add embedding layer.

mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/clip_by_norm_no_div_square_sum_fusion.cc

@@ -59,7 +59,7 @@ const AnfNodePtr ClipByNormNoDivSquareSumFusion::Process(const FuncGraphPtr &gra
 
   auto prim = std::make_shared<Primitive>(kClipByNormNoDivSumOpName);
   MS_EXCEPTION_IF_NULL(prim);
-  std::vector<AnfNodePtr> inputs = {NewValueNode(prim), input, constant_select, constant_greater, constant_maximum};
+  std::vector<AnfNodePtr> inputs = {NewValueNode(prim), input, constant_greater, constant_select, constant_maximum};
   auto fusion_node = graph->NewCNode(inputs);
   MS_EXCEPTION_IF_NULL(fusion_node);
   auto types = {AnfAlgo::GetOutputInferDataType(node, 0)};

mindspore/nn/layer/basic.py

@@ -260,6 +260,7 @@ def _is_float_dtype(dtype):
         return True
     return False
 
+
 class ClipByNorm(Cell):
     r"""
     Clips tensor values to a maximum :math:`L_2`-norm.
@@ -271,6 +272,9 @@ class ClipByNorm(Cell):
         \text{output}(X) = \frac{\text{clip_norm} * X}{L_2(X)},
 
     where :math:`L_2(X)` is the :math:`L_2`-norm of :math:`X`.
+    Args:
+        axis (Union[None, int, tuple(int)): Compute the L2-norm along the Specific dimension.
+                                            Default: None, all dimensions to calculate.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape N-D. The type must be float32 or float16.
@@ -287,8 +291,14 @@ class ClipByNorm(Cell):
 
     """
 
-    def __init__(self):
+    def __init__(self, axis=None):
         super(ClipByNorm, self).__init__()
+        if axis is None:
+            axis = ()
+        if isinstance(axis, tuple):
+            for idx, item in enumerate(axis):
+                Validator.check_value_type("axis[%d]" % idx, item, [int], self.cls_name)
+        self.axis = Validator.check_value_type('axis', axis, [int, tuple], self.cls_name)
         self.reduce_sum = P.ReduceSum(keep_dims=True)
         self.select_ = P.Select()
         self.greater_ = P.Greater()
@@ -305,7 +315,7 @@ class ClipByNorm(Cell):
     def construct(self, x, clip_norm):
         """add ms_function decorator for pynative mode"""
         mul_x = F.square(x)
-        l2sum = self.cast(self.reduce_sum(mul_x), mstype.float32)
+        l2sum = self.cast(self.reduce_sum(mul_x, self.axis), mstype.float32)
         cond = self.greater_(l2sum, 0)
         ones_ = self.fill(self.dtype(cond), self.shape(cond), 1.0)
         l2sum_safe = self.select_(cond, l2sum, self.cast(ones_, self.dtype(l2sum)))
@@ -318,7 +328,9 @@ class ClipByNorm(Cell):
             intermediate = x * clip_norm
 
         max_norm = self.max_op(l2norm, clip_norm)
-        values_clip = self.cast(intermediate, mstype.float32) / self.expand_dims(max_norm, -1)
+        if self.axis is None:
+            max_norm = self.expand_dims(max_norm, -1)
+        values_clip = self.cast(intermediate, mstype.float32) / max_norm
         values_clip = self.reshape(values_clip, self.shape(x))
         values_clip = identity(values_clip)
         return values_clip

mindspore/nn/layer/embedding.py

@@ -16,16 +16,21 @@
 import mindspore.common.dtype as mstype
 from mindspore.common.tensor import Tensor
 from mindspore.ops import operations as P
+from mindspore.ops import functional as F
 from mindspore.common.parameter import Parameter
 from mindspore.common.initializer import initializer
 from mindspore.communication.management import get_group_size
 from mindspore.context import ParallelMode
 from mindspore.parallel._utils import _get_parallel_mode
+from mindspore._checkparam import Rel
 from mindspore._checkparam import Validator as validator
+from mindspore.ops.primitive import constexpr
+from .basic import ClipByNorm
 from ..cell import Cell
 
