#!/usr/bin/env python
# coding=utf-8
"""
Benchmark write_weight_scale_to_buffer for AMX MOE operators.

Supports:
- FP8: FP8 weights (1 byte) + float32 scales (block-wise)
- FP8_PERCHANNEL: FP8 weights (1 byte) + float32 per-channel scales
- BF16: Native BF16 weights (2 bytes), no scales

Usage:
    python bench_write_buffer.py          # Run all modes
    python bench_write_buffer.py fp8      # Run FP8 only
    python bench_write_buffer.py fp8_perchannel  # Run FP8 per-channel only
    python bench_write_buffer.py bf16     # Run BF16 only
"""
import json
import os
import platform
import subprocess
import sys
import time

from tqdm import tqdm

sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "build"))

from kt_kernel import kt_kernel_ext
import torch

# Benchmark parameters
expert_num = 256
num_experts_per_tok = 8
gpu_tp_count = 2

warm_up_iter = 30
test_iter = 70

gpu_experts_num = expert_num

hidden_size = 7168
intermediate_size = 2048
group_size = 128  # FP8 uses 128x128 block-wise scales
max_len = 1

physical_to_logical_map = torch.arange(expert_num, dtype=torch.int64, device="cpu").contiguous()
CPUInfer = kt_kernel_ext.CPUInfer(80)


def get_git_commit():
    result = {}
    try:
        commit = subprocess.check_output(["git", "rev-parse", "HEAD"]).decode("utf-8").strip()
        commit_msg = subprocess.check_output(["git", "log", "-1", "--pretty=%B"]).decode("utf-8").strip()
        result["commit"] = commit
        result["commit_message"] = commit_msg
        dirty_output = subprocess.check_output(["git", "status", "--porcelain"]).decode("utf-8").strip()
        result["dirty"] = bool(dirty_output)
        if dirty_output:
            result["dirty_files"] = dirty_output.splitlines()
    except Exception as e:
        result["error"] = str(e)
    return result


def get_system_info():
    info = {}
    info["system_name"] = platform.uname().system
    info["node_name"] = platform.uname().node
    info["cpu_core_count"] = os.cpu_count()
    if os.path.exists("/proc/cpuinfo"):
        with open("/proc/cpuinfo", "r") as f:
            for line in f:
                if "model name" in line:
                    info["cpu_model"] = line.split(":", 1)[1].strip()
                    break
    if os.path.exists("/proc/meminfo"):
        with open("/proc/meminfo", "r") as f:
            for line in f:
                if "MemTotal" in line:
                    mem_kb = float(line.split(":", 1)[1].split()[0])
                    info["memory_size_GB"] = round(mem_kb / (1024 * 1024), 2)
                    break
    return info


script_path = os.path.abspath(__file__)
script_dir = os.path.dirname(script_path)
script_name = os.path.splitext(os.path.basename(script_path))[0]
json_path = os.path.join(script_dir, script_name + ".jsonl")


def record_results(result, filename=json_path):
    with open(filename, "a") as f:
        f.write(json.dumps(result) + "\n")


def div_up(a, b):
    return (a + b - 1) // b


# ==============================================================================
# FP8 Functions
# ==============================================================================


def allocate_weights_fp8():
    per_mat_weight_bytes = hidden_size * intermediate_size
    n_blocks_n_gate_up = div_up(intermediate_size, group_size)
    n_blocks_k = div_up(hidden_size, group_size)
    per_mat_scale_elems_gate_up = n_blocks_n_gate_up * n_blocks_k
    per_mat_scale_elems_down = n_blocks_k * n_blocks_n_gate_up

