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import ast
import os
import subprocess
import time
import pytest
from tests.test_inference_servers import run_h2ogpt_docker
from tests.utils import wrap_test_forked, get_inf_server, get_inf_port
from src.utils import download_simple
results_file = "./benchmarks/perf.json"
@pytest.mark.skipif(not os.getenv('BENCHMARK'),
reason="Only for benchmarking")
@pytest.mark.parametrize("backend", [
# 'transformers',
# 'text-generation-inference',
'text-generation-inference-',
])
@pytest.mark.parametrize("base_model", [
'h2oai/h2ogpt-4096-llama2-7b-chat',
'h2oai/h2ogpt-4096-llama2-13b-chat',
'h2oai/h2ogpt-4096-llama2-70b-chat',
])
@pytest.mark.parametrize("task", [
# 'summary',
# 'generate',
'summary_and_generate'
])
@pytest.mark.parametrize("bits", [
16,
8,
4,
], ids=[
"16-bit",
"8-bit",
"4-bit",
])
@pytest.mark.parametrize("ngpus", [
0,
1,
2,
4,
8,
], ids=[
"CPU",
"1 GPU",
"2 GPUs",
"4 GPUs",
"8 GPUs",
])
@pytest.mark.need_tokens
@wrap_test_forked
def test_perf_benchmarks(backend, base_model, task, bits, ngpus):
reps = 3
bench_dict = locals()
from datetime import datetime
import json
import socket
os.environ['CUDA_VISIBLE_DEVICES'] = "" if ngpus == 0 else "0" if ngpus == 1 else ",".join([str(x) for x in range(ngpus)])
import torch
n_gpus = torch.cuda.device_count()
if n_gpus != ngpus:
return
git_sha = (
subprocess.check_output("git rev-parse HEAD", shell=True)
.decode("utf-8")
.strip()
)
bench_dict["date"] = datetime.now().strftime("%m/%d/%Y %H:%M:%S")
bench_dict["git_sha"] = git_sha[:8]
bench_dict["n_gpus"] = n_gpus
from importlib.metadata import version
bench_dict["transformers"] = str(version('transformers'))
bench_dict["bitsandbytes"] = str(version('bitsandbytes'))
bench_dict["cuda"] = str(torch.version.cuda)
bench_dict["hostname"] = str(socket.gethostname())
gpu_list = [torch.cuda.get_device_name(i) for i in range(n_gpus)]
# get GPU memory, assumes homogeneous system
cmd = 'nvidia-smi -i 0 -q | grep -A 1 "FB Memory Usage" | cut -d: -f2 | tail -n 1'
o = subprocess.check_output(cmd, shell=True, timeout=15)
mem_gpu = o.decode("utf-8").splitlines()[0].strip() if n_gpus else 0
bench_dict["gpus"] = "%d x %s (%s)" % (n_gpus, gpu_list[0], mem_gpu) if n_gpus else "CPU"
assert all([x == gpu_list[0] for x in gpu_list])
print(bench_dict)
# launch server(s)
docker_hash1 = None
docker_hash2 = None
max_new_tokens = 4096
try:
h2ogpt_args = dict(base_model=base_model,
chat=True, gradio=True, num_beams=1, block_gradio_exit=False, verbose=True,
load_half=bits == 16 and n_gpus,
load_8bit=bits == 8,
load_4bit=bits == 4,
langchain_mode='MyData',
use_auth_token=True,
max_new_tokens=max_new_tokens,
use_gpu_id=ngpus == 1,
use_safetensors=True,
score_model=None,
)
if backend == 'transformers':
from src.gen import main
main(**h2ogpt_args)
elif backend == 'text-generation-inference':
if bits != 16:
return
from tests.test_inference_servers import run_docker
# HF inference server
gradio_port = get_inf_port()
inf_port = gradio_port + 1
inference_server = 'http://127.0.0.1:%s' % inf_port
docker_hash1 = run_docker(inf_port, base_model, low_mem_mode=False) # don't do low-mem, since need tokens for summary
os.system('docker logs %s | tail -10' % docker_hash1)
# h2oGPT server
docker_hash2 = run_h2ogpt_docker(gradio_port, base_model, inference_server=inference_server, max_new_tokens=max_new_tokens)
time.sleep(30) # assumes image already downloaded, else need more time
os.system('docker logs %s | tail -10' % docker_hash2)
elif backend == 'text-generation-inference-':
if bits != 16:
return
from tests.test_inference_servers import run_docker
# HF inference server
gradio_port = get_inf_port()
inf_port = gradio_port + 1
inference_server = 'http://127.0.0.1:%s' % inf_port
docker_hash1 = run_docker(inf_port, base_model, low_mem_mode=False) # don't do low-mem, since need tokens for summary
from src.gen import main
main(**h2ogpt_args)
else:
raise NotImplementedError("backend %s not implemented" % backend)
# get file for client to upload
url = 'https://cdn.openai.com/papers/whisper.pdf'
test_file1 = os.path.join('/tmp/', 'whisper1.pdf')
download_simple(url, dest=test_file1)
# PURE client code
from gradio_client import Client
client = Client(get_inf_server())
if "summary" in task:
