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[Unified Checkpoint] Fix expert parallel (PaddlePaddle#9821)
* fix expert parallel * fix split_param for expert parallel * add filter_sync_parameters
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| # Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # 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. | ||
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| import os | ||
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| import numpy as np | ||
| import pytest | ||
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| from paddlenlp.utils.downloader import get_path_from_url_with_filelock | ||
| from tests.parallel_launch import TestMultipleGpus | ||
| from tests.testing_utils import require_paddle_at_least_8_gpu, skip_for_none_ce_case | ||
| from tests.trainer.test_unified_checkpoint import remove_ckpt, remove_logs | ||
| from tests.trainer.trainer_utils import get_pretrain_arguments | ||
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| environment_variables = { | ||
| "NCCL_ALGO": "Tree", | ||
| "NVIDIA_TF32_OVERRIDE": "0", | ||
| "NCCL_IB_TIMEOUT": "22", | ||
| "NCCL_DEBUG": "INFO", | ||
| "FLAGS_embedding_deterministic": "1", | ||
| "FLAGS_cudnn_deterministic": "1", | ||
| "Flags_mp_aysnc_allreduce": "1", | ||
| "Flags_skip_mp_c_identity": "1", | ||
| "FLAGS_shard_norm_align_dp": "0", | ||
| "FLAGS_shard_use_reduce": "1", | ||
| "test_ci_no_save_model": "1", | ||
| } | ||
|
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| moe_arguments = { | ||
| "model_name_or_path": "__internal_testing__/unified-ckpt-qwen2moe", | ||
| "dataset_name_or_path": "./unified_checkpoint/peft_input/data/", | ||
| "output_dir": "./unified_checkpoint/checkpoints/qwen2moe_sft_ckpts", | ||
| "per_device_train_batch_size": 1, | ||
| "gradient_accumulation_steps": 8, | ||
| "per_device_eval_batch_size": 8, | ||
| "eval_accumulation_steps": 16, | ||
| "learning_rate": 3e-04, | ||
| "max_steps": 10, | ||
| "save_steps": 6, | ||
| "warmup_steps": 30, | ||
| "logging_steps": 1, | ||
| "evaluation_strategy": "no", | ||
| "save_strategy": "steps", | ||
| "src_length": 1024, | ||
| "max_length": 2048, | ||
| "bf16": "true", | ||
| "fp16_opt_level": "O2", | ||
| "do_train": "true", | ||
| "do_eval": "false", | ||
| "disable_tqdm": "true", | ||
| "eval_with_do_generation": "false", | ||
| "recompute": "true", | ||
| "recompute_granularity": "full", | ||
| "save_total_limit": 1, | ||
| "tensor_parallel_degree": 1, | ||
| "pipeline_parallel_degree": 1, | ||
| "sharding": "", | ||
| "lora": "false", | ||
| "zero_padding": "false", | ||
| "use_flash_attention": "false", | ||
| "unified_checkpoint": 1, | ||
| "continue_training": 0, | ||
| "sequence_parallel": 0, | ||
| } | ||
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| def check_acc(log_dir="log"): | ||
| file_path = os.path.join(log_dir, "workerlog.n0.c0") | ||
| cmd = "grep -a 'global_step: 10' " + file_path + " | awk -F ',' '{print $2}' | awk '{print $6}'" | ||
| import subprocess | ||
|
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| res = subprocess.check_output(cmd, shell=True, text=True) | ||
| res = [float(x) for x in res.split()] | ||
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| return res | ||
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| seed = 2024 | ||
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| rng = np.random.default_rng(seed=seed) | ||
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| @pytest.mark.xdist_group(name="UC") | ||
| class TestUnifiedCheckpointBase(TestMultipleGpus): | ||
| @classmethod | ||
| @property | ||
| def __test__(cls): | ||
| return cls != TestUnifiedCheckpointBase | ||
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| def setUp(self): | ||
| """ | ||
| 1. update runfirst and rerun to run defined different config | ||
| 2. update need_allclose to True if you want to check the result | ||
| 3. update rtol to the relative value you want to check | ||
| """ | ||
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| self.configs = get_pretrain_arguments(moe_arguments) | ||
| os.environ.update(environment_variables) | ||
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| file_ = "https://bj.bcebos.com/paddlenlp/datasets/examples/AdvertiseGen.tar.gz" | ||
| input_dir = "unified_checkpoint/peft_input/" | ||
| os.makedirs(input_dir, exist_ok=True) | ||
| file_path = os.path.join(input_dir, "AdvertiseGen.tar.gz") | ||
| if not os.path.exists(file_path): | ||
| get_path_from_url_with_filelock(file_, root_dir=input_dir) | ||
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| self.need_allclose = True | ||
| self.rtol = 1e-7 | ||
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| self.run_file = "llm/run_finetune.py" | ||
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| def runfirst(self, train_args): | ||
| self.run_n1c8(self.run_file, **train_args) | ||
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| def rerun(self, train_args): | ||
| self.run_n1c8(self.run_file, **train_args) | ||
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| @require_paddle_at_least_8_gpu | ||
| def testTP4DP2(self): | ||
| remove_logs() | ||
| remove_ckpt(moe_arguments["output_dir"]) | ||
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| train_args = self.configs["TP4DP2"] | ||
| self.runfirst(train_args) | ||
| self.rerun(train_args) | ||
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| if self.need_allclose: | ||
| res = check_acc() | ||
| assert len(res) == 2 | ||
| np.testing.assert_allclose(res[0], res[1], self.rtol) | ||
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| @skip_for_none_ce_case | ||
| @require_paddle_at_least_8_gpu | ||
| def testTP2Sharding4(self): | ||
| remove_logs() | ||
| remove_ckpt(moe_arguments["output_dir"]) | ||
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| train_args = self.configs["TP2Sharding4"] | ||
| self.runfirst(train_args) | ||
| self.rerun(train_args) | ||
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| if self.need_allclose: | ||
| res = check_acc() | ||
| assert len(res) == 2 | ||
| np.testing.assert_allclose(res[0], res[1], self.rtol) | ||
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| @pytest.mark.xdist_group(name="UC") | ||
| class TestUnifiedCheckpointFull(TestUnifiedCheckpointBase): | ||
| @skip_for_none_ce_case | ||
| @require_paddle_at_least_8_gpu | ||
| def testTP2Sharding4V2(self): | ||
| remove_logs() | ||
| remove_ckpt(moe_arguments["output_dir"]) | ||
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| train_args = self.configs["TP2Sharding4"] | ||
| train_args.update({"sharding_parallel_config": "split_param"}) | ||
| train_args.update({"amp_master_grad": True}) | ||
| self.runfirst(train_args) | ||
| self.rerun(train_args) | ||
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| if self.need_allclose: | ||
| res = check_acc() | ||
| assert len(res) == 2 | ||
| np.testing.assert_allclose(res[0], res[1], self.rtol) |
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