[Lang] MatrixNdarray refactor part6: Add scalarization for LocalLoadStmt & GlobalLoadStmt with TensorType

python/taichi/lang/ast/ast_transformer.py

@@ -15,8 +15,9 @@
 from taichi.lang.ast.symbol_resolver import ASTResolver
 from taichi.lang.exception import TaichiSyntaxError, TaichiTypeError
 from taichi.lang.field import Field
+from taichi.lang.impl import current_cfg
 from taichi.lang.matrix import (Matrix, MatrixType, Vector, _PyScopeMatrixImpl,
-                                _TiScopeMatrixImpl)
+                                _TiScopeMatrixImpl, make_matrix)
 from taichi.lang.snode import append
 from taichi.lang.util import in_taichi_scope, is_taichi_class, to_taichi_type
 from taichi.types import (annotations, ndarray_type, primitive_types,
@@ -114,6 +115,12 @@ def build_Assign(ctx, node):
     @staticmethod
     def build_assign_slice(ctx, node_target, values, is_static_assign):
         target = ASTTransformer.build_Subscript(ctx, node_target, get_ref=True)
+        if current_cfg().real_matrix:
+            if isinstance(node_target.value.ptr,
+                          any_array.AnyArray) and isinstance(
+                              values, (list, tuple)):
+                values = make_matrix(values)
+
         if isinstance(node_target.value.ptr, Matrix):
             if isinstance(node_target.value.ptr._impl, _TiScopeMatrixImpl):
                 target._assign(values)

python/taichi/lang/impl.py

@@ -16,7 +16,7 @@
 from taichi.lang.kernel_arguments import SparseMatrixProxy
 from taichi.lang.matrix import (Matrix, MatrixField, MatrixNdarray, MatrixType,
                                 Vector, _IntermediateMatrix,
-                                _MatrixFieldElement, make_matrix)
+                                _MatrixFieldElement)
 from taichi.lang.mesh import (ConvType, MeshElementFieldProxy, MeshInstance,
                               MeshRelationAccessProxy,
                               MeshReorderedMatrixFieldProxy,

taichi/analysis/offline_cache_util.cpp

@@ -66,6 +66,7 @@ static std::vector<std::uint8_t> get_offline_cache_key_of_compile_config(
   serializer(config->experimental_auto_mesh_local);
   serializer(config->auto_mesh_local_default_occupacy);
   serializer(config->real_matrix);
+  serializer(config->real_matrix_scalarize);
   serializer.finalize();
 
   return serializer.data;

taichi/python/export_lang.cpp

@@ -203,6 +203,8 @@ void export_lang(py::module &m) {
                      &CompileConfig::ndarray_use_cached_allocator)
       .def_readwrite("use_mesh", &CompileConfig::use_mesh)
       .def_readwrite("real_matrix", &CompileConfig::real_matrix)
+      .def_readwrite("real_matrix_scalarize",
+                     &CompileConfig::real_matrix_scalarize)
       .def_readwrite("cc_compile_cmd", &CompileConfig::cc_compile_cmd)
       .def_readwrite("cc_link_cmd", &CompileConfig::cc_link_cmd)
       .def_readwrite("quant_opt_store_fusion",

taichi/transforms/scalarize.cpp

@@ -8,10 +8,14 @@ TLANG_NAMESPACE_BEGIN
 
 class Scalarize : public IRVisitor {
  public:
+  DelayedIRModifier modifier_;
+
   Scalarize(IRNode *node) {
     allow_undefined_visitor = true;
     invoke_default_visitor = false;
     node->accept(this);
+
+    modifier_.modify_ir();
   }
 
   /*
@@ -51,18 +55,75 @@ class Scalarize : public IRVisitor {
       int num_elements = val_tensor_type->get_num_elements();
       for (int i = 0; i < num_elements; i++) {
         auto const_stmt = std::make_unique<ConstStmt>(
-            TypedConstant(stmt->val->ret_type.get_element_type(), i));
+            TypedConstant(get_data_type<int32>(), i));
 
         auto ptr_offset_stmt =
             std::make_unique<PtrOffsetStmt>(stmt->dest, const_stmt.get());
         auto scalarized_stmt = std::make_unique<T>(ptr_offset_stmt.get(),
                                                    matrix_init_stmt->values[i]);
 
-        stmt->insert_before_me(std::move(const_stmt));
-        stmt->insert_before_me(std::move(ptr_offset_stmt));
-        stmt->insert_before_me(std::move(scalarized_stmt));
+        modifier_.insert_before(stmt, std::move(const_stmt));
+        modifier_.insert_before(stmt, std::move(ptr_offset_stmt));
+        modifier_.insert_before(stmt, std::move(scalarized_stmt));
+      }
+      modifier_.erase(stmt);
+    }
+  }
+
+  /*
+
+    Before:
+      TensorType<4 x i32> val = LoadStmt(TensorType<4 x i32>* src)
+
+    After:
+      i32* addr0 = PtrOffsetStmt(TensorType<4 x i32>* src, 0)
+      i32* addr1 = PtrOffsetStmt(TensorType<4 x i32>* src, 1)
+      i32* addr2 = PtrOffsetStmt(TensorType<4 x i32>* src, 2)
+      i32* addr3 = PtrOffsetStmt(TensorType<4 x i32>* src, 3)
+
+      i32 val0 = LoadStmt(addr0)
+      i32 val1 = LoadStmt(addr1)
+      i32 val2 = LoadStmt(addr2)
+      i32 val3 = LoadStmt(addr3)
+
+      tmp = MatrixInitStmt(val0, val1, val2, val3)
+
+      stmt->replace_all_usages_with(tmp)
+  */
+  template <typename T>
+  void scalarize_load_stmt(T *stmt) {
+    auto src_dtype = stmt->src->ret_type.ptr_removed();
+    if (src_dtype->template is<TensorType>()) {
+      // Needs scalarize
+      auto src_tensor_type = src_dtype->template as<TensorType>();
+
+      std::vector<Stmt *> matrix_init_values;
+      int num_elements = src_tensor_type->get_num_elements();
+
+      for (size_t i = 0; i < num_elements; i++) {
+        auto const_stmt = std::make_unique<ConstStmt>(
+            TypedConstant(get_data_type<int32>(), i));
+
+        auto ptr_offset_stmt =
+            std::make_unique<PtrOffsetStmt>(stmt->src, const_stmt.get());
+        auto scalarized_stmt = std::make_unique<T>(ptr_offset_stmt.get());
+
+        matrix_init_values.push_back(scalarized_stmt.get());
+
+        modifier_.insert_before(stmt, std::move(const_stmt));
+        modifier_.insert_before(stmt, std::move(ptr_offset_stmt));
+        modifier_.insert_before(stmt, std::move(scalarized_stmt));
       }
-      stmt->parent->erase(stmt);
+
+      auto matrix_init_stmt =
+          std::make_unique<MatrixInitStmt>(matrix_init_values);
+
+      matrix_init_stmt->ret_type = src_dtype;
+
+      stmt->replace_usages_with(matrix_init_stmt.get());
+      modifier_.insert_before(stmt, std::move(matrix_init_stmt));
+
+      modifier_.erase(stmt);
     }
   }
 
@@ -107,6 +168,14 @@ class Scalarize : public IRVisitor {
   void visit(LocalStoreStmt *stmt) override {
     scalarize_store_stmt<LocalStoreStmt>(stmt);
   }
+
+  void visit(GlobalLoadStmt *stmt) override {
+    scalarize_load_stmt<GlobalLoadStmt>(stmt);
+  }
+
+  void visit(LocalLoadStmt *stmt) override {
+    scalarize_load_stmt<LocalLoadStmt>(stmt);
+  }
 };
 
 namespace irpass {

tests/cpp/transforms/scalarize_test.cpp

@@ -9,7 +9,7 @@ namespace lang {
 
 // Basic tests within a basic block
 template <typename T>
-void test_scalarize() {
+void test_store_scalarize() {
   TestProgram test_prog;
   test_prog.setup();
 
@@ -76,9 +76,69 @@ void test_scalarize() {
   EXPECT_EQ(block->statements[16]->is<T>(), true);
 }
 
