Support mrope triton kernel and add unit test (#11722)

Co-authored-by: luoyuan.luo <luoyuan.luo@antgroup.com>
Co-authored-by: b8zhong <b8zhong@uwaterloo.ca>

python/sglang/srt/layers/rotary_embedding.py

@@ -7,6 +7,8 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
+import triton
+import triton.language as tl
 
 from sglang.srt.custom_op import CustomOp
 from sglang.srt.utils import (
@@ -1033,6 +1035,188 @@ def apply_interleaved_rope(x: torch.Tensor, mrope_section: list[int]) -> torch.T
     return x_t
 
 
+@triton.jit
+def _triton_mrope_forward(
+    q_ptr,
+    k_ptr,
+    cos,
+    sin,
+    num_tokens,
+    n_qh: tl.constexpr,
+    n_kh: tl.constexpr,
+    hd: tl.constexpr,
+    rd: tl.constexpr,
+    pad_n_qh: tl.constexpr,
+    pad_n_kh: tl.constexpr,
+    pad_hd: tl.constexpr,
+    mrope_section_t: tl.constexpr,
+    mrope_section_h: tl.constexpr,
+    mrope_section_w: tl.constexpr,
+    is_interleaved: tl.constexpr,
+):
+    # Adapted from
+    # https://github.com/linkedin/Liger-Kernel/blob/main/src/liger_kernel/ops/qwen2vl_mrope.py
+    # This version supports flatten input tensors from vllm
+    # and supports cos and sin cache with shape (3, num_tokens, head_dim // 2)
+    # instead of (3, bsz, seq_len, head_dim), also supports interleaved rotary
+    pid = tl.program_id(0)
+    # locate start address
+    q_ptr = q_ptr + pid * (n_qh * hd)
+    k_ptr = k_ptr + pid * (n_kh * hd)
+
+    # ####################################################################
+    # get the cos(mθ_{i...d/2}) and sin(mθ_{i...d/2}) for token position
+    # m of this program instance
+    # ####################################################################
+    # Note: cos and sin now have shape (3, num_tokens, head_dim // 2)
+
+    # Updated stride calculation for half head_dim
+    half_rd = rd // 2
+    t_cos = cos + pid * half_rd
+    h_cos = t_cos + num_tokens * half_rd
+    w_cos = h_cos + num_tokens * half_rd
+    t_sin = sin + pid * half_rd
+    h_sin = t_sin + num_tokens * half_rd
+    w_sin = h_sin + num_tokens * half_rd
+
+    # Updated offsets for half head_dim
+    cos_offsets = tl.arange(0, pad_hd // 2)
+    if is_interleaved:
+        h_mask = ((cos_offsets % 3) == 1) & (cos_offsets <= 3 * mrope_section_h)
+        w_mask = ((cos_offsets % 3) == 2) & (cos_offsets <= 3 * mrope_section_w)
+        t_mask = ~(h_mask | w_mask)
+    else:
+        t_end = mrope_section_t
+        h_end = t_end + mrope_section_h
+        t_mask = cos_offsets < mrope_section_t
+        h_mask = (t_end <= cos_offsets) & (cos_offsets < h_end)
+        w_mask = (h_end <= cos_offsets) & (cos_offsets < half_rd)
+
+    t_cos_row = tl.load(t_cos + cos_offsets, mask=t_mask, other=0)
+    h_cos_row = tl.load(h_cos + cos_offsets, mask=h_mask, other=0)
+    w_cos_row = tl.load(w_cos + cos_offsets, mask=w_mask, other=0)
+    t_sin_row = tl.load(t_sin + cos_offsets, mask=t_mask, other=0)
+    h_sin_row = tl.load(h_sin + cos_offsets, mask=h_mask, other=0)
+    w_sin_row = tl.load(w_sin + cos_offsets, mask=w_mask, other=0)
+
+    cos_row = t_cos_row + h_cos_row + w_cos_row
+    sin_row = t_sin_row + h_sin_row + w_sin_row
+
+    # ####################################################################
+    # 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
+    first_half_q_offsets = (
+        tl.arange(0, pad_n_qh)[:, None] * hd + tl.arange(0, pad_hd // 2)[None, :]
+    )
