File size: 9,688 Bytes
4ba4c08 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 |
import torch
from torch import nn
from typing import Optional, Tuple, Union
import transformers
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, rotate_half
import math
try:
from xformers import ops as xops
except ImportError:
xops = None
print(
"Xformers is not installed correctly. If you want to use memory_efficient_attention use the following command to install Xformers\npip install xformers."
)
STORE_KV_BEFORE_ROPE = False
USE_MEM_EFF_ATTENTION = False
ALPHA = 1.0
AUTO_COEFF = 1.0
SCALING_FACTOR = None
def apply_rotary_pos_emb_single(q, 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)
return q_embed
def xformers_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()
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]
if STORE_KV_BEFORE_ROPE is False:
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
else:
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
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states = apply_rotary_pos_emb_single(query_states, cos, sin, position_ids)
position_ids = torch.arange(kv_seq_len, dtype=torch.long, device=cos.device)
position_ids = position_ids.unsqueeze(0).view(-1, kv_seq_len)
key_states = apply_rotary_pos_emb_single(key_states, cos, sin, position_ids)
if xops is not None and USE_MEM_EFF_ATTENTION:
attn_weights = None
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
attn_bias = None if (query_states.size(1)==1 and key_states.size(1)>1) else xops.LowerTriangularMask()
attn_output = xops.memory_efficient_attention(
query_states, key_states, value_states, attn_bias=attn_bias, p=0)
else:
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
attn_weights = torch.max(
attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min, device=attn_weights.device)
)
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
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)
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
old_init = transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
t = t / self.scaling_factor
freqs = torch.einsum("i,j->ij", t, self.ntk_inv_freq.to(device))
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
def adaptive_ntk_init(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=None):
self.alpha = ALPHA
if SCALING_FACTOR is None:
self.scaling_factor = scaling_factor or 1.0
else:
self.scaling_factor = SCALING_FACTOR
if isinstance(ALPHA,(float,int)):
base = base * ALPHA ** (dim / (dim-2))
self.base = base
elif ALPHA=='auto':
self.base = base
else:
raise ValueError(ALPHA)
old_init(self, dim, max_position_embeddings, base, device)
self.ntk_inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
self._set_cos_sin_cache = _set_cos_sin_cache
self._set_cos_sin_cache(
self, seq_len=max_position_embeddings, device=self.ntk_inv_freq.device, dtype=torch.get_default_dtype()
)
def adaptive_ntk_forward(self, x, seq_len=None):
if seq_len > self.max_seq_len_cached:
if isinstance(self.alpha,(float,int)):
self._set_cos_sin_cache(self, seq_len=seq_len, device=x.device, dtype=x.dtype)
elif self.alpha=='auto':
t = torch.arange(seq_len, device=x.device, dtype=torch.float32)
t = t / self.scaling_factor
dim = self.dim
alpha = (seq_len / (self.max_position_embeddings/2) - 1) * AUTO_COEFF
base = self.base * alpha ** (dim / (dim-2))
ntk_inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(x.device) / dim ))
freqs = torch.einsum("i,j->ij", t, ntk_inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
cos_cached = emb.cos()[None, None, :, :]
sin_cached = emb.sin()[None, None, :, :]
return (
cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype)
)
return (
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype)
)
def apply_attention_patch(
use_memory_efficient_attention=False,
store_kv_before_rope=False
):
global USE_MEM_EFF_ATTENTION, STORE_KV_BEFORE_ROPE
if use_memory_efficient_attention is True and xops is not None:
USE_MEM_EFF_ATTENTION = use_memory_efficient_attention
print("USE_MEM_EFF_ATTENTION: ",USE_MEM_EFF_ATTENTION)
STORE_KV_BEFORE_ROPE = store_kv_before_rope
print("STORE_KV_BEFORE_ROPE:", STORE_KV_BEFORE_ROPE)
transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward
def apply_ntk_scaling_patch(alpha: Union[float,str], scaling_factor: Optional[float] = None):
global ALPHA
global SCALING_FACTOR
ALPHA = alpha
SCALING_FACTOR = scaling_factor
try:
ALPHA = float(ALPHA)
except ValueError:
if ALPHA!="auto":
raise ValueError(f"Alpha can only be a float or 'auto', but given {ALPHA}")
print(f"Apply NTK scaling with ALPHA={ALPHA}")
if scaling_factor is None:
print(f"The value of scaling factor will be read from model config file, or set to 1.")
else:
print(f"Warning: scaling factor is set to {SCALING_FACTOR}. \
If you set the value by hand, do not forget to update \
max_position_embeddings in the model config file.")
transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__ = adaptive_ntk_init
if hasattr(transformers.models.llama.modeling_llama,'LlamaLinearScalingRotaryEmbedding'):
transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding.__init__ = adaptive_ntk_init
transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.forward = adaptive_ntk_forward |