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1# Copyright 2021 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15
16import re
17import numpy as np
18import mindspore as ms
19import mindspore.nn as nn
20from mindspore import Tensor
21from mindspore import context
22from mindspore.common.api import _cell_graph_executor
23from mindspore.ops import operations as P
24from mindspore.common.parameter import Parameter
25
26context.set_context(mode=context.GRAPH_MODE)
27
28class DenseMutMulNet(nn.Cell):
29    def __init__(self):
30        super(DenseMutMulNet, self).__init__()
31        self.fc1 = nn.Dense(128, 768)
32        self.fc2 = nn.Dense(128, 768)
33        self.fc3 = nn.Dense(128, 768)
34        self.fc4 = nn.Dense(768, 768, has_bias=False)
35        self.relu4 = nn.ReLU()
36        self.relu5 = nn.ReLU()
37        self.transpose = P.Transpose()
38        self.matmul1 = P.MatMul()
39        self.matmul2 = P.MatMul()
40        self.fc4.matmul.shard(((1, 1), (8, 1)))
41
42    def construct(self, x):
43        q = self.fc1(x)
44        k = self.fc2(x)
45        v = self.fc3(x)
46        k = self.transpose(k, (1, 0))
47        c = self.relu4(self.matmul1(q, k))
48        s = self.relu5(self.matmul2(c, v))
49        s = self.fc4(s)
50        return s
51
52class MulNegTwoOutputNet(nn.Cell):
53    def __init__(self):
54        super().__init__()
55        self.mul = P.Mul().shard(((2, 4), (2, 4)))
56        self.neg = P.Neg().shard(((2, 4),))
57        self.mul_weight = Parameter(Tensor(np.ones([32, 128]), dtype=ms.float32), name="weight")
58
59    def construct(self, x):
60        out1 = self.mul(x, self.mul_weight)
61        out2 = self.neg(out1)
62        return out1, out2
63
64class ReshapeMatMulNet(nn.Cell):
65    def __init__(self, strategy1, strategy2):
66        super().__init__()
67        self.reshape = P.Reshape()
68        self.matmul = P.MatMul().shard(strategy2)
69        self.matmul_weight = Parameter(Tensor(np.ones([28, 64]), dtype=ms.float32), name="weight")
70    # x (64, 4, 7)
71    def construct(self, x):
72        out = self.reshape(x, (64, 28))
73        out = self.matmul(out, self.matmul_weight)
74        return out
75
76class MatMulReshapeNet(nn.Cell):
77    def __init__(self, strategy1, strategy2):
78        super().__init__()
79        self.reshape = P.Reshape()
80        self.matmul = P.MatMul().shard(strategy1)
81        self.matmul_weight = Parameter(Tensor(np.ones([28, 64]), dtype=ms.float32), name="weight")
82    # x (128, 28)
83    def construct(self, x):
84        out = self.matmul(x, self.matmul_weight)
85        out = self.reshape(out, (64, -1))
86        return out
87
88class ReshapeMulNet(nn.Cell):
89    def __init__(self):
90        super().__init__()
91        self.reshape = P.Reshape()
92        self.mul = P.Mul().shard(((1, 2, 4), (2, 4)))
93        self.mul_weight = Parameter(Tensor(np.ones([128, 96]), dtype=ms.float32), name="weight")
94
95    def construct(self, x):
96        weight = self.reshape(self.mul_weight, (1, 128, 96))
97        out = self.mul(weight, self.mul_weight)
98        return out
99
100class ParallelMulNet(nn.Cell):
101    def __init__(self, dense_in_channel=2048, dense_out_channel=250):
102        super().__init__()
103        weight_np = np.full((dense_out_channel, dense_in_channel), 0.01, dtype=np.float32)
104        bias_np = np.full((dense_out_channel,), 0.01, dtype=np.float32)
105        self.flat = nn.Flatten()
106        self.dense = nn.Dense(in_channels=dense_in_channel,
107                              out_channels=dense_out_channel,
108                              weight_init=Tensor(weight_np),
109                              bias_init=Tensor(bias_np),
110                              has_bias=True)
111        self.mul = P.Mul()
112    def construct(self, inputs):
113        x = self.flat(inputs)
114        x = self.dense(x)
115        x = self.mul(x, x)
116        return x
117
118def compile_graph(x, net):
119    net.set_auto_parallel()
120    net.set_train(False)
121    _cell_graph_executor.compile(net, x, auto_parallel_mode=True)
122    strategies = _cell_graph_executor._get_shard_strategy(net)
123    return strategies
124
125def compile_graph_two_input(x, y, net):
126    net.set_auto_parallel()
127    net.set_train(False)
128    _cell_graph_executor.compile(net, x, y, auto_parallel_mode=True)
129    strategies = _cell_graph_executor._get_shard_strategy(net)
130    return strategies
131
132
133def test_dense_relu_semi_auto():
