904 lines
39 KiB
Python
904 lines
39 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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Diffusion Model Runner for vLLM-Omni.
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Handles model loading, compilation, caching, and execution of diffusion model
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forward passes. This follows the AR pattern where the Runner handles all
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model-related operations.
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"""
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from __future__ import annotations
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import copy
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import time
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from collections.abc import Callable
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from contextlib import AbstractContextManager, nullcontext
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from typing import TYPE_CHECKING, Any, cast
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import torch
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from torch.profiler import record_function
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from vllm.config import LoadConfig, VllmConfig
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from vllm.logger import init_logger
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from vllm.utils.mem_utils import DeviceMemoryProfiler, GiB_bytes
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from vllm_omni.diffusion.cache.cachedit import cache_summary
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from vllm_omni.diffusion.cache.prompt_embed_cache import (
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install_prompt_embed_cache,
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resolve_prompt_embed_cache_config,
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)
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from vllm_omni.diffusion.cache.selector import get_cache_backend
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from vllm_omni.diffusion.compile import regionally_compile
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from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
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from vllm_omni.diffusion.forward_context import set_forward_context
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from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
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from vllm_omni.diffusion.models.interface import (
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SupportsPromptUpdate,
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adopt_request_scoped_cache_dit,
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is_request_scoped_cache_dit_enabled,
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supports_prompt_update,
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supports_step_execution,
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)
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from vllm_omni.diffusion.offloader import get_offload_backend
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from vllm_omni.diffusion.registry import _NO_CACHE_ACCELERATION
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from vllm_omni.diffusion.request import OmniDiffusionRequest
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from vllm_omni.diffusion.sched.interface import DiffusionSchedulerOutput, KVPrefetchJob
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from vllm_omni.diffusion.vae_optimizations import (
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optimize_pipeline_vaes,
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use_pipeline_vae_fast_path,
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)
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from vllm_omni.diffusion.worker.input_batch import InputBatch, scatter_latents
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from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
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from vllm_omni.diffusion.worker.utils import (
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BatchRunnerOutput,
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RunnerOutput,
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StepRequestState,
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attach_stage_durations,
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clear_pipeline_stage_durations,
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consume_pipeline_stage_durations,
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merge_stage_durations,
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)
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from vllm_omni.distributed.omni_connectors.kv_transfer_manager import OmniKVTransferManager
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from vllm_omni.inputs.data import OmniDiffusionSamplingParams
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from vllm_omni.platforms import current_omni_platform
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from vllm_omni.worker.omni_connector_model_runner_mixin import OmniConnectorModelRunnerMixin
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if TYPE_CHECKING:
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from vllm_omni.inputs.data import OmniInteractionPrompt
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logger = init_logger(__name__)
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def _normalize_pipeline_outputs(
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outputs: object,
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*,
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expected_count: int,
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allow_single_output: bool,
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pipeline_name: str,
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) -> list[DiffusionOutput]:
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if isinstance(outputs, DiffusionOutput):
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if allow_single_output and expected_count == 1:
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return [outputs]
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raise RuntimeError(
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f"{pipeline_name}.forward returned a single DiffusionOutput; "
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"request-batch forward must return list[DiffusionOutput]."
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)
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if not isinstance(outputs, list):
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raise RuntimeError(
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f"{pipeline_name}.forward returned {type(outputs).__name__}; "
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"expected DiffusionOutput or list[DiffusionOutput]."
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)
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if len(outputs) != expected_count:
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raise RuntimeError(
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f"{pipeline_name}.forward returned {len(outputs)} outputs for {expected_count} requests; "
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"expected exactly one DiffusionOutput per request."
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)
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bad_index = next((idx for idx, output in enumerate(outputs) if not isinstance(output, DiffusionOutput)), None)
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if bad_index is not None:
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raise RuntimeError(
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f"{pipeline_name}.forward returned list item {bad_index} with type "
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f"{type(outputs[bad_index]).__name__}; expected DiffusionOutput."
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)
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return outputs
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class DiffusionModelRunner(OmniConnectorModelRunnerMixin):
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"""
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Model runner that handles model loading and execution for diffusion models.
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This class follows the AR pattern where the Runner handles all model-related
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operations including loading, compilation, offloading, caching, and execution.
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The Worker only handles infrastructure (device, distributed env).
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"""
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def __init__(
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self,
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vllm_config: VllmConfig,
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od_config: OmniDiffusionConfig,
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device: torch.device,
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):
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"""
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Initialize the diffusion model runner.
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Args:
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vllm_config: vLLM configuration.
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od_config: OmniDiffusion configuration.
