docs: replace torch.distributed.run by torchrun

ISSUES.md

@@ -152,7 +152,7 @@ You are not required to read the following guidelines before opening an issue. H
 
    ```bash
     cd examples/seq2seq
-    python -m torch.distributed.launch --nproc_per_node=2 ./finetune_trainer.py \
+    torchrun --nproc_per_node=2 ./finetune_trainer.py \
     --model_name_or_path sshleifer/distill-mbart-en-ro-12-4 --data_dir wmt_en_ro \
     --output_dir output_dir --overwrite_output_dir \
     --do_train --n_train 500 --num_train_epochs 1 \

docs/source/de/run_scripts.md

@@ -130,7 +130,7 @@ Der [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) unt
 - Legen Sie die Anzahl der zu verwendenden GPUs mit dem Argument `nproc_per_node` fest.
 
 ```bash
-python -m torch.distributed.launch \
+torchrun \
     --nproc_per_node 8 pytorch/summarization/run_summarization.py \
     --fp16 \
     --model_name_or_path t5-small \

docs/source/en/main_classes/deepspeed.md

@@ -287,7 +287,7 @@ The information in this section isn't not specific to the DeepSpeed integration
 
 For the duration of this section let's assume that you have 2 nodes with 8 gpus each. And you can reach the first node with `ssh hostname1` and second node with `ssh hostname2`, and both must be able to reach each other via ssh locally without a password. Of course, you will need to rename these host (node) names to the actual host names you are working with.
 
-#### The torch.distributed.run launcher
+#### The torch.distributed.run(torchrun) launcher
 
 
 For example, to use `torch.distributed.run`, you could do:

docs/source/en/main_classes/trainer.md

@@ -206,7 +206,7 @@ Let's discuss how you can tell your program which GPUs are to be used and in wha
 When using [`DistributedDataParallel`](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html) to use only a subset of your GPUs, you simply specify the number of GPUs to use. For example, if you have 4 GPUs, but you wish to use the first 2 you can do:
 
 ```bash
-python -m torch.distributed.launch --nproc_per_node=2  trainer-program.py ...
+torchrun --nproc_per_node=2  trainer-program.py ...
 ```
 
 if you have either [`accelerate`](https://github.com/huggingface/accelerate) or [`deepspeed`](https://github.com/microsoft/DeepSpeed) installed you can also accomplish the same by using one of:
@@ -233,15 +233,15 @@ If you have multiple GPUs and you'd like to use only 1 or a few of those GPUs, s
 For example, let's say you have 4 GPUs: 0, 1, 2 and 3. To run only on the physical GPUs 0 and 2, you can do:
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,2 python -m torch.distributed.launch trainer-program.py ...
+CUDA_VISIBLE_DEVICES=0,2 torchrun trainer-program.py ...
 ```
 
 So now pytorch will see only 2 GPUs, where your physical GPUs 0 and 2 are mapped to `cuda:0` and `cuda:1` correspondingly.
 
 You can even change their order:
 
 ```bash
-CUDA_VISIBLE_DEVICES=2,0 python -m torch.distributed.launch trainer-program.py ...
+CUDA_VISIBLE_DEVICES=2,0 torchrun trainer-program.py ...
 ```
 
 Here your physical GPUs 0 and 2 are mapped to `cuda:1` and `cuda:0` correspondingly.
@@ -263,7 +263,7 @@ As with any environment variable you can, of course, export those instead of add
 
 ```bash
 export CUDA_VISIBLE_DEVICES=0,2
-python -m torch.distributed.launch trainer-program.py ...
+torchrun trainer-program.py ...
 ```
 
 but this approach can be confusing since you may forget you set up the environment variable earlier and not understand why the wrong GPUs are used. Therefore, it's a common practice to set the environment variable just for a specific run on the same command line as it's shown in most examples of this section.

docs/source/en/perf_hardware.md

@@ -134,7 +134,7 @@ Here is the full benchmark code and outputs:
 ```bash
 # DDP w/ NVLink
 
-rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
+rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 torchrun \
 --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path gpt2 \
 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train \
 --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
@@ -143,7 +143,7 @@ rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch
 
 # DDP w/o NVLink
 
-rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 NCCL_P2P_DISABLE=1 python -m torch.distributed.launch \
+rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 NCCL_P2P_DISABLE=1 torchrun \
 --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path gpt2 \
 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train
 --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200

docs/source/en/perf_train_gpu_many.md

@@ -153,7 +153,7 @@ python examples/pytorch/language-modeling/run_clm.py \
 
