【PaddlePaddle Hackathon】1、在 Paddle 中新增 AdaptiveLogSoftmaxWithLoss

python/paddle/fluid/tests/unittests/test_adaptive_log_softmax_with_loss.py

@@ -0,0 +1,143 @@
+#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+import unittest
+import numpy as np
+import paddle
+from paddle import nn
+from paddle.nn import functional as F
+
+
+class TestNNAdaptiveLogSoftmaxWithLossAPI(unittest.TestCase):
+    def setUp(self):
+        paddle.disable_static()
+
+    def test_error(self):
+        # args validation
+        with self.assertRaises(ValueError):
+            _ = nn.AdaptiveLogSoftmaxWithLoss(16, 20, [5, 15, 15], div_value=2.)
+
+        with self.assertRaises(ValueError):
+            _ = nn.AdaptiveLogSoftmaxWithLoss(16, 20, [5, 15, 10], div_value=2.)
+
+        with self.assertRaises(ValueError):
+            _ = nn.AdaptiveLogSoftmaxWithLoss(16, 20, [5, 10, 25], div_value=2.)
+
+        with self.assertRaisesRegex(ValueError,
+                                    "cutoffs should be a sequence of unique,"):
+            _ = nn.AdaptiveLogSoftmaxWithLoss(16, 20, [5, 10, 20], div_value=2.)
+        # not raise
+        _ = nn.AdaptiveLogSoftmaxWithLoss(16, 20, [5, 10, 19], div_value=2.)
+
+    def test_shape(self):
+        # input shapes
+        with self.assertRaisesRegex(
+                RuntimeError, r"Input and target should have the same size"):
+            asfm = nn.AdaptiveLogSoftmaxWithLoss(
+                16, 20, [5, 10, 15], div_value=2.)
+            x = paddle.randn((2, 16))
+            y = paddle.to_tensor([0, 5, 10])
+            asfm(x, y)
+
+        # out-of-bound targets
+        with self.assertRaisesRegex(RuntimeError,
+                                    r"Target values should be in"):
+            asfm = nn.AdaptiveLogSoftmaxWithLoss(
+                16, 20, [5, 10, 15], div_value=2.)
+            x = paddle.randn((128, 16))
+            y = paddle.randint(low=21, high=200, shape=[128])
+            asfm(x, y)
+
+    def test_cluster(self):
+        # cluster sizes
+        asfm = nn.AdaptiveLogSoftmaxWithLoss(16, 20, [5, 10, 15], div_value=2.)
+        x = paddle.randn((128, 16))
+        y = paddle.randint(low=0, high=20, shape=[128])
+
+        self.assertEqual(
+            asfm.head.weight.shape,
+            [16, 5 + 3])  # 5 targets in head, 3 clusters, dimensionality 16
+        self.assertEqual(asfm.tail[0][1].weight.shape,
+                         [8, 5])  # 5 targets in this cluster, dimensionality 8
+        self.assertEqual(asfm.tail[1][1].weight.shape, [4, 5])
+        self.assertEqual(asfm.tail[2][1].weight.shape, [2, 5])
+
+        self.assertEqual(asfm(x, y).output.shape, [128])
+
+    def test_log_probs(self):
+        # log_probs actually returns log_proba
+        asfm = nn.AdaptiveLogSoftmaxWithLoss(8, 4, [2], div_value=2.)
+        x = paddle.randn((4, 8))
+        logprob_out = asfm.log_prob(x)
+        np.testing.assert_array_almost_equal(
+            paddle.exp(logprob_out).sum(1), paddle.ones([4]))
+
+        # forward returns the same thing as log_probs
+        for v in [0, 1, 2, 3]:
+            y = paddle.full((4, ), v, dtype='int64')
+            out, loss = asfm(x, y)
+            np.testing.assert_array_almost_equal(
+                out,
+                logprob_out.gather(y.unsqueeze(1), 1).slice([1], [0],
+                                                            [1]).squeeze())
+            np.testing.assert_array_almost_equal(loss,
+                                                 F.nll_loss(logprob_out, y))
+
+    def test_correct(self):
+        # predict
+        x = paddle.abs(paddle.randn((64, 8)))
+
+        # argmax in shortlist
+        asfm = nn.AdaptiveLogSoftmaxWithLoss(
+            8, 10, [4, 8], div_value=2., head_bias=True)
+        asfm.head.weight.detach().abs()
+        asfm.head.bias.detach().abs()
+        asfm.head.weight.detach()[asfm.shortlist_size:, :] *= 0.
+
+        out = asfm.predict(x)
+        np.testing.assert_array_almost_equal(
+            out, asfm.log_prob(x).argmax(axis=1))
+
+        # argmax outside of shortlist
+        asfm = nn.AdaptiveLogSoftmaxWithLoss(
+            8, 10, [4, 8], div_value=2., head_bias=True)
+        asfm.head.weight.detach().abs()
+        asfm.head.bias.detach().abs()
+        asfm.head.weight.detach()[:asfm.shortlist_size, :] *= 0.
+
+        out = asfm.predict(x)
+        np.testing.assert_array_almost_equal(
+            out, asfm.log_prob(x).argmax(axis=1))
+
+        # half of the argmax in shortlist, half in clusters
+        asfm = nn.AdaptiveLogSoftmaxWithLoss(
+            8, 10, [4, 8], div_value=2., head_bias=True)
+        asfm.head.weight.detach().abs()
+        asfm.head.bias.detach().abs()
+
+        x[:32, :asfm.shortlist_size] *= 0.
+        x[32:, asfm.shortlist_size:] *= 0.
+
+        asfm.head.weight.detach()[:asfm.shortlist_size,
+                                  asfm.shortlist_size:] *= 0.
+        asfm.head.weight.detach()[asfm.shortlist_size:, :
+                                  asfm.shortlist_size] *= 0.
+
+        out = asfm.predict(x)
+        np.testing.assert_array_almost_equal(
+            out, asfm.log_prob(x).argmax(axis=1))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/nn/__init__.py

@@ -90,6 +90,7 @@
 from .layer.conv import Conv2DTranspose  # noqa: F401
 from .layer.conv import Conv3DTranspose  # noqa: F401
 
+from .layer.loss import AdaptiveLogSoftmaxWithLoss  # noqa: F401
 from .layer.loss import BCEWithLogitsLoss  # noqa: F401
 from .layer.loss import CrossEntropyLoss  # noqa: F401
 from .layer.loss import HSigmoidLoss  # noqa: F401
@@ -276,6 +277,7 @@ def weight_norm(*args):
            'Conv3DTranspose',
            'Flatten',
            'AdaptiveAvgPool1D',
+           'AdaptiveLogSoftmaxWithLoss',
            'Tanhshrink',
            'HSigmoidLoss',
            'PReLU',

python/paddle/nn/layer/__init__.py

@@ -57,6 +57,7 @@
 from .pooling import AdaptiveMaxPool2D  # noqa: F401
 from .pooling import AdaptiveMaxPool3D  # noqa: F401
 from .pooling import MaxUnPool2D  # noqa: F401
+from .loss import AdaptiveLogSoftmaxWithLoss  # noqa: F401
 from .conv import Conv1D  # noqa: F401
 from .conv import Conv2D  # noqa: F401
 from .conv import Conv3D  # noqa: F401