64 lines
2.6 KiB
Markdown
64 lines
2.6 KiB
Markdown
# Benchmark tool for transformers int4 (separate 1st token and rest)
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`benchmark_util.py` is used to provide a simple benchmark tool for transformer int4 model to calculate 1st token performance and the rest on CPU and GPU.
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## CPU Usage
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Just put this file into your benchmark directory, and then wrap your transformer int4 model with `BenchmarkWrapper` (`model = BenchmarkWrapper(model)`).
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Take `chatglm-6b` as an example:
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```python
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import torch
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from bigdl.llm.transformers import AutoModel
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from transformers import AutoTokenizer
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from benchmark_util import BenchmarkWrapper
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model_path ='THUDM/chatglm-6b'
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model = AutoModel.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True)
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model = BenchmarkWrapper(model, do_print=True)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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prompt = "今天睡不着怎么办"
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with torch.inference_mode():
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input_ids = tokenizer.encode(prompt, return_tensors="pt")
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output = model.generate(input_ids, do_sample=False, max_new_tokens=32)
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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```
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Output will be like:
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```bash
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=========First token cost xx.xxxxs=========
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=========Last token cost average xx.xxxxs (31 tokens in all)=========
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```
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## GPU Usage
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Just put this file into your benchmark directory, and then wrap your transformer int4 model with `BenchmarkWrapper` (`model = BenchmarkWrapper(model)`).
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Take `chatglm-6b` as an example:
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```python
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import torch
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import intel_extension_for_pytorch as ipex
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from bigdl.llm.transformers import AutoModel
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from transformers import AutoTokenizer
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from benchmark_util import BenchmarkWrapper
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model_path ='THUDM/chatglm-6b'
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model = AutoModel.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True)
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model = model.to('xpu')
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model = BenchmarkWrapper(model, do_print=True)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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prompt = "今天睡不着怎么办"
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with torch.inference_mode():
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# wamup two times as use ipex
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for i in range(2):
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
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output = model.generate(input_ids, do_sample=False, max_new_tokens=32)
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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# collect performance data now
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for i in range(5):
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
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output = model.generate(input_ids, do_sample=False, max_new_tokens=32)
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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```
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Output will be like:
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```bash
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=========First token cost xx.xxxxs=========
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=========Last token cost average xx.xxxxs (31 tokens in all)=========
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```
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