 ```
 rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \
-python -m torch.distributed.launch --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
+torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
 --model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
 --do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
 
@@ -164,7 +164,7 @@ python -m torch.distributed.launch --nproc_per_node 2 examples/pytorch/language-
 
 ```
 rm -r /tmp/test-clm; NCCL_P2P_DISABLE=1 CUDA_VISIBLE_DEVICES=0,1 \
-python -m torch.distributed.launch --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
+torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
 --model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
 --do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
 

docs/source/en/run_scripts.md

@@ -130,7 +130,7 @@ The [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) sup
 - Set the number of GPUs to use with the `nproc_per_node` argument.
 
 ```bash
-python -m torch.distributed.launch \
+torchrun \
     --nproc_per_node 8 pytorch/summarization/run_summarization.py \
     --fp16 \
     --model_name_or_path t5-small \

docs/source/es/run_scripts.md

@@ -130,7 +130,7 @@ python examples/tensorflow/summarization/run_summarization.py  \
 - Establece la cantidad de GPU que se usará con el argumento `nproc_per_node`.
 
 ```bash
-python -m torch.distributed.launch \
+torchrun \
     --nproc_per_node 8 pytorch/summarization/run_summarization.py \
     --fp16 \
     --model_name_or_path t5-small \

docs/source/it/perf_hardware.md

@@ -134,7 +134,7 @@ Ecco il codice benchmark completo e gli output:
 ```bash
 # DDP w/ NVLink
 
-rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
+rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 torchrun \
 --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path gpt2 \
 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train \
 --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
@@ -143,7 +143,7 @@ rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch
 
 # DDP w/o NVLink
 
-rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 NCCL_P2P_DISABLE=1 python -m torch.distributed.launch \
+rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 NCCL_P2P_DISABLE=1 torchrun \
 --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path gpt2 \
 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train
 --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200

docs/source/it/run_scripts.md

@@ -130,7 +130,7 @@ Il [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) supp
 - Imposta un numero di GPU da usare con l'argomento `nproc_per_node`.
 
 ```bash
-python -m torch.distributed.launch \
+torchrun \
     --nproc_per_node 8 pytorch/summarization/run_summarization.py \
     --fp16 \
     --model_name_or_path t5-small \

docs/source/ja/main_classes/trainer.md

@@ -196,7 +196,7 @@ _python_、_numpy_、および _pytorch_ の RNG 状態は、そのチェック
 [`DistributedDataParallel`](https://pytorch.org/docs/stable/generated/torch.nn.Parallel.DistributedDataParallel.html) を使用して GPU のサブセットのみを使用する場合、使用する GPU の数を指定するだけです。 。たとえば、GPU が 4 つあるが、最初の 2 つを使用したい場合は、次のようにします。
 
 ```bash
-python -m torch.distributed.launch --nproc_per_node=2  trainer-program.py ...
+torchrun --nproc_per_node=2  trainer-program.py ...
 ```
 
 [`accelerate`](https://github.com/huggingface/accelerate) または [`deepspeed`](https://github.com/microsoft/DeepSpeed) がインストールされている場合は、次を使用して同じことを達成することもできます。の一つ：
@@ -223,15 +223,15 @@ deepspeed --num_gpus 2 trainer-program.py ...
 たとえば、4 つの GPU (0、1、2、3) があるとします。物理 GPU 0 と 2 のみで実行するには、次のようにします。
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,2 python -m torch.distributed.launch trainer-program.py ...
+CUDA_VISIBLE_DEVICES=0,2 torchrun trainer-program.py ...
 ```
 
 したがって、pytorch は 2 つの GPU のみを認識し、物理 GPU 0 と 2 はそれぞれ `cuda:0` と `cuda:1` にマッピングされます。
 
 順序を変更することもできます。
 
 ```bash
-CUDA_VISIBLE_DEVICES=2,0 python -m torch.distributed.launch trainer-program.py ...
+CUDA_VISIBLE_DEVICES=2,0 torchrun trainer-program.py ...
 ```
 
