[core] Support custom ascendc kernels in vllm-ascend (#233)

This PR add custom ascendc kernel rotary_embedding support in
vllm-ascend, related CMakeLists and setuptools is also added in this PR.

Related: https://github.com/vllm-project/vllm-ascend/issues/156

---------

Signed-off-by: ganyi <pleaplusone.gy@gmail.com>

tests/ops/test_rotary_embedding.py

@@ -0,0 +1,204 @@
+# Adapted from
+# https://github.com/vllm-project/vllm/blob/main/vllm/tests/kernels/test_rotary_embedding.py
+# Copyright 2023 The vLLM team.
+
+# Copyright (c) Huawei Technologies Co., Ltd. 2024-2025. All rights reserved.
+
+from typing import Optional, Tuple, Union
+
+import pytest
+import torch
+import torch.nn as nn
+import torch_npu  # noqa: F401
+
+import vllm_ascend.platform  # noqa: F401
+
+# Only Neox style true scenario is supported for now
+IS_NEOX_STYLE = [True]
+DTYPES = [torch.half]
+HEAD_SIZES = [64, 96, 128, 256]
+ROTARY_DIMS = [None, 32]  # None means rotary dim == head size
+NUM_HEADS = [17]  # Arbitrary values for testing
+BATCH_SIZES = [5]  # Arbitrary values for testing
+SEQ_LENS = [11, 4096]  # Arbitrary values for testing
+SEEDS = [0]
+DEVICES = [f"npu:{0}"]
+# Set tolerance to 1 for quant ops
+DEFAULT_ATOL = 1e-3
+DEFAULT_RTOL = 1e-3
+
+
+def _apply_rotary_emb(
+    x: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    is_neox_style: bool,
+) -> torch.Tensor:
+    """
+    Args:
+        x: [num_tokens, num_heads, head_size]
+        cos: [num_tokens, head_size // 2]
+        sin: [num_tokens, head_size // 2]
+        is_neox_style: Whether to use the Neox-style or GPT-J-style rotary
+            positional embeddings.
+    """
+    cos = cos.unsqueeze(-2).to(x.dtype)
+    sin = sin.unsqueeze(-2).to(x.dtype)
+    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)
+
+
+# adapted from https://github.com/vllm-project/vllm/vllm/model_executor/layers/rotary_embedding.py
+class RotaryEmbedding(nn.Module):
+    """Original rotary positional embedding."""
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        dtype: torch.dtype,
+    ) -> None:
+        super().__init__()
+        self.head_size = head_size
+        self.rotary_dim = rotary_dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        self.is_neox_style = is_neox_style
+        self.dtype = dtype
+
+        cache = self._compute_cos_sin_cache()
+        cache = cache.to(dtype)
+        self.cos_sin_cache: torch.Tensor
+        self.register_buffer("cos_sin_cache", cache, persistent=False)
+
+    def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
+        """Compute the inverse frequency."""
+        # NOTE(woosuk): To exactly match the HF implementation, we need to
+        # use CPU to compute the cache and then move it to GPU. However, we
+        # create the cache on GPU for faster initialization. This may cause
+        # a slight numerical difference between the HF implementation and ours.
+        inv_freq = 1.0 / (base**(torch.arange(
+            0, self.rotary_dim, 2, dtype=torch.float) / self.rotary_dim))
+        return inv_freq
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        """Compute the cos and sin cache."""
+        inv_freq = self._compute_inv_freq(self.base)
+        t = torch.arange(self.max_position_embeddings, dtype=torch.float)
+
+        freqs = torch.einsum("i,j -> ij", t, inv_freq)
+        cos = freqs.cos()
+        sin = freqs.sin()
+        cache = torch.cat((cos, sin), dim=-1)
+        return cache
+
+    def forward_native(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        offsets: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """A PyTorch-native implementation of forward()."""
+        if offsets is not None:
+            positions = positions + offsets
+        positions = positions.flatten()
+        num_tokens = positions.shape[0]
+        cos_sin = self.cos_sin_cache.index_select(0, positions)
+        cos, sin = cos_sin.chunk(2, dim=-1)
+
+        query_shape = query.shape
+        query = query.view(num_tokens, -1, self.head_size)
+        query_rot = query[..., :self.rotary_dim]
+        query_pass = query[..., self.rotary_dim:]
+        query_rot = _apply_rotary_emb(query_rot, cos, sin, self.is_neox_style)
+        query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
+
+        key_shape = key.shape
+        key = key.view(num_tokens, -1, self.head_size)
+        key_rot = key[..., :self.rotary_dim]
+        key_pass = key[..., self.rotary_dim:]
+        key_rot = _apply_rotary_emb(key_rot, cos, sin, self.is_neox_style)
+        key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
+        return query, key
+
+
+# test with leading dimension and merge seqlen and batch_size as num_tokens
+# TODO(ganyi): open this test in the future
+@pytest.mark.skip(
+    reason=
+    "skip this test by default for now because of ci issue, will enable it in the future"
+)
+@pytest.mark.parametrize("is_neox_style", IS_NEOX_STYLE)
+@pytest.mark.parametrize("batch_size", BATCH_SIZES)
+@pytest.mark.parametrize("seq_len", SEQ_LENS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+@pytest.mark.parametrize("head_size", HEAD_SIZES)
+@pytest.mark.parametrize("rotary_dim", ROTARY_DIMS)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.parametrize("device", DEVICES)
+@torch.inference_mode()
+def test_rotary_embedding_quant_with_leading_dim(
+    is_neox_style: bool,
+    batch_size: int,
+    seq_len: int,
+    num_heads: int,
+    head_size: int,
+    rotary_dim: Optional[int],
+    dtype: torch.dtype,
+    seed: int,
+    device: str,
+    max_position: int = 8192,
+    base: int = 10000,
+) -> None:
+    if rotary_dim is None:
+        rotary_dim = head_size
+
+    torch.set_default_device(device)
+    if rotary_dim is None:
+        rotary_dim = head_size
+    rope = RotaryEmbedding(head_size, rotary_dim, max_position, base,
+                           is_neox_style, dtype)
+    rope = rope.to(dtype=dtype)
+    num_tokens = batch_size * seq_len
+    positions = torch.randint(0, max_position, (batch_size * seq_len, ))
+    qkv_tensor = torch.randn(num_tokens,
+                             num_heads * head_size * 3,
+                             dtype=dtype)
+    query, key, _ = qkv_tensor.split(
+        [num_heads * head_size, num_heads * head_size, num_heads * head_size],
+        dim=-1,
+    )
+
+    ref_query, ref_key = rope.forward_native(positions, query, key)
+    torch.ops._C.rotary_embedding(
+        positions,
+        query,
+        key,
+        rope.head_size,
+        rope.cos_sin_cache,
+        rope.is_neox_style,
+    )
+
+    # Compare the results.
+    torch.testing.assert_close(query,
+                               ref_query,
+                               atol=DEFAULT_ATOL,
+                               rtol=DEFAULT_RTOL)
+    torch.testing.assert_close(key,
+                               ref_key,
+                               atol=DEFAULT_ATOL,
+                               rtol=DEFAULT_RTOL)