^jddlZddlmZddlmZddlmZddlmZddl m Z e je Z Gdd eZeGd d ZeGd d eZeGddeZeGddeZy)N) dataclass)Enum)PathLike)Any)loggingceZdZdZdZdZdZy) ExportFormatz]Identifies the export backend. Stored in [`ExportConfigMixin`] for serialisation round-trips. executorchdynamoonnxN)__name__ __module__ __qualname____doc__ EXECUTORCHDYNAMOONNXi/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/exporters/configs.pyr r sgJ F Drr cHeZdZUdZeed<edZdee e ffdZ dZ y)ExportConfigMixina Base class for all export configuration dataclasses. Provides `to_dict` / `from_dict` serialisation so configs can be saved and round-tripped without knowing the concrete subclass. The `export_format` field identifies the subclass during deserialisation. export_formatc|di|}|S)aa Instantiates a [`ExportConfigMixin`] from a Python dictionary of parameters. Args: config_dict (`dict[str, Any]`): Dictionary that will be used to instantiate the configuration object. Returns: [`ExportConfigMixin`]: The configuration object instantiated from those parameters. rr)cls config_dictconfigs r from_dictzExportConfigMixin.from_dict/s#{# rreturnc@tj|jS)z Serializes this instance to a Python dictionary. Returns: `dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance. )copydeepcopy__dict__selfs rto_dictzExportConfigMixin.to_dict>s}}T]]++rc#TK|jjEd{y7w)N)r$itemsr%s r__iter__zExportConfigMixin.__iter__Gs==&&(((s (&(N) rrrrr __annotations__ classmethodrdictstrrr'r*rrrrr#s:   ,c3h,)rrc|eZdZUdZej Zeed<dZe ed<dZ e ed<dZ e e efdzed<dZe ed<y) DynamoConfigao Configuration class for exporting models via `torch.export`. Args: dynamic (`bool`, *optional*, defaults to `False`): Whether to export with dynamic (symbolic) shapes. When `True` and `dynamic_shapes` is not set, all tensor dimensions are set to `Dim.AUTO` automatically. strict (`bool`, *optional*, defaults to `False`): Whether to enable strict mode in `torch.export`. Runs the full symbolic trace and catches more errors, but is slower and more likely to fail on complex models. dynamic_shapes (`dict[str, Any]`, *optional*): Explicit per-input dynamic shape specifications passed to `torch.export`. Takes precedence over `dynamic`. prefer_deferred_runtime_asserts_over_guards (`bool`, *optional*, defaults to `False`): When `True`, data-dependent shape guards are emitted as runtime asserts in the exported graph instead of failing the export at trace time when a guard wouldn't hold across the full symbolic shape range. Most transformer LLMs need this set to `True` when using fine-grained ``Dim(min=, max=)`` bounds. Not needed with ``dynamic=True`` / ``Dim.AUTO``, where ``torch.export`` infers shape relations instead of verifying them against the user-stated bounds. rFdynamicstrictNdynamic_shapes+prefer_deferred_runtime_asserts_over_guards)rrrrr rrr+r1boolr2r3r-r.rr4rrrr0r0KsO0#/"5"5M<5GTFD,0NDcNT)08=/=rr0ceZdZUdZej Zeed<dZe e zdzed<dZ e e e fdzed<dZedzed<dZeed<dZeed <dZeed <d Zeed <y) OnnxConfigu> Configuration class for exporting models to ONNX via `torch.onnx.export`. Inherits all fields from [`DynamoConfig`] (`dynamic`, `strict`, `dynamic_shapes`, `prefer_deferred_runtime_asserts_over_guards`). Args: output_path (`str` or `PathLike`, *optional*): Output path for the `.onnx` file. When `None` (default) the exported model is kept in memory as an `ONNXProgram` and not written to disk. opset_version (`int`, *optional*): ONNX opset version to target. Defaults to the latest opset supported by the installed `onnxscript` version. external_data (`bool`, *optional*, defaults to `True`): Store large weight tensors in a separate `.onnx_data` sidecar file instead of embedding them in the protobuf. Required for models whose weights exceed the 2 GB protobuf limit. optimize (`bool`, *optional*, defaults to `True`): Run `onnxscript` optimisation passes (constant folding, dead-code elimination, …) on the exported graph. Disable for models that hit upstream `onnxscript` optimiser bugs. export_params (`bool`, *optional*, defaults to `True`): Embed model weights in the ONNX graph. Set to `False` to export a weight-free graph (weights must be supplied at runtime). keep_initializers_as_inputs (`bool`, *optional*, defaults to `False`): Expose weight initializers as explicit graph inputs. Required by some older ONNX runtimes (opset < 9). rN output_pathr3 opset_versionT external_dataoptimize export_paramsFkeep_initializers_as_inputs)rrrrr rrr+r8r.rr3r-rr9intr:r5r;r<r=rrrr7r7msz<#/"3"3M<3)-Kx$&-,0NDcNT)0 $M3:$M4HdM4(--rr7cBeZdZUdZej Zeed<dZe ed<y)ExecutorchConfigu Configuration class for exporting models to ExecuTorch format. Inherits all fields from [`DynamoConfig`] (`dynamic`, `strict`, `dynamic_shapes`, `prefer_deferred_runtime_asserts_over_guards`). Args: backend (`str`, *optional*, defaults to `"xnnpack"`): Target ExecuTorch backend. Supported values: - `"xnnpack"` — CPU inference via the XNNPACK library (default; runs anywhere). - `"cuda"` — GPU inference via the ExecuTorch CUDA backend. rxnnpackbackendN) rrrrr rrr+rBr.rrrr@r@s# #/"9"9M<9GSrr@)r" dataclassesrenumrosrtypingrutilsr get_loggerrloggerr rr0r7r@rrrrJs !   H %4 $)$) $)N >$> >B '.'. '.T | r