    gate_q = (
        torch.randint(0, 256, (expert_num * per_mat_weight_bytes,), dtype=torch.uint8, device="cuda")
        .to("cpu")
        .contiguous()
    )
    up_q = (
        torch.randint(0, 256, (expert_num * per_mat_weight_bytes,), dtype=torch.uint8, device="cuda")
        .to("cpu")
        .contiguous()
    )
    down_q = (
        torch.randint(0, 256, (expert_num * per_mat_weight_bytes,), dtype=torch.uint8, device="cuda")
        .to("cpu")
        .contiguous()
    )
    gate_scale = (
        torch.randn(expert_num * per_mat_scale_elems_gate_up, dtype=torch.float32, device="cuda").to("cpu").contiguous()
    )
    up_scale = (
        torch.randn(expert_num * per_mat_scale_elems_gate_up, dtype=torch.float32, device="cuda").to("cpu").contiguous()
    )
    down_scale = (
        torch.randn(expert_num * per_mat_scale_elems_down, dtype=torch.float32, device="cuda").to("cpu").contiguous()
    )

    return {
        "gate_q": gate_q,
        "up_q": up_q,
        "down_q": down_q,
        "gate_scale": gate_scale,
        "up_scale": up_scale,
        "down_scale": down_scale,
        "per_mat_weight_bytes": per_mat_weight_bytes,
        "per_mat_scale_elems_gate_up": per_mat_scale_elems_gate_up,
        "per_mat_scale_elems_down": per_mat_scale_elems_down,
    }


def allocate_weights_fp8_perchannel():
    per_mat_weight_bytes = hidden_size * intermediate_size
    per_mat_scale_elems_gate_up = intermediate_size
    per_mat_scale_elems_down = hidden_size

    gate_q = (
        torch.randint(0, 256, (expert_num * per_mat_weight_bytes,), dtype=torch.uint8, device="cuda")
        .to("cpu")
        .contiguous()
    )
    up_q = (
        torch.randint(0, 256, (expert_num * per_mat_weight_bytes,), dtype=torch.uint8, device="cuda")
        .to("cpu")
        .contiguous()
    )
    down_q = (
        torch.randint(0, 256, (expert_num * per_mat_weight_bytes,), dtype=torch.uint8, device="cuda")
        .to("cpu")
        .contiguous()
    )
    gate_scale = (
        torch.randn(expert_num * per_mat_scale_elems_gate_up, dtype=torch.float32, device="cuda").to("cpu").contiguous()
    )
    up_scale = (
        torch.randn(expert_num * per_mat_scale_elems_gate_up, dtype=torch.float32, device="cuda").to("cpu").contiguous()
    )
    down_scale = (
        torch.randn(expert_num * per_mat_scale_elems_down, dtype=torch.float32, device="cuda").to("cpu").contiguous()
    )

    return {
        "gate_q": gate_q,
        "up_q": up_q,
        "down_q": down_q,
        "gate_scale": gate_scale,
        "up_scale": up_scale,
        "down_scale": down_scale,
        "per_mat_weight_bytes": per_mat_weight_bytes,
        "per_mat_scale_elems_gate_up": per_mat_scale_elems_gate_up,
        "per_mat_scale_elems_down": per_mat_scale_elems_down,
    }


def build_moe_fp8(layer_idx=0):
    """Build a single FP8 MOE instance."""
    weights = allocate_weights_fp8()

    config = kt_kernel_ext.moe.MOEConfig(expert_num, num_experts_per_tok, hidden_size, intermediate_size)
    config.max_len = max_len
    config.layer_idx = layer_idx
    config.quant_config.bits = 8
    config.quant_config.group_size = group_size
    config.quant_config.zero_point = False
    config.pool = CPUInfer.backend_
    config.gate_proj = weights["gate_q"].data_ptr()
    config.up_proj = weights["up_q"].data_ptr()
    config.down_proj = weights["down_q"].data_ptr()
    config.gate_scale = weights["gate_scale"].data_ptr()
    config.up_scale = weights["up_scale"].data_ptr()
    config.down_scale = weights["down_scale"].data_ptr()

    moe = kt_kernel_ext.moe.AMXFP8_MOE(config)
    CPUInfer.submit(moe.load_weights_task(physical_to_logical_map.data_ptr()))
    CPUInfer.sync()

