forked from mindspore-Ecosystem/mindspore
308 lines
12 KiB
Python
308 lines
12 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import re
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore.common.api import _executor
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from mindspore.ops import operations as P
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from mindspore.common.parameter import Parameter
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context.set_context(mode=context.GRAPH_MODE)
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class DenseMutMulNet(nn.Cell):
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def __init__(self):
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super(DenseMutMulNet, self).__init__()
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self.fc1 = nn.Dense(128, 768)
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self.fc2 = nn.Dense(128, 768)
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self.fc3 = nn.Dense(128, 768)
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self.fc4 = nn.Dense(768, 768, has_bias=False)
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self.relu4 = nn.ReLU()
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self.relu5 = nn.ReLU()
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self.transpose = P.Transpose()
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self.matmul1 = P.MatMul()
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self.matmul2 = P.MatMul()
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self.fc4.matmul.shard(((1, 1), (8, 1)))
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def construct(self, x):
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q = self.fc1(x)
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k = self.fc2(x)
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v = self.fc3(x)
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k = self.transpose(k, (1, 0))
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c = self.relu4(self.matmul1(q, k))
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s = self.relu5(self.matmul2(c, v))
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s = self.fc4(s)
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return s
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class MulNegTwoOutputNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.mul = P.Mul().shard(((2, 4), (2, 4)))
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self.neg = P.Neg().shard(((2, 4),))
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self.mul_weight = Parameter(Tensor(np.ones([32, 128]), dtype=ms.float32), name="weight")
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def construct(self, x):
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out1 = self.mul(x, self.mul_weight)
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out2 = self.neg(out1)
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return out1, out2
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class ReshapeMatMulNet(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.reshape = P.Reshape()
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self.matmul = P.MatMul().shard(strategy2)
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self.matmul_weight = Parameter(Tensor(np.ones([28, 64]), dtype=ms.float32), name="weight")
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# x (64, 4, 7)
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def construct(self, x):
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out = self.reshape(x, (64, 28))
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out = self.matmul(out, self.matmul_weight)
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return out
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class MatMulReshapeNet(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.reshape = P.Reshape()
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self.matmul = P.MatMul().shard(strategy1)
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self.matmul_weight = Parameter(Tensor(np.ones([28, 64]), dtype=ms.float32), name="weight")
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# x (128, 28)
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def construct(self, x):
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out = self.matmul(x, self.matmul_weight)
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out = self.reshape(out, (64, -1))
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return out
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class ReshapeMulNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.reshape = P.Reshape()
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self.mul = P.Mul().shard(((1, 2, 4), (2, 4)))
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self.mul_weight = Parameter(Tensor(np.ones([128, 96]), dtype=ms.float32), name="weight")
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def construct(self, x):
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weight = self.reshape(self.mul_weight, (1, 128, 96))
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out = self.mul(weight, self.mul_weight)
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return out
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class ParallelMulNet(nn.Cell):
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def __init__(self, dense_in_channel=2048, dense_out_channel=250):
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super().__init__()
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weight_np = np.full((dense_out_channel, dense_in_channel), 0.01, dtype=np.float32)
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bias_np = np.full((dense_out_channel,), 0.01, dtype=np.float32)
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self.flat = nn.Flatten()
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self.dense = nn.Dense(in_channels=dense_in_channel,
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out_channels=dense_out_channel,
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weight_init=Tensor(weight_np),
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bias_init=Tensor(bias_np),
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has_bias=True)
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self.mul = P.Mul()
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def construct(self, inputs):
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x = self.flat(inputs)
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x = self.dense(x)
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x = self.mul(x, x)
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return x
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def compile_graph(x, net):
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net.set_auto_parallel()
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net.set_train(False)
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_executor.compile(net, x, auto_parallel_mode=True)
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strategies = _executor._get_shard_strategy(net)
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return strategies
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def compile_graph_two_input(x, y, net):
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net.set_auto_parallel()
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net.set_train(False)
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_executor.compile(net, x, y, auto_parallel_mode=True)
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strategies = _executor._get_shard_strategy(net)
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return strategies
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def test_dense_relu_semi_auto():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel", full_batch=False)
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net = DenseMutMulNet()
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x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
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strategies = compile_graph(x, net)
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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assert v[0][0] == 8
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def test_dense_relu_semi_auto_full_batch():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel", full_batch=True)
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net = DenseMutMulNet()
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x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
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strategies = compile_graph(x, net)
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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assert v[0][0] == 1
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def test_dense_relu_auto():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel", full_batch=False)
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net = DenseMutMulNet()
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x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
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strategies = compile_graph(x, net)
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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assert v[0][0] == 8
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def test_dense_relu_auto_full_batch():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel", full_batch=True)
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net = DenseMutMulNet()
