New ir support data transfer (#54763)

* add kernel dialect

* change DenseTensorTypeStorage to DenseTensorType

* add test case`

* add first pd_op to kernel dialect

* lower pd op to kernel dialect

* update

* update

* remove useless code

* add attrite print test

* fix bug

* update

* update

* update

* update

* polish code

* fix bug

* polish  code  and add python test

* add test

* fix test error

* add env flag

* fix bug

* revert test env

* change cc_test_old to cc_test

* fix build_static bug

* fix type test error

* udpate cmake

* disable test in windows

* update

* update

* fix bug

* split file

* fix conflict

* polish code and fix conflict

* support place transformer

* finish bug

* add gpu flags

* fix with cuda macro

* update

* add scope guard

* polish code

paddle/fluid/framework/new_executor/interpreter/interpreter_util.cc

@@ -985,9 +985,9 @@ void BuildOpFuncList(
                           .data();
 
     VLOG(6) << "finish process infer meta context";
-    auto t1 =
-        phi::KernelFactory::Instance().SelectKernel(kernel_name, kernel_key);
-    op_func_node.phi_kernel_ = new phi::Kernel(t1);
+    auto t1 = phi::KernelFactory::Instance().SelectKernelOrThrowError(
+        kernel_name, kernel_key);
+    op_func_node.phi_kernel_ = new phi::Kernel(t1.kernel);
 
     PADDLE_ENFORCE_EQ(op_func_node.phi_kernel_->IsValid(),
                       true,

paddle/fluid/framework/new_executor/new_ir_interpreter.cc

@@ -962,9 +962,12 @@ void NewIRInterpreter::RunInstruction(const Instruction& instr_node) {
     if (instr_node.PreDefineContext()) {
       VLOG(5) << "run new ir selected kernel";
       auto op_func_node = const_cast<OpFuncNode*>((instr_node.OpFunc()));
+      VLOG(5) << "begin to run op " << op_func_node->phi_op_name_;
       op_func_node->infer_shape_interface_->infer_shape_(
           &(op_func_node->infer_meta_context_));
+      VLOG(5) << "after run infer meta";
       (*(op_func_node->phi_kernel_))(&(op_func_node->kernel_context_));
+      VLOG(5) << "after run kernel";
     } else if (!instr_node.IsArtificial()) {
       RunOperator(instr_node);
       CheckGC(instr_node);

paddle/fluid/ir/pass/pd_op_to_kernel_pass.cc

@@ -22,8 +22,10 @@
 #include "paddle/fluid/ir/dialect/kernel_type.h"
 #include "paddle/fluid/ir/dialect/op_yaml_info_util.h"
 #include "paddle/fluid/ir/dialect/pd_attribute.h"
+#include "paddle/fluid/ir/dialect/pd_dialect.h"
 #include "paddle/fluid/ir/dialect/utils.h"
 #include "paddle/fluid/ir/interface/op_yaml_info.h"
+#include "paddle/phi/api/lib/data_transform.h"
 #include "paddle/phi/api/lib/kernel_dispatch.h"
 #include "paddle/phi/common/place.h"
 #include "paddle/phi/core/compat/convert_utils.h"
@@ -31,6 +33,8 @@
 namespace paddle {
 namespace dialect {
 
+const int init_on_gpu_threashold = 1000;
+
 phi::KernelKey GetKernelKey(
     ir::Operation* op,
     const phi::Place& place,
@@ -100,9 +104,33 @@ phi::KernelKey GetKernelKey(
     // parse all the input tensor
     if (tensor_input_number == 0 || op->name() == "pd.full_") {
       // all the information have to get from attribute and context
+
+      if (op->name() == "pd.uniform") {
+        // try to process uniform, use shape to determin backend
+        // TODO(phlrain): shuold support other initilize op
+        auto define_op = op->operand(0).source().GetDefiningOp();
+        if (define_op->name() == "pd.full_int_array") {
+          auto shape = define_op->attributes()
+                           .at("value")
+                           .dyn_cast<dialect::IntArrayAttribute>()
+                           .data()
+                           .GetData();
+
+          size_t numel = 1;
+          for (auto& s : shape) {
+            numel *= s;
+          }
+          if (numel > init_on_gpu_threashold) {
+            kernel_backend = phi::Backend::GPU;
+          }
+        }
+      }
+
+      if (kernel_backend == phi::Backend::UNDEFINED) {
         kernel_backend = paddle::experimental::ParseBackend(place);
       }
     }
+  }
 
   if (op->num_operands() > 0) {
     paddle::experimental::detail::KernelKeyParser kernel_key_parser;
@@ -166,6 +194,7 @@ std::unique_ptr<ir::Program> PdOpLowerToKernelPass(ir::Program* prog) {
   phi::Place cpu_place(phi::AllocationType::CPU);
 
