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Diffstat (limited to 'candle-transformers/src/models/bert.rs')
| -rw-r--r-- | candle-transformers/src/models/bert.rs | 568 |
1 files changed, 568 insertions, 0 deletions
diff --git a/candle-transformers/src/models/bert.rs b/candle-transformers/src/models/bert.rs new file mode 100644 index 00000000..3f164a3a --- /dev/null +++ b/candle-transformers/src/models/bert.rs @@ -0,0 +1,568 @@ +use candle::{DType, Device, Result, Tensor}; +use candle_nn::{Embedding, Module, VarBuilder}; +use serde::Deserialize; + +pub const DTYPE: DType = DType::F32; + +#[derive(Debug, Clone, Copy, PartialEq, Eq, Deserialize)] +#[serde(rename_all = "lowercase")] +enum HiddenAct { + Gelu, + Relu, +} + +struct HiddenActLayer { + act: HiddenAct, + span: tracing::Span, +} + +impl HiddenActLayer { + fn new(act: HiddenAct) -> Self { + let span = tracing::span!(tracing::Level::TRACE, "hidden-act"); + Self { act, span } + } + + fn forward(&self, xs: &Tensor) -> candle::Result<Tensor> { + let _enter = self.span.enter(); + match self.act { + // TODO: The all-MiniLM-L6-v2 model uses "gelu" whereas this is "gelu_new", this explains some + // small numerical difference. + // https://github.com/huggingface/transformers/blob/cd4584e3c809bb9e1392ccd3fe38b40daba5519a/src/transformers/activations.py#L213 + HiddenAct::Gelu => xs.gelu(), + HiddenAct::Relu => xs.relu(), + } + } +} + +#[derive(Debug)] +pub struct Linear { + weight: Tensor, + bias: Option<Tensor>, + span: tracing::Span, +} + +impl Linear { + pub fn new(weight: Tensor, bias: Option<Tensor>) -> Self { + let span = tracing::span!(tracing::Level::TRACE, "linear"); + Self { weight, bias, span } + } + + pub fn forward(&self, x: &Tensor) -> candle::Result<Tensor> { + let _enter = self.span.enter(); + let w = match x.dims() { + &[bsize, _, _] => self.weight.broadcast_left(bsize)?.t()?, + _ => self.weight.t()?, + }; + let x = x.matmul(&w)?; + match &self.bias { + None => Ok(x), + Some(bias) => x.broadcast_add(bias), + } + } +} + +#[derive(Debug)] +pub struct LayerNorm { + weight: Tensor, + bias: Tensor, + eps: f64, + span: tracing::Span, +} + +impl LayerNorm { + pub fn new(weight: Tensor, bias: Tensor, eps: f64) -> Self { + let span = tracing::span!(tracing::Level::TRACE, "layer-norm"); + Self { + weight, + bias, + eps, + span, + } + } + + pub fn forward(&self, x: &Tensor) -> Result<Tensor> { + let _enter = self.span.enter(); + let x_dtype = x.dtype(); + let internal_dtype = match x_dtype { + DType::F16 | DType::BF16 => DType::F32, + d => d, + }; + let (_bsize, _seq_len, hidden_size) = x.dims3()?; + let x = x.to_dtype(internal_dtype)?; + let mean_x = (x.sum_keepdim(2)? / hidden_size as f64)?; + let x = x.broadcast_sub(&mean_x)?; + let norm_x = (x.sqr()?.sum_keepdim(2)? / hidden_size as f64)?; + let x_normed = x.broadcast_div(&(norm_x + self.eps)?.sqrt()?)?; + let x = x_normed + .to_dtype(x_dtype)? + .broadcast_mul(&self.weight)? + .broadcast_add(&self.bias)?; + Ok(x) + } +} +#[derive(Debug, Clone, Copy, PartialEq, Eq, Deserialize, Default)] +#[serde(rename_all = "lowercase")] +enum PositionEmbeddingType { + #[default] + Absolute, +} + +// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/configuration_bert.py#L1 +#[derive(Debug, Clone, PartialEq, Deserialize)] +pub struct Config { + vocab_size: usize, + hidden_size: usize, + num_hidden_layers: usize, + num_attention_heads: usize, + intermediate_size: usize, + hidden_act: HiddenAct, + hidden_dropout_prob: f64, + max_position_embeddings: usize, + type_vocab_size: usize, + initializer_range: f64, + layer_norm_eps: f64, + pad_token_id: usize, + #[serde(default)] + position_embedding_type: PositionEmbeddingType, + #[serde(default)] + use_cache: bool, + classifier_dropout: Option<f64>, + model_type: Option<String>, +} + +impl Default for Config { + fn default() -> Self { + Self { + vocab_size: 30522, + hidden_size: 768, + num_hidden_layers: 12, + num_attention_heads: 12, + intermediate_size: 3072, + hidden_act: HiddenAct::Gelu, + hidden_dropout_prob: 0.1, + max_position_embeddings: 512, + type_vocab_size: 2, + initializer_range: 0.02, + layer_norm_eps: 