use irange for loops 1 (pytorch#66741)

Summary: Pull Request resolved: pytorch#66741 Modified loops in files under fbsource/fbcode/caffe2/ from the format `for(TYPE var=x0;var<x_max;x++)` to the format `for(const auto var: irange(xmax))` This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand. Test Plan: Sandcastle Reviewed By: ngimel Differential Revision: D31705360 fbshipit-source-id: 7115f76e381ad2d98584eb534961c3cbb957ebaa
jakeharmon8 · Oct 19, 2021 · 0a07488 · 0a07488
1 parent 72803db
commit 0a07488
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Showing 100 changed files with 513 additions and 428 deletions.
diff --git a/android/pytorch_android/src/main/cpp/pytorch_jni_common.cpp b/android/pytorch_android/src/main/cpp/pytorch_jni_common.cpp
@@ -4,6 +4,7 @@
 #include <string>
 
 #include <c10/core/MemoryFormat.h>
+#include <c10/util/irange.h>
 
 #include <fbjni/ByteBuffer.h>
 #include <fbjni/fbjni.h>
@@ -97,7 +98,7 @@ static at::Tensor newAtTensor(
   std::vector<int64_t> shapeVec{};
   shapeVec.reserve(rank);
   auto numel = 1;
-  for (auto i = 0; i < rank; ++i) {
+  for (const auto i : c10::irange(rank)) {
     shapeVec.push_back(shapeArr[i]);
     numel *= shapeArr[i];
   }
@@ -521,7 +522,7 @@ at::IValue JIValue::JIValueToAtIValue(
 
     std::vector<at::IValue> elements;
     elements.reserve(n);
-    for (auto i = 0; i < n; ++i) {
+    for (const auto i : c10::irange(n)) {
       auto jivalue_element = jarray->getElement(i);
       auto element = JIValue::JIValueToAtIValue(jivalue_element);
       elements.push_back(std::move(element));
@@ -535,7 +536,7 @@ at::IValue JIValue::JIValueToAtIValue(
     size_t n = jArrayPinned.size();
     c10::List<bool> list{};
     list.reserve(n);
-    for (size_t i = 0; i < n; ++i) {
+    for (const auto i : c10::irange(n)) {
       list.push_back(jArrayPinned[i]);
     }
     return at::IValue{std::move(list)};
@@ -547,7 +548,7 @@ at::IValue JIValue::JIValueToAtIValue(
     size_t n = jArrayPinned.size();
     c10::List<int64_t> list{};
     list.reserve(n);
-    for (size_t i = 0; i < n; ++i) {
+    for (const auto i : c10::irange(n)) {
       list.push_back(jArrayPinned[i]);
     }
     return at::IValue{std::move(list)};
@@ -559,7 +560,7 @@ at::IValue JIValue::JIValueToAtIValue(
     size_t n = jArrayPinned.size();
     c10::List<double> list{};
     list.reserve(n);
-    for (size_t i = 0; i < n; ++i) {
+    for (const auto i : c10::irange(n)) {
       list.push_back(jArrayPinned[i]);
     }
     return at::IValue{std::move(list)};
@@ -572,7 +573,7 @@ at::IValue JIValue::JIValueToAtIValue(
     size_t n = jArray->size();
     c10::List<at::Tensor> list{};
     list.reserve(n);
-    for (size_t i = 0; i < n; ++i) {
+    for (const auto i : c10::irange(n)) {
       list.push_back(
           TensorHybrid::newAtTensorFromJTensor(jArray->getElement(i)));
     }
@@ -594,7 +595,7 @@ at::IValue JIValue::JIValueToAtIValue(
     c10::impl::GenericList list{c10::unshapedType(first_element.type())};
     list.reserve(n);
     list.push_back(first_element);
-    for (auto i = 1; i < n; ++i) {
+    for (const auto i : c10::irange(1, n)) {
       auto jivalue_element = jarray->getElement(i);
       auto element = JIValue::JIValueToAtIValue(jivalue_element);
       list.push_back(element);

diff --git a/android/pytorch_android/src/main/cpp/pytorch_jni_lite.cpp b/android/pytorch_android/src/main/cpp/pytorch_jni_lite.cpp
@@ -6,6 +6,7 @@
 #include <fbjni/ByteBuffer.h>
 #include <fbjni/fbjni.h>
 
