From a340bba4c3b38a55675afe5c561b048ca543f65d Mon Sep 17 00:00:00 2001 From: FuryMartin <41051953+FuryMartin@users.noreply.github.com> Date: Wed, 3 Jul 2024 22:07:52 +0800 Subject: [PATCH 01/10] add proposal: cloud-edge-collaboration-inference-for-llm Signed-off-by: Yu Fan --- ...ud-edge-collaboration-inference-for-llm.md | 258 ++++++++++++++++++ .../images/image-20240530163452865.png | Bin 0 -> 60140 bytes .../images/image-20240531115217381.png | Bin 0 -> 114662 bytes .../images/image-20240531121038701.png | Bin 0 -> 537871 bytes .../images/image-20240531121339480.png | Bin 0 -> 95794 bytes .../images/image-20240531131022026.png | Bin 0 -> 163142 bytes .../images/image-20240531131935416.png | Bin 0 -> 62115 bytes .../images/image-20240602112744993.png | Bin 0 -> 79782 bytes .../images/image-20240703174544686.png | Bin 0 -> 183957 bytes .../images/image-20240703211823035.png | Bin 0 -> 99960 bytes .../images/image-20240703213446953.png | Bin 0 -> 247298 bytes 11 files changed, 258 insertions(+) create mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md create mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/images/image-20240530163452865.png create mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/images/image-20240531115217381.png create mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/images/image-20240531121038701.png create mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/images/image-20240531121339480.png create mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/images/image-20240531131022026.png create mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/images/image-20240531131935416.png create mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/images/image-20240602112744993.png create mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/images/image-20240703174544686.png create mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/images/image-20240703211823035.png create mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/images/image-20240703213446953.png diff --git a/docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md b/docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md new file mode 100644 index 00000000..27dd1ff1 --- /dev/null +++ b/docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md @@ -0,0 +1,258 @@ +* [Cloud-Edge collaborative inference for LLM based on KubeEdge-Ianvs](#incremental-learning) + * [Motivation](#motivation) + * [Goals](#goals) + * [Proposal](#proposal) + * [Use Cases](#use-cases) + * [Design Details](#design-details) + * [Benchmark Construction](#benchmark-construction) + * [LLM Background](#llm-background) + * [LLM Architecture](#llm-architecture) + * [LLM Overhead Analysis](#llm-overhead-analysis) + * [Efficient Inference with LLM](#efficient-inference-with-llm) + * [Collaboration Strategies](#collaboration-strategies) + * [Query Routing Strategy](#query-routing-strategy) + * [Speculative Decoding Strategy](#speculative-decoding-strategy) + * [Summary](#summary) + * [Road Map](#road-map) + * [References](#references) + +# Cloud-Edge collaborative inference for LLM based on KubeEdge-Ianvs +## Motivation + +Large language model (LLM) technologies represented by GPT-4, LLaMA-2, and Qwen have demonstrated high usability in a wide range of tasks due to their powerful semantic understanding capabilities, bringing great hope for the realization of general artificial intelligence. + +Currently, such LLMs have billions or even trillions of parameters, requiring massive computing power for training and deployment. Therefore, they are often deployed in cloud computing centers and serving via APIs. However, such service paradigm faces many drawbacks: on one hand, the response time of cloud-based LLM is usually very long; on the other hand, uploading user data to the cloud may lead to additional privacy risks and retraining risks $^{[1]}$. + +Introducing edge computing into the inference process of large language models will help address these issues. Edge computing is a computational architecture characterized by privacy security and low communication latency that can transfer data processing tasks and services from centralized data centers to network edges with advantages such as low latency, energy efficiency, privacy protection, and bandwidth savings $^{[2]}$. + +Based on the type of device, there are three task offloading strategies for deploying LLMs on edge and cloud: $^{[3]}$: + +1. Full-cloud: Strong model performance but weak illusion; long response time; privacy risk. +2. Full-edge: Weak model performance but strong illusion; short response time; privacy security. +3. Edge-cloud collaboration: Balances the strengths and weaknesses of full-cloud and full-edge while ensuring fast response time and privacy protection under the premise of maintaining model performance. + +Edge-cloud collaboration is a more practical way to benefit from edge computing for LLM inference. As the industry's first distributed collaborative AI benchmarking platform, Ianvs should consider adopting the above edge-cloud collaboration strategy to improve the efficiency of LLM systems in emerging computing scenarios. + + +### Goals + + +* Task 1: Implement a benchmark for an LLM task in KubeEdge-Ianvs (e.g., user Q&A, code generation, or text translation). +* Task 2:Implement an query routing example of LLM cloud-edge collaborative inference in KubeEdge-Ianvs. +* Advanced Task: Implement *speculative decoding* for LLM cloud-edge collaborative inference. + +## Proposal + +We propose KubeEdge-Ianvs to adopt the edge-cloud collaboration strategy to enhance LLM system efficiency according to emerging computational scenarios' needs. + +### Use Cases + +* Users can get qualified answers from LLMs with lower latency and privacy risk by deploying LLMs using the cloud-edge collaborative strategy. + +## Design Details + +The architecture of this proposal is shown in the figure below. We leverage the existed *TestEnvManager*, *TestCaseController* and *StoryManager* in Ianvs. For LLM inference, we also add a serving module named *ModelServer* to use the state-of-the-art inference frameworks to deploy LLM services. + +- In *TestEnvManager*, we plan to add MMLU and CMMLU as LLM benchmark and *Accuracy*, *Latency*, *Throughput*, *Bandwith* as metrics. + +- In *TestCaseController*, we plan to add two cloud-edge collaboration algorithm named *Query Routing* and *Speculative Decoding*. +- In *StoryManager*, we plan to show Leaderboard and Test Report for users. +- In *ModelServer*, we plan to support small-scale models such as Qwen-1.8B and large-scale models such as Qwen-72B-Chat. We plan to support serving frameworks like PyTorch and vLLM with low bit quantization compatibility. + +image-20240703213446953 + +### Benchmark Construction + +#### Construction Method + +Compared to traditional DNN models, the evaluation of LLM faces unprecedented challenges. On one hand, it requires consideration from multiple aspects such as knowledge understanding, logical inference, role-playing, and professional skills; on the other hand, the evaluation list itself has data leakage issues. Emerging LLMs may incorporate evaluation list data into training data, leading to inaccurate evaluations. + +The evaluation of LLM is a massive system engineering project. In order to align with the reliability of evaluations in academia and industry, it is recommended to introduce OpenCompass into KubeEdge-Ianvs as an implementation method for Benchmark and integrate one or several sets of evaluations. + +OpenCompass was released by Shanghai Artificial Intelligence Laboratory and integrates multiple dimensions of evaluation lists including Language, Knowledge, Reasoning, Examination, Safety Code Understanding Long Context etc., which can broadly reflect the capabilities of LLMs and has become an important tool for evaluating LLM$^{[4]}$. + +#### Metric Selection + +Among the supported lists in OpenCompass mentioned above,Examination、Knowledge、Reasoning、Code have strong reference significance and can more fully reflect the abilities of LLMs. + +For simplicity, we can choose MMLU / CMMLU in Examination category as our evaluation benchmark. + +### LLM Background + +#### LLM Architecture + +The current mainstream LLM architecture is Decoder-only, which requires two stages of Prefill and Decoding $^{[5]}$. + +In the Prefill stage, the user input tokens $(x_1,\cdots,x_n)$ are taken as input to generate the first token $x_{n+1}$. + +In the Decoding stage, results are generated iteratively token by token until an end tag is produced or the maximum output length is reached. + + + +As Decoding is iterative, in order to reduce redundant calculations, a key-value pair KV-Cache is used to cache previous processing results so that only new tokens are included for computation. + +#### LLM Overhead Analysis + +LLM's characteristic feature is its large memory overhead. Taking the Llama2 series as an example, full-precision model memory usage ranges from tens of gigabytes to several hundred gigabytes, posing significant challenges for deploying LLM. + +image-20240531115217381 + +Furthermore, LLM also incurs high time overhead. This is because its generation process operates in an autoregressive manner and can only generate serially token by token. As a result, GPU utilization rates are not high and serious communication bottlenecks occur. + +#### Efficient Inference with LLM + +To alleviate memory usage issues, some studies have proposed low-bit quantization strategies such as llm.int8(), which can achieve 8-bit or 4-bit quantization while maintaining almost unchanged precision levels for reducing memory consumption with LLM $^{[6]}$. + +image-20240531121339480 + +In order to improve GPU utilization during inference with LLM models, some research has adopted Continuous batching strategy where multiple inferences are combined into a single batch for unified inference and utilize pipelining scheduling to enhance efficiency such as vLLM $^{[7]}$. + +image-20240531121038701 + +Additionally, in order to overcome Python's inherent limitations, certain engineering implementations have utilized C++ rewriting of the inference service for LLN models like llama.cpp. The primary goal of these frameworks is to implement inference on various hardware platforms supporting multiple types of LLN models integrating model compression quantization and deployment functions beneficial for deployment on edge devices $^{[8]}$. + +### Collaboration Strategies + +The edge cloud collaboration strategy mainly includes three types of strategies: slicing strategy, query routing strategy and speculative decoding strategy. + +1. The slicing strategy refers to slicing the LLM according to the computing power requirements and time overhead of each layer, and allocating it to be executed on edge servers and cloud servers. The slicing strategy can solve the privacy issues of requests, but communication overhead is still significant. +2. The query routing strategy involves deploying a small model on the edge server and a large model on the cloud side, routing user requests to the edge or cloud based on performance needs. The collaboration strategy can greatly reduce communication overhead, but there are still some privacy issues as some requests will go to the cloud. +3. The speculative decoding strategy also involves deploying a small model on the edge server and a large model on the cloud side. Different from query routing, this strategy needs the small model and large model to collaboration through all the generation process of one query. + +#### Query Routing Strategy + +This project aims to implement a collaboration strategy as shown in the following figure. + +image-20240602112744993 + +The core of this strategy is designing a routing model $R$ that can predict the performance of user request $q$ in both the large cloud model $L$ and small edge model $S$. This model takes request $q$ as input and outputs two labels, 0 or 1, representing preference for either edge or cloud. This is essentially a text classification task which can be accomplished by training an NLP model. Training such a model involves considerations such as selecting models, datasets, loss functions, evaluation metrics etc. + +##### Model Selection + +Typically BERT completes text classification tasks. There are many variants of BERT models including BERT-large, ALBERT, RoBERTa etc. Based on relevant literature references we plan to fine-tune a RoBERTa variant for completing text classification tasks$^{[3]}$. RoBERTa is an excellent multilingual variant of BERT with better performance across multiple metrics compared to other models; hence it's planned for adoption. + +With LLM coming into existence there have been examples using LLM for text classification tasks too. We could also attempt fine-tuning a lightweight LLM like TinyLlama for performing classification tasks. + +##### Dataset + +To fine-tune our models we need training data formatted as follows: + +| ID | Query | Label | +| ---- | ----------------------------------- | ----- | +| 0 | 1+1 = ? | 0 | +| 1 | Explain the principles behind BERT. | 1 | +| ... | ... | ... | + +We could refer Hybrid LLMs$^{[3]}$, use MixInstruct dataset generation queries from MixInstruct, which is an extensive collection real instructions covering various tasks aiding classifiers in obtaining strong generalization capabilities. We can extract around 10K instructions from MixInstruct dataset where each instruction $q_i$ would be answered by both edge servering small model and cloud-serving large model with responses $A_s$ and $A_l$. + +For responses $A_s$ and $A_l$ we could evaluate their quality using [BART Score](https://github.com/neulab/BARTScore) getting quality scores $S_s$ and $S_l$ respectively where BART score measures textual generation quality easily obtainable through code snippet provided below: + +```python +>>> from bart_score import BARTScorer +>>> bart_scorer = BARTScorer(device='cuda:0', checkpoint='facebook/bart-large-cnn') +>>> bart_scorer.score(['This is interesting'], ['This is fun'], batch_size=4) # generation scores from first list texts vs second list texts. +[out] +[-2.510652780532837] +``` + +Then we can get label $y_i$ through the formula below: +$$ +y_i = \begin{cases} +0 &\text{if} ~~~ S_s \geq S_l - \epsilon \\ +1 &\text{if} ~~~ S_s < S_l - \epsilon +\end{cases} +$$ +where $\epsilon$ represents permissible performance loss margin. + +##### Loss Function + +We could utilize binary cross entropy function during the training process of the classifier: +$$ +\mathcal{L}(w)=-\frac{1}{N} \sum_{i=1}^{N}\left(y_{i} \log \left(p_{w}\left(q_{i}\right)\right)+\left(1-y_{i}\right) \log \left(1-p_{w}\left(q_{i}\right)\right)\right) +$$ +where $p_w(q_i)$ denotes logits generated by the classifier. + +##### Evaluation Metrics + +Classifier's evaluation may involve *Accuracy*, *Recall* , *F-score* etc. To verify the overall performance of the system, we can use the Benchmark built with OpenCompass to conduct comprehensive performance testing after setting up the whole system to obtain its performance on typical test sets such as MMLU. + +As for the whole cloud-edge collaboration inference process, we should also consider *Latency*, *Throughput*, *Bandwith*. + +##### Lifelong Learning + +In a long-running cloud-based collaborative inference system for LLM, we can collect user feedback for each generated answer and continuously use the feedback as training data to train the classifier, thereby improving the decision-making ability of the classifier to higher and higher. + +image-20240703211823035 + +#### Speculative Decoding Strategy + +Speculative decoding serves as an acceleration method during LLM decoding phase. + +The core idea behind speculative decoding lies in utilizing smaller-scale models predicting future multiple words quickly during decoding followed by parallel validation via larger-scale models; if validation fails then re-generation by larger-scale occurs. In traditional auto-regressive decoding phase, every token is generated one at time, which limits the speed of generation. Speculative decoding combines fast predictions made by smaller size models along with parallel validations done by larger ones and significantly speeds up the generation.