[numpy] [DO NOT MERGE] Fix d2l chapters 9 and 13 (apache#15246)

* Add npx batch_dot and topk

* Text embedding uses numpy

* Fix SoftmaxCrossEntropyLoss with np

* Fix sentiment cnn

* Fix pylint

* Fix dot attention

* Fix seq2seq attention

* Add np.tile

* Fix transformer

* Fix ci

* Fix ci and rebase

python/mxnet/_numpy_op_doc.py

@@ -86,3 +86,26 @@ def _np_zeros_like(a):
         Array of zeros with the same shape and type as `a`.
     """
     pass
+
+
+def _np_repeat(a, repeats, axis=None):
+    """Repeat elements of an array.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array.
+    repeats : int or array of ints
+        The number of repetitions for each element.  `repeats` is broadcasted
+        to fit the shape of the given axis.
+    axis : int, optional
+        The axis along which to repeat values.  By default, use the
+        flattened input array, and return a flat output array.
+
+    Returns
+    -------
+    repeated_array : ndarray
+        Output array which has the same shape as `a`, except along
+        the given axis.
+    """
+    pass

python/mxnet/contrib/text/embedding.py

@@ -35,6 +35,9 @@
 from ... import ndarray as nd
 from ... import registry
 from ... import base
+from ...util import is_np_array
+from ... import numpy as _mx_np
+from ... import numpy_extension as _mx_npx
 
 
 def register(embedding_cls):
@@ -295,12 +298,15 @@ def _load_embedding(self, pretrained_file_path, elem_delim, init_unknown_vec, en
                     tokens.add(token)
 
         self._vec_len = vec_len
-        self._idx_to_vec = nd.array(all_elems).reshape((-1, self.vec_len))
+        array_fn = _mx_np.array if is_np_array() else nd.array
+        self._idx_to_vec = array_fn(all_elems).reshape((-1, self.vec_len))
 
         if loaded_unknown_vec is None:
-            self._idx_to_vec[C.UNKNOWN_IDX] = init_unknown_vec(shape=self.vec_len)
+            init_val = init_unknown_vec(shape=self.vec_len)
+            self._idx_to_vec[C.UNKNOWN_IDX] =\
+                init_val.as_np_ndarray() if is_np_array() else init_val
         else:
-            self._idx_to_vec[C.UNKNOWN_IDX] = nd.array(loaded_unknown_vec)
+            self._idx_to_vec[C.UNKNOWN_IDX] = array_fn(loaded_unknown_vec)
 
     def _index_tokens_from_vocabulary(self, vocabulary):
         self._token_to_idx = vocabulary.token_to_idx.copy() \
@@ -328,7 +334,8 @@ def _set_idx_to_vec_by_embeddings(self, token_embeddings, vocab_len, vocab_idx_t
         """
 
         new_vec_len = sum(embed.vec_len for embed in token_embeddings)
-        new_idx_to_vec = nd.zeros(shape=(vocab_len, new_vec_len))
+        zeros_fn = _mx_np.zeros if is_np_array() else nd.zeros
+        new_idx_to_vec = zeros_fn(shape=(vocab_len, new_vec_len))
 
         col_start = 0
         # Concatenate all the embedding vectors in token_embeddings.
@@ -397,7 +404,13 @@ def get_vecs_by_tokens(self, tokens, lower_case_backup=False):
                        else self.token_to_idx.get(token.lower(), C.UNKNOWN_IDX)
                        for token in tokens]
 
-        vecs = nd.Embedding(nd.array(indices), self.idx_to_vec, self.idx_to_vec.shape[0],
+        if is_np_array():
+            embedding_fn = _mx_npx.Embedding
+            array_fn = _mx_np.array
+        else:
+            embedding_fn = nd.Embedding
+            array_fn = nd.array
+        vecs = embedding_fn(array_fn(indices), self.idx_to_vec, self.idx_to_vec.shape[0],
                             self.idx_to_vec.shape[1])
 
         return vecs[0] if to_reduce else vecs
@@ -425,7 +438,8 @@ def update_token_vectors(self, tokens, new_vectors):
             if not isinstance(tokens, list):
                 tokens = [tokens]
             if len(new_vectors.shape) == 1:
-                new_vectors = new_vectors.expand_dims(0)
+                expand_dims_fn = _mx_np.expand_dims if is_np_array() else nd.expand_dims
+                new_vectors = expand_dims_fn(new_vectors, axis=0)
 
         else:
             assert isinstance(new_vectors, nd.NDArray) and len(new_vectors.shape) == 2, \
@@ -444,7 +458,8 @@ def update_token_vectors(self, tokens, new_vectors):
                                  '`unknown_token` %s in `tokens`. This is to avoid unintended '
                                  'updates.' % (token, self.idx_to_token[C.UNKNOWN_IDX]))
 
-        self._idx_to_vec[nd.array(indices)] = new_vectors
+        array_fn = _mx_np.array if is_np_array() else nd.array
+        self._idx_to_vec[array_fn(indices)] = new_vectors
 
