chore: import upstream snapshot with attribution

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

@@ -0,0 +1,227 @@
+import time
+import uuid
+from abc import ABC
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Literal, Optional, Union
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+# Image API protocol models
+class ImageResponseData(BaseModel):
+    b64_json: Optional[str] = None
+    url: Optional[str] = None
+    revised_prompt: Optional[str] = None
+    file_path: Optional[str] = None
+
+
+class ImageResponse(BaseModel):
+    id: str
+    created: int = Field(default_factory=lambda: int(time.time()))
+    data: List[ImageResponseData]
+    peak_memory_mb: Optional[float] = None
+    inference_time_s: Optional[float] = None
+
+
+class ImageGenerationsRequest(BaseModel):
+    model_config = ConfigDict(extra="allow")
+
+    prompt: str
+    model: Optional[str] = None
+    n: Optional[int] = 1
+    quality: Optional[str] = "auto"
+    response_format: Optional[str] = "url"  # url | b64_json
+    size: Optional[str] = "1024x1024"  # e.g., 1024x1024
+    style: Optional[str] = "vivid"
+    background: Optional[str] = "auto"  # transparent | opaque | auto
+    output_format: Optional[str] = None  # png | jpeg | webp
+    user: Optional[str] = None
+    # SGLang extensions
+    width: Optional[int] = None
+    height: Optional[int] = None
+    num_inference_steps: Optional[int] = None
+    guidance_scale: Optional[float] = None
+    true_cfg_scale: Optional[float] = (
+        None  # for CFG vs guidance distillation (e.g., QwenImage)
+    )
+    seed: Optional[Union[int, List[int]]] = None
+    generator_device: Optional[str] = "cuda"
+    negative_prompt: Optional[str] = None
+    output_quality: Optional[str] = "default"
+    output_compression: Optional[int] = None
+    enable_teacache: Optional[bool] = False
+    max_sequence_length: Optional[int] = None
+    flow_shift: Optional[float] = None
+    # Upscaling
+    enable_upscaling: Optional[bool] = False
+    upscaling_model_path: Optional[str] = None
+    upscaling_scale: Optional[int] = 4
+    diffusers_kwargs: Optional[Dict[str, Any]] = None  # kwargs for diffusers backend
+    # Performance profiling
+    perf_dump_path: Optional[str] = None
+    # Progressive resolution generation
+    progressive_mode: Optional[str] = None
+    progressive_levels: Optional[int] = None
+    progressive_delta: Optional[float] = None
+
+
+# Video API protocol models
+class VideoResponse(BaseModel):
+    id: str
+    object: str = "video"
+    model: str = "sora-2"
+    status: str = "queued"
+    progress: int = 0
+    created_at: int = Field(default_factory=lambda: int(time.time()))
+    size: str = ""
+    seconds: str = "4"
+    quality: str = "standard"
+    url: Optional[str] = None
+    remixed_from_video_id: Optional[str] = None
+    completed_at: Optional[int] = None
+    expires_at: Optional[int] = None
+    error: Optional[Dict[str, Any]] = None
+    file_path: Optional[str] = None
+    file_paths: Optional[List[str]] = None
+    num_outputs: Optional[int] = None
+    peak_memory_mb: Optional[float] = None
+    inference_time_s: Optional[float] = None
+    action: Optional[Dict[str, Any]] = None
+
+
+class VideoGenerationsRequest(BaseModel):
+    model_config = ConfigDict(extra="allow")
+
+    prompt: str
+    input_reference: Optional[str] = None
+    reference_url: Optional[str] = None
+    video_path: Optional[str] = None
+    video_url: Optional[str] = None
+    model: Optional[str] = None
+    n: Optional[int] = 1
+    num_outputs_per_prompt: Optional[int] = None
+    seconds: Optional[int] = 4
+    size: Optional[str] = ""
+    fps: Optional[int] = None
+    num_frames: Optional[int] = None
+    seed: Optional[Union[int, List[int]]] = None
+    generator_device: Optional[str] = "cuda"
+    # SGLang extensions
+    width: Optional[int] = None
+    height: Optional[int] = None
+    num_inference_steps: Optional[int] = None
