export

torch_to_nnef.export

export_model_to_nnef

export_model_to_nnef(model: torch.nn.Module, args, file_path_export: T.Union[Path, str], inference_target: InferenceTarget, input_names: T.Optional[T.List[str]] = None, output_names: T.Optional[T.List[str]] = None, compression_level: T.Optional[int] = 0, log_level: int = log.INFO, nnef_variable_naming_scheme: VariableNamingScheme = DEFAULT_VARNAME_SCHEME, check_io_names_qte_match: bool = True, debug_bundle_path: T.Optional[Path] = None, custom_extensions: T.Optional[T.List[str]] = None, allow_same_io_names: bool = False) -> Path

Main entrypoint of this library.

Export any torch.nn.Module to NNEF file format archive

Parameters:

Name	Type	Description	Default
`model`	`Module`	a nn.Module that have a `.forward` function with only tensor arguments and outputs (no tuple, list, dict or objects) Only this function will be serialized	required
`args`		a flat ordered list of tensors for each forward inputs of `model` this list can not be of dynamic size (at serialization it will be fixed to quantity of tensor provided) WARNING! tensor size in args will increase export time so take that in consideration for dynamic axes	required
`file_path_export`	`Union[Path, str]`	target path for the exported model. Accepted forms are: - ".../model.nnef" → base path; creates: • directory when `compression_level is None` • archive "model.nnef.tar" when `compression_level == 0` • archive "model.nnef.tgz" when `compression_level in 1..9` - ".../model.nnef.tgz" → treated as a request to use base name "model.nnef"; the actual artifact still follows the rule above (directory, .tar, or .tgz) depending on `compression_level`. Any other suffix pattern is rejected.	required
`inference_target`	`InferenceTarget`	can be `torch_to_nnef.TractNNEF` or `torch_to_nnef.KhronosNNEF` for each you can specify version targeted: - KhronosNNEF is the least maintained so far, and is checked against nnef-tools PyTorch interpreter - TractNNEF is our main focus at SONOS, it is checked against tract inference engine among key paramters there is feature_flags: Optional[Set[str]], that may contains tract specifics dynamic_axes: Optional By default the exported model will have the shapes of all input and output tensors set to exactly match those given in args. To specify axes of tensors as dynamic (i.e. known only at runtime) set dynamic_axes to a dict with schema: KEY (str): an input or output name. Each name must also be provided in input_names or output_names. VALUE (dict or list): If a dict, keys are axis indices and values are axis names. If a list, each element is an axis index.	required
`input_names`	`Optional[List[str]]`	Optional list of names for args, it replaces variable inputs names traced from graph (if set it must have the same size as number of args)	`None`
`output_names`	`Optional[List[str]]`	Optional list of names for outputs of `model.forward`, it replaces variable output names traced from graph (if set it must have the same size as number of outputs)	`None`
`compression_level`	`Optional[int]`	Optional[int] = 0 If None, writes an uncompressed `.nnef` directory. If 0, writes an uncompressed tar archive `.nnef.tar`. If 1..9, writes a gzip-compressed tar archive `.nnef.tgz` with the given compression level.	`0`
`log_level`	`int`	int, logger level for `torch_to_nnef` following Python standard logging level can be set to: INFO, WARN, DEBUG ...	`INFO`
`nnef_variable_naming_scheme`	`VariableNamingScheme`	Possible choices NNEF variables naming schemes are: - "raw": Taking variable names from traced graph debugName directly - "natural_verbose": that try to provide nn.Module exported variable naming consistency - "natural_verbose_camel": that try to provide nn.Module exported variable naming consistency but with more consice camelCase variable pattern - "numeric": that try to be as concise as possible	`DEFAULT_VARNAME_SCHEME`
`check_io_names_qte_match`	`bool`	(default: True) During the tracing process of the torch graph One or more input provided can be removed if not contributing to generate outputs while check_io_names_qte_match is True we ensure that this input and output quantity remain constant with numbers in `input_names` and `output_names`.	`True`
`debug_bundle_path`	`Optional[Path]`	Optional[Path] if specified it should create an archive bundle with all needed information to allow easier debug.	`None`
`custom_extensions`	`Optional[List[str]]`	Optional[List[str]] allow to add a set of extensions as defined in (https://registry.khronos.org/NNEF/specs/1.0/nnef-1.0.5.html) Useful to set specific extensions like for example: 'extension tract_assert S >= 0' those assertion allows to add limitation on dynamic shapes that are not expressed in traced graph (like for example maximum number of tokens for an LLM)	`None`
`allow_same_io_names`	`bool`	bool by default input and output names must be different to avoid simplification of the graph that would merge those tensors silently. If you really want to have same names for inputs and outputs set this flag to True. Some libs like 'nvidia/nemo' use this pattern. (note that it only make sense if it's a no operation)	`False`

Returns:

Name	Type	Description
`Path`	`Path`	the path to the exported artifact. - If `compression_level is None`: returns the `.nnef` directory path. - If `compression_level == 0`: returns the `.nnef.tar` archive path. - If `compression_level in 1..9`: returns the `.nnef.tgz` archive path.

