Skip to content

GitLab

"docs/vscode:/vscode.git/clone" did not exist on "7e0107c395b24fa55e080dee3903552430008b48"
Unverified Commit 6d55f60e authored by Yineng Zhang's avatar Yineng Zhang Committed by GitHub
Browse files

Revert "[1/2] Optimizations and refactors about quant kernel (#9534)" (#10292)

parent 033b75f5
Expand all Show whitespace changes
Inline Side-by-side
Showing with 335 additions and 1002 deletions
python/sglang/srt/bench_utils.py
View file @ 6d55f60e
import os
import re
import sys
from contextlib import nullcontext
......@@ -109,8 +108,7 @@ def bench_kineto(
if not with_multiple_kernels:
for name in kernel_names:
assert (
sum([int(re.search(name, line) is not None) for line in prof_lines])
== 1
sum([name in line for line in prof_lines]) == 1
), f"Errors of the kernel {name} in the profiling table (table: {prof_lines})"
# Save chrome traces
......@@ -124,7 +122,7 @@ def bench_kineto(
total_time = 0
total_num = 0
for line in prof_lines:
if re.search(name, line) is not None:
if name in line:
time_str = line.split()[-2]
num_str = line.split()[-1]
for unit, scale in units.items():
......
python/sglang/srt/layers/quantization/fp8_kernel.py
View file @ 6d55f60e
......@@ -43,17 +43,11 @@ _is_cpu = is_cpu()
_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
if _is_cuda:
from sgl_kernel import sgl_per_tensor_quant_fp8, sgl_per_token_quant_fp8
# Temporary
try:
from sgl_kernel import sgl_per_token_group_quant_8bit
enable_sgl_per_token_group_quant_8bit = True
except ImportError:
from sgl_kernel import sgl_per_token_group_quant_fp8
enable_sgl_per_token_group_quant_8bit = False
from sgl_kernel import (
sgl_per_tensor_quant_fp8,
sgl_per_token_group_quant_fp8,
sgl_per_token_quant_fp8,
)
if _is_hip:
if _use_aiter:
......@@ -502,21 +496,6 @@ def sglang_per_token_group_quant_fp8(
)
if x.shape[0] > 0:
# Temporary
if enable_sgl_per_token_group_quant_8bit:
sgl_per_token_group_quant_8bit(
x,
x_q,
x_s,
group_size,
eps,
fp8_min,
fp8_max,
scale_ue8m0,
fuse_silu_and_mul,
masked_m,
)
else:
sgl_per_token_group_quant_fp8(
x, x_q, x_s, group_size, eps, fp8_min, fp8_max, scale_ue8m0
)
......
python/sglang/srt/layers/quantization/int8_kernel.py
View file @ 6d55f60e
......@@ -12,13 +12,7 @@ from sglang.srt.utils import get_device_name, is_cuda
_is_cuda = is_cuda()
if _is_cuda:
# Temporary
try:
from sgl_kernel import sgl_per_token_group_quant_8bit
except ImportError:
from sgl_kernel import (
sgl_per_token_group_quant_int8 as sgl_per_token_group_quant_8bit,
)
from sgl_kernel import sgl_per_token_group_quant_int8
logger = logging.getLogger(__name__)
......@@ -210,7 +204,7 @@ def sglang_per_token_group_quant_int8(
dtype=torch.float32,
)
sgl_per_token_group_quant_8bit(x, x_q, x_s, group_size, eps, int8_min, int8_max)
sgl_per_token_group_quant_int8(x, x_q, x_s, group_size, eps, int8_min, int8_max)
return x_q, x_s
......
