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test / tests /test_perf_benchmarks.py

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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)