    buffer_shapes = {
        "per_mat_weight_bytes": weights["per_mat_weight_bytes"],
        "per_mat_scale_elems_gate_up": weights["per_mat_scale_elems_gate_up"],
        "per_mat_scale_elems_down": weights["per_mat_scale_elems_down"],
    }

    return moe, buffer_shapes, weights


def build_moe_fp8_perchannel(layer_idx=0):
    """Build a single FP8 per-channel MOE instance."""
    weights = allocate_weights_fp8_perchannel()

    config = kt_kernel_ext.moe.MOEConfig(expert_num, num_experts_per_tok, hidden_size, intermediate_size)
    config.max_len = max_len
    config.layer_idx = layer_idx
    config.quant_config.bits = 8
    config.quant_config.group_size = 0
    config.quant_config.zero_point = False
    config.quant_config.per_channel = True
    config.pool = CPUInfer.backend_
    config.gate_proj = weights["gate_q"].data_ptr()
    config.up_proj = weights["up_q"].data_ptr()
    config.down_proj = weights["down_q"].data_ptr()
    config.gate_scale = weights["gate_scale"].data_ptr()
    config.up_scale = weights["up_scale"].data_ptr()
    config.down_scale = weights["down_scale"].data_ptr()

    moe = kt_kernel_ext.moe.AMXFP8PerChannel_MOE(config)
    CPUInfer.submit(moe.load_weights_task(physical_to_logical_map.data_ptr()))
    CPUInfer.sync()

    buffer_shapes = {
        "per_mat_weight_bytes": weights["per_mat_weight_bytes"],
        "per_mat_scale_elems_gate_up": weights["per_mat_scale_elems_gate_up"],
        "per_mat_scale_elems_down": weights["per_mat_scale_elems_down"],
    }

    return moe, buffer_shapes, weights


def allocate_buffers_fp8(buffer_shapes):
    """Allocate output buffers for FP8 single expert."""
    per_mat_weight_bytes = buffer_shapes["per_mat_weight_bytes"]
    per_mat_scale_elems_gate_up = buffer_shapes["per_mat_scale_elems_gate_up"]
    per_mat_scale_elems_down = buffer_shapes["per_mat_scale_elems_down"]

    weight_bytes_per_expert_per_tp = per_mat_weight_bytes // gpu_tp_count
    scale_elems_per_expert_per_tp_gate_up = per_mat_scale_elems_gate_up // gpu_tp_count
    scale_elems_per_expert_per_tp_down = per_mat_scale_elems_down // gpu_tp_count

    w13_weight_bufs = [torch.empty(2 * weight_bytes_per_expert_per_tp, dtype=torch.uint8) for _ in range(gpu_tp_count)]
    w13_scale_bufs = [
        torch.empty(2 * scale_elems_per_expert_per_tp_gate_up, dtype=torch.float32) for _ in range(gpu_tp_count)
    ]
    w2_weight_bufs = [torch.empty(weight_bytes_per_expert_per_tp, dtype=torch.uint8) for _ in range(gpu_tp_count)]
    w2_scale_bufs = [torch.empty(scale_elems_per_expert_per_tp_down, dtype=torch.float32) for _ in range(gpu_tp_count)]

    buffer_ptrs = {
        "w13_weight_ptrs": [buf.data_ptr() for buf in w13_weight_bufs],
        "w13_scale_ptrs": [buf.data_ptr() for buf in w13_scale_bufs],
        "w2_weight_ptrs": [buf.data_ptr() for buf in w2_weight_bufs],
        "w2_scale_ptrs": [buf.data_ptr() for buf in w2_scale_bufs],
    }

    keep_tensors = {
        "w13_weight_bufs": w13_weight_bufs,
        "w13_scale_bufs": w13_scale_bufs,
        "w2_weight_bufs": w2_weight_bufs,
        "w2_scale_bufs": w2_scale_bufs,
    }

