ayo/ipex-llm
1
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 1

Merge harness (#9319)

Browse source 
* add harness patch and llb script

* add readme

* add license

* use patch instead

* update readme

* rename tests to evaluation

* fix typo

* remove nano dependency

* add original harness link

* rename title of usage

* rename BigDLGPULM as BigDLLM

* empty commit to rerun job
This commit is contained in:
Chen, Zhentao 2023-11-02 15:14:19 +08:00 committed by GitHub
parent 63b2556ce2
commit d4dffbdb62
3 changed files with 261 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

28
python/llm/dev/benchmark/harness/README.md Normal file
View file

@ -0,0 +1,28 @@
# Harness Evalution
[Harness evalution](https://github.com/EleutherAI/lm-evaluation-harness) allows users to eaisly get accuracy on various datasets. Here we have enabled harness evalution with BigDL-LLM under
[Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) settings.
Before running, make sure to have [bigdl-llm](../../../README.md) installed.
## Install Harness
```bash
git clone https://github.com/EleutherAI/lm-evaluation-harness.git
cd lm-evaluation-harness
git checkout e81d3cc
pip install -e .
git apply ../bigdl-llm.patch
cd ..
```
## Run
run `python llb.py`. `llb.py` combines some arguments in `main.py` to make evalutions easier. The mapping of arguments is defined as a dict in [`llb.py`](llb.py).
### Evaluation on CPU
```python
python llb.py --model bigdl-llm --pretrained /path/to/model --precision nf3 int4 nf4 --device cpu --tasks hellaswag arc mmlu truthfulqa --output_dir results/output
```
### Evaluation on Intel GPU
```python
python llb.py --model bigdl-llm --pretrained /path/to/model --precision nf3 int4 nf4 --device xpu --tasks hellaswag arc mmlu truthfulqa --output_dir results/output
```
## Results
We follow [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) to record our metrics, `acc_norm` for `hellaswag` and `arc_challenge`, `mc2` for `truthful_qa` and `acc` for `mmlu`. For `mmlu`, there are 57 subtasks which means users may need to average them manually to get final result.

151
python/llm/dev/benchmark/harness/bigdl-llm.patch Normal file
View file

@ -0,0 +1,151 @@
diff --git a/lm_eval/models/__init__.py b/lm_eval/models/__init__.py
index 8ca27fac..6b581487 100644
--- a/lm_eval/models/__init__.py
+++ b/lm_eval/models/__init__.py
@@ -4,6 +4,7 @@ from . import anthropic_llms
from . import huggingface
from . import textsynth
from . import dummy
+from . import bigdl_llm
MODEL_REGISTRY = {
"hf": gpt2.HFLM,
@@ -15,6 +16,7 @@ MODEL_REGISTRY = {
"anthropic": anthropic_llms.AnthropicLM,
"textsynth": textsynth.TextSynthLM,
"dummy": dummy.DummyLM,
+ "bigdl-llm": bigdl_llm.BigDLLM
}
diff --git a/lm_eval/models/bigdl_llm.py b/lm_eval/models/bigdl_llm.py
new file mode 100644
index 00000000..74010da3
--- /dev/null
+++ b/lm_eval/models/bigdl_llm.py
@@ -0,0 +1,124 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+# this code is copied from llama2 example test, and added performance test
+import os
+import multiprocessing
+
+from bigdl.llm.transformers import AutoModel, AutoModelForCausalLM
+
+import torch
+from typing import Optional, Union
+from lm_eval.base import BaseLM
+
+from transformers import AutoTokenizer, LlamaTokenizer
+
+def _get_dtype(
+ dtype: Union[str, torch.dtype]
+) -> torch.dtype:
+ """Converts `dtype` from `str` to torch.dtype when possible. Does not use an instantiated HF AutoConfig"""
+ if isinstance(dtype, str) and dtype != "auto":
+ # Convert `str` args torch dtype: `float16` -> `torch.float16`
+ _torch_dtype = getattr(torch, dtype)
+ else:
+ _torch_dtype = dtype
+ return _torch_dtype
+
+class BigDLLM(BaseLM):
+ def __init__(
+ self,
+ device="xpu",
+ pretrained="gpt2",
+ revision="main",
+ low_cpu_mem_usage=None,
+ subfolder=None,
+ tokenizer=None,
+ batch_size=1,
+ load_in_8bit: Optional[bool] = False,
+ trust_remote_code: Optional[bool] = False,
+ load_in_low_bit=None,
+ dtype: Optional[Union[str, torch.dtype]] = "auto",
+ ):
+ super().__init__()
+
+ assert isinstance(pretrained, str)
+ assert isinstance(batch_size, (int,str))
+ if device == 'xpu':
+ import intel_extension_for_pytorch as ipex
+ model = AutoModelForCausalLM.from_pretrained(pretrained,
+ load_in_low_bit=load_in_low_bit,
+ optimize_model=True,
+ trust_remote_code=True,
+ use_cache=True,
+ torch_dtype=_get_dtype(dtype))
+ print(model) # print model to check precision
+ self._device = device
+ self.model = model.to(device)
+
+ self.tokenizer = LlamaTokenizer.from_pretrained(pretrained, trust_remote_code=True)
+
+ # setup for automatic batch size detection
+ if batch_size == 'auto':
+ self.batch_size_per_gpu = batch_size
+ else:
+ self.batch_size_per_gpu = int(batch_size)
+
+ @property
+ def eot_token_id(self):
+ # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
+ return self.model.token_eos()
+
+ @property
+ def max_length(self):
+ return 2048 # TODO: how to get this from config
+
+ @property
+ def max_gen_toks(self):
+ return 256
+
+ @property
+ def batch_size(self):
+ # TODO: fix multi-gpu
+ return self.batch_size_per_gpu # * gpus
+
+ @property
+ def device(self):
+ # TODO: fix multi-gpu
+ return torch.device(self._device)
+
+ def tok_encode(self, string: str):
+ input_ids = self.tokenizer.encode(string)
+ return input_ids
+
+ def tok_decode(self, tokens):
+ return self.tokenizer.decode(output[0], skip_special_tokens=True)
+
+ def _model_call(self, inps):
+ """
+ inps: a torch tensor of shape [batch, sequence]
+ the size of sequence may vary from call to call
+
+ returns: a torch tensor of shape [batch, sequence, vocab] with the
+ logits returned from the model
+ """
+ with torch.inference_mode():
+ inps = inps.to(self.device)
+ res = self.model(inps)[0]
+ return res
+
+ def _model_generate(self, context, max_length, eos_token_id):
+ return self.model(context, max_tokens=max_length, stop=["Q:", "\n"], echo=True)
\ No newline at end of file

