[BIG] name change

pytorch_transformers/tests/optimization_test.py

@@ -0,0 +1,91 @@
+# 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 torch
+
+from pytorch_transformers import BertAdam
+from pytorch_transformers import OpenAIAdam
+from pytorch_transformers.optimization import ConstantLR, WarmupLinearSchedule, WarmupConstantSchedule, \
+    WarmupCosineWithWarmupRestartsSchedule, WarmupCosineWithHardRestartsSchedule, WarmupCosineSchedule
+import numpy as np
+
+
+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(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 = BertAdam(params=[w], lr=2e-1,
+                                          weight_decay=0.0,
+                                          max_grad_norm=-1)
+        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):
+    def test_bert_sched_init(self):
+        m = torch.nn.Linear(50, 50)
+        optim = BertAdam(m.parameters(), lr=0.001, warmup=.1, t_total=1000, schedule=None)
+        self.assertTrue(isinstance(optim.param_groups[0]["schedule"], ConstantLR))
+        optim = BertAdam(m.parameters(), lr=0.001, warmup=.1, t_total=1000, schedule="none")
+        self.assertTrue(isinstance(optim.param_groups[0]["schedule"], ConstantLR))
+        optim = BertAdam(m.parameters(), lr=0.001, warmup=.01, t_total=1000)
+        self.assertTrue(isinstance(optim.param_groups[0]["schedule"], WarmupLinearSchedule))
+        # shouldn't fail
+
+    def test_openai_sched_init(self):
+        m = torch.nn.Linear(50, 50)
+        optim = OpenAIAdam(m.parameters(), lr=0.001, warmup=.1, t_total=1000, schedule=None)
+        self.assertTrue(isinstance(optim.param_groups[0]["schedule"], ConstantLR))
+        optim = OpenAIAdam(m.parameters(), lr=0.001, warmup=.1, t_total=1000, schedule="none")
+        self.assertTrue(isinstance(optim.param_groups[0]["schedule"], ConstantLR))
+        optim = OpenAIAdam(m.parameters(), lr=0.001, warmup=.01, t_total=1000)
+        self.assertTrue(isinstance(optim.param_groups[0]["schedule"], WarmupLinearSchedule))
+        # shouldn't fail
+
+
+class WarmupCosineWithRestartsTest(unittest.TestCase):
+    def test_it(self):
+        m = WarmupCosineWithWarmupRestartsSchedule(warmup=0.05, t_total=1000., cycles=5)
+        x = np.arange(0, 1000)
+        y = [m.get_lr(xe) for xe in x]
+        y = np.asarray(y)
+        expected_zeros = y[[0, 200, 400, 600, 800]]
+        print(expected_zeros)
+        expected_ones = y[[50, 250, 450, 650, 850]]
+        print(expected_ones)
+        self.assertTrue(np.allclose(expected_ones, 1))
+        self.assertTrue(np.allclose(expected_zeros, 0))
+
+
+if __name__ == "__main__":
+    unittest.main()