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synced 2025-06-16 10:38:54 +00:00
Updated quantized phi model (#2099)
* Quantized phi in a separate file. * Add the quantized phi example + rework the model code. * Improve the phi model. * Get some generation out. * Use the appropriate rope shape. * Tweak the default prompt. --------- Co-authored-by: Jane Doe <jane.doe@example.org>
This commit is contained in:
Laurent Mazare
@ -37,6 +37,7 @@ pub mod quantized_mistral;
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pub mod quantized_mixformer;
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pub mod quantized_moondream;
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pub mod quantized_mpt;
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pub mod quantized_phi;
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pub mod quantized_recurrent_gemma;
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pub mod quantized_rwkv_v5;
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pub mod quantized_rwkv_v6;
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288
candle-transformers/src/models/quantized_phi.rs
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288
candle-transformers/src/models/quantized_phi.rs
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@ -0,0 +1,288 @@
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use std::collections::HashMap;
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use candle::quantized::gguf_file;
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use candle::quantized::QTensor;
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use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
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use candle_nn::{Embedding, LayerNorm};
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pub const MAX_SEQ_LEN: usize = 4096;
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#[derive(Debug, Clone)]
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struct QLinear {
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inner: candle::quantized::QMatMul,
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bias: Tensor,
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span: tracing::Span,
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}
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impl QLinear {
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fn new<R: std::io::Read + std::io::Seek>(
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ct: &gguf_file::Content,
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r: &mut R,
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name: &str,
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device: &Device,
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) -> Result<Self> {
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let span = tracing::span!(tracing::Level::TRACE, "qmatmul");
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let w = ct.tensor(r, &format!("{name}.weight"), device)?;
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let b = ct.tensor(r, &format!("{name}.bias"), device)?;
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let inner = candle::quantized::QMatMul::from_qtensor(w)?;
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let bias = b.dequantize(device)?;
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Ok(Self { inner, bias, span })
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}
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}
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impl Module for QLinear {
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fn forward(&self, xs: &Tensor) -> Result<Tensor> {
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let _enter = self.span.enter();
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self.inner.forward(xs)?.broadcast_add(&self.bias)
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}
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}
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#[derive(Debug, Clone)]
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struct Mlp {
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ffn_up: QLinear,
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ffn_down: QLinear,
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}
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impl Module for Mlp {
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fn forward(&self, xs: &Tensor) -> Result<Tensor> {
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xs.apply(&self.ffn_up)?.gelu()?.apply(&self.ffn_down)
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}
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}
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#[derive(Debug, Clone)]
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struct LayerWeights {
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attn_qkv: QLinear,
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attn_output: QLinear,
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attn_norm: LayerNorm,
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mlp: Mlp,
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n_head: usize,
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n_kv_head: usize,
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head_dim: usize,
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cos: Tensor,
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sin: Tensor,
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rope_dim: usize,
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neg_inf: Tensor,
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kv_cache: Option<(Tensor, Tensor)>,
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span_attn: tracing::Span,
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span_rot: tracing::Span,
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}
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fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: &Tensor) -> Result<Tensor> {
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let shape = mask.shape();
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let m = mask.where_cond(&on_true.broadcast_as(shape.dims())?, on_false)?;
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Ok(m)
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}
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impl LayerWeights {
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fn apply_rotary_emb(&self, xs: &Tensor, index_pos: usize) -> Result<Tensor> {
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let _enter = self.span_rot.enter();
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let (_b_sz, _n_head, seq_len, _n_embd) = xs.dims4()?;
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let xs_rot = xs.i((.., .., .., ..self.rope_dim))?;
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let xs_pass = xs.i((.., .., .., self.rope_dim..))?;
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let cos = self.cos.narrow(0, index_pos, seq_len)?;
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let sin = self.sin.narrow(0, index_pos, seq_len)?;
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let xs_rot = candle_nn::rotary_emb::rope(&xs_rot.contiguous()?, &cos, &sin)?;
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Tensor::cat(&[&xs_rot, &xs_pass], D::Minus1)
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}
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fn forward_attn(
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&mut self,
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x: &Tensor,
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mask: Option<&Tensor>,
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index_pos: usize,
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) -> Result<Tensor> {
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let _enter = self.span_attn.enter();
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let (b_sz, seq_len, n_embd) = x.dims3()?;
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let qkv =
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self.attn_qkv
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.forward(x)?
