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[misc] update pre-commit and run all files (#4752)

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* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
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2
colossalai/legacy/zero/gemini/ophooks/_shard_grad_ophook.py
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@@ -22,7 +22,7 @@ class ShardGradMemTracerHook(BaseOpHook):
def pre_bwd_exec(self, module: torch.nn.Module, input, output):
for param in module.parameters():
assert hasattr(param, '_sharded_grad')
assert hasattr(param, "_sharded_grad")
param._sharded_grad.setup()
def post_bwd_exec(self, module: torch.nn.Module, input):

8
colossalai/legacy/zero/gemini/ophooks/_shard_param_ophook.py
View File

@@ -19,25 +19,25 @@ class ShardParamHook(BaseOpHook):
def pre_fwd_exec(self, module: torch.nn.Module, *args):
for param in module.parameters():
assert hasattr(param, 'ca_attr')
assert hasattr(param, "ca_attr")
param.ca_attr.gather()
param.data = param.ca_attr.payload()
def post_fwd_exec(self, module: torch.nn.Module, *args):
for param in module.parameters():
assert hasattr(param, 'ca_attr')
assert hasattr(param, "ca_attr")
param.ca_attr.shard()
param.data = param.ca_attr.payload()
def pre_bwd_exec(self, module: torch.nn.Module, input, output):
for param in module.parameters():
assert hasattr(param, 'ca_attr')
assert hasattr(param, "ca_attr")
param.ca_attr.gather()
param.data = param.ca_attr.payload()
def post_bwd_exec(self, module: torch.nn.Module, input):
for param in module.parameters():
assert hasattr(param, 'ca_attr')
assert hasattr(param, "ca_attr")
param.ca_attr.shard()
param.data = param.ca_attr.payload()

14
colossalai/legacy/zero/gemini/ophooks/runtime_mem_tracer_hook.py
View File

@@ -15,8 +15,7 @@ class TrainingPhase(Enum):
BACKWARD = 1
class GradMemStats():
class GradMemStats:
def __init__(self) -> None:
self.unreleased_grad_flag = {}
self.unreleased_grad_volume = 0
@@ -26,8 +25,7 @@ class GradMemStats():
self.unreleased_grad_volume = 0
class GradMemTracerHook():
class GradMemTracerHook:
def __init__(self, grad_stats: GradMemStats):
self.grad_hook_list = []
self._grad_stats = grad_stats
@@ -50,7 +48,6 @@ class GradMemTracerHook():
class ParamMemTracerHook(ColoParamOpHook):
def __init__(self, memstats: MemStats, gradstats: GradMemStats) -> None:
super().__init__()
self._training_phase = TrainingPhase.FORWARD
@@ -79,10 +76,9 @@ class ParamMemTracerHook(ColoParamOpHook):
if cur_dev == "cpu":
if p.grad is not None and p.grad.device.type == "cpu":
raise NotImplementedError("Only run in forward propagation")
p.data = torch.empty(p.data.shape,
device="cuda",
dtype=p.data.dtype,
requires_grad=p.data.requires_grad)
p.data = torch.empty(
p.data.shape, device="cuda", dtype=p.data.dtype, requires_grad=p.data.requires_grad
)
elif cur_dev == "cuda":
alloc_storage(p.data)

15
colossalai/legacy/zero/gemini/ophooks/utils.py
View File

@@ -48,7 +48,6 @@ def _apply_to_tensors_only(module, functional, backward_function, outputs):
class PreBackwardFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, module, pre_backward_function, outputs):
ctx.module = module
@@ -64,7 +63,6 @@ class PreBackwardFunction(torch.autograd.Function):
class PostBackwardFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, module, pre_backward_function, output):
ctx.module = module
@@ -84,16 +82,15 @@ class PostBackwardFunction(torch.autograd.Function):
return (None, None) + args
def register_ophooks_recursively(module: torch.nn.Module,
ophook_list: List[BaseOpHook],
name: str = "",
filter_fn: Optional[Callable] = None):
def register_ophooks_recursively(
module: torch.nn.Module, ophook_list: List[BaseOpHook], name: str = "", filter_fn: Optional[Callable] = None
):
r"""Recursively register pre/post hooks for all submodules in the module in FWD and BWD."""
assert isinstance(module, torch.nn.Module)
assert isinstance(ophook_list, (list, tuple))
assert len(ophook_list) > 0, 'expected at least 1 hook in the argument ophook_list but found 0'
assert len(ophook_list) > 0, "expected at least 1 hook in the argument ophook_list but found 0"
for hook in ophook_list:
assert (isinstance(hook, BaseOpHook))
assert isinstance(hook, BaseOpHook)
# Add hooks for submodules
for child_name, child in module.named_children():
@@ -118,7 +115,6 @@ def register_ophooks_recursively(module: torch.nn.Module,
hook.post_fwd_exec(submodule, *args)
def _pre_backward_module_hook(submodule, inputs, output):
def _run_before_backward_function(submodule):
for hook in ophook_list:
assert isinstance(submodule, torch.nn.Module)
@@ -127,7 +123,6 @@ def register_ophooks_recursively(module: torch.nn.Module,
return _apply_to_tensors_only(submodule, PreBackwardFunction, _run_before_backward_function, output)
def _post_backward_module_hook(submodule, inputs):
def _run_after_backward_function(submodule):
for hook in ophook_list:
assert isinstance(submodule, torch.nn.Module)