5037af1033
Signed-off-by: AndyZhou952 <jzhoubc@connect.ust.hk> Signed-off-by: Andy Zhou <jzhoubc@connect.ust.hk> Signed-off-by: dongbo910220 <1275604947@qq.com> Signed-off-by: Andy Zhou <46011930+AndyZhou952@users.noreply.github.com> Signed-off-by: Samit <285365963@qq.com> Co-authored-by: dongbo910220 <1275604947@qq.com> Co-authored-by: Samit <285365963@qq.com>
632 lines
26 KiB
Python
632 lines
26 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import time
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from collections import OrderedDict
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import torch
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import torch.nn as nn
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from vllm.logger import init_logger
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from vllm.lora.layers import BaseLayerWithLoRA
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from vllm.lora.lora_model import LoRAModel
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from vllm.lora.lora_weights import LoRALayerWeights, PackedLoRALayerWeights
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from vllm.lora.peft_helper import PEFTHelper
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from vllm.lora.request import LoRARequest
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from vllm.lora.utils import (
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get_adapter_absolute_path,
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get_supported_lora_modules,
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replace_submodule,
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)
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from vllm.model_executor.layers.linear import MergedColumnParallelLinear, QKVParallelLinear
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from vllm_omni.config.lora import LoRAConfig
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from vllm_omni.diffusion.lora.utils import (
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_expand_expected_modules_for_packed_layers,
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_match_target_modules,
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from_layer_diffusion,
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)
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from vllm_omni.lora.utils import stable_lora_int_id
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logger = init_logger(__name__)
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class DiffusionLoRAManager:
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"""Manager for LoRA adapters in diffusion models.
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Reuses vLLM's LoRA infrastructure, adapted for diffusion pipelines.
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Uses LRU cache management similar to LRUCacheLoRAModelManager.
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"""
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def __init__(
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self,
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pipeline: nn.Module,
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device: torch.device,
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dtype: torch.dtype,
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max_cached_adapters: int = 1,
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lora_path: str | None = None,
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lora_scale: float = 1.0,
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):
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"""
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Initialize the DiffusionLoRAManager.
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Args:
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max_cached_adapters: Maximum number of LoRA adapters to keep in the
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CPU-side cache (LRU). This mirrors vLLM's `max_cpu_loras` and is
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exposed to users via `OmniDiffusionConfig.max_cpu_loras`.
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"""
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self.pipeline = pipeline
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self.device = device
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self.dtype = dtype
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# Cache supported/expected module suffixes once, before any layer
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# replacement happens. After LoRA layers are injected, the original
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# LinearBase layers become submodules named "*.base_layer", and calling
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# vLLM's get_supported_lora_modules() again would incorrectly yield
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# "base_layer" instead of the real target module suffixes.
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self._supported_lora_modules = self._compute_supported_lora_modules()
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self._packed_modules_mapping = self._compute_packed_modules_mapping()
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self._expected_lora_modules = _expand_expected_modules_for_packed_layers(
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self._supported_lora_modules,
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self._packed_modules_mapping,
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)
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# LRU-style cache management
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self.max_cached_adapters = max_cached_adapters # max_cpu_loras
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self._registered_adapters: dict[int, LoRAModel] = {} # adapter_id -> LoRAModel
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self._active_adapter_id: int | None = None
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self._adapter_scales: dict[int, float] = {} # adapter_id -> external scale
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# LRU cache tracking (adapter_id -> last_used_time)
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self._adapter_access_order: OrderedDict[int, float] = OrderedDict()
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# Pinned adapters are not evicted
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self._pinned_adapters: set[int] = set()
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# track replaced modules
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# key: full module name (component.module.path); value: LoRA layer
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self._lora_modules: dict[str, BaseLayerWithLoRA] = {}
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# Track the maximum LoRA rank we've allocated buffers for.
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self._max_lora_rank: int = 0
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logger.info(
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"Initializing DiffusionLoRAManager: device=%s, dtype=%s, max_cached_adapters=%d, static_lora_path=%s",
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device,
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dtype,
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max_cached_adapters,
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lora_path,
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)
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if lora_path is not None:
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logger.info("Loading LoRA during initialization from %s with scale %.2f", lora_path, lora_scale)
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init_request = LoRARequest(
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lora_name="static",
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lora_int_id=stable_lora_int_id(lora_path),
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lora_path=lora_path,
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)
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self.set_active_adapter(init_request, lora_scale)
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def _compute_supported_lora_modules(self) -> set[str]:
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"""Compute supported LoRA module suffixes for this pipeline.
