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[legacy] clean up legacy code (#4743)

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* [legacy] remove outdated codes of pipeline (#4692)

* [legacy] remove cli of benchmark and update optim (#4690)

* [legacy] remove cli of benchmark and update optim

* [doc] fix cli doc test

* [legacy] fix engine clip grad norm

* [legacy] remove outdated colo tensor (#4694)

* [legacy] remove outdated colo tensor

* [test] fix test import

* [legacy] move outdated zero to legacy (#4696)

* [legacy] clean up utils (#4700)

* [legacy] clean up utils

* [example] update examples

* [legacy] clean up amp

* [legacy] fix amp module

* [legacy] clean up gpc (#4742)

* [legacy] clean up context

* [legacy] clean core, constants and global vars

* [legacy] refactor initialize

* [example] fix examples ci

* [example] fix examples ci

* [legacy] fix tests

* [example] fix gpt example

* [example] fix examples ci

* [devops] fix ci installation

* [example] fix examples ci
This commit is contained in:
Hongxin Liu
2023-09-18 16:31:06 +08:00
committed by GitHub
parent 32e7f99416
commit b5f9e37c70
342 changed files with 2919 additions and 4182 deletions
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23
examples/tutorial/sequence_parallel/data/bert_helper.py
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@@ -1,7 +1,8 @@
from colossalai.core import global_context as gpc
from colossalai.context import ParallelMode
import torch
from colossalai.legacy.context import ParallelMode
from colossalai.legacy.core import global_context as gpc
_MAX_DATA_DIM = 5
@@ -22,7 +23,8 @@ def _build_key_size_numel_dictionaries(keys, data):
# Move to GPU and broadcast.
sizes_cuda = torch.cuda.LongTensor(sizes)
torch.distributed.broadcast(sizes_cuda, gpc.get_ranks_in_group(ParallelMode.TENSOR)[0],
torch.distributed.broadcast(sizes_cuda,
gpc.get_ranks_in_group(ParallelMode.TENSOR)[0],
group=gpc.get_group(ParallelMode.TENSOR))
# Move back to cpu and unpack.
@@ -60,19 +62,15 @@ def broadcast_data(keys, data, datatype):
"""
# Build (key, size) and (key, number of elements) dictionaries along
# with the total number of elements on all ranks.
key_size, key_numel, total_numel = _build_key_size_numel_dictionaries(keys,
data)
key_size, key_numel, total_numel = _build_key_size_numel_dictionaries(keys, data)
# Pack on rank zero.
if not gpc.is_initialized(ParallelMode.TENSOR) or gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# Check that all keys have the same data type.
# Flatten the data associated with the keys
flatten_data = torch.cat(
[data[key].contiguous().view(-1) for key in keys], dim=0).cuda()
flatten_data = torch.cat([data[key].contiguous().view(-1) for key in keys], dim=0).cuda()
else:
flatten_data = torch.empty(total_numel,
device=torch.cuda.current_device(),
dtype=datatype)
flatten_data = torch.empty(total_numel, device=torch.cuda.current_device(), dtype=datatype)
# Broadcast
torch.distributed.broadcast(flatten_data,
@@ -139,7 +137,7 @@ def get_batch_for_sequence_parallel(data_iterator):
seq_length = data_b['text'].size(1)
sub_seq_length = seq_length // local_world_size
sub_seq_start = local_rank * sub_seq_length
sub_seq_end = (local_rank+1) * sub_seq_length
sub_seq_end = (local_rank + 1) * sub_seq_length
#
# # Unpack.
tokens = data_b['text'][:, sub_seq_start:sub_seq_end].long()
@@ -156,10 +154,9 @@ class SequenceParallelDataIterator:
def __init__(self, data_iter):
self.data_iter = data_iter
def __iter__(self):
return self.data_iter
def __next__(self):
return get_batch_for_sequence_parallel(self.data_iter)
return get_batch_for_sequence_parallel(self.data_iter)