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[doc] use full path for run_qa.py (#34914)

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use full path for run_qa.py
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Fanli Lin 2024-11-27 01:23:44 +08:00 committed by GitHub
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3 changed files with 5 additions and 6 deletions
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5
docs/source/en/perf_infer_cpu.md
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@ -42,7 +42,6 @@ Enable BetterTransformer with the [`PreTrainedModel.to_bettertransformer`] metho
from transformers import AutoModelForCausalLM from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder") model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder")
model.to_bettertransformer()
``` ```
## TorchScript ## TorchScript
@ -54,7 +53,7 @@ For a gentle introduction to TorchScript, see the [Introduction to PyTorch Torch
With the [`Trainer`] class, you can enable JIT mode for CPU inference by setting the `--jit_mode_eval` flag: With the [`Trainer`] class, you can enable JIT mode for CPU inference by setting the `--jit_mode_eval` flag:
```bash ```bash
python run_qa.py \ python examples/pytorch/question-answering/run_qa.py \
--model_name_or_path csarron/bert-base-uncased-squad-v1 \ --model_name_or_path csarron/bert-base-uncased-squad-v1 \
--dataset_name squad \ --dataset_name squad \
--do_eval \ --do_eval \
@ -86,7 +85,7 @@ pip install intel_extension_for_pytorch
Set the `--use_ipex` and `--jit_mode_eval` flags in the [`Trainer`] class to enable JIT mode with the graph optimizations: Set the `--use_ipex` and `--jit_mode_eval` flags in the [`Trainer`] class to enable JIT mode with the graph optimizations:
```bash ```bash
python run_qa.py \ python examples/pytorch/question-answering/run_qa.py \
--model_name_or_path csarron/bert-base-uncased-squad-v1 \ --model_name_or_path csarron/bert-base-uncased-squad-v1 \
--dataset_name squad \ --dataset_name squad \
--do_eval \ --do_eval \

2
docs/source/en/perf_train_cpu.md
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@ -51,7 +51,7 @@ To enable auto mixed precision with IPEX in Trainer, users should add `use_ipex`
Take an example of the use cases on [Transformers question-answering](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering) Take an example of the use cases on [Transformers question-answering](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering)
- Training with IPEX using BF16 auto mixed precision on CPU: - Training with IPEX using BF16 auto mixed precision on CPU:
<pre> python run_qa.py \ <pre> python examples/pytorch/question-answering/run_qa.py \
--model_name_or_path google-bert/bert-base-uncased \ --model_name_or_path google-bert/bert-base-uncased \
--dataset_name squad \ --dataset_name squad \
--do_train \ --do_train \

4
docs/source/en/perf_train_cpu_many.md
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@ -75,7 +75,7 @@ The following command enables training with 2 processes on one Xeon node, with o
export CCL_WORKER_COUNT=1 export CCL_WORKER_COUNT=1
export MASTER_ADDR=127.0.0.1 export MASTER_ADDR=127.0.0.1
mpirun -n 2 -genv OMP_NUM_THREADS=23 \ mpirun -n 2 -genv OMP_NUM_THREADS=23 \
python3 run_qa.py \ python3 examples/pytorch/question-answering/run_qa.py \
--model_name_or_path google-bert/bert-large-uncased \ --model_name_or_path google-bert/bert-large-uncased \
--dataset_name squad \ --dataset_name squad \
--do_train \ --do_train \
@ -104,7 +104,7 @@ Now, run the following command in node0 and **4DDP** will be enabled in node0 an
export MASTER_ADDR=xxx.xxx.xxx.xxx #node0 ip export MASTER_ADDR=xxx.xxx.xxx.xxx #node0 ip
mpirun -f hostfile -n 4 -ppn 2 \ mpirun -f hostfile -n 4 -ppn 2 \
-genv OMP_NUM_THREADS=23 \ -genv OMP_NUM_THREADS=23 \
python3 run_qa.py \ python3 examples/pytorch/question-answering/run_qa.py \
--model_name_or_path google-bert/bert-large-uncased \ --model_name_or_path google-bert/bert-large-uncased \
--dataset_name squad \ --dataset_name squad \
--do_train \ --do_train \