diff --git a/python/paddle/tests/hapi_mnist_bf16_static.py b/python/paddle/tests/hapi_mnist_bf16_static.py
new file mode 100644
index 0000000000000000000000000000000000000000..7eb4d61a21ee12c5357e8af911017b1523d78dba
--- /dev/null
+++ b/python/paddle/tests/hapi_mnist_bf16_static.py
@@ -0,0 +1,126 @@
+# 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.
+
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import paddle
+
+from paddle import Model, set_device
+from paddle.static import InputSpec as Input
+from paddle.metric import Accuracy
+from paddle.vision.datasets import MNIST
+from paddle.vision.models import LeNet
+import paddle.static.amp as amp
+import random
+from paddle import callbacks
+import argparse
+import ast
+
+SEED = 2
+paddle.seed(SEED)
+paddle.framework.random._manual_program_seed(SEED)
+np.random.seed(SEED)
+random.seed(SEED)
+
+paddle.enable_static()
+set_device('cpu')
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("Lenet BF16 train static script")
+    parser.add_argument(
+        '-bf16',
+        '--bf16',
+        type=ast.literal_eval,
+        default=False,
+        help="whether use bf16")
+    args = parser.parse_args()
+    return args
+
+
+class MnistDataset(MNIST):
+    def __init__(self, mode, return_label=True):
+        super(MnistDataset, self).__init__(mode=mode)
+        self.return_label = return_label
+
+    def __getitem__(self, idx):
+        img = np.reshape(self.images[idx], [1, 28, 28])
+        if self.return_label:
+            return img, np.array(self.labels[idx]).astype('int64')
+        return img,
+
+    def __len__(self):
+        return len(self.images)
+
+
+def compute_accuracy(pred, gt):
+    pred = np.argmax(pred, -1)
+    gt = np.array(gt)
+
+    correct = pred[:, np.newaxis] == gt
+
+    return np.sum(correct) / correct.shape[0]
+
+
+def main(args):
+    print('download training data and load training data')
+    train_dataset = MnistDataset(mode='train', )
+    val_dataset = MnistDataset(mode='test', )
+    test_dataset = MnistDataset(mode='test', return_label=False)
+
+    im_shape = (-1, 1, 28, 28)
+    batch_size = 64
+
+    inputs = [Input(im_shape, 'float32', 'image')]
+    labels = [Input([None, 1], 'int64', 'label')]
+
+    model = Model(LeNet(), inputs, labels)
+    optim = paddle.optimizer.SGD(learning_rate=0.001)
+    if args.bf16:
+        optim = amp.bf16.decorate_bf16(
+            optim,
+            amp_lists=amp.bf16.AutoMixedPrecisionListsBF16(
+                custom_bf16_list={
+                    'matmul_v2', 'pool2d', 'relu', 'scale', 'elementwise_add',
+                    'reshape2', 'slice', 'reduce_mean', 'conv2d'
+                }, ))
+
+    # Configuration model
+    model.prepare(optim, paddle.nn.CrossEntropyLoss(), Accuracy())
+    # Training model #
+    if args.bf16:
+        print('Training BF16')
+    else:
+        print('Training FP32')
+    model.fit(train_dataset, epochs=2, batch_size=batch_size, verbose=1)
+    eval_result = model.evaluate(val_dataset, batch_size=batch_size, verbose=1)
+
+    output = model.predict(
+        test_dataset, batch_size=batch_size, stack_outputs=True)
+
+    np.testing.assert_equal(output[0].shape[0], len(test_dataset))
+
+    acc = compute_accuracy(output[0], val_dataset.labels)
+
+    print("acc", acc)
+    print("eval_result['acc']", eval_result['acc'])
+
+    np.testing.assert_allclose(acc, eval_result['acc'])
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)