Tensor construction codemod - 1/3 (#14828)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14828

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

Reviewed By: bddppq

Differential Revision: D13335160

fbshipit-source-id: a3ae4c5a86bfbdaf2d5aa14e0eef57255e829fd4

diff --git a/caffe2/experiments/operators/fully_connected_op_decomposition.h b/caffe2/experiments/operators/fully_connected_op_decomposition.h
index 955177a..432189d 100644 (file)
--- a/caffe2/experiments/operators/fully_connected_op_decomposition.h
+++ b/caffe2/experiments/operators/fully_connected_op_decomposition.h
@@ -43,7 +43,7 @@ class FullyConnectedOpDecomp final : public Operator<Context> {
     const auto& U = Input(1);
     const auto& V = Input(2);
     const auto& b = Input(3);
-    auto* Y = Output(0);
+
     //auto* buffer_ptr = Output(1);
     // Size M * middle;
     //auto& multi_buffer_ = *buffer_ptr;
@@ -64,15 +64,17 @@ class FullyConnectedOpDecomp final : public Operator<Context> {
     int middle = U.dim32(0);
     CAFFE_ENFORCE_EQ(K, V.dim32(0));
     CAFFE_ENFORCE_EQ(N, b.dim32(0));
+    std::vector<int64_t> dims;
     if (X.dim() > 1) {
-      Y->Resize(M, N);
+      dims = {M, N};
       multi_buffer_.Resize(M, middle);
     } else {
-      Y->Resize(N);
+      dims = {N};
       multi_buffer_.Resize(middle);
     }
-  // The col buffer is stored in CHW order as well - kernel_dim, and the height
-  // and width.
+    auto* Y = Output(0, dims, at::dtype<T>());
+    // The col buffer is stored in CHW order as well - kernel_dim, and the
+    // height and width.
     //  multi_buffer_.Resize(M, middle);
     T* multi_buffer_data = multi_buffer_.template mutable_data<T>();
     //  X * V * tans(U)
@@ -138,10 +140,10 @@ class FullyConnectedDecompGradientOp : public Operator<Context> {
     }
     auto* dU = Output(0);
     auto* dV = Output(1);
-    auto* db = Output(2);
+
     dU->ResizeLike(U);
     dV->ResizeLike(V);
-    db->Resize(N);
+    auto* db = Output(2, {N}, at::dtype<T>());
 
     // Compute dU
     // first compute X * V

diff --git a/caffe2/experiments/operators/fully_connected_op_prune.h b/caffe2/experiments/operators/fully_connected_op_prune.h
index 4bd7fc0..179fb67 100644 (file)
--- a/caffe2/experiments/operators/fully_connected_op_prune.h
+++ b/caffe2/experiments/operators/fully_connected_op_prune.h
@@ -135,7 +135,7 @@ namespace caffe2 {
           const auto& W = Input(1);
           const auto& Mask = Input(2);
           const auto& b = Input(3);
-          auto* Y = Output(0);
+
           CAFFE_ENFORCE_GE(X.dim(), 1);
           CAFFE_ENFORCE_GE(W.dim(), 2);
           if (X.dim() > 2 || W.dim() > 2) {
@@ -151,11 +151,13 @@ namespace caffe2 {
           int N = W.dim32(0);
           CAFFE_ENFORCE_EQ(K, W.numel() / W.dim32(0));
           CAFFE_ENFORCE_EQ(N, b.dim32(0));
+          std::vector<int64_t> dims;
           if (X.dim() > 1) {
-            Y->Resize(M, N);
+            dims = {M, N};
           } else {
-            Y->Resize(N);
+            dims = {N};
           }
+          auto* Y = Output(0, dims, at::dtype<T>());
           // W * x
           math::Gemm<T, Context, Engine>(
               CblasNoTrans, CblasTrans, M, N, K, 1, X.template data<T>(),
@@ -176,8 +178,7 @@ namespace caffe2 {
               bias_multiplier_.template data<T>(), b.template data<T>(), 1,
               Y->template mutable_data<T>(), &context_);
           if (OutputSize() == 2){
-            auto* Comp_rate = Output(1);
-            Comp_rate->Resize(vector<int64_t>());
+            auto* Comp_rate = Output(1, vector<int64_t>(), at::dtype<T>());
             T* comp_data = Comp_rate->template mutable_data<T>();
             math::Sum<T, Context>(
                 Mask.numel(), Mask.template data<T>(), comp_data, &context_);
@@ -251,9 +252,9 @@ namespace caffe2 {
             DCHECK_EQ(N, dY.numel());
           }
           auto* dW = Output(0);
-          auto* db = Output(1);
+
           dW->ResizeLike(W);
-          db->Resize(N);
+          auto* db = Output(1, {N}, at::dtype<T>());
 
