[CPU] fix _weight_int8pack_mm with large output shape (#158341)

**Summary**
`_weight_int8pack_mm` on CPU may cause segmentation fault if output shape is large (i.e., M * N is large). It's because the kernel compute output buffer address by
```c++
auto* C_ptr = C_data + mb_start * N + nb_start;
```
where both `mb_start` and `N` are `int` and when they are large their product may overflow.
The solution is simple: declare these variables as `int64_t` so that the product won't overflow.

**Test plan**
```
pytest -sv test/test_linalg.py -k test__int8_mm_large_shape
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/158341
Approved by: https://github.com/mingfeima, https://github.com/drisspg

test/test_linalg.py

@@ -7811,6 +7811,34 @@ scipy_lobpcg  | {eq_err_scipy:10.2e}  | {eq_err_general_scipy:10.2e}  | {iters2:
         mean_err = ((res - ref).abs() / ref).mean()
         self.assertTrue(mean_err < 0.05)
 
+    @slowTest
+    @onlyCPU
+    def test__int8_mm_large_shape(self, device):
+        torch.manual_seed(1)
+        m = 65536
+        k = 64
+        n = 50400
+        a = torch.rand((m, k), dtype=torch.bfloat16, device=device)
+        b = torch.rand((n, k), dtype=torch.bfloat16, device=device)
+
+        def convert_weight_to_int8pack(b):
+            b_int8pack, b_scales, _ = _dynamically_quantize_per_channel(
+                b, -128, 127, torch.int8
+            )
+            return b_int8pack, b_scales
+
+        def weight_int8pack_mm(a, b_int8pack, b_scales):
+            return torch._weight_int8pack_mm(
+                a, b_int8pack, b_scales
+            )
+
+        b_int8pack, b_scales = convert_weight_to_int8pack(b)
+        res = weight_int8pack_mm(a, b_int8pack, b_scales)
+        ref = torch.mm(a, b.transpose(0, 1))
+
+        mean_err = ((res - ref).abs() / ref).mean()
+        self.assertTrue(mean_err < 0.05)
+
     @onlyCPU
     @parametrize("m", [32, 35, 36, 40, 64])
     @parametrize("k", [32, 35, 36, 40, 64])