[MLU]add mlu batch_norm kernel pytest (#39071)

python/paddle/fluid/tests/unittests/mlu/test_batch_norm_op_mlu_v2.py

+# 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 unittest
+import numpy as np
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
+import paddle.fluid as fluid
+import sys
+sys.path.append("..")
+from op_test import OpTest, _set_use_system_allocator
+from paddle.fluid.framework import grad_var_name
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
+import paddle
+
+
+class TestBatchNorm(unittest.TestCase):
+    def test_name(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_mlu():
+            places.append(fluid.MLUPlace(0))
+        for p in places:
+            with fluid.dygraph.guard(p):
+                batch_norm1d = paddle.nn.BatchNorm1D(1, name="test")
+
+    def test_error(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_mlu():
+            places.append(fluid.MLUPlace(0))
+        for p in places:
+            #paddle.disable_static()
+            x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
+            x_data_3 = np.random.random(size=(2, 1, 3)).astype('float32')
+
+            def error1d_dataformat():
+                x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
+                batch_norm1d = paddle.nn.BatchNorm1D(1, data_format='NCDHW')
+                batch_norm1d(fluid.dygraph.to_variable(x_data_4))
+
+            def error2d_dataformat():
+                x_data_3 = np.random.random(size=(2, 1, 3)).astype('float32')
+                batch_norm2d = paddle.nn.BatchNorm2D(1, data_format='NCDHW')
+                batch_norm2d(fluid.dygraph.to_variable(x_data_3))
+
+            def error3d_dataformat():
+                x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
+                batch_norm3d = paddle.nn.BatchNorm3D(1, data_format='NCL')
+                batch_norm3d(fluid.dygraph.to_variable(x_data_4))
+
+            def error1d():
+                x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
+                batch_norm1d = paddle.nn.BatchNorm1D(1)
+                batch_norm1d(fluid.dygraph.to_variable(x_data_4))
+
+            def error2d():
+                x_data_3 = np.random.random(size=(2, 1, 3)).astype('float32')
+                batch_norm2d = paddle.nn.BatchNorm2D(1)
+                batch_norm2d(fluid.dygraph.to_variable(x_data_3))
+
+            def error3d():
+                x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
+                batch_norm3d = paddle.nn.BatchNorm3D(1)
+                batch_norm3d(fluid.dygraph.to_variable(x_data_4))
+
+            with fluid.dygraph.guard(p):
+                self.assertRaises(ValueError, error1d)
+                self.assertRaises(ValueError, error2d)
+                self.assertRaises(ValueError, error3d)
+                self.assertRaises(ValueError, error1d_dataformat)
+                self.assertRaises(ValueError, error2d_dataformat)
+                self.assertRaises(ValueError, error3d_dataformat)
+
+    def test_dygraph(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_mlu():
+            places.append(fluid.MLUPlace(0))
+        for p in places:
+            shape = [4, 10, 4, 4]
+
+            def compute_v1(x, is_test, trainable_statistics):
+                with fluid.dygraph.guard(p):
+                    bn = fluid.dygraph.BatchNorm(
+                        shape[1],
+                        is_test=is_test,
+                        trainable_statistics=trainable_statistics)
+                    y = bn(fluid.dygraph.to_variable(x))
+                return y.numpy()
+
+            def compute_v2(x):
+                with fluid.dygraph.guard(p):
+                    bn = paddle.nn.BatchNorm2D(shape[1])
+                    y = bn(fluid.dygraph.to_variable(x))
+                return y.numpy()
+
+            def compute_v3(x, is_test, trainable_statistics):
+                with fluid.dygraph.guard(p):
+                    bn = fluid.dygraph.BatchNorm(
+                        shape[1],
+                        is_test=is_test,
+                        param_attr=fluid.ParamAttr(
+                            initializer=fluid.initializer.Constant(1.0),
+                            trainable=False),
+                        bias_attr=fluid.ParamAttr(
+                            initializer=fluid.initializer.Constant(0.0),
+                            trainable=False),
+                        trainable_statistics=trainable_statistics)
+                    y = bn(fluid.dygraph.to_variable(x))
+                return y.numpy()
+
+            def compute_v4(x):
+                with fluid.dygraph.guard(p):
+                    bn = paddle.nn.BatchNorm2D(
+                        shape[1], weight_attr=False, bias_attr=False)
+                    y = bn(fluid.dygraph.to_variable(x))
+                return y.numpy()
+
+            x = np.random.randn(*shape).astype("float32")
+            y1 = compute_v1(x, False, False)
+            y2 = compute_v2(x)
+            y3 = compute_v3(x, False, False)
+            y4 = compute_v4(x)
+            self.assertTrue(np.allclose(y1, y2))
+            self.assertTrue(np.allclose(y3, y4))
+
+    def test_static(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_mlu():
+            places.append(fluid.MLUPlace(0))
+        for p in places:
+            exe = fluid.Executor(p)
+            shape = [4, 10, 16, 16]
+
+            def compute_v1(x_np, is_test, trainable_statistics):
+                with program_guard(Program(), Program()):
+                    bn = fluid.dygraph.BatchNorm(
+                        shape[1],
+                        is_test=is_test,
