!3648 Add some testing cases for mindspore.profiler

Merge pull request !3648 from 张毅辉/zyh_profiler_test
This commit is contained in:
mindspore-ci-bot 2020-08-04 14:23:15 +08:00 committed by Gitee
commit bac1781539
26 changed files with 621 additions and 0 deletions

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op_name:Default/Cast-op6 op_type:Cast input_id:0 input_format:DefaultFormat input_data_type:40 input_shape:"32,3,224,224" output_id:0 output_format:DefaultFormat output_data_type:39 output_shape:"32,3,224,224"
op_name:Default/TransData-op7 op_type:TransData input_id:0 input_format:DefaultFormat input_data_type:39 input_shape:"32,3,224,224" output_id:0 output_format:NC1HWC0 output_data_type:39 output_shape:"32,1,224,224,16"
op_name:Default/network-WithLossCell/_backbone-ResNet/conv1-Conv2d/Cast-op5 op_type:Cast input_id:0 input_format:FracZ input_data_type:40 input_shape:"49,4,16,16" output_id:0 output_format:FracZ output_data_type:39 output_shape:"49,4,16,16"
op_name:Default/network-WithLossCell/_backbone-ResNet/layer1-SequentialCell/0-ResidualBlock/conv1-Conv2d/Cast-op28 op_type:Cast input_id:0 input_format:FracZ input_data_type:40 input_shape:"4,4,16,16" output_id:0 output_format:FracZ output_data_type:39 output_shape:"4,4,16,16"

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Default/Cast-op6 32 51517 0
Default/TransData-op7 32 51518 0
Default/network-WithLossCell/_backbone-ResNet/conv1-Conv2d/Cast-op5 32 51519 0
Default/network-WithLossCell/_backbone-ResNet/layer1-SequentialCell/0-ResidualBlock/conv1-Conv2d/Cast-op28 4 51522 0

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{
"sampling_interval": 10,
"op_info": [
{
"op_id": 4,
"op_type": "TFReader",
"num_workers": 4,
"metrics": null,
"children": [3]
},
{
"op_id": 3,
"op_type": "TFReader",
"num_workers": 4,
"metrics": {
"output_queue": {
"size": [10, 20, 30],
"length": 64
}
},
"children": null
},
{
"op_id": 2,
"op_type": "TFReader",
"num_workers": 4,
"metrics": {
"output_queue": {
"size": [10, 20, 30],
"length": 64
}
},
"children": null
},
{
"op_id": 1,
"op_type": "Shuffle",
"num_workers": 1,
"metrics": {
"output_queue": {
"size": [10, 20, 30],
"length": 64
}
},
"children": [2, 4]
},
{
"op_id": 0,
"op_type": "Batch",
"num_workers": 4,
"metrics": null,
"children": [1]
}
]
}

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{
"sampling_interval": 10,
"op_info": [
{
"op_id": 3,
"op_type": "TFReader",
"num_workers": 4,
"metrics": {
"output_queue": {
"size": [10, 20, 30],
"length": 64
}
},
"children": null
},
{
"op_id": 2,
"op_type": "TFReader",
"num_workers": 4,
"metrics": {
"output_queue": {
"size": [10, 20, 30],
"length": 64
}
},
"children": null
},
{
"op_id": 1,
"op_type": "Shuffle",
"num_workers": 1,
"metrics": {
"output_queue": {
"size": [10, 20, 30],
"length": 64
}
},
"children": [2, 3]
},
{
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"op_type": "Batch",
"num_workers": 4,
"metrics": null,
"children": [1]
}
]
}

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op_name:Default/Cast-op6 op_type:Cast input_id:0 input_format:DefaultFormat input_data_type:40 input_shape:"32,3,224,224" output_id:0 output_format:DefaultFormat output_data_type:39 output_shape:"32,3,224,224"
op_name:Default/TransData-op7 op_type:TransData input_id:0 input_format:DefaultFormat input_data_type:39 input_shape:"32,3,224,224" output_id:0 output_format:NC1HWC0 output_data_type:39 output_shape:"32,1,224,224,16"
op_name:Default/network-WithLossCell/_backbone-ResNet/conv1-Conv2d/Cast-op5 op_type:Cast input_id:0 input_format:FracZ input_data_type:40 input_shape:"49,4,16,16" output_id:0 output_format:FracZ output_data_type:39 output_shape:"49,4,16,16"
op_name:Default/network-WithLossCell/_backbone-ResNet/layer1-SequentialCell/0-ResidualBlock/conv1-Conv2d/Cast-op28 op_type:Cast input_id:0 input_format:FracZ input_data_type:40 input_shape:"4,4,16,16" output_id:0 output_format:FracZ output_data_type:39 output_shape:"4,4,16,16"

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1 Default/Cast-op6
2 Default/TransData-op7

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Default/Cast-op6 32 1 0
Default/TransData-op7 32 2 0
Default/network-WithLossCell/_backbone-ResNet/conv1-Conv2d/Cast-op5 32 3 0
Default/network-WithLossCell/_backbone-ResNet/layer1-SequentialCell/0-ResidualBlock/conv1-Conv2d/Cast-op28 4 4 0

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serial_number node_type_name total_time(ms) dispatch_time(ms) run_start run_end
1 InitData 1.567 0.1 2298200409 2298200538
2 GetNext 0.989 0.087 2302769932 2302769980
3 TruncatedNormal 1.566 0.105 4098200409 4098200538
AI CPU Total Time(ms): 4.122000

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op_name:Default/Cast-op6 op_type:Cast input_id:0 input_format:DefaultFormat input_data_type:40 input_shape:"32,3,224,224" output_id:0 output_format:DefaultFormat output_data_type:39 output_shape:"32,3,224,224"
op_name:Default/TransData-op7 op_type:TransData input_id:0 input_format:DefaultFormat input_data_type:39 input_shape:"32,3,224,224" output_id:0 output_format:NC1HWC0 output_data_type:39 output_shape:"32,1,224,224,16"
op_name:Default/network-WithLossCell/_backbone-ResNet/conv1-Conv2d/Cast-op5 op_type:Cast input_id:0 input_format:FracZ input_data_type:40 input_shape:"49,4,16,16" output_id:0 output_format:FracZ output_data_type:39 output_shape:"49,4,16,16"
op_name:Default/network-WithLossCell/_backbone-ResNet/layer1-SequentialCell/0-ResidualBlock/conv1-Conv2d/Cast-op28 op_type:Cast input_id:0 input_format:FracZ input_data_type:40 input_shape:"4,4,16,16" output_id:0 output_format:FracZ output_data_type:39 output_shape:"4,4,16,16"