    return buffer_ptrs, keep_tensors


def allocate_buffers_fp8_perchannel(buffer_shapes):
    """Allocate output buffers for FP8 per-channel single expert."""
    per_mat_weight_bytes = buffer_shapes["per_mat_weight_bytes"]
    per_mat_scale_elems_gate_up = buffer_shapes["per_mat_scale_elems_gate_up"]
    per_mat_scale_elems_down = buffer_shapes["per_mat_scale_elems_down"]

    weight_bytes_per_expert_per_tp = per_mat_weight_bytes // gpu_tp_count
    scale_elems_per_expert_per_tp_gate_up = per_mat_scale_elems_gate_up // gpu_tp_count
    scale_elems_per_expert_per_tp_down = per_mat_scale_elems_down

    w13_weight_bufs = [torch.empty(2 * weight_bytes_per_expert_per_tp, dtype=torch.uint8) for _ in range(gpu_tp_count)]
    w13_scale_bufs = [
        torch.empty(2 * scale_elems_per_expert_per_tp_gate_up, dtype=torch.float32) for _ in range(gpu_tp_count)
    ]
    w2_weight_bufs = [torch.empty(weight_bytes_per_expert_per_tp, dtype=torch.uint8) for _ in range(gpu_tp_count)]
    w2_scale_bufs = [torch.empty(scale_elems_per_expert_per_tp_down, dtype=torch.float32) for _ in range(gpu_tp_count)]

    buffer_ptrs = {
        "w13_weight_ptrs": [buf.data_ptr() for buf in w13_weight_bufs],
        "w13_scale_ptrs": [buf.data_ptr() for buf in w13_scale_bufs],
        "w2_weight_ptrs": [buf.data_ptr() for buf in w2_weight_bufs],
        "w2_scale_ptrs": [buf.data_ptr() for buf in w2_scale_bufs],
    }

    keep_tensors = {
        "w13_weight_bufs": w13_weight_bufs,
        "w13_scale_bufs": w13_scale_bufs,
        "w2_weight_bufs": w2_weight_bufs,
        "w2_scale_bufs": w2_scale_bufs,
    }

    return buffer_ptrs, keep_tensors


# ==============================================================================
# BF16 Functions
# ==============================================================================


def allocate_weights_bf16():
    per_mat_weight_elems = hidden_size * intermediate_size
    per_mat_weight_bytes = per_mat_weight_elems * 2  # BF16 = 2 bytes

    gate_proj = (
        torch.randn(expert_num * per_mat_weight_elems, dtype=torch.bfloat16, device="cuda").to("cpu").contiguous()
    )
    up_proj = torch.randn(expert_num * per_mat_weight_elems, dtype=torch.bfloat16, device="cuda").to("cpu").contiguous()
    down_proj = (
        torch.randn(expert_num * per_mat_weight_elems, dtype=torch.bfloat16, device="cuda").to("cpu").contiguous()
    )

    return {
        "gate_proj": gate_proj,
        "up_proj": up_proj,
        "down_proj": down_proj,
        "per_mat_weight_bytes": per_mat_weight_bytes,
        "per_mat_weight_elems": per_mat_weight_elems,
    }


def build_moe_bf16(layer_idx=0):
    """Build a single BF16 MOE instance."""
    weights = allocate_weights_bf16()

    config = kt_kernel_ext.moe.MOEConfig(expert_num, num_experts_per_tok, hidden_size, intermediate_size)
    config.max_len = max_len
    config.layer_idx = layer_idx
    config.pool = CPUInfer.backend_
    config.gate_proj = weights["gate_proj"].data_ptr()
    config.up_proj = weights["up_proj"].data_ptr()
    config.down_proj = weights["down_proj"].data_ptr()
    config.gate_scale = 0
    config.up_scale = 0
    config.down_scale = 0

    moe = kt_kernel_ext.moe.AMXBF16_MOE(config)
    CPUInfer.submit(moe.load_weights_task(physical_to_logical_map.data_ptr()))
    CPUInfer.sync()

    buffer_shapes = {
        "per_mat_weight_bytes": weights["per_mat_weight_bytes"],
        "per_mat_weight_elems": weights["per_mat_weight_elems"],
    }

