File size: 6,672 Bytes
1faccd4 | 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 | # Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Generate responses given a dataset of prompts
"""
import os
import aiohttp
import hydra
import numpy as np
import ray
os.environ["NCCL_DEBUG"] = "WARN"
os.environ["TOKENIZERS_PARALLELISM"] = "true"
# os.environ['TORCH_COMPILE_DISABLE'] = '1'
import asyncio
from pprint import pprint
import pandas as pd
from omegaconf import OmegaConf
from openai.types.chat import ChatCompletion
from verl.utils.hdfs_io import makedirs
from verl.workers.rollout.replica import get_rollout_replica_class
async def start_server(config):
tp_size = config.actor_rollout_ref.rollout.tensor_model_parallel_size
num_replicas = (config.trainer.n_gpus_per_node * config.trainer.nnodes) // tp_size
rollout_config = config.actor_rollout_ref.rollout
model_config = config.actor_rollout_ref.model
# create standalone rollout server
rollout_server_class = get_rollout_replica_class(config.actor_rollout_ref.rollout.name)
rollout_servers = [
rollout_server_class(
replica_rank=replica_rank,
config=rollout_config,
model_config=model_config,
gpus_per_node=config.trainer.n_gpus_per_node,
)
for replica_rank in range(num_replicas)
]
await asyncio.gather(*[server.init_standalone() for server in rollout_servers])
server_handles = [server._server_handle for server in rollout_servers]
server_addresses = [server._server_address for server in rollout_servers]
assert len(server_handles) == num_replicas
assert len(server_addresses) == num_replicas
return server_handles, server_addresses
async def submit_request(server_address, **chat_complete_request):
try:
extra_headers = chat_complete_request.pop("extra_headers", {})
timeout = aiohttp.ClientTimeout(total=None)
session = aiohttp.ClientSession(timeout=timeout)
async with session.post(
url=f"http://{server_address}/v1/chat/completions",
headers={"Authorization": "Bearer token-abc123", **extra_headers},
json=chat_complete_request,
) as resp:
data = await resp.json()
return ChatCompletion(**data)
finally:
await session.close()
async def generate_per_replica(server_address, model_path: str, n_samples: int, sampling_params: dict, chat_lst: list):
# here we should sample n_samples for each chat_lst.
# we use aiohttp to avoid hang in AsyncOpenAI when the number of requests is large.
# client = AsyncOpenAI(
# api_key="123-abc",
# base_url=f"http://{server_address}/v1",
# )
chat_complete_request = [
{
"model": model_path,
"messages": messages,
**sampling_params,
}
for messages in chat_lst
for _ in range(n_samples)
]
tasks = [submit_request(server_address, **req) for req in chat_complete_request]
results = await asyncio.gather(*tasks)
return results
async def generate(
server_addresses: list, model_path: str, n_samples: int, sampling_params: dict, chat_numpy: np.ndarray
):
num_replicas = len(server_addresses)
chat_sub_array = np.array_split(chat_numpy, num_replicas)
chat_sub_array = [chat.tolist() for chat in chat_sub_array]
assert len(server_addresses) == len(chat_sub_array)
results = await asyncio.gather(
*[
generate_per_replica(server_addresses[i], model_path, n_samples, sampling_params, chat_sub_array[i])
for i in range(num_replicas)
]
)
return results
@hydra.main(config_path="config", config_name="ppo_trainer", version_base=None)
def main(config):
ray.init(runtime_env={"env_vars": {"TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_USE_V1": "1"}})
pprint(OmegaConf.to_container(config, resolve=True)) # resolve=True will eval symbol values
OmegaConf.resolve(config)
n_samples = config.actor_rollout_ref.rollout.n
if config.actor_rollout_ref.rollout.temperature == 0.0:
assert n_samples == 1, "When temperature=0, n_samples must be 1."
assert n_samples >= 1, "n_samples should always >= 1"
sampling_params = {
"temperature": config.actor_rollout_ref.rollout.temperature,
"top_p": config.actor_rollout_ref.rollout.top_p,
# "top_k": config.actor_rollout_ref.rollout.top_k,
"max_tokens": config.actor_rollout_ref.rollout.response_length,
}
from omegaconf import ListConfig
train_files = config.data.train_files
if not isinstance(train_files, list | ListConfig):
train_files = [train_files]
# read dataset. Note that the dataset should directly contain chat template format (e.g., a list of dictionary)
datasets = []
for train_file in train_files:
dataset = pd.read_parquet(train_file)
datasets.append(dataset)
# concat dataset
dataset = pd.concat(datasets, axis=0, ignore_index=True)
chat_lst = dataset[config.data.prompt_key].tolist()
chat_lst = [chat.tolist() for chat in chat_lst]
chat_numpy = np.array(chat_lst)
# start native server
server_handles, server_addresses = asyncio.run(start_server(config))
# run generate
gen_results = asyncio.run(
generate(server_addresses, config.actor_rollout_ref.model.path, n_samples, sampling_params, chat_numpy)
)
# reshape results into a numpy array
import itertools
results = list(itertools.chain.from_iterable(gen_results))
# extract content from results
results = np.array([result.choices[0].message.content for result in results])
results = np.reshape(results, (-1, n_samples))
assert results.shape == (len(chat_lst), n_samples)
results = results.tolist()
# add to the data frame
dataset["responses"] = results
# write to a new parquet
output_dir = os.path.dirname(config.data.output_path)
makedirs(output_dir, exist_ok=True)
print(f"Saving results to {config.data.output_path}")
dataset.to_parquet(config.data.output_path)
if __name__ == "__main__":
main()
|