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Default/Cast-op6 32 51517 0
Default/TransData-op7 32 51518 0
Default/network-WithLossCell/_backbone-ResNet/conv1-Conv2d/Cast-op5 32 51519 0
Default/network-WithLossCell/_backbone-ResNet/layer1-SequentialCell/0-ResidualBlock/conv1-Conv2d/Cast-op28 4 51522 0

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full_op_time,execution_time
Default/AtomicAddrClean-op104,0.00133
Default/AtomicAddrClean-op105,0.000987
Default/AtomicAddrClean-op106,0.001129
Default/Cast-op10,0.00466
Default/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/Cast-op12,0.002366
Gradients/Default/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/gradConv2D/Cast-op53,0.004879
Default/TransData-op11,0.006366
Gradients/Default/network-WithLossCell/_backbone-LeNet5/gradReshape/TransData-op44,0.006782
Default/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/Conv2D-op13,0.05651
Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/MatMul-op9,0.370864
1 full_op_time execution_time
2 Default/AtomicAddrClean-op104 0.00133
3 Default/AtomicAddrClean-op105 0.000987
4 Default/AtomicAddrClean-op106 0.001129
5 Default/Cast-op10 0.00466
6 Default/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/Cast-op12 0.002366
7 Gradients/Default/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/gradConv2D/Cast-op53 0.004879
8 Default/TransData-op11 0.006366
9 Gradients/Default/network-WithLossCell/_backbone-LeNet5/gradReshape/TransData-op44 0.006782
10 Default/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/Conv2D-op13 0.05651
11 Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/MatMul-op9 0.370864

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op_type,execution_time,execution_frequency,percent
AtomicAddrClean,0.007283,6,0.49
Cast,0.053395,13,3.63
TransData,0.121800,5,8.23
Conv2D,0.063656,2,4.33
MatMul,1.085982,9,73.80
1 op_type execution_time execution_frequency percent
2 AtomicAddrClean 0.007283 6 0.49
3 Cast 0.053395 13 3.63
4 TransData 0.121800 5 8.23
5 Conv2D 0.063656 2 4.33
6 MatMul 1.085982 9 73.80

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task_id,stream_id,block_dim,full_op_name,op_name,op_type,subgraph,op_info
51517,0,32,Default/Cast-op6,Cast-op6,Cast,Default,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""32,3,224,224""}, ""output_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT16"", ""shape"": ""32,3,224,224""}}"
51518,0,32,Default/TransData-op7,TransData-op7,TransData,Default,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT16"", ""shape"": ""32,3,224,224""}, ""output_0"": {""format"": ""NC1HWC0"", ""data_type"": ""NUMBER_TYPE_FLOAT16"", ""shape"": ""32,1,224,224,16""}}"
51519,0,32,Default/network-WithLossCell/_backbone-ResNet/conv1-Conv2d/Cast-op5,Cast-op5,Cast,Default,"{""input_0"": {""format"": ""FracZ"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""49,4,16,16""}, ""output_0"": {""format"": ""FracZ"", ""data_type"": ""NUMBER_TYPE_FLOAT16"", ""shape"": ""49,4,16,16""}}"
51522,0,4,Default/network-WithLossCell/_backbone-ResNet/layer1-SequentialCell/0-ResidualBlock/conv1-Conv2d/Cast-op28,Cast-op28,Cast,Default,"{""input_0"": {""format"": ""FracZ"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""4,4,16,16""}, ""output_0"": {""format"": ""FracZ"", ""data_type"": ""NUMBER_TYPE_FLOAT16"", ""shape"": ""4,4,16,16""}}"
1 task_id stream_id block_dim full_op_name op_name op_type subgraph op_info
2 51517 0 32 Default/Cast-op6 Cast-op6 Cast Default {"input_0": {"format": "DefaultFormat", "data_type": "NUMBER_TYPE_FLOAT32", "shape": "32,3,224,224"}, "output_0": {"format": "DefaultFormat", "data_type": "NUMBER_TYPE_FLOAT16", "shape": "32,3,224,224"}}
3 51518 0 32 Default/TransData-op7 TransData-op7 TransData Default {"input_0": {"format": "DefaultFormat", "data_type": "NUMBER_TYPE_FLOAT16", "shape": "32,3,224,224"}, "output_0": {"format": "NC1HWC0", "data_type": "NUMBER_TYPE_FLOAT16", "shape": "32,1,224,224,16"}}
4 51519 0 32 Default/network-WithLossCell/_backbone-ResNet/conv1-Conv2d/Cast-op5 Cast-op5 Cast Default {"input_0": {"format": "FracZ", "data_type": "NUMBER_TYPE_FLOAT32", "shape": "49,4,16,16"}, "output_0": {"format": "FracZ", "data_type": "NUMBER_TYPE_FLOAT16", "shape": "49,4,16,16"}}
5 51522 0 4 Default/network-WithLossCell/_backbone-ResNet/layer1-SequentialCell/0-ResidualBlock/conv1-Conv2d/Cast-op28 Cast-op28 Cast Default {"input_0": {"format": "FracZ", "data_type": "NUMBER_TYPE_FLOAT32", "shape": "4,4,16,16"}, "output_0": {"format": "FracZ", "data_type": "NUMBER_TYPE_FLOAT16", "shape": "4,4,16,16"}}