+    first_half_k_offsets = (
+        tl.arange(0, pad_n_kh)[:, None] * hd + tl.arange(0, pad_hd // 2)[None, :]
+    )
+    first_q_mask = (tl.arange(0, pad_n_qh)[:, None] < n_qh) & (
+        tl.arange(0, pad_hd // 2)[None, :] < rd // 2
+    )
+    first_k_mask = (tl.arange(0, pad_n_kh)[:, None] < n_kh) & (
+        tl.arange(0, pad_hd // 2)[None, :] < rd // 2
+    )
+
+    q_tile_1 = tl.load(q_ptr + first_half_q_offsets, mask=first_q_mask, other=0).to(
+        sin_row.dtype
+    )
+    k_tile_1 = tl.load(k_ptr + first_half_k_offsets, mask=first_k_mask, other=0).to(
+        sin_row.dtype
+    )
+
+    # right half of the head
+    second_half_q_offsets = first_half_q_offsets + (rd // 2)
+    second_half_k_offsets = first_half_k_offsets + (rd // 2)
+    second_q_mask = first_q_mask
+    second_k_mask = first_k_mask
+
+    q_tile_2 = tl.load(q_ptr + second_half_q_offsets, mask=second_q_mask, other=0).to(
+        sin_row.dtype
+    )
+    k_tile_2 = tl.load(k_ptr + second_half_k_offsets, mask=second_k_mask, other=0).to(
+        sin_row.dtype
+    )
+
+    # y = [x1, x2] * [cos, cos] + [-x2, x1] * [sin, sin]
+    # Since cos and sin are now half-size,
+    # we use the same cos_row and sin_row for both halves
+    new_q_tile_1 = q_tile_1 * cos_row - q_tile_2 * sin_row
+    tl.store(q_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_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_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_ptr + second_half_k_offsets, new_k_tile_2, mask=second_k_mask)
+
+
+def triton_mrope(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    mrope_section: list[int],
+    head_size: int,
+    rotary_dim: int,
+    mrope_interleaved: bool,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """The mrope triton kernel.
+
+    Args:
+        q: [num_tokens, num_heads * head_size]
+        k: [num_tokens, num_kv_heads * head_size]
+        cos: [3, num_tokens, head_size //2 ]
+            (T/H/W positions with multimodal inputs)
+        sin: [3, num_tokens, head_size //2 ]
+            (T/H/W positions with multimodal inputs)
+        mrope_section: [t, h, w]
+        head_size: int
+    """
+    n_row, n_q_head_head_dim = q.shape
+    assert (
+        n_q_head_head_dim % head_size == 0
+    ), f"q shape {n_q_head_head_dim} must be divisible by head_size {head_size}"
+    n_q_head = n_q_head_head_dim // head_size
+    assert (
+        k.shape[1] % head_size == 0
+    ), f"k shape {k.shape[1]} must be divisible by head_size {head_size}"
+    n_kv_head = k.shape[1] // head_size
+    pad_hd = triton.next_power_of_2(head_size)
+    pad_n_q_head = triton.next_power_of_2(n_q_head)
+    pad_n_kv_head = triton.next_power_of_2(n_kv_head)
+
+    # ensure tensors passed into the kernel are contiguous.
+    # It will be no-op if they are already contiguous
+    q = q.contiguous()
+    k = k.contiguous()
+    cos = cos.contiguous()
+    sin = sin.contiguous()
+
+    _triton_mrope_forward[(n_row,)](
+        q,
+        k,
+        cos,
+        sin,
+        n_row,
+        n_q_head,
+        n_kv_head,
+        head_size,
+        rotary_dim,
+        pad_n_q_head,
+        pad_n_kv_head,
+        pad_hd,
+        mrope_section[0],
+        mrope_section[1],
+        mrope_section[2],
+        mrope_interleaved,
+    )
+    return q, k
+
+
 class MRotaryEmbedding(RotaryEmbedding):
     """Rotary Embedding with Multimodal Sections."""
 
