Add support for gemma-2. (huggingface#2425)

* Add gemma-2. * Support a couple more models. * Sliding window support. * Example + readme updates. * Update the main readme.
smartyhouses · Aug 17, 2024 · c1b9e07 · c1b9e07
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diff --git a/README.md b/README.md
@@ -65,7 +65,7 @@ We also provide a some command line based examples using state of the art models
 - [Falcon](./candle-examples/examples/falcon/): general LLM.
 - [Codegeex4](./candle-examples/examples/codegeex4-9b/): Code completion,code interpreter,web search,fuction calling,repository-level
 - [GLM4](./candle-examples/examples/glm4/): Open Multilingual Multimodal Chat LMs by THUDM
-- [Gemma](./candle-examples/examples/gemma/): 2b and 7b general LLMs from Google Deepmind.
+- [Gemma v1 and v2](./candle-examples/examples/gemma/): 2b and 7b+/9b general LLMs from Google Deepmind.
 - [RecurrentGemma](./candle-examples/examples/recurrent-gemma/): 2b and 7b
   Griffin based models from Google that mix attention with a RNN like state.
 - [Phi-1, Phi-1.5, Phi-2, and Phi-3](./candle-examples/examples/phi/): 1.3b,
@@ -208,7 +208,7 @@ If you have an addition to this list, please submit a pull request.
         - StarCoder, StarCoder2.
         - Phi 1, 1.5, 2, and 3.
         - Mamba, Minimal Mamba
-        - Gemma 2b and 7b.
+        - Gemma v1 2b and 7b+, v2 2b and 9b.
         - Mistral 7b v0.1.
         - Mixtral 8x7b v0.1.
         - StableLM-3B-4E1T, StableLM-2-1.6B, Stable-Code-3B.

diff --git a/candle-examples/examples/gemma/README.md b/candle-examples/examples/gemma/README.md
@@ -1,27 +1,27 @@
 # candle-gemma: 2b and 7b LLMs from Google DeepMind
 
 [Gemma](https://ai.google.dev/gemma/docs) is a collection of lightweight open
-models published by Google Deepmind with a 2b and a 7b variant.
-
-In order to use the example below, you have to accept the license on the
-[HuggingFace Hub Gemma repo](https://huggingface.co/google/gemma-7b) and set up
-your access token via the [HuggingFace cli login
-command](https://huggingface.co/docs/huggingface_hub/guides/cli#huggingface-cli-login).
+models published by Google Deepmind with a 2b and a 7b variant for the first
+version, and a 2b and a 9b variant for v2.
 
 ## Running the example
 
 ```bash
-$ cargo run --example gemma --release -- --prompt "fn count_primes(max_n: usize)"
-fn count_primes(max_n: usize) -> usize {
-    let mut primes = vec![true; max_n];
-    for i in 2..=max_n {
-        if primes[i] {
-            for j in i * i..max_n {
-                primes[j] = false;
-             }
-         }
-    }
-    primes.len()
-}
+$ cargo run --example gemma --features cuda -r -- \
+    --prompt "Here is a proof that square root of 2 is not rational: "
+
+Here is a proof that square root of 2 is not rational:
+
+Let us assume it to be rational. Then, we can write √2 = p/q where q ≠ 0 and p and q are integers with no common factors other than 1. Squaring both sides gives us (p/q)^2 = 2 or p^2/q^2 = 2. This implies that p^2 is divisible by 2, which means that p must be even. Let us write p = 2m where m is an integer. Substituting this in the above equation we get:
+
+(p^2)/q^2 = 2 or (4m^2)/q^2 = 2 or q^2/2m^2 = 1 which implies that q^2 must be divisible by 2, and hence q is even. This contradicts our assumption that p and q have no common factors other than 1. Hence we conclude that √2 cannot be rational.
 ```
 
+## Access restrictions
+
+In order to use the v1 examples, you have to accept the license on the
+[HuggingFace Hub Gemma repo](https://huggingface.co/google/gemma-7b) and set up
+your access token via the [HuggingFace cli login
+command](https://huggingface.co/docs/huggingface_hub/guides/cli#huggingface-cli-login).
+
+
diff --git a/candle-examples/examples/gemma/main.rs b/candle-examples/examples/gemma/main.rs
@@ -7,7 +7,8 @@ extern crate accelerate_src;
 use anyhow::{Error as E, Result};
 use clap::Parser;
 
