Datasets:

Launch80
/

Qwopus3.8-27B-Coder-Flash-quality-eval

#!/usr/bin/env python3
"""GPTQ INT4 body quantization of a BF16 Qwen3.8-27B (qwen3_5) checkpoint with GPTQModel 7.3.2 on XPU.
  python quant_body.py --src /src --dst /dst --calib /calib --nsamples 512 --seqlen 2048 [--layers N]
Body linears -> GPTQ INT4 sym g128, desc_act off, act_group_aware, damp 0.05 (mikeinnyc/SergiioB
layout). GPTQModel's qwen3_5 definition already leaves in_proj_a/in_proj_b, the vision tower and
mtp.* untouched; lm_head=False keeps the head dense BF16, so the result is the greglechin-style
body that research/hybrid-bake/make_dense_from_gptq.py expects.
Calibration (seed 608), disjoint from every eval set (wikitext-2 TEST, HumanEval/MBPP, etc.):
  40 % C4 en validation shard 0   - concatenated 2048-token windows
  40 % codeparrot-clean-valid     - concatenated 2048-token windows of real source files
  20 % ultrachat_200k test_sft    - whole conversations through the chat template, cut at seqlen
With --tools FILE (hermes-function-calling-v1 func-calling.json) the mix becomes 30 % C4, 40 % code,
10 % ultrachat, 20 % tool-call conversations (half with parallel calls) rendered through the model's
own template with tools= (calib_tools.py), each cut to a window that holds its first assistant turn.
Added 2026-11-02 after BFCL parallel lost 25/100 items: the first calibration set had no tool calls.
The exact token ids are written to <dst>/calib-body.jsonl (+ sha256 in quant-meta.json) so the
run can be reproduced bit-for-bit on the inputs side.
--layers N is the smoke test: a symlinked copy of src whose config keeps only the first N decoder
layers, to check loading, the XPU path and peak host RSS before the multi-hour full run.
"""
import argparse, ctypes, glob, gzip, hashlib, json, os, random, resource, shutil, sys, threading, time

ap = argparse.ArgumentParser()
ap.add_argument("--src", required=True); ap.add_argument("--dst", required=True)
ap.add_argument("--calib", required=True, help="dir with c4/, codeparrot/, ultrachat/")
ap.add_argument("--nsamples", type=int, default=512); ap.add_argument("--seqlen", type=int, default=2048)
ap.add_argument("--layers", type=int, default=0, help="smoke test: keep only the first N decoder layers")
ap.add_argument("--tools", default="", help="hermes func-calling.json: add a 20 %% tool-call slice")
ap.add_argument("--damp", type=float, default=0.05)
ap.add_argument("--offload", default="/dst/.offload")
ap.add_argument("--sync-finalize", action="store_true",
                help="drain each layer's pack/offload finalizers before the next layer (bounds host RAM)")
ap.add_argument("--calib-device", default="", help="keep cached calibration activations here (e.g. xpu:0) instead of host RAM")
ap.add_argument("--malloc-trim", action="store_true",
                help="call glibc malloc_trim(0) every 20 s (run with MALLOC_ARENA_MAX=2): returns freed heap "
                     "pages that GPTQModel's worker-thread arenas otherwise keep, which grew host RAM per layer")
a = ap.parse_args()
random.seed(608)
os.makedirs(a.dst, exist_ok=True)
T0 = time.time()


def mem_logger():
    while True:
        peak = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1e6
        cur = next(int(l.split()[1]) for l in open("/proc/self/status") if l.startswith("VmRSS")) / 1e6
        avail = next(int(l.split()[1]) for l in open("/proc/meminfo") if l.startswith("MemAvailable")) / 1e6
        off = sum(os.path.getsize(os.path.join(r, f)) for r, _, fs in os.walk(a.offload) for f in fs) / 1e9 if os.path.isdir(a.offload) else 0
        print(f"[mem] t={time.time()-T0:6.0f}s rss={cur:5.1f} peak={peak:5.1f} GB host_avail={avail:5.1f} GB offload_disk={off:5.1f} GB", flush=True)
        if a.malloc_trim:
            ctypes.CDLL("libc.so.6").malloc_trim(0)
        time.sleep(20)


