[gemini]remove registered gradients hooks (hpcaitech#5696)

* fix gemini

fix gemini

* fix

fix

colossalai/zero/gemini/chunk/manager.py

@@ -20,7 +20,12 @@ class ChunkManager:
  init_device (torch.device): optional, the device on which the chunk is initialized. The default is None.
  """
 
- def __init__(self, chunk_configuration, init_device: Optional[torch.device] = None) -> None:
+ def __init__(
+ self,
+ chunk_configuration,
+ init_device: Optional[torch.device] = None,
+ reuse_fp16_chunk: bool = True,
+ ) -> None:
  self.device = init_device or get_accelerator().get_current_device()
  self.dp_degree_chunk_size_dict: Dict[int, int] = dict()
  self.kwargs_config = chunk_configuration
@@ -33,6 +38,10 @@ def __init__(self, chunk_configuration, init_device: Optional[torch.device] = No
  self.accessed_chunks: Set[Chunk] = set()
  self.accessed_mem: int = 0
  self.total_mem: Dict[str, int] = {"cpu": 0, "cuda": 0}
+ self.reuse_fp16_chunk = reuse_fp16_chunk
+ # Whether model is accumulating gradients,
+ self.accumulating_grads = False
+ self.overflow_counter = 0
 
  def register_tensor(
  self,

colossalai/zero/gemini/chunk/utils.py

@@ -19,6 +19,7 @@ def init_chunk_manager(
  model: nn.Module,
  init_device: Optional[torch.device] = None,
  hidden_dim: Optional[int] = None,
+ reuse_fp16_chunk: bool = True,
  verbose: bool = False,
  **kwargs,
 ) -> ChunkManager:
@@ -50,5 +51,9 @@ def init_chunk_manager(
  )
  dist.barrier()
 
- chunk_manager = ChunkManager(config_dict, init_device)
+ chunk_manager = ChunkManager(
+ config_dict,
+ init_device,
+ reuse_fp16_chunk=reuse_fp16_chunk,
+ )
  return chunk_manager

colossalai/zero/gemini/gemini_ddp.py

@@ -98,8 +98,14 @@ def __init__(
  verbose: bool = False,
  ) -> None:
  assert mixed_precision in (torch.float16, torch.bfloat16)
+ reuse_fp16_chunk = master_weights if not enable_gradient_accumulation else False
+ self.enable_gradient_accumulation = enable_gradient_accumulation
  if chunk_config_dict is not None:
- self.chunk_manager = ChunkManager(chunk_config_dict, chunk_init_device)
+ self.chunk_manager = ChunkManager(
+ chunk_config_dict,
+ chunk_init_device,
+ reuse_fp16_chunk=reuse_fp16_chunk,
+ )
  else:
  # some ugly hotfix for the compatibility with Lightning
  if search_range_m is None:
@@ -112,6 +118,7 @@ def __init__(
  min_chunk_size_m=min_chunk_size_m,
  strict_ddp_flag=strict_ddp_mode,
  process_group=zero_group,
+ reuse_fp16_chunk=reuse_fp16_chunk,
  verbose=verbose,
  )
  self.gemini_manager = GeminiManager(
@@ -128,7 +135,6 @@ def __init__(
  self.param_op_hook = GeminiZeROHook(self.gemini_manager)
  self.fp32_params: List[torch.Tensor] = list()
  self.fp16_params: List[ColoParameter] = list()
- self.overflow_counter = 0
  self.grads_device: Dict[torch.Tensor, torch.device] = dict()
  self.param2name: Dict[nn.Parameter, str] = dict()
  self.name2param: Dict[str, nn.Parameter] = dict()
@@ -137,14 +143,8 @@ def __init__(
  self.zero_group = zero_group or _get_default_group()
  self.extra_dp_group = extra_dp_group
 
