Merge pull request #25 from iotamudelta/master

Merge from upstream

Author: iotamudelta

torch/csrc/Module.cpp

@@ -282,12 +282,26 @@ PyObject *THPModule_hasDistributed(PyObject *_unused)
 #endif
 }
 
+void DLPack_Capsule_Destructor(PyObject* data) {
+  HANDLE_TH_ERRORS
+  DLManagedTensor * dlMTensor = (DLManagedTensor *)PyCapsule_GetPointer(data, "dltensor");
+  if (dlMTensor) {
+    // the dlMTensor has not been consumed, call deleter ourselves
+    dlMTensor->deleter(const_cast<DLManagedTensor*>(dlMTensor));
+  } else {
+    // the dlMTensor has been consumed
+    // PyCapsule_GetPointer has set an error indicator
+    PyErr_Clear();
+  }
+  END_HANDLE_TH_ERRORS_RET()
+}
+
 PyObject *THPModule_toDLPack(PyObject *_unused, PyObject *data)
 {
   HANDLE_TH_ERRORS
   THPUtils_assert(THPVariable_Check(data), "data must be a Tensor");
   DLManagedTensor* dlMTensor = at::toDLPack(THPVariable_UnpackData(data));
-  return PyCapsule_New(dlMTensor, "dltensor", NULL);
+  return PyCapsule_New(dlMTensor, "dltensor", DLPack_Capsule_Destructor);
   END_HANDLE_TH_ERRORS
 }
 

torch/nn/utils/clip_grad.py

@@ -29,12 +29,12 @@ def clip_grad_norm_(parameters, max_norm, norm_type=2):
         total_norm = 0
         for p in parameters:
             param_norm = p.grad.data.norm(norm_type)
-            total_norm += param_norm ** norm_type
+            total_norm += param_norm.item() ** norm_type
         total_norm = total_norm ** (1. / norm_type)
     clip_coef = max_norm / (total_norm + 1e-6)
     if clip_coef < 1:
         for p in parameters:
-            p.grad.data.mul_(clip_coef.item())
+            p.grad.data.mul_(clip_coef)
     return total_norm