[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

colossalai/pipeline/schedule/interleaved_pp.py

@@ -11,8 +11,8 @@ from colossalai.accelerator import get_accelerator
 from colossalai.interface import OptimizerWrapper
 from colossalai.pipeline.p2p import PipelineP2PCommunication, create_send_metadata
 from colossalai.pipeline.stage_manager import PipelineStageManager
+from colossalai.quantization.fp8 import cast_from_fp8_pipeline, cast_to_fp8_pipeline
 from colossalai.utils import get_current_device
-from colossalai.quantization.fp8 import cast_to_fp8_pipeline, cast_from_fp8_pipeline
 
 from ._utils import detach, get_batch_size, get_micro_batch, merge_batch, model_forward, retain_grad, to_device
 from .base import PipelineSchedule
@@ -59,6 +59,7 @@ class InterleavedSchedule(PipelineSchedule):
         self.grad_metadata_recv = None
 
         self.fp8_communication = fp8_communication
+
     def load_batch(self, data_iter: Iterable, device: Optional[torch.device] = None) -> None:
         """Load a batch from data iterator.
 

colossalai/pipeline/schedule/one_f_one_b.py

@@ -10,8 +10,8 @@ from colossalai.accelerator import get_accelerator
 from colossalai.interface import ModelWrapper, OptimizerWrapper
 from colossalai.pipeline.p2p import PipelineP2PCommunication, create_send_metadata
 from colossalai.pipeline.stage_manager import PipelineStageManager
+from colossalai.quantization.fp8 import cast_from_fp8_pipeline, cast_to_fp8_pipeline
 from colossalai.utils import get_current_device
-from colossalai.quantization.fp8 import cast_to_fp8_pipeline, cast_from_fp8_pipeline
 
 from ._utils import (
     detach,
@@ -172,6 +172,7 @@ class OneForwardOneBackwardSchedule(PipelineSchedule):
 
             if self.fp8_communication:
                 cast_from_fp8_pipeline(output_tensor, del_metadata=False)
+
     def send_backward(self, input_tensor_grad: Any, prev_rank: int = None) -> None:
         """Sends the gradient tensor to the previous stage in pipeline.
            For 1F1B.

colossalai/quantization/fp8.py

@@ -1,4 +1,4 @@
-from typing import Any, Callable, List, Optional, Tuple, Union
+from typing import Any
 
 import torch
 import torch.distributed as dist
@@ -107,7 +107,6 @@ def all_reduce_fp8(tensor: torch.Tensor, fp8_format="e4m3") -> None:
     tensor.data = tensor_out.view(input_shape).to(input_type)
 
 
-
 def cast_to_fp8_pipeline(inp: Any) -> None:
     """
     Cast the hidden_states tensor of inp object to fp8 format before p2p communication in pipeline.
@@ -121,7 +120,7 @@ def cast_to_fp8_pipeline(inp: Any) -> None:
     if type(inp) == torch.Tensor:
         return
 
-    assert 'hidden_states' in inp, 'required by pipeline parallelism.'
+    assert "hidden_states" in inp, "required by pipeline parallelism."
     inp_tensor = inp["hidden_states"]
 
     min_val, max_val = inp_tensor.aminmax()
@@ -137,7 +136,7 @@ def cast_to_fp8_pipeline(inp: Any) -> None:
 
     finfo = torch.finfo(fp8_type)
     scale = torch.tensor(1.0).to(inp_tensor.device) if amax == 0.0 else finfo.max / amax.float()
-    q_tensor = (inp_tensor.data.float() * scale)
+    q_tensor = inp_tensor.data.float() * scale
     # Todo: Currently we use fp8_view_type <float16, bfloat16> to indicate which fp8 format is used. This is a temporary workaround due to 'Only support tensor for fast send'.
     #  inp_tensor needs to be a float datatype to avoid error during gradient placement.
     inp_tensor.data = q_tensor.to(fp8_type).view(fp8_view_type)
@@ -145,7 +144,6 @@ def cast_to_fp8_pipeline(inp: Any) -> None:
     inp["fp8_scale"] = scale.float().reciprocal()
 
 
-
 def cast_from_fp8_pipeline(inp: Any, del_metadata=True) -> None:
     """
     Cast the FP8 encoded hidden_states tensor back to original dtype after p2p communication in pipeline.
@@ -156,7 +154,7 @@ def cast_from_fp8_pipeline(inp: Any, del_metadata=True) -> None:
     if type(inp) == torch.Tensor:
         return
 
-    assert 'hidden_states' in inp, 'required by pipeline parallelism.'
+    assert "hidden_states" in inp, "required by pipeline parallelism."
     inp_tensor = inp["hidden_states"]
     scale = inp["fp8_scale"]