refactor(cpu-offload): use ruff format

Signed-off-by: dbyoung18 <yang5.yang@intel.com>

Author: dbyoung18

benchmarks/benchmark_low_bit_adam.py

@@ -53,7 +53,9 @@
 
     OPTIM_MAP.update(
         AdamW4bitLpmm=partial(lpmm.optim.AdamW, fused=True),
-        AdamW4bitRank1Lpmm=partial(lpmm.optim.AdamW, qconfig=argparse.Namespace(scale_type="rank1")),
+        AdamW4bitRank1Lpmm=partial(
+            lpmm.optim.AdamW, qconfig=argparse.Namespace(scale_type="rank1")
+        ),
     )
 
 except ImportError:
@@ -71,8 +73,12 @@ def get_lr(self, step: int) -> float:
         if step < self.warmup_steps:
             return self.lr * step / self.warmup_steps
         if step < self.total_steps:
-            progress = (step - self.warmup_steps) / (self.total_steps - self.warmup_steps)
-            return self.final_lr + 0.5 * (self.lr - self.final_lr) * (1 + math.cos(progress * math.pi))
+            progress = (step - self.warmup_steps) / (
+                self.total_steps - self.warmup_steps
+            )
+            return self.final_lr + 0.5 * (self.lr - self.final_lr) * (
+                1 + math.cos(progress * math.pi)
+            )
         return self.final_lr
 
 
@@ -96,7 +102,9 @@ def get_parser():
     parser.add_argument("--weight_decay", type=float, default=0)
     parser.add_argument("--optim_kwargs", type=json.loads, default=dict())
     parser.add_argument("--cosine_lr_scheduler", action="store_true")
-    parser.add_argument("--optim_cpu_offload", choices=["ao", "ao_offload_grads", "deepspeed"])
+    parser.add_argument(
+        "--optim_cpu_offload", choices=["ao", "ao_offload_grads", "deepspeed"]
+    )
 
     parser.add_argument("--project")
     parser.add_argument("--run_name", default="debug")
@@ -114,11 +122,15 @@ def get_dloader(args, training: bool):
         transforms.extend([v2.Resize(256), v2.CenterCrop(224)])
 
     transforms.append(v2.ToDtype(torch.float32, scale=True))
-    transforms.append(v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))
+    transforms.append(
+        v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    )
     transforms = v2.Compose(transforms)
 
     # use dataset from HF so download is fast
-    ds = datasets.load_dataset("timm/resisc45", split="train" if training else "validation")
+    ds = datasets.load_dataset(
+        "timm/resisc45", split="train" if training else "validation"
+    )
     ds = ds.select_columns(["image", "label"])
     ds.set_transform(lambda x: dict(image=transforms(x["image"]), label=x["label"]))
 
@@ -168,8 +180,12 @@ def evaluate_model(model, args):
     if args.full_bf16:
         assert args.amp == "none", "When --full_bf16 is set, --amp must be none"
     if args.optim_cpu_offload == "deepspeed":
-        assert args.amp == "none", "When using DeepSpeed ZeRO-Offload, --amp must be none"
-        assert args.optim == "AdamW", "When using DeepSpeed ZeRO-Offload, --optim must be AdamW"
+        assert (
+            args.amp == "none"
+        ), "When using DeepSpeed ZeRO-Offload, --amp must be none"
+        assert (
+            args.optim == "AdamW"
+        ), "When using DeepSpeed ZeRO-Offload, --optim must be AdamW"
     if args.profile:
         args.n_epochs = 1
     if args.seed is not None:
@@ -189,7 +205,9 @@ def evaluate_model(model, args):
     dloader = get_dloader(args, True)
     print(f"Train dataset: {len(dloader.dataset):,} images")
 
