add new auto mix presicion testcase and check the cast num

Signed-off-by: zhushujing <zhushujing@huawei.com>

tests/st/mix_precision/test_mix_precision.py

@@ -13,14 +13,31 @@
 # limitations under the License.
 """Test network turn on mix_precision."""
 
+import os
+import re
 import pytest
 import numpy as np
+from mindspore.common import dtype
 from mindspore import nn
 from mindspore import ops
 from mindspore import amp
 from mindspore import Tensor
 from mindspore import context
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
+from mindspore.train.model import Model
+from utils import FakeData
+from utils import allclose_nparray
+from utils import FakeDataInitMode
+from utils import find_newest_validateir_file
+from utils import clean_all_ir_files
+
+
+def read_validateir_file(path_folder):
+    filename = find_newest_validateir_file(path_folder)
+    with open(os.path.join(filename), 'r') as f:
+        contend = f.read()
+    clean_all_ir_files(path_folder)
+    return contend
 
 
 class Net(nn.Cell):
@@ -62,7 +79,7 @@ class Net(nn.Cell):
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
-def test_auto_mix_precision():
+def test_sit_auto_mix_precision_train_o3():
     input_data = np.random.randn(32, 3, 224, 224).astype(np.float64)
     label_data = np.random.randn(32, 10).astype(np.float32)
     # graph mode
@@ -87,3 +104,74 @@ def test_auto_mix_precision():
                                                          drop_overflow_update=False))
     out_pynative = train_network_pynative(Tensor(input_data), Tensor(label_data))
     assert np.allclose(out.asnumpy(), out_pynative.asnumpy(), 0.001, 0.001)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_sit_auto_mix_precision_model_o0():
+    input_data = np.random.randn(32, 3, 224, 224).astype(np.float32)
+    dataset1 = FakeData(size=32,
+                        batch_size=32,
+                        image_size=(3, 224, 224),
+                        num_classes=10,
+                        fakedata_mode=FakeDataInitMode.OnesInit)
+    dataset1.set_label_data_type(np.float16)
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    context.set_context(save_graphs=True, save_graphs_path='./test_amp_o0')
+    net = Net(3, 10)
+    net.to_float(dtype.float16)
+    opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.001, momentum=0.0009)
+    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
+    model = Model(net, loss, opt, amp_level="O0")
+    model.train(1, dataset1, dataset_sink_mode=False)
+    contend = read_validateir_file('./test_amp_o0')
+    castnum = re.findall("Cast", contend)
+    assert len(castnum) == 17
+    model.predict(Tensor(input_data))
+    contend = read_validateir_file('./test_amp_o0')
+    castnum = re.findall("Cast", contend)
+    assert len(castnum) == 11
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_sit_auto_mix_precision_model_o2():
+    input_data = np.random.randn(32, 3, 224, 224).astype(np.float32)
+    dataset1 = FakeData(size=32,
+                        batch_size=32,
+                        image_size=(3, 224, 224),
+                        num_classes=10,
+                        fakedata_mode=FakeDataInitMode.OnesInit)
+    dataset2 = FakeData(size=32,
+                        batch_size=32,
+                        image_size=(3, 224, 224),
+                        num_classes=10,
+                        fakedata_mode=FakeDataInitMode.OnesInit)
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    context.set_context(save_graphs=True, save_graphs_path='./test_amp_o2')
+    net = Net(3, 10)
+    opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.001, momentum=0.0009)
+    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
+    model = Model(net, loss, opt, amp_level="O2")
+    model.train(1, dataset1, dataset_sink_mode=False)
+    contend = read_validateir_file('./test_amp_o2')
+    castnum = re.findall("Cast", contend)
+    assert len(castnum) == 14
+    out_graph = model.predict(Tensor(input_data))
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    net_pynative = Net(3, 10)
+    opt_pynative = nn.Momentum(params=net_pynative.trainable_params(), learning_rate=0.001, momentum=0.0009)
+    loss_pynative = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
+    model_pynative = Model(net_pynative, loss_pynative, opt_pynative, amp_level="O2")
+    model_pynative.train(1, dataset2, dataset_sink_mode=False)
+    out_pynative = model_pynative.predict(Tensor(input_data))
+    allclose_nparray(out_graph.asnumpy(), out_pynative.asnumpy(), 0.001, 0.001)

