Tensor construction codemod(ResizeLike) - 3/7 (#15122)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15122

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

Reviewed By: dzhulgakov

Differential Revision: D13419643

fbshipit-source-id: 65b5a037b94d458b944d51f790ba2829db1fb530

diff --git a/caffe2/mpi/mpi_ops.h b/caffe2/mpi/mpi_ops.h
index 0580a77..a40ac31 100644 (file)
--- a/caffe2/mpi/mpi_ops.h
+++ b/caffe2/mpi/mpi_ops.h
@@ -70,8 +70,7 @@ class MPIReduceOp final : public Operator<Context> {
   bool RunOnDevice() override {
     MPI_Comm comm = OperatorBase::Input<MPICommonWorldWrapper>(0).comm();
     auto& input = Input(1);
-    auto* output = Output(0);
-    output->ResizeLike(input);
+    auto* output = Output(0, input.sizes(), at::dtype<T>());
     MPI_CHECK(MPI_Reduce(
         const_cast<T*>(input.template data<T>()),
         output->template mutable_data<T>(),
@@ -123,8 +122,7 @@ class MPIAllreduceOp final : public Operator<Context> {
   bool RunOnDevice() override {
     MPI_Comm comm = OperatorBase::Input<MPICommonWorldWrapper>(0).comm();
     auto& input = Input(1);
-    auto* output = Output(0);
-    output->ResizeLike(input);
+    auto* output = Output(0, input.sizes(), at::dtype<T>());
     void* source;
     if (output->template mutable_data<T>() == input.template data<T>()) {
       // We are doing in-place call. Special case handling.

diff --git a/caffe2/operators/activation_ops_cudnn.h b/caffe2/operators/activation_ops_cudnn.h
index 8492026..6d65f04 100644 (file)
--- a/caffe2/operators/activation_ops_cudnn.h
+++ b/caffe2/operators/activation_ops_cudnn.h
@@ -68,8 +68,8 @@ class CuDNNActivationOp final : public CuDNNActivationOpBase {
   template <typename T>
   bool DoRunWithType() {
     const auto& X = Input(0);
-    auto* Y = Output(0);
-    Y->ResizeLike(X);
+
+    auto* Y = Output(0, X.sizes(), at::dtype<T>());
     if (X.numel() == 0) {
       Y->template mutable_data<T>();
       return true;
@@ -107,8 +107,8 @@ class CuDNNActivationGradientOp final : public CuDNNActivationOpBase {
   bool DoRunWithType() {
     const auto& Y = Input(0);
     const auto& dY = Input(1);
-    auto* dX = Output(0);
-    dX->ResizeLike(Y);
+
+    auto* dX = Output(0, Y.sizes(), at::dtype<T>());
     if (Y.numel() == 0) {
       dX->template mutable_data<T>();
       return true;

diff --git a/caffe2/operators/affine_channel_op.cc b/caffe2/operators/affine_channel_op.cc
index d20e399..c0df42f 100644 (file)
--- a/caffe2/operators/affine_channel_op.cc
+++ b/caffe2/operators/affine_channel_op.cc
@@ -54,8 +54,8 @@ template <>
 bool AffineChannelGradientOp<float, CPUContext>::RunOnDeviceWithOrderNCHW() {
   const auto& dY = Input(0);
   const auto& scale = is_learnable_ ? Input(2) : Input(1);
-  auto* dX = Output(0);
-  dX->ResizeLike(dY);
+
+  auto* dX = Output(0, dY.sizes(), at::dtype<float>());
   const int N = dY.dim32(0);
   const int C = dY.dim32(1);
   const int HxW = dY.numel() / (N * C);
@@ -75,10 +75,9 @@ bool AffineChannelGradientOp<float, CPUContext>::RunOnDeviceWithOrderNCHW() {
   if (is_learnable_) {
     const auto& X = Input(1);
     const float* X_data = X.data<float>();
-    auto* dscale = Output(1);
-    auto* dbias = Output(2);
-    dscale->ResizeLike(scale);
-    dbias->ResizeLike(scale);
+
+    auto* dscale = Output(1, scale.sizes(), at::dtype<float>());
+    auto* dbias = Output(2, scale.sizes(), at::dtype<float>());
     AffineChannelScaleBiasBackwardNCHW<float>(
         N,
         C,
@@ -95,8 +94,8 @@ template <>
 bool AffineChannelGradientOp<float, CPUContext>::RunOnDeviceWithOrderNHWC() {
   const auto& dY = Input(0);
   const auto& scale = is_learnable_ ? Input(2) : Input(1);
-  auto* dX = Output(0);
-  dX->ResizeLike(dY);
+
+  auto* dX = Output(0, dY.sizes(), at::dtype<float>());
   const int ndim = dY.dim();
   const int C = dY.dim32(ndim - 1);
   const int rows = dY.numel() / C;
@@ -115,10 +114,9 @@ bool AffineChannelGradientOp<float, CPUContext>::RunOnDeviceWithOrderNHWC() {
     const float* X_data = X.data<float>();
     const int N = X.dim32(0);
     const int HxW = rows / N;
-    auto* dscale = Output(1);
-    auto* dbias = Output(2);
-    dscale->ResizeLike(scale);
-    dbias->ResizeLike(scale);
+
+    auto* dscale = Output(1, scale.sizes(), at::dtype<float>());
+    auto* dbias = Output(2, scale.sizes(), at::dtype<float>());
     AffineChannelScaleBiasBackwardNHWC<float>(
         N,
         C,

