[Kernel] Upgrade xDNN to v1.5.2 and make AMX_FP16 work

cmake/xdnn.cmake

@@ -26,9 +26,9 @@ include(ExternalProject)
 
 # cmake-format: off
 ExternalProject_Add(xdnn_lib
-  URL               https://github.com/intel/xFasterTransformer/releases/download/IntrinsicGemm/xdnn_v1.5.1.tar.gz
-  URL_HASH          MD5=9ac7a7031b542eca2d9ec80d4c0f8be2
-  TIMEOUT           60
+  URL               https://github.com/intel/xFasterTransformer/releases/download/IntrinsicGemm/xdnn_v1.5.2.tar.gz
+  URL_HASH          MD5=884f2e1e2c846ff19f33c889681f8dc2
+  TIMEOUT           120
   SOURCE_DIR        ${CMAKE_SOURCE_DIR}/3rdparty/xdnn
   CONFIGURE_COMMAND ""
   BUILD_COMMAND     ""

src/kernels/attention_kernels.h

@@ -19,6 +19,7 @@
 #include <omp.h>
 #include "aligned_type.h"
 #include "amx_sgemm_bf16bf16bf16.h"
+#include "amx_sgemm_f16f16f16.h"
 #include "bfloat16.h"
 #include "compile_util.h"
 #include "copy_util.h"
@@ -107,9 +108,23 @@ void small_amx_gemm_16bits_compute(int m, int n, int k, T *A, int lda, T *packed
 
     if (std::is_same_v<T, bfloat16_t>) {
         xdnn_small_amx_sgemm_bf16bf16bf16_compute(
-                m, n, k, (XDNN_BF16 *)A, lda, (XDNN_BF16 *)packedB, (XDNN_BF16 *)C, ldc);
+                m, n, k, (XDNN_BF16 *)A, lda, (XDNN_BF16 *)packedB, ldb, (XDNN_BF16 *)C, ldc);
     } else {
-        //xdnn_small_amx_sgemm_f16f16f16_compute(m, n, k, (XDNN_FP16 *)A, lda, (XDNN_FP16 *)packedB, ldb, (XDNN_FP16 *)C, ldc);
+        xdnn_small_amx_sgemm_f16f16f16_compute(m, n, k, (XDNN_FP16 *)A, lda, (XDNN_FP16 *)packedB, ldb, (XDNN_FP16 *)C, ldc);
+    }
+}
+
+template <typename T>
+void small_softmax(T *data, float scale, int elements) {
+    static_assert(std::is_same_v<T, float> || std::is_same_v<T, bfloat16_t> || std::is_same_v<T, float16_t>,
+            "Unsupported data type for small_softmax");
+
+    if constexpr (std::is_same_v<T, float>) {
+        small_softmax_f32(data, scale, elements);
+    } else if constexpr (std::is_same_v<T, bfloat16_t>) {
+        small_softmax_bf16((XDNN_BF16 *)data, scale, elements);
+    } else if constexpr (std::is_same_v<T, float16_t>) {
+        DecoderUtil::computeSoftmax(data, scale, elements);
     }
 }
 
@@ -266,7 +281,7 @@ void selfAttention_SeparateCopy(T *output, T *query, T *key, T *value, int qHead
         for (int seq = 0; seq < endSeq - startSeq; ++seq) {
             int elements = startSeq + seq + 1;
             if (alibiSlopes == nullptr) {
-                small_softmax_bf16((XDNN_BF16 *)(C + seq * ldc), scale, elements);
+                small_softmax(C + seq * ldc, scale, elements);
             } else {
                 DecoderUtil::alibiSoftmax(C + seq * ldc, scale, alibiSlopes[i], elements);
             }
@@ -416,7 +431,7 @@ void selfAttention_FusedCopy(T *output, T *query, T *key, T *value, int qHeadNum
                 for (int seq = 0; seq < endSeq - startSeq; ++seq) {
                     int elements = startSeq + seq + 1;
                     if (alibiSlopes == nullptr) {
-                        small_softmax_bf16((XDNN_BF16 *)(C + seq * ldc), scale, elements);
+                        small_softmax(C + seq * ldc, scale, elements);
                     } else {
                         DecoderUtil::alibiSoftmax(C + seq * ldc, scale, alibiSlopes[i], elements);
                     }
@@ -765,7 +780,7 @@ void crossAttnByHead(T *output, const T *query, const T *key, const T *value, in
                 for (int seq = 0; seq < queryLen; ++seq) {
                     int elements = pastSeqLens[b] + seq + 1;
                     if (alibiSlopes == nullptr) {
-                        small_softmax_f32(S + seq * keyLen, scale, elements);
+                        small_softmax(S + seq * keyLen, scale, elements);
                     } else {
                         DecoderUtil::alibiSoftmax(S + seq * keyLen, scale, alibiSlopes[i], elements);
                     }

src/utils/decoder_util.h

@@ -336,6 +336,49 @@ class DecoderUtil {
         }
     }
 
+    // General version
+    static void computeSoftmax(float16_t *data, float scale, int size) {
+        int vecs = (size + 15) / 16; // how many avx512 vectors
+        __mmask16 tailMask = (size % 16 == 0 ? 0xffff : (1 << (size % 16)) - 1); // mask of last vector
+
+        __m512 vsum = _mm512_set1_ps(0);
+
+        // maxVal is used to avoid exp(x) = inf
+        float maxVal = std::numeric_limits<float>::lowest();
+        __m512 vmax = _mm512_set1_ps(maxVal);
+        __m512 vfactor = _mm512_set1_ps(scale);
+
+        int i = 0;
+        for (i = 0; i < vecs; ++i) {
+            __mmask16 k = (i == vecs - 1 ? tailMask : 0xffff);
+            __m512 vx = xft::load_avx512(k, data + i * 16);
+            vmax = _mm512_mask_max_ps(vmax, k, vmax, vx * vfactor);
+        }
+
+        maxVal = _mm512_reduce_max_ps(vmax);
+        vmax = _mm512_set1_ps(maxVal);
+
+        // Compute vexp(vx - vmax) and sum it
+        for (i = 0; i < vecs; ++i) {
+            __mmask16 k = (i == vecs - 1 ? tailMask : 0xffff);
+            __m512 vx = xft::load_avx512(k, data + i * 16);
+            vx = BertUtil::vexp(vx * vfactor - vmax);
+            xft::store_avx512(data + i * 16, k, vx);
+            vsum = _mm512_mask_add_ps(vsum, k, vsum, vx);
+        }
+
+        float sum = _mm512_reduce_add_ps(vsum);
+        __m512 vrsum = _mm512_set1_ps(1.0f / sum);
+
+        // Compute exp/sum(exp) and store
+        for (i = 0; i < vecs; ++i) {
+            __mmask16 k = (i == vecs - 1 ? tailMask : 0xffff);
+            __m512 vx = xft::load_avx512(k, data + i * 16);
+            vx = vx * vrsum;
+            xft::store_avx512(data + i * 16, k, vx);
+        }
+    }
+
     // Softmax: skip the calculation when attention mask is the lowest value
     static void softmaxSkipMask(float *data, const float *attnMask, int size, float scale) {
         int vecs = (size + 15) / 16; // how many avx512 vectors