Source code for block_zoo.math.ElementWisedMultiply3D

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT license.

import torch
import torch.nn as nn
import logging

from ..BaseLayer import BaseConf
from utils.DocInherit import DocInherit
from utils.exceptions import ConfigurationError
import copy

[docs]class ElementWisedMultiply3DConf(BaseConf): """ Configuration of ElementWisedMultiply3D layer """ # init the args def __init__(self,**kwargs): super(ElementWisedMultiply3DConf, self).__init__(**kwargs) #set default params #@DocInherit #def default(self):
[docs] @DocInherit def declare(self): self.num_of_inputs = 2 self.input_ranks = [3,3]
[docs] @DocInherit def inference(self): self.output_dim = copy.deepcopy(self.input_dims[0]) if self.input_dims[0][-1] != 1: self.output_dim[-1] = self.input_dims[0][-1] else: self.output_dim[-1] = self.input_dims[1][-1] super(ElementWisedMultiply3DConf, self).inference()
[docs] @DocInherit def verify(self): super(ElementWisedMultiply3DConf, self).verify()
# # to check if the ranks of all the inputs are equal # rank_equal_flag = True # for i in range(len(self.input_ranks)): # if self.input_ranks[i] != self.input_ranks[0]: # rank_equal_flag = False # break # if rank_equal_flag == False: # raise ConfigurationError("For layer ElementWisedMultiply3D, the ranks of each inputs should be equal!")
[docs]class ElementWisedMultiply3D(nn.Module): """ ElementWisedMultiply3D layer to do Element-Wised Multiply of two sequences(3D representation) Args: layer_conf (ElementWisedMultiply3DConf): configuration of a layer """ def __init__(self, layer_conf): super(ElementWisedMultiply3D, self).__init__() self.layer_conf = layer_conf logging.warning("The length ElementWisedMultiply3D layer returns is the length of first input")
[docs] def forward(self, *args): """ process input Args: *args: (Tensor): string, string_len, string2, string2_len e.g. string (Tensor): [batch_size, seq_len, dim], string_len (Tensor): [batch_size] Returns: Tensor: [batch_size, seq_len, output_dim], [batch_size] """ dim_flag = True input_dims = list(self.layer_conf.input_dims) if (args[0].shape[1] * args[0].shape[2]) != (args[2].shape[1] * args[2].shape[2]): if args[0].shape[1] == args[2].shape[1] and (input_dims[1][-1] == 1 or input_dims[0][-1] == 1): dim_flag = True else: dim_flag = False if dim_flag == False: raise ConfigurationError("For layer ElementWisedMultiply3D, the dimensions of each inputs should be equal or 1 ,or the elements number of two inputs (expect for the first dimension) should be equal") return torch.addcmul(torch.zeros(args[0].size()).to('cuda'),1,args[0],args[2]),args[1]