add anakin fc op converter (#15965)

paddle/fluid/inference/anakin/convert/fc.cc

@@ -13,6 +13,16 @@
 // limitations under the License.
 
 #include "paddle/fluid/inference/anakin/convert/fc.h"
+#include <algorithm>
+
+using anakin::graph::GraphGlobalMem;
+using anakin::AK_FLOAT;
+using anakin::Precision;
+using anakin::saber::NV;
+using anakin::saber::X86;
+using anakin::saber::Shape;
+using anakin::PBlock;
+using anakin::PTuple;
 
 namespace paddle {
 namespace inference {
@@ -23,15 +33,39 @@ void FcOpConverter::operator()(const framework::proto::OpDesc &op,
   framework::OpDesc op_desc(op, nullptr);
   PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
   PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1);
-  PADDLE_ENFORCE_EQ(op_desc.Input("Out").size(), 1);
+  PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);
 
   auto x_name = op_desc.Input("X").front();
-  PADDLE_ENFORCE(x_name.size() > 0);
+  auto op_name = op_desc.Type() + ":" + op_desc.Output("Out").front();
   auto *y_v = scope.FindVar(op_desc.Input("Y").front());
   PADDLE_ENFORCE_NOT_NULL(y_v);
   auto *y_t = y_v->GetMutable<framework::LoDTensor>();
 
-  auto shape = framework::vectorize2int(y_t->dims());
+  auto input_name = op_desc.Input("X").front();
+  auto output_name = op_desc.Output("Out").front();
+
+  auto weight_shape = framework::vectorize2int(y_t->dims());
+  engine_->AddOp(op_name, "Dense", {input_name}, {output_name});
+  engine_->AddOpAttr(op_name, "bias_term", false);
+  engine_->AddOpAttr(op_name, "axis", 1);
+  int out_dim = weight_shape[1];
+  engine_->AddOpAttr(op_name, "out_dim", out_dim);
+
+  weight_shape.push_back(1);
+  weight_shape.push_back(1);
+  Shape anakin_shape(weight_shape);
+
+  framework::LoDTensor weight_tensor;
+  weight_tensor.Resize(y_t->dims());
+  TensorCopySync((*y_t), platform::CPUPlace(), &weight_tensor);
+
+  auto *weight1 =
+      GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(anakin_shape);
+  float *cpu_data = static_cast<float *>(weight1->h_tensor().mutable_data());
+  std::copy_n(weight_tensor.data<float>(), weight_tensor.numel(), cpu_data);
+  weight1->d_tensor().set_shape(anakin_shape);
+  weight1->d_tensor().copy_from(weight1->h_tensor());
+  engine_->AddOpAttr(op_name, "weight_1", *weight1);
 }
 
 }  // namespace anakin

paddle/fluid/inference/anakin/convert/test_fc_op.cc

@@ -22,14 +22,16 @@ namespace inference {
 namespace anakin {
 
 TEST(fc_op, test) {
-  auto it = OpRegister::instance()->Get("fc");
-  ASSERT_TRUE(it != nullptr);
+  auto fc_converter = OpRegister::instance()->Get("fc");
+  ASSERT_TRUE(fc_converter != nullptr);
+  // Registrar<FcOpConverter> register_fc("fc");
+  // auto fc = std::make_shared<FcOpConverter>();
 
   std::unordered_set<std::string> parameters({"mul_y"});
   framework::Scope scope;
   AnakinConvertValidation validator(parameters, scope);
   validator.DeclInputVar("mul_x", {1, 1, 1, 1});
-  validator.DeclParamVar("mul_y", {1, 1, 1, 2});
+  validator.DeclParamVar("mul_y", {1, 2});
   validator.DeclOutputVar("mul_out", {1, 1, 1, 2});
 
   // Prepare Op description

paddle/fluid/inference/anakin/convert/ut_helper.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <map>
 #include <memory>
 #include <string>
 #include <unordered_map>
@@ -127,6 +128,7 @@ class AnakinConvertValidation {
       engine_->SetInputShape(input, t_shape);
     }
     engine_->Optimize();
+    engine_->InitGraph();
   }
 
   // We use the set 'neglected_output' here, because some Ops like batch norm,
@@ -138,16 +140,47 @@ class AnakinConvertValidation {
     platform::CUDADeviceContext ctx(place_);
     op_->Run(scope_, place_);
 
+    // std::vector<framework::LoDTensor> input_vector;
+    // std::vector<framework::LoDTensor> output_vector;
+    std::map<std::string, framework::LoDTensor*> inputs;
+    for (const auto& input : op_desc_->InputArgumentNames()) {
+      if (parameters_.count(input)) continue;
+      auto* var = scope_.FindVar(input);
+      auto tensor = var->GetMutable<framework::LoDTensor>();
+      inputs.insert({input, tensor});
+    }
+
+    std::map<std::string, framework::LoDTensor*> outputs;
+    std::vector<std::vector<float>> fluid_outputs;
     for (const auto& output : op_desc_->OutputArgumentNames()) {
       if (neglected_output.count(output)) continue;
       std::vector<float> fluid_out;
       auto* var = scope_.FindVar(output);
-      auto* tensor = var->GetMutable<framework::LoDTensor>();
+      auto tensor = var->GetMutable<framework::LoDTensor>();
       framework::TensorToVector(*tensor, ctx, &fluid_out);
+      fluid_outputs.push_back(fluid_out);
 
