refactor: Remove dead code from utils/common.py (#20668)

python/sglang/srt/utils/common.py

@@ -85,7 +85,6 @@ from PIL import Image
 from starlette.routing import Mount
 from torch import nn
 from torch.library import Library
-from torch.profiler import ProfilerActivity, profile, record_function
 from torch.utils._contextlib import _DecoratorContextManager
 from typing_extensions import Literal
 
@@ -363,17 +362,6 @@ def get_int_env_var(name: str, default: int = 0) -> int:
         return default
 
 
-def get_float_env_var(name: str, default: float = 0.0) -> float:
-    # FIXME: move your environment variable to sglang.srt.environ
-    value = os.getenv(name)
-    if value is None or not value.strip():
-        return default
-    try:
-        return float(value)
-    except ValueError:
-        return default
-
-
 def support_triton(backend: str) -> bool:
     return backend not in ["torch_native", "intel_amx"]
 
@@ -700,85 +688,6 @@ def set_random_seed(seed: int) -> None:
         torch.cuda.manual_seed_all(seed)
 
 
-def decode_video_base64(video_base64):
-    from PIL import Image
-
-    # Decode the base64 string
-    video_bytes = pybase64.b64decode(video_base64, validate=True)
-
-    # Placeholder for the start indices of each PNG image
-    img_starts = []
-
-    frame_format = "PNG"  # str(os.getenv('FRAME_FORMAT', "JPEG"))
-
-    assert frame_format in [
-        "PNG",
-        "JPEG",
-    ], "FRAME_FORMAT must be either 'PNG' or 'JPEG'"
-
-    if frame_format == "PNG":
-        # Find each PNG start signature to isolate images
-        i = 0
-        while i < len(video_bytes) - 7:  # Adjusted for the length of the PNG signature
-            # Check if we found the start of a PNG file
-            if (
-                video_bytes[i] == 0x89
-                and video_bytes[i + 1] == 0x50
-                and video_bytes[i + 2] == 0x4E
-                and video_bytes[i + 3] == 0x47
-                and video_bytes[i + 4] == 0x0D
-                and video_bytes[i + 5] == 0x0A
-                and video_bytes[i + 6] == 0x1A
-                and video_bytes[i + 7] == 0x0A
-            ):
-                img_starts.append(i)
-                i += 8  # Skip the PNG signature
-            else:
-                i += 1
-    else:
-        # Find each JPEG start (0xFFD8) to isolate images
-        i = 0
-        while (
-            i < len(video_bytes) - 1
-        ):  # Adjusted for the length of the JPEG SOI signature
-            # Check if we found the start of a JPEG file
-            if video_bytes[i] == 0xFF and video_bytes[i + 1] == 0xD8:
-                img_starts.append(i)
-                # Move to the next byte to continue searching for the next image start
-                i += 2
-            else:
-                i += 1
-
-    frames = []
-    for start_idx in img_starts:
-        # Assuming each image is back-to-back, the end of one image is the start of another
-        # The last image goes until the end of the byte string
-        end_idx = (
-            img_starts[img_starts.index(start_idx) + 1]
-            if img_starts.index(start_idx) + 1 < len(img_starts)
-            else len(video_bytes)
-        )
-        img_bytes = video_bytes[start_idx:end_idx]
-
-        # Convert bytes to a PIL Image
-        img = Image.open(BytesIO(img_bytes))
-
-        # Convert PIL Image to a NumPy array
-        frame = np.array(img)
-
-        # Append the frame to the list of frames
-        frames.append(frame)
-
-    # Ensure there's at least one frame to avoid errors with np.stack
-    if frames:
-        return np.stack(frames, axis=0), img.size
-    else:
-        return np.array([]), (
-            0,
-            0,
-        )  # Return an empty array and size tuple if no frames were found
-
-
 def load_audio(
     audio_file: str, sr: Optional[int] = None, mono: bool = True
 ) -> np.ndarray:
@@ -1341,70 +1250,6 @@ def point_to_point_pyobj(
     return []
 
 
-step_counter = 0
-
-
-def pytorch_profile(name, func, *args, data_size=-1):
-    """
-    Args:
-        name (string): the name of recorded function.
-        func: the function to be profiled.
-        args: the arguments of the profiled function.
-        data_size (int): some measurement of the computation complexity.
-            Usually, it could be the batch size.
-    """
-    global step_counter
-    os.makedirs("trace", exist_ok=True)
-    with profile(
-        activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
-        # schedule=torch.profiler.schedule(wait=1, warmup=1, active=3, repeat=2),
-        # on_trace_ready=tensorboard_trace_handler('./log_dir'),
-        record_shapes=True,
-        profile_memory=True,
-        with_stack=True,
-    ) as prof:
-        with record_function(name):
-            with open(f"trace/size_{step_counter}.json", "w") as f:
-                json.dump({"size": data_size}, f)
-            result = func(*args)
-    prof.export_chrome_trace(f"trace/{name}_{step_counter}.json")
-    step_counter += 1
-    return result
-
-
-def dump_to_file(dirpath, name, value):
-    from sglang.srt.distributed import get_tensor_model_parallel_rank
-
-    if get_tensor_model_parallel_rank() != 0:
-        return
-
-    os.makedirs(dirpath, exist_ok=True)
-    if value.dtype is torch.bfloat16:
-        value = value.float()
-    value = value.cpu().numpy()
-    output_filename = os.path.join(dirpath, f"pytorch_dump_{name}.npy")
-    logger.info(f"Dump a tensor to {output_filename}. Shape = {value.shape}")
-    np.save(output_filename, value)
-
-
-def is_triton_3():
-    return triton.__version__.startswith("3.")
-
-
-def maybe_torch_compile(*args, **kwargs):
-    """
-    torch.compile does not work for triton 2.2.0, which is needed in xlm1's jax.
-    Therefore, we disable it here.
-    """
-
-    def decorator(func):
-        if is_triton_3():
-            return torch.compile(*args, **kwargs)(func)
-        return func
-
-    return decorator
-
-
 def delete_directory(dirpath):
     try:
         # This will remove the directory and all its contents