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[sgl-kernel] per token group quant support COLUMN MAJOR (#4817)

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This commit is contained in:
Xiaoyu Zhang
2025-04-03 09:29:59 +08:00
committed by GitHub
parent 31da75abed
commit 2c8fd99363
3 changed files with 252 additions and 80 deletions
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254
sgl-kernel/tests/test_per_token_group_quant_8bit.py
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@@ -9,12 +9,12 @@ from sgl_kernel import sgl_per_token_group_quant_fp8, sgl_per_token_group_quant_
from sglang.srt.utils import is_hip
is_hip_ = is_hip()
fp8_type_ = torch.float8_e4m3fnuz if is_hip_ else torch.float8_e4m3fn
_is_hip = is_hip()
fp8_type_ = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
@triton.jit
def _per_token_group_quant_8bit(
def _per_token_group_quant_fp8(
# Pointers to inputs and output
y_ptr,
y_q_ptr,
@@ -25,15 +25,16 @@ def _per_token_group_quant_8bit(
N,
# Avoid to divide zero
eps,
# Information for 8bit data type (int8 or fp8_type_)
max_8bit,
min_8bit,
# Information for float8
fp8_min,
fp8_max,
# Meta-parameters
BLOCK: tl.constexpr,
):
"""A Triton-accelerated function to perform per-token-group quantization on a
tensor.
This function converts the tensor values into 8bit values.
This function converts the tensor values into float8 values.
"""
# Map the program id to the row of X and Y it should compute.
g_id = tl.program_id(0)
@@ -47,8 +48,57 @@ def _per_token_group_quant_8bit(
y = tl.load(y_ptr + cols, mask=mask, other=0.0).to(tl.float32)
# Quant
_absmax = tl.maximum(tl.max(tl.abs(y)), eps)
y_s = _absmax / max_8bit
y_q = tl.clamp(y / y_s, min_8bit, max_8bit).to(y_q_ptr.dtype.element_ty)
y_s = _absmax / fp8_max
y_s_inv = 1.0 / y_s
y_q = tl.clamp(y * y_s_inv, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)
tl.store(y_q_ptr + cols, y_q, mask=mask)
tl.store(y_s_ptr, y_s)
@triton.jit
def _per_token_group_quant_fp8_colmajor(
# Pointers to inputs and output
y_ptr,
y_q_ptr,
y_s_ptr,
group_size,
# Num columns of y
y_num_columns,
# Stride from one column to the next of y_s
y_s_col_stride,
# Avoid to divide zero
eps,
# Information for float8
fp8_min,
fp8_max,
# Meta-parameters
BLOCK: tl.constexpr,
):
"""A Triton-accelerated function to perform per-token-group
quantization on a tensor.
This function converts the tensor values into float8 values.
"""
# Map the program id to the row of X and Y it should compute.
g_id = tl.program_id(0)
y_ptr += g_id * group_size
y_q_ptr += g_id * group_size
# Convert g_id the flattened block coordinate to 2D so we can index
# into the output y_scales matrix
blocks_per_row = y_num_columns // group_size
scale_col = g_id % blocks_per_row
scale_row = g_id // blocks_per_row
y_s_ptr += scale_col * y_s_col_stride + scale_row
cols = tl.arange(0, BLOCK) # group_size <= BLOCK
mask = cols < group_size
y = tl.load(y_ptr + cols, mask=mask, other=0.0).to(tl.float32)
# Quant
_absmax = tl.maximum(tl.max(tl.abs(y)), eps)
y_s = _absmax / fp8_max
y_q = tl.clamp(y / y_s, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)
tl.store(y_q_ptr + cols, y_q, mask=mask)
tl.store(y_s_ptr, y_s)
@@ -57,17 +107,22 @@ def _per_token_group_quant_8bit(
def triton_per_token_group_quant_8bit(
x: torch.Tensor,
group_size: int,
dst_dtype: torch.dtype,
eps: float = 1e-10,
dtype: torch.dtype = fp8_type_,
column_major_scales: bool = False,
scale_tma_aligned: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Function to perform per-token-group quantization on an input tensor `x`.
