irange-ify 11 (pytorch#62121)

Summary: Pull Request resolved: pytorch#62121

Test Plan: Sandcastle

Reviewed By: ngimel

Differential Revision: D29879701

fbshipit-source-id: 5c51879c88fa6a5790db241c8b33ec0dc4b177ca

Author: r-barnes

torch/csrc/jit/python/init.cpp

@@ -98,6 +98,7 @@
 #include <torch/csrc/jit/tensorexpr/tensorexpr_init.h>
 
 #include <c10/macros/Export.h>
+#include <c10/util/irange.h>
 #include <c10/util/signal_handler.h>
 #include <caffe2/serialize/inline_container.h>
 
@@ -475,7 +476,7 @@ void initJITBindings(PyObject* module) {
             // we want full shape specialization. The alternative would be to
             // have a "complete type inference" function in ArguemntSpecCreator.
             auto g_inputs = graph->inputs();
-            for (size_t i = 0; i < inputs.size(); ++i) {
+            for (const auto i : c10::irange(inputs.size())) {
               if (stack[i].isTensor()) {
                 g_inputs[i]->setType(stack[i].type());
               }
@@ -491,7 +492,7 @@ void initJITBindings(PyObject* module) {
               stack.push_back(toTypeInferredIValue(obj));
             }
             auto g_inputs = graph->inputs();
-            for (size_t i = 0; i < inputs.size(); ++i) {
+            for (const auto i : c10::irange(inputs.size())) {
               if (stack[i].isTensor()) {
                 g_inputs[i]->setType(stack[i].type());
               }
@@ -1164,7 +1165,7 @@ void initJITBindings(PyObject* module) {
               [operations, symbol](py::args args, py::kwargs kwargs) {
                 std::vector<py::handle> overloaded_args;
                 size_t total_arg_num = args.size() + kwargs.size();
-                for (size_t i = 0; i < args.size(); ++i) {
+                for (const auto i : c10::irange(args.size())) {
                   is_tensor_and_append_overloaded(
                       args[i].ptr(), &overloaded_args);
                   is_tensor_list_and_append_overloaded(
@@ -1380,7 +1381,7 @@ void initJITBindings(PyObject* module) {
     py::function f = py::cast<py::function>(args[0]);
     py::tuple args_tup(args.size() - 1);
 
-    for (size_t i = 1; i < args.size(); ++i) {
+    for (const auto i : c10::irange(1, args.size())) {
       args_tup[i - 1] = args[i];
     }
 

torch/csrc/jit/python/pybind.h

@@ -4,6 +4,7 @@
 
 #include <ATen/core/interned_strings.h>
 #include <ATen/core/ivalue.h>
+#include <c10/util/irange.h>
 #include <torch/csrc/DynamicTypes.h>
 #include <torch/csrc/THP.h>
 #include <torch/csrc/autograd/variable.h>
@@ -214,7 +215,7 @@ namespace jit {
 
 static inline py::tuple tuple_tail(const py::tuple& tup) {
   py::tuple r(tup.size() - 1);
-  for (size_t i = 1; i < tup.size(); i++) {
+  for (const auto i : c10::irange(1, tup.size())) {
     r[i - 1] = tup[i];
   }
   return r;

torch/csrc/jit/python/pybind_utils.cpp

@@ -82,7 +82,7 @@ IValue toIValue(py::handle obj, const TypePtr& type, c10::optional<int32_t> N) {
       }
       std::vector<IValue> values;
       values.reserve(tuple_size);
-      for (size_t i = 0; i < tuple_size; ++i) {
+      for (const auto i : c10::irange(tuple_size)) {
         values.push_back(toIValue(tuple[i], elem_types[i]));
       }
       return tuple_type->name()

torch/csrc/jit/python/pybind_utils.h

@@ -38,6 +38,7 @@
 #endif
 #include <c10/util/Exception.h>
 #include <c10/util/Optional.h>
+#include <c10/util/irange.h>
 
