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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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4
candle-transformers/src/models/chinese_clip/vision_model.rs
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@ -6,7 +6,7 @@
//! - 💻 [Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP)
//! - 💻 [GH](https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/chinese_clip/modeling_chinese_clip.py_
use candle::{DType, IndexOp, Module, Result, Shape, Tensor, D};
use candle::{Context, DType, IndexOp, Module, Result, Shape, Tensor, D};
use candle_nn as nn;
use super::{Activation, EncoderConfig};
@ -363,7 +363,7 @@ impl ChineseClipVisionTransformer {
.apply(&self.pre_layer_norm)?;
let mut result = self.encoder.output_hidden_states(&hidden_states, None)?;
let encoder_outputs = result.last().unwrap();
let encoder_outputs = result.last().context("no last")?;
let pooled_output = encoder_outputs.i((.., 0, ..))?;
result.push(self.final_layer_norm.forward(&pooled_output)?.clone());
Ok(result)

4
candle-transformers/src/models/clip/vision_model.rs
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@ -6,7 +6,7 @@
//! https://github.com/openai/CLIP
//! https://github.com/huggingface/transformers/tree/f6fa0f0bf0796ac66f201f23bdb8585de1609add/src/transformers/models/clip
use candle::{IndexOp, Result, Shape, Tensor, D};
use candle::{Context, IndexOp, Result, Shape, Tensor, D};
use candle_nn as nn;
use candle_nn::Module;
use nn::Conv2dConfig;
@ -149,7 +149,7 @@ impl ClipVisionTransformer {
.apply(&self.embeddings)?
.apply(&self.pre_layer_norm)?;
let mut result = self.encoder.output_hidden_states(&hidden_states, None)?;
let encoder_outputs = result.last().unwrap();
let encoder_outputs = result.last().context("no last")?;
let pooled_output = encoder_outputs.i((.., 0, ..))?;
result.push(self.final_layer_norm.forward(&pooled_output)?.clone());
Ok(result)

4
candle-transformers/src/models/efficientnet.rs
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@ -3,7 +3,7 @@
//! See:
//! - ["EfficientBERT: Progressively Searching Multilayer Perceptron Architectures for BERT"](https://arxiv.org/abs/2201.00462)
//!
use candle::{Result, Tensor, D};
use candle::{Context, Result, Tensor, D};
use candle_nn as nn;
use nn::{Module, VarBuilder};
@ -289,7 +289,7 @@ impl EfficientNet {
pub fn new(p: VarBuilder, configs: Vec<MBConvConfig>, nclasses: usize) -> Result<Self> {
let f_p = p.pp("features");
let first_in_c = configs[0].input_channels;
let last_out_c = configs.last().unwrap().out_channels;
let last_out_c = configs.last().context("no last")?.out_channels;
let final_out_c = 4 * last_out_c;
let init_cna = ConvNormActivation::new(f_p.pp(0), 3, first_in_c, 3, 2, 1)?;
let nconfigs = configs.len();

4
candle-transformers/src/models/fastvit.rs
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@ -5,7 +5,7 @@
//!
//! Implementation based on [timm model](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/fastvit.py)
use candle::{DType, Result, Tensor, D};
use candle::{Context, DType, Result, Tensor, D};
use candle_nn::{
batch_norm, conv2d, conv2d_no_bias, linear, linear_no_bias, ops::sigmoid, ops::softmax,
BatchNorm, Conv2d, Conv2dConfig, Func, VarBuilder,
@ -178,7 +178,7 @@ fn squeeze_and_excitation(
// based on the _fuse_bn_tensor method in timm
// see https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/byobnet.py#L602
fn fuse_conv_bn(weights: &Tensor, bn: BatchNorm) -> Result<(Tensor, Tensor)> {
let (gamma, beta) = bn.weight_and_bias().unwrap();
let (gamma, beta) = bn.weight_and_bias().context("no weight-bias")?;
let mu = bn.running_mean();
let sigma = (bn.running_var() + bn.eps())?.sqrt();
let gps = (gamma / sigma)?;

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
}

4
candle-transformers/src/models/segformer.rs
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@ -15,7 +15,7 @@
//!
use crate::models::with_tracing::{conv2d, linear, Conv2d, Linear};
use candle::{Module, ModuleT, Result, Tensor, D};
use candle::{Context, Module, ModuleT, Result, Tensor, D};
use candle_nn::{conv2d_no_bias, layer_norm, Activation, Conv2dConfig, VarBuilder};
use serde::Deserialize;
use std::collections::HashMap;
@ -633,7 +633,7 @@ impl ImageClassificationModel {
impl Module for ImageClassificationModel {
fn forward(&self, x: &Tensor) -> Result<Tensor> {
let all_hidden_states = self.segformer.forward(x)?;
let hidden_states = all_hidden_states.last().unwrap();
let hidden_states = all_hidden_states.last().context("no last")?;
let hidden_states = hidden_states.flatten_from(2)?.permute((0, 2, 1))?;
let mean = hidden_states.mean(1)?;
self.classifier.forward(&mean)