# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import functools import os import unittest from unittest import mock import torch import torch.nn as nn import torch.nn.functional as F from fairscale.nn import FullyShardedDataParallel as FSDP from fairscale.nn import auto_wrap, default_auto_wrap_policy, enable_wrap, wrap from fairscale.utils.testing import DummyProcessGroup class TestAutoWrap(unittest.TestCase): def setUp(self) -> None: version = torch.__version__.split(".")[:2] major, minor = int(version[0]), int(version[1]) if major < 1 or (major == 1 and minor < 6): raise unittest.SkipTest("Need pytorch version >= 1.6 due to autocast") self.process_group = DummyProcessGroup(rank=0, size=1) def test_wrap(self): with enable_wrap(wrapper_cls=FSDP, flatten_parameters=False, process_group=self.process_group): layer = wrap(nn.Linear(5, 5)) assert isinstance(layer, FSDP) assert layer.flatten_parameters is False def test_wrap_disabled_outside_context(self): layer = wrap(nn.Linear(5, 5)) assert isinstance(layer, nn.Linear) def test_wrap_override_defaults(self): with enable_wrap(wrapper_cls=FSDP, flatten_parameters=False, process_group=self.process_group): layer = wrap(nn.Linear(5, 5), flatten_parameters=True) assert isinstance(layer, FSDP) assert layer.flatten_parameters def test_auto_wrap(self): """ Test to ensure with auto wrap, we wrap child modules correctly based on the min_num_params. ``nn.Linear(5, 5)`` does not exceed the bucket size, but combined they do. """ with enable_wrap(wrapper_cls=FSDP, process_group=self.process_group, flatten_parameters=False): sequential = nn.Sequential( nn.Linear(5, 5), nn.Linear(5, 5), nn.Sequential(nn.Linear(5, 5), nn.Linear(5, 5)) ) my_auto_wrap_policy = functools.partial(default_auto_wrap_policy, min_num_params=40) model = auto_wrap(sequential, auto_wrap_policy=my_auto_wrap_policy) assert isinstance(model, FSDP) assert isinstance(model.module[0], nn.Linear) assert isinstance(model.module[1], nn.Linear) assert isinstance(model.module[2], FSDP) assert isinstance(model.module[2].module[0], nn.Linear) assert isinstance(model.module[2].module[1], nn.Linear) def test_auto_wrap_preset_exclude_wrap(self): """ Test to ensure excluded modules are not wrapped, regardless if the total param size is greater than the min_num_params. """ with enable_wrap(wrapper_cls=FSDP, process_group=self.process_group, flatten_parameters=False): sequential = nn.ModuleList([nn.Linear(5, 5), nn.Linear(5, 5)]) my_auto_wrap_policy = functools.partial(default_auto_wrap_policy, min_num_params=40) model = auto_wrap(sequential, auto_wrap_policy=my_auto_wrap_policy) assert isinstance(model, nn.ModuleList) assert isinstance(model[0], nn.Linear) assert isinstance(model[1], nn.Linear) def test_auto_wrap_preset_exclude_wrap_include_children(self): """ Test to ensure excluded modules are not wrapped, but children are if param size is greater than min_num_params """ with enable_wrap(wrapper_cls=FSDP, process_group=self.process_group, flatten_parameters=False): sequential = nn.ModuleList([nn.Linear(10, 10)]) my_auto_wrap_policy = functools.partial(default_auto_wrap_policy, min_num_params=40) model = auto_wrap(sequential, auto_wrap_policy=my_auto_wrap_policy) assert isinstance(model, nn.ModuleList) assert isinstance(model[0], FSDP) def test_auto_wrap_preset_force_leaf(self): """ Test to ensure force-leaf modules are not wrapped, and children are not wrapped. """ with enable_wrap(wrapper_cls=FSDP, process_group=self.process_group, flatten_parameters=False): sequential = nn.Sequential(nn.Linear(10, 10), nn.MultiheadAttention(100, 1)) my_auto_wrap_policy = functools.partial(default_auto_wrap_policy, min_num_params=40) model = auto_wrap(sequential, auto_wrap_policy=my_auto_wrap_policy) assert isinstance(model.module[0], FSDP) # Assert children of multihead attention are not wrapped assert isinstance(model.module[1], nn.MultiheadAttention) assert isinstance(model.module[1].out_proj, nn.Linear) def test_auto_wrap_preset_force_leaf_custom(self): """ Test to ensure force-leaf modules are not wrapped. """ my_auto_wrap_policy = functools.partial( default_auto_wrap_policy, min_num_params=40, force_leaf_modules=default_auto_wrap_policy.FORCE_LEAF_MODULES.union({nn.Linear}), ) with enable_wrap( auto_wrap_policy=my_auto_wrap_policy, wrapper_cls=FSDP, process_group=self.process_group, flatten_parameters=False, ): sequential = nn.Sequential(nn.Linear(10, 10), nn.ModuleList([nn.Linear(10, 10)])) model = auto_wrap(sequential) # Model was wrapped in FSDP as no inner modules were wrapped. assert isinstance(model, FSDP) assert isinstance(model.module[0], nn.Linear) assert isinstance(model.module[1], nn.ModuleList) # todo: currently complains that address is in use, not sure why since I clear the proc group. # def test_auto_wrap_smoke(self): # self._auto_wrap_smoke_test(enable_mixed_precision=False) def test_auto_wrap_smoke_autocast(self): """ Ensure we can do a forward/backward through an auto-wrapped model. """ self._auto_wrap_smoke_test(enable_mixed_precision=True) @mock.patch.dict(os.environ, {"MASTER_ADDR": "localhost", "MASTER_PORT": "12345"}, clear=True) @unittest.skipIf(not torch.cuda.is_available(), "Test Requires CUDA") def _auto_wrap_smoke_test(self, enable_mixed_precision): from torch.cuda.amp import autocast device = torch.device("cuda") torch.cuda.set_device(0) torch.distributed.init_process_group(backend="nccl", rank=0, world_size=1) with enable_wrap(wrapper_cls=FSDP, mixed_precision=enable_mixed_precision): sequential = nn.Sequential( nn.Linear(5, 5), nn.Linear(5, 5), nn.Sequential(nn.Linear(5, 5), nn.Linear(5, 5)) ) my_auto_wrap_policy = functools.partial(default_auto_wrap_policy, min_num_params=40) model = auto_wrap(sequential, auto_wrap_policy=my_auto_wrap_policy) model.to(device) input = torch.rand((1, 5), dtype=torch.float).to(device) with autocast(enabled=enable_mixed_precision): output = model(input) loss = F.mse_loss(input, output) loss.backward() torch.distributed.destroy_process_group()