use oss config for clang-format

pytorch · Jul 8, 2019 · c0f348d · c0f348d
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diff --git a/.clang-format b/.clang-format
@@ -0,0 +1,155 @@
+---
+Language:        Cpp
+# BasedOnStyle:  Google
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+AlignAfterOpenBracket: Align
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+AlignConsecutiveDeclarations: false
+AlignEscapedNewlines: Left
+AlignOperands:   true
+AlignTrailingComments: true
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+AllowShortFunctionsOnASingleLine: All
+#AllowShortLambdasOnASingleLine: All
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+AlwaysBreakAfterReturnType: None
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+AlwaysBreakTemplateDeclarations: Yes
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+BinPackParameters: true
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+  SplitEmptyNamespace: true
+BreakBeforeBinaryOperators: None
+BreakBeforeBraces: Attach
+BreakBeforeInheritanceComma: false
+BreakInheritanceList: BeforeColon
+BreakBeforeTernaryOperators: true
+BreakConstructorInitializersBeforeComma: false
+BreakConstructorInitializers: BeforeColon
+BreakAfterJavaFieldAnnotations: false
+BreakStringLiterals: true
+ColumnLimit:     80
+CommentPragmas:  '^ IWYU pragma:'
+CompactNamespaces: false
+ConstructorInitializerAllOnOneLineOrOnePerLine: true
+ConstructorInitializerIndentWidth: 4
+ContinuationIndentWidth: 4
+Cpp11BracedListStyle: true
+DerivePointerAlignment: true
+DisableFormat:   false
+ExperimentalAutoDetectBinPacking: false
+FixNamespaceComments: true
+ForEachMacros:
+  - foreach
+  - Q_FOREACH
+  - BOOST_FOREACH
+IncludeBlocks:   Regroup
+IncludeCategories:
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+    Priority:        2
+  - Regex:           '^<.*\.h>'
+    Priority:        1
+  - Regex:           '^<.*'
+    Priority:        2
+  - Regex:           '.*'
+    Priority:        3
+IncludeIsMainRegex: '([-_](test|unittest))?$'
+IndentCaseLabels: true
+IndentPPDirectives: None
+IndentWidth:     2
+IndentWrappedFunctionNames: false
+JavaScriptQuotes: Leave
+JavaScriptWrapImports: true
+KeepEmptyLinesAtTheStartOfBlocks: false
+MacroBlockBegin: ''
+MacroBlockEnd:   ''
+MaxEmptyLinesToKeep: 1
+NamespaceIndentation: None
+ObjCBinPackProtocolList: Never
+ObjCBlockIndentWidth: 2
+ObjCSpaceAfterProperty: false
+ObjCSpaceBeforeProtocolList: true
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+PenaltyBreakBeforeFirstCallParameter: 1
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+PenaltyBreakFirstLessLess: 120
+PenaltyBreakString: 1000
+PenaltyBreakTemplateDeclaration: 10
+PenaltyExcessCharacter: 1000000
+PenaltyReturnTypeOnItsOwnLine: 200
+PointerAlignment: Left
+RawStringFormats:
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+    Delimiters:
+      - cc
+      - CC
+      - cpp
+      - Cpp
+      - CPP
+      - 'c++'
+      - 'C++'
+    CanonicalDelimiter: ''
+    BasedOnStyle:    google
+  - Language:        TextProto
+    Delimiters:
+      - pb
+      - PB
+      - proto
+      - PROTO
+    EnclosingFunctions:
+      - EqualsProto
+      - EquivToProto
+      - PARSE_PARTIAL_TEXT_PROTO
+      - PARSE_TEST_PROTO
+      - PARSE_TEXT_PROTO
+      - ParseTextOrDie
+      - ParseTextProtoOrDie
+    CanonicalDelimiter: ''
+    BasedOnStyle:    google
+ReflowComments:  true
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+SortUsingDeclarations: true
+SpaceAfterCStyleCast: false
+#SpaceAfterLogicalNot: false
+SpaceAfterTemplateKeyword: true
+SpaceBeforeAssignmentOperators: true
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+SpaceBeforeCtorInitializerColon: true
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diff --git a/torch_xla/csrc/batch_norm.cpp b/torch_xla/csrc/batch_norm.cpp
@@ -1,4 +1,5 @@
 #include "torch_xla/csrc/batch_norm.h"
+
 #include "torch_xla/csrc/helpers.h"
 
 namespace torch_xla {

diff --git a/torch_xla/csrc/cross_replica_reduces.cpp b/torch_xla/csrc/cross_replica_reduces.cpp
@@ -1,6 +1,7 @@
 #include "torch_xla/csrc/cross_replica_reduces.h"
 
