/
ppo.py
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/
ppo.py
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import math
from pathlib import Path
import copy
from tqdm import tqdm
from functools import partial
from collections import deque, namedtuple
from random import randrange
from beartype import beartype
from beartype.typing import List, Optional, Callable, Deque
import torch
from torch import nn
import torch.nn.functional as F
from torch.optim import Adam
from torch.utils.data import Dataset, DataLoader
from torch.nn.utils.rnn import pad_sequence
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
from palm_rlhf_pytorch.palm import PaLM
from palm_rlhf_pytorch.reward import RewardModel
from palm_rlhf_pytorch.optimizer import get_optimizer
from palm_rlhf_pytorch.utils import masked_mean, eval_decorator
from accelerate import Accelerator
# actor critic - PaLM with lora
PPOActionCriticReturn = namedtuple('PPOActionCriticReturn', [
'actions',
'sequence',
'mask',
'prompt_mask',
'action_logits',
'values'
])
@beartype
class ActorCritic(nn.Module):
def __init__(
self,
palm: PaLM,
critic_palm: Optional[PaLM] = None,
pooled_values = False,
actor_lora = True,
critic_lora = True,
actor_lora_r = 8,
critic_lora_r = 8,
actor_lora_scope = 'actor',
critic_lora_scope = 'critic',
actor_dropout = 0.,
critic_dropout = 0.
):
super().__init__()
self.actor_palm = palm
self.critic_palm = critic_palm
if not exists(self.critic_palm):
self.critic_palm = copy.deepcopy(palm)
self.actor_palm.set_dropout(actor_dropout)
self.critic_palm.set_dropout(critic_dropout)
self.actor_lora = actor_lora
self.critic_lora = critic_lora
self.actor_lora_scope = actor_lora_scope if actor_lora else None
self.critic_lora_scope = critic_lora_scope if critic_lora else None
if self.actor_lora:
self.actor_palm.add_finetune_params(actor_lora_scope, lora_r = actor_lora_r)
if self.critic_lora:
self.critic_palm.add_finetune_params(critic_lora_scope, lora_r = critic_lora_r)
self.pooled_values = pooled_values
self.value_head = nn.Sequential(
nn.Linear(palm.dim, 1),
Rearrange('... 1 -> ...')
)
nn.init.zeros_(self.value_head[0].bias)
nn.init.orthogonal_(self.value_head[0].weight, gain = math.sqrt(2))
def actor_parameters(self):
if not self.actor_lora:
return self.actor_palm.parameters()
return [
*self.actor_palm.finetune_parameters(self.actor_lora_scope)
]
def critic_parameters(self):
if not self.actor_lora:
return [*self.critic_palm.parameters(), *self.value_head.parameters()]
return [
*self.critic_palm.finetune_parameters(self.critic_lora_scope),
*self.value_head.parameters()
]
@torch.no_grad()
@eval_decorator
def generate(
self,
state,
max_seq_len,
eos_token = None,
return_values = False,
**kwargs
):
actions = self.actor_palm.generate(
max_seq_len,
prompt = state,
eos_token = eos_token,
finetune_scope = self.actor_lora_scope,
use_tqdm = True,
**kwargs
)
sequence = torch.cat((state, actions), dim = -1)
action_len = actions.shape[-1]
state_len = state.shape[-1]
prompt_mask = torch.arange(sequence.shape[-1], device = state.device) < state_len
prompt_mask = repeat(prompt_mask, 'n -> b n', b = sequence.shape[0])
action_mask = ~prompt_mask
mask = None
if exists(eos_token):
mask = ((sequence == eos_token).cumsum(dim = -1) == 0)
mask = F.pad(mask, (1, -1), value = True) # include eos token
action_mask &= mask
action_logits, value = self.forward(
sequence,
mask = action_mask,
return_values = return_values
)
return PPOActionCriticReturn(
actions,
sequence,
mask,
prompt_mask,
action_logits,
value
)
def forward(
self,
x,
mask = None,
return_values = True
):
action_logits = self.actor_palm(
x,
finetune_scope = self.actor_lora_scope
)
if not return_values:
return action_logits, None
critic_embeds = self.critic_palm(
x,
return_only_embedding = True,
finetune_scope = self.critic_lora_scope
)
if self.pooled_values:
critic_embeds = shift(critic_embeds, shift = 1, dim = -2)
critic_embeds = masked_mean(critic_embeds, mask, dim = 1)
