134 lines
4.9 KiB
Python
134 lines
4.9 KiB
Python
# Copyright The Lightning AI team.
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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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from unittest import mock
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from unittest.mock import ANY, MagicMock, Mock
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import pytest
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import torch
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import torch.nn as nn
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from tests_fabric.helpers.runif import RunIf
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from torch.optim import Adam
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from lightning.fabric.strategies import FSDPStrategy
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from lightning.fabric.strategies.fsdp import _FSDPBackwardSyncControl
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from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_1_12
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if _TORCH_GREATER_EQUAL_1_12:
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from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload, FullyShardedDataParallel, MixedPrecision
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@mock.patch("lightning.fabric.strategies.fsdp._TORCH_GREATER_EQUAL_1_12", False)
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def test_fsdp_support(*_):
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with pytest.raises(NotImplementedError, match="`FSDPStrategy` is supported from PyTorch v1.12.0"):
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FSDPStrategy()
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@RunIf(min_torch="1.12")
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def test_fsdp_custom_mixed_precision():
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"""Test that passing a custom mixed precision config works."""
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config = MixedPrecision()
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strategy = FSDPStrategy(mixed_precision=config)
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assert strategy.mixed_precision_config == config
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@RunIf(min_torch="1.12")
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def test_fsdp_cpu_offload():
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"""Test the different ways cpu offloading can be enabled."""
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# bool
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strategy = FSDPStrategy(cpu_offload=True)
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assert strategy.cpu_offload == CPUOffload(offload_params=True)
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# dataclass
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config = CPUOffload()
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strategy = FSDPStrategy(cpu_offload=config)
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assert strategy.cpu_offload == config
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@RunIf(min_torch="1.12")
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def test_fsdp_setup_optimizer_validation():
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"""Test that `setup_optimizer()` validates the param groups and reference to FSDP parameters."""
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module = nn.Linear(2, 2)
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strategy = FSDPStrategy(parallel_devices=[torch.device("cpu")])
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bad_optimizer = Adam([{"params": [module.weight]}, {"params": [module.bias], "lr": 1e-3}])
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with pytest.raises(ValueError, match="does not support multiple param groups"):
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strategy.setup_optimizer(bad_optimizer)
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bad_optimizer = Adam(module.parameters())
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with pytest.raises(ValueError, match="The optimizer does not seem to reference any FSDP parameter"):
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strategy.setup_optimizer(bad_optimizer)
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@RunIf(min_torch="1.12")
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def test_fsdp_no_backward_sync():
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"""Test that the backward sync control calls `.no_sync()`, and only on a module wrapped in
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FullyShardedDataParallel."""
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strategy = FSDPStrategy()
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assert isinstance(strategy._backward_sync_control, _FSDPBackwardSyncControl)
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with pytest.raises(
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TypeError, match="is only possible if the module passed to .* is wrapped in `FullyShardedDataParallel`"
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):
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with strategy._backward_sync_control.no_backward_sync(Mock()):
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pass
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module = MagicMock(spec=FullyShardedDataParallel)
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with strategy._backward_sync_control.no_backward_sync(module):
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pass
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module.no_sync.assert_called_once()
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@RunIf(min_torch="1.12")
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@mock.patch("lightning.fabric.strategies.fsdp._TORCH_GREATER_EQUAL_1_13", False)
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def test_fsdp_activation_checkpointing_support():
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"""Test that we error out if activation checkpointing requires a newer PyTorch version."""
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with pytest.raises(ValueError, match="Activation checkpointing requires torch >= 1.13.0"):
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FSDPStrategy(activation_checkpointing=Mock())
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@RunIf(min_torch="1.13")
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def test_fsdp_activation_checkpointing():
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"""Test that the FSDP strategy can apply activation checkpointing to the given layers."""
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class Block1(nn.Linear):
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pass
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class Block2(nn.Linear):
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pass
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class Model(nn.Module):
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def __init__(self):
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super().__init__()
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self.layer0 = nn.Sequential(Block1(4, 4), Block1(5, 5))
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self.layer1 = Block2(2, 2)
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self.layer2 = nn.Linear(3, 3)
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strategy = FSDPStrategy(activation_checkpointing=Block1)
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assert strategy._activation_checkpointing == [Block1]
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strategy = FSDPStrategy(activation_checkpointing=[Block1, Block2])
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assert strategy._activation_checkpointing == [Block1, Block2]
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strategy._parallel_devices = [torch.device("cuda", 0)]
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with mock.patch(
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"torch.distributed.fsdp.fully_sharded_data_parallel.FullyShardedDataParallel"
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) as fsdp_mock, mock.patch(
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"torch.distributed.algorithms._checkpoint.checkpoint_wrapper.apply_activation_checkpointing"
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) as ckpt_mock:
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strategy.setup_module(Model())
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ckpt_mock.assert_called_with(fsdp_mock(), checkpoint_wrapper_fn=ANY, check_fn=ANY)
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