Upload 02_export_quant_b.py

02_export_quant_b.py

@@ -0,0 +1,202 @@
+#!/usr/bin/env python
+"""
+02_export_quant_b.py  — WEG B
+Gemma 4 E4B (gfp78/gemma4-bund-merged) -> EIN ONNX-Decoder -> q4f16
+
+Unterschied zu 01: Der Wrapper nimmt input_ids statt inputs_embeds.
+Der Decoder erzeugt Embeddings UND Per-Layer-Embeddings (PLE) intern selbst.
+Kein separates embed_tokens.onnx, kein Reverse-Lookup.
+"""
+
+import gc
+import os
+from pathlib import Path
+
+import torch
+import onnx
+
+os.environ.setdefault("HF_HOME", "/root/hf")
+
+MODEL_ID = "gfp78/gemma4-bund-merged"
+OUT = Path("/root/train/gemma4-bund-onnx")
+OUT.mkdir(parents=True, exist_ok=True)
+
+FP32_ONNX = OUT / "decoder_model_merged.onnx"
+FP32_DATA = "decoder_model_merged.onnx_data"
+Q4_ONNX = OUT / "decoder_model_merged_q4f16.onnx"
+Q4_DATA = "decoder_model_merged_q4f16.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()
+print("hidden_size:", lm.config.hidden_size)
+
+
+# ------------------------------------------------- B) Cache-Geometrie messen
+log("B) Trockenlauf: Cache-Layer + Head-Dims")
+with torch.no_grad():
+    probe = lm(input_ids=torch.tensor([[1, 2, 3, 4]]), use_cache=True, return_dict=True)
+
+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(f"  layer {i:2d}: n_kv_heads={k.shape[1]}, head_dim={k.shape[3]}")
+
+del probe, pkv
+gc.collect()
+
+
+# ---------------------------------------------------------------- C) Wrapper
+class DecoderWrapper(torch.nn.Module):
+    """input_ids + attention_mask + position_ids + past -> logits + present"""
+
+    def __init__(self, lm, lm_head, n_cache):
+        super().__init__()
+        self.lm = lm
+        self.lm_head = lm_head
+        self.n_cache = n_cache
+
+    def forward(self, input_ids, 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(
+            input_ids=input_ids,              # <-- Weg B: ids, nicht embeds
+            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 (echte Token-ID, keine Nullen)")
+B, S, P = 1, 1, 1
+dummy_ids = torch.tensor([[42]], dtype=torch.long)
+dummy_mask = torch.ones(B, P + S, dtype=torch.long)
+dummy_pos = torch.tensor([[P]], dtype=torch.long)
+
+dummy_past = []
+for (n_kv, hd) in KV_SHAPES:
+    dummy_past.append(torch.zeros(B, n_kv, P, hd, dtype=torch.float32))
+    dummy_past.append(torch.zeros(B, n_kv, P, hd, dtype=torch.float32))
+
+input_names = ["input_ids", "attention_mask", "position_ids"]
+output_names = ["logits"]
+dynamic_axes = {
+    "input_ids": {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)
+        dynamic_axes[pn] = {0: "batch", 2: "past_seq"}
+        dynamic_axes[on] = {0: "batch", 2: "total_seq"}
+
+log("C) torch.onnx.export laeuft — LANGE STILLE IST NORMAL")
+with torch.no_grad():
+    torch.onnx.export(
+        wrapper,
+        (dummy_ids, dummy_mask, dummy_pos, *dummy_past),
+        str(FP32_ONNX),
+        input_names=input_names,
+        output_names=output_names,
+        dynamic_axes=dynamic_axes,
+        opset_version=17,
+        do_constant_folding=True,
+        dynamo=False,
+    )
+print("Export geschrieben.")
+
+del model, lm, lm_head, wrapper, dummy_past
+gc.collect()
+
+
+# --------------------------------------------------------- D) Konsolidierung
+log("D) Konsolidierung (finaler Name direkt, NIE danach umbenennen)")
+m = onnx.load(str(FP32_ONNX), load_external_data=True)
+onnx.save_model(m, str(FP32_ONNX), save_as_external_data=True,
+                all_tensors_to_one_file=True, location=FP32_DATA, size_threshold=1024)
+del m
+gc.collect()
+
+for f in OUT.iterdir():
+    if f.name.startswith("onnx__") or f.name.startswith("_"):
+        f.unlink()
+os.system(f"df -h /root; free -g; ls -la {OUT}")
+
+onnx.checker.check_model(str(FP32_ONNX))
+print("fp32-Graph valide.")
+
+
+# ------------------------------------------------------------ E) q4f16
+log("E) q4f16-Quantisierung")
+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)
+
+model_fp32 = onnx.load(str(FP32_ONNX), load_external_data=True)
+cfg = DefaultWeightOnlyQuantConfig(block_size=32, is_symmetric=True, accuracy_level=4)
+quant = Q(model_fp32, algo_config=cfg)
+quant.process()
+
+qm = quant.model.model if hasattr(quant.model, "model") else quant.model
+onnx.save_model(qm, str(Q4_ONNX), save_as_external_data=True,
+                all_tensors_to_one_file=True, location=Q4_DATA, size_threshold=1024)
+print("q4f16 geschrieben.")
+
+del model_fp32, quant, qm
+gc.collect()
+
+
+# ------------------------------------------------------- F) Verifikation
+log("F) Verifikation")
+onnx.checker.check_model(str(Q4_ONNX))
+size_mb = (OUT / Q4_DATA).stat().st_size / 1e6
+print(f"{Q4_DATA}: {size_mb:.0f} MB")
+print("!! >3500 MB = Browser-Limit gefaehrdet" if size_mb > 3500 else "OK: browsertauglich")
+
+import onnxruntime as ort
+sess = ort.InferenceSession(str(Q4_ONNX), providers=["CPUExecutionProvider"])
+print("Session OK. Inputs:", len(sess.get_inputs()), "Outputs:", len(sess.get_outputs()))
+
+tok.save_pretrained(str(OUT.parent / "gemma4-bund-final"))
+log("FERTIG — jetzt SOFORT auf HF pushen, /root ist fluechtig!")