Davids048andClaude Opus 5.5
6c776a219b
ResolvedGeneratorConfig becomes the runtime config of every mode. Resolution
builds the model's PipelineConfig once, freezes it, and attaches it as
resolved_config.pipeline_config; it also exposes mode, training_mode, and
inference_mode. VideoGenerator, fastvideo serve, the OpenAI server state,
the executors, the worker, and ComposedPipelineBase receive the resolved
config instead of a FastVideoArgs, and generator_config_to_fastvideo_args
is no longer on the inference path.
- Schema: ExecutionMode and WorkloadType move to fastvideo/api/schema.py;
typed homes for mode, output_type, boundary_ratio, master_port,
moba_config, and the LTX-2 refine settings; Enum parsing.
- Resolution: steps for the refine preset overrides, flat experimental
keys, the V-MoBA config, the validation that check_fastvideo_args did,
the num_gpus bump, the model's vae_tiling default, and a final
fill_runtime_defaults step for typed fields no earlier step decides.
- fastvideo/api/training_schema.py: TrainingRunConfig and
PreprocessRunConfig roots, their resolvers, and a --config plus dotted
override loader.
- fastvideo/api/device_policy.py: the device offload policy as functions
that return an overridden config; the worker keeps the result. The LTX-2
and MiniMax-H3 checkpoint defaults rebind the pipeline's config.
- fastvideo/api/flat_name_fallback.py answers the flat FastVideoArgs names
on the resolved config until the readers move to typed paths.
Golden changes: the decisions of the steps above are recorded, and
FastVideoArgs.ltx2_vae_tiling holds the model's vae_tiling (it held None;
no runtime code reads it). Against the 3f6893a0 goldens every
FastVideoArgs field, PipelineConfig, and SamplingParam matches, except
disable_autocast and boundary_ratio, which FastVideoArgs held at their
defaults because flattening routed them only to PipelineConfig.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
549 lines
22 KiB
Python
549 lines
22 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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from collections.abc import Iterator, Mapping
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from copy import deepcopy
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from dataclasses import fields, is_dataclass
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from pathlib import Path
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from typing import Any, get_args, get_type_hints
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from fastvideo.api.inference_resolution import resolve_inference_config
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from fastvideo.api.overrides import apply_overrides, normalize_overrides
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from fastvideo.api.parser import config_to_dict, load_raw_config, parse_config
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from fastvideo.api.resolution import ResolvedGeneratorConfig
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from fastvideo.api.request_metadata import (
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EXPLICIT_PATHS_ATTR,
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bind_generation_request_raw,
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get_explicit_paths,
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reset_tracking_roots,
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)
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from fastvideo.api.schema import (
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FLAT_NAME,
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CompileConfig,
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ContinuationState,
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GenerationRequest,
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GeneratorConfig,
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)
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from fastvideo.api.sampling_param import SamplingParam
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from fastvideo.fastvideo_args import FastVideoArgs
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from fastvideo.pipelines.basic.ltx2.stage_overrides import (
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refine_preset_override_fields,
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refine_stage_override_fields,
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)
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_MISSING = object()
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REQUEST_BATCH_EXTRA_PASSTHROUGH_FIELDS = (
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"ltx2_audio_latents",
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"ltx2_audio_clean_latent",
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"ltx2_audio_denoise_mask",
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"audio_num_frames",
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"video_position_offset_sec",
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"vsa_mode",
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"vsa_dense_first_n_steps",
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"vsa_dense_layers",
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)
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# The VideoGenerator.from_pretrained keywords besides model_path, config, and log_queue. Every other setting goes
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# through VideoGenerator.from_config at its typed path.
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FROM_PRETRAINED_KWARGS = frozenset({
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"num_gpus",
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"revision",
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"trust_remote_code",
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"distributed_executor_backend",
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"tp_size",
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"sp_size",
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"hsdp_replicate_dim",
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"hsdp_shard_dim",
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"dist_timeout",
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"use_fsdp_inference",
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"disable_autocast",
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"enable_stage_verification",
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"dit_cpu_offload",
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"dit_layerwise_offload",
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"text_encoder_cpu_offload",
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"image_encoder_cpu_offload",
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"vae_cpu_offload",
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"pin_cpu_memory",
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"enable_torch_compile",
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"torch_compile_kwargs",
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"lora_path",
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"lora_strength",
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"output_type",
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"nvfp4_fa4",
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})
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# torch.compile kwargs that map to first-class CompileConfig fields.
