Fix segfault with torch.orgqr. (pytorch#46700)

Summary:
Fixes pytorch#41768

The fault was that a NULL `tau` would get passed to LAPACK function. This PR fixes that by checking whether the `tau` contains 0 elements at the beginning of the function.

Pull Request resolved: pytorch#46700

Reviewed By: albanD

Differential Revision: D24616427

Pulled By: mruberry

fbshipit-source-id: 92e8f1489b113c0ceeca6e54dea8b810a51a63c3

Author: v0dro

aten/src/TH/generic/THLapack.cpp

@@ -2,7 +2,6 @@
 #define TH_GENERIC_FILE "TH/generic/THLapack.cpp"
 #else
 
-
 TH_EXTERNC void dgels_(char *trans, int *m, int *n, int *nrhs, double *a, int *lda, double *b, int *ldb, double *work, int *lwork, int *info);
 TH_EXTERNC void sgels_(char *trans, int *m, int *n, int *nrhs, float *a, int *lda, float *b, int *ldb, float *work, int *lwork, int *info);
 TH_EXTERNC void dgeev_(char *jobvl, char *jobvr, int *n, double *a, int *lda, double *wr, double *wi, double* vl, int *ldvl, double *vr, int *ldvr, double *work, int *lwork, int *info);

aten/src/TH/generic/THTensorLapack.cpp

@@ -410,7 +410,8 @@ void THTensor_(orgqr)(THTensor *ra_, THTensor *a, THTensor *tau)
 {
   if (a == NULL) a = ra_;
   THArgCheck(THTensor_nDimension(a) == 2, 1, "'input' should be 2 dimensional");
-  THArgCheck(!a->is_empty(), 1, "'input' should not be empty");
+  THArgCheck(!a->is_empty(), 2, "'input' should not be empty");
+  THArgCheck(!tau->is_empty(), 3, "'tau' should not be empty");
 
   THTensor *ra__ = NULL;
   ra__ = THTensor_(cloneColumnMajor)(ra_, a);

test/test_torch.py

@@ -15539,7 +15539,8 @@ def test_orgqr_errors(self, device):
             ((10,), (2,), r"'input' should be 2 dimensional"),
             ((10, 6), (20,), r"input.size\(1\) must be greater than or equal to input2.size\(0\)"),
             ((6, 10), (5,), r"input.size\(0\) must be greater than or equal to input.size\(1\)"),
-            ((0, 0), (0,), r"'input' should not be empty")
+            ((0, 0), (0,), r"'input' should not be empty"),
+            ((2, 2), (2, 0,), r"'tau' should not be empty")
         ]
         for a_size, tau_size, error_regex in test_cases:
             a = torch.rand(*a_size, device=device)