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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 import fp8_division
from .common import FP8_MAX_VALUE, SCALE_MIN_THRES, convert_str_to_fp8, get_configs_io_block
"""Element-wise Add, useful for backward"""
"""Input1 (Residual) uses full-precision/BF16"""
"""Input2 (Backbone) uses full-precision/BF16"""
"""Output1 uses full-precision/BF16"""
"""Output2 uses per-tensor 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_add_Ifp_Ifp_Ofp_Opt_kernel(
output1_ptr, # output
output2_scale_ptr,
input1_ptr, # input
input2_ptr, # input
M,
N,
SN,
QB: tl.constexpr,
fp8_max, # shape
input1_stride_0,
input1_stride_1, # input1 stride
input2_stride_0,
input2_stride_1, # input2 stride
output1_stride_0,
output1_stride_1, # output stride
s_output2_stride_0,
s_output2_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),
)
input1 = tl.load(input1_block_ptr)
input1 = input1.to(tl.float32)
input1 = tl.reshape(input1, (BLOCK_M, BLOCK_SN, QB))
# --- 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),
)
input2 = tl.load(input2_block_ptr)
input2 = input2.to(tl.float32)
input2 = tl.reshape(input2, (BLOCK_M, BLOCK_SN, QB))
# Actual Calculation of Add
add_output = input1 + input2
# Quantize the grad 1 - Scale calculation
abs_add_output = tl.abs(add_output)
max_val = tl.max(abs_add_output, axis=2) + SCALE_MIN_THRES
scale_output2 = max_val / fp8_max
scale_output2 = tl.reshape(scale_output2, (BLOCK_M, BLOCK_SN, 1))
# save the fp add output
fp_add_output = add_output.to(output1_ptr.type.element_ty)
fp_add_output = tl.reshape(fp_add_output, (BLOCK_M, BLOCK_N))
# pointers
output1_block_ptr = tl.make_block_ptr(
base=output1_ptr,
shape=(M, N),
strides=(output1_stride_0, output1_stride_1),
offsets=(pid_dim0 * BLOCK_M, pid_dim1 * BLOCK_N),
block_shape=(BLOCK_M, BLOCK_N),
order=(1, 0),
)
tl.store(output1_block_ptr, fp_add_output, boundary_check=(0, 1))
# Quantize
scale_output2 = scale_output2.to(output2_scale_ptr.type.element_ty)
scale_output2 = tl.reshape(scale_output2, (BLOCK_M, BLOCK_SN))
# pointers
scale_output2_ptr = tl.make_block_ptr(
base=output2_scale_ptr,
shape=(M, SN),
strides=(s_output2_stride_0, s_output2_stride_1),
offsets=(pid_dim0 * BLOCK_M, pid_dim1 * BLOCK_SN),
block_shape=(BLOCK_M, BLOCK_SN),
order=(1, 0),
)
tl.store(scale_output2_ptr, scale_output2, boundary_check=(0, 1))
def fp8_add_Ifp_Ifp_Ofp_Opt(x1, x2, QB, fp8type, stochastic=False): # suppose x1 is full precision or BF16
# Change batched 3D input to 2D
batched = False
if len(x1.shape) == 3:
assert len(x2.shape) == 3
batched = True
BS = x1.shape[0]
x1 = x1.reshape(-1, x1.shape[-1])
x2 = x2.reshape(-1, x2.shape[-1])
# defining the input and output tensor
M, N = x1.shape
SN = N // QB
assert x1.shape == x2.shape
if isinstance(fp8type, str):
fp8type = convert_str_to_fp8[fp8type]
y1 = torch.empty_like(x1, dtype=torch.bfloat16)
s_y2 = torch.empty((M, SN), dtype=torch.bfloat16, device=x2.device)
fp8MaxValue = FP8_MAX_VALUE[fp8type] # E4M3 and E5M2 have different max value
grid = lambda META: (triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]),)
_fp8_add_Ifp_Ifp_Ofp_Opt_kernel[grid](
y1,
s_y2,
x1,
x2,
M,
N,
SN,
QB,
fp8MaxValue,
x1.stride(0),
x1.stride(1),
x2.stride(0),
x2.stride(1),
y1.stride(0),
y1.stride(1),
s_y2.stride(0),
s_y2.stride(1),
SCALE_MIN_THRES=SCALE_MIN_THRES,
)
s_y2_max = s_y2.max()
qy2, s_y2_max = fp8_division(y1, QB, fp8type, s_y2_max, stochastic=stochastic) # reuse the floating point output y1
# Recover 2D to 3D
if batched:
y1 = y1.reshape(BS, -1, y1.shape[-1])
qy2 = qy2.reshape(BS, -1, qy2.shape[-1])
s_y2 = s_y2.reshape(BS, -1, s_y2.shape[-1])
return y1, (qy2, s_y2_max, s_y2)
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