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_COMPILE_TYPED_KEYS = ("backend", "fullgraph", "mode", "dynamic")
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# LTX-2 refine flat kwargs (init + per-request) known to FastVideoArgs.
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_LTX2_REFINE_FLAT_KEYS = (refine_preset_override_fields() | refine_stage_override_fields())
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def normalize_generator_config(config: GeneratorConfig | Mapping[str, Any], ) -> GeneratorConfig:
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if isinstance(config, GeneratorConfig):
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return config
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return parse_config(GeneratorConfig, config)
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def load_generator_config_from_file(
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path: str | Path,
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overrides: list[str] | Mapping[str, Any] | None = None,
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) -> GeneratorConfig:
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raw = load_raw_config(path)
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normalized_overrides = normalize_overrides(overrides)
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if _looks_like_run_or_serve_config(raw):
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if normalized_overrides:
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raw = apply_overrides(raw, normalized_overrides)
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return parse_config(GeneratorConfig, raw["generator"])
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if normalized_overrides:
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adjusted = normalized_overrides
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if all(key.startswith("generator.") for key in adjusted):
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adjusted = {key[len("generator."):]: value for key, value in adjusted.items()}
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raw = apply_overrides(raw, adjusted)
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return parse_config(GeneratorConfig, raw)
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def from_pretrained_kwargs_to_config(
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model_path: str,
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kwargs: Mapping[str, Any],
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) -> GeneratorConfig:
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"""Build a ``GeneratorConfig`` from ``VideoGenerator.from_pretrained`` keyword arguments.
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A keyword that a schema field declares as its flat name sets that field. ``torch_compile_kwargs`` is split across
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``engine.compile``, and the keywords without a typed field are kept in ``pipeline.experimental``. A keyword outside
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``FROM_PRETRAINED_KWARGS`` raises ``TypeError`` that names the typed path to use with ``from_config``.
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"""
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unsupported = sorted(set(kwargs) - FROM_PRETRAINED_KWARGS)
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if unsupported:
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paths = ", ".join(f"{key} -> {_typed_path_of_keyword(key, kwargs[key])}" for key in unsupported)
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raise TypeError("VideoGenerator.from_pretrained(...) does not accept these keywords; pass them to "
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f"VideoGenerator.from_config(...) at these config paths: {paths}")
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raw: dict[str, Any] = {"model_path": model_path}
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for key, value in kwargs.items():
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if key == "torch_compile_kwargs":
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remaining: dict[str, Any] = (dict(deepcopy(value)) if isinstance(value, Mapping) else {})
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for first_class in _COMPILE_TYPED_KEYS:
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if first_class in remaining:
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_set_dotted_path(raw, ["engine", "compile", first_class], remaining.pop(first_class))
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if remaining:
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_set_dotted_path(raw, ["engine", "compile", "extras"], remaining)
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elif key in _FLAT_NAME_FIELDS:
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_set_dotted_path(raw, _FLAT_NAME_FIELDS[key][0].split("."), value)
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else:
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_set_dotted_path(raw, ["pipeline", "experimental", key], deepcopy(value))
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return parse_config(GeneratorConfig, raw)
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def _typed_path_of_keyword(key: str, value: Any) -> str:
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"""The ``GeneratorConfig`` path that holds the setting of a flat ``FastVideoArgs`` keyword."""
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if key in _FLAT_NAME_FIELDS and not (key == "pipeline_config" and not isinstance(value, str)):
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return _FLAT_NAME_FIELDS[key][0]
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for prefix, section in _COMPONENT_OVERRIDE_PREFIXES.items():
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if key.startswith(prefix):
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return f"pipeline.{section}.{key[len(prefix):]}"
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return f"pipeline.experimental.{key}"
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def generator_config_to_fastvideo_args(
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config: GeneratorConfig | Mapping[str, Any] | ResolvedGeneratorConfig, ) -> FastVideoArgs:
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"""Resolve a ``GeneratorConfig`` and flatten it into a ``FastVideoArgs``.
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A config that is not resolved yet goes through :func:`resolve_inference_config` first. The keywords are the ones
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that :func:`generator_kwargs` builds.
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"""
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resolved = config if isinstance(config, ResolvedGeneratorConfig) else resolve_inference_config(config)
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return FastVideoArgs.from_kwargs(**generator_kwargs(resolved), resolved_config=resolved)
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def generator_kwargs(resolved: ResolvedGeneratorConfig) -> dict[str, Any]:
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"""Flatten a resolved config into the flat keywords that build its ``PipelineConfig``.