$^{[9]}$ + +image-20240531131935416 + +Speculative Decoding requires probability distributions being identical between smaller and larger models, which can ensure high likelihoods between tokens generated by smaller and larger models. Consistent predicted tokens should be retained while inconsistent ones should be discarded . + +For most cases, the majority part tokens predicted by smaller scaled modes get adopted. The worst case is that all tokens predicted rejected. however due faster speeds brought about these additional time costs remain minimal relative accelerated effects brought about . + +Over past few years several researches have delved into speculative decodings depicted below : + +image-20240531131022026 + +Additionally HuggingFace has implemented open source solution related Speculative Decoding available here : [Assisted Generation](https://huggingface.co/blog/assisted-generation). + +### Summary + +Task 1 is planned to be implemented as follows: + +- Integrate OpenCompass to build the evaluation of MMLU and CMMLU benchmarks. + +Task 2 is planned to be implemented as follows: + +- Model selection: Qwen-1.8B model will be used at the edge, and Qwen-72B-Chat model will be used in the cloud. +- Classifier design: Train a RoBERTa-base as a classifier. If time allows, also train a TinyLlama for classifier comparison. +- Inference optimization: Plan to use low-bit quantization technology to achieve 4-bit and 8-bit quantized deployment, and introduce llama.cpp or vllm to form the inference framework. + +Advanced task is planned to be implemented as follows: + +- Combine HuggingFace code examples to complete speculative decoding of LLM. +- Implement the speculative decoding process in the edge-cloud collaborative framework used in Task 2. + + +## Roadmap + +### July + +- Study code and interfaces of Ianvs. +- Task 1: build a benchmark for LLM. + +### August + +- Task 2: build a cloud-edge collaborative inference example for LLM. +- Try advanced task: build a *speculative decoding* example for cloud-edge collaborative inference. + +### Septembter + +- Write detailed docs for this proposal. +- PR and merge. + +## References + +[1] Z. Zhou *et al.*, “Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing,” *Proceedings of the IEEE*, vol. 107, no. 8, pp. 1738–1762, Aug. 2019 + +[2] M. Xu *et al.*, “Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services,” *IEEE Communications Surveys & Tutorials*, pp. 1–1, 2024 + +[3] D. Ding *et al.*, “Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing.” arXiv, Apr. 22, 2024. Accessed: May 30, 2024. + +[4] O. Contributors, “OpenCompass: a universal evaluation platform for foundation models.” 2023. [Online]. Available: https://github.com/open-compass/opencompass + +[5] M. Zhang *et al.*, “EdgeShard: Efficient LLM Inference via Collaborative Edge Computing.” arXiv, May 23, 2024. + +[6] T. Dettmers, M. Lewis, Y. Belkada, and L. Zettlemoyer, “LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale.” arXiv, Nov. 10, 2022. + +[7] W. Kwon *et al.*, “Efficient Memory Management for Large Language Model Serving with PagedAttention.” arXiv, Sep. 12, 2023. Accessed: Jan. 31, 2024. + +[8] G. Gerganov, “ggerganov/llama.cpp.” May 31, 2024. Accessed: May 31, 2024. [Online]. Available: https://github.com/ggerganov/llama.cpp + +[9] H. Xia *et al.*, “Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding.” arXiv, Feb. 20, 2024. 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z`5E~j*^FKH;Aipn$-ZGuK)Epmu2Uz>jiaGb)-;%1&e?7TI(6BQ$Sqo*vbqD5qc zB-h9UGJP; z2`qjVm!5*($tV;ofZTDVMPAFj$%xY$XMnnG6e5ihZDqc|b?oo(z273Btf5Nkj4>zR zYZ<&jkmguTbl7nrzI)9}O-VV6;Q;cQaD9!cs-KTf5Ts9Lwsi$nT1+ee@7CE!NW2L5 zETiMN!|36KrBv$Cg_wgKh`jQ95~@Y~O^uB9mz=s)exu{{>j8X1;>5+Ru#+PmTG_nd za=F1+r-n>PdmN@*=5%IQb87bD^5x6>I<*=xx1po%wrGb1s@$yPa!-M5R{ZpI5ap#M z#0R)utn(f2mrHWU28x3lKjwNBsuu_~HNG_J8b_%;+@; WOy&C-Pi!VGY!}6JN4~*<|NIxu`9=x= literal 0 HcmV?d00001 From 167895b23f35f6ed4b706f1a00a721648e0da8c6 Mon Sep 17 00:00:00 2001 From: FuryMartin <41051953+FuryMartin@users.noreply.github.com> Date: Thu, 4 Jul 2024 14:47:26 +0800 Subject: [PATCH 02/10] Update proposal: cloud-edge-collaboration-inference-for-llm Signed-off-by: Yu Fan --- ...ud-edge-collaboration-inference-for-llm.md | 90 +++++++++---------- 1 file changed, 43 insertions(+), 47 deletions(-) diff --git a/docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md b/docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md index 27dd1ff1..a3052d6e 100644 --- a/docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md +++ b/docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md @@ -1,22 +1,23 @@ -* [Cloud-Edge collaborative inference for LLM based on KubeEdge-Ianvs](#incremental-learning) - * [Motivation](#motivation) - * [Goals](#goals) - * [Proposal](#proposal) - * [Use Cases](#use-cases) - * [Design Details](#design-details) - * [Benchmark Construction](#benchmark-construction) - * [LLM Background](#llm-background) - * [LLM Architecture](#llm-architecture) - * [LLM Overhead Analysis](#llm-overhead-analysis) - * [Efficient Inference with LLM](#efficient-inference-with-llm) - * [Collaboration Strategies](#collaboration-strategies) - * [Query Routing Strategy](#query-routing-strategy) - * [Speculative Decoding Strategy](#speculative-decoding-strategy) - * [Summary](#summary) - * [Road Map](#road-map) - * [References](#references) +* [Cloud-Edge collaborative inference for LLM based on KubeEdge-Ianvs](#cloud-edge-collaborative-inference-for-llm-based-on-kubeedge-ianvs) + * [Motivation](#motivation) + * [Goals](#goals) + * [Proposal](#proposal) + * [Use Cases](#use-cases) + * [Design Details](#design-details) + * [Benchmark Construction](#benchmark-construction) + * [LLM Background](#llm-background) + * [LLM Architecture](#llm-architecture) + * [LLM Overhead Analysis](#llm-overhead-analysis) + * [Efficient Inference with LLM](#efficient-inference-with-llm) + * [Collaboration Strategies](#collaboration-strategies) + * [Query Routing Strategy](#query-routing-strategy) + * [Speculative Decoding Strategy](#speculative-decoding-strategy) + * [Summary](#summary) + * [Road Map](#road-map) + * [References](#references) # Cloud-Edge collaborative inference for LLM based on KubeEdge-Ianvs + ## Motivation Large language model (LLM) technologies represented by GPT-4, LLaMA-2, and Qwen have demonstrated high usability in a wide range of tasks due to their powerful semantic understanding capabilities, bringing great hope for the realization of general artificial intelligence. @@ -38,7 +39,7 @@ Edge-cloud collaboration is a more practical way to benefit from edge computing * Task 1: Implement a benchmark for an LLM task in KubeEdge-Ianvs (e.g., user Q&A, code generation, or text translation). -* Task 2:Implement an query routing example of LLM cloud-edge collaborative inference in KubeEdge-Ianvs. +* Task 2:Implement an *query routing* example of LLM cloud-edge collaborative inference in KubeEdge-Ianvs. * Advanced Task: Implement *speculative decoding* for LLM cloud-edge collaborative inference. ## Proposal @@ -113,23 +114,25 @@ Additionally, in order to overcome Python's inherent limitations, certain engine ### Collaboration Strategies -The edge cloud collaboration strategy mainly includes three types of strategies: slicing strategy, query routing strategy and speculative decoding strategy. +The edge cloud collaboration strategy mainly includes three types of strategies: *slicing* strategy, *query routing* strategy and *speculative decoding* strategy. -1. The slicing strategy refers to slicing the LLM according to the computing power requirements and time overhead of each layer, and allocating it to be executed on edge servers and cloud servers. The slicing strategy can solve the privacy issues of requests, but communication overhead is still significant. -2. The query routing strategy involves deploying a small model on the edge server and a large model on the cloud side, routing user requests to the edge or cloud based on performance needs. The collaboration strategy can greatly reduce communication overhead, but there are still some privacy issues as some requests will go to the cloud. -3. The speculative decoding strategy also involves deploying a small model on the edge server and a large model on the cloud side. Different from query routing, this strategy needs the small model and large model to collaboration through all the generation process of one query. +1. The *slicing* strategy refers to slicing the LLM according to the computing power requirements and time overhead of each layer, and allocating it to be executed on edge servers and cloud servers. The slicing strategy can solve the privacy issues of requests, but communication overhead is still significant. +2. The *query routing* strategy involves deploying a small model on the edge server and a large model on the cloud side, routing user requests to the edge or cloud based on performance needs. The collaboration strategy can greatly reduce communication overhead, but there are still some privacy issues as some requests will go to the cloud. +3. The *speculative decoding* strategy also involves deploying a small model on the edge server and a large model on the cloud side. Different from query routing, this strategy needs the small model and large model to collaboration through all the generation process of one query. #### Query Routing Strategy -This project aims to implement a collaboration strategy as shown in the following figure. +The principle of *query routing* strategy is shown in the following figure. image-20240602112744993 -The core of this strategy is designing a routing model $R$ that can predict the performance of user request $q$ in both the large cloud model $L$ and small edge model $S$. This model takes request $q$ as input and outputs two labels, 0 or 1, representing preference for either edge or cloud. This is essentially a text classification task which can be accomplished by training an NLP model. Training such a model involves considerations such as selecting models, datasets, loss functions, evaluation metrics etc. +The core of this strategy is designing a routing model $R$ that can predict the performance of user request $q$ in both the large cloud model $L$ and small edge model $S$. This model takes request $q$ as input and outputs two labels, 0 or 1, representing preference for either edge or cloud. + +This is essentially a text classification task which can be accomplished by training an NLP model. Training such a model involves considerations such as selecting models, datasets, loss functions, evaluation metrics etc. ##### Model Selection -Typically BERT completes text classification tasks. There are many variants of BERT models including BERT-large, ALBERT, RoBERTa etc. Based on relevant literature references we plan to fine-tune a RoBERTa variant for completing text classification tasks$^{[3]}$. RoBERTa is an excellent multilingual variant of BERT with better performance across multiple metrics compared to other models; hence it's planned for adoption. +Typically BERT completes text classification tasks. There are many variants of BERT models including BERT-large, ALBERT, RoBERTa etc. Based on relevant references we plan to fine-tune a RoBERTa variant for completing text classification tasks$^{[3]}$. RoBERTa is an excellent multilingual variant of BERT with better performance across multiple metrics compared to other models. With LLM coming into existence there have been examples using LLM for text classification tasks too. We could also attempt fine-tuning a lightweight LLM like TinyLlama for performing classification tasks. @@ -143,9 +146,9 @@ To fine-tune our models we need training data formatted as follows: | 1 | Explain the principles behind BERT. | 1 | | ... | ... | ... | -We could refer Hybrid LLMs$^{[3]}$, use MixInstruct dataset generation queries from MixInstruct, which is an extensive collection real instructions covering various tasks aiding classifiers in obtaining strong generalization capabilities. We can extract around 10K instructions from MixInstruct dataset where each instruction $q_i$ would be answered by both edge servering small model and cloud-serving large model with responses $A_s$ and $A_l$. +We could refer to Hybrid LLMs$^{[3]}$ and use MixInstruct dataset to generate queries. This dataset contains a wide range of real instructions for various tasks, which helps classifiers develop strong generalization capabilities. We can extract around 10K instructions from MixInstruct dataset where each instruction $q_i$ would be answered by both edge-serving small model and cloud-serving large model with responses $A_s$ and $A_l$. -For responses $A_s$ and $A_l$ we could evaluate their quality using [BART Score](https://github.com/neulab/BARTScore) getting quality scores $S_s$ and $S_l$ respectively where BART score measures textual generation quality easily obtainable through code snippet provided below: +For responses $A_s$ and $A_l$ we could evaluate their quality using [BART Score](https://github.com/neulab/BARTScore) getting quality scores $S_s$ and $S_l$ respectively where BART score can measure textual generation quality easily through code snippet provided below: ```python >>> from bart_score import BARTScorer @@ -156,20 +159,15 @@ For responses $A_s$ and $A_l$ we could evaluate their quality using [BART Score] ``` Then we can get label $y_i$ through the formula below: -$$ -y_i = \begin{cases} -0 &\text{if} ~~~ S_s \geq S_l - \epsilon \\ -1 &\text{if} ~~~ S_s < S_l - \epsilon -\end{cases} -$$ + +$$ y_i = \begin{cases} 0 &\text{if} ~~~ S_s \geq S_l - \epsilon \\\\ 1 &\text{if} ~~~ S_s < S_l - \epsilon\end{cases}$$ + where $\epsilon$ represents permissible performance loss margin. ##### Loss Function We could utilize binary cross entropy function during the training process of the classifier: -$$ -\mathcal{L}(w)=-\frac{1}{N} \sum_{i=1}^{N}\left(y_{i} \log \left(p_{w}\left(q_{i}\right)\right)+\left(1-y_{i}\right) \log \left(1-p_{w}\left(q_{i}\right)\right)\right) -$$ +$$\mathcal{L}(w)=-\frac{1}{N} \sum_{i=1}^{N}\bigg[y_{i} \log \big(p_{w}(q_{i})\big)+(1-y_{i}) \log \big(1-p_{w}(q_{i})\big)\bigg]$$ where $p_w(q_i)$ denotes logits generated by the classifier. ##### Evaluation Metrics @@ -186,9 +184,11 @@ In a long-running cloud-based collaborative inference system for LLM, we can col #### Speculative Decoding Strategy -Speculative decoding serves as an acceleration method during LLM decoding phase. +Speculative decoding serves as an acceleration method during LLM decoding phase. Over the past few years, several studies have explored speculative decoding, as shown below: -The core idea behind speculative decoding lies in utilizing smaller-scale models predicting future multiple words quickly during decoding followed by parallel validation via larger-scale models; if validation fails then re-generation by larger-scale occurs. In traditional auto-regressive decoding phase, every token is generated one at time, which limits the speed of generation. Speculative decoding combines fast predictions made by smaller size models along with parallel validations done by larger ones and significantly speeds up the generation.