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import os
import subprocess
import sys
import time
import traceback
os.environ.setdefault("CHIPFORMER_WROM_HEADS", "320")
R = {"sections": {}}
def sh(cmd, tail=600):
t = time.time()
p = subprocess.run(["bash", "-lc", cmd], capture_output=True, text=True)
out = ((p.stdout or "") + (p.stderr or ""))[-tail:]
print(f"[{time.time() - t:.0f}s rc={p.returncode}] {out}", flush=True)
return p.returncode, out
def safe(name):
def deco(fn):
try:
R["sections"][name] = fn()
print(f"OK {name}: {json.dumps(R['sections'][name])[:300]}", flush=True)
except Exception as e:
R["sections"][name] = {"error": f"{type(e).__name__}: {e}",
"tb": traceback.format_exc()[-800:]}
print(f"FAIL {name}: {type(e).__name__}: {e}", flush=True)
return fn
return deco
@safe("hardware")
def hardware():
import torch
g = subprocess.run(["nvidia-smi", "--query-gpu=name,memory.total,driver_version",
"--format=csv,noheader"], capture_output=True, text=True).stdout.strip()
name, mem, drv = [s.strip() for s in g.split(",")]
return {"gpu": name, "vram_gb": round(int(mem.split()[0]) / 1024, 1), "driver": drv,
"torch": torch.__version__, "cuda": torch.version.cuda,
"capability": ".".join(map(str, torch.cuda.get_device_capability())),
"cpus": os.cpu_count(),
"ram_gb": round(int(open("/proc/meminfo").readline().split()[1]) / 1048576, 1),
"python": sys.version.split()[0]}
A = R["sections"]
@safe("setup")
def setup():
sh("apt-get -qq install -y clang lld >/dev/null 2>&1; clang --version | head -1")
rc1, _ = sh("cd /content && rm -rf qwenputer && git clone -q https://github.com/divagr18/qwenputer "
"&& cd qwenputer && git checkout -q 2717232")
rc2, _ = sh("cd /content && rm -rf bundle && mkdir bundle && unzip -q colab_bundle.zip -d bundle "
"&& cp -r bundle/colab/patches/* qwenputer/ && cp bundle/colab/tl/* tl/")
rc3, out = sh("cd /content/tl && python3 export_full.py 2>&1 | tail -1")
rc4, _ = sh("cd /content/tl && clang --target=riscv32-unknown-elf -march=rv32im_zba -mabi=ilp32 "
"-O2 -DNTOK=40 -nostdlib -nostartfiles -static -fuse-ld=lld "
"-T /content/qwenputer/experimental/riscv/guests/stories260k/vd.ld "
"-I /content/qwenputer/experimental/riscv/chipformer/rv/env -I . "
"-o tl40.elf /content/qwenputer/experimental/riscv/chipformer/rv/env/crt0.S "
"tl.c vd2.S wrom_tl.c minic.c 2>&1 | grep ' error' | head -2")
return {"export": out.strip(), "rc": [rc1, rc2, rc3, rc4]}
@safe("model")
def model():
from huggingface_hub import snapshot_download
p = snapshot_download("Maykeye/TinyLLama-v0",
allow_patterns=["model.safetensors", "config.json", "tokenizer.json"])
for f in ("model.safetensors", "config.json", "tokenizer.json"):
subprocess.run(["cp", f"{p}/{f}", f"/content/tl/{f}"], check=True)
from safetensors import safe_open
n = 0
with safe_open("/content/tl/model.safetensors", "pt") as f:
for k in f.keys():
m = 1
for d in f.get_slice(k).get_shape():
m *= d
n += m
return {"params": n, "bf16_mb": round(n * 2 / 2 ** 20, 2), "int4_mb": round(n * 0.5 / 2 ** 20, 2),
"fp32_mb": round(n * 4 / 2 ** 20, 2)}
@safe("trace")
def trace():
import collections
from chipformer.rv import elf, ref
from chipformer.rv import spec as S
m = ref.Machine.from_program(elf.program("/content/tl/tl40.elf"))
mix = collections.Counter()
toks = 0
t0 = time.time()
while not m.halted and m.ram[0xE000 // 4 + 100] == 0:
w = m.rom[(m.pc - S.ROM_BASE) >> 2] if m.n != S.N_MAX else None
mix[ref.decode(w).op if w is not None else "R"] += 1
toks += len(m.record())
tot = sum(mix.values())
return {"instructions_one_token": tot, "trace_tokens_one_token": toks,
"cpu_seconds": round(time.time() - t0, 1),
"tokens_per_instruction": round(toks / tot, 3),
"mix": {k: v for k, v in mix.most_common(12)},
"vdot": mix["VDOT"], "vdot_pct": round(100 * mix["VDOT"] / tot, 1),
"overhead_pct": round(100 * (tot - mix["VDOT"]) / tot, 1)}
@safe("executor")
def executor():
import numpy as np