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task_id,stream_id,block_dim,full_op_name,op_name,op_type,subgraph,op_info
30290,0,1,Default/AtomicAddrClean-op104,AtomicAddrClean-op104,AtomicAddrClean,Default,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": """"}}"
30295,0,1,Default/AtomicAddrClean-op105,AtomicAddrClean-op105,AtomicAddrClean,Default,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""10""}}"
30300,0,1,Default/AtomicAddrClean-op106,AtomicAddrClean-op106,AtomicAddrClean,Default,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""84""}}"
30268,0,32,Default/Cast-op10,Cast-op10,Cast,Default,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""32,1,32,32""}, ""output_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT16"", ""shape"": ""32,1,32,32""}}"
30271,0,9,Default/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/Cast-op12,Cast-op12,Cast,Default,"{""input_0"": {""format"": ""FracZ"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""25,1,16,16""}, ""output_0"": {""format"": ""FracZ"", ""data_type"": ""NUMBER_TYPE_FLOAT16"", ""shape"": ""25,1,16,16""}}"
30320,0,32,Gradients/Default/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/gradConv2D/Cast-op53,Cast-op53,Cast,Gradients,"{""input_0"": {""format"": ""NC1HWC0"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""32,1,28,28,16""}, ""output_0"": {""format"": ""NC1HWC0"", ""data_type"": ""NUMBER_TYPE_FLOAT16"", ""shape"": ""32,1,28,28,16""}}"
30269,0,32,Default/TransData-op11,TransData-op11,TransData,Default,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT16"", ""shape"": ""32,1,32,32""}, ""output_0"": {""format"": ""NC1HWC0"", ""data_type"": ""NUMBER_TYPE_FLOAT16"", ""shape"": ""32,1,32,32""}}"
30308,0,32,Gradients/Default/network-WithLossCell/_backbone-LeNet5/gradReshape/TransData-op44,TransData-op44,TransData,Gradients,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT16"", ""shape"": ""32,16,5,5""}, ""output_0"": {""format"": ""NC1HWC0"", ""data_type"": ""NUMBER_TYPE_FLOAT16"", ""shape"": ""32,1,5,5,16""}}"
30272,0,32,Default/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/Conv2D-op13,Conv2D-op13,Conv2D,Default,"{""input_0"": {""format"": ""NC1HWC0"", ""data_type"": ""NUMBER_TYPE_FLOAT16"", ""shape"": ""32,1,32,32,16""}, ""input_1"": {""format"": ""FracZ"", ""data_type"": ""NUMBER_TYPE_FLOAT16"", ""shape"": ""25,1,16,16""}, ""output_0"": {""format"": ""NC1HWC0"", ""data_type"": ""NUMBER_TYPE_FLOAT16"", ""shape"": ""32,1,28,28,16""}}"
30286,0,1,Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/MatMul-op9,MatMul-op9,MatMul,Default,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""32,120""}, ""input_1"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""84,120""}, ""input_2"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""84""}, ""output_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""32,84""}}"
1 task_id stream_id block_dim full_op_name op_name op_type subgraph op_info
2 30290 0 1 Default/AtomicAddrClean-op104 AtomicAddrClean-op104 AtomicAddrClean Default {"input_0": {"format": "DefaultFormat", "data_type": "NUMBER_TYPE_FLOAT32", "shape": ""}}
3 30295 0 1 Default/AtomicAddrClean-op105 AtomicAddrClean-op105 AtomicAddrClean Default {"input_0": {"format": "DefaultFormat", "data_type": "NUMBER_TYPE_FLOAT32", "shape": "10"}}
4 30300 0 1 Default/AtomicAddrClean-op106 AtomicAddrClean-op106 AtomicAddrClean Default {"input_0": {"format": "DefaultFormat", "data_type": "NUMBER_TYPE_FLOAT32", "shape": "84"}}
5 30268 0 32 Default/Cast-op10 Cast-op10 Cast Default {"input_0": {"format": "DefaultFormat", "data_type": "NUMBER_TYPE_FLOAT32", "shape": "32,1,32,32"}, "output_0": {"format": "DefaultFormat", "data_type": "NUMBER_TYPE_FLOAT16", "shape": "32,1,32,32"}}
6 30271 0 9 Default/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/Cast-op12 Cast-op12 Cast Default {"input_0": {"format": "FracZ", "data_type": "NUMBER_TYPE_FLOAT32", "shape": "25,1,16,16"}, "output_0": {"format": "FracZ", "data_type": "NUMBER_TYPE_FLOAT16", "shape": "25,1,16,16"}}
7 30320 0 32 Gradients/Default/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/gradConv2D/Cast-op53 Cast-op53 Cast Gradients {"input_0": {"format": "NC1HWC0", "data_type": "NUMBER_TYPE_FLOAT32", "shape": "32,1,28,28,16"}, "output_0": {"format": "NC1HWC0", "data_type": "NUMBER_TYPE_FLOAT16", "shape": "32,1,28,28,16"}}
8 30269 0 32 Default/TransData-op11 TransData-op11 TransData Default {"input_0": {"format": "DefaultFormat", "data_type": "NUMBER_TYPE_FLOAT16", "shape": "32,1,32,32"}, "output_0": {"format": "NC1HWC0", "data_type": "NUMBER_TYPE_FLOAT16", "shape": "32,1,32,32"}}
9 30308 0 32 Gradients/Default/network-WithLossCell/_backbone-LeNet5/gradReshape/TransData-op44 TransData-op44 TransData Gradients {"input_0": {"format": "DefaultFormat", "data_type": "NUMBER_TYPE_FLOAT16", "shape": "32,16,5,5"}, "output_0": {"format": "NC1HWC0", "data_type": "NUMBER_TYPE_FLOAT16", "shape": "32,1,5,5,16"}}
10 30272 0 32 Default/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/Conv2D-op13 Conv2D-op13 Conv2D Default {"input_0": {"format": "NC1HWC0", "data_type": "NUMBER_TYPE_FLOAT16", "shape": "32,1,32,32,16"}, "input_1": {"format": "FracZ", "data_type": "NUMBER_TYPE_FLOAT16", "shape": "25,1,16,16"}, "output_0": {"format": "NC1HWC0", "data_type": "NUMBER_TYPE_FLOAT16", "shape": "32,1,28,28,16"}}
11 30286 0 1 Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/MatMul-op9 MatMul-op9 MatMul Default {"input_0": {"format": "DefaultFormat", "data_type": "NUMBER_TYPE_FLOAT32", "shape": "32,120"}, "input_1": {"format": "DefaultFormat", "data_type": "NUMBER_TYPE_FLOAT32", "shape": "84,120"}, "input_2": {"format": "DefaultFormat", "data_type": "NUMBER_TYPE_FLOAT32", "shape": "84"}, "output_0": {"format": "DefaultFormat", "data_type": "NUMBER_TYPE_FLOAT32", "shape": "32,84"}}

