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SK-TDA4VM: I can't compile any sample model on EdgeAI TIDL Tools

Taekmin Lee
Expert 1445 points
Part Number: SK-TDA4VM

Hi,

I tried to model compilation on PC using EdgeAI TIDL Tools.

I setup environment for the model compilation and tried to model compile using TI's sample models(ONNX, TFLite).
But result is failed.(Also not working edgeAI Apps on EVM) model compilation shows below warning messages:

WARNING: [TIDL_E_DATAFLOW_INFO_NULL] ti_cnnperfsim.out fails to allocate memory in MSMC. 
Please look into perfsim log. 
This model can only be used on PC emulation, it will get fault on target.

I checked the perfsim log(model-artifacts/cl-ort-resnet18-v1/tempDir/191_tidl_io_.perf_sim_config.txt) file.

# Size of L2 SRAM Memory in KB which can be used by TIDL, Recommended value is
# 448KB considering that 64KB of L2 shall be configured as cache. TIDL test bench
# configures L2 cache as 64 KB, so any value higher than 448 KB would require
# user to change the L2 cache setting in TIDL test bench
L2MEMSIZE_KB           = 448
# Size of L3 (MSMC) SRAM Memory in KB which can be used by TIDL
MSMCSIZE_KB            = 7968
#ID for a Device, TDA4VMID = 0, TIDL_TDA4AEP = 1,  TIDL_TDA4AM = 2, TIDL_TDA4AMPlus = 3
DEVICE_NAME            = 0
ENABLE_PERSIT_WT_ALLOC = 1
DDRFREQ_MHZ            = 4266
FILENAME_NET     = /home/edgeai-tidl-tools/model-artifacts/cl-ort-resnet18-v1/tempDir/191_tidl_net.bin
FILEFORMAT_NET     = -1
OUTPUT_DIR     = /home/edgeai-tidl-tools/model-artifacts/cl-ort-resnet18-v1/tempDir/191_tidl_net.bin

I can't find compile option about cache size in EdgeAI TIDL Tools.

tidl_tools.log 
Available execution providers :  ['TIDLExecutionProvider', 'TIDLCompilationProvider', 'CPUExecutionProvider']

Running 1 Models - ['cl-ort-resnet18-v1']

2022-03-22 11:14:23.113709000 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer4.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113749200 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer4.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113756200 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer4.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113761400 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer4.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113766500 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer3.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113771900 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer1.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113776700 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer1.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113781300 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer4.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113785800 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer3.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113790500 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer3.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113795400 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113800400 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer1.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113804900 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer2.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113809800 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer1.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113814600 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer2.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113819200 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer2.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113824200 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer2.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113829300 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer2.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113834200 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer3.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2022-03-22 11:14:23.113846100 [W:onnxruntime:, graph.cc:3106 CleanUnusedInitializers] Removing initializer 'layer3.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
tidl_tools_path                                 = /home/edgeai-tidl-tools/tidl_tools 
artifacts_folder                                = ../../../model-artifacts//cl-ort-resnet18-v1/ 
tidl_tensor_bits                                = 8 
debug_level                                     = 3 
num_tidl_subgraphs                              = 16 
tidl_denylist                                   = 
tidl_calibration_accuracy_level                 = 7 
tidl_calibration_options:num_frames_calibration = 2 
tidl_calibration_options:bias_calibration_iterations = 5 
power_of_2_quantization                         = 2 
enable_high_resolution_optimization             = 0 
pre_batchnorm_fold                              = 1 
add_data_convert_ops                          = 3 
output_feature_16bit_names_list                 =  
m_params_16bit_names_list                       =  
reserved_compile_constraints_flag               = 1601 
ti_internal_reserved_1                          = 

 ****** WARNING : Network not identified as Object Detection network - Ignore if network is not OD *****

Supported TIDL layer type ---            Cast --  
Supported TIDL layer type ---             Add --  
Supported TIDL layer type ---             Mul --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---         MaxPool --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---             Add --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---             Add --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---             Add --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---             Add --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---             Add --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---             Add --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---             Add --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type ---            Conv --  
Supported TIDL layer type ---             Add --  
Supported TIDL layer type ---            Relu --  
Supported TIDL layer type --- GlobalAveragePool --  
Supported TIDL layer type ---         Flatten --  
Supported TIDL layer type ---            Gemm --  

