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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
try:
from .common import FP8_MAX_VALUE, SCALE_MIN_THRES, get_configs_io_block
except:
from common import FP8_MAX_VALUE, SCALE_MIN_THRES, get_configs_io_block
"""SiLU Activation Forward"""
"""Input uses 1 * 16 group quantization"""
"""Output uses 1 * 16 group quantization"""
"""The input can be 2D or 3D, but the calculation is performed in 2D"""
@triton.autotune(
configs=[] + get_configs_io_block(),
key=[
"N",
],
)
@triton.heuristics(
{
"BLOCK_SN": lambda args: args["BLOCK_N"] // args["QB"],
}
)
@triton.jit
def _fp8_silu_forward_kernel(
output_ptr,
output_scale_ptr, # output
input_ptr,
input_scale_ptr, # input
M,
N,
SN,
QB: tl.constexpr,
fp8_max, # shape
input_stride_0,
input_stride_1, # input stride
s_input_stride_0,
s_input_stride_1, # scale of input 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
# 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 ptr
scale_input_ptr = tl.make_block_ptr(
base=input_scale_ptr,
shape=(M, SN),
strides=(s_input_stride_0, s_input_stride_1),
offsets=(pid_dim0 * BLOCK_M, pid_dim1 * BLOCK_SN),
block_shape=(BLOCK_M, BLOCK_SN),
order=(1, 0),
)
input = tl.load(input_block_ptr)
scale_input = tl.load(scale_input_ptr)
input = input.to(tl.float32)
scale_input = scale_input.to(tl.float32)
# Dequantize and silu calculation
scale_input = tl.reshape(scale_input, (BLOCK_M, BLOCK_SN, 1))
input = tl.reshape(input, (BLOCK_M, BLOCK_SN, QB))
input = input * scale_input
# Actual Calculation of SiLU
sigmoid = 1 / (1.0 + libdevice.exp(-input))
silu_output = sigmoid * input
# Quantize Scale calculation
abs_output = tl.abs(silu_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
silu_output = tl.fdiv(silu_output, scale_output)
silu_output = silu_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))
silu_output = tl.reshape(silu_output, (BLOCK_M, BLOCK_N))
# debug
# silu_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, silu_output, boundary_check=(0, 1))
tl.store(scale_output_ptr, scale_output, boundary_check=(0, 1))
def fp8_silu_forward(x, s_x, QB):
# Change batched 3D input to 2D
batched = False
if len(x.shape) == 3:
assert len(s_x.shape) == 3
batched = True
BS = x.shape[0]
x = x.reshape(-1, x.shape[-1])
s_x = s_x.reshape(-1, s_x.shape[-1])
# defining the input and output tensor
M, N = x.shape
_, SN = s_x.shape # assume the shape of quantization block size is always 1 * G
y = torch.empty_like(x, dtype=x.dtype)
s_y = torch.empty_like(s_x, dtype=s_x.dtype)
fp8MaxValue = FP8_MAX_VALUE[x.dtype] # E4M3 and E5M2 have different max value
grid = lambda META: (triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]),)
_fp8_silu_forward_kernel[grid](
y,
s_y,
x,
s_x,
M,
N,
SN,
QB,
fp8MaxValue,
x.stride(0),
x.stride(1),
s_x.stride(0),
s_x.stride(1),
y.stride(0),
y.stride(1),
s_y.stride(0),
s_y.stride(1),
SCALE_MIN_THRES=SCALE_MIN_THRES,
)
# Recover 2D to 3D
if batched:
y = y.reshape(BS, -1, y.shape[-1])
s_y = s_y.reshape(BS, -1, s_y.shape[-1])
return y, s_y
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