onnx_diagnostic.helpers.onnx_helper

onnx_diagnostic.helpers.onnx_helper.check_model_ort(onx: ModelProto, providers: str | List[Any] | None = None, dump_file: str | None = None) → onnxruntime.InferenceSession[source]

Loads a model with onnxruntime.

Parameters:

  • onx – ModelProto

  • providers – list of providers, None fur CPU, cpu for CPU, cuda for CUDA

  • dump_file – if not empty, dumps the model into this file if an error happened

Returns:

InferenceSession

onnx_diagnostic.helpers.onnx_helper.convert_endian(tensor: TensorProto) → None[source]

Call to convert endianness of raw data in tensor.

Args:

tensor: TensorProto to be converted.

onnx_diagnostic.helpers.onnx_helper.dtype_to_tensor_dtype(dt: dtype | torch.dtype) → int[source]

Converts a torch dtype or numpy dtype into a onnx element type.

Parameters:

to – dtype

Returns:

onnx type

onnx_diagnostic.helpers.onnx_helper.from_array_extended(tensor: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], name: str | None = None) → TensorProto[source]

Converts an array into a onnx.TensorProto.

Parameters:

  • tensor – numpy array or torch tensor

  • name – name

Returns:

TensorProto

onnx_diagnostic.helpers.onnx_helper.from_array_ml_dtypes(arr: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], name: str | None = None) → TensorProto[source]

Converts a numpy array to a tensor def assuming the dtype is defined in ml_dtypes.

Args:

arr: a numpy array. name: (optional) the name of the tensor.

Returns:

TensorProto: the converted tensor def.

onnx_diagnostic.helpers.onnx_helper.get_onnx_signature(model: ModelProto) → Tuple[Tuple[str, Any], ...][source]

Produces a tuple of tuples corresponding to the signatures.

Parameters:

model – model

Returns:

signature

onnx_diagnostic.helpers.onnx_helper.np_dtype_to_tensor_dtype(dt: dtype) → int[source]

Converts a numpy dtype into a onnx element type.

Parameters:

to – dtype

Returns:

onnx type

onnx_diagnostic.helpers.onnx_helper.onnx_dtype_name(itype: int) → str[source]

Returns the ONNX name for a specific element type.

<<<

import onnx
from onnx_diagnostic.helpers.onnx_helper import onnx_dtype_name

itype = onnx.TensorProto.BFLOAT16
print(onnx_dtype_name(itype))
print(onnx_dtype_name(7))

>>>

    BFLOAT16
    INT64
onnx_diagnostic.helpers.onnx_helper.onnx_dtype_to_np_dtype(itype: int) → Any[source]

Converts an onnx type into a to numpy dtype. That includes ml_dtypes dtypes.

Parameters:

to – onnx dtype

Returns:

numpy dtype

onnx_diagnostic.helpers.onnx_helper.onnx_dtype_to_torch_dtype(itype: int) → torch.dtype[source]

Converts an onnx type into a torch dtype.

Parameters:

to – onnx dtype

Returns:

torch dtype

onnx_diagnostic.helpers.onnx_helper.onnx_find(onx: str | ModelProto, verbose: int = 0, watch: Set[str] | None = None) → List[NodeProto | TensorProto][source]

Looks for node producing or consuming some results.

Parameters:

  • onx – model

  • verbose – verbosity

  • watch – names to search for

Returns:

list of nodes

onnx_diagnostic.helpers.onnx_helper.onnx_lighten(onx: str | ModelProto, verbose: int = 0) → Tuple[ModelProto, Dict[str, Dict[str, float]]][source]

Creates a model without big initializers but stores statistics into dictionaries. The function can be reversed with onnx_diagnostic.helpers.onnx_helper.onnx_unlighten(). The model is modified inplace.

Parameters:

  • onx – model

  • verbose – verbosity

Returns:

new model, statistics

onnx_diagnostic.helpers.onnx_helper.onnx_unlighten(onx: str | ModelProto, stats: Dict[str, Dict[str, float]] | None = None, verbose: int = 0) → ModelProto[source]

Function fixing the model produced by function onnx_diagnostic.helpers.onnx_helper.onnx_lighten(). The model is modified inplace.

Parameters:

  • onx – model

  • stats – statistics, can be None if onx is a file, then it loads the file <filename>.stats, it assumes it is json format

  • verbose – verbosity

Returns:

new model, statistics

onnx_diagnostic.helpers.onnx_helper.pretty_onnx(onx: FunctionProto | GraphProto | ModelProto | ValueInfoProto | str, with_attributes: bool = False, highlight: Set[str] | None = None, shape_inference: bool = False) → str[source]

Displays an onnx prot in a better way.

Parameters:

  • with_attributes – displays attributes as well, if only a node is printed

  • highlight – to highlight some names

  • shape_inference – run shape inference before printing the model

Returns:

text

onnx_diagnostic.helpers.onnx_helper.proto_from_tensor(arr: torch.Tensor, name: str | None = None, verbose: int = 0) → TensorProto[source]

Converts a torch Tensor into a TensorProto.

Parameters:

  • arr – tensor

  • verbose – display the type and shape

Returns:

a TensorProto

onnx_diagnostic.helpers.onnx_helper.tensor_dtype_to_np_dtype(tensor_dtype: int) → dtype[source]

Converts a TensorProto’s data_type to corresponding numpy dtype. It can be used while making tensor.

Parameters:

tensor_dtype – TensorProto’s data_type

Returns:

numpy’s data_type

onnx_diagnostic.helpers.onnx_helper.to_array_extended(proto: TensorProto) → Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes][source]

Converts onnx.TensorProto into a numpy array.

onnx_diagnostic.helpers.onnx_helper.torch_dtype_to_onnx_dtype(to: torch.dtype) → int[source]

Converts a torch dtype into a onnx element type.

Parameters:

to – torch dtype

Returns:

onnx type

onnx_diagnostic.helpers.onnx_helper.type_info(itype: int, att: str)[source]

Returns the minimum or maximum value for a type.

Parameters:

  • itype – onnx type

  • att – ‘min’ or ‘max’

Returns:

value