# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """ cache-dit integration backend for vllm-omni. This module provides a CacheDiTBackend class to enable cache-dit acceleration on diffusion pipelines in vllm-omni, supporting both single and dual-transformer architectures. """ import functools from collections.abc import Callable from contextlib import ExitStack from typing import Any, Optional import cache_dit import torch from cache_dit import BlockAdapter, DBCacheConfig, ForwardPattern, ParamsModifier, TaylorSeerCalibratorConfig from cache_dit.caching.block_adapters import FakeDiffusionPipeline from cache_dit.caching.cache_adapters.cache_adapter import CachedAdapter from cache_dit.caching.cache_blocks.pattern_0_1_2 import CachedBlocks_Pattern_0_1_2 from cache_dit.caching.cache_contexts import BasicCacheConfig from cache_dit.caching.cache_contexts.cache_manager import CachedContextManager from vllm.logger import init_logger from vllm_omni.diffusion.cache.base import CacheBackend from vllm_omni.diffusion.data import DiffusionCacheConfig, OmniDiffusionConfig logger = init_logger(__name__) # Small helper to centralize cache-dit summaries. def cache_summary(pipeline: Any, details: bool = True) -> None: cache_dit.summary(pipeline.transformer, details=details) if hasattr(pipeline, "transformer_2"): cache_dit.summary(pipeline.transformer_2, details=details) # Registry of custom cache-dit enablers for specific models # Maps pipeline names to their cache-dit enablement functions # Models in this registry require custom handling (e.g., dual-transformer architectures) # Will be populated after function definitions CUSTOM_DIT_ENABLERS: dict[str, Callable] = {} def _build_db_cache_config(cache_config: Any) -> DBCacheConfig: """Build DBCacheConfig with optional SCM (Step Computation Masking) support. Args: cache_config: DiffusionCacheConfig instance. Returns: DBCacheConfig instance with SCM support if configured. """ return DBCacheConfig( # we will refresh the context when gets num_inference_steps in the first inference request num_inference_steps=None, Fn_compute_blocks=cache_config.Fn_compute_blocks, Bn_compute_blocks=cache_config.Bn_compute_blocks, max_warmup_steps=cache_config.max_warmup_steps, max_cached_steps=cache_config.max_cached_steps, max_continuous_cached_steps=cache_config.max_continuous_cached_steps, residual_diff_threshold=cache_config.residual_diff_threshold, ) def enable_cache_for_wan22(pipeline: Any, cache_config: Any) -> Callable[[int], None]: """Enable cache-dit for Wan2.2 dual-transformer architecture. Wan2.2 uses two transformers (transformer and transformer_2) that need to be enabled together using BlockAdapter. Args: pipeline: The Wan2.2 pipeline instance. cache_config: DiffusionCacheConfig instance with cache configuration. Returns: A refresh function that can be called to update cache context with new num_inference_steps. """ cache_dit.enable_cache( BlockAdapter( transformer=[ pipeline.transformer, pipeline.transformer_2, ], blocks=[ pipeline.transformer.blocks, pipeline.transformer_2.blocks, ], forward_pattern=[ ForwardPattern.Pattern_2, ForwardPattern.Pattern_2, ], params_modifiers=[ # high-noise transformer only have 30% steps ParamsModifier( cache_config=DBCacheConfig().reset( max_warmup_steps=cache_config.max_warmup_steps, max_cached_steps=cache_config.max_cached_steps, ), ), ParamsModifier( cache_config=DBCacheConfig().reset( max_warmup_steps=2, max_cached_steps=20, ), ), ], has_separate_cfg=True, ), cache_config=DBCacheConfig( Fn_compute_blocks=cache_config.Fn_compute_blocks, Bn_compute_blocks=cache_config.Bn_compute_blocks, max_warmup_steps=cache_config.max_warmup_steps, max_cached_steps=cache_config.max_cached_steps, max_continuous_cached_steps=cache_config.max_continuous_cached_steps, residual_diff_threshold=cache_config.residual_diff_threshold, num_inference_steps=None, ), ) # from