perf(imperative/data): improve dataloader preformance

GitOrigin-RevId: 7d8d52aaeb

imperative/python/megengine/data/sampler.py

@@ -2,6 +2,7 @@
 import collections.abc
 import math
 from abc import ABC, abstractmethod
+from itertools import count
 from typing import Any, Generator, Iterator, List, Union
 
 import numpy as np
@@ -126,13 +127,15 @@ class MapSampler(Sampler):
         if self.world_size > 1:
             indices = self.scatter(indices)
 
-        step, length = self.batch_size, len(indices)
-        batch_index = [indices[i : i + step] for i in range(0, length, step)]
+        batch = []
+        for idx in indices:
+            batch.append(idx)
+            if len(batch) == self.batch_size:
+                yield batch
+                batch = []
 
-        if self.drop_last and len(batch_index[-1]) < self.batch_size:
-            batch_index.pop()
-
-        return iter(batch_index)
+        if len(batch) > 0 and not self.drop_last:
+            yield batch
 
 
 class StreamSampler(Sampler):
@@ -151,10 +154,18 @@ class StreamSampler(Sampler):
         self.batch_size = batch_size
 
     def __iter__(self):
-        return self
+        return self.batch()
 
-    def __next__(self):
-        return iter(range(self.batch_size))
+    def batch(self):
+        batch = []
+        for idx in self.sample():
+            batch.append(idx)
+            if len(batch) == self.batch_size:
+                yield batch
+                batch = []
+
+    def sample(self):
+        return count(start=0)
 
 
 class SequentialSampler(MapSampler):

imperative/python/test/unit/data/test_dataloader.py

@@ -1,4 +1,12 @@
 # -*- coding: utf-8 -*-
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+import math
 import os
 import platform
 import time
@@ -7,7 +15,7 @@ import numpy as np
 import pytest
 
 from megengine.data.collator import Collator
-from megengine.data.dataloader import DataLoader
+from megengine.data.dataloader import DataLoader, get_worker_info
 from megengine.data.dataset import ArrayDataset, StreamDataset
 from megengine.data.sampler import RandomSampler, SequentialSampler, StreamSampler
 from megengine.data.transform import (
@@ -29,14 +37,10 @@ def init_dataset():
 
 def test_dataloader_init():
     dataset = init_dataset()
-    with pytest.raises(ValueError):
-        dataloader = DataLoader(dataset, num_workers=2, divide=True)
     with pytest.raises(ValueError):
         dataloader = DataLoader(dataset, num_workers=-1)
     with pytest.raises(ValueError):
         dataloader = DataLoader(dataset, timeout=-1)
-    with pytest.raises(ValueError):
-        dataloader = DataLoader(dataset, num_workers=0, divide=True)
 
     dataloader = DataLoader(dataset)
     assert isinstance(dataloader.sampler, SequentialSampler)
@@ -54,10 +58,8 @@ def test_dataloader_init():
 
 
 class MyStream(StreamDataset):
-    def __init__(self, number, batch=False, error_foramt=False, block=False):
+    def __init__(self, number, block=False):
         self.number = number
-        self.batch = batch
-        self.error_format = error_foramt
         self.block = block
 
     def __iter__(self):
@@ -65,22 +67,14 @@ class MyStream(StreamDataset):
             if self.block:
                 for _ in range(10):
                     time.sleep(1)
-            if self.batch:
-                data = np.random.randint(0, 256, (2, 2, 2, 3), dtype="uint8")
-                yield (True, (data, [cnt, cnt - self.number]))
-            else:
-                data = np.random.randint(0, 256, (2, 2, 3), dtype="uint8")
-                if self.error_format:
-                    yield (data, cnt)
-                else:
-                    yield (False, (data, cnt))
+            data = np.random.randint(0, 256, (2, 2, 3), dtype="uint8")
+            yield (data, cnt)
         raise StopIteration
 
 
-@pytest.mark.parametrize("batch", [True, False])
 @pytest.mark.parametrize("num_workers", [0, 2])
-def test_stream_dataloader(batch, num_workers):
-    dataset = MyStream(100, batch=batch)
+def test_stream_dataloader(num_workers):
+    dataset = MyStream(100)
     sampler = StreamSampler(batch_size=4)
     dataloader = DataLoader(
         dataset,
@@ -90,7 +84,6 @@ def test_stream_dataloader(batch, num_workers):
     )
 
     check_set = set()
-
     for step, data in enumerate(dataloader):
         if step == 10:
             break
@@ -101,18 +94,9 @@ def test_stream_dataloader(batch, num_workers):
             check_set.add(i)
 