-    model = timm.create_model(args.model, pretrained=True, num_classes=45, **args.model_kwargs)
+    model = timm.create_model(
+        args.model, pretrained=True, num_classes=45, **args.model_kwargs
+    )
     if args.checkpoint_activations:
         model.set_grad_checkpointing()
     if args.full_bf16:
@@ -231,9 +249,15 @@ def evaluate_model(model, args):
         optim_cls = OPTIM_MAP[args.optim]
 
         if args.optim_cpu_offload == "ao":
-            optim_cls = partial(low_bit_optim.CPUOffloadOptimizer, optimizer_class=optim_cls)
+            optim_cls = partial(
+                low_bit_optim.CPUOffloadOptimizer, optimizer_class=optim_cls
+            )
         elif args.optim_cpu_offload == "ao_offload_grads":
-            optim_cls = partial(low_bit_optim.CPUOffloadOptimizer, optimizer_class=optim_cls, offload_gradients=True)
+            optim_cls = partial(
+                low_bit_optim.CPUOffloadOptimizer,
+                optimizer_class=optim_cls,
+                offload_gradients=True,
+            )
 
         optim = optim_cls(
             model.parameters(),
@@ -250,17 +274,23 @@ def evaluate_model(model, args):
     step = 0
     for epoch_idx in range(args.n_epochs):
         model.train()
-        pbar = tqdm(dloader, dynamic_ncols=True, desc=f"Epoch {epoch_idx + 1}/{args.n_epochs}")
+        pbar = tqdm(
+            dloader, dynamic_ncols=True, desc=f"Epoch {epoch_idx + 1}/{args.n_epochs}"
+        )
 
         with torch.profiler.profile() if args.profile else nullcontext() as prof:
             for batch in pbar:
                 if args.full_bf16:
                     batch["image"] = batch["image"].bfloat16()
                 if args.channels_last:
-                    batch["image"] = batch["image"].to(memory_format=torch.channels_last)
+                    batch["image"] = batch["image"].to(
+                        memory_format=torch.channels_last
+                    )
 
                 with get_amp_ctx(args.amp, _DEVICE):
-                    loss = F.cross_entropy(model(batch["image"].to(_DEVICE)), batch["label"].to(_DEVICE))
+                    loss = F.cross_entropy(
+                        model(batch["image"].to(_DEVICE)), batch["label"].to(_DEVICE)
+                    )
 
                 if args.optim_cpu_offload == "deepspeed":
                     model.backward(loss)
@@ -279,7 +309,9 @@ def evaluate_model(model, args):
                     log_dict = dict(loss=loss.item(), lr=optim.param_groups[0]["lr"])
                     if step > 0:
                         t1 = time.perf_counter()
-                        log_dict["imgs_per_second"] = args.batch_size * log_interval / (t1 - t0)
+                        log_dict["imgs_per_second"] = (
+                            args.batch_size * log_interval / (t1 - t0)
+                        )
                         t0 = t1
                     logger.log(log_dict, step=step)
 
@@ -300,7 +332,9 @@ def evaluate_model(model, args):
 
         else:
             val_acc = evaluate_model(model, args)
-            print(f"Epoch {epoch_idx + 1}/{args.n_epochs}: val_acc={val_acc.item() * 100:.2f}")
+            print(
+                f"Epoch {epoch_idx + 1}/{args.n_epochs}: val_acc={val_acc.item() * 100:.2f}"
+            )
             logger.log(dict(val_acc=val_acc), step=step)
 
     peak_mem = getattr(torch, _DEVICE).max_memory_allocated() / 1e9

test/prototype/test_low_bit_optim.py

@@ -95,7 +95,9 @@ def test_bf16_stochastic_round(self, device, compile):
         x = torch.rand(32, device=device) * 100
         x_rep = x.view(-1, 1).repeat(1, 100_000)
 
-        func = torch.compile(_fp32_to_bf16_sr, fullgraph=True, dynamic=False, disable=not compile)
+        func = torch.compile(
+            _fp32_to_bf16_sr, fullgraph=True, dynamic=False, disable=not compile
+        )
         x_rep_bf16 = func(x_rep)
         assert x_rep_bf16.dtype is torch.bfloat16
 
@@ -170,8 +172,13 @@ def test_subclass_slice(self, subclass, shape, device):
         tensor = subclass.zeros(shape, device=device)
         offset = shape[0] // 2
 
