From e1fed50ddc9948698bcb5845bb59cc84826756e1 Mon Sep 17 00:00:00 2001 From: dm4 Date: Wed, 25 Dec 2024 01:14:50 +0800 Subject: [PATCH] [Example] ggml: add Qwen2-VL example Signed-off-by: dm4 --- .github/workflows/llama.yml | 17 ++ wasmedge-ggml/qwen2vl/Cargo.toml | 8 + wasmedge-ggml/qwen2vl/README.md | 47 +++++ wasmedge-ggml/qwen2vl/src/main.rs | 197 ++++++++++++++++++ .../qwen2vl/wasmedge-ggml-qwen2vl.wasm | Bin 0 -> 2275385 bytes 5 files changed, 269 insertions(+) create mode 100644 wasmedge-ggml/qwen2vl/Cargo.toml create mode 100644 wasmedge-ggml/qwen2vl/README.md create mode 100644 wasmedge-ggml/qwen2vl/src/main.rs create mode 100755 wasmedge-ggml/qwen2vl/wasmedge-ggml-qwen2vl.wasm diff --git a/.github/workflows/llama.yml b/.github/workflows/llama.yml index c0b9cd3..f1a243b 100644 --- a/.github/workflows/llama.yml +++ b/.github/workflows/llama.yml @@ -301,6 +301,23 @@ jobs: default \ $'[INST] <>\nYou are a helpful, respectful and honest assistant. Always output JSON format string.\n<>\nGive me a JSON array of Apple products.[/INST]' + - name: Qwen2-VL + run: | + test -f ~/.wasmedge/env && source ~/.wasmedge/env + cd wasmedge-ggml/qwen2-vl + curl -LO https://huggingface.co/second-state/Qwen2-VL-2B-Instruct-GGUF/resolve/main/Qwen2-VL-2B-Instruct-vision-encoder.gguf + curl -LO https://huggingface.co/second-state/Qwen2-VL-2B-Instruct-GGUF/resolve/main/Qwen2-VL-2B-Instruct-Q5_K_M.gguf + curl -LO https://llava-vl.github.io/static/images/monalisa.jpg + cargo build --target wasm32-wasi --release + time wasmedge --dir .:. \ + --env n_gpu_layers="$NGL" \ + --nn-preload default:GGML:AUTO:Qwen2-VL-2B-Instruct-Q5_K_M.gguf \ + --env mmproj=Qwen2-VL-2B-Instruct-vision-encoder.gguf \ + --env image=monalisa.jpg \ + target/wasm32-wasi/release/wasmedge-ggml-qwen2vl.wasm \ + default \ + <|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|>{image_placeholder}<|vision_end|>what is in this picture?<|im_end|>\n<|im_start|>assistant\n" + - name: Build llama-stream run: | cd wasmedge-ggml/llama-stream diff --git a/wasmedge-ggml/qwen2vl/Cargo.toml b/wasmedge-ggml/qwen2vl/Cargo.toml new file mode 100644 index 0000000..122469d --- /dev/null +++ b/wasmedge-ggml/qwen2vl/Cargo.toml @@ -0,0 +1,8 @@ +[package] +name = "wasmedge-ggml-qwen2vl" +version = "0.1.0" +edition = "2021" + +[dependencies] +serde_json = "1.0" +wasmedge-wasi-nn = "0.7.1" diff --git a/wasmedge-ggml/qwen2vl/README.md b/wasmedge-ggml/qwen2vl/README.md new file mode 100644 index 0000000..1143a4e --- /dev/null +++ b/wasmedge-ggml/qwen2vl/README.md @@ -0,0 +1,47 @@ +# Qwen-2VL Example For WASI-NN with GGML Backend + +> [!NOTE] +> Please refer to the [wasmedge-ggml/README.md](../README.md) for the general introduction and the setup of the WASI-NN plugin with GGML backend. This document will focus on the specific example of the Qwen2-VL model. + +## Get Qwen2-VL Model + +In this example, we are going to use the pre-converted [Qwen2-VL-2B](https://huggingface.co/second-state/Qwen2-VL-2B-Instruct-GGUF/tree/main) model. + +Download the model: + +```bash +curl -LO https://huggingface.co/second-state/Qwen2-VL-2B-Instruct-GGUF/resolve/main/Qwen2-VL-2B-Instruct-vision-encoder.gguf +curl -LO https://huggingface.co/second-state/Qwen2-VL-2B-Instruct-GGUF/resolve/main/Qwen2-VL-2B-Instruct-Q5_K_M.gguf +``` + +## Prepare the Image + +Download the image you want to perform inference on: + +```bash +curl -LO https://llava-vl.github.io/static/images/monalisa.jpg +``` + +## Parameters + +> [!NOTE] +> Please check the parameters section of [wasmedge-ggml/README.md](https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters) first. + +In Qwen2-VL inference, we recommend to use the `ctx-size` at least `4096` for better results. + +```rust +options.insert("ctx-size", Value::from(4096)); +``` + +## Execute + +Execute the WASM with the `wasmedge` using the named model feature to preload a large model: + +```bash +wasmedge --dir .:. \ + --nn-preload default:GGML:AUTO:Qwen2-VL-2B-Instruct-Q5_K_M.gguf \ + --env mmproj=Qwen2-VL-2B-Instruct-vision-encoder.gguf \ + --env