From fd91b8281480233cb814443a49095ea4046db0a5 Mon Sep 17 00:00:00 2001
From: hjchen2 <chenhoujiangcug@gmail.com>
Date: Thu, 20 Dec 2018 16:06:07 +0800
Subject: [PATCH] Refine: pass input's lod info to output for quantize and
 dequantize op

---
 src/operators/kernel/arm/dequantize_kernel.cpp | 5 +++--
 src/operators/kernel/arm/quantize_kernel.cpp   | 5 +++--
 src/operators/math/sequence2batch.h            | 4 ++--
 src/operators/op_param.h                       | 8 ++++----
 4 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/src/operators/kernel/arm/dequantize_kernel.cpp b/src/operators/kernel/arm/dequantize_kernel.cpp
index 2c13cac1a6..c9dfd6f936 100644
--- a/src/operators/kernel/arm/dequantize_kernel.cpp
+++ b/src/operators/kernel/arm/dequantize_kernel.cpp
@@ -30,8 +30,8 @@ bool DequantizeKernel<CPU, float>::Init(DequantizeParam<CPU> *param) {
 
 template <>
 void DequantizeKernel<CPU, float>::Compute(const DequantizeParam<CPU> &param) {
-  const Tensor *input = param.input_;
-  Tensor *output = param.output_;
+  const LoDTensor *input = param.input_;
+  LoDTensor *output = param.output_;
   float activation_scale = param.activation_scale_->data<float>()[0];
   float weight_scale = param.weight_scale_;
   const int32_t *x = input->data<const int32_t>();
@@ -72,6 +72,7 @@ void DequantizeKernel<CPU, float>::Compute(const DequantizeParam<CPU> &param) {
   for (size_t i = 0; i < size; ++i) {
     y[i] = x[i] * scale;
   }
+  output->set_lod(input->lod());
 }
 
 }  // namespace operators
diff --git a/src/operators/kernel/arm/quantize_kernel.cpp b/src/operators/kernel/arm/quantize_kernel.cpp
index 03f4ac81fb..97ffa05c86 100644
--- a/src/operators/kernel/arm/quantize_kernel.cpp
+++ b/src/operators/kernel/arm/quantize_kernel.cpp
@@ -186,8 +186,8 @@ bool QuantizeKernel<CPU, float>::Init(QuantizeParam<CPU> *param) {
 
 template <>
 void QuantizeKernel<CPU, float>::Compute(const QuantizeParam<CPU> &param) {
-  const Tensor *input = param.input_;
-  Tensor *output = param.output_;
+  const LoDTensor *input = param.input_;
+  LoDTensor *output = param.output_;
   Tensor *output_scale = param.online_scale_;
   float max_abs = 0.f;
   if (param.offline_) {
@@ -212,6 +212,7 @@ void QuantizeKernel<CPU, float>::Compute(const QuantizeParam<CPU> &param) {
       LOG(kLOG_ERROR) << "round type is not supported.";
       break;
   }
+  output->set_lod(input->lod());
 }
 
 }  // namespace operators
diff --git a/src/operators/math/sequence2batch.h b/src/operators/math/sequence2batch.h
index 42b369f7dc..537f2326d0 100644
--- a/src/operators/math/sequence2batch.h
+++ b/src/operators/math/sequence2batch.h
@@ -69,10 +69,10 @@ class LoDTensor2BatchFunctor {
 
     auto lods = lod_tensor.lod();
     PADDLE_MOBILE_ENFORCE((lods.size() == 1UL),
-                          "Only support one level sequence now.");
+                          "Only support 1 level sequence, but %d is given",
+                          lods.size());
 
     const auto& lod = lods[0];
-
     std::vector<SeqInfo> seq_info;
     for (size_t seq_id = 0; seq_id < lod.size() - 1; ++seq_id) {
       int length = lod[seq_id + 1] - lod[seq_id];
diff --git a/src/operators/op_param.h b/src/operators/op_param.h
index adc725e25f..643bd65ee0 100644
--- a/src/operators/op_param.h
+++ b/src/operators/op_param.h
@@ -2589,9 +2589,9 @@ class QuantizeParam : public OpParam {
 
  public:
   // op input
-  RType *input_;
+  GType *input_;
   // op output
-  RType *output_;
+  GType *output_;
   RType *online_scale_;
   // quantize offline scale
   RType *offline_scale_;
@@ -2625,9 +2625,9 @@ class DequantizeParam : public OpParam {
 
  public:
   // op input
-  RType *input_;
+  GType *input_;
   // op output
-  RType *output_;
+  GType *output_;
   RType *activation_scale_;
   float weight_scale_;
 };
-- 
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