    return moe, buffer_shapes, weights


def allocate_buffers_bf16(buffer_shapes):
    """Allocate output buffers for BF16 single expert (no scales)."""
    per_mat_weight_bytes = buffer_shapes["per_mat_weight_bytes"]

    weight_bytes_per_expert_per_tp = per_mat_weight_bytes // gpu_tp_count

    w13_weight_bufs = [torch.empty(2 * weight_bytes_per_expert_per_tp, dtype=torch.uint8) for _ in range(gpu_tp_count)]
    w2_weight_bufs = [torch.empty(weight_bytes_per_expert_per_tp, dtype=torch.uint8) for _ in range(gpu_tp_count)]
    # Dummy scale buffers (not used for BF16 but needed for interface)
    w13_scale_bufs = [torch.empty(1, dtype=torch.float32) for _ in range(gpu_tp_count)]
    w2_scale_bufs = [torch.empty(1, dtype=torch.float32) for _ in range(gpu_tp_count)]

    buffer_ptrs = {
        "w13_weight_ptrs": [buf.data_ptr() for buf in w13_weight_bufs],
        "w13_scale_ptrs": [buf.data_ptr() for buf in w13_scale_bufs],
        "w2_weight_ptrs": [buf.data_ptr() for buf in w2_weight_bufs],
        "w2_scale_ptrs": [buf.data_ptr() for buf in w2_scale_bufs],
    }

    keep_tensors = {
        "w13_weight_bufs": w13_weight_bufs,
        "w13_scale_bufs": w13_scale_bufs,
        "w2_weight_bufs": w2_weight_bufs,
        "w2_scale_bufs": w2_scale_bufs,
    }

    return buffer_ptrs, keep_tensors


# ==============================================================================
# Benchmark Functions
# ==============================================================================


def bench_write_buffer(quant_mode: str):
    """Benchmark write_weight_scale_to_buffer for specified quant mode."""
    print(f"\n{'='*60}")
    print(f"{quant_mode.upper()} write_weight_scale_to_buffer benchmark")
    print(f"{'='*60}")

    if quant_mode == "fp8":
        bytes_per_elem = 1.0
        moe_0, buffer_shapes, keep_tensors_0 = build_moe_fp8(layer_idx=0)
        moe_1, _, keep_tensors_1 = build_moe_fp8(layer_idx=1)
        buffer_ptrs, buffer_keep = allocate_buffers_fp8(buffer_shapes)

        # Calculate total bytes including scales
        total_weights = hidden_size * intermediate_size * expert_num * 3
        total_scale_bytes = (
            (buffer_shapes["per_mat_scale_elems_gate_up"] * 2 + buffer_shapes["per_mat_scale_elems_down"])
            * expert_num
            * 4
        )
        bytes_per_call = total_weights + total_scale_bytes

    elif quant_mode == "fp8_perchannel":
        bytes_per_elem = 1.0
        moe_0, buffer_shapes, keep_tensors_0 = build_moe_fp8_perchannel(layer_idx=0)
        moe_1, _, keep_tensors_1 = build_moe_fp8_perchannel(layer_idx=1)
        buffer_ptrs, buffer_keep = allocate_buffers_fp8_perchannel(buffer_shapes)

        total_weights = hidden_size * intermediate_size * expert_num * 3
        total_scale_bytes = (
            (buffer_shapes["per_mat_scale_elems_gate_up"] * 2 + buffer_shapes["per_mat_scale_elems_down"])
            * expert_num
            * 4
        )
        bytes_per_call = total_weights + total_scale_bytes

    elif quant_mode == "bf16":
        bytes_per_elem = 2.0
        moe_0, buffer_shapes, keep_tensors_0 = build_moe_bf16(layer_idx=0)
        moe_1, _, keep_tensors_1 = build_moe_bf16(layer_idx=1)
        buffer_ptrs, buffer_keep = allocate_buffers_bf16(buffer_shapes)