values = self.value_head(critic_embeds)
return action_logits, values
# data
Memory = namedtuple('Memory', [
'sequence',
'prompt_mask',
'mask',
'action_prob',
'action_log_prob',
'reward',
'value'
])
@beartype
class ExperienceDataset(Dataset):
def __init__(
self,
data: List[torch.Tensor],
device = None
):
super().__init__()
self.data = data
self.device = device
def __len__(self):
return self.data[0].shape[0]
def __getitem__(self, ind):
return tuple(map(lambda t: t[ind].to(self.device), self.data))
def create_dataloader(data, batch_size, shuffle = True, device = None, **kwargs):
ds = ExperienceDataset(data, device = device)
return DataLoader(ds, batch_size = batch_size, shuffle = shuffle, **kwargs)
# helper functions
def exists(val):
return val is not None
def default(val, d):
if exists(val):
return val
return d() if callable(d) else d
def masked_normalize(t, eps = 1e-5, mask = None, dim = None):
dim = default(dim, tuple(range(t.ndim)))
kwargs = dict(dim = dim, keepdim = True)
mean = masked_mean(t, mask = mask, **kwargs)
mean_centered = t - mean
var = masked_mean(mean_centered ** 2, mask = mask, **kwargs)
return mean_centered * var.clamp(min = eps).rsqrt()
def pad_sequence_fixed(sequences, *args, **kwargs):
first_el = sequences[0]
has_no_dimension = first_el.ndim == 0
# if no dimensions, add a single dimension
if has_no_dimension:
sequences = tuple(map(lambda t: t[None], sequences))
out = pad_sequence(sequences, *args, **kwargs)
if has_no_dimension:
out = rearrange(out, '... 1 -> ...')
return out
def log(t, eps = 1e-20):
return torch.log(t.clamp(min = eps))
def log_prob(prob, indices):
assert prob.shape[:2] == indices.shape, f'preceding shapes of prob {prob.shape[:2]} and indices {indices.shape} must match'
return log(prob.gather(-1, indices[..., None])).squeeze(-1)
def shift(t, value = 0, shift = 1, dim = -1):
zeros = (0, 0) * (-dim - 1)
return F.pad(t, (*zeros, shift, -shift), value = value)
def masked_entropy(prob, dim = -1, mask = None):
entropies = (prob * log(prob)).sum(dim = -1)
return masked_mean(entropies, mask = mask).mean()
def masked_kl_div(prob1, prob2, mask = None):
"""
need to account for variable sequence lengths, therefore not using the built-in functional version
"""
kl_divs = (prob1 * (log(prob1) - log(prob2))).sum(dim = -1)
if not exists(mask):
return kl_divs.mean()
return masked_mean(kl_divs, mask).mean()
def clipped_value_loss(values, rewards, old_values, clip):
value_clipped = old_values + (values - old_values).clamp(-clip, clip)
value_loss_1 = (value_clipped.flatten() - rewards) ** 2
value_loss_2 = (values.flatten() - rewards) ** 2
return torch.mean(torch.max(value_loss_1, value_loss_2))
# rlhf trainer
@beartype
class RLHFTrainer(nn.Module):
def __init__(
self,
*,
prompts: Optional[List[str]] = None,
prompts_path: Optional[str] = None,
prompt_token_ids: Optional[torch.Tensor] = None,
tokenizer: Callable = None,
palm: PaLM,
reward_model: RewardModel,
critic_palm: Optional[PaLM] = None,
actor_critic: Optional[ActorCritic] = None,
actor_lr = 1e-4,
critic_lr = 1e-4,
actor_wd = 0.,
critic_wd = 0.,
actor_adam_eps = 1e-7,
critic_adam_eps = 1e-7,
actor_lora = True,
critic_lora = True,
actor_lora_r = 8,
critic_lora_r = 8,
critic_pooled_values = True,
actor_dropout = 0.,
critic_dropout = 0.,
betas = (0.9, 0.999),
max_norm = None,
eps_clip = 0.2,
value_clip = 0.4,
beta_s = .01,
pad_value = 0.,
minibatch_size = 16,
epochs = 1,
kl_div_loss_weight = 0.1, # between old action probs and new action probs - not sure what the right value is
accelerate_kwargs: dict = {},
use_lion = False
):
super().__init__()
self.accelerate = Accelerator(**accelerate_kwargs)
# take care of prompts -> token ids
assert (exists(prompts) + exists(prompts_path) + exists(prompt_token_ids)) == 1
if exists(prompts_path):
path = Path(prompts_path)
prompts = path.read_text().split('\n')
if exists(prompts):
assert len(prompts) > 0, 'no prompts'
