ColossalAI/colossalai/kernel/triton/no_pad_rotary_embedding.py

import torch
import triton
import triton.language as tl

"""
# Base autotune if needed
@triton.autotune(
    configs=[
        triton.Config({'BLOCK_HEAD':4,"BLOCK_TOKENS":4,},num_warps=4),
        triton.Config({'BLOCK_HEAD':4,"BLOCK_TOKENS":8,},num_warps=8),
        triton.Config({'BLOCK_HEAD':8,"BLOCK_TOKENS":8,},num_warps=8),
        triton.Config({'BLOCK_HEAD':4,"BLOCK_TOKENS":4,},num_warps=16),
        triton.Config({'BLOCK_HEAD':4,"BLOCK_TOKENS":4,},num_warps=32),
        triton.Config({'BLOCK_HEAD':16,"BLOCK_TOKENS":16,},num_warps=4),
        triton.Config({'BLOCK_HEAD':8,"BLOCK_TOKENS":16,},num_warps=8),
    ],
    key=['HEAD_DIM','q_total_tokens','Q_HEAD_NUM']
)
"""


@triton.jit
def rotary_embedding_kernel(
    q,
    k,
    cos,
    sin,
    q_token_stride,
    q_head_stride,
    k_token_stride,
    k_head_stride,
    head_dim_stride,
    cos_token_stride,
    cos_stride,
    q_total_tokens,
    Q_HEAD_NUM: tl.constexpr,
    K_HEAD_NUM: tl.constexpr,
    HEAD_DIM: tl.constexpr,
    BLOCK_HEAD: tl.constexpr,
    BLOCK_TOKENS: tl.constexpr,
):
    block_head_index = tl.program_id(0)
    block_token_index = tl.program_id(1)

    tokens_range = block_token_index * BLOCK_TOKENS + tl.arange(0, BLOCK_TOKENS)
    head_range = block_head_index * BLOCK_HEAD + tl.arange(0, BLOCK_HEAD)

    dim_range0 = tl.arange(0, HEAD_DIM // 2)
    dim_range1 = tl.arange(HEAD_DIM // 2, HEAD_DIM)

    off_q0 = (
        tokens_range[:, None, None] * q_token_stride
        + head_range[None, :, None] * q_head_stride
        + dim_range0[None, None, :] * head_dim_stride
    )
    off_q1 = (
        tokens_range[:, None, None] * q_token_stride
        + head_range[None, :, None] * q_head_stride
        + dim_range1[None, None, :] * head_dim_stride
    )
    off_k0 = (
        tokens_range[:, None, None] * k_token_stride
        + head_range[None, :, None] * k_head_stride
        + dim_range0[None, None, :] * head_dim_stride
    )
    off_k1 = (
        tokens_range[:, None, None] * k_token_stride
        + head_range[None, :, None] * k_head_stride
        + dim_range1[None, None, :] * head_dim_stride
    )

    loaded_q0 = tl.load(
        q + off_q0,
        mask=((head_range[None, :, None] < Q_HEAD_NUM) & (tokens_range[:, None, None] < q_total_tokens)),
        other=0.0,
    )
    loaded_q1 = tl.load(
        q + off_q1,
        mask=((head_range[None, :, None] < Q_HEAD_NUM) & (tokens_range[:, None, None] < q_total_tokens)),
        other=0.0,
    )

    loaded_k0 = tl.load(
        k + off_k0,
        mask=((head_range[None, :, None] < K_HEAD_NUM) & (tokens_range[:, None, None] < q_total_tokens)),
        other=0.0,
    )

    loaded_k1 = tl.load(
        k + off_k1,
        mask=((head_range[None, :, None] < K_HEAD_NUM) & (tokens_range[:, None, None] < q_total_tokens)),
        other=0.0,
    )

    off_cos_sin = tokens_range[:, None] * cos_token_stride + dim_range0[None, :] * cos_stride

    loaded_cos = tl.load(cos + off_cos_sin, mask=(tokens_range[:, None] < q_total_tokens), other=0.0)
    loaded_sin = tl.load(sin + off_cos_sin, mask=(tokens_range[:, None] < q_total_tokens), other=0.0)

    out_q0 = loaded_q0 * loaded_cos[:, None, :] - loaded_q1 * loaded_sin[:, None, :]
    out_q1 = loaded_q0 * loaded_sin[:, None, :] + loaded_q1 * loaded_cos[:, None, :]

    out_k0 = loaded_k0 * loaded_cos[:, None, :] - loaded_k1 * loaded_sin[:, None, :]
    out_k1 = loaded_k0 * loaded_sin[:, None, :] + loaded_k1 * loaded_cos[:, None, :]

    # concat
    tl.store(
        q + off_q0,
        out_q0,
        mask=((head_range[None, :, None] < Q_HEAD_NUM) & (tokens_range[:, None, None] < q_total_tokens)),
    )
    tl.store(
        q + off_q1,
        out_q1,
        mask=((head_range[None, :, None] < Q_HEAD_NUM) & (tokens_range[:, None, None] < q_total_tokens)),
    )
    tl.store(
        k + off_k0,
        out_k0,
        mask=((head_range[None, :, None] < K_HEAD_NUM) & (tokens_range[:, None, None] < q_total_tokens)),
    )
    tl.store(
        k + off_k1,
        out_k1,
        mask=((head_range[None, :, None] < K_HEAD_NUM) & (tokens_range[:, None, None] < q_total_tokens)),
    )


@torch.no_grad()
def rotary_embedding(
    q: torch.Tensor,
    k: torch.Tensor,
    cos: torch.Tensor,
    sin: torch.Tensor,
):
    """
    Args:
        q: query tensor, [total_tokens, head_num, head_dim]
        k: key tensor, [total_tokens, head_num, head_dim]
        cos: cosine for rotary embedding, [max_position_len, head_dim]
        sin: sine for rotary embedding, [max_position_len, head_dim]
        lengths [num_seqs]
    """
    q_total_tokens, q_head_num, head_dim = q.shape
    assert q.size(0) == k.size(0)
    BLOCK_HEAD = 4
    BLOCK_TOKENS = 4
    grid = lambda META: (triton.cdiv(q_head_num, META["BLOCK_HEAD"]), triton.cdiv(q_total_tokens, META["BLOCK_TOKENS"]))

    if head_dim >= 256:
        num_warps = 32
    elif head_dim >= 128:
        num_warps = 16
    else:
        num_warps = 4

    q_token_stride = q.stride(0)
    q_head_stride = q.stride(1)
    head_dim_stride = q.stride(2)

    k_token_stride = k.stride(0)
    k_head_stride = k.stride(1)

    k_head_num = q.shape[1]

    cos_token_stride = cos.stride(0)
    cos_stride = cos.stride(1)

    rotary_embedding_kernel[grid](
        q,
        k,
        cos,
        sin,
        q_token_stride,
        q_head_stride,
        k_token_stride,
        k_head_stride,
        head_dim_stride,
        cos_token_stride,
        cos_stride,
        q_total_tokens,
        Q_HEAD_NUM=q_head_num,
        K_HEAD_NUM=k_head_num,
        HEAD_DIM=head_dim,
        BLOCK_HEAD=BLOCK_HEAD,
        BLOCK_TOKENS=BLOCK_TOKENS,
        num_warps=num_warps,
    )

    return