@@ -1086,8 +1270,17 @@ class MRotaryEmbedding(RotaryEmbedding):
                     f"Corrected mrope_section: {self.mrope_section} (sum={sum(self.mrope_section)})"
                 )
 
+    def _match_cos_sin_cache_dtype(self, query: torch.Tensor) -> None:
+        # __setattr__ in nn.Module (called by `self.cos_sin_cache = ...`)
+        # is expensive, so avoid calling it if possible
+        if (
+            self.cos_sin_cache.device != query.device
+            or self.cos_sin_cache.dtype != query.dtype
+        ):
+            self.cos_sin_cache = self.cos_sin_cache.to(query.device, dtype=query.dtype)
+
     @torch.compile(dynamic=True, backend=get_compiler_backend())
-    def forward(
+    def forward_native(
         self,
         positions: torch.Tensor,
         query: torch.Tensor,
@@ -1141,6 +1334,51 @@ class MRotaryEmbedding(RotaryEmbedding):
         key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
         return query, key
 
+    def forward(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        fused_set_kv_buffer_arg: Optional[FusedSetKVBufferArg] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        assert positions.ndim == 1 or positions.ndim == 2
+        assert key is not None
+
+        self._match_cos_sin_cache_dtype(query)
+        num_tokens = positions.shape[-1]
+        cos_sin = self.cos_sin_cache[positions]
+        cos, sin = cos_sin.chunk(2, dim=-1)
+        query_shape = query.shape
+        key_shape = key.shape
+        if positions.ndim == 2:
+            assert self.mrope_section
+
+            q, k = triton_mrope(
+                query,
+                key,
+                cos,
+                sin,
+                self.mrope_section,
+                self.head_size,
+                self.rotary_dim,
+                self.mrope_interleaved,
+            )
+
+            return q.reshape(query_shape), k.reshape(key_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 = 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
+
     # Copied from https://github.com/huggingface/transformers/blob/c8e0e603de9b3d49161a15fe6e8ea84badfb5d02/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L1439
     @staticmethod
     def get_rope_index(