 
-def test_stream_dataloader_error():
-    dataset = MyStream(100, error_foramt=True)
-    sampler = StreamSampler(batch_size=4)
-    dataloader = DataLoader(dataset, sampler)
-    with pytest.raises(AssertionError, match=r".*tuple.*"):
-        data_iter = iter(dataloader)
-        next(data_iter)
-
-
 @pytest.mark.parametrize("num_workers", [0, 2])
 def test_stream_dataloader_timeout(num_workers):
-    dataset = MyStream(100, False, block=True)
+    dataset = MyStream(100, block=True)
     sampler = StreamSampler(batch_size=4)
 
     dataloader = DataLoader(dataset, sampler, num_workers=num_workers, timeout=2)
@@ -140,17 +124,6 @@ def test_dataloader_parallel():
         dataset,
         sampler=RandomSampler(dataset, batch_size=4, drop_last=False),
         num_workers=2,
-        divide=False,
-    )
-    for (data, label) in dataloader:
-        assert data.shape == (4, 1, 32, 32)
-        assert label.shape == (4,)
-
-    dataloader = DataLoader(
-        dataset,
-        sampler=RandomSampler(dataset, batch_size=4, drop_last=False),
-        num_workers=2,
-        divide=True,
     )
     for (data, label) in dataloader:
         assert data.shape == (4, 1, 32, 32)
@@ -205,7 +178,7 @@ def test_dataloader_parallel_worker_exception():
         transform=FakeErrorTransform(),
         num_workers=2,
     )
-    with pytest.raises(RuntimeError, match=r"worker.*died"):
+    with pytest.raises(RuntimeError, match=r"exited unexpectedly"):
         data_iter = iter(dataloader)
         batch_data = next(data_iter)
 
@@ -213,26 +186,23 @@ def test_dataloader_parallel_worker_exception():
 def _multi_instances_parallel_dataloader_worker():
     dataset = init_dataset()
 
-    for divide_flag in [True, False]:
-        train_dataloader = DataLoader(
-            dataset,
-            sampler=RandomSampler(dataset, batch_size=4, drop_last=False),
-            num_workers=2,
-            divide=divide_flag,
-        )
-        val_dataloader = DataLoader(
-            dataset,
-            sampler=RandomSampler(dataset, batch_size=10, drop_last=False),
-            num_workers=2,
-            divide=divide_flag,
-        )
-        for idx, (data, label) in enumerate(train_dataloader):
-            assert data.shape == (4, 1, 32, 32)
-            assert label.shape == (4,)
-            if idx % 5 == 0:
-                for val_data, val_label in val_dataloader:
-                    assert val_data.shape == (10, 1, 32, 32)
-                    assert val_label.shape == (10,)
+    train_dataloader = DataLoader(
+        dataset,
+        sampler=RandomSampler(dataset, batch_size=4, drop_last=False),
+        num_workers=2,
+    )
+    val_dataloader = DataLoader(
+        dataset,
+        sampler=RandomSampler(dataset, batch_size=10, drop_last=False),
+        num_workers=2,
+    )
+    for idx, (data, label) in enumerate(train_dataloader):
+        assert data.shape == (4, 1, 32, 32)
+        assert label.shape == (4,)
+        if idx % 5 == 0:
+            for val_data, val_label in val_dataloader:
+                assert val_data.shape == (10, 1, 32, 32)
+                assert val_label.shape == (10,)
 
 
 def test_dataloader_parallel_multi_instances():
@@ -261,18 +231,81 @@ def test_dataloader_parallel_multi_instances_multiprocessing():
         assert p.exitcode == 0
 
 
-@pytest.mark.parametrize("num_workers", [0, 2])
-def test_timeout_event(num_workers):
-    def cb():
-        return (True, (np.zeros(shape=(2, 2, 2, 3)), np.ones(shape=(2,))))
+def partition(ls, size):
+    return [ls[i : i + size] for i in range(0, len(ls), size)]
 
