# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """ Diffusion Model Runner for vLLM-Omni. Handles model loading, compilation, caching, and execution of diffusion model forward passes. This follows the AR pattern where the Runner handles all model-related operations. """ from __future__ import annotations import copy import gc import time from collections.abc import Callable from contextlib import AbstractContextManager, nullcontext from typing import TYPE_CHECKING, Any, cast import torch from torch.profiler import record_function from vllm.config import LoadConfig, VllmConfig from vllm.logger import init_logger from vllm.utils.mem_utils import DeviceMemoryProfiler, GiB_bytes from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec from vllm_omni.diffusion.attention.layer import Attention from vllm_omni.diffusion.cache.cachedit import cache_summary from vllm_omni.diffusion.cache.prompt_embed_cache import ( install_prompt_embed_cache, resolve_prompt_embed_cache_config, ) from vllm_omni.diffusion.cache.selector import get_cache_backend from vllm_omni.diffusion.compile import regionally_compile from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig from vllm_omni.diffusion.diffusion_kv.config import DiffusionKVCacheMode from vllm_omni.diffusion.diffusion_kv.metadata import DiffusionKVMetadata from vllm_omni.diffusion.diffusion_kv.model_runner_backend import DiffusionKVModelRunnerBackend from vllm_omni.diffusion.diffusion_kv.paged_attention_adapter import ( DiffusionPagedAttentionRow, PreparedDiffusionPagedAttentionBatch, ) from vllm_omni.diffusion.forward_context import set_forward_context from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader from vllm_omni.diffusion.models.interface import ( SupportsPromptUpdate, adopt_request_scoped_cache_dit, is_request_scoped_cache_dit_enabled, supports_prompt_update, supports_step_execution, ) from vllm_omni.diffusion.offloader import enable_offload_backend from vllm_omni.diffusion.registry import _NO_CACHE_ACCELERATION from vllm_omni.diffusion.request import OmniDiffusionRequest from vllm_omni.diffusion.sched.interface import ( CachedRequestData, DiffusionSchedulerOutput, KVPrefetchJob, NewRequestData, validate_new_request_data_identity, ) from vllm_omni.diffusion.worker.input_batch import InputBatch, scatter_latents from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch from vllm_omni.diffusion.worker.utils import ( BatchRunnerOutput, RunnerOutput, StepRequestState, attach_stage_durations, clear_pipeline_stage_durations, consume_pipeline_stage_durations, merge_stage_durations, ) from vllm_omni.distributed.omni_connectors.kv_transfer_manager import OmniKVTransferManager from vllm_omni.inputs.data import OmniDiffusionSamplingParams from vllm_omni.platforms import current_omni_platform from vllm_omni.worker.omni_connector_model_runner_mixin import OmniConnectorModelRunnerMixin if TYPE_CHECKING: from vllm_omni.inputs.data import OmniInteractionPrompt logger = init_logger(__name__) def _dit_any_rank_failed(local_failed: bool) -> bool: """All-reduce a per-request failure flag across the DiT process group. Every DiT rank must reach this point in lockstep; the caller wraps ``pipeline.prepare_encode`` so both the success and the exception paths call the helper. In single-rank execution this collapses to the local flag with no collectives issued. """ if not torch.distributed.is_initialized(): return local_failed try: from vllm_omni.diffusion.distributed.parallel_state import get_dit_group group = get_dit_group() except (AssertionError, ImportError): group = None if group is None: return local_failed signal = torch.tensor(1 if local_failed else 0, dtype=torch.int32) if current_omni_platform.is_available(): signal = signal.to(device=current_omni_platform.device_type) torch.distributed.all_reduce(signal, op=torch.distributed.ReduceOp.MAX, group=group) return bool(signal.item()) def _normalize_pipeline_outputs( outputs: object, *, expected_count: int, allow_single_output: bool, pipeline_name: str, ) -> list[DiffusionOutput]: if isinstance(outputs, DiffusionOutput): if allow_single_output and expected_count == 1: return [outputs] raise RuntimeError( f"{pipeline_name}.forward returned a single DiffusionOutput; " "request-batch forward must return list[DiffusionOutput]." ) if not isinstance(outputs, list): raise RuntimeError( f"{pipeline_name}.forward returned {type(outputs).__name__}; " "expected DiffusionOutput or list[DiffusionOutput]." ) if len(outputs) != expected_count: raise RuntimeError( f"{pipeline_name}.forward returned {len(outputs)} outputs for {expected_count} requests; " "expected exactly one DiffusionOutput per request." ) bad_index = next((idx for idx, output in enumerate(outputs) if not isinstance(output, DiffusionOutput)), None) if bad_index is not None: raise RuntimeError( f"{pipeline_name}.forward returned list item {bad_index} with type " f"{type(outputs[bad_index]).