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Every field of the resolved config that declares a flat name and holds a value other than ``None`` becomes the
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keyword of that name. The paths in ``_SPECIALLY_MAPPED_PATHS`` are converted below; ``pipeline.preset_overrides``
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and then ``pipeline.experimental`` are applied last, so their keys win over the typed fields.
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"""
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normalized = resolved.to_config()
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unsupported = []
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if normalized.pipeline.preset is not None:
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unsupported.append("pipeline.preset")
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if normalized.pipeline.preset_version is not None:
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unsupported.append("pipeline.preset_version")
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if normalized.pipeline.components.vae_weights is not None:
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unsupported.append("pipeline.components.vae_weights")
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if unsupported:
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joined = ", ".join(unsupported)
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raise NotImplementedError(f"VideoGenerator compatibility adapter does not support {joined} yet")
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kwargs: dict[str, Any] = {}
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for flat_name, (dotted_path, _) in _FLAT_NAME_FIELDS.items():
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value = _read_dotted_path(normalized, dotted_path.split("."))
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if value is not _MISSING and value is not None:
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kwargs[flat_name] = deepcopy(value)
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kwargs["torch_compile_kwargs"] = _compile_config_to_torch_kwargs(normalized.engine.compile)
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quantization = normalized.engine.quantization
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if quantization is not None and quantization.transformer_quant is not None:
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# Resolve the typed quant name to a concrete ``QuantizationConfig``
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# instance and pin it on ``dit_config.quant_config``. The legacy
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# path expected callers to do this themselves via
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# ``pipeline_config.dit_config.quant_config = NVFP4Config()``; the
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# typed surface accepts a string and does the wiring here so
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# downstream code can rely on a single source of truth.
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from fastvideo.layers.quantization import get_quantization_config
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_resolved_quant_cls = get_quantization_config(quantization.transformer_quant)
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kwargs["transformer_quant"] = _resolved_quant_cls()
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for prefix, section in _COMPONENT_OVERRIDE_PREFIXES.items():
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for field_name, value in getattr(normalized.pipeline, section).items():
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kwargs[f"{prefix}{field_name}"] = deepcopy(value)
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preset_overrides = deepcopy(normalized.pipeline.preset_overrides)
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refine = preset_overrides.pop("refine", None)
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if isinstance(refine, Mapping):
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for key in _LTX2_REFINE_FLAT_KEYS:
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if key in refine:
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kwargs[f"ltx2_refine_{key}"] = refine[key]
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if "enabled" in refine:
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kwargs["refine_enabled"] = refine["enabled"]
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kwargs.update(preset_overrides)
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kwargs.update(deepcopy(normalized.pipeline.experimental))
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return kwargs
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def normalize_generation_request(request: GenerationRequest | Mapping[str, Any], ) -> GenerationRequest:
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normalized = (request if isinstance(request, GenerationRequest) else parse_config(GenerationRequest, request))
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if not hasattr(normalized, EXPLICIT_PATHS_ATTR):
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# Request wasn't bound through the parser (e.g. constructed
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# directly). Treat every currently-set field as explicit.
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bind_generation_request_raw(normalized, _serialize_generation_request(normalized))
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return normalized
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def request_to_sampling_param(
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request: GenerationRequest,
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*,
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model_path: str,
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) -> SamplingParam:
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if request.plan is not None:
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raise NotImplementedError("GenerationRequest.plan is not wired into VideoGenerator yet")
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sampling_param = SamplingParam.from_pretrained(model_path)
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if request.state is not None:
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_validate_continuation_state(request.state)
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sampling_param.continuation_state = request.state
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if request.output.return_state:
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sampling_param.return_continuation_state = True
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updates = explicit_request_updates(request)
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for key, value in updates.items():
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if hasattr(sampling_param, key):
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setattr(sampling_param, key, deepcopy(value))
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elif key in REQUEST_BATCH_EXTRA_PASSTHROUGH_FIELDS:
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continue
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elif value == _SCHEMA_DEFAULT_UPDATES.get(key, _MISSING):
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# Schema-default field that isn't on SamplingParam; tolerated
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# because direct GenerationRequest(...) construction has no
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# way to distinguish "user set" from "schema default".