sgl-kernel/benchmark/bench_mrope.py

+# Adapted from vLLM benchmark_mrope.py
+
+# This script benchmarks the mrope kernel (mainly for Qwen2VL and Qwen2.5VL models).
+# It generates test data, runs benchmarks, and saves results to a CSV file.
+#
+# The CSV file (named with current date/time) contains these columns:
+# model_name, tp_size, num_tokens, num_heads, num_kv_heads, head_dim, max_position,
+# rope_theta, is_neox_style, rope_scaling, dtype, torch_mean, torch_median, torch_p99,
+# torch_min, torch_max, triton_mean, triton_median, triton_p99, triton_min, triton_max,
+# speedup
+#
+# == Usage Examples ==
+#
+# Single model benchmark:
+# python3 benchmark_mrope.py --model-name Qwen/Qwen2.5-VL-7B-Instruct --tp-size 8 \
+#   --warmup-iter 10 --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024
+
+import argparse
+import time
+from typing import Any
+
+import numpy as np
+import torch
+from transformers import AutoConfig
+
+from sglang.srt.layers.rotary_embedding import get_rope
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def get_model_config(model_name: str):
+    """Get model configuration parameters"""
+    config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+    return config
+
+
+def generate_test_data(
+    num_tokens: int,
+    num_q_heads: int,
+    num_kv_heads: int,
+    head_size: int,
+    max_position_embeddings: int,
+    dtype: torch.dtype,
+    device: torch.device,
+):
+    """Generate test data for given configuration."""
+    # Create 2D positions (3, num_tokens) for multimodal case
+    positions = torch.randint(
+        0, max_position_embeddings // 4, (3, num_tokens), device=device
+    )
+
+    # Create query and key tensors
+    query = torch.randn(num_tokens, num_q_heads * head_size, dtype=dtype, device=device)
+    key = torch.randn(num_tokens, num_kv_heads * head_size, dtype=dtype, device=device)
+
+    return positions, query, key
+
+
+def calculate_stats(times: list[float]) -> dict[str, float]:
+    """Calculate statistics from a list of times."""
+    times_array = np.array(times)
+    return {
+        "mean": np.mean(times_array),
+        "median": np.median(times_array),
+        "p99": np.percentile(times_array, 99),
+        "min": np.min(times_array),
+        "max": np.max(times_array),
+    }
+
+
+def benchmark_mrope(
+    model_name: str,
+    num_tokens: int,
+    head_dim: int,
+    tp_size: int,
+    num_heads: int,
+    num_kv_heads: int,
+    max_position: int = 8192,
+    rope_theta: float = 10000,
+    is_neox_style: bool = True,
+    rope_scaling: dict[str, Any] = None,
+    dtype: torch.dtype = torch.bfloat16,
+    seed: int = 0,
+    warmup_iter: int = 10,
+    benchmark_iter: int = 100,
+):
+    torch.manual_seed(seed)
+    torch.set_default_device(device)
+    # the parameters to compute the q k v size based on tp_size
+    mrope_helper_class = get_rope(
+        head_size=head_dim,
+        rotary_dim=head_dim,
+        max_position=max_position,
+        base=rope_theta,
+        is_neox_style=is_neox_style,
+        rope_scaling=rope_scaling,
+        dtype=dtype,
+    ).to(device=device)
+
+    print(80 * "=")
+    print(
+        f"Evaluating model: {model_name} "
+        f"with tp_size: {tp_size} "
+        f"and num_tokens: {num_tokens}, "
+        f"dtype: {dtype}"
+    )
+
+    # create q k v input tensors
+    # create rotary pos emb input tensors
+    positions, query, key = generate_test_data(
+        num_tokens, num_heads, num_kv_heads, head_dim, max_position, dtype, device
+    )
+
+    # Warm up
+    for _ in range(warmup_iter):
+        mrope_helper_class.forward_native(
+            positions,
+            query.clone(),
+            key.clone(),
+        )
+
+        mrope_helper_class.forward(
+            positions,
+            query.clone(),
+            key.clone(),
+        )
+
+    torch.cuda.synchronize()
+
+    # Time reference implementation
+    torch_times = []
+    for _ in range(benchmark_iter):
+        query_clone = query.clone()
+        key_clone = key.clone()
+        torch.cuda.synchronize()
+        start_time = time.time()
