mirror of
https://github.com/huggingface/candle.git
synced 2025-06-16 02:38:10 +00:00
Addressing a lot of comments.
This commit is contained in:
@ -597,6 +597,7 @@ pub fn call_last_softmax(
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length: usize,
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elements_to_sum: usize,
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input: &Buffer,
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input_offset: usize,
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output: &Buffer,
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) -> Result<(), MetalKernelError> {
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let pipeline = kernels.load_pipeline(device, Source::Reduce, kernel_name)?;
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@ -604,7 +605,10 @@ pub fn call_last_softmax(
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encoder.wait_for_fence(&kernels.fence);
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encoder.set_compute_pipeline_state(&pipeline);
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set_params!(encoder, (length, elements_to_sum, input, output));
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set_params!(
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encoder,
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(length, elements_to_sum, (input, input_offset), output)
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);
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let out_length = length / elements_to_sum;
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@ -312,7 +312,7 @@ fn run_affine<T: Clone>(v: &[T], mul: f64, add: f64) -> Vec<T> {
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&device,
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command_buffer,
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&kernels,
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"affine_float",
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"affine_f32",
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size,
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&input,
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&output,
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@ -346,7 +346,7 @@ fn run_affine_strided<T: Clone>(
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&device,
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command_buffer,
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&kernels,
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"affine_float_strided",
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"affine_f32_strided",
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shape,
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&input,
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strides,
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@ -608,6 +608,7 @@ fn run_softmax<T: Clone + std::fmt::Debug>(v: &[T], last_dim: usize, name: &'sta
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v.len(),
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last_dim,
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&input,
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0,
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&output,
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)
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.unwrap();
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@ -622,7 +623,7 @@ fn reduce_sum() {
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let v = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0];
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let out_length = 1;
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let results = run_reduce(&v, out_length, "fast_sum_float");
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let results = run_reduce(&v, out_length, "fast_sum_f32");
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assert_eq!(approx(results, 4), vec![21.0]);
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}
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@ -631,7 +632,7 @@ fn reduce_sum2() {
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let v = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0];
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let out_length = 2;
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let results = run_reduce(&v, out_length, "fast_sum_float");
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let results = run_reduce(&v, out_length, "fast_sum_f32");
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assert_eq!(approx(results, 4), vec![6.0, 15.0]);
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}
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@ -639,7 +640,7 @@ fn reduce_sum2() {
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fn softmax() {
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let v = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0];
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let last_dim = 6;
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let results = run_softmax(&v, last_dim, "softmax_float");
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let results = run_softmax(&v, last_dim, "softmax_f32");
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assert_eq!(
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approx(results, 4),
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vec![0.0043, 0.0116, 0.0315, 0.0858, 0.2331, 0.6337]
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@ -651,7 +652,7 @@ fn softmax() {
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for i in 0..n {
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v[i * last_dim] = 20.0;
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}
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let results = run_softmax(&v, last_dim, "softmax_float");
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let results = run_softmax(&v, last_dim, "softmax_f32");
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let results = approx(results, 4);
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println!("{results:?}");
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assert_eq!(
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@ -665,7 +666,7 @@ fn softmax() {
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let v = vec![0.0f32, 1.0, 2.0, 3.0, 4.0, 5.0];
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let last_dim = 6;
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let results = run_softmax(&v, last_dim, "softmax_float");
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let results = run_softmax(&v, last_dim, "softmax_f32");
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assert_eq!(
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approx(results, 4),
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vec![0.0043, 0.0116, 0.0315, 0.0858, 0.2331, 0.6337]
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@ -673,7 +674,7 @@ fn softmax() {
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let v = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0];
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let last_dim = 3;
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let results = run_softmax(&v, last_dim, "softmax_float");
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let results = run_softmax(&v, last_dim, "softmax_f32");
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assert_eq!(
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approx(results, 4),
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vec![0.0900, 0.2447, 0.6652, 0.0900, 0.2447, 0.6652]
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@ -684,7 +685,7 @@ fn softmax() {
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.map(|v| f16::from_f32(*v))
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.collect::<Vec<_>>();
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let last_dim = 6;
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let results = run_softmax(&v, last_dim, "softmax_half");
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let results = run_softmax(&v, last_dim, "softmax_f16");
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assert_eq!(
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approx_f16(results, 4),
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vec![0.0043, 0.0116, 0.0316, 0.0858, 0.2332, 0.6338]
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@ -695,7 +696,7 @@ fn softmax() {
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.map(|v| bf16::from_f32(*v))
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.collect::<Vec<_>>();
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let last_dim = 6;
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let results = run_softmax(&v, last_dim, "softmax_bfloat");
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let results = run_softmax(&v, last_dim, "softmax_bf16");
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assert_eq!(
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approx_bf16(results, 4),
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vec![0.0043, 0.0116, 0.0315, 0.0859, 0.2324, 0.6328]
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