# upload file(s). Can be list or single file
test_file_local, test_file_server = client.predict(test_file1, api_name='/upload_api')
assert os.path.normpath(test_file_local) != os.path.normpath(test_file_server)
chunk = True
chunk_size = 512
langchain_mode = 'MyData'
embed = True
loaders = tuple([None, None, None, None, None])
extract_frames = 1
llava_prompt = ''
h2ogpt_key = ''
res = client.predict(test_file_server,
chunk, chunk_size, langchain_mode, embed,
*loaders,
extract_frames,
llava_prompt,
h2ogpt_key,
api_name='/add_file_api')
assert res[0] is None
assert res[1] == langchain_mode
# assert os.path.basename(test_file_server) in res[2]
assert res[3] == ''
# ask for summary, need to use same client if using MyData
api_name = '/submit_nochat_api' # NOTE: like submit_nochat but stable API for string dict passing
kwargs = dict(langchain_mode=langchain_mode,
langchain_action="Summarize", # uses full document, not vectorDB chunks
top_k_docs=4, # -1 == entire pdf
document_subset='Relevant',
document_choice='All',
max_new_tokens=max_new_tokens,
max_time=300,
do_sample=False,
prompt_summary='Summarize into single paragraph',
system_prompt='',
)
t0 = time.time()
for r in range(reps):
res = client.predict(
str(dict(kwargs)),
api_name=api_name,
)
t1 = time.time()
time_taken = (t1 - t0) / reps
res = ast.literal_eval(res)
response = res['response']
sources = res['sources']
size_summary = os.path.getsize(test_file1)
# print(response)
print("Time to summarize %s bytes into %s bytes: %.4f" % (size_summary, len(response), time_taken))
bench_dict["summarize_input_len_bytes"] = size_summary
bench_dict["summarize_output_len_bytes"] = len(response)
bench_dict["summarize_time"] = time_taken
# bench_dict["summarize_tokens_per_sec"] = res['tokens/s']
assert 'my_test_pdf.pdf' in sources
if "generate" in task:
api_name = '/submit_nochat_api' # NOTE: like submit_nochat but stable API for string dict passing
kwargs = dict(prompt_summary="Write a poem about water.")
t0 = time.time()
for r in range(reps):
res = client.predict(
str(dict(kwargs)),
api_name=api_name,
)
t1 = time.time()
time_taken = (t1 - t0) / reps
res = ast.literal_eval(res)
response = res['response']
# print(response)
print("Time to generate %s bytes: %.4f" % (len(response), time_taken))
bench_dict["generate_output_len_bytes"] = len(response)
bench_dict["generate_time"] = time_taken
# bench_dict["generate_tokens_per_sec"] = res['tokens/s']
except BaseException as e:
if 'CUDA out of memory' in str(e):
e = "OOM"
bench_dict["exception"] = str(e)
else:
raise
finally:
if bench_dict["backend"] == "text-generation-inference-":
# Fixup, so appears as same
bench_dict["backend"] = "text-generation-inference"
if 'summarize_time' in bench_dict or 'generate_time' in bench_dict or bench_dict.get('exception') == "OOM":
with open(results_file, mode="a") as f:
f.write(json.dumps(bench_dict) + "\n")
if "text-generation-inference" in backend:
if docker_hash1:
os.system("docker stop %s" % docker_hash1)
if docker_hash2:
os.system("docker stop %s" % docker_hash2)
@pytest.mark.skip("run manually")
def test_plot_results():
import pandas as pd
import json
res = []
with open(results_file) as f:
for line in f.readlines():
entry = json.loads(line)
res.append(entry)
X = pd.DataFrame(res)
X.to_csv(results_file + ".csv", index=False)
result_cols = ['summarization time [sec]', 'generation speed [tokens/sec]']
X[result_cols[0]] = X['summarize_time']
X[result_cols[1]] = X['generate_output_len_bytes'] / 4 / X['generate_time']
with open(results_file.replace(".json", ".md"), "w") as f:
for backend in pd.unique(X['backend']):
print("# Backend: %s" % backend, file=f)
for base_model in pd.unique(X['base_model']):
print("## Model: %s (%s)" % (base_model, backend), file=f)
for n_gpus in sorted(pd.unique(X['n_gpus'])):
XX = X[(X['base_model'] == base_model) & (X['backend'] == backend) & (X['n_gpus'] == n_gpus)]
if XX.shape[0] == 0:
continue
print("### Number of GPUs: %s" % n_gpus, file=f)
XX.drop_duplicates(subset=['bits', 'gpus'], keep='last', inplace=True)
XX = XX.sort_values(['bits', result_cols[1]], ascending=[False, False])
XX['exception'] = XX['exception'].astype(str).replace("nan", "")
print(XX[['bits', 'gpus', result_cols[0], result_cols[1], 'exception']].to_markdown(index=False), file=f)
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