From: Jerry Zhang <jerryzh@fb.com>
Date: Thu, 20 Dec 2018 23:28:12 +0000 (-0800)
Subject: Tensor construction codemod(ResizeLike) - 7/7 (#15087)
X-Git-Tag: accepted/tizen/6.5/unified/20211028.231830~2136
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=ed5b584f65eaf792761dd9e265e6cd668c11fb93;p=platform%2Fupstream%2Fpytorch.git

Tensor construction codemod(ResizeLike) - 7/7 (#15087)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15087

Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407

Reviewed By: ezyang

Differential Revision: D13419765

fbshipit-source-id: 34d695309a66723281429610a12544598c507d74
---

diff --git a/caffe2/operators/swish_op.cc b/caffe2/operators/swish_op.cc
index e7a764f..18cdf32 100644
--- a/caffe2/operators/swish_op.cc
+++ b/caffe2/operators/swish_op.cc
@@ -24,10 +24,10 @@ bool SwishGradientOp<CPUContext>::DoRunWithType() {
   auto& Xin = Input(X);
   auto& Yin = Input(Y);
   auto& DYin = Input(DY);
-  auto* DXout = Output(DX);
+
   CAFFE_ENFORCE_EQ(Xin.numel(), Yin.numel());
   CAFFE_ENFORCE_EQ(DYin.numel(), Yin.numel());
-  DXout->ResizeLike(Yin);
+  auto* DXout = Output(DX, Yin.sizes(), at::dtype<float>());
 
   const float* Xdata = Xin.template data<float>();
   const float* Ydata = Yin.template data<float>();
diff --git a/caffe2/operators/thresholded_relu_op.cc b/caffe2/operators/thresholded_relu_op.cc
index 4d0aa51..d9ca8e6 100644
--- a/caffe2/operators/thresholded_relu_op.cc
+++ b/caffe2/operators/thresholded_relu_op.cc
@@ -8,8 +8,8 @@ namespace caffe2 {
 template <>
 bool ThresholdedReluOp<float, CPUContext>::RunOnDevice() {
   auto& X = Input(0);
-  auto* Y = Output(0);
-  Y->ResizeLike(X);
+
+  auto* Y = Output(0, X.sizes(), at::dtype<float>());
 
   ConstEigenVectorArrayMap<float> Xvec(X.data<float>(), X.numel());
   EigenVectorArrayMap<float> Yvec(
@@ -30,9 +30,9 @@ template <>
 bool ThresholdedReluGradientOp<float, CPUContext>::RunOnDevice() {
   auto& Y = Input(0);
   auto& dY = Input(1);
-  auto* dX = Output(0);
+
   CAFFE_ENFORCE_EQ(dY.numel(), Y.numel());
-  dX->ResizeLike(Y);
+  auto* dX = Output(0, Y.sizes(), at::dtype<float>());
 
   const float* Ydata = Y.data<float>();
   const float* dYdata = dY.data<float>();
diff --git a/caffe2/operators/unique_ops.cc b/caffe2/operators/unique_ops.cc
index 003fe13..a992342 100644
--- a/caffe2/operators/unique_ops.cc
+++ b/caffe2/operators/unique_ops.cc
@@ -30,8 +30,8 @@ bool UniqueOp<CPUContext>::DoRunWithType() {
 
   int* remapping = nullptr;
   if (REMAPPING < OutputSize()) {
-    auto* remappingTensor = Output(REMAPPING);
-    remappingTensor->ResizeLike(inputTensor);
+    auto* remappingTensor =
+        Output(REMAPPING, inputTensor.sizes(), at::dtype<int>());
     remapping = remappingTensor->template mutable_data<int>();
   }
 
