onnx_diagnostic.reference¶
submodules
modules
ExtendedReferenceEvaluator¶
- class onnx_diagnostic.reference.ExtendedReferenceEvaluator(proto: Any, opsets: Dict[str, int] | None = None, functions: List[ReferenceEvaluator | FunctionProto] | None = None, verbose: int = 0, new_ops: List[type[OpRun]] | None = None, **kwargs)[source]¶
This class replaces the python implementation by custom implementation. The evaluator allows to test scenarios outside what an onnx backend bound to the official onnx operators definition could do such as optimization patterns involving onnxruntime contrib operators.
from onnx_diagnostic.reference import ExtendedReferenceEvaluator ref = ExtendedReferenceEvaluator(...)
The class overloads or adds the following operators by default:
<<<
import pprint from onnx_diagnostic.reference import ExtendedReferenceEvaluator pprint.pprint(ExtendedReferenceEvaluator.default_ops)
>>>
[<class 'onnx_diagnostic.reference.ops.op_add_add_mul_mul.AddAdd'>, <class 'onnx_diagnostic.reference.ops.op_add_add_mul_mul.AddMul'>, <class 'onnx_diagnostic.reference.ops.op_add_add_mul_mul.AddSharedInput'>, <class 'onnx_diagnostic.reference.ops.op_attention.Attention'>, <class 'onnx_diagnostic.reference.ops.op_average_pool_grad.AveragePoolGrad'>, <class 'onnx_diagnostic.reference.ops.op_bias_softmax.BiasSoftmax'>, <class 'onnx_diagnostic.reference.ops.op_concat.Concat'>, <class 'onnx_diagnostic.reference.ops.op_cast_like.CastLike_15'>, <class 'onnx_diagnostic.reference.ops.op_cast_like.CastLike_19'>, <class 'onnx_diagnostic.reference.ops.op_complex.ComplexModule'>, <class 'onnx_diagnostic.reference.ops.op_constant_of_shape.ConstantOfShape'>, <class 'onnx_diagnostic.reference.ops.op_fused_matmul.FusedMatMul'>, <class 'onnx_diagnostic.reference.ops.op_gather.Gather'>, <class 'onnx_diagnostic.reference.ops.op_gather_elements.GatherElements'>, <class 'onnx_diagnostic.reference.ops.op_gather_grad.GatherGrad'>, <class 'onnx_diagnostic.reference.ops.op_scatternd_of_shape.MaskedScatterNDOfShape'>, <class 'onnx_diagnostic.reference.ops.op_memcpy_host.MemcpyFromHost'>, <class 'onnx_diagnostic.reference.ops.op_memcpy_host.MemcpyToHost'>, <class 'onnx_diagnostic.reference.ops.op_add_add_mul_mul.MulAdd'>, <class 'onnx_diagnostic.reference.ops.op_add_add_mul_mul.MulMul'>, <class 'onnx_diagnostic.reference.ops.op_add_add_mul_mul.MulSharedInput'>, <class 'onnx_diagnostic.reference.ops.op_mul_sigmoid.MulSigmoid'>, <class 'onnx_diagnostic.reference.ops.op_add_add_mul_mul.MulSub'>, <class 'onnx_diagnostic.reference.ops.op_negxplus1.NegXplus1'>, <class 'onnx_diagnostic.reference.ops.op_qlinear_conv.QLinearConv'>, <class 'onnx_diagnostic.reference.ops.op_qlinear_average_pool.QLinearAveragePool'>, <class 'onnx_diagnostic.reference.ops.op_quick_gelu.QuickGelu'>, <class 'onnx_diagnostic.reference.ops.op_replace_zero.ReplaceZero'>, <class 'onnx_diagnostic.reference.ops.op_rotary.Rotary'>, <class 'onnx_diagnostic.reference.ops.op_scatter_elements.ScatterElements'>, <class 'onnx_diagnostic.reference.ops.op_scatternd_of_shape.ScatterNDOfShape'>, <class 'onnx_diagnostic.reference.ops.op_simplified_layer_normalization.SimplifiedLayerNormalization'>, <class 'onnx_diagnostic.reference.ops.op_skip_layer_normalization.SkipLayerNormalization'>, <class 'onnx_diagnostic.reference.ops.op_slice.Slice_1'>, <class 'onnx_diagnostic.reference.ops.op_slice.Slice_10'>, <class 'onnx_diagnostic.reference.ops.op_add_add_mul_mul.SubMul'>, <class 'onnx_diagnostic.reference.ops.op_complex.ToComplex'>, <class 'onnx_diagnostic.reference.ops.op_transpose_cast.Transpose2DCastFP16'>, <class 'onnx_diagnostic.reference.ops.op_transpose_cast.Transpose2DCastFP32'>, <class 'onnx_diagnostic.reference.ops.op_tri_matrix.TriMatrix'>]
OnnxruntimeEvaluator¶
- class onnx_diagnostic.reference.OnnxruntimeEvaluator(proto: str | FunctionProto | ModelProto | GraphProto | NodeProto | OnnxruntimeEvaluator, session_options: SessionOptions | None = None, providers: str | List[str] | None = None, nvtx: bool = False, enable_profiling: bool = False, graph_optimization_level: GraphOptimizationLevel | bool = None, log_severity_level: int | None = None, log_verbosity_level: int | None = None, optimized_model_filepath: str | None = None, disable_aot_function_inlining: bool | None = None, use_training_api: bool = False, verbose: int = 0, local_functions: Dict[Tuple[str, str], FunctionProto | ModelProto | GraphProto | NodeProto | OnnxruntimeEvaluator] | None = None, ir_version: int = 10, opsets: int | Dict[str, int] | None = None)[source]¶
This class loads an onnx model and the executes one by one the nodes with onnxruntime. This class is mostly meant for debugging.
- Parameters:
proto – proto or filename
session_options – options
providers – providers
nvtx – enable nvidia events
providers – None, “CPU”, “CUDA” or a list of providers
graph_optimization_level – see
onnxruntime.SessionOptions
log_severity_level – see
onnxruntime.SessionOptions
log_verbosity_level – see
onnxruntime.SessionOptions
optimized_model_filepath – see
onnxruntime.SessionOptions
disable_aot_function_inlining – see
onnxruntime.SessionOptions
use_training_api – use onnxruntime-traning API
verbose – verbosity
local_functions – additional local function
ir_version – ir version to use when unknown
opsets – opsets to use when unknown
- run(outputs: List[str] | None, feed_inputs: Dict[str, Any], intermediate: bool = False) Dict[str, Any] | List[Any] [source]¶
Runs the model. It only works with numpy arrays.
- Parameters:
outputs – required outputs or None for all
feed_inputs – inputs
intermediate – returns all output instead of the last ones
- Returns:
outputs, as a list if return_all is False, as a dictionary if return_all is True