Preliminary subgraphs created = 1 
Final number of subgraphs created are : 1, - Offloaded Nodes - 52, Total Nodes - 52 
Running runtimes graphviz - /home/edgeai-tidl-tools/tidl_tools/tidl_graphVisualiser_runtimes.out ../../../model-artifacts//cl-ort-resnet18-v1//allowedNode.txt ../../../model-artifacts//cl-ort-resnet18-v1//tempDir/graphvizInfo.txt ../../../model-artifacts//cl-ort-resnet18-v1//tempDir/runtimes_visualization.svg 
*** In TIDL_createStateImportFunc *** 
Compute on node : TIDLExecutionProvider_TIDL_0_0
  0,            Cast, 1, 1, input.1Net_IN, TIDL_cast_in
  1,             Add, 2, 1, TIDL_cast_in, TIDL_Scale_In
  2,             Mul, 2, 1, TIDL_Scale_In, input.1
  3,            Conv, 3, 1, input.1, 124
  4,            Relu, 1, 1, 124, 125
  5,         MaxPool, 1, 1, 125, 126
  6,            Conv, 3, 1, 126, 128
  7,            Relu, 1, 1, 128, 129
  8,            Conv, 3, 1, 129, 131
  9,             Add, 2, 1, 131, 132
 10,            Relu, 1, 1, 132, 133
 11,            Conv, 3, 1, 133, 135
 12,            Relu, 1, 1, 135, 136
 13,            Conv, 3, 1, 136, 138
 14,             Add, 2, 1, 138, 139
 15,            Relu, 1, 1, 139, 140
 16,            Conv, 3, 1, 140, 142
 17,            Relu, 1, 1, 142, 143
 18,            Conv, 3, 1, 143, 145
 19,            Conv, 3, 1, 140, 147
 20,             Add, 2, 1, 145, 148
 21,            Relu, 1, 1, 148, 149
 22,            Conv, 3, 1, 149, 151
 23,            Relu, 1, 1, 151, 152
 24,            Conv, 3, 1, 152, 154
 25,             Add, 2, 1, 154, 155
 26,            Relu, 1, 1, 155, 156
 27,            Conv, 3, 1, 156, 158
 28,            Relu, 1, 1, 158, 159
 29,            Conv, 3, 1, 159, 161
 30,            Conv, 3, 1, 156, 163
 31,             Add, 2, 1, 161, 164
 32,            Relu, 1, 1, 164, 165
 33,            Conv, 3, 1, 165, 167
 34,            Relu, 1, 1, 167, 168
 35,            Conv, 3, 1, 168, 170
 36,             Add, 2, 1, 170, 171
 37,            Relu, 1, 1, 171, 172
 38,            Conv, 3, 1, 172, 174
 39,            Relu, 1, 1, 174, 175
 40,            Conv, 3, 1, 175, 177
 41,            Conv, 3, 1, 172, 179
 42,             Add, 2, 1, 177, 180
 43,            Relu, 1, 1, 180, 181
 44,            Conv, 3, 1, 181, 183
 45,            Relu, 1, 1, 183, 184
 46,            Conv, 3, 1, 184, 186
 47,             Add, 2, 1, 186, 187
 48,            Relu, 1, 1, 187, 188
 49, GlobalAveragePool, 1, 1, 188, 189
 50,         Flatten, 1, 1, 189, 190
 51,            Gemm, 3, 1, 190, 191

Input tensor name -  input.1Net_IN 
Output tensor name - 191 
In TIDL_onnxRtImportInit subgraph_name=191
Layer 0, subgraph id 191, name=191
Layer 1, subgraph id 191, name=input.1Net_IN
In TIDL_runtimesOptimizeNet: LayerIndex = 54, dataIndex = 53 