__name__}; expected DiffusionOutput." ) return outputs class DiffusionModelRunner(OmniConnectorModelRunnerMixin): """ Model runner that handles model loading and execution for diffusion models. This class follows the AR pattern where the Runner handles all model-related operations including loading, compilation, offloading, caching, and execution. The Worker only handles infrastructure (device, distributed env). """ def __init__( self, vllm_config: VllmConfig, od_config: OmniDiffusionConfig, device: torch.device, ): """ Initialize the diffusion model runner. Args: vllm_config: vLLM configuration. od_config: OmniDiffusion configuration. device: The device to run on. """ self.vllm_config = vllm_config self.od_config = od_config self.device = device self.pipeline: Any | None = None self.cache_backend: Any | None = None self.offload_backend: Any | None = None self.prompt_embed_cache: Any | None = None self.input_batch: InputBatch | None = None self.model_memory_usage = 0 self.diffusion_kv_backend = DiffusionKVModelRunnerBackend( vllm_config=vllm_config, od_config=od_config, device=device, ) # Compatibility view for callers that inspect the installed config; # physical ownership remains in ``diffusion_kv_backend``. self.kv_cache_config: KVCacheConfig | None = None # Cache for per-request stepwise state. self.state_cache: dict[str, StepRequestState] = {} # Initialize KV cache manager for connector management. self.kv_transfer_manager = OmniKVTransferManager.from_od_config(od_config) # Prefetch covers TP / SP / CFG-Parallel / HSDP. Disabled when a CFG # companion KV collector is set (that KV is not backgrounded). has_cfg_companion_kv = getattr(od_config, "cfg_kv_collect_func", None) is not None self._kv_prefetch_enabled = ( bool(self.kv_transfer_manager.config.enable_kv_async_prefetch) and not has_cfg_companion_kv and self.kv_transfer_manager.config.need_recv_cache ) @property def _target_device(self) -> torch.device | None: return getattr(self.pipeline, "device", None) def _validate_diffusion_kv_metadata( self, *, request_id: str, metadata: DiffusionKVMetadata | None, ) -> None: cache_mode = getattr(self.od_config, "diffusion_kv_mode", DiffusionKVCacheMode.DENSE_LEGACY) if cache_mode is DiffusionKVCacheMode.PAGED_SCHEDULER and metadata is None: raise ValueError(f"paged_scheduler request {request_id!r} requires Diffusion KV metadata") if cache_mode is not DiffusionKVCacheMode.PAGED_SCHEDULER and metadata is not None: raise ValueError(f"{cache_mode.value} request {request_id!r} must not carry Diffusion KV metadata") if metadata is not None and metadata.request_id != request_id: raise ValueError( f"Diffusion KV metadata request mismatch: expected={request_id!r}, got={metadata.request_id!r}" ) def _compile_transformer(self, attr_name: str) -> None: """Compile a transformer attribute on the pipeline with torch.compile.""" model = getattr(self.pipeline, attr_name, None) if model is None: return compile_granularity = self.od_config.diffusion_compile_granularity compile_dynamic = self.od_config.diffusion_compile_dynamic try: if compile_granularity == "full": model.compile(dynamic=compile_dynamic) compiled_model = model else: compiled_model = regionally_compile(model, dynamic=compile_dynamic) setattr(self.pipeline, attr_name, compiled_model) except Exception as e: logger.warning( "Model runner: %s torch.compile setup for %s failed before activation: %s. " "Continuing with the uncompiled model; lazy compilation errors can still " "surface on the first request.", compile_granularity, attr_name, e, ) return logger.info( "Model runner: %s configured for lazy %s torch.compile with dynamic=%s; " "compilation errors may surface on the first request.", attr_name, compile_granularity, compile_dynamic, ) def load_model( self, memory_pool_context_fn: Callable[[str], AbstractContextManager[Any]] | None = None, load_format: str = "default", custom_pipeline_name: str | None = None, ) -> None: """ Launch the diffusion pipeline, applying compilation, offloading, and caching. Args: memory_pool_context_fn: Optional function that returns a context manager for memory pool allocation (used for sleep mode). load_format: Format for loading model weights. Supported formats: - "default" (default): Automatically detect and use the default format based on configuration - "custom_pipeline": Init model from a custom pipeline class specified by `custom_pipeline_name` - "dummy": Skip actual weight loading, useful for testing and custom pipelines that don't require default weights. custom_pipeline_name: Optional custom pipeline class name to use. """ if load_format == "dummy": return current_omni_platform.init_diffusion_model_runner_runtime( vllm_config=self.vllm_config, od_config=self.od_config, device=self.device, ) load_device = ( "cpu" if self.od_config.enable_cpu_offload or self.od_config.enable_layerwise_offload or getattr(self.od_config, "enable_distributed_layerwise_offload", False) else str(self.device) ) def get_memory_context() -> AbstractContextManager[Any]: if memory_pool_context_fn is not None: return memory_pool_context_fn("weights") return nullcontext() # Load model within forward context load_config = LoadConfig() model_loader = DiffusersPipelineLoader(load_config, od_config=self.od_config) time_before_load = time.perf_counter() with get_memory_context(): with DeviceMemoryProfiler() as m: self.pipeline = model_loader.load_model( load_device=load_device, load_format=load_format, custom_pipeline_name=custom_pipeline_name, device=self.device, ) time_after_load = time.perf_counter() logger.info( "Model loading took %.4f GiB and %.6f seconds", m.consumed_memory / GiB_bytes, time_after_load - time_before_load, ) self.model_memory_usage = int(m.consumed_memory) logger.info("Model runner: Model loaded successfully.") if self.od_config.streaming_output and not getattr(self.od_config, "step_execution", False): logger.warning("streaming_output=True requires step_execution=True; enabling step execution.") self.od_config.step_execution = True if getattr(self.od_config, "step_execution", False) and not self._supports_step_mode(): raise ValueError( "step_execution=True requires a pipeline implementing " "prepare_encode(), denoise_step(), step_scheduler(), and post_decode(); " f"{self.od_config.model_class_name} does not support that contract." ) if self.od_config.streaming_output and not self._supports_step_mode(): raise ValueError( "streaming_output=True requires step execution support; " f"{self.od_config.model_class_name} does not support that contract." ) # The offloader owns loader-plan handoff and startup recovery. The # runner only receives the pipeline/backend pair that is ready to use. self.pipeline, self.offload_backend = enable_offload_backend( self.od_config, self.pipeline, device=self.device, ) # Apply torch.compile if not in eager mode if not self.od_config.enforce_eager: if current_omni_platform.supports_torch_inductor(): if hasattr(self.pipeline, "setup_compile"): try: self.pipeline.setup_compile() except Exception as exc: logger.warning( "Model runner: setup_compile() failed (%s); running without compile.", exc, ) else: transformer_attrs = getattr(self.pipeline, "_dit_modules", None) if not transformer_attrs: transformer_attrs = ("transformer", "transformer_2") for attr_name in transformer_attrs: self._compile_transformer(attr_name) else: logger.warning( "Model runner: Platform %s does not support torch inductor, skipping torch.compile.", current_omni_platform.get_torch_device(), ) # Setup cache backend self.cache_backend = get_cache_backend(self.od_config.cache_backend, self.od_config.cache_config) if self.cache_backend is not None: if self.od_config.model_class_name in _NO_CACHE_ACCELERATION: logger.warning( "Cache backend '%s' is not supported for %s; disabling cache acceleration.", self.od_config.cache_backend, self.od_config.model_class_name, ) self.cache_backend = None self.od_config.cache_backend = None else: # Install configured cache capability once at startup. A model # may explicitly adopt the enabled Cache-DiT backend and then # own all later request-boundary enable/disable transitions. self.cache_backend.enable(self.pipeline) if str(self.od_config.cache_backend).lower() == "cache_dit" and adopt_request_scoped_cache_dit( self.pipeline, self.cache_backend, ): logger.info( "Pipeline %s owns request-scoped Cache-DiT transitions.", type(self.pipeline).__name__, ) self.cache_backend = None # Install prompt-embedding cache (transparent wrapper around # ``pipeline.encode_prompt``). Enabled via config or env var; a no-op # when the pipeline does not expose ``encode_prompt``. enable_pec, pec_size = resolve_prompt_embed_cache_config( enable=getattr(self.od_config, "enable_prompt_embed_cache", False), max_size=getattr(self.od_config, "prompt_embed_cache_size", 32), ) if enable_pec: self.prompt_embed_cache = install_prompt_embed_cache( self.pipeline, max_size=pec_size, enabled=True, model_tag=self.od_config.model_class_name, ) logger.info("Model runner: Initialization complete.") def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]: """Collect native specs from cache-enabled loaded attention modules.""" if self.od_config.diffusion_kv_mode is not DiffusionKVCacheMode.PAGED_SCHEDULER: return {} if self.pipeline is None: raise RuntimeError("Model must be loaded before collecting Diffusion KV cache specs") cache_layers: dict[str, tuple[Attention, KVCacheSpec]] = {} for layer_name, module in self.pipeline.named_modules(): if not isinstance(module, Attention): continue spec = module.get_kv_cache_spec(self.vllm_config) if spec is not None: cache_layers[layer_name] = (module, spec) if not cache_layers: raise RuntimeError( "paged_scheduler Diffusion KV found no cache-enabled Attention modules " f"in {type(self.pipeline).