-use candle_transformers::models::gemma::{Config, Model};
+use candle_transformers::models::gemma::{Config as Config1, Model as Model1};
+use candle_transformers::models::gemma2::{Config as Config2, Model as Model2};
 
 use candle::{DType, Device, Tensor};
 use candle_examples::token_output_stream::TokenOutputStream;
@@ -38,6 +39,46 @@ enum Which {
     CodeInstruct2B,
     #[value(name = "code-7b-it")]
     CodeInstruct7B,
+    #[value(name = "2-2b")]
+    BaseV2_2B,
+    #[value(name = "2-2b-it")]
+    InstructV2_2B,
+    #[value(name = "2-9b")]
+    BaseV2_9B,
+    #[value(name = "2-9b-it")]
+    InstructV2_9B,
+}
+
+impl Which {
+    fn is_v1(&self) -> bool {
+        match self {
+            Self::Base2B
+            | Self::Base7B
+            | Self::Instruct2B
+            | Self::Instruct7B
+            | Self::InstructV1_1_2B
+            | Self::InstructV1_1_7B
+            | Self::CodeBase2B
+            | Self::CodeBase7B
+            | Self::CodeInstruct2B
+            | Self::CodeInstruct7B => true,
+            Self::BaseV2_2B | Self::InstructV2_2B | Self::BaseV2_9B | Self::InstructV2_9B => false,
+        }
+    }
+}
+
+enum Model {
+    V1(Model1),
+    V2(Model2),
+}
+
+impl Model {
+    fn forward(&mut self, input_ids: &Tensor, pos: usize) -> candle::Result<Tensor> {
+        match self {
+            Self::V1(m) => m.forward(input_ids, pos),
+            Self::V2(m) => m.forward(input_ids, pos),
+        }
+    }
 }
 
 struct TextGeneration {
@@ -191,7 +232,7 @@ struct Args {
     repeat_last_n: usize,
 
     /// The model to use.
-    #[arg(long, default_value = "2b")]
+    #[arg(long, default_value = "2-2b")]
     which: Which,
 
     #[arg(long)]
@@ -239,6 +280,10 @@ fn main() -> Result<()> {
             Which::CodeBase7B => "google/codegemma-7b".to_string(),
             Which::CodeInstruct2B => "google/codegemma-2b-it".to_string(),
             Which::CodeInstruct7B => "google/codegemma-7b-it".to_string(),
+            Which::BaseV2_2B => "google/gemma-2-2b".to_string(),
+            Which::InstructV2_2B => "google/gemma-2-2b-it".to_string(),
+            Which::BaseV2_9B => "google/gemma-2-9b".to_string(),
+            Which::InstructV2_9B => "google/gemma-2-9b-it".to_string(),
         },
     };
     let repo = api.repo(Repo::with_revision(
@@ -263,7 +308,6 @@ fn main() -> Result<()> {
     };
     println!("retrieved the files in {:?}", start.elapsed());
     let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
-    let config: Config = serde_json::from_reader(std::fs::File::open(config_filename)?)?;
 
     let start = std::time::Instant::now();
     let device = candle_examples::device(args.cpu)?;
@@ -273,7 +317,15 @@ fn main() -> Result<()> {
         DType::F32
     };
     let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
-    let model = Model::new(args.use_flash_attn, &config, vb)?;
+    let model = if args.which.is_v1() {
+        let config: Config1 = serde_json::from_reader(std::fs::File::open(config_filename)?)?;
+        let model = Model1::new(args.use_flash_attn, &config, vb)?;
+        Model::V1(model)
+    } else {
+        let config: Config2 = serde_json::from_reader(std::fs::File::open(config_filename)?)?;
+        let model = Model2::new(args.use_flash_attn, &config, vb)?;
+        Model::V2(model)
+    };
 
     println!("loaded the model in {:?}", start.elapsed());