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Further randn tweaks: use the appropriate rng rather than the f64 one, some cleanup. (#383)
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@ -9,23 +9,6 @@ fn zeros(device: &Device) -> Result<()> {
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Ok(())
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}
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fn randn_hasneg(device: &Device) -> Result<()> {
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let s = 200;
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let t = Tensor::randn(
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0f32,
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1f32, s
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as usize,
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&Device::Cpu
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)?
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.to_vec1::<f32>()?;
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for i in t {
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if i < 0. {
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return Ok(())
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}
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}
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panic!("randn failed to generate a negative number")
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}
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fn add_mul(device: &Device) -> Result<()> {
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let tensor = Tensor::new(&[3f32, 1., 4.], device)?;
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let dim1 = tensor.dims1()?;
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@ -866,7 +849,6 @@ fn broadcasting(device: &Device) -> Result<()> {
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}
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test_device!(zeros, zeros_cpu, zeros_gpu);
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test_device!(randn_hasneg, randn_hasneg_cpu, randn_hasneg_gpu);
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test_device!(add_mul, add_mul_cpu, add_mul_gpu);
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test_device!(tensor_2d, tensor_2d_cpu, tensor_2d_gpu);
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test_device!(narrow, narrow_cpu, narrow_gpu);
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@ -887,3 +869,14 @@ test_device!(index_select, index_select_cpu, index_select_gpu);
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test_device!(index_add, index_add_cpu, index_add_gpu);
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test_device!(gather, gather_cpu, gather_gpu);
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test_device!(scatter_add, scatter_add_cpu, scatter_add_gpu);
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// There was originally a bug on the CPU implementation for randn
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// https://github.com/huggingface/candle/issues/381
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#[test]
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fn randn_hasneg() -> Result<()> {
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let t = Tensor::randn(0f32, 1f32, 200, &Device::Cpu)?.to_vec1::<f32>()?;
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if t.iter().all(|&v| v >= 0.) {
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candle_core::bail!("all values in tensors are non-negative")
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}
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Ok(())
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}
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