[Feature] rewrite rope kernel; remove flashinfer dependencies (#18844)
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
This commit is contained in:
DarkSharpness
@@ -1,47 +1,20 @@
|
||||
import time
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
from sgl_kernel import FusedSetKVBufferArg as FusedSetKVBufferArgKernel
|
||||
from sgl_kernel import (
|
||||
apply_rope_with_cos_sin_cache_inplace as apply_rope_with_cos_sin_cache_inplace_kernel,
|
||||
)
|
||||
|
||||
from sglang.jit_kernel.rope import FusedSetKVBufferArg as FusedSetKVBufferArgJit
|
||||
from sglang.jit_kernel.rope import (
|
||||
apply_rope_with_cos_sin_cache_inplace as apply_rope_with_cos_sin_cache_inplace_jit,
|
||||
)
|
||||
|
||||
DEVICE = "cuda"
|
||||
DTYPE = torch.bfloat16
|
||||
MAX_SEQ_LEN = 131072 # common seq length
|
||||
ROPE_BASE = 10000.0
|
||||
CACHE_SIZE = 1024 * 128
|
||||
|
||||
|
||||
@triton.jit
|
||||
def burn_kernel(out_ptr, iters: tl.constexpr):
|
||||
pid = tl.program_id(0)
|
||||
x = tl.full((), pid + 1, dtype=tl.uint32)
|
||||
|
||||
a = tl.full((), 1664525, dtype=tl.uint32)
|
||||
c = tl.full((), 1013904223, dtype=tl.uint32)
|
||||
sh = tl.full((), 13, dtype=tl.uint32)
|
||||
|
||||
for _ in range(iters):
|
||||
x = x * a + c
|
||||
x = x ^ (x >> sh)
|
||||
|
||||
if pid == 0:
|
||||
tl.store(out_ptr, x)
|
||||
|
||||
|
||||
def triton_burn(ms: float, grid=(256,)):
|
||||
iters = int(ms * 20000)
|
||||
out = torch.empty((), device="cuda", dtype=torch.uint32)
|
||||
burn_kernel[grid](out, iters=iters)
|
||||
return out
|
||||
|
||||
|
||||
def create_cos_sin_cache(rotary_dim, max_position_embeddings, base, dtype):
|
||||
def create_cos_sin_cache(
|
||||
rotary_dim: int,
|
||||
max_position: int = MAX_SEQ_LEN,
|
||||
base: float = ROPE_BASE,
|
||||
) -> torch.Tensor:
|
||||
"""Create cos/sin cache compatible with SGLang layout: [max_pos, rotary_dim]."""
|
||||
inv_freq = 1.0 / (
|
||||
base
|
||||
** (
|
||||
@@ -49,253 +22,223 @@ def create_cos_sin_cache(rotary_dim, max_position_embeddings, base, dtype):
|
||||
/ rotary_dim
|
||||
)
|
||||
)
|
||||
|
||||
t = torch.arange(max_position_embeddings, dtype=torch.float32, device=DEVICE)
|
||||
freqs = torch.einsum("i,j -> ij", t, inv_freq)
|
||||
t = torch.arange(max_position, dtype=torch.float32, device=DEVICE)
|
||||
freqs = torch.einsum("i,j->ij", t, inv_freq)
|
||||
cos = freqs.cos()
|
||||
sin = freqs.sin()
|
||||
cache = torch.cat((cos, sin), dim=-1)
|
||||
cache = torch.cat((cos, sin), dim=-1) # [max_pos, rotary_dim]
|
||||
return cache
|
||||
|
||||
|
||||
@pytest.mark.parametrize("bs", [1, 8])
|
||||
@pytest.mark.parametrize("seq_len", [1, 512])
|
||||
@pytest.mark.parametrize("num_qo_heads", [1, 16])
|
||||
@pytest.mark.parametrize("num_kv_heads", [1, 16])
|
||||
@pytest.mark.parametrize("head_dim", [64, 512])
|
||||
