add optimizer_parallel level0 case

tests/st/auto_parallel/optimizer_parallel.py

@@ -0,0 +1,328 @@
+# 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.
+# ============================================================================
+
+import os
+import numpy as np
+
+from mindspore.communication.management import init
+from mindspore.communication.management import release
+from mindspore.communication.management import get_rank
+from mindspore.communication.management import get_group_size
+from mindspore.nn import Cell
+from mindspore.nn import ReLU
+from mindspore.nn import Dense
+from mindspore.nn import Flatten
+from mindspore.nn import Momentum
+import mindspore.ops.operations as P
+from mindspore.train.serialization import load_param_into_net
+from mindspore.train.callback import CheckpointConfig
+from mindspore.train.callback import ModelCheckpoint
+from mindspore.train.serialization import load_checkpoint
+
+from mindspore.nn import SoftmaxCrossEntropyWithLogits
+from mindspore.train import Model
+from mindspore.parallel import set_algo_parameters
+from mindspore import Tensor
+from mindspore.common.parameter import Parameter
+from mindspore import context
+from mindspore.context import ParallelMode
+
+context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
+
+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_ckpt_files(folder_path):
+    if os.path.exists(folder_path):
+        for file_name in os.listdir(folder_path):
+            if file_name.endswith('.ckpt') or file_name.endswith('.meta'):
+                os.remove(os.path.join(folder_path, file_name))
+
+
+def find_newest_ckpt_file(folder_path):
+    ckpt_files = map(lambda f: os.path.join(folder_path, f),
+                     filter(lambda f: f.endswith('.ckpt'),
+                            os.listdir(folder_path)))
+    return max(ckpt_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(backend_name='hccl')
+            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):
+        _ = 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
+
+
+class OptimizerSemiAutoAndAutoParallel6Net(Cell):
+    def __init__(self, strategy_dict=None):
+        super().__init__()
+        shared_np = np.full((16, 1, 32, 32), 0.5, dtype=np.float32)
+        self.shared_weight = Parameter(Tensor(shared_np), name='shared_weight')
+        self.fc1 = Dense(in_channels=1024,
+                         out_channels=116,
+                         weight_init='ones',
+                         bias_init='ones',
+                         has_bias=True)
+        self.relu = ReLU()
+        self.sigmoid = P.Sigmoid()
+        self.add1 = P.TensorAdd()
+        self.add2 = P.TensorAdd()
+        self.mul1 = P.Mul().add_prim_attr('primitive_target', 'CPU')
+        self.mul2 = P.Mul()
+        self.mul3 = P.Mul()
+        self.flatten = Flatten()
+
+        mul2_weight_np = np.full((16, 116), 1, dtype=np.float32)
+        self.mul2_weight = Parameter(Tensor(mul2_weight_np), name='mul2_weight')
+
+        mul3_weight_np = np.full((16, 116), 1, dtype=np.float32)
+        self.mul3_weight = Parameter(Tensor(mul3_weight_np), name='mul3_weight')
+
+        if strategy_dict is not None:
+            self.add1.shard(strategy_dict['add1'])
+            self.mul1.shard(strategy_dict['mul1'])
+            self.fc1.matmul.shard(strategy_dict['fc1_matmul'])
+            self.fc1.bias_add.shard(strategy_dict['fc1_bias_add'])
+            self.mul2.shard(strategy_dict['mul2'])
+            self.mul3.shard(strategy_dict['mul3'])
+
+    def construct(self, inputs):
+        relu = self.relu(inputs)
+        sigmoid = self.sigmoid(inputs)
+        add1 = self.add1(relu, self.shared_weight)
+        mul = self.mul1(sigmoid, self.shared_weight)
+        add2 = self.add2(add1, mul)
+        flatten = self.flatten(add2)
+        dense = self.fc1(flatten)
+        mul2 = self.mul2(dense, self.mul2_weight)
+        out = self.mul3(mul2, self.mul3_weight)
+        return out
+
+
+class OptimizerSemiAutoAndAutoParallelFactory:
+    def __init__(self, net, strategy_dict=None):
+        self.parallel_ckpt = None
+        self.optimizer_parallel_ckpt = None
+        self.net = net
+        self.strategy_dict = strategy_dict
+        self.global_rank_id = None
+        self._set_parallel_env()
+        self._init_parallel()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        return
+
+    def __del__(self):
+        self._release_parallel()
+
+    def _set_parallel_env(self):
+        if 'RANK_ID' in os.environ:
+            self.global_rank_id = int(os.environ['RANK_ID'])
+
+    def _init_parallel(self):
+        self._init_parallel_flag = False
+        init(backend_name='hccl')
+        self._init_parallel_flag = True
+
+    def _release_parallel(self):
+        if self._init_parallel_flag:
+            release()
+
+    def _model_train_and_save_ckpt(self, net, dataset, epoch):
+        self.opt = Momentum(learning_rate=0.01, momentum=0.9, params=net.get_parameters())
+        self.loss_fn = SoftmaxCrossEntropyWithLogits(reduction='mean')
+        self.model = Model(network=net,
+                           loss_fn=self.loss_fn,
+                           optimizer=self.opt)
+        ckpt_config = CheckpointConfig(keep_checkpoint_max=1)
+        ckpt_path = './rank_{}_ckpt'.format(self.global_rank_id)
+        ckpt_callback = ModelCheckpoint(prefix='parallel', directory=ckpt_path,
+                                        config=ckpt_config)
+        clean_all_ckpt_files(ckpt_path)
+        self.model.train(epoch=epoch,
+                         train_dataset=dataset,
+                         callbacks=[ckpt_callback],
+                         dataset_sink_mode=False)
+        newest_ckpt_file = find_newest_ckpt_file(ckpt_path)
+        return load_checkpoint(newest_ckpt_file)
+
+    def mindspore_auto_parallel_impl(self,
+                                     dataset,
+                                     epoch,
+                                     device_num):
+        set_algo_parameters(fully_use_devices=False)
+        context.reset_auto_parallel_context()
+        context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL,
+                                          device_num=device_num)
+        parallel_mode_net = self.net(self.strategy_dict)
+        self.parallel_ckpt = self._model_train_and_save_ckpt(net=parallel_mode_net,
+                                                             dataset=dataset, epoch=epoch)
+        context.reset_auto_parallel_context()
+
+    def mindspore_optimizer_auto_parallel_impl(self,
+                                               dataset,
+                                               epoch,
+                                               device_num):
+        set_algo_parameters(fully_use_devices=False)
+        context.reset_auto_parallel_context()
+        context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL,
+                                          device_num=device_num,
+                                          enable_parallel_optimizer=True)
+        parallel_mode_net = self.net(self.strategy_dict)
+        self.optimizer_parallel_ckpt = self._model_train_and_save_ckpt(net=parallel_mode_net,
+                                                                       dataset=dataset, epoch=epoch)
+        context.reset_auto_parallel_context()
+
+    def checkpoint_cmp(self, inputs_np):
+        optimizer_parallel_net = self.net(self.strategy_dict)
+        load_param_into_net(optimizer_parallel_net, self.optimizer_parallel_ckpt)
+        optimizer_parallel_out = optimizer_parallel_net(Tensor(inputs_np))
+
+        parallel_net = self.net(self.strategy_dict)
+        load_param_into_net(parallel_net, self.parallel_ckpt)
+        parallel_out = parallel_net(Tensor(inputs_np))
+        allclose_nparray(optimizer_parallel_out.asnumpy(), parallel_out.asnumpy(), 0.001, 0.001)
+
+def test_optimizer_parallel_auto_4p_6_parameter_same_strategy_1_1_2_1_momentum():
+    inputs_np = np.random.randn(16, 1, 32, 32).astype(np.float32)
+    dataset = FakeData(size=32,
+                       batch_size=4,
+                       image_size=(1, 32, 32),
+                       use_parallel=True,
+                       num_classes=116)
+    strategy_dict = {'add1': ((1, 1, 2, 1), (1, 1, 2, 1)),
+                     'mul1': ((1, 1, 2, 1), (1, 1, 2, 1)),
+                     'fc1_matmul': ((1, 2), (1, 2)),
+                     'fc1_bias_add': ((1, 2), (2,)),
+                     'mul2': ((1, 2), (1, 2)),
+                     'mul3': ((1, 2), (1, 2))}
+    fact = OptimizerSemiAutoAndAutoParallelFactory(net=OptimizerSemiAutoAndAutoParallel6Net,
+                                                   strategy_dict=strategy_dict)
+    fact.mindspore_auto_parallel_impl(dataset=dataset, epoch=2, device_num=4)
+    fact.mindspore_optimizer_auto_parallel_impl(dataset=dataset, epoch=2, device_num=4)
+    fact.checkpoint_cmp(inputs_np=inputs_np)