It converts the tensor values into signed float8 values and returns the
quantized tensor along with the scaling factor used for quantization.
Args:
x: The input tenosr with ndim >= 2.
group_size: The group size used for quantization.
eps: The minimum to avoid dividing zero.
dtype: The dype of output tensor. Note that only `torch.float8_e4m3fn` is supported for now.
dtype: The dype of output tensor.
Returns:
Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the scaling factor for quantization.
"""
@@ -76,41 +131,79 @@ def triton_per_token_group_quant_8bit(
), "the last dimension of `x` cannot be divisible by `group_size`"
assert x.is_contiguous(), "`x` is not contiguous"
if dst_dtype == torch.int8:
iinfo = torch.iinfo(dst_dtype)
max_8bit = iinfo.max
min_8bit = iinfo.min
if dtype == torch.int8:
finfo = torch.iinfo(dtype)
else:
finfo = torch.finfo(dst_dtype)
max_8bit = finfo.max
min_8bit = finfo.min
finfo = torch.finfo(dtype)
x_q = torch.empty_like(x, device=x.device, dtype=dst_dtype)
fp8_max = finfo.max
if _is_hip:
if dtype == torch.int8:
fp8_max = 127.0
else:
fp8_max = 224.0
fp8_min = -fp8_max
x_q = torch.empty_like(x, device=x.device, dtype=dtype)
M = x.numel() // group_size
N = group_size
x_s = torch.empty(
x.shape[:-1] + (x.shape[-1] // group_size,),
device=x.device,
dtype=torch.float32,
)
if column_major_scales:
if scale_tma_aligned:
# aligned to 4 * sizeof(float)
aligned_size = (x.shape[-2] + 3) // 4 * 4
x_s = torch.empty(
x.shape[:-2] + (x.shape[-1] // group_size, aligned_size),
device=x.device,
dtype=torch.float32,
).permute(-1, -2)[: x.shape[-2], :]
else:
x_s = torch.empty(
(x.shape[-1] // group_size,) + x.shape[:-1],
device=x.device,
dtype=torch.float32,
).permute(-1, -2)
else:
x_s = torch.empty(
x.shape[:-1] + (x.shape[-1] // group_size,),
device=x.device,
dtype=torch.float32,
)
BLOCK = triton.next_power_of_2(N)
# heuristics for number of warps
num_warps = min(max(BLOCK // 256, 1), 8)
num_stages = 1
_per_token_group_quant_8bit[(M,)](
x,
x_q,
x_s,
group_size,
N,
eps,
max_8bit,
min_8bit,
BLOCK=BLOCK,
num_warps=num_warps,
num_stages=num_stages,
)
if column_major_scales:
_per_token_group_quant_fp8_colmajor[(M,)](
x,
x_q,
x_s,
group_size,
x.shape[1],
x_s.stride(1),
eps,
fp8_min=fp8_min,
fp8_max=fp8_max,
BLOCK=BLOCK,
num_warps=num_warps,
num_stages=num_stages,
)
else:
_per_token_group_quant_fp8[(M,)](
x,
x_q,
x_s,
group_size,
N,
eps,
fp8_min=fp8_min,
fp8_max=fp8_max,
BLOCK=BLOCK,
num_warps=num_warps,
num_stages=num_stages,
)
return x_q, x_s
@@ -118,28 +211,48 @@ def triton_per_token_group_quant_8bit(
def sglang_per_token_group_quant_8bit(
x: torch.Tensor,
group_size: int,
dst_dtype: torch.dtype,
eps: float = 1e-10,
dtype: torch.dtype = fp8_type_,
column_major_scales: bool = False,
scale_tma_aligned: bool = False,
):
assert (
x.shape[-1] % group_size == 0
), "the last dimension of `x` cannot be divisible by `group_size`"
assert x.is_contiguous(), "`x` is not contiguous"
x_q = torch.empty_like(x, device=x.device, dtype=dst_dtype)
x_s = torch.empty(
