# 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 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_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 compile_callable_with_fallback, regionally_compile from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig 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 get_offload_backend from vllm_omni.diffusion.offloader.module_collector import ModuleDiscovery from vllm_omni.diffusion.registry import _NO_CACHE_ACCELERATION from vllm_omni.diffusion.request import OmniDiffusionRequest from vllm_omni.diffusion.sched.interface import DiffusionSchedulerOutput, KVPrefetchJob 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 _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 # 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 _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 _compile_vae_decodes(self) -> None: """Compile declared VAE decode callables with eager fallback.""" assert self.pipeline is not None modules = ModuleDiscovery.discover(self.pipeline) compiled_count = 0 for name, vae in zip(modules.vae_names, modules.vaes, strict=True): decode = getattr(vae, "decode", None) if not callable(decode): logger.debug("Model runner: VAE component %s has no decode callable; skipping compile.", name) continue label = f"VAE component {name}.decode" compiled_decode, activated = compile_callable_with_fallback( decode, label=label, mode="default", fullgraph=False, dynamic=None, ) if not activated: continue setattr(vae, "decode", compiled_decode) compiled_count += 1 if compiled_count: logger.info( "Model runner: configured lazy torch.compile for %d VAE decode callable(s); " "startup warmup will compile the real request path.", compiled_count, ) else: logger.info("Model runner: no compile-compatible VAE decode callable was discovered.") 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, ) 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." ) # Apply CPU offloading self.offload_backend = get_offload_backend(self.od_config, device=self.device) if self.offload_backend is not None: logger.info(f" Enabling offloader backend: {self.offload_backend.__class__.__name__}") self.offload_backend.enable(self.pipeline) # Apply torch.compile if not in eager mode if not self.od_config.enforce_eager: if current_omni_platform.supports_torch_inductor(): pipeline_owns_compile = hasattr(self.pipeline, "setup_compile") if pipeline_owns_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) if self.od_config.diffusion_compile_vae and not pipeline_owns_compile: self._compile_vae_decodes() 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 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 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: 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, ) -> 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: 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: 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, ) -> 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. """ 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 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``. """ 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", ) # ------------------------------------------------------------------ # 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]) -> InputBatch: # process new reqs for state in states: if state.request_id in new_request_ids: 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), ) input_batch = InputBatch.make_batch( states, cached_batch=getattr(self, "input_batch", None), ) self.input_batch = input_batch return input_batch 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.""" assert self.pipeline is not None, "Model not loaded. Call load_model() first." 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.") 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 new_request_ids and not had_active_states and is_primary and current_omni_platform.is_available(): current_omni_platform.reset_peak_memory_stats() input_batch = self._prepare_batch_inputs(states, new_request_ids) 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, ) runner_output_list = [] 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 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(error=str(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: 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, ) self._update_states_after(states, input_batch, pipeline_interrupted) 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)