threading.Thread(target=mem_logger, daemon=True).start()


def tensor_census():
    # debug (QB_CENSUS=1): every 60 s, the largest groups of live CPU tensors by (shape, dtype)
    import gc, collections, torch
    while True:
        time.sleep(60)
        groups = collections.Counter(); seen = set()
        for o in gc.get_objects():
            try:
                if torch.is_tensor(o) and o.device.type == "cpu" and o.numel() > 1_000_000:
                    st = o.untyped_storage(); key = st.data_ptr()
                    if key in seen:
                        continue
                    seen.add(key); groups[(tuple(o.shape), str(o.dtype))] += st.nbytes()
            except Exception:
                pass
        top = ", ".join(f"{s}{d.replace('torch.', '')}={b / 1e9:.2f}GB" for (s, d), b in groups.most_common(6))
        print(f"[census] t={time.time()-T0:6.0f}s total={sum(groups.values()) / 1e9:.1f}GB {top}", flush=True)


if os.environ.get("QB_CENSUS") == "1":
    threading.Thread(target=tensor_census, daemon=True).start()

src = a.src
if a.layers:
    src = "/tmp/smoke-src"
    shutil.rmtree(src, ignore_errors=True); os.makedirs(src)
    for fn in os.listdir(a.src):
        if fn != "config.json":
            os.symlink(os.path.join(a.src, fn), os.path.join(src, fn))
    cfg = json.load(open(os.path.join(a.src, "config.json")))
    tc = cfg.get("text_config", cfg)
    tc["num_hidden_layers"] = a.layers
    if "layer_types" in tc:
        tc["layer_types"] = tc["layer_types"][:a.layers]
    json.dump(cfg, open(os.path.join(src, "config.json"), "w"), indent=2)
    print(f"smoke test: {a.layers} layers via {src}", flush=True)

from transformers import AutoTokenizer
tok = AutoTokenizer.from_pretrained(src)


def windows(texts, n):
    out, buf = [], []
    for t in texts:
        buf += tok(t, add_special_tokens=False)["input_ids"] + [tok.eos_token_id]
        while len(buf) >= a.seqlen and len(out) < n:
            out.append(buf[:a.seqlen]); buf = buf[a.seqlen:]
        if len(out) >= n:
            break
    return out


n_tools = int(a.nsamples * 0.2) if a.tools else 0
n_c4, n_code = int(a.nsamples * (0.3 if a.tools else 0.4)), int(a.nsamples * 0.4)
n_chat = a.nsamples - n_c4 - n_code - n_tools

c4 = [json.loads(l)["text"] for l in gzip.open(glob.glob(f"{a.calib}/c4/**/*.json.gz", recursive=True)[0], "rt")]
random.shuffle(c4)
code = [json.loads(l)["content"] for l in gzip.open(glob.glob(f"{a.calib}/codeparrot/*.json.gz")[0], "rt")]
code = code[:int(len(code) * 0.9)]  # last 10 % (file order) is the ppl-code / kld eval set
random.shuffle(code)
import pandas as pd
uc = pd.read_parquet(sorted(glob.glob(f"{a.calib}/ultrachat/**/test_sft*.parquet", recursive=True))[0])
uc = uc.sample(frac=1.0, random_state=608)