$^{[9]}$ +image-20240531131022026 + +The core idea behind speculative decoding lies in utilizing smaller-scale models predicting future multiple words quickly during decoding followed by parallel validation via larger-scale models; if validation fails then re-generation by larger-scale occurs. In traditional auto-regressive decoding phase, every token is generated one at time, which limits the speed of generation. Speculative decoding combines fast predictions made by smaller size models along with parallel validations done by larger ones and significantly speeds up the generation $^{[9]}$. image-20240531131935416 @@ -196,11 +196,7 @@ Speculative Decoding requires probability distributions being identical between For most cases, the majority part tokens predicted by smaller scaled modes get adopted. The worst case is that all tokens predicted rejected. however due faster speeds brought about these additional time costs remain minimal relative accelerated effects brought about . -Over past few years several researches have delved into speculative decodings depicted below : - -image-20240531131022026 - -Additionally HuggingFace has implemented open source solution related Speculative Decoding available here : [Assisted Generation](https://huggingface.co/blog/assisted-generation). +HuggingFace has implemented open source solution related to Speculative Decoding, which is available at [Assisted Generation](https://huggingface.co/blog/assisted-generation). ### Summary @@ -212,15 +208,15 @@ Task 2 is planned to be implemented as follows: - Model selection: Qwen-1.8B model will be used at the edge, and Qwen-72B-Chat model will be used in the cloud. - Classifier design: Train a RoBERTa-base as a classifier. If time allows, also train a TinyLlama for classifier comparison. -- Inference optimization: Plan to use low-bit quantization technology to achieve 4-bit and 8-bit quantized deployment, and introduce llama.cpp or vllm to form the inference framework. +- Inference optimization: Plan to use low-bit quantization technology to achieve 4-bit and 8-bit quantized deployment, and introduce vllm to form the inference framework. Advanced task is planned to be implemented as follows: - Combine HuggingFace code examples to complete speculative decoding of LLM. -- Implement the speculative decoding process in the edge-cloud collaborative framework used in Task 2. +- Implement the speculative decoding strategy in the edge-cloud collaborative framework. -## Roadmap +## Road map ### July @@ -255,4 +251,4 @@ Advanced task is planned to be implemented as follows: [8] G. Gerganov, “ggerganov/llama.cpp.” May 31, 2024. Accessed: May 31, 2024. [Online]. Available: https://github.com/ggerganov/llama.cpp -[9] H. Xia *et al.*, “Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding.” arXiv, Feb. 20, 2024. Accessed: May 31, 2024. \ No newline at end of file +[9] H. Xia *et al.*, “Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding.” arXiv, Feb. 20, 2024. Accessed: May 31, 2024. From 863c8127c42c0b7810101b55579b232d5d89040a Mon Sep 17 00:00:00 2001 From: FuryMartin <41051953+FuryMartin@users.noreply.github.com> Date: Thu, 18 Jul 2024 10:10:30 +0800 Subject: [PATCH 03/10] Update proposal: cloud-edge-collaboration Signed-off-by: Yu Fan --- ...ud-edge-collaboration-inference-for-llm.md | 85 ++++++++++++------ .../images/image-20240718083459836.png | Bin 0 -> 258471 bytes .../images/image_20240718085103296.png | Bin 0 -> 158915 bytes 3 files changed, 58 insertions(+), 27 deletions(-) create mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/images/image-20240718083459836.png create mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/images/image_20240718085103296.png diff --git a/docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md b/docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md index a3052d6e..4fe7cfd7 100644 --- a/docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md +++ b/docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md @@ -1,23 +1,33 @@ -* [Cloud-Edge collaborative inference for LLM based on KubeEdge-Ianvs](#cloud-edge-collaborative-inference-for-llm-based-on-kubeedge-ianvs) - * [Motivation](#motivation) - * [Goals](#goals) - * [Proposal](#proposal) - * [Use Cases](#use-cases) - * [Design Details](#design-details) - * [Benchmark Construction](#benchmark-construction) - * [LLM Background](#llm-background) - * [LLM Architecture](#llm-architecture) - * [LLM Overhead Analysis](#llm-overhead-analysis) - * [Efficient Inference with LLM](#efficient-inference-with-llm) - * [Collaboration Strategies](#collaboration-strategies) - * [Query Routing Strategy](#query-routing-strategy) - * [Speculative Decoding Strategy](#speculative-decoding-strategy) - * [Summary](#summary) - * [Road Map](#road-map) - * [References](#references) +- [Cloud-Edge collaborative inference for LLM based on KubeEdge-Ianvs](#cloud-edge-collaborative-inference-for-llm-based-on-kubeedge-ianvs) + - [Motivation](#motivation) + - [Goals](#goals) + - [Proposal](#proposal) + - [Use Cases](#use-cases) + - [Design Details](#design-details) + - [Benchmark Construction](#benchmark-construction) + - [Construction Method](#construction-method) + - [Metric Selection](#metric-selection) + - [Constructure Detail](#constructure-detail) + - [LLM Background](#llm-background) + - [LLM Architecture](#llm-architecture) + - [LLM Overhead Analysis](#llm-overhead-analysis) + - [Efficient Inference with LLM](#efficient-inference-with-llm) + - [Collaboration Strategies](#collaboration-strategies) + - [Query Routing Strategy](#query-routing-strategy) + - [Model Selection](#model-selection) + - [Dataset](#dataset) + - [Loss Function](#loss-function) + - [Evaluation Metrics](#evaluation-metrics) + - [Lifelong Learning](#lifelong-learning) + - [Speculative Decoding Strategy](#speculative-decoding-strategy) + - [Summary](#summary) + - [Roadmap](#roadmap) + - [July](#july) + - [August](#august) + - [Septembter](#septembter) + - [References](#references) # Cloud-Edge collaborative inference for LLM based on KubeEdge-Ianvs - ## Motivation Large language model (LLM) technologies represented by GPT-4, LLaMA-2, and Qwen have demonstrated high usability in a wide range of tasks due to their powerful semantic understanding capabilities, bringing great hope for the realization of general artificial intelligence. @@ -60,7 +70,7 @@ The architecture of this proposal is shown in the figure below. We leverage the - In *StoryManager*, we plan to show Leaderboard and Test Report for users. - In *ModelServer*, we plan to support small-scale models such as Qwen-1.8B and large-scale models such as Qwen-72B-Chat. We plan to support serving frameworks like PyTorch and vLLM with low bit quantization compatibility. -image-20240703213446953 +image-20240718083459836.png ### Benchmark Construction @@ -78,6 +88,20 @@ Among the supported lists in OpenCompass mentioned above,Examination、Knowled For simplicity, we can choose MMLU / CMMLU in Examination category as our evaluation benchmark. +### Constructure Detail + +Edge-cloud collaborative reasoning requires a clear understanding of the performance of edge-side models and cloud-side models. To this end, we need to support evaluations for three types of architectures: + +- Edge only +- Cloud only +- Cloud-Edge collaboration + +In order to achieve unified data distribution between the edge and the cloud, we plan to deploy Benchmark on a small server with a public IP address and distribute dataset data to the edge and cloud in the form of WebSocket. Once these data are distributed to the corresponding devices, inference will be performed by the models on the devices, and the responses obtained will be sent back to Benchmark Server for statistical analysis and metric calculation. + +image_20240718085103 + +We are aware that the bandwidth resources at the edge of the cloud may be limited, which could cause inconvenience for transferring large datasets. However, considering that LLM datasets are often pure text and occupy a small volume, the bandwidth requirement for transmission is relatively low. In contrast, large language model inference is a computationally intensive task, and the time taken for inference upon receiving data will far exceed the time taken for transmission. Therefore, the bottleneck of the system lies mainly in LLM inference rather than benchmark data transmission, so bandwidth will not have a significant impact. + ### LLM Background #### LLM Architecture @@ -159,15 +183,22 @@ For responses $A_s$ and $A_l$ we could evaluate their quality using [BART Score] ``` Then we can get label $y_i$ through the formula below: - -$$ y_i = \begin{cases} 0 &\text{if} ~~~ S_s \geq S_l - \epsilon \\\\ 1 &\text{if} ~~~ S_s < S_l - \epsilon\end{cases}$$ - +$$ +y_i = \begin{cases} +0 &\text{if} ~~~ S_s \geq S_l - \epsilon \\ +1 &\text{if} ~~~ S_s < S_l - \epsilon +\end{cases} +$$ where $\epsilon$ represents permissible performance loss margin. +According to recent generative evaluation progress, we can also consider using LLMs like GPT-4 to give scores for the generated response. The best solution needs to wait for the results of the experiment. + ##### Loss Function We could utilize binary cross entropy function during the training process of the classifier: -$$\mathcal{L}(w)=-\frac{1}{N} \sum_{i=1}^{N}\bigg[y_{i} \log \big(p_{w}(q_{i})\big)+(1-y_{i}) \log \big(1-p_{w}(q_{i})\big)\bigg]$$ +$$ +\mathcal{L}(w)=-\frac{1}{N} \sum_{i=1}^{N}\bigg[y_{i} \log \big(p_{w}(q_{i})\big)+(1-y_{i}) \log \big(1-p_{w}(q_{i})\big)\bigg] +$$ where $p_w(q_i)$ denotes logits generated by the classifier. ##### Evaluation Metrics @@ -188,7 +219,7 @@ Speculative decoding serves as an acceleration method during LLM decoding phase. image-20240531131022026 -The core idea behind speculative decoding lies in utilizing smaller-scale models predicting future multiple words quickly during decoding followed by parallel validation via larger-scale models; if validation fails then re-generation by larger-scale occurs. In traditional auto-regressive decoding phase, every token is generated one at time, which limits the speed of generation. Speculative decoding combines fast predictions made by smaller size models along with parallel validations done by larger ones and significantly speeds up the generation $^{[9]}$. +The core idea behind speculative decoding lies in utilizing smaller-scale models predicting future multiple words quickly during decoding followed by parallel validation via larger-scale models; if validation fails then re-generation by larger-scale occurs. In traditional auto-regressive decoding phase, every token is generated one at time, which limits the speed of generation. Speculative decoding combines fast predictions made by smaller size models along with parallel validations done by larger ones and significantly speeds up the generation.$^{[9]}$ image-20240531131935416 @@ -206,7 +237,7 @@ Task 1 is planned to be implemented as follows: Task 2 is planned to be implemented as follows: -- Model selection: Qwen-1.8B model will be used at the edge, and Qwen-72B-Chat model will be used in the cloud. +- Model selection: Qwen-1.8B model will be used at the edge, and Qwen-72B-Chat model will be used in the cloud. (For simplicity, also may choose to use LLM service like GPT-4, GLM-4, Claude as our cloud model) - Classifier design: Train a RoBERTa-base as a classifier. If time allows, also train a TinyLlama for classifier comparison. - Inference optimization: Plan to use low-bit quantization technology to achieve 4-bit and 8-bit quantized deployment, and introduce vllm to form the inference framework. @@ -216,7 +247,7 @@ Advanced task is planned to be implemented as follows: - Implement the speculative decoding strategy in the edge-cloud collaborative framework. -## Road map +## Roadmap ### July @@ -251,4 +282,4 @@ Advanced task is planned to be implemented as follows: [8] G. Gerganov, “ggerganov/llama.cpp.” May 31, 2024. Accessed: May 31, 2024. [Online]. Available: https://github.com/ggerganov/llama.cpp -[9] H. Xia *et al.*, “Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding.” arXiv, Feb. 20, 2024. Accessed: May 31, 2024. +[9] H. Xia *et al.*, “Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding.” arXiv, Feb. 20, 2024. 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zMuJAd#X5=1X+Ga(;#ofv*JikKp#Ms)e3|i%e9+Zj<*ol4ETbvg<-R-F_b)veY>2E% zc$fD}s4A+N9X4a$dN+P!Xw@Z12OGa-@eED!8XZVijZ2K%HhVEkF6M_f;+-Dbmd|J0DUuOF2a zf?YTEdY?wBuu$BVA?Pf%x|$R$r^}6AA>OlRq#)$Bb9zNreKaK|q2WY^L6Q9k=Bu_D z;Zpg|Yg<{RTTr|+x00g2nS6d}SY>)i3fn2{7}JP5X)Ru8?%RHhtS&T}J3P_1 zKJEp`#a23VFYMuN_TNNjDx!DEiSjCa*=Z_rU`U$@4Dx;p~ymk({gHryS;19@MRhLzzYp}BIk=i14hAo zQN^rH*iVLFp|7IRi)Kcb9?Ji|Bb-XYM0ZGPDP*kOy8i(KV()hE;40-9oF}0OM0MEO z%pkj|VbT$s`K;9>jo%K!8c~jVWhiCo$FL>q zE9J_^xLmBXk3NOxm&#uBQ+V|*Tf;`d`t~tw;X(hs9`HwB@jra^&qw}~@jp0Ee`f6e fEMvu&h1)7TJHzD9v)wh7|L$bxcB%Tp)gOKblgt2} literal 0 HcmV?d00001 From b3e7673c980b193dad84429bbc5e4e4895914eb2 Mon Sep 17 00:00:00 2001 From: Yu Fan Date: Thu, 1 Aug 2024 16:56:04 +0800 Subject: [PATCH 04/10] Update proposal: cloud-edge-collaboration Signed-off-by: Yu Fan --- ...ud-edge-collaboration-inference-for-llm.md | 255 ++++++++++++++++-- .../images/image-20240703213446953.png | Bin 247298 -> 0 bytes .../images/image-20240718083459836.png | Bin 258471 -> 0 bytes .../images/image-20240801165243035.png | Bin 0 -> 158263 bytes 4 files changed, 236 insertions(+), 19 deletions(-) delete mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/images/image-20240703213446953.png delete mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/images/image-20240718083459836.png create mode 100644 docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/images/image-20240801165243035.png diff --git a/docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md b/docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md index 4fe7cfd7..160b39c4 100644 --- a/docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md +++ b/docs/proposals/algorithms/cloud-edge-collaboration-inference-for-llm/cloud-edge-collaboration-inference-for-llm.md @@ -4,10 +4,12 @@ - [Proposal](#proposal) - [Use Cases](#use-cases) - [Design Details](#design-details) + - [Implementation Detail](#implementation-detail) + - [Change of Ianvs](#change-of-ianvs) + - [Example Implementation](#example-implementation) - [Benchmark Construction](#benchmark-construction) - [Construction Method](#construction-method) - [Metric Selection](#metric-selection) - - [Constructure Detail](#constructure-detail) - [LLM Background](#llm-background) - [LLM Architecture](#llm-architecture) - [LLM Overhead Analysis](#llm-overhead-analysis) @@ -62,15 +64,244 @@ We propose KubeEdge-Ianvs to adopt the edge-cloud collaboration strategy to enha ## Design Details -The architecture of this proposal is shown in the figure below. We leverage the existed *TestEnvManager*, *TestCaseController* and *StoryManager* in Ianvs. For LLM inference, we also add a serving module named *ModelServer* to use the state-of-the-art inference frameworks to deploy LLM services. +The architecture of this proposal is shown in the figure below. We leverage the existed *TestEnvManager*, *TestCaseController* and *StoryManager* in Ianvs. - In *TestEnvManager*, we plan to add MMLU and CMMLU as LLM benchmark and *Accuracy*, *Latency*, *Throughput*, *Bandwith* as metrics. - - In *TestCaseController*, we plan to add two cloud-edge collaboration algorithm named *Query Routing* and *Speculative Decoding*. - In *StoryManager*, we plan to show Leaderboard and Test Report for users. -- In *ModelServer*, we plan to support small-scale models such as Qwen-1.8B and large-scale models such as Qwen-72B-Chat. We plan to support serving frameworks like PyTorch and vLLM with low bit quantization compatibility. -image-20240718083459836.png +image-20240801165243035.png + +### Implementation Detail + +#### Change of Ianvs + +For implementation details, we plan to add a module named `llm_joint_inference` under `core/testcasecontroller/algorithm/paradigm`: + +- `query_routing.py` serves as the entry