1e-12, + pad_token_id: 0, + position_embedding_type: PositionEmbeddingType::Absolute, + use_cache: true, + classifier_dropout: None, + model_type: Some("bert".to_string()), + } + } +} + +impl Config { + fn _all_mini_lm_l6_v2() -> Self { + // https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/blob/main/config.json + Self { + vocab_size: 30522, + hidden_size: 384, + num_hidden_layers: 6, + num_attention_heads: 12, + intermediate_size: 1536, + hidden_act: HiddenAct::Gelu, + hidden_dropout_prob: 0.1, + max_position_embeddings: 512, + type_vocab_size: 2, + initializer_range: 0.02, + layer_norm_eps: 1e-12, + pad_token_id: 0, + position_embedding_type: PositionEmbeddingType::Absolute, + use_cache: true, + classifier_dropout: None, + model_type: Some("bert".to_string()), + } + } +} + +fn embedding(vocab_size: usize, hidden_size: usize, vb: VarBuilder) -> Result<Embedding> { + let embeddings = vb.get((vocab_size, hidden_size), "weight")?; + Ok(Embedding::new(embeddings, hidden_size)) +} + +fn linear(size1: usize, size2: usize, vb: VarBuilder) -> Result<Linear> { + let weight = vb.get((size2, size1), "weight")?; + let bias = vb.get(size2, "bias")?; + Ok(Linear::new(weight, Some(bias))) +} + +struct Dropout { + #[allow(dead_code)] + pr: f64, +} + +impl Dropout { + fn new(pr: f64) -> Self { + Self { pr } + } + + fn forward(&self, x: &Tensor) -> Result<Tensor> { + // TODO + Ok(x.clone()) + } +} + +fn layer_norm(size: usize, eps: f64, vb: VarBuilder) -> Result<LayerNorm> { + let (weight, bias) = match (vb.get(size, "weight"), vb.get(size, "bias")) { + (Ok(weight), Ok(bias)) => (weight, bias), + (Err(err), _) | (_, Err(err)) => { + if let (Ok(weight), Ok(bias)) = (vb.get(size, "gamma"), vb.get(size, "beta")) { + (weight, bias) + } else { + return Err(err); + } + } + }; + Ok(LayerNorm::new(weight, bias, eps)) +} + +// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L180 +struct BertEmbeddings { + word_embeddings: Embedding, + position_embeddings: Option<Embedding>, + token_type_embeddings: Embedding, + layer_norm: LayerNorm, + dropout: Dropout, + span: tracing::Span, +} + +impl BertEmbeddings { + fn load(vb: VarBuilder, config: &Config) -> Result<Self> { + let word_embeddings = embedding( + config.vocab_size, + config.hidden_size, + vb.pp("word_embeddings"), + )?; + let position_embeddings = embedding( + config.max_position_embeddings, + config.hidden_size, + vb.pp("position_embeddings"), + )?; + let token_type_embeddings = embedding( + config.type_vocab_size, + config.hidden_size, + vb.pp("token_type_embeddings"), + )?; + let layer_norm = layer_norm( + config.hidden_size, + config.layer_norm_eps, + vb.pp("LayerNorm"), + )?; + Ok(Self { + word_embeddings, + position_embeddings: Some(position_embeddings), + token_type_embeddings, + layer_norm, + dropout: Dropout::new(config.hidden_dropout_prob), + span: tracing::span!(tracing::Level::TRACE, "embeddings"), + }) + } + + fn forward(&self, input_ids: &Tensor, token_type_ids: &Tensor) -> Result<Tensor> { + let _enter = self.span.enter(); + let (_bsize, seq_len) = input_ids.dims2()?; + let input_embeddings = self.word_embeddings.forward(input_ids)?; + let token_type_embeddings = self.token_type_embeddings.forward(token_type_ids)?; + let mut embeddings = (&input_embeddings + token_type_embeddings)?; + if let Some(position_embeddings) = &self.position_embeddings { + // TODO: Proper absolute positions? + let position_ids = (0..seq_len as u32).collect::<Vec<_>>(); + let position_ids = Tensor::new(&position_ids[..], input_ids.device())?; + embeddings = embeddings.broadcast_add(&position_embeddings.forward(&position_ids)?)? + } + let embeddings = self.layer_norm.forward(&embeddings)?; + let embeddings = self.dropout.forward(&embeddings)?; + Ok(embeddings) + } +} + +struct BertSelfAttention { + query: Linear, + key: Linear, + value: Linear, + dropout: Dropout, + num_attention_heads: usize, + attention_head_size: usize, + span: tracing::Span, + span_softmax: tracing::Span, +} + +impl BertSelfAttention { + fn load(vb: VarBuilder, 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 hidden_size = config.hidden_size; + let query = linear(hidden_size, all_head_size, vb.pp("query"))?; + let value = linear(hidden_size, all_head_size, vb.pp("value"))?; + let key = linear(hidden_size, all_head_size, vb.pp("key"))?; + Ok(Self { + query, + key, + value, + dropout, + num_attention_heads: config.num_attention_heads, + attention_head_size, + span: tracing::span!