- self.reuse_fp16_chunk = master_weights
  self.master_weights = master_weights
 
- self.enable_gradient_accumulation = enable_gradient_accumulation
- if self.enable_gradient_accumulation:
- self.reuse_fp16_chunk = False
- self.accumulating_grads = False # Whether model is accumulating gradients
-
  self._logger = get_dist_logger()
 
  if self.gemini_manager._premade_memstats_:
@@ -178,7 +178,29 @@ def __init__(
  if is_ddp_ignored(p):
  continue
  if p.requires_grad:
- p.register_hook(partial(self.grad_handle, p))
+ p._grad_handle = p.register_hook(
+ partial(
+ GeminiDDP.grad_handle,
+ chunk_manager=self.chunk_manager,
+ param2name=self.param2name,
+ grads_device=self.grads_device,
+ master_weights=self.master_weights,
+ enable_gradient_accumulation=self.enable_gradient_accumulation,
+ p=p,
+ )
+ )
+
+ def remove_hooks(self):
+ for p in self.module.parameters():
+ if is_ddp_ignored(p):
+ continue
+ if p.requires_grad:
+ assert hasattr(p, "_grad_handle")
+ p._grad_handle.remove()
+ delattr(p, "_grad_handle")
+
+ def __del__(self):
+ self.remove_hooks()
 
  def parameters(self, recurse: bool = True):
  return self.module.parameters(recurse)
@@ -324,8 +346,8 @@ def _post_backward(self):
  f"{error_str}",
  )
  self._setup_grads_ptr()
- if self.enable_gradient_accumulation and not self.accumulating_grads:
- self.accumulating_grads = True # Turn on the state of gradient accumulation.
+ if self.enable_gradient_accumulation and not self.chunk_manager.accumulating_grads:
+ self.chunk_manager.accumulating_grads = True # Turn on the state of gradient accumulation.
  self._logger.debug(
  f"comp cuda demand time: {self.gemini_manager._comp_cuda_demand_time}, layout time: {self.gemini_manager._layout_time}, evict time: {self.gemini_manager._evict_time}, CPU->CUDA vol: {self.gemini_manager._h2d_volume}B, CUDA->CPU vol: {self.gemini_manager._d2h_volume}"
  )
@@ -340,25 +362,34 @@ def backward(self, loss: torch.Tensor):
  def backward_by_grad(self, tensor, grad):
  raise RuntimeError("Gemini is not compatible with pipeline. backward_by_grad shoudn't be called in Gemini.")
 
- def grad_handle(self, p, grad):
+ @staticmethod
+ def grad_handle(
+ grad,
+ chunk_manager: ChunkManager,
+ param2name: Dict,
+ grads_device: Dict,
+ master_weights: bool,
+ enable_gradient_accumulation: bool,
+ p: nn.Parameter,
+ ):
  setattr(p, "_gemini_reduced", True)
  empty_grad = torch.empty_like(grad)
  free_storage(empty_grad)
  with torch._C.DisableTorchFunction():
- chunk = self.chunk_manager.get_chunk(p)
+ chunk = chunk_manager.get_chunk(p)
  if chunk.tensors_info[p].state != TensorState.HOLD_AFTER_BWD:
  raise RuntimeError(
- f"Parameter `{self.param2name[p]}` failed at the gradient reduction. "
+ f"Parameter `{param2name[p]}` failed at the gradient reduction. "
  "Some unsupported torch function is operated upon this parameter."
  )
  grad_chunk = chunk
- if not self.reuse_fp16_chunk:
- if not self.accumulating_grads:
- grad_chunk = self.chunk_manager.init_grad_chunk(chunk)
+ if not chunk_manager.reuse_fp16_chunk:
+ if not chunk_manager.accumulating_grads:
+ grad_chunk = chunk_manager.init_grad_chunk(chunk)
  else:
  assert chunk.grad_chunk is not None
- if chunk.grad_chunk not in self.chunk_manager.accessed_chunks:
- grad_chunk = self.chunk_manager.rearrange_accumulated_grad_chunk(chunk)
+ if chunk.grad_chunk not in chunk_manager.accessed_chunks:
+ grad_chunk = chunk_manager.rearrange_accumulated_grad_chunk(chunk)
  else:
  grad_chunk = chunk.grad_chunk
  chunk.grad_chunk.l2_norm = None
@@ -371,33 +402,33 @@ def grad_handle(self, p, grad):
  chunk.tensor_trans_state(p, TensorState.HOLD)
 
