mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-01-26 17:20:01 +00:00
0f9ecaec04
* Tool calls support from mainline * update cmake * revert api for /completions * Fix broken thinking process for gpt-oss * add missing args and fix webui bugs * add missing args and fix webui bugs2 * Fix reasoning format error * add usage * change default post_sampling_probs to true * add back generated_text * Remove server endpoints tests * add log * Chat fixes * Remove logs * webui: revert extra handling of thinking process --------- Co-authored-by: firecoperana <firecoperana> Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
106 lines
3.9 KiB
Python
Executable File
106 lines
3.9 KiB
Python
Executable File
#!/usr/bin/env python
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'''
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This script fetches all the models used in the server tests.
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This is useful for slow tests that use larger models, to avoid them timing out on the model downloads.
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It is meant to be run from the root of the repository.
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Example:
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python scripts/fetch_server_test_models.py
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( cd examples/server/tests && ./tests.sh -v -x -m slow )
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'''
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import ast
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import glob
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import logging
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import os
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from typing import Generator
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from pydantic import BaseModel
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from typing import Optional
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import subprocess
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class HuggingFaceModel(BaseModel):
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hf_repo: str
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hf_file: Optional[str] = None
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class Config:
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frozen = True
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def collect_hf_model_test_parameters(test_file) -> Generator[HuggingFaceModel, None, None]:
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try:
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with open(test_file) as f:
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tree = ast.parse(f.read())
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except Exception as e:
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logging.error(f'collect_hf_model_test_parameters failed on {test_file}: {e}')
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return
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for node in ast.walk(tree):
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if isinstance(node, ast.FunctionDef):
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for dec in node.decorator_list:
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if isinstance(dec, ast.Call) and isinstance(dec.func, ast.Attribute) and dec.func.attr == 'parametrize':
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param_names = ast.literal_eval(dec.args[0]).split(",")
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if "hf_repo" not in param_names:
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continue
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raw_param_values = dec.args[1]
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if not isinstance(raw_param_values, ast.List):
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logging.warning(f'Skipping non-list parametrize entry at {test_file}:{node.lineno}')
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continue
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hf_repo_idx = param_names.index("hf_repo")
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hf_file_idx = param_names.index("hf_file") if "hf_file" in param_names else None
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for t in raw_param_values.elts:
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if not isinstance(t, ast.Tuple):
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logging.warning(f'Skipping non-tuple parametrize entry at {test_file}:{node.lineno}')
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continue
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yield HuggingFaceModel(
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hf_repo=ast.literal_eval(t.elts[hf_repo_idx]),
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hf_file=ast.literal_eval(t.elts[hf_file_idx]) if hf_file_idx is not None else None)
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if __name__ == '__main__':
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logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
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models = sorted(list(set([
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model
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for test_file in glob.glob('examples/server/tests/unit/test_*.py')
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for model in collect_hf_model_test_parameters(test_file)
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])), key=lambda m: (m.hf_repo, m.hf_file))
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logging.info(f'Found {len(models)} models in parameterized tests:')
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for m in models:
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logging.info(f' - {m.hf_repo} / {m.hf_file}')
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cli_path = os.environ.get(
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'LLAMA_CLI_BIN_PATH',
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os.path.join(
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os.path.dirname(__file__),
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'../build/bin/Release/llama-cli.exe' if os.name == 'nt' else '../build/bin/llama-cli'))
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for m in models:
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if '<' in m.hf_repo or (m.hf_file is not None and '<' in m.hf_file):
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continue
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if m.hf_file is not None and '-of-' in m.hf_file:
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logging.warning(f'Skipping model at {m.hf_repo} / {m.hf_file} because it is a split file')
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continue
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logging.info(f'Using llama-cli to ensure model {m.hf_repo}/{m.hf_file} was fetched')
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cmd = [
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cli_path,
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'-hfr', m.hf_repo,
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*([] if m.hf_file is None else ['-hff', m.hf_file]),
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'-n', '1',
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'-p', 'Hey',
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'--no-warmup',
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'--log-disable',
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'-no-cnv']
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if m.hf_file != 'tinyllamas/stories260K.gguf' and 'Mistral-Nemo' not in m.hf_repo:
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cmd.append('-fa')
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try:
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subprocess.check_call(cmd)
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except subprocess.CalledProcessError:
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logging.error(f'Failed to fetch model at {m.hf_repo} / {m.hf_file} with command:\n {" ".join(cmd)}')
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exit(1)
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