 #include <algorithm>
 #include <cstddef>
@@ -706,7 +707,7 @@ inline py::object toPyObject(IValue ivalue) {
   } else if (ivalue.isList()) {
     auto list = std::move(ivalue).toList();
     py::list t{list.size()};
-    for (size_t i = 0; i < list.size(); ++i) {
+    for (const auto i : c10::irange(list.size())) {
       t[i] = toPyObject(IValue{list.get(i)});
     }
     return std::move(t);
@@ -715,7 +716,7 @@ inline py::object toPyObject(IValue ivalue) {
     const auto& elements = tuple->elements();
 
     py::tuple t{elements.size()};
-    for (size_t i = 0; i < elements.size(); ++i) {
+    for (const auto i : c10::irange(elements.size())) {
       t[i] = toPyObject(IValue{elements.at(i)});
     }
 
@@ -783,7 +784,7 @@ inline py::object toPyObject(IValue ivalue) {
 
     const auto numAttrs = classType->numAttributes();
 
-    for (size_t slot = 0; slot < numAttrs; slot++) {
+    for (const auto slot : c10::irange(numAttrs)) {
       const auto& attrName = classType->getAttributeName(slot);
       IValue v = obj->getSlot(slot);
       py::setattr(pyObj, attrName.c_str(), toPyObject(std::move(v)));
@@ -916,7 +917,7 @@ inline py::object createPyObjectForStack(Stack&& stack) {
 
   // If there is more than one return value, pop them into a py::tuple.
   py::tuple return_values(stack.size());
-  for (size_t ret = 0; ret < return_values.size(); ++ret) {
+  for (const auto ret : c10::irange(return_values.size())) {
     return_values[ret] = toPyObject(std::move(stack[ret]));
   }
 
@@ -935,7 +936,7 @@ inline Stack evilDeprecatedBadCreateStackDoNotUse(
   }
   Stack result;
   result.reserve(tuple.size() + reserve_extra_space);
-  for (size_t i = 0; i < inputs.size(); ++i) {
+  for (const auto i : c10::irange(inputs.size())) {
     result.push_back(toIValue(std::move(tuple[i]), inputs[i]->type()));
   }
   return result;

torch/csrc/jit/python/python_arg_flatten.cpp

@@ -1,3 +1,4 @@
+#include <c10/util/irange.h>
 #include <torch/csrc/jit/python/python_arg_flatten.h>
 #include <torch/csrc/utils/python_strings.h>
 #include <torch/csrc/utils/six.h>
@@ -33,8 +34,9 @@ template <typename T>
 py::object cast_handle_sequence(std::vector<py::handle> objs) {
   auto num_objs = objs.size();
   T sequence{num_objs};
-  for (size_t i = 0; i < num_objs; ++i)
+  for (const auto i : c10::irange(num_objs)) {
     sequence[i] = py::reinterpret_borrow<py::object>(objs[i]);
+  }
   return sequence;
 }
 
@@ -109,15 +111,16 @@ template <typename T>
 py::object cast_sequence(std::vector<py::object> objs) {
   auto num_objs = objs.size();
   T sequence{num_objs};
-  for (size_t i = 0; i < num_objs; ++i)
+  for (const auto i : c10::irange(num_objs)) {
     sequence[i] = std::move(objs[i]);
+  }
   return std::move(sequence);
 }
 
 py::object cast_dict(std::vector<py::object> objs) {
   auto num_objs = objs.size();
   py::dict sequence = {};
-  for (size_t i = 0; i < num_objs; ++i) {
+  for (const auto i : c10::irange(num_objs)) {
     py::tuple obj = py::reinterpret_borrow<py::tuple>(objs[i]);
     sequence[obj[0]] = obj[1];
   }

torch/csrc/jit/python/python_arg_flatten.h

@@ -1,6 +1,7 @@
 #pragma once
 
 #include <c10/util/hash.h>
+#include <c10/util/irange.h>
 #include <torch/csrc/autograd/variable.h>
 #include <torch/csrc/jit/python/pybind.h>
 
