From 3153a201ddc698cd4b474784cedafafc99284317 Mon Sep 17 00:00:00 2001
From: dingminghui <dingminghui@cambricon.com>
Date: Sun, 15 Mar 2020 18:05:31 +0800
Subject: [PATCH] feat(leaky_relu): support leaky_relu

---
 lite/kernels/mlu/bridges/act_op.cc      | 29 ++++++++++++++++++++-----
 lite/kernels/mlu/bridges/act_op_test.cc |  3 ++-
 2 files changed, 25 insertions(+), 7 deletions(-)

diff --git a/lite/kernels/mlu/bridges/act_op.cc b/lite/kernels/mlu/bridges/act_op.cc
index 396d859194..5d6e2f9ca0 100644
--- a/lite/kernels/mlu/bridges/act_op.cc
+++ b/lite/kernels/mlu/bridges/act_op.cc
@@ -31,20 +31,34 @@ int ActConverter(void* ctx, OpLite* op, KernelBase* kernel) {
   VLOG(3) << "[MLU] Converting " + op_type + "...";
 
   // Create act node and set params from op
+  auto fp_type = graph->FPType();
   auto x_var_name = op_info->Input("X").front();
   auto out_var_name = op_info->Output("Out").front();
   auto output = scope->FindVar(out_var_name)->GetMutable<Tensor>();
   auto output_dims = output->dims().Vectorize();
   auto output_tensor = graph->AddNode(
-      out_var_name, output_dims, CNML_TENSOR, CNML_NHWC, graph->FPType());
+      out_var_name, output_dims, CNML_TENSOR, CNML_NHWC, fp_type);
   CHECK(graph->HasNode(x_var_name));
   auto input_tensor = graph->GetNode(x_var_name);
-  cnmlActiveFunction_t act_type = OpTypeToCNMLActType(op_type);
   cnmlBaseOp_t activation_op;
-  CNML_CALL(cnmlCreateActiveOp(&activation_op,
-                               act_type,
-                               input_tensor->mlu_tensor(),
-                               output_tensor->mlu_tensor()));
+  if (op_type == "leaky_relu") {
+    auto alpha = op_info->GetAttr<float>("alpha");
+    std::vector<int64_t> shape = {1, 1, 1, 1};
+    std::string alpha_var_name = string_format("leaky_relu_alpha_%p", op);
+    auto alpha_tensor =
+        graph->AddNode(alpha_var_name, shape, CNML_CONST, CNML_NHWC, fp_type);
+    graph->BindConstRawData(alpha_var_name, &alpha, 1, true);
+    CNML_CALL(cnmlCreatePreluOp(&activation_op,
+                                input_tensor->mlu_tensor(),
+                                output_tensor->mlu_tensor(),
+                                alpha_tensor->mlu_tensor()));
+  } else {
+    cnmlActiveFunction_t act_type = OpTypeToCNMLActType(op_type);
+    CNML_CALL(cnmlCreateActiveOp(&activation_op,
+                                 act_type,
+                                 input_tensor->mlu_tensor(),
+                                 output_tensor->mlu_tensor()));
+  }
   graph->FuseOp(activation_op);
   return SUCCESS;
 }
@@ -59,3 +73,6 @@ REGISTER_SUBGRAPH_BRIDGE(sigmoid,
                          paddle::lite::subgraph::mlu::ActConverter);
 REGISTER_SUBGRAPH_BRIDGE(relu, kMLU, paddle::lite::subgraph::mlu::ActConverter);
 REGISTER_SUBGRAPH_BRIDGE(tanh, kMLU, paddle::lite::subgraph::mlu::ActConverter);
+REGISTER_SUBGRAPH_BRIDGE(leaky_relu,
+                         kMLU,
+                         paddle::lite::subgraph::mlu::ActConverter);
diff --git a/lite/kernels/mlu/bridges/act_op_test.cc b/lite/kernels/mlu/bridges/act_op_test.cc
index 91a09bfb03..7cec0529e4 100644
--- a/lite/kernels/mlu/bridges/act_op_test.cc
+++ b/lite/kernels/mlu/bridges/act_op_test.cc
@@ -134,7 +134,7 @@ void test_act(std::vector<int64_t> x_shape, std::string op_type) {
 
 TEST(MLUBridges, activation) {
   std::vector<std::vector<int64_t>> shapes{{1}, {2, 3}, {1, 2, 3, 4}};
-  std::vector<std::string> types{"sigmoid", "relu", "tanh"};
+  std::vector<std::string> types{"sigmoid", "relu", "tanh", "leaky_relu"};
   for (auto x_shape : shapes) {
     for (auto op_type : types) {
       test_act(x_shape, op_type);
@@ -150,3 +150,4 @@ TEST(MLUBridges, activation) {
 USE_SUBGRAPH_BRIDGE(sigmoid, kMLU)
 USE_SUBGRAPH_BRIDGE(relu, kMLU)
 USE_SUBGRAPH_BRIDGE(tanh, kMLU)
+USE_SUBGRAPH_BRIDGE(leaky_relu, kMLU)
-- 
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