+#include <c10/util/irange.h>
 #include <torch/csrc/jit/mobile/import.h>
 #include <torch/csrc/jit/mobile/module.h>
 #include <torch/script.h>
@@ -157,7 +158,7 @@ class PytorchJni : public facebook::jni::HybridClass<PytorchJni> {
     std::vector<at::IValue> inputs{};
     size_t n = jinputs->size();
     inputs.reserve(n);
-    for (size_t i = 0; i < n; i++) {
+    for (const auto i : c10::irange(n)) {
       at::IValue atIValue = JIValue::JIValueToAtIValue(jinputs->getElement(i));
       if (at::kVulkan == deviceType_) {
         inputs.push_back(
@@ -186,7 +187,7 @@ class PytorchJni : public facebook::jni::HybridClass<PytorchJni> {
     std::vector<at::IValue> inputs{};
     size_t n = jinputs->size();
     inputs.reserve(n);
-    for (size_t i = 0; i < n; i++) {
+    for (const auto i : c10::irange(n)) {
       at::IValue atIValue = JIValue::JIValueToAtIValue(jinputs->getElement(i));
       if (at::kVulkan == deviceType_) {
         inputs.push_back(

diff --git a/aten/src/ATen/BatchingRegistrations.cpp b/aten/src/ATen/BatchingRegistrations.cpp
@@ -3,6 +3,7 @@
 #include <ATen/BatchedFallback.h>
 #include <ATen/native/ResizeCommon.h>
 #include <ATen/ATen.h>
+#include <c10/util/irange.h>
 
 namespace at {
 
@@ -329,7 +330,7 @@ Tensor permute_batching_rule(const Tensor& self, IntArrayRef dims) {
 
   VmapDimVector all_dims_physical;
   all_dims_physical.reserve(self_physical.tensor().dim());
-  for (int64_t bdim = 0; bdim < self_physical.numBatchDims(); bdim++) {
+  for (const auto bdim : c10::irange(self_physical.numBatchDims())) {
     all_dims_physical.push_back(bdim);
   }
   all_dims_physical.insert(

diff --git a/aten/src/ATen/CPUApplyUtils.h b/aten/src/ATen/CPUApplyUtils.h
@@ -2,6 +2,7 @@
 
 #include <ATen/Parallel.h>
 #include <ATen/TensorUtils.h>
+#include <c10/util/irange.h>
 #include <limits>
 #include <utility>
 #include <cstring>
@@ -130,7 +131,7 @@ inline Tensor sort_strides(Tensor& tensor_) {
   IntArrayRef strides = tensor_.strides();
   std::vector<int64_t> indices;
   indices.reserve(tensor_.ndimension());
-  for (int64_t i = 0; i < tensor_.ndimension(); i++) {
+  for (const auto i : c10::irange(tensor_.ndimension())) {
     indices.push_back(i);
   }
   std::sort(indices.begin(), indices.end(), [&strides](int64_t i1, int64_t i2) {
@@ -196,7 +197,7 @@ inline bool _all_equal_numel(at::ArrayRef<Tensor> tensors) {
   if (tensors.size() == 0)
     return true;
   int64_t all_numel = tensors[0].numel();
-  for (size_t i = 1; i < tensors.size(); i++) {
+  for (const auto i : c10::irange(1, tensors.size())) {
     if (tensors[i].numel() != all_numel)
       return false;
   }

diff --git a/aten/src/ATen/Context.h b/aten/src/ATen/Context.h
@@ -11,6 +11,7 @@
 #include <c10/util/Exception.h>
 #include <c10/core/impl/DeviceGuardImplInterface.h>
 #include <c10/core/QEngine.h>
+#include <c10/util/irange.h>
 
 #include <memory>
 #include <mutex>
@@ -351,7 +352,7 @@ static inline void manual_seed(uint64_t seed) {
   // available. In that case, we must not seed CUDA; it will fail!
   const auto num_gpus = detail::getCUDAHooks().getNumGPUs();
   if (hasCUDA() && num_gpus > 0) {
-    for (int i = 0; i < num_gpus; i++) {
+    for (const auto i : c10::irange(num_gpus)) {
       auto cuda_gen = globalContext().defaultGenerator(
         Device(at::kCUDA, static_cast<c10::DeviceIndex>(i))
       );

diff --git a/aten/src/ATen/ExpandUtils.cpp b/aten/src/ATen/ExpandUtils.cpp
@@ -197,7 +197,7 @@ std::vector<int64_t> infer_dense_strides(IntArrayRef tensor_sizes, IntArrayRef t
   // compute output strides which preserves the input tensor's memory layout
   std::vector<int64_t> out_strides(ndim);
   int64_t curr_stride = 1;
-  for (size_t i = 0; i < ndim; ++i) {
+  for (const auto i : c10::irange(ndim)) {
     int64_t idx = perm[i];
     out_strides[idx] = curr_stride;
     // Note: for size 0, we simply treated it as 1, it really doesn't matter here

diff --git a/aten/src/ATen/ExpandUtils.h b/aten/src/ATen/ExpandUtils.h
@@ -4,6 +4,7 @@
 #include <ATen/Tensor.h>
 #include <c10/util/Exception.h>
 #include <c10/util/MaybeOwned.h>
+#include <c10/util/irange.h>
 
