128 lines
5.3 KiB
Python
128 lines
5.3 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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import os
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from unittest import mock
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from unittest.mock import MagicMock, Mock
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import pytest
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import torch.nn
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import torch.nn as nn
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from lightning.fabric.accelerators import XLAAccelerator
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from lightning.fabric.plugins import XLAPrecision
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from lightning.fabric.strategies import XLAFSDPStrategy
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from lightning.fabric.strategies.xla_fsdp import _activation_checkpointing_auto_wrapper, _XLAFSDPBackwardSyncControl
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from torch.optim import Adam
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from tests_fabric.helpers.runif import RunIf
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@RunIf(min_torch="2.0", tpu=True)
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def test_xla_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 = XLAFSDPStrategy(
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parallel_devices=XLAAccelerator.get_parallel_devices(XLAAccelerator.auto_device_count()),
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)
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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 XLAFSDP parameter"):
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strategy.setup_optimizer(bad_optimizer)
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@RunIf(min_torch="2.0", tpu=True)
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def test_xla_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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XlaFullyShardedDataParallel."""
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from torch_xla.distributed.fsdp import XlaFullyShardedDataParallel
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strategy = XLAFSDPStrategy()
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assert isinstance(strategy._backward_sync_control, _XLAFSDPBackwardSyncControl)
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with pytest.raises(
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TypeError, match="is only possible if the module passed to .* is wrapped in `XlaFullyShardedDataParallel`"
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), strategy._backward_sync_control.no_backward_sync(object()):
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pass
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module = MagicMock(spec=XlaFullyShardedDataParallel)
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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="2.0", tpu=True)
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def test_xla_fsdp_grad_clipping_value_error():
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strategy = XLAFSDPStrategy()
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with pytest.raises(NotImplementedError, match="does not support to clip gradients by value"):
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strategy.clip_gradients_value(Mock(), Mock(), Mock())
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_rank_properties_access(xla_available):
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"""Test that the strategy returns the expected values depending on whether we're in the main process or not."""
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strategy = XLAFSDPStrategy()
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strategy.cluster_environment = Mock()
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# we're in the main process, no processes have been launched yet
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assert not strategy._launched
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assert strategy.global_rank == 0
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assert strategy.local_rank == 0
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assert strategy.node_rank == 0
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assert strategy.world_size == 1
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# simulate we're in a worker process
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strategy._launched = True
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assert strategy.global_rank == strategy.cluster_environment.global_rank()
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assert strategy.local_rank == strategy.cluster_environment.local_rank()
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assert strategy.node_rank == strategy.cluster_environment.node_rank()
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assert strategy.world_size == strategy.cluster_environment.world_size()
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def test_xla_fsdp_policy(xla_available):
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strategy = XLAFSDPStrategy(foo=1)
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assert strategy._fsdp_kwargs == {"foo": 1}
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strategy = XLAFSDPStrategy(auto_wrap_policy={torch.nn.Linear})
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kwargs = strategy._parse_fsdp_kwargs()
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assert set(kwargs) == {"auto_wrap_policy", "compute_dtype"}
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assert kwargs["auto_wrap_policy"].func._mock_name == "transformer_auto_wrap_policy"
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assert kwargs["compute_dtype"] is torch.float32
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strategy = XLAFSDPStrategy(activation_checkpointing_policy={torch.nn.Linear})
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_ = strategy._parse_fsdp_kwargs()
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kwargs = strategy._parse_fsdp_kwargs() # ensure it's idempotent
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assert set(kwargs) == {"auto_wrapper_callable", "compute_dtype"}
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assert kwargs["auto_wrapper_callable"].func is _activation_checkpointing_auto_wrapper
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assert kwargs["compute_dtype"] is torch.float32
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strategy = XLAFSDPStrategy(
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accelerator=Mock(),
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auto_wrap_policy={torch.nn.Linear},
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activation_checkpointing_policy={torch.nn.Linear},
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precision=XLAPrecision("bf16-true"),
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)
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kwargs = strategy._parse_fsdp_kwargs()
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assert set(kwargs) == {"auto_wrap_policy", "auto_wrapper_callable", "compute_dtype"}
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assert kwargs["auto_wrap_policy"].func._mock_name == "transformer_auto_wrap_policy"
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assert kwargs["auto_wrapper_callable"].func is _activation_checkpointing_auto_wrapper
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assert kwargs["compute_dtype"] is torch.bfloat16
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strategy.teardown()
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strategy = XLAFSDPStrategy(activation_checkpointing_policy={torch.nn.Linear}, auto_wrapper_callable="foo")
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with pytest.raises(ValueError, match="cannot set both"):
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strategy._parse_fsdp_kwargs()
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strategy = XLAFSDPStrategy(activation_checkpointing_policy="foo")
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with pytest.raises(TypeError, match="must be a set"):
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strategy._parse_fsdp_kwargs()
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