https://github.com/vipshop/cache-dit/pull/542 def _split_inference_steps(num_inference_steps: int) -> tuple[int, int]: """Split inference steps into high-noise and low-noise steps for Wan2.2. This is an internal helper function specific to Wan2.2's dual-transformer architecture that uses boundary_ratio to determine the split point. Args: num_inference_steps: Total number of inference steps. Returns: A tuple of (num_high_noise_steps, num_low_noise_steps). """ if pipeline.boundary_ratio is not None: boundary_timestep = pipeline.boundary_ratio * pipeline.scheduler.config.num_train_timesteps else: boundary_timestep = None # Set timesteps to calculate the split device = next(pipeline.transformer.parameters()).device pipeline.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = pipeline.scheduler.timesteps num_high_noise_steps = 0 # high-noise steps for transformer for t in timesteps: if boundary_timestep is None or t >= boundary_timestep: num_high_noise_steps += 1 # low-noise steps for transformer_2 num_low_noise_steps = num_inference_steps - num_high_noise_steps return num_high_noise_steps, num_low_noise_steps def refresh_cache_context(pipeline: Any, num_inference_steps: int, verbose: bool = True) -> None: """Refresh cache context for both transformers with new num_inference_steps. Args: pipeline: The Wan2.2 pipeline instance. num_inference_steps: New number of inference steps. """ num_high_noise_steps, num_low_noise_steps = _split_inference_steps(num_inference_steps) # Refresh context for high-noise transformer if cache_config.scm_steps_mask_policy is None: # cache_dit.refresh_context(pipeline.transformer, num_inference_steps=num_high_noise_steps, verbose=verbose) cache_dit.refresh_context( pipeline.transformer, num_inference_steps=num_high_noise_steps, verbose=verbose, ) cache_dit.refresh_context( pipeline.transformer_2, num_inference_steps=num_low_noise_steps, verbose=verbose, ) else: cache_dit.refresh_context( pipeline.transformer, cache_config=DBCacheConfig().reset( num_inference_steps=num_high_noise_steps, steps_computation_mask=cache_dit.steps_mask( mask_policy=cache_config.scm_steps_mask_policy, total_steps=num_high_noise_steps ), steps_computation_policy=cache_config.scm_steps_policy, ), verbose=verbose, ) cache_dit.refresh_context( pipeline.transformer_2, cache_config=DBCacheConfig().reset( num_inference_steps=num_low_noise_steps, steps_computation_mask=cache_dit.steps_mask( mask_policy=cache_config.scm_steps_mask_policy, total_steps=num_low_noise_steps ), steps_computation_policy=cache_config.scm_steps_policy, ), verbose=verbose, ) return refresh_cache_context def enable_cache_for_longcat_image(pipeline: Any, cache_config: Any) -> Callable[[int], None]: """Enable cache-dit for LongCatImage pipeline. Args: pipeline: The LongCatImage pipeline instance. cache_config: DiffusionCacheConfig instance with cache configuration. """ # Build DBCacheConfig for transformer db_cache_config = _build_db_cache_config(cache_config) calibrator = None if cache_config.enable_taylorseer: taylorseer_order = cache_config.taylorseer_order calibrator = TaylorSeerCalibratorConfig(taylorseer_order=taylorseer_order) logger.info(f"TaylorSeer enabled with order={taylorseer_order}") # Build ParamsModifier for transformer modifier = ParamsModifier( cache_config=db_cache_config, calibrator_config=calibrator, ) logger.info( f"Enabling cache-dit on LongCatImage transformer with BlockAdapter: " f"Fn={db_cache_config.Fn_compute_blocks}, " f"Bn={db_cache_config.Bn_compute_blocks}, " f"W={db_cache_config.max_warmup_steps}, " ) # Enable cache-dit using BlockAdapter for transformer cache_dit.enable_cache( ( BlockAdapter( transformer=pipeline.transformer, blocks=[ pipeline.transformer.transformer_blocks, pipeline.transformer.single_transformer_blocks, ], forward_pattern=[ForwardPattern.Pattern_1, ForwardPattern.Pattern_1], params_modifiers=[modifier], ) ), cache_config=db_cache_config, ) def