assert exists(tokenizer), 'tokenizer must be passed in if raw text prompts are given'
prompt_token_ids = tokenizer(prompts)
self.pad_value = pad_value # token pad value
self.num_prompts = prompt_token_ids.shape[0]
self.register_buffer('prompt_token_ids', prompt_token_ids)
# models
self.palm = palm
if not exists(actor_critic):
actor_critic = ActorCritic(
palm = palm,
critic_palm = critic_palm,
actor_lora = actor_lora,
critic_lora = critic_lora,
actor_lora_r = actor_lora_r,
critic_lora_r = critic_lora_r,
pooled_values = critic_pooled_values,
actor_dropout = actor_dropout,
critic_dropout = critic_dropout
).to(palm.device)
self.actor_critic = actor_critic
self.reward_model = reward_model.eval()
# train hyperparameters
self.epochs = epochs
self.minibatch_size = minibatch_size
self.max_norm = max_norm
self.kl_div_loss_weight = kl_div_loss_weight
# optimizers
self.actor_optim = get_optimizer(actor_critic.actor_parameters(), lr = actor_lr, wd = actor_wd, betas = betas, eps = actor_adam_eps, use_lion = use_lion)
self.critic_optim = get_optimizer(actor_critic.critic_parameters(), lr = critic_lr, wd = critic_wd, betas = betas, eps = critic_adam_eps, use_lion = use_lion)
# ppo hyperparams
self.eps_clip = eps_clip
self.value_clip = value_clip
self.beta_s = beta_s
# prepare with accelerator
(
self.actor_critic,
self.reward_model,
self.actor_optim,
self.critic_optim
) = self.accelerate.prepare(
self.actor_critic,
self.reward_model,
self.actor_optim,
self.critic_optim
)
def print(self, msg):
return self.accelerate.print(msg)
def save(self, filepath = './checkpoint.pt'):
torch.save(self.actor_critic.state_dict(), filepath)
def load(self, filepath = './checkpoint.pt'):
state_dict = torch.load(filepath)
self.actor_critic.load_state_dict(state_dict)
@property
def device(self):
return self.accelerate.device
@torch.no_grad()
def generate(
self,
max_seq_len,
*args,
prompt,
num_samples = 4, # sample 4 per prompt and select the one with highest reward
**kwargs
):
assert prompt.ndim == 1, 'only one prompt allowed at a time for now'
prompt = repeat(prompt, 'n -> b n', b = num_samples)
actor_critic = self.accelerate.unwrap_model(self.actor_critic)
reward_model = self.accelerate.unwrap_model(self.reward_model)
actor_critic.eval()
(
actions,
sequences,
mask,
prompt_mask,
action_logits,
_
) = actor_critic.generate(
prompt,
*args,
max_seq_len = max_seq_len,
return_values = False,
**kwargs
)
rewards = reward_model(
sequences,
prompt_mask = prompt_mask,
mask = mask,
sample = True
)
best_sequence_index = rewards.topk(1, dim = -1).indices
best_sequence = sequences[best_sequence_index]
best_sequence = rearrange(best_sequence, '1 ... -> ...')
return best_sequence
def learn(
self,
memories: Deque[Memory]
):
# stack all data stored in the memories
all_memories_stacked_and_padded = list(map(partial(pad_sequence_fixed, batch_first = True), zip(*memories)))
# prepare dataloader for policy phase training
dl = create_dataloader(all_memories_stacked_and_padded, self.minibatch_size, device = self.device)
self.actor_critic.train()
# PPO training
for _ in range(self.epochs):
for (
sequences,
prompt_masks,
masks,
old_action_probs,
old_log_probs,
rewards,
old_values
) in dl:
action_masks = ~prompt_masks & masks
action_logits, values = self.actor_critic(
sequences,
mask = action_masks
)
action_logits = shift(action_logits, shift = 1, dim = -2) # need to shift along sequence dimension by 1, since actions start from the last prompt (state) token
action_len = old_log_probs.shape[-1]
action_probs = action_logits.softmax(dim = -1)
action_log_probs = log_prob(action_probs, sequences)
action_log_probs = action_log_probs[:, -action_len:]
# calculate entropies, taking into account which part of the sequence is actually an action
entropies = masked_entropy(action_probs, mask = action_masks)
# calculate kl div between old action probs and new ones, taking into account which part of the sequence is action or not
kl_div_loss = 0.
