Merge pull request #1912 from ken77921/softmax-cpr

FEA: Integrate softmax cpr to the latest RecBole version

recbole/model/sequential_recommender/__init__.py

@@ -8,6 +8,7 @@
 from recbole.model.sequential_recommender.fpmc import FPMC
 from recbole.model.sequential_recommender.gcsan import GCSAN
 from recbole.model.sequential_recommender.gru4rec import GRU4Rec
+from recbole.model.sequential_recommender.gru4reccpr import GRU4RecCPR
 from recbole.model.sequential_recommender.gru4recf import GRU4RecF
 from recbole.model.sequential_recommender.gru4reckg import GRU4RecKG
 from recbole.model.sequential_recommender.hgn import HGN
@@ -20,6 +21,7 @@
 from recbole.model.sequential_recommender.repeatnet import RepeatNet
 from recbole.model.sequential_recommender.s3rec import S3Rec
 from recbole.model.sequential_recommender.sasrec import SASRec
+from recbole.model.sequential_recommender.sasreccpr import SASRecCPR
 from recbole.model.sequential_recommender.sasrecf import SASRecF
 from recbole.model.sequential_recommender.shan import SHAN
 from recbole.model.sequential_recommender.sine import SINE

recbole/model/sequential_recommender/gru4reccpr.py

@@ -0,0 +1,339 @@
+# -*- coding: utf-8 -*-
+# @Time   : 2020/8/17 19:38
+# @Author : Yujie Lu
+# @Email  : [email protected]
+
+# UPDATE:
+# @Time   : 2020/8/19, 2020/10/2
+# @Author : Yupeng Hou, Yujie Lu
+# @Email  : [email protected], [email protected]
+
+# UPDATE:
+# @Time   : 2023/11/24
+# @Author : Haw-Shiuan Chang
+# @Email  : [email protected]
+
+r"""
+GRU4Rec + Softmax-CPR
+################################################
+
+Reference:
+    Yong Kiam Tan et al. "Improved Recurrent Neural Networks for Session-based Recommendations." in DLRS 2016.
+    Haw-Shiuan Chang, Nikhil Agarwal, and Andrew McCallum "To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders" in WSDM 2024
+
+
+"""
+
+import torch
+from torch import nn
+from torch.nn.init import xavier_uniform_, xavier_normal_
+
+import torch.nn.functional as F
+import math
+from recbole.model.abstract_recommender import SequentialRecommender
+from recbole.model.loss import BPRLoss
+import sys
+
+def gelu(x):
+    return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
+
+class GRU4RecCPR(SequentialRecommender):
+    r"""GRU4Rec is a model that incorporate RNN for recommendation.
+
+    Note:
+
+        Regarding the innovation of this article,we can only achieve the data augmentation mentioned
+        in the paper and directly output the embedding of the item,
+        in order that the generation method we used is common to other sequential models.
+    """
+
+    def __init__(self, config, dataset):
+        super(GRU4RecCPR, self).__init__(config, dataset)
+
+        # load parameters info
+        self.hidden_size = config['hidden_size']
+        self.embedding_size = config['embedding_size']
+        self.loss_type = config['loss_type']
+        self.num_layers = config['num_layers']
+        self.dropout_prob = config['dropout_prob']
+
+        self.n_facet_all = config['n_facet_all'] #added for mfs
+        self.n_facet = config['n_facet'] #added for mfs
+        self.n_facet_window = config['n_facet_window'] #added for mfs
+        self.n_facet_hidden = min(config['n_facet_hidden'], config['num_layers']) #config['n_facet_hidden'] #added for mfs
+        self.n_facet_MLP = config['n_facet_MLP'] #added for mfs
+        self.n_facet_context = config['n_facet_context'] #added for dynamic partioning
+        self.n_facet_reranker = config['n_facet_reranker'] #added for dynamic partioning
+        self.n_facet_emb = config['n_facet_emb'] #added for dynamic partioning
+        assert self.n_facet_MLP <= 0 #-1 or 0
+        assert self.n_facet_window <= 0
+        self.n_facet_window = - self.n_facet_window
+        self.n_facet_MLP = - self.n_facet_MLP
+        self.softmax_nonlinear='None' #added for mfs
+        self.use_out_emb = config['use_out_emb'] #added for mfs
+        self.only_compute_loss = True #added for mfs
+
+        self.dense = nn.Linear(self.hidden_size, self.embedding_size)
+        out_size = self.embedding_size
+
+        self.n_embd = out_size
+
+        self.use_proj_bias = config['use_proj_bias'] #added for mfs
+        self.weight_mode = config['weight_mode'] #added for mfs
+        self.context_norm = config['context_norm'] #added for mfs
+        self.post_remove_context = config['post_remove_context'] #added for mfs
+        self.reranker_merging_mode = config['reranker_merging_mode'] #added for mfs
+        self.partition_merging_mode = config['partition_merging_mode'] #added for mfs
+        self.reranker_CAN_NUM = [int(x) for x in str(config['reranker_CAN_NUM']).split(',')]
+        assert self.use_proj_bias is not None
+        self.candidates_from_previous_reranker = True
+        if self.weight_mode == 'max_logits':
+            self.n_facet_effective = 1
+        else:
+            self.n_facet_effective = self.n_facet
+
+        assert self.n_facet + self.n_facet_context + self.n_facet_reranker*len(self.reranker_CAN_NUM) + self.n_facet_emb == self.n_facet_all
+        assert self.n_facet_emb == 0 or self.n_facet_emb == 2
+
+
+        hidden_state_input_ratio = 1 + self.n_facet_MLP #1 + 1
+        self.MLP_linear = nn.Linear(self.n_embd * (self.n_facet_hidden * (self.n_facet_window+1) ), self.n_embd * self.n_facet_MLP) # (hid_dim*2) -> (hid_dim)
+        total_lin_dim = self.n_embd * hidden_state_input_ratio
+        self.project_arr = nn.ModuleList([nn.Linear(total_lin_dim, self.n_embd, bias=self.use_proj_bias) for i in range(self.n_facet_all)])
+
+        self.project_emb = nn.Linear(self.n_embd, self.n_embd, bias=self.use_proj_bias)
+        if len(self.weight_mode) > 0:
+            self.weight_facet_decoder = nn.Linear(self.n_embd * hidden_state_input_ratio, self.n_facet_effective)
+            self.weight_global = nn.Parameter( torch.ones(self.n_facet_effective) )
+
+        self.c = 123
+
+        # define layers and loss
+        self.emb_dropout = nn.Dropout(self.dropout_prob)
+        self.gru_layers = nn.GRU(
+            input_size=self.embedding_size,
+            hidden_size=self.hidden_size,
+            num_layers=self.num_layers,
+            bias=False,
+            batch_first=True,
+        )
+        self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0)
+
+
+        if self.use_out_emb:
+            self.out_item_embedding = nn.Linear(out_size, self.n_items, bias = False)
+        else:
+            self.out_item_embedding = self.item_embedding
+            self.out_item_embedding.bias = None
+
+        if self.loss_type == 'BPR':
+            print("current softmax-cpr code does not support BPR loss")
+            sys.exit(0)
+        elif self.loss_type == 'CE':
+            self.loss_fct = nn.CrossEntropyLoss()
+        else:
+            raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!")
+
+        # parameters initialization
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Embedding):
+            xavier_normal_(module.weight)
+        elif isinstance(module, nn.GRU):
+            xavier_uniform_(module.weight_hh_l0)
+            xavier_uniform_(module.weight_ih_l0)
+
+    def forward(self, item_seq, item_seq_len):
+        item_seq_emb = self.item_embedding(item_seq)
+        item_seq_emb_dropout = self.emb_dropout(item_seq_emb)
+        gru_output, _ = self.gru_layers(item_seq_emb_dropout)
+        gru_output = self.dense(gru_output)
+        return gru_output
+
+    def get_facet_emb(self,input_emb, i):
+        return self.project_arr[i](input_emb)
+
+    def calculate_loss_prob(self, interaction, only_compute_prob=False):
+        item_seq = interaction[self.ITEM_SEQ]
+        item_seq_len = interaction[self.ITEM_SEQ_LEN]
+        last_layer_hs = self.forward(item_seq, item_seq_len)
+        all_hidden_states = [last_layer_hs]
+        if self.loss_type != 'CE':
+            print("current softmax-cpr code does not support BPR or the losses other than cross entropy")
+            sys.exit(0)
+        else:  # self.loss_type = 'CE'
+            test_item_emb = self.out_item_embedding.weight
+            test_item_bias = self.out_item_embedding.bias
+
+            '''mfs code starts'''
+            device = all_hidden_states[0].device
+
+            hidden_emb_arr = []
+            # h_facet_hidden -> H, n_face_window -> W, here 1 and 0
+            for i in range(self.n_facet_hidden):
+                #print('all_hidden_states length is {}. i is {}'.format(len(all_hidden_states), i))
+                hidden_states = all_hidden_states[-(i+1)] #i-th hidden-state embedding from the top
+                device = hidden_states.device
+                hidden_emb_arr.append(hidden_states)
+                for j in range(self.n_facet_window):
+                    bsz, seq_len, hidden_size = hidden_states.size() #bsz -> , seq_len -> , hidden_size -> 768 in GPT-small?
+                    if j+1 < hidden_states.size(1):
+                        shifted_hidden = torch.cat( (torch.zeros( (bsz, (j+1), hidden_size), device = device), hidden_states[:,:-(j+1),:]), dim = 1)
+                    else:
+                        shifted_hidden = torch.zeros( (bsz, hidden_states.size(1), hidden_size), device = device)
+                    hidden_emb_arr.append(shifted_hidden)
+            #hidden_emb_arr -> (W*H, bsz, seq_len, hidden_size)
+
+
+            #n_facet_MLP -> 1
+            if self.n_facet_MLP > 0:
+                stacked_hidden_emb_raw_arr = torch.cat(hidden_emb_arr, dim=-1) #(bsz, seq_len, W*H*hidden_size)
+                hidden_emb_MLP = self.MLP_linear(stacked_hidden_emb_raw_arr) #bsz, seq_len, hidden_size
+                stacked_hidden_emb_arr_raw = torch.cat([hidden_emb_arr[0], gelu(hidden_emb_MLP)], dim=-1) #bsz, seq_len, 2*hidden_size
+            else:
+                stacked_hidden_emb_arr_raw = hidden_emb_arr[0]
+
+            #Only use the hidden state corresponding to the last word
+            stacked_hidden_emb_arr = stacked_hidden_emb_arr_raw[:,-1,:].unsqueeze(dim=1)
+
+            #list of linear projects per facet
+            projected_emb_arr = []
+            #list of final logits per facet
+            facet_lm_logits_arr = []
+
+            rereanker_candidate_token_ids_arr = []
+            for i in range(self.n_facet):
+                projected_emb = self.get_facet_emb(stacked_hidden_emb_arr, i) #(bsz, seq_len, hidden_dim)
+                projected_emb_arr.append(projected_emb) 
+                lm_logits = F.linear(projected_emb, test_item_emb, test_item_bias)
+                facet_lm_logits_arr.append(lm_logits)
+                if i < self.n_facet_reranker and not self.candidates_from_previous_reranker:
+                    candidate_token_ids = []
+                    for j in range(len(self.reranker_CAN_NUM)):
+                        _, candidate_token_ids_ = torch.topk(lm_logits, self.reranker_CAN_NUM[j])
+                        candidate_token_ids.append(candidate_token_ids_)
+                    rereanker_candidate_token_ids_arr.append(candidate_token_ids)
+
+            for i in range(self.n_facet_reranker):
+                for j in range(len(self.reranker_CAN_NUM)):
+                    projected_emb = self.get_facet_emb(stacked_hidden_emb_arr, self.n_facet+i*len(self.reranker_CAN_NUM) + j) #(bsz, seq_len, hidden_dim)
+                    projected_emb_arr.append(projected_emb)
+
+            for i in range(self.n_facet_context):
+                projected_emb = self.get_facet_emb(stacked_hidden_emb_arr, self.n_facet+self.n_facet_reranker*len(self.reranker_CAN_NUM)+i) #(bsz, seq_len, hidden_dim)
+                projected_emb_arr.append(projected_emb)
+
+            #to generate context-based embeddings for words in input
+            for i in range(self.n_facet_emb):
+                projected_emb = self.get_facet_emb(stacked_hidden_emb_arr_raw, self.n_facet + self.n_facet_context + self.n_facet_reranker*len(self.reranker_CAN_NUM) + i) #(bsz, seq_len, hidden_dim)
+                projected_emb_arr.append(projected_emb)
+
+            for i in range(self.n_facet_reranker):
+                bsz, seq_len, hidden_size = projected_emb_arr[i].size()
+                for j in range(len(self.reranker_CAN_NUM)):
+                    if self.candidates_from_previous_reranker:
+                        _, candidate_token_ids = torch.topk(facet_lm_logits_arr[i], self.reranker_CAN_NUM[j]) #(bsz, seq_len, topk)
+                    else:
+                        candidate_token_ids = rereanker_candidate_token_ids_arr[i][j]
+                    logit_hidden_reranker_topn = (projected_emb_arr[self.n_facet + i*len(self.reranker_CAN_NUM) + j].unsqueeze(dim=2).expand(bsz, seq_len, self.reranker_CAN_NUM[j], hidden_size) * test_item_emb[candidate_token_ids, :] ).sum(dim=-1) #(bsz, seq_len, emb_size) x (bsz, seq_len, topk, emb_size) -> (bsz, seq_len, topk)
+                    if test_item_bias is not None:
+                        logit_hidden_reranker_topn +=test_item_bias[candidate_token_ids]
+                    if self.reranker_merging_mode == 'add':
+                        #print("inside reranker")
+                        facet_lm_logits_arr[i].scatter_add_(2, candidate_token_ids, logit_hidden_reranker_topn) #(bsz, seq_len, vocab_size) <- (bsz, seq_len, topk) x (bsz, seq_len, topk)
+                    else:
+                        facet_lm_logits_arr[i].scatter_(2, candidate_token_ids, logit_hidden_reranker_topn) #(bsz, seq_len, vocab_size) <- (bsz, seq_len, topk) x (bsz, seq_len, topk)
+
+            for i in range(self.n_facet_context):
+                bsz, seq_len_1, hidden_size = projected_emb_arr[i].size()
+                bsz, seq_len_2 = item_seq.size()
+                logit_hidden_context = (projected_emb_arr[self.n_facet + self.n_facet_reranker*len(self.reranker_CAN_NUM) + i].unsqueeze(dim=2).expand(-1,-1,seq_len_2,-1) * test_item_emb[item_seq, :].unsqueeze(dim=1).expand(-1,seq_len_1,-1,-1)  ).sum(dim=-1)
+                if test_item_bias is not None:
+                    logit_hidden_context += test_item_bias[item_seq].unsqueeze(dim=1).expand(-1,seq_len_1,-1)
+                logit_hidden_pointer = 0
+                if self.n_facet_emb == 2:
+                    logit_hidden_pointer = ( projected_emb_arr[-2][:,-1,:].unsqueeze(dim=1).unsqueeze(dim=1).expand(-1,seq_len_1,seq_len_2,-1) * projected_emb_arr[-1].unsqueeze(dim=1).expand(-1,seq_len_1,-1,-1) ).sum(dim=-1)
+
+                item_seq_expand = item_seq.unsqueeze(dim=1).expand(-1,seq_len_1,-1)
+                only_new_logits = torch.zeros_like(facet_lm_logits_arr[i])
+                if self.context_norm:
+                    only_new_logits.scatter_add_(dim=2, index=item_seq_expand, src=logit_hidden_context+logit_hidden_pointer)
+                    item_count = torch.zeros_like(only_new_logits) + 1e-15
+                    item_count.scatter_add_(dim=2, index=item_seq_expand,src=torch.ones_like(item_seq_expand).to(dtype=item_count.dtype))
+                    only_new_logits = only_new_logits / item_count
+                else:
+                    only_new_logits.scatter_add_(dim=2, index=item_seq_expand, src=logit_hidden_context)
+                    item_count = torch.zeros_like(only_new_logits) + 1e-15
+                    item_count.scatter_add_(dim=2, index=item_seq_expand,src=torch.ones_like(item_seq_expand).to(dtype=item_count.dtype))
+                    only_new_logits = only_new_logits / item_count
+                    only_new_logits.scatter_add_(dim=2, index=item_seq_expand, src=logit_hidden_pointer)
+
+                if self.partition_merging_mode == 'replace':
+                    facet_lm_logits_arr[i].scatter_(dim=2, index=item_seq_expand, src=torch.zeros_like(item_seq_expand).to(dtype=facet_lm_logits_arr[i].dtype) )
+                    facet_lm_logits_arr[i] = facet_lm_logits_arr[i] + only_new_logits
+                elif self.partition_merging_mode == 'add':
+                    facet_lm_logits_arr[i] = facet_lm_logits_arr[i] + only_new_logits
+                elif self.partition_merging_mode == 'half':
+                    item_in_context = torch.ones_like(only_new_logits)
+                    item_in_context.scatter_(dim=2, index=item_seq_expand,src= 2 * torch.ones_like(item_seq_expand).to(dtype=item_count.dtype))
+                    facet_lm_logits_arr[i] = facet_lm_logits_arr[i] / item_in_context + only_new_logits
+
+
+            weight = None
+            if self.weight_mode == 'dynamic':
+                weight = self.weight_facet_decoder(stacked_hidden_emb_arr).softmax(dim=-1) #hidden_dim*hidden_input_state_ration -> n_facet_effective
+            elif self.weight_mode == 'static':
+                weight = self.weight_global.softmax(dim=-1) #torch.ones(n_facet_effective)
+            elif self.weight_mode == 'max_logits':
+                stacked_facet_lm_logits = torch.stack(facet_lm_logits_arr, dim=0)
+                facet_lm_logits_arr = [stacked_facet_lm_logits.amax(dim=0)]
+
+            prediction_prob = 0
+
+            for i in range(self.n_facet_effective):
+                facet_lm_logits = facet_lm_logits_arr[i]
+                if self.softmax_nonlinear == 'sigsoftmax': #'None' here
+                    facet_lm_logits_sig = torch.exp(facet_lm_logits - facet_lm_logits.max(dim=-1,keepdim=True)[0]) * (1e-20 + torch.sigmoid(facet_lm_logits))
+                    facet_lm_logits_softmax = facet_lm_logits_sig / facet_lm_logits_sig.sum(dim=-1,keepdim=True)
+                elif self.softmax_nonlinear == 'None':
+                    facet_lm_logits_softmax = facet_lm_logits.softmax(dim=-1) #softmax over final logits
+                if self.weight_mode == 'dynamic':
+                    prediction_prob += facet_lm_logits_softmax * weight[:,:,i].unsqueeze(-1)
+                elif self.weight_mode == 'static':
+                    prediction_prob += facet_lm_logits_softmax * weight[i]
+                else:
+                    prediction_prob += facet_lm_logits_softmax / self.n_facet_effective #softmax over final logits/1
+            if not only_compute_prob:
+                inp = torch.log(prediction_prob.view(-1, self.n_items)+1e-8)
+                pos_items = interaction[self.POS_ITEM_ID]
+                loss_raw = self.loss_fct(inp, pos_items.view(-1))
+                loss = loss_raw.mean()
+            else:
+                loss = None
+            return loss, prediction_prob.squeeze(dim=1)
+
+    def calculate_loss(self, interaction):
+        loss, prediction_prob = self.calculate_loss_prob(interaction)
+        return loss
+
+    def predict(self, interaction):
+        print("Current softmax cpr code does not support negative sampling in an efficient way just like RepeatNet.", file=sys.stderr)
+        assert False #If you can accept slow running time, uncomment this line.
+        loss, prediction_prob = self.calculate_loss_prob(interaction, only_compute_prob=True)
+        if self.post_remove_context:
+            item_seq = interaction[self.ITEM_SEQ]
+            prediction_prob.scatter_(1, item_seq, 0)
+        test_item = interaction[self.ITEM_ID]
+        prediction_prob = prediction_prob.unsqueeze(-1)
+        # batch_size * num_items * 1
+        scores = self.gather_indexes(prediction_prob, test_item).squeeze(-1)
+        return scores
+
+    def full_sort_predict(self, interaction):
+        loss, prediction_prob = self.calculate_loss_prob(interaction)
+        item_seq = interaction[self.ITEM_SEQ]
+        if self.post_remove_context:
+            prediction_prob.scatter_(1, item_seq, 0)
+        return prediction_prob
+