nemo_automodel.components.models.qwen3_moe.layers

Module Contents

Classes

Name	Description
`Qwen3MoeAttention`	Qwen3 MoE attention (query/key per-head RMSNorm + RoPE) compatible with TE/SDPA backends.

Data

logger

API

class nemo_automodel.components.models.qwen3_moe.layers.Qwen3MoeAttention(
    config: transformers.models.qwen3_moe.configuration_qwen3_moe.Qwen3MoeConfig,
    backend: nemo_automodel.components.models.common.BackendConfig
)

Bases: Module

Qwen3 MoE attention (query/key per-head RMSNorm + RoPE) compatible with TE/SDPA backends.

Shapes:

Input: x -> [B, S, H]
Projections: q: [B, S, n_heads, head_dim] k/v: [B, S, n_kv_heads, head_dim] -> repeated to n_heads via groups
Output: [B, S, H]

head_dim

k_norm

k_proj

num_heads

= config.num_attention_heads

num_kv_heads

= config.num_key_value_heads

o_proj

q_norm

q_proj

v_proj

nemo_automodel.components.models.qwen3_moe.layers.Qwen3MoeAttention._forward_impl(
    x: torch.Tensor,
    freqs_cis: torch.Tensor,
    attention_mask: torch.Tensor | None = None,
    attn_kwargs: typing.Any = {}
) -> torch.Tensor

nemo_automodel.components.models.qwen3_moe.layers.Qwen3MoeAttention.forward(
    x: torch.Tensor,
    freqs_cis: torch.Tensor,
    attention_mask: torch.Tensor | None = None,
    attn_kwargs: typing.Any = {}
) -> torch.Tensor

nemo_automodel.components.models.qwen3_moe.layers.Qwen3MoeAttention.init_weights(
    buffer_device: torch.device,
    init_std: float = 0.02
)

nemo_automodel.components.models.qwen3_moe.layers.logger = logging.getLogger(__name__)