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vLLM's get_supported_lora_modules() returns suffixes for LinearBase
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modules. After this manager replaces layers with BaseLayerWithLoRA
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wrappers, those LinearBase modules become nested under ".base_layer",
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which would cause get_supported_lora_modules() to return "base_layer".
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To make adapter loading stable across multiple adapters, we also accept
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suffixes from existing BaseLayerWithLoRA wrappers and drop "base_layer"
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when appropriate.
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"""
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supported = set(get_supported_lora_modules(self.pipeline))
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has_lora_wrappers = False
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for name, module in self.pipeline.named_modules():
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if isinstance(module, BaseLayerWithLoRA):
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has_lora_wrappers = True
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supported.add(name.split(".")[-1])
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if has_lora_wrappers:
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supported.discard("base_layer")
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return supported
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def _compute_packed_modules_mapping(self) -> dict[str, list[str]]:
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"""Collect packed->sublayer mappings from the diffusion model.
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vLLM models declare `packed_modules_mapping` on the model class. For
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diffusion pipelines, we attach the same mapping on the transformer
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module(s) that implement packed (fused) projections, so LoRA loading can
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accept checkpoints trained against the logical sub-projections.
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"""
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mapping: dict[str, list[str]] = {}
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for module in self.pipeline.modules():
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packed = getattr(module, "packed_modules_mapping", None)
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if not isinstance(packed, dict):
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continue
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for packed_name, sub_names in packed.items():
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if not isinstance(packed_name, str) or not packed_name:
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continue
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if not isinstance(sub_names, (list, tuple)) or not all(isinstance(s, str) for s in sub_names):
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continue
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sub_names_list = list(sub_names)
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if not sub_names_list:
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continue
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existing = mapping.get(packed_name)
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if existing is None:
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mapping[packed_name] = sub_names_list
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elif existing != sub_names_list:
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logger.warning(
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"Conflicting packed_modules_mapping for %s: %s vs %s; using %s",
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packed_name,
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existing,
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sub_names_list,
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existing,
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)
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return mapping
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def _get_packed_sublayer_suffixes(self, packed_module_suffix: str, n_slices: int) -> list[str] | None:
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sub_suffixes = self._packed_modules_mapping.get(packed_module_suffix)
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if not sub_suffixes:
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return None
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if len(sub_suffixes) != n_slices:
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logger.warning(
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"packed_modules_mapping[%s] has %d slices but layer expects %d; skipping sublayer lookup",
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packed_module_suffix,
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len(sub_suffixes),
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n_slices,
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)
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return None
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return sub_suffixes
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def set_active_adapter(self, lora_request: LoRARequest | None, lora_scale: float = 1.0) -> None:
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"""Set the active LoRA adapter for the pipeline.
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Args:
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lora_request: The LoRA request, or None to deactivate all adapters.
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lora_scale: The external scale for the LoRA adapter.
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"""
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if lora_request is None:
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logger.debug("No lora_request provided, deactivating all LoRA adapters")
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self._deactivate_all_adapters()
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return
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adapter_id = lora_request.lora_int_id
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logger.debug(
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"Setting active adapter: id=%d, name=%s, path=%s, scale=%.2f, cache_size=%d/%d",
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adapter_id,
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lora_request.lora_name,
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lora_request.lora_path,
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lora_scale,
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len(self._registered_adapters),
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self.max_cached_adapters,
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)
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if adapter_id not in self._registered_adapters:
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logger.info("Loading new adapter: id=%d, name=%s", adapter_id, lora_request.lora_name)
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self.add_adapter(lora_request, lora_scale)
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else:
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logger.debug("Adapter %d already loaded, activating", adapter_id)
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# update access order
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self._adapter_scales[adapter_id] = lora_scale
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self._adapter_access_order[adapter_id] = time.time()
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self._adapter_access_order.move_to_end(adapter_id)
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self._activate_adapter(adapter_id)
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def _load_adapter(
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self,
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lora_request: LoRARequest,
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) -> tuple[LoRAModel, PEFTHelper]:
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if not self._expected_lora_modules:
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raise ValueError("No supported LoRA modules found in the diffusion pipeline.")