point for the query routing algorithm (the algorithm planned for implementation in Task 2); + +- `speculative_decoding.py` serves as the entry point for the speculative decoding algorithm (the algorithm planned for implementation in advanced tasks); + +- `sedna_joint_inference.py` is a modified version of the `JointInference` module from `sedna`, which we will modify internally to support collaborative inference with LLMs (in the future, this file should be located in `KubeEdge/sedna` instead of `KubeEdge/ianvs`). + +The folder structure will be as follows: + +```bash +. +├── testcasecontroller +│ ├── algorithm +│ │ └── paradigm +│ │ ├── __init__.py +│ │ ├── base.py +│ │ ├── llm_joint_inference +│ │ │ ├── __init__.py +│ │ │ ├── query_routing.py +│ │ │ ├── speculative_decoding.py +│ │ │ └── sedna_joint_inference.py +``` + +We will add enumerations for these two algorithms in `ParadigmType` within `core/common/constant`. + +```python +class ParadigmType(Enum): + SINGLE_TASK_LEARNING = "singletasklearning" + INCREMENTAL_LEARNING = "incrementallearning" + MULTIEDGE_INFERENCE = "multiedgeinference" + LIFELONG_LEARNING = "lifelonglearning" + LLM_QUERY_ROUTING = "llmqueryrouting" + LLM_SPECULATIVE_DECODING = "llmsepeculativedecoding" +``` + +We will also modify the `build_paradigm_job()` function in `core/testcasecontroller/algorithm/base.py` to add support for these two algorithms: + +```python +from llm_joint_inference.sedna_joint_inference import JointInference + +class ParadigmBase: + def build_paradigm_job(self, paradigm_type): + if paradigm_type == ParadigmType.LLM_QUERY_ROUTING.value: + # Sedna 中的 JointInference 接口 + # 与 IncrementalLearning、LifelongLearning 算法与 Sedna 接口的对齐方式保持一致 + return JointInference( + estimator=self.module_instances.get( + ModuleType.BASEMODEL.value), + hard_example_mining=self.module_instances.get( + ModuleType.HARD_EXAMPLE_MINING.value) + ) + if paradigm_type == ParadigmType.LLM_SPECULATIVE_DECODING.value: + # 预留接口,与 LLM Speculative Decoding 的具体实现有关,后续进阶任务中可进一步考虑 + pass + + return None +``` + +Needed fields will also be added to `core/testcasecontroller/algorithm/algorithm.py` to support the new algorithm. + +```python +from core.testcasecontroller.algorithm.paradigm import ( + SingleTaskLearning, + IncrementalLearning, + MultiedgeInference, + LifelongLearning, + LLM_QUERY_ROUTING, + LLM_SPECULATIVE_DECODING +) + +class Algorithm: + def paradigm(self, workspace: str, **kwargs): + if self.paradigm_type == ParadigmType.LLM_QUERY_ROUTING.value: + return JointInference(workspace, **config) + if self.paradigm_type == ParadigmType.LLM_SPECULATIVE_DECODING.value: + # 预留接口 + pass + + return None +``` + +#### Example Implementation + +We will add a folder named `cloud-edge-collaborative-inference-for-llm` under the `examples/` directory, which will contain two subprojects: `query-routing` and `speculative-decoding`. + +```bash +├── cloud-edge-collaborative-inference-for-llm +│ ├── README.md +│ ├── benchmarkingjob.yaml +│ ├── testalgorithms +│ │ ├── query-routing +│ │ │ ├── basemodel.py +│ │ │ ├── test_queryrouting.yaml +│ │ │ ├── hard_example_mining.py +│ │ │ ├── models +│ │ │ │ ├── joint_inference.py +│ │ │ │ └── llm.py +│ │ └── sepeculative-decoding +│ │ │ └── ... +│ └── testenv +│ ├── acc.py +│ ├── lantency.py +│ ├── throughput.py +│ ├── bandwith.py +│ └── testenv.yaml +``` + +We will create the following content in `query-routing/basemodel.py` to implement the registration of the query-routing base class. + +```python +from models.joint_inference import JointInference, BigModelService +from models.llm import LLM_HuggingFace, LLM_vLLM, LLM_via_API, + +@ClassFactory.register(ClassType.GENERAL, alias="query-routing") +class BaseModel: + def __init__(self, **kwargs): + self.kwargs = kwargs + self.backend = kwargs.get("backend", "huggingface") + self.quantization = kwargs.get("quantization", "full") + # 'backend' means serving framework: "huggingface", "vllm" + # 'quantization' means quantization mode:"full","4-bit","8-bit" + + def load(self, model_url=None): + # TODO BigModelService needs to be edited to remove app start progress. + # TODO quantization needs to be passed to estimator. + + if self.backend == "huggingface": + estimator = LLM_HuggingFace(model_url, self.quantization) + elif self.backend == "vllm": + estimator = LLM_vLLM(model_url, self.quantization) + else: + raise Exception(f"Backend {self.backend} is not supported") + + # TODO cloud service must be configured in JointInference + # TODO JointInference needs to be rewrite to support. + self.pipeline = JointInference( + estimator=estimator, + hard_example_mining={ + "method": "BERT-Classifier", + "param": { + "xxxxx": 0.9 + } + } + ) + print("BaseModel load") + + def predict(self, data, input_shape=None, **kwargs): + print("BaseModel predict") + answer_list = [] + for line in data: + response = self.pipeline.inference(line) + answer_list.append(response) + return answer_list +``` + +We will also construct a BERT classifier in `query-routing/hard_example_mining` to achieve the classification of hard examples. + +```python +@ClassFactory.register(ClassType.HEM, alias="BERT-Classifier") +class BERTFilter(BaseFilter, abc.ABC): + def __init__(self, model_path, **kwargs): + self.classifier = pipeline( + "text-classification", + model=model_path, + trust_remote_code=True + ) + + def _predict(self, data): + result = self.classifier(data) + return result + + def __call__(self, data=None) -> bool: + return self._predict(data) +``` + +The content of `benchmarkingjob.yaml` is as follows: + +```python +benchmarkingjob: + name: "benchmarkingjob" + workspace: "/root/autodl-fs/ianvs/workspace" + testenv: "./examples/cloud-edge-collaborative-inference-for-llm/testenv/testenv.yaml" + test_object: + type: "algorithms" + algorithms: + - name: "query-routing" + url: "./examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/gen/gen_algorithm.yaml" + rank: + sort_by: [ { "acc": "descend" } ] + visualization: + mode: "selected_only" + method: "print_table" + + selected_dataitem: + paradigms: [ "llmqueryrouting" ] + modules: [ "all" ] + hyperparameters: [ "all" ] + metrics: [ "acc" , "latency", "throughput", "bandwith"] + + save_mode: "selected_and_all" +``` + +The content of `test_queryrouting.yaml` is as follows: + +```python +algorithm: + paradigm_type: "llmqueryrouting" + modules: + - type: "basemodel" + name: "query-routing" + url: "./examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/basemodel.py" + hyperparameters: + - backend: + values: + - "huggingface" + - quantization: + values: + - "4-bit" + - other_parameters + - type: "query-routing" + name: "HardSampleMining" + url: "./examples/robot/lifelong_learning_bench/testalgorithms/rfnet/hard_sample_mining.py" + hyperparameters: + - other_parameters +``` ### Benchmark Construction @@ -88,20 +319,6 @@ Among the supported lists in OpenCompass mentioned above,Examination、Knowled For simplicity, we can choose MMLU / CMMLU in Examination category as our evaluation benchmark. -### Constructure Detail - -Edge-cloud collaborative reasoning requires a clear understanding of the performance of edge-side models and cloud-side models. To this end, we need to support evaluations for three types of architectures: - -- Edge only -- Cloud only -- Cloud-Edge collaboration - -In order to achieve unified data distribution between the edge and the cloud, we plan to deploy Benchmark on a small server with a public IP address and distribute dataset data to the edge and cloud in the form of WebSocket. Once these data are distributed to the corresponding devices, inference will be performed by the models on the devices, and the responses obtained will be sent back to Benchmark Server for statistical analysis and metric calculation. - -image_20240718085103 - -We are aware that the bandwidth resources at the edge of the cloud may be limited, which could cause inconvenience for transferring large datasets. However, considering that LLM datasets are often pure text and occupy a small volume, the bandwidth requirement for transmission is relatively low. In contrast, large language model inference is a computationally intensive task, and the time taken for inference upon receiving data will far exceed the time taken for transmission. 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    7!KFM1ctt4JdFUgH}8w{_8%9=Bs z2g&@vQ@k-Kc@{Izy~uV<(*9lR z1&FI;T}%eq>E2g61_UBDo6sXweLf3-L)#rMq=K~CNPS_=$bE+L8g*D12a3vuz;iu% zlFMGZh9Z~!q`p$(o=IQfWG+4~Kfe~(n+0U1=tYuw9j6T1s(30fC6Z_WdMUew}9SOA5NH}r?SopkjC4S>}sNCK8E+= zuO?2eS|Zmfn+fYAzaTD|`&@|BkclZdOzJNShdarnzL?ocqCKIu4#1fNGa58~o^il4 z2JI2NymhjzLr*iX(NT!AuE>GfIgeoG3Rhu3kW7b1f#rQ&+Y(FU%i%#=!e1W3zzF{s z5<45H3PWbYHJ;ok;M;bgA;GX}$3G#yPj_y#2&Q%4EtNk;`%{=enhRXiX9`VUB=fN~=JhW5r;<3$Yk76C*inWC@Ch-DnOf*UH zFwIQd(jguEHXr;(9V;d6^eqt^6U(=x@zBL){JZfL>Ou$Z)bx5`>Ng5&xjJ zEZmoV4NznNiY`bgP1-F8MLD0jf4{B_W1`Wxug4!95rb8u<8#J zFhdfqrT(aeP%;@?9HfRMDo=PwWEi|2z;Wb~%zJg_7mgVbH0ehkERSonRQ@X66xMNR z)>c@fOg+{wGOCV9tws@S1(yF~#9%e`<{IqKnN1`jz3n=|2A%u34g1*WiP3LWj;*Z1 z7t3)%R6tLRx@K0T0!}hsTkGQPb>Qa8Cq`3w$)Q!li`q_ebuQ^a0d4~`5^5WjAl>1Z z6DRqo31>xvrNqZAF?0Xm5$CV!0~u?Yz!hysygFR1N!9k1ngCH+BTm{?&FHjBTwY>YGGG18r+%x$_93KEYBo-0 z(WOOIt5L2@#YI-QO&IznA~7!@S8Ws%ENQe#%&0P%Fnayo(S8eYk&GkEI+=rM1T&o9 z@sk8EH~2SkAix&4M0%s2^pO2)irrJ8>JbZ8M4wBh`b<@u7V%XNHWr@8U?L7|46cK o-f3tb5Y(WOpgY=C3!+(8oghuHAYA65=Aia{BfLG<0B9xpKYiR Date: Tue, 13 Aug 2024 22:07:13 +0800 Subject: [PATCH 08/10] fix: import error and interface alignment Signed-off-by: Yu Fan --- .../algorithm/paradigm/base.py | 3 +- .../joint_inference/joint_inference.py | 17 +++--- .../sedna_joint_inference.py | 27 ++++++--- .../benchmarkingjob.yaml | 2 + .../query-routing/cloud_model.py | 9 +-- .../query-routing/edge_model.py | 28 +++++---- .../query-routing/hard_sample_mining.py | 20 ++++--- .../query-routing/models/__init__.py | 3 +- .../query-routing/models/api_llm.py | 23 ++++---- .../query-routing/models/base_llm.py | 30 +++++++--- .../query-routing/models/huggingface_llm.py | 16 ++--- .../query-routing/models/vllm_llm.py | 26 +++++--- .../query-routing/test_queryrouting.yaml | 59 +++++++++++++------ .../testenv/acc.py | 9 +-- .../testenv/result_parser.py | 12 ++++ .../testenv/testenv.yaml | 7 ++- 16 files changed, 183 insertions(+), 108 deletions(-) rename core/testcasecontroller/algorithm/paradigm/{joint_inference => }/sedna_joint_inference.py (93%) create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/testenv/result_parser.py diff --git a/core/testcasecontroller/algorithm/paradigm/base.py b/core/testcasecontroller/algorithm/paradigm/base.py index 3162bb65..da21cc56 100644 --- a/core/testcasecontroller/algorithm/paradigm/base.py +++ b/core/testcasecontroller/algorithm/paradigm/base.py @@ -18,8 +18,7 @@ from sedna.core.incremental_learning import IncrementalLearning from sedna.core.lifelong_learning import LifelongLearning -from joint_inference.sedna_joint_inference import JointInference - +from .sedna_joint_inference import JointInference from core.common.constant import ModuleType, ParadigmType diff --git a/core/testcasecontroller/algorithm/paradigm/joint_inference/joint_inference.py b/core/testcasecontroller/algorithm/paradigm/joint_inference/joint_inference.py index b8267c01..e57a3a55 100644 --- a/core/testcasecontroller/algorithm/paradigm/joint_inference/joint_inference.py +++ b/core/testcasecontroller/algorithm/paradigm/joint_inference/joint_inference.py @@ -20,7 +20,7 @@ from core.common.constant import ParadigmType from core.testcasecontroller.algorithm.paradigm.base import ParadigmBase from sedna.common.class_factory import ClassFactory, ClassType -from sedna_joint_inference import JointInference as SednaJointInference +# from sedna_joint_inference import JointInference as SednaJointInference class JointInference(ParadigmBase): """ @@ -51,8 +51,7 @@ class JointInference(ParadigmBase): def __init__(self, workspace, **kwargs): ParadigmBase.__init__(self, workspace, **kwargs) self.kwargs = kwargs - self.initial_model = kwargs.get("initial_model_url") - self.hard_example_mining_mode = kwargs.get("hard_example_mining_mode", "mining_edge_or_cloud") + self.hard_example_mining_mode = kwargs.get("hard_example_mining_mode", "mining-then-inference") def run(self): """ @@ -68,17 +67,21 @@ def run(self): job = self.build_paradigm_job(ParadigmType.JOINT_INFERENCE.value) - inference_result = self._inference(job, self.initial_model) + inference_result = self._inference(job) return inference_result, self.system_metric_info - def _inference(self, job : SednaJointInference, trained_model): + def _inference(self, job): # Ianvs API inference_dataset = self.dataset.load_data(self.dataset.test_url, "inference") inference_output_dir = os.path.join(self.workspace, "output/inference/") os.environ["RESULT_SAVED_URL"] = inference_output_dir + batch_infer_res = [] + + for data in inference_dataset.x: # inference via sedna JointInference API - infer_res = job.inference(inference_dataset.x, mining_mode=self.hard_example_mining_mode) + infer_res = job.inference(data, mining_mode=self.hard_example_mining_mode) + batch_infer_res.append(infer_res) - return infer_res \ No newline at end of file + return batch_infer_res # (is_hard_example, res, edge_result, cloud_result) \ No newline at end of file diff --git a/core/testcasecontroller/algorithm/paradigm/joint_inference/sedna_joint_inference.py b/core/testcasecontroller/algorithm/paradigm/sedna_joint_inference.py similarity index 93% rename from core/testcasecontroller/algorithm/paradigm/joint_inference/sedna_joint_inference.py rename to core/testcasecontroller/algorithm/paradigm/sedna_joint_inference.py index 91e693c8..46eca523 100644 --- a/core/testcasecontroller/algorithm/paradigm/joint_inference/sedna_joint_inference.py +++ b/core/testcasecontroller/algorithm/paradigm/sedna_joint_inference.py @@ -9,8 +9,9 @@ from sedna.service.client import ModelClient, LCReporter from sedna.common.constant import K8sResourceKind from sedna.core.base import JobBase +import re -__all__ = ("JointInference", "BigModelService") +HUGGINGFACE_PATH_PATTERN = r'^[a-zA-Z0-9][\w\-]*/[a-zA-Z0-9][\w\-\.]*$' # Currently rename "JointInference" to "SednaJointInference" class JointInference(JobBase): @@ -50,6 +51,8 @@ class JointInference(JobBase): def __init__(self, estimator=None, cloud=None, hard_example_mining: dict = None): super(JointInference, self).__init__(estimator=estimator) + + self.job_kind = K8sResourceKind.JOINT_INFERENCE_SERVICE.value self.local_ip = get_host_ip() @@ -70,10 +73,13 @@ def __init__(self, estimator=None, cloud=None, hard_example_mining: dict = None) if callable(self.estimator): self.estimator = self.estimator() - if not os.path.exists(self.model_path): + + check_huggingface_repo = lambda x: bool(re.match(HUGGINGFACE_PATH_PATTERN, x)) + + if not os.path.exists(self.model_path) and not check_huggingface_repo(self.model_path): raise FileExistsError(f"{self.model_path} miss") else: - self.estimator.load(self.model_path) + self.estimator.load(model_url=self.model_path) # If cloud is None, then initialize ModelClint as cloud. # Otherwise, we will regard the cloud as a client implemented by the user. @@ -94,8 +100,10 @@ def __init__(self, estimator=None, cloud=None, hard_example_mining: dict = None) if not hard_example_mining: hard_example_mining = self.get_hem_algorithm_from_config() if hard_example_mining: - hem = hard_example_mining.get("method", "IBT") - hem_parameters = hard_example_mining.get("param", {}) + # hem = hard_example_mining.get("method", "IBT") + hem = "BERT" + hem_parameters = {} + # hem_parameters = hard_example_mining.get("param", {}) self.hard_example_mining_algorithm = ClassFactory.get_cls( ClassType.HEM, hem )(**hem_parameters) @@ -140,14 +148,15 @@ def _get_edge_result(self, data, callback_func, **kwargs): return res, edge_result def _get_cloud_result(self, data, post_process, **kwargs): + try: cloud_result = self.cloud.inference( data.tolist(), post_process=post_process, **kwargs) except Exception as err: self.log.error(f"get cloud result error: {err}") - else: - res = cloud_result - + + res = deepcopy(cloud_result) + self.lc_reporter.update_for_collaboration_inference() return res, cloud_result @@ -208,7 +217,7 @@ def inference(self, data=None, post_process=None, **kwargs): if self.hard_example_mining_algorithm is None: raise ValueError("Hard example mining algorithm is not set.") - is_hard_example = self.hard_example_mining_algorithm(res) + is_hard_example = self.hard_example_mining_algorithm(data) if is_hard_example: if not sepeculative_decoding: res, cloud_result = self._get_cloud_result(data, post_process=post_process, **kwargs) diff --git a/examples/cloud-edge-collaborative-inference-for-llm/benchmarkingjob.yaml b/examples/cloud-edge-collaborative-inference-for-llm/benchmarkingjob.yaml index 182dd85b..c8099a4c 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/benchmarkingjob.yaml +++ b/examples/cloud-edge-collaborative-inference-for-llm/benchmarkingjob.yaml @@ -5,6 +5,8 @@ benchmarkingjob: # "~/" cannot be identified, so must be relative path or absoulute path workspace: "./workspace" + hard_example_mining_mode: "mining-then-inference" + # the url address of test environment configuration file; string type; # the file format supports yaml/yml; testenv: "./examples/cloud-edge-collaborative-inference-for-llm/testenv/testenv.yaml" diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/cloud_model.