mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-28 20:25:22 +08:00
grpo_trainer shoudl be done
This commit is contained in:
Goekdeniz-Guelmez
parent
6c58aa995c
commit
80bcf68956
@ -229,47 +229,6 @@ def iterate_batches(dataset, tokenizer, batch_size, max_seq_length, train=False)
|
||||
break
|
||||
|
||||
|
||||
def evaluate_grpo(
|
||||
model,
|
||||
ref_model,
|
||||
dataset,
|
||||
tokenizer,
|
||||
batch_size,
|
||||
num_batches,
|
||||
beta: float,
|
||||
epslion: float,
|
||||
group_size: int,
|
||||
max_seq_length,
|
||||
loss: callable = grpo_loss,
|
||||
iterate_batches: callable = iterate_batches
|
||||
):
|
||||
all_losses = 0
|
||||
ntokens = 0
|
||||
|
||||
index_iterator = iter(range(num_batches)) if num_batches != -1 else iter(int, 1)
|
||||
|
||||
for _, batch in zip(
|
||||
index_iterator,
|
||||
iterate_batches(
|
||||
dataset=dataset,
|
||||
tokenizer=tokenizer,
|
||||
batch_size=batch_size,
|
||||
max_seq_length=max_seq_length,
|
||||
),
|
||||
):
|
||||
losses, toks = loss(
|
||||
model,
|
||||
*batch
|
||||
)
|
||||
all_losses += losses * toks
|
||||
ntokens += toks
|
||||
mx.eval(all_losses, ntokens)
|
||||
|
||||
all_losses = mx.distributed.all_sum(all_losses, stream=mx.cpu)
|
||||
ntokens = mx.distributed.all_sum(ntokens, stream=mx.cpu)
|
||||
|
||||
return (all_losses / ntokens).item()
|
||||
|
||||
def evaluate_grpo(
|
||||
model,
|
||||
ref_model,
|
||||
@ -299,10 +258,7 @@ def evaluate_grpo(
|
||||
max_seq_length=max_seq_length,
|
||||
),
|
||||
):
|
||||
# Extract prompts from the batch (assuming the batch contains 'prompts')
|
||||
prompts = batch.get("prompts", None)
|
||||
|
||||
# Call the loss function with the correct arguments
|
||||
prompts = batch
|
||||
losses, toks, metrics = loss(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
@ -313,15 +269,25 @@ def evaluate_grpo(
|
||||
epslion=epslion,
|
||||
ref_model=ref_model
|
||||
)
|
||||
|
||||
all_losses += losses * toks
|
||||
ntokens += toks
|
||||
|
||||
if all_metrics is None:
|
||||
all_metrics = {k: v * toks for k, v in metrics.items()}
|
||||
else:
|
||||
for k, v in metrics.items():
|
||||
all_metrics[k] += v * toks
|
||||
|
||||
mx.eval(all_losses, ntokens)
|
||||
|
||||
all_losses = mx.distributed.all_sum(all_losses, stream=mx.cpu)
|
||||
ntokens = mx.distributed.all_sum(ntokens, stream=mx.cpu)
|
||||
all_metrics = {k: mx.distributed.all_sum(v) for k, v in all_metrics.items()}
|
||||
|
||||
return (all_losses / ntokens).item()
|
||||
avg_metrics = {k: (v / ntokens).item() for k, v in all_metrics.items()}
|
||||
avg_loss = (all_losses / ntokens).item()
|
||||
|
||||
return avg_loss, ntokens, avg_metrics
|
||||
|
||||
|
||||
def train(
|
||||
@ -335,7 +301,7 @@ def train(
|
||||
iterate_batches: callable = iterate_batches,
|
||||
training_callback: TrainingCallback = None,
|
||||
):
|
||||
print(f"Starting training..., iters: {args.iters}")
|
||||
print(f"Starting GRPO training..., iters: {args.iters}")
|
||||
world = mx.distributed.init()
|
||||
world_size = world.size()
|
||||
rank = world.rank()
|
||||
@ -349,7 +315,7 @@ def train(
|
||||
|
||||
def step(batch):
|
||||
# Forward and backward pass
|
||||
(lvalue, toks), grad = loss_value_and_grad(model, *batch)
|
||||
(loss, toks, metrics), grad = loss_value_and_grad(model, *batch)
|
||||
|
||||
# All reduce the gradients if running in distributed mode
|
||||
grad = average_gradients(grad)
|
||||
@ -357,18 +323,22 @@ def train(
|
||||
# Model update
|
||||
optimizer.update(model, grad)
|
||||
|
||||
return lvalue, toks
|
||||
return loss, toks, metrics
|
||||
|
||||
loss_value_and_grad = nn.value_and_grad(model, loss)
|
||||
|
||||
# Save initial model weights as reference
|
||||
ref_weights = {k: v.copy() for k, v in model.parameters().items()}
|
||||
|
||||
losses = 0
|
||||
n_tokens = 0
|
||||
steps = 0
|
||||
trained_tokens = 0
|
||||
# Main training loop
|
||||
accumulated_metrics = {
|
||||
'rewards': 0,
|
||||
'rewards_std': 0,
|
||||
'grouped_rewards': 0,
|
||||
'grouped_rewards_std': 0,
|
||||
'kl': 0
|
||||
}
|
||||
|
||||
start = time.perf_counter()