diff --git a/caffe2/operators/utility_ops.h b/caffe2/operators/utility_ops.h
index 0e03bfb..d80f4ec 100644
--- a/caffe2/operators/utility_ops.h
+++ b/caffe2/operators/utility_ops.h
@@ -255,12 +255,19 @@ class SumOp : public Operator<Context> {
   template <typename T, typename M>
   bool DoRunWithType() {
     auto& input0 = Input(0);
-    auto* output = Output(0);
+
     if (InputSize() == 1) {
-      output->CopyFrom(input0, true /*async*/);
+      // TODO: better TensorOptions argument passing(e.g. default argument)
+      OutputTensorCopyFrom(
+          0,
+          // I'll change the order of argument in another diff, so that we don't
+          // need to write this
+          at::dtype(input0.dtype()),
+          input0,
+          true /*async*/);
       return true;
     }
-    output->ResizeLike(input0);
+    auto* output = Output(0, input0.sizes(), at::dtype<T>());
     T* output_data = output->template mutable_data<T>();
     // Dimension checking
     for (int i = 1; i < InputSize(); ++i) {
@@ -331,10 +338,9 @@ class WeightedSumOp : public Operator<Context> {
     CAFFE_ENFORCE_GT(X0.numel(), 0);
     CAFFE_ENFORCE_EQ(weight0.numel(), 1);
     const int size = X0.numel();
-    auto* Y = Output(0);
-    if (Y != &X0) {
-      Y->ResizeLike(X0);
-    }
+    // Note: removed Aliasing check, since Output already has
+    // caching capability
+    auto* Y = Output(0, X0.sizes(), at::dtype<T>());
     T* Y_data = Y->template mutable_data<T>();
     if (input_size == 2) {
       math::Scale<float, T>(
@@ -346,15 +352,14 @@ class WeightedSumOp : public Operator<Context> {
       return true;
     }
     const auto& X1 = Input(2);
-    CAFFE_ENFORCE_NE(
-        &X1,
-        Y,
+    CAFFE_ENFORCE(
+        !IsInputOutputAlias(2, 0),
         "Input #2 is the same as output. If you want to do in-place updates, "
         "put the output as input #0.");
     const auto& weight1 = Input(3);
     CAFFE_ENFORCE_EQ(X1.numel(), size);
     CAFFE_ENFORCE_EQ(weight1.numel(), 1);
-    if (Y != &X0) {
+    if (!IsInputOutputAlias(0, 0)) {
       context_.template CopySameDevice<T>(size, X0.template data<T>(), Y_data);
     }
     math::Axpby<float, T, Context>(
@@ -371,7 +376,7 @@ class WeightedSumOp : public Operator<Context> {
       const std::string err_msg = "Input #" + to_string(i) +
           " is the same as output. If you want to do in-place updates, "
           "put the output as input #0.";
-      CAFFE_ENFORCE_NE(&Xi, Y, err_msg);
+      CAFFE_ENFORCE(!IsInputOutputAlias(i, 0), err_msg);
       const auto& weighti = Input(i + 1);
       CAFFE_ENFORCE_EQ(Xi.numel(), size);
       CAFFE_ENFORCE_EQ(weighti.numel(), 1);
@@ -410,8 +415,8 @@ class WeightedSumGradientOp : public Operator<Context> {
     for (int i = 0; i < InputSize() / 2; i++) {
       auto& cur_w = Input(2 * i + 2);
       CAFFE_ENFORCE_EQ(cur_w.numel(), 1);
-      auto* cur_dX = Output(i);
-      cur_dX->ResizeLike(dY);
+
+      auto* cur_dX = Output(i, dY.sizes(), at::dtype<DstType>());
 