   ir::IrContext* ctx = ir::IrContext::Instance();
+  ctx->GetOrRegisterDialect<paddle::dialect::PaddleDialect>();
   ctx->GetOrRegisterDialect<paddle::dialect::PaddleKernelDialect>();
 
   std::unordered_map<ir::Operation*, ir::Operation*> map_op_pair;
@@ -220,22 +249,68 @@ std::unique_ptr<ir::Program> PdOpLowerToKernelPass(ir::Program* prog) {
     // constuct input
     std::vector<ir::OpResult> vec_inputs;
 
-    if ((*it)->name() != "pd.full" && (*it)->num_operands() > 0) {
-      for (size_t i = 0; i < (*it)->num_operands(); ++i) {
-        auto cur_in = (*it)->operand(i).source();
-        auto new_in = map_value_pair.at(cur_in);
-
-        vec_inputs.push_back(new_in);
-      }
-    }
-
     paddle::dialect::OpYamlInfoInterface op_info_interface =
         (*it)->dyn_cast<paddle::dialect::OpYamlInfoInterface>();
     std::string kernel_fn_str;
+    std::vector<paddle::dialect::OpInputInfo> input_info;
     if (op_info_interface) {
       auto op_info_res = op_info_interface.GetOpInfo();
       auto runtime_info = std::get<3>(op_info_res);
       kernel_fn_str = runtime_info.kernel_func[0];
+      input_info = std::get<0>(op_info_res);
+    }
+
+    if ((*it)->num_operands() > 0) {
+      for (size_t i = 0; i < (*it)->num_operands(); ++i) {
+        auto cur_in = (*it)->operand(i).source();
+        auto new_in = map_value_pair.at(cur_in);
+
+        auto new_in_type = new_in.type();
+
+        auto& kernel = phi::KernelFactory::Instance().SelectKernelWithGPUDNN(
+            kernel_fn_str, kernel_key);
+
+        if (kernel.IsValid()) {
+          if (new_in_type.isa<dialect::AllocatedDenseTensorType>()) {
+            // allocated type
+            auto place =
+                new_in_type.dyn_cast<dialect::AllocatedDenseTensorType>()
+                    .place();
+
+            if ((i < input_info.size()) &&
+                (!input_info[i].is_mutable_attribute) &&
+                (place != phi::TransToPhiPlace(kernel_key.backend()))) {
+              if (paddle::experimental::NeedTransformPlace(
+                      place, kernel.InputAt(i).backend, {})) {
+                VLOG(6) << "need trans from " << place << " to "
+                        << kernel_key.backend();
+                // build memcopy op
+                auto copy_kernel_key = kernel_key;
+                copy_kernel_key.set_backend(phi::Backend::GPU);
+                std::unordered_map<std::string, ir::Attribute> op1_attribute{
+                    {"op_name", ir::StrAttribute::get(ctx, "pd.memcpy_h2d")},
+                    {"kernel_name", ir::StrAttribute::get(ctx, "memcpy_h2d")},
+                    {"kernel_key",
+                     dialect::KernelAttribute::get(ctx, copy_kernel_key)},
+                    {"dst_place_type", ir::Int32Attribute::get(ctx, 1)}};
+
+                ir::Operation* op1 = ir::Operation::Create(
+                    {new_in}, op1_attribute, {new_in_type}, op1_info);
+
+                program->block()->push_back(op1);
+
+                new_in = op1->result(0);
+              }
+            }
+          } else if (new_in_type.isa<ir::VectorType>()) {
+            // [ todo need update here, support combine data transfomer]
+          } else {
+            PADDLE_THROW(phi::errors::Unimplemented(
+                "only support allocated dense tensor type for now"));
+          }
+        }
+        vec_inputs.push_back(new_in);
+      }
     }
 
     std::unordered_map<std::string, ir::Attribute> op1_attribute{

paddle/phi/api/lib/data_transform.cc

@@ -35,23 +35,6 @@ inline bool NeedTransformDataType(const DataType& input,
           target == DataType::COMPLEX64 || target == DataType::COMPLEX128);
 }
 
