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# Copyright 2022 MosaicML LLM Foundry authors
# SPDX-License-Identifier: Apache-2.0
import logging
import os
import sys
import time
import warnings
from typing import Any, Dict, List, Optional, Union
import pandas as pd
import torch
from composer.loggers.logger_destination import LoggerDestination
from composer.models.base import ComposerModel
from composer.trainer import Trainer
from composer.utils import dist, get_device, reproducibility
from omegaconf import DictConfig, ListConfig
from omegaconf import OmegaConf as om
from transformers import (AutoModelForCausalLM, PreTrainedTokenizerBase,
T5ForConditionalGeneration)
from llmfoundry.models import MPTForCausalLM
from llmfoundry.models.model_registry import COMPOSER_MODEL_REGISTRY
from llmfoundry.utils.builders import (build_icl_data_and_gauntlet,
build_logger, build_tokenizer)
from llmfoundry.utils.config_utils import pop_config, process_init_device
def load_peft_model(model_cfg: DictConfig, tokenizer: PreTrainedTokenizerBase,
num_retries: int) -> Optional[ComposerModel]:
try:
from peft import PeftModel
except ImportError as e:
raise ImportError(
f'Error importing from peft. Run `pip install -e .[gpu,peft]`. \n {e}'
)
model_registry = {
'mpt_causal_lm': MPTForCausalLM,
'hf_causal_lm': AutoModelForCausalLM,
'hf_prefix_lm': AutoModelForCausalLM,
'hf_t5': T5ForConditionalGeneration,
}
retries = 0
while retries < num_retries:
try:
trust_remote_code = model_cfg.get('trust_remote_code', True)
use_auth_token = model_cfg.get('use_auth_token', False)
model = model_registry[model_cfg.name].from_pretrained(
model_cfg.pretrained_model_name_or_path,
trust_remote_code=trust_remote_code,
use_auth_token=use_auth_token,
)
peft_model = PeftModel.from_pretrained(
model, model_cfg.pretrained_lora_id_or_path)
composer_model_wrapper = COMPOSER_MODEL_REGISTRY[model_cfg.name](
peft_model, tokenizer)
return composer_model_wrapper
except Exception as e:
retries += 1
if retries >= num_retries:
raise e
else:
print(
f'Got exception {str(e)} while loading model {model_cfg.name}. {num_retries-retries} retries remaining'
)
def load_model(model_cfg: DictConfig, tokenizer: PreTrainedTokenizerBase,
fsdp_config: Optional[Dict],
num_retries: int) -> Optional[ComposerModel]:
init_context = process_init_device(model_cfg, fsdp_config)
retries = 0
with init_context:
while retries < num_retries:
try:
composer_model = COMPOSER_MODEL_REGISTRY[model_cfg.name](
model_cfg, tokenizer)
return composer_model
except Exception as e:
retries += 1
if retries >= num_retries:
raise e
else:
print(
f'Got exception {str(e)} while loading model {model_cfg.name}. {num_retries-retries} retries remaining'
)
def evaluate_model(
model_cfg: DictConfig,
dist_timeout: Union[float, int],
run_name: str,
seed: int,
icl_tasks: Union[str, ListConfig],
max_seq_len: int,
device_eval_batch_size: int,
eval_gauntlet_config: Optional[Union[str, DictConfig]],
fsdp_config: Optional[Dict],
num_retries: int,
loggers_cfg: Dict[str, Any],
python_log_level: Optional[str],
precision: str,
eval_gauntlet_df: Optional[pd.DataFrame],
icl_subset_num_batches: Optional[int],
):
print(f'Evaluating model: {model_cfg.model_name}', flush=True)
# Build tokenizer and model
tokenizer_cfg: Dict[str,
Any] = om.to_container(model_cfg.tokenizer,
resolve=True) # type: ignore
tokenizer_name = tokenizer_cfg['name']
tokenizer_kwargs = tokenizer_cfg.get('kwargs', {})
tokenizer = build_tokenizer(tokenizer_name, tokenizer_kwargs)
evaluators, logger_keys, eval_gauntlet_callback = build_icl_data_and_gauntlet(
icl_tasks, eval_gauntlet_config, tokenizer, device_eval_batch_size,
max_seq_len, icl_subset_num_batches)
callbacks = []
if eval_gauntlet_callback is not None:
callbacks.append(eval_gauntlet_callback)
loggers: List[LoggerDestination] = [
build_logger(name, logger_cfg)
for name, logger_cfg in loggers_cfg.items()
]
if fsdp_config and model_cfg.model.get('load_in_8bit', False):
raise ValueError(
'The FSDP config block is not supported when loading ' +
'Hugging Face models in 8bit.')
if hasattr(model_cfg.model, 'pretrained_lora_id_or_path'):
composer_model = load_peft_model(model_cfg.model, tokenizer,
num_retries)
else:
composer_model = load_model(model_cfg.model, tokenizer, fsdp_config,
num_retries)
if eval_gauntlet_df is None and eval_gauntlet_callback is not None:
eval_gauntlet_df = pd.DataFrame(
columns=['model_name', 'average'] +
[t.name for t in eval_gauntlet_callback.categories])
load_path = model_cfg.get('load_path', None)
if model_cfg.model.name == 'mpt_causal_lm' and load_path is None:
raise ValueError(
'MPT causal LMs require a load_path to the checkpoint for model evaluation.'
