onnx_diagnostic.torch_onnx.sbs¶
- onnx_diagnostic.torch_onnx.sbs.prepare_args_kwargs(torch_results: Dict[str, Any], node: Node) Tuple[Tuple[Any, ...], Dict[str, Any]][source]¶
Prepares args and kwargs before executing a fx node.
- Parameters:
torch_results – existing results
node – node to execute
- Returns:
new args and kwargs
- onnx_diagnostic.torch_onnx.sbs.run_aligned(ep: ExportedProgram, onx: ModelProto | FunctionProto, args: Tuple[Tensor, ...], check_conversion_cls: Dict[str, Any] | type, kwargs: Dict[str, Any] | None = None, verbose: int = 0) Iterator[Tuple[Any, ...]][source]¶
Runs in parallel both the exported program and the onnx proto and looks for discrepancies. The function does match on result names so it assumes the exported program and the onnx model have the same names for equivalent results.
- Parameters:
ep – exported program
onx – model or function proto
args – input args
check_conversion_cls – defines the runtime to use for this task
kwargs – input kwargs
verbose – verbosity level
- Returns:
a list of tuples containing the results, they come in tuple,
Example:
<<<
import pprint import pandas import torch from onnx_diagnostic.reference import ( # This can be replace by any runtime taking NodeProto as an input. ExtendedReferenceEvaluator as ReferenceEvaluator, ) from onnx_diagnostic.torch_onnx.sbs import run_aligned class Model(torch.nn.Module): def forward(self, x): ry = x.abs() rz = ry.exp() rw = rz + 1 ru = rw.log() + rw return ru def post_process(obs): dobs = dict(zip(["ep_id_node", "onnx_id_node", "ep_name", "onnx_name"], obs)) dobs["err_abs"] = obs[-1]["abs"] dobs["err_rel"] = obs[-1]["rel"] return dobs x = torch.randn((5, 4)) Model()(x) # to make sure the model is running ep = torch.export.export( Model(), (x,), dynamic_shapes=({0: torch.export.Dim("batch")},) ) onx = torch.onnx.export( Model(), (x,), dynamic_shapes=({0: torch.export.Dim("batch")},), dynamo=True ).model_proto results = list( map( post_process, run_aligned( ep, onx, (x,), check_conversion_cls=dict(cls=ReferenceEvaluator, atol=1e-5, rtol=1e-5), verbose=1, ), ), ) print("------------") print("final results") df = pandas.DataFrame(results) print(df)
>>>
/home/xadupre/github/onnxscript/onnxscript/converter.py:823: FutureWarning: 'onnxscript.values.Op.param_schemas' is deprecated in version 0.1 and will be removed in the future. Please use '.op_signature' instead. param_schemas = callee.param_schemas() /home/xadupre/github/onnxscript/onnxscript/converter.py:823: FutureWarning: 'onnxscript.values.OnnxFunction.param_schemas' is deprecated in version 0.1 and will be removed in the future. Please use '.op_signature' instead. param_schemas = callee.param_schemas() [torch.onnx] Obtain model graph for `Model()` with `torch.export.export(..., strict=False)`... [torch.onnx] Obtain model graph for `Model()` with `torch.export.export(..., strict=False)`... ✅ [torch.onnx] Run decomposition... [torch.onnx] Run decomposition... ✅ [torch.onnx] Translate the graph into ONNX... [torch.onnx] Translate the graph into ONNX... ✅ [run_aligned] +onnx-input: x: T1s5x4[-2.37814998626709,1.8000156879425049:A0.2102274749428034] [run_aligned] run ep.graph.nodes[0]: placeholder -> 'x' [run_aligned] +torch x=T1s5x4[-2.37814998626709,1.8000156879425049:A0.2102274749428034] [run_aligned] run ep.graph.nodes[1]: call_function[aten.abs.default] -> 'abs_1' [run_aligned] +torch abs_1=T1s5x4[0.0842517837882042,2.37814998626709:A0.8864509236067534] [run_aligned] run onx.graph.node[0]: Abs(x) -> abs_1 [run_aligned] +onnx abs_1=A1s5x4[0.0842517837882042,2.37814998626709:A0.8864509236067534] [run_aligned] =common results abs_1: abs=0.0, rel=0.0 [run_aligned] run ep.graph.nodes[2]: call_function[aten.exp.default] -> 'exp' [run_aligned] +torch exp=T1s5x4[1.0879027843475342,10.784932136535645:A2.931220865249634] [run_aligned] run onx.graph.node[1]: Exp(abs_1) -> exp [run_aligned] +onnx exp=A1s5x4[1.0879027843475342,10.784933090209961:A2.931220918893814] [run_aligned] =common results exp: abs=9.5367431640625e-07, rel=9.311585534838043e-08, n=20.0 [run_aligned] run ep.graph.nodes[3]: call_function[aten.add.Tensor] -> 'add' [run_aligned] +torch add=T1s5x4[2.087902784347534,11.784932136535645:A3.9312208533287047] [run_aligned] run ep.graph.nodes[4]: call_function[aten.log.default] -> 'log' [run_aligned] +torch log=T1s5x4[0.7361600995063782,2.4668216705322266:A1.2638569533824922] [run_aligned] run onx.graph.node[2]: Constant() -> scalar_tensor_default [run_aligned] +onnx scalar_tensor_default=A1s=1.0 [run_aligned] run onx.graph.node[3]: Add(exp, scalar_tensor_default) -> add_6 [run_aligned] +onnx add_6=A1s5x4[2.087902784347534,11.784933090209961:A3.9312209248542787] [run_aligned] run onx.graph.node[4]: Log(add_6) -> log [run_aligned] +onnx log=A1s5x4[0.7361600995063782,2.4668219089508057:A1.26385697722435] [run_aligned] =common results log: abs=2.384185791015625e-07, rel=1.4452490643151165e-07, n=20.0 [run_aligned] run ep.graph.nodes[5]: call_function[aten.add.Tensor] -> 'add_1' [run_aligned] +torch add_1=T1s5x4[2.8240628242492676,14.251753807067871:A5.195077753067016] [run_aligned] run ep.graph.nodes[6]: output -> 'output' [run_aligned] +torch output=(T1s5x4[2.8240628242492676,14.251753807067871:A5.195077753067016],) [run_aligned] run onx.graph.node[5]: Add(log, add_6) -> add_13 [run_aligned] +onnx add_13=A1s5x4[2.8240628242492676,14.251754760742188:A5.1950778603553776] [run_aligned] =common results* add_1/add_13: abs=9.5367431640625e-07, rel=1.5597590704800244e-07, n=20.0 ------------ final results ep_id_node onnx_id_node ep_name onnx_name err_abs err_rel 0 1 0 abs_1 abs_1 0.000000e+00 0.000000e+00 1 2 1 exp exp 9.536743e-07 9.311586e-08 2 4 4 log log 2.384186e-07 1.445249e-07 3 6 5 add_13 add_1 9.536743e-07 1.559759e-07
- onnx_diagnostic.torch_onnx.sbs.run_fx_node(node: Node, args: Tuple[Any, ...], kwargs: Dict[str, Any] | None = None) Tuple[Any, ...][source]¶
Executes a node
- Parameters:
node – runs a node
args – unnamed inputs to the node
kwargs – named inputs to the node
- Returns:
results