Model creation.

candle-examples/examples/bert/main.rs

@@ -1,9 +1,11 @@
 #![allow(dead_code)]
 
 use anyhow::Result as R;
-use candle::{Result, Tensor};
+use candle::{DType, Device, Result, Tensor};
 
-#[derive(Debug, Clone, PartialEq, Eq)]
+const DTYPE: DType = DType::F32;
+
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
 enum HiddenAct {
     Gelu,
     Relu,
@@ -18,7 +20,7 @@ impl HiddenAct {
     }
 }
 
-#[derive(Debug, Clone, PartialEq, Eq)]
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
 enum PositionEmbeddingType {
     Absolute,
 }
@@ -94,11 +96,13 @@ impl Linear {
     }
 }
 
-struct Dropout {}
+struct Dropout {
+    pr: f64,
+}
 
 impl Dropout {
-    fn new() -> Self {
-        Self {}
+    fn new(pr: f64) -> Self {
+        Self { pr }
     }
 
     fn forward(&self, x: &Tensor) -> Result<Tensor> {
@@ -140,6 +144,31 @@ struct BertEmbeddings {
 }
 
 impl BertEmbeddings {
+    fn load(device: &Device, config: &Config) -> Result<Self> {
+        let word_embeddings =
+            Tensor::zeros((config.vocab_size, config.hidden_size), DTYPE, device)?;
+        let position_embeddings = Tensor::zeros(
+            (config.max_position_embeddings, config.hidden_size),
+            DTYPE,
+            device,
+        )?;
+        let token_type_embeddings =
+            Tensor::zeros((config.type_vocab_size, config.hidden_size), DTYPE, device)?;
+        let layer_norm = Tensor::zeros((), DTYPE, device)?;
+        let position_ids: Vec<_> = (0..config.max_position_embeddings as u32).collect();
+        let position_ids = Tensor::new(&position_ids[..], device)?.unsqueeze(0)?;
+        let token_type_ids = position_ids.zeros_like()?;
+        Ok(Self {
+            word_embeddings: Embedding::new(word_embeddings),
+            position_embeddings: Some(Embedding::new(position_embeddings)),
+            token_type_embeddings: Embedding::new(token_type_embeddings),
+            layer_norm: LayerNorm::new(layer_norm),
+            dropout: Dropout::new(config.hidden_dropout_prob),
+            position_ids,
+            token_type_ids,
+        })
+    }
+
     fn forward(&self, input_ids: &Tensor, token_type_ids: &Tensor) -> Result<Tensor> {
         let input_embeddings = self.word_embeddings.forward(input_ids)?;
         let token_type_embeddings = self.token_type_embeddings.forward(token_type_ids)?;
@@ -163,6 +192,26 @@ struct BertSelfAttention {
 }
 
 impl BertSelfAttention {
+    fn load(device: &Device, config: &Config) -> Result<Self> {
+        let attention_head_size = config.hidden_size / config.num_attention_heads;
+        let all_head_size = config.num_attention_heads * attention_head_size;
+        let dropout = Dropout::new(config.hidden_dropout_prob);
+        let query = Tensor::zeros((config.hidden_size, all_head_size), DTYPE, device)?;
+        let query = Linear::new(query);
+        let value = Tensor::zeros((config.hidden_size, all_head_size), DTYPE, device)?;
+        let value = Linear::new(value);
+        let key = Tensor::zeros((config.hidden_size, all_head_size), DTYPE, device)?;
+        let key = Linear::new(key);
+        Ok(Self {
+            query,
+            key,
+            value,
+            dropout,
+            num_attention_heads: config.num_attention_heads,
+            attention_head_size,
+        })
+    }
+
     fn transpose_for_scores(&self, xs: &Tensor) -> Result<Tensor> {
         let mut new_x_shape = xs.dims().to_vec();
         new_x_shape.pop();
@@ -199,6 +248,19 @@ struct BertSelfOutput {
 }
 
