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# Copyright (c) 2025 NVIDIA CORPORATION.
# Licensed under the MIT license.
# Adapted from https://github.com/NVlabs/VILA/tree/main under the Apache 2.0 license.
# LICENSE is in incl_licenses directory.
import torch
# 4 block
import triton
import triton.language as tl
from triton.language.extra.cuda import libdevice
try:
from .common import FP8_MAX_VALUE, SCALE_MIN_THRES, convert_fp8_to_embit, convert_str_to_fp8
except:
from common import FP8_MAX_VALUE, SCALE_MIN_THRES, convert_fp8_to_embit, convert_str_to_fp8
"""Quantize Operator"""
"""Input uses 1 * 16 group quantization"""
"""Output uses full-precision/BF16"""
"""The input can be 2D or 3D, but the calculation is performed in 2D"""
# The kernel with 1 load operation and 4 store operation
def get_configs_io_block():
configs = []
for nstages in [3, 4, 5]:
for block_m in [32, 64, 128]:
for block_n in [32, 64, 128]:
for nwarps in [4, 8, 16]:
if block_m == 64 and block_n == 64:
continue
configs.append(
triton.Config(
{"BLOCK_M": block_m, "BLOCK_N": block_n},
num_stages=nstages,
num_warps=nwarps,
)
)
return configs
@triton.autotune(
configs=[] + get_configs_io_block(),
key=[
"N",
],
)
@triton.heuristics(
{
"BLOCK_SN": lambda args: args["BLOCK_N"] // args["QB"],
}
)
@triton.jit
def _fp8_division_kernel(
output_ptr, # output
input_ptr,
input_scale_ptr, # input
noise_ptr, # noise for stochastic
M,
N,
SN,
QB: tl.constexpr,
fp8_max,
e_bit,
m_bit, # shape
input_stride_0,
input_stride_1, # input stride
output_stride_0,
output_stride_1, # output stride
SCALE_MIN_THRES: tl.constexpr, # We do not use it since we believe SCALE_MIN_THRES should be used in previous kernel when calculating scaling factor
STOCHASTIC: tl.constexpr,
BLOCK_M: tl.constexpr,
BLOCK_N: tl.constexpr,
BLOCK_SN: tl.constexpr,
): # CUDA block size
# Block PID
pid = tl.program_id(0)
NUM_BLOCK_N = tl.cdiv(N, BLOCK_N)
pid_dim0 = pid // NUM_BLOCK_N
pid_dim1 = pid % NUM_BLOCK_N
# pointers
input_block_ptr = tl.make_block_ptr(
base=input_ptr,
shape=(M, N),
strides=(input_stride_0, input_stride_1),
offsets=(pid_dim0 * BLOCK_M, pid_dim1 * BLOCK_N),
block_shape=(BLOCK_M, BLOCK_N),
order=(1, 0),
)
input = tl.load(input_block_ptr, boundary_check=(0, 1))
input = input.to(tl.float32)
scale_output = tl.load(input_scale_ptr)
scale_output = scale_output.to(tl.float32)
output = tl.reshape(input, (BLOCK_M, BLOCK_SN, QB))
# Quantize Scale calculation
# Quantize
output = tl.div_rn(output, scale_output)
output = tl.reshape(output, (BLOCK_M, BLOCK_N))
if STOCHASTIC:
noise_block_ptr = tl.make_block_ptr(
base=noise_ptr,
shape=(M, N),
strides=(input_stride_0, input_stride_1),
offsets=(pid_dim0 * BLOCK_M, pid_dim1 * BLOCK_N),
block_shape=(BLOCK_M, BLOCK_N),
order=(1, 0),
)
noise = tl.load(noise_block_ptr, boundary_check=(0, 1))
output = _stochastic_rounding(output, noise, e_bit, m_bit)
output = output.to(output_ptr.type.element_ty)
# pointers
output_block_ptr = tl.make_block_ptr(
base=output_ptr,
shape=(M, N),
strides=(output_stride_0, output_stride_1),
offsets=(pid_dim0 * BLOCK_M, pid_dim1 * BLOCK_N),
block_shape=(BLOCK_M, BLOCK_N),
order=(1, 0),
)
tl.store(output_block_ptr, output, boundary_check=(0, 1))
@triton.jit
def _stochastic_rounding(output, noise, e_bit, m_bit):
subnormal_min = tl.exp2(2 - tl.exp2(e_bit - 1) - m_bit)
# subnormal_should_be = tl.exp2(2 - tl.exp2(e_bit) - 1)
output_int32 = tl.cast(output, tl.int32, bitcast=True)
output_int32 = output_int32 & 0x7F800000
output_float32 = tl.cast(output_int32, tl.float32, bitcast=True)
output_exp = tl.maximum(output_float32, subnormal_min)