(tracing::Level::TRACE, "self-attn"), + span_softmax: tracing::span!(tracing::Level::TRACE, "softmax"), + }) + } + + fn transpose_for_scores(&self, xs: &Tensor) -> Result<Tensor> { + let mut new_x_shape = xs.dims().to_vec(); + new_x_shape.pop(); + new_x_shape.push(self.num_attention_heads); + new_x_shape.push(self.attention_head_size); + let xs = xs.reshape(new_x_shape.as_slice())?.transpose(1, 2)?; + xs.contiguous() + } + + fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> { + let _enter = self.span.enter(); + let query_layer = self.query.forward(hidden_states)?; + let key_layer = self.key.forward(hidden_states)?; + let value_layer = self.value.forward(hidden_states)?; + + let query_layer = self.transpose_for_scores(&query_layer)?; + let key_layer = self.transpose_for_scores(&key_layer)?; + let value_layer = self.transpose_for_scores(&value_layer)?; + + let attention_scores = query_layer.matmul(&key_layer.t()?)?; + let attention_scores = (attention_scores / (self.attention_head_size as f64).sqrt())?; + let attention_probs = { + let _enter_sm = self.span_softmax.enter(); + candle_nn::ops::softmax(&attention_scores, candle::D::Minus1)? + }; + let attention_probs = self.dropout.forward(&attention_probs)?; + + let context_layer = attention_probs.matmul(&value_layer)?; + let context_layer = context_layer.transpose(1, 2)?.contiguous()?; + let context_layer = context_layer.flatten_from(candle::D::Minus2)?; + Ok(context_layer) + } +} + +struct BertSelfOutput { + dense: Linear, + layer_norm: LayerNorm, + dropout: Dropout, + span: tracing::Span, +} + +impl BertSelfOutput { + fn load(vb: VarBuilder, config: &Config) -> Result<Self> { + let dense = linear(config.hidden_size, config.hidden_size, vb.pp("dense"))?; + let layer_norm = layer_norm( + config.hidden_size, + config.layer_norm_eps, + vb.pp("LayerNorm"), + )?; + let dropout = Dropout::new(config.hidden_dropout_prob); + Ok(Self { + dense, + layer_norm, + dropout, + span: tracing::span!(tracing::Level::TRACE, "self-out"), + }) + } + + fn forward(&self, hidden_states: &Tensor, input_tensor: &Tensor) -> Result<Tensor> { + let _enter = self.span.enter(); + let hidden_states = self.dense.forward(hidden_states)?; + let hidden_states = self.dropout.forward(&hidden_states)?; + self.layer_norm.forward(&(hidden_states + input_tensor)?) + } +} + +// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L392 +struct BertAttention { + self_attention: BertSelfAttention, + self_output: BertSelfOutput, + span: tracing::Span, +} + +impl BertAttention { + fn load(vb: VarBuilder, config: &Config) -> Result<Self> { + let self_attention = BertSelfAttention::load(vb.pp("self"), config)?; + let self_output = BertSelfOutput::load(vb.pp("output"), config)?; + Ok(Self { + self_attention, + self_output, + span: tracing::span!(tracing::Level::TRACE, "attn"), + }) + } + + fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> { + let _enter = self.span.enter(); + let self_outputs = self.self_attention.forward(hidden_states)?; + let attention_output = self.self_output.forward(&self_outputs, hidden_states)?; + Ok(attention_output) + } +} + +// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L441 +struct BertIntermediate { + dense: Linear, + intermediate_act: HiddenActLayer, + span: tracing::Span, +} + +impl BertIntermediate { + fn load(vb: VarBuilder, config: &Config) -> Result<Self> { + let dense = linear(config.hidden_size, config.intermediate_size, vb.pp("dense"))?; + Ok(Self { + dense, + intermediate_act: HiddenActLayer::new(config.hidden_act), + span: tracing::span!