 #include <vector>
+
 #include "torch_xla/csrc/helpers.h"
 
 namespace torch_xla {

diff --git a/torch_xla/csrc/init_python_bindings.cpp b/torch_xla/csrc/init_python_bindings.cpp
@@ -242,52 +242,47 @@ void InitXlaModuleBindings(py::module m) {
   m.def("_xla_set_default_device",
         [](const std::string& device) { return SetCurrentDevice(device); });
   m.def("_xla_get_default_device", []() { return GetCurrentDevice(); });
-  m.def(
-      "_xla_sync_multi",
-      [](const std::vector<at::Tensor>& tensors,
-         const std::vector<std::string>& devices, bool wait,
-         bool sync_xla_data) {
-        NoGilSection nogil;
-        SyncTensors(tensors, devices, wait, sync_xla_data);
-      },
-      py::arg("tensors"), py::arg("devices"), py::arg("wait") = true,
-      py::arg("sync_xla_data") = true);
-  m.def(
-      "_xla_sync_live_tensors",
-      [](const std::string& device, const std::vector<std::string>& devices,
-         bool wait) {
-        NoGilSection nogil;
-        SyncLiveTensors(device, devices, wait);
-      },
-      py::arg("device") = "", py::arg("devices"), py::arg("wait") = true);
-  m.def(
-      "_xla_step_marker",
-      [](const std::string& device, const std::vector<std::string>& devices,
-         bool wait) {
-        NoGilSection nogil;
-        StepMarker(device, devices, wait);
-      },
-      py::arg("device") = "", py::arg("devices"), py::arg("wait") = true);
+  m.def("_xla_sync_multi",
+        [](const std::vector<at::Tensor>& tensors,
+           const std::vector<std::string>& devices, bool wait,
+           bool sync_xla_data) {
+          NoGilSection nogil;
+          SyncTensors(tensors, devices, wait, sync_xla_data);
+        },
+        py::arg("tensors"), py::arg("devices"), py::arg("wait") = true,
+        py::arg("sync_xla_data") = true);
+  m.def("_xla_sync_live_tensors",
+        [](const std::string& device, const std::vector<std::string>& devices,
+           bool wait) {
+          NoGilSection nogil;
+          SyncLiveTensors(device, devices, wait);
+        },
+        py::arg("device") = "", py::arg("devices"), py::arg("wait") = true);
+  m.def("_xla_step_marker",
+        [](const std::string& device, const std::vector<std::string>& devices,
+           bool wait) {
+          NoGilSection nogil;
+          StepMarker(device, devices, wait);
+        },
+        py::arg("device") = "", py::arg("devices"), py::arg("wait") = true);
   m.def("_xla_counter_value", [](const std::string& name) -> py::object {
     xla::metrics::CounterData* data = xla::metrics::GetCounter(name);
     return data != nullptr ? py::cast<int64_t>(data->Value()) : py::none();
   });
   m.def("_xla_metrics_report",
         []() { return xla::metrics::CreateMetricReport(); });
-  m.def(
-      "_xla_tensors_report",
-      [](size_t nodes_threshold, const std::string& device) {
-        return GetLiveTensorsReport(nodes_threshold, device);
-      },
-      py::arg("nodes_threshold") = 100, py::arg("device") = "");
-  m.def(
-      "_xla_set_use_full_mat_mul_precision",
-      [](bool use_full_mat_mul_precision) {
-        XlaHelpers::set_mat_mul_precision(use_full_mat_mul_precision
-                                              ? xla::PrecisionConfig::HIGHEST
-                                              : xla::PrecisionConfig::DEFAULT);
-      },
-      py::arg("use_full_mat_mul_precision") = true);
+  m.def("_xla_tensors_report",
+        [](size_t nodes_threshold, const std::string& device) {
+          return GetLiveTensorsReport(nodes_threshold, device);
+        },
+        py::arg("nodes_threshold") = 100, py::arg("device") = "");
+  m.def("_xla_set_use_full_mat_mul_precision",
+        [](bool use_full_mat_mul_precision) {
+          XlaHelpers::set_mat_mul_precision(
+              use_full_mat_mul_precision ? xla::PrecisionConfig::HIGHEST
+                                         : xla::PrecisionConfig::DEFAULT);
+        },
+        py::arg("use_full_mat_mul_precision") = true);
 }
 
