chore: import upstream snapshot with attribution

test/quantization/test_imperative_skip_op.py

@@ -0,0 +1,77 @@
+#   copyright (c) 2022 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 os
+import sys
+import unittest
+
+import numpy as np
+
+sys.path.append("../../quantization")
+from imperative_test_utils import (
+    ImperativeLenetWithSkipQuant,
+    fix_model_dict,
+    train_lenet,
+)
+
+import paddle
+from paddle.framework import core, set_flags
+from paddle.optimizer import Adam
+from paddle.quantization import ImperativeQuantAware
+
+INFER_MODEL_SUFFIX = ".pdmodel"
+INFER_PARAMS_SUFFIX = ".pdiparams"
+os.environ["CPU_NUM"] = "1"
+if core.is_compiled_with_cuda():
+    set_flags({"FLAGS_cudnn_deterministic": True})
+
+
+class TestImperativeOutSclae(unittest.TestCase):
+    def test_out_scale_acc(self):
+        paddle.disable_static()
+        seed = 1000
+        lr = 0.1
+
+        qat = ImperativeQuantAware()
+
+        np.random.seed(seed)
+        reader = paddle.batch(
+            paddle.dataset.mnist.test(), batch_size=512, drop_last=True
+        )
+
+        lenet = ImperativeLenetWithSkipQuant()
+        lenet = fix_model_dict(lenet)
+        qat.quantize(lenet)
+
+        adam = Adam(learning_rate=lr, parameters=lenet.parameters())
+        dynamic_loss_rec = []
+        lenet.train()
+        loss_list = train_lenet(lenet, reader, adam)
+
+        lenet.eval()
+
+        path = "./save_dynamic_quant_infer_model/lenet"
+        save_dir = "./save_dynamic_quant_infer_model"
+
+        paddle.enable_static()
+
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+        else:
+            place = core.CPUPlace()
+        exe = paddle.static.Executor(place)
+
+
+if __name__ == '__main__':
+    unittest.main()