The following is an example log with NM_LOGGING_LEVEL=diagnose running a super_resolution network, where we only support running 70% of it. Different portions of the log are explained in Parsing an Example Log.
onnx_filename : test-models/cv-resolution/super_resolution/none-bsd300-onnx-repo/model.onnx
[ INFO neuralmagic.py: 112 - neuralmagic_create() ] Construct network from ONNX = test-models/cv-resolution/super_resolution/none-bsd300-onnx-repo/model.onnx
NeuralMagic WAND version: 1.0.0.96ce2f6cb23b8ab377012ed9ef38d3da3b9f5313 (optimized) (system=avx512, binary=avx512)
[nm_ort 7f4fbbd3f740 >DIAGNOSE< operator() /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/nm_execution_provider.cc:104] == NMExecutionProvider::GetCapability ==
Printing GraphViewer torch-jit-export:
Node 0: Conv
Node 1: Relu
Node 2: Conv
Node 3: Relu
Node 4: Conv
Node 5: Relu
Node 6: Conv
Node 7: Reshape
Node 8: Transpose
Node 9: Reshape
[nm_ort 7f4fbbd3f740 >DIAGNOSE< unsupported /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/ops.cc:60] Unsupported Reshape , const shape greater than 5D
[nm_ort 7f4fbbd3f740 >DIAGNOSE< construct_subgraphs /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/subgraphs.cc:595] == Constructing subgraphs from graph info
[nm_ort 7f4fbbd3f740 >WARN< construct_subgraphs /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/subgraphs.cc:604] Cannot support patterns, defaulting to non-pattern-matched subgraphs
[nm_ort 7f4fbbd3f740 >DIAGNOSE< supported_subgraphs /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/subgraphs.cc:644] == Beginning new subgraph ==
[nm_ort 7f4fbbd3f740 >DIAGNOSE< supported_subgraphs /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/subgraphs.cc:667] Runtime inputs for subgraph:
[nm_ort 7f4fbbd3f740 >DIAGNOSE< supported_subgraphs /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/subgraphs.cc:679] input (required)
[nm_ort 7f4fbbd3f740 >DIAGNOSE< supported_subgraphs /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/subgraphs.cc:684] Printing subgraph:
Node 0: Conv
Node 1: Relu
Node 2: Conv
Node 3: Relu
Node 4: Conv
Node 5: Relu
Node 6: Conv
[nm_ort 7f4fbbd3f740 >DIAGNOSE< supported_subgraphs /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/subgraphs.cc:706] == Translating subgraph NM_Subgraph_1 to NM intake graph.
[nm_ort 7f4fbbd3f740 >DIAGNOSE< supported_subgraphs /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/subgraphs.cc:715] ( L1 graph
( values:
(10 float [ 1, 64, 224, 224 ])
(11 float [ 1, 64, 224, 224 ])
(12 float [ 1, 64, 224, 224 ])
(13 float [ 1, 32, 224, 224 ])
(14 float [ 1, 32, 224, 224 ])
(15 float [ 1, 9, 224, 224 ])
(9 float [ 1, 64, 224, 224 ])
(conv1.bias float [ 64 ])
(conv1.weight float [ 64, 1, 5, 5 ])
(conv2.bias float [ 64 ])
(conv2.weight float [ 64, 64, 3, 3 ])
(conv3.bias float [ 32 ])
(conv3.weight float [ 32, 64, 3, 3 ])
(conv4.bias float [ 9 ])
(conv4.weight float [ 9, 32, 3, 3 ])
(input float [ 1, 1, 224, 224 ])
)
( operations:
(Constant conv1.bias (constant float [ 64 ]))
(Constant conv1.weight (constant float [ 64, 1, 5, 5 ]))
(Constant conv2.bias (constant float [ 64 ]))
(Constant conv2.weight (constant float [ 64, 64, 3, 3 ]))