       math::Scale<float, DstType, Context>(
           size,
diff --git a/caffe2/operators/utility_ops_cudnn.cc b/caffe2/operators/utility_ops_cudnn.cc
index b50c500..bd04f30 100644
--- a/caffe2/operators/utility_ops_cudnn.cc
+++ b/caffe2/operators/utility_ops_cudnn.cc
@@ -45,10 +45,10 @@ class CuDNNWeightedSumOp : public Operator<CUDAContext> {
     CAFFE_ENFORCE_EQ(weight0.numel(), 1);
     const int input_size = X0.numel();
     SetTensorDescriptor(cudnnTypeWrapper<T>::type, input_size);
-    auto* Y = Output(0);
-    if (Y != &X0) {
-      Y->ResizeLike(X0);
-    }
+
+    // Note: removed Aliasing check, since Output already has
+    // caching capability
+    auto* Y = Output(0, X0.sizes(), at::dtype<T>());
     T* Y_data = Y->template mutable_data<T>();
     T alpha = convert::To<float, T>(0.0f);
     T beta = convert::To<float, T>(0.0f);
@@ -65,9 +65,8 @@ class CuDNNWeightedSumOp : public Operator<CUDAContext> {
       return true;
     }
     const auto& X1 = Input(2);
-    CAFFE_ENFORCE_NE(
-        &X1,
-        Y,
+    CAFFE_ENFORCE(
+        !IsInputOutputAlias(2, 0),
         "Input #2 is the same as output. If you want to do in-place updates, "
         "put the output as input #0.");
     const auto& weight1 = Input(3);
@@ -75,7 +74,7 @@ class CuDNNWeightedSumOp : public Operator<CUDAContext> {
     CAFFE_ENFORCE_EQ(weight1.numel(), 1);
     CopyWeightToHost<T>(weight1.template data<float>(), &alpha);
     CopyWeightToHost<T>(weight0.template data<float>(), &beta);
-    if (Y == &X0) {
+    if (IsInputOutputAlias(0, 0)) {
       CUDNN_ENFORCE(cudnnAddTensor(
           cudnn_wrapper_.inline_cudnn_handle(),
           &alpha,
@@ -105,7 +104,7 @@ class CuDNNWeightedSumOp : public Operator<CUDAContext> {
       const std::string err_msg = "Input #" + to_string(i) +
           " is the same as output. If you want to do in-place updates, "
           "put the output as input #0.";
-      CAFFE_ENFORCE_NE(&Xi, Y, err_msg);
+      CAFFE_ENFORCE(!IsInputOutputAlias(i, 0), err_msg);
       const auto& weighti = Input(i + 1);
       CAFFE_ENFORCE_EQ(Xi.numel(), input_size);
       CAFFE_ENFORCE_EQ(weighti.numel(), 1);
diff --git a/caffe2/quantization/server/conv_relu_op.cc b/caffe2/quantization/server/conv_relu_op.cc
index bd41404..e3a3cd4 100644
--- a/caffe2/quantization/server/conv_relu_op.cc
+++ b/caffe2/quantization/server/conv_relu_op.cc
@@ -20,8 +20,8 @@ bool ConvReluOp<T, Context>::RunOnDeviceWithOrderNCHW() {
       BlobGetMutableTensor(local_output_blobs_[0], Context::GetDeviceType());
   const T* output_local_data = local_output->template data<T>();
 
-  Tensor* output = Operator<Context>::Output(0);
-  output->ResizeLike(*local_output);
+  Tensor* output =
+      Operator<Context>::Output(0, local_output->sizes(), at::dtype<T>());
   T* output_data = output->template mutable_data<T>();
 #ifdef _OPENMP
 #pragma omp parallel for
@@ -51,8 +51,8 @@ bool ConvReluOp<T, Context>::RunOnDeviceWithOrderNHWC() {
       BlobGetMutableTensor(local_output_blobs_[0], Context::GetDeviceType());
   const T* output_local_data = local_output->template data<T>();
 
-  Tensor* output = Operator<Context>::Output(0);
-  output->ResizeLike(*local_output);
+  Tensor* output =
+      Operator<Context>::Output(0, local_output->sizes(), at::dtype<T>());
   T* output_data = output->template mutable_data<T>();
 #ifdef _OPENMP
 #pragma omp parallel for
diff --git a/caffe2/quantization/server/fully_connected_fake_lowp_op.cc b/caffe2/quantization/server/fully_connected_fake_lowp_op.cc
index 2380014..ae08461 100644
--- a/caffe2/quantization/server/fully_connected_fake_lowp_op.cc
+++ b/caffe2/quantization/server/fully_connected_fake_lowp_op.cc
@@ -179,9 +179,7 @@ bool FullyConnectedGradientFakeLowpFPOp<Q, Context, Engine, TransposeWeight>::
   CAFFE_ENFORCE(M * K == X.size());
   CAFFE_ENFORCE(K * N == W.size());
 