image=monalisa.jpg \ + --env ctx_size=4096 \ + wasmedge-ggml-qwen2vl.wasm default +``` diff --git a/wasmedge-ggml/qwen2vl/src/main.rs b/wasmedge-ggml/qwen2vl/src/main.rs new file mode 100644 index 0000000..73356dd --- /dev/null +++ b/wasmedge-ggml/qwen2vl/src/main.rs @@ -0,0 +1,197 @@ +use serde_json::Value; +use std::collections::HashMap; +use std::env; +use std::io; +use wasmedge_wasi_nn::{ + self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, + TensorType, +}; + +fn read_input() -> String { + loop { + let mut answer = String::new(); + io::stdin() + .read_line(&mut answer) + .expect("Failed to read line"); + if !answer.is_empty() && answer != "\n" && answer != "\r\n" { + return answer.trim().to_string(); + } + } +} + +fn get_options_from_env() -> HashMap<&'static str, Value> { + let mut options = HashMap::new(); + + // Required parameters for llava + if let Ok(val) = env::var("mmproj") { + options.insert("mmproj", Value::from(val.as_str())); + } else { + eprintln!("Failed to get mmproj model."); + std::process::exit(1); + } + if let Ok(val) = env::var("image") { + options.insert("image", Value::from(val.as_str())); + } else { + eprintln!("Failed to get the target image."); + std::process::exit(1); + } + + // Optional parameters + if let Ok(val) = env::var("enable_log") { + options.insert("enable-log", serde_json::from_str(val.as_str()).unwrap()); + } else { + options.insert("enable-log", Value::from(false)); + } + if let Ok(val) = env::var("ctx_size") { + options.insert("ctx-size", serde_json::from_str(val.as_str()).unwrap()); + } else { + options.insert("ctx-size", Value::from(4096)); + } + if let Ok(val) = env::var("n_gpu_layers") { + options.insert("n-gpu-layers", serde_json::from_str(val.as_str()).unwrap()); + } else { + options.insert("n-gpu-layers", Value::from(0)); + } + options +} + +fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { + context.set_input(0, TensorType::U8, &[1], &data) +} + +fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { + // Preserve for 4096 tokens with average token length 6 + const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; + let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; + let mut output_size = context + .get_output(index, &mut output_buffer) + .expect("Failed to get output"); + output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); + + String::from_utf8_lossy(&output_buffer[..output_size]).to_string() +} + +fn get_output_from_context(context: &GraphExecutionContext) -> String { + get_data_from_context(context, 0) +} + +fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { + serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") +} + +fn main() { + let args: Vec = env::args().collect(); + let model_name: &str = &args[1]; + + // Set options for the graph. Check our README for more details: + // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters + let options = get_options_from_env(); + // You could also set the options manually like this: + + // Create graph and initialize context. + let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) + .config(serde_json::to_string(&options).expect("Failed to serialize options")) + .build_from_cache(model_name) + .expect("Failed to build graph"); + let mut context = graph + .init_execution_context() + .expect("Failed to init context"); + + // If there is a third argument, use it as the prompt and enter non-interactive mode. + // This is mainly for the CI workflow. + if args.len() >= 3 { + let prompt = &args[2]; + // Set the prompt. + println!("Prompt:\n{}", prompt); + let tensor_data = prompt.as_bytes().to_vec(); + context + .set_input(0, TensorType::U8, &[1], &tensor_data) + .expect("Failed to set input"); + println!("Response:"); + + // Get the number of input tokens and llama.cpp versions. + let input_metadata = get_metadata_from_context(&context); + println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); + println!( + "[INFO] llama_build_number: {}", + input_metadata["llama_build_number"] + ); + println!