246 lines
7.8 KiB
Python
246 lines
7.8 KiB
Python
"""
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Copyright 2023-2024 SGLang Team
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import json
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import multiprocessing
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import os
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from dataclasses import dataclass
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from typing import List, Union
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import torch
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import torch.nn.functional as F
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from sglang.srt.server import Runtime
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from sglang.srt.utils import is_generation_model
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DEFAULT_PROMPTS = [
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# the output of gemma-2-2b from SRT is unstable on the commented prompt
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# "The capital of France is",
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"The capital of the United Kindom is",
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"Today is a sunny day and I like",
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"AI is a field of computer science focused on",
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"Apple is red. Banana is Yellow. " * 800 + "Apple is",
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]
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dirpath = os.path.dirname(__file__)
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with open(os.path.join(dirpath, "long_prompt.txt"), "r") as f:
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long_prompt = f.read()
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DEFAULT_PROMPTS.append(long_prompt)
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NUM_TOP_LOGPROBS = 5
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def get_dtype_str(torch_dtype):
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if torch_dtype is torch.float16:
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return "float16"
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else:
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raise NotImplementedError()
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@dataclass
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class ModelOutput:
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output_strs: List[str] = None
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output_ids: List[int] = None
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top_input_logprobs: List[torch.Tensor] = None
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top_output_logprobs: List[torch.Tensor] = None
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embed_logits: List[torch.Tensor] = None
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class HFRunner:
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def __init__(
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self,
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model_path,
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torch_dtype=torch.float16,
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is_generation_model=None,
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):
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self.in_queue = multiprocessing.Queue()
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self.out_queue = multiprocessing.Queue()
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self.model_proc = multiprocessing.Process(
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target=self.start_model_process,
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args=(
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self.in_queue,
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self.out_queue,
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model_path,
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torch_dtype,
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is_generation_model,
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),
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)
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self.model_proc.start()
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def start_model_process(
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self, in_queue, out_queue, model_path, torch_dtype, is_generation_model
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):
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self.tokenizer = AutoTokenizer.from_pretrained(
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model_path,
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torch_dtype=torch_dtype,
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trust_remote_code=True,
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)
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self.is_generation_model = (
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is_generation_model(model_path)
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if is_generation_model is None
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else is_generation_model
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)
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if self.is_generation_model:
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self.model = AutoModelForCausalLM.from_pretrained(
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model_path,
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torch_dtype=torch_dtype,
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low_cpu_mem_usage=True,
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trust_remote_code=True,
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).cuda()
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else:
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from sentence_transformers import SentenceTransformer
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self.model = SentenceTransformer(
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model_path,
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model_kwargs={"torch_dtype": torch_dtype},
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)
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while True:
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prompts, max_new_tokens = in_queue.get()
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if prompts is not None:
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if self.is_generation_model:
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output_strs = []
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prefill_logprobs = []
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for p in prompts:
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if isinstance(p, str):
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input_ids = self.tokenizer.encode(
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p, return_tensors="pt"
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).cuda()
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else:
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input_ids = torch.tensor([p], device="cuda")
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output_ids = self.model.generate(
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input_ids, do_sample=False, max_new_tokens=max_new_tokens
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)
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output_strs.append(
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self.tokenizer.decode(output_ids[0][len(input_ids[0]) :])
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)
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logits = self.model.forward(input_ids).logits[0]
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logprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
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logprobs, top_indices = torch.topk(
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logprobs, k=NUM_TOP_LOGPROBS, dim=-1
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)
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# print("index", top_indices)
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prefill_logprobs.append(logprobs.tolist())
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del logits
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del logprobs
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out_queue.put(
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ModelOutput(
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output_strs=output_strs, top_input_logprobs=prefill_logprobs
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)
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)
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else:
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logits = self.model.encode(prompts).tolist()
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out_queue.put(ModelOutput(embed_logits=logits))
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def forward(
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self,
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prompts: Union[List[str], List[torch.Tensor]] = DEFAULT_PROMPTS,
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max_new_tokens=8,
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):
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self.in_queue.put((prompts, max_new_tokens))
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return self.out_queue.get()
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def terminate(self):
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self.model_proc.terminate()
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self.in_queue = self.out_queue = None
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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self.model_proc.terminate()
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self.in_queue = self.out_queue = None
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class SRTRunner:
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def __init__(
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self,
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model_path,
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tp_size=1,
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torch_dtype=torch.float16,
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is_generation_model=None,
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port=5157,
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):
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self.is_generation_model = (
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is_generation_model(model_path)
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if is_generation_model is None
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else is_generation_model
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)
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self.runtime = Runtime(
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model_path=model_path,
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tp_size=tp_size,
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dtype=get_dtype_str(torch_dtype),
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port=port,
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mem_fraction_static=0.7,
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)
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def forward(
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self,
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prompts: Union[List[str], List[torch.Tensor]] = DEFAULT_PROMPTS,
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max_new_tokens=8,
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):
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if self.is_generation_model:
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# the return value contains logprobs from prefill
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output_strs = []
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top_input_logprobs = []
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sampling_params = {"max_new_tokens": max_new_tokens, "temperature": 0}
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for prompt in prompts:
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response = self.runtime.generate(
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prompt,
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sampling_params=sampling_params,
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return_logprob=True,
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top_logprobs_num=NUM_TOP_LOGPROBS,
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)
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response = json.loads(response)
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output_strs.append(response["text"])
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top_input_logprobs.append(
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[
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[tup[0] for tup in x[:NUM_TOP_LOGPROBS]]
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for x in response["meta_info"]["input_top_logprobs"][1:]
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]
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+ [
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[
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tup[0]
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for tup in response["meta_info"]["output_top_logprobs"][0][
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:NUM_TOP_LOGPROBS
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]
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]
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]
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)
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return ModelOutput(
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output_strs=output_strs, top_input_logprobs=top_input_logprobs
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)
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else:
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response = self.runtime.encode(prompts)
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response = json.loads(response)
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logits = [x["embedding"] for x in response]
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return ModelOutput(embed_logits=logits)
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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self.runtime.shutdown()
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del self.runtime
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