Add code for DDP tutorial series [PR 3 / 3] (pytorch#1069)

* Adds files for minGPT training with DDP

* filtered-clone, update script path, update readme

* add refs to karpathy's repo

* add training data

* add AMP training

* delete raw data file, update index.rst

* Update gpt2_train_cfg.yaml

distributed/minGPT-ddp/README.md

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+# minGPT-DDP
+
+Code accompanying the tutorial at https://pytorch.org/tutorials/intermediate/ddp_minGPT.html for training a GPT-like model with Distributed Data Parallel (DDP) in PyTorch. 
+
+Files marked with an asterisk (*) are adapted from the minGPT repo (https://github.com/karpathy/minGPT). 
+
+- [trainer.py](mingpt/trainer.py) includes the Trainer class that runs the distributed training iterations on the model with the provided dataset.
+- [model.py *](mingpt/model.py) defines the model architecture.
+- [char_dataset.py *](mingpt/char_dataset.py) contains the `Dataset`class for a character-level dataset.
+- [gpt2_train_cfg.yaml](mingpt/gpt2_train_cfg.yaml) contains the configurations for data, model, optimizer and training run.
+- [main.py](mingpt/main.py) is the entry point to the trainig job. It sets up the DDP process group, reads all the configurations and runs the training job.
+- [slurm/](mingpt/slurm) contains files for setting up an AWS cluster and the slurm script to run multinode training.

distributed/minGPT-ddp/mingpt/char_dataset.py

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+import torch
+from torch.utils.data import Dataset
+import fsspec
+from dataclasses import dataclass
+
+"""
+Adapted from https://github.com/karpathy/minGPT/blob/master/projects/chargpt/chargpt.py
+"""
+
+@dataclass
+class DataConfig:
+    path: str = None
+    block_size: int = None
+    train_split: float = None
+    truncate: float = 1.0
+
+class CharDataset(Dataset):
+
+    def __init__(self, data_cfg: DataConfig): #data_path: str, block_size):
+        data = fsspec.open(data_cfg.path).open().read().decode('utf-8')
+        data = data[ : int(len(data) * data_cfg.truncate)]
+
+        chars = sorted(list(set(data)))
+        data_size, vocab_size = len(data), len(chars)
+        print('Data has %d characters, %d unique.' % (data_size, vocab_size))
+
+        self.stoi = {ch: i for i, ch in enumerate(chars)}
+        self.itos = {i: ch for i, ch in enumerate(chars)}
+        self.block_size = data_cfg.block_size
+        self.vocab_size = vocab_size
+        self.data = data
+
+    def __len__(self):
+        return len(self.data) - self.block_size
+
+    def __getitem__(self, idx):
+        # grab a chunk of (block_size + 1) characters from the data
+        chunk = self.data[idx:idx + self.block_size + 1]
+        # encode every character to an integer
+        dix = [self.stoi[s] for s in chunk]
+        x = torch.tensor(dix[:-1], dtype=torch.long)
+        y = torch.tensor(dix[1:], dtype=torch.long)
+        return x, y