[cpu][bench] Add CPU paged attention benchmarks

benchmarks/kernels/cpu/benchmark_cpu_attn.py

@@ -0,0 +1,272 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import functools
+import time
+
+import numpy as np
+import torch
+
+from vllm._custom_ops import (
+    cpu_attention_with_kv_cache,
+    cpu_attn_get_scheduler_metadata,
+    cpu_attn_reshape_and_cache,
+)
+from vllm.platforms import CpuArchEnum, current_platform
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.v1.attention.backends.cpu_attn import CPUAttentionBackend, _get_attn_isa
+
+
+def get_attn_isa(
+    block_size: int | None = None,
+    dtype: torch.dtype | None = None,
+):
+    if block_size and dtype:
+        return _get_attn_isa(dtype, block_size)
+    else:
+        if current_platform.get_cpu_architecture() == CpuArchEnum.ARM:
+            return "neon"
+        elif torch._C._cpu._is_amx_tile_supported():
+            return "amx"
+        else:
+            return "vec"
+
+
+# rand number generation takes too much time, cache rand tensors
+@functools.lru_cache(maxsize=128, typed=False)
+def tensor_cache(
+    elem_num: int,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    tensor = torch.randn(elem_num, dtype=dtype)
+    return tensor
+
+
+@torch.inference_mode()
+def main(
+    seq_lens: list[tuple[int, int]],
+    num_heads: tuple[int, int],
+    head_size: int,
+    sliding_window: int = None,
+    dtype: torch.dtype = torch.bfloat16,
+    block_size: int = 128,
+    num_blocks: int = 4096,
+    use_sink: bool = False,
+    enable_kv_split: bool = False,
+    isa: str | None = None,
+    seed: int = 0,
+    iters: int = 20,
+) -> None:
+    current_platform.seed_everything(seed)
+    num_seqs = len(seq_lens)
+    query_lens = [x[0] for x in seq_lens]
+    kv_lens = [x[1] for x in seq_lens]
+    num_query_heads = num_heads[0]
+    num_kv_heads = num_heads[1]
+    assert num_query_heads % num_kv_heads == 0
+    max_kv_len = max(kv_lens)
+    window_size = (sliding_window - 1, 0) if sliding_window is not None else (-1, -1)
+    scale = head_size**-0.5
+    token_num = sum(query_lens)
+
+    if isa is None:
+        isa = get_attn_isa(block_size, dtype)
+
+    s_aux = (
+        15 * torch.rand((num_query_heads,), dtype=torch.bfloat16) if use_sink else None
+    )
+
+    query = tensor_cache(
+        elem_num=token_num * num_query_heads * head_size,
+        dtype=dtype,
+    )
+    query = query.view(
+        token_num,
+        num_query_heads,
+        head_size,
+    )
+
+    key_value = tensor_cache(
+        elem_num=2 * num_blocks * num_kv_heads * block_size * head_size,
+        dtype=dtype,
+    )
+    key_value = key_value.view(
+        2,
+        num_blocks,
+        block_size,
+        num_kv_heads,
+        head_size,
+    )
+    key_cache, value_cache = key_value.unbind(0)
+
+    # KV cache for CPU attention
+    packed_key_cache = torch.empty(
+        num_blocks, num_kv_heads, block_size, head_size, dtype=dtype
+    )
+    packed_value_cache = torch.empty_like(packed_key_cache)
+
+    cu_query_lens = torch.tensor([0] + query_lens, dtype=torch.int32).cumsum(
+        dim=0, dtype=torch.int32
+    )
+    kv_lens_tensor = torch.tensor(kv_lens, dtype=torch.int32)
+    max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
+    block_tables = torch.randint(
+        0, num_blocks, (num_seqs, max_num_blocks_per_seq), dtype=torch.int32
+    )
+
+    # use reshape_and_cache to pack key_cache and value_cache
+    slot_mapping = torch.arange(0, num_blocks * block_size, dtype=torch.int64)
+    cpu_attn_reshape_and_cache(
+        key=key_cache.view(-1, num_kv_heads, head_size),
+        value=value_cache.view(-1, num_kv_heads, head_size),
+        key_cache=packed_key_cache,
+        value_cache=packed_value_cache,
+        slot_mapping=slot_mapping,
+        isa=isa,
+    )
+
+    metadata = cpu_attn_get_scheduler_metadata(
+        num_reqs=num_seqs,
+        num_heads=num_query_heads,
+        num_kv_heads=num_kv_heads,
+        head_dim=head_size,
+        seq_lens=kv_lens_tensor,
+        dtype=dtype,
+        query_start_loc=cu_query_lens,
+        causal=True,
+        sliding_window_size=sliding_window if sliding_window is not None else -1,
+        isa=isa,
+        enable_kv_split=enable_kv_split,
+    )
+
+    out_with_split = torch.empty_like(query)
