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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
from copy import deepcopy
import torch
import torch.nn as nn
from ._quantize import fp8_quantize
from ._quantize_pertensor import fp8_quantize_pertensor
class Coat_quantize_bgn(nn.Module):
def __init__(self, args=None, layer_type=""):
super().__init__()
self.args = deepcopy(args)
self.fp8type = self.args.fabit
self.layer_type = layer_type
def forward(self, input):
if self.training:
return Coat_quantize_bgn_func.apply(input, self.args.group_size, self.fp8type)
else:
return input, None, None
class Coat_quantize_bgn_func(torch.autograd.Function):
@staticmethod
def forward(ctx, input, group_size, fp8type):
"""
(Qoutput, Oscale) uses 1 * 16 quantization
"""
Qoutput, Oscale = fp8_quantize(input, group_size, fp8type)
# For autograd
Qoutput = Qoutput.view(torch.float8_e4m3fn)
ctx.saved = group_size
return input, Qoutput, Oscale
@staticmethod
def backward(ctx, grad_output, Qgrad_output, Gscale):
"""
(Qgrad_output, Gscale) uses 1 * 16 quantization
"""
return grad_output, None, None
class Coat_quantize_end(nn.Module):
def __init__(self, args=None, layer_type=""):
super().__init__()
self.args = deepcopy(args)
self.fp8type = self.args.babit
self.layer_type = layer_type
def forward(self, input, Qinput, Iscale):
if self.training:
return Coat_quantize_end_func.apply(input, Qinput, Iscale, self.args.group_size, self.fp8type)
else:
return input
class Coat_quantize_end_func(torch.autograd.Function):
@staticmethod
def forward(ctx, input, Qinput, Iscale, group_size, fp8type):
"""
(Qinput, Iscale) uses 1 * 16 quantization
"""
ctx.saved = group_size, fp8type
return input
@staticmethod
def backward(ctx, grad_output):
"""
(Qgrad_output, Gscale) uses per-tensor quantization
"""
group_size, fp8type = ctx.saved
Qgrad_output, Gscale, Gscale_g16 = fp8_quantize_pertensor(grad_output, group_size, fp8type, stochastic=False)
# For autograd
Qgrad_output = Qgrad_output.view(torch.float8_e4m3fn)
return grad_output, Qgrad_output, Gscale_g16, None, None
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