AVX version of the vecdot for q4_0. (#474)

* AVX version of the vecdot for q4_0.

* Tweak the avx bits.

* Add a qmatmul benchmark.

* Fix the quantized test.

candle-core/examples/cpu_benchmarks.rs

@@ -5,6 +5,7 @@ extern crate intel_mkl_src;
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
 
+use candle_core::quantized::GgmlType;
 use candle_core::{Device, Result, Tensor, D};
 use clap::{Parser, Subcommand};
 
@@ -81,6 +82,27 @@ impl Benchmark for Matmul {
     const ITERS: usize = 100;
 }
 
+// This benchmark is similar to:
+// https://github.com/ggerganov/llama.cpp/blob/master/examples/benchmark/benchmark-matmult.cpp
+struct QMatMul;
+impl Benchmark for QMatMul {
+    type PreProcessData = (candle_core::quantized::QMatMul, Tensor);
+    type RunResult = Tensor;
+    fn preprocess() -> Result<Self::PreProcessData> {
+        let zeros = vec![candle_core::quantized::k_quants::BlockQ4_0::zeros(); 4096 * 11008 / 32];
+        let mm = candle_core::quantized::QTensor::new(zeros, (4096, 11008));
+        let mm = candle_core::quantized::QMatMul::from_qtensor(mm);
+        let arg = Tensor::randn(0f32, 1., (128, 11008), &Device::Cpu)?;
+        Ok((mm, arg))
+    }
+
+    fn run_one(d: &Self::PreProcessData) -> Result<Self::RunResult> {
+        d.0.forward(&d.1)
+    }
+
+    const ITERS: usize = 100;
+}
+
 struct Softmax;
 impl Benchmark for Softmax {
     type PreProcessData = Tensor;
@@ -116,6 +138,7 @@ enum Task {
     Conv1d,
     Conv2d,
     Matmul,
+    Qmatmul,
     Softmax,
 }
 
@@ -137,6 +160,7 @@ fn main() -> Result<()> {
         Task::Conv2d => run::<Conv2d>(args.iters)?,
         Task::Matmul => run::<Matmul>(args.iters)?,
         Task::Softmax => run::<Softmax>(args.iters)?,
+        Task::Qmatmul => run::<QMatMul>(args.iters)?,
     }
     Ok(())
 }

candle-core/src/quantized/k_quants.rs

@@ -1,3 +1,8 @@
+#[cfg(target_arch = "x86")]
+use core::arch::x86::*;
+#[cfg(target_arch = "x86_64")]
+use core::arch::x86_64::*;
+
 use super::GgmlDType;
 use crate::Result;
 use half::f16;
@@ -620,6 +625,48 @@ impl GgmlType for BlockQ8K {
     }
 }
 
+#[cfg(target_feature = "avx")]
+#[inline(always)]
+unsafe fn sum_i16_pairs_float(x: __m256i) -> __m256 {
+    let ones = _mm256_set1_epi16(1);
+    let summed_pairs = _mm256_madd_epi16(ones, x);
+    _mm256_cvtepi32_ps(summed_pairs)
+}
+
+#[cfg(target_feature = "avx")]
+#[inline(always)]
+unsafe fn mul_sum_us8_pairs_float(ax: __m256i, sy: __m256i) -> __m256 {
+    let dot = _mm256_maddubs_epi16(ax, sy);
+    sum_i16_pairs_float(dot)
+}
+
+#[cfg(target_feature = "avx")]
+#[inline(always)]
+unsafe fn hsum_float_8(x: __m256) -> f32 {
+    let mut res = _mm256_extractf128_ps(x, 1);
+    res = _mm_add_ps(res, _mm256_castps256_ps128(x));
+    res = _mm_add_ps(res, _mm_movehl_ps(res, res));
+    res = _mm_add_ss(res, _mm_movehdup_ps(res));
+    _mm_cvtss_f32(res)
+}
+
+#[cfg(target_feature = "avx")]
+#[inline(always)]
+unsafe fn bytes_from_nibbles_32(rsi: *const u8) -> __m256i {
+    let tmp = _mm_loadu_si128(rsi as *const __m128i);
+    let bytes = _mm256_insertf128_si256::<1>(_mm256_castsi128_si256(tmp), _mm_srli_epi16(tmp, 4));
+    let low_mask = _mm256_set1_epi8(0xF);
+    _mm256_and_si256(low_mask, bytes)
+}
+
+#[cfg(target_feature = "avx")]
+#[inline(always)]
+unsafe fn mul_sum_i8_pairs_float(x: __m256i, y: __m256i) -> __m256 {
+    let ax = _mm256_sign_epi8(x, x);
+    let sy = _mm256_sign_epi8(y, x);
+    mul_sum_us8_pairs_float(ax, sy)
+}
+
 impl GgmlType for BlockQ4_0 {
     const DTYPE: GgmlDType = GgmlDType::Q4_0;
     const BLCK_SIZE: usize = QK4_0;
@@ -685,7 +732,35 @@ impl GgmlType for BlockQ4_0 {
         Ok(())
     }
 
