Add the new gemma models. (#2023)

* Add the new gemma models.

* Revert the lightning changes.

* Support for the 1.1 models.

candle-core/src/quantized/metal.rs

@@ -149,8 +149,11 @@ impl QMetalStorage {
         let (n, k) = self_shape.dims2()?;
         let mut dst_shape = src_shape.dims().to_vec();
 
+        // We always use a single batch dimension and stack all the tensors in the batch on the
+        // second dimension as the implementation in candle-metal-kernels doesn't handle batch
+        // properly.
         let (b, m) = match dst_shape.len() {
-            3 => (dst_shape[0], dst_shape[1]),
+            3 => (1, dst_shape[0] * dst_shape[1]),
             2 => (1, dst_shape[0]),
             n => crate::bail!("Invalid rank {n} for quantized matmul metal"),
         };

candle-examples/examples/gemma/main.rs

@@ -16,6 +16,22 @@ use candle_transformers::generation::LogitsProcessor;
 use hf_hub::{api::sync::Api, Repo, RepoType};
 use tokenizers::Tokenizer;
 
+#[derive(Clone, Debug, Copy, PartialEq, Eq, clap::ValueEnum)]
+enum Which {
+    #[value(name = "2b")]
+    Base2B,
+    #[value(name = "7b")]
+    Base7B,
+    #[value(name = "2b-it")]
+    Instruct2B,
+    #[value(name = "7b-it")]
+    Instruct7B,
+    #[value(name = "1.1-2b-it")]
+    InstructV1_1_2B,
+    #[value(name = "1.1-7b-it")]
+    InstructV1_1_7B,
+}
+
 struct TextGeneration {
     model: Model,
     device: Device,
@@ -165,6 +181,10 @@ struct Args {
     /// The context size to consider for the repeat penalty.
     #[arg(long, default_value_t = 64)]
     repeat_last_n: usize,
+
+    /// The model to use.
+    #[arg(long, default_value = "2b")]
+    which: Which,
 }
 
 fn main() -> Result<()> {
@@ -196,14 +216,15 @@ fn main() -> Result<()> {
     let start = std::time::Instant::now();
     let api = Api::new()?;
     let model_id = match &args.model_id {
-        Some(model_id) => match model_id.as_str() {
-            "7b-it" => "google/gemma-7b-it".to_string(),
-            "7b" => "google/gemma-7b".to_string(),
-            "2b-it" => "google/gemma-2b-it".to_string(),
-            "2b" => "google/gemma-2b".to_string(),
-            _ => model_id.to_string(),
+        Some(model_id) => model_id.to_string(),
+        None => match args.which {
+            Which::InstructV1_1_2B => "google/gemma-1.1-2b-it".to_string(),
+            Which::InstructV1_1_7B => "google/gemma-1.1-7b-it".to_string(),
+            Which::Base2B => "google/gemma-2b".to_string(),
+            Which::Base7B => "google/gemma-7b".to_string(),
+            Which::Instruct2B => "google/gemma-2b-it".to_string(),
+            Which::Instruct7B => "google/gemma-7b-it".to_string(),
         },
-        None => "google/gemma-2b".to_string(),
     };
     let repo = api.repo(Repo::with_revision(
         model_id,

candle-metal-kernels/src/lib.rs

@@ -406,7 +406,7 @@ pub fn call_copy2d(
     );
 
     let width: usize = d1 * d2;
-    let (thread_group_count, thread_group_size) = linear_split(&pipeline, width / 4);
+    let (thread_group_count, thread_group_size) = linear_split(&pipeline, width);
 
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);

candle-metal-kernels/src/unary.metal

@@ -112,7 +112,6 @@ kernel void FN_NAME( \
     device TYPENAME *output, \
     uint tid [[ thread_position_in_grid ]] \
 ) { \
-    tid *= 4; \
     if (tid >= d1 * d2) { \
         return; \
     } \
@@ -121,9 +120,6 @@ kernel void FN_NAME( \
     size_t src_idx = idx1 * src_s + idx2; \
     size_t dst_idx = idx1 * dst_s + idx2; \
     output[dst_idx] = input[src_idx]; \
-    output[dst_idx+1] = input[src_idx+1]; \
-    output[dst_idx+2] = input[src_idx+2]; \
-    output[dst_idx+3] = input[src_idx+3]; \
 }
 
 COPY2D(copy2d_f32, float)

candle-transformers/src/models/gemma.rs

@@ -11,6 +11,7 @@ fn default_max_position_embeddings() -> usize {
 pub struct Config {
     pub attention_bias: bool,
     pub head_dim: usize,
+    #[serde(alias = "hidden_activation")]
     pub hidden_act: candle_nn::Activation,
     pub hidden_size: usize,
     pub intermediate_size: usize,

candle-transformers/src/models/quantized_metavoice.rs

@@ -235,6 +235,7 @@ pub mod transformer {
                 xs = layer.forward(&xs, pos, &mask)?
             }
             xs.narrow(1, seqlen - 1, 1)?
+                .contiguous()?
                 .apply(&self.norm)?
                 .apply(&self.output)
         }