 #include <functional>
 #include <sstream>
@@ -266,7 +267,7 @@ inline std::vector<Tensor> expand_outplace(TensorList to_expand) {
   // expands a list of Tensors; ignores undefined (null) tensors
   bool first = true;
   DimVector sizes;
-  for (size_t i = 0; i < to_expand.size(); ++i) {
+  for (const auto i : c10::irange(to_expand.size())) {
     if (!to_expand[i].defined()) {
       continue;
     } else if (first) {
@@ -278,7 +279,7 @@ inline std::vector<Tensor> expand_outplace(TensorList to_expand) {
   }
 
   std::vector<Tensor> result(to_expand.size());
-  for (size_t i = 0; i < to_expand.size(); ++i) {
+  for (const auto i : c10::irange(to_expand.size())) {
     if (!to_expand[i].defined()) {
       continue;
     } else if (to_expand[i].sizes().equals(sizes)) {
@@ -299,7 +300,7 @@ static inline Tensor sum_to(Tensor tensor, const IntArrayRef shape) {
   c10::SmallVector<int64_t, 8> reduce_dims;
   const at::IntArrayRef sizes = tensor.sizes();
   const int64_t leading_dims = sizes.size() - shape.size();
-  for (int64_t i = 0; i < leading_dims; ++i) {
+  for (const auto i : c10::irange(leading_dims)) {
     reduce_dims.push_back(i);
   }
   for (int64_t i = leading_dims; i < static_cast<int64_t>(sizes.size()); ++i) {
@@ -320,7 +321,7 @@ static inline bool is_expandable_to(IntArrayRef shape, IntArrayRef desired) {
   if (ndim > target_dim) {
     return false;
   }
-  for (size_t i = 0; i < ndim; i++) {
+  for (const auto i : c10::irange(ndim)) {
     int64_t size = shape[ndim - i - 1];
     int64_t target = desired[target_dim - i - 1];
     if (size != target && size != 1) {

diff --git a/aten/src/ATen/MemoryOverlap.cpp b/aten/src/ATen/MemoryOverlap.cpp
@@ -1,6 +1,7 @@
 #include <ATen/MemoryOverlap.h>
 #include <ATen/core/TensorBase.h>
 #include <c10/core/Layout.h>
+#include <c10/util/irange.h>
 
 namespace at {
 
@@ -17,7 +18,7 @@ MemOverlap has_internal_overlap(TensorImpl* t) {
 
   auto strides = t->strides();
   auto sizes = t->sizes();
-  for (size_t i = 0; i < strides.size(); ++i) {
+  for (const auto i : c10::irange(strides.size())) {
     if (strides[i] == 0 && sizes[i] > 1) {
       return MemOverlap::YES;
     }

diff --git a/aten/src/ATen/NamedTensorUtils.cpp b/aten/src/ATen/NamedTensorUtils.cpp
@@ -225,7 +225,7 @@ std::vector<Dimname> compute_squeeze_outnames(const Tensor& tensor) {
   }
   std::vector<Dimname> outnames;
   auto tensor_names = tensor.names();
-  for (int64_t d = 0; d < tensor.dim(); d++) {
+  for (const auto d : c10::irange(tensor.dim())) {
     if (tensor.sizes()[d] != 1) {
       outnames.push_back(tensor_names[d]);
     }
@@ -242,7 +242,7 @@ std::vector<Dimname> compute_diagonal_outnames(
   }
   std::vector<Dimname> outnames;
   auto tensor_names = tensor.names();
-  for (int64_t d = 0; d < tensor.dim(); d++) {
+  for (const auto d : c10::irange(tensor.dim())) {
     if (d == dim1 || d == dim2) {
       continue;
     }

diff --git a/aten/src/ATen/ParallelNative.cpp b/aten/src/ATen/ParallelNative.cpp
@@ -6,6 +6,7 @@
 
 #ifndef C10_MOBILE
 #include <c10/core/thread_pool.h>
+#include <c10/util/irange.h>
 #else
 #include <caffe2/utils/threadpool/pthreadpool-cpp.h>
 #endif // C10_MOBILE
@@ -87,7 +88,7 @@ TaskThreadPoolBase& _get_intraop_pool() {
 // `fn` will be called with params: (thread_pool_task_id, task_id).
 void _run_with_pool(const std::function<void(int, size_t)>& fn, size_t range) {
 #ifndef C10_MOBILE
-  for (size_t i = 1; i < range; ++i) {
+  for (const auto i : c10::irange(1, range)) {
     _get_intraop_pool().run([fn, i]() { fn((int)i, i); });
   }
   // Run the first task on the current thread directly.

