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Move the common quantized-nn code to a shared module. (#1063)
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87
candle-transformers/src/quantized_nn.rs
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87
candle-transformers/src/quantized_nn.rs
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use crate::models::with_tracing::QMatMul;
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use crate::quantized_var_builder::VarBuilder;
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use candle::{Module, Result, Tensor};
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#[derive(Debug)]
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pub struct Embedding {
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inner: candle_nn::Embedding,
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span: tracing::Span,
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}
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impl Embedding {
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pub fn new(d1: usize, d2: usize, vb: VarBuilder) -> Result<Self> {
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let embeddings = vb.get((d1, d2), "weight")?.dequantize(vb.device())?;
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let inner = candle_nn::Embedding::new(embeddings, d2);
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let span = tracing::span!(tracing::Level::TRACE, "embedding");
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Ok(Self { inner, span })
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}
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pub fn embeddings(&self) -> &Tensor {
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self.inner.embeddings()
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}
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}
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impl Module for Embedding {
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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)
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}
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}
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#[derive(Debug)]
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pub struct Linear {
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weight: QMatMul,
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bias: Option<Tensor>,
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}
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impl Module for Linear {
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fn forward(&self, x: &Tensor) -> candle::Result<Tensor> {
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let x = x.apply(&self.weight)?;
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match &self.bias {
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None => Ok(x),
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Some(bias) => x.broadcast_add(bias),
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}
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}
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}
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pub fn linear(in_dim: usize, out_dim: usize, vb: VarBuilder) -> Result<Linear> {
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let bias = vb.get(out_dim, "bias")?.dequantize(vb.device())?;
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let weight = QMatMul::new(in_dim, out_dim, vb)?;
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Ok(Linear {
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weight,
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bias: Some(bias),
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})
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}
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pub fn layer_norm(size: usize, eps: f64, vb: VarBuilder) -> Result<candle_nn::LayerNorm> {
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let weight = vb.get(size, "weight")?.dequantize(vb.device())?;
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let bias = vb.get(size, "bias")?.dequantize(vb.device())?;
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Ok(candle_nn::LayerNorm::new(weight, bias, eps))
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}
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pub fn linear_no_bias(in_dim: usize, out_dim: usize, vb: VarBuilder) -> Result<Linear> {
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let weight = QMatMul::new(in_dim, out_dim, vb)?;
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Ok(Linear { weight, bias: None })
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}
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#[derive(Debug)]
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pub struct RmsNorm {
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inner: candle_nn::RmsNorm,
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span: tracing::Span,
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}
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impl RmsNorm {
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pub fn new(size: usize, eps: f64, vb: VarBuilder) -> Result<Self> {
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let span = tracing::span!(tracing::Level::TRACE, "rms-norm");
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let weight = vb.get(size, "weight")?.dequantize(vb.device())?;
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let inner = candle_nn::RmsNorm::new(weight, eps);
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Ok(Self { inner, span })
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}
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}
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impl Module for RmsNorm {
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fn forward(&self, x: &Tensor) -> Result<Tensor> {
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let _enter = self.span.enter();
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self.inner.forward(x)
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}
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}
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