diff --git a/caffe2/operators/affine_channel_op.h b/caffe2/operators/affine_channel_op.h
index 3f89a26..38a6565 100644 (file)
--- a/caffe2/operators/affine_channel_op.h
+++ b/caffe2/operators/affine_channel_op.h
@@ -32,18 +32,17 @@ class AffineChannelOp final : public Operator<Context> {
     const auto& X = Input(0);
     const auto& scale = Input(1);
     const auto& bias = Input(2);
-    auto* Y = Output(0);
+
     if (is_learnable_) {
-      CAFFE_ENFORCE_NE(
-          Y,
-          &X,
+      CAFFE_ENFORCE(
+          !IsInputOutputAlias(0, 0),
           "In-place affine_channel_op is not supported when "
           "is_learnable = true.");
     }
     const int N = X.dim32(0);
     const int C = X.dim32(1);
     const int HxW = X.numel() / (N * C);
-    Y->ResizeLike(X);
+    auto* Y = Output(0, X.sizes(), at::dtype<T>());
     math::AffineChannel<T, Context, StorageOrder::NCHW>(
         N,
         C,
@@ -60,11 +59,10 @@ class AffineChannelOp final : public Operator<Context> {
     const auto& X = Input(0);
     const auto& scale = Input(1);
     const auto& bias = Input(2);
-    auto* Y = Output(0);
+
     if (is_learnable_) {
-      CAFFE_ENFORCE_NE(
-          Y,
-          &X,
+      CAFFE_ENFORCE(
+          !IsInputOutputAlias(0, 0),
           "In-place affine_channel_op is not supported when "
           "is_learnable = true.");
     }
@@ -72,7 +70,8 @@ class AffineChannelOp final : public Operator<Context> {
     const int N = X.dim32(0);
     const int C = X.dim32(ndim - 1);
     const int HxW = X.numel() / (N * C);
-    Y->ResizeLike(X);
+    auto* Y =
+        Output(0, X.sizes(), at::dtype<T>());
     math::AffineChannel<T, Context, StorageOrder::NHWC>(
         N,
         C,

diff --git a/caffe2/operators/batch_box_cox_op.cc b/caffe2/operators/batch_box_cox_op.cc
index 031cf8e..95901f4 100644 (file)
--- a/caffe2/operators/batch_box_cox_op.cc
+++ b/caffe2/operators/batch_box_cox_op.cc
@@ -79,8 +79,7 @@ bool BatchBoxCoxOp<CPUContext>::DoRunWithType() {
   auto N = data.size(0);
   auto D = data.size_from_dim(1);
 
-  auto* output = Output(0);
-  output->ResizeLike(Input(DATA));
+  auto* output = Output(0, Input(DATA).sizes(), at::dtype<T>());
   auto* output_ptr = output->template mutable_data<T>();
 
   if (data.numel() <= 0) {

diff --git a/caffe2/operators/batch_gather_ops.h b/caffe2/operators/batch_gather_ops.h
index 46ab990..277efab 100644 (file)
--- a/caffe2/operators/batch_gather_ops.h
+++ b/caffe2/operators/batch_gather_ops.h
@@ -58,7 +58,6 @@ class BatchGatherGradientOp final : public Operator<Context> {
     auto& data = Input(DATA);
     auto& indices = Input(INDICES);
     auto& grad = Input(GRAD);
-    auto* output = Output(0);
 
     // ONNX allows negative axis to index from the back, valid range: [-r, r].
     int axis = axis_;
@@ -76,7 +75,7 @@ class BatchGatherGradientOp final : public Operator<Context> {
           "batch gather outer dimensions should match");
     }
 