 ここでは、物理 GPU 0 と 2 がそれぞれ`cuda:1`と`cuda:0`にマッピングされています。
@@ -253,7 +253,7 @@ CUDA_VISIBLE_DEVICES= python trainer-program.py ...
 
 ```bash
 export CUDA_VISIBLE_DEVICES=0,2
-python -m torch.distributed.launch trainer-program.py ...
+torchrun trainer-program.py ...
 ```
 
 ただし、この方法では、以前に環境変数を設定したことを忘れて、なぜ間違った GPU が使用されているのか理解できない可能性があるため、混乱を招く可能性があります。したがって、このセクションのほとんどの例で示されているように、同じコマンド ラインで特定の実行に対してのみ環境変数を設定するのが一般的です。

docs/source/ja/perf_hardware.md

@@ -139,7 +139,7 @@ NVLinkを使用すると、トレーニングが約23％速く完了すること
 ```bash
 # DDP w/ NVLink
 
-rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
+rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 torchrun \
 --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path gpt2 \
 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train \
 --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
@@ -148,7 +148,7 @@ rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch
 
 # DDP w/o NVLink
 
-rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 NCCL_P2P_DISABLE=1 python -m torch.distributed.launch \
+rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 NCCL_P2P_DISABLE=1 torchrun \
 --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path gpt2 \
 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train
 --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200

docs/source/ja/perf_train_gpu_many.md

@@ -143,15 +143,15 @@ python examples/pytorch/language-modeling/run_clm.py \
 
 # DDP w/ NVlink
 rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \
-python -m torch.distributed.launch --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
+torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
 --model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
 --do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
 
 {'train_runtime': 101.9003, 'train_samples_per_second': 1.963, 'epoch': 0.69}
 
 # DDP w/o NVlink
 rm -r /tmp/test-clm; NCCL_P2P_DISABLE=1 CUDA_VISIBLE_DEVICES=0,1 \
-python -m torch.distributed.launch --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
+torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
 --model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
 --do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
 

docs/source/ja/run_scripts.md

@@ -140,7 +140,7 @@ python examples/tensorflow/summarization/run_summarization.py  \
 以下は提供されたBashコードです。このコードの日本語訳をMarkdown形式で記載します。
 
 ```bash
-python -m torch.distributed.launch \
+torchrun \
     --nproc_per_node 8 pytorch/summarization/run_summarization.py \
     --fp16 \
     --model_name_or_path t5-small \

docs/source/ko/perf_hardware.md

@@ -135,7 +135,7 @@ NVLink 사용 시 훈련이 약 23% 더 빠르게 완료됨을 확인할 수 있
 ```bash
 # DDP w/ NVLink
 
-rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
+rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 torchrun \
 --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path gpt2 \
 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train \
 --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
@@ -144,7 +144,7 @@ rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch
 
 # DDP w/o NVLink
 
-rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 NCCL_P2P_DISABLE=1 python -m torch.distributed.launch \
+rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 NCCL_P2P_DISABLE=1 torchrun \
 --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path gpt2 \
 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train
 --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200

docs/source/ko/perf_train_gpu_many.md

@@ -145,15 +145,15 @@ python examples/pytorch/language-modeling/run_clm.py \
 
 # DDP w/ NVlink
 rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \
-python -m torch.distributed.launch --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
+torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
 --model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
 --do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
 
 {'train_runtime': 101.9003, 'train_samples_per_second': 1.963, 'epoch': 0.69}
 
 # DDP w/o NVlink
 rm -r /tmp/test-clm; NCCL_P2P_DISABLE=1 CUDA_VISIBLE_DEVICES=0,1 \
-python -m torch.distributed.launch --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
+torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
 --model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
 --do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
 

docs/source/ko/run_scripts.md

@@ -141,7 +141,7 @@ python examples/tensorflow/summarization/run_summarization.py  \
 - `nproc_per_node` 인수를 추가해 사용할 GPU 개수를 설정합니다.
 
