From a07c5443fc570911946700c60a6433b04461d00e Mon Sep 17 00:00:00 2001 From: zhulinJulia24 <145004780+zhulinJulia24@users.noreply.github.com> Date: Mon, 26 Feb 2024 15:39:17 +0800 Subject: [PATCH] add more model case into daily test --- tests/test_hf_model.py | 162 +++++++++++++++++++++++++++++++++++++++-- 1 file changed, 156 insertions(+), 6 deletions(-) diff --git a/tests/test_hf_model.py b/tests/test_hf_model.py index 2f7a06b..ea45fd5 100644 --- a/tests/test_hf_model.py +++ b/tests/test_hf_model.py @@ -1,6 +1,8 @@ import pytest import torch -from transformers import AutoModelForCausalLM, AutoTokenizer +from auto_gptq.modeling import BaseGPTQForCausalLM +from PIL import Image +from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer prompts = ['你好', "what's your name"] @@ -16,8 +18,8 @@ class TestChat: @pytest.mark.parametrize( 'model_name', [ - 'internlm/internlm2-chat-7b', - 'internlm/internlm2-chat-7b-sft', + 'internlm/internlm2-chat-7b', 'internlm/internlm2-chat-7b-sft', + 'internlm/internlm2-chat-1_8b', 'internlm/internlm2-chat-1_8b-sft' ], ) def test_demo_default(self, model_name): @@ -29,11 +31,13 @@ class TestChat: model_name, torch_dtype=torch.float16, trust_remote_code=True).cuda() model = model.eval() + history = [] for prompt in prompts: - response, history = model.chat(tokenizer, prompt, history=[]) + response, history = model.chat(tokenizer, prompt, history=history) print(response) assert_model(response) + history = [] for prompt in prompts: length = 0 for response, history in model.stream_chat(tokenizer, @@ -50,8 +54,8 @@ class TestBase: @pytest.mark.parametrize( 'model_name', [ - 'internlm/internlm2-7b', - 'internlm/internlm2-base-7b', + 'internlm/internlm2-7b', 'internlm/internlm2-base-7b', + 'internlm/internlm2-1_8b' ], ) def test_demo_default(self, model_name): @@ -78,3 +82,149 @@ class TestBase: skip_special_tokens=True) print(output) assert_model(output) + + +class TestMath: + """Test cases for base model.""" + + @pytest.mark.parametrize( + 'model_name', + ['internlm/internlm2-math-7b', 'internlm/internlm2-math-base-7b'], + ) + def test_demo_default(self, model_name): + tokenizer = AutoTokenizer.from_pretrained(model_name, + trust_remote_code=True) + # Set `torch_dtype=torch.float16` to load model in float16, otherwise + # it will be loaded as float32 and might cause OOM Error. + model = AutoModelForCausalLM.from_pretrained( + model_name, trust_remote_code=True, + torch_dtype=torch.float16).cuda() + model = model.eval() + model = model.eval() + response, history = model.chat(tokenizer, + '1+1=', + history=[], + meta_instruction='') + print(response) + assert_model(response) + + +class TestMMModel: + """Test cases for base model.""" + + @pytest.mark.parametrize( + 'model_name', + [ + 'internlm/internlm-xcomposer2-7b', + ], + ) + def test_demo_default(self, model_name): + tokenizer = AutoTokenizer.from_pretrained(model_name, + trust_remote_code=True) + # Set `torch_dtype=torch.float16` to load model in float16, otherwise + # it will be loaded as float32 and might cause OOM Error. + if '4bit' in model_name: + model = InternLMXComposer2QForCausalLM.from_quantized( + model_name, trust_remote_code=True, device='cuda:0').eval() + tokenizer = AutoTokenizer.from_pretrained(model_name, + trust_remote_code=True) + else: + model = AutoModelForCausalLM.from_pretrained( + model_name, torch_dtype=torch.float16, + trust_remote_code=True).cuda() + tokenizer = AutoTokenizer.from_pretrained(model_name, + trust_remote_code=True) + + model = model.eval() + img_path_list = [ + 'tests/panda.jpg', + 'tests/bamboo.jpeg', + ] + images = [] + for img_path in img_path_list: + image = Image.open(img_path).convert('RGB') + image = model.vis_processor(image) + images.append(image) + image = torch.stack(images) + query = ' please write an article ' \ + + 'based on the images. Title: my favorite animal.' + with torch.cuda.amp.autocast(): + response, history = model.chat(tokenizer, + query=query, + image=image, + history=[], + do_sample=False) + print(response) + assert len(response) != 0 + assert 'panda' in response + + query = ' 请根据图片写一篇作文：我最喜欢的小动物。' \ + + '要求：选准角度，确定立意，明确文体，自拟标题。' + with torch.cuda.amp.autocast(): + response, history = model.chat(tokenizer, + query=query, + image=image, + history=[], + do_sample=False) + print(response) + assert len(response) != 0 + assert '熊猫' in response + + +class TestMMVlModel: + """Test cases for base model.""" + + @pytest.mark.parametrize( + 'model_name', + [ + 'internlm/internlm-xcomposer2-vl-7b', + 'internlm/internlm-xcomposer2-vl-7b-4bit' + ], + ) + def test_demo_default(self, model_name): + tokenizer = AutoTokenizer.from_pretrained(model_name, + trust_remote_code=True) + + torch.set_grad_enabled(False) + + # init model and tokenizer + if '4bit' in model_name: + model = InternLMXComposer2QForCausalLM.from_quantized( + model_name, trust_remote_code=True, device='cuda:0').eval() + tokenizer = AutoTokenizer.from_pretrained(model_name, + trust_remote_code=True) + else: + model = AutoModel.from_pretrained( + model_name, trust_remote_code=True).cuda().eval() + tokenizer = AutoTokenizer.from_pretrained(model_name, + trust_remote_code=True) + + query = 'Please describe this image in detail.' + image = 'tests/image.webp' + with torch.cuda.amp.autocast(): + response, _ = model.chat(tokenizer, + query=query, + image=image, + history=[], + do_sample=False) + print(response) + assert len(response) != 0 + assert 'Oscar Wilde' in response + assert 'Live life with no excuses, travel with no regret' in response + + +class InternLMXComposer2QForCausalLM(BaseGPTQForCausalLM): + layers_block_name = 'model.layers' + outside_layer_modules = [ + 'vit', + 'vision_proj', + 'model.tok_embeddings', + 'model.norm', + 'output', + ] + inside_layer_modules = [ + ['attention.wqkv.linear'], + ['attention.wo.linear'], + ['feed_forward.w1.linear', 'feed_forward.w3.linear'], + ['feed_forward.w2.linear'], + ]