 ************** Frame index 1 : Running float import ************* 
In TIDL_runtimesPostProcessNet 
WARNING: [TIDL_E_DATAFLOW_INFO_NULL] ti_cnnperfsim.out fails to allocate memory in MSMC. Please look into perfsim log. This model can only be used on PC emulation, it will get fault on target.
****************************************************
**          1 WARNINGS          0 ERRORS          **
****************************************************
************ in TIDL_subgraphRtCreate ************ 
  0.0s:  VX_ZONE_INIT:Enabled
 0.6s:  VX_ZONE_ERROR:Enabled
 0.7s:  VX_ZONE_WARNING:Enabled
 0.962s:  VX_ZONE_INIT:[tivxInit:178] Initialization Done !!!
TIDL_initDebugTraceParams Done 
Alg Alloc for Layer # -    0
Alg Alloc for Layer # -    1
Alg Alloc for Layer # -    2
Alg Alloc for Layer # -    3
Alg Alloc for Layer # -    4
Alg Alloc for Layer # -    5
Alg Alloc for Layer # -    6
Alg Alloc for Layer # -    7
Alg Alloc for Layer # -    8
Alg Alloc for Layer # -    9
Alg Alloc for Layer # -   10
Alg Alloc for Layer # -   11
Alg Alloc for Layer # -   12
Alg Alloc for Layer # -   13
Alg Alloc for Layer # -   14
Alg Alloc for Layer # -   15
Alg Alloc for Layer # -   16
Alg Alloc for Layer # -   17
Alg Alloc for Layer # -   18
Alg Alloc for Layer # -   19
Alg Alloc for Layer # -   20
Alg Alloc for Layer # -   21
Alg Alloc for Layer # -   22
Alg Alloc for Layer # -   23
Alg Alloc for Layer # -   24
Alg Alloc for Layer # -   25
Alg Alloc for Layer # -   26
Alg Alloc for Layer # -   27
Alg Alloc for Layer # -   28
Alg Alloc for Layer # -   29
Alg Alloc for Layer # -   30
Alg Alloc for Layer # -   31
Alg Alloc for Layer # -   32
Alg Alloc for Layer # -   33
Alg Alloc for Layer # -   34

TIDL Memory requiement 
MemRecNum , Space     , Attribute ,    SizeinBytes 
 0         , DDR       , Persistent,    15208      
 1         , DDR       , Persistent,    136        
 2         , DDR       , Scratch   ,    16384      
 3         , DDR       , Scratch   ,    4096       
 4         , DDR       , Scratch   ,    57344      
 5         , DDR       , Persistent,    103328     
 6         , DDR       , Scratch   ,    15355804   
 7         , DDR       , Scratch   ,    256        
 8         , DDR       , Scratch   ,    4990208    
 9         , DDR       , Scratch   ,    26616832   
 10        , DDR       , Persistent,    5431680    
 11        , DDR       , Persistent,    441536     
 12        , DDR       , Scratch   ,    256        
 13        , DDR       , Persistent,    128        
NOTE: Memory requirement in host emulation can be different from the same on EVM
      To get the actual TIDL memory requirement make sure to run on EVM with 
      writeTraceLevel = 0

