add a self attention test case

tests/ut/python/parallel/test_loss_and_o2_level.py

@@ -0,0 +1,121 @@
+# 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 numpy as np
+
+import mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import Cell, Momentum
+from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
+from mindspore.ops import operations as P
+from mindspore.train import Model
+from tests.dataset_mock import MindData
+
+
+class Dataset(MindData):
+    def __init__(self, predict, label, length=3):
+        super(Dataset, self).__init__(size=length)
+        self.predict = predict
+        self.label = label
+        self.index = 0
+        self.length = length
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.index >= self.length:
+            raise StopIteration
+        self.index += 1
+        return self.predict, self.label
+
+    def reset(self):
+        self.index = 0
+
+
+class Net(Cell):
+    def __init__(self, mul_weight, strategy1=None, strategy2=None):
+        super().__init__()
+        self.mul = P.Mul().shard(strategy1)
+        self.neg = P.Neg().shard(strategy2)
+        self.mul_weight = Parameter(mul_weight, "w1")
+
+    def construct(self, x):
+        out = self.mul(x, self.mul_weight)
+        out = self.neg(out)
+        return out
+
+
+_x = Tensor(np.ones([32, 128]), dtype=ms.float32)
+_b = Tensor(np.ones([32]), dtype=ms.int32)
+_w1 = Tensor(np.ones([512, 128]), dtype=ms.float32)
+
+
+def compile_net(net):
+    context.set_context(save_graphs=True)
+    learning_rate = 0.1
+    momentum = 0.9
+    epoch_size = 2
+    dataset = Dataset(_x, _b)
+    loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+    opt = Momentum(net.trainable_params(), learning_rate, momentum)
+    model = Model(net, loss, optimizer=opt, amp_level="O2")
+    model.train(epoch_size, dataset, dataset_sink_mode=False)
+    context.reset_auto_parallel_context()
+
+
+def test_neg_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1), (16, 1))
+    strategy2 = ((16, 1),)
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_neg_model_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 16), (1, 16))
+    strategy2 = ((1, 16),)
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_neg_hybrid_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((4, 4), (4, 4))
+    strategy2 = ((4, 4),)
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_neg_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
+    net = Net(_w1)
+    compile_net(net)
+
+
+def test_neg_repeat_calc():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((4, 4), (4, 4))
+    strategy2 = ((2, 2),)
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_neg_repeat_calc2():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((4, 2), (4, 2))
+    strategy2 = ((4, 4),)
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)

tests/ut/python/parallel/test_self_attention.py

@@ -0,0 +1,149 @@
+# Copyright 2019 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 numpy as np
+
+import mindspore as ms
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import context
+from mindspore.common.api import _executor
+from mindspore.context import set_auto_parallel_context
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
+from tests.ut.python.ops.test_math_ops import VirtualLoss
+
+
+grad_all = C.GradOperation(get_all=True)
+
+
+class NetWithLoss(nn.Cell):
+    def __init__(self, network):
+        super(NetWithLoss, self).__init__()
+        self.loss = VirtualLoss()
+        self.network = network
+
+    def construct(self, x):
+        predict = self.network(x)
+        return self.loss(predict)
+
+
+class GradWrap(nn.Cell):
+    def __init__(self, network):
+        super(GradWrap, self).__init__()
+        self.network = network
+
+    def construct(self, x):
+        return grad_all(self.network)(x)
+
+
+def compile_net(net, x):
+    net.set_auto_parallel()
+    _executor.compile(net, x)
+
+
+class Net(nn.Cell):
+    def __init__(self, strategy1, strategy2, strategy3, strategy4, strategy5):
+        super().__init__()
+        self.query_w = Parameter(initializer(
+            "normal", [8, 16], ms.float32), name='query')
+        self.query = P.MatMul().shard(strategy1)
+
+        self.key_w = Parameter(initializer(
+            "normal", [8, 16], ms.float32), name='key')
+        self.key = P.MatMul().shard(strategy2)
+
+        self.value_w = Parameter(initializer(
+            "normal", [8, 16], ms.float32), name='value')
+        self.value = P.MatMul().shard(strategy3)
+
+        self.score = P.MatMul().shard(strategy4)
+        self.context = P.MatMul().shard(strategy5)
+        self.transpose1 = P.Transpose()
+        self.transpose2 = P.Transpose()
+        self.relu = P.ReLU()
+
+    def construct(self, x):
+        q = self.query(x, self.query_w)
+        k = self.key(x, self.key_w)
+        v = self.value(x, self.value_w)
+
+        k = self.transpose1(k, (1, 0))
+        s = self.score(q, k)
+
+        v = self.transpose2(v, (1, 0))
+        c = self.context(v, s)
+        out = self.relu(c)
+
+        return out
+
+
+def test_self_attention_standalone():
+    set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="stand_alone")
+    net = GradWrap(NetWithLoss(
+        Net(None, None, None, None, None)))
+
+    x = Tensor(np.ones([32, 8]), dtype=ms.float32)
+
+    compile_net(net, x)
+
+
+def test_self_attention_semi():
+    set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((2, 2), (2, 2))
+    strategy3 = ((2, 2), (2, 2))
+    strategy4 = ((2, 4), (4, 1))
+    strategy5 = ((2, 1), (1, 4))
+
+    net = GradWrap(NetWithLoss(
+        Net(strategy1, strategy2, strategy3, strategy4, strategy5)))
+
+    x = Tensor(np.ones([32, 8]), dtype=ms.float32)
+
+    compile_net(net, x)
+
+
+def test_self_attention_dp():
+    set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    strategy1 = ((8, 1), (1, 1))
+    strategy2 = ((8, 1), (1, 1))
+    strategy3 = ((8, 1), (1, 1))
+    strategy4 = ((8, 1), (1, 1))
+    strategy5 = ((8, 1), (1, 1))
+
+    net = GradWrap(NetWithLoss(
+        Net(strategy1, strategy2, strategy3, strategy4, strategy5)))
+
+    x = Tensor(np.ones([32, 8]), dtype=ms.float32)
+
+    compile_net(net, x)
+
+
+def test_self_attention_auto():
+    set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="auto_parallel")
+    net = GradWrap(NetWithLoss(
+        Net(None, None, None, None, None)))
+
+    x = Tensor(np.ones([32, 8]), dtype=ms.float32)
+
+    compile_net(net, x)