Examples:

For example this function can be used to export as simple perceptron model:

>>> import os
>>> import tarfile
>>> import tempfile
>>> from torch import nn
>>> mod = nn.Sequential(nn.Linear(1, 5), nn.ReLU())
>>> export_path = tempfile.mktemp(suffix=".nnef.tgz")
>>> inference_target = TractNNEF.latest()
>>> _ = export_model_to_nnef(
...   mod,
...   torch.rand(3, 1),
...   export_path,
...   inference_target,
...   compression_level=0,
...   input_names=["inp"],
...   output_names=["out"]
... )
>>> os.chdir(export_path.rsplit("/", maxsplit=1)[0])
>>> tarfile.open(export_path).extract("graph.nnef")
>>> "graph network(inp) -> (out)" in open("graph.nnef").read()
True

export_tensors_from_disk_to_nnef

export_tensors_from_disk_to_nnef(store_filepath: T.Union[Path, str], output_dir: T.Union[Path, str], filter_key: T.Optional[T.Callable[[str], bool]] = None, fn_check_found_tensors: T.Optional[T.Callable[[T.Dict[str, _Tensor]], bool]] = None, map_location: T.Union[str, torch.device] = 'cpu') -> T.Dict[str, _Tensor]

Export any statedict or safetensors file torch.Tensors to NNEF .dat file.

Parameters:

Name	Type	Description	Default
`store_filepath`	`Union[Path, str]`	the filepath that hold the .safetensors , .pt or .bin containing the state dict	required
`output_dir`	`Union[Path, str]`	directory to dump the NNEF tensor .dat files	required
`filter_key`	`Optional[Callable[[str], bool]]`	An optional function to filter specific keys to be exported	`None`
`fn_check_found_tensors`	`Optional[Callable[[Dict[str, _Tensor]], bool]]`	post checking function to ensure all requested tensors have effectively been dumped	`None`
`map_location`	`Union[str, device]`	device mapping used by torch.load for .pt/.pth/.bin files (default: "cpu").	`'cpu'`

Returns:

Type	Description
`Dict[str, _Tensor]`	a dict of tensor name as key and torch.Tensor values, identical to `torch_to_nnef.export.export_tensors_to_nnef`

Examples:

Simple filtered example

>>> import tempfile
>>> from torch import nn
>>> class Mod(nn.Module):
...     def __init__(self):
...         super().__init__()
...         self.a = nn.Linear(1, 5)
...         self.b = nn.Linear(5, 1)
...
...     def forward(self, x):
...         return self.b(self.a(x))
>>> mod = Mod()
>>> pt_path = tempfile.mktemp(suffix=".pt")
>>> nnef_dir = tempfile.mkdtemp(suffix="_nnef")
>>> torch.save(mod.state_dict(), pt_path)
>>> def check(ts):
...     assert all(_.startswith("a.") for _ in ts)
>>> exported_tensors = export_tensors_from_disk_to_nnef(
...     pt_path,
...     nnef_dir,
...     lambda x: x.startswith("a."),
...     check
... )
>>> list(exported_tensors.keys())
['a.weight', 'a.bias']

export_tensors_to_nnef

export_tensors_to_nnef(name_to_torch_tensors: T.Dict[str, _Tensor], output_dir: Path) -> T.Dict[str, _Tensor]

Export any torch.Tensors list to NNEF .dat file.

Parameters:

Name	Type	Description	Default
`name_to_torch_tensors`	`Dict[str, _Tensor]`	dict A map of name (that will be used to define .dat filename) and tensor values (that can also be special torch_to_nnef tensors)	required
`output_dir`	`Path`	directory to dump the NNEF tensor .dat files	required

Returns:

Type	Description
`Dict[str, _Tensor]`	a dict of tensor name as key and torch.Tensor values, identical to `torch_to_nnef.export.export_tensors_to_nnef`

Examples:

Simple example

>>> import tempfile
>>> from torch import nn
>>> class Mod(nn.Module):
...     def __init__(self):
...         super().__init__()
...         self.a = nn.Linear(1, 5)
...         self.b = nn.Linear(5, 1)
...
...     def forward(self, x):
...         return self.b(self.a(x))
>>> mod = Mod()
>>> nnef_dir = tempfile.mkdtemp(suffix="_nnef")
>>> exported_tensors = export_tensors_to_nnef(
...     {k: v for k, v in mod.named_parameters() if k.startswith("b.")},
...     nnef_dir,
... )
>>> list(exported_tensors.keys())
['b.weight', 'b.bias']

iter_torch_tensors_from_disk

iter_torch_tensors_from_disk(store_filepath: Path, filter_key: T.Optional[T.Callable[[str], bool]] = None, map_location: T.Union[str, torch.device] = 'cpu') -> T.Iterator[T.Tuple[str, _Tensor]]

Iter on torch tensors from disk .safetensors, .pt, pth, .bin.

Parameters:

Name	Type	Description	Default
`store_filepath`	`Path`	path to the container file holding PyTorch tensors (.pt, .pth, .bin and .safetensors)	required
`filter_key`	`Optional[Callable[[str], bool]]`	if set, this function filter over tensor by name stored in those format	`None`
`map_location`	`Union[str, device]`	device mapping used by torch.load for .pt/.pth/.bin files (default: "cpu").	`'cpu'`

Yields:

Type	Description
`str`	provide each tensor that are validated by filter within store filepath
`_Tensor`	one at a time as tuple with name first then the torch.Tensor itself