refresh_cache_context(pipeline: Any, num_inference_steps: int, verbose: bool = True) -> None: """Refresh cache context for the transformer with new num_inference_steps. Args: pipeline: The LongCatImage pipeline instance. num_inference_steps: New number of inference steps. """ if cache_config.scm_steps_mask_policy is None: cache_dit.refresh_context(pipeline.transformer, num_inference_steps=num_inference_steps, verbose=verbose) else: cache_dit.refresh_context( pipeline.transformer, cache_config=DBCacheConfig().reset( num_inference_steps=num_inference_steps, steps_computation_mask=cache_dit.steps_mask( mask_policy=cache_config.scm_steps_mask_policy, total_steps=num_inference_steps, ), steps_computation_policy=cache_config.scm_steps_policy, ), verbose=verbose, ) return refresh_cache_context def enable_cache_for_flux(pipeline: Any, cache_config: Any) -> Callable[[int], None]: """Enable cache-dit for Flux.1-dev pipeline. Args: pipeline: The Flux pipeline instance. cache_config: DiffusionCacheConfig instance with cache configuration. Returns: A refresh function that can be called with a new ``num_inference_steps`` to update the cache context for the pipeline. """ # Build DBCacheConfig for transformer db_cache_config = _build_db_cache_config(cache_config) calibrator = None if cache_config.enable_taylorseer: taylorseer_order = cache_config.taylorseer_order calibrator = TaylorSeerCalibratorConfig(taylorseer_order=taylorseer_order) logger.info(f"TaylorSeer enabled with order={taylorseer_order}") # Build ParamsModifier for transformer modifier = ParamsModifier( cache_config=db_cache_config, calibrator_config=calibrator, ) logger.info( f"Enabling cache-dit on Flux transformer with BlockAdapter: " f"Fn={db_cache_config.Fn_compute_blocks}, " f"Bn={db_cache_config.Bn_compute_blocks}, " f"W={db_cache_config.max_warmup_steps}, " ) # Enable cache-dit using BlockAdapter for transformer cache_dit.enable_cache( ( BlockAdapter( transformer=pipeline.transformer, blocks=[ pipeline.transformer.transformer_blocks, pipeline.transformer.single_transformer_blocks, ], forward_pattern=[ForwardPattern.Pattern_1, ForwardPattern.Pattern_1], params_modifiers=[modifier], ) ), cache_config=db_cache_config, ) def refresh_cache_context(pipeline: Any, num_inference_steps: int, verbose: bool = True) -> None: """Refresh cache context for the transformer with new num_inference_steps. Args: pipeline: The Flux pipeline instance. num_inference_steps: New number of inference steps. """ if cache_config.scm_steps_mask_policy is None: cache_dit.refresh_context(pipeline.transformer, num_inference_steps=num_inference_steps, verbose=verbose) else: cache_dit.refresh_context( pipeline.transformer, cache_config=DBCacheConfig().reset( num_inference_steps=num_inference_steps, steps_computation_mask=cache_dit.steps_mask( mask_policy=cache_config.scm_steps_mask_policy, total_steps=num_inference_steps, ), steps_computation_policy=cache_config.scm_steps_policy, ), verbose=verbose, ) return refresh_cache_context def enable_cache_for_sd3(pipeline: Any, cache_config: Any) -> Callable[[int], None]: """Enable cache-dit for StableDiffusion3Pipeline. Args: pipeline: The StableDiffusion3 pipeline instance. cache_config: DiffusionCacheConfig instance with cache configuration. """ # Build DBCacheConfig for transformer db_cache_config = _build_db_cache_config(cache_config) calibrator = None if cache_config.enable_taylorseer: taylorseer_order = cache_config.taylorseer_order calibrator = TaylorSeerCalibratorConfig(taylorseer_order=taylorseer_order) logger.info(f"TaylorSeer enabled with order={taylorseer_order}") # Build ParamsModifier for transformer modifier = ParamsModifier( cache_config=db_cache_config, calibrator_config=calibrator, ) logger.info( f"Enabling cache-dit on StableDiffusion3 transformer with BlockAdapter: " f"Fn={db_cache_config.Fn_compute_blocks}, " f"Bn={db_cache_config.Bn_compute_blocks}, " f"W={db_cache_config.max_warmup_steps}, " ) # Enable cache-dit using BlockAdapter for transformer cache_dit.enable_cache( ( BlockAdapter( transformer=pipeline.transformer, blocks=pipeline.transformer.transformer_blocks, forward_pattern=ForwardPattern.Pattern_1, params_modifiers=[modifier], ) ), cache_config=db_cache_config, ) def refresh_cache_context(pipeline: Any, num_inference_steps: int, verbose: bool = True) -> None: """Refresh cache context for the transformer with new num_inference_steps. Args: pipeline: The LongCatImage pipeline instance. num_inference_steps: New number of inference steps. """ if cache_config.scm_steps_mask_policy is None: cache_dit.refresh_context(pipeline.transformer, num_inference_steps=num_inference_steps, verbose=verbose) else: cache_dit.refresh_context( pipeline.transformer, cache_config=DBCacheConfig().reset( num_inference_steps=num_inference_steps, steps_computation_mask=cache_dit.steps_mask( mask_policy=cache_config.scm_steps_mask_policy, total_steps=num_inference_steps, ), steps_computation_policy=cache_config.scm_steps_policy, ), verbose=verbose, ) return refresh_cache_context def enable_cache_for_dit(pipeline: Any, cache_config: Any) -> Callable[[int], None]: """Enable cache-dit for regular single-transformer DiT models. Args: pipeline: The diffusion pipeline instance. cache_config: DiffusionCacheConfig instance with cache configuration. Returns: A refresh function that can be called to update cache context with new num_inference_steps. """ # Build DBCacheConfig with optional SCM support db_cache_config = _build_db_cache_config(cache_config) # Build calibrator config if TaylorSeer is enabled calibrator_config = None if cache_config.enable_taylorseer: taylorseer_order = cache_config.taylorseer_order calibrator_config = TaylorSeerCalibratorConfig(taylorseer_order=taylorseer_order) logger.info(f"TaylorSeer enabled with order={taylorseer_order}") logger.info( f"Enabling cache-dit on transformer: " f"Fn={db_cache_config.Fn_compute_blocks}, " f"Bn={db_cache_config.Bn_compute_blocks}, " f"W={db_cache_config.max_warmup_steps}, " ) # Enable cache-dit on the transformer cache_dit.enable_cache( pipeline.transformer, cache_config=db_cache_config, calibrator_config=calibrator_config, ) def refresh_cache_context(pipeline: Any, num_inference_steps: int, verbose: bool = True) -> None: """Refresh cache context for the transformer with new num_inference_steps. Args: pipeline: The diffusion pipeline instance. num_inference_steps: New number of inference steps. """ if cache_config.scm_steps_mask_policy is None: cache_dit.refresh_context(pipeline.transformer, num_inference_steps=num_inference_steps, verbose=verbose) else: cache_dit.refresh_context( pipeline.transformer, cache_config=DBCacheConfig().reset( num_inference_steps=num_inference_steps, steps_computation_mask=cache_dit.steps_mask( mask_policy=cache_config.scm_steps_mask_policy, total_steps=num_inference_steps, ), steps_computation_policy=cache_config.scm_steps_policy, ), verbose=verbose, ) return refresh_cache_context class BagelCachedContextManager(CachedContextManager): """ Custom CachedContextManager for Bagel that safely handles NaiveCache objects (mapped to encoder_hidden_states) by skipping tensor operations on them. """ @torch.compiler.disable def apply_cache( self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor = None, prefix: str = "Bn", encoder_prefix: str = "Bn_encoder", ) -> tuple[torch.Tensor, torch.Tensor | None]: # Allow Bn and Fn prefix to be used for residual cache. if "Bn" in prefix: hidden_states_prev = self.get_Bn_buffer(prefix) else: hidden_states_prev = self.get_Fn_buffer(prefix) assert hidden_states_prev is not None, f"{prefix}_buffer must be set before" if self.is_cache_residual(): hidden_states = hidden_states_prev + hidden_states else: # If cache is