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import threading
from typing import Any, Iterable, List, Optional
import torch
from diffusers.utils import logging
from .scheduler import BaseAsyncScheduler, async_retrieve_timesteps
from .wrappers import ThreadSafeImageProcessorWrapper, ThreadSafeTokenizerWrapper, ThreadSafeVAEWrapper
logger = logging.get_logger(__name__)
class RequestScopedPipeline:
DEFAULT_MUTABLE_ATTRS = [
"_all_hooks",
"_offload_device",
"_progress_bar_config",
"_progress_bar",
"_rng_state",
"_last_seed",
"latents",
]
def __init__(
self,
pipeline: Any,
mutable_attrs: Optional[Iterable[str]] = None,
auto_detect_mutables: bool = True,
tensor_numel_threshold: int = 1_000_000,
tokenizer_lock: Optional[threading.Lock] = None,
wrap_scheduler: bool = True,
):
self._base = pipeline
self.unet = getattr(pipeline, "unet", None)
self.vae = getattr(pipeline, "vae", None)
self.text_encoder = getattr(pipeline, "text_encoder", None)
self.components = getattr(pipeline, "components", None)
self.transformer = getattr(pipeline, "transformer", None)
if wrap_scheduler and hasattr(pipeline, "scheduler") and pipeline.scheduler is not None:
if not isinstance(pipeline.scheduler, BaseAsyncScheduler):
pipeline.scheduler = BaseAsyncScheduler(pipeline.scheduler)
self._mutable_attrs = list(mutable_attrs) if mutable_attrs is not None else list(self.DEFAULT_MUTABLE_ATTRS)
self._tokenizer_lock = tokenizer_lock if tokenizer_lock is not None else threading.Lock()
self._vae_lock = threading.Lock()
self._image_lock = threading.Lock()
self._auto_detect_mutables = bool(auto_detect_mutables)
self._tensor_numel_threshold = int(tensor_numel_threshold)
self._auto_detected_attrs: List[str] = []
def _detect_kernel_pipeline(self, pipeline) -> bool:
kernel_indicators = [
"text_encoding_cache",
"memory_manager",
"enable_optimizations",
"_create_request_context",
"get_optimization_stats",
]
return any(hasattr(pipeline, attr) for attr in kernel_indicators)
def _make_local_scheduler(self, num_inference_steps: int, device: Optional[str] = None, **clone_kwargs):
base_sched = getattr(self._base, "scheduler", None)
if base_sched is None:
return None
if not isinstance(base_sched, BaseAsyncScheduler):
wrapped_scheduler = BaseAsyncScheduler(base_sched)
else:
wrapped_scheduler = base_sched
try:
return wrapped_scheduler.clone_for_request(
num_inference_steps=num_inference_steps, device=device, **clone_kwargs
)
except Exception as e:
logger.debug(f"clone_for_request failed: {e}; trying shallow copy fallback")
try:
if hasattr(wrapped_scheduler, "scheduler"):
try:
copied_scheduler = copy.copy(wrapped_scheduler.scheduler)
return BaseAsyncScheduler(copied_scheduler)
except Exception:
return wrapped_scheduler
else:
copied_scheduler = copy.copy(wrapped_scheduler)
return BaseAsyncScheduler(copied_scheduler)
except Exception as e2:
logger.warning(
f"Shallow copy of scheduler also failed: {e2}. Using original scheduler (*thread-unsafe but functional*)."
)
return wrapped_scheduler
def _autodetect_mutables(self, max_attrs: int = 40):
if not self._auto_detect_mutables:
return []
if self._auto_detected_attrs:
return self._auto_detected_attrs
candidates: List[str] = []
seen = set()
for name in dir(self._base):
if name.startswith("__"):
continue
if name in self._mutable_attrs:
continue
if name in ("to", "save_pretrained", "from_pretrained"):
continue
try:
val = getattr(self._base, name)
except Exception:
continue
import types
if callable(val) or isinstance(val, (types.ModuleType, types.FunctionType, types.MethodType)):
continue
if isinstance(val, (dict, list, set, tuple, bytearray)):
candidates.append(name)
seen.add(name)
else:
# try Tensor detection
try:
if isinstance(val, torch.Tensor):
if val.numel() <= self._tensor_numel_threshold:
candidates.append(name)
seen.add(name)
else:
logger.debug(f"Ignoring large tensor attr '{name}', numel={val.numel()}")
except Exception:
continue
if len(candidates) >= max_attrs:
break
self._auto_detected_attrs = candidates
logger.debug(f"Autodetected mutable attrs to clone: {self._auto_detected_attrs}")
return self._auto_detected_attrs
def _is_readonly_property(self, base_obj, attr_name: str) -> bool:
try:
cls = type(base_obj)
descriptor = getattr(cls, attr_name, None)
if isinstance(descriptor, property):
return descriptor.fset is None
if hasattr(descriptor, "__set__") is False and descriptor is not None:
return False
except Exception:
pass
return False
def _clone_mutable_attrs(self, base, local):
attrs_to_clone = list(self._mutable_attrs)
attrs_to_clone.extend(self._autodetect_mutables())
EXCLUDE_ATTRS = {
"components",
}
for attr in attrs_to_clone:
if attr in EXCLUDE_ATTRS:
logger.debug(f"Skipping excluded attr '{attr}'")
continue
if not hasattr(base, attr):
continue
if self._is_readonly_property(base, attr):
logger.debug(f"Skipping read-only property '{attr}'")
continue
try:
val = getattr(base, attr)
except Exception as e:
logger.debug(f"Could not getattr('{attr}') on base pipeline: {e}")
continue
try:
if isinstance(val, dict):
setattr(local, attr, dict(val))
elif isinstance(val, (list, tuple, set)):
setattr(local, attr, list(val))
elif isinstance(val, bytearray):
setattr(local, attr, bytearray(val))
else:
# small tensors or atomic values
if isinstance(val, torch.Tensor):
if val.numel() <= self._tensor_numel_threshold:
setattr(local, attr, val.clone())
else:
# don't clone big tensors, keep reference
setattr(local, attr, val)
else:
try:
setattr(local, attr, copy.copy(val))
except Exception:
setattr(local, attr, val)
except (AttributeError, TypeError) as e:
logger.debug(f"Skipping cloning attribute '{attr}' because it is not settable: {e}")
continue
except Exception as e:
logger.debug(f"Unexpected error cloning attribute '{attr}': {e}")
continue
def _is_tokenizer_component(self, component) -> bool:
if component is None:
return False
tokenizer_methods = ["encode", "decode", "tokenize", "__call__"]
has_tokenizer_methods = any(hasattr(component, method) for method in tokenizer_methods)
class_name = component.__class__.__name__.lower()
has_tokenizer_in_name = "tokenizer" in class_name
tokenizer_attrs = ["vocab_size", "pad_token", "eos_token", "bos_token"]
has_tokenizer_attrs = any(hasattr(component, attr) for attr in tokenizer_attrs)
return has_tokenizer_methods and (has_tokenizer_in_name or has_tokenizer_attrs)
def _should_wrap_tokenizers(self) -> bool:
return True
def generate(self, *args, num_inference_steps: int = 50, device: Optional[str] = None, **kwargs):
local_scheduler = self._make_local_scheduler(num_inference_steps=num_inference_steps, device=device)
try:
local_pipe = copy.copy(self._base)
except Exception as e:
logger.warning(f"copy.copy(self._base) failed: {e}. Falling back to deepcopy (may increase memory).")
local_pipe = copy.deepcopy(self._base)
try:
if (
hasattr(local_pipe, "vae")
and local_pipe.vae is not None
and not isinstance(local_pipe.vae, ThreadSafeVAEWrapper)
):
local_pipe.vae = ThreadSafeVAEWrapper(local_pipe.vae, self._vae_lock)
if (
hasattr(local_pipe, "image_processor")
and local_pipe.image_processor is not None
and not isinstance(local_pipe.image_processor, ThreadSafeImageProcessorWrapper)
):
local_pipe.image_processor = ThreadSafeImageProcessorWrapper(
local_pipe.image_processor, self._image_lock
)
except Exception as e:
logger.debug(f"Could not wrap vae/image_processor: {e}")
if local_scheduler is not None:
try:
timesteps, num_steps, configured_scheduler = async_retrieve_timesteps(
local_scheduler.scheduler,
num_inference_steps=num_inference_steps,
device=device,
return_scheduler=True,
**{k: v for k, v in kwargs.items() if k in ["timesteps", "sigmas"]},
)
final_scheduler = BaseAsyncScheduler(configured_scheduler)
setattr(local_pipe, "scheduler", final_scheduler)
except Exception:
logger.warning("Could not set scheduler on local pipe; proceeding without replacing scheduler.")
self._clone_mutable_attrs(self._base, local_pipe)
original_tokenizers = {}
if self._should_wrap_tokenizers():
try:
for name in dir(local_pipe):
if "tokenizer" in name and not name.startswith("_"):
tok = getattr(local_pipe, name, None)
if tok is not None and self._is_tokenizer_component(tok):
if not isinstance(tok, ThreadSafeTokenizerWrapper):
original_tokenizers[name] = tok
wrapped_tokenizer = ThreadSafeTokenizerWrapper(tok, self._tokenizer_lock)
setattr(local_pipe, name, wrapped_tokenizer)
if hasattr(local_pipe, "components") and isinstance(local_pipe.components, dict):
for key, val in local_pipe.components.items():
if val is None:
continue
if self._is_tokenizer_component(val):
if not isinstance(val, ThreadSafeTokenizerWrapper):
original_tokenizers[f"components[{key}]"] = val
wrapped_tokenizer = ThreadSafeTokenizerWrapper(val, self._tokenizer_lock)
local_pipe.components[key] = wrapped_tokenizer
except Exception as e:
logger.debug(f"Tokenizer wrapping step encountered an error: {e}")
result = None
cm = getattr(local_pipe, "model_cpu_offload_context", None)
try:
if callable(cm):
try:
with cm():
result = local_pipe(*args, num_inference_steps=num_inference_steps, **kwargs)
except TypeError:
try:
with cm:
result = local_pipe(*args, num_inference_steps=num_inference_steps, **kwargs)
except Exception as e:
logger.debug(f"model_cpu_offload_context usage failed: {e}. Proceeding without it.")
result = local_pipe(*args, num_inference_steps=num_inference_steps, **kwargs)
else:
result = local_pipe(*args, num_inference_steps=num_inference_steps, **kwargs)
return result
finally:
try:
for name, tok in original_tokenizers.items():
if name.startswith("components["):
key = name[len("components[") : -1]
if hasattr(local_pipe, "components") and isinstance(local_pipe.components, dict):
local_pipe.components[key] = tok
else:
setattr(local_pipe, name, tok)
except Exception as e:
logger.debug(f"Error restoring original tokenizers: {e}")
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