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Consolidate the with-tracing usage. (#1234)

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This commit is contained in:
Laurent Mazare
2023-11-01 19:21:36 +01:00
committed by GitHub
parent 693fad511c
commit 1704f1b3ae
4 changed files with 8 additions and 102 deletions
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36
candle-transformers/src/models/bert.rs
View File

@ -1,3 +1,4 @@
use super::with_tracing::{linear, Linear};
use candle::{DType, Device, Result, Tensor};
use candle_nn::{Embedding, Module, VarBuilder};
use serde::Deserialize;
@ -32,35 +33,6 @@ impl HiddenActLayer {
}
}
#[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 }
}
}
impl Module for Linear {
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,
@ -184,12 +156,6 @@ fn embedding(vocab_size: usize, hidden_size: usize, vb: VarBuilder) -> Result<Em
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,

22
candle-transformers/src/models/llama.rs
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@ -1,3 +1,4 @@
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 serde::Deserialize;
@ -81,21 +82,6 @@ impl Config {
}
}
// We wrap the `Linear` layer here to add some tracing so that it's easier to profile the resulting
// model.
#[derive(Debug)]
pub struct Linear {
inner: candle_nn::Linear,
span: tracing::Span,
}
impl Linear {
fn forward(&self, x: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
self.inner.forward(x)
}
}
#[derive(Clone)]
pub struct Cache {
masks: Arc<Mutex<HashMap<usize, Tensor>>>,
@ -150,12 +136,6 @@ impl Cache {
}
}
fn linear(size1: usize, size2: usize, vb: VarBuilder) -> Result<Linear> {
let span = tracing::span!(tracing::Level::TRACE, "linear");
let inner = candle_nn::linear_no_bias(size1, size2, vb)?;
Ok(Linear { inner, span })
}
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))

24
candle-transformers/src/models/segment_anything/mod.rs
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@ -1,3 +1,4 @@
pub use crate::models::with_tracing::Linear;
use candle::{Result, Tensor};
use candle_nn::{Module, VarBuilder};
@ -9,13 +10,11 @@ pub mod tiny_vit;
pub mod transformer;
pub fn linear(vb: VarBuilder, in_dim: usize, out_dim: usize, bias: bool) -> Result<Linear> {
let inner = if bias {
candle_nn::linear(in_dim, out_dim, vb)?
if bias {
crate::models::with_tracing::linear(in_dim, out_dim, vb)
} else {
candle_nn::linear_no_bias(in_dim, out_dim, vb)?
};
let span = tracing::span!(tracing::Level::TRACE, "linear");
Ok(Linear { inner, span })
crate::models::with_tracing::linear_no_bias(in_dim, out_dim, vb)
}
}
#[derive(Debug)]
@ -85,16 +84,3 @@ impl Module for MlpBlock {
.apply(&self.lin2)
}
}
#[derive(Debug)]
pub struct Linear {
inner: candle_nn::Linear,
span: tracing::Span,
}
impl Module for Linear {
fn forward(&self, x: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
self.inner.forward(x)
}
}

28
candle-transformers/src/models/whisper/model.rs
View File

@ -1,4 +1,5 @@
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};
@ -6,33 +7,6 @@ fn embedding(vocab_size: usize, hidden_size: usize, vb: VarBuilder) -> Result<Em
let embeddings = vb.get((vocab_size, hidden_size), "weight")?;
Ok(Embedding::new(embeddings, hidden_size))
}
//
// We wrap the `Linear` layer here to add some tracing so that it's easier to profile the resulting
// model.
#[derive(Debug, Clone)]
pub struct Linear {
inner: candle_nn::Linear,
span: tracing::Span,
}
impl Linear {
fn forward(&self, x: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
self.inner.forward(x)
}
}
fn linear(size1: usize, size2: usize, vb: VarBuilder) -> Result<Linear> {
let span = tracing::span!(tracing::Level::TRACE, "linear");
let inner = candle_nn::linear(size1, size2, vb)?;
Ok(Linear { inner, span })
}
fn linear_no_bias(size1: usize, size2: usize, vb: VarBuilder) -> Result<Linear> {
let span = tracing::span!(tracing::Level::TRACE, "linear");
let inner = candle_nn::linear_no_bias(size1, size2, vb)?;
Ok(Linear { inner, span })
}
fn conv1d(
in_channels: usize,