Alg Init for Layer # -    0 out of   34
Alg Init for Layer # -    1 out of   34
Alg Init for Layer # -    2 out of   34
Alg Init for Layer # -    3 out of   34
Alg Init for Layer # -    4 out of   34
Alg Init for Layer # -    5 out of   34
Alg Init for Layer # -    6 out of   34
Alg Init for Layer # -    7 out of   34
Alg Init for Layer # -    8 out of   34
Alg Init for Layer # -    9 out of   34
Alg Init for Layer # -   10 out of   34
Alg Init for Layer # -   11 out of   34
Alg Init for Layer # -   12 out of   34
Alg Init for Layer # -   13 out of   34
Alg Init for Layer # -   14 out of   34
Alg Init for Layer # -   15 out of   34
Alg Init for Layer # -   16 out of   34
Alg Init for Layer # -   17 out of   34
Alg Init for Layer # -   18 out of   34
Alg Init for Layer # -   19 out of   34
Alg Init for Layer # -   20 out of   34
Alg Init for Layer # -   21 out of   34
Alg Init for Layer # -   22 out of   34
Alg Init for Layer # -   23 out of   34
Alg Init for Layer # -   24 out of   34
Alg Init for Layer # -   25 out of   34
Alg Init for Layer # -   26 out of   34
Alg Init for Layer # -   27 out of   34
Alg Init for Layer # -   28 out of   34
Alg Init for Layer # -   29 out of   34
Alg Init for Layer # -   30 out of   34
Alg Init for Layer # -   31 out of   34
Alg Init for Layer # -   32 out of   34
Alg Init for Layer # -   33 out of   34
Alg Init for Layer # -   34 out of   34
************ TIDL_subgraphRtCreate done ************ 
 *******   In TIDL_subgraphRtInvoke  ******** 
TIDL_activate is called with handle : 2c010190 
Starting Layer # -    1
   0         1.00000        13.00000       255.00000 6
Processing Layer # -    1
   1         1.00000        13.00000       255.00000 6
End of Layer # -    1 with outPtrs[0] = 0x7f015e95a010
Starting Layer # -    2
Processing Layer # -    2
   2         1.00000         0.00000         3.24673 6
End of Layer # -    2 with outPtrs[0] = 0x7f015e9f3a10
Starting Layer # -    3
Processing Layer # -    3
   3         1.00000         0.00000         3.24673 6
End of Layer # -    3 with outPtrs[0] = 0x7f015ed1fd10
Starting Layer # -    4
Processing Layer # -    4
   4         1.00000         0.00000         1.37021 6
End of Layer # -    4 with outPtrs[0] = 0x7f015edf2010
Starting Layer # -    5
Processing Layer # -    5
   5         1.00000        -2.85065         2.32687 6
End of Layer # -    5 with outPtrs[0] = 0x7f015eec4310
Starting Layer # -    6
Processing Layer # -    6
   6         1.00000         0.00000         3.78996 6
End of Layer # -    6 with outPtrs[0] = 0x7f015ef88310
Starting Layer # -    7
Processing Layer # -    7
   7         1.00000         0.00000         2.41517 6
End of Layer # -    7 with outPtrs[0] = 0x7f015edf2010
Starting Layer # -    8
Processing Layer # -    8
   8         1.00000        -4.03907         3.50151 6
End of Layer # -    8 with outPtrs[0] = 0x7f015eec4310
Starting Layer # -    9
Processing Layer # -    9
   9         1.00000         0.00000         4.79530 6
End of Layer # -    9 with outPtrs[0] = 0x7f015f05a610
Starting Layer # -   10
Processing Layer # -   10
  10         1.00000         0.00000         2.11946 6
End of Layer # -   10 with outPtrs[0] = 0x7f015f12c910
Starting Layer # -   11
Processing Layer # -   11
  11         1.00000        -2.99241         2.95832 6
End of Layer # -   11 with outPtrs[0] = 0x7f015f19cf10
Starting Layer # -   12
Processing Layer # -   12
  12         1.00000        -2.36892         1.84864 6
End of Layer # -   12 with outPtrs[0] = 0x7f015f1fef10
Starting Layer # -   13
Processing Layer # -   13
  13         1.00000         0.00000         3.29244 6
End of Layer # -   13 with outPtrs[0] = 0x7f015f260f10
Starting Layer # -   14
Processing Layer # -   14
  14         1.00000         0.00000         1.89355 6
End of Layer # -   14 with outPtrs[0] = 0x7f015f12c910
Starting Layer # -   15
Processing Layer # -   15
  15         1.00000        -2.87346         2.74429 6
End of Layer # -   15 with outPtrs[0] = 0x7f015f1fef10
Starting Layer # -   16
Processing Layer # -   16
  16         1.00000         0.00000         4.25615 6
End of Layer # -   16 with outPtrs[0] = 0x7f015f2d1510
Starting Layer # -   17
Processing Layer # -   17
  17         1.00000         0.00000         2.57455 6
End of Layer # -   17 with outPtrs[0] = 0x7f015f341b10
Starting Layer # -   18
Processing Layer # -   18
  18         1.00000        -1.81724         3.67904 6
End of Layer # -   18 with outPtrs[0] = 0x7f015f381710
Starting Layer # -   19
Processing Layer # -   19
  19         1.00000        -1.22743         0.52273 6
End of Layer # -   19 with outPtrs[0] = 0x7f015f3b2710
Starting Layer # -   20
Processing Layer # -   20
  20         1.00000         0.00000         3.24563 6
End of Layer # -   20 with outPtrs[0] = 0x7f015f3e3710
Starting Layer # -   21
Processing Layer # -   21
  21         1.00000         0.00000         1.52359 6
End of Layer # -   21 with outPtrs[0] = 0x7f015f341b10
Starting Layer # -   22
Processing Layer # -   22
  22         1.00000        -2.17227         2.12657 6
End of Layer # -   22 with outPtrs[0] = 0x7f015f3b2710
Starting Layer # -   23
Processing Layer # -   23
  23         1.00000         0.00000         2.62711 6
End of Layer # -   23 with outPtrs[0] = 0x7f015f423310
Starting Layer # -   24
Processing Layer # -   24
  24         1.00000         0.00000         1.54402 6
End of Layer # -   24 with outPtrs[0] = 0x7f015f462f10
Starting Layer # -   25
Processing Layer # -   25
  25         1.00000        -2.26611         2.34110 6
End of Layer # -   25 with outPtrs[0] = 0x7f015f48af10
Starting Layer # -   26
Processing Layer # -   26
  26         1.00000        -2.26553         1.66404 6
End of Layer # -   26 with outPtrs[0] = 0x7f015f4a3710
Starting Layer # -   27
Processing Layer # -   27
  27         1.00000         0.00000         2.53993 6
End of Layer # -   27 with outPtrs[0] = 0x7f015f4bbf10
Starting Layer # -   28
Processing Layer # -   28
  28         1.00000         0.00000         1.61172 6
End of Layer # -   28 with outPtrs[0] = 0x7f015f462f10
Starting Layer # -   29
Processing Layer # -   29
  29         1.00000        -9.35423        16.83671 6
End of Layer # -   29 with outPtrs[0] = 0x7f015f4a3710
Starting Layer # -   30
Processing Layer # -   30
  30         1.00000         0.00000        16.88544 6
End of Layer # -   30 with outPtrs[0] = 0x7f015f4e3f10
Starting Layer # -   31
Processing Layer # -   31
  31         1.00000         0.00000         6.78721 6
End of Layer # -   31 with outPtrs[0] = 0x7f015f4fc710
Starting Layer # -   32
Processing Layer # -   32
  32         1.00000        -7.60278        23.56697 6
End of Layer # -   32 with outPtrs[0] = 0x7f015f4fcf10
Starting Layer # -   33
Processing Layer # -   33
  33         1.00000        -7.60278        23.56697 6
End of Layer # -   33 with outPtrs[0] = 0x11259290