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logger.debug("Supported LoRA modules: %s", self._expected_lora_modules)
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lora_path = get_adapter_absolute_path(lora_request.lora_path)
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logger.debug("Resolved LoRA path: %s", lora_path)
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peft_helper = PEFTHelper.from_local_dir(
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lora_path,
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max_position_embeddings=None, # no need in diffusion
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tensorizer_config_dict=lora_request.tensorizer_config_dict,
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)
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logger.info(
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"Loaded PEFT config: r=%d, lora_alpha=%d, target_modules=%s",
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peft_helper.r,
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peft_helper.lora_alpha,
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peft_helper.target_modules,
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)
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lora_model = LoRAModel.from_local_checkpoint(
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lora_path,
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expected_lora_modules=self._expected_lora_modules,
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peft_helper=peft_helper,
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lora_model_id=lora_request.lora_int_id,
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device="cpu", # consistent w/ vllm's behavior
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dtype=self.dtype,
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model_vocab_size=None,
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tensorizer_config_dict=lora_request.tensorizer_config_dict,
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weights_mapper=None,
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)
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logger.info(
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"Loaded LoRA model: id=%d, num_modules=%d, modules=%s",
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lora_model.id,
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len(lora_model.loras),
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list(lora_model.loras.keys()),
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)
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for lora in lora_model.loras.values():
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lora.optimize() # ref: _create_merged_loras_inplace, internal scaling
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return lora_model, peft_helper
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def _get_packed_modules_list(self, module: nn.Module) -> list[str]:
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"""Return a packed_modules_list suitable for vLLM LoRA can_replace_layer().
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Diffusion transformers frequently use packed projection layers like
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QKVParallelLinear (fused QKV). vLLM's LoRA replacement logic relies on
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`packed_modules_list` length to decide between single-slice vs packed
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LoRA layer implementations.
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"""
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if isinstance(module, QKVParallelLinear):
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# Treat diffusion QKV as a 3-slice packed projection by default.
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return ["q", "k", "v"]
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if isinstance(module, MergedColumnParallelLinear):
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# 2-slice packed projection (e.g. fused MLP projections).
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return ["0", "1"]
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return []
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def _replace_layers_with_lora(self, peft_helper: PEFTHelper) -> None:
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self._ensure_max_lora_rank(peft_helper.r)
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target_modules = getattr(peft_helper, "target_modules", None)
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target_modules_list: list[str] | None = None
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target_modules_pattern: str | None = None
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if isinstance(target_modules, str) and target_modules:
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target_modules_pattern = target_modules
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elif isinstance(target_modules, list) and target_modules:
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target_modules_list = target_modules
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def _matches_target(module_name: str) -> bool:
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if target_modules_pattern is not None:
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import regex as re
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return re.search(target_modules_pattern, module_name) is not None
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if target_modules_list is None:
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return True
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return _match_target_modules(module_name, target_modules_list)
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# dummy lora config
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lora_config = LoRAConfig(
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max_lora_rank=self._max_lora_rank,
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max_loras=1,
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max_cpu_loras=self.max_cached_adapters,
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lora_dtype=self.dtype,
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fully_sharded_loras=False,
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)
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for component_name in ("transformer", "transformer_2", "dit"):
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if not hasattr(self.pipeline, component_name):
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continue
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component = getattr(self.pipeline, component_name)
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if not isinstance(component, nn.Module):
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continue
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for module_name, module in component.named_modules(remove_duplicate=False):
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# Don't recurse into already-replaced LoRA wrappers. Their
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# original LinearBase lives under "base_layer", and replacing
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# that again would nest LoRA wrappers and break execution.
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if isinstance(module, BaseLayerWithLoRA) or "base_layer" in module_name.split("."):
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continue
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full_module_name = f"{component_name}.{module_name}"
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if full_module_name in self._lora_modules:
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logger.debug("Layer %s already replaced, skipping", full_module_name)
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continue
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packed_modules_list = self._get_packed_modules_list(module)
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if target_modules_pattern is not None or target_modules_list is not None:
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should_replace = _matches_target(full_module_name)
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if not should_replace and len(packed_modules_list) > 1:
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prefix, _, packed_suffix = full_module_name.rpartition(".")
