update to use newest llama model

Author: cometyang

crates/llm-chain-llama-sys/llama.cpp

@@ -759,14 +759,21 @@ fn bindgen_test_layout_llama_token_data_array() {
         )
     );
 }
+
+const LLAMA_MAX_DEVICES: usize = 1;
 pub type llama_progress_callback =
     ::std::option::Option<unsafe extern "C" fn(progress: f32, ctx: *mut ::std::os::raw::c_void)>;
 #[repr(C)]
 #[derive(Debug, Copy, Clone)]
 pub struct llama_context_params {
     pub n_ctx: ::std::os::raw::c_int,
-    pub n_parts: ::std::os::raw::c_int,
+    pub n_batch: ::std::os::raw::c_int,
+    pub n_gpu_layers: ::std::os::raw::c_int,
+    pub main_gpu: ::std::os::raw::c_int,
+    pub tensor_split: [::std::os::raw::c_float; LLAM_MAX_DEVICES],
+    
     pub seed: ::std::os::raw::c_int,
+    
     pub f16_kv: bool,
     pub logits_all: bool,
     pub vocab_only: bool,
@@ -800,16 +807,7 @@ fn bindgen_test_layout_llama_context_params() {
             stringify!(n_ctx)
         )
     );
-    assert_eq!(
-        unsafe { ::std::ptr::addr_of!((*ptr).n_parts) as usize - ptr as usize },
-        4usize,
-        concat!(
-            "Offset of field: ",
-            stringify!(llama_context_params),
-            "::",
-            stringify!(n_parts)
-        )
-    );
+
     assert_eq!(
         unsafe { ::std::ptr::addr_of!((*ptr).seed) as usize - ptr as usize },
         8usize,

crates/llm-chain-llama-sys/src/bindings.rs

@@ -1,5 +1,7 @@
 use llm_chain::{executor, parameters, prompt};
-
+use llm_chain::options;
+use llm_chain::options::{ModelRef, Options};
+use std::{env::args, error::Error};
 /// This example demonstrates how to use the llm-chain-llama crate to generate text using a
 /// LLaMA model.
 ///
@@ -9,11 +11,42 @@ use llm_chain::{executor, parameters, prompt};
 /// cargo run --example simple /models/llama
 #[tokio::main(flavor = "current_thread")]
 async fn main() -> Result<(), Box<dyn std::error::Error>> {
-    let exec = executor!(llama)?;
+    let raw_args: Vec<String> = args().collect();
+    let args = match &raw_args.len() {
+      2 => (raw_args[1].as_str(), "Rust is a cool programming language because"),
+      3 => (raw_args[1].as_str(), raw_args[2].as_str()),
+      _ => panic!("Usage: cargo run --release --example simple <path to model> <optional prompt>")
+    };
+
+    let model_path = args.0;
+    let prompt = args.1;
+    let opts = options!(
+        Model: ModelRef::from_path(model_path),
+        ModelType: "llama",
+        MaxContextSize: 512_usize,
+        NThreads: 4_usize,
+        MaxTokens: 0_usize,
+        TopK: 40_i32,
+        TopP: 0.95,
+        TfsZ: 1.0,
+        TypicalP: 1.0,
+        Temperature: 0.8,
+        RepeatPenalty: 1.1,
+        RepeatPenaltyLastN: 64_usize,
+        FrequencyPenalty: 0.0,
+        PresencePenalty: 0.0,
+        Mirostat: 0_i32,
+        MirostatTau: 5.0,
+        MirostatEta: 0.1,
+        PenalizeNl: true,
+        StopSequence: vec!["\n".to_string()]
+    );
+    let exec = executor!(llama, opts.clone())?;
 
-    let res = prompt!("The Colors of the Rainbow are (in order): ")
+    let res = prompt!(prompt)
         .run(&parameters!(), &exec)
         .await?;
+    
     println!("{}", res.to_immediate().await?);
     Ok(())
 }

crates/llm-chain-llama/examples/simple.rs

@@ -20,11 +20,15 @@ use serde::{Deserialize, Serialize};
 #[error("LLAMA.cpp returned error-code {0}")]
 pub struct LLAMACPPErrorCode(i32);
 
