.begin_request(
+        [encode_stage, denoise_stage],
+        SimpleNamespace(is_warmup=True),
+        server_args,
+    )
+
+    manager.before_stage(encode_stage, 0, SimpleNamespace(is_warmup=True), server_args)
+    manager.begin_stage()
+    manager.end_stage()
+    manager.before_stage(denoise_stage, 1, SimpleNamespace(is_warmup=True), server_args)
+
+    assert manager._warmup_phase_peaks[
+        "0:encode:prefetch:transformer"
+    ] == WarmupPhasePeak(("transformer",), 7, used_components=("transformer",))
+    assert manager._warmup_phase_peaks["0:encode:between"] == WarmupPhasePeak((), 7)
+
+
+def test_warmup_splits_sequential_component_transition(monkeypatch):
+    device_module = SimpleNamespace(
+        is_available=lambda: True,
+        reset_peak_memory_stats=Mock(),
+        max_memory_allocated=lambda: 7,
+        memory_allocated=lambda: 2,
+    )
+    monkeypatch.setattr(torch, "get_device_module", lambda: device_module)
+    monkeypatch.setattr(current_platform, "is_cuda", lambda: True)
+
+    first = ComponentUse("stage", "text_encoder")
+    second = ComponentUse("stage", "transformer")
+    stage = _Stage(first, second)
+    modules = {
+        "text_encoder": torch.nn.Linear(2, 2),
+        "transformer": torch.nn.Linear(2, 2),
+    }
+    pipeline = SimpleNamespace(
+        modules=modules,
+        _stage_name_mapping={"stage": stage},
+        component_residency_strategies={},
+    )
+    server_args = _server_args()
+    manager = ComponentResidencyManager(pipeline, server_args)
+    manager.strategy_for = Mock(return_value=Mock())
+    manager.refresh_pipeline(pipeline)
+    manager.begin_request([stage], SimpleNamespace(is_warmup=True), server_args)
+    manager.before_stage(stage, 0, SimpleNamespace(is_warmup=True), server_args)
+
+    manager.begin_use(first)
+    manager.begin_use(second)
+    manager._record_warmup_phase_peak()
+
+    assert manager._warmup_phase_peaks["0:stage:transition:text_encoder->idle"] == (
+        WarmupPhasePeak(("text_encoder",), 7, used_components=("text_encoder",))
+    )
+    assert manager._warmup_phase_peaks["0:stage:transition:idle->transformer"] == (
+        WarmupPhasePeak(("transformer",), 7, used_components=("transformer",))
+    )
+
+
 def _manager_for_stage(stage, modules):
     pipeline = SimpleNamespace(
         modules=modules,

python/sglang/multimodal_gen/test/unit/test_diffusion_generator_shutdown.py

@@ -3,8 +3,8 @@ from types import SimpleNamespace
 from unittest.mock import Mock, patch
 
 from sglang.multimodal_gen.runtime.entrypoints import diffusion_generator as dg
+from sglang.multimodal_gen.runtime.entrypoints.control_requests import ShutdownReq
 from sglang.multimodal_gen.runtime.entrypoints.diffusion_generator import DiffGenerator
-from sglang.multimodal_gen.runtime.entrypoints.utils import ShutdownReq
 
 
 class _FakeProcess:

python/sglang/multimodal_gen/test/unit/test_dp_routing.py

@@ -6,7 +6,10 @@ and a realtime session always lands on the same replica it started on.
 
