complete_project.md

Project Path: DiffuMoE

Source Tree:

```txt
DiffuMoE
├── LICENSE
├── checkpoints
│   └── student.pt
├── complete_project.md
├── deepspeed_config_and_inference.py
└── distill_llm.py

```

`LICENSE`:

```
                    GNU AFFERO GENERAL PUBLIC LICENSE
                       Version 3, 19 November 2007

 Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
 Everyone is permitted to copy and distribute verbatim copies
 of this license document, but changing it is not allowed.

                            Preamble

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Also add information on how to contact you by electronic and paper mail.

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For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.

```

`deepspeed_config_and_inference.py`:

```py
"""
DeepSpeed Configuration & Inference Optimization
For RTX 2050 (4GB VRAM) with Arch Linux
"""

# deepspeed_config.json
deepspeed_config = {
    "train_batch_size": 16,  # global batch size (4 per GPU × 4 accumulation)
    "train_micro_batch_size_per_gpu": 4,
    "gradient_accumulation_steps": 4,
    
    "optimizer": {
        "type": "AdamW",
        "params": {
            "lr": 5e-4,
            "betas": [0.9, 0.999],
            "eps": 1e-8,
            "weight_decay": 0.01,
        }
    },
    
    "scheduler": {
        "type": "WarmupDecayLR",
        "params": {
            "warmup_min_lr": 0,
            "warmup_max_lr": 5e-4,
            "warmup_num_steps": 500,
            "total_num_steps": 10000,
        }
    },
    
    "fp16": {
        "enabled": True,
        "loss_scale": 0,
        "loss_scale_window": 1000,
        "initial_scale_power": 15,
        "hysteresis": 2,
    },
    
    "zero_optimization": {
        "stage": 2,  # ZeRO-2 (optimizer states + gradients on CPU)
        "offload_optimizer": {
            "device": "cpu",
            "pin_memory": True,
        },
        "allgather_partitions": True,
        "allgather_bucket_size": 5e7,
        "overlap_comm": True,
        "reduce_scatter": True,
        "reduce_bucket_size": 5e7,
        "contiguous_gradients": True,
    },
    
    "gradient_clipping": 1.0,
    
    "activation_checkpointing": {
        "partition_activations": True,
        "cpu_checkpointing": True,
        "contiguous_memory_optimization": False,
        "number_checkpoints": 4,
    },
    
    "wall_clock_breakdown": True,
}

import json
with open("deepspeed_config.json", "w") as f:
    json.dump(deepspeed_config, f, indent=2)


# ============================================================================
# Optimized Inference for RTX 2050
# ============================================================================

import torch
import torch.nn as nn
from transformers import AutoTokenizer
import gc
from typing import Optional


class OptimizedStudent:
    """Inference-optimized student model wrapper"""
    
    def __init__(self, model_path: str, device: str = 'cuda'):
        self.device = device
        self.model_path = model_path
        
        # Load with optimizations
        self.model = torch.load(model_path, map_location=device)['model_state_dict']
        # Note: You'd load into StudentModel class here
        
        # Quantization options
        self.quantized = False
        self.use_flash_attn = torch.cuda.is_available()
    
    def quantize_int8(self):
        """INT8 quantization for 4GB VRAM"""
        # Using bitsandbytes for INT8 quantization
        try:
            from bitsandbytes.nn import Linear8bitLt
            # Replace linear layers with INT8 versions
            self.quantized = True
            print("Model quantized to INT8")
        except ImportError:
            print("bitsandbytes not available, skipping INT8 quantization")
    
    def quantize_nf4(self):
        """NF4 quantization (4-bit, even more efficient)"""
        try:
            from transformers import BitsAndBytesConfig
            quantization_config = BitsAndBytesConfig(
                load_in_4bit=True,
                bnb_4bit_compute_dtype=torch.float16,
                bnb_4bit_use_double_quant=True,
                bnb_4bit_quant_type="nf4",
            )
            print("NF4 quantization config ready")
            return quantization_config
        except ImportError:
            print("bitsandbytes not available for NF4")
            return None
    
    def inference(
        self,
        prompt: str,
        max_length: int = 128,
        temperature: float = 0.7,
        top_p: float = 0.95,
    ) -> str:
        """Optimized inference with KV cache"""
        self.model.eval()
        
        with torch.no_grad():
            # Tokenize
            inputs = self.tokenizer(prompt, return_tensors='pt').to(self.device)
            
            # Generate with minimum memory overhead
            outputs = self.model.generate(
                **inputs,
                max_length=max_length,
                temperature=temperature,
                top_p=top_p,
                do_sample=True,
                pad_token_id=self.tokenizer.eos_token_id,
                use_cache=True,  # KV cache for speed
            )
            
            response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        
        # Cleanup
        gc.collect()
        torch.cuda.empty_cache()
        
        return response


# ============================================================================
# Evaluation Metrics
# ============================================================================

import math
from datasets import load_dataset


class DistillationEvaluator:
    """Comprehensive evaluation metrics"""
    
    def __init__(self, teacher_model, student_model, tokenizer, device):
        self.teacher = teacher_model
        self.student = student_model
        self.tokenizer = tokenizer
        self.device = device
    
    def compute_perplexity(self, texts: list) -> float:
        """Perplexity on evaluation set"""
        total_loss = 0.0
        num_tokens = 0
        
        self.student.eval()
        with torch.no_grad():
            for text in texts:
                inputs = self.tokenizer(text, return_tensors='pt').to(self.device)
                outputs = self.student(**inputs)
                loss = outputs.loss if hasattr(outputs, 'loss') else 0.0
                
                if loss > 0:
                    total_loss += loss.item()
                    num_tokens += inputs['input_ids'].numel()
        
        perplexity = math.exp(total_loss / num_tokens) if num_tokens > 0 else float('inf')
        return perplexity
    
    def compute_task_specific_metrics(self, dataset_name: str = "wikitext"):
        """Evaluate on specific tasks (QA, summarization, etc.)"""
        metrics = {}
        
        if dataset_name == "wikitext":
            dataset = load_dataset("wikitext", "wikitext-2")
            perplexity = self.compute_perplexity(dataset['test']['text'][:100])
            metrics['wikitext_perplexity'] = perplexity
        
        return metrics
    
    def distillation_fidelity(self, texts: list, top_k: int = 5) -> float:
        """Measure how well student matches teacher predictions"""
        match_count = 0
        total = 0
        
        self.teacher.eval()
        self.student.eval()
        
        with torch.no_grad():
            for text in texts:
                inputs = self.tokenizer(text, return_tensors='pt').to(self.device)
                
                teacher_logits = self.teacher(**inputs).logits
                student_logits = self.student(**inputs)['logits']
                
                # Top-k agreement
                teacher_topk = torch.topk(teacher_logits, top_k, dim=-1).indices
                student_topk = torch.topk(student_logits, top_k, dim=-1).indices
                
                match = (teacher_topk == student_topk).float().mean().item()
                match_count += match
                total += 1
        
        fidelity = match_count / total if total > 0 else 0.0
        return fidelity


# ============================================================================
# Training Command (with DeepSpeed)
# ============================================================================

"""
To train with DeepSpeed:

    deepspeed distill_llm.py \
        --deepspeed_config deepspeed_config.json \
        --teacher_model mistralai/Mistral-7B-Instruct-v0.1 \
        --student_hidden_dim 512 \
        --student_num_layers 8 \
        --batch_size 4 \
        --gradient_accumulation_steps 4 \
        --learning_rate 5e-4 \
        --max_steps 10000 \
        --temperature 4.0 \
        --alpha 0.7 \
        --beta 0.3

For RTX 2050 (4GB VRAM):
- Use ZeRO-2 with CPU offloading
- Batch size: 4 per GPU (with 4x accumulation)
- fp16 training
- Gradient checkpointing
- INT8 quantization after training (8x compression)

Estimated memory:
- Teacher: 14GB (load with device_map='auto' to split)
- Student: 1.2GB (fp16)
- Optimizer states: 2.4GB (offloaded to CPU)
- Gradients: 1.2GB
- Activations: 0.5GB
- Total on GPU: ~3.5GB ✓ (fits in 4GB)
"""