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@ -0,0 +1,5 @@
op_id,op_type,num_workers,output_queue_size,output_queue_average_size,output_queue_length,output_queue_usage_rate,sample_interval,parent_id,children_id
0,Batch,4,,,,,10,,[1]
1,Shuffle,1,"[10, 20, 30]",20.0,64,0.3125,10,0,"[2, 3]"
2,TFReader,4,"[10, 20, 30]",20.0,64,0.3125,10,1,
3,TFReader,4,"[10, 20, 30]",20.0,64,0.3125,10,1,
1 op_id op_type num_workers output_queue_size output_queue_average_size output_queue_length output_queue_usage_rate sample_interval parent_id children_id
2 0 Batch 4 10 [1]
3 1 Shuffle 1 [10, 20, 30] 20.0 64 0.3125 10 0 [2, 3]
4 2 TFReader 4 [10, 20, 30] 20.0 64 0.3125 10 1
5 3 TFReader 4 [10, 20, 30] 20.0 64 0.3125 10 1

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@ -0,0 +1,55 @@
{
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"op_info": [
{
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"op_type": "TFReader",
"num_workers": 4,
"metrics": null,
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},
{
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"op_type": "TFReader",
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"metrics": {
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}
},
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},
{
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"metrics": {
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}
},
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},
{
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"op_type": "Shuffle",
"num_workers": 1,
"metrics": {
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}
},
"children": [2, 4]
},
{
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"op_type": "Batch",
"num_workers": 4,
"metrics": null,
"children": [1]
}
]
}