tests/st/mix_precision/utils.py

@@ -0,0 +1,157 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+""" create train dataset. """
+
+import os
+import re
+import numpy as np
+from mindspore.communication.management import init
+from mindspore.communication.management import get_rank
+from mindspore.communication.management import get_group_size
+from mindspore import Tensor
+
+
+def _count_unequal_element(data_expected, data_me, rtol, atol):
+    assert data_expected.shape == data_me.shape
+    total_count = len(data_expected.flatten())
+    error = np.abs(data_expected - data_me)
+    greater = np.greater(error, atol + np.abs(data_me) * rtol)
+    loss_count = np.count_nonzero(greater)
+    assert (loss_count / total_count) < rtol, \
+        "\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}". \
+            format(data_expected[greater], data_me[greater], error[greater])
+
+
+def allclose_nparray(data_expected, data_me, rtol, atol, equal_nan=True):
+    if np.any(np.isnan(data_expected)):
+        assert np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan)
+    elif not np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan):
+        _count_unequal_element(data_expected, data_me, rtol, atol)
+    else:
+        assert True
+
+
+def clean_all_ir_files(folder_path):
+    if os.path.exists(folder_path):
+        for file_name in os.listdir(folder_path):
+            if file_name.endswith('.ir') or file_name.endswith('.dat') or file_name.endswith('.dot'):
+                os.remove(os.path.join(folder_path, file_name))
+
+
+def find_newest_validateir_file(folder_path):
+    validate_files = map(lambda f: os.path.join(folder_path, f),
+                         filter(lambda f: re.match(r'\d+_validate_\d+.ir', f), os.listdir(folder_path)))
+    return max(validate_files, key=os.path.getctime)
+
+
+class FakeDataInitMode:
+    RandomInit = 0
+    OnesInit = 1
+    UniqueInit = 2
+    ZerosInit = 3
+
+
+class FakeData:
+    def __init__(self, size=1024, batch_size=32, image_size=(3, 224, 224),
+                 num_classes=10, random_offset=0, use_parallel=False,
+                 fakedata_mode=FakeDataInitMode.RandomInit):
+        self.size = size
+        self.rank_batch_size = batch_size
+        self.total_batch_size = self.rank_batch_size
+        self.random_offset = random_offset
+        self.image_size = image_size
+        self.num_classes = num_classes
+        self.rank_size = 1
+        self.rank_id = 0
+        self.batch_index = 0
+        self.image_data_type = np.float32
+        self.label_data_type = np.float32
+        self.is_onehot = True
+        self.fakedata_mode = fakedata_mode
+
+        if use_parallel is True:
+            init()
+            self.rank_size = get_group_size()
+            self.rank_id = get_rank()
+
+        self.total_batch_size = self.rank_batch_size * self.rank_size
+
+        assert (self.size % self.total_batch_size) == 0
+
+        self.total_batch_data_size = (self.rank_size, self.rank_batch_size) + image_size
+
+    def get_dataset_size(self):
+        return int(self.size / self.total_batch_size)
+
+    def get_repeat_count(self):
+        return 1
+
+    def set_image_data_type(self, data_type):
+        self.image_data_type = data_type
+
+    def set_label_data_type(self, data_type):
+        self.label_data_type = data_type
+
+    def set_label_onehot(self, is_onehot=True):
+        self.is_onehot = is_onehot
+
+    def create_tuple_iterator(self, num_epochs=-1, do_copy=True):
+        _ = num_epochs
+        return self
+
+    def __getitem__(self, batch_index):
+        if batch_index * self.total_batch_size >= len(self):
+            raise IndexError("{} index out of range".format(self.__class__.__name__))
+        rng_state = np.random.get_state()
+        np.random.seed(batch_index + self.random_offset)
+        if self.fakedata_mode == FakeDataInitMode.OnesInit:
+            img = np.ones(self.total_batch_data_size)
+        elif self.fakedata_mode == FakeDataInitMode.ZerosInit:
+            img = np.zeros(self.total_batch_data_size)
+        elif self.fakedata_mode == FakeDataInitMode.UniqueInit:
+            total_size = 1
+            for i in self.total_batch_data_size:
+                total_size = total_size * i
+            img = np.reshape(np.arange(total_size) * 0.0001, self.total_batch_data_size)
+        else:
+            img = np.random.randn(*self.total_batch_data_size)
+        target = np.random.randint(0, self.num_classes, size=(self.rank_size, self.rank_batch_size))
+        np.random.set_state(rng_state)
+        img = img[self.rank_id]
+        target = target[self.rank_id]
+        img_ret = img.astype(self.image_data_type)
+        target_ret = target.astype(self.label_data_type)
+        if self.is_onehot:
+            target_onehot = np.zeros(shape=(self.rank_batch_size, self.num_classes))
+            target_onehot[np.arange(self.rank_batch_size), target] = 1
+            target_ret = target_onehot.astype(self.label_data_type)
+        return Tensor(img_ret), Tensor(target_ret)
+
+    def __len__(self):
+        return self.size
+
+    def __iter__(self):
+        self.batch_index = 0
+        return self
+
+    def reset(self):
+        self.batch_index = 0
+
+    def __next__(self):
+        if self.batch_index * self.total_batch_size < len(self):
+            data = self[self.batch_index]
+            self.batch_index += 1
+            return data
+        raise StopIteration