__name__}" ) return self.diffusion_kv_backend.register_kv_cache_layers(cache_layers) def set_kv_cache_config(self, kv_cache_config: KVCacheConfig) -> None: """Physically initialize the Engine-generated rank-local config.""" self.diffusion_kv_backend.initialize_kv_cache(kv_cache_config) self.kv_cache_config = self.diffusion_kv_backend.kv_cache_config def install_diffusion_kv_metadata(self, metadata: DiffusionKVMetadata) -> bool: return self.diffusion_kv_backend.install_diffusion_kv_metadata(metadata) def get_diffusion_kv_row( self, request_id: str, sequence_id: int | None, context_id: str | None = None, ) -> int: return self.diffusion_kv_backend.get_diffusion_kv_row(request_id, sequence_id, context_id) def remove_diffusion_kv_requests(self, request_ids: list[str]) -> int: return self.diffusion_kv_backend.remove_diffusion_kv_requests(request_ids) def refresh_diffusion_kv_block_table_layout(self) -> None: self.diffusion_kv_backend.refresh_block_table_layout() def prepare_paged_attention_batch( self, rows: list[DiffusionPagedAttentionRow] | tuple[DiffusionPagedAttentionRow, ...], ) -> PreparedDiffusionPagedAttentionBatch: """Prepare model-specific query/write spans against installed Worker rows.""" return self.diffusion_kv_backend.prepare_paged_attention_batch(rows) def activate_paged_attention(self, batch: PreparedDiffusionPagedAttentionBatch): """Expose a prepared paged batch to Omni Attention for one forward.""" return self.diffusion_kv_backend.activate_paged_attention(batch) def clear_prompt_embed_cache(self) -> None: """Evict all cached text-encoder outputs (e.g. between training epochs). Kept primarily for extension purposes. """ if self.prompt_embed_cache is not None: self.prompt_embed_cache.clear() def get_prompt_embed_cache_stats(self) -> dict | None: """Return hit/miss statistics for the prompt-embedding cache, if enabled. Kept primarily for extension purposes. """ if self.prompt_embed_cache is None: return None return self.prompt_embed_cache.stats() def _sample_peak_memory_mb(self) -> float: """Return peak GPU memory for the current forward pass in MB. Must be called immediately after the measured forward/step work, with reset_peak_memory_stats() called just before it, so the measurement reflects the current execution slice and not the global historical maximum. Uses max_memory_reserved (CUDA memory pool high-water mark) rather than max_memory_allocated so that allocator fragmentation is also visible. See: https://docs.pytorch.org/docs/stable/generated/torch.cuda.memory.max_memory_reserved.html """ peak_reserved_bytes = current_omni_platform.max_memory_reserved() peak_allocated_bytes = current_omni_platform.max_memory_allocated() peak_memory_mb = peak_reserved_bytes / (1024**2) peak_reserved_gb = peak_reserved_bytes / (1024**3) peak_allocated_gb = peak_allocated_bytes / (1024**3) pool_overhead_gb = peak_reserved_gb - peak_allocated_gb logger.debug( "Peak GPU memory (this request): %.2f GB reserved, %.2f GB allocated, %.2f GB pool overhead (%.1f%%)", peak_reserved_gb, peak_allocated_gb, pool_overhead_gb, pool_overhead_gb / peak_reserved_gb * 100 if peak_reserved_gb > 0 else 0.0, ) return peak_memory_mb def _prepare_request_for_forward( self, req: OmniDiffusionRequest, *, od_config: OmniDiffusionConfig, kv_prefetch_job: KVPrefetchJob | None = None, use_prefetch: bool = False, ) -> None: # Receive AR KV. Single-request execution can use the prefetch path: # consume prior-forward payload, sync-fallback on miss; request-batch # execution keeps the synchronous per-request receive path. kv_recv_t0 = time.perf_counter() if use_prefetch and self._kv_prefetch_enabled: self.kv_transfer_manager.consume_and_distribute_kv_cache( req, target_device=self._target_device, ) else: self.kv_transfer_manager.receive_multi_kv_cache_distributed( req, cfg_kv_collect_func=getattr(od_config, "cfg_kv_collect_func", None), target_device=self._target_device if use_prefetch else getattr(self.pipeline, "device", None), ) kv_recv_ms = (time.perf_counter() - kv_recv_t0) * 1000 req.kv_recv_ms = kv_recv_ms logger.debug("KV recv for %s %.1fms", req.request_id, kv_recv_ms) # Kick off the next request's prefetch (+ H2D) to overlap this forward. if use_prefetch and self._kv_prefetch_enabled and kv_prefetch_job is not None: self.kv_transfer_manager.start_prefetch(kv_prefetch_job, self._target_device) self._initialize_generator(req.sampling_params) def _initialize_generator(self, sampling_params: OmniDiffusionSamplingParams) -> None: if sampling_params.generator is None and sampling_params.seed is not None: if sampling_params.generator_device is not None: gen_device = sampling_params.generator_device elif self.device.type == "cpu": gen_device = "cpu" else: gen_device = self.device sampling_params.generator = torch.Generator(device=gen_device).manual_seed(sampling_params.seed) def _refresh_cache_for_requests( self, reqs: list[OmniDiffusionRequest], *, od_config: OmniDiffusionConfig, ) -> None: first_req = reqs[0] if self.cache_backend is None or not self.cache_backend.is_enabled(): return # Refresh cache context if needed. Batch admission groups requests by # RequestBatchSamplingParamsKey, so the first request's num_inference_steps applies # to the whole runner batch. num_inference_steps = first_req.sampling_params.num_inference_steps if num_inference_steps is None and od_config.cache_backend in ( "tea_cache", "step_cache", ): # When num_inference_steps is None, some pipelines defer to their # own defaults. TeaCache refresh ignores this value; step_cache # refresh is a no-op because per-chunk state resets in the denoise # loop. Use the pipeline default when available to keep refresh # behavior aligned with single-request execution. num_inference_steps = getattr(self.pipeline, "num_inference_steps", 0) or 0 if num_inference_steps is not None: self.cache_backend.refresh(self.pipeline, num_inference_steps) else: logger.warning( "Failed to refresh the