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/cloud_model.py index eb68dfe0..c622447a 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/cloud_model.py +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/cloud_model.py @@ -24,11 +24,12 @@ from sedna.common.config import Context from sedna.common.class_factory import ClassType, ClassFactory -from models import HuggingfaceLLM, VllmLLM, APIBasedLLM +from models import HuggingfaceLLM, APIBasedLLM, VllmLLM from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto +os.environ['BACKEND_TYPE'] = 'TORCH' logging.disable(logging.WARNING) @@ -44,8 +45,4 @@ def __init__(self, **kwargs): ) def inference(self, data, input_shape=None, **kwargs): - answer_list = [] - for line in data: - response = self.model.inference(line) - answer_list.append(response) - return answer_list \ No newline at end of file + return self.client.inference(data) \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/edge_model.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/edge_model.py index 0c05aa8e..ff48d63d 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/edge_model.py +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/edge_model.py @@ -24,13 +24,21 @@ from sedna.common.config import Context from sedna.common.class_factory import ClassType, ClassFactory -from models import HuggingfaceLLM, VllmLLM, APIBasedLLM +from models import HuggingfaceLLM, APIBasedLLM, VllmLLM from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto +os.environ['BACKEND_TYPE'] = 'TORCH' + +MODEL_URL = "Qwen/Qwen2-1.5B-Instruct" logging.disable(logging.WARNING) +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" +) +logger = logging.getLogger("EdgeModel") __all__ = ["BaseModel"] @@ -54,21 +62,21 @@ def _set_config(self): # EdgeModel URL, see at https://github.com/kubeedge/sedna/blob/ac623ab32dc37caa04b9e8480dbe1a8c41c4a6c2/lib/sedna/core/base.py#L132 parameters["MODEL_URL"] = self.model_url - def load(self, model_url=None): + def load(self, **kwargs): + # Align with Sedna's TorchBackend interface, see at https://github.com/kubeedge/sedna/blob/ac623ab32dc37caa04b9e8480dbe1a8c41c4a6c2/lib/sedna/backend/torch/__init__.py#L55-L67 if self.backend == "huggingface": - self.model = HuggingfaceLLM(model_url, self.quantization) + self.model = HuggingfaceLLM() elif self.backend == "vllm": - self.model = VllmLLM(model_url, self.quantization) + self.model = VllmLLM() else: raise Exception(f"Backend {self.backend} is not supported") - self.model.load(model_url=model_url) + logger.info(f"Using Backend: {self.backend}") + self.model.load(model_url=self.model_url) # TODO cloud service must be configured in JointInference def predict(self, data, input_shape=None, **kwargs): - answer_list = [] - for line in data: - response = self.model.inference(line) - answer_list.append(response) - return answer_list \ No newline at end of file + answer_list = self.model.inference(data) + + return answer_list diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/hard_sample_mining.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/hard_sample_mining.py index 65b67f4d..955e81d7 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/hard_sample_mining.py +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/hard_sample_mining.py @@ -49,16 +49,18 @@ def data_check(cls, data): @ClassFactory.register(ClassType.HEM, alias="BERT") class BERTFilter(BaseFilter, abc.ABC): - def __init__(self, model_path, **kwargs): - self.classifier = pipeline( - "text-classification", - model=model_path, - trust_remote_code=True - ) + def __init__(self, **kwargs): + # self.classifier = pipeline( + # "text-classification", + # model=model_path, + # trust_remote_code=True + # ) + pass def _predict(self, data): - result = self.classifier(data) - return result + # result = self.classifier(data) + # return result + return False def __call__(self, data=None) -> bool: - return self._predict(data) \ No newline at end of file + return False#self._predict(data) \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/__init__.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/__init__.py index 103c53bd..548b62e7 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/__init__.py +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/__init__.py @@ -1,3 +1,4 @@ from .api_llm import APIBasedLLM from .huggingface_llm import HuggingfaceLLM -from .vllm_llm import VllmLLM \ No newline at end of file +from .vllm_llm import VllmLLM +from .base_llm import BaseLLM \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/api_llm.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/api_llm.py index 2bcaf915..88ed2672 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/api_llm.py +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/api_llm.py @@ -1,13 +1,13 @@ import os from openai import OpenAI -from base_llm import BaseLLM +from models.base_llm import BaseLLM from sedna.core.joint_inference.joint_inference import BigModelService class APIBasedLLM(BaseLLM): def __init__(self, model_name, **kwargs) -> None: - api_key=os.environ.get("OPENAI_API_KEY"), + api_key=os.environ.get("OPENAI_API_KEY") base_url=os.environ.get("OPENAI_BASE_URL") self.model = model_name @@ -17,16 +17,8 @@ def __init__(self, model_name, **kwargs) -> None: ) def _infer(self, prompt, system=None): - if system: - messages = [ - {"role": "system", "content": system}, - {"role": "user", "content": prompt} - ] - else: - messages = [ - {"role": "user", "content": prompt} - ] - + messages = self.get_message_chain(prompt, system) + self.chat_completion = self.client.chat.completions.create( messages = messages, model=self.model, @@ -35,4 +27,9 @@ def _infer(self, prompt, system=None): response = self.chat_completion.choices[0].message.content return response - \ No newline at end of file + +if __name__ == '__main__': + llm = APIBasedLLM(model_name="gpt-4o-mini") + data = ["你好吗?介绍一下自己"] + res = llm.inference(data) + print(res) \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/base_llm.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/base_llm.py index a8175f71..b5a2ce3b 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/base_llm.py +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/base_llm.py @@ -1,17 +1,33 @@ class BaseLLM: def __init__(self, **kwargs) -> None: - BaseLLM.__init__(self, **kwargs) self.quantization = kwargs.get("quantization", "full") def load(self): raise NotImplementedError - def inference(self, datas): - answer_list = [] - for line in datas: - response = self._infer(line) - answer_list.append(response) - return answer_list + def inference(self, data): + if isinstance(data, list): + return [self._infer(line) for line in data] + elif isinstance(data, str): + return self._infer(data) + else: + raise ValueError(f"DataType {type(data)} is not supported, it must be `list` or `str`") + + def get_message_chain(self, prompt, system = None): + system = "You are a helpful assistant, please help sovle user's question. " + + if system: + messages = [ + {"role": "system", "content": system}, + {"role": "user", "content": prompt} + ] + else: + messages = [ + {"role": "user", "content": prompt} + ] + + return messages + def _infer(self, data): raise NotImplementedError \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/huggingface_llm.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/huggingface_llm.py index 806068e6..8cb49da5 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/huggingface_llm.py +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/huggingface_llm.py @@ -1,5 +1,5 @@ from transformers import AutoModelForCausalLM, AutoTokenizer -from base_llm import BaseLLM +from models.base_llm import BaseLLM device = "cuda" @@ -9,11 +9,11 @@ def __init__(self, **kwargs) -> None: def load(self, model_url): self.model = AutoModelForCausalLM.from_pretrained( - model_url,#"/root/autodl-tmp/Qwen/Qwen-1_8B-Chat", + model_url, torch_dtype="auto", device_map="auto", trust_remote_code=True, - quantization = self.quantization # Need to align with HF API + # quantization = self.quantization # Need to align with HF API ) self.tokenizer = AutoTokenizer.from_pretrained( model_url, @@ -21,15 +21,7 @@ def load(self, model_url): ) def _infer(self, prompt, system=None): - if system: - messages = [ - {"role": "system", "content": system}, - {"role": "user", "content": prompt} - ] - else: - messages = [ - {"role": "user", "content": prompt} - ] + messages = self.get_message_chain(prompt, system) text = self.tokenizer.apply_chat_template( messages, tokenize=False, diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/vllm_llm.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/vllm_llm.py index 79e4fbdf..b2489f7a 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/vllm_llm.py +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/vllm_llm.py @@ -1,5 +1,6 @@ from vllm import LLM, SamplingParams -from base_llm import BaseLLM +from transformers import AutoTokenizer +from models.base_llm import BaseLLM device = "cuda" @@ -11,15 +12,26 @@ def load(self, model_url): self.model = LLM( model=model_url, trust_remote_code=True, - quantization=self.quantization # TODO need to align with vllm API + dtype="float16", + max_model_len=1024 + #quantization=self.quantization # TODO need to align with vllm API + ) + self.tokenizer = AutoTokenizer.from_pretrained( + model_url, + trust_remote_code=True ) def _infer(self, prompt, system=None): - sampling_params = SamplingParams( - temperature=0.8, - top_p=0.95, - max_tokens=2048 + messages = self.get_message_chain(prompt, system) + + text = self.tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True ) - outputs = self.model.generate(prompt, sampling_params) + + sampling_params = SamplingParams(temperature=0.7, top_p=0.8, repetition_penalty=1.05, max_tokens=512) + + outputs = self.model.generate([text], sampling_params) response = outputs[0].outputs[0].text return response \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/test_queryrouting.yaml b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/test_queryrouting.yaml index 5adb1be8..46f6dfc2 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/test_queryrouting.yaml +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/test_queryrouting.yaml @@ -16,16 +16,29 @@ algorithm: # This defines the edge model name: "EdgeModel" # the url address of python module; string type; - url: "./examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/edgemodel.py" + url: "./examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/edge_model.py" hyperparameters: - - model_name: "Qwen/Qwen-1.8B-Chat" - - backend: "huggingface" - - quantization: "4-bit" - - config: - temperature: 0.7 - top_p: 5 - max_token: 2048 + # name of the hyperparameter; string type; + - model_name: + values: + - "Qwen/Qwen2-1.5B-Instruct" + - backend: + values: + - "huggingface" + - quantization: + values: + - "full" + # - config: + # temperature: + # values: + # - 0.7 + # top_p: + # values: + # - 5 + # max_token: + # values: + # - 2048 - type: "apimodel" # name of python module; string type; @@ -33,20 +46,30 @@ algorithm: # This defines the edge model name: "CloudModel" # the url address of python module; string type; - url: "./examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/cloudmodel.py" + url: "./examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/cloud_model.py" hyperparameters: # name of the hyperparameter; string type; - - model_name: "GPT-4o-mini" - - config: - temperature: 0.7 - top_p: 5 - max_token: 2048 + - model_name: + values: + - "gpt-4o-mini" + # - config: + # temperature: + # values: + # - 0.7 + # top_p: + # values: + # - 5 + # max_token: + # values: + # - 2048 - type: "hard_example_mining" # name of python module; string type; - name: "HardSampleMining" + name: "BERT" # the url address of python module; string type; - url: "./examples/robot/lifelong_learning_bench/testalgorithms/rfnet/hard_sample_mining.py" - hyperparameters: - - example_config: 0 \ No newline at end of file + url: "./examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/hard_sample_mining.py" + # hyperparameters: + # - example_config: + # values: + # - 0 \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testenv/acc.py b/examples/cloud-edge-collaborative-inference-for-llm/testenv/acc.py index 3285a93a..6f09d891 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/testenv/acc.py +++ b/examples/cloud-edge-collaborative-inference-for-llm/testenv/acc.py @@ -13,6 +13,7 @@ # limitations under the License. from sedna.common.class_factory import ClassType, ClassFactory +from result_parser import JointInferenceResult __all__ = ["acc"] @@ -29,12 +30,12 @@ def get_last_letter(input_string): # 如果没有找到字母,返回None return None - @ClassFactory.register(ClassType.GENERAL, alias="acc") def acc(y_true, y_pred): - y_pred = [get_last_letter(pred) for pred in y_pred] - print(y_true) - print(y_pred) + + infer_res = [JointInferenceResult.from_list(*pred) for pred in y_pred] + + y_pred = [get_last_letter(pred.result) for pred in infer_res] # 使用列表推导来比较两个列表中的元素是否相同 same_elements = [y_pred[i] == y_true[i] for i in range(len(y_pred))] diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testenv/result_parser.py b/examples/cloud-edge-collaborative-inference-for-llm/testenv/result_parser.py new file mode 100644 index 00000000..4fdf99d5 --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/testenv/result_parser.py @@ -0,0 +1,12 @@ +from dataclasses import dataclass + +@dataclass +class JointInferenceResult: + is_hard_example : bool + result : str + edge_result: str + cloud_result: str + + @classmethod + def from_list(cls, *args): + return cls(*args) \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testenv/testenv.yaml b/examples/cloud-edge-collaborative-inference-for-llm/testenv/testenv.yaml index 250ae024..7c9843b1 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/testenv/testenv.yaml +++ b/examples/cloud-edge-collaborative-inference-for-llm/testenv/testenv.yaml @@ -2,13 +2,14 @@ testenv: # dataset configuration dataset: # the url address of train dataset index; string type; - train_data: "/root/autodl-fs/ianvs-dev/dataset/llm_simple_qa/train_data/data.jsonl" + train_data: "./dataset/train_data/data.jsonl" # the url address of test dataset index; string type; - test_data: "/root/autodl-fs/ianvs-dev/dataset/llm_simple_qa/test_data/data.jsonl" + test_data: "./dataset/test_data/data.jsonl" + # metrics configuration for test case's evaluation; list type; metrics: # metric name; string type; - name: "acc" # the url address of python file - url: "./examples/llm/singletask_learning_bench/simple_qa/testenv/acc.py" + url: "./examples/cloud-edge-collaborative-inference-for-llm/testenv/acc.py" From e3531b74392d93cc4db30072edc21e2a527240e1 Mon Sep 17 00:00:00 2001 From: Yu Fan Date: Thu, 15 Aug 2024 19:15:41 +0800 Subject: [PATCH 09/10] add: edge-rate metric for joint inference example Signed-off-by: Yu Fan --- .../benchmarkingjob.yaml | 2 +- .../testalgorithms/query-routing/edge_model.py | 2 +- .../query-routing/hard_sample_mining.py | 4 +++- .../testenv/edge_rate.py | 12 ++++++++++++ .../testenv/testenv.yaml | 3 +++ 5 files changed, 20 insertions(+), 3 deletions(-) create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/testenv/edge_rate.py diff --git a/examples/cloud-edge-collaborative-inference-for-llm/benchmarkingjob.yaml b/examples/cloud-edge-collaborative-inference-for-llm/benchmarkingjob.yaml index c8099a4c..dd6ca275 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/benchmarkingjob.yaml +++ b/examples/cloud-edge-collaborative-inference-for-llm/benchmarkingjob.yaml @@ -60,7 +60,7 @@ benchmarkingjob: # 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: [ "acc" , "latency", "throughput", "bandwith"] + metrics: [ "acc" , "edge-rate"] # model of save selected and all dataitems in workspace; string type; # currently the options of value are as follows: diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/edge_model.