目录

使用 Transformers 的 Pipeline 进行推理

安装依赖包

pip install datasets evaluate transformers[sentencepiece]

英文情感分类

from transformers import pipeline

classifier = pipeline("sentiment-analysis")
classifier(
    [
        "I've been waiting for a HuggingFace course my whole life.",
        "I hate this so much!",
    ]
)

No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).
Using a pipeline without specifying a model name and revision in production is not recommended.
[{'label': 'POSITIVE', 'score': 0.9598048329353333},
 {'label': 'NEGATIVE', 'score': 0.9994558691978455}]

中文情感分类

模型：uer/roberta-base-finetuned-dianping-chinese

from transformers import AutoModelForSequenceClassification,AutoTokenizer,pipeline

checkpoint = "uer/roberta-base-finetuned-dianping-chinese"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
text_classification = pipeline('sentiment-analysis', tokenizer=tokenizer, model=model)

raw_inputs = [
    "我一直都在等待 HuggingFace 课程。",
    "我非常讨厌这个！",
]
for _ in range(500):
    text_classification(raw_inputs)

[{'label': 'positive (stars 4 and 5)', 'score': 0.8151589035987854},
 {'label': 'negative (stars 1, 2 and 3)', 'score': 0.9962126016616821}]

Pipeline 内部分解

Pipeline 将预处理、推理和后处理三部分组合在一起

❶ 预处理


from transformers import AutoTokenizer

checkpoint = "uer/roberta-base-finetuned-dianping-chinese"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

raw_inputs = [
    "我一直都在等待 HuggingFace 课程。",
    "我非常讨厌这个！",
]
inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors="pt")
print(inputs)

结果：

{
    'input_ids': tensor([
        [  101,  2769,   671,  4684,  6963,  1762,  5023,  2521, 12199,  9949,  8221, 12122,  6440,  4923,   511,   102],
        [  101,  2769,  7478,  2382,  6374,  1328,  6821,   702,  8013,   102,     0,     0,     0,     0,     0,     0]
    ]), 
    'attention_mask': tensor([
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]
    ])
}

❷ 推理

from transformers import AutoModelForSequenceClassification

checkpoint = "uer/roberta-base-finetuned-dianping-chinese"
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)

outputs = model(**inputs)
print(outputs.logits)

结果：

tensor([[-0.7019,  0.6344],
        [ 2.8446, -2.7260]], grad_fn=<AddmmBackward0>)

logits 不是概率，是模型最后一层输出的原始非标准化分数。

❸ 后处理

labels = model.config.id2label
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

for input, prediction in zip(raw_inputs, predictions):
    score = torch.max(prediction).item()
    label = labels[torch.argmax(prediction).item()]
    print(f"input: {input}")
    print(f"label: {label.upper()}, score: {score}")
    print()

结果：

input: 我一直都在等待 HuggingFace 课程。
label: POSITIVE (STARS 4 AND 5), score: 0.7918757796287537

input: 我非常讨厌这个！
label: NEGATIVE (STARS 1, 2 AND 3), score: 0.9962064027786255

LLM.int8 节省显存 & 加速推理

安装依赖包

pip install bitsandbytes accelerate scipy

from transformers import AutoModelForSequenceClassification,AutoTokenizer,pipeline

checkpoint = "uer/roberta-base-finetuned-dianping-chinese"

tokenizer = AutoTokenizer.from_pretrained(checkpoint, device_map='auto', load_in_8bit=True)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, load_in_8bit=True)
text_classification = pipeline('sentiment-analysis', tokenizer=tokenizer, model=model)

raw_inputs = [
    "我一直都在等待 HuggingFace 课程。",
    "我非常讨厌这个！",
]
outputs = text_classification(raw_inputs)
print(outputs)

[{'label': 'positive (stars 4 and 5)', 'score': 0.8151589035987854}, 
 {'label': 'negative (stars 1, 2 and 3)', 'score': 0.9962126016616821}]

Colab 中测量执行时间

安装插件

pip install ipython-autotime

在 代码单元格 中增加代码

%load_ext autotime

不使用 LLM.int8

%load_ext autotime
from transformers import AutoModelForSequenceClassification,AutoTokenizer,pipeline

checkpoint = "uer/roberta-base-finetuned-dianping-chinese"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
text_classification = pipeline('sentiment-analysis', tokenizer=tokenizer, model=model)

raw_inputs = [
    "我一直都在等待 HuggingFace 课程。",
    "我非常讨厌这个！",
]
for _ in range(500):
    outputs = text_classification(raw_inputs)

print('显存：', model.get_memory_footprint())

显存： 204546564
time: 2min 21s

使用 LLM.int8

%load_ext autotime
from transformers import AutoModelForSequenceClassification,AutoTokenizer,pipeline

checkpoint = "uer/roberta-base-finetuned-dianping-chinese"

tokenizer = AutoTokenizer.from_pretrained(checkpoint, device_map='auto', load_in_8bit=True)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, load_in_8bit=True)
text_classification = pipeline('sentiment-analysis', tokenizer=tokenizer, model=model)

raw_inputs = [
    "我一直都在等待 HuggingFace 课程。",
    "我非常讨厌这个！",
]
for _ in range(500):
    text_classification(raw_inputs)

print('显存：', model.get_memory_footprint())

显存： 119022084
time: 1min 46s

总结

  • 不使用 LLM.int8：显存： 205MB；用时： 141秒
  • 使用 LLM.int8：显存： 119MB；用时： 106秒

显存减少📉 72%，速度提升📈 33%