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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.
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
import torch
# 4 block
import triton
import triton.language as tl
from triton.language.extra.cuda import libdevice
from ._division_transpose import fp8_division_transpose
from .common import FP8_MAX_VALUE, SCALE_MIN_THRES, get_configs_io_block
"""Element-wise Multiplication Forward"""
"""Input1 (Gate) uses 1 * 16 group quantization"""
"""Input2 (Up) uses 1 * 16 group quantization"""
"""Output uses per-tensor quantization"""
"""The input can be 2D or 3D, but the calculation is performed in 2D"""
fp8_max_value = {
torch.float8_e4m3fn: 448,
torch.float8_e5m2: 57344,
}
@triton.autotune(
configs=[] + get_configs_io_block(),
key=[
"N",
],
)
@triton.heuristics(
{
"BLOCK_SN": lambda args: args["BLOCK_N"] // args["QB"],
}
)
@triton.jit
def fp8_mul_forward_kernel(
output_ptr,
output_scale_ptr, # output
input1_ptr,
input1_scale_ptr, # input
input2_ptr,
input2_scale_ptr, # input
M,
N,
SN,
QB: tl.constexpr,
fp8_max, # shape
input1_stride_0,
input1_stride_1, # input1 stride
s_input1_stride_0,
s_input1_stride_1, # scale of input1 stride
input2_stride_0,
input2_stride_1, # input2 stride
s_input2_stride_0,
s_input2_stride_1, # scale of input2 stride
output_stride_0,
output_stride_1, # output stride
s_output_stride_0,
s_output_stride_1, # scale of output stride
SCALE_MIN_THRES: 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
# --- The first input ---
input1_block_ptr = tl.make_block_ptr(
base=input1_ptr,
shape=(M, N),
strides=(input1_stride_0, input1_stride_1),
offsets=(pid_dim0 * BLOCK_M, pid_dim1 * BLOCK_N),
block_shape=(BLOCK_M, BLOCK_N),
order=(1, 0),
)
# input ptr
scale_input1_ptr = tl.make_block_ptr(
base=input1_scale_ptr,
shape=(M, SN),
strides=(s_input1_stride_0, s_input1_stride_1),
offsets=(pid_dim0 * BLOCK_M, pid_dim1 * BLOCK_SN),
block_shape=(BLOCK_M, BLOCK_SN),
order=(1, 0),
)
input1 = tl.load(input1_block_ptr)
scale_input1 = tl.load(scale_input1_ptr)
input1 = input1.to(tl.float32)
scale_input1 = scale_input1.to(tl.float32)
# Dequantize and mul calculation
scale_input1 = tl.reshape(scale_input1, (BLOCK_M, BLOCK_SN, 1))
input1 = tl.reshape(input1, (BLOCK_M, BLOCK_SN, QB))
input1 = input1 * scale_input1
# --- The second input ---
input2_block_ptr = tl.make_block_ptr(
base=input2_ptr,
shape=(M, N),
strides=(input2_stride_0, input2_stride_1),
offsets=(pid_dim0 * BLOCK_M, pid_dim1 * BLOCK_N),
block_shape=(BLOCK_M, BLOCK_N),
order=(1, 0),
)
# input ptr
scale_input2_ptr = tl.make_block_ptr(
base=input2_scale_ptr,
shape=(M, SN),
strides=(s_input2_stride_0, s_input2_stride_1),
offsets=(pid_dim0 * BLOCK_M, pid_dim1 * BLOCK_SN),
block_shape=(BLOCK_M, BLOCK_SN),
order=(1, 0),
)
input2 = tl.load(input2_block_ptr)
scale_input2 = tl.load(scale_input2_ptr)
input2 = input2.to(tl.float32)
scale_input2 = scale_input2.to(tl.float32)
# Dequantize and mul calculation
scale_input2 = tl.reshape(scale_input2, (BLOCK_M, BLOCK_SN, 1))
input2 = tl.reshape(input2, (BLOCK_M, BLOCK_SN, QB))
input2 = input2 * scale_input2
# Actual Calculation of SiLU
mul_output = input1 * input2
# Quantize Scale calculation
abs_output = tl.abs(mul_output)
max_val = tl.max(abs_output, axis=2) + SCALE_MIN_THRES
scale_output = max_val / fp8_max
scale_output = tl.reshape(scale_output, (BLOCK_M, BLOCK_SN, 1))
# Quantize
# mul_output = tl.fdiv(mul_output, scale_output) # do not quantize the output since it should use per-tensor quantization afterwards
mul_output = mul_output.to(output_ptr.type.element_ty)
scale_output = scale_output.to(output_scale_ptr.type.element_ty)
scale_output = tl.reshape(scale_output, (BLOCK_M, BLOCK_SN))
mul_output = tl.reshape(mul_output, (BLOCK_M, BLOCK_N))
# debug
# mul_output = input
# scale_output = scale_input
# 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),
)
scale_output_ptr = tl.make_block_ptr(
base=output_scale_ptr,
shape=(M, SN),
strides=(s_output_stride_0, s_output_stride_1),
offsets=(pid_dim0 * BLOCK_M, pid_dim1 * BLOCK_SN),
block_shape=(BLOCK_M, BLOCK_SN),
order=(1, 0),
)
tl.store(output_block_ptr, mul_output, boundary_check=(0, 1))
tl.store(scale_output_ptr, scale_output, boundary_check=(0, 1))
def fp8_mul_forward(x1, s_x1, x2, s_x2, QB, transpose_output_2d=False):
# Change batched 3D input to 2D
batched = False
if len(x1.shape) == 3:
assert len(s_x1.shape) == 3
batched = True
BS = x1.shape[0]
x1 = x1.reshape(-1, x1.shape[-1])
s_x1 = s_x1.reshape(-1, s_x1.shape[-1])
x2 = x2.reshape(-1, x2.shape[-1])
s_x2 = s_x2.reshape(-1, s_x2.shape[-1])
# defining the input and output tensor
M, N = x1.shape
_, SN = s_x1.shape # assume the shape of quantization block size is always 1 * G
assert x1.shape == x2.shape
assert s_x1.shape == s_x2.shape
y = torch.empty_like(x1, dtype=torch.bfloat16)
s_y = torch.empty_like(s_x1, dtype=s_x1.dtype)
fp8MaxValue = fp8_max_value[x1.dtype] # E4M3 and E5M2 have different max value
grid = lambda META: (triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]),)
fp8_mul_forward_kernel[grid](
y,
s_y,
x1,
s_x1,
x2,
s_x2,
M,
N,
SN,
QB,
fp8MaxValue,
x1.stride(0),
x1.stride(1),
s_x1.stride(0),
s_x1.stride(1),
x2.stride(0),
x2.stride(1),
s_x2.stride(0),
s_x2.stride(1),
y.stride(0),
y.stride(1),
s_y.stride(0),
s_y.stride(1),
SCALE_MIN_THRES=SCALE_MIN_THRES,
)
s_y_max = s_y.max()
qy, s_y_max, qy_t = fp8_division_transpose(y, QB, x2.dtype, s_y_max)
qy = qy.to(x2.dtype)
qy_t = qy_t.to(x2.dtype)
# Recover 2D to 3D
if batched:
y = y.reshape(BS, -1, y.shape[-1])
qy = qy.reshape(BS, -1, qy.shape[-1])
if not transpose_output_2d:
qy_t = qy_t.reshape(BS, -1, qy.shape[-1])
return qy, s_y_max, qy_t
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