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Change default optimization level to All (from Basic) (#2745)

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* change default optimization level to All (from Basic)

* fix test

* fix c# test
This commit is contained in:
Ashwini Khade 2019-12-30 12:31:44 -08:00 committed by GitHub
parent 90b708f8a9
commit 56bb503c2f
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10 changed files with 18 additions and 16 deletions
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4
csharp/src/Microsoft.ML.OnnxRuntime/SessionOptions.cs
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@ -345,7 +345,7 @@ namespace Microsoft.ML.OnnxRuntime
private int _interOpNumThreads = 0; // set to what is set in C++ SessionOptions by default;
/// <summary>
/// Sets the graph optimization level for the session. Default is set to ORT_ENABLE_BASIC.
/// Sets the graph optimization level for the session. Default is set to ORT_ENABLE_ALL.
/// </summary>
public GraphOptimizationLevel GraphOptimizationLevel
{
@ -359,7 +359,7 @@ namespace Microsoft.ML.OnnxRuntime
_graphOptimizationLevel = value;
}
}
private GraphOptimizationLevel _graphOptimizationLevel = GraphOptimizationLevel.ORT_ENABLE_BASIC;
private GraphOptimizationLevel _graphOptimizationLevel = GraphOptimizationLevel.ORT_ENABLE_ALL;
/// <summary>
/// Sets the execution mode for the session. Default is set to ORT_SEQUENTIAL.

2
csharp/test/Microsoft.ML.OnnxRuntime.Tests/InferenceTest.cs
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@ -41,7 +41,7 @@ namespace Microsoft.ML.OnnxRuntime.Tests
Assert.Equal(LogLevel.Verbose, opt.LogVerbosityLevel);
Assert.Equal(0, opt.IntraOpNumThreads);
Assert.Equal(0, opt.InterOpNumThreads);
Assert.Equal(GraphOptimizationLevel.ORT_ENABLE_BASIC, opt.GraphOptimizationLevel);
Assert.Equal(GraphOptimizationLevel.ORT_ENABLE_ALL, opt.GraphOptimizationLevel);
// try setting options
opt.ExecutionMode = ExecutionMode.ORT_PARALLEL;

2
csharp/tools/Microsoft.ML.OnnxRuntime.PerfTool/Program.cs
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@ -33,7 +33,7 @@ namespace Microsoft.ML.OnnxRuntime.PerfTool
public bool ParallelExecution { get; set; } = false;
[Option('o', "optimization_level", Required = false, HelpText = "Optimization Level. Default is 1, partial optimization.")]
public GraphOptimizationLevel OptimizationLevel { get; set; } = GraphOptimizationLevel.ORT_ENABLE_BASIC;
public GraphOptimizationLevel OptimizationLevel { get; set; } = GraphOptimizationLevel.ORT_ENABLE_ALL;
}
class Program

4
docs/ONNX_Runtime_Graph_Optimizations.md
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@ -15,9 +15,11 @@ Graph optimizations are divided in three levels:
The optimizations belonging to one level are performed after the optimizations of the previous level have been applied (e.g., extended optimizations are applied after basic optimizations have been applied).
**All optimizations are enabled by default.**
### Basic Graph Optimizations
These are semantics-preserving graph rewrites which remove redundant nodes and redundant computation. These optimizations are enabled by default. They run before graph partitioning and thus apply to all the execution providers. Available basic graph optimizations are as follows:
These are semantics-preserving graph rewrites which remove redundant nodes and redundant computation. They run before graph partitioning and thus apply to all the execution providers. Available basic graph optimizations are as follows:
* Constant Folding: Statically computes parts of the graph that rely only on constant initializers. This eliminates the need to compute them during runtime.

4
docs/ONNX_Runtime_Perf_Tuning.md
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@ -86,9 +86,9 @@ sess_options.graph_optimization_level = rt.GraphOptimizationLevel.ORT_ENABLE_ALL
* Sequential vs Parallel Execution
* `sess_options.execution_mode = rt.ExecutionMode.ORT_SEQUENTIAL` controls whether then operators in the graph should run sequentially or in parallel. Usually when a model has many branches, setting this option to false will provide better performance.
* When `sess_options.execution_mode = rt.ExecutionMode.ORT_PARALLEL`, you can set `sess_options.inter_op_num_threads` to control the
number of threads used to parallelize the execution of the graph (across nodes).
* sess_options.graph_optimization_level = rt.GraphOptimizationLevel.ORT_ENABLE_ALL. Default is ORT_ENABLE_BASIC(1). Please see [onnxruntime_c_api.h](../include/onnxruntime/core/session/onnxruntime_c_api.h#L241) (enum GraphOptimizationLevel) for the full list of all optimization levels. For details regarding available optimizations and usage please refer to the [Graph Optimizations Doc](../docs/ONNX_Runtime_Graph_Optimizations.md).
* sess_options.graph_optimization_level = rt.GraphOptimizationLevel.ORT_ENABLE_ALL. Default is ORT_ENABLE_All. Please see [onnxruntime_c_api.h](../include/onnxruntime/core/session/onnxruntime_c_api.h#L241) (enum GraphOptimizationLevel) for the full list of all optimization levels. For details regarding available optimizations and usage please refer to the [Graph Optimizations Doc](../docs/ONNX_Runtime_Graph_Optimizations.md).
### MKL_DNN/nGraph/MKL_ML Execution Provider
MKL_DNN, MKL_ML and nGraph all depends on openmp for parallization. For those execution providers, we need to use the openmp enviroment variable to tune the performance.

