From 7829adf9eb4c762aaf013badfbbe0c75cbdb1802 Mon Sep 17 00:00:00 2001 From: Frank-lilinjie Date: Sun, 29 Sep 2024 18:45:47 +0800 Subject: [PATCH] LLM_Agent_Benchmark Signed-off-by: Frank-lilinjie delete proposal in this branch Signed-off-by: Frank-lilinjie fix: deprecate outdated interface of pandas Signed-off-by: Yu Fan fix: pylint R0917 error Signed-off-by: Yu Fan fix the issuse of Chinese comments Signed-off-by: Frank-lilinjie parent 5fd70d227a32a0e444787da97d23b7707b0fbee1 author Frank-lilinjie 1730356419 +0800 committer Frank-lilinjie 1730365262 +0800 parent 5fd70d227a32a0e444787da97d23b7707b0fbee1 author Frank-lilinjie 1730356419 +0800 committer Frank-lilinjie 1730365240 +0800 Print replaced with logging Signed-off-by: Frank-lilinjie add a proposal of Smart Coding benchmark suite Signed-off-by: boX <442572328@qq.com> update and improve the proposal Improve the architecture diagram Signed-off-by: boX <442572328@qq.com> update and improve the proposal Signed-off-by: boX <442572328@qq.com> add Proposal for Large Language Model Edge Benchmark Suite: Implementation on KubeEdge-lanvs Signed-off-by: yexiaochuan add Proposal for Large Language Model Edge Benchmark Suite: Implementation on KubeEdge-lanvs Signed-off-by: yexiaochuan llm suite benchmark implement Signed-off-by: yexiaochuan Revert "llm suite benchmark implement" This reverts commit f341b0f2806acf787c65d62d33317f8a998b028d. Signed-off-by: yexiaochuan Fix llm edge benchmark suite description and highlight edge in the name of the documents Signed-off-by: yexiaochuan fix pylint 3.9 too-many-positional-arguments Signed-off-by: yexiaochuan Trigger CI Signed-off-by: yexiaochuan Trigger CI Signed-off-by: yexiaochuan Fix pylint disable R0913 Signed-off-by: yexiaochuan Fix pylint disable R0917 Signed-off-by: yexiaochuan Revert: Remove extra pylint fix changes Signed-off-by: yexiaochuan chore: Empty commit to trigger CI Signed-off-by: yexiaochuan add: Heterogeneous Multi-Edge Collaborative Neural Network Inference for High Mobility Scenarios: Base on KubeEdge-Ianvs proposal Signed-off-by: wyoung1 coding for ospp Signed-off-by: wyoung1 fix pylint Signed-off-by: wyoung1 fix pylint issue Signed-off-by: wyoung1 add: add a simple demo of feadereated learning in ianvs Signed-off-by: Marchons Signed-off-by: Marchons <1286360646@qq.com> Revert "OSPP: Development of Federated Incremental Learning for Label Scarcity: Base on KubeEdge-Ianvs" Revert "Merge pull request #160 from kubeedge/revert-143-dev_script" This reverts commit 4f01ee54b6a802fd4712bece1f244a5b0d824824, reversing changes made to 95530519bb8e73b88daeae44523733e5cb3aaf0e. Signed-off-by: Marchons <1286360646@qq.com> add Impl for llm edge benchmark suite Signed-off-by: yexiaochuan adapt Impl in core for llm edge benchmark suite Signed-off-by: yexiaochuan Fix impl on singletast_learning with compression Signed-off-by: yexiaochuan chore: trigger CI Signed-off-by: yexiaochuan CI: fix pylint warnings Signed-off-by: yexiaochuan CI: fix pylint warnings Signed-off-by: yexiaochuan fix: update comments and configuration parameters Signed-off-by: yexiaochuan fix core of dataset Signed-off-by: Frank-lilinjie --- ...KubeEdge-Ianvs Cloud-Edge Collaboration.md | 120 ------------------ .../images/Q2A_task_plan.png | Bin 326585 -> 0 bytes .../images/agent-overview.png | Bin 139506 -> 0 bytes .../images/bert_score.png | Bin 280783 -> 0 bytes .../images/continual_llm_agent.png | Bin 197817 -> 0 bytes .../images/llm_agent_ianvs.png | Bin 159902 -> 0 bytes .../images/personalized_agent.png | Bin 460871 -> 0 bytes examples/llm-agent/config/config.json | 11 ++ examples/llm-agent/config/train_config.json | 11 ++ .../singletask_learning_bench/README.md | 115 +++++++++++++++++ .../benchmarkingjob.yaml | 66 ++++++++++ .../testalgorithms/basemodel.py | 104 +++++++++++++++ .../testalgorithms/test_algorithm.yaml | 16 +++ .../testenv/rouge.py | 43 +++++++ .../testenv/testenv.yaml | 20 +++ 