sgl-kernel/benchmark/bench_per_token_group_quant_8bit.py
View file @ 6d55f60e
import itertools
import os
import time
from functools import partial
from pathlib import Path
import torch
import triton
from sgl_kernel.test_utils import create_per_token_group_quant_test_data
from sglang.srt.bench_utils import bench_kineto
from sglang.srt.layers.quantization.fp8_kernel import (
......@@ -21,231 +19,78 @@ from sglang.srt.utils import is_hip
_is_hip = is_hip()
fp8_type_ = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
mode_concentrated = os.environ.get("SGLANG_BENCH_MODE", "") == "concentrated"
if int(os.environ.get("SGLANG_NSYS_PROFILING", "0")):
# configs = [[
# 768,
# 16384,
# 128,
# None,
# fp8_type_,
# dict(
# column_major_scales=True,
# scale_tma_aligned=True,
# scale_ue8m0=True,
# fuse_silu_and_mul=False,
# masked_layout_mode=None,
# ),
# ]]
configs = [
[
768 * 8,
2048,
128,
48,
fp8_type_,
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=True,
# masked_layout_mode=None,
masked_layout_mode="balanced",
# masked_layout_mode="extreme",
),
]
]
elif mode_concentrated:
configs = list(
itertools.product(
[768],
[1536, 7168, 16384],
[128],
[None],
[fp8_type_],
[
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=False,
masked_layout_mode=None,
),
],
)
) + list(
itertools.product(
[768 * 8],
[2048],
[128],
[48],
[fp8_type_],
[
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=True,
masked_layout_mode=None,
),
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=True,
masked_layout_mode="balanced",
),
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=True,
masked_layout_mode="imbalanced",
),
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=True,
masked_layout_mode="extreme",
),
],
)
)
else:
configs = list(
itertools.product(
[1, 4, 16, 64, 256, 768, 2048, 8192, 16384],
[1536, 7168, 16384],
[128],
[None],
[fp8_type_],
[
num_tokens_range = [1, 4, 16, 64, 256, 768, 2048, 8192, 16384]
hidden_dim_range = [1536, 7168, 18432] # For DeepSeek V3/R1
group_size_range = [128] # For DeepSeek V3/R1
# TODO test int8
dst_dtype_range = [fp8_type_]
flags_range = [
dict(
column_major_scales=False,
scale_tma_aligned=False,
scale_ue8m0=False,
fuse_silu_and_mul=False,
masked_layout_mode=None,
),
dict(
column_major_scales=True,
scale_tma_aligned=False,
scale_ue8m0=False,
fuse_silu_and_mul=False,
masked_layout_mode=None,
),
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=False,
fuse_silu_and_mul=False,
masked_layout_mode=None,
),
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=False,
masked_layout_mode=None,
),
],
)
) + list(
]
configs = list(
itertools.product(
[1 * 8, 4 * 8, 64 * 8, 256 * 8, 768 * 8],
[2048],
[128],
[8, 16, 32, 48],
[fp8_type_],
[
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=True,
masked_layout_mode=None,
),
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=True,
masked_layout_mode="balanced",
),
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=True,
masked_layout_mode="imbalanced",
),
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=True,
masked_layout_mode="extreme",
),
],
)
num_tokens_range,
hidden_dim_range,
group_size_range,
dst_dtype_range,
flags_range,
)
)
@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=[
"num_tokens",
"hidden_dim",
"group_size",
"num_ranks",
"dst_dtype",
"flags",
],
x_names=["num_tokens", "hidden_dim", "group_size", "dst_dtype", "flags"],
x_vals=configs,
line_arg="provider",
line_vals=["triton", "sglang"],
# Triton has multi kernels and we only report the time for the core one
line_names=["Triton (Inaccurate)", "SGL Kernel"],
line_names=["Triton", "SGL Kernel"],
styles=[("blue", "-"), ("green", "-")],
ylabel="us",
plot_name="per-token-group-quant-8bit-performance",
args={},
)
)
def benchmark(
num_tokens, hidden_dim, group_size, num_ranks, dst_dtype, flags, provider
):
print(
f"Testing: {num_tokens=} {hidden_dim=} {group_size=} {num_ranks=} {dst_dtype=} {flags=} {provider=}"
)
def benchmark(num_tokens, hidden_dim, group_size, dst_dtype, flags, provider):
if flags["scale_ue8m0"] and group_size != 128:
return
x, masked_m = create_per_token_group_quant_test_data(
num_tokens=num_tokens, hidden_dim=hidden_dim, num_ranks=num_ranks, flags=flags
)
device = torch.device("cuda")
x = torch.randn(num_tokens, hidden_dim, device=device, dtype=torch.bfloat16)
fn, kernel_names = {
"triton": (
triton_per_token_group_quant_8bit,
"_per_token_group_quant_8bit|_silu_and_mul_post_quant_kernel",
),
"triton": (triton_per_token_group_quant_8bit, "_per_token_group_quant_fp8"),
"sglang": (
sglang_per_token_group_quant_8bit,
"per_token_group_quant_8bit_kernel",
),
}[provider]
bench_fn = lambda: fn(
x=x,
masked_m=masked_m,
group_size=group_size,
dst_dtype=dst_dtype,
**{k: v for k, v in flags.items() if k not in ["masked_layout_mode"]},
)
bench_fn = lambda: fn(x=x, group_size=group_size, dst_dtype=dst_dtype, **flags)
time_s = bench_kineto(
bench_fn, kernel_names=kernel_names, num_tests=300 if mode_concentrated else 30
)
time_s = bench_kineto(bench_fn, kernel_names=kernel_names)
return time_s * 1e6
......
