[SYCL][Matrix] Add the 8 bit type variants

SYCL/Matrix/joint_matrix_bf16.cpp

@@ -11,8 +11,6 @@
 // RUN: %CPU_RUN_PLACEHOLDER %t.out
 // RUN: %GPU_RUN_PLACEHOLDER %t.out
 
-// XFAIL: *
-
 #include <CL/sycl.hpp>
 #include <iostream>
 
@@ -22,7 +20,7 @@ using namespace sycl::ext::oneapi::experimental::matrix;
 #define SG_SZ 8
 
 #define TM 8
-#define TN SG_SIZE
+#define TN SG_SZ
 #define TK 16
 
 template <typename T, size_t NUM_ROWS, size_t NUM_COLS> struct big_matrix {

SYCL/Matrix/joint_matrix_ss_int8.cpp

@@ -0,0 +1,178 @@
+//==-------- joint_matrix_ss_int8.cpp  - DPC++ joint_matrix------------ ----==//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+// REQUIRES: matrix
+
+// RUN: %clangxx -fsycl %s -o %t.out
+// RUN: %CPU_RUN_PLACEHOLDER %t.out
+// RUN: %GPU_RUN_PLACEHOLDER %t.out
+
+#include <CL/sycl.hpp>
+#include <iostream>
+
+using namespace sycl;
+using namespace sycl::ext::oneapi::experimental::matrix;
+
+#define SG_SZ 8
+
+#define TM 8
+#define TN SG_SZ
+#define TK 32
+
+template <typename T, size_t NUM_ROWS, size_t NUM_COLS> struct big_matrix {
+public:
+  T *mat;
+
+public:
+  T *get_data() { return mat; }
+  void set_data(T *data) { mat = data; }
+  big_matrix(T *data) : mat(data) {}
+};
+
+template <typename T1, typename T2, size_t NUM_ROWS_A, size_t NUM_COLS_A,
+          size_t NUM_ROWS_B, size_t NUM_COLS_B, size_t NUM_ROWS_C,
+          size_t NUM_COLS_C>
+void matrix_multiply(big_matrix<T1, NUM_ROWS_C, NUM_COLS_C> &C,
+                     big_matrix<T2, NUM_ROWS_A, NUM_COLS_A> &A,
+                     big_matrix<T2, NUM_ROWS_B, NUM_COLS_B> &B) {
+  size_t M = NUM_ROWS_C;
+  size_t N = NUM_COLS_C;
+  size_t K = NUM_COLS_A;
+  // B => K/4 x N*4, A => M x K, C => M, N
+  // stride should be X's cols, e.g., B's stirde = N*4
+  assert(NUM_ROWS_C == NUM_ROWS_A && NUM_COLS_A == NUM_ROWS_B * 4);
+  size_t NDRangeM = M / TM;
+  size_t NDRangeN = N / TN;
+  buffer<int8_t, 2> bufA(A.get_data(), range<2>(M, K));
+  buffer<int8_t, 2> bufB(B.get_data(), range<2>(K, N));
+  buffer<int32_t, 2> bufC(C.get_data(), range<2>(M, N));
+
+  queue q;
+  q.submit([&](handler &cgh) {
+     auto accC = bufC.get_access<access::mode::read_write>(cgh);
+     auto accA = bufA.get_access<access::mode::read_write>(cgh);
+     auto accB = bufB.get_access<access::mode::read_write>(cgh);
+
+     cgh.parallel_for<class imatrix>(
+         nd_range<2>({NDRangeM, NDRangeN * SG_SZ}, {1, 1 * SG_SZ}),
+         [accA, accB, accC, M, N, K](nd_item<2> spmd_item)
+
+         {
+           // The submatrix API has to be accessed by all the workitems in a
+           // subgroup these functions will be called once by the subgroup no
+           // code divergence between the workitems
+           const auto global_idx = spmd_item.get_global_id(0);
+           const auto global_idy = spmd_item.get_global_id(1);
+           const auto sg_startx = global_idx - spmd_item.get_local_id(0);
+           const auto sg_starty = global_idy - spmd_item.get_local_id(1);
+
+           ext::oneapi::sub_group sg = spmd_item.get_sub_group();
+           joint_matrix<int8_t, TM, TK> sub_a(sg);
+           // For B, since current implementation does not support non-packed
+           // layout, users need to specify the updated VNNI sizes along with
+           // the packed_b layout. By default, the layout is row_major and size
+           // is (TK, TN).
