Generic implementation of vecdot for q80. (#596)

* Generic implementation of vecdot for q80.

* Add support for code-llama 7b.

* Support more code-llama.
This commit is contained in:
Laurent Mazare
2023-08-25 09:04:05 +01:00
committed by GitHub
parent d8ba0452dc
commit c093b03d51
2 changed files with 41 additions and 7 deletions

View File

@ -421,8 +421,24 @@ impl GgmlType for BlockQ8_0 {
Ok(())
}
fn vec_dot(_: usize, _: &[Self], _: &[Self::VecDotType]) -> Result<f32> {
todo!()
fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
let qk = QK8_0;
if n % QK8_0 != 0 {
crate::bail!("vec_dot_q8_0_q8_0: {n} is not divisible by {qk}")
}
// Generic implementation.
let mut sumf = 0f32;
for (xs, ys) in xs.iter().zip(ys.iter()) {
let sum_i = xs
.qs
.iter()
.zip(ys.qs.iter())
.map(|(&x, &y)| x as i32 * y as i32)
.sum::<i32>();
sumf += sum_i as f32 * f16::to_f32(xs.d) * f16::to_f32(ys.d)
}
Ok(sumf)
}
}

View File

@ -195,10 +195,10 @@ impl WeightMap {
}
}
fn precomput_freqs_cis(head_dim: usize) -> Result<(Tensor, Tensor)> {
fn precomput_freqs_cis(head_dim: usize, freq_base: f32) -> Result<(Tensor, Tensor)> {
let theta: Vec<_> = (0..head_dim)
.step_by(2)
.map(|i| 1f32 / 10000f32.powf(i as f32 / head_dim as f32))
.map(|i| 1f32 / freq_base.powf(i as f32 / head_dim as f32))
.collect();
let theta = Tensor::new(theta.as_slice(), &Device::Cpu)?;
let idx_theta = Tensor::arange(0, MAX_SEQ_LEN as u32, &Device::Cpu)?
@ -214,7 +214,7 @@ impl ModelWeights {
fn from_ggml(mut ct: ggml_file::Content, gqa: usize) -> Result<Self> {
let cpu = &Device::Cpu;
let head_dim = (ct.hparams.n_embd / ct.hparams.n_head) as usize;
let (cos, sin) = precomput_freqs_cis(head_dim)?;
let (cos, sin) = precomput_freqs_cis(head_dim, 10000.)?;
let tok_embeddings = ct.remove("tok_embeddings.weight")?;
let tok_embeddings = tok_embeddings.dequantize(cpu)?;
let norm = RmsNorm::new(ct.remove("norm.weight")?, 1e-5)?;
@ -287,7 +287,10 @@ impl ModelWeights {
// Strangely this value is generally 1e-6 in GGUF file but used to be 1e-5 by default.
let rms_norm_eps = md_get("llama.attention.layer_norm_rms_epsilon")?.to_f32()?;
let (cos, sin) = precomput_freqs_cis(rope_dim)?;
let rope_freq_base = md_get("llama.rope.freq_base")
.and_then(|m| m.to_f32())
.unwrap_or(10000f32);
let (cos, sin) = precomput_freqs_cis(rope_dim, rope_freq_base)?;
let tok_embeddings = ct.tensor(reader, "token_embd.weight")?;
let tok_embeddings = tok_embeddings.dequantize(cpu)?;
@ -399,6 +402,12 @@ enum Which {
L13bChat,
#[value(name = "70b-chat")]
L70bChat,
#[value(name = "7b-code")]
L7bCode,
#[value(name = "13b-code")]
L13bCode,
#[value(name = "32b-code")]
L34bCode,
}
#[derive(Parser, Debug)]
@ -486,6 +495,9 @@ impl Args {
"TheBloke/Llama-2-70B-Chat-GGML",
"llama-2-70b-chat.ggmlv3.q4_0.bin",
),
Which::L7bCode => ("TheBloke/CodeLlama-7B-GGUF", "codellama-7b.Q8_0.gguf"),
Which::L13bCode => ("TheBloke/CodeLlama-13B-GGUF", "codellama-13b.Q8_0.gguf"),
Which::L34bCode => ("TheBloke/CodeLlama-34B-GGUF", "codellama-34b.Q8_0.gguf"),
};
let api = hf_hub::api::sync::Api::new()?;
let api = api.model(repo.to_string());
@ -607,7 +619,13 @@ fn main() -> anyhow::Result<()> {
);
println!("params: {:?}", model.hparams);
let default_gqa = match args.which {
Which::L7b | Which::L13b | Which::L7bChat | Which::L13bChat => 1,
Which::L7b
| Which::L13b
| Which::L7bChat
| Which::L13bChat
| Which::L7bCode
| Which::L13bCode
| Which::L34bCode => 1,
Which::L70b | Which::L70bChat => 8,
};
ModelWeights::from_ggml(model, args.gqa.unwrap_or(default_gqa))?