not residual, we use the hidden states directly hidden_states = hidden_states_prev hidden_states = hidden_states.contiguous() if encoder_hidden_states is not None: if "Bn" in encoder_prefix: encoder_hidden_states_prev = self.get_Bn_encoder_buffer(encoder_prefix) else: encoder_hidden_states_prev = self.get_Fn_encoder_buffer(encoder_prefix) if encoder_hidden_states_prev is not None: if self.is_encoder_cache_residual(): # FIX: Check if encoder_hidden_states is a tensor before adding if isinstance(encoder_hidden_states, torch.Tensor) and isinstance( encoder_hidden_states_prev, torch.Tensor ): encoder_hidden_states = encoder_hidden_states_prev + encoder_hidden_states else: # If encoder cache is not residual, we use the encoder hidden states directly encoder_hidden_states = encoder_hidden_states_prev # FIX: Check if encoder_hidden_states is a tensor before calling contiguous if isinstance(encoder_hidden_states, torch.Tensor): encoder_hidden_states = encoder_hidden_states.contiguous() return hidden_states, encoder_hidden_states class BagelCachedBlocks(CachedBlocks_Pattern_0_1_2): """ Custom CachedBlocks for Bagel that safely handles NaiveCache objects by adding isinstance checks in call_Mn_blocks and compute_or_prune. """ def call_Mn_blocks( self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, *args, **kwargs, ): original_hidden_states = hidden_states original_encoder_hidden_states = encoder_hidden_states for block in self._Mn_blocks(): hidden_states = block( hidden_states, encoder_hidden_states, *args, **kwargs, ) hidden_states, encoder_hidden_states = self._process_block_outputs(hidden_states, encoder_hidden_states) # compute hidden_states residual hidden_states = hidden_states.contiguous() hidden_states_residual = hidden_states - original_hidden_states if ( encoder_hidden_states is not None and original_encoder_hidden_states is not None and isinstance(encoder_hidden_states, torch.Tensor) # FIX: Added Check ): encoder_hidden_states = encoder_hidden_states.contiguous() encoder_hidden_states_residual = encoder_hidden_states - original_encoder_hidden_states else: encoder_hidden_states_residual = None return ( hidden_states, encoder_hidden_states, hidden_states_residual, encoder_hidden_states_residual, ) def compute_or_prune( self, block_id: int, # Block index in the transformer blocks # Below are the inputs to the block block, # The transformer block to be executed hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, *args, **kwargs, ): # NOTE: Although Bagel likely won't use pruning, implementing safe version just in case. # Copy-pasted from original but adding checks. original_hidden_states = hidden_states original_encoder_hidden_states = encoder_hidden_states can_use_prune = self._maybe_prune( block_id, hidden_states, prefix=f"{self.cache_prefix}_{block_id}_Fn_original", ) torch._dynamo.graph_break() if can_use_prune: self.context_manager.add_pruned_step() hidden_states, encoder_hidden_states = self.context_manager.apply_prune( hidden_states, encoder_hidden_states, prefix=( f"{self.cache_prefix}_{block_id}_Bn_residual" if self.context_manager.is_cache_residual() else f"{self.cache_prefix}_Bn_hidden_states" ), encoder_prefix=( f"{self.cache_prefix}_{block_id}_Bn_encoder_residual" if self.context_manager.is_encoder_cache_residual() else f"{self.cache_prefix}_{block_id}_Bn_encoder_hidden_states" ), ) torch._dynamo.graph_break() else: # Normal steps: Compute the block and cache the residuals. hidden_states = block( hidden_states, encoder_hidden_states, *args, **kwargs, ) hidden_states, encoder_hidden_states = self._process_block_outputs(hidden_states, encoder_hidden_states) if not self._skip_prune(block_id): hidden_states = hidden_states.contiguous() hidden_states_residual = hidden_states - original_hidden_states if ( encoder_hidden_states is not None and original_encoder_hidden_states is not None and