mirror of
https://github.com/Vision-CAIR/MiniGPT-4.git
synced 2025-04-07 19:40:45 +00:00
195 lines
9.3 KiB
Python
195 lines
9.3 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 RouteMoELayer(nn.Module):
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def __init__(self, hidden_size, expert, num_experts, num_beams=2, layer_judge=None, route_method="pre-route"):
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# remove hash list
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nn.Module.__init__(self)
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self.num_experts = num_experts
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self.experts = nn.ModuleList([copy.deepcopy(expert) for i in range(num_experts)])
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self.num_beams = num_beams
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self.hidden_size = hidden_size
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self.layer_judge = layer_judge
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self.route_method = route_method
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if self.route_method == "pre-route":
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self.gate = nn.Linear(hidden_size, num_experts, bias=False).float()
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elif self.route_method == "post-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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# self.gates = nn.ModuleList([copy.deepcopy(gate) for i in range(num_experts)])
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def forward_gate(self, x):
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"""
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x : torch.Size([bz*num_beams, 32, 768]) or torch.Size([bz, 32, 768])
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prob_gate : torch.Size([bz*num_beams, 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, 32, 768])
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x_average = x_masked.sum(1) / attention_mask.unsqueeze(-1).sum(1) # torch.Size([bz*num_beams, 768])
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logits_gate = self.gate(x_average) # torch.Size([bz*num_beams, num_experts])
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prob_gate = F.softmax(logits_gate, dim=-1) # torch.Size([bz*num_beams, num_experts])
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return prob_gate
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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=='pre-route':
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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_beams, 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_beams * batch_size) # torch.Size([bz * num_beams])
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expert_route = gate.view(self.num_beams * batch_size).unsqueeze(1) # torch.Size([bz * num_beams,1])
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beam_idx = None
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else:
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if self.layer_judge=='first' and self.route_method == 'post-route':
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batch_size = batch_size
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next_scores_raw1 = torch.exp(current_scores_log) # torch.Size([bz, num_experts])
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else:
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batch_size = int(batch_size // self.num_beams)
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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_beams * self.num_experts
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) # torch.Size([bz, num_beams*num_experts])
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next_scores, next_experts = torch.topk(next_scores_raw1, self.num_beams, dim=1, largest=True, sorted=True)
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# next_scores torch.Size([bz, num_beams])
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# next_tokens torch.Size([bz, num_beams])
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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_experts
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ex_id = expert_id % self.num_experts
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effective_beam_id = batch_idx*self.num_beams + 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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if self.layer_judge=='first' and self.route_method == 'post-route':
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beam_scores = next_scores.view(self.num_beams * batch_size) # torch.Size([bz * num_beams])
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expert_route = next_experts.view(self.num_beams * batch_size)
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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.new([x[1] for x in next_batch_beam]).unsqueeze(-1)
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beam_idx = expert_route.new([int(x[2]/self.num_beams) for x in next_batch_beam])
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expert_route = beam_experts
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else:
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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_expert_ffn(self, x, expert_select, beam_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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"""
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# add_1212 l2_normalization
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# normalized_tensor = torch.nn.functional.normalize(beam_scores, p=2, dim=0) # L2 Normalization torch.Size([bz, topk])
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# tmp_prob = normalized_tensor.unsqueeze(-1).unsqueeze(-1)
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outputs = list()
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for i in range(x.shape[0]):
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output_x = self.experts[expert_select[i]].forward(x[i])
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outputs.append(output_x.unsqueeze(0))
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candidate_output = torch.cat(outputs)
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# candidate_output = candidate_output * tmp_prob
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return candidate_output # torch.Size([bz*num_beams, 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, 5]
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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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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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candidate_output = self.forward_expert_ffn(x, current_expert_select, beam_scores) # [bz*num_beams, 32,768]
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return candidate_output, beam_scores, expert_route, beam_idx
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def forward_post_route(self, x, beam_scores, expert_route, use_log=True):
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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, 32, 768])
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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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outputs = list()
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logits_gate_lst = list()
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for expert_idx in range(self.num_experts):
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output_x = forward_expert(x_masked, expert_idx)
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outputs.append(output_x.unsqueeze(0))
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output_x_aver = output_x.sum(1) / attention_mask.unsqueeze(-1).sum(1) # torch.Size([bz*num_beam, 768])
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# gate_score = self.gates[expert_idx](output_x_aver)
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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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candidate_output = torch.cat(outputs) # torch.Size([num_expert, bz*num_beam, 32, 768])
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logits_gate = torch.cat(logits_gate_lst,dim=1)# torch.Size([bz*num_beam, num_expert])
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current_scores = F.softmax(logits_gate, dim=-1) # torch.Size([bz*num_beam, num_experts])
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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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batch_size = x.shape[0] # bz*num_beam
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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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# beam_scores torch.Size([bz*num_beam])
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# expert_route torch.Size([bz*num_beam, layer_n])
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current_select_expert = expert_route[:,-1]
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output = list()
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for i in range(beam_idx.shape[0]):
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b_idx = beam_idx[i]
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ex_idx = current_select_expert[i]
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ex_out = candidate_output[ex_idx, b_idx, :,:]
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output.append(ex_out.unsqueeze(0))
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final_output = torch.concat(output, dim=0)
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return final_output, beam_scores, expert_route, beam_idx
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def forward(self, x, attention_mask, beam_scores, expert_route, use_log=True):
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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':
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candidate_output, beam_scores, expert_route, beam_idx = self.forward_pre_route(x, beam_scores, expert_route, use_log=True)
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elif self.route_method == "post-route":
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candidate_output, beam_scores, expert_route, beam_idx = self.forward_post_route(x, beam_scores, expert_route, use_log=True)
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return candidate_output, beam_scores, expert_route, beam_idx
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