@pytest.mark.parametrize("rotary_dim", [64, 128])
|
||||
@pytest.mark.parametrize("interleave", [False, True])
|
||||
@pytest.mark.parametrize("enable_pdl", [False, True])
|
||||
@pytest.mark.parametrize("save_kv_cache", [False, True])
|
||||
@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16, torch.float32])
|
||||
# ---------------------------------------------------------------------------
|
||||
# Implementation wrappers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def sglang_jit_rope(
|
||||
q: torch.Tensor,
|
||||
k: torch.Tensor,
|
||||
cos_sin_cache: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
is_neox: bool,
|
||||
) -> None:
|
||||
from sglang.jit_kernel.rope import apply_rope_inplace
|
||||
|
||||
apply_rope_inplace(q, k, cos_sin_cache, positions, is_neox=is_neox)
|
||||
|
||||
|
||||
def flashinfer_rope(
|
||||
q: torch.Tensor,
|
||||
k: torch.Tensor,
|
||||
cos_sin_cache: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
is_neox: bool,
|
||||
) -> None:
|
||||
from flashinfer.rope import apply_rope_with_cos_sin_cache_inplace
|
||||
|
||||
head_size = q.shape[-1]
|
||||
# flashinfer expects [nnz, num_heads * head_size]
|
||||
q_2d = q.view(q.shape[0], -1)
|
||||
k_2d = k.view(k.shape[0], -1)
|
||||
apply_rope_with_cos_sin_cache_inplace(
|
||||
positions=positions,
|
||||
query=q_2d,
|
||||
key=k_2d,
|
||||
head_size=head_size,
|
||||
cos_sin_cache=cos_sin_cache,
|
||||
is_neox=is_neox,
|
||||
)
|
||||
|
||||
|
||||
def torch_impl_rope(
|
||||
q: torch.Tensor,
|
||||
k: torch.Tensor,
|
||||
cos_sin_cache: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
is_neox: bool,
|
||||
) -> None:
|
||||
# TODO: implement a pure-PyTorch reference for extra coverage
|
||||
pass
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Test parameters
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
BS_LIST = [2**x for x in range(12)]
|
||||
BS_LIST += [x + 1 for x in BS_LIST] # odd sizes to stress non-aligned paths
|
||||
NUM_KV_HEADS_LIST = [1, 2, 8]
|
||||
GQA_RATIO = [1, 4, 8]
|
||||
ROPE_DIM_LIST = [64, 128, 256, 512]
|
||||
IS_NEOX_LIST = [False, True]
|
||||
DTYPE_LIST = [torch.bfloat16, torch.float16]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("batch_size", BS_LIST)
|
||||
@pytest.mark.parametrize("gqa_ratio", GQA_RATIO)
|
||||
@pytest.mark.parametrize("num_kv_heads", NUM_KV_HEADS_LIST)
|
||||
@pytest.mark.parametrize("rope_dim", ROPE_DIM_LIST)
|
||||
@pytest.mark.parametrize("is_neox", IS_NEOX_LIST)
|
||||
@pytest.mark.parametrize("dtype", DTYPE_LIST)
|
||||
def test_rope(
|
||||
bs,
|
||||
seq_len,
|
||||
num_qo_heads,
|
||||
num_kv_heads,
|
||||
head_dim,
|
||||
rotary_dim,
|
||||
interleave: bool,
|
||||
enable_pdl: bool,
|
||||
save_kv_cache: bool,
|
||||
batch_size: int,
|
||||
gqa_ratio: int,
|
||||
num_kv_heads: int,
|
||||
rope_dim: int,
|
||||
is_neox: bool,
|
||||