  grad_chunk.tensor_trans_state(p, TensorState.READY_FOR_REDUCE)
- if not self.accumulating_grads:
- grad_chunk.copy_tensor_to_chunk_slice(p, grad, update_ptr=self.reuse_fp16_chunk)
+ if not chunk_manager.accumulating_grads:
+ grad_chunk.copy_tensor_to_chunk_slice(p, grad, update_ptr=chunk_manager.reuse_fp16_chunk)
  else:
  grad_chunk.add_tensor_to_chunk_slice(p, grad)
- reduced = self.chunk_manager.reduce_chunk(grad_chunk)
+ reduced = chunk_manager.reduce_chunk(grad_chunk)
  if reduced:
- if not self.reuse_fp16_chunk:
+ if not chunk_manager.reuse_fp16_chunk:
  if chunk.keep_gathered:
- self.chunk_manager.fake_release_chunk(chunk)
+ chunk_manager.fake_release_chunk(chunk)
  else:
- self.chunk_manager.release_chunk(chunk)
+ chunk_manager.release_chunk(chunk)
  if grad_chunk.is_gathered:
  grad_chunk.cuda_global_chunk.div_(chunk.pg_size)
- if self.extra_dp_group is not None:
+ if chunk.extra_dp_group is not None:
  grad_chunk.cuda_global_chunk.div_(chunk.extra_dp_size)
  else:
  grad_chunk.cuda_shard.div_(chunk.pg_size)
- if self.extra_dp_group is not None:
+ if chunk.extra_dp_group is not None:
  grad_chunk.cuda_shard.div_(chunk.extra_dp_size)
  # check overflow elements
- self.overflow_counter += grad_chunk.has_inf_or_nan
+ chunk_manager.overflow_counter += grad_chunk.has_inf_or_nan
  # record l2 norm for gradient clipping. flag is bound to fp16 chunk
  if chunk.l2_norm_flag:
  grad_chunk.set_l2_norm()
- self.chunk_manager.move_chunk(grad_chunk, self.grads_device[p], force_copy=True)
- if not (self.master_weights) or (self.enable_gradient_accumulation):
- self.chunk_manager.move_chunk(chunk, self.grads_device[p], force_copy=True)
+ chunk_manager.move_chunk(grad_chunk, grads_device[p], force_copy=True)
+ if not (master_weights) or (enable_gradient_accumulation):
+ chunk_manager.move_chunk(chunk, grads_device[p], force_copy=True)
  return empty_grad
 
  def zero_grad(self, set_to_none: bool = False) -> None:
@@ -513,11 +544,11 @@ def _save_to_state_dict(self, destination, prefix, keep_vars, only_rank_0=True):
 
  # get copies of fp32 parameters in CPU
  # as memory of fp16_params may be reused by grad, it's not reliable, we should use fp32_params and convert to fp16
- params = self.fp32_params if self.reuse_fp16_chunk else self.fp16_params
+ params = self.fp32_params if self.chunk_manager.reuse_fp16_chunk else self.fp16_params
  param_to_save_data = self._get_param_to_save_data(params, only_rank_0)
  # get the mapping between copies and fp16 parameters
  p_mapping = dict()
- if self.reuse_fp16_chunk:
+ if self.chunk_manager.reuse_fp16_chunk:
  for p, fp32_p in zip(self.fp16_params, self.fp32_params):
  name = self.param2name[p]
  assert fp32_p in param_to_save_data, "Parameter '{}' is neglected in the chunk list".format(name)
@@ -713,7 +744,7 @@ def load_parameter(chunk_slice, data):
  name = self.param2name[p]
  fp32_to_name[fp32_p] = name
 
- params_to_load = self.fp32_params if self.reuse_fp16_chunk else self.fp16_params
+ params_to_load = self.fp32_params if self.chunk_manager.reuse_fp16_chunk else self.fp16_params
  chunk_list = self.chunk_manager.get_chunks(params_to_load)
  for chunk in chunk_list:
  temp_chunk = get_temp_total_chunk_on_cuda(chunk, self.mixed_precision)
@@ -728,7 +759,9 @@ def load_parameter(chunk_slice, data):
  shard_fn = tensor.shard_fn
  gather_fn = tensor.gather_fn
 