diffusion transformer cache; backend %s " "currently requires num_inference_steps to be passed explicitly", od_config.cache_backend, ) def _runner_output_from_outputs( self, reqs: list[OmniDiffusionRequest], outputs: list[DiffusionOutput], ) -> BatchRunnerOutput: for i in range(len(reqs)): # Carry the runner-measured KV-recv timing onto the output so the # engine's step_streaming can surface it as diffusion_kv_load_s. outputs[i].kv_recv_ms = reqs[i].kv_recv_ms return BatchRunnerOutput.from_list( [ RunnerOutput( request_id=reqs[i].request_id, step_index=None, finished=True, result=outputs[i], ) for i in range(len(reqs)) ] ) def _execute_request_list( self, reqs: list[OmniDiffusionRequest], *, od_config: OmniDiffusionConfig, allow_single_output: bool, require_request_batch_support: bool, kv_prefetch_job: KVPrefetchJob | None = None, record_name: str, record_output_peak_memory: bool = True, ) -> BatchRunnerOutput: assert self.pipeline is not None, "Model not loaded. Call load_model() first." if not reqs: return BatchRunnerOutput.from_list([]) for req in reqs: if req.prompt is None: raise ValueError("Cannot execute model with empty prompt") if require_request_batch_support and not getattr(self.pipeline, "supports_request_batch", False): raise RuntimeError(f"{type(self.pipeline).__name__} does not support request-batch forward.") # Use no_grad() for HSDP compatibility, inference_mode() otherwise for # better perf. HSDP2's fully_shard pre-forward hooks need tensor version # counters, which inference tensors do not track. use_hsdp = od_config.parallel_config.use_hsdp use_distributed_offload = getattr(self.od_config, "enable_distributed_layerwise_offload", False) grad_context = torch.no_grad() if (use_hsdp or use_distributed_offload) else torch.inference_mode() with grad_context: for req in reqs: self._prepare_request_for_forward( req, od_config=od_config, kv_prefetch_job=kv_prefetch_job, use_prefetch=allow_single_output, ) self._refresh_cache_for_requests(reqs, od_config=od_config) batch = DiffusionRequestBatch(requests=reqs) is_primary = not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0 if is_primary and record_output_peak_memory: current_omni_platform.reset_peak_memory_stats() with set_forward_context(vllm_config=self.vllm_config, omni_diffusion_config=od_config): with record_function(record_name): raw_outputs = self.pipeline.forward(batch) outputs = _normalize_pipeline_outputs( raw_outputs, expected_count=len(reqs), allow_single_output=allow_single_output, pipeline_name=type(self.pipeline).__name__, ) if is_primary and outputs and record_output_peak_memory: batch_peak_memory_mb = self._sample_peak_memory_mb() for output in outputs: output.peak_memory_mb = max(output.peak_memory_mb, batch_peak_memory_mb) # Log prompt-embed cache activity; hits/misses accumulate across requests. prompt_embed_cache = getattr(self, "prompt_embed_cache", None) if is_primary and prompt_embed_cache is not None: logger.debug("prompt-embed cache: %s", prompt_embed_cache.stats()) runner_cache_dit_enabled = self.cache_backend is not None and self.cache_backend.is_enabled() if ( od_config.cache_backend == "cache_dit" and od_config.enable_cache_dit_summary and (runner_cache_dit_enabled or is_request_scoped_cache_dit_enabled(self.pipeline)) ): cache_summary(self.pipeline, details=True) return self._runner_output_from_outputs(reqs, outputs) def _attach_stepwise_metrics( self, state: StepRequestState, output: DiffusionOutput, ) -> None: merge_stage_durations( state, consume_pipeline_stage_durations(self.pipeline), ) attach_stage_durations(state, output) def execute_model( self, req: OmniDiffusionRequest, kv_prefetch_job: KVPrefetchJob | None = None, diffusion_kv_metadata: DiffusionKVMetadata | None = None, ) -> DiffusionOutput: """ Execute a forward pass for the given requests. Args: req: A diffusion request containing a list of prompts to process. Returns: DiffusionOutput with generated results. Note: We use torch.no_grad() for HSDP because HSDP2's fully_shard requires access to tensor version counters in pre_forward hooks, which inference tensors do not track. For non-HSDP inference, we use torch.inference_mode() for better performance. """ self._validate_diffusion_kv_metadata( request_id=req.request_id, metadata=diffusion_kv_metadata, ) installed_request = False if diffusion_kv_metadata is not None: self.install_diffusion_kv_metadata(diffusion_kv_metadata) installed_request = True try: runner_output = self._execute_request_list( [req], od_config=self.od_config, allow_single_output=True, require_request_batch_support=False, kv_prefetch_job=kv_prefetch_job, record_name="pipeline_forward", ) output = runner_output.runner_outputs[0].result assert output is not None return output finally: if installed_request: self.remove_diffusion_kv_requests([req.request_id]) def profile_run(self, requests: list[OmniDiffusionRequest]) -> None: """Run the maximum per-rank request batch for memory profiling. This deliberately bypasses Scheduler admission and Diffusion KV metadata validation because cache capacity has not been sized yet. It otherwise uses the normal execution-mode path so model inputs, collective