 import itertools
 
-from sglang.multimodal_gen.runtime.entrypoints.utils import SetLoraReq, ShutdownReq
+from sglang.multimodal_gen.runtime.entrypoints.control_requests import (
+    SetLoraReq,
+    ShutdownReq,
+)
 from sglang.multimodal_gen.runtime.pipelines_core import Req
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import OutputBatch
 from sglang.multimodal_gen.runtime.scheduler_client import (

python/sglang/multimodal_gen/test/unit/test_ipc_a2a_lifecycle.py

@@ -56,3 +56,38 @@ def test_peer_cuda_device_uses_the_ulysses_group_mapping():
         patch(f"{_IPC}.dist.all_gather_object", side_effect=gather),
     ):
         assert _peer_cuda_device(group, rank=1, device=3) == 2
+
+
+def test_drop_staging_clears_cached_buffers_and_nothing_else():
+    from collections import OrderedDict
+
+    from sglang.multimodal_gen.runtime.distributed.device_communicators.ipc_a2a import (
+        IpcA2AState,
+    )
+
+    state = IpcA2AState()
+    state.inited = True
+    state.calls = 7
+    state.staging = OrderedDict(
+        {(4, 4, "bf16"): ("local", "peer"), (8, 8, "bf16"): ("l", "p")}
+    )
+
+    state.drop_staging()
+
+    assert state.staging == OrderedDict()
+    assert state.inited is True and state.calls == 7
+    state.drop_staging()  # idempotent on an empty cache
+
+
+def test_drop_a2a_staging_buffers_clears_the_ulysses_cache():
+    import torch
+
+    from sglang.multimodal_gen.runtime.layers import usp
+
+    usp._A2A_STAGING_BUFFERS[("qkv", torch.float16, 0)] = torch.empty(
+        8, dtype=torch.float16
+    )
+    with patch.object(torch.cuda, "is_available", return_value=False):
+        usp.drop_a2a_staging_buffers()
+        usp.drop_a2a_staging_buffers()  # idempotent on an empty cache
+    assert usp._A2A_STAGING_BUFFERS == {}

python/sglang/multimodal_gen/test/unit/test_launch_server_shutdown.py

@@ -4,7 +4,7 @@ from unittest.mock import Mock, patch
 
 from sglang.multimodal_gen.runtime import launch_server as ls
 from sglang.multimodal_gen.runtime.disaggregation.roles import RoleType
-from sglang.multimodal_gen.runtime.entrypoints.utils import ShutdownReq
+from sglang.multimodal_gen.runtime.entrypoints.control_requests import ShutdownReq
 
 
 class _FakeProcess:

python/sglang/multimodal_gen/test/unit/test_performance_metrics.py

@@ -1,11 +1,16 @@
 import json
+from types import SimpleNamespace
 from unittest.mock import Mock, patch
 
 import pytest
 import torch
 
 import sglang.multimodal_gen.runtime.managers.gpu_worker as gpu_worker_module
+import sglang.multimodal_gen.runtime.managers.memory_managers.component_manager as component_manager_module
 from sglang.multimodal_gen.runtime.managers.gpu_worker import GPUWorker
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
+    WarmupPhasePeak,
+)
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import OutputBatch
 from sglang.multimodal_gen.runtime.platforms import current_platform
 from sglang.multimodal_gen.runtime.utils.perf_logger import (
@@ -23,6 +28,16 @@ from sglang.multimodal_gen.test.server.testcase_configs import (
 )
 
 
+@pytest.fixture(autouse=True)
+def _isolate_global_component_residency_manager(monkeypatch):
+    # Worker paths exercised here create the process-global residency manager
+    # for their fake pipeline; leaving it behind hands its modules to later
+    # tests (the worker prefers the global manager's placement modules).
+    monkeypatch.setattr(
+        component_manager_module, "_GLOBAL_COMPONENT_RESIDENCY_MANAGER", None
+    )
+
+
 def _perf_record(memory_snapshots: dict[str, dict]) -> RequestPerfRecord:
     return RequestPerfRecord(
         request_id="request",
@@ -35,6 +50,27 @@ def _perf_record(memory_snapshots: dict[str, dict]) -> RequestPerfRecord:
     )
 
