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Add a conv1d benchmark based on the whisper sizes. (#377)

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* Add a conv1d benchmark based on the whisper sizes.

* Enforce the batch-dim in conv1d.
This commit is contained in:
Laurent Mazare
2023-08-09 21:27:03 +02:00
committed by GitHub
parent 653ec5abc1
commit fcfdcbd337
4 changed files with 29 additions and 19 deletions
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13
candle-core/src/tensor.rs
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@ -773,18 +773,7 @@ impl Tensor {
/// Applies a 1D convolution over the input tensor.
pub fn conv1d(&self, kernel: &Self, padding: usize, stride: usize) -> Result<Self> {
let (c_out, c_in_k, k_size) = kernel.dims3()?;
let (b_size, c_in, l_in) = match *self.dims() {
[b_size, c_in, l_in] => (Some(b_size), c_in, l_in),
[c_in, l_in] => (None, c_in, l_in),
_ => Err(Error::Conv1dInvalidArgs {
inp_shape: self.shape().clone(),
k_shape: kernel.shape().clone(),
padding,
stride,
msg: "input rank is not 2 or 3",
}
.bt())?,
};
let (b_size, c_in, l_in) = self.dims3()?;
if c_in != c_in_k {
Err(Error::Conv1dInvalidArgs {
inp_shape: self.shape().clone(),