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)