-        torch.testing.assert_close(tensor.dequantize()[:offset], tensor[:offset].dequantize())
-        torch.testing.assert_close(tensor.dequantize()[offset:offset*2], tensor[offset:offset*2].dequantize())
+        torch.testing.assert_close(
+            tensor.dequantize()[:offset], tensor[:offset].dequantize()
+        )
+        torch.testing.assert_close(
+            tensor.dequantize()[offset : offset * 2],
+            tensor[offset : offset * 2].dequantize(),
+        )
 
     @pytest.mark.skipif(bnb is None, reason="bitsandbytes is not available")
     @pytest.mark.skipif(
@@ -189,7 +196,9 @@ def test_optim_8bit_correctness(self, optim_name):
         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)
@@ -246,7 +255,7 @@ def test_optim_4bit_correctness(self, optim_name):
 
     @pytest.mark.skipif(
         not torch.cuda.is_available() and not torch.xpu.is_available(),
-        reason="optim CPU offload requires CUDA or XPU"
+        reason="optim CPU offload requires CUDA or XPU",
     )
     @parametrize("offload_grad,grad_accum", [(False, 1), (False, 2), (True, 1)])
     def test_optim_cpu_offload_correctness(self, offload_grad, grad_accum):
@@ -282,13 +291,15 @@ def test_optim_cpu_offload_correctness(self, offload_grad, grad_accum):
 
     @pytest.mark.skipif(
         not torch.cuda.is_available() and not torch.xpu.is_available(),
-        reason="optim CPU offload requires CUDA or XPU"
+        reason="optim CPU offload requires CUDA or XPU",
     )
     def test_optim_cpu_offload_save_load(self):
         device = _DEVICES[-1]
         model1 = nn.Sequential(nn.Linear(32, 1024), nn.ReLU(), nn.Linear(1024, 128))
         model1.to(device)
-        optim1 = low_bit_optim.CPUOffloadOptimizer(model1.parameters(), torch.optim.AdamW)
+        optim1 = low_bit_optim.CPUOffloadOptimizer(
+            model1.parameters(), torch.optim.AdamW
+        )
 
         for _ in range(2):
             x = torch.randn(4, 32, device=device)
@@ -303,7 +314,9 @@ def test_optim_cpu_offload_save_load(self):
 
         # 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):
@@ -387,7 +400,11 @@ def _test_fsdp2(self, optim_cls):
         import torch.utils._pytree as pytree
         from torch.distributed._composable.fsdp import fully_shard
         from torch.distributed.tensor import DTensor
-        from torch.testing._internal.distributed._tensor.common_dtensor import ModelArgs, Transformer, TransformerBlock
+        from torch.testing._internal.distributed._tensor.common_dtensor import (
+            ModelArgs,
+            Transformer,
+            TransformerBlock,
+        )
 
         batch_size = 3
         vocab_size = 1024
@@ -460,7 +477,10 @@ def _test_fsdp2(self, optim_cls):
 
         subclasses = (OptimState4bit, OptimState8bit, OptimStateFp8)
 
-        for v1, v2 in zip(pytree.tree_iter(resumed_fsdp_optim.state_dict()), pytree.tree_iter(fsdp_optim.state_dict())):
+        for v1, v2 in zip(
+            pytree.tree_iter(resumed_fsdp_optim.state_dict()),
+            pytree.tree_iter(fsdp_optim.state_dict()),
+        ):
             assert v1.__class__ == v2.__class__, (v1.__class__, v2.__class__)
             if isinstance(v1, DTensor):
                 v1 = v1.to_local()

torchao/prototype/low_bit_optim/cpu_offload.py

@@ -41,7 +41,10 @@ def __init__(
         self.param_d2h_map = dict()
         self.optim_dict = dict()
         self.device = get_available_devices()[-1]
-        assert self.device in ["cuda", "xpu"], "CPU Offload currently only supports CUDA & XPU"
+        assert self.device in [
+            "cuda",
+            "xpu",
+        ], "CPU Offload currently only supports CUDA & XPU"
         self.stream = getattr(torch, self.device).Stream()
 
         # the queue maintains the order which param we should do optim step on first.