Add a simple Module trait and implement it for the various nn layers (#500)

* Start adding the module trait.

* Use the module trait.

* Implement module for qmatmul.

2 files changed, 2 insertions, 2 deletions

diff --git a/candle-wasm-examples/llama2-c/src/model.rs b/candle-wasm-examples/llama2-c/src/model.rs
index 2c867793..3fedb1d3 100644
--- a/candle-wasm-examples/llama2-c/src/model.rs
+++ b/candle-wasm-examples/llama2-c/src/model.rs
@@ -1,5 +1,5 @@
 use candle::{DType, Device, IndexOp, Result, Tensor, D};
-use candle_nn::{rms_norm, Embedding, Linear, RmsNorm, VarBuilder};
+use candle_nn::{rms_norm, Embedding, Linear, Module, RmsNorm, VarBuilder};
 use std::collections::HashMap;
 use std::sync::{Arc, Mutex};
 
diff --git a/candle-wasm-examples/whisper/src/model.rs b/candle-wasm-examples/whisper/src/model.rs
index 9f3d92f5..3470c3d6 100644
--- a/candle-wasm-examples/whisper/src/model.rs
+++ b/candle-wasm-examples/whisper/src/model.rs
@@ -3,7 +3,7 @@
 // back when using RUST_LIB_BACKTRACE=1.
 use anyhow::Result;
 use candle::{Device, Tensor};
-use candle_nn::{Conv1d, Conv1dConfig, Embedding, LayerNorm, VarBuilder};
+use candle_nn::{Conv1d, Conv1dConfig, Embedding, LayerNorm, Module, VarBuilder};
 use serde::Deserialize;
 
 // The names in comments correspond to the original implementation: