wehub-resource-sync
209 lines
6.9 KiB
Python
209 lines
6.9 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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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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import os
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import tempfile
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import unittest
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import collective.test_communication_api_base as test_base
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import numpy as np
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import paddle
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import paddle.distributed as dist
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from paddle.distributed.flex_checkpoint.dcp.load_state_dict import (
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get_checkpoint_files,
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get_rank_to_files,
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)
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from paddle.distributed.flex_checkpoint.dcp.utils import (
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flatten_state_dict,
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unflatten_state_dict,
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)
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os.environ['FLAGS_enable_pir_api'] = '0'
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class TestDistCheckpointUtils(test_base.CommunicationTestDistBase):
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def setUp(self):
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super().setUp(num_of_devices=2, timeout=120, nnode=1)
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self._default_envs = {}
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self._changeable_envs = {"backend": ["gpu"]}
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def test_flatten_mapping(self):
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envs_list = test_base.gen_product_envs_list(
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self._default_envs, self._changeable_envs
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)
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for envs in envs_list:
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ckpt_path_tmp = tempfile.TemporaryDirectory()
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ckpt_path = ckpt_path_tmp.name
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envs["ckpt_path"] = ckpt_path
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self.run_test_case(
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"semi_auto_parallel_checkpoint_flatten_mapping.py",
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user_defined_envs=envs,
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)
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ckpt_path_tmp.cleanup()
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def test_dedup_tensor(self):
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envs_list = test_base.gen_product_envs_list(
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self._default_envs, self._changeable_envs
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)
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for envs in envs_list:
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ckpt_path_tmp = tempfile.TemporaryDirectory()
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ckpt_path = ckpt_path_tmp.name
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envs["ckpt_path"] = ckpt_path
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self.run_test_case(
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"semi_auto_parallel_checkpoint_dedup_tensor.py",
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user_defined_envs=envs,
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)
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ckpt_path_tmp.cleanup()
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def test_flatten_state_dict(self):
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state_dict = {
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"model": {
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"a.0": paddle.to_tensor([1, 2]),
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"b": paddle.to_tensor([3, 4]),
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},
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"optimizer": {
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"c": paddle.to_tensor([5, 6]),
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"d.2": paddle.to_tensor([7, 8]),
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},
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}
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expected_flat_state_dict = {
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"model.a.0": paddle.to_tensor([1, 2]),
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"model.b": paddle.to_tensor([3, 4]),
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"optimizer.c": paddle.to_tensor([5, 6]),
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"optimizer.d.2": paddle.to_tensor([7, 8]),
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}
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flat_state_dict, mapping = flatten_state_dict(state_dict)
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self.assertTrue(len(expected_flat_state_dict) == len(flat_state_dict))
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for k, v in flat_state_dict.items():
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self.assertTrue(isinstance(v, paddle.Tensor))
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self.assertTrue(k in expected_flat_state_dict)
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np.testing.assert_equal(
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v.numpy(), expected_flat_state_dict[k].numpy()
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)
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recover_state_dict = unflatten_state_dict(flat_state_dict, mapping)
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def check_state_dict(d1, d2):
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self.assertTrue(len(d1) == len(d2))
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self.assertTrue(type(d1) == type(d2))
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if isinstance(d1, dict):
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for k in d1:
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self.assertTrue(k in d2)
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check_state_dict(d1[k], d2[k])
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elif isinstance(d1, paddle.Tensor):
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np.testing.assert_equal(d1.numpy(), d2.numpy())
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else:
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raise ValueError(f"Invalid type of state_dict:{d1} != {d2}")
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check_state_dict(recover_state_dict, state_dict)
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def test_get_rank_to_files(self):
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process_group = None
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use_dist = False
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ckpt_dir_tmp = tempfile.TemporaryDirectory()
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ckpt_dir = ckpt_dir_tmp.name
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state_dict = {
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"w1": paddle.to_tensor([1, 2]),
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"w2": paddle.to_tensor([3, 4]),
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}
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dist.save_state_dict(state_dict, ckpt_dir)
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metadata_files, local_load_files = get_checkpoint_files(ckpt_dir)
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metadata_list = []
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for metadata_file in metadata_files:
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metadata_list.append(
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paddle.load(os.path.join(ckpt_dir, metadata_file))
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)
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new_state_dict = {
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"w1": paddle.to_tensor([1, 2]),
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"w2": paddle.to_tensor([3, 4]),
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}
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(
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rank_to_files,
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mw_name_compatibility_mapping,
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) = get_rank_to_files(
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metadata_list,
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local_load_files,
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new_state_dict,
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process_group,
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use_dist,
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)
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self.assertTrue(len(rank_to_files) == 1 and 0 in rank_to_files)
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self.assertTrue(rank_to_files[0] == ["0_0.distcp"])
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self.assertTrue(len(mw_name_compatibility_mapping) == 0)
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new_state_dict = {
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"w1": paddle.to_tensor([1, 2]),
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"w3": paddle.to_tensor([3, 4]),
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}
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(
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rank_to_files,
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mw_name_compatibility_mapping,
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) = get_rank_to_files(
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metadata_list,
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local_load_files,
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new_state_dict,
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process_group,
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use_dist,
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)
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self.assertTrue(len(rank_to_files) == 1 and 0 in rank_to_files)
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self.assertTrue(rank_to_files[0] == ["0_0.distcp"])
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self.assertTrue(len(mw_name_compatibility_mapping) == 0)
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new_state_dict = {
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"w3": paddle.to_tensor([3, 4]),
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"w4": paddle.to_tensor([5, 6]),
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}
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(
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rank_to_files,
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mw_name_compatibility_mapping,
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) = get_rank_to_files(
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metadata_list,
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local_load_files,
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new_state_dict,
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process_group,
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use_dist,
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)
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self.assertTrue(len(rank_to_files) == 0)
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self.assertTrue(len(mw_name_compatibility_mapping) == 0)
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ckpt_dir_tmp.cleanup()
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class TestMergeCheckpoint(test_base.CommunicationTestDistBase):
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def setUp(self):
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super().setUp(num_of_devices=1, timeout=120, nnode=1)
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self._default_envs = {}
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self._changeable_envs = {"backend": ["gpu"]}
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def test_merge_skip(self):
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envs_list = test_base.gen_product_envs_list(
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self._default_envs, self._changeable_envs
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)
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for envs in envs_list:
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ckpt_path_tmp = tempfile.TemporaryDirectory()
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ckpt_path = ckpt_path_tmp.name
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envs["ckpt_path"] = ckpt_path
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self.run_test_case(
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"semi_merge_shard_state_dict.py",
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user_defined_envs=envs,
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)
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ckpt_path_tmp.cleanup()
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if __name__ == "__main__":
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unittest.main()
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