82 lines
2.2 KiB
Python
82 lines
2.2 KiB
Python
"""
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Usage:
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python hidden_states_server.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 requests
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import torch
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from sglang.test.test_utils import is_in_ci
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from sglang.utils import terminate_process, wait_for_server
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if is_in_ci():
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from docs.backend.patch import launch_server_cmd
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else:
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from sglang.utils import launch_server_cmd
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def main():
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# Launch the server
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server_process, port = launch_server_cmd(
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"python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-1.5B-instruct --host 0.0.0.0"
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)
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wait_for_server(f"http://localhost:{port}")
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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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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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json_data = {
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"text": prompts,
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"sampling_params": sampling_params,
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"return_hidden_states": True,
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}
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response = requests.post(
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f"http://localhost:{port}/generate",
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json=json_data,
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)
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terminate_process(server_process)
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outputs = response.json()
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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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if __name__ == "__main__":
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main()
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