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sub_suffixes = self._get_packed_sublayer_suffixes(packed_suffix, len(packed_modules_list))
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if sub_suffixes is not None:
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for sub_suffix in sub_suffixes:
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sub_full_name = f"{prefix}.{sub_suffix}" if prefix else sub_suffix
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if _matches_target(sub_full_name):
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should_replace = True
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break
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if not should_replace:
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continue
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lora_layer = from_layer_diffusion(
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layer=module,
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max_loras=1,
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lora_config=lora_config,
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packed_modules_list=packed_modules_list,
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model_config=None,
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)
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if lora_layer is not module and isinstance(lora_layer, BaseLayerWithLoRA):
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replace_submodule(component, module_name, lora_layer)
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self._lora_modules[full_module_name] = lora_layer
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logger.debug("Replaced layer: %s -> %s", full_module_name, type(lora_layer).__name__)
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def _ensure_max_lora_rank(self, min_rank: int) -> None:
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"""Ensure LoRA buffers can accommodate adapters up to `min_rank`.
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We allocate per-layer LoRA buffers once when we first replace layers.
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If a later adapter has a larger rank, we need to reinitialize those
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buffers and re-apply the currently active adapter.
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"""
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if min_rank <= self._max_lora_rank:
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return
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if min_rank <= 0:
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raise ValueError(f"Invalid LoRA rank: {min_rank}")
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logger.info("Increasing max LoRA rank: %d -> %d", self._max_lora_rank, min_rank)
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self._max_lora_rank = min_rank
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if not self._lora_modules:
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return
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lora_config = LoRAConfig(
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max_lora_rank=self._max_lora_rank,
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max_loras=1,
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max_cpu_loras=self.max_cached_adapters,
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lora_dtype=self.dtype,
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fully_sharded_loras=False,
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)
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# Recreate per-layer buffers with the new maximum rank.
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for lora_layer in self._lora_modules.values():
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lora_layer.create_lora_weights(max_loras=1, lora_config=lora_config, model_config=None)
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# Re-apply active adapter if needed (buffers were reset).
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if self._active_adapter_id is not None:
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active_id = self._active_adapter_id
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self._active_adapter_id = None
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self._activate_adapter(active_id)
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def _get_lora_weights(
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self,
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lora_model: LoRAModel,
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full_module_name: str,
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) -> LoRALayerWeights | PackedLoRALayerWeights | None:
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"""Best-effort lookup for LoRA weights by name.
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Tries:
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- Full module name (e.g. transformer.blocks.0.attn.to_qkv)
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- Relative name without the top-level component (e.g. blocks.0.attn.to_qkv)
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- Suffix-only name (e.g. to_qkv)
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"""
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lora_weights = lora_model.get_lora(full_module_name)
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if lora_weights is not None:
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return lora_weights
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component_relative_name = full_module_name.split(".", 1)[-1] if "." in full_module_name else full_module_name
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lora_weights = lora_model.get_lora(component_relative_name)
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if lora_weights is not None:
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return lora_weights
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module_suffix = full_module_name.split(".")[-1]
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return lora_model.get_lora(module_suffix)
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def _activate_adapter(self, adapter_id: int) -> None:
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if self._active_adapter_id == adapter_id:
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logger.debug("Adapter %d already active, skipping", adapter_id)
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return
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logger.info("Activating adapter: id=%d", adapter_id)
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lora_model = self._registered_adapters[adapter_id]
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# activate weights in each LoRA layer
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for full_module_name, lora_layer in self._lora_modules.items():
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lora_weights = self._get_lora_weights(lora_model, full_module_name)
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if lora_weights is None:
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n_slices = getattr(lora_layer, "n_slices", 1)
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if n_slices > 1:
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prefix, _, packed_suffix = full_module_name.rpartition(".")
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sub_suffixes = self._get_packed_sublayer_suffixes(packed_suffix, n_slices)
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if sub_suffixes is None:
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lora_layer.reset_lora(0)
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continue
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sub_loras: list[LoRALayerWeights | None] = []
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any_found = False
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for sub_suffix in sub_suffixes:
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sub_full_name = f"{prefix}.{sub_suffix}" if prefix else sub_suffix
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sub_lora = self._get_lora_weights(lora_model, sub_full_name)
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if sub_lora is not None:
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any_found = True
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# Packed layers expect plain (non-packed) subloras.