samples = windows(c4, n_c4) + windows(code, n_code)
if a.tools:
    sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
    import calib_tools
    conv = calib_tools.render(tok, a.tools)
    random.Random(608).shuffle(conv)
    par = [c for c in conv if "</tool_call>\n<tool_call>" in c][:n_tools // 2]
    single = [c for c in conv if "</tool_call>\n<tool_call>" not in c and "<tool_call>" in c][:n_tools - len(par)]
    end_tok = tok.convert_tokens_to_ids("<|im_end|>")
    for c in par + single:
        ids = tok(c, add_special_tokens=False)["input_ids"]
        # end of the first assistant turn that calls a tool: keep it, plus what follows, inside the window
        k = c.find("<tool_call>"); e = len(tok(c[:k], add_special_tokens=False)["input_ids"])
        e = next((i for i in range(e, len(ids)) if ids[i] == end_tok), len(ids) - 1) + 1
        lo = 0 if e + 64 <= a.seqlen else max(0, min(e + 64, len(ids)) - a.seqlen)
        samples.append(list(ids[lo:lo + a.seqlen]))
    assert len(samples) == n_c4 + n_code + n_tools, "tool slice short"
for _, r in uc.iterrows():
    if len(samples) >= a.nsamples:
        break
    msgs = [{"role": m["role"], "content": m["content"]} for m in r["messages"]]
    # render to text, then tokenize: apply_chat_template(tokenize=True) returns a BatchEncoding in transformers 5
    ids = tok(tok.apply_chat_template(msgs, tokenize=False), add_special_tokens=False)["input_ids"]
    if len(ids) >= 256:
        samples.append(list(ids[:a.seqlen]))
assert len(samples) == a.nsamples, f"built {len(samples)} calibration samples, wanted {a.nsamples}"

with open(os.path.join(a.dst, "calib-body.jsonl"), "w") as f:
    for s in samples:
        f.write(json.dumps(s) + "\n")
calib_sha = hashlib.sha256(open(os.path.join(a.dst, "calib-body.jsonl"), "rb").read()).hexdigest()
print(f"calibration: {len(samples)} samples (c4 {n_c4}, code {n_code}, chat {n_chat}, tools {n_tools}), "
      f"{sum(map(len, samples))} tokens, sha256 {calib_sha[:16]}", flush=True)
calib = [{"input_ids": s, "attention_mask": [1] * len(s)} for s in samples]

from gptqmodel import GPTQModel, QuantizeConfig
import gptqmodel, torch

qcfg = QuantizeConfig(bits=4, group_size=128, sym=True, desc_act=False, act_group_aware=True,
                      damp_percent=a.damp, true_sequential=True, lm_head=False,
                      offload_to_disk=True, offload_to_disk_path=a.offload,
                      wait_for_submodule_finalizers=a.sync_finalize,
                      **({"calibration_data_device": a.calib_device} if a.calib_device else {}))
print("QuantizeConfig:", qcfg, flush=True)
model = GPTQModel.load(src, qcfg, device="xpu:0")
t = time.time()
model.quantize(calib, batch_size=1, tokenizer=tok)
quant_s = time.time() - t
model.save(a.dst)
shutil.rmtree(a.offload, ignore_errors=True)

meta = {"src": a.src, "layers": a.layers or "all", "nsamples": a.nsamples, "seqlen": a.seqlen,
        "mix": {"c4": n_c4, "code": n_code, "chat": n_chat, "tools": n_tools}, "tools_src": "NousResearch/hermes-function-calling-v1@dae3e1d func-calling.json" if a.tools else None, "seed": 608, "damp_percent": a.damp,
        "calib_sha256": calib_sha, "calib_device": a.calib_device or "default", "sync_finalize": a.sync_finalize, "malloc_trim": a.malloc_trim, "malloc_arena_max": os.environ.get("MALLOC_ARENA_MAX"), "gptqmodel": gptqmodel.__version__, "torch": torch.__version__,
        "quantize_seconds": round(quant_s), "total_seconds": round(time.time() - T0),
        "peak_rss_gb": round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1e6, 2)}
json.dump(meta, open(os.path.join(a.dst, "quant-meta.json"), "w"), indent=2)
print("DONE", json.dumps(meta), flush=True)