15 files changed, 386 insertions(+), 120 deletions(-) delete mode 100644 docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md delete mode 100644 docs/proposals/algorithms/single-task-learning/images/Q2A_task_plan.png delete mode 100644 docs/proposals/algorithms/single-task-learning/images/agent-overview.png delete mode 100644 docs/proposals/algorithms/single-task-learning/images/bert_score.png delete mode 100644 docs/proposals/algorithms/single-task-learning/images/continual_llm_agent.png delete mode 100644 docs/proposals/algorithms/single-task-learning/images/llm_agent_ianvs.png delete mode 100644 docs/proposals/algorithms/single-task-learning/images/personalized_agent.png create mode 100644 examples/llm-agent/config/config.json create mode 100644 examples/llm-agent/config/train_config.json create mode 100644 examples/llm-agent/singletask_learning_bench/README.md create mode 100644 examples/llm-agent/singletask_learning_bench/benchmarkingjob.yaml create mode 100644 examples/llm-agent/singletask_learning_bench/testalgorithms/basemodel.py create mode 100644 examples/llm-agent/singletask_learning_bench/testalgorithms/test_algorithm.yaml create mode 100644 examples/llm-agent/singletask_learning_bench/testenv/rouge.py create mode 100644 examples/llm-agent/singletask_learning_bench/testenv/testenv.yaml diff --git a/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md deleted file mode 100644 index e8e347ca..00000000 --- a/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md +++ /dev/null @@ -1,120 +0,0 @@ -# Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration - -# 1 Motivation - -## 1.1 Background - -### 1.1.1 LLM is Not Equivalent to AGI - -Large Language Models (LLMs) possess excellent reasoning and thinking abilities due to extensive training data and various training methods. However, current LLMs are not truly Artificial General Intelligence (AGI). The following are three obstacles that prevent LLMs from achieving AGI: - -1. **Lack of support for multimodal input**. AGI requires models to solve complex real-world multimodal tasks like humans and possess lifelong learning capabilities. LLMs only support single-modal text input and do not support the integration of multimodal inputs such as video or voice. - -2. **Inability to decompose and plan complex tasks**. In the real world, humans decompose complex problems into multiple sub-tasks and possess task-planning abilities when solving them. LLMs do not have this capability. - -3. **Inferior to expert models in specific domains**. Although LLMs demonstrate powerful zero-shot or few-shot learning capabilities, they are weaker compared to expert models fine-tuned for specific domains. - -This project aims to address the above issues by developing a multi-expert intelligent agent system based on LLM Agents, where the LLM serves as the system's controller (analogous to the human brain). By connecting, managing, and invoking expert models, the system completes complex tasks. Specifically, the LLM's text understanding ability is used to decompose user instructions, distribute different instructions to task-specific expert models, and link the inputs and outputs of various experts to collaboratively complete complex tasks. The system employs the LLM as a controller to receive text and multimodal inputs, understand user requirements through text, and plan sub-tasks. By leveraging the strengths of expert models, the system completes a complex task step by step, addressing the obstacles preventing LLMs from achieving AGI. - -### 1.1.2 Edge Requirements for Personalized LLM Agent - -This project aims to provide personalized LLM Agent services for edge users, facing three challenges: privacy, timeliness, and efficiency. - -1. **Privacy**: Uploading users' personal data to the cloud poses security risks of data breaches. -2. **Timeliness**: Edge computing allows edge users to receive faster feedback. -3. **Efficiency**: Edge computing power is limited, making the deployment of LLM models costly. - -This project aims to achieve a personalized LLM Agent by utilizing a cloud-edge collaborative framework, combining answers from cloud-based large models with those generated based on privacy data at the edge. We plan to develop a personalized LLM Agent based on the KubeEdge-lanvs cloud-edge collaborative platform for lifelong learning (maintaining user-specific data in the knowledge base, with lifelong learning supporting different models on different edges). This system integrates the generalization capabilities of large cloud-based LLMs with personalized user data on edge devices to generate high-quality, personalized responses. - -## 1.2 Goals - -1. Research evaluation benchmarks for LLM and LLM Agents. - -2. Develop a personalized LLM Agent based on lifelong learning using the KubeEdge-lanvs cloud-edge collaborative platform." - -# 2. Proposal - -## 2.1 LLM Agent - -agent - -In an LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components: - -- Planning - - Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks. - - Reflection and refinement: The agent can do self-criticism and self-reflection on past actions, learn from mistakes, and refine them for future steps, thereby improving the quality of final results. -- Memory - - Short-term memory: I would consider all the in-context learning as utilizing short-term memory of the model to learn. - - Long-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval. -- Tool use - - The agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources, and more. - -## 2.2 Cloud-Edge Collaboration - -LLM agent Continual learning - - - -Large-scale LLM agent models possess powerful generalization capabilities, enabling them to output personalized task plans based on different user instructions. However, edge devices have limited computing resources and cannot support the operation of large-scale LLM agent models. If a small-scale LLM agent can learn from the habits of different users and perform comparably to large-scale LLM agents, this issue can be resolved. Since user data is streamed and large amounts of training data cannot be immediately obtained, continuous learning of the small-scale edge LLM agent is necessary. - -Therefore, this project proposes a **Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaborative Lifelong Learning**. The goal is to enable small-scale edge LLM agents to gradually achieve capabilities comparable to large-scale models by leveraging the guidance of large-scale cloud LLM agent models. Specifically, edge users provide a small number of instructions to both the cloud and edge LLM agent models. The output of the cloud LLM agent model serves as pseudo-labels for the edge LLM agent, which is gradually trained through knowledge distillation. This process transforms the edge model into a **Personalized LLM Agent**. - -# 3 Design Detail - -## 3.1 Demo - -Personal_Instruction - -This project presents three scenarios, which are audio task, human posture classification task and face and object recognition task. - -What happens if you put a trained Agent model in a mismatched scenario? Here are two examples: -1. Set ***Agent #1*** to ***Scene #3***: Data source does not match; -2. Place ***Agent #3*** in ***Scene #2***: A task planning error occurred. - -## 3.2 DataSet - -### 3.2.1 Q2A Task-plan Dataset - -"Q2A (Query to Answer) Task-plan Dataset is a custom dataset created for this project. This dataset includes multiple user instructions and the task planning results generated by the LLM. We can use several data points as prompts, leveraging the few-shot capability of the pretrained LLM to fine-tune it, enabling it to perform task planning and become an LLM Agent." - -Q2A - -## 3.3 Ianvs Implement - -llm_agent_ianvs - -This project implements automated task definition and task orchestration through the