Network Cycles 0 
 Layer,   Layer Cycles,kernelOnlyCycles, coreLoopCycles,LayerSetupCycles,dmaPipeupCycles, dmaPipeDownCycles, PrefetchCycles,copyKerCoeffCycles,LayerDeinitCycles,LastBlockCycles, paddingTrigger,    paddingWait,LayerWithoutPad,LayerHandleCopy,   BackupCycles,  RestoreCycles,
     1,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,
     2,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,
     3,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,
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**********  Frame Index 1 : Running float inference **********
*******   In TIDL_subgraphRtInvoke  ******** 
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 ----------------------- TIDL Process with REF_ONLY FLOW ------------------------

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*******  TIDL_subgraphRtInvoke done  ******** 

**********  Frame Index 2 : Running fixed point mode for calibration **********
In TIDL_runtimesPostProcessNet 

~~~~~Running TIDL in PC emulation mode to collect Activations range for each layer~~~~~

~~~~~Running TIDL in PC emulation mode to collect Activations range for each layer~~~~~

 
 
 *****************   Calibration iteration number 0 completed ************************ 
 
 
 

~~~~~Running TIDL in PC emulation mode to collect Activations range for each layer~~~~~

 
 
 *****************   Calibration iteration number 1 completed ************************ 
 
 
 

~~~~~Running TIDL in PC emulation mode to collect Activations range for each layer~~~~~

 
 