+    #[cfg(target_feature = "avx")]
+    fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
+        let qk = QK8_0;
+        let nb = n / qk;
+        if n % QK8_0 != 0 {
+            crate::bail!("vec_dot_q4_0_q8_0: {n} is not divisible by {qk}")
+        }
+        if nb % 2 != 0 {
+            crate::bail!("vec_dot_q4_0_q8_0: {nb} is not even")
+        }
+
+        unsafe {
+            // Generic implementation.
+            let mut acc = _mm256_setzero_ps();
+            for (x, y) in xs.iter().zip(ys.iter()) {
+                let d = _mm256_set1_ps(f16::to_f32(x.d) * f16::to_f32(y.d));
+                let bx = bytes_from_nibbles_32(x.qs.as_ptr());
+                let off = _mm256_set1_epi8(8);
+                let bx = _mm256_sub_epi8(bx, off);
+                let by = _mm256_loadu_si256(y.qs.as_ptr() as *const __m256i);
+                let q = mul_sum_i8_pairs_float(bx, by);
+                acc = _mm256_fmadd_ps(d, q, acc);
+            }
+            Ok(hsum_float_8(acc))
+        }
+    }
+
     // https://github.com/ggerganov/llama.cpp/blob/b5ffb2849d23afe73647f68eec7b68187af09be6/ggml.c#L2361C10-L2361C122
+    #[cfg(not(target_feature = "avx"))]
     fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
         let qk = QK8_0;
         let nb = n / qk;

candle-core/tests/quantized_tests.rs

@@ -16,10 +16,10 @@ fn quantized_matmul() -> Result<()> {
     k_quants::BlockQ4_0::from_float(&rhs, &mut rhs_t)?;
     k_quants::matmul((m, k, n), &lhs, &rhs_t, &mut dst)?;
     assert_eq!(
-        dst,
+        dst.iter().map(|x| x.round()).collect::<Vec<_>>(),
         &[
-            85120.43, 214561.61, 345454.9, 474748.1, 213474.94, 604465.25, 1000686.4, 1388317.3,
-            341875.88, 994283.0, 1655708.8, 2301518.3
+            85120.0, 214562.0, 345455.0, 474748.0, 213475.0, 604465.0, 1000686.0, 1388317.0,
+            341876.0, 994283.0, 1655709.0, 2301518.0
         ]
     );
     let mm = tensor_lhs.matmul(&tensor_rhs)?;
@@ -36,11 +36,11 @@ fn quantized_matmul() -> Result<()> {
     let matmul = quantized::QMatMul::from_qtensor(qtensor);
     let res = matmul.forward(&tensor_lhs)?;
     assert_eq!(
-        res.to_vec2::<f32>()?,
+        to_vec2_round(&res, 0)?,
         &[
-            [85120.43, 214561.61, 345454.9, 474748.1],
-            [213474.94, 604465.25, 1000686.4, 1388317.3],
-            [341875.88, 994283.0, 1655708.8, 2301518.3]
+            [85120.0, 214562.0, 345455.0, 474748.0],
+            [213475.0, 604465.0, 1000686.0, 1388317.0],
+            [341876.0, 994283.0, 1655709.0, 2301518.0]
         ]
     );
 
@@ -64,10 +64,10 @@ fn quantized_matmul_neg() -> Result<()> {
     k_quants::BlockQ4_0::from_float(&rhs, &mut rhs_t)?;
     k_quants::matmul((m, k, n), &lhs, &rhs_t, &mut dst)?;
     assert_eq!(
-        dst,
+        dst.iter().map(|x| x.round()).collect::<Vec<_>>(),
         &[
-            243524.14, -19596.34, -285051.3, -549814.94, 23776.629, 21650.926, 19397.924,
-            18366.586, -196472.1, 63011.6, 324584.56, 587901.9
+            243524.0, -19596.0, -285051.0, -549815.0, 23777.0, 21651.0, 19398.0, 18367.0,
+            -196472.0, 63012.0, 324585.0, 587902.0
         ]
     );
     let mm = tensor_lhs.matmul(&tensor_rhs)?;