if self.kl_div_loss_weight > 0:
kl_div_loss = masked_kl_div(action_probs, old_action_probs, mask = action_masks) * self.kl_div_loss_weight
# handle non-pooled values
normalize_kwargs = dict()
if old_values.ndim == 2:
old_values, values = map(lambda t: shift(t, shift = 1, dim = -2), (old_values, values))
old_values = old_values[:, -action_len:]
values = values[:, -action_len:]
rewards = rearrange(rewards, 'b -> b 1')
normalize_kwargs = dict(dim = -1, mask = action_masks[:, -action_len:])
if values.ndim < rewards.ndim:
values = rearrange(values, '... -> ... 1')
# calculate clipped surrogate objective, classic PPO loss
ratios = (action_log_probs - old_log_probs).exp()
advantages = masked_normalize(rewards - old_values, **normalize_kwargs)
if advantages.ndim == 1:
advantages = rearrange(advantages, 'b -> b 1')
surr1 = ratios * advantages
surr2 = ratios.clamp(1 - self.eps_clip, 1 + self.eps_clip) * advantages
policy_loss = - torch.min(surr1, surr2) - self.beta_s * entropies
# combine losses
loss = policy_loss.mean() + kl_div_loss
# update actor
self.accelerate.backward(loss)
self.print(f'policy_loss: {loss.item():.3f}')
if exists(self.max_norm):
self.accelerator.clip_grad_norm_(self.actor_critic.actor_parameters(), self.max_norm)
self.actor_optim.step()
self.actor_optim.zero_grad()
# calculate value loss and update value network separate from policy network
value_loss = clipped_value_loss(values, rewards, old_values, self.value_clip)
value_loss = value_loss.mean()
self.print(f'critic_loss: {value_loss.item():.3f}')
self.accelerate.backward(value_loss)
if exists(self.max_norm):
self.accelerator.clip_grad_norm_(self.actor_critic.critic_parameters(), self.max_norm)
self.critic_optim.step()
self.critic_optim.zero_grad()
def train(
self,
num_episodes = 50000,
max_timesteps = 500,
update_timesteps = 5000,
max_batch_size = 16,
max_seq_len = 2048,
eos_token = None,
temperature = 1.
):
device = self.device
time = 0
memories = deque([])
for eps in tqdm(range(num_episodes), desc = 'episodes'):
for timestep in range(max_timesteps):
time += 1
# select a bunch of random states (prompts)
# and get the action (sampled sequence from palm as well as the action probs)
# also calculate the reward using reward model and store
rand_prompt_index = randrange(0, self.num_prompts)
state = self.prompt_token_ids[rand_prompt_index]
# remove padding from state
state_mask = state != self.pad_value
state = state[state_mask]
# get predicted sequence
(
actions,
sequence,
mask,
prompt_mask,
action_logits,
value
) = self.actor_critic.generate(
rearrange(state, 'n -> 1 n'),
max_seq_len = max_seq_len,
eos_token = eos_token,
temperature = temperature,
return_values = True
)
action_logits = shift(action_logits, shift = 1, dim = -2) # need to shift along sequence dimension by 1, since actions start from the last prompt (state) token
action_prob = action_logits.softmax(dim = -1)
action_len = actions.shape[-1]
action_log_prob = log_prob(action_prob, sequence)
action_log_prob = action_log_prob[:, -action_len:]
actions = rearrange(actions, '1 ... -> ...')
# get reward as given by supervised trained reward model
sequence = torch.cat((state, actions), dim = 0)
prompt_length = len(state)
prompt_mask = torch.arange(sequence.shape[-1], device = device) < prompt_length
sequence = rearrange(sequence, 'n -> 1 n')
prompt_mask = rearrange(prompt_mask, 'n -> 1 n')
mask = default(mask, lambda: torch.ones(sequence.shape, dtype = torch.bool, device = device))
reward = self.reward_model(
sequence,
prompt_mask = prompt_mask,
mask = mask,
sample = True
)
detach_to_cpu_ = lambda t: rearrange(t.detach().cpu(), '1 ... -> ...')
# store memory for learning
memories.append(Memory(*map(detach_to_cpu_, (
sequence,
prompt_mask,
mask,
action_prob,
action_log_prob,
reward,
value
))))
# learn from the stored memories
if time % update_timesteps == 0:
self.learn(memories)
memories.clear()
print('rlhf training complete')