text-generation-inference/router/src/config.rs
Daniël de Kok b5bc6e5c4e
Add GPT-2 with flash attention (#1889)
# What does this PR do?

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This change adds `FlashGPT2ForCausalLM` and wires it up. The model
itself is pretty straightforward, the main difference from other models
is that it uses trained position embeddings and that all weight matrices
are transposed compared to other models (due to the use of Conv1D in the
upstream model).


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Fixes # (issue)


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2024-05-15 13:31:22 +02:00

200 lines
6.0 KiB
Rust

use serde::{Deserialize, Serialize};
#[derive(Clone, Debug, Serialize, Deserialize)]
#[serde(tag = "model_type")]
#[serde(rename_all = "snake_case")]
pub struct LlavaNext {
text_config: TextConfig,
vision_config: VisionConfig,
image_grid_pinpoints: Vec<(usize, usize)>,
}
fn get_anyres_image_grid_shape(
height: usize,
width: usize,
grid_pinpoints: &[(usize, usize)],
patch_size: usize,
) -> (usize, usize) {
let (height, width) = select_best_resolution(height, width, grid_pinpoints);
(height / patch_size, width / patch_size)
}
/// Selects the best resolution from a list of possible resolutions based on the original size.
/// This is done by calculating the effective and wasted resolution for each possible resolution.
/// The best fit resolution is the one that maximizes the effective resolution and minimizes the wasted resolution.
fn select_best_resolution(
original_height: usize,
original_width: usize,
possible_resolutions: &[(usize, usize)],
) -> (usize, usize) {
let mut best_fit = None;
let mut max_effective_resolution = 0;
let mut min_wasted_resolution = f32::NEG_INFINITY;
for (height, width) in possible_resolutions {
let wscale = *width as f32 / original_width as f32;
let hscale = *height as f32 / original_height as f32;
// f32 partial ord.
let scale = if wscale > hscale { hscale } else { wscale };
let downscaled_width = (*width as f32 * scale) as usize;
let downscaled_height = (*height as f32 * scale) as usize;
let effective_resolution = std::cmp::min(
downscaled_width * downscaled_height,
original_width * original_height,
);
let wasted_resolution = (width * height) - effective_resolution;
if effective_resolution > max_effective_resolution
|| (effective_resolution == max_effective_resolution
&& (wasted_resolution as f32) < min_wasted_resolution)
{
max_effective_resolution = effective_resolution;
min_wasted_resolution = wasted_resolution as f32;
best_fit = Some((*height, *width));
}
}
best_fit.unwrap_or((original_height, original_width))
}
fn get_unpadded_features(
height: usize,
width: usize,
npatches: usize,
num_patch_height: usize,
num_patch_width: usize,
) -> (usize, usize) {
let current_height = npatches * num_patch_height;
let current_width = npatches * num_patch_width;
let aspect_ratio: f64 = width as f64 / height as f64;
let current_aspect_ratio: f64 = current_width as f64 / current_height as f64;
let (current_height, current_width) = if aspect_ratio > current_aspect_ratio {
let new_height = (height * current_width) / width;
(new_height, current_width)
} else {
let new_width = (width * current_height) / height;
(current_height, new_width)
};
let unpadded_features = current_height * current_width;
let newline_features = current_height;
(unpadded_features, newline_features)
}
impl LlavaNext {
pub fn get_number_of_features(&self, height: usize, width: usize) -> usize {
let image_size = self.vision_config.image_size;
let patch_size = self.vision_config.patch_size;
assert!(image_size % patch_size == 0);
let npatches = image_size / patch_size;
let (num_patch_height, num_patch_width) =
get_anyres_image_grid_shape(height, width, &self.image_grid_pinpoints, image_size);
let (unpadded_features, newline_features) =
get_unpadded_features(height, width, npatches, num_patch_height, num_patch_width);
// The base patch covers the entire image
let base_features = npatches.pow(2);
unpadded_features + newline_features + base_features
}
}
#[derive(Clone, Debug, Serialize, Deserialize)]
#[serde(tag = "model_type")]
#[serde(rename_all = "snake_case")]
pub struct ClipVisionModel {
image_size: usize,
patch_size: usize,
}
#[derive(Clone, Debug, Serialize, Deserialize)]
#[serde(tag = "model_type")]
#[serde(rename_all = "snake_case")]
pub struct Idefics2 {}
impl Idefics2 {
pub fn get_number_of_features(&self, _height: usize, _width: usize) -> usize {
320
}
}
#[derive(Clone, Debug, Serialize, Deserialize)]
#[serde(tag = "model_type")]
#[serde(rename_all = "snake_case")]
pub enum Config {
LlavaNext(LlavaNext),
ClipVisionModel(ClipVisionModel),
Mistral,
Idefics,
Idefics2(Idefics2),
Ssm,
GptBigcode,
Santacoder,
Bloom,
Mpt,
Gpt2,
GptNeox,
Phi,
#[serde(rename = "phi-msft")]
PhiMsft,
Phi3,
Llama,
Baichuan,
Gemma,
Cohere,
Drbx,
Falcon,
Mixtral,
Starcoder2,
Qwen2,
Opt,
T5,
}
#[derive(Clone, Debug, Serialize, Deserialize)]
#[serde(rename_all = "snake_case")]
pub struct TextConfig {}
#[derive(Clone, Debug, Serialize, Deserialize)]
#[serde(rename_all = "snake_case")]
pub struct VisionConfig {
image_size: usize,
patch_size: usize,
}
#[cfg(test)]
mod test {
use super::*;
#[test]
fn test_llava_next_features() {
let config = LlavaNext {
text_config: TextConfig {},
vision_config: VisionConfig {
image_size: 336,
patch_size: 14,
},
image_grid_pinpoints: vec![
(336, 672),
(672, 336),
(672, 672),
(1008, 336),
(336, 1008),
],
};
let slots = config.get_number_of_features(20, 20);
assert_eq!(slots, 1176);
let slots = config.get_number_of_features(640, 640);
assert_eq!(slots, 2928);
let slots = config.get_number_of_features(480, 640);
assert_eq!(slots, 2340);
let slots = config.get_number_of_features(899, 1024);
assert_eq!(slots, 2634);
let slots = config.get_number_of_features(1024, 899);
assert_eq!(slots, 2640);
let slots = config.get_number_of_features(1067, 1600);
assert_eq!(slots, 2144);
}
}