sgl-kernel/csrc/common_extension.cc
View file @ 6d55f60e
......@@ -121,9 +121,14 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
m.impl("fp8_blockwise_scaled_mm", torch::kCUDA, &fp8_blockwise_scaled_mm);
m.def(
"sgl_per_token_group_quant_8bit(Tensor input, Tensor output_q, Tensor output_s, int group_size,"
" float eps, float fp8_min, float fp8_max, bool scale_ue8m0, bool fuse_silu_and_mul, Tensor? masked_m) -> ()");
m.impl("sgl_per_token_group_quant_8bit", torch::kCUDA, &sgl_per_token_group_quant_8bit);
"sgl_per_token_group_quant_fp8(Tensor input, Tensor output_q, Tensor output_s, int group_size,"
" float eps, float fp8_min, float fp8_max, bool scale_ue8m0) -> ()");
m.impl("sgl_per_token_group_quant_fp8", torch::kCUDA, &sgl_per_token_group_quant_fp8);
m.def(
"sgl_per_token_group_quant_int8(Tensor input, Tensor output_q, Tensor output_s, int group_size,"
" float eps, float int8_min, float int8_max) -> ()");
m.impl("sgl_per_token_group_quant_int8", torch::kCUDA, &sgl_per_token_group_quant_int8);
m.def("sgl_per_tensor_quant_fp8(Tensor input, Tensor output_q, Tensor output_s, bool is_static) -> ()");
m.impl("sgl_per_tensor_quant_fp8", torch::kCUDA, &sgl_per_tensor_quant_fp8);
......
sgl-kernel/include/sgl_kernel_ops.h
View file @ 6d55f60e
......@@ -207,17 +207,23 @@ torch::Tensor fp8_blockwise_scaled_mm(
const torch::Dtype& out_dtype);
void scaled_fp4_quant(
torch::Tensor& output, torch::Tensor const& input, torch::Tensor& output_scale, torch::Tensor const& input_scale);
void sgl_per_token_group_quant_8bit(
void sgl_per_token_group_quant_fp8(
at::Tensor input,
at::Tensor output_q,
at::Tensor output_s,
int64_t group_size,
double eps,
double min_8bit,
double max_8bit,
bool scale_ue8m0,
bool fuse_silu_and_mul,
const std::optional<torch::Tensor>& masked_m);
double fp8_min,
double fp8_max,
bool scale_ue8m0);
void sgl_per_token_group_quant_int8(
at::Tensor input,
at::Tensor output_q,
at::Tensor output_s,
int64_t group_size,
double eps,
double int8_min,
double int8_max);
void sgl_per_tensor_quant_fp8(at::Tensor input, at::Tensor output_q, at::Tensor output_s, bool is_static);
void sgl_per_token_quant_fp8(at::Tensor input, at::Tensor output_q, at::Tensor output_s);
void bmm_fp8(
......
sgl-kernel/python/sgl_kernel/__init__.py
View file @ 6d55f60e
......@@ -58,7 +58,8 @@ from sgl_kernel.gemm import (
scaled_fp4_grouped_quant,
scaled_fp4_quant,
sgl_per_tensor_quant_fp8,
sgl_per_token_group_quant_8bit,
sgl_per_token_group_quant_fp8,
sgl_per_token_group_quant_int8,
sgl_per_token_quant_fp8,
shuffle_rows,
silu_and_mul_scaled_fp4_grouped_quant,
......
