mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-27 03:05:20 +08:00
392 lines
13 KiB
Python
392 lines
13 KiB
Python
import time
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from pathlib import Path
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from dataclasses import dataclass, field
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import mlx.nn as nn
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import mlx.core as mx
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import numpy as np
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from mlx.utils import tree_flatten
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from mlx.nn.utils import average_gradients
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from .trainer import TrainingArgs, grad_checkpoint, TrainingCallback
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@dataclass
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class ORPOTrainingArgs(TrainingArgs):
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beta: float = field(
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default=0.1,
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metadata={"help": "Temperature parameter for DPO training."}
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)
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reward_scaling: float = field(
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default=1.0,
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metadata={"help": "Scaling factor for offline rewards."}
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)
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def orpo_loss(
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model,
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chosen: mx.array,
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rejected: mx.array,
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chosen_masks: mx.array,
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rejected_masks: mx.array,
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chosen_rewards: mx.array,
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rejected_rewards: mx.array,
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beta: float,
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reward_scaling: float = 1.0,
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):
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"""
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Calculate ORPO loss using pre-computed rewards.
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Args:
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model: Policy model
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chosen: Chosen sequence tokens
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rejected: Rejected sequence tokens
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chosen_masks: Attention masks for chosen sequences
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rejected_masks: Attention masks for rejected sequences
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chosen_rewards: Pre-computed rewards for chosen sequences
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rejected_rewards: Pre-computed rewards for rejected sequences
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beta: Temperature parameter
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reward_scaling: Scaling factor for rewards
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Returns:
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Loss value, rewards, and number of tokens.
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"""
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def make_predictions(model, x, mask):
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inputs = x[:, :-1]
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targets = x[:, 1:]
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logits = model(inputs)
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logits = logits.astype(mx.float32)
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return -nn.losses.cross_entropy(logits, targets) * mask[:, :-1]
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# Calculate log probabilities for policy model
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policy_chosen_scores = make_predictions(model, chosen, chosen_masks)
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policy_rejected_scores = make_predictions(model, rejected, rejected_masks)
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# Scale the pre-computed rewards
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chosen_rewards = chosen_rewards * reward_scaling
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rejected_rewards = rejected_rewards * reward_scaling
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# ORPO uses the reward difference directly
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reward_diff = chosen_rewards - rejected_rewards
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# Calculate ORPO loss using logistic function
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policy_diff = policy_chosen_scores.sum(-1) - policy_rejected_scores.sum(-1)
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loss = -nn.log_sigmoid(beta * (policy_diff * reward_diff))
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loss = mx.mean(loss)
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# Calculate number of tokens for logging
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num_tokens = (chosen_masks.sum() + rejected_masks.sum())
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# Calculate rewards for logging
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avg_chosen_reward = mx.mean(chosen_rewards)
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avg_rejected_reward = mx.mean(rejected_rewards)
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reward = mx.stack([avg_chosen_reward, avg_rejected_reward])
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return loss, reward, num_tokens
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def evaluate_orpo(
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model,
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dataset,
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tokenizer,
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batch_size,
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num_batches,
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beta: float,
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reward_scaling: float = 1.0,
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max_seq_length=2048,
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):
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"""
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Evaluation function for ORPO.
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"""
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all_losses = 0
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all_rewards = mx.zeros((2,))
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ntokens = 0
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index_iterator = iter(range(num_batches)) if num_batches != -1 else iter(int, 1)
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for _, batch in zip(
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index_iterator,
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iterate_orpo_batches(
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dataset=dataset,
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tokenizer=tokenizer,
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batch_size=batch_size,
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max_seq_length=max_seq_length,
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),
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):
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chosen, rejected, chosen_masks, rejected_masks, chosen_rewards, rejected_rewards = batch
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loss, reward, toks = orpo_loss(
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model=model,
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chosen=chosen,
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rejected=rejected,
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chosen_masks=chosen_masks,
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rejected_masks=rejected_masks,
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chosen_rewards=chosen_rewards,
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rejected_rewards=rejected_rewards,
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beta=beta,
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reward_scaling=reward_scaling,
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)
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all_losses += loss * toks
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all_rewards += reward
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ntokens += toks
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mx.eval(all_losses, all_rewards, ntokens)
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all_losses = mx.distributed.all_sum(all_losses)
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all_rewards = mx.distributed.all_sum(all_rewards)
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ntokens = mx.distributed.all_sum(ntokens)
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return (all_losses / ntokens).item(), all_rewards.tolist()
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def iterate_orpo_batches(dataset, tokenizer, batch_size, max_seq_length, train=False):
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"""
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Modified batch iterator for ORPO that includes pre-computed rewards.
