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* Enable single card sync engine * enable ipex-llm optimizations for vllm * enable optimizations for lm_head * Fix chatglm multi-reference problem * Remove duplicate layer * LLM: Update vLLM to v0.5.4 (#11746) * Enable single card sync engine * enable ipex-llm optimizations for vllm * enable optimizations for lm_head * Fix chatglm multi-reference problem * update 0.5.4 api_server * add dockerfile * fix * fix * refine * fix --------- Co-authored-by: gc-fu <guancheng.fu@intel.com> * Add vllm-0.5.4 Dockerfile (#11838) * Update BIGDL_LLM_SDP_IGNORE_MASK in start-vllm-service.sh (#11957) * Fix vLLM not convert issues (#11817) (#11918) * Fix not convert issues * refine Co-authored-by: Guancheng Fu <110874468+gc-fu@users.noreply.github.com> * Fix glm4-9b-chat nan error on vllm 0.5.4 (#11969) * init * update mlp forward * fix minicpm error in vllm 0.5.4 * fix dependabot alerts (#12008) * Update 0.5.4 dockerfile (#12021) * Add vllm awq loading logic (#11987) * [ADD] Add vllm awq loading logic * [FIX] fix the module.linear_method path * [FIX] fix quant_config path error * Enable Qwen padding mlp to 256 to support batch_forward (#12030) * Enable padding mlp * padding to 256 * update style * Install 27191 runtime in 0.5.4 docker image (#12040) * fix rebase error * fix rebase error * vLLM: format for 0.5.4 rebase (#12043) * format * Update model_convert.py * Fix serving docker related modifications (#12046) * Fix undesired modifications (#12048) * fix * Refine offline_inference arguments --------- Co-authored-by: Xiangyu Tian <109123695+xiangyuT@users.noreply.github.com> Co-authored-by: Jun Wang <thoughts.times@gmail.com> Co-authored-by: Wang, Jian4 <61138589+hzjane@users.noreply.github.com> Co-authored-by: liu-shaojun <johnssalyn@outlook.com> Co-authored-by: Shaojun Liu <61072813+liu-shaojun@users.noreply.github.com>
63 lines
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2.3 KiB
Python
63 lines
No EOL
2.3 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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# Some parts of this file is adapted from
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# https://github.com/vllm-project/vllm/blob/v0.2.1.post1/examples/offline_inference.py
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# which is licensed under Apache License 2.0
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#
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# Copyright 2023 The vLLM team. All rights reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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from vllm import SamplingParams
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from ipex_llm.vllm.xpu.engine import IPEXLLMClass as LLM
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# Sample prompts.
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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 a sampling params object.
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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# Create an LLM.
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llm = LLM(model="YOUR_MODEL",
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device="xpu",
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dtype="float16",
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enforce_eager=True,
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load_in_low_bit="fp8",
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tensor_parallel_size=1,
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max_model_len=2000,
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max_num_batched_tokens=2000)
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# Generate texts from the prompts. The output is a list of RequestOutput objects
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# that contain the prompt, generated text, and other information.
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outputs = llm.generate(prompts, sampling_params)
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# Print the outputs.
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") |