118 lines
4.1 KiB
Python
118 lines
4.1 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 time
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from unittest.mock import Mock
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import pytest
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import torch.nn
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from lightning.fabric.utilities.init import (
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_EmptyInit,
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_has_meta_device_parameters_or_buffers,
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_materialize_meta_tensors,
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)
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from tests_fabric.helpers.runif import RunIf
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@RunIf(min_cuda_gpus=1)
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def test_empty_init(monkeypatch):
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"""Test that `_EmptyInit()` skips initialization and allocates uninitialized memory."""
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init_mock = Mock()
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monkeypatch.setattr(torch.Tensor, "uniform_", init_mock)
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with _EmptyInit(enabled=True):
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torch.nn.Linear(2, 2, device="cuda")
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init_mock.assert_not_called()
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with _EmptyInit(enabled=False):
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torch.nn.Linear(2, 2, device="cuda")
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init_mock.assert_called()
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@RunIf(min_cuda_gpus=1)
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def test_empty_init_speed():
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"""Test that `_EmptyInit()` is faster than regular initialization."""
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t0 = time.perf_counter()
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with _EmptyInit(enabled=False):
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torch.nn.Linear(10000, 10000, device="cuda")
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torch.cuda.synchronize()
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normal_init_time = time.perf_counter() - t0
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t0 = time.perf_counter()
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with _EmptyInit(enabled=True):
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torch.nn.Linear(10000, 10000, device="cuda")
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torch.cuda.synchronize()
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empty_init_time = time.perf_counter() - t0
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assert normal_init_time > 2 * empty_init_time
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def test_materialize_meta_tensors():
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class Submodule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.l = torch.nn.Linear(1, 1)
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class MyModel(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.register_buffer("buf", torch.tensor(0))
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self.l = torch.nn.Linear(1, 1)
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self.inner = Submodule()
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with torch.device("meta"):
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model = MyModel()
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with pytest.raises(TypeError, match="MyModel.reset_parameters` method is implemented"):
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_materialize_meta_tensors(model, torch.device("cpu"))
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class MyModel2(MyModel):
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def reset_parameters(self):
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self.buf = torch.empty_like(self.buf)
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with torch.device("meta"):
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model = MyModel2()
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_materialize_meta_tensors(model, torch.device("cpu"))
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assert model.buf.device.type == "cpu"
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assert len(list(model.parameters())) == 4
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assert all(p.device.type == "cpu" for p in model.parameters())
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def test_has_meta_device_parameters_or_buffers():
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"""Test that the `_has_meta_device_parameters_or_buffers` function can find meta-device parameters in models and
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optimizers."""
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class BufferModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.register_buffer("buffer", torch.ones(2, device="meta"))
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# nn.Module
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module = torch.nn.Linear(2, 2)
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meta_module = torch.nn.Linear(2, 2, device="meta")
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buffer_meta_module = BufferModule()
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assert not _has_meta_device_parameters_or_buffers(module)
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assert _has_meta_device_parameters_or_buffers(meta_module)
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assert _has_meta_device_parameters_or_buffers(torch.nn.Sequential(module, meta_module, torch.nn.ReLU()))
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assert _has_meta_device_parameters_or_buffers(buffer_meta_module)
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# optim.Optimizer
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optimizer = torch.optim.SGD(module.parameters(), lr=0.1)
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meta_optimizer = torch.optim.SGD(meta_module.parameters(), lr=0.1)
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assert not _has_meta_device_parameters_or_buffers(optimizer)
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assert _has_meta_device_parameters_or_buffers(meta_optimizer)
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# unsupported objects
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with pytest.raises(TypeError, match="Expected `torch.nn.Module` or `torch.optim.Optimizer`"):
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_has_meta_device_parameters_or_buffers(None)
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