 }  // namespace

diff --git a/torch_xla/csrc/matrix.cpp b/torch_xla/csrc/matrix.cpp
@@ -1,4 +1,5 @@
 #include "torch_xla/csrc/matrix.h"
+
 #include "tensorflow/compiler/xla/client/lib/constants.h"
 #include "tensorflow/compiler/xla/client/lib/matrix.h"
 #include "torch_xla/csrc/helpers.h"

diff --git a/torch_xla/csrc/ops/adaptive_avg_pool2d.cpp b/torch_xla/csrc/ops/adaptive_avg_pool2d.cpp
@@ -1,4 +1,5 @@
 #include "torch_xla/csrc/ops/adaptive_avg_pool2d.h"
+
 #include "tensorflow/compiler/xla/xla_client/debug_macros.h"
 #include "tensorflow/compiler/xla/xla_client/util.h"
 #include "torch_xla/csrc/lowering_context.h"
@@ -26,10 +27,9 @@ xla::Shape NodeOutputShape(
 
 AdaptiveAvgPool2d::AdaptiveAvgPool2d(const Value& input,
                                      std::vector<xla::int64> output_size)
-    : Node(
-          ir::OpKind(at::aten::adaptive_avg_pool2d), {input},
-          [&]() { return NodeOutputShape(input, output_size); },
-          /*num_outputs=*/1, xla::util::MHash(output_size)),
+    : Node(ir::OpKind(at::aten::adaptive_avg_pool2d), {input},
+           [&]() { return NodeOutputShape(input, output_size); },
+           /*num_outputs=*/1, xla::util::MHash(output_size)),
       output_size_(std::move(output_size)) {}
 
 NodePtr AdaptiveAvgPool2d::Clone(OpList operands) const {

diff --git a/torch_xla/csrc/ops/arg_max.cpp b/torch_xla/csrc/ops/arg_max.cpp
@@ -20,10 +20,9 @@ xla::Shape NodeOutputShape(const Value& input, xla::int64 dim, bool keepdim) {
 }  // namespace
 
 ArgMax::ArgMax(const Value& input, xla::int64 dim, bool keepdim)
-    : Node(
-          ir::OpKind(at::aten::argmax), {input},
-          [&]() { return NodeOutputShape(input, dim, keepdim); },
-          /*num_outputs=*/1, xla::util::MHash(dim, keepdim)),
+    : Node(ir::OpKind(at::aten::argmax), {input},
+           [&]() { return NodeOutputShape(input, dim, keepdim); },
+           /*num_outputs=*/1, xla::util::MHash(dim, keepdim)),
       dim_(dim),
       keepdim_(keepdim) {}
 

diff --git a/torch_xla/csrc/ops/arg_min.cpp b/torch_xla/csrc/ops/arg_min.cpp
@@ -20,10 +20,9 @@ xla::Shape NodeOutputShape(const Value& input, xla::int64 dim, bool keepdim) {
 }  // namespace
 
 ArgMin::ArgMin(const Value& input, xla::int64 dim, bool keepdim)
-    : Node(
-          ir::OpKind(at::aten::argmin), {input},
-          [&]() { return NodeOutputShape(input, dim, keepdim); },
-          /*num_outputs=*/1, xla::util::MHash(dim, keepdim)),
+    : Node(ir::OpKind(at::aten::argmin), {input},
+           [&]() { return NodeOutputShape(input, dim, keepdim); },
+           /*num_outputs=*/1, xla::util::MHash(dim, keepdim)),
       dim_(dim),
       keepdim_(keepdim) {}
 

diff --git a/torch_xla/csrc/ops/as_strided.cpp b/torch_xla/csrc/ops/as_strided.cpp
@@ -45,12 +45,11 @@ xla::Shape NodeOutputShape(const Value& input,
 