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@ -0,0 +1,22 @@
step_num,start_point,end_point,total,fp_point,bp_point,iteration_interval,fp_and_bp,tail,stream_10_parallel_0_start_point,stream_10_parallel_0_end_point,stream_10_parallel_0,stream_10_parallel_1_start_point,stream_10_parallel_1_end_point,stream_10_parallel_1,stream_10_parallel_2_start_point,stream_10_parallel_2_end_point,stream_10_parallel_2,stream_11_parallel_0_start_point,stream_11_parallel_0_end_point,stream_11_parallel_0
1,45000030081,45004033128,4003047,45000030081,45001733025,0,1702944,2300103,45000042679,45000060275,17596,45001048152,45001346254,298102,45002247411,45002448354,200943,45000049687,45000075987,26300
2,45004033128,45017085658,13052530,45013070937,45014785314,9037809,1714377,2300344,45013085379,45013105429,20050,45014087119,45014385136,298017,45015297166,45015504449,207283,45013084925,45013118334,33409
3,45017085658,45030119392,13033734,45026116231,45027818443,9030573,1702212,2300949,45026131909,45026150554,18645,45027134392,45027430418,296026,45028337093,45028537767,200674,45026129217,45026160937,31720
4,45030119392,45043158607,13039215,45039152348,45040856975,9032956,1704627,2301632,45039169890,45039188966,19076,45040169338,45040466770,297432,45041374122,45041567754,193632,45039171681,45039193865,22184
5,45043158607,45056198128,13039521,45052190932,45053898028,9032325,1707096,2300100,45052207675,45052222642,14967,45053204442,45053505540,301098,45054413207,45054616536,203329,45052201931,45052237599,35668
6,45056198128,45069239564,13041436,45065233106,45066939463,9034978,1706357,2300101,45065245482,45065272534,27052,45066248423,45066546419,297996,45067455113,45067659145,204032,45065245817,45065279896,34079
7,45069239564,45082281383,13041819,45078274997,45079980193,9035433,1705196,2301190,45078293910,45078312935,19025,45079287754,45079593841,306087,45080492957,45080691395,198438,45078292067,45078322277,30210
8,45082281383,45095336378,13054995,45091321488,45093036084,9040105,1714596,2300294,45091338628,45091359138,20510,45092338469,45092638994,300525,45093554195,45093747470,193275,45091341356,45091369667,28311
9,45095336378,45108372225,13035847,45104363079,45106071009,9026701,1707930,2301216,45104374524,45104400088,25564,45105378751,45105683029,304278,45106587481,45106785336,197855,45104382131,45104410852,28721
10,45108372225,45121412413,13040188,45117401873,45119111301,9029648,1709428,2301112,45117417721,45117439668,21947,45118413083,45118718050,304967,45119629347,45119829996,200649,45117421502,45117446718,25216
11,45121412413,45134477662,13065249,45130459598,45132175723,9047185,1716125,2301939,45130478168,45130498936,20768,45131477957,45131775220,297263,45132691645,45132893707,202062,45130470285,45130501652,31367
12,45134477662,45147533298,13055636,45143521860,45145232553,9044198,1710693,2300745,45143533787,45143557293,23506,45144533554,45144841545,307991,45145744997,45145952255,207258,45143537383,45143563466,26083
13,45147533298,45160588134,13054836,45156570201,45158286694,9036903,1716493,2301440,45156581069,45156609506,28437,45157581617,45157880841,299224,45158806166,45158999875,193709,45156589050,45156615664,26614
14,45160588134,45173640064,13051930,45169625906,45171339426,9037772,1713520,2300638,45169637432,45169661754,24322,45170639482,45170940949,301467,45171853721,45172056606,202885,45169644605,45169673410,28805
15,45173640064,45186671634,13031570,45182666696,45184371430,9026632,1704734,2300204,45182678355,45182698471,20116,45183679568,45183981082,301514,45184887156,45185083035,195879,45182680062,45182708455,28393
16,45186671634,45199720448,13048814,45195714716,45197420410,9043082,1705694,2300038,45195728993,45195754646,25653,45196732493,45197028048,295555,45197934921,45198139237,204316,45195733069,45195764102,31033
17,45199720448,45212762605,13042157,45208758416,45210460864,9037968,1702448,2301741,45208771010,45208790367,19357,45209773548,45210074988,301440,45210978277,45211173577,195300,45208773143,45208803280,30137
18,45212762605,45225814601,13051996,45221801814,45223514580,9039209,1712766,2300021,45221815911,45221839644,23733,45222819211,45223114544,295333,45224031469,45224234043,202574,45221812106,45221849103,36997
19,45225814601,45238848430,13033829,45234842015,45236548356,9027414,1706341,2300074,45234855444,45234876469,21025,45235853358,45236160825,307467,45237063061,45237260964,197903,45234857141,45234882976,25835
20,45238848430,45251899738,13051308,45247879385,45249598280,9030955,1718895,2301458,45247896725,45247917316,20591,45248896361,45249193681,297320,45250117916,45250315651,197735,45247894228,45247926723,32495
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1 step_num start_point end_point total fp_point bp_point iteration_interval fp_and_bp tail stream_10_parallel_0_start_point stream_10_parallel_0_end_point stream_10_parallel_0 stream_10_parallel_1_start_point stream_10_parallel_1_end_point stream_10_parallel_1 stream_10_parallel_2_start_point stream_10_parallel_2_end_point stream_10_parallel_2 stream_11_parallel_0_start_point stream_11_parallel_0_end_point stream_11_parallel_0
2 1 45000030081 45004033128 4003047 45000030081 45001733025 0 1702944 2300103 45000042679 45000060275 17596 45001048152 45001346254 298102 45002247411 45002448354 200943 45000049687 45000075987 26300
3 2 45004033128 45017085658 13052530 45013070937 45014785314 9037809 1714377 2300344 45013085379 45013105429 20050 45014087119 45014385136 298017 45015297166 45015504449 207283 45013084925 45013118334 33409
4 3 45017085658 45030119392 13033734 45026116231 45027818443 9030573 1702212 2300949 45026131909 45026150554 18645 45027134392 45027430418 296026 45028337093 45028537767 200674 45026129217 45026160937 31720
5 4 45030119392 45043158607 13039215 45039152348 45040856975 9032956 1704627 2301632 45039169890 45039188966 19076 45040169338 45040466770 297432 45041374122 45041567754 193632 45039171681 45039193865 22184
6 5 45043158607 45056198128 13039521 45052190932 45053898028 9032325 1707096 2300100 45052207675 45052222642 14967 45053204442 45053505540 301098 45054413207 45054616536 203329 45052201931 45052237599 35668
7 6 45056198128 45069239564 13041436 45065233106 45066939463 9034978 1706357 2300101 45065245482 45065272534 27052 45066248423 45066546419 297996 45067455113 45067659145 204032 45065245817 45065279896 34079
8 7 45069239564 45082281383 13041819 45078274997 45079980193 9035433 1705196 2301190 45078293910 45078312935 19025 45079287754 45079593841 306087 45080492957 45080691395 198438 45078292067 45078322277 30210
9 8 45082281383 45095336378 13054995 45091321488 45093036084 9040105 1714596 2300294 45091338628 45091359138 20510 45092338469 45092638994 300525 45093554195 45093747470 193275 45091341356 45091369667 28311
10 9 45095336378 45108372225 13035847 45104363079 45106071009 9026701 1707930 2301216 45104374524 45104400088 25564 45105378751 45105683029 304278 45106587481 45106785336 197855 45104382131 45104410852 28721
11 10 45108372225 45121412413 13040188 45117401873 45119111301 9029648 1709428 2301112 45117417721 45117439668 21947 45118413083 45118718050 304967 45119629347 45119829996 200649 45117421502 45117446718 25216
12 11 45121412413 45134477662 13065249 45130459598 45132175723 9047185 1716125 2301939 45130478168 45130498936 20768 45131477957 45131775220 297263 45132691645 45132893707 202062 45130470285 45130501652 31367
13 12 45134477662 45147533298 13055636 45143521860 45145232553 9044198 1710693 2300745 45143533787 45143557293 23506 45144533554 45144841545 307991 45145744997 45145952255 207258 45143537383 45143563466 26083
14 13 45147533298 45160588134 13054836 45156570201 45158286694 9036903 1716493 2301440 45156581069 45156609506 28437 45157581617 45157880841 299224 45158806166 45158999875 193709 45156589050 45156615664 26614
15 14 45160588134 45173640064 13051930 45169625906 45171339426 9037772 1713520 2300638 45169637432 45169661754 24322 45170639482 45170940949 301467 45171853721 45172056606 202885 45169644605 45169673410 28805
16 15 45173640064 45186671634 13031570 45182666696 45184371430 9026632 1704734 2300204 45182678355 45182698471 20116 45183679568 45183981082 301514 45184887156 45185083035 195879 45182680062 45182708455 28393
17 16 45186671634 45199720448 13048814 45195714716 45197420410 9043082 1705694 2300038 45195728993 45195754646 25653 45196732493 45197028048 295555 45197934921 45198139237 204316 45195733069 45195764102 31033
18 17 45199720448 45212762605 13042157 45208758416 45210460864 9037968 1702448 2301741 45208771010 45208790367 19357 45209773548 45210074988 301440 45210978277 45211173577 195300 45208773143 45208803280 30137
19 18 45212762605 45225814601 13051996 45221801814 45223514580 9039209 1712766 2300021 45221815911 45221839644 23733 45222819211 45223114544 295333 45224031469 45224234043 202574 45221812106 45221849103 36997
20 19 45225814601 45238848430 13033829 45234842015 45236548356 9027414 1706341 2300074 45234855444 45234876469 21025 45235853358 45236160825 307467 45237063061 45237260964 197903 45234857141 45234882976 25835
21 20 45238848430 45251899738 13051308 45247879385 45249598280 9030955 1718895 2301458 45247896725 45247917316 20591 45248896361 45249193681 297320 45250117916 45250315651 197735 45247894228 45247926723 32495
22 - 45121436513 45134482124 13045611 45130471874 45132181322 9035360 1709449 2300802 45130486422 45130508229 21808 45131486785 45131787364 300579 45132697369 45132897305 199936 45130487458 45130517315 29857

