Llama 3.1 (#38)

* Add Llama 3.1

* Use provided max_seq_len and remove llama3_1 feature flag

* Fix llama3 tokenizer w/ special tokens

* Add Llama3 enum for model selection

* Add end of turn token w/ stop tokens

* Add Llama-3.1-8B-Instruct link

* Add llama 3.1 tokenizer new tokens

* Update to burn 0.14 and add import feature flag

* Add cuda backend

* Add known issues

* Add llama 3.1 link

* Add llama 3.1 community license agreement

* Remove space

* Remove deprecated note regarding binary weights (we use mpk now)

* Add missing cuda backend mention

llama-burn/Cargo.toml

@@ -16,12 +16,15 @@ tiny = ["dep:tokenizers"]
 # Example feature flags (backend selection)
 tch-cpu = ["burn/tch"]
 tch-gpu = ["burn/tch"]
+cuda = ["burn/cuda-jit"]
 wgpu = ["burn/wgpu"]
 
+# To import pytorch weights
+import = ["burn-import"]
+
 [dependencies]
-# Note: default-features = false is needed to disable std
-burn = { git = "https://github.com/tracel-ai/burn", rev = "a53f459f205889a22ecea3713bbae12d3de7eb0c", default-features = false }
-burn-import = { git = "https://github.com/tracel-ai/burn", rev = "a53f459f205889a22ecea3713bbae12d3de7eb0c" }
+burn = { version = "0.14.0", default-features = false, features = ["std"] }
+burn-import = { version = "0.14.0", optional = true }
 itertools = { version = "0.12.1", default-features = false, features = [
     "use_alloc",
 ] }
@@ -46,5 +49,5 @@ rand = { version = "0.8.5", default-features = false, features = [
 ] } # std_rng is for no_std
 
 [dev-dependencies]
-burn = { git = "https://github.com/tracel-ai/burn", rev = "a53f459f205889a22ecea3713bbae12d3de7eb0c" }
+burn = { version = "0.14.0", default-features = false }
 clap = { version = "4.5.4", features = ["derive"] }

llama-burn/NOTICES.md

@@ -8,6 +8,8 @@ derived from. The use of the following resources complies with the licenses prov
 The model implementation was adapted from the original
 [Llama 3 implementation](https://github.com/meta-llama/llama3), which is distributed under the
 [Meta Llama 3 Community License Agreement](https://github.com/meta-llama/llama3/blob/main/LICENSE).
+The Llama 3.1 model is distributed under the
+[Llama 3.1 Community License Agreement](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE).
 
 The TinyLlama implementation is derived from the same code, but its weights and tokenizers were
 adapted from the [original implementation](https://github.com/jzhang38/TinyLlama) distributed under

llama-burn/README.md

@@ -4,7 +4,8 @@
 
 The popular Llama LLM is here!
 
-This repository contains the [Llama 3](https://github.com/meta-llama/llama3) and
+This repository contains the [Llama 3.1](https://github.com/meta-llama/llama-models/),
+[Llama 3](https://github.com/meta-llama/llama3) and
 [TinyLlama](https://github.com/jzhang38/TinyLlama) implementations with their corresponding
 tokenizers. You can find the [Burn](https://github.com/tracel-ai/burn) implementation for the Llama
 variants in [src/llama.rs](src/llama.rs).
@@ -23,9 +24,7 @@ llama-burn = { git = "https://github.com/tracel-ai/models", package = "llama-bur
 If you want to use Llama 3 or TinyLlama (including pre-trained weights if default features are
 active), enable the corresponding feature flag.
 
-> **Important:** these features require `std`. Note that the weights have been saved in the binary
-> format, which is more compact and faster to save & load, but might not be compatible in future
-> versions if the Burn data schema were to evolve.
+> **Important:** these features require `std`.
 
 #### Llama 3
 
@@ -47,7 +46,7 @@ The [chat completion example](examples/chat.rs) initializes a Llama model from t
 file and generates a sequence of text based on the input prompt. The instruction-tuned model is
 loaded for dialogue applications, so the prompt is automatically formatted for chat completion.
 
-The example can be executed on the `tch` backend (CUDA or CPU) or `wgpu`.
+The example can be executed on the `tch` backend (CUDA or CPU), `cuda` or `wgpu`.
 
 | Argument        | Description                                                                                                    |
 | :-------------- | :------------------------------------------------------------------------------------------------------------- |
@@ -83,9 +82,16 @@ Using the `wgpu` backend:
 cargo run --release --features llama3,wgpu --example chat
 ```
 
+Using the `cuda` backend:
+
+```sh
+cargo run --release --features llama3,cuda --example chat
+```
+
 **Built with Meta Llama 3.** This example uses the
-[Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
-instruction-tuned model. Note that the [base pre-trained Llama-3 model](./src/pretrained.rs#L77) is
+[Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) (default)
+and [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
+instruction-tuned models. Note that the [base pre-trained Llama-3 model](./src/pretrained.rs#L77) is
 also available if you wish to use it in your application.
 
 #### TinyLlama
@@ -109,6 +115,18 @@ Using the `wgpu` backend:
 cargo run --release --features tiny,wgpu --example chat
 ```
 