x.shape[:-1] + (x.shape[-1] // group_size,),
device=x.device,
dtype=torch.float32,
)
x_q = torch.empty_like(x, device=x.device, dtype=dtype)
M = x.numel() // group_size
N = group_size
if column_major_scales:
if scale_tma_aligned:
# aligned to 4 * sizeof(float)
aligned_size = (x.shape[-2] + 3) // 4 * 4
x_s = torch.empty(
x.shape[:-2] + (x.shape[-1] // group_size, aligned_size),
device=x.device,
dtype=torch.float32,
).permute(-1, -2)[: x.shape[-2], :]
else:
x_s = torch.empty(
(x.shape[-1] // group_size,) + x.shape[:-1],
device=x.device,
dtype=torch.float32,
).permute(-1, -2)
else:
x_s = torch.empty(
x.shape[:-1] + (x.shape[-1] // group_size,),
device=x.device,
dtype=torch.float32,
)
if dst_dtype == torch.int8:
iinfo = torch.iinfo(dst_dtype)
if dtype == torch.int8:
iinfo = torch.iinfo(dtype)
int8_max = iinfo.max
int8_min = iinfo.min
sgl_per_token_group_quant_int8(x, x_q, x_s, group_size, eps, int8_min, int8_max)
else:
f8_info = torch.finfo(dst_dtype)
f8_info = torch.finfo(dtype)
fp8_max = f8_info.max
fp8_min = f8_info.min
sgl_per_token_group_quant_fp8(x, x_q, x_s, group_size, eps, fp8_min, fp8_max)
@@ -148,30 +261,55 @@ def sglang_per_token_group_quant_8bit(
@pytest.mark.parametrize(
"batch_size, seq_len, group_size, dst_dtype",
"num_tokens, hidden_dim, group_size, dst_dtype, column_major_scales, scale_tma_aligned",
list(
itertools.product(
[1, 2, 4, 8, 16, 32, 64, 128], # batch_size
[64, 128, 256, 512, 1024, 2048], # seq_len
[16, 32, 64, 128, 256], # group_size
[127, 128, 512, 1024, 4096, 8192], # num_tokens
[256, 512, 1024, 2048, 4096], # hidden_dim
[8, 16, 32, 64, 128], # group_size
[torch.int8, fp8_type_], # dtype
[False, True], # column_major_scales
[False, True], # scale_tma_aligned
)
),
)
def test_per_token_group_quant_compare_implementations(
batch_size, seq_len, group_size, dst_dtype
def test_per_token_group_quant_with_column_major(
num_tokens,
hidden_dim,
group_size,
dst_dtype,
column_major_scales,
scale_tma_aligned,
):
x = torch.randn(
(batch_size, seq_len, group_size * 2), device="cuda", dtype=torch.float16
if not column_major_scales and scale_tma_aligned:
return
x = torch.randn(num_tokens, hidden_dim, device="cuda", dtype=torch.float16)
x_q_triton, x_s_triton = triton_per_token_group_quant_8bit(
x,
group_size,
eps=1e-10,
dtype=dst_dtype,
column_major_scales=column_major_scales,
scale_tma_aligned=scale_tma_aligned,
)
x_q_triton, x_s_triton = triton_per_token_group_quant_8bit(x, group_size, dst_dtype)
x_q_sglang, x_s_sglang = sglang_per_token_group_quant_8bit(x, group_size, dst_dtype)
x_q_sglang, x_s_sglang = sglang_per_token_group_quant_8bit(
x,
group_size,
eps=1e-10,
dtype=dst_dtype,
column_major_scales=column_major_scales,
scale_tma_aligned=scale_tma_aligned,
)
assert torch.allclose(
x_q_triton.to(torch.float32), x_q_sglang.to(torch.float32), rtol=1e-3, atol=1e-5
)
assert torch.allclose(x_s_triton, x_s_sglang, rtol=1e-3, atol=1e-5)
assert torch.allclose(
x_s_triton.contiguous(), x_s_sglang.contiguous(), rtol=1e-3, atol=1e-5
)
if __name__ == "__main__":