dtype: torch.dtype,
|
||||
) -> None:
|
||||
if head_dim < rotary_dim:
|
||||
pytest.skip(f"{head_dim=} < {rotary_dim=}")
|
||||
if not save_kv_cache and enable_pdl:
|
||||
pytest.skip(f"({save_kv_cache=}, {enable_pdl=}) is not allowed")
|
||||
|
||||
q = torch.randn(bs * seq_len, num_qo_heads * head_dim, device=DEVICE, dtype=dtype)
|
||||
k = torch.randn(bs * seq_len, num_kv_heads * head_dim, device=DEVICE, dtype=dtype)
|
||||
v = torch.randn(bs * seq_len, num_kv_heads * head_dim, device=DEVICE, dtype=dtype)
|
||||
|
||||
KV_POOL_SIZE = bs * seq_len * 2
|
||||
k_buffer = torch.zeros(
|
||||
KV_POOL_SIZE, num_kv_heads, head_dim, device=DEVICE, dtype=dtype
|
||||
num_qo_heads = num_kv_heads * gqa_ratio
|
||||
q = torch.randn(batch_size, num_qo_heads, rope_dim, device=DEVICE, dtype=dtype)
|
||||
k = torch.randn(batch_size, num_kv_heads, rope_dim, device=DEVICE, dtype=dtype)
|
||||
positions = torch.randint(
|
||||
0, MAX_SEQ_LEN, (batch_size,), device=DEVICE, dtype=torch.int64
|
||||
)
|
||||
v_buffer = torch.zeros(
|
||||
KV_POOL_SIZE, num_kv_heads, head_dim, device=DEVICE, dtype=dtype
|
||||
)
|
||||
out_cache_loc = torch.randperm(KV_POOL_SIZE, dtype=torch.int64, device=DEVICE)[
|
||||
: bs * seq_len
|
||||
].clone()
|
||||
cos_sin_cache = create_cos_sin_cache(rope_dim)
|
||||
|
||||
pos_ids = torch.arange(seq_len, device=DEVICE).repeat(bs)
|
||||
q_fi, k_fi = q.clone(), k.clone()
|
||||
q_jit, k_jit = q.clone(), k.clone()
|
||||
|
||||
max_seq_len = seq_len
|
||||
base = 10000
|
||||
cos_sin_cache = create_cos_sin_cache(rotary_dim, max_seq_len, base, dtype)
|
||||
flashinfer_rope(q_fi, k_fi, cos_sin_cache, positions, is_neox)
|
||||
sglang_jit_rope(q_jit, k_jit, cos_sin_cache, positions, is_neox)
|
||||
|
||||
q_jit = q.clone()
|
||||
k_jit = k.clone()
|
||||
v_jit = v.clone()
|
||||
k_buffer_jit = k_buffer.clone()
|
||||
v_buffer_jit = v_buffer.clone()
|
||||
out_cache_loc_jit = out_cache_loc.clone()
|
||||
fused_set_kv_buffer_arg_jit = FusedSetKVBufferArgJit(
|
||||
value=v_jit,
|
||||
k_buffer=k_buffer_jit.view(k_buffer_jit.shape[0], -1),
|
||||
v_buffer=v_buffer_jit.view(v_buffer_jit.shape[0], -1),
|
||||
k_scale=None,
|
||||
v_scale=None,
|
||||
cache_loc=out_cache_loc_jit,
|
||||
)
|
||||
|
||||
q_kernel = q.clone()
|
||||
k_kernel = k.clone()
|
||||
v_kernel = v.clone()
|
||||
k_buffer_kernel = k_buffer.clone()
|
||||
v_buffer_kernel = v_buffer.clone()
|
||||
out_cache_loc_kernel = out_cache_loc.clone()
|
||||
fused_set_kv_buffer_arg_kernel = FusedSetKVBufferArgKernel(
|
||||
value=v_kernel,
|
||||
k_buffer=k_buffer_kernel.view(k_buffer_kernel.shape[0], -1),
|
||||
v_buffer=v_buffer_kernel.view(v_buffer_kernel.shape[0], -1),
|
||||
k_scale=None,
|
||||
v_scale=None,
|
||||
cache_loc=out_cache_loc_kernel,
|
||||
)
|
||||
|
||||
stream_jit = torch.cuda.Stream()
|
||||
stream_kernel = torch.cuda.Stream()
