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Quantized support for stable-lm2. (#1654)

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* Quantized support for stable-lm2.

* Quantized support for v2-zephyr.
This commit is contained in:
Laurent Mazare
2024-02-04 11:57:05 +01:00
committed by GitHub
parent 58cc896e69
commit 50be8a98ba
3 changed files with 36 additions and 10 deletions
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4
candle-examples/examples/stable-lm/README.md
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@ -10,7 +10,9 @@ order to be able to use it.
Other available models are Stable-Code-3B, StableLM-2 and Zephyr variants. Other available models are Stable-Code-3B, StableLM-2 and Zephyr variants.
StableLM-2 uses a Tiktoken based GPT-3.5/GPT-4 tokenizer not supported by Candle, so to run it you can download a somewhat compatible [tokenizer.json](https://huggingface.co/Xenova/gpt-4/resolve/main/tokenizer.json?download=true) StableLM-2 uses a Tiktoken based GPT-3.5/GPT-4 tokenizer not supported by
Candle, so to run it you can download a somewhat compatible
[tokenizer.json](https://huggingface.co/Xenova/gpt-4/resolve/main/tokenizer.json?download=true)
and pass it via the --tokenizer-file argument. and pass it via the --tokenizer-file argument.
## Running some example ## Running some example

29
candle-examples/examples/stable-lm/main.rs
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@ -162,7 +162,7 @@ struct Args {
seed: u64, seed: u64,
/// The length of the sample to generate (in tokens). /// The length of the sample to generate (in tokens).
#[arg(long, short = 'n', default_value_t = 100)] #[arg(long, short = 'n', default_value_t = 1000)]
sample_len: usize, sample_len: usize,
#[arg(long)] #[arg(long)]
@ -171,7 +171,7 @@ struct Args {
#[arg(long, default_value = "main")] #[arg(long, default_value = "main")]
revision: String, revision: String,
#[arg(long, default_value = "v1-orig")] #[arg(long, default_value = "v2")]
which: Which, which: Which,
#[arg(long)] #[arg(long)]
@ -239,7 +239,14 @@ fn main() -> Result<()> {
)); ));
let tokenizer_filename = match args.tokenizer_file { let tokenizer_filename = match args.tokenizer_file {
Some(file) => std::path::PathBuf::from(file), Some(file) => std::path::PathBuf::from(file),
None => repo.get("tokenizer.json")?, None => match args.which {
Which::V1Orig | Which::V1 | Which::V1Zephyr | Which::Code => {
repo.get("tokenizer.json")?
}
Which::V2 | Which::V2Zephyr => api
.model("lmz/candle-stablelm".to_string())
.get("tokenizer-gpt4.json")?,
},
}; };
let filenames = match args.weight_files { let filenames = match args.weight_files {
Some(files) => files Some(files) => files
@ -247,8 +254,20 @@ fn main() -> Result<()> {
.map(std::path::PathBuf::from) .map(std::path::PathBuf::from)
.collect::<Vec<_>>(), .collect::<Vec<_>>(),
None => match (args.which, args.quantized) { None => match (args.which, args.quantized) {
(Which::V1Orig, true) => vec![repo.get("model-q4k.gguf")?], (Which::V1Orig | Which::V1, true) => vec![repo.get("model-q4k.gguf")?],
(Which::V1 | Which::V1Zephyr | Which::V2 | Which::V2Zephyr | Which::Code, true) => { (Which::V2, true) => {
let gguf = api
.model("lmz/candle-stablelm".to_string())
.get("stablelm-2-1_6b-q4k.gguf")?;
vec![gguf]
}
(Which::V2Zephyr, true) => {
let gguf = api
.model("lmz/candle-stablelm".to_string())
.get("stablelm-2-zephyr-1_6b-q4k.gguf")?;
vec![gguf]
}
(Which::V1Zephyr | Which::Code, true) => {
anyhow::bail!("Quantized {:?} variant not supported.", args.which) anyhow::bail!("Quantized {:?} variant not supported.", args.which)
} }
(Which::V1Orig | Which::V1 | Which::V1Zephyr | Which::V2 | Which::V2Zephyr, false) => { (Which::V1Orig | Which::V1 | Which::V1Zephyr | Which::V2 | Which::V2Zephyr, false) => {

13
candle-transformers/src/models/quantized_stable_lm.rs
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@ -1,4 +1,4 @@
use crate::quantized_nn::{layer_norm, linear_no_bias, Embedding, Linear}; use crate::quantized_nn::{layer_norm, linear, linear_no_bias, Embedding, Linear};
pub use crate::quantized_var_builder::VarBuilder; pub use crate::quantized_var_builder::VarBuilder;
use candle::{DType, Device, Module, Result, Tensor, D}; use candle::{DType, Device, Module, Result, Tensor, D};
use candle_nn::{Activation, LayerNorm}; use candle_nn::{Activation, LayerNorm};
@ -67,9 +67,14 @@ impl Attention {
let head_dim = cfg.head_dim(); let head_dim = cfg.head_dim();
let num_heads = cfg.num_attention_heads; let num_heads = cfg.num_attention_heads;
let num_kv_heads = cfg.num_key_value_heads; let num_kv_heads = cfg.num_key_value_heads;
let q_proj = linear_no_bias(hidden_sz, num_heads * head_dim, vb.pp("q_proj"))?; let linear_layer = if cfg.use_qkv_bias {
let k_proj = linear_no_bias(hidden_sz, num_kv_heads * head_dim, vb.pp("k_proj"))?; linear
let v_proj = linear_no_bias(hidden_sz, num_kv_heads * head_dim, vb.pp("v_proj"))?; } else {
linear_no_bias
};
let q_proj = linear_layer(hidden_sz, num_heads * head_dim, vb.pp("q_proj"))?;
let k_proj = linear_layer(hidden_sz, num_kv_heads * head_dim, vb.pp("k_proj"))?;
let v_proj = linear_layer(hidden_sz, num_kv_heads * head_dim, vb.pp("v_proj"))?;
let o_proj = linear_no_bias(num_heads * head_dim, hidden_sz, vb.pp("o_proj"))?; let o_proj = linear_no_bias(num_heads * head_dim, hidden_sz, vb.pp("o_proj"))?;
Ok(Self { Ok(Self {
q_proj, q_proj,