 
+def test_request_metrics_attributes_steps_and_iterations_to_active_stage():
+    metrics = RequestMetrics("request")
+    metrics.active_stage_name = "ShapeStage"
+
+    metrics.record_step(0.1)
+    metrics.record_stage_iterations(4, 50)
+    metrics.active_stage_name = "PaintStage"
+    metrics.record_step(0.2)
+    metrics.record_stage_iterations(4, 30)
+
+    assert metrics.steps == [100.0, 200.0]
+    assert metrics.steps_by_stage == {
+        "ShapeStage": [100.0],
+        "PaintStage": [200.0],
+    }
+    assert metrics.stage_iterations == {
+        "ShapeStage": (4, 50),
+        "PaintStage": (4, 30),
+    }
+
+
 def test_performance_summary_separates_load_and_runtime_peaks():
     summary = PerformanceSummary.from_req_perf_record(
         _perf_record(
@@ -62,13 +98,14 @@ def test_worker_records_replica_load_and_runtime_peaks():
     worker.is_output_rank = True
     worker._load_peak_reserved_mb = 4096.0
     worker._runtime_peak_reserved_mb = 0.0
+    worker._warmup_peak_reserved_mb = 0.0
     worker._load_peak_allocated_mb = 3000.0
     worker._runtime_peak_allocated_mb = 0.0
     output = OutputBatch()
     metrics = RequestMetrics("request")
     replica_group = Mock()
     replica_group.all_reduce.return_value = torch.tensor(
-        [5120.0, 3584.0, 3500.0, 2560.0], dtype=torch.float64
+        [5120.0, 3584.0, 6144.0, 3500.0, 2560.0], dtype=torch.float64
     )
     snapshots = [
         MemorySnapshot(0.0, 0.0, 2048.0, 3072.0),
@@ -97,6 +134,88 @@ def test_worker_records_replica_load_and_runtime_peaks():
     assert metrics.memory_snapshots["runtime_peak"].peak_reserved_mb == 3584.0
     assert metrics.memory_snapshots["load_peak"].peak_allocated_mb == 3500.0
     assert metrics.memory_snapshots["runtime_peak"].peak_allocated_mb == 2560.0
+    assert metrics.memory_snapshots["warmup_peak"].peak_reserved_mb == 6144.0
+
+
+def test_server_warmup_preserves_peak_after_managed_stage_timeline():
+    worker = GPUWorker.__new__(GPUWorker)
+    worker._auto_residency_warmup_records = []
+    residency_manager = Mock()
+    residency_manager.take_warmup_phase_peaks.return_value = {
+        "0:denoise:use:transformer": WarmupPhasePeak(("transformer",), 8)
+    }
+    residency_manager.current_device_components.return_value = ("transformer",)
+    device_module = Mock()
+    device_module.max_memory_allocated.return_value = 9
+    device_module.max_memory_reserved.return_value = 12
+    req = SimpleNamespace(
+        width=64,
+        height=64,
+        num_frames=1,
+        num_inference_steps=1,
+        metrics=None,
+    )
+
+    with (
+        patch.object(torch, "get_device_module", return_value=device_module),
+        patch.object(
+            gpu_worker_module,
+            "peek_global_component_residency_manager",
+            return_value=residency_manager,
+        ),
+    ):
+        worker._record_server_warmup_memory(
+            req=req,
+            workload=(128, 96, 9, 2),
+            baseline_allocated_bytes=3,
+            succeeded=True,
+        )
+
+    record = worker._auto_residency_warmup_records[0]
+    assert record.peak_allocated_bytes == 9
+    assert record.peak_reserved_bytes == 12
+    assert (record.width, record.height, record.num_frames) == (128, 96, 9)
+    assert record.num_inference_steps == 2
+    assert record.phase_peak_allocated_bytes["request:untracked"] == 9
+    assert record.phase_active_components["request:untracked"] == ("transformer",)
+
+
+def test_server_warmup_does_not_treat_allocator_cache_as_untracked_live_memory():
+    worker = GPUWorker.__new__(GPUWorker)
+    worker._auto_residency_warmup_records = []
+    residency_manager = Mock()
+    residency_manager.take_warmup_phase_peaks.return_value = {
+        "0:denoise:use:transformer": WarmupPhasePeak(("transformer",), 8)
+    }
+    residency_manager.current_device_components.return_value = ("transformer",)
+    device_module = Mock()
+    device_module.max_memory_allocated.return_value = 8
+    device_module.max_memory_reserved.return_value = 12
+    req = SimpleNamespace(
+        width=64,
+        height=64,
+        num_frames=1,
+        num_inference_steps=1,
+        metrics=None,
+    )
+
+    with (
+        patch.object(torch, "get_device_module", return_value=device_module),
+        patch.object(
+            gpu_worker_module,
+            "peek_global_component_residency_manager",
+            return_value=residency_manager,
+        ),
+    ):
+        worker._record_server_warmup_memory(
+            req=req,
+            workload=(64, 64, 1, 1),
+            baseline_allocated_bytes=3,
+            succeeded=True,
+        )
+
+    record = worker._auto_residency_warmup_records[0]
+    assert "request:untracked" not in record.phase_peak_allocated_bytes
 
