Merge branch 'main' into angelayi/serialize_pytree

docs/source/en/_toctree.yml

@@ -135,6 +135,8 @@
     title: Overview
   - local: quantization
     title: Quantization
+  - local: trainer
+    title: Trainer
   - sections:
     - local: perf_train_gpu_one
       title: Methods and tools for efficient training on a single GPU
@@ -149,7 +151,7 @@
     - local: perf_train_tpu_tf
       title: Training on TPU with TensorFlow
     - local: perf_train_special
-      title: Training on Specialized Hardware
+      title: PyTorch training on Apple silicon
     - local: perf_hardware
       title: Custom hardware for training
     - local: hpo_train

docs/source/en/model_doc/mixtral.md

@@ -20,7 +20,7 @@ rendered properly in your Markdown viewer.
 
 Mixtral-8x7B is Mistral AI's second Large Language Model (LLM). 
 
-The Mixtral model was proposed in the by the [Mistral AI](https://mistral.ai/) team.
+The Mixtral model was proposed by the [Mistral AI](https://mistral.ai/) team.
 
 It was introduced in the [Mixtral of Experts blogpost](https://mistral.ai/news/mixtral-of-experts/) with the following introduction:
 

docs/source/en/model_doc/vipllava.md

@@ -37,13 +37,13 @@ Tips:
 - For better results, we recommend users to prompt the model with the correct prompt format: 
 
 ```bash
-"USER: <image>\n<prompt>ASSISTANT:"
+A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.###Human: <image>\n<prompt>###Assistant:
 ```
 
 For multiple turns conversation:
 
 ```bash
-"USER: <image>\n<prompt1>ASSISTANT: <answer1>USER: <prompt2>ASSISTANT: <answer2>USER: <prompt3>ASSISTANT:"
+A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.###Human: <image>\n<prompt1>###Assistant: <answer1>###Human: <prompt2>###Assistant:
 ```
 
 The original code can be found [here](https://github.com/mu-cai/ViP-LLaVA).

docs/source/en/perf_train_special.md

@@ -13,12 +13,51 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# Training on Specialized Hardware
+# PyTorch training on Apple silicon
 
-<Tip>
+Previously, training models on a Mac was limited to the CPU only. With the release of PyTorch v1.12, you can take advantage of training models with Apple's silicon GPUs for significantly faster performance and training. This is powered in PyTorch by integrating Apple's Metal Performance Shaders (MPS) as a backend. The [MPS backend](https://pytorch.org/docs/stable/notes/mps.html) implements PyTorch operations as custom Metal shaders and places these modules on a `mps` device.
 
- Note: Most of the strategies introduced in the [single GPU section](perf_train_gpu_one) (such as mixed precision training or gradient accumulation) and [multi-GPU section](perf_train_gpu_many) are generic and apply to training models in general so make sure to have a look at it before diving into this section.
+<Tip warning={true}>
+
+Some PyTorch operations are not implemented in MPS yet and will throw an error. To avoid this, you should set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU kernels instead (you'll still see a `UserWarning`).
+
+<br>
+
+If you run into any other errors, please open an issue in the [PyTorch](https://github.com/pytorch/pytorch/issues) repository because the [`Trainer`] only integrates the MPS backend.
 
 </Tip>
 
-This document will be completed soon with information on how to train on specialized hardware.
+With the `mps` device set, you can:
+
+* train larger networks or batch sizes locally
+* reduce data retrieval latency because the GPU's unified memory architecture allows direct access to the full memory store
+* reduce costs because you don't need to train on cloud-based GPUs or add additional local GPUs
+
+Get started by making sure you have PyTorch installed. MPS acceleration is supported on macOS 12.3+.
+
+```bash
+pip install torch torchvision torchaudio
+```
+
+[`TrainingArguments`] uses the `mps` device by default if it's available which means you don't need to explicitly set the device. For example, you can run the [run_glue.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py) script with the MPS backend automatically enabled without making any changes.
+
+```diff
+export TASK_NAME=mrpc
+
+python examples/pytorch/text-classification/run_glue.py \
+  --model_name_or_path bert-base-cased \
+  --task_name $TASK_NAME \
+- --use_mps_device \
+  --do_train \
+  --do_eval \
+  --max_seq_length 128 \
+  --per_device_train_batch_size 32 \
+  --learning_rate 2e-5 \
+  --num_train_epochs 3 \
+  --output_dir /tmp/$TASK_NAME/ \
+  --overwrite_output_dir
+```
+
+Backends for [distributed setups](https://pytorch.org/docs/stable/distributed.html#backends) like `gloo` and `nccl` are not supported by the `mps` device which means you can only train on a single GPU with the MPS backend.
+
+You can learn more about the MPS backend in the [Introducing Accelerated PyTorch Training on Mac](https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/) blog post.

docs/source/en/tasks/semantic_segmentation.md

@@ -276,8 +276,7 @@ You could also create and use your own dataset if you prefer to train with the [
                                      "label": sorted(label_paths)})
          dataset = dataset.cast_column("image", Image())
          dataset = dataset.cast_column("label", Image())
-
-     return dataset
+         return dataset
 
      # step 1: create Dataset objects
      train_dataset = create_dataset(image_paths_train, label_paths_train)