-inline bool NeedTransformPlace(const phi::Place& input,
-                               const Backend& target,
-                               const TransformFlag& transform_flag) {
-  // NOTE(dev): The default value of TransformFlag is True, if it is set with
-  // False
-  // somewhere such as ops.yaml or backward.yaml that means we should skip data
-  // transform. Because "stop_transform_" has highest priority.
-  if (!transform_flag.need_trans_backend()) {
-    return false;
-  }
-  bool ret = input.GetType() == AllocationType::GPUPINNED ||
-             (target != Backend::ALL_BACKEND &&
-              phi::TransToPhiBackend(input) !=
-                  (target != Backend::GPUDNN ? target : Backend::GPU));
-  return ret;
-}
-
 inline bool NeedTransformLayout(const DataLayout& input,
                                 const DataLayout& target,
                                 const phi::Place& place,

paddle/phi/api/lib/data_transform.h

@@ -118,5 +118,22 @@ void TransDataBackend(const phi::SelectedRows* tensor,
                       Backend target_backend,
                       phi::SelectedRows* out);
 
+inline bool NeedTransformPlace(const phi::Place& input,
+                               const Backend& target,
+                               const TransformFlag& transform_flag) {
+  // NOTE(dev): The default value of TransformFlag is True, if it is set with
+  // False
+  // somewhere such as ops.yaml or backward.yaml that means we should skip data
+  // transform. Because "stop_transform_" has highest priority.
+  if (!transform_flag.need_trans_backend()) {
+    return false;
+  }
+  bool ret = input.GetType() == AllocationType::GPUPINNED ||
+             (target != Backend::ALL_BACKEND &&
+              phi::TransToPhiBackend(input) !=
+                  (target != Backend::GPUDNN ? target : Backend::GPU));
+  return ret;
+}
+
 }  // namespace experimental
 }  // namespace paddle

paddle/phi/api/yaml/legacy_ops.yaml

@@ -591,6 +591,24 @@
     func : mean
   backward : mean_grad
 
+- op : memcpy_d2h
+  args : (Tensor x, int dst_place_type)
+  output : Tensor
+  infer_meta :
+    func : UnchangedInferMeta
+    param : [x]
+  kernel :
+    func : memcpy_d2h
+
+- op : memcpy_h2d
+  args : (Tensor x, int dst_place_type)
+  output : Tensor
+  infer_meta :
+    func : UnchangedInferMeta
+    param : [x]
+  kernel :
+    func : memcpy_h2d
+
 - op : min
   args : (Tensor x, IntArray axis={}, bool keepdim=false)
   output : Tensor(out)

paddle/phi/core/kernel_factory.cc

@@ -88,6 +88,7 @@ const Kernel& KernelFactory::SelectKernel(const std::string& kernel_name,
   if (iter == kernels_.end()) {
     return empty_kernel;
   }
+
   auto kernel_iter = iter->second.find(kernel_key);
   if (kernel_iter == iter->second.end() &&
       kernel_key.layout() != phi::DataLayout::ALL_LAYOUT) {
@@ -95,6 +96,50 @@ const Kernel& KernelFactory::SelectKernel(const std::string& kernel_name,
         kernel_key.backend(), phi::DataLayout::ALL_LAYOUT, kernel_key.dtype());
     kernel_iter = iter->second.find(any_layout_kernel_key);
   }
+
+#if defined(PADDLE_WITH_CUSTOM_DEVICE)
+  if (kernel_iter == iter->second.end() &&
+      kernel_key.backend() > phi::Backend::NUM_BACKENDS) {
+    kernel_iter = iter->second.find({phi::Backend::CUSTOM,
+                                     phi::DataLayout::ALL_LAYOUT,
+                                     kernel_key.dtype()});
+  }
+#endif
+
+  if (kernel_iter == iter->second.end()) {
+    return empty_kernel;
+  }
+
+  return kernel_iter->second;
+}
+
+const Kernel& KernelFactory::SelectKernelWithGPUDNN(
+    const std::string& kernel_name, const KernelKey& const_kernel_key) const {
+  auto iter = kernels_.find(kernel_name);
+  if (iter == kernels_.end()) {
+    return empty_kernel;
+  }
+  KernelKey kernel_key = KernelKey(const_kernel_key);
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+  if (kernel_key.backend() == Backend::GPUDNN) {
+    auto kernel_iter = iter->second.find(
+        {Backend::GPUDNN, phi::DataLayout::ALL_LAYOUT, kernel_key.dtype()});
+    if (kernel_iter != iter->second.end()) {
+      return kernel_iter->second;
+    }
+    kernel_key =
+        KernelKey(Backend::GPU, kernel_key.layout(), kernel_key.dtype());
+  }
+#endif
+
+  auto kernel_iter = iter->second.find(kernel_key);
+  if (kernel_iter == iter->second.end() &&
+      kernel_key.layout() != phi::DataLayout::ALL_LAYOUT) {
+    phi::KernelKey any_layout_kernel_key(
+        kernel_key.backend(), phi::DataLayout::ALL_LAYOUT, kernel_key.dtype());
+    kernel_iter = iter->second.find(any_layout_kernel_key);
+  }
+
 #if defined(PADDLE_WITH_CUSTOM_DEVICE)
   if (kernel_iter == iter->second.end() &&
       kernel_key.backend() > phi::Backend::NUM_BACKENDS) {

paddle/phi/core/kernel_factory.h

@@ -330,6 +330,9 @@ class KernelFactory {
   const Kernel& SelectKernel(const std::string& kernel_name,
                              const KernelKey& kernel_key) const;
 