 
 def test_baseline_config_loads_per_scenario_peak_vram(tmp_path):
@@ -249,6 +368,16 @@ def test_peak_vram_validation_enforces_allocated_when_baselined():
         with pytest.raises(AssertionError, match="Runtime Peak VRAM"):
             validator.validate_peak_vram(reserved_drift, 10_000.0, 10_000.0)
 
+        reserved_drift.warmup_peak_vram_mb = 12_000.0
+        with pytest.raises(AssertionError, match="Warmup Peak VRAM"):
+            validator.validate_peak_vram(
+                reserved_drift,
+                10_000.0,
+                10_000.0,
+                expected_warmup_peak_vram_mb=10_000.0,
+                expected_runtime_peak_allocated_mb=8_000.0,
+            )
+
 
 @pytest.mark.parametrize(
     ("load_peak_vram_mb", "runtime_peak_vram_mb", "message"),
@@ -308,3 +437,138 @@ def test_results_json_merges_retry_sessions(tmp_path):
 
     results = json.loads(path.read_text(encoding="utf-8"))
     assert {item["test_name"] for item in results} == {"first", "second"}
+
+
+def _warmup_batch_for_iterations(steps: int, target_steps: int):
+    from sglang.multimodal_gen.runtime.utils.perf_logger import RequestMetrics
+
+    metrics = RequestMetrics(request_id="probe")
+    metrics.suppress_stage_breakdown = False
+    metrics.active_stage_name = "DenoisingStage"
+    return SimpleNamespace(
+        metrics=metrics,
+        num_inference_steps=steps,
+        extra={"warmup_target_num_inference_steps": target_steps},
+        is_warmup=True,
+    )
+
+
+def test_stage_formula_records_probe_and_default_iterations():
+    from sglang.multimodal_gen.runtime.pipelines_core.stages.base import (
+        record_default_workload_iterations,
+    )
+
+    batch = _warmup_batch_for_iterations(steps=4, target_steps=50)
+    stage = SimpleNamespace(default_workload_iterations=lambda batch, steps: steps - 1)
+    record_default_workload_iterations(stage, batch)
+    assert batch.metrics.stage_iterations == {"DenoisingStage": (3, 49)}
+
+
+def test_fixed_schedule_formula_records_the_same_count_twice():
+    from sglang.multimodal_gen.runtime.pipelines_core.stages.base import (
+        record_default_workload_iterations,
+    )
+
+    batch = _warmup_batch_for_iterations(steps=4, target_steps=50)
+    stage = SimpleNamespace(default_workload_iterations=lambda batch, steps: 8)
+    record_default_workload_iterations(stage, batch)
+    assert batch.metrics.stage_iterations == {"DenoisingStage": (8, 8)}
+
+
+def test_explicit_loop_record_wins_over_the_formula():
+    from sglang.multimodal_gen.runtime.pipelines_core.stages.base import (
+        record_default_workload_iterations,
+    )
+
+    batch = _warmup_batch_for_iterations(steps=4, target_steps=50)
+    batch.metrics.record_stage_iterations(12, 12)
+    stage = SimpleNamespace(default_workload_iterations=lambda batch, steps: steps)
+    record_default_workload_iterations(stage, batch)
+    assert batch.metrics.stage_iterations == {"DenoisingStage": (12, 12)}
+
+
+def test_stage_without_a_formula_records_nothing():
+    from sglang.multimodal_gen.runtime.pipelines_core.stages.base import (
+        record_default_workload_iterations,
+    )
+
+    batch = _warmup_batch_for_iterations(steps=4, target_steps=50)