communication, backend workspaces, denoising, and decode allocations contribute to the observed peak. Step execution profiles one fused ``InputBatch`` instead of sequential single-request forwards. """ if not requests: raise ValueError("Diffusion memory profiling requires at least one request.") runner_output: BatchRunnerOutput | None = None request_ids = [request.request_id for request in requests] try: if getattr(self.od_config, "step_execution", False): scheduler_output = DiffusionSchedulerOutput( step_id=0, scheduled_new_reqs=[ NewRequestData(request_id=request.request_id, req=request) for request in requests ], scheduled_cached_reqs=CachedRequestData.make_empty(), finished_req_ids=set(), num_running_reqs=len(requests), num_waiting_reqs=0, ) runner_output = self._execute_stepwise( scheduler_output, validate_kv_metadata=False, record_output_peak_memory=False, ) else: runner_output = self._execute_request_list( requests, od_config=self.od_config, allow_single_output=len(requests) == 1, require_request_batch_support=len(requests) > 1, record_name="pipeline_memory_profile", # The enclosing native memory_profiling context owns the # peak counters. Resetting them here would discard request # preparation allocations and understate the budget. record_output_peak_memory=False, ) current_omni_platform.synchronize() finally: for request_id in request_ids: self.state_cache.pop(request_id, None) self.input_batch = None del runner_output gc.collect() def execute_model_batch( self, scheduler_output: DiffusionSchedulerOutput, od_config: OmniDiffusionConfig, ) -> BatchRunnerOutput: """Execute scheduled request-mode requests through the batch forward path. Builds a ``DiffusionRequestBatch`` from scheduled new requests, runs per-request setup, and calls ``pipeline.forward(batch)``. The pipeline must declare ``supports_request_batch = True``. """ for new_req in scheduler_output.scheduled_new_reqs: validate_new_request_data_identity(new_req) self._validate_diffusion_kv_metadata( request_id=new_req.req.request_id, metadata=new_req.diffusion_kv_metadata, ) installed_request_ids: list[str] = [] try: for new_req in scheduler_output.scheduled_new_reqs: if new_req.diffusion_kv_metadata is not None: self.install_diffusion_kv_metadata(new_req.diffusion_kv_metadata) installed_request_ids.append(new_req.request_id) reqs = [nr.req for nr in scheduler_output.scheduled_new_reqs] return self._execute_request_list( reqs, od_config=od_config, allow_single_output=False, require_request_batch_support=True, record_name="pipeline_forward_batch", ) finally: if installed_request_ids: self.remove_diffusion_kv_requests(installed_request_ids) # ------------------------------------------------------------------ # Step-wise execution # ------------------------------------------------------------------ def _supports_step_mode(self) -> bool: """Return whether current pipeline supports step execution.""" return self.pipeline is not None and supports_step_execution(self.pipeline) def _update_states(self, scheduler_output: DiffusionSchedulerOutput) -> tuple[list[StepRequestState], list[str]]: """Step-before update: cleanup finished requests and get/create one running state.""" for request_id in scheduler_output.finished_req_ids: self.state_cache.pop(request_id, None) resolved: list[StepRequestState] = [] new_request_ids: list[str] = [] try: # process new requests for sched_new_req in scheduler_output.scheduled_new_reqs: request_id = sched_new_req.request_id new_request_ids.append(request_id) if request_id in self.state_cache: raise ValueError(f"Received duplicate new-request payload for cached request {request_id}.") new_state = StepRequestState( request_id=request_id, sampling=copy.deepcopy(sched_new_req.req.sampling_params), prompt=sched_new_req.req.prompt, kv_sender_info=sched_new_req.req.kv_sender_info, prepared_layout=getattr(sched_new_req.req, "prepared_layout", None), ) state_req = copy.copy(sched_new_req.req) state_req.sampling_params = new_state.sampling self.kv_transfer_manager.receive_multi_kv_cache_distributed( state_req, cfg_kv_collect_func=getattr(self.od_config, "cfg_kv_collect_func", None), target_device=self._target_device, ) self.state_cache[request_id] = new_state resolved.append(new_state) # process cached requests for request_id in scheduler_output.scheduled_cached_reqs.request_ids: state = self.state_cache.get(request_id) if state is None: raise ValueError(f"Missing cached state for request {request_id}.") resolved.append(state) except Exception: for request_id in new_request_ids: self.state_cache.pop(request_id, None) raise return resolved, new_request_ids def _prepare_batch_inputs( self, states: list[StepRequestState], new_request_ids: list[str], ) -> tuple[list[StepRequestState], InputBatch | None, list[RunnerOutput]]: # process new reqs prepared_states: list[StepRequestState] = [] error_outputs: list[RunnerOutput] = [] for state in states: if state.request_id in new_request_ids: # Everything that runs before ``_dit_any_rank_failed`` must be # inside the try: an exception in ``_initialize_generator`` or # ``clear_pipeline_stage_durations`` on one rank would skip the # all-reduce