 *****************   Calibra
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 ----------------------- TIDL Process with REF_ONLY FLOW ------------------------

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------------------ Network Compiler Traces -----------------------------
successful Memory allocation

Running_Model :  cl-ort-resnet18-v1  


 
Completed_Model :     1, Name : cl-ort-resnet18-v1                                , Total time :    8936.09, Offload Time :    1380.89 , DDR RW MBs : 0, Output File : py_out_cl-ort-resnet18-v1_ADE_val_00001801.jpg

I attached log files. (set debug_level 3)

I have read README and proceeding in order. What is my problem?
If the sample model compilation fails, is there a problem with my environment?
I tried to it on VM(Ubuntu), WSL2 Docker(Ubuntu), result is all same.

I wondered if it was a problem with the sample model.
so I copied the model(ONR-CL-6100-resNet18) file from EVM to PC. And try to compile, but it's failed.
Also shows same message that "fails to allocate memory..."

Regards,
Lee.

  • 0
    TI__Mastermind 36492 points

    Hi Lee,

    Is this on SK-TDA4VM or EVM?

    For reference, I will link the page for SK-TDA4VM: https://www.ti.com/tool/SK-TDA4VM and EVM: https://www.ti.com/tool/J721EXSOMXEVM.

    Regards,

    Takuma

  • 0 in reply to Takuma Fujiwara
    Expert 1445 points

    Hi Takuma,

    It's SK-TDA4VM.

    I try to TFLite CL model compile, and it works on SK-TDA4VM's edgeAI Apps.
    but other OD model, ONNX model not work on EdgeAI Apps.

    Regards,
    Lee

  • 0 in reply to Taekmin Lee
    TI__Mastermind 36492 points

    Hi Lee,

    We will look into this internally.

    In the meantime, could you try checking out the 08.00.01.10 tag for edgeai-tidl-tools using "git checkout 08.00.01.10" and try compiling again? This could be due to some mismatch in the SDK version number being used in edgeai-tidl-tools on the PC and on the SK board. 

    Regards,

    Takuma

  • 0 in reply to Takuma Fujiwara
    Expert 1445 points

    Hi Takuma,

    I try to model compile again after "git checkout 08.00.01.10".
    Still below message show.

    WARNING: [TIDL_E_DATAFLOW_INFO_NULL] ti_cnnperfsim.out fails to allocate memory in MSMC. 
Please look into perfsim log. 
This model can only be used on PC emulation, it will get fault on target.

    And compiled model not working on SK-TDA4VM(EdgeAI Apps, EdgeAI TIDL Tools)
    In my opinion, the reason why the model does not work in SK-TDA4VM is because of that message.

    I attached log message of EdgeAI TIDL Tools's example on SK-TDA4VM
    (If using disable_offload option, the model works.)

    root@j7-evm:/opt/edgeai-tidl-tools/examples/osrt_python/tfl# python3 tflrt_delegate.py
Running 1 Models - ['ssd_mobilenet_v2_300_float']