+
+        mrope_helper_class.forward_native(
+            positions,
+            query_clone,
+            key_clone,
+        )
+
+        torch.cuda.synchronize()
+        torch_times.append(time.time() - start_time)
+
+    # Time triton kernel implementation
+    triton_times = []
+    for _ in range(benchmark_iter):
+        query_clone = query.clone()
+        key_clone = key.clone()
+        torch.cuda.synchronize()
+        start_time = time.time()
+        mrope_helper_class.forward(
+            positions,
+            query_clone,
+            key_clone,
+        )
+        torch.cuda.synchronize()
+        triton_times.append(time.time() - start_time)
+
+    # Calculate statistics
+    torch_stats = calculate_stats(torch_times)
+    triton_stats = calculate_stats(triton_times)
+    print(f"\nPerformance for config ({num_tokens}, {num_heads}, {num_kv_heads}):")
+
+    print(
+        f"Torch implementation: "
+        f"mean={torch_stats['mean']:.8f}s, "
+        f"median={torch_stats['median']:.8f}s, "
+        f"p99={torch_stats['p99']:.8f}s"
+    )
+
+    print(
+        f"Triton implementation: "
+        f"mean={triton_stats['mean']:.8f}s, "
+        f"median={triton_stats['median']:.8f}s, "
+        f"p99={triton_stats['p99']:.8f}s"
+    )
+
+    print(
+        f"Triton Speedup over Torch: {torch_stats['mean'] / triton_stats['mean']:.8f}x"
+    )
+
+    return torch_stats, triton_stats
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Benchmark the rotary embedding kernels."
+    )
+    parser.add_argument("--model-name", type=str, default="")
+    parser.add_argument("--tp-size", type=int, default=1)
+    parser.add_argument("--warmup-iter", type=int, default=10)
+    parser.add_argument("--benchmark-iter", type=int, default=100)
+    parser.add_argument("--dtype", type=str, choices=["bfloat16"], default="bfloat16")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--num-tokens", type=int, nargs="+", required=False)
+    parser.add_argument("--trust-remote-code", action="store_true")
+    args = parser.parse_args()
+    print(args)
+
+    model_tp_dict = {}
+    if args.model_name == "":
+        model_tp_dict = {
+            "Qwen/Qwen2-VL-2B-Instruct": [1],
+            "Qwen/Qwen2-VL-7B-Instruct": [1],
+            "Qwen/Qwen2-VL-72B-Instruct": [2, 4, 8],
+            "Qwen/Qwen2.5-VL-3B-Instruct": [1, 2, 4, 8],
+            "Qwen/Qwen2.5-VL-7B-Instruct": [1, 2, 4, 8],
+            "Qwen/Qwen2.5-VL-72B-Instruct": [2, 4, 8],
+        }
+    else:
+        model_tp_dict[args.model_name] = [args.tp_size]
+
+    if args.num_tokens is None:
+        num_tokens_list = [2**i for i in range(0, 18)]
+    else:
+        num_tokens_list = args.num_tokens
+
+    for model_name, tp_list in model_tp_dict.items():
+        for tp_size in tp_list:
+            config = get_model_config(model_name)
+            # get the model config
+            total_num_kv_heads = config.num_key_value_heads
+            total_num_heads = config.num_attention_heads
+            num_heads = total_num_heads // tp_size
+            num_kv_heads = max(1, total_num_kv_heads // tp_size)
+            head_dim = config.hidden_size // total_num_heads
+            is_neox_style = True
+            rope_theta = config.rope_theta
+            max_position = config.max_position_embeddings
+
+            for num_tokens in num_tokens_list:
+                benchmark_mrope(
+                    model_name=model_name,
+                    num_tokens=num_tokens,
+                    head_dim=head_dim,
+                    tp_size=tp_size,
+                    num_heads=num_heads,
+                    num_kv_heads=num_kv_heads,
+                    max_position=max_position,
+                    rope_theta=rope_theta,
+                    is_neox_style=is_neox_style,
+                    rope_scaling=config.rope_scaling,
+                    dtype=getattr(torch, args.dtype),
+                    seed=args.seed,
+                    warmup_iter=args.warmup_iter,
+                    benchmark_iter=args.benchmark_iter,
+                )

