chore: import upstream snapshot with attribution

test/dygraph_to_static/test_partial_program.py

@@ -0,0 +1,205 @@
+# Copyright (c) 2020 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
+from dygraph_to_static_utils import (
+    Dy2StTestBase,
+    test_ast_only,
+)
+from test_fetch_feed import Linear
+
+import paddle
+
+SEED = 2020
+
+
+def nested_input(x, y):
+    sum_res = x + y[0]
+
+    z_elem = y[3]['z']
+    sub_res = z_elem[0] - z_elem[1]
+
+    mul_res = y[-1]['d']['da'] * y[-1]['d']['dc']
+    mean_func = paddle.mean
+    out = mean_func(sub_res) + mean_func(sum_res) + mean_func(mul_res)
+    return out
+
+
+def nested_output(x, y):
+    sum_res = x + y
+    sub_res = x - y
+    mul_res = x * y
+
+    out = {}
+    out['z'] = sum_res
+    out['a'] = [sub_res, 64, [mul_res, "cmd"]]
+    return out
+
+
+def fake_data(shape):
+    x_data = np.random.random(shape).astype('float32')
+    return paddle.to_tensor(x_data)
+
+
+class TestWithNestedInput(Dy2StTestBase):
+    def setUp(self):
+        self.x = None
+        self.y = None
+
+    def fake_input(self):
+        self.x = fake_data([10, 16])
+        self.y = [
+            fake_data([10, 16]),
+            "preprocess_cmd",
+            64,
+            {
+                'z': [fake_data([10, 12]), fake_data([10, 12])],
+                'c': fake_data([10, 10]),
+                'd': {'da': 12, 'dc': fake_data([10, 10])},
+            },
+        ]
+
+    def _run(self, to_static):
+        if self.x is None or self.y is None:
+            self.fake_input()
+
+        if to_static:
+            out = paddle.jit.to_static(nested_input, full_graph=True)(
+                self.x, self.y
+            )
+        else:
+            out = nested_input(self.x, self.y)
+
+        return out.numpy()
+
+    def test_nest(self):
+        dygraph_res = self._run(to_static=False)
+        static_res = self._run(to_static=True)
+        np.testing.assert_allclose(dygraph_res, static_res, rtol=1e-05)
+
+
+class TestWithNestedOutput(Dy2StTestBase):
+    def setUp(self):
+        self.x = None
+        self.y = None
+
+    def _run(self, to_static):
+        if self.x is None or self.y is None:
+            self.x = fake_data([10, 16])
+            self.y = fake_data([10, 16])
+
+        if to_static:
+            out = paddle.jit.to_static(nested_output, full_graph=True)(
+                self.x, self.y
+            )
+        else:
+            out = nested_output(self.x, self.y)
+
+        return out
+
+    def test_nest(self):
+        dygraph_res = self._run(to_static=False)
+        dygraph_res = paddle.utils.flatten(dygraph_res)
+
+        static_res = self._run(to_static=True)
+        static_res = paddle.utils.flatten(static_res)
+
+        self.assertTrue(len(dygraph_res) == len(static_res))
+
+        for dy_var, st_var in zip(dygraph_res, static_res):
+            if isinstance(dy_var, paddle.Tensor):
+                np.testing.assert_allclose(
+                    dy_var.numpy(), st_var.numpy(), rtol=1e-05
+                )
+            else:
+                self.assertTrue(dy_var, st_var)
+
+
+class TestWithTrainAndEval(Dy2StTestBase):
+    @test_ast_only
+    def test_switch_eval_and_train(self):
+        linear_net = Linear()
+        linear_net = paddle.jit.to_static(linear_net, full_graph=True)
+        x_data = np.random.random((4, 10)).astype('float32')
+        x = paddle.to_tensor(x_data)
+        linear_net(x)
+
+        _, train_partial_layer = linear_net.forward.program_cache.last()[-1]
+        # check default mode is for training
+        self.assertEqual(
+            train_partial_layer.program,
+            train_partial_layer.train_program,
+        )
+
+        # switch to run test program after `eval()`
+        linear_net.eval()
+        linear_net(x)
+        _, eval_partial_layer = linear_net.forward.program_cache.last()[-1]
+        self.assertEqual(
+            eval_partial_layer.program, eval_partial_layer.infer_program
+        )
+
+        # switch back into training
+        linear_net.train()
+        linear_net(x)
+        self.assertEqual(
+            train_partial_layer.program, train_partial_layer.train_program
+        )
+
+
+class TestWithNoGrad(Dy2StTestBase):
+    @test_ast_only
+    def test_with_no_grad(self):
+        linear_net = Linear()
+        linear_net = paddle.jit.to_static(linear_net, full_graph=True)
+        x_data = np.random.random((5, 10)).astype('float32')
+        x = paddle.to_tensor(x_data)
+
+        with paddle.no_grad():
+            linear_net.train()
+            linear_net(x)
+            _, partial_layer = linear_net.forward.program_cache.last()[-1]
+            self.assertEqual(partial_layer.program, partial_layer.train_program)
+
+
+class GPT2LMHeadModel(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.embedding0 = paddle.nn.Embedding(20, 16)
+        self.embedding1 = paddle.nn.Embedding(20, 32)
+        self.lm_head_weight = paddle.to_tensor(
+            np.random.rand(2, 3).astype('float32')
+        )
+
+    def forward(self, x):
+        x = paddle.reshape(x, shape=[-1, 6])
+        x1, x2, x3 = paddle.split(x=x, axis=1, num_or_sections=3)
+        return x1
+
+
+class TestPruneUnusedParamInProgram(Dy2StTestBase):
+    def test_prune(self):
+        input_ids = np.array([[15, 11, 6, 3, 18, 13]]).astype("float32")
+
+        model = paddle.jit.to_static(GPT2LMHeadModel())
+        model.eval()
+        input_ids = paddle.to_tensor(input_ids)
+        out = model(input_ids)
+        np.testing.assert_array_equal(out.numpy(), [[15, 11]])
+
+
+if __name__ == '__main__':
+    unittest.main()