Unified pipeline for models & support phi3 model

README.md

@@ -13,11 +13,25 @@ Efficient, easy-to-use platform for inference and serving local LLMs including a
 - Efficient management of key-value cache with PagedAttention.
 - Continuous batching.
 
-### Pipelines
-- Llama
-    - 7b
-    - 13b
-    - 70b
+## Develop Status
+
+Currently, candle-vllm supports chat serving for the following models.
+
+| Model ID | Model Type | Supported | Speed (A100, BF16)
+|--|--|--|--|
+| #1 | **LLAMA/LLAMA2/LLaMa3** |✅|71 tks/s (7B)|
+| #2 | Mistral |TBD|TBD|
+| #3 | Phi (v1, v1.5, v2) |TBD|TBD|
+| #4 | **Phi-3 （3.8B, 7B）** |✅|99 tks/s (3.8B)|
+| #5 | Yi |TBD|TBD|
+| #6 | StableLM |TBD|TBD|
+| #7 | BigCode/StarCode |TBD|TBD|
+| #8 | ChatGLM |TBD|TBD|
+| #9 | QWen |TBD|TBD|
+| #10 | Google Gemma |TBD|TBD|
+| #11 | Blip-large (Multimodal) |TBD|TBD|
+| #12 | Moondream-2 (Multimodal LLM) |TBD|TBD|
+
 
 ## Demo Chat with candle-vllm (71 tokens/s, LLaMa2 7B, bf16, on A100)
 <img src="./res/candle-vllm-demo.gif" width="90%" height="90%" >

src/lib.rs

@@ -3,7 +3,7 @@ use candle::Result;
 use candle_core as candle;
 use clap::Subcommand;
 use openai::pipelines::{
-    llama::{LlamaLoader, LlamaSpecificConfig},
+    pipeline::{DefaultLoader, SpecificConfig},
     ModelLoader,
 };
 
@@ -29,6 +29,13 @@ pub enum ModelSelected {
         #[arg(long)]
         repeat_last_n: usize,
     },
+
+    /// Select the phi3 3.8b model.
+    Phi3 {
+        /// Control the application of repeat penalty for the last n tokens
+        #[arg(long)]
+        repeat_last_n: usize,
+    },
 }
 
 impl ToString for ModelSelected {
@@ -37,33 +44,41 @@ impl ToString for ModelSelected {
             ModelSelected::Llama7b { repeat_last_n: _ } => "llama7b".to_string(),
             ModelSelected::Llama13b { repeat_last_n: _ } => "llama13b".to_string(),
             ModelSelected::Llama70b { repeat_last_n: _ } => "llama70b".to_string(),
+            ModelSelected::Phi3 { repeat_last_n: _ } => "phi3".to_string(),
         }
     }
 }
 
 pub fn get_model_loader<'a>(selected_model: ModelSelected) -> (Box<dyn ModelLoader<'a>>, String) {
     match selected_model {
         ModelSelected::Llama7b { repeat_last_n } => (
-            Box::new(LlamaLoader::new(
-                LlamaSpecificConfig::new(repeat_last_n),
+            Box::new(DefaultLoader::new(
+                SpecificConfig::new(repeat_last_n),
                 "llama7b".to_string(),
             )),
             "meta-llama/Llama-2-7b-chat-hf".to_string(),
         ),
         ModelSelected::Llama13b { repeat_last_n } => (
-            Box::new(LlamaLoader::new(
-                LlamaSpecificConfig::new(repeat_last_n),
+            Box::new(DefaultLoader::new(
+                SpecificConfig::new(repeat_last_n),
                 "llama13b".to_string(),
             )),
             "meta-llama/Llama-2-13b-chat-hf".to_string(),
         ),
         ModelSelected::Llama70b { repeat_last_n } => (
-            Box::new(LlamaLoader::new(
-                LlamaSpecificConfig::new(repeat_last_n),
+            Box::new(DefaultLoader::new(
+                SpecificConfig::new(repeat_last_n),
                 "llama70b".to_string(),
             )),
             "meta-llama/Llama-2-70b-chat-hf".to_string(),
         ),
+        ModelSelected::Phi3 { repeat_last_n } => (
+            Box::new(DefaultLoader::new(
+                SpecificConfig::new(repeat_last_n),
+                "phi3".to_string(),
+            )),
+            "microsoft/Phi-3-mini-4k-instruct".to_string(),
+        ),
     }
 }
 