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/edge_model.py index ff48d63d..708ee604 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/edge_model.py +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/edge_model.py @@ -60,7 +60,7 @@ def _set_config(self): # Some parameters are passed to Sedna through environment variables parameters = os.environ # EdgeModel URL, see at https://github.com/kubeedge/sedna/blob/ac623ab32dc37caa04b9e8480dbe1a8c41c4a6c2/lib/sedna/core/base.py#L132 - parameters["MODEL_URL"] = self.model_url + parameters["model_path"] = self.model_url def load(self, **kwargs): # Align with Sedna's TorchBackend interface, see at https://github.com/kubeedge/sedna/blob/ac623ab32dc37caa04b9e8480dbe1a8c41c4a6c2/lib/sedna/backend/torch/__init__.py#L55-L67 diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/hard_sample_mining.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/hard_sample_mining.py index 955e81d7..9f9a6da3 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/hard_sample_mining.py +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/hard_sample_mining.py @@ -63,4 +63,6 @@ def _predict(self, data): return False def __call__(self, data=None) -> bool: - return False#self._predict(data) \ No newline at end of file + import random + res = bool(random.randint(0,1)) + return res#self._predict(data) \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testenv/edge_rate.py b/examples/cloud-edge-collaborative-inference-for-llm/testenv/edge_rate.py new file mode 100644 index 00000000..78229525 --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/testenv/edge_rate.py @@ -0,0 +1,12 @@ +from sedna.common.class_factory import ClassType, ClassFactory +from result_parser import JointInferenceResult + +@ClassFactory.register(ClassType.GENERAL, alias="edge-rate") +def edge_rate(y_true, y_pred): + + infer_res = [JointInferenceResult.from_list(*pred) for pred in y_pred] + + y_pred = [pred.is_hard_example for pred in infer_res] + + return 1 - sum(y_pred) / len(y_pred) + diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testenv/testenv.yaml b/examples/cloud-edge-collaborative-inference-for-llm/testenv/testenv.yaml index 7c9843b1..c3a51f37 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/testenv/testenv.yaml +++ b/examples/cloud-edge-collaborative-inference-for-llm/testenv/testenv.yaml @@ -13,3 +13,6 @@ testenv: - name: "acc" # the url address of python file url: "./examples/cloud-edge-collaborative-inference-for-llm/testenv/acc.py" + + - name: "edge-rate" + url: "./examples/cloud-edge-collaborative-inference-for-llm/testenv/edge_rate.py" From 0efb86a08681af4ca90e43b80af455d3ad068a48 Mon Sep 17 00:00:00 2001 From: Yu Fan Date: Sat, 17 Aug 2024 01:15:40 +0800 Subject: [PATCH 10/10] add: README.md for cloud-edge joint inference example Signed-off-by: Yu Fan --- .../README.md | 100 ++++++++++++++---- .../requirements.txt | 4 + requirements.txt | 5 +- 3 files changed, 89 insertions(+), 20 deletions(-) create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/requirements.txt diff --git a/examples/cloud-edge-collaborative-inference-for-llm/README.md b/examples/cloud-edge-collaborative-inference-for-llm/README.md index 934bd79a..f19ed674 100644 --- a/examples/cloud-edge-collaborative-inference-for-llm/README.md +++ b/examples/cloud-edge-collaborative-inference-for-llm/README.md @@ -1,20 +1,65 @@ -# README +# Quick Start -## Simple QA +Before using this example, you need to have the device ready: -### Prepare Data +One machine is all you need, i.e., a laptop or a virtual machine is sufficient and a cluster is not necessary -The data of simple-qa example structure is: +- 2 CPUs or more + +- 1 GPU with at least 4GB of memory, depends on the tested model + +- 4GB+ free memory, depends on algorithm and simulation setting + +- 10GB+ free disk space + +- Internet connection for GitHub and pip, etc + +- Python 3.8+ environment + +## Step 1. Ianvs Preparation +```bash +# Create a new conda environment with Python>=3.8 (venv users can do it in their own way). +conda create -n ianvs-experiment python=3.8 + +# Activate our environment +conda activate ianvs-experiment + +# Clone Ianvs Repo +git clone https://github.com/kubeedge/ianvs.git +cd ianvs + +# Install a modified sedna wheel (a small bug and dependencies was fixed) +wget https://github.com/FuryMartin/sedna/releases/download/v0.4.1.1/sedna-0.4.1.1-py3-none-any.whl +pip install sedna-0.4.1.1-py3-none-any.whl + +# Install dependencies for this example. +pip install examples/cloud-edge-collaborative-inference-for-llm/requirements.txt + +# Install dependencies for Ianvs Core. +pip install requirements.txt + +# Install ianvs +python setup.py install +``` + +## Step 2. Dataset and Model Preparation + +### Dataset Configuration + +You need to create a dataset folder in`ianvs/` in the following structure. ``` . -├── test_data -│ └── data.jsonl -└── train_data - └── data.jsonl +├── dataset + ├── test_data + │ └── data.jsonl + └── train_data + └── data.jsonl ``` -`train_data/data.jsonl` is empty, and the `test_data/data.jsonl` is as follows: +Leave `train_data/data.jsonl` as empty. + +Fill the `test_data/data.jsonl` as follows: ``` {"question": "如果小明有5个苹果,他给了小华3个,那么小明还剩下多少个苹果?\nA. 2个\nB. 3个\nC. 4个\nD. 5个", "answer": "A"} @@ -29,26 +74,43 @@ The data of simple-qa example structure is: {"question": "下列哪个图形的周长最长?\nA. 正方形\nB. 长方形\nC. 圆形\nD. 三角形", "answer": "C"} ``` -### Prepare Environment +Then, check the path of `train_data` and `test_dat` in +`examples/cloud-edge-collaborative-inference-for-llm/testenv/testenv.yaml`. + +- If you created the `dataset` folder inside `ianvs/` as mentioned earlier, then the relative path is correct and does not need to be modified. + +- If your `dataset` is created in a different location, please use an absolute path, and using `~` to represent the home directory is not supported. -You need to install the changed-sedna package, which added `JsonlDataParse` in `sedna.datasources` +### Model Configuration -Replace the file in `yourpath/anaconda3/envs/ianvs/lib/python3.x/site-packages/sedna` with `examples/resources/sedna-with-jsonl.zip` +The models are configured in `examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/test_queryrouting.yaml`. +If you just want to run this example quickly, you can skip the configuration. -### Run Ianvs +The default model is `Qwen/Qwen2-1.5B-Instruct`, which is configured by `model_name`. We use transformers to load the model, which is completely consistent with most LLMs you can see. If you don't have this model, HuggingFace will download it automatically. + +This example support two LLM serving framework: HuggingFace and vllm. You can choose one and change the value of `backend`. + +Besides, this example support different quatization methods. You can pass "full", "4-bit", "8-bit" to the value of `quantization` + + +## Step 3. Run Ianvs Run the following command: `ianvs -f examples/llm/singletask_learning_bench/simple_qa/benchmarkingjob.yaml` -## OpenCompass Evaluation - -### Prepare Environment +After several seconds, you will see the following output: -`pip install examples/resources/opencompass-0.2.5-py3-none-any.whl` +```bash ++------+---------------+-----+-----------+----------------+-----------+------------+---------------------+--------------------------+-------------------+------------------------+---------------------+---------------------+--------------------------------------------------------------------------------+ +| rank | algorithm | acc | edge-rate | paradigm | basemodel | apimodel | hard_example_mining | basemodel-model_name | basemodel-backend | basemodel-quantization | apimodel-model_name | time | url | ++------+---------------+-----+-----------+----------------+-----------+------------+---------------------+--------------------------+-------------------+------------------------+---------------------+---------------------+--------------------------------------------------------------------------------+ +| 1 | query-routing | 1.0 | 0.4 | jointinference | EdgeModel | CloudModel | BERT | Qwen/Qwen2-1.5B-Instruct | huggingface | full | gpt-4o-mini | 2024-08-17 01:02:45 | ./workspace/benchmarkingjob/query-routing/493d14ea-5bf1-11ef-bf9b-755996a48c84 | ++------+---------------+-----+-----------+----------------+-----------+------------+---------------------+--------------------------+-------------------+------------------------+---------------------+---------------------+--------------------------------------------------------------------------------+ +``` -### Run Evaluation +Ianvs will output a `rank.csv` and `selected_rank.csv` in `ianvs/workspace`, which will record the test results of each test. -`python run_op.py examples/llm/singletask_learning_bench/simple_qa/testalgorithms/gen/op_eval.py` +You can modify the relevant model parameters in `examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/test_queryrouting.yaml`, conduct multiple tests, and compare the results of different configurations. diff --git a/examples/cloud-edge-collaborative-inference-for-llm/requirements.txt b/examples/cloud-edge-collaborative-inference-for-llm/requirements.txt new file mode 100644 index 00000000..57f1fe78 --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/requirements.txt @@ -0,0 +1,4 @@ +vllm +transformers +openai +accelerate \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 9f7cec56..2e8d4ff6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1 +1,4 @@ -prettytable~=2.5.0 # BSD \ No newline at end of file +prettytable~=2.5.0 # BSD +scikit-learn +numpy +pandas \ No newline at end of file

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zDdH0*mTX^pyylS{b&}MxocTLuRQryh3-brKk`LUjNeEiP&s4C)Lfe~{pJ68xOX(sQe1FciR1gXf-jltmlt&K_5biA cR>v%f7%En}I{u~Xh$?1oY-Ln<==W>?3s&4Sxc~qF literal 0 HcmV?d00001 From 4ac560b42e9431999f1fb087eed4664175d43026 Mon Sep 17 00:00:00 2001 From: Yu Fan Date: Sat, 3 Aug 2024 03:35:33 +0800 Subject: [PATCH 05/10] add: introduce Joint Inference as a new paradigm Signed-off-by: Yu Fan --- core/common/constant.py | 2 + .../testcasecontroller/algorithm/algorithm.py | 4 + .../algorithm/paradigm/__init__.py | 1 + .../algorithm/paradigm/base.py | 11 + .../paradigm/joint_inference/__init__.py | 16 ++ .../joint_inference/joint_inference.py | 84 +++++++ .../joint_inference/sedna_joint_inference.py | 227 ++++++++++++++++++ 7 files changed, 345 insertions(+) create mode 100644 core/testcasecontroller/algorithm/paradigm/joint_inference/__init__.py create mode 100644 core/testcasecontroller/algorithm/paradigm/joint_inference/joint_inference.py create mode 100644 core/testcasecontroller/algorithm/paradigm/joint_inference/sedna_joint_inference.py diff --git a/core/common/constant.py b/core/common/constant.py index 5444e289..d81bdd9d 100644 --- a/core/common/constant.py +++ b/core/common/constant.py @@ -35,6 +35,7 @@ class ParadigmType(Enum): INCREMENTAL_LEARNING = "incrementallearning" MULTIEDGE_INFERENCE = "multiedgeinference" LIFELONG_LEARNING = "lifelonglearning" + JOINT_INFERENCE = "jointinference" class ModuleType(Enum): @@ -42,6 +43,7 @@ class ModuleType(Enum): Algorithm module type. """ BASEMODEL = "basemodel" + APIMODEL = "apimodel" # HEM HARD_EXAMPLE_MINING = "hard_example_mining" diff --git a/core/testcasecontroller/algorithm/algorithm.py b/core/testcasecontroller/algorithm/algorithm.py index cb2d9b7b..fdf44375 100644 --- a/core/testcasecontroller/algorithm/algorithm.py +++ b/core/testcasecontroller/algorithm/algorithm.py @@ -24,6 +24,7 @@ IncrementalLearning, MultiedgeInference, LifelongLearning, + JointInference ) from core.testcasecontroller.generation_assistant import get_full_combinations @@ -103,6 +104,9 @@ def paradigm(self, workspace: str, **kwargs): if self.paradigm_type == ParadigmType.LIFELONG_LEARNING.value: return LifelongLearning(workspace, **config) + + if self.paradigm_type == ParadigmType.JOINT_INFERENCE.value: + return JointInference(workspace, **config) return None diff --git a/core/testcasecontroller/algorithm/paradigm/__init__.py b/core/testcasecontroller/algorithm/paradigm/__init__.py index c966bd38..ca64867c 100644 --- a/core/testcasecontroller/algorithm/paradigm/__init__.py +++ b/core/testcasecontroller/algorithm/paradigm/__init__.py @@ -17,3 +17,4 @@ from .singletask_learning import SingleTaskLearning from .multiedge_inference import MultiedgeInference from .lifelong_learning import LifelongLearning +from .joint_inference import JointInference diff --git a/core/testcasecontroller/algorithm/paradigm/base.py b/core/testcasecontroller/algorithm/paradigm/base.py index cf36cd4e..3162bb65 100644 --- a/core/testcasecontroller/algorithm/paradigm/base.py +++ b/core/testcasecontroller/algorithm/paradigm/base.py @@ -18,6 +18,7 @@ from sedna.core.incremental_learning import IncrementalLearning from sedna.core.lifelong_learning import LifelongLearning +from joint_inference.sedna_joint_inference import JointInference from core.common.constant import ModuleType, ParadigmType @@ -125,5 +126,15 @@ def build_paradigm_job(self, paradigm_type): # pylint: disable=E1101 if paradigm_type == ParadigmType.MULTIEDGE_INFERENCE.value: return self.modules_funcs.get(ModuleType.BASEMODEL.value)() + + if paradigm_type == ParadigmType.JOINT_INFERENCE.value: + return JointInference( + estimator=self.module_instances.get( + ModuleType.BASEMODEL.value), + cloud=self.module_instances.get( + ModuleType.APIMODEL.value), + hard_example_mining=self.module_instances.get( + ModuleType.HARD_EXAMPLE_MINING.value) + ) return None diff --git a/core/testcasecontroller/algorithm/paradigm/joint_inference/__init__.py b/core/testcasecontroller/algorithm/paradigm/joint_inference/__init__.py new file mode 100644 index 00000000..c61116ba --- /dev/null +++ b/core/testcasecontroller/algorithm/paradigm/joint_inference/__init__.py @@ -0,0 +1,16 @@ +# Copyright 2022 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# pylint: disable=missing-module-docstring +from .joint_inference import JointInference diff --git a/core/testcasecontroller/algorithm/paradigm/joint_inference/joint_inference.py b/core/testcasecontroller/algorithm/paradigm/joint_inference/joint_inference.py new file mode 100644 index 00000000..b8267c01 --- /dev/null +++ b/core/testcasecontroller/algorithm/paradigm/joint_inference/joint_inference.py @@ -0,0 +1,84 @@ +# Copyright 2022 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Cloud-Edge Joint Inference""" + +# Ianvs imports +import os +from copy import deepcopy +from core.common.constant import ParadigmType +from core.testcasecontroller.algorithm.paradigm.base import ParadigmBase +from sedna.common.class_factory import ClassFactory, ClassType +from sedna_joint_inference import JointInference as SednaJointInference + +class JointInference(ParadigmBase): + """ + Cloud-Edge-JointInference: + provide the flow of multi-edge inference paradigm. + Notes: + 1. Ianvs serves as testing tools for test objects, e.g., algorithms. + 2. Ianvs does NOT include code directly on test object. + 3. Algorithms serve as typical test objects in Ianvs + and detailed algorithms are thus NOT included in this Ianvs python file. + 4. As for the details of example test objects, e.g., algorithms, + please refer to third party packages in Ianvs example. + For example, AI workflow and interface pls refer to sedna + (sedna docs: https://sedna.readthedocs.io/en/latest/api/lib/index.html), + and module implementation pls refer to `examples' test algorithms`, + e.g., basemodel.py, hard_example_mining.py. + + Parameters + --------- + workspace: string + the output required for multi-edge inference paradigm. + kwargs: dict + config required for the test process of multi-edge inference paradigm, + e.g.: algorithm modules, dataset, initial model, etc. + + """ + + def __init__(self, workspace, **kwargs): + ParadigmBase.__init__(self, workspace, **kwargs) + self.kwargs = kwargs + self.initial_model = kwargs.get("initial_model_url") + self.hard_example_mining_mode = kwargs.get("hard_example_mining_mode", "mining_edge_or_cloud") + + def run(self): + """ + run the test flow of multi-edge inference paradigm. + + Returns + ------ + test result: numpy.ndarray + system metric info: dict + information needed to compute system metrics. + + """ + + job = self.build_paradigm_job(ParadigmType.JOINT_INFERENCE.value) + + inference_result = self._inference(job, self.initial_model) + + return inference_result, self.system_metric_info + + def _inference(self, job : SednaJointInference, trained_model): + # Ianvs API + inference_dataset = self.dataset.load_data(self.dataset.test_url, "inference") + inference_output_dir = os.path.join(self.workspace, "output/inference/") + os.environ["RESULT_SAVED_URL"] = inference_output_dir + + # inference via sedna JointInference API + infer_res = job.inference(inference_dataset.x, mining_mode=self.hard_example_mining_mode) + + return infer_res \ No newline at end of file diff --git a/core/testcasecontroller/algorithm/paradigm/joint_inference/sedna_joint_inference.py b/core/testcasecontroller/algorithm/paradigm/joint_inference/sedna_joint_inference.py new file mode 100644 index 00000000..91e693c8 --- /dev/null +++ b/core/testcasecontroller/algorithm/paradigm/joint_inference/sedna_joint_inference.py @@ -0,0 +1,227 @@ + +# Sena imports +import os +from copy import deepcopy + +from sedna.common.utils import get_host_ip +from sedna.common.class_factory import ClassFactory, ClassType +from sedna.service.server import InferenceServer +from sedna.service.client import ModelClient, LCReporter +from sedna.common.constant import K8sResourceKind +from sedna.core.base import JobBase + +__all__ = ("JointInference", "BigModelService") + +# Currently rename "JointInference" to "SednaJointInference" +class JointInference(JobBase): + """ + Sedna provide a framework make sure under the condition of limited + resources on the edge, difficult inference tasks are offloaded to the + cloud to improve the overall performance, keeping the throughput. + + Parameters + ---------- + estimator : Instance + An instance with the high-level API that greatly simplifies + machine learning programming. Estimators encapsulate training, + evaluation, prediction, and exporting for your model. + hard_example_mining : Dict + HEM algorithms with parameters which has registered to ClassFactory, + see `sedna.algorithms.hard_example_mining` for more detail. + + Examples + -------- + >>> Estimator = keras.models.Sequential() + >>> ji_service = JointInference( + estimator=Estimator, + hard_example_mining={ + "method": "IBT", + "param": { + "threshold_img": 0.9 + } + } + ) + + Notes + ----- + Sedna provide an interface call `get_hem_algorithm_from_config` to build + the `hard_example_mining` parameter from CRD definition. + """ + + def __init__(self, estimator=None, cloud=None, hard_example_mining: dict = None): + super(JointInference, self).__init__(estimator=estimator) + self.job_kind = K8sResourceKind.JOINT_INFERENCE_SERVICE.value + self.local_ip = get_host_ip() + + report_msg = { + "name": self.worker_name, + "namespace": self.config.namespace, + "ownerName": self.job_name, + "ownerKind": self.job_kind, + "kind": "inference", + "results": [] + } + period_interval = int(self.get_parameters("LC_PERIOD", "30")) + self.lc_reporter = LCReporter(lc_server=self.config.lc_server, + message=report_msg, + period_interval=period_interval) + self.lc_reporter.setDaemon(True) + self.lc_reporter.start() + + if callable(self.estimator): + self.estimator = self.estimator() + if not os.path.exists(self.model_path): + raise FileExistsError(f"{self.model_path} miss") + else: + self.estimator.load(self.model_path) + + # If cloud is None, then initialize ModelClint as cloud. + # Otherwise, we will regard the cloud as a client implemented by the user. + if cloud is None: + self.remote_ip = self.get_parameters( + "BIG_MODEL_IP", self.local_ip) + self.port = int(self.get_parameters("BIG_MODEL_PORT", "5000")) + + self.cloud = ModelClient( + service_name=self.job_name, + host=self.remote_ip, + port=self.port + ) + else: + self.cloud = cloud + + self.hard_example_mining_algorithm = None + if not hard_example_mining: + hard_example_mining = self.get_hem_algorithm_from_config() + if hard_example_mining: + hem = hard_example_mining.get("method", "IBT") + hem_parameters = hard_example_mining.get("param", {}) + self.hard_example_mining_algorithm = ClassFactory.get_cls( + ClassType.HEM, hem + )(**hem_parameters) + + @classmethod + def get_hem_algorithm_from_config(cls, **param): + """ + get the `algorithm` name and `param` of hard_example_mining from crd + + Parameters + ---------- + param : Dict + update value in parameters of hard_example_mining + + Returns + ------- + dict + e.g.: {"method": "IBT", "param": {"threshold_img": 0.5}} + + Examples + -------- + >>> JointInference.get_hem_algorithm_from_config( + threshold_img=0.9 + ) + {"method": "IBT", "param": {"threshold_img": 0.9}} + """ + return cls.parameters.get_algorithm_from_api( + algorithm="HEM", + **param + ) + + + def _get_edge_result(self, data, callback_func, **kwargs): + edge_result = self.estimator.predict(data, **kwargs) + res = deepcopy(edge_result) + + if callback_func: + res = callback_func(res) + + self.lc_reporter.update_for_edge_inference() + + return res, edge_result + + def _get_cloud_result(self, data, post_process, **kwargs): + try: + cloud_result = self.cloud.inference( + data.tolist(), post_process=post_process, **kwargs) + except Exception as err: + self.log.error(f"get cloud result error: {err}") + else: + res = cloud_result + + self.lc_reporter.update_for_collaboration_inference() + + return res, cloud_result + + + def inference(self, data=None, post_process=None, **kwargs): + """ + Inference task with JointInference + + Parameters + ---------- + data: BaseDataSource + datasource use for inference, see + `sedna.datasources.BaseDataSource` for more detail. + post_process: function or a registered method + effected after `estimator` inference. + kwargs: Dict + parameters for `estimator` inference, + Like: `ntree_limit` in Xgboost.XGBClassifier + + Returns + ------- + if is hard sample : bool + inference result : object + result from little-model : object + result from big-model: object + """ + + callback_func = None + if callable(post_process): + callback_func = post_process + elif post_process is not None: + callback_func = ClassFactory.get_cls( + ClassType.CALLBACK, post_process) + + # Try to obtain mining_mode, with the default value being edge_mining_cloud, + # which means performing edge inference first, then mining difficult cases, + # and uploading to the cloud if necessary. + mining_mode = kwargs.get("mining_mode", "inference-then-mining") + + is_hard_example = False + sepeculative_decoding = False + + edge_result, cloud_result = None, None + + if mining_mode == "inference-then-mining": + res, edge_result = self._get_edge_result(data, callback_func, **kwargs) + + if self.hard_example_mining_algorithm is None: + raise ValueError("Hard example mining algorithm is not set.") + + is_hard_example = self.hard_example_mining_algorithm(res) + if is_hard_example: + res, cloud_result = self._get_cloud_result(data, post_process=post_process, **kwargs) + + elif mining_mode == "mining-then-inference": + # First conduct hard example mining, and then decide whether to execute on the edge or in the cloud. + if self.hard_example_mining_algorithm is None: + raise ValueError("Hard example mining algorithm is not set.") + + is_hard_example = self.hard_example_mining_algorithm(res) + if is_hard_example: + if not sepeculative_decoding: + res, cloud_result = self._get_cloud_result(data, post_process=post_process, **kwargs) + else: + # do speculative_decoding + pass + else: + res, edge_result = self._get_edge_result(data, callback_func, **kwargs) + + else: + raise ValueError( + "Mining Mode must be in ['mining-then-inference', 'inference-then-mining']" + ) + + return [is_hard_example, res, edge_result, cloud_result] + From 68240172bf2c6492c827327300d387430744fc38 Mon Sep 17 00:00:00 2001 From: Yu Fan Date: Sat, 3 Aug 2024 03:36:54 +0800 Subject: [PATCH 06/10] add: example for LLM joint inference Signed-off-by: Yu Fan --- .../README.md | 54 ++++++++++++++ .../benchmarkingjob.yaml | 73 ++++++++++++++++++ .../query-routing/cloud_model.py | 51 +++++++++++++ .../query-routing/edge_model.py | 74 +++++++++++++++++++ .../query-routing/hard_sample_mining.py | 64 ++++++++++++++++ .../query-routing/models/__init__.py | 3 + .../query-routing/models/api_llm.py | 38 ++++++++++ .../query-routing/models/base_llm.py | 17 +++++ .../query-routing/models/huggingface_llm.py | 50 +++++++++++++ .../query-routing/models/vllm_llm.py | 25 +++++++ .../query-routing/test_queryrouting.yaml | 52 +++++++++++++ .../testenv/acc.py | 45 +++++++++++ .../testenv/testenv.yaml | 14 ++++ 13 files changed, 560 insertions(+) create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/README.md create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/benchmarkingjob.yaml create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/cloud_model.py create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/edge_model.py create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/hard_sample_mining.py create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/__init__.py create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/api_llm.py create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/base_llm.py create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/huggingface_llm.py create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/vllm_llm.py create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/test_queryrouting.yaml create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/testenv/acc.py create mode 100644 examples/cloud-edge-collaborative-inference-for-llm/testenv/testenv.yaml diff --git a/examples/cloud-edge-collaborative-inference-for-llm/README.md b/examples/cloud-edge-collaborative-inference-for-llm/README.md new file mode 100644 index 00000000..934bd79a --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/README.md @@ -0,0 +1,54 @@ +# README + +## Simple QA + +### Prepare Data + +The data of simple-qa example structure is: + +``` +. +├── test_data +│ └── data.jsonl +└── train_data + └── data.jsonl +``` + +`train_data/data.jsonl` is empty, and the `test_data/data.jsonl` is as follows: + +``` +{"question": "如果小明有5个苹果,他给了小华3个,那么小明还剩下多少个苹果?\nA. 2个\nB. 3个\nC. 4个\nD. 5个", "answer": "A"} +{"question": "下列哪个数是最小的质数?\nA. 0\nB. 1\nC. 2\nD. 4", "answer": "C"} +{"question": "一个长方形的长是10厘米,宽是5厘米,它的周长是多少厘米?\nA. 20厘米\nB. 30厘米\nC. 40厘米\nD. 50厘米", "answer": "B"} +{"question": "下列哪个分数是最接近1的?\nA. 1/2\nB. 3/4\nC. 4/5\nD. 5/6", "answer": "D"} +{"question": "如果一个数加上10等于30,那么这个数是多少?\nA. 20\nB. 21\nC. 22\nD. 23", "answer": "A"} +{"question": "下列哪个算式的结果最大?\nA. 3 + 4\nB. 5 - 2\nC. 6 * 2\nD. 7 ÷ 2", "answer": "C"} +{"question": "一个班级有24个学生,如果每个学生都带了2本书,那么总共有多少本书?\nA. 48本\nB. 36本\nC. 24本\nD. 12本", "answer": "A"} +{"question": "下列哪个是正确的乘法口诀?\nA. 三三得七\nB. 四四十六\nC. 五五二十五\nD. 六六三十六", "answer": "B"} +{"question": "如果一个数是另一个数的3倍,并且这个数是15,那么另一个数是多少?\nA. 5\nB. 10\nC. 15\nD. 45", "answer": "A"} +{"question": "下列哪个图形的周长最长?\nA. 正方形\nB. 长方形\nC. 圆形\nD. 三角形", "answer": "C"} +``` + +### Prepare Environment + +You need to install the changed-sedna package, which added `JsonlDataParse` in `sedna.datasources` + +Replace the file in `yourpath/anaconda3/envs/ianvs/lib/python3.x/site-packages/sedna` with `examples/resources/sedna-with-jsonl.zip` + + +### Run Ianvs + +Run the following command: + +`ianvs -f examples/llm/singletask_learning_bench/simple_qa/benchmarkingjob.yaml` + +## OpenCompass Evaluation + +### Prepare Environment + +`pip install examples/resources/opencompass-0.2.5-py3-none-any.whl` + +### Run Evaluation + +`python run_op.py examples/llm/singletask_learning_bench/simple_qa/testalgorithms/gen/op_eval.py` + diff --git a/examples/cloud-edge-collaborative-inference-for-llm/benchmarkingjob.yaml b/examples/cloud-edge-collaborative-inference-for-llm/benchmarkingjob.yaml new file mode 100644 index 00000000..182dd85b --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/benchmarkingjob.yaml @@ -0,0 +1,73 @@ +benchmarkingjob: + # job name of bechmarking; string type; + name: "benchmarkingjob" + # the url address of job workspace that will reserve the output of tests; string type; + # "~/" cannot be identified, so must be relative path or absoulute path + workspace: "./workspace" + + # the url address of test environment configuration file; string type; + # the file format supports yaml/yml; + testenv: "./examples/cloud-edge-collaborative-inference-for-llm/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: "query-routing" + # the url address of test algorithm configuration file; string type; + # the file format supports yaml/yml; + url: "./examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/test_queryrouting.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: [ { "acc": "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: [ "acc" , "latency", "throughput", "bandwith"] + + # model of save selected and all dataitems in workspace; 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" + + + + + + diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/cloud_model.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/cloud_model.py new file mode 100644 index 00000000..eb68dfe0 --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/cloud_model.py @@ -0,0 +1,51 @@ +# Copyright 2022 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import, division, print_function + +import os +import tempfile +import time +import zipfile +import logging + +import numpy as np +from sedna.common.config import Context +from sedna.common.class_factory import ClassType, ClassFactory + +from models import HuggingfaceLLM, VllmLLM, APIBasedLLM + +from transformers import AutoModelForCausalLM, AutoTokenizer +device = "cuda" # the device to load the model onto + + +logging.disable(logging.WARNING) + +__all__ = ["BaseModel"] + +@ClassFactory.register(ClassType.GENERAL, alias="CloudModel") +class BaseModel: + def __init__(self, **kwargs): + # The API KEY and API URL are confidential data and should not be written in yaml. + self.client = APIBasedLLM( + model_name = kwargs.get("model_name", "gpt-4o-mini"), + config = kwargs.get("config", None), + ) + + def inference(self, data, input_shape=None, **kwargs): + answer_list = [] + for line in data: + response = self.model.inference(line) + answer_list.append(response) + return answer_list \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/edge_model.