noise_rescale = tl.exp2(m_bit) + (output_exp == subnormal_min) * (
1 - tl.exp2(m_bit)
) # 2^m_bit for normal, 1 for subnormal
noise = output_exp * noise / noise_rescale
sign = 1 - 2 * libdevice.signbit(output)
output = tl.abs(output) + noise
# tl.device_print("out", output)
# tl.device_print("noise", noise)
minmax_ratio = 2 + (output_exp == subnormal_min) * (tl.exp2(m_bit) - 2) # 2 for normal, and 2^M for subnormal
output = sign * tl.clamp(output, min=output_exp, max=minmax_ratio * output_exp)
return output
def fp8_division(x, QB, fp8type, s_y=None, stochastic=False):
# Change batched 3D input to 2D
batched = False
if len(x.shape) == 3:
batched = True
BS = x.shape[0]
x = x.reshape(-1, x.shape[-1])
if stochastic:
noise = torch.empty_like(x, dtype=torch.float32).uniform_(-0.5, 0.5)
else:
noise = None
# defining the input and output tensor
M, N = x.shape
SN = N // QB
if isinstance(fp8type, str):
fp8type = convert_str_to_fp8[fp8type]
y = torch.empty_like(x, dtype=fp8type)
fp8MaxValue = FP8_MAX_VALUE[fp8type] # E4M3 and E5M2 have different max value
e_bit, m_bit = convert_fp8_to_embit[fp8type]
if s_y is None:
s_y = (x.abs().max() + SCALE_MIN_THRES) / fp8MaxValue
grid = lambda META: (triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]),)
_fp8_division_kernel[grid](
y,
x,
s_y,
noise,
M,
N,
SN,
QB,
fp8MaxValue,
e_bit,
m_bit,
x.stride(0),
x.stride(1),
y.stride(0),
y.stride(1),
SCALE_MIN_THRES=SCALE_MIN_THRES,
STOCHASTIC=stochastic,
)
# Recover 2D to 3D
if batched:
y = y.reshape(BS, -1, y.shape[-1])
return y, s_y # y_t is expected to be 2D tensor
# I change the dtype of both the input tensor and the output tensor. I use torch.float32, torch.float16, and torch.fp8
configs = []
for SL in [8192]:
configs.append(
triton.testing.Benchmark( # test different matrix size influence
x_names=["CDIM"],
x_vals=[1024, 2048, 4096, 8192],
line_arg="provider",
line_vals=["triton", "torch"],
line_names=["triton", "torch"],
styles=[("blue", "-"), ("green", "-")],
ylabel="time-cost",
plot_name=f"FP8gelu<SL={SL}>",
args={"BS": 4, "SL": SL, "QB": 16, "fp8type": torch.float8_e4m3fn, "mode": "time-consuming"},
)
)
@triton.testing.perf_report(configs)
def bench_load_store(
BS, SL, CDIM, QB, fp8type, provider, mode="forward"
): # I only use triton as the provider, and mode when benchmarking
# create data
x = torch.randn(BS, SL, CDIM).cuda()
_qx = x.reshape(BS, SL, CDIM // QB, QB)
sx = _qx.abs().amax(dim=(3)) / FP8_MAX_VALUE[fp8type]
sx = sx.to(torch.bfloat16)
_qx = (_qx / sx.unsqueeze(3)).to(fp8type)
qx = _qx.reshape(BS, SL, CDIM)
quantiles = [0.5, 0.2, 0.8]
# utility functions
if provider == "triton":
def y_fwd():
fp8_division(qx, sx, QB)
if provider == "torch":
torch_gelu = torch.nn.SiLU()
def y_fwd():
return torch_gelu(x)
# forward pass
if mode == "time-consuming":
convert_func = lambda ms: ms
ms, min_ms, max_ms = triton.testing.do_bench(y_fwd, quantiles=quantiles, rep=100)
# backward pass
if mode == "gbps":
convert_func = lambda ms: 2 * x.numel() * x.element_size() / ms * 1e-6
ms, min_ms, max_ms = triton.testing.do_bench(y_fwd, quantiles=quantiles, rep=100)
return convert_func(ms), convert_func(max_ms), convert_func(min_ms)
def validity_check(BS, SL, CDIM, QB, fp8type=torch.float8_e4m3fn):
# create data
x = torch.randn(BS * SL, CDIM).cuda()
# torch result
avg_output_triton = torch.zeros_like(x)
# triton result
for _ in range(100):
x_triton, s_triton, x_triton_t = fp8_division(x, QB, "E4M3", stochastic=False)
output_triton = x_triton.float() * s_triton
avg_output_triton = avg_output_triton + output_triton
avg_output_triton /= 100
import IPython
IPython.embed()