src/main.rs

@@ -4,8 +4,8 @@ use actix_web::{App, HttpServer};
 use candle_core::{DType, Device};
 use candle_examples;
 use candle_vllm::openai::openai_server::chat_completions;
-use candle_vllm::openai::pipelines::llama::LlamaModelPaths;
 use candle_vllm::openai::pipelines::llm_engine::LLMEngine;
+use candle_vllm::openai::pipelines::pipeline::DefaultModelPaths;
 use candle_vllm::openai::responses::APIError;
 use candle_vllm::openai::OpenAIServerData;
 use candle_vllm::scheduler::cache_engine::CacheConfig;
@@ -77,7 +77,7 @@ async fn main() -> Result<(), APIError> {
     let (loader, model_id) = get_model_loader(args.command);
 
     let paths = match &args.weight_path {
-        Some(path) => Box::new(LlamaModelPaths {
+        Some(path) => Box::new(DefaultModelPaths {
             tokenizer_filename: (path.to_owned() + "tokenizer.json").into(),
             config_filename: (path.to_owned() + "config.json").into(),
             filenames: hub_load_local_safetensors(path, "model.safetensors.index.json").unwrap(),
@@ -100,16 +100,16 @@ async fn main() -> Result<(), APIError> {
     let num_gpu_blocks = args.kvcache_mem_gpu * SIZE_IN_MB
         / dsize
         / args.block_size
-        / config.get_num_kv_heads()
+        / config.num_key_value_heads
         / config.get_head_size()
-        / config.get_num_hidden_layers()
+        / config.num_hidden_layers
         / 2;
     let num_cpu_blocks = args.kvcache_mem_cpu * SIZE_IN_MB
         / dsize
         / args.block_size
-        / config.get_num_kv_heads()
+        / config.num_key_value_heads
         / config.get_head_size()
-        / config.get_num_hidden_layers()
+        / config.num_hidden_layers
         / 2;
     let cache_config = CacheConfig {
         block_size: args.block_size,

src/openai/conversation/default_conversation.rs

@@ -17,6 +17,7 @@ pub enum SeparatorStyle {
     NoColonTwo,
     AddNewLineSingle,
     Llama2,
+    Phi,
     ChatGLM,
     ChatML,
     ChatIntern,
@@ -242,6 +243,29 @@ impl Conversation for DefaultConversation {
                 accum
             }
 
+            SeparatorStyle::Phi => {
+                let mut accum = "".to_string();
+                for (i, message) in self.messages.iter().enumerate() {
+                    let Message((_role, message)) = message;
+                    if _role.clone() == self.roles.0 {
+                        //user message
+                        if let Some(message) = message {
+                            accum += &format!("<|user|> {message}<|end|>");
+                        } else {
+                            accum += &format!("<|user|> <|end|");
+                        }
+                    } else if _role.clone() == self.roles.1 {
+                        //assistant message
+                        if let Some(message) = message {
+                            accum += &format!("<|assistant|>{message}<|end|>");
+                        }
+                    } else if i == 0 && !system_prompt.is_empty() {
+                        accum += &system_prompt;
+                    }
+                }
+                accum
+            }
+
             SeparatorStyle::ChatGLM => {
                 let round_add_n = if self.name == "chatglm2" { 1 } else { 0 };
 

src/openai/models/llama.rs

@@ -1,4 +1,4 @@
-use super::ConfigLike;
+use super::Config;
 use crate::paged_attention::input_metadata::InputMetadata;
 use crate::paged_attention::PagedAttention;
 use candle::{DType, Device, IndexOp, Result, Tensor, D};
@@ -23,37 +23,10 @@ pub struct LlamaConfig {
     pub eos_token_id: Option<u32>,
 }
 
-impl LlamaConfig {
-    pub fn num_key_value_heads(&self) -> usize {
-        self.num_key_value_heads.unwrap_or(self.num_attention_heads)
-    }
-}
-
 fn default_rope() -> f32 {
     10_000.0
 }
 
-impl ConfigLike for LlamaConfig {
-    fn get_num_kv_heads(&self) -> usize {
-        self.num_key_value_heads.unwrap_or(self.num_attention_heads)
-    }
-    fn get_hidden_size(&self) -> usize {
-        self.hidden_size
-    }
-    fn get_num_hidden_layers(&self) -> usize {
-        self.num_hidden_layers
-    }
-    fn get_num_attention_heads(&self) -> usize {
-        self.num_attention_heads
-    }
-    fn get_vocab_size(&self) -> usize {
-        self.vocab_size
-    }
-    fn get_sliding_window(&self) -> Option<usize> {
-        None
-    }
-}
-
 impl LlamaConfig {
     pub fn into_config(self, use_flash_attn: bool) -> Config {
         Config {
@@ -68,80 +41,13 @@ impl LlamaConfig {
             use_flash_attn,
             bos_token_id: self.bos_token_id,
             eos_token_id: self.eos_token_id,
+            max_seq_len: MAX_SEQ_LEN,
+            sliding_window: None,
+            hidden_act: None,
         }
     }
 }
 
