mirror of https://github.com/InternLM/InternLM
update
zhulin1
parent
7af5da56b9
commit
4f565ac16d
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@ -42,7 +42,7 @@ jobs:
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pip install torch==2.2.2 torchvision==0.17.2 --index-url https://download.pytorch.org/whl/cu118
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pip install /mnt/petrelfs/qa-caif-cicd/resource/flash_attn-2.5.8+cu118torch2.2cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
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pip install sentencepiece auto-gptq==0.6.0 lmdeploy[all]
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srun -p ${SLURM_PARTITION} --kill-on-bad-exit=1 --job-name=${GITHUB_RUN_ID}-${GITHUB_JOB} --gpus-per-task=2 pytest -s -v --color=yes ./tests/test_hf_model.py
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srun -p ${SLURM_PARTITION} --kill-on-bad-exit=1 --job-name=${GITHUB_RUN_ID}-${GITHUB_JOB} --gpus-per-task=2 pytest -s -v -m tmp --color=yes ./tests/test_hf_model.py
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conda deactivate
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- name: remove_env
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if: always()
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@ -1,6 +1,7 @@
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import pytest
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import torch
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from auto_gptq.modeling import BaseGPTQForCausalLM
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from bs4 import BeautifulSoup
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from lmdeploy import TurbomindEngineConfig, pipeline
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from PIL import Image
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from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer
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@ -279,3 +280,181 @@ class InternLMXComposer2QForCausalLM(BaseGPTQForCausalLM):
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['feed_forward.w1.linear', 'feed_forward.w3.linear'],
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['feed_forward.w2.linear'],
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]
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@pytest.mark.tmp
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class TestXcomposer2d5Model:
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"""Test cases for base model."""
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@pytest.mark.parametrize(
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'model_name',
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[
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'internlm/internlm-xcomposer2d5-7b',
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],
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)
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def test_high_resolution_default(self, model_name):
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torch.set_grad_enabled(False)
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# init model and tokenizer
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model = AutoModel.from_pretrained(
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model_name, torch_dtype=torch.bfloat16,
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trust_remote_code=True).cuda().eval()
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tokenizer = AutoTokenizer.from_pretrained(model_name,
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trust_remote_code=True)
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model.tokenizer = tokenizer
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query = 'Analyze the given image in a detail manner'
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image = ['/mnt/petrelfs/qa-caif-cicd/github_runner/examples/dubai.png']
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with torch.autocast(device_type='cuda', dtype=torch.float16):
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response, _ = model.chat(tokenizer,
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query,
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image,
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do_sample=False,
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num_beams=3,
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use_meta=True)
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print(response)
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assert len(response) > 100
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assert 'dubai' in response.lower()
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@pytest.mark.parametrize(
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'model_name',
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[
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'internlm/internlm-xcomposer2d5-7b',
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],
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)
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def test_introduce_web_default(self, model_name):
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torch.set_grad_enabled(False)
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# init model and tokenizer
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model = AutoModel.from_pretrained(
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model_name, torch_dtype=torch.bfloat16,
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trust_remote_code=True).cuda().eval()
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tokenizer = AutoTokenizer.from_pretrained(model_name,
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trust_remote_code=True)
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model.tokenizer = tokenizer
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query = '''A website for Research institutions. The name is Shanghai
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AI lab. Top Navigation Bar is blue.Below left, an image shows the
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logo of the lab. In the right, there is a passage of text below that
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describes the mission of the laboratory.There are several images to
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show the research projects of Shanghai AI lab.'''
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with torch.autocast(device_type='cuda', dtype=torch.float16):
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response = model.write_webpage(
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query,
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seed=202,
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task='Instruction-aware Webpage Generation',
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repetition_penalty=3.0)
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print(response)
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assert len(response) > 100
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assert is_html_code(response)
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assert 'href' in response.lower()
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@pytest.mark.parametrize(
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'model_name',
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[
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'internlm/internlm-xcomposer2d5-7b',
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],
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)
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def test_resume_to_webset_default(self, model_name):
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torch.set_grad_enabled(False)
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# init model and tokenizer
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model = AutoModel.from_pretrained(
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model_name, torch_dtype=torch.bfloat16,
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trust_remote_code=True).cuda().eval()
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tokenizer = AutoTokenizer.from_pretrained(model_name,
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trust_remote_code=True)
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model.tokenizer = tokenizer
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# the input should be a resume in markdown format
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query = '/mnt/petrelfs/qa-caif-cicd/github_runner/examples/resume.md'
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with torch.autocast(device_type='cuda', dtype=torch.float16):
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response = model.resume_2_webpage(query,
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seed=202,
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repetition_penalty=3.0)
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print(response)
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assert len(response) > 100
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assert is_html_code(response)
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assert 'href' in response.lower()
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@pytest.mark.parametrize(
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'model_name',
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[
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'internlm/internlm-xcomposer2d5-7b',
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],
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)
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def test_screen_to_webset_default(self, model_name):
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torch.set_grad_enabled(False)
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# init model and tokenizer
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model = AutoModel.from_pretrained(
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model_name, torch_dtype=torch.bfloat16,
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trust_remote_code=True).cuda().eval()
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tokenizer = AutoTokenizer.from_pretrained(model_name,
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trust_remote_code=True)
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model.tokenizer = tokenizer
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query = 'Generate the HTML code of this web image with Tailwind CSS.'
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image = ['./examples/screenshot.jpg']
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with torch.autocast(device_type='cuda', dtype=torch.float16):
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response = model.resume_2_webpage(query,
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image,
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seed=202,
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repetition_penalty=3.0)
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print(response)
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assert len(response) > 100
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assert is_html_code(response)
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assert 'href' in response.lower()
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@pytest.mark.parametrize(
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'model_name',
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[
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'internlm/internlm-xcomposer2d5-7b',
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],
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)
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def test_write_artical_default(self, model_name):
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torch.set_grad_enabled(False)
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# init model and tokenizer
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model = AutoModel.from_pretrained(
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'internlm/internlm-xcomposer2d5-7b',
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torch_dtype=torch.bfloat16,
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trust_remote_code=True).cuda().eval()
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tokenizer = AutoTokenizer.from_pretrained(
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'internlm/internlm-xcomposer2d5-7b', trust_remote_code=True)
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model.tokenizer = tokenizer
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query = '''阅读下面的材料，根据要求写作。 电影《长安三万里》的出现让人感慨，影片并未将重点全落在大唐风华上，
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也展现了恢弘气象的阴暗面，即旧门阀的资源垄断、朝政的日益衰败与青年才俊的壮志难酬。高适仕进无门，只能回乡>沉潜修行。
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李白虽得玉真公主举荐，擢入翰林，但他只是成为唐玄宗的御用文人，不能真正实现有益于朝政的志意。然而，片中高潮部分《将进酒》一节，
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人至中年、挂着肚腩的李白引众人乘仙鹤上天，一路从水面、瀑布飞升至银河进入仙>宫，李白狂奔着与仙人们碰杯，最后大家纵身飞向漩涡般的九重天。
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肉身的微贱、世路的“天生我材必有用，坎坷，拘不住精神的高蹈。“天生我材必有用，千金散尽还复来。” 古往今来，身处闲顿、遭受挫折、被病痛折磨，
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很多人都曾经历>了人生的“失意”，却反而成就了他们“诗意”的人生。对正在追求人生价值的当代青年来说，如何对待人生中的缺憾和困顿?诗意人生中又
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有怎样的自我坚守和自我认同?请结合“失意”与“诗意”这两个关键词写一篇文章。 要求:选准角度，确定>立意，明确文体，自拟标题;不要套作，不得抄
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袭;不得泄露个人信息;不少于 800 字。'''
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with torch.autocast(device_type='cuda', dtype=torch.float16):
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response = model.write_artical(query, seed=8192)
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print(response)
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assert len(response) > 100
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assert '。' in response and '诗' in response
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query = '''Please write a blog based on the title: French Pastries:
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A Sweet Indulgence'''
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with torch.autocast(device_type='cuda', dtype=torch.float16):
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response = model.write_artical(query, seed=8192)
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print(response)
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assert len(response) > 100
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assert ' ' in response and 'a' in response
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def is_html_code(html_code):
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try:
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soup = BeautifulSoup(html_code, 'lxml')
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if soup.find('html'):
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print('HTML appears to be well-formed.')
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return True
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else:
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print('There was an issue with the HTML structure.')
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return False
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except Exception as e:
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print('Error parsing HTML:', str(e))
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return False
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