Simpler repro for the neon optimization issue + bugfix (#1544)

* Simpler repro for the neon optimization issue.

* Bugfix for q4k.

* Improve the fix, share the dot-prod bit.

* Clippy fixes.

* Fix for q6k.

* Also fix for q2k.

* Use the new shared dotprod.

* Add more testing.

candle-core/tests/quantized_tests.rs

@@ -1,4 +1,5 @@
 use candle_core::{
+    bail,
     quantized::{self, GgmlDType},
     test_utils::to_vec2_round,
     Device, Module, Result, Tensor,
@@ -265,7 +266,8 @@ fn compare_with_error(values: &[f32], expected: &[f32], tolerance: f32) {
     }
 }
 
-/// Creates a vector simillarly to the one used in GGML unit tests: https://github.com/ggerganov/llama.cpp/blob/master/tests/test-quantize-fns.cpp#L26-L30
+/// Creates a vector similar to the ones used in GGML unit tests:
+/// https://github.com/ggerganov/llama.cpp/blob/master/tests/test-quantize-fns.cpp#L26-L30
 fn create_ggml_like_vector(offset: f32) -> Vec<f32> {
     (0..GGML_TEST_SIZE)
         .map(|i| 0.1 + 2.0 * (i as f32 + offset).cos())
@@ -284,14 +286,15 @@ fn calculate_rmse(a: &[f32], b: &[f32]) -> f32 {
     sum / a.len() as f32
 }
 
-/// Mirrores the GGML quanitzation unit test: https://github.com/ggerganov/llama.cpp/blob/master/tests/test-quantize-fns.cpp#L43-L50
+/// Similar to the GGML quantization unit test:
+/// https://github.com/ggerganov/llama.cpp/blob/master/tests/test-quantize-fns.cpp#L43-L50
 fn ggml_quantization_error_test<T: GgmlType>(max_error: f32) -> Result<()> {
     let src = create_ggml_like_vector(0.0);
     let mut dst = vec![0.0; GGML_TEST_SIZE];
     let _quant = quantize_roundtrip::<T>(src.as_slice(), dst.as_mut_slice())?;
     let error = calculate_rmse(src.as_slice(), dst.as_slice());
     if error > max_error {
-        candle_core::bail!(
+        bail!(
             "Quantization error {} exceeds max error {}",
             error,
             max_error
@@ -487,54 +490,66 @@ fn ggml_reference_matmul_error(dtype: GgmlDType) -> Result<f32> {
         GgmlDType::Q5K => 0.000740,
         GgmlDType::Q6K => 0.000952,
         GgmlDType::Q4_0 => 0.001143,
-        GgmlDType::Q4_1 => 0.007784,
+        GgmlDType::Q4_1 => 0.008,
         GgmlDType::Q5_0 => 0.001353,
-        GgmlDType::Q5_1 => 0.001363,
+        GgmlDType::Q5_1 => 0.00149,
         GgmlDType::Q8_0 => 0.000092,
 
         // Not from the ggml repo.
         GgmlDType::Q8K => 0.00065,
-        _ => candle_core::bail!("No GGML results for quantization type {dtype:?}",),
+        _ => bail!("No GGML results for quantization type {dtype:?}",),
     };
     Ok(err)
 }
 
-/// Mirrores the GGML matmul unit test: https://github.com/ggerganov/llama.cpp/blob/master/tests/test-quantize-fns.cpp#L76-L91
+/// Similar to the GGML matmul unit test:
+/// https://github.com/ggerganov/llama.cpp/blob/master/tests/test-quantize-fns.cpp#L76-L91
 fn ggml_matmul_error_test<T: GgmlType>() -> Result<()> {
     let a = create_ggml_like_vector(0.0);
     let b = create_ggml_like_vector(1.0);
+    ggml_matmul_error_test_::<T>(a.as_slice(), b.as_slice(), 1.0)?;
+    // Another example that is more likely to trigger the overflow reported in #1526
+    let a = (0..GGML_TEST_SIZE)
+        .map(|i| i as f32 / GGML_TEST_SIZE as f32)
+        .collect::<Vec<_>>();
+    let b = (0..GGML_TEST_SIZE)
+        .map(|i| i as f32 / GGML_TEST_SIZE as f32)
+        .collect::<Vec<_>>();
+    ggml_matmul_error_test_::<T>(a.as_slice(), b.as_slice(), 2.0)?;
+    Ok(())
+}
+
+fn ggml_matmul_error_test_<T: GgmlType>(a: &[f32], b: &[f32], err_m: f32) -> Result<()> {
     let length = a.len();
 
     let mut a_quant = vec![T::zeros(); length / T::BLCK_SIZE];
     let mut b_quant = vec![T::VecDotType::zeros(); length / T::VecDotType::BLCK_SIZE];
-    T::from_float(&a, &mut a_quant)?;
-    T::VecDotType::from_float(&b, &mut b_quant)?;
+    T::from_float(a, &mut a_quant)?;
+    T::VecDotType::from_float(b, &mut b_quant)?;
 
     let result = T::vec_dot(length, &a_quant, &b_quant)?;
     let result_unopt = T::vec_dot_unopt(length, &a_quant, &b_quant)?;
-    let reference_result = vec_dot_reference(&a, &b);
+    let reference_result = vec_dot_reference(a, b);
 
     if (result - result_unopt).abs() / length as f32 > 1e-6 {
-        candle_core::bail!(
+        bail!(
             "the opt and unopt vec-dot returned different values, opt {result}, unopt {result_unopt}"
         )
     }
 
     let error = (result - reference_result).abs() / length as f32;
 
-    let ggml_error = ggml_reference_matmul_error(T::DTYPE)?;
+    let ggml_error = ggml_reference_matmul_error(T::DTYPE)? * err_m;
 
     if !error.is_finite() || error > GGML_MAX_DOT_PRODUCT_ERROR {
-        candle_core::bail!(
-            "Dot product error {error} exceeds max error {GGML_MAX_DOT_PRODUCT_ERROR}",
-        );
+        bail!("Dot product error {error} exceeds max error {GGML_MAX_DOT_PRODUCT_ERROR}",);
     }
 
     // We diverge slightly due to different rounding behavior / f16 to f32 conversions in GGML
     // => we use a slightly higher error threshold
     const ERROR_LENIENCY: f32 = 0.00001;
     if error - ERROR_LENIENCY > ggml_error {
-        candle_core::bail!(
+        bail!(
             "Dot product error {} exceeds ggml reference error {}",
             error,
             ggml_error
@@ -543,6 +558,16 @@ fn ggml_matmul_error_test<T: GgmlType>() -> Result<()> {
     Ok(())
 }
 
+#[test]
+fn quantized_mm() -> Result<()> {
+    ggml_matmul_error_test::<k_quants::BlockQ4_0>()?;
+    ggml_matmul_error_test::<k_quants::BlockQ4_1>()?;
+    ggml_matmul_error_test::<k_quants::BlockQ5_0>()?;
+    ggml_matmul_error_test::<k_quants::BlockQ5_1>()?;
+    ggml_matmul_error_test::<k_quants::BlockQ8_0>()?;
+    Ok(())
+}
+
 /// generates random tensors of size `m x k` and `n x k` and calculates their expected matrix multiplication result.
 fn get_random_tensors(
     m: usize,