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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 os
import time
from copy import deepcopy
from dataclasses import dataclass
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd.function import Function
from ..utils import quant_get_local_rank
from ._division_transpose import fp8_division_transpose
from ._quantize_pertensor_transpose import fp8_quantize_pertensor_transpose
from .linear import fp8_linear_backward, fp8_linear_forward
@dataclass
class DefaultArgs:
fabit: int
fwbit: int
bobit: int
class FP8Linear(nn.Linear):
def __init__(self, in_features, out_features, bias=True, device=None, args=None, layer_idx=0):
super().__init__(in_features, out_features, bias, device)
if args is None: # I do not want to pass a new argument to OLMo so just use this method
args = DefaultArgs(
fabit=os.environ["FABIT_FP8Linear"],
fwbit=os.environ["FWBIT_FP8Linear"],
bobit=os.environ["BOBIT_FP8Linear"],
)
self.args = deepcopy(args)
if quant_get_local_rank() == 0:
print(f"[qlinear debug] Apply QLinear, {layer_idx}")
self.layer_idx = layer_idx
self.layer_name = None
def forward(self, Input):
if self.training:
# if False:
output = QuantLinearTE.apply(Input, self.weight, self.bias, self.args, self.layer_name)
else:
output = F.linear(Input, self.weight, self.bias)
return output
# if int(os.environ.get("LOCAL_RANK")) == 0:
# import IPython
# IPython.embed()
# else:
# import time
# time.sleep(1000)
# class QuantLinearTE(Function):
# @staticmethod
# def forward(ctx, input, weight, bias, args, layer_type):
# ctx.saved = input, weight, bias, args, layer_type
# return F.linear(input, weight, bias)
# @staticmethod
# def backward(ctx, grad_output):
# input, weight, bias, args, layer_type = ctx.saved
# C_in = input.shape[-1]
# C_out = grad_output.shape[-1]
# grad_output_flatten = grad_output.reshape(-1, C_out)
# input_flatten = input.reshape(-1, C_in)
# if grad_output_flatten.dtype == input_flatten.dtype:
# grad_weight = grad_output_flatten.t().mm(input_flatten)
# else:
# grad_weight = grad_output_flatten.float().t().mm(input_flatten)
# if grad_output_flatten.dtype == weight.dtype:
# grad_input = grad_output_flatten.mm(weight)
# else:
# grad_input = grad_output_flatten.float().mm(weight)
# if bias is not None:
# grad_bias = grad_output_flatten.sum(0)
# else:
# grad_bias = None
# grad_input_transform = grad_input.reshape(input.size())
# return grad_input_transform, grad_weight, grad_bias, None, None
class QuantLinearTE(Function):
@staticmethod
@torch.amp.custom_fwd(device_type="cuda", cast_inputs=torch.bfloat16)
def forward(ctx, input, weight, bias, args, layer_name):
time_bench = os.getenv("TIME_BENCH")
if time_bench:
start_1 = torch.cuda.Event(enable_timing=True)
start_1.record()
# Qinput, Iscale, Qinput_t = fp8_division_transpose(input, 16, args.fabit)
Qinput, Iscale, Qinput_t = fp8_quantize_pertensor_transpose(input, 16, args.fabit, transpose_output_2d=True)
if time_bench:
end_1 = torch.cuda.Event(enable_timing=True)
end_1.record()
start_2 = torch.cuda.Event(enable_timing=True)
start_2.record()
# Qweight, Wscale, Qweight_t = fp8_division_transpose(weight, 16, args.fwbit)
Qweight, Wscale, Qweight_t = fp8_quantize_pertensor_transpose(weight, 16, args.fwbit, transpose_output_2d=True)