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Works with pre-tokenized input data.
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"""
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# Sort pairs by length of the chosen response
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idx = sorted(range(len(dataset)), key=lambda idx: len(dataset[idx]['chosen']))
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if len(dataset) < batch_size:
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raise ValueError(
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f"Dataset must have at least batch_size={batch_size}"
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f" examples but only has {len(dataset)}."
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)
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step = mx.distributed.init().size()
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if batch_size % step != 0:
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raise ValueError("The batch size must be divisible by the number of workers")
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batch_idx = [
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idx[i : i + batch_size : step]
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for i in range(0, len(idx) - batch_size + 1, batch_size)
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]
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while True:
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indices = np.random.permutation(len(batch_idx)) if train else range(len(batch_idx))
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for i in indices:
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batch = [dataset[j] for j in batch_idx[i]]
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# Get lengths assuming data is already tokenized
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chosen_lengths = [len(x['chosen']) for x in batch]
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rejected_lengths = [len(x['rejected']) for x in batch]
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max_length = max(max(chosen_lengths), max(rejected_lengths))
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if max_length > max_seq_length:
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print(
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f"[WARNING] Sequences longer than {max_seq_length} tokens "
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f"will be truncated."
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)
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pad_to = 8
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max_length_in_batch = pad_to * ((max_length + pad_to - 1) // pad_to)
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max_length_in_batch = min(max_length_in_batch, max_seq_length)
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chosen_arr = np.zeros((batch_size // step, max_length_in_batch), np.int32)
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rejected_arr = np.zeros((batch_size // step, max_length_in_batch), np.int32)
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chosen_masks = np.zeros((batch_size // step, max_length_in_batch), np.float32)
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rejected_masks = np.zeros((batch_size // step, max_length_in_batch), np.float32)
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# Always use binary rewards
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chosen_rewards = np.ones((batch_size // step,), np.float32)
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rejected_rewards = np.zeros((batch_size // step,), np.float32)
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for j in range(batch_size // step):
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# Use pre-tokenized sequences directly
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chosen_length = min(chosen_lengths[j], max_seq_length)
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chosen_arr[j, :chosen_length] = batch[j]['chosen'][:chosen_length]
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chosen_masks[j, :chosen_length] = 1.0
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rejected_length = min(rejected_lengths[j], max_seq_length)
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rejected_arr[j, :rejected_length] = batch[j]['rejected'][:rejected_length]
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rejected_masks[j, :rejected_length] = 1.0
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yield (mx.array(chosen_arr), mx.array(rejected_arr),
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mx.array(chosen_masks), mx.array(rejected_masks),
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mx.array(chosen_rewards), mx.array(rejected_rewards))
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if not train:
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break
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def train_orpo(
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model,
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tokenizer,
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optimizer,
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train_dataset,
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val_dataset,
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args: ORPOTrainingArgs = ORPOTrainingArgs(),
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training_callback: TrainingCallback = None,
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):
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"""
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Training function for ORPO.