+
' Please check your yaml and the model_cfg to ensure that load_path is set.'
)
assert composer_model is not None
trainer = Trainer(
run_name=run_name,
seed=seed,
model=composer_model,
callbacks=callbacks,
loggers=loggers,
precision=precision,
fsdp_config=fsdp_config,
load_path=load_path,
load_weights_only=True,
progress_bar=False,
log_to_console=True,
dist_timeout=dist_timeout,
python_log_level=python_log_level,
)
if torch.cuda.is_available():
torch.cuda.synchronize()
a = time.time()
trainer.eval(eval_dataloader=evaluators)
if torch.cuda.is_available():
torch.cuda.synchronize()
b = time.time()
print(f'Ran {model_cfg.model_name} eval in: {b-a} seconds')
return (trainer, logger_keys, eval_gauntlet_callback, eval_gauntlet_df)
def main(cfg: DictConfig):
om.resolve(cfg)
model_configs: ListConfig = pop_config(cfg, 'models', must_exist=True)
eval_gauntlet_config: Optional[Union[str, DictConfig]] = pop_config(
cfg, 'eval_gauntlet', must_exist=False, default_value=None)
if eval_gauntlet_config is None:
eval_gauntlet_config = pop_config(cfg,
'model_gauntlet',
must_exist=False,
default_value=None)
if eval_gauntlet_config:
print(
'Use of the key `model_gauntlet` is deprecated, please use the key `eval_gauntlet`'
)
fsdp_dict_cfg: Optional[DictConfig] = pop_config(cfg,
'fsdp_config',
must_exist=False,
default_value=None)
fsdp_config: Optional[Dict] = om.to_container(
fsdp_dict_cfg,
resolve=True) if fsdp_dict_cfg is not None else None # type: ignore
assert isinstance(fsdp_config, Dict) or fsdp_config is None
# Mandatory Evaluation Parameters
icl_tasks: Union[str, ListConfig] = pop_config(cfg,
'icl_tasks',
must_exist=True)
max_seq_len: int = pop_config(cfg, 'max_seq_len', must_exist=True)
device_eval_batch_size: int = pop_config(cfg,
'device_eval_batch_size',
must_exist=True)
precision: str = pop_config(cfg,
'precision',
must_exist=False,
default_value=None)
python_log_level: Optional[str] = pop_config(cfg,
'python_log_level',
must_exist=False,
default_value='debug')
# Optional Evaluation Parameters with default values
seed: int = pop_config(cfg, 'seed', must_exist=False, default_value=17)
dist_timeout: Union[float, int] = pop_config(cfg,
'dist_timeout',
must_exist=False,
default_value=600.0)
default_run_name: str = os.environ.get('RUN_NAME', 'llm')
run_name: str = pop_config(cfg,
'run_name',
must_exist=False,
default_value=default_run_name)
num_retries: int = pop_config(cfg,
'num_retries',
must_exist=False,
default_value=3)
loggers_cfg: Dict[str, Any] = pop_config(cfg,
'loggers',
must_exist=False,
default_value={})
icl_subset_num_batches: int = pop_config(cfg,
'icl_subset_num_batches',
must_exist=False,
default_value=None)
# Pop out interpolation variables.
pop_config(cfg, 'model_name_or_path', must_exist=False, default_value=None)
# Warn for unused parameters
for key in cfg:
warnings.warn(
f'Unused parameter {key} found in cfg. Please check your yaml to ensure this parameter is necessary.'