 __all__ = ['Embedding', 'EmbeddingLookup']
 
+
 class Embedding(Cell):
     r"""
     A simple lookup table that stores embeddings of a fixed dictionary and size.
@@ -45,7 +50,8 @@ class Embedding(Cell):
             Refer to class `initializer` for the values of string when a string
             is specified. Default: 'normal'.
         dtype (:class:`mindspore.dtype`): Data type of input. Default: mindspore.float32.
-
+        padding_idx (int, None): When the padding_idx encounters index, the output embedding vector of this index
+                                 will be initialized to zero. Default: None. The feature is inactivated.
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(\text{batch_size}, \text{input_length})`. The elements of
           the Tensor must be integer and not larger than vocab_size. Otherwise the corresponding embedding vector will
@@ -63,16 +69,24 @@ class Embedding(Cell):
         >>> output.shape
         (8, 128, 768)
     """
-    def __init__(self, vocab_size, embedding_size, use_one_hot=False, embedding_table='normal', dtype=mstype.float32):
+
+    def __init__(self, vocab_size, embedding_size, use_one_hot=False, embedding_table='normal',
+                 dtype=mstype.float32, padding_idx=None):
         super(Embedding, self).__init__()
-        validator.check_subclass("dtype", dtype, mstype.number_type, self.cls_name)
+        self.vocab_size = validator.check_value_type('vocab_size', vocab_size, [int], self.cls_name)
+        self.embedding_size = validator.check_value_type('embedding_size', embedding_size, [int], self.cls_name)
         validator.check_value_type('use_one_hot', use_one_hot, [bool], self.cls_name)
-        self.vocab_size = vocab_size
-        self.embedding_size = embedding_size
+        validator.check_subclass("dtype", dtype, mstype.number_type, self.cls_name)
         self.use_one_hot = use_one_hot
-        self.embedding_table = Parameter(initializer(embedding_table, [vocab_size, embedding_size]),
-                                         name='embedding_table')
         self.dtype = dtype
+        self.init_tensor = initializer(embedding_table, [vocab_size, embedding_size])
+        self.padding_idx = padding_idx
+        if padding_idx is not None:
+            self.padding_idx = validator.check_int_range(padding_idx, 0, vocab_size, Rel.INC_BOTH,
+                                                         "padding_idx", self.cls_name)
+            self.init_tensor = self.init_tensor.to_tensor().asnumpy()
+            self.init_tensor[self.padding_idx] = 0
+        self.embedding_table = Parameter(self.init_tensor, name='embedding_table')
         self.expand = P.ExpandDims()
         self.reshape_flat = P.Reshape()
         self.shp_flat = (-1,)
@@ -99,16 +113,17 @@ class Embedding(Cell):
         return output
 
     def extend_repr(self):
-        s = 'vocab_size={}, embedding_size={},' \
-            'use_one_hot={}, ' \
-            'embedding_table={}, dtype={}'.format(
-                self.vocab_size,
-                self.embedding_size,
-                self.use_one_hot,
-                self.embedding_table,
-                self.dtype)
+        s = 'vocab_size={}, embedding_size={}, use_one_hot={}, embedding_table={}, dtype={}, padding_idx={}'.format(
+            self.vocab_size, self.embedding_size, self.use_one_hot, self.embedding_table, self.dtype, self.padding_idx)
         return s
 
+
+@constexpr
+def _make_axis_range(start, end):
+    axis = tuple(range(start, end))
+    return axis
+
+
 class EmbeddingLookup(Cell):
     r"""
     Returns a slice of input tensor based on the specified indices.
@@ -120,8 +135,7 @@ class EmbeddingLookup(Cell):
         When 'target' is set to 'DEVICE', this module will use P.GatherV2() which
         specified 'axis = 0' to lookup table.
         In field slice mode, the manual_shapes must be given. It is a tuple ,where
-        the element is vocab[i], vocab[i] is the row numbers for i-th
-        part.
+        the element is vocab[i], vocab[i] is the row numbers for i-th part.
 
     Args:
         vocab_size (int): Size of the dictionary of embeddings.
@@ -132,6 +146,8 @@ class EmbeddingLookup(Cell):
         slice_mode (str): The slicing way in semi_auto_parallel/auto_parallel. The value must get through
             nn.EmbeddingLookup. Default: nn.EmbeddingLookup.BATCH_SLICE.
         manual_shapes (tuple): The accompaniment array in field slice mode.
+        max_norm (Union[float, None]): A maximum clipping value. The data type must be float16, float32
+                                       or None. Default: None
 
     Inputs:
         - **input_indices** (Tensor) - The shape of tensor is :math:`(y_1, y_2, ..., y_S)`.
@@ -152,7 +168,7 @@ class EmbeddingLookup(Cell):
     TABLE_COLUMN_SLICE = "table_column_slice"
 
     def __init__(self, vocab_size, embedding_size, param_init='normal',
-                 target='CPU', slice_mode='batch_slice', manual_shapes=None):
+                 target='CPU', slice_mode='batch_slice', manual_shapes=None, max_norm=None):
         super(EmbeddingLookup, self).__init__()
         self.target = target
         if target not in ('CPU', 'DEVICE'):
@@ -160,7 +176,9 @@ class EmbeddingLookup(Cell):
                              + str(target) + ', should be one of values in \'CPU\', \'DEVICE\'.')
         self.gatherv2 = P.GatherV2()
         self.embeddinglookup = P.EmbeddingLookup().add_prim_attr('primitive_target', 'CPU')
-        self.embedding_table = Parameter(initializer(param_init, [vocab_size, embedding_size]),
+        self.vocab_size = validator.check_value_type('vocab_size', vocab_size, [int], self.cls_name)
+        self.embedding_size = validator.check_value_type('embedding_size', embedding_size, [int], self.cls_name)
+        self.embedding_table = Parameter(initializer(param_init, [self.vocab_size, self.embedding_size]),
                                          name='embedding_table')
         parallel_mode = _get_parallel_mode()
         is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
@@ -188,10 +206,18 @@ class EmbeddingLookup(Cell):
             if is_auto_parallel:
                 raise ValueError("slice_mode should support mode in nn.EmbeddingLookup, but get "
                                  + str(slice_mode))
+        self.max_norm = max_norm
+        if self.max_norm is not None:
+            self.max_norm = validator.check_positive_float(self.max_norm, 'max_norm', self.cls_name)
+            self.max_norm = Tensor(self.max_norm, dtype=mstype.float32)
 
