from ..config import cfg from ..utils.distributed import fix_unset_envs, ddp_model fix_unset_envs() if cfg.trainer.backend == "deepspeed": from .deepspeed import Engine elif cfg.trainer.backend == "local": from .base import Engine from .base import Engines, TrainFeeder, default_feeder, Engine as LocalEngine from ..models import get_models, get_model from ..utils import wrapper as ml from ..models.lora import apply_lora, lora_load_state_dict import torch import re deepspeed_available = False try: import deepspeed deepspeed_available = True except Exception as e: pass from functools import cache @cache def load_engines(training=True): models = get_models(cfg.models, training=training) engines = dict() for name, model in models.items(): state = None stats = None lora = None inferencing = cfg.mode == "inferencing" or not model.config.training or not training backend = cfg.inference.backend if inferencing else cfg.trainer.backend loads_state_dict = cfg.trainer.load_state_dict # or inferencing checkpoint_path = cfg.ckpt_dir / name / "latest" # automatically load from state dict if one is provided, but no DeepSpeed checkpoint is present load_path = cfg.ckpt_dir / name / "fp32.pth" # actually use the lora-specific checkpoint if available if cfg.lora is not None: checkpoint_path = cfg.ckpt_dir / cfg.lora.full_name / "latest" # to handle the issue of training with deepspeed, but inferencing with local if checkpoint_path.exists() and backend == "local": tag = open(checkpoint_path).read() checkpoint_path = cfg.ckpt_dir / cfg.lora.full_name / tag / "state.pth" if not loads_state_dict and not checkpoint_path.exists() and load_path.exists(): print("Checkpoint missing, but weights found.") loads_state_dict = True # load state early if loads_state_dict: state = torch.load(load_path, map_location=torch.device(cfg.device)) # check if config is defined in state, and re-initialize the model if "config" in state and False: print("Model config definition in weights, re-loading...") config_state = state["config"] model = get_model( config=cfg.model.__class__( *config_state ), training=training ) hyper_config = model.config optimizer = None lr_scheduler = None dtype = cfg.inference.dtype if inferencing else cfg.trainer.dtype amp = cfg.inference.amp if inferencing else cfg.trainer.amp ddp = cfg.trainer.ddp engine_class = LocalEngine if backend == "local" else Engine # apply model replacers if cfg.optimizations.replace and cfg.optimizations.linear: model.model = ml.replace_linear( model.model ) if cfg.optimizations.replace and cfg.optimizations.embedding: model.model = ml.replace_embedding( model.model ) for lora in cfg.loras: model.model = apply_lora( model.model, rank = lora.rank, alpha = lora.alpha, policy = model.config.lora_policy, use_parametrize = lora.parametrize ) if inferencing: model.config.training = False if not inferencing and (backend == "local" or (backend == "deepspeed" and cfg.hyperparameters.torch_optimizer)): optimizer_class = None scheduler_class = None params = { "lr": cfg.hyperparameters.learning_rate, } if cfg.hyperparameters.optimizer.lower() == "adamw": params["betas"] = (0.9, 0.96) params["eps"] = 1e-07 params["weight_decay"] = 0.01 # for dadaptation since it has Adam only if ml.AdamW == ml.Adam: params["decouple"] = True optimizer_class = ml.AdamW elif cfg.hyperparameters.optimizer.lower() == "sgd": optimizer = ml.SGD elif cfg.hyperparameters.optimizer.lower() == "prodigy": optimizer_class = ml.Prodigy params['d_coef'] = params['lr'] params['lr'] = 1.0 elif cfg.hyperparameters.optimizer.lower() == "adagrad": optimizer_class = ml.Adagrad else: raise ValueError(f'Optimizer specified not implemented: {cfg.hyperparameters.optimizer}') params.update(cfg.hyperparameters.optimizer_params) optimizer = optimizer_class( [ param for name, param in model.named_parameters() if name not in model.config.frozen_params ], **params, ) if cfg.hyperparameters.scheduler.lower() == "schedulefree": if cfg.hyperparameters.optimizer.lower() == "adamw": scheduler_class = ml.schedulefree.AdamWScheduleFree elif cfg.hyperparameters.optimizer.lower() == "sgd": scheduler_class = ml.schedulefree.SGDScheduleFree else: raise ValueError(f'ScheduleFree not implemented with requested optimizer: {cfg.hyperparameters.optimizer}') optimizer = scheduler_class( [ param for name, param in model.named_parameters() if name not in model.config.frozen_params ], lr = params['lr'], warmup_steps = cfg.hyperparameters.warmup_steps ) """ # set up our LR scheduler here """ if inferencing: optimizer = None lr_scheduler = None # load state dict if requested / required if loads_state_dict: # state dict is not just the module, extract the extra trainer details if "stats" in state: stats = state["stats"] # do not load stats if we're training a LoRA if cfg.lora is not None or cfg.trainer.restart_step_count: stats = None if "module" in state: state = state["module"] # maintain compat if I change variable names insert = {} erase = [] for k in state.keys(): key = re.sub(r'^retnet\.', "model.", k) if k != key: insert[key] = state[k] erase.append(k) for k in insert.keys(): state[k] = insert[k] for k in erase: del state[k] # resize embeddings if "text_emb.weight" in state: state["text_emb.weight"] = ml.resize_weight( state["text_emb.weight"], model.config.text_tokens ) if "rvq_l_emb.weight" in state: state["rvq_l_emb.weight"] = ml.resize_weight( state["rvq_l_emb.weight"], model.config.resp_levels ) model.load_state_dict(state, strict=cfg.trainer.strict_loading) # load lora weights if exists if cfg.lora is not None: lora_path = cfg.ckpt_dir / cfg.lora.full_name / "lora.pth" if lora_path.exists(): print( "Loaded LoRA state dict:", lora_path ) state = torch.load(lora_path, map_location=torch.device(cfg.device)) state = state['lora' if 'lora' in state else 'module'] lora_load_state_dict( model, state ) # wrap if DDP is requested if ddp: model = ddp_model(model) # deepspeed inferencing elif backend == "local" and inferencing and deepspeed_available and cfg.trainer.deepspeed.inferencing: #and sys.platform.startswith("win"): engine_class = LocalEngine model = deepspeed.init_inference(model=model, mp_size=1, replace_with_kernel_inject=True, dtype=dtype if not amp else torch.float32).module # use base engine if requested engines[name] = engine_class( model=model, optimizer=optimizer, lr_scheduler=lr_scheduler, hyper_config=hyper_config, stats=stats ) engines = Engines(engines) engines.setup() # this might bite me in the ass since technically this doesn't handle one engine loading fine but another engine not if not cfg.trainer.load_state_dict: engines.load_checkpoint(training=not inferencing) # freeze requested params for name, engine in engines.items(): engine.freeze(freeze_all=False) return engines