o ?߱i@sdZddlZddlmZddlmZ  ddededededBd edBd eddeefeeeeefBf d d Z    ddededej dej de dedBdedBdedBdee Bd e fddZ dS)zo Imaginaire4 Attention Subpackage: Unified implementation for all Attention implementations. Varlen utilities N)Tensor)is_torch_compilingquerykeyvalue seqlens_Q seqlens_KVreturnc Cstrtd|jd|jdks|jd|jdkr2td|jdd|jdd|jdd|du|duArEtd|d|d|durO|durOd S|dusUJ|dus[Jt|tret|tsitd |j|jksu|j|jkrtd |jd |jd |jd|jtj ks|jtj krtd|jd|jd| dks| dkrtd| d| d|jd|jdkrtd|jd|jd|jddkrtd|jd|jd|jddkrtd|jdd| | krdks JJ|jd|jdkrdks!JJ|j|jkr1tj ks4JJ| }| }tj dgtj |jd}tj||dtj gdd}tj||dtj gdd} t|tssJt|ts{J|| ||fS)NzRunning 'generate_varlen_parameters' in a torch-compiled region is disallowed as it results in graph breaks. Please consider calling ahead of time and pass 'cumulative_seqlen_{Q,KV}' and 'max_seqlen_{Q,KV}' instead of 'seqlens_{Q,KV}' to 'attention'. rz9Q, K, and V must match in batch size, got query.shape[0]=z, key.shape[0]=z, value.shape[0]=.z]Variable length Attention requires both of seqlens_Q and seqlens_KV to be set, got seqlens_Q=z , seqlens_KV=)NNrrz.seqlens_Q and seqlens_KV must both be tensors.zQseqlens_Q and seqlens_KV must be on the same device as QKV, but seqlens_Q.device=z, seqlens_KV.device=z, query.device=zOseqlens_Q and seqlens_KV must both be torch.int32 tensors, got seqlens_Q.dtype=z, seqlens_KV.dtype=zGseqlens_Q and seqlens_KV must both be 1-D tensors, got seqlens_Q.dim()=z, seqlens_KV.dim()=zAseqlens_Q and seqlens_KV must match in size, got seqlens_Q.shape=z, seqlens_KV.shape=zPseqlens_Q and seqlens_KV must contain at least one element, got seqlens_Q.shape=zfVariable length attention only supports sequence-packed memory layout (batch = 1), got query.shape[0]=)dtypedevice)dim)r RuntimeErrorshape ValueError isinstancerr r torchint32rmaxitemtensorcatcumsumtoint) rrrrr max_seqlen_Q max_seqlen_KVzcumulative_seqlen_Qcumulative_seqlen_KVr!Z/data/cameron/vidgen/cosmos-predict2.5/cosmos_predict2/_src/imaginaire/attention/varlen.pygenerate_varlen_parameterss ((  (,&    r#Ftoken_layout_listhead_dimr r requires_gradwindow_size_list stride_list dilation_list is_causalc Osddlm} m} | std| dstdddlm} |dur)dd |D}ng}t||D]\}}td d t||D}||q0| | ||||||ddd|||d | }|S) a Configures metadata for variable-length multi-dimensional attention operations. This function prepares the metadata needed for varlen/varsized sparse attention, including backend selection and tile configurations. The metadata should be generated ahead of time (outside of torch.compile regions) and reused across forward/backward passes. **Requires NATTEN >= 0.21.6.dev1** Parameters: token_layout_list (list): List of token layout tuples describing the spatial arrangement of tokens for each sequence. For example, for 2D attention with two sequences of sizes (H1, W1) and (H2, W2), pass [(H1, W1), (H2, W2)]. head_dim (int): Attention head dimension. device (torch.device): Target device for runtime. dtype (torch.dtype): Tensor element type. requires_grad (bool): Whether tensors will require backward pass. window_size_list (list | None): Per-sequence window sizes for variable kernel sizes. stride_list (list | None): Per-sequence stride values for variable strides. dilation_list (list | None): Per-sequence dilation values for variable dilations. is_causal (tuple | bool): Toggle causal masking. Default is False. Returns: dict: Runtime metadata for varlen operations. This dict should be passed to `natten_multi_dimensional_attention_varlen` as the `metadata` parameter. r)natten_supportednatten_version_satisfiesz5generate_multi_dim_varlen_parameters requires NATTEN.z 0.21.6.dev1zgenerate_multi_dim_varlen_parameters requires NATTEN >= 0.21.6.dev1. Please upgrade NATTEN to use varlen/varsized attention features.)configure_varlenNcSsg|]}|qSr!r!).0 token_layoutr!r!r" sz8generate_multi_dim_varlen_parameters..css$|] \}}|dkr |n|VqdS)rNr!)r.kxr!r!r" s"z7generate_multi_dim_varlen_parameters..) r$r%r r r&r* kernel_sizestridedilationZkernel_size_listr(r)) 0cosmos_predict2._src.imaginaire.attention.nattenr+r,rZ natten.varlenr-ziptupleappend)r$r%r r r&r'r(r)r*argskwargsr+r,r-Zwindow_size_list_filtered window_sizer/Zwindow_size_filteredmetadatar!r!r"$generate_multi_dim_varlen_parameters{s@1   r?)NN)NNNF)__doc__rr/cosmos_predict2._src.imaginaire.attention.utilsrr9rr#listr r booldictr?r!r!r!r"sX   d