Make AT_ASSERT/AT_ERROR non-printf based, other tweaks (#7104)

* Make AT_ASSERT/AT_ERROR non-printf based, other tweaks

- AT_ASSERT/AT_ERROR don't take printf strings anymore; instead,
  they take a comma-separated list of things you wanted to print
  (bringing it inline with Caffe2's conventions).

  Instead of AT_ASSERT(x == 0, "%d is not zero", x)
  you write AT_ASSERT(x == 0, x, " is not zero")

  This is done by way of a new variadic template at::str(), which
  takes a list of arguments and cats their string reps (as per
  operator<<) together.

- A bunch of the demangling logic that was in Error.h is now
  moved to Error.cpp (better header hygiene.)  Also, demangle
  has been moved out to its own helper function, and also
  a new helper demangle_type (from Caffe2) added.

- A bunch of AT_ASSERT converted into AT_CHECK, to more properly
  convey which checks can be caused by user error, and which are
  due to logic error in ATen.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* CR

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Fix test failure.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* buildfix

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* More fixes.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* One more fix

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Try harder

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

2 files changed, 14 insertions, 18 deletions

diff --git a/tools/autograd/templates/Functions.cpp b/tools/autograd/templates/Functions.cpp
index 279f2c3012..aeae0dc792 100644
--- a/tools/autograd/templates/Functions.cpp
+++ b/tools/autograd/templates/Functions.cpp
@@ -36,14 +36,14 @@ struct IndexRangeGenerator {
 };
 
 void copy_range(variable_list& out, IndexRange range, const Tensor & t) {
-  AT_ASSERT(range.second <= out.size(), "range out of bounds");
-  AT_ASSERT(range.second - range.first == 1, "inconsistent range for Tensor output");
+  AT_ASSERT(range.second <= out.size());
+  AT_ASSERTM(range.second - range.first == 1, "inconsistent range for Tensor output");
   out[range.first] = t;
 }
 
 void copy_range(variable_list& out, IndexRange range, at::ArrayRef<Tensor> t) {
-  AT_ASSERT(range.second <= out.size(), "range out of bounds");
-  AT_ASSERT(range.second - range.first == t.size(), "inconsistent range for TensorList output");
+  AT_ASSERT(range.second <= out.size());
+  AT_ASSERTM(range.second - range.first == t.size(), "inconsistent range for TensorList output");
   std::copy(t.begin(), t.end(), out.begin() + range.first);
 }
 
@@ -972,7 +972,7 @@ Tensor logdet_backward(const Tensor & grad, const Tensor& self, const Tensor& lo
 Tensor slogdet_backward(const std::vector<torch::autograd::Variable> &grads,
                         const Tensor& self,
                         const Tensor& signdet, const Tensor& logabsdet) {
-  AT_ASSERT(!grads[0].defined(), "slogdet's sign output should never have gradient");
+  AT_ASSERTM(!grads[0].defined(), "slogdet's sign output should never have gradient");
   auto signdet_val = signdet.toCDouble();
   if (signdet_val != 0 /* det != 0, invertible */) {
     return grads[1] * self.inverse().t();
@@ -1275,7 +1275,7 @@ std::tuple<Tensor, Tensor, Tensor> batchnorm_double_backward(
 
   if (output_mask[0] && !ggO.defined()) ggO = at::zeros_like(gO);
   if (output_mask[1] && !gG.defined()) {
-    AT_ASSERT(affine, "gamma should always be defined when it requires grad");
+    AT_ASSERTM(affine, "gamma should always be defined when it requires grad");
     gG = at::zeros_like(gamma);
   }
   if (output_mask[2] && !gI.defined()) gI = at::zeros_like(input);
diff --git a/tools/autograd/templates/VariableType.cpp b/tools/autograd/templates/VariableType.cpp
index 9846170564..6235fc8fa1 100644
--- a/tools/autograd/templates/VariableType.cpp
+++ b/tools/autograd/templates/VariableType.cpp
@@ -157,12 +157,10 @@ std::vector<at::Type*> VariableType::allTypes() {
 
 Variable & VariableType::checked_cast_variable(const Tensor & t, const char * name, int pos) {
   if (!t.defined()) {
-    AT_ERROR("Expected a Tensor of type Variable but found an undefined Tensor for argument #%d '%s'",
-        pos, name);
+    AT_ERROR("Expected a Tensor of type Variable but found an undefined Tensor for argument #", pos, " '", name, "'");
   }
   if (!isVariableType(t.type())) {
-    AT_ERROR("Expected object of type Variable but found type %s for argument #%d '%s'",
-        t.type().toString(), pos, name);
+    AT_ERROR("Expected object of type Variable but found type ", t.type().toString(), " for argument #", pos, " '", name, "'");
   }
   return as_variable_ref(const_cast<Tensor&>(t));
 }
@@ -187,14 +185,12 @@ std::vector<at::Tensor> VariableType::unpack(at::TensorList tl, const char *name
   for (size_t i = 0; i < tl.size(); ++i) {
     const auto &t = tl[i];
     if (!t.defined()) {
-      AT_ERROR("Expected a Tensor of type Variable but found an undefined Tensor at position #%d "
-                    "for iterable argument #%d '%s'",
-                    i, pos, name);
+      AT_ERROR("Expected a Tensor of type Variable but found an undefined Tensor at position #", i, " "
+                    "for iterable argument #", pos, " '", name, "'");
     }
     if (!isVariableType(t.type())) {
-      AT_ERROR("Expected object of type Variable but found type %s at position #%d "
-                    "for iterable argument #%d '%s'",
-                    t.type().toString(), i, pos, name);
+      AT_ERROR("Expected object of type Variable but found type ", t.type().toString(), " at position #", i, " "
+                    "for iterable argument #", pos, " '", name, "'");
     }
     ret[i] = static_cast<const Variable&>(t).data();
   }
@@ -288,8 +284,8 @@ static void check_inplace(const Tensor& tensor) {
 
 static void throw_error_out_requires_grad(const char* name) {
   AT_ERROR(
-      "%s(): functions with out=... arguments don't support automatic differentiation, "
-      "but one of the arguments requires grad.", name);
+      name, "(): functions with out=... arguments don't support automatic differentiation, "
+      "but one of the arguments requires grad.");
 }
 
 static void rebase_history(Tensor& tensor, std::shared_ptr<Function> grad_fn) {