+    stage = SimpleNamespace(default_workload_iterations=lambda batch, steps: None)
+    record_default_workload_iterations(stage, batch)
+    assert batch.metrics.stage_iterations == {}
+
+
+def test_warmup_peak_stays_out_of_the_runtime_peak():
+    from unittest import mock
+
+    worker = gpu_worker_module.GPUWorker.__new__(gpu_worker_module.GPUWorker)
+    worker._runtime_peak_reserved_mb = 0.0
+    worker._runtime_peak_allocated_mb = 0.0
+    worker._warmup_peak_reserved_mb = 0.0
+    worker.is_output_rank = False
+    with (
+        mock.patch.object(
+            gpu_worker_module.current_platform, "is_cpu", return_value=False
+        ),
+        mock.patch.object(
+            gpu_worker_module,
+            "capture_memory_snapshot",
+            side_effect=[
+                MemorySnapshot(0.0, 0.0, 35000.0, 40000.0),
+                MemorySnapshot(0.0, 0.0, 15000.0, 20000.0),
+            ],
+        ),
+    ):
+        worker._record_output_peak_memory(SimpleNamespace(), is_warmup=True)
+        worker._record_output_peak_memory(SimpleNamespace(), is_warmup=False)
+    assert worker._warmup_peak_reserved_mb == 40000.0
+    assert worker._runtime_peak_reserved_mb == 20000.0
+    assert worker._runtime_peak_allocated_mb == 15000.0
+
+
+def _warmup_req(*, probe: bool = False) -> SimpleNamespace:
+    extra = {"auto_residency_full_shape_probe": True} if probe else {}
+    return SimpleNamespace(is_warmup=True, extra=extra)
+
+
+def test_worker_releases_the_probe_pool_before_the_next_request(monkeypatch):
+    import sglang.multimodal_gen.runtime.distributed.device_communicators.ipc_a2a as ipc_a2a_module
+
+    calls = []
+    fake_device = SimpleNamespace(empty_cache=lambda: calls.append("empty_cache"))
+    monkeypatch.setattr(
+        gpu_worker_module.torch, "get_device_module", lambda: fake_device
+    )
+    monkeypatch.setattr(type(current_platform), "is_cpu", lambda self: False)
+    monkeypatch.setattr(type(current_platform), "is_mps", lambda self: False)
+    monkeypatch.setattr(
+        ipc_a2a_module.IPC_A2A, "drop_staging", lambda: calls.append("drop_staging")
+    )
+    worker = GPUWorker.__new__(GPUWorker)
+    worker._release_warmup_pool_before_serving = False
+
+    worker._release_warmup_pool(_warmup_req())
+    worker._release_warmup_pool(_warmup_req(probe=True))
+    assert calls == []
+
+    # the bounded re-warm after the probe regrows the pool from empty, and the
+    # IPC staging buffers sized for the probe's messages go with it
+    worker._release_warmup_pool(_warmup_req())
+    assert calls == ["drop_staging", "empty_cache"]
+
+    worker._release_warmup_pool(SimpleNamespace(is_warmup=False, extra={}))
+    assert calls == ["drop_staging", "empty_cache"]
+
+
+def test_worker_keeps_the_pool_when_no_probe_ran(monkeypatch):
+    calls = []
+    fake_device = SimpleNamespace(empty_cache=lambda: calls.append("empty_cache"))
+    monkeypatch.setattr(
+        gpu_worker_module.torch, "get_device_module", lambda: fake_device
+    )
+    worker = GPUWorker.__new__(GPUWorker)
+    worker._release_warmup_pool_before_serving = False
+
+    worker._release_warmup_pool(_warmup_req())
+    worker._release_warmup_pool(SimpleNamespace(is_warmup=False, extra={}))
+    assert calls == []