+           joint_matrix<int8_t, TK, TN, matrix_layout::packed_b> sub_b(sg);
+           joint_matrix<int32_t, TM, TN> sub_c(sg);
+
+           joint_matrix_load(sg, sub_c,
+                             accC.get_pointer() + (sg_startx * TM) * N +
+                                 sg_starty / SG_SZ * TN,
+                             N, matrix_layout::row_major);
+           for (int k = 0; k < K / TK; k += 1) {
+             joint_matrix_load(
+                 sg, sub_a, accA.get_pointer() + (sg_startx * TM) * K + k * TK,
+                 K, matrix_layout::packed_a);
+             // Assuming B data is already in VNNI format.
+             joint_matrix_load(sg, sub_b,
+                               accB.get_pointer() + (k * TK / 4) * (N * 4) +
+                                   sg_starty / SG_SZ * TN * 4,
+                               N * 4, matrix_layout::packed_b);
+             sub_c = joint_matrix_mad(sg, sub_a, sub_b, sub_c);
+           }
+           joint_matrix_store(sg, sub_c,
+                              accC.get_pointer() + (sg_startx * TM) * N +
+                                  sg_starty / SG_SZ * TN,
+                              N, matrix_layout::row_major);
+         }); // parallel for
+   }).wait();
+}
+
+static constexpr size_t MATRIX_M = TM * 2;
+static constexpr size_t MATRIX_N = TN * 2;
+static constexpr size_t MATRIX_K = TK * 2;
+int8_t A[MATRIX_M][MATRIX_K];
+int8_t B[MATRIX_K / 4][MATRIX_N * 4];
+int32_t C[MATRIX_M][MATRIX_N];
+int32_t D[MATRIX_M][MATRIX_N];
+
+void matrix_multiply_ref(int32_t *A_mem, int32_t *B_mem, int32_t *C_mem, int M,
+                         int N, int K) {
+  // tiling
+  for (int m = 0; m < M; m++)
+    for (int n = 0; n < N; n++) {
+      for (int k = 0; k < K; k++) {
+        char *va = (char *)(A_mem + m * K + k);
+        char *vb = (char *)(B_mem + k * N + n);
+        int acc = *(C_mem + m * N + n);
+        for (int i = 0; i < 4; i++) {
+          acc += (va[i] * vb[i]);
+        }
+        *(C_mem + m * N + n) = acc;
+      }
+    }
+}
+
+int main() {
+  for (int i = 0; i < MATRIX_M; i++) {
+    for (int j = 0; j < MATRIX_K; j++) {
+      A[i][j] = i + 2 * j;
+    }
+  }
+  for (int i = 0; i < MATRIX_K / 4; i++) {
+    for (int j = 0; j < MATRIX_N * 4; j++) {
+      B[i][j] = i + j;
+    }
+  }
+  for (int i = 0; i < MATRIX_M; i++) {
+    for (int j = 0; j < MATRIX_N; j++) {
+      C[i][j] = 1;
+      D[i][j] = 1;
+    }
+  }
+
+  big_matrix<int32_t, MATRIX_M, MATRIX_N> MC((int32_t *)&C);
+  big_matrix<int32_t, MATRIX_M, MATRIX_N> MD((int32_t *)&D);
+  big_matrix<int8_t, MATRIX_M, MATRIX_K> MA((int8_t *)&A);
+  big_matrix<int8_t, MATRIX_K / 4, MATRIX_N * 4> MB((int8_t *)&B);
+  matrix_multiply(MC, MA, MB);
+  matrix_multiply_ref((int32_t *)A, (int32_t *)B, (int32_t *)D, MATRIX_M,
+                      MATRIX_N, MATRIX_K / 4);
+
+  bool res = true;
+  for (int i = 0; i < MATRIX_M; i++) {
+    for (int j = 0; j < MATRIX_N; j++) {
+      if (C[i][j] != D[i][j])
+        res = false;
+    }
+  }
+  if (res)
+    std::cout << "passed\n";
+  else
+    std::cout << "failed\n";
+  for (int i = 0; i < MATRIX_M; i++) {
+    for (int j = 0; j < MATRIX_N; j++)
+      std::cout << C[i][j] << ", ";
+    std::cout << "\n";
+  }
+  std::cout << std::endl;
+  for (int i = 0; i < MATRIX_M; i++) {
+    for (int j = 0; j < MATRIX_N; j++)
+      std::cout << D[i][j] << ", ";
+    std::cout << "\n";
+  }
+}