-#[derive(Debug, Clone)]
-pub struct Config {
-    pub hidden_size: usize,
-    pub intermediate_size: usize,
-    pub vocab_size: usize,
-    pub num_hidden_layers: usize,
-    pub num_attention_heads: usize,
-    pub num_key_value_heads: usize,
-    pub use_flash_attn: bool,
-    pub rms_norm_eps: f64,
-    pub rope_theta: f32,
-    pub bos_token_id: Option<u32>,
-    pub eos_token_id: Option<u32>,
-}
-
-impl Config {
-    pub fn config_7b_v1(use_flash_attn: bool) -> Self {
-        Self {
-            hidden_size: 4096,
-            intermediate_size: 11008,
-            vocab_size: 32000,
-            num_hidden_layers: 32,
-            num_attention_heads: 32,
-            num_key_value_heads: 32,
-            use_flash_attn,
-            rms_norm_eps: 1e-6,
-            rope_theta: 10_000.0,
-            bos_token_id: None,
-            eos_token_id: None,
-        }
-    }
-
-    pub fn config_7b_v2(use_flash_attn: bool) -> Self {
-        Self {
-            hidden_size: 4096,
-            intermediate_size: 11008,
-            vocab_size: 32000,
-            num_hidden_layers: 32,
-            num_attention_heads: 32,
-            num_key_value_heads: 32,
-            use_flash_attn,
-            rms_norm_eps: 1e-5,
-            rope_theta: 10_000.0,
-            bos_token_id: None,
-            eos_token_id: None,
-        }
-    }
-}
-
-impl ConfigLike for Config {
-    fn get_num_kv_heads(&self) -> usize {
-        self.num_key_value_heads
-    }
-    fn get_hidden_size(&self) -> usize {
-        self.hidden_size
-    }
-    fn get_num_hidden_layers(&self) -> usize {
-        self.num_hidden_layers
-    }
-    fn get_num_attention_heads(&self) -> usize {
-        self.num_attention_heads
-    }
-    fn get_vocab_size(&self) -> usize {
-        self.vocab_size
-    }
-    fn get_sliding_window(&self) -> Option<usize> {
-        None
-    }
-}
-
 #[derive(Debug, Clone)]
 pub struct Cache {
     masks: HashMap<usize, Tensor>,
@@ -159,9 +65,9 @@ impl Cache {
             .map(|i| 1f32 / config.rope_theta.powf(i as f32 / n_elem as f32))
             .collect();
         let theta = Tensor::new(theta.as_slice(), device)?;
-        let idx_theta = Tensor::arange(0, MAX_SEQ_LEN as u32, device)?
+        let idx_theta = Tensor::arange(0, config.max_seq_len as u32, device)?
             .to_dtype(DType::F32)?
-            .reshape((MAX_SEQ_LEN, 1))?
+            .reshape((config.max_seq_len, 1))?
             .matmul(&theta.reshape((1, theta.elem_count()))?)?;
         let cos = idx_theta.cos()?.to_dtype(dtype)?;
         let sin = idx_theta.sin()?.to_dtype(dtype)?;

src/openai/models/mod.rs

@@ -1,13 +1,26 @@
 pub mod llama;
+pub mod phi3;
 
-pub trait ConfigLike {
-    fn get_num_kv_heads(&self) -> usize;
-    fn get_hidden_size(&self) -> usize;
-    fn get_num_hidden_layers(&self) -> usize;
-    fn get_num_attention_heads(&self) -> usize;
-    fn get_vocab_size(&self) -> usize;
-    fn get_sliding_window(&self) -> Option<usize>;
-    fn get_head_size(&self) -> usize {
-        self.get_hidden_size() / self.get_num_attention_heads()
+#[derive(Debug, Clone)]
+pub struct Config {
+    pub hidden_size: usize,
+    pub intermediate_size: usize,
+    pub vocab_size: usize,
+    pub num_hidden_layers: usize,
+    pub num_attention_heads: usize,
+    pub num_key_value_heads: usize,
+    pub use_flash_attn: bool,
+    pub rms_norm_eps: f64,
+    pub rope_theta: f32,
+    pub bos_token_id: Option<u32>,
+    pub eos_token_id: Option<u32>,
+    pub max_seq_len: usize,
+    pub sliding_window: Option<usize>,
+    pub hidden_act: Option<candle_nn::Activation>,
+}
+
+impl Config {
+    pub fn get_head_size(&self) -> usize {
+        self.hidden_size / self.num_attention_heads
     }
 }