66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
"""
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Usage:
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python hidden_states.py
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Note that each time you change the `return_hidden_states` parameter,
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the cuda graph will be recaptured, which might lead to a performance hit.
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So avoid getting hidden states and completions alternately.
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"""
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import torch
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import sglang as sgl
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def main():
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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# Create an LLM.
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llm = sgl.Engine(
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model_path="Alibaba-NLP/gte-Qwen2-1.5B-instruct",
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)
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sampling_params = {
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"temperature": 0.8,
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"top_p": 0.95,
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"max_new_tokens": 10,
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}
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outputs = llm.generate(
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prompts, sampling_params=sampling_params, return_hidden_states=True
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)
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llm.shutdown()
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for prompt, output in zip(prompts, outputs):
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for i in range(len(output["meta_info"]["hidden_states"])):
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output["meta_info"]["hidden_states"][i] = torch.tensor(
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output["meta_info"]["hidden_states"][i], dtype=torch.bfloat16
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)
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print("===============================")
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print(
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f"Prompt: {prompt}\n"
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f"Generated text: {output['text']}\n"
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f"Prompt_Tokens: {output['meta_info']['prompt_tokens']}\t"
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f"Completion_tokens: {output['meta_info']['completion_tokens']}"
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)
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print("Hidden states: ")
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hidden_states = torch.cat(
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[
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i.unsqueeze(0) if len(i.shape) == 1 else i
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for i in output["meta_info"]["hidden_states"]
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]
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)
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print(hidden_states)
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print()
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# The __main__ condition is necessary here because we use "spawn" to create subprocesses
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# Spawn starts a fresh program every time, if there is no __main__, it will run into infinite loop to keep spawning processes from sgl.Engine
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if __name__ == "__main__":
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main()
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