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.reshape((b_sz, seq_len, 3, self.n_head, self.head_dim))?;
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let q = qkv.i((.., .., 0))?.transpose(1, 2)?;
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let k = qkv.i((.., .., 1))?.transpose(1, 2)?;
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let v = qkv.i((.., .., 2))?.transpose(1, 2)?;
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// This call to contiguous ensures that the fast kernel can be called below. It's
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// actually a no-op except when processing the initial prompt so has no significant
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// impact on performance.
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let v = v.contiguous()?;
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let q = self.apply_rotary_emb(&q, index_pos)?.contiguous()?;
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let k = self.apply_rotary_emb(&k, index_pos)?;
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let (k, v) = match &self.kv_cache {
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None => (k.contiguous()?, v.contiguous()?),
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Some((k_cache, v_cache)) => {
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if index_pos == 0 {
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(k.contiguous()?, v.contiguous()?)
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} else {
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let k = Tensor::cat(&[k_cache, &k], 2)?;
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let v = Tensor::cat(&[v_cache, &v], 2)?;
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(k.contiguous()?, v.contiguous()?)
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}
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}
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};
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self.kv_cache = Some((k.clone(), v.clone()));
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let k = crate::utils::repeat_kv(k, self.n_head / self.n_kv_head)?;
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let v = crate::utils::repeat_kv(v, self.n_head / self.n_kv_head)?;
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let att = (q.matmul(&k.t()?)? / (self.head_dim as f64).sqrt())?;
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let att = match mask {
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None => att,
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Some(mask) => {
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let mask = mask.broadcast_as(att.shape())?;
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masked_fill(&att, &mask, &self.neg_inf)?
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}
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};
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let att = candle_nn::ops::softmax_last_dim(&att)?;
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// Convert to contiguous as matmul doesn't support strided vs for now.
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let y = att.matmul(&v.contiguous()?)?;
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let y = y.transpose(1, 2)?.reshape(&[b_sz, seq_len, n_embd])?;
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let y = self.attn_output.forward(&y)?;
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Ok(y)
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}
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}
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#[derive(Debug, Clone)]
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pub struct ModelWeights {
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tok_embeddings: Embedding,
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layers: Vec<LayerWeights>,
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output_norm: LayerNorm,
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output: QLinear,
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masks: HashMap<usize, Tensor>,
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span: tracing::Span,
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span_output: tracing::Span,
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}
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fn precomput_freqs_cis(
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head_dim: usize,
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freq_base: f32,
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device: &Device,
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) -> Result<(Tensor, Tensor)> {
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let theta: Vec<_> = (0..head_dim)
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.step_by(2)
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.map(|i| 1f32 / freq_base.powf(i as f32 / head_dim as f32))
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.collect();
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let theta = Tensor::new(theta.as_slice(), device)?;
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let idx_theta = Tensor::arange(0, MAX_SEQ_LEN as u32, device)?
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.to_dtype(DType::F32)?
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.reshape((MAX_SEQ_LEN, 1))?
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.matmul(&theta.reshape((1, theta.elem_count()))?)?;
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let cos = idx_theta.cos()?;
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let sin = idx_theta.sin()?;
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Ok((cos, sin))
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}
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fn layer_norm(w: QTensor, b: QTensor, eps: f64) -> Result<LayerNorm> {
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let w = w.dequantize(&w.device())?;
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let b = b.dequantize(&b.device())?;
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let ln = LayerNorm::new(w, b, eps);
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Ok(ln)
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}
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impl ModelWeights {
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pub fn from_gguf<R: std::io::Seek + std::io::Read>(
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ct: gguf_file::Content,
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reader: &mut R,
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device: &Device,
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) -> Result<Self> {
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let md_get = |s: &str| match ct.metadata.get(s) {
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None => candle::bail!("cannot find {s} in metadata"),
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Some(v) => Ok(v),
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};
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// Parameter extraction from metadata.
