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import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Dict, Tuple
import uvicorn
import copy
from configs.model_config import SystemConfig, PathConfig
from src.model.llm import DecoderOnlyTransformer
from src.model.config import ModelConfig
from tokenizers import Tokenizer
app = FastAPI(
title="Generative AI Project - Alignment Service",
description="SFT & DPO Microservice supporting multi-GPU training",
version="1.0.0"
)
@app.get("/health")
def health():
"""Lightweight liveness probe used by the gateway."""
return {"status": "ok", "service": "alignment"}
class SFTTrainRequest(BaseModel):
sft_data: List[Dict[str, str]]
epochs: int = 3
lr: float = 2e-5
batch_size: int = 2
class DPOTrainRequest(BaseModel):
dpo_data: List[Dict[str, str]]
epochs: int = 3
lr: float = 5e-6
beta: float = 0.1
batch_size: int = 2
class SFTDataset(Dataset):
def __init__(self, data: List[Dict[str, str]], tokenizer: Tokenizer, max_len: int = 512):
from src.common.prompts import format_prompt, resolve_eos_id, resolve_pad_id
self.samples = []
eos_id = resolve_eos_id(tokenizer)
pad_id = resolve_pad_id(tokenizer)
for item in data:
prompt = format_prompt(item["prompt"])
response = item["response"]
enc_prompt = tokenizer.encode(prompt).ids
enc_response = tokenizer.encode(response).ids
if eos_id is not None:
enc_response = enc_response + [eos_id]
input_ids = enc_prompt + enc_response
labels = [-100] * len(enc_prompt) + enc_response
if len(input_ids) > max_len:
input_ids = input_ids[:max_len]
labels = labels[:max_len]
else:
pad_len = max_len - len(input_ids)
input_ids = input_ids + [pad_id] * pad_len
labels = labels + [-100] * pad_len
self.samples.append((torch.tensor(input_ids, dtype=torch.long), torch.tensor(labels, dtype=torch.long)))
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
return self.samples[idx]
class DPODataset(Dataset):
def __init__(self, data: List[Dict[str, str]], tokenizer: Tokenizer, max_len: int = 512):
from src.common.prompts import format_prompt, resolve_eos_id, resolve_pad_id
self.samples = []
pad_token = resolve_pad_id(tokenizer)
eos_id = resolve_eos_id(tokenizer)
for item in data:
prompt = format_prompt(item["prompt"])
chosen = item["chosen"]
rejected = item["rejected"]
enc_prompt = tokenizer.encode(prompt).ids
enc_chosen = tokenizer.encode(chosen).ids
enc_rejected = tokenizer.encode(rejected).ids
if eos_id is not None:
enc_chosen = enc_chosen + [eos_id]
enc_rejected = enc_rejected + [eos_id]
def pad(ids, labels):
if len(ids) > max_len:
return ids[:max_len], labels[:max_len]
pad_len = max_len - len(ids)
return ids + [pad_token] * pad_len, labels + [-100] * pad_len
c_input_ids, c_labels = pad(enc_prompt + enc_chosen, [-100] * len(enc_prompt) + enc_chosen)
r_input_ids, r_labels = pad(enc_prompt + enc_rejected, [-100] * len(enc_prompt) + enc_rejected)
self.samples.append({
"c_ids": torch.tensor(c_input_ids, dtype=torch.long),
"c_labels": torch.tensor(c_labels, dtype=torch.long),
"r_ids": torch.tensor(r_input_ids, dtype=torch.long),
"r_labels": torch.tensor(r_labels, dtype=torch.long)
})
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
return self.samples[idx]
def get_log_probs(logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_mask = (shift_labels != -100)
dummy_labels = shift_labels.clone()
dummy_labels[shift_labels == -100] = 0
log_probs = F.log_softmax(shift_logits, dim=-1)
per_token_log_probs = torch.gather(log_probs, dim=-1, index=dummy_labels.unsqueeze(-1)).squeeze(-1)
return (per_token_log_probs * loss_mask).sum(dim=-1)
def wrap_model_for_device(model: nn.Module) -> nn.Module:
"""Wraps model in DataParallel dynamically if multiple GPUs are detected."""
model = model.to(SystemConfig.DEVICE)
if SystemConfig.DEVICE == "cuda" and SystemConfig.NUM_GPUS > 1:
print(f"[ACCELERATION] Wrapping model in torch.nn.DataParallel across {SystemConfig.NUM_GPUS} GPUs.")
model = nn.DataParallel(model)
return model
@app.post("/sft")
def train_sft(payload: SFTTrainRequest):
if not os.path.exists(PathConfig.PRETRAINED_BASE_MODEL):
raise HTTPException(status_code=400, detail="Base model checkpoint not found. Run pretraining first.")