View File

@ -0,0 +1,42 @@
step_num,start_point,end_point,total,fp_point,bp_point,iteration_interval,fp_and_bp,tail,stream_10_parallel_0_start_point,stream_10_parallel_0_end_point,stream_10_parallel_0,stream_10_parallel_1_start_point,stream_10_parallel_1_end_point,stream_10_parallel_1,stream_10_parallel_2_start_point,stream_10_parallel_2_end_point,stream_10_parallel_2,stream_11_parallel_0_start_point,stream_11_parallel_0_end_point,stream_11_parallel_0
1,45000025226,45004034753,4009527,45000025226,45001734362,0,1709136,2300391,45000044023,45000060886,16863,45001043581,45001343373,299792,45002254048,45002452830,198782,45000043807,45000065736,21929
2,45004034753,45017091420,13056667,45013073790,45014789509,9039037,1715719,2301911,45013085205,45013104210,19005,45014086339,45014393261,306922,45015299546,45015501808,202262,45013085040,45013119810,34770
3,45017091420,45030144372,13052952,45026123867,45027843651,9032447,1719784,2300721,45026138546,45026154524,15978,45027135742,45027437486,301744,45028363120,45028560901,197781,45026136046,45026171363,35317
4,45030144372,45043184486,13040114,45039173149,45040883087,9028777,1709938,2301399,45039190927,45039209948,19021,45040185915,45040484897,298982,45041399754,45041594775,195021,45039192768,45039221423,28655
5,45043184486,45056241064,13056578,45052223555,45053940709,9039069,1717154,2300355,45052241736,45052262186,20450,45053239605,45053540866,301261,45054452604,45054654505,201901,45052233932,45052265774,31842
6,45056241064,45069291346,13050282,45065278144,45066991121,9037080,1712977,2300225,45065293660,45065316136,22476,45066289480,45066589910,300430,45067511002,45067701731,190729,45065293679,45065321296,27617
7,45069291346,45082344927,13053581,45078335376,45080043268,9044030,1707892,2301659,45078353164,45078365382,12218,45079354748,45079648384,293636,45080557453,45080760374,202921,45078353030,45078384530,31500
8,45082344927,45095382554,13037627,45091368697,45093080797,9023770,1712100,2301757,45091381244,45091405208,23964,45092382630,45092684285,301655,45093590961,45093796698,205737,45091381199,45091413840,32641
9,45095382554,45108433947,13051393,45104419947,45106132133,9037393,1712186,2301814,45104432587,45104457476,24889,45105431458,45105735476,304018,45106651213,45106845305,194092,45104435207,45104466677,31470
10,45108433947,45121486591,13052644,45117469353,45119185969,9035406,1716616,2300622,45117483627,45117504869,21242,45118483411,45118788540,305129,45119696660,45119898575,201915,45117485587,45117510985,25398
11,45121486591,45134546571,13059980,45130528618,45132244809,9042027,1716191,2301762,45130539730,45130561122,21392,45131538695,45131846715,308020,45132759789,45132960848,201059,45130545378,45130569412,24034
12,45134546571,45147608222,13061651,45143597023,45145307273,9050452,1710250,2300949,45143615771,45143631460,15689,45144610592,45144910736,300144,45145818642,45146024326,205684,45143613528,45143640223,26695
13,45147608222,45160663790,13055568,45156648696,45158362923,9040474,1714227,2300867,45156663193,45156685466,22273,45157661576,45157963074,301498,45158881212,45159074431,193219,45156667038,45156694912,27874
14,45160663790,45173707626,13043836,45169694535,45171407246,9030745,1712711,2300380,45169710667,45169727936,17269,45170705802,45171013806,308004,45171924100,45172120273,196173,45169708524,45169739038,30514
15,45173707626,45186754860,13047234,45182750254,45184454036,9042628,1703782,2300824,45182765445,45182789799,24354,45183761335,45184065169,303834,45184973312,45185170444,197132,45182769451,45182799598,30147
16,45186754860,45199798718,13043858,45195792271,45197497908,9037411,1705637,2300810,45195804771,45195827915,23144,45196804016,45197108243,304227,45198013357,45198209858,196501,45195806656,45195841674,35018
17,45199798718,45212854993,13056275,45208834355,45210553378,9035637,1719023,2301615,45208850179,45208865588,15409,45209851018,45210151436,300418,45211073169,45211271792,198623,45208847052,45208876998,29946
18,45212854993,45225893712,13038719,45221888939,45223593704,9033946,1704765,2300008,45221901732,45221924983,23251,45222908795,45223203590,294795,45224105803,45224313354,207551,45221899792,45221938802,39010
19,45225893712,45238941242,13047530,45234926295,45236640454,9032583,1714159,2300788,45234938628,45234957237,18609,45235942710,45236239983,297273,45237159532,45237356140,196608,45234938330,45234976170,37840
20,45238941242,45251979177,13037935,45247977674,45249678116,9036432,1700442,2301061,45247990919,45248013476,22557,45248991451,45249294742,303291,45250195733,45250395760,200027,45247988950,45248024969,36019
21,45251979177,45265018752,13039575,45261005416,45262718472,9026239,1713056,2300280,0,0,0,0,0,0,0,0,0,0,0,0
22,45265018752,45278062782,13044030,45274047185,45275762095,9028433,1714910,2300687,0,0,0,0,0,0,0,0,0,0,0,0
23,45278062782,45291105708,13042926,45287094000,45288805223,9031218,1711223,2300485,0,0,0,0,0,0,0,0,0,0,0,0
24,45291105708,45304155918,13050210,45300150844,45301854040,9045136,1703196,2301878,0,0,0,0,0,0,0,0,0,0,0,0
25,45304155918,45317206695,13050777,45313191948,45314905714,9036030,1713766,2300981,0,0,0,0,0,0,0,0,0,0,0,0
26,45317206695,45330265105,13058410,45326256021,45327964581,9049326,1708560,2300524,0,0,0,0,0,0,0,0,0,0,0,0
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28,45343324012,45356374571,13050559,45352366211,45354073401,9042199,1707190,2301170,0,0,0,0,0,0,0,0,0,0,0,0
29,45356374571,45369429514,13054943,45365417827,45367128283,9043256,1710456,2301231,0,0,0,0,0,0,0,0,0,0,0,0
30,45369429514,45382479199,13049685,45378476397,45380177297,9046883,1700900,2301902,0,0,0,0,0,0,0,0,0,0,0,0
31,45382479199,45395530376,13051177,45391510137,45393229377,9030938,1719240,2300999,0,0,0,0,0,0,0,0,0,0,0,0
32,45395530376,45408571765,13041389,45404559082,45406270720,9028706,1711638,2301045,0,0,0,0,0,0,0,0,0,0,0,0
33,45408571765,45421635175,13063410,45417619223,45419334221,9047458,1714998,2300954,0,0,0,0,0,0,0,0,0,0,0,0
34,45421635175,45434672219,13037044,45430669445,45432371312,9034270,1701867,2300907,0,0,0,0,0,0,0,0,0,0,0,0
35,45434672219,45447714036,13041817,45443704548,45445413852,9032329,1709304,2300184,0,0,0,0,0,0,0,0,0,0,0,0
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37,45460765153,45473829105,13063952,45469808281,45471527400,9043128,1719119,2301705,0,0,0,0,0,0,0,0,0,0,0,0
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39,45486884190,45499928571,13044381,45495917628,45497627921,9033438,1710293,2300650,0,0,0,0,0,0,0,0,0,0,0,0
40,45499928571,45512973815,13045244,45508968990,45510673699,9040419,1704709,2300116,0,0,0,0,0,0,0,0,0,0,0,0
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1 step_num start_point end_point total fp_point bp_point iteration_interval fp_and_bp tail stream_10_parallel_0_start_point stream_10_parallel_0_end_point stream_10_parallel_0 stream_10_parallel_1_start_point stream_10_parallel_1_end_point stream_10_parallel_1 stream_10_parallel_2_start_point stream_10_parallel_2_end_point stream_10_parallel_2 stream_11_parallel_0_start_point stream_11_parallel_0_end_point stream_11_parallel_0
2 1 45000025226 45004034753 4009527 45000025226 45001734362 0 1709136 2300391 45000044023 45000060886 16863 45001043581 45001343373 299792 45002254048 45002452830 198782 45000043807 45000065736 21929
3 2 45004034753 45017091420 13056667 45013073790 45014789509 9039037 1715719 2301911 45013085205 45013104210 19005 45014086339 45014393261 306922 45015299546 45015501808 202262 45013085040 45013119810 34770
4 3 45017091420 45030144372 13052952 45026123867 45027843651 9032447 1719784 2300721 45026138546 45026154524 15978 45027135742 45027437486 301744 45028363120 45028560901 197781 45026136046 45026171363 35317
5 4 45030144372 45043184486 13040114 45039173149 45040883087 9028777 1709938 2301399 45039190927 45039209948 19021 45040185915 45040484897 298982 45041399754 45041594775 195021 45039192768 45039221423 28655
6 5 45043184486 45056241064 13056578 45052223555 45053940709 9039069 1717154 2300355 45052241736 45052262186 20450 45053239605 45053540866 301261 45054452604 45054654505 201901 45052233932 45052265774 31842
7 6 45056241064 45069291346 13050282 45065278144 45066991121 9037080 1712977 2300225 45065293660 45065316136 22476 45066289480 45066589910 300430 45067511002 45067701731 190729 45065293679 45065321296 27617
8 7 45069291346 45082344927 13053581 45078335376 45080043268 9044030 1707892 2301659 45078353164 45078365382 12218 45079354748 45079648384 293636 45080557453 45080760374 202921 45078353030 45078384530 31500
9 8 45082344927 45095382554 13037627 45091368697 45093080797 9023770 1712100 2301757 45091381244 45091405208 23964 45092382630 45092684285 301655 45093590961 45093796698 205737 45091381199 45091413840 32641
10 9 45095382554 45108433947 13051393 45104419947 45106132133 9037393 1712186 2301814 45104432587 45104457476 24889 45105431458 45105735476 304018 45106651213 45106845305 194092 45104435207 45104466677 31470
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42 - 45251983006 45265032725 13049720 45261020353 45262731761 9037347 1711408 2300964 21986676455 21986686280 9825 21987163213 21987310272 147058 21987754537 21987851587 97050 21986676441 21986691731 15290