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"""
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print(f"Starting ORPO training..., iters: {args.iters}")
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world = mx.distributed.init()
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world_size = world.size()
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rank = world.rank()
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if world_size > 1:
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print(f"Node {rank} of {world_size}")
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if args.grad_checkpoint:
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grad_checkpoint(model.layers[0])
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state = [model.state, optimizer.state]
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def step(batch):
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chosen, rejected, chosen_masks, rejected_masks, chosen_rewards, rejected_rewards = batch
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(loss, reward, toks), grad = loss_value_and_grad(
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model,
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chosen,
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rejected,
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chosen_masks,
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rejected_masks,
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chosen_rewards,
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rejected_rewards
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)
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grad = average_gradients(grad)
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optimizer.update(model, grad)
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return loss, reward, toks
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def loss_wrapper(model, chosen, rejected, chosen_masks, rejected_masks,
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chosen_rewards, rejected_rewards):
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return orpo_loss(
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model=model,
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chosen=chosen,
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rejected=rejected,
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chosen_masks=chosen_masks,
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rejected_masks=rejected_masks,
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chosen_rewards=chosen_rewards,
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rejected_rewards=rejected_rewards,
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beta=args.beta,
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reward_scaling=args.reward_scaling
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)
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loss_value_and_grad = nn.value_and_grad(model, loss_wrapper)
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# Training loop with progress tracking
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losses = 0
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rewards = mx.zeros((2,))
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n_tokens = 0
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steps = 0
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trained_tokens = 0
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start = time.perf_counter()
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for it, batch in zip(
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range(1, args.iters + 1),
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iterate_orpo_batches( # reuse DPO batch iterator
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dataset=train_dataset,
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tokenizer=tokenizer,
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batch_size=args.batch_size,
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max_seq_length=args.max_seq_length,
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train=True,
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),
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):
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# Evaluate if needed
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if it == 1 or it % args.steps_per_eval == 0 or it == args.iters:
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stop = time.perf_counter()
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val_loss, val_rewards = evaluate_orpo(
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model=model,
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dataset=val_dataset,
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tokenizer=tokenizer,
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batch_size=args.batch_size,
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num_batches=args.val_batches,
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max_seq_length=args.max_seq_length,
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beta=args.beta,
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reward_scaling=args.reward_scaling,
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)
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val_time = time.perf_counter() - stop
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if rank == 0:
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print(
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f"Iter {it}: "
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f"Val loss {val_loss:.3f}, "
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f"Val chosen reward {val_rewards[0]:.3f}, "
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f"Val rejected reward {val_rewards[1]:.3f}, "
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f"Val took {val_time:.3f}s",
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flush=True,
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)
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if training_callback is not None:
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training_callback.on_val_loss_report({
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"iteration": it,
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"val_loss": val_loss,
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"val_chosen_reward": val_rewards[0],
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"val_rejected_reward": val_rewards[1],
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"val_time": val_time,
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})
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start = time.perf_counter()
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# Training step
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loss, reward, toks = step(batch)
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losses += loss
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rewards += reward
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n_tokens += toks
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steps += 1
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mx.eval(state, losses, rewards, n_tokens)
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# Report training metrics if needed
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if it % args.steps_per_report == 0 or it == args.iters:
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stop = time.perf_counter()
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train_loss = mx.distributed.all_sum(losses).item() / (steps * world_size)
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train_rewards = [r / (steps * world_size) for r in mx.distributed.all_sum(rewards).tolist()]
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n_tokens = mx.distributed.all_sum(n_tokens).item()
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learning_rate = optimizer.learning_rate.item()
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it_sec = args.steps_per_report / (stop - start)
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tokens_sec = float(n_tokens) / (stop - start)
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trained_tokens += n_tokens
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peak_mem = mx.metal.get_peak_memory() / 1e9
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if rank == 0:
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print(
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f"Iter {it}: Train loss {train_loss:.3f}, "
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f"Chosen reward {train_rewards[0]:.3f}, "
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f"Rejected reward {train_rewards[1]:.3f}, "
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f"Learning Rate {learning_rate:.3e}, "
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f"It/sec {it_sec:.3f}, "
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f"Tokens/sec {tokens_sec:.3f}, "
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f"Trained Tokens {trained_tokens}, "
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f"Peak mem {peak_mem:.3f} GB",
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flush=True,
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)
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if training_callback is not None:
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training_callback.on_train_loss_report({
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"iteration": it,
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"train_loss": train_loss,
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"train_chosen_reward": train_rewards[0],
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"train_rejected_reward": train_rewards[1],
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"learning_rate": learning_rate,
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"iterations_per_second": it_sec,
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"tokens_per_second": tokens_sec,
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"trained_tokens": trained_tokens,
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"peak_memory": peak_mem,
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})
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losses = 0
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rewards = mx.zeros((2,))
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n_tokens = 0
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steps = 0
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start = time.perf_counter()
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# Save model weights if needed
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if it % args.steps_per_save == 0:
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adapter_weights = dict(tree_flatten(model.trainable_parameters()))
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mx.save_safetensors(str(args.adapter_file), adapter_weights)
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checkpoint = (
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Path(args.adapter_file).parent / f"{it:07d}_adapters.safetensors"
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)
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mx.save_safetensors(str(checkpoint), adapter_weights)
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print(
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f"Iter {it}: Saved adapter weights to "
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f"{args.adapter_file} and {checkpoint}."
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)
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# Save final weights
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adapter_weights = dict(tree_flatten(model.trainable_parameters()))
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mx.save_safetensors(str(args.adapter_file), adapter_weights)
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print(f"Saved final weights to {args.adapter_file}.") |