 impl BertSelfOutput {
+    fn load(device: &Device, config: &Config) -> Result<Self> {
+        let dense = Tensor::zeros((config.hidden_size, config.hidden_size), DTYPE, device)?;
+        let dense = Linear::new(dense);
+        let layer_norm = Tensor::zeros((), DTYPE, device)?;
+        let layer_norm = LayerNorm::new(layer_norm);
+        let dropout = Dropout::new(config.hidden_dropout_prob);
+        Ok(Self {
+            dense,
+            layer_norm,
+            dropout,
+        })
+    }
+
     fn forward(&self, hidden_states: &Tensor, input_tensor: &Tensor) -> Result<Tensor> {
         let hidden_states = self.dense.forward(hidden_states)?;
         let hidden_states = self.dropout.forward(&hidden_states)?;
@@ -213,6 +275,15 @@ struct BertAttention {
 }
 
 impl BertAttention {
+    fn load(device: &Device, config: &Config) -> Result<Self> {
+        let self_attention = BertSelfAttention::load(device, config)?;
+        let self_output = BertSelfOutput::load(device, config)?;
+        Ok(Self {
+            self_attention,
+            self_output,
+        })
+    }
+
     fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
         let self_outputs = self.self_attention.forward(hidden_states)?;
         let attention_output = self.self_output.forward(&self_outputs, hidden_states)?;
@@ -227,6 +298,19 @@ struct BertIntermediate {
 }
 
 impl BertIntermediate {
+    fn load(device: &Device, config: &Config) -> Result<Self> {
+        let dense = Tensor::zeros(
+            (config.hidden_size, config.intermediate_size),
+            DTYPE,
+            device,
+        )?;
+        let dense = Linear::new(dense);
+        Ok(Self {
+            dense,
+            intermediate_act: config.hidden_act,
+        })
+    }
+
     fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
         let hidden_states = self.dense.forward(hidden_states)?;
         self.intermediate_act.forward(&hidden_states)
@@ -241,6 +325,23 @@ struct BertOutput {
 }
 
 impl BertOutput {
+    fn load(device: &Device, config: &Config) -> Result<Self> {
+        let dense = Tensor::zeros(
+            (config.intermediate_size, config.hidden_size),
+            DTYPE,
+            device,
+        )?;
+        let dense = Linear::new(dense);
+        let layer_norm = Tensor::zeros((), DTYPE, device)?;
+        let layer_norm = LayerNorm::new(layer_norm);
+        let dropout = Dropout::new(config.hidden_dropout_prob);
+        Ok(Self {
+            dense,
+            layer_norm,
+            dropout,
+        })
+    }
+
     fn forward(&self, hidden_states: &Tensor, input_tensor: &Tensor) -> Result<Tensor> {
         let hidden_states = self.dense.forward(hidden_states)?;
         let hidden_states = self.dropout.forward(&hidden_states)?;
@@ -256,6 +357,17 @@ struct BertLayer {
 }
 
 impl BertLayer {
+    fn load(device: &Device, config: &Config) -> Result<Self> {
+        let attention = BertAttention::load(device, config)?;
+        let intermediate = BertIntermediate::load(device, config)?;
+        let output = BertOutput::load(device, config)?;
+        Ok(Self {
+            attention,
+            intermediate,
+            output,
+        })
+    }
+
     fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
         let attention_output = self.attention.forward(hidden_states)?;
         // TODO: Support cross-attention?
@@ -275,6 +387,13 @@ struct BertEncoder {
 }
 
 impl BertEncoder {
+    fn load(device: &Device, config: &Config) -> Result<Self> {
+        let layers = (0..config.num_hidden_layers)
+            .map(|_index| BertLayer::load(device, config))
+            .collect::<Result<Vec<_>>>()?;
+        Ok(BertEncoder { layers })
+    }
+
     fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
         let mut hidden_states = hidden_states.clone();
         // Use a loop rather than a fold as it's easier to modify when adding debug/...
@@ -292,6 +411,15 @@ struct BertModel {
 }
 
 impl BertModel {
+    fn load(device: &Device, config: &Config) -> Result<Self> {
+        let embeddings = BertEmbeddings::load(device, config)?;
+        let encoder = BertEncoder::load(device, config)?;
+        Ok(Self {
+            embeddings,
+            encoder,
+        })
+    }
+
     fn forward(&self, input_ids: &Tensor, position_ids: &Tensor) -> Result<Tensor> {
         let embedding_output = self.embeddings.forward(input_ids, position_ids)?;
         let sequence_output = self.encoder.forward(&embedding_output)?;