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/edge_model.py new file mode 100644 index 00000000..0c05aa8e --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/edge_model.py @@ -0,0 +1,74 @@ +# Copyright 2022 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import, division, print_function + +import os +import tempfile +import time +import zipfile +import logging + +import numpy as np +from sedna.common.config import Context +from sedna.common.class_factory import ClassType, ClassFactory + +from models import HuggingfaceLLM, VllmLLM, APIBasedLLM + +from transformers import AutoModelForCausalLM, AutoTokenizer +device = "cuda" # the device to load the model onto + + +logging.disable(logging.WARNING) + +__all__ = ["BaseModel"] + +@ClassFactory.register(ClassType.GENERAL, alias="EdgeModel") +class BaseModel: + """ + This is actually the Edge Model. + """ + def __init__(self, **kwargs): + self.kwargs = kwargs + self.model_url = kwargs.get("model_name", None) + self.backend = kwargs.get("backend", "huggingface") + self.quantization = kwargs.get("quantization", "full") + self._set_config() + # 'backend' means serving framework: "huggingface", "vllm" + # 'quantization' means quantization mode:"full","4-bit","8-bit" + + def _set_config(self): + # Some parameters are passed to Sedna through environment variables + parameters = os.environ + # EdgeModel URL, see at https://github.com/kubeedge/sedna/blob/ac623ab32dc37caa04b9e8480dbe1a8c41c4a6c2/lib/sedna/core/base.py#L132 + parameters["MODEL_URL"] = self.model_url + + def load(self, model_url=None): + if self.backend == "huggingface": + self.model = HuggingfaceLLM(model_url, self.quantization) + elif self.backend == "vllm": + self.model = VllmLLM(model_url, self.quantization) + else: + raise Exception(f"Backend {self.backend} is not supported") + + self.model.load(model_url=model_url) + + # TODO cloud service must be configured in JointInference + + def predict(self, data, input_shape=None, **kwargs): + answer_list = [] + for line in data: + response = self.model.inference(line) + answer_list.append(response) + return answer_list \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/hard_sample_mining.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/hard_sample_mining.py new file mode 100644 index 00000000..65b67f4d --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/hard_sample_mining.py @@ -0,0 +1,64 @@ +# Copyright 2021 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Hard Example Mining Algorithms""" + +import abc +import math +import random +from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline +from sedna.common.class_factory import ClassFactory, ClassType + +__all__ = ('ThresholdFilter', 'CrossEntropyFilter', 'IBTFilter') + +class BaseFilter(metaclass=abc.ABCMeta): + """The base class to define unified interface.""" + + def __call__(self, infer_result=None): + """ + predict function, judge the sample is hard or not. + + Parameters + ---------- + infer_result : array_like + prediction result + + Returns + ------- + is_hard_sample : bool + `True` means hard sample, `False` means not. + """ + raise NotImplementedError + + @classmethod + def data_check(cls, data): + """Check the data in [0,1].""" + return 0 <= float(data) <= 1 + + +@ClassFactory.register(ClassType.HEM, alias="BERT") +class BERTFilter(BaseFilter, abc.ABC): + def __init__(self, model_path, **kwargs): + self.classifier = pipeline( + "text-classification", + model=model_path, + trust_remote_code=True + ) + + def _predict(self, data): + result = self.classifier(data) + return result + + def __call__(self, data=None) -> bool: + return self._predict(data) \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/__init__.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/__init__.py new file mode 100644 index 00000000..103c53bd --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/__init__.py @@ -0,0 +1,3 @@ +from .api_llm import APIBasedLLM +from .huggingface_llm import HuggingfaceLLM +from .vllm_llm import VllmLLM \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/api_llm.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/api_llm.py new file mode 100644 index 00000000..2bcaf915 --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/api_llm.py @@ -0,0 +1,38 @@ +import os +from openai import OpenAI + +from base_llm import BaseLLM +from sedna.core.joint_inference.joint_inference import BigModelService + +class APIBasedLLM(BaseLLM): + def __init__(self, model_name, **kwargs) -> None: + + api_key=os.environ.get("OPENAI_API_KEY"), + base_url=os.environ.get("OPENAI_BASE_URL") + + self.model = model_name + self.client = OpenAI( + api_key=api_key, + base_url=base_url + ) + + def _infer(self, prompt, system=None): + if system: + messages = [ + {"role": "system", "content": system}, + {"role": "user", "content": prompt} + ] + else: + messages = [ + {"role": "user", "content": prompt} + ] + + self.chat_completion = self.client.chat.completions.create( + messages = messages, + model=self.model, + ) + + response = self.chat_completion.choices[0].message.content + + return response + \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/base_llm.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/base_llm.py new file mode 100644 index 00000000..a8175f71 --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/base_llm.py @@ -0,0 +1,17 @@ +class BaseLLM: + def __init__(self, **kwargs) -> None: + BaseLLM.__init__(self, **kwargs) + self.quantization = kwargs.get("quantization", "full") + + def load(self): + raise NotImplementedError + + def inference(self, datas): + answer_list = [] + for line in datas: + response = self._infer(line) + answer_list.append(response) + return answer_list + + def _infer(self, data): + raise NotImplementedError \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/huggingface_llm.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/huggingface_llm.py new file mode 100644 index 00000000..806068e6 --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/huggingface_llm.py @@ -0,0 +1,50 @@ +from transformers import AutoModelForCausalLM, AutoTokenizer +from base_llm import BaseLLM + +device = "cuda" + +class HuggingfaceLLM(BaseLLM): + def __init__(self, **kwargs) -> None: + BaseLLM.__init__(self, **kwargs) + + def load(self, model_url): + self.model = AutoModelForCausalLM.from_pretrained( + model_url,#"/root/autodl-tmp/Qwen/Qwen-1_8B-Chat", + torch_dtype="auto", + device_map="auto", + trust_remote_code=True, + quantization = self.quantization # Need to align with HF API + ) + self.tokenizer = AutoTokenizer.from_pretrained( + model_url, + trust_remote_code=True + ) + + def _infer(self, prompt, system=None): + if system: + messages = [ + {"role": "system", "content": system}, + {"role": "user", "content": prompt} + ] + else: + messages = [ + {"role": "user", "content": prompt} + ] + text = self.tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True + ) + model_inputs = self.tokenizer([text], return_tensors="pt").to(device) + + generated_ids = self.model.generate( + model_inputs.input_ids, + max_new_tokens=512 + ) + generated_ids = [ + output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) + ] + + response = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] + + return response \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/vllm_llm.py b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/vllm_llm.py new file mode 100644 index 00000000..79e4fbdf --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/models/vllm_llm.py @@ -0,0 +1,25 @@ +from vllm import LLM, SamplingParams +from base_llm import BaseLLM + +device = "cuda" + +class VllmLLM(BaseLLM): + def __init__(self, **kwargs) -> None: + BaseLLM.__init__(self, **kwargs) + + def load(self, model_url): + self.model = LLM( + model=model_url, + trust_remote_code=True, + quantization=self.quantization # TODO need to align with vllm API + ) + + def _infer(self, prompt, system=None): + sampling_params = SamplingParams( + temperature=0.8, + top_p=0.95, + max_tokens=2048 + ) + outputs = self.model.generate(prompt, sampling_params) + response = outputs[0].outputs[0].text + return response \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/test_queryrouting.yaml b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/test_queryrouting.yaml new file mode 100644 index 00000000..5adb1be8 --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/test_queryrouting.yaml @@ -0,0 +1,52 @@ +algorithm: + # paradigm name; string type; + # currently the options of value are as follows: + # 1> "singletasklearning" + # 2> "incrementallearning" + paradigm_type: "jointinference" + + # algorithm module configuration in the paradigm; list type; + modules: + # kind of algorithm module; string type; + # currently the options of value are as follows: + # 1> "basemodel" + - type: "basemodel" + # name of python module; string type; + # example: basemodel.py has BaseModel module that the alias is "FPN" for this benchmarking; + # This defines the edge model + name: "EdgeModel" + # the url address of python module; string type; + url: "./examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/edgemodel.py" + + hyperparameters: + - model_name: "Qwen/Qwen-1.8B-Chat" + - backend: "huggingface" + - quantization: "4-bit" + - config: + temperature: 0.7 + top_p: 5 + max_token: 2048 + + - type: "apimodel" + # name of python module; string type; + # example: basemodel.py has BaseModel module that the alias is "FPN" for this benchmarking; + # This defines the edge model + name: "CloudModel" + # the url address of python module; string type; + url: "./examples/cloud-edge-collaborative-inference-for-llm/testalgorithms/query-routing/cloudmodel.py" + + hyperparameters: + # name of the hyperparameter; string type; + - model_name: "GPT-4o-mini" + - config: + temperature: 0.7 + top_p: 5 + max_token: 2048 + + - type: "hard_example_mining" + # name of python module; string type; + name: "HardSampleMining" + # the url address of python module; string type; + url: "./examples/robot/lifelong_learning_bench/testalgorithms/rfnet/hard_sample_mining.py" + hyperparameters: + - example_config: 0 \ No newline at end of file diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testenv/acc.py b/examples/cloud-edge-collaborative-inference-for-llm/testenv/acc.py new file mode 100644 index 00000000..3285a93a --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/testenv/acc.py @@ -0,0 +1,45 @@ +# Copyright 2022 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from sedna.common.class_factory import ClassType, ClassFactory + +__all__ = ["acc"] + +def get_last_letter(input_string): + # 检查输入是否为空或只包含非字母字符 + if not input_string or not any(char.isalpha() for char in input_string): + return None + + # 倒序遍历字符串,找到最后一个字母 + for char in reversed(input_string): + if 'A' <= char <= 'D': + return char + + # 如果没有找到字母,返回None + return None + + +@ClassFactory.register(ClassType.GENERAL, alias="acc") +def acc(y_true, y_pred): + y_pred = [get_last_letter(pred) for pred in y_pred] + print(y_true) + print(y_pred) + + # 使用列表推导来比较两个列表中的元素是否相同 + same_elements = [y_pred[i] == y_true[i] for i in range(len(y_pred))] + + # 计算相同元素的数量 + acc = sum(same_elements) / len(same_elements) + + return acc diff --git a/examples/cloud-edge-collaborative-inference-for-llm/testenv/testenv.yaml b/examples/cloud-edge-collaborative-inference-for-llm/testenv/testenv.yaml new file mode 100644 index 00000000..250ae024 --- /dev/null +++ b/examples/cloud-edge-collaborative-inference-for-llm/testenv/testenv.yaml @@ -0,0 +1,14 @@ +testenv: + # dataset configuration + dataset: + # the url address of train dataset index; string type; + train_data: "/root/autodl-fs/ianvs-dev/dataset/llm_simple_qa/train_data/data.jsonl" + # the url address of test dataset index; string type; + test_data: "/root/autodl-fs/ianvs-dev/dataset/llm_simple_qa/test_data/data.jsonl" + + # metrics configuration for test case's evaluation; list type; + metrics: + # metric name; string type; + - name: "acc" + # the url address of python file + url: "./examples/llm/singletask_learning_bench/simple_qa/testenv/acc.py" From 6f25da505e951634f274c1d312e470b0c02ee740 Mon Sep 17 00:00:00 2001 From: Yu Fan Date: Tue, 13 Aug 2024 19:11:36 +0800 Subject: [PATCH 07/10] add: support jsonl dataset Signed-off-by: Yu Fan --- core/common/constant.py | 1 + core/testenvmanager/dataset/dataset.py | 39 +++++++++++++++--- .../third_party/sedna-with-jsonl.zip | Bin 0 -> 372096 bytes 3 files changed, 34 insertions(+), 6 deletions(-) create mode 100644 examples/resources/third_party/sedna-with-jsonl.zip diff --git a/core/common/constant.py b/core/common/constant.py index d81bdd9d..436c9028 100644 --- a/core/common/constant.py +++ b/core/common/constant.py @@ -25,6 +25,7 @@ class DatasetFormat(Enum): CSV = "csv" TXT = "txt" JSON = "json" + JSONL = "jsonl" class ParadigmType(Enum): diff --git a/core/testenvmanager/dataset/dataset.py b/core/testenvmanager/dataset/dataset.py index 16bd038f..02d6b1a2 100644 --- a/core/testenvmanager/dataset/dataset.py +++ b/core/testenvmanager/dataset/dataset.py @@ -18,7 +18,7 @@ import tempfile import pandas as pd -from sedna.datasources import CSVDataParse, TxtDataParse, JSONDataParse +from sedna.datasources import CSVDataParse, TxtDataParse, JSONDataParse, JsonlDataParse from core.common import utils from core.common.constant import DatasetFormat @@ -38,12 +38,22 @@ class Dataset: def __init__(self, config): self.train_url: str = "" self.test_url: str = "" + self.train_index: str = "" + self.test_index: str = "" + self.train_data: str = "" + self.test_data: str = "" self.label: str = "" self._parse_config(config) def _check_fields(self): - self._check_dataset_url(self.train_url) - self._check_dataset_url(self.test_url) + if self.train_index: + self._check_dataset_url(self.train_index) + if self.test_index: + self._check_dataset_url(self.test_index) + if self.train_data: + self._check_dataset_url(self.train_data) + if self.test_data: + self._check_dataset_url(self.test_data) def _parse_config(self, config): for attr, value in config.items(): @@ -103,6 +113,13 @@ def _process_index_file(self, file_url): return None + def _process_data_file(self, file_url): + file_format = utils.get_file_format(file_url) + if file_format == DatasetFormat.JSONL.value: + return file_url + + return None + def process_dataset(self): """ process dataset: @@ -111,9 +128,15 @@ def process_dataset(self): in the index file(e.g.: txt index file). """ + if self.train_index: + self.train_url = self._process_index_file(self.train_index) + else: + self.train_url = self._process_data_file(self.train_data) - self.train_url = self._process_index_file(self.train_url) - self.test_url = self._process_index_file(self.test_url) + if self.test_index: + self.test_url = self._process_index_file(self.test_index) + else: + self.test_url = self._process_data_file(self.test_data) # pylint: disable=too-many-arguments def split_dataset(self, dataset_url, dataset_format, ratio, method="default", @@ -404,4 +427,8 @@ def load_data(cls, file: str, data_type: str, label=None, use_raw=False, feature data = JSONDataParse(data_type=data_type, func=feature_process) data.parse(file) - return data + if data_format == DatasetFormat.JSONL.value: + data = JsonlDataParse(data_type=data_type, func=feature_process) + data.parse(file) + + return data \ No newline at end of file diff --git a/examples/resources/third_party/sedna-with-jsonl.zip 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