# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo # SPDX-License-Identifier: Apache-2.0 # Adapted from vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/utils.py import argparse import ctypes import importlib import importlib.util import inspect import math import os import signal import socket import sys import threading import traceback from collections.abc import Callable from dataclasses import dataclass, fields, is_dataclass from functools import lru_cache, partial, wraps from typing import Any, TypeVar, cast import cloudpickle import torch import yaml from remote_pdb import RemotePdb from torch.distributed.fsdp import MixedPrecisionPolicy import sglang.multimodal_gen.envs as envs from sglang.multimodal_gen.runtime.utils.logging_utils import ( SortedHelpFormatter, init_logger, ) logger = init_logger(__name__) T = TypeVar("T") # TODO(will): used to convert server_args.precision to torch.dtype. Find a # cleaner way to do this. PRECISION_TO_TYPE = { "fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16, } STR_BACKEND_ENV_VAR: str = "SGLANG_DIFFUSION_ATTENTION_BACKEND" STR_ATTN_CONFIG_ENV_VAR: str = "SGLANG_DIFFUSION_ATTENTION_CONFIG" def find_nccl_library() -> str: """ We either use the library file specified by the `VLLM_NCCL_SO_PATH` environment variable, or we find the library file brought by PyTorch. After importing `torch`, `libnccl.so.2`, `librccl.so.1` or `libmccl.so.2` can be found by `ctypes` automatically. """ so_file = envs.SGLANG_DIFFUSION_NCCL_SO_PATH # manually load the nccl library if so_file: logger.info( "Found nccl from environment variable SGLANG_DIFFUSION_NCCL_SO_PATH=%s", so_file, ) else: if torch.version.cuda is not None: so_file = "libnccl.so.2" elif torch.version.hip is not None: so_file = "librccl.so.1" elif hasattr(torch.version, "musa") and torch.version.musa is not None: so_file = "libmccl.so.2" else: raise ValueError("NCCL only supports CUDA, ROCm and MUSA backends.") logger.info("Found nccl from library %s", so_file) return str(so_file) prev_set_stream = torch.cuda.set_stream _current_stream = None def _patched_set_stream(stream: torch.cuda.Stream | None) -> None: global _current_stream _current_stream = stream if stream is not None: prev_set_stream(stream) torch.cuda.set_stream = _patched_set_stream def current_stream() -> torch.cuda.Stream | None: """ replace `torch.cuda.current_stream()` with `sglang.multimodal_gen.utils.current_stream()`. it turns out that `torch.cuda.current_stream()` is quite expensive, as it will construct a new stream object at each call. here we patch `torch.cuda.set_stream` to keep track of the current stream directly, so that we can avoid calling `torch.cuda.current_stream()`. the underlying hypothesis is that we do not call `torch._C._cuda_setStream` from C/C++ code. """ from sglang.multimodal_gen.runtime.platforms import current_platform # For non-CUDA platforms, return None if not current_platform.is_cuda_alike(): return None global _current_stream if _current_stream is None: # when this function is called before any stream is set, # we return the default stream. # On ROCm using the default 0 stream in combination with RCCL # is hurting performance. Therefore creating a dedicated stream # per process _current_stream = ( torch.cuda.Stream() if current_platform.is_rocm() else torch.cuda.current_stream() ) return _current_stream class StoreBoolean(argparse.Action): def __init__(self, option_strings, dest, default=False, required=False, help=None): super().__init__( option_strings=option_strings, dest=dest, nargs="?", const=True, default=default, required=required, help=help, ) def __call__(self, parser, namespace, values, option_string=None): if values is None: setattr(namespace, self.dest, True) elif isinstance(values, str): if values.lower() == "true": setattr(namespace, self.dest, True) elif values.lower() == "false": setattr(namespace, self.dest, False) else: raise ValueError( f"Invalid boolean value: {values}. " "Expected 'true' or 'false'." ) else: setattr(namespace, self.dest, bool(values)) class FlexibleArgumentParser(argparse.ArgumentParser): """ArgumentParser that allows both underscore and dash in names.""" def __init__(self, *args, **kwargs) -> None: # Set the default 'formatter_class' to SortedHelpFormatter if "formatter_class" not in kwargs: kwargs["formatter_class"] = SortedHelpFormatter super().