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continue
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else:
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raise ValueError(f"Request field {key!r} is not supported by sampling params for {model_path}")
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sampling_param.__post_init__()
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sampling_param.check_sampling_param()
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return sampling_param
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def expand_request_prompt_batch(request: GenerationRequest, ) -> list[GenerationRequest]:
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if not isinstance(request.prompt, list):
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return [request]
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requests: list[GenerationRequest] = []
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for index, prompt in enumerate(request.prompt):
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single_request = deepcopy(request)
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# deepcopy preserves the tracking-root cycle, but re-pin roots
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# defensively so that subsequent setattrs record on the copy.
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reset_tracking_roots(single_request)
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single_request.prompt = prompt
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_fan_out_batched_input_value(request, single_request, "image_path", index)
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_fan_out_batched_input_value(request, single_request, "video_path", index)
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requests.append(single_request)
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return requests
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def _looks_like_run_or_serve_config(raw: Mapping[str, Any]) -> bool:
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return isinstance(raw.get("generator"), Mapping)
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def _schema_fields(config_type: type, prefix: str = "") -> Iterator[tuple[str, Any, Any]]:
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"""Yield ``(dotted path, field, annotation)`` for every field under ``config_type`` that is not a nested config.
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A field whose type is a dataclass, or an optional dataclass, is a nested config and is walked into.
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"""
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type_hints = get_type_hints(config_type)
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for config_field in fields(config_type):
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dotted_path = f"{prefix}{config_field.name}"
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annotation = type_hints[config_field.name]
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nested = [arg for arg in (annotation, *get_args(annotation)) if isinstance(arg, type) and is_dataclass(arg)]
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if nested:
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yield from _schema_fields(nested[0], f"{dotted_path}.")
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else:
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yield dotted_path, config_field, annotation
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# Flat keyword name -> (dotted path, annotation) for every GeneratorConfig field that declares a flat name.
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_FLAT_NAME_FIELDS: dict[str, tuple[str, Any]] = {
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config_field.metadata[FLAT_NAME]: (dotted_path, annotation)
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for dotted_path, config_field, annotation in _schema_fields(GeneratorConfig) if FLAT_NAME in config_field.metadata
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}
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# GeneratorConfig fields without a flat name, and how generator_config_to_fastvideo_args carries each one.
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_SPECIALLY_MAPPED_PATHS: dict[str, str] = {
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**{
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f"engine.compile.{key}": "merged into torch_compile_kwargs"
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for key in (*_COMPILE_TYPED_KEYS, "extras")
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},
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"engine.quantization.transformer_quant": "resolved to a QuantizationConfig instance",
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"pipeline.preset": "not supported",
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"pipeline.preset_version": "not supported",
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"pipeline.components.vae_weights": "not supported",
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"pipeline.ltx2.refine.image_crf": "no flat keyword; copied from pipeline.preset_overrides.refine",
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"pipeline.ltx2.refine.video_position_offset_sec": "no flat keyword; copied from pipeline.preset_overrides.refine",
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"pipeline.dit": "keys passed as dit_config.<key>",
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"pipeline.vae": "keys passed as vae_config.<key>",
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"pipeline.preset_overrides": "keys passed as flat keywords; refine keys renamed to ltx2_refine_*",
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"pipeline.experimental": "keys passed as flat keywords",
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}
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# Flat keyword prefix for a component config override -> the PipelineSelection dict that holds the overrides.
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_COMPONENT_OVERRIDE_PREFIXES = {
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"dit_config.": "dit",
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"vae_config.": "vae",
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}
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def _compile_config_to_torch_kwargs(compile_config: CompileConfig, ) -> dict[str, Any]:
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"""Flatten typed ``CompileConfig`` back to a ``torch_compile_kwargs``
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dict that the legacy ``FastVideoArgs`` path still expects.
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Typed first-class fields (:attr:`backend`, :attr:`fullgraph`,
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:attr:`mode`, :attr:`dynamic`) are only emitted when the user set
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them explicitly (non-``None``). ``extras`` is merged on top for any
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uncommon kwargs.
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"""
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out: dict[str, Any] = {}
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for key in _COMPILE_TYPED_KEYS:
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value = getattr(compile_config, key)
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if value is not None:
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out[key] = value
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if compile_config.extras:
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out.update(deepcopy(compile_config.extras))
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return out
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def request_to_batch_extra(request: GenerationRequest) -> dict[str, Any]:
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"""Extract typed-request extensions consumed through ``ForwardBatch.extra``."""