Running_Model :  ssd_mobilenet_v2_300_float

 Number of subgraphs:1 , 104 nodes delegated out of 104 nodes

APP: Init ... !!!
MEM: Init ... !!!
MEM: Initialized DMA HEAP (fd=5) !!!
MEM: Init ... Done !!!
IPC: Init ... !!!
IPC: Init ... Done !!!
REMOTE_SERVICE: Init ... !!!
REMOTE_SERVICE: Init ... Done !!!
  4163.409801 s: GTC Frequency = 200 MHz
APP: Init ... Done !!!
  4163.409835 s:  VX_ZONE_INIT:Enabled
  4163.409841 s:  VX_ZONE_ERROR:Enabled
  4163.409846 s:  VX_ZONE_WARNING:Enabled
  4163.410311 s:  VX_ZONE_INIT:[tivxInitLocal:130] Initialization Done !!!
  4163.412694 s:  VX_ZONE_INIT:[tivxHostInitLocal:86] Initialization Done for HOST !!!
  4163.455765 s:  VX_ZONE_ERROR:[ownContextSendCmd:815] Command ack message returned failure cmd_status: -1
  4163.455792 s:  VX_ZONE_ERROR:[ownContextSendCmd:851] tivxEventWait() failed.
  4163.455816 s:  VX_ZONE_ERROR:[ownNodeKernelInit:538] Target kernel, TIVX_CMD_NODE_CREATE failed for node TIDLNode
  4163.455823 s:  VX_ZONE_ERROR:[ownNodeKernelInit:539] Please be sure the target callbacks have been registered for this core
  4163.455829 s:  VX_ZONE_ERROR:[ownNodeKernelInit:540] If the target callbacks have been registered, please ensure no errors are occurring within the create callback of this kernel
  4163.455837 s:  VX_ZONE_ERROR:[ownGraphNodeKernelInit:583] kernel init for node 0, kernel com.ti.tidl ... failed !!!
  4163.455846 s:  VX_ZONE_ERROR:[vxVerifyGraph:2055] Node kernel init failed
  4163.455852 s:  VX_ZONE_ERROR:[vxVerifyGraph:2109] Graph verify failed
TIDL_RT_OVX: ERROR: Verifying TIDL graph ... Failed !!!
TIDL_RT_OVX: ERROR: Verify OpenVX graph failed
  4163.574673 s:  VX_ZONE_ERROR:[ownContextSendCmd:815] Command ack message returned failure cmd_status: -1
  4163.574700 s:  VX_ZONE_ERROR:[ownContextSendCmd:851] tivxEventWait() failed.
  4163.574723 s:  VX_ZONE_ERROR:[ownNodeKernelInit:538] Target kernel, TIVX_CMD_NODE_CREATE failed for node TIDLNode
  4163.574729 s:  VX_ZONE_ERROR:[ownNodeKernelInit:539] Please be sure the target callbacks have been registered for this core
  4163.574735 s:  VX_ZONE_ERROR:[ownNodeKernelInit:540] If the target callbacks have been registered, please ensure no errors are occurring within the create callback of this kernel
  4163.574743 s:  VX_ZONE_ERROR:[ownGraphNodeKernelInit:583] kernel init for node 0, kernel com.ti.tidl ... failed !!!
  4163.574752 s:  VX_ZONE_ERROR:[vxVerifyGraph:2055] Node kernel init failed
  4163.574758 s:  VX_ZONE_ERROR:[vxVerifyGraph:2109] Graph verify failed
  4163.574861 s:  VX_ZONE_ERROR:[ownGraphScheduleGraphWrapper:820] graph is not in a state required to be scheduled
  4163.574868 s:  VX_ZONE_ERROR:[vxProcessGraph:755] schedule graph failed
  4163.574873 s:  VX_ZONE_ERROR:[vxProcessGraph:760] wait graph failed
ERROR: Running TIDL graph ... Failed !!!
     

    Regard,
    Lee.

  • 0 in reply to Taekmin Lee
    TI__Mastermind 22145 points

    Can you share the model which gives this issue? a corresponding change in model compilation script to reproduce this issue

  • 0 in reply to kumar.desappan
    Expert 1445 points

    Hi kumar,

    I just using TI's sample model. not use my custom model.
    I attached models using google drive.

    drive.google.com/.../view

    Regards,
    Lee.

  • +1 in reply to Taekmin Lee
    TI__Mastermind 22145 points

    Hi Lee,

    All the default models are tested using github actions as well . Please find the below latest test log  to compare against your run.

    https://github.com/TexasInstruments/edgeai-tidl-tools/actions/runs/2014520509

  • 0 in reply to kumar.desappan
    Expert 1445 points

    Hi kumar,

    Okay. i will check it. and update this thread after check up.

    Regards,
    Lee.

  • 0 in reply to Taekmin Lee
    Expert 1445 points

    Hi kumar,

    I checked test log and try to using Dockerfile.
    The model compilation was successful without warning message that above i mentioned!

    When configuring my docker environment, I configured the environment using setup.sh after configuring the Ubuntu 18.04 container.
    I think there seems to be a difference between configuring the environment with Dockerfile and configuring the environment with setup.sh.
    I tried to setup environment on VirtualBox(Ubuntu VM), native Ubuntu and WSL2. but all environment fails model compile.

    Thanks!

    Regards,
    Lee.