 AsStrided::AsStrided(const Value& input, std::vector<xla::int64> size,
                      c10::optional<xla::int64> storage_offset)
-    : Node(
-          ir::OpKind(at::aten::as_strided), {input},
-          [&]() { return NodeOutputShape(input, size, storage_offset); },
-          /*num_outputs=*/1,
-          xla::util::MHash(size,
-                           OptionalOr<xla::int64>(storage_offset, 0x311bd6))),
+    : Node(ir::OpKind(at::aten::as_strided), {input},
+           [&]() { return NodeOutputShape(input, size, storage_offset); },
+           /*num_outputs=*/1,
+           xla::util::MHash(size,
+                            OptionalOr<xla::int64>(storage_offset, 0x311bd6))),
       size_(std::move(size)),
       storage_offset_(storage_offset) {}
 

diff --git a/torch_xla/csrc/ops/as_strided.h b/torch_xla/csrc/ops/as_strided.h
@@ -1,9 +1,9 @@
 #pragma once
 
-#include <vector>
-
 #include <c10/util/Optional.h>
 
+#include <vector>
+
 #include "tensorflow/compiler/xla/types.h"
 #include "torch_xla/csrc/ir.h"
 

diff --git a/torch_xla/csrc/ops/avg_pool_nd.cpp b/torch_xla/csrc/ops/avg_pool_nd.cpp
@@ -50,16 +50,15 @@ AvgPoolNd::AvgPoolNd(const Value& input, xla::int64 spatial_dim_count,
                      std::vector<xla::int64> stride,
                      std::vector<xla::int64> padding, bool ceil_mode,
                      bool count_include_pad)
-    : Node(
-          ir::OpKind(AvgPoolNdSymbol(spatial_dim_count)), {input},
-          [&]() {
-            return NodeOutputShape(input, spatial_dim_count, kernel_size,
-                                   stride, padding, ceil_mode,
-                                   count_include_pad);
-          },
-          /*num_outputs=*/1,
-          xla::util::MHash(spatial_dim_count, kernel_size, stride, padding,
-                           ceil_mode, count_include_pad)),
+    : Node(ir::OpKind(AvgPoolNdSymbol(spatial_dim_count)), {input},
+           [&]() {
+             return NodeOutputShape(input, spatial_dim_count, kernel_size,
+                                    stride, padding, ceil_mode,
+                                    count_include_pad);
+           },
+           /*num_outputs=*/1,
+           xla::util::MHash(spatial_dim_count, kernel_size, stride, padding,
+                            ceil_mode, count_include_pad)),
       spatial_dim_count_(spatial_dim_count),
       kernel_size_(std::move(kernel_size)),
       stride_(std::move(stride)),

diff --git a/torch_xla/csrc/ops/avg_pool_nd_backward.cpp b/torch_xla/csrc/ops/avg_pool_nd_backward.cpp
@@ -49,16 +49,15 @@ AvgPoolNdBackward::AvgPoolNdBackward(
     const Value& grad_output, const Value& input, xla::int64 spatial_dim_count,
     std::vector<xla::int64> kernel_size, std::vector<xla::int64> stride,
     std::vector<xla::int64> padding, bool ceil_mode, bool count_include_pad)
-    : Node(
-          OpKind(AvgNdBackwardSymbol(spatial_dim_count)), {grad_output, input},
-          [&]() {
-            return NodeOutputShape(grad_output, input, spatial_dim_count,
-                                   kernel_size, stride, padding, ceil_mode,
-                                   count_include_pad);
-          },
-          /*num_outputs=*/1,
-          xla::util::MHash(spatial_dim_count, kernel_size, stride, padding,
-                           ceil_mode, count_include_pad)),
+    : Node(OpKind(AvgNdBackwardSymbol(spatial_dim_count)), {grad_output, input},
+           [&]() {
+             return NodeOutputShape(grad_output, input, spatial_dim_count,
+                                    kernel_size, stride, padding, ceil_mode,
+                                    count_include_pad);
+           },
+           /*num_outputs=*/1,
+           xla::util::MHash(spatial_dim_count, kernel_size, stride, padding,
+                            ceil_mode, count_include_pad)),
       spatial_dim_count_(spatial_dim_count),
       kernel_size_(std::move(kernel_size)),
       stride_(std::move(stride)),