205 lines
9.6 KiB
Python
205 lines
9.6 KiB
Python
# Copyright 2024 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Implement a multiprocess PPOCritic
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"""
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import itertools
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from typing import Iterable
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import torch
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import torch.distributed
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from torch import nn, optim
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from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
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from verl import DataProto
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from verl.trainer.ppo import core_algos
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from verl.workers.critic import BasePPOCritic
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from verl.utils.py_functional import append_to_dict
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from verl.utils.torch_functional import masked_mean
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from verl.utils.ulysses import ulysses_pad_and_slice_inputs, gather_outpus_and_unpad
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from verl.utils.seqlen_balancing import rearrange_micro_batches, get_reverse_idx
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from flash_attn.bert_padding import pad_input, unpad_input, rearrange, index_first_axis
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__all__ = ['DataParallelPPOCritic']
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class DataParallelPPOCritic(BasePPOCritic):
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def __init__(self, config, critic_module: nn.Module, critic_optimizer: optim.Optimizer):
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super().__init__(config=config)
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self.critic_module = critic_module
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self.critic_optimizer = critic_optimizer
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self.use_remove_padding = self.config.model.get('use_remove_padding', False)
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print(f'Critic use_remove_padding={self.use_remove_padding}')
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assert self.config.ppo_mini_batch_size % self.config.ppo_micro_batch_size == 0
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self.gradient_accumulation = self.config.ppo_mini_batch_size // self.config.ppo_micro_batch_size
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self.ulysses_sequence_parallel_size = self.config.get('ulysses_sequence_parallel_size', 1)
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def _forward_micro_batch(self, micro_batch):
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response_length = micro_batch['responses'].size(-1)
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with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
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input_ids = micro_batch['input_ids']
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batch, seqlen = input_ids.shape
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attention_mask = micro_batch['attention_mask']
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position_ids = micro_batch['position_ids']
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if self.use_remove_padding:
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input_ids_rmpad, indices, *_ = unpad_input(input_ids.unsqueeze(-1),
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attention_mask) # input_ids_rmpad (total_nnz, ...)
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input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz)
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# unpad the position_ids to align the rotary
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position_ids_rmpad = index_first_axis(rearrange(position_ids.unsqueeze(-1), "b s ... -> (b s) ..."),
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indices).transpose(0, 1)
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# pad and slice the inputs if sp > 1
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if self.ulysses_sequence_parallel_size > 1:
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input_ids_rmpad, position_ids_rmpad, pad_size = ulysses_pad_and_slice_inputs(input_ids_rmpad, \
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position_ids_rmpad, \
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sp_size=self.ulysses_sequence_parallel_size)
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# only pass input_ids and position_ids to enable flash_attn_varlen
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output = self.critic_module(input_ids=input_ids_rmpad,
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attention_mask=None,
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position_ids=position_ids_rmpad,
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use_cache=False) # prevent model thinks we are generating
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values_rmpad = output.logits
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values_rmpad = values_rmpad.squeeze(0) # (total_nnz)
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# gather output if sp > 1
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if self.ulysses_sequence_parallel_size > 1:
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values_rmpad = gather_outpus_and_unpad(values_rmpad,
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gather_dim=0,
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unpad_dim=0,
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padding_size=pad_size)
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# pad it back
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values = pad_input(values_rmpad, indices=indices, batch=batch, seqlen=seqlen).squeeze(-1)
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values = values[:, -response_length - 1:-1]
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else:
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output = self.critic_module(input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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use_cache=False) # prevent model thinks we are generating
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values = output.logits
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values = values[:, -response_length - 1:-1].squeeze(-1)
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return values
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def _optimizer_step(self):
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assert self.config.grad_clip is not None
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if isinstance(self.critic_module, FSDP):
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grad_norm = self.critic_module.clip_grad_norm_(self.config.grad_clip)
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else:
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grad_norm = torch.nn.utils.clip_grad_norm_(self.critic_module.parameters(), max_norm=self.config.grad_clip)
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self.critic_optimizer.step()
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return grad_norm
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def compute_values(self, data: DataProto) -> torch.Tensor:
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self.critic_module.eval()
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micro_batch_size = data.meta_info['micro_batch_size']
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select_keys = ['responses', 'input_ids', 'attention_mask', 'position_ids']
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batch = data.select(batch_keys=select_keys).batch
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use_dynamic_bsz = data.meta_info['use_dynamic_bsz']
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if use_dynamic_bsz:
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# split using dynamic bsz
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max_token_len = data.meta_info['max_token_len'] * self.ulysses_sequence_parallel_size
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micro_batches, indices = rearrange_micro_batches(batch=batch, max_token_len=max_token_len)
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else:
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micro_batches = batch.split(micro_batch_size)
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values_lst = []
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for micro_batch in micro_batches:
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with torch.no_grad():
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values = self._forward_micro_batch(micro_batch)
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values_lst.append(values)
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values = torch.concat(values_lst, dim=0)
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responses = data.batch['responses']
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attention_mask = data.batch['attention_mask']
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response_length = responses.size(1)
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values = values * attention_mask[:, -response_length - 1:-1]
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if use_dynamic_bsz:
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indices = list(itertools.chain.from_iterable(indices))
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assert len(indices) == values.size(0), f"{len(indices)} vs. {values.size()}"
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revert_indices = torch.tensor(get_reverse_idx(indices), dtype=torch.long)
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values = values[revert_indices]
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return values
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def update_critic(self, data: DataProto):
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# make sure we are in training mode
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self.critic_module.train()
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metrics = {}
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select_keys = ['input_ids', 'responses', 'attention_mask', 'position_ids', 'values', 'returns']
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batch = data.select(batch_keys=select_keys).batch
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# Split to make minibatch iterator for updating the actor
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# See PPO paper for details. https://arxiv.org/abs/1707.06347
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dataloader = batch.split(self.config.ppo_mini_batch_size)
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for batch_idx, data in enumerate(dataloader):
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# split batch into micro_batches
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mini_batch = data
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if self.config.use_dynamic_bsz:
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max_token_len = self.config.ppo_max_token_len_per_gpu * self.ulysses_sequence_parallel_size
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micro_batches, _ = rearrange_micro_batches(batch=mini_batch, max_token_len=max_token_len)
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else:
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micro_batches = mini_batch.split(self.config.ppo_micro_batch_size)
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self.critic_optimizer.zero_grad()
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for data in micro_batches:
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data = data.cuda() # critic device is cpu when using offload
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input_ids = data['input_ids']
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responses = data['responses']
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attention_mask = data['attention_mask']
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position_ids = data['position_ids']
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values = data['values']
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returns = data['returns']
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response_length = responses.size(1)
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eos_mask = attention_mask[:, -response_length - 1:-1]
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vpreds = self._forward_micro_batch(data)
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# assert not torch.any(torch.isnan(vpreds)).item()
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vf_loss, vf_clipfrac = core_algos.compute_value_loss(vpreds=vpreds,
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values=values,
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returns=returns,
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eos_mask=eos_mask,
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cliprange_value=self.config.cliprange_value)
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loss = vf_loss / self.gradient_accumulation
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loss.backward()
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data = {
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'critic/vf_loss': vf_loss.detach().item(),
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'critic/vf_clipfrac': vf_clipfrac.detach().item(),
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'critic/vpred_mean': masked_mean(vpreds, eos_mask).detach().item(),
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}
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append_to_dict(metrics, data)
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grad_norm = self._optimizer_step()
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data = {'critic/grad_norm': grad_norm.detach().item()}
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append_to_dict(metrics, data)
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self.critic_optimizer.zero_grad()
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return metrics
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