	#!/usr/bin/env python3
	"""GPTQ INT4 body quantization of a BF16 Qwen3.8-27B (qwen3_5) checkpoint with GPTQModel 7.3.2 on XPU.

	python quant_body.py --src /src --dst /dst --calib /calib --nsamples 512 --seqlen 2048 [--layers N]

	Body linears -> GPTQ INT4 sym g128, desc_act off, act_group_aware, damp 0.05 (mikeinnyc/SergiioB
	layout). GPTQModel's qwen3_5 definition already leaves in_proj_a/in_proj_b, the vision tower and
	mtp.* untouched; lm_head=False keeps the head dense BF16, so the result is the greglechin-style
	body that research/hybrid-bake/make_dense_from_gptq.py expects.

	Calibration (seed 608), disjoint from every eval set (wikitext-2 TEST, HumanEval/MBPP, etc.):
	40 % C4 en validation shard 0 - concatenated 2048-token windows
	40 % codeparrot-clean-valid - concatenated 2048-token windows of real source files
	20 % ultrachat_200k test_sft - whole conversations through the chat template, cut at seqlen
	With --tools FILE (hermes-function-calling-v1 func-calling.json) the mix becomes 30 % C4, 40 % code,
	10 % ultrachat, 20 % tool-call conversations (half with parallel calls) rendered through the model's
	own template with tools= (calib_tools.py), each cut to a window that holds its first assistant turn.
	Added 2026-11-02 after BFCL parallel lost 25/100 items: the first calibration set had no tool calls.
	The exact token ids are written to <dst>/calib-body.jsonl (+ sha256 in quant-meta.json) so the
	run can be reproduced bit-for-bit on the inputs side.

	--layers N is the smoke test: a symlinked copy of src whose config keeps only the first N decoder
	layers, to check loading, the XPU path and peak host RSS before the multi-hour full run.
	"""
	import argparse, ctypes, glob, gzip, hashlib, json, os, random, resource, shutil, sys, threading, time

	ap = argparse.ArgumentParser()
	ap.add_argument("--src", required=True); ap.add_argument("--dst", required=True)
	ap.add_argument("--calib", required=True, help="dir with c4/, codeparrot/, ultrachat/")
	ap.add_argument("--nsamples", type=int, default=512); ap.add_argument("--seqlen", type=int, default=2048)
	ap.add_argument("--layers", type=int, default=0, help="smoke test: keep only the first N decoder layers")
	ap.add_argument("--tools", default="", help="hermes func-calling.json: add a 20 %% tool-call slice")
	ap.add_argument("--damp", type=float, default=0.05)
	ap.add_argument("--offload", default="/dst/.offload")
	ap.add_argument("--sync-finalize", action="store_true",
	help="drain each layer's pack/offload finalizers before the next layer (bounds host RAM)")
	ap.add_argument("--calib-device", default="", help="keep cached calibration activations here (e.g. xpu:0) instead of host RAM")
	ap.add_argument("--malloc-trim", action="store_true",
	help="call glibc malloc_trim(0) every 20 s (run with MALLOC_ARENA_MAX=2): returns freed heap "
	"pages that GPTQModel's worker-thread arenas otherwise keep, which grew host RAM per layer")
	a = ap.parse_args()
	random.seed(608)
	os.makedirs(a.dst, exist_ok=True)
	T0 = time.time()


	def mem_logger():
	while True:
	peak = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1e6
	cur = next(int(l.split()[1]) for l in open("/proc/self/status") if l.startswith("VmRSS")) / 1e6
	avail = next(int(l.split()[1]) for l in open("/proc/meminfo") if l.startswith("MemAvailable")) / 1e6
	off = sum(os.path.getsize(os.path.join(r, f)) for r, _, fs in os.walk(a.offload) for f in fs) / 1e9 if os.path.isdir(a.offload) else 0
	print(f"[mem] t={time.time()-T0:6.0f}s rss={cur:5.1f} peak={peak:5.1f} GB host_avail={avail:5.1f} GB offload_disk={off:5.1f} GB", flush=True)
	if a.malloc_trim:
	ctypes.CDLL("libc.so.6").malloc_trim(0)
	time.sleep(20)