|
||||
|
||||
triton_burn(10, grid=(1024,))
|
||||
r = torch.randn_like(q)
|
||||
r_jit, r_kernel = r.clone(), r.clone()
|
||||
torch.cuda.synchronize()
|
||||
|
||||
with torch.cuda.stream(stream_jit):
|
||||
# Test if rotary_embedding runs on stream_jit
|
||||
triton_burn(10, grid=(1024,))
|
||||
q_jit = q_jit + r_jit
|
||||
apply_rope_with_cos_sin_cache_inplace_jit(
|
||||
positions=pos_ids,
|
||||
query=q_jit,
|
||||
key=k_jit,
|
||||
head_size=head_dim,
|
||||
cos_sin_cache=cos_sin_cache,
|
||||
is_neox=(not interleave),
|
||||
fused_set_kv_buffer_arg=(
|
||||
fused_set_kv_buffer_arg_jit if save_kv_cache else None
|
||||
),
|
||||
enable_pdl=enable_pdl,
|
||||
)
|
||||
|
||||
with torch.cuda.stream(stream_kernel):
|
||||
triton_burn(10, grid=(1024,))
|
||||
q_kernel = q_kernel + r_kernel
|
||||
apply_rope_with_cos_sin_cache_inplace_kernel(
|
||||
positions=pos_ids,
|
||||
query=q_kernel,
|
||||
key=k_kernel,
|
||||
head_size=head_dim,
|
||||
cos_sin_cache=cos_sin_cache,
|
||||
is_neox=(not interleave),
|
||||
fused_set_kv_buffer_arg=(
|
||||
fused_set_kv_buffer_arg_kernel if save_kv_cache else None
|
||||
),
|
||||
enable_pdl=enable_pdl,
|
||||
)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
|
||||
atol = 1e-3 if dtype != torch.float32 else 1e-6
|
||||
rtol = 1e-3 if dtype != torch.float32 else 1e-6
|
||||
torch.testing.assert_close(q_jit, q_kernel, atol=atol, rtol=rtol)
|
||||
torch.testing.assert_close(k_jit, k_kernel, atol=atol, rtol=rtol)
|
||||
torch.testing.assert_close(k_buffer_jit, k_buffer_kernel, atol=atol, rtol=rtol)
|
||||
torch.testing.assert_close(v_buffer_jit, v_buffer_kernel, atol=atol, rtol=rtol)
|
||||
atol = rtol = 1e-2
|
||||
triton.testing.assert_close(q_fi, q_jit, atol=atol, rtol=rtol)
|
||||
triton.testing.assert_close(k_fi, k_jit, atol=atol, rtol=rtol)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("bs", [8])
|
||||
@pytest.mark.parametrize("seq_len", [256, 512, 1024])
|
||||
@pytest.mark.parametrize("num_qo_heads", [16])
|
||||
@pytest.mark.parametrize("num_kv_heads", [16])
|
||||
@pytest.mark.parametrize("head_dim", [64])
|
||||
@pytest.mark.parametrize("rotary_dim", [64])
|
||||
@pytest.mark.parametrize("interleave", [False])
|
||||
@pytest.mark.parametrize("enable_pdl", [False])
|
||||
@pytest.mark.parametrize("save_kv_cache", [False])
|
||||
@pytest.mark.parametrize("dtype", [torch.bfloat16])
|
||||
def test_bench_rope(
|
||||
bs,
|
||||
seq_len,
|
||||
num_qo_heads,
|
||||
num_kv_heads,
|
||||
head_dim,
|
||||
rotary_dim,
|
||||
interleave: bool,
|
||||
enable_pdl: bool,
|
||||
save_kv_cache: bool,
|
||||
dtype: torch.dtype,
|
||||
@pytest.mark.parametrize("dtype", [torch.int32, torch.int64])
|
||||
def test_rope_position_dtypes(dtype: torch.dtype) -> None:
|
||||
"""Ensure both int32 and int64 position tensors work correctly."""
