Handle pollution of MAX, MIN and CHECK macros. (pytorch#11805)

Summary:
Pull Request resolved: pytorch#11805

Some of our headers in Caffe2 pollute the macro namespace with things like MAX,
MIN, CHECK, so I renamed these in places where this is a problem.

This patch courtesy of gchanan, extracted out of pytorch#11721

Reviewed By: Yangqing

Differential Revision: D9917757

fbshipit-source-id: 17fc692ca04b208dcb8ae00731ed60e393284f7c

aten/src/TH/THTensorCopy.cpp

@@ -1,6 +1,8 @@
 #include "THTensor.hpp"
 #include "THVector.h"
 
+#include <algorithm>
+
 #include "generic/THTensorCopy.cpp"
 #include "THGenerateAllTypes.h"
 

aten/src/TH/generic/THTensorCopy.cpp

@@ -26,13 +26,11 @@ int THTensor_(copyTransposeValid)(THTensor *tensor, THTensor *src) {
 // special case copy where tensor is contiguous and src is a transposed matrix
 // This can be generalized to most copies, but it's tricker
 void THTensor_(copyTranspose)(THTensor *tensor, THTensor *src) {
-  #define MIN(x, y) (((x) < (y)) ? (x) : (y))
-  #define MAX(x, y) (((x) > (y)) ? (x) : (y))
 
 #ifdef TH_REAL_IS_BYTE
-  const int BLOCK_SZ = 120;
+  const int64_t BLOCK_SZ = 120;
 #else
-  const int BLOCK_SZ = 60;
+  const int64_t BLOCK_SZ = 60;
 #endif
 
   THTensor *buf = THTensor_(newWithSize2d)(BLOCK_SZ, BLOCK_SZ);
@@ -48,19 +46,19 @@ void THTensor_(copyTranspose)(THTensor *tensor, THTensor *src) {
       scalar_t *spo = sp + R + C * NR;
       scalar_t *rpo = rp + C + R * NC;
 
-      int nr = MIN(NR - R, BLOCK_SZ);
-      int nc = MIN(NC - C, BLOCK_SZ);
+      int nr = std::min(NR - R, BLOCK_SZ);
+      int nc = std::min(NC - C, BLOCK_SZ);
 
       // 1. copy columns from src to buf
       for (int c = 0; c < nc; c++) {
         memcpy(bp + c * BLOCK_SZ, spo + c * NR, nr * sizeof(scalar_t));
       }
 
       // 2. transpose buf in place
-      int rc_max = MAX(nr, nc);
-      int rc_min = MIN(nr, nc);
+      int rc_max = std::max(nr, nc);
+      int rc_min = std::min(nr, nc);
       for (int r = 0; r < rc_max; r++) {
-        int end = MIN(r, rc_min);
+        int end = std::min(r, rc_min);
         for (int c = 0; c < end; c++) {
           scalar_t tmp = bp[r + BLOCK_SZ * c];
           bp[r + BLOCK_SZ * c] = bp[r * BLOCK_SZ + c];
@@ -75,8 +73,6 @@ void THTensor_(copyTranspose)(THTensor *tensor, THTensor *src) {
     }
   }
   c10::raw::intrusive_ptr::decref(buf);
-  #undef MIN
-  #undef MAX
 }
 
 void THTensor_(copy)(THTensor *tensor, THTensor *src)

torch/csrc/cuda/nccl.cpp

@@ -28,7 +28,7 @@ struct NcclCommList {
   int ndevices;
   NcclCommList(const std::vector<int>& devices)
     : comms(new ncclComm_t[devices.size()]), ndevices(devices.size()) {
-    CHECK(ncclCommInitAll(comms.get(), devices.size(), devices.data()));
+    NCCL_CHECK(ncclCommInitAll(comms.get(), devices.size(), devices.data()));
   }
   NcclCommList(NcclCommList&& foo) = default;
   ~NcclCommList() {
@@ -219,7 +219,7 @@ void broadcast(TensorList tensors, const stream_list& streams, const comm_list&
     AT_CHECK(static_cast<uint64_t>(numel) <= static_cast<uint64_t>(count_max),
              "Broadcast tensor has ", numel, " elements, which exceeds the "
              "maximum NCCL supports (", count_max, ")");
-    CHECK(ncclBcast(tensors[i].data_ptr(), numel, data_type, 0, comms[i], stream));
+    NCCL_CHECK(ncclBcast(tensors[i].data_ptr(), numel, data_type, 0, comms[i], stream));
   }
 #else
   throw std::runtime_error("PyTorch built without NCCL support");

torch/csrc/cuda/nccl.h

@@ -12,7 +12,7 @@ namespace detail {
 
 void throw_nccl_error(ncclResult_t status);
 