-      size_t fluid_out_size = fluid_out.size();
-      for (size_t i = 0; i < fluid_out_size; i++) {
+      // size_t fluid_out_size = fluid_out.size();
+      /*for (size_t i = 0; i < fluid_out_size; i++) {
         std::cout << fluid_out[i] << std::endl;
+      }*/
+      outputs.insert({output, tensor});
+    }
+
+    engine_->Execute(inputs, outputs);
+    int i_output = 0;
+    for (const auto& output : op_desc_->OutputArgumentNames()) {
+      if (neglected_output.count(output)) continue;
+      std::vector<float> anakin_out;
+      auto* var = scope_.FindVar(output);
+      auto tensor = var->GetMutable<framework::LoDTensor>();
+      framework::TensorToVector(*tensor, ctx, &anakin_out);
+
+      size_t anakin_out_size = anakin_out.size();
+      auto fluid_out = fluid_outputs[i_output++];
+      for (size_t i = 0; i < anakin_out_size; i++) {
+        LOG(INFO) << "Output[" << i << "]: anakin[" << anakin_out[i] << "], "
+                  << "fluid[" << fluid_out[i] << "]";
       }
     }
   }

paddle/fluid/inference/anakin/test_anakin_engine.cc

@@ -46,47 +46,51 @@ class TestAnakinEngine : public ::testing::Test {
 
 void TestAnakinEngine::SetUp() {
   engine_.reset(new AnakinEngine<NV, Precision::FP32>(true));
+}
+
+TEST_F(TestAnakinEngine, Execute) {
+  engine_->AddOp("op1", "Dense", {"x"}, {"y"});
+  engine_->AddOpAttr("op1", "out_dim", 2);
+  engine_->AddOpAttr("op1", "bias_term", false);
+  engine_->AddOpAttr("op1", "axis", 1);
+  std::vector<int> shape = {1, 1, 1, 2};
+  Shape tmp_shape(shape);
+  // PBlock<NV> weight1(tmp_shape);
+  auto *weight1 =
+      GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(tmp_shape);
+  // auto *weight1 = new PBlock<NV>(tmp_shape, AK_FLOAT);
+
+  float *cpu_data = static_cast<float *>(weight1->h_tensor().mutable_data());
+  cpu_data[0] = 2.;
+  weight1->d_tensor().set_shape(tmp_shape);
+  weight1->d_tensor().copy_from(weight1->h_tensor());
+  engine_->AddOpAttr("op1", "weight_1", *weight1);
 
-  TEST_F(TestAnakinEngine, Execute) {
-    engine_->AddOp("op1", "Dense", {"x"}, {"y"});
-    engine_->AddOpAttr("op1", "out_dim", 2);
-    engine_->AddOpAttr("op1", "bias_term", false);
-    engine_->AddOpAttr("op1", "axis", 1);
-    std::vector<int> shape = {1, 1, 1, 2};
-    Shape tmp_shape(shape);
-    auto *weight1 =
-        GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(tmp_shape);
-
-    float *cpu_data = static_cast<float *>(weight1->h_tensor().mutable_data());
-    cpu_data[0] = 2.;
-    weight1->d_tensor().set_shape(tmp_shape);
-    weight1->d_tensor().copy_from(weight1->h_tensor());
-    engine_->AddOpAttr("op1", "weight_1", *weight1);
-
-    engine_->Freeze();
-    engine_->SetInputShape("x", {1, 1, 1, 1});
-    engine_->Optimize();
-    engine_->InitGraph();
-    framework::LoDTensor x;
-    framework::LoDTensor y;
-    x.Resize({1, 1, 1, 1});
-    y.Resize({1, 1, 1, 2});
-    auto *x_data = x.mutable_data<float>(platform::CUDAPlace());
-    float x_data_cpu[] = {1.};
-    cudaMemcpy(x_data, x_data_cpu, sizeof(float), cudaMemcpyHostToDevice);
-
-    std::map<std::string, framework::LoDTensor *> inputs = {{"x", &x}};
-    auto *y_data = y.mutable_data<float>(platform::CUDAPlace());
-    std::map<std::string, framework::LoDTensor *> outputs = {{"y", &y}};
-
-    engine_->Execute(inputs, outputs);
-    auto *y_data_gpu = y_data;
-    float y_data_cpu[2];
-    cudaMemcpy(y_data_cpu, y_data_gpu, sizeof(float) * 2,
-               cudaMemcpyDeviceToHost);
-    LOG(INFO) << "output value: " << y_data_cpu[0] << ", " << y_data_cpu[1];
-  }
+  engine_->Freeze();
+  // PTuple<int> input_shape = {1};
+  // engine_->AddOpAttr("x", "input_shape", input_shape);
+  engine_->SetInputShape("x", {1, 1, 1, 1});
+  engine_->Optimize();
+  engine_->InitGraph();
+  framework::LoDTensor x;
+  framework::LoDTensor y;
+  x.Resize({1, 1, 1, 1});
+  y.Resize({1, 1, 1, 2});
+  auto *x_data = x.mutable_data<float>(platform::CUDAPlace());
+  float x_data_cpu[] = {1.};
+  cudaMemcpy(x_data, x_data_cpu, sizeof(float), cudaMemcpyHostToDevice);
+
+  std::map<std::string, framework::LoDTensor *> inputs = {{"x", &x}};
+  auto *y_data = y.mutable_data<float>(platform::CUDAPlace());
+  std::map<std::string, framework::LoDTensor *> outputs = {{"y", &y}};
+
+  engine_->Execute(inputs, outputs);
+  auto *y_data_gpu = y_data;
+  float y_data_cpu[2];
+  cudaMemcpy(y_data_cpu, y_data_gpu, sizeof(float) * 2, cudaMemcpyDeviceToHost);
+  LOG(INFO) << "output value: " << y_data_cpu[0] << ", " << y_data_cpu[1];
 }
+
 }  // namespace anakin
 }  // namespace inference
 }  // namespace paddle