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if isinstance(sub_lora, PackedLoRALayerWeights):
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sub_lora = None
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sub_loras.append(sub_lora if isinstance(sub_lora, LoRALayerWeights) else None)
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if not any_found:
|
|
lora_layer.reset_lora(0)
|
|
continue
|
|
|
|
scale = self._adapter_scales.get(adapter_id, 1.0)
|
|
lora_a_list: list[torch.Tensor | None] = []
|
|
lora_b_list: list[torch.Tensor | None] = []
|
|
for sub_lora in sub_loras:
|
|
if sub_lora is None:
|
|
lora_a_list.append(None)
|
|
lora_b_list.append(None)
|
|
continue
|
|
lora_a_list.append(sub_lora.lora_a)
|
|
lora_b_list.append(sub_lora.lora_b * scale)
|
|
|
|
lora_layer.set_lora(index=0, lora_a=lora_a_list, lora_b=lora_b_list)
|
|
logger.debug(
|
|
"Activated packed LoRA for %s via submodules=%s (scale=%.2f)",
|
|
full_module_name,
|
|
sub_suffixes,
|
|
scale,
|
|
)
|
|
else:
|
|
lora_layer.reset_lora(0)
|
|
continue
|
|
|
|
scale = self._adapter_scales.get(adapter_id, 1.0)
|
|
|
|
# Packed LoRA weights already provide per-slice tensors.
|
|
if isinstance(lora_weights, PackedLoRALayerWeights):
|
|
lora_a_list = lora_weights.lora_a
|
|
lora_b_list = [
|
|
None if b is None else b * scale # type: ignore[operator]
|
|
for b in lora_weights.lora_b
|
|
]
|
|
lora_layer.set_lora(index=0, lora_a=lora_a_list, lora_b=lora_b_list)
|
|
logger.debug(
|
|
"Activated packed LoRA for %s (scale=%.2f)",
|
|
full_module_name,
|
|
scale,
|
|
)
|
|
continue
|
|
|
|
# Fused (non-packed) weights: if the layer is multi-slice, split B.
|
|
n_slices = getattr(lora_layer, "n_slices", 1)
|
|
if n_slices > 1:
|
|
output_slices = getattr(lora_layer, "output_slices", None)
|
|
if output_slices is None:
|
|
lora_layer.reset_lora(0)
|
|
continue
|
|
|
|
total = sum(output_slices)
|
|
if lora_weights.lora_b.shape[0] != total:
|
|
logger.warning(
|
|
"Skipping LoRA for %s due to shape mismatch: lora_b[0]=%d != sum(output_slices)=%d",
|
|
full_module_name,
|
|
lora_weights.lora_b.shape[0],
|
|
total,
|
|
)
|
|
lora_layer.reset_lora(0)
|
|
continue
|
|
|
|
b_splits = list(torch.split(lora_weights.lora_b, list(output_slices), dim=0))
|
|
lora_a_list = [lora_weights.lora_a] * n_slices
|
|
lora_b_list = [b * scale for b in b_splits]
|
|
lora_layer.set_lora(index=0, lora_a=lora_a_list, lora_b=lora_b_list)
|
|
logger.debug(
|
|
"Activated fused LoRA for packed layer %s (scale=%.2f)",
|
|
full_module_name,
|
|
scale,
|
|
)
|
|
continue
|
|
|
|
scaled_lora_b = lora_weights.lora_b * scale
|
|
lora_layer.set_lora(index=0, lora_a=lora_weights.lora_a, lora_b=scaled_lora_b)
|
|
logger.debug(
|
|
"Activated LoRA for %s: lora_a shape=%s, lora_b shape=%s, scale=%.2f",
|
|
full_module_name,
|
|
lora_weights.lora_a.shape,
|
|
lora_weights.lora_b.shape,
|
|
scale,
|
|
)
|
|
|
|
self._active_adapter_id = adapter_id
|
|
|
|
def _deactivate_all_adapters(self) -> None:
|
|
logger.info("Deactivating all adapters: %d layers", len(self._lora_modules))
|
|
for lora_layer in self._lora_modules.values():
|
|
lora_layer.reset_lora(0)
|
|
self._active_adapter_id = None
|
|