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let head_count = md_get("phi2.attention.head_count")?.to_u32()? as usize;
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let head_count_kv = md_get("phi2.attention.head_count_kv")?.to_u32()? as usize;
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let block_count = md_get("phi2.block_count")?.to_u32()? as usize;
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let embedding_length = md_get("phi2.embedding_length")?.to_u32()? as usize;
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let rope_dim = md_get("phi2.rope.dimension_count")?.to_u32()? as usize;
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let ln_eps = md_get("phi2.attention.layer_norm_epsilon")?.to_f32()? as f64;
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let (cos, sin) = precomput_freqs_cis(rope_dim, 10_000., device)?;
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let neg_inf = Tensor::new(f32::NEG_INFINITY, device)?;
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let tok_embeddings = ct.tensor(reader, "token_embd.weight", device)?;
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let tok_embeddings = tok_embeddings.dequantize(device)?;
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let output_norm = layer_norm(
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ct.tensor(reader, "output_norm.weight", device)?,
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ct.tensor(reader, "output_norm.bias", device)?,
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ln_eps,
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)?;
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let output = QLinear::new(&ct, reader, "output", device)?;
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let mut layers = Vec::with_capacity(block_count);
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for layer_idx in 0..block_count {
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let prefix = format!("blk.{layer_idx}");
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let ffn_up = QLinear::new(&ct, reader, &format!("{prefix}.ffn_up"), device)?;
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let ffn_down = QLinear::new(&ct, reader, &format!("{prefix}.ffn_down"), device)?;
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let mlp = Mlp { ffn_up, ffn_down };
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let attn_norm = layer_norm(
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ct.tensor(reader, &format!("{prefix}.attn_norm.weight"), device)?,
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ct.tensor(reader, &format!("{prefix}.attn_norm.bias"), device)?,
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ln_eps,
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)?;
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let span_attn = tracing::span!(tracing::Level::TRACE, "attn");
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let span_rot = tracing::span!(tracing::Level::TRACE, "attn-rot");
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layers.push(LayerWeights {
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attn_qkv: QLinear::new(&ct, reader, &format!("{prefix}.attn_qkv"), device)?,
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attn_output: QLinear::new(&ct, reader, &format!("{prefix}.attn_output"), device)?,
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attn_norm,
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mlp,
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n_head: head_count,
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n_kv_head: head_count_kv,
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head_dim: embedding_length / head_count,
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cos: cos.clone(),
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sin: sin.clone(),
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rope_dim,
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neg_inf: neg_inf.clone(),
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kv_cache: None,
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span_attn,
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span_rot,
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})
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}
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let span = tracing::span!(tracing::Level::TRACE, "model");
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let span_output = tracing::span!(tracing::Level::TRACE, "output");
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Ok(Self {
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tok_embeddings: Embedding::new(tok_embeddings, embedding_length),
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layers,
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output_norm,
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output,
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masks: HashMap::new(),
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span,
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span_output,
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})
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}
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fn mask(&mut self, t: usize, device: &Device) -> Result<Tensor> {
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if let Some(mask) = self.masks.get(&t) {
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Ok(mask.clone())
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} else {
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let mask: Vec<_> = (0..t)
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.flat_map(|i| (0..t).map(move |j| u8::from(j > i)))
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.collect();
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let mask = Tensor::from_slice(&mask, (t, t), device)?;
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self.masks.insert(t, mask.clone());
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Ok(mask)
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}
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}
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pub fn forward(&mut self, xs: &Tensor, index_pos: usize) -> Result<Tensor> {
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let (_b_sz, seq_len) = xs.dims2()?;
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let mask = if seq_len == 1 {
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None
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} else {
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Some(self.mask(seq_len, xs.device())?)
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};
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let _enter = self.span.enter();
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let mut xs = self.tok_embeddings.forward(xs)?;
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for layer in self.layers.iter_mut() {
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let residual = &xs;
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let xs_norm = xs.apply(&layer.attn_norm)?;
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let attn_outputs = layer.forward_attn(&xs_norm, mask.as_ref(), index_pos)?;
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let feed_forward_hidden_states = layer.mlp.forward(&xs_norm)?;
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xs = (attn_outputs + feed_forward_hidden_states + residual)?
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}
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let xs = xs.apply(&self.output_norm)?.i((.., seq_len - 1, ..))?;
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let _enter = self.span_output.enter();
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self.output.forward(&xs)
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}
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}
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