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!9085 Fix MultiFieldEmbedding Doc Error 

From: @huangxinjing
Reviewed-by: 
Signed-off-by:
This commit is contained in:
mindspore-ci-bot 2020-11-27 19:02:30 +08:00 committed by Gitee
parent 3bed28822d 6aea4ba4a7
commit 3874160faf
2 changed files with 7 additions and 8 deletions
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2
mindspore/core/abstract/prim_others.cc
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@ -495,7 +495,7 @@ AbstractBasePtr InferImplCast(const AnalysisEnginePtr &, const PrimitivePtr &pri
AbstractBasePtr InferImplExpandDims(const AnalysisEnginePtr &, const PrimitivePtr &primitive, AbstractBasePtr InferImplExpandDims(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list) { const AbstractBasePtrList &args_spec_list) {
const std::string op_name = primitive->name(); const std::string op_name = primitive->name();
CheckArgsSize(op_name, args_spec_list, 2); CheckArgsSize(op_name, args_spec_list, 1);
auto x = CheckArg<AbstractTensor>(op_name, args_spec_list, 0); auto x = CheckArg<AbstractTensor>(op_name, args_spec_list, 0);
MS_EXCEPTION_IF_NULL(x); MS_EXCEPTION_IF_NULL(x);
MS_EXCEPTION_IF_NULL(x->shape()); MS_EXCEPTION_IF_NULL(x->shape());

13
mindspore/nn/layer/embedding.py
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@ -273,8 +273,8 @@ class EmbeddingLookup(Cell):
class MultiFieldEmbeddingLookup(EmbeddingLookup): class MultiFieldEmbeddingLookup(EmbeddingLookup):
r""" r"""
Returns a slice of input tensor based on the specified indices based on the field ids. This operation Returns a slice of input tensor based on the specified indices and the field ids. This operation
supports looking up embeddings within multi hot and one hot fields simultaneously. supports looking up embeddings using multi hot and one hot fields simultaneously.
Note: Note:
When 'target' is set to 'CPU', this module will use When 'target' is set to 'CPU', this module will use
@ -282,13 +282,13 @@ class MultiFieldEmbeddingLookup(EmbeddingLookup):
specified 'offset = 0' to lookup table. specified 'offset = 0' to lookup table.
When 'target' is set to 'DEVICE', this module will use P.GatherV2() which When 'target' is set to 'DEVICE', this module will use P.GatherV2() which
specified 'axis = 0' to lookup table. specified 'axis = 0' to lookup table.
The vectors with the same field_ids will be combined by the `operator`, such as `SUM`, `MAX` and The vectors with the same field_ids will be combined by the 'operator', such as 'SUM', 'MAX' and
`MEAN`. Ensure the input_values of the padded id is zero, so that they can be ignored. The final 'MEAN'. Ensure the input_values of the padded id is zero, so that they can be ignored. The final
output will be zeros if the sum of absolute weight of the field is zero. This class only output will be zeros if the sum of absolute weight of the field is zero. This class only
supports ['table_row_slice', 'batch_slice' and 'table_column_slice'] supports ['table_row_slice', 'batch_slice' and 'table_column_slice']
Args: Args:
vocab_size (int): Size of the dictionary of embeddings. vocab_size (int): The size of the dictionary of embeddings.
embedding_size (int): The size of each embedding vector. embedding_size (int): The size of each embedding vector.
field_size (int): The field size of the final outputs. field_size (int): The field size of the final outputs.
param_init (str): The initialize way of embedding table. Default: 'normal'. param_init (str): The initialize way of embedding table. Default: 'normal'.
@ -296,7 +296,7 @@ class MultiFieldEmbeddingLookup(EmbeddingLookup):
['DEVICE', 'CPU']. Default: 'CPU'. ['DEVICE', 'CPU']. Default: 'CPU'.
slice_mode (str): The slicing way in semi_auto_parallel/auto_parallel. The value must get through slice_mode (str): The slicing way in semi_auto_parallel/auto_parallel. The value must get through
nn.EmbeddingLookup. Default: nn.EmbeddingLookup.BATCH_SLICE. nn.EmbeddingLookup. Default: nn.EmbeddingLookup.BATCH_SLICE.
feature_num_list (tuple): The accompaniment array in field slice mode. feature_num_list (tuple): The accompaniment array in field slice mode. This is unused currently.
max_norm (Union[float, None]): A maximum clipping value. The data type must be float16, float32 max_norm (Union[float, None]): A maximum clipping value. The data type must be float16, float32
or None. Default: None or None. Default: None
sparse (bool): Using sparse mode. When 'target' is set to 'CPU', 'sparse' has to be true. Default: True. sparse (bool): Using sparse mode. When 'target' is set to 'CPU', 'sparse' has to be true. Default: True.
@ -410,7 +410,6 @@ class MultiFieldEmbeddingLookup(EmbeddingLookup):
batch_size = self.shape(input_indices)[0] batch_size = self.shape(input_indices)[0]
num_segments = batch_size * self.field_size num_segments = batch_size * self.field_size
bias = Range(0, num_segments, self.field_size)() bias = Range(0, num_segments, self.field_size)()
bias = self.reshape(bias, (self.field_size, -1)) bias = self.reshape(bias, (self.field_size, -1))
field_ids = self.bias_add(field_ids, bias) field_ids = self.bias_add(field_ids, bias)