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# 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.
# ============================================================================
"""Unit test for profiler."""
import os
RAW_DATA_BASE = os.path.realpath(os.path.join(os.path.dirname(__file__), '../../data/profiler_data'))
RAW_DATA = os.path.realpath(os.path.join(RAW_DATA_BASE, 'JOB1'))
RAW_DATA_JOB2 = os.path.realpath(os.path.join(RAW_DATA_BASE, 'JOB2'))
PROFILER_DIR = os.path.realpath(os.path.join(RAW_DATA_BASE, 'profiler'))

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# 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.
# ============================================================================

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# 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.
# ============================================================================
"""Test the aicpu parser."""
import os
import tempfile
import shutil
from unittest import TestCase
from mindspore.profiler.parser.aicpu_data_parser import DataPreProcessParser
def get_result(file_path):
"""
Get result from the aicpu file.
Args:
file_path (str): The aicpu file path.
Returns:
list[list], the parsed aicpu information.
"""
result = []
try:
file = open(file_path, 'r')
result.append(file.read())
return result
finally:
if file:
file.close()
class TestAicpuParser(TestCase):
"""Test the class of Aicpu Parser."""
def setUp(self) -> None:
"""Initialization before test case execution."""
self.profiling_dir = os.path.realpath(os.path.join(os.path.dirname(__file__),
'../../../data/profiler_data/'
'JOB_AICPU/data'))
self.expect_dir = os.path.realpath(os.path.join(os.path.dirname(__file__),
'../../../data/profiler_data/'
'JOB_AICPU/expect'))
self.output_path = tempfile.mkdtemp(prefix='output_data_preprocess_aicpu_')
self.output_file = os.path.join(self.output_path, 'output_data_preprocess_aicpu_0.txt')
self.expect_file = os.path.join(self.expect_dir, 'output_data_preprocess_aicpu_0.txt')
def test_aicpu_parser(self):
"""Test the class of Aicpu Parser."""
data = DataPreProcessParser(self.profiling_dir, self.output_file)
data.execute()
expect_result = get_result(self.expect_file)
result = get_result(self.output_file)
shutil.rmtree(self.output_path)
assert expect_result == result
def test_aicpu_parser_file_not_exist(self):
"""Test the class of Aicpu Parser."""
profiling_dir = os.path.realpath(os.path.join(self.profiling_dir, 'data'))
data = DataPreProcessParser(profiling_dir, self.output_file)
data.execute()
shutil.rmtree(self.output_path)

