File size: 4,618 Bytes
6c5f29f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | from __future__ import annotations
import json
import os
import subprocess
from pathlib import Path
import modal
ROOT = Path(__file__).resolve().parent.parent
REMOTE_ROOT = "/root/project"
REMOTE_RESULTS = "/results"
REMOTE_HF_CACHE = "/root/.cache/huggingface"
IGNORE = [
".git",
".git-archives",
"__pycache__",
"dreamerv3/.venv",
"dreamerv3/pilot_logs",
"dreamerv3/smoke_logs",
"dreamerv3/cw_modal_runs",
"dreamerv3/paper_runs_smoke",
"results*",
"seq_results",
]
app = modal.App("llm-memory-longmemeval")
results_volume = modal.Volume.from_name("llm-memory-longmemeval-results", create_if_missing=True)
hf_cache_volume = modal.Volume.from_name("llm-memory-longmemeval-hf-cache", create_if_missing=True)
image = (
modal.Image.debian_slim(python_version="3.11")
.apt_install("git")
.pip_install(
"torch>=2.4.0",
"transformers>=4.51.0",
"accelerate>=1.6.0",
"scikit-learn>=1.5.0",
"matplotlib>=3.9.0",
"sentencepiece>=0.2.0",
"safetensors>=0.4.5",
"huggingface_hub[hf_transfer]>=0.30.2",
)
.env(
{
"PYTHONUNBUFFERED": "1",
"HF_HUB_ENABLE_HF_TRANSFER": "1",
"TOKENIZERS_PARALLELISM": "false",
}
)
.add_local_dir(ROOT, REMOTE_ROOT, copy=True, ignore=IGNORE)
)
def _stream_subprocess(command: list[str], cwd: str, env: dict[str, str], logfile: str) -> None:
Path(logfile).parent.mkdir(parents=True, exist_ok=True)
with open(logfile, "w", encoding="utf-8") as stream:
process = subprocess.Popen(
command,
cwd=cwd,
env=env,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
text=True,
bufsize=1,
)
assert process.stdout is not None
for line in process.stdout:
print(line, end="")
stream.write(line)
return_code = process.wait()
if return_code:
raise subprocess.CalledProcessError(return_code, command)
@app.function(
image=image,
gpu="L4",
cpu=8,
memory=32768,
timeout=60 * 60 * 4,
volumes={
REMOTE_RESULTS: results_volume,
REMOTE_HF_CACHE: hf_cache_volume,
},
)
def run_validation(
budget_frac: float = 0.20,
run_generation: bool = True,
generation_per_type: int = 20,
reader_model: str = "Qwen/Qwen2.5-1.5B-Instruct",
prompt_word_budget: int = 1600,
max_new_tokens: int = 48,
) -> dict:
env = os.environ.copy()
env["PYTHONPATH"] = REMOTE_ROOT + os.pathsep + env.get("PYTHONPATH", "")
env["HF_HOME"] = REMOTE_HF_CACHE
env["HF_HUB_CACHE"] = str(Path(REMOTE_HF_CACHE) / "hub")
env["TRANSFORMERS_CACHE"] = str(Path(REMOTE_HF_CACHE) / "hub")
run_name = f"longmemeval_budget_{str(budget_frac).replace('.', 'p')}"
if run_generation:
run_name += "_gen"
output_dir = f"{REMOTE_RESULTS}/{run_name}"
logfile = f"{output_dir}/stdout.log"
command = [
"python",
"llm_memory_validation/bsc_longmemeval.py",
"--output-dir",
output_dir,
"--budget-frac",
str(budget_frac),
"--topk",
"5",
"--generation-per-type",
str(generation_per_type),
"--prompt-word-budget",
str(prompt_word_budget),
"--max-new-tokens",
str(max_new_tokens),
"--reader-model",
reader_model,
]
if run_generation:
command.append("--run-generation")
_stream_subprocess(command, cwd=REMOTE_ROOT, env=env, logfile=logfile)
results_volume.commit()
hf_cache_volume.commit()
summary_path = Path(output_dir) / "summary.json"
report_path = Path(output_dir) / "REPORT.md"
payload = {
"run_name": run_name,
"output_dir": output_dir,
"summary": json.loads(summary_path.read_text(encoding="utf-8")),
"report_md": report_path.read_text(encoding="utf-8"),
"stdout_log": logfile,
}
return payload
@app.local_entrypoint()
def main(
budget_frac: float = 0.20,
run_generation: bool = True,
generation_per_type: int = 20,
reader_model: str = "Qwen/Qwen2.5-1.5B-Instruct",
prompt_word_budget: int = 1600,
max_new_tokens: int = 48,
) -> None:
payload = run_validation.remote(
budget_frac=budget_frac,
run_generation=run_generation,
generation_per_type=generation_per_type,
reader_model=reader_model,
prompt_word_budget=prompt_word_budget,
max_new_tokens=max_new_tokens,
)
print(json.dumps(payload, indent=2))
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