python/sglang/multimodal_gen/test/unit/test_warmup_probe_degradation.py

@@ -0,0 +1,179 @@
+"""A warmup probe that does not fit is retried smaller instead of abandoned."""
+
+from types import SimpleNamespace
+
+from sglang.multimodal_gen.configs.sample.sampling_params import SamplingParams
+from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import Req
+from sglang.multimodal_gen.runtime.warmup_request_builder import lighten_warmup_req
+
+
+def _server_args(temporal_compression_ratio: int = 4) -> SimpleNamespace:
+    arch_config = SimpleNamespace(
+        temporal_compression_ratio=temporal_compression_ratio,
+        vae_scale_factor=8,
+        spatial_compression_ratio=8,
+    )
+    return SimpleNamespace(
+        pipeline_class_name=None,
+        pipeline_config=SimpleNamespace(
+            vae_config=SimpleNamespace(arch_config=arch_config),
+            vae_scale_factor=8,
+        ),
+    )
+
+
+def _req(width: int, height: int, num_frames: int) -> Req:
+    return Req(
+        sampling_params=SamplingParams(
+            width=width, height=height, num_frames=num_frames
+        )
+    )
+
+
+class TestLightenWarmupReq:
+    def test_video_probe_halves_latent_frames_first(self):
+        lighter = lighten_warmup_req(_server_args(), _req(832, 480, 17))
+        assert (lighter.width, lighter.height) == (832, 480)
+        assert lighter.num_frames == 9
+
+    def test_frames_step_down_to_a_single_frame(self):
+        server_args = _server_args()
+        frames = []
+        req = _req(832, 480, 17)
+        for _ in range(4):
+            req = lighten_warmup_req(server_args, req)
+            if req is None:
+                break
+            frames.append(req.num_frames)
+        assert frames[:3] == [9, 5, 1]
+
+    def test_image_probe_halves_the_area(self):
+        lighter = lighten_warmup_req(_server_args(), _req(1024, 1024, 1))
+        assert lighter.num_frames == 1
+        assert lighter.width * lighter.height <= 1024 * 1024 // 2
+        assert lighter.width % 16 == 0 and lighter.height % 16 == 0
+
+    def test_the_original_request_is_left_alone(self):
+        req = _req(832, 480, 17)
+        lighten_warmup_req(_server_args(), req)
+        assert req.num_frames == 17
+
+    def test_a_probe_at_the_floor_cannot_shrink(self):
+        assert lighten_warmup_req(_server_args(), _req(16, 16, 1)) is None
+
+    def test_frames_follow_the_model_frame_contract(self):
+        # LongLive2-style contract: latent frames come in causal blocks of 8,
+        # so with a temporal ratio of 4 only 29, 61, 93, ... frames are valid.
+        server_args = _server_args()
+
+        def adjust_num_frames(num_frames: int) -> int:
+            latent = (num_frames - 1) // 4 + 1
+            if latent % 8 == 0:
+                return num_frames
+            return (max(8, latent // 8 * 8) - 1) * 4 + 1
+
+        server_args.pipeline_config.adjust_num_frames = adjust_num_frames
+
+        lighter = lighten_warmup_req(server_args, _req(960, 928, 61))
+        assert lighter.num_frames == 29
+        assert (lighter.width, lighter.height) == (960, 928)
+
+        # At the smallest valid frame count the probe shrinks the area instead.
+        floor = lighten_warmup_req(server_args, lighter)
+        assert floor.num_frames == 29
+        assert floor.width * floor.height <= 960 * 928 // 2
+
+
+def _record(width: int, height: int, num_frames: int, *, peak_gib: float):