(tracing::Level::TRACE, "inter"), + }) + } + + fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> { + let _enter = self.span.enter(); + let hidden_states = self.dense.forward(hidden_states)?; + let ys = self.intermediate_act.forward(&hidden_states)?; + Ok(ys) + } +} + +// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L456 +struct BertOutput { + dense: Linear, + layer_norm: LayerNorm, + dropout: Dropout, + span: tracing::Span, +} + +impl BertOutput { + fn load(vb: VarBuilder, config: &Config) -> Result<Self> { + let dense = linear(config.intermediate_size, config.hidden_size, vb.pp("dense"))?; + let layer_norm = layer_norm( + config.hidden_size, + config.layer_norm_eps, + vb.pp("LayerNorm"), + )?; + let dropout = Dropout::new(config.hidden_dropout_prob); + Ok(Self { + dense, + layer_norm, + dropout, + span: tracing::span!(tracing::Level::TRACE, "out"), + }) + } + + fn forward(&self, hidden_states: &Tensor, input_tensor: &Tensor) -> Result<Tensor> { + let _enter = self.span.enter(); + let hidden_states = self.dense.forward(hidden_states)?; + let hidden_states = self.dropout.forward(&hidden_states)?; + self.layer_norm.forward(&(hidden_states + input_tensor)?) + } +} + +// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L470 +struct BertLayer { + attention: BertAttention, + intermediate: BertIntermediate, + output: BertOutput, + span: tracing::Span, +} + +impl BertLayer { + fn load(vb: VarBuilder, config: &Config) -> Result<Self> { + let attention = BertAttention::load(vb.pp("attention"), config)?; + let intermediate = BertIntermediate::load(vb.pp("intermediate"), config)?; + let output = BertOutput::load(vb.pp("output"), config)?; + Ok(Self { + attention, + intermediate, + output, + span: tracing::span!(tracing::Level::TRACE, "layer"), + }) + } + + fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> { + let _enter = self.span.enter(); + let attention_output = self.attention.forward(hidden_states)?; + // TODO: Support cross-attention? + // https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L523 + // TODO: Support something similar to `apply_chunking_to_forward`? + let intermediate_output = self.intermediate.forward(&attention_output)?; + let layer_output = self + .output + .forward(&intermediate_output, &attention_output)?; + Ok(layer_output) + } +} + +// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L556 +struct BertEncoder { + layers: Vec<BertLayer>, + span: tracing::Span, +} + +impl BertEncoder { + fn load(vb: VarBuilder, config: &Config) -> Result<Self> { + let layers = (0..config.num_hidden_layers) + .map(|index| BertLayer::load(vb.pp(&format!("layer.{index}")), config)) + .collect::<Result<Vec<_>>>()?; + let span = tracing::span!(tracing::Level::TRACE, "encoder"); + Ok(BertEncoder { layers, span }) + } + + fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> { + let _enter = self.span.enter(); + let mut hidden_states = hidden_states.clone(); + // Use a loop rather than a fold as it's easier to modify when adding debug/... + for layer in self.layers.iter() { + hidden_states = layer.forward(&hidden_states)? + } + Ok(hidden_states) + } +} + +// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L874 +pub struct BertModel { + embeddings: BertEmbeddings, + encoder: BertEncoder, + pub device: Device, + span: tracing::Span, +} + +impl BertModel { + pub fn load(vb: VarBuilder, config: &Config) -> Result<Self> { + let (embeddings, encoder) = match ( + BertEmbeddings::load(vb.pp("embeddings"), config), + BertEncoder::load(vb.pp("encoder"), config), + ) { + (Ok(embeddings), Ok(encoder)) => (embeddings, encoder), + (Err(err), _) | (_, Err(err)) => { + if let Some(model_type) = &config.model_type { + if let (Ok(embeddings), Ok(encoder)) = ( + BertEmbeddings::load(vb.pp(&format!("{model_type}.embeddings")), config), + BertEncoder::load(vb.pp(&format!("{model_type}.encoder")), config), + ) { + (embeddings, encoder) + } else { + return Err(err); + } + } else { + return Err(err); + } + } + }; + Ok(Self { + embeddings, + encoder, + device: vb.device().clone(), + span: tracing::span!(tracing::Level::TRACE, "model"), + }) + } + + pub fn forward(&self, input_ids: &Tensor, token_type_ids: &Tensor) -> Result<Tensor> { + let _enter = self.span.enter(); + let embedding_output = self.embeddings.forward(input_ids, token_type_ids)?; + let sequence_output = self.encoder.forward(&embedding_output)?; + Ok(sequence_output) + } +} |