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[doc] Fix typo under colossalai and doc(#3618)

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* Fixed several spelling errors under colossalai

* Fix the spelling error in colossalai and docs directory

* Cautious Changed the spelling error under the example folder

* Update runtime_preparation_pass.py

revert autograft to autograd

* Update search_chunk.py

utile to until

* Update check_installation.py

change misteach to mismatch in line 91

* Update 1D_tensor_parallel.md

revert to perceptron

* Update 2D_tensor_parallel.md

revert to perceptron in line 73

* Update 2p5D_tensor_parallel.md

revert to perceptron in line 71

* Update 3D_tensor_parallel.md

revert to perceptron in line 80

* Update README.md

revert to resnet in line 42

* Update reorder_graph.py

revert to indice in line 7

* Update p2p.py

revert to megatron in line 94

* Update initialize.py

revert to torchrun in line 198

* Update routers.py

change to detailed in line 63

* Update routers.py

change to detailed in line 146

* Update README.md

revert  random number in line 402
This commit is contained in:
digger-yu
2023-04-26 11:38:43 +08:00
committed by GitHub
parent e1b0a78afa
commit b9a8dff7e5
72 changed files with 158 additions and 158 deletions
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6
colossalai/tensor/utils.py
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@@ -18,7 +18,7 @@ def all_gather_simulator(target_pair):
Argument:
target_pair(Tuple[int, List[int]]): The first element is the dimension of tensor to be sharded,
and the second element decribes which logical axis will be sharded in that dimension.
and the second element describes which logical axis will be sharded in that dimension.
'''
_, shard_list = target_pair
new_shard_list = shard_list[:-1]
@@ -36,7 +36,7 @@ def all_to_all_simulator(f_target_pair, b_target_pair):
Therefore, if the behind shard_list is not None, we just extend it to the front shard_list.
Argument:
target_pair(Tuple[int, List[int]]): The first element is the dimension of tensor to be sharded,
and the second element decribes which logical axis will be sharded in that dimension.
and the second element describes which logical axis will be sharded in that dimension.
e.g.:
all-to-all(S0, S1) -> [S01, R]
all-to-all(S0, R) -> [R, S0]
@@ -46,7 +46,7 @@ def all_to_all_simulator(f_target_pair, b_target_pair):
Argument:
target_pair(Tuple[int, List[int]]): The first element is the dimension of tensor to be sharded,
and the second element decribes which logical axis will be sharded in that dimension.
and the second element describes which logical axis will be sharded in that dimension.
'''
_, f_shard_list = f_target_pair
_, b_shard_list = b_target_pair