+Using the `cuda` backend:
+
+```sh
+cargo run --release --features tiny,cuda --example chat
+```
+
 This example uses the
 [TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
 instruction-tuned model based on the Llama2 architecture and tokenizer.
+
+## Known Issues
+
+Based on your hardware and the model selected, the `wgpu` backend might not be able to successfully
+run the model due to the current memory management strategy. With `cuda` selected, the precision is
+set to `f32` due to compilation errors with `f16`.

llama-burn/examples/chat.rs

@@ -8,6 +8,9 @@ use llama_burn::{
     tokenizer::Tokenizer,
 };
 
+#[cfg(feature = "llama3")]
+use clap::ValueEnum;
+
 const DEFAULT_PROMPT: &str = "How many helicopters can a human eat in one sitting?";
 
 #[derive(Parser, Debug)]
@@ -26,7 +29,7 @@ pub struct Config {
     max_seq_len: usize,
 
     /// The number of new tokens to generate (i.e., the number of generation steps to take).
-    #[arg(long, short = 'n', default_value_t = 50)]
+    #[arg(long, short = 'n', default_value_t = 65)]
     sample_len: usize,
 
     /// The seed to use when generating random samples.
@@ -36,6 +39,23 @@ pub struct Config {
     /// The input prompt.
     #[arg(short, long, default_value_t = String::from(DEFAULT_PROMPT))]
     prompt: String,
+
+    /// The Llama 3 model version.
+    #[cfg(feature = "llama3")]
+    #[arg(long, default_value = "llama-3.1-8b-instruct")]
+    version: Llama3,
+}
+
+#[cfg(feature = "llama3")]
+#[derive(Clone, Debug, ValueEnum)]
+/// Llama-3 model variants to load.
+enum Llama3 {
+    /// Llama-3-8B-Instruct.
+    #[value(name = "llama-3-8b-instruct")]
+    V3Instruct,
+    /// Llama-3.1-8B-Instruct.
+    #[value(name = "llama-3.1-8b-instruct")]
+    V31Instruct,
 }
 
 pub fn generate<B: Backend, T: Tokenizer>(
@@ -76,7 +96,7 @@ pub fn chat<B: Backend>(args: Config, device: Device<B>) {
     #[cfg(feature = "tiny")]
     {
         // TinyLlama-1.1B Chat v1.0
-        let mut llama = LlamaConfig::tiny_llama_pretrained::<B>(&device).unwrap();
+        let mut llama = LlamaConfig::tiny_llama_pretrained::<B>(args.max_seq_len, &device).unwrap();
         println!("Processing prompt: {}", prompt);
 
         // Prompt formatting for chat model
@@ -95,8 +115,15 @@ pub fn chat<B: Backend>(args: Config, device: Device<B>) {
 
     #[cfg(feature = "llama3")]
     {
-        // Llama-3-8B-Instruct
-        let mut llama = LlamaConfig::llama3_8b_pretrained::<B>(true, &device).unwrap();
+        // Llama-3-8B-Instruct or Llama-3.1-8B-Instruct
+        let mut llama = match args.version {
+            Llama3::V3Instruct => {
+                LlamaConfig::llama3_8b_pretrained::<B>(args.max_seq_len, &device).unwrap()
+            }
+            Llama3::V31Instruct => {
+                LlamaConfig::llama3_1_8b_pretrained::<B>(args.max_seq_len, &device).unwrap()
+            }
+        };
         println!("Processing prompt: {}", prompt);
 
         // Prompt formatting for chat model
@@ -156,6 +183,19 @@ mod wgpu {
     }
 }
 
+#[cfg(feature = "cuda")]
+mod cuda {
+    use super::*;
+    use burn::backend::{cuda_jit::CudaDevice, CudaJit};
+
+    pub fn run(args: Config) {
+        let device = CudaDevice::default();
+
+        // NOTE: compilation errors in f16
+        chat::<CudaJit<f32, i32>>(args, device);
+    }
+}
+
 pub fn main() {
     // Parse arguments
     let args = Config::parse();
@@ -166,4 +206,6 @@ pub fn main() {
     tch_cpu::run(args);
     #[cfg(feature = "wgpu")]
     wgpu::run(args);
+    #[cfg(feature = "cuda")]
+    cuda::run(args);
 }

llama-burn/src/cache.rs

@@ -1,11 +1,5 @@
 use burn::tensor::{backend::Backend, Tensor};
 
-/// All Llama-3 models support sequence length up to 8192 tokens.
-pub(crate) const MAX_SEQ_LEN: usize = 8192;
-
-// /// All Llama-2 models support sequence length up to 4096 tokens.
-// pub(crate) const MAX_SEQ_LEN_V2: usize = 4096;
-
 // Adapted from `burn::nn::cache`
 enum CacheState<T> {
     Value(T),
@@ -39,11 +33,6 @@ pub(crate) struct AutoregressiveCache<B: Backend> {
 impl<B: Backend> AutoregressiveCache<B> {
     /// Creates a new empty cache.
     pub fn new(max_seq_len: usize) -> Self {
-        assert!(
-            max_seq_len <= MAX_SEQ_LEN,
-            "Maximum sequence length must not exceed {MAX_SEQ_LEN}"
-        );
-
         Self {
             cache: TensorCache::empty(),
             max_seq_len,