mirror of
https://github.com/Vision-CAIR/MiniGPT-4.git
synced 2025-04-19 04:00:46 +00:00
331 lines
15 KiB
Python
331 lines
15 KiB
Python
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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 MoELayer(nn.Module):
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def __init__(self, hidden_size, expert, num_experts, route_method, topk=1, use_balance_loss=True, weight_type='l2_norm'):
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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.route_method = route_method
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self.topk = topk
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self.use_balance_loss = use_balance_loss
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self.weight_type = weight_type
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if route_method in ["gate-token", "gate-sentence"]:
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self.gate = nn.Linear(hidden_size, num_experts, bias=False).float()
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elif route_method in ["gate-sentence-post"]:
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gate = nn.Linear(hidden_size, 1, bias=False).float()
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# self.gates = nn.ModuleList([copy.deepcopy(gate) for i in range(num_experts)])
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self.gate = gate
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else:
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raise KeyError("Routing method not supported.")
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def _balancing_loss(self, prob_gate, num_tokens):
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# From MOEBERT
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# compute the load balancing loss
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# prob_gate，是 [bz, num_expert]，每个样本被分配给每个expert的概率
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# 等价于 VMOE 中 _gshard_auxiliary_loss
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P = prob_gate.mean(0) # torch.Size([num_expert]) 每个expert被分配到样本的平均概率
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temp = num_tokens.float()
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f = temp / temp.sum(0, keepdim=True) # 每个expert被分配的sample比例
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balance_loss = self.num_experts * torch.sum(P * f)
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return balance_loss
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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 _forward_gate_token(self, x):
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bsz, seq_len, dim = x.size()
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x = x.view(-1, dim)
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logits_gate = self.gate(x)
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prob_gate = F.softmax(logits_gate, dim=-1)
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gate = torch.argmax(prob_gate, dim=-1)
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order = gate.argsort(0)
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num_tokens = F.one_hot(gate, self.num_experts).gt(0).sum(0)
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gate_load = num_tokens.clone()
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x = x[order] # reorder according to expert number
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x = x.split(num_tokens.tolist(), dim=0) # a list of length self.num_experts
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# compute the load balancing loss
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P = prob_gate.mean(0)
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temp = num_tokens.float()
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f = temp / temp.sum(0, keepdim=True)
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balance_loss = self.num_experts * torch.sum(P * f)
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prob_gate = prob_gate.gather(dim=1, index=gate.unsqueeze(1))
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prob_gate = prob_gate[order]
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prob_gate = prob_gate.split(num_tokens.tolist(), dim=0)
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def forward_expert(input_x, prob_x, expert_idx):
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input_x = self.experts[expert_idx].forward(input_x)
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input_x = input_x * prob_x
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return input_x
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x = [forward_expert(x[i], prob_gate[i], i) for i in range(self.num_experts)]
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x = torch.vstack(x)
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x = x[order.argsort(0)] # restore original order
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x = x.view(bsz, seq_len, dim)
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return x, balance_loss, gate_load
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def _forward_gate_sentence_top1_raw(self, x, attention_mask):
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"""
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x: query_attention_output , torch.Size([bz, 32, 768])
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attention_mask: torch.ones([bz, 32])
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### Notice:
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the raw version of expert_attention_mask is the extended_attention_mask,
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which will be add to attention_score directly
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the values of extended_attention_mask are -0.0 or -10000
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it should be adjust to 1/0 version to be processed by experts
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"""
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attention_mask = torch.ones(attention_mask.shape[0], attention_mask.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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x_average = x_masked.sum(1) / attention_mask.unsqueeze(-1).sum(1) # torch.Size([bz, 768])
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logits_gate = self.gate(x_average) # torch.Size([bz, num_experts])
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prob_gate = F.softmax(logits_gate, dim=-1) # torch.Size([bz, num_experts])
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gate = torch.argmax(prob_gate, dim=-1) # torch.Size([bz])
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order = gate.argsort(0)
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num_sentences = F.one_hot(gate, self.num_experts).gt(0).sum(0)
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gate_load = num_sentences.clone()
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x = x[order] # reorder according to expert number
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x = x.split(num_sentences.tolist(), dim=0) # a list of length self.num_experts
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# compute the load balancing loss
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P = prob_gate.mean(0)
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temp = num_sentences.float()
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f = temp / temp.sum(0, keepdim=True)
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balance_loss = self.num_experts * torch.sum(P * f)
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prob_gate = prob_gate.gather(dim=1, index=gate.unsqueeze(1))
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prob_gate = prob_gate[order]
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prob_gate = prob_gate.split(num_sentences.tolist(), dim=0)
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def forward_expert(input_x, prob_x, expert_idx):
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input_x = self.experts[expert_idx].forward(input_x)
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input_x = input_x * prob_x.unsqueeze(-1)
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return input_x
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result = []
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for i in range(self.num_experts):
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if x[i].size(0) > 0:
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result.append(forward_expert(x[i], prob_gate[i], i))
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result = torch.vstack(result)
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result = result[order.argsort(0)] # restore original order
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return result, balance_loss, gate_load
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def _forward_gate_sentence_post(self, x, attention_mask):
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"""
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x: query_attention_output; torch.Size([bz, 32, 768])
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attention_mask: torch.ones([bz, 32])
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bz = 4
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x = torch.randn(bz,32,768)
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attention_mask = torch.ones([bz, 32])
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"""
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attention_mask = torch.ones(attention_mask.shape[0], attention_mask.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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# input_x += torch.randn(4,32,768)
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# return input_x
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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, 768])
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# gate_acore = 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, 32, 768])
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logits_gate = torch.cat(logits_gate_lst,dim=1)# torch.Size([bz, num_expert])
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prob_gate = F.softmax(logits_gate, dim=-1) # torch.Size([bz, num_experts])
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topk_values, gate = torch.topk(prob_gate, self.topk, dim=1) # gate, 每个样本被分配的expert: torch.Size([bz, topk])
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num_sentences = F.one_hot(gate, self.num_experts).sum(1).gt(0).sum(0) # 每个expert被分配的样本数 torch.Size([num_expert])
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gate_load = num_sentences.clone()
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# load balancing loss
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if self.use_balance_loss:
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balance_loss = self._balancing_loss(prob_gate, num_sentences)
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else:
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balance_loss = 0.0
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# importance loss
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importance_loss = self._importance_auxiliary_loss(prob_gate)
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# output_average = candidate_output.sum(2) / candidate_attn_mask.unsqueeze(-1).sum(2) # torch.Size([num_expert, bz, 768])
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# output_average = torch.permute(output_average, (1, 0, 2)) # torch.Size([bz, num_expert, 768])
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# logits_gate = self.gate(output_average) # torch.Size([bz, num_experts, 1])
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prob_gate_topk = torch.zeros_like(prob_gate)
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prob_gate_topk.scatter_(1, gate, topk_values)
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if self.weight_type == 'average':
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# torch.Size([bz, num_expert]) 未选中的expert prob_gate_norm为0
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prob_gate_normalized = prob_gate_topk / prob_gate_topk.sum(dim=1, keepdim=True)
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elif self.weight_type == 'raw_prob':
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prob_gate_normalized = prob_gate_topk
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elif self.weight_type == 'softmax_norm':
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prob_gate_normalized = F.softmax(prob_gate_topk, dim=-1) # torch.Size([bz, num_expert])
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candidate_output_ad = torch.permute(candidate_output, (1, 0, 2, 3)) # torch.Size([bz, num_expert, 32, 768])
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results = prob_gate_normalized.unsqueeze(-1).unsqueeze(-1) * candidate_output_ad # torch.Size([bz, num_expert, 32, 768])
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moe_result = torch.sum(results, dim=1) # torch.Size([bz, 32, 768])
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# import pdb;pdb.set_trace()
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return moe_result, (balance_loss+importance_loss), prob_gate_normalized
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# def _forward_gate_sentence(self, x, attention_mask):
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# attention_mask = torch.ones(attention_mask.shape[0], attention_mask.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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# x_average = x_masked.sum(1) / attention_mask.unsqueeze(-1).sum(1)
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# logits_gate = self.gate(x_average)
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# prob_gate = F.softmax(logits_gate, dim=-1)
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# gate = torch.argmax(prob_gate, dim=-1)
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# order = gate.argsort(0)
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# num_sentences = F.one_hot(gate, self.num_experts).gt(0).sum(0)
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# gate_load = num_sentences.clone()
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# x = x[order] # reorder according to expert number
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# x = x.split(num_sentences.tolist(), dim=0) # a list of length self.num_experts
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# # compute the load balancing loss
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# P = prob_gate.mean(0)
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# temp = num_sentences.float()
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# f = temp / temp.sum(0, keepdim=True)
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# balance_loss = self.num_experts * torch.sum(P * f)
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# prob_gate = prob_gate.gather(dim=1, index=gate.unsqueeze(1))
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# prob_gate = prob_gate[order]
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# prob_gate = prob_gate.split(num_sentences.tolist(), dim=0)
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# def forward_expert(input_x, prob_x, expert_idx):
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# input_x = self.experts[expert_idx].forward(input_x)
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# input_x = input_x * prob_x.unsqueeze(-1)
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# return input_x
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# result = []
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# for i in range(self.num_experts):
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# if x[i].size(0) > 0:
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# result.append(forward_expert(x[i], prob_gate[i], i))
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# result = torch.vstack(result)
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# result = result[order.argsort(0)] # restore original order
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# return result, balance_loss, gate_load
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def _forward_gate_sentence(self, x, attention_mask):
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"""
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x: query_attention_output , torch.Size([bz, 32, 768])
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attention_mask: torch.ones([bz, 32])
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### Notice:
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the raw version of expert_attention_mask is the extended_attention_mask,
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which will be add to attention_score directly
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the values of extended_attention_mask are -0.0 or -10000
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it should be adjust to 1/0 version to be processed by experts
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"""
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attention_mask = torch.ones(attention_mask.shape[0], attention_mask.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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x_average = x_masked.sum(1) / attention_mask.unsqueeze(-1).sum(1) # torch.Size([bz, 768])
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logits_gate = self.gate(x_average) # torch.Size([bz, num_experts])
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prob_gate = F.softmax(logits_gate, dim=-1) # torch.Size([bz, num_experts])
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select_prob_gate, gate = torch.topk(prob_gate, self.topk, dim=1) # gate, 每个样本被分配的expert: torch.Size([bz, topk])
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# 这里用l2 norm 去加权
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if self.weight_type == 'l2_norm':
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# actually neigther dim=0 nor dim=1 is right
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normalized_tensor = torch.nn.functional.normalize(select_prob_gate, p=2, dim=1) # L2 Normalization torch.Size([bz, topk])
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elif self.weight_type == 'l2_norm_0':
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normalized_tensor = torch.nn.functional.normalize(select_prob_gate, p=2, dim=0) # L2 Normalization torch.Size([bz, topk])
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elif self.weight_type == 'average':
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normalized_tensor = select_prob_gate / select_prob_gate.sum(dim=1, keepdim=True)
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elif self.weight_type == 'raw_prob':
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normalized_tensor = select_prob_gate
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num_sentences = F.one_hot(gate, self.num_experts).sum(1).gt(0).sum(0) # 每个expert被分配的样本数 torch.Size([num_expert])
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gate_load = num_sentences.clone()
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# load balancing loss
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if self.use_balance_loss:
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balance_loss = self._balancing_loss(prob_gate, num_sentences)
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else:
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balance_loss = 0.0
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# importance loss
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importance_loss = self._importance_auxiliary_loss(prob_gate)
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# forward experts
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def forward_expert(input_x, expert_idx):
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input_x = self.experts[expert_idx].forward(input_x)
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return input_x
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result_lst = list()
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for i in range(self.topk):
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# top1、top2... 分别为一组，进行gate分组之后过expert，然后乘以概率后相加
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tmp_gate = gate[:,i]
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tmp_prob = normalized_tensor[:,i].unsqueeze(-1).unsqueeze(-1)
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order = tmp_gate.argsort(0)
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num_sentences_t = F.one_hot(tmp_gate, self.num_experts).gt(0).sum(0)
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x1 = x[order] # reorder according to expert number
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x1 = x1.split(num_sentences_t.tolist(), dim=0) # a list of length self.num_experts
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result = []
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for i in range(self.num_experts):
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if x1[i].size(0) > 0:
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result.append(forward_expert(x1[i], i))
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result = torch.vstack(result)
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result = result[order.argsort(0)] # restore original order
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result_lst.append(result * tmp_prob) # result * prob
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# result_lst.append(result) # result * prob # add_1212
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|
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|
moe_result = sum(result_lst)
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|
return moe_result, (balance_loss+importance_loss), gate
|
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|
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|
def _forward_sentence_single_expert(self, x, attention_mask):
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x_masked = x * attention_mask.unsqueeze(-1)
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|
x_average = x_masked.sum(1) / attention_mask.unsqueeze(-1).sum(1)
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logits_gate = self.gate(x_average)
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|
prob_gate = F.softmax(logits_gate, dim=-1)
|
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|
gate = torch.argmax(prob_gate, dim=-1)
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|
|
|
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|
|
gate_load = F.one_hot(gate, self.num_experts).gt(0).sum(0)
|
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|
|
x = self.experts[gate.cpu().item()].forward(x)
|
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|
|
return x, 0.0, gate_load
|
|||
|
|
|
|||
|
|
def forward(self, x, attention_mask):
|
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|
|
if self.route_method == "gate-token":
|
|||
|
|
x, balance_loss, gate_load = self._forward_gate_token(x)
|
|||
|
|
elif self.route_method == "gate-sentence":
|
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|
|
if x.size(0) == 1:
|
|||
|
|
x, balance_loss, gate_load = self._forward_sentence_single_expert(x, attention_mask)
|
|||
|
|
else:
|
|||
|
|
x, balance_loss, gate_load = self._forward_gate_sentence(x, attention_mask)
|
|||
|
|
elif self.route_method == "gate-sentence-post":
|
|||
|
|
x, balance_loss, gate_load = self._forward_gate_sentence_post(x, attention_mask)
|
|||
|
|
else:
|
|||
|
|
raise KeyError("Routing method not supported.")
|
|||
|
|
# import pdb; pdb.set_trace()
|
|||
|
|
return x, balance_loss, gate_load
|