if time_bench:
end_2 = torch.cuda.Event(enable_timing=True)
end_2.record()
start_3 = torch.cuda.Event(enable_timing=True)
start_3.record()
ctx.saved = Qinput_t, Iscale, Qweight_t, Wscale, bias, args, layer_name
fc_output = fp8_linear_forward(Qinput, Iscale, Qweight, Wscale, False, 0, bias)
if time_bench:
end_3 = torch.cuda.Event(enable_timing=True)
end_3.record()
start_4 = torch.cuda.Event(enable_timing=True)
start_4.record()
output = F.linear(input, weight, bias)
end_4 = torch.cuda.Event(enable_timing=True)
end_4.record()
torch.cuda.synchronize()
if quant_get_local_rank() == 0:
print(
f"[Forward] Part 1: {start_1.elapsed_time(end_1):.6f} ms | Part 2: {start_2.elapsed_time(end_2):.6f} ms | Part 3: {start_3.elapsed_time(end_3):.6f} ms | "
f"FP8: {start_1.elapsed_time(end_3):.6f} | BF16: {start_4.elapsed_time(end_4):.6f} | Input shape: {input.shape} | Weight shape: {weight.shape}"
)
return fc_output
@staticmethod
@torch.amp.custom_bwd(device_type="cuda")
def backward(ctx, grad_output):
Qinput_t, Iscale, Qweight_t, Wscale, bias, args, layer_name = ctx.saved
time_bench = os.getenv("TIME_BENCH")
if time_bench:
start_1 = torch.cuda.Event(enable_timing=True)
start_1.record()
# Qgrad_output, Gscale, Qgrad_output_t = fp8_division_transpose(grad_output, 16, args.bobit, stochastic=False)
Qgrad_output, Gscale, Qgrad_output_t = fp8_quantize_pertensor_transpose(
grad_output, 16, args.bobit, stochastic=False, transpose_output_2d=True
)
if time_bench:
end_1 = torch.cuda.Event(enable_timing=True)
end_1.record()
start_2 = torch.cuda.Event(enable_timing=True)
start_2.record()
grad_input, grad_weight = fp8_linear_backward(
Qinput_t,
Iscale,
Qgrad_output,
Gscale,
Qgrad_output_t,
Qweight_t,
Wscale,
16,
bias,
stochastic=False,
dgrad_quantize=False,
)
if time_bench:
end_2 = torch.cuda.Event(enable_timing=True)
end_2.record()
start_3 = torch.cuda.Event(enable_timing=True)
start_3.record()
if bias is not None:
grad_bias = grad_output.reshape(-1, grad_output.shape[-1]).sum(0)
else:
grad_bias = None
if time_bench:
end_3 = torch.cuda.Event(enable_timing=True)
end_3.record()
# ========== BF16 ==========
C_in = Qinput_t.shape[0]
C_out = grad_output.shape[-1]
grad_output_flatten = grad_output.reshape(-1, C_out)
input_flatten = Qinput_t.t().reshape(-1, C_in).to(torch.bfloat16)
weight = Qweight_t.t().to(torch.bfloat16)
start_4 = torch.cuda.Event(enable_timing=True)
start_4.record()
if grad_output_flatten.dtype == input_flatten.dtype:
_grad_weight = grad_output_flatten.t().mm(input_flatten)
else:
_grad_weight = grad_output_flatten.float().t().mm(input_flatten)
if grad_output_flatten.dtype == weight.dtype:
_grad_input = grad_output_flatten.mm(weight)
else:
_grad_input = grad_output_flatten.float().mm(weight)
end_4 = torch.cuda.Event(enable_timing=True)
end_4.record()
torch.cuda.synchronize()
if quant_get_local_rank() == 0:
print(
f"[Backward] Part 1: {start_1.elapsed_time(end_1):.6f} ms | Part 2: {start_2.elapsed_time(end_2):.6f} ms | Part 3: {start_3.elapsed_time(end_3):.6f} ms | "
f"FP8: {start_1.elapsed_time(end_3):.6f} | BF16: {start_4.elapsed_time(end_4):.6f} | Input shape: {Qinput_t.shape} | Weight shape: {weight.shape}"
)
return grad_input, grad_weight, grad_bias, None, None
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