nemo_automodel._transformers.auto_model

Module Contents

Classes

_BaseNeMoAutoModelClass	Drop-in replacement for _BaseAutoModelClass that includes custom-kernels.
NeMoAutoModelForCausalLM	Drop-in replacement for transformers.AutoModelForCausalLM that includes custom-kernels.
NeMoAutoModelForImageTextToText	Drop-in replacement for transformers.AutoModelForImageTextToText with custom-kernels.
NeMoAutoModelForSequenceClassification	Drop-in replacement for transformers.AutoModelForSequenceClassification with custom-kernels.
NeMoAutoModelForTextToWaveform	Drop-in replacement for transformers.AutoModelForTextToWaveform with custom-kernels.

Functions

_assert_same_signature	Raise AssertionError if the two call signatures differ.
_patch_attention	Wrap the forward method of obj in an sdap_kernel context manager.
_patch_liger_kernel	Patches a model with liger-kernel and sdpa_kernel
_get_next_fallback_attn	Get the next attention implementation in the priority list, in reverse order.
_prepare_hf_config_and_flag	Resolve trust_remote_code default, fetch HF config and determine if model is HF-based.
_pop_tp_cp_has_packed	Extract and remove TP/CP/packed flags from kwargs.
_apply_preload_overrides	Compute final attention implementation and liger-kernel flag based on TP/CP and packed sequence constraints.
_verify_sdpa_support	Validate SDPA support when CP is enabled for HF models.

Data

logger

API

nemo_automodel._transformers.auto_model.logger

‘getLogger(…)’

nemo_automodel._transformers.auto_model._assert_same_signature(original, patched)

Raise AssertionError if the two call signatures differ.

nemo_automodel._transformers.auto_model._patch_attention(obj, sdpa_method=None)

Wrap the forward method of obj in an sdap_kernel context manager.

Parameters:

• obj – Any object with a .forward(*args, **kwargs) method.

• sdpa_method (list[SDPBackend], optional) – Ordered list of SDPBackend implementations to attempt. If None, defaults to [CUDNN_ATTENTION, FLASH_ATTENTION, EFFICIENT_ATTENTION, MATH].

Returns:

The same obj with its .forward method patched.

nemo_automodel._transformers.auto_model._patch_liger_kernel(model)

Patches a model with liger-kernel and sdpa_kernel

Parameters:

• model (nn.Module) – the model to patch

• use_liger_kernel (bool) – Applies liger-kernel to model Default True.

• use_sdpa_patching (bool) – Enables model patching with SDPA kernel optim. Default True.

• sdpa_method (list[SDPBackend], optional) – Ordered list of SDPBackend implementations to attempt. If None, defaults to [CUDNN_ATTENTION, FLASH_ATTENTION, EFFICIENT_ATTENTION, MATH].

Returns:

the patched model

Return type:

nn.Module

nemo_automodel._transformers.auto_model._get_next_fallback_attn(attn_implementation: str) → str

Get the next attention implementation in the priority list, in reverse order.

If a model does not support a given attention implementation, the next implementation in the priority list is returned.

If the current attention implementation is not in the priority list, it uses eager.

Parameters:

attn_implementation (str) – The current attention implementation.

Returns:

The next attention implementation in the priority list.

Return type:

str

nemo_automodel._transformers.auto_model._prepare_hf_config_and_flag(

pretrained_model_name_or_path,

force_hf,

kwargs,

)

Resolve trust_remote_code default, fetch HF config and determine if model is HF-based.

nemo_automodel._transformers.auto_model._pop_tp_cp_has_packed(kwargs)

Extract and remove TP/CP/packed flags from kwargs.

nemo_automodel._transformers.auto_model._apply_preload_overrides(

is_hf_model,

tp_size,

cp_size,

has_packed_sequence,

attn_implementation,

use_liger_kernel,

)

Compute final attention implementation and liger-kernel flag based on TP/CP and packed sequence constraints.

nemo_automodel._transformers.auto_model._verify_sdpa_support(model, is_hf_model, cp_size)

Validate SDPA support when CP is enabled for HF models.

class nemo_automodel._transformers.auto_model._BaseNeMoAutoModelClass

Bases: transformers.models.auto.auto_factory._BaseAutoModelClass

Drop-in replacement for _BaseAutoModelClass that includes custom-kernels.

The class only overrides from_pretrained and from_config to add the optional use_liger_kernel flag. If the flag is True (default) and the Liger kernel is available, the model’s attention layers are monkey-patched in place. If patching fails for any reason, the call is retried once with use_liger_kernel=False so that users still obtain a functional model.