tokenizer = Tokenizer.from_file(PathConfig.TOKENIZER_PATH)
checkpoint = torch.load(PathConfig.PRETRAINED_BASE_MODEL, map_location=SystemConfig.DEVICE)
model_config_dict = checkpoint.get('model_config', checkpoint.get('args'))
model_config = ModelConfig(**model_config_dict) if isinstance(model_config_dict, dict) else model_config_dict
raw_model = DecoderOnlyTransformer(model_config)
model_state = checkpoint.get('model', checkpoint.get('model_state_dict'))
raw_model.load_state_dict(model_state)
model = wrap_model_for_device(raw_model)
model.train()
dataset = SFTDataset(payload.sft_data, tokenizer, max_len=model_config.block_size)
dataloader = DataLoader(dataset, batch_size=payload.batch_size, shuffle=True)
optimizer = torch.optim.AdamW(model.parameters(), lr=payload.lr)
print("Starting SFT training stage...")
for epoch in range(payload.epochs):
epoch_loss = 0.0
for inputs, labels in dataloader:
inputs, labels = inputs.to(SystemConfig.DEVICE), labels.to(SystemConfig.DEVICE)
optimizer.zero_grad()
logits, _ = model(inputs)
shift_logits = logits[..., :-1, :].contiguous().view(-1, model_config.vocab_size)
shift_labels = labels[..., 1:].contiguous().view(-1)
loss = F.cross_entropy(shift_logits, shift_labels, ignore_index=-100)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
print(f"SFT Epoch {epoch+1}/{payload.epochs} | Loss: {epoch_loss/len(dataloader):.4f}")
state_to_save = model.module.state_dict() if isinstance(model, nn.DataParallel) else model.state_dict()
torch.save({"model_state_dict": state_to_save, "args": model_config_dict}, PathConfig.SFT_MODEL_CHECKPOINT)
return {"status": "SFT Complete", "checkpoint_path": PathConfig.SFT_MODEL_CHECKPOINT}
@app.post("/dpo")
def train_dpo(payload: DPOTrainRequest):
if not os.path.exists(PathConfig.SFT_MODEL_CHECKPOINT):
raise HTTPException(status_code=400, detail="SFT checkpoint not found. Run SFT first.")
tokenizer = Tokenizer.from_file(PathConfig.TOKENIZER_PATH)
checkpoint = torch.load(PathConfig.SFT_MODEL_CHECKPOINT, map_location=SystemConfig.DEVICE)
model_config_dict = checkpoint.get('model_config', checkpoint.get('args'))
model_config = ModelConfig(**model_config_dict) if isinstance(model_config_dict, dict) else model_config_dict
policy_raw = DecoderOnlyTransformer(model_config)
model_state = checkpoint.get('model', checkpoint.get('model_state_dict'))
policy_raw.load_state_dict(model_state)
policy_model = wrap_model_for_device(policy_raw)
policy_model.train()
ref_raw = DecoderOnlyTransformer(model_config)
ref_raw.load_state_dict(copy.deepcopy(model_state))
ref_model = wrap_model_for_device(ref_raw)
ref_model.eval()
dataset = DPODataset(payload.dpo_data, tokenizer, max_len=model_config.block_size)
dataloader = DataLoader(dataset, batch_size=payload.batch_size, shuffle=True)
optimizer = torch.optim.AdamW(policy_model.parameters(), lr=payload.lr)
print("Starting DPO training stage...")
for epoch in range(payload.epochs):
epoch_loss = 0.0
for batch in dataloader:
optimizer.zero_grad()
c_ids, c_labels = batch["c_ids"].to(SystemConfig.DEVICE), batch["c_labels"].to(SystemConfig.DEVICE)
r_ids, r_labels = batch["r_ids"].to(SystemConfig.DEVICE), batch["r_labels"].to(SystemConfig.DEVICE)
p_c_logits, _ = policy_model(c_ids)
p_r_logits, _ = policy_model(r_ids)
policy_chosen_logps = get_log_probs(p_c_logits, c_labels)
policy_rejected_logps = get_log_probs(p_r_logits, r_labels)
with torch.no_grad():
ref_c_logits, _ = ref_model(c_ids)
ref_r_logits, _ = ref_model(r_ids)
ref_chosen_logps = get_log_probs(ref_c_logits, c_labels)
ref_rejected_logps = get_log_probs(ref_r_logits, r_labels)
pi_log_ratio = policy_chosen_logps - policy_rejected_logps
ref_log_ratio = ref_chosen_logps - ref_rejected_logps
logits_diff = pi_log_ratio - ref_log_ratio
loss = -F.logsigmoid(payload.beta * logits_diff).mean()
loss.backward()
optimizer.step()
epoch_loss += loss.item()
print(f"DPO Epoch {epoch+1}/{payload.epochs} | Loss: {epoch_loss/len(dataloader):.4f}")
state_to_save = policy_model.module.state_dict() if isinstance(policy_model, nn.DataParallel) else policy_model.state_dict()
torch.save({"model_state_dict": state_to_save, "args": model_config_dict}, PathConfig.DPO_MODEL_CHECKPOINT)
return {"status": "DPO Alignment Complete", "checkpoint_path": PathConfig.DPO_MODEL_CHECKPOINT}
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8002)
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