# Copyright 2020-present the HuggingFace Inc. team.
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is modified from
# https://github.com/huggingface/transformers/blob/main/src/transformers
import collections
import copy
import os
from typing import Any, Optional
import numpy as np
import paddle
import paddle.distributed as dist
from paddle.distributed import fleet
from paddlenlp.utils.log import logger
__all__ = [
"distributed_concat",
"paddle_pad_and_concatenate",
"nested_concat",
"nested_detach",
"nested_numpify",
"nested_truncate",
]
def distributed_concat(tensor: Any, num_total_examples: Optional[int] = None) -> Any:
try:
if isinstance(tensor, (tuple, list)):
return type(tensor)(distributed_concat(t, num_total_examples) for t in tensor)
output_tensors = []
dist.all_gather(output_tensors, tensor)
output_tensors = [t if len(t.shape) > 0 else t.reshape_([-1]) for t in output_tensors]
concat = paddle.concat(output_tensors, axis=0)
# truncate the dummy elements added by SequentialDistributedSampler
if num_total_examples is not None:
concat = concat[:num_total_examples]
return concat
except AssertionError:
raise AssertionError("Not currently using distributed training")
[文档]def paddle_pad_and_concatenate(tensor1, tensor2, padding_index=-100):
"""Concatenates `tensor1` and `tensor2` on first axis, applying padding on the second if necessary."""
if len(tensor1.shape) == 1 or tensor1.shape[1] == tensor2.shape[1]:
return paddle.concat((tensor1, tensor2), axis=0)
# raise ValueError("Error")
# Let's figure out the new shape
new_shape = (tensor1.shape[0] + tensor2.shape[0], max(tensor1.shape[1], tensor2.shape[1])) + tuple(
tensor1.shape[2:]
)
# Now let's fill the result tensor
# result = tensor1.new_full(new_shape, padding_index)
result = paddle.full(new_shape, padding_index, dtype=tensor1.dtype)
result[: tensor1.shape[0], : tensor1.shape[1]] = tensor1
result[tensor1.shape[0] :, : tensor2.shape[1]] = tensor2
return result
def numpy_pad_and_concatenate(array1, array2, padding_index=-100):
"""Concatenates `array1` and `array2` on first axis, applying padding on the second if necessary."""
if len(array1.shape) == 1 or array1.shape[1] == array2.shape[1]:
return np.concatenate((array1, array2), axis=0)
# Let's figure out the new shape
new_shape = (array1.shape[0] + array2.shape[0], max(array1.shape[1], array2.shape[1])) + array1.shape[2:]
# Now let's fill the result tensor
result = np.full_like(array1, padding_index, shape=new_shape)
result[: array1.shape[0], : array1.shape[1]] = array1
result[array1.shape[0] :, : array2.shape[1]] = array2
return result
[文档]def nested_concat(tensors, new_tensors, padding_index=-100):
"""
Concat the `new_tensors` to `tensors` on the first dim and pad them on the second if needed. Works for tensors or
nested list/tuples of tensors.
"""
assert type(tensors) == type(
new_tensors
), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}."
if isinstance(tensors, (list, tuple)):
return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n in zip(tensors, new_tensors))
elif isinstance(tensors, paddle.Tensor):
return paddle_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)
elif isinstance(tensors, np.ndarray):
return numpy_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)
else:
raise TypeError(f"Unsupported type for concatenation: got {type(tensors)}")
[文档]def nested_detach(tensors):
"Detach `tensors` (even if it's a nested list/tuple of tensors)."
if isinstance(tensors, (list, tuple)):
return type(tensors)(nested_detach(t) for t in tensors)
return tensors.detach()
[文档]def nested_numpify(tensors):
"Numpify `tensors` (even if it's a nested list/tuple of tensors)."
if isinstance(tensors, (list, tuple)):
return type(tensors)(nested_numpify(t) for t in tensors)
t = tensors.cpu()
if t.dtype == paddle.float16:
t = t.cast(paddle.float32)
return t.cpu().numpy()
[文档]def nested_truncate(tensors, limit):
"Truncate `tensors` at `limit` (even if it's a nested list/tuple of tensors)."
if isinstance(tensors, (list, tuple)):
return type(tensors)(nested_truncate(t, limit) for t in tensors)
return tensors[:limit]
def distributed_isfile(filename):
"""Check all machine nodes. return False if no machine have such file."""
