File size: 4,540 Bytes
1c60c10 f2a2029 1c60c10 f2a2029 37293dc f2a2029 37293dc f2a2029 e8717d3 f2a2029 37293dc f2a2029 1c60c10 f2a2029 37293dc f2a2029 37293dc f2a2029 37293dc f2a2029 37293dc f2a2029 37293dc 1c60c10 f2a2029 1c60c10 f2a2029 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 |
"""Flash attention monkey patch for llama model"""
# copied from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/llama_flash_attn_monkey_patch.py
from typing import Optional, Tuple
import torch
import transformers
from einops import rearrange
from flash_attn.bert_padding import pad_input, unpad_input
from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: 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]]]:
"""Input shape: Batch x Time x Channel
attention_mask: [bsz, q_len]
"""
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)
)
# [bsz, q_len, nh, hd]
# [bsz, nh, q_len, hd]
kv_seq_len = key_states.shape[-2]
assert past_key_value is None, "past_key_value is not supported"
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]
assert not output_attentions, "output_attentions is not supported"
assert not use_cache, "use_cache is not supported"
# Flash attention codes from
# https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py
# transform the data into the format required by flash attention
qkv = torch.stack(
[query_states, key_states, value_states], dim=2
) # [bsz, nh, 3, q_len, hd]
qkv = qkv.transpose(1, 3) # [bsz, q_len, 3, nh, hd]
# We have disabled _prepare_decoder_attention_mask in LlamaModel
# the attention_mask should be the same as the key_padding_mask
key_padding_mask = attention_mask
if key_padding_mask is None:
qkv = rearrange(qkv, "b s ... -> (b s) ...")
max_s = q_len
cu_q_lens = torch.arange(
0,
(bsz + 1) * q_len,
step=q_len,
dtype=torch.int32,
device=qkv.device,
)
output = flash_attn_unpadded_qkvpacked_func(
qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
)
output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
else:
nheads = qkv.shape[-2]
# pylint: disable=invalid-name
x = rearrange(qkv, "b s three h d -> b s (three h d)")
x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
x_unpad = rearrange(
x_unpad,
"nnz (three h d) -> nnz three h d",
three=3,
h=nheads,
)
output_unpad = flash_attn_unpadded_qkvpacked_func(
x_unpad,
cu_q_lens,
max_s,
0.0,
softmax_scale=None,
causal=True,
)
output = rearrange(
pad_input(
rearrange(output_unpad, "nnz h d -> nnz (h d)"),
indices,
bsz,
q_len,
),
"b s (h d) -> b s h d",
h=nheads,
)
return (
self.o_proj(rearrange(output, "b s h d -> b s (h d)")),
None,
None,
)
# Disable the transformation of the attention mask in LlamaModel as the flash attention
# requires the attention mask to be the same as the key_padding_mask
def _prepare_decoder_attention_mask(
self,
attention_mask,
input_shape,
inputs_embeds,
past_key_values_length,
): # pylint: disable=unused-argument
# [bsz, seq_len]
return attention_mask
def replace_llama_attn_with_flash_attn():
transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = ( # pylint: disable=protected-access
_prepare_decoder_attention_mask
)
transformers.models.llama.modeling_llama.LlamaAttention.forward = forward
|