LLM Agent on Ianvs and evaluates it using the self-built Q2A dataset. Specifically, in the Test Environment Manager, we integrate the Q2A and DAHLIA datasets, as well as three evaluation metrics: task decomposition, tool invocation, and parameter prediction, to establish a benchmark for LLM Agent evaluation. In the Test Case Controller, we perform automated task definition and task orchestration, providing user instructions to the LLM Agent and outputting task plan results. In the Story Manager, we set up the output of test reports. - -## 3.4 Metrics -### 3.4.1 Task Decomposition -#### Rouge-N -The ROUGE-N metric is a method used to evaluate the quality of automatic summarization or machine translation, focusing on recall, which is the degree of overlap between the system-generated text and the reference text. The "N" in ROUGE-N represents the length of the longest common subsequence considered. Here is the calculation formula for ROUGE-N: -$$ ROUGE\_N=\frac{\displaystyle\sum^{}_{S\in\{ReferenceSummaries\}}\sum^{}_{grams_n\in S}Count_{match} (gram_n) }{\displaystyle\sum^{}_{S\in\{ReferenceSummaries\}}\sum^{}_{grams_n\in S}Count (gram_n)} $$ - -#### Bertscore -[Bert_score](https://github.com/Tiiiger/bert_score?tab=readme-ov-file#readme) leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference sentences by cosine similarity. It has been shown to correlate with human judgment on sentence-level and system-level evaluation. Moreover, BERTScore computes precision, recall, and F1 measures, which can be useful for evaluating different language generation tasks. - -bert_score - - -$$ R_{\mathrm{BERT}}=\frac{1}{|x|} \sum_{x_{i} \in x} \max _{\hat{x}_{j} \in \hat{x}} \mathbf{x}_{i}^{\top} \hat{\mathbf{x}}_{j}$$ -$$P_{\mathrm{BERT}}=\frac{1}{|\hat{x}|} \sum_{\hat{x}_{j} \in \hat{x}} \max _{x_{i} \in x} \mathbf{x}_{i}^{\top} \hat{\mathbf{x}}_{j}$$ -$$F_{\mathrm{BERT}}=2 \frac{P_{\mathrm{BERT}} \cdot R_{\mathrm{BERT}}}{P_{\mathrm{BERT}}+R_{\mathrm{BERT}}} $$ - - -# 4 Roadmap - -## July - -- Research benchmarks for evaluating LLM Agent; -- Commit Proposal for Kubeedge/Ianvs - -## August - -- Implement LLM Agent Benchmark based on Kubeedge/Ianvs; - -## September - -- Implement the Personalized LLM Agent framework on Kubedege/Ianvs -- Commit the codes for Kubeedge/Ianvs - diff --git a/docs/proposals/algorithms/single-task-learning/images/Q2A_task_plan.png 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"token_factor": 32, + "half_model": true, + "token_padding": "right", + "trust_remote": true, + "device": "auto", + "output_dir": "./checkpoint" + } \ No newline at end of file diff --git a/examples/llm-agent/config/train_config.json b/examples/llm-agent/config/train_config.json new file mode 100644 index 00000000..df757133 --- /dev/null +++ b/examples/llm-agent/config/train_config.json @@ -0,0 +1,11 @@ +{ + "per_device_train_batch_size":5, + "logging_steps":50, + "num_train_epochs":2, + "output_dir":"./checkpoint", + "half_lora":"True", + "learning_rate":2e-4, + "weight_decay":0.01, + "save_strategy":"epoch", + "save_total_limit":10 +} \ No newline at end of file diff --git a/examples/llm-agent/singletask_learning_bench/README.md b/examples/llm-agent/singletask_learning_bench/README.md new file mode 100644 index 00000000..14eca6f7 --- /dev/null +++ b/examples/llm-agent/singletask_learning_bench/README.md @@ -0,0 +1,115 @@ +# Quick Start about Personalized LLM Agent + +Welcome to Ianvs! Ianvs aims to test the performance of distributed synergy AI solutions following recognized standards, in order to facilitate more efficient and effective development. Quick start helps you to test your algorithm on Ianvs with a simple example of industrial defect detection. You can reduce manual procedures to just a few steps so that you can build and start your distributed synergy AI solution development within minutes. + +Before using Ianvs, you might want to have the device ready: + +- One machine is all you need, i.e., a laptop or a virtual machine is sufficient and a cluster is not necessary +- 2 CPUs or more +- 4GB+ free memory depends on the algorithm and simulation setting +- 10GB+ free disk space +- Internet connection for GitHub and pip, etc +- Python 3.6+ installed + +In this example, we are using the Linux platform with Python 3.7.1. If you are using Windows, most steps should still apply but a few like commands and package requirements might be different. + +The proposal for this demo: [Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration](https://github.com/Frank-lilinjie/ianvs/blob/main/docs/proposals/algorithms/single-task-learning/Personalized%20LLM%20Agent%20based%20on%20KubeEdge-Ianvs%20Cloud-Edge%20Collaboration.md) + +## Step 1. Ianvs Preparation + +First, we download the code of Ianvs. Assuming that we are using `/ianvs` as workspace, Ianvs can be cloned with `Git` as: + +```shell +mkdir /ianvs +cd /ianvs #One might use another path preferred + +mkdir project +cd project +git clone https://github.com/kubeedge/ianvs.git +``` + +Then, we install third-party dependencies for ianvs. + +**Attention**: The project requires updating the sedna.zip in the file to sednaJsonForAgent.zip. + +```shell +sudo apt-get update +sudo apt-get install libgl1-mesa-glx -y +python -m pip install --upgrade pip + +cd ianvs +python -m pip install ./examples/resources/third_party/* +python -m pip install -r requirements.txt +``` + +We are now ready to install Ianvs. + +```shell +python setup.py install +``` + +## Step 2. Dataset and Model Preparation + +In this case, we have provided datasets for three different scenarios: human pose detection, environmental sound classification, and facial recognition. These datasets are generated by GPT-4. They adhere to the standard Agent data structure, featuring two distinct roles: User and Assistant. Their content corresponds to the prompt and label, respectively. You can customize your dataset in a similar format. + +- Place the dataset at the following path: `./examples/LLM-Agent-Benchmark/dataset/` + +- Place the configuration file at the following path: `./examples/LLM-Agent-Benchmark/config/` + +- Place the pre-trained model at the following path: `./examples/LLM-Agent-Benchmark/pretrains/` + +- Place the evaluate at the following path: `./examples/LLM-Agent-Benchmark/evaluate/`. The source code can be obtained from [Evaluate](https://github.com/huggingface/evaluate) + +The pretrain model used in the current case originates from [bloom-1b4-zh](https://huggingface.co/Langboat/bloom-1b4-zh) + +The file path for the project is as follows: + +``` +-ianvs + |-.... + |-examples + |-... + |-LLM-Agent-Benchmark + |-config + |-dataset + |-evaluate + |-pretrains + |-singletask_learning_bench + |-testalgorithms + |-basemodel.py + |-test_algorithm.yaml + |-testenv + |-rouge.py + |-testenv.yaml + |-benchmarkingjob.yaml + |-README.md +``` + + + +## Step 3. Ianvs Execution and Presentation + +We are now ready to run the ianvs for benchmarking. + +```shell +cd /ianvs/project + +ianvs -f ./examples/LLM-Agent-Benchmark/singletask_learning_bench/benchmarkingjob.yaml +``` + +Finally, the user can check the result of benchmarking on the console and also in the output path( e.g. `/ianvs/lifelong_learning_bench/workspace`) defined in the benchmarking config file ( e.g. `benchmarkingjob.yaml`). In this quick start, we have done all configurations for you and the interested readers can refer to [benchmarkingJob.