sgl-kernel/python/sgl_kernel/gemm.py
View file @ 6d55f60e
......@@ -98,7 +98,7 @@ def dsv3_fused_a_gemm(
return output
def sgl_per_token_group_quant_8bit(
def sgl_per_token_group_quant_fp8(
input: torch.Tensor,
output_q: torch.Tensor,
output_s: torch.Tensor,
......@@ -106,21 +106,24 @@ def sgl_per_token_group_quant_8bit(
eps: float,
fp8_min: float,
fp8_max: float,
scale_ue8m0: bool = False,
fuse_silu_and_mul: bool = False,
masked_m: Optional[torch.Tensor] = None,
scale_ue8m0: bool,
) -> None:
torch.ops.sgl_kernel.sgl_per_token_group_quant_8bit.default(
input,
output_q,
output_s,
group_size,
eps,
fp8_min,
fp8_max,
scale_ue8m0,
fuse_silu_and_mul,
masked_m,
torch.ops.sgl_kernel.sgl_per_token_group_quant_fp8.default(
input, output_q, output_s, group_size, eps, fp8_min, fp8_max, scale_ue8m0
)
def sgl_per_token_group_quant_int8(
input: torch.Tensor,
output_q: torch.Tensor,
output_s: torch.Tensor,
group_size: int,
eps: float,
int8_min: float,
int8_max: float,
) -> None:
torch.ops.sgl_kernel.sgl_per_token_group_quant_int8.default(
input, output_q, output_s, group_size, eps, int8_min, int8_max
)
......
sgl-kernel/python/sgl_kernel/test_utils.py deleted 100644 → 0
View file @ 033b75f5
import torch
def create_per_token_group_quant_test_data(num_tokens, hidden_dim, num_ranks, flags):
device = torch.device("cuda")
dtype = torch.bfloat16
seed = num_tokens * 10000 + hidden_dim
gen_cpu = torch.Generator(device="cpu")
gen_cpu.manual_seed(seed)
gen_cuda = torch.Generator(device="cuda")
gen_cuda.manual_seed(seed)
if flags["fuse_silu_and_mul"]:
effective_hidden_dim = hidden_dim * 2
else:
effective_hidden_dim = hidden_dim
del hidden_dim
if (masked_layout_mode := flags["masked_layout_mode"]) is not None:
num_max_dispatch_tokens_per_rank = 768
num_global_experts = 288
num_local_experts, remainder = divmod(num_global_experts, num_ranks)
assert remainder == 0
# mimic DeepEP low_latency_dispatch output
x = torch.randn(
num_local_experts,
num_max_dispatch_tokens_per_rank * num_ranks,
effective_hidden_dim,
device=device,
dtype=dtype,
generator=gen_cuda,
)
if masked_layout_mode == "balanced":
masked_m = _compute_balanced_split(num_tokens, num_local_experts)
elif masked_layout_mode == "imbalanced":
masked_m = _compute_imbalanced_split(
num_tokens, num_local_experts, gen_cpu=gen_cpu
)
elif masked_layout_mode == "extreme":
masked_m = torch.tensor(
[num_tokens] + [0] * (num_local_experts - 1), dtype=torch.int
)
else:
raise NotImplementedError
print(f"{masked_layout_mode=} {masked_m=} {x.shape=}")
masked_m = masked_m.to(device)
return x, masked_m
else:
x = torch.randn(
num_tokens,
effective_hidden_dim,
device=device,
dtype=dtype,
generator=gen_cuda,
)
x[torch.randn(x.shape, device=device, generator=gen_cuda) < 0.001] *= 10
return x, None
def _compute_balanced_split(total: int, arr_len: int):
base = total // arr_len
remainder = total % arr_len
ans = [base + 1 if i < remainder else base for i in range(arr_len)]
assert sum(ans) == total
return torch.tensor(ans, dtype=torch.int)
def _compute_imbalanced_split(
total: int, arr_len: int, gen_cpu, dtype=torch.int
) -> list[int]:
# can use `rand ** 2`, `rand ** 3`, etc, to change how imbalanced it is
noise_raw = torch.rand(arr_len, generator=gen_cpu) ** 3
noise = noise_raw / noise_raw.sum()
ans = (noise * total).round().to(dtype)
diff = total - ans.sum().item()
while diff != 0:
idx = torch.randint(0, arr_len, (1,), generator=gen_cpu).item()
if diff > 0:
ans[idx] += 1
diff -= 1
elif diff < 0 and ans[idx] > 0:
ans[idx] -= 1
diff += 1
assert sum(ans) == total
return ans
def assert_all_close_or_tiny_diff(a: torch.Tensor, b: torch.Tensor):
assert (a.shape == b.shape) and (
a.dtype == b.dtype
), f"{a.shape=} {b.shape=} {a.dtype=} {b.dtype=}"
numel = a.numel()
if a.dtype == torch.float8_e4m3fn:
a_u8 = a.view(torch.uint8)
b_u8 = b.view(torch.uint8)
diff_u8 = (a_u8.to(torch.int16) - b_u8.to(torch.int16)).abs()
count_diff_sign = ((a_u8 >= 0) & (b_u8 < 0)).sum().item()
