318 lines
13 KiB
Python
318 lines
13 KiB
Python
# Copyright 2023 The HuggingFace Team. 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 itertools
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import os
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import unittest
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from copy import deepcopy
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from functools import partial
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from parameterized import parameterized
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import tests.trainer.test_trainer
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from tests.trainer.test_trainer import TrainerIntegrationCommon # noqa
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from transformers import is_torch_available
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from transformers.testing_utils import (
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TestCasePlus,
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backend_device_count,
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execute_subprocess_async,
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mockenv_context,
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require_accelerate,
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require_fsdp,
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require_torch_accelerator,
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require_torch_multi_accelerator,
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slow,
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torch_device,
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)
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from transformers.trainer_callback import TrainerState
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from transformers.trainer_utils import FSDPOption, set_seed
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from transformers.utils import is_accelerate_available, is_torch_bf16_available_on_device
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if is_torch_available():
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from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_1
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from transformers.trainer import FSDP_MODEL_NAME
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else:
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is_torch_greater_or_equal_than_2_1 = False
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# default torch.distributed port
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DEFAULT_MASTER_PORT = "10999"
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dtypes = ["fp16"]
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if is_torch_bf16_available_on_device(torch_device):
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dtypes += ["bf16"]
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sharding_strategies = ["full_shard", "shard_grad_op"]
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state_dict_types = ["FULL_STATE_DICT", "SHARDED_STATE_DICT"]
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set_seed(42)
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params = list(itertools.product(sharding_strategies, dtypes))
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def get_master_port(real_launcher=False):
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"""
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When using a single gpu launcher emulation (i.e. not deepspeed or python -m torch.distributed)
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the issue is that once the port is tied it can't be used anywhere else outside of this process,
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since torch.dist doesn't free the port until the process exits. Therefore for the sake of being
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able to run both emulated launcher and normal launcher tests we need 2 distinct ports.
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This function will give the right port in the right context. For real launcher it'll give the
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base port, for emulated launcher it'll give the base port + 1. In both cases a string is
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returned.
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Args:
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`real_launcher`: whether a real launcher is going to be used, or the emulated one
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"""
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master_port_base = os.environ.get("DS_TEST_PORT", DEFAULT_MASTER_PORT)
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if not real_launcher:
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master_port_base = str(int(master_port_base) + 1)
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return master_port_base
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if is_torch_available():
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from tests.trainer.test_trainer import ( # noqa
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RegressionModelConfig,
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RegressionPreTrainedModel,
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)
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# hack to restore original logging level pre #21700
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get_regression_trainer = partial(tests.trainer.test_trainer.get_regression_trainer, log_level="info")
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require_fsdp_version = require_fsdp
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if is_accelerate_available():
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from accelerate.utils.constants import (
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FSDP_PYTORCH_VERSION,
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FSDP_SHARDING_STRATEGY,
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)
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require_fsdp_version = partial(require_fsdp, min_version=FSDP_PYTORCH_VERSION)
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def get_launcher(distributed=False, use_accelerate=False):
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# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
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# - it won't be able to handle that
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# 2. for now testing with just 2 gpus max (since some quality tests may give different
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# results with mode gpus because we use very little data)
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num_gpus = min(2, backend_device_count(torch_device)) if distributed else 1
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master_port = get_master_port(real_launcher=True)
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if use_accelerate:
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return f"""accelerate launch
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--num_processes {num_gpus}
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--main_process_port {master_port}
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--use_fsdp
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--fsdp_auto_wrap_policy TRANSFORMER_BASED_WRAP
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--fsdp_state_dict_type SHARDED_STATE_DICT
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--fsdp_transformer_layer_cls_to_wrap BertLayer""".split()
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return f"torchrun --nnodes 1 --nproc-per-node {num_gpus} --master-port {master_port}".split()
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def _parameterized_custom_name_func(func, param_num, param):
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# customize the test name generator function as we want both params to appear in the sub-test
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# name, as by default it shows only the first param
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param_based_name = parameterized.to_safe_name("_".join(str(x) for x in param.args))
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return f"{func.__name__}_{param_based_name}"
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@require_accelerate
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@require_torch_accelerator
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@require_fsdp_version
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class TrainerIntegrationFSDP(TestCasePlus, TrainerIntegrationCommon):
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def setUp(self):
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super().setUp()