TODO(@akoumpa): extend this beyond liger_kernel.

Notes:

• No changes are made to the model’s public API; forward signatures, generation utilities, and weight shapes remain identical.

• Only decoder-style (causal) architectures are currently supported by the Liger patch. Unsupported models will silently fall back.

classmethod from_pretrained(

pretrained_model_name_or_path,

*model_args,

use_liger_kernel: bool = True,

use_sdpa_patching: bool = True,

sdpa_method: Optional[List[torch.nn.attention.SDPBackend]] = None,

torch_dtype='auto',

attn_implementation: str = 'flash_attention_2',

quantization_config=None,

force_hf: bool = False,

**kwargs,

) → transformers.PreTrainedModel

Instantiate and (optionally) patch a causal-language model.

This is a light wrapper around transformers.AutoModelForCausalLM.from_pretrained that can automatically apply Liger and/or SDPA (scaled-dot-product attention) kernel optimizations.

Parameters:

• pretrained_model_name_or_path (str | os.PathLike) – Hugging Face hub repo ID or local path accepted by AutoModelForCausalLM.from_pretrained.

• *model_args – Positional arguments forwarded verbatim to AutoModelForCausalLM.from_pretrained.

• use_liger_kernel (bool, default=True) – If True, try to patch the model with Liger kernels for faster inference/training.

• use_sdpa_patching (bool, default=True) – If True, patch the model with SDPA-based attention optimizations.

• sdpa_method (list[SDPBackend] | None, optional) – Explicit list of SDPA back-ends to consider when use_sdpa_patching=True.

• torch_dtype (str | torch.dtype | Literal["auto"], default="auto") – Data type passed to the underlying from_pretrained call.

• attn_implementation (str, default="flash_attention_2") – Desired attention implementation; forwarded to the HF config.

• quantization_config (optional) – BitsAndBytesConfig configuration object that specifies all quantization settings. If provided, quantization will be applied to the model.

• force_hf (bool, default=False) – If True, force the use of HF model implementation. If False, the model will be loaded using the custom model implementation if available.

• **kwargs – Additional keyword arguments forwarded verbatim to AutoModelForCausalLM.from_pretrained.

Returns:

The loaded (and possibly patched) model instance.

Return type:

transformers.PreTrainedModel

Warns:

• UserWarning – Emitted when use_liger_kernel=True but the Liger

• package is unavailable.

.. rubric:: Notes

If kernel patching fails, the partially constructed model is deleted and the method recurses once with use_liger_kernel=False or use_sdpa_patching=False

classmethod from_config(

config,

*model_args,

use_liger_kernel: bool = True,

use_sdpa_patching: bool = True,

sdpa_method: Optional[List[torch.nn.attention.SDPBackend]] = None,

torch_dtype: Union[str, torch.dtype] = 'auto',

attn_implementation: str = 'flash_attention_2',

quantization_config=None,

force_hf: bool = False,

**kwargs,

) → transformers.PreTrainedModel

Instantiate a model from a transformers.PretrainedConfig and optionally patch it with Liger or SDPA-optimized kernels.

Parameters:

• config (transformers.PretrainedConfig) – The configuration object used to build the model.

• *model_args – Positional arguments forwarded to the underlying transformers.AutoModelForCausalLM.from_config call.

• use_liger_kernel (bool, optional) – If True, tries to patch the instantiated model with Liger optimized attention kernels. Defaults to True.

• use_sdpa_patching (bool, optional) – If True, applies in-place SDPA (Scaled-Dot-Product-Attention) kernel optimizations wherever possible. Defaults to True.

• sdpa_method (Optional[List[SDPBackend]], optional) – One or multiple SDPA back-ends to prefer when applying SDPA patching. When None, the default backend resolution logic is used. Defaults to None.

• attn_implementation (str, optional) – Specifies which attention implementation to use (e.g., "flash_attention_2", "eager"). Only applied when the base model supports this kwarg. Defaults to "flash_attention_2".

• force_hf (bool, default=False) – If True, force the use of HF model implementation. If False, the model will be loaded using the custom model implementation if available.

• **kwargs – Additional keyword arguments forwarded to the superclass constructor and underlying from_config logic.

Returns:

The instantiated (and possibly kernel-patched) model.

Return type:

transformers.PreTrainedModel

.. rubric:: Notes

If kernel patching fails, the partially constructed model is deleted and the method recurses once with use_liger_kernel=False or use_sdpa_patching=False

class nemo_automodel._transformers.auto_model.NeMoAutoModelForCausalLM

Bases: nemo_automodel._transformers.auto_model._BaseNeMoAutoModelClass, transformers.AutoModelForCausalLM

Drop-in replacement for transformers.AutoModelForCausalLM that includes custom-kernels.