SYCL/Matrix/joint_matrix_su_int8.cpp

@@ -0,0 +1,180 @@
+//==-------- joint_matrix_su_int8.cpp  - DPC++ joint_matrix------------ ----==//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+// REQUIRES: matrix
+
+// RUN: %clangxx -fsycl %s -o %t.out
+// RUN: %CPU_RUN_PLACEHOLDER %t.out
+// RUN: %GPU_RUN_PLACEHOLDER %t.out
+
+#include <CL/sycl.hpp>
+#include <iostream>
+
+using namespace sycl;
+using namespace sycl::ext::oneapi::experimental::matrix;
+
+#define SG_SZ 8
+
+#define TM 8
+#define TN SG_SZ
+#define TK 32
+
+template <typename T, size_t NUM_ROWS, size_t NUM_COLS> struct big_matrix {
+public:
+  T *mat;
+
+public:
+  T *get_data() { return mat; }
+  void set_data(T *data) { mat = data; }
+  big_matrix(T *data) : mat(data) {}
+};
+
+template <typename T1, typename T2, typename T3, size_t NUM_ROWS_A,
+          size_t NUM_COLS_A, size_t NUM_ROWS_B, size_t NUM_COLS_B,
+          size_t NUM_ROWS_C, size_t NUM_COLS_C>
+void matrix_multiply(big_matrix<T1, NUM_ROWS_C, NUM_COLS_C> &C,
+                     big_matrix<T2, NUM_ROWS_A, NUM_COLS_A> &A,
+                     big_matrix<T3, NUM_ROWS_B, NUM_COLS_B> &B) {
+  size_t M = NUM_ROWS_C;
+  size_t N = NUM_COLS_C;
+  size_t K = NUM_COLS_A;
+  // B => K/4 x N*4, A => M x K, C => M, N
+  // stride should be X's cols, e.g., B's stirde = N*4
+  assert(NUM_ROWS_C == NUM_ROWS_A && NUM_COLS_A == NUM_ROWS_B * 4);
+  size_t NDRangeM = M / TM;
+  size_t NDRangeN = N / TN;
+  buffer<int8_t, 2> bufA(A.get_data(), range<2>(M, K));
+  buffer<uint8_t, 2> bufB(B.get_data(), range<2>(K, N));
+  buffer<int32_t, 2> bufC(C.get_data(), range<2>(M, N));
+
+  queue q;
+  q.submit([&](handler &cgh) {
+     auto accC = bufC.get_access<access::mode::read_write>(cgh);
+     auto accA = bufA.get_access<access::mode::read_write>(cgh);
+     auto accB = bufB.get_access<access::mode::read_write>(cgh);
+
+     cgh.parallel_for<class imatrix>(
+         nd_range<2>({NDRangeM, NDRangeN * SG_SZ}, {1, 1 * SG_SZ}),
+         [accA, accB, accC, M, N, K](nd_item<2> spmd_item)
+
+         {
+           // The submatrix API has to be accessed by all the workitems in a
+           // subgroup these functions will be called once by the subgroup no
+           // code divergence between the workitems
+           const auto global_idx = spmd_item.get_global_id(0);
+           const auto global_idy = spmd_item.get_global_id(1);
+           const auto sg_startx = global_idx - spmd_item.get_local_id(0);
+           const auto sg_starty = global_idy - spmd_item.get_local_id(1);
+
+           ext::oneapi::sub_group sg = spmd_item.get_sub_group();
+           joint_matrix<int8_t, TM, TK> sub_a(sg);
+           // For B, since current implementation does not support non-packed
+           // layout, users need to specify the updated VNNI sizes along with
+           // the packed_b layout. By default, the layout is row_major and size
+           // is (TK, TN).
+           joint_matrix<uint8_t, TK, TN, matrix_layout::packed_b> sub_b(sg);
+           joint_matrix<int32_t, TM, TN> sub_c(sg);
+
+           // AMX: 8 register tiles : 1k byte size, SMmaxxSKmax =16x64
+           // strideX = X's cols, so strideC = N, strideA = K, strideB = N*4
+           joint_matrix_load(sg, sub_c,
