File size: 4,433 Bytes
713dc9d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 |
#%% PACKAGES & MODULES
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from inference import prepare_for_lwm
from input_preprocess import tokenizer
from lwm_model import lwm
import numpy as np
#%% PARAMETERS
n_epochs = 100
n_layers = 12
n_heads = 12
d_model = 64
d_ff = d_model * 4
d_k = d_model // n_heads
d_v = d_model // n_heads
dropout = 0.1
max_len = 129
element_length = 16
batch_size = 64
train_ratio = 0.7
val_ratio = 0.2
device = 'cuda' if torch.cuda.is_available() else 'cpu'
#%% PRE-TRAINING DATA GENERATION
# The following DeepMIMO scenarios are not enough for pre-training a
# Transformer-based foundation model like LWM. Add more scenarios for
# more effective pre-training. The instruction for reproducing the actual
# dataset used for pre-training LWM can be found in the Huggingface forum.
scenario_names = np.array([
"city_18_denver", "city_15_indianapolis", "city_19_oklahoma",
"city_12_fortworth", "city_11_santaclara", "city_7_sandiego"
])
scenario_idxs = np.array([0, 1, 2, 3, 4, 5])
selected_scenario_names = scenario_names[scenario_idxs]
preprocessed_chs = tokenizer(
selected_scenario_names=selected_scenario_names,
manual_data=None,
gen_raw=False)
#%% DATALOADER
train_size = int(train_ratio * len(preprocessed_chs))
val_size = int(val_ratio * len(preprocessed_chs))
test_size = len(preprocessed_chs) - val_size - train_size
train_data, val_data, test_data = torch.utils.data.random_split(
preprocessed_chs, [train_size, val_size, test_size]
)
train_loader = prepare_for_lwm(train_data, device, batch_size=batch_size, shuffle=True)
val_loader = prepare_for_lwm(val_data, device, batch_size=batch_size, shuffle=True)
test_loader = prepare_for_lwm(test_data, device, batch_size=batch_size, shuffle=True)
# %% Model
load_model = False
model = lwm()
model.to(device)
if load_model:
model_name = 'models/pretrained_model.pth'
model.load_state_dict(torch.load(model_name))
print(f"Model loaded from {model_name}")
# Loss function
criterionMLM = nn.MSELoss()
# %% Optimizer and Scheduler
adaptive_lr = False
optimizer = optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-5)
scheduler = (
optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min')
if adaptive_lr
else StepLR(optimizer, step_size=10, gamma=0.9)
)
# %% Training
training_loss = []
validation_loss = []
def train(model, dataloader, optimizer, scheduler=None, device="cuda"):
model.train()
running_loss = 0.0
criterionMCM = nn.MSELoss()
for idx, batch in enumerate(dataloader):
input_ids = batch[0].to(device)
masked_tokens = batch[1].to(device)
masked_pos = batch[2].to(device)
optimizer.zero_grad()
logits_lm, _ = model(input_ids, masked_pos)
loss_lm = criterionMCM(logits_lm, masked_tokens)
loss = loss_lm / torch.var(masked_tokens)
loss.backward()
optimizer.step()
if scheduler is not None:
scheduler.step()
running_loss += loss.item()
average_loss = running_loss / len(dataloader)
return average_loss
def validate(model, dataloader, device="cuda"):
model.eval()
running_loss = 0.0
criterionMCM = nn.MSELoss()
with torch.no_grad():
for idx, batch in enumerate(dataloader):
input_ids = batch[0].to(device)
masked_tokens = batch[1].to(device)
masked_pos = batch[2].to(device)
logits_lm, _ = model(input_ids, masked_pos)
loss_lm = criterionMCM(logits_lm, masked_tokens)
loss = loss_lm / torch.var(masked_tokens)
running_loss += loss.item()
average_loss = running_loss / len(dataloader)
return average_loss
# %% Training Loop
for epoch in range(n_epochs):
print(f"Epoch {epoch + 1}/{n_epochs}")
# Training step
train_loss = train(model, train_loader, optimizer, scheduler, device)
training_loss.append(train_loss)
print(f"Training Loss: {train_loss:.4f}")
# Validation step
if val_loader is not None:
val_loss = validate(model, val_loader, device)
validation_loss.append(val_loss)
print(f"Validation Loss: {val_loss:.4f}")
|