Fix for weights-only load

stack-info: PR: #1228, branch: drisspg/stack/19

Author: drisspg

test/prototype/test_low_bit_optim.py

@@ -19,7 +19,11 @@
     quantize_4bit_with_qmap,
     _fp32_to_bf16_sr,
 )
-from torchao.utils import TORCH_VERSION_AT_LEAST_2_3, TORCH_VERSION_AT_LEAST_2_4, TORCH_VERSION_AT_LEAST_2_6
+from torchao.utils import (
+    TORCH_VERSION_AT_LEAST_2_3,
+    TORCH_VERSION_AT_LEAST_2_4,
+    TORCH_VERSION_AT_LEAST_2_6,
+)
 
 try:
     import bitsandbytes as bnb
@@ -85,7 +89,9 @@ def test_bf16_stochastic_round(self, device, compile):
         x_rep = x.view(-1, 1).repeat(1, 100_000)
 
         if compile:
-            x_rep_bf16 = torch.compile(_fp32_to_bf16_sr, fullgraph=True, dynamic=False)(x_rep)
+            x_rep_bf16 = torch.compile(_fp32_to_bf16_sr, fullgraph=True, dynamic=False)(
+                x_rep
+            )
         else:
             x_rep_bf16 = _fp32_to_bf16_sr(x_rep)
 
@@ -96,8 +102,13 @@ def test_bf16_stochastic_round(self, device, compile):
 
 
 class TestOptim(TestCase):
-    @pytest.mark.skipif(not TORCH_VERSION_AT_LEAST_2_3, reason="requires PyTorch >= 2.3")
-    @parametrize("optim_name", ["Adam8bit", "AdamW8bit", "Adam4bit", "AdamW4bit", "AdamFp8", "AdamWFp8"])
+    @pytest.mark.skipif(
+        not TORCH_VERSION_AT_LEAST_2_3, reason="requires PyTorch >= 2.3"
+    )
+    @parametrize(
+        "optim_name",
+        ["Adam8bit", "AdamW8bit", "Adam4bit", "AdamW4bit", "AdamFp8", "AdamWFp8"],
+    )
     @parametrize("dtype", [torch.float32, torch.bfloat16])
     @parametrize("device", _DEVICES)
     def test_optim_smoke(self, optim_name, dtype, device):
@@ -120,7 +131,7 @@ def test_optim_smoke(self, optim_name, dtype, device):
         # test serialization. also test the case CUDA optim loads CPU state dict
         with tempfile.NamedTemporaryFile() as f:
             torch.save(optim.state_dict(), f.name)
-            state_dict = torch.load(f.name, map_location="cpu")
+            state_dict = torch.load(f.name, map_location="cpu", weights_only=True)
 
         model2 = copy.deepcopy(model)
         optim2 = getattr(low_bit_optim, optim_name)(model2.parameters())
@@ -141,19 +152,28 @@ def test_optim_smoke(self, optim_name, dtype, device):
             torch.testing.assert_close(p2, p1)
 
     @pytest.mark.skipif(bnb is None, reason="bitsandbytes is not available")
-    @pytest.mark.skipif(not torch.cuda.is_available(), reason="bitsandbytes 8-bit Adam only works for CUDA")
-    @pytest.mark.skipif(not TORCH_VERSION_AT_LEAST_2_3, reason="requires PyTorch >= 2.3")
+    @pytest.mark.skipif(
+        not torch.cuda.is_available(),
+        reason="bitsandbytes 8-bit Adam only works for CUDA",
+    )
+    @pytest.mark.skipif(
+        not TORCH_VERSION_AT_LEAST_2_3, reason="requires PyTorch >= 2.3"
+    )
     @parametrize("optim_name", ["Adam8bit", "AdamW8bit"])
     def test_optim_8bit_correctness(self, optim_name):
         device = "cuda"
-        model1 = nn.Sequential(nn.Linear(32, 1024), nn.ReLU(), nn.Linear(1024, 128)).to(device)
+        model1 = nn.Sequential(nn.Linear(32, 1024), nn.ReLU(), nn.Linear(1024, 128)).to(
+            device
+        )
         model2 = copy.deepcopy(model1)
 
         # https://github.com/bitsandbytes-foundation/bitsandbytes/releases/tag/v0.44.0
         block_size = 256 if Version(bnb.__version__) >= Version("0.44.0") else 2048
 
         optim1 = getattr(bnb.optim, optim_name)(model1.parameters())
-        optim2 = getattr(low_bit_optim, optim_name)(model2.parameters(), block_size=block_size)
+        optim2 = getattr(low_bit_optim, optim_name)(
+            model2.parameters(), block_size=block_size
+        )
 
         for _ in range(2):
             x = torch.randn(4, 32, device=device)
@@ -173,12 +193,18 @@ def test_optim_8bit_correctness(self, optim_name):
 