134    context.reset_auto_parallel_context()
135    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel",
136                                      dataset_strategy="data_parallel")
137    net = DenseMutMulNet()
138    x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
139    strategies = compile_graph(x, net)
140    for (k, v) in strategies.items():
141        if re.search('VirtualOutput-op', k) is not None:
142            assert v[0][0] == 8
143
144def test_dense_relu_semi_auto_full_batch():
145    context.reset_auto_parallel_context()
146    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel",
147                                      dataset_strategy="full_batch")
148    net = DenseMutMulNet()
149    x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
150    strategies = compile_graph(x, net)
151    for (k, v) in strategies.items():
152        if re.search('VirtualOutput-op', k) is not None:
153            assert v[0][0] == 1
154
155def test_dense_relu_auto():
156    context.reset_auto_parallel_context()
157    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel",
158                                      dataset_strategy="data_parallel")
159    net = DenseMutMulNet()
160    x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
161    strategies = compile_graph(x, net)
162    for (k, v) in strategies.items():
163        if re.search('VirtualOutput-op', k) is not None:
164            assert v[0][0] == 8
165
166def test_dense_relu_auto_full_batch():
167    context.reset_auto_parallel_context()
168    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel",
169                                      dataset_strategy="full_batch")
170    net = DenseMutMulNet()
171    x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
172    strategies = compile_graph(x, net)
173    for (k, v) in strategies.items():
174        if re.search('VirtualOutput-op', k) is not None:
175            assert v[0][0] == 1
176
177def test_mul_neg_two_output_semi_auto():
178    context.reset_auto_parallel_context()
179    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel",
180                                      dataset_strategy="data_parallel")
181    net = MulNegTwoOutputNet()
182    x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
183    strategies = compile_graph(x, net)
184    count = 0
185    for (k, v) in strategies.items():
186        if re.search('VirtualOutput-op', k) is not None:
187            count += 1
188            assert v[0][0] == 8
189    assert count == 2
190
191def test_mul_neg_two_output_semi_auto_full_batch():
192    context.reset_auto_parallel_context()
193    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel",
194                                      dataset_strategy="full_batch")
195    net = MulNegTwoOutputNet()
196    x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
197    strategies = compile_graph(x, net)
198    count = 0
199    for (k, v) in strategies.items():
200        if re.search('VirtualOutput-op', k) is not None:
201            count += 1
202            assert v[0][0] == 1
203    assert count == 2
204
205def test_mul_neg_two_output_auto():
206    context.reset_auto_parallel_context()
207    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel",
208                                      dataset_strategy="data_parallel")
209    net = MulNegTwoOutputNet()
210    x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
211    strategies = compile_graph(x, net)
212    count = 0
213    for (k, v) in strategies.items():
214        if re.search('VirtualOutput-op', k) is not None:
215            count += 1
216            assert v[0][0] == 8
217    assert count == 2
218
219def test_mul_neg_two_output_full_batch():
220    context.reset_auto_parallel_context()
221    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel",
222                                      dataset_strategy="full_batch")
223    net = MulNegTwoOutputNet()
224    x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
225    strategies = compile_graph(x, net)
226    count = 0
227    for (k, v) in strategies.items():
228        if re.search('VirtualOutput-op', k) is not None:
229            count += 1
230            assert v[0][0] == 1
231    assert count == 2
232
233def test_reshape_matmul_semi_auto():
234    context.reset_auto_parallel_context()
235    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel",
236                                      dataset_strategy="data_parallel")
237    strategy1 = None
238    strategy2 = ((1, 1), (1, 8))
