add advice about dealing with non-invertable hessians (#875)

Signed-off-by: Kyle Sayers <kylesayrs@gmail.com>

src/llmcompressor/modifiers/quantization/gptq/utils/gptq_wrapper.py

@@ -164,13 +164,20 @@ def compress(
         Losses = torch.zeros(self.rows, device=self.dev)
 
         # compute inverse hessian in place to save memory
-        damp = percdamp * torch.mean(torch.diag(self.H))
-        diag = torch.arange(self.columns, device=self.dev)
-        self.H[diag, diag] += damp
-        self.H = torch.linalg.cholesky(self.H)
-        self.H = torch.cholesky_inverse(self.H)
-        self.H = torch.linalg.cholesky(self.H, upper=True)
-        Hinv = self.H
+        try:
+            damp = percdamp * torch.mean(torch.diag(self.H))
+            diag = torch.arange(self.columns, device=self.dev)
+            self.H[diag, diag] += damp
+            self.H = torch.linalg.cholesky(self.H)
+            self.H = torch.cholesky_inverse(self.H)
+            self.H = torch.linalg.cholesky(self.H, upper=True)
+            Hinv = self.H
+        except torch._C._LinAlgError:
+            raise ValueError(
+                "Failed to invert hessian due to numerical instability. Consider "
+                "increasing GPTQModifier.dampening_frac, increasing the number "
+                "of calibration samples, or shuffling the calibration dataset"
+            )
 
         # See section 3.4 of https://arxiv.org/abs/2203.07259
         for i1 in range(0, self.columns, blocksize):