Merge branch 'master' of github.com:keras-team/keras

keras/backend/jax/core.py

@@ -61,11 +61,11 @@ def convert_to_tensor(x, dtype=None, sparse=None):
         if dtype and dtype != x.dtype:
             return x.value.astype(dtype)
         return x.value
-    return jnp.array(x, dtype=dtype)
+    return jnp.asarray(x, dtype=dtype)
 
 
 def convert_to_numpy(x):
-    return np.array(x)
+    return np.asarray(x)
 
 
 def is_tensor(x):

keras/backend/jax/trainer.py

@@ -904,11 +904,10 @@ def distribute_single_value(d):
 
 
 class JAXEpochIterator(EpochIterator):
-    def _get_iterator(self, return_type="auto"):
-        if return_type in ("np", "auto"):
-            # enable prefetching when using numpy_iterator
-            return self._prefetch_numpy_iterator(super()._get_iterator("np"))
-        return super()._get_iterator(return_type)
+    def _get_iterator(self):
+        return self._prefetch_numpy_iterator(
+            self.data_adapter.get_jax_iterator()
+        )
 
     def _prefetch_numpy_iterator(self, numpy_iterator):
         """Shard and prefetch batches on device.

keras/backend/numpy/trainer.py

@@ -198,7 +198,7 @@ def append_to_outputs(batch_outputs, outputs):
         self.stop_predicting = False
         callbacks.on_predict_begin()
         outputs = None
-        for step, data in epoch_iterator.enumerate_epoch(return_type="np"):
+        for step, data in epoch_iterator.enumerate_epoch():
             callbacks.on_predict_batch_begin(step)
             batch_outputs = self.predict_function(data)
             outputs = append_to_outputs(batch_outputs, outputs)
@@ -242,7 +242,7 @@ def evaluate(
 
         if not all(layer.built for layer in self._flatten_layers()):
             # Build the model on one batch of data.
-            for _, data in epoch_iterator.enumerate_epoch(return_type="np"):
+            for _, data in epoch_iterator.enumerate_epoch():
                 data_batch = data[0]
                 self._symbolic_build(data_batch)
                 break
@@ -264,7 +264,7 @@ def evaluate(
         callbacks.on_test_begin()
         logs = None
         self.reset_metrics()
-        for step, data in epoch_iterator.enumerate_epoch(return_type="np"):
+        for step, data in epoch_iterator.enumerate_epoch():
             callbacks.on_test_batch_begin(step)
             logs = self.test_function(data)
             callbacks.on_test_batch_end(step, self._pythonify_logs(logs))

keras/backend/tensorflow/core.py

@@ -126,7 +126,7 @@ def convert_to_numpy(x):
         x.set_shape(x_shape)
     elif isinstance(x, tf.IndexedSlices):
         x = tf.convert_to_tensor(x)
-    return np.array(x)
+    return np.asarray(x)
 
 
 def is_tensor(x):

keras/backend/tensorflow/trainer.py

@@ -629,14 +629,17 @@ class TFEpochIterator(EpochIterator):
     def __init__(self, distribute_strategy=None, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self._distribute_strategy = distribute_strategy
-        dataset = self.data_adapter.get_tf_dataset()
+        dataset = self._get_iterator()
         if not isinstance(dataset, tf.distribute.DistributedDataset):
             dataset = self._distribute_strategy.experimental_distribute_dataset(
                 dataset
             )
         self._distributed_dataset = dataset
         self._steps_seen = 0
 
+    def _get_iterator(self):
+        return self.data_adapter.get_tf_dataset()
+
     def enumerate_epoch(self):
         if self.steps_per_epoch:
             if not self._current_iterator:

keras/backend/torch/trainer.py

@@ -1,5 +1,3 @@
-import collections
-import itertools
 import warnings
 
 import numpy as np
@@ -10,7 +8,6 @@
 from keras import backend
 from keras import callbacks as callbacks_module
 from keras import optimizers as optimizers_module
-from keras.trainers import data_adapters
 from keras.trainers import trainer as base_trainer
 from keras.trainers.data_adapters import data_adapter_utils
 from keras.trainers.epoch_iterator import EpochIterator
@@ -496,40 +493,5 @@ def predict_on_batch(self, x):
 
 
 class TorchEpochIterator(EpochIterator):
-    def _get_iterator(self, return_type="auto"):
-        if return_type == "auto" and isinstance(
-            self.data_adapter, data_adapters.TorchDataLoaderAdapter
-        ):
-            return self.data_adapter.get_torch_dataloader()
-        elif return_type in ("np", "auto"):
-            # enable prefetching when using numpy_iterator
-            return self._prefetch_numpy_iterator(super()._get_iterator("np"))
-        return super()._get_iterator(return_type)
-
-    def _prefetch_numpy_data(self, data):
-        return tree.map_structure(backend.convert_to_tensor, data)
-
-    def _prefetch_numpy_iterator(self, numpy_iterator):
-        """Prefetch batches on device.
-
-        The idea has been borrowed from
-        `torchtnt.utils.data.CudaDataPrefetcher`
-
-        This utility takes an iterator and returns a new iterator which fills an
-        on device prefetch buffer. Eager prefetching can improve the performance
-        of training loops significantly by overlapping compute and data
-        transfer.
-        """
-        queue = collections.deque()
-
-        # If you're training on GPUs, 2 is generally the best choice because
-        # this guarantees that you can overlap a training step on GPU with a
-        # data prefetch step on CPU.
-        def enqueue(n=2):
-            for data in itertools.islice(numpy_iterator, n):
-                queue.append(self._prefetch_numpy_data(data))
-
-        enqueue(n=2)  # TODO: should we make `n` configurable?
-        while queue:
-            yield queue.popleft()
-            enqueue(1)
+    def _get_iterator(self):
+        return self.data_adapter.get_torch_dataloader()

keras/layers/normalization/batch_normalization_test.py

@@ -92,8 +92,10 @@ def test_correctness(
         broadcast_shape = [1] * len(input_shape)
         broadcast_shape[axis] = input_shape[axis]
         out = backend.convert_to_numpy(out)
-        out -= np.reshape(backend.convert_to_numpy(layer.beta), broadcast_shape)
-        out /= np.reshape(
+        out = out - np.reshape(
+            backend.convert_to_numpy(layer.beta), broadcast_shape
+        )
+        out = out / np.reshape(
             backend.convert_to_numpy(layer.gamma), broadcast_shape
         )
 