	threading.Thread(target=mem_logger, daemon=True).start()


	def tensor_census():
	# debug (QB_CENSUS=1): every 60 s, the largest groups of live CPU tensors by (shape, dtype)
	import gc, collections, torch
	while True:
	time.sleep(60)
	groups = collections.Counter(); seen = set()
	for o in gc.get_objects():
	try:
	if torch.is_tensor(o) and o.device.type == "cpu" and o.numel() > 1_000_000:
	st = o.untyped_storage(); key = st.data_ptr()
	if key in seen:
	continue
	seen.add(key); groups[(tuple(o.shape), str(o.dtype))] += st.nbytes()
	except Exception:
	pass
	top = ", ".join(f"{s}{d.replace('torch.', '')}={b / 1e9:.2f}GB" for (s, d), b in groups.most_common(6))
	print(f"[census] t={time.time()-T0:6.0f}s total={sum(groups.values()) / 1e9:.1f}GB {top}", flush=True)


	if os.environ.get("QB_CENSUS") == "1":
	threading.Thread(target=tensor_census, daemon=True).start()

	src = a.src
	if a.layers:
	src = "/tmp/smoke-src"
	shutil.rmtree(src, ignore_errors=True); os.makedirs(src)
	for fn in os.listdir(a.src):
	if fn != "config.json":
	os.symlink(os.path.join(a.src, fn), os.path.join(src, fn))
	cfg = json.load(open(os.path.join(a.src, "config.json")))
	tc = cfg.get("text_config", cfg)
	tc["num_hidden_layers"] = a.layers
	if "layer_types" in tc:
	tc["layer_types"] = tc["layer_types"][:a.layers]
	json.dump(cfg, open(os.path.join(src, "config.json"), "w"), indent=2)
	print(f"smoke test: {a.layers} layers via {src}", flush=True)

	from transformers import AutoTokenizer
	tok = AutoTokenizer.from_pretrained(src)


	def windows(texts, n):
	out, buf = [], []
	for t in texts:
	buf += tok(t, add_special_tokens=False)["input_ids"] + [tok.eos_token_id]
	while len(buf) >= a.seqlen and len(out) < n:
	out.append(buf[:a.seqlen]); buf = buf[a.seqlen:]
	if len(out) >= n:
	break
	return out


	n_tools = int(a.nsamples * 0.2) if a.tools else 0
	n_c4, n_code = int(a.nsamples * (0.3 if a.tools else 0.4)), int(a.nsamples * 0.4)
	n_chat = a.nsamples - n_c4 - n_code - n_tools

	c4 = [json.loads(l)["text"] for l in gzip.open(glob.glob(f"{a.calib}/c4/*/.json.gz", recursive=True)[0], "rt")]
	random.shuffle(c4)
	code = [json.loads(l)["content"] for l in gzip.open(glob.glob(f"{a.calib}/codeparrot/*.json.gz")[0], "rt")]
	code = code[:int(len(code) * 0.9)] # last 10 % (file order) is the ppl-code / kld eval set
	random.shuffle(code)
	import pandas as pd
	uc = pd.read_parquet(sorted(glob.glob(f"{a.calib}/ultrachat/*/test_sft.parquet", recursive=True))[0])
	uc = uc.sample(frac=1.0, random_state=608)

