76 lines
No EOL
2.9 KiB
Python
76 lines
No EOL
2.9 KiB
Python
from typing import List, Optional, Tuple
|
|
|
|
import torch
|
|
from torch import nn
|
|
|
|
import transformers
|
|
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
|
|
import torch
|
|
from torch import nn
|
|
import torch.nn.functional as F
|
|
import math
|
|
import transformers
|
|
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
|
|
|
|
|
|
def forward_2(
|
|
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()
|
|
|
|
query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
|
key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
|
value_states = self.v_proj(hidden_states).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)
|
|
|
|
assert not output_attentions, "output_attentions is not supported"
|
|
assert not use_cache, "use_cache is not supported"
|
|
assert past_key_value is None, "past_key_value is not supported"
|
|
|
|
|
|
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,dropout_p=0.0, is_causal=True)
|
|
attn_weights = None
|
|
|
|
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)
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
|
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
if not output_attentions:
|
|
attn_weights = None
|
|
|
|
return attn_output, attn_weights, past_key_value
|
|
|
|
|
|
def _prepare_decoder_attention_mask(self, attention_mask, input_shape,
|
|
inputs_embeds, past_key_values_length):
|
|
# [bsz, seq_len]
|
|
return attention_mask
|
|
|
|
def replace_llama_attn_with_flash_attn():
|
|
transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = _prepare_decoder_attention_mask
|
|
transformers.models.llama.modeling_llama.LlamaAttention.forward = forward_2
|
|
|