+                             accC.get_pointer() + (sg_startx * TM) * N +
+                                 sg_starty / SG_SZ * TN,
+                             N, matrix_layout::row_major);
+           for (int k = 0; k < K / TK; k += 1) {
+             joint_matrix_load(
+                 sg, sub_a, accA.get_pointer() + (sg_startx * TM) * K + k * TK,
+                 K, matrix_layout::packed_a);
+             // Assuming B data is already in VNNI format.
+             joint_matrix_load(sg, sub_b,
+                               accB.get_pointer() + (k * TK / 4) * (N * 4) +
+                                   sg_starty / SG_SZ * TN * 4,
+                               N * 4, matrix_layout::packed_b);
+             sub_c = joint_matrix_mad(sg, sub_a, sub_b, sub_c);
+           }
+           joint_matrix_store(sg, sub_c,
+                              accC.get_pointer() + (sg_startx * TM) * N +
+                                  sg_starty / SG_SZ * TN,
+                              N, matrix_layout::row_major);
+         }); // parallel for
+   }).wait();
+}
+
+static constexpr size_t MATRIX_M = TM * 2;
+static constexpr size_t MATRIX_N = TN * 2;
+static constexpr size_t MATRIX_K = TK * 2;
+int8_t A[MATRIX_M][MATRIX_K];
+uint8_t B[MATRIX_K / 4][MATRIX_N * 4];
+int32_t C[MATRIX_M][MATRIX_N];
+int32_t D[MATRIX_M][MATRIX_N];
+
+void matrix_multiply_ref(int32_t *A_mem, int32_t *B_mem, int32_t *C_mem, int M,
+                         int N, int K) {
+  // tiling
+  for (int m = 0; m < M; m++)
+    for (int n = 0; n < N; n++) {
+      for (int k = 0; k < K; k++) {
+        char *va = (char *)(A_mem + m * K + k);
+        char *vb = (char *)(B_mem + k * N + n);
+        int acc = *(C_mem + m * N + n);
+        for (int i = 0; i < 4; i++) {
+          acc += (va[i] * vb[i]);
+        }
+        *(C_mem + m * N + n) = acc;
+      }
+    }
+}
+
+int main() {
+  for (int i = 0; i < MATRIX_M; i++) {
+    for (int j = 0; j < MATRIX_K; j++) {
+      A[i][j] = i + 2 * j;
+    }
+  }
+  for (int i = 0; i < MATRIX_K / 4; i++) {
+    for (int j = 0; j < MATRIX_N * 4; j++) {
+      B[i][j] = i + j;
+    }
+  }
+  for (int i = 0; i < MATRIX_M; i++) {
+    for (int j = 0; j < MATRIX_N; j++) {
+      C[i][j] = 1;
+      D[i][j] = 1;
+    }
+  }
+
+  big_matrix<int32_t, MATRIX_M, MATRIX_N> MC((int32_t *)&C);
+  big_matrix<int32_t, MATRIX_M, MATRIX_N> MD((int32_t *)&D);
+  big_matrix<int8_t, MATRIX_M, MATRIX_K> MA((int8_t *)&A);
+  big_matrix<uint8_t, MATRIX_K / 4, MATRIX_N * 4> MB((uint8_t *)&B);
+  matrix_multiply(MC, MA, MB);
+  matrix_multiply_ref((int32_t *)A, (int32_t *)B, (int32_t *)D, MATRIX_M,
+                      MATRIX_N, MATRIX_K / 4);
+
+  bool res = true;
+  for (int i = 0; i < MATRIX_M; i++) {
+    for (int j = 0; j < MATRIX_N; j++) {
+      if (C[i][j] != D[i][j])
+        res = false;
+    }
+  }
+  if (res)
+    std::cout << "passed\n";
+  else
+    std::cout << "failed\n";
+  for (int i = 0; i < MATRIX_M; i++) {
+    for (int j = 0; j < MATRIX_N; j++)
+      std::cout << C[i][j] << ", ";
+    std::cout << "\n";
+  }
+  std::cout << std::endl;
+  for (int i = 0; i < MATRIX_M; i++) {
+    for (int j = 0; j < MATRIX_N; j++)
+      std::cout << D[i][j] << ", ";
+    std::cout << "\n";
+  }
+}