#!/usr/bin/env python3
"""
Dequantize FP8 weights to BF16 in-place for DeepSeek V4 Pro mixed-precision model.

For each FP8 weight tensor (float8_e4m3fn) paired with a block-wise scale
(float8_e8m0fnu), reconstructs the BF16 weight as:
    bf16_weight = fp8_weight * scale_expanded

After dequantization, FP8Linear.forward() sees element_size() > 1 and
falls back to F.linear(), avoiding the broken FP8 kernel paths on Blackwell.

Preserves all BF16 and FP32 tensors unchanged.
Removes the now-unnecessary scale tensors from the output.
"""

import os, glob, json, shutil
from safetensors import safe_open
from safetensors.torch import save_file
import torch

FP8_WEIGHT_DTYPE = torch.float8_e4m3fn
FP8_SCALE_DTYPE = torch.float8_e8m0fnu
BLOCK_SIZE = (128, 128)


def dequantize_weight(fp8_weight: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
    """Dequantize block-wise FP8 weight to BF16.
    
    fp8_weight: (out_features, in_features) float8_e4m3fn
    scale: (out_features//128, in_features//128) float8_e8m0fnu or float32
    """
    scale_f32 = scale.float()
    
    out_features, in_features = fp8_weight.shape
    scale_expanded = scale_f32.repeat_interleave(BLOCK_SIZE[0], dim=0).repeat_interleave(BLOCK_SIZE[1], dim=1)
    scale_expanded = scale_expanded[:out_features, :in_features]
    
    weight_bf16 = fp8_weight.float() * scale_expanded
    return weight_bf16.to(torch.bfloat16)


def dequantize_model(model_dir: str, out_dir: str):
    os.makedirs(out_dir, exist_ok=True)

    # Copy non-safetensor files
    for f in os.listdir(model_dir):
        fp = os.path.join(model_dir, f)
        if not f.endswith(".safetensors") and os.path.isfile(fp):
            shutil.copy2(fp, os.path.join(out_dir, f))
            print(f"Copied {f}")

    safetensor_files = sorted(glob.glob(os.path.join(model_dir, "*.safetensors")))
    total = len(safetensor_files)
    
    # First pass: build map of scale_key -> weight_key
    # Pattern: layers.X.attn.Y.scale -> layers.X.attn.Y.weight
    scales_map = {}
    
    print("Scanning for FP8 weight+scale pairs...")
    for f in safetensor_files:
        with safe_open(f, framework="pt") as sf:
            for key in sf.keys():
                if key.endswith(".scale"):
                    weight_key = key[:-len(".scale")] + ".weight"
                    scales_map[weight_key] = key
    print(f"Found {len(scales_map)} FP8 weight+scale pairs")
    
    # Second pass: dequantize and save
    fp8_dequantized = 0
    fp8_scales_removed = 0
    scale_keys_global = set(scales_map.values())
    
    for i, f in enumerate(safetensor_files):
        tensors = {}
        scales_in_shard = {}
        fp8_weights_in_shard = {}
        
        with safe_open(f, framework="pt") as sf:
            for key in sf.keys():
                t = sf.get_tensor(key)
                
                if key in scale_keys_global:
                    # This is a scale tensor - save for dequantization
                    scales_in_shard[key] = t
                elif key in scales_map and t.dtype == FP8_WEIGHT_DTYPE:
                    # FP8 weight that has a corresponding scale
                    fp8_weights_in_shard[key] = t
                else:
                    # Regular tensor (BF16, FP32, or FP8 without scale) - keep as is
                    tensors[key] = t
        
        # Dequantize FP8 weights
        for weight_key, fp8_w in fp8_weights_in_shard.items():
            scale_key = scales_map[weight_key]
            scale = scales_in_shard.get(scale_key)
            if scale is not None:
                bf16_weight = dequantize_weight(fp8_w, scale)
                tensors[weight_key] = bf16_weight
                fp8_dequantized += 1
                del scales_in_shard[scale_key]
                fp8_scales_removed += 1
            else:
                # Scale not in this shard (shouldn't happen but handle gracefully)
                print(f"WARNING: scale {scale_key} not found in same shard as {weight_key}")
                tensors[weight_key] = fp8_w  # keep as-is
        
        out_path = os.path.join(out_dir, os.path.basename(f))
        save_file(tensors, out_path)
        del tensors, scales_in_shard, fp8_weights_in_shard
        
        if (i + 1) % 10 == 0 or i == total - 1:
            print(f"[{i+1}/{total}] {os.path.basename(f)} (dequantized {fp8_dequantized} FP8, removed {fp8_scales_removed} scales)")

    # Update config
    cfg_path = os.path.join(out_dir, "config.json")
    if os.path.exists(cfg_path):
        cfg = json.load(open(cfg_path))
        cfg["torch_dtype"] = "bfloat16"
        cfg["_experts_implementation"] = "eager"
        if "quantization_config" in cfg:
            del cfg["quantization_config"]
        json.dump(cfg, open(cfg_path, "w"), indent=2)
        print(f"Updated config.json: torch_dtype=bfloat16, _experts_implementation=eager")

    print(f"\nDone! Dequantized {fp8_dequantized} FP8 weights, removed {fp8_scales_removed} scale tensors")


if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser(description="Dequantize FP8 weights to BF16")
    parser.add_argument("model_dir", help="Path to mixed-precision model")
    parser.add_argument("out_dir", help="Path to write dequantized BF16 model")
    args = parser.parse_args()
    dequantize_model(args.model_dir, args.out_dir)