!11763 add testcases of hcom

From: @rainyhorse
Reviewed-by: @chenfei52,@ginfung,@zh_qh
Signed-off-by: @zh_qh

tests/st/hcom/env_hcom.sh

@@ -0,0 +1,22 @@
+#!/bin/bash
+# Copyright 2021 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.
+# ============================================================================
+LOCAL_HIAI=/usr/local/HiAI
+export TBE_IMPL_PATH=${LOCAL_HIAI}/runtime/ops/op_impl/built-in/ai_core/tbe/impl/
+export LD_LIBRARY_PATH=${LOCAL_HIAI}/runtime/lib64/:${LD_LIBRARY_PATH}
+export PATH=${LOCAL_HIAI}/runtime/ccec_compiler/bin/:${PATH}
+export PYTHONPATH=${LOCAL_HIAI}/runtime/ops/op_impl/built-in/ai_core/tbe/:${PYTHONPATH}
+export DEVICE_MEMORY_CAPACITY=1073741824000
+export NOT_FULLY_USE_DEVICES=off

tests/st/hcom/hcom_sparsetensor.py

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+# Copyright 2021 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 get_rank
+from mindspore import Tensor
+from mindspore import Parameter
+from mindspore import context
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.train import Model
+from mindspore.context import ParallelMode
+from mindspore.communication.management import init
+from mindspore.communication.management import get_group_size
+
+
+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_class=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_class = num_class
+        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:
+            if 'CONTEXT_DEVICE_TARGET' in os.environ and os.environ['CONTEXT_DEVICE_TARGET'] == 'GPU':
+                init(backend_name='nccl')
+            else:
+                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_reeat_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=False):
+        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_class, 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_class))
+            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 NetWithSparseGatherV2(nn.Cell):
+    def __init__(self, strategy=None, sparse=True):
+        super(NetWithSparseGatherV2, self).__init__()
+        self.axis = 0
+        self.sparse = sparse
+        if sparse:
+            self.weight = Parameter(Tensor(np.ones([8, 8]).astype(np.float32)), name="weight")
+            self.gather = P.SparseGatherV2()
+        else:
+            self.weight = Parameter(Tensor(np.ones([8, 8]).astype(np.float32)), name="weight")
+            self.gather = P.GatherV2()
+        if strategy is not None:
+            self.gather.shard(strategy)
+
+    def construct(self, indices):
+        x = self.gather(self.weight, indices, self.axis)
+        return x
+
+    def train_mindspore_impl(self, indices, epoch, batch_size, use_parallel=True):
+        ds = FakeData(size=8, batch_size=batch_size, num_class=8, image_size=(), use_parallel=use_parallel)
+        ds.set_image_data_type(np.int32)
+        net = self
+        net.set_train()
+        loss = nn.SoftmaxCrossEntropyWithLogits()
+        optimizer = nn.Adam(net.trainable_params())
+        optimizer.target = "CPU"
+        model = Model(net, loss, optimizer)
+        for _ in range(epoch):
+            model.train(1, ds, dataset_sink_mode=False)
+        output = net(indices)
+        return output
+
+
+def test_allreduce_sparsegatherv2_adam_auto_parallel():
+    context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
+    init(backend_name='hccl')
+    context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, device_num=8, gradients_mean=True)
+    indices = Tensor(np.array([0, 1, 2, 3, 4, 5, 6, 7]).astype(np.int32))
+    epoch = 3
+    batch_size = 1
+    context.set_context(enable_sparse=True)
+    net = NetWithSparseGatherV2(sparse=True)
+    output_sparse = net.train_mindspore_impl(indices, epoch, batch_size)
+    net = NetWithSparseGatherV2(sparse=False)
+    output = net.train_mindspore_impl(indices, epoch, batch_size)
+    assert np.allclose(output.asnumpy(), output_sparse.asnumpy(), 0.001, 0.001)

tests/st/hcom/run_hcom_sparsetensor.sh

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+#!/bin/bash
+# Copyright 2021 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 $0)"
+  pwd
+)
+CONFIG_PATH=/home/workspace/mindspore_config
+export DEVICE_NUM=8
+export RANK_SIZE=$DEVICE_NUM
+source ${BASE_PATH}/env_hcom.sh
+unset SLOG_PRINT_TO_STDOUT
+export MINDSPORE_HCCL_CONFIG_PATH=$CONFIG_PATH/hccl/rank_table_${DEVICE_NUM}p.json
+
+process_pid=()
+for ((i = 0; i < $DEVICE_NUM; i++)); do
+  rm -rf ${BASE_PATH}/hcom_sparsetensor${i}
+  mkdir ${BASE_PATH}/hcom_sparsetensor${i}
+  cp -r ${BASE_PATH}/hcom_sparsetensor.py ${BASE_PATH}/hcom_sparsetensor${i}/
+  cd ${BASE_PATH}/hcom_sparsetensor${i}
+  export RANK_ID=${i}
+  export DEVICE_ID=${i}
+  echo "start training for device $i"
+  env >env$i.log
+  pytest -s -v hcom_sparsetensor.py >test_hcom_sparsetensor_8p_log$i.log 2>&1 &
+  process_pid[${i}]=$(echo $!)
+done
+
+for ((i = 0; i < ${DEVICE_NUM}; i++)); do
+  wait ${process_pid[i]}
+  status=$(echo $?)
+  if [ "${status}" != "0" ]; then
+    echo "[ERROR] test_hcom_sparsetensor failed. status: ${status}"
+    exit 1
+  else
+    echo "[INFO] test_hcom_sparsetensor success."
+  fi
+done
+
+exit 0

tests/st/hcom/test_hcom_sparsetensor.py

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+# Copyright 2021 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_allreduce_sparsegatherv2_adam_auto_parallel():
+    sh_path = os.path.split(os.path.realpath(__file__))[0]
+    ret = os.system(f"sh {sh_path}/run_hcom_sparsetensor.sh")
+    assert ret == 0