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Goekdeniz-Guelmez 2025-02-24 22:20:07 +01:00
parent e4eac9c97b
commit 53185c7f3d
3 changed files with 30 additions and 16 deletions
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3
llms/mlx_lm/lora.py
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@ -387,7 +387,8 @@ def evaluate_model(args, model: nn.Module, tokenizer: TokenizerWrapper, test_set
test_ppl = math.exp(test_loss) test_ppl = math.exp(test_loss)
print(f"Test loss {test_loss:.3f}, Test ppl {test_ppl:.3f}, Rewards: {test_rewards[0]:.3f}, {test_rewards[1]:.3f}") rewards_str = ", ".join([f"{k}: {v:.3f}" for k, v in test_rewards.items()])
print(f"Test loss {test_loss:.3f}, Test ppl {test_ppl:.3f}, Rewards: {rewards_str}")
else: else:
test_loss = evaluate( test_loss = evaluate(
model=model, model=model,

2
llms/mlx_lm/tuner/grpo_reward_functions.py
View File

@ -55,7 +55,7 @@ def r1_soft_format_reward_func(prompts: list, completions: list, answer: list, *
def r1_strict_format_reward_func(prompts: list, completions: list, answer: list, **kwargs) -> list[float]: def r1_strict_format_reward_func(prompts: list, completions: list, answer: list, **kwargs) -> list[float]:
if not completions: if not completions:
return [0.0] * len(prompts) return [0.0] * len(prompts)
pattern = r"<think>\n.*?\n</think>\n<answer>*?</answer>" pattern = r"<think> .*? </think><answer> .*? </answer>"
matches = [bool(re.search(pattern, r)) if r else False for r in completions] matches = [bool(re.search(pattern, r)) if r else False for r in completions]
return [0.5 if match else 0.0 for match in matches] return [0.5 if match else 0.0 for match in matches]

27
llms/mlx_lm/tuner/grpo_trainer.py
View File

@ -10,7 +10,7 @@ import mlx.core as mx
import mlx.nn as nn import mlx.nn as nn
import numpy as np import numpy as np
from .grpo_reward_functions import r1_accuracy_reward_func, r1_int_reward_func, r1_strict_format_reward_func, r1_soft_format_reward_func, r1_count_xml, RewardFunctions from .grpo_reward_functions import r1_accuracy_reward_func, r1_int_reward_func, r1_strict_format_reward_func, r1_soft_format_reward_func, r1_count_xml,r1_extract_xml_answer, RewardFunctions
from .trainer import grad_checkpoint, TrainingArgs, TrainingCallback, average_gradients from .trainer import grad_checkpoint, TrainingArgs, TrainingCallback, average_gradients
from ..utils import generate_step from ..utils import generate_step
from ..models import cache from ..models import cache
@ -177,8 +177,10 @@ def grpo_loss(
prompt_tensor, prompt_tensor,
max_tokens, max_tokens,
tokenizer, tokenizer,
group_size group_size,
temperature=temperature
) )
model.train()
else: else:
completions = generate_grpo( completions = generate_grpo(
model, model,
@ -327,8 +329,13 @@ def grpo_loss(
} }
if is_validation: if is_validation:
print(f"\nValidation sample generation:\n{all_completion_texts}\n") print("\n=== Validation Sample Details ===")
print(f"Validation sample answer:\n{answer_text[-1]}\n") print(f"\n📝 Generation:\n{all_completion_texts[-1]}")
print("\n" + "="*10 + "\n")
print(f"\n✅ Answer:\n{answer_text[-1]}")
print("\n" + "="*10 + "\n")
print(f"\n🔍 Extracted Answer:\n{r1_extract_xml_answer(all_completion_texts[-1])}")
print("\n" + "="*30 + "\n")
mx.metal.clear_cache() mx.metal.clear_cache()
return loss, sequence_lengths.sum(), metrics return loss, sequence_lengths.sum(), metrics
@ -396,7 +403,13 @@ def evaluate_grpo(
max_seq_length: int, max_seq_length: int,
max_tokens: int, max_tokens: int,
temperature: float, temperature: float,
reward_funcs: Optional[List[RewardFunctions]] = None, reward_funcs: Optional[List[RewardFunctions]] = [
r1_accuracy_reward_func,
r1_int_reward_func,
r1_strict_format_reward_func,
r1_soft_format_reward_func,
r1_count_xml
],
loss_fn: callable = grpo_loss, loss_fn: callable = grpo_loss,
iterate_batches: callable = iterate_grpo_batches iterate_batches: callable = iterate_grpo_batches
): ):
@ -550,7 +563,7 @@ def train_grpo(
val_time = time.perf_counter() - stop val_time = time.perf_counter() - stop
if rank == 0: if rank == 0:
val_metrics_str = ( val_metrics_str = (
f"Val loss {val_loss:.8f}, " f"Val loss {val_loss:.3f}, "
f"Val total_rewards_mean {val_metrics['total_rewards_mean']:.3f}, " f"Val total_rewards_mean {val_metrics['total_rewards_mean']:.3f}, "
f"Val total_rewards_std {val_metrics['total_rewards_std']:.3f}, " f"Val total_rewards_std {val_metrics['total_rewards_std']:.3f}, "
f"Val grouped_rewards_mean {val_metrics['grouped_rewards_mean']:.3f}, " f"Val grouped_rewards_mean {val_metrics['grouped_rewards_mean']:.3f}, "
@ -605,7 +618,7 @@ def train_grpo(
if rank == 0: if rank == 0:
train_metrics_str = ( train_metrics_str = (
f"Train loss {train_loss:.8f}, " f"Train loss {train_loss:.3f}, "
f"Total rewards mean {avg_metrics['total_rewards_mean']:.3f}, " f"Total rewards mean {avg_metrics['total_rewards_mean']:.3f}, "
f"Total rewards std {avg_metrics['total_rewards_std']:.3f}, " f"Total rewards std {avg_metrics['total_rewards_std']:.3f}, "
f"Grouped rewards mean {avg_metrics['grouped_rewards_mean']:.3f}, " f"Grouped rewards mean {avg_metrics['grouped_rewards_mean']:.3f}, "