231 lines
11 KiB
Python
231 lines
11 KiB
Python
import transformers
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from transformers import get_scheduler
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import torch
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from torch.utils import tensorboard
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import os
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import shutil
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import math
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from tqdm.auto import tqdm
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import gc
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from arguments import DataArguments, ModelArguments, TrainingArguments
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from datamodules import create_data_module_s2s, create_data_module, create_data_module_hub
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from tokenizer import get_tokenizer
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from dyntrainmodel import DyntrainModel
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def save_model(model, global_step: int, output_dir: str, max_checkpoints: int = 0):
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output_chkpt_dir = f"step_{global_step}" if global_step >= 0 else ""
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output_dir = os.path.join(output_dir, output_chkpt_dir)
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model.save_pretrained(output_dir)
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if max_checkpoints > 0:
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files = [f for f in os.listdir(output_dir) if os.path.isdir(os.path.join(output_dir, f)) and f.startswith("step_")]
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def extract_step(filename):
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tokens = filename.split('_')
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return int(tokens[1])
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if len(files) > max_checkpoints:
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min_step = min(map(extract_step, files))
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delete_checkpoit_dir = os.path.join(output_dir, f"step_{min_step}")
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print(f"there are more than {max_checkpoints} checkpints saved, deleting {delete_checkpoit_dir}")
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shutil.rmtree(delete_checkpoit_dir)
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def get_optimizer(dyamic_parameters: list[torch.nn.Parameter], static_parameters: list[torch.nn.Parameter] | None, lr: float, static_lr: float,
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weight_decay: float, eps: float, adam8bit: bool):
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parameters = list[dict]()
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parameters.extend({'params': p} for p in dyamic_parameters if p.requires_grad)
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param_ids = set([id(p['params']) for p in parameters])
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if static_parameters is not None:
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for param in static_parameters:
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if param.requires_grad and id(param) not in param_ids:
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parameters.append({'params': param, 'lr': static_lr})
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param_ids.add(id(param))
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if not adam8bit:
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optimizer = torch.optim.AdamW(parameters, weight_decay=weight_decay, lr=lr, eps=training_args.adam_epsilon)
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else:
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try:
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import bitsandbytes as bnb
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except ImportError:
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raise ImportError("To use 8-bit Adam, bitsandbytes must be available")
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optimizer = bnb.optim.AdamW8bit(parameters, weight_decay=weight_decay, lr=lr, eps=eps)
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return optimizer
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def evaluate(model: DyntrainModel, tokenizer,
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dataloader: torch.utils.data.DataLoader, globalstep: int,
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log_writer: tensorboard.SummaryWriter, eval_prompt: str | None = None):
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print("*** Eval ***")
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loss = torch.zeros((1), device="cuda:0")
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model.model.eval()
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for batch in dataloader:
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for key in batch:
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batch[key] = batch[key].to("cuda:0")
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outputs = model.model(**batch)
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loss += outputs.loss
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loss = loss / len(dataloader)
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log_writer.add_scalar("Loss/Eval", loss, globalstep)
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print(f"Eval Loss {loss.item()}")
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return loss.item()
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if eval_prompt is not None:
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input_ids = tokenizer(eval_prompt, return_tensors="pt").input_ids.to(model.devices[0])
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attention_mask = torch.ones(input_ids.shape, device=model.devices[0], requires_grad=False)
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outputs = model.generate(input_ids, attention_mask=attention_mask, do_sample=True, temperature=1, max_new_tokens=100)
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response_decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
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print(f"Eval generation: {response_decoded}")
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log_writer.add_text("Text/Eval", response_decoded, globalstep)
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def train(model_args: ModelArguments, data_args: DataArguments, training_args: TrainingArguments):
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log_writer = tensorboard.SummaryWriter()
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model = DyntrainModel(model_args.model_name_or_path, training_args.cache_dir, target_active_params=int(training_args.max_instant_params * 1e6),
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reshuffle_fraction=training_args.churn_percent / 100.0, gradient_checkpointing=True, trust_remote_code=True,
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quantize=model_args.quantize)
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devices = list(torch.device(i) for i in range(0, torch.cuda.device_count()))
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model.toDevices(devices)
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model.reshuffleActive()
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model.balanceActive()
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paramter_count = sum(p.numel() for p in model.model.parameters())
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active_paramter_count = sum(p.numel() for p in model.model.parameters() if p.requires_grad)
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static_parameter_count = model.staticParameterCount() if training_args.train_non_linear_layers else 0
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print(f"Training model with {paramter_count / 1e6}m parameters and {active_paramter_count / 1e6}m"
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f"instantanous active paramters of which {static_parameter_count} are static")
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tokenizer = get_tokenizer(model.model, training_args.cache_dir, model_args)
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if data_args.dataset.endswith("json"):
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print("Loading dataset in s2s mode")
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data_module = create_data_module_s2s(tokenizer, data_args, training_args.do_train, training_args.do_eval, False)
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elif data_args.data_from_hub:
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data_module = create_data_module_hub(tokenizer, data_args, training_args.do_train, training_args.do_eval, False)
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else:
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print("Loading dataset in txt mode")
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data_module = create_data_module(tokenizer, data_args, training_args.do_train, training_args.do_eval, False)