@@ -75,7 +76,7 @@ static inline std::ostream& operator<<(
     out << ", device=" << meta_device.index();
   }
   out << ") {";
-  for (size_t i = 0; i < meta.sizes.size(); ++i) {
+  for (const auto i : c10::irange(meta.sizes.size())) {
     if (i > 0)
       out << ", ";
     out << meta.sizes[i];
@@ -89,7 +90,7 @@ static inline std::ostream& operator<<(
     const IODescriptor& desc) {
   out << desc.structure << "\n";
   out << "  with grad_enabled=" << desc.grad_enabled << "\n";
-  for (size_t i = 0; i < desc.metadata.size(); ++i) {
+  for (const auto i : c10::irange(desc.metadata.size())) {
     out << "  with v" << i << " having type " << desc.metadata[i] << "\n";
   }
   return out;

torch/csrc/jit/python/python_list.cpp

@@ -1,4 +1,5 @@
 #include <ATen/core/ivalue.h>
+#include <c10/util/irange.h>
 #include <pybind11/detail/common.h>
 #include <pybind11/pytypes.h>
 #include <torch/csrc/jit/python/pybind_utils.h>
@@ -133,7 +134,8 @@ void initScriptListBindings(PyObject* module) {
 
             auto seq = std::make_shared<ScriptList>(self->type());
 
-            for (size_t i = 0; i < slicelength; ++i) {
+            for (const auto i : c10::irange(slicelength)) {
+              (void)i; // Suppress unused variable warning
               seq->append(self->getItem(start));
               start += step;
             }
@@ -172,7 +174,7 @@ void initScriptListBindings(PyObject* module) {
                   "Left and right hand size of slice assignment have different sizes");
             }
 
-            for (size_t i = 0; i < slicelength; ++i) {
+            for (const auto i : c10::irange(slicelength)) {
               try {
                 self->setItem(
                     start, toIValue(value[i], self->type()->getElementType()));

torch/csrc/jit/python/python_tracer.cpp

@@ -10,6 +10,7 @@
 #include <torch/csrc/utils/python_strings.h>
 
 #include <c10/util/Exception.h>
+#include <c10/util/irange.h>
 
 #include <sstream>
 
@@ -89,7 +90,7 @@ std::pair<std::shared_ptr<Graph>, Stack> createGraphByTracing(
       [&func](Stack inputs) -> Stack {
         size_t num_func_inputs = inputs.size();
         py::tuple py_inputs(num_func_inputs);
-        for (size_t i = 0; i < num_func_inputs; ++i) {
+        for (const auto i : c10::irange(num_func_inputs)) {
           py_inputs[i] = py::cast(inputs[i]);
         }
         auto out = func(*py_inputs);

torch/csrc/jit/python/script_init.cpp

@@ -18,6 +18,7 @@
 #include <torch/csrc/jit/testing/file_check.h>
 
 #include <c10/util/intrusive_ptr.h>
+#include <c10/util/irange.h>
 #include <torch/csrc/jit/frontend/parser.h>
 #include <torch/csrc/jit/frontend/tracer.h>
 #include <torch/csrc/jit/ir/constants.h>
@@ -175,7 +176,7 @@ void checkOverloadDecl(const Decl& new_decl, const Decl& old_decl) {
       "Overload must have same number of parameters\n",
       new_decl.range(),
       old_decl.range());
-  for (size_t i = 0; i < new_decl.params().size(); ++i) {
+  for (const auto i : c10::irange(new_decl.params().size())) {
     TORCH_INTERNAL_ASSERT(
         new_params[i].ident().name() == old_params[i].ident().name(),
         "Overload parameters must have the same names\n",
@@ -311,7 +312,7 @@ static Decl mergeDefaultsAndExtraParametersToOverloadDecl(
       overload_decl.range(),
       impl_decl.range());
 
-  for (size_t i = 0; i < overload_params.size(); ++i) {
+  for (const auto i : c10::irange(overload_params.size())) {
     auto overload_name = overload_params[i].ident().name();
     auto impl_name = impl_params[i].ident().name();
     if (overload_name != impl_name) {
@@ -586,7 +587,7 @@ bool ivalue_tags_match(const Module& lhs, const Module& rhs) {
     } else if (item.a.isList()) {
       auto al = item.a.toList();
       auto bl = item.b.toList();
-      for (size_t i = 0; i < al.size(); ++i) {
+      for (const auto i : c10::irange(al.size())) {
         work.emplace_back(Work{al.get(i), bl.get(i)});
       }
     } else if (item.a.isGenericDict()) {