Musicgen text embeddings. (#726)

* Musicgen text embeddings.

* Bugfix for layer norm.

* Proper position bias.

* Expose the weights.

candle-examples/examples/musicgen/main.rs

@@ -18,7 +18,7 @@ mod t5_model;
 use musicgen_model::{GenConfig, MusicgenForConditionalGeneration};
 
 use anyhow::{Error as E, Result};
-use candle::DType;
+use candle::{DType, Tensor};
 use candle_nn::VarBuilder;
 use clap::Parser;
 use hf_hub::{api::sync::Api, Repo, RepoType};
@@ -39,6 +39,12 @@ struct Args {
     /// The tokenizer config.
     #[arg(long)]
     tokenizer: Option<String>,
+
+    #[arg(
+        long,
+        default_value = "90s rock song with loud guitars and heavy drums"
+    )]
+    prompt: String,
 }
 
 fn main() -> Result<()> {
@@ -53,7 +59,10 @@ fn main() -> Result<()> {
             .get("tokenizer.json")?,
     };
     let mut tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
-    let _tokenizer = tokenizer.with_padding(None).with_truncation(None);
+    let tokenizer = tokenizer
+        .with_padding(None)
+        .with_truncation(None)
+        .map_err(E::msg)?;
 
     let model = match args.model {
         Some(model) => std::path::PathBuf::from(model),
@@ -69,6 +78,18 @@ fn main() -> Result<()> {
     let model = model.deserialize()?;
     let vb = VarBuilder::from_safetensors(vec![model], DTYPE, &device);
     let config = GenConfig::small();
-    let _model = MusicgenForConditionalGeneration::load(vb, config)?;
+    let model = MusicgenForConditionalGeneration::load(vb, config)?;
+
+    let tokens = tokenizer
+        .encode(args.prompt.as_str(), true)
+        .map_err(E::msg)?
+        .get_ids()
+        .to_vec();
+    println!("tokens: {tokens:?}");
+    let tokens = Tensor::new(tokens.as_slice(), &device)?.unsqueeze(0)?;
+    println!("{tokens:?}");
+    let embeds = model.text_encoder.forward(&tokens)?;
+    println!("{embeds}");
+
     Ok(())
 }

candle-examples/examples/musicgen/musicgen_model.rs

@@ -370,9 +370,9 @@ impl MusicgenForCausalLM {
 
 #[derive(Debug)]
 pub struct MusicgenForConditionalGeneration {
-    text_encoder: crate::t5_model::T5EncoderModel,
-    audio_encoder: crate::encodec_model::EncodecModel,
-    decoder: MusicgenForCausalLM,
+    pub text_encoder: crate::t5_model::T5EncoderModel,
+    pub audio_encoder: crate::encodec_model::EncodecModel,
+    pub decoder: MusicgenForCausalLM,
     cfg: GenConfig,
 }
 

candle-examples/examples/musicgen/t5_model.rs

@@ -96,10 +96,9 @@ impl T5LayerNorm {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let dtype = xs.dtype();
         let xs_f32 = xs.to_dtype(DType::F32)?;
-        let xs2_f32 = (&xs_f32 * &xs_f32)?;
-        let sum_xs2_f32 = xs2_f32.sum_keepdim(D::Minus1)?;
-        let variance = xs2_f32.broadcast_div(&sum_xs2_f32)?;
-        let xs = (xs / (variance + self.variance_epsilon)?.sqrt()?)?;
+        // variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        let variance = xs_f32.sqr()?.mean_keepdim(D::Minus1)?;
+        let xs = xs.broadcast_div(&(variance + self.variance_epsilon)?.sqrt()?)?;
         let xs = xs.to_dtype(dtype)?;
         let xs = xs.broadcast_mul(&self.weight)?;
         Ok(xs)
@@ -167,6 +166,9 @@ struct T5Attention {
     n_heads: usize,
     d_kv: usize,
     relative_attention_bias: Option<Embedding>,
+    relative_attention_num_buckets: usize,
+    relative_attention_max_distance: usize,
+    inner_dim: usize,
 }
 
 impl T5Attention {
@@ -194,6 +196,9 @@ impl T5Attention {
             n_heads: cfg.num_heads,
             d_kv: cfg.d_kv,
             relative_attention_bias,
+            relative_attention_num_buckets: cfg.relative_attention_num_buckets,
+            relative_attention_max_distance: cfg.relative_attention_max_distance,
+            inner_dim,
         })
     }
 
@@ -206,17 +211,53 @@ impl T5Attention {
         let v = self.v.forward(xs)?;
         let q = q
             .reshape((b_sz, seq_len, self.n_heads, self.d_kv))?
-            .transpose(1, 2)?;
+            .transpose(1, 2)?
+            .contiguous()?;
         let k = k
             .reshape((b_sz, seq_len, self.n_heads, self.d_kv))?
-            .transpose(1, 2)?;
+            .transpose(1, 2)?
+            .contiguous()?;
         let v = v
             .reshape((b_sz, seq_len, self.n_heads, self.d_kv))?
-            .transpose(1, 2)?;
+            .transpose(1, 2)?
+            .contiguous()?;
         let scores = q.matmul(&k.t()?)?;
-        // TODO: position_bias_masked
+
+        let scores = match &self.relative_attention_bias {
+            None => scores,
+            Some(relative_attention_bias) => {
+                let query_length = seq_len;
+                let key_length = seq_len;
+                // This only handles the bidirectional case.
+                let num_buckets = self.relative_attention_num_buckets / 2;
+                let relative_position = (0..query_length as u32)
+                    .map(|i| {
+                        (0..key_length as u32)
+                            .map(|j| {
+                                if i < j {
+                                    j - i + num_buckets as u32
+                                } else {
+                                    i - j
+                                }
+                            })
+                            .collect::<Vec<u32>>()
+                    })
+                    .collect::<Vec<Vec<_>>>();
+                let relative_buckets = Tensor::new(relative_position, q.device())?;
+                let position_bias = relative_attention_bias
+                    .forward(&relative_buckets)?
+                    .permute((2, 0, 1))?
+                    .unsqueeze(0)?;
+                (scores + position_bias)?
+                // TODO: position_bias_masked?
+            }
+        };
+
         let attn_weights = candle_nn::ops::softmax(&scores, D::Minus1)?;
         let attn_output = attn_weights.matmul(&v)?;
+        let attn_output = attn_output
+            .transpose(1, 2)?
+            .reshape((b_sz, seq_len, self.inner_dim))?;
         let attn_output = self.o.forward(&attn_output)?;
         Ok(attn_output)
     }
@@ -324,7 +365,7 @@ impl T5Stack {
 
     fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {
         let input_embeds = self.shared.as_ref().forward(input_ids)?;
-        let (_b_sz, _seq_len) = input_embeds.dims2()?;
+        let (_b_sz, _seq_len) = (input_embeds.dim(0)?, input_embeds.dim(1)?);
 
         let mut hidden_states = input_embeds;
         for block in self.block.iter() {

candle-nn/src/linear.rs

@@ -29,6 +29,14 @@ impl Linear {
     pub fn new(weight: Tensor, bias: Option<Tensor>) -> Self {
         Self { weight, bias }
     }
+
+    pub fn weight(&self) -> &Tensor {
+        &self.weight
+    }
+
+    pub fn bias(&self) -> Option<&Tensor> {
+        self.bias.as_ref()
+    }
 }
 
 impl super::Module for Linear {