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[Unified Checkpoint] update merge tensor parallel #8856

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Merged
wawltor merged 1 commit into from
Aug 1, 2024
Merged

[Unified Checkpoint] update merge tensor parallel #8856

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61 changes: 51 additions & 10 deletions paddlenlp/trainer/plugins/unified_checkpoint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1735,6 +1735,37 @@ def filter_params(model_to_save, state_dict, is_optimizer=False):
return filter_tensor_list


def merge_large_tensor_parallel(tensor, tp_group, tp_action, dst_rank, is_dst):
num_rows = tensor.shape[0]
num_splits = 4
parts = np.array_split(np.arange(num_rows), num_splits)
splits = [len(part) for part in parts]
split_parts = np.insert(np.cumsum(splits), 0, 0)
split_tensors = []
for i in range(num_splits):
if get_env_device() == "xpu":
ret = distributed_allgather(tensor[split_parts[i] : split_parts[i + 1], :], group=tp_group, offload=False)
else:
ret = distributed_gather(
tensor[split_parts[i] : split_parts[i + 1], :], dst=dst_rank, group=tp_group, offload=False
)
# Copy to CPUPlace temporarily, may lower speed.
if ret is not None:
ret = [t.cpu() for t in ret]
split_tensors.append(ret)
concat_tensors = []
if is_dst:
for i in range(tp_group.nranks):
tmp = []
for j in range(num_splits):
tmp.append(split_tensors[j][i])
concat_tensors.append(paddle.concat(tmp))
tensor = tp_action(concat_tensors)
else:
tensor = None
return tensor


def merge_tensor_parallel_with_shard(state_dict, tp_actions, all_filter_keys):
hcg = fleet.get_hybrid_communicate_group()
tp_group = hcg.get_model_parallel_group()
Expand All @@ -1757,12 +1788,17 @@ def merge_tensor_parallel_with_shard(state_dict, tp_actions, all_filter_keys):
key = filter_keys[i]
tensor = state_dict[key]
if key in tp_actions:
if get_env_device() == "xpu":
ret = distributed_allgather(tensor, group=tp_group, offload=False)
# Get tensor size
tensor_bytes = tensor.numel().item() * dtype_byte_size(tensor.dtype) * tp_group.nranks
if tensor_bytes >= 5 * 1024 * 1024 * 1024: # temporarily set 5GB as threshold
tensor = merge_large_tensor_parallel(tensor, tp_group, tp_actions[key], j, is_dst)
else:
ret = distributed_gather(tensor, dst=j, group=tp_group, offload=False)
action = tp_actions.pop(key)
tensor = action(ret) if is_dst else None
if get_env_device() == "xpu":
ret = distributed_allgather(tensor, group=tp_group, offload=False)
else:
ret = distributed_gather(tensor, dst=j, group=tp_group, offload=False)
action = tp_actions.pop(key)
tensor = action(ret) if is_dst else None
else:
tensor = tensor._copy_to(DEST_PLACE, False) if is_dst else None

Expand Down Expand Up @@ -1797,12 +1833,17 @@ def merge_tensor_parallel_for_optimizer(state_dict, tp_actions, all_filter_keys)
if tensor.numel().item() == 1:
tensor = tensor._copy_to(DEST_PLACE, False) if is_dst else None # Need broadcast when loaded
else:
if get_env_device() == "xpu":
ret = distributed_allgather(tensor, group=tp_group, offload=False)
# Get tensor size
tensor_bytes = tensor.numel().item() * dtype_byte_size(tensor.dtype) * tp_group.nranks
if tensor_bytes >= 5 * 1024 * 1024 * 1024: # temporarily set 5GB as threshold
tensor = merge_large_tensor_parallel(tensor, tp_group, tp_actions[model_key], j, is_dst)
else:
ret = distributed_gather(tensor, dst=j, group=tp_group, offload=False)
action = tp_actions[model_key]
tensor = action(ret) if is_dst else None
if get_env_device() == "xpu":
ret = distributed_allgather(tensor, group=tp_group, offload=False)
else:
ret = distributed_gather(tensor, dst=j, group=tp_group, offload=False)
action = tp_actions[model_key]
tensor = action(ret) if is_dst else None
else:
tensor = tensor._copy_to(DEST_PLACE, False) if is_dst else None

Expand Down