llama_xformers_attention.py

import torch
import torch.nn as nn

from typing import Optional, Tuple

from transformers.models.llama.modeling_llama import LlamaAttention

class LlamaXFormersAttention(LlamaAttention):
 def 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]]]:
 if "padding_mask" in kwargs:
 warnings.warn(
 "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
 )

 bsz, q_len, _ = hidden_states.size()

 if self.config.pretraining_tp > 1:
 key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
 query_slices = self.q_proj.weight.split(
 (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
 )
 key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
 value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)

 query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
 query_states = torch.cat(query_states, dim=-1)

 key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
 key_states = torch.cat(key_states, dim=-1)

 value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
 value_states = torch.cat(value_states, dim=-1)

 else:
 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

 key_states = repeat_kv(key_states, self.num_key_value_groups)
 value_states = repeat_kv(value_states, self.num_key_value_groups)

 attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

 if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
 raise ValueError(
 f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
 f" {attn_weights.size()}"
 )

 if attention_mask is not None:
 if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
 raise ValueError(
 f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
 )
 attn_weights = attn_weights + attention_mask

 # upcast attention to fp32
 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
 attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
 attn_output = torch.matmul(attn_weights, value_states)

 if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
 raise ValueError(
 f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
 f" {attn_output.size()}"
 )

 attn_output = attn_output.transpose(1, 2).contiguous()

 attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

 if self.config.pretraining_tp > 1:
 attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
 o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
 attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
 else:
 attn_output = self.o_proj(attn_output)

 if not output_attentions:
 attn_weights = None

 return attn_output, attn_weights, past_key_value