+                        trainable_statistics=trainable_statistics)
+                    x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
+                    y = bn(x)
+                    exe.run(fluid.default_startup_program())
+                    r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
+                return r
+
+            def compute_v2(x_np):
+                with program_guard(Program(), Program()):
+                    bn = paddle.nn.BatchNorm2D(shape[1])
+                    x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
+                    y = bn(x)
+                    exe.run(fluid.default_startup_program())
+                    r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
+                return r
+
+            x = np.random.randn(*shape).astype("float32")
+            y1 = compute_v1(x, False, False)
+            y2 = compute_v2(x)
+            self.assertTrue(np.allclose(y1, y2))
+
+
+class TestBatchNormChannelLast(unittest.TestCase):
+    def setUp(self):
+        self.original_dtyep = paddle.get_default_dtype()
+        paddle.set_default_dtype("float32")
+        self.places = [fluid.CPUPlace()]
+        if core.is_compiled_with_mlu():
+            self.places.append(fluid.MLUPlace(0))
+
+    def tearDown(self):
+        paddle.set_default_dtype(self.original_dtyep)
+
+    def test_1d(self):
+        for p in self.places:
+            with fluid.dygraph.guard(p):
+                x = paddle.randn([2, 6, 4])
+                net1 = paddle.nn.BatchNorm1D(4, data_format="NLC")
+                net2 = paddle.nn.BatchNorm1D(4)
+                net2.weight = net1.weight
+                net2.bias = net1.bias
+                y1 = net1(x)
+                channel_first_x = paddle.transpose(x, [0, 2, 1])
+                y2 = net2(channel_first_x)
+                y2 = paddle.transpose(y2, [0, 2, 1])
+                self.assertEqual(
+                    np.allclose(
+                        y1.numpy(), y2.numpy(), atol=1e-07), True)
+
+    def test_2d(self):
+        for p in self.places:
+            with fluid.dygraph.guard(p):
+                x = paddle.randn([2, 6, 6, 4])
+                net1 = paddle.nn.BatchNorm2D(4, data_format="NHWC")
+                net2 = paddle.nn.BatchNorm2D(4)
+                net2.weight = net1.weight
+                net2.bias = net1.bias
+                y1 = net1(x)
+                channel_first_x = paddle.transpose(x, [0, 3, 1, 2])
+                y2 = net2(channel_first_x)
+                y2 = paddle.transpose(y2, [0, 2, 3, 1])
+                self.assertEqual(
+                    np.allclose(
+                        y1.numpy(), y2.numpy(), atol=1e-07), True)
+
+    def test_3d(self):
+        for p in self.places:
+            with fluid.dygraph.guard(p):
+                x = paddle.randn([2, 6, 6, 6, 4])
+                net1 = paddle.nn.BatchNorm3D(4, data_format="NDHWC")
+                net2 = paddle.nn.BatchNorm3D(4)
+                net2.weight = net1.weight
+                net2.bias = net1.bias
+                y1 = net1(x)
+                channel_first_x = paddle.transpose(x, [0, 4, 1, 2, 3])
+                y2 = net2(channel_first_x)
+                y2 = paddle.transpose(y2, [0, 2, 3, 4, 1])
+                self.assertEqual(
+                    np.allclose(
+                        y1.numpy(), y2.numpy(), atol=1e-07), True)
+                # res = np.allclose(y1.numpy(), y2.numpy())
+                # if res == False:
+                #   np.savetxt("./y1.txt", y1.numpy().flatten(), fmt='%.10f', delimiter='\n')
+                #   np.savetxt("./y2.txt", y2.numpy().flatten(), fmt='%.10f', delimiter='\n')
+                # self.assertEqual(res, True)
+
+
+class TestBatchNormUseGlobalStats(unittest.TestCase):
+    def setUp(self):
+        self.places = [fluid.CPUPlace()]
+        if core.is_compiled_with_mlu():
+            self.places.append(fluid.MLUPlace(0))
+        self.init_test()
+
+    ### train mode
+    def init_test(self):
+        self.use_global_stats = True
+        self.trainable_statistics = False
+
+    def test_global_stats(self):
+        for p in self.places:
+            with fluid.dygraph.guard(p):
+                x = paddle.randn([2, 6, 6, 4])
+                net1 = paddle.fluid.dygraph.BatchNorm(
+                    6,
+                    param_attr=fluid.ParamAttr(
+                        initializer=fluid.initializer.Constant(1.0)),
+                    use_global_stats=self.use_global_stats,
+                    trainable_statistics=self.trainable_statistics)
+                net2 = paddle.nn.BatchNorm2D(
+                    6, use_global_stats=self.use_global_stats)
+                net2.weight = net1.weight
+                net2.bias = net1.bias
+                if self.trainable_statistics == True:
+                    net1.training = False
+                    net2.training = False
+                y1 = net1(x)
+                y2 = net2(x)
+                self.assertEqual(np.allclose(y1.numpy(), y2.numpy()), True)
+
+
+class TestBatchNormUseGlobalStatsCase1(TestBatchNormUseGlobalStats):
+    ### test mode
+    def init_test(self):
+        self.use_global_stats = False
+        self.trainable_statistics = True
+
+
+class TestBatchNormUseGlobalStatsCase2(TestBatchNormUseGlobalStats):
+    ### train mode
+    def init_test(self):
+        self.use_global_stats = False
+        self.trainable_statistics = False
+
+
+class TestBatchNormUseGlobalStatsCase3(TestBatchNormUseGlobalStats):
+    ### test mode
+    def init_test(self):
+        self.use_global_stats = True
+        self.trainable_statistics = True
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    unittest.main()