[BIG] pytorch-transformers => transformers

transformers/tests/optimization_test.py

@@ -0,0 +1,145 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import unittest
+import os
+import pytest
+
+from transformers import is_torch_available
+
+if is_torch_available():
+    import torch
+
+    from transformers import (AdamW, ConstantLRSchedule, WarmupConstantSchedule,
+                                    WarmupCosineSchedule, WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
+else:
+    pytestmark = pytest.mark.skip("Require Torch")
+
+from .tokenization_tests_commons import TemporaryDirectory
+
+
+def unwrap_schedule(scheduler, num_steps=10):
+    lrs = []
+    for _ in range(num_steps):
+        scheduler.step()
+        lrs.append(scheduler.get_lr())
+    return lrs
+
+def unwrap_and_save_reload_schedule(scheduler, num_steps=10):
+    lrs = []
+    for step in range(num_steps):
+        scheduler.step()
+        lrs.append(scheduler.get_lr())
+        if step == num_steps // 2:
+            with TemporaryDirectory() as tmpdirname:
+                file_name = os.path.join(tmpdirname, 'schedule.bin')
+                torch.save(scheduler.state_dict(), file_name)
+
+                state_dict = torch.load(file_name)
+                scheduler.load_state_dict(state_dict)
+    return lrs
+
+class OptimizationTest(unittest.TestCase):
+
+    def assertListAlmostEqual(self, list1, list2, tol):
+        self.assertEqual(len(list1), len(list2))
+        for a, b in zip(list1, list2):
+            self.assertAlmostEqual(a, b, delta=tol)
+
+    def test_adam_w(self):
+        w = torch.tensor([0.1, -0.2, -0.1], requires_grad=True)
+        target = torch.tensor([0.4, 0.2, -0.5])
+        criterion = torch.nn.MSELoss()
+        # No warmup, constant schedule, no gradient clipping
+        optimizer = AdamW(params=[w], lr=2e-1, weight_decay=0.0)
+        for _ in range(100):
+            loss = criterion(w, target)
+            loss.backward()
+            optimizer.step()
+            w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
+            w.grad.zero_()
+        self.assertListAlmostEqual(w.tolist(), [0.4, 0.2, -0.5], tol=1e-2)
+
+
+class ScheduleInitTest(unittest.TestCase):
+    m = torch.nn.Linear(50, 50) if is_torch_available() else None
+    optimizer = AdamW(m.parameters(), lr=10.) if is_torch_available() else None
+    num_steps = 10
+
+    def assertListAlmostEqual(self, list1, list2, tol):
+        self.assertEqual(len(list1), len(list2))
+        for a, b in zip(list1, list2):
+            self.assertAlmostEqual(a, b, delta=tol)
+
+    def test_constant_scheduler(self):
+        scheduler = ConstantLRSchedule(self.optimizer)
+        lrs = unwrap_schedule(scheduler, self.num_steps)
+        expected_learning_rates = [10.] * self.num_steps
+        self.assertEqual(len(lrs[0]), 1)
+        self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
+
+        scheduler = ConstantLRSchedule(self.optimizer)
+        lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
+        self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
+
+    def test_warmup_constant_scheduler(self):
+        scheduler = WarmupConstantSchedule(self.optimizer, warmup_steps=4)
+        lrs = unwrap_schedule(scheduler, self.num_steps)
+        expected_learning_rates = [2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]
+        self.assertEqual(len(lrs[0]), 1)
+        self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
+
+        scheduler = WarmupConstantSchedule(self.optimizer, warmup_steps=4)
+        lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
+        self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
+
+    def test_warmup_linear_scheduler(self):
+        scheduler = WarmupLinearSchedule(self.optimizer, warmup_steps=2, t_total=10)
+        lrs = unwrap_schedule(scheduler, self.num_steps)
+        expected_learning_rates = [5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25, 0.0]
+        self.assertEqual(len(lrs[0]), 1)
+        self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
+
+        scheduler = WarmupLinearSchedule(self.optimizer, warmup_steps=2, t_total=10)
+        lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
+        self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
+
+    def test_warmup_cosine_scheduler(self):
+        scheduler = WarmupCosineSchedule(self.optimizer, warmup_steps=2, t_total=10)
+        lrs = unwrap_schedule(scheduler, self.num_steps)
+        expected_learning_rates = [5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38, 0.0]
+        self.assertEqual(len(lrs[0]), 1)
+        self.assertListAlmostEqual([l[0] for l in lrs], expected_learning_rates, tol=1e-2)
+
+        scheduler = WarmupCosineSchedule(self.optimizer, warmup_steps=2, t_total=10)
+        lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
+        self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
+
+    def test_warmup_cosine_hard_restart_scheduler(self):
+        scheduler = WarmupCosineWithHardRestartsSchedule(self.optimizer, warmup_steps=2, cycles=2, t_total=10)
+        lrs = unwrap_schedule(scheduler, self.num_steps)
+        expected_learning_rates = [5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46, 0.0]
+        self.assertEqual(len(lrs[0]), 1)
+        self.assertListAlmostEqual([l[0] for l in lrs], expected_learning_rates, tol=1e-2)
+
+        scheduler = WarmupCosineWithHardRestartsSchedule(self.optimizer, warmup_steps=2, cycles=2, t_total=10)
+        lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
+        self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
+
+if __name__ == "__main__":
+    unittest.main()