trainers_num = int(os.getenv("PADDLE_TRAINERS_NUM", "1"))
if trainers_num <= 1:
return os.path.isfile(filename)
else:
local_rank = int(os.getenv("PADDLE_RANK_IN_NODE", 0))
file_count = paddle.zeros([1], dtype="int64")
if local_rank == 0 and os.path.isfile(filename):
file_count += 1
paddle.distributed.all_reduce(file_count)
return file_count >= 1
def distributed_file(filename):
trainers_num = int(os.getenv("PADDLE_TRAINERS_NUM", "1"))
if trainers_num <= 1:
return filename
else:
local_rank = int(os.getenv("PADDLE_RANK_IN_NODE", 0))
found_file = paddle.to_tensor([2**20], dtype="int64")
if local_rank == 0 and os.path.isfile(filename):
found_file = paddle.to_tensor([paddle.distributed.get_rank()], dtype="int64")
tensor_list = []
paddle.distributed.all_gather(tensor_list, found_file)
src = paddle.min(paddle.concat(tensor_list)).item()
file_object_list = [None]
if paddle.distributed.get_rank() == src:
file_object_list = [open(filename, "rb").read()]
paddle.distributed.broadcast_object_list(file_object_list, src=src)
file_object = file_object_list[0]
if local_rank == 0 and not os.path.isfile(filename):
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
with open(filename, "wb") as f:
f.write(file_object)
paddle.distributed.barrier()
return filename
TensorHolder = collections.namedtuple("TensorHolder", ["shape", "dtype", "name"])
def nested_reduce_tensor(tensor):
if isinstance(tensor, dict):
# copy tensor since it will be inplace modified dict
tensor = copy.copy(tensor)
for key in list(tensor.keys()):
tensor[key] = nested_reduce_tensor(tensor[key])
if isinstance(tensor, (tuple, list)):
return type(tensor)(nested_reduce_tensor(t) for t in tensor)
if isinstance(tensor, paddle.Tensor):
return TensorHolder(tensor.shape, tensor.dtype, tensor.name)
return tensor
def nested_empty_tensor(tensor):
if isinstance(tensor, dict):
for key in list(tensor.keys()):
tensor[key] = nested_empty_tensor(tensor[key])
if isinstance(tensor, list):
return type(tensor)(nested_empty_tensor(t) for t in tensor)
# TensorHolder is tuple
if isinstance(tensor, TensorHolder):
t = paddle.empty(tensor.shape, dtype=tensor.dtype, name=tensor.name)
t.name = tensor.name
return t
return tensor
def nested_broadcast_tensor(tensor, src=0, group=None):
if isinstance(tensor, dict):
for key in list(tensor.keys()):
tensor[key] = nested_broadcast_tensor(tensor[key], src=src, group=group)
if isinstance(tensor, list):
return type(tensor)(nested_broadcast_tensor(t, src=src, group=group) for t in tensor)
if isinstance(tensor, paddle.Tensor):
paddle.distributed.broadcast(tensor, src=src, group=group, sync_op=True)
return tensor
def broadcast_dp_optimizer(state_dict):
if paddle.distributed.get_world_size() <= 1:
return state_dict
logger.info("Start broadcast optimizer in data parallel group.")
try:
hcg = fleet.get_hybrid_communicate_group()
dp_group = hcg.get_data_parallel_group()
src_rank = hcg.get_data_parallel_group_src_rank()
process_rank = paddle.distributed.get_rank()
# Don't broadcast optimizer for dp rank is 1.
if dp_group.nranks <= 1:
return state_dict
except:
dp_group = None
src_rank = 0
process_rank = paddle.distributed.get_rank()
if process_rank == src_rank:
if state_dict is None:
logger.warning(
f"Your local rank {paddle.distributed.get_rank()} must have a state_dict. dp_rank:{process_rank}, src_rank:{src_rank}"
)
fake_state_dict = [nested_reduce_tensor(state_dict)]
else:
if state_dict is not None:
logger.warning(
f"Your local rank {paddle.distributed.get_rank()} are forbidden to have a state_dict. dp_rank:{process_rank}, src_rank:{src_rank}"
)
fake_state_dict = [None]
paddle.distributed.broadcast_object_list(
fake_state_dict,
src=src_rank,
group=dp_group,
)
fake_state_dict = fake_state_dict[0]
if process_rank != src_rank:
state_dict = nested_empty_tensor(fake_state_dict)
state_dict = nested_broadcast_tensor(state_dict, src=src_rank, group=dp_group)
return state_dict
