Fix bad use of null-like value and internal links. (#4171) (#4177)

* Correct wrong link address.

* Fix bad use of none-like value in _quantize_utils.py.

---------

Co-authored-by: Ye Ting <[email protected]>

Author: Hanyu-Jin

intel_extension_for_pytorch/quantization/_quantize_utils.py

@@ -286,6 +286,7 @@ def _patched_module_call(self, *args, **kwargs):
 
                     hook_type = get_module_hook_type(parent_module, cur_module)
                     if hook_type is HookType.OP_HOOKS:
+                        assert parent_module is not None
                         parent_qstate: AutoQuantizationState = (
                             parent_module._auto_quant_state
                         )  # type: ignore[union-attr, assignment]
@@ -674,6 +675,7 @@ def _patched_module_call(self, *args, **kwargs):
                     hook_type = get_module_hook_type(parent_module, cur_module)
                     if hook_type is HookType.OP_HOOKS:
                         # before hooks
+                        assert parent_module is not None
                         qstate: AutoQuantizationState = (
                             parent_module._auto_quant_state
                         )  # type: ignore[union-attr, assignment]

scripts/tools/queryop/query_op.py

@@ -6,9 +6,8 @@
 "pytorch: https://github.com/pytorch/pytorch/tree/release/2.1"
 
 if key == 'XPU':
-"Please set up your XPU environment: install pytorch-2.1 and ipex GPU",
-"pytorch: https://github.com/intel-innersource/frameworks.ai.pytorch.private-gpu.git"
-"ipex GPU: rebase/ipex-2.1 https://github.com/intel-innersource/frameworks.ai.pytorch.ipex-gpu.git"
+"Please set up your XPU environment: install pytorch-2.1 and Intel® Extension for PyTorch* GPU"
+"Intel® Extension for PyTorch* GPU: https://github.com/intel/intel-extension-for-pytorch/tree/xpu-main"
 
 usage example:
 if key == CPU or CUDA:

tests/cpu/test_weight_prepack.py

@@ -483,7 +483,6 @@ def test_conv3d_training(self):
         # TODO: add inference case.
 
     def _test_conv_nc11_base(self, dim):
-        # related issue: https://github.com/intel-innersource/frameworks.ai.pytorch.ipex-cpu/pull/86.
         channels_last = torch.channels_last if dim == 2 else torch.channels_last_3d
         test_dtypes = [torch.float]
         if core.onednn_has_bf16_support():

tests/gpu/examples/test_inplace_binary_and_relu.py

@@ -3,14 +3,6 @@
 from torch.testing._internal.common_utils import TestCase
 import intel_extension_for_pytorch  # noqa
 
-"""
-    Motivation: When running block format ResNet-XX with using inplaced binary add and relu, 
-                layer output will be flushed to shape zero. In this UT, it may throw a segmentfault error.
-    This issue was fixed in commit: 193ef42, 
-    PR: https://github.com/intel-innersource/frameworks.ai.pytorch.ipex-gpu/pull/668/commits
-"""
-
-
 dpcpp_device = torch.device("xpu")
 cpu_device = torch.device("cpu")
 

tests/gpu/examples/test_reshape.py

@@ -6,13 +6,6 @@
 
 class TestTorchMethod(TestCase):
     def test_reshape_cat(self, dtype=torch.float):
-        """
-        Issue: When run SSD-ResNet50 with block format, it raised a shape mismatch error in cat op.
-        Root cause: There was no to_plain_if_needed() in reshape_alias.
-        Without it, the size of tensor was changed by reshape but dims was kept unchanged.
-        It led to the shape mismatch error in the next op cat.
-        Fixed PR: https://github.com/intel-innersource/frameworks.ai.pytorch.ipex-gpu/pull/709
-        """
         with torch.xpu.onednn_layout():
             input1 = torch.randn([32, 3, 300, 300]).to("xpu")
             conv1 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False).to(