-    dataset = MyStream(100, block=True)
+
+class MyPreStream(StreamDataset):
+    def __init__(self, number, block=False):
+        self.number = [i for i in range(number)]
+        self.block = block
+        self.data = []
+        for i in range(100):
+            self.data.append(np.random.randint(0, 256, (2, 2, 3), dtype="uint8"))
+
+    def __iter__(self):
+        worker_info = get_worker_info()
+        per_worker = int(math.ceil((len(self.data)) / float(worker_info.worker)))
+        pre_data = iter(partition(self.data, per_worker)[worker_info.idx])
+        pre_cnt = partition(self.number, per_worker)[worker_info.idx]
+        for cnt in pre_cnt:
+            if self.block:
+                for _ in range(10):
+                    time.sleep(1)
+            yield (next(pre_data), cnt)
+        raise StopIteration
+
+
+@pytest.mark.skipif(
+    platform.system() == "Windows",
+    reason="dataloader do not support parallel on windows",
+)
+def test_prestream_dataloader_multiprocessing():
+    dataset = MyPreStream(100)
     sampler = StreamSampler(batch_size=4)
+    dataloader = DataLoader(
+        dataset,
+        sampler,
+        Compose([Normalize(mean=(103, 116, 123), std=(57, 57, 58)), ToMode("CHW")]),
+        num_workers=2,
+        parallel_stream=True,
+    )
+
+    check_set = set()
+
+    for step, data in enumerate(dataloader):
+        if step == 10:
+            break
+        assert data[0].shape == (4, 3, 2, 2)
+        assert data[1].shape == (4,)
+        for i in data[1]:
+            assert i not in check_set
+            check_set.add(i)
+
+
+@pytest.mark.skipif(
+    platform.system() == "Windows",
+    reason="dataloader do not support parallel on windows",
+)
+def test_predataloader_parallel_worker_exception():
+    dataset = MyPreStream(100)
+
+    class FakeErrorTransform(Transform):
+        def __init__(self):
+            pass
+
+        def apply(self, input):
+            raise RuntimeError("test raise error")
+            return input
 
     dataloader = DataLoader(
-        dataset, sampler, num_workers=num_workers, timeout=2, timeout_event=cb
+        dataset,
+        sampler=StreamSampler(batch_size=4),
+        transform=FakeErrorTransform(),
+        num_workers=2,
+        parallel_stream=True,
     )
-    for _, data in enumerate(dataloader):
-        np.testing.assert_equal(data[0], np.zeros(shape=(4, 2, 2, 3)))
-        np.testing.assert_equal(data[1], np.ones(shape=(4,)))
-        break
+    with pytest.raises(RuntimeError, match=r"exited unexpectedly"):
+        data_iter = iter(dataloader)
+        batch_data = next(data_iter)
+        print(batch_data.shape)

imperative/python/test/unit/data/test_pre_dataloader.py

@@ -1,5 +1,13 @@
 # -*- coding: utf-8 -*-
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 import gc
+import math
 import os
 import platform
 import time
@@ -8,7 +16,7 @@ import numpy as np
 import pytest
 
 from megengine.data.collator import Collator
-from megengine.data.dataloader import DataLoader
+from megengine.data.dataloader import DataLoader, get_worker_info
 from megengine.data.dataset import ArrayDataset, StreamDataset
 from megengine.data.sampler import RandomSampler, SequentialSampler, StreamSampler
 from megengine.data.transform import (
@@ -30,14 +38,10 @@ def init_dataset():
 
 def test_dataloader_init():
     dataset = init_dataset()
-    with pytest.raises(ValueError):
-        dataloader = DataLoader(dataset, num_workers=2, divide=True)
     with pytest.raises(ValueError):
         dataloader = DataLoader(dataset, num_workers=-1)
     with pytest.raises(ValueError):
         dataloader = DataLoader(dataset, timeout=-1)
-    with pytest.raises(ValueError):
-        dataloader = DataLoader(dataset, num_workers=0, divide=True)
 
     dataloader = DataLoader(dataset, preload=True)
     assert isinstance(dataloader.sampler, SequentialSampler)
@@ -59,10 +63,8 @@ def test_dataloader_init():
 
 
 class MyStream(StreamDataset):
-    def __init__(self, number, batch=False, error_foramt=False, block=False):
+    def __init__(self, number, block=False):
         self.number = number
-        self.batch = batch
-        self.error_format = error_foramt
         self.block = block
 
     def __iter__(self):
@@ -70,22 +72,14 @@ class MyStream(StreamDataset):
             if self.block:
                 for _ in range(10):
                     time.sleep(1)
-            if self.batch:
-                data = np.random.randint(0, 256, (2, 2, 2, 3), dtype="uint8")
-                yield (True, (data, [cnt, cnt - self.number]))
-            else:
-                data = np.random.randint(0, 256, (2, 2, 3), dtype="uint8")
-                if self.error_format:
-                    yield (data, cnt)
-                else:
-                    yield (False, (data, cnt))
+            data = np.random.randint(0, 256, (2, 2, 3), dtype="uint8")
+            yield (data, cnt)
         raise StopIteration
 
 
-@pytest.mark.parametrize("batch", [True, False])
 @pytest.mark.parametrize("num_workers", [0, 2])
-def test_stream_dataloader(batch, num_workers):
-    dataset = MyStream(100, batch=batch)
+def test_stream_dataloader(num_workers):
+    dataset = MyStream(100)
     sampler = StreamSampler(batch_size=4)
     dataloader = DataLoader(
         dataset,
@@ -107,18 +101,9 @@ def test_stream_dataloader(batch, num_workers):
             check_set.add(i)
 
