Spaces:
Runtime error
Runtime error
File size: 8,242 Bytes
fa6856c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 |
import json
import math
import os
import sys
from itertools import islice
import numpy as np
import torch
import tritonclient.grpc as client_util
from datasets import load_dataset
from huggingface_hub import snapshot_download
from torch import nn
from transformers import AutoModelForCausalLM, AutoTokenizer
from tritonclient.utils import np_to_triton_dtype
import trlx
from trlx.data.default_configs import (
ModelConfig,
OptimizerConfig,
PPOConfig,
SchedulerConfig,
TokenizerConfig,
TrainConfig,
TRLConfig,
)
default_config = TRLConfig(
train=TrainConfig(
seq_length=1024,
epochs=10000,
total_steps=10000,
batch_size=4,
checkpoint_interval=10000,
eval_interval=500,
pipeline="PromptPipeline",
trainer="AcceleratePPOTrainer",
checkpoint_dir="checkpoints/ppo_hh",
),
model=ModelConfig(model_path="EleutherAI/gpt-j-6B", num_layers_unfrozen=2),
tokenizer=TokenizerConfig(tokenizer_path="EleutherAI/gpt-j-6B", truncation_side="left"),
optimizer=OptimizerConfig(name="adamw", kwargs=dict(lr=8e-6, betas=(0.9, 0.95), eps=1.0e-8, weight_decay=1.0e-6)),
scheduler=SchedulerConfig(name="cosine_annealing", kwargs=dict(T_max=10000, eta_min=8e-6)),
method=PPOConfig(
name="PPOConfig",
num_rollouts=64,
chunk_size=16,
ppo_epochs=4,
init_kl_coef=0.05,
target=6,
horizon=10000,
gamma=1,
lam=0.95,
cliprange=0.2,
cliprange_value=0.2,
vf_coef=1,
scale_reward="running",
ref_mean=None,
ref_std=None,
cliprange_reward=10,
gen_kwargs=dict(
max_new_tokens=128,
top_k=0,
top_p=1.0,
do_sample=True,
),
),
)
config_name = os.environ.get("CONFIG_NAME")
if config_name == "125M":
default_config.train.batch_size = 32
default_config.train.total_steps = 1500
default_config.train.checkpoint_dir = "checkpoints/ppo_hh_125M"
default_config.model.model_path = "Dahoas/pythia-125M-static-sft"
default_config.tokenizer.tokenizer_path = "EleutherAI/gpt-neox-20b"
default_config.method.num_rollouts = 128
elif config_name == "1B":
default_config.train.batch_size = 8
default_config.train.total_steps = 2500
default_config.optimizer.kwargs["lr"] = 6e-6
default_config.scheduler.kwargs["eta_min"] = 6e-6
default_config.train.checkpoint_dir = "checkpoints/ppo_hh_1B"
default_config.model.model_path = "Dahoas/pythia-1B-static-sft"
default_config.tokenizer.tokenizer_path = "EleutherAI/gpt-neox-20b"
default_config.method.chunk_size = 16
elif config_name == "6B":
default_config.train.batch_size = 4
default_config.train.seq_length = 512
default_config.train.total_steps = 6000
default_config.train.checkpoint_dir = "checkpoints/ppo_hh_6B"
default_config.model.model_path = "Dahoas/pythia-6B-static-sft"
default_config.tokenizer.tokenizer_path = "EleutherAI/gpt-neox-20b"
default_config.method.chunk_size = 16
elif config_name == "20B":
default_config.train.seq_length = 512
default_config.train.batch_size = 1
default_config.train.total_steps = 8000
default_config.optimizer.kwargs["lr"] = 1e-6
default_config.scheduler.kwargs["eta_min"] = 1e-6
default_config.train.checkpoint_dir = "checkpoints/ppo_hh_20B"
default_config.model.model_path = "EleutherAI/gpt-neox-20b"
default_config.tokenizer.tokenizer_path = "EleutherAI/gpt-neox-20b"
default_config.method.num_rollouts = 16
default_config.method.chunk_size = 4
default_config.method.ppo_epochs = 2
def prepare_tensor(name: str, input):
t = client_util.InferInput(name, input.shape, np_to_triton_dtype(input.dtype))
t.set_data_from_numpy(input)
