github/ColossalAI
1
0
Fork 0
mirror of https://github.com/hpcaitech/ColossalAI.git synced 2025-08-20 08:53:22 +00:00
Code Issues Packages Projects Releases Wiki Activity

handle empty index

Browse Source
This commit is contained in:
Tong Li 2025-05-15 18:30:27 +08:00 committed by YeAnbang
parent 88f49ddc5e
commit 6ebd813b5f
2 changed files with 45 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
applications/ColossalChat/coati/distributed/consumer.py
View File

@ -113,7 +113,6 @@ class BaseConsumer:
) as pbar: ) as pbar:
for step in pbar: for step in pbar:
i = 0 i = 0
allow_sync_model = True
for _ in range(self.num_recv_per_update): for _ in range(self.num_recv_per_update):
# receive data from producers # receive data from producers
for r in range(self.num_producers): for r in range(self.num_producers):
@ -140,7 +139,6 @@ class BaseConsumer:
loss = self.step(i, pbar, **batch) loss = self.step(i, pbar, **batch)
self.buffer = self.buffer[self.dp_size * self.minibatch_size :] self.buffer = self.buffer[self.dp_size * self.minibatch_size :]
if loss is not None: if loss is not None:
allow_sync_model = True
pbar.set_postfix({"loss": loss}) pbar.set_postfix({"loss": loss})
i += 1 i += 1
if self.lr_scheduler is not None: if self.lr_scheduler is not None:
@ -154,7 +152,6 @@ class BaseConsumer:
print(f"Saved model checkpoint at step {step + 1} in folder {save_path}") 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 episode != self.num_episodes - 1 or step != self.num_update_per_episode - 1:
if allow_sync_model:
if self.pp_size > 1: if self.pp_size > 1:
print( print(
f"[T{dist.get_rank()}] Sync model PP stage {self.pp_rank} episode {episode} step {step}" f"[T{dist.get_rank()}] Sync model PP stage {self.pp_rank} episode {episode} step {step}"
@ -178,7 +175,6 @@ class BaseConsumer:
) )
del state_dict del state_dict
torch.cuda.empty_cache() torch.cuda.empty_cache()
allow_sync_model = True
@ray.remote @ray.remote

24
applications/ColossalChat/coati/distributed/grpo_consumer.py
View File

@ -218,7 +218,29 @@ class GRPOConsumer(BaseConsumer):
if self.grpo_config.get("dynamic_batching", True): if self.grpo_config.get("dynamic_batching", True):
need_update = self.effective_prompt_count >= self.batch_size * self.dp_size need_update = self.effective_prompt_count >= self.batch_size * self.dp_size
excessive_prompts = self.effective_prompt_count - self.batch_size * self.dp_size excessive_prompts = self.effective_prompt_count - self.batch_size * self.dp_size
assert excessive_prompts <= 0, "Debug: Excessive prompts should always be less than 0. Bug!!!!"
if excessive_prompts > 0:
excessive_prompts_per_rank = excessive_prompts // self.dp_size
# Only count excessive prompts if they are greater than 1 per rank.
# TODO: customize excessive prompts calculation.
if excessive_prompts_per_rank != 0:
# Mask excessive prompts to False
true_indices = torch.nonzero(effective_prompts_mask)
# Make sure the indices are not empty.
if true_indices.numel() > 0:
true_indices = true_indices.squeeze()
if excessive_prompts_per_rank <= len(true_indices):
excessive_prompts_idx = true_indices[-excessive_prompts_per_rank:]
else:
excessive_prompts_idx = true_indices
effective_prompts_mask[excessive_prompts_idx] = False
for mask_idx in range(len(effective_prompts_mask)):
if effective_prompts_mask[mask_idx] == False:
# Update loss mask.
loss_mask[mask_idx] = False
else:
excessive_prompts_idx = torch.empty([0])
else: else:
# If dynamic batching is disabled, we need to use all samples for training. # If dynamic batching is disabled, we need to use all samples for training.
need_update = (step_idx + 1) % self.num_microbatches == 0 need_update = (step_idx + 1) % self.num_microbatches == 0