     @classmethod
     def _check_pretrained_file_names(cls, pretrained_file_name):

python/mxnet/gluon/block.py

@@ -553,7 +553,7 @@ def __call__(self, *args):
         for hook in self._forward_hooks.values():
             hook(self, args, out)
         if _mx_npx.is_np_array():
-            _check_all_np_ndarrays(_flatten(out, "output")[0])
+            _check_all_np_ndarrays(out)
         return out
 
     def forward(self, *args):

python/mxnet/gluon/loss.py

@@ -357,17 +357,28 @@ def __init__(self, axis=-1, sparse_label=True, from_logits=False, weight=None,
         self._sparse_label = sparse_label
         self._from_logits = from_logits
 
-    @_adapt_np_array
     def hybrid_forward(self, F, pred, label, sample_weight=None):
+        if is_np_array():
+            log_softmax = F.npx.log_softmax
+            pick = F.npx.pick
+        else:
+            log_softmax = F.log_softmax
+            pick = F.pick
         if not self._from_logits:
-            pred = F.log_softmax(pred, self._axis)
+            pred = log_softmax(pred, self._axis)
         if self._sparse_label:
-            loss = -F.pick(pred, label, axis=self._axis, keepdims=True)
+            loss = -pick(pred, label, axis=self._axis, keepdims=True)
         else:
             label = _reshape_like(F, label, pred)
-            loss = -F.sum(pred * label, axis=self._axis, keepdims=True)
+            loss = -(pred * label).sum(axis=self._axis, keepdims=True)
         loss = _apply_weighting(F, loss, self._weight, sample_weight)
-        return F.mean(loss, axis=self._batch_axis, exclude=True)
+        if is_np_array():
+            if F is ndarray:
+                return loss.mean(axis=tuple(range(1, loss.ndim)))
+            else:
+                return F.npx.batch_flatten(loss).mean(axis=1)
+        else:
+            return loss.mean(axis=self._batch_axis, exclude=True)
 
 
 SoftmaxCELoss = SoftmaxCrossEntropyLoss

python/mxnet/gluon/nn/basic_layers.py

@@ -264,12 +264,13 @@ def __init__(self, rate, axes=(), **kwargs):
         self._rate = rate
         self._axes = axes
 
-    @_adapt_np_array
     def hybrid_forward(self, F, x):
         if self._rate > 0:
-            return F.Dropout(x, p=self._rate, axes=self._axes, name='fwd', cudnn_off=False)
+            dropout = F.npx.Dropout if is_np_array() else F.Dropout
+            return dropout(x, p=self._rate, axes=self._axes, name='fwd', cudnn_off=False)
         else:
-            return F.identity(x)
+            copy = F.np.copy if is_np_array() else F.identity
+            return copy(x)
 
     def __repr__(self):
         s = '{name}(p = {_rate}, axes={_axes})'
@@ -359,8 +360,9 @@ def cast(self, dtype):
             dtype = 'float32'
         super(BatchNorm, self).cast(dtype)
 
-    @_adapt_np_array
     def hybrid_forward(self, F, x, gamma, beta, running_mean, running_var):
+        if is_np_array():
+            F = F.npx
         return F.BatchNorm(x, gamma, beta, running_mean, running_var,
                            name='fwd', **self._kwargs)
 
@@ -611,10 +613,10 @@ def __init__(self, axis=-1, epsilon=1e-5, center=True, scale=True,
                                     shape=(in_channels,), init=beta_initializer,
                                     allow_deferred_init=True)
 
-    @_adapt_np_array
     def hybrid_forward(self, F, data, gamma, beta):
-        norm_data = F.LayerNorm(data, gamma=gamma, beta=beta, axis=self._axis, eps=self._epsilon)
-        return norm_data
+        if is_np_array():
+            F = F.npx
+        return F.LayerNorm(data, gamma=gamma, beta=beta, axis=self._axis, eps=self._epsilon)
 
     def __repr__(self):
         s = '{name}({content}'

python/mxnet/gluon/parameter.py

@@ -369,7 +369,10 @@ def _reduce(self):
         ctx = context.cpu()
         if self._stype == 'default':
             block = self.list_data()
-            data = ndarray.add_n(*(w.copyto(ctx).as_nd_ndarray() for w in block)) / len(block)
+            if is_np_array():
+                data = sum([w.copyto(ctx) for w in block]) / len(block)
+            else:
+                data = ndarray.add_n(*(w.copyto(ctx) for w in block)) / len(block)
         else:
             # fetch all rows for 'row_sparse' param
             all_row_ids = ndarray.arange(0, self.shape[0], dtype='int64', ctx=ctx)

python/mxnet/gluon/utils.py

@@ -18,6 +18,8 @@
 # coding: utf-8
 # pylint: disable=
 """Parallelization utility optimizer."""
+from __future__ import absolute_import
+
 __all__ = ['split_data', 'split_and_load', 'clip_global_norm',
            'check_sha1', 'download']
 
