[object Object]

[object Object][object Object]undefined[object Object]

• Description: Perform single-channel rotary position encoding computation.

• Formula:

  • [object Object]Atlas A3 training products/Atlas A3 inference products[object Object], [object Object]Atlas A2 training products/Atlas A2 inference products[object Object]:

  (1) Half mode (mode equals 0):

  $$x1 = x[..., : x.shape[-1] // 2]$$ $$x2 = x[..., x.shape[-1] // 2 :]$$ $$x\_rotate = torch.cat((-x2, x1), dim=-1)$$ $$y = x * cos + x\_rotate * sin$$

  (2) Interleave mode (mode equals 1):

  $$x1 = x[..., ::2].view(-1, 1)$$ $$x2 = x[..., 1::2].view(-1, 1)$$ $$x\_rotate = torch.cat((-x2, x1), dim=-1).view(x.shape[0], x.shape[1], x.shape[2], x.shape[3])$$ $$y = x * cos + x\_rotate * sin$$

  (3) Quarter mode (mode equals 2):

  $$x1 = x[..., : x.shape[-1] // 4]$$ $$x2 = x[..., x.shape[-1] // 4 : x.shape[-1] // 2]$$ $$x3 = x[..., x.shape[-1] // 2 : x.shape[-1] // 4 * 3]$$ $$x4 = x[..., x.shape[-1] // 4 * 3 :]$$ $$x\_rotate = torch.cat((-x2, x1, -x4, x3), dim=-1)$$ $$y = x * cos + x\_rotate * sin$$

  (4) interleave-half mode (mode equals 3), this mode first extracts the odd-positioned inputs to the first half and the even-positioned inputs to the second half, then performs the half processing:

  $$x1 = x[..., ::2]$$ $$x2 = x[..., 1::2]$$ $$x\_part1 = torch.cat((x1, x2), dim=-1)$$ $$x\_part2 = torch.cat((-x2, x1), dim=-1)$$ $$y = x\_part1 * cos + x\_part2 * sin$$

[object Object]

Each operator has calls. You must first call [object Object] to obtain the input parameters and calculate the required workspace size according to the process, and then call [object Object] to perform the computation.

[object Object]

• Parameters:

  [object Object]
  • Parameter mode constraints:
    • [object Object]Atlas A3 training products/Atlas A3 inference products[object Object], [object Object]Atlas A2 training products/Atlas A2 inference products[object Object]: 0=half, 1=interleave.

• Returns:

  [object Object]: status code. For details, see .

  The first-phase API implements input parameter verification. The following errors may be thrown.

  [object Object]

[object Object]

• Parameters:

  [object Object]

• Returns:

  [object Object]: status code. For details, see .

[object Object]

• Deterministic Computation:

  • [object Object] defaults to deterministic implementation.

  • [object Object]Atlas A3 training products/Atlas A3 inference products[object Object], [object Object]Atlas A2 training products/Atlas A2 inference products[object Object]:

    The input tensor [object Object] supports BNSD, BSND, SBND, and TND layouts. The D-dimensional sizes of the input tensor [object Object], [object Object], [object Object], and output tensor [object Object] must be the same, satisfy D < 896, and must be a multiple of 2. The shapes of the input tensor [object Object] and the output tensor [object Object] must be exactly the same. The shapes of the input tensors [object Object] and [object Object] must be exactly the same.

    • Half mode:
      • B, N < 1000.
      • When [object Object] is BNSD, [object Object] and [object Object] support 11SD, B1SD, and BNSD.
        • When (D/2) % (32/inputDtypeSize) == 0, it must satisfy B N <= S 8.
        • When (D/2) % (32/inputDtypeSize) != 0, it must satisfy B N 2 <= (S + coreNum -1) / coreNum or D >= 80.
      • When [object Object] is BSND, [object Object] and [object Object] support 1S1D, BS1D, BSND.
      • When [object Object] is SBND, [object Object] and [object Object] support S11D, SB1D, SBND.
      • When [object Object] is TND, [object Object] and [object Object] support T1D and TND.
    • Interleave mode:
      • B * N < 1000 (N<1000 when x is TND)
      • When [object Object] is BNSD, [object Object] and [object Object] support 11SD.
      • When [object Object] is BSND, [object Object] and [object Object] support 1S1D.
      • When [object Object] is SBND, [object Object] and [object Object] support S11D.
      • When [object Object] is TND, [object Object] and [object Object] support T1D.

[object Object]

The following example is for reference only. For details, see .