|
||||
batch_size, num_qo_heads, num_kv_heads, rope_dim = 16384, 16, 2, 128
|
||||
is_neox = True
|
||||
|
||||
q = torch.randn(batch_size, num_qo_heads, rope_dim, device=DEVICE, dtype=DTYPE)
|
||||
k = torch.randn(batch_size, num_kv_heads, rope_dim, device=DEVICE, dtype=DTYPE)
|
||||
positions = torch.randint(0, MAX_SEQ_LEN, (batch_size,), device=DEVICE, dtype=dtype)
|
||||
cos_sin_cache = create_cos_sin_cache(rope_dim)
|
||||
|
||||
q_fi, k_fi = q.clone(), k.clone()
|
||||
q_jit, k_jit = q.clone(), k.clone()
|
||||
|
||||
flashinfer_rope(q_fi, k_fi, cos_sin_cache, positions.long(), is_neox)
|
||||
sglang_jit_rope(q_jit, k_jit, cos_sin_cache, positions, is_neox)
|
||||
|
||||
atol = rtol = 1e-2
|
||||
triton.testing.assert_close(q_fi, q_jit, atol=atol, rtol=rtol)
|
||||
triton.testing.assert_close(k_fi, k_jit, atol=atol, rtol=rtol)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("batch_size", BS_LIST)
|
||||
@pytest.mark.parametrize("is_neox", IS_NEOX_LIST)
|
||||
@pytest.mark.parametrize("rope_dim", [64, 80, 96, 128])
|
||||
@pytest.mark.parametrize("head_dim", [64, 128, 256])
|
||||
def test_partial_rope(batch_size: int, is_neox: bool, rope_dim: int, head_dim: int):
|
||||
if head_dim < rope_dim:
|
||||
pytest.skip("Invalid config: head_dim must be >= rope_dim.")
|
||||
num_qo_heads, num_kv_heads = 8, 2
|
||||
|
||||
q = torch.randn(batch_size, num_qo_heads, head_dim, device=DEVICE, dtype=DTYPE)
|
||||
k = torch.randn(batch_size, num_kv_heads, head_dim, device=DEVICE, dtype=DTYPE)
|
||||
positions = torch.randint(0, MAX_SEQ_LEN, (batch_size,), device=DEVICE)
|
||||
cos_sin_cache = create_cos_sin_cache(rope_dim)
|
||||
|
||||
q_fi, k_fi = q.clone(), k.clone()
|
||||
q_jit, k_jit = q.clone(), k.clone()
|
||||
rope = ..., slice(rope_dim) # NOTE: flashinfer by default apply to first rope_dim
|
||||
|
||||
flashinfer_rope(q_fi, k_fi, cos_sin_cache, positions.long(), is_neox)
|
||||
sglang_jit_rope(q_jit[rope], k_jit[rope], cos_sin_cache, positions, is_neox)
|
||||
|
||||
atol = rtol = 1e-2
|
||||
triton.testing.assert_close(q_fi, q_jit, atol=atol, rtol=rtol)
|
||||
triton.testing.assert_close(k_fi, k_jit, atol=atol, rtol=rtol)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("batch_size", BS_LIST)
|
||||
@pytest.mark.parametrize("gqa_ratio", GQA_RATIO)
|
||||
@pytest.mark.parametrize("num_kv_heads", NUM_KV_HEADS_LIST)
|
||||
@pytest.mark.parametrize("rope_dim", ROPE_DIM_LIST)
|
||||
@pytest.mark.parametrize("is_neox", IS_NEOX_LIST)
|
||||
def test_fused_rope_store(
|
||||
batch_size: int,
|
||||
gqa_ratio: int,
|
||||
num_kv_heads: int,
|
||||
rope_dim: int,
|
||||
is_neox: bool,
|
||||
) -> None:
|
||||
if head_dim < rotary_dim:
|
||||
pytest.skip(f"{head_dim=} < {rotary_dim=}")
|
||||
if not save_kv_cache and enable_pdl:
|
||||
pytest.skip(f"({save_kv_cache=}, {enable_pdl=}) is not allowed")
|
||||
"""Test fused RoPE + KV cache store against separate RoPE + manual store."""
|
||||
from sglang.jit_kernel.rope import apply_rope_inplace_with_kvcache
|
||||
|
||||
q = torch.randn(bs * seq_len, num_qo_heads * head_dim, device=DEVICE, dtype=dtype)
|
||||
k = torch.randn(bs * seq_len, num_kv_heads * head_dim, device=DEVICE, dtype=dtype)
|
||||
v = torch.randn(bs * seq_len, num_kv_heads * head_dim, device=DEVICE, dtype=dtype)
|
||||
num_qo_heads = num_kv_heads * gqa_ratio
|
||||
dtype = DTYPE
|
||||
|
||||
KV_POOL_SIZE = bs * seq_len * 2
|
||||
k_buffer = torch.zeros(
|
||||
KV_POOL_SIZE, num_kv_heads, head_dim, device=DEVICE, dtype=dtype
|
||||
q = torch.randn(batch_size, num_qo_heads, rope_dim, device=DEVICE, dtype=dtype)
|
||||
k = torch.randn(batch_size, num_kv_heads, rope_dim, device=DEVICE, dtype=dtype)
|
||||
v = torch.randn(batch_size, num_kv_heads, rope_dim, device=DEVICE, dtype=dtype)
|
||||
positions = torch.randint(
|
||||
0, MAX_SEQ_LEN, (batch_size,), device=DEVICE, dtype=torch.int64
|
||||
)
|
||||
v_buffer = torch.zeros(
|
||||
KV_POOL_SIZE, num_kv_heads, head_dim, device=DEVICE, dtype=dtype
|
||||
out_loc = torch.randperm(CACHE_SIZE, device=DEVICE, dtype=torch.int64)[:batch_size]
|
||||
cos_sin_cache = create_cos_sin_cache(rope_dim)
|
||||
|
||||
row_size = num_kv_heads * rope_dim
|
||||
k_cache_ref = torch.zeros(CACHE_SIZE, row_size, device=DEVICE, dtype=dtype)
|
||||
v_cache_ref = torch.zeros(CACHE_SIZE, row_size, device=DEVICE, dtype=dtype)
|
||||
k_cache_fused = torch.zeros(CACHE_SIZE, row_size, device=DEVICE, dtype=dtype)
|
||||
v_cache_fused = torch.zeros(CACHE_SIZE, row_size, device=DEVICE, dtype=dtype)
|
||||
|
||||
# --- reference: separate RoPE then manual scatter ---
|
||||
q_ref, k_ref = q.clone(), k.clone()
|
||||
flashinfer_rope(q_ref, k_ref, cos_sin_cache, positions, is_neox)
|
||||
k_cache_ref[out_loc] = k_ref.view(batch_size, -1)
|
||||
v_cache_ref[out_loc] = v.view(batch_size, -1)
|
||||
|
||||
# --- fused kernel ---
|
||||
q_fused, k_fused = q.clone(), k.clone()
|
||||
v_fused = v.clone()
|
||||
apply_rope_inplace_with_kvcache(
|
||||
q_fused,
|
||||
k_fused,
|
||||
v_fused,
|
||||
k_cache_fused,
|
||||
v_cache_fused,
|
||||
cos_sin_cache,
|
||||
positions,
|
||||
out_loc,
|
||||
is_neox=is_neox,
|
||||
)
|
||||
out_cache_loc = torch.randperm(KV_POOL_SIZE, dtype=torch.int64, device=DEVICE)[
|
||||
: bs * seq_len
|
||||
].clone()
|
||||
|
||||
pos_ids = torch.arange(seq_len, device=DEVICE).repeat(bs)
|
||||
|
||||
max_seq_len = seq_len
|
||||
base = 10000
|
||||
cos_sin_cache = create_cos_sin_cache(rotary_dim, max_seq_len, base, dtype)
|
||||
|
||||
q_jit = q.clone()
|
||||
k_jit = k.clone()
|
||||
v_jit = v.clone()
|
||||
k_buffer_jit = k_buffer.clone()
|
||||
v_buffer_jit = v_buffer.clone()
|
||||
out_cache_loc_jit = out_cache_loc.clone()
|
||||
|
||||
q_kernel = q.clone()
|
||||
k_kernel = k.clone()
|
||||
v_kernel = v.clone()
|
||||
k_buffer_kernel = k_buffer.clone()
|
||||
v_buffer_kernel = v_buffer.clone()
|
||||
out_cache_loc_kernel = out_cache_loc.clone()
|
||||
|
||||
jit_args = {
|
||||
"positions": pos_ids,
|
||||
"query": q_jit,
|
||||
"key": k_jit,
|
||||
"head_size": head_dim,
|
||||
"cos_sin_cache": cos_sin_cache,
|
||||
"is_neox": (not interleave),
|
||||
"fused_set_kv_buffer_arg": None,
|
||||
"enable_pdl": enable_pdl,
|
||||
}
|
||||
jit_time = bench_rope(
|
||||
apply_rope_with_cos_sin_cache_inplace_jit,
|
||||
jit_args,
|
||||
atol = rtol = 1e-2
|
||||
# q should match RoPE-only result
|
||||
triton.testing.assert_close(q_ref, q_fused, atol=atol, rtol=rtol)
|
||||
# k_cache should contain the rotated k
|
||||
triton.testing.assert_close(
|
||||
k_cache_ref[out_loc], k_cache_fused[out_loc], atol=atol, rtol=rtol
|
||||
)
|
||||
kernel_args = {
|
||||
"positions": pos_ids,
|
||||
"query": q_kernel,
|
||||
"key": k_kernel,
|
||||
"head_size": head_dim,
|
||||
"cos_sin_cache": cos_sin_cache,
|
||||
"is_neox": (not interleave),
|
||||
"fused_set_kv_buffer_arg": None,
|
||||
"enable_pdl": enable_pdl,
|
||||
}
|
||||
kernel_time = bench_rope(
|
||||
apply_rope_with_cos_sin_cache_inplace_kernel,
|
||||
kernel_args,
|
||||
)
|
||||
print(f"\nPerformance Test - Batch={bs}, SeqLen={seq_len}")
|
||||
print(f"JIT: {jit_time*1000:.9f}ms, SGL: {kernel_time*1000:.9f}ms")
|
||||
if kernel_time > 0:
|
||||
speedup = kernel_time / jit_time if jit_time > 0 else float("inf")
|
||||
print(f"Speedup (SGL/JIT): {speedup:.2f}x")
|
||||
|
||||
|
||||
def bench_rope(fn, args):
|
||||
warmup = 10
|
||||
iteration = 100
|
||||
for _ in range(warmup):
|
||||
fn(**args)
|
||||
torch.cuda.synchronize()
|
||||
start_time = time.time()
|
||||
for _ in range(iteration):
|
||||
fn(**args)
|
||||
torch.cuda.synchronize()
|
||||
return (time.time() - start_time) / iteration
|
||||
# v_cache should be an exact copy
|
||||
assert torch.all(v_cache_ref[out_loc] == v_cache_fused[out_loc]), "v_cache mismatch"
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
pytest.main([__file__])
|
||||
pytest.main([__file__, "-v"])
|
||||
|
||||
Reference in New Issue
Block a user