#!/usr/bin/env python
"""
03_export_quant_c.py — WEG C (Decoder-only, ohne Embedding-Gewichte)

Der Decoder bekommt inputs_embeds UND per_layer_inputs (4D) von aussen.
Damit landen die 14 GB Gather-Gewichte (embed_tokens + embed_tokens_per_layer)
NICHT im Graphen -> fp32 ~10 GB -> q4f16 ~2 GB.

Das Embed-Modell wird NICHT exportiert: das Stock-embed_tokens_q4f16.onnx von
onnx-community/gemma-4-E4B-it-ONNX ist bitidentisch (LoRA hat nur
q/k/v/o/gate/up/down_proj beruehrt) und wird einfach danebengelegt.

Start IMMER mit nohup:
  nohup python 03_export_quant_c.py > export_c.log 2>&1 &
"""

import gc
import os
from pathlib import Path

import torch
import onnx

os.environ.setdefault("HF_HOME", "/root/hf")

MODEL_ID = "/root/gemma4-bund-merged"
STOCK = "onnx-community/gemma-4-E4B-it-ONNX"
OUT = Path("/root/train/gemma4-bund-final")
ONNX_DIR = OUT / "onnx"
ONNX_DIR.mkdir(parents=True, exist_ok=True)

FP32 = ONNX_DIR / "decoder_model_merged.onnx"
FP32_DATA = "decoder_model_merged.onnx_data"
Q4 = ONNX_DIR / "decoder_model_merged_q4f16.onnx"
Q4_DATA = "decoder_model_merged_q4f16.onnx_data"
FP16 = ONNX_DIR / "decoder_model_merged_fp16.onnx"
FP16_DATA = "decoder_model_merged_fp16.onnx_data"


def log(m):
    print(f"\n=== {m}", flush=True)


# ------------------------------------------------------------------ A) laden
log("A) Modell laden")
from transformers import AutoTokenizer, AutoModelForImageTextToText, DynamicCache

tok = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForImageTextToText.from_pretrained(
    MODEL_ID, dtype=torch.float32, device_map="cpu")
model.eval()

lm = model.model.language_model
lm_head = model.lm_head if hasattr(model, "lm_head") else model.get_output_embeddings()
HIDDEN = lm.config.hidden_size
print("hidden_size:", HIDDEN)


# ------------------------------------------------- B) Cache-Geometrie messen
log("B) Trockenlauf")
with torch.no_grad():
    ids = torch.tensor([[1, 2, 3, 4]])
    emb = lm.get_input_embeddings()(ids)
    ple = lm.get_per_layer_inputs(ids, emb)
    print("per_layer_inputs Shape:", tuple(ple.shape), "(erwartet: 1,4,42,256)")
    probe = lm(inputs_embeds=emb, per_layer_inputs=ple, use_cache=True, return_dict=True)

PLE_SHAPE = tuple(ple.shape[2:])          # (42, 256)
pkv = probe.past_key_values
N_CACHE = len(pkv.layers)
print("n_cache_layers:", N_CACHE, "(erwartet: 24)")

KV_SHAPES = []
for i in range(N_CACHE):
    k = pkv.layers[i].keys
    KV_SHAPES.append((int(k.shape[1]), int(k.shape[3])))
print("head_dims:", sorted({s[1] for s in KV_SHAPES}), "(erwartet: [256, 512])")

del probe, pkv, emb, ple, ids
gc.collect()


# ---------------------------------------------------------------- C) Wrapper
class DecoderWrapper(torch.nn.Module):
    def __init__(self, lm, lm_head, n_cache):
        super().__init__()
        self.lm, self.lm_head, self.n_cache = lm, lm_head, n_cache

    def forward(self, inputs_embeds, per_layer_inputs,
                attention_mask, position_ids, *past):
        cache = None
        if len(past) == 2 * self.n_cache and past[0].shape[2] > 0:
            cache = DynamicCache(config=self.lm.config)
            for i in range(self.n_cache):
                cache.update(past[2 * i], past[2 * i + 1], i)

        out = self.lm(
            inputs_embeds=inputs_embeds,
            per_layer_inputs=per_layer_inputs,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=cache,
            use_cache=True,
            return_dict=True,
        )
        logits = self.lm_head(out.last_hidden_state)
        present = []
        for i in range(self.n_cache):
            present.append(out.past_key_values.layers[i].keys)
            present.append(out.past_key_values.layers[i].values)
        return (logits, *present)


wrapper = DecoderWrapper(lm, lm_head, N_CACHE).eval()

log("C) Dummy-Inputs")
B, S, P = 1, 1, 1
# ECHTE Embeddings (keine Nullen) — Gemma 4 prueft die Konsistenz
with torch.no_grad():
    d_ids = torch.tensor([[42]], dtype=torch.long)
    d_emb = lm.get_input_embeddings()(d_ids).detach()
    d_ple = lm.get_per_layer_inputs(d_ids, d_emb).detach()

d_mask = torch.ones(B, P + S, dtype=torch.long)
d_pos = torch.tensor([[P]], dtype=torch.long)
d_past = []
for (n_kv, hd) in KV_SHAPES:
    d_past += [torch.zeros(B, n_kv, P, hd), torch.zeros(B, n_kv, P, hd)]

input_names = ["inputs_embeds", "per_layer_inputs", "attention_mask", "position_ids"]
output_names = ["logits"]
dyn = {
    "inputs_embeds":    {0: "batch", 1: "seq"},
    "per_layer_inputs": {0: "batch", 1: "seq"},
    "attention_mask":   {0: "batch", 1: "total"},
    "position_ids":     {0: "batch", 1: "seq"},
    "logits":           {0: "batch", 1: "seq"},
}
for i in range(N_CACHE):
    for kv in ("key", "value"):
        pn, on = f"past_key_values.{i}.{kv}", f"present.{i}.{kv}"
        input_names.append(pn)
        output_names.append(on)
        dyn[pn] = {0: "batch", 2: "past_seq"}
        dyn[on] = {0: "batch", 2: "total_seq"}

log("C) torch.onnx.export — LANGE STILLE IST NORMAL (30-60 Min)")
with torch.no_grad():
    torch.onnx.export(
        wrapper, (d_emb, d_ple, d_mask, d_pos, *d_past), str(FP32),
        input_names=input_names, output_names=output_names, dynamic_axes=dyn,
        opset_version=17, do_constant_folding=True, dynamo=False,
    )
print("Export geschrieben.")