( + "[INFO] Number of input tokens: {}", + input_metadata["input_tokens"] + ); + + // Get the output. + context.compute().expect("Failed to compute"); + let output = get_output_from_context(&context); + println!("{}", output.trim()); + + // Retrieve the output metadata. + let metadata = get_metadata_from_context(&context); + println!( + "[INFO] Number of input tokens: {}", + metadata["input_tokens"] + ); + println!( + "[INFO] Number of output tokens: {}", + metadata["output_tokens"] + ); + std::process::exit(0); + } + + let mut saved_prompt = String::new(); + let system_prompt = String::from("You are a helpful assistant."); + let image_placeholder = ""; + + loop { + println!("USER:"); + let input = read_input(); + + // Qwen2VL prompt format: <|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n<|vision_start|>{image_placeholder}<|vision_end|>{user_prompt}<|im_end|>\n<|im_start|>assistant\n"; + if saved_prompt.is_empty() { + saved_prompt = format!( + "<|im_start|>system\n{}<|im_end|>\n<|im_start|>user\n<|vision_start|>{}<|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n", + system_prompt, image_placeholder, input + ); + } else { + saved_prompt = format!( + "{}<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n", + saved_prompt, input + ); + } + + // Set prompt to the input tensor. + set_data_to_context(&mut context, saved_prompt.as_bytes().to_vec()) + .expect("Failed to set input"); + + // Execute the inference. + let mut reset_prompt = false; + match context.compute() { + Ok(_) => (), + Err(Error::BackendError(BackendError::ContextFull)) => { + println!("\n[INFO] Context full, we'll reset the context and continue."); + reset_prompt = true; + } + Err(Error::BackendError(BackendError::PromptTooLong)) => { + println!("\n[INFO] Prompt too long, we'll reset the context and continue."); + reset_prompt = true; + } + Err(err) => { + println!("\n[ERROR] {}", err); + std::process::exit(1); + } + } + + // Retrieve the output. + let mut output = get_output_from_context(&context); + println!("ASSISTANT:\n{}", output.trim()); + + // Update the saved prompt. + if reset_prompt { + saved_prompt.clear(); + } else { + output = output.trim().to_string(); + saved_prompt = format!("{}{}<|im_end|>\n", saved_prompt, output); + } + } +} diff --git a/wasmedge-ggml/qwen2vl/wasmedge-ggml-qwen2vl.wasm b/wasmedge-ggml/qwen2vl/wasmedge-ggml-qwen2vl.wasm new file mode 100755 index 0000000000000000000000000000000000000000..3a4025fc15ca0d77b0cd85d1caf631ce37d21490 GIT binary patch literal 2275385 zcmeFa4cuK-edoK^-sk=0c}^feASnABGc(E6RC-GjCfYIkxynn?k#=l9PH*o8Mw!a< zAd-+G?L_iO5{wl!Rm7;Yjfw^pDk>^PcH;T@Uw{3IV^lwB8Psuht z>R(>!{MbV+{Q3k6hL4w%SKlu8KfI~F%g=2pKYw^vmUydbb6feR;Xm-y^-uOMuXzz3 zlgmTvm-9z8Urr_Q=aT-im!5aY1?QZ9@r%xX@%fw2yYQTgF6#W+`CHC;$**s@_}8~Y z&844LT=vFl;5W~I{Cutd`oeQwy7>ayY8|Ae z&gSzjdf`i6{J5=k&%fx>3pUf`#{+3up6Sp~wJ+ZMk{5u<3${d2^8Vze#JP49#q}g{ zoko&$`BU$ak|g~&uFuq?q+d_!^?KY+;?`_bi)&F|`CD-khd&Wd{U}cA2_ND{qvq2a z?MA!Zr)+~S{1+#USl{hsuq;W63Md{o;yVBHhqgONiv3AcHGllS4gHCuK5s5+My~EU zksuSWwWQyV`}|YWKdxS{MNz+BZ!|i5mgM!7O<>a_{?MBmnQ1_EM^TO6dPGFm1W-x)m^eOGSkRy2R@9pAVs{OH>xqk& zHQMNoY;kda)NKxv#*5E?@k=)UMjSQHxn#?Eo3}(W-E+=)@p%_qbk3zOx!{G-aqZWE zxy*In;7&Q}7oPr0#}3Y0_KW}F=brYQ|9IAO&p7k+=Y2Z6;qH!K?!FQKpTF{d z{pxT0=MN>n`Lh4E^)~mxWWRf@`=tB6dyTsydBpvm`|s|(?q>H9_xtXH?!)dz_aS$S zyTN_H{eim)HTP=wD!0q+bKi2i-P_#G_#N(6_gn7m?m+x*ceQ)Hd$ap1_XhVSx5vHF zUF*K-e%rmpz0>`<`=Yzgec64>qr|d^6KO@$t}rilikU0Cx01#IO%+JN9(2}j=IluZd<%D-k3$J<2`S7tW7n zNTR-Vag?^w28k<3oXy(vYocgkyUF2G3pX-i-z>xF}x)#DS?0;#}3 z>WYv$oE31sscg0NBkxdvzMkFh`)4SmS8I~c0j1B*2mHP&XW@Ca;r=YR~gJjPHMd~aZ9#RX+8=#dQ9#!22$z}fOTuH#P z>@l|~Yn%?%j~X_R{j|L|8FG1Mr*@236J5%5N~JDGo~i-wI- z*@i-8PMy77;==9+NI@r&|{?+pk7A4s%9}sGtH8WX_kJfoJeNVG|NnyR5i5w|1Sz-YeG)v&G2YKpvzk!c!|fz6vwkDUA)E!s=*j;o{hpBX1&_sC}>hW`$*% zwH|f7{-eysLjUEw@3AID;TZybYE{HWD8mDtZhB@1S`;9%9BLBR6jBuMpYu$d9e>3B;Uzz_@Ryh%MjL^7^(R-L(^@NG=}^#=CxDA*V{lQZ zQK~y)XpDSd1!9k4vtKbyKv61&+_`>+4~!Mne$EfeWt_!A@%(e~(h+{&7vw70G>V@E z5``vZzc!g4HsBT%Ni=GviSbD@?RY-PYJ8<)8c6sU+h90=I zGoM{YqLaqS3hv8ZhCMI_^cB18vYauq%O^i=Ek13V_%zPTc~t`$T=7c{R;Gui_oE); 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