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. + +| rank | algorithm | rouge1 | rouge2 | rougeL | paradigm | basemodel | basemodel-config | basemodel-train_config | time | url | +| ---- | --------- | -------- | -------- | -------- | ------------------ | --------- | ------------------------------------------------- | ------------------------------------------------------- | ------------------- | ------------------------------------------------------------ | +| 1 | LLM_agent | 0.401155 | 0.310173 | 0.401876 | singletasklearning | LLM_agent | ./examples/LLM-Agent-Benchmark/config/config.json | ./examples/LLM-Agent-Benchmark/config/train_config.json | 2024-09-24 15:08:17 | ./workspace/benchmarkingjob/LLM_agent/adb8baf8-7a43-11ef-960e-b07b25dd6922 | + +This ends the quick start experiment. + +# What is next + +If any problems happen, the user can refer to [the issue page on Github](https://github.com/kubeedge/ianvs/issues) for help and are also welcome to raise any new issue. + +Enjoy your journey on Ianvs! + + + diff --git a/examples/llm-agent/singletask_learning_bench/benchmarkingjob.yaml b/examples/llm-agent/singletask_learning_bench/benchmarkingjob.yaml new file mode 100644 index 00000000..4bb6f873 --- /dev/null +++ b/examples/llm-agent/singletask_learning_bench/benchmarkingjob.yaml @@ -0,0 +1,66 @@ +benchmarkingjob: + # job name of bechmarking; string type; + name: "benchmarkingjob" + # the url address of job workspace that will reserve the output of tests; string type; + workspace: "./workspace" + + # the url address of test environment configuration file; string type; + # the file format supports yaml/yml; + testenv: "./examples/LLM-Agent-Benchmark/singletask_learning_bench/testenv/testenv.yaml" + + # the configuration of test object + test_object: + # test type; string type; + # currently the option of value is "algorithms",the others will be added in succession. + type: "algorithms" + # test algorithm configuration files; list type; + algorithms: + # algorithm name; string type; + - name: "LLM_agent" + # the url address of test algorithm configuration file; string type; + # the file format supports yaml/yml + url: "./examples/LLM-Agent-Benchmark/singletask_learning_bench/testalgorithms/test_algorithm.yaml" + + # the configuration of ranking leaderboard + rank: + # rank leaderboard with metric of test case's evaluation and order ; list type; + # the sorting priority is based on the sequence of metrics in the list from front to back; + sort_by: [{ "rouge1": "descend" }] + + # visualization configuration + visualization: + # mode of visualization in the leaderboard; string type; + # There are quite a few possible dataitems in the leaderboard. Not all of them can be shown simultaneously on the screen. + # In the leaderboard, we provide the "selected_only" mode for the user to configure what is shown or is not shown. + mode: "selected_only" + # method of visualization for selected dataitems; string type; + # currently the options of value are as follows: + # 1> "print_table": print selected dataitems; + method: "print_table" + + # selected dataitem configuration + # The user can add his/her interested dataitems in terms of "paradigms", "modules", "hyperparameters" and "metrics", + # so that the selected columns will be shown. + selected_dataitem: + # currently the options of value are as follows: + # 1> "all": select all paradigms in the leaderboard; + # 2> paradigms in the leaderboard, e.g., "singletasklearning" + paradigms: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all modules in the leaderboard; + # 2> modules in the leaderboard, e.g., "basemodel" + modules: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all hyperparameters in the leaderboard; + # 2> hyperparameters in the leaderboard, e.g., "momentum" + hyperparameters: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all metrics in the leaderboard; + # 2> metrics in the leaderboard, e.g., "F1_SCORE" + metrics: ["rouge1","rouge2","rougeL"] + + # model of save selected and all dataitems in workspace `./rank` ; string type; + # currently the options of value are as follows: + # 1> "selected_and_all": save selected and all dataitems; + # 2> "selected_only": save selected dataitems; + save_mode: "selected_and_all" \ No newline at end of file diff --git a/examples/llm-agent/singletask_learning_bench/testalgorithms/basemodel.py