        # BF16: only weights, no scales
        bytes_per_call = hidden_size * intermediate_size * expert_num * 3 * 2  # BF16 = 2 bytes

    else:
        raise ValueError(f"Unsupported quant_mode: {quant_mode}")

    moes = [moe_0, moe_1]

    # Warm-up
    for _ in tqdm(range(warm_up_iter), desc=f"[{quant_mode.upper()}] Warm-up"):
        for moe_idx, moe in enumerate(moes):
            for expert_id in range(gpu_experts_num):
                CPUInfer.submit(
                    moe.write_weight_scale_to_buffer_task(gpu_tp_count=gpu_tp_count, expert_id=expert_id, **buffer_ptrs)
                )
                CPUInfer.sync()

    # Benchmark
    total_time = 0
    for iter_idx in tqdm(range(test_iter), desc=f"[{quant_mode.upper()}] Testing"):
        start = time.perf_counter()
        for moe_idx, moe in enumerate(moes):
            for expert_id in range(gpu_experts_num):
                CPUInfer.submit(
                    moe.write_weight_scale_to_buffer_task(gpu_tp_count=gpu_tp_count, expert_id=expert_id, **buffer_ptrs)
                )
                CPUInfer.sync()
        end = time.perf_counter()
        iter_time = end - start
        total_time += iter_time
        # print(f"  Iter {iter_idx}: {iter_time*1000:.2f} ms")
        time.sleep(0.3)

    # bytes_per_call is for one MOE, we have 2 MOEs
    bytes_per_iter = bytes_per_call * 2
    time_per_iter_ms = total_time / test_iter * 1000
    bandwidth_gbs = bytes_per_iter * test_iter / total_time / 1e9

    print(f"\n{'='*60}")
    print(f"{quant_mode.upper()} write_weight_scale_to_buffer Results (2 MOEs alternating)")
    print(f"{'='*60}")
    print(f"Time per iteration: {time_per_iter_ms:.2f} ms")
    print(f"Bandwidth: {bandwidth_gbs:.2f} GB/s")
    print(f"Experts per MOE: {gpu_experts_num}, MOEs: 2")
    print(f"Time per expert: {time_per_iter_ms/(gpu_experts_num*2)*1000:.2f} us")

    result = {
        "op": f"write_weight_scale_to_buffer_{quant_mode}",
        "quant_mode": quant_mode,
        "time_per_iteration_ms": time_per_iter_ms,
        "bandwidth_GBs": bandwidth_gbs,
        "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
        "test_parameters": {
            "expert_num": expert_num,
            "hidden_size": hidden_size,
            "intermediate_size": intermediate_size,
            "gpu_tp_count": gpu_tp_count,
            "bytes_per_iter": bytes_per_iter,
            "num_moes": 2,
        },
    }
    if quant_mode == "fp8":
        result["test_parameters"]["group_size"] = group_size

    result.update(get_git_commit())
    result.update(get_system_info())
    record_results(result)

    return bandwidth_gbs


def main(quant_modes=None):
    """Run benchmarks for specified quant modes."""
    if quant_modes is None:
        quant_modes = ["fp8", "fp8_perchannel", "bf16"]

    results = {}
    for mode in quant_modes:
        try:
            bandwidth = bench_write_buffer(mode)
            results[mode] = f"PASSED ({bandwidth:.2f} GB/s)"
        except Exception as e:
            results[mode] = f"FAILED: {e}"
            import traceback

            traceback.print_exc()

    print("\n" + "=" * 60)
    print("SUMMARY")
    print("=" * 60)
    for mode, result in results.items():
        print(f"  {mode.upper()}: {result}")


if __name__ == "__main__":
    if len(sys.argv) > 1:
        mode = sys.argv[1].lower()
        if mode in ["fp8", "fp8_perchannel", "bf16"]:
            main([mode])
        else:
            print(f"Unknown mode: {mode}. Use 'fp8', 'fp8_perchannel' or 'bf16'")
            sys.exit(1)
    else:
        main()