__init__(*args, **kwargs) def parse_args( # type: ignore[override] self, args=None, namespace=None ) -> argparse.Namespace: if args is None: args = sys.argv[1:] if any(arg.startswith("--config") for arg in args): args = self._pull_args_from_config(args) # Convert underscores to dashes and vice versa in argument names processed_args = [] for arg in args: if arg.startswith("--"): if "=" in arg: key, value = arg.split("=", 1) key = "--" + key[len("--") :].replace("_", "-") processed_args.append(f"{key}={value}") else: processed_args.append("--" + arg[len("--") :].replace("_", "-")) elif arg.startswith("-O") and arg != "-O" and len(arg) == 2: # allow -O flag to be used without space, e.g. -O3 processed_args.append("-O") processed_args.append(arg[2:]) else: processed_args.append(arg) namespace = super().parse_args(processed_args, namespace) # Track which arguments were explicitly provided namespace._provided = set() i = 0 while i < len(args): arg = args[i] if arg.startswith("--"): # Handle --key=value format if "=" in arg: key = arg.split("=")[0][2:].replace("-", "_") namespace._provided.add(key) i += 1 # Handle --key value format else: key = arg[2:].replace("-", "_") namespace._provided.add(key) # Skip the value if there is one if i + 1 < len(args) and not args[i + 1].startswith("-"): i += 2 else: i += 1 else: i += 1 return namespace # type: ignore[no-any-return] def _pull_args_from_config(self, args: list[str]) -> list[str]: """Method to pull arguments specified in the config file into the command-line args variable. The arguments in config file will be inserted between the argument list. example: ```yaml port: 12323 tensor-parallel-size: 4 ``` ```python $: vllm {serve,chat,complete} "facebook/opt-12B" \ --config config.yaml -tp 2 $: args = [ "serve,chat,complete", "facebook/opt-12B", '--config', 'config.yaml', '-tp', '2' ] $: args = [ "serve,chat,complete", "facebook/opt-12B", '--port', '12323', '--tp-size', '4', '-tp', '2' ] ``` Please note how the config args are inserted after the sub command. this way the order of priorities is maintained when these are args parsed by super(). """ index = -1 config_arg = None for i, arg in enumerate(args): if arg.startswith("--config"): if index != -1: raise ValueError("More than one config file specified!") index = i config_arg = arg if config_arg is None: return args args_before_config = args[:index] if "=" in config_arg: file_path = config_arg.split("=", 1)[1] args_after_config = args[index + 1 :] else: if index == len(args) - 1: raise ValueError( "No config file specified! " "Please check your command-line arguments." ) file_path = args[index + 1] args_after_config = args[index + 2 :] config_args = self._load_config_file(file_path) # 0th index is for {serve,chat,complete} # followed by model_tag (only for serve) # followed by config args # followed by rest of cli args. # maintaining this order will enforce the precedence # of cli > config > defaults if args[0] == "serve": if index == 1: raise ValueError( "No model_tag specified! Please check your command-line" " arguments." ) command = args_before_config[0] model_tag = args_before_config[1] other_args_before = args_before_config[2:] args = ( [command, model_tag] + config_args + other_args_before + args_after_config ) else: command = args_before_config[0] other_args_before = args_before_config[1:] args = [command] + config_args + other_args_before + args_after_config return args def _load_config_file(self, file_path: str) -> list[str]: """Loads a yaml file and returns the key value pairs as a flattened list with argparse like pattern ```yaml port: 12323 tensor-parallel-size: 4 vae_config: load_encoder: false load_decoder: true ``` returns: processed_args: list[str] = [ '--port': '12323', '--tp-size': '4', '--vae-config.load-encoder': 'false', '--vae-config.load-decoder': 'true' ] """ extension: str = file_path.split(".")[-1] if extension not in ("yaml", "yml", "json"): raise ValueError( "Config file must be of a yaml/yml/json type.