     # this will not run in CI because we can't install lpmm
     @pytest.mark.skipif(lpmm is None, reason="lpmm is not available")
-    @pytest.mark.skipif(not torch.cuda.is_available(), reason="lpmm 4-bit Adam only works for CUDA")
-    @pytest.mark.skipif(not TORCH_VERSION_AT_LEAST_2_3, reason="requires PyTorch >= 2.3")
+    @pytest.mark.skipif(
+        not torch.cuda.is_available(), reason="lpmm 4-bit Adam only works for CUDA"
+    )
+    @pytest.mark.skipif(
+        not TORCH_VERSION_AT_LEAST_2_3, reason="requires PyTorch >= 2.3"
+    )
     @parametrize("optim_name", ["Adam4bit", "AdamW4bit"])
     def test_optim_4bit_correctness(self, optim_name):
         device = "cuda"
-        model1 = nn.Sequential(nn.Linear(32, 1024), nn.ReLU(), nn.Linear(1024, 128)).to(device)
+        model1 = nn.Sequential(nn.Linear(32, 1024), nn.ReLU(), nn.Linear(1024, 128)).to(
+            device
+        )
         model2 = copy.deepcopy(model1)
 
         # lpmm doesn't have Adam. use AdamW with no weight decay instead.
@@ -206,17 +232,25 @@ def test_optim_4bit_correctness(self, optim_name):
         for p1, p2 in zip(model1.parameters(), model2.parameters()):
             torch.testing.assert_close(p2, p1, rtol=1e-5, atol=1e-5)
 
-    @pytest.mark.skipif(not torch.cuda.is_available(), reason="optim CPU offload requires CUDA")
+    @pytest.mark.skipif(
+        not torch.cuda.is_available(), reason="optim CPU offload requires CUDA"
+    )
     @parametrize("offload_grad,grad_accum", [(False, 1), (False, 2), (True, 1)])
     def test_optim_cpu_offload_correctness(self, offload_grad, grad_accum):
         device = "cuda"
-        model1 = nn.Sequential(nn.Linear(32, 1024), nn.ReLU(), nn.Linear(1024, 128)).to(device)
-        model1[0].requires_grad_(False)  # make sure it can work in the presence of non-trainable params
+        model1 = nn.Sequential(nn.Linear(32, 1024), nn.ReLU(), nn.Linear(1024, 128)).to(
+            device
+        )
+        model1[0].requires_grad_(
+            False
+        )  # make sure it can work in the presence of non-trainable params
         model2 = copy.deepcopy(model1)
 
         optim1 = torch.optim.AdamW(model1.parameters())
         optim2 = low_bit_optim.CPUOffloadOptimizer(
-            model2.parameters(), torch.optim.AdamW, offload_gradients=offload_grad,
+            model2.parameters(),
+            torch.optim.AdamW,
+            offload_gradients=offload_grad,
         )
 
         for _ in range(2):
@@ -234,11 +268,17 @@ def test_optim_cpu_offload_correctness(self, offload_grad, grad_accum):
         for p1, p2 in zip(model1.parameters(), model2.parameters()):
             torch.testing.assert_close(p2, p1)
 
-    @pytest.mark.skipif(not torch.cuda.is_available(), reason="optim CPU offload requires CUDA")
+    @pytest.mark.skipif(
+        not torch.cuda.is_available(), reason="optim CPU offload requires CUDA"
+    )
     def test_optim_cpu_offload_save_load(self):
         device = "cuda"
-        model1 = nn.Sequential(nn.Linear(32, 1024), nn.ReLU(), nn.Linear(1024, 128)).to(device)
-        optim1 = low_bit_optim.CPUOffloadOptimizer(model1.parameters(), torch.optim.AdamW)
+        model1 = nn.Sequential(nn.Linear(32, 1024), nn.ReLU(), nn.Linear(1024, 128)).to(
+            device
+        )
+        optim1 = low_bit_optim.CPUOffloadOptimizer(
+            model1.parameters(), torch.optim.AdamW
+        )
 
         for _ in range(2):
             x = torch.randn(4, 32, device=device)
@@ -249,11 +289,13 @@ def test_optim_cpu_offload_save_load(self):
         # save checkpoint. make sure it can be serialized by torch.save()
         with tempfile.NamedTemporaryFile() as file:
             torch.save(optim1.state_dict(), file.name)
-            state_dict = torch.load(file.name, map_location="cpu")
+            state_dict = torch.load(file.name, map_location="cpu", weights_only=True)
 
