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#!/usr/bin/env python3
import os
import csv
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
get_linear_schedule_with_warmup
)
from peft import PeftModel
from torch.cuda.amp import autocast, GradScaler
from tqdm.auto import tqdm
from multiprocessing import freeze_support
class TripletDataset(Dataset):
def __init__(self, path):
self.samples = []
with open(path, newline="") as f:
reader = csv.DictReader(f)
for row in reader:
a_ids = torch.tensor(list(map(int, row["a_ids"].split())), dtype=torch.long)
a_mask = torch.tensor(list(map(int, row["a_mask"].split())), dtype=torch.long)
p_ids = torch.tensor(list(map(int, row["p_ids"].split())), dtype=torch.long)
p_mask = torch.tensor(list(map(int, row["p_mask"].split())), dtype=torch.long)
n_ids = torch.tensor(list(map(int, row["n_ids"].split())), dtype=torch.long)
n_mask = torch.tensor(list(map(int, row["n_mask"].split())), dtype=torch.long)
self.samples.append((a_ids, a_mask, p_ids, p_mask, n_ids, n_mask))
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
return self.samples[idx]
class GemmaTripletModel(nn.Module):
def __init__(self, peft_model):
super().__init__()
self.peft = peft_model
H = peft_model.config.hidden_size
self.proj = nn.Sequential(
nn.Linear(H, 512),
nn.ReLU(),
nn.Linear(512, 256),
)
def forward(self, ids, mask):
out = self.peft.base_model( # PeftModel stores underlying model as .base_model
input_ids=ids,
attention_mask=mask,
output_hidden_states=True,
return_dict=True
)
last = out.hidden_states[-1] # (B, T, H)
pooled = last.mean(dim=1) # mean pooling
z = self.proj(pooled)
norm = z.norm(p=2, dim=1, keepdim=True).clamp_min(1e-6)
return z / norm
def collate_fn(batch):
return tuple(torch.stack(x) for x in zip(*batch))
def main():
# --- Config ---
MODEL_NAME = "google/gemma-3-1b-pt"
STAGE1_DIR = "stage1_simcse/final"
TRAIN_FILE = "train.csv"
VAL_FILE = "val.csv"
BATCH_SIZE = 12
LR = 1e-5
WEIGHT_DECAY = 0.01
NUM_EPOCHS = 3
MARGIN = 0.2
OUTPUT_DIR = "phase2_triplet_amp"
SEED = 42
os.makedirs(OUTPUT_DIR, exist_ok=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.manual_seed(SEED)
# --- Tokenizer & PEFT Model (load Stage 1) ---
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)
base = AutoModelForCausalLM.from_pretrained(MODEL_NAME, attn_implementation="eager")
peft_model = PeftModel.from_pretrained(base, STAGE1_DIR) # loads your Stage 1 LoRA weights
# --- Embed + Projector ---
model = GemmaTripletModel(peft_model).to(device)
# --- Datasets & Loaders ---
train_ds = TripletDataset(TRAIN_FILE)
val_ds = TripletDataset(VAL_FILE)
train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_fn)
val_loader = DataLoader(val_ds, batch_size=BATCH_SIZE, shuffle=False, collate_fn=collate_fn)
# --- Optimizer, Scheduler, AMP ---
optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)
total_steps = len(train_loader) * NUM_EPOCHS
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=int(0.1 * total_steps),
num_training_steps=total_steps
)
scaler = GradScaler()
# --- Loss ---
triplet_loss = nn.TripletMarginLoss(margin=MARGIN, p=2)
# --- Training Loop ---
for epoch in range(1, NUM_EPOCHS + 1):
model.train()
running_loss = 0.0
for a_ids, a_mask, p_ids, p_mask, n_ids, n_mask in tqdm(train_loader, desc=f"Train {epoch}", unit="batch"):
a_ids, a_mask = a_ids.to(device), a_mask.to(device)
p_ids, p_mask = p_ids.to(device), p_mask.to(device)
n_ids, n_mask = n_ids.to(device), n_mask.to(device)
optimizer.zero_grad()
with autocast():
emb_a = model(a_ids, a_mask)
emb_p = model(p_ids, p_mask)
emb_n = model(n_ids, n_mask)
loss = triplet_loss(emb_a, emb_p, emb_n)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
scheduler.step()
running_loss += loss.item()
print(f"Epoch {epoch} Train Loss: {running_loss/len(train_loader):.6f}")
# --- Validation ---
model.eval()
val_loss = 0.0
with torch.no_grad():
for a_ids, a_mask, p_ids, p_mask, n_ids, n_mask in tqdm(val_loader, desc=f"Val {epoch}", unit="batch"):
a_ids, a_mask = a_ids.to(device), a_mask.to(device)
p_ids, p_mask = p_ids.to(device), p_mask.to(device)
n_ids, n_mask = n_ids.to(device), n_mask.to(device)
with autocast():
emb_a = model(a_ids, a_mask)
emb_p = model(p_ids, p_mask)
emb_n = model(n_ids, n_mask)
val_loss += triplet_loss(emb_a, emb_p, emb_n).item()
print(f"Epoch {epoch} Val Loss: {val_loss/len(val_loader):.6f}")
# --- Checkpoint LoRA only ---
ckpt_dir = os.path.join(OUTPUT_DIR, f"epoch{epoch}")
peft_model.save_pretrained(ckpt_dir)
tokenizer.save_pretrained(ckpt_dir)
# --- Final Save ---
final_dir = os.path.join(OUTPUT_DIR, "final")
os.makedirs(final_dir, exist_ok=True)
peft_model.save_pretrained(final_dir)
tokenizer.save_pretrained(final_dir)
print("Phase 2 complete. Checkpoints in", OUTPUT_DIR)
if __name__ == "__main__":
freeze_support()
main()
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