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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from typing import Optional, Tuple | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| def baichuan2_norm_head_forward(self, hidden_states): | |
| norm_weight = nn.functional.normalize(self.weight) | |
| return nn.functional.linear(hidden_states, norm_weight) | |
| def rotate_half(x): | |
| """Rotates half the hidden dims of the input.""" | |
| x1 = x[..., :x.shape[-1] // 2] | |
| x2 = x[..., x.shape[-1] // 2:] | |
| return torch.cat((-x2, x1), dim=-1) | |
| def apply_rotary_pos_emb(q, k, cos_, sin_, position_ids): | |
| cos = cos_.squeeze(1).squeeze(0) # [seq_len, dim] | |
| sin = sin_.squeeze(1).squeeze(0) # [seq_len, dim] | |
| cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] | |
| sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] | |
| q_embed = (q.float() * cos) + (rotate_half(q.float()) * sin) | |
| k_embed = (k.float() * cos) + (rotate_half(k.float()) * sin) | |
| return q_embed.to(q.dtype), k_embed.to(k.dtype) | |
| def baichuan_7b_attn_forward( | |
| self, | |
| hidden_states: torch.Tensor, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.LongTensor] = None, | |
| past_key_value: Optional[Tuple[torch.Tensor]] = None, | |
| output_attentions: bool = False, | |
| use_cache: bool = False, | |
| ) -> Tuple[torch.Tensor, Optional[torch.Tensor], | |
| Optional[Tuple[torch.Tensor]]]: | |
| bsz, q_len, _ = hidden_states.size() | |
| proj = self.W_pack(hidden_states) | |
| proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose( | |
| 0, -2).squeeze(-2) | |
| query_states = proj[0].view(bsz, q_len, self.num_heads, | |
| self.head_dim).transpose(1, 2) | |
| key_states = proj[1].view(bsz, q_len, self.num_heads, | |
| self.head_dim).transpose(1, 2) | |
| value_states = proj[2].view(bsz, q_len, self.num_heads, | |
| self.head_dim).transpose(1, 2) | |
| kv_seq_len = key_states.shape[-2] | |
| if past_key_value is not None: | |
| kv_seq_len += past_key_value[0].shape[-2] | |
| cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) | |
| query_states, key_states = apply_rotary_pos_emb(query_states, key_states, | |
| cos, sin, position_ids) | |
| # [bsz, nh, t, hd] | |
| if past_key_value is not None: | |
| # reuse k, v, self_attention | |
| key_states = torch.cat([past_key_value[0], key_states], dim=2) | |
| value_states = torch.cat([past_key_value[1], value_states], dim=2) | |
| past_key_value = (key_states, value_states) if use_cache else None | |
| attn_output = F.scaled_dot_product_attention( | |
| query_states, key_states, value_states, attn_mask=attention_mask) | |
| attn_output = attn_output.transpose(1, 2) | |
| attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) | |
| attn_output = self.o_proj(attn_output) | |
| return attn_output, None, past_key_value | |
| def baichuan_13b_attn_forward( | |
| self, | |
| hidden_states: torch.Tensor, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| past_key_value: Optional[Tuple[torch.Tensor]] = None, | |
| output_attentions: bool = False, | |
| use_cache: bool = False, | |
| ) -> Tuple[torch.Tensor, Optional[torch.Tensor], | |
| Optional[Tuple[torch.Tensor]]]: | |
| bsz, q_len, _ = hidden_states.size() | |
| proj = self.W_pack(hidden_states) | |
| proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose( | |
| 0, -2).squeeze(-2) | |
| query_states = proj[0].view(bsz, q_len, self.num_heads, | |
| self.head_dim).transpose(1, 2) | |
| key_states = proj[1].view(bsz, q_len, self.num_heads, | |
| self.head_dim).transpose(1, 2) | |
| value_states = proj[2].view(bsz, q_len, self.num_heads, | |
| self.head_dim).transpose(1, 2) | |
| kv_seq_len = key_states.shape[-2] | |
| if past_key_value is not None: | |
| kv_seq_len += past_key_value[0].shape[-2] | |
| if past_key_value is not None: | |
| # reuse k, v, self_attention | |
| key_states = torch.cat([past_key_value[0], key_states], dim=2) | |
| value_states = torch.cat([past_key_value[1], value_states], dim=2) | |
| past_key_value = (key_states, value_states) if use_cache else None | |
| if attention_mask is not None: | |
| if q_len == 1: # inference with cache | |
| if len(attention_mask.size()) == 4: | |
| attention_mask = attention_mask[:, :, -1:, :] | |
| else: | |
| attention_mask = attention_mask[:, -1:, :] | |
| attn_output = F.scaled_dot_product_attention( | |
| query_states, key_states, value_states, attn_mask=attention_mask) | |
| attn_output = attn_output.transpose(1, 2) | |
| attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) | |
| attn_output = self.o_proj(attn_output) | |
| return attn_output, None, past_key_value | |