mirror of
https://github.com/guillaume-be/rust-bert.git
synced 2024-09-11 12:55:34 +03:00
Shallow clone optimization (#243)
* Fixed Clippy warnings * Shallow clone optimization (reduce tensor copy) * Updated changelog and fixed Clippy warnings
This commit is contained in:
parent
6f1888e8f9
commit
ba584653bc
@ -130,7 +130,7 @@ impl AlbertLayerGroup {
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None
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};
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let mut hidden_state = hidden_states.copy();
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let mut hidden_state = hidden_states.shallow_clone();
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let mut attention_weights: Option<Tensor>;
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for layer in &self.layers {
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@ -28,8 +28,8 @@ pub struct LayerState {
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impl Clone for LayerState {
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fn clone(&self) -> Self {
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LayerState {
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prev_key: self.prev_key.copy(),
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prev_value: self.prev_value.copy(),
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prev_key: self.prev_key.shallow_clone(),
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prev_value: self.prev_value.shallow_clone(),
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}
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}
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}
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@ -138,8 +138,8 @@ impl BartAttention {
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let new_layer_state = if self.store_cache {
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Some(LayerState {
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prev_key: key_states.copy(),
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prev_value: value_states.copy(),
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prev_key: key_states.shallow_clone(),
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prev_value: value_states.shallow_clone(),
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})
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} else {
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None
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@ -109,7 +109,7 @@ impl EncoderLayer {
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let output: Tensor = output.apply_t(&self.dropout, train) + x;
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let output = output.apply(&self.self_attention_layer_norm);
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let residual = output.copy();
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let residual = output.shallow_clone();
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let output = (self.activation.get_fn())(&output.apply(&self.fc1));
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let output = output
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.apply_t(&self.activation_dropout, train)
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@ -337,7 +337,7 @@ impl<T: BertEmbedding> BertModel<T> {
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let encoder_hidden_states = encoder_hidden_states.as_ref().unwrap();
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let encoder_hidden_states_shape = encoder_hidden_states.size();
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let encoder_mask = match encoder_mask {
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Some(value) => value.copy(),
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Some(value) => value.shallow_clone(),
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None => Tensor::ones(
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&[
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encoder_hidden_states_shape[0],
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@ -416,7 +416,7 @@ impl BertPredictionHeadTransform {
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}
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pub fn forward(&self, hidden_states: &Tensor) -> Tensor {
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((&self.activation.get_fn())(&hidden_states.apply(&self.dense))).apply(&self.layer_norm)
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(self.activation.get_fn()(&hidden_states.apply(&self.dense))).apply(&self.layer_norm)
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}
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}
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@ -205,7 +205,7 @@ impl DebertaDisentangledSelfAttention {
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let r_pos = if query_layer_size[1] != key_layer_size[1] {
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build_relative_position(key_layer_size[1], key_layer_size[1], query_layer.device())
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} else {
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relative_pos.copy()
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relative_pos.shallow_clone()
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};
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let p2c_pos = (-r_pos + attention_span).clamp(0, attention_span * 2 - 1);
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let mut p2c_att = key_layer
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@ -333,10 +333,10 @@ impl DebertaV2Encoder {
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}
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attention_weights = layer_output.1;
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if let Some(attentions) = all_attentions.borrow_mut() {
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attentions.push(attention_weights.as_ref().unwrap().copy());
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attentions.push(attention_weights.as_ref().unwrap().shallow_clone());
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};
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if let Some(hidden_states) = all_hidden_states.borrow_mut() {
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hidden_states.push(output_states.as_ref().unwrap().copy());
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hidden_states.push(output_states.as_ref().unwrap().shallow_clone());
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};
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}
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@ -150,7 +150,6 @@ impl Transformer {
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None
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};
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// let mut hidden_state = input.copy();
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let mut hidden_state: Option<Tensor> = None;
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let mut attention_weights: Option<Tensor>;
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@ -175,7 +175,7 @@ impl Attention {
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}
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None => (key, value),
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};
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let present = Tensor::stack(&[key.transpose(-2, -1), value.copy()], 0);
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let present = Tensor::stack(&[key.transpose(-2, -1), value.shallow_clone()], 0);
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let (a, attentions) = self.attention(&query, &key, &value, attention_mask, train);
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let a = self
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@ -386,7 +386,7 @@ impl Gpt2Model {
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(
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value
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.iter()
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.map(|v| Some(v.copy()))
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.map(|v| Some(v.shallow_clone()))
