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Use candle_nn::embedding instead of local copies in a few models. (#1562)

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This commit is contained in:
Jani Monoses
2024-01-10 22:36:27 +02:00
committed by GitHub
parent d3bdd788cf
commit 63944714f2
5 changed files with 6 additions and 31 deletions
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7
candle-transformers/src/models/bert.rs
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@ -1,6 +1,6 @@
use super::with_tracing::{layer_norm, linear, LayerNorm, Linear};
use candle::{DType, Device, Result, Tensor};
use candle_nn::{Embedding, Module, VarBuilder};
use candle_nn::{embedding, Embedding, Module, VarBuilder};
use serde::Deserialize;
pub const DTYPE: DType = DType::F32;
@ -112,11 +112,6 @@ impl Config {
}
}
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))
}
struct Dropout {
#[allow(dead_code)]
pr: f64,

7
candle-transformers/src/models/bigcode.rs
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@ -1,5 +1,5 @@
use candle::{DType, Device, IndexOp, Result, Tensor, D};
use candle_nn::{Embedding, LayerNorm, Linear, Module, VarBuilder};
use candle_nn::{embedding, Embedding, LayerNorm, Linear, Module, VarBuilder};
fn linear(size1: usize, size2: usize, bias: bool, vb: VarBuilder) -> Result<Linear> {
let weight = vb.get((size2, size1), "weight")?;
@ -11,11 +11,6 @@ fn linear(size1: usize, size2: usize, bias: bool, vb: VarBuilder) -> Result<Line
Ok(Linear::new(weight, bias))
}
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 layer_norm(size: usize, eps: f64, vb: VarBuilder) -> Result<LayerNorm> {
let weight = vb.get(size, "weight")?;
let bias = vb.get(size, "bias")?;

7
candle-transformers/src/models/falcon.rs
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@ -1,5 +1,5 @@
use candle::{DType, Device, Result, Tensor, D};
use candle_nn::{Embedding, LayerNorm, Linear, Module, VarBuilder};
use candle_nn::{embedding, Embedding, LayerNorm, Linear, Module, VarBuilder};
const MAX_SEQ_LEN: usize = 5000;
@ -27,11 +27,6 @@ fn layer_norm(size: usize, eps: f64, vb: VarBuilder) -> Result<LayerNorm> {
Ok(LayerNorm::new(weight, bias, eps))
}
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))
}
// https://raw.githubusercontent.com/huggingface/transformers/030c863aaa0165e98352b61697430bf69bf33755/src/transformers/models/falcon/configuration_falcon.py
#[derive(Debug)]
pub struct Config {

9
candle-transformers/src/models/llama.rs
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@ -1,6 +1,6 @@
use super::with_tracing::{linear_no_bias as linear, Linear};
use candle::{DType, Device, IndexOp, Result, Tensor, D};
use candle_nn::{Embedding, Module, VarBuilder};
use candle_nn::{embedding, Embedding, Module, VarBuilder};
use serde::Deserialize;
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
@ -136,11 +136,6 @@ impl Cache {
}
}
fn embedding(cfg: &Config, vb: VarBuilder) -> Result<Embedding> {
let embeddings = vb.get((cfg.vocab_size, cfg.hidden_size), "weight")?;
Ok(Embedding::new(embeddings, cfg.hidden_size))
}
struct RmsNorm {
inner: candle_nn::RmsNorm,
span: tracing::Span,
@ -409,7 +404,7 @@ impl Llama {
}
pub fn load(vb: VarBuilder, cache: &Cache, cfg: &Config) -> Result<Self> {
let wte = embedding(cfg, vb.pp("model.embed_tokens"))?;
let wte = embedding(cfg.vocab_size, cfg.hidden_size, vb.pp("model.embed_tokens"))?;
let lm_head = linear(cfg.hidden_size, cfg.vocab_size, vb.pp("lm_head"))?;
let ln_f = RmsNorm::load(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("model.norm"))?;
let blocks: Vec<_> = (0..cfg.num_hidden_layers)

7
candle-transformers/src/models/whisper/model.rs
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@ -1,12 +1,7 @@
use super::Config;
use crate::models::with_tracing::{linear, linear_no_bias, Linear};
use candle::{Device, IndexOp, Result, Tensor, D};
use candle_nn::{Conv1d, Conv1dConfig, Embedding, LayerNorm, Module, VarBuilder};
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))
}
use candle_nn::{embedding, Conv1d, Conv1dConfig, Embedding, LayerNorm, Module, VarBuilder};
fn conv1d(
in_channels: usize,