           // Compute dW
           math::Gemm<T, Context, Engine>(

diff --git a/caffe2/experiments/operators/fully_connected_op_sparse.h b/caffe2/experiments/operators/fully_connected_op_sparse.h
index 2f66822..a1d91d4 100644 (file)
--- a/caffe2/experiments/operators/fully_connected_op_sparse.h
+++ b/caffe2/experiments/operators/fully_connected_op_sparse.h
@@ -106,7 +106,7 @@ class FullyConnectedOp_SPARSE final : public Operator<Context> {
     const auto& jw = Input(3);
     // Notice that we do not need to transpose b
     const auto& b = Input(4);
-    auto* Yt = Output(0); // transposed Y
+    // transposed Y
     // here we assume X is k-by-m
     CAFFE_ENFORCE_EQ(Xt.dim(), 2);
     CAFFE_ENFORCE_EQ(b.dim(), 1);
@@ -117,7 +117,7 @@ class FullyConnectedOp_SPARSE final : public Operator<Context> {
     // number of outputs.
     int N = iw.dim32(0)-1;
     CAFFE_ENFORCE_EQ(N, b.dim32(0));
-    Yt->Resize(shape(N, M));
+    auto* Yt = Output(0, shape(N, M), at::dtype<T>());
 
     // Y' = W * X';
     Sparse_mm<T, Context>(

diff --git a/caffe2/experiments/operators/funhash_op.h b/caffe2/experiments/operators/funhash_op.h
index 4ea41ad..0ccfeac 100644 (file)
--- a/caffe2/experiments/operators/funhash_op.h
+++ b/caffe2/experiments/operators/funhash_op.h
@@ -75,8 +75,7 @@ class FunHashOp : public Operator<Context> {
       ++n_segments;
     }
 
-    auto* output = Output(0);
-    output->Resize(n_segments, num_outputs_);
+    auto* output = Output(0, {n_segments, num_outputs_}, at::dtype<T>());
 
     T* output_data = output->template mutable_data<T>();
 

diff --git a/caffe2/experiments/operators/sparse_funhash_op.h b/caffe2/experiments/operators/sparse_funhash_op.h
index b6d240c..e8a1cba 100644 (file)
--- a/caffe2/experiments/operators/sparse_funhash_op.h
+++ b/caffe2/experiments/operators/sparse_funhash_op.h
@@ -74,8 +74,7 @@ class SparseFunHashOp : public Operator<Context> {
       ++n_segments;
     }
 
-    auto* output = Output(0);
-    output->Resize(n_segments, num_outputs_);
+    auto* output = Output(0, {n_segments, num_outputs_}, at::dtype<T>());
 
     T* output_data = output->template mutable_data<T>();
 
@@ -176,12 +175,11 @@ class SparseFunHashGradientOp : public Operator<Context> {
     int64_t num_nz_ent = seg.size(0);
 
     int64_t grad_weight_size = num_nz_ent * num_outputs_ * num_alpha;
-    auto* grad_weight_val = Output(0);
-    grad_weight_val->Resize(grad_weight_size);
+
+    auto* grad_weight_val = Output(0, {grad_weight_size}, at::dtype<T>());
     T* grad_weight_val_data = grad_weight_val->template mutable_data<T>();
 