logger.debug("All adapters deactivated")
|
|
|
|
def _evict_if_needed(self) -> None:
|
|
while len(self._registered_adapters) > self.max_cached_adapters:
|
|
# Pick LRU among non-pinned adapters
|
|
evict_candidates = [aid for aid in self._adapter_access_order.keys() if aid not in self._pinned_adapters]
|
|
if not evict_candidates:
|
|
logger.warning(
|
|
"Cache full (%d) but all adapters are pinned; cannot evict. "
|
|
"Increase max_cached_adapters or unpin adapters.",
|
|
self.max_cached_adapters,
|
|
)
|
|
break
|
|
|
|
lru_adapter_id = evict_candidates[0]
|
|
logger.info(
|
|
"Evicting LRU adapter: id=%d (cache: %d/%d)",
|
|
lru_adapter_id,
|
|
len(self._registered_adapters),
|
|
self.max_cached_adapters,
|
|
)
|
|
self.remove_adapter(lru_adapter_id)
|
|
|
|
def add_adapter(self, lora_request: LoRARequest, lora_scale: float = 1.0) -> bool:
|
|
"""
|
|
Add a new adapter to the cache without activating it.
|
|
"""
|
|
adapter_id = lora_request.lora_int_id
|
|
|
|
if adapter_id in self._registered_adapters:
|
|
logger.debug("Adapter %d already registered, skipping", adapter_id)
|
|
return False
|
|
|
|
logger.info("Adding new adapter: id=%d, name=%s", adapter_id, lora_request.lora_name)
|
|
lora_model, peft_helper = self._load_adapter(lora_request)
|
|
self._registered_adapters[adapter_id] = lora_model
|
|
self._adapter_scales[adapter_id] = lora_scale
|
|
|
|
self._replace_layers_with_lora(peft_helper)
|
|
|
|
self._adapter_access_order[adapter_id] = time.time()
|
|
self._adapter_access_order.move_to_end(adapter_id)
|
|
|
|
# evict if cache full
|
|
self._evict_if_needed()
|
|
|
|
logger.debug(
|
|
"Adapter %d added, cache size: %d/%d", adapter_id, len(self._registered_adapters), self.max_cached_adapters
|
|
)
|
|
return True
|
|
|
|
def remove_adapter(self, adapter_id: int) -> bool:
|
|
"""
|
|
Remove an adapter from the cache.
|
|
"""
|
|
if adapter_id not in self._registered_adapters:
|
|
logger.debug("Adapter %d not found, cannot remove", adapter_id)
|
|
return False
|
|
|
|
logger.info("Removing adapter: id=%d", adapter_id)
|
|
if self._active_adapter_id == adapter_id:
|
|
self._deactivate_all_adapters()
|
|
|
|
del self._registered_adapters[adapter_id]
|
|
self._adapter_scales.pop(adapter_id, None)
|
|
self._adapter_access_order.pop(adapter_id, None)
|
|
self._pinned_adapters.discard(adapter_id)
|
|
logger.debug(
|
|
"Adapter %d removed, cache size: %d/%d",
|
|
adapter_id,
|
|
len(self._registered_adapters),
|
|
self.max_cached_adapters,
|
|
)
|
|
return True
|
|
|
|
def list_adapters(self) -> list[int]:
|
|
"""Return list of registered adapter ids."""
|
|
return list(self._registered_adapters.keys())
|
|
|
|
def pin_adapter(self, adapter_id: int) -> bool:
|
|
"""Mark an adapter as pinned so it will not be evicted."""
|
|
if adapter_id not in self._registered_adapters:
|
|
logger.debug("Adapter %d not found, cannot pin", adapter_id)
|
|
return False
|
|
self._pinned_adapters.add(adapter_id)
|
|
# Touch access order so it is most recently used
|
|
self._adapter_access_order[adapter_id] = time.time()
|
|
self._adapter_access_order.move_to_end(adapter_id)
|
|
logger.info("Pinned adapter id=%d (won't be evicted)", adapter_id)
|
|
return True
|