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device: The device to run on.
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"""
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self.vllm_config = vllm_config
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self.od_config = od_config
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self.device = device
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self.pipeline: Any | None = None
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self.cache_backend: Any | None = None
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self.offload_backend: Any | None = None
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self.prompt_embed_cache: Any | None = None
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self.input_batch: InputBatch | None = None
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# Cache for per-request stepwise state.
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self.state_cache: dict[str, StepRequestState] = {}
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# Initialize KV cache manager for connector management.
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self.kv_transfer_manager = OmniKVTransferManager.from_od_config(od_config)
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# Prefetch covers TP / SP / CFG-Parallel / HSDP. Disabled when a CFG
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# companion KV collector is set (that KV is not backgrounded).
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has_cfg_companion_kv = getattr(od_config, "cfg_kv_collect_func", None) is not None
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self._kv_prefetch_enabled = (
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bool(self.kv_transfer_manager.config.enable_kv_async_prefetch)
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and not has_cfg_companion_kv
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and self.kv_transfer_manager.config.need_recv_cache
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)
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@property
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def _target_device(self) -> torch.device | None:
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return getattr(self.pipeline, "device", None)
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def _compile_transformer(self, attr_name: str) -> None:
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"""Compile a transformer attribute on the pipeline with torch.compile."""
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model = getattr(self.pipeline, attr_name, None)
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if model is None:
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return
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compile_granularity = self.od_config.diffusion_compile_granularity
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compile_dynamic = self.od_config.diffusion_compile_dynamic
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try:
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if compile_granularity == "full":
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model.compile(dynamic=compile_dynamic)
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compiled_model = model
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else:
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compiled_model = regionally_compile(model, dynamic=compile_dynamic)
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setattr(self.pipeline, attr_name, compiled_model)
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except Exception as e:
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logger.warning(
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"Model runner: %s torch.compile setup for %s failed before activation: %s. "
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"Continuing with the uncompiled model; lazy compilation errors can still "
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"surface on the first request.",
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compile_granularity,
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attr_name,
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e,
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)
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return
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logger.info(
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"Model runner: %s configured for lazy %s torch.compile with dynamic=%s; "
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"compilation errors may surface on the first request.",
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attr_name,
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compile_granularity,
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compile_dynamic,
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)
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def load_model(
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self,
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memory_pool_context_fn: Callable[[str], AbstractContextManager[Any]] | None = None,
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load_format: str = "default",
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custom_pipeline_name: str | None = None,
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) -> None:
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"""
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Launch the diffusion pipeline, applying compilation, offloading, and caching.
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Args:
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memory_pool_context_fn: Optional function that returns a context manager
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for memory pool allocation (used for sleep mode).
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load_format: Format for loading model weights. Supported formats:
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- "default" (default): Automatically detect and use the default format based on configuration
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- "custom_pipeline": Init model from a custom pipeline class specified by `custom_pipeline_name`
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- "dummy": Skip actual weight loading, useful for testing and custom pipelines that
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don't require default weights.
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custom_pipeline_name: Optional custom pipeline class name to use.
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"""
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if load_format == "dummy":
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return
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current_omni_platform.init_diffusion_model_runner_runtime(
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vllm_config=self.vllm_config,
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od_config=self.od_config,
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device=self.device,
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)
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load_device = (
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"cpu"
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if self.od_config.enable_cpu_offload
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or self.od_config.enable_layerwise_offload
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or getattr(self.od_config, "enable_distributed_layerwise_offload", False)
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else str(self.device)
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)
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def get_memory_context() -> AbstractContextManager[Any]:
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if memory_pool_context_fn is not None:
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return memory_pool_context_fn("weights")
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return nullcontext()
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# Load model within forward context
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load_config = LoadConfig()
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model_loader = DiffusersPipelineLoader(load_config, od_config=self.od_config)
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time_before_load = time.perf_counter()
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with get_memory_context():
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with DeviceMemoryProfiler() as m:
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self.pipeline = model_loader.load_model(
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load_device=load_device,
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load_format=load_format,
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custom_pipeline_name=custom_pipeline_name,
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device=self.device,
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)
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# Keep precision rematerialization in the tagged weights pool
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# so sleep level 1 can offload every resulting parameter.
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optimize_pipeline_vaes(self.pipeline, self.od_config)
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time_after_load = time.perf_counter()
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logger.info(
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"Model loading took %.4f GiB and %.6f seconds",
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m.consumed_memory / GiB_bytes,
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time_after_load - time_before_load,
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)
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logger.info("Model runner: Model loaded successfully.")