+
+    def run_benchmark(iters: int) -> list[float]:
+        times = []
+        for _ in range(iters):
+            start_time = time.perf_counter_ns()
+            cpu_attention_with_kv_cache(
+                query=query,
+                key_cache=packed_key_cache,
+                value_cache=packed_value_cache,
+                output=out_with_split,
+                query_start_loc=cu_query_lens,
+                seq_lens=kv_lens_tensor,
+                scale=scale,
+                causal=True,
+                alibi_slopes=None,
+                sliding_window=window_size,
+                block_table=block_tables,
+                softcap=0,
+                scheduler_metadata=metadata,
+                s_aux=s_aux,
+            )
+            end_time = time.perf_counter_ns()
+            times.append((end_time - start_time) / 1e6)
+        return times
+
+    # warmup
+    run_benchmark(5)
+    # benchmark
+    times = run_benchmark(iters)
+
+    time_min = min(times)
+    time_max = max(times)
+    time_mean = np.mean(times)
+    time_std = np.std(times)
+
+    print("\tmin (ms) = ", time_min)
+    print("\tmax (ms) = ", time_max)
+    print("\tmean (ms) = ", time_mean)
+    print("\tstd = ", time_std)
+    print("\tmedian (ms) = ", np.median(times))
+
+
+def generate_seq_lens(
+    batch_size: int,
+    q_len_min: int,
+    q_len_max: int,
+    kv_len_min: int,
+    kv_len_max: int,
+    seed: int = 0,
+) -> list[tuple[int, int]]:
+    assert 1 <= q_len_min <= q_len_max
+    assert 1 <= kv_len_min <= kv_len_max
+    assert kv_len_max >= q_len_min
+
+    g = torch.Generator(device="cpu").manual_seed(seed)
+
+    def rint(lo: int, hi: int) -> int:
+        return torch.randint(lo, hi + 1, (1,), generator=g).item()
+
+    seq_lens: list[tuple[int, int]] = []
+    for _ in range(batch_size):
+        # ensure q <= kv
+        kv = rint(max(kv_len_min, q_len_min), kv_len_max)
+        q = rint(q_len_min, min(q_len_max, kv))
+        seq_lens.append((q, kv))
+
+    return seq_lens
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(description="Benchmark the paged attention kernel.")
+    parser.add_argument("--batch-size", type=int, default=64)
+    parser.add_argument("--q-len-min", type=int, default=512)
+    parser.add_argument("--q-len-max", type=int, default=512)
+    parser.add_argument("--kv-len-min", type=int, default=512)
+    parser.add_argument("--kv-len-max", type=int, default=512)
+    parser.add_argument("--num-blocks", type=int, default=4096)
+
+    parser.add_argument("--sliding-window", type=int, default=None)
+    parser.add_argument("--num-query-heads", type=int, default=32)
+    parser.add_argument("--num-kv-heads", type=int, default=8)
+    parser.add_argument(
+        "--head-size",
+        type=int,
+        choices=CPUAttentionBackend.get_supported_head_sizes(),
+        default=128,
+    )
+    parser.add_argument("--enable-kv-split", action="store_true")
+    parser.add_argument("--block-size", type=int, choices=[32, 64, 128], default=128)
+    parser.add_argument(
+        "--dtype", type=str, choices=["half", "bfloat16", "float"], default="bfloat16"
+    )
+    parser.add_argument("--use-sink", action="store_true")
+    parser.add_argument(
+        "--isa", type=str, choices=["vec", "neon", "amx", "vec16"], default=None
+    )
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--iters", type=int, default=20)
+
+    args = parser.parse_args()
+    print(args)
+
+    seq_lens = generate_seq_lens(
+        args.batch_size,
+        args.q_len_min,
+        args.q_len_max,
+        args.kv_len_min,
+        args.kv_len_max,
+        args.seed,
+    )
+
+    print("batch (query len, kv len) = ", seq_lens)
+
+    main(
+        seq_lens=seq_lens,
+        num_heads=(args.num_query_heads, args.num_kv_heads),
+        head_size=args.head_size,
+        sliding_window=args.sliding_window,
+        dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
+        block_size=args.block_size,
+        num_blocks=args.num_blocks,
+        use_sink=args.use_sink,
+        enable_kv_split=args.enable_kv_split,
+        isa=args.isa
+        if args.isa is not None
+        else get_attn_isa(args.block_size, STR_DTYPE_TO_TORCH_DTYPE[args.dtype]),
+        seed=args.seed,
+        iters=args.iters,
+    )