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Add a Context trait similar to anyhow::Context. (#2676)

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* Add a Context trait similar to anyhow::Context.

* Switch two unwrap to context.
This commit is contained in:
Laurent Mazare
2024-12-22 09:18:13 +01:00
committed by GitHub
parent 5c2f893e5a
commit 62ced44ea9
13 changed files with 97 additions and 41 deletions
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22
candle-transformers/src/models/llava/mod.rs
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@ -14,7 +14,7 @@ use crate::models::clip::vision_model::{ClipVisionConfig, ClipVisionTransformer}
use crate::models::llama::{Cache, Llama};
use crate::models::with_tracing::linear;
use candle::{bail, Device, IndexOp, Result, Tensor};
use candle::{bail, Context, Device, IndexOp, Result, Tensor};
use candle_nn::{seq, Activation, Module, Sequential, VarBuilder};
use fancy_regex::Regex;
use utils::get_anyres_image_grid_shape;
@ -145,7 +145,7 @@ impl ClipVisionTower {
let config = if config.is_none() {
ClipVisionConfig::clip_vit_large_patch14_336()
} else {
config.clone().unwrap()
config.clone().context("no config")?
};
let select_layer = match select_layer {
-1 | -2 => select_layer,
@ -262,14 +262,14 @@ impl LLaVA {
let image_features = if mm_patch_merge_type == "flat" {
image_features
.iter()
.map(|x| x.flatten(0, 1).unwrap())
.collect::<Vec<Tensor>>()
.map(|x| x.flatten(0, 1))
.collect::<Result<Vec<Tensor>>>()?
} else if mm_patch_merge_type.starts_with("spatial") {
let mut new_image_features = Vec::new();
for (image_idx, image_feature) in image_features.iter().enumerate() {
let new_image_feature = if image_feature.dims()[0] > 1 {
let base_image_feature = image_feature.get(0).unwrap();
let patch_image_feature = image_feature.i(1..).unwrap();
let base_image_feature = image_feature.get(0)?;
let patch_image_feature = image_feature.i(1..)?;
let height = self.clip_vision_tower.num_patches_per_side();
let width = height;
assert_eq!(height * width, base_image_feature.dims()[0]);
@ -313,16 +313,12 @@ impl LLaVA {
};
Tensor::cat(&[base_image_feature, new_image_feature], 0)?
} else {
let new_image_feature = image_feature.get(0).unwrap();
let new_image_feature = image_feature.get(0)?;
if mm_patch_merge_type.contains("unpad") {
Tensor::cat(
&[
new_image_feature,
self.image_newline.clone().unsqueeze(0).unwrap(),
],
&[new_image_feature, self.image_newline.clone().unsqueeze(0)?],
0,
)
.unwrap()
)?
} else {
new_image_feature
}