)
reproducibility.seed_all(seed)
dist.initialize_dist(get_device(None), timeout=dist_timeout)
if python_log_level is not None:
logging.basicConfig(
# Example of format string
# 2022-06-29 11:22:26,152: rank0[822018][MainThread]: INFO: Message here
format=
f'%(asctime)s: rank{dist.get_global_rank()}[%(process)d][%(threadName)s]: %(levelname)s: %(name)s: %(message)s'
)
logging.getLogger('llmfoundry').setLevel(python_log_level.upper())
eval_gauntlet_df = None
models_df = None
composite_scores = None
for model_cfg in model_configs:
(trainer, logger_keys, eval_gauntlet_callback,
eval_gauntlet_df) = evaluate_model(
model_cfg=model_cfg,
dist_timeout=dist_timeout,
run_name=run_name,
seed=seed,
icl_tasks=icl_tasks,
max_seq_len=max_seq_len,
device_eval_batch_size=device_eval_batch_size,
eval_gauntlet_config=eval_gauntlet_config,
fsdp_config=fsdp_config,
num_retries=num_retries,
loggers_cfg=loggers_cfg,
python_log_level=python_log_level,
precision=precision,
eval_gauntlet_df=eval_gauntlet_df,
icl_subset_num_batches=icl_subset_num_batches)
if eval_gauntlet_callback is not None:
composite_scores = eval_gauntlet_callback.eval_after_all(
trainer.state, trainer.logger)
benchmark_to_taxonomy = {}
if eval_gauntlet_callback is not None:
for t in eval_gauntlet_callback.categories:
for b in t.benchmarks:
benchmark_to_taxonomy[b.name] = t.name
model_results = calculate_markdown_results(logger_keys, trainer,
benchmark_to_taxonomy,
model_cfg.model_name)
if models_df is None:
models_df = model_results
else:
models_df = pd.concat([models_df, model_results], ignore_index=True)
if eval_gauntlet_df is not None and eval_gauntlet_callback is not None:
assert composite_scores is not None
row = {'model_name': model_cfg['model_name']}
row.update({
t.name:
composite_scores.get(f'icl/metrics/eval_gauntlet/{t.name}',
None)
for t in eval_gauntlet_callback.categories
})
row.update({
'average':
composite_scores[f'icl/metrics/eval_gauntlet/average']
})
eval_gauntlet_df = pd.concat(
[eval_gauntlet_df, pd.DataFrame([row])], ignore_index=True)
print(f'Printing gauntlet results for all models')
print(
eval_gauntlet_df.sort_values(
'average', ascending=False).to_markdown(index=False))
print(f'Printing complete results for all models')
assert models_df is not None
print(models_df.to_markdown(index=False))
def calculate_markdown_results(logger_keys: List[str], trainer: Trainer,
benchmark_to_taxonomy: Dict[str, str],
model_name: str):
results = {}
for key in logger_keys:
# dl_name is either 2-tuple (benchmark_name, num_fewshot)
# or 3-tuple (benchmark_name, num_fewshot, subcategory)
dl_name, metric_name = key.split('/')[1:-1], key.split('/')[-1]
if 'Accuracy' not in metric_name:
continue
metric = trainer.state.eval_metrics.get('/'.join(dl_name),
{}).get(metric_name, None)
if metric is None:
continue
if dl_name[1] not in results:
results[dl_name[1]] = {}
if dl_name[0] not in results[dl_name[1]]:
results[dl_name[1]][dl_name[0]] = {}
if metric_name not in results[dl_name[1]][dl_name[0]]:
results[dl_name[1]][dl_name[0]][metric_name] = []
results[dl_name[1]][dl_name[0]][metric_name].append({
'val': metric.compute(),
'subcat': dl_name[-1] if len(dl_name) == 3 else 'no_subcat'
})
df = pd.DataFrame(columns=[
'Category', 'Benchmark', 'Subtask', 'Accuracy', 'Number few shot',
'Model'
])
for num_shot in results:
for benchmark in results[num_shot]:
for metric in results[num_shot][benchmark]:
subscores = results[num_shot][benchmark][metric]
if len(subscores) == 1:
row = {
'Category': benchmark_to_taxonomy.get(benchmark, ''),
'Benchmark': benchmark,
'Subtask': None,
'Accuracy': subscores[0]['val'],
'Number few shot': num_shot,
'Model': model_name
}
df = pd.concat([df, pd.DataFrame([row])], ignore_index=True)
else:
row = {
'Category':
benchmark_to_taxonomy.get(benchmark, ''),
'Benchmark':
benchmark,
'Subtask':
'Average',
'Accuracy':
sum(s['val'] for s in subscores) / len(subscores),
'Number few shot':
num_shot,
'Model':
model_name
}
df = pd.concat([df, pd.DataFrame([row])], ignore_index=True)
for sub in subscores:
row = {
'Category':
benchmark_to_taxonomy.get(benchmark, ''),
'Benchmark':
None,
'Subtask':
sub['subcat'],
'Accuracy':
sub['val'],
'Number few shot':
num_shot,
'Model':
model_name
}
df = pd.concat([df, pd.DataFrame([row])],
ignore_index=True)
return df
if __name__ == '__main__':
yaml_path, args_list = sys.argv[1], sys.argv[2:]
with open(yaml_path) as f:
yaml_cfg = om.load(f)
cli_cfg = om.from_cli(args_list)
cfg = om.merge(yaml_cfg, cli_cfg)
assert isinstance(cfg, DictConfig)
main(cfg)
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