2
onnxruntime/core/framework/session_options.h
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@ -52,7 +52,7 @@ struct SessionOptions {
unsigned max_num_graph_transformation_steps = 10; // TODO choose a good default here?
// set graph optimization level
TransformerLevel graph_optimization_level = TransformerLevel::Level1;
TransformerLevel graph_optimization_level = TransformerLevel::Level3;
// controls the size of the thread pool used to parallelize the execution of tasks within individual nodes (ops)
int intra_op_num_threads = 0;

6
onnxruntime/python/onnxruntime_pybind_state.cc
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@ -587,7 +587,7 @@ Applies to session load, initialization, etc. Default is 0.)pbdoc")
.def_property(
"graph_optimization_level",
[](const SessionOptions* options) -> GraphOptimizationLevel {
GraphOptimizationLevel retval = ORT_ENABLE_BASIC;
GraphOptimizationLevel retval = ORT_ENABLE_ALL;
switch (options->graph_optimization_level) {
case onnxruntime::TransformerLevel::Default:
retval = ORT_DISABLE_ALL;
@ -602,8 +602,8 @@ Applies to session load, initialization, etc. Default is 0.)pbdoc")
retval = ORT_ENABLE_ALL;
break;
default:
retval = ORT_ENABLE_BASIC;
LOGS_DEFAULT(WARNING) << "Got invalid graph optimization level; defaulting to ORT_ENABLE_BASIC";
retval = ORT_ENABLE_ALL;
LOGS_DEFAULT(WARNING) << "Got invalid graph optimization level; defaulting to ORT_ENABLE_ALL";
break;
}
return retval;

2
onnxruntime/test/onnx/main.cc
View file

@ -41,7 +41,7 @@ void usage() {
"Default: 'cpu'.\n"
"\t-x: Use parallel executor, default (without -x): sequential executor.\n"
"\t-d [device_id]: Specifies the device id for multi-device (e.g. GPU). The value should > 0\n"
"\t-o [optimization level]: Default is 1. Valid values are 0 (disable), 1 (basic), 2 (extended), 99 (all).\n"
"\t-o [optimization level]: Default is 99. Valid values are 0 (disable), 1 (basic), 2 (extended), 99 (all).\n"
"\t\tPlease see onnxruntime_c_api.h (enum GraphOptimizationLevel) for the full list of all optimization levels. "
"\n"
"\t-h: help\n"

2
onnxruntime/test/perftest/test_configuration.h
View file

@ -47,7 +47,7 @@ struct RunConfig {
ExecutionMode execution_mode{ExecutionMode::ORT_SEQUENTIAL};
int intra_op_num_threads{0};
int inter_op_num_threads{0};
GraphOptimizationLevel optimization_level{ORT_ENABLE_EXTENDED};
GraphOptimizationLevel optimization_level{ORT_ENABLE_ALL};
std::basic_string<ORTCHAR_T> optimized_model_path;
};

6
onnxruntime/test/python/onnxruntime_test_python.py
View file

@ -521,10 +521,10 @@ class TestInferenceSession(unittest.TestCase):
def testGraphOptimizationLevel(self):
opt = onnxrt.SessionOptions()
self.assertEqual(opt.graph_optimization_level, onnxrt.GraphOptimizationLevel.ORT_ENABLE_BASIC)
# default should be basic optimization
opt.graph_optimization_level = onnxrt.GraphOptimizationLevel.ORT_ENABLE_ALL
self.assertEqual(opt.graph_optimization_level, onnxrt.GraphOptimizationLevel.ORT_ENABLE_ALL)
# default should be all optimization
opt.graph_optimization_level = onnxrt.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
self.assertEqual(opt.graph_optimization_level, onnxrt.GraphOptimizationLevel.ORT_ENABLE_EXTENDED)
sess = onnxrt.InferenceSession(self.get_name("logicaland.onnx"), sess_options=opt)
a = np.array([[True, True], [False, False]], dtype=np.bool)
b = np.array([[True, False], [True, False]], dtype=np.bool)