diff --git a/colossalai/gemini/update/chunkv2.py b/colossalai/gemini/update/chunkv2.py
index b1bbfce5e..25f7858ea 100644
--- a/colossalai/gemini/update/chunkv2.py
+++ b/colossalai/gemini/update/chunkv2.py
@@ -9,6 +9,7 @@ from colossalai.gemini.chunk import TensorState, STATE_TRANS, TensorInfo, ChunkF
class ChunkV2:
+
def __init__(self,
chunk_size: int,
process_group: ColoProcessGroup,
@@ -49,9 +50,9 @@ class ChunkV2:
self.dtype = dtype
device = init_device or get_current_device()
- self.chunk_temp = torch.zeros(chunk_size, dtype=dtype, device=device) # keep all zero
- self.chunk_total = None # we force chunk_total located in CUDA
- self.cuda_shard = None # using two attributes for the better interpretation
+ self.chunk_temp = torch.zeros(chunk_size, dtype=dtype, device=device) # keep all zero
+ self.chunk_total = None # we force chunk_total located in CUDA
+ self.cuda_shard = None # using two attributes for the better interpretation
self.cpu_shard = None
self.is_gathered = True
@@ -71,7 +72,7 @@ class ChunkV2:
# so their computation patterns are the same as that of the parameters in DDP
self.keep_gathered = keep_gathered
if self.keep_gathered:
- pin_memory = False # since this chunk is gathered, it doesn't need to pin
+ pin_memory = False # since this chunk is gathered, it doesn't need to pin
# if pin_memory is True, we allocate a piece of CPU pin-memory
# for it all the time
@@ -137,9 +138,9 @@ class ChunkV2:
if new_utilized_size > self.chunk_size:
raise ChunkFullError
- self.chunk_temp[self.utilized_size: new_utilized_size].copy_(tensor.data.flatten())
+ self.chunk_temp[self.utilized_size:new_utilized_size].copy_(tensor.data.flatten())
assert type(self.chunk_temp) == torch.Tensor, "copy_tensor_to_chunk_slice must use a torch tensor"
- tensor.data = self.chunk_temp[self.utilized_size: new_utilized_size].view(tensor.shape)
+ tensor.data = self.chunk_temp[self.utilized_size:new_utilized_size].view(tensor.shape)
# record all the information about the tensor
self.num_tensors += 1
@@ -177,11 +178,9 @@ class ChunkV2:
shard_dev = torch.device('cpu')
if self.pin_memory or shard_dev.type == 'cpu':
- self.cpu_shard = torch.empty(self.shard_size,
- dtype=self.dtype,
- pin_memory=self.pin_memory)
+ self.cpu_shard = torch.empty(self.shard_size, dtype=self.dtype, pin_memory=self.pin_memory)
self.cpu_shard.copy_(self.cuda_shard)
- self.cpu_vis_flag = True # cpu_shard has been visited
+ self.cpu_vis_flag = True # cpu_shard has been visited
if shard_dev.type == 'cpu':
self.cuda_shard = None
@@ -260,8 +259,7 @@ class ChunkV2:
# we use all-reduce here
dist.all_reduce(self.chunk_total, group=self.torch_pg)
else:
- self.cuda_shard = torch.empty(
- self.shard_size, dtype=self.dtype, device=get_current_device())
+ self.cuda_shard = torch.empty(self.shard_size, dtype=self.dtype, device=get_current_device())
input_list = list(torch.chunk(self.chunk_total, chunks=self.pg_size, dim=0))
dist.reduce_scatter(self.cuda_shard, input_list, group=self.torch_pg)
@@ -330,10 +328,10 @@ class ChunkV2:
Check if the chunk has inf or nan values in CUDA.
"""
if self.is_gathered:
- valid_tensor = self.chunk_total[: self.utilized_size]
+ valid_tensor = self.chunk_total[:self.utilized_size]
else:
- assert self.cuda_shard is not None # only check in CUDA
- valid_tensor = self.cuda_shard[: self.valid_end]
+ assert self.cuda_shard is not None # only check in CUDA
+ valid_tensor = self.cuda_shard[:self.valid_end]
return torch.isinf(valid_tensor).any().item() | torch.isnan(valid_tensor).any().item()
@@ -346,8 +344,7 @@ class ChunkV2:
self.chunk_total = self.cuda_shard
else:
alloc_storage(self.chunk_total)
- gather_list = list(torch.chunk(
- input=self.chunk_total, chunks=self.pg_size, dim=0))
+ gather_list = list(torch.chunk(input=self.chunk_total, chunks=self.pg_size, dim=0))
dist.all_gather(gather_list, self.cuda_shard, self.torch_pg)
self.cuda_shard = None
@@ -361,11 +358,9 @@ class ChunkV2:
# sanity check
assert self.cuda_shard is None
- self.cuda_shard = torch.empty(self.shard_size,
- dtype=self.dtype,
- device=self.chunk_total.device)
+ self.cuda_shard = torch.empty(self.shard_size, dtype=self.dtype, device=self.chunk_total.device)
- self.cuda_shard.copy_(self.chunk_total[self.shard_begin: self.shard_end])
+ self.cuda_shard.copy_(self.chunk_total[self.shard_begin:self.shard_end])
free_storage(self.chunk_total)
self.is_gathered = False
@@ -412,15 +407,15 @@ class ChunkV2:
def __repr__(self, detailed: bool = False):
output = [
"AgChunk Information:\n",
- "\tchunk size: {}, chunk dtype: {}, process group size: {}\n".format(
- self.chunk_size, self.dtype, self.pg_size),
+ "\tchunk size: {}, chunk dtype: {}, process group size: {}\n".format(self.chunk_size, self.dtype,
+ self.pg_size),
"\t# of tensors: {}, utilized size: {}, utilized percentage: {:.2f}\n".format(
self.num_tensors, self.utilized_size, self.utilized_size / self.chunk_size)
]
def print_tensor(tensor, prefix=''):
- output.append("{}shape: {}, dtype: {}, device: {}\n".format(
- prefix, tensor.shape, tensor.dtype, tensor.device))
+ output.append("{}shape: {}, dtype: {}, device: {}\n".format(prefix, tensor.shape, tensor.dtype,
+ tensor.device))
if self.chunk_temp is not None:
output.append("\tchunk temp:\n")