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# 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.
# ============================================================================
"""Test the framework parser module."""
import csv
import os
import shutil
import tempfile
from unittest import mock
import pytest
from mindspore.profiler.common.exceptions.exceptions import \
ProfilerFileNotFoundException
from mindspore.profiler.parser.framework_parser import FrameworkParser
from tests.ut.python.profiler import PROFILER_DIR, RAW_DATA_BASE
def get_framework_result(file_path):
"""
Get framework result from the framework file.
Args:
file_path (str): The framework file path.
Returns:
list[list], the parsed framework information.
"""
result = []
with open(file_path, 'r') as file:
csv_reader = csv.reader(file)
for row in csv_reader:
result.append(row)
return result
class TestFrameworkParser:
"""Test the class of `FrameworkParser`."""
def setup_method(self):
"""Initialization before test case execution."""
with mock.patch.object(FrameworkParser, '_raw_data_dir', RAW_DATA_BASE):
self._output_path_1 = tempfile.mkdtemp(prefix='test_framework_parser_')
self._parser_1 = FrameworkParser('JOB1', '0', self._output_path_1)
self._output_path_2 = tempfile.mkdtemp(prefix='test_framework_parser_')
self._parser_2 = FrameworkParser('JOB2', '0', self._output_path_2)
self._output_path_4 = tempfile.mkdtemp(prefix='test_framework_parser_')
self._parser_4 = FrameworkParser('JOB4', '0', self._output_path_4)
def teardown_method(self) -> None:
"""Clear up after test case execution."""
shutil.rmtree(self._output_path_1)
shutil.rmtree(self._output_path_2)
shutil.rmtree(self._output_path_4)
def test_save_path(self):
"""Test the querying save path function."""
expect_result = os.path.join(self._output_path_1, 'framework_raw_0.csv')
assert expect_result == self._parser_1.save_path
expect_result = os.path.join(self._output_path_2, 'framework_raw_0.csv')
assert expect_result == self._parser_2.save_path
def test_point_info(self):
"""Test the querying point info function."""
expect_result = {
1: 'Default/Cast-op6',
2: 'Default/TransData-op7'
}
assert expect_result == self._parser_4.point_info
def test_to_task_id_full_op_name_dict(self):
"""Test the querying task id and full operator name dict function."""
expect_result = {
'51517': 'Default/Cast-op6',
'51518': 'Default/TransData-op7',
'51519': 'Default/network-WithLossCell/_backbone-ResNet/conv1-Conv2d/Cast-op5',
'51522': 'Default/network-WithLossCell/_backbone-ResNet/'
'layer1-SequentialCell/0-ResidualBlock/conv1-Conv2d/Cast-op28'
}
assert expect_result == self._parser_1.to_task_id_full_op_name_dict()
assert expect_result == self._parser_2.to_task_id_full_op_name_dict()
expect_result = {
'0_1': 'Default/Cast-op6',
'0_2': 'Default/TransData-op7',
'0_3': 'Default/network-WithLossCell/_backbone-ResNet/conv1-Conv2d/Cast-op5',
'0_4': 'Default/network-WithLossCell/_backbone-ResNet/layer1-SequentialCell/'
'0-ResidualBlock/conv1-Conv2d/Cast-op28'
}
assert expect_result == self._parser_4.to_task_id_full_op_name_dict()
def test_parse(self):
"""Test the parse function."""
expect_framework_file = os.path.join(PROFILER_DIR, 'framework_raw_0.csv')
expect_framework_file = os.path.realpath(expect_framework_file)
expect_result = get_framework_result(expect_framework_file)
self._parser_1.parse()
framework_file = os.path.join(self._output_path_1, 'framework_raw_0.csv')
result = get_framework_result(framework_file)
assert expect_result == result
self._parser_2.parse()
framework_file = os.path.join(self._output_path_2, 'framework_raw_0.csv')
result = get_framework_result(framework_file)
assert expect_result == result
@mock.patch('os.listdir')
@mock.patch('os.path.isdir')
def test_create_framework_parser_fail_1(self, *args):
"""Test the function of fail to create framework parser."""
args[0].return_value = True
args[1].return_value = []
with pytest.raises(ProfilerFileNotFoundException) as exc_info:
FrameworkParser('JOB1', '0')
assert exc_info.value.error_code == '50546084'
assert exc_info.value.message == 'The file <Framework> not found.'

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# 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.
# ============================================================================
"""Test the minddata pipeline parser module."""
import csv
import os
import shutil
import tempfile
from mindspore.profiler.parser.minddata_pipeline_parser import \
MinddataPipelineParser
from tests.ut.python.profiler import PROFILER_DIR, RAW_DATA, RAW_DATA_JOB2
def get_minddata_pipeline_result(file_path):
"""
Get minddata pipeline result from the minddata pipeline file.
Args:
file_path (str): The minddata pipeline file path.
Returns:
list[list], the parsed minddata pipeline information.
"""
result = []
with open(file_path, 'r') as file:
csv_reader = csv.reader(file)
for row in csv_reader:
result.append(row)
return result
class TestMinddataPipelineParser:
"""Test the class of `MinddataPipelineParser`."""
def setup_method(self):
"""Initialization before test case execution."""
self._output_path_1 = tempfile.mkdtemp(
prefix='test_minddata_pipeline_parser_'
)
self._parser_1 = MinddataPipelineParser(
RAW_DATA, '0', self._output_path_1
)
self._output_path_2 = tempfile.mkdtemp(
prefix='test_minddata_pipeline_parser_'
)
self._parser_2 = MinddataPipelineParser(
RAW_DATA_JOB2, '0', self._output_path_2
)
def teardown_method(self) -> None:
"""Clear up after test case execution."""
shutil.rmtree(self._output_path_1)
shutil.rmtree(self._output_path_2)
def test_save_path(self):
"""Test the querying save path function."""
expect_result = os.path.join(
self._output_path_1, 'minddata_pipeline_raw_0.csv'
)
assert expect_result == self._parser_1.save_path
def test_parse(self):
"""Test the parse function."""
expect_pipeline_file = os.path.join(
PROFILER_DIR, 'minddata_pipeline_raw_0.csv'
)
expect_result = get_minddata_pipeline_result(expect_pipeline_file)
self._parser_1.parse()
pipeline_file = os.path.join(
self._output_path_1, 'minddata_pipeline_raw_0.csv'
)
result = get_minddata_pipeline_result(pipeline_file)
assert expect_result == result
self._parser_2.parse()
pipeline_file = os.path.join(
self._output_path_2, 'minddata_pipeline_raw_0.csv'
)
result = get_minddata_pipeline_result(pipeline_file)
assert expect_result == result