count_tiny_diff = (diff_u8 == 1).sum().item()
count_large_diff = (diff_u8 >= 2).sum().item()
elif a.dtype == torch.int8:
diff = (a.to(torch.int16) - a.to(torch.int16)).abs()
count_diff_sign = ((a >= 0) & (b < 0)).sum().item()
count_tiny_diff = (diff == 1).sum().item()
count_large_diff = (diff >= 2).sum().item()
else:
raise NotImplementedError
assert (
(count_diff_sign == 0)
and (count_large_diff == 0)
and (
(count_tiny_diff / numel < 0.005)
or ((count_tiny_diff / numel < 0.04) and (numel <= 4096))
)
), f"{count_diff_sign=} {count_tiny_diff=} {count_large_diff=} {numel=} {a=} {b=}"
sgl-kernel/tests/test_per_token_group_quant_8bit.py
View file @ 6d55f60e
import itertools
import os
import time
from pathlib import Path
import pytest
import torch
from sgl_kernel.test_utils import (
assert_all_close_or_tiny_diff,
create_per_token_group_quant_test_data,
)
from sglang.srt.layers.quantization import deep_gemm_wrapper
from sglang.srt.layers.quantization.fp8_kernel import (
per_token_group_quant_8bit as triton_per_token_group_quant_8bit,
)
from sglang.srt.layers.quantization.fp8_kernel import sglang_per_token_group_quant_8bit
from sglang.srt.utils import get_bool_env_var, is_hip
from sglang.srt.layers.quantization.utils import assert_fp8_all_close
from sglang.srt.utils import is_hip
_is_hip = is_hip()
fp8_type_ = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
configs = list(
@pytest.mark.parametrize(
"num_tokens, hidden_dim, group_size, dst_dtype, flags",
list(
itertools.product(
[1, 4, 16, 64, 127, 128, 512, 1024, 4096, 8192], # num_tokens
[128, 256, 384, 512, 1024, 1536, 1664, 2048, 4096, 7168, 16384], # hidden_dim
[16, 32, 64, 128], # group_size
[None], # num_ranks
[fp8_type_, torch.int8], # dtype
[127, 128, 512, 1024, 4096, 8192], # num_tokens
[256, 512, 1024, 2048, 4096], # hidden_dim
[8, 16, 32, 64, 128], # group_size
# TODO test int8
[fp8_type_], # dtype
[
dict(
column_major_scales=False,
scale_tma_aligned=False,
scale_ue8m0=False,
fuse_silu_and_mul=False,
masked_layout_mode=None,
),
dict(
column_major_scales=True,
scale_tma_aligned=False,
scale_ue8m0=False,
fuse_silu_and_mul=False,
masked_layout_mode=None,
),
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=False,
fuse_silu_and_mul=False,
masked_layout_mode=None,
),
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=False,
masked_layout_mode=None,
),
],
)
) + list(
itertools.product(
[1, 4, 1 * 8, 4 * 8, 64 * 8, 256 * 8, 768 * 8],
# TODO support more
[2048],
[128],
[8, 16, 32, 48],
[fp8_type_],
[
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=True,
masked_layout_mode=None,
),
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=True,
masked_layout_mode="balanced",
),
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=True,
masked_layout_mode="imbalanced",
),
dict(
column_major_scales=True,
scale_tma_aligned=True,
scale_ue8m0=True,
fuse_silu_and_mul=True,
masked_layout_mode="extreme",
),
],
)
)
@pytest.mark.parametrize(
"num_tokens, hidden_dim, group_size, num_ranks, dst_dtype, flags", configs
)
def test_per_token_group_quant_with_column_major(
num_tokens,
hidden_dim,
group_size,
num_ranks,
dst_dtype,
flags,
):
print(
f"{num_tokens=} {hidden_dim=} {group_size=} {num_ranks=} {dst_dtype=} {flags=}"
)
arch_major, _ = torch.cuda.get_device_capability(torch.cuda.current_device())
if flags["scale_ue8m0"] and (arch_major <= 9):
pytest.skip("Only Blackwell need ue8m0 fusion")
return
if (flags["scale_ue8m0"] and (group_size != 128)) or (
(dst_dtype == torch.int8) and flags["column_major_scales"]
):
if flags["scale_ue8m0"] and ((group_size != 128) or (hidden_dim % 512 != 0)):
pytest.skip()
return
if flags["scale_ue8m0"] and not deep_gemm_wrapper.DEEPGEMM_BLACKWELL:
pytest.skip("scale_ue8m0 only supported on Blackwell")
return
x, masked_m = create_per_token_group_quant_test_data(
num_tokens=num_tokens, hidden_dim=hidden_dim, num_ranks=num_ranks, flags=flags
)