 
-def test_stream_dataloader_error():
-    dataset = MyStream(100, error_foramt=True)
-    sampler = StreamSampler(batch_size=4)
-    dataloader = DataLoader(dataset, sampler, preload=True)
-    with pytest.raises(AssertionError, match=r".*tuple.*"):
-        data_iter = iter(dataloader)
-        next(data_iter)
-
-
 @pytest.mark.parametrize("num_workers", [0, 2])
 def test_stream_dataloader_timeout(num_workers):
-    dataset = MyStream(100, False, block=True)
+    dataset = MyStream(100, block=True)
     sampler = StreamSampler(batch_size=4)
 
     dataloader = DataLoader(
@@ -150,18 +135,6 @@ def test_dataloader_parallel():
         dataset,
         sampler=RandomSampler(dataset, batch_size=4, drop_last=False),
         num_workers=2,
-        divide=False,
-        preload=True,
-    )
-    for (data, label) in dataloader:
-        assert data._tuple_shape == (4, 1, 32, 32)
-        assert label._tuple_shape == (4,)
-
-    dataloader = DataLoader(
-        dataset,
-        sampler=RandomSampler(dataset, batch_size=4, drop_last=False),
-        num_workers=2,
-        divide=True,
         preload=True,
     )
     for (data, label) in dataloader:
@@ -219,7 +192,7 @@ def test_dataloader_parallel_worker_exception():
         num_workers=2,
         preload=True,
     )
-    with pytest.raises(RuntimeError, match=r"worker.*died"):
+    with pytest.raises(RuntimeError, match=r"exited unexpectedly"):
         data_iter = iter(dataloader)
         batch_data = next(data_iter)
 
@@ -227,28 +200,25 @@ def test_dataloader_parallel_worker_exception():
 def _multi_instances_parallel_dataloader_worker():
     dataset = init_dataset()
 
-    for divide_flag in [True, False]:
-        train_dataloader = DataLoader(
-            dataset,
-            sampler=RandomSampler(dataset, batch_size=4, drop_last=False),
-            num_workers=2,
-            divide=divide_flag,
-            preload=True,
-        )
-        val_dataloader = DataLoader(
-            dataset,
-            sampler=RandomSampler(dataset, batch_size=10, drop_last=False),
-            num_workers=2,
-            divide=divide_flag,
-            preload=True,
-        )
-        for idx, (data, label) in enumerate(train_dataloader):
-            assert data._tuple_shape == (4, 1, 32, 32)
-            assert label._tuple_shape == (4,)
-            if idx % 5 == 0:
-                for val_data, val_label in val_dataloader:
-                    assert val_data._tuple_shape == (10, 1, 32, 32)
-                    assert val_label._tuple_shape == (10,)
+    train_dataloader = DataLoader(
+        dataset,
+        sampler=RandomSampler(dataset, batch_size=4, drop_last=False),
+        num_workers=2,
+        preload=True,
+    )
+    val_dataloader = DataLoader(
+        dataset,
+        sampler=RandomSampler(dataset, batch_size=10, drop_last=False),
+        num_workers=2,
+        preload=True,
+    )
+    for idx, (data, label) in enumerate(train_dataloader):
+        assert data._tuple_shape == (4, 1, 32, 32)
+        assert label._tuple_shape == (4,)
+        if idx % 5 == 0:
+            for val_data, val_label in val_dataloader:
+                assert val_data._tuple_shape == (10, 1, 32, 32)
+                assert val_label._tuple_shape == (10,)
 
 
 def test_dataloader_parallel_multi_instances():
@@ -276,25 +246,3 @@ def test_dataloader_parallel_multi_instances_multiprocessing():
     for p in processes:
         p.join()
         assert p.exitcode == 0
-
-
-@pytest.mark.parametrize("num_workers", [0, 2])
-def test_timeout_event(num_workers):
-    def cb():
-        return (True, (np.zeros(shape=(2, 2, 2, 3)), np.ones(shape=(2,))))
-
-    dataset = MyStream(100, block=True)
-    sampler = StreamSampler(batch_size=4)
-
-    dataloader = DataLoader(
-        dataset,
-        sampler,
-        num_workers=num_workers,
-        timeout=2,
-        timeout_event=cb,
-        preload=True,
-    )
-    for _, data in enumerate(dataloader):
-        np.testing.assert_equal(data[0], np.zeros(shape=(4, 2, 2, 3)))
-        np.testing.assert_equal(data[1], np.ones(shape=(4,)))
-        break