github/ColossalAI
1
0
Fork 0
mirror of https://github.com/hpcaitech/ColossalAI.git synced 2025-09-09 04:50:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

merge

Browse Source
This commit is contained in:
YeAnbang
2025-05-14 18:13:47 +08:00
parent 50070c1e84 aca547623f
commit 094f119b3a
4 changed files with 123 additions and 93 deletions
Download Patch File Download Diff File Expand all files Collapse all files

69
applications/ColossalChat/coati/distributed/consumer.py
View File

@@ -106,9 +106,14 @@ class BaseConsumer:
f"Consumer{self.rank} num_update: {self.num_update_per_episode}, num_recv: {self.num_recv_per_update}, nmb: {self.num_microbatches}"
)
for episode in range(self.num_episodes):
with tqdm(range(self.num_update_per_episode), desc=f"Episode {episode}", disable=self.rank != 0) as pbar:
with tqdm(
range(self.num_update_per_episode),
desc=f"Episode {episode} with rollout step(s)",
disable=self.rank != 0,
) as pbar:
for step in pbar:
i = 0
allow_sync_model = False
for _ in range(self.num_recv_per_update):
# receive data from producers
for r in range(self.num_producers):
@@ -126,15 +131,15 @@ class BaseConsumer:
]
batch = bind_batch(batches)
batch = post_recv(batch)
loss, num_excessive_prompts = self.step(i, pbar, **batch)
self.buffer = (
self.buffer[
(self.dp_rank + 1) * self.minibatch_size
- num_excessive_prompts : (self.dp_rank + 1) * self.minibatch_size
]
+ self.buffer[self.dp_size * self.minibatch_size :]
)
loss, excessive_prompts_idx = self.step(i, pbar, **batch)
if excessive_prompts_idx is not None:
excessive_prompts = [self.buffer[idx] for idx in excessive_prompts_idx]
self.buffer = excessive_prompts + self.buffer[self.dp_size * self.minibatch_size :]
else:
self.buffer = self.buffer[self.dp_size * self.minibatch_size :]
if loss is not None:
allow_sync_model = True
pbar.set_postfix({"loss": loss})
i += 1
if self.lr_scheduler is not None:
@@ -148,29 +153,31 @@ class BaseConsumer:
print(f"Saved model checkpoint at step {step + 1} in folder {save_path}")
if episode != self.num_episodes - 1 or step != self.num_update_per_episode - 1:
if self.pp_size > 1:
print(
f"[T{dist.get_rank()}] Sync model PP stage {self.pp_rank} episode {episode} step {step}"
)
else:
print(f"[T{dist.get_rank()}] Sync model episode {episode} step {step}")
torch.cuda.empty_cache()
state_dict = self.state_dict()
if self.pp_size > 1:
if self.tp_rank == 0 and self.dp_rank == 0:
ray_broadcast_tensor_dict(
state_dict,
src=self.num_producers,
device=self.device,
group_name=f"sync_model_{self.pp_rank}",
if allow_sync_model:
if self.pp_size > 1:
print(
f"[T{dist.get_rank()}] Sync model PP stage {self.pp_rank} episode {episode} step {step}"
)
else:
if self.rank == 0:
ray_broadcast_tensor_dict(
state_dict, src=self.num_producers, device=self.device, group_name="sync_model"
)
del state_dict
torch.cuda.empty_cache()
else:
print(f"[T{dist.get_rank()}] Sync model episode {episode} step {step}")
torch.cuda.empty_cache()
state_dict = self.state_dict()
if self.pp_size > 1:
if self.tp_rank == 0 and self.dp_rank == 0:
ray_broadcast_tensor_dict(
state_dict,
src=self.num_producers,
device=self.device,
group_name=f"sync_model_{self.pp_rank}",
)
else:
if self.rank == 0:
ray_broadcast_tensor_dict(
state_dict, src=self.num_producers, device=self.device, group_name="sync_model"
)
del state_dict
torch.cuda.empty_cache()
allow_sync_model = False
@ray.remote