xc-llm-ascend/vllm_ascend/ops/triton/rope.py

#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
#

import torch
from vllm.triton_utils import tl, triton

from vllm_ascend.ops.triton.triton_utils import get_vectorcore_num


# TODO(whx-sjtu): Add tiling of n_q_head and n_kv_head to support more models.
# I only have tested this kernel on Deepseek V3.2 and Qwen3-Next.
# For models with larger n_q_head and n_kv_head such as GLM 4.6, this is not supported yet.
@triton.jit
def _triton_rope(
    q_ptr,
    q_row_stride,
    k_ptr,
    k_row_stride,
    cos,
    cos_row_stride,
    sin,
    sin_row_stride,
    num_tokens,
    n_qh: tl.constexpr,
    n_kh: tl.constexpr,
    hd: tl.constexpr,
    rope_dim: tl.constexpr,
    pad_n_qh: tl.constexpr,
    pad_n_kh: tl.constexpr,
    pad_rope_dim: tl.constexpr,
    BLOCK_SIZE: tl.constexpr,
    IS_NEOX_STYLE: tl.constexpr,
):
    """
    This triton kernel applies rotary embedding on q and k.
    It supports rope_dim != head_dim scenario.
    It supports both neox style and non-neox style rope computation.

    Input tensor layout assumptions:

    q size: (num_tokens, num_q_heads, head_dim)
    q stride: (num_q_heads * head_dim, head_dim, 1)
    k size: (num_tokens, num_kv_heads, head_dim)
    k stride: (num_kv_heads * head_dim, head_dim, 1)
    cos/sin size: (num_tokens, rope_dim/2)
    cos/sin stride: (rope_dim/2, 1)

    Different compute pattern of IS_NEOX_STYLE:

    if IS_NEOX_STYLE:
        x1, x2 = torch.chunk(x, 2, dim=-1)
    else:
        x1 = x[..., ::2]
        x2 = x[..., 1::2]
    o1 = x1 * cos - x2 * sin
    o2 = x2 * cos + x1 * sin
    if IS_NEOX_STYLE:
        return torch.cat((o1, o2), dim=-1)
    else:
        return torch.stack((o1, o2), dim=-1).flatten(-2)
    """
    pid = tl.program_id(0).to(tl.int64)
    row_block_size = tl.num_programs(0)

    for row_idx in tl.range(pid, num_tokens, row_block_size):
        q_start_ptr = q_ptr + row_idx * q_row_stride
        k_start_ptr = k_ptr + row_idx * k_row_stride

        # ####################################################################
        # get the cos(mθ_{i...d/2}) and sin(mθ_{i...d/2}) for token position
        # m of this program instance
        # ####################################################################
        cos_start_ptr = cos + row_idx * cos_row_stride
        sin_start_ptr = sin + row_idx * sin_row_stride

        cos_offsets = tl.arange(0, pad_rope_dim // 2)
        cos_mask = cos_offsets < (rope_dim // 2)
        cos_row = tl.load(cos_start_ptr + cos_offsets, mask=cos_mask, other=0).to(tl.float32)
        sin_row = tl.load(sin_start_ptr + cos_offsets, mask=cos_mask, other=0).to(tl.float32)

        # ####################################################################
        # Load the left and right half of q and k for the current
        # program instance (i.e. for the current token) separately
        # ####################################################################
        # left half of the head
        if IS_NEOX_STYLE:
            first_half_q_offsets = tl.arange(0, pad_n_qh)[:, None] * hd + tl.arange(0, pad_rope_dim // 2)[None, :]
            first_half_k_offsets = tl.arange(0, pad_n_kh)[:, None] * hd + tl.arange(0, pad_rope_dim // 2)[None, :]
        else:
            first_half_q_offsets = tl.arange(0, pad_n_qh)[:, None] * hd + (2 * tl.arange(0, pad_rope_dim // 2)[None, :])
            first_half_k_offsets = tl.arange(0, pad_n_kh)[:, None] * hd + (2 * tl.arange(0, pad_rope_dim // 2)[None, :])

