291 lines
14 KiB
Python
291 lines
14 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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Single Process Actor
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"""
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import itertools
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from typing import Iterable, Tuple
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import torch
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from torch import nn
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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.actor import BasePPOActor
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from verl.utils.py_functional import append_to_dict
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from verl.utils.torch_functional import logprobs_from_logits, 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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import verl.utils.torch_functional as verl_F
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from flash_attn.bert_padding import pad_input, unpad_input, rearrange, index_first_axis
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__all__ = ['DataParallelPPOActor']
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class DataParallelPPOActor(BasePPOActor):
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def __init__(
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self,
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config,
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actor_module: nn.Module,
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actor_optimizer: torch.optim.Optimizer = None,
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):
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"""When optimizer is None, it is Reference Policy"""
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super().__init__(config)
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self.actor_module = actor_module
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self.actor_optimizer = actor_optimizer
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self.use_remove_padding = self.config.get('use_remove_padding', False)
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print(f'Actor use_remove_padding={self.use_remove_padding}')
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self.ulysses_sequence_parallel_size = self.config.ulysses_sequence_parallel_size
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self.use_ulysses_sp = self.ulysses_sequence_parallel_size > 1
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self.compute_entropy_from_logits = torch.compile(verl_F.entropy_from_logits, dynamic=True)
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def _forward_micro_batch(self, micro_batch, temperature) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Returns:
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entropy: # (bs, response_len)
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log_probs: # (bs, response_len)
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"""
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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_size, 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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# for compute the log_prob
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input_ids_rmpad_rolled = torch.roll(input_ids_rmpad, shifts=-1, dims=1) # (1, total_nnz)
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# pad and slice the inputs if sp > 1
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if self.use_ulysses_sp:
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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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input_ids_rmpad_rolled, _, _ = ulysses_pad_and_slice_inputs(input_ids_rmpad_rolled, None,
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self.ulysses_sequence_parallel_size)
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input_ids_rmpad_rolled = input_ids_rmpad_rolled.squeeze(0) # ((total_nnz / sp) + pad)
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# only pass input_ids and position_ids to enable flash_attn_varlen
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output = self.actor_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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logits_rmpad = output.logits.squeeze(0) # (total_nnz, vocab_size)
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logits_rmpad.div_(temperature)
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# compute entropy
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entropy_rmpad = self.compute_entropy_from_logits(logits_rmpad) # ((total_nnz / sp) + pad)
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# if use_sp: ((total_nnz / sp) + pad) ; if not use_sp: (batch, seqlen)
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log_probs = logprobs_from_logits(logits=logits_rmpad, labels=input_ids_rmpad_rolled)
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# gather log_prob if sp > 1
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if self.use_ulysses_sp:
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# gather and unpad for the ulysses sp
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log_probs = gather_outpus_and_unpad(log_probs, gather_dim=0, unpad_dim=0, padding_size=pad_size)
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entropy_rmpad = gather_outpus_and_unpad(entropy_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 back to (bsz, seqlen)
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full_entropy = pad_input(hidden_states=entropy_rmpad.unsqueeze(-1),
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indices=indices,
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batch=batch_size,
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seqlen=seqlen)
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full_log_probs = pad_input(hidden_states=log_probs.unsqueeze(-1),
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indices=indices,
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batch=batch_size,
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seqlen=seqlen)
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# only return response part:
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entropy = full_entropy.squeeze(-1)[:, -response_length - 1:-1] # (bsz, response_length)
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log_probs = full_log_probs.squeeze(-1)[:, -response_length - 1:-1] # (bsz, response_length)
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else: # not using rmpad and no ulysses sp
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output = self.actor_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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logits = output.logits
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logits.div_(temperature)
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logits = logits[:, -response_length - 1:-1] # (bsz, response_length)
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log_probs = logprobs_from_logits(logits, micro_batch['responses'])
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entropy = verl_F.entropy_from_logits(logits) # (bsz, response_length)
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return entropy, log_probs
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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.actor_module, FSDP):
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grad_norm = self.actor_module.clip_grad_norm_(max_norm=self.config.grad_clip)
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else:
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grad_norm = torch.nn.utils.clip_grad_norm_(self.actor_module.parameters(), max_norm=self.config.grad_clip)
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self.actor_optimizer.step()
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return grad_norm
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def compute_log_prob(self, data: DataProto) -> torch.Tensor:
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"""Compute the log probability of the responses given input_ids, attention_mask and position_ids
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Args:
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data (DataProto): a DataProto containing keys
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``input_ids``: tensor of shape [batch_size, sequence_length]. torch.int64. Note that input_ids is the
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concatenation of prompt and response. Note that ``sequence_length = prompt_length + response_length``.
