mirror of
https://github.com/saymrwulf/pytorch.git
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5f57be7571
Migrate function call in test to eliminate warning message in below and reduce the chance of test fail when methods removed - from deprecated `save_state_dict` change to `save` - from deprecated `load_state_dict` change to `load` Warning message: ```bash pytorch/test/distributed/checkpoint/test_fsdp_model_state.py:37: FutureWarning: `save_state_dict` is deprecated and will be removed in future versions.Please use `save` instead. ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/134938 Approved by: https://github.com/wz337, https://github.com/fegin
99 lines
3.3 KiB
Python
99 lines
3.3 KiB
Python
# Owner(s): ["oncall: distributed"]
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import torch
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import torch.distributed as dist
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import torch.distributed.checkpoint as dist_cp
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from torch.distributed.checkpoint.default_planner import (
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DefaultLoadPlanner,
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DefaultSavePlanner,
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)
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from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
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from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
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from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
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from torch.testing._internal.common_utils import run_tests
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from torch.testing._internal.distributed._tensor.common_dtensor import (
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DTensorTestBase,
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with_comms,
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)
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from torch.testing._internal.distributed.checkpoint_utils import with_temp_dir
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class FsdpModelStateCheckpoint(DTensorTestBase):
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@property
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def backend(self):
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return "cpu:gloo,cuda:nccl"
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def _test_fsdp_model_state(self, process_group) -> None:
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CHECKPOINT_DIR = self.temp_dir
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model = FSDP(torch.nn.Linear(8, 8, device="meta"))
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model(torch.rand(8, 8, device=dist.get_rank())).sum().backward()
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with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT):
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state_dict = {
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"model": model.state_dict(),
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}
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dist_cp.save(
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state_dict=state_dict,
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storage_writer=dist_cp.FileSystemWriter(CHECKPOINT_DIR),
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planner=DefaultSavePlanner(),
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)
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model_2 = FSDP(
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torch.nn.Linear(8, 8, device="meta"), process_group=process_group
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)
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with FSDP.summon_full_params(model):
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with FSDP.summon_full_params(model_2):
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self.assertNotEqual(model.weight, model_2.weight)
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self.assertNotEqual(model.bias, model_2.bias)
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# now load the model and ensure the values are the same
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with FSDP.state_dict_type(model_2, StateDictType.SHARDED_STATE_DICT):
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state_dict = {
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"model": model_2.state_dict(),
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}
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dist_cp.load(
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state_dict=state_dict,
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storage_reader=dist_cp.FileSystemReader(CHECKPOINT_DIR),
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planner=DefaultLoadPlanner(),
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)
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model_2.load_state_dict(state_dict["model"])
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with FSDP.summon_full_params(model):
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with FSDP.summon_full_params(model_2):
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self.assertEqual(model.weight, model_2.weight)
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self.assertEqual(model.bias, model_2.bias)
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@with_comms
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@skip_if_lt_x_gpu(2)
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@with_temp_dir
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def test_fsdp_model_state_no_resharding(self):
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self._test_fsdp_model_state(process_group=None)
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def _create_new_dist_group(self):
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world_size = dist.get_world_size()
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group1 = [i for i in range(world_size) if i % 2 == 0]
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group2 = [i for i in range(world_size) if i % 2 != 0]
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# create new fsdp group for resharding
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fsdp_0 = dist.new_group(ranks=group1)
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fsdp_1 = dist.new_group(ranks=group2)
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if dist.get_rank() % 2 == 0:
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my_fsdp = fsdp_0
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else:
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my_fsdp = fsdp_1
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return my_fsdp
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@with_comms
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@skip_if_lt_x_gpu(4)
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@with_temp_dir
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def test_fsdp_model_state_with_resharding(self):
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self._test_fsdp_model_state(process_group=self._create_new_dist_group())
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if __name__ == "__main__":
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run_tests()
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