119 lines
4.7 KiB
Python
119 lines
4.7 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 sys
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from unittest.mock import Mock
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import lightning.fabric
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import pytest
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import torch
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import torch.distributed
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from lightning.fabric.connector import _Connector
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from lightning.fabric.plugins.precision.transformer_engine import TransformerEnginePrecision
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def test_transformer_engine_plugin(monkeypatch):
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module = lightning.fabric.plugins.precision.transformer_engine
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if module._TRANSFORMER_ENGINE_AVAILABLE:
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pytest.skip("Assumes transformer_engine is unavailable")
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monkeypatch.setattr(module, "_TRANSFORMER_ENGINE_AVAILABLE", lambda: True)
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transformer_engine_mock = Mock()
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monkeypatch.setitem(sys.modules, "transformer_engine", transformer_engine_mock)
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monkeypatch.setitem(sys.modules, "transformer_engine.pytorch", Mock())
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recipe_mock = Mock()
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monkeypatch.setitem(sys.modules, "transformer_engine.common.recipe", recipe_mock)
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connector = _Connector(precision="transformer-engine")
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assert isinstance(connector.precision, TransformerEnginePrecision)
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assert connector.precision.weights_dtype is torch.bfloat16
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connector = _Connector(precision="transformer-engine-float16")
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assert connector.precision.weights_dtype is torch.float16
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recipe_mock.reset_mock()
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precision = TransformerEnginePrecision(weights_dtype=torch.float32)
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connector = _Connector(plugins=precision)
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assert connector.precision is precision
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assert precision.weights_dtype == torch.float32
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recipe_mock.DelayedScaling.assert_called_once_with()
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recipe_mock.reset_mock()
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recipe = {"foo": 0, "fp8_format": "HYBRID"}
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precision = TransformerEnginePrecision(weights_dtype=torch.float16, recipe=recipe)
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connector = _Connector(plugins=precision)
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assert connector.precision is precision
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recipe_mock.DelayedScaling.assert_called_once_with(foo=0, fp8_format=recipe_mock.Format.HYBRID)
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assert isinstance(recipe["fp8_format"], str) # not modified
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# same logic as in `test_default_dtype_is_restored`
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assert torch.get_default_dtype() is torch.float32
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with pytest.raises(RuntimeError, match="foo"), precision.module_init_context():
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assert torch.get_default_dtype() is not torch.float32
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raise RuntimeError("foo")
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assert torch.get_default_dtype() is torch.float32
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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, 3)
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.l1 = torch.nn.Linear(16, 48)
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self.l2 = torch.nn.LayerNorm(1)
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self.l3 = SubModule()
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model = MyModule()
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precision.replace_layers = False
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precision.convert_module(model)
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assert isinstance(model.l1, torch.nn.Linear)
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assert model.l1.weight.dtype == torch.float16
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assert isinstance(model.l3.l, torch.nn.Linear)
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assert isinstance(model.l2, torch.nn.LayerNorm)
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precision.replace_layers = True
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setattr_mock = Mock()
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model.__setattr__ = setattr_mock
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with pytest.warns(match="divisible by 8 and 16"):
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precision.convert_module(model)
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mock_calls = setattr_mock.mock_calls
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assert len(mock_calls) == 2
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assert mock_calls[0][1][0] == "l1"
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assert mock_calls[1][1][0] == "l2"
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assert mock_calls[0][1][1]._extract_mock_name() == "mock.pytorch.Linear()"
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assert mock_calls[1][1][1]._extract_mock_name() == "mock.pytorch.LayerNorm()"
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precision.replace_layers = False
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with precision.module_init_context():
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model = MyModule()
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assert isinstance(model.l1, torch.nn.Linear)
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assert isinstance(model.l2, torch.nn.LayerNorm)
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assert isinstance(model.l3.l, torch.nn.Linear)
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class TELinearMock(Mock):
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...
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class TELayerNormMock(Mock):
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...
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transformer_engine_mock.pytorch.Linear = TELinearMock
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transformer_engine_mock.pytorch.LayerNorm = TELayerNormMock
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precision.replace_layers = True
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with precision.module_init_context():
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assert torch.get_default_dtype() == torch.float16
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model = MyModule()
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assert isinstance(model.l1, TELinearMock)
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assert isinstance(model.l2, TELayerNormMock)
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assert isinstance(model.l3.l, TELinearMock)
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