here while every peer proceeds into it, and the # peers then hang on the NCCL collective until timeout. per_req_exc: BaseException | None = None try: self._initialize_generator(state.sampling) clear_pipeline_stage_durations(self.pipeline) # encode self.pipeline.prepare_encode(state) merge_stage_durations( state, consume_pipeline_stage_durations(self.pipeline), ) except Exception as exc: per_req_exc = exc # Pipelines that do rank-0-only work (e.g. MiniMax H3 # reference-video prep) must broadcast per-request failures # internally so downstream collectives stay in step; even so, # cross-check that every DiT rank agrees so a rank-local error # (or a future pipeline that omits the guard) does not leave # the process group half-way through a new request. if _dit_any_rank_failed(per_req_exc is not None): self.state_cache.pop(state.request_id, None) if per_req_exc is None: per_req_exc = RuntimeError( f"Stepwise preparation failed on another DiT rank for {state.request_id}" ) logger.error( "Stepwise request preparation failed for %s: %s", state.request_id, per_req_exc, exc_info=isinstance(per_req_exc, Exception), ) error_outputs.append( RunnerOutput( request_id=state.request_id, step_index=state.step_index, finished=True, result=DiffusionOutput.from_exception(per_req_exc), ) ) continue prepared_states.append(state) if not prepared_states: return prepared_states, None, error_outputs input_batch = InputBatch.make_batch( prepared_states, cached_batch=getattr(self, "input_batch", None), ) self.input_batch = input_batch return prepared_states, input_batch, error_outputs def _update_states_after( self, states: list[StepRequestState], input_batch: InputBatch, interrupted: bool = False, ) -> None: """Step-after update: clear cached state for completed request.""" gathered_latents = torch.cat([state.latents for state in states], dim=0) if ( input_batch.latents.size() == gathered_latents.size() and input_batch.latents.dtype == gathered_latents.dtype and input_batch.latents.device == gathered_latents.device ): input_batch.latents.copy_(gathered_latents) else: input_batch.latents = gathered_latents.clone() self.input_batch = input_batch scatter_latents(states, input_batch) for state in states: if interrupted or state.request_denoise_completed: self.state_cache.pop(state.request_id, None) def execute_stepwise(self, scheduler_output: DiffusionSchedulerOutput) -> BatchRunnerOutput: """Execute one step for one scheduled request and return runner output.""" return self._execute_stepwise( scheduler_output, validate_kv_metadata=True, record_output_peak_memory=True, ) def _execute_stepwise( self, scheduler_output: DiffusionSchedulerOutput, *, validate_kv_metadata: bool, record_output_peak_memory: bool, ) -> BatchRunnerOutput: """Execute one step with explicit validation and profiling policy.""" assert self.pipeline is not None, "Model not loaded. Call load_model() first." for new_req in scheduler_output.scheduled_new_reqs: validate_new_request_data_identity(new_req) if validate_kv_metadata: self._validate_diffusion_kv_metadata( request_id=new_req.req.request_id, metadata=new_req.diffusion_kv_metadata, ) if not self._supports_step_mode(): raise ValueError("Current pipeline does not support step execution.") # Stepwise mode only supports the basic state-driven denoise path for now. # Request-mode extras such as cache backends, editing inputs, and # similar features are not supported here yet. if self.od_config.cache_backend not in (None, "none"): raise ValueError("Step mode does not support cache_backend yet.") # Scheduler metadata is installed only for newly admitted requests; # cached requests continue to use the row installed on their first # step. Paged rows are retired here for scheduler-finished requests so # a subsequent wave can reuse the native table slots. Dense execution # must not issue Worker KV cleanup side effects. if ( getattr(self.od_config, "diffusion_kv_mode", DiffusionKVCacheMode.DENSE_LEGACY) is DiffusionKVCacheMode.PAGED_SCHEDULER and scheduler_output.finished_req_ids ): self.remove_diffusion_kv_requests(list(scheduler_output.finished_req_ids)) installed_request_ids: list[str] = [] try: for new_req in scheduler_output.scheduled_new_reqs: if new_req.diffusion_kv_metadata is not None: self.install_diffusion_kv_metadata(new_req.diffusion_kv_metadata) installed_request_ids.append(new_req.request_id) return self._execute_stepwise_core( scheduler_output, record_output_peak_memory=record_output_peak_memory, ) except Exception: if installed_request_ids: self.remove_diffusion_kv_requests(installed_request_ids) raise def _execute_stepwise_core( self, scheduler_output: DiffusionSchedulerOutput, *, record_output_peak_memory: bool, ) -> BatchRunnerOutput: """Run the denoise step after metadata admission and row installation.""" use_hsdp = self.od_config.parallel_config.use_hsdp grad_context = torch.no_grad() if use_hsdp else torch.inference_mode() with grad_context: had_active_states = bool(self.state_cache) states, new_request_ids = self._update_states(scheduler_output) is_primary = not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0 if ( record_output_peak_memory and