\ %s supplied", extension, ) processed_args: list[str] = [] config: dict[str, Any] = {} try: with open(file_path) as config_file: config = yaml.safe_load(config_file) except Exception as ex: logger.error( "Unable to read the config file at %s. \ Make sure path is correct", file_path, ) raise ex store_boolean_arguments = [ action.dest for action in self._actions if isinstance(action, StoreBoolean) ] def process_dict(prefix: str, d: dict[str, Any]): for key, value in d.items(): full_key = f"{prefix}.{key}" if prefix else key if isinstance(value, bool) and full_key not in store_boolean_arguments: if value: processed_args.append("--" + full_key) else: processed_args.append("--" + full_key) processed_args.append("false") elif isinstance(value, list): processed_args.append("--" + full_key) for item in value: processed_args.append(str(item)) elif isinstance(value, dict): process_dict(full_key, value) else: processed_args.append("--" + full_key) processed_args.append(str(value)) process_dict("", config) return processed_args def warn_for_unimplemented_methods(cls: type[T]) -> type[T]: """ A replacement for `abc.ABC`. When we use `abc.ABC`, subclasses will fail to instantiate if they do not implement all abstract methods. Here, we only require `raise NotImplementedError` in the base class, and log a warning if the method is not implemented in the subclass. """ original_init = cls.__init__ def find_unimplemented_methods(self: object): unimplemented_methods = [] for attr_name in dir(self): # bypass inner method if attr_name.startswith("_"): continue try: attr = getattr(self, attr_name) # get the func of callable method if callable(attr): attr_func = attr.__func__ except AttributeError: continue src = inspect.getsource(attr_func) if "NotImplementedError" in src: unimplemented_methods.append(attr_name) if unimplemented_methods: method_names = ",".join(unimplemented_methods) msg = f"Methods {method_names} not implemented in {self}" logger.warning(msg) @wraps(original_init) def wrapped_init(self, *args, **kwargs) -> None: original_init(self, *args, **kwargs) find_unimplemented_methods(self) type.__setattr__(cls, "__init__", wrapped_init) return cls def align_to(value: int, alignment: int) -> int: """align height, width according to alignment Args: value (int): height or width alignment (int): target alignment factor Returns: int: the aligned value """ return int(math.ceil(value / alignment) * alignment) def resolve_obj_by_qualname(qualname: str) -> Any: """ Resolve an object by its fully qualified name. """ module_name, obj_name = qualname.rsplit(".", 1) module = importlib.import_module(module_name) return getattr(module, obj_name) # From vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/utils.py def import_pynvml(): """ Historical comments: libnvml.so is the library behind nvidia-smi, and pynvml is a Python wrapper around it. We use it to get GPU status without initializing CUDA context in the current process. Historically, there are two packages that provide pynvml: - `nvidia-ml-py` (https://pypi.org/project/nvidia-ml-py/): The official wrapper. It is a dependency of sglang-diffusion, and is installed when users install sglang-diffusion. It provides a Python module named `pynvml`. - `pynvml` (https://pypi.org/project/pynvml/): An unofficial wrapper. Prior to version 12.0, it also provides a Python module `pynvml`, and therefore conflicts with the official one which is a standalone Python file. This causes errors when both of them are installed. Starting from version 12.0, it migrates to a new module named `pynvml_utils` to avoid the conflict. It is so confusing that many packages in the community use the unofficial one by mistake, and we have to handle this case. For example, `nvcr.io/nvidia/pytorch:24.12-py3` uses the unofficial one, and it will cause errors, see the issue https://github.com/vllm-project/vllm/issues/12847 for example. After all the troubles, we decide to copy the official `pynvml` module to our codebase, and use it directly. """ import sglang.multimodal_gen.third_party.pynvml as pynvml return pynvml def update_environment_variables(envs: dict[str, str]): for k, v in envs.items(): if k in os.environ and os.environ[k] != v: logger.warning( "Overwriting environment variable %s " "from '%s' to '%s'", k, os.environ[k], v, ) os.environ[k] = v def run_method( obj: Any, method: str | bytes | Callable, args: tuple[Any], kwargs: dict[str, Any] ) -> Any: """ Run a method of an object with the given arguments and