import torch
from chipformer.rv import core, elf, fastexec, ref
from chipformer.rv import spec as S
c = core.build()
prog = elf.program("/content/tl/tl40.elf")
want = ref.Machine.from_program(prog).run(10 ** 8)
out = {"circuit": c.stats(), "reference_tokens": len(want)}
core.snapshot(c, ref.Machine.from_program(prog))
N = 200000
fr = fastexec.FreeRun(c, 1, "cuda", False, v2=True)
fr.load(0, S.tok("GO"))
t0 = time.time()
got = fr.run(N)[0]
torch.cuda.synchronize()
dt = time.time() - t0
exp = np.array(want[1:N + 1])
bad = np.nonzero(got != exp)[0]
out.update(tokens=N, seconds=round(dt, 1), tokens_per_second=round(N / dt, 1),
mismatches=int(len(bad)),
first_mismatch=None if not len(bad) else int(bad[0]),
minutes_per_model_token=round(2470000 / (N / dt) / 60, 1))
return out
@safe("merge")
def merge():
from huggingface_hub import snapshot_download
p = snapshot_download("Qwen/Qwen3.5-4B", allow_patterns=["*.safetensors", "*.json", "*.jinja"])
rc, out = sh(f"cd /content/qwenputer/experimental/riscv && CHIPFORMER_WROM_HEADS=320 "
f"python3 -m chipformer.rv.merge {p} --out /content/merged 2>&1 | tail -4", 1500)
size = subprocess.run(["bash", "-lc", "du -sm /content/merged | cut -f1"],
capture_output=True, text=True).stdout.strip()
import re
layers = re.findall(r"'layers': (\d+)", out)
return {"rc": rc, "size_mb": int(size or 0), "circuit_layers": int(layers[0]) if layers else None,
"tail": out.strip()[-300:]}
@safe("degradation")
def degradation():
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("/content/merged")
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B", dtype=torch.bfloat16,
device_map="cuda").eval()
merged = AutoModelForCausalLM.from_pretrained("/content/merged", dtype=torch.bfloat16,
device_map="cuda").eval()
def logsm(z):
z = z - z.max(-1, keepdim=True).values
return z - torch.log(torch.exp(z).sum(-1, keepdim=True))
texts = ["The capital of France is Paris, and the river that runs through it is the Seine.",
"In 1969, humans first landed on the Moon, a milestone of the space race.",
"A neural network learns by adjusting its weights to reduce a loss function.",
"The recipe calls for two cups of flour, one egg, and a pinch of salt.",
"Photosynthesis converts light energy into chemical energy inside chloroplasts.",
"The committee approved the budget after a long debate about public transport.",
"Mount Everest rises 8,849 metres above sea level on the border of Nepal and China.",
"She opened the book, read the first page twice, and put it down again."]
rows, kls, pos = [], [], 0
with torch.no_grad():
for t in texts:
ids = tok(t, return_tensors="pt").to("cuda")
a, b = base(**ids).logits[0].float(), merged(**ids).logits[0].float()
n = min(a.shape[-1], b.shape[-1])
a, b = a[..., :n], b[..., :n]
k = (torch.exp(logsm(a)) * (logsm(a) - logsm(b))).sum(-1).mean().item()
agree = (a.argmax(-1) == b.argmax(-1)).float().mean().item()
rows.append({"kw": round(k, 6), "top1": round(agree, 3), "positions": int(a.shape[0])})
kls.append(k)
pos += int(a.shape[0])
ids = tok("The capital of France is", return_tensors="pt").to("cuda")
with torch.no_grad():
out = merged.generate(**ids, max_new_tokens=20, do_sample=False)
text = tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True)
merged.generate(**ids, max_new_tokens=9, do_sample=False)
torch.cuda.synchronize()
t0 = time.time()
merged.generate(**ids, max_new_tokens=32, do_sample=False)
torch.cuda.synchronize()
dt = time.time() - t0
return {"per_text": rows, "kl_mean": round(sum(kls) / len(kls), 6), "positions": pos,
"sample_continuation": text, "decode_tok_s": round(32 / dt, 2),
"peak_vram_gb": round(torch.cuda.max_memory_allocated() / 2 ** 30, 1),
"base_layers": len(base.model.layers), "merged_layers": len(merged.model.layers)}
json.dump(R, open("/content/results.json", "w"), indent=1)
print("RESULTS " + json.dumps(R), flush=True)
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