@@ -39,6 +41,7 @@ class requests_failed_to_import(object):
 
 from .. import ndarray
 from ..util import is_np_shape, is_np_array, wraps_safely
+from .. import numpy as _mx_np  # pylint: disable=reimported
 
 
 def split_data(data, num_slice, batch_axis=0, even_split=True):
@@ -112,15 +115,14 @@ def split_and_load(data, ctx_list, batch_axis=0, even_split=True):
     list of NDArray
         Each corresponds to a context in `ctx_list`.
     """
-    # TODO(junwu): temp solution for supporting np.ndarray
-    # rewrite this using np ops
+    array_fn = _mx_np.array if is_np_array() else ndarray.array
     if not isinstance(data, ndarray.NDArray):
-        data = ndarray.array(data, ctx=ctx_list[0])
+        data = array_fn(data, ctx=ctx_list[0])
     if len(ctx_list) == 1:
-        if is_np_array():
-            data = data.as_np_ndarray()
         return [data.as_in_context(ctx_list[0])]
 
+    # TODO(junwu): temp solution for supporting np.ndarray
+    # rewrite this using np ops
     slices = split_data(data, len(ctx_list), batch_axis, even_split)
     if is_np_array():
         slices = [i.as_np_ndarray() for i in slices]
@@ -445,7 +447,7 @@ def _check_same_symbol_type(symbols):
     Raise type error if the types are different. Return the class of
     the symbols."""
     from ..symbol.numpy import _Symbol as np_symbol
-    from ..symbol import Symbol as classic_symbol
+    from ..symbol import Symbol as nd_symbol
     is_np_sym = bool(isinstance(symbols[0], np_symbol))
     for s in symbols[1:]:
         if is_np_sym != isinstance(s, np_symbol):
@@ -460,18 +462,25 @@ def _check_same_symbol_type(symbols):
                             'on each of them; if you want classic ndarray output(s) from the '
                             'computation graph, please convert all the numpy symbols in the list '
                             'to classic symbols by calling `as_nd_ndarray()` on each of them.')
-    return np_symbol if is_np_sym else classic_symbol
+    return np_symbol if is_np_sym else nd_symbol
 
 
 def _check_all_np_ndarrays(out):
-    """Check if ndarrays in out are all np.ndarray"""
+    """Check if ndarrays/symbols in out are all np.ndarray/np._Symbol."""
     from ..numpy import ndarray as np_ndarray
     from ..symbol.numpy import _Symbol as np_symbol
-    assert isinstance(out, (list, tuple))
-    for array in out:
-        if not isinstance(array, (np_ndarray, np_symbol)):
-            raise TypeError('Expected np.ndarray or np._Symbol type in output, while received type '
-                            '{}'.format(str(type(array))))
+    from ..symbol import Symbol as nd_symbol
+    from ..ndarray import NDArray as nd_ndarray
+
+    # pylint: disable=no-else-raise
+    if isinstance(out, (nd_ndarray, nd_symbol)) and not isinstance(out, (np_ndarray, np_symbol)):
+        raise TypeError("Block's output ndarrays/symbols must be of type `mxnet.numpy.ndarray`"
+                        " or `mxnet.symbol.numpy._Symbol`, while got output type {}"
+                        .format(str(type(out))))
+    elif isinstance(out, (list, tuple)):
+        for i in out:
+            _check_all_np_ndarrays(i)
+    # pylint: enable=no-else-raise
 
 
 def _to_classic_arrays(*args, **kwargs):

python/mxnet/ndarray/numpy/_op.py

@@ -26,7 +26,7 @@
 
 __all__ = ['zeros', 'ones', 'maximum', 'minimum', 'stack', 'arange', 'argmax',
            'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'concatenate',
-           'clip', 'split', 'swapaxes', 'expand_dims']
+           'clip', 'split', 'swapaxes', 'expand_dims', 'tile']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -593,3 +593,39 @@ def split(ary, indices_or_sections, axis=0):
     if not isinstance(ret, list):
         raise NotImplementedError('single output from split is not supported yet...')
     return ret
+
+
+@set_module('mxnet.ndarray.numpy')
+def tile(A, reps):
+    """
+    Construct an array by repeating A the number of times given by reps.
+
+    If `reps` has length ``d``, the result will have dimension of
+    ``max(d, A.ndim)``.
+
+    If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new
+    axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication,
+    or shape (1, 1, 3) for 3-D replication. If this is not the desired
+    behavior, promote `A` to d-dimensions manually before calling this
+    function.
+
+    If ``A.ndim > d``, `reps` is promoted to `A`.ndim by pre-pending 1's to it.
+    Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as
+    (1, 1, 2, 2).
+
+    Note : Although tile may be used for broadcasting, it is strongly
+    recommended to use numpy's broadcasting operations and functions.
+
+    Parameters
+    ----------
+    A : ndarray
+        The input array.
+    reps : tuple of integers
+        The number of repetitions of `A` along each axis.
+
+    Returns
+    -------
+    c : ndarray
+        The tiled output array.
+    """
+    return _npi.tile(A, reps)