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Tensor construction codemod(ResizeLike) - 1/7 (#15073)

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Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15073

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

Reviewed By: dzhulgakov

Differential Revision: D13419563

fbshipit-source-id: 8c284405fa3a867303216df876ee6b20d8a46551
This commit is contained in:
Jerry Zhang 2018-12-19 21:34:36 -08:00 committed by Facebook Github Bot
parent 2db742fc95
commit 3fc889e976
5 changed files with 14 additions and 20 deletions
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4
caffe2/cuda_rtc/pool_op_rtc_gpu.cc
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@ -257,8 +257,8 @@ class MaxPoolGradientRTCOp final : public ConvPoolOpBase<CUDAContext> {
auto& Y = Input(1);
auto& dY = Input(2);
CAFFE_ENFORCE_EQ(dY.dim(), 4);
auto* dX = Output(0);
dX->ResizeLike(X);
auto* dX = Output(0, X.sizes(), at::dtype<float>());
ConvPoolOpBase<CUDAContext>::ComputePads({X.dim32(2), X.dim32(3)});
if (input_dims_ != X.sizes()) {
VLOG(1) << "MaxPoolGradient RTC recompiling";

9
caffe2/experiments/operators/fully_connected_op_decomposition.h
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@ -138,11 +138,9 @@ class FullyConnectedDecompGradientOp : public Operator<Context> {
DCHECK_EQ(X.dim(), 1);
DCHECK_EQ(N, dY.numel());
}
auto* dU = Output(0);
auto* dV = Output(1);
dU->ResizeLike(U);
dV->ResizeLike(V);
auto* dU = Output(0, U.sizes(), at::dtype<T>());
auto* dV = Output(1, V.sizes(), at::dtype<T>());
auto* db = Output(2, {N}, at::dtype<T>());
// Compute dU
@ -189,8 +187,7 @@ class FullyConnectedDecompGradientOp : public Operator<Context> {
&context_);
// Compute dX if necessary.
if (OutputSize() == 4) {
auto* dX = Output(3);
dX->ResizeLike(X);
auto* dX = Output(3, X.sizes(), at::dtype<T>());
dx_buffer_.Resize(M, middle);
T* dx_buffer_data = dx_buffer_.template mutable_data<T>();
math::Gemm<T, Context, Engine>(

10
caffe2/experiments/operators/fully_connected_op_prune.h
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@ -220,7 +220,7 @@ namespace caffe2 {
auto* Ag_dW_ptr = Output(4);
auto& Ag_dW = *Ag_dW_ptr;
// it is also the Input(5)
auto* mask_seq_auto = Output(5);
// how about get threshold
auto& thres = Input(6);
//TODO(wyiming): check comp_lb is a float
@ -251,9 +251,8 @@ namespace caffe2 {
DCHECK_EQ(X.dim(), 1);
DCHECK_EQ(N, dY.numel());
}
auto* dW = Output(0);
dW->ResizeLike(W);
auto* dW = Output(0, W.sizes(), at::dtype<T>());
auto* db = Output(1, {N}, at::dtype<T>());
// Compute dW
@ -292,7 +291,7 @@ namespace caffe2 {
Ag_dW.template mutable_data<T>(),
sum_buffer_.template mutable_data<T>(),
&context_);
mask_seq_auto->ResizeLike(W);
auto* mask_seq_auto = Output(5, W.sizes(), at::dtype<T>());
T* mask_seq = mask_seq_auto->template mutable_data<T>();
math::Set<T, Context>(N*K, static_cast<T>(0),
mask_seq_auto->template mutable_data<T>(), &context_);
@ -338,8 +337,7 @@ namespace caffe2 {
&context_);
// Compute dX if necessary.
if (OutputSize() == 7) {
auto* dX = Output(6);
dX->ResizeLike(X);
auto* dX = Output(6, X.sizes(), at::dtype<T>());
math::Gemm<T, Context, Engine>(
CblasNoTrans, CblasNoTrans, M, K, N, 1,
dY.template data<T>(), W.template data<T>(),

7
caffe2/experiments/operators/funhash_op.h
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@ -164,8 +164,8 @@ class FunHashGradientOp : public Operator<Context> {
if (adaptive_) {
const auto& alpha = Input(5);
num_alpha = alpha.size(0);
auto* grad_alpha = Output(1);
grad_alpha->ResizeLike(alpha);
auto* grad_alpha = Output(1, alpha.sizes(), at::dtype<T>());
grad_alpha_data = grad_alpha->template mutable_data<T>();
memset(grad_alpha_data, 0, sizeof(T) * num_alpha);
}
@ -175,8 +175,7 @@ class FunHashGradientOp : public Operator<Context> {
int64_t num_weight = weight.size(0);
int64_t num_nz_ent = seg.size(0);
auto* grad_weight = Output(0);
grad_weight->ResizeLike(weight);
auto* grad_weight = Output(0, weight.sizes(), at::dtype<T>());
T* grad_weight_data = grad_weight->template mutable_data<T>();
const auto* grad_out_data = grad_out.template data<T>();

4
caffe2/experiments/operators/sparse_funhash_op.h
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@ -163,8 +163,8 @@ class SparseFunHashGradientOp : public Operator<Context> {
if (adaptive_) {
const auto& alpha = Input(5);
num_alpha = alpha.size(0);
auto* grad_alpha = Output(2);
grad_alpha->ResizeLike(alpha);
auto* grad_alpha = Output(2, alpha.sizes(), at::dtype<T>());
grad_alpha_data = grad_alpha->template mutable_data<T>();
memset(grad_alpha_data, 0, sizeof(T) * num_alpha);
}