diff --git a/aten/src/ATen/SparseTensorImpl.h b/aten/src/ATen/SparseTensorImpl.h
@@ -3,6 +3,7 @@
 #include <ATen/Tensor.h>
 #include <c10/core/TensorImpl.h>
 #include <c10/util/Exception.h>
+#include <c10/util/irange.h>
 
 namespace at {
 struct TORCH_API SparseTensorImpl : public TensorImpl {
@@ -109,15 +110,15 @@ struct TORCH_API SparseTensorImpl : public TensorImpl {
       bool shrinking_dense_dim = false;
       auto sparse_size_original = sizes().slice(0, sparse_dim);
       auto sparse_size_new = size.slice(0, sparse_dim);
-      for (int64_t i = 0; i < sparse_dim; i++) {
+      for (const auto i : c10::irange(sparse_dim)) {
         if (sparse_size_new[i] < sparse_size_original[i]) {
           shrinking_sparse_dims = true;
           break;
         }
       }
       auto dense_size_original = sizes().slice(sparse_dim);
       auto dense_size_new = size.slice(sparse_dim);
-      for (int64_t i = 0; i < dense_dim; i++) {
+      for (const auto i : c10::irange(dense_dim)) {
         if (dense_size_new[i] < dense_size_original[i]) {
           shrinking_dense_dim = true;
           break;

diff --git a/aten/src/ATen/SparseTensorUtils.cpp b/aten/src/ATen/SparseTensorUtils.cpp
@@ -3,6 +3,7 @@
 #include <ATen/ATen.h>
 #include <ATen/SparseTensorImpl.h>
 #include <ATen/Parallel.h>
+#include <c10/util/irange.h>
 
 namespace at { namespace sparse {
 
@@ -98,7 +99,7 @@ Tensor coo_to_csr(const int64_t* indices, int64_t dim, int64_t nnz) {
     at::parallel_for(0, nnz, 10000, [&](int64_t start, int64_t end) {
       // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
       int64_t h, hp0, hp1;
-      for (auto i = start; i < end; i++) {
+      for (const auto i : c10::irange(start, end)) {
         hp0 = indices[i];
         hp1 = (i+1 == nnz) ?  dim : indices[i+1];
         if (hp0 != hp1) {

diff --git a/aten/src/ATen/TensorIndexing.cpp b/aten/src/ATen/TensorIndexing.cpp
@@ -1,6 +1,7 @@
 #include <ATen/TensorIndexing.h>
 
 #include <c10/util/Exception.h>
+#include <c10/util/irange.h>
 
 namespace at {
 namespace indexing {
@@ -31,7 +32,7 @@ std::ostream& operator<<(std::ostream& stream, const TensorIndex& tensor_index)
 
 std::ostream& operator<<(std::ostream& stream, const std::vector<TensorIndex>& tensor_indices) {
   stream << "(";
-  for (size_t i = 0; i < tensor_indices.size(); i++) {
+  for (const auto i : c10::irange(tensor_indices.size())) {
     stream << tensor_indices[i];
     if (i < tensor_indices.size() - 1) stream << ", ";
   }

diff --git a/aten/src/ATen/TensorIndexing.h b/aten/src/ATen/TensorIndexing.h
@@ -1,6 +1,7 @@
 #pragma once
 
 #include <c10/util/Optional.h>
+#include <c10/util/irange.h>
 #include <ATen/core/TensorBody.h>
 #include <ATen/ExpandUtils.h>
 #include <ATen/Functions.h>
@@ -335,7 +336,7 @@ static inline Tensor scalarToTensor(const Scalar& v, const TensorOptions& option
 // strip away unit dimensions from the left of 'src'
 static inline IntArrayRef slicePrefix1sSize(const IntArrayRef& sizes) {
   size_t first_non1_src = sizes.size();
-  for (size_t i = 0; i < sizes.size(); ++i) {
+  for (const auto i : c10::irange(sizes.size())) {
     if (sizes[i] != 1) {
       first_non1_src = i;
       break;
@@ -439,7 +440,7 @@ static inline Tensor applySlicing(
     "too many indices for tensor of dimension ", (int)self_sizes.size());
 
   Tensor result = self;
-  for (size_t i = 0; i < indices.size(); i++) {
+  for (const auto i : c10::irange(indices.size())) {
     auto& obj = indices[i];
     result = handleDimInMultiDimIndexing(
       /*prev_dim_result=*/result,