[AOTI] fix pointer_to_list (pytorch#138806)

Fixes the `pointer_to_list` function to take `*(ptr + i)` instead of `*ptr`.
This fixes the runtime error when running INT8 yolo-v7.

Pull Request resolved: pytorch#138806
Approved by: https://github.com/jgong5, https://github.com/desertfire
ghstack dependencies: pytorch#138691

Author: chunyuan-w

test/inductor/test_cpu_cpp_wrapper.py

@@ -303,6 +303,12 @@ class BaseTest(NamedTuple):
             for func in dir(test_mkldnn_pattern_matcher.TestPatternMatcher())
             if func.startswith("test_qlinear")
         ],
+        BaseTest(
+            "test_qconv2d_with_concat",
+            "cpu",
+            test_mkldnn_pattern_matcher.TestPatternMatcher(),
+            condition=torch.backends.mkldnn.is_available() and not IS_WINDOWS,
+        ),
         BaseTest(
             "test_dynamic_qlinear",
             "cpu",

test/inductor/test_mkldnn_pattern_matcher.py

@@ -1089,6 +1089,59 @@ def matcher_check_fn():
                 matcher_check_fn=matcher_check_fn,
             )
 
+    @skipIfNoDynamoSupport
+    @skipIfNoONEDNN
+    def test_qconv2d_with_concat_cpu(self):
+        channel_1 = 32
+        channel_2 = 16
+        channel_3 = 8
+        channel_4 = int(channel_2 * 2 + channel_3)
+
+        class Model(torch.nn.Module):
+            def __init__(
+                self,
+            ):
+                super().__init__()
+                self.conv1 = torch.nn.Conv2d(
+                    channel_1, channel_2, 1, stride=1, dilation=1, padding=0
+                )
+                self.conv2 = torch.nn.Conv2d(
+                    channel_1, channel_2, 1, stride=1, dilation=1, padding=0
+                )
+                self.conv3 = torch.nn.Conv2d(
+                    channel_2, channel_3, 3, stride=1, dilation=1, padding=1
+                )
+
+                self.conv = torch.nn.Conv2d(
+                    channel_4, channel_2, 1, stride=1, dilation=1, padding=0
+                )
+
+            def forward(self, x: torch.Tensor):
+                x1 = self.conv1(x)
+                x2 = self.conv2(x)
+                x3 = self.conv3(x2)
+                res = torch.cat([x1, x2, x3], dim=1)
+                res = self.conv(res)
+                return res
+
+        mod = Model().eval()
+        v = torch.randn(
+            (8, channel_1, 40, 40), dtype=torch.float32, requires_grad=False
+        )
+
+        def matcher_check_fn():
+            self.assertEqual(
+                counters["inductor"]["qconv2d_weight_prepack_matcher_count"], 4
+            )
+            self.assertEqual(counters["inductor"]["qconv2d_unary_matcher_count"], 3)
+
+        self._test_common(
+            mod,
+            (v,),
+            check_quantization=True,
+            matcher_check_fn=matcher_check_fn,
+        )
+
     @skipIfNoDynamoSupport
     @skipIfNoONEDNN
     def test_qconv2d_add_2(self):

torch/csrc/inductor/aoti_torch/utils.h

@@ -148,7 +148,7 @@ inline std::vector<at::Tensor> pointer_to_list(
   std::vector<at::Tensor> result;
   result.reserve(len);
   for (int64_t i = 0; i < len; i++) {
-    result.emplace_back(*tensor_handle_to_tensor_pointer(*ptr));
+    result.emplace_back(*tensor_handle_to_tensor_pointer(ptr[i]));
   }
   return result;
 }