     def construct(self, indices):
         if self.target == "CPU":
             out = self.embeddinglookup(self.embedding_table, indices, 0)
         else:
             out = self.gatherv2(self.embedding_table, indices, 0)
+        if self.max_norm is not None:
+            axis = _make_axis_range(F.rank(indices), F.rank(out))
+            clip_by_norm = ClipByNorm(axis)
+            out = clip_by_norm(out, self.max_norm)
         return out

tests/ut/python/ops/test_ops.py

@@ -228,6 +228,44 @@ class Moments(nn.Cell):
         return mean, variance
 
 
+class ClipByNorm(nn.Cell):
+    """ClipByNorm net definition"""
+
+    def __init__(self, axis=None):
+        super(ClipByNorm, self).__init__()
+        self.clip_by_norm = nn.ClipByNorm(axis=axis)
+
+    def construct(self, input_x, max_norm):
+        norm = self.clip_by_norm(input_x, max_norm)
+        return norm
+
+
+class Embedding(nn.Cell):
+    """Embedding net definition"""
+
+    def __init__(self, vocab_size, embedding_size, padding_idx=None):
+        super(Embedding, self).__init__()
+        self.embedding = nn.Embedding(vocab_size=vocab_size, embedding_size=embedding_size,
+                                      padding_idx=padding_idx)
+
+    def construct(self, index):
+        res = self.embedding(index)
+        return res
+
+
+class EmbeddingLookup(nn.Cell):
+    """EmbeddingLookup net definition"""
+
+    def __init__(self, vocab_size, embedding_size, max_norm=None):
+        super(EmbeddingLookup, self).__init__()
+        self.embedding_lookup = nn.EmbeddingLookup(vocab_size=vocab_size, embedding_size=embedding_size,
+                                                   max_norm=max_norm)
+
+    def construct(self, index):
+        res = self.embedding_lookup(index)
+        return res
+
+
 class CountNonZero(nn.Cell):
     """CountNonZero net definition"""
 
@@ -1082,6 +1120,32 @@ test_case_math_ops = [
         'desc_inputs': [Tensor(np.array([[True, False, False], [False, True, True]])),
                         [2, 3], [2, 3]],
         'desc_bprop': [[2, 3]]}),
+    ('ClipByNorm_1', {
+        'block': ClipByNorm(),
+        'desc_inputs': [Tensor(np.random.rand(3, 16, 5, 4).astype(np.float32)),
+                        Tensor(np.array([0.01]).astype(np.float32))],
+        'skip': ['backward']}),
+    ('ClipByNorm_2', {
+        'block': ClipByNorm(axis=0),
+        'desc_inputs': [Tensor(np.random.rand(3, 16, 5, 4).astype(np.float32)),
+                        Tensor(np.array([0.01]).astype(np.float32))],
+        'skip': ['backward']}),
+    ('Embedding_1', {
+        'block': Embedding(vocab_size=10, embedding_size=3),
+        'desc_inputs': [Tensor(np.array([0, 2, 2, 7]).astype(np.int32))],
+        'skip': ['backward']}),
+    ('Embedding_2', {
+        'block': Embedding(vocab_size=10, embedding_size=3, padding_idx=2),
+        'desc_inputs': [Tensor(np.array([0, 2, 2, 7]).astype(np.int32))],
+        'skip': ['backward']}),
+    ('EmbeddingLookup_1', {
+        'block': EmbeddingLookup(vocab_size=10, embedding_size=3),
+        'desc_inputs': [Tensor(np.array([0, 2, 2, 7]).astype(np.int32))],
+        'skip': ['backward']}),
+    ('EmbeddingLookup_2', {
+        'block': EmbeddingLookup(vocab_size=10, embedding_size=3, max_norm=0.01),
+        'desc_inputs': [Tensor(np.array([0, 2, 2, 7]).astype(np.int32))],
+        'skip': ['backward']}),
     ('Moments', {
         'block': Moments(axis=(), keep_dims=False),
         'desc_inputs': [Tensor(np.random.rand(3, 16, 5, 4).astype(np.float32))],