From b8b1b6888b9fdd6bacb4c6c3e0c72d507d80574a Mon Sep 17 00:00:00 2001 From: Song Jiaming Date: Fri, 25 Aug 2023 14:31:45 +0800 Subject: [PATCH] [LLM] Performance test (#8796) --- .github/workflows/llm_performance_tests.yml | 66 +++++++++++++ python/llm/dev/benchmark/benchmark_util.py | 11 ++- .../dev/benchmark/pipelines/llama2_test.py | 94 +++++++++++++++++++ .../llm/dev/benchmark/run-benchmark-tests.sh | 22 +++++ python/llm/setup.py | 2 +- 5 files changed, 191 insertions(+), 4 deletions(-) create mode 100644 .github/workflows/llm_performance_tests.yml create mode 100644 python/llm/dev/benchmark/pipelines/llama2_test.py create mode 100644 python/llm/dev/benchmark/run-benchmark-tests.sh diff --git a/.github/workflows/llm_performance_tests.yml b/.github/workflows/llm_performance_tests.yml new file mode 100644 index 00000000..a9312481 --- /dev/null +++ b/.github/workflows/llm_performance_tests.yml @@ -0,0 +1,66 @@ +name: LLM Performance Test + +# Cancel previous runs in the PR when you push new commits +concurrency: + group: ${{ github.workflow }}-llm-performance-tests-${{ github.event.pull_request.number || github.run_id }} + cancel-in-progress: true + +# Controls when the action will run. +on: + schedule: + - cron: '00 13 * * *' # GMT time, 13:00 GMT == 21:00 China + pull_request: + branches: [ main ] + paths: + - '.github/workflows/llm_performance_tests.yml' + - '.github/workflows/llm-binary-build.yml' + - '.github/actions/llm/setup-llm-env/action.yml' + - '.github/actions/llm/remove-llm-env/action.yml' + - '.github/actions/llm/download-llm-binary/action.yml' + workflow_dispatch: + workflow_call: + +# A workflow run is made up of one or more jobs that can run sequentially or in parallel +jobs: + llm-cpp-build: + uses: ./.github/workflows/llm-binary-build.yml + llm-performance-test: + needs: llm-cpp-build + strategy: + fail-fast: false + matrix: + python-version: ["3.9"] + instruction: ["AVX512"] + runs-on: [ self-hosted, llm, perf ] + env: + THREAD_NUM: 24 + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install --upgrade setuptools==58.0.4 + python -m pip install --upgrade wheel + + - name: Download llm binary + uses: ./.github/actions/llm/download-llm-binary + + - name: Run LLM install (all) test + uses: ./.github/actions/llm/setup-llm-env + env: + ANALYTICS_ZOO_ROOT: ${{ github.workspace }} + + - name: Run LLM Performance test + env: + ANALYTICS_ZOO_ROOT: ${{ github.workspace }} + run: + bash python/llm/dev/benchmark/run-benchmark-tests.sh + + # - name: Clean up test environment + # uses: ./.github/actions/llm/remove-llm-env + # env: + # ANALYTICS_ZOO_ROOT: ${{ github.workspace }} diff --git a/python/llm/dev/benchmark/benchmark_util.py b/python/llm/dev/benchmark/benchmark_util.py index 06b2e654..a5d40a27 100644 --- a/python/llm/dev/benchmark/benchmark_util.py +++ b/python/llm/dev/benchmark/benchmark_util.py @@ -510,8 +510,9 @@ class BenchmarkWrapper: learn more about decoding strategies refer to the [text generation strategies guide](../generation_strategies). """ - def __init__(self, model): + def __init__(self, model, do_print=True): self.model = model + self.do_print = do_print print(self.model.__class__) def __getattr__(self, attr): @@ -2445,9 +2446,13 @@ class BenchmarkWrapper: if this_peer_finished and not synced_gpus: break - print(f"=========First token cost {first_token_time:.4f}s=========") + if self.do_print: + print(f"=========First token cost {first_token_time:.4f}s=========") if len(last_token_time) > 1: - print(f"=========Rest tokens cost average {np.mean(last_token_time):.4f}s ({len(last_token_time)} tokens in all)=========") + self.first_cost = first_token_time + self.rest_cost_mean = np.mean(last_token_time) + if self.do_print: + print(f"=========Rest tokens cost average {self.rest_cost_mean:.4f}s ({len(last_token_time)} tokens in all)=========") if streamer is not None: streamer.end() diff --git a/python/llm/dev/benchmark/pipelines/llama2_test.py b/python/llm/dev/benchmark/pipelines/llama2_test.py new file mode 100644 index 00000000..0eadf9e2 --- /dev/null +++ b/python/llm/dev/benchmark/pipelines/llama2_test.py @@ -0,0 +1,94 @@ +# +# 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 torch +import time +import argparse + +from