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https://github.com/Vision-CAIR/MiniGPT-4.git
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277 lines
14 KiB
Python
277 lines
14 KiB
Python
import copy
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import pickle
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class UniRouteMoELayer(nn.Module):
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def __init__(self, hidden_size, expert, num_experts, num_beams=2, layer_judge=None, route_method="pre-route", weight_type="ffn_prob"):
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# remove hash list
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nn.Module.__init__(self)
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self.num_experts = num_experts #(1+other)
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self.num_route_experts = num_experts-1
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self.num_beams = num_beams
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self.num_route_beam = num_beams-1
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self.experts = nn.ModuleList([copy.deepcopy(expert) for i in range(num_experts)])
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self.hidden_size = hidden_size
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self.layer_judge = layer_judge
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self.weight_type = weight_type
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self.route_method = route_method
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if self.route_method in ['pre-route-uni', 'uni-cls-gate-route']:
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self.gate = nn.Linear(hidden_size, self.num_route_experts, bias=False).float()
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elif self.route_method in ["post-route-uni",'uni-cls-route', 'uni-cls-query-route', 'uni-cls-cross-route']:
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gate = nn.Linear(hidden_size, 1, bias=False).float()
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self.gate = gate
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def _importance_auxiliary_loss(self, prob_gate):
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# From VMOE
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# _importance_auxiliary_loss
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axis = tuple(range(prob_gate.ndim - 1)) # All except last.
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importance_per_expert = torch.sum(prob_gate, dim=axis)
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std_importance_per_expert = torch.std(importance_per_expert)
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mean_importance_per_expert = torch.mean(importance_per_expert)
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# Compute coefficient of variation (i.e. std/mean) squared.
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return (std_importance_per_expert / mean_importance_per_expert)**2
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def beam_search(self, current_scores_log, beam_scores, expert_route, batch_size):
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if self.layer_judge=='first' and self.route_method in ['pre-route-uni', 'post-route-uni','uni-cls-route', 'uni-cls-query-route', 'uni-cls-cross-route','uni-cls-gate-route']:
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# current_scores_log torch.Size([bz, num_experts-1])
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assert beam_scores==None and expert_route==None
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current_scores = torch.exp(current_scores_log)
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topk_values, gate = torch.topk(current_scores, self.num_route_beam, dim=1) # gate, 每个样本被分配的expert: torch.Size([bz, topk])
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beam_scores = topk_values.view(self.num_route_beam * batch_size) # torch.Size([bz * num_beams])
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expert_route = gate.view(self.num_route_beam * batch_size).unsqueeze(1) # torch.Size([bz * num_beams,1])
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beam_idx = torch.tensor(range(self.num_route_beam * batch_size))
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else:
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batch_size = int(batch_size // self.num_route_beam)
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next_scores_raw = current_scores_log + torch.log(beam_scores).unsqueeze(1) # torch.Size([4*3, 5]) # 取log 之后，可以直接相加概率
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next_scores_exp = torch.exp(next_scores_raw)
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next_scores_raw1 = next_scores_exp.view(
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batch_size, self.num_route_beam * self.num_route_experts
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) # torch.Size([bz, num_route_beam*num_route_experts])
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next_scores, next_experts = torch.topk(next_scores_raw1, self.num_route_beam, dim=1, largest=True, sorted=True)
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# next_tokens torch.Size([bz, num_route_beam])
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next_batch_beam = list()
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for batch_idx in range(batch_size):
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next_sent_beam = list()
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for rank, (expert_id, expert_score) in enumerate(
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zip(next_experts[batch_idx], next_scores[batch_idx])
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):
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expert_id = expert_id.item()
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beam_id = expert_id // self.num_route_experts
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ex_id = expert_id % self.num_route_experts
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effective_beam_id = batch_idx*self.num_route_beam + beam_id
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next_sent_beam.append((expert_score, ex_id, effective_beam_id))
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next_batch_beam.extend(next_sent_beam)
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beam_scores = beam_scores.new([x[0] for x in next_batch_beam])
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beam_experts = expert_route[:,-1].new([x[1] for x in next_batch_beam])
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beam_idx = expert_route[:,-1].new([x[2] for x in next_batch_beam])
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pre_route = expert_route[beam_idx,:]
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expert_route = torch.cat([pre_route, beam_experts.unsqueeze(1)], dim=-1)
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return beam_scores, expert_route, beam_idx
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def forward_gate(self, x):
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"""
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TODO: Pre forward gate
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x : torch.Size([bz*(num_beams-1), 32, 768]) or torch.Size([bz, 32, 768])
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prob_gate : torch.Size([bz*(num_beams-1), num_experts]) or torch.Size([bz, num_experts])
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"""
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attention_mask = torch.ones(x.shape[0], x.shape[1]).to(x.device)
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x_masked = x * attention_mask.unsqueeze(-1) # torch.Size([bz*(num_beams-1), 32, 768])
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x_average = torch.mean(x_masked, dim=1) # torch.Size([bz*(num_beams-1), 768])
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logits_gate = self.gate(x_average) # torch.Size([bz*(num_beams-1), num_experts])
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prob_gate = F.softmax(logits_gate, dim=-1) # torch.Size([bz*(num_beams-1), num_experts])
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return prob_gate
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def forward_expert_ffn(self, x, expert_select, current_scores):
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"""
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x_repeat : [bz*num_beams, 32,768]
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expert_select : [bz*num_beams]
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current_scores : [bz*num_beams, num_experts] / [bz, num_experts]
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"""
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# import pdb;pdb.set_trace()
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outputs = list()
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for i in range(self.num_experts-1):
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output_x = self.experts[i].forward(x)
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outputs.append(output_x.unsqueeze(1))
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candidate_output = torch.cat(outputs, dim=1)
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expert_select_matrix = F.one_hot(expert_select, self.num_experts)
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if self.weight_type == 'ffn_prob':
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tmp_prob = current_scores * expert_select_matrix
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candidate_output = candidate_output * tmp_prob.unsqueeze(-1).unsqueeze(-1)
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else:
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candidate_output = candidate_output * expert_select_matrix.unsqueeze(-1).unsqueeze(-1)
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output = torch.sum(candidate_output, dim=1)
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# import pdb;pdb.set_trace()
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return output # torch.Size([bz*(num_beams-1), 32, 768])
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def forward_pre_route(self, x, beam_scores, expert_route, use_log=True):
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current_scores = self.forward_gate(x) # [bz, num_beams] / [bz*num_beams, num_beams]
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importance_loss = self._importance_auxiliary_loss(current_scores)
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if use_log:
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current_scores_log = torch.log(current_scores) # 取log之后可以直接相加
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else:
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current_scores_log = current_scores
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# import pdb;pdb.set_trace()
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batch_size, num_tokens = x.shape[0], x.shape[1]
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beam_scores, expert_route, beam_idx = self.beam_search(current_scores_log, beam_scores, expert_route, batch_size)
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current_expert_select = expert_route[:,-1]
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if self.layer_judge=='first': # expand first dim to batch_size * num_beams
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replicated_tensor = x.unsqueeze(1).expand(batch_size, self.num_beams, num_tokens, self.hidden_size)
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x = replicated_tensor.contiguous().view(-1, num_tokens, self.hidden_size) # [bz*num_beams, 32,768]
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current_scores_t = current_scores.unsqueeze(1).expand(batch_size, self.num_beams, self.num_experts)
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current_scores = current_scores_t.contiguous().view(-1, self.num_experts) # [bz*num_beams, num_experts]
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input_x = x[beam_idx]
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candidate_output = self.forward_expert_ffn(input_x, current_expert_select, current_scores) # [bz*num_beams, 32,768]
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# import pdb;pdb.set_trace()
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return candidate_output, beam_scores, expert_route, beam_idx, importance_loss
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def calculate_cls_gate_score(self, cls_hidden, output_x):
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if self.route_method == 'uni-cls-route':
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# cls_hidden = [bz, 768]
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gate_score = self.gate(cls_hidden) # bz, 1
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elif self.route_method == 'uni-cls-query-route': # add cls_hiddin on query_token mean pool hidden
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mean_output = torch.mean(output_x, dim=1) # bz, 768
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gate_score = self.gate(mean_output+cls_hidden) # bz, 1
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elif self.route_method == 'uni-cls-cross-route':
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# cls_hidden as Q, output_x as K, V calculate scaled dot-product attention between Q and K and V
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# cls_hidden: bz, 768
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# output_x: bz, 32, 768
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Q = cls_hidden.unsqueeze(1) # bz, 1, 768
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K = output_x # bz, 32, 768
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V = output_x # bz, 32, 768
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# scaled dot-product attention
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QK = torch.matmul(Q, K.transpose(-1, -2)) / (K.size(-1) ** 0.5) # bz, 1, 32
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QK = F.softmax(QK, dim=-1) # bz, 1, 32
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gate_score = torch.matmul(QK, V) # bz, 1, 768
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gate_score = gate_score.squeeze(1) # bz, 768
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gate_score = self.gate(gate_score) # bz, 1
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return gate_score
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def adjust_cls_hidden(self, cls_hidden, output_x):
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if cls_hidden.shape[0]/self.num_beams == output_x.shape[0]/self.num_route_beam:
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cls_hidden_lst = list()
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for i in range(cls_hidden.shape[0]):
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if i % self.num_beams != 0:
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cls_hidden_lst.append(cls_hidden[i,:])
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cls_hidden = torch.stack(cls_hidden_lst)
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return cls_hidden
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def forward_route_uni(self, x, beam_scores, expert_route, cls_hidden=None):
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if beam_scores == None:
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batch_size = x.shape[0]
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x_masked, x_uniexpert = x, x # torch.Size([bz, 32, 768])
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elif x.shape[0]/self.num_beams == beam_scores.shape[0]/self.num_route_beam:
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batch_size = int(x.shape[0]/self.num_beams)
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select_universal = [i*self.num_beams+self.num_route_beam for i in range(batch_size)]
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select_expert = [ x for x in range(batch_size*self.num_beams) if x not in select_universal]
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x_masked, x_uniexpert = x[select_expert],x[select_universal]
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num_tokens = x.shape[1]
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def forward_expert(input_x, expert_idx):
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output_x = self.experts[expert_idx].forward(input_x)
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return output_x
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####################
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### route expert
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####################
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if cls_hidden is not None:
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cls_hidden = self.adjust_cls_hidden(cls_hidden, x_masked)
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outputs = list()
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logits_gate_lst = list()
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for expert_idx in range(self.num_route_experts): # num_expert-1
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output_x = forward_expert(x_masked, expert_idx)
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if self.route_method == 'post-route-uni':
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output_x_aver = torch.mean(output_x, dim=1)
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gate_score = self.gate(output_x_aver)
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logits_gate_lst.append(gate_score)
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elif self.route_method in ['uni-cls-route', 'uni-cls-query-route', 'uni-cls-cross-route'] and cls_hidden is not None:
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gate_score = self.calculate_cls_gate_score(cls_hidden, output_x)
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logits_gate_lst.append(gate_score)
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outputs.append(output_x.unsqueeze(0))
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candidate_output_raw = torch.cat(outputs) # torch.Size([num_expert-1, bz*(num_beam-1), 32, 768])
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if self.route_method == 'uni-cls-gate-route':
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# universal expert with cls_hidden state into nn.Linear(768,num_experts-1)
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logits_gate = self.gate(cls_hidden)
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current_scores = F.softmax(logits_gate, dim=-1) # torch.Size([bz*(num_beam-1), num_expert-1])
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else:
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logits_gate = torch.cat(logits_gate_lst,dim=1)# torch.Size([bz*(num_beam-1), num_expert-1])
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current_scores = F.softmax(logits_gate, dim=-1) # torch.Size([bz*(num_beam-1), num_expert-1])
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current_scores_log = torch.log(current_scores) # 取log之后可以直接相加 torch.Size([bz*(num_beam-1), num_expert-1])
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importance_loss = self._importance_auxiliary_loss(current_scores)
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beam_scores, expert_route, beam_idx = self.beam_search(current_scores_log, beam_scores, expert_route, current_scores_log.shape[0])
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# beam_scores torch.Size([bz*(num_beam-1)]), expert_route torch.Size([bz*(num_beam-1), layer_n])
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current_select_expert = expert_route[:,-1] # torch.Size([bz*(num_beam-1)])
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if self.layer_judge == 'first':
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replicated_tensor = candidate_output_raw.unsqueeze(2).expand(self.num_route_experts, batch_size, self.num_route_beam, num_tokens, self.hidden_size)
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candidate_output_raw = replicated_tensor.contiguous().view(self.num_route_experts, -1, num_tokens, self.hidden_size) # [bz*num_beams, 32,768]
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current_scores_t = current_scores.unsqueeze(1).expand(batch_size, self.num_route_beam, self.num_route_experts)
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current_scores = current_scores_t.contiguous().view(-1, self.num_route_experts) # [bz*(num_beams-1), num_experts-1]
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candidate_output = candidate_output_raw.permute(1, 0, 2, 3)[beam_idx] # torch.Size([8, 2, 32, 768])
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expert_select_matrix = F.one_hot(current_select_expert, self.num_route_experts)
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if self.weight_type == 'ffn_prob':
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tmp_prob = current_scores[beam_idx] * expert_select_matrix
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output = candidate_output * tmp_prob.unsqueeze(-1).unsqueeze(-1)
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else:
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output = candidate_output * expert_select_matrix.unsqueeze(-1).unsqueeze(-1)
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experts_output = torch.sum(output, dim=1) # [bz*num_beams-1, 32, 768]
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####################
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### universal expert
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####################
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uni_output = forward_expert(x_uniexpert, self.num_experts-1) # [bz, 32, 768]
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####################
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### Combine expert
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####################
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output = list()
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for i in range(batch_size):
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expert_tmp = experts_output[i*self.num_route_beam: i*self.num_route_beam+self.num_route_beam,:,:]
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combine_tmp = torch.cat((expert_tmp, uni_output[i].unsqueeze(0)))
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output.append(combine_tmp)
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final_output = torch.cat(output) # [bz*num_beam, 32 ,768]
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# import pdb; pdb.set_trace()
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return final_output, beam_scores, expert_route, beam_idx, importance_loss
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def forward(self, x, attention_mask, beam_scores, expert_route, cls_hidden):
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"""
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if first_layer: x [bz, 32, 768]
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else: x [bz*num_beams, 32, 768]
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"""
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if self.route_method == 'pre-route-uni':
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candidate_output, beam_scores, expert_route, beam_idx, importance_loss = self.forward_pre_route(x, beam_scores, expert_route)
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elif self.route_method in ['post-route-uni', 'uni-cls-route', 'uni-cls-query-route', 'uni-cls-cross-route','uni-cls-gate-route']:
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candidate_output, beam_scores, expert_route, beam_idx, importance_loss = self.forward_route_uni(x, beam_scores, expert_route, cls_hidden=cls_hidden)
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return candidate_output, beam_scores, expert_route, beam_idx, importance_loss
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