tests/st/auto_parallel/run_optimizer_parallel.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+# 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.
+# ============================================================================
+set -e
+BASE_PATH=$(cd "$(dirname $0)"; pwd)
+CONFIG_PATH=/home/workspace/mindspore_config
+export DEVICE_NUM=4
+export RANK_SIZE=$DEVICE_NUM
+source ${BASE_PATH}/env.sh
+unset SLOG_PRINT_TO_STDOUT
+export MINDSPORE_HCCL_CONFIG_PATH=$CONFIG_PATH/hccl/rank_tabel_4p/rank_table_${DEVICE_NUM}p_1.json
+export LD_LIBRARY_PATH=/usr/local/Ascend/opp/op_impl/built-in/ai_core/tbe/op_tiling:${LD_LIBRARY_PATH}
+export ASCEND_OPP_PATH=/usr/local/Ascend/opp/
+
+process_pid=()
+for((i=0; i<$DEVICE_NUM; i++)); do
+    rm -rf ${BASE_PATH}/optimizer_parallel${i}
+    mkdir ${BASE_PATH}/optimizer_parallel${i}
+    cp -r ${BASE_PATH}/optimizer_parallel.py  ${BASE_PATH}/optimizer_parallel${i}/
+    cd ${BASE_PATH}/optimizer_parallel${i}
+    export RANK_ID=${i}
+    export DEVICE_ID=${i}
+    echo "start training for device $i"
+    env > env$i.log
+    pytest -s -v optimizer_parallel.py::test_optimizer_parallel_auto_4p_6_parameter_same_strategy_1_1_2_1_momentum > optimizer_parallel$i.log 2>&1 &
+    process_pid[${i}]=`echo $!`
+done
+
+for((i=0; i<${DEVICE_NUM}; i++)); do
+    wait ${process_pid[i]}
+    status=`echo $?`
+    if [ "${status}" != "0" ]; then
+        echo "[ERROR] test_optimizer_parallel failed. status: ${status}"
+        exit 1
+    else
+        echo "[INFO] test_optimizer_parallel success."
+    fi
+done
+
+exit 0

tests/st/auto_parallel/test_optimizer_parallel.py

@@ -0,0 +1,27 @@
+# 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.
+# ============================================================================
+import os
+import pytest
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_single
+def test_sit_optimizer_parallel():
+    sh_path = os.path.split(os.path.realpath(__file__))[0]
+    ret = os.system(f"sh {sh_path}/run_optimizer_parallel.sh")
+    os.system(f"grep -E 'ERROR|error' {sh_path}/optimizer_parallel*/optimizer_parallel*log -C 3")
+    assert ret == 0