109 lines
3.1 KiB
Python
109 lines
3.1 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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Trajectory tracker can be inserted into code to save the intermediate results.
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The results will be dump to hdfs for offline comparison.
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Each process will have a client that first move all the tensors to CPU
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"""
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from verl.utils.hdfs_io import makedirs, copy
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import torch
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import os
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import ray
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import io
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import tempfile
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from collections import deque
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remote_copy = ray.remote(copy)
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@ray.remote
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def save_to_hdfs(data: io.BytesIO, name, hdfs_dir, verbose):
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filename = name + '.pth'
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with tempfile.TemporaryDirectory() as tmpdirname:
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local_filepath = os.path.join(tmpdirname, filename)
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with open(local_filepath, 'wb') as f:
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f.write(data.getbuffer())
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# upload to hdfs
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if verbose:
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print(f'Saving {local_filepath} to {hdfs_dir}')
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try:
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copy(local_filepath, hdfs_dir)
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except Exception as e:
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print(e)
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@ray.remote
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class TrajectoryTracker():
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def __init__(self, hdfs_dir, verbose) -> None:
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self.hdfs_dir = hdfs_dir
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makedirs(hdfs_dir)
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self.verbose = verbose
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self.handle = deque()
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def dump(self, data: io.BytesIO, name):
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# get a temp file and write to it
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self.handle.append(save_to_hdfs.remote(data, name, self.hdfs_dir, self.verbose))
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def wait_for_hdfs(self):
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while len(self.handle) != 0:
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future = self.handle.popleft()
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ray.get(future)
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def dump_data(data, name):
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enable = os.getenv('VERL_ENABLE_TRACKER', '0') == '1'
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if not enable:
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return
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buffer = io.BytesIO()
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torch.save(data, buffer)
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tracker = get_trajectory_tracker()
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ray.get(tracker.dump.remote(buffer, name))
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def get_trajectory_tracker():
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hdfs_dir = os.getenv('VERL_TRACKER_HDFS_DIR', default=None)
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verbose = os.getenv('VERL_TRACKER_VERBOSE', default='0') == '1'
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assert hdfs_dir is not None
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tracker = TrajectoryTracker.options(name="global_tracker", get_if_exists=True,
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lifetime="detached").remote(hdfs_dir, verbose)
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return tracker
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if __name__ == '__main__':
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# testing
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os.environ['VERL_ENABLE_TRACKER'] = '1'
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os.environ['VERL_TRACKER_HDFS_DIR'] = '~/debug/test'
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@ray.remote
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def process(iter):
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data = {'obs': torch.randn(10, 20)}
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dump_data(data, f'process_{iter}_obs')
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ray.init()
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output_lst = []
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for i in range(10):
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output_lst.append(process.remote(i))
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out = ray.get(output_lst)
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tracker = get_trajectory_tracker()
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ray.get(tracker.wait_for_hdfs.remote())
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