-static inline void CHECK(ncclResult_t status) {
+static inline void NCCL_CHECK(ncclResult_t status) {
   if (status != ncclSuccess) {
     throw_nccl_error(status);
   }
@@ -21,12 +21,12 @@ static inline void CHECK(ncclResult_t status) {
 struct AutoNcclGroup {
   AutoNcclGroup() {
 #if defined(NCCL_MAJOR) && (NCCL_MAJOR >= 2)
-    CHECK(ncclGroupStart());
+    NCCL_CHECK(ncclGroupStart());
 #endif
   }
   ~AutoNcclGroup() {
 #if defined(NCCL_MAJOR) && (NCCL_MAJOR >= 2)
-    CHECK(ncclGroupEnd());
+    NCCL_CHECK(ncclGroupEnd());
 #endif
   }
 };

torch/csrc/cuda/python_nccl.cpp

@@ -26,7 +26,7 @@ PyObject * THCPModule_nccl_version(PyObject *self, PyObject *args) {
 PyObject * THCPModule_nccl_unique_id(PyObject *self, PyObject *args) {
   HANDLE_TH_ERRORS
   ncclUniqueId id;
-  CHECK(ncclGetUniqueId(&id));
+  NCCL_CHECK(ncclGetUniqueId(&id));
   return PyBytes_FromStringAndSize((char*)&id, NCCL_UNIQUE_ID_BYTES);
   END_HANDLE_TH_ERRORS
 }
@@ -109,7 +109,7 @@ PyObject * THCPModule_nccl_init_rank(PyObject *self, PyObject *args) {
   memcpy(&commId, id, NCCL_UNIQUE_ID_BYTES);
   ncclComm_t comm;
   with_no_gil([&]{
-    CHECK(ncclCommInitRank(&comm, nranks, commId, rank));
+    NCCL_CHECK(ncclCommInitRank(&comm, nranks, commId, rank));
   });
   return PyCapsule_New(comm, COMM_CAPSULE_NAME, &destroy_nccl_comm);
   END_HANDLE_TH_ERRORS
@@ -149,7 +149,7 @@ PyObject * THCPModule_nccl_reduce(PyObject *self, PyObject *args) {
       int device = inputs[i].get_device();
       device_guard.set_index(device);
       auto stream = (streams[i] == nullptr) ? nullptr : THCStream_stream(streams[i]);
-      CHECK(ncclReduce(inputs[i].data_ptr(), outputs[i].data_ptr(),
+      NCCL_CHECK(ncclReduce(inputs[i].data_ptr(), outputs[i].data_ptr(),
            count, data_type, (ncclRedOp_t) op, root, comms[i], stream));
     }
   });
@@ -191,7 +191,7 @@ PyObject * THCPModule_nccl_all_reduce(PyObject *self, PyObject *args) {
       int device = inputs[i].get_device();
       device_guard.set_index(device);
       auto stream = (streams[i] == nullptr) ? nullptr : THCStream_stream(streams[i]);
-      CHECK(ncclAllReduce(inputs[i].data_ptr(), outputs[i].data_ptr(),
+      NCCL_CHECK(ncclAllReduce(inputs[i].data_ptr(), outputs[i].data_ptr(),
           count, data_type, (ncclRedOp_t) op, comms[i], stream));
     }
   });
@@ -255,10 +255,10 @@ PyObject * THCPModule_nccl_all_gather(PyObject *self, PyObject *args) {
       device_guard.set_index(device);
       auto stream = (streams[i] == nullptr) ? nullptr : THCStream_stream(streams[i]);
     #if defined(NCCL_MAJOR) && (NCCL_MAJOR >= 2)
-      CHECK(ncclAllGather(inputs[i].data_ptr(), outputs[i].data_ptr(),
+      NCCL_CHECK(ncclAllGather(inputs[i].data_ptr(), outputs[i].data_ptr(),
         count, data_type, comms[i], stream));
     #else
-      CHECK(ncclAllGather(inputs[i].data_ptr(), count, data_type,
+      NCCL_CHECK(ncclAllGather(inputs[i].data_ptr(), count, data_type,
         outputs[i].data_ptr(), comms[i], stream));
     #endif
     }
@@ -299,7 +299,7 @@ PyObject * THCPModule_nccl_reduce_scatter(PyObject *self, PyObject *args) {
       int device = inputs[i].get_device();
       device_guard.set_index(device);
       auto stream = (streams[i] == nullptr) ? nullptr : THCStream_stream(streams[i]);
-      CHECK(ncclReduceScatter(inputs[i].data_ptr(), outputs[i].data_ptr(),
+      NCCL_CHECK(ncclReduceScatter(inputs[i].data_ptr(), outputs[i].data_ptr(),
           count, data_type, (ncclRedOp_t) op, comms[i], stream));
     }
   });