         # resume training
         model2 = copy.deepcopy(model1)
-        optim2 = low_bit_optim.CPUOffloadOptimizer(model2.parameters(), torch.optim.AdamW)
+        optim2 = low_bit_optim.CPUOffloadOptimizer(
+            model2.parameters(), torch.optim.AdamW
+        )
         optim2.load_state_dict(state_dict)
 
         for _ in range(2):
@@ -273,13 +315,17 @@ def test_optim_cpu_offload_save_load(self):
     def test_optim_bf16_stochastic_round_correctness(self):
         device = "cuda" if torch.cuda.is_available() else "cpu"
         torch.manual_seed(2024)
-        model1 = nn.Sequential(nn.Linear(32, 1024), nn.ReLU(), nn.Linear(1024, 128)).to(device)
+        model1 = nn.Sequential(nn.Linear(32, 1024), nn.ReLU(), nn.Linear(1024, 128)).to(
+            device
+        )
         model2 = copy.deepcopy(model1).bfloat16()
 
         # small LR so that weight update is small
         # when bf16_stochastic_round=False, the test will fail after 1 iteration
         optim1 = torch.optim.AdamW(model1.parameters(), lr=1e-5)
-        optim2 = low_bit_optim._AdamW(model2.parameters(), lr=1e-5, bf16_stochastic_round=True)
+        optim2 = low_bit_optim._AdamW(
+            model2.parameters(), lr=1e-5, bf16_stochastic_round=True
+        )
 
         # overfit on this sample
         x = torch.randn(4, 32, device=device)
@@ -299,15 +345,19 @@ def test_optim_bf16_stochastic_round_correctness(self):
             optim2.step()
             optim2.zero_grad()
 
-            torch.testing.assert_close(loss1, loss2, msg=lambda msg: f"Iteration {idx}. {msg}")
+            torch.testing.assert_close(
+                loss1, loss2, msg=lambda msg: f"Iteration {idx}. {msg}"
+            )
 
 
 class TestFSDP2(FSDPTest):
     @property
     def world_size(self) -> int:
         return 2
 
-    @pytest.mark.skipif(not TORCH_VERSION_AT_LEAST_2_6, reason="PyTorch>=2.6 is required.")
+    @pytest.mark.skipif(
+        not TORCH_VERSION_AT_LEAST_2_6, reason="PyTorch>=2.6 is required."
+    )
     @skip_if_lt_x_gpu(2)
     def test_fsdp2(self):
         optim_classes = [low_bit_optim.AdamW8bit, low_bit_optim.AdamW4bit]
@@ -363,7 +413,9 @@ def _test_fsdp2(self, optim_cls):
             base_loss.backward()
             for param in base_model.parameters():
                 if param.grad is not None:
-                    torch.distributed.all_reduce(param.grad, op=torch.distributed.ReduceOp.AVG)
+                    torch.distributed.all_reduce(
+                        param.grad, op=torch.distributed.ReduceOp.AVG
+                    )
             base_optim.step()
             self.assertEqual(fsdp_loss, base_loss)
 

torchao/prototype/low_bit_optim/subclass_4bit.py

@@ -3,9 +3,18 @@
 import torch
 from torch import Tensor
 from torch.utils._python_dispatch import return_and_correct_aliasing
-from torchao.utils import TorchAOBaseTensor, TORCH_VERSION_AT_LEAST_2_4
+from torchao.utils import (
+    TorchAOBaseTensor,
+    TORCH_VERSION_AT_LEAST_2_4,
+    TORCH_VERSION_AT_LEAST_2_5,
+)
 
-from .quant_utils import create_dynamic_map, scale_tensor, quantize_4bit_with_qmap, dequant_with_qmap
+from .quant_utils import (
+    create_dynamic_map,
+    scale_tensor,
+    quantize_4bit_with_qmap,
+    dequant_with_qmap,
+)
 
 
 aten = torch.ops.aten
@@ -55,8 +64,12 @@ def __tensor_flatten__(self):
         return self.tensor_attrs, [self.signed, self._shape]
 
     @classmethod
-    def __tensor_unflatten__(cls, tensor_data_dict, tensor_attributes, outer_size=None, outer_stride=None):
-        return cls(*[tensor_data_dict[name] for name in cls.tensor_attrs], *tensor_attributes)
+    def __tensor_unflatten__(
+        cls, tensor_data_dict, tensor_attributes, outer_size=None, outer_stride=None
+    ):
+        return cls(
+            *[tensor_data_dict[name] for name in cls.tensor_attrs], *tensor_attributes
+        )
 
     def dequantize(self, output_dtype=None):
         codes = torch.stack([self.codes >> 4, self.codes & 0b1111], dim=-1)  # unpack
@@ -85,6 +98,7 @@ def __repr__(self):
 # in pre-2.4, calling .to(device, dtype) will not dispatch aten._to_copy.default when
 # dtype is the same but device is different. thus, we must override .to() method instead.
 if not TORCH_VERSION_AT_LEAST_2_4:
+
     def _to(self, *args, **kwargs):
         # ignore other args/kwargs
         device = kwargs.pop("device", None)
@@ -158,16 +172,20 @@ def _(func, types, args, kwargs):
     if len(shape) == 1 and shape[0] == -1:
         return OptimState4bit(x.codes, x.scale, x.qmap, x.signed, (x.numel(),))
 
