mstar.model.vjepa2.components.layers

mstar.model.vjepa2.components.layers#

Shared transformer building blocks for V-JEPA 2 encoder and predictor.

Ports VJEPA2MLP, VJEPA2RopeAttention, VJEPA2Layer from HuggingFace transformers/models/vjepa2/modeling_vjepa2.py. Uses eager (matmul+softmax) attention — no SDPA auto-selection or gradient checkpointing — to keep numerics bit-reproducible against the reference implementation.

Weight layout per layer (matches HF checkpoint keys):

norm1.{weight,bias} attention.{query,key,value}.{weight,bias} attention.proj.{weight,bias} norm2.{weight,bias} mlp.fc1.{weight,bias} mlp.fc2.{weight,bias}

Classes

VJEPA2Layer(config, hidden_size, ...)

One transformer block: pre-norm self-attention + pre-norm MLP with residuals.

VJEPA2MLP(config, hidden_size, mlp_ratio)

VJEPA2RopeAttention(config, hidden_size, ...)

Self-attention with 3D rotary positional encoding.
class mstar.model.vjepa2.components.layers.VJEPA2Layer(config, hidden_size, num_attention_heads, mlp_ratio)[source]#

Bases: Module

One transformer block: pre-norm self-attention + pre-norm MLP with residuals.

Parameters:
forward(hidden_states, position_mask=None)[source]#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters:
Return type:

Tensor

class mstar.model.vjepa2.components.layers.VJEPA2MLP(config, hidden_size, mlp_ratio)[source]#

Bases: Module

Parameters:
forward(hidden_state)[source]#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters:

hidden_state (Tensor)

Return type:

Tensor

class mstar.model.vjepa2.components.layers.VJEPA2RopeAttention(config, hidden_size, num_attention_heads)[source]#

Bases: Module

Self-attention with 3D rotary positional encoding.

Q/K/V are separate nn.Linear projections (matches HF checkpoint key layout: attention.{query,key,value,proj}.*). RoPE is applied to queries and keys, split into depth/height/width axes derived from each token’s position id.

Parameters:
apply_rotary_embeddings(qk, pos_ids)[source]#
Parameters:
Return type:

Tensor

forward(hidden_states, position_mask=None)[source]#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters:
Return type:

Tensor

get_position_ids(x, masks=None)[source]#
Parameters:
Return type:

tuple[Tensor, Tensor, Tensor]