o iq@sZddlmZmZddlZddlmZddlmmZGdddZ Gdddej Z dS))DictTupleNc @s:eZdZdZddZdedededejdejf d d Z d S) PositionGetterabGenerates and caches 2D spatial positions for patches in a grid. This class efficiently manages the generation of spatial coordinates for patches in a 2D grid, caching results to avoid redundant computations. Attributes: position_cache: Dictionary storing precomputed position tensors for different grid dimensions. cCs i|_dS)z7Initializes the position generator with an empty cache.N)position_cache)selfrG/data/cameron/da3_repo/src/depth_anything_3/model/dinov2/layers/rope.py__init__"s zPositionGetter.__init__ batch_sizeheightwidthdevicereturnc Csr||f|jvr"tj||d}tj||d}t||}||j||f<|j||f}|d||d|ddS)aGenerates spatial positions for a batch of patches. Args: batch_size: Number of samples in the batch. height: Height of the grid in patches. width: Width of the grid in patches. device: Target device for the position tensor. Returns: Tensor of shape (batch_size, height*width, 2) containing y,x coordinates for each position in the grid, repeated for each batch item. r )rtorcharangecartesian_prodviewexpandclone) rr r r r y_coordsx_coords positionsZcached_positionsrrr__call__&s  zPositionGetter.__call__N) __name__ __module__ __qualname____doc__r intrr Tensorrrrrrrs rc seZdZdZddedeffdd Zded ed ejd ej d e ej ej ff d dZ e dej d ej fddZdej dej dej dej d ej f ddZdej dej d ej fddZZS)RotaryPositionEmbedding2Da2D Rotary Position Embedding implementation. This module applies rotary position embeddings to input tokens based on their 2D spatial positions. It handles the position-dependent rotation of features separately for vertical and horizontal dimensions. Args: frequency: Base frequency for the position embeddings. Default: 100.0 scaling_factor: Scaling factor for frequency computation. Default: 1.0 Attributes: base_frequency: Base frequency for computing position embeddings. scaling_factor: Factor to scale the computed frequencies. frequency_cache: Cache for storing precomputed frequency components. Y@? frequencyscaling_factorcs t||_||_i|_dS)zInitializes the 2D RoPE module.N)superr base_frequencyr'frequency_cache)rr&r' __class__rrr Ps  z"RotaryPositionEmbedding2D.__init__dimseq_lenr dtyperc Cs||||f}||jvrRtjd|d|d|}d|j|}tj|||jd}td||} | |} tj| | fdd} | |} | |} | | f|j|<|j|S) apComputes frequency components for rotary embeddings. Args: dim: Feature dimension (must be even). seq_len: Maximum sequence length. device: Target device for computations. dtype: Data type for the computed tensors. Returns: Tuple of (cosine, sine) tensors for frequency components. rrrr%)r r/zi,j->ijrr-) r*rrfloatr)r/einsumtocatcossin) rr-r.r r/ cache_key exponentsZinv_freqranglesZcos_componentsZsin_componentsrrr_compute_frequency_componentsWs    z7RotaryPositionEmbedding2D._compute_frequency_componentsxcCsH|jd}|dd|df|d|ddf}}tj| |fddS)zPerforms feature rotation by splitting and recombining feature dimensions. Args: x: Input tensor to rotate. Returns: Rotated feature tensor. r.Nrr0)shaperr4)r; feature_dimx1x2rrr_rotate_featuresxs *z*RotaryPositionEmbedding2D._rotate_featurestokensrcos_compsin_compcCs^t||dddddddf}t||dddddddf}|||||S)a]Applies 1D rotary position embeddings along one dimension. Args: tokens: Input token features. positions: Position indices. cos_comp: Cosine components for rotation. sin_comp: Sine components for rotation. Returns: Tokens with applied rotary position embeddings. N)F embeddingr@)rrArrBrCr5r6rrr_apply_1d_ropes$$z(RotaryPositionEmbedding2D._apply_1d_ropec Cs|dddks Jd|jdkr|jddksJd|dd}t|d}||||j|j\}}|jddd\}}| ||d ||}| ||d ||}t j ||fddS) aIApplies 2D rotary position embeddings to input tokens. Args: tokens: Input tensor of shape (batch_size, n_heads, n_tokens, dim). The feature dimension (dim) must be divisible by 4. positions: Position tensor of shape (batch_size, n_tokens, 2) containing the y and x coordinates for each token. Returns: Tensor of same shape as input with applied 2D rotary position embeddings. Raises: AssertionError: If input dimensions are invalid or positions are malformed. rrrzFeature dimension must be evenz3Positions must have shape (batch_size, n_tokens, 2)rr0).r).r) sizendimr<r!maxr:r r/chunkrFrr4) rrArr=Z max_positionrBrCZvertical_featuresZhorizontal_featuresrrrforwards"   z!RotaryPositionEmbedding2D.forward)r$r%)rrrr r1r r!rr r/rr"r: staticmethodr@rFrL __classcell__rrr+rr#?s8 !  $r#) typingrrrtorch.nnnnZtorch.nn.functional functionalrDrModuler#rrrrs  (