[PyTorch] Add weights_only=False for torch.load (#1374)

add weights_only=False for torch.load

Signed-off-by: Charlene Yang <[email protected]>

tests/pytorch/test_float8tensor.py

@@ -339,7 +339,7 @@ def test_serialization(
         del x_fp8, byte_stream
 
         # Deserialize tensor
-        x_fp8 = torch.load(io.BytesIO(x_bytes))
+        x_fp8 = torch.load(io.BytesIO(x_bytes), weights_only=False)
         del x_bytes
 
         # Check results

tests/pytorch/test_sanity.py

@@ -1101,7 +1101,7 @@ def get_model(dtype, config):
 
         del block
         block = get_model(dtype, config)
-        block.load_state_dict(torch.load(path))
+        block.load_state_dict(torch.load(path, weights_only=False))
         torch.set_rng_state(_cpu_rng_state_new)
         torch.cuda.set_rng_state(_cuda_rng_state_new)
 

tests/pytorch/test_torch_save_load.py

@@ -124,7 +124,7 @@ def forward(self, inp, weight):
     torch.save(model_in.state_dict(), tmp_filename)
 
     model_out = Test_TE_Export(precision, True)
-    model_out.load_state_dict(torch.load(tmp_filename))
+    model_out.load_state_dict(torch.load(tmp_filename, weights_only=False))
     model_out.eval()
 
     # scaling fwd
@@ -263,7 +263,7 @@ def test_fp8_model_checkpoint(
     # to load the fp8 metadata before loading tensors.
     #
     # Load checkpoint
-    model.load_state_dict(torch.load(io.BytesIO(model_bytes)))
+    model.load_state_dict(torch.load(io.BytesIO(model_bytes), weights_only=False))
     del model_bytes
 
     # Check that loaded model matches saved model
@@ -450,7 +450,7 @@ def train_step(
                 torch.testing.assert_close(m_model.scale_inv, m_ref["scale_inv"], **exact_tols)
 
     # Load checkpoint
-    model.load_state_dict(torch.load(io.BytesIO(model_bytes)))
+    model.load_state_dict(torch.load(io.BytesIO(model_bytes), weights_only=False))
     del model_bytes
 
     # Check that new model's FP8 metadata matches saved model