bigdl.llm.transformers import AutoModelForCausalLM +from transformers import LlamaTokenizer + + +import os +benchmark_util_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') +import sys +sys.path.append(benchmark_util_path) +from benchmark_util import BenchmarkWrapper + +# you could tune the prompt based on your own model, +# here the prompt tuning refers to https://huggingface.co/georgesung/llama2_7b_chat_uncensored#prompt-style +LLAMA2_PROMPT_FORMAT = """### HUMAN: +{prompt} + +### RESPONSE: +""" + +if __name__ == '__main__': + + parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model') + parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Llama-2-7b-chat-hf", + help='The huggingface repo id for the Llama2 (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded' + ', or the path to the huggingface checkpoint folder') + parser.add_argument('--prompt', type=str, default="What is AI?", + help='Prompt to infer') + parser.add_argument('--n-predict', type=int, default=32, + help='Max tokens to predict') + + args = parser.parse_args() + model_path = args.repo_id_or_model_path + + # Load model in 4 bit, + # which convert the relevant layers in the model into INT4 format + model = AutoModelForCausalLM.from_pretrained(model_path, + load_in_4bit=True, + trust_remote_code=True) + + + model = BenchmarkWrapper(model, do_print=False) + + # Load tokenizer + tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True) + + # Generate predicted tokens + with torch.inference_mode(): + prompt = LLAMA2_PROMPT_FORMAT.format(prompt=args.prompt) + input_ids = tokenizer.encode(prompt, return_tensors="pt") + st = time.time() + # if your selected model is capable of utilizing previous key/value attentions + # to enhance decoding speed, but has `"use_cache": false` in its model config, + # it is important to set `use_cache=True` explicitly in the `generate` function + # to obtain optimal performance with BigDL-LLM INT4 optimizations + output = model.generate(input_ids, + max_new_tokens=args.n_predict) + end = time.time() + output_str = tokenizer.decode(output[0], skip_special_tokens=True) + print(f'Inference time: {end-st} s') + print('-'*20, 'Prompt', '-'*20) + print(prompt) + print('-'*20, 'Output', '-'*20) + print(output_str) + + assert "AI is a term" in output_str, "output is not as expected, the correctness may be wrong." + llama2_baseline = os.getenv('LLAMA2_BASELINE') + if llama2_baseline is None: + print('baseline is not set, skipping baseline validation') + else: + llama2_baseline = float(llama2_baseline) + ratio = model.rest_cost_mean / llama2_baseline + assert ratio < 1.1, f"performance did not meet baseline, the cost is {(ratio - 1) * 100}% higher than the baseline" + diff --git a/python/llm/dev/benchmark/run-benchmark-tests.sh b/python/llm/dev/benchmark/run-benchmark-tests.sh new file mode 100644 index 00000000..1fa8032e --- /dev/null +++ b/python/llm/dev/benchmark/run-benchmark-tests.sh @@ -0,0 +1,22 @@ +# Performance tests usually use dedicated machines, see below to set env vars, e.g. model paths +# The following environment variables should be ready +# ORIGINAL_LLAMA2_PATH +# LLAMA2_BASELINE +# LLM_DIR + +if [ -z "$THREAD_NUM" ]; then + THREAD_NUM=2 +fi +export OMP_NUM_THREADS=$THREAD_NUM + +######## LLAMA2 +# transformers + +if [ ! -d $ORIGINAL_LLAMA2_PATH ]; then + echo "Directory $ORIGINAL_LLAMA2_PATH not found. Downloading from FTP server..." + wget -r -nH --no-verbose --cut-dirs=1 $LLM_FTP_URL/${ORIGINAL_LLAMA2_PATH:2} -P $LLM_DIR +fi + +echo ">>> Testing LLAMA2 transformers API" +taskset -c 0-$((THREAD_NUM - 1)) python python/llm/dev/benchmark/pipelines/llama2_test.py --repo-id-or-model-path $ORIGINAL_LLAMA2_PATH + diff --git a/python/llm/setup.py b/python/llm/setup.py index 8f2a3cd4..4d749c92 100644 --- a/python/llm/setup.py +++ b/python/llm/setup.py @@ -50,7 +50,7 @@ llm_home = os.path.join(os.path.dirname(os.path.abspath(__file__)), "src") github_artifact_dir = os.path.join(llm_home, '../llm-binary') libs_dir = os.path.join(llm_home, "bigdl", "llm", "libs") CONVERT_DEP = ['numpy >= 1.22', 'torch', - 'transformers >= 4.31.0', 'sentencepiece', + 'transformers == 4.31.0', 'sentencepiece', 'accelerate', 'tabulate'] windows_binarys = [ "llama.dll",