new_request_ids and not had_active_states and is_primary and current_omni_platform.is_available() ): current_omni_platform.reset_peak_memory_stats() states, input_batch, runner_output_list = self._prepare_batch_inputs(states, new_request_ids) if input_batch is None: return BatchRunnerOutput.from_list(runner_output_list) attn_metadata = {} with set_forward_context( vllm_config=self.vllm_config, omni_diffusion_config=self.od_config, attn_metadata=attn_metadata, ): clear_pipeline_stage_durations(self.pipeline) noise_pred = self.pipeline.denoise_step(input_batch, states=states) denoise_stage_durations = consume_pipeline_stage_durations(self.pipeline) for state in states: merge_stage_durations( state, denoise_stage_durations, ) pipeline_interrupted = getattr(self.pipeline, "interrupt", False) if noise_pred is None and pipeline_interrupted: for state in states: runner_output_list.append( RunnerOutput( request_id=state.request_id, step_index=state.step_index, finished=True, result=DiffusionOutput(error="stepwise denoise interrupted"), ) ) else: offset = 0 for req in states: row_num = req.latents.shape[0] try: self.pipeline.step_scheduler( req, noise_pred[offset : offset + row_num] if noise_pred is not None else None ) if self.od_config.streaming_output: should_decode = req.chunk_denoise_completed or req.request_denoise_completed else: should_decode = req.denoise_completed if should_decode: clear_pipeline_stage_durations(self.pipeline) result = self.pipeline.post_decode(req) if result is not None: self._attach_stepwise_metrics( req, result, ) else: result = None # finished should be computed after post_decode() advanced chunk_index finished = ( req.request_denoise_completed if self.od_config.streaming_output else req.denoise_completed ) runner_output_list.append( RunnerOutput( request_id=req.request_id, step_index=req.step_index, finished=finished, result=result, ) ) offset = offset + row_num except Exception as per_req_exc: offset = offset + row_num self.state_cache.pop(req.request_id, None) logger.error( "Stepwise per-request error for %s: %s", req.request_id, per_req_exc, exc_info=True, ) runner_output_list.append( RunnerOutput( request_id=req.request_id, step_index=req.step_index, finished=True, result=DiffusionOutput.from_exception(per_req_exc), ) ) if noise_pred is not None and offset != noise_pred.shape[0]: raise ValueError( f"Stepwise noise_pred consumed {offset} rows, " f"but batched noise_pred has {noise_pred.shape[0]} rows." ) if is_primary and record_output_peak_memory: batch_peak_memory_mb = self._sample_peak_memory_mb() states_by_id = {state.request_id: state for state in states} for state in states: state.peak_memory_mb = max(state.peak_memory_mb, batch_peak_memory_mb) for runner_output in runner_output_list: if runner_output.result is None: continue state = states_by_id.get(runner_output.request_id) if state is None: continue runner_output.result.peak_memory_mb = max( runner_output.result.peak_memory_mb, state.peak_memory_mb, ) terminal_request_ids = [ runner_output.request_id for runner_output in runner_output_list if runner_output.finished ] self._update_states_after(states, input_batch, pipeline_interrupted) if ( getattr(self.od_config, "diffusion_kv_mode", DiffusionKVCacheMode.DENSE_LEGACY) is DiffusionKVCacheMode.PAGED_SCHEDULER and terminal_request_ids ): self.remove_diffusion_kv_requests(terminal_request_ids) return BatchRunnerOutput.from_list(runner_output_list) def submit_interaction( self, request_id: str, interaction: OmniInteractionPrompt, ) -> None: """Route a midway interaction to the matching active stepwise request feature.""" assert self.pipeline is not None, "Model not loaded. Call load_model() first." if not self.od_config.streaming_output: raise ValueError("submit_interaction requires streaming_output=True") if not self._supports_step_mode(): raise ValueError("submit_interaction requires step execution support") event = interaction.get("event") if isinstance(event, dict) and "prompt" in event and "multi_modal_data" not in event: # Is a prompt update interaction. self._submit_prompt_update_interaction(request_id, interaction) return raise NotImplementedError( "Only text-only prompt update interactions with 'event.prompt' and optional " "'transition_chunks' are supported in this release" ) def _submit_prompt_update_interaction( self, request_id: str, interaction: OmniInteractionPrompt, ) -> None: """Queue a prompt-update interaction for an active stepwise request.""" if not supports_prompt_update(self.pipeline): raise ValueError(f"prompt_update is not supported by pipeline {self.od_config.model_class_name!r}") state = self.state_cache.get(request_id) if state is None: raise ValueError(f"No active request state for prompt_update: {request_id!r}") event = cast(dict[str, Any], interaction.get("event")) prompt = event["prompt"] transition_chunks = interaction.get("transition_chunks") event_id = interaction.get("event_id") if not isinstance(event_id, str) or not event_id: raise ValueError("event_id must be non-empty") pipeline = cast(SupportsPromptUpdate, self.pipeline) pipeline.prepare_prompt_update(state, prompt, event_id, transition_chunks)