app.py

"""
BitNet b1.58 2B4T — CPU-Only Inference Explorer
================================================
A Gradio demo showcasing Microsoft's first open-source native 1-bit LLM.
All inference runs on CPU — no GPU required.

Paper: https://arxiv.org/abs/2504.12285
Model: https://huggingface.co/microsoft/bitnet-b1.58-2B-4T
"""

import os
import time
import threading
import psutil
import torch
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer

# ─── Configuration ───────────────────────────────────────────────────────────
MODEL_ID = "microsoft/bitnet-b1.58-2B-4T"
MAX_CONTEXT = 4096

# ─── Load Model (CPU-only) ──────────────────────────────────────────────────
print(f"Loading {MODEL_ID} on CPU...")
t0 = time.time()

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="cpu",
    low_cpu_mem_usage=True,
)
model.eval()

load_time = time.time() - t0
proc = psutil.Process(os.getpid())
model_mem_gb = proc.memory_info().rss / 1024**3

print(f"✓ Model loaded in {load_time:.1f}s | RSS: {model_mem_gb:.2f} GB")

# ─── System Info ─────────────────────────────────────────────────────────────
cpu_count = psutil.cpu_count(logical=True)
total_ram = psutil.virtual_memory().total / 1024**3

SYSTEM_INFO = f"""### System
| Metric | Value |
|---|---|
| CPU cores | {cpu_count} |
| Total RAM | {total_ram:.1f} GB |
| Model RSS | {model_mem_gb:.2f} GB |
| Load time | {load_time:.1f}s |
| Weights | 1.58-bit ternary ({{-1, 0, +1}}) |
| Activations | 8-bit integer |
| Context | {MAX_CONTEXT} tokens |
"""

# ─── Paper benchmark table (from Table 1 of the paper) ──────────────────────
PAPER_TABLE = """### Published Benchmarks (from the paper)

| Benchmark | LLaMA 3.2 1B | Gemma-3 1B | Qwen2.5 1.5B | SmolLM2 1.7B | **BitNet 2B** |
|---|---|---|---|---|---|
| **Memory** | 2 GB | 1.4 GB | 2.6 GB | 3.2 GB | **0.4 GB** |
| **CPU Latency** | 48ms | 41ms | 65ms | 67ms | **29ms** |
| **Energy/token** | 0.258J | 0.186J | 0.347J | 0.425J | **0.028J** |
| ARC-Challenge | 37.8 | 38.4 | 46.7 | 43.5 | **49.9** |
| WinoGrande | 59.5 | 58.5 | 62.8 | 69.0 | **71.9** |
| GSM8K | 38.2 | 31.2 | 56.8 | 45.1 | **58.4** |
| MMLU | 45.6 | 39.9 | **60.3** | 49.2 | 53.2 |
| HumanEval+ | 31.1 | 37.2 | **50.6** | 28.0 | 38.4 |
| **Average** | 44.9 | 43.7 | **55.2** | 48.7 | 54.2 |

*BitNet uses 5-13× less memory and 6-9× less energy than comparable models.*

> ⚠️ **Note:** This demo uses the `transformers` library, which does **not** include
> the specialized `bitnet.cpp` kernels. For the CPU latency numbers shown above,
> use [bitnet.cpp](https://github.com/microsoft/BitNet) with the GGUF weights.
"""

# ─── Architecture explainer ──────────────────────────────────────────────────
ARCHITECTURE_MD = """### How BitNet b1.58 Works

```
Standard Transformer          →  BitNet b1.58
─────────────────────          ─────────────────
FP16/BF16 weights (16 bits)   →  Ternary weights: {-1, 0, +1} (1.58 bits)
FP16 activations               →  INT8 activations (absmax per-token)
nn.Linear                      →  BitLinear (absmean quantization)
SwiGLU activation               →  Squared ReLU (ReLU²)
LayerNorm                       →  SubLN normalization
Standard MatMul                 →  Additions only (no multiplications!)
```

**Key Insight:** Since weights are only -1, 0, or +1, matrix multiplication
becomes pure addition/subtraction. This is why CPUs can run BitNet models
so efficiently — you don't need floating-point multiply hardware at all.

**Training:** The model was trained **from scratch** with this quantization,
not post-training quantized. This is crucial — native 1-bit training preserves
quality far better than quantizing a pre-trained FP16 model down to 1-bit.

**3-Stage Training Pipeline:**
1. **Pre-training** on 4T tokens (text, code, synthetic math)
2. **SFT** on instruction-following datasets
3. **DPO** for alignment with human preferences
"""

# ─── Generation functions ────────────────────────────────────────────────────

def chat_respond(message, history, system_prompt, max_new_tokens, temperature, top_p):
    """Streaming chat with live token/sec stats."""
    messages = [{"role": "system", "content": system_prompt}]
    for item in history:
        messages.append(item)
    messages.append({"role": "user", "content": message})

    inputs = tokenizer.apply_chat_template(
        messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
    )

    # Truncate to max context
    if inputs.shape[1] > MAX_CONTEXT - max_new_tokens:
        inputs = inputs[:, -(MAX_CONTEXT - max_new_tokens):]