+    guidance_scale: Optional[float] = None
+    guidance_scale_2: Optional[float] = None
+    true_cfg_scale: Optional[float] = (
+        None  # for CFG vs guidance distillation (e.g., QwenImage)
+    )
+    negative_prompt: Optional[str] = None
+    max_sequence_length: Optional[int] = None
+    flow_shift: Optional[float] = None
+    enable_teacache: Optional[bool] = False
+    # Frame interpolation
+    enable_frame_interpolation: Optional[bool] = False
+    frame_interpolation_exp: Optional[int] = 1  # 1=2×, 2=4×
+    frame_interpolation_scale: Optional[float] = 1.0
+    frame_interpolation_model_path: Optional[str] = None
+    # Upscaling
+    enable_upscaling: Optional[bool] = False
+    upscaling_model_path: Optional[str] = None
+    upscaling_scale: Optional[int] = 4
+    output_quality: Optional[str] = "default"
+    output_compression: Optional[int] = None
+    output_path: Optional[str] = None
+    diffusers_kwargs: Optional[Dict[str, Any]] = None  # kwargs for diffusers backend
+    # Performance profiling
+    perf_dump_path: Optional[str] = None
+
+
+class VideoListResponse(BaseModel):
+    data: List[VideoResponse]
+    object: str = "list"
+
+
+class VideoRemixRequest(BaseModel):
+    prompt: str
+
+
+class RealtimeVideoGenerationsRequest(VideoGenerationsRequest):
+    type: Literal["init"]
+    # WebSocket does not support multipart/form-data image uploads
+    first_frame: Optional[bytes | str] = None
+    condition_inputs: Optional[Dict[str, Any]] = None
+    max_chunks: Optional[int] = Field(default=None, ge=1)
+    seed: Optional[int] = 42
+    guidance_scale: Optional[float] = 1.0
+    size: Optional[str] = "832x480"
+    profile: Optional[bool] = False
+    num_profiled_timesteps: Optional[int] = None
+    profile_all_stages: Optional[bool] = False
+    realtime_output_format: Optional[Literal["raw", "webp", "jpeg"]] = None
+    realtime_preview_max_width: Optional[int] = None
+    realtime_output_pacing: Optional[bool] = False
+    realtime_causal_sink_size: Optional[int] = None
+    realtime_causal_kv_cache_num_frames: Optional[int] = None
+
+
+class RealtimeEvent(BaseModel):
+    type: Literal["event"]
+    kind: str
+    payload: Any = None
+    event_id: Optional[int] = None
+
+
+# Mesh API protocol models
+class MeshResponse(BaseModel):
+    id: str
+    object: str = "mesh"
+    model: str = ""
+    status: str = "queued"
+    progress: int = 0
+    created_at: int = Field(default_factory=lambda: int(time.time()))
+    format: str = "glb"
+    url: Optional[str] = None
+    completed_at: Optional[int] = None
+    expires_at: Optional[int] = None
+    error: Optional[Dict[str, Any]] = None
+    file_path: Optional[str] = None
+    file_size_bytes: Optional[int] = None
+    peak_memory_mb: Optional[float] = None
+    inference_time_s: Optional[float] = None
+
+
+class MeshGenerationsRequest(BaseModel):
+    prompt: str = "generate 3d mesh"
+    input_image: Optional[str] = None
+    model: Optional[str] = None
+    seed: Optional[Union[int, List[int]]] = None
+    generator_device: Optional[str] = "cuda"
+    num_inference_steps: Optional[int] = None
+    guidance_scale: Optional[float] = None
+    negative_prompt: Optional[str] = None
+    output_format: Optional[str] = "glb"
+
+
+class MeshListResponse(BaseModel):
+    data: List[MeshResponse]
+    object: str = "list"
+
+
+@dataclass
+class BaseReq(ABC):
+    rid: Optional[Union[str, List[str]]] = field(default=None, kw_only=True)
+    http_worker_ipc: Optional[str] = field(default=None, kw_only=True)
+
+    def regenerate_rid(self):
+        """Generate a new request ID and return it."""
+        if isinstance(self.rid, list):
+            self.rid = [uuid.uuid4().hex for _ in range(len(self.rid))]
+        else:
+            self.rid = uuid.uuid4().hex
+        return self.rid
+
+
+@dataclass
+class VertexGenerateReqInput(BaseReq):
+    instances: List[dict]
+    parameters: Optional[dict] = None