|
||||
for it, batch in zip(
|
||||
range(1, args.iters + 1),
|
||||
@ -384,7 +354,7 @@ def train(
|
||||
# is always measured before any training.
|
||||
if it == 1 or it % args.steps_per_eval == 0 or it == args.iters:
|
||||
stop = time.perf_counter()
|
||||
val_loss = evaluate(
|
||||
val_loss, val_ntokens, val_metrics = evaluate(
|
||||
model=model,
|
||||
dataset=val_dataset,
|
||||
loss=loss,
|
||||
@ -398,61 +368,74 @@ def train(
|
||||
if rank == 0:
|
||||
print(
|
||||
f"Iter {it}: "
|
||||
f"Val loss {val_loss:.3f}, "
|
||||
f"Val loss {val_loss:.8f}, "
|
||||
f"Val rewards {val_metrics['rewards']:.3f}, "
|
||||
f"Val rewards_std {val_metrics['rewards_std']:.3f}, "
|
||||
f"Val grouped_rewards {val_metrics['grouped_rewards']:.3f}, "
|
||||
f"Val grouped_rewards_std {val_metrics['grouped_rewards_std']:.3f}, "
|
||||
f"Val kl {val_metrics['kl']:.3f}, "
|
||||
f"Val took {val_time:.3f}s",
|
||||
flush=True,
|
||||
)
|
||||
|
||||
if training_callback is not None:
|
||||
val_info = {
|
||||
training_callback.on_val_loss_report({
|
||||
"iteration": it,
|
||||
"val_loss": val_loss,
|
||||
**{f"val_{k}": v for k, v in val_metrics.items()},
|
||||
"val_time": val_time,
|
||||
}
|
||||
training_callback.on_val_loss_report(val_info)
|
||||
})
|
||||
|
||||
start = time.perf_counter()
|
||||
|
||||
lvalue, toks = step(batch)
|
||||
losses += lvalue
|
||||
loss, toks, metrics = step(batch)
|
||||
losses += loss
|
||||
n_tokens += toks
|
||||
steps += 1
|
||||
for k, v in metrics.items():
|
||||
accumulated_metrics[k] += v
|
||||
mx.eval(state, losses, n_tokens)
|
||||
|
||||
# Report training loss if needed
|
||||
if it % args.steps_per_report == 0 or it == args.iters:
|
||||
stop = time.perf_counter()
|
||||
|
||||
train_loss = mx.distributed.all_sum(losses, stream=mx.cpu).item()
|
||||
train_loss /= steps * mx.distributed.init().size()
|
||||
avg_metrics = {k: v / (steps * world_size) for k, v in accumulated_metrics.items()}
|
||||
n_tokens = mx.distributed.all_sum(n_tokens, stream=mx.cpu).item()
|
||||
learning_rate = optimizer.learning_rate.item()
|
||||
it_sec = args.steps_per_report / (stop - start)
|
||||
tokens_sec = float(n_tokens) / (stop - start)
|
||||
trained_tokens += n_tokens
|
||||
peak_mem = mx.metal.get_peak_memory() / 1e9
|
||||
|
||||
if rank == 0:
|
||||
print(
|
||||
f"Iter {it}: Train loss {train_loss:.3f}, "
|
||||
f"Iter {it}: Train loss {train_loss:.8f}, "
|
||||
f"Rewards {avg_metrics['rewards']:.3f}, "
|
||||
f"Rewards_std {avg_metrics['rewards_std']:.3f}, "
|
||||
f"Grouped Rewards {avg_metrics['grouped_rewards']:.3f}, "
|
||||
f"Grouped Rewards {avg_metrics['grouped_rewards']:.3f}, "
|
||||
f"Grouped Rewards_std {val_metrics['grouped_rewards_std']:.3f}, "
|
||||
f"KL {val_metrics['kl']:.3f}, "
|
||||
f"Learning Rate {learning_rate:.3e}, "
|
||||
f"It/sec {it_sec:.3f}, "
|
||||
f"Tokens/sec {tokens_sec:.3f}, "
|
||||
f"Trained Tokens {trained_tokens}, "
|
||||
f"Peak mem {peak_mem:.3f} GB",
|
||||
flush=True,
|
||||
)
|
||||
|
||||
if training_callback is not None:
|
||||
train_info = {
|
||||
training_callback.on_train_loss_report({
|
||||
"iteration": it,
|
||||
"train_loss": train_loss,
|
||||
**{f"train_{k}": v for k, v in avg_metrics.items()},
|
||||
"learning_rate": learning_rate,
|
||||
"iterations_per_second": it_sec,
|
||||
"tokens_per_second": tokens_sec,
|
||||
"trained_tokens": trained_tokens,
|
||||
"peak_memory": peak_mem,
|
||||
}
|
||||
training_callback.on_train_loss_report(train_info)
|
||||
})
|
||||
|
||||
losses = 0
|
||||
n_tokens = 0
|
||||
|
||||
Loading…
Reference in New Issue
Block a user