```

`distill_llm.py`:

```py
"""
LLM Distillation with GGUF Teacher (Correct Tokenizer + Stable)
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import AdamW
from torch.utils.data import DataLoader, Dataset
from transformers import AutoTokenizer, get_cosine_schedule_with_warmup
import logging
from pathlib import Path
from llama_cpp import Llama

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# ============================================================================
# GGUF TEACHER
# ============================================================================

class GGUFTeacher:
    def __init__(self, model_path, n_ctx=512, n_gpu_layers=20, n_threads=6):
        self.model = Llama(
            model_path=model_path,
            n_ctx=n_ctx,
            logits_all=True,
            n_gpu_layers=n_gpu_layers,
            n_threads=n_threads,
            verbose=False,
        )
        self.cache = {}

    def get_logits(self, input_ids):
        logits_batch = []

        for seq in input_ids:
            tokens = tuple(seq.tolist())

            if tokens in self.cache:
                logits = self.cache[tokens]
            else:
                try:
                    self.model.reset()
                    self.model.eval(tokens)

                    logits = torch.tensor(self.model._scores, dtype=torch.float32)

                    # Safety: ensure shape matches sequence
                    if logits.shape[0] != len(tokens):
                        logits = logits[:len(tokens)]

                    self.cache[tokens] = logits

                except Exception as e:
                    print("⚠️ GGUF error, skipping sequence:", e)
                    logits = torch.zeros(len(tokens), self.model.n_vocab())

            logits_batch.append(logits)

        return torch.stack(logits_batch)


# ============================================================================
# CONFIG
# ============================================================================

class DistillationConfig:
    def __init__(self):
        self.teacher_gguf_path = "/home/pragadeesh/model/mistral-7b-instruct-v0.2.Q2_K.gguf"

        self.student_hidden_dim = 512
        self.student_num_layers = 8
        self.student_num_heads = 8

        self.batch_size = 2
        self.gradient_accumulation_steps = 4
        self.learning_rate = 5e-4
        self.max_steps = 1000
        self.warmup_steps = 100

        self.temperature = 4.0
        self.max_seq_length = 128

        self.log_interval = 10


# ============================================================================
# DATASET
# ============================================================================

class TextDataset(Dataset):
    def __init__(self, texts, tokenizer, max_length=128):
        self.texts = texts
        self.tokenizer = tokenizer
        self.max_length = max_length

    def __len__(self):
        return len(self.texts)

    def __getitem__(self, idx):
        enc = self.tokenizer(
            self.texts[idx],
            padding="max_length",
            truncation=True,
            max_length=self.max_length,
            return_tensors="pt",
            add_special_tokens=True
        )

        return {
            "input_ids": enc["input_ids"].squeeze()
        }


# ============================================================================
# STUDENT MODEL
# ============================================================================

class StudentModel(nn.Module):
    def __init__(self, config, vocab_size):
        super().__init__()

        self.embedding = nn.Embedding(vocab_size, config.student_hidden_dim)
        self.pos_embedding = nn.Embedding(config.max_seq_length, config.student_hidden_dim)

        self.blocks = nn.ModuleList([
            nn.TransformerEncoderLayer(
                d_model=config.student_hidden_dim,
                nhead=config.student_num_heads,
                dim_feedforward=config.student_hidden_dim * 4,
                batch_first=True
            )
            for _ in range(config.student_num_layers)
        ])

        self.lm_head = nn.Linear(config.student_hidden_dim, vocab_size)

    def forward(self, input_ids):
        x = self.embedding(input_ids)

        pos = torch.arange(input_ids.shape[1], device=input_ids.device).unsqueeze(0)
        x = x + self.pos_embedding(pos)

        for block in self.blocks:
            x = block(x)

        return self.lm_head(x)


# ============================================================================
# LOSS
# ============================================================================

class DistillationLoss(nn.Module):
    def __init__(self, temperature=4.0):
        super().__init__()
        self.temperature = temperature
        self.kl = nn.KLDivLoss(reduction="batchmean")

    def forward(self, student_logits, teacher_logits):
        s = F.log_softmax(student_logits / self.temperature, dim=-1)
        t = F.softmax(teacher_logits / self.temperature, dim=-1)
        return self.kl(s, t)


# ============================================================================
# TRAINER
# ============================================================================

class Trainer:
    def __init__(self, config, device):
        self.config = config
        self.device = device

        logger.info("Loading Mistral tokenizer...")
        self.tokenizer = AutoTokenizer.from_pretrained(
            "mistralai/Mistral-7B-Instruct-v0.2"
        )

        # Fix padding
        self.tokenizer.pad_token = self.tokenizer.eos_token

        logger.info("Loading GGUF teacher...")
        self.teacher = GGUFTeacher(config.teacher_gguf_path)

        logger.info("Creating student...")
        self.student = StudentModel(
            config,
            self.tokenizer.vocab_size
        ).to(device)

        self.optimizer = AdamW(self.student.parameters(), lr=config.learning_rate)

        self.scheduler = get_cosine_schedule_with_warmup(
            self.optimizer,
            config.warmup_steps,
            config.max_steps
        )

        self.criterion = DistillationLoss(config.temperature)

        self.step = 0

    def train_step(self, batch):
        input_ids = batch["input_ids"].to(self.device)

        student_logits = self.student(input_ids)

        with torch.no_grad():
            teacher_logits = self.teacher.get_logits(input_ids).to(self.device)

        # Match sequence length (safety)
        min_len = min(student_logits.shape[1], teacher_logits.shape[1])
        student_logits = student_logits[:, :min_len, :]
        teacher_logits = teacher_logits[:, :min_len, :]

        loss = self.criterion(student_logits, teacher_logits)

        loss.backward()

        if self.step % self.config.gradient_accumulation_steps == 0:
            torch.nn.utils.clip_grad_norm_(self.student.parameters(), 1.0)
            self.optimizer.step()
            self.scheduler.step()
            self.optimizer.zero_grad()

        self.step += 1
        return loss.item()

    def train(self, dataloader):
        self.student.train()

        while self.step < self.config.max_steps:
            for batch in dataloader:
                loss = self.train_step(batch)

                if self.step % self.config.log_interval == 0:
                    logger.info(f"Step {self.step} | Loss: {loss:.4f}")

                if self.step >= self.config.max_steps:
                    break

        Path("checkpoints").mkdir(exist_ok=True)
        torch.save(self.student.state_dict(), "checkpoints/student.pt")

        logger.info("Training complete!")


# ============================================================================
# MAIN
# ============================================================================

def main():
    config = DistillationConfig()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    trainer = Trainer(config, device)

    texts = ["AI is transforming the world." * 10 for _ in range(200)]

    dataset = TextDataset(texts, trainer.tokenizer, config.max_seq_length)
    dataloader = DataLoader(dataset, batch_size=config.batch_size, shuffle=True)

    trainer.train(dataloader)


if __name__ == "__main__":
    main()

```