nemo_automodel.components.models.gpt_oss.state_dict_adapter

Module Contents

Classes

Name	Description
`GPTOSSStateDictAdapter`	-

Data

FP4_VALUES

API

class nemo_automodel.components.models.gpt_oss.state_dict_adapter.GPTOSSStateDictAdapter(
    config: transformers.GptOssConfig,
    moe_config: nemo_automodel.components.moe.config.MoEConfig,
    backend: nemo_automodel.components.models.common.BackendConfig,
    dtype: torch.dtype = torch.bfloat16
)

Bases: StateDictAdapter

hf_to_internal_map

internal_to_hf_map

nemo_automodel.components.models.gpt_oss.state_dict_adapter.GPTOSSStateDictAdapter._apply_key_mapping(
    state_dict: dict[str, typing.Any],
    mapping: dict[str, str]
) -> dict[str, typing.Any]

nemo_automodel.components.models.gpt_oss.state_dict_adapter.GPTOSSStateDictAdapter._convert_moe_packed_tensors(
    blocks,
    scales,
    dtype: torch.dtype = torch.bfloat16,
    rows_per_chunk: int = 32768 * 1024
) -> torch.Tensor

Convert the mxfp4 weights to bfloat16.

Source: https://github.com/huggingface/transformers/blob/869735d37d0f929311ac6611728c482a4414ba8c/src/transformers/integrations/mxfp4.py#L77

nemo_automodel.components.models.gpt_oss.state_dict_adapter.GPTOSSStateDictAdapter._dequantize_block_scale_tensors(
    state_dict: dict[str, typing.Any]
) -> dict[str, typing.Any]

nemo_automodel.components.models.gpt_oss.state_dict_adapter.GPTOSSStateDictAdapter.convert_single_tensor_to_hf(
    fqn: str,
    tensor: typing.Any,
    kwargs = {}
) -> list[tuple[str, typing.Any]]

Convert a single tensor from native format to HuggingFace format.

Parameters:

fqn

str

Fully qualified name of the tensor in native format

tensor

Any

The tensor to convert

**kwargs

Defaults to {}

Additional arguments for conversion

Returns: list[tuple[str, Any]]

List of (fqn, tensor) tuples in HuggingFace format

nemo_automodel.components.models.gpt_oss.state_dict_adapter.GPTOSSStateDictAdapter.from_hf(
    hf_state_dict: dict[str, typing.Any],
    device_mesh: typing.Optional[torch.distributed.device_mesh.DeviceMesh] = None,
    kwargs = {}
) -> dict[str, typing.Any]

Convert HF checkpoint to native format in-place.

Apply key mappings from HF to internal format
Dequantize block/scale tensors (freeing originals)

Operates in-place on the input dict to avoid allocating a full copy, reducing peak memory from 2x to ~1x model size.

nemo_automodel.components.models.gpt_oss.state_dict_adapter.GPTOSSStateDictAdapter.to_hf(
    state_dict: dict[str, typing.Any],
    exclude_key_regex: typing.Optional[str] = None,
    quantization: bool = False,
    kwargs = {}
) -> dict[str, typing.Any]

Convert from native model state dict to HuggingFace format.

nemo_automodel.components.models.gpt_oss.state_dict_adapter.FP4_VALUES = [+0.0, +0.5, +1.0, +1.5, +2.0, +3.0, +4.0, +6.0, -0.0, -0.5, -1.0, -1.5, -2.0, -...