test/srt/rotary_embedding/test_mrope.py

+from typing import NamedTuple
+
+import pytest
+import torch
+from packaging.version import Version
+from transformers import AutoConfig
+from transformers import __version__ as TRANSFORMERS_VERSION
+
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.utils import (
+    cpu_has_amx_support,
+    is_cpu,
+    is_cuda,
+    is_hip,
+    is_npu,
+    is_xpu,
+)
+
+_is_cuda = is_cuda()
+_is_hip = is_hip()
+_is_cpu = is_cpu()
+_is_cpu_amx_available = cpu_has_amx_support()
+_is_npu = is_npu()
+_is_xpu = is_xpu()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def generate_test_data(
+    num_tokens: int,
+    num_q_heads: int,
+    num_kv_heads: int,
+    head_size: int,
+    max_position_embeddings: int,
+    dtype: torch.dtype,
+    device: torch.device,
+):
+    """Generate test data for given configuration."""
+    torch.manual_seed(42)
+    # Create 2D positions (3, num_tokens) for multimodal case
+    positions = torch.randint(
+        0, max_position_embeddings // 4, (3, num_tokens), device=device
+    )
+
+    # Create query and key tensors
+    query = torch.randn(num_tokens, num_q_heads * head_size, dtype=dtype, device=device)
+    key = torch.randn(num_tokens, num_kv_heads * head_size, dtype=dtype, device=device)
+
+    return positions, query, key
+
+
+class MRoPETestInfo(NamedTuple):
+    model_name: str
+    atol: float = 1e-2
+    rtol: float = 1.6e-2
+    marks: list[pytest.MarkDecorator] = []
+
+
+TRANSFORMERS_BASE_VERSION = Version(TRANSFORMERS_VERSION).base_version
+
+MODELS_TO_TEST = [
+    MRoPETestInfo(model_name="Qwen/Qwen2-VL-7B-Instruct"),
+    MRoPETestInfo(model_name="Qwen/Qwen2-VL-72B-Instruct"),
+    MRoPETestInfo(model_name="Qwen/Qwen2.5-VL-72B-Instruct"),
+]
+
+num_tokens_list = [11, 8192]
+
+
+@pytest.mark.skipif(not _is_cuda, reason="Skipping CUDA/ROCm only tests.")
+@pytest.mark.parametrize(
+    "model_info, model_name",
+    [
+        pytest.param(test_config, test_config.model_name, marks=test_config.marks)
+        for test_config in MODELS_TO_TEST
+    ],
+)
+@pytest.mark.parametrize("tp_size", [1, 2])
+@pytest.mark.parametrize("dtype", [torch.bfloat16])
+@pytest.mark.parametrize("num_tokens", num_tokens_list)
+def test_mrope(
+    model_name: str,
+    model_info: MRoPETestInfo,
+    tp_size: int,
+    dtype: torch.dtype,
+    num_tokens: int,
+):
+    atol = model_info.atol
+    rtol = model_info.rtol
+
+    config = AutoConfig.from_pretrained(model_name)
+    config = config.get_text_config()
+
+    # get the model config
+    total_num_kv_heads = config.num_key_value_heads
+    total_num_heads = config.num_attention_heads
+    num_heads = total_num_heads // tp_size
+    num_kv_heads = max(1, total_num_kv_heads // tp_size)
+    head_dim = (
+        config.head_dim
+        if hasattr(config, "head_dim")
+        else config.hidden_size // total_num_heads
+    )
+    is_neox_style = True
+
+    rope_theta = config.rope_theta
+    max_position = config.max_position_embeddings
+    partial_rotary_factor = getattr(config, "partial_rotary_factor", 1.0)
+    rotary_dim = int(head_dim * partial_rotary_factor)
+
+    mrope_helper_class = get_rope(
+        head_size=head_dim,
+        rotary_dim=rotary_dim,
+        max_position=max_position,
+        base=rope_theta,
+        is_neox_style=is_neox_style,
+        rope_scaling=config.rope_scaling,
+        dtype=dtype,
+    ).to(device=device)
+
+    # create q k v input tensors
+    # create rotary pos emb input tensors
+    positions, query, key = generate_test_data(
+        num_tokens, num_heads, num_kv_heads, head_dim, max_position, dtype, device
+    )
+
+    query_native, key_native = mrope_helper_class.forward_native(
+        positions,
+        query.clone(),
+        key.clone(),
+    )
+
+    query_cuda, key_cuda = mrope_helper_class.forward(
+        positions,
+        query.clone(),
+        key.clone(),
+    )
+
+    torch.testing.assert_close(query_native, query_cuda, atol=atol, rtol=rtol)
+    torch.testing.assert_close(key_native, key_cuda, atol=atol, rtol=rtol)

test/srt/run_suite.py

@@ -77,6 +77,7 @@ suites = {
         TestFile("test_eval_fp8_accuracy.py", 303),
         TestFile("test_fa3.py", 376),
         # TestFile("test_flashmla.py", 352),
+        TestFile("rotary_embedding/test_mrope.py", 300),
         TestFile("test_function_call_parser.py", 10),
         TestFile("test_fused_moe.py", 30),
         TestFile("test_gpt_oss_1gpu.py", 600),