isinstance(encoder_hidden_states, torch.Tensor) # FIX: Added Check ): encoder_hidden_states = encoder_hidden_states.contiguous() encoder_hidden_states_residual = encoder_hidden_states - original_encoder_hidden_states else: encoder_hidden_states_residual = None self.context_manager.set_Fn_buffer( original_hidden_states, prefix=f"{self.cache_prefix}_{block_id}_Fn_original", ) if self.context_manager.is_cache_residual(): self.context_manager.set_Bn_buffer( hidden_states_residual, prefix=f"{self.cache_prefix}_{block_id}_Bn_residual", ) else: self.context_manager.set_Bn_buffer( hidden_states, prefix=f"{self.cache_prefix}_{block_id}_Bn_hidden_states", ) if encoder_hidden_states_residual is not None: if self.context_manager.is_encoder_cache_residual(): self.context_manager.set_Bn_encoder_buffer( encoder_hidden_states_residual, prefix=f"{self.cache_prefix}_{block_id}_Bn_encoder_residual", ) else: self.context_manager.set_Bn_encoder_buffer( encoder_hidden_states_residual, prefix=f"{self.cache_prefix}_{block_id}_Bn_encoder_hidden_states", ) torch._dynamo.graph_break() return hidden_states, encoder_hidden_states class BagelCachedAdapter(CachedAdapter): """ Custom CachedAdapter for Bagel that uses BagelCachedContextManager and BagelCachedBlocks. """ @classmethod def create_context( cls, block_adapter: BlockAdapter, **context_kwargs, ) -> tuple[list[str], list[dict[str, Any]]]: # Override to use BagelCachedContextManager BlockAdapter.assert_normalized(block_adapter) if BlockAdapter.is_cached(block_adapter.pipe): return block_adapter.pipe # Check context_kwargs context_kwargs = cls.check_context_kwargs(block_adapter, **context_kwargs) # Each Pipeline should have it's own context manager instance. cache_config: BasicCacheConfig = context_kwargs.get("cache_config", None) assert cache_config is not None, "cache_config can not be None." # Apply cache on pipeline: wrap cache context pipe_cls_name = block_adapter.pipe.__class__.__name__ # USE CUSTOM CONTEXT MANAGER context_manager = BagelCachedContextManager( name=f"{pipe_cls_name}_{hash(id(block_adapter.pipe))}", persistent_context=isinstance(block_adapter.pipe, FakeDiffusionPipeline), ) flatten_contexts, contexts_kwargs = cls.modify_context_params(block_adapter, **context_kwargs) block_adapter.pipe._context_manager = context_manager # instance level if not context_manager.persistent_context: original_call = block_adapter.pipe.__class__.__call__ @functools.wraps(original_call) def new_call(self, *args, **kwargs): with ExitStack() as stack: # cache context will be reset for each pipe inference for context_name, context_kwargs in zip(flatten_contexts, contexts_kwargs): stack.enter_context( context_manager.enter_context( context_manager.reset_context( context_name, **context_kwargs, ), ) ) outputs = original_call(self, *args, **kwargs) cls.apply_stats_hooks(block_adapter) return outputs block_adapter.pipe.__class__.__call__ = new_call block_adapter.pipe.__class__._original_call = original_call else: # Init persistent cache context for transformer for context_name, context_kwargs in zip(flatten_contexts, contexts_kwargs): context_manager.reset_context( context_name, **context_kwargs, ) block_adapter.pipe.__class__._is_cached = True cls.apply_params_hooks(block_adapter, contexts_kwargs) return flatten_contexts, contexts_kwargs @classmethod def collect_unified_blocks( cls, block_adapter: BlockAdapter, contexts_kwargs: list[dict], ) -> list[dict[str, torch.nn.ModuleList]]: # Override to use BagelCachedBlocks BlockAdapter.assert_normalized(block_adapter) total_cached_blocks: list[dict[str, torch.nn.ModuleList]] = [] assert hasattr(block_adapter.pipe, "_context_manager") # Skipping isinstance check for ContextManager._supported_managers to avoid import issues for i in range(len(block_adapter.transformer)): unified_blocks_bind_context = {} for j in range(len(block_adapter.blocks[i])): cache_config: BasicCacheConfig = contexts_kwargs[i * len(block_adapter.blocks[i]) + j]["cache_config"] # Directly instantiate BagelCachedBlocks unified_blocks_bind_context[block_adapter.unique_blocks_name[i][j]] = torch.nn.ModuleList( [ BagelCachedBlocks( # 0. Transformer blocks configuration block_adapter.blocks[i][j], transformer=block_adapter.transformer[i], forward_pattern=block_adapter.forward_pattern[i][j], check_forward_pattern=block_adapter.check_forward_pattern, check_num_outputs=block_adapter.check_num_outputs, # 1. Cache/Prune context configuration cache_prefix=block_adapter.blocks_name[i][j], cache_context=block_adapter.unique_blocks_name[i][j], context_manager=block_adapter.pipe._context_manager, cache_type=cache_config.cache_type, ) ] ) total_cached_blocks.append(unified_blocks_bind_context) return total_cached_blocks def enable_cache_for_bagel(pipeline: Any, cache_config: Any) -> Callable[[int], None]: """Enable cache-dit for Bagel model (via OmniDiffusion pipeline). Args: pipeline: The OmniDiffusion pipeline instance. cache_config: DiffusionCacheConfig instance with cache configuration. Returns: A refresh function that can be called to update cache context with new num_inference_steps. """ # Build DBCacheConfig db_cache_config = _build_db_cache_config(cache_config) # Build calibrator config if TaylorSeer is enabled calibrator_config = None if cache_config.enable_taylorseer: taylorseer_order = cache_config.taylorseer_order calibrator_config = TaylorSeerCalibratorConfig(taylorseer_order=taylorseer_order) logger.info(f"TaylorSeer enabled with order={taylorseer_order}") # Access the transformer: BagelPipeline -> Qwen2MoTForCausalLM -> Qwen2MoTModel # BagelPipeline has self.language_model which is Qwen2MoTForCausalLM # Qwen2MoTForCausalLM has self.model which is Qwen2MoTModel transformer = pipeline.language_model.model logger.info( f"Enabling cache-dit on Bagel transformer: " f"Fn={db_cache_config.Fn_compute_blocks}, " f"Bn={db_cache_config.Bn_compute_blocks}, " f"W={db_cache_config.max_warmup_steps}, " ) # Enable cache-dit on the transformer # Pattern_0 corresponds to (hidden_states, encoder_hidden_states) input, output # Custom adapter for Bagel to handle NaiveCache correctly # from vllm_omni.diffusion.cache.bagel_cache_adapter import BagelCachedAdapter # No longer needed BagelCachedAdapter.apply( BlockAdapter( transformer=transformer, blocks=transformer.layers, forward_pattern=ForwardPattern.Pattern_0, ), cache_config=db_cache_config, calibrator_config=calibrator_config, ) def refresh_cache_context(pipeline: Any, num_inference_steps: int, verbose: bool = True) -> None: transformer = pipeline.language_model.model if cache_config.scm_steps_mask_policy is None: cache_dit.refresh_context(transformer, num_inference_steps=num_inference_steps, verbose=verbose) else: cache_dit.refresh_context( transformer, cache_config=DBCacheConfig().reset( num_inference_steps=num_inference_steps, steps_computation_mask=cache_dit.steps_mask( mask_policy=cache_config.scm_steps_mask_policy, total_steps=num_inference_steps, ), steps_computation_policy=cache_config.scm_steps_policy, ), verbose=verbose, ) return refresh_cache_context # Register custom cache-dit enablers after function definitions CUSTOM_DIT_ENABLERS.update( { "Wan22Pipeline": enable_cache_for_wan22, "Wan22I2VPipeline": enable_cache_for_wan22, "Wan22TI2VPipeline": enable_cache_for_wan22, "FluxPipeline": enable_cache_for_flux, "LongCatImagePipeline": enable_cache_for_longcat_image, "LongCatImageEditPipeline": enable_cache_for_longcat_image, "StableDiffusion3Pipeline": enable_cache_for_sd3, "BagelPipeline": enable_cache_for_bagel, } ) class CacheDiTBackend(CacheBackend): """Backend class for cache-dit acceleration on diffusion pipelines. This class implements cache-dit acceleration (DBCache, SCM, TaylorSeer) using the cache-dit library. It inherits from CacheBackend and provides a unified