# print("hack data!!!")
# x = torch.full_like(x, fill_value=100)
x = torch.randn(num_tokens, hidden_dim, device="cuda", dtype=torch.bfloat16)
execute_kwargs = dict(
x=x,
masked_m=masked_m,
group_size=group_size,
eps=1e-10,
dst_dtype=dst_dtype,
**{k: v for k, v in flags.items() if k not in ["masked_layout_mode"]},
**flags,
)
def _postprocess(x_q, x_s):
if masked_m is not None:
print(f"Mask tokens after {masked_m} to be zero")
for i in range(len(masked_m)):
x_q[i, masked_m[i] :, :] = 0
x_s[i, masked_m[i] :, :] = 0
return x_q, x_s
x_q_triton, x_s_triton = triton_per_token_group_quant_8bit(**execute_kwargs)
x_q_sglang, x_s_sglang = sglang_per_token_group_quant_8bit(**execute_kwargs)
x_q_triton, x_s_triton = _postprocess(
*triton_per_token_group_quant_8bit(**execute_kwargs)
)
x_q_sglang, x_s_sglang = _postprocess(
*sglang_per_token_group_quant_8bit(**execute_kwargs)
)
# torch.set_printoptions(profile="full")
# print(f"{x_q_triton=}")
# print(f"{x_s_triton=}")
# print(f"{x_q_sglang=}")
# print(f"{x_s_sglang=}")
# torch.set_printoptions(profile="default")
try:
assert_all_close_or_tiny_diff(x_q_triton, x_q_sglang)
assert_fp8_all_close(x_q_triton, x_q_sglang)
torch.testing.assert_close(
x_s_triton.contiguous(),
x_s_sglang.contiguous(),
......@@ -165,35 +91,6 @@ def test_per_token_group_quant_with_column_major(
atol=1e-5,
msg=lambda message: message + f" {x_s_triton=} {x_s_sglang=}",
)
except AssertionError:
# torch.set_printoptions(profile="full")
print(
f"{x.shape=} {x_q_triton.shape=} {x_s_triton.shape=} {x_q_sglang.shape=} {x_s_sglang.shape=}"
)
print(f"{x=}")
print(f"{masked_m=}")
print(f"{x_q_triton=}")
print(f"{x_s_triton=}")
print(f"{x_q_sglang=}")
print(f"{x_s_sglang=}")
# torch.set_printoptions(profile="default")
# if (d := os.environ.get("SGLANG_DUMP_TEST_ERROR_DIR", "")) != "":
# import matplotlib.pyplot as plt
#
# base_stem = time.time()
# for name, value in [
# ("x_q", x_q_triton != x_q_sglang),
# ("x_s", x_s_triton != x_s_sglang),
# ]:
# value = value.reshape((-1, value.shape[-1]))
# plt.figure(figsize=(20, 20))
# plt.imshow((value * 1.0).cpu().numpy())
# p = Path(d) / f"{base_stem}_{name}.png"
# print(f"Write diff to {p}", flush=True)
# plt.savefig(p)
raise
if __name__ == "__main__":
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment