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memmnet.lua
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memmnet.lua
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-- Copyright (c) 2015-present, Facebook, Inc.
-- All rights reserved.
--
-- This source code is licensed under the BSD-style license found in the
-- LICENSE file in the root directory of this source tree. An additional grant
-- of patent rights can be found in the PATENTS file in the same directory.
require "cutorch"
require "nngraph"
require "torch"
local memmnet={};
memmnet.Data=require("data")
function memmnet:MeMM_()
local inputs={}
local memory
memory=nn.Identity()()
table.insert(inputs,memory)
local question=nn.Identity()()
table.insert(inputs,question)
local answer_v=nn.Identity()()
table.insert(inputs,answer_v)
local context_mask=nn.Identity()()
local context_mask_p = nn.MulConstant(1e8)(nn.AddConstant(-1)(context_mask))
table.insert(inputs,context_mask)
local word_mask,word_mask_p
word_mask=nn.Identity()()
word_mask_p = nn.MulConstant(1e8)(nn.AddConstant(-1)(word_mask))
table.insert(inputs,word_mask)
local u=question;
local store={};
local sen_atten;
local attens={}
for i=1,self.params.N_hop do
if i==1 then
u=nn.Replicate(1,3)(u);
else
u=nn.Replicate(1,3)(store[i-1]);
end
sen_atten=nn.MM()({memory,u});
sen_atten=nn.Sum(3)(sen_atten);
sen_atten = nn.CAddTable(){sen_atten, context_mask_p}
sen_atten=nn.SoftMax()(sen_atten);
table.insert(attens,sen_atten);
sen_atten=nn.Replicate(1,2)(sen_atten);
local attent_vect=nn.MM()({sen_atten,memory});
attent_vect=nn.Sum(2)(attent_vect)
store[i]=nn.CAddTable()({attent_vect,u}):annotate{name = 'hopvec' .. i}
end
local atten;
local expand=nn.Replicate(1,3)(store[self.params.N_hop]);
atten=nn.MM()({answer_v,expand});
atten=nn.Sum(3)(atten);
atten = nn.CAddTable(){atten, word_mask_p}
pred=nn.LogSoftMax()(atten);
local outputs={}
table.insert(outputs,pred)
local pred_response;
if self.params.FP then
local response_v=nn.Identity()();
table.insert(inputs,response_v)
local beta_v=nn.Identity()();
table.insert(inputs,beta_v)
atten=nn.Replicate(1,2)(nn.Exp()(pred));
local attent_vect=nn.MM()({atten,beta_v});
attent_vect=nn.Sum(2)(attent_vect);
local combine_v=nn.CAddTable()({attent_vect,store[self.params.N_hop]});
combine_v=nn.Replicate(1,3)(combine_v);
local h2rank=nn.MM()({response_v,combine_v});
h2rank=nn.Sum(3)(h2rank);
pred_response=nn.LogSoftMax()(h2rank);
table.insert(outputs,pred_response)
end
local module=nn.gModule(inputs,outputs);
return module:cuda();
end
function memmnet:LookUpTable_()
local inputs={};
local context_word=nn.Identity()();
table.insert(inputs,context_word)
local question_word=nn.Identity()();
table.insert(inputs,question_word)
local question_table=
self.word_table:clone('weight','bias');
local question_v=nn.Sum(2)(question_table(question_word));
local answer_word=nn.Identity()();
local answer_table=self.word_table:clone('weight','bias');
table.insert(inputs,answer_word)
local answer_v=nn.Sum(2)(answer_table(answer_word))
local context_table=self.word_table:clone('weight','bias');
local context_v=nn.Sum(2)(context_table(context_word));
if self.params.FP then
local response_word=nn.Identity()();
table.insert(inputs,response_word)
response_table=self.word_table:clone('weight','bias');
response_v=nn.Sum(2)(response_table(response_word));
local potential_answer_beta=nn.Identity()()
table.insert(inputs,potential_answer_beta)
potential_answer_beta_table=self.word_table:clone('weight','bias');
potential_answer_beta_v=nn.Sum(2)(potential_answer_beta_table(potential_answer_beta));
end
local module;
local context_length
if self.params.FP then
module=nn.gModule(inputs,{context_v,question_v,answer_v,response_v,potential_answer_beta_v});
else module=nn.gModule(inputs,{context_v,question_v,answer_v});
end
return module:cuda();
end
function memmnet:PrepareNegativeResponse(Dataset)
for i,instance in pairs(Dataset)do
if instance.response:size(1)>self.Data.MaxResponseLength then
self.Data.MaxResponseLength=instance.response:size(1);
end
end
self.response_word=torch.Tensor(#Dataset*(1+self.params.negative),self.Data.MaxResponseLength):fill(1)
for i,ex in pairs(Dataset)do
local line_index=(i-1)*(1+self.params.negative)+1
self.response_word:sub(line_index,line_index,1,ex.response:size(1)):copy(ex.response)
for j=1,self.params.negative do
local line_index=(i-1)*(1+self.params.negative)+1+j;
local negative_response=self.Data.responsePool[torch.random(#self.Data.responsePool)];
self.response_word:sub(line_index,line_index,1,negative_response:size(1)):copy(negative_response)
end
self.Data.responsePool[torch.random(#self.Data.responsePool)]=ex.response;
end
self.response_word=self.response_word:cuda()
end
function memmnet:prepareAnswer(Dataset)
local max_length=-1;
for i=1,#Dataset do
local ex=Dataset[i]
local length=#Dataset[i].AnswerCandidate;
if length>max_length then
max_length=length;
end
end
self.answer_word=torch.Tensor(
#Dataset,max_length):fill(1):cuda();
self.answer_mask=torch.Tensor(
#Dataset,max_length):fill(0):cuda();
for i=1,#Dataset do
local ex=Dataset[i]
self.answer_word[{{i},{1,#ex.AnswerCandidate}}]
:copy(torch.Tensor(ex.AnswerCandidate));
self.answer_mask[{{i},{1,#ex.AnswerCandidate}}]:fill(1);
end
self.answer_word=torch.reshape(self.answer_word,
self.answer_word:size(1)*self.answer_word:size(2),1);
if self.params.FP then
self.beta_word=torch.ones(self.answer_word:size()):cuda();
for i=1,self.params.batch_size do
local ex=Dataset[i];
if ex.AnswerCandidateReverse[ex.answer[1]]~=nil then
self.beta_word[(i-1)*max_length+
ex.AnswerCandidateReverse[ex.answer[1]]]=self.params.token_size;
end
end
self.beta_word=torch.cat(self.answer_word,self.beta_word,2);
end
end
function memmnet:PrepareContextVector(Dataset)
self.context_length=-100
self.context_num=-100
for i,instance in pairs(Dataset)do
if #instance["hist_x"]>self.context_num then
self.context_num=#instance["hist_x"]
end
for j,v in pairs(instance["hist_x"])do
if v:size(1)>self.context_length then
self.context_length=v:size(1)
end
end
end
self.context_mask=torch.Tensor(#Dataset,self.context_num):fill(0):cuda()
for i,instance in pairs(Dataset)do
self.context_mask:sub(i,i,1,#instance.hist_x):fill(1);
local context_Mat=torch.Tensor(self.context_num,self.context_length):fill(1):cuda()
for j,v in pairs(Dataset[i].hist_x)do
context_Mat:sub(j,j,1,v:size(1)):copy(v);
end
if i==1 then
self.context_word=context_Mat;
else
self.context_word=torch.cat(self.context_word,context_Mat,1);
end
end
end
function memmnet:PrepareQues(Dataset)
local max_length=-1;
for i,instance in pairs(Dataset)do
if instance.question:size(1)>max_length then
max_length=instance.question:size(1)
end
end
self.query_word=torch.Tensor(#Dataset,max_length):fill(1);
for i,instance in pairs(Dataset)do
self.query_word:sub(i,i,1,instance.question:size(1)):copy(instance.question);
end
self.query_word=self.query_word:cuda()
end
function memmnet:prepareData(Dataset)
self:PrepareContextVector(Dataset)
self:PrepareQues(Dataset)
self.reward_vector=torch.Tensor(#Dataset):cuda();
for i,instance in pairs(Dataset)do
self.reward_vector[i]=instance.r[1]
end
self:prepareAnswer(Dataset)
if self.params.FP then
self:PrepareNegativeResponse(Dataset);
end
end
function memmnet:Reshape2Dto3D(vector,n1)
return torch.reshape(vector,
n1,vector:size(1)/n1,
self.params.dimension);
end
function memmnet:Reshape3Dto2D(vector)
return torch.reshape(vector,
vector:size(1)*vector:size(2),
vector:size(3))
end
function memmnet:Forward()
local vector_output;
if self.params.FP then
vector_output=self.LookUpTable:forward({
self.context_word,self.query_word,self.answer_word,
self.response_word,self.beta_word})
else
vector_output=self.LookUpTable:forward({
self.context_word,self.query_word,self.answer_word})
end
self.context_v=vector_output[1]
self.question_v=vector_output[2]
self.answer_v=vector_output[3]
self.response_v=vector_output[4]
self.beta_v=vector_output[5]
self.context_v=self:Reshape2Dto3D(self.context_v,self.n_instance)
self.answer_v=self:Reshape2Dto3D(self.answer_v,self.n_instance)
if self.params.FP then
self.response_v=self:Reshape2Dto3D(self.response_v,self.n_instance)
self.beta_v=self:Reshape2Dto3D(self.beta_v,self.n_instance)
end
local pred,pred_response;
if self.params.FP then
local output=self.MeMM:forward({
self.context_v,self.question_v,self.answer_v,
self.context_mask,self.answer_mask,
self.response_v,self.beta_v});
pred=output[1]
pred_response=output[2]
else
local output=self.MeMM:forward({
self.context_v,self.question_v,self.answer_v,
self.context_mask,self.answer_mask});
pred=output
end
return pred,pred_response
end
function memmnet:Backward(d_pred,d_pred_response)
local grad_inputs;
local d_context_v,d_question_v,d_answer_v
if self.params.FP then
grad_inputs=self.MeMM:backward({
self.context_v,self.question_v,self.answer_v,
self.context_mask,self.answer_mask,
self.response_v,self.beta_v},
{d_pred,d_pred_response})
else
grad_inputs=self.MeMM:backward({
self.context_v,self.question_v,self.answer_v,
self.context_mask,self.answer_mask},d_pred);
end
local d_context_v=grad_inputs[1];
local d_question_v=grad_inputs[2];
local d_answer_v=grad_inputs[3];
d_context_v=self:Reshape3Dto2D(d_context_v)
d_answer_v=self:Reshape3Dto2D(d_answer_v)
if self.params.FP then
local d_response_v=grad_inputs[6];
local d_beta_v=grad_inputs[7];
d_response_v=self:Reshape3Dto2D(d_response_v)
d_beta_v=self:Reshape3Dto2D(d_beta_v)
self.LookUpTable:backward({
self.context_word,self.query_word,self.answer_word,
self.response_word,self.beta_word},
{d_context_v,d_question_v,d_answer_v,
d_response_v,d_beta_v})
else
self.LookUpTable:backward({
self.context_word,self.query_word,self.answer_word},
{d_context_v,d_question_v,d_answer_v})
end
end
function memmnet:test(file)
self.total_instance_RBI=0
local options_in_total=0;
local batch_data_;
if file=="dev" then
batch_data_=self.Data.devData;
self.model_flag="dev";
elseif file=="test" then
self.model_flag="test"
batch_data_=self.Data.testData;
end
local right=0;
local output_f
for i=1,torch.floor(#batch_data_/self.params.batch_size) do
self.total_instance_RBI=self.total_instance_RBI+self.params.batch_size
local Begin=(i-1)*self.params.batch_size+1;
local End=i*self.params.batch_size;
if End>#batch_data_ then
End=#batch_data_;
end
batch_data={}
for j=Begin,End do
batch_data[j-Begin+1]=batch_data_[j];
end
self.n_instance=#batch_data
self:prepareData(batch_data)
local pred,_=self:Forward()
local max_p,max_index=torch.max(pred,2);
for j=1,self.params.batch_size do
local AnswerCandidate=batch_data[j].AnswerCandidate;
local predict_index=max_index[j][1]
if not (#batch_data[j].answers==1 and batch_data[j].answers[1]==1)
and batch_data[j].answers[AnswerCandidate[predict_index]]~=nil then
right=right+1;
end
end
end
self.model_flag="train"
return right/self.total_instance_RBI;
end
function memmnet:Initial(params_)
self.params=params_;
self.Data:process_data(self.params)
if self.params.dataset=="movieQA" then
self.params.token_size=200000
else
self.params.token_size=self.Data.dict.size+100;
--consider features such as time features
end
self.word_table=
nn.LookupTable(self.params.token_size,self.params.dimension):cuda();
self.word_table:reset(self.params.init_weight)
self.word_table.weight[1]:zero()
--dummy token, always zero
self.LookUpTable=self:LookUpTable_();
self.Modules={}
self.Modules[#self.Modules+1]=self.LookUpTable;
self.MeMM=self:MeMM_();
self.n_instance=self.params.batch_size;
self.current_lr=self.params.lr;
end
function memmnet:batch_train(batch_data)
for i=1,#self.Modules do
self.Modules[i]:zeroGradParameters()
end
self.n_instance=#batch_data
self:prepareData(batch_data);
local pred,pred_response=self:Forward() -- last argument is not used
local d_pred=torch.Tensor(pred:size()):fill(0):cuda();
local d_pred_response
if self.params.policyGrad or (not self.params.policyGrad and not self.params.FP) then
for i=1,self.reward_vector:size(1) do
local right_answer_index=batch_data[i].AnswerCandidateReverse[batch_data[i].answer[1]]
-- index of bot's answer
if self.params.policyGrad then
if right_answer_index~=nil then
if self.reward_vector[i] == 1 then
d_pred[i][right_answer_index] = -1
end
end
else
-- imitation learning
d_pred[i][right_answer_index] = -1
end
end
end
--error("")
-- FP
if self.params.FP then
d_pred_response=torch.Tensor(pred_response:size()):fill(0):cuda();
for i=1,pred_response:size(1)do
d_pred_response[i][1]=-1;
end
end
self:Backward(d_pred,d_pred_response)
self:update()
end
function memmnet:update()
local lr=self.current_lr;
local grad_norm=0;
for i=1,#self.Modules do
local p,dp=self.Modules[i]:parameters()
for j,m in pairs(dp) do
grad_norm=grad_norm+m:norm()^2;
end
end
grad_norm=grad_norm^0.5;
if grad_norm>self.params.thres then
lr=lr*self.params.thres/grad_norm;
end
for i=1,#self.Modules do
self.Modules[i]:updateParameters(lr);
end
self.word_table.weight[1]:zero()
for i,v in pairs(self.Modules[1].modules) do
if v.weight~=nil then
v.weight[1]:zero();
--token number 1 is a dummy token
end
end
end
function memmnet:train()
local timer=torch.Timer()
self.iter=0;
local best_dev_acc=-10;
local final_test_acc=0;
if self.params.dataset=="movieQA" then
self.Data.trainData=self.Data:sortData(self.Data.trainData,"hist_x")
end
print(#self.Data.trainData)
print(#self.Data.trainData)
print(#self.Data.trainData)
while true do
self.model_flag="train"
self.iter=self.iter+1;
print("iter "..self.iter)
if self.iter==self.params.N_iter then
break;
end
if self.iter%self.params.iter_halve_lr==0 then
if self.params.dataset=="babi" then
self.current_lr=self.current_lr/2
print(self.current_lr)
end
end
local time1=timer:time().real;
for k=1,math.floor(#self.Data.trainData/self.params.batch_size) do
local batch_data={}
if self.params.dataset=="movieQA" then
local start_index=torch.random(#self.Data.trainData)
while start_index+self.params.batch_size>=#self.Data.trainData do
start_index=torch.random(#self.Data.trainData)
end
for i=start_index,start_index+self.params.batch_size-1 do
batch_data[#batch_data+1]=self.Data.trainData[i];
end
else
for i=1,self.params.batch_size do
local index=torch.random(#self.Data.trainData);
batch_data[i]=self.Data.trainData[index];
end
end
self:batch_train(batch_data)
end
local time2=timer:time().real;
local acc_dev=self:test("dev")
print("acc_dev "..acc_dev)
if acc_dev>=best_dev_acc then
best_dev_acc=acc_dev;
local acc_test=self:test("test")
final_test_acc=acc_test;
end
local acc_test=self:test("test")
end
print("test_acc "..final_test_acc)
return final_test_acc,best_dev_acc;
end
return memmnet