+    from sglang.multimodal_gen.runtime.managers.memory_managers.auto_residency import (
+        WarmupMemoryRecord,
+    )
+
+    return WarmupMemoryRecord(
+        width=width,
+        height=height,
+        num_frames=num_frames,
+        baseline_allocated_bytes=2 << 30,
+        peak_allocated_bytes=int(peak_gib * (1 << 30)),
+        succeeded=True,
+    )
+
+
+class TestFitAutoResidencyProbe:
+    def test_probe_shrinks_until_its_extrapolated_peak_fits(self):
+        from sglang.multimodal_gen.runtime.managers.gpu_worker import (
+            fit_auto_residency_probe,
+        )
+
+        fitted, estimate, steps = fit_auto_residency_probe(
+            _req(1280, 720, 81),
+            records=[_record(832, 480, 81, peak_gib=20.0)],
+            free_bytes=40 << 30,
+            total_bytes=80 << 30,
+            server_args=_server_args(),
+        )
+        assert steps >= 1
+        assert (fitted.width, fitted.height) == (1280, 720)
+        assert fitted.num_frames < 81
+        assert estimate is not None and estimate <= 40 << 30
+
+    def test_probe_that_fits_runs_at_full_shape(self):
+        from sglang.multimodal_gen.runtime.managers.gpu_worker import (
+            fit_auto_residency_probe,
+        )
+
+        fitted, _, steps = fit_auto_residency_probe(
+            _req(1280, 720, 81),
+            records=[_record(832, 480, 81, peak_gib=20.0)],
+            free_bytes=79 << 30,
+            total_bytes=80 << 30,
+            server_args=_server_args(),
+        )
+        assert steps == 0
+        assert fitted.num_frames == 81
+
+    def test_probe_never_shrinks_below_the_bounded_warmup_shape(self):
+        from sglang.multimodal_gen.runtime.managers.gpu_worker import (
+            fit_auto_residency_probe,
+        )
+
+        # Nothing fits the extrapolation, but the bounded 832x480x17f warmup
+        # already ran, so the ladder (81 -> 41 -> 21 -> 9 frames) stops at the
+        # first shape at or below it instead of reaching a 16x16x1f probe.
+        fitted, _, steps = fit_auto_residency_probe(
+            _req(832, 480, 81),
+            records=[_record(832, 480, 17, peak_gib=30.0)],
+            free_bytes=8 << 30,
+            total_bytes=80 << 30,
+            server_args=_server_args(),
+        )
+        assert steps >= 1
+        assert (fitted.width, fitted.height) == (832, 480)
+        assert fitted.num_frames == 9
+
+    def test_without_a_trusted_estimate_the_probe_runs_as_requested(self):
+        from sglang.multimodal_gen.runtime.managers.gpu_worker import (
+            fit_auto_residency_probe,
+        )
+
+        fitted, estimate, steps = fit_auto_residency_probe(
+            _req(1280, 720, 81),
+            records=[],
+            free_bytes=1 << 30,
+            total_bytes=80 << 30,
+            server_args=_server_args(),
+        )
+        assert (steps, estimate) == (0, None)
+        assert fitted.num_frames == 81
+
+
+class TestOutOfMemoryClassification:
+    def test_allocation_failures_from_libraries_count_as_out_of_memory(self):
+        from sglang.multimodal_gen.runtime.server_warmup import _is_out_of_memory
+
+        assert _is_out_of_memory("CUDA error: out of memory")
+        assert _is_out_of_memory("cuBLAS error: CUBLAS_STATUS_ALLOC_FAILED")
+        assert _is_out_of_memory(
+            "RuntimeError: cudaErrorMemoryAllocation: out of memory"
+        )
+        assert not _is_out_of_memory("shape mismatch in attention")