Hz/fx fix (#3383)

* Update dependency_version.json

* fix fx ut

* Update dependency_version.json

* Update dependency_version.json

* Update dependency_version.json

* fix fp8 test

---------

Co-authored-by: DiweiSun <[email protected]>

Author: zhuhaozhe

dependency_version.json

@@ -7,13 +7,13 @@
     "commit": "llvmorg-16.0.6"
   },
   "pytorch": {
-    "version": "2.6.0.dev20241028+cpu"
+    "version": "2.6.0.dev20241124+cpu"
   },
   "torchaudio": {
-    "version": "2.5.0.dev20241028+cpu"
+    "version": "2.5.0.dev20241121+cpu"
   },
   "torchvision": {
-    "version": "0.20.0.dev20241028+cpu"
+    "version": "0.20.0.dev20241121+cpu"
   },
   "torch-ccl": {
     "commit": "torch_ccl_dev_2.6",

tests/cpu/test_fp8_autocast.py

@@ -193,7 +193,7 @@ def forward(self, x):
         fp8_linear_with_calibration = MyModel()
         fp8_linear_with_calibration = prepare_fp8(fp8_linear_with_calibration)
         fp8_linear_with_calibration.load_state_dict(
-            torch.load("fp8_linear_inference.pt")
+            torch.load("fp8_linear_inference.pt", weights_only=False)
         )
         fp8_linear_with_calibration.eval()
 

tests/cpu/test_fx_optimization.py

@@ -104,7 +104,7 @@ def test_concat_linear_hf_bert(self):
         config = AutoConfig.from_pretrained(loc + "/bert-base-config.json")
         model = AutoModelForCausalLM.from_config(config)
         model.eval()
-        inputs = torch.load(loc + "/bert-inputs.pt")
+        inputs = torch.load(loc + "/bert-inputs.pt", weights_only=False)
         gm = hf_symbolic_trace(model, input_names=list(inputs.keys()))
         ref_out = gm(**inputs)
         concat_gm = ipex.fx.concat_linear.concat_linear(copy.deepcopy(gm), inplace=True)
@@ -125,7 +125,7 @@ def test_automatically_apply_concat_linear_with_ipex_optimize(self):
         loc = os.path.dirname(os.path.abspath(__file__))
         config = AutoConfig.from_pretrained(loc + "/bert-base-config.json")
         base_model = AutoModelForCausalLM.from_config(config).eval()
-        inputs = torch.load(loc + "/bert-inputs.pt")
+        inputs = torch.load(loc + "/bert-inputs.pt", weights_only=False)
         for dtype in [torch.float, torch.bfloat16]:
             for inplace in [True, False]:
                 model = copy.deepcopy(base_model)