    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

    gen_kwargs = dict(
        input_ids=inputs,
        attention_mask=torch.ones_like(inputs),
        pad_token_id=tokenizer.eos_token_id,
        streamer=streamer,
        max_new_tokens=int(max_new_tokens),
        do_sample=temperature > 0,
        use_cache=True,
    )
    if temperature > 0:
        gen_kwargs["temperature"] = float(temperature)
        gen_kwargs["top_p"] = float(top_p)

    t0 = time.perf_counter()
    thread = threading.Thread(target=model.generate, kwargs=gen_kwargs)
    thread.start()

    response = ""
    tok_count = 0
    for chunk in streamer:
        response += chunk
        tok_count += 1
        elapsed = time.perf_counter() - t0
        tps = tok_count / elapsed if elapsed > 0 else 0
        stats = f"\n\n---\n*⏱ {tok_count} tokens · {tps:.1f} tok/s · {elapsed:.1f}s*"
        yield response + stats

    thread.join()


def single_benchmark(prompt, max_new_tokens):
    """Run a single non-streaming generation with detailed stats."""
    messages = [
        {"role": "system", "content": "You are a helpful AI assistant."},
        {"role": "user", "content": prompt},
    ]
    inputs = tokenizer.apply_chat_template(
        messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
    )
    input_len = inputs.shape[1]

    mem_before = proc.memory_info().rss / 1024**3

    t0 = time.perf_counter()
    with torch.no_grad():
        output = model.generate(
            inputs,
            attention_mask=torch.ones_like(inputs),
            pad_token_id=tokenizer.eos_token_id,
            max_new_tokens=int(max_new_tokens),
            do_sample=False,
            use_cache=True,
        )
    elapsed = time.perf_counter() - t0

    mem_after = proc.memory_info().rss / 1024**3
    n_generated = output.shape[-1] - input_len
    tps = n_generated / elapsed if elapsed > 0 else 0

    response = tokenizer.decode(output[0][input_len:], skip_special_tokens=True)

    stats_md = f"""### Benchmark Results

| Metric | Value |
|---|---|
| Input tokens | {input_len} |
| Output tokens | {n_generated} |
| Total time | {elapsed:.2f}s |
| **Tokens/sec** | **{tps:.2f}** |
| Avg ms/token | {(elapsed/n_generated*1000):.1f}ms |
| Memory before | {mem_before:.2f} GB |
| Memory after | {mem_after:.2f} GB |
| Memory delta | {(mem_after - mem_before)*1000:.1f} MB |
"""
    return response, stats_md


# ─── Build Gradio UI ─────────────────────────────────────────────────────────

HEADER = """# 🧬 BitNet b1.58 2B4T — CPU-Only Inference Explorer

**The first open-source native 1-bit LLM** by Microsoft Research.
All weights are ternary {-1, 0, +1} — no floating-point multiplications needed.

| | |
|---|---|
| 📄 [Paper](https://arxiv.org/abs/2504.12285) | 🤗 [Model](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T) |
| 💻 [bitnet.cpp](https://github.com/microsoft/BitNet) (38K+ ⭐) | 📊 2B params · 4T training tokens · 1.1 GB weights |
"""

with gr.Blocks(
    title="BitNet b1.58 2B4T — CPU Inference Explorer",
) as demo:

    gr.Markdown(HEADER)

    with gr.Tabs():
        # ── Tab 1: Chat ──────────────────────────────────────────────────
        with gr.Tab("💬 Chat", id="chat"):
            chat = gr.ChatInterface(
                fn=chat_respond,
                description="Chat with BitNet b1.58 on CPU. Token/sec stats shown after each response.",
                additional_inputs=[
                    gr.Textbox(
                        value="You are a helpful, concise AI assistant.",
                        label="System Prompt",
                    ),
                    gr.Slider(1, 2048, value=256, step=1, label="Max New Tokens"),
                    gr.Slider(0.0, 2.0, value=0.7, step=0.05, label="Temperature (0 = greedy)"),
                    gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p"),
                ],
                examples=[
                    ["Explain what a 1-bit LLM is in 3 sentences."],
                    ["Write a Python function to find the nth Fibonacci number."],
                    ["What are the pros and cons of running AI on CPUs vs GPUs?"],
                    ["Solve: If 3x + 7 = 22, what is x?"],
                ],
                cache_examples=False,
            )

        # ── Tab 2: Benchmark ─────────────────────────────────────────────
        with gr.Tab("📊 Benchmark", id="bench"):
            gr.Markdown("### Run a single-shot benchmark (greedy decoding)")
            with gr.Row():
                with gr.Column(scale=2):
                    bench_prompt = gr.Textbox(
                        value="Write a detailed explanation of how transformer neural networks work, covering attention mechanisms, positional encoding, and the training process.",
                        label="Prompt",
                        lines=3,
                    )
                    bench_tokens = gr.Slider(16, 512, value=128, step=16, label="Max New Tokens")
                    bench_btn = gr.Button("🚀 Run Benchmark", variant="primary")
                with gr.Column(scale=1):
                    bench_stats = gr.Markdown("*Click 'Run Benchmark' to start*")

            bench_output = gr.Textbox(label="Generated Text", lines=10, interactive=False)
            bench_btn.click(
                fn=single_benchmark,
                inputs=[bench_prompt, bench_tokens],
                outputs=[bench_output, bench_stats],
            )

        # ── Tab 3: Paper Results ─────────────────────────────────────────
        with gr.Tab("📈 Paper Results", id="paper"):
            gr.Markdown(PAPER_TABLE)

        # ── Tab 4: Architecture ──────────────────────────────────────────
        with gr.Tab("🏗️ Architecture", id="arch"):
            gr.Markdown(ARCHITECTURE_MD)

        # ── Tab 5: System Info ───────────────────────────────────────────
        with gr.Tab("⚙️ System", id="sys"):
            gr.Markdown(SYSTEM_INFO)


if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, theme=gr.themes.Soft())