keyword arguments. If the method is string, it will be converted to a method using getattr. If the method is serialized bytes and will be deserialized using cloudpickle. If the method is a callable, it will be called directly. """ if isinstance(method, bytes): func = partial(cloudpickle.loads(method), obj) elif isinstance(method, str): try: func = getattr(obj, method) except AttributeError: raise NotImplementedError( f"Method {method!r} is not" " implemented." ) from None else: func = partial(method, obj) # type: ignore return func(*args, **kwargs) def shallow_asdict(obj) -> dict[str, Any]: if not is_dataclass(obj): raise TypeError("Expected dataclass instance") return {f.name: getattr(obj, f.name) for f in fields(obj)} # TODO: validate that this is fine def kill_itself_when_parent_died() -> None: # if sys.platform == "linux": # sigkill this process when parent worker manager dies PR_SET_PDEATHSIG = 1 import platform if platform.system() == "Linux": libc = ctypes.CDLL("libc.so.6") libc.prctl(PR_SET_PDEATHSIG, signal.SIGKILL) # elif platform.system() == "Darwin": # libc = ctypes.CDLL("libc.dylib") # logger.warning("kill_itself_when_parent_died is only supported in linux.") else: logger.warning("kill_itself_when_parent_died is only supported in linux.") def get_exception_traceback() -> str: etype, value, tb = sys.exc_info() err_str = "".join(traceback.format_exception(etype, value, tb)) return err_str class TypeBasedDispatcher: def __init__(self, mapping: list[tuple[type, Callable]]): self._mapping = mapping def __call__(self, obj: Any): for ty, fn in self._mapping: if isinstance(obj, ty): return fn(obj) raise ValueError(f"Invalid object: {obj}") # For non-torch.distributed debugging def remote_breakpoint() -> None: with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) s.bind(("localhost", 0)) # Let the OS pick an ephemeral port. port = s.getsockname()[1] RemotePdb(host="localhost", port=port).set_trace() @dataclass class MixedPrecisionState: param_dtype: torch.dtype | None = None reduce_dtype: torch.dtype | None = None output_dtype: torch.dtype | None = None compute_dtype: torch.dtype | None = None mp_policy: MixedPrecisionPolicy | None = None # Thread-local storage for mixed precision state _mixed_precision_state = threading.local() def get_mixed_precision_state() -> MixedPrecisionState: """Get the current mixed precision state.""" if not hasattr(_mixed_precision_state, "state"): raise ValueError("Mixed precision state not set") return cast(MixedPrecisionState, _mixed_precision_state.state) def set_mixed_precision_policy( param_dtype: torch.dtype, reduce_dtype: torch.dtype, output_dtype: torch.dtype | None = None, mp_policy: MixedPrecisionPolicy | None = None, ): """Set mixed precision policy globally. Args: param_dtype: Parameter dtype used for training reduce_dtype: Reduction dtype used for gradients output_dtype: Optional output dtype """ state = MixedPrecisionState( param_dtype=param_dtype, reduce_dtype=reduce_dtype, output_dtype=output_dtype, mp_policy=mp_policy, ) _mixed_precision_state.state = state def get_compute_dtype() -> torch.dtype: """Get the current compute dtype from mixed precision policy.""" if not hasattr(_mixed_precision_state, "state"): return torch.get_default_dtype() else: state = get_mixed_precision_state() return state.param_dtype def dict_to_3d_list( mask_strategy: dict[str, Any] | None = None, t_max: int | None = None, l_max: int | None = None, h_max: int | None = None, ) -> list[list[list[torch.Tensor | None]]]: """ Convert a dictionary of mask indices to a 3D list of tensors. Args: mask_strategy: keys are "t_l_h", values are torch.Tensor masks. t_max, l_max, h_max: if provided (all three), force the output shape to (t_max, l_max, h_max). If all three are None, infer shape from the data. """ # Case 1: no data, but fixed shape requested if mask_strategy is None: assert ( t_max is not None and l_max is not None and h_max is not None ), "If mask_strategy is None, you must provide t_max, l_max, and h_max" return [ [[None for _ in range(h_max)] for _ in range(l_max)] for _ in range(t_max) ] # Parse all keys into integer tuples indices = [tuple(map(int, key.split("_"))) for key in mask_strategy] # Decide on dimensions if t_max is None and l_max is None and h_max is None: # fully