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``attention_mask``: tensor of shape [batch_size, sequence_length]. torch.int64.
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``position_ids``: tensor of shape [batch_size, sequence_length]. torch.int64.
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``responses``: tensor of shape [batch_size, response_length]. torch.int64.
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Returns:
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torch.Tensor: the log_prob tensor
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"""
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# set to eval
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self.actor_module.eval()
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micro_batch_size = data.meta_info['micro_batch_size']
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temperature = data.meta_info['temperature'] # temperature must be in the data.meta_info to avoid slient error
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use_dynamic_bsz = data.meta_info['use_dynamic_bsz']
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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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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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log_probs_lst = []
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for micro_batch in micro_batches:
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with torch.no_grad():
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_, log_probs = self._forward_micro_batch(micro_batch, temperature=temperature)
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log_probs_lst.append(log_probs)
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log_probs = torch.concat(log_probs_lst, dim=0)
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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) == log_probs.size(0), f"{len(indices)} vs. {log_probs.size()}"
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revert_indices = torch.tensor(get_reverse_idx(indices), dtype=torch.long)
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log_probs = log_probs[revert_indices]
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return log_probs
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def update_policy(self, data: DataProto):
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# make sure we are in training mode
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self.actor_module.train()
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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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temperature = data.meta_info['temperature'] # temperature must be in the data.meta_info to avoid slient error
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select_keys = ['responses', 'input_ids', 'attention_mask', 'position_ids', 'old_log_probs', 'advantages']
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if self.config.state_masking:
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select_keys.append('loss_mask')
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if self.config.use_kl_loss:
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select_keys.append('ref_log_prob')
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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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metrics = {}
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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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# split batch into micro_batches
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micro_batches = mini_batch.split(self.config.ppo_micro_batch_size)
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self.actor_optimizer.zero_grad()
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for data in micro_batches:
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data = data.cuda() # actor device is cpu when using offload
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responses = data['responses']
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response_length = responses.size(1)
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attention_mask = data['attention_mask']
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response_mask = attention_mask[:, -response_length:]
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if self.config.state_masking:
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response_mask = data['loss_mask']
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old_log_prob = data['old_log_probs']
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advantages = data['advantages']
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clip_ratio = self.config.clip_ratio
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entropy_coeff = self.config.entropy_coeff
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# all return: (bsz, response_length)
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entropy, log_prob = self._forward_micro_batch(micro_batch=data, temperature=temperature)
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pg_loss, pg_clipfrac, ppo_kl = core_algos.compute_policy_loss(old_log_prob=old_log_prob,
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log_prob=log_prob,
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advantages=advantages,
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eos_mask=response_mask,
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cliprange=clip_ratio)
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# compute entropy loss from entropy
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entropy_loss = verl_F.masked_mean(entropy, response_mask)
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# compute policy loss
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policy_loss = pg_loss - entropy_loss * entropy_coeff
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if self.config.use_kl_loss:
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ref_log_prob = data['ref_log_prob']
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# compute kl loss
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kld = core_algos.kl_penalty(logprob=log_prob,
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ref_logprob=ref_log_prob,
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kl_penalty=self.config.kl_loss_type)
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kl_loss = masked_mean(kld, response_mask)
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policy_loss = policy_loss + kl_loss * self.config.kl_loss_coef
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metrics['actor/kl_loss'] = kl_loss.detach().item()
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metrics['actor/kl_coef'] = self.config.kl_loss_coef
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loss = policy_loss / self.gradient_accumulation
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loss.backward()
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data = {
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'actor/entropy_loss': entropy_loss.detach().item(),
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'actor/pg_loss': pg_loss.detach().item(),
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'actor/pg_clipfrac': pg_clipfrac.detach().item(),
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'actor/ppo_kl': ppo_kl.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 = {'actor/grad_norm': grad_norm.detach().item()}
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append_to_dict(metrics, data)
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self.actor_optimizer.zero_grad()
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return metrics
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