chore: import upstream snapshot with attribution

test/sequence/test_sequence_softmax_op.py

@@ -0,0 +1,101 @@
+#   Copyright (c) 2018 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 sys
+import unittest
+
+import numpy as np
+from op_test import OpTest
+
+sys.path.append("../legacy_test")
+from test_softmax_op import stable_softmax
+
+from paddle.base import core
+
+
+class TestSequenceSoftmaxOp(OpTest):
+    def setUp(self):
+        self.op_type = "sequence_softmax"
+        self.use_cudnn = False
+        self.init_op_type()
+        self.dtype = "float32" if core.is_compiled_with_rocm() else "float64"
+        x = np.random.uniform(0.1, 1, (110, 1)).astype(self.dtype)
+        self.init_lod()
+        out = np.zeros((110, 1)).astype(self.dtype)
+        offset = 0
+        for i in range(len(self.lod[0])):
+            if self.lod[0][i] == 0:
+                continue
+            sub_x = x[offset : offset + self.lod[0][i], :]
+            sub_x = sub_x.reshape(1, self.lod[0][i])
+            sub_out = stable_softmax(sub_x)
+            out[offset : offset + self.lod[0][i], :] = sub_out.reshape(
+                self.lod[0][i], 1
+            )
+            offset += self.lod[0][i]
+
+        self.inputs = {"X": (x, self.lod)}
+        self.outputs = {"Out": out}
+        self.attrs = {
+            'use_cudnn': self.use_cudnn,
+        }
+
+    def init_lod(self):
+        self.lod = [[40, 10, 30, 30]]
+
+    def init_op_type(self):
+        pass
+
+    def test_check_output(self):
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_output_with_place(place, atol=1e-5, check_dygraph=False)
+        else:
+            self.check_output(check_dygraph=False)
+
+    def test_check_grad(self):
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(place, ["X"], "Out", check_dygraph=False)
+        else:
+            self.check_grad(["X"], "Out", check_dygraph=False)
+
+
+# ----------------cudnn Sequencesoftmax----------------
+@unittest.skipIf(
+    not core.is_compiled_with_cuda(), "core is not compiled with CUDA"
+)
+class TestSequenceSoftmaxCUDNNOp(TestSequenceSoftmaxOp):
+    def init_op_type(self):
+        self.use_cudnn = True
+
+
+class TestSequenceSoftmaxOpSeqLen0Case0(TestSequenceSoftmaxOp):
+    def init_lod(self):
+        self.lod = [[40, 0, 40, 30]]
+
+
+class TestSequenceSoftmaxOpSeqLen0Case1(TestSequenceSoftmaxOp):
+    def init_lod(self):
+        self.lod = [[0, 40, 70, 0]]
+
+
+class TestSequenceSoftmaxOpSeqLen0Case2(TestSequenceSoftmaxOp):
+    def init_lod(self):
+        self.lod = [[0, 0, 0, 110]]
+
+
+if __name__ == "__main__":
+    unittest.main()