+  const Kernel& SelectKernelWithGPUDNN(const std::string& kernel_name,
+                                       const KernelKey& kernel_key) const;
+
   KernelKeyMap SelectKernelMap(const std::string& kernel_name) const;
 
   const KernelArgsDef& GetFirstKernelArgsDef(

test/cpp/new_executor/standalone_executor_new_ir_test.cc

@@ -158,5 +158,81 @@ TEST(StandaloneExecutor, run_2) {
   EXPECT_EQ(res3, true);
 }
 
+#ifdef PADDLE_WITH_CUDA
+TEST(StandaloneExecutor, data_transfer) {
+  ir::IrContext* ctx = ir::IrContext::Instance();
+  ctx->GetOrRegisterDialect<paddle::dialect::PaddleDialect>();
+  ir::Program program(ctx);
+  ir::Builder builder(ctx, program.block());
+  ir::Block* block = program.block();
+
+  // Def: A = paddle::dialect::UniformOp(std::vector<int64_t> shape,
+  // phi::DataType dtype, float min, float max, int seed, phi::Place place)
+  paddle::dialect::UniformOp uniform1 =
+      builder.Build<paddle::dialect::UniformOp>(std::vector<int64_t>{1},
+                                                phi::DataType::FLOAT32,
+                                                0.0,
+                                                1.0,
+                                                2,
+                                                phi::CPUPlace());
+  EXPECT_EQ(uniform1->result(0).type().isa<paddle::dialect::DenseTensorType>(),
+            true);
+  EXPECT_EQ(block->size(), 4u);
+
+  // Def: B = paddle::dialect::UniformOp(...)
+  paddle::dialect::UniformOp uniform2 =
+      builder.Build<paddle::dialect::UniformOp>(std::vector<int64_t>{100, 100},
+                                                phi::DataType::FLOAT32,
+                                                0.0,
+                                                1.0,
+                                                2,
+                                                phi::CPUPlace());
+  EXPECT_EQ(uniform2->result(0).type().isa<paddle::dialect::DenseTensorType>(),
+            true);
+  EXPECT_EQ(block->size(), 8u);
+
+  // Def: C = paddle::dialect::AddOp(ir::OpResult x_, ir::OpResult y_)
+  paddle::dialect::AddOp add = builder.Build<paddle::dialect::AddOp>(
+      uniform1->result(0), uniform2->result(0));
+  EXPECT_EQ(add->result(0).type().isa<paddle::dialect::DenseTensorType>(),
+            true);
+  EXPECT_EQ(block->size(), 9u);
+
+  program.Print(std::cout);
+
+  auto kernel_program = paddle::dialect::PdOpLowerToKernelPass(&program);
+
+  kernel_program->Print(std::cout);
+
+  auto place = platform::CPUPlace();
+  Scope scope;
+
+  ProgramDesc prog_desc;
+
+  InterpreterCore test_core(place, std::move(kernel_program), &scope);
+
+  test_core.Run({});
+
+  auto out_tensor = scope.Var("inner_var_9")->Get<phi::DenseTensor>();
+
+  auto& pool = phi::DeviceContextPool::Instance();
+  phi::DenseTensor out;
+  phi::DeviceContext* dev_ctx = pool.Get(out_tensor.place());
+  phi::Copy(*dev_ctx, out_tensor, place, true, &out);
+
+  bool res0 = simple_cmp(out.data<float>()[0], 0.903649);
+  bool res1 = simple_cmp(out.data<float>()[1], 1.07367);
+  bool res2 = simple_cmp(out.data<float>()[2], 1.10631);
+  bool res3 = simple_cmp(out.data<float>()[3], 1.68683);
+  std::cerr << out.data<float>()[0] << "\t" << out.data<float>()[1] << "\t"
+            << out.data<float>()[2] << "\t" << out.data<float>()[3]
+            << std::endl;
+  EXPECT_EQ(res0, true);
+  EXPECT_EQ(res1, true);
+  EXPECT_EQ(res2, true);
+  EXPECT_EQ(res3, true);
+}
+#endif
+
 }  // namespace framework
 }  // namespace paddle