[format] applied code formatting on changed files in pull request 5088 (#5127)

Co-authored-by: github-actions <github-actions@github.com>

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -28,8 +28,8 @@ from colossalai.shardformer import ShardConfig, ShardFormer
 from colossalai.shardformer.layer.utils import SeqParallelUtils
 from colossalai.shardformer.policies.base_policy import Policy
 from colossalai.tensor.d_tensor.api import is_distributed_tensor
-from colossalai.zero.low_level import LowLevelZeroOptimizer
 from colossalai.utils.device import get_current_device
+from colossalai.zero.low_level import LowLevelZeroOptimizer
 
 from .pp_plugin_base import PipelinePluginBase
 
@@ -385,7 +385,9 @@ class HybridParallelNaiveOptimizer(OptimizerWrapper):
 
                 total_norm_exponentiated += grad_norm_exponentiated
 
-            total_norm_exponentiated_cuda = torch.tensor([float(total_norm_exponentiated)], device=get_current_device(), dtype=torch.float32)
+            total_norm_exponentiated_cuda = torch.tensor(
+                [float(total_norm_exponentiated)], device=get_current_device(), dtype=torch.float32
+            )
             if self.tp_size > 1:
                 # compute norm in tp process group
                 dist.all_reduce(tensor=total_norm_exponentiated_cuda, op=dist.ReduceOp.SUM, group=self.tp_pg)
@@ -586,7 +588,9 @@ class HybridParallelAMPOptimizer(MixedPrecisionOptimizer):
 
                 total_norm_exponentiated += grad_norm_exponentiated
 
-            total_norm_exponentiated_cuda = torch.tensor([float(total_norm_exponentiated)], device=get_current_device(), dtype=torch.float32)
+            total_norm_exponentiated_cuda = torch.tensor(
+                [float(total_norm_exponentiated)], device=get_current_device(), dtype=torch.float32
+            )
             if self.tp_size > 1:
                 # compute norm in tp process group
                 dist.all_reduce(tensor=total_norm_exponentiated_cuda, op=dist.ReduceOp.SUM, group=self.tp_pg)
@@ -837,7 +841,9 @@ class HybridParallelZeroOptimizer(LowLevelZeroOptimizer):
 
                 total_norm_exponentiated += grad_norm_exponentiated
 
-            total_norm_exponentiated_cuda = torch.tensor([float(total_norm_exponentiated)], device=get_current_device(), dtype=torch.float32)
+            total_norm_exponentiated_cuda = torch.tensor(
+                [float(total_norm_exponentiated)], device=get_current_device(), dtype=torch.float32
+            )
             if dp_size > 1:
                 # compute norm in dp process group
                 dist.all_reduce(tensor=total_norm_exponentiated_cuda, op=dist.ReduceOp.SUM, group=self.dp_pg)

tests/test_booster/test_plugin/test_gemini_plugin.py

@@ -1,7 +1,7 @@
 from contextlib import nullcontext
 from typing import Optional
-import pytest
 
+import pytest
 import torch
 import torch.distributed as dist
 
@@ -11,8 +11,6 @@ from colossalai.booster.plugin import GeminiPlugin
 from colossalai.fx import is_compatible_with_meta
 from colossalai.lazy.lazy_init import LazyInitContext
 from colossalai.nn.optimizer import HybridAdam
-from colossalai.tensor.d_tensor.api import clear_layout_converter
-from colossalai.shardformer.layer.utils import Randomizer
 from colossalai.tensor.colo_parameter import ColoParameter
 from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
 from tests.kit.model_zoo import model_zoo
@@ -26,7 +24,13 @@ def run_fn(init_method, model_fn, data_gen_fn, output_transform_fn, zero_size, t
             ctx = nullcontext()
         extra_dp_size = dist.get_world_size() // (zero_size * tp_size)
         enable_all_optimization = True if tp_size > 1 else False
-        plugin = GeminiPlugin(max_norm=1.0, initial_scale=2**5, tp_size=tp_size, extra_dp_size=extra_dp_size, enable_all_optimization=enable_all_optimization)
+        plugin = GeminiPlugin(
+            max_norm=1.0,
+            initial_scale=2**5,
+            tp_size=tp_size,
+            extra_dp_size=extra_dp_size,
+            enable_all_optimization=enable_all_optimization,
+        )
         booster = Booster(plugin=plugin)
         with ctx:
             model = model_fn()
@@ -66,7 +70,9 @@ def run_fn(init_method, model_fn, data_gen_fn, output_transform_fn, zero_size, t
 @parameterize("init_method", ["none"])
 @parameterize("zero_size", [2])
 @parameterize("tp_size", [2])
-def check_gemini_plugin(subset: str, init_method: str = "none", early_stop: bool = True, zero_size: int = 1, tp_size: int = 1):
+def check_gemini_plugin(
+    subset: str, init_method: str = "none", early_stop: bool = True, zero_size: int = 1, tp_size: int = 1
+):
     """check gemini plugin over model zoo
 
     Args:
@@ -161,6 +167,7 @@ def run_dist(rank, world_size, port, early_stop: bool = True):
 def test_gemini_plugin(early_stop: bool = True):
     spawn(run_dist, 4, early_stop=early_stop)
 
+
 @pytest.mark.largedist
 @rerun_if_address_is_in_use()
 def test_gemini_plugin_3d(early_stop: bool = True):