b/examples/llm-agent/singletask_learning_bench/testalgorithms/basemodel.py new file mode 100644 index 00000000..5c7567f8 --- /dev/null +++ b/examples/llm-agent/singletask_learning_bench/testalgorithms/basemodel.py @@ -0,0 +1,104 @@ +import os +import zipfile +import logging +from transformers import AutoTokenizer,AutoModelForCausalLM +import torch +from peft import LoraConfig,get_peft_model,TaskType,PeftModel +from transformers import AutoModelForCausalLM,TrainingArguments,Trainer,pipeline,AutoTokenizer,DataCollatorForSeq2Seq +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.common.config import Context +from sedna.common.file_ops import FileOps +from sedna.common.log import LOGGER +from functools import partial +import datasets +import json +import os + +logging.disable(logging.WARNING) + +__all__ = ["BaseModel"] + +os.environ['BACKEND_TYPE'] = 'TORCH' + +@ClassFactory.register(ClassType.GENERAL, alias="LLM_agent") +class BaseModel: + def __init__(self, **kwargs): + config=kwargs.get("config") + with open(config, 'r', encoding='utf-8') as file: + self.config = json.load(file) + train_config=kwargs.get("train_config") + with open(train_config, 'r', encoding='utf-8') as file: + self.train_config = json.load(file) + + self.tokenizer_dir = self.config["tokenizer_dir"] + self.auth_token=self.config["auth_token"] + self.token_factor=self.config["token_factor"] + self.MAX_LENGTH = 128 + self.model = AutoModelForCausalLM.from_pretrained(self.tokenizer_dir, use_auth_token=self.auth_token,device_map=self.config["device"],trust_remote_code=self.config["trust_remote"]) + self.tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_dir,token=self.auth_token) + + def train(self, train_data, **kwargs): + preprocessed_data = [] + for x, y in zip(train_data.x, train_data.y): + sample = type('Sample', (object,), {'x': x, 'y': y})() + processed_sample = self.preprocess(sample, self.MAX_LENGTH, self.tokenizer) + preprocessed_data.append(processed_sample) + + train_dataset = datasets.Dataset.from_dict({ + "input_ids": [d["input_ids"][1] for d in preprocessed_data], + "attention_mask": [d["attention_mask"][1] for d in preprocessed_data], + "labels": [d["labels"][1] for d in preprocessed_data] + }) + config_lora=LoraConfig(task_type=TaskType.CAUSAL_LM, + lora_alpha = 1, + lora_dropout = 0.0 + ) + model=get_peft_model(self.model,config_lora) + half = self.train_config["half_lora"] + if half==True: + model=model.half() + del self.train_config["half_lora"] + args=TrainingArguments(adam_epsilon=(1e-4 if half else 1e-8) + ,**self.train_config) + trainer=Trainer(model=model,args=args,data_collator=DataCollatorForSeq2Seq(tokenizer=self.tokenizer,padding=True),train_dataset=train_dataset, eval_dataset=None) + trainer.train() + self.model = trainer.model + return self.model + + from transformers import pipeline + def predict(self, data, **kwargs): + pipe=pipeline("text2text-generation",model=self.model,tokenizer=self.tokenizer) + y_pred = pipe(data) + return y_pred + + + def evaluate(self, data, **kwargs): + pass + + + def load(self, model_url, **kwargs): + if model_url: + logging.info("load model url: ",model_url) + + def save(self, model_path = None): + pass + + + def preprocess(self, samples, MAX_LENGTH, tokenizer): + input_ids,attention_mask,labels=[],[],[] + prompt=samples.x + plan=samples.y + instruction=tokenizer("\n".join(["user: ",prompt])+"\n\nassistant: ",add_special_tokens=False) + response=tokenizer(plan,add_special_tokens=False) + input_ids=instruction["input_ids"]+response["input_ids"]+[tokenizer.eos_token_id] + attention_mask=instruction["attention_mask"]+response["attention_mask"]+[1] + labels=len(instruction["input_ids"])*[-100]+response["input_ids"]+[tokenizer.eos_token_id] + if len(labels)>MAX_LENGTH: + input_ids=input_ids[:MAX_LENGTH] + attention_mask=attention_mask[:MAX_LENGTH] + labels=labels[:MAX_LENGTH] + return { + "input_ids":[[None],input_ids], + "attention_mask":[[None],attention_mask], + "labels":[[None],labels] + } diff --git a/examples/llm-agent/singletask_learning_bench/testalgorithms/test_algorithm.yaml b/examples/llm-agent/singletask_learning_bench/testalgorithms/test_algorithm.yaml new file mode 100644 index 00000000..c2273ca3 --- /dev/null +++ b/examples/llm-agent/singletask_learning_bench/testalgorithms/test_algorithm.yaml @@ -0,0 +1,16 @@ +algorithm: + paradigm_type: "singletasklearning" + # initial_model_url: "./models/530_exp3_2.pth" + + modules: + - type: "basemodel" + name: "LLM_agent" + url: "./examples/LLM-Agent-Benchmark/singletask_learning_bench/testalgorithms/basemodel.py" + + hyperparameters: + - config: + values: + - "./examples/LLM-Agent-Benchmark/config/config.json" + - train_config: + values: + - "./examples/LLM-Agent-Benchmark/config/train_config.json" \ No newline at end of file diff --git a/examples/llm-agent/singletask_learning_bench/testenv/rouge.py b/examples/llm-agent/singletask_learning_bench/testenv/rouge.py new file mode 100644 index 00000000..9a547707 --- /dev/null +++ b/examples/llm-agent/singletask_learning_bench/testenv/rouge.py @@ -0,0 +1,43 @@ +import evaluate +import numpy as np +from sedna.common.class_factory import ClassType, ClassFactory +from transformers import AutoTokenizer,AutoModelForCausalLM +import logging + +@ClassFactory.register(ClassType.GENERAL, alias="rouge1") +def rouge1(y_true, y_pred, **kwargs): + rouge=evaluate.load('./examples/LLM-Agent-Benchmark/evaluate/metrics/rouge') + y_prednew=[] + for i in range(len(y_pred)): + y_prednew.append(y_pred[i]["generated_text"]) + rou_score = rouge.compute(predictions = y_prednew, references=y_true, use_aggregator=True) + rouge1 = rou_score['rouge1'] * 10 + return rouge1 + +@ClassFactory.register(ClassType.GENERAL, alias="rouge2") +def rouge2(y_true, y_pred, **kwargs): + rouge=evaluate.load('./examples/LLM-Agent-Benchmark/evaluate/metrics/rouge') + y_prednew=[] + for i in range(len(y_pred)): + y_prednew.append(y_pred[i]["generated_text"]) + rou_score = rouge.compute(predictions = y_prednew, references=y_true, use_aggregator=True) + rouge2 = rou_score['rouge2'] * 10 + return rouge2 + +@ClassFactory.register(ClassType.GENERAL, alias="rougeL") +def rougeL(y_true, y_pred, **kwargs): + rouge=evaluate.load('./examples/LLM-Agent-Benchmark/evaluate/metrics/rouge') + y_prednew=[] + for i in range(len(y_pred)): + y_prednew.append(y_pred[i]["generated_text"]) + rou_score = rouge.compute(predictions = y_prednew, references=y_true, use_aggregator=True) + rougeL = rou_score['rougeL'] * 10 + return rougeL + +def calculate_mean(lst): + logging.info(lst) + if not isinstance(lst,list): + return lst + if not lst: + return None + return sum(lst) / len(lst) \ No newline at end of file diff --git a/examples/llm-agent/singletask_learning_bench/testenv/testenv.yaml b/examples/llm-agent/singletask_learning_bench/testenv/testenv.yaml new file mode 100644 index 00000000..34fafdba --- /dev/null +++ b/examples/llm-agent/singletask_learning_bench/testenv/testenv.yaml @@ -0,0 +1,20 @@ +testenv: + # dataset configuration + dataset: + # the url address of train dataset index; string type; + train_url: "./examples/LLM-Agent-Benchmark/dataset/activity_classification.json" + # the url address of test dataset index; string type; + test_url: "./examples/LLM-Agent-Benchmark/dataset/activity_classification.json" + # model eval configuration of incremental learning; + + metrics: + # metric name; string type; + - name: "rouge1" + # the url address of python file + url: "./examples/LLM-Agent-Benchmark/singletask_learning_bench/testenv/rouge.py" + - name: "rouge2" + # the url address of python file + url: "./examples/LLM-Agent-Benchmark/singletask_learning_bench/testenv/rouge.py" + - name: "rougeL" + # the url address of python file + url: "./examples/LLM-Agent-Benchmark/singletask_learning_bench/testenv/rouge.py" \ No newline at end of file