[Perf][DreamZero]dreamzero supports CUDA graph, torch.compile, and DiT Caching. (#4213)

Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>

examples/offline_inference/dreamzero/client_schedule.py

@@ -0,0 +1,129 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""DreamZero AR client frame schedule aligned with upstream ``test_client_AR.py``."""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+import numpy as np
+
+# Frame schedule constants (matching upstream debug_inference.py / test_client_AR.py).
+ACTION_HORIZON = 24
+DEFAULT_ACTION_DIM = 8
+RELATIVE_OFFSETS = [-23, -16, -8, 0]
+DEFAULT_NUM_AR_CHUNKS = 15
+
+CAMERA_FILES = {
+    "observation/exterior_image_0_left": "exterior_image_1_left.mp4",
+    "observation/exterior_image_1_left": "exterior_image_2_left.mp4",
+    "observation/wrist_image_left": "wrist_image_left.mp4",
+}
+
+
+def build_frame_schedule(
+    total_frames: int,
+    num_chunks: int,
+    *,
+    logger: logging.Logger | None = None,
+) -> list[list[int]]:
+    """Build the frame index schedule for multi-frame chunks.
+
+    Args:
+        total_frames: Number of frames available in each camera video.
+        num_chunks: Number of 4-frame chunks to schedule after the initial frame.
+
+    Returns:
+        A list of frame-index lists. Each inner list has four indices. The
+        returned list may be shorter than ``num_chunks`` when the videos run
+        out of frames (upstream stops early instead of erroring).
+    """
+    chunks: list[list[int]] = []
+    current_frame = 23
+    for _ in range(num_chunks):
+        indices = [max(current_frame + offset, 0) for offset in RELATIVE_OFFSETS]
+        if indices[-1] >= total_frames:
+            if logger is not None:
+                logger.info(
+                    "Frame %s >= %s, stopping at %s chunks",
+                    indices[-1],
+                    total_frames,
+                    len(chunks),
+                )
+            break
+        chunks.append(indices)
+        current_frame += ACTION_HORIZON
+    return chunks
+
+
+def make_obs_from_video(
+    camera_frames: dict[str, np.ndarray],
+    frame_indices: list[int],
+    *,
+    prompt: str,
+    session_id: str,
+) -> dict[str, Any]:
+    """Build an observation dict from real video frames.
+
+    For one frame each image key is ``(H, W, 3)``. For four frames each key is
+    ``(4, H, W, 3)``.
+    """
+    obs: dict[str, Any] = {}
+    for camera_key, all_frames in camera_frames.items():
+        selected = all_frames[frame_indices]
+        obs[camera_key] = selected[0] if len(frame_indices) == 1 else selected
+
+    obs["observation/joint_position"] = np.zeros(7, dtype=np.float32)
+    obs["observation/cartesian_position"] = np.zeros(6, dtype=np.float32)
+    obs["observation/gripper_position"] = np.zeros(1, dtype=np.float32)
+    obs["prompt"] = prompt
+    obs["session_id"] = session_id
+    return obs
+
+
+def build_ar_observations(
+    camera_frames: dict[str, np.ndarray],
+    *,
+    prompt: str,
+    session_id: str,
+    num_chunks: int = DEFAULT_NUM_AR_CHUNKS,
+    repeat_chunk_observations: bool = False,
+    logger: logging.Logger | None = None,
+) -> list[dict[str, Any]]:
+    """Build the AR observation sequence used by upstream ``test_client_AR.py``.
+
+    Step 0 sends frame ``[0]``. Each subsequent step sends one 4-frame chunk.
+
+    ``num_chunks`` counts only the 4-frame chunks after the initial frame, not
+    the total number of inferences.
+    """
+    if num_chunks < 0:
+        raise ValueError("num_chunks must be non-negative")
+
+    total_frames = min(frames.shape[0] for frames in camera_frames.values())
+    observations = [
+        make_obs_from_video(
+            camera_frames,
+            [0],
+            prompt=prompt,
+            session_id=session_id,
+        )
+    ]
+
+    chunk_schedule = build_frame_schedule(total_frames, num_chunks, logger=logger)
+    if repeat_chunk_observations and chunk_schedule and len(chunk_schedule) < num_chunks:
+        while len(chunk_schedule) < num_chunks:
+            chunk_schedule.append(chunk_schedule[-1])
+
+    for frame_indices in chunk_schedule:
+        observations.append(
+            make_obs_from_video(
+                camera_frames,
+                frame_indices,
+                prompt=prompt,
+                session_id=session_id,
+            )
+        )
+    return observations