interface for managing cache-dit acceleration on diffusion models. Attributes: config: Cache configuration (DiffusionCacheConfig instance), inherited from CacheBackend. enabled: Whether cache-dit is enabled on this pipeline, inherited from CacheBackend. _refresh_func: Internal refresh function for updating cache context. _last_num_inference_steps: Last num_inference_steps used for refresh optimization. """ def __init__(self, cache_config: Any = None): """Initialize the cache-dit backend. Args: cache_config: Cache configuration (DiffusionCacheConfig instance, dict, or None). If None or empty, uses default DiffusionCacheConfig(). """ # Use default config if cache_config is not provided or is empty if cache_config is None: config = DiffusionCacheConfig() elif isinstance(cache_config, dict): # Convert dict to DiffusionCacheConfig, using defaults for missing keys config = DiffusionCacheConfig.from_dict(cache_config) else: config = cache_config # Initialize base class with normalized config super().__init__(config) # Cache-dit specific attributes self._refresh_func: Callable[[Any, int, bool], None] | None = None self._last_num_inference_steps: int | None = None def enable(self, pipeline: Any) -> None: """Enable cache-dit on the pipeline if configured. This method applies cache-dit acceleration to the appropriate transformer(s) in the pipeline. It handles both single-transformer and dual-transformer architectures (e.g., Wan2.2). Args: pipeline: The diffusion pipeline instance. """ # Extract pipeline name from pipeline pipeline_name = pipeline.__class__.__name__ # Check if this model has a custom cache-dit enabler if pipeline_name in CUSTOM_DIT_ENABLERS: logger.info(f"Using custom cache-dit enabler for model: {pipeline_name}") self._refresh_func = CUSTOM_DIT_ENABLERS[pipeline_name](pipeline, self.config) else: # For regular single-transformer models self._refresh_func = enable_cache_for_dit(pipeline, self.config) self.enabled = True logger.info(f"Cache-dit enabled successfully on {pipeline_name}") def refresh(self, pipeline: Any, num_inference_steps: int, verbose: bool = True) -> None: """Refresh cache context with new num_inference_steps. This method updates the cache context when num_inference_steps changes during inference. For dual-transformer models (e.g., Wan2.2), it automatically splits the steps based on boundary_ratio. Args: pipeline: The diffusion pipeline instance. num_inference_steps: New number of inference steps. verbose: Whether to log refresh operations. """ if not self.enabled or self._refresh_func is None: logger.warning("Cache-dit is not enabled. Cannot refresh cache context.") return # Only refresh if num_inference_steps has changed if self._last_num_inference_steps is None or num_inference_steps != self._last_num_inference_steps: if verbose: logger.info(f"Refreshing cache context for transformer with num_inference_steps: {num_inference_steps}") self._refresh_func(pipeline, num_inference_steps, verbose) self._last_num_inference_steps = num_inference_steps def is_enabled(self) -> bool: """Check if cache-dit is enabled on this pipeline. Returns: True if cache-dit is enabled, False otherwise. """ return self.enabled def may_enable_cache_dit(pipeline: Any, od_config: OmniDiffusionConfig) -> Optional["CacheDiTBackend"]: """Enable cache-dit on the pipeline if configured (convenience function). This is a convenience function that creates and enables a CacheDiTBackend. For new code, consider using CacheDiTBackend directly. Args: pipeline: The diffusion pipeline instance. od_config: OmniDiffusionConfig with cache configuration. Returns: A CacheDiTBackend instance if cache-dit is enabled, None otherwise. """ if od_config.cache_backend != "cache-dit" or not od_config.cache_config: return None backend = CacheDiTBackend(od_config.cache_config) backend.enable(pipeline) return backend if backend.is_enabled() else None