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x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
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strategies = compile_graph(x, net)
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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assert v[0][0] == 1
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def test_mul_neg_two_output_semi_auto():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel", full_batch=False)
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net = MulNegTwoOutputNet()
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x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
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strategies = compile_graph(x, net)
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count = 0
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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count += 1
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assert v[0][0] == 8
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assert count == 2
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def test_mul_neg_two_output_semi_auto_full_batch():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel", full_batch=True)
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net = MulNegTwoOutputNet()
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x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
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strategies = compile_graph(x, net)
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count = 0
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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count += 1
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assert v[0][0] == 1
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assert count == 2
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def test_mul_neg_two_output_auto():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel", full_batch=False)
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net = MulNegTwoOutputNet()
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x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
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strategies = compile_graph(x, net)
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count = 0
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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count += 1
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assert v[0][0] == 8
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assert count == 2
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def test_mul_neg_two_output_full_batch():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel", full_batch=True)
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net = MulNegTwoOutputNet()
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x = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
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strategies = compile_graph(x, net)
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count = 0
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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count += 1
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assert v[0][0] == 1
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assert count == 2
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def test_reshape_matmul_semi_auto():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel", full_batch=False)
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strategy1 = None
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strategy2 = ((1, 1), (1, 8))
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net = ReshapeMatMulNet(strategy1, strategy2)
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x = Tensor(np.ones([64, 4, 7]), ms.float32)
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strategies = compile_graph(x, net)
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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assert v[0][0] == 8
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def test_reshape_matmul_auto():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel", full_batch=False)
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strategy1 = None
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strategy2 = ((1, 1), (1, 8))
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net = ReshapeMatMulNet(strategy1, strategy2)
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x = Tensor(np.ones([64, 4, 7]), ms.float32)
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strategies = compile_graph(x, net)
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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assert v[0][0] == 8
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def test_matmul_reshape_semi_auto():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel", full_batch=False)
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strategy2 = None
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strategy1 = ((1, 1), (1, 8))
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net = MatMulReshapeNet(strategy1, strategy2)
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x = Tensor(np.ones([128, 28]), ms.float32)
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strategies = compile_graph(x, net)
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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assert v[0][0] == 8
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def test_matmul_reshape_auto():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel", full_batch=False)
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strategy2 = None
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strategy1 = ((1, 1), (1, 8))
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net = MatMulReshapeNet(strategy1, strategy2)
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x = Tensor(np.ones([128, 28]), ms.float32)
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strategies = compile_graph(x, net)
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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assert v[0][0] == 8
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def test_reshape_mul_semi_auto():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel", full_batch=True)
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net = ReshapeMulNet()
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x = Tensor(np.ones([64, 4]), ms.float32)
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strategies = compile_graph(x, net)
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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assert v[0][0] == 1
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def test_reshape_mul_auto():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel", full_batch=True)
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net = ReshapeMulNet()
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x = Tensor(np.ones([64, 4]), ms.float32)
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strategies = compile_graph(x, net)
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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assert v[0][0] == 1
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def test_scalar_output_semi_auto():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel", full_batch=False)
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net = ParallelMulNet()
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loss_fn = nn.SoftmaxCrossEntropyWithLogits(reduction='mean')
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eval_net = nn.WithEvalCell(net, loss_fn)
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x = Tensor(np.ones([4096, 1, 2, 1024]).astype(np.float32)*0.01)
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label = Tensor(np.ones([4096, 250]).astype(np.float32)*0.01)
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strategies = compile_graph_two_input(x, label, eval_net)
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count = 0
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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assert v[0][0] == 8
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count += 1
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assert count == 1
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def test_scalar_output_auto():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel", full_batch=False)
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net = ParallelMulNet()
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loss_fn = nn.SoftmaxCrossEntropyWithLogits(reduction='mean')
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eval_net = nn.WithEvalCell(net, loss_fn)
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x = Tensor(np.ones([4096, 1, 2, 1024]).astype(np.float32)*0.01)
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label = Tensor(np.ones([4096, 250]).astype(np.float32)*0.01)
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strategies = compile_graph_two_input(x, label, eval_net)
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count = 0
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for (k, v) in strategies.items():
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if re.search('VirtualOutput-op', k) is not None:
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assert v[0][0] == 8
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count += 1
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assert count == 1
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