Sync from v0.13
This commit is contained in:
210
benchmarks/kernels/benchmark_reshape_and_cache_flash.py
Normal file
210
benchmarks/kernels/benchmark_reshape_and_cache_flash.py
Normal file
@@ -0,0 +1,210 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
import random
|
||||
import time
|
||||
|
||||
import torch
|
||||
from tabulate import tabulate
|
||||
|
||||
from vllm import _custom_ops as ops
|
||||
from vllm.attention.ops.triton_reshape_and_cache_flash import (
|
||||
triton_reshape_and_cache_flash,
|
||||
)
|
||||
from vllm.logger import init_logger
|
||||
from vllm.platforms import current_platform
|
||||
from vllm.utils.argparse_utils import FlexibleArgumentParser
|
||||
from vllm.utils.torch_utils import (
|
||||
STR_DTYPE_TO_TORCH_DTYPE,
|
||||
create_kv_caches_with_random_flash,
|
||||
)
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
@torch.inference_mode()
|
||||
def run_benchmark(
|
||||
num_tokens: int,
|
||||
num_heads: int,
|
||||
head_size: int,
|
||||
block_size: int,
|
||||
num_blocks: int,
|
||||
dtype: torch.dtype,
|
||||
kv_cache_dtype: str,
|
||||
kv_cache_layout: str,
|
||||
num_iters: int,
|
||||
implementation: str,
|
||||
benchmark_mode: str,
|
||||
device: str = "cuda",
|
||||
) -> float:
|
||||
"""Return latency (seconds) for given num_tokens."""
|
||||
|
||||
if kv_cache_dtype == "fp8" and head_size % 16:
|
||||
raise ValueError("fp8 kv-cache requires head_size to be a multiple of 16.")
|
||||
|
||||
if implementation not in ("cuda", "triton"):
|
||||
raise ValueError(
|
||||
f"Unsupported implementation: {implementation}. "
|
||||
"Only 'cuda' and 'triton' are supported."
|
||||
)
|
||||
if implementation == "triton" and kv_cache_layout == "HND":
|
||||
return float("nan") # Triton does not support HND layout yet.
|
||||
|
||||
current_platform.seed_everything(42)
|
||||
torch.set_default_device(device)
|
||||
|
||||
# create random key / value tensors [T, H, D].
|
||||
key = torch.randn(num_tokens, num_heads, head_size, dtype=dtype, device=device)
|
||||
value = torch.randn_like(key)
|
||||
|
||||
# prepare the slot mapping.
|
||||
# each token is assigned a unique slot in the KV-cache.
|
||||
num_slots = block_size * num_blocks
|
||||
if num_tokens > num_slots:
|
||||
raise ValueError("num_tokens cannot exceed the total number of cache slots")
|
||||
slot_mapping_lst = random.sample(range(num_slots), num_tokens)
|
||||
slot_mapping = torch.tensor(slot_mapping_lst, dtype=torch.long, device=device)
|
||||
|
||||
key_caches, value_caches = create_kv_caches_with_random_flash(
|
||||
num_blocks,
|
||||
block_size,
|
||||
1, # num_layers
|
||||
num_heads,
|
||||
head_size,
|
||||
kv_cache_dtype,
|
||||
dtype,
|
||||
device=device,
|
||||
cache_layout=kv_cache_layout,
|
||||
)
|
||||
key_cache, value_cache = key_caches[0], value_caches[0]
|
||||
# to free unused memory
|
||||
del key_caches, value_caches
|
||||
|
||||
# compute per-kernel scaling factors for fp8 conversion (if used).
|
||||
k_scale = (key.amax() / 64.0).to(torch.float32)
|
||||
v_scale = (value.amax() / 64.0).to(torch.float32)
|
||||
|
||||
if implementation == "cuda":
|
||||
function_under_test = lambda: ops.reshape_and_cache_flash(
|
||||
key, # noqa: F821
|
||||
value, # noqa: F821
|
||||
key_cache, # noqa: F821
|
||||
value_cache, # noqa: F821
|
||||
slot_mapping, # noqa: F821
|
||||
kv_cache_dtype,
|
||||
k_scale,
|
||||
v_scale,
|
||||
)
|
||||
else:
|
||||
function_under_test = lambda: triton_reshape_and_cache_flash(
|
||||
key, # noqa: F821
|
||||
value, # noqa: F821
|
||||
key_cache, # noqa: F821
|
||||
value_cache, # noqa: F821
|
||||
slot_mapping, # noqa: F821
|
||||
kv_cache_dtype,
|
||||
k_scale,
|
||||
v_scale,
|
||||
)
|
||||
if benchmark_mode == "cudagraph":
|
||||
g = torch.cuda.CUDAGraph()
|
||||
with torch.cuda.graph(g):
|
||||
function_under_test()
|
||||
torch.cuda.synchronize()
|
||||
function_under_test = lambda: g.replay()
|
||||
|
||||
def run_cuda_benchmark(n_iters: int) -> float:
|
||||
nonlocal key, value, key_cache, value_cache, slot_mapping
|
||||
torch.cuda.synchronize()
|
||||
start = time.perf_counter()
|
||||
for _ in range(n_iters):
|
||||
function_under_test()
|
||||
torch.cuda.synchronize()
|
||||
end = time.perf_counter()
|
||||
return (end - start) / n_iters
|
||||
|
||||
# warm-up
|
||||
run_cuda_benchmark(3)
|
||||
|
||||
lat = run_cuda_benchmark(num_iters)
|
||||
|
||||
# free tensors to mitigate OOM when sweeping
|
||||
del key, value, key_cache, value_cache, slot_mapping
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
return lat
|
||||
|
||||
|
||||
def main(args):
|
||||
rows = []
|
||||
for layout in ["NHD", "HND"]:
|
||||
for exp in range(1, 17):
|
||||
n_tok = 2**exp
|
||||
lat = run_benchmark(
|
||||
num_tokens=n_tok,
|
||||
num_heads=args.num_heads,
|
||||
head_size=args.head_size,
|
||||
block_size=args.block_size,
|
||||
num_blocks=args.num_blocks,
|
||||
dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
|
||||
kv_cache_dtype=args.kv_cache_dtype,
|
||||
kv_cache_layout=layout,
|
||||
num_iters=args.iters,
|
||||
implementation=args.implementation,
|
||||
benchmark_mode=args.mode,
|
||||
device="cuda",
|
||||
)
|
||||
rows.append([n_tok, layout, f"{lat * 1e6:.3f}"])
|
||||
|
||||
print(
|
||||
f"Benchmark results for implementation {args.implementation}"
|
||||
f" (measuring with {args.mode}):"
|
||||
)
|
||||
print(tabulate(rows, headers=["num_tokens", "layout", "latency (µs)"]))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = FlexibleArgumentParser()
|
||||
|
||||
parser.add_argument("--num-heads", type=int, default=128)
|
||||
parser.add_argument(
|
||||
"--head-size",
|
||||
type=int,
|
||||
choices=[64, 80, 96, 112, 120, 128, 192, 256],
|
||||
default=128,
|
||||
)
|
||||
parser.add_argument("--block-size", type=int, choices=[16, 32], default=16)
|
||||
parser.add_argument("--num-blocks", type=int, default=128 * 512)
|
||||
|
||||
parser.add_argument(
|
||||
"--dtype",
|
||||
type=str,
|
||||
choices=["half", "bfloat16", "float"],
|
||||
default="bfloat16",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--kv-cache-dtype",
|
||||
type=str,
|
||||
choices=["auto", "fp8"],
|
||||
default="auto",
|
||||
)
|
||||
|
||||
parser.add_argument("--iters", type=int, default=100)
|
||||
|
||||
parser.add_argument(
|
||||
"--implementation",
|
||||
type=str,
|
||||
choices=["cuda", "triton"],
|
||||
default="cuda",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--mode",
|
||||
type=str,
|
||||
choices=["cudagraph", "no_graph"],
|
||||
default="cudagraph",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
main(args)
|
||||
Reference in New Issue
Block a user