239    net = ReshapeMatMulNet(strategy1, strategy2)
240    x = Tensor(np.ones([64, 4, 7]), ms.float32)
241    strategies = compile_graph(x, net)
242    for (k, v) in strategies.items():
243        if re.search('VirtualOutput-op', k) is not None:
244            assert v[0][0] == 8
245
246def test_reshape_matmul_auto():
247    context.reset_auto_parallel_context()
248    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel",
249                                      dataset_strategy="data_parallel")
250    strategy1 = None
251    strategy2 = ((1, 1), (1, 8))
252    net = ReshapeMatMulNet(strategy1, strategy2)
253    x = Tensor(np.ones([64, 4, 7]), ms.float32)
254    strategies = compile_graph(x, net)
255    for (k, v) in strategies.items():
256        if re.search('VirtualOutput-op', k) is not None:
257            assert v[0][0] == 8
258
259def test_matmul_reshape_semi_auto():
260    context.reset_auto_parallel_context()
261    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel",
262                                      dataset_strategy="data_parallel")
263    strategy2 = None
264    strategy1 = ((1, 1), (1, 8))
265    net = MatMulReshapeNet(strategy1, strategy2)
266    x = Tensor(np.ones([128, 28]), ms.float32)
267    strategies = compile_graph(x, net)
268    for (k, v) in strategies.items():
269        if re.search('VirtualOutput-op', k) is not None:
270            assert v[0][0] == 8
271
272def test_matmul_reshape_auto():
273    context.reset_auto_parallel_context()
274    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel",
275                                      dataset_strategy="data_parallel")
276    strategy2 = None
277    strategy1 = ((1, 1), (1, 8))
278    net = MatMulReshapeNet(strategy1, strategy2)
279    x = Tensor(np.ones([128, 28]), ms.float32)
280    strategies = compile_graph(x, net)
281    for (k, v) in strategies.items():
282        if re.search('VirtualOutput-op', k) is not None:
283            assert v[0][0] == 8
284
285def test_reshape_mul_semi_auto():
286    context.reset_auto_parallel_context()
287    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel",
288                                      dataset_strategy="full_batch")
289    net = ReshapeMulNet()
290    x = Tensor(np.ones([64, 4]), ms.float32)
291    strategies = compile_graph(x, net)
292    for (k, v) in strategies.items():
293        if re.search('VirtualOutput-op', k) is not None:
294            assert v[0][0] == 1
295
296def test_reshape_mul_auto():
297    context.reset_auto_parallel_context()
298    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel",
299                                      dataset_strategy="full_batch")
300    net = ReshapeMulNet()
301    x = Tensor(np.ones([64, 4]), ms.float32)
302    strategies = compile_graph(x, net)
303    for (k, v) in strategies.items():
304        if re.search('VirtualOutput-op', k) is not None:
305            assert v[0][0] == 1
306
307def test_scalar_output_semi_auto():
308    context.reset_auto_parallel_context()
309    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel",
310                                      dataset_strategy="data_parallel")
311    net = ParallelMulNet()
312    loss_fn = nn.SoftmaxCrossEntropyWithLogits(reduction='mean')
313    eval_net = nn.WithEvalCell(net, loss_fn)
314    x = Tensor(np.ones([4096, 1, 2, 1024]).astype(np.float32)*0.01)
315    label = Tensor(np.ones([4096, 250]).astype(np.float32)*0.01)
316    strategies = compile_graph_two_input(x, label, eval_net)
317    count = 0
318    for (k, v) in strategies.items():
319        if re.search('VirtualOutput-op', k) is not None:
320            assert v[0][0] == 8
321            count += 1
322    assert count == 1
323
324def test_scalar_output_auto():
325    context.reset_auto_parallel_context()
326    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel",
327                                      dataset_strategy="data_parallel")
328    net = ParallelMulNet()
329    loss_fn = nn.SoftmaxCrossEntropyWithLogits(reduction='mean')
330    eval_net = nn.WithEvalCell(net, loss_fn)
331    x = Tensor(np.ones([4096, 1, 2, 1024]).astype(np.float32)*0.01)
332    label = Tensor(np.ones([4096, 250]).astype(np.float32)*0.01)
333    strategies = compile_graph_two_input(x, label, eval_net)
334    count = 0
335    for (k, v) in strategies.items():
336        if re.search('VirtualOutput-op', k) is not None:
337            assert v[0][0] == 8
338            count += 1
339    assert count == 1
340