        first_q_mask = (tl.arange(0, pad_n_qh)[:, None] < n_qh) & (
            tl.arange(0, pad_rope_dim // 2)[None, :] < (rope_dim // 2)
        )
        first_k_mask = (tl.arange(0, pad_n_kh)[:, None] < n_kh) & (
            tl.arange(0, pad_rope_dim // 2)[None, :] < (rope_dim // 2)
        )
        q_tile_1 = tl.load(q_start_ptr + first_half_q_offsets, mask=first_q_mask, other=0).to(sin_row.dtype)
        k_tile_1 = tl.load(k_start_ptr + first_half_k_offsets, mask=first_k_mask, other=0).to(sin_row.dtype)

        # right half of the head
        if IS_NEOX_STYLE:
            second_half_q_offsets = first_half_q_offsets + (rope_dim // 2)
            second_half_k_offsets = first_half_k_offsets + (rope_dim // 2)
        else:
            second_half_q_offsets = first_half_q_offsets + 1
            second_half_k_offsets = first_half_k_offsets + 1
        second_q_mask = first_q_mask
        second_k_mask = first_k_mask
        q_tile_2 = tl.load(q_start_ptr + second_half_q_offsets, mask=second_q_mask, other=0).to(sin_row.dtype)
        k_tile_2 = tl.load(k_start_ptr + second_half_k_offsets, mask=second_k_mask, other=0).to(sin_row.dtype)

        # y = [x1, x2] * [cos, cos] + [-x2, x1] * [sin, sin]
        new_q_tile_1 = q_tile_1 * cos_row - q_tile_2 * sin_row
        tl.store(q_start_ptr + first_half_q_offsets, new_q_tile_1, mask=first_q_mask)
        new_q_tile_2 = q_tile_2 * cos_row + q_tile_1 * sin_row
        tl.store(q_start_ptr + second_half_q_offsets, new_q_tile_2, mask=second_q_mask)

        new_k_tile_1 = k_tile_1 * cos_row - k_tile_2 * sin_row
        tl.store(k_start_ptr + first_half_k_offsets, new_k_tile_1, mask=first_k_mask)
        new_k_tile_2 = k_tile_2 * cos_row + k_tile_1 * sin_row
        tl.store(k_start_ptr + second_half_k_offsets, new_k_tile_2, mask=second_k_mask)


def rope_forward_triton(
    q: torch.Tensor,
    k: torch.Tensor,
    cos: torch.Tensor,
    sin: torch.Tensor,
    rope_dim: int = -1,
    is_neox_style: bool = True,
) -> tuple[torch.Tensor, torch.Tensor]:
    if not q.is_contiguous():
        q = q.contiguous()
    if not k.is_contiguous():
        k = k.contiguous()

    num_tokens, n_q_head, head_dim = q.shape
    n_kv_head = k.shape[1]
    cos = cos.view(num_tokens, -1)
    sin = sin.view(num_tokens, -1)
    if rope_dim == -1:
        # If rope_dim is not specified, we assume that input cos/sin is not
        # duplicated to rope_dim, which means rope_dim == cos.shape[-1] * 2
        rope_dim = cos.shape[-1] * 2
    assert rope_dim <= head_dim
    pad_rope_dim = triton.next_power_of_2(rope_dim)
    pad_n_q_head = triton.next_power_of_2(n_q_head)
    pad_n_kv_head = triton.next_power_of_2(n_kv_head)
    BLOCK_SIZE = max(pad_n_q_head, pad_n_kv_head)
    num_vectorcore = get_vectorcore_num()
    n_row = min(num_tokens, num_vectorcore)

    _triton_rope[(n_row,)](
        q,
        q.stride(0),
        k,
        k.stride(0),
        cos,
        cos.stride(0),
        sin,
        sin.stride(0),
        num_tokens,
        n_q_head,
        n_kv_head,
        head_dim,
        rope_dim,
        pad_n_q_head,
        pad_n_kv_head,
        pad_rope_dim,
        BLOCK_SIZE=BLOCK_SIZE,
        IS_NEOX_STYLE=is_neox_style,
    )
    return q, k