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Quantized moondream implementation and BOS token (#1980)

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* moondream implementation

* add moondream example

* change config default activation

* Add assets and integrate phi mixformer with example

* Make use of kv cache and fix seq_len bug; Clean up example code

* Add README link to example

* Remove pos_embed scaling; Remove assets; Add to README; Expand VisionConfig

* Delete image

* Use apply instead of forward

* Pass bos token at the beginning of tensor.

* Quantize moondream.

* Forward with image bos token.

* Clippy.

* Use q4_0 quantization.

* Add pointers for sequence and tokens; Remove seq_len conditional
This commit is contained in:
Santiago Medina
2024-04-01 10:37:54 -07:00
committed by GitHub
parent 308ea070ed
commit ea0d8d3753
6 changed files with 393 additions and 32 deletions
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93
candle-examples/examples/moondream/main.rs
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@ -9,11 +9,19 @@ use clap::Parser;
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::{generation::LogitsProcessor, models::moondream};
use candle_transformers::{
generation::LogitsProcessor,
models::{moondream, quantized_moondream},
};
use tokenizers::Tokenizer;
enum Model {
Moondream(moondream::Model),
Quantized(quantized_moondream::Model),
}
struct TextGeneration {
model: moondream::Model,
model: Model,
device: Device,
tokenizer: Tokenizer,
logits_processor: LogitsProcessor,
@ -25,7 +33,7 @@ struct TextGeneration {
impl TextGeneration {
#[allow(clippy::too_many_arguments)]
fn new(
model: moondream::Model,
model: Model,
tokenizer: Tokenizer,
seed: u64,
temp: Option<f64>,
@ -64,6 +72,14 @@ impl TextGeneration {
let mut tokens = tokens.get_ids().to_vec();
let mut generated_tokens = 0usize;
// Moondream tokenizer bos_token is "<|endoftext|>"
// https://huggingface.co/vikhyatk/moondream2/blob/main/special_tokens_map.json
let bos_token = match self.tokenizer.get_vocab(true).get("<|endoftext|>") {
Some(token) => *token,
None => anyhow::bail!("cannot find the BOS token"),
};
// eos_token is "END"
// https://github.com/vikhyat/moondream/blob/a9d788a20d1543fb1479edc54106e88cff7759d3/moondream/moondream.py#L100
let eos_token = match self.tokenizer.get_vocab(true).get("END") {
Some(token) => *token,
None => anyhow::bail!("cannot find the EOS token"),
@ -75,11 +91,24 @@ impl TextGeneration {
let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
let logits = if index > 0 {
self.model.text_model.forward(&input)?
match self.model {
Model::Moondream(ref mut model) => model.text_model.forward(&input)?,
Model::Quantized(ref mut model) => model.text_model.forward(&input)?,
}
} else {
self.model
.text_model
.forward_with_img(&input, image_embeds)?
let bos_token = Tensor::new(&[bos_token], &self.device)?.unsqueeze(0)?;
match self.model {
Model::Moondream(ref mut model) => {
model
.text_model
.forward_with_img(&bos_token, &input, image_embeds)?
}
Model::Quantized(ref mut model) => {
model
.text_model
.forward_with_img(&bos_token, &input, image_embeds)?
}
}
};
let logits = logits.squeeze(0)?.to_dtype(DType::F32)?;
let logits = if self.repeat_penalty == 1. {
@ -142,7 +171,7 @@ struct Args {
top_p: Option<f64>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
#[arg(long, default_value_t = 0)]
seed: u64,
#[arg(long, default_value_t = 5000)]
@ -156,12 +185,15 @@ struct Args {
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
#[arg(long, default_value = "vikhyatk/moondream2")]
model_id: String,
#[arg(long)]
model_id: Option<String>,
#[arg(long, default_value = "main")]
revision: String,
#[arg(long)]
quantized: bool,
#[arg(long)]
model_file: Option<String>,
@ -216,14 +248,30 @@ async fn main() -> anyhow::Result<()> {
let start = std::time::Instant::now();
let api = hf_hub::api::tokio::Api::new()?;
let model_id = match args.model_id {
Some(model_id) => model_id.to_string(),
None => {
if args.quantized {
"santiagomed/candle-moondream".to_string()
} else {
"vikhyatk/moondream2".to_string()
}
}
};
let repo = api.repo(hf_hub::Repo::with_revision(
args.model_id,
model_id,
hf_hub::RepoType::Model,
args.revision,
));
let model_file = match args.model_file {
Some(m) => m.into(),
None => repo.get("model.safetensors").await?,
None => {
if args.quantized {
repo.get("model-q4_0.gguf").await?
} else {
repo.get("model.safetensors").await?
}
}
};
let tokenizer = match args.tokenizer_file {
Some(m) => m.into(),
@ -234,22 +282,35 @@ async fn main() -> anyhow::Result<()> {
let start = std::time::Instant::now();
let device = candle_examples::device(args.cpu)?;
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
let config = moondream::Config::v2();
let model = moondream::Model::new(&config, vb)?;
let model = if args.quantized {
let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(
&model_file,
&device,
)?;
let model = quantized_moondream::Model::new(&config, vb)?;
Model::Quantized(model)
} else {
let vb =
unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
let model = moondream::Model::new(&config, vb)?;
Model::Moondream(model)
};
println!("loaded the model in {:?}", start.elapsed());
let start = std::time::Instant::now();
let image = load_image(args.image)?.to_device(&device)?;
let image_embeds = image.unsqueeze(0)?;
let image_embeds = image_embeds.apply(model.vision_encoder())?;
let image_embeds = match model {
Model::Moondream(ref m) => image_embeds.apply(m.vision_encoder())?,
Model::Quantized(ref m) => image_embeds.apply(m.vision_encoder())?,
};
println!(
"loaded and encoded the image {image:?} in {:?}",
start.elapsed()
);
let prompt = format!("\n\nQuestion: {0}\n\nAnswer:", args.prompt);
let mut pipeline = TextGeneration::new(
model,
tokenizer,