-    auto* grad_weight_ind = Output(1);
-    grad_weight_ind->Resize(grad_weight_size);
+    auto* grad_weight_ind = Output(1, {grad_weight_size}, at::dtype<int64_t>());
     auto* grad_weight_ind_data =
         grad_weight_ind->template mutable_data<int64_t>();
 

diff --git a/caffe2/experiments/operators/sparse_matrix_reshape_op.h b/caffe2/experiments/operators/sparse_matrix_reshape_op.h
index 89e96b3..e48665f 100644 (file)
--- a/caffe2/experiments/operators/sparse_matrix_reshape_op.h
+++ b/caffe2/experiments/operators/sparse_matrix_reshape_op.h
@@ -101,10 +101,9 @@ class SparseMatrixReshapeOp : public Operator<Context> {
     CAFFE_ENFORCE(
         old_row.numel() == nnz,
         "Column and row tensors must have the same size.");
-    auto* new_col = Output(0);
-    auto* new_row = Output(1);
-    new_col->Resize(nnz);
-    new_row->Resize(nnz);
+
+    auto* new_col = Output(0, {nnz}, at::dtype<int64_t>());
+    auto* new_row = Output(1, {nnz}, at::dtype<int>());
 
     const auto* old_col_data = old_col.template data<int64_t>();
     const auto* old_row_data = old_row.template data<int>();

diff --git a/caffe2/experiments/operators/tt_contraction_op.h b/caffe2/experiments/operators/tt_contraction_op.h
index af28779..73865d1 100644 (file)
--- a/caffe2/experiments/operators/tt_contraction_op.h
+++ b/caffe2/experiments/operators/tt_contraction_op.h
@@ -40,7 +40,6 @@ class TTContractionOp final : public Operator<Context> {
   bool RunOnDevice() override {
     const auto& A = Input(0);
     const auto& B = Input(1);
-    auto* C = Output(0);
 
     CAFFE_ENFORCE(A.dim() == 2, A.dim());
 
@@ -58,7 +57,7 @@ class TTContractionOp final : public Operator<Context> {
     int64_t D_ = B_size / (K_ * N_);
 
     int64_t C_size = D_ * M_ * N_;
-    C->Resize(vector<int64_t>{C_size});
+    auto* C = Output(0, vector<int64_t>{C_size}, at::dtype<T>());
 
     int64_t B_stride = K_ * N_;
     int64_t C_stride = M_ * N_;
@@ -103,16 +102,14 @@ class TTContractionGradientOp final : public Operator<Context> {
     const auto& G = Input(0);
     const auto& A = Input(1);
     const auto& B = Input(2);
-    auto* dA = Output(0);
-    auto* dB = Output(1);
 
     int64_t G_size = G.numel();
     int64_t D_ = G_size / (M_ * N_);
 
     int64_t dB_size = D_ * K_ * N_;
 
-    dA->Resize(A.sizes());
-    dB->Resize(B.sizes());
+    auto* dA = Output(0, A.sizes(), at::dtype<T>());
+    auto* dB = Output(1, B.sizes(), at::dtype<T>());
 
     int64_t B_stride = K_ * N_;
     int64_t G_stride = M_ * N_;

diff --git a/caffe2/experiments/operators/tt_pad_op.h b/caffe2/experiments/operators/tt_pad_op.h
index e25159d..b482e38 100644 (file)
--- a/caffe2/experiments/operators/tt_pad_op.h
+++ b/caffe2/experiments/operators/tt_pad_op.h
@@ -44,8 +44,7 @@ class TTPadOp final : public Operator<Context> {
     auto X_dim0 = X.size(0);
     auto X_dim1 = X.size(1);
 
-    auto* X_orig_dim0 = Output(1);
-    X_orig_dim0->Resize(1);
+    auto* X_orig_dim0 = Output(1, {1}, at::dtype<int64_t>());
     *X_orig_dim0->template mutable_data<int64_t>() = X_dim0;
 
     if (X_dim0 % scale_ != 0) {