+template <typename T>
+void test_load_scalarize() {
+  TestProgram test_prog;
+  test_prog.setup();
+
+  auto block = std::make_unique<Block>();
+
+  auto func = []() {};
+  auto kernel =
+      std::make_unique<Kernel>(*test_prog.prog(), func, "fake_kernel");
+  block->kernel = kernel.get();
+
+  auto &type_factory = TypeFactory::get_instance();
+
+  /*
+    TensorType<4 x i32>* %1 = ExternalPtrStmt()
+    TensorType<4 x i32>  %2 = LoadStmt(%1)
+  */
+  Type *tensor_type = type_factory.get_tensor_type(
+      {2, 2}, type_factory.get_primitive_type(PrimitiveTypeID::i32));
+  auto argload_stmt = block->push_back<ArgLoadStmt>(0 /*arg_id*/, tensor_type);
+
+  std::vector<Stmt *> indices = {};
+  Stmt *src_stmt = block->push_back<ExternalPtrStmt>(
+      argload_stmt, indices);  // fake ExternalPtrStmt
+  src_stmt->ret_type = type_factory.get_pointer_type(tensor_type);
+
+  block->push_back<T>(src_stmt);
+
+  irpass::scalarize(block.get());
+
+  EXPECT_EQ(block->size(), 1 /*argload*/ + 1 /*external_ptr*/ + 4 /*const*/ +
+                               4 /*ptroffset*/ + 4 /*load*/ +
+                               1 /*matrix_init*/);
+
+  // Check for scalarized statements
+  EXPECT_EQ(block->statements[2]->is<ConstStmt>(), true);
+  EXPECT_EQ(block->statements[3]->is<PtrOffsetStmt>(), true);
+  EXPECT_EQ(block->statements[4]->is<T>(), true);
+
+  EXPECT_EQ(block->statements[5]->is<ConstStmt>(), true);
+  EXPECT_EQ(block->statements[6]->is<PtrOffsetStmt>(), true);
+  EXPECT_EQ(block->statements[7]->is<T>(), true);
+
+  EXPECT_EQ(block->statements[8]->is<ConstStmt>(), true);
+  EXPECT_EQ(block->statements[9]->is<PtrOffsetStmt>(), true);
+  EXPECT_EQ(block->statements[10]->is<T>(), true);
+
+  EXPECT_EQ(block->statements[11]->is<ConstStmt>(), true);
+  EXPECT_EQ(block->statements[12]->is<PtrOffsetStmt>(), true);
+  EXPECT_EQ(block->statements[13]->is<T>(), true);
+
+  EXPECT_EQ(block->statements[14]->is<MatrixInitStmt>(), true);
+}
+
 TEST(Scalarize, ScalarizeStore) {
-  test_scalarize<GlobalStoreStmt>();
-  test_scalarize<LocalStoreStmt>();
+  test_store_scalarize<GlobalStoreStmt>();
+  test_store_scalarize<LocalStoreStmt>();
+}
+
+TEST(Scalarize, ScalarizeLoad) {
+  test_load_scalarize<GlobalLoadStmt>();
+  test_load_scalarize<LocalLoadStmt>();
 }
 
 }  // namespace lang

tests/python/test_matrix.py

@@ -817,3 +817,41 @@ def foo() -> ti.types.matrix(2, 2, ti.f32):
         return a @ b.transpose()
 
     assert foo() == [[1.0, 2.0], [2.0, 4.0]]
+
+
+@test_utils.test(arch=[ti.cuda, ti.cpu],
+                 real_matrix=True,
+                 real_matrix_scalarize=True)
+def test_store_scalarize():
+    @ti.kernel
+    def func(a: ti.types.ndarray()):
+        for i in range(5):
+            a[i] = [[i, i + 1], [i + 2, i + 3]]
+
+    x = ti.Matrix.ndarray(2, 2, ti.i32, shape=5)
+    func(x)
+
+    assert (x[0] == [[0, 1], [2, 3]])
+    assert (x[1] == [[1, 2], [3, 4]])
+    assert (x[2] == [[2, 3], [4, 5]])
+    assert (x[3] == [[3, 4], [5, 6]])
+    assert (x[4] == [[4, 5], [6, 7]])
+
+
+@test_utils.test(arch=[ti.cuda, ti.cpu],
+                 real_matrix=True,
+                 real_matrix_scalarize=True)
+def test_load_store_scalarize():
+    @ti.kernel
+    def func(a: ti.types.ndarray()):
+        for i in range(3):
+            a[i] = [[i, i + 1], [i + 2, i + 3]]
+
+        a[3] = a[1]
+        a[4] = a[2]
+
+    x = ti.Matrix.ndarray(2, 2, ti.i32, shape=5)
+    func(x)
+
+    assert (x[3] == [[1, 2], [3, 4]])
+    assert (x[4] == [[2, 3], [4, 5]])