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master_port = get_master_port(real_launcher=False)
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self.dist_env_1_gpu = {
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"MASTER_ADDR": "localhost",
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"MASTER_PORT": master_port,
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"RANK": "0",
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"LOCAL_RANK": "0",
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"WORLD_SIZE": "1",
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}
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self.fsdp_config = {
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"backward_prefetch": "backward_pre",
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"forward_prefetch": "False",
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"limit_all_gathers": "False",
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"use_orig_params": "True",
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"sync_module_states": "True",
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"cpu_ram_efficient_loading": "True",
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"activation_checkpointing": "False",
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"min_num_params": 1,
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}
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def tearDown(self):
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super().tearDown()
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@parameterized.expand(params, name_func=_parameterized_custom_name_func)
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def test_fsdp_config(self, sharding_strategy, dtype):
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output_dir = self.get_auto_remove_tmp_dir()
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kwargs = {
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"output_dir": output_dir,
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"train_len": 128,
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"save_steps": 5,
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"learning_rate": 0.1,
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"fsdp": f"{sharding_strategy} offload auto_wrap",
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"fsdp_config": self.fsdp_config,
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}
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kwargs[dtype] = True
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with mockenv_context(**self.dist_env_1_gpu):
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trainer = get_regression_trainer(**kwargs)
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self.assertEqual(trainer.args.fsdp[0], sharding_strategy)
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self.assertEqual(trainer.args.fsdp[1], FSDPOption.OFFLOAD)
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self.assertEqual(trainer.args.fsdp[2], FSDPOption.AUTO_WRAP)
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for k, v in trainer.args.fsdp_config.items():
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self.assertEqual(v, self.fsdp_config[k])
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self.assertEqual(os.environ.get("ACCELERATE_USE_FSDP", "false"), "true")
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@parameterized.expand(params, name_func=_parameterized_custom_name_func)
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def test_fsdp_config_transformers_auto_wrap(self, sharding_strategy, dtype):
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output_dir = self.get_auto_remove_tmp_dir()
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fsdp_config = deepcopy(self.fsdp_config)
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del fsdp_config["min_num_params"]
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fsdp_config["transformer_layer_cls_to_wrap"] = "BertLayer"
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kwargs = {
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"output_dir": output_dir,
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"train_len": 128,
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"save_steps": 5,
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"learning_rate": 0.1,
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"fsdp": f"{sharding_strategy} offload auto_wrap",
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"fsdp_config": fsdp_config,
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}
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kwargs[dtype] = True
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prefix = "FSDP_"
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with mockenv_context(**self.dist_env_1_gpu):
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trainer = get_regression_trainer(**kwargs)
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self.assertEqual(trainer.args.fsdp[0], sharding_strategy)
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self.assertEqual(trainer.args.fsdp[1], FSDPOption.OFFLOAD)
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self.assertEqual(trainer.args.fsdp[2], FSDPOption.AUTO_WRAP)
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fsdp_sharding_strategy = (
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str(FSDP_SHARDING_STRATEGY.index(sharding_strategy.upper()) + 1)
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if is_accelerate_available("0.26.0")
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else sharding_strategy.upper()
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)
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self.assertEqual(os.environ[f"{prefix}SHARDING_STRATEGY"], fsdp_sharding_strategy)
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self.assertEqual(os.environ[f"{prefix}OFFLOAD_PARAMS"], "true")
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self.assertEqual(os.environ[f"{prefix}AUTO_WRAP_POLICY"], "TRANSFORMER_BASED_WRAP")
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self.assertEqual(
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os.environ[f"{prefix}TRANSFORMER_CLS_TO_WRAP"], ",".join(fsdp_config["transformer_layer_cls_to_wrap"])
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)
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self.assertEqual(os.environ[f"{prefix}BACKWARD_PREFETCH"], fsdp_config["backward_prefetch"].upper())
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self.assertEqual(os.environ[f"{prefix}FORWARD_PREFETCH"], fsdp_config["forward_prefetch"])
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self.assertEqual(os.environ[f"{prefix}USE_ORIG_PARAMS"], fsdp_config["use_orig_params"])
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self.assertEqual(os.environ[f"{prefix}SYNC_MODULE_STATES"], fsdp_config["sync_module_states"])
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self.assertEqual(
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os.environ[f"{prefix}CPU_RAM_EFFICIENT_LOADING"], fsdp_config["cpu_ram_efficient_loading"]
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)
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self.assertEqual(os.environ.get("ACCELERATE_USE_FSDP", "false"), "true")
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@parameterized.expand(params, name_func=_parameterized_custom_name_func)
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@require_torch_multi_accelerator
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@slow
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def test_basic_run(self, sharding_strategy, dtype):
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launcher = get_launcher(distributed=True, use_accelerate=False)
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output_dir = self.get_auto_remove_tmp_dir()
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args = self.get_base_args(output_dir, 1, 50).split() + [f"--{dtype}"]
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fsdp_args = ["--fsdp", f"{sharding_strategy} auto_wrap", "--fsdp_transformer_layer_cls_to_wrap", "BertLayer"]
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script = [f"{self.examples_dir_str}/pytorch/text-classification/run_glue.py"]
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cmd = launcher + script + args + fsdp_args
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execute_subprocess_async(cmd, env=self.get_env())
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@parameterized.expand(dtypes)
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@require_torch_multi_accelerator
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@slow
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@unittest.skipIf(not is_torch_greater_or_equal_than_2_1, reason="This test on pytorch 2.0 takes 4 hours.")