 ```bash
-python -m torch.distributed.launch \
+torchrun \
     --nproc_per_node 8 pytorch/summarization/run_summarization.py \
     --fp16 \
     --model_name_or_path t5-small \

docs/source/pt/run_scripts.md

@@ -131,7 +131,7 @@ O [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) ofere
 - Defina o número de GPUs a serem usadas com o argumento `nproc_per_node`.
 
 ```bash
-python -m torch.distributed.launch \
+torchrun \
     --nproc_per_node 8 pytorch/summarization/run_summarization.py \
     --fp16 \
     --model_name_or_path t5-small \

docs/source/zh/perf_hardware.md

@@ -135,7 +135,7 @@ GPU1    PHB      X      0-11            N/A
 ```bash
 # DDP w/ NVLink
 
-rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
+rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 torchrun \
 --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path gpt2 \
 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train \
 --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
@@ -144,7 +144,7 @@ rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch
 
 # DDP w/o NVLink
 
-rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 NCCL_P2P_DISABLE=1 python -m torch.distributed.launch \
+rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 NCCL_P2P_DISABLE=1 torchrun \
 --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path gpt2 \
 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train
 --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200

docs/source/zh/run_scripts.md

@@ -133,7 +133,7 @@ python examples/tensorflow/summarization/run_summarization.py  \
 
 
 ```bash
-python -m torch.distributed.launch \
+torchrun \
     --nproc_per_node 8 pytorch/summarization/run_summarization.py \
     --fp16 \
     --model_name_or_path t5-small \

examples/legacy/question-answering/README.md

@@ -18,7 +18,7 @@ in Huang et al. [Improve Transformer Models with Better Relative Position Embedd
 
 ```bash
 export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
+torchrun --nproc_per_node=8 ./examples/question-answering/run_squad.py \
     --model_name_or_path zhiheng-huang/bert-base-uncased-embedding-relative-key-query \
     --dataset_name squad \
     --do_train \
@@ -46,7 +46,7 @@ gpu training leads to the f1 score of 90.71.
 
 ```bash
 export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
+torchrun --nproc_per_node=8 ./examples/question-answering/run_squad.py \
     --model_name_or_path zhiheng-huang/bert-large-uncased-whole-word-masking-embedding-relative-key-query \
     --dataset_name squad \
     --do_train \
@@ -68,7 +68,7 @@ Training with the above command leads to the f1 score of 93.52, which is slightl
 Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD1.1:
 
 ```bash
-python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
+torchrun --nproc_per_node=8 ./examples/question-answering/run_squad.py \
     --model_name_or_path bert-large-uncased-whole-word-masking \
     --dataset_name squad \
     --do_train \

examples/legacy/seq2seq/README.md

@@ -140,7 +140,7 @@ python finetune_trainer.py --help
 
 For multi-gpu training use `torch.distributed.launch`, e.g. with 2 gpus:
 ```bash
-python -m torch.distributed.launch --nproc_per_node=2  finetune_trainer.py ...
+torchrun --nproc_per_node=2  finetune_trainer.py ...
 ```
 
 **At the moment, `Seq2SeqTrainer` does not support *with teacher* distillation.**
@@ -214,7 +214,7 @@ because it uses SortishSampler to minimize padding. You can also use it on 1 GPU
 `{type_path}.source` and `{type_path}.target`. Run `./run_distributed_eval.py --help` for all clargs.
 
 ```bash
-python -m torch.distributed.launch --nproc_per_node=8  run_distributed_eval.py \
+torchrun --nproc_per_node=8  run_distributed_eval.py \
     --model_name sshleifer/distilbart-large-xsum-12-3  \
     --save_dir xsum_generations \
     --data_dir xsum \

examples/pytorch/README.md

@@ -98,7 +98,7 @@ the [Trainer API](https://huggingface.co/transformers/main_classes/trainer.html)
 use the following command:
 
 ```bash
-python -m torch.distributed.launch \
+torchrun \
     --nproc_per_node number_of_gpu_you_have path_to_script.py \
 	--all_arguments_of_the_script
 ```
@@ -107,7 +107,7 @@ As an example, here is how you would fine-tune the BERT large model (with whole
 classification MNLI task using the `run_glue` script, with 8 GPUs:
 
 ```bash
-python -m torch.distributed.launch \
+torchrun \
     --nproc_per_node 8 pytorch/text-classification/run_glue.py \
     --model_name_or_path bert-large-uncased-whole-word-masking \
     --task_name mnli \