From 0d008110e7a4fe2eb5d90cfd8fed6b9e2f294e00 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 29 May 2025 10:16:55 +0000 Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../coati/distributed/consumer.py | 40 +++++++++++++------ .../coati/distributed/grpo_consumer.py | 10 +++-- 2 files changed, 34 insertions(+), 16 deletions(-) diff --git a/applications/ColossalChat/coati/distributed/consumer.py b/applications/ColossalChat/coati/distributed/consumer.py index 690a10608..22de6911e 100644 --- a/applications/ColossalChat/coati/distributed/consumer.py +++ b/applications/ColossalChat/coati/distributed/consumer.py @@ -117,20 +117,28 @@ class BaseConsumer: # receive data from producers for r in range(self.num_producers): print(f"[T{dist.get_rank()}] Recv data episode {episode} step {step} from {r}") - raw_batch = ray_broadcast_tensor_dict(None, src=0, device=self.device, group_name=f"sync_data_{r}") + raw_batch = ray_broadcast_tensor_dict( + None, src=0, device=self.device, group_name=f"sync_data_{r}" + ) # calculate group reward et al. filtering. As only the filtered group will be used for training (which is incomplete), # we need to calculate the metrics before filtering here for logging # [batch_size, num_generations, ...] -> [batch_size * num_generations, ...] - raw_batch_with_reward = self.calculate_reward({k:v.view(-1, v.size(-1)) if k!='temperature' else v for k, v in raw_batch.items()}) - raw_batch_with_reward = {k: v.view(-1, self.num_generations, v.size(-1)) if k!='temperature' else v for k, v in raw_batch_with_reward.items()} - # [batch_size, num_generations] -> [batch_size] - reward = raw_batch_with_reward["reward"][:,:,0] - format_acc = raw_batch_with_reward["format_acc"][:,:,0] - ans_acc = raw_batch_with_reward["ans_acc"][:,:,0] - response_len = ( - (raw_batch_with_reward["response_idx"][:, :, 1] - raw_batch_with_reward["response_idx"][:, :, 0] + 1) - .type(torch.float32) + raw_batch_with_reward = self.calculate_reward( + {k: v.view(-1, v.size(-1)) if k != "temperature" else v for k, v in raw_batch.items()} ) + raw_batch_with_reward = { + k: v.view(-1, self.num_generations, v.size(-1)) if k != "temperature" else v + for k, v in raw_batch_with_reward.items() + } + # [batch_size, num_generations] -> [batch_size] + reward = raw_batch_with_reward["reward"][:, :, 0] + format_acc = raw_batch_with_reward["format_acc"][:, :, 0] + ans_acc = raw_batch_with_reward["ans_acc"][:, :, 0] + response_len = ( + raw_batch_with_reward["response_idx"][:, :, 1] + - raw_batch_with_reward["response_idx"][:, :, 0] + + 1 + ).type(torch.float32) effective_group_mask = None if self.filter_range is not None and self.grpo_config.get("dynamic_batching", True): # filter the group based on the reward and accuracy @@ -138,11 +146,15 @@ class BaseConsumer: effective_group_mask = torch.logical_and( group_ans_acc_mean > self.filter_range[0], group_ans_acc_mean < self.filter_range[1] ) - raw_batch_with_reward = unbind_batch(raw_batch_with_reward) # List[Dict[str, torch.Tensor]] + raw_batch_with_reward = unbind_batch(raw_batch_with_reward) # List[Dict[str, torch.Tensor]] for group_idx, group_with_reward in enumerate(raw_batch_with_reward): self.buffer.append( [ - group_with_reward if effective_group_mask is None or effective_group_mask[group_idx] else None, + ( + group_with_reward + if effective_group_mask is None or effective_group_mask[group_idx] + else None + ), reward[group_idx], format_acc[group_idx], ans_acc[group_idx], @@ -160,7 +172,9 @@ class BaseConsumer: effective_group_to_raw_group_mapping[len(effective_group_to_raw_group_mapping)] = ( buffer_idx ) - print(f"[T{dist.get_rank()}] Collect Effective Prompt: {len(effective_group_to_raw_group_mapping)}/{self.dp_size * self.minibatch_size}") + print( + f"[T{dist.get_rank()}] Collect Effective Prompt: {len(effective_group_to_raw_group_mapping)}/{self.dp_size * self.minibatch_size}" + ) while len(effective_group_to_raw_group_mapping) >= self.dp_size * self.minibatch_size: # on each dp_rank, we use minibatch_size effective samples to form a batch diff --git a/applications/ColossalChat/coati/distributed/grpo_consumer.py b/applications/ColossalChat/coati/distributed/grpo_consumer.py index 451947b44..89fae6fc7 100644 --- a/applications/ColossalChat/coati/distributed/grpo_consumer.py +++ b/applications/ColossalChat/coati/distributed/grpo_consumer.py @@ -211,10 +211,12 @@ class GRPOConsumer(BaseConsumer): loss_mask, action_mask[:, -1] == False, ) - if self.filter_range is not None and self.grpo_config.get("dynamic_batching", False)==False: + if self.filter_range is not None and self.grpo_config.get("dynamic_batching", False) == False: # filter out samples with reward outside the range # if dynamic batching is enabled, we filter out out of range groups before training - group_ans_acc_mean = ans_acc.view(-1, self.num_generations).mean(dim=1).repeat_interleave(self.num_generations, dim=-1) + group_ans_acc_mean = ( + ans_acc.view(-1, self.num_generations).mean(dim=1).repeat_interleave(self.num_generations, dim=-1) + ) loss_mask = torch.logical_and( loss_mask, torch.logical_and( @@ -454,7 +456,9 @@ class GRPOConsumer(BaseConsumer): raw_batch_ans_acc_mean = torch.cat(self.raw_train_batch_ans_acc, dim=0).mean().cpu().item() raw_batch_response_len = torch.cat(self.raw_train_batch_response_len, dim=0) raw_batch_response_len_mean = raw_batch_response_len.mean().cpu().item() - overlength_samples_ratio = (raw_batch_response_len >= action_mask.size(-1)).to(float).mean().cpu().item() # not an exact figure, but a close estimate + overlength_samples_ratio = ( + (raw_batch_response_len >= action_mask.size(-1)).to(float).mean().cpu().item() + ) # not an exact figure, but a close estimate self.raw_train_batch_reward = [] self.raw_train_batch_format_acc = [] self.raw_train_batch_ans_acc = []