-  auto* dW = Output(0);
-
-  dW->ResizeLike(W);
+  auto* dW = Output(0, W.sizes(), at::dtype<T_DW>());
   auto* db = Output(1, {N}, at::dtype<T_DB>());
 
   if (X.size() == 0) {
@@ -198,9 +196,7 @@ bool FullyConnectedGradientFakeLowpFPOp<Q, Context, Engine, TransposeWeight>::
         &context_);
 
     if (OutputSize() == 3) {
-      auto* dX = Output(2);
-      dX->ResizeLike(X);
-      dX->template mutable_data<T_DX>();
+      Output(2, X.sizes(), at::dtype<T_DX>());
     }
 
     return true;
@@ -270,8 +266,7 @@ bool FullyConnectedGradientFakeLowpFPOp<Q, Context, Engine, TransposeWeight>::
 
   // Compute dX
   if (OutputSize() == 3) {
-    auto* dX = Output(2);
-    dX->ResizeLike(X);
+    auto* dX = Output(2, X.sizes(), at::dtype<T_DX>());
     math::Gemm<T_DX, Context, Engine>(
         CblasNoTrans,
         TransposeWeight ? CblasNoTrans : CblasTrans,
diff --git a/caffe2/sgd/clip_tensor_op.h b/caffe2/sgd/clip_tensor_op.h
index 5792051..34d6686 100644
--- a/caffe2/sgd/clip_tensor_op.h
+++ b/caffe2/sgd/clip_tensor_op.h
@@ -29,8 +29,7 @@ class ClipTensorByScalingOp final : public Operator<Context> {
     const auto* input_tensor_data = input_tensor.template data<float>();
     const auto* val_data = val.template data<float>();
 
-    auto* clipped = Output(0);
-    clipped->ResizeLike(input_tensor);
+    auto* clipped = Output(0, input_tensor.sizes(), at::dtype<float>());
     float* clipped_tensor_data = clipped->template mutable_data<float>();
 
     if (InputSize() > 2) {
diff --git a/caffe2/sgd/yellowfin_op.h b/caffe2/sgd/yellowfin_op.h
index 5c6f67f..622caa8 100644
--- a/caffe2/sgd/yellowfin_op.h
+++ b/caffe2/sgd/yellowfin_op.h
@@ -157,9 +157,9 @@ for (int i = 0; i < param_tensor.dim(); ++i) {
 
 // Output data
 
-#define CAFFE2_YF_READ_OUTPUT(OUTPUT_NAME, VAR_NAME)         \
-  auto VAR_NAME##_out_tensor = Output(OUTPUT_##OUTPUT_NAME); \
-  VAR_NAME##_out_tensor->ResizeLike(VAR_NAME##_tensor);      \
+#define CAFFE2_YF_READ_OUTPUT(OUTPUT_NAME, VAR_NAME)                           \
+  auto VAR_NAME##_out_tensor =                                                 \
+      Output(OUTPUT_##OUTPUT_NAME, VAR_NAME##_tensor.sizes(), at::dtype<T>()); \
   VAR_NAME##_out_ = VAR_NAME##_out_tensor->template mutable_data<T>();
 
     CAFFE2_YF_READ_OUTPUT(PARAM, param)
diff --git a/modules/detectron/batch_permutation_op.cc b/modules/detectron/batch_permutation_op.cc
index 4d53035..60916e5 100644
--- a/modules/detectron/batch_permutation_op.cc
+++ b/modules/detectron/batch_permutation_op.cc
@@ -78,7 +78,6 @@ template <>
 bool BatchPermutationOp<float, CPUContext>::RunOnDevice() {
   const auto& X = Input(0);
   const auto& indices = Input(1);
-  auto* Y = Output(0);
 
   CAFFE_ENFORCE_EQ(indices.dim(), 1, "indices must be 1-d");
   CAFFE_ENFORCE_EQ(
@@ -90,7 +89,7 @@ bool BatchPermutationOp<float, CPUContext>::RunOnDevice() {
     indices.dim32(0),
     ")");
 
-  Y->ResizeLike(X);
+  auto* Y = Output(0, X.sizes(), at::dtype<float>());
 
   const int N = X.dim32(0);
   const int C = X.dim32(1);