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dataset = {k: v for k, v in data_module.items() if k != 'predict_dataset'}
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train_dataloader = torch.utils.data.DataLoader(
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dataset['train_dataset'],
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shuffle=True,
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collate_fn=dataset['data_collator'],
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batch_size=training_args.per_device_train_batch_size
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) if dataset['train_dataset'] is not None else None
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if training_args.do_eval:
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eval_dataloader = torch.utils.data.DataLoader(
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dataset['eval_dataset'],
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shuffle=True,
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collate_fn=dataset['data_collator'],
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batch_size=training_args.per_device_train_batch_size
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)
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dynamic_param_ratio = (model.staticParameterCount() + model.dynamicParameterCount()) / model.dynamicParameterCount()
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steps_per_epoch = math.ceil(len(train_dataloader) / training_args.gradient_accumulation_steps) if train_dataloader is not None else 1
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total_steps = steps_per_epoch * training_args.epochs
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optimizer = get_optimizer(model.dynamicParameters(),
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model.staticParameters() if training_args.train_non_linear_layers else None,
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training_args.learning_rate,
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training_args.learning_rate / dynamic_param_ratio,
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training_args.weight_decay,
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training_args.adam_epsilon,
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training_args.adam8bit)
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lr_scheduler = get_scheduler(
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name=training_args.lr_scheduler_type,
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optimizer=optimizer,
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num_warmup_steps=training_args.warmup_steps,
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num_training_steps=total_steps
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)
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if training_args.do_train:
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progress_bar = tqdm(range(total_steps))
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global_step = 0
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model.model.train()
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for epoch in range(0, training_args.epochs):
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model.model.train()
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print("*** Train ***")
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print(f'Vram used for model before training starts: {torch.cuda.memory_allocated()/(1024.0*1024.0)}')
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for step, batch in enumerate(train_dataloader):
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for key in batch:
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batch[key] = batch[key].to("cuda:0")
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outputs = model.model(**batch)
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loss = outputs.loss / training_args.gradient_accumulation_steps
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loss.backward()
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if (step + 1) % training_args.gradient_accumulation_steps == 0 or step + 1 == len(train_dataloader):
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if global_step % training_args.logging_steps == 0:
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log_writer.add_scalar("Loss/train", loss, global_step)
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optimizer.step()
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lr_scheduler.step()
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model.model.zero_grad()
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if global_step % 5 == 0:
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print(f"Train Loss {loss.item()}")
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if global_step % training_args.reshufle_steps == 0 and training_args.max_instant_params != 0:
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print("Reshuffleing")
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lr_scheduler.optimizer = None
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del optimizer
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# distance, error = model.getDistanceAndErrorSample()
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# log_writer.add_histogram("Distances/Train", distance, max_bins=50)
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# log_writer.add_histogram("Errors/Train", error, max_bins=50)
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model.reshuffleActive()
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model.balanceActive()
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log_writer.add_scalar("Parameters/train", model.activeParameterCount(), global_step)
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optimizer = get_optimizer(model.dynamicParameters(),
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model.staticParameters() if training_args.train_non_linear_layers else None,
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training_args.learning_rate,
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training_args.learning_rate / dynamic_param_ratio,
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training_args.weight_decay,
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training_args.adam_epsilon,
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training_args.adam8bit)
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lr_scheduler.optimizer = optimizer
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global_step += 1
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progress_bar.update()
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if global_step > 0:
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if global_step % training_args.save_steps == 0:
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save_model(model.model, global_step, training_args.output_dir, training_args.max_checkpoints)
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if training_args.eval_steps > 0 and global_step % training_args.eval_steps == 0:
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evaluate(model, tokenizer, eval_dataloader, global_step, log_writer, training_args.eval_prompt)
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if training_args.flush_allocator:
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gc.collect()
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torch.cuda.empty_cache()
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if training_args.do_eval and training_args.eval_steps == -1:
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evaluate(model, tokenizer, eval_dataloader, global_step, log_writer, training_args.eval_prompt)
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# Evaluation
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if training_args.do_eval:
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evaluate(model, tokenizer, eval_dataloader, global_step, log_writer, training_args.eval_prompt)
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save_model(model.model, global_step, training_args.output_dir)
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return
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if __name__ == "__main__":
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hfparser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
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model_args, data_args, training_args, extra_args = hfparser.parse_args_into_dataclasses(return_remaining_strings=True)
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print("Model Arguments:")
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print(model_args)
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print("\nData Arguments:")
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print(data_args)
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print("\nTraining Arguments:")
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print(training_args)
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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train(model_args, data_args, training_args)
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