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def test_basic_run_with_cpu_offload(self, dtype):
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launcher = get_launcher(distributed=True, use_accelerate=False)
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output_dir = self.get_auto_remove_tmp_dir()
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args = self.get_base_args(output_dir, 1, 50).split() + [f"--{dtype}", "--max_steps", "10"]
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fsdp_args = ["--fsdp", "full_shard auto_wrap offload", "--fsdp_transformer_layer_cls_to_wrap", "BertLayer"]
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script = [f"{self.examples_dir_str}/pytorch/text-classification/run_glue.py"]
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cmd = launcher + script + args + fsdp_args
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execute_subprocess_async(cmd, env=self.get_env())
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@parameterized.expand(state_dict_types, name_func=_parameterized_custom_name_func)
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@require_torch_multi_accelerator
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@slow
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def test_training_and_can_resume_normally(self, state_dict_type):
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output_dir = self.get_auto_remove_tmp_dir("./xxx", after=False)
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sharding_strategy = "full_shard"
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use_accelerate = state_dict_type == "SHARDED_STATE_DICT"
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launcher = get_launcher(True, use_accelerate=use_accelerate)
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args = self.get_base_args(output_dir, 2, 25).split()
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script = [f"{self.examples_dir_str}/pytorch/text-classification/run_glue.py"]
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logs = self.run_cmd_and_get_logs(use_accelerate, sharding_strategy, launcher, script, args, output_dir)
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# resume from ckpt
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checkpoint = os.path.join(output_dir, "checkpoint-115")
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resume_args = args + f"--resume_from_checkpoint {checkpoint}".split()
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is_fsdp_ckpt = os.path.isdir(checkpoint) and (
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# this checks the FSDP state dict when `SHARDED_STATE_DICT` is used
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any(
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FSDP_MODEL_NAME in folder_name
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for folder_name in os.listdir(checkpoint)
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if os.path.isdir(os.path.join(checkpoint, folder_name))
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)
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# this checks the FSDP state dict when `FULL_STATE_DICT` is used
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or os.path.isfile(os.path.join(checkpoint, f"{FSDP_MODEL_NAME}.bin"))
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)
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self.assertTrue(is_fsdp_ckpt)
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logs_resume = self.run_cmd_and_get_logs(
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use_accelerate, sharding_strategy, launcher, script, resume_args, output_dir
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)
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for log, log1 in zip(logs, logs_resume):
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if "learning_rate" in log:
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self.assertAlmostEqual(log["learning_rate"], log1["learning_rate"], delta=1e-5)
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def run_cmd_and_get_logs(self, use_accelerate, sharding_strategy, launcher, script, args, output_dir):
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if not use_accelerate:
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fsdp_args = [
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"--fsdp",
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f"{sharding_strategy} auto_wrap",
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"--fsdp_transformer_layer_cls_to_wrap",
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"BertLayer",
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]
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cmd = launcher + script + args + fsdp_args
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else:
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fsdp_config = f"""
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--fsdp_sharding_strategy {FSDP_SHARDING_STRATEGY.index(sharding_strategy.upper()) + 1}
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""".split()
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cmd = launcher + fsdp_config + script + args
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# keep for quick debug
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# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
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execute_subprocess_async(cmd, env=self.get_env())
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logs = TrainerState.load_from_json(os.path.join(output_dir, "trainer_state.json")).log_history
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return logs
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def get_base_args(self, output_dir, num_epochs, logging_steps):
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return f"""
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--model_name_or_path google-bert/bert-base-cased
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--task_name mrpc
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--output_dir {output_dir}
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--overwrite_output_dir
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--do_train
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--max_seq_length 128
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--per_device_train_batch_size 16
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--learning_rate 5e-5
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--num_train_epochs {num_epochs}
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--lr_scheduler_type cosine
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--logging_steps {logging_steps}
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--save_strategy epoch
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--do_eval
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--eval_strategy epoch
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--report_to none
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"""
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