[wip/s2s] DistributedSortishSampler (#7056)

examples/seq2seq/finetune.py

@@ -3,7 +3,6 @@ import glob
 import logging
 import os
 import time
-import warnings
 from collections import defaultdict
 from pathlib import Path
 from typing import Dict, List, Tuple
@@ -67,6 +66,8 @@ class SummarizationModule(BaseTransformer):
     default_val_metric = "rouge2"
 
     def __init__(self, hparams, **kwargs):
+        if hparams.sortish_sampler and hparams.gpus > 1:
+            hparams.replace_sampler_ddp = False
         super().__init__(hparams, num_labels=None, mode=self.mode, **kwargs)
         use_task_specific_params(self.model, "summarization")
         save_git_info(self.hparams.output_dir)
@@ -93,9 +94,6 @@ class SummarizationModule(BaseTransformer):
             "val": self.hparams.val_max_target_length,
             "test": self.hparams.test_max_target_length,
         }
-        if self.hparams.sortish_sampler and self.hparams.gpus > 1:
-            self.hparams.sortish_sampler = False
-            warnings.warn("ignoring sortish_sampler as it is unsupported on multiple GPUs")
         assert self.target_lens["train"] <= self.target_lens["val"], f"target_lens: {self.target_lens}"
         assert self.target_lens["train"] <= self.target_lens["test"], f"target_lens: {self.target_lens}"
 
@@ -257,8 +255,7 @@ class SummarizationModule(BaseTransformer):
         dataset = self.get_dataset(type_path)
         sampler = None
         if self.hparams.sortish_sampler and type_path == "train":
-            assert self.hparams.gpus <= 1  # this should never break because of the assertion in __init__
-            sampler = dataset.make_sortish_sampler(batch_size)
+            sampler = dataset.make_sortish_sampler(batch_size, distributed=self.hparams.gpus > 1)
             shuffle = False
 
         dataloader = DataLoader(

examples/seq2seq/test_seq2seq_examples.py

@@ -149,9 +149,9 @@ class TestSummarizationDistiller(unittest.TestCase):
             no_teacher=True,
             freeze_encoder=True,
             gpus=2,
-            sortish_sampler=False,
+            sortish_sampler=True,
         )
-        self._test_distiller_cli(updates)
+        self._test_distiller_cli(updates, check_contents=False)
 
     def test_distill_no_teacher(self):
         updates = dict(student_encoder_layers=2, student_decoder_layers=1, no_teacher=True)

examples/seq2seq/utils.py

 import itertools
 import json
 import linecache
+import math
 import os
 import pickle
 from logging import getLogger
@@ -10,6 +11,7 @@ from typing import Callable, Dict, Iterable, List, Union
 import git
 import numpy as np
 import torch
+import torch.distributed as dist
 from rouge_score import rouge_scorer, scoring
 from sacrebleu import corpus_bleu
 from torch import nn
@@ -111,8 +113,11 @@ class AbstractSeq2SeqDataset(Dataset):
     def get_char_lens(data_file):
         return [len(x) for x in Path(data_file).open().readlines()]
 
-    def make_sortish_sampler(self, batch_size):
-        return SortishSampler(self.src_lens, batch_size)
+    def make_sortish_sampler(self, batch_size, distributed=False):
+        if distributed:
+            return DistributedSortishSampler(self, batch_size)
+        else:
+            return SortishSampler(self.src_lens, batch_size)
 
     def __getitem__(self, item):
         raise NotImplementedError("You must implement this")
@@ -191,24 +196,77 @@ class SortishSampler(Sampler):
     def __init__(self, data, batch_size):
         self.data, self.bs = data, batch_size
 
-    def key(self, i):
-        return self.data[i]
-
     def __len__(self) -> int:
         return len(self.data)
 
     def __iter__(self):
-        idxs = np.random.permutation(len(self.data))
-        sz = self.bs * 50
-        ck_idx = [idxs[i : i + sz] for i in range(0, len(idxs), sz)]
-        sort_idx = np.concatenate([sorted(s, key=self.key, reverse=True) for s in ck_idx])
-        sz = self.bs
-        ck_idx = [sort_idx[i : i + sz] for i in range(0, len(sort_idx), sz)]
-        max_ck = np.argmax([self.key(ck[0]) for ck in ck_idx])  # find the chunk with the largest key,
-        ck_idx[0], ck_idx[max_ck] = ck_idx[max_ck], ck_idx[0]  # then make sure it goes first.
-        sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=np.int)
-        sort_idx = np.concatenate((ck_idx[0], sort_idx))
-        return iter(sort_idx)
+        return iter(sortish_sampler_indices(self.data, self.bs))
+
+
+def sortish_sampler_indices(data: List, bs: int) -> np.array:
+    "Go through the text data by order of src length with a bit of randomness. From fastai repo."
+
+    def key_fn(i):
+        return data[i]
+
+    idxs = np.random.permutation(len(data))
+    sz = bs * 50
+    ck_idx = [idxs[i : i + sz] for i in range(0, len(idxs), sz)]
+    sort_idx = np.concatenate([sorted(s, key=key_fn, reverse=True) for s in ck_idx])
+    sz = bs
+    ck_idx = [sort_idx[i : i + sz] for i in range(0, len(sort_idx), sz)]
+    max_ck = np.argmax([key_fn(ck[0]) for ck in ck_idx])  # find the chunk with the largest key,
+    ck_idx[0], ck_idx[max_ck] = ck_idx[max_ck], ck_idx[0]  # then make sure it goes first.
+    sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=np.int)
+    sort_idx = np.concatenate((ck_idx[0], sort_idx))
+    return sort_idx
+
+
+class DistributedSortishSampler(Sampler):
+    """Copied from torch DistributedSampler"""
+
+    def __init__(self, dataset, batch_size, num_replicas=None, rank=None):
+        if num_replicas is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            num_replicas = dist.get_world_size()
+        if rank is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            rank = dist.get_rank()
+        self.dataset = dataset
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.epoch = 0
+        self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
+        self.total_size = self.num_samples * self.num_replicas
+        self.batch_size = batch_size
+
+    def __iter__(self) -> Iterable:
+        g = torch.Generator()
+        g.manual_seed(self.epoch)
+        available_indices = self.get_indices_for_rank()  # indices[self.rank: self.total_size: self.num_replicas]
+
+        sortish_data = [self.dataset.src_lens[i] for i in available_indices]
+        sortish_indices = sortish_sampler_indices(sortish_data, self.batch_size)
+        indices = [available_indices[i] for i in sortish_indices]
+        assert len(indices) == self.num_samples
+        return iter(indices)
+
+    def get_indices_for_rank(self) -> np.array:
+        indices = list(range(len(self.dataset)))
+        # add extra samples to make it evenly divisible
+        indices += indices[: (self.total_size - len(indices))]
+        assert len(indices) == self.total_size
+        # subsample
+        available_indices = indices[self.rank : self.total_size : self.num_replicas]
+        return available_indices
+
+    def __len__(self):
+        return self.num_samples
+
+    def set_epoch(self, epoch):
+        self.epoch = epoch
 
 
 logger = getLogger(__name__)