- parameter_name = fp32_to_name[tensor] if self.reuse_fp16_chunk else self.param2name[tensor]
+ parameter_name = (
+ fp32_to_name[tensor] if self.chunk_manager.reuse_fp16_chunk else self.param2name[tensor]
+ )
  parameter_slice = temp_chunk[tensor_info.offset : tensor_info.end]
  load(
  parameter_name,
@@ -900,7 +933,7 @@ def state_dict_shard(
  gathered_param = param if keep_vars else param.detach()
  else:
  # as memory of fp16 param may be reused, we should use fp32 param and then convert to fp16
- param_to_save = fp16_to_fp32[param] if self.reuse_fp16_chunk else param
+ param_to_save = fp16_to_fp32[param] if self.chunk_manager.reuse_fp16_chunk else param
  if param_to_save not in gathered_param_buffer:
  chunk = self.chunk_manager.get_chunk(param_to_save)
  gathered_param_buffer.update(self._get_chunk_to_save_data(chunk, only_rank_0))

colossalai/zero/gemini/gemini_optimizer.py

@@ -62,10 +62,10 @@ def __init__(
  self.module = module
 
  def check_local_overflow(self) -> bool:
- return self.module.overflow_counter > 0
+ return self.module.chunk_manager.overflow_counter > 0
 
  def pre_zero_grad(self) -> None:
- self.module.overflow_counter = 0
+ self.module.chunk_manager.overflow_counter = 0
 
 
 class GeminiOptimizer(OptimizerWrapper):
@@ -202,7 +202,7 @@ def _set_grad_ptr(self):
  chunk16 = self.param_to_chunk16[fake_param]
  begin, end = self.param_to_range[fake_param]
 
- grad_chunk16 = chunk16 if self.module.reuse_fp16_chunk else chunk16.grad_chunk
+ grad_chunk16 = chunk16 if self.module.chunk_manager.reuse_fp16_chunk else chunk16.grad_chunk
  fake_param.data = grad_chunk16.payload[begin:end]
  fake_param.grad = fake_param.data
 
@@ -221,14 +221,14 @@ def _update_fp16_params(self):
 
  def _clear_global_norm(self) -> None:
  for c16 in self.chunk16_set:
- grad_chunk = c16 if self.module.reuse_fp16_chunk else c16.grad_chunk
+ grad_chunk = c16 if self.module.chunk_manager.reuse_fp16_chunk else c16.grad_chunk
  grad_chunk.l2_norm = None
 
  def _calc_global_norm(self) -> float:
  norm_sqr: float = 0.0
  group_to_norm = dict()
  for c16 in self.chunk16_set:
- grad_chunk = c16 if self.module.reuse_fp16_chunk else c16.grad_chunk
+ grad_chunk = c16 if self.module.chunk_manager.reuse_fp16_chunk else c16.grad_chunk
  assert grad_chunk.l2_norm is not None
 
  if grad_chunk.is_gathered:
@@ -275,7 +275,7 @@ def step(self, *args, **kwargs):
  self._logger.info(f"Found overflow. Skip step")
  self._clear_global_norm() # clear recorded norm
  self.zero_grad() # reset all gradients
- if self.module.reuse_fp16_chunk:
+ if self.module.chunk_manager.reuse_fp16_chunk:
  self._update_fp16_params()
  return
 
@@ -288,7 +288,7 @@ def step(self, *args, **kwargs):
  self.zero_grad()
  if self.module.master_weights:
  self._update_fp16_params()
- self.module.accumulating_grads = False
+ self.module.chunk_manager.accumulating_grads = False
  return ret
 
  def clip_grad_norm(self, model: torch.nn.Module, max_norm: float, norm_type: float = 2.0):

tests/test_zero/test_gemini/test_fwd_bwd.py

@@ -26,7 +26,7 @@ def check_grad(model: GeminiDDP, torch_model: torch.nn.Module):
  chunk_manager = model.chunk_manager
  param_list = [p for p in model.parameters()]
  chunk_list = chunk_manager.get_chunks(param_list)
- if not model.reuse_fp16_chunk:
+ if not model.chunk_manager.reuse_fp16_chunk:
  chunk_list = [chunk.grad_chunk for chunk in chunk_list]
  for chunk in chunk_list:
  chunk_manager.access_chunk(chunk)