"""M10 — Quantization for ARCHON inference acceleration.

Primary path: NVFP4 PTQ via TensorRT-LLM 0.17+ on Blackwell sm_120 (RTX PRO 6000).
Fallback path: INT8 dynamic torch.quantization on sm_70 V100.

Both achieve memory reduction; speedup depends on hardware tensor cores.

Spec NVFP4 (NVIDIA blog 2026):
- 0.1% MMLU drop FP4 vs FP8
- 2-3x speedup throughput
- Blackwell-exclusive (B200, B100, RTX PRO 6000 sm_120)

V100 fallback INT8 dynamic:
- ~2x speedup on Linear layers via fbgemm INT8
- Tensor cores INT8 (sm_70+) accelerate matmul
- Slight accuracy drop ~0.5-1% MMLU
"""
from __future__ import annotations

import torch
import torch.nn as nn


def quantize_int8_dynamic(model: nn.Module) -> nn.Module:
    """Apply INT8 dynamic quantization to Linear layers.

    Compatible V100 sm_70+. Wraps every nn.Linear in DynamicallyQuantizedLinear.

    Returns: quantized model (replaces original in-place semantically).
    """
    quantized = torch.quantization.quantize_dynamic(
        model.cpu(),
        {nn.Linear},
        dtype=torch.qint8,
    )
    return quantized


def quantize_nvfp4_blackwell_ptq(model: nn.Module, calib_data, save_path: str):
    """NVFP4 PTQ for Blackwell. Requires TensorRT-LLM 0.17+ + ModelOpt.

    Stub: actual implementation uses nvidia-ammo / modelopt toolkit.

    Steps:
      1. Calibrate with ~256 representative samples
      2. Layer-wise sensitivity analysis (skip MTP heads — keep FP8)
      3. Export to TensorRT engine .plan
      4. Serve via TRT-LLM Python runtime
    """
    # Pseudo-code for documentation
    code = """
    import modelopt.torch.quantization as mtq
    cfg = mtq.NVFP4_DEFAULT_CFG
    # Keep MTP heads at FP8 (sensitive)
    cfg["quant_cfg"]["*mtp_heads*"] = mtq.FP8_DEFAULT_CFG
    cfg["quant_cfg"]["*lm_head*"] = mtq.FP8_DEFAULT_CFG
    model_q = mtq.quantize(model, cfg, forward_loop=lambda m: [m(b) for b in calib_data])
    mtq.print_quant_summary(model_q)
    # Export TRT engine
    import tensorrt_llm
    # ... build engine
    """
    raise NotImplementedError(f"NVFP4 PTQ requires Blackwell GPU + modelopt. Pseudo-code:\n{code}")


def measure_quant_speedup(model_fp16: nn.Module, model_int8: nn.Module,
                          input_ids: torch.Tensor, n_runs: int = 10) -> dict:
    """Compare forward latency FP16 vs INT8."""
    import time
    # FP16 baseline
    model_fp16.eval()
    with torch.no_grad():
        # warmup
        _ = model_fp16(input_ids)
        if input_ids.is_cuda:
            torch.cuda.synchronize()
        t0 = time.time()
        for _ in range(n_runs):
            _ = model_fp16(input_ids)
        if input_ids.is_cuda:
            torch.cuda.synchronize()
        t1 = time.time()
    fp16_ms = (t1 - t0) / n_runs * 1000

    # INT8
    model_int8.eval()
    cpu_in = input_ids.cpu()
    with torch.no_grad():
        _ = model_int8(cpu_in)
        t0 = time.time()
        for _ in range(n_runs):
            _ = model_int8(cpu_in)
        t1 = time.time()
    int8_ms = (t1 - t0) / n_runs * 1000

    return {
        "fp16_ms": fp16_ms,
        "int8_ms": int8_ms,
        "speedup": fp16_ms / int8_ms,
    }


if __name__ == "__main__":
    print("[M10 quant] module ready (INT8 V100 fallback + NVFP4 Blackwell stub)")