nemo_automodel.components.models.qwen3

Dense Qwen3 model support.

Submodules

nemo_automodel.components.models.qwen3.model

Package Contents

Classes

Name	Description
`Qwen3ForCausalLM`	Dense Qwen3 causal LM with packed THD context parallelism.

API

class nemo_automodel.components.models.qwen3.model.Qwen3ForCausalLM(
    config: transformers.Qwen3Config,
    backend: nemo_automodel.components.models.common.BackendConfig | None = None
)

Bases: HFCheckpointingMixin, Qwen3PreTrainedModel, GenerationMixin

Dense Qwen3 causal LM with packed THD context parallelism.

_keep_in_fp32_modules

= ['rotary_emb']

_pp_plan

= {'lm_head': (['hidden_states'], ['logits'])}

_tied_weights_keys

= {'lm_head.weight': 'model.embed_tokens.weight'}

_tp_plan

= {'lm_head': 'colwise_rep'}

_uses_hf_attention

bool

BSHD and NEAT use HF dispatch; TE handles only pre-packed THD.

backend

= backend or BackendConfig()

lm_head

model

= Qwen3Model(config=config, backend=(self.backend))

tie_word_embeddings_support

TieSupport = TieSupport.BOTH

vocab_size

= config.vocab_size

nemo_automodel.components.models.qwen3.model.Qwen3ForCausalLM.forward(
    input_ids: torch.LongTensor | None = None,
    attention_mask: torch.Tensor | None = None,
    position_ids: torch.LongTensor | None = None,
    past_key_values: transformers.cache_utils.Cache | None = None,
    inputs_embeds: torch.FloatTensor | None = None,
    labels: torch.LongTensor | None = None,
    use_cache: bool | None = None,
    output_attentions: bool | None = None,
    output_hidden_states: bool | None = None,
    return_dict: bool | None = None,
    cache_position: torch.LongTensor | None = None,
    logits_to_keep: int | torch.Tensor = 0,
    kwargs: transformers.processing_utils.Unpack[transformers.utils.TransformersKwargs] = {}
) -> transformers.modeling_outputs.CausalLMOutputWithPast

Run causal LM projection for BSHD or packed THD inputs.

Parameters:

input_ids

torch.LongTensor | NoneDefaults to None

Token IDs [B, S] or packed local IDs [T].

attention_mask

torch.Tensor | NoneDefaults to None

Optional padded mask. THD uses cu_seqlens.

position_ids

torch.LongTensor | NoneDefaults to None

Position IDs [B, S] or packed local IDs [T].

past_key_values

Cache | NoneDefaults to None

Optional BSHD KV cache; unsupported for THD.

inputs_embeds

torch.FloatTensor | NoneDefaults to None

Optional hidden inputs [B, S, H] or [T, H].

labels

torch.LongTensor | NoneDefaults to None

Optional labels [B, S] or packed [T].

use_cache

bool | NoneDefaults to None

Whether to update the BSHD KV cache.

output_attentions

bool | NoneDefaults to None

Whether to request attention outputs.

output_hidden_states

bool | NoneDefaults to None

Whether to return per-layer hidden states.

return_dict

bool | NoneDefaults to None

Whether to return CausalLMOutputWithPast.

cache_position

torch.LongTensor | NoneDefaults to None

Optional BSHD cache positions [S].

logits_to_keep

int | torch.TensorDefaults to 0

Positions to project from hidden size H to vocabulary size V.

**kwargs

Unpack[TransformersKwargs]Defaults to {}

THD metadata. cu_seqlens is [N + 1] and CP adds cp_size and cp_rank.

Returns: CausalLMOutputWithPast

Causal LM output with BSHD logits [B, S, V]. Packed local

nemo_automodel.components.models.qwen3.model.Qwen3ForCausalLM.get_input_embeddings() -> torch.nn.Module

nemo_automodel.components.models.qwen3.model.Qwen3ForCausalLM.get_output_embeddings() -> torch.nn.Module

nemo_automodel.components.models.qwen3.model.Qwen3ForCausalLM.set_input_embeddings(
    value: torch.nn.Module
) -> None

nemo_automodel.components.models.qwen3.model.Qwen3ForCausalLM.set_output_embeddings(
    new_embeddings: torch.nn.Module
) -> None

nemo_automodel.components.models.qwen3.model.Qwen3ForCausalLM.tie_weights(
    _args: object = (),
    _kwargs: object = {}
) -> None