c3c736d488
Signed-off-by: Jinheng Li <ahengljh@gmail.com> Signed-off-by: natureofnature <wzliu@connect.hku.hk> Co-authored-by: ahengljh <ahengljh@users.noreply.github.com> Co-authored-by: natureofnature <wzliu@connect.hku.hk>
59 lines
2.5 KiB
Python
59 lines
2.5 KiB
Python
# adapted from sglang and fastvideo
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import random
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from dataclasses import dataclass, field
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from vllm_omni.inputs.data import OmniDiffusionSamplingParams, OmniPromptType
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@dataclass
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class OmniDiffusionRequest:
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"""
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Complete state passed through the pipeline execution.
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This dataclass contains the prompts and sampling parameters for the diffusion pipeline
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execution. It also contains a request_id for other components to trace this request and its outputs.
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"""
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# TODO(will): double check that args are separate from server_args
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# properly. Also maybe think about providing an abstraction for pipeline
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# specific arguments.
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# data_type: DataType
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prompts: list[OmniPromptType] # Actually supporting str-based prompts
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sampling_params: OmniDiffusionSamplingParams
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request_ids: list[str] = field(default_factory=list)
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request_id: str | None = None
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kv_sender_info: dict | None = None
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def __post_init__(self):
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"""Initialize dependent fields after dataclass initialization."""
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# When neither a generator nor a seed is provided, assign a random seed
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# so that all ranks derive the same generator state.
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if self.sampling_params.generator is None and self.sampling_params.seed is None:
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self.sampling_params.seed = random.randint(0, 2**31 - 1)
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# Detect whether user explicitly provided guidance_scale.
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# The sentinel default is 0.0 (false-like); any truthy value means
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# the caller set it intentionally. We must resolve this BEFORE
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# auto-filling guidance_scale_2, otherwise the sentinel leaks into
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# guidance_scale_2.
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if self.sampling_params.guidance_scale:
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self.sampling_params.guidance_scale_provided = True
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else:
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self.sampling_params.guidance_scale = 1.0
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# Set do_classifier_free_guidance based on guidance scale and negative prompt
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if self.sampling_params.guidance_scale > 1.0 and any(
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(not isinstance(p, str) and p.get("negative_prompt")) for p in self.prompts
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):
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self.sampling_params.do_classifier_free_guidance = True
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# Auto-fill guidance_scale_2 from the (now-resolved) guidance_scale
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# so downstream code always has a valid value.
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if self.sampling_params.guidance_scale_2 is None:
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self.sampling_params.guidance_scale_2 = self.sampling_params.guidance_scale
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