chore: import upstream snapshot with attribution

test/dygraph_to_static/test_cache_program.py

@@ -0,0 +1,203 @@
+#   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
+from collections import Counter, OrderedDict
+
+import numpy as np
+from dygraph_to_static_utils import (
+    Dy2StTestBase,
+    enable_to_static_guard,
+    test_ast_only,
+)
+from test_fetch_feed import Linear, Pool2D
+
+import paddle
+from paddle.jit.dy2static import convert_to_static
+
+
+class TestCacheProgram(Dy2StTestBase):
+    def setUp(self):
+        self.batch_num = 5
+        self.dygraph_class = Pool2D
+        self.data = np.random.random((1, 2, 4, 4)).astype('float32')
+
+    @test_ast_only
+    def test_cache(self):
+        prev_ops, cur_ops = Counter(), Counter()
+        prev_out, cur_out = None, None
+        static_net = paddle.jit.to_static(self.dygraph_class())
+        for batch_id in range(self.batch_num):
+            out = static_net(paddle.to_tensor(self.data))
+            # Check outputs
+            prev_out = cur_out
+            cur_out = out
+            # Check forward ops
+            prev_ops = cur_ops
+
+            cur_ops = Counter(
+                [
+                    op.name()
+                    for op in static_net.forward.concrete_program.main_program.global_block().ops
+                ]
+            )
+
+            if batch_id > 0:
+                prev_out_numpy = (
+                    prev_out[0].numpy()
+                    if isinstance(prev_out, (tuple, list))
+                    else prev_out.numpy()
+                )
+                cur_out_numpy = (
+                    cur_out[0].numpy()
+                    if isinstance(cur_out, (tuple, list))
+                    else cur_out.numpy()
+                )
+                np.testing.assert_allclose(
+                    prev_out_numpy,
+                    cur_out_numpy,
+                    rtol=1e-05,
+                    err_msg=f'Output in previous batch is {prev_out_numpy}\n Output in current batch is \n{cur_out_numpy}',
+                )
+                self.assertEqual(prev_ops, cur_ops)
+
+
+class TestCacheProgram2(TestCacheProgram):
+    def setUp(self):
+        self.batch_num = 5
+        self.dygraph_class = Linear
+        self.data = np.random.random((4, 10)).astype('float32')
+
+
+class TestCacheProgramWithDictInput(TestCacheProgram):
+    def setUp(self):
+        class DummyModel(paddle.nn.Layer):
+            def __init__(self):
+                super().__init__()
+                self.linear = paddle.nn.Linear(3, 4)
+
+            def forward(self, x_dict):
+                x, y = x_dict["x"], x_dict["y"]
+                return (x * y).sum()
+
+        self.batch_num = 2
+        self.dygraph_class = DummyModel
+        self.data = [
+            {"x": paddle.randn(7, 3), "y": paddle.randn(1, 3)},
+            {"y": paddle.randn(1, 3), "x": paddle.randn(7, 3)},
+        ]
+
+    @test_ast_only
+    def test_cache(self):
+        static_net = paddle.jit.to_static(self.dygraph_class(), full_graph=True)
+        _ = static_net(self.data[0])
+        cache1 = OrderedDict({**static_net.forward._program_cache._caches})
+        _ = static_net(self.data[1])
+        cache2 = static_net.forward._program_cache._caches
+
+        self.assertEqual(
+            cache1,
+            cache2,
+            msg=f"\ncache1({cache1})\n should be equal to \ncache2({cache2})",
+        )
+
+
+class TestCacheProgramWithOptimizer(Dy2StTestBase):
+    def setUp(self):
+        self.dygraph_class = Linear
+        self.data = np.random.random((4, 10)).astype('float32')
+        self.batch_num = 5
+
+    def train_static(self):
+        with enable_to_static_guard(True):
+            return self.train()
+
+    def train_dygraph(self):
+        with enable_to_static_guard(False):
+            return self.train()
+
+    def train(self):
+        static_net = paddle.jit.to_static(self.dygraph_class())
+        adam = paddle.optimizer.Adam(
+            learning_rate=0.001, parameters=static_net.parameters()
+        )
+        loss_data = []
+        for batch_id in range(self.batch_num):
+            input = paddle.to_tensor(self.data)
+            pred, avg_loss = static_net(input)
+
+            loss_data.append(avg_loss.numpy())
+            avg_loss.backward()
+            adam.minimize(avg_loss)
+            static_net.clear_gradients()
+
+        return loss_data
+
+    def test_with_optimizer(self):
+        dygraph_loss = self.train_dygraph()
+        static_loss = self.train_static()
+        np.testing.assert_allclose(
+            dygraph_loss,
+            static_loss,
+            rtol=1e-05,
+            err_msg=f'dygraph is {dygraph_loss}\n static_res is \n{static_loss}',
+        )
+
+
+def simple_func(x):
+    inputs = paddle.assign(x)
+    mean = paddle.mean(inputs)
+    return mean
+
+
+class TestConvertWithCache(Dy2StTestBase):
+    def test_cache(self):
+        static_func = convert_to_static(simple_func)
+        # Get transformed function from cache.
+        cached_func = convert_to_static(simple_func)
+        self.assertTrue(id(static_func), id(cached_func))
+
+
+def sum_even_until_limit(max_len, limit):
+    ret_sum = paddle.to_tensor(np.zeros(1).astype('int32'))
+    for i in range(max_len):
+        if i % 2 > 0:
+            continue
+        elif i > limit:
+            break
+
+        ret_sum += i
+    return ret_sum
+
+
+def sum_under_while(limit):
+    i = paddle.to_tensor(np.zeros(1).astype('int32'))
+    ret_sum = paddle.to_tensor(np.zeros(1).astype('int32'))
+    while i <= limit:
+        ret_sum += i
+        i += 1
+    return ret_sum
+
+
+class TestToOutputWithCache(Dy2StTestBase):
+    def test_output(self):
+        ret = paddle.jit.to_static(sum_even_until_limit)(80, 10)
+        self.assertEqual(ret.numpy(), 30)
+
+        ret = paddle.jit.to_static(sum_under_while)(100)
+        self.assertEqual(ret.numpy(), 5050)
+
+
+if __name__ == '__main__':
+    unittest.main()