-    raise ValueError(f"{x.__class__.__name__} only supports .view() with same shape or shape=[-1]")
+    raise ValueError(
+        f"{x.__class__.__name__} only supports .view() with same shape or shape=[-1]"
+    )
 
 
 # this is needed for DTensor.full_tensor()
-@OptimState4bit.implements([
-    c10d_functional.all_gather_into_tensor.default,
-    _c10d_functional.all_gather_into_tensor.default,
-    c10d_functional.wait_tensor.default,
-    _c10d_functional.wait_tensor.default,
-])
+@OptimState4bit.implements(
+    [
+        c10d_functional.all_gather_into_tensor.default,
+        _c10d_functional.all_gather_into_tensor.default,
+        c10d_functional.wait_tensor.default,
+        _c10d_functional.wait_tensor.default,
+    ]
+)
 def _(func, types, args, kwargs):
     x = args[0]
     if not isinstance(x, OptimState4bit):
@@ -181,3 +199,10 @@ def _(func, types, args, kwargs):
 
     # assume tensors from all ranks have the same signedness
     return OptimState4bit(codes, scale, x.qmap.clone(), x.signed, shape)
+
+
+if TORCH_VERSION_AT_LEAST_2_5:
+    # Needed to load Float8Tensor with weights_only = True
+    from torch.serialization import add_safe_globals
+
+    add_safe_globals([OptimState4bit])

torchao/prototype/low_bit_optim/subclass_8bit.py

@@ -1,9 +1,18 @@
 import torch
 from torch import Tensor
 from torch.utils._python_dispatch import return_and_correct_aliasing
-from torchao.utils import TorchAOBaseTensor, TORCH_VERSION_AT_LEAST_2_4
+from torchao.utils import (
+    TorchAOBaseTensor,
+    TORCH_VERSION_AT_LEAST_2_4,
+    TORCH_VERSION_AT_LEAST_2_5,
+)
 
-from .quant_utils import create_dynamic_map, scale_tensor, quantize_8bit_with_qmap, dequant_with_qmap
+from .quant_utils import (
+    create_dynamic_map,
+    scale_tensor,
+    quantize_8bit_with_qmap,
+    dequant_with_qmap,
+)
 
 
 aten = torch.ops.aten
@@ -46,8 +55,12 @@ def __tensor_flatten__(self):
         return self.tensor_attrs, [self.signed]
 
     @classmethod
-    def __tensor_unflatten__(cls, tensor_data_dict, tensor_attributes, outer_size=None, outer_stride=None):
-        return cls(*[tensor_data_dict[name] for name in cls.tensor_attrs], *tensor_attributes)
+    def __tensor_unflatten__(
+        cls, tensor_data_dict, tensor_attributes, outer_size=None, outer_stride=None
+    ):
+        return cls(
+            *[tensor_data_dict[name] for name in cls.tensor_attrs], *tensor_attributes
+        )
 
     def dequantize(self, output_dtype=None):
         float_data = dequant_with_qmap(self.codes, self.qmap, self.scale)
@@ -72,6 +85,7 @@ def __repr__(self):
 # in pre-2.4, calling .to(device, dtype) will not dispatch aten._to_copy.default when
 # dtype is the same but device is different. thus, we must override .to() method instead.
 if not TORCH_VERSION_AT_LEAST_2_4:
+
     def _to(self, *args, **kwargs):
         # ignore other args/kwargs
         device = kwargs.pop("device", None)
@@ -136,12 +150,14 @@ def _(func, types, args, kwargs):
 
 
 # this is needed for DTensor.full_tensor()
-@OptimState8bit.implements([
-    c10d_functional.all_gather_into_tensor.default,
-    _c10d_functional.all_gather_into_tensor.default,
-    c10d_functional.wait_tensor.default,
-    _c10d_functional.wait_tensor.default,
-])
+@OptimState8bit.implements(
+    [
+        c10d_functional.all_gather_into_tensor.default,
+        _c10d_functional.all_gather_into_tensor.default,
+        c10d_functional.wait_tensor.default,
+        _c10d_functional.wait_tensor.default,
+    ]
+)
 def _(func, types, args, kwargs):
     x = args[0]
     if not isinstance(x, OptimState8bit):
@@ -154,3 +170,10 @@ def _(func, types, args, kwargs):
         x.qmap.clone(),
         x.signed,
     )
+
+
+if TORCH_VERSION_AT_LEAST_2_5:
+    # Needed to load Float8Tensor with weights_only = True
+    from torch.serialization import add_safe_globals
+
+    add_safe_globals([OptimState8bit])