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if self.od_config.streaming_output and not getattr(self.od_config, "step_execution", False):
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logger.warning("streaming_output=True requires step_execution=True; enabling step execution.")
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self.od_config.step_execution = True
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if getattr(self.od_config, "step_execution", False) and not self._supports_step_mode():
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raise ValueError(
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"step_execution=True requires a pipeline implementing "
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"prepare_encode(), denoise_step(), step_scheduler(), and post_decode(); "
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f"{self.od_config.model_class_name} does not support that contract."
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)
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if self.od_config.streaming_output and not self._supports_step_mode():
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raise ValueError(
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"streaming_output=True requires step execution support; "
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f"{self.od_config.model_class_name} does not support that contract."
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)
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# Apply CPU offloading
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self.offload_backend = get_offload_backend(self.od_config, device=self.device)
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if self.offload_backend is not None:
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logger.info(f" Enabling offloader backend: {self.offload_backend.__class__.__name__}")
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self.offload_backend.enable(self.pipeline)
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# Apply torch.compile if not in eager mode
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if not self.od_config.enforce_eager:
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if current_omni_platform.supports_torch_inductor():
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if hasattr(self.pipeline, "setup_compile"):
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try:
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self.pipeline.setup_compile()
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except Exception as exc:
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logger.warning(
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"Model runner: setup_compile() failed (%s); running without compile.",
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exc,
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)
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else:
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transformer_attrs = getattr(self.pipeline, "_dit_modules", None)
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if not transformer_attrs:
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transformer_attrs = ("transformer", "transformer_2")
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for attr_name in transformer_attrs:
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self._compile_transformer(attr_name)
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else:
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logger.warning(
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"Model runner: Platform %s does not support torch inductor, skipping torch.compile.",
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current_omni_platform.get_torch_device(),
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)
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# Setup cache backend
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self.cache_backend = get_cache_backend(self.od_config.cache_backend, self.od_config.cache_config)
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if self.cache_backend is not None:
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if self.od_config.model_class_name in _NO_CACHE_ACCELERATION:
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logger.warning(
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"Cache backend '%s' is not supported for %s; disabling cache acceleration.",
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self.od_config.cache_backend,
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self.od_config.model_class_name,
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)
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self.cache_backend = None
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self.od_config.cache_backend = None
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else:
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# Install configured cache capability once at startup. A model
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# may explicitly adopt the enabled Cache-DiT backend and then
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# own all later request-boundary enable/disable transitions.
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self.cache_backend.enable(self.pipeline)
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if str(self.od_config.cache_backend).lower() == "cache_dit" and adopt_request_scoped_cache_dit(
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self.pipeline,
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self.cache_backend,
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):
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logger.info(
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"Pipeline %s owns request-scoped Cache-DiT transitions.",
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type(self.pipeline).__name__,
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)
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self.cache_backend = None
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# Install prompt-embedding cache (transparent wrapper around
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# ``pipeline.encode_prompt``). Enabled via config or env var; a no-op
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# when the pipeline does not expose ``encode_prompt``.
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enable_pec, pec_size = resolve_prompt_embed_cache_config(
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enable=getattr(self.od_config, "enable_prompt_embed_cache", False),
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max_size=getattr(self.od_config, "prompt_embed_cache_size", 32),
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)
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if enable_pec:
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self.prompt_embed_cache = install_prompt_embed_cache(
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self.pipeline,
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max_size=pec_size,
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enabled=True,
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model_tag=self.od_config.model_class_name,
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)
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logger.info("Model runner: Initialization complete.")
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def clear_prompt_embed_cache(self) -> None:
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"""Evict all cached text-encoder outputs (e.g. between training epochs).
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Kept primarily for extension purposes.
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"""
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if self.prompt_embed_cache is not None:
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self.prompt_embed_cache.clear()
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def get_prompt_embed_cache_stats(self) -> dict | None:
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"""Return hit/miss statistics for the prompt-embedding cache, if enabled.
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Kept primarily for extension purposes.
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"""
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if self.prompt_embed_cache is None:
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return None
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return self.prompt_embed_cache.stats()
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def _sample_peak_memory_mb(self) -> float:
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"""Return peak GPU memory for the current forward pass in MB.
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Must be called immediately after the measured forward/step work, with
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reset_peak_memory_stats() called just before it, so the measurement
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reflects the current execution slice and not the global historical
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maximum.
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Uses max_memory_reserved (CUDA memory pool high-water mark) rather than
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max_memory_allocated so that allocator fragmentation is also visible.