-    output->ResizeLike(data);
+    auto* output = Output(0, data.sizes(), at::dtype<TData>());
     TData* out_data = output->template mutable_data<TData>();
     if (data.numel() <= 0) {
       return true;

diff --git a/caffe2/operators/batch_moments_op.h b/caffe2/operators/batch_moments_op.h
index 560a7a5..18d64e4 100644 (file)
--- a/caffe2/operators/batch_moments_op.h
+++ b/caffe2/operators/batch_moments_op.h
@@ -72,12 +72,12 @@ class BatchMomentsGradientOp final : public Operator<Context> {
     const auto& dmu = Input(0);
     const auto& dvar = Input(1);
     const auto& X = Input(2);
-    auto* dX = Output(0);
+
     const int ndim = X.dim();
     const int N = X.dim32(0);
     const int C = order_ == StorageOrder::NCHW ? X.dim32(1) : X.dim32(ndim - 1);
     const int HxW = X.numel() / (N * C);
-    dX->ResizeLike(X);
+    auto* dX = Output(0, X.sizes(), at::dtype<T>());
     const T* dmu_data = dmu.template data<T>();
     const T* dvar_data = dvar.template data<T>();
     const T* X_data = X.template data<T>();

diff --git a/caffe2/operators/bbox_transform_op.cc b/caffe2/operators/bbox_transform_op.cc
index 6b82af5..a4298ed 100644 (file)
--- a/caffe2/operators/bbox_transform_op.cc
+++ b/caffe2/operators/bbox_transform_op.cc
@@ -91,7 +91,6 @@ bool BBoxTransformOp<float, CPUContext>::RunOnDevice() {
   const auto& roi_in = Input(0);
   const auto& delta_in = Input(1);
   const auto& iminfo_in = Input(2);
-  auto* box_out = Output(0);
 
   const int box_dim = rotated_ ? 5 : 4;
   const int N = roi_in.dim32(0);
@@ -132,7 +131,7 @@ bool BBoxTransformOp<float, CPUContext>::RunOnDevice() {
   Eigen::Map<const ERArrXXf> iminfo(
       iminfo_in.data<float>(), iminfo_in.size(0), iminfo_in.size(1));
 
-  box_out->ResizeLike(delta_in);
+  auto* box_out = Output(0, delta_in.sizes(), at::dtype<float>());
   Eigen::Map<ERArrXXf> new_boxes(
       box_out->template mutable_data<float>(),
       box_out->dim32(0),

diff --git a/caffe2/operators/bisect_percentile_op.h b/caffe2/operators/bisect_percentile_op.h
index becede5..131239e 100644 (file)
--- a/caffe2/operators/bisect_percentile_op.h
+++ b/caffe2/operators/bisect_percentile_op.h
@@ -64,8 +64,8 @@ class BisectPercentileOp final : public Operator<Context> {
     const float* raw_data = raw.template data<float>();
 
     // Output
-    auto* pct = Output(PCT);
-    pct->ResizeLike(raw);
+
+    auto* pct = Output(PCT, raw.sizes(), at::dtype<float>());
     float* pct_output = pct->template mutable_data<float>();
 
     // Compute percentile for each raw feature value

diff --git a/caffe2/operators/boolean_mask_ops.cc b/caffe2/operators/boolean_mask_ops.cc
index 2bcf71a..f23e655 100644 (file)
--- a/caffe2/operators/boolean_mask_ops.cc
+++ b/caffe2/operators/boolean_mask_ops.cc
@@ -20,7 +20,7 @@ class BooleanMaskLengthsOp final : public Operator<Context> {
   bool DoRunWithType() {
     auto& lengths = Input(0);
     auto& mask = Input(1);
-    auto* lengthsOut = Output(0);
+
     CAFFE_ENFORCE(lengths.dim() == 1);
     CAFFE_ENFORCE(mask.dim() == 1);
     const auto* lengthsPtr = lengths.template data<T>();
@@ -28,7 +28,7 @@ class BooleanMaskLengthsOp final : public Operator<Context> {
     auto totalLength =
         std::accumulate(lengthsPtr, lengthsPtr + lengths.numel(), 0);
     CAFFE_ENFORCE(mask.numel() == totalLength);
-    lengthsOut->ResizeLike(lengths);
+    auto* lengthsOut = Output(0, lengths.sizes(), at::dtype<T>());
     auto* lengthsOutPtr = lengthsOut->template mutable_data<T>();
     int p = 0;
     for (int i = 0; i < lengths.numel(); ++i) {
@@ -365,8 +365,7 @@ bool SequenceMaskOp<CPUContext>::DoRunWithType() {
     window_centers = &Input(1);
   }
 
-  auto* output = Output(0);
-  output->ResizeLike(*input);
+  auto* output = Output(0, input->sizes(), at::dtype<T>());
 
   const auto canonical_axis = input->canonical_axis_index(axis_);