The class only overrides from_pretrained and from_config to add the optional use_liger_kernel flag. If the flag is True (default) and the Liger kernel is available, the model’s attention layers are monkey-patched in place. If patching fails for any reason, the call is retried once with use_liger_kernel=False so that users still obtain a functional model.

TODO(@akoumpa): extend this beyond liger_kernel.

Notes:

• No changes are made to the model’s public API; forward signatures, generation utilities, and weight shapes remain identical.

• Only decoder-style (causal) architectures are currently supported by the Liger patch. Unsupported models will silently fall back.

Examples:

model = NeMoAutoModelForCausalLM.from_pretrained(“gpt2”) # try Liger model = NeMoAutoModelForCausalLM.from_pretrained( … “gpt2”, use_liger_kernel=False) # skip Liger

class nemo_automodel._transformers.auto_model.NeMoAutoModelForImageTextToText

Bases: nemo_automodel._transformers.auto_model._BaseNeMoAutoModelClass, transformers.AutoModelForImageTextToText

Drop-in replacement for transformers.AutoModelForImageTextToText with custom-kernels.

The class only overrides from_pretrained and from_config to add the optional use_liger_kernel flag. If the flag is True (default) and the Liger kernel is available, the model’s attention layers are monkey-patched in place. If patching fails for any reason, the call is retried once with use_liger_kernel=False so that users still obtain a functional model.

@akoumpa: currently only supporting liger_kernel for demonstration purposes.

Notes:

• No changes are made to the model’s public API; forward signatures, generation utilities, and weight shapes remain identical.

• Only decoder-style (causal) architectures are currently supported by the Liger patch. Unsupported models will silently fall back.

Examples:

model = NeMoAutoModelForImageTextToText.from_pretrained(“Qwen/Qwen2.5-VL-3B-Instruct”) # try Liger model = NeMoAutoModelForImageTextToText.from_pretrained( … “Qwen/Qwen2.5-VL-3B-Instruct”, use_liger_kernel=False) # skip Liger

class nemo_automodel._transformers.auto_model.NeMoAutoModelForSequenceClassification

Bases: nemo_automodel._transformers.auto_model._BaseNeMoAutoModelClass, transformers.AutoModelForSequenceClassification

Drop-in replacement for transformers.AutoModelForSequenceClassification with custom-kernels.

The class only overrides from_pretrained and from_config to add the optional use_liger_kernel flag. If the flag is True (default) and the Liger kernel is available, the model’s attention layers are monkey-patched in place. If patching fails for any reason, the call is retried once with use_liger_kernel=False so that users still obtain a functional model.

@akoumpa: currently only supporting liger_kernel for demonstration purposes.

Notes:

• No changes are made to the model’s public API; forward signatures, generation utilities, and weight shapes remain identical.

• Only decoder-style (causal) architectures are currently supported by the Liger patch. Unsupported models will silently fall back.

Examples:

model = NeMoAutoModelForSequenceClassification.from_pretrained(“bert-base-uncased”) # try Liger model = NeMoAutoModelForSequenceClassification.from_pretrained( … “bert-base-uncased”, use_liger_kernel=False) # skip Liger

class nemo_automodel._transformers.auto_model.NeMoAutoModelForTextToWaveform

Bases: nemo_automodel._transformers.auto_model._BaseNeMoAutoModelClass, transformers.AutoModelForTextToWaveform

Drop-in replacement for transformers.AutoModelForTextToWaveform with custom-kernels.

The class only overrides from_pretrained and from_config to add the optional use_liger_kernel flag. If the flag is True (default) and the Liger kernel is available, the model’s attention layers are monkey-patched in place. If patching fails for any reason, the call is retried once with use_liger_kernel=False so that users still obtain a functional model.

@akoumpa: currently only supporting liger_kernel for demonstration purposes.

Notes:

• No changes are made to the model’s public API; forward signatures, generation utilities, and weight shapes remain identical.

• Only decoder-style (causal) architectures are currently supported by the Liger patch. Unsupported models will silently fall back.

Examples:

model = NeMoAutoModelForTextToWaveform.from_pretrained(“facebook/musicgen-small”) # try Liger model = NeMoAutoModelForTextToWaveform.from_pretrained( … “facebook/musicgen-small”, use_liger_kernel=False) # skip Liger