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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from typing import Optional, Tuple | |
| import torch | |
| import torch.nn.functional as F | |
| 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): | |
| # The first two dimensions of cos and sin are always 1, | |
| # so we can `squeeze` them. | |
| 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 * cos) + (rotate_half(q) * sin) | |
| k_embed = (k * cos) + (rotate_half(k) * sin) | |
| return q_embed, k_embed | |
| def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: | |
| """This is the equivalent of torch.repeat_interleave(x, dim=1, | |
| repeats=n_rep). | |
| The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to | |
| (batch, num_attention_heads, seqlen, head_dim) | |
| """ | |
| batch, num_key_value_heads, slen, head_dim = hidden_states.shape | |
| if n_rep == 1: | |
| return hidden_states | |
| hidden_states = hidden_states[:, :, | |
| None, :, :].expand(batch, | |
| num_key_value_heads, | |
| n_rep, slen, head_dim) | |
| return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, | |
| head_dim) | |
| def yi_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, | |
| **kwargs, | |
| ) -> Tuple[torch.Tensor, Optional[torch.Tensor], | |
| Optional[Tuple[torch.Tensor]]]: | |
| bsz, q_len, _ = hidden_states.size() | |
| query_states = self.q_proj(hidden_states) | |
| key_states = self.k_proj(hidden_states) | |
| value_states = self.v_proj(hidden_states) | |
| query_states = query_states.view(bsz, q_len, self.num_heads, | |
| self.head_dim).transpose(1, 2) | |
| key_states = key_states.view(bsz, q_len, self.num_key_value_heads, | |
| self.head_dim).transpose(1, 2) | |
| value_states = value_states.view(bsz, q_len, self.num_key_value_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) | |
| 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 | |
| # repeat k/v heads if n_kv_heads < n_heads | |
| key_states = repeat_kv(key_states, self.num_key_value_groups) | |
| value_states = repeat_kv(value_states, self.num_key_value_groups) | |
| # use flash attention implemented by pytorch | |
| attn_output = F.scaled_dot_product_attention( | |
| query_states, key_states, value_states, attn_mask=attention_mask) | |
| attn_output = attn_output.transpose(1, 2).contiguous() | |
| attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) | |
| attn_output = self.o_proj(attn_output) | |
| # Due to the implementation of the PyTorch version of flash attention, | |
| # even when the output_attentions flag is set to True, it is not possible | |
| # to return the attn_weights. | |
| return attn_output, None, past_key_value | |