142 lines
4.3 KiB
Python
142 lines
4.3 KiB
Python
# Copyright The PyTorch Lightning 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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import pytest
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import torch
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import torch.distributed as dist
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import torch.multiprocessing as mp
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import tests.helpers.utils as tutils
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from pytorch_lightning.core.step_result import Result
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from pytorch_lightning.metrics import Metric
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class DummyMetric(Metric):
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def __init__(self):
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super().__init__()
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self.add_state("x", torch.tensor(0), dist_reduce_fx="sum")
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def update(self, x):
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self.x += x
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def compute(self):
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return self.x
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def _setup_ddp(rank, worldsize):
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import os
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os.environ["MASTER_ADDR"] = "localhost"
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# initialize the process group
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dist.init_process_group("gloo", rank=rank, world_size=worldsize)
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def _ddp_test_fn(rank, worldsize):
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_setup_ddp(rank, worldsize)
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torch.tensor([1.0])
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metric_a = DummyMetric()
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metric_b = DummyMetric()
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metric_c = DummyMetric()
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# dist_sync_on_step is False by default
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result = Result()
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for epoch in range(3):
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cumulative_sum = 0
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for i in range(5):
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metric_a(i)
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metric_b(i)
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metric_c(i)
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cumulative_sum += i
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result.log('a', metric_a, on_step=True, on_epoch=True)
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result.log('b', metric_b, on_step=False, on_epoch=True)
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result.log('c', metric_c, on_step=True, on_epoch=False)
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batch_log = result.get_batch_log_metrics()
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batch_expected = {"a_step": i, "a": i, "c": i}
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assert set(batch_log.keys()) == set(batch_expected.keys())
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for k in batch_expected.keys():
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assert batch_expected[k] == batch_log[k]
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epoch_log = result.get_epoch_log_metrics()
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# assert metric state reset to default values
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assert metric_a.x == metric_a._defaults['x']
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assert metric_b.x == metric_b._defaults['x']
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assert metric_c.x == metric_c._defaults['x']
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epoch_expected = {"b": cumulative_sum * worldsize, "a_epoch": cumulative_sum * worldsize}
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assert set(epoch_log.keys()) == set(epoch_expected.keys())
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for k in epoch_expected.keys():
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assert epoch_expected[k] == epoch_log[k]
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@pytest.mark.skipif(sys.platform == "win32", reason="DDP not available on windows")
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def test_result_reduce_ddp():
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"""Make sure result logging works with DDP"""
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tutils.reset_seed()
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tutils.set_random_master_port()
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worldsize = 2
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mp.spawn(_ddp_test_fn, args=(worldsize, ), nprocs=worldsize)
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def test_result_metric_integration():
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metric_a = DummyMetric()
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metric_b = DummyMetric()
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metric_c = DummyMetric()
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result = Result()
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for epoch in range(3):
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cumulative_sum = 0
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for i in range(5):
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metric_a(i)
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metric_b(i)
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metric_c(i)
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cumulative_sum += i
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result.log('a', metric_a, on_step=True, on_epoch=True)
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result.log('b', metric_b, on_step=False, on_epoch=True)
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result.log('c', metric_c, on_step=True, on_epoch=False)
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batch_log = result.get_batch_log_metrics()
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batch_expected = {"a_step": i, "a": i, "c": i}
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assert set(batch_log.keys()) == set(batch_expected.keys())
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for k in batch_expected.keys():
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assert batch_expected[k] == batch_log[k]
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epoch_log = result.get_epoch_log_metrics()
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# assert metric state reset to default values
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assert metric_a.x == metric_a._defaults['x']
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assert metric_b.x == metric_b._defaults['x']
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assert metric_c.x == metric_c._defaults['x']
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epoch_expected = {"b": cumulative_sum, "a_epoch": cumulative_sum}
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assert set(epoch_log.keys()) == set(epoch_expected.keys())
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for k in epoch_expected.keys():
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assert epoch_expected[k] == epoch_log[k]
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