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return {
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key: deepcopy(value)
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for key, value in explicit_request_updates(request).items() if key in REQUEST_BATCH_EXTRA_PASSTHROUGH_FIELDS
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}
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def explicit_request_raw(request: GenerationRequest) -> dict[str, Any]:
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"""The fields that the caller or operator wrote in ``request``, as a nested raw request mapping.
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Like :func:`explicit_request_updates`, it uses the paths tracked during parsing, but it keeps the sections
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(``sampling``, ``stage_overrides``, and so on) instead of flattening them.
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"""
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assert hasattr(request, EXPLICIT_PATHS_ATTR), ("GenerationRequest reached explicit_request_raw without tracking; "
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"route it through normalize_generation_request or parse_config")
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return _build_sparse_raw_from_paths(request, get_explicit_paths(request))
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def explicit_request_updates(request: GenerationRequest) -> dict[str, Any]:
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"""Project a ``GenerationRequest`` down to *explicitly set* fields only.
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Returns a flat kwargs dict suitable for merging into a generator call.
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The projection uses ``_fastvideo_explicit_paths`` (populated during
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``parse_config`` / raw binding) so schema defaults on the dataclass
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are **not** emitted — only paths the caller/operator actually wrote.
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This is what makes ``ServeConfig.default_request`` work as an
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operator-pinned baseline rather than a full override: a YAML with just
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``sampling.seed: 42`` yields ``{"seed": 42}``, not the full sampling
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config with its 15 schema defaults.
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Precondition: the request must carry ``_fastvideo_explicit_paths`` —
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populated by :func:`fastvideo.api.parser.parse_config` or
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:func:`fastvideo.api.compat.normalize_generation_request`. Calling on
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a raw ``GenerationRequest()`` asserts.
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"""
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assert hasattr(request,
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EXPLICIT_PATHS_ATTR), ("GenerationRequest reached explicit_request_updates without tracking; "
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"every entry point must route through normalize_generation_request "
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"or parse_config first")
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paths = get_explicit_paths(request)
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raw = _build_sparse_raw_from_paths(request, paths)
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return _extract_request_updates(raw)
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def _build_sparse_raw_from_paths(
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request: GenerationRequest,
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paths: frozenset[str],
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) -> dict[str, Any]:
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result: dict[str, Any] = {}
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for path in paths:
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parts = path.split(".")
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value = _read_dotted_path(request, parts)
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if value is _MISSING:
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continue
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_set_dotted_path(result, parts, deepcopy(value))
|
|
return result
|
|
|
|
|
|
def _read_dotted_path(obj: Any, parts: list[str]) -> Any:
|
|
for part in parts:
|
|
if is_dataclass(obj) and not isinstance(obj, type):
|
|
if not hasattr(obj, part):
