Merge branch 'pytorch:main' into optim_compile

ruff.toml

@@ -12,6 +12,8 @@ include = [
     "test/dtypes/test_affine_quantized_float.py",
     "test/dtypes/test_nf4.py",
     "test/prototype/low_bit_optim/**.py",
+    "torchao/utils.py",
+
 ]
 
 lint.ignore = ["E731"]

test/dtypes/test_nf4.py

@@ -170,11 +170,10 @@ def test_load_from_state_dicts(self, dtype: torch.dtype):
         assert base_mod.param.block_size == 32
         assert base_mod.param.scaler_block_size == 2
 
-    @unittest.skipIf(not torch.cuda.is_available(), "Need cuda for test")
     @parametrize("dtype", [torch.bfloat16, torch.float16, torch.float32])
     def test_load_from_nf4_same_meta(self, dtype: torch.dtype):
         """Tests loading to and from different module state dicts"""
-        input_tensor = torch.rand(64, device="cuda", dtype=dtype)
+        input_tensor = torch.rand(64, dtype=dtype)
         base_mod = self.TestMod(input_tensor, 32, 2)
         state_dict = base_mod.state_dict()
         saved_state_dict = self.save_state_dict_to_buffer(state_dict)
@@ -184,11 +183,10 @@ def test_load_from_nf4_same_meta(self, dtype: torch.dtype):
         assert other_mod.param.block_size == 32
         assert other_mod.param.scaler_block_size == 2
 
-    @unittest.skipIf(not torch.cuda.is_available(), "Need cuda for test")
     @parametrize("dtype", [torch.bfloat16, torch.float16, torch.float32])
     def test_load_from_nf4_diff_meta(self, dtype: torch.dtype):
         """Tests loading to and from different module state dicts"""
-        input_tensor = torch.rand(128, device="cuda", dtype=dtype)
+        input_tensor = torch.rand(128, dtype=dtype)
         base_mod = self.TestMod(input_tensor, 32, 2)
         state_dict = base_mod.state_dict()
         saved_state_dict = self.save_state_dict_to_buffer(state_dict)

test/float8/test_base.py

@@ -632,7 +632,7 @@ def test_pad_inner_dim(self, base_dtype, use_fast_accum):
         with pytest.raises(
             RuntimeError,
             match=re.escape(
-                "Expected trailing dimension of mat1 to be divisible by 16 but got mat1 shape: (16x41."
+                "Expected trailing dimension of mat1 to be divisible by 16 but got mat1 shape: (16x41)."
             ),
         ):
             a_fp8 @ b_fp8

torchao/dtypes/nf4tensor.py

@@ -10,6 +10,8 @@
 from torch._prims_common import make_contiguous_strides_for
 from torch.distributed.device_mesh import DeviceMesh
 
+from torchao.utils import TORCH_VERSION_AT_LEAST_2_5
+
 aten = torch.ops.aten
 
 c10d_functional = torch.ops.c10d_functional
@@ -1043,3 +1045,7 @@ def nf4_constructor(
         quantized_data,
         nf4,
     )
+
+
+if TORCH_VERSION_AT_LEAST_2_5:
+    torch.serialization.add_safe_globals([NF4Tensor])