return t
def create_reward_fn(): # noqa: C901
reward_tokenizer = AutoTokenizer.from_pretrained("gpt2")
reward_tokenizer.pad_token = reward_tokenizer.eos_token
reward_tokenizer.truncation_side = "left"
triton_host = os.environ.get("TRITON_HOST")
if triton_host:
triton_url, triton_model = triton_host.split("/")
client = client_util.InferenceServerClient(url=triton_url, verbose=False)
def reward_fn(samples, prompts, outputs):
samples = [s + reward_tokenizer.eos_token for s in samples]
input = reward_tokenizer(samples, padding=True, max_length=1024)
mbs = 24
out = []
for i in range(math.ceil(len(samples) / mbs)):
batch_ixs = slice(i * mbs, (i + 1) * mbs)
input_ids = np.array(input.input_ids[batch_ixs], dtype=np.int32)
result = client.infer(triton_model, [prepare_tensor("input_ids", input_ids)])
rewards = result.as_numpy("rewards")
out.extend(rewards)
return out
elif os.environ.get("RANK", "0") == "0":
class RewardModel(nn.Module):
def __init__(self, checkpoint_path, eos_token_id):
super().__init__()
model = AutoModelForCausalLM.from_pretrained(checkpoint_path)
self.transformer = model.transformer
self.v_head = nn.Linear(model.config.n_embd, 1, bias=False)
self.eos_token_id = eos_token_id
def forward(self, input_ids):
states = self.transformer(input_ids)[0]
rewards = self.v_head(states).squeeze(-1)
ends = torch.argmax((input_ids == self.eos_token_id).float(), dim=1).view(-1, 1)
returns = torch.gather(rewards, 1, ends).squeeze(-1)
return returns
reward_model = RewardModel("EleutherAI/gpt-j-6B", reward_tokenizer.eos_token_id)
directory = snapshot_download("Dahoas/gptj-rm-static", revision="676bfd4d")
for fpath in os.listdir(directory):
if fpath.endswith(".pt") or fpath.endswith(".bin"):
checkpoint = os.path.join(directory, fpath)
break
reward_model.load_state_dict(torch.load(checkpoint))
reward_model.eval()
reward_model.requires_grad_(False)
reward_device = torch.cuda.device_count() - 1
reward_model = reward_model.half().to(reward_device)
reward_batch_size = 48
delta_reward = True
def get_reward(samples):
input = reward_tokenizer(
samples,
padding=True,
truncation=True,
max_length=reward_tokenizer.max_len_single_sentence,
return_tensors="pt",
).to(reward_device)
mbs = reward_batch_size
out = []
for i in range(math.ceil(len(samples) / mbs)):
batch_ixs = slice(i * mbs, (i + 1) * mbs)
input_ids = input.input_ids[batch_ixs]
rewards = reward_model(input_ids)
out.extend(rewards)
return torch.hstack(out)
def reward_fn(samples, prompts, original_output, **kwargs):
samples = [s + reward_tokenizer.eos_token for s in samples]
rewards = get_reward(samples)
if not delta_reward:
return rewards
original_samples = [p + o + reward_tokenizer.eos_token for p, o in zip(prompts, original_output)]
original_rewards = get_reward(original_samples)
return rewards - original_rewards
else:
reward_fn = True
return reward_fn
def main(hparams={}):
config = TRLConfig.update(default_config, hparams)
dataset = load_dataset("Dahoas/rm-static")
prompts = [{"prompt": x["prompt"], "original_output": x["chosen"]} for x in dataset["train"]]
eval_prompts = [{"prompt": x["prompt"], "original_output": x["chosen"]} for x in islice(dataset["test"], 280)]
reward_fn = create_reward_fn()
trlx.train(
prompts=prompts,
eval_prompts=eval_prompts,
reward_fn=reward_fn,
config=config,
stop_sequences=["Human:", "human:", "Assistant:", "assistant:"],
)
if __name__ == "__main__":
hparams = {} if len(sys.argv) == 1 else json.loads(sys.argv[1])
main(hparams)
|