	samples = windows(c4, n_c4) + windows(code, n_code)
	if a.tools:
	sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
	import calib_tools
	conv = calib_tools.render(tok, a.tools)
	random.Random(608).shuffle(conv)
	par = [c for c in conv if "</tool_call>\n<tool_call>" in c][:n_tools // 2]
	single = [c for c in conv if "</tool_call>\n<tool_call>" not in c and "<tool_call>" in c][:n_tools - len(par)]
	end_tok = tok.convert_tokens_to_ids("<\|im_end\|>")
	for c in par + single:
	ids = tok(c, add_special_tokens=False)["input_ids"]
	# end of the first assistant turn that calls a tool: keep it, plus what follows, inside the window
	k = c.find("<tool_call>"); e = len(tok(c[:k], add_special_tokens=False)["input_ids"])
	e = next((i for i in range(e, len(ids)) if ids[i] == end_tok), len(ids) - 1) + 1
	lo = 0 if e + 64 <= a.seqlen else max(0, min(e + 64, len(ids)) - a.seqlen)
	samples.append(list(ids[lo:lo + a.seqlen]))
	assert len(samples) == n_c4 + n_code + n_tools, "tool slice short"
	for _, r in uc.iterrows():
	if len(samples) >= a.nsamples:
	break
	msgs = [{"role": m["role"], "content": m["content"]} for m in r["messages"]]
	# render to text, then tokenize: apply_chat_template(tokenize=True) returns a BatchEncoding in transformers 5
	ids = tok(tok.apply_chat_template(msgs, tokenize=False), add_special_tokens=False)["input_ids"]
	if len(ids) >= 256:
	samples.append(list(ids[:a.seqlen]))
	assert len(samples) == a.nsamples, f"built {len(samples)} calibration samples, wanted {a.nsamples}"

	with open(os.path.join(a.dst, "calib-body.jsonl"), "w") as f:
	for s in samples:
	f.write(json.dumps(s) + "\n")
	calib_sha = hashlib.sha256(open(os.path.join(a.dst, "calib-body.jsonl"), "rb").read()).hexdigest()
	print(f"calibration: {len(samples)} samples (c4 {n_c4}, code {n_code}, chat {n_chat}, tools {n_tools}), "
	f"{sum(map(len, samples))} tokens, sha256 {calib_sha[:16]}", flush=True)
	calib = [{"input_ids": s, "attention_mask": [1] * len(s)} for s in samples]

	from gptqmodel import GPTQModel, QuantizeConfig
	import gptqmodel, torch

	qcfg = QuantizeConfig(bits=4, group_size=128, sym=True, desc_act=False, act_group_aware=True,
	damp_percent=a.damp, true_sequential=True, lm_head=False,
	offload_to_disk=True, offload_to_disk_path=a.offload,
	wait_for_submodule_finalizers=a.sync_finalize,
	**({"calibration_data_device": a.calib_device} if a.calib_device else {}))
	print("QuantizeConfig:", qcfg, flush=True)
	model = GPTQModel.load(src, qcfg, device="xpu:0")
	t = time.time()
	model.quantize(calib, batch_size=1, tokenizer=tok)
	quant_s = time.time() - t
	model.save(a.dst)
	shutil.rmtree(a.offload, ignore_errors=True)

	meta = {"src": a.src, "layers": a.layers or "all", "nsamples": a.nsamples, "seqlen": a.seqlen,
	"mix": {"c4": n_c4, "code": n_code, "chat": n_chat, "tools": n_tools}, "tools_src": "NousResearch/hermes-function-calling-v1@dae3e1d func-calling.json" if a.tools else None, "seed": 608, "damp_percent": a.damp,
	"calib_sha256": calib_sha, "calib_device": a.calib_device or "default", "sync_finalize": a.sync_finalize, "malloc_trim": a.malloc_trim, "malloc_arena_max": os.environ.get("MALLOC_ARENA_MAX"), "gptqmodel": gptqmodel.__version__, "torch": torch.__version__,
	"quantize_seconds": round(quant_s), "total_seconds": round(time.time() - T0),
	"peak_rss_gb": round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1e6, 2)}
	json.dump(meta, open(os.path.join(a.dst, "quant-meta.json"), "w"), indent=2)
	print("DONE", json.dumps(meta), flush=True)