+const LLAMA_MAX_DEVICES: usize = 1; // corresponding to constant in llama.h
 // Represents the configuration parameters for a LLamaContext.
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct ContextParams {
     pub n_ctx: i32,
-    pub n_parts: i32,
+    pub n_batch: i32,
+    pub n_gpu_layers: i32,
+    pub main_gpu: i32,
+    pub tensor_split: [f32; LLAMA_MAX_DEVICES],
     pub seed: i32,
     pub f16_kv: bool,
     pub vocab_only: bool,
@@ -56,7 +60,10 @@ impl From<ContextParams> for llama_context_params {
     fn from(params: ContextParams) -> Self {
         llama_context_params {
             n_ctx: params.n_ctx,
-            n_parts: params.n_parts,
+            n_batch: params.n_batch,
+            n_gpu_layers: params.n_gpu_layers,
+            main_gpu: params.main_gpu,
+            tensor_split: params.tensor_split,
             seed: params.seed,
             f16_kv: params.f16_kv,
             logits_all: false,
@@ -74,7 +81,10 @@ impl From<llama_context_params> for ContextParams {
     fn from(params: llama_context_params) -> Self {
         ContextParams {
             n_ctx: params.n_ctx,
-            n_parts: params.n_parts,
+            n_batch: params.n_batch,
+            n_gpu_layers: params.n_gpu_layers,
+            main_gpu: params.main_gpu,
+            tensor_split: params.tensor_split,
             seed: params.seed,
             f16_kv: params.f16_kv,
             vocab_only: params.vocab_only,

crates/llm-chain-llama/src/context.rs

@@ -51,7 +51,7 @@ impl Executor {
 
     // Run the LLAMA model with the provided input and generate output.
     // Executes the model with the provided input and context parameters.
-    fn run_model(&self, input: LlamaInvocation) -> Output {
+    async fn run_model(&self, input: LlamaInvocation) -> Output {
         let (sender, output) = Output::new_stream();
         // Tokenize the stop sequence and input prompt.
         let context = self.context.clone();
@@ -62,7 +62,6 @@ impl Executor {
             async move {
                 let context_size = context_size;
                 let context = context.lock().await;
-
                 let tokenized_stop_prompt = tokenize(
                     &context,
                     input
@@ -87,7 +86,7 @@ impl Executor {
 
                 // Embd contains the prompt and the completion. The longer the prompt, the shorter the completion.
                 let mut embd = tokenized_input.clone();
-
+                
                 // Evaluate the prompt in full.
                 bail!(
                     context
@@ -180,7 +179,7 @@ impl Executor {
                     }
                 }
             }
-        });
+        }).await.unwrap().await;
 
         output
     }
@@ -210,7 +209,7 @@ impl ExecutorTrait for Executor {
     async fn execute(&self, options: &Options, prompt: &Prompt) -> Result<Output, ExecutorError> {
         let invocation = LlamaInvocation::new(self.get_cascade(options), prompt)
             .ok_or(ExecutorError::InvalidOptions)?;
-        Ok(self.run_model(invocation))
+        Ok(self.run_model(invocation).await)
     }
 
     fn tokens_used(

crates/llm-chain-llama/src/executor.rs

@@ -43,7 +43,8 @@ impl LlamaInvocation {
     pub(crate) fn new(opt: OptionsCascade, prompt: &Prompt) -> Option<LlamaInvocation> {
         opt_extract!(opt, n_threads, NThreads);
         opt_extract!(opt, n_tok_predict, MaxTokens);
-        opt_extract!(opt, token_bias, TokenBias);
+        // Skip TokenBias for now
+        //opt_extract!(opt, token_bias, TokenBias);
         opt_extract!(opt, top_k, TopK);
         opt_extract!(opt, top_p, TopP);
         opt_extract!(opt, tfs_z, TfsZ);
@@ -59,8 +60,8 @@ impl LlamaInvocation {
         opt_extract!(opt, penalize_nl, PenalizeNl);
         opt_extract!(opt, stop_sequence, StopSequence);
 
-        let logit_bias = token_bias.as_i32_f32_hashmap()?;
-
+        let logit_bias =  HashMap::<i32,f32>::new();// token_bias.as_i32_f32_hashmap()?;
+        
         Some(LlamaInvocation {
             n_threads: *n_threads as i32,
             n_tok_predict: *n_tok_predict,