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.collect::<Vec<Option<Tensor>>>(),
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value[0].size()[3],
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)
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@ -399,7 +399,7 @@ impl Gpt2Model {
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};
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let position_ids = match position_ids {
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Some(value) => value.copy(),
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Some(value) => value.shallow_clone(),
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None => Tensor::arange_start(
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layer_past_length,
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seq_length + layer_past_length,
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@ -29,8 +29,8 @@ pub struct LayerState {
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impl Clone for LayerState {
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fn clone(&self) -> Self {
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LayerState {
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prev_key: self.prev_key.copy(),
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prev_value: self.prev_value.as_ref().map(|value| value.copy()),
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prev_key: self.prev_key.shallow_clone(),
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prev_value: self.prev_value.as_ref().map(|value| value.shallow_clone()),
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}
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}
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}
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@ -211,8 +211,8 @@ impl GptNeoSelfAttention {
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};
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let layer_state = Some(LayerState {
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prev_key: key.copy(),
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prev_value: Some(value.copy()),
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prev_key: key.shallow_clone(),
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prev_value: Some(value.shallow_clone()),
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});
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let (attention_output, attention_weights) =
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@ -107,7 +107,7 @@ impl MBartEncoderLayer {
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.forward_t(&output, None, encoder_attention_mask, None, train);
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let output: Tensor = output.apply_t(&self.dropout, train) + x;
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let residual = output.copy();
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let residual = output.shallow_clone();
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let output = output.apply(&self.final_layer_norm);
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let output = (self.activation.get_fn())(&output.apply(&self.fc1));
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let output = output
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@ -221,7 +221,7 @@ impl OpenAiGptModel {
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let seq_length = input_shape[1];
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let position_ids = match position_ids {
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Some(value) => value.copy(),
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Some(value) => value.shallow_clone(),
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None => Tensor::arange(seq_length, (Int64, input_embeddings.device())).unsqueeze(0),
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};
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@ -507,7 +507,7 @@ pub(crate) mod private_generation_utils {
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&Tensor::arange_start(1, vocab_size, (Int64, logits.device())),
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&sorted_indices_to_remove
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.slice(1, 0, vocab_size - 1, 1)
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.copy(),
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.shallow_clone(),
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);
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let _ = sorted_indices_to_remove.index_fill_(
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1,
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@ -749,8 +749,8 @@ pub(crate) mod private_generation_utils {
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let (bad_word_ids_length_1, bad_word_ids_length_greater_than_1) =
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self.split_bad_word_ids(gen_opt.bad_word_ids);
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let mut static_bad_words_mask: Option<Tensor> = None;
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let mut attention_mask = attention_mask.copy();
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let mut input_ids = input_ids.copy();
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let mut attention_mask = attention_mask.shallow_clone();
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let mut input_ids = input_ids.shallow_clone();
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let mut past: Cache = Cache::None;
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let mut outputs: Tensor;
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let mut current_length = cur_len;
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@ -759,10 +759,10 @@ pub(crate) mod private_generation_utils {
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while current_length < gen_opt.max_length {
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let prepared_input = self.prepare_inputs_for_generation(
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input_ids.copy(),
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input_ids.shallow_clone(),
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encoder_outputs.as_ref(),
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past,
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attention_mask.copy(),
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attention_mask.shallow_clone(),
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);
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let temp = self
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.get_model()
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@ -1020,10 +1020,10 @@ pub(crate) mod private_generation_utils {
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);
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}
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let prepared_input = self.prepare_inputs_for_generation(
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input_ids.copy(),
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input_ids.shallow_clone(),
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encoder_outputs.as_ref(),
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past,
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attention_mask.copy(),
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attention_mask.shallow_clone(),
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);
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let temp = self
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.get_model()
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@ -1240,10 +1240,10 @@ pub(crate) mod private_generation_utils {
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saved_beam_scores.as_ref().map(|step_wise_scores| {
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Tensor::stack(step_wise_scores, 1)
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.get(effective_beam_id)
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.copy()
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.shallow_clone()
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});