del model, lm, lm_head, wrapper, d_past, d_emb, d_ple
gc.collect()


# --------------------------------------------------------- D) Konsolidierung
log("D) Konsolidierung")
os.system(f"du -sh {ONNX_DIR}; df -h /root")
m = onnx.load(str(FP32), load_external_data=True)
onnx.save_model(m, str(FP32), save_as_external_data=True,
                all_tensors_to_one_file=True, location=FP32_DATA,
                size_threshold=1024)
del m
gc.collect()

for f in ONNX_DIR.iterdir():
    if f.name.startswith("onnx__") or f.name.startswith("lm.") or f.name.startswith("_"):
        f.unlink()
os.system(f"df -h /root; ls -la {ONNX_DIR}")
try:
    onnx.checker.check_model(str(FP32))
    print("fp32 valide.")
except Exception as e:
    print("checker uebersprungen (>2GB-Falle):", type(e).__name__)


# ------------------------------------------------------------------ E) q4f16
# ---------- D.5) fp32 -> fp16 (VOR Quantisierung)
log("D.5) fp32 -> fp16")
from onnxconverter_common import float16
m32 = onnx.load(str(FP32), load_external_data=True)
m16 = float16.convert_float_to_float16(
    m32, keep_io_types=False, disable_shape_infer=True)
onnx.save_model(m16, str(FP16), save_as_external_data=True,
                all_tensors_to_one_file=True, location=FP16_DATA,
                size_threshold=1024)
del m32, m16
gc.collect()
FP32.unlink(missing_ok=True)
(ONNX_DIR / FP32_DATA).unlink(missing_ok=True)
print("fp16 geschrieben.")


log("E) q4f16")
try:
    from onnxruntime.quantization.matmul_nbits_quantizer import (
        MatMulNBitsQuantizer as Q, DefaultWeightOnlyQuantConfig)
except ImportError:
    from onnxruntime.quantization.matmul_4bits_quantizer import (
        MatMul4BitsQuantizer as Q, DefaultWeightOnlyQuantConfig)

mf = onnx.load(str(FP16), load_external_data=True)
quant = Q(mf, algo_config=DefaultWeightOnlyQuantConfig(
    block_size=32, is_symmetric=True, accuracy_level=4))
quant.process()
qm = quant.model.model if hasattr(quant.model, "model") else quant.model
onnx.save_model(qm, str(Q4), save_as_external_data=True,
                all_tensors_to_one_file=True, location=Q4_DATA, size_threshold=1024)
del mf, quant, qm
gc.collect()

# fp32 wegwerfen — sonst laeuft die Disk beim Upload voll
FP16.unlink(missing_ok=True)
(ONNX_DIR / FP16_DATA).unlink(missing_ok=True)


# ---------------------------------------------------- F) Stock-Embed + Config
log("F) Stock-Embed holen + Tokenizer/Config schreiben")
from huggingface_hub import hf_hub_download
import shutil, json

for fn in ("onnx/embed_tokens_q4f16.onnx", "onnx/embed_tokens_q4f16.onnx_data"):
    try:
        p = hf_hub_download(STOCK, fn)
        shutil.copy(p, ONNX_DIR / Path(fn).name)
        print("geholt:", fn)
    except Exception as e:
        print("nicht vorhanden (evtl. ok):", fn, e)

tok.save_pretrained(str(OUT))
from transformers import AutoConfig
c = AutoConfig.from_pretrained(MODEL_ID)
c.save_pretrained(str(OUT))

cp = OUT / "config.json"
cfg = json.load(open(cp))
cfg["transformers.js_config"] = {
    "dtype": "q4f16",
    "use_external_data_format": {
        "decoder_model_merged_q4f16.onnx": True,
        "embed_tokens_q4f16.onnx": True,
    },
    "kv_cache_dtype": "float16",
}
json.dump(cfg, open(cp, "w"), indent=2)
print("transformers.js_config geschrieben.")


# ------------------------------------------------------------ G) Verifikation
log("G) Verifikation")
try:
    onnx.checker.check_model(str(Q4))
except Exception as e:
    print("checker uebersprungen:", type(e).__name__)
size = (ONNX_DIR / Q4_DATA).stat().st_size / 1e6
print(f"decoder q4f16 data: {size:.0f} MB")
print("!! >3500 MB = Browser-Limit" if size > 3500 else "OK: browsertauglich")

import onnxruntime as ort
s = ort.InferenceSession(str(Q4), providers=["CPUExecutionProvider"])
print("Inputs:", [i.name for i in s.get_inputs()][:4], "... total", len(s.get_inputs()))
os.system(f"du -sh {OUT}; ls -la {ONNX_DIR}")

log("FERTIG. JETZT SOFORT auf HF pushen — /root ist fluechtig!")
print(f"  hf upload gfp78/gemma4-bund-onnx {OUT} . --repo-type model")