(Constant conv3.bias (constant float [ 32 ]))
(Constant conv3.weight (constant float [ 32, 64, 3, 3 ]))
(Constant conv4.bias (constant float [ 9 ]))
(Constant conv4.weight (constant float [ 9, 32, 3, 3 ]))
(Input input (io 0))
(Conv input -> 9 (conv kernel = [ 64, 1, 5, 5 ] bias = [ 64 ] padding = {{2, 2}, {2, 2}} strides = {1, 1}))
(Elementwise 9 -> 10 (calc Relu))
(Conv 10 -> 11 (conv kernel = [ 64, 64, 3, 3 ] bias = [ 64 ] padding = {{1, 1}, {1, 1}} strides = {1, 1}))
(Elementwise 11 -> 12 (calc Relu))
(Conv 12 -> 13 (conv kernel = [ 32, 64, 3, 3 ] bias = [ 32 ] padding = {{1, 1}, {1, 1}} strides = {1, 1}))
(Elementwise 13 -> 14 (calc Relu))
(Conv 14 -> 15 (conv kernel = [ 9, 32, 3, 3 ] bias = [ 9 ] padding = {{1, 1}, {1, 1}} strides = {1, 1}))
(Output 15 (io 0))
)
)
[nm_ort 7f4fbbd3f740 >DIAGNOSE< supported_subgraphs /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/subgraphs.cc:723] == Compiling NM_Subgraph_1 with batch size 1 using 18 cores.
[7f4fbbd3f740 >DIAGNOSE< allocate_buffers_pass ./src/include/wand/engine/units/planner.hpp:49] compiler: total buffer size = 25690112/33918976, ratio = 0.757396
[nm_ort 7f4fbbd3f740 >DIAGNOSE< supported_subgraphs /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/subgraphs.cc:644] == Beginning new subgraph ==
[nm_ort 7f4fbbd3f740 >WARN< supported_subgraphs /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/subgraphs.cc:652] Filtered subgraph was empty, ignoring subgraph.
[nm_ort 7f4fbbd3f740 >DIAGNOSE< operator() /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/nm_execution_provider.cc:122] Created 1 compiled subgraphs.
[nm_ort 7f4fbbd3f740 >DIAGNOSE< validate_minimum_supported_fraction /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/utility/graph_util.cc:321] == NM Execution Provider supports 70% of the network
[nm_ort 7f4fbbd3f740 >DIAGNOSE< operator() /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/nm_execution_provider.cc:129] == End NMExecutionProvider::GetCapability ==
[nm_ort 7f4fbbd3f740 >DIAGNOSE< operator() /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/nm_execution_provider.cc:140] == NMExecutionProvider::Compile ==
[nm_ort 7f4fbbd3f740 >DIAGNOSE< operator() /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/nm_execution_provider.cc:157] Graph #0: 1 inputs and 1 outputs
[nm_ort 7f4fbbd3f740 >DIAGNOSE< operator() /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/nm_execution_provider.cc:276] == End NMExecutionProvider::Compile ==
Generating 1 random inputs
-- 1 random input of shape = [1, 1, 224, 224]
[ INFO execute.py: 242 - nm_exec_test_iters() ] Starting tests
[ INFO neuralmagic.py: 121 - neuralmagic_execute() ] Executing TEST_1
[ INFO neuralmagic.py: 124 - neuralmagic_execute() ] [1] input_data.shape = (1, 1, 224, 224)
[ INFO neuralmagic.py: 126 - neuralmagic_execute() ] -- START
[nm_ort 7f4fbbd3f740 >DIAGNOSE< operator() /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/nm_execution_provider.cc:265] ORT NM EP compute_func: 6.478 ms
[ INFO neuralmagic.py: 130 - neuralmagic_execute() ] -- FINISH
[ INFO neuralmagic.py: 132 - neuralmagic_execute() ] [output] output_data.shape = (1, 1, 672, 672)