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hypotheses[batch_index as usize].add(
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input_ids.get(effective_beam_id).copy(),
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input_ids.get(effective_beam_id).shallow_clone(),
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beam_token_score,
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saved_beam_scores,
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);
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@ -1301,7 +1301,7 @@ pub(crate) mod private_generation_utils {
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}
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if let Some(scores_output) = saved_beam_scores.as_mut() {
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scores_output.push(beam_scores.copy());
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scores_output.push(beam_scores.shallow_clone());
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}
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if done.iter().all(|&x| x) {
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break;
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@ -2105,10 +2105,10 @@ impl Clone for BeamHypotheses {
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.map(|(score, tensor, scores_tensor)| {
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(
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*score,
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tensor.copy(),
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tensor.shallow_clone(),
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scores_tensor
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.as_ref()
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.map(|scores_tensor| scores_tensor.copy()),
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.map(|scores_tensor| scores_tensor.shallow_clone()),
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)
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})
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.collect::<Vec<(f64, Tensor, Option<Tensor>)>>(),
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@ -31,8 +31,8 @@ pub struct LayerState {
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impl Clone for LayerState {
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fn clone(&self) -> Self {
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LayerState {
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prev_key: self.prev_key.copy(),
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prev_value: self.prev_value.copy(),
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prev_key: self.prev_key.shallow_clone(),
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prev_value: self.prev_value.shallow_clone(),
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}
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}
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}
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@ -27,7 +27,7 @@ use tch::{nn, Device, Kind, Tensor};
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fn ngram_attention_bias(sequence_length: i64, ngram: i64, device: Device, kind: Kind) -> Tensor {
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let left_block = Tensor::ones(&[ngram, sequence_length, sequence_length], (kind, device))
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* get_negative_infinity(kind).unwrap();
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let right_block = left_block.copy();
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let right_block = left_block.shallow_clone();
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for stream_idx in 0..ngram {
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let _ = right_block.get(stream_idx).fill_diagonal_(0, false);
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let _ = left_block.get(stream_idx).triu_(-stream_idx + 1);
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@ -68,7 +68,7 @@ impl ProphetNetPositionalEmbeddings {
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attention_mask.cumsum(1, Kind::Int64) * attention_mask + self.padding_idx
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}
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}
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Some(value) => value.copy(),
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Some(value) => value.shallow_clone(),
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};
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(calc_position_ids.apply(&self.embeddings), calc_position_ids)
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@ -40,10 +40,10 @@ impl Clone for LayerState {
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let prev_buckets = self
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.prev_buckets
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.as_ref()
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.map(|prev_buckets| prev_buckets.copy());
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.map(|prev_buckets| prev_buckets.shallow_clone());
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LayerState {
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prev_buckets,
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prev_states: self.prev_states.copy(),
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prev_states: self.prev_states.shallow_clone(),
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}
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}
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}
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@ -417,7 +417,7 @@ impl LSHSelfAttention {
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None,
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)?;
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let key_value_bucket_idx = look_adjacent(
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query_bucket_idx.copy(),
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query_bucket_idx.shallow_clone(),
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self.num_chunks_before,
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self.num_chunks_after,
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);
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@ -446,7 +446,7 @@ impl LSHSelfAttention {
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(query_bucket_idx, key_value_bucket_idx)
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} else {
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(
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sorted_bucket_indices_per_hash.copy(),
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sorted_bucket_indices_per_hash.shallow_clone(),
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sorted_bucket_indices_per_hash,
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)
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};
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@ -849,7 +849,7 @@ impl LSHSelfAttention {
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false
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}
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} {
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(sorted_bucket_idx.unwrap().copy(), None)
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(sorted_bucket_idx.unwrap().shallow_clone(), None)
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} else {
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(
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Tensor::arange(sequence_length, (Kind::Int64, query_key_vectors.device()))
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@ -1121,7 +1121,7 @@ impl LocalSelfAttention {
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self.num_attention_heads,
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None,
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)?;
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let key_indices = query_indices.copy();
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let key_indices = query_indices.shallow_clone();
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key_vectors = look_adjacent(key_vectors, self.num_chunks_before, self.num_chunks_after);