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See: https://docs.pytorch.org/docs/stable/generated/torch.cuda.memory.max_memory_reserved.html
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"""
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peak_reserved_bytes = current_omni_platform.max_memory_reserved()
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peak_allocated_bytes = current_omni_platform.max_memory_allocated()
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peak_memory_mb = peak_reserved_bytes / (1024**2)
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peak_reserved_gb = peak_reserved_bytes / (1024**3)
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peak_allocated_gb = peak_allocated_bytes / (1024**3)
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pool_overhead_gb = peak_reserved_gb - peak_allocated_gb
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logger.debug(
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"Peak GPU memory (this request): %.2f GB reserved, %.2f GB allocated, %.2f GB pool overhead (%.1f%%)",
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peak_reserved_gb,
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peak_allocated_gb,
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pool_overhead_gb,
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pool_overhead_gb / peak_reserved_gb * 100 if peak_reserved_gb > 0 else 0.0,
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)
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return peak_memory_mb
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def _prepare_request_for_forward(
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self,
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req: OmniDiffusionRequest,
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*,
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od_config: OmniDiffusionConfig,
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kv_prefetch_job: KVPrefetchJob | None = None,
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use_prefetch: bool = False,
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) -> None:
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# Receive AR KV. Single-request execution can use the prefetch path:
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# consume prior-forward payload, sync-fallback on miss; request-batch
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# execution keeps the synchronous per-request receive path.
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kv_recv_t0 = time.perf_counter()
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if use_prefetch and self._kv_prefetch_enabled:
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self.kv_transfer_manager.consume_and_distribute_kv_cache(
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req,
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target_device=self._target_device,
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)
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else:
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self.kv_transfer_manager.receive_multi_kv_cache_distributed(
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req,
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cfg_kv_collect_func=getattr(od_config, "cfg_kv_collect_func", None),
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target_device=self._target_device if use_prefetch else getattr(self.pipeline, "device", None),
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)
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kv_recv_ms = (time.perf_counter() - kv_recv_t0) * 1000
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logger.debug("KV recv for %s %.1fms", req.request_id, kv_recv_ms)
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# Kick off the next request's prefetch (+ H2D) to overlap this forward.
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if use_prefetch and self._kv_prefetch_enabled and kv_prefetch_job is not None:
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self.kv_transfer_manager.start_prefetch(kv_prefetch_job, self._target_device)
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self._initialize_generator(req.sampling_params)
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def _initialize_generator(self, sampling_params: OmniDiffusionSamplingParams) -> None:
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if sampling_params.generator is None and sampling_params.seed is not None:
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if sampling_params.generator_device is not None:
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gen_device = sampling_params.generator_device
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elif self.device.type == "cpu":
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gen_device = "cpu"
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else:
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gen_device = self.device
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sampling_params.generator = torch.Generator(device=gen_device).manual_seed(sampling_params.seed)
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def _refresh_cache_for_requests(
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self,
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reqs: list[OmniDiffusionRequest],
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*,
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od_config: OmniDiffusionConfig,
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) -> None:
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first_req = reqs[0]
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if self.cache_backend is None or not self.cache_backend.is_enabled():
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return
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# Refresh cache context if needed. Batch admission groups requests by
|
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# RequestBatchSamplingParamsKey, so the first request's num_inference_steps applies
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# to the whole runner batch.
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num_inference_steps = first_req.sampling_params.num_inference_steps
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if num_inference_steps is None and od_config.cache_backend in (
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"tea_cache",
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"step_cache",
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):
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# When num_inference_steps is None, some pipelines defer to their
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# own defaults. TeaCache refresh ignores this value; step_cache
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# refresh is a no-op because per-chunk state resets in the denoise
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# loop. Use the pipeline default when available to keep refresh
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# behavior aligned with single-request execution.
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num_inference_steps = getattr(self.pipeline, "num_inference_steps", 0) or 0
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if num_inference_steps is not None:
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self.cache_backend.refresh(self.pipeline, num_inference_steps)
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else:
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logger.warning(
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"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.")
|
|
|
|
quality = getattr(reqs[0].sampling_params, "quality", None)
|
|
if any(getattr(req.sampling_params, "quality", None) != quality for req in reqs[1:]):
|
|
raise RuntimeError("A diffusion worker batch cannot mix request quality levels.")
|
|
use_fast_vae = quality == "high"
|
|
|
|
# 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):
|
|
with use_pipeline_vae_fast_path(self.pipeline, use_fast_vae):
|
|
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)
|
|
with use_pipeline_vae_fast_path(
|
|
self.pipeline,
|
|
getattr(req.sampling, "quality", None) == "high",
|
|
):
|
|
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)
|