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upgrade half library to fix rand (#2806)

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fix lints
This commit is contained in:
Mike Seddon
2025-03-14 19:01:54 +11:00
committed by GitHub
parent 111edbc4ea
commit c930ab7e1a
9 changed files with 38 additions and 37 deletions
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22
candle-nn/tests/ops.rs
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@ -83,7 +83,7 @@ fn rms_norml(device: &Device) -> Result<()> {
let (b_size, seq_len, head_dim) = (24, 70, 64);
let el_count = b_size * seq_len * head_dim;
let mut rng = StdRng::seed_from_u64(299792458);
let src: Vec<f32> = (0..el_count).map(|_| rng.gen::<f32>()).collect();
let src: Vec<f32> = (0..el_count).map(|_| rng.random::<f32>()).collect();
let tensor = Tensor::new(src, device)?.reshape((b_size, seq_len, head_dim))?;
let alpha = Tensor::ones(head_dim, candle::DType::F32, device)?;
let t = candle_nn::ops::rms_norm(&tensor, &alpha, 1e-5)?;
@ -130,7 +130,7 @@ fn layer_norml(device: &Device) -> Result<()> {
let (b_size, seq_len, head_dim) = (24, 70, 64);
let el_count = b_size * seq_len * head_dim;
let mut rng = StdRng::seed_from_u64(299792458);
let src: Vec<f32> = (0..el_count).map(|_| rng.gen::<f32>()).collect();
let src: Vec<f32> = (0..el_count).map(|_| rng.random::<f32>()).collect();
let tensor = Tensor::new(src, device)?.reshape((b_size, seq_len, head_dim))?;
let alpha = Tensor::ones(head_dim, candle::DType::F32, device)?;
let beta = Tensor::zeros(head_dim, candle::DType::F32, device)?;
@ -161,12 +161,12 @@ fn ropei(device: &Device) -> Result<()> {
let (b_size, num_head, seq_len, head_dim) = (2, 5, 10, 16);
let el_count = b_size * num_head * seq_len * head_dim;
let mut rng = StdRng::seed_from_u64(299792458);
let src: Vec<f32> = (0..el_count).map(|_| rng.gen::<f32>()).collect();
let src: Vec<f32> = (0..el_count).map(|_| rng.random::<f32>()).collect();
let cos: Vec<f32> = (0..seq_len * head_dim / 2)
.map(|_| rng.gen::<f32>())
.map(|_| rng.random::<f32>())
.collect();
let sin: Vec<f32> = (0..seq_len * head_dim / 2)
.map(|_| rng.gen::<f32>())
.map(|_| rng.random::<f32>())
.collect();
let src = Tensor::from_vec(src, (b_size, num_head, seq_len, head_dim), device)?;
let cos = Tensor::from_vec(cos, (seq_len, head_dim / 2), device)?;
@ -188,12 +188,12 @@ fn rope(device: &Device) -> Result<()> {
let (b_size, num_head, seq_len, head_dim) = (2, 5, 10, 16);
let el_count = b_size * num_head * seq_len * head_dim;
let mut rng = StdRng::seed_from_u64(299792458);
let src: Vec<f32> = (0..el_count).map(|_| rng.gen::<f32>()).collect();
let src: Vec<f32> = (0..el_count).map(|_| rng.random::<f32>()).collect();
let cos: Vec<f32> = (0..seq_len * head_dim / 2)
.map(|_| rng.gen::<f32>())
.map(|_| rng.random::<f32>())
.collect();
let sin: Vec<f32> = (0..seq_len * head_dim / 2)
.map(|_| rng.gen::<f32>())
.map(|_| rng.random::<f32>())
.collect();
let src = Tensor::from_vec(src, (b_size, num_head, seq_len, head_dim), device)?;
let cos = Tensor::from_vec(cos, (seq_len, head_dim / 2), device)?;
@ -215,12 +215,12 @@ fn rope_thd(device: &Device) -> Result<()> {
let (b_size, num_head, seq_len, head_dim) = (2, 5, 10, 16);
let el_count = b_size * num_head * seq_len * head_dim;
let mut rng = StdRng::seed_from_u64(299792458);
let src: Vec<f32> = (0..el_count).map(|_| rng.gen::<f32>()).collect();
let src: Vec<f32> = (0..el_count).map(|_| rng.random::<f32>()).collect();
let cos: Vec<f32> = (0..seq_len * head_dim / 2)
.map(|_| rng.gen::<f32>())
.map(|_| rng.random::<f32>())
.collect();
let sin: Vec<f32> = (0..seq_len * head_dim / 2)
.map(|_| rng.gen::<f32>())
.map(|_| rng.random::<f32>())
.collect();
let src = Tensor::from_vec(src, (b_size, num_head, seq_len, head_dim), device)?;
let cos = Tensor::from_vec(cos, (seq_len, head_dim / 2), device)?;