@@ -200,8 +202,12 @@ def test_trainable_behavior(self):
             out = layer(x, training=True)
 
         out = backend.convert_to_numpy(out)
-        out -= np.reshape(backend.convert_to_numpy(layer.beta), (1, 1, 1, 3))
-        out /= np.reshape(backend.convert_to_numpy(layer.gamma), (1, 1, 1, 3))
+        out = out - np.reshape(
+            backend.convert_to_numpy(layer.beta), (1, 1, 1, 3)
+        )
+        out = out / np.reshape(
+            backend.convert_to_numpy(layer.gamma), (1, 1, 1, 3)
+        )
 
         self.assertAllClose(np.mean(out, axis=(0, 1, 2)), 0.0, atol=1e-3)
         self.assertAllClose(np.std(out, axis=(0, 1, 2)), 1.0, atol=1e-3)

keras/trainers/data_adapters/array_data_adapter.py

@@ -6,6 +6,7 @@
 from keras import backend
 from keras.trainers.data_adapters import data_adapter_utils
 from keras.trainers.data_adapters.data_adapter import DataAdapter
+from keras.utils.dataset_utils import is_torch_tensor
 from keras.utils.nest import lists_to_tuples
 
 try:
@@ -98,13 +99,23 @@ def __init__(
 
     def get_numpy_iterator(self):
         inputs = self._inputs
-        if self._shuffle:
+        if self._shuffle and self._shuffle != "batch":
             inputs = data_adapter_utils.sync_shuffle(
                 inputs, num_samples=self._num_samples
             )
         for i in range(self._size):
-            start, stop = i * self._batch_size, (i + 1) * self._batch_size
-            yield tree.map_structure(lambda x: x[start:stop], inputs)
+            start = i * self._batch_size
+            stop = min((i + 1) * self._batch_size, self._num_samples)
+            if self._shuffle == "batch":
+
+                def slice_and_shuffle(x):
+                    return data_adapter_utils.sync_shuffle(
+                        x[start:stop], num_samples=(stop - start)
+                    )
+
+                yield tree.map_structure(slice_and_shuffle, inputs)
+            else:
+                yield tree.map_structure(lambda x: x[start:stop], inputs)
 
     def get_tf_dataset(self):
         from keras.utils.module_utils import tensorflow as tf
@@ -237,6 +248,62 @@ def shuffle_batch(*batch):
         dataset = dataset.with_options(options)
         return dataset.prefetch(tf.data.AUTOTUNE)
 
+    def get_jax_iterator(self):
+        return data_adapter_utils.get_jax_iterator(self.get_numpy_iterator())
+
+    def get_torch_dataloader(self):
+        import torch
+
+        from keras.backend.torch.core import convert_to_tensor
+
+        class ArrayDataset(torch.utils.data.Dataset):
+            def __init__(self, array):
+                self.array = array
+
+            def __getitem__(self, index):
+                def slice_and_convert(x):
+                    return convert_to_tensor(x[index])
+
+                return tree.map_structure(slice_and_convert, self.array)
+
+            def __len__(self):
+                return len(self.array[0])
+
+        class RandomBatchSampler(torch.utils.data.Sampler):
+            def __init__(self, sampler):
+                self.sampler = sampler
+
+            def __iter__(self):
+                for batch in self.sampler:
+                    yield [batch[i] for i in torch.randperm(len(batch))]
+
+            def __len__(self):
+                return len(self.sampler)
+
+        if self._shuffle == "batch":
+            batch_sampler = RandomBatchSampler(
+                torch.utils.data.BatchSampler(
+                    range(self._num_samples),
+                    batch_size=self._batch_size,
+                    drop_last=False,
+                )
+            )
+        elif self._shuffle:
+            batch_sampler = torch.utils.data.BatchSampler(
+                torch.utils.data.RandomSampler(range(self._num_samples)),
+                batch_size=self._batch_size,
+                drop_last=False,
+            )
+        else:
+            batch_sampler = torch.utils.data.BatchSampler(
+                torch.utils.data.SequentialSampler(range(self._num_samples)),
+                batch_size=self._batch_size,
+                drop_last=False,
+            )
+
+        dataset = ArrayDataset(self._inputs)
+        return torch.utils.data.DataLoader(dataset, batch_sampler=batch_sampler)
+
     @property
     def num_batches(self):
         return self._size
@@ -315,7 +382,9 @@ def convert_single_array(x):
             # `torch.Tensor`, as well as any other tensor-like object that has
             # added numpy support.
             if hasattr(x, "__array__"):
-                x = backend.convert_to_numpy(x)
+                if is_torch_tensor(x):
+                    x = x.cpu()
+                x = np.asarray(x)
             else:
                 raise ValueError(
                     "Expected a NumPy array, tf.Tensor, tf.RaggedTensor, "