|
|
return _MISSING
|
|
obj = getattr(obj, part)
|
|
elif isinstance(obj, Mapping):
|
|
if part not in obj:
|
|
return _MISSING
|
|
obj = obj[part]
|
|
else:
|
|
return _MISSING
|
|
return obj
|
|
|
|
|
|
def _set_dotted_path(
|
|
target: dict[str, Any],
|
|
parts: list[str],
|
|
value: Any,
|
|
) -> None:
|
|
cursor = target
|
|
for part in parts[:-1]:
|
|
nxt = cursor.get(part)
|
|
if not isinstance(nxt, dict):
|
|
nxt = {}
|
|
cursor[part] = nxt
|
|
cursor = nxt
|
|
cursor[parts[-1]] = value
|
|
|
|
|
|
def _extract_request_updates(raw: Mapping[str, Any]) -> dict[str, Any]:
|
|
updates: dict[str, Any] = {}
|
|
if "negative_prompt" in raw:
|
|
updates["negative_prompt"] = deepcopy(raw["negative_prompt"])
|
|
|
|
for section_name in ("inputs", "sampling", "runtime", "output"):
|
|
section = raw.get(section_name)
|
|
if not isinstance(section, Mapping):
|
|
continue
|
|
for key, value in section.items():
|
|
updates[key] = deepcopy(value)
|
|
|
|
stage_overrides = raw.get("stage_overrides")
|
|
if stage_overrides:
|
|
updates.update(_flatten_stage_overrides(stage_overrides))
|
|
|
|
extensions = raw.get("extensions")
|
|
if isinstance(extensions, Mapping):
|
|
for key, value in extensions.items():
|
|
updates[key] = deepcopy(value)
|
|
|
|
return updates
|
|
|
|
|
|
def _flatten_stage_overrides(stage_overrides: Any) -> dict[str, Any]:
|
|
if not isinstance(stage_overrides, Mapping):
|
|
raise ValueError("GenerationRequest.stage_overrides must be a mapping")
|
|
|
|
flattened: dict[str, Any] = {}
|
|
for stage_name, overrides in stage_overrides.items():
|
|
if not isinstance(overrides, Mapping):
|
|
raise ValueError(f"GenerationRequest.stage_overrides.{stage_name} must be a mapping")
|
|
for key, value in overrides.items():
|
|
if key in flattened and flattened[key] != value:
|
|
raise ValueError(f"Conflicting stage override for {key!r} across stages")
|
|
flattened[key] = deepcopy(value)
|
|
return flattened
|
|
|
|
|
|
def _serialize_generation_request(request: GenerationRequest) -> dict[str, Any]:
|
|
return deepcopy(config_to_dict(request))
|
|
|
|
|
|
_SCHEMA_DEFAULT_UPDATES = _extract_request_updates(config_to_dict(GenerationRequest()))
|
|
|
|
_KNOWN_CONTINUATION_KINDS: set[str] = set()
|
|
|
|
|
|
def register_continuation_kind(kind: str) -> None:
|
|
"""Register a :class:`ContinuationState.kind` as recognized.
|
|
|
|
PR 7 wires the envelope through; per-kind payload deserializers live
|
|
with each model family (e.g. ``fastvideo.pipelines.basic.ltx2.
|
|
continuation.LTX2ContinuationState``). The registry lets the
|
|
public-API compat layer validate the kind early, before the state
|
|
reaches the pipeline.
|
|
"""
|
|
if not isinstance(kind, str) or not kind:
|
|
raise ValueError("ContinuationState kind must be a non-empty string")
|
|
_KNOWN_CONTINUATION_KINDS.add(kind)
|
|
|
|
|
|
def _validate_continuation_state(state: ContinuationState) -> None:
|
|
if not isinstance(state.kind, str) or not state.kind:
|
|
raise ValueError("GenerationRequest.state.kind must be a non-empty string; got "
|
|
f"{state.kind!r}")
|
|
if not isinstance(state.payload, Mapping):
|
|
raise ValueError(f"GenerationRequest.state.payload must be a mapping; got "
|
|
f"{type(state.payload).__name__}")
|
|
if state.kind not in _KNOWN_CONTINUATION_KINDS:
|
|
known = sorted(_KNOWN_CONTINUATION_KINDS)
|
|
raise ValueError(f"Unknown ContinuationState kind {state.kind!r}; registered "
|
|
f"kinds: {known}. Import the model family that owns this kind "
|
|
"(e.g. `import fastvideo.pipelines.basic.ltx2.continuation`) "
|
|
"to register it, or drop the state field.")
|
|
|
|
|
|
def _fan_out_batched_input_value(
|
|
source_request: GenerationRequest,
|
|
target_request: GenerationRequest,
|
|
field_name: str,
|
|
index: int,
|
|
) -> None:
|
|
value = getattr(source_request.inputs, field_name)
|
|
if not isinstance(value, list):
|
|
return
|
|
_validate_batched_input_length(source_request.prompt, value, field_name)
|
|
setattr(target_request.inputs, field_name, deepcopy(value[index]))
|
|
|
|
|
|
def _validate_batched_input_length(
|
|
prompts: str | list[str] | None,
|
|
values: list[Any],
|
|
field_name: str,
|
|
) -> None:
|
|
if not isinstance(prompts, list):
|
|
return
|
|
if len(values) != len(prompts):
|
|
raise ValueError(f"GenerationRequest.inputs.{field_name} must have the same length as request.prompt")
|
|
|
|
|
|
__all__ = [
|
|
"FROM_PRETRAINED_KWARGS",
|
|
"REQUEST_BATCH_EXTRA_PASSTHROUGH_FIELDS",
|
|
"explicit_request_raw",
|
|
"explicit_request_updates",
|
|
"from_pretrained_kwargs_to_config",
|
|
"generator_config_to_fastvideo_args",
|
|
"generator_kwargs",
|
|
"load_generator_config_from_file",
|
|
"normalize_generation_request",
|
|
"normalize_generator_config",
|
|
"register_continuation_kind",
|
|
"request_to_batch_extra",
|
|
"request_to_sampling_param",
|
|
]
|