xi=~SrSSKJrJr SSKJr \"SS55r\"SS55r\"SS 55rg ) z\ Training Configuration Classes Contains dataclasses for LoRA and training configurations. ) dataclassfield)Listcv\rSrSr%SrSr\\S'Sr\\S'Sr \ \S'\ "S S 9r \ \\S 'S r\\S 'SrSrg) LoRAConfig alConfiguration for LoRA (Low-Rank Adaptation) training. Attributes: r: LoRA rank (dimension of low-rank matrices) alpha: LoRA scaling factor (alpha/r determines the scaling) dropout: Dropout probability for LoRA layers target_modules: List of module names to apply LoRA to bias: Whether to train bias parameters ("none", "all", or "lora_only") ralphag?dropoutc /SQ$N)q_projk_projv_projo_projrO/mnt/workspace/acestep.cpp/tests/../../ACE-Step-1.5/acestep/training/configs.pyLoRAConfig.?rdefault_factorytarget_modulesnonebiascvURURURURURS.$)z&Convert to dictionary for PEFT config.)r lora_alpha lora_dropoutrr)r r r rrselfs rto_dictLoRAConfig.to_dicts3** LL"11II   rrN)__name__ __module__ __qualname____firstlineno____doc__r int__annotations__r r floatrrrstrrr$__static_attributes__rrrrr sPAsJE3OGU %7!NDID# rrc\rSrSr%SrSr\\S'Sr\\S'Sr \\S'S r \ \S 'S r \ \S 'S r \ \S 'S r\ \S '\"SS9r\\\S'S r\ \S'S r\ \S'S r\ \S'S r\ \S'SrSrg) LoKRConfig)z5Configuration for LoKr (Low-Rank Kronecker) training.@ linear_dim linear_alphafactorFdecompose_both use_tucker use_scalarweight_decomposec /SQ$rrrrrrLoKRConfig.4rrrr full_matrix bypass_moders_loraunbalanced_factorizationc URURURURURUR UR URURURURURS. $)z)Convert to dictionary for LyCORIS config. r4r6r8r9r:r;r<rr?r@rArBrDr"s rr$LoKRConfig.to_dict<sp// --kk"11//// $ 5 5"11++++||(,(E(E  rrN)r&r'r(r)r*r4r+r,r6r8r9boolr:r;r<rrrr.r?r@rArBr$r/rrrr1r1)s?JL#FC ND JJ"d" %7!NDIKKGT%*d* rr1c^\rSrSr%SrSr\\S'Sr\ \S'Sr \\S'S r \ \S 'S r \ \S 'S r \ \S'Sr\ \S'S r\ \S'Sr\\S'Sr\\S'Sr\\S'Sr\\S'Sr\\S'Sr\ \S'Sr\\S'S r\ \S'S r\\S!'S"r\ \S#'S r\\S$'S%r\\S&'Sr\ \S''S(r\\S)'S.S+jr S,r!S-r"g*)/TrainingConfigNaConfiguration for LoRA training process. Training uses: - Device-aware mixed precision (bf16 on CUDA/XPU, fp16 on MPS, fp32 on CPU) - Discrete timesteps from turbo shift=3.0 schedule (8 steps) - Randomly samples one of 8 timesteps per training step: [1.0, 0.9545, 0.9, 0.8333, 0.75, 0.6429, 0.5, 0.3] Attributes: shift: Timestep shift factor (fixed at 3.0 for turbo model) num_inference_steps: Number of inference steps (fixed at 8 for turbo) learning_rate: Initial learning rate batch_size: Training batch size gradient_accumulation_steps: Number of gradient accumulation steps max_epochs: Maximum number of training epochs save_every_n_epochs: Save checkpoint every N epochs warmup_steps: Number of warmup steps for learning rate scheduler weight_decay: Weight decay for optimizer max_grad_norm: Maximum gradient norm for clipping mixed_precision: Preferred precision mode for logging/config tracking seed: Random seed for reproducibility output_dir: Directory to save checkpoints and logs g@shiftr num_inference_stepsg-C6? learning_rate batch_sizegradient_accumulation_stepsd max_epochs save_every_n_epochs warmup_stepsg{Gz? weight_decay? max_grad_normbf16mixed_precisionFuse_fp8gradient_checkpointing*seedz ./lora_output output_dir num_workersT pin_memoryprefetch_factorpersistent_workerspin_memory_devicelog_every_n_steps val_splitNcLSURs=::aS:d O [S5eg)NrhrWz val_split must be in [0.0, 1.0).)ri ValueErrorr"s r __post_init__TrainingConfig.__post_init__s$dnn*s*?@ @+rc40SUR_SUR_SUR_SUR_SUR_SUR _SUR _SUR_S UR_S UR_S UR_S UR_S UR_SUR_SUR_SUR_SUR _UR"UR$UR&UR(UR*S.E$)zConvert to dictionary.rJrKrLrNrPrRrTrUrVrXrZr[r\r^r_r`ra)rcrdrfrgri)rJrKrLrNrPrRrTrUrVrXrZr[r\r^r_r`rarcrdrfrgrir"s rr$TrainingConfig.to_dicts^ TZZ !4#;#;  T//  $//  *4+K+K  $//   "4#;#;  D--  D--  T//  t33  t||  %d&A&A  DII  $// 4++! " $//# $ $33"&"9"9!%!7!7!%!7!7-  rr)returnN)#r&r'r(r)r*rJr-r,rKr+rLrNrPrRrTrUrVrXrZr.r[rFr\r^r_r`rarcrdrfrgrirlr$r/rrrrHrHNs0E5  M5J'((J!!L#L%M5!OS!GT#(D(D#N%J%KJOS##s sIuA rrHN) r* dataclassesrrtypingrrr1rHrrrrssa )     : ! !  ! H S S  S r