flashinfer.rope.apply_rope_pos_ids¶

flashinfer.rope.apply_rope_pos_ids(q: torch.Tensor, k: torch.Tensor, pos_ids: torch.Tensor, rotary_dim: int | None = None, interleave: bool = False, rope_scale: float = 1, rope_theta: float = 10000.0) → Tuple[torch.Tensor, torch.Tensor]¶

Apply rotary embedding to a batch of queries/keys (stored as RaggedTensor). cos/sin values are computed on the fly inside the kernel.

We use indptr to denote the start pointer of each segment in the batch, the i-th segment the query of the i-th segment is q[indptr[i]:indptr[i+1]] and the key of the i-th segment is k[indptr[i]:indptr[i+1]], the first element of indptr is always 0 and the last element of indptr is the total number of queries/keys in the batch. Please see Ragged Tensor tutorial for more details about the ragged tensor.

Parameters:

q (torch.Tensor) – Query ragged tensor, shape: (nnz, num_q_heads, head_dim)`, where ``nnz is the last element of indptr.
k (torch.Tensor) – Key ragged tensor, shape: (nnz, num_k_heads, head_dim), where nnz is the last element of indptr.
pos_ids (torch.Tensor) – Position indices, shape: (batch_size + 1).
rotary_dim (Optional[int]) – The dimensions to apply RoPE, if None, we apply RoPE to the entire head dimension, otherwise, we apply RoPE to the first rotary_dim dimensions, default: None.
interleave (bool) –
Whether to use interleaved layout in the last dimension, default: False.
- If True, the last dimension of the query/key tensor is interleaved, i.e., we rotate the even dimensions ([..., ::2]) and odd dimensions ([..., 1::2]).
- If False, the last dimension of the query/key tensor is not interleaved, i.e., we rorate the first half dimensions ([..., :head_dim//2]) and the second half dimensions ([..., head_dim//2:]).
rope_scale (float) – The scaling factor used in the rope embedding, default: 1.
rope_theta (float) – The theta value used in the rope embedding, default: 1e4.

Returns:

q_rope (torch.Tensor) – The rotated query tensor, shape: (nnz, num_q_heads, head_dim).
k_rope (torch.Tensor) – The rotated key tensor, shape: (nnz, num_k_heads, head_dim).