dynamic: infer from data max_timesteps_idx = max(t for t, _, _ in indices) + 1 max_layer_idx = max(l for _, l, _ in indices) + 1 # noqa: E741 max_head_idx = max(h for _, _, h in indices) + 1 else: # require all three to be provided assert t_max is not None and l_max is not None and h_max is not None, ( "Either supply none of (t_max, l_max, h_max) to infer dimensions, " "or supply all three to fix the shape." ) max_timesteps_idx = t_max max_layer_idx = l_max max_head_idx = h_max # Preallocate result = [ [[None for _ in range(max_head_idx)] for _ in range(max_layer_idx)] for _ in range(max_timesteps_idx) ] # Fill in, skipping any out-of-bounds entries for key, value in mask_strategy.items(): t, l, h = map(int, key.split("_")) # noqa: E741 if ( 0 <= t < max_timesteps_idx and 0 <= l < max_layer_idx and 0 <= h < max_head_idx ): result[t][l][h] = value # else: silently ignore any key that doesn't fit return result def set_random_seed(seed: int) -> None: from sglang.multimodal_gen.runtime.platforms import current_platform current_platform.seed_everything(seed) @lru_cache(maxsize=1) def is_vsa_available() -> bool: return importlib.util.find_spec("vsa") is not None @lru_cache(maxsize=1) def is_vmoba_available() -> bool: if importlib.util.find_spec("kernel.csrc.attn.vmoba_attn.vmoba") is None: return False try: import flash_attn return flash_attn.__version__ >= "2.7.4" except Exception: return False # adapted from: https://github.com/Wan-Video/Wan2.2/blob/main/wan/utils/utils.py def masks_like( tensors, zero=False, generator=None, p=0.2 ) -> tuple[list[torch.Tensor], list[torch.Tensor]]: """ Generate binary masks for Text-to-Image-to-Video (TI2V) tasks. Creates masks to control which frames should be preserved vs replaced. Primarily used to fix the first frame to the input image while generating other frames. Args: tensors: List of tensors with shape [C, T, H, W] zero: If True, set first frame (dim 1, index 0) to zero. Default: False generator: Optional random generator for stochastic masking p: Probability of applying special noise when generator is provided. Default: 0.2 Returns: Tuple of two lists of tensors: - When zero=False: Both lists contain all-ones tensors - When zero=True (no generator): First frame set to 0, others to 1 - When zero=True (with generator): First frame set to small random values with probability p Example: >>> latent = torch.randn(48, 69, 96, 160) # [C, T, H, W] >>> _, mask = masks_like([latent], zero=True) >>> # mask[0][:, 0] == 0 (first frame) >>> # mask[0][:, 1:] == 1 (other frames) >>> blended = (1.0 - mask[0]) * image + mask[0] * latent >>> # Result: first frame = image, other frames = latent """ assert isinstance(tensors, list) out1 = [torch.ones(u.shape, dtype=u.dtype, device=u.device) for u in tensors] out2 = [torch.ones(u.shape, dtype=u.dtype, device=u.device) for u in tensors] if zero: if generator is not None: for u, v in zip(out1, out2, strict=False): random_num = torch.rand( 1, generator=generator, device=generator.device ).item() if random_num < p: u[:, 0] = ( torch.normal( mean=-3.5, std=0.5, size=(1,), device=u.device, generator=generator, ) .expand_as(u[:, 0]) .exp() ) v[:, 0] = torch.zeros_like(v[:, 0]) else: u[:, 0] = u[:, 0] v[:, 0] = v[:, 0] else: for u, v in zip(out1, out2, strict=False): u[:, 0] = torch.zeros_like(u[:, 0]) v[:, 0] = torch.zeros_like(v[:, 0]) return out1, out2 # adapted from: https://github.com/Wan-Video/Wan2.2/blob/main/wan/utils/utils.py def best_output_size(w, h, dw, dh, expected_area): # float output size ratio = w / h ow = (expected_area * ratio) ** 0.5 oh = expected_area / ow # process width first ow1 = int(ow // dw * dw) oh1 = int(expected_area / ow1 // dh * dh) assert ow1 % dw == 0 and oh1 % dh == 0 and ow1 * oh1 <= expected_area ratio1 = ow1 / oh1 # process height first oh2 = int(oh // dh * dh) ow2 = int(expected_area / oh2 // dw * dw) assert oh2 % dh == 0 and ow2 % dw == 0 and ow2 * oh2 <= expected_area ratio2 = ow2 / oh2 # compare ratios if max(ratio / ratio1, ratio1 / ratio) < max(ratio / ratio2, ratio2 / ratio): return ow1, oh1 else: return ow2, oh2 def calculate_dimensions(target_area, ratio): width = math.sqrt(target_area * ratio) height = width / ratio width = round(width / 32) * 32 height = round(height / 32) * 32 return width, height, None