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value_vectors =
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@ -1130,7 +1130,7 @@ impl LocalSelfAttention {
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look_adjacent(key_indices, self.num_chunks_before, self.num_chunks_after);
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(query_indices, key_indices)
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} else {
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(indices.copy(), indices.copy())
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(indices.shallow_clone(), indices.shallow_clone())
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};
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let mut query_key_dots = query_vectors.matmul(&key_vectors.transpose(-1, -2));
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@ -1356,7 +1356,7 @@ impl ReformerAttention {
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if original_sequence_length > 1 {
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Some(buckets_value.slice(3, 0, original_sequence_length, 1))
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} else {
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Some(buckets_value.copy())
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Some(buckets_value.shallow_clone())
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}
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} else {
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Some(Tensor::cat(
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@ -291,8 +291,8 @@ impl ReformerEncoder {
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original_sequence_length: i64,
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train: bool,
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) -> Result<ReformerModelOutput, RustBertError> {
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let mut hidden_state = hidden_states.copy();
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let mut attention_output = hidden_states.copy();
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let mut hidden_state = hidden_states.shallow_clone();
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let mut attention_output = hidden_states.shallow_clone();
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let mut all_hidden_states: Option<Vec<Tensor>> = if self.output_hidden_states {
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Some(Vec::with_capacity(self.layers.len()))
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} else {
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@ -30,8 +30,8 @@ pub struct LayerState {
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impl Clone for LayerState {
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fn clone(&self) -> Self {
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LayerState {
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prev_key: self.prev_key.copy(),
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prev_value: self.prev_value.copy(),
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prev_key: self.prev_key.shallow_clone(),
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prev_value: self.prev_value.shallow_clone(),
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}
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}
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}
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@ -176,15 +176,15 @@ impl T5Attention {
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k = Tensor::cat(&[&layer_state.prev_key, &k], 2);
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v = Tensor::cat(&[&layer_state.prev_value, &v], 2);
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} else {
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k = layer_state.prev_key.copy();
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v = layer_state.prev_value.copy();
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k = layer_state.prev_key.shallow_clone();
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v = layer_state.prev_value.shallow_clone();
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}
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};
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layer_state = if self.is_decoder & self.store_cache {
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Some(LayerState {
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prev_key: k.copy(),
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prev_value: v.copy(),
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prev_key: k.shallow_clone(),
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prev_value: v.shallow_clone(),
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})
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} else {
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None
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|
@ -434,7 +434,7 @@ impl T5Stack {
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let encoder_hidden_states = encoder_hidden_states.as_ref().unwrap();
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let encoder_hidden_states_shape = encoder_hidden_states.size();
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let encoder_mask = match encoder_attention_mask {
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Some(value) => value.copy(),
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Some(value) => value.shallow_clone(),
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None => Tensor::ones(
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&[
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encoder_hidden_states_shape[0],
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|
@ -29,7 +29,7 @@ pub struct LayerState {
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impl Clone for LayerState {
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fn clone(&self) -> Self {
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LayerState {
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prev_content: self.prev_content.copy(),
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prev_content: self.prev_content.shallow_clone(),
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}
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}
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}
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|
@ -67,7 +67,7 @@ impl XLNetFeedForward {
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pub fn forward_t(&self, input: &Tensor, train: bool) -> Tensor {
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let output = input.apply(&self.layer_1);
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let output: Tensor = (&self.activation.get_fn())(&output);
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let output: Tensor = self.activation.get_fn()(&output);
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let output = output
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.apply_t(&self.dropout, train)
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.apply(&self.layer_2)
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|
@ -516,9 +516,9 @@ impl XLNetModel {
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&[m_len, batch_size],
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(Kind::Int64, token_type_ids_value.device()),
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);
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Tensor::cat(&[mem_pad, token_type_ids_value.copy()], 0)
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Tensor::cat(&[mem_pad, token_type_ids_value.shallow_clone()], 0)
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} else {
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token_type_ids_value.copy()
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token_type_ids_value.shallow_clone()
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};
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let seg_mat = token_type_ids_value
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.unsqueeze(-1)
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|
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