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Use F32 for the reduce ops. (#105)

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This commit is contained in:
Laurent Mazare
2023-07-07 17:55:21 +01:00
committed by GitHub
parent e923b3adc2
commit 03dffe9ecc
1 changed files with 6 additions and 1 deletions
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7
candle-examples/examples/falcon/model.rs
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@ -122,12 +122,15 @@ impl LayerNorm {
} }
fn forward(&self, x: &Tensor) -> Result<Tensor> { fn forward(&self, x: &Tensor) -> Result<Tensor> {
let dtype = x.dtype();
let (_bsize, _seq_len, hidden_size) = x.shape().r3()?; let (_bsize, _seq_len, hidden_size) = x.shape().r3()?;
let x = x.to_dtype(DType::F32)?;
let mean_x = (x.sum(&[2])? / hidden_size as f64)?; let mean_x = (x.sum(&[2])? / hidden_size as f64)?;
let x = x.broadcast_sub(&mean_x)?; let x = x.broadcast_sub(&mean_x)?;
let norm_x = ((&x * &x)?.sum(&[2])? / hidden_size as f64)?; let norm_x = ((&x * &x)?.sum(&[2])? / hidden_size as f64)?;
let x_normed = x.broadcast_div(&(norm_x + self.eps)?.sqrt()?)?; let x_normed = x.broadcast_div(&(norm_x + self.eps)?.sqrt()?)?;
let x = x_normed let x = x_normed
.to_dtype(dtype)?
.broadcast_mul(&self.weight)? .broadcast_mul(&self.weight)?
.broadcast_add(&self.bias)?; .broadcast_add(&self.bias)?;
Ok(x) Ok(x)
@ -470,7 +473,9 @@ impl FalconAttention {
let attention_scores = (query.matmul(&key.t()?)? * self.inv_norm_factor)?; let attention_scores = (query.matmul(&key.t()?)? * self.inv_norm_factor)?;
let attention_scores = attention_scores let attention_scores = attention_scores
.broadcast_add(&mask.squeeze(1)?)? .broadcast_add(&mask.squeeze(1)?)?
.softmax(D::Minus1)?; .to_dtype(DType::F32)?
.softmax(D::Minus1)?
.to_dtype(x.dtype())?;
let attn_output = attention_scores let attn_output = attention_scores
.matmul(&value)? .matmul(&value)?
.reshape((b_sz, self.num_heads, seq_len, head_dim))? .reshape((b_sz, self.num_heads, seq_len, head_dim))?