82
python/llm/dev/benchmark/harness/llb.py Normal file
View file

@ -0,0 +1,82 @@
#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# this code is copied from llama2 example test, and added performance test
import argparse
import os
import subprocess
task_cmd = "--num_fewshot {} --tasks {}"
task_map = {
"hellaswag": task_cmd.format(10, "hellaswag"),
"arc": task_cmd.format(25, "arc_challenge"),
"truthfulqa": task_cmd.format(0, "truthfulqa_mc"),
"mmlu": task_cmd.format(5, "hendrycksTest-abstract_algebra,hendrycksTest-anatomy,hendrycksTest-astronomy,hendrycksTest-business_ethics,hendrycksTest-clinical_knowledge,hendrycksTest-college_biology,hendrycksTest-college_chemistry,hendrycksTest-college_computer_science,hendrycksTest-college_mathematics,hendrycksTest-college_medicine,hendrycksTest-college_physics,hendrycksTest-computer_security,hendrycksTest-conceptual_physics,hendrycksTest-econometrics,hendrycksTest-electrical_engineering,hendrycksTest-elementary_mathematics,hendrycksTest-formal_logic,hendrycksTest-global_facts,hendrycksTest-high_school_biology,hendrycksTest-high_school_chemistry,hendrycksTest-high_school_computer_science,hendrycksTest-high_school_european_history,hendrycksTest-high_school_geography,hendrycksTest-high_school_government_and_politics,hendrycksTest-high_school_macroeconomics,hendrycksTest-high_school_mathematics,hendrycksTest-high_school_microeconomics,hendrycksTest-high_school_physics,hendrycksTest-high_school_psychology,hendrycksTest-high_school_statistics,hendrycksTest-high_school_us_history,hendrycksTest-high_school_world_history,hendrycksTest-human_aging,hendrycksTest-human_sexuality,hendrycksTest-international_law,hendrycksTest-jurisprudence,hendrycksTest-logical_fallacies,hendrycksTest-machine_learning,hendrycksTest-management,hendrycksTest-marketing,hendrycksTest-medical_genetics,hendrycksTest-miscellaneous,hendrycksTest-moral_disputes,hendrycksTest-moral_scenarios,hendrycksTest-nutrition,hendrycksTest-philosophy,hendrycksTest-prehistory,hendrycksTest-professional_accounting,hendrycksTest-professional_law,hendrycksTest-professional_medicine,hendrycksTest-professional_psychology,hendrycksTest-public_relations,hendrycksTest-security_studies,hendrycksTest-sociology,hendrycksTest-us_foreign_policy,hendrycksTest-virology,hendrycksTest-world_religions")
}
prec_to_arg = {
"bigdl-llm": {
"int4": "load_in_low_bit=sym_int4",
"nf4": "load_in_low_bit=nf4",
"nf3": "load_in_low_bit=nf3",
"fp8": "load_in_low_bit=fp8",
"fp4": "load_in_low_bit=fp4",
"bf16": "dtype=bfloat16",
"fp16": "dtype=float16",
},
"hf-causal": {
"nf4": "bnb_type=nf4",
"bf16": "dtype=bfloat16",
"fp16": "dtype=float16",
}
}
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", required=True, type=str)
parser.add_argument("--pretrained", required=True, type=str)
parser.add_argument("--precision", required=True, nargs='+', type=str)
parser.add_argument("--device", required=True, type=str)
parser.add_argument("--batch", default=1, type=int)
parser.add_argument("--tasks", required=True, nargs='+', type=str)
parser.add_argument("--output_dir", type=str)
args = parser.parse_args()
print(args.model)
print(args.tasks)
basic_cmd = "python lm-evaluation-harness/main.py --model {} --model_args pretrained={},{} --no_cache --device {} --batch_size {} {} --output_path {} "
os.makedirs(args.output_dir, exist_ok=True)
index = 1
total = len(args.precision) * len(args.tasks)
for prec in args.precision:
prec_arg = prec_to_arg[args.model][prec]
for task in args.tasks:
output_path = f"{args.model}_{prec}_{args.device}_{task}"
task_arg = task_map[task]
cmd_exec = basic_cmd.format(args.model, args.pretrained, prec_arg, args.device, args.batch,
task_arg, f"{args.output_dir}/{output_path}")
print(f"Running job {index}/{total}:\n{cmd_exec}")
index += 1
with open(f"{args.output_dir}/log_{output_path}.txt", "w") as f:
return_code = subprocess.call(cmd_exec, shell=True, stderr=f, stdout=f)
if return_code == 0:
print("Successful")
else:
print("Failed")
main()