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a402b9b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | import torch
import triton
import triton.language as tl
from sgl_kernel import concat_mla_k as concat_mla_k_cuda
DEVICE = triton.runtime.driver.active.get_active_torch_device()
num_local_heads = 128
qk_nope_head_dim = 128
qk_rope_head_dim = 64
def create_data(num_tokens):
k_nope_container = torch.randn(
(num_tokens, num_local_heads, qk_nope_head_dim + 128),
dtype=torch.bfloat16,
device="cuda",
)
k_nope = k_nope_container[:, :, :qk_nope_head_dim]
k_rope_container = torch.randn(
(num_tokens, 1, 128 + qk_rope_head_dim), dtype=torch.bfloat16, device="cuda"
)
k_rope = k_rope_container[:, :, -qk_rope_head_dim:]
k = torch.empty(
(num_tokens, num_local_heads, qk_nope_head_dim + qk_rope_head_dim),
dtype=torch.bfloat16,
device="cuda",
)
return dict(k=k, k_nope=k_nope, k_rope=k_rope)
def fn_torch(k, k_nope, k_rope):
k[..., :qk_nope_head_dim] = k_nope
k[..., qk_nope_head_dim:] = k_rope
def fn_hack_non_strided(k, k_nope, k_rope):
k_flatten_view = k.flatten()
k_flatten_view[: k_nope.numel()] = k_nope.flatten()
k2 = k_flatten_view[k_nope.numel() :].view(k_rope.numel(), -1)
k2 = k_rope.flatten()[:, None]
@torch.compile(dynamic=True)
def fn_torch_compiled(k, k_nope, k_rope):
return fn_torch(k, k_nope, k_rope)
def fn_cuda(k, k_nope, k_rope):
concat_mla_k_cuda(k, k_nope, k_rope)
@triton.jit
def fn_triton_kernel(
k_ptr,
k_nope_ptr,
k_rope_ptr,
num_tokens,
QK_NOPE_HEAD_DIM: tl.constexpr,
QK_ROPE_HEAD_DIM: tl.constexpr,
NUM_LOCAL_HEADS: tl.constexpr,
K_NOPE_STRIDE_0: tl.constexpr,
K_NOPE_STRIDE_1: tl.constexpr,
K_STRIDE_0: tl.constexpr,
K_STRIDE_1: tl.constexpr,
K_ROPE_STRIDE_0: tl.constexpr,
BLOCK_ROWS: tl.constexpr,
):
pid = tl.program_id(axis=0)
token_id = pid * BLOCK_ROWS + tl.arange(0, BLOCK_ROWS)
token_mask = token_id < num_tokens
head_id = tl.arange(0, NUM_LOCAL_HEADS)
# nope
nope_sub_id = tl.arange(0, QK_NOPE_HEAD_DIM)
offs_nope = (
token_id[:, None, None] * K_NOPE_STRIDE_0
+ head_id[None, :, None] * K_NOPE_STRIDE_1
+ nope_sub_id[None, None, :]
)
offs_k = (
token_id[:, None, None] * K_STRIDE_0
+ head_id[None, :, None] * K_STRIDE_1
+ nope_sub_id[None, None, :]
)
vals_nope = tl.load(k_nope_ptr + offs_nope, mask=token_mask[:, None, None])
tl.store(k_ptr + offs_k, vals_nope, mask=token_mask[:, None, None])
# rope
rope_sub_id = tl.arange(0, QK_ROPE_HEAD_DIM)
offs_rope = token_id[:, None, None] * K_ROPE_STRIDE_0 + rope_sub_id[None, None, :]
offs_k = (
token_id[:, None, None] * K_STRIDE_0
+ head_id[None, :, None] * K_STRIDE_1
+ rope_sub_id[None, None, :]
+ QK_NOPE_HEAD_DIM
)
vals_rope = tl.load(k_rope_ptr + offs_rope, mask=token_mask[:, None, None])
tl.store(k_ptr + offs_k, vals_rope, mask=token_mask[:, None, None])
def fn_triton(k, k_nope, k_rope):
assert k.device == DEVICE and k_nope.device == DEVICE and k_rope.device == DEVICE
num_tokens, _, _ = k.shape
grid = lambda meta: (triton.cdiv(num_tokens, meta["BLOCK_ROWS"]),)
fn_triton_kernel[grid](
k,
k_nope,
k_rope,
num_tokens,
QK_NOPE_HEAD_DIM=qk_nope_head_dim,
QK_ROPE_HEAD_DIM=qk_rope_head_dim,
NUM_LOCAL_HEADS=num_local_heads,
K_NOPE_STRIDE_0=k_nope.stride(0),
K_NOPE_STRIDE_1=k_nope.stride(1),
K_STRIDE_0=k.stride(0),
K_STRIDE_1=k.stride(1),
K_ROPE_STRIDE_0=k_rope.stride(0),
BLOCK_ROWS=16,
)
def execute_and_get_output(f, data):
data["k"].zero_()
f(**data)
assert data["k"].sum().item() != 0
return data["k"].clone()
torch.manual_seed(0)
data = create_data(num_tokens=32768)
output_ref = execute_and_get_output(fn_torch, data)
output_exp = execute_and_get_output(fn_cuda, data)
# print(output_ref)
# print(output_exp)
if not torch.all(output_ref == output_exp):
abs_delta = torch.abs(output_ref - output_exp)
raise AssertionError(
f"{output_ref=} {output_exp=} "
f"{abs_delta=} "
f"{torch.argwhere(abs_delta != 0.0)=} "
)
@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=["num_tokens"], # Argument names to use as an x-axis for the plot.
x_vals=[
2048,
4096,
8192,
16384,
32768,
], # Different possible values for `x_name`.
x_log=False, # x axis is logarithmic.
line_arg="provider", # Argument name whose value corresponds to a different line in the plot.
line_vals=[
"torch",
"torch_compiled",
"triton",
"hack_non_strided",
"cuda",
], # Possible values for `line_arg`.
line_names=[
"torch",
"torch_compiled",
"triton",
"hack_non_strided",
"cuda",
], # Label name for the lines.
plot_name="vector-add-performance", # Name for the plot. Used also as a file name for saving the plot.
args={}, # Values for function arguments not in `x_names` and `y_name`.
)
)
def benchmark(num_tokens, provider):
data = create_data(num_tokens=num_tokens)
quantiles = [0.5, 0.2, 0.8]
fn = {
"torch": fn_torch,
"torch_compiled": fn_torch_compiled,
"triton": fn_triton,
"hack_non_strided": fn_hack_non_strided,
"cuda": fn_cuda,
}[provider]
ms, min_